research articles on diabetes mellitus

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• 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

New Aspects of Diabetes Research and Therapeutic Development

Affiliations.

• 1 Department of Pharmacology (L.S.S.), Division of Metabolism, Endocrinology, and Diabetes, Department of Internal Medicine (L.S.S., S.A.S., E.M.W.), and Brehm Diabetes Center (L.S.S., S.A.S., E.M.W.), University of Michigan Medical School, Ann Arbor, Michigan; and VA Ann Arbor Healthcare System, Ann Arbor, Michigan (S.A.S.) [email protected]; [email protected]; [email protected].
• 2 Department of Pharmacology (L.S.S.), Division of Metabolism, Endocrinology, and Diabetes, Department of Internal Medicine (L.S.S., S.A.S., E.M.W.), and Brehm Diabetes Center (L.S.S., S.A.S., E.M.W.), University of Michigan Medical School, Ann Arbor, Michigan; and VA Ann Arbor Healthcare System, Ann Arbor, Michigan (S.A.S.).
• PMID: 34193595
• PMCID: PMC8274312
• DOI: 10.1124/pharmrev.120.000160

Both type 1 and type 2 diabetes mellitus are advancing at exponential rates, placing significant burdens on health care networks worldwide. Although traditional pharmacologic therapies such as insulin and oral antidiabetic stalwarts like metformin and the sulfonylureas continue to be used, newer drugs are now on the market targeting novel blood glucose-lowering pathways. Furthermore, exciting new developments in the understanding of beta cell and islet biology are driving the potential for treatments targeting incretin action, islet transplantation with new methods for immunologic protection, and the generation of functional beta cells from stem cells. Here we discuss the mechanistic details underlying past, present, and future diabetes therapies and evaluate their potential to treat and possibly reverse type 1 and 2 diabetes in humans. SIGNIFICANCE STATEMENT: Diabetes mellitus has reached epidemic proportions in the developed and developing world alike. As the last several years have seen many new developments in the field, a new and up to date review of these advances and their careful evaluation will help both clinical and research diabetologists to better understand where the field is currently heading.

Trial registration: ClinicalTrials.gov NCT02239354 .

U.S. Government work not protected by U.S. copyright.

Publication types

• Research Support, N.I.H., Extramural
• Research Support, Non-U.S. Gov't
• Research Support, U.S. Gov't, Non-P.H.S.
• Diabetes Mellitus, Type 1*
• Diabetes Mellitus, Type 2* / drug therapy
• Hypoglycemic Agents / therapeutic use
• Hypoglycemic Agents

Associated data

• ClinicalTrials.gov/NCT02239354

Grants and funding

• R01 DK108921/DK/NIDDK NIH HHS/United States
• P30 DK020572/DK/NIDDK NIH HHS/United States
• U01 DK127747/DK/NIDDK NIH HHS/United States
• I01 BX004444/BX/BLRD VA/United States
• R01 DK046409/DK/NIDDK NIH HHS/United States

Recent Advances

ADA-funded researchers use the money from their awards to conduct critical diabetes research. In time, they publish their findings in order to inform fellow scientists of their results, which ensures that others will build upon their work. Ultimately, this cycle drives advances to prevent diabetes and to help people burdened by it. In 2018 alone, ADA-funded scientists published over 200 articles related to their awards!

Identification of a new player in type 1 diabetes risk

Type 1 diabetes is caused by an autoimmune attack of insulin-producing beta-cells. While genetics and the environment are known to play important roles, the underlying factors explaining why the immune system mistakenly recognize beta-cells as foreign is not known. Now, Dr. Delong has discovered a potential explanation. He found that proteins called Hybrid Insulin Peptides (HIPs) are found on beta-cells of people with type 1 diabetes and are recognized as foreign by their immune cells. Even after diabetes onset, immune cells are still present in the blood that attack these HIPs.

Next, Dr. Delong wants to determine if HIPs can serve as a biomarker or possibly even targeted to prevent or treat type 1 diabetes. Baker, R. L., Rihanek, M., Hohenstein, A. C., Nakayama, M., Michels, A., Gottlieb, P. A., Haskins, K., & Delong, T. (2019). Hybrid Insulin Peptides Are Autoantigens in Type 1 Diabetes. Diabetes , 68 (9), 1830–1840.

Understanding the biology of body-weight regulation in children

Determining the biological mechanisms regulating body-weight is important for preventing type 2 diabetes. The rise in childhood obesity has made this even more urgent. Behavioral studies have demonstrated that responses to food consumption are altered in children with obesity, but the underlying biological mechanisms are unknown. This year, Dr. Schur tested changes in brain and hormonal responses to a meal in normal-weight and obese children. Results from her study show that hormonal responses in obese children are normal following a meal, but responses within the brain are reduced. The lack of response within the brain may predispose them to overconsumption of food or difficulty with weight-loss.

With this information at hand, Dr. Schur wants to investigate how this information can be used to treat obesity in children and reduce diabetes.

Roth, C. L., Melhorn, S. J., Elfers, C. T., Scholz, K., De Leon, M. R. B., Rowland, M., Kearns, S., Aylward, E., Grabowski, T. J., Saelens, B. E., & Schur, E. A. (2019). Central Nervous System and Peripheral Hormone Responses to a Meal in Children. The Journal of Clinical Endocrinology and Metabolism , 104 (5), 1471–1483.

A novel molecule to improve continuous glucose monitoring

To create a fully automated artificial pancreas, it is critical to be able to quantify blood glucose in an accurate and stable manner. Current ways of continuously monitoring glucose are dependent on the activity of an enzyme which can change over time, meaning the potential for inaccurate readings and need for frequent replacement or calibration. Dr. Wang has developed a novel molecule that uses a different, non-enzymatic approach to continuously monitor glucose levels in the blood. This new molecule is stable over long periods of time and can be easily integrated into miniaturized systems.

Now, Dr. Wang is in the process of patenting his invention and intends to continue research on this new molecule so that it can eventually benefit people living with diabetes.

Wang, B. , Chou, K.-H., Queenan, B. N., Pennathur, S., & Bazan, G. C. (2019). Molecular Design of a New Diboronic Acid for the Electrohydrodynamic Monitoring of Glucose. Angewandte Chemie (International Ed. in English) , 58 (31), 10612–10615.

Addressing the legacy effect of diabetes

Several large clinical trials have demonstrated the importance of tight glucose control for reducing diabetes complications. However, few studies to date have tested this in the real-world, outside of a controlled clinical setting. In a study published this year, Dr. Laiteerapong found that indeed in a real-world setting, people with lower hemoglobin A1C levels after diagnosis had significantly lower vascular complications later on, a phenomenon known as the ‘legacy effect’ of glucose control. Her research noted the importance of early intervention for the best outcomes, as those with the low A1C levels just one-year after diagnosis had significantly lower vascular disease risk compared to people with higher A1C levels.

With these findings in hand, physicians and policymakers will have more material to debate and determine the best course of action for improving outcomes in people newly diagnosed with diabetes.

Laiteerapong, N. , Ham, S. A., Gao, Y., Moffet, H. H., Liu, J. Y., Huang, E. S., & Karter, A. J. (2019). The Legacy Effect in Type 2 Diabetes: Impact of Early Glycemic Control on Future Complications (The Diabetes & Aging Study). Diabetes Care , 42 (3), 416–426.

A new way to prevent immune cells from attacking insulin-producing beta-cells

Replacing insulin-producing beta-cells that have been lost in people with type 1 diabetes is a promising strategy to restore control of glucose levels. However, because the autoimmune disease is a continuous process, replacing beta-cells results in another immune attack if immunosorbent drugs are not used, which carry significant side-effects. This year, Dr. Song reported on the potential of an immunotherapy he developed that prevents immune cells from attacking beta-cells and reduces inflammatory processes. This immunotherapy offers several potential benefits, including eliminating the need for immunosuppression, long-lasting effects, and the ability to customize the treatment to each patient.

The ability to suppress autoimmunity has implications for both prevention of type 1 diabetes and improving success rates of islet transplantation.

Haque, M., Lei, F., Xiong, X., Das, J. K., Ren, X., Fang, D., Salek-Ardakani, S., Yang, J.-M., & Song, J . (2019). Stem cell-derived tissue-associated regulatory T cells suppress the activity of pathogenic cells in autoimmune diabetes. JCI Insight , 4 (7).

A new target to improve insulin sensitivity

The hormone insulin normally acts like a ‘key’, traveling through the blood and opening the cellular ‘lock’ to enable the entry of glucose into muscle and fat cells. However, in people with type 2 diabetes, the lock on the cellular door has, in effect, been changed, meaning insulin isn’t as effective. This phenomenon is called insulin resistance. Scientists have long sought to understand what causes insulin resistance and develop therapies to enable insulin to work correctly again. This year, Dr. Summers determined an essential role for a molecule called ceramides as a driver of insulin resistance in mice. He also presented a new therapeutic strategy for lowering ceramides and reversing insulin resistance. His findings were published in one of the most prestigious scientific journals, Science .

Soon, Dr. Summers and his team will attempt to validate these findings in humans, with the ultimate goal of developing a new medication to help improve outcomes in people with diabetes.

Chaurasia, B., Tippetts, T. S., Mayoral Monibas, R., Liu, J., Li, Y., Wang, L., Wilkerson, J. L., Sweeney, C. R., Pereira, R. F., Sumida, D. H., Maschek, J. A., Cox, J. E., Kaddai, V., Lancaster, G. I., Siddique, M. M., Poss, A., Pearson, M., Satapati, S., Zhou, H., … Summers, S. A. (2019). Targeting a ceramide double bond improves insulin resistance and hepatic steatosis. Science (New York, N.Y.) , 365 (6451), 386–392.

Determining the role of BPA in type 2 diabetes risk

Many synthetic chemicals have infiltrated our food system during the period in which rates of diabetes has surged. Data has suggested that one particular synthetic chemical, bisphenol A (BPA), may be associated with increased risk for developing type 2 diabetes. However, no study to date has determined whether consumption of BPA alters the progression to type 2 diabetes in humans. Results reported this year by Dr. Hagobian demonstrated that indeed when BPA is administered to humans in a controlled manner, there is an immediate, direct effect on glucose and insulin levels.

Now, Dr. Hagobian wants to conduct a larger clinical trial including exposure to BPA over a longer period of time to determine precisely how BPA influences glucose and insulin. Such results are important to ensure the removal of chemicals contributing to chronic diseases, including diabetes.

Hagobian, T. A. , Bird, A., Stanelle, S., Williams, D., Schaffner, A., & Phelan, S. (2019). Pilot Study on the Effect of Orally Administered Bisphenol A on Glucose and Insulin Response in Nonobese Adults. Journal of the Endocrine Society , 3 (3), 643–654.

Investigating the loss of postmenopausal protection from cardiovascular disease in women with type 1 diabetes

On average, women have a lower risk of developing heart disease compared to men. However, research has shown that this protection is lost in women with type 1 diabetes. The process of menopause increases rates of heart disease in women, but it is not known how menopause affects women with type 1 diabetes in regard to risk for developing heart disease. In a study published this year, Dr. Snell-Bergeon found that menopause increased risk markers for heart disease in women with type 1 diabetes more than women without diabetes.

Research has led to improved treatments and significant gains in life expectancy for people with diabetes and, as a result, many more women are reaching the age of menopause. Future research is needed to address prevention and treatment options.

Keshawarz, A., Pyle, L., Alman, A., Sassano, C., Westfeldt, E., Sippl, R., & Snell-Bergeon, J. (2019). Type 1 Diabetes Accelerates Progression of Coronary Artery Calcium Over the Menopausal Transition: The CACTI Study. Diabetes Care , 42 (12), 2315–2321.

Identification of a potential therapy for diabetic neuropathy related to type 1 and type 2 diabetes

Diabetic neuropathy is a type of nerve damage that is one of the most common complications affecting people with diabetes. For some, neuropathy can be mild, but for others, it can be painful and debilitating. Additionally, neuropathy can affect the spinal cord and the brain. Effective clinical treatments for neuropathy are currently lacking. Recently, Dr. Calcutt reported results of a new potential therapy that could bring hope to the millions of people living with diabetic neuropathy. His study found that a molecule currently in clinical trials for the treatment of depression may be valuable for diabetic neuropathy, particularly the type affecting the brain.

Because the molecule is already in clinical trials, there is the potential that it can benefit patients sooner than later.

Jolivalt, C. G., Marquez, A., Quach, D., Navarro Diaz, M. C., Anaya, C., Kifle, B., Muttalib, N., Sanchez, G., Guernsey, L., Hefferan, M., Smith, D. R., Fernyhough, P., Johe, K., & Calcutt, N. A. (2019). Amelioration of Both Central and Peripheral Neuropathy in Mouse Models of Type 1 and Type 2 Diabetes by the Neurogenic Molecule NSI-189. Diabetes , 68 (11), 2143–2154.

ADA-funded researcher studying link between ageing and type 2 diabetes

One of the most important risk factors for developing type 2 diabetes is age. As a person gets older, their risk for developing type 2 diabetes increases. Scientists want to better understand the relationship between ageing and diabetes in order to determine out how to best prevent and treat type 2 diabetes. ADA-funded researcher Rafael Arrojo e Drigo, PhD, from the Salk Institute for Biological Studies, is one of those scientists working hard to solve this puzzle.

Recently, Dr. Arrojo e Drigo published results from his research in the journal Cell Metabolism . The goal of this specific study was to use high-powered microscopes and novel cellular imaging tools to determine the ‘age’ of different cells that reside in organs that control glucose levels, including the brain, liver and pancreas. He found that, in mice, the cells that make insulin in the pancreas – called beta-cells – were a mosaic of both old and young cells. Some beta-cells appeared to be as old as the animal itself, and some were determined to be much younger, indicating they recently underwent cell division.

Insufficient insulin production by beta-cells is known to be a cause of type 2 diabetes. One reason for this is thought to be fewer numbers of functional beta-cells. Dr. Arrojo e Drigo believes that people with or at risk for diabetes may have fewer ‘young’ beta-cells, which are likely to function better than old ones. Alternatively, if we can figure out how to induce the production of younger, high-functioning beta-cells in the pancreas, it could be a potential treatment for people with diabetes.

In the near future, Dr. Arrojo e Drigo’s wants to figure out how to apply this research to humans. “The next step is to look for molecular or morphological features that would allow us to distinguish a young cell from and old cell,” Dr. Arrojo e Drigo said.

The results from this research are expected to provide a unique insight into the life-cycle of beta-cells and pave the way to novel therapeutic avenues for type 2 diabetes.

Watch a video of Dr. Arrojo e Drigo explaining his research!

Arrojo E Drigo, R. , Lev-Ram, V., Tyagi, S., Ramachandra, R., Deerinck, T., Bushong, E., … Hetzer, M. W. (2019). Age Mosaicism across Multiple Scales in Adult Tissues. Cell Metabolism , 30 (2), 343-351.e3.

Researcher identifies potential underlying cause of type 1 diabetes

Type 1 diabetes occurs when the immune system mistakenly recognizes insulin-producing beta-cells as foreign and attacks them. The result is insulin deficiency due to the destruction of the beta-cells. Thankfully, this previously life-threatening condition can be managed through glucose monitoring and insulin administration. Still, therapies designed to address the underlying immunological cause of type 1 diabetes remain unavailable.

Conventional approaches have focused on suppressing the immune system, which has serious side effects and has been mostly unsuccessful. The American Diabetes Association recently awarded a grant to Dr. Kenneth Brayman, who proposed to take a different approach. What if instead of suppressing the whole immune system, we boost regulatory aspects that already exist in the system, thereby reigning in inappropriate immune cell activation and preventing beta-cell destruction? His idea focused on a molecule called immunoglobulin M (IgM), which is responsible for limiting inflammation and regulating immune cell development.

In a paper published in the journal Diabetes , Dr. Brayman and a team of researchers reported exciting findings related to this approach. They found that supplementing IgM obtained from healthy mice into mice with type 1 diabetes selectively reduced the amount of autoreactive immune cells known to target beta-cells for destruction. Amazingly, this resulted in reversal of new-onset diabetes. Importantly, the authors of the study determined this therapy is translatable to humans. IgM isolated from healthy human donors also prevented the development of type 1 diabetes in a humanized mouse model of type 1 diabetes.

The scientists tweaked the original experiment by isolating IgM from mice prone to developing type 1 diabetes, but before it actually occurred. When mice with newly onset diabetes were supplemented with this IgM, their diabetes was not reversed. This finding suggests that in type 1 diabetes, IgM loses its capacity to serve as a regulator of immune cells, which may be contribute to the underlying cause of the disease.

Future studies will determine exactly how IgM changes its regulatory properties to enable diabetes development. Identification of the most biologically optimal IgM will facilitate transition to clinical applications of IgM as a potential therapeutic for people with type 1 diabetes.    Wilson, C. S., Chhabra, P., Marshall, A. F., Morr, C. V., Stocks, B. T., Hoopes, E. M., Bonami, R.H., Poffenberger, G., Brayman, K.L. , Moore, D. J. (2018). Healthy Donor Polyclonal IgM’s Diminish B Lymphocyte Autoreactivity, Enhance Treg Generation, and Reverse T1D in NOD Mice. Diabetes .

ADA-funded researcher designs community program to help all people tackle diabetes

Diabetes self-management and support programs are important adjuncts to traditional physician directed treatment. These community-based programs aim to give people with diabetes the knowledge and skills necessary to effectively self-manage their condition. While several clinical trials have demonstrated the value of diabetes self-management programs in terms of improving glucose control and reducing health-care costs, whether this also occurs in implemented programs outside a controlled setting is unclear, particularly in socially and economically disadvantaged groups.

Lack of infrastructure and manpower are often cited as barriers to implementation of these programs in socioeconomically disadvantaged communities. ADA-funded researcher Dr. Briana Mezuk addressed this challenge in a study recently published in The Diabetes Educator . Dr. Mezuk partnered with the YMCA to evaluate the impact of the Diabetes Control Program in Richmond, Virginia. This community-academic partnership enabled both implementation and evaluation of the Diabetes Control Program in socially disadvantaged communities, who are at higher risk for developing diabetes and the complications that accompany it.

Dr. Mezuk had two primary research questions: (1) What is the geographic and demographic reach of the program? and (2) Is the program effective at improving diabetes management and health outcomes in participants? Over a 12-week study period, Dr. Mezuk found that there was broad geographic and demographic participation in the program. The program had participants from urban, suburban and rural areas, most of which came from lower-income zip codes. HbA1C, mental health and self-management behaviors all improved in people taking part in the Greater Richmond Diabetes Control Program. Results from this study demonstrate the value of diabetes self-management programs and their potential to broadly improve health outcomes in socioeconomically diverse communities. Potential exists for community-based programs to address the widespread issue of outcome disparities related to diabetes.  Mezuk, B. , Thornton, W., Sealy-Jefferson, S., Montgomery, J., Smith, J., Lexima, E., … Concha, J. B. (2018). Successfully Managing Diabetes in a Community Setting: Evidence from the YMCA of Greater Richmond Diabetes Control Program. The Diabetes Educator , 44 (4), 383–394.

Using incentives to stimulate behavior changes in youth at risk for developing diabetes

Once referred to as ‘adult-onset diabetes’, incidence of type 2 diabetes is now rapidly increasing in America’s youth. Unfortunately, children often do not have the ability to understand how everyday choices impact their health. Could there be a way to change a child’s eating behaviors? Davene Wright, PhD, of Seattle Children’s Hospital was granted an Innovative Clinical or Translational Science award to determine whether using incentives, directed by parents, can improve behaviors related to diabetes risk. A study published this year in Preventive Medicine Reports outlined what incentives were most desirable and feasible to implement. A key finding was that incentives should be tied to behavior changes and not to changes in body-weight.

With this information in hand, Dr. Wright now wants to see if incentives do indeed change a child’s eating habits and risk for developing type 2 diabetes. She is also planning to test whether an incentive program can improve behavior related to diabetes management in youth with type 1 diabetes. Jacob-Files, E., Powell, J., & Wright, D. R. (2018). Exploring parent attitudes around using incentives to promote engagement in family-based weight management programs. Preventive Medicine Reports , 10 , 278–284.

Determining the genetic risk for gestational diabetes

Research has identified more than 100 genetic variants linked to risk for developing type 2 diabetes in humans. However, the extent to which these same genetic variants might affect a woman’s probability for getting gestational diabetes has not been investigated.

Pathway to Stop Diabetes ® Accelerator awardee Marie-France Hivert, MD, of Harvard University set out to answer this critical question. Dr. Hivert found that indeed genetic determinants of type 2 diabetes outside of pregnancy are also strong risk factors for gestational diabetes. This study was published in the journal Diabetes .

The implications? Because of this finding, doctors in the clinic may soon be able to identify women at risk for getting gestational diabetes and take proactive steps to prevent it. Powe, C. E., Nodzenski, M., Talbot, O., Allard, C., Briggs, C., Leya, M. V., … Hivert, M.-F. (2018). Genetic Determinants of Glycemic Traits and the Risk of Gestational Diabetes Mellitus. Diabetes , 67 (12), 2703–2709.

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• Open access
• Published: 13 April 2024

Novel anthropometric indices for predicting type 2 diabetes mellitus

• Erfan Sadeghi 1 ,
• Alireza Khodadadiyan 2 ,
• Seyed Ali Hosseini 3 ,
• Sayed Mohsen Hosseini 4 ,
• Ashraf Aminorroaya 5 ,
• Massoud Amini 5 &
• Sara Javadi 6  

BMC Public Health volume  24 , Article number:  1033 ( 2024 ) Cite this article

Metrics details

This study aimed to compare anthropometric indices to predict type 2 diabetes mellitus (T2DM) among first-degree relatives of diabetic patients in the Iranian community.

In this study, information on 3483 first-degree relatives (FDRs) of diabetic patients was extracted from the database of the Endocrinology and Metabolism Research Center of Isfahan University of Medical Sciences. Overall, 2082 FDRs were included in the analyses. A logistic regression model was used to evaluate the association between anthropometric indices and the odds of having diabetes. Furthermore, a receiver operating characteristic (ROC) curve was applied to estimate the optimal cutoff point based on the sensitivity and specificity of each index. In addition, the indices were compared based on the area under the curve (AUC).

The overall prevalence of diabetes was 15.3%. The optimal cutoff points for anthropometric measures among men were 25.09 for body mass index (BMI) (AUC = 0.573), 0.52 for waist-to-height ratio (WHtR) (AUC = 0.648), 0.91 for waist-to-hip ratio (WHR) (AUC = 0.654), 0.08 for a body shape index (ABSI) (AUC = 0.599), 3.92 for body roundness index (BRI) (AUC = 0.648), 27.27 for body adiposity index (BAI) (AUC = 0.590), and 8 for visceral adiposity index (VAI) (AUC = 0.596). The optimal cutoff points for anthropometric indices were 28.75 for BMI (AUC = 0.610), 0.55 for the WHtR (AUC = 0.685), 0.80 for the WHR (AUC = 0.687), 0.07 for the ABSI (AUC = 0.669), 4.34 for the BRI (AUC = 0.685), 39.95 for the BAI (AUC = 0.583), and 6.15 for the VAI (AUC = 0.658). The WHR, WHTR, and BRI were revealed to have fair AUC values and were relatively greater than the other indices for both men and women. Furthermore, in women, the ABSI and VAI also had fair AUCs. However, BMI and the BAI had the lowest AUC values among the indices in both sexes.

The WHtR, BRI, VAI, and WHR outperformed other anthropometric indices in predicting T2DM in first-degree relatives (FDRs) of diabetic patients. However, further investigations in different populations may need to be implemented to justify their widespread adoption in clinical practice.

Peer Review reports

In recent decades, T2DM has increasingly become a significant public health issue globally, especially in the past few decades. The prevalence of T2DM has increased to 11.6% globally, impacting a population of more than 100 million adults [ 1 ]. One of the most important risk factors for T2DM is obesity. There is a growing recognition that obesity is a modifiable risk factor for prediabetes, and T2DM has various aspects according to its extent, pattern, timing, and duration [ 2 ]. Moreover, not only are FDRs of individuals with diabetes at greater risk than second-degree relatives, but they also exhibit increased whole-body insulin resistance and decreased muscle glucose uptake [ 3 ]. In epidemiological studies, anthropometric indices have been utilized to measure obesity because of their simplicity and utility [ 4 ].

Classic anthropometric indices include BMI, waist circumference (WC), waist-to-hip ratio (WHR), and waist-to-height ratio (WHtR) [ 4 , 5 , 6 ]. BMI is a simple index of weight-to-height that is commonly used to classify overweight and obesity in adults [ 7 ]. Studies have shown that BMI is not able to distinguish muscle tissue from fat accumulation, so it cannot reflect abdominal fat. Recently, BMI has been criticized because it does not accurately measure body weight and fat directly but relies on body weight alone [ 8 ]. Among traditional anthropometric indices, the WHR and WHtR are indices of central obesity and are correlated with visceral body fat [ 9 ]. In addition, abdominal obesity was measured by waist circumference (WC). According to the study by Jamar et al., WHtR predicts insulin resistance more precisely than WC or BMI [ 10 ]. Furthermore, based on analyses from similar studies, optimal cutoff values of the WHtR were used to predict diabetes [ 11 , 12 ]. However, some published studies have reported BMI or WC as the best predictors of diabetes [ 4 , 13 , 14 , 15 ].

Novel indices, such as the body shape index (ABSI), body roundness index (BRI), and visceral adiposity index (VAI), have been proposed as alternative indicators of obesity [ 4 ]. The ABSI is a new anthropometric index based on normalizing WC to BMI and height [ 16 ]. According to the literature, the ABSI, which is independent of BMI by design, provides efficient risk stratification for underweight and obese individuals. However, we are not sure whether the ABSI could also predict the new onset of diabetes mellitus (DM) in our population [ 17 ]. The BRI is a potential alternative measure for evaluating obesity in individuals with T2DM [ 4 ]. In addition, the BRI is an indicator of obesity and is based on body fat (BF) and body fat percentage (BF%) [ 18 ]. This index is closely associated with diabetes risk and was used to identify diabetes in a cross-sectional study [ 19 , 20 ]. According to one study, BRI can predict development of diabetes based on height, weight, waist circumference, and hip circumference [ 4 ].

Due to the difficulties of assessing BMI at the nutritional level and its limited accuracy, Bergman et al. developed the body adiposity index (BAI) for adults as an alternative new parameter for evaluating body composition based on height in meters and hip circumference in centimeters [ 21 ]. Bozorgmanesh et al. reported that the VAI, an indicator of visceral fat dysfunction, has good predictive performance for diabetes in Iran [ 22 ] and is also a sex-specific index that indirectly reflects visceral adipose function [ 23 , 24 ]. Another study has shown that the VAI is a good predictor of T2DM [ 25 ]. Cutoff points for anthropometric indices such as the BRI, BAI, and VAI are not unified among different populations [ 26 , 27 , 28 ]. However, no comprehensive agreement has been reached on the best anthropometric index for predicting the development of T2DM in FDRs of diabetic patients. The present study aimed to compare anthropometric indices for predicting T2DM among first-degree relatives of diabetic patients in the Iranian community.

Study participants

In this study, baseline information on 3483 FDRs of diabetic patients was extracted from the database of the Endocrinology and Metabolism Research Center of Isfahan University of Medical Sciences, known as the Isfahan Diabetes Prevention Study (IDPS), the details of which have been presented elsewhere [ 29 , 30 ]. In summary, the IDPS is an ongoing longitudinal study initiated between 2003 and 2005 in Isfahan, central Iran. The primary aim of this study was to examine the potential risk factors for diabetes in individuals with a family history of T2DM. During the evaluations, participants underwent physical measurements and laboratory tests, including a standard 75-g, 2-hour oral glucose tolerance test (OGTT). Diabetes status was defined as having a fasting plasma glucose (FPG) level equal to or higher than 126 mg/dL, a 2-hour plasma glucose level equal to or higher than 200 mg/dL, or a HbA1c level equal to or higher than 6.5%. Normal status was defined as having an FPG level below 100 mg/dL, a 2-hour plasma glucose level below 140 mg/dL, or an HbA1c level below 6.0%. The participants also completed a questionnaire on their health status and various factors potentially associated with the risk of diabetes. Follow-up assessments adhered to standard medical care for diabetes [ 31 ], focusing on gathering updated information on demographics, physical measurements, lifestyle factors, and newly diagnosed diabetes cases. Participants with a normal baseline OGTT result underwent repeat testing at least every 3 years, while those with abnormal results usually underwent annual repeat testing. The inclusion criteria were siblings and children of type 2 diabetes patients aged 30 to 70 years. We excluded participants who had a prediabetic baseline status defined as impaired fasting glucose (IFG) (FPG: 100–125 mg/dL and 2-h plasma glucose < 140 mg/dL) or impaired fasting glucose (IGT) (FPG < 126 mg/dL, but with 2-h plasma glucose concentration ≥ 140 and < 200 mg/dL) or HbA1c 6.0–6.49% [ 32 ] or were missing data, resulting in the exclusion of 1401 participants. All participants signed informed written consent for their participation. The present study was conducted based on the principles of the Declaration of Helsinki and the approval of the ethics committee of Isfahan University of Medical Sciences.

Measurements

The participants’ height and weight were measured in light clothing using a Seca weighting scales and stadiometer. The BMI was calculated by dividing weight in kilogram (kg) by height squared in meter (m2) [ 33 ]. To measure waist circumference (WC), the midpoint between the lowest point of the rib and the top edge of the iliac crest was measured [ 34 ]. Hip circumference (HC) was utilized to quantify the horizontal extent or placement of the hip protrusion. Tape measures were used to measure WC and HC to the nearest 0.1 cm [ 35 ]. The WHR and WHtR were calculated as WC divided by HC and WC divided by height, respectively [ 36 , 37 , 38 ].

Other indices were calculated using the following formulas:

Statistical analysis

Anthropometric indices are presented as the mean (standard deviation) and were compared between diabetic patients and nondiabetic patients using Student’s t test. Due to the differences in the scale of the indices, we standardized them so that we could easily compare their effects. Therefore, we first computed the sample mean and standard deviation of the indices separately for all males and females. Then, z-scores were calculated as follow: (measurement value—mean) / standard deviation. The association of T2DM risk and anthropometric indices were examined using univariate logistic regression with T2DM status as the binary dependent variable, and anthropometric indices as the independent variables. Moreover, a receiver operating characteristic (ROC) curve analysis was performed to estimate the diagnostic parameters to compare the discrimination ability of the anthropometric indices, and to determine the optimal cutoff points of the indices based on the Youden index. The Statistical Packages for Social Sciences (SPSS) version 24 and MedCalc version 20.104 were used for data analysis. P values < 0.05 were considered to indicate statistical significance.

A total of 2082 FDR subjects, ranging from 30 to 70 years old, were included in the present study, of whom 318 (15.3%) had diabetes (103 male and 215 female). The mean age of the males was 43.17 ± 7.20 years, while that of the females was 43.18 ± 6.10 years. For both the male and female groups, Table  1 shows that the mean values of almost all indices were significantly greater in the T2DM group than in the normal control group ( P  < 0.05). The logistic regression model revealed that all of the indices were significantly associated with increased risk of T2DM; for instance, each one-unit increase in BMI z-score was associated with increased the risk of T2DM by 33% in males (OR = 1.33, 95% CI = [1.07, 1.64], P  = 0.008) and each one-unit increase in the WHtR z-score was associated with increased the risk of T2DM by 90% (OR = 1.90, 95% CI = [1.64, 2.20], P  < 0.001) in females (Table  2 ).

Figure  1 presents the ROC curves for the anthropometric indices of men and women. Table  3 lists the diagnostic parameters, including the sensitivity, specificity, optimal cutoff values, P value, and area under the curve (AUC), of the anthropometric indices for predicting T2DM according to sex. Furthermore, in women, the area under the curve (AUC) values of all the incidences were significantly greater than those in men. Table 3 presents the associations between z-scores for various anthropometric indices (namely, BMI, WHR, WHtR, ABSI, BAI, BRI, and VAI) and risk of diabetes. According to the confidence intervals in Table 3 , the WHR, WHtR, and BRI were the strongest predictors of T2DM risk in both the male and female groups. BMI and BAI were the weakest predictors for both the male and female groups compared to the other indices.

ROC curve for the anthropometric indices in male ( a ) and female ( b )

The present study aimed to delineate the relationship between different anthropometric indices and diabetes risk. Our baseline data from the 14-year cohort of FDRs of T2DM patients among Iranian patients revealed that, in both women and men, the BRI, BMI, BAI, WHtR, ABSI, WHR and VAI were significantly greater in the T2DM group than in the non-T2DM group. In women, almost all the indices mentioned above had moderate sensitivity and specificity. However, in men, these indices had high sensitivity but low specificity. The WHR, WHtR, and BRI were the strongest predictors in both men and women, with cutoffs of 0.91, 0.52, and 27.27 in men, respectively, and 0.80, 0.55, and 39.95 in women.

As mentioned before, compared with the other indices, the WHR, WHtR, and BRI were the strongest predictors of T2D risk, while BMI and BAI were the weakest predictors among both the male and female groups. While BMI and the BAI had high sensitivity (86.40 and 81%, respectively), they had relatively low specificity (27.60 and 36.79%, respectively) for predicting T2D risk in men. Even though BMI and the BAI are not good predictors of a diabetes diagnosis in men, these two indices, as well as other indices, have high sensitivity. In other words, all these indices had a relatively low false-positive rate in the diagnosis of diabetes in men, which indicates the capability of these indices to diagnose diabetes. In women, our results showed that the ABSI and BAI, along with the VAI, had relatively moderate specificity. In other words, these patients do not have high false positives, which indicates their ability to diagnose nondiabetic individuals. In total, the three indices WHR, WHtR, and BRI seem to be better at distinguishing diabetic patients from nondiabetic patients.

In the present study, the ABSI index in men was not a good predictor of T2DM risk, which is consistent with the results of Yang. et al. study. However, this index performed well among women. Furthermore, that study revealed BMI to be a stronger predictor of WC, WHtR, VAI, and BRI, which contradicts the results of our study. The different target populations may also explain this difference [ 4 ].

Several researchers suggest combining anthropometric indices to better predict T2DM risk [ 41 ], while others note increased specificity but decreased sensitivity and positive predictive value when using joint measures [ 42 ]. The VAI is calculated using both anthropometric indices (WC and BMI) and laboratory parameters (HDL-C and TG) [ 23 , 40 ]. This index is positively correlated with visceral adipose tissue and insulin resistance, with its value in predicting T2DM having been shown in both Caucasian [ 40 ] and Asian populations [ 43 ]. In the present study, we found that the VAI had moderate sensitivity and specificity, indicating that it must be used in combination with the patient’s clinical profile. Furthermore, its AUC was near that of simpler indices, meaning that it may not necessarily be worth evaluating when simpler indices are available. These findings are in line with a similar study on a similar population, which concluded that while the VAI is a robust predictor of T2DM, its predictive power resembles that of BMI, WC, WHtR, and WHR [ 44 ]. This concept is also supported by the findings of a large, four-year study on an adult Chinese population [ 45 ]. Hence, while the superiority of the VAI over other anthropometric indices has emerged as a common theme in recent years [ 46 ], the extent to which it can improve clinical practice is unclear.

In the cohort study of Zafari et al. conducted in Tehran, the derived cutoff values for BMI, WC, WHtR, WHR, and HC were 25.56 kg/m2, 89 cm, 0.52, 0.91, and 96 cm, respectively, in males and 27.12 kg/m2, 87 cm, 0.56, 0.83, and 103 cm, respectively, in females. Among these indices, the WHtR had the greatest discriminatory power [ 42 ]. Our study’s cutoff points were slightly different, possibly due to population differences. In Germany, stronger associations were established between indices that reflect abdominal obesity (WC and WHtR) and incident T2DM than between BMI and weight, with WHtR being the strongest predictor [ 47 ]; our results are in general agreement with this concept.

A number of similar studies have been conducted on Asian populations. In the Jinchang Cohort Study, Ding et al. reported that the AUC of BMI was greater than that of WC and WHtR in predicting T2DM in Asians. The cutoff points for BMI, WC, and WHtR for predicting T2DM were 24.6 kg/m2, 89.5 cm, and 0.52, respectively, in men and 23.4 kg/m2, 76.5 cm, and 0.47, respectively [ 12 ]. Yang et al. reported that BMI, WC, the WHtR, the VAI, and the BRI were positively associated with incident T2DM risk in an elderly Chinese population, with BMI representing the strongest predictor in both men and women (AUC = 0.655 and 0.635, respectively) [ 4 ]. Our results suggested a higher cutoff for BMI, in line with the findings of a previous study. In a previous study, the strongest predictor of T2DM incidence was the WHtR in men and BMI in women [ 48 ]. BMI has maintained its popularity in the clinic over the years, with strong evidence in favor of its independent link with T2DM [ 49 ]. However, in two large cohort studies from the USA, the WHtR performed better than BMI in predicting T2DM [ 50 ]. Hence, variations between populations must be considered in clinical decision-making, with the value of indices varying in each population. An interesting prospect is the use of modified indices for each population, for example, the Chinese VAI (CVAI), which performed better than the VAI, BMI, WC, WHR, and WHtR in predicting both prediabetes and T2DM in Chinese adults [ 51 ].

The present study has encountered some limitations. Firstly, we used secondary data for this study and did not have control over data collection or the ability to add new information. Another limitation of this study is that due to the unavailability of several indicators, such as ankle and hand circumference or arm circumference, we could not evaluate other new anthropometric indices. Another limitation of our study is that information on postmenopausal women was not available to the researchers. Therefore, further investigations might be required to examine whether menopause and stratification of women based on the menopause status can mediate the association of anthropometric indices and risk of T2DM. The other key limitation of this study was its lack of evaluation of the effects of anthropometric indices on prediabetes, which may be valuable for guiding screening interventions. Nonetheless, the extensive study period and relatively large sample size provided valuable findings. Future studies should focus more on prediabetes to improve screening and prevention rather than disease diagnosis. Population-based modifications to the VAI formula may also be worth exploring.

Conclusions

The WHtR, BRI, VAI, and WHR outperform the more conventional anthropometric indices in predicting T2DM in FDRs of diabetic patients in this population. Notably, the WHtR, BRI, VAI, and WHR were significantly greater in the T2DM group than in the non-T2DM group. Nonetheless, WHtR and WHR are more practical and relatively simpler to calculate and evaluate, as compared to Visceral Adiposity Index (VAI) and Body Roundness Index (BRI), making them more accessible for healthcare professionals and individuals. Therefore, it is recommended to prioritize the use of WHtR and WHR in T2DM prediction. However, the nuanced sex-specific variations in sensitivity and specificity suggest that a tailored approach may be crucial in clinical applications. These indices, which are finely tuned to capture the intricacies of abdominal obesity and visceral adiposity, have emerged as powerful indicators. Nonetheless, the extent of its superiority in justifying its widespread use in clinical practice remains questionable. In essence, our study not only substantiates the importance of specific anthropometric indices in predicting T2DM risk but also opens the door to a future where personalized risk assessment tools may redefine how we approach preventive strategies.

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.

Abbreviations

First-degree relatives

Type 2 diabetes mellitus

• Diabetes mellitus
• Body mass index

World Health Organization

Waist circumference

• Waist-to-height ratio
• Waist-to-hip ratio

A Body shape index

• Body roundness index
• Body adiposity index
• Visceral adiposity index

Hip circumference

Receiver operating characteristic

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Acknowledgments

We thank all participants and staff members at the Isfahan Endocrine and Metabolism Research Center.

This study was performed without any funding or financial support.

Author information

Authors and affiliations.

Department of Biostatistics, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran

Erfan Sadeghi

Department of Cardiovascular Research Centre, Shiraz University of Medical Sciences, Shiraz, Iran

Alireza Khodadadiyan

School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran

Seyed Ali Hosseini

Department of Biostatistics & Epidemiology, School of Public Health, Isfahan University of Medical Sciences, Isfahan, Iran

Sayed Mohsen Hosseini

Isfahan Endocrine and Metabolism Research Center, Isfahan University of Medical Sciences, Isfahan, Iran

Ashraf Aminorroaya & Massoud Amini

Shiraz University of Medical Sciences, Shiraz, Iran

Sara Javadi

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E.S Designed and directed the project. Conceived and designed the analysis. Performed the analysis. Contributed to the interpretation of the results. Wrote and edited the manuscript. A.Kh Conceived and designed the analysis. Contributed to the interpretation of the results. Contributed to the numerical calculations. Edited and commented on the manuscript. SA.H Conceived and designed the analysis. Contributed to the interpretation of the results. Contributed to the numerical calculations. Edited and commented on the manuscript. SM.H Conceived and designed the analysis. Conceived and designed the analysis. Contributed to the interpretation of the results. Edited and commented on the manuscript. A.A Conceived and designed the analysis. Contributed to the interpretation of the results. Edited and commented on the manuscript. M.A Conceived and designed the analysis. Contributed to the interpretation of the results. Edited and commented on the manuscript. S.J Designed and directed the project. Conceived and designed the analysis. Performed the analysis. Contributed to the interpretation of the results. Wrote and edited the manuscript.

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Sadeghi, E., Khodadadiyan, A., Hosseini, S.A. et al. Novel anthropometric indices for predicting type 2 diabetes mellitus. BMC Public Health 24 , 1033 (2024). https://doi.org/10.1186/s12889-024-18541-7

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ORIGINAL RESEARCH article

Risk factors for erectile dysfunction in diabetes mellitus: a systematic review and meta-analysis.

• 1 Department of Urology, First Affiliated Hospital of Xinjiang Medical University, Urumqi, China
• 2 Department of Pancreatic Surgery, First Affiliated Hospital of Xinjiang Medical University, Urumqi, China
• 3 Department of Cardiac Surgery, First Affiliated Hospital of Xinjiang Medical University, Urumqi, China

Background: Previous studies have established that diabetes mellitus (DM) markedly raises the risk of developing erectile dysfunction (ED). Despite extensive investigations, the risk factors associated with ED in diabetic men have yet to be unequivocally determined, owing to incongruent and inconclusive results reported in various studies.

Objective: The objective of this systematic review and meta-analysis was to assess the risk factors for ED in men with DM.

Methods: A comprehensive systematic review was conducted, encompassing studies published in the PubMed, Scopus and Embase databases up to August 24th, 2023. All studies examining the risk factors of ED in patients with DM were included in the analysis. To identify significant variations among the risk factors, odds ratios (ORs) and their corresponding 95% confidence intervals (CIs) were employed. The risk of bias was evaluated using the Newcastle-Ottawa Scale(NOS) for longitudinal studies and the Agency for Healthcare Research and Quality Scale(AHRQ) for cross-sectional studies.

Results: A total of 58 studies, including a substantial participant pool of 66,925 individuals diagnosed with DM, both with or without ED, were included in the meta-analysis. Mean age (OR: 1.31, 95% CI=1.24-1.37), smoking status (OR: 1.32, 95% CI=1.18-1.47), HbA1C (OR: 1.44, 95% CI=1.28-1.62), duration of DM (OR: 1.39, 95% CI=1.29-1.50), diabetic neuropathy (OR: 3.47, 95% CI=2.16-5.56), diabetic retinopathy (OR: 3.01, 95% CI=2.02-4.48), diabetic foot (OR: 3.96, 95% CI=2.87-5.47), cardiovascular disease (OR: 1.92, 95% CI=1.71-2.16), hypertension (OR: 1.74, 95% CI=1.52-2.00), microvascular disease (OR: 2.14, 95% CI=1.61-2.85), vascular disease (OR: 2.75, 95% CI=2.35-3.21), nephropathy (OR: 2.67, 95% CI=2.06-3.46), depression (OR: 1.82, 95% CI=1.04-3.20), metabolic syndrome (OR: 2.22, 95% CI=1.98-2.49), and diuretic treatment (OR: 2.42, 95% CI=1.38-4.22) were associated with increased risk factors of ED in men with DM.

Conclusion: Our study indicates that in men with DM, several risk factors for ED have been identified, including mean age, HbA1C, duration of DM, diabetic neuropathy, diabetic retinopathy, diabetic foot, cardiovascular disease, hypertension, microvascular disease, vascular disease, nephropathy, depression, metabolic syndrome, and diuretic treatment. By clarifying the connection between these risk factors and ED, clinicians and scientific experts can intervene and address these risk factors, ultimately reducing the occurrence of ED and improving patient management.

Introduction

Diabetes mellitus stands as a prevalent and formidable non-communicable disease that profoundly impacts the health and well-being of individuals, their families, and broader societies. DM represents a substantial global burden, exerting a significant impact on morbidity and mortality rates, and stands as the ninth leading cause of death worldwide ( 1 ). Epidemiological investigations have revealed a remarkable upsurge in the prevalence and mortality associated with DM from 2007 to 2017 ( 2 ). Projections suggest that by 2030, an estimated 10.2% of the global population will be affected by this chronic condition ( 3 ).

ED refers to the repetitive or persistent inability to attain and/or sustain an adequate level of erectile function required for satisfactory sexual intercourse ( 4 ). In individuals with DM, this condition typically emerges from the intricate interplay of neurogenic, vasogenic, and psychological factors, which are closely interlinked with the chronic complications related to DM ( 5 ). The prevalence of DM has rapidly increased due to higher consumption of high-sugar diets and decreased physical activity as a result of social development. The prevalence of ED among diabetic patients exhibits significant variation, spanning from 35% to 90% ( 6 ). Furthermore, in the United States, the total direct cost of evaluating ED treatment is estimated to be $400 million, with approximately a quarter of this amount linked to DM and obesity ( 7 ).

A recent study examined the association between DM and ED, treatment options, and diabetes-related ED, incorporating 106 relevant studies in the review ( 8 ). This extensive inclusion of studies highlights the widespread interest and significance of the association between DM and ED as a current and highly pertinent topic. Men with DM often contend with several comorbidities that serve as independent risk factors for ED, including advancing age, obesity, smoking, cardiovascular disease(CVD), hypertension, metabolic syndrome, and dyslipidemia ( 9 , 10 ). In addition, the presence of diabetic complications such as diabetic retinopathy and diabetic foot can further precipitate the development of ED ( 5 ). The effects of ED reach far beyond physical symptoms, encompassing significant psychosocial and clinical implications. These implications are linked to men’s social interactions, emotional and psychological well-being, as well as their relationships with their partners. Nevertheless, it is important to highlight that ED stands as one of the most treatable complications of DM, with a success rate exceeding 95% in treatment outcomes ( 11 ).

ED is a prevalent complication of DM and high-quality meta-analyses and ED guidelines ( 12 ) have recognized DM as a significant risk factor. However, there remains a notable gap in the literature regarding a comprehensive analysis and synthesis of the various risk factors associated with ED in men affected by DM. Thus, we conducted a comprehensive exploration of the risk factors for ED in the diabetic population, aiming to furnish clinicians and preventive physicians with valuable insights for averting the onset of ED.

Materials and methods

This meta-analysis adheres to the 2020 guidelines outlined in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) ( 13 ). The study protocol has been registered with the international prospective register of systematic reviews (PROSPERO) under the registration number CRD42023495323.

Search strategy

A comprehensive systematic review was conducted, encompassing studies published in the PubMed, Scopus and Embase databases up to August 24th, 2023. Relevant studies derived from the references of the studies included in the initial search, along with significant reviews and systematic reviews pertinent to this field, were also comprehensively assessed to ensure comprehensive coverage of the literature. By utilizing a combination of medical subject heading (MeSH) terms and text words, we devised a preliminary search strategy that incorporated the following terms: “Diabet”; “insulin”; “resistance glucose”; “Intolerant”; “diabetes mellitus”; “T1DM”; “T2DM”; “Erectile Dysfunction”; “Impotence”. The comprehensive search strategy employed for all databases can be found in Supplementary Material 1 .

Study selection criteria

Three researchers (AW, AT, and L-WT) independently assessed all articles for eligibility and cross-validated their findings. Any discrepancies were resolved through discussion or consultation with the senior authors (DD).The inclusion criteria for the selected articles were as follows: (1) diagnosis of DM was conducted by either a specialist clinician, a qualified health manager, or through analysis of database data adhering to internationally recognized diagnostic criteria. (2) studies investigating risk factors for ED in men with DM. (3) studies involving male participants aged 18 years or older, and publications in English, irrespective of study design(longitudinal or cross-sectional). And (4) any studies that provided OR, relative risks (RR), hazard ratios (HR) with 95% CIs, or sufficient data to facilitate the calculation of these values. The following exclusion criteria were applied: (1) no control group was established in the study; (2) reviews, letters, conference abstracts, case reports, case series, or editorials; (3) duplicates, animal studies, non-English articles, or articles for which full-text access could not be obtained were excluded. When multiple articles from a single study reported on the same endpoint, only the data representing the longest follow-up period were extracted. Furthermore, in studies that reported multivariate adjusted effects, we extracted results from models that controlled for the most significant potential confounders. In cases where studies did not report an effect result or where data could only be extracted from baseline, we computed the effect result using a fourfold table and defined the result as unadjusted.

Data extraction and quality assessment

Data extraction from each article was performed by three independent observers (AW, AT, and L-WT). Any discrepancies were resolved either by a third observer (DD) or through consensus among the observers. The extracted data included the first author’s name, year of publication, country of origin, study design, sample size, number of participants, mean age, ascertainment of DM and ED, type of DM, pharmaceutical treatments, outcomes, and other relevant factors.

The quality and methodological robustness of the included longitudinal studies were assessed by three researchers (AW, AT, and L-WT) using the Newcastle-Ottawa Scale (NOS) ( 14 ). As for the cross-sectional studies, these three researchers utilized the guidelines provided by the Agency for Healthcare Research and Quality (AHRQ) to evaluate their methodological rigor ( 15 ).

Statistical analyses

Data analysis was performed using STATA software version 12.0 (STATA Corporation, Texas, USA). The primary outcome of this study examines the risk factors for ED in diabetic patients, while the secondary outcome focuses on conducting subgroup analyses to stratify risk factors based on the type of DM, age, and other relevant factors. Given the substantial representation of cross-sectional studies, ORs were used as effect sizes, and findings from a combination of longitudinal and cross-sectional studies were integrated to enhance the generalizability of our study, drawing upon insights from previous research endeavors ( 16 , 17 ). Additionally, we conducted separate subgroup analyses to rigorously examine the results of studies, thereby enhancing the robustness of our findings. Heterogeneity in the study was evaluated through Cochrane’s Q test and I 2 statistics. The fixed-effect model was adopted when P ≥ 0.1 and I 2 ≤ 50%, while the random-effect model was utilized for cases when P < 0.1 and I 2 > 50%. Further subgroup analyses were conducted using a comprehensive dataset including more than 10 studies to investigate potential causes of heterogeneity. We conducted sensitivity analyses by excluding individual studies and assessing their impact on the overall pooled results. Furthermore, we performed funnel plot analysis and evaluated publication bias using the Egger tests. Statistical significance was defined as a p-value less than 0.05 for all two-sided statistical tests.

The initial search involved a systematic review of a vast array of 7,885 studies, including 2,694 from PubMed, 2,496 from Scopus, and 2,666 from Embase. Furthermore, an additional 29 studies were identified through alternative sources. After the removal of 1,417 duplicated studies and the exclusion of an additional 4,504 based on the evaluation of their title and abstract content, a rigorous assessment was conducted on 1,964 studies for full-text evaluation. Finally, 58 articles met the criteria for inclusion in the meta-analysis and literature review. The process employed to identify eligible articles is depicted in Figure 1 .

Figure 1 Flow chart of study selection.

Of the 58 studies ultimately included, a comprehensive analysis revealed a total of 37 identified risk factors, encompassing the following categories: demographic and lifestyle characteristics (mean age, BMI, weight, waist-to-hip ratio, alcohol consumption, smoking status, lower income, physical activity, sitting time); laboratory analyses (systolic blood pressure, low HDL cholesterol, estimated glomerular filtration rate, testosterone levels, Hemoglobin, microalbuminuria); diabetes-related complications (HbA1c levels, inadequate glycemic control, duration of DM, diabetic neuropathy, diabetic retinopathy, Diabetic foot); and medical history and symptomatology (CVD, hypertension, microvascular disease, vascular disease, nephropathy, depression, premature ejaculation, atherogenic dyslipidemia, reduced libido, metabolic syndrome, hyperuricemia, nocturia, cardiorespiratory fitness diuretics, ACE inhibitors, injectable insulin).

Study characteristics

During the conclusive analysis, 22 of the 37 identified risk factors were subjected to meta-analysis, indicating that they were each supported by a minimum of two included studies. As there was an insufficient number of eligible studies, a meta-analysis could not be conducted on the 15 risk factors. Therefore, Table 1 presents the raw data extracted from the individual articles that were included in this study. All included articles, spanning from 1996 to 2023, encompassed a total cohort of 66,925 participants. Among the studies, 9 were conducted in Europe, 26 in Asia, 10 in North America, 1 in South America, 2 in Oceania, and 10 in Africa. The mean age of subjects ranged from 18.0 to 78.8 years. Table 2 shows the characteristics of the included articles and the quality of each. Table 1 presents the results of a meta-analysis or original outcome analysis that evaluates the influence of 37 identified risk factors on the occurrence of ED in men with DM.

Table 1 Categorical analysis on the correlation between risk factors for erectile dysfunction and diabetes mellitus.

Table 2 Characteristics of studies included in the meta-analysis.

Demographic and lifestyle characteristics

The meta-analysis encompassed 43, 16, 2, 16, and 3 studies investigating the mean age, BMI, alcohol consumption, smoking status, and physical activity factors, respectively. Among these, mean age (OR: 1.31, 95% CI=1.24-1.37) and smoking status (OR: 1.32, 95% CI=1.18-1.47) were identified as significant risk factors, while BMI, alcohol consumption, and physical activity did not show significance(P ≥.05). Significant heterogeneity is present in both the mean age factor and smoking status factor(I 2 = 94.8% and 64.1%, respectively). Furthermore, we observed a significant publication bias in relation to the mean age factor (Egger’s test: P <.001). However, when employing the trim and filling method, the results remained stable after applying the necessary adjustments. Additionally, the smoking status factor displayed no significant bias (Egger’s test: P = .631).The results of all meta-analyses involving demographic and lifestyle characteristics factors are presented in Supplementary Figures 1 - 12 .

Laboratory analyses

low HDL cholesterol, testosterone, and microalbuminuria factors were analyzed in two separate articles for meta-analysis. The results revealed no significant heterogeneity between low HDL cholesterol and microalbuminuria factors(I 2 = 0% and 0%, respectively), prompting the utilization of the fixed-effect model. This model yielded significant results, indicating that both low HDL cholesterol(OR: 8.86, 95% CI=3.64-21.57) and microalbuminuria(OR: 3.77, 95% CI=1.98-7.18) were substantial risk factors. However, no discernible association was found between testosterone and the occurrence of ED(P = .727). The results of all meta-analyses involving Laboratory analyses factors are presented in Supplementary Figures 13 - 15 .

Diabetes-related complications

Meta-analyses were conducted on the factors of HbA1C(OR: 1.44, 95% CI=1.28-1.62), Duration of DM(OR: 1.39, 95% CI=1.29-1.50), Diabetic neuropathy(OR: 3.47, 95% CI=2.16-5.56), Diabetic retinopathy(OR: 3.01, 95% CI=2.02-4.48), and Diabetic foot(OR: 3.96, 95% CI=2.87-5.47), with a total of 26, 30, 4, 4, and 2 studies included, respectively. The findings demonstrated that these factors were substantiated as risk factors associated with an increased occurrence of ED in diabetic men. Significant heterogeneity was detected among the factors of HbA1C, Duration of DM, Diabetic neuropathy, and Diabetic retinopathy(I 2 = 87.0%, 95.2%,72.1% and 75.6%, respectively), while no significant heterogeneity was observed for the Diabetic foot factor(I 2 = 0%). Evidence of publication bias was identified in the studies examining the HbA1C and Duration of DM factors, as indicated by the results of the Egger’s test (P = 0.024, <.001, respectively). However, when employing the trim and filling method, the results remained stable after applying the necessary adjustments. The results of all meta-analyses involving Diabetes-related complications factors are presented in Supplementary Figures 16 - 26 .

Medical history and symptomatology

The meta-analysis conducted on CVD(OR: 1.92, 95% CI=1.71-2.16), hypertension(OR: 1.74, 95% CI=1.52-2.00), microvascular disease(OR: 2.14, 95% CI=1.61-2.85), vascular disease(OR: 2.75, 95% CI=2.35-3.21), nephropathy(OR: 2.67, 95% CI=2.06-3.46), depression(OR: 1.82, 95% CI=1.04-3.20), atherogenic dyslipidemia(OR: 2.22, 95% CI=1.98-2.49), metabolic syndrome(OR: 2.22, 95% CI=1.98-2.49), and diuretic treatment(OR: 2.42, 95% CI=1.38-4.22) revealed that these factors pose a significant risk for ED in diabetic men. Analyses of factors such as hypertension, microvascular disease, depression, and diuretic treatment exhibited considerable heterogeneity(I 2 = 63.3%, 77.8%,87.7% and 68.6%, respectively). Conversely, analyses of factors such as CVD, vascular disease, nephropathy, atherogenic dyslipidemia, and metabolic syndrome demonstrated no significant heterogeneity. As a result, fixed-effect models were employed in these cases(I 2 = 25.3%, 0%,30.0%, 46.0%, and 0%, respectively). No evidence of publication bias was detected in the results pertaining to the hypertension and microvascular disease factors(P = .527, = .296, respectively). The results of all meta-analyses involving medical history and symptomatology factors are presented in Supplementary Figures 26 - 38 .

Subgroup analysis and sensitivity analyses

Ration could possibly serve as a contributing factor to the observed heterogeneity in BMI, HbA1C, and microvascular disease factors. Furthermore, the utilization of medication for DM may be a potential source of heterogeneity in the relationship between smoking status factors, hypertension factors, and the development of ED. Lastly, the study design employed could be a plausible source of heterogeneity in the associations between BMI, smoking status, and microvascular disease factors. Significantly, we observed a notably higher incidence of ED within the African subgroup of the diabetic population, particularly in relation to mean age(OR: 2.38, 95% CI=1.52-5.26), duration of DM(OR: 3.16, 95% CI=1.41-7.08), and hypertension(OR: 2.23, 95% CI=1.50-3.31) factors. In addition, sensitivity analysis showed that our findings were reliable.

Our study, a Comprehensive Systematic Review and Meta-Analysis, has shed light on the multitude of risk factors associated with ED in men with DM. Notably, we have identified several key risk factors, including mean age, HbA1C levels, duration of DM, presence of diabetic neuropathy, retinopathy, foot complications, CVD, hypertension, microvascular complications, vascular disease, nephropathy, depression, metabolic syndrome, and diuretic treatment. Our findings significantly emphasize the heightened incidence of ED among individuals within the African subgroup of the diabetic population. Notably, mean age, duration of DM, and hypertension emerge as influential contributing factors to this phenomenon.

Heterogeneity was observed in the meta-analysis of certain factors, including mean age, smoking status, and others. To explore the potential sources of heterogeneity, we conducted subgroup analyses based on various parameters. In the subgroup analysis encompassing BMI, smoking status, HbA1C, hypertension, and microvascular disease factors, our observations indicate that heterogeneity in the meta-analysis results of these factors may stem from the subgroups of region, diabetes types, and study design. Regrettably, our analyses did not reveal a significant source of heterogeneity in the results of subgroup analyses regarding mean age and duration of DM factors. In the context of conducting a meta-analysis that encompasses a substantial number of studies, it is inevitable to encounter high heterogeneity. On one hand, the vast number of studies reflects the inclusion of diverse possibilities from various sources. On the other hand, in our pursuit of incorporating a comprehensive range of risk factors to provide a broader perspective, certain quality control measures had to be relaxed, which may have introduced heterogeneity due to the inclusion of lower-quality studies under less standardized study designs. We have acknowledged and outlined the limitations of our study, which detail the reasons behind the heterogeneity. Upon thorough examination of our data, we have identified the presence of publication bias in the meta-analysis pertaining to mean age, HbA1C, and duration of DM factors. To mitigate this issue, we firstly expanded our literature search to include not only mainstream academic databases but also gray literature, unpublished studies, and conference proceedings. Then, we used the trim and filling method to validate the results, which showed that the results were still stable after applying the necessary adjustments.

Our subgroup analysis revealed a significant increase in the incidence of ED among African subgroups of the diabetic population, particularly in relation to factors such as mean age (OR: 2.38, 95% CI = 1.52 – 5.26), duration of diabetes (OR: 3.16, 95% CI = 1.41 – 7.08), and hypertension (OR: 2.23, 95% CI = 1.50 – 3.31). Several factors contribute to this increased risk of ED in African populations. Firstly, the high prevalence of chronic diseases, including CVD and hypertension, along with infectious diseases like malaria and AIDS, in specific African regions, collectively contribute to the development of ED. These diseases pose a significant burden on African regions, exacerbating the incidence of ED ( 76 ). Furthermore, it is worth noting that certain regions experience a significant economic disparity when compared to developed regions in Europe and the US. This disparity has far-reaching implications, encompassing various aspects such as the quality of medical and healthcare services, education, and food safety ( 77 ). Previous studies have established a strong correlation between these factors and the prevalence of ED ( 78 ). Finally, cultural and social contexts also play a role in the higher risk of ED among African populations. In certain African cultures, male sexual competence is considered a symbol of honor and dignity. Consequently, men may experience anxiety and stress regarding their sexual ability, which can further affect their sexual function ( 79 ).

The relationship between DM and ED has garnered significant attention in the realm of ED-related research. Conducted as a comprehensive exploration of the medical and psychosocial factors associated with erectile dysfunction, the Massachusetts Male Aging Study uncovered a significant finding: diabetic patients exhibited a threefold age-adjusted likelihood of developing ED compared to non-diabetic patients ( 80 ). In 2017, Kouidrat et al. carried out an extensive meta-analysis consisting of 145 studies. The analysis revealed prevalence rates of 37.5%, 66.3%, and 57.7% for ED in individuals with type 1, type 2, and both types of diabetes, respectively ( 81 ). Recently, a review conducted by Giuseppe Defeudis and colleagues ( 82 ) on the definition and incidence of ED in patients with DM, the influence of DM complications and treatment on ED, served as inspiration for our study. Building upon this research, we employed more objective statistical tools to delve deeper into the distinct impact of these influencing factors on ED.

Advancing age is associated with a notable decline in organ function as well as reductions in male sex hormones. Additionally, the aging process often coincides with the simultaneous presence of other risk factors for ED. There exist misconceptions suggesting that advancing age leads to diminished sexual interest and desire. However, despite a reduction in sexual activity attributable to declining physical vigor associated with aging, engagement in sexual behavior remains prevalent among older demographics ( 83 ). In an epidemiological study carried out in the UK, results indicated that as many as 84.5% of men aged 60–69 years reported participating in sexual activity, while the percentage stood at 59.3% for men aged 70–79 years ( 84 ). Our study not only provides compelling evidence for this perspective, but our subgroup analysis also reveals a noteworthy finding: populations from Africa may exhibit a heightened susceptibility to the impact of advancing age on ED ( Table 3 ).

Table 3 Subgroup analysis of the correlation between risk factors for erectile dysfunction and diabetes mellitus.

In contrast to prior research regarding risk factors for ED ( 85 ), our study identified that BMI does not significantly contribute to ED risk. Likewise, physical activity was found to have limited efficacy in mitigating the development of ED. On one hand, it is plausible that BMI may not accurately reflect the extent of obesity in individuals, and on the other hand, managing body size and fat content may not effectively reduce ED risk in diabetic individuals without adequate glycemic control. Our findings align with this interpretation, as they underscore the significance of diabetic complications and glycemic control in relation to ED ( 86 ). Notably, the influence of smoking on ED remains considerable, underscoring its ongoing relevance. Therefore, quitting smoking represents an effective strategy for preventing and managing ED, even among individuals with DM.

Sufficient levels of androgens are crucial for erectile function. Androgens act peripherally, influencing erectile mechanisms by upholding the integrity of penile structures and regulating vasodilation in the penis ( 87 ). Two comprehensive meta-analyses, encompassing 850 diabetic men and 2000 non-diabetic individuals ( 88 ), as well as 1,822 diabetic men and 10,009 non-diabetic individuals, revealed markedly lower total testosterone levels in diabetic men compared to controls ( 89 ). This association has been linked to reduced levels of sex hormone binding globulin in individuals with DM. Our findings indicate that testosterone may not be a significant risk factor, aligning with previous reviews by Corona et al. ( 90 ), which suggest that testosterone replacement improves sexual symptoms in patients with prediabetes or newly diagnosed DM, but not in subjects with established diabetes. This phenomenon is attributed to the masking effect of diabetes-related vascular disease and neuropathy on the impact of replacement therapy.

Our observations indicate that the risk of experiencing ED is more prominently associated with diabetic complications rather than the duration of diabetes itself. These findings suggest that the duration of DM should not be perceived as the sole determinant of ED, and that the key factors contributing to heightened risk are inadequate glycemic control and the development of complications stemming from suboptimal treatment approaches. A randomized controlled study substantiates our perspective, which examined the impact of intensive glucose control on the risk of subsequent ED in 280 men with a history of diabetes ranging from 1 to 15 years and minor complications. Those initially randomized to intensive glucose control demonstrated a significantly reduced risk of ED compared to the usual care group (OR 0.33; 95% CI 0.18, 0.60) ( 91 ).

Previous animal and human studies have demonstrated that glycemic control plays a crucial role in regulating levels of systemic testosterone and Derived Factor-1 alpha. Notably, diabetic animals and humans exhibited significantly reduced levels of these two factors, whereas glycemic control effectively reversed this decline. This finding suggests that maintaining proper glycemic control mitigates the risk of ED in diabetic individuals by improving endothelial damage and enhancing protective mechanisms ( 92 ).We regret to note that only one study has investigated the outcomes of poor glycemic control as a risk factor. Consequently, we were unable to conduct a meta-analysis on this aspect. However, it is inferred that individuals with complications may be more prone to also have poor glycemic control. In individuals with diabetic retinopathy, there is an up-regulation of pro-inflammatory cytokines, which also hasten the progression of atherosclerosis. This leads to compromised blood flow to penile arterioles. Moreover, diabetic retinopathy signifies a more severe peripheral nerve complication of diabetes, undeniably exerting a detrimental impact on the erectile nerve ( 26 ). Similarly, the development of diabetic neuropathy is intricately linked to the underlying processes of microangiopathy and neurotoxicity, which manifest through a multitude of mechanisms ( 93 ). These mechanisms encompass heightened oxidative stress, accumulation of advanced glycation end products, impaired axonal transport, elevated flow through the polyol pathway, and the resulting detrimental impact on vascular nerve injury ( 94 ).

Penile erection is a complex process that involves the intricate interplay of neurovascular and psychological factors, regulating the balance between cavernous smooth muscle contraction and relaxation ( 95 ). The etiology of ED encompasses organic factors (such as neurogenic, vasogenic, steroid-induced, and drug-induced) as well as psychological factors ( 96 ). Vascular diseases, including CVD, microvascular and peripheral vascular sclerosis, and injury, are recognized as the primary organic causes of ED ( 97 ), while psychogenic ED is primarily attributed to psychological factors, social interpersonal relationships, and psychiatric diseases, all of which can exacerbate the occurrence of psychogenic ED. Hyperglycemia is frequently linked to impaired vasodilator signals, excessive smooth muscle cell contraction, and venous occlusive disorders—all of which are mechanisms contributing to ED in patients with DM ( 98 ). In addition, prolonged hyperglycemia leads to elevated oxidative stress due to factors such as inflammation, heightened production of reactive oxygen species, hyperhomocysteinemia, and reduced cellular antioxidants ( 99 ). These effects may be exacerbated by the presence of additional risk factors associated with both organic and psychological causes of ED. Our findings strongly align with this perspective, and through our meta-analysis of medical histories, we consistently observed significant impact results.

In our review of current published literature, we have identified certain risk factors that were not addressed in our article. This was due to the fact that some studies did not align with our inclusion and exclusion criteria, and others were not designed for quantitative meta-analysis. It is important to note that despite their omission from our study, these risk factors are of significance. Specifically, we would like to highlight risk factors such as abdominal obesity ( 100 ); waist circumference ( 101 );Hypogonadism ( 102 ); Cardiovascular medications ( 103 ) encompass a range of pharmacological interventions, such as angiotensin-converting enzyme (ACE) inhibitors, calcium channel blockers, beta-blockers, and diuretics ( 5 ) that were not covered in our analysis. We recommend that future high-quality longitudinal studies with wide-ranging scopes investigate the association of these factors with ED.

A notable strength of this study lies in its status as the most comprehensive meta-analysis to date examining the risk of ED in diabetic men. Initially, our study produced noteworthy findings in African subgroups with multiple risk factors. Nevertheless, to date, there is a lack of published studies stratified by different racial/ethnic populations to ascertain the impact of diverse demographics on the incidence of ED in diabetic men. Secondly, it is important to acknowledge the significant heterogeneity observed across several of our studies. While we have attempted to address this through subgroup analysis, it is vital to recognize the limitations inherent in our interpretation of these findings. Finally, the exclusive inclusion of English literature introduces the potential for selection bias, thereby possibly limiting the ability of certain studies to conduct meta-analyses and confining them to providing solely original data.

Our study indicates that in men with DM, several risk factors for ED have been identified, including mean age, HbA1C, duration of DM, diabetic neuropathy, diabetic retinopathy, diabetic foot, cardiovascular disease, hypertension, microvascular disease, vascular disease, nephropathy, depression, metabolic syndrome, and diuretic treatment. By clarifying the connection between these risk factors and ED, clinicians and scientific experts can intervene and address these risk factors, ultimately reducing the occurrence of ED and improving patient management.

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 authors.

Ethics statement

In our current study, we solely relied on publicly accessible summary studies, and ethical approval as well as consent from participants were obtained through the original studies.

Author contributions

DD: Conceptualization, Investigation, Methodology, Resources, Writing – original draft. AW: Investigation, Methodology, Resources, Writing – original draft. AT: Investigation, Methodology, Resources, Writing – original draft. LWT: Investigation, Methodology, Resources, Writing – original draft. AZ: Conceptualization, Funding acquisition, Supervision, Writing – review & editing. MR: Conceptualization, Funding acquisition, Supervision, Writing – review & editing.

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This research was supported by Xinjiang Uygur Autonomous Region Regional Collaborative Innovation Special Science and Technology Assistance Program FOUNDATION(No.2022E02129); National Natural Science Foundation of China (NSFC 82260139).

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fendo.2024.1368079/full#supplementary-material

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101. Corona G, Rastrelli G, Filippi S, Vignozzi L, Mannucci E, Maggi M. Erectile dysfunction and central obesity: an Italian perspective. Asian J Androl . (2014) 16:581–91. doi: 10.4103/1008-682X.126386

102. Kamenov ZA. A comprehensive review of erectile dysfunction in men with diabetes. Exp Clin Endocrinol Diabetes . (2015) 123:141–58. doi: 10.1055/s-0034-1394383

103. Dilixiati D, Cao R, Mao Y, Li Y, Dilimulati D, Azhati B, et al. Association between cardiovascular disease and risk of female sexual dysfunction: A systematic review and meta-analysis. Eur J Prev Cardiol . (2024), zwae042. doi: 10.1093/eurjpc/zwae042

Keywords: diabetes mellitus, erectile dysfunction, risk factors, meta-analysis, sexual dysfuction

Citation: Dilixiati D, Waili A, Tuerxunmaimaiti A, Tao L, Zebibula A and Rexiati M (2024) Risk factors for erectile dysfunction in diabetes mellitus: a systematic review and meta-analysis. Front. Endocrinol. 15:1368079. doi: 10.3389/fendo.2024.1368079

Received: 09 January 2024; Accepted: 19 March 2024; Published: 04 April 2024.

Reviewed by:

Copyright © 2024 Dilixiati, Waili, Tuerxunmaimaiti, Tao, Zebibula and Rexiati. 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: Abudureheman Zebibula, [email protected] ; Mulati Rexiati, [email protected]

† These authors share first authorship

Disclaimer: 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.

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How can I plan what to eat or drink when I have diabetes?

How can physical activity help manage my diabetes, what can i do to reach or maintain a healthy weight, should i quit smoking, how can i take care of my mental health, clinical trials for healthy living with diabetes.

Healthy living is a way to manage diabetes . To have a healthy lifestyle, take steps now to plan healthy meals and snacks, do physical activities, get enough sleep, and quit smoking or using tobacco products.

Healthy living may help keep your body’s blood pressure , cholesterol , and blood glucose level, also called blood sugar level, in the range your primary health care professional recommends. Your primary health care professional may be a doctor, a physician assistant, or a nurse practitioner. Healthy living may also help prevent or delay health problems  from diabetes that can affect your heart, kidneys, eyes, brain, and other parts of your body.

Making lifestyle changes can be hard, but starting with small changes and building from there may benefit your health. You may want to get help from family, loved ones, friends, and other trusted people in your community. You can also get information from your health care professionals.

What you choose to eat, how much you eat, and when you eat are parts of a meal plan. Having healthy foods and drinks can help keep your blood glucose, blood pressure, and cholesterol levels in the ranges your health care professional recommends. If you have overweight or obesity, a healthy meal plan—along with regular physical activity, getting enough sleep, and other healthy behaviors—may help you reach and maintain a healthy weight. In some cases, health care professionals may also recommend diabetes medicines that may help you lose weight, or weight-loss surgery, also called metabolic and bariatric surgery.

Choose healthy foods and drinks

There is no right or wrong way to choose healthy foods and drinks that may help manage your diabetes. Healthy meal plans for people who have diabetes may include

• dairy or plant-based dairy products
• nonstarchy vegetables
• protein foods
• whole grains

Try to choose foods that include nutrients such as vitamins, calcium , fiber , and healthy fats . Also try to choose drinks with little or no added sugar , such as tap or bottled water, low-fat or non-fat milk, and unsweetened tea, coffee, or sparkling water.

Try to plan meals and snacks that have fewer

• foods high in saturated fat
• foods high in sodium, a mineral found in salt
• sugary foods , such as cookies and cakes, and sweet drinks, such as soda, juice, flavored coffee, and sports drinks

Your body turns carbohydrates , or carbs, from food into glucose, which can raise your blood glucose level. Some fruits, beans, and starchy vegetables—such as potatoes and corn—have more carbs than other foods. Keep carbs in mind when planning your meals.

You should also limit how much alcohol you drink. If you take insulin  or certain diabetes medicines , drinking alcohol can make your blood glucose level drop too low, which is called hypoglycemia . If you do drink alcohol, be sure to eat food when you drink and remember to check your blood glucose level after drinking. Talk with your health care team about your alcohol-drinking habits.

Find the best times to eat or drink

Talk with your health care professional or health care team about when you should eat or drink. The best time to have meals and snacks may depend on

• what medicines you take for diabetes
• what your level of physical activity or your work schedule is
• whether you have other health conditions or diseases

Ask your health care team if you should eat before, during, or after physical activity. Some diabetes medicines, such as sulfonylureas  or insulin, may make your blood glucose level drop too low during exercise or if you skip or delay a meal.

Plan how much to eat or drink

You may worry that having diabetes means giving up foods and drinks you enjoy. The good news is you can still have your favorite foods and drinks, but you might need to have them in smaller portions  or enjoy them less often.

For people who have diabetes, carb counting and the plate method are two common ways to plan how much to eat or drink. Talk with your health care professional or health care team to find a method that works for you.

Carb counting

Carbohydrate counting , or carb counting, means planning and keeping track of the amount of carbs you eat and drink in each meal or snack. Not all people with diabetes need to count carbs. However, if you take insulin, counting carbs can help you know how much insulin to take.

Plate method

The plate method helps you control portion sizes  without counting and measuring. This method divides a 9-inch plate into the following three sections to help you choose the types and amounts of foods to eat for each meal.

• Nonstarchy vegetables—such as leafy greens, peppers, carrots, or green beans—should make up half of your plate.
• Carb foods that are high in fiber—such as brown rice, whole grains, beans, or fruits—should make up one-quarter of your plate.
• Protein foods—such as lean meats, fish, dairy, or tofu or other soy products—should make up one quarter of your plate.

If you are not taking insulin, you may not need to count carbs when using the plate method.

Work with your health care team to create a meal plan that works for you. You may want to have a diabetes educator  or a registered dietitian  on your team. A registered dietitian can provide medical nutrition therapy , which includes counseling to help you create and follow a meal plan. Your health care team may be able to recommend other resources, such as a healthy lifestyle coach, to help you with making changes. Ask your health care team or your insurance company if your benefits include medical nutrition therapy or other diabetes care resources.

Talk with your health care professional before taking dietary supplements

There is no clear proof that specific foods, herbs, spices, or dietary supplements —such as vitamins or minerals—can help manage diabetes. Your health care professional may ask you to take vitamins or minerals if you can’t get enough from foods. Talk with your health care professional before you take any supplements, because some may cause side effects or affect how well your diabetes medicines work.

Research shows that regular physical activity helps people manage their diabetes and stay healthy. Benefits of physical activity may include

• lower blood glucose, blood pressure, and cholesterol levels
• better heart health
• healthier weight
• better mood and sleep
• better balance and memory

Talk with your health care professional before starting a new physical activity or changing how much physical activity you do. They may suggest types of activities based on your ability, schedule, meal plan, interests, and diabetes medicines. Your health care professional may also tell you the best times of day to be active or what to do if your blood glucose level goes out of the range recommended for you.

Do different types of physical activity

People with diabetes can be active, even if they take insulin or use technology such as insulin pumps .

Try to do different kinds of activities . While being more active may have more health benefits, any physical activity is better than none. Start slowly with activities you enjoy. You may be able to change your level of effort and try other activities over time. Having a friend or family member join you may help you stick to your routine.

The physical activities you do may need to be different if you are age 65 or older , are pregnant , or have a disability or health condition . Physical activities may also need to be different for children and teens . Ask your health care professional or health care team about activities that are safe for you.

Aerobic activities

Aerobic activities make you breathe harder and make your heart beat faster. You can try walking, dancing, wheelchair rolling, or swimming. Most adults should try to get at least 150 minutes of moderate-intensity physical activity each week. Aim to do 30 minutes a day on most days of the week. You don’t have to do all 30 minutes at one time. You can break up physical activity into small amounts during your day and still get the benefit. 1

Strength training or resistance training

Strength training or resistance training may make your muscles and bones stronger. You can try lifting weights or doing other exercises such as wall pushups or arm raises. Try to do this kind of training two times a week. 1

Balance and stretching activities

Balance and stretching activities may help you move better and have stronger muscles and bones. You may want to try standing on one leg or stretching your legs when sitting on the floor. Try to do these kinds of activities two or three times a week. 1

Some activities that need balance may be unsafe for people with nerve damage or vision problems caused by diabetes. Ask your health care professional or health care team about activities that are safe for you.

Stay safe during physical activity

Staying safe during physical activity is important. Here are some tips to keep in mind.

Drink liquids

Drinking liquids helps prevent dehydration , or the loss of too much water in your body. Drinking water is a way to stay hydrated. Sports drinks often have a lot of sugar and calories , and you don’t need them for most moderate physical activities.

Avoid low blood glucose

Check your blood glucose level before, during, and right after physical activity. Physical activity often lowers the level of glucose in your blood. Low blood glucose levels may last for hours or days after physical activity. You are most likely to have low blood glucose if you take insulin or some other diabetes medicines, such as sulfonylureas.

Ask your health care professional if you should take less insulin or eat carbs before, during, or after physical activity. Low blood glucose can be a serious medical emergency that must be treated right away. Take steps to protect yourself. You can learn how to treat low blood glucose , let other people know what to do if you need help, and use a medical alert bracelet.

Avoid high blood glucose and ketoacidosis

Taking less insulin before physical activity may help prevent low blood glucose, but it may also make you more likely to have high blood glucose. If your body does not have enough insulin, it can’t use glucose as a source of energy and will use fat instead. When your body uses fat for energy, your body makes chemicals called ketones .

High levels of ketones in your blood can lead to a condition called diabetic ketoacidosis (DKA) . DKA is a medical emergency that should be treated right away. DKA is most common in people with type 1 diabetes . Occasionally, DKA may affect people with type 2 diabetes  who have lost their ability to produce insulin. Ask your health care professional how much insulin you should take before physical activity, whether you need to test your urine for ketones, and what level of ketones is dangerous for you.

Take care of your feet

People with diabetes may have problems with their feet because high blood glucose levels can damage blood vessels and nerves. To help prevent foot problems, wear comfortable and supportive shoes and take care of your feet  before, during, and after physical activity.

If you have diabetes, managing your weight  may bring you several health benefits. Ask your health care professional or health care team if you are at a healthy weight  or if you should try to lose weight.

If you are an adult with overweight or obesity, work with your health care team to create a weight-loss plan. Losing 5% to 7% of your current weight may help you prevent or improve some health problems  and manage your blood glucose, cholesterol, and blood pressure levels. 2 If you are worried about your child’s weight  and they have diabetes, talk with their health care professional before your child starts a new weight-loss plan.

You may be able to reach and maintain a healthy weight by

• following a healthy meal plan
• consuming fewer calories
• being physically active
• getting 7 to 8 hours of sleep each night 3

If you have type 2 diabetes, your health care professional may recommend diabetes medicines that may help you lose weight.

Online tools such as the Body Weight Planner  may help you create eating and physical activity plans. You may want to talk with your health care professional about other options for managing your weight, including joining a weight-loss program  that can provide helpful information, support, and behavioral or lifestyle counseling. These options may have a cost, so make sure to check the details of the programs.

Your health care professional may recommend weight-loss surgery  if you aren’t able to reach a healthy weight with meal planning, physical activity, and taking diabetes medicines that help with weight loss.

If you are pregnant , trying to lose weight may not be healthy. However, you should ask your health care professional whether it makes sense to monitor or limit your weight gain during pregnancy.

Both diabetes and smoking —including using tobacco products and e-cigarettes—cause your blood vessels to narrow. Both diabetes and smoking increase your risk of having a heart attack or stroke , nerve damage , kidney disease , eye disease , or amputation . Secondhand smoke can also affect the health of your family or others who live with you.

If you smoke or use other tobacco products, stop. Ask for help . You don’t have to do it alone.

Feeling stressed, sad, or angry can be common for people with diabetes. Managing diabetes or learning to cope with new information about your health can be hard. People with chronic illnesses such as diabetes may develop anxiety or other mental health conditions .

Learn healthy ways to lower your stress , and ask for help from your health care team or a mental health professional. While it may be uncomfortable to talk about your feelings, finding a health care professional whom you trust and want to talk with may help you

• lower your feelings of stress, depression, or anxiety
• manage problems sleeping or remembering things
• see how diabetes affects your family, school, work, or financial situation

Ask your health care team for mental health resources for people with diabetes.

Sleeping too much or too little may raise your blood glucose levels. Your sleep habits may also affect your mental health and vice versa. People with diabetes and overweight or obesity can also have other health conditions that affect sleep, such as sleep apnea , which can raise your blood pressure and risk of heart disease.

NIDDK conducts and supports clinical trials in many diseases and conditions, including diabetes. The trials look to find new ways to prevent, detect, or treat disease and improve quality of life.

What are clinical trials for healthy living with diabetes?

Clinical trials—and other types of clinical studies —are part of medical research and involve people like you. When you volunteer to take part in a clinical study, you help health care professionals and researchers learn more about disease and improve health care for people in the future.

Researchers are studying many aspects of healthy living for people with diabetes, such as

• how changing when you eat may affect body weight and metabolism
• how less access to healthy foods may affect diabetes management, other health problems, and risk of dying
• whether low-carbohydrate meal plans can help lower blood glucose levels
• which diabetes medicines are more likely to help people lose weight

Find out if clinical trials are right for you .

Watch a video of NIDDK Director Dr. Griffin P. Rodgers explaining the importance of participating in clinical trials.

What clinical trials for healthy living with diabetes are looking for participants?

You can view a filtered list of clinical studies on healthy living with diabetes that are federally funded, open, and recruiting at www.ClinicalTrials.gov . You can expand or narrow the list to include clinical studies from industry, universities, and individuals; however, the National Institutes of Health does not review these studies and cannot ensure they are safe for you. Always talk with your primary health care professional before you participate in a clinical study.

This content is provided as a service of the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), part of the National Institutes of Health. NIDDK translates and disseminates research findings to increase knowledge and understanding about health and disease among patients, health professionals, and the public. Content produced by NIDDK is carefully reviewed by NIDDK scientists and other experts.

NIDDK would like to thank: Elizabeth M. Venditti, Ph.D., University of Pittsburgh School of Medicine.

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Type 2 diabetes mellitus, the most frequent subtype of diabetes, is a disease characterized by high levels of blood glucose (hyperglycaemia). It arises from a resistance to and relative deficiency of the pancreatic β-cell hormone insulin.

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Thiazolidinediones (TZDs) are potent insulin-sensitizing drugs, but their use is accompanied by adverse side-effects. Rohm et al. now report that TZD-stimulated macrophages release miR-690-containing vesicles that improve insulin sensitization and bypass unwanted side-effects.

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Metformin induces a Lac-Phe gut–brain signalling axis

The mechanism by which metformin affects food intake remains controversial. Now, two studies link metformin treatment with the induction of the appetite-suppressing metabolite N -lactoyl-phenylalanine, which is produced by the intestine.

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Adipose tissue macrophages secrete small extracellular vesicles that mediate rosiglitazone-induced insulin sensitization

Rohm et al. show that small extracellular vesicles from adipose tissue macrophages from obese rosiglitazone-treated mice ameliorate glucose tolerance and insulin sensitivity in obese mice, while circumventing the adverse effects of rosiglitazone.

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Diagnose und Management der Osteoporose bei Diabetes mellitus (Update 2023)

Gemeinsame Leitlinie der Österreichischen Gesellschaft für Knochen- und Mineralstoffwechsel und der Österreichischen Diabetes Gesellschaft

Diagnosis and management of patients with diabetes and co-existing osteoporosis (Update 2023)

Common guideline of the Austrian Society for Bone and Mineral Research and the Austrian Diabetes Society

• leitlinien für die praxis
• Open access
• Published: 20 April 2023
• Volume 135 , pages 207–224, ( 2023 )

Cite this article

You have full access to this open access article

• Christian Muschitz 1 , 2 ,
• Alexandra Kautzky-Willer 3 ,
• Yvonne Winhofer 3 ,
• Martina Rauner 4 ,
• Judith Haschka 2 , 5 ,
• Daniel Cejka 6 ,
• Robert Wakolbinger-Habel 2 , 7 &
• Peter Pietschmann 8  

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Zusammenfassung

Diabetes mellitus und Osteoporose zählen zu den häufigsten chronischen Erkrankungen und kommen deshalb beide häufig in ein und demselben Individuum vor. Da die Prävalenz beider mit steigendem Alter zunimmt, wird in Anbetracht der Altersstruktur unserer Bevölkerung deren Häufigkeit zunehmen.

Patient:innen mit Diabetes haben ein erhöhtes Risiko für Fragilitätsfrakturen. Die Pathophysiologie ist unklar und vermutlich multifaktoriell.

Longitudinale Studien haben den Nachweis erbracht, dass das Fracture Risk Assessment Tool (FRAX) und die Knochendichte (BMD) mittels DXA (T-score) Messungen und einem eventuell vorhandenen Trabecular Bone Score (TBS) das individuelle Frakturrisiko vorhersagen können. Hierfür muss allerdings eine Adjustierung vorgenommen werden, um das Risiko nicht zu unterschätzen.

Es gibt derzeit aus osteologischer Sicht noch nicht den optimalen Ansatz, da es keine Studien mit rein diabetischen Patient:innen und Osteoporose gibt.

Patient:innen mit Diabetes mellitus und einem erhöhten Frakturrisiko sollten genauso wie Patient:innen ohne Diabetes und einem erhöhten Frakturrisiko behandelt werden.

Der Vitamin-D-Spiegel sollte auf jeden Fall immer optimiert werden und auf eine ausreichende Kalziumaufnahme (vorzugsweise durch die Nahrung) ist zu achten.

Bei der Wahl der antihyperglykämischen Therapie sollten Substanzen mit nachgewiesen negativem Effekt auf den Knochen weggelassen werden. Bei Vorliegen einer Fragilitätsfraktur ist auf jeden Fall – unabhängig von allen vorliegenden Befunden – eine langfristige spezifische osteologische Therapie indiziert.

Zur Prävention von Fragilitätsfrakturen sind antiresorptive Medikamente die erste Wahl, entsprechend den nationalen Erstattungskriterien auch anabole Medikamente. Das Therapiemonitoring soll im Einklang mit der nationalen Osteoporose Leitlinie erfolgen.

Fragility fractures are increasingly recognized as a complication of both type 1 and type 2 diabetes, with fracture risk that increases with disease duration and poor glycemic control. The identification and management of fracture risk in these patients remains challenging. This manuscript explores the clinical characteristics of bone fragility in adults with diabetes and highlights recent studies that have evaluated areal bone mineral density (BMD), bone microstructure and material properties, biochemical markers, and fracture prediction algorithms (FRAX) in these patients. It further reviews the impact of diabetes drugs on bone tissue as well as the efficacy of osteoporosis treatments in this population. An algorithm for the identification and management of diabetic patients at increased fracture risk is proposed.

Avoid common mistakes on your manuscript.

Epidemiologie des Diabetes mellitus und osteoporotische Fragilitäts-Frakturen

Diabetes mellitus und Osteoporose zählen zu den häufigsten chronischen Erkrankungen und kommen deshalb beide häufig in ein und demselben Individuum vor, weshalb davon ausgegangen wird, dass sie in Zusammenhang stehen. Da die Prävalenz beider mit steigendem Alter zunimmt, wird in Anbetracht der Altersstruktur unserer Bevölkerung deren Häufigkeit zunehmen.

Rezente Metaanalysen mit rund 140.000 Patient:innen Footnote 1 mit Typ 1 Diabetes (T1DM) zeigen ein gepooltes relatives Risiko (RR) für eine Fraktur von 3,16, für eine Hüftfraktur von 3,78 und für eine vertebrale Fraktur von 2,88. Das RR einer hüftgelenksnahen Fraktur bei Frauen mit T1DM im Vergleich zu Frauen ohne Diabetes beträgt 5,19 [ 1 ]. Das Frakturrisiko steigt mit zunehmendem Lebensalter an, speziell Hüftfrakturen treten bei T1DM etwa 10–15 Jahre früher auf [ 2 ].

Bei Typ 2 Diabetes (T2DM) weisen Populations-basierte Daten von rund 33.000 Patient:innen den T2DM als stärksten Prädiktor für niedrig-traumatische (= osteoporotische) Frakturen bei Männern (RR 2,38) und bei Frauen (RR 1,87) aus [ 3 ]. Mit einer durchschnittlichen Odds-Ratio (OR) für Frakturen von 1,5 ist der T2DM nur für rund 4 % aller osteoporotischen Frakturen ursächlich in Zusammenhang zu bringen. Dem gegenüber ist allerdings die global steigende Inzidenz von Patient:innen mit T2DM (rund 425 Mio. sowie rund 320 Mio. mit einer gestörten Glucose-Toleranz) gegenüber zu stellen. Zusätzlich zu diesem direkten Risiko kommen noch weitere klinische Risikofaktoren (clinical risk factors, CRF), die mit einem Diabetes einhergehen (z. B. multiple Stürze, Neuro- und Retinopathie, etc.) und das individuelle Frakturrisiko zusätzlich erhöhen (Tab.  1 und  2 ; [ 4 ]).

Diabetes assoziierte Risikofaktoren für Frakturen

Diabetes per se ist ein klinischer Risikofaktor für ein erhöhtes Frakturrisiko (Tab.  3 ). Bei T2DM spielen das Alter und die Dauer des Diabetes eine wichtige Rolle. Sowohl bei Frauen als auch bei Männern > 40 Jahren ist T2DM ein unabhängiger Risikofaktor für sämtliche osteoporotische Frakturen (Hazard Ratio, HR 1,32). Das Alter beeinflusst das Risiko dahin gehend, dass jüngere Patient:innen ein höheres Risiko für Hüftfrakturen haben (HR Alter < 60 Jahre: 4,67; HR Alter 60–69 Jahre: 2,68; HR Alter 70–79 Jahre: 1,57; HR Alter > 80 Jahre: 1,42) [ 5 ]. Entscheidend ist außerdem die Dauer der Erkrankung. In den ersten fünf Jahren der Erkrankung kommt es zu keiner Erhöhung des relativen Risikos (ein protektiver Effekt vermehrter Fettmasse wird diskutiert), das Risiko folgt allerdings einem biphasischen Verlauf mit einem zweiten Gipfel jenseits von zehn Jahren Erkrankungsdauer (HR 1,47) [ 6 ].

Die glykämische Kontrolle ist wichtig für die Beurteilung des individuellen Frakturrisikos. Ein HbA1c > 7 % führt zu einem raschen Anstieg des Risikos mit einer erhöhten Mortalität nach Frakturen [ 7 ]. Zusätzlich hat eine schlechte glykämische Kontrolle einen negativen Einfluss auf die Mikroarchitektur des Knochens mit mikrovaskulären Komplikationen in diesem Organsystem [ 8 ].

Diabetes, Niere & Knochen

Diabetes mellitus (DM) ist eine häufige Ursache für chronische Niereninsuffizienz (chronic kidney disease – CKD) auf Basis einer diabetischen Nephropathie (diabetic kidney disease – DKD). In Österreich ist DM (Typ 1 und 2) die häufigste Ursache für terminales Nierenversagen. Etwa 25 % aller Patient:innen, die in Österreich eine Nierenersatztherapie benötigen (Dialyse oder Transplantation), haben eine DKD als Grunderkrankung [ 1 ]. Sowohl DM als auch CKD erhöhen das Risiko für osteoporotische Frakturen. Im Vergleich zu nierengesunden Patient:innen mit DM haben Patient:innen mit DM und einer DKD ein etwa 1,4 bis 1,7-fach erhöhtes Risiko für osteoporotische Frakturen, einschließlich Hüftfrakturen [ 2 , 3 ].

Als Basis jeder Behandlung von Patient:innen mit DKD wird eine Lebensstilmodifikation empfohlen [ 4 ]. Einige dieser Lebensstil-Empfehlungen wie sportliche Betätigung und Rauchstop sind auch aus osteologischer Sicht günstig. Darüber hinaus wird für die meisten Patient:innen mit einer DKD eine Therapie mittels ACE-Hemmern/Angiotensin-Rezeptor-Blockern (ACEi/ARB) in Kombination mit SGLT-2-Inhibitoren empfohlen [ 4 ].

In den letzten Jahren haben mehrere randomisierte prospektive Studien gezeigt, dass SGLT‑2 Inhibitoren sowohl in der Primärprävention (Vermeidung einer DKD) als auch in der Sekundärprävention (Behandlung einer bestehenden DKD) signifikante und klinisch relevante Vorteile bezüglich verlangsamter Progression der Niereninsuffizienz (verlangsamter GFR-Verlust), Reduktion der Albuminurie, Reduktion von akutem Nierenversagen und Reduktion des Auftretens einer terminalen (dialysepflichtigen) Niereninsuffizienz, bringen [ 5 , 6 , 7 , 8 , 9 , 10 , 11 ].

In einer SGLT-2-Inhibitor-Studie (Akronym: CANVAS) wurde eine erhöhte Rate osteoporotischer Frakturen unter Canagliflozin versus Placebo (hazard ratio 1,53) beobachtet [ 9 ]. Interessanter Weise kommt es unter SGLT-2-Inhibitor-Therapie zu einem (teilweise passageren und inter-individuell stark variierendem) Anstieg von Serum-Phosphat (vermutlich durch vermehrte renale Re-Absorption), Parathormon (PTH) und fibroblast growth-factor 23 (FGF-23) sowie einem korrespondierenden Abfall von Calcitriol (1,25-OH-D3) [ 12 , 13 ]. Diese Auswirkungen einer SGLT-2-Inhibitor-Therapie auf den Mineralstoffwechsel könnten unter Umständen nachteilige Effekte auf die Knochengesundheit haben und auch eine Erklärung für die beobachtete Erhöhung der Frakturrate unter Canagliflozin sein.

In einer anderen prospektiven randomisierten Studie mit Canagliflozin wurde jedoch keine erhöhte Frakturrate gegenüber Plazebo gefunden [ 10 ], ebensowenig wie in allen weiteren Studien mit anderen SGLT-2-Inhibitoren (Empagliflozin [ 5 , 6 ], Dapagliflozin [ 7 , 8 , 14 ], Ertugliflozin [ 15 ]). Auch in real-world-Analysen basierend auf Verschreibungsdaten und Gesundheitsdaten von Krankenversicherungen, Meta-Analysen und Analysen von Pharmakovigilanzmeldungen findet sich kein Hinweis für eine erhöhte Frakturrate unter einer Behandlung mit SGLT-2-Inhibitoren [ 16 , 17 , 18 , 19 ].

Zusammengefasst sind SGLT-2-Inhibitoren eine Standardtherapie zur Prävention und Behandlung einer DKD und haben keinen Einfluss auf das Frakturrisiko.

Einfluss der Behandlung des Diabetes auf das Frakturrisiko

Das Verhältnis zwischen Diabetes und Knochenfragilität und die Identifizierung jener Patient:innen mit einem erhöhten Risiko für Frakturen wird zusätzlich durch die Eigenschaften antiglykämischer Medikamente auf das Skelett beeinflusst (Tab.  1 ). Obwohl es keine einzige prospektive Studie mit einem primären Studienziel in Bezug auf Therapie des Diabetes und Knochenfragilität gibt, zeigen Daten aus epidemiologischen und Beobachtungsstudien ein heterogenes Muster von teilweise positiven, aber auch negativen Effekten auf den Knochenstoffwechsel.

Lebensstilfaktoren

Eine Veränderung des Lebensstils ist – nicht nur bei der Erkrankung Diabetes – eine der Säulen der nicht-medikamentösen Therapie. Grundsätzlich ist ein Gewichtsverlust, sofern keine Gegenmaßnahmen gesetzt werden, immer mit dem Verlust von Muskel- und Knochenmasse verbunden. Sarkopenie und sarkopene Adipositas sind Risikofaktoren für Stürze und Gebrechlichkeit, daher ist immer auf eine ausreichende alimentäre Zufuhr von Proteinen und progressives Widerstandstraining zu achten.

Körperliche Aktivität während einer gezielten Gewichtsabnahme verbessert die Lebensqualität und senkt gleichzeitig zirkulierende Sclerostin-Spiegel, unabhängig vom Alter der Patient:innen [ 20 ].

Andere nicht-pharmakologische Maßnahmen sind – wie bei vielen anderen Erkrankungen – die Vermeidung von Nikotin und übermäßigem Alkoholgenuss.

Ein Vitamin D Mangel ist sowohl beim T1DM als auch beim T2DM mit hoher Prävalenz vorhanden. Obwohl der direkte Beweis für die Wirksamkeit eines optimierten Vitamin D Spiegels bei Adipositas bzw. Diabetes und/oder Insulin-Resistenz noch nicht in Studien als primärer Endpunkt nachgewiesen wurde, ist doch davon auszugehen, dass Patient:innen mit Diabetes ähnlich wie nicht-diabetische Kollektive davon profitieren. Ein adäquater Vitamin D Spiegel und eine suffiziente Aufnahme von Calcium (vorzugsweise über die Nahrung) sind daher eine Grundvoraussetzung – auch im Hinblick auf die Prävention eines sekundären Hyperparathyreoidismus. Möglicherweise sind anfänglich höhere Einzeldosen von Cholecalciferol notwendig, um einen suffizienten Spiegel zu erreichen [ 21 ]. Eine Supplementation hat allerdings keinen Schutz vor Frakturen, Sturz oder klinisch relevante Effekte auf die Knochendichte gezeigt [ 22 ].

Glykämische Kontrolle

Bei Patient:innen mit Diabetes besteht zusätzlich eine Fallneigung, welche wahrscheinlich zum erhöhten Frakturrisiko beiträgt. Die periphere Neuropathie, die Retinopathie mit Visusverschlechterung, vermehrte Stürze in der Anamnese, die Tendenz zu hypoglykämen Episoden, die Hypo- oder Hypertension bzw. die autonome Neuropathie sind hier beispielhaft zu nennen.

Eine eng eingestellte glykämische Kontrolle (HbA1c < 7 %) verringert das Frakturrisiko bei Diabetes, vor allem bei älteren Patient:innen. Allerdings ist sowohl die Hypoglykämie als auch die Hyperglykämie mit einem erhöhten Risiko für Fragilitätsfrakturen assoziiert, wahrscheinlich durch unterschiedliche Mechanismen [ 23 ]. Vor allem bei älteren Patient:innen mit Diabetes wird daher – um das Risiko für hypoglykäme Episoden zu vermeiden – eine weniger stringente Einstellung des Diabetes empfohlen, um das Sturzrisiko zu senken (EASD/ADA Guidelines) [ 24 ].

Effekte antihyperglykämischer Therapie auf den Knochen

In vitro Studien zeigen einen positiven Effekt von Metformin auf den Knochen durch Steigerung der Knochenmasse und Knochenstärke, einer Reduktion von AGEs (Advanced Glycolysation Endproducts) sowie einer Stimulation der Osteoblastogenese und einer verminderten Apoptose von Osteoblasten [ 25 , 26 , 27 ]. Präklinische und klinische Daten bestätigen einen neutralen bis positiven osteogenen Effekt in Bezug auf Frakturen und bei ketogener Ernährung, sodass diese Medikation in Bezug auf die Knochenqualität als sicher zu werten ist [ 28 , 29 , 30 , 31 , 32 ].

SGLT-2-Inhibitoren

Natrium-Glucose-Cotransporter 2 (SGLT2)-Inhibitoren gelten aufgrund ihrer zusätzlichen kardio-reno-protektiven Effekte als wichtige Substanzen in der Therapie des Typ 2 Diabetes. Sie hemmen im proximalen Tubulus die Reabsorption von Glucose und führen gleichzeitig zu einer vermehrten Reabsorption von Phosphat. Damit kann es potenziell zu einer Störung der Calcium-Phosphat-Homöostase kommen. Unter der Behandlung mit Dapagliflozin konnten leichte Anstiege von Magnesium, Phosphat und Parathormon gezeigt werden, jedoch ohne konsekutiven Effekt auf Serum-Calcium oder Vitamin D [ 33 , 34 ]. Unter Dapagliflozin konnte zudem kein negativer Effekt auf den Knochenstoffwechsel oder die Knochendichte nachgewiesen werden 51. Sicherheitshinweise hinsichtlich verstärkter Knochenresorption und erhöhtem Frakturrisiko fanden sich lediglich bei der Behandlung mit Canagliflozin, wodurch eine FDA Warnung erfolgte. Daher sollte, wie auch in der täglichen Praxis üblich, einer Behandlung mit Dapagliflozin und Empagliflozin der Vorzug gegeben werden [ 35 , 36 ]. Zusammenfassend fand sich in großen Meta-Analysen kein negativer Einfluss auf die Knochendichte sowie kein erhöhtes Frakturrisiko unter SGLT-2-Inhibitor Therapie [ 37 ].

Inkretinmimetika

Glp-1-rezeptoragonisten.

GLP-1-Rezeptoragonisten werden je nach Wirkdauer einmal täglich oder einmal wöchentlich verabreicht. Neben ihrer ausgeprägten blutzuckersenkenden Potenz, zeigen sie zudem positive Effekte auf das Körpergewicht und einen kardiovaskulären Benefit, weswegen jene mit nachgewiesenem kardiovaskulärem Benefit vor allem bei Patient:innen mit etablierten kardiovaskulären Erkrankungen oder einem hohen Risiko hierfür eingesetzt werden sollten.

Ein positiver Effekt der GLP-1R-Agonisten konnte durch eine gesteigerte Proliferation mesenchymaler Stammzellen, einer Adipozytendifferenzierung und einer verminderten Sclerostin Exprimierung gezeigt werden [ 38 , 39 ]. Klinische Studien zeigten einen neutralen Effekt auf die Knochendichte unter GLP-1R-Agonisten Therapie [ 40 ]. Die aktuelle Datenlage zeigt kein erhöhtes Frakturrisiko durch den Einsatz von GLP-.1-RA; im Gegenteil, es gibt zunehmend Evidenz auf einen anti-osteoporotischen Effekt durch Verbesserung der Knochendichte und Qualität sowie Hemmung der Knochenresorption [ 41 , 42 , 43 ].

DPP-IV-Hemmer

Dipeptidyl-Peptidase-4-Inhibitoren (DPP4-Inhibitoren) zeigen bei T2DM ein mehrheitlich neutrales Sicherheitsprofil für das Skelettsystem. Während präklinische Studien eine Reduktion der Knochenresorption und eine Zunahme von trabekulärem und kortikalem Knochenvolumen unter DPP4-Inhibitor Therapie zeigten [ 25 , 26 , 38 , 39 , 40 , 44 , 45 ], ist in klinischen Studien eine neutraler bis positiver Effekt hinsichtlich Frakturprävention unter DPP-4-Inhibitor Therapie beschrieben, wenngleich keine dieser Untersuchungen die Frakturprävention als primären Endpunkt hatten und die Fallzahl an Frakturen gering war [ 46 , 47 , 48 ]. Zusammenfassend zeigt die aktuelle Datenlage einen neutralen bis positiven Effekt auf die Reduktion des Frakturrisikos.

Sulfonylharnstoffe

Unter Behandlung mit Sulfonylharnstoffen wurde eine gesteigerte Osteoblastenproliferation und Differenzierung in vitro gezeigt [ 25 , 28 ]. Epidemiologische Daten wiederum zeigen unterschiedliche Ergebnisse in Bezug auf das Organsystem Knochen, mit einem neutralen bis positiven Effekt auf das Frakturrisiko. Daten zu Veränderungen der Knochendichte liegen nicht vor. Eine Auswertung von Hypoglykämie-assoziierten Events fand einen Zusammenhang mit einem erhöhten Risiko für Sturz-assoziierte Frakturen [ 46 ]. Zusammenfassend kann daher das Frakturrisiko als reduziert, mit Ausnahme des indirekt erhöhten Sturzrisikos bei Hypoglykämie, zusammengefasst werden [ 28 , 31 , 47 ].

Thiazolidinedione

Thiazolidinedione interagieren mit dem peroxisome proliferator-activated receptor (PPAR)γ, was zu einer Dysbalance zugunsten der Differenzierung von Adipozyten und zu Lasten der Differenzierung von Osteoblasten führt. Darüber hinaus kommt es zu einer gesteigerten Osteoklastogenese [ 25 , 26 , 48 , 49 , 50 ]. Eine große epidemiologische Auswertung von über 32000 Patient:innen mit Diabetes Mellitus Typ 2 bestätigte das erhöhte Frakturrisiko unter Behandlung mit Thiazolidinedionen, insbesondere für periphere Frakturen und bei Frauen unter 64 Jahren [ 28 , 51 ]. Eine rezente Metaanalyse bestätigt ebenfalls den negativen Einfluss sowohl von Rosiglitazon, als auch von Pioglitazon auf den Knochen [ 32 ]. Es wird daher empfohlen, bei allen Patient:innen mit erhöhtem Risikoprofil für Fragilitätsfrakturen, insbesondere bei postmenopausalen Frauen, primär nicht einzusetzen [ 31 , 52 , 53 ].

In präklinischen Studien konnte ein anaboler Effekt von Insulin auf den Knochen nachgewiesen werden. Im Kontrast dazu findet sich jedoch in klinischen Studien ein erhöhtes Frakturrisiko bei Patient:innen mit T2DM unter Insulin Therapie, insbesondere für nicht-vertebrale Frakturen [ 54 , 55 , 56 , 57 ]. Eine rezente epidemiologische Analyse in Österreich konnte das deutlich erhöhte Risiko für Hüftfrakturen bei Patient:innen unter Insulintherapie bestätigen [ 58 ]. Ursächlich muss die längere Krankheitsdauer und/oder eine schlechtere glykämische Kontrolle in Zusammenhang mit allen Sekundärkomplikationen der Erkrankung (Retinopathie, Neuropathie, Nephropathie, etc.) berücksichtig werden. Patient:innen unter einer Insulin-Therapie haben zudem indirekt durch Hypoglykämie-induzierte Stürze ein erhöhtes Risiko [ 59 ]. Wenig Evidenz liegt zu Veränderung der Knochenmineraldichte vor, wobei hier ein neutraler bis negativer Effekt insbesondere bei Männern zu beobachten war [ 60 ]. Zusammenfassend muss daher die Insulin Therapie als Risikofaktor für ein erhöhtes Frakturrisiko gesehen werden [ 28 ].

DXA-Knochendichtemessung

Die Knochendichtemessung mittels DXA (dual energy x‑ray absorptiometry) ist nach wie vor der Goldstandard. Die Definition einer Osteoporose von einem T‑score ≤ −2,5 basiert auf einer Definition der WHO aus dem Jahr 1994 und definiert die Erkrankung, jedoch nicht die individuelle Interventionsschwelle [ 61 ].

Die Mehrzahl der Studien bei Patient:innen mit T1DM zeigen, dass die BMD bei dieser Patient:innenpopulation deutlich vermindert ist [ 62 ]. Aufgrund der meist vorherrschenden Adipositas als Risikofaktor bei T2DM wäre grundsätzlich davon auszugehen, dass ein hoher Body Mass Index (BMI) und eine hohe BMD positiv miteinander korrelieren. Daher haben Patient:innen mit einem T2DM in der Regel eine 5–10 % höhere BMD im Vergleich zur nicht-diabetischen gesunden Population. Die höhere BMD ist vor allem beim jüngeren männlichen Geschlecht vorherrschend – interessanter Weise auch bei höheren HbA1c Werten. Die höhere BMD ist vor allem am Gewichts-tragenden Knochen zu sehen, jedoch nicht am Radius.

Die relativ höhere BMD bei T2DM schützt die Patient:innen jedoch nicht vor Frakturen. Die Mehrzahl der Patient:innen mit Frakturen haben einen T‑score im osteopenen Bereich, also einem T‑score > −2,5 [ 63 ]. Bei Frauen mit T2DM ist das individuelle Frakturrisiko im Gegensatz zu Frauen ohne Diabetes in etwa 0,5 T-scores als Korrekturfaktor tiefer als der tatsächliche Messwert anzusetzen (Abb.  1 ). Obwohl zahlreiche Studien in dieser Patient:innenpopulation bestätigen, dass die DXA-Messung systematisch das Frakturrisiko unterschätzt, werden unter Berücksichtigung dieses Korrekturfaktors vor allem ältere Patient:innen adäquat stratifiziert [ 64 ].

Evaluation des Frakturrisikos bei Patient:innen mit Diabetes. a Bei Diabetes ist das Frakturrisiko bei einem T‑score ≤ −2,0 gleich hoch wie beim nicht-diabetischen Patient:innen mit einem T‑score ≤ −2,5. b In Österreich gilt folgende nationale Interventionsschwelle im FRAX: 10-Jahreswahrscheinlichkeit für major osteoporotic fracture: ≥ 20 %, hip fracture: ≥ 5 %. z. B. mit TBS Korrektur oder der CRF „Rheumatoide Arthritis“ wird auf „Ja“ gesetzt. d Zu diesen Frakturen zählen: Humerusfraktur, Schambeinastfraktur, Clavicula, Rippen (= nicht-vertebrale, nicht-Hüft Frakturen) (From: Diagnose und Management der Osteoporose bei Diabetes mellitus (Update 2019))

Manche Studien bestätigen einen schnelleren Verlust an BMD auch an Gewichts-tragenden Knochen (z. B. Hüfte) unter T2DM als möglichen Grund für die erhöhte Frakturrate [ 65 ].

Mit dem Trabecular Bone Score (TBS) steht eine Methode zur Verfügung, um aus einer zweidimensionalen DXA-Untersuchung Informationen über die Knochenmikrostruktur der Lendenwirbelsäule zu generieren. Diese einheitslose Zahl spiegelt anhand der Analyse von Grauwert-Variogrammen der radiologischen Messung der Lendenwirbelkörper L1–L4 mit einer hohen Korrelation die trabekuläre Mikroarchitektur bei Osteoporose unabhängig von der BMD wider [ 66 ]. Der TBS-Software kann direkt im Rahmen der Messung mit, oder auch retrospektiv den Score berechnen, wodurch sich keine zusätzliche Strahlenbelastung für den Patient:innen ergibt. Im Gegensatz zur DXA-Methode ist der TBS bei Patient:innen mit T2DM tiefer als in einer nicht-diabetischen Population. In einer großen Populations-basierten Untersuchung wurden Patient:innen mit einem TBS von < 1230 als Risikopatient:innen für Osteoporose-assoziierte Frakturen eingestuft, bei einem TBS von 1230–1310 ein mittleres Risiko. Bei T2DM fanden sich in Studien TBS Werte zwischen 1100 und 1200 [ 67 ].

Der TBS ist bei Patient:innen mit guter glykämischer Kontrolle höher und tiefer bei einem schlecht eingestellten T2DM. Der TBS ist somit ein unabhängiger Prädiktor für das Frakturrisiko bei Diabetes (HR 1,27) bzw. auch ohne Diabetes (HR 1,31) [ 68 ].

Alternative Methoden wie etwa der Ultraschall am Calcaneus oder am Radius zeigen inkonklusive Ergebnisse bei T2DM [ 69 ].

Mikroarchitektur und Knochenqualität

Die BMD alleine – vor allem beim T2DM – erklärt nicht die erhöhte skeletale Fragilität. Sowohl in MRT-Untersuchungen als auch mittels HR-pQCT (high resolution peripheral quantitative computed tomography) am Radius (nicht Gewichts-tragender Knochen) und an der Tibia (Gewichts-tragender Knochen) zeigt sich beim T2DM eine verschlechterte Mikroarchitektur. Die Trabekel beim T2DM sind im Vergleich zum nicht-diabetischen Patient:innen eher hypertrophiert. Im trabekulären Netzwerk finden sich allerdings auch größere Löcher, zusätzlich ist die kortikale Porosität (bis zu 16 %) gegenüber Patient:innen ohne T2DM erhöht. Die strukturelle Alteration mit hoher Heterogenität ist an der endokortikalen Übergangszone besonders ausgeprägt („Trabekularisierung der Kortikalis“). Zusätzlich gibt es einen geschlechtsspezifischen Unterschied mit schlechteren Werten beim weiblichen Geschlecht [ 70 , 71 ].

Aufgrund dieser strukturellen Defizite bei T2DM ist die Knochenfestigkeit sowie die Steifigkeit und die Elastizität des Knochens in virtuellen FEA (finite element analysis) Untersuchungen vermindert. Microindentations-Untersuchungen zeigen zusätzlich strukturelle Einschränkungen als Ausdruck veränderter Kollagenverlinkungen in der Knochenmatrix aufgrund vermehrter AGEs (advanced gylcation endproducts). Diese Verlinkungen führen dazu, dass der Knochen an Elastizität und Flexibilität verliert. In Summe haben diese Untersuchungsergebnisse den Begriff der „Diabetoporose“ geprägt ([ 72 ]; Abb.  2 ).

Effekte des T2DM auf den Knochenstoffwechsel. Auf systemischer Ebene ist T2DM mit einem niedrigen Vitamin D-Spiegel, hoher Glukosewerte, sowie eingeschränkter physischer Aktivität und einen erhöhten Sturzrisiko verbunden, alle, welche zu einer erhöhten Frakturrate führen. Auf zellulärer Ebene ist vor allem die Anzahl der Adipozyten erhöht, wobei die Anzahl der Osteoblasten sowie die Anzahl und Funktion der Gefäße vermindert sind. Osteozyten werden zur vermehrten Produktion von Sclerostin, DKK‑1, Periostin, FGF-23 und RANKL animiert, welche die Osteoblastendifferenzierung hemmen und die Osteoklastengeneration fördern. Zusätzlich fördern pro-inflammatorische M1-Makrophagen die Osteoklastengeneration und hemmen Osteoblasten. Verschieden miRNA-Signaturen sind auch bei Diabetes verändert und könnten zur Pathogenese des Knochenverlusts beitragen. T2DM hat auch direkte Effekte auf die Knochenmatrix. So werden vermehrt advanced glycation endproducts (AGE) in die Kollagenmatrix eingebaut, die so zur Versteifung der Matrix beiträgt. Außerdem führt T2DM zur einer niedrigeren Mineral:Matrix-Ratio und zu einer poröseren kortikalen Knochenstruktur, welches die mechanische Stabilität des Knochens beeinträchtigt. (From: Diagnose und Management der Osteoporose bei Diabetes mellitus (Update 2019))

Knochenstoffwechsel: Histomorphometrie und Serum Marker

Der Goldstandard zur Untersuchung des lokalen Knochenstoffwechsels ist die Histomorphometrie aus bioptischen Proben. Zur Abschätzung der tatsächlichen Aktivität ist einer der wichtigen Parameter die Formationsrate bezogen auf eine Referenzoberfläche der Biopsie (BFR/BS, bone formation rate/bone surface). Bei diabetischen Patient:innen ist dieser Parameter im trabekulären, endokortikalen und intrakortikalen Bereich um bis zu 70–80 % vermindert [ 73 ].

In der Mehrzahl der Studien wurde bei Patient:innen mit T2DM eine verminderte Aktivität von serologischen Formationsmarkern (procollagen type I N‑terminal propeptide, PINP; osteocalcin OC) und Resorptionsmarkern (C telopeptide, CTX; tartrate-resistant acid phosphatase 5, TRAP5b) nachgewiesen [ 25 ]. Der Zusammenhang zwischen dem low bone turnover und der gleichzeitig nachgewiesenen strukturellen Alteration (kortikale Porosität) ist zum gegenwärtigen Zeitpunkt unklar.

Alternative (experimentelle) biochemische Marker der Knochenfragilität

Bei Diabetes ist in der Knochenmatrix der Gehalt von Pentosidin, dem am häufigsten vorhandenen AGE, im Vergleich zu nicht-diabetischen Menschen deutlich erhöht. Erhöhte Pentosidin-Spiegel im Knochen und im Serum korrelieren negativ mit der biomechanischen Stärke des Knochens (Abb.  2 ). Untersuchungen bestätigen erhöhte Werte von AGEs und auch von sRAGE (soluble receptors for advanced glycation endproducts) als prädiktiven Faktor für eine erhöhte Inzidenz von klinischen und vertebralen Frakturen unabhängig von der BMD [ 74 ].

Sclerostin, der endogene Inhibitor des Wnt/β-Catenin Signalweges und somit der Knochenformation durch Osteoblasten, ist bei T2DM deutlich erhöht (Abb.  2 ). Die Höhe der Sclerostin-Spiegel korreliert bei diesen Patient:innen mit der Inzidenz von Fragilitätsfrakturen [ 75 ]. Bei T1DM verhalten sich die Sclerostin-Spiegel genau gegenläufig zur Inzidenz von Frakturen. Patient:innen im obersten Drittel der gemessenen Spiegel hatten ein um 81 % geringeres Frakturrisiko verglichen mit Patient:innen mit Spiegeln im untersten Drittel [ 76 ]. Ob nun eine Erhöhung zirkulierender Sclerostin-Spiegel direkt die Dysfunktion von Osteozyten widerspiegelt und/oder ein Marker für eine zusätzliche Angiopathie sind, bleibt derzeit noch unbeantwortet [ 77 ].

Neben Sclerostin scheint auch ein weiterer Wnt-Inhibitor, Dickkopf‑1 (DKK-1) eine Rolle beim Knochenverlust bei Diabetes zu spielen (Abb.  2 ). Serumspiegel von DKK‑1 sind sowohl bei Kindern mit T1D also auch bei Erwachsenen mit T2DM erhöht [ 78 , 79 , 80 ]. Das Ausschalten von DKK‑1 in osteogenen Zellen in Mäusen führte zu einer Reduktion des durch T1DM-mediierten [ 81 ] Knochenverlusts.

Serum Periostin bzw. dessen Fragmente sind mit einem erhöhten Frakturrisiko bei nicht-diabetischen Patient:innen vergesellschaftet. Derzeit laufen Studien in großen diabetischen Populationen zur Evaluation dieses Markers [ 82 ]. Die Bestimmung von Serum exsosomalen microRNA (miRNA) Signaturen erscheint nicht nur bei diabetischen Populationen zukünftig eine entsprechende Option zu werden ([ 83 , 84 ]; Abb.  2 ).

Basisprophylaxe mit Vitamin D und Kalzium

Die Vitamin D- und Kalziumsubstitution ist sowohl eine eigenständige Therapiemöglichkeit der Osteoporose als auch die absolut notwendige Basis jeder spezifischen Osteoporosetherapie.

Eine ausreichende Versorgung mit Vitamin D ist eine wichtige Voraussetzung für die Knochengesundheit. Eine 25-OH-Vitamin D Serumkonzentration < 20 ng/ml (50 nmol/l) ist mit einem erhöhten Risiko für proximale Femurfrakturen und nichtvertebralen Frakturen verbunden [ 85 ].

Zur Therapie eingesetzt wird Cholecalciferol (Vitamin D3); 1 μg Vitamin D3 entspricht 40 IE Vitamin D3. Die Einnahme soll mit den Mahlzeiten erfolgen, da dies die Resorption verbessert.

Die Tagesdosis (z. B. 800 IE) kann auch als Wochenäquivalent gegeben werden (entsprechend zB. 5600 IE einmal wöchentlich). Im Einzelfall kann bei Malabsorption eine parenterale (intramuskuläre) Gabe von 100.000 IE Cholecalciferol notwendig sein. Die Gabe der aktiven Form von Vitamin D – Calcitriol (1,25-Dihydroxycholecalciferol) – ist nur bei schwerer Niereninsuffizienz indiziert.

Es gibt Hinweise auf eine positive Beeinflussung der diabetischen Insulinresistenz durch Vitamin D Supplementierung [ 86 ].

Eine ausreichende Kalziumzufuhr, primär über die Nahrung, ist sicherzustellen.

Patient:innen mit Osteoporose (mit und ohne spezifischer Osteoporosetherapie) sollen daher täglich 1000 mg Kalzium aufnehmen, vorzugsweise über die Nahrung. Ist dies nicht möglich, sind Kalziumsupplemente erforderlich. Pro Einnahme wird eine Dosis von maximal 500 mg Kalziumsupplement empfohlen [ 61 ].

Calcium-Supplementierung könnte bei Menschen mit Diabetes möglicherweise zusätzliche positive Effekte bewirken, wie Linderung der Insulinresistenz, Verbesserung der Insulinsekretion, sowie Reduktion von Lipogenese und Entzündung [ 87 ].

Spezifische Osteoporosetherapie bei Diabetes

Keine einzige randomisierte Studie hatte bisher als Endpunkt die Wirksamkeit einer spezifischen Osteoporosetherapie bei Patient:innen mit T2DM. Daher basieren die Empfehlungen für das Management von Patient:innen mit Diabetes und einem erhöhten Frakturrisiko auf empirischen Daten und der klinischen Erfahrung. Die klinische Evidenz in Bezug auf die Effizienz einer antiresorptiven oder anabolen Osteoporosetherapie bei gleichzeitigem Diabetes beruht daher auf post hoc Analysen von Subgruppen in großen randomisierten Osteoporose Studien und auch einer kleinen Anzahl von Observationsstudien [ 88 ].

Grundsätzlich sind sämtliche Medikamente zur Behandlung der Osteoporose auch bei Patient:innen mit einem manifesten Diabetes möglich und zugelassen. Da sowohl der Diabetes mellitus als auch die Osteoporose eine chronische Erkrankung mit einem dauerhaft erhöhten Risiko für sekundäre Komplikationen sind, ist die Indikation für eine langfristige Behandlung indiziert.

Bisphosphonate

Bisphosphonate (Alendronat, Risedronat, Ibandronat, Zoledronat) sind potente Inhibitoren der Knochenresorption. Sie werden an metabolisch aktiven Umbaueinheiten im Knochen abgelagert und bewirken eine Apoptose von Osteoklasten. Die Resorptionsaktivität wird im Gesamtskelett deutlich gedämpft und das Frakturrisiko reduziert.

Oral werden Bisphosphonate nur in geringem Ausmaß (maximal 3 %) resorbiert; die Einnahme erfolgt stets nüchtern in ausreichendem Abstand zur Nahrungsaufnahme, mit ausreichend Wasser und in aufrechter Körperhaltung, um Irritationen der Ösophagusschleimhaut zu vermeiden.

Bei intravenöser Bisphosphonatgabe kann, überwiegend bei erstmaliger Verabreichung, eine sogenannte „Akutphasereaktion“ – im Wesentlichen ein grippeähnliches Zustandsbild mit Fieber und Muskelschmerzen – auftreten, die in der Regel innerhalb von 36 h nach intravenöser Gabe beginnt und dann 24–48 h anhält.

Bei allen Bisphosphonaten stellen die Hypokalzämie, eine erhebliche Nierenfunktionseinschränkung oder eine Gravidität eine Kontraindikation dar.

Bisphosphonate haben eine lange Verweildauer im Knochen. Residuale Wirkungen auf den Knochenstoffwechsel lassen sich auch nach Beendigung der Bisphosphonattherapie nachweisen. Das Auftreten von atypischen Femurfrakturen ist sehr selten, scheint aber unter einer Langzeitgabe mit Bisphosphonaten zuzunehmen. Kiefernekrosen sind bei dieser für Osteoporose zugelassenen Therapie eine mutmaßlich seltene Nebenwirkung. Eine Kontrolle des Zahnstatus ist allerdings vor Therapiebeginn empfehlenswert.

Es gibt keine durch Frakturdaten validierten individuellen Entscheidungskriterien für die Wiederaufnahme einer Therapie nach einer Therapiepause oder einen weiteren Therapieverzicht in Abhängigkeit von Veränderungen der BMD, der Knochenumbaumarker oder anderer messtechnischer oder klinischer Kriterien. Datenbankanalysen geben allerdings Hinweise auf einen Wiederanstieg des Knochenbruchrisikos nach Absetzen einer Bisphosphonattherapie [ 61 ].

Denosumab ist ein monoklonaler Antikörper gegen RANKL, der die Reifung und Aktivierung der Osteoklasten hemmt. Es wird alle sechs Monate subkutan verabreicht und wird nicht renal eliminiert.

Bei der Behandlung der postmenopausalen Osteoporose ist eine Reduktion von vertebralen und nichtvertebralen Frakturen inklusive proximaler Femurfrakturen in Studien bis zu 10 Jahre nachgewiesen. Die Wirkung ist unabhängig von einer eventuellen Vorbehandlung mit Bisphosphonaten [ 89 ]. Die Behandlungsdauer ist unklar. Nach Absetzen von Denosumab scheint es im Gegensatz zu den Bisphosphonaten zu einem raschen Anstieg des Knochenumbaus und in weiterer Folge zu einer Abnahme der Knochenmineraldichte zu kommen. Kiefernekrosen und atypische Femurfrakturen sind bei dieser für Osteoporose zugelassenen Therapie eine mutmaßlich sehr seltene Nebenwirkung [ 61 ].

Raloxifen ist ein selektiver Östrogenrezeptor-Modulator (SERM), der die Knochenresorption hemmt und das Frakturrisiko für vertebrale Frakturen reduziert (nicht für nicht-vertebrale Frakturen und proximale Femurfrakturen). Raloxifen ist zugelassen für die Prävention und für die Therapie der Osteoporose bei postmenopausalen Frauen.

Ein bedeutender zusätzlicher Effekt ist die Reduktion des relativen Risikos eines invasiven (Östrogenrezeptor-positiven) Mammakarzinoms um 79 %. Eine unerwünschte Nebenwirkung ist die Erhöhung des thromboembolischen Risikos [ 61 ].

Teriparatid

Teriparatid, ein aminoterminales Fragment des Parathormons, wird einmal täglich subkutan über 24 Monate angewandt. Der osteoanabole Effekt beruht auf einer Beschleunigung der Reifung und Stimulierung von Osteoblasten.

Im Anschluss an die anabole Reaktion des Knochens kommt es nach Beendigung der Teriparatid-Therapie wiederum zu einem gesteigerten Knochenabbau, weshalb eine sofortige Anschlussbehandlung mit einem Antiresorptivum (Bisphosphonat, Denosumab, SERM) unbedingt notwendig ist [ 61 ].

Neue/zukünftige Osteoporose Medikamente

Romosozumab, ein Anti-Sclerostin Antikörper, verbessert die BMD und die Knochenstärke im diabetischen Rattenmodell. Studiendaten bei postmenopausalen Frauen mit einem erhöhten Knochenbruchrisiko zeigen eine außergewöhnlich starke Zunahme der BMD bei monatlicher Applikation. Daher könnte dieser Antikörper, der derzeit in Österreich in der Roten Box ist, zukünftig eine neue Behandlungsoption auch bei T2DM in der entsprechenden Indikation darstellen [ 90 , 91 ]. Vor Beginn einer Therapie mit Romosozumab ist eine exakte Anamnese auf kardiovaskuläre Events zu erheben, da diese eine Kontraindikation darstellen.

Management einer erhöhten Knochenfragilität bei Diabetes

Die Kriterien für den Beginn einer osteologischen Therapie bei Diabetes basieren entweder auf einer prävalenten Fragilitätsfraktur (unabhängig von der BMD) und/oder auf einer verminderten BMD. Das diagnostische Kriterium der Osteoporose in der DXA-Messung (T-score < 2,5) ist nicht mit der individuellen Therapieschwelle gleich zu setzen [ 61 , 92 ].

Die wichtigste Entscheidungshilfe für den Beginn einer Therapie ist auch beim Patient:innen mit Diabetes eine prävalente Fragilitätsfraktur. Das Ziel ist jedoch, die Patient:innen vor der ersten niedrig-traumatischen Fraktur zu schützen.

BMD Interventionsschwelle

Bei Patient:innen mit einem manifesten T2DM unterschätzt die DXA-Messung das individuelle Frakturrisiko. Aktuell wird daher bei diesen Patient:innen eine Anhebung der Interventionsschwelle auf einen T‑score von −2,0 an der Lendenwirbelsäule (kumulativ L1–L4) oder an der Hüfte (Schenkelhals bzw. gesamte Hüfte) empfohlen, um der DXA-basierten Unterschätzung der Knochenfragilität entgegenzuwirken (Abb.  3 ). Diese Anhebung ist jedoch nur in westlichen Populationen zu empfehlen. Patient:innen aus Asien oder dem Nahen/Mittleren Osten haben bei Alters- und Geschlechts-adjustierter BMD niedrigere Frakturraten, die sich auch bei manifestem Diabetes auswirken.

Strategien zur Behandlung von T2DM aus diabetologischer und osteologischer Sicht. DPP4  Dipeptidyl Peptidase‑4 Inhibitor, GLP‑1  Glucagon-like peptide‑1 Analoga, TZD  Tiazolidindione, SGLT2  Natrium-Glucose Cotransporter 2 – Canagliflozin, CKD-MBD  chronic kidney disease – metabolic bone disease, T2DM  Diabetes mellitus Typ 2, Vitamin   D 25-OH Vitamin   D3  Cholecalciferol, GLP1-RA  Glucagon-like peptide-1-Rezeptoragonisten, ÖDG LL  Leitlinie der Österreichischen Diabetes Gesellschaft. a Unter Berücksichtigung weiterer klinischer Merkmale entsprechend der ÖDG-LL zur Antihyperglykämischen Therapie bei T2DM. (From: Diagnose und Management der Osteoporose bei Diabetes mellitus (Update 2019))

Patient:innen mit einem ausgeprägten Verlust an BMD in zwei konsekutiven Messungen (= > 5 % in zwei Jahren) sollten schon bei Werten nahe der Interventionsschwelle prophylaktisch behandelt werden.

FRAX, das WHO zertifizierte Fracture Risk Assessment Tool, implementiert nationale Frakturdaten und besteht aus 12 dichotomisierten Fragen (die 12. Frage zu BMD ist optional) [ 93 ]. Dem FRAX liegen klinische Risikofaktoren (clinical risk factor, CRF) zugrunde, die in randomisierten Studien ein erhöhtes individuelles Risiko für Fragilitätsfrakturen sind. FRAX berechnet zwei Werte: (a) eine 10-Jahreswahrscheinlichkeit für alle osteoporotischen Frakturen und (b) eine 10-Jahreswahrscheinlichkeit für eine osteoporotische Hüftfraktur. Entsprechend der Österreichischen Leitlinie zur Behandlung der Osteoporose wird ab einem Risiko von (a) ≥ 20 % bzw. (b) ≥ 5 % prophylaktisch eine knochenspezifische Therapie empfohlen.

Diabetes per se ist im FRAX kein eigener CRF, daher unterschätzt auch dieses diagnostische System die Frakturwahrscheinlichkeit bei einem manifesten Diabetes. Diabetes per se ist allerdings ein starker Risikofaktor für eine osteoporotische Fraktur, auch nach Korrektur aller CRFs und BMD [ 94 ]. FRAX bietet auch die Möglichkeit, die BMD-Werte mittels TBS-Korrektur zu rechnen. Vor allem bei T2DM führt dies zu einer Verbesserung der Vorhersagewahrscheinlichkeit.

Berechnungen mit dem FRAX Rechner haben gezeigt, dass es weitere Korrekturmöglichkeiten gibt, um sich bei T2DM unter Verwendung dieses Risikorechners dem individuellen Frakturrisiko zu nähern. Eine Möglichkeit ist (a) die Erhöhung des Patient:innenalters um 10 Jahre, da der Risikofaktor Diabetes bei dieser Korrektur in etwa dem Risikofaktor Alter (als etablierter CRF im FRAX) entspricht. Die andere Möglichkeit ist (b) die Verminderung des gemessenen T‑cores am Schenkelhals um 0,5 Standardabweichungen zu verringern (z. B. T‑core −2,4 statt den tatsächlich gemessenen −1,9). Eine weitere Möglichkeit wäre es, (c) Rheumatoide Arthritis als CRF zu nehmen. Der Untersucher sollte sich für eine der drei Möglichkeiten entscheiden, jedoch nicht alle drei Optionen verwenden.

Nach aktueller Datenlage bieten diese drei Optionen trotz aller methodischen Limitationen derzeit die beste Lösung, sich dem tatsächlichen individuellen Frakturrisiko bei T2DM zu nähern. Die größte Trefferwahrscheinlichkeit bietet die Verwendung des CRF Rheumatoide Arthritis und wird daher derzeit auch von der Task Force Diabetes der International Osteoporosis Foundation empfohlen ([ 88 ]; Tab.  3 ).

Stellenwert der Physikalischen Medizin und Rehabilitation bei Prophylaxe und Therapie

Rehabilitative problemstellung.

Diabetes birgt neben der erhöhten Gefahr für Frakturen ein Risiko für zahlreiche weitere Folgeerscheinungen, wie koronare Herzerkrankung, Schlaganfall, periphere vaskuläre Komplikationen, Neuropathie, oder Retinopathie [ 95 , 96 ]. Diese Folgeerscheinungen erhöhen nicht nur die Mortalität, sondern bewirken auch Dekonditionierung, erhöhtes Sturzrisiko und zunehmenden Verlust der Fähigkeit, Aktivitäten des täglichen Lebens (ATLs) selbstständig durchzuführen. Deshalb ist es wichtig, die Krankheit möglichst frühzeitig rehabilitativ zu beeinflussen.

Körperliche Aktivität resultiert generell in einer gesundheitswirksamen Verbesserung der körperlichen Leistungsfähigkeit, besonders profitieren jedoch Personen mit metabolischem Syndrom, da die positive Beeinflussung der Insulinresistenz einen zentralen Wirkungsmechanismus der Trainingstherapie darstellt [ 97 ].

Es gilt bei der Planung und Durchführung der Bewegungstherapie einige potenzielle Folgen des Diabetes mellitus zu berücksichtigen, wie Retinopathie, Neuropathie, kardiale Erkrankungen oder Hypoglykämien [ 97 ].

In Tab.  4 findet sich eine „best practice“ Empfehlung der Autoren dieser Leitlinie, die sich sowohl für Prophylaxe als auch Therapie eignen.

Die folgenden Referenzen verweisen auf trainingstherapeutische Empfehlungen weiterer nationaler und internationaler Leitlinien: [ 85 , 97 , 98 , 99 ].

Krafttraining

Durch regelmäßiges Krafttraining kommt es über einen zusätzlichen Glucosetransporter zu einer Steigerung der zellulären Glucoseaufnahme, was in einer Erhöhung des Grundumsatzes und somit günstigen Gewichtsentwicklung resultiert. Dies ist insbesondere bei sarkopenen adipösen („sarcopenic obesity“) Patient:innen von Bedeutung [ 97 ].

Krafttraining steigert die Muskelmasse und wirkt osteoanabol [ 100 , 101 ]. Sollte aufgrund kardiorespiratorischer Einschränkungen nur ein geringer Trainingsumfang möglich sein, gilt Krafttraining im Vergleich zu Ausdauertraining als leichter einsetzbar [ 97 ]. Zusätzlich scheint sich Krafttraining positiv auf das Sturzrisiko auszuwirken [ 102 ].

In einem diabetischen Rattenmodell mit via Elektrostimulation simuliertem Krafttraining zeigten sich Verbesserungen von Knochendichte und Mikroarchitektur [ 103 ]. Bei diabetischen Kindern kam es nach einem 9‑monatigen Training (2 ×/Woche 90 min, Ballsport, springen, Gymnastik) zu einer Verbesserung der Ganzkörper- und lumbalen Knochendichte [ 104 ]. Bei älteren Personen mit Typ 2 Diabetes wurden nach 12-monatigem Gewichtsreduktionsprogramm kombiniert mit progressivem Krafttraining (3 ×/Woche, 3 Sätze, 8–10 Wiederholungen mit 75–85 % des 1‑Wiederholungsmaximums; erste 6 Monate supervidiert mit freien Gewichten und Geräten, zweite 6 Monate heimbasiert mit Kurzhanteln und Gewichtsmanschetten) eine Stabilisierung der Knochendichte beschrieben, verglichen mit reiner Gewichtsreduktion [ 105 ]. Bei postmenopausalen Frauen mit Prädiabetes und Typ 2 Diabetes kam es durch ein 32-wöchiges Training (Gehen, Wassergymnastik und Krafttraining: Kurzhanteln, Gewichtsmanschetten, 6 Übungen, 3 Sätze, 15–20 Wiederholungen) zu einer Verbesserung der Knochendichte am Wardschen Dreieck [ 106 ]. Eine aktuelle Studie bei Personen mit Typ 2 Diabetes fand nach einem 12-monatigem Trainingsprogramm (5–6 ×/w aerobes Training, sowie 2–3 ×/w 30 min Krafttraining ohne detailliertes Schema) von einer Stabilisierung der Knochendichte [ 107 ].

Ausdauertraining

Ausdauertraining bewirkt eine Effizienzsteigerung der Aufnahme und Aufnahme und des Metabolismus von Glukose in der Muskulatur. Dies resultiert in einer Verbesserung der Insulinresistenz [ 97 ].

Weiters könnte aerobes Ausdauertraining eine positive Wirkung auf Knochenstoffwechselparameter haben [ 108 , 109 ]. Zudem könnte es den altersbedingten Verlust an Knochenmasse verlangsamen, eine eindeutige positive Wirkung auf die Knochendichte wurde jedoch nicht berichtet [ 110 , 111 ]. Jedenfalls scheint regelmäßiges aerobes Training, welches in höherer aerober Fitness resultiert, die Erholung nach intensiven Aktivitäten zu beschleunigen [ 112 ].

Balancetraining

Balancetraining ist eine wirksame Strategie, um das Risiko für Stürze zu reduzieren [ 113 ]. Dies ist insbesondere bei Menschen mit Diabetes von großer Bedeutung, da in dieser Personengruppe nicht nur ein erhöhtes Sturzrisiko, sondern auch die Angst vor Stürzen weit verbreitet sind [ 114 , 115 ]. Eine rezente Arbeit konnte zeigen, dass ein 3‑monatiges Balancetraining eine deutliche Verbesserung zahlreicher Parameter der Stabilität sowie des Sturzrisikos bewirkt [ 115 ].

Beweglichkeitstraining

Eine Verbesserung der Beweglichkeit geht mit einem geringeren Risiko für Stürze einher [ 116 ].

Weitere Maßnahmen

Ergotherapeutische Interventionen können dabei helfen, nicht nur die glykämische Kontrolle, sondern auch die psychosoziale Situation zu verbessern. Insbesondere konnten Verbesserungen des HbA1c und der Diabetes-bezogenen Lebensqualität gezeigt werden [ 117 ].

Rehabilitation nach Frakturen

In der Rehabilitation von Personen mit osteoporotischen Frakturen ist es wichtig, die orthopädisch-traumatologischen Aspekte mit den oben erwähnten Maßnahmen zu kombinieren. Menschen mit Diabetes weisen hierbei eine hohe Vulnerabilität auf. Dies spiegelt sich nach operativer Versorgung von Schenkelhalsfrakturen in einem häufigeren Transfer an ICU-Einheiten, häufigerer Wiederaufnahme im Spital nach erfolgter Entlassung sowie einer höheren ein-Jahres-Mortalität wider [ 118 ].

Wirbelkörperfraktur

Bei osteoporotischen Wirbelkörperfrakturen sollten eine multimodale Schmerztherapie (medikamentös und physikalisch), bei unzureichender Stabilität eine Miederversorgung (meist für 8–12 Wochen) sowie ein rasches Wiedererlangen der Mobilität erfolgen, um Komplikationen der Immobilität, wie Pneumonie oder Dekonditionierung, zu verhindern [ 119 ].

In der Frühphase erfolgen im Rahmen der Bewegungstherapie neben der Remobilisierung auch Instruktionen bzgl. Ergonomie (zB. en bloc Drehen), isometrische Stabilisierungsübungen zur Verhinderung einer Kyphosierung, sowie ggf. ein stufenweiser Abbau des Mieders [ 120 ].

Weiters sollte ein Training der Balance erfolgen, um das Risiko für weitere Stürze zu reduzieren, sowie die Beseitigung von Stolperfallen und das Absetzen von Sturz-begünstigenden Medikamenten [ 120 ].

Funktionelle Orthesen können Schmerzen lindern, die Rückenmuskulatur aktivieren und die Aktivitäten des täglichen Lebens (activities of daily living, ADL) erleichtern [ 119 ].

Bei osteoporotischen Wirbelkörperfrakturen kommt es im Allgemeinen gemäß radiologischer Kriterien nach etwa drei bis vier Monaten zur Ausheilung [ 121 ]. Bei Diabetes mellitus besteht jedoch eine verzögerte Frakturheilung [ 122 ]. Jedenfalls soll ab der radiologisch bestätigten Heilung auch an der Beweglichkeit der Wirbelsäule gearbeitet werden.

Kommt es aufgrund der Fraktur zu funktionellen Einschränkungen, sollten ergotherapeutische Interventionen bzgl. ADL und Hilfsmittel erfolgen, um die Selbstständigkeit wiederzuerlangen. Bei schwerwiegenden Einschränkungen ist ggf. eine pflegerische Unterstützung und/oder psychosoziale Intervention erforderlich [ 120 ].

Nach osteoporotischen Wirbelkörperfrakturen kann eine individualisierte und supervidierte Trainingstherapie vorsichtig ab etwa 4–6 Wochen (in Abhängigkeit der klinischen Beschwerden und dem radiologischen Heilungsverlauf) begonnen werden [ 123 , 124 ]. Hierbei soll in der Akutphase (erste 3 Monate) der Fokus auf die Aktivierung und Verbesserung der Ausdauer der Rückenstrecker gelegt werden, sowie auf ein Balancetraining. Zusätzlich können beispielsweise die Kniestrecker und/oder Kniebeuger ohne fortgeleitete Belastung der Wirbelsäule (z. B. mittels leg extension bzw. leg curl) trainiert werden. Die Deutsche Gesellschaft für Orthopädie und Unfallchirurgie (DGOU) empfiehlt bereits ab 6 Wochen mit einem intensivierten Muskelaufbautraining mit Geräten zu beginnen [ 124 ]. Jedenfalls soll in der subakuten Phase (ab ca. 3 Monaten, bzw. ab erfolgter Frakturheilung) das Balancetraining intensiviert und um funktionelles Training bzw. Krafttraining ergänzt werden [ 123 ].

Supervidiertes Krafttraining bei postmenopausalen Frauen [ 125 ] und Männern [ 126 ] mit sehr niedriger Knochendichte scheint sicher zu sein. Insbesondere wurde in den LIFTMOR-Studien von einer Verbesserung von Kyphosen berichtet, es traten keine neuen Frakturen auf und es kam auch zu keinem Progress prävalenter Frakturen.

Schenkelhalsfraktur

Die Schenkelhalsfraktur ist meist ein Zeichen für komplexe Funktionsstörungen. Deshalb sind die rehabilitativen Ziele vielfältig und es sollten medikamentöse, diätetische, bewegungs- und ergotherapeutische Maßnahmen sowie physikalische Modalitäten kombiniert werden [ 127 , 128 ].

Eine Atemtherapie zur Pneumonieprophylaxe sollte frühzeitig zum Einsatz kommen, unserer Meinung nach bereits präoperativ. Die Remobilisierung soll rasch erfolgen, bereits ab dem ersten postoperativen Tag und mindestens einmal täglich [ 129 ].

Die Belastbarkeit sowie die freigegebenen Bewegungsumfänge variieren abhängig von der Operationsmethode. Die Bewegungstherapie gestaltet sich außerdem entsprechend den Phasen der Bindegewebsheilung [ 128 , 130 ]: In der Akutphase (Tage 0–5) stehen Atemtherapie, entstauende Maßnahmen, passiv/assistive Mobilisation und Stehversuche im Vordergrund, wohingegen in der Proliferationsphase (Tage 5–21) an der weiteren Mobilisierung, Koordinations- und Sensomotoriktraining, Verbesserung der Beweglichkeit, und des Ganges gearbeitet wird. In der Konsolidierungsphase (Tage 21–60) erfolgen v. a. Narbentherapie, Kräftigung und Sturzprophylaxe. In der Umbauphase liegt der Schwerpunkt auf der Medizinischen Trainingstherapie.

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Muschitz, C., Kautzky-Willer, A., Winhofer, Y. et al. Diagnose und Management der Osteoporose bei Diabetes mellitus (Update 2023). Wien Klin Wochenschr 135 (Suppl 1), 207–224 (2023). https://doi.org/10.1007/s00508-022-02118-8

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What Is Diabetes Mellitus?

Diabetes mellitus—more commonly known as diabetes —is a chronic disease that occurs when you have higher than normal levels of blood glucose (or, blood sugar). Glucose is the body’s main source of energy. Too much glucose can lead to symptoms such as fatigue , feeling thirsty, and blurry vision.

Research estimates that 11% of the U.S. population has some form of diabetes. Fortunately, there are several treatments that can help you manage diabetes, such as lifestyle changes and medications. Learning more about diabetes and understanding how to manage your condition can help prevent long-term complications and improve your quality of life.

Types of Diabetes

There are several different types of diabetes. While the symptoms, diagnostic process, and treatment options have some overlap, each type of diabetes is specific. These types include:

• Type 1 diabetes (T1D): About 5% to 10% of people with diabetes have this form. Historically, people sometimes called type 1 diabetes “juvenile diabetes” or “insulin-dependent diabetes.” This is because type 1 diabetes often starts during childhood and early adolescence—though anyone can develop T1D at any age. 
• Type 2 diabetes (T2D): About 90% to 95% of people with diabetes have what is called type 2 diabetes. Oftentimes, this type develops in adulthood, but some children can also have T2D.
• Type 3c diabetes: Roughly 4% to 5% of people with diabetes this type. Researchers believe that some sort of physical damage to the pancreas can lead to type 3c diabetes. It's worth noting that this type is sometimes misdiagnosed as type 2 diabetes.
• Gestational diabetes : Due to physical and hormonal changes during pregnancy , some people develop diabetes when they're expecting. This type of diabetes usually goes away after the baby is born. However, people who have had gestational diabetes have a higher risk of eventually developing type 2 diabetes in the future.

Generally, there is overlap between the symptoms of the different types of diabetes. However, each type can present some unique symptoms that you should know. Here's how to recognize the differences.

Type 1 Diabetes Symptoms

Symptoms of T1D often develop quickly, in a matter of just a few weeks. It's also common to experience more severe symptoms if you have this type. Some hallmark characteristics of T1D include:

• Feeling extremely hungry
• Being very thirsty
• Needing to use the bathroom more often
• Unintentional weight loss
• Blurry vision

People with type 1 diabetes may also eventually notice symptoms of a diabetes complication called diabetic ketoacidosis, or DKA. This complication can cause vomiting , stomach pain, rapid breathing, and fruity-smelling breath. 

Type 2 Diabetes Symptoms

With T2D, it can be common to not experience symptoms at first. When symptoms do gradually begin, they may be a result of elevated blood sugar levels or from damage to the organs that diabetes can cause. If you suspect you may have type 2 diabetes, here are some symptoms to keep in mind:

• Extreme fatigue
• Repeated infections or sores and cuts that heal slowly
• Feeling thirsty
• Urinating frequently
• Tingling , numbness, or pain in the hands and feet
• Dark patches on the skin
• Irritability or other mood changes

Type 3c Diabetes Symptoms

Most people with type 3c diabetes also experience some symptoms of type 1 and type 2 diabetes. But, those with type 3c diabetes can also have symptoms that appear as a result of damage to the pancreas. These include:

• Fatty stools (poops)
• Stomach pain

Gestational Diabetes Symptoms

Gestational diabetes usually doesn’t cause any symptoms. If you do develop this condition while pregnant, you may however notice symptoms such as being thirstier than normal or needing to urinate more frequently. 

Your pancreas makes a hormone called insulin . This hormone plays an important role in signaling cells to allow glucose from your blood to enter the cells in your muscles, fat, and liver . Your muscles, fat, and liver can then use glucose as energy for your body. You get glucose from the food you eat. After eating a meal, your blood sugar naturally rises. When this happens, your pancreas creates insulin and releases it into your blood to lower your blood sugar and keep it in a normal range.

When something interferes with your pancreas' ability to produce insulin, your blood sugar levels can stay elevated, increasing your risk of having too much glucose in your blood. Excess blood sugar can lead to the onset of diabetes symptoms. All types of diabetes occur as a result of a problem with your pancreas. What exactly leads to issues with your pancreas and your body's ability to produce insulin depends on the type of diabetes you have.

What Causes Type 1 Diabetes?

Scientists believe that type 1 diabetes occurs because of an abnormal autoimmune response, which causes your immune system to attack your pancreas by mistake. This immune system response specifically destroys healthy cells in the pancreas that are responsible for making insulin. Once these cells become damaged, your pancreas can't process insulin normally which can increase your risk of having high blood sugar levels and developing diabetes symptoms. 

What Causes Type 2 Diabetes?

With type 2 diabetes , the cells in your body stop responding normally to insulin. As a result, they become what's called " insulin resistant ." Your pancreas may initially make more insulin to try to help keep your blood sugar levels low. But eventually, your pancreas isn't able to make the insulin your body needs and the amount of glucose in your blood rises.

It's not completely clear what can cause insulin resistance. However, researchers believe that having a family history of diabetes, carrying higher amounts of adipose tissue (or, body fat around your waist), and living a sedentary lifestyle can increase your likelihood of becoming insulin resistant. When this happens, your body can't use glucose normally—which leads to elevated levels of blood sugar and boosts your risk of developing T2D symptoms.

What Causes Type 3c Diabetes?

Type 3c diabetes occurs as a result of broader damage to your pancreas. Your pancreas can become damaged for several reasons including chronic pancreatitis , cystic fibrosis, and pancreatic cancer. These conditions can lower your pancreas' ability to function normally and cause problems with producing insulin.

If you suspect you have symptoms of diabetes or have a family history of the condition, it's good practice to see your healthcare provider for proper testing. Your provider will ask you about your personal and family medical history, learn about your symptoms and lifestyle habits, and perform a physical exam to assess whether they should order additional tests.

The two most common diagnostic tests for diabetes include:

• Fasting glucose test: Measures the glucose in your blood to see if it is elevated after at least 8 hours without food. Over 126 milligrams of glucose per deciliter of blood (mg/dL) may indicate that you have diabetes. It's worth noting that 100 to 125 mg/dL is a sign of having prediabetes.
• Hemoglobin A1C test: Estimates how elevated your blood glucose has been over the last three months. A normal A1C level is under 5.7%. Receiving a result of 5.7% to 6.4% means that you have prediabetes—which occurs when you have higher than average blood sugar, but not high enough where you have diabetes. An A1C level of 6.5% or more can result in a diabetes diagnosis.

Your healthcare provider may also use the following tests:

• Random plasma glucose test: While not as helpful or reliable of a test for a diabetes diagnosis, your healthcare provider may use this test if you haven't fasted.
• Glucose challenge test: A test that providers more commonly use to check for gestational diabetes. This test involves measuring the amount of glucose in your blood after you drink something very sweet.

If your healthcare provider suspects you have T1D, they may order additional testing, such as a blood test that checks if your body is producing antibodies against the cells in your pancreas. These antibodies can help diagnose T1D, but are not found in your body if you have T2D.

If you receive a diagnosis for a type of diabetes , your healthcare provider can help you understand your treatment options and support you as you learn to manage your condition. There is no cure for diabetes, but the goal of treatment is to keep your blood sugar within a normal range.

Making small, healthy changes to your diet and increasing your movement or physical activity can have positive effects on lowering your insulin resistance. In fact, some people with T2D specifically can manage their condition with these lifestyle changes without the need to take medications.

Your healthcare provider will often discuss a comprehensive treatment plan which includes a combination of both lifestyle changes and medical treatments, such as non-insulin medications and insulin treatments.

Non-Insulin Medications

If lifestyle changes aren't giving you the results you're aiming for, medication can help you manage your diabetes. Non-insulin medications are a common treatment for people with T2D. There are currently several medications on the market to help you keep your blood sugar levels in a normal range. The most common drug healthcare providers prescribe first is called Glucophage ( metformin )—mostly because of its low cost, minimal side effects, and ability to promote weight loss.

If treatment with Glucophage and lifestyle changes aren’t enough, The American Academy of Family Physicians recommends adding one of the following classes of oral medications to your treatment plan if you have T2D. Examples of these medications include:

• Diabeta (glyburide)
• Avandia (rosiglitazone)
• Invokana (canagliflozin)
• Januvia (sitagliptin)

However, other options are available, including some injectable medications such as Ozempic or Wegovy (semaglutide). If you are interested in trying injectable treatments, ask your healthcare provider if this is a good option for you.

Insulin Treatments

Insulin treatment is the standard treatment option for people with T1D. You can receive insulin treatments via a needle and syringe, an insulin pen, or a pump. It's worth noting that insulin treatments are not available in the form of a pill. Eventually, many people with type 2 diabetes may also need some type of insulin to manage their condition, especially if their diabetes progresses. 

Different types of insulin are available. Your healthcare provider can help you figure out what type of insulin treatments you need and how often you should take them. If you are on insulin treatment, your healthcare provider will recommend doing regular check-ins to ensure that your blood sugar levels are staying close to your target goal.

How to Prevent Diabetes

There is no known way to prevent type 1 diabetes—mostly because researchers don't know what triggers type 1 diabetes. However, healthcare providers recommend incorporating dietary changes and increasing your physical activity to prevent type 2 diabetes. This is especially important if you have a family history of the condition, carry excess adipose tissue around the waist, or have prediabetes .

Any type of physical exercise that you can do regularly can help you reduce your risk. This may include options such as strength training, aerobic activity (e.g., swimming ), walking , sports, or even completing household chores. The most important thing is to find ways to move your body more frequently and in ways that you enjoy.

Dietary changes are also important to prevent the onset of T2D symptoms. Some recommendations from the American Diabetes Association include:

• Eating more whole grains and high-fiber foods
• Reducing your intake of foods high in carbohydrates and increasing your protein intake
• Avoiding sugar-sweetened beverages and foods high in sugar

It's important to note that weight isn’t the only indicator of your overall health. However, for some people, losing a bit of weight (if needed) can help their body become less resistant to insulin. One study found that for every kilogram (about 2.2 lbs) that people lost, they reduced their risk of diabetes by about 16%.

Complications

Diabetes can lead to several different complications. That's why it's essential to manage your condition and work with your healthcare provider to find the treatment options that are right for you. Some of these complications include:

• Diabetic ketoacidosis (DKA): Diabetic ketoacidosis is a potentially life-threatening complication that is more common in type 1 diabetes than in type 2 diabetes. The condition can happen when your body doesn't have enough insulin and has to use fat for energy instead. This causes ketones (or, chemicals that your liver produces when it breaks down fat) to develop and build up in your body. The production of ketones may happen if you become sick or miss an insulin shot. Unfortunately, if your condition isn't well-managed, DKA can sometimes lead to diabetic coma or death. 
• Hypoglycemia: Living with diabetes can also cause you to develop hypoglycemia —a condition that occurs when you have very low blood sugar. It may sound counterintuitive to diabetes, but this condition may occur if you took too much insulin or drank an excessive amount of alcohol. If this condition becomes severe, you might develop serious symptoms such as a seizure.
• Organ damage: In the long-term, elevated blood sugar from any type of diabetes damages multiple organ systems, especially if you aren't managing your condition well or following your treatment plan. As a result, diabetes can cause damage to your kidneys, eyes, nerves, blood vessels, and heart—while also increasing your risk of having a heart attack or stroke .

Living With Diabetes

Getting a diabetes diagnosis can feel discouraging and difficult. This condition often requires a lot of management and lifestyle changes. You’ll want to be proactive about your choices to prevent short-term and long-term complications. It can seem overwhelming when you are first diagnosed—and that's OK. Your healthcare team can help you understand diabetes and support you in managing your condition.

Living a full life with diabetes is possible. The following tips may help as you navigate your diagnosis:

• Follow your healthcare provider’s instructions on medications and blood sugar monitoring
• Check your feet regularly for signs of damage or infection
• Get regular eye exams to check for eye complications 
• Move your body for 150 minutes or more per week, if possible
• Make small, but important changes to your diet, such as eating fewer foods high in sugar and more foods high in protein
• Work with your healthcare provider to manage any underlying health conditions (e.g., high blood pressure) that may be increasing your risk of diabetes-related complications
• Talk to your loved ones, join a support group, or meet with a mental health professional to learn how to navigate your condition, receive support, and take care of your emotional well-being

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