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• How to Write Discussions and Conclusions

The discussion section contains the results and outcomes of a study. An effective discussion informs readers what can be learned from your experiment and provides context for the results.

What makes an effective discussion?

When you’re ready to write your discussion, you’ve already introduced the purpose of your study and provided an in-depth description of the methodology. The discussion informs readers about the larger implications of your study based on the results. Highlighting these implications while not overstating the findings can be challenging, especially when you’re submitting to a journal that selects articles based on novelty or potential impact. Regardless of what journal you are submitting to, the discussion section always serves the same purpose: concluding what your study results actually mean.

A successful discussion section puts your findings in context. It should include:

• the results of your research,
• a discussion of related research, and
• a comparison between your results and initial hypothesis.

Tip: Not all journals share the same naming conventions.

You can apply the advice in this article to the conclusion, results or discussion sections of your manuscript.

Our Early Career Researcher community tells us that the conclusion is often considered the most difficult aspect of a manuscript to write. To help, this guide provides questions to ask yourself, a basic structure to model your discussion off of and examples from published manuscripts. 

Questions to ask yourself:

• Was my hypothesis correct?
• If my hypothesis is partially correct or entirely different, what can be learned from the results? 
• How do the conclusions reshape or add onto the existing knowledge in the field? What does previous research say about the topic? 
• Why are the results important or relevant to your audience? Do they add further evidence to a scientific consensus or disprove prior studies? 
• How can future research build on these observations? What are the key experiments that must be done? 
• What is the “take-home” message you want your reader to leave with?

How to structure a discussion

Trying to fit a complete discussion into a single paragraph can add unnecessary stress to the writing process. If possible, you’ll want to give yourself two or three paragraphs to give the reader a comprehensive understanding of your study as a whole. Here’s one way to structure an effective discussion:

Writing Tips

While the above sections can help you brainstorm and structure your discussion, there are many common mistakes that writers revert to when having difficulties with their paper. Writing a discussion can be a delicate balance between summarizing your results, providing proper context for your research and avoiding introducing new information. Remember that your paper should be both confident and honest about the results! 

• Read the journal’s guidelines on the discussion and conclusion sections. If possible, learn about the guidelines before writing the discussion to ensure you’re writing to meet their expectations. 
• Begin with a clear statement of the principal findings. This will reinforce the main take-away for the reader and set up the rest of the discussion. 
• Explain why the outcomes of your study are important to the reader. Discuss the implications of your findings realistically based on previous literature, highlighting both the strengths and limitations of the research. 
• State whether the results prove or disprove your hypothesis. If your hypothesis was disproved, what might be the reasons? 
• Introduce new or expanded ways to think about the research question. Indicate what next steps can be taken to further pursue any unresolved questions. 
• If dealing with a contemporary or ongoing problem, such as climate change, discuss possible consequences if the problem is avoided. 
• Be concise. Adding unnecessary detail can distract from the main findings. 

Don’t

• Rewrite your abstract. Statements with “we investigated” or “we studied” generally do not belong in the discussion. 
• Include new arguments or evidence not previously discussed. Necessary information and evidence should be introduced in the main body of the paper. 
• Apologize. Even if your research contains significant limitations, don’t undermine your authority by including statements that doubt your methodology or execution. 
• Shy away from speaking on limitations or negative results. Including limitations and negative results will give readers a complete understanding of the presented research. Potential limitations include sources of potential bias, threats to internal or external validity, barriers to implementing an intervention and other issues inherent to the study design. 
• Overstate the importance of your findings. Making grand statements about how a study will fully resolve large questions can lead readers to doubt the success of the research. 

Snippets of Effective Discussions:

Consumer-based actions to reduce plastic pollution in rivers: A multi-criteria decision analysis approach

Identifying reliable indicators of fitness in polar bears

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The purpose of the discussion section is to interpret and describe the significance of your findings in relation to what was already known about the research problem being investigated and to explain any new understanding or insights that emerged as a result of your research. The discussion will always connect to the introduction by way of the research questions or hypotheses you posed and the literature you reviewed, but the discussion does not simply repeat or rearrange the first parts of your paper; the discussion clearly explains how your study advanced the reader's understanding of the research problem from where you left them at the end of your review of prior research.

Annesley, Thomas M. “The Discussion Section: Your Closing Argument.” Clinical Chemistry 56 (November 2010): 1671-1674; Peacock, Matthew. “Communicative Moves in the Discussion Section of Research Articles.” System 30 (December 2002): 479-497.

Importance of a Good Discussion

The discussion section is often considered the most important part of your research paper because it:

• Most effectively demonstrates your ability as a researcher to think critically about an issue, to develop creative solutions to problems based upon a logical synthesis of the findings, and to formulate a deeper, more profound understanding of the research problem under investigation;
• Presents the underlying meaning of your research, notes possible implications in other areas of study, and explores possible improvements that can be made in order to further develop the concerns of your research;
• Highlights the importance of your study and how it can contribute to understanding the research problem within the field of study;
• Presents how the findings from your study revealed and helped fill gaps in the literature that had not been previously exposed or adequately described; and,
• Engages the reader in thinking critically about issues based on an evidence-based interpretation of findings; it is not governed strictly by objective reporting of information.

Annesley Thomas M. “The Discussion Section: Your Closing Argument.” Clinical Chemistry 56 (November 2010): 1671-1674; Bitchener, John and Helen Basturkmen. “Perceptions of the Difficulties of Postgraduate L2 Thesis Students Writing the Discussion Section.” Journal of English for Academic Purposes 5 (January 2006): 4-18; Kretchmer, Paul. Fourteen Steps to Writing an Effective Discussion Section. San Francisco Edit, 2003-2008.

Structure and Writing Style

I.  General Rules

These are the general rules you should adopt when composing your discussion of the results :

• Do not be verbose or repetitive; be concise and make your points clearly
• Avoid the use of jargon or undefined technical language
• Follow a logical stream of thought; in general, interpret and discuss the significance of your findings in the same sequence you described them in your results section [a notable exception is to begin by highlighting an unexpected result or a finding that can grab the reader's attention]
• Use the present verb tense, especially for established facts; however, refer to specific works or prior studies in the past tense
• If needed, use subheadings to help organize your discussion or to categorize your interpretations into themes

II.  The Content

The content of the discussion section of your paper most often includes :

• Explanation of results : Comment on whether or not the results were expected for each set of findings; go into greater depth to explain findings that were unexpected or especially profound. If appropriate, note any unusual or unanticipated patterns or trends that emerged from your results and explain their meaning in relation to the research problem.
• References to previous research : Either compare your results with the findings from other studies or use the studies to support a claim. This can include re-visiting key sources already cited in your literature review section, or, save them to cite later in the discussion section if they are more important to compare with your results instead of being a part of the general literature review of prior research used to provide context and background information. Note that you can make this decision to highlight specific studies after you have begun writing the discussion section.
• Deduction : A claim for how the results can be applied more generally. For example, describing lessons learned, proposing recommendations that can help improve a situation, or highlighting best practices.
• Hypothesis : A more general claim or possible conclusion arising from the results [which may be proved or disproved in subsequent research]. This can be framed as new research questions that emerged as a consequence of your analysis.

III.  Organization and Structure

Keep the following sequential points in mind as you organize and write the discussion section of your paper:

• Think of your discussion as an inverted pyramid. Organize the discussion from the general to the specific, linking your findings to the literature, then to theory, then to practice [if appropriate].
• Use the same key terms, narrative style, and verb tense [present] that you used when describing the research problem in your introduction.
• Begin by briefly re-stating the research problem you were investigating and answer all of the research questions underpinning the problem that you posed in the introduction.
• Describe the patterns, principles, and relationships shown by each major findings and place them in proper perspective. The sequence of this information is important; first state the answer, then the relevant results, then cite the work of others. If appropriate, refer the reader to a figure or table to help enhance the interpretation of the data [either within the text or as an appendix].
• Regardless of where it's mentioned, a good discussion section includes analysis of any unexpected findings. This part of the discussion should begin with a description of the unanticipated finding, followed by a brief interpretation as to why you believe it appeared and, if necessary, its possible significance in relation to the overall study. If more than one unexpected finding emerged during the study, describe each of them in the order they appeared as you gathered or analyzed the data. As noted, the exception to discussing findings in the same order you described them in the results section would be to begin by highlighting the implications of a particularly unexpected or significant finding that emerged from the study, followed by a discussion of the remaining findings.
• Before concluding the discussion, identify potential limitations and weaknesses if you do not plan to do so in the conclusion of the paper. Comment on their relative importance in relation to your overall interpretation of the results and, if necessary, note how they may affect the validity of your findings. Avoid using an apologetic tone; however, be honest and self-critical [e.g., in retrospect, had you included a particular question in a survey instrument, additional data could have been revealed].
• The discussion section should end with a concise summary of the principal implications of the findings regardless of their significance. Give a brief explanation about why you believe the findings and conclusions of your study are important and how they support broader knowledge or understanding of the research problem. This can be followed by any recommendations for further research. However, do not offer recommendations which could have been easily addressed within the study. This would demonstrate to the reader that you have inadequately examined and interpreted the data.

IV.  Overall Objectives

The objectives of your discussion section should include the following: I.  Reiterate the Research Problem/State the Major Findings

Briefly reiterate the research problem or problems you are investigating and the methods you used to investigate them, then move quickly to describe the major findings of the study. You should write a direct, declarative, and succinct proclamation of the study results, usually in one paragraph.

II.  Explain the Meaning of the Findings and Why They are Important

No one has thought as long and hard about your study as you have. Systematically explain the underlying meaning of your findings and state why you believe they are significant. After reading the discussion section, you want the reader to think critically about the results and why they are important. You don’t want to force the reader to go through the paper multiple times to figure out what it all means. If applicable, begin this part of the section by repeating what you consider to be your most significant or unanticipated finding first, then systematically review each finding. Otherwise, follow the general order you reported the findings presented in the results section.

III.  Relate the Findings to Similar Studies

No study in the social sciences is so novel or possesses such a restricted focus that it has absolutely no relation to previously published research. The discussion section should relate your results to those found in other studies, particularly if questions raised from prior studies served as the motivation for your research. This is important because comparing and contrasting the findings of other studies helps to support the overall importance of your results and it highlights how and in what ways your study differs from other research about the topic. Note that any significant or unanticipated finding is often because there was no prior research to indicate the finding could occur. If there is prior research to indicate this, you need to explain why it was significant or unanticipated. IV.  Consider Alternative Explanations of the Findings

It is important to remember that the purpose of research in the social sciences is to discover and not to prove . When writing the discussion section, you should carefully consider all possible explanations for the study results, rather than just those that fit your hypothesis or prior assumptions and biases. This is especially important when describing the discovery of significant or unanticipated findings.

V.  Acknowledge the Study’s Limitations

It is far better for you to identify and acknowledge your study’s limitations than to have them pointed out by your professor! Note any unanswered questions or issues your study could not address and describe the generalizability of your results to other situations. If a limitation is applicable to the method chosen to gather information, then describe in detail the problems you encountered and why. VI.  Make Suggestions for Further Research

You may choose to conclude the discussion section by making suggestions for further research [as opposed to offering suggestions in the conclusion of your paper]. Although your study can offer important insights about the research problem, this is where you can address other questions related to the problem that remain unanswered or highlight hidden issues that were revealed as a result of conducting your research. You should frame your suggestions by linking the need for further research to the limitations of your study [e.g., in future studies, the survey instrument should include more questions that ask..."] or linking to critical issues revealed from the data that were not considered initially in your research.

NOTE: Besides the literature review section, the preponderance of references to sources is usually found in the discussion section . A few historical references may be helpful for perspective, but most of the references should be relatively recent and included to aid in the interpretation of your results, to support the significance of a finding, and/or to place a finding within a particular context. If a study that you cited does not support your findings, don't ignore it--clearly explain why your research findings differ from theirs.

V.  Problems to Avoid

• Do not waste time restating your results . Should you need to remind the reader of a finding to be discussed, use "bridge sentences" that relate the result to the interpretation. An example would be: “In the case of determining available housing to single women with children in rural areas of Texas, the findings suggest that access to good schools is important...," then move on to further explaining this finding and its implications.
• As noted, recommendations for further research can be included in either the discussion or conclusion of your paper, but do not repeat your recommendations in the both sections. Think about the overall narrative flow of your paper to determine where best to locate this information. However, if your findings raise a lot of new questions or issues, consider including suggestions for further research in the discussion section.
• Do not introduce new results in the discussion section. Be wary of mistaking the reiteration of a specific finding for an interpretation because it may confuse the reader. The description of findings [results section] and the interpretation of their significance [discussion section] should be distinct parts of your paper. If you choose to combine the results section and the discussion section into a single narrative, you must be clear in how you report the information discovered and your own interpretation of each finding. This approach is not recommended if you lack experience writing college-level research papers.
• Use of the first person pronoun is generally acceptable. Using first person singular pronouns can help emphasize a point or illustrate a contrasting finding. However, keep in mind that too much use of the first person can actually distract the reader from the main points [i.e., I know you're telling me this--just tell me!].

Analyzing vs. Summarizing. Department of English Writing Guide. George Mason University; Discussion. The Structure, Format, Content, and Style of a Journal-Style Scientific Paper. Department of Biology. Bates College; Hess, Dean R. "How to Write an Effective Discussion." Respiratory Care 49 (October 2004); Kretchmer, Paul. Fourteen Steps to Writing to Writing an Effective Discussion Section. San Francisco Edit, 2003-2008; The Lab Report. University College Writing Centre. University of Toronto; Sauaia, A. et al. "The Anatomy of an Article: The Discussion Section: "How Does the Article I Read Today Change What I Will Recommend to my Patients Tomorrow?” The Journal of Trauma and Acute Care Surgery 74 (June 2013): 1599-1602; Research Limitations & Future Research . Lund Research Ltd., 2012; Summary: Using it Wisely. The Writing Center. University of North Carolina; Schafer, Mickey S. Writing the Discussion. Writing in Psychology course syllabus. University of Florida; Yellin, Linda L. A Sociology Writer's Guide . Boston, MA: Allyn and Bacon, 2009.

Writing Tip

Don’t Over-Interpret the Results!

Interpretation is a subjective exercise. As such, you should always approach the selection and interpretation of your findings introspectively and to think critically about the possibility of judgmental biases unintentionally entering into discussions about the significance of your work. With this in mind, be careful that you do not read more into the findings than can be supported by the evidence you have gathered. Remember that the data are the data: nothing more, nothing less.

MacCoun, Robert J. "Biases in the Interpretation and Use of Research Results." Annual Review of Psychology 49 (February 1998): 259-287; Ward, Paulet al, editors. The Oxford Handbook of Expertise . Oxford, UK: Oxford University Press, 2018.

Another Writing Tip

Don't Write Two Results Sections!

One of the most common mistakes that you can make when discussing the results of your study is to present a superficial interpretation of the findings that more or less re-states the results section of your paper. Obviously, you must refer to your results when discussing them, but focus on the interpretation of those results and their significance in relation to the research problem, not the data itself.

Azar, Beth. "Discussing Your Findings."  American Psychological Association gradPSYCH Magazine (January 2006).

Yet Another Writing Tip

Avoid Unwarranted Speculation!

The discussion section should remain focused on the findings of your study. For example, if the purpose of your research was to measure the impact of foreign aid on increasing access to education among disadvantaged children in Bangladesh, it would not be appropriate to speculate about how your findings might apply to populations in other countries without drawing from existing studies to support your claim or if analysis of other countries was not a part of your original research design. If you feel compelled to speculate, do so in the form of describing possible implications or explaining possible impacts. Be certain that you clearly identify your comments as speculation or as a suggestion for where further research is needed. Sometimes your professor will encourage you to expand your discussion of the results in this way, while others don’t care what your opinion is beyond your effort to interpret the data in relation to the research problem.

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How to Write the Discussion Section of a Research Paper

The discussion section of a research paper analyzes and interprets the findings, provides context, compares them with previous studies, identifies limitations, and suggests future research directions.

Updated on September 15, 2023

Structure your discussion section right, and you’ll be cited more often while doing a greater service to the scientific community. So, what actually goes into the discussion section? And how do you write it?

The discussion section of your research paper is where you let the reader know how your study is positioned in the literature, what to take away from your paper, and how your work helps them. It can also include your conclusions and suggestions for future studies.

First, we’ll define all the parts of your discussion paper, and then look into how to write a strong, effective discussion section for your paper or manuscript.

Discussion section: what is it, what it does

The discussion section comes later in your paper, following the introduction, methods, and results. The discussion sets up your study’s conclusions. Its main goals are to present, interpret, and provide a context for your results.

What is it?

The discussion section provides an analysis and interpretation of the findings, compares them with previous studies, identifies limitations, and suggests future directions for research.

This section combines information from the preceding parts of your paper into a coherent story. By this point, the reader already knows why you did your study (introduction), how you did it (methods), and what happened (results). In the discussion, you’ll help the reader connect the ideas from these sections.

Why is it necessary?

The discussion provides context and interpretations for the results. It also answers the questions posed in the introduction. While the results section describes your findings, the discussion explains what they say. This is also where you can describe the impact or implications of your research.

Adds context for your results

Most research studies aim to answer a question, replicate a finding, or address limitations in the literature. These goals are first described in the introduction. However, in the discussion section, the author can refer back to them to explain how the study's objective was achieved. 

Shows what your results actually mean and real-world implications

The discussion can also describe the effect of your findings on research or practice. How are your results significant for readers, other researchers, or policymakers?

What to include in your discussion (in the correct order)

A complete and effective discussion section should at least touch on the points described below.

Summary of key findings

The discussion should begin with a brief factual summary of the results. Concisely overview the main results you obtained.

Begin with key findings with supporting evidence

Your results section described a list of findings, but what message do they send when you look at them all together?

Your findings were detailed in the results section, so there’s no need to repeat them here, but do provide at least a few highlights. This will help refresh the reader’s memory and help them focus on the big picture.

Read the first paragraph of the discussion section in this article (PDF) for an example of how to start this part of your paper. Notice how the authors break down their results and follow each description sentence with an explanation of why each finding is relevant. 

State clearly and concisely

Following a clear and direct writing style is especially important in the discussion section. After all, this is where you will make some of the most impactful points in your paper. While the results section often contains technical vocabulary, such as statistical terms, the discussion section lets you describe your findings more clearly. 

Interpretation of results

Once you’ve given your reader an overview of your results, you need to interpret those results. In other words, what do your results mean? Discuss the findings’ implications and significance in relation to your research question or hypothesis.

Analyze and interpret your findings

Look into your findings and explore what’s behind them or what may have caused them. If your introduction cited theories or studies that could explain your findings, use these sources as a basis to discuss your results.

For example, look at the second paragraph in the discussion section of this article on waggling honey bees. Here, the authors explore their results based on information from the literature.

Unexpected or contradictory results

Sometimes, your findings are not what you expect. Here’s where you describe this and try to find a reason for it. Could it be because of the method you used? Does it have something to do with the variables analyzed? Comparing your methods with those of other similar studies can help with this task.

Context and comparison with previous work

Refer to related studies to place your research in a larger context and the literature. Compare and contrast your findings with existing literature, highlighting similarities, differences, and/or contradictions.

How your work compares or contrasts with previous work

Studies with similar findings to yours can be cited to show the strength of your findings. Information from these studies can also be used to help explain your results. Differences between your findings and others in the literature can also be discussed here. 

How to divide this section into subsections

If you have more than one objective in your study or many key findings, you can dedicate a separate section to each of these. Here’s an example of this approach. You can see that the discussion section is divided into topics and even has a separate heading for each of them. 

Limitations

Many journals require you to include the limitations of your study in the discussion. Even if they don’t, there are good reasons to mention these in your paper.

Why limitations don’t have a negative connotation

A study’s limitations are points to be improved upon in future research. While some of these may be flaws in your method, many may be due to factors you couldn’t predict.

Examples include time constraints or small sample sizes. Pointing this out will help future researchers avoid or address these issues. This part of the discussion can also include any attempts you have made to reduce the impact of these limitations, as in this study .

How limitations add to a researcher's credibility

Pointing out the limitations of your study demonstrates transparency. It also shows that you know your methods well and can conduct a critical assessment of them.  

Implications and significance

The final paragraph of the discussion section should contain the take-home messages for your study. It can also cite the “strong points” of your study, to contrast with the limitations section.

Restate your hypothesis

Remind the reader what your hypothesis was before you conducted the study. 

How was it proven or disproven?

Identify your main findings and describe how they relate to your hypothesis.

How your results contribute to the literature

Were you able to answer your research question? Or address a gap in the literature?

Future implications of your research

Describe the impact that your results may have on the topic of study. Your results may show, for instance, that there are still limitations in the literature for future studies to address. There may be a need for studies that extend your findings in a specific way. You also may need additional research to corroborate your findings. 

Sample discussion section

This fictitious example covers all the aspects discussed above. Your actual discussion section will probably be much longer, but you can read this to get an idea of everything your discussion should cover.

Our results showed that the presence of cats in a household is associated with higher levels of perceived happiness by its human occupants. These findings support our hypothesis and demonstrate the association between pet ownership and well-being. 

The present findings align with those of Bao and Schreer (2016) and Hardie et al. (2023), who observed greater life satisfaction in pet owners relative to non-owners. Although the present study did not directly evaluate life satisfaction, this factor may explain the association between happiness and cat ownership observed in our sample.

Our findings must be interpreted in light of some limitations, such as the focus on cat ownership only rather than pets as a whole. This may limit the generalizability of our results.

Nevertheless, this study had several strengths. These include its strict exclusion criteria and use of a standardized assessment instrument to investigate the relationships between pets and owners. These attributes bolster the accuracy of our results and reduce the influence of confounding factors, increasing the strength of our conclusions. Future studies may examine the factors that mediate the association between pet ownership and happiness to better comprehend this phenomenon.

This brief discussion begins with a quick summary of the results and hypothesis. The next paragraph cites previous research and compares its findings to those of this study. Information from previous studies is also used to help interpret the findings. After discussing the results of the study, some limitations are pointed out. The paper also explains why these limitations may influence the interpretation of results. Then, final conclusions are drawn based on the study, and directions for future research are suggested.

How to make your discussion flow naturally

If you find writing in scientific English challenging, the discussion and conclusions are often the hardest parts of the paper to write. That’s because you’re not just listing up studies, methods, and outcomes. You’re actually expressing your thoughts and interpretations in words.

• How formal should it be?
• What words should you use, or not use?
• How do you meet strict word limits, or make it longer and more informative?

Always give it your best, but sometimes a helping hand can, well, help. Getting a professional edit can help clarify your work’s importance while improving the English used to explain it. When readers know the value of your work, they’ll cite it. We’ll assign your study to an expert editor knowledgeable in your area of research. Their work will clarify your discussion, helping it to tell your story. Find out more about AJE Editing.

Adam Goulston, PsyD, MS, MBA, MISD, ELS

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How to Write a Discussion Section for a Research Paper

We’ve talked about several useful writing tips that authors should consider while drafting or editing their research papers. In particular, we’ve focused on  figures and legends , as well as the Introduction ,  Methods , and  Results . Now that we’ve addressed the more technical portions of your journal manuscript, let’s turn to the analytical segments of your research article. In this article, we’ll provide tips on how to write a strong Discussion section that best portrays the significance of your research contributions.

What is the Discussion section of a research paper?

In a nutshell,  your Discussion fulfills the promise you made to readers in your Introduction . At the beginning of your paper, you tell us why we should care about your research. You then guide us through a series of intricate images and graphs that capture all the relevant data you collected during your research. We may be dazzled and impressed at first, but none of that matters if you deliver an anti-climactic conclusion in the Discussion section!

Are you feeling pressured? Don’t worry. To be honest, you will edit the Discussion section of your manuscript numerous times. After all, in as little as one to two paragraphs ( Nature ‘s suggestion  based on their 3,000-word main body text limit), you have to explain how your research moves us from point A (issues you raise in the Introduction) to point B (our new understanding of these matters). You must also recommend how we might get to point C (i.e., identify what you think is the next direction for research in this field). That’s a lot to say in two paragraphs!

So, how do you do that? Let’s take a closer look.

What should I include in the Discussion section?

As we stated above, the goal of your Discussion section is to  answer the questions you raise in your Introduction by using the results you collected during your research . The content you include in the Discussions segment should include the following information:

• Remind us why we should be interested in this research project.
• Describe the nature of the knowledge gap you were trying to fill using the results of your study.
• Don’t repeat your Introduction. Instead, focus on why  this  particular study was needed to fill the gap you noticed and why that gap needed filling in the first place.
• Mainly, you want to remind us of how your research will increase our knowledge base and inspire others to conduct further research.
• Clearly tell us what that piece of missing knowledge was.
• Answer each of the questions you asked in your Introduction and explain how your results support those conclusions.
• Make sure to factor in all results relevant to the questions (even if those results were not statistically significant).
• Focus on the significance of the most noteworthy results.
• If conflicting inferences can be drawn from your results, evaluate the merits of all of them.
• Don’t rehash what you said earlier in the Results section. Rather, discuss your findings in the context of answering your hypothesis. Instead of making statements like “[The first result] was this…,” say, “[The first result] suggests [conclusion].”
• Do your conclusions line up with existing literature?
• Discuss whether your findings agree with current knowledge and expectations.
• Keep in mind good persuasive argument skills, such as explaining the strengths of your arguments and highlighting the weaknesses of contrary opinions.
• If you discovered something unexpected, offer reasons. If your conclusions aren’t aligned with current literature, explain.
• Address any limitations of your study and how relevant they are to interpreting your results and validating your findings.
• Make sure to acknowledge any weaknesses in your conclusions and suggest room for further research concerning that aspect of your analysis.
• Make sure your suggestions aren’t ones that should have been conducted during your research! Doing so might raise questions about your initial research design and protocols.
• Similarly, maintain a critical but unapologetic tone. You want to instill confidence in your readers that you have thoroughly examined your results and have objectively assessed them in a way that would benefit the scientific community’s desire to expand our knowledge base.
• Recommend next steps.
• Your suggestions should inspire other researchers to conduct follow-up studies to build upon the knowledge you have shared with them.
• Keep the list short (no more than two).

How to Write the Discussion Section

The above list of what to include in the Discussion section gives an overall idea of what you need to focus on throughout the section. Below are some tips and general suggestions about the technical aspects of writing and organization that you might find useful as you draft or revise the contents we’ve outlined above.

Technical writing elements

• Embrace active voice because it eliminates the awkward phrasing and wordiness that accompanies passive voice.
• Use the present tense, which should also be employed in the Introduction.
• Sprinkle with first person pronouns if needed, but generally, avoid it. We want to focus on your findings.
• Maintain an objective and analytical tone.

Discussion section organization

• Keep the same flow across the Results, Methods, and Discussion sections.
• We develop a rhythm as we read and parallel structures facilitate our comprehension. When you organize information the same way in each of these related parts of your journal manuscript, we can quickly see how a certain result was interpreted and quickly verify the particular methods used to produce that result.
• Notice how using parallel structure will eliminate extra narration in the Discussion part since we can anticipate the flow of your ideas based on what we read in the Results segment. Reducing wordiness is important when you only have a few paragraphs to devote to the Discussion section!
• Within each subpart of a Discussion, the information should flow as follows: (A) conclusion first, (B) relevant results and how they relate to that conclusion and (C) relevant literature.
• End with a concise summary explaining the big-picture impact of your study on our understanding of the subject matter. At the beginning of your Discussion section, you stated why  this  particular study was needed to fill the gap you noticed and why that gap needed filling in the first place. Now, it is time to end with “how your research filled that gap.”

Discussion Part 1: Summarizing Key Findings

Begin the Discussion section by restating your  statement of the problem  and briefly summarizing the major results. Do not simply repeat your findings. Rather, try to create a concise statement of the main results that directly answer the central research question that you stated in the Introduction section . This content should not be longer than one paragraph in length.

Many researchers struggle with understanding the precise differences between a Discussion section and a Results section . The most important thing to remember here is that your Discussion section should subjectively evaluate the findings presented in the Results section, and in relatively the same order. Keep these sections distinct by making sure that you do not repeat the findings without providing an interpretation.

Phrase examples: Summarizing the results

• The findings indicate that …
• These results suggest a correlation between A and B …
• The data present here suggest that …
• An interpretation of the findings reveals a connection between…

Discussion Part 2: Interpreting the Findings

What do the results mean? It may seem obvious to you, but simply looking at the figures in the Results section will not necessarily convey to readers the importance of the findings in answering your research questions.

The exact structure of interpretations depends on the type of research being conducted. Here are some common approaches to interpreting data:

• Identifying correlations and relationships in the findings
• Explaining whether the results confirm or undermine your research hypothesis
• Giving the findings context within the history of similar research studies
• Discussing unexpected results and analyzing their significance to your study or general research
• Offering alternative explanations and arguing for your position

Organize the Discussion section around key arguments, themes, hypotheses, or research questions or problems. Again, make sure to follow the same order as you did in the Results section.

Discussion Part 3: Discussing the Implications

In addition to providing your own interpretations, show how your results fit into the wider scholarly literature you surveyed in the  literature review section. This section is called the implications of the study . Show where and how these results fit into existing knowledge, what additional insights they contribute, and any possible consequences that might arise from this knowledge, both in the specific research topic and in the wider scientific domain.

Questions to ask yourself when dealing with potential implications:

• Do your findings fall in line with existing theories, or do they challenge these theories or findings? What new information do they contribute to the literature, if any? How exactly do these findings impact or conflict with existing theories or models?
• What are the practical implications on actual subjects or demographics?
• What are the methodological implications for similar studies conducted either in the past or future?

Your purpose in giving the implications is to spell out exactly what your study has contributed and why researchers and other readers should be interested.

Phrase examples: Discussing the implications of the research

• These results confirm the existing evidence in X studies…
• The results are not in line with the foregoing theory that…
• This experiment provides new insights into the connection between…
• These findings present a more nuanced understanding of…
• While previous studies have focused on X, these results demonstrate that Y.

Step 4: Acknowledging the limitations

All research has study limitations of one sort or another. Acknowledging limitations in methodology or approach helps strengthen your credibility as a researcher. Study limitations are not simply a list of mistakes made in the study. Rather, limitations help provide a more detailed picture of what can or cannot be concluded from your findings. In essence, they help temper and qualify the study implications you listed previously.

Study limitations can relate to research design, specific methodological or material choices, or unexpected issues that emerged while you conducted the research. Mention only those limitations directly relate to your research questions, and explain what impact these limitations had on how your study was conducted and the validity of any interpretations.

Possible types of study limitations:

• Insufficient sample size for statistical measurements
• Lack of previous research studies on the topic
• Methods/instruments/techniques used to collect the data
• Limited access to data
• Time constraints in properly preparing and executing the study

After discussing the study limitations, you can also stress that your results are still valid. Give some specific reasons why the limitations do not necessarily handicap your study or narrow its scope.

Phrase examples: Limitations sentence beginners

• “There may be some possible limitations in this study.”
• “The findings of this study have to be seen in light of some limitations.”
•  “The first limitation is the…The second limitation concerns the…”
•  “The empirical results reported herein should be considered in the light of some limitations.”
• “This research, however, is subject to several limitations.”
• “The primary limitation to the generalization of these results is…”
• “Nonetheless, these results must be interpreted with caution and a number of limitations should be borne in mind.”

Discussion Part 5: Giving Recommendations for Further Research

Based on your interpretation and discussion of the findings, your recommendations can include practical changes to the study or specific further research to be conducted to clarify the research questions. Recommendations are often listed in a separate Conclusion section , but often this is just the final paragraph of the Discussion section.

Suggestions for further research often stem directly from the limitations outlined. Rather than simply stating that “further research should be conducted,” provide concrete specifics for how future can help answer questions that your research could not.

Phrase examples: Recommendation sentence beginners

• Further research is needed to establish …
• There is abundant space for further progress in analyzing…
• A further study with more focus on X should be done to investigate…
• Further studies of X that account for these variables must be undertaken.

Consider Receiving Professional Language Editing

As you edit or draft your research manuscript, we hope that you implement these guidelines to produce a more effective Discussion section. And after completing your draft, don’t forget to submit your work to a professional proofreading and English editing service like Wordvice, including our manuscript editing service for  paper editing , cover letter editing , SOP editing , and personal statement proofreading services. Language editors not only proofread and correct errors in grammar, punctuation, mechanics, and formatting but also improve terms and revise phrases so they read more naturally. Wordvice is an industry leader in providing high-quality revision for all types of academic documents.

For additional information about how to write a strong research paper, make sure to check out our full  research writing series !

Wordvice Writing Resources

• How to Write a Research Paper Introduction 
• Which Verb Tenses to Use in a Research Paper
• How to Write an Abstract for a Research Paper
• How to Write a Research Paper Title
• Useful Phrases for Academic Writing
• Common Transition Terms in Academic Papers
• Active and Passive Voice in Research Papers
• 100+ Verbs That Will Make Your Research Writing Amazing
• Tips for Paraphrasing in Research Papers

Additional Academic Resources

•   Guide for Authors.  (Elsevier)
•  How to Write the Results Section of a Research Paper.  (Bates College)
•   Structure of a Research Paper.  (University of Minnesota Biomedical Library)
•   How to Choose a Target Journal  (Springer)
•   How to Write Figures and Tables  (UNC Writing Center)

Library Guides

Dissertations 5: findings, analysis and discussion: home.

• Results/Findings

Alternative Structures

The time has come to show and discuss the findings of your research. How to structure this part of your dissertation? 

Dissertations can have different structures, as you can see in the dissertation  structure  guide.

Dissertations organised by sections

Many dissertations are organised by sections. In this case, we suggest three options. Note that, if within your course you have been instructed to use a specific structure, you should do that. Also note that sometimes there is considerable freedom on the structure, so you can come up with other structures too. 

A) More common for scientific dissertations and quantitative methods:

- Results chapter 

- Discussion chapter

Example: 

• Introduction
• Literature review
• Methodology
• (Recommendations)

if you write a scientific dissertation, or anyway using quantitative methods, you will have some  objective  results that you will present in the Results chapter. You will then interpret the results in the Discussion chapter.  

B) More common for qualitative methods

- Analysis chapter. This can have more descriptive/thematic subheadings.

- Discussion chapter. This can have more descriptive/thematic subheadings.

• Case study of Company X (fashion brand) environmental strategies 
• Successful elements
• Lessons learnt
• Criticisms of Company X environmental strategies 
• Possible alternatives

C) More common for qualitative methods

- Analysis and discussion chapter. This can have more descriptive/thematic titles.

• Case study of Company X (fashion brand) environmental strategies 

If your dissertation uses qualitative methods, it is harder to identify and report objective data. Instead, it may be more productive and meaningful to present the findings in the same sections where you also analyse, and possibly discuss, them. You will probably have different sections dealing with different themes. The different themes can be subheadings of the Analysis and Discussion (together or separate) chapter(s). 

Thematic dissertations

If the structure of your dissertation is thematic ,  you will have several chapters analysing and discussing the issues raised by your research. The chapters will have descriptive/thematic titles. 

• Background on the conflict in Yemen (2004-present day)
• Classification of the conflict in international law  
• International law violations
• Options for enforcement of international law
• Next: Results/Findings >>
• Last Updated: Aug 4, 2023 2:17 PM
• URL: https://libguides.westminster.ac.uk/c.php?g=696975

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Organizing Academic Research Papers: 8. The Discussion

• Purpose of Guide
• Design Flaws to Avoid
• Glossary of Research Terms
• Narrowing a Topic Idea
• Broadening a Topic Idea
• Extending the Timeliness of a Topic Idea
• Academic Writing Style
• Choosing a Title
• Making an Outline
• Paragraph Development
• Executive Summary
• Background Information
• The Research Problem/Question
• Theoretical Framework
• Citation Tracking
• Content Alert Services
• Evaluating Sources
• Primary Sources
• Secondary Sources
• Tertiary Sources
• What Is Scholarly vs. Popular?
• Qualitative Methods
• Quantitative Methods
• Using Non-Textual Elements
• Limitations of the Study
• Common Grammar Mistakes
• Avoiding Plagiarism
• Footnotes or Endnotes?
• Further Readings
• Annotated Bibliography
• Dealing with Nervousness
• Using Visual Aids
• Grading Someone Else's Paper
• How to Manage Group Projects
• Multiple Book Review Essay
• Reviewing Collected Essays
• About Informed Consent
• Writing Field Notes
• Writing a Policy Memo
• Writing a Research Proposal
• Acknowledgements

The purpose of the discussion is to interpret and describe the significance of your findings in light of what was already known about the research problem being investigated, and to explain any new understanding or fresh insights about the problem after you've taken the findings into consideration. The discussion will always connect to the introduction by way of the research questions or hypotheses you posed and the literature you reviewed, but it does not simply repeat or rearrange the introduction; the discussion should always explain how your study has moved the reader's understanding of the research problem forward from where you left them at the end of the introduction.

Importance of a Good Discussion

This section is often considered the most important part of a research paper because it most effectively demonstrates your ability as a researcher to think critically about an issue, to develop creative solutions to problems based on the findings, and to formulate a deeper, more profound understanding of the research problem you are studying.

The discussion section is where you explore the underlying meaning of your research , its possible implications in other areas of study, and the possible improvements that can be made in order to further develop the concerns of your research.

This is the section where you need to present the importance of your study and how it may be able to contribute to and/or fill existing gaps in the field. If appropriate, the discussion section is also where you state how the findings from your study revealed new gaps in the literature that had not been previously exposed or adequately described.

This part of the paper is not strictly governed by objective reporting of information but, rather, it is where you can engage in creative thinking about issues through evidence-based interpretation of findings. This is where you infuse your results with meaning.

Kretchmer, Paul. Fourteen Steps to Writing to Writing an Effective Discussion Section . San Francisco Edit, 2003-2008.

Structure and Writing Style

I.  General Rules

These are the general rules you should adopt when composing your discussion of the results :

• Do not be verbose or repetitive.
• Be concise and make your points clearly.
• Avoid using jargon.
• Follow a logical stream of thought.
• Use the present verb tense, especially for established facts; however, refer to specific works and references in the past tense.
• If needed, use subheadings to help organize your presentation or to group your interpretations into themes.

II.  The Content

The content of the discussion section of your paper most often includes :

• Explanation of results : comment on whether or not the results were expected and present explanations for the results; go into greater depth when explaining findings that were unexpected or especially profound. If appropriate, note any unusual or unanticipated patterns or trends that emerged from your results and explain their meaning.
• References to previous research : compare your results with the findings from other studies, or use the studies to support a claim. This can include re-visiting key sources already cited in your literature review section, or, save them to cite later in the discussion section if they are more important to compare with your results than being part of the general research you cited to provide context and background information.
• Deduction : a claim for how the results can be applied more generally. For example, describing lessons learned, proposing recommendations that can help improve a situation, or recommending best practices.
• Hypothesis : a more general claim or possible conclusion arising from the results [which may be proved or disproved in subsequent research].

III. Organization and Structure

Keep the following sequential points in mind as you organize and write the discussion section of your paper:

• Think of your discussion as an inverted pyramid. Organize the discussion from the general to the specific, linking your findings to the literature, then to theory, then to practice [if appropriate].
• Use the same key terms, mode of narration, and verb tense [present] that you used when when describing the research problem in the introduction.
• Begin by briefly re-stating the research problem you were investigating and answer all of the research questions underpinning the problem that you posed in the introduction.
• Describe the patterns, principles, and relationships shown by each major findings and place them in proper perspective. The sequencing of providing this information is important; first state the answer, then the relevant results, then cite the work of others. If appropriate, refer the reader to a figure or table to help enhance the interpretation of the data. The order of interpreting each major finding should be in the same order as they were described in your results section.
• A good discussion section includes analysis of any unexpected findings. This paragraph should begin with a description of the unexpected finding, followed by a brief interpretation as to why you believe it appeared and, if necessary, its possible significance in relation to the overall study. If more than one unexpected finding emerged during the study, describe each them in the order they appeared as you gathered the data.
• Before concluding the discussion, identify potential limitations and weaknesses. Comment on their relative importance in relation to your overall interpretation of the results and, if necessary, note how they may affect the validity of the findings. Avoid using an apologetic tone; however, be honest and self-critical.
• The discussion section should end with a concise summary of the principal implications of the findings regardless of statistical significance. Give a brief explanation about why you believe the findings and conclusions of your study are important and how they support broader knowledge or understanding of the research problem. This can be followed by any recommendations for further research. However, do not offer recommendations which could have been easily addressed within the study. This demonstrates to the reader you have inadequately examined and interpreted the data.

IV.  Overall Objectives

The objectives of your discussion section should include the following: I.  Reiterate the Research Problem/State the Major Findings

Briefly reiterate for your readers the research problem or problems you are investigating and the methods you used to investigate them, then move quickly to describe the major findings of the study. You should write a direct, declarative, and succinct proclamation of the study results.

II.  Explain the Meaning of the Findings and Why They are Important

No one has thought as long and hard about your study as you have. Systematically explain the meaning of the findings and why you believe they are important. After reading the discussion section, you want the reader to think about the results [“why hadn’t I thought of that?”]. You don’t want to force the reader to go through the paper multiple times to figure out what it all means. Begin this part of the section by repeating what you consider to be your most important finding first.

III.  Relate the Findings to Similar Studies

No study is so novel or possesses such a restricted focus that it has absolutely no relation to other previously published research. The discussion section should relate your study findings to those of other studies, particularly if questions raised by previous studies served as the motivation for your study, the findings of other studies support your findings [which strengthens the importance of your study results], and/or they point out how your study differs from other similar studies. IV.  Consider Alternative Explanations of the Findings

It is important to remember that the purpose of research is to discover and not to prove . When writing the discussion section, you should carefully consider all possible explanations for the study results, rather than just those that fit your prior assumptions or biases.

V.  Acknowledge the Study’s Limitations

It is far better for you to identify and acknowledge your study’s limitations than to have them pointed out by your professor! Describe the generalizability of your results to other situations, if applicable to the method chosen, then describe in detail problems you encountered in the method(s) you used to gather information. Note any unanswered questions or issues your study did not address, and.... VI.  Make Suggestions for Further Research

Although your study may offer important insights about the research problem, other questions related to the problem likely remain unanswered. Moreover, some unanswered questions may have become more focused because of your study. You should make suggestions for further research in the discussion section.

NOTE: Besides the literature review section, the preponderance of references to sources in your research paper are usually found in the discussion section . A few historical references may be helpful for perspective but most of the references should be relatively recent and included to aid in the interpretation of your results and/or linked to similar studies. If a study that you cited disagrees with your findings, don't ignore it--clearly explain why the study's findings differ from yours.

V.  Problems to Avoid

• Do not waste entire sentences restating your results . Should you need to remind the reader of the finding to be discussed, use "bridge sentences" that relate the result to the interpretation. An example would be: “The lack of available housing to single women with children in rural areas of Texas suggests that...[then move to the interpretation of this finding].”
• Recommendations for further research can be included in either the discussion or conclusion of your paper but do not repeat your recommendations in the both sections.
• Do not introduce new results in the discussion. Be wary of mistaking the reiteration of a specific finding for an interpretation.
• Use of the first person is acceptable, but too much use of the first person may actually distract the reader from the main points.

Analyzing vs. Summarizing. Department of English Writing Guide. George Mason University; Discussion . The Structure, Format, Content, and Style of a Journal-Style Scientific Paper. Department of Biology. Bates College; Hess, Dean R. How to Write an Effective Discussion. Respiratory Care 49 (October 2004); Kretchmer, Paul. Fourteen Steps to Writing to Writing an Effective Discussion Section . San Francisco Edit, 2003-2008; The Lab Report . University College Writing Centre. University of Toronto; Summary: Using it Wisely . The Writing Center. University of North Carolina; Schafer, Mickey S. Writing the Discussion . Writing in Psychology course syllabus. University of Florida; Yellin, Linda L. A Sociology Writer's Guide. Boston, MA: Allyn and Bacon, 2009.

Writing Tip

Don’t Overinterpret the Results!

Interpretation is a subjective exercise. Therefore, be careful that you do not read more into the findings than can be supported by the evidence you've gathered. Remember that the data are the data: nothing more, nothing less.

Another Writing Tip

Don't Write Two Results Sections!

One of the most common mistakes that you can make when discussing the results of your study is to present a superficial interpretation of the findings that more or less re-states the results section of your paper. Obviously, you must refer to your results when discussing them, but focus on the interpretion of those results, not just the data itself.

Azar, Beth. Discussing Your Findings.  American Psychological Association gradPSYCH Magazine (January 2006)

Yet Another Writing Tip

Avoid Unwarranted Speculation!

The discussion section should remain focused on the findings of your study. For example, if you studied the impact of foreign aid on increasing levels of education among the poor in Bangladesh, it's generally not appropriate to speculate about how your findings might apply to populations in other countries without drawing from existing studies to support your claim. If you feel compelled to speculate, be certain that you clearly identify your comments as speculation or as a suggestion for where further research is needed. Sometimes your professor will encourage you to expand the discussion in this way, while others don’t care what your opinion is beyond your efforts to interpret the data.

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Thank you. payment completed., you will receive an email from us to confirm your registration, please click the link in the email to activate your account., there was error during payment, orcid profile found in public registry, download history, how to write the analysis and discussion chapters in qualitative (ssah) research.

• Charlesworth Author Services
• 11 November, 2021

While it is more common for Science, Technology, Engineering and Mathematics (STEM) researchers to write separate, distinct chapters for their data/ results and analysis/ discussion , the same sections can feel less clearly defined for a researcher in Social Sciences, Arts and Humanities (SSAH). This article will look specifically at some useful approaches to writing the analysis and discussion chapters in qualitative/SSAH research.

Note : Most of the differences in approaches to research, writing, analysis and discussion come down, ultimately, to differences in epistemology – how we approach, create and work with knowledge in our respective fields. However, this is a vast topic that deserves a separate discussion.

Look for emerging themes and patterns

The ‘results’ of qualitative research can sometimes be harder to pinpoint than in quantitative research. You’re not dealing with definitive numbers and results in the same way as, say, a scientist conducting experiments that produce measurable data. Instead, most qualitative researchers explore prominent, interesting themes and patterns emerging from their data – that could comprise interviews, textual material or participant observation, for example. 

You may find that your data presents a huge number of themes, issues and topics, all of which you might find equally significant and interesting. In fact, you might find yourself overwhelmed by the many directions that your research could take, depending on which themes you choose to study in further depth. You may even discover issues and patterns that you had not expected , that may necessitate having to change or expand the research focus you initially started off with.

It is crucial at this point not to panic. Instead, try to enjoy the many possibilities that your data is offering you. It can be useful to remind yourself at each stage of exactly what you are trying to find out through this research.

What exactly do you want to know? What knowledge do you want to generate and share within your field?

Then, spend some time reflecting upon each of the themes that seem most interesting and significant, and consider whether they are immediately relevant to your main, overarching research objectives and goals.

Suggestion: Don’t worry too much about structure and flow at the early stages of writing your discussion . It would be a more valuable use of your time to fully explore the themes and issues arising from your data first, while also reading widely alongside your writing (more on this below). As you work more intimately with the data and develop your ideas, the overarching narrative and connections between those ideas will begin to emerge. Trust that you’ll be able to draw those links and craft the structure organically as you write.

Let your data guide you

A key characteristic of qualitative research is that the researchers allow their data to ‘speak’ and guide their research and their writing. Instead of insisting too strongly upon the prominence of specific themes and issues and imposing their opinions and beliefs upon the data, a good qualitative researcher ‘listens’ to what the data has to tell them.

Again, you might find yourself having to address unexpected issues or your data may reveal things that seem completely contradictory to the ideas and theories you have worked with so far. Although this might seem worrying, discovering these unexpected new elements can actually make your research much richer and more interesting. 

Suggestion: Allow yourself to follow those leads and ask new questions as you work through your data. These new directions could help you to answer your research questions in more depth and with greater complexity; or they could even open up other avenues for further study, either in this or future research.

Work closely with the literature

As you analyse and discuss the prominent themes, arguments and findings arising from your data, it is very helpful to maintain a regular and consistent reading practice alongside your writing. Return to the literature that you’ve already been reading so far or begin to check out new texts, studies and theories that might be more appropriate for working with any new ideas and themes arising from your data.

Reading and incorporating relevant literature into your writing as you work through your analysis and discussion will help you to consistently contextualise your research within the larger body of knowledge. It will be easier to stay focused on what you are trying to say through your research if you can simultaneously show what has already been said on the subject and how your research and data supports, challenges or extends those debates. By drawing from existing literature , you are setting up a dialogue between your research and prior work, and highlighting what this research has to add to the conversation.

Suggestion : Although it might sometimes feel tedious to have to blend others’ writing in with yours, this is ultimately the best way to showcase the specialness of your own data, findings and research . Remember that it is more difficult to highlight the significance and relevance of your original work without first showing how that work fits into or responds to existing studies. 

In conclusion

The discussion chapters form the heart of your thesis and this is where your unique contribution comes to the forefront. This is where your data takes centre-stage and where you get to showcase your original arguments, perspectives and knowledge. To do this effectively needs you to explore the original themes and issues arising from and within the data, while simultaneously contextualising these findings within the larger, existing body of knowledge of your specialising field. By striking this balance, you prove the two most important qualities of excellent qualitative research : keen awareness of your field and a firm understanding of your place in it.

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Home » Research Results Section – Writing Guide and Examples

Research Results Section – Writing Guide and Examples

Table of Contents

Research Results

Research results refer to the findings and conclusions derived from a systematic investigation or study conducted to answer a specific question or hypothesis. These results are typically presented in a written report or paper and can include various forms of data such as numerical data, qualitative data, statistics, charts, graphs, and visual aids.

Results Section in Research

The results section of the research paper presents the findings of the study. It is the part of the paper where the researcher reports the data collected during the study and analyzes it to draw conclusions.

In the results section, the researcher should describe the data that was collected, the statistical analysis performed, and the findings of the study. It is important to be objective and not interpret the data in this section. Instead, the researcher should report the data as accurately and objectively as possible.

Structure of Research Results Section

The structure of the research results section can vary depending on the type of research conducted, but in general, it should contain the following components:

• Introduction: The introduction should provide an overview of the study, its aims, and its research questions. It should also briefly explain the methodology used to conduct the study.
• Data presentation : This section presents the data collected during the study. It may include tables, graphs, or other visual aids to help readers better understand the data. The data presented should be organized in a logical and coherent way, with headings and subheadings used to help guide the reader.
• Data analysis: In this section, the data presented in the previous section are analyzed and interpreted. The statistical tests used to analyze the data should be clearly explained, and the results of the tests should be presented in a way that is easy to understand.
• Discussion of results : This section should provide an interpretation of the results of the study, including a discussion of any unexpected findings. The discussion should also address the study’s research questions and explain how the results contribute to the field of study.
• Limitations: This section should acknowledge any limitations of the study, such as sample size, data collection methods, or other factors that may have influenced the results.
• Conclusions: The conclusions should summarize the main findings of the study and provide a final interpretation of the results. The conclusions should also address the study’s research questions and explain how the results contribute to the field of study.
• Recommendations : This section may provide recommendations for future research based on the study’s findings. It may also suggest practical applications for the study’s results in real-world settings.

Outline of Research Results Section

The following is an outline of the key components typically included in the Results section:

I. Introduction

• A brief overview of the research objectives and hypotheses
• A statement of the research question

II. Descriptive statistics

• Summary statistics (e.g., mean, standard deviation) for each variable analyzed
• Frequencies and percentages for categorical variables

III. Inferential statistics

• Results of statistical analyses, including tests of hypotheses
• Tables or figures to display statistical results

IV. Effect sizes and confidence intervals

• Effect sizes (e.g., Cohen’s d, odds ratio) to quantify the strength of the relationship between variables
• Confidence intervals to estimate the range of plausible values for the effect size

V. Subgroup analyses

• Results of analyses that examined differences between subgroups (e.g., by gender, age, treatment group)

VI. Limitations and assumptions

• Discussion of any limitations of the study and potential sources of bias
• Assumptions made in the statistical analyses

VII. Conclusions

• A summary of the key findings and their implications
• A statement of whether the hypotheses were supported or not
• Suggestions for future research

Example of Research Results Section

An Example of a Research Results Section could be:

• This study sought to examine the relationship between sleep quality and academic performance in college students.
• Hypothesis : College students who report better sleep quality will have higher GPAs than those who report poor sleep quality.
• Methodology : Participants completed a survey about their sleep habits and academic performance.

II. Participants

• Participants were college students (N=200) from a mid-sized public university in the United States.
• The sample was evenly split by gender (50% female, 50% male) and predominantly white (85%).
• Participants were recruited through flyers and online advertisements.

III. Results

• Participants who reported better sleep quality had significantly higher GPAs (M=3.5, SD=0.5) than those who reported poor sleep quality (M=2.9, SD=0.6).
• See Table 1 for a summary of the results.
• Participants who reported consistent sleep schedules had higher GPAs than those with irregular sleep schedules.

IV. Discussion

• The results support the hypothesis that better sleep quality is associated with higher academic performance in college students.
• These findings have implications for college students, as prioritizing sleep could lead to better academic outcomes.
• Limitations of the study include self-reported data and the lack of control for other variables that could impact academic performance.

V. Conclusion

• College students who prioritize sleep may see a positive impact on their academic performance.
• These findings highlight the importance of sleep in academic success.
• Future research could explore interventions to improve sleep quality in college students.

Example of Research Results in Research Paper :

Our study aimed to compare the performance of three different machine learning algorithms (Random Forest, Support Vector Machine, and Neural Network) in predicting customer churn in a telecommunications company. We collected a dataset of 10,000 customer records, with 20 predictor variables and a binary churn outcome variable.

Our analysis revealed that all three algorithms performed well in predicting customer churn, with an overall accuracy of 85%. However, the Random Forest algorithm showed the highest accuracy (88%), followed by the Support Vector Machine (86%) and the Neural Network (84%).

Furthermore, we found that the most important predictor variables for customer churn were monthly charges, contract type, and tenure. Random Forest identified monthly charges as the most important variable, while Support Vector Machine and Neural Network identified contract type as the most important.

Overall, our results suggest that machine learning algorithms can be effective in predicting customer churn in a telecommunications company, and that Random Forest is the most accurate algorithm for this task.

Example 3 :

Title : The Impact of Social Media on Body Image and Self-Esteem

Abstract : This study aimed to investigate the relationship between social media use, body image, and self-esteem among young adults. A total of 200 participants were recruited from a university and completed self-report measures of social media use, body image satisfaction, and self-esteem.

Results: The results showed that social media use was significantly associated with body image dissatisfaction and lower self-esteem. Specifically, participants who reported spending more time on social media platforms had lower levels of body image satisfaction and self-esteem compared to those who reported less social media use. Moreover, the study found that comparing oneself to others on social media was a significant predictor of body image dissatisfaction and lower self-esteem.

Conclusion : These results suggest that social media use can have negative effects on body image satisfaction and self-esteem among young adults. It is important for individuals to be mindful of their social media use and to recognize the potential negative impact it can have on their mental health. Furthermore, interventions aimed at promoting positive body image and self-esteem should take into account the role of social media in shaping these attitudes and behaviors.

Importance of Research Results

Research results are important for several reasons, including:

• Advancing knowledge: Research results can contribute to the advancement of knowledge in a particular field, whether it be in science, technology, medicine, social sciences, or humanities.
• Developing theories: Research results can help to develop or modify existing theories and create new ones.
• Improving practices: Research results can inform and improve practices in various fields, such as education, healthcare, business, and public policy.
• Identifying problems and solutions: Research results can identify problems and provide solutions to complex issues in society, including issues related to health, environment, social justice, and economics.
• Validating claims : Research results can validate or refute claims made by individuals or groups in society, such as politicians, corporations, or activists.
• Providing evidence: Research results can provide evidence to support decision-making, policy-making, and resource allocation in various fields.

How to Write Results in A Research Paper

Here are some general guidelines on how to write results in a research paper:

• Organize the results section: Start by organizing the results section in a logical and coherent manner. Divide the section into subsections if necessary, based on the research questions or hypotheses.
• Present the findings: Present the findings in a clear and concise manner. Use tables, graphs, and figures to illustrate the data and make the presentation more engaging.
• Describe the data: Describe the data in detail, including the sample size, response rate, and any missing data. Provide relevant descriptive statistics such as means, standard deviations, and ranges.
• Interpret the findings: Interpret the findings in light of the research questions or hypotheses. Discuss the implications of the findings and the extent to which they support or contradict existing theories or previous research.
• Discuss the limitations : Discuss the limitations of the study, including any potential sources of bias or confounding factors that may have affected the results.
• Compare the results : Compare the results with those of previous studies or theoretical predictions. Discuss any similarities, differences, or inconsistencies.
• Avoid redundancy: Avoid repeating information that has already been presented in the introduction or methods sections. Instead, focus on presenting new and relevant information.
• Be objective: Be objective in presenting the results, avoiding any personal biases or interpretations.

When to Write Research Results

Here are situations When to Write Research Results”

• After conducting research on the chosen topic and obtaining relevant data, organize the findings in a structured format that accurately represents the information gathered.
• Once the data has been analyzed and interpreted, and conclusions have been drawn, begin the writing process.
• Before starting to write, ensure that the research results adhere to the guidelines and requirements of the intended audience, such as a scientific journal or academic conference.
• Begin by writing an abstract that briefly summarizes the research question, methodology, findings, and conclusions.
• Follow the abstract with an introduction that provides context for the research, explains its significance, and outlines the research question and objectives.
• The next section should be a literature review that provides an overview of existing research on the topic and highlights the gaps in knowledge that the current research seeks to address.
• The methodology section should provide a detailed explanation of the research design, including the sample size, data collection methods, and analytical techniques used.
• Present the research results in a clear and concise manner, using graphs, tables, and figures to illustrate the findings.
• Discuss the implications of the research results, including how they contribute to the existing body of knowledge on the topic and what further research is needed.
• Conclude the paper by summarizing the main findings, reiterating the significance of the research, and offering suggestions for future research.

Purpose of Research Results

The purposes of Research Results are as follows:

• Informing policy and practice: Research results can provide evidence-based information to inform policy decisions, such as in the fields of healthcare, education, and environmental regulation. They can also inform best practices in fields such as business, engineering, and social work.
• Addressing societal problems : Research results can be used to help address societal problems, such as reducing poverty, improving public health, and promoting social justice.
• Generating economic benefits : Research results can lead to the development of new products, services, and technologies that can create economic value and improve quality of life.
• Supporting academic and professional development : Research results can be used to support academic and professional development by providing opportunities for students, researchers, and practitioners to learn about new findings and methodologies in their field.
• Enhancing public understanding: Research results can help to educate the public about important issues and promote scientific literacy, leading to more informed decision-making and better public policy.
• Evaluating interventions: Research results can be used to evaluate the effectiveness of interventions, such as treatments, educational programs, and social policies. This can help to identify areas where improvements are needed and guide future interventions.
• Contributing to scientific progress: Research results can contribute to the advancement of science by providing new insights and discoveries that can lead to new theories, methods, and techniques.
• Informing decision-making : Research results can provide decision-makers with the information they need to make informed decisions. This can include decision-making at the individual, organizational, or governmental levels.
• Fostering collaboration : Research results can facilitate collaboration between researchers and practitioners, leading to new partnerships, interdisciplinary approaches, and innovative solutions to complex problems.

Advantages of Research Results

Some Advantages of Research Results are as follows:

• Improved decision-making: Research results can help inform decision-making in various fields, including medicine, business, and government. For example, research on the effectiveness of different treatments for a particular disease can help doctors make informed decisions about the best course of treatment for their patients.
• Innovation : Research results can lead to the development of new technologies, products, and services. For example, research on renewable energy sources can lead to the development of new and more efficient ways to harness renewable energy.
• Economic benefits: Research results can stimulate economic growth by providing new opportunities for businesses and entrepreneurs. For example, research on new materials or manufacturing techniques can lead to the development of new products and processes that can create new jobs and boost economic activity.
• Improved quality of life: Research results can contribute to improving the quality of life for individuals and society as a whole. For example, research on the causes of a particular disease can lead to the development of new treatments and cures, improving the health and well-being of millions of people.

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• How to Write a Discussion Section | Tips & Examples

How to Write a Discussion Section | Tips & Examples

Published on 21 August 2022 by Shona McCombes . Revised on 25 October 2022.

The discussion section is where you delve into the meaning, importance, and relevance of your results .

It should focus on explaining and evaluating what you found, showing how it relates to your literature review , and making an argument in support of your overall conclusion . It should not be a second results section .

There are different ways to write this section, but you can focus your writing around these key elements:

• Summary: A brief recap of your key results
• Interpretations: What do your results mean?
• Implications: Why do your results matter?
• Limitations: What can’t your results tell us?
• Recommendations: Avenues for further studies or analyses

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Table of contents

What not to include in your discussion section, step 1: summarise your key findings, step 2: give your interpretations, step 3: discuss the implications, step 4: acknowledge the limitations, step 5: share your recommendations, discussion section example.

There are a few common mistakes to avoid when writing the discussion section of your paper.

• Don’t introduce new results: You should only discuss the data that you have already reported in your results section .
• Don’t make inflated claims: Avoid overinterpretation and speculation that isn’t directly supported by your data.
• Don’t undermine your research: The discussion of limitations should aim to strengthen your credibility, not emphasise weaknesses or failures.

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Start this section by reiterating your research problem  and concisely summarising your major findings. Don’t just repeat all the data you have already reported – aim for a clear statement of the overall result that directly answers your main  research question . This should be no more than one paragraph.

Many students struggle with the differences between a discussion section and a results section . The crux of the matter is that your results sections should present your results, and your discussion section should subjectively evaluate them. Try not to blend elements of these two sections, in order to keep your paper sharp.

• The results indicate that …
• The study demonstrates a correlation between …
• This analysis supports the theory that …
• The data suggest  that …

The meaning of your results may seem obvious to you, but it’s important to spell out their significance for your reader, showing exactly how they answer your research question.

The form of your interpretations will depend on the type of research, but some typical approaches to interpreting the data include:

• Identifying correlations , patterns, and relationships among the data
• Discussing whether the results met your expectations or supported your hypotheses
• Contextualising your findings within previous research and theory
• Explaining unexpected results and evaluating their significance
• Considering possible alternative explanations and making an argument for your position

You can organise your discussion around key themes, hypotheses, or research questions, following the same structure as your results section. Alternatively, you can also begin by highlighting the most significant or unexpected results.

• In line with the hypothesis …
• Contrary to the hypothesised association …
• The results contradict the claims of Smith (2007) that …
• The results might suggest that x . However, based on the findings of similar studies, a more plausible explanation is x .

As well as giving your own interpretations, make sure to relate your results back to the scholarly work that you surveyed in the literature review . The discussion should show how your findings fit with existing knowledge, what new insights they contribute, and what consequences they have for theory or practice.

Ask yourself these questions:

• Do your results support or challenge existing theories? If they support existing theories, what new information do they contribute? If they challenge existing theories, why do you think that is?
• Are there any practical implications?

Your overall aim is to show the reader exactly what your research has contributed, and why they should care.

• These results build on existing evidence of …
• The results do not fit with the theory that …
• The experiment provides a new insight into the relationship between …
• These results should be taken into account when considering how to …
• The data contribute a clearer understanding of …
• While previous research has focused on  x , these results demonstrate that y .

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Even the best research has its limitations. Acknowledging these is important to demonstrate your credibility. Limitations aren’t about listing your errors, but about providing an accurate picture of what can and cannot be concluded from your study.

Limitations might be due to your overall research design, specific methodological choices , or unanticipated obstacles that emerged during your research process.

Here are a few common possibilities:

• If your sample size was small or limited to a specific group of people, explain how generalisability is limited.
• If you encountered problems when gathering or analysing data, explain how these influenced the results.
• If there are potential confounding variables that you were unable to control, acknowledge the effect these may have had.

After noting the limitations, you can reiterate why the results are nonetheless valid for the purpose of answering your research question.

• The generalisability of the results is limited by …
• The reliability of these data is impacted by …
• Due to the lack of data on x , the results cannot confirm …
• The methodological choices were constrained by …
• It is beyond the scope of this study to …

Based on the discussion of your results, you can make recommendations for practical implementation or further research. Sometimes, the recommendations are saved for the conclusion .

Suggestions for further research can lead directly from the limitations. Don’t just state that more studies should be done – give concrete ideas for how future work can build on areas that your own research was unable to address.

• Further research is needed to establish …
• Future studies should take into account …
• Avenues for future research include …

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• Open access
• Published: 09 May 2024

An exploration into the causal relationships between educational attainment, intelligence, and wellbeing: an observational and two-sample Mendelian randomisation study

• J. M. Armitage 1 ,
• R. E. Wootton 2 , 3 ,
• O. S. P. Davis 4 &
• C. M. A. Haworth 2  

npj Mental Health Research volume  3 , Article number:  23 ( 2024 ) Cite this article

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• Epidemiology
• Genetics research

Educational attainment is associated with a range of positive outcomes, yet its impact on wellbeing is unclear, and complicated by high correlations with intelligence. We use genetic and observational data to investigate for the first time, whether educational attainment and intelligence are causally and independently related to wellbeing. Results from our multivariable Mendelian randomisation demonstrated a positive causal impact of a genetic predisposition to higher educational attainment on wellbeing that remained after accounting for intelligence, and a negative impact of intelligence that was independent of educational attainment. Observational analyses suggested that these associations may be subject to sex differences, with benefits to wellbeing greater for females who attend higher education compared to males. For intelligence, males scoring more highly on measures related to happiness were those with lower intelligence. Our findings demonstrate a unique benefit for wellbeing of staying in school, over and above improving cognitive abilities, with benefits likely to be greater for females compared to males.

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Introduction.

In most societies, education provides young people with the knowledge, skills, and socialisation necessary to prepare for adult life. The number of years spent in schooling can therefore be an important determinant of later outcomes and functioning, as evidenced by greater occupational status and income, marriage, and health 1 , 2 . The extent to which some of these relationships are causal, however, remains less clear. Educational attainment has been shown to causally impact smoking, sedentary behaviours, and Body Mass Index 3 , as well as the risk of suicide attempts, insomnia, and major depressive disorder 4 . Yet also fundamental to health and success is wellbeing 5 , but the causal impact of educational attainment on wellbeing remains unexplored.

Wellbeing is broadly defined as relating to feelings of satisfaction and happiness 6 . Observational studies investigating the impact of educational attainment on wellbeing have produced mixed results, with evidence to suggest both direct 7 and indirect effects 2 , as well as both positive and negative influences 8 . Indirect effects of education refer to those that occur via mechanisms other than education itself, such as through income, employment, marriage, children, or health 2 . Positive indirect influences of educational attainment on wellbeing have been noted largely through income, with males and females experiencing benefits of education through increased earnings 2 . Some sex differences have been noted for other indirect paths, like employment, whereby the wellbeing of educated males but not females is heightened through being employed 2 . When these indirect paths are not accounted for, associations between educational attainment and wellbeing have been shown to be negative 2 , suggesting that education exerts its benefits through many different channels. Most of the findings to date however, are based on samples from Australia, with just one study to date investigating associations in a UK sample 9 . This study found little effect of educational attainment on happiness, and little impact of a reform that raised the school leaving age. No study has jointly considered the role of intelligence and schooling on overall wellbeing.

Intelligence is often used to refer to the many facets of cognitive functioning, including memory and learning, processing speed, as well as abstract, verbal, and spatial reasoning 10 . These abilities are all interrelated and highly correlated with educational attainment 11 , yet observational findings have suggested associations with wellbeing may differ to those found for educational attainment. In particular, associations between intelligence and wellbeing are often positive, but switch to negative after accounting for other correlated factors like income and parental education 12 , 13 . This has been suggested to reflect the greater expectations that come with being highly intelligent and a higher earner 12 . The correlational nature of these studies, however, does not permit causal inferences for either the direct or indirect effects.

Determining whether associations are causal or driven by unobserved or imprecisely measured confounders is crucial to establishing true and unbiased effects. Mendelian randomisation (MR) is a study design that uses summary-level genetic data to assess potentially causal relationships 14 . The methods of MR enable control over both confounding and reverse causality, and can be extended to multivariable MR when teasing apart the combined and independent effects of highly correlated variables, like educational attainment and intelligence.

So far, MR studies have revealed that despite their high correlation, intelligence and educational attainment exert independent causal effects on some health and economic outcomes 3 , 11 . Extending such findings to wellbeing could therefore help to inform best practice for maximising optimal functioning. In particular, if associations between educational attainment and wellbeing are largely accounted for by intelligence, policy makers would benefit from focusing less on keeping students in higher education, and more on improving cognitive abilities. If, however, educational attainment exerts a direct positive impact on wellbeing, policy makers would benefit from extending requirements to remain in further education.

Few studies to date have studied causal associations with wellbeing 15 , and even fewer have made use of the latest genetic instrument for wellbeing 16 . This instrument combines four wellbeing related traits (life satisfaction, positive affect, neuroticism, and depressive symptoms), which are referred to collectively as the wellbeing spectrum. This phenotype has been associated with more genetic signals than previous genetic analyses based on positive affect and life satisfaction alone 17 . The first part of this study therefore makes use of this instrument for wellbeing in univariable and multivariable MR to test for the first time, whether educational attainment and intelligence are causally and independently related to wellbeing. Bidirectional associations are also explored as findings have shown that wellbeing not only results from successful outcomes, but it also precedes them 18 . Understanding whether associations work both ways could therefore highlight important paths to improving overall functioning.

One drawback of using the MR design is that estimates are not time bound, meaning implications for intervention may be less clear. The second part of this study therefore supplements genetic findings using longitudinal observational data. The aim is to understand the impact of educational attainment and intelligence on wellbeing in emerging adulthood, a critical life stage for establishing identity and adult mental health. Such analyses aimed to also further scrutinise the relationship between educational attainment and wellbeing to clarify possible sex differences 8 , 19 , non-linear trends 20 , and moderating effects of intelligence.

Univariable MR testing causal associations between educational attainment and intelligence

Prior to investigating effects on wellbeing, univariable MR was first used to confirm the bidirectional relationship between educational attainment and intelligence 11 . Analyses revealed strong causal effects of educational attainment on intelligence, and vice versa (Supplementary Table 1 ). Effect sizes were twofold greater for educational attainment on intelligence, aligning with previous findings 11 . There was also strong evidence of heterogeneity in the causal estimates for both directions, also replicating previous findings.

Univariable MR testing causal associations between educational attainment and wellbeing

Univariable MR analyses exploring total causal effects of educational attainment on wellbeing provided evidence of a small positive impact (Fig. 1 ). For every standard deviation (SD) increase in years of schooling, which equates to 3.6 years of schooling, there was a 0.057 (95% CI = 0.042, 0.074) increase in wellbeing, as assessed using the wellbeing spectrum. There was also evidence of a causal impact of wellbeing on educational attainment. Analyses revealed that a SD increase in wellbeing predicted a 0.206 (95% CI = 0.071, 0.341) increase in the number of years schooling (see Supplementary Table 2 ).

The main analysis is the inverse variance weighted estimate. The MR-Egger, Weighted median, and weighted mode represent sensitivity analyses.

Neither of these findings replicated using MR-Egger (see Fig. 1 ), which as explained in the methods, is unlikely to be a result of directional pleiotropy as the MR-Egger intercept did not differ from zero. The funnel plots also provided evidence of balanced pleiotropy and there was no evidence in the forest plots to suggest that associations were strongly driven by one single-nucleotide polymorphism (SNP) (see Supplementary Figs. 1 and 2 ). Steiger filtering revealed that all educational attainment SNPs were more associated with educational attainment than wellbeing, and sensitivity analyses removing 4 wellbeing SNPs that explained more of the variance in educational attainment revealed consistent results (see Supplementary Table 3 ), suggesting minimal bias from reverse causation. Instead, given the large confidence intervals and the low regression dilution statistic (See Supplementary Table 4 ), it is likely that MR-Egger results can be accounted for by measurement error.

Analyses predicting subjective happiness and life satisfaction using the trait-specific estimates from the model-averaging GWAMA 16 revealed largely consistent findings, with effect sizes most similar to the full wellbeing spectrum for analyses predicting life satisfaction (see Supplementary Tables 5 to 6). As anticipated, results for neuroticism and depression produced associations in the opposite direction (Supplementary Tables 7 and 8 ).

Univariable MR testing causal associations between intelligence and wellbeing

Univariable MR analyses investigating associations between intelligence and wellbeing revealed no causal effects of intelligence on wellbeing, but evidence of a causal impact of wellbeing on intelligence (Fig. 1 ). Effect sizes were similar to those found for educational attainment, with a SD increase in wellbeing predictive of a 0.199 (95% CI = 0.014, 0.390) increase in intelligence (Supplementary Table 2 ). This did not replicate using MR-Egger, but was consistent across other sensitivity analyses. As per analyses on educational attainment, findings also provided no evidence of bias due to directional horizontal pleiotropy (see Supplementary Fig. 3 ). Analyses conducted after removing 11 wellbeing SNPs following Steiger filtering also revealed largely consistent results (see Supplementary Table 3 ).

Multivariable MR

Results from the multivariable MR analysis revealed independent causal effects of both educational attainment and intelligence on wellbeing (see Fig. 2 ), however, findings were in the opposite direction to one another. For educational attainment, a one SD increase in years of schooling (3.6 years) predicted a 0.103 (95% CI = 0.05, 0.16) increase in wellbeing, controlling for the effects of intelligence, while intelligence predicted a 0.04 (95% CI = −0.08, −0.01) decrease in wellbeing, controlling for years of schooling. These findings were both larger than those found in the univariable models and were generated despite relatively weak instruments (F-statistic = 7.94 for intelligence and F-statistic = 7.23 for educational attainment). These F-statistics are lower than those in the univariable analyses due to estimating the impact of the SNPs on one exposure, conditioning on the other 21 .

This figure shows that years of schooling has positive independent (multivariable) and total (univariable) causal effects on wellbeing. In contrast, intelligence has negative independent (multivariable) but not total (univariable) causal effects on wellbeing.

Findings from the multivariable MR-Egger analyses produced the same pattern of results as above for both exposures (Table 1 ), and the MR-Egger intercept provided no evidence of directional pleiotropy. All univariable and multivariable MR findings also remained after adjustment for multiple testing using the Benjamini–Hochberg procedure 22 . Raw p-values are therefore reported to ensure consistency with the wider MR literature 23 .

To further test the bi-directional relationship between educational attainment and wellbeing, we performed an additional multivariable MR analysis in which we investigated intelligence and well-being on years of schooling. Findings revealed that wellbeing was independently associated with years of schooling after accounting for intelligence, predicting a 0.193 (95% CI = 0.07, 0.31) increase in years of schooling, and intelligence was independently associated with years of schooling after accounting for wellbeing, predicting a 0.44 (95% CI = 0.40, 0.48) increase in years of schooling.

Observational findings

Descriptives.

Observational analyses were conducted using the Avon Longitudinal Study of Parents and Children (ALSPAC 24 ). Among participants with data on educational attainment, intelligence, and wellbeing, approximately 66.7% had a university degree. Individuals who had a university degree scored significantly higher on the intelligence test at 8 years old (mean = 112.21, SD = 14.75, range = 62−148) compared to individuals without a university degree (mean = 99.07, SD = 14.93, range = 45−138), according to a Welch two sample t test, t(1879) = −22.2, p  < 0.001.

Subjective happiness scores in the samples averaged 4.89 (range = 1 to 7), while life satisfaction scores averaged 24.25 (range = 5 to 35). Happiness scores were not significantly different among those with (mean = 4.89, SD = 1.27) or without (mean = 4.89, SD = 1.31) a university degree, but those with a degree had significantly higher life satisfaction scores (mean = 24.78, SD = 6.65) compared to those without a degree (mean = 23.09, SD = 7.36), t(2591) = 6.99, p  < 0.001. Further information to wellbeing, educational attainment, and intelligence can be found in the supplementary (see Supplementary Tables 9 and 10 ).

Testing linear associations

Analyses revealed that higher educational attainment, indexed by having at least a university degree, was not associated with subjective happiness, but did predict increased life satisfaction (Table 2 ). After including adjustments for main and interactive effects of sex, findings showed that females who completed university had significantly higher life satisfaction than those who did not, with differences appearing greater than those noted between males with and without a degree (see Fig. 3 ). For subjective happiness, the direction of effects was the opposite for the two sexes, with females more likely to experience positive benefits to their subjective happiness if they completed university, whereas male graduates were more at risk of lower subjective happiness (see Fig. 3 ). These opposing results likely explain the absence of effects noted in models unadjusted for sex.

This figure shows differences between male and female subjective happiness and life satisfaction for those with higher intelligence (1 SD above the mean), and differences in subjective happiness for those with and without a university degree, and for life satisfaction for those with a degree.

Unadjusted models exploring the impact of intelligence revealed that as intelligence scores increased, subjective happiness declined, while life satisfaction increased (Table 2 ). After adding an interaction term between intelligence and sex, associations with life satisfaction remained, and associations with subjective happiness became positive. This suggests a moderating effect of sex, which is supported by plots of the findings (see Fig. 3 ). Males scoring more highly on the subjective happiness scale were those with lower intelligence scores (see Fig. 3 ).

All findings, including those for intelligence and educational attainment, remained after adjustment for multiple testing, and all findings replicated after adjustment for attrition (see Table 2 for adjusted results, and Supplementary Table 11 for unstandardised estimates).

Testing non-linear associations and moderating effects

When testing the relationship between intelligence and wellbeing for non-linearity, there was no evidence to suggest that non-linear models fit the data better than the linear models (see Supplementary Table 12 ). There was also no clear evidence to suggest moderating effects of educational attainment, with no strong interactions found between educational attainment and intelligence in analyses predicting subjective happiness (β = 0.001, SE = 0.003, p  = 0.721) or life satisfaction (β = 0.024, SE = 0.018, p  = 0.197). Analyses also revealed that family income is unlikely to explain associations between educational attainment and wellbeing, and between intelligence and wellbeing (see Supplementary Table 13 ).

This study was the first to combine genetic and observational data to test for causal associations between educational attainment, intelligence, and wellbeing. The MR results suggest that the relationship between educational attainment and wellbeing is bidirectional, with the magnitude of effects greater for wellbeing on educational attainment than vice versa. Findings also revealed that the causal and protective effect of staying in school is independent of intelligence, but may be greater for females relative to males.

Investigations into intelligence showed that wellbeing has a positive causal impact on intelligence, but intelligence a negative impact on wellbeing. These negative effects were only found after adjusting for educational attainment, implying either a direct and independent role, or that independent effects are in the opposite direction to the combined effects. Observational findings confirmed the direction of this effect for associations with subjective happiness but not life satisfaction, however, as per educational attainment, there were underlying sex differences. Together the findings stress the importance of staying in education over and above cognitive abilities for wellbeing.

Our MR finding that individuals who are genetically inclined to stay on and complete more years of schooling will have greater wellbeing was implied in previous observational studies 1 , 19 but not in a previous MR study 9 . The previous MR study found little impact of educational attainment on happiness. This was not found using positive affect in our MR analyses, however, previous findings do align with the current observational findings. These suggested that completing more years of schooling may positively impact life satisfaction but not happiness, however, males and females may be affected differently. The previous MR study by Davies and colleagues 9 adjusted for sex differences, which likely explains the different results to our MR findings.

Similar sex differences to our study have been reported previously in observational studies, with associations between schooling and happiness stronger among females relative to males 8 . This study in combination with the present findings suggest that females gain more to their wellbeing from continuing their education compared to males. One explanation for this could be due to underlying differences in socialisation.

Studies have shown that socialising has a greater impact on happiness among females relative to males 25 . Education has been referred to as an “institutionalised form of social resource” 4 and is an important determinant of social relations 26 . Spending more years in education therefore brings increased opportunities for not only developing cognitive skills, but also wider cultural awareness and social networks 4 . It is possible that this increased socialisation explains why females respond more positively to prolonged education than males.

Another possibility is that spending more years in education alters habits, practices, and health-related choices more favourably among females. Individuals genetically inclined to complete more years of schooling are more likely to engage in vigorous physical activity and less likely to engage in sedentary behaviour 3 . Educated females but not males have also been shown to be at a reduced risk of obesity 27 . Given the positive associations between BMI and wellbeing 15 , it is possible that sex differences in health behaviours contribute to the differential gains in the impact of education on wellbeing. Further research should attempt to understand these sex differences further to ensure more targeted support for males and females in schools. It is possible that males who remain in higher education would benefit from additional wellbeing support compared to females.

Our findings for intelligence add to the literature by providing causal evidence of the previously demonstrated negative associations with wellbeing 12 , 13 . In line with the current study, previous research also revealed a switch from positive to negative associations between intelligence and wellbeing after controlling for later-life outcomes like education attainment 12 . It has previously been suggested that this “residual” effect of intelligence on wellbeing may reflect the greater expectations of those high in intelligence with more education 12 . However, unlike previous observational research, the current findings were able to more directly rule out confounding of educational attainment to establish a causal and independent role for intelligence. The findings suggest it is possible that while educational attainment serves a protective function for those high in intelligence, the negative impact of lack of education is most detrimental for those with high intelligence. In other words, intelligence may negatively impact wellbeing among those who do not stay in education and who may be viewed as under-achievers.

The direct negative impact of intelligence on wellbeing may also reflect an underlying predisposition towards rumination and worry that is often reported among highly intelligent individuals 28 . It has been suggested that those high in intelligence have exaggerated physiological, neurological, and psychological responses to environmental stress that puts them at increased risk of mental health problems 29 . These reactions are more prevalent among those at the extreme end of the intelligence scale, which may explain why analyses using intelligence, but not educational attainment, produced negative associations with wellbeing. It is likely that such pupils may feel increased academic strain and pressure, and would benefit from additional wellbeing support at school.

It is also possible that different health behaviours underlie those high in intelligence compared to those who chose to stay on in higher education. Genetic studies of intelligence have revealed that unlike educational attainment, a genetic disposition towards higher intelligence is associated with reduced vigorous physical activity 3 . Intense physical activity is positively related to wellbeing across the lifespan 29 , 30 and may therefore explain the positive association between education and wellbeing, and negative association between education and intelligence.

The finding that higher wellbeing positively predicts both intelligence and years of schooling aligns with previous research which has shown that adolescents with increased wellbeing tend to perform better academically 31 . By using a causal design, the current study reduces bias from reverse causality and confounding to provide support for improving wellbeing in schools 32 . The finding that wellbeing and educational attainment have a bidirectional relationship suggests that interventions aimed at improving wellbeing in schools could encourage further education and improved cognitive skills, and these in turn, could improve wellbeing in later life. Similarly, by keeping students engaged in school and increasing the likelihood of further education, wellbeing is likely to be improved, which could further increase the potential for higher education. Together these findings highlight their reciprocal relationship.

This study used both genetic and observational data to triangulate and provide further insight into associations between educational attainment, intelligence, and wellbeing. By using both univariable and multivariable MR, our study was able to investigate whether causal relations reflect direct or indirect effects. This is particularly important as the longer an individual spends in schooling, the greater their adult intelligence 33 . Thus, by using a multivariable design it was possible to separate such effects. Observational analyses were also adjusted for attrition and selective participation, helping to reduce the potential for bias. Some limitations of this study, however should be noted.

The first is that the MR analyses used GWAS data that included large samples from the UK Biobank 16 . Participants in the UK Biobank are generally more educated than the general population, which may have reduced the generalisability of the causal effect estimates. Given the cost of education in several of the studies contributing to the GWASs (that identify the genetic instrument), the effects of education may also be picking up socioeconomic effects. Previous MR studies on educational attainment have shown that after reweighting for sample selection, there is minimal impact of educational biases on the overall estimates 9 . Nevertheless, it is important that findings are interpreted in light of this potential selection bias, and that researchers are mindful of possible confounding by socioeconomic status.

The MR findings should also be interpreted in light of assortative mating and dynastic effects. Findings have shown that individuals are more likely to select a mate with a similar educational background 34 and intelligence level 35 . This can lead to enriched educational or intelligence associated SNPs, as previously shown 36 , and may inflate subsequent MR estimates 37 . Dynastic effects can also bias MR estimates. Research has shown that parental educational level and family socioeconomic status predict the educational outcomes of their offspring 38 . Such dynastic effects as well as assortative mating can be investigated using a within-family design that adjusts for transmitted and non-transmitted alleles 39 . However, this was not possible in the current study as sufficient genotyped family data were not available. Nevertheless, a consistent result across MR estimates and observational analyses reduces the likelihood that MR estimates are confounding by characteristics that are transmitted across generations.

Further limitations of the current MR findings are that effect sizes relating to wellbeing are difficult to interpret. This is due to the nature of the meta-analytic findings which use multiple measures and phenotypes. While this is useful in testing whether or not there are possible causal effects, additional analyses using other methods are needed to estimate effect sizes. In addition, MR results using the intelligence GWAS from ref. 40 . used UKBiobank samples that conditioned on socioeconomic status. Sensitivity analyses conducted after removing these samples produced consistent results but it is important that main analyses are interpreted with some caution.

The current observational findings should also be interpreted in light of some limitations. The only available information relating to educational attainment was whether or not individuals had at least a university degree. While detailed information has recently been collected on educational qualifications in ALSPAC for this age group, this data has not yet been released. Analyses were therefore unable to explore non-linear or cumulative effects of years of schooling, meaning it is not possible to ascertain whether a particular level of education confers an advantage or disadvantage for wellbeing. Such knowledge could have important implications for guiding and supporting students who continue their education to post-graduate level. Nevertheless, previous findings have shown that using years of education or a “Graduates versus non-graduates” proxy of education makes minimal difference to overall results 2 .

Other possible limitations are that wellbeing was assessed at 26 years, four years after the average person graduates from university. While research has shown that the gap in happiness between the educated and less educated widens as individuals age, this gap does not appear until around 35 years of age 41 . This is suggested to reflect a time in which uncertainties and student loan debt repayments may be reduced. Further longitudinal research should explore trajectories of mental health and wellbeing following completion of higher education to gain a more in-depth understanding of the long-term outcomes of education. This could also aid insight into differences noted between associations with educational attainment and either subjective happiness or life satisfaction.

Unlike subjective happiness, life satisfaction captures cognitive evaluations of one’s life. When reporting on life satisfaction, participants are therefore required to draw comparisons between their actual and desired life situation. It is possible that positive effects of educational attainment and intelligence on life satisfaction therefore reflect the fulfilment of years of hard work. Indeed, findings have shown that factors related to individual prosperity, including income and possessions, predict increased life satisfaction but not feelings of happiness 42 . Measures of subjective happiness do not require cognitive processing but capture immediate and accessible feelings of pleasure. Such feelings may be less influenced by the accumulation of factors gained from education and more influenced by immediate sensations like perceived general health. Young adults in the current study may have been transitioning into their new role in either employment, parenthood, or another life domain, and thus have been exposed to increased stress. This could have resulted in lower happiness levels at that time. Further investigation into the role of educational attainment on subjective happiness at earlier or later stages of life may lead to different estimates.

Overall, the findings from this study suggest important avenues for further research. While steps were taken to triangulate and improve the interpretation of the MR results, future research should consider using repeated measures of wellbeing to understand how causal effects may unfold over time. Research should also attempt to understand the factors underlying positive effects of educational attainment on wellbeing, and should consider additional mediating factors. This will be key to further dissecting the causal pathway and could reveal subtle differences between predictors of life satisfaction and subjective happiness 42 , and factors specific to wellbeing at specific life stages.

The degree to which educational attainment is driven by educational achievement (the grades you get) or other non-cognitive skills also requires further investigation. Unlike educational attainment, educational achievement is assessed using test and examination results. While highly correlated with cognitive ability 43 , educational outcomes reflect more than just intelligence 44 , 45 . These non-cognitive abilities, such as self-control, emotion regulation, grit and motivation, may explain why some remain in education where others do not, even if they do not excel academically or intellectually. Understanding more about the educational attainment phenotype and its drivers could yield important insight into why effects of educational attainment and intelligence may differ. This could have implications for both intervention and policy.

A final priority for further study is to ensure replication in other countries and among other ancestries. The average number of years spent in education differs worldwide 46 , and there exists significant global variability in wellbeing across sex 47 . Wellbeing has also changed over time, with some evidence to suggest population declines in subjective happiness 48 . The current observational findings are limited to individuals born between 1991 and 1992. Those following the typical education trajectory would therefore have graduated from university in 2012 or 2013. Research has shown that the time in which an individual graduates can predict wellbeing, with those graduating in times of higher unemployment more likely to have lower life satisfaction 49 . This needs to be accounted for when investigating more recent effects of educational attainment, particularly in light of the COVID-19 pandemic and on-going economic uncertainty. The pandemic caused significant distress to many due to unprecedented changes to economic situations and education systems. The implications of which for young people’s future education is not certain but remains a public health priority. Our findings add further weight to this and stress the importance of staying in school over and above cognitive abilities for good wellbeing.

To conclude, our findings demonstrate a unique benefit for wellbeing of staying in school, over and above improving cognitive abilities. Benefits are likely to be greater for females relative to males, suggesting other interventions may be necessary to improve the wellbeing of males who remain in education. The finding that intelligence has a direct negative impact on wellbeing suggests that students high in intelligence may be at risk of increased academic stress, and may therefore benefit from additional wellbeing support to alleviate these pressures. Schools aiming to improve student wellbeing more widely should focus less on improving cognitive abilities, and more on keeping students engaged in school.

Principles of Mendelian Randomisation

Mendelian Randomisation (MR) is an instrumental variable method that uses natural genetic variation to study the causal effect of an exposure on an outcome 50 , 51 . The principles rely on Mendel’s law of segregation and independent assortment such that individuals inherit alleles that are independent of confounding traits and are randomly allocated at conception. Much like a Randomised Control Trial (RCT), the random segregation of participants, or alleles in the case of MR, are independent of any confounding variables, meaning confounding factors are assumed to be balanced across the two groups (see Fig. 4 taken from Davey Smith and Ebrahim 52 ). Any differences that arise are therefore attributed to causal effects, providing that certain assumptions are met.

Analogy between Mendelian randomisation (MR) and randomsed controlled trial (RCT).

Assumptions of MR

MR is based on the three key assumptions; (1) The instrument must be robustly associated with the exposure of interest; (2) The instrument must not be associated with factors that may confound the association between the exposure and the outcome; (3) If there is a causal effect of an exposure on an outcome, then genetic variants associated with the exposure should also predict the outcome, through the exposure only. If this last assumption is violated and genetic variants act on a second exposure that influences the outcome, this is known as pleiotropy. Some forms of pleiotropy, such as vertical pleiotropy, satisfy the principles of MR and do not inflict bias. This is because such pleiotropy occurs when genetic variants predict a primary and a secondary exposure which are both on the same causal pathway to the outcome (see Fig. 5a ). This is the mechanism assumed in MR. If, however, the genetic variants act on the second exposure through a pathway other than through the primary exposure, this is known as horizontal pleiotropy (see Fig. 4b ). This can lead to biased estimates in MR if not accounted for.

A Directed Acyclic Graph (DAG) demonstrating vertical and horizontal pleiotropy in associations between an exposure and an outcome.

Many of the above assumptions rarely hold in MR, particularly where large numbers of genetic variants, whose functions are often unknown, are used as instruments 53 . This is because these can make pleiotropic pathways more likely. Fortunately, there are measures that can be taken to improve the reliability of MR, including running alternative versions of MR that make different assumptions about pleiotropy, as well as multivariable MR 54 .

In traditional univariable MR, where the total effects of an exposure are investigated on an outcome, a second highly correlated exposure that is influenced by the same genetic variants would violate the assumptions of MR. An extension of MR, known as multivariable MR, allows exposures to be causally related provided the effects of the genetic variants are independent of the outcome 21 . Such an approach allows investigation into whether the two correlated exposures are causally related to the outcome, and whether such associations are independent of one another.

Genetic data

To conduct an MR study, researchers must decide whether to use a one- or two-sample approach. Two sample MR requires two independent study samples, one is used to provide estimates for associations between genetic markers and the exposure, and the other for associations between genetic markers and the outcome. Benefit of using a two-sample approach include that it provides more power and pleiotropy sensitivity analyses. However, it comes with the additional assumption that the two samples represent separate participants from similar populations 3 . Genome wide association studies for this MR study were therefore carefully selected to ensure sample overlap was minimal.

Data for educational attainment was taken from the Years of Schooling GWAS 17 This meta-analysed summary statistics from 64 samples, covering 15 different countries, all of European descent. Years of schooling were mapped and categorised across samples according to the 1997 International Standard Classification of Education (ISCED) scale 55 . This initial analysis identified 74 single nucleotide polymorphisms (SNPs) that were independently associated with years of schooling ( m  = 14.3, SD  = 3.6) after adjustment for sex and ancestry principal components. A polygenic score constructed from the measured SNPs explained around 3.2% of the variance in educational attainment.

GWAS data were subsequently combined with those of 111,349 participants from the UK-Biobank (UKB) 17 . This replication resulted in a GWAS sample of 405,072 participants, and increased the number of associated genetic variants from 74 to 162. The current study, however, used data from the original discovery GWAS as opposed to the larger replication to reduce sample overlap (from 34% to 9%). Analyses were repeated using the larger replication cohort to ensure consistency (see Supplementary Table 14 ). It is important to note that while a larger and more recent meta-GWAS is available for educational attainment 56 , these samples largely overlap with those of the wellbeing GWAS used in the present study. Sensitivity analyses using MRLap, which is a method that accounts for potential sample overlap 57 , suggested estimates for univariable MR were similar to those using SNP estimates including23andMe (see Supplementary Table 15 ).

For intelligence, data were derived from the largest GWAS of intelligence to date ( n  = 269,867) 40 . This study was based on 14 cohorts that assessed intelligence using various neurocognitive tests of logical, verbal, spatial, and technical ability. Despite the different assessments, all cohorts extracted a single sum, mean, or factor score which was used to index general intelligence. Correlations across cohort measures were on average 0.67. Overall, the GWAS identified 242 lead SNPs associated with intelligence at genome-wide significance. Polygenic scores derived from these SNPs explained up to 5.2% of the variance in intelligence in four independent samples 40 .

Wellbeing data were taken from a multivariate genome-wide-association meta-analysis (GWAMA) of wellbeing 16 . This used the widely documented genetic overlap between four traits, life satisfaction, positive affect, depression, and neuroticism, to identify genetic variants associated with wellbeing. Two novel and complementary methods: An N-weighted multivariate GWAMA (N-GWAMA) and a model-averaging GWAMA (MA-GWAMA) approach were used. The N-GWAMA investigated a unitary effect of all traits, referred to collectively as the wellbeing spectrum, while MA-GWAMA relaxed the assumption of a unitary effect to study trait-specific estimates. Findings from the N-GWAMA revealed 231 independent SNPs associated with the wellbeing spectrum, while the MA-GWAMA resulted in 148 independent loci for life satisfaction and 191 for positive affect. The incremental R 2 for these SNPs was slightly lower than those derived from the N-GWAMA, therefore the current study used estimates related to the wellbeing spectrum. In particular, polygenic scores constructed from the N-GWAMA and MA-GWAMA explained 0.94% and 0.92% of the variance in life satisfaction and 1.10% and 1.06 of the variance in positive affect. Follow-up analyses were carried out to explore specific estimates for the individual wellbeing components (Supplementary Tables 5 to 8 ).

Approximately 11% of the individuals from the wellbeing GWAS sample were also included in the educational attainment GWAS sample 17 , and around 8% in the intelligence GWAS sample 40 . This overlap is similar to previous MR studies investigating wellbeing 15 .

Genetic instrument construction

Genetic variants included were those that passed the genome-wide level of significance ( p  < 5 × 10 −8 ) and were independent. Clumping was performed to ensure independence at r 2 < 0.001 within an 10,000 kb window. Data harmonisation was then performed using the TwoSampleMR package 58 , where allele frequencies were used to align palindromic SNPs down to a minor allele frequency of 0.42. For univariable MR analyses, instrument strength was calculated using an F statistic greater than 10 59 . For multivariable analyses, the Sanderson–Windmeijer partial F-statistic was used 60 .

Following data harmonisation, analyses exploring causal effects of educational attainment on intelligence used a total of 63 SNPs ( F  = 38.44), while analyses exploring the impact of intelligence on educational attainment used 144 SNPs ( F  = 42.72). Note that an F greater than 10 indicates the analysis is unlikely to suffer from bias due to a weak instrument 61 .

Analyses exploring the total causal effects of educational attainment on wellbeing used 54 SNPs that were available following data harmonisation ( F  = 38.88). For analyses exploring total causal effects of wellbeing on educational attainment, there were 147 SNPs available following data harmonisation ( F  = 40.78). Of these, 90 SNPs (61.2%) formed part of the original 232 SNPs identified in the wellbeing GWAS.

Analyses testing causal effects of intelligence on wellbeing used 126 SNPs following data harmonisation ( F  = 43.35). Analyses testing causal effects of wellbeing on intelligence used 128 SNPs ( F  = 40.83), of which 71 (55.4%) formed part of the original 232 lead SNPs in wellbeing GWAS.

As per the univariable MR analyses, variants were selected for the multivariable MR if they passed the genome-wide level of significance ( p  < 0.001 and 10,000 kb were used as conditions of clumping), and palindromic SNPs were aligned using a minor allele frequency of 0.42. Note that SNPS selected for multivariable MR are the exposure SNPs that are associated with the outcome, conditional on the other exposure. This resulted in 151 SNPs available for the multivariable MR analysis, a full list of which can be found in the Supplementary Table 14 .

Statistical analyses

Univariable mr.

Univariable MR analyses were conducted using the TwoSampleMR package, version 0.5.6 in R 61 . These analyses were used to test for causal associations between educational attainment and intelligence, and between wellbeing and the two exposures: educational attainment and intelligence. All univariable analyses were run using four different versions of two-sample MR. The inverse variance weighted (IVW) method was used as the main analysis as this assumes no directional pleiotropy, with sensitivity analyses including mendelian randomisation-Egger (MR-Egger), weighted median, and weighted mode. These have each been described in detail elsewhere 62 , 63 , but were included here as each makes a different assumption about pleiotropy. A consistent effect across the different methods can therefore provide more confidence that the assumptions are valid. In addition, a simulation extrapolation (SIMEX) correction was applied to MR-Egger estimates to correct coefficients where regression dilution was lower than 0.9 54 . A consistent result across these provides further support for a true causal effect.

To further assess the robustness of the results, heterogeneity was estimated using Cochran’s Q 64 . Tests of heterogeneity reveal how consistent the causal estimate is across SNPs, which can be used as an indicator of pleiotropy. Based on previous findings, it was anticipated that heterogeneity would be high 11 . High pleiotropy will only impose bias if it is directional and horizontal, and therefore the MR Egger intercept was used to check for evidence of directional/horizontal pleiotropy. A multiplicative random effects IVW regression was also chosen to adjust for this. Steiger filtering was conducted where more than one SNP explained more of the variance in the outcome than the exposure, which could suggest possible reverse causation.

As a sensitivity check, we repeated analyses using the intelligence GWAS without the UKB. This was because samples using the UKB conditioned on socioeconomic status.

Multivariable MR was then used to estimate the direct effects of educational attainment and intelligence on wellbeing, independent of the other. We also performed two additional multivariable MR analyses: (1) intelligence and well-being on years of schooling; (2) years of schooling and well-being on intelligence. All analyses were run using the MVMR package 21 and the MendelianRandomization package (Rees et al., 2017) in R. As per the univariable analyses, heterogeneity was checked using Cochran’s Q, and conditional F statistics using the Sanderson–Windmeijer partial F-statistic 60 .

Observational analyses

Observational data were taken from the Avon Longitudinal Study of Parents and Children (ALSPAC 24 ) a prospective cohort study based in the United Kingdom. Pregnant women residing in the former Avon area were enrolled if they had an expected delivery date between April 1991 and December 1992 65 . The initial cohort consisted of 14,062 live births but has since increased to 14,901 children following further recruitment 66 . Data gathered from 22 years and onwards were collected and managed using REDCap electronic data capture tools hosted at the University of Bristol 67 . REDCap (Research Electronic Data Capture) is a secure, web-based software platform designed to support data capture for research studies. Please note that the study website contains details of all the data that is available through a fully searchable data dictionary and variable search tool ( http://www.bristol.ac.uk/alspac/researchers/our-data/ ).

Participants included in the current study were those who completed a measure of educational attainment at age 26, an intelligence assessment at age 8, as well as relevant wellbeing measures at age 26 (see Supplementary Tables 10 , 15 , and 16 for further information about the measures, and Supplementary Fig. 4 for a flowchart of data availability). In total, there were 2844 participants with complete data on intelligence, wellbeing, and educational attainment. The wellbeing of participants with complete data on either intelligence ( n  = 3179) or educational attainment ( n  = 3788) did not differ (see Supplementary Table 9 ), therefore analyses were conducted on the two predictors separately to maximise power.

Ethical approval for the ALSPAC study was obtained from ALSPAC Ethics and Law Committee and the Local Research Ethics Committees. Informed consent for the use of data collected via questionnaires and clinics was obtained from participants following the recommendations of the ALSPAC Ethics and Law Committee at the time.

Educational attainment was based on university degree completion. Participants responded to the item, ‘Do you have a university degree?’ which was included in the Life@26 questionnaire sent to 9230 (66%) participants in ALSPAC. While detailed information was collected on educational qualifications in ALSPAC for this age group, this data has not yet been released.

In total, 4029 completed the questionnaire, reflecting a 43.7% response rate. Answers included ‘yes’ ( n  = 2452), ‘no’ ( n  = 1377) or ‘still at university’ ( n  = 200). Those who responded ‘still at university’ were excluded from analyses. This is because individuals at university at 26 years would not necessarily represent those who followed the typical educational trajectory. For example, individuals may have taken a break from education and returned, or may be re-taking courses. Including such individuals may therefore have skewed analyses or created noise between the observational findings and those from the MR, which were based on years of schooling. Thus those with the highest number of total years would reflect those who earned a PhD degree at university. This could not be guaranteed among the current cohort of individual’s still studying due to the unavailability of further information.

Intelligence was assessed at the Focus at 8 clinic using the Wechsler Intelligence Scale for Children (WISC-III 68 ). The WISC comprises of ten subtests, including five verbal tests and five performance tests, as well as a forwards/backwards digit span test. The overall continuous score represents the total scaled scores across verbal and performance tests which were calculated using the WISC manual.

Wellbeing was captured at 26 years using the Subjective Happiness Scale 69 , the Satisfaction with Life Scale 70 , and the Meaning in Life Scale 71 . The current study focused on the Subjective Happiness Scale and the Satisfaction with Life Scale to ensure a close replication of the MR study. The Subjective Happiness Scale includes 4 items, with overall scores reflective of greater subjective happiness. The scale has high internal consistency and test-retest reliability, and is suitable for different age, occupational, and cultural groups 69 . The Satisfaction with Life Scale is a 5-item measure that was designed to capture cognitive judgments of one’s life satisfaction as opposed to positive affect 70 . Answers are coded so that a higher overall score reflects greater life satisfaction. Correlations between life satisfaction and subjective happiness were r  = 0.65. Both wellbeing measures were z-standardised to facilitate comparisons between the two.

In an attempt to first replicate the MR findings, separate linear regression models were first run. These investigated associations between educational attainment and wellbeing, and between intelligence and wellbeing. Wellbeing was assessed using subjective happiness and life satisfaction, with each ran as a separate regression. Analyses were repeated after including an interaction between sex and the predictor to test for possible sex differences.

All linear models were corrected for multiple testing using Benjamini Hochberg False Discovery Rate (FDR 22 ). This was based on a total of 62 tests to include models adjusted for attrition and missing data.

As educational attainment was recorded using a binary response, analyses checking for possible non-linearity were conducted for intelligence only. Models investigating associations between intelligence and wellbeing included either a quadratic, cubic, or quartic polynomial term, as per previous research focused on mental health in young adulthood 72 . Additional analyses also explored non-linearity using spline regressions. This is because polynomial terms may not be flexible enough to capture the relationship between intelligence and wellbeing as they impose a global structure on all of the data. Spline regressions were therefore included within a Generalised Additive Model (GAM) which was run using the ‘mgcv’ R package 73 . The model of best fit was determined using the Akaike information criterion (AIC) and Bayesian information criterion (BIC), as previously recommended 74 .

To further investigate possible factors driving associations with wellbeing, a linear model was run with an interaction term between the two predictors (educational attainment*intelligence). This was used to provide insight into the extent to which the relationship between intelligence and wellbeing is moderated by educational attainment and vice versa. Two interaction models were run, one predicting subjective happiness and one predicting life satisfaction. Finally, to test if any associations were explained by income, as noted in previous studies 2 , we repeated analyses after adjustment for family income.

The impact of attrition in the observational analyses was investigated using inverse probability weighting (IPW) and multiple imputation, as per previous studies using ALSPAC 75 . Multiple imputation was conducted using the Chained Equations (MICE) package 76 . Based on Rubin’s rules 77 , 60 imputations were conducted. The variables selected to impute data have been previously associated with missingness in ALSPAC and can be found in Supplementary Table 16 . It was important that analyses accounted for missing data as there was some evidence to suggest selective attrition (see Supplementary Table 15 ).

Data availability

All data sources used for the MR SNP-exposure and SNP-outcome associations are publicly available. Summary data from the Okbay et al. 17 Years of Schooling GWAS were downloaded from the SSGAC website SSGAC Login (thessgac.com), and the summary data for the intelligence GWAS 40 were obtained from the CNCR website GWAS Summary Statistics | CTG (cncr.nl). Summary statistics for the wellbeing GWAS, excluding results from 23AndMe cohort, were downloaded from https://surfdrive.surf.nl/files/index.php/s/Ow1qCDpFT421ZOO The observational ALSPAC data used in this study is not publicly available because the informed consent does not allow data to be made freely available through any third party maintained public repository. Data used for this submission, however, can be made available on request to the ALSPAC Executive. Please refer to the ALSPAC data management plan which describes the policy regarding data sharing. This is through a system of managed open access. Full instructions for applying for data access can be found here: http://www.bristol.ac.uk/alspac/researchers/access/ . The ALSPAC study website contains details of all the data that are available ( http://www.bristol.ac.uk/alspac/researchers/our-data/ ), and a comprehensive list of grants funding is also available on the ALSPAC website ( http://www.bristol.ac.uk/alspac/external/documents/grant-acknowledgements.pdf ).

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Acknowledgements

We are extremely grateful to all the families who took part in this study, the midwives for their help in recruiting them, and the whole ALSPAC team, which includes interviewers, computer and laboratory technicians, clerical workers, research scientists, volunteers, managers, receptionists and nurses. Thank you also to the cohorts that made their GWAS summary data publicly available. The UK Medical Research Council and Wellcome (Grant ref: 102215/2/13/2) and the University of Bristol provide core support for ALSPAC. A comprehensive list of grants funding is available on the ALSPAC website ( http://www.bristol.ac.uk/alspac/external/documents/grant-acknowledgements.pdf ). J.M.A. is supported by the Wolfson Centre for Young People’s Mental Health at Cardiff University. R.E.W. is supported by a postdoctoral fellowship from the South-Eastern Norway Regional Health Authority (2020024). C.M.A.H. is supported by a Philip Leverhulme Prize. This publication is the work of the authors, and JMA will serve as guarantor for the contents of this paper.

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J.M.A. and C.M.A.H. defined the research question. Funding acquisition and data collection for the wellbeing material in ALSPAC were performed by C.M.A.H. Data curation, formal analysis and investigation were performed by J.M.A., R.E.W. assisted with the MR data analysis. J.M.A., R.E.W., C.M.A.H., and O.S.P.D. contributed to the interpretation of the data. The original draft of the manuscript was written by J.M.A., and all authors read and approved the final manuscript.

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Armitage, J.M., Wootton, R.E., Davis, O.S.P. et al. An exploration into the causal relationships between educational attainment, intelligence, and wellbeing: an observational and two-sample Mendelian randomisation study. npj Mental Health Res 3 , 23 (2024). https://doi.org/10.1038/s44184-024-00066-x

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Older adults at greater risk for Alzheimer’s disease show stronger associations between sleep apnea severity in REM sleep and verbal memory

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Obstructive sleep apnea (OSA) increases risk for cognitive decline and Alzheimer’s disease (AD). While the underlying mechanisms remain unclear, hypoxemia during OSA has been implicated in cognitive impairment. OSA during rapid eye movement (REM) sleep is usually more severe than in non-rapid eye movement (NREM) sleep, but the relative effect of oxyhemoglobin desaturation during REM versus NREM sleep on memory is not completely characterized. Here, we examined the impact of OSA, as well as the moderating effects of AD risk factors, on verbal memory in a sample of middle-aged and older adults with heightened AD risk.

Eighty-one adults (mean age:61.7 ± 6.0 years, 62% females, 32% a polipoprotein E ε4 allele ( APOE4) carriers, and 70% with parental history of AD) underwent clinical polysomnography including assessment of OSA. OSA features were derived in total, NREM, and REM sleep. REM-NREM ratios of OSA features were also calculated. Verbal memory was assessed with the Rey Auditory Verbal Learning Test (RAVLT). Multiple regression models evaluated the relationships between OSA features and RAVLT scores while adjusting for sex, age, time between assessments, education years, body mass index (BMI), and APOE4 status or parental history of AD. The significant main effects of OSA features on RAVLT performance and the moderating effects of AD risk factors (i.e., sex, age, APOE4 status, and parental history of AD) were examined.

Apnea–hypopnea index (AHI), respiratory disturbance index (RDI), and oxyhemoglobin desaturation index (ODI) during REM sleep were negatively associated with RAVLT total learning and long-delay recall. Further, greater REM-NREM ratios of AHI, RDI, and ODI (i.e., more events in REM than NREM) were related to worse total learning and recall. We found specifically that the negative association between REM ODI and total learning was driven by adults 60 + years old. In addition, the negative relationships between REM-NREM ODI ratio and total learning, and REM-NREM RDI ratio and long-delay recall were driven by APOE4 carriers.

Greater OSA severity, particularly during REM sleep, negatively affects verbal memory, especially for people with greater AD risk. These findings underscore the potential importance of proactive screening and treatment of REM OSA even if overall AHI appears low.

Obstructive sleep apnea (OSA) is characterized by recurrent pharyngeal airway collapses that cause complete (apneas) or partial (hypopneas) cessations of airflow that lead to sleep fragmentation and intermittent hypoxemia [ 1 ]. Meta-analyses have shown that sleep-disordered breathing (SDB), including OSA, increases incidence of Alzheimer’s disease (AD), and people with AD were five times more likely to have OSA [ 2 , 3 ]. Proposed mechanisms suggest that OSA accelerates expression of AD pathologies, medial temporal lobe (MTL) degeneration, and memory impairment through OSA-related hypoxemia [ 4 , 5 , 6 , 7 , 8 ]. Though, some have reported that the cognitive consequences of OSA are diminished in older age [ 9 ]. Verbal memory deficits, specifically learning and recall of word list, is considered as the most sensitive marker of early cognitive changes associated with AD [ 10 , 11 , 12 ], and OSA-related hypoxemia could exacerbate AD risk through its impact on the hippocampus, a brain region critical for the formation and processing of episodic memories and is especially vulnerable to injury from oxygen deprivation [ 13 , 14 ]. While OSA severity has been linked to poor verbal memory performance, especially word lists learning tests [ 8 , 15 ], the exact OSA features driving these relationships, as well as whether respiratory events occurring during non-rapid eye movement (NREM) or rapid eye movement (REM) sleep are more damaging, have remained unclear.

During REM sleep, there is higher neurometabolic demand in regions impacted in early AD compared to NREM sleep [ 16 , 17 , 18 ]. In addition, during REM sleep, there is an increased susceptibility of upper airway collapse due to inhibition of the genioglossus muscle (the major upper airway dilator muscle that helps stabilize breathing) [ 19 ]. There are also lower hypoxic and hypercapnic respiratory drives during REM sleep which results in longer durations of apneas and hypopneas, and more instances of oxyhemoglobin desaturation than in NREM sleep [ 20 ]. These specific features of REM sleep and OSA events during REM sleep allude to the possibility that OSA during REM sleep may impart greater cognitive consequences, however, this has yet to be fully examined.

Furthermore, AD risk factors, such as older age, female sex, and apolipoprotein E ε4 ( APOE4 ) genotype have been implicated in OSA as well. OSA and AD are both more prevalent in the aging population [ 21 ]. Roughly 40–80% of people with AD carry at least one APOE4 allele and older adults with APOE4 may have increased risk for SDB, although this has not been consistently reported [ 22 , 23 ]. Moreover, the effects of APOE4 on the associations between OSA and memory have remained unclear [ 24 , 25 ], though it appears that OSA’s effects on memory networks may be stronger in β-amyloid positive older adults [ 26 ]. There are also sex differences in both AD and OSA risk. Women are nearly twice as likely to be diagnosed with AD and have a more severe disease progression that is characterized by faster memory decline and more pathological tau accumulation [ 27 , 28 , 29 , 30 ]. While men are at increased risk for OSA, OSA prevalence substantially rises in post-menopausal women, yet women typically remain underdiagnosed [ 31 , 32 ]. There is some evidence that suggests women with OSA may develop stronger OSA-related memory impairments than men with OSA, though, results have been inconsistent [ 33 , 34 , 35 ]. OSA is also expressed differentially by sex, with apneas and hypopneas more likely to occur in REM sleep in women than men [ 36 , 37 ]. Thus, there is a possibility of interplay between OSA and AD risk factors of age, sex, and APOE4 , that may synergistically cause stronger memory impairments. However, these interactions have yet to be fully examined.

Here, in the current study, we sought to examine OSA expression separately during REM and NREM sleep as it related to verbal memory, and whether AD risk factors moderated these relationships. We combined clinical polysomnography (PSG) with verbal memory measured by the Rey Auditory Verbal Learning Test (RAVLT) in a cohort of middle- and older- aged adults enriched for parental history and genetic risk for AD. We aimed to extend the current literature by testing the following hypotheses: 1) greater OSA severity, particularly during REM sleep, is associated with impaired verbal learning and delayed recall and 2) that in significant associations, the relationships are stronger among older adults, women, and/or individuals with increased genetic and parental risk for AD.

Clinical methods

One hundred fifteen cognitively unimpaired middle- and older-aged adults from the Wisconsin Alzheimer’s Disease Research Center (ADRC) Clinical Core, a prospective cohort study enriched for probable parental history of AD relative to the general population [ 38 ], were enrolled in a sub-study, the Predicting Alzheimer’s from Metabolic Markers and Sleep (PAMMS). From their ADRC visit, participants underwent cognitive assessments of declarative and semantic memory, attention, executive function, language, and visuospatial processing using the National Alzheimer’s Coordinating Center Uniform Data Set (UDS) neuropsychological battery version 3 and additional assessments [ 39 ]. Clinical diagnosis of cognitively unimpaired status was determined using the 2011 National Institute on Aging-Alzheimer’s Association (NIA-AA) workgroup diagnostic criteria and confirmed by multidisciplinary consensus conference (Table S 1 ) [ 40 , 41 ].

Of the 115 participants in PAMMS, 89 underwent a subcomponent of the study with polysomnography (PSG) with high-density EEG (hdEEG). This portion of the study excluded for individuals with a past history or current neurological, psychiatric, medical conditions, or treatments that impacted their cognition, or hindered their ability to complete any aspects of the study protocol, taking medications known to influence sleep or sleep electroencephalography (EEG), including antipsychotic medications, non-selective serotonin reuptake inhibitors (SSRIs) antidepressants, neuroleptics, chronic anxiolytics, sedative hypnotics, and stimulants, and was currently undergoing treatment for SDB (e.g. continuous positive airway pressure). Participants completed PSG with hdEEG approximately within 1 year of cognitive assessment. RAVLT scores were taken from their cognitive assessment at the ADRC. In addition, participant’s APOE 4 genotyped by DNA extraction from whole blood samples using competitive allele-specific PCR based KASP genotyping for rs429358, as previously reported [ 42 ]. The final sample size consisted of 81 participants with valid PSG data and RAVLT scores (e.g., oxyhemoglobin saturation levels not recorded the whole night would be considered invalid data).

Polysomnography

To assess sleep and OSA severity, participants underwent clinical PSG with 256-channel hdEEG. A thorough description of PSG with hdEEG recording and sleep scoring has been previously described [ 43 ]. From PSG, sleep architecture measures of total sleep time (TST), time in bed (TIB), sleep onset latency, wake after sleep onset (WASO), and percent of TST spent in N1, N2, N3, and REM were derived. Additionally, clinical measures reflecting sleep disorder characteristics were calculated, including apnea–hypopnea index (AHI; number of apneas and hypopneas per hour), respiratory disturbance index (RDI; number of apneas, hypopneas, and respiratory-related arousals per hour), oxyhemoglobin desaturation index (ODI; number of oxyhemoglobin desaturations ≥ 4% per hour). The AHI is a traditional measure for OSA diagnosis, with the American Academy of Sleep Medicine (AASM) clinical criterion of an AHI ≥ 5 for at least mild OSA [ 44 , 45 ]. The RDI captures additional information regarding respiratory events that do not meet criteria for a hypopnea, yet still lead to an arousal, and thus disrupt continuous sleep [ 44 , 45 ]. The AASM criteria for OSA diagnosis is RDI ≥ 5, if daytime sleepiness is present, and RDI ≥ 15 if not. The ODI is a clinically informative measure on the frequency of drops in oxyhemoglobin saturation levels, a marker of intermittent hypoxemia, which has been associated with poor cardiovascular outcomes, including elevated stroke risk, and increased risk of mortality [ 46 , 47 , 48 ].

Furthermore, nadir blood oxyhemoglobin saturation level, mean blood oxyhemoglobin saturation, duration of time spent with < 90% blood oxyhemoglobin saturation, and periodic leg movements during sleep index (PLMSI; number of PLMs per hour) were analyzed. These measures besides PLMSI, also reflects the degree of hypoxemia that occurs during sleep, and are additionally diagnostically important for OSA [ 45 , 47 ]. This study included participants with and without OSA and used the clinical measures of OSA as continuous variables for statistical analyses.

In addition, AHI, RDI, ODI, and duration of time spent with < 90% oxyhemoglobin saturation in REM and NREM sleep were also derived. To measure whether an individual had more REM or NREM OSA features throughout the night, ratios of AHI, RDI, and ODI between REM and NREM sleep were also calculated [ 49 , 50 , 51 , 52 ]. The NREM/REM ratios provide context of whether there is predominance of OSA events in a specific sleep stage across the sleep period. There are well characterized physiological differences in REM versus NREM sleep; specifically, OSA in REM sleep leads to more severe OSA events and has been linked to poor cardiovascular outcomes [ 7 , 16 , 17 , 18 , 19 , 20 , 53 , 54 , 55 , 56 ]. While some studies reported no significant differences between those with OSA events predominantly in NREM sleep versus REM sleep in clinical features such as BMI, daytime sleepiness, and depression [ 50 , 51 ], there have been established physiological and polysomnographic differences in those that express OSA events more in REM sleep and in those that express OSA events more in NREM sleep [ 51 , 52 ]. For instance, in predominant NREM OSA, ventilatory control is worse with higher loop gain, whereas in predominant REM OSA, the upper airway is more collapsible. Furthermore, people with more predominant NREM OSA had longer sleep onset latency, less sleep efficiency, and lower mean oxyhemoglobin saturation. There has yet to be an examination of the ratio of REM OSA events to NREM OSA event as it relates to memory.

Rey Auditory Verbal Learning Test (RAVLT)

The RAVLT is a standard neuropsychological assessment for verbal memory that it MTL-dependent, a sensitive marker of memory impairment in preclinical AD, and commonly used in AD research and clinical practice for diagnosis [ 57 , 58 , 59 , 60 ]. Further, OSA severity has been linked to poor verbal memory performance [ 8 ].

The test includes one learning phase, two recall phases, and one recognition phase. During the learning phase, a list of 15 words is read to the participant five times, and the participant repeat the words they remember after each trial. An interference list of 15 words is then read aloud once, and the short-delay recall ability is assessed after the interference list. Long-delay recall is then assessed after 20 min. A total learning score is derived by summing the number of remembered words in trials 1 through 5 (range: 0–75). Short-delay recall was measured as total number of words recalled after the interference list (range: 0–15), and long-delay recall was measured as total number of words after the 20-min delay (range: 0–15).

Statistical analyses

The purpose of this study was to comprehensively examine the distinct features of OSA (e.g., number of OSA-related events per hour or time spent during sleep with blood oxyhemoglobin levels in hypoxemia) across the whole night and broken down by distinct sleep stages (NREM versus REM), since there are physiological differences in these brain states [ 16 , 17 , 18 , 19 , 20 ]. Thus, the OSA characteristics that were analyzed were AHI, RDI, and ODI, and sleep duration spent with < 90% blood oxyhemoglobin saturation for total sleep, REM sleep, and NREM sleep. Total sleep nadir blood oxyhemoglobin saturation, total sleep mean blood oxyhemoglobin saturation, and WASO were also analyzed. RAVLT measures included total learning, short-delay recall, and long-delay recall. Normality of variables were analyzed with a Shapiro–Wilk test. All AHI, RDI, and ODI measures were log-transformed with a constant added to meet normality assumptions.

Independent sample t-tests were conducted to analyze group differences by sex, APOE4 status, and parental history of AD. Student’s t-tests were used if assumptions of normality and variance were met. Mann Whitney U-tests were used if assumptions of normality were violated. Kendall rank correlation was conducted on the associations between age and OSA characteristics. Paired samples t-tests were used to analyze REM versus NREM AHI, RDI, ODI, and duration spent with < 90% blood oxyhemoglobin saturation. Student’s t-test was used if assumptions of normality and Wilcoxon signed-rank test was used if assumptions of normality were violated.

Multiple linear regression models were used to analyze the relationships between OSA characteristics (predictors) and RAVLT (outcomes), controlling for sex, age, time between PSG and RAVLT, APOE4 status, body mass index (BMI), and years of education. One participant’s BMI was not measured and another's APOE4 status was not obtained, and thus were not included in statistical analyses that included those measures. Across all 15 models, the Benjamin-Hochberg method for False Discovery Rate (FDR) correction was used to correct multiple comparisons [ 61 ]. Regressions were repeated, substituting APOE4 status for parental history of AD.

To further understand the relative impact of OSA during REM sleep against NREM sleep on verbal memory performance, post hoc analyses included using the Steiger’s Z test to directly compare the correlation strengths of the associations between REM and NREM sleep apnea features and RAVLT scores [ 62 ]. Also, REM-NREM AHI, RDI, and ODI ratios were calculated, and the ratios were log-transformed with a constant added to meet normality assumptions. Multiple linear regression models were then used to analyze the associations between the OSA feature by sleep stage ratios and RAVLT scores while controlling for the same covariates. Regressions were repeated substituting APOE4 status for parental history of AD as a covariate.

For significant sleepstage findings, we wanted to account for the possible effects of sleep duration in that sleep stage on the significant associations between OSA features and RAVLT measures. Thus, we conducted follow-up analyses that included percent of time spent in that sleep stage in the models. Also, to disentangle whether total number of OSA events in that sleep stage or sleep duration in that sleep stage (i.e., the values that go into calculating AHI/RDI/ODI) was driving the detected effects, we conducted linear regression models with those separate measures as predictors. Total number of apnea/hypopneas, respiratory disturbances, and oxyhemoglobin desaturations during REM sleep were all log-transformed to meet assumptions of normality.

For significant associations between OSA characteristics and verbal memory, we investigated the moderating effects of sex, age, APOE4 status, and parental history of AD ( APOE4 status and parental history of AD in separate regression models) on those relationships. To further probe significant interactions with either APOE4 status or parental history of AD, we grouped participants into three groups consisting of 1) people with no APOE4 or parental history of AD, 2) people with either APOE 4 or parental history of AD, and 3) people with both APOE4 and parental history of AD. Analysis of covariance (ANCOVA) was used to analyze interactions between AD risk group and OSA features as it predicted verbal memory while controlling for the same covariates mentioned above. Slopes of the relationship between OSA features and verbal memory between the 3 groups were compared and Tukey’s method was used to correct for multiple comparison [ 63 ]. For significant interactions with age, Johnson-Neyman intervals and simple slope analyses were used to determine how much of the sample was driving the significant moderating effect [ 64 , 65 , 66 ]. All statistical analysis was conducted on JASP (Version 0.17.3) and RStudio (Version 2021.09.2).

Sample characteristics

Participant demographics and RAVLT scores of the 81 participants are shown in Table  1 . Sleep architecture and OSA characteristics are shown in Table  2 . In this sample, the average age was 61.7 ± 6.0 years (age range: 44–88 years), 60% participants were female, 32.5% of them were APOE4 carriers, 69.1% had parental history of AD, and 26.3% were both APOE4 carriers and had parental history of AD. The average time between PSG and RAVLT assessments was 0.31 ± 0.50 years. Nearly half of the cohort (44.44%) had OSA (AHI ≥ 5/h) and 16.05% had moderate or severe OSA (AHI ≥ 15/h). OSA severity was significantly higher in REM sleep than in NREM sleep (AHI: (t(80) = 7.91, p <  0.001), RDI: (t(81) = 5.11, p <  0.001), ODI: (t(81) = 9.64, p <  0.001). Remarkably, the duration spent with blood oxyhemoglobin levels < 90% did not differ significantly between REM and NREM sleep stages (1.83 ± 4.57 versus 2.55 ± 7.21, z = 0.06, p =  0.96), despite the fact that participants spent significantly more of the sleep period in NREM than in REM sleep stages (NREM:279.49 ± 57.71 min versus REM:59.80 ± 28.69 min, t(80) = 36.17, p <  0.001). This resulted in the proportion of time spent with blood oxyhemoglobin levels < 90% being significantly higher during REM than in NREM sleep stages (REM: 0.04 ± 0.11 versus NREM:0.01 ± 0.03, z = 3.68, p <  0.001).

Sex, age, APOE4 status, parental history of AD effects on OSA

Overall, male participants had more severe OSA than females (see Table S 2 ). However, females did have a significantly higher REM-NREM ODI ratio (t(79) = 2.45, p =  0.02). There were no significant associations observed between age and any OSA characteristics (all p >  0.10; see Table S 3 ). APOE4 carriers had significantly lower overall AHI, RDI, and ODI; REM AHI, as well as lower NREM AHI and ODI when compared to APOE4 non-carriers (all p <  0.05; see Table S 4 ), indicating that in this cohort, APOE4 carriers did not have greater OSA severity compared to non-carriers. There was also no significant difference in OSA characteristics among participants with and without a parental history of AD (all p >  0.30; see Table S 5 ). These findings indicate that those with higher AD risk did not show evidence of greater OSA severity in this cognitively intact cohort.

Sex, age, APOE4 status, parental history of AD effects on verbal memory

As previously reported, females had higher RAVLT scores than males (all p <  0.05; see Table S 6 ) [ 67 ]. Age and verbal memory were not significantly correlated (all p >  0.10; see Table S 7 ) and RAVLT scores did not significantly differ between APOE4 carriers and non-carriers (all p >  0.20; see Table S 8 ). Interestingly, participants with parental history of AD performed better across the RAVLT compared to those without parental history of AD (all p <  0.002; see Table S 9 ).

Associations between sleep apnea and verbal memory and total learning

Total AHI (b = -4.47, p =  0.09), RDI (b = -3.70, p =  0.17, and ODI (b = -5.17, p =  0.09) were not significantly associated with total learning. Similar results were observed when substituting APOE4 status with parental history of AD in the models. When testing OSA metrics by sleep stage, we found that REM AHI (b = -4.84, p =  0.01, FDR-corrected p =  0.07), REM RDI (b = -5.65, p =  0.01, FDR-corrected p =  0.06), and REM ODI (b = -7.91, p =  0.001, FDR-corrected p =  0.02) were all significantly associated with total learning, with REM ODI as the only significant predictor following FDR correction (Fig.  1 A-C). In the models with parental history of AD as the covariate instead of APOE4 status, REM RDI and REM ODI were significant predictors of total learning after FDR correction (all FDR-corrected p <  0.05). However, these same features in NREM sleep were not significantly associated with total learning performance after adjusting for covariates (NREM AHI: (b = -1.32, p =  0.63), NREM RDI: (b = -1.47, p =  0.56), NREM ODI: (b = -2.41, p =  0.40)). In addition, WASO, TST nadir and average oxyhemoglobin desaturation, and duration spent with < 90% blood oxyhemoglobin saturation in TST, NREM, and REM were not significantly associated with total learning (all p >  0.40; See Table S 10 for full statistical details). Similar insignificant results were found with models that included parental history of AD instead of APOE4 status (all ps > 0.30; See Table S 11 for full statistical details).

Scatter plots showing the relationships of ( A ) AHI, (B ) RDI, and ( C ) ODI during REM sleep to RAVLT total learning scores while controlling for age, sex, time between assessments, years of education, BMI, and APOE4 status

Steiger’s Z test revealed that correlations between total learning and AHI (z = 1.66, p =  0.10), RDI (z = 1.29, p =  0.20), and ODI (z = 1.51, p =  0.13) did not differ significantly between REM and NREM sleep. Although there were no significant differences in correlation strengths between REM and NREM OSA features in their associations with RAVLT total learning, the regression models indicated that REM OSA features, especially REM ODI, were significant predictors of verbal learning deficits, whereas NREM OSA features were not. Further, multiple regression models revealed that the ratios of REM-NREM AHI (b = -6.07, p =  0.01), RDI (b = -6.65, p =  0.01), and ODI (b = -7.42, p =  0.01) were significantly associated with total learning (Fig.  2 A-C; see Table S 12 ). Consistent results were found in models with parental history of AD instead of APOE4 status as the covariate (all p <  0.02; see Table S 13 ). These findings indicated that greater OSA severity during REM sleep in comparison to NREM sleep was associated with diminished total learning performance.

Scatter plots showing the relationships of REM-NREM ( A ) AHI, ( B ) RDI, and ( C ) ODI ratios to RAVLT total learning scores while controlling for age, sex, time between assessments, years of education, BMI, and APOE4 status

Associations between sleep apnea and short-delay recall

Overall AHI (b = -0.84, p =  0.27), RDI (b = -0.44, p =  0.56), and ODI (b = -0.80, p =  0.37) were not significantly related to short-delay recall. REM AHI (b = -1.38, p =  0.01, FDR-corrected p =  0.21) and REM ODI (b = -1.54, p =  0.03, FDR-corrected p =  0.23) were associated with short-delay recall but were no longer significant after FDR correction (See Table S 14 for full statistical details of all predictors). In models with parental history of AD instead of APOE4 status, REM AHI was associated with short-delay recall ( p =  0.04; FDR-corrected p =  0.48), but not after FDR correction (See Table S 15 for full statistical details of all predictors). Steiger’s Z test revealed a significant difference in the correlation strengths between short-delay recall and AHI during REM sleep versus during NREM sleep (z = 2.81, p =  0.005), demonstrating that REM AHI was more strongly associated with short-delay recall than NREM AHI. Moreover, multiple regression models indicated that REM-NREM AHI (b = -2.38, p <  0.001), RDI (b = -1.77, p =  0.02), and ODI (b = -2.02, p =  0.03) ratios were negatively associated with short-delay recall, both in models featuring APOE4 status (See Table S 16 ) and parental history of AD as covariates (all p <  0.03; See Table S 17 ). Thus, individuals with more severe sleep apnea had worse short-delay recall, particularly if OSA was more prevalent during REM sleep as opposed to during NREM sleep.

Associations between sleep apnea and long-delay recall

Total AHI, RDI, and ODI were not significantly associated with long-delay recall (all p >  0.07). However, REM AHI (b = -1.96, p =  0.001, FDR-corrected p =  0.01), REM RDI (b = -1.83, p =  0.01, FDR-corrected p =  0.04), and REM ODI (b = -2.46, p =  0.001, FDR-corrected p =  0.02) were all significantly associated with worse long-delay recall (Fig.  3 A-C). In models with parental history of AD instead of APOE4 status as the covariate, REM AHI, REM RDI, and REM ODI remained significant predictors (all FDR corrected p <  0.05). Demonstrating specificity, these same OSA parameters during NREM sleep were not significantly predictive of long-delay recall (NREM AHI (b = 0.07, p =  0.93), NREM RDI (b = -0.70, p =  0.93), NREM ODI (b = -0.08, p =  0.93). Nadir and average oxyhemoglobin desaturation during total sleep, duration spent with < 90% blood oxyhemoglobin saturation across total sleep and in NREM and REM sleep stages, and WASO were also not significant predictors (all p >  0.12; See Table S 18 for full statistical results). Similar insignificant results were found with models with NREM OSA severity predicting long-delay recall that included parental history of AD instead of APOE4 status (See Table S 19 for full statistical results).

Scatter plots showing the relationships of ( A ) AHI, ( B ) RDI, and ( C ) ODI during REM sleep to RAVLT long-delay scores while controlling for age, sex, time between assessments, years of education, BMI, and APOE4 status 

Steiger’s Z tests revealed significant differences in the correlation strengths between long-delay recall and REM AHI (z = 2.90, p =  0.004) and REM ODI (z = 2.14, p =  0.03) versus NREM features, but not in RDI (z = 1.85, p =  0.07), indicating that the frequency of events and extent of oxyhemoglobin desaturations in REM sleep were more strongly associated with long-delay recall than in NREM sleep. Further, multiple regression models showed that the ratios of REM-NREM AHI (b = -2.95, p <  0.001), RDI (b = -2.62, p =  0.001), and ODI (b = -2.94, p <  0.001) were significantly negatively associated with long-delay recall (Fig.  4 A-C; See Table S 20 ). Results from models with parental history of AD in place of APOE4 status were similar (all p <  0.003; See Table S 21 ). These results complement our initial findings by demonstrating that that worse verbal memory learning and recall performance were specifically associated with greater OSA severity during REM sleep and not during NREM sleep.

Scatter plots showing the relationships of REM-NREM ( A ) AHI, ( B ) RDI, and ( C ) ODI ratios to RAVLT long-delay scores while controlling for age, sex, time between assessments, years of education, BMI, and APOE4 status

Given the significant associations between REM OSA features and verbal memory, we wanted to account for the possible influence of percentage of REM sleep in these models. We found that even when controlling for percentage of REM sleep, REM AHI, RDI, and ODI, in addition to NREM-REM AHI, NREM-REM RDI, and NREM-REM ODI ratios were still associated with RAVLT measures (all p >  0.05; See Tables S 22 and S 23 for full statistical details). Furthermore, we probed whether total number of OSA events during REM sleep or total duration of REM sleep (i.e., the two values that go into calculating OSA indices) may be driving the significant associations between REM OSA features and verbal memory. We found that REM sleep duration was not significantly associated with RAVLT performance (all p >  0.25; see Tables S 24 and S 25 ). In contrast, total amount of apneas and hypopneas in REM sleep was associated with RAVLT total learning, short-delay, and long-delay significantly or on a trend level (all p <  0.09; See Tables S 26 and S 27 for full statistical details). Total number of respiratory-related arousals in addition to apneas/hypopneas (the total number of events in RDI) in REM sleep were also related to RAVLT total learning and long-delay recall (ps < 0.05; See Tables S 28 and S 29 for full statistical details). Lastly, total number of oxyhemoglobin desaturations during REM sleep was associated with total learning and long-delay recall (all p <  0.05; see Tables S 30 and S 31 for full statistical details). All models controlled for the same covariates with either APOE 4 status or parental history of AD. These findings suggest that the impact of OSA during REM sleep on verbal memory is more strongly associated with the OSA-related events themselves rather than their effects on REM sleep duration, per se. As a control analysis, we also analyzed whether periodic limb movement of sleep index (PMLSI) was associated with RAVLT performance. We found that there was no significant association between PLSMI and verbal memory (all p <  0.06).

The moderating effects of AD risk factors on verbal memory

Next, we examined the moderating influence of sex, age, and genetic and familial risk of AD on the significant relationships between OSA variables (i.e., OSA indices in REM sleep and REM-NREM ratios) and verbal memory. We found that APOE4 carriers demonstrated a significant association between REM-NREM ODI ratio and total learning (b = -18.17, p <  0.01) as opposed to non-carriers (b = -4.12, p =  0.17; Fig.  5 A). Full statistical details with all interactions between OSA features and AD risk factors predicting RAVLT total learning (with APOE4 status as a covariate in the models) are presented in Table S 32 . In models with parental history of AD as the covariate, age significantly moderated the association between REM ODI and total learning (b = -0.47, p <  0.05). There were significant effects at the mean age and at 1SD above the mean age (all p <  0.01; Fig.  5 B), with 80% of the sample in the significant moderating range (Figure S 1 ). Full statistical details with all interactions between OSA features and AD risk factors predicting RAVLT total learning (with parental history of AD as a covariate in the models) are presented in Table S 33 . Taken together, these findings indicate that the negative impact of oxyhemoglobin desaturations during REM sleep (relative to NREM sleep) on RAVLT total learning was more pronounced in those that were APOE4 carriers and in those aged 60 or older. Further, REM-NREM RDI ratio was significantly associated with long-delay recall in APOE4 carriers (b = -5.42, p <  0.01) but not in those without APOE4 (b = -1.67, p =  0.06; Fig.  5 C). Full statistical details with all interactions between OSA features and AD risk factors predicting long-delay recall (with APOE4 status as a covariate in the models) are presented in Table S 34 . There were no significant interactions between OSA factors and AD risk factors on long-delay recall when parental history of AD was a covariate in the models (See Table S 35 ). This suggests that the association between higher REM-NREM RDI and long-delay recall was specific to individuals with genetic risk for AD.

A The association between REM-NREM ODI ratio and RAVLT total learning scores was significantly moderated by APOE4 status. Only the APOE4 carriers showed that more oxyhemoglobin saturations during REM sleep as opposed to NREM sleep was related to worse learning performance. B The association between REM ODI and RAVLT total learning scores was moderated by age. A significant moderating effect was present at the mean age and 1 SD above the mean age (in 80% of the sample). C The association between REM-NREM RDI ratio and RAVLT long-delay recall score was moderated by APOE4 status. A significant moderating effect was observed for only APOE4 carriers in that, more respiratory events during REM sleep than in NREM sleep was associated with fewer words remembered after a 20-min delay

Lastly, we binned participants into the 3 groups based on presence or absence of APOE4 status and parental history of AD: 1) people with no AD risk factors, 2) people with either APOE4 status or parental history of AD, and 3) people with both AD risk factors. We then used ANCOVA models to examine interactions between OSA characteristics and AD risk factor groups as it related to verbal memory. In individuals with both AD risk factors, we found that higher REM RDI (b = -14.08, 95%CI:[-21.53, -6.63]), REM-NREM RDI ratio (b = -23.20, 95%CI:[-35.34, -11.06]) and REM-NREM ODI (b = -23.49, 95%CI:[-35.46, -11.51]) were significantly associated with worse total learning (See Figure S 2 and Tables S 36 -S 38 for contrast testing). Similarly, in individuals that were both APOE4 positive and had parental history of AD, higher REM-NREM RDI ratio was significantly associated with lower long-delay recall (b = -8.27, 95%CI:[-12.13, -4.42]; See Figure S 3 and Table S 39 for contrast testing results). Overall, these findings suggest that more OSA-related events in REM sleep (relative to NREM sleep) strongly impaired word list learning and recall, especially for those that had both parental and genetic risk for AD.

In this study, we assessed the relationships between OSA features and verbal memory performance of a word list, and tested the moderating effects of biological sex, age, APOE4 status, and parental history of AD on these relationships. We found that greater OSA severity during REM sleep was associated with worse word list learning and delay memory recall in a cohort of cognitively unimpaired middle- and older- aged adults enriched for AD risk. Additionally, more oxyhemoglobin desaturations during REM sleep versus NREM sleep were associated with worse learning performance, specifically in those that were older than 60 years old and APOE4 carriers. Further, more respiratory events and arousals during REM sleep, as opposed to during NREM sleep, had a greater negative impact on recall performance for those who were APOE4 carriers. The negative effects of OSA during REM sleep, specifically respiratory disturbances and oxyhemoglobin desaturations, on verbal memory seemed to be most prominent in those that had a parent with AD and was an APOE4 carrier. Since AD risk factors (e.g., female sex, older age, or genetic or familial risk) were not associated with more severe OSA in this current study, these findings were not simply driven by increased OSA severity in individuals with AD risk factors. Though it is possible this could be related to lower survival from conversion to mild cognitive impairment (MCI) or AD in older adults with AD risk and more severe OSA [ 9 , 21 , 68 , 69 ]. That being said, our results support the hypothesis that the memory consequences of OSA are particularly important for cognitively intact older adults with AD risk factors (older age, APOE4 positivity, and parental history of AD), particularly when OSA events occur during REM sleep.

OSA predominantly expressed in REM sleep is a common condition and REM-sleep related physiological changes lead to increased susceptibility to airway collapse, with longer durations of apneas and hypopneas and more severe oxyhemoglobin desaturations [ 19 , 20 , 53 , 70 ]. This is consistent with our findings that demonstrated higher AHI, RDI, and ODI scores during REM sleep relative to NREM sleep and extends the current literature by demonstrating that OSA events in REM sleep were more strongly linked with verbal memory performance than OSA events in NREM sleep. However, future investigations comparing samples enriched for more severe REM or NREM OSA are needed to determine whether it is specifically REM OSA severity that negatively impacts verbal memory performance.

REM OSA may impact verbal memory learning and recall via active disruption of memory processing or through long-term damage to brain structures and brain network function relevant for memory processing during REM sleep. While considerable attention has been given to the role of NREM sleep features in memory processing [ 71 ], there is evidence that REM sleep also supports memory. Neuroplastic processes needed for both memory consolidation and forgetting has been observed during REM sleep, in addition, hippocampal replay also occurs during this sleep stage [ 72 , 73 ]. Further, behaviorally, REM sleep has been linked to both emotional and spatial navigational memory [ 74 ]. Metabolic demand is also greater during REM sleep as opposed to wake and NREM sleep, including in memory-relevant regions, such as the MTL [ 17 , 18 ]. Therefore, OSA events in REM sleep could potentially cause memory deficits through both 1) transient disruptions in cerebral glucose metabolism in memory networks actively supporting memory processing during REM sleep and 2) long term degeneration of memory networks resultant from the presence of repeated hypoxia during high metabolic demand.

Varga and colleagues demonstrated the acute cognitive consequences of REM OSA in which they found that when withdrawing positive airway pressure (PAP) treatment specifically during REM sleep, spatial memory performance was reduced when compared to continued PAP treatment during REM sleep [ 16 ]. Although, the impact of withdrawing treatment during NREM sleep was not assessed, these findings indicated that REM OSA could cause transient MTL dysfunction by actively disrupting memory formation and consolidation even prior to neurodegenerative processes.

The effects of intermittent hypoxemia during OSA is a likely contributor to the hippocampal atrophy reported in people with OSA and explains memory impairments observed in OSA [ 5 , 6 , 7 , 8 , 9 , 13 , 14 ]. A possible mechanism of OSA’s impact on the hippocampus is through the presence of AD pathologies, with evidence supporting that hypoxemia exacerbates expression of both β-amyloid and tau that will in turn cause neurodegeneration and cognitive deficits [ 75 , 76 , 77 ]. Another potential mechanism is that hypoxia and sleep fragmentation specifically in REM sleep could accelerate neurodegeneration and cognitive decline via a vascular pathway [ 54 , 55 , 56 , 78 , 79 ]. REM sleep is characterized by increased sympathetic activation, decreased vagal tone, and cardiovascular instability, and REM OSA has been linked to poor cardiovascular health [ 20 , 54 , 55 , 56 ]. While REM sleep has shown to have high cerebral blood flow in memory-relevant brain areas, REM OSA severity has also been associated with reduced regional cerebral blood flow in those regions [ 78 , 79 ]. In addition, older adults with cardiovascular risk factors were more likely to have memory deficits [ 80 ]. Thus, it is possible that the compounded effects of REM OSA and vascular dysfunction greatly increases oxidative stress, neuroinflammation, blood brain barrier breakdown, and/or endothelial dysfunction causing neurodegenerative-associated memory deficits in older adults [ 20 , 77 ].

We found that AD risk factors including older age and both parental and genetic risk for AD all exacerbated the effect of OSA severity during REM sleep on word list learning and recall. While it has been reported that the associations between OSA and cognition are weaker in older age, our findings suggested in contrast, that the relationship between oxygen desaturations in REM sleep and verbal memory were actually strongest in older individuals [ 9 ]. As our cohort consisted of individuals with undiagnosed OSA and we are unaware of the true age of OSA onset, it is quite possible that some of the older participants may have had untreated OSA longer than the younger participants. We thus cannot discount that our findings may be more related to the consequences of the duration of untreated OSA than age of OSA onset, per se.

While it is remains unclear whether APOE4 status increases risk for SDB, our findings suggest that APOE4 carriers may be more vulnerable to the impact of OSA, especially during REM sleep, on memory function. Other studies have reported similar findings in that, in APOE4 carriers, OSA severity was associated with worse memory and executive function and had increased odds of cognitive decline [ 25 , 81 , 82 , 83 ]. Furthermore, disrupted sleep and APOE4 status may synergistically exacerbate expression of hallmark AD pathologies of β-amyloid and tau [ 84 , 85 ].

The combined effects of parental history of AD and APOE4 positivity has shown to have strong negative effects on learning and memory [ 86 , 87 ]. In addition, older age, family history of AD, and APOE4 status have been linked to a smaller hippocampus and greater accumulation of pathological β-amyloid and tau [ 88 , 89 ]. Moreover, in a subset of this cohort, we found that increasing age was related to elevated cerebral spinal fluid (CSF) markers of tau phosphorylation and neuroinflammation, which were then associated with impaired sleep-dependent memory [ 43 ]. This points to the possibility that accumulation of AD pathologies might be intensified by REM-related OSA leading to poor memory function, with the effects strongest or even just specific to those that are older and with parental and/or genetic risk for AD. Alternatively, REM OSA may contribute to cognitive impairment through cerebrovascular disease, and may be a factor to the common comorbidity of AD and vascular cognitive impairment [ 90 ]. Prospective studies will be necessary to investigate whether REM OSA accelerates expression of AD pathologies or promotes cognitive impairment through cerebrovascular dysfunction, or both, as well as why individuals with AD risk factors and OSA may be more cognitively impaired.

We did not find sex-specific effects in the associations between OSA and memory. In this specific cohort, males presented with more severe NREM and REM OSA, and had worse verbal memory performance than females. The lack of a sex effect could be due to a cognitively healthy sample that included females with a less severe OSA presentation. While females have increased risk for AD and present with greater levels of pathological tau in regions associated with AD compared to males, it is possible that the negative effects of OSA on verbal memory performance may be more exaggerated only once women are tau and/or β-amyloid positive, due to the female verbal memory advantage [ 28 , 29 , 67 , 91 , 92 ]. This verbal memory advantage tends to be diminished when women progress from MCI to AD who present with steeper memory decline than men [ 30 ]. In support of this possibility, the average age of this cohort was < 65 years old and in a subsample of 58 participants from this cohort, they were almost entirely β-amyloid and tau negative (based on CSF assessment) [ 43 ]. Future studies are needed that combine multimodal neuroimaging, sleep apnea testing, and other cognitive measures, to examine this in more detail. Regardless, it is important to state that despite the cohort being largely β-amyloid and tau negative, AD risk still remained a significant moderator of OSA-memory relationships, indicating that these effects cannot be entirely explained by and may even precede β-amyloid positivity, despite recent findings [ 26 ].

Some limitations of this study should be addressed. This was a cross-sectional study that found correlational relationships between OSA characteristics during REM sleep and verbal memory. Longitudinal studies will be necessary to examine how treatment of REM-related OSA would affect memory decline and progression to MCI or AD. Given the study sample size and number of analyses computed, it is possible that the study was underpowered to detect some significant associations. However, the focus of the current study was to contrast the relative strengths of associations between OSA features and memory when events occurred during NREM or REM sleep. While we think these effects are likely robust, it will be important to replicate these findings in a larger study. Further, the memory testing and sleep measurements did not typically occur on the same day. While we controlled for time between measurements, this study does not directly address memory processing that occurs over a night of sleep, but rather informs upon sleep abnormalities and memory associations at the trait level of individual differences. Another limitation is that this study had exclusions of multiple medications that are commonly taken by older adults, which could potentially bias the sample and reduce the generalizability of the results. It is also important to note that this cohort was mostly White (88%) and that these findings may not be generalizable, since racial/ethnic disparities and differences exist for both OSA and AD risk factors and the relationships between these risk factors are not well studied in underrepresented populations [ 93 , 94 ].

Our findings further support the possibility that OSA could be a modifiable risk factor for AD through its impact on one of the more sensitive markers of AD, impairment in word list learning and recall. A future direction is to extend our analyses to examine OSA’s relationship with other cognitive measures that are impaired in AD, including verbal memory of stories, nonverbal memory, and executive function. This will further elucidate OSA as a contributor to AD and provide further support that treatment may reduce risk for cognitive decline [ 9 ]. There is some evidence that continuous positive airway pressure (CPAP) adherence decreases the odds of AD dementia and slowed cognitive decline [ 95 , 96 , 97 ]. Importantly, a systematic review reported that PAP treatment adherence only covers mostly the first half of the night, which could potentially leave much of REM sleep OSA untreated, since REM sleep dominates the latter half of the night [ 98 ]. It will be critical for future investigations to examine whether more aggressive OSA treatment that covers the entire sleep period would mitigate cognitive impairment and AD risk in individuals with OSA. With growth of the aging population, there is a need for interventions targeting prevention of MCI and AD, and early diagnosis and effective treatment of OSA may be one approach that could reduce risk for neurodegenerative diseases and cognitive dysfunction associated with AD.

In conclusion, these findings suggest that more severe OSA during REM sleep and more REM OSA events as opposed to NREM OSA events were linked to worse verbal memory performance. This relationship was particularly true for older adults and individuals with a genetic risk for and parental history of AD. This suggests that the negative memory consequences of OSA, specifically when OSA events occur during REM sleep, are particularly impactful in individuals with multiple AD risk factors. The findings emphasize the importance of a thorough OSA screening with sleep recording capable of assessing sleep stage specific expression of OSA, as certain individuals may have high REM AHI, while presenting with a low overall AHI. This is particularly important given that most ambulatory, non-PSG methods for assessing OSA do not include the capacity to assess REM versus NREM sleep specific OSA expression. Without sleep stage characterization of OSA, individuals that are more susceptible to memory decline, especially those with AD risk factors, may miss the opportunity to be referred for comprehensive neurological/neuropsychological evaluation and aggressive OSA treatment that may delay cognitive decline and/or AD onset.

Availability of data and materials

The data are available upon reasonable request and can be obtained by completing a Wisconsin Alzheimer’s Disease Research Center resource request: https://www.adrc.wisc.edu/apply-resources .

Abbreviations

Alzheimer’s disease

Alzheimer’s Disease Research Center

Apnea–hypopnea Index

Analysis of covariance

Apolipoprotein E ε4

Body mass index

Continuous Positive Airway Pressure

Cerebral spinal fluid

False Discovery Rate

High-density electroencephalography

Mild cognitive impairment

Medial temporal lobe

Non-rapid eye movement

Oxyhemoglobin Desaturation Index

Obstructive Sleep Apnea

Positive airway pressure

Periodic leg movements during sleep index

Rey Auditory Verbal Learning Test

Respiratory Disturbance Index

Rapid eye movement

Sleep Disordered Breathing

Total sleep time

Time in bed

Wake after sleep onset

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Acknowledgements

We would like to thank the research participants and staff of the Wisconsin ADRC and Wisconsin Sleep for their contributions to the study.

This research was supported by grants R56 AG052698, R01 AG027161, R01 AG021155, ADRC P50 AG033514, R01 AG037639, K01 AG068353, and National Research Service Award F31 AG048732 from the National Institute on Aging, and by the Clinical and Translational Science Award (CTSA) program, through the NIH National Center for Advancing Translational Sciences (NCATS), grant UL1TR000427.

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Kitty K. Lui

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Kitty K. Lui, Abhishek Dave, Bryce A. Mander & Ruth M. Benca

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K.K.L analyzed the data and wrote the manuscript. A.D. and M.G.C. aided in analyzing the data and writing the manuscript. K.E.S. conducted the experiments, overseeing sleep data collection as part of her dissertation, and aided in data analysis and writing the manuscript. B.A.R. aided in sleep study data collection, provided data analytic tools, and aided in manuscript preparation. M.H. aided in collection and analysis of demographic, and neuropsychological data. C.T. aided in data collection. C.M.C., O.C.O., S.A., and S.C.J. aided data collection, neuropsychological screening, and manuscript preparation. B.B.B. aided study design, provided the subject pool, and aided in data analysis and manuscript preparation. R.M.B. designed the study, oversaw clinical sleep screening procedures, aided in data collection and analysis, and aided writing the manuscript. Lastly, B.A.M. aided in data analysis and writing the manuscript.

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Correspondence to Bryce A. Mander or Ruth M. Benca .

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Dr. Mander has served as a consultant for Eisai Co., Ltd. Dr. Benca has served as a consultant for Eisai, Genomind, Idorsia, Jazz, Merck, and Sunovion. Dr. Riedner has several patents related to sleep technology jointly held by the Wisconsin Alumni Research Foundation and Philips Healthcare, and in addition to grant support, has given several lectures sponsored by Philips Healthcare.

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Lui, K.K., Dave, A., Sprecher, K.E. et al. Older adults at greater risk for Alzheimer’s disease show stronger associations between sleep apnea severity in REM sleep and verbal memory. Alz Res Therapy 16 , 102 (2024). https://doi.org/10.1186/s13195-024-01446-3

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Research Implications & Recommendations

A Plain-Language Explainer With Examples + FREE Template

By: Derek Jansen (MBA) | Reviewers: Dr Eunice Rautenbach | May 2024

The research implications and recommendations are closely related but distinctly different concepts that often trip students up. Here, we’ll unpack them using plain language and loads of examples , so that you can approach your project with confidence.

Overview: Implications & Recommendations

• What are research implications ?
• What are research recommendations ?
• Examples of implications and recommendations
• The “ Big 3 ” categories
• How to write the implications and recommendations
• Template sentences for both sections
• Key takeaways

Implications & Recommendations 101

Let’s start with the basics and define our terms.

At the simplest level, research implications refer to the possible effects or outcomes of a study’s findings. More specifically, they answer the question, “ What do these findings mean?” . In other words, the implications section is where you discuss the broader impact of your study’s findings on theory, practice and future research.

This discussion leads us to the recommendations section , which is where you’ll propose specific actions based on your study’s findings and answer the question, “ What should be done next?” . In other words, the recommendations are practical steps that stakeholders can take to address the key issues identified by your study.

In a nutshell, then, the research implications discuss the broader impact and significance of a study’s findings, while recommendations provide specific actions to take, based on those findings. So, while both of these components are deeply rooted in the findings of the study, they serve different functions within the write up.

Need a helping hand?

Examples: Implications & Recommendations

The distinction between research implications and research recommendations might still feel a bit conceptual, so let’s look at one or two practical examples:

Let’s assume that your study finds that interactive learning methods significantly improve student engagement compared to traditional lectures. In this case, one of your recommendations could be that schools incorporate more interactive learning techniques into their curriculums to enhance student engagement.

Let’s imagine that your study finds that patients who receive personalised care plans have better health outcomes than those with standard care plans. One of your recommendations might be that healthcare providers develop and implement personalised care plans for their patients.

Now, these are admittedly quite simplistic examples, but they demonstrate the difference (and connection ) between the research implications and the recommendations. Simply put, the implications are about the impact of the findings, while the recommendations are about proposed actions, based on the findings.

The “Big 3” Categories

Now that we’ve defined our terms, let’s dig a little deeper into the implications – specifically, the different types or categories of research implications that exist.

Broadly speaking, implications can be divided into three categories – theoretical implications, practical implications and implications for future research .

Theoretical implications relate to how your study’s findings contribute to or challenge existing theories. For example, if a study on social behaviour uncovers new patterns, it might suggest that modifications to current psychological theories are necessary.

Practical implications , on the other hand, focus on how your study’s findings can be applied in real-world settings. For example, if your study demonstrated the effectiveness of a new teaching method, this would imply that educators should consider adopting this method to improve learning outcomes.

Practical implications can also involve policy reconsiderations . For example, if a study reveals significant health benefits from a particular diet, an implication might be that public health guidelines be re-evaluated.

Last but not least, there are the implications for future research . As the name suggests, this category of implications highlights the research gaps or new questions raised by your study. For example, if your study finds mixed results regarding a relationship between two variables, it might imply the need for further investigation to clarify these findings.

To recap then, the three types of implications are the theoretical, the practical and the implications on future research. Regardless of the category, these implications feed into and shape the recommendations , laying the foundation for the actions you’ll propose.

How To Write The  Sections

Now that we’ve laid the foundations, it’s time to explore how to write up the implications and recommendations sections respectively.

Let’s start with the “ where ” before digging into the “ how ”. Typically, the implications will feature in the discussion section of your document, while the recommendations will be located in the conclusion . That said, layouts can vary between disciplines and institutions, so be sure to check with your university what their preferences are.

For the implications section, a common approach is to structure the write-up based on the three categories we looked at earlier – theoretical, practical and future research implications. In practical terms, this discussion will usually follow a fairly formulaic sentence structure – for example:

This research provides new insights into [theoretical aspect], indicating that…

The study’s outcomes highlight the potential benefits of adopting [specific practice] in..

This study raises several questions that warrant further investigation, such as…

Moving onto the recommendations section, you could again structure your recommendations using the three categories. Alternatively, you could structure the discussion per stakeholder group – for example, policymakers, organisations, researchers, etc.

Again, you’ll likely use a fairly formulaic sentence structure for this section. Here are some examples for your inspiration: 

Based on the findings, [specific group] should consider adopting [new method] to improve…

To address the issues identified, it is recommended that legislation should be introduced to…

Researchers should consider examining [specific variable] to build on the current study’s findings.

Remember, you can grab a copy of our tried and tested templates for both the discussion and conclusion sections over on the Grad Coach blog. You can find the links to those, as well as loads of other free resources, in the description 🙂

FAQs: Implications & Recommendations

How do i determine the implications of my study.

To do this, you’ll need to consider how your findings address gaps in the existing literature, how they could influence theory, practice, or policy, and the potential societal or economic impacts.

When thinking about your findings, it’s also a good idea to revisit your introduction chapter, where you would have discussed the potential significance of your study more broadly. This section can help spark some additional ideas about what your findings mean in relation to your original research aims. 

Should I discuss both positive and negative implications?

Absolutely. You’ll need to discuss both the positive and negative implications to provide a balanced view of how your findings affect the field and any limitations or potential downsides.

Can my research implications be speculative?

Yes and no. While implications are somewhat more speculative than recommendations and can suggest potential future outcomes, they should be grounded in your data and analysis. So, be careful to avoid overly speculative claims.

How do I formulate recommendations?

Ideally, you should base your recommendations on the limitations and implications of your study’s findings. So, consider what further research is needed, how policies could be adapted, or how practices could be improved – and make proposals in this respect.

How specific should my recommendations be?

Your recommendations should be as specific as possible, providing clear guidance on what actions or research should be taken next. As mentioned earlier, the implications can be relatively broad, but the recommendations should be very specific and actionable. Ideally, you should apply the SMART framework to your recommendations.

Can I recommend future research in my recommendations?

Absolutely. Highlighting areas where further research is needed is a key aspect of the recommendations section. Naturally, these recommendations should link to the respective section of your implications (i.e., implications for future research).

Wrapping Up: Key Takeaways

We’ve covered quite a bit of ground here, so let’s quickly recap.

• Research implications refer to the possible effects or outcomes of a study’s findings.
• The recommendations section, on the other hand, is where you’ll propose specific actions based on those findings.
• You can structure your implications section based on the three overarching categories – theoretical, practical and future research implications.
• You can carry this structure through to the recommendations as well, or you can group your recommendations by stakeholder.

Remember to grab a copy of our tried and tested free dissertation template, which covers both the implications and recommendations sections. If you’d like 1:1 help with your research project, be sure to check out our private coaching service, where we hold your hand throughout the research journey, step by step.

Psst... there’s more!

This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

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• Open access
• Published: 09 May 2024

Exploring age and gender variations in root canal morphology of maxillary premolars in Saudi sub population: a cross-sectional CBCT study

• Mohmed Isaqali Karobari 1 , 2 ,
• Azhar Iqbal 3 ,
• Rumesa Batul 1 ,
• Abdul Habeeb Adil 1 ,
• Jamaluddin Syed 4 , 5 ,
• Hmoud Ali Algarni 3 ,
• Meshal Aber Alonazi 3 &
• Tahir Yusuf Noorani 6 , 7  

BMC Oral Health volume  24 , Article number:  543 ( 2024 ) Cite this article

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In complex teeth like maxillary premolars, endodontic treatment success depends on a complete comprehension of root canal anatomy. The research on mandibular premolars’ root canal anatomy has been extensive and well-documented in existing literature. However, there appears to be a notable gap in available data concerning the root canal anatomy of maxillary premolars. This study aimed to explore the root canal morphology of maxillary premolars using cone-beam computed tomography (CBCT) imaging, considering age and gender variations.

From 500 patient CBCT scans, 787 maxillary premolar teeth were evaluated. The sample was divided by gender and age (10–20, 21–30, 31–40, 41–50, 51–60, and 61 years and older). Ahmed et al. classification system was used to record root canal morphology.

The most frequent classifications for right maxillary 1st premolars were 2 MPM 1 B 1 L 1 (39.03%) and 1 MPM 1 (2.81%), while the most frequent classifications for right maxillary 2nd premolars were 2 MPM 1 B 1 L 1 (39.08%) and 1 MPM 1 (17.85%). Most of the premolars typically had two roots (left maxillary first premolars: 81.5%, left maxillary second premolars: 82.7%, right maxillary first premolars: 74.4%, right maxillary second premolars: 75.7%). Left and right maxillary 1st premolars for classes 1 MPM 1 and 1 MPM 1–2−1 showed significant gender differences. For classifications 1 MPM 1 and 1 MPM 1–2−1 , age-related changes were seen in the left and right maxillary first premolars.

This study provides novel insights into the root canal anatomy of maxillary premolars within the Saudi population, addressing a notable gap in the literature specific to this demographic. Through CBCT imaging and analysis of large sample sizes, the complex and diverse nature of root canal morphology in these teeth among Saudi individuals is elucidated. The findings underscore the importance of CBCT imaging in precise treatment planning and decision-making tailored to the Saudi population. Consideration of age and gender-related variations further enhances understanding and aids in personalized endodontic interventions within this demographic.

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Introduction

The morphology and variability of root canal systems play a crucial role in the success of endodontic treatment [ 1 , 2 ]. Understanding the intricacies of root canal anatomy is essential for effective diagnosis, treatment planning, and applying appropriate techniques. The research on mandibular premolars’ root canal anatomy has been extensive and well-documented in existing literature [ 3 , 4 ]. However, there appears to be a notable gap in available data concerning the root canal anatomy of maxillary premolars [ 5 , 6 , 7 , 8 , 9 ].

Maxillary premolars present unique challenges due to their anatomical complexity, including multiple canals, isthmuses, and accessory canals [ 10 , 11 ]. Accurately identifying and classifying root canal systems in maxillary premolars is crucial for diagnosis and achieving optimal treatment outcomes [ 12 ].

Despite the importance of understanding root canal morphology, there remains a gap in knowledge concerning maxillary premolars. This lack of comprehensive information on the root canal morphology of maxillary premolars hinders endodontic practitioners’ ability to deliver precise and successful treatments [ 13 ]. This study aims to fill this gap by conducting an investigation using cone-beam computed tomography (CBCT) imaging. CBCT, as a non-invasive and highly accurate imaging technique, offers the advantage of providing detailed three-dimensional representations of root canal systems, which were previously not easily achievable through conventional radiographs [ 14 ]. The high-resolution images obtained through CBCT will provide valuable data to enhance the knowledge and clinical management of root canal anatomy in these teeth, leading to better-informed treatment decisions and reduced complications [ 4 , 15 ].

By analyzing a large sample size of CBCT images, we aim to comprehensively understand the root canal configuration in maxillary premolars, considering factors such as age and gender [ 16 ]. The findings of this study will contribute to enhancing the knowledge and clinical management of root canal anatomy in maxillary premolars, improving treatment success rates, and reducing complications.

By elucidating the variations and complexities of root canal morphology in maxillary premolars, this study will aid dental professionals in making informed decisions regarding treatment approaches, instrument selection, and the application of advanced endodontic techniques [ 17 , 18 ]. Furthermore, the results will provide valuable insights for dental educators, researchers, and students, facilitating the development of standardized protocols and guidelines for managing root canal systems in maxillary premolars.

Methodology

Study design.

This study employed a retrospective cross-sectional design to comprehensively investigate the root canal morphology of maxillary premolars using cone-beam computed tomography (CBCT) imaging. This design allows for the examination of a large sample size and facilitates the analysis of root canal anatomy variations among different age groups and genders. By retrospectively analyzing CBCT images, the study aimed to elucidate the complex root canal anatomy of maxillary premolars and identify potential factors influencing their variability.

Ethical consideration

Ethical approval was obtained from the Local Committee of Bioethics for Research at the Dentistry College, King Abdul-Aziz University (Ethical Approval No. 025-02-22). Informed consent was obtained from the Committee of Bioethics for Research, College of Dentistry, King Abdul-Aziz University, Jeddah, Saudi Arabia, considering the retrospective nature of the study. This ensured that the study adhered to ethical standards and protected the rights and confidentiality of the participants. Additionally, the study complied with all relevant regulations and guidelines regarding the use of patient data for research purposes.

Sample size determination

The sample size for this study was determined using G Power 3.1.9.4 software, considering a chi-square test for goodness-of-fit, statistical power analysis, and an a priori approach. A comprehensive sample of 500 patient records was obtained, resulting in the evaluation of 787 maxillary premolar teeth. This large sample size enhances the statistical power of the study and allows for robust analysis of root canal morphology variations. It also increases the generalizability of the findings to the target population.

Inclusion and exclusion criteria

Inclusion criteria were carefully defined to ensure the selection of appropriate teeth for analysis. Healthy maxillary premolars with small carious or restorative crowns, fully formed root apex, and defect-free radiographic images were included in the study. Exclusion criteria were applied to eliminate potential confounding factors, including root canal-treated teeth, fractured upper and lower posterior teeth, post and core restorations, calcification, resorption defects, and anomalies of crown and root. These criteria helped ensure the homogeneity of the study sample and the validity of the results.

Imaging technique

CBCT images were acquired using the iCAT scanner system (Imaging Sciences International, Hatfield, PA, USA), a widely recognized and reliable imaging device in dentistry. Standardized imaging parameters (120 KVp, 5–7 mA) were employed to ensure consistent image quality across all scans. The use of CBCT allowed for the acquisition of detailed three-dimensional representations of root canal anatomy, enabling precise analysis and classification. High-resolution images obtained through CBCT provided valuable data for evaluating root canal morphology.

Calibration and reliability

Prior to data collection, calibration was conducted involving an expert endodontist and an observer. The observer underwent rigorous training to accurately identify and classify root canal morphology. Calibration involved the examination of 50 CBCT images, with discrepancies resolved through discussion to achieve consensus. The kappa test was utilized to determine the level of agreement between observers, and intra- and interobserver reliability was assessed. Furthermore, specimens were assessed independently by observers following calibration to minimize bias and ensure consistency in the evaluations. A high kappa value (0.8) was obtained, indicating substantial to almost perfect reliability, thereby ensuring the validity of the data collected. This rigorous calibration process helped minimize observer bias and enhance the reliability of the study findings.

Root and canal analysis

Root canal morphology was recorded and classified according to the classification system proposed by Ahmed et al. in 2017. This classification system provides a standardized framework for describing root canal configurations, facilitating comparisons across studies. The obtained CBCT images were meticulously analyzed, with root canal morphology recorded for each maxillary premolar (Fig.  1 ). The images were divided into age groups (10 to 20, 21 to 30, 31 to 40, 41 to 50, 51 to 60, and 61 years above) and categorized by gender (males and females) to explore variations in root canal anatomy. Detailed analysis of each image was conducted to identify the number of roots, canals, and any anatomical variations present.

New classification system for root canal morphology of maxillary left second premolar classified using the new classification system, described as code 1 25 1 . The code consists of three components, the tooth number – Yellow color arrow, number of roots – blue color arrow and the root canal configuration – green color arrow. The number of roots is added as a superscript before the tooth number, so it is single root and tooth number (25). Description of root canal configuration is written as superscript after the tooth number on the course of the root canal starting from the orifices [O], passing through the canal [C], ending by the foramen [F], so it is single canal

Statistical analysis

Statistical analysis was performed using SPSS version 26 software. Descriptive statistics, including mean frequency and standard deviation, were calculated to summarize the data. The association between root canal morphology and age/gender was analyzed using the chi-square test or Fisher exact test, depending on the distribution of the data. Significance levels were set at p  ≤ 0.05 to determine the statistical significance of the findings. Additionally, subgroup analyses were conducted to explore potential interactions between age, gender, and root canal morphology.

The distribution of maxillary premolars according to Ahmed’s classification was examined. Table  1 presents the distribution of premolars based on the classification categories. For right maxillary 1st premolars, the majority belonged to 2 MPM 1 B 1 L 1 (39.03%) and 1 MPM 1 (2.81%) categories. Similarly, for right maxillary 2nd premolars, 2 MPM 1 B 1 L 1 (39.08%) and 1 MPM 1 (17.85%) were the most prevalent categories.

Table  2 displays the distribution of maxillary premolars based on the number of roots. The majority of premolars had two roots (73.33% for left maxillary 1st premolars, 24.45% for left maxillary 2nd premolars, 74.03% for right maxillary 1st premolars, and 24.32% for right maxillary 2nd premolars) (Figs.  2 , 3 and 4 ).

CBCT View (Sagittal and axial) of left maxillary second premolar showing the code 1 MPM 1

CBCT View (Sagittal and axial) maxillary first and second premolars showing the canal variations

CBCT View (Sagittal and axial) maxillary first and second premolars showing the canal variations in more than one root

Tables  3 and 4 present the distribution of left and right maxillary 1st and 2nd premolars, respectively, based on gender. In Table  3 , significant gender differences were observed for the classification 1 MPM 1 ( p  = 0.515) and 1 MPM 1–2−1 ( p  = 0.010*) for both left maxillary 1st and 2nd premolars. The number of males and females for MPM 1 in left maxillary 1st premolars was 121 and 88, respectively, while for 1 MPM 1 in left maxillary 2nd premolars, it was 111 and 72, respectively. Similarly, for 1 MPM 1–2−1 in left maxillary 1st premolars, the number of males and females was 3 and 3, respectively, whereas for left maxillary 2nd premolars, it was 30 and 21, respectively.

Table  4 indicates significant gender differences for the classification MPM 1 ( p  = 0.032*) and 1 MPM 1–2−1 ( p  = 0.003*) in the right maxillary 1st premolars. The number of males and females for 1 MPM 1 in the right maxillary 1st premolars was 122 and 84, respectively, while for 1 MPM 1 in the right maxillary 2nd premolars, it was 115 and 70, respectively. Additionally, the number of males and females for 1 MPM 1–2−1 in right maxillary 1st premolars was 10 and 11, respectively, whereas, for right maxillary 2nd premolars, it was 33 and 18, respectively.

Tables  5 and 6 demonstrate the distribution of left and right maxillary 1st and 2nd premolars, respectively, based on age groups. In Table  5 , significant differences were observed for the classification 1 MPM 1 ( p  = 0.053) and 1 MPM 1–2−1 ( p  = 0.002*) in left maxillary 1st premolars. The number of premolars in each age group for 1 MPM 1 in left maxillary 1st premolars ranged from 1 to 7, whereas for 1 MPM 1–2−1, it ranged from 0 to 3. For left maxillary 2nd premolars, significant differences were observed for the classification 1 MPM 1 ( p  = 0.002*) and 1 MPM 1–2−1 ( p  = 0.002*). The number of premolars in each age group for 1 MPM 1 in left maxillary 2nd premolars ranged from 6 to 38, whereas for 1 MPM 1–2−1, it ranged from 4 to 23.

In Table  6 , significant differences were observed for the classification 1 MPM 1 ( p  = 0.055) and MPM 1 ( p  = 0.002*) in the right maxillary 1st and 2nd premolars, respectively. The number of premolars in each age group for 1 MPM 1 in the right maxillary 1st premolars ranged from 1 to 6, whereas for 1 MPM 1–2−1, it ranged from 0 to 15. For right maxillary 2nd premolars, significant differences were observed for the classification 1 MPM 1 ( p  = 0.002*) and 1 MPM 1–2−1 ( p  = 0.002*). The number of premolars in each age group for 1 MPM 1 in the right maxillary 2nd premolars ranged from 6 to 36, whereas for 1 MPM 1–2−1 , it ranged from 3 to 15.

The present study aimed to investigate the root canal morphology of maxillary premolars using cone-beam computed tomography (CBCT) imaging. By analyzing a large sample size of CBCT images, we sought to provide a comprehensive understanding of the complex and variable root canal configuration in maxillary premolars, considering factors such as gender and age.

As mentioned in the literature [ 11 , 19 ], our findings revealed a diverse range of root canal configurations in maxillary premolars. Multiple canals, isthmuses, and accessory canals in these teeth pose a challenge to endodontic treatment, as it necessitates thorough exploration, disinfection, and meticulous instrumentation [ 20 ]. Recognizing such complex anatomy underscores the importance of employing advanced imaging techniques, such as CBCT, to accurately visualize and assess root canal morphology [ 21 , 22 ].

In our study, age emerged as a significant factor influencing the root canal morphology of maxillary premolars. The categorization into different age groups allowed for a nuanced exploration of these variations, corroborating previous research [ 23 , 24 , 25 ]. The age-specific analysis revealed noteworthy trends in the prevalence of certain root canal configurations. For instance, in left maxillary 1st premolars, the marginal significance ( p  = 0.053) for 1MPM1 suggests a potential shift in root canal anatomy with increasing age. This finding prompts further investigation into the underlying reasons for such variations across age groups. Similarly, the significant difference ( p  = 0.002*) observed in 1MPM1-2-1 in both left and right maxillary 1st premolars indicates distinct patterns in root canal morphology among different age brackets. This finding raises questions about whether these differences are attributed to developmental changes, wear and tear, or other factors associated with aging. These age-related changes can be attributed to factors such as dentin deposition and secondary dentin formation, which may alter the shape and complexity of the root canal system over time. Therefore, endodontists should consider these age-related variations when planning and performing root canal procedures, particularly in older patients [ 26 ]. Younger age groups may exhibit features associated with incomplete root development and open apices, while older age groups may show signs of maturation, closure of apices, and increased calcification [ 27 ]. The correlations between age-related changes in root canal morphology and systemic conditions enhance the clinical context. Systemic factors, such as hormonal changes, metabolic disorders, or medication use, may influence dental development and impact root canal anatomy differently across age groups [ 28 ]. Practitioners should consider these age-related nuances during treatment planning and execution, adjusting their approaches to accommodate the potential variations in root canal anatomy. For example, younger patients may exhibit different anatomical features compared to older individuals, influencing decisions related to instrumentation and obturation techniques.

Furthermore, our study identified gender-based differences in root canal morphology. This finding aligns with Ahmed et al. [ 19 ], who reported similar gender differences in maxillary premolars. Their study revealed a higher prevalence of multiple canals in males than females, which supports our observations of significant gender variations in root canal morphology. However, it is worth noting that Ahmed et al. did not mention the specific classification code 1 MPM 1–2−1 in their study, making a direct comparison somewhat limited.

Likewise, Cleghorn et al. [ 11 ] found that the prevalence of multiple canals in maxillary first premolars ranged from 30 to 73%, a range consistent with our findings. Shi et al., while studying the Chinese population [ 23 ], also noted significant differences in the number of roots and gender in both maxillary first and second premolars.

In a study conducted by Mashyakhy et al. [ 29 ] in a Saudi population, highly statistically significant differences in canal configurations were observed between genders in maxillary teeth. Similarly, Martins et al. [ 30 ] reported a gender difference in the root canal morphology of the Portuguese population. However, it is essential to mention that some contrasting results were found in specific subpopulations. For instance, no significant difference in root canal morphology was noted in the Malaysian subpopulation [ 31 ] and the German subpopulation [ 32 ].

In summary, our study adds to the existing body of literature by providing further evidence of gender-related variations in root canal morphology, and it is in line with previous research in this field.

This study’s utilization of CBCT imaging provided valuable insights into the three-dimensional morphology of maxillary premolars. CBCT has emerged as a powerful diagnostic tool in endodontics, enabling the visualization of intricate root canal anatomy [ 33 ]. Accurately assessing root canal morphology facilitates precise treatment planning, guiding clinicians in determining the appropriate access, instrumentation, and obturation techniques [ 34 ]. The present study has several advantages, reinforcing its conclusions’ reliability and veracity. First and foremost, a large sample size was used in the study, with 500 cone-beam computed tomography (CBCT) images in total, 1230 maxillary premolars included. This large sample size improves the study’s statistical power and broadens the applicability of the results to the intended population.

The study employed qualified endodontists and observers calibrated to evaluate root canal morphology to achieve precise and reliable analysis. To determine the classification of root canal morphology, 50 CBCT images were examined as part of the calibration process. The research boosted the consistency and accuracy of the results by creating a smooth decision-making process that reduced the possibility of observer bias.

In the present study, a standardized classification scheme was used. This classification system offers a reliable and standardized method for classifying root canal morphology. The study’s findings may be easily compared and integrated with those of other research utilizing the same approach because it used a recognized classification system. Understanding root canal morphology in maxillary premolars is ultimately enhanced by this, making it easier for future research and enabling meta-analyses.

Additionally, the study compared its findings to pertinent literature, enabling a thorough interpretation of the data in light of earlier research. The study offers important insights into the heterogeneity of root canal morphology in maxillary premolars by comparing the consistency or divergence of results across different populations and studies. The scientific knowledge base is expanded, and this topic is better understood thanks to the comparative method.

Strengths of our study

One of the key strengths of our study is the large sample size, which enhances the statistical power and generalizability of our findings. Additionally, the utilization of cone-beam computed tomography (CBCT) imaging allowed for detailed three-dimensional analysis of root canal morphology, providing valuable insights into the complexity of maxillary premolars. Our rigorous calibration process, involving expert endodontists and observers, ensured the reliability and accuracy of our data collection and analysis. Furthermore, by considering age and gender variations, we were able to explore the influence of demographic factors on root canal anatomy, contributing to a more nuanced understanding of this topic.

Limitations

Despite these strengths, our study also has several limitations that warrant consideration. Firstly, the retrospective nature of the study may introduce selection bias and limit the generalizability of the findings. Additionally, the study focused on a specific population, which may limit its applicability to other ethnic groups or regions. Furthermore, the reliance on CBCT imaging, while providing detailed anatomical information, is subject to radiation exposure and cost constraints. Moreover, the inclusion and exclusion criteria applied in the study may have inadvertently excluded certain teeth or patient populations, potentially affecting the representativeness of the sample.

Future research endeavors should explore the relationship between root canal morphology and treatment outcomes in maxillary premolars to enhance our knowledge further. Long-term follow-up studies can provide valuable insights into the success rates and potential complications associated with different root canal configurations. Furthermore, advancements in imaging modalities and treatment techniques, such as guided endodontics and regenerative approaches, hold promise for overcoming the challenges posed by complex root canal anatomy.

This study provides novel insights into the root canal anatomy of maxillary premolars within the Saudi population, addressing a notable gap in the literature specific to this demographic. Through CBCT imaging and analysis of large sample sizes, the complex and diverse nature of root canal morphology in these teeth among Saudi individuals is elucidated. The findings underscore the importance of CBCT imaging in precise treatment planning and decision-making tailored to the Saudi population. Consideration of age and gender-related variations further enhances understanding and aids in personalized endodontic interventions within this demographic. Moving forward, these findings inform clinical practice within the Saudi community, emphasizing the need for customized approaches to optimize treatment outcomes.

Data availability

All data supporting the findings of this study are available from the corresponding author upon reasonable request.

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Mohmed Isaqali Karobari, Rumesa Batul & Abdul Habeeb Adil

Department of Restorative Dentistry & Endodontics, Faculty of Dentistry, University of Puthisastra, Phnom Penh, 12211, Cambodia

Mohmed Isaqali Karobari

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Azhar Iqbal, Hmoud Ali Algarni & Meshal Aber Alonazi

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Conception and design of the study: MIK, and TYN. Acquisition of data: AZ and SJ. Analysis and interpretation of data: RB and AHA. Drafting the article: MIK, RB, AHA and SJ. Revising it critically for important intellectual content: MIK, AZ, HAA, MAA and TYN. All authors approved the final submitted version.

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Ethical approval for this retrospective study was obtained from the Local Committee of Bioethics for Research at the Dentistry College, King Abdul-Aziz University, with Ethical Approval No. 025-02-22. Informed consent was obtained from the Committee of Bioethics for Research, College of Dentistry, King Abdul-Aziz University, Jeddah, Saudi Arabia, considering the study’s retrospective nature. Before any investigation or treatment, the patients signed a general consent form, allowing the use of findings in future studies and publications without revealing personal information. The informed consent was obtained from all subjects and/or their legal guardian(s).

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Karobari, M.I., Iqbal, A., Batul, R. et al. Exploring age and gender variations in root canal morphology of maxillary premolars in Saudi sub population: a cross-sectional CBCT study. BMC Oral Health 24 , 543 (2024). https://doi.org/10.1186/s12903-024-04310-w

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Artificial intelligence in strategy

Can machines automate strategy development? The short answer is no. However, there are numerous aspects of strategists’ work where AI and advanced analytics tools can already bring enormous value. Yuval Atsmon is a senior partner who leads the new McKinsey Center for Strategy Innovation, which studies ways new technologies can augment the timeless principles of strategy. In this episode of the Inside the Strategy Room podcast, he explains how artificial intelligence is already transforming strategy and what’s on the horizon. This is an edited transcript of the discussion. For more conversations on the strategy issues that matter, follow the series on your preferred podcast platform .

Joanna Pachner: What does artificial intelligence mean in the context of strategy?

Yuval Atsmon: When people talk about artificial intelligence, they include everything to do with analytics, automation, and data analysis. Marvin Minsky, the pioneer of artificial intelligence research in the 1960s, talked about AI as a “suitcase word”—a term into which you can stuff whatever you want—and that still seems to be the case. We are comfortable with that because we think companies should use all the capabilities of more traditional analysis while increasing automation in strategy that can free up management or analyst time and, gradually, introducing tools that can augment human thinking.

Joanna Pachner: AI has been embraced by many business functions, but strategy seems to be largely immune to its charms. Why do you think that is?

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Yuval Atsmon: You’re right about the limited adoption. Only 7 percent of respondents to our survey about the use of AI say they use it in strategy or even financial planning, whereas in areas like marketing, supply chain, and service operations, it’s 25 or 30 percent. One reason adoption is lagging is that strategy is one of the most integrative conceptual practices. When executives think about strategy automation, many are looking too far ahead—at AI capabilities that would decide, in place of the business leader, what the right strategy is. They are missing opportunities to use AI in the building blocks of strategy that could significantly improve outcomes.

I like to use the analogy to virtual assistants. Many of us use Alexa or Siri but very few people use these tools to do more than dictate a text message or shut off the lights. We don’t feel comfortable with the technology’s ability to understand the context in more sophisticated applications. AI in strategy is similar: it’s hard for AI to know everything an executive knows, but it can help executives with certain tasks.

When executives think about strategy automation, many are looking too far ahead—at AI deciding the right strategy. They are missing opportunities to use AI in the building blocks of strategy.

Joanna Pachner: What kind of tasks can AI help strategists execute today?

Yuval Atsmon: We talk about six stages of AI development. The earliest is simple analytics, which we refer to as descriptive intelligence. Companies use dashboards for competitive analysis or to study performance in different parts of the business that are automatically updated. Some have interactive capabilities for refinement and testing.

The second level is diagnostic intelligence, which is the ability to look backward at the business and understand root causes and drivers of performance. The level after that is predictive intelligence: being able to anticipate certain scenarios or options and the value of things in the future based on momentum from the past as well as signals picked in the market. Both diagnostics and prediction are areas that AI can greatly improve today. The tools can augment executives’ analysis and become areas where you develop capabilities. For example, on diagnostic intelligence, you can organize your portfolio into segments to understand granularly where performance is coming from and do it in a much more continuous way than analysts could. You can try 20 different ways in an hour versus deploying one hundred analysts to tackle the problem.

Predictive AI is both more difficult and more risky. Executives shouldn’t fully rely on predictive AI, but it provides another systematic viewpoint in the room. Because strategic decisions have significant consequences, a key consideration is to use AI transparently in the sense of understanding why it is making a certain prediction and what extrapolations it is making from which information. You can then assess if you trust the prediction or not. You can even use AI to track the evolution of the assumptions for that prediction.

Those are the levels available today. The next three levels will take time to develop. There are some early examples of AI advising actions for executives’ consideration that would be value-creating based on the analysis. From there, you go to delegating certain decision authority to AI, with constraints and supervision. Eventually, there is the point where fully autonomous AI analyzes and decides with no human interaction.

Because strategic decisions have significant consequences, you need to understand why AI is making a certain prediction and what extrapolations it’s making from which information.

Joanna Pachner: What kind of businesses or industries could gain the greatest benefits from embracing AI at its current level of sophistication?

Yuval Atsmon: Every business probably has some opportunity to use AI more than it does today. The first thing to look at is the availability of data. Do you have performance data that can be organized in a systematic way? Companies that have deep data on their portfolios down to business line, SKU, inventory, and raw ingredients have the biggest opportunities to use machines to gain granular insights that humans could not.

Companies whose strategies rely on a few big decisions with limited data would get less from AI. Likewise, those facing a lot of volatility and vulnerability to external events would benefit less than companies with controlled and systematic portfolios, although they could deploy AI to better predict those external events and identify what they can and cannot control.

Third, the velocity of decisions matters. Most companies develop strategies every three to five years, which then become annual budgets. If you think about strategy in that way, the role of AI is relatively limited other than potentially accelerating analyses that are inputs into the strategy. However, some companies regularly revisit big decisions they made based on assumptions about the world that may have since changed, affecting the projected ROI of initiatives. Such shifts would affect how you deploy talent and executive time, how you spend money and focus sales efforts, and AI can be valuable in guiding that. The value of AI is even bigger when you can make decisions close to the time of deploying resources, because AI can signal that your previous assumptions have changed from when you made your plan.

Joanna Pachner: Can you provide any examples of companies employing AI to address specific strategic challenges?

Yuval Atsmon: Some of the most innovative users of AI, not coincidentally, are AI- and digital-native companies. Some of these companies have seen massive benefits from AI and have increased its usage in other areas of the business. One mobility player adjusts its financial planning based on pricing patterns it observes in the market. Its business has relatively high flexibility to demand but less so to supply, so the company uses AI to continuously signal back when pricing dynamics are trending in a way that would affect profitability or where demand is rising. This allows the company to quickly react to create more capacity because its profitability is highly sensitive to keeping demand and supply in equilibrium.

Joanna Pachner: Given how quickly things change today, doesn’t AI seem to be more a tactical than a strategic tool, providing time-sensitive input on isolated elements of strategy?

Yuval Atsmon: It’s interesting that you make the distinction between strategic and tactical. Of course, every decision can be broken down into smaller ones, and where AI can be affordably used in strategy today is for building blocks of the strategy. It might feel tactical, but it can make a massive difference. One of the world’s leading investment firms, for example, has started to use AI to scan for certain patterns rather than scanning individual companies directly. AI looks for consumer mobile usage that suggests a company’s technology is catching on quickly, giving the firm an opportunity to invest in that company before others do. That created a significant strategic edge for them, even though the tool itself may be relatively tactical.

Joanna Pachner: McKinsey has written a lot about cognitive biases  and social dynamics that can skew decision making. Can AI help with these challenges?

Yuval Atsmon: When we talk to executives about using AI in strategy development, the first reaction we get is, “Those are really big decisions; what if AI gets them wrong?” The first answer is that humans also get them wrong—a lot. [Amos] Tversky, [Daniel] Kahneman, and others have proven that some of those errors are systemic, observable, and predictable. The first thing AI can do is spot situations likely to give rise to biases. For example, imagine that AI is listening in on a strategy session where the CEO proposes something and everyone says “Aye” without debate and discussion. AI could inform the room, “We might have a sunflower bias here,” which could trigger more conversation and remind the CEO that it’s in their own interest to encourage some devil’s advocacy.

We also often see confirmation bias, where people focus their analysis on proving the wisdom of what they already want to do, as opposed to looking for a fact-based reality. Just having AI perform a default analysis that doesn’t aim to satisfy the boss is useful, and the team can then try to understand why that is different than the management hypothesis, triggering a much richer debate.

In terms of social dynamics, agency problems can create conflicts of interest. Every business unit [BU] leader thinks that their BU should get the most resources and will deliver the most value, or at least they feel they should advocate for their business. AI provides a neutral way based on systematic data to manage those debates. It’s also useful for executives with decision authority, since we all know that short-term pressures and the need to make the quarterly and annual numbers lead people to make different decisions on the 31st of December than they do on January 1st or October 1st. Like the story of Ulysses and the sirens, you can use AI to remind you that you wanted something different three months earlier. The CEO still decides; AI can just provide that extra nudge.

Joanna Pachner: It’s like you have Spock next to you, who is dispassionate and purely analytical.

Yuval Atsmon: That is not a bad analogy—for Star Trek fans anyway.

Joanna Pachner: Do you have a favorite application of AI in strategy?

Yuval Atsmon: I have worked a lot on resource allocation, and one of the challenges, which we call the hockey stick phenomenon, is that executives are always overly optimistic about what will happen. They know that resource allocation will inevitably be defined by what you believe about the future, not necessarily by past performance. AI can provide an objective prediction of performance starting from a default momentum case: based on everything that happened in the past and some indicators about the future, what is the forecast of performance if we do nothing? This is before we say, “But I will hire these people and develop this new product and improve my marketing”— things that every executive thinks will help them overdeliver relative to the past. The neutral momentum case, which AI can calculate in a cold, Spock-like manner, can change the dynamics of the resource allocation discussion. It’s a form of predictive intelligence accessible today and while it’s not meant to be definitive, it provides a basis for better decisions.

Joanna Pachner: Do you see access to technology talent as one of the obstacles to the adoption of AI in strategy, especially at large companies?

Yuval Atsmon: I would make a distinction. If you mean machine-learning and data science talent or software engineers who build the digital tools, they are definitely not easy to get. However, companies can increasingly use platforms that provide access to AI tools and require less from individual companies. Also, this domain of strategy is exciting—it’s cutting-edge, so it’s probably easier to get technology talent for that than it might be for manufacturing work.

The bigger challenge, ironically, is finding strategists or people with business expertise to contribute to the effort. You will not solve strategy problems with AI without the involvement of people who understand the customer experience and what you are trying to achieve. Those who know best, like senior executives, don’t have time to be product managers for the AI team. An even bigger constraint is that, in some cases, you are asking people to get involved in an initiative that may make their jobs less important. There could be plenty of opportunities for incorpo­rating AI into existing jobs, but it’s something companies need to reflect on. The best approach may be to create a digital factory where a different team tests and builds AI applications, with oversight from senior stakeholders.

The big challenge is finding strategists to contribute to the AI effort. You are asking people to get involved in an initiative that may make their jobs less important.

Joanna Pachner: Do you think this worry about job security and the potential that AI will automate strategy is realistic?

Yuval Atsmon: The question of whether AI will replace human judgment and put humanity out of its job is a big one that I would leave for other experts.

The pertinent question is shorter-term automation. Because of its complexity, strategy would be one of the later domains to be affected by automation, but we are seeing it in many other domains. However, the trend for more than two hundred years has been that automation creates new jobs, although ones requiring different skills. That doesn’t take away the fear some people have of a machine exposing their mistakes or doing their job better than they do it.

Joanna Pachner: We recently published an article about strategic courage in an age of volatility  that talked about three types of edge business leaders need to develop. One of them is an edge in insights. Do you think AI has a role to play in furnishing a proprietary insight edge?

Yuval Atsmon: One of the challenges most strategists face is the overwhelming complexity of the world we operate in—the number of unknowns, the information overload. At one level, it may seem that AI will provide another layer of complexity. In reality, it can be a sharp knife that cuts through some of the clutter. The question to ask is, Can AI simplify my life by giving me sharper, more timely insights more easily?

Joanna Pachner: You have been working in strategy for a long time. What sparked your interest in exploring this intersection of strategy and new technology?

Yuval Atsmon: I have always been intrigued by things at the boundaries of what seems possible. Science fiction writer Arthur C. Clarke’s second law is that to discover the limits of the possible, you have to venture a little past them into the impossible, and I find that particularly alluring in this arena.

AI in strategy is in very nascent stages but could be very consequential for companies and for the profession. For a top executive, strategic decisions are the biggest way to influence the business, other than maybe building the top team, and it is amazing how little technology is leveraged in that process today. It’s conceivable that competitive advantage will increasingly rest in having executives who know how to apply AI well. In some domains, like investment, that is already happening, and the difference in returns can be staggering. I find helping companies be part of that evolution very exciting.

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