where to download research papers free

15 Best Websites to Download Research Papers for Free

Is your thirst for knowledge limited by expensive subscriptions? Explore the best websites to download research papers for free and expand your academic reach.

With paywalls acting like impenetrable fortresses, accessing scholarly articles becomes a herculean task. However, a beacon of hope exists in the form of free-access platforms, quenching our thirst for intellectual wisdom. Let’s set sail on this scholarly journey.

Table of Contents

Today’s champions of academia aren’t just about offering free access, they uphold ethics and copyright respectability. Let’s delve into these repositories that are reshaping the academia world. You can download free research papers from any of the following websites.

Best Websites to Download Research Papers

#1. sci-hub – best for accessing paywalled academic papers.

Despite its contentious standing, Sci-Hub offers an invaluable service to knowledge-seekers. While navigating the tightrope between access and legality, it represents a game-changing force in the world of academic research.

Source: https://www.sci-hub.se

#2. Library Genesis (Libgen) – Best for a Wide Range of Books and Articles

It’s not just a repository, but a vibrant confluence of multiple disciplines and interests, catering to the unique intellectual appetite of each knowledge seeker.

What are the benefits of Libgen?

Source: https://libgen.is

#3. Unpaywall – Best for Legal Open Access Versions of Scholarly Articles

What are the benefits of Unpaywall?

#4. Directory of Open Access Journals (DOAJ) – Best for Peer-Reviewed Open Access Journals

Source: https://doaj.org

#5. Open Access Button – Best for Free Versions of Paywalled Articles

What are the benefits of Open Access Button?

#6. Science Open – Best for a Wide Variety of Open Access Scientific Research

Consider Science Open as a bustling town square in the city of scientific knowledge, where scholars from all walks of life gather, discuss, and dissect over 60 million articles. 

You might also like:

#7. CORE – Best for Open Access Content Across Disciplines

With its unparalleled aggregation and comprehensive access, CORE embodies the grand orchestra of global research. It stands as an essential tool in the modern researcher’s toolkit.

#8. ERIC – Best for Education Research

What are the benefits of ERIC?

#9. PaperPanda – Best for Free Access to Research Papers

It’s like having a personal research assistant, guiding you through the maze of scholarly literature.

#10. Citationsy Archives – Best for Research Papers from Numerous Fields

Source: https://citationsy.com

#11. OA.mg – Best for Direct Download Links to Open Access Papers

Source: https://oa.mg

#12. Social Science Research Network (SSRN) – Best for Social Sciences and Humanities Research

SSRN serves as an invaluable resource for researchers in the social sciences and humanities, fostering a community that drives innovation and advancements in these fields.

#13. Project Gutenberg – Best for Free Access to eBooks

Project Gutenberg serves as a testament to the power of literature and the accessibility of knowledge. It enables readers worldwide to embark on intellectual journeys through its extensive collection of free eBooks.

#14. PLOS (Public Library of Science) – Best for Open Access to Scientific and Medical Research

As a leading publisher of open access research, PLOS fosters the dissemination of cutting-edge scientific discoveries to a global audience. 

#15. arXiv.org – Best for Preprints in Science, Mathematics, and Computer Science

In a world where knowledge is king, accessing a research paper shouldn’t feel like an impossible task. Thanks to these free and innovative websites, we can escape the barriers of paywalls and dive into a vast ocean of intellectual wealth. 

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Download Research Papers for Free: Legal and Ethical Methods

14 Legal Ways to Download Research Papers for Free: The Ultimate Guide

Are you a student, researcher, or curious individual looking to access scholarly articles without breaking the bank? You’re in luck! This comprehensive guide will walk you through various legal and ethical methods to Download Research Papers for Free. We’ll cover everything from open-access databases to contacting authors directly, ensuring you have all the tools to fuel your academic pursuits.

Why Access to Research Papers Matters

Before we dive into the methods, let’s quickly address why free access to research papers is so crucial:

• Advancing knowledge: Open access to research promotes the spread of ideas and accelerates scientific progress.
• Equalizing opportunities: Free access levels the playing field for researchers and students worldwide, regardless of their financial resources.
• Encouraging collaboration: When research is freely available, it’s easier for scientists to build upon each other’s work and collaborate across institutions.

Now, let’s explore the various ways you can legally and ethically obtain research papers without spending a dime.

10 Legal Ways to Download Research Papers for Free: The Ultimate Guide

1. leverage open access databases.

Open-access databases are treasure troves of freely available scholarly articles. Here are some of the best options:

• PubMed Central (PMC): A free full-text archive of biomedical and life sciences journal literature.
• Directory of Open Access Journals ( DOAJ ): A community-curated online directory that indexes high-quality, open-access, peer-reviewed journals.
• arXiv: A repository of electronic preprints for physics, mathematics, computer science, and related fields.
• CORE: The world’s largest collection of open-access research papers.

Pro tip: Many of these databases offer email alerts for new papers in your area of interest, helping you stay up-to-date with the latest research.

2. Utilize Academic Search Engines

Specialized academic search engines can help you find both open-access and potentially accessible papers:

• Google Scholar: The most popular academic search engine, with features like cited by and related articles.
• Microsoft Academic: A free public web search engine for academic publications and literature.
• Semantic Scholar: An AI-powered research tool for scientific literature.

These search engines often provide direct links to free full-text versions when available or point you towards institutional repositories.

3. Explore Institutional Repositories

Many universities and research institutions maintain their own repositories of scholarly work produced by their faculty and students. These repositories often make papers freely available to the public. Try searching for “[University Name] repository” to find these goldmines of information.

4. Check Author’s Websites and Social Media

Many researchers maintain personal websites or profiles on academic social networks where they share their work. Try searching for the author’s name followed by their institution or area of expertise. Platforms to check include:

• ResearchGate
• Academia.edu

5. Contact the Authors Directly

If you can’t find a free version of a paper, don’t hesitate to reach out to the authors. Most researchers are happy to share their work and may send you a copy of their paper. Look for the corresponding author’s email address in the paper’s abstract or contact information.

6. Use Browser Extensions

Several browser extensions can help you find free versions of paywalled articles:

• Unpaywall: A legal and simple tool that searches for free versions of scholarly articles.
• Open Access Button: Searches for free, legal copies of research papers.
• Kopernio: Helps you access PDF versions of scientific articles.

7. Take Advantage of Preprint Servers

Preprint servers host early versions of research papers before they undergo peer review. While these papers should be approached with caution, they can be valuable sources of cutting-edge research:

• bioRxiv: For life sciences
• chemRxiv: For chemistry and related fields
• SocArXiv: For social sciences

8. Utilize Your Library’s Resources

Don’t forget about your local library! Many public and university libraries offer:

• Access to academic databases
• Interlibrary loan services
• Remote access to digital resources

Even if you’re not currently a student, some libraries offer cards to community members that include database access.

9. Explore Sci-Hub Alternatives

While Sci-Hub is popular, it operates in a legal grey area. Instead, consider these alternatives:

• Open Access Button: A legal tool that helps you request access to research papers.
• Lazy Scholar: A browser extension that finds free full-text PDF versions of articles.
• Unpaywall: Another legal alternative that finds open-access versions of articles.

10. Stay Informed About Open Access Initiatives

Keep an eye on developments in the open access movement. Initiatives like Plan S are working to make all publicly funded research freely available. Following these developments can help you stay ahead of the curve in accessing free research.

Ethical Considerations and Best Practices

While accessing free research papers, it’s crucial to keep these ethical considerations in mind:

• Respect copyright laws and publisher agreements.
• Use obtained papers for personal research and educational purposes only.
• Properly cite all sources in your work.
• Support open access initiatives when possible.

Accessing research papers for free is not only possible but also increasingly important in our interconnected world. By utilizing the methods outlined in this guide, you can tap into a vast wealth of knowledge without breaking the bank. Remember to always respect copyright laws and support the open access movement to ensure that knowledge remains freely accessible to all.

14 Websites to Download Research Paper for Free – 2024 – Alternative Methods

Collecting and reading relevant research articles to one’s research areas is important for PhD scholars. However, downloading a research paper is one of the most difficult tasks for any research scholar. You must pay for access to high-quality research materials or subscribe to the journal or publication. In this article, ilovephd lists the top 14 websites to download research papers, journals, books, datasets, patents, and conference proceedings for free.

Check the 14 best free websites to download and read research papers listed below:

Sci-Hub is a website link with over 64.5 million academic papers and articles available for direct download. It bypasses publisher paywalls by allowing access through educational institution proxies.  To download papers Sci-Hub  stores papers in its repository, this storage is called Library Genesis (LibGen) or Library Genesis Proxy 2024. It helps researchers to download free articles by simply using the Digital Object Identifier (DOI) of the article.

Visit: Working Sci-Hub Proxy Links – 2024

2. Z-Library

The Z-Library clones Library Genesis, a shadow library project. Z-Library facilitates file sharing of scholarly journal articles, academic texts, and general-interest books (including some copyrighted materials). While most of its books come from Library Genesis, further expanding the collection, users can also directly upload content to the site. This user-contributed content helps to make literature even more widely available. Additionally, individuals can donate to the website’s repository, furthering their mission of free access.

Z-Library claims to have a massive collection, boasting more than 10,139,382 Books books and 84,837,646 Articles articles as of April 25, 2024. According to the project’s page for academic publications (at booksc.org), it aspires to be “the world’s largest e-book library” as well as “the world’s largest scientific papers repository.” Interestingly, Z-Library also describes itself as a donation-based non-profit organization.

Visit Z-Library – You can Download 70,000,000+ scientific articles for free

3. Library Genesis

The Library Genesis aggregator is a community aiming to collect and catalog item descriptions for the most scientific, scientific, and technical directions, as well as file metadata. In addition to the descriptions, the aggregator contains only links to third-party resources hosted by users. All information posted on the website is collected from publicly available public Internet resources and is intended solely for informational purposes.

Visit: libgen.li

4. Unpaywall – Free Research Paper Download

Unpaywall harvests Open Access content from over 50,000 publishers and repositories, and makes it easy to find, track, and use. It is integrated into thousands of library systems, search platforms, and other information products worldwide. If you’re involved in scholarly communication, there’s a good chance you’ve already used Unpaywall data.

Unpaywall is run by OurResearch, a nonprofit dedicated to making scholarships more accessible to everyone. Open is our passion. So it’s only natural our source code is open, too.

Visit: unpaywall.org

5. GetTheResearch.org

GetTheResearch.org is an  Artificial Intelligence(AI)  powered search engine for searching and understanding  scientific articles  for researchers and scientists. It was developed as a part of the  Unpaywall  project. Unpaywall is a database of 23,329,737 free scholarly Open Access(OA) articles from over 50,000 publishers and repositories, and make it easy to find, track, and use.

Visit: Find and Understand 25 Million Peer-Reviewed Research Papers for Free

6. Directory of Open Access Journals (DOAJ)

DOAJ (Directory of Open Access Journals) was launched in 2003 with 300 open-access journals. Today, this independent index contains almost 17,500 peer-reviewed, open-access journals covering all areas of science, technology, medicine, social sciences, arts, and humanities. Open-access journals from all countries and in all languages are accepted for indexing.

DOAJ is financially supported by many libraries, publishers, and other like-minded organizations. Supporting DOAJ demonstrates a firm commitment to open access and the infrastructure that supports it.

Visit: doaj.org

7. Researcher

The Researcher is a free journal-finding mobile application that helps you to read new journal papers every day that are relevant to your research. It is the most popular mobile application used by more than 3 million scientists and researchers to keep themselves updated with the latest academic literature.

Visit: 10 Best Apps for Graduate Students 

8. Science Open

ScienceOpen  is a discovery platform with interactive features for scholars to enhance their research in the open, make an impact, and receive credit for it. It provides context-building services for publishers, to bring researchers closer to the content than ever before. These advanced search and discovery functions, combined with post-publication peer review, recommendation, social sharing, and collection-building features make  ScienceOpen  the only research platform you’ll ever need.

Visit: scienceopen.com

OA.mg is a search engine for academic papers. Whether you are looking for a specific paper, or for research from a field, or all of an author’s works – OA.mg is the place to find it.

Visit: oa.mg

10. Internet Archive Scholar

Internet Archive Scholar (IAS) is a full-text search index that includes over 25 million research articles and other scholarly documents preserved in the Internet Archive. The collection spans from digitized copies of eighteenth-century journals through the latest Open Access conference proceedings and pre-prints crawled from the World Wide Web.

Visit: Sci hub Alternative – Internet Archive Scholar

11. Citationsy Archives

Citationsy was founded in 2017 after the reference manager Cenk was using at the time, RefMe, was shut down. It was immediately obvious that the reason people loved RefMe — a clean interface, speed, no ads, and simplicity of use — did not apply to CiteThisForMe. It turned out to be easier than anticipated to get a rough prototype up.

Visit: citationsy.com

CORE is the world’s largest aggregator of open-access research papers from repositories and journals. It is a not-for-profit service dedicated to the open-access mission. We serve the global network of repositories and journals by increasing the discoverability and reuse of open-access content.

It provides solutions for content management, discovery, and scalable machine access to research. Our services support a wide range of stakeholders, specifically researchers, the general public, academic institutions, developers, funders, and companies from a diverse range of sectors including but not limited to innovators, AI technology companies, digital library solutions, and pharma.

Visit: core.ac.uk

13. Dimensions

The database called “Dimensions” covers millions of research publications connected by more than 1.6 billion citations, supporting grants, datasets, clinical trials, patents, and policy documents.

Dimensions is the most comprehensive research grants database that links grants to millions of resulting publications, clinical trials, and patents. It

provides up-to-the-minute online attention data via Altmetric, showing you how often publications and clinical trials are discussed around the world. 226m Altmetric mentions with 17m links to publications.

Dimensions include datasets from repositories such as Figshare, Dryad, Zenodo, Pangaea, and many more. It hosts millions of patents with links to other citing patents as well as to publications and supporting grants.

Visit: dimensions.ai

14. PaperPanda – Download Research Papers for Free

PaperPanda is a Chrome extension that uses some clever logic and the Panda’s detective skills to find you the research paper PDFs you need. Essentially, when you activate PaperPanda it finds the DOI of the paper from the current page, and then goes and searches for it. It starts by querying various Open Access repositories like OpenAccessButton, OaDoi, SemanticScholar, Core, ArXiV , and the Internet Archive. You can also set your university library’s domain in the settings (this feature is in the works and coming soon). PaperPanda will then automatically search for the paper through your library. You can also set a different custom domain in the settings.

Visit: PaperPanda

I hope this article will help you to know some of the best websites to download research papers and journals for free. By utilizing open-access databases, free search tools, and potentially even your local university library, you can access a wealth of valuable scholarly information without infringing on a copyright. Remember, ethical practices in research paper downloading are important, so always prioritize legal access to materials whenever possible. Happy researching!

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Get around this paywall in a flash: DOI: 10.1126/science.196.4287.293 URL: http://science.sciencemag.org/content/196/4287/293/tab-pdf PMC (Pubmed Central) ID: PMC4167664 Pubmed ID: 17756097 Title: Ribulose bisphosphate carboxylase: a two-layered, square-shaped molecule of symmetry 422 Citation: Baker, T. S., Eisenberg, D., & Eiserling, F. (1977). Ribulose Bisphosphate Carboxylase: A Two-Layered, Square-Shaped Molecule of Symmetry 422. Science, 196(4287), 293-295. doi:10.1126/science.196.4287.293 or try your favourite citation format (Harvard, Bibtex, etc).

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How To Download Research Papers For Free: Sci-hub, LibGen, etc.

One of the biggest problems about accessing research papers is the cost. At times, you may have encountered the right papers for your research, only to be frustrated that it needs to be paid for. 

There are many ways to download research papers for free, using websites like Oa.mg, LibGen, and more. This post will talk about these platforms, so you can go try it out yourself.

Open Access vs Paywalled Research Papers

There may be many research papers around, but there are some that remain behind paywalls. While the demand for open access to research is undeniable, certain factors contribute to the persistence of paywalled content.

Publishing Companies Need The Funds

Publishers like Elsevier and Wiley operate on a model where subscription fees and paywalls helps to pay for costs such as:

• peer review,
• typesetting, and
• maintaining digital platforms.

This economic structure ensures the sustainability of publishing houses but limits access to those without the means to pay. 

Protect Copyright Laws

Copyright laws further entrench the paywall system. Publishers hold the rights to the vast majority of journal articles, making it illegal to distribute copyrighted material without consent.

This legal framework underpins the operation of paywalls, despite the ethical debate surrounding access to publicly funded research.

In response, platforms like PaperPanda and Unpaywall have emerged, utilizing clever logic and browser extensions to find open access versions of papers, leveraging repositories like the Directory of Open Access Journals.

Paid Papers Seem To Have Higher Value

The perceived value of peer-reviewed journal articles also plays a role. Academic institutions and researchers place high regard on published work, often equating it with career advancement and credibility.

This prestige associated with peer-reviewed publications incentivizes researchers to publish in traditional journals, despite their papers going to be behind a paywall.

Open access platforms and repositories strive to balance this by offering peer-reviewed articles for free, challenging the traditional valuation of scholarly work.

Despite these challenges, the landscape is shifting. Open access initiatives are gaining traction, challenging the traditional publishing model and advocating for free access to research.

As the academic community and the public demand more equitable access to knowledge, the future might see a paradigm shift towards a more open and accessible repository of human understanding.

Best Websites To Download Research Papers For Free

If you are looking to dive into the vast ocean of academic knowledge without hitting a paywall, certain websites are akin to hidden treasures.

These platforms offer free access to millions of research papers and journal articles, covering various areas of science and beyond.

Often dubbed as the “Pirate Bay” of scientific articles, Sci-Hub breaks down the barriers to knowledge by providing free access to research papers that are otherwise locked behind paywalls.

Founded by Alexandra Elbakyan in 2011, this website uses donated institutional logins to bypass publisher restrictions, offering a direct download button for the paper you’re after.

It’s a controversial but popular choice to download papers, with a repository that includes articles from nearly every field of research. Users simply need to find the DOI (Digital Object Identifier) of the paper they want, and Sci-Hub does the rest.

Library Genesis (LibGen)

This is more than just a repository for scientific papers; it’s a comprehensive database of:

• academic books,
• comics, and

Library Genesis offers a wide range of academic and non-academic content, making it a versatile resource for researchers, students, and the general public alike. 

The platform operates on the principle of sharing knowledge freely, and you can easily find and download PDFs of the research papers you need.

This is a free browser extension for Chrome and Firefox that provides legal access to millions of open access research papers.

When you stumble upon a paper online, Unpaywall’s clever logic checks various open access repositories and finds you a legal, freely available copy. 

It’s like having a digital detective at your disposal, dedicated to finding open-access versions of paywalled articles.

Directory of Open Access Journals (DOAJ)

The DOAJ is an online directory that indexes and provides access to high-quality, open access, peer-reviewed journals.

It covers all subjects and languages, making it an invaluable tool for researchers worldwide.

The directory is meticulously curated, ensuring that all listed journals adhere to a stringent open access policy. For those seeking reputable sources, this is a go-to place to find open access research papers across disciplines.

OA.mg is a tool designed to facilitate free access to scientific papers that are otherwise behind paywalls.

It operates by leveraging the open access movement’s resources, indexing millions of freely available research papers.

To obtain a paper, you typically need the DOI (Digital Object Identifier) of the desired article. By entering this DOI into OA.mg, the platform searches through various open access repositories and databases to find a legally accessible version of the paper.

This service simplifies the process of finding open access versions of research papers, making academic literature more accessible to everyone.

Utilizing some of the most advanced search algorithms, PaperPanda operates by querying various open access repositories to find you the research paper pdfs you need.

It’s especially useful for those who don’t have the DOI of a paper, as PaperPanda’s search capabilities can locate papers based on:

• author names, or

The platform aims to democratize access to scientific literature by making it as straightforward as possible to find and download research papers for free.

Download Research Papers For Free

Each of these websites plays a crucial role in the ongoing push towards open access, ensuring that scientific knowledge is available to anyone curious enough to seek it out.

Whether you’re conducting a literature review, working on a thesis, or simply indulging in a personal quest for knowledge, these platforms can provide you with the resources you need, free of charge.

Dr Andrew Stapleton has a Masters and PhD in Chemistry from the UK and Australia. He has many years of research experience and has worked as a Postdoctoral Fellow and Associate at a number of Universities. Although having secured funding for his own research, he left academia to help others with his YouTube channel all about the inner workings of academia and how to make it work for you.

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The top list of academic search engines

1. Google Scholar

4. science.gov, 5. semantic scholar, 6. baidu scholar, get the most out of academic search engines, frequently asked questions about academic search engines, related articles.

Academic search engines have become the number one resource to turn to in order to find research papers and other scholarly sources. While classic academic databases like Web of Science and Scopus are locked behind paywalls, Google Scholar and others can be accessed free of charge. In order to help you get your research done fast, we have compiled the top list of free academic search engines.

Google Scholar is the clear number one when it comes to academic search engines. It's the power of Google searches applied to research papers and patents. It not only lets you find research papers for all academic disciplines for free but also often provides links to full-text PDF files.

• Coverage: approx. 200 million articles
• Abstracts: only a snippet of the abstract is available
• Related articles: ✔
• References: ✔
• Cited by: ✔
• Links to full text: ✔
• Export formats: APA, MLA, Chicago, Harvard, Vancouver, RIS, BibTeX

BASE is hosted at Bielefeld University in Germany. That is also where its name stems from (Bielefeld Academic Search Engine).

• Coverage: approx. 136 million articles (contains duplicates)
• Abstracts: ✔
• Related articles: ✘
• References: ✘
• Cited by: ✘
• Export formats: RIS, BibTeX

CORE is an academic search engine dedicated to open-access research papers. For each search result, a link to the full-text PDF or full-text web page is provided.

• Coverage: approx. 136 million articles
• Links to full text: ✔ (all articles in CORE are open access)
• Export formats: BibTeX

Science.gov is a fantastic resource as it bundles and offers free access to search results from more than 15 U.S. federal agencies. There is no need anymore to query all those resources separately!

• Coverage: approx. 200 million articles and reports
• Links to full text: ✔ (available for some databases)
• Export formats: APA, MLA, RIS, BibTeX (available for some databases)

Semantic Scholar is the new kid on the block. Its mission is to provide more relevant and impactful search results using AI-powered algorithms that find hidden connections and links between research topics.

• Coverage: approx. 40 million articles
• Export formats: APA, MLA, Chicago, BibTeX

Although Baidu Scholar's interface is in Chinese, its index contains research papers in English as well as Chinese.

• Coverage: no detailed statistics available, approx. 100 million articles
• Abstracts: only snippets of the abstract are available
• Export formats: APA, MLA, RIS, BibTeX

RefSeek searches more than one billion documents from academic and organizational websites. Its clean interface makes it especially easy to use for students and new researchers.

• Coverage: no detailed statistics available, approx. 1 billion documents
• Abstracts: only snippets of the article are available
• Export formats: not available

Consider using a reference manager like Paperpile to save, organize, and cite your references. Paperpile integrates with Google Scholar and many popular databases, so you can save references and PDFs directly to your library using the Paperpile buttons:

Google Scholar is an academic search engine, and it is the clear number one when it comes to academic search engines. It's the power of Google searches applied to research papers and patents. It not only let's you find research papers for all academic disciplines for free, but also often provides links to full text PDF file.

Semantic Scholar is a free, AI-powered research tool for scientific literature developed at the Allen Institute for AI. Sematic Scholar was publicly released in 2015 and uses advances in natural language processing to provide summaries for scholarly papers.

BASE , as its name suggest is an academic search engine. It is hosted at Bielefeld University in Germany and that's where it name stems from (Bielefeld Academic Search Engine).

CORE is an academic search engine dedicated to open access research papers. For each search result a link to the full text PDF or full text web page is provided.

Science.gov is a fantastic resource as it bundles and offers free access to search results from more than 15 U.S. federal agencies. There is no need any more to query all those resources separately!

Open Access. Powered by Scholars. Published by Universities. ®

Digital Commons Network ™

The Digital Commons Network brings together free, full-text scholarly articles from hundreds of universities and colleges worldwide. Curated by university librarians and their supporting institutions, the Network includes a growing collection of peer-reviewed journal articles, book chapters, dissertations, working papers, conference proceedings, and other original scholarly work.

Sci-Hub is the most controversial project in today science. The goal of Sci-Hub is to provide free and unrestricted access to all scientific knowledge ever published in journal or book form.

Today the circulation of knowledge in science is restricted by high prices. Many students and researchers cannot afford academic journals and books that are locked behind paywalls. Sci-Hub emerged in 2011 to tackle this problem. Since then, the website has revolutionized the way science is being done.

Sci-Hub is helping millions of students and researchers, medical professionals, journalists and curious people in all countries to unlock access to knowledge. The mission of Sci-Hub is to fight every obstacle that prevents open access to knowledge: be it legal, technical or otherwise.

To get more information visit the about Sci-Hub section.

Thank you for joining Sci-Hub mailing list!

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Top 11 Websites for Free Research Paper Downloads

For PhD researchers, it’s critical to gather and read research publications that are pertinent to their areas of study. However, downloading a research paper is one of the most challenging chores for any research scholar. To gain access to high-quality research resources, one needs to pay a fee or subscribe to a journal or publication. In this post, We have shown you how to get a research paper for free.

Sci-Hub was originally launched by Alexandra Elbakyan, a Kazakhstani graduate student, in 2011. It is a website known for providing access to various academic articles and papers using educational institution access and its own collection of downloaded articles and papers. In fact, you can download almost 99% of all scientific papers and articles in existence on Sci-Hub.

Many internet service providers (especially in developed countries) have blocked it at present.  Sci-Hub’s own statistics show that the chances of a request for download being successful are 99%. It processes more than 200,000 requests every day.

How to use Sci-Hub?

• Visit https://sci-hub.se/ (Use a VPN to access it if blocked.) You can also checkout Visit: Working Sci-Hub Proxy Links – 2022 ( https://www.ilovephd.com/working-sci-hub-proxy-links-updated/ )
• Enter the full name of the DOI, URL, or URL in the paper that you would like to download.
• Select”Open” or click the “Open” click.

2. Library Genesis

Library Genesis (Libgen) is a file-sharing based shadow library website for scholarly journal articles, academic and general-interest books, images, comics, audiobooks, and magazines. The site enables free access to content that is otherwise paywalled or not digitized elsewhere. This website was threatened with legal action by Elsevier one of the largest publishing companies of technical, scientific medical and scientific research papers in the year 2015.

You can find a research paper or book on Library Genesis by following the steps given below:

• Visit Library Genesis’ official website (libgen.li).
• Type the name of whatever you’re looking for into the search field, and click the “search!” button.
• Click on the name of a book or research paper in the list of results, and choose one of the available mirrors.
• Proceed to download the book or research paper and save it to your device.

3. Z-Library

Z-Library is a clone of Library Genesis, a shadow library project that allows users to share scholarly journal articles, academic texts, and general-interest books via file sharing (some of which are pirated). The majority of its books come from Library Genesis, however, some are posted directly to the site by individuals.

Individuals can also donate to the website’s repository to make literature more widely available. Z-library claims to have more than 10,139,382 Books and 84,837,646 Articles articles as of April 25, 2022.

The steps to download Z-Library books for free are as follows:

Step 1: Go to the Z-Library website ( https://singlelogin.me/ )  and Sign In.

Step 2: Browse through the categories or use the search bar to find the book you want.

Step 3: Click on the book to open it.

Step 4: Click on the download button to download the book.

4. Unpaywall

This is a huge database that contains more than 21 million academic works from over fifty thousand content repositories as well as publishers. The content in the database is replicated from government resources so downloading them is legal. The authors claim they are able to access around 80-85 percent of all scientific papers accessible on their website. 

You can utilize Google’s Chrome extension to quickly get them at any time. 

In order to do this, you have to follow the instructions listed below:

• Visit https://unpaywall.org/products/extension
• Select on the “Add the Chrome” button. Chrome” option.
• Simply click “Add the store to Chrome” in the Chrome Web Store page in addition.
• Keep an eye on the extension until it is installed.
• After installing the extension, it will work automatically and will appear whenever you go to the site of a paywalled research paper in the database of Unpaywall’s open databases. All you have just click on the green Unpaywall button to allow the article to be displayed immediately.

5. Directory of Open Access Journals

A multidisciplinary, community-curated directory, the Directory of Open Access Journals (DOAJ) gives researchers access to high-quality peer-reviewed journals. It has archived more than two million articles from 17,193 journals, allowing you to either browse by subject or search by keyword.

The site was launched in 2003 with the aim of increasing the visibility of OA scholarly journals online. Content on the site covers subjects from science, to law, to fine arts, and everything in between. DOAJ has a commitment to “increase the visibility, accessibility, reputation, usage and impact of quality, peer-reviewed, OA scholarly research journals globally, regardless of discipline, geography or language.”

It can be used to search for and download research papers for free:

• Visit: https://doaj.org/
• Input your keywords in the search field , then hit enter.
• Choose the research paper you wish to download.
• Hit on the “Full Text” button that is located just below the abstract.

6.ScienceOpen

ScienceOpen offers a professional network platform for academics that gives access to more than 40 million research papers from all fields of science. Although you do need to register to view the full text of articles, registration is free. The advanced search function is highly detailed, allowing you to find exactly the research you’re looking for. You can also bookmark articles for later research. There are extensive networking options, including your Science Open profile, a forum for interacting with other researchers, the ability to track your usage and citations, and an interactive bibliography. Users have the ability to review articles and provide their knowledge and insight within the community.

To search for research papers with the help of Science open:

• Go to: http://about.scienceopen.com/ .
• Select on the “green “Search” button located in the upper right corner.
• Enter your search terms into the search box. In addition to the keywords, you can look up authors’ collections, journals publishers, as well as others.

OA.mg is a search engine for academic papers. Whether you are looking for a specific paper, or for research from a field, or all of an author’s works – OA.mg is the place to find it. Research papers can be found by using OA.mg by following these steps:

• Follow the link below: https://oa.mg
• You can enter your keywords or DOI number into the search field that is available there.
• Select on the “search” button, and wait for results to show up.
• In the search results Download any research document you require by clicking this link for download.

8.Citationsy Archives

Citationsy Archives allows you to look up journals and papers to download, download them, and (obviously) incorporate them into your work.It is important to note that you can access Citationsy Archives with or without an account. 

All you have to do is make a request, and it will then search for the exact phrase in all research papers around the world and show the pertinent matches to you. Click on each of them to view more information, and then access it directly from the search results. 

The platform also allows you to download the papers using a number of different and totally open access and legal options. 

Use Citationsy Archives from https://citationsy.com/archives/

CORE is the world’s largest aggregator of open access research papers from repositories and journals. It is a not-for-profit service dedicated to the open access mission. They serve the global network of repositories and journals by increasing the discoverability and reuse of open access content.

To find a research article using CORE:

• Visit: https://core.ac.uk/
• Enter your search terms into the search box.
• Hit the “Search” link.
• Select on the “Get PDF” button to download any research document you are looking for.

10. PaperPanda

PaperPanda is a Chrome extension that uses some clever logic and the Panda’s detective skills to find you the research paper PDFs you need. Essentially, when you activate PaperPanda it finds the DOI of the paper from the current page, and then goes and searches for it. It starts by querying various Open Access repositories like OpenAccessButton, OaDoi, SemanticScholar, Core, ArXiV, and the Internet Archive. You can also set your university libraries domain in the settings (this feature is in the works and coming soon). PaperPanda will then automatically search for the paper through your library. You can also set a different custom domain in the settings.

11.Dimensions

Dimensions covers millions of research publications connected by more than 1.6 billion citations, supporting grants, datasets, clinical trials, patents and policy documents. Dimensions is the most comprehensive research grants database which links grants to millions of resulting publications, clinical trials and patents.

Dimensions includes datasets from repositories such as Figshare, Dryad, Zenodo, Pangaea, and many more. It hosts millions of patents with links to other citing patents as well as to publications and supporting grants.

Visit: https://www.dimensions.ai/

https://www.scribendi.com/academy/articles/free_online_journal_and_research_databases.en.html

https://gauravtiwari.org/download-research-papers-for-free/

8 Sites to Download Research Papers for Free – 2020

https://microbiologynote.com/12-top-websites-to-download-research-papers-for-free/

14 Websites to Download Research Paper for Free – 2023

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Z-Library is legal? You can Download 70,000,000+ scientific articles for free

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First, pin PaperPanda to your toolbar like this:

When you’re on a page for a research paper, click the little panda icon in the toolbar., the panda will then run off and find the pdf for you., open settings to adjust what domain paperpanda uses to find your papers..

Z-Library Project - Search & Download Free Books | ZLibrary

Z-library is a free online library containing over 100 million books. Anyone can download e-books from our website without registration and in many formats.

Welcome to ZLibrary, a comprehensive digital library platform designed to provide free access to a vast collection of e-books and articles spanning various genres and topics. In this ultimate guide, we'll explore the benefits of using ZLibrary over traditional libraries and other online sources, helping you discover, download, and maximize your digital reading experience.

What is Z Library?

Z Library is a digital library that allows users to download books for free. The website has many books covering various genres, including fiction, non-fiction, textbooks, and research papers. Users can search for books by author, title, or ISBN, and the website also provides recommendations based on the user's search history.

Exploring ZLibrary's Collection

Variety of content.

No registration or fees are required, and the books are available in ePub, Kindle, HTML, and plain text formats. Browse our 10 million+ titles or use a powerful search to find exactly what you're looking for.

User-friendly Search Options

Navigating ZLibrary's vast collection is a breeze, thanks to its user-friendly search options. You can conduct a simple keyword search or use advanced search filters to refine your query by language, publication year, or file format.

Content Sources

ZLibrary's impressive selection comes from collaborations with libraries and publishers, and user uploads, ensuring a continually growing and diverse collection of materials for you to explore.

Benefits of Using Z-Library

One of the significant advantages of using Z-Library is the cost-saving aspect. Books can be expensive, and even borrowing from the library can incur fees. With Z-Library, users can access a vast selection of books for free. The website also provides the convenience of accessing books online, making it easy to read on the go or from the comfort of your home.

In addition to cost-saving and convenience, Z-Library also provides a vast selection of books. With over 6 million books available, there is something for everyone. Whether you're looking for a classic novel, a textbook for school, or a research paper for work, Z-Library has you covered.

How to Use ZLibrary.to?

To use ZLibrary, users need to create an account. Creating an account is easy and free, allowing users to save their search history and bookmark books for future reference. Once users have created an account, they can search for books by author, title, or ISBN. The website will then provide a list of books that match the search criteria. Users can then download the book in the format of their choice, including PDF, EPUB, and MOBI.

ZLibrary also provides some helpful tools and features for users. For example, users can filter their search results by language, year of publication, and category. The website also has a rating system, allowing users to see the top-rated books in each category.

Frequently Asked Questions

1 . Is it legal to download books from Z Library?

ZLibrary operates under the concept of fair use, which allows users to access copyrighted material for educational purposes. However, we recommend checking your local laws before downloading any material.

2 . What formats are available for book downloads?

Z-Library offers books in various formats, including PDF, EPUB, and MOBI.

3 . Is it safe to download books from Z-Library?

ZLibrary takes user safety seriously and ensures that all books are virus-free.

4 . Can I upload content to ZLibrary?

Yes, users can contribute to ZLibrary's collection by uploading content. However, it's crucial to respect copyright and intellectual property rights.

5 . How can I support ZLibrary?

You can support ZLibrary through donations, sharing the platform with others, or providing feedback to help improve the user experience. You should place the backlink to our website when sharing the content from Zlibrary.to.

6 . What are the download limits, and can they be increased?

ZLibrary imposes download limits to maintain resources for all users. Creating an account and logging in can grant you access to

7 . How often is Z Library updated with new books?

Z-Library is updated regularly with new books. However, the frequency of updates may vary depending on various factors, such as the availability of new books and the website's policies.

8 . Can I access Z Library from my mobile device?

Yes, ZLib is mobile-friendly and can be accessed from any device with an internet connection.

9 . What if I can't find the book I'm looking for on ZLibrary?

If you can't find the book you're looking for on ZLibrary, you can request the book from ZLibrary:hope , and the contributors may add it to their collection if it's available.

Z Library is a fantastic resource for book lovers looking to expand their reading collection without breaking the bank. With a vast selection of books, cost-saving benefits, and convenient online access, it's no wonder why ZLibrary.to is so popular. So what are you waiting for? Sign up for a free account and start downloading books today!

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ScienceOpen offers content hosting, context building and marketing services for publishers. See our tailored offerings

• For academic publishers  to promote journals and interdisciplinary collections
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ScienceOpen offers state-of-the-art technology and a range of solutions and services

• For faculties and research groups  to promote and share your work
• For research institutes  to build up your own branding for OA publications
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Make an impact and build your research profile in the open with ScienceOpen

• Search and discover relevant research in over 95 million Open Access articles and article records
• Share your expertise and get credit by publicly reviewing any article
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• Create a topical Collection  to advance your research field

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Launching a new open access journal or an open access press? ScienceOpen now provides full end-to-end open access publishing solutions – embedded within our smart interactive discovery environment. A modular approach allows open access publishers to pick and choose among a range of services and design the platform that fits their goals and budget.

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What can a Researcher do on ScienceOpen?

ScienceOpen provides researchers with a wide range of tools to support their research – all for free. Here is a short checklist to make sure you are getting the most of the technological infrastructure and content that we have to offer. What can a researcher do on ScienceOpen? Continue reading “What can a Researcher do on ScienceOpen?”   

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Research paper

Research paper is a written report which contains the results of original scientific research (primary research article) or the review of published scientific papers on one or several science topics (review article). In primary research articles, the authors give vital information about the research that allows other members of the scientific community to evaluate it, reproduce science experiments, and also to assess the reasoning and conclusions drawn from them. Review articles are designed to analyze, evaluate, summarize or synthesize research already conducted in primary academic sources. Quite often, a science article combines these two types of scientific text, including the overview and original parts.

Currently, the number of scientific articles in open access is growing fast, but all of them are spread on numerous science websites on the Internet, and therefore it is hard for a researcher to find the necessary information for new science discoveries or download PDF due to the unreliability of websites.

CyberLeninka is intended to solve this problem. We provide platform, which aggregates a lot of free articles from various open access peer-reviewed journals . And our global goal is to build new research infrastructure for academia.

Directory of open access articles based on OECD fields of science and technology

• Medical and Health sciences
• Basic medicine
• Clinical medicine
• Health sciences
• Health biotechnology
• Natural sciences
• Mathematics
• Computer and information sciences
• Physical sciences
• Chemical sciences
• Earth and related environmental sciences
• Biological sciences
• Engineering and Technology
• Civil engineering
• Electrical engineering, electronic engineering, information engineering
• Mechanical engineering
• Chemical engineering
• Materials engineering
• Medical engineering
• Environmental engineering
• Environmental biotechnology
• Industrial biotechnology
• Nano technology
• Agricultural sciences
• Agriculture, forestry, and fisheries
• Animal and dairy science
• Veterinary science
• Agricultural biotechnology
• Social sciences
• Economics and business
• Educational sciences
• Political science
• Social and economic geography
• Media and communications
• History and archaeology
• Languages and literature
• Philosophy, ethics and religion
• Arts, history of arts, performing arts, music

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How to Write a Research Paper: A Step by Step Writing Guide

Published on July 2, 2024 by Hannah Skaggs . Revised on August 19, 2024.

A research paper explores and evaluates previously and newly gathered information on a topic, then offers evidence for an argument. It follows academic writing standards, and virtually every college student will write at least one. Research papers are also integral to scientific fields, among others, as the most reliable way to share knowledge.

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Table of contents

How to write a research paper in 7 steps:, how quillbot tools can help, frequently asked questions about how to write a research paper.

So you’re sitting at your computer, staring at a blank document. Now what? How do you write a research paper?

Here are the 7 steps you need to take in order to write a stellar academic research paper.

1. Understand your goal

2. choose your topic, 3. research your topic, 4. build an outline and thesis statement, 5. write your first draft, 6. cite your sources, 7. edit and proofread.

Trying to write a research paper without understanding the guidelines is like trying to bake a cake without following a recipe. You’re likely to use the wrong ingredients and techniques and therefore get poor results.

Instead, closely examine the requirements of the assignment:

• Rubric and assignment instructions—provided by your instructor
• Required components and length—abstract, literature review, reference list, etc.
• Style guide for citations and body text—MLA, APA, CMOS, AMA, or other
• Formatting requirements—double spacing, margins, etc. (often depend on style guide)
• Deadline and how to submit—date and time, file format, etc.

You might find it helpful to create a checklist that you can use to review your work in step 7. If you have any questions after looking over these elements, ask your instructor before you go any further. It will save you the time and effort of redoing everything later. Once you understand your goal the next steps in writing a research paper are as follows.

If you haven’t been assigned a topic for your research paper, you’ll need to choose one. These are some questions you can ask yourself to narrow it down:

• What am I interested in? Choosing a topic you like will make the work easier.
• What specific aspect of this topic can I focus on? A good research paper topic is not so general that you can’t say something new about it, but not so specific that you can’t find quality information on it.
• Will I have enough material to work with? You need to be able to discuss evidence both for and against your position.
• What question(s) do I want to answer? This question will help you focus your research, and its answer might even be your main idea. It’s a good idea to add a few sub-research questions that you might dig into.
• What unique perspective can I offer? Think about what topic gives you a chance to add new ideas to the existing research.

To answer the above, you may need to spend some time glancing through the available studies and resources online or in a library, depending on your potential topic(s). Don’t forget to write down any sources that you look through so you can properly cite them later on.

Once you’ve settled on a topic your next in how to write a research paper is, to begin the preliminary research. You can take a deeper dive into some sources you examined while choosing your topic. Look for data and evidence that answer the questions you developed in step 2. Critically examine a variety of reputable sources that both support and contradict your own view.

As you conduct research, remember to record citation information, including direct quotes and page numbers. You may be tempted to leave that task for later and just focus on gathering information from further research, but if you do, you’ll regret it. You’re already looking at the sources now; why waste time making a second trip?

Failing to cite a source used in your research paper means that you’ve plagiarized the uncited work, which can lead to a number of consequences, academic and otherwise.

You’ve collected all the information you need in your research paper, and now it’s time to organize it to take the reader on a journey from uninformed to informed.

Writing a research paper outline is like turn-by-turn GPS directions that guide the reader to the conclusion you reached during your research. Chart your course before you start writing so you can organize your paper cohesively and avoid missing anything.

A useful outline breaks your research paper into sections with a logical flow. It can include as much or as little detail as you need to organize your thoughts and evidence, but it should include the key points you plan to cover and any relevant information that you don’t want to miss.

Your thesis statement is like the address of the destination. As part of the research paper’s introduction (and included in the outline), it broadly tells the reader where you’re going in the paper. In the thesis statement, you answer the question that inspired your research and summarize your main points in a sentence or two.

Now that you’ve outlined what you’ll say, put words on the page. Yes, it’s that simple. Ignore the urge to censor your thoughts or revise your phrasing and focus on getting your ideas down.

You don’t have to start with the introduction; start wherever you feel the most inspired. You can make sure everything flows together once all the sections are finished.

Related Read: How to Write a Research Paper Conclusion

The key to learning how to write a research paper is learning how to cite sources . Depending on the style guide you’re following, you may need to create in-text citations , a Works Cited page, a reference list, a bibliography, or footnotes. Pay close attention to what information is included and how citations are punctuated and formatted.

Citing sources can be tedious, so tackle it only when you’re alert and feeling well so you can follow the guidelines to a T. Making a mistake can be too easy when you’re tired and can even lead to accidental plagiarism .

All research papers rely on existing information and research; not citing a source properly can lead to serious consequences, which can include assignment or grade failure, expulsion, or even jail time.

Make sure all sources are properly cited.

You may have thought the real writing for a research paper was done in step 5, but the truth is you’re just getting to it. However, you’re also at the last step for writing a research paper—so close to the finish line.

To edit effectively, you must consider both the big picture and the details. It may be easier to divide these views into two editing stages, and you may want to go through each stage more than once. The big-picture stage considers major points like these:

• Organization and headings
• Flow of ideas and arguments
• Support for the main research question or thesis statement

Once you’ve finished reviewing larger issues, you can move on to the finer points:

• Grammar, spelling, and punctuation
• Clear and precise word choices and sentence structure
• Transition words and phrases
• Definition of terms that may be unfamiliar to readers
• Citations, both in-text and full
• The requirements listed in Step 1 for writing a research papaer

At both stages, remove anything that doesn’t support your thesis and add value, from entire sections to unnecessary words. Cutting can be the most painful part of the writing process, but also the most effective.

Conversely, you may need to add content if your paper is too short after cutting or if you realize some parts need a bit more fleshing out.

Expert editing tip: Don’t procrastinate! Start writing your research paper as soon as possible so that you can give yourself downtime between editing rounds. Taking a break for a few days can help you see the paper with new eyes and catch mistakes that you may have repeatedly overlooked .

Once you’ve finished the editing and think your research paper is ready to submit, proofread it. Read through the whole thing again slowly and with a critical eye to catch any lingering errors.

Still, feel like you need expert academic writing help? QuillBot offers an assortment of writing tools that can assist you as you learn how to write a good research paper.

• Plagiarism Checker and Citation Generator to cite your sources properly
• Grammar Checker to make sure your grammar, spelling , and punctuation are correct
• Paraphraser to understand ideas you read in your research and improve the fluency of your writing
• Summarizer to compose a potential thesis statement and concluding sentences for paragraphs and sections
• QuillBot Flow to work on your research paper from start to finish

Writing a research paper requires a lot of work, and we’re here to help.

You might be a native or multilingual English speaker and you might be writing in US, UK, Canadian, or Australian English. But no matter what, you can follow these seven steps and write a world-class research paper with QuillBot.

Happy research and writing!

Most research papers contain at least an introduction and sections for methodology, results, discussion, and references. Many also include an abstract and a literature review. Some other common elements are a title page, a table of contents, tables and figures, and appendices.

As you research, write down citation information for any sources you plan to use. Record quotes and ideas carefully, along with the page numbers where you found them. You can write them on note cards, on paper, or in a digital document.

When writing your first draft, include enough citation information in the text to ensure accurate referencing. After finishing the draft, you can go through your paper and add the full citations, following the style guide.

QuillBot’s Citation Generator can help you automatically generate in-text citations and a reference list for your paper.

Finally, use QuillBot’s Plagiarism Checker to double-check your work and avoid plagiarism .

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• Published: 14 August 2024

Nonlinear dynamics of multi-omics profiles during human aging

• Xiaotao Shen   ORCID: orcid.org/0000-0002-9608-9964 1 , 2 , 3   na1 ,
• Chuchu Wang   ORCID: orcid.org/0000-0003-2015-7331 4 , 5   na1 ,
• Xin Zhou   ORCID: orcid.org/0000-0001-8089-4507 1 , 6 ,
• Wenyu Zhou 1 ,
• Daniel Hornburg   ORCID: orcid.org/0000-0002-6618-7774 1 ,
• Si Wu 1 &
• Michael P. Snyder   ORCID: orcid.org/0000-0003-0784-7987 1 , 6  

Nature Aging ( 2024 ) Cite this article

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Aging is a complex process associated with nearly all diseases. Understanding the molecular changes underlying aging and identifying therapeutic targets for aging-related diseases are crucial for increasing healthspan. Although many studies have explored linear changes during aging, the prevalence of aging-related diseases and mortality risk accelerates after specific time points, indicating the importance of studying nonlinear molecular changes. In this study, we performed comprehensive multi-omics profiling on a longitudinal human cohort of 108 participants, aged between 25 years and 75 years. The participants resided in California, United States, and were tracked for a median period of 1.7 years, with a maximum follow-up duration of 6.8 years. The analysis revealed consistent nonlinear patterns in molecular markers of aging, with substantial dysregulation occurring at two major periods occurring at approximately 44 years and 60 years of chronological age. Distinct molecules and functional pathways associated with these periods were also identified, such as immune regulation and carbohydrate metabolism that shifted during the 60-year transition and cardiovascular disease, lipid and alcohol metabolism changes at the 40-year transition. Overall, this research demonstrates that functions and risks of aging-related diseases change nonlinearly across the human lifespan and provides insights into the molecular and biological pathways involved in these changes.

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Aging is a complex and multifactorial process of physiological changes strongly associated with various human diseases, including cardiovascular diseases (CVDs), diabetes, neurodegeneration and cancer 1 . The alterations of molecules (including transcripts, proteins, metabolites and cytokines) are critically important to understand the underlying mechanism of aging and discover potential therapeutic targets for aging-related diseases. Recently, the development of high-throughput omics technologies has enabled researchers to study molecular changes at the system level 2 . A growing number of studies have comprehensively explored the molecular changes that occur during aging using omics profiling 3 , 4 , and most focus on linear changes 5 . It is widely recognized that the occurrence of aging-related diseases does not follow a proportional increase with age. Instead, the risk of these diseases accelerates at specific points throughout the human lifespan 6 . For example, in the United States, the prevalence of CVDs (encompassing atherosclerosis, stroke and myocardial infarction) is approximately 40% between the ages of 40 and 59, increases to about 75% between 60 and 79 and reaches approximately 86% in individuals older than 80 years 7 . Similarly, also in the United States, the prevalence of neurodegenerative diseases, such as Parkinson’s disease and Alzheimer’s disease, exhibits an upward trend as well as human aging progresses, with distinct turning points occurring around the ages of 40 and 65, respectively 8 , 9 , 10 . Some studies also found that brain aging followed an accelerated decline in flies 11 and chimpanzees 12 that lived past middle age and advanced age.

The observation of a nonlinear increase in the prevalence of aging-related diseases implies that the process of human aging is not a simple linear trend. Consequently, investigating the nonlinear changes in molecules will likely reveal previously unreported molecular signatures and mechanistic insights. Some studies examined the nonlinear alterations of molecules during human aging 13 . For instance, nonlinear changes in RNA and protein expression related to aging have been documented 14 , 15 , 16 . Moreover, certain DNA methylation sites have exhibited nonlinear changes in methylation intensity during aging, following a power law pattern 17 . Li et al. 18 identified the 30s and 50s as transitional periods during women’s aging. Although aging patterns are thought to reflect the underlying biological mechanisms, the comprehensive landscape of nonlinear changes of different types of molecules during aging remains largely unexplored. Remarkably, the global monitoring of nonlinear changing molecular profiles throughout human aging has yet to be fully used to extract basic insights into the biology of aging.

In the present study, we conducted a comprehensive deep multi-omics profiling on a longitudinal human cohort comprising 108 individuals aged from 25 years to 75 years. The cohort was followed over a span of several years (median, 1.7 years), with the longest monitoring period for a single participant reaching 6.8 years (2,471 days). Various types of omics data were collected from the participants’ biological samples, including transcriptomics, proteomics, metabolomics, cytokines, clinical laboratory tests, lipidomics, stool microbiome, skin microbiome, oral microbiome and nasal microbiome. The investigation explored the changes occurring across different omics profiles during human aging. Remarkably, many molecular markers and biological pathways exhibited a nonlinear pattern throughout the aging process, thereby providing valuable insight into periods of dramatic alterations during human aging.

Most of the molecules change nonlinearly during aging

We collected longitudinal biological samples from 108 participants over several years, with a median tracking period of 1.7 years and a maximum period of 6.8 years, and conducted multi-omics profiling on the samples. The participants were sampled every 3–6 months while healthy and had diverse ethnic backgrounds and ages ranging from 25 years to 75 years (median, 55.7 years). The participants’ body mass index (BMI) ranged from 19.1 kg m −2 to 40.8 kg m −2 (median, 28.2 kg m −2 ). Among the participants, 51.9% were female (Fig. 1a and Extended Data Fig. 1a–d ). For each visit, we collected blood, stool, skin swab, oral swab and nasal swab samples. In total, 5,405 biological samples (including 1,440 blood samples, 926 stool samples, 1,116 skin swab samples, 1,001 oral swab samples and 922 nasal swab samples) were collected. The biological samples were used for multi-omics data acquisition (including transcriptomics from peripheral blood mononuclear cells (PBMCs), proteomics from plasma, metabolomics from plasma, cytokines from plasma, clinical laboratory tests from plasma, lipidomics from plasma, stool microbiome, skin microbiome, oral microbiome and nasal microbiome; Methods ). In total, 135,239 biological features (including 10,346 transcripts, 302 proteins, 814 metabolites, 66 cytokines, 51 clinical laboratory tests, 846 lipids, 52,460 gut microbiome taxons, 8,947 skin microbiome taxons, 8,947 oral microbiome taxons and 52,460 nasal microbiome taxons) were acquired, resulting in 246,507,456,400 data points (Fig. 1b and Extended Data Fig. 1e,f ). The average sampling period and number of samples for each participant were 626 days and 47 samples, respectively. Notably, one participant was deeply monitored for 6.8 years (2,471 days), during which 367 samples were collected (Fig. 1c ). Overall, this extensive and longitudinal multi-omics dataset enables us to examine the molecular changes that occur during the human aging process. The detailed characteristics of all participants are provided in the Supplementary Data . For each participant, the omics data were aggregated and averaged across all healthy samples to represent the individual’s mean value, as detailed in the Methods section. Compared to cross-sectional cohorts, which have only a one-time point sample from each participant, our longitudinal dataset, which includes multiple time point samples from each participant, is more robust for detecting complex aging-related changes in molecules and functions. This is because analysis of multi-time point samples can detect participants’ baseline and robustly evaluate individuals’ longitudinal molecular changes.

a , The demographics of the 108 participants in the study are presented. b , Sample collection and multi-omics data acquisition of the cohort. Four types of biological samples were collected, and 10 types of omics data were acquired. c , Collection time range and sample numbers for each participant. The top x axis represents the collection range for each participant (read line), and the bottom x axis represents the sample number for each participant (bar plot). Bars are color-coded by omics type. d , Significantly changed molecules and microbes during aging were detected using the Spearman correlation approach ( P  < 0.05). The P values were not adjusted ( Methods ). Dots are color-coded by omics type. e , Differential expressional molecules/microbes in different age ranges compared to baseline (25–40 years old, two-sided Wilcoxon test, P  < 0.05). The P values were not adjusted ( Methods ). f , The linear changing molecules comprised only a small part of dysregulated molecules in at least one age range. g , Heatmap depicting the nonlinear changing molecules and microbes during human aging.

We included samples only from healthy visits and adjusted for confounding factors (for example, BMI, sex, insulin resistance/insulin sensitivity (IRIS) and ethnicity; Extended Data Fig. 1a–d ), allowing us to discern the molecules and microbes genuinely associated with aging ( Methods ). Two common and traditional approaches, linear regression and Spearman correlation, were first used to identify the linear changing molecules during human aging 5 . The linear regression method is commonly used for linear changing molecules. As expected, both approaches have very high consistent results for each type of omics data (Supplementary Fig. 1a ). For convenience, the Spearman correlation approach was used in the analysis. Interestingly, only a small portion of all the molecules and microbes (749 out of 11,305, 6.6%; only genus level was used for microbiome data; Methods ) linearly changed during human aging (Fig. 1d and Supplementary Fig. 1b ), consistent with our previous studies 5 ( Methods ). Next, we examined nonlinear effects by categorizing all participants into distinct age stages according to their ages and investigated the dysregulated molecules within each age stage compared to the baseline (25–40 years old; Methods ). Interestingly, using this approach, 81.03% of molecules (9,106 out of 11,305) exhibited changes in at least one age stage compared to the baseline (Fig. 1e and Extended Data Fig. 2a ). Remarkably, the percentage of linear changing molecules was relatively small compared to the overall dysregulated molecules during aging (mean, 16.2%) (Fig. 1f and Extended Data Fig. 2b ). To corroborate our findings, we employed a permutation approach to calculate permutated P values, which yielded consistent results ( Methods ). The heatmap depicting all dysregulated molecules also clearly illustrates pronounced nonlinear changes (Fig. 1g ). Taken together, these findings strongly suggest that a substantial number of molecules and microbes undergo nonlinear changes throughout human aging.

Clustering reveals nonlinear multi-omics changes during aging

Next, we assessed whether the multi-omics data collected from the longitudinal cohort could serve as reliable indicators of the aging process. Our analysis revealed a substantial correlation between a significant proportion of the omics data and the ages of the participants (Fig. 2a ). Particularly noteworthy was the observation that, among all the omics data examined, metabolomics, cytokine and oral microbiome data displayed the strongest association with age (Fig. 2a and Extended Data Fig. 3a–c ). Partial least squares (PLS) regression was further used to compare the strength of the age effect across different omics data types. The results are consistent with the results presented above in Fig. 2a ( Methods ). These findings suggest the potential utility of these datasets as indicators of the aging process while acknowledging that further research is needed for validation 4 . As the omics data are not accurately matched across all the samples, we then smoothed the omics data using our previously published approach 19 ( Methods and Supplementary Fig. 2a–c ). Next, to reveal the specific patterns of molecules that change during human aging, we then grouped all the molecules with similar trajectories using an unsupervised fuzzy c-means clustering approach 19 ( Methods , Fig. 3b and Supplementary Fig. 2d,e ). We identified 11 clusters of molecular trajectories that changed during aging, which ranged in size from 638 to 1,580 molecules/microbes (Supplementary Fig. 2f and Supplementary Data ). We found that most molecular patterns exhibit nonlinear changes, indicating that aging is not a linear process (Fig. 2b ). Among the 11 identified clusters, three distinct clusters (2, 4 and 5) displayed compelling, straightforward and easily understandable patterns that spanned the entire lifespan (Fig. 2c ). Most molecules within these three clusters primarily consist of transcripts (Supplementary Fig. 2f ), which is expected because transcripts dominate the multi-omics data (8,556 out of 11,305, 75.7%). Cluster 4 exhibits a relatively stable pattern until approximately 60 years of age, after which it shows a rapid decrease (Fig. 2c ). Conversely, clusters 2 and 5 display fluctuations before 60 years of age, followed by a sharp increase and an upper inflection point at approximately 55–60 years of age (Fig. 2c ). We also attempted to observe this pattern of molecular change during aging individually. The participant with the longest follow-up period of 6.8 years (Fig. 1c ) approached the age of 60 years (range, 59.5–66.3 years; Extended Data Fig. 1g ), and it was not possible to identify obvious patterns in this short time window (Supplementary Fig. 2g ). Tracking individuals longitudinally over longer periods (decades) will be required to observe these trajectories at an individual level.

a , Spearman correlation (cor) between the first principal component and ages for each type of omics data. The shaded area around the regression line represents the 95% confidence interval. b , The heatmap shows the molecular trajectories in 11 clusters during human aging. The right stacked bar plots show the percentages of different kinds of omics data, and the right box plots show the correlation distribution between features and ages ( n  = 108 participants). c , Three notable clusters of molecules that exhibit clear and straightforward nonlinear changes during human aging. The top stacked bar plots show the percentages of different kinds of omics data, and the top box plots show the correlation distribution between features and ages ( n  = 108 participants). The box plot shows the median (line), interquartile range (IQR) (box) and whiskers extending to 1.5 × IQR. Bars and lines are color-coded by omics type. Abs, absolute.

a , Pathway enrichment and module analysis for each transcriptome cluster. The left panel is the heatmap for the pathways that undergo nonlinear changes across aging. The right panel is the pathway similarity network ( Methods ) ( n  = 108 participants). b , Pathway enrichment for metabolomics in each cluster. Enriched pathways and related metabolites are illustrated (Benjamini–Hochberg-adjusted P  < 0.05). c , Four clinical laboratory tests that change during human aging: blood urea nitrogen, serum/plasma glucose, mean corpuscular hemoglobin and red cell distribution width ( n  = 108 participants). The box plot shows the median (line), interquartile range (IQR) (box) and whiskers extending to 1.5 × IQR.

Although confounders, including sex, were corrected before analysis ( Methods ), we acknowledge that the age range for menopause in females is typically between 45 years and 55 years of age 20 , which is very close to the major transition points in all three clusters (Fig. 2c ). Therefore, we conducted further investigation into whether the menopausal status of females in the dataset contributed to the observed transition point at approximately 55 years of age (Fig. 2c ) by performing separate clustering analyses on the male and female datasets. Surprisingly, both the male and female datasets exhibited similar clusters, as illustrated in Extended Data Fig. 4a . This suggests that the transition point observed at approximately 55 years of age is not solely attributed to female menopause but, rather, represents a common phenomenon in the aging process of both sexes. This result is consistent with previous studies 14 , 15 , further supporting the notion that this transition point is a major characteristic feature of human aging. Moreover, to investigate the possibility that the transcriptomics data might skew the results toward transcriptomic changes as age-related factors, we conducted two additional clustering analyses—one focusing solely on transcriptomic data and another excluding it. Interestingly, both analyses yielded nearly identical three-cluster configurations, as observed using the complete omics dataset (Extended Data Fig. 4b ). This reinforces the robustness of the identified clusters and confirms that they are consistent across various omics platforms, not just driven by transcriptomic data.

Nonlinear changes in function and disease risk during aging

To gain further insight into the biological functions associated with the nonlinear changing molecules within the three identified clusters, we conducted separate functional analyses for transcriptomics, proteomics and metabolomics datasets for all three clusters. In brief, we constructed a similarity network using enriched pathways from various databases (Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and Reactome) and identified modules to eliminate redundant annotations. We then used all modules from different databases to reduce redundancy further using the same approach and define the final functional modules ( Methods , Extended Data Fig. 4c and Supplementary Data ). We identified some functional modules that were reported in previous studies, but we defined their more accurate patterns of change during human aging. Additionally, we also found previously unreported potential functional modules during human aging ( Supplementary Data ). For instance, in cluster 2, we identified a transcriptomic module associated with GTPase activity (adjusted P  = 1.64 × 10 −6 ) and histone modification (adjusted P   =  6.36 × 10 −7 ) (Fig. 3a ). Because we lack epigenomic data in this study, our findings should be validated through additional experiments in the future. GTPase activity is closely correlated with programmed cell death (apoptosis), and some previous studies showed that this activity increases during aging 21 . Additionally, histone modifications have been demonstrated to increase during human aging 22 . In cluster 4, we identified one transcriptomics module associated with oxidative stress; this module includes antioxidant activity, oxygen carrier activity, oxygen binding and peroxidase activity (adjusted P  = 0.029) (Fig. 3a ). Previous studies demonstrated that oxidative stress and many reactive oxygen species (ROS) are positively associated with increased inflammation in relation to aging 23 . In cluster 5, the first transcriptomics module is associated with mRNA stability, which includes mRNA destabilization (adjusted P   =  0.0032), mRNA processing (adjusted P   =  3.2 × 10 −4 ), positive regulation of the mRNA catabolic process (adjusted P   =  1.46 × 10 −4 ) and positive regulation of the mRNA metabolic process (adjusted P   =  0.00177) (Fig. 3a ). Previous studies showed that mRNA turnover is associated with aging 24 . The second module is associated with autophagy (Fig. 3a ), which increases during human aging, as demonstrated in previous studies 25 .

In addition, we also identified certain modules in the clusters that suggest a nonlinear increase in several disease risks during human aging. For instance, in cluster 2, where components increase gradually and then rapidly after age 60, the phenylalanine metabolism pathway (adjusted P   =  4.95 × 10 −4 ) was identified (Fig. 3b ). Previous studies showed that aging is associated with a progressive increase in plasma phenylalanine levels concomitant with cardiac dysfunction, and dysregulated phenylalanine catabolism is a factor that triggers deviations from healthy cardiac aging trajectories 26 . Additionally, C-X-C motif chemokine 5 (CXCL5 or ENA78) from proteomics data, which has higher concentrations in atherosclerosis 27 , is also detected in cluster 2 ( Supplementary Data ). The clinical laboratory test blood urea nitrogen, which provides important information about kidney function, is also detected in cluster 2 (Fig. 3c ). This indicates that kidney function nonlinearly decreases during aging. Furthermore, the clinical laboratory test for serum/plasma glucose, a marker of type 2 diabetes (T2D), falls within cluster 2. This is consistent with and supported by many previous studies demonstrating that aging is a major risk factor for T2D 28 . Collectively, these findings suggest a nonlinear escalation in the risk of cardiovascular and kidney diseases and T2D with advancing age, particularly after the age of 60 years (Fig. 2c ).

The identified modules in cluster 4 also indicate a nonlinear increase in disease risks. For instance, the unsaturated fatty acids biosynthesis pathway (adjusted P   =  4.71 × 10 −7 ) is decreased in cluster 4. Studies have shown that unsaturated fatty acids are helpful in reducing CVD risk and maintaining brain function 29 , 30 . The pathway of alpha-linolenic acid and linolenic acid metabolism (adjusted P   =  1.32 × 10 −4 ) can reduce aging-associated diseases, such as CVD 31 . We also detected the caffeine metabolism pathway (adjusted P   =  7.34 × 10 −5 ) in cluster 4, which suggests that the ability to metabolize caffeine decreases during aging. Additionally, the cytokine MCP1 (chemokine (C-C motif) ligand 2 (CCL2)), a member of the CC chemokine family, plays an important immune regulatory role and is also in cluster 4 ( Supplementary Data ). These findings further support previous observations and highlight the nonlinear increase in age-related disease risk as individuals age.

Cluster 5 comprises the clinical tests of mean corpuscular hemoglobin and red cell distribution width (Fig. 3c ). These tests assess the average hemoglobin content per red blood cell and the variability in the size and volume of red blood cells, respectively. These findings align with the aforementioned transcriptomic data, which suggest a nonlinear reduction in the oxygen-carrying capacity associated with the aging process.

Aside from these three distinct clusters (Fig. 2c ), we also conducted pathway enrichment analysis across all other eight clusters, which displayed highly nonlinear trajectories, employing the same method (Fig. 2b and Supplementary Data ). Notably, cluster 11 exhibited a consistent increase up until the age of 50, followed by a decline until the age of 56, after which no substantial changes were observed up to the age of 75. A particular transcriptomics module related to DNA repair was identified, encompassing three pathways: positive regulation of double-strand break repair (adjusted P   =  0.042), peptidyl−lysine acetylation (adjusted P   =  1.36 × 10 −5 ) and histone acetylation (adjusted P   =  3.45 × 10 −4 ) (Extended Data Fig. 4d ). These three pathways are critical in genomic stability, gene expression and metabolic balances, with their levels diminishing across the human lifespan 32 , 33 , 34 . Our findings reveal a nonlinear alteration across the human lifespan in these pathways, indicating an enhancement in DNA repair capabilities before the age of 50, a marked reduction between the ages of 50 and 56 and stabilization after that until the age of 75. The pathway enrichment results for all clusters are detailed in the Supplementary Data .

Altogether, the comprehensive functional analysis offers valuable insights into the nonlinear changes observed in molecular profiles and their correlations with biological functions and disease risks across the human lifespan. Our findings reveal that individuals aged 60 and older exhibit increased susceptibility to CVD, kidney issues and T2D. These results carry important implications for both the diagnosis and prevention of these diseases. Notably, many clinically actionable markers were identified, which have the potential for improved healthcare management and enhanced overall well-being of the aging population.

Uncovering waves of aging-related molecules during aging

Although the trajectory clustering approaches described above effectively identify nonlinear changing molecules and microbes that exhibit clear and compelling patterns throughout human aging, it may not be as effective in capturing substantial changes that occur at specific chronological aging periods. In such cases, alternative analytical approaches may be necessary to detect and characterize these dynamics. To gain a comprehensive understanding of changes in multi-omics profiling during human aging, we used a modified version of the DE-SWAN algorithm 14 , as described in the Methods section. This algorithm identifies dysregulated molecules and microbes throughout the human lifespan by analyzing molecule levels within 20-year windows and comparing two groups in 10-year parcels while sliding the window incrementally from young to old ages 14 . Using this approach and multiomics data, we detected changes at specific stages of lifespan and uncovered the sequential effects of aging. Our analysis revealed thousands of molecules exhibiting changing patterns throughout aging, forming distinct waves, as illustrated in Fig. 3a . Notably, we observed two prominent crests occurring around the ages of 45 and 65, respectively (Fig. 4a ). Notably, too, these crests were consistent with findings from a previous study that included only proteomics data 14 . Specifically, crest 2 aligns with our previous trajectory clustering result, indicating a turning point at approximately 60 years of age (Fig. 2c ).

a , Number of molecules and microbes differentially expressed during aging. Two local crests at the ages of 44 years and 60 years were identified. b , c , The same waves were detected using different q value ( b ) and window ( c ) cutoffs. d , The number of molecules/microbes differentially expressed for different types of omics data during human aging.

To demonstrate the significance of the two crests, we employed different q value cutoffs and sliding window parameters, which consistently revealed the same detectable waves (Fig. 4b,c and Supplementary Fig. 4a,b ). Furthermore, when we permuted the ages of individuals, the crests disappeared (Supplementary Figs. 3a and 4c ) ( Methods ). These observations highlight the robustness of the two major waves of aging-related molecular changes across the human lifespan. Although we already accounted for confounders before our statistical analysis, we took additional steps to explore their impact. Specifically, we investigated whether confounders, such as insulin sensitivity, sex and ethnicity, differed between the two crests across various age ranges. As anticipated, these confounders did not show significant differences across other age brackets (Supplementary Fig. 4d ). This further supports our conclusion that the observed differences in the two crests are attributable to age rather than other confounding variables.

The identified crests represent notable milestones in the aging process and suggest specific age ranges where substantial molecular alterations occur. Therefore, we investigated the age-related waves for each type of omics data. Interestingly, most types of omics data exhibited two distinct crests that were highly robust (Fig. 3b and Supplementary Fig. 4 ). Notably, the proteomics data displayed two age-related crests at ages around 40 years and 60 years. Only a small overlap was observed between our dataset and the results from the previous study (1,305 proteins versus 302 proteins, with only 75 proteins overlapping). The observed pattern in our study was largely consistent with the previous findings 14 . However, our finding that many types of omics data, including transcriptomics, proteomics, metabolomics, cytokine, gut microbiome, skin microbiome and nasal microbiome, exhibit these waves, often with a similar pattern as the proteomics data (Fig. 4d ), supports the hypothesis that aging-related changes are not limited to a specific omics layer but, rather, involve a coordinated and systemic alteration across multiple molecular components. Identifying consistent crests across different omics data underscores the robustness and reliability of these molecular milestones in the aging process.

Next, we investigated the roles and functions of dysregulated molecules within two distinct crests. Notably, we found that the two crests related to aging predominantly consisted of the same molecules (Supplementary Fig. 6 ). To focus on the unique biological functions associated with each crest and eliminate commonly occurring molecules, we removed background molecules present in most stages. To explore the specific biological functions associated with each type of omics data (transcriptomics, proteomics and metabolomics) for both crests, we employed the function annotation approach described above ( Methods ). In brief, we constructed a similarity network of enriched pathways and identified modules to remove redundant annotations (Supplementary Fig. 6 and Extended Data Fig. 5a,b ). Furthermore, we applied the same approach to all modules to reduce redundancy and identify the final functional modules ( Methods and Extended Data Fig. 6a ). Our analysis revealed significant changes in multiple modules associated with the two crests (Extended Data Fig. 6b–d ). To present the results clearly, Fig. 5a displays the top 20 pathways (according to adjusted P value) for each type of omics data, and the Supplementary Data provides a comprehensive list of all identified functional modules.

a , Pathway enrichment and biological functional module analysis for crests 1 and 2. Dots and lines are color-coded by omics type. b , The overlapping of molecules between two crests and three clusters.

Interestingly, the analysis identifies many dysregulated functional modules in crests 1 and 2, indicating a nonlinear risk for aging-related diseases. In particular, several modules associated with CVD were identified in both crest 1 and crest 2 (Fig. 5a ), which is consistent with the above results (Fig. 3b ). For instance, the dysregulation of platelet degranulation (crest 1: adjusted P   =  1.77 × 10 −30 ; crest 2: adjusted P   =  1.73 × 10 −26 ) 35 , 36 , complement cascade (crest 1: adjusted P   =  3.84 × 10 −30 ; crest 2: adjusted P   =  2.02 × 10 −28 ), complement and coagulation cascades (crest 1: adjusted P   =  1.78 × 10 −46 ; crest 2: adjusted P   =  2.02 × 10 −28 ) 37 , 38 , protein activation cascade (crest 1: adjusted P   =  1.56 × 10 −17 ; crest 2: adjusted P   =  1.61 × 10 −8 ) and protease binding (crest 1: adjusted P   =  2.7 × 10 −6 ; crest 2: adjusted P   =  0.0114) 39 have various effects on the cardiovascular system and can contribute to various CVDs. Furthermore, blood coagulation (crest 1: adjusted P   =  1.48 × 10 −28 ; crest 2: adjusted P   =  9.10 × 10 −17 ) and fibrinolysis (crest 1: adjusted P   =  2.11 × 10 −15 ; crest 2: adjusted P   =  1.64 × 10 −10 ) were also identified, which are essential processes for maintaining blood fluidity, and dysregulation in these modules can lead to thrombotic and cardiovascular events 40 , 41 . We also identified certain dysregulated metabolic modules associated with CVD. For example, aging has been linked to an incremental rise in plasma phenylalanine levels (crest 1: adjusted P   =  9.214 × 10 −4 ; crest 2: adjusted P   =  0.0453), which can contribute to the development of cardiac hypertrophy, fibrosis and dysfunction 26 . Branched-chain amino acids (BCAAs), including valine, leucine and isoleucine (crest 1: adjusted P : not significant (NS); crest 2: adjusted P   =  0.0173), have also been implicated in CVD development 42 , 43 and T2D, highlighting their relevance in CVD pathophysiology. Furthermore, research suggests that alpha-linolenic acid (ALA) and linoleic acid metabolism (crest 1: adjusted P : NS; crest 2: adjusted P   =  0.0217) may be protective against coronary heart disease 44 , 45 . Our investigation also identified lipid metabolism modules that are associated with CVD, including high-density lipoprotein (HDL) remodeling (crest 1: adjusted P   =  1.073 × 10 −8 ; crest 2: adjusted P   =  2.589 × 10 −9 ) and glycerophospholipid metabolism (crest 1: adjusted P : NS; crest 2: adjusted P   =  0.0033), which influence various CVDs 46 , 47 , 48 .

In addition, the dysregulation of skin and muscle stability was found to be increased at crest 1 and crest 2, as evidenced by the identification of numerous modules associated with these processes (Fig. 5a,b ). This suggests that the aging of skin and muscle is markedly accelerated at crest 1 and crest 2. The extracellular matrix (ECM) provides structural stability, mechanical strength, elasticity and hydration to the tissues and cells, and the ECM of the skin is mainly composed of collagen, elastin and glycosaminoglycans (GAGs) 49 . Phosphatidylinositols are a class of phospholipids that have various roles in cytoskeleton organization 50 . Notably, the dysregulation of ECM structural constituent (crest 1: adjusted P   =  3.32 × 10 −8 ; crest 2: adjusted P   =  1.61 × 10 −8 ), GAG binding (crest 1: adjusted P   =  1.805 × 10 −8 ; crest 2: adjusted P   =  4.093 × 10 −6 ) and phosphatidylinositol binding (crest 1: adjusted P   =  3.391 × 10 −6 ; crest 2: adjusted P   =  7.832 × 10 −6 ) were identified 51 , 52 . We also identified cytolysis (crest 1: adjusted P   =  2.973 × 10 −5 ; crest 2: adjusted P : NS), which can increase water loss 53 . The dysregulated actin binding (crest 1: adjusted P   =  3.536 × 10 −8 ; crest 2: adjusted P   =  3.435 × 10 −9 ), actin filament organization (crest 1: adjusted P   =  8.406 × 10 −9 ; crest 2: adjusted P   =  1.157 × 10 −9 ) and regulation of actin cytoskeleton (crest 1: adjusted P   =  0.00090242; crest 2: adjusted P   =  6.788 × 10 −4 ) were identified, which affect the structure and function of various tissues 54 , 55 , 56 , 57 , 58 . Additionally, cell adhesion is the attachment of a cell to another cell or to ECM via adhesion molecules 59 . We identified the positive regulation of cell adhesion (crest 1: adjusted P   =  3.618 × 10 −5 ; crest 2: adjusted P   =  8.272 × 10 −9 ) module, which can prevent or delay skin aging 60 , 61 . Threonine can affect sialic acid production, which is involved in cell adhesion 62 . We also identified the glycine, serine and threonine metabolism (crest 1: adjusted P : NS; crest 2: adjusted P   =  0.00506) 62 . Additionally, scavenging of heme from plasma was identified (crest 1: adjusted P   =  1.176 × 10 −11 ; crest 2: adjusted P   =  1.694 × 10 −8 ), which can modulate skin aging as excess-free heme can damage cellular components 63 , 64 . Rho GTPases regulate a wide range of cellular responses, including changes to the cytoskeleton and cell adhesion (RHO GTPase cycle, crest 1: adjusted P   =  9.956 × 10 −10 ; crest 2: adjusted P   =  1.546 × 10 −5 ) 65 . In relation to muscle, previous studies demonstrated that muscle mass decreases by approximately 3–8% per decade after the age of 30, with an even higher decline rate after the age of 60, which consistently coincides with the observed second crest 66 . Interestingly, we identified dysregulation in the module associated with the structural constituent of muscle (crest 1: adjusted P   =  0.00565; crest 2: adjusted P   =  0.0162), consistent with previous findings 66 . Furthermore, we identified the pathway associated with caffeine metabolism (crest 1: adjusted P   =  0.00378; crest 2: adjusted P   =  0.0162), which is consistent with our observations above (Fig. 2b ) and implies that the capacity to metabolize caffeine undergoes a notable alteration not only around 60 years of age but also around the age of 40 years.

In crest 1, we identified specific modules associated with lipid and alcohol metabolism. Previous studies established that lipid metabolism declines with human aging 67 . Our analysis revealed several modules related to lipid metabolism, including plasma lipoprotein remodeling (crest 1: adjusted P   =  2.269 × 10 −9 ), chylomicron assembly (crest 1: adjusted P   =  9.065 × 10 −7 ) and ATP-binding cassette (ABC) transporters (adjusted P   =  1.102 × 10 −4 ). Moreover, we discovered a module linked to alcohol metabolism (alcohol binding, adjusted P   =  8.485 × 10 −7 ), suggesting a decline in alcohol metabolization efficiency with advancing age, particularly around the age of 40, when it significantly diminishes. In crest 2, we observed prominent modules related to immune dysfunction, encompassing acute-phase response (adjusted P   =  2.851 × 10 −8 ), antimicrobial humoral response (adjusted P   =  2.181 × 10 −5 ), zymogen activation (adjusted P   =  4.367 × 10 −6 ), complement binding (adjusted P   =  0.002568), mononuclear cell differentiation (adjusted P   =  9.352 × 10 −8 ), viral process (adjusted P   =  5.124 × 10 −7 ) and regulation of hemopoiesis (adjusted P   =  3.522 × 10 −7 ) (Fig. 5a ). Age-related changes in the immune system, collectively known as immunosenescence, have been extensively documented 68 , 69 , 70 , and our results demonstrate a rapid decline at age 60. Furthermore, we also identified modules associated with kidney function (glomerular filtration, adjusted P   =  0.00869) and carbohydrate metabolism (carbohydrate binding, adjusted P   =  0.01045). Our previous findings indicated a decline in kidney function around the age of 60 years (Fig. 3c ), as did the present result of this observation. Previous studies indicated the influence of carbohydrates on aging, characterized by the progressive decline of physiological functions and increased susceptibility to diseases over time 71 , 72 .

In summary, our analysis identifies many dysregulated functional modules identified in both crest 1 and crest 2 that underlie the risk for various diseases and alterations of biological functions. Notably, we observed an overlap of dysregulated functional modules among clusters 2, 4 and 6 because they overlap at the molecular level, as depicted in Fig. 5b . This indicates that certain molecular components are shared among these clusters and the identified crests. However, it is important to note that numerous molecules are specific to each of the two approaches employed in our study. This suggests that these two approaches complement each other in identifying nonlinear changes in molecules and functions during human aging. By using both approaches, we were able to capture a more comprehensive understanding of the molecular alterations associated with aging and their potential implications for diseases.

Analyzing a longitudinal multi-omics dataset involving 108 participants, we successfully captured the dynamic and nonlinear molecular changes that occur during human aging. Our study’s strength lies in the comprehensive nature of the dataset, which includes multiple time point samples for each participant. This longitudinal design enhances the reliability and robustness of our findings compared to cross-sectional studies with only one time point sample for each participant. The first particularly intriguing finding from our analysis is that only a small fraction of molecules (6.6%) displayed linear changes throughout human aging (Fig. 1d ). This observation is consistent with previous research and underscores the limitations of relying solely on linear regression to understand the complexity of aging-related molecular changes 5 . Instead, our study revealed that a considerable number of molecules (81.0%) exhibited nonlinear patterns (Fig. 1e ). Notably, this nonlinear trend was observed across all types of omics data with remarkably high consistency (Fig. 1e,g ), highlighting the widespread functionally relevant nature of these dynamic changes. By unveiling the nonlinear molecular alterations associated with aging, our research contributes to a more comprehensive understanding of the aging process and its molecular underpinnings.

To further investigate the nonlinear changing molecules observed in our study, we employed a trajectory clustering approach to group molecules with similar temporal patterns. This analysis revealed the presence of three distinct clusters (Fig. 2c ) that exhibited clear and compelling patterns across the human lifespan. These clusters suggest that there are specific age ranges, such as around 60 years old, where distinct and extensive molecular changes occur (Fig. 2c ). Functional analysis revealed several modules that exhibited nonlinear changes during human aging. For example, we identified a module associated with oxidative stress, which is consistent with previous studies linking oxidative stress to the aging process 23 (Fig. 3a ). Our analysis indicates that this pathway increases significantly after the age of 60 years. In cluster 5, we identified a transcriptomics module associated with mRNA stabilization and autophagy (Fig. 3a ). Both of these processes have been implicated in the aging process and are involved in maintaining cellular homeostasis and removing damaged components. Furthermore, our analysis uncovered nonlinear changes in disease risk across aging. In cluster 2, we identified the phenylalanine metabolism pathway (Fig. 3b ), which has been associated with cardiac dysfunction during aging 26 . Additionally, we found that the clinical laboratory tests blood urea nitrogen and serum/plasma glucose increase significantly with age (cluster 2; Fig. 3c ), indicating a nonlinear decline in kidney function and an increased risk of T2D with age, with a critical threshold occurring approximately at the age of 60 years. In cluster 4, we identified pathways related to cardiovascular health, such as the biosynthesis of unsaturated fatty acids and caffeine metabolism (Fig. 3b ). Overall, our study provides compelling evidence for the existence of nonlinear changes in molecular profiles during human aging. By elucidating the specific functional modules and disease-related pathways that exhibit such nonlinear changes, we contribute to a better understanding of the complex molecular dynamics underlying the aging process and its implications for disease risk.

Although the trajectory clustering approach proves effective in identifying molecules that undergo nonlinear changes, it may not be as proficient in capturing substantial alterations that occur at specific time points without exhibiting a consistent pattern in other stages. We then employed a modified version of the DE-SWAN algorithm 14 to comprehensively investigate changes in multi-omics profiling throughout human aging. This approach enabled us to identify waves of dysregulated molecules and microbes across the human lifespan. Our analysis revealed two prominent crests occurring around the ages of 40 years and 60 years, which were consistent across various omics data types, suggesting their universal nature (Fig. 4a,e ). Notably, in the proteomics data, we observed crests around the ages of 40 years and 60 years, which aligns approximately with a previous study (which reported crests at ages 34 years, 60 years and 78 years) 14 . Due to the age range of our cohort being 25–75 years, we did not detect the third peak. Furthermore, the differences in proteomics data acquisition platforms (mass spectrometry versus SomaScan) 14 , 73 resulted in different identified proteins, with only a small overlap (1,305 proteins versus 302 proteins, of which only 75 were shared). This discrepancy may explain the age variation of the first crest identified in the two studies (approximately 10 years). However, despite the differences in the two proteomics datasets, the wave patterns observed in both studies were highly similar 14 (Fig. 4a ). Remarkably, by considering multiple omics data types, we consistently identified similar crests for each type, indicating the universality of these waves of change across plasma molecules and microbes from various body sites (Fig. 4e and Supplementary Fig. 3 ).

The analysis of molecular functionality in the two distinct crests revealed the presence of several modules, indicating a nonlinear increase in the risks of various diseases (Fig. 5a ). Both crest 1 and crest 2 exhibit the identification of multiple modules associated with CVD, which aligns with the aforementioned findings (Fig. 3b ). Moreover, we observed an escalated dysregulation in skin and muscle functioning in both crest 1 and crest 2. Additionally, we identified a pathway linked to caffeine metabolism, indicating a noticeable alteration in caffeine metabolization not only around the age of 60 but also around the age of 40. This shift may be due to either a metabolic shift or a change in caffeine consumption. In crest 1, we also identified specific modules associated with lipid and alcohol metabolism, whereas crest 2 demonstrated prominent modules related to immune dysfunction. Furthermore, we also detected modules associated with kidney function and carbohydrate metabolism, which is consistent with our above results. These findings reinforce our previous observations regarding a decline in kidney function around the age of 60 years (Fig. 3c ) while shedding light on the impact of dysregulated functional modules in both crest 1 and crest 2, suggesting nonlinear changes in disease risk and functional dysregulation. Notably, we identified an overlap of dysregulated functional modules among clusters 2, 4 and 6, indicating molecular-level similarities between these clusters and the identified crests (Fig. 5b ). This suggests the presence of shared molecular components among these clusters and crests. However, it is crucial to note that there are also numerous molecules specific to each of the two approaches employed in our study, indicating that these approaches complement each other in identifying nonlinear changes in molecules and functions during human aging.

The present research is subject to certain constraints. We accounted for many basic characteristics (confounders) of participants in the cohort; but because this study primarily reflects between-individual differences, there may be additional confounders due to the different age distributions of the participants. For example, we identified a notable decrease in oxygen carrier activity around age 60 (Figs. 2c and 3a ) and marked variations in alcohol and caffeine metabolism around ages 40 and 60 (Fig. 3a ). However, these findings might be shaped by participants’ lifestyle—that is, physical activity and their alcohol and caffeine intake. Regrettably, we do not have such detailed behavioral data for the entire group, necessitating validation in upcoming research. Although initial BMI and insulin sensitivity measurements were available at cohort entry, subsequent metrics during the observation span were absent, marking a study limitation.

A further constraint is our cohort’s modest size, encompassing merely 108 individuals (eight individuals between 25 years and 40 years of age), which hampers the full utilization of deep learning and may affect the robustness of the identification of nonlinear changing features in Fig. 1e . Although advanced computational techniques, including deep learning, are pivotal for probing nonlinear patterns, our sample size poses restrictions. Expanding the cohort size in subsequent research would be instrumental in harnessing the full potential of machine learning tools. Another limitation of our study is that the recruitment of participants was within the community around Stanford University, driven by rigorous sample collection procedures and the substantial expenses associated with setting up a longitudinal cohort. Although our participants exhibited a considerable degree of ethnic age and biological sex diversity (Fig. 1a and Supplementary Data ), it is important to acknowledge that our cohort may not fully represent the diversity of the broader population. The selectivity of our cohort limits the generalizability of our findings. Future studies should aim to include a more diverse cohort to enhance the external validity and applicability of the results.

In addition, the mean observation span for participants was 626 days, which is insufficient for detailed inflection point analyses. Our cohort’s age range of 25–70 years lacks individuals who lie outside of this range. The molecular nonlinearity detected might be subject to inherent variations or oscillations, a factor to consider during interpretation. Our analysis has not delved into the nuances of the dynamical systems theory, which provides a robust mathematical framework for understanding observed behaviors. Delving into this theory in future endeavors may yield enhanced clarity and interpretation of the data.

Moreover, it should be noted that, in our study, the observed nonlinear molecular changes occurred across individuals of varying ages rather than within the same individuals. This is attributed to the fact that, despite our longitudinal study, the follow-up period for our participants was relatively brief for following aging patterns (median, 1.7 years; Extended Data Fig. 1g ). Such a timeframe is inadequate for detecting nonlinear molecular changes that unfold over decades throughout the human lifespan. Addressing this limitation in future research is essential.

Lastly, our study’s molecular data are derived exclusively from blood samples, casting doubt on its direct relevance to specific tissues, such as the skin or muscles. We propose that blood gene expression variations might hint at overarching physiological alterations, potentially impacting the ECM in tissues, including skin and muscle. Notably, some blood-based biomarkers and transcripts have demonstrated correlations with tissue modifications, inflammation and other elements influencing the ECM across diverse tissues 74 , 75 .

In our future endeavors, the definitive confirmation of our findings hinges on determining if nonlinear molecular patterns align with nonlinear changes in functional capacities, disease occurrences and mortality hazards. For a holistic grasp of this, amalgamating multifaceted data from long-term cohort studies covering several decades becomes crucial. Such data should encompass molecular markers, comprehensive medical records, functional assessments and mortality data. Moreover, employing cutting-edge statistical techniques is vital to intricately decipher the ties between these nonlinear molecular paths and health-centric results.

In summary, the unique contribution of our study lies not merely in reaffirming the nonlinear nature of aging but also in the depth and breadth of the multi-omics data that we analyzed. Our study goes beyond stating that aging is nonlinear by identifying specific patterns, inflection points and potential waves in aging across multiple layers of biological data during human aging. Identifying specific clusters with distinct patterns, functional implications and disease risks enhances our understanding of the aging process. By considering the nonlinear dynamics of aging-related changes, we can gain insights into specific periods of significant changes (around age 40 and age 60) and the molecular mechanisms underlying age-related diseases, which could lead to the development of early diagnosis and prevention strategies. These comprehensive multi-omics data and the approach allow for a more nuanced understanding of the complexities involved in the aging process, which we think adds value to the existing body of research. However, further research is needed to validate and expand upon these findings, potentially incorporating larger cohorts to capture the full complexity of aging.

The participant recruitment, sample collection, data acquisition and data processing were documented in previous studies conducted by Zhou et al. 76 , Ahadi et al. 5 , Schüssler-Fiorenza Rose et al. 77 , Hornburg et al. 78 and Zhou et al. 79 .

Participant recruitment

Participants provided informed written consent for the study under research protocol 23602, which was approved by the Stanford University institutional review board. This study adheres to all relevant ethical regulations, ensuring informed consents were obtained from all participants. All participants consented to publication of potentially identifiable information. The cohort comprised 108 participants who underwent follow-up assessments. Exclusion criteria encompassed conditions such as anemia, kidney disease, a history of CVD, cancer, chronic inflammation or psychiatric illnesses as well as any prior bariatric surgery or liposuction. Each participant who met the eligibility criteria and provided informed consent underwent a one-time modified insulin suppression test to quantify insulin-mediated glucose uptake at the beginning of the enrollment 76 . The steady-state plasma glucose (SSPG) levels served as a direct indicator of each individual’s insulin sensitivity in processing a glucose load. We categorized individuals with SSPG levels below 150 mg dl −1 as insulin sensitive and those with levels of 150 mg dl −1 or higher as insulin resistant 80 , 81 . Thirty-eight participants were missing SSPG values, rendering their insulin resistance or sensitivity status undetermined. We also collected fasting plasma glucose (FPG) data for 69 participants at enrollment. Based on the FPG levels, two participants were identified as having diabetes at enrollment, with FPG levels exceeding 126 mg dl −1 ( Supplementary Data ). Additionally, we measured hemoglobin A1C (HbA1C) levels during each visit, using it as a marker for average glucose levels over the past 3 months: 6.5% or higher indicates diabetes. Accordingly, four participants developed diabetes during the study period. At the beginning of the enrollment, BMI was also measured for each participant. Participants received no compensation.

Comprehensive sample collection was conducted during the follow-up period, and multi-omics data were acquired (Fig. 1b ). For each visit, the participants self-reported as healthy or non-healthy 76 . To ensure accuracy and minimize the impact of confounding factors, only samples from individuals classified as healthy were selected for subsequent analysis.

Transcriptomics

Transcriptomic profiling was conducted on flash-frozen PBMCs. RNA isolation was performed using a QIAGEN All Prep kit. Subsequently, RNA libraries were assembled using an input of 500 ng of total RNA. In brief, ribosomal RNA (rRNA) was selectively eliminated from the total RNA pool, followed by purification and fragmentation. Reverse transcription was carried out using a random primer outfitted with an Illumina-specific adaptor to yield a cDNA library. A terminal tagging procedure was used to incorporate a second adaptor sequence. The final cDNA library underwent amplification. RNA sequencing libraries underwent sequencing on an Illumina HiSeq 2000 platform. Library quantification was performed via an Agilent Bioanalyzer and Qubit fluorometric quantification (Thermo Fisher Scientific) using a high-sensitivity dsDNA kit. After normalization, barcoded libraries were pooled at equimolar ratios into a multiplexed sequencing library. An average of 5–6 libraries were processed per HiSeq 2000 lane. Standard Illumina pipelines were employed for image analysis and base calling. Read alignment to the hg19 reference genome and personal exomes was achieved using the TopHat package, followed by transcript assembly and expression quantification via HTseq and DESeq2. In the realm of data pre-processing, genes with an average read count across all samples lower than 0.5 were excluded. Samples exhibiting an average read count lower than 0.5 across all remaining genes were likewise removed. For subsequent global variance and correlation assessments, genes with an average read count of less than 1 were eliminated.

Plasma sample tryptic peptides were fractionated using a NanoLC 425 System (SCIEX) operating at a flow rate of 5 μl min −1 under a trap-elute configuration with a 0.5 × 10 mm ChromXP column (SCIEX). The liquid chromatography gradient was programmed for a 43-min run, transitioning from 4% to 32% of mobile phase B, with an overall run time of 1 h. Mobile phase A consisted of water with 0.1% formic acid, and mobile phase B was formulated with 100% acetonitrile and 0.1% formic acid. An 8-μg aliquot of non-depleted plasma was loaded onto a 15-cm ChromXP column. Mass spectrometry analysis was executed employing SWATH acquisition on a TripleTOF 6600 system. A set of 100 variable Q1 window SWATH acquisition methods was designed in high-sensitivity tandem mass spectrometry (MS/MS) mode. Subsequent data analysis included statistical scoring of peak groups from individual runs via pyProphet 82 , followed by multi-run alignment through TRIC60, ultimately generating a finalized data matrix with a false discovery rate (FDR) of 1% at the peptide level and 10% at the protein level. Protein quantitation was based on the sum of the three most abundant peptide signals for each protein. Batch effect normalization was achieved by subtracting principal components that primarily exhibited batch-associated variation, using Perseus software v.1.4.2.40.

Untargeted metabolomics

A ternary solvent system of acetone, acetonitrile and methanol in a 1:1:1 ratio was used for metabolite extraction. The extracted metabolites were dried under a nitrogen atmosphere and reconstituted in a 1:1 methanol:water mixture before analysis. Metabolite profiles were generated using both hydrophilic interaction chromatography (HILIC) and reverse-phase liquid chromatography (RPLC) under positive and negative ion modes. Thermo Q Exactive Plus mass spectrometers were employed for HILIC and RPLC analyses, respectively, in full MS scan mode. MS/MS data were acquired using quality control (QC) samples. For the HILIC separations, a ZIC-HILIC column was used with mobile phase solutions of 10 mM ammonium acetate in 50:50 and 95:5 acetonitrile:water ratios. In the case of RPLC, a Zorbax SBaq column was used, and the mobile phase consisted of 0.06% acetic acid in water and methanol. Metabolic feature detection was performed using Progenesis QI software. Features from blanks and those lacking sufficient linearity upon dilution were excluded. Only features appearing in more than 33% of the samples were retained for subsequent analyses, and any missing values were imputed using the k -nearest neighbors approach. We employed locally estimated scatterplot smoothing (LOESS) normalization 83 to correct the metabolite-specific signal drift over time. The metid package 84 was used for metabolite annotation.

Cytokine data

A panel of 62 human cytokines, chemokines and growth factors was analyzed in EDTA-anticoagulated plasma samples using Luminex-based multiplex assays with conjugated antibodies (Affymetrix). Raw fluorescence measurements were standardized to median fluorescence intensity values and subsequently subjected to variance-stabilizing transformation to account for batch-related variations. As previously reported 76 , data points characterized by background noise, termed CHEX, that deviate beyond five standard deviations from the mean (mean ± 5 × s.d.) were excluded from the analyses.

Clinical laboratory test

The tests encompassed a comprehensive metabolic panel, a full blood count, glucose and HbA1C levels, insulin assays, high-sensitivity C-reactive protein (hsCRP), immunoglobulin M (IgM) and lipid, kidney and liver panels.

Lipid extraction and quantification procedures were executed in accordance with established protocols 78 . In summary, complex lipids were isolated from 40 μl of EDTA plasma using a solvent mixture comprising methyl tertiary-butyl ether, methanol and water, followed by a biphasic separation. Subsequent lipid analysis was conducted on the Lipidyzer platform, incorporating a differential mobility spectrometry device (SelexION Technology) and a QTRAP 5500 mass spectrometer (SCIEX).

Immediately after arrival, samples were stored at −80 °C. Stool and nasal samples were processed and sequenced in-house at the Jackson Laboratory for Genomic Medicine, whereas oral and skin samples were outsourced to uBiome for additional processing. Skin and oral samples underwent 30 min of beads-beating lysis, followed by a silica-guanidinium thiocyanate-based nucleic acid isolation protocol. The V4 region of the 16S rRNA gene was amplified using specific primers, after which the DNA was barcoded and sequenced on an Illumina NextSeq 500 platform via a 2 × 150-bp paired-end protocol. Similarly, stool and nasal samples were processed for 16S rRNA V1–V3 region amplification using a different set of primers and sequenced on an Illumina MiSeq platform. For data processing, the raw sequencing data were demultiplexed using BCL2FASTQ software and subsequently filtered for quality. Reads with a Q-score lower than 30 were excluded. The DADA2 R package was used for further sequence data processing, which included filtering out reads with ambiguous bases and errors, removing chimeras and aligning sequences against a validated 16S rRNA gene database. Relative abundance calculations for amplicon sequence variants (ASVs) were performed, and samples with inadequate sequencing depth (<1,000 reads) were excluded. Local outlier factor (LOF) was calculated for each point on a depth-richness plot, and samples with abnormal LOF were removed. In summary, rigorous procedures were followed in both the collection and processing stages, leveraging automated systems and specialized software to ensure the quality and integrity of the microbiome data across multiple body sites.

Statistics and reproducibility

For all data processing, statistical analysis and data visualization tasks, RStudio, along with R language (v.4.2.1), was employed. A comprehensive list of the packages used can be found in the Supplementary Note . The Benjamini–Hochberg method was employed to account for multiple comparisons. Spearman correlation coefficients were calculated using the R functions ‘cor’ and ‘cor.test’. Principal-component analysis (PCA) was conducted using the R function ‘princomp’. Before all the analyses, the confounders, such as BMI, sex, IRIS and ethnicity, were adjusted using the previously published method 19 . In brief, we used the intensity of each feature as the dependent variable (Y) and the confounding factors as the independent variables (X) to build a linear regression model. The residuals from this model were then used as the adjusted values for that specific feature.

All the omics data were acquired randomly. No statistical methods were used to predetermine the sample size, but our sample sizes are similar to those reported in previous publications 5 , 76 , 77 , 78 , 79 , and no data were excluded from the analyses. Additionally, the investigators were blinded to allocation during experiments and outcome assessment to the conditions of the experiments. Data distribution was assumed to be normal, but this was not formally tested.

The icons used in figures are from iconfont.cn, which can be used for non-commercial purposes under the MIT license ( https://pub.dev/packages/iconfont/license ).

Cross-sectional dataset generation

The ‘cross-sectional’ dataset was created by briefly extracting information from the longitudinal dataset. The mean value was calculated to represent each molecule’s intensity for each participant. Similarly, the age of each participant was determined by calculating the mean value of ages across all sample collection time points.

Linear changing molecule detection

We detected linear changing molecules during human aging using Spearman correlation and linear regression modeling. The confounders, such as BMI, sex, IRIS and ethnicity, were adjusted using the previously published method 19 . Our analysis revealed a high correlation between these two approaches in identifying such molecules. Based on these findings, we used the Spearman correlation approach to showcase the linear changing molecules during human aging. The permutation test was also used to get the permutated P values for each feature. In brief, each feature was subjected to sample label shuffling followed by a recalculation of the Spearman correlation. This process was reiterated 10,000 times, yielding 10,000 permuted Spearman correlations. The original Spearman correlation was then compared against these permuted values to obtain the permuted P values.

Dysregulated molecules compared to baseline during human aging

To depict the dysregulated molecules during human aging compared to the baseline, we categorized the participants into different age stages based on their ages. The baseline stage was defined as individuals aged 25–40 years. For each age stage group, we employed the Wilcoxon test to identify dysregulated molecules in comparison to the baseline, considering a significance threshold of P  < 0.05. Before the statistical analysis, all the confounders were corrected. Subsequently, we visualized the resulting dysregulated molecules at different age stages using a Sankey plot. The permutation test was also used to get the permutated P values for each feature. In brief, we shuffled the sample labels and recalculated the absolute mean difference between the two groups, against which the actual absolute mean difference was benchmarked to derive the permuted P values. To identify the molecules and microbes that exhibited significant changes at any given age stage, we adjusted the P values for each feature by multiplying them by 6. This adjustment adheres to the Bonferroni correction method, ensuring a rigorous evaluation of statistical significance.

Evaluation of the age reflected by different types of omics data

To assess whether each type of omics data accurately reflects the ages of individuals in our dataset, we conducted a PCA. Subsequently, we computed the Spearman correlation coefficient between the ages of participants and the first principal component (PC1). The absolute value of this coefficient was used to evaluate the degree to which the omics data reflect the ages (Fig. 2a ). PLS regression was also used to compare the strength of the age effect to the different omics data types. In brief, the ‘pls’ function from the R package mixOmics was used to construct the regression model between omics data and ages. Then, the ‘perf’ function was used to assess the performance of all the modules with sevenfold cross-validation. The R 2 was extracted to assess the strength of the age effect on the different omics data types.

To accommodate the varying time points of biological and omics data, we employed the LOESS approach. This approach allowed us to smooth and predict the multi-omics data at specific time points (that is, every half year) 14 , 85 . In brief, for each molecule, we fitted a LOESS regression model. During the fitting process, the LOESS argument ‘span’ was optimized through cross-validation. This ensured that the LOESS model provided an accurate and non-overfitting fit to the data (Supplementary Fig. 2a,b ). Once we obtained the LOESS prediction model, we applied it to predict the intensity of each molecule at every half-year time point.

Trajectory clustering analysis

To conduct trajectory clustering analysis, we employed the fuzzy c-means clustering approach available in the R package ‘Mfuzz.’ This approach was previously described in our publication 19 . The analysis proceeded in several steps. First, the omics data were auto-scaled to ensure comparable ranges. Next, we computed the minimum centroid distances for a range of cluster numbers, specifically from 2 to 22, in step 1. These minimum centroid distances served as a cluster validity index, helping us determine the optimal cluster number. Based on predefined rules, we selected the optimal cluster number. To refine the accuracy of this selection, we merged clusters with center expression data correlations greater than 0.8 into a single cluster. This step aimed to capture similar patterns within the data. The resulting optimal cluster number was then used for the fuzzy c-means clustering. Only molecules with memberships above 0.5 were retained within each cluster for further analysis. This threshold ensured that the molecules exhibited a strong association with their assigned cluster and contributed considerably to the cluster’s characteristics.

Pathway enrichment analysis and functional module identification

Transcriptomics and proteomics pathway enrichment.

Pathway enrichment analysis was conducted using the ‘clusterProfiler’ R package 86 . The GO, KEGG and Reactome databases were used. The P values were adjusted using the Benjamini–Hochberg method, with a significance threshold set at <0.05. To minimize redundant enriched pathways and GO terms, we employed a series of analyses. First, for enriched GO terms, we used the ‘Wang’ algorithm from the R package ‘simplifyEnrichment’ to calculate the similarity between GO terms. Only connections with a similarity score greater than 0.7 were retained to construct the GO term similarity network. Subsequently, community analysis was performed using the ‘igraph’ R package to partition the network into distinct modules. The GO term with the smallest enrichment adjusted P value was chosen as the representative within each module. The same approach was applied to the enriched KEGG and Reactome pathways, with one slight modification. In this case, the ‘jaccard’ algorithm was used to calculate the similarity between pathways, and a similarity cutoff of 0.5 was employed for the Jaccard index. After removing redundant enriched pathways, we combined all the remaining GO terms and pathways. Subsequently, we calculated the similarity between these merged entities using the Jaccard index. This similarity analysis aimed to capture the overlap and relationships between the different GO terms and pathways. Using the same approach as before, we performed community analysis to identify distinct biological functional modules based on the merged GO terms and pathways.

Identification of functional modules

First, we used the ‘Wang’ algorithm for the GO database and the ‘jaccard’ algorithm for the KEGG and Reactome databases to calculate the similarity between pathways. The enriched pathways served as nodes in a similarity network, with edges representing the similarity between two nodes. Next, we employed the R package ‘igraph’ to identify modules within the network based on edge betweenness. By gradually removing edges with the highest edge betweenness scores, we constructed a hierarchical map known as a dendrogram, representing a rooted tree of the graph. The leaf nodes correspond to individual pathways, and the root node represents the entire graph 87 . We then merged pathways within each module, selecting the pathway with the smallest adjusted P value to represent the module. After this step, we merged pathways from all three databases into modules. Subsequently, we repeated the process by calculating the similarity between modules from all three databases using the ‘jaccard’ algorithm. Once again, we employed the same approach described above to identify the functional modules.

Metabolomics pathway enrichment

To perform pathway enrichment analysis for metabolomics data, we used the human KEGG pathway database. This database was obtained from KEGG using the R package ‘massDatabase’ 88 . For pathway enrichment analysis, we employed the hypergeometric distribution test from the ‘TidyMass’ project 89 . This statistical test allowed us to assess the enrichment of metabolites within each pathway. To account for multiple tests, P values were adjusted using the Benjamini–Hochberg method. We considered pathways with Benjamini–Hochberg-adjusted P values lower than 0.05 as significantly enriched.

Modified DE-SWAN

The DE-SWAN algorithm 14 was used. To begin, a unique age is selected as the center of a 20-year window. Molecule levels in individuals younger than and older than that age are compared using the Wilcoxon test to assess differential expression. P values are calculated for each molecule, indicating the significance of the observed differences. To ensure sufficient sample sizes for statistical analysis in each time window, the initial window ranges from ages 25 to 50. The left half of this window covers ages 25–40, whereas the right half spans ages 41–50. The window then moves in one-year steps; this is why Fig. 4 displays an age range of 40–65 years. To account for multiple comparisons, these P values are adjusted using Benjamini–Hochberg correction. To evaluate the robustness and relevance of the DE-SWAN results, the algorithm is tested with various parcel widths, including 15 years, 20 years, 25 years and 30 years. Additionally, different q value thresholds, such as <0.0001, <0.001, <0.01 and <0.05, are applied. By comparing the results obtained with these different parameters to results obtained by chance, we can assess the significance of the findings. To generate random results for comparison, the phenotypes of the individuals are randomly permuted, and the modified DE-SWAN algorithm is applied to the permuted dataset. This allows us to determine whether the observed results obtained with DE-SWAN are statistically significant and not merely a result of chance.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

The raw data used in this study can be accessed without any restrictions on the National Institutes of Health Human Microbiome 2 project site ( https://portal.hmpdacc.org ). Both the raw and processed data are also available on the Stanford iPOP site ( http://med.stanford.edu/ipop.html ). Researchers and interested individuals can visit these websites to access the data. For further details and inquiries about the study, we recommend contacting the corresponding author, who can provide additional information and address any specific questions related to the research.

Code availability

The statistical analysis and data processing in this study were performed using R v.4.2.1, along with various base packages and additional packages. Detailed information about the specific packages used can be found in the Supplementary Note , which accompanies the manuscript. Furthermore, all the custom scripts developed for this study have been made openly accessible and can be found on the GitHub repository at https://github.com/jaspershen-lab/ipop_aging . By visiting this repository, researchers and interested individuals can access and use the custom scripts for their own analyses or to replicate the study’s findings.

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Acknowledgements

We sincerely thank all the research participants for their dedicated involvement in this study. We also thank A. Chen and L. Stainton for their valuable administrative assistance. Additionally, we are deeply grateful to A.T. Brunger’s support for this work. This work was supported by National Institutes of Health (NIH) grants U54DK102556 (M.P.S.), R01 DK110186-03 (M.P.S.), R01HG008164 (M.P.S.), NIH S10OD020141 (M.P.S.), UL1 TR001085 (M.P.S.) and P30DK116074 (M.P.S.) and by the Stanford Data Science Initiative (M.P.S.). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

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These authors contributed equally: Xiaotao Shen, Chuchu Wang.

Authors and Affiliations

Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA

Xiaotao Shen, Xin Zhou, Wenyu Zhou, Daniel Hornburg, Si Wu & Michael P. Snyder

Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore

Xiaotao Shen

School of Chemistry, Chemical Engineering and Biotechnology, Singapore, Singapore

Howard Hughes Medical Institute, Stanford University, Stanford, CA, USA

Chuchu Wang

Department of Molecular and Cellular Physiology, Stanford University, Stanford, CA, USA

Stanford Center for Genomics and Personalized Medicine, Stanford, CA, USA

Xin Zhou & Michael P. Snyder

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Contributions

X.S. and M.P.S. conceptualized and designed the study. X.Z. and W.Z. prepared the microbiome data. D.H. and W.S. prepared the lipidomics data. X.S. and C.W. conducted the data analysis. X.S. and C.W. prepared the figures. X.S., C.W. and M.P.S. contributed to the writing and revision of the manuscript, with input from other authors. M.S. and X.S. supervised the overall study.

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Correspondence to Michael P. Snyder .

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Competing interests.

M.P.S. is a co-founder of Personalis, SensOmics, Qbio, January AI, Filtricine, Protos and NiMo and is on the scientific advisory boards of Personalis, SensOmics, Qbio, January AI, Filtricine, Protos, NiMo and Genapsys. D.H. has a financial interest in PrognomIQ and Seer. All other authors have no competing interests.

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Extended data

Extended data fig. 1 demographic data of all the participants in the study..

a , The ages positively correlate with BMI. The shaded area around the regression line represents the 95% confidence interval. b , Gender with age. c , Ethnicity with age. d , Insulin response with age. e , biological sample collection for all the participants. f , Overlap of the different kinds of omics data. g , The age range for each participant in this study.

Extended Data Fig. 2 Most of the molecules change nonlinearly during human aging.

a , Differential expressional microbes in different age ranges compared to baselines (25 – 40 years old, two-sided Wilcoxon test, p -value < 0.05). b , Most of the linear changing molecules and microbiota are also included in the molecules/microbes that significantly dysregulated at least one age range.

Extended Data Fig. 3 Omics data can represent aging.

PCA score plot of metabolomics data ( a ), cytokine ( b ), and oral microbiome ( c ).

Extended Data Fig. 4 Functional analysis of molecules in different clusters.

a , The Jaccard index between clusters from different datasets. b , The overlap between clusters using different types of omics data. c , Functional module detection and identification. d , Functional analysis of nonlinear changing molecules for all clusters.

Extended Data Fig. 5 Function annotation for significantly dysregulated molecules in crest 1 and 2.

a , Transcriptomics data. b , Proteomics data. c , Metabolomics data.

Extended Data Fig. 6 Pathways enrichment results for crest 1 and 2.

a , The final functional modules identified for Crest 1 and 2. b , The pathway enrichment analysis results for transcriptomics data. c , The pathway enrichment analysis results for proteomics data. d , The pathway enrichment results for metabolomics data.

Supplementary information

Supplementary figs. 1–6, reporting summary, supplementary data analysis results of the study., rights and permissions.

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Shen, X., Wang, C., Zhou, X. et al. Nonlinear dynamics of multi-omics profiles during human aging. Nat Aging (2024). https://doi.org/10.1038/s43587-024-00692-2

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DOI : https://doi.org/10.1038/s43587-024-00692-2

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