diabetes mellitus type 2 essay

Type 2 Diabetes Essay

Introduction.

Diabetes is a health condition that is developed when sugar level in the blood increases above normal levels. The two major types of diabetes are type 1 diabetes and type 2 diabetes. Type 2 diabetes is more prevalent than type 1 diabetes. This essay discusses some of the most frequently asked questions about type 2 diabetes through a sample dialogue between a patient and a doctor.

Patient: What is type 2 Diabetes and how is it developed?

Doctor: Type 2 diabetes can be described as a complication in the metabolic processes characterized by a relative shortage of insulin and high levels of glucose in the blood (Barnett, 2011). It differs from type 1 diabetes where there is a complete deficiency of insulin caused by destruction of pancreatic islet cells.

In addition, type 2 diabetes is more common in adults unlike type 1 diabetes which is prevalent amongst young people. The typical symptoms of type 2 diabetes include: recurrent urination, excessive thirst, and persistent hunger (Wilson &Mehra, 1997).

Type 2 diabetes is caused by a mixture of lifestyle and hereditary factors. Even though some factors, like nutrition and obesity, are under individual control, others like femininity, old age, and genetics are not. Sedentary lifestyle, poor nutrition and stress are the major causes of Type 2 diabetes.

Particularly, excessive consumption of sugar and fats increases the risk of infection. Genetic factors have been linked to this condition. For instance, research indicates that if one identical twin is infected, there is a 90% probability of the other twin getting infected. Nutritional condition of a mother for the period of fetal growth can as well lead to this condition. Inadequate sleep is associated with Type 2 diabetes since it affects the process of metabolism (Hawley & Zierath, 2008).

Patient: How is type 2 Diabetes transmitted?

Doctor: Type 2 diabetes cannot be transmitted from one individual to another, since it is not caused by micro-organisms that can be spread. Instead, it is a health condition where the body is unable to create sufficient insulin to maintain the blood sugar level.

Nevertheless, a child from diabetic parents is likely to develop the complication due to genetic inheritance. According to Hanas & Fox (2007), there are some genes that may result in diabetes. As in 2011, research showed that there are more than thirty-six genes that increase the risk of type 2 diabetes infection.

These genes represent 10 per cent of the entire hereditary component of the complication. For instance, a gene referred to as TCF7L2 allele, increases the probability of diabetes occurrence by 1.5 times. It is the greatest threat amongst the genetic invariants. Children from diabetic parents are, therefore, likely to get infected since genes are transferrable from parents to the offspring.

Patient: How is type 2 Diabetes treated?

Doctor: The first step in the treatment of type 2 diabetes is consumption of healthy diet. This involves avoiding excessive consumption of foods that contain sugar and fats as they are likely to increase the levels of sugar in the blood. In addition, getting involved in physical activity and losing excessive weight are also important.

These management practices are recommended because they lower insulin resistance and improve the body cells’ response to insulin. Eating healthy food and physical activity also lower the level of sugar in the blood. There are also pills and other medications that can be injected when these lifestyle changes do not regulate the blood sugar (Roper, 2006).

Type2 diabetes pills function in different ways. Some pills work by lowering insulin resistance while some raise the level of insulin in the blood or decrease the rate of food digestion. Even though the non-insulin injected medicines for this condition work in complex ways, essentially, they lower the levels of blood glucose after injection.

Insulin injection treatment basically raises the insulin level in the blood. Another treatment for type 2 diabetes is weight loss surgery that is recommended for obese people. This treatment has been proved effective since most of the patients can maintain regular levels of sugar in their blood after surgery (Codario, 2011).

Multiple prescriptions can be applied in controlling the levels of blood sugar. Actually, combination treatment is a popular remedy for Type 2 diabetes. If a single therapy is not sufficient, a health care provider may prescribe two or more different kinds of pills.

For instance, individuals with type 2 diabetes have high fat levels in the blood and high blood pressure. Therefore, doctors can prescribe medicines for treatment of these conditions at the same time. The kind of medication prescribed depends on the health condition of the patient (Ganz, 2005).

Patient: What are the chances of survival?

Doctor: Diabetes is one of the major causes of deaths in the United States each year. Statistics indicates that it contributes to approximately 100,000 deaths every year. In the United States, there are over 20 million reported cases of diabetes, the majority being Type 2 diabetes. Proper remedy including change of lifestyle and medications is known to improve the health condition of a patient. If properly used together, lifestyle changes and medication can increase the chances of survival of a patient by up to 85 per cent (Rosenthal, 2009).

Barnett, H. (2011). Type 2 diabetes. Oxford: Oxford University Press.

Codario, A. (2011). Type 2 diabetes, pre-diabetes, and the metabolic syndrome. Totowa, N.J: Humana Press.

Ganz, M. (2005). Prevention of Type 2 Diabetes . Chichester: John Wiley & Sons.

Hanas, R., & Fox, C. (2007). Type 2 diabetes in adults of all ages. London: Class Health.

Hawley, A., & Zierath, R. (2008). Physical activity and type 2 diabetes: Therapeutic effects and mechanisms of action. Champaign, IL: Human Kinetics.

Roper, R. (2006). Type 2 diabetes: The adrenal gland disease : the cause of type 2 diabetes and a nutrition program that takes control! . Bloomington, IN: AuthorHouse.

Rosenthal, S. (2009). The Canadian type 2 diabetes sourcebook. Mississauga, Ont: J. Wiley & Sons Canada.

Wilson, L., & Mehra, V. (1997). Managing the patient with type II diabetes . Gaithersburg, Md: Aspen Publishers.

• Chicago (A-D)
• Chicago (N-B)

IvyPanda. (2019, February 4). Type 2 Diabetes. https://ivypanda.com/essays/type-2-diabetes-2/

"Type 2 Diabetes." IvyPanda , 4 Feb. 2019, ivypanda.com/essays/type-2-diabetes-2/.

IvyPanda . (2019) 'Type 2 Diabetes'. 4 February.

IvyPanda . 2019. "Type 2 Diabetes." February 4, 2019. https://ivypanda.com/essays/type-2-diabetes-2/.

1. IvyPanda . "Type 2 Diabetes." February 4, 2019. https://ivypanda.com/essays/type-2-diabetes-2/.

Bibliography

IvyPanda . "Type 2 Diabetes." February 4, 2019. https://ivypanda.com/essays/type-2-diabetes-2/.

• Insulin Injection: Drug Properties and Injection Process
• Doctors' Reluctance to Prescribe Birth Control Pills to Early Adolescents
• Endocrine Disorders: The Diabetic
• The Diabetic Plate and Healthy Nutrition
• How to Self-Administer Insulin Injection
• The History of the Pill and Feminism
• Management of Patients With Diabetic Ketoacidosis
• Stontioum-90 in Plasma
• Diabetic Diet and Food Restrictions
• R-insulin: Article Critique
• Type I Diabetes: Pathogenesis and Treatment
• Human Body Organ Systems Disorders: Diabetes
• Informative Speech on Hyperthyroidism
• Are there Occasions in the Delivery of Health Care when Deception is Warranted?
• Xtra Mile: Roles of Medical Directors

• Reference Manager
• Simple TEXT file

People also looked at

Hypothesis and theory article, type 2 diabetes mellitus: a pathophysiologic perspective.

• Department of Medicine, Duke University, Durham, NC, United States

Type 2 Diabetes Mellitus (T2DM) is characterized by chronically elevated blood glucose (hyperglycemia) and elevated blood insulin (hyperinsulinemia). When the blood glucose concentration is 100 milligrams/deciliter the bloodstream of an average adult contains about 5–10 grams of glucose. Carbohydrate-restricted diets have been used effectively to treat obesity and T2DM for over 100 years, and their effectiveness may simply be due to lowering the dietary contribution to glucose and insulin levels, which then leads to improvements in hyperglycemia and hyperinsulinemia. Treatments for T2DM that lead to improvements in glycemic control and reductions in blood insulin levels are sensible based on this pathophysiologic perspective. In this article, a pathophysiological argument for using carbohydrate restriction to treat T2DM will be made.

Introduction

Type 2 Diabetes Mellitus (T2DM) is characterized by a persistently elevated blood glucose, or an elevation of blood glucose after a meal containing carbohydrate ( 1 ) ( Table 1 ). Unlike Type 1 Diabetes which is characterized by a deficiency of insulin, most individuals affected by T2DM have elevated insulin levels (fasting and/or post glucose ingestion), unless there has been beta cell failure ( 2 , 3 ). The term “insulin resistance” (IR) has been used to explain why the glucose levels remain elevated even though there is no deficiency of insulin ( 3 , 4 ). Attempts to determine the etiology of IR have involved detailed examinations of molecular and intracellular pathways, with attribution of cause to fatty acid flux, but the root cause has been elusive to experts ( 5 – 7 ).

Table 1 . Definition of type 2 diabetes mellitus.

How Much Glucose Is in the Blood?

Keeping in mind that T2DM involves an elevation of blood glucose, it is important to understand how much glucose is in the blood stream to begin with, and then the factors that influence the blood glucose—both exogenous and endogenous factors. The amount of glucose in the bloodstream is carefully controlled—approximately 5–10 grams in the bloodstream at any given moment, depending upon the size of the person. To calculate this, multiply 100 milligrams/deciliter × 1 gram/1,000 milligrams × 10 deciliters/1 liter × 5 liters of blood. The “zeros cancel” and you are left with 5 grams of glucose if the individual has 5 liters of blood. Since red blood cells represent about 40% of the blood volume, and the glucose is in equilibrium, there may be an extra 40% glucose because of the red blood cell reserve ( 8 ). Adding the glucose from the serum and red blood cells totals about 5–10 grams of glucose in the entire bloodstream.

Major Exogenous Factors That Raise the Blood Glucose

Dietary carbohydrate is the major exogenous factor that raises the blood glucose. When one considers that it is common for an American in 2021 to consume 200–300 grams of carbohydrate daily, and most of this carbohydrate is digested and absorbed as glucose, the body absorbs and delivers this glucose via the bloodstream to the cells while attempting to maintain a normal blood glucose level. Thinking of it in this way, if 200–300 grams of carbohydrates is consumed in a day, the bloodstream that holds 5–10 grams of glucose and has a concentration of 100 milligrams/deciliter, is the conduit through which 200,000–300,000 milligrams (200 grams = 200,000 milligrams) passes over the course of a day.

Major Endogenous Factors That Raise the Blood Glucose

There are many endogenous contributors that raise the blood glucose. There are at least 3 different hormones that increase glucose levels: glucagon, epinephrine, and cortisol. These hormones increase glucose levels by increasing glycogenolysis and gluconeogenesis ( 9 ). Without any dietary carbohydrate, the normal human body can generate sufficient glucose though the mechanism of glucagon secretion, gluconeogenesis, glycogen storage and glycogenolysis ( 10 ).

Major Exogenous Factors That Lower the Blood Glucose

A reduction in dietary carbohydrate intake can lower the blood glucose. An increase in activity or exercise usually lowers the blood glucose ( 11 ). There are many different medications, employing many mechanisms to lower the blood glucose. Medications can delay sucrose and starch absorption (alpha-glucosidase inhibitors), slow gastric emptying (GLP-1 agonists, DPP-4 inhibitors) enhance insulin secretion (sulfonylureas, meglitinides, GLP-1 agonists, DPP-4 inhibitors), reduce gluconeogenesis (biguanides), reduce insulin resistance (biguanides, thiazolidinediones), and increase urinary glucose excretion (SGLT-2 inhibitors). The use of medications will also have possible side effects.

Major Endogenous Factors That Lower the Blood Glucose

The major endogenous mechanism to lower the blood glucose is to deliver glucose into the cells (all cells can use glucose). If the blood glucose exceeds about 180 milligrams/deciliter, then loss of glucose into the urine can occur. The blood glucose is reduced by cellular uptake using glut transporters ( 12 ). Some cells have transporters that are responsive to the presence of insulin to activate (glut4), others have transporters that do not require insulin for activation. Insulin-responsive glucose transporters in muscle cells and adipose cells lead to a reduction in glucose levels—especially after carbohydrate-containing meals ( 13 ). Exercise can increase the glucose utilization in muscle, which then increases glucose cellular uptake and reduce the blood glucose levels. During exercise, when the metabolic demands of skeletal muscle can increase more than 100-fold, and during the absorptive period (after a meal), the insulin-responsive glut4 transporters facilitate the rapid entry of glucose into muscle and adipose tissue, thereby preventing large fluctuations in blood glucose levels ( 13 ).

Which Cells Use Glucose?

Glucose can used by all cells. A limited number of cells can only use glucose, and are “glucose-dependent.” It is generally accepted that the glucose-dependent cells include red blood cells, white blood cells, and cells of the renal papilla. Red blood cells have no mitochondria for beta-oxidation, so they are dependent upon glucose and glycolysis. White blood cells require glucose for the respiratory burst when fighting infections. The cells of the inner renal medulla (papilla) are under very low oxygen tension, so therefore must predominantly use glucose and glycolysis. The low oxygen tension is a result of the countercurrent mechanism of urinary concentration ( 14 ). These glucose-dependent cells have glut transporters that do not require insulin for activation—i.e., they do not need insulin to get glucose into the cells. Some cells can use glucose and ketones, but not fatty acids. The central nervous system is believed to be able to use glucose and ketones for fuel ( 15 ). Other cells can use glucose, ketones, and fatty acids for fuel. Muscle, even cardiac muscle, functions well on fatty acids and ketones ( 16 ). Muscle cells have both non-insulin-responsive and insulin-responsive (glut4) transporters ( 12 ).

Possible Dual Role of an Insulin-Dependent Glucose-Transporter (glut4)

A common metaphor is to think of the insulin/glut transporter system as a key/lock mechanism. Common wisdom states that the purpose of insulin-responsive glut4 transporters is to facilitate glucose uptake when blood insulin levels are elevated. But, a lock serves two purposes: to let someone in and/or to keep someone out . So, one of the consequences of the insulin-responsive glut4 transporter is to keep glucose out of the muscle and adipose cells, too, when insulin levels are low. The cells that require glucose (“glucose-dependent”) do not need insulin to facilitate glucose entry into the cell (non-insulin-responsive transporters). In a teleological way, it would “make no sense” for cells that require glucose to have insulin-responsive glut4 transporters. Cells that require glucose have glut1, glut2, glut3, glut5 transporters—none of which are insulin-responsive (Back to the key/lock metaphor, it makes no sense to have a lock on a door that you want people to go through). At basal (low insulin) conditions, most glucose is used by the brain and transported by non-insulin-responsive glut1 and glut3. So, perhaps one of the functions of the insulin-responsive glucose uptake in muscle and adipose to keep glucose OUT of the these cells at basal (low insulin) conditions, so that the glucose supply can be reserved for the tissue that is glucose-dependent (blood cells, renal medulla).

What Causes IR and T2DM?

The current commonly espoused view is that “Type 2 diabetes develops when beta-cells fail to secrete sufficient insulin to keep up with demand, usually in the context of increased insulin resistance.” ( 17 ). Somehow, the beta cells have failed in the face of insulin resistance. But what causes insulin resistance? When including the possibility that the environment may be part of the problem, is it possible that IR is an adaptive (protective) response to excess glucose availability? From the perspective that carbohydrate is not an essential nutrient and the change in foods in recent years has increased the consumption of refined sugar and flour, maybe hyperinsulinemia is the cause of IR and T2DM, as cells protect themselves from excessive glucose and insulin levels.

Insulin Is Already Elevated in IR and T2DM

Clinical experience of most physicians using insulin to treat T2DM over time informs us that an escalation of insulin dose is commonly needed to achieve glycemic control (when carbohydrate is consumed). When more insulin is given to someone with IR, the IR seems to get worse and higher levels of insulin are needed. I have the clinical experience of treating many individuals affected by T2DM and de-prescribing insulin as it is no longer needed after consuming a diet without carbohydrate ( 18 ).

Diets Without Carbohydrate Reverse IR and T2DM

When dietary manipulation was the only therapy for T2DM, before medications were available, a carbohydrate-restricted diet was used to treat T2DM ( 19 – 21 ). Clinical experience of obesity medicine physicians and a growing number of recent studies have demonstrated that carbohydrate-restricted diets reverse IR and T2DM ( 18 , 22 , 23 ). Other methods to achieve caloric restriction also have these effects, like calorie-restricted diets and bariatric surgery ( 24 , 25 ). There may be many mechanisms by which these approaches may work: a reduction in glucose, a reduction in insulin, nutritional ketosis, a reduction in metabolic syndrome, or a reduction in inflammation ( 26 ). Though there may be many possible mechanisms, let's focus on an obvious one: a reduction in blood glucose. Let's assume for a moment that the excessive glucose and insulin leads to hyperinsulinemia and this is the cause of IR. On a carbohydrate-restricted diet, the reduction in blood glucose leads to a reduction in insulin. The reduction in insulin leads to a reduction in insulin resistance. The reduction in insulin leads to lipolysis. The resulting lowering of blood glucose, insulin and body weight reverses IR, T2DM, AND obesity. These clinical observations strongly suggest that hyperinsulinemia is a cause of IR and T2DM—not the other way around.

What Causes Atherosclerosis?

For many years, the metabolic syndrome has been described as a possible cause of atherosclerosis, but there are no RCTs directly targeting metabolic syndrome, and the current drug treatment focuses on LDL reduction, so its importance remains controversial. A recent paper compared the relative importance of many risk factors in the prediction of the first cardiac event in women, and the most powerful predictors were diabetes, metabolic syndrome, smoking, hypertension and BMI ( 27 ). The connection between dietary carbohydrate and fatty liver is well-described ( 28 ). The connection between fatty liver and atherosclerosis is well-described ( 29 ). It is very possible that the transport of excess glucose to the adipose tissue via lipoproteins creates the particles that cause the atherosclerotic damage (small LDL) ( Figure 1 ) ( 30 – 32 ). This entire process of dietary carbohydrate leading to fatty liver, leading to small LDL, is reversed by a diet without carbohydrate ( 26 , 33 , 34 ).

Figure 1 . Key aspects of the interconnection between glucose and lipoprotein metabolism.

Reducing dietary carbohydrate in the context of a low carbohydrate, ketogenic diet reduces hyperglycemia and hyperinsulinemia, IR and T2DM. In the evaluation of an individual for a glucose abnormality, measure the blood glucose and insulin levels. If the insulin level (fasting or after a glucose-containing meal) is high, do not give MORE insulin—instead, use an intervention to lower the insulin levels. Effective ways to reduce insulin resistance include lifestyle, medication, and surgical therapies ( 23 , 35 ).

The search for a single cause of a complex problem is fraught with difficulty and controversy. I am not hypothesizing that excessive dietary carbohydrate is the only cause of IR and T2DM, but that it is a cause, and quite possibly the major cause. How did such a simple explanation get overlooked? I believe it is very possible that the reductionistic search for intracellular molecular mechanisms of IR and T2DM, the emphasis on finding pharmaceutical (rather than lifestyle) treatments, the emphasis on the treatment of high total and LDL cholesterol, and the fear of eating saturated fat may have misguided a generation of researchers and clinicians from the simple answer that dietary carbohydrate, when consumed chronically in amounts that exceeds an individual's ability to metabolize them, is the most common cause of IR, T2DM and perhaps even atherosclerosis.

While there has historically been a concern about the role of saturated fat in the diet as a cause of heart disease, most nutritional experts now cite the lack of evidence implicating dietary saturated fat as the reason for lack of concern of it in the diet ( 36 ).

The concept of comparing medications that treat IR by insulin-sensitizers or by providing insulin itself was tested in the Bari-2D study ( 37 ). Presumably in the context of consuming a standard American diet, this study found no significant difference in death rates or major cardiovascular events between strategies of insulin sensitization or insulin provision.

While lifestyle modification may be ideal to prevent or cure IR and T2DM, for many people these changes are difficult to learn and/or maintain. Future research should be directed toward improving adherence to all effective lifestyle or medication treatments. Future research is also needed to assess the effect of carbohydrate restriction on primary or secondary prevention of outcomes of cardiovascular disease.

Data Availability Statement

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author/s.

Author Contributions

The author confirms being the sole contributor of this work and has approved it for publication.

Conflict of Interest

EW receives royalties from popular diet books and is founder of a company based on low-carbohydrate diet principles (Adapt Your Life, Inc.).

Publisher's Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

1. American Diabetes Association. Classification and diagnosis of diabetes. Diabetes Care . (2016) 39 (Suppl. 1):S13–22. doi: 10.2337/dc16-S005

PubMed Abstract | CrossRef Full Text | Google Scholar

2. Bogardus C, Lillioja S, Howard BV, Reaven G, Mott D. Relationships between insulin secretion, insulin action, and fasting plasma glucose concentration in nondiabetic and noninsulin-dependent diabetic subjects. J Clin Invest. (1984) 74:1238–46. doi: 10.1172/JCI111533

3. Reaven GM. Compensatory hyperinsulinemia and the development of an atherogenic lipoprotein profile: the price paid to maintain glucose homeostasis in insulin-resistant individuals. Endocrinol Metab Clin North Am. (2005) 34:49–62. doi: 10.1016/j.ecl.2004.12.001

4. DeFronzo RA, Ferrannini E. Insulin resistance. A multifaceted syndrome responsible for NIDDM, obesity, hypertension, dyslipidemia, and atherosclerotic cardiovascular disease. Diabetes Care. (1991) 14:173–94. doi: 10.2337/diacare.14.3.173

5. Eckel RH, Grundy SM, Zimmet PZ. The metabolic syndrome. Lancet. (2005) 365:1415–28. doi: 10.1016/S0140-6736(05)66378-7

6. Yaribeygi H, Farrokhi FR, Butler AE, Sahebkar A. Insulin resistance: review of the underlying molecular mechanisms. J Cell Physiol. (2019) 234:8152–61. doi: 10.1002/jcp.27603

7. Shulman GI. Cellular mechanisms of insulin resistance. J Clin Invest. (2000) 106:171–6. doi: 10.1172/JCI10583

8. Guizouarn H, Allegrini B. Erythroid glucose transport in health and disease. Pflugers Arch. (2020) 472:1371–83. doi: 10.1007/s00424-020-02406-0

9. Petersen MC, Vatner DF, Shulman GI. Regulation of hepatic glucose metabolism in health and disease. Nat Rev Endocrinol. (2017) 13:572–87. doi: 10.1038/nrendo.2017.80

10. Tondt J, Yancy WS, Westman EC. Application of nutrient essentiality criteria to dietary carbohydrates. Nutr Res Rev. (2020) 33:260–70. doi: 10.1017/S0954422420000050

11. Colberg SR, Hernandez MJ, Shahzad F. Blood glucose responses to type, intensity, duration, and timing of exercise. Diabetes Care. (2013) 36:e177. doi: 10.2337/dc13-0965

12. Mueckler M, Thorens B. The SLC2 (GLUT) family of membrane transporters. Mol Aspects Med. (2013) 34:121–38. doi: 10.1016/j.mam.2012.07.001

13. Bryant NJ, Govers R, James DE. Regulated transport of the glucose transporter GLUT4. Nat Rev Mol Cell Biol. (2002) 3:267–77. doi: 10.1038/nrm782

14. Epstein FH. Oxygen and renal metabolism. Kidney Int. (1997) 51:381–5. doi: 10.1038/ki.1997.50

15. Cahill GF. Fuel metabolism in starvation. Annu Rev Nutr. (2006) 26:1–22. doi: 10.1146/annurev.nutr.26.061505.111258

16. Murashige D, Jang C, Neinast M, Edwards JJ, Cowan A, Hyman MC, et al. Comprehensive quantification of fuel use by the failing and nonfailing human heart. Science. (2020) 370:364–8. doi: 10.1126/science.abc8861

17. Skyler JS, Bakris GL, Bonifacio E, Darsow T, Eckel RH, Groop L, et al. Differentiation of diabetes by pathophysiology, natural history, and prognosis. Diabetes. (2017) 66:241–55. doi: 10.2337/db16-0806

18. Westman EC, Yancy WS, Mavropoulos JC, Marquart M, McDuffie JR. The effect of a low-carbohydrate, ketogenic diet versus a low-glycemic index diet on glycemic control in type 2 diabetes mellitus. Nutr Metab. (2008) 5:36. doi: 10.1186/1743-7075-5-36

CrossRef Full Text | Google Scholar

19. Allen F. The treatment of diabetes. Boston Med Surg J. (1915) 172:241–7. doi: 10.1056/NEJM191502181720702

20. Osler W, McCrae T. The Principles and Practice of Medicine . 9th ed. New York and London: Appleton & Company (1923).

21. Lennerz BS, Koutnik AP, Azova S, Wolfsdorf JI, Ludwig DS. Carbohydrate restriction for diabetes: rediscovering centuries-old wisdom. J Clin Invest. (2021) 131:e142246. doi: 10.1172/JCI142246

22. Steelman GM, Westman EC. Obesity: Evaluation and Treatment Essentials . 2nd ed. Boca Raton: CRC Press, Taylor & Francis Group (2016). 340 p.

23. Athinarayanan SJ, Adams RN, Hallberg SJ, McKenzie AL, Bhanpuri NH, Campbell WW, et al. Long-term effects of a novel continuous remote care intervention including nutritional ketosis for the management of type 2 diabetes: a 2-year non-randomized clinical trial. Front Endocrinol. (2019) 10:348. doi: 10.3389/fendo.2019.00348

24. Lim EL, Hollingsworth KG, Aribisala BS, Chen MJ, Mathers JC, Taylor R. Reversal of type 2 diabetes: normalisation of beta cell function in association with decreased pancreas and liver triacylglycerol. Diabetologia. (2011) 54:2506–14. doi: 10.1007/s00125-011-2204-7

25. Isbell JM, Tamboli RA, Hansen EN, Saliba J, Dunn JP, Phillips SE, et al. The importance of caloric restriction in the early improvements in insulin sensitivity after Roux-en-Y gastric bypass surgery. Diabetes Care. (2010) 33:1438–42. doi: 10.2337/dc09-2107

26. Bhanpuri NH, Hallberg SJ, Williams PT, McKenzie AL, Ballard KD, Campbell WW, et al. Cardiovascular disease risk factor responses to a type 2 diabetes care model including nutritional ketosis induced by sustained carbohydrate restriction at 1 year: an open label, non-randomized, controlled study. Cardiovasc Diabetol. (2018) 17:56. doi: 10.1186/s12933-018-0698-8

27. Dugani SB, Moorthy MV, Li C, Demler OV, Alsheikh-Ali AA, Ridker PM, et al. Association of lipid, inflammatory, and metabolic biomarkers with age at onset for incident coronary heart disease in women. JAMA Cardiol. (2021) 6:437–47. doi: 10.1001/jamacardio.2020.7073

28. Duwaerts CC, Maher JJ. Macronutrients and the adipose-liver axis in obesity and fatty liver. Cell Mol Gastroenterol Hepatol. (2019) 7:749–61. doi: 10.1016/j.jcmgh.2019.02.001

29. Zhang L, She Z-G, Li H, Zhang X-J. Non-alcoholic fatty liver disease: a metabolic burden promoting atherosclerosis. Clin Sci Lond Engl. (1979) 134:1775–99. doi: 10.1042/CS20200446

30. Horton TJ, Drougas H, Brachey A, Reed GW, Peters JC, Hill JO. Fat and carbohydrate overfeeding in humans: different effects on energy storage. Am J Clin Nutr. (1995) 62:19–29. doi: 10.1093/ajcn/62.1.19

31. Packard C, Caslake M, Shepherd J. The role of small, dense low density lipoprotein (LDL): a new look. Int J Cardiol. (2000) 74 (Suppl. 1):S17–22. doi: 10.1016/S0167-5273(99)00107-2

32. Borén J, Chapman MJ, Krauss RM, Packard CJ, Bentzon JF, Binder CJ, et al. Low-density lipoproteins cause atherosclerotic cardiovascular disease: pathophysiological, genetic, and therapeutic insights: a consensus statement from the European Atherosclerosis Society Consensus Panel. Eur Heart J. (2020) 41:2313–30. doi: 10.1093/eurheartj/ehz962

33. Yancy WS, Olsen MK, Guyton JR, Bakst RP, Westman EC. A low-carbohydrate, ketogenic diet versus a low-fat diet to treat obesity and hyperlipidemia: a randomized, controlled trial. Ann Intern Med. (2004) 140:769. doi: 10.7326/0003-4819-140-10-200405180-00006

34. Tendler D, Lin S, Yancy WS, Mavropoulos J, Sylvestre P, Rockey DC, et al. The effect of a low-carbohydrate, ketogenic diet on nonalcoholic fatty liver disease: a pilot study. Dig Dis Sci. (2007) 52:589–93. doi: 10.1007/s10620-006-9433-5

35. Pories WJ, Swanson MS, MacDonald KG, Long SB, Morris PG, Brown BM, et al. Who would have thought it? An operation proves to be the most effective therapy for adult-onset diabetes mellitus. Ann Surg. (1995) 222:339–50. doi: 10.1097/00000658-199509000-00011

36. Astrup A, Magkos F, Bier DM, Brenna JT, de Oliveira Otto MC, Hill JO, et al. Saturated fats and health: a reassessment and proposal for food-based recommendations: JACC state-of-the-art review. J Am Coll Cardiol. (2020) 76:844–57. doi: 10.1016/j.jacc.2020.05.077

37. A randomized trial of therapies for type 2 diabetes and coronary artery disease. N Engl J Med . (2009) 360:2503–15. doi: 10.1056/NEJMoa0805796

Keywords: type 2 diabetes, insulin resistance, pre-diabetes, carbohydrate-restricted diets, hyperinsulinemia, hyperglycemia

Citation: Westman EC (2021) Type 2 Diabetes Mellitus: A Pathophysiologic Perspective. Front. Nutr. 8:707371. doi: 10.3389/fnut.2021.707371

Received: 09 May 2021; Accepted: 20 July 2021; Published: 10 August 2021.

Reviewed by:

Copyright © 2021 Westman. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Eric C. Westman, ewestman@duke.edu

This article is part of the Research Topic

Carbohydrate-restricted Nutrition and Diabetes Mellitus

Essay on Diabetes for Students and Children

500+ words essay on diabetes.

Diabetes is a very common disease in the world. But people may never realize, how did they get diabetes and what will happen to them and what will they go through. It may not be your problem but you have to show respect and care for the one who has diabetes. It can help them and also benefited you to know more about it and have a better understanding of it. Diabetes is a metabolic disorder which is identified by the high blood sugar level. Increased blood glucose level damages the vital organs as well as other organs of the human’s body causing other potential health ailments.

Types of Diabetes

Diabetes  Mellitus can be described in two types:

Description of two types of Diabetes Mellitus are as follows

1) Type 1 Diabetes Mellitus is classified by a deficiency of insulin in the blood. The deficiency is caused by the loss of insulin-producing beta cells in the pancreas. This type of diabetes is found more commonly in children. An abnormally high or low blood sugar level is a characteristic of this type of Diabetes.

Most patients of type 1 diabetes require regular administration of insulin. Type 1 diabetes is also hereditary from your parents. You are most likely to have type 1 diabetes if any of your parents had it. Frequent urination, thirst, weight loss, and constant hunger are common symptoms of this.

2) Type 2 Diabetes Mellitus is characterized by the inefficiency of body tissues to effectively respond to insulin because of this it may be combined by insulin deficiency. Type 2 diabetes mellitus is the most common type of diabetes in people.

People with type 2 diabetes mellitus take medicines to improve the body’s responsiveness to insulin or to reduce the glucose produced by the liver. This type of diabetes mellitus is generally attributed to lifestyle factors like – obesity, low physical activity, irregular and unhealthy diet, excess consumption of sugar in the form of sweets, drinks, etc.

Get the huge list of more than 500 Essay Topics and Ideas

Causes of Diabetes

By the process of digestion, food that we eat is broken down into useful compounds. One of these compounds is glucose, usually referred to as blood sugar. The blood performs the job of carrying glucose to the cells of the body. But mere carrying the glucose to the cells by blood isn’t enough for the cells to absorb glucose.

This is the job of the Insulin hormone. Pancreas supply insulin in the human body. Insulin acts as a bridge for glucose to transit from blood to the body cells. The problem arises when the pancreas fails to produce enough insulin or the body cells for some reason do not receive the glucose. Both the cases result in the excess of glucose in the blood, which is referred to as Diabetes or Diabetes Mellitus.

Symptoms of Diabetes

Most common symptoms of diabetes are fatigue, irritation, stress, tiredness, frequent urination and headache including loss of strength and stamina, weight loss, increase in appetite, etc.

Levels of Diabetes

There are two types of blood sugar levels – fasting blood sugar level and postprandial blood sugar level. The fasting sugar level is the sugar level that we measure after fasting for at least eight hours generally after an overnight fast. Blood sugar level below 100 mg/dL before eating food is considered normal. Postprandial glucose level or PP level is the sugar level which we measure after two hours of eating.

The PP blood sugar level should be below 140 mg/dL, two hours after the meals. Though the maximum limit in both the cases is defined, the permissible levels may vary among individuals. The range of the sugar level varies with people. Different people have different sugar level such as some people may have normal fasting sugar level of 60 mg/dL while some may have a normal value of 90 mg/dL.

Effects of Diabetes

Diabetes causes severe health consequences and it also affects vital body organs. Excessive glucose in blood damages kidneys, blood vessels, skin resulting in various cardiovascular and skin diseases and other ailments. Diabetes damages the kidneys, resulting in the accumulation of impurities in the body.

It also damages the heart’s blood vessels increasing the possibility of a heart attack. Apart from damaging vital organs, diabetes may also cause various skin infections and the infection in other parts of the body. The prime cause of all type of infections is the decreased immunity of body cells due to their inability to absorb glucose.

Diabetes is a serious life-threatening disease and must be constantly monitored and effectively subdued with proper medication and by adapting to a healthy lifestyle. By following a healthy lifestyle, regular checkups, and proper medication we can observe a healthy and long life.

Customize your course in 30 seconds

Which class are you in.

• Travelling Essay
• Picnic Essay
• Our Country Essay
• My Parents Essay
• Essay on Favourite Personality
• Essay on Memorable Day of My Life
• Essay on Knowledge is Power
• Essay on Gurpurab
• Essay on My Favourite Season
• Essay on Types of Sports

Leave a Reply Cancel reply

Your email address will not be published. Required fields are marked *

Download the App

Home Essay Examples Health Type 2 Diabetes

Understanding Type 2 Diabetes

• Category Health
• Subcategory Disease
• Topic Type 2 Diabetes

Diabetes mellitus is a common metabolic disease that is classified by hyperglycemia due to disruption of normal insulin activity (American Diabetes Association [ADA], 2010). Hyperglycemia can be caused by disruption of insulin production or the body may be unable to respond to insulin (Toniolo et al. 2019). There are various forms of diabetes, for example, Type 1, Type 2, and gestational diabetes. The first type of diabetes is an autoimmune disease that results in complete insulin deficiency caused by the attack of B-cells in the pancreas (ADA, 2010).

While the second type of diabetes is classified as only relative insulin deficiency, typically caused by a secretion defect or insulin resistance (ADA, 2010). Gestational diabetes is restricted to pregnancy and typically goes away after delivery, but does increase the risk for later developing type 2 (ADA, 2010). However, type 2 diabetes is responsible for up to 90% of diagnosed cases (Chireh & D’Arcy, 2019). Common symptoms for diabetes include unexpected weight loss, fatigue, frequent urination, dry mouth, and losing feeling in your feet (Ramachandran, 2014). There are various risk factors for developing type 2 diabetes. The greatest risk factor is obesity, which is typically caused by poor diet and an inactive lifestyle (Reinehr, 2013). Cases of type 2 diabetes has increased greatly in the last 20 years in both children and adults, resulting in a need for prevention awareness (Reinehr, 2013). Type 2 diabetes is expensive and requires a lot of self-monitoring in order to maintain healthy insulin levels, which can put a lot of stress onto affected individuals and their loved ones. According to the American Diabetes Association (2018), the financial weight of diabetes was $327 billion in 2017, which is a rise of $82 billion within the last six years. Since cases and costs are increasing, this disease should be considered as a serious health issue. The concern of type 2 diabetes in adults can be better understood from analyzing the causes, diagnosis, and available treatments.

Our writers can write you a new plagiarism-free essay on any topic

The exact cause of Type 2 diabetes is undetermined, there are multiple well-known factors that contribute to its development. The most common risk factor for Type 2 diabetes is obesity, which can likely cause the body to develop resistance to insulin (ADA, 2010). A combination of inefficient diet choices and absence of physical activity can be a source of obesity. Another known risk factor of diabetes type 2 is genetics. A family history increases the risk of development, in fact it can increase by up to 40% when parents are diagnosed (Toniolo et al, 2019). While family history increases the risk of developing diabetes, it may have both genetic and environmental dynamics. This means that obese family members may impact the risk of their loved ones by normalizing poor diet choices and sedentary routines. However, genetics can impact the risk by passing down effected alleles like PPARD and PPARGC1A (Temelkova-Kurktschiev et al. 2011). These certain alleles can negatively influence how successful healthy diet choices and physical activity are to these individuals. Which means that even if effected individuals make proactive decisions on diet choices and staying physically active, their bodies do not properly show it and may result in obesity. In addition, certain alleles can be the source of insulin deficiency by reducing the body’s ability to respond to insulin. Type 2 diabetes can be attributed to various environmental and genetic factors.

A variety of serious health problems can arise as a result of type 2 diabetes. The most common cause of death for this form of diabetes is cardiovascular disease (Wu et al. 2014). Cardiovascular disease is a serious health problem because the heart is detrimental to the wellness of the body. Other increased health issues include diabetic neuropathy, nephropathy, and retinopath (Wu et al. 2014). The complication of diabetic neuropathy is losing feeling in body parts due to nerve damage (Wu et al. 2014). Foot amputations are a common consequence of diabetic neuropathy because it causes patients to lose awareness of their feet, making them vulnerable to infections. And untreated infections can spread throughout the body, becoming more serious complications. Nephropathy is a kidney disease complication that occurs when protein is found in urine (Wu et al. 2014). Another complication can result in vessels of the retina being damaged, possibly causing hemorrhage or fluid pooling in the retina (Wu et al. 2014). These health issues can progress slowly so it is important to be aware of the possible complications in order to be prepared. It has been found that the number of health issues is reduced by early detection (Aschner et al. 2016). A simple way to avoid serious health problems is staying alert and on top of your diagnosis. This means keeping up with doctor appointments and doing routine checks on your body, especially foot care in order to catch any problems early. Type 2 diabetes is a serious chronic disease that when untreated can cause severe health issues.

Most common available treatments for type 2 diabetes include lifestyle modifications, oral medicines, and insulin injections. Primary care providers have multiple treatment options available for patients with diabetes that range from prescription drugs to devices (Aschner et al. 2016). The most effective treatment to reduce high levels of glucose in blood is insulin injections (Wu et al. 2014). Insulin is a hormone that regulates glucose levels in blood by converting glucose to cellular energy. So these insulin injections allow people with high levels of glucose in their bodies return to normal levels. A few oral medications that can help regulate type 2 diabetes include biguanides, sulfonylureas, and thiazolidinediones (Wu et al. 2014). These types of medications work in different ways varying from decreasing glucose production to insulin sensitizers to increasing secretion of insulin (Wu et al. 2014). There are various treatment options, yet the most vital part is patient cooperation. Patients with diabetes need to take an active role once they are diagnosed because most treatments are self-monitored and lifestyle changes depend on their actions. There is a diversity in available treatments for diabetes that are individually determined for each patient based on their severity and ability to manage.

Type 2 Diabetes is a chronic disease that impacts people all over the world. While there is not one definite cause of this form of diabetes, genetic and environmental factors have been found to increase the risk (ADA, 2010). Practicing a healthy lifestyle by choosing nutritious diets and maintaining physical activity can reduce both the risk of development and the progression of serious health complications. Cardiovascular disease, kidney disease, nerve loss, and retina damage are serious health issues that can arise from diabetes (Wu et al. 2014). Without proper self monitoring, such serious health issues can slowly progress and turn into life threatening consequences. While oral medicines and insulin injections are common treatments, lifestyle modification is a non-hormonal treatment option available as well (Wu et al. 2014). Primary care providers commonly come into contact with diabetes and it is their responsibility to determine the best fit treatment for each patient. It is very important to understand the risk factors, complications, and treatment options of type 2 diabetes because it is a very common disease.

• American Diabetes Association (2010). Diagnosis and classification of diabetes mellitus. Diabetes care, 33 (1), S62–S69. https://doi.org/10.2337/dc10-S062
• American Diabetes Association (2018). Economic Costs of Diabetes in the U.S. in 2017. Diabetes care, 41 (5), 917-928. Doi: 10.2337/dci18-0007
• Aschner, P. M., Muñoz, O. M., Girón, D., García, O. M., Fernández-Ávila, D. G., Casas, L. Á., Bohórquez, L. F., Arango T, C. M., Carvajal, L., Ramírez, D. A., Sarmiento, J. G., Colon, C. A., Correa G, N. F., Alarcón R, P., & Bustamante S, Á. A. (2016). Clinical practice guideline for the prevention, early detection, diagnosis, management and follow up of type 2 diabetes mellitus in adults. Colombia Medica (Cali, Colombia), 47(2), 109–131.
• Chireh, B., & D’Arcy, C. (2019). Shared and unique risk factors for depression and diabetes mellitus in a longitudinal study, implications for prevention: an analysis of a longitudinal population sample aged ⩾45 years. Therapeutic Advances in Endocrinology and Metabolism, 10, 10-15. https://doi.org/10.1177/2042018819865828
• Ramachandran A. (2014). Know the signs and symptoms of diabetes. The Indian Journal of Medical Research, 140(5), 579–581.
• Reinehr, Thomas (2013). Type 2 diabetes mellitus in children and adolescents. World Journal of Diabetes 4 (6), 270-281. https://doi.org/10.4239/wjd.v4.i6.270
• Temelkova-Kurktschiev, T., Stefanov, T. (2011). Lifestyle and genetics in obesity and type 2 diabetes. Experimental Clinical Endocrinology Diabetes 2012 120 (1), 1-6. DOI: 10.1055/s-0031-1285832
• Toniolo, A., Cassani, G., Puggioni, A., Rossi, A., Colombo, A., Onodera, T., & Ferrannini, E. (2019). The diabetes pandemic and associated infections: suggestions for clinical microbiology. Reviews in medical microbiology: A Journal of the Pathological Society of Great Britain and Ireland, 30(1), 1–17. Doi: 10.1097/MRM.0000000000000155
• Wu, Y., Ding, Y., Tanaka, Y., & Zhang, W. (2014). Risk factors contributing to type 2 diabetes and recent advances in the treatment and prevention. International journal of medical sciences, 11 (11), 1185–1200. https://doi.org/10.7150/ijms.10001

We have 98 writers available online to start working on your essay just NOW!

Related Topics

Related essays.

By clicking "Send essay" you agree to our Terms of service and Privacy statement . We will occasionally send you account related emails.

By clicking "Receive essay" you agree to our Terms of service and Privacy statement . We will occasionally send you account related emails.

We can edit this one and make it plagiarism-free in no time

We use cookies to give you the best experience possible. By continuing we’ll assume you board with our cookie policy .

• Previous Article
• Next Article

Acknowledgments

Connected content.

In a special series of the ADA Journals' podcast Diabetes Core Update , host Dr. Neil Skolnik interviews special guests and authors of this clinical compendium issue. Listen now at Special Podcast Series: Focus on Diabetes or view the interviews on YouTube at A Practice Guide to Diabetes-Related Eye Care .

Summary and Conclusion

• Split-Screen
• Article contents
• Figures & tables
• Supplementary Data
• Peer Review
• Open the PDF for in another window
• Cite Icon Cite
• Get Permissions

Thomas W. Gardner; Summary and Conclusion. ADA Clinical Compendia 1 July 2022; 2022 (3): 20. https://doi.org/10.2337/db20223-20

Download citation file:

• Ris (Zotero)
• Reference Manager

Diabetes is a multifactorial disease process, and its long-term management requires the active involvement of people with diabetes and their families, as well as a large multidisciplinary care team to ensure optimal health, quality of life, and productivity. Keeping up with new medications, emerging technology, and evolving treatment recommendations can be challenging, and the language and care processes commonly used by practitioners in one discipline may be less familiar to other diabetes care professionals.

In the realm of diabetes-related eye care, our ability to prevent the progression of diabetes-related retinal disease and thereby preserve vision has never been greater. However, far too many people with diabetes still are not receiving appropriate screening to identify eye disease early and ensure its timely treatment.

It is our hope that this compendium has provided information and guidance to improve communication and encourage collaboration between eye care professionals and other diabetes health care professionals and allow them to more effectively cooperate to reduce barriers to care and improve both the ocular and systemic health of their shared patients.

Editorial and project management services were provided by Debbie Kendall of Kendall Editorial in Richmond, VA.

Dualities of Interest

B.A.C. is a consultant for Genentech and Regeneron. S.A.R. is a speaker for Allergan, Inc., and VSP Vision Care. No other potential conflicts of interest relevant to this compendium were reported.

Author Contributions

All authors researched and wrote their respective sections. Lead author T.W.G. reviewed all content and is the guarantor of this work.

The opinions expressed are those of the authors and do not necessarily reflect those of VSP Vision Care, Regeneron, or the American Diabetes Association. The content was developed by the authors and does not represent the policy or position of the American Diabetes Association, any of its boards or committees, or any of its journals or their editors or editorial boards.

Email alerts

• Online ISSN 2771-6880
• Print ISSN 2771-6872
• Diabetes Care
• Clinical Diabetes
• Diabetes Spectrum
• Standards of Medical Care in Diabetes
• Scientific Sessions Abstracts
• BMJ Open Diabetes Research & Care
• ShopDiabetes.org
• ADA Professional Books

Clinical Compendia

• Clinical Compendia Home
• Latest News
• DiabetesPro SmartBrief
• Special Collections
• DiabetesPro®
• Diabetes Food Hub™
• Insulin Affordability
• Know Diabetes By Heart™
• About the ADA
• Journal Policies
• For Reviewers
• Advertising in ADA Journals
• Reprints and Permission for Reuse
• Copyright Notice/Public Access Policy
• ADA Professional Membership
• ADA Member Directory
• Diabetes.org
• X (Twitter)
• Cookie Policy
• Accessibility
• Terms & Conditions
• Get Adobe Acrobat Reader
• © Copyright American Diabetes Association

This Feature Is Available To Subscribers Only

Sign In or Create an Account

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• My Account Login
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Open access
• Published: 18 April 2024

Assessing the predictive value of insulin resistance indices for metabolic syndrome risk in type 2 diabetes mellitus patients

• Hadi Bazyar 1 , 2 ,
• Ahmad Zare Javid 3 , 4 ,
• Mahmood Reza Masoudi 5 ,
• Fatemeh Haidari 6 ,
• Zeinab Heidari 7 ,
• Sohrab Hajializadeh 1 ,
• Vahideh Aghamohammadi 8 &
• Mehdi Vajdi 9  

Scientific Reports volume  14 , Article number:  8917 ( 2024 ) Cite this article

320 Accesses

Metrics details

• Endocrinology
• Medical research

Limited research has explored the effectiveness of insulin resistance (IR) in forecasting metabolic syndrome (MetS) risk, especially within the Iranian population afflicted with type 2 diabetes mellitus (T2DM). The present investigation aimed to assess the efficacy of IR indices in predicting the risk of MetS among T2DM patients. Convenient sampling was utilized to select four hundred subjects with T2DM. Metabolic factors and IR indices, including the Waist Circumference-Triglyceride Index (WTI), Triglyceride and Glucose Index (TyG index), the product of TyG index and abdominal obesity indices, and the Metabolic Score for Insulin Resistance (METS-IR), were evaluated. Logistic regression, coupled with modeling, was employed to explore the risk of MetS. The predictive performance of the indices for MetS stratified by sex was evaluated via receiver operating characteristic (ROC) curve analysis and estimation of the area under the curve (AUC) values. The TyG-Waist Circumference (TyG-WC) index exhibited the largest AUCs in both males (0.91) and females (0.93), while the TyG-Body Mass Index (TyG-BMI) demonstrated the smallest AUCs (0.77 in males and 0.74 in females). All indices significantly predicted the risk of MetS in all subjects before and after adjustment (p < 0.001 for all). The TyG-WC index demonstrated the highest odds ratios for MetS (8.06, 95% CI 5.41–12.00). In conclusion, all IR indices assessed in this study effectively predicted the risk of MetS among Iranian patients with T2DM, with the TyG-WC index emerging as the most robust predictor across both genders.

Similar content being viewed by others

Development and validation of a new algorithm for improved cardiovascular risk prediction

What is the pipeline for future medications for obesity?

Genome-wide characterization of circulating metabolic biomarkers

Introduction.

Insulin resistance (IR) arises from inadequate physiological responses due to reduced sensitivity of peripheral tissues to insulin, leading to elevated insulin levels through compensatory mechanisms involving pancreatic β-cell insulin production 1 . Predominantly affecting muscle, liver, and adipose tissue, IR onset in muscle tissue stems from immune-induced inflammatory changes and excess free fatty acids. With impaired glucose uptake by muscles, surplus glucose is redirected to the liver, triggering increased lipogenesis and release of free fatty acids, thereby promoting fat accumulation outside adipose tissue and exacerbating IR 2 , 3 . In individuals with compromised insulin signaling, such as those with type 2 diabetes mellitus (T2DM), insulin fails to suppress hepatic gluconeogenesis, even in the fed state, critically influencing blood glucose regulation 4 . Recognized as a major risk factor for metabolic syndrome (MetS), T2DM, and cardiovascular diseases (CVD), early detection of IR is vital for preventing these conditions 5 , 6 , 7 . While the glucose clamp technique serves as the gold standard for quantifying IR 8 , its complexity, cost, and invasiveness limit its routine use in laboratories 9 , 10 . Therefore, simpler methods like the homeostasis model assessment of insulin resistance (HOMA-IR) have been widely adopted since its proposal in 1985 11 . However, challenges with insulin measurement availability and standardization have prompted the exploration of alternative IR prediction approaches, including lipid ratios and visceral fat index (VAI) 9 , 12 , 13 . The triglyceride-glucose index (TyG index), derived from circulating triglyceride and glucose concentrations, has emerged as a promising tool for IR assessment, outperforming HOMA-IR in predictive accuracy 13 , 14 , 15 . Its strong correlation with IR, high diagnostic sensitivity and specificity, and ease of clinical application make it particularly valuable 9 , 10 , 12 , 13 , 14 . Additionally, obesity, prevalent among individuals with T2DM, is closely linked to IR. Anthropometric measures such as body mass index (BMI), waist circumference (WC), and waist-to-height ratio (WHtR) are commonly used due to their practicality. Combined TyG-related parameters, such as TyG-BMI and TyG-WC, exhibit superior performance compared to the standalone TyG index in IR evaluation 16 , 17 , 18 . Simultaneous consideration of WC and TG values, known as the waist circumference-triglyceride index (WTI), offers enhanced effectiveness in investigating MetS, T2DM, and CVD prevalence compared to individual parameters 19 , 20 , 21 . Moreover, the majority of individuals with T2DM are overweight or obese, and it is anticipated that a significant proportion of them will develop MetS 22 , 23 .

Given the scarcity of research on IR index effectiveness in predicting MetS risk among Iranian T2DM patients, this study seeks to evaluate the predictive capacity of IR indices, including WTI, TyG index, the product of TyG index and abdominal obesity indices, and METS-IR, in this population.

The prevalence of Metabolic Syndrome (MetS), as per the International Diabetes Federation (IDF) criteria, was found to be 63.3% in the study sample. The demographic and clinical characteristics of subjects across quartiles of the Triglyceride-Glucose (TyG) index scores are presented in Tables 1 and 2 . Compared to individuals in quartile 4, those in quartile 1 exhibited significantly lower values for weight, Body Mass Index (BMI), Waist Circumference (WC), Hip Circumference (HC), Fasting Blood Glucose (FBG), Hemoglobin A1C (HbA1C), Triglycerides (TG), Total Cholesterol (TC), Low-Density Lipoprotein Cholesterol (LDL-C), LDL/HDL Cholesterol ratio (LDL.HDL-c), Atherogenic Index of Plasma (AIP), Systolic Blood Pressure (SBP), Diastolic Blood Pressure (DBP), Mean Arterial Pressure (MAP), prevalence of MetS, TyG index score, TyG-BMI, TyG-WC, TyG-Waist-to-Hip Ratio (TyG-WHR), TyG-Waist-to-Height Ratio (TyG-WHtR), Waist Circumference-Triglyceride Index (WTI), and Metabolic Score for Insulin Resistance (METS-IR) (p < 0.001). Post-hoc pairwise comparisons revealed a significant reduction in FBG, HbA1C, TG, TC, LDL-C, AIP, TyG index score, TyG-BMI, TyG-WC, TyG-WHR, TyG-WHtR, and WTI in the third quartile compared to the fourth quartile (p < 0.001). Additionally, a significant reduction in mean weight, BMI, WC, HC, FBG, TG, LDL-C, LDL.HDL-c, AIP, TyG index score, TyG-BMI, TyG-WC, TyG-WHR, TyG-WHtR, WTI, and METS-IR were observed in the second quartile of TyG index score compared to the third and fourth quartiles (p < 0.001). Conversely, a significant increase in HDL-C was observed in the first quartile compared to the fourth quartile (p < 0.001).

Optimal cut-off values for IR indices in predicting MetS risk in patients with T2DM are presented in Table 3 . The predictive performance of anthropometric indices (TyG index, TyG-BMI, TyG-WC, TyG-WHR, TyG-WHtR, WTI, and METS-IR) for MetS, stratified by sex, was evaluated using receiver operating characteristic (ROC) curve analysis, and the corresponding area under the curve (AUC) values are depicted in Figs.  1 , 2 , 3 , 4 , 5 .

Roc Curve for TyG index ( a male, b female, c total).

Roc Curve for TyG-BMI index ( a male, b female, c total).

Roc Curve for TyG.WC index ( a male, b female, c total).

Roc Curve for total TyG-WHR index ( a male, b female, c total).

Roc Curve for total TyG-WHtR index ( a male, b female, c total).

The TyG-WC index exhibited the largest AUCs in both males and females (0.91 and 0.93, respectively) (Fig.  3 , Table 3 ), while the TyG-BMI demonstrated the smallest AUCs (0.77 in males and 0.74 in females) (Fig.  2 , Table 3 ).

Odds ratios (95% CI) for MetS, with IR indices as independent variables among participants, are presented in Table 4 . All indices significantly predicted the risk of MetS in all subjects before and after adjustment (p < 0.001). The TyG-WC index presented the highest odds ratios for MetS (8.06, 95% CI 5.41–12.00).

The coexistence of Metabolic Syndrome (MetS) in diabetic individuals is associated with the development of both microvascular and macrovascular complications, as evidenced by previous research 24 , 25 . Within our study sample, MetS was found to be prevalent in 63.3% of participants. Notably, all insulin resistance (IR) indices investigated demonstrated predictive potential for MetS risk. Among these indices, the TyG-WC index exhibited the most pronounced area under the curve (AUC) values and highest odds ratios for MetS among patients diagnosed with Type 2 Diabetes Mellitus (T2DM).

In line with our findings, prior investigations have also demonstrated the predictive capability of the TyG index for Metabolic Syndrome (MetS). The predictive capacity of the TyG index can be elucidated by mechanisms involving glucotoxicity and lipotoxicity, alongside the intimate associations of its constituent components (triglycerides and fasting plasma glucose) with insulin resistance, a pivotal factor in MetS pathogenesis 18 , 26 , 27 , 28 . However, combining the TyG index with measures of adiposity such as body mass index (BMI) and waist circumference (WC) may enhance predictive accuracy 29 . Indeed, in our study, the composite of the TyG index with abdominal obesity indices such as WC and waist-to-height ratio (WHtR) demonstrated higher odds ratios for MetS compared to the TyG index alone. Khan et al. revealed that the TyG index, with its robust area under the curve (AUC) of 0.764, outperforms other traditional markers such as fasting blood glucose, triglycerides, small dense LDL-c, non-HDL-c, and HOMA-IR in predicting MetS 26 . Similarly, Gui et al. demonstrated the predictive potential of various obesity- and lipid-related indices for MetS in middle-aged and older adults, with TyG-BMI and the Chinese visceral adiposity index (CVAI) emerging as the most effective markers for predicting MetS in men and women, respectively 30 . In a cross-sectional study, Raimi et al. assessed the utility of the TyG index in identifying MetS among Nigerians, concluding that it was effective in predicting MetS. Furthermore, combining anthropometric and TyG index indicators enhanced predictive accuracy, consistent with our findings 18 . Similarly, in our study, TyG-WC and TyG-WHtR exhibited the largest AUCs in both genders, with overall AUC values higher than those reported by Raimi et al. This suggests that TyG-WC and TyG-WHtR may possess greater predictive utility in our population. Both waist circumference (WC) and waist-to-height ratio (WHtR) serve as markers of visceral adiposity, which correlates more strongly with cardiovascular disease (CVD) risk than BMI, a measure of overall obesity 31 . Notably, WHtR, corrected for height, may offer superior predictive capability compared to WC alone. Indeed, previous studies have demonstrated that WHtR identifies individuals at early health risks more effectively than a composite index combining BMI and WC 18 , 32 , 33 . Moreover, in a study by Laurindo et al. conducted among the Brazilian population, the TyG-WC index exhibited the largest AUC (0.849) for detecting MetS using IR indices 34 . Differences in AUC values between studies may be attributed to differences in mean fasting plasma glucose and triglyceride levels, variation in study populations (diabetic versus non-diabetic individuals), and ethnic diversity.

Mao et al. conducted a study aiming to identify the optimal predictors and cut-off points for Metabolic Syndrome (MetS) among Chinese adults with Type 2 Diabetes Mellitus (T2DM). Their findings indicated that TyG-WC was the most effective predictor of MetS among women, while BMI emerged as the best predictor for both genders combined 35 . In contrast, our study revealed that TyG-WC was the superior predictor of MetS for both women and men. Another study utilizing data from the 2013–2016 US National Health and Nutrition Examination Survey found TyG-WC to be more robust in predicting MetS among the non-Hispanic population, though gender-specific analysis was not conducted 36 . Our findings demonstrated that TyG-WC outperformed TyG-BMI in MetS prediction, with TyG-WC exhibiting the largest area under the curve (AUC) and TyG-BMI the smallest. Body Mass Index (BMI) is commonly regarded as a general indicator of obesity, while waist circumference (WC) is considered a measure of central obesity 37 . However, the distribution of adipose tissue, particularly visceral fat, holds greater significance in metabolic dysfunction and insulin resistance. WC is closely associated with cardiometabolic risks 38 , highlighting its importance in predicting MetS. Moreover, in a study by Song et al., in addition to MetS, the product of the TyG index and anthropometric indices was also employed for predicting non-alcoholic fatty liver disease and Type 2 Diabetes Mellitus. TyG-WC exhibited superiority over TyG-BMI in predicting non-alcoholic fatty liver disease, further emphasizing the utility of WC as a predictor of metabolic disorders 39 .

In the present study, both the Waist-Triglyceride Index (WTI) and Metabolic Syndrome-Insulin Resistance (METS-IR) significantly predicted the risk of Metabolic Syndrome (MetS) in all participants, both before and after adjusting for relevant factors. Yang et al. highlighted the Waist-Triglyceride (WT) index, calculated as the product of waist circumference (WC) and triglyceride levels, as strongly associated with coronary heart disease risk 40 . Additionally, the WT index demonstrated effectiveness in screening for MetS in individuals with Type 2 Diabetes Mellitus (T2DM) 41 . Recently, Liu et al. introduced a modified form of the WT index, termed WTI, which exhibited a robust ability to identify MetS 42 . Similarly, Endukuru et al. demonstrated that WTI had the highest predictive ability for detecting low high-density lipoprotein cholesterol (HDL-C), elevated blood pressure, and high triglyceride levels in women compared to other indices 43 . Several studies have also demonstrated the high predictive capacity of WTI for discriminating MetS 44 , 45 . The METS-IR was developed by Chavolla et al. to evaluate insulin sensitivity, validated against the euglycemic–hyperinsulinemic clamp. Moreover, they found that METS-IR was associated with ectopic fat accumulation and could better predict incident T2DM than the triglyceride to high-density lipoprotein cholesterol ratio (TG/HDL-C) and TyG index in the Mexican population 46 . Han et al. investigated the association of various insulin resistance indicators, including METS-IR, TG/HDL-C, TyG-BMI, and TyG index, with serum uric acid levels in patients with T2DM, revealing significant associations between all indices and serum uric acid levels 47 . Furthermore, Zhang et al. demonstrated that METS-IR could predict the incidence of major adverse cardiovascular events in individuals with ischemic cardiomyopathy and T2DM 48 . It has been reported that METS-IR is strongly associated with hypertension even in individuals with normal weight 49 . Pathophysiological studies have elucidated that insulin resistance can perturb the lipid metabolism of the entire body, increase cardiac lipotoxicity, and induce oxidative stress and endothelial dysfunction, ultimately culminating in dyslipidemia, hypertension, and cardiovascular disease 50 .

Variations in the literature may stem from differences in chosen anthropometric indices, gender, ethnicity, underlying conditions, participant age, confounder variables, and criteria used to define Metabolic Syndrome (MetS), such as those by WHO, IDF, ATP III, and AHA/NHLBI. A limitation of our study is its cross-sectional design, which doesn't establish causality. Additionally, our focus on the Iranian population may limit generalizability. However, our study is the first to explore IR indices in predicting MetS risk among Iranian T2DM patients, and it includes both genders and employs multivariable logistic regression across three models.

All IR indices examined predicted MetS risk in our study, with the TyG-WC index emerging as the most effective predictor for both genders among Iranian T2DM patients.

Study design and participants

In this cross-sectional investigation, 400 Iranian patients diagnosed with Type 2 Diabetes Mellitus (T2DM) were prospectively enrolled from the Endocrine and Metabolism Clinic of Golestan Hospital, located in Ahvaz City, during the period spanning from March to May 2023. Patients were selected utilizing a convenient consecutive sampling method. Inclusion criteria comprised willingness to participate, age between 18 and 60 years, and a minimum of 2 years since the diagnosis of T2DM. Exclusion criteria consisted of insulin usage, pregnancy or lactation, smoking, alcohol consumption, incomplete demographic or anthropometric data, adherence to specialized diets, recent intake of antioxidant supplements within the last 3 months, and presence of comorbidities such as renal, hepatic, thyroidal, neoplastic, HIV, or infectious diseases.

A structured questionnaire was employed to collect demographic and baseline characteristics, encompassing sociodemographic factors such as gender, age, educational level, occupation, ethnicity, duration of diabetes, physical activity, and medication history. The study protocol adhered to the principles outlined in the Declaration of Helsinki and was approved by the Ethics Committee in Research of Sirjan University of Medical Sciences (Ethical code: IR.SIRUMS.REC.1401.017, Approval date: 18-03-2023). Written informed consent was obtained from all participants before their involvement. The sample size was determined based on the study conducted by Zhang et al. 51 and the utilization of the TyG-WHtR index, employing the formula (n = (z1 − a/2) 2 . SD 2 /d 2 ) with a precision (d) of 0.05, a standard deviation (SD) of 0.45, and a confidence level of 95%, resulting in a final sample size of 400 subjects.

Definition of MetS

Metabolic Syndrome (MetS) was defined according to the criteria established by the International Diabetes Federation (IDF), which includes the presence of central obesity, defined as a waist circumference (WC) equal to or greater than 95 cm for both genders based on guidelines provided by the Iranian National Obesity Committee 52 , in addition to meeting two or more of the following criteria: fasting blood glucose (FBG) levels equal to or greater than 100 mg/dL, or receiving medications for hyperglycemia; triglyceride (TG) levels equal to or greater than 150 mg/dL, or receiving medications for hypertriglyceridemia; low levels of high-density lipoprotein cholesterol (HDL-C), defined as less than 40 mg/dL in men and less than 50 mg/dL in women, or receiving drug treatment for low HDL-C; and elevated blood pressure, indicated by systolic blood pressure (SBP) equal to or greater than 130 mmHg or diastolic blood pressure (DBP) equal to or greater than 85 mmHg, or receiving drug treatment for hypertension 53 .

Blood pressure (BP) measurement

Blood pressure (BP) measurements were taken by a trained professional following a 20-min rest period for the patients, between 8:00 and 9:00 AM. This procedure was iterated thrice consecutively, and the average of the three successive readings was utilized for analysis. The mean arterial pressure (MAP) and pulse pressure (PP) were calculated employing the following formulas 54 :

where SBP represents systolic blood pressure and DBP represents diastolic blood pressure, both measured in millimeters of mercury (mmHg).

Biochemical assessment

Serum levels of fasting blood glucose (FBG) with a coefficient of variation (CV) interassay of 1.2% and lipid profile parameters, including triglycerides (TG) with a CV interassay of 1.6%, total cholesterol (TC) with a CV interassay of 2%, high-density lipoprotein cholesterol (HDL-C) with a CV interassay of 1.8%, low-density lipoprotein cholesterol (LDL-C) with a CV interassay of 1.29%, and very low-density lipoprotein (VLDL), were measured following a 12-h fasting period. Blood samples of 5 cc were drawn from each participant. FBG and the lipid profile were determined utilizing the enzymatic method with Pars Azmoon kits (Tehran, Iran) and analyzed on an auto analyzer (Hitachi 902, Japan). The Atherogenic Index of Plasma (AIP) was calculated using the logarithm of the TG to HDL-C ratio 55 . Hemoglobin A1c (HbA1c) levels in whole blood were quantified via automated high-performance liquid chromatography (HPLC) utilizing an exchange ion method with a DS5 set (DREW, United Kingdom).

Measurement of anthropometric indices and physical activity

All anthropometric assessments were conducted by a trained professional. Weight was measured using a digital scale manufactured in Japan with a precision of 0.1 kg, with participants asked to remove their shoes and wear minimal clothing. Height was determined using a tape measure with a precision of 0.5 cm. Body Mass Index (BMI) was calculated using the formula: weight in kilograms divided by the square of height in meters. Waist circumference (WC) was measured at the narrowest point of the torso with a precision of 0.5 cm, while hip circumference (HC) was assessed at the most prominent part of the hip area using a tape measure 56 , 57 , 58 . Waist-to-hip ratio (WHR) was computed by dividing WC by HC. Additionally, Waist-to-height ratio (WHtR) was obtained by dividing WC by height 59 .

Formulas for calculating novel indices of insulin resistance (IR) were applied as follows 18 , 36 :

Physical activity levels were assessed using the International Physical Activity Questionnaire (IPAQ), and results were reported as metabolic equivalent hours per week (METs hr/wk) 60 .

Statistical analysis

Data analysis was conducted using SPSS version 23 software. The normal distribution of the data was assessed using the Kolmogorov–Smirnov statistical test. Quantitative variables were compared between two groups using the independent t-test, while qualitative variables were compared using the chi-square test. Differences in variables across quartiles of the Triglyceride and glucose index (TyG index) were examined using One-way ANOVA with Post hoc (Least Significant Difference, LSD) analysis. To investigate the risk of Metabolic Syndrome (MetS), logistic regression was utilized, incorporating models with both crude and adjusted effects for potential confounding factors such as age, gender, ethnicity, educational level, occupation, duration of disease, physical activity, and medication usage. The predictive capacity of anthropometric indices (TyG index, TyG-BMI, TyG-WC, TyG-WHR, TyG-WHtR, Waist circumference-triglyceride index (WTI), and Metabolic Syndrome-Insulin Resistance (METS-IR)) for MetS stratified by sex was evaluated through receiver operating characteristic (ROC) curve analysis, with the area under the curve (AUC) values calculated. Figures  1 , 2 , 3 , 4 , 5 depict the results, highlighting the best predictors for both genders alongside their optimal threshold values. Quantitative data are presented as mean ± standard deviation (SD), while qualitative data are expressed as frequencies (percentages). A significance level of p < 0.05 was considered statistically significant.

Ethics declarations

The research protocol was in accordance with the guidelines of the Declaration of Helsinki. The Ethics Committee in Research of Sirjan University of Medical Sciences approved the study protocol (Ethical code: IR.SIRUMS.REC.1401.017, Approval date: 18-03-2023). The informed written consent form was acquired from all subjects at the starting of the study. For illiterate participants, informed consent was obtained from their guardian/legally authorized representative.

Data availability

All data and materials are fully presented in the manuscript.

Abbreviations

Area under the curve

Body mass index

Homeostasis model assessment of insulin resistance

Insulin resistance

Mean arterial pressure

• Metabolic syndrome

Metabolic score for insulin resistance

Pulse pressure

Receiver operating characteristic

• Type 2 diabetes mellitus

Triglyceride and glucose index

TyG-waist circumference

TyG-body mass index

Visceral fat index

Waist circumference

Waist to height ratio

Waist circumference-triglyceride index

Cerf, M. E. Beta cell dysfunction and insulin resistance. Front. Endocrinol. 4 , 43179 (2013).

Article   Google Scholar  

Bugianesi, E., McCullough, A. J. & Marchesini, G. Insulin resistance: A metabolic pathway to chronic liver disease. Hepatology 42 , 987–1000 (2005).

Article   CAS   PubMed   Google Scholar  

Freeman, A. M. & Pennings, N. Insulin resistance. (2018).

Hatting, M., Tavares, C. D., Sharabi, K., Rines, A. K. & Puigserver, P. Insulin regulation of gluconeogenesis. Ann. N. Y. Acad. Sci. 1411 , 21–35 (2018).

Article   ADS   CAS   PubMed   Google Scholar  

Gast, K. B., Tjeerdema, N., Stijnen, T., Smit, J. W. & Dekkers, O. M. Insulin resistance and risk of incident cardiovascular events in adults without diabetes: Meta-analysis. PLoS ONE 7 , e52036 (2012).

Article   ADS   CAS   PubMed   PubMed Central   Google Scholar  

Nilsson, P. M., Tuomilehto, J. & Rydén, L. The metabolic syndrome–What is it and how should it be managed?. Eur. J. Prev. Cardiol. 26 , 33–46 (2019).

Article   PubMed   Google Scholar  

Xia, C. et al. Lipid accumulation product is a powerful index for recognizing insulin resistance in non-diabetic individuals. Eur. J. Clin. Nutr. 66 , 1035–1038 (2012).

DeFronzo, R. A., Tobin, J. D. & Andres, R. Glucose clamp technique: A method for quantifying insulin secretion and resistance. Am. J. Physiol.-Endocrinol. Metab. 237 , E214 (1979).

Article   CAS   Google Scholar  

Du, T. et al. Clinical usefulness of lipid ratios, visceral adiposity indicators, and the triglycerides and glucose index as risk markers of insulin resistance. Cardiovasc. Diabetol. 13 , 1–10 (2014).

Matthews, D. R. et al. Homeostasis model assessment: Insulin resistance and β-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia 28 , 412–419 (1985).

Miller, W. G. et al. Toward standardization of insulin immunoassays. Clin. Chem. 55 , 1011–1018 (2009).

Ren, X. et al. Association between triglyceride to HDL-C ratio (TG/HDL-C) and insulin resistance in Chinese patients with newly diagnosed type 2 diabetes mellitus. PLoS ONE 11 , e0154345 (2016).

Article   PubMed   PubMed Central   Google Scholar  

Kim-Dorner, S.-J., Deuster, P. A., Zeno, S. A., Remaley, A. T. & Poth, M. Should triglycerides and the triglycerides to high-density lipoprotein cholesterol ratio be used as surrogates for insulin resistance?. Metabolism 59 , 299–304 (2010).

Guerrero-Romero, F. et al. The product of triglycerides and glucose, a simple measure of insulin sensitivity. Comparison with the euglycemic-hyperinsulinemic clamp. J. Clin. Endocrinol. Metab. 95 , 3347–3351 (2010).

Simental-Mendía, L. E., Rodríguez-Morán, M. & Guerrero-Romero, F. The product of fasting glucose and triglycerides as surrogate for identifying insulin resistance in apparently healthy subjects. Metab. Syndr. Relat. Disord. 6 , 299–304 (2008).

Er, L.-K. et al. Triglyceride glucose-body mass index is a simple and clinically useful surrogate marker for insulin resistance in nondiabetic individuals. PLoS ONE 11 , e0149731 (2016).

Article   MathSciNet   PubMed   PubMed Central   Google Scholar  

Zheng, S. et al. Triglyceride glucose-waist circumference, a novel and effective predictor of diabetes in first-degree relatives of type 2 diabetes patients: Cross-sectional and prospective cohort study. J. Transl. Med. 14 , 1–10 (2016).

Article   ADS   Google Scholar  

Raimi, T. H. et al. Triglyceride-glucose index and related parameters predicted metabolic syndrome in Nigerians. Metab. Syndr. Relat. Disord. 19 , 76–82 (2021).

Article   CAS   PubMed   PubMed Central   Google Scholar  

Zhao, K. et al. Association between the hypertriglyceridemic waist phenotype and prediabetes in Chinese adults aged 40 years and older. J. Diabetes Res. 2018 , 1–9 (2018).

Google Scholar  

Sam, S. et al. Hypertriglyceridemic waist phenotype predicts increased visceral fat in subjects with type 2 diabetes. Diabetes Care 32 , 1916–1920 (2009).

Tanko, L. B. et al. Enlarged waist combined with elevated triglycerides is a strong predictor of accelerated atherogenesis and related cardiovascular mortality in postmenopausal women. Circulation 111 , 1883–1890 (2005).

Cardenas, V. et al. Depression is associated with increased risk for metabolic syndrome in latinos with type 2 diabetes. Am. J. Geriatr. Psychiatry 25 , 646–653 (2017).

Nsiah, K., Shang, V. O., Boateng, K. A. & Mensah, F. Prevalence of metabolic syndrome in type 2 diabetes mellitus patients. Int. J. Appl. Basic Med. Res. 5 , 133–138 (2015).

Katsiki, N., Anagnostis, P., Kotsa, K., Goulis, D. G. & Mikhailidis, D. P. Obesity, metabolic syndrome and the risk of microvascular complications in patients with diabetes mellitus. Curr. Pharm. Des. 25 , 2051–2059 (2019).

Lee, M.-Y. et al. Association between metabolic syndrome and microvascular and macrovascular disease in type 2 diabetic mellitus. Am. J. Med. Sci. 355 , 342–349 (2018).

Article   ADS   PubMed   Google Scholar  

Khan, S. H. et al. Metabolic clustering of risk factors: Evaluation of triglyceride-glucose index (TyG index) for evaluation of insulin resistance. Diabetol. Metab. Syndr. 10 , 1–8 (2018).

Mirr, M., Skrypnik, D., Bogdański, P. & Owecki, M. Newly proposed insulin resistance indexes called TyG-NC and TyG-NHtR show efficacy in diagnosing the metabolic syndrome. J. Endocrinol. Investig. 44 , 2831–2843 (2021).

Shin, K.-A. Triglyceride and glucose (TyG) index is a clinical surrogate marker for the diagnosis of metabolic syndrome. Biomed. Sci. Lett. 23 , 348–354 (2017).

Khamseh, M. E. et al. Triglyceride glucose index and related parameters (triglyceride glucose-body mass index and triglyceride glucose-waist circumference) identify nonalcoholic fatty liver and liver fibrosis in individuals with overweight/obesity. Metab. Syndr. Relat. Disord. 19 , 167–173 (2021).

Gui, J., Li, Y., Yuan, T. & Zhang, L. Obesity-and lipid-related indices as a predictor of obesity metabolic syndrome in a national cohort study. Front. Public Health 11 , 1073824 (2023).

Nalini, M. et al. Comparing anthropometric indicators of visceral and general adiposity as determinants of overall and cardiovascular mortality. Arch. Iran. Med. 22 , 301 (2019).

PubMed   Google Scholar  

Ashwell, M. & Gibson, S. Waist-to-height ratio as an indicator of ‘early health risk’: Simpler and more predictive than using a ‘matrix’based on BMI and waist circumference. BMJ Open 6 , e010159 (2016).

Gibson, S. & Ashwell, M. A simple cut-off for waist-to-height ratio (0· 5) can act as an indicator for cardiometabolic risk: Recent data from adults in the Health Survey for England. Br. J. Nutr. 123 , 681–690 (2020).

Fornari Laurindo, L. et al. Detection of metabolic syndrome using insulin resistance indexes: A cross-sectional observational cohort study. Endocrines 4 , 257–268 (2023).

Mao, J., Gan, S., Zhou, Q., Zhou, H. & Deng, Z. Optimal anthropometric indicators and cut points for predicting metabolic syndrome in Chinese patients with type 2 diabetes mellitus by gender. Diabetes Metab. Syndr. Obes. 16 , 505–514 (2023).

Okosun, I. S., Okosun, B., Lyn, R. & Airhihenbuwa, C. Surrogate indexes of insulin resistance and risk of metabolic syndrome in non-Hispanic White, non-Hispanic Black and Mexican American. Diabetes Metab. Syndr. Clin. Res. Rev. 14 , 3–9 (2020).

Kissebah, A. H. et al. Relation of body fat distribution to metabolic complications of obesity. J. Clin. Endocrinol. Metab. 54 , 254–260 (1982).

Pouliot, M.-C. et al. Waist circumference and abdominal sagittal diameter: best simple anthropometric indexes of abdominal visceral adipose tissue accumulation and related cardiovascular risk in men and women. Am. J. Cardiol. 73 , 460–468 (1994).

Song, S., Son, D.-H., Baik, S.-J., Cho, W.-J. & Lee, Y.-J. Triglyceride glucose-waist circumference (TyG-WC) is a reliable marker to predict non-alcoholic fatty liver disease. Biomedicines 10 , 2251 (2022).

Yang, R., Liu, X., Lin, Z. & Zhang, G. Correlation study on waist circumference-triglyceride (WT) index and coronary artery scores in patients with coronary heart disease. Eur. Rev. Med. Pharmacol. Sci. 19 , 113–118 (2015).

CAS   PubMed   Google Scholar  

Ma, C.-M. et al. Three novel obese indicators perform better in monitoring management of metabolic syndrome in type 2 diabetes. Sci. Rep. 7 , 9843 (2017).

Article   ADS   PubMed   PubMed Central   Google Scholar  

Liu, P. J., Lou, H. P. & Zhu, Y. N. Screening for metabolic syndrome using an integrated continuous index consisting of waist circumference and triglyceride: A preliminary cross-sectional study. Diabetes Metab. Syndr. Obes. 18 , 2899–2907 (2020).

Endukuru, C. K., Gaur, G. S., Dhanalakshmi, Y., Sahoo, J. & Vairappan, B. Cut-off values and clinical efficacy of body roundness index and other novel anthropometric indices in identifying metabolic syndrome and its components among Southern-Indian adults. Diabetol. Int. 13 , 188–200 (2022).

Yin, Q., Zheng, J., Cao, Y., Yan, X. & Zhang, H. Evaluation of novel obesity and lipid-related indices as indicators for the diagnosis of metabolic syndrome and Premetabolic syndrome in Chinese women with polycystic ovary syndrome. Int. J. Endocrinol. 2021 , 1–6 (2021).

Adejumo, E. N. et al. Anthropometric parameter that best predict metabolic syndrome in South west Nigeria. Diabetes Metab. Syndr. Clin. Res. Rev. 13 , 48–54 (2019).

Bello-Chavolla, O. Y. et al. METS-IR, a novel score to evaluate insulin sensitivity, is predictive of visceral adiposity and incident type 2 diabetes. Eur. J. Endocrinol. 178 , 533–544 (2018).

Han, R., Zhang, Y. & Jiang, X. Relationship between four non-insulin-based indexes of insulin resistance and serum uric acid in patients with type 2 diabetes: A cross-sectional study. Diabetes Metab. Syndr. Obes. Targets Ther. 9 , 1461–1471 (2022).

Zhang, X. et al. Metabolic score for insulin resistance (METS-IR) predicts adverse cardiovascular events in patients with type 2 diabetes and ischemic cardiomyopathy. Diabetes Metab. Syndr. Obes. 5 , 1283–1295 (2023).

Liu, X. Z., Fan, J. & Pan, S. J. METS-IR, a novel simple insulin resistance indexes, is associated with hypertension in normal-weight Chinese adults. J. Clin. Hypertens. 21 , 1075–1081 (2019).

Yang, W. et al. The metabolic score for insulin resistance (METS-IR) predicts cardiovascular disease and its subtypes in patients with hypertension and obstructive sleep apnea. Clin. Epidemiol. 15 , 177–189 (2023).

Zhang, X. et al. Association of metabolic syndrome with TyG index and TyG-related parameters in an urban Chinese population: A 15-year prospective study. Diabetol.Metab. Syndr. 14 , 1 (2022).

AZIZI, F. et al. Appropriate waist circumference cut-off points among Iranian adults: the first report of the Iranian National Committee of Obesity. (2010).

KG, A. International diabetes federation task force on epidemiology and prevention; hational heart, lung, and blood institute; American heart association; world heart federation; international atherosclerosis society; international association for the study of obesity: harmonizing the metabolic syndrome: a joint interim statement of the international diabetes federation task force on epidemiology and prevention; national heart, lung, and blood institute; American heart association; world heart federation; international atherosclerosis society; and international association for the study of obesity. Circulation 120 , 1640–1645 (2009).

Bazyar, H. et al. The effects of rutin supplement on blood pressure markers, some serum antioxidant enzymes, and quality of life in patients with type 2 diabetes mellitus compared with placebo. Front. Nutr. https://doi.org/10.3389/fnut.2023.1214420 (2023).

Dobiasˇova, M. Vol. 50 1113–1115 (Oxford University Press, 2004).

Haidari, F., Aghamohammadi, V., Mohammadshahi, M., Ahmadi-Angali, K. & Asghari-Jafarabadi, M. Whey protein supplementation reducing fasting levels of anandamide and 2-AG without weight loss in pre-menopausal women with obesity on a weight-loss diet. Trials 21 , 1–10 (2020).

Haidari, F., Aghamohammadi, V., Mohammadshahi, M. & Ahmadi-Angali, K. Effect of whey protein supplementation on levels of endocannabinoids and some of metabolic risk factors in obese women on a weight-loss diet: A study protocol for a randomized controlled trial. Nutr. J. 16 , 1–5 (2017).

Behbahani, H. B. et al. Glycemic index, glycemic load, dietary insulin index, and dietary insulin load in relation to cardiometabolic risk factors among participants with atherosclerosis: a cross-sectional study. BMC Nutr. 9 , 98 (2023).

Yoo, E.-G. Waist-to-height ratio as a screening tool for obesity and cardiometabolic risk. Korean J. Pediatr. 59 , 425 (2016).

Aadahl, M. & Jørgensen, T. Validation of a new self-report instrument for measuring physical activity. Med. Sci. Sports Exerc. 35 , 1196–1202 (2003).

Download references

Acknowledgements

The authors extend their appreciation to Endocrinology and Metabolism clinic employees of Golestan Hospital and also the laboratory staff of Golestan Hospital in Ahvaz.

This work has been financially supported by the Student Research Committee of Sirjan School of Medical Sciences (No: 401000024).

Author information

Authors and affiliations.

Student Research Committee, Sirjan School of Medical Sciences, Sirjan, Iran

Hadi Bazyar & Sohrab Hajializadeh

Department of Public Health, Sirjan School of Medical Sciences, Sirjan, Iran

Hadi Bazyar

Nutrition and Metabolic Diseases Research Center, Clinical Sciences Research Institute, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran

Ahmad Zare Javid

Department of Nutrition, School of Allied Medical Sciences, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran

Sirjan School of Medical Sciences, Sirjan, Iran

Mahmood Reza Masoudi

School of Health, Medical and Applied Sciences, Central Queensland University, Brisbane, Australia

Fatemeh Haidari

Student Research Committee, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran

Zeinab Heidari

Department of Nutrition, Khalkhal University of Medical Science, Khalkhal, Iran

Vahideh Aghamohammadi

Department of Community Nutrition, School of Nutrition and Food Science, Isfahan University of Medical Sciences, Isfahan, Iran

Mehdi Vajdi

You can also search for this author in PubMed   Google Scholar

Contributions

HB: all steps of research and writing the manuscript, A-ZJ: drafting of the manuscript, MR-M: drafting of the manuscript, FH: revision of the manuscript, ZH: drafting and revision of the manuscript, SH: drafting of the manuscript, VA: corresponding author, supervision and all steps of research and writing the manuscript, MV: co-corresponding and all steps of research. All authors reviewed the manuscript. The final version of the manuscript was approved by all authors.

Corresponding authors

Correspondence to Vahideh Aghamohammadi or Mehdi Vajdi .

Competing interests

The authors declare no competing interests.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Cite this article.

Bazyar, H., Zare Javid, A., Masoudi, M.R. et al. Assessing the predictive value of insulin resistance indices for metabolic syndrome risk in type 2 diabetes mellitus patients. Sci Rep 14 , 8917 (2024). https://doi.org/10.1038/s41598-024-59659-3

Download citation

Received : 31 October 2023

Accepted : 12 April 2024

Published : 18 April 2024

DOI : https://doi.org/10.1038/s41598-024-59659-3

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Insulin resistance indices

By submitting a comment you agree to abide by our Terms and Community Guidelines . If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here .

Loading metrics

Open Access

Peer-reviewed

Research Article

Serum brain-derived neurotrophic factor levels in type 2 diabetes mellitus patients and its association with cognitive impairment: A meta-analysis

Contributed equally to this work with: Wan-li He, Fei-xia Chang

Roles Conceptualization, Data curation, Formal analysis, Methodology, Writing – original draft

Affiliation Department of Medical Imaging Center, Gansu Provincial Maternal and Child Care Hospital (Gansu Provincial Central Hospital), Lanzhou, Gansu, China

Roles Conceptualization, Data curation, Investigation, Methodology, Writing – original draft

Roles Data curation, Formal analysis, Investigation, Writing – original draft, Writing – review & editing

Roles Investigation, Methodology, Visualization, Writing – original draft, Writing – review & editing

Roles Methodology, Visualization, Writing – original draft, Writing – review & editing

Affiliation Department of Medical Imaging Center, Gansu Provincial Maternal and Child Care Hospital, Lanzhou, Gansu, China

Roles Data curation, Project administration, Visualization, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliation Department of Radiology, Gansu Provincial Hospital, Lanzhou, Gansu, China

• Wan-li He, 
• Fei-xia Chang, 
• Tao Wang, 
• Bi-xia Sun, 
• Rui-rong Chen, 
• Lian-ping Zhao

• Published: April 22, 2024
• https://doi.org/10.1371/journal.pone.0297785
• Peer Review
• Reader Comments

To compare the serum levels of brain-derived neurotrophic factor (BDNF) in type 2 diabetes mellitus (T2DM) patients with healthy controls (HC) and evaluate the BDNF levels in T2DM patients with/without cognitive impairment.

PubMed, EMBASE, and the Cochrane Library databases were searched for the published English literature on BDNF in T2DM patients from inception to December 2022. The BDNF data in the T2DM and HC groups were extracted, and the study quality was evaluated using the Agency for Healthcare Research and Quality. A meta-analysis of the pooled data was conducted using Review Manager 5.3 and Stata 12.0 software.

A total of 18 English articles fulfilled with inclusion criteria. The standard mean difference of the serum BDNF level was significantly lower in T2DM than that in the HC group (SMD: -2.04, z = 11.19, P <0.001). Besides, T2DM cognitive impairment group had a slightly lower serum BDNF level compared to the non-cognitive impairment group (SMD: -2.59, z = 1.87, P = 0.06).

BDNF might be involved in the neuropathophysiology of cerebral damage in T2DM, especially cognitive impairment in T2DM.

Citation: He W-l, Chang F-x, Wang T, Sun B-x, Chen R-r, Zhao L-p (2024) Serum brain-derived neurotrophic factor levels in type 2 diabetes mellitus patients and its association with cognitive impairment: A meta-analysis. PLoS ONE 19(4): e0297785. https://doi.org/10.1371/journal.pone.0297785

Editor: Purvi Purohit, All India Institute of Medical Sciences, INDIA

Received: July 25, 2023; Accepted: January 12, 2024; Published: April 22, 2024

Copyright: © 2024 He et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the manuscript and its Supporting Information files.

Funding: The author(s) received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

1. Introduction

Type 2 diabetes mellitus (T2DM) is a chronic systemic metabolic disorder seriously affecting human health, which is triggered by genetic predisposition and environmental factors [ 1 ]. International Diabetes Federation estimates that T2DM occurs in over 400 million people and it is one of the largest epidemics worldwide [ 2 ]. T2DM manifesting through fasting and post-prandial hyperglycemia can induce various life-threatening co-morbidities and complications such as diabetic neuropathy and diabetic nephropathy [ 3 , 4 ]. Cognitive dysfunction is an important complication observed in type T2DM patients [ 5 ]. In addition, T2DM is an important risk factor implicated in cognitive deficits except aging and neurodegenerative disorder [ 6 ]. T2DM patients have a greater decline in cognitive function than those without T2DM [ 7 ]. Besides, it is reported that T2DM accelerates brain aging and cognitive decline [ 8 ]. T2DM is significantly associated with an increased risk of dementia and a large portion of T2DM patients with cognitive impairment eventually progress to dementia [ 9 , 10 ], which may represent a consequence of brain-specific insulin resistance and impaired glucose regulation [ 11 ]. However, the pathophysiological mechanisms of cerebral impairment in T2DM remain elucidated.

Brain-derived neurotrophic factor (BDNF), a member of the neurotrophic family of proteins, is most widely distributed in the central nervous system (CNS) [ 12 ]. It plays an important role in protecting neurons and synaptic activity [ 13 ]. BDNF was released from the brain to peripheral circulation [ 14 ], and there is a correlation between BDNF in serum and CNS, providing an alternative measure of BDNF changes [ 15 ]. Alternation of BDNF is observed in the pathophysiological basis of many neurodegenerative and psychiatric disorders [ 16 ], including Alzheimer’s disease and depression [ 17 , 18 ]. Furthermore, the serum BDNF is a useful biomarker for executive cognitive impairment in schizophrenia patients [ 19 , 20 ]. In addition, the BDNF Val66Met polymorphism may be a major factor in the susceptibility to cognitive impairment which affects the secretion of mature BDNF [ 21 ]. A meta-analysis suggests that BDNF Val66Met is associated with cognitive impairment in Parkinson’s disease [ 22 ], confirming that BDNF is a risk factor for this disorder [ 23 ]. Furthermore, BDNF is related to the regulation of glucose levels [ 24 ]. Exogenous BDNF reduces blood glucose concentrations and glycated hemoglobin in obese diabetic mice [ 25 ], which is consistent with the finding that there was a positive correlation between BDNF and insulin sensitivity [ 26 ]. Previous studies have revealed the relationship between serum BDNF and diabetic conditions in T2DM patients with controversial results [ 27 – 34 ]. However, the precise role of BDNF in the development of T2DM patients as well as in cognitive function remains unclear.

Therefore, our study aims to explore the alteration tendency of the serum BDNF levels in T2DM patients with or without cognitive impairment using meta-analysis with a comprehensive evaluation of relevant literature. The current study will provide a basic foundation for further investigating the neuropathophysiological mechanisms of cerebral damage in T2DM.

2.1. Literature search and selection

A systemic search strategy was used to identify the relevant studies published in PubMed, EMBASE, and the Cochrane Library from inception to December 2022. We applied a search strategy based on the combination of relevant terms. Two independent investigators acquired articles and sequentially screened their titles and abstracts for eligibility. Then, full texts of articles deemed potentially eligible were acquired. Any disagreement would be solved via discussion with the help of a third senior investigator. A screening guide was used to ensure that the selection criteria were constantly applied.

Inclusion criteria: (1) clinical cross-sectional studies concerning the quantitative values of serum BDNF level in T2DM patients; (2) sufficient data were available for mean and standard deviation analysis of BDNF level; (3) original research. Exclusion criteria: (1) review, abstracts only, letters, comments, guidelines, and case reports; (2) studies in vitro or in animal models; (3) duplicate publications; (4) incomplete data.

2.2. Quality evaluation and data extraction

Agency for Healthcare Research and Quality (AHRQ) was used to evaluate the quality of the included cross-sectional studies. The AHRQ included 8 items with a total score of 8 points. Two independent researchers assessed the quality of the literature and reached a consensus after consultation when necessary.

Calculate standard deviation from confidence interval:

(2) Calculate the standard deviation from an interquartile range:

(3) Calculate standard deviation by p -value:

2.3. Statistical analysis

All the meta-analyses were performed on Review Manager 5.3 and STATA12.0 with a significance level of P <0.05. To calculate the effect size for each study, the summary standard mean difference (SMD) and 95% confidence interval were applied to evaluate the serum BDNF values between T2DM and healthy control (HC), T2DM with or without cognitive impairment. Pooled SMD and corresponding 95% confidence interval were calculated using the inverse variances method. Heterogeneity was estimated using the Cochran Q ( P ) and the inconsistency index where a P value less than 0.05 and I 2 value greater than 50% indicated the presence of significant heterogeneity across the enrolled studies. If notable heterogeneity was observed, a random-effect model was applied and subgroup analyses were used to determine factors that contributed to the heterogeneity and to explore how those factors influenced the results. Subgroup analysis was stratified by the BDNF measuring instruments brand (China or USA; same brand in China or USA), ethnicity (Asian or European), and population [adults or the aged (years≥60)]. In addition, sensitivity analysis was performed to evaluate the reliability of included studies using STATA 12.0. The Egger’s test and the Begg’s test were applied to evaluate potential publication bias using STATA 12.0.

3.1. Search and selection results

The main search strategy is illustrated in Table 1 . Studies selection was managed using EndNote X7. A total of 678 records were initially identified, but only 501 records remained after the elimination of duplicates. Only 51 records were remaining after screening titles, and subsequently, 29 records remained after reading the abstract. After reading full texts, 11 articles with incomplete data were excluded and finally, 18 articles were enrolled. The flow diagram is shown in Fig 1 .

• PPT PowerPoint slide
• PNG larger image
• TIFF original image

https://doi.org/10.1371/journal.pone.0297785.g001

https://doi.org/10.1371/journal.pone.0297785.t001

3.2. Characteristics and quality evaluation

Eighteen articles were included in the meta-analysis. The basic characteristics and quality evaluation of the studies are shown in Table 2 . Among them, 17 articles had T2DM and HC groups, and 3 articles divided the T2DM group into two subgroups according to the presence of cognitive impairment. Of the 18 articles included, 13 were done in China, 2 in Japan, and 1 in each of the following countries (USA, Italy, and Turkey). The sample’s mean age was >18 years in 15 articles and >60 years in 3 articles. The diagnostic criteria of T2DM as recommended by the World Health Organization were adopted in 11 articles; whereas the American Diabetes Association was employed in 1 article, but the remaining articles were not mentioned. Measurement of BDNF using ELISA in 17 articles, but 1 article was not mentioned. All of the 18 included studies were cross-sectional studies. Based on the quality evaluation of AHRQ, 11 studies scored 8, 4 studies scored 7, 1 study scored 5, and 1 study scored 4.

https://doi.org/10.1371/journal.pone.0297785.t002

3.3. Meta-analysis

We compared the BDNF level between T2DM and HC groups ( P < 0.001, I 2 = 99%), and between the T2DM with or without cognitive impairment groups ( P < 0.001, I 2 = 90%) using a random-effect model since the heterogeneity test showed the I 2 value >50%.

Seventeen articles contained 2966 T2DM cases and 3580 HCs. The serum BDNF level in the T2DM group was significantly lower than that in the HC group [SMD: -2.04, z = 11.19, P < 0.001] ( Fig 2A ) . The number of T2DM patients with or without cognitive impairment was 672 and 1913, respectively. The serum BDNF levels in T2DM with cognitive impairment group had a marginal difference from those without cognitive impairment [SMD: -2.59, z = 1.87, P = 0.06] ( Fig 2B ) .

(A) The different BDNF levels between T2DM and HC. (B) The different BDNF levels in T2DM patients with or without cognitive impairment. Abbreviations: BDNF, brain-derived neurotrophic factor; T2DM, type 2 diabetes mellitus; HC, healthy controls.

https://doi.org/10.1371/journal.pone.0297785.g002

3.4. Sensitivity analysis

Sensitivity analyses were conducted to evaluate the robustness of the findings by excluding 1 study at a time to assess if the results were driven by any one study. The significance of the meta-analysis outcome for T2DM and HC group changed when ruling out any one of 6/17 studies and the results also changed in T2DM with or without cognitive impairment group after ruling out 1/3 study, suggesting the results were unstable.

3.5. Subgroup analysis

Subgroup analysis based on the BDNF measuring instruments (either China or USA) exhibited that there were significant differences in BDNF values between T2DM and HC (China: P = 0.05; USA: P < 0.001; Total: P < 0.001), with large heterogeneity (China: P < 0.001 and I 2 = 100%; USA: P < 0.001 and I 2 = 99%; Total: P < 0.001 and I 2 = 99%) ( Fig 3 ) . Then, subgroup analysis was performed on the same instrument brand in China or the USA, respectively and similar results were observed (China: P <0.001; USA: P = 0.002; Total: P <0.001). The heterogeneity was only observed in the same brand from the USA, but not in the same brand from China [China: (P = 0.84 and I 2 = 0%; USA: P < 0.001 and I 2 = 98%; Total: P < 0.001 and I 2 = 96%)] ( Fig 4 ) . Subgroup analysis based on ethnicity and population distribution presented that the BDNF values were significantly different, except for the European (Asian: P < 0.001; European: P = 0.59; Total: P < 0.001). In addition, there was significant difference in the adults in T2DM and HC, except for the aged (adults: P < 0.001; the aged: P = 0.25; Total: P < 0.001), and there were no marked decrease in heterogeneity (Asian: P < 0.001 and I 2 = 99%; European: P < 0.001 and I 2 = 99%; Total: P < 0.001 and I 2 = 99%) (Adults: P < 0.001 and I 2 = 99%; the aged: P = 0.03 and I 2 = 80%; Total: P < 0.001 and I 2 = 99%) (Figs 5 and 6 ) .

https://doi.org/10.1371/journal.pone.0297785.g003

https://doi.org/10.1371/journal.pone.0297785.g004

https://doi.org/10.1371/journal.pone.0297785.g005

https://doi.org/10.1371/journal.pone.0297785.g006

3.6. Publication bias analysis

After Egger’s and Begg’s test, the studies of T2DM and HC group, T2DM with or without cognitive impairment group showed no significant publication bias ( P = 0.606, P = 0.672; P = 0.202, P = 1.000).

4. Discussion

Our study is the first meta-analysis to evaluate the levels of serum BDNF in T2DM patients and HCs and compare the levels between T2DM patients with or without cognitive impairment. We found that the serum BDNF levels were lower in T2DM compared with HC. Furthermore, the serum BDNF levels had a decreasing tendency in T2DM patients with cognitive impairment compared with those without cognitive impairment.

The BDNF plays a key role in the pathophysiology of T2DM due to improving glucose metabolism and insulin sensitivity [ 44 – 46 ]. Previous studies have reported that T2DM patients exhibited significantly lower levels of serum BDNF compared with normal controls [ 27 – 32 , 43 ], which is consistent with our research. Additionally, the cerebral output of BDNF is inhibited under hyperglycemia, logically decreased serum BDNF may be detected in the uncontrolled T2DM patients [ 14 ]. This is in line with the findings that there is an inverse correlation between serum BDNF levels and long-standing diabetes, in males and aged T2DM patients [ 43 ]. Interestingly, upregulated serum BDNF levels in T2DM patients were also reported [ 33 , 34 ]. Such discrepancy is possibly related to physical exercise, obesity, and a balanced diet in T2DM patients [ 47 – 50 ]. In addition, the serum BDNF levels are increased in T2DM patients who received metformin treatment [ 3 ]. This may also link to a compensatory mechanism of serum BDNF release in T2DM [ 33 ], which is supported by the findings that the upregulated serum BDNF levels control blood glucose in newly diagnosed T2DM patients, but this control ability might be lost in a long term T2DM patients [ 35 ]. Our explanation is further supported by a resting state fMRI report showing enhanced functional connectivity of the left hippocampus (a major source of BDNF) with the left inferior frontal gyrus in the early stage of T2DM, which might contribute to adaptive compensation of hippocampal function [ 51 ]. Taken together, the serum BDNF could be a useful biological marker to monitor the development of T2DM and the cerebral impairment in T2DM.

T2DM has reduced the number of new neurons in the hippocampus, and hippocampal neurogenesis plays an important role in learning and memory function throughout life [ 52 ]. The Hippocampal perhaps regulates BDNF to provide neuroprotection and control of synaptic interactions [ 53 – 56 ]. In the present meta-analysis, the serum BDNF levels presented a decreasing tendency in T2DM patients with cognitive impairment compared with those patients without cognitive impairment. Such downregulation was also observed in Alzheimer’s disease, showing that the serum BDNF levels may be involved in the progression of cognitive impairment [ 57 ]. Such findings showed that serum BDNF levels may be involved in the progression of cognitive impairment in patients with T2DM. Thus, the reduction of BDNF might contribute to the neuropathophysiology of brain damage in T2DM, especially relating to cognitive impairment in T2DM.

However, substantial heterogeneity existed in the present meta-analysis. The heterogeneity could be generated from related factors, including the different brands of instruments for measuring BDNF, times and methods of blood collection, population distributions, and ethnicities. Such heterogeneity has been eliminated in the subgroup analysis by comparing the data from the same brands of instruments.

There are some limitations in the study. Firstly, the meta-analysis mostly included Chinese Han populations, which may not reflect the entire population/race. Secondly, different diagnostic criteria for diabetes were applied which might also compromise the data analysis. Although internationally recognized scales were utilized, the lack of a standard protocol for cognitive impairment could lead to inconsistent results.

In conclusion, the present meta-analysis suggests that the decrease in serum BDNF levels in T2DM patients has resolved the inconsistencies in previous studies. The serum BDNF levels in T2DM patients with cognitive impairment had a downward trend compared with those patients without cognitive impairment. Moreover, the reduction of serum BDNF may be a vital neuropathophysiological mechanism of cognitive impairment in T2DM patients.

Supporting information

S1 raw data..

https://doi.org/10.1371/journal.pone.0297785.s001

S2 Raw data.

https://doi.org/10.1371/journal.pone.0297785.s002

• View Article
• PubMed/NCBI
• Google Scholar

• Open access
• Published: 24 April 2024

Effects of sodium-glucose cotransporter 2 inhibitors on bone metabolism in patients with type 2 diabetes mellitus: a systematic review and meta-analysis

• Jing Wang 1 ,
• Yang Li 3 &
• Chen Lei 4  

BMC Endocrine Disorders volume  24 , Article number:  52 ( 2024 ) Cite this article

139 Accesses

Metrics details

Sodium glucose cotransporter 2 (SGLT2) inhibitors are widely used in type 2 diabetes mellitus (T2DM) therapy. The impact of SGLT2 inhibitors on bone metabolism has been widely taken into consideration. But there are controversial results in the study on the effect of SGLT2 inhibitors on bone metabolism in patients with T2DM. Therefore, we aimed to examine whether and to what extent SGLT2 inhibitors affect bone metabolism in patients with T2DM.

A literature search of randomized controlled trials (RCTs) was conducted through PubMed, Web of Science, Embase, Cochrane databases, and Scopus from inception until 15 April 2023. Eligible RCTs compared the effects of SGLT2 inhibitors versus placebo on bone mineral density and bone metabolism in patients with T2DM. To evaluate the differences between groups, a meta-analysis was conducted using the random effects inverse-variance model by utilizing standardized mean differences (SMD).

Through screening, 25 articles were finally included, covering 22,828 patients. The results showed that, compared with placebo, SGLT2 inhibitors significantly increased parathyroid hormone (PTH, SMD = 0.13; 95%CI: 0.06, 0.20), and cross-linked C-terminal telopeptides of type I collagen (CTX, SMD = 0.11; 95%CI: 0.01, 0.21) in patients with T2DM, decreased serum alkaline phosphatase levels (ALP, SMD = -0.06; 95%CI: -0.10, -0.03), and had no significant effect on bone mineral density (BMD), procollagen type 1 N-terminal propeptide (P1NP), 25-hydroxy vitamin D, tartrate resistant acid phosphatase-5b (TRACP-5b) and osteocalcin.

Conclusions

SGLT2 inhibitors may negatively affect bone metabolism by increasing serum PTH, CTX, and decreasing serum ALP. This conclusion needs to be verified by more studies due to the limited number and quality of included studies.

Systematic review registration

PROSPERO, identifier CRD42023410701

Peer Review reports

Research in context

SGLT2 inhibitors have been widely used in clinical practice for their good cardiorenal protective and hypoglycemic effects. However, their effects on bones are still controversial. The drug has been shown to have a potential adverse effect on bone in multiple animal experiments. However, in the latest meta-analysis, it was not found that the risk of fracture increased in patients with type 2 diabetes mellitus (T2DM) treated with SGLT2 inhibitors.

Can SGLT2 inhibitors affect bone mineral density and bone metabolism in patients with T2DM?

We found that SGLT2 inhibitors may have a negative effect on bone in patients with T2DM.

When T2DM is treated in clinical work, doctors will pay more attention to the monitoring of bone safety. And we provided a reference for the use of SGLT2 inhibitors.

Introduction

It is well known that type 2 diabetes mellitus (T2DM) is characterized by persistently elevated blood glucose or elevated postprandial blood glucose containing carbohydrates [ 1 ]. As a chronic non-communicable disease, its prevalence is increasing worldwide, especially related to the gradual entry of people into an aging society, high calorie intake, and a sedentary lifestyle [ 2 ]. Recent studies have shown that in addition to the cardiovascular, ocular, renal and neurological complications of the disease in patients, bone strength is also impaired and leads to an increased risk of fractures [ 3 ]. The presence of T2DM is associated with a prevalent metabolic disorder that has detrimental effects on bone metabolism, leading to an increased susceptibility to fractures [ 4 , 5 ]. Among the various types of osteoporotic fractures, individuals with T2DM face a heightened risk for hip fractures, which are considered the most severe, as well as limb fractures such as those occurring in the leg or ankle [ 6 ].

The anti-diabetic drugs currently applied clinically have certain effects on the bone metabolism of patients [ 7 ]. Sodium–glucose cotransporter 2 (SGLT2) inhibitor is one of the new hypoglycemic drugs. It can reduce glucose re-absorption by inhibiting SGLT2 in proximal tubules of the kidney, thus promoting urine glucose excretion and reducing blood glucose [ 8 ]. In recent years, studies on the effects of SGLT2 inhibitors on bone metabolism have been continuously released, and the existing relationship between the two is still controversial. Theoretically, SGLT2 inhibitors increase renal tubular reabsorption of phosphate and serum parathyroid hormone concentration [ 9 ].

Considering the significant economic and social burden caused by bone health issues and associated fracture risks, it is imperative to conduct a comprehensive evaluation of the impact of SGLT2 inhibitors on fractures and bone metabolism. In view of the fact that there are still controversial results in the study on the effect of SGLT2 inhibitors on bone metabolism in patients with T2DM, we conducted a systematic and comprehensive analysis of the existing research results in order to provide reference for the selection of SGLT2 inhibitors in the treatment of T2DM in clinical work.

Protocol and registration

The protocol of this systematic review and meta-analysis has been registered in PROSPERO (registration no. CRD42023410701).

Eligibility criteria

We included randomized controlled trials (RCTs) comparing the efficacy of SGLT2 inhibitors versus placebo, in English only. Eligible participants were adults with T2DM, regardless of background hypoglycemic therapy. Interventions should last for at least 12 weeks and the outcomes should include at least one of bone mineral density or bone metabolism.

Search strategy

We searched PubMed, Web of Science, Embase, Cochrane databases, and Scopus on 15 April 2023 for English-language studies. Detailed information about our search strategy was presented in the electronic supplementary material (Table S1 ). To avoid omitting any eligible studies, any terms related to “SGLT2 inhibitor” were searched.

Selection process

All search results were downloaded into EndNote (version X9, Thomson Reuters, Philadelphia, PA, USA) to eliminate duplication. Two reviewers independently performed a preliminary screening of the title and abstract. Remaining articles were read through the full text to determine inclusion, and the reasons for excluded articles were recorded. Any disagreements were resolved by a third reviewer. Articles that could not get the required data were also excluded. Articles for which the required data were not available after contacting the corresponding author were also excluded.

Data collection and risk of bias assessment

Data extraction was done by two independent reviewers and arbitrated by a third reviewer. The relevant information extracted from the included articles mainly included: (1) Basic information: first author, publication year, sample size, and the number of experimental and control groups. (2) Characteristics of research subjects: gender, age, glycated hemoglobin, BMI, SGLT2 inhibitor type and dose, and duration of treatment; (3) Outcomes: Mean ± standard deviation (SD) of post-treatment relative baseline changes in bone mineral density (BMD) and bone metabolism-related indicators including parathyroid hormone (PTH), cross-linked C-terminal telopeptides of type I collagen (CTX), alkaline phosphatase (ALP), 25-hydroxy vitamin D, procollagen type 1 N-terminal propeptide (P1NP), osteocalcin, and Tartrate resistant acid phosphatase-5b (TRACP-5b); (4) Relevant information described in the literature that can be used to assess the risk of bias.

The risk of bias will be assessed by two authors independently using the RoB2 tool for the included RCTs [ 10 ]. Using the RoB2 tool, we will assess domains such as randomization process, assignment and adhering to intervention, missing data and measurement of outcome, and finally categorize the studies as having a low, some concern, or high risk of bias.

Statistical analysis

We will pool the results using a random-effects meta-analysis, using standard mean difference (SMD) for continuous outcomes, and calculate 95% confidence interval (CI). A p -value < 0.05 was considered statistically significant. The Chi-square test combined with I-value analysis was used to judge the heterogeneity among the articles. When the heterogeneity of the studies in each group was relatively large ( P  < 0.05, I 2  ≥ 50%), the source of heterogeneity needed to be clarified. Subsequent subgroup analysis or sensitivity analysis was conducted to explain the reasons for heterogeneity. Egger’s tests were performed to assess publication bias. R (version 4.2.3) and the statistical package ‘meta’ were used for analysis.

Search results

According to the established retrieval strategy, we screened a total of 8554 studies from 5 databases. After a series of screenings, 25 studies ultimately met the eligibility criteria, totaling 22,828 unique participants. Twenty-three studies included in the analysis were RCTs [ 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 ], and two studies were for RCTs Pooled analysis [ 34 , 35 ] (Fig.  1 ).

Flow diagram of the identification of eligible trials

Study characteristics

The study characteristics were summarized in Table  1 . A total of 22,828 participants from 25 RCTs were randomly assigned to one of five SGLT2 inhibitors (canagliflozin, dapagliflozin, empagliflozin, ipragliflozin, and ertugliflozin) or placebo. Sample sizes for individual trials ranged from 40 to 12,620 participants, and the average trial duration was 55 weeks (range 12–104 weeks).

The risk of bias in the 25 RCTs is summarized in Fig.  2 . Most of the trials included in the meta-analysis were judged to have a low risk of bias.

Risk of bias assessments of included studies

Meta-analysis results

• Bone mineral density

A total of 3 studies [ 11 , 24 , 26 ] reported the effects of SGLT2 inhibitors on BMD in patients with T2DM. The results of the overall and subgroup meta-analysis are presented in Fig.  3 . There was no significant difference in BMD after treatment between the SGLT2 inhibitor group and the placebo group (SMD = -0.02; 95%CI: -0.09, 0.05). In subgroup analyses of bone sites, there was also no significant change in BMD in the two groups (lumbar spine, SMD = − 0.02, 95%CI: −0.13, 0.10; femoral neck, SMD = 0.05, 95%CI: −0.11, 0.22; total hip, SMD = -0.08, 95%CI: −0.27, 0.12; and distal forearm, SMD = − 0.06, 95%CI: −0.18, 0.06). No evidence of publication bias was observed (Table S2 ).

Meta-analysis of the effect of Sodium–glucose cotransporter 2 (SGLT2) inhibitors on BMD compared with placebo. BMD, bone mineral density

• Bone metabolism

13 studies [ 11 , 12 , 13 , 14 , 15 , 16 , 19 , 23 , 24 , 28 , 31 , 32 , 35 ] reported PTH levels after SGLT2 inhibitor treatment (Fig.  4 ). 7 papers compared CTX [ 11 , 19 , 23 , 24 , 26 , 28 , 32 ] and 25-hydroxy vitamin D [ 11 , 14 , 15 , 23 , 31 , 32 , 35 ] levels after treatment (Fig.  5 A-B). 15 papers [ 11 , 15 , 16 , 18 , 20 , 21 , 22 , 25 , 27 , 29 , 30 , 34 , 35 ] reported ALP levels after treatment (Fig.  6 ). 3 papers compared P1NP [ 11 , 14 , 24 ] and osteocalcin [ 14 , 26 , 32 ] levels after treatment (Fig.  7 A-B). 2 papers [ 23 , 28 ] reported TRACP-5b levels after treatment (Fig.  7 C). Except for osteocalcin ( P  = 0.02, I 2  = 75%), no significant heterogeneity was observed. Meta results showed that, compared with placebo, SGLT2 inhibitors significantly increased PTH levels (SMD = 0.13; 95%CI: 0.06, 0.20) and CTX levels (SMD = 0.11; 95%CI: 0.01, 0.21), while significantly decreased ALP levels (SMD = -0.06; 95%CI: -0.10, -0.03). However, there was no significant difference in 25-hydroxy vitamin D (SMD = 0.09; 95%CI: 0.00, 0.18), P1NP (SMD = 0.13; 95%CI: -0.02, 0.28), osteocalcin (SMD = 0.19; 95%CI: -0.16, 0.54), and TRACP-5b (SMD = 0.05; 95%CI: -0.17, 0.28) after treatment between the SGLT2 inhibitor group and the placebo group.

Meta-analysis of the effect of Sodium–glucose cotransporter 2 (SGLT2) inhibitors on PTH compared with placebo. PTH, parathyroid hormone

Meta-analysis of the effect of Sodium–glucose cotransporter 2 (SGLT2) inhibitors on CTX ( A ) and 25-hydroxy vitamin D ( B ) compared with placebo. CTX, Cross-linked C-terminal telopeptides of type I collagen

Meta-analysis of the effect of Sodium–glucose cotransporter 2 (SGLT2) inhibitors on ALP compared with placebo. ALP, Alkaline phosphatase

Meta-analysis of the effect of Sodium–glucose cotransporter 2 (SGLT2) inhibitors on P1NP ( A ), osteocalcin ( B ) and TRACP-5b ( C ) compared with placebo. P1NP, Procollagen type 1 N-terminal propeptide; TRACP-5b, Tartrate resistant acid phosphatase-5b

In addition, no evidence of publication bias was observed for any of the above outcomes (Table S2 ).

The combined detection of BMD and bone turnover markers can be used to evaluate bone metabolism in patients. However, the changes of bone turnover markers are more sensitive [ 36 ]. In this study, after a comprehensive literature search and analysis, 25 studies were finally included for meta-analysis. Our results suggested that SGLT2 inhibitors had no significant effect on BMD in patients with T2DM compared to placebo. However, due to the short follow-up period and limited number of the RCTs included in the studies, more long-term studies are needed to accurately determine the impact of SGLT2 inhibitors on BMD.

In terms of bone metabolism, we observed that SGLT2 inhibitors significantly increased serum PTH and CTX levels and decreased serum ALP levels in patients with T2DM. This presents a seemingly paradoxical situation, as it is traditionally understood that elevated levels of PTH normally stimulate bone formation, which in turn increases levels of ALP, the active marker of bone formation [ 37 ]. This reflects the discrepancy between increased PTH levels and decreased ALP levels in patients using SGLT2 inhibitors underscores the complexity of the drugs’ impact on bone metabolism. It suggests a multifactorial influence involving immediate metabolic changes, differential effects on bone remodeling phases, the intricate role of RAAS activation, and the body’s broader compensatory responses [ 38 ]. In addition, no statistically significant effect of SGLT2 inhibitors on P1NP, TRACP-5b, 25-hydroxy vitamin D, and osteocalcin was observed in this study. However, although CTX and ALP levels change significantly in the meta-analysis, no single report shows a significant increase in CTX and only one study found a significant reduction of ALP. The reason for these phenomena can be attributed to the short duration of the study. The studies included this time are up to just over 3 months (104 days). Current research suggests that short-term studies (3 months) may not sufficiently capture significant changes in bone metabolism markers due to the physiological lag between alterations in glucose metabolism and their impact on bone remodeling processes []. In contrast, studies extending beyond 6 to 12 months are considered more likely to demonstrate meaningful changes in these markers [ 37 , 39 ]. Further research, particularly studies with longer follow-up periods and detailed analyses of bone quality and turnover markers, is needed to fully elucidate these relationships.

The exact mechanism of the negative effects of SGLT2 inhibitors on bone health remains unknown. A study has shown that SGLT2 is not expressed in either the osteoblast lineage or the osteoclast lineage [ 40 ]. SGLT1 was detected in MC3T3-E1 differentiated osteoblasts, but its expression level was low. Therefore, the effects of these drugs on bone may be indirect [ 41 ]. SGLT2 inhibitors destroy serum calcium, phosphate, and vitamin D homeostasis [ 42 ]. As reabsorption of sodium in the proximal renal tubules decreases, the activity of sodium-phosphate co-transporters at the apical membrane increases. Serum phosphate levels further increase, inducing parathyroid cells and osteoblasts to secrete PTH and fibroblast growth factor 23 (FGF23). PTH causes bone resorption. While FGF23 promotes urinary phosphate excretion, inhibition of 1-αhydroxylase causes a decrease in 1,25-dihydroxvitamin D levels [ 43 ]. The decrease in blood sodium concentration can also directly affect osteoclasts, leading to an increase in bone fragility [ 44 ]. In the opposite way, calcium is reabsorbed by sodium-calcium cotransporters. The inhibition of SGLT2 leads to increased excretion of urine glucose and urine calcium, and the decrease of serum calcium causes secondary hyperparathyroidism [ 9 ]. It has been verified that the main results in our study suggested SGLT2 inhibitors could significantly increase serum PTH. Unfortunately, there are no more clinical studies reporting the effects of SGLT2 inhibitors on FGF23 in patients with T2DM.

SGLT2 inhibitors provide modest weight loss. A reduction in mechanical pressure on the bone tissue may decrease bone density and enhance bone turnover [ 45 ]. This may partly explain the reduction in total hip bone density in T2DM patients with canagliflozin. Weight loss also decreases aromatase activity, resulting in decreased estradiol levels that severely affect bone density and bone turnover [ 46 , 47 ]. In addition to the indirect effects of SGLT2 inhibitors on bone metabolism, adverse events associated with these agents due to osmotic diuresis and volume consumption (orthostatic hypotension, postural dizziness, etc.) may increase the risk of falls and fractures [ 48 ].

There are some limitations to consider in this study. Most studies containing SGLT2 inhibitors focused on the cardiorenal effects. The main outcomes did not include bone health or relevant data were not shown. Therefore, some types of SGLT2 inhibitors received few articles and participants. Important confounding factors such as diet, exercise level, and solar radiation were not reported in some original studies and cannot be corrected. Since T2DM requires a combination of drugs in most cases, the background treatment for each patient cannot be unified, and there may be other drugs that also affect bones, leading to error in the results.

Although further studies are needed, the results of our study have demonstrated the possible negative effects of SGLT2 inhibitors on bone health in patients with T2DM. However, there is still a lack of human studies regarding the effects of SGLT2 inhibitors on bone microarchitectural changes in patients with T2DM. Further preclinical or clinical data are needed to elucidate the effects on bone matrix mineralization and collagen fiber distribution. SGLT2 inhibitors have a good hypoglycemic effect and cardiorenal protection, but they may have a secondary effect on bone turnover. The long-term safety of this effect on bones deserves continued monitoring as the use of this drug becomes more routine in patients with T2DM.

Data availability

Datasets used in this article are available from the corresponding author on reasonable request.

Abbreviations

Alkaline phosphatase

Cross-linked C-terminal telopeptides of type I collagen

Fibroblast growth factor 23

Procollagen type 1 N-terminal propeptide

Parathyroid hormone

Randomized controlled trials

Sodium–glucose cotransporter 2

Standard mean difference

Tartrate resistant acid phosphatase-5b

Vijan S. Type 2 diabetes. Ann Intern Med. 2019;171(9):ITC65–80.

Article   PubMed   Google Scholar  

Picke AK, Campbell G, Napoli N, Hofbauer LC, Rauner M. Update on the impact of type 2 diabetes mellitus on bone metabolism and material properties. Endocr Connections. 2019;8(3):R55–70.

Article   CAS   Google Scholar  

Koromani F, Ghatan S, van Hoek M, Zillikens MC, Oei EHG, Rivadeneira F, Oei L. Type 2 diabetes mellitus and vertebral fracture risk. Curr Osteoporos Rep. 2021;19(1):50–7.

Article   PubMed   PubMed Central   Google Scholar  

Nilsson AG, Sundh D, Johansson L, Nilsson M, Mellstrom D, Rudang R, Zoulakis M, Wallander M, Darelid A, Lorentzon M. Type 2 diabetes mellitus is associated with better bone microarchitecture but lower bone material strength and poorer physical function in elderly women: a population-based study. J Bone Min Res. 2017;32(5):1062–71.

Article   Google Scholar  

Ahmad OS, Leong A, Miller JA, Morris JA, Forgetta V, Mujammami M, Richards JB. A Mendelian randomization study of the effect of type-2 diabetes and glycemic traits on bone mineral density. J Bone Min Res. 2017;32(5):1072–81.

Liu J, Cao L, Qian YW, Chen ZX, Guo SF, Sun WQ, He ZR. The association between risk of limb fracture and type 2 diabetes mellitus. Oncotarget. 2018;9(58):31302–10.

Mabilleau G, Bouvard B. Update on: effects of anti-diabetic drugs on bone metabolism. Expert Rev Endocrinol Metabolism. 2020;15(6):415–30.

Lupsa BC, Kibbey RG, Inzucchi SE. Ketones: the double-edged sword of SGLT2 inhibitors? Diabetologia. 2023;66(1):23–32.

Blau JE, Taylor SI. Adverse effects of SGLT2 inhibitors on bone health. Nat Rev Nephrol. 2018;14(8):473–4.

Article   CAS   PubMed   PubMed Central   Google Scholar  

Sterne JAC, Savovic J, Page MJ, Elbers RG, Blencowe NS, Boutron I, Cates CJ, Cheng HY, Corbett MS, Eldridge SM, et al. RoB 2: a revised tool for assessing risk of bias in randomised trials. BMJ. 2019;366:l4898.

Bolinder J, Ljunggren O, Johansson L, Wilding J, Langkilde AM, Sjöström CD, Sugg J, Parikh S. Dapagliflozin maintains glycaemic control while reducing weight and body fat mass over 2 years in patients with type 2 diabetes mellitus inadequately controlled on metformin. Diabetes Obes Metabolism. 2014;16(2):159–69.

Bailey CJ, Villegas ECM, Woo V, Tang W, Ptaszynska A, List JF. Efficacy and safety of dapagliflozin monotherapy in people with type 2 diabetes: a randomized double-blind placebo-controlled 102-week trial. Diabet Med. 2015;32(4):531–41.

Article   CAS   PubMed   Google Scholar  

Kohan DE. Long-term study of patients with type 2 diabetes and moderate renal impairment shows that dapagliflozin reduces weight and blood pressure but does not improve glycemic control. Kidney Int. 2014;85(4):962–71.

Rosenstock J, Vico M, Wei L, Salsali A, List JF. Effects of dapagliflozin, an SGLT2 inhibitor, on HbA(1c), body weight, and hypoglycemia risk in patients with type 2 diabetes inadequately controlled on pioglitazone monotherapy. Diabetes Care. 2012;35(7):1473–8.

Wilding JPH, Woo V, Soler NG, Pahor AP, Sugg J, Rohwedder K, Parikh S. Long-term efficacy of dapagliflozin in patients with type 2 diabetes mellitus receiving high doses of insulin a randomized trial. Ann Intern Med. 2012;156(6):405–15.

Araki E, Onishi Y, Asano M, Kim H, Yajima T. Efficacy and safety of dapagliflozin over 1 year as add-on to insulin therapy in Japanese patients with type 2 diabetes: the DAISY (Dapagliflozin added to patients under InSulin therapY) trial. Diabetes Obes Metabolism. 2017;19(4):562–70.

Schumm-Draeger PM, Burgess L, Korányi L, Hruba V, Hamer-Maansson JE, de Bruin TWA. Twice-daily dapagliflozin co-administered with metformin in type 2 diabetes: a 16-week randomized, placebo-controlled clinical trial. Diabetes Obes Metabolism. 2015;17(1):42–51.

Wilding JP, Ferrannini E, Fonseca VA, Wilpshaar W, Dhanjal P, Houzer A. Efficacy and safety of ipragliflozin in patients with type 2 diabetes inadequately controlled on metformin: a dose-finding study. Diabetes Obes Metab. 2013;15(5):403–9.

Lu CH, Min KW, Chuang LM, Kokubo S, Yoshida S, Cha BS. Efficacy, safety, and tolerability of ipragliflozin in Asian patients with type 2 diabetes mellitus and inadequate glycemic control with metformin: results of a phase 3 randomized, placebo-controlled, double-blind, multicenter trial. J Diabetes Invest. 2016;7(3):366–73.

Han KA, Chon S, Chung CH, Lim S, Lee KW, Baik S, Jung CH, Kim DS, Park KS, Yoon KH, et al. Efficacy and safety of ipragliflozin as an add-on therapy to sitagliptin and metformin in Korean patients with inadequately controlled type 2 diabetes mellitus: a randomized controlled trial. Diabetes Obes Metab. 2018;20(10):2408–15.

Fonseca VA, Ferrannini E, Wilding JP, Wilpshaar W, Dhanjal P, Ball G, Klasen S. Active- and placebo-controlled dose-finding study to assess the efficacy, safety, and tolerability of multiple doses of ipragliflozin in patients with type 2 diabetes mellitus. J Diabetes Complicat. 2013;27(3):268–73.

Min KW, Ku BJ, Lee JH, Kim MS, Ahn KJ, Lee MK, Kokubo S, Yoshida S, Cho HJ, Cha BS. Addition of ipragliflozin to metformin treatment in Korean patients with type 2 diabetes mellitus: subgroup analysis of a phase 3 trial. Diabetes Metabolism J. 2017;41(2):135–45.

Kashiwagi A, Kazuta K, Takinami Y, Yoshida S, Utsuno A, Nagase I. Ipragliflozin improves glycemic control in Japanese patients with type 2 diabetes mellitus: the BRIGHTEN study. Diabetol Int. 2015;6(1):8–18.

Gallo S, Raji A, Calle RA, Pong A, Meyer C. The effects of ertugliflozin on β-cell function: pooled analysis from four phase 3 randomized controlled studies. Diabetes Obes Metabolism. 2020;22(12):2267–75.

Ji L, Han P, Liu Y, Yang G, Dieu Van NK, Vijapurkar U, Qiu R, Meininger G. Canagliflozin in Asian patients with type 2 diabetes on metformin alone or metformin in combination with sulphonylurea. Diabetes Obes Metab. 2015;17(1):23–31.

Bilezikian JP, Watts NB, Usiskin K, Polidori D, Fung A, Sullivan D, Rosenthal N. Evaluation of bone mineral density and bone biomarkers in patients with type 2 diabetes treated with canagliflozin. J Clin Endocrinol Metab. 2016;101(1):44–51.

Yale JF, Bakris G, Cariou B, Yue D, David-Neto E, Xi L, Figueroa K, Wajs E, Usiskin K, Meininger G. Efficacy and safety of canagliflozin in subjects with type 2 diabetes and chronic kidney disease. Diabetes Obes Metab. 2013;15(5):463–73.

Rosenstock J, Aggarwal N, Polidori D, Zhao Y, Arbit D, Usiskin K, Capuano G, Canovatchel W. Dose-ranging effects of canagliflozin, a sodium-glucose cotransporter 2 inhibitor, as add-on to metformin in subjects with type 2 diabetes. Diabetes Care. 2012;35(6):1232–8.

Rodbard HW, Seufert J, Aggarwal N, Cao A, Fung A, Pfeifer M, Alba M. Efficacy and safety of titrated canagliflozin in patients with type 2 diabetes mellitus inadequately controlled on metformin and sitagliptin. Diabetes Obes Metab. 2016;18(8):812–9.

Sha S, Polidori D, Heise T, Natarajan J, Farrell K, Wang SS, Sica D, Rothenberg P, Plum-Mörschel L. Effect of the sodium glucose co-transporter 2 inhibitor canagliflozin on plasma volume in patients with type 2 diabetes mellitus. Diabetes Obes Metab. 2014;16(11):1087–95.

Sone H, Kaneko T, Shiki K, Tachibana Y, Pfarr E, Lee J, Tajima N. Efficacy and safety of empagliflozin as add-on to insulin in Japanese patients with type 2 diabetes: a randomized, double-blind, placebo-controlled trial. Diabetes Obes Metab. 2020;22(3):417–26.

Rau M, Thiele K, Hartmann NUK, Möllmann J, Wied S, Hohl M, Marx N, Lehrke M. Effects of empagliflozin on markers of calcium and phosphate homeostasis in patients with type 2 diabetes– data from a randomized, placebo-controlled study. Bone Rep. 2022;16.

Kullmann S, Hummel J, Wagner R, Dannecker C, Vosseler A, Fritsche L, Veit R, Kantartzis K, Machann J, Birkenfeld AL, et al. Empagliflozin improves insulin sensitivity of the hypothalamus in humans with prediabetes: a randomized, double-blind, placebo-controlled, phase 2 trial. Diabetes Care. 2022;45(2):398–406.

Usiskin K, Kline I, Fung A, Mayer C, Meininger G. Safety and tolerability of canagliflozin in patients with type 2 diabetes mellitus: pooled analysis of phase 3 study results. Postgrad Med. 2014;126(3):16–34.

Kohler S, Zeller C, Iliev H, Kaspers S. Safety and tolerability of empagliflozin in patients with type 2 diabetes: pooled analysis of phase I–III clinical trials. Adv Therapy. 2017;34(7):1707–26.

Brown JP, Don-Wauchope A, Douville P, Albert C, Vasikaran SD. Current use of bone turnover markers in the management of osteoporosis. Clin Biochem. 2022:109–110:1–10.

Greenblatt MB, Tsai JN, Wein MN. Bone turnover markers in the diagnosis and monitoring of metabolic bone disease. Clin Chem. 2017;63(2):464–74.

Dong B, Lv R, Wang J, Che L, Wang Z, Huai Z, Wang Y, Xu L. The extraglycemic effect of SGLT-2is on mineral and bone metabolism and bone fracture. Front Endocrinol (Lausanne). 2022;13:918350.

Tu MY, Chen HL, Tung YT, Kao CC, Hu FC, Chen CM. Short-term effects of Kefir-fermented milk consumption on bone Mineral density and bone metabolism in a Randomized Clinical Trial of osteoporotic patients. PLoS ONE. 2015;10(12):e0144231.

Shaffner J, Chen B, Malhotra DK, Dworkin LD, Gong R. Therapeutic targeting of SGLT2: a new era in the treatment of diabetes and diabetic kidney disease. Front Endocrinol (Lausanne). 2021;12:749010.

Thrailkill KM, Nyman JS, Bunn RC, Uppuganti S, Thompson KL, Lumpkin CK, Kalaitzoglou E, Fowlkes JL. The impact of SGLT2 inhibitors, compared with insulin, on diabetic bone disease in a mouse model of type 1 diabetes. Bone. 2017;94:141–51.

Ye Y, Zhao C, Liang J, Yang Y, Yu M, Qu X. Effect of sodium-glucose co-transporter 2 inhibitors on bone metabolism and fracture risk. Front Pharmacol. 2019;9.

Razzaque MS. Interactions between FGF23 and vitamin D. Endocr Connections. 2022;11(10):e220239.

Barsony J, Sugimura Y, Verbalis JG. Osteoclast response to low extracellular sodium and the mechanism of hyponatremia-induced bone loss *. J Biol Chem. 2011;286(12):10864–75.

Watanabe-Takano H, Ochi H, Chiba A, Matsuo A, Kanai Y, Fukuhara S, Ito N, Sako K, Miyazaki T, Tainaka K, et al. Mechanical load regulates bone growth via periosteal osteocrin. Cell Rep. 2021;36(2):109380.

Schmitz D, Ek WE, Berggren E, Höglund J, Karlsson T, Johansson Å. Genome-wide association study of estradiol levels and the causal effect of estradiol on bone mineral density. J Clin Endocrinol Metab. 2021;106(11):e4471–86.

Schwartz AV, Johnson KC, Kahn SE, Shepherd JA, Nevitt MC, Peters AL, Walkup MP, Hodges A, Williams CC, Bray GA. Effect of 1 year of an intentional weight loss intervention on bone mineral density in type 2 diabetes: results from the look AHEAD randomized trial. J bone Mineral Research: Official J Am Soc Bone Mineral Res. 2012;27(3):619–27.

Watts NB, Bilezikian JP, Usiskin K, Edwards R, Desai M, Law G, Meininger G. Effects of canagliflozin on fracture risk in patients with type 2 diabetes mellitus. J Clin Endocrinol Metabolism. 2016;101(1):157–66.

Download references

Acknowledgements

Not applicable.

This study was supported by Key Science and technology project in Ningxia (2020BFG02011); Key Science and technology project in Ningxia (2023BEG02022); Ningxia natural science foundation (2023AAC03614) and Ningxia natural science foundation (2023AAC03597).

Author information

Authors and affiliations.

Office of Academic Research, General Hospital of Ningxia Medical University, 750004, Yinchuan, Ningxia, China

Department of Nutrition, General Hospital of Ningxia Medical University, 750004, Yinchuan, Ningxia, China

First Clinical Medical College, Ningxia Medical University, 750004, Yinchuan, Ningxia, China

Department of Geriatrics and Special Needs, General Hospital of Ningxia Medical University, No. 804 South Shengli Street, 750004, Yinchuan, Ningxia, China

You can also search for this author in PubMed   Google Scholar

Contributions

XL, CL and JH designed the study. XL and YL identified and acquired reports of trials and extracted data. HT, QD, WS, SZ, YS and JH performed all data analyses, checked for statistical inconsistency, and interpreted data. HT, QD, WS, SZ, YS and JH contributed to data interpretation. HT drafted the report and all other authors critically reviewed the report. All authors approved the final version of manuscript. JH is the guarantor of this work.

Corresponding author

Correspondence to Chen Lei .

Ethics declarations

Ethics approval and consent to participate, patient consent for publication, competing interests.

The authors declare no competing interests.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1

Rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ . The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Cite this article.

Wang, J., Li, X., Li, Y. et al. Effects of sodium-glucose cotransporter 2 inhibitors on bone metabolism in patients with type 2 diabetes mellitus: a systematic review and meta-analysis. BMC Endocr Disord 24 , 52 (2024). https://doi.org/10.1186/s12902-024-01575-8

Download citation

Received : 14 December 2023

Accepted : 01 April 2024

Published : 24 April 2024

DOI : https://doi.org/10.1186/s12902-024-01575-8

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Type 2 diabetes mellitus
• SGLT2 inhibitor
• Meta-analysis

BMC Endocrine Disorders

ISSN: 1472-6823

• General enquiries: [email protected]

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• World J Diabetes
• v.6(6); 2015 Jun 25

Diabetes mellitus: The epidemic of the century

Correspondence to: Akram T Kharroubi, PhD, Associate Professor of Biochemistry and Endocrinology, Dean of Faculty of Health Professions, Department of Medical Laboratory Sciences, Faculty of Health Professions, Al-Quds University, P.O. Box 51000, Abed Elhamaid Shoman Street, Beit Hanina-Jerusalem, Jerusalem 91000, Palestine. [email protected]

Telephone: +972-2-2791243 Fax: +972-2-2791243

The epidemic nature of diabetes mellitus in different regions is reviewed. The Middle East and North Africa region has the highest prevalence of diabetes in adults (10.9%) whereas, the Western Pacific region has the highest number of adults diagnosed with diabetes and has countries with the highest prevalence of diabetes (37.5%). Different classes of diabetes mellitus, type 1, type 2, gestational diabetes and other types of diabetes mellitus are compared in terms of diagnostic criteria, etiology and genetics. The molecular genetics of diabetes received extensive attention in recent years by many prominent investigators and research groups in the biomedical field. A large array of mutations and single nucleotide polymorphisms in genes that play a role in the various steps and pathways involved in glucose metabolism and the development, control and function of pancreatic cells at various levels are reviewed. The major advances in the molecular understanding of diabetes in relation to the different types of diabetes in comparison to the previous understanding in this field are briefly reviewed here. Despite the accumulation of extensive data at the molecular and cellular levels, the mechanism of diabetes development and complications are still not fully understood. Definitely, more extensive research is needed in this field that will eventually reflect on the ultimate objective to improve diagnoses, therapy and minimize the chance of chronic complications development.

Core tip: Diabetes mellitus is rising to an alarming epidemic level. Early diagnosis of diabetes and prediabetes is essential using recommended hemoglobin A1c criteria for different types except for gestational diabetes. Screening for diabetes especially in underdeveloped countries is essential to reduce late diagnosis. Diabetes development involves the interaction between genetic and non-genetic factors. Biomedical research continues to provide new insights in our understanding of the mechanism of diabetes development that is reviewed here. Recent studies may provide tools for the use of several genes as targets for risk assessment, therapeutic strategies and prediction of complications.

DEFINITION OF DIABETES MELLITUS

Diabetes mellitus is a group of metabolic diseases characterized by chronic hyperglycemia resulting from defects in insulin secretion, insulin action, or both. Metabolic abnormalities in carbohydrates, lipids, and proteins result from the importance of insulin as an anabolic hormone. Low levels of insulin to achieve adequate response and/or insulin resistance of target tissues, mainly skeletal muscles, adipose tissue, and to a lesser extent, liver, at the level of insulin receptors, signal transduction system, and/or effector enzymes or genes are responsible for these metabolic abnormalities. The severity of symptoms is due to the type and duration of diabetes. Some of the diabetes patients are asymptomatic especially those with type 2 diabetes during the early years of the disease, others with marked hyperglycemia and especially in children with absolute insulin deficiency may suffer from polyuria, polydipsia, polyphagia, weight loss, and blurred vision. Uncontrolled diabetes may lead to stupor, coma and if not treated death, due to ketoacidosis or rare from nonketotic hyperosmolar syndrome[ 1 - 3 ].

CLASSIFICATION OF DIABETES MELLITUS

Although classification of diabetes is important and has implications for the treatment strategies, this is not an easy task and many patients do not easily fit into a single class especially younger adults[ 1 , 4 - 6 ] and 10% of those initially classified may require revision[ 7 ]. The classical classification of diabetes as proposed by the American Diabetes Association (ADA) in 1997 as type 1, type 2, other types, and gestational diabetes mellitus (GDM) is still the most accepted classification and adopted by ADA[ 1 ]. Wilkin[ 8 ] proposed the accelerator hypothesis that argues “type 1 and type 2 diabetes are the same disorder of insulin resistance set against different genetic backgrounds”[ 9 ]. The difference between the two types relies on the tempo, the faster tempo reflecting the more susceptible genotype and earlier presentation in which obesity, and therefore, insulin resistance, is the center of the hypothesis. Other predictors of type 1 diabetes include increased height growth velocity[ 10 , 11 ] and impaired glucose sensitivity of β cells[ 12 ]. The implications of increased free radicals, oxidative stress, and many metabolic stressors in the development, pathogenesis and complications of diabetes mellitus[ 13 - 18 ] are very strong and well documented despite the inconsistency of the clinical trials using antioxidants in the treatment regimens of diabetes[ 19 - 21 ]. The female hormone 17-β estradiol acting through the estrogen receptor-α (ER-α) is essential for the development and preservation of pancreatic β cell function since it was clearly demonstrated that induced oxidative stress leads to β-cell destruction in ER-α knockout mouse. The ER-α receptor activity protects pancreatic islets against glucolipotoxicity and therefore prevents β-cell dysfunction[ 22 ].

TYPE 1 DIABETES MELLITUS

Autoimmune type 1 diabetes.

This type of diabetes constitutes 5%-10% of subjects diagnosed with diabetes[ 23 ] and is due to destruction of β cells of the pancreas[ 24 , 25 ]. Type 1 diabetes accounts for 80%-90% of diabetes in children and adolescents[ 2 , 26 ]. According to International Diabetes Federation (IDF), the number of youth (0-14 years) diagnosed with type 1 diabetes worldwide in 2013 was 497100 (Table ​ (Table1) 1 ) and the number of newly diagnosed cases per year was 78900[ 27 ]. These figures do not represent the total number of type 1 diabetes patients because of the high prevalence of type 1 diabetes in adolescence and adults above 14 years of age. One reported estimate of type 1 diabetes in the United States in 2010 was 3 million[ 28 , 29 ]. The number of youth in the United States younger than 20 years with type 1 diabetes was estimated to be 166984 in the year 2009[ 30 ]. The prevalence of type 1 diabetes in the world is not known but in the United States in youth younger than 20 years was 1.93 per 1000 in 2009 (0.35-2.55 in different ethnic groups) with 2.6%-2.7% relative annual increase[ 26 , 31 ]. Type 1 diabetes is mainly due to an autoimmune destruction of the pancreatic β cells through T-cell mediated inflammatory response (insulitis) as well as a humoral (B cell) response[ 25 ]. The presence of autoantibodies against the pancreatic islet cells is the hallmark of type 1 diabetes, even though the role of these antibodies in the pathogenesis of the disease is not clear. These autoantibodies include islet cell autoantibodies, and autoantibodies to insulin (IAA), glutamic acid decarboxylase (GAD, GAD65), protein tyrosine phosphatase (IA2 and IA2β) and zinc transporter protein (ZnT8A)[ 32 ]. These pancreatic autoantibodies are characteristics of type 1 diabetes and could be detected in the serum of these patients months or years before the onset of the disease[ 33 ]. Autoimmune type 1 diabetes has strong HLA associations, with linkage to DR and DQ genes. HLA-DR/DQ alleles can be either predisposing or protective[ 1 ]. This autoimmune type 1 diabetes is characterized by the absence of insulin secretion and is more dominant in children and adolescents.

Number of subjects with type 1 diabetes in children (0-14 years), with diabetes in adults (20-79 years) and with hyperglycemia (type 2 or gestational diabetes) in pregnancy (20-49 years)

Data extracted from International Diabetes Federation Diabetes Atlas, 6th ed, 2013.

In addition to the importance of genetic predisposition in type 1 diabetes, several environmental factors have been implicated in the etiology of the disease[ 9 , 33 ]. Viral factors include congenital rubella[ 34 , 35 ], viral infection with enterovirus, rotavirus, herpes virus, cytomegalovirus, endogenous retrovirus[ 36 , 37 ] and Ljungan virus. Other factors include low vitamin D levels[ 38 ], prenatal exposure to pollutants, improved hygiene and living conditions decreased childhood infections in countries with high socioeconomic status leading to increased autoimmune diseases (hygiene hypothesis), early infant nutrition such as using cow’s milk formula instead of breast feeding[ 39 ] in addition to insulin resistance in early childhood due to obesity or increased height growth velocity. The role of environmental factors remains controversial[ 40 ]. Recent evidence supported the causative effect of viral infections in diabetes[ 41 - 43 ].

Type 1 diabetes often develops suddenly and can produce symptoms such as polydipsia, polyuria, enuresis, lack of energy, extreme tiredness, polyphagia, sudden weight loss, slow-healing wounds, recurrent infections and blurred vision[ 27 ] with severe dehydration and diabetic ketoacidosis in children and adolescents. The symptoms are more severe in children compared to adults. These autoimmune type 1 diabetes patients are also prone to other autoimmune disorders such as Graves’ disease, Hashimoto’s thyroiditis, Addison’s disease, vitiligo, celiac sprue, autoimmune hepatitis, myasthenia gravis, and pernicious anemia[ 1 ]. The complete dependence on insulin of type 1 diabetes patients may be interrupted by a honeymoon phase which lasts weeks to months or in some cases 2-3 years. In some children, the requirement for insulin therapy may drop to a point where insulin therapy could be withdrawn temporarily without detectable hyperglycemia[ 44 ].

Idiopathic type 1 diabetes

A rare form of type 1 diabetes of unknown origin (idiopathic), less severe than autoimmune type 1 diabetes and is not due to autoimmunity has been reported. Most patients with this type are of African or Asian descent and suffer from varying degrees of insulin deficiency and episodic ketoacidosis[ 45 ].

Fulminant type 1 diabetes

This is a distinct form of type 1 diabetes, first described in the year 2000, and has some common features with idiopathic type 1 diabetes being non-immune mediated[ 46 , 47 ]. It is characterized by ketoacidosis soon after the onset of hyperglycemia, high glucose levels (≥ 288 mg/dL) with undetectable levels of serum C-peptide, an indicator of endogenous insulin secretion[ 48 ]. It has been described mainly in East Asian countries and accounted for approximately 20% of acute-onset type 1 diabetes patients in Japan (5000-7000 cases) with an extremely rapid and almost complete beta-cell destruction resulting in nearly no residual insulin secretion[ 48 , 49 ]. Both genetic and environmental factors, especially viral infection, have been implicated in the disease. Anti-viral immune response may trigger the destruction of pancreatic beta cells through the accelerated immune reaction with no detectable autoantibodies against pancreatic beta cells[ 48 , 50 ]. Association of fulminant type 1 diabetes with pregnancy has also been reported[ 51 ].

TYPE 2 DIABETES MELLITUS

The global prevalence of diabetes in adults (20-79 years old) according to a report published in 2013 by the IDF was 8.3% (382 million people), with 14 million more men than women (198 million men vs 184 million women), the majority between the ages 40 and 59 years and the number is expected to rise beyond 592 million by 2035 with a 10.1% global prevalence. With 175 million cases still undiagnosed, the number of people currently suffering from diabetes exceeds half a billion. An additional 21 million women are diagnosed with hyperglycemia during pregnancy. The Middle East and North Africa region has the highest prevalence of diabetes (10.9%), however, Western Pacific region has the highest number of adults diagnosed with diabetes (138.2 millions) and has also countries with the highest prevalence (Figure ​ (Figure1 1 )[ 27 ]. Low- and middle-income countries encompass 80% of the cases, “where the epidemic is gathering pace at alarming rates”[ 27 ]. Despite the fact that adult diabetes patients are mainly type 2 patients, it is not clear whether the reported 382 million adults diagnosed with diabetes also include type 1 diabetes patients.

Comparative prevalence of diabetes in adults (20-79 years) in countries with high prevalence (≥ 10%). Data extracted from International Diabetes Federation Diabetes Atlas, 6th ed, 2013.

More than 90%-95% of diabetes patients belong to this type and most of these patients are adults. The number of youth (less than 20 years) with type 2 diabetes in the United States in the year 2009 was 0.46 in 1000 and accounted for approximately 20% of type 2 diabetes in youth[ 26 ]. The increased incidence of type 2 diabetes in youth is mainly due to the change in the lifestyle of the children in terms of more sedentary life and less healthy food. Obesity is the major reason behind insulin resistance which is mainly responsible for type 2 diabetes[ 52 - 54 ]. The ADA recommends screening of overweight children and adolescence to detect type 2 diabetes[ 55 , 56 ]. The prevalence of obesity in children in on the rise[ 6 ] which is probably the main reason for the increased incidence of type 2 diabetes in the young (30.3% overall increase in type 2 diabetes in children and adolescence between 2001 and 2009)[ 26 ].

Insulin resistance in type 2 diabetes patients increases the demand for insulin in insulin-target tissues. In addition to insulin resistance, the increased demand for insulin could not be met by the pancreatic β cells due to defects in the function of these cells[ 18 ]. On the contrary, insulin secretion decreases with the increased demand for insulin by time due to the gradual destruction of β cells[ 57 ] that could transform some of type 2 diabetes patients from being independent to become dependent on insulin. Most type 2 diabetes patients are not dependent on insulin where insulin secretion continues and insulin depletion rarely occurs. Dependence on insulin is one of the major differences from type 1 diabetes. Other differences include the absence of ketoacidosis in most patients of type 2 diabetes and autoimmune destruction of β cells does not occur. Both type 1 and type 2 diabetes have genetic predisposition, however, it is stronger in type 2 but the genes are more characterized in type 1 (the TCF7L2 gene is strongly associated with type 2 diabetes)[ 58 ]. Due to the mild symptoms of type 2 diabetes in the beginning, its diagnosis is usually delayed for years especially in countries where regular checkup without symptoms is not part of the culture. This delay in diagnosis could increase the incidence of long-term complications in type 2 diabetes patients since hyperglycemia is not treated during this undiagnosed period.

In addition to diabetes, insulin resistance has many manifestations that include obesity, nephropathy, essential hypertension, dyslipidemia (hypertriglyceridemia, low HDL, decreased LDL particle diameter, enhanced postprandial lipemia and remnant lipoprotein accumulation), ovarian hyperandrogenism and premature adrenarche, non-alcoholic fatty liver disease and systemic inflammation[ 6 , 54 ]. The presence of type 2 diabetes in children and adolescence who are not obese[ 59 - 61 ], the occasional severe dehydration and the presence of ketoacidosis in some pediatric patients with type 2 diabetes[ 55 ] had led to the misclassification of type 2 to type 1 diabetes.

Some patients with many features of type 2 diabetes have some type 1 characteristics including the presence of islet cell autoantibodies or autoantibodies to GAD65 are classified as a distinct type of diabetes called latent autoimmune diabetes in adults (LADA)[ 62 ]. People diagnosed with LADA do not require insulin treatment. In a recent study, Hawa et al[ 63 ] reported 7.1% of European patients with type 2 diabetes with a mean age of 62 years, tested positive for GAD autoantibodies and the prevalence of LADA was higher in patients diagnosed with diabetes at a younger age. This classification of LADA as a distinct type of diabetes is still controversial[ 6 , 64 - 66 ].

Insulin resistance and signaling

Defects in the insulin-dependent substrate proteins IRS-1 and IRS-2 mediated signaling pathway are implicated in the development of metabolic disorders, mainly diabetes. This pathway mediates the cellular response to insulin and involves a large array of insulin-stimulated protein kinases including the serine/threonine kinase AKT and protein kinase C (PKC) that phosphorylate a large number of Ser/Thr residues in the insulin receptor substrate (IRS) proteins involved in the metabolic response to insulin[ 67 ]. In addition, other non-insulin dependent kinases including the AMP-activated protein kinase, c-Jun N-terminal protein kinase and G protein-coupled receptor kinase 2 that are activated under various conditions can phosphorylate the two insulin responsive substrates[ 67 - 71 ]. Disruption in the AKT and PKC kinases is central to the development of diabetes[ 72 ] and is associated with all major features of the disease including hyperinsulinemia, dyslipidemia and insulin resistance[ 73 ]. Replacing the wild type IRS-1 with a mutant version of the protein having alanine instead of tyrosine in three locations using genetic knock-in approach provided evidence to the central role of IRS-1 phosphorylation in the development of insulin resistance[ 74 ]. Using a similar approach to generate IRS-1 mutant with a single mutation involving a specific tyrosine residue, confirmed the role of IRS-1 phosphorylation in the development of insulin resistance pathogenesis[ 75 ]. The large cumulative evidence indicates a complex array of factors including environmental factors[ 76 ] and a wide range of cellular disturbances in glucose and lipid metabolism in various tissues[ 77 ] contribute to the development of insulin resistance. This condition generates complex cellular metabolic changes in a variety of tissues, mainly liver and muscles, that include the inability of the liver to transport and dispose glucose, control glucose production via gluconeogenesis, impaired storage of glucose as glycogen, de novo lipogenesis and hypertriglyceridemia[ 77 ]. Among the factors implicated in the development of insulin resistance, obesity is the most predominant risk factor leading to insulin insensitivity and diabetes which involves several mechanisms that participate in the pathogenesis of the disease[ 78 ]. Obesity-induced insulin resistance is directly linked to increased nutrient flux and energy accumulation in tissues that directly affect cell responsiveness to insulin[ 77 ]. However, it seems that other insulin-independent mechanisms are involved in the overall metabolic disturbances of glucose homeostasis and diabetes including activities in extra-hepatic tissues in addition to the central role of liver.

OTHER TYPES OF DIABETES MELLITUS

Monogenic diabetes.

Characterization of the genetic etiology of diabetes enables more appropriate treatment, better prognosis, and counseling[ 79 ]. Monogenic diabetes is due to a genetic defect in single genes in pancreatic β cells which results in disruption of β cell function or a reduction in the number of β cells. Conventionally, monogenic diabetes is classified according to the age of onset as neonatal diabetes before the age of six months or Maturity Onset Diabetes of the Young (MODY) before the age of 25 years. However, certain familial defects are manifested in neonatal diabetes, MODY or adult onset diabetes[ 2 , 9 , 80 ]. Others believe that classification of diabetes as MODY and neonatal diabetes is obsolete and monogenic diabetes is currently used relating specific genetic etiologies with their specific treatment implications[ 79 ]. Beta cell differentiation depends on the expression of the homeodomain transcription factor PDX1 where mutation in the gene results in early onset diabetes (MODY) and its expression decreases before the onset of diabetes[ 81 ]. The angiopoietin-like protein 8 (ANGPTL8) may represent a potential “betatrophin” that acts to promote the proliferation of beta cells, however, studies using mice lacking the ANGPTL8 active gene or overexpressed protein indicated that it did not seem to play a role in beta cells proliferation[ 82 ].

Mitochondrial diabetes is due to a point mutation in the mitochondrial DNA associated with deafness and maternal transmission of the mutant DNA can result in maternally-inherited diabetes[ 1 , 83 ].

Mutations that result in mutant insulin or the inability to convert proinsulin to insulin result in glucose intolerance in some of these cases. Genetic defects in the insulin receptor or in the signal transduction pathway of insulin have been demonstrated to result in hyperinsulinemia and modest hyperglycemia to severe diabetes[ 1 ].

Disease of the exocrine pancreas

Damage of the β cells of the pancreas due to diffused injury of the pancreas can cause diabetes. This damage could be due to pancreatic carcinoma, pancreatitis, infection, pancreatectomy, and trauma[ 1 ]. Atrophy of the exocrine pancreas leads to progressive loss of the β cells[ 84 ]. Accumulation of fat in the pancreas or pancreatic steatosis could lead to diabetes due to decreased insulin secretion but may require a long time before the damage to β cells occurs[ 85 ]. In most cases, extensive damage of the pancreas is required before diabetes occurs and the exocrine function of the pancreas is decreased in these patients[ 86 ]. Cirrhosis in cystic fibrosis may contribute to insulin resistance and diabetes[ 2 ].

Hormones and drugs

Diabetes has been found in patients with endocrine diseases that secrete excess hormones like growth hormone, glucocorticoids, glucagon and epinephrine in certain endocrinopathies like acromegaly, Cushing’s syndrome, glucagonoma, and pheochromocytoma, respectively[ 1 ]. Some of these hormones are used as drugs such as glucocorticoids to suppress the immune system and in chemotherapy and growth hormone to treat children with stunted growth.

Genetic syndromes

Diabetes has been detected in patients with various genetic syndromes such as Down syndrome, Klinefelter syndrome, Turner syndrome and Wolfram syndrome[ 1 ].

PREDIABETES

Individuals with prediabetes do not meet the criteria of having diabetes but are at high risk to develop type 2 diabetes in the future. According to the ADA Expert Committee, individuals are defined to have prediabetes if they have either impaired fasting plasma glucose (IFG) levels between 100-125 mg/dL (5.6-6.9 mmol/L) or impaired glucose tolerance test (IGT) with 2-h plasma glucose levels in the oral glucose tolerance test (OGTT) of 140-199 mg/dL (7.8-11.0 mmol/L). The World Health Organization (WHO) still adopts the range for IFG from 110-125 mg/dL (6.1-6.9 mmol/L). Prediabetes has been shown to correlate with increased cardiovascular mortality[ 87 , 88 ] and cancer[ 89 ]. The definition of prediabetes with the indicated cut off values is misleading since lower levels of glucose in the normal range are still correlated with cardiovascular disease in a continuous glycemic risk perspective[ 90 ]. In accordance with the recommendation of the ADA in 2009 to use hemoglobin A1c (HbA1c) to diagnose diabetes, ADA also recommended the use of an HbA1c (5.7%-6.4%) to diagnose prediabetes[ 91 ]. The number of people with IGT according to IDF was 316 million in 2013 (global prevalence 6.9% in adults) and is expected to rise to 471 million in 2030[ 27 ]. According to a report in 2014 by the Center for Disease Control and Prevention, 86 million Americans (1 out of 3) have prediabetes[ 92 ]. Four of the top ten countries with the highest prevalence of prediabetes are in the Middle East Arab States of the Gulf (Kuwait, Qatar, UAE and Bahrin with prevalence of 17.9%, 17.1%, 16.6% and 16.3%, respectively)[ 27 ]. The number of people diagnosed with prediabetes is different according to the method and criteria used to diagnose prediabetes. The number of people with prediabetes defined by IFG 100-125 mg/dL is 4-5 folds higher than those diagnosed using the WHO criteria of 110-125 mg/dL[ 93 ]. Diabetes and prediabetes diagnosed using an HbA1c criteria give different estimates compared to methods using FPG or OGTT. Higher percentages of prediabetes were diagnosed using HbA1c compared to FPG[ 94 - 96 ]. Prediabetes is associated with metabolic syndrome and obesity (especially abdominal or visceral obesity), dyslipidemia with high triglycerides and/or low HDL cholesterol, and hypertension[ 97 ]. Not all individuals with prediabetes develop diabetes in the future, exercise with a reduction of weight 5%-10% reduces the risk of developing diabetes considerably (40%-70%)[ 98 ]. Individuals with an HbA1c of 6.0%-6.5% have twice the risk of developing diabetes (25%-50%) in five years compared to those with an HbA1c of 5.5%-6.0%[ 99 ].

DIAGNOSTIC CRITERIA FOR DIABETES MELLITUS

Diabetes mellitus is diagnosed using either the estimation of plasma glucose (FPG or OGTT) or HbA1c. Estimation of the cut off values for glucose and HbA1c is based on the association of FPG or HbA1c with retinopathy. Fasting plasma glucose of ≥ 126 mg/dL (7.0 mmol/L), plasma glucose after 2-h OGTT ≥ 200 mg/dL (11.1 mmol/L), HbA1c ≥ 6.5% (48 mmol/mol) or a random plasma glucose ≥ 200 mg/dL (11.1 mmol/L) along with symptoms of hyperglycemia is diagnostic of diabetes mellitus. In addition to monitor the treatment of diabetes, HbA1c has been recommended to diagnose diabetes by the International Expert Committee in 2009[ 100 ] and endorsed by ADA[ 101 ], the Endocrine Society, the WHO[ 102 ] and many scientists and related organizations all over the world. The advantages and disadvantages of the different tests used to diagnose diabetes have been reviewed by Sacks et al[ 103 ]. The advantages of using HbA1c over FPG to diagnose diabetes include greater convenience and preanalytical stability, lower CV (3.6%) compared to FPG (5.7%) and 2h OGTT (16.6%), stronger correlation with microvascular complications especially retinopathy, and a marker for glycemic control and glycation of proteins which is the direct link between diagnosis of diabetes and its complications[ 104 - 109 ]. It is recommended to repeat the HbA1c test in asymptomatic patients within two weeks to reaffirm a single apparently diagnostic result[ 110 ].

A cut off value for HbA1c of ≥ 6.5% (48 mmol/mol) has been endorsed by many countries and different ethnic groups, yet ethnicity seems to affect the cut off values to diagnose diabetes[ 111 , 112 ]. Cut-off values of 5.5% (37 mmol/mol)[ 113 ] and 6.5% (48 mmol/mol)[ 114 ] have been reported in a Japanese study, 6.0% (42 mmol/mol) in the National Health and Nutrition Examination Survey (NHANES III), 6.2% (44 mmol/mol) in a Pima Indian study, 6.3% (45 mmol/mol) in an Egyptian study as reported by Davidson[ 105 ]; and three cut-off values for Chinese[ 112 ]. The Australians recommended the use of two cut-off values: ≤ 5.5% to “rule-out” and ≥ 7.0% to “rule-in” diabetes[ 115 ]. Variations in the prevalence of diabetes[ 94 , 116 - 119 ] and prediabetes[ 120 ] due to ethnicity have been documented. Most studies diagnosed less subjects with diabetes using HbA1c compared to FPG or OGTT[ 121 - 123 ]. Yet, other studies reported more subjects diagnosed with diabetes using HbA1c[ 96 , 124 - 126 ].

GESTATIONAL DIABETES

Hyperglycemia in pregnancy whether in the form of type 2 diabetes diagnosed before or during pregnancy or in the form gestational diabetes has an increased risk of adverse maternal, fetal and neonatal outcome. Mothers with gestational diabetes and babies born to such mothers have increased risk of developing diabetes later in life. Hyperglycemia in pregnancy is responsible for the increased risk for macrosomia (birth weight ≥ 4.5 kg), large for gestational age births, preeclampsia, preterm birth and cesarean delivery due to large babies[ 127 ]. Risk factors for gestational diabetes include obesity, personal history of gestational diabetes, family history of diabetes, maternal age, polycystic ovary syndrome, sedentary life, and exposure to toxic factors[ 3 ].

Diagnosis of type 2 diabetes before or during pregnancy is based on criteria mentioned before. Fasting plasma glucose ≥ 126 mg/dL (7.0 mmol/L) or 2-h plasma glucose ≥ 200 mg/dL (11.1 mmol/L) after a 75 g oral glucose load. However, gestational diabetes has been diagnosed at 24-28 wk of gestation in women not previously diagnosed with diabetes using two approaches: the first approach is based on the “one-step” International Association of the Diabetes and Pregnancy Study Groups (IADPSG) consensus[ 128 ] and recently adopted by WHO[ 129 ]. Gestational diabetes is diagnosed using this method by FPG ≥ 92 mg/dL (5.1 mmol/L), 1-h plasma glucose after a 75 g glucose load ≥ 180 mg/dL (10.0 mmol/L) or 2-h plasma glucose after a 75 g glucose load ≥ 153 mg/dL (8.5 mmol/L). This criteria is derived from the Hyperglycemia and Adverse Pregnancy Outcome (HAPO) study[ 127 ] even though the HAPO study showed a continuous relationship between hyperglycemia and adverse short-term pregnancy outcome with no threshold reported[ 130 ]. The second approach is used in the United States and is based on the “two-step” NIH consensus[ 131 ]. In the first step 1-h plasma glucose after a 50 g glucose load under nonfasting state ≥ 140 mg/dL (7.8 mmol/L) is followed by a second step under fasting conditions after a 100 g glucose load for those who screened abnormal in the first step. The diagnosis of gestational diabetes is made when at least two of the four plasma glucose levels are met. The four plasma glucose levels according to Carpenter/Coustan criteria are: FPG ≥ 95 mg/dL (5.3 mmol/L); 1-h ≥ 180 mg/dL (10.0 mmol/L); 2-h ≥ 155 mg/dL (8.6 mmol/L); and 3-h ≥ 140 mg/dL (7.8 mmol/L)[ 1 ].

The use IADPSC criteria in comparison with the Carpenter/Coustan criteria was associated with a 3.5-fold increase in GDM prevalence as well as significant improvements in pregnancy outcomes, and was cost-effective[ 132 ]. In another retrospective cohort study of women diagnosed with gestational diabetes, Ethridge et al[ 133 ] have shown that newborns of women diagnosed with gestational diabetes by IADPSG approach have greater measures of fetal overgrowth compared with Carpenter-Coustan “two-step” approach neonates. A strategy of using fasting plasma glucose as a screening test and to determine the need for OGTT is valid[ 134 , 135 ]. According to Sacks[ 136 ], correlation of glucose concentrations and the risk of subsequent complications will eventually lead to universal guidelines.

The use of ADA/WHO cut off value of HbA1c ≥ 6.5% (48 mmol/mol) to diagnose gestational diabetes is not recommended by the “one step” IADPSC criteria or the “two-step” NIH criteria. Further investigation is required in light of recent reports on HbA1c in combination with OGTT and its usefulness to predict adverse effect of gestational diabetes or obviate the use OGTT in all women with gestational diabetes[ 137 - 141 ].

DIABETES AND GENETICS

Diabetes is a complex disease that involves a wide range of genetic and environmental factors. Over the past several years, many studies have focused on the elucidation of the wide spectrum of genes that played a role in the molecular mechanism of diabetes development[ 142 - 144 ]. However, despite the vast flow of genetic information including the identification of many gene mutations and a large array of single nucleotide polymorphisms (SNPs) in many genes involved in the metabolic pathways that affect blood glucose levels, the exact genetic mechanism of diabetes remains elusive[ 145 , 146 ]. Evidently, a major complication is the fact that a single gene mutation or polymorphism will not impose the same effect among different individuals within a population or different populations. This variation is directly or indirectly affected by the overall genetic background at the individual, family or population levels that are potentially further complicated by interaction with highly variable environmental modifier factors[ 147 , 148 ].

Molecular genetics and type 2 diabetes

One of the major focuses of biomedical research is to delineate the collective and broad genetic variants in the human genome that are involved in the development of diabetes. This major effort will potentially provide the necessary information to understand the molecular genetics of the different forms of diabetes including type 1, type 2 and monogenic neonatal diabetes among individuals of all populations and ethnic groups. Despite the fact that linkage and association studies allowed the identification and characterization of many candidate genes that are associated with type 2 diabetes[ 144 , 149 , 150 ], however, not all of these genes showed consistent and reproducible association with the disease[ 151 ]. Genome wide association studies (GWAS) in various populations identified 70 loci associated with type 2 diabetes and revealed positive linkage of many mutations and SNPs that influence the expression and physiological impact of the related proteins and risk to develop type 2 diabetes. One study involved several thousand type 2 diabetes patients and control subjects from the United Kingdom allowed the identification of several diabetes putative loci positioned in and around the CDKAL1 , CDKN2A/B , HHEX/IDE and SLC30A8 genes in addition to the contribution of a large number of other genetic variants that are involved in the development of the disease[ 152 ]. Two similar studies from the Finns and Swedish populations and the United States resulted in the identification of similar single nucleotide variants[ 153 ] that are linked to the risk of acquiring type 2 diabetes[ 154 , 155 ]. The study in the United States population included in addition to type 2 diabetes, the association of the identified SNPs with the level of triglycerides in the tested subjects[ 155 ]. These SNPs are located near several candidate genes including IGFBP2 and CDKAL1 and other genes in addition to several other variants that are located near or in genes firmly associated with the risk of acquiring type 2 diabetes. Other GWAS analysis studies were performed in the Chinese, Malays, and Asian-Indian populations which are distinct from the European and United States populations in addition to meta-analysis of data from other populations in the region revealed relevant findings among patients with European ancestry[ 156 ]. The results of the combined analysis showed significant association of SNPs in the CDKAL1 , CDKN2A/B , HHEX , KCNQ1 and SLC30A8 genes after adjustment with gender and body mass index. More recently, meta-analysis of GWAS data involving African American type 2 diabetes patients identified similar loci to the previous studies with the addition of two novel loci, HLA-B and INS-IGF[ 157 ]. These results provide strong evidence of common genetic determinants including common specific genes that are linked to diabetes. A small list of specific genetic markers seem strongly associated with the risk of developing type 2 diabetes including the TCF7L2 [ 158 ] and CAPN10 [ 159 , 160 ] genes which also play a significant role in the risk and pathogenesis of the disease[ 158 , 159 ]. The association of TCF7L2 gene variants with type 2 diabetes and its mechanism of action received special attention by several investigators[ 161 , 162 ]. Over expression of the protein was shown to decrease the sensitivity of beta islet cells to secrete insulin[ 163 , 164 ] and was more precisely involved in the regulation of secretary granule fusion that constitute a late event in insulin secretion pathway[ 165 ]. The role of TCF7L2 in insulin secretion was partially clarified[ 166 ] that involves modifying the effect of incretins on insulin secretion by lowering the sensitivity of beta cells to incretins. Several other genes have been found to be significantly associated with the risk of developing type 2 diabetes including a specific SNP in a hematopoietically-expressed homeobox ( HHEX ) gene[ 167 ]. The islet zinc transporter protein (SLC30A8)[ 168 ] showed positive correlation with the risk of developing type 2 diabetes where variant mutations in this gene seem protective against the disease which provides a potential tool for therapy[ 169 ]. More recently, a low frequency variant of the HNF1A identified by whole exome sequencing was associated with the risk of developing type 2 diabetes among the Latino population and potentially may serve as a screening tool[ 170 ]. Genetic variants and specific combined polymorphisms in the interleukin and related genes including interlukin-6 ( IL-6 ), tumor necrosis factor-α and IL-10 genes were found to be associated with greater risk of developing type 2 diabetes[ 171 ], in addition to genetic variants in the genes for IL12B , IL23R and IL23A genes[ 172 ]. In a study involving the hormone sensitive lipase responsible for lipolysis in adipose tissues, a deletion null mutation, which resulted in the absence of the protein from adipocytes, was reported to be associated with diabetes[ 173 ]. Nine specific rare variants in the peroxisome proliferator-activated receptor gamma ( PPARG ) gene that resulted in loss of the function of the protein in adipocytes differentiation, were significantly associated with the risk of developing type 2 diabetes[ 174 ]. In addition, certain SNPs in the alpha 2A adrenergic receptor ( ADRA2A ) gene, involved in the sympathetic nervous system control of insulin secretion and lipolysis, were found to be associated with obesity and type 2 diabetes[ 175 ]. Link analysis between the melatonin MT2 receptor ( MTNR1B ) gene, a G-protein coupled receptor, identified 14 mutant variants from 40 known variants revealed by exome sequencing, to be positively linked with type 2 diabetes[ 176 ]. The authors suggested that mutations in the MT2 gene could provide a tool with other related genes in modifying therapy for type 2 diabetes patients based on their specific genetic background to formulate personalized therapies which potentially may ensures the optimum response. Interestingly, mutations in the clock[ 177 , 178 ] and Bmal1 [ 179 ] transcription factor genes which are involved in beta cells biological clock affecting growth, survival and synaptic vesicle assembly in these cells, resulted in reduced insulin secretion and diabetes. Evidently, prominent metabolic functions involve the production of specific reactive metabolites, leading to oxidative stress, which affect lipids, proteins and other biological compounds leading to serious damage in various tissues and organs. Mutations and SNPs in the antioxidant genes, including superoxide dismutase, catalase and glutathione peroxidase, that decrease their activity are implicated in the risk and pathogenesis of type 2 diabetes[ 180 ]. The metabolic syndrome was shown to be associated with the development of type 2 diabetes in a population that is described as highly endogenous especially in individuals over 45 years of age[ 181 ]. Since consanguinity marriages is high in this population, screening for this syndrome among families could provide an informative marker on the risk of developing type 2 diabetes[ 181 ].

Molecular genetics of type 1 diabetes

Even though type 1 diabetes is basically described as an autoimmune disease that results in the destruction of pancreatic beta cells, however, single gene mutations and SNPs have been found to be associated with the susceptibility to this type of diabetes. Initially, two gene mutations were linked to the development of type 1 diabetes including the autoimmune regulator ( AIRE ) gene which affect the immune tolerance to self antigens leading to autoimmunity[ 182 ] and the FOXP3 gene which results in defective regulatory T cells[ 183 ]. In addition, a mutation in the histone deacetylase SIRTI gene predominantly expressed in beta cells involved in the regulation of insulin secretion[ 184 ] and played a role in modulating the sensitivity of peripheral tissues to insulin[ 185 ] was detected in type 1 diabetes patients[ 186 ]. Recently, additional mutations and SNPs in the CTLA-4 +49A/G and HLA-DQB1 and INS gene VNTR alleles were found to be associated with type 1 diabetes, which have the advantage of differentiating between Latent autoimmune type 1 diabetes and type 2 diabetes[ 187 ]. The HLA-DQB1, in combination with HLA-DR alleles and a polymorphism in PTPN22 gene seem to be associated with the age onset of late type 1 diabetes[ 188 , 189 ]. Two specific polymorphisms in the promoter region of a transmembrane protein (DC-SIGN) gene expressed in macrophages and played an important role of T- cell activation and inflammation were found to be protective against type 1 diabetes[ 190 ]. An innovative non-parametric SNP enrichment tool using summary GWAS DATA allowed the identification of association between several transcription factors and type 1 diabetes and are located in a type 1 diabetes susceptibility region[ 191 ]. Nine SNP variants in several genes associated with type 1 diabetes, not including the major histocompatibility gene region, were identified using extensive GWAS analysis[ 192 ]. Furthermore, several novel SNPs in a region in chromosome 16 located in the CLEC16A gene were shown to be associated with type 1 diabetes and seem to function through the reduced expression of DEX1 in B lymphoblastoid cells[ 193 ]. Since more than 40 regions in the human genome were identified to be associated with the susceptibility to type 1 diabetes[ 194 - 196 ], a weighted risk model was developed utilizing selected genes SNPs could be used for testing infants for these genetic markers that could provide insights in the susceptibility to type 1 diabetes development or safe prevention of the disease among young children[ 197 ].

Molecular genetics of monogenic diabetes

A large array of genes were identified to be involved in the development of monogenic diabetes[ 80 ] which represent about 2%-5% of diabetes patients. Monogenic diabetes results primarily from gene defects that lead to a decrease in beta cell number or function. Monogenic diabetes genes were identified using linkage studies or code for proteins that directly affected glucose homeostasis. The majority of genes responsible for monogenetic diabetes code for either transcription factors that participate in the control of nuclear gene expression or proteins that are located on the cell membrane, cytoplasm and endoplasmic reticulum, proteins involved in insulin synthesis and secretion, exocrine pancreatic proteins and autoimmune diabetes proteins[ 80 ]. The collective function of these proteins is their participation in glucose metabolism at different levels. Evidently, the hierarchy of a specific gene in the overall glucose metabolism pathway determines the onset of diabetes in the patient and whether it is neonataly expressed or have late onset expression (adulthood). Consequently, molecular defects in the structure and function of these genes lead to the disturbance of plasma glucose level, the primary pathological sign of diabetes. The molecular mechanism of permanent neonatal diabetes mellitus (PNDP) in addition to MODY explains the observed phenotype of monogenetic diabetes that involves loss of function of the expressed mutant protein. The first gene implicated in monogenic diabetes was the glucokinase ( GCK ) gene[ 198 ] which functions as a pancreatic sensor for blood glucose where more than 70 mutations in the gene were identified that affected its activity[ 199 ]. A recent study on GCK gene mutations causing neonatal and childhood diabetes showed that the majority of mutations resulted in the loss of the enzyme function primarily due to protein instability[ 148 , 150 ]. Two hepatocytes nuclear factor genes that code for the HNF4A and HNF1A transcription factors were closely associated with MODY1 and MODY2[ 148 , 149 ]. Definitely, a whole list of other genes involved in monogenic diabetes are either overlooked or included in the genetic determinants of type 1 and type 2 diabetes which will be identified and clarified through more careful future studies.

MOLECULAR GENETICS OF DIABETES COMPLICATIONS

In addition to the genetic determinants of diabetes, several gene mutations and polymorphisms have been associated with the clinical complications of diabetes. The cumulative data on diabetes patients with a variety of micro- and macrovascular complications support the presence of strong genetic factors involved in the development of various complications[ 200 ]. A list of genes have been reported that are associated with diabetes complications including ACE and AKR1B1 in nephropathy, VEGF and AKRB1 in retinopathy and ADIPOQ and GLUL in cardiovascular diseases[ 200 ]. A study on Chinese patients revealed a single SNP in the promoter region of the smooth muscle actin ( ACTA2 ) gene correlates with the degree of coronary artery stenosis in type 2 diabetes patients[ 201 ]. Furthermore, the alpha kinase 1 gene ( ALPK1 ) identified as a susceptibility gene for chronic kidney disease by GWAS[ 202 ], was demonstrated in type 2 diabetes patients[ 203 ]. Three additional genes have been strongly correlated with this risk of diabetic retinopathy (DR) including the vascular endothelial growth receptor, aldose reductase and the receptor for advanced glycation products genes[ 204 ] where specific polymorphisms in these genes seem to increase the risk of DR development in diabetes patients[ 204 ]. A significant differential proteome (involving 56 out of 252 proteins) is evident that characterizes vitreous samples obtained from diabetes patients with the complication in comparison to diabetes patients without the complication and control individuals[ 205 ]. Interestingly, a large portion of these proteins (30 proteins) belong to the kallikrein-kinin, coagulation and complement systems including complement C3, complement factor 1, prothrombin, alpha-1-antitrypsin and antithrombin III that are elevated in diabetic patients with retinopathy[ 205 ]. In addition, 2 single nucleotides polymorphisms in the human related B7-I gene seem to mediate podocyte injury in diabetic nephropathy[ 206 ]. Furthermore, increased concentration of the ligand of B7-1 correlates with the progression of end-stage renal disease (ESRD) in diabetes patients[ 206 ]. These results indicate that B7-I inhibition may serve as a potential target for diabetes nephropathy prevention and/or treatment. Recently, it was shown that direct correlation is evident between circulating levels of tumor necrosis factors 1 and 2 and increased risk of ESRD in American Indian patients[ 207 ]. The link between diabetes and proper bone development and health is evident. Studies using animal models with major significant reduction in insulin receptor (IR) in osteoprogenitor cells resulted in thin and rod-like weak bones with high risk of fractures[ 208 ]. Similar findings were observed in animal models with bone-specific IR knockdown animals which points to the central role of IR in the proper development of bones[ 208 ]. Type 2 diabetes is also associated with mitochondrial dysfunction in adipose tissues. Using knockout animal models of specific mitochondrial genes led to significant reduction in key electron transport complexes expression and eventually adipocytes death[ 209 ]. These animals exhibited Insulin resistance in addition to other complications that can potentially lead to cardiovascular disease[ 209 ].

Diabetes mellitus is the epidemic of the century and without effective diagnostic methods at an early stage, diabetes will continue to rise. This review focuses on the types of diabetes and the effective diagnostic methods and criteria to be used for diagnosis of diabetes and prediabetes. Evidently, diabetes is a complex disease with a large pool of genes that are involved in its development. The precise identification of the genetic bases of diabetes potentially provides an essential tool to improve diagnoses, therapy (more towards individualized patient targeted therapy) and better effective genetic counseling. Furthermore, our advanced knowledge of the association between medical genetics and the chronic complications of diabetes, will provide an additional advantage to delay or eradicate these complications that impose an immense pressure on patient’s quality of life and the significantly rising cost of health-care services.

Conflict-of-interest: The authors declare that there is no conflict of interest associated with this manuscript.

Open-Access: This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/

Peer-review started: November 23, 2014

First decision: February 7, 2015

Article in press: April 14, 2015

P- Reviewer: Hegardt FG, Surani S, Traub M S- Editor: Gong XM L- Editor: A E- Editor: Wang CH