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Global, regional, and national burden of diabetes from 1990 to 2021, with projections of prevalence to 2050: a systematic analysis for the Global Burden of Disease Study 2021

• Watkins et al.

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Interpretation

Introduction, non-fatal outcomes, ylls, ylds, and dalys, risk-attributable burden, forecasting, uncertainty and presentation of results, geographical locations reported, code availability, role of the funding source, total diabetes prevalence.

Sex-specific total diabetes prevalence

Age-specific total diabetes prevalence.

Type-specific diabetes prevalence

Total diabetes burden: ylls, ylds, and dalys.

• Open table in a new tab

Type 2 diabetes risk factors

Diabetes prevalence over time: 1990 to 2021, and forecasts to 2050

Data sharing

Declaration of interests, acknowledgments, supplementary material (1), related hub.

Global Burden of Disease The Global Burden of Disease study provides the most comprehensive global health estimates, examining worldwide, national, and regional trends for mortality and morbidity from major diseases, injuries, and risk factors to understand the health challenges of the 21st century.

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New Research Sheds Light on Cause of Type 2 Diabetes

St. Petersburg, Fla. – September 12, 2023 – Scientists at Johns Hopkins All Children’s Hospital, along with an international team of researchers, are shedding new light on the causes of Type 2 diabetes. The new research, published in the journal Nature Communications , offers a potential strategy for developing new therapies that could restore dysfunctional pancreatic beta-cells or, perhaps, even prevent Type 2 diabetes from developing.

The new study shows that the beta-cells of Type 2 diabetes patients are deficient in a cell trafficking protein called “phosphatidylinositol transfer protein alpha” (or PITPNA), which can promote the formation of “little packages,” or intracellular granules containing insulin. These structures facilitate processing and maturation of insulin “cargo.” By restoring PITPNA in the Type 2 deficient beta-cells, production of insulin granule is restored and this reverses many of the deficiencies associated with beta-cell failure and Type 2 diabetes.

Researchers say it’s important to understand how specific genes regulate pancreatic beta-cell function, including those that mediate insulin granule production and maturation like PITPNA to provide therapeutic options for people.

Matthew Poy, Ph.D. , an associate professor of Medicine and Biological Chemistry in the Johns Hopkins University School of Medicine and leader of the Johns Hopkins All Children’s team within the  Institute for Fundamental Biomedical Research , was lead researcher on the study. He adds that follow-up work is now focused on whether PITPNA can enhance the functionality of stem-cell-derived pancreatic beta-cells. Since stem cell-based therapies are still in their relatively early stages of clinical development, it appears a great deal of the potential of this approach remains untapped. Poy believes that increasing levels of PITPNA in stem cell-derived beta-cells is an approach that could enhance the ability to produce and release mature insulin prior to transplantation in diabetic subjects.

“Our dream is that increasing PITPNA could improve the efficacy and potency of beta-like stem cells,” Poy says. “This is where our research is heading, but we have to discover whether the capacity of these undifferentiated stem cells that can be converted into many different cell types can be optimized — and to what level — to be converted into healthy insulin producing beta-cells. The goal would be to find a cure for type 2 diabetes.”

Read more about this groundbreaking research.

This study was funded through grants from the  Johns Hopkins All Children’s Foundation , the  National Institute of Health, the Robert A. Welch Foundation, the Helmholtz Gemeinschaft , the European Foundation for the Study of Diabetes, the  Swedish Science Council , the  NovoNordisk Foundation  and the  Deutsche Forschungsgemeinschaft .     About Johns Hopkins All Children’s Hospital Johns Hopkins All Children’s Hospital in St. Petersburg is a leader in children’s health care, combining a legacy of compassionate care focused solely on children since 1926 with the innovation and experience of one of the world’s leading health care systems. The 259-bed teaching hospital, stands at the forefront of discovery, leading innovative research to cure and prevent childhood diseases while training the next generation of pediatric experts. With a network of Johns Hopkins All Children’s Outpatient Care centers and collaborative care provided by All Children’s Specialty Physicians at regional hospitals, Johns Hopkins All Children’s brings care closer to home. Johns Hopkins All Children’s Hospital consistently keeps the patient and family at the center of care while continuing to expand its mission in treatment, research, education and advocacy. For more information, visit HopkinsAllChildrens.org .

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At a glance

• The National Diabetes Statistics Report provides up-to-date information on the prevalence and incidence of diabetes and prediabetes, risk factors for complications, acute and long-term complications, deaths, and costs.
• Data in the report can help focus efforts to prevent and control diabetes across the United States. This report is continually updated as data become available. This report fulfills the requirement mandated by the Catalyst to Better Diabetes Care Act of 2009 (Section 10407 of Public Law 111-148).

Fast Facts on Diabetes

• Total: 38.4 million people have diabetes (11.6% of the U.S. population)
• Diagnosed: 29.7 million people, including 29.4 million adults
• Undiagnosed: 8.7 million people (22.8% of adults with diabetes are undiagnosed)

Prediabetes

• Total: 97.6 million people aged 18 years or older have prediabetes (38.0% of the adult U.S. population)
• 65 years or older: 27.2 million people aged 65 years or older (48.8%) have prediabetes

Methods and tables ‎

Prevalence of both diagnosed and undiagnosed diabetes.

Among the U.S. population overall, crude estimates for 2021 were:

• 38.4 million people of all ages—or 11.6% of the U.S. population—had diabetes.
• 38.1 million adults aged 18 years or older—or 14.7% of all U.S. adults—had diabetes (Table 1a; Table 1b).
• 8.7 million adults aged 18 years or older who met laboratory criteria for diabetes were not aware of or did not report having diabetes (undiagnosed diabetes, Table 1b). This number represents 3.4% of all U.S. adults (Table 1a) and 22.8% of all U.S. adults with diabetes.
• The percentage of adults with diabetes increased with age, reaching 29.2% among those aged 65 years or older (Table 1a).

Table 1a. Estimated crude prevalence of diagnosed diabetes, undiagnosed diabetes, and total diabetes among adults aged 18 years or older, United States, 2017–2020

Notes: CI = confidence interval. Time period 2017–2020 covers January 2017 through March 2020 only. Diagnosed diabetes was based on self-report. Undiagnosed diabetes was based on fasting plasma glucose and A1C levels among people self-reporting no diabetes. Numbers for subgroups may not add up to the total because of rounding. Age-adjusted estimates are presented in Appendix Table 1 . Data source: 2017–March 2020 National Health and Nutrition Examination Survey.

Table 1b. Estimated number of adults aged 18 years or older with diagnosed diabetes, undiagnosed diabetes, and total diabetes, United States, 2021

Notes: CI = confidence interval. Estimated numbers for 2021 were derived from percentages for 2017–March 2020 applied to July 1, 2021, U.S. resident population estimates from the U.S. Census Bureau (See detailed methods and data sources ). Diagnosed diabetes was based on self-report. Undiagnosed diabetes was based on fasting plasma glucose and A1C levels among people self-reporting no diabetes. Numbers for subgroups may not add up to the total because of rounding.

Data sources: 2017–March 2020 National Health and Nutrition Examination Survey; 2021 U.S. Census Bureau data.

Trends in prevalence of diagnosed diabetes, undiagnosed diabetes, and total diabetes

• During 2001–2020, the age-adjusted prevalence of total diabetes significantly increased among adults aged 18 years or older (Figure 1).
• Prevalence estimates for total diabetes were 10.3% in 2001–2004 and 13.2% in 2017–2020 ( Appendix Table 2 ).
• During this period, the age-adjusted prevalence significantly increased for diagnosed diabetes. No significant change in undiagnosed diabetes prevalence was found (Figure 1; Appendix Table 2 ).

Figure 1. Trends in age-adjusted prevalence of diagnosed diabetes, undiagnosed diabetes, and total diabetes among adults aged 18 years or older, United States, 2001–2020

Notes: Diagnosed diabetes was based on self-report. Undiagnosed diabetes was based on fasting plasma glucose and A1C levels among people self-reporting no diabetes. Time period 2017–2020 covers January 2017 through March 2020 only.

Prevalence of diagnosed diabetes

• 29.7 million people of all ages—or 8.9% of the U.S. population—had diagnosed diabetes.
• 352,000 children and adolescents younger than age 20 years—or 35 per 10,000 U.S. youths—had diagnosed diabetes. This includes 304,000 with type 1 diabetes.
• 1.7 million adults aged 20 years or older—or 5.7% of all U.S. adults with diagnosed diabetes—reported both having type 1 diabetes and using insulin.
• 3.6 million adults aged 20 years or older—or 12.3% of all U.S. adults with diagnosed diabetes—started using insulin within a year of their diagnosis.

Among U.S. adults aged 18 years or older, age-adjusted data for 2019–2021 indicated the following:

• For both men and women, prevalence of diagnosed diabetes was highest among American Indian and Alaska Native adults (13.6%), followed by non-Hispanic Black adults (12.1%), adults of Hispanic origin (11.7%), non-Hispanic Asian adults (9.1%) and non-Hispanic White adults (6.9%) ( Appendix Table 3 ).
• Prevalence varied significantly by education level, which is an indicator of socioeconomic status. Specifically, 13.1% of adults with less than a high school education had diagnosed diabetes versus 9.1% of those with a high school education and 6.9% of those with more than a high school education ( Appendix Table 3 ).
• Adults with family income above 500% of the federal poverty level had the lowest prevalence for both men (6.3%) and women (3.9%) ( Appendix Table 3 ).
• For both men and women, prevalence was higher among adults living in nonmetropolitan areas compared to those in metropolitan areas (Figure 2; Appendix Table 3 ).

Figure 2. Age-adjusted estimated prevalence of diagnosed diabetes by metropolitan residence and sex for adults aged 18 years or older, United States, 2019–2021

Note: Error bars represent upper and lower bounds of the 95% confidence interval.

Table 2. Crude prevalence of diagnosed diabetes by detailed race and ethnicity among adults aged 18 years or older, United States, 2019–2021

Note: CI = confidence interval. Data sources: National Center for Health Statistics; 2019–2021 National Health Interview Survey.

County-level prevalence among adults

Among U.S. adults aged 20 years or older, age-adjusted, county-level data indicated:

• In 2021, estimates of diagnosed diabetes prevalence varied across U.S. counties, ranging from 4.4% to 17.9% (Figure 3).
• Median county-level prevalence of diagnosed diabetes increased from 6.3% in 2004 to 8.3% in 2021.

Figure 3. Age-adjusted, county-level prevalence of diagnosed diabetes among adults aged 20 years or older, United States, 2004 and 2021

Incidence of newly diagnosed diabetes

Incidence among adults.

Among U.S. adults aged 18 years or older, crude estimates for 2021 were:

• 1.2 million new cases of diabetes—or 5.9 per 1,000 people—were diagnosed (Table 3).
• Compared to adults aged 18 to 44 years, incidence rates of diagnosed diabetes were higher among adults aged 45 to 64 years and those aged 65 years and older (Table 3).

Among U.S. adults aged 18 years or older, age-adjusted data for 2019–2021 indicated:

• Compared to non-Hispanic White adults and Asian adults, incidence estimates were higher for non-Hispanic Black adults and Hispanic adults ( Appendix Table 4 ).
• Incidence rates of diagnosed diabetes were higher among those with less than high school education and those with high school education only compared to adults with more than high school education ( Appendix Table 4 ).
• Incidence was similar among adults living in metropolitan and nonmetropolitan areas ( Appendix Table 4 ).

Table 3. Estimated crude incidence of diagnosed diabetes among adults aged 18 years or older, United States, 2019–2021

CI = confidence interval.

a Population estimates for 2021 were derived from rates for 2019–2021 applied to July 1, 2021 U.S. resident population estimates from the U.S. Census Bureau (See Appendix B: Detailed Methods ).

b Rates were calculated using 2021 data only.

Data sources: 2019–2021 National Health Interview Survey and 2021 U.S. Census Bureau data.

Trends in incidence among adults

• Among adults aged 18 years or older, the age-adjusted incidence of diagnosed diabetes was similar in 2000 (6.2 per 1,000 adults) and 2021 (5.8 per 1,000 adults). A significant decreasing trend in incidence was detected after 2008 (8.4 per 1,000 adults) through 2021. (Figure 4).

Figure 4. Trends in age-adjusted incidence of diagnosed diabetes among adults aged 18 years or older, United States, 2000–2021

Notes: Data shown are estimated incidence rates (solid blue line) and 95% confidence intervals (shaded). Joinpoint identified in 2008 (see Appendix B: Detailed Methods and Data Sources ). Because of changes to the survey design and survey instruments after 2018, comparisons of the 2000–2018 and 2019–2021 data should be examined with caution.

County-level incidence among adults

Among US adults aged 20 years or older, age-adjusted, county-level data indicated:

• Estimates of diagnosed diabetes incidence varied across U.S. counties, ranging from 2.2 to 53.5 per 1,000 people in 2020 (for more detail, see U.S. Diabetes Surveillance System ).
• Median county-level incidence of diagnosed diabetes was 9.7 and 9.0 per 1,000 people in 2004 and 2020, respectively (for more detail, see U.S. Diabetes Surveillance System ).

Incidence among children and adolescents

Data from the SEARCH for Diabetes in Youth study indicated that, during 2017–2018, the estimated annual number of newly diagnosed cases in the United States included:

• 18,169 children and adolescents younger than age 20 years with type 1 diabetes.
• 5,293 children and adolescents aged 10 to 19 years with type 2 diabetes.

Trends in incidence among children and adolescents

Among U.S. children and adolescents aged younger than 20 years, modeled data in Figure 5 showed:

• For the period 2002–2018, overall incidence of type 1 diabetes significantly increased.
• Non-Hispanic Asian or Pacific Islander children and adolescents had the largest significant increases in incidence of type 1 diabetes, followed by Hispanic and non-Hispanic Black children and adolescents.
• Non-Hispanic White children and adolescents had the highest incidence of type 1 diabetes across all years.

Among U.S. children and adolescents aged 10 to 19 years, modeled data in Figure 5 showed:

• For the entire period 2002–2018, overall incidence of type 2 diabetes significantly increased.
• Incidence of type 2 diabetes significantly increased for all racial and ethnic groups, especially Asian or Pacific Islander, Hispanic, and non-Hispanic Black children and adolescents.
• Non-Hispanic Black children and adolescents had the highest incidence of type 2 diabetes across all years.

Figure 5. Trends in incidence of type 1 and type 2 diabetes in children and adolescents, overall and by race and ethnicity, 2002–2018

Note: Adapted from Wagenknecht LE et al 1 . Data are model-adjusted incidence estimates (see Appendix B: Detailed Methods and Data Sources ).

Prevalence of prediabetes among adults

• An estimated 97.6 million adults aged 18 years or older had prediabetes in 2021 (Table 4).
• 38.0% of all U.S. adults had prediabetes, based on their fasting glucose or A1C level (Table 4).
• 19.0% of adults with prediabetes reported being told by a health professional that they had this condition (Table 4).

Among U.S. adults aged 18 years or older, age-adjusted data for 2017–2020 indicated:

• 10.8% of adults had prediabetes, based on both elevated fasting plasma glucose and A1C levels ( Appendix Table 5 ).
• A higher percentage of men (41.0%) than women (32.0%) had prediabetes, based on their fasting glucose or A1C level ( Appendix Table 6 ).
• Prevalence of prediabetes (based on fasting glucose or A1C level) was similar among all racial and ethnic groups and education levels ( Appendix Table 6 ).

Table 4. Estimated number, percentage, and awareness of prediabetes a among adults aged 18 years or older, United States, 2017–2020 and 2021

Notes: CI = confidence interval. Data are crude estimates (see Appendix B: Detailed Methods and Data Sources ). Time period 2017–2020 covers January 2017 through March 2020 only.

a Prediabetes was defined as fasting plasma glucose values of 100 to 125 mg/dL or A1C values of 5.7% to 6.4%.

b Prediabetes awareness was based on self-report and estimated only among adults with prediabetes.

Trends in prevalence of prediabetes among adults

• There were no significant changes in age-adjusted prevalence of prediabetes from 2005–2008 to 2017–2020 ( Appendix Table 7 ). About one-third of U.S. adults had prediabetes over the entire period.
• Among adults with prediabetes, the age-adjusted percentage aware that they had this condition increased from 6.5% in 2005–2008 to 17.4% in 2017–2020 ( Appendix Table 7 ).

Risk factors for diabetes-related complications

Among U.S. adults aged 18 years or older with diagnosed diabetes, crude estimates for 2017–2020 shown in Appendix Table 8 were:

• 22.1% were tobacco users based on self-report or levels of serum cotinine.
• 14.6% reported current cigarette smoking.
• 36.0% had quit smoking but had a history of smoking at least 100 cigarettes in their lifetime.

Overweight and obesity

• 26.9% were overweight (BMI of 25.0 to 29.9 kg/m 2 ).
• 47.1% had obesity (BMI of 30.0 to 39.9 kg/m 2 ).
• 15.7% had extreme obesity (BMI of 40.0 kg/m 2 or higher).

Physical inactivity

• 31.9% were physically inactive, defined as getting less than 10 minutes a week of moderate or vigorous activity in each physical activity category of work, leisure time, and transportation.
• 22.9% had an A1C value of 7.0% to 7.9%.
• 11.5% had an A1C value of 8.0% to 9.0%.
• 13.0% had an A1C value higher than 9.0%.
• 10.4% of adults aged 18–44 years had A1C levels of 10% or higher, compared to 9.4% of those aged 45–64 years and 2.6% of those aged 65 years or older ( Appendix Table 9 ).

High blood pressure

• 80.6% had a systolic blood pressure of 130 mmHg or higher or diastolic blood pressure of 80 mmHg or higher or were on prescription medication for their high blood pressure ( Appendix Table 8 ).
• 70.8% had a systolic blood pressure of 140 mmHg or higher or diastolic blood pressure of 90 mmHg or higher or were on prescription medication for their high blood pressure ( Appendix Table 8 ).

High cholesterol*

• 19.9% had a non-HDL level of 130 to 159 mg/dL.
• 11.5% had a non-HDL level of 160 to 189 mg/dL.
• 8.0% had a non-HDL level of 190 mg/dL or higher.

* Non-high-density lipoprotein cholesterol (non-HDL) contains all the atherogenic lipoproteins, including low-density lipoprotein cholesterol (LDL), very-low-density lipoprotein, lipoprotein(a), and others. Growing evidence supports non-HDL as a better predictor of cardiovascular disease risk than LDL 2 .

Preventing diabetes-related complications

Among U.S. adults aged 18 years or older with diagnosed diabetes, crude data for 2017–2020 shown in Appendix Table 10 indicated:

Usual source for diabetes care

• 78.8% reported having at least one usual source of diabetes care, such as a doctor or other health care professional.

Physical activity

• 24.1% met the recommended goal of at least 150 minutes per week of leisure-time physical activity.

Weight management

• 73.1% reported managing or losing weight to lower their risk for developing certain diseases.

Statin treatment

• 57.8% of adults aged 40–75 years were on statin therapy.

A1C, blood pressure, cholesterol, and smoking (ABCs)

• 11.1% met all these criteria: A1C value <7.0%, blood pressure <130/80 mmHg, non-HDL cholesterol <130 mg/dL, and being a nonsmoker (Table 5).
• 36.8% met all these criteria: A1C value <8.0%, blood pressure <140/90 mmHg, non-HDL cholesterol <160 mg/dL, and being a nonsmoker (Table 5).

Table 5. Crude percentage of adults aged 18 years or older with diagnosed diabetes meeting all ABCs goals , United States, 2017–2020 3 4

Notes: ABCs = A1C, blood pressure, cholesterol, and smoking. CI = confidence interval. Estimates are crude percentages and 95% confidence intervals.

Data source: 2017–2020 National Health and Nutrition Examination Survey.

Among U.S. adults aged 18 years or older with diagnosed diabetes, crude data for 2021 indicated:

• 94.2% (95% CI, 93.0–95.2) received a blood test for A1C.
• 96.8% (95% CI, 95.8–97.5) had their blood pressure checked.
• 93.0% (95% CI, 91.8–94.1) had their cholesterol checked.

Vaccinations

• 65.9% (95% CI, 63.8–68.0) had received an influenza vaccination in the past year.
• 8.9% (95% CI, 7.6–10.4) had received one COVID-19 vaccination.
• 63.8% (95% CI, 61.5–66.1) had received two COVID-19 vaccinations.
• 8.7% (95% CI, 7.6–10.1) had received more than two COVID-19 vaccinations.
• 35.9% (95% CI, 32.2–39.8) of adults aged 18–59 years had ever received a hepatitis B vaccination.
• 50.7% (95% CI, 48.6–52.9) had ever received a pneumococcal vaccination.

Coexisting conditions and complications

Emergency department visits.

In 2020, about 16.8 million emergency department visits were reported with diabetes as any listed diagnosis among adults aged 18 years or older (Table 6), including:

• 267,000 for hyperglycemic crisis (11.4 per 1,000 adults with diabetes).
• 202,000 for hypoglycemia (8.6 per 1,000 adults with diabetes).

Table 6. Number and rate of emergency department visits per 1,000 adults aged 18 years or older with diabetes for selected causes, United States, 2019 and 2020

Note: CI = confidence interval. Numbers rounded to the nearest thousand. Data sources: 2019 and 2020 National Emergency Department Sample; 2019 and 2020 National Health Interview Survey.

In 2020, of the emergency department visits with diabetes as any listed diagnosis among U.S. adults aged 18 years or older, disposition data (see Appendix B: Detailed Methods and Data Sources ) indicated:

• 54.9% were treated and released; 38.4% were admitted to the hospital; 2.5% were transferred to another hospital; 2.6% were transferred to a skilled nursing facility, an intermediate care facility, or home with home health care; 1.3% left against medical advice; 0.3% died; and <0.1% had unknown disposition but were not admitted to a hospital.
• Of those ED visits involving hypoglycemia, 66.8% were treated and released, 25.1% were admitted to the hospital, and <0.1% died.
• Of the ED visits involving hyperglycemic crisis, 8.4% were treated and released, 84.4% were admitted to the hospital, and <0.1% died.

Hospitalizations

In 2020, a total of 7.86 million hospital discharges were reported with diabetes as any listed diagnosis among U.S. adults aged 18 years or older (335.4 per 1,000 adults with diabetes) (Table 7). These discharges included:

• 368,000 for ischemic heart disease (15.7 per 1,000 adults with diabetes).
• 321,000 for stroke (13.7 per 1,000 adults with diabetes).
• 160,000 for a lower-extremity amputation (6.8 per 1,000 adults with diabetes).
• 232,000 for hyperglycemic crisis (9.9 per 1,000 adults with diabetes).
• 51,000 for hypoglycemia (2.2 per 1,000 adults with diabetes).

Table 7. Number and rate of hospitalizations per 1,000 adults aged 18 years or older with diabetes for selected causes, United States, 2019 and 2020

Notes: CI = confidence interval. Numbers rounded to the nearest thousand. Data sources: 2019 and 2020 National Inpatient Sample; 2019 and 2020 National Health Interview Survey.

Kidney disease

Among U.S. adults aged 18 years or older with diagnosed diabetes, crude data for 2017–2020 shown in Appendix Table 11 indicated:

• 15.7% had moderate to severe CKD (stage 3 or 4).
• 23.1% of non-Hispanic Black adults, 17.2% of non-Hispanic White adults, and 8.9% of Hispanic adults had moderate to severe CKD (stage 3 or 4).
• 32.5% with moderate to severe CKD (stage 3 or 4) were aware of their kidney disease.
• 40.9% had chronic kidney disease (CKD, stages 1–4), based on the 2009 CKD-EPI eGFR equation, which included a factor for non-Hispanic Black race.
• A total of 61,522 people developed end-stage kidney disease with diabetes as the primary cause.
• Crude incidence of end-stage kidney disease with diabetes as the primary cause was 192.7 per 1 million population (61,522 new cases). Adjusted for age group, sex, and racial or ethnic group, the rate was 179.5 per 1 million people.
• The proportion of end-stage kidney disease with diabetes listed as the primary cause was 39.2% (307,385 out of 783,594 people). As a result, diabetes was the leading cause of end-stage kidney disease, followed by high blood pressure (26.7%), glomerulonephritis (14.6%), and cystic kidney disease (5.0%).

Vision disability

• Diabetes is the leading cause of new cases of blindness among adults aged 18–64 years.
• 10.1% (95% CI, 9.6%–11.3%) reported severe vision difficulty or blindness.
• In 2021, diabetes was the eighth leading cause of death in the United States. This finding is based on 103,294 death certificates in which diabetes was listed as the underlying cause of death (crude rate, 31.1 per 100,000 people).
• In 2021, there were 399,401 death certificates with diabetes listed as the underlying or contributing cause of death (crude rate, 120.3 per 100,000 people).
• The total direct and indirect estimated costs* of diagnosed diabetes in the United States in 2022 was $413 billion.
• Total direct estimated costs of diagnosed diabetes increased from $227 billion in 2012 to $307 billion in 2022 (2022 dollars). Total indirect costs increased from $89 billion to $106 billion in the same period (2022 dollars).
• From 2012 to 2022, excess medical costs per person associated with diabetes increased from $10,179 to $12,022 (2022 dollars).

* Direct costs = medical costs; indirect costs = lost productivity from work-related absenteeism, reduced productivity at work and at home, unemployment from chronic disability, and premature mortality.

• Wagenknecht LE, Lawrence JM, Isom S, et al. Trends in incidence of youth-onset type 1 and type 2 diabetes in the USA, 2002-18: results from the population-based SEARCH for Diabetes in Youth study. Lancet Diabetes Endocrinol . 2023;11(4):242–250. doi: 10.1016/S2213-8587(23)00025-6
• Su X, Kong Y, Peng D. Evidence for changing lipid management strategy to focus on non-high density lipoprotein cholesterol. Lipids Health Dis . 2019;18(1):134.
• American Diabetes Association. Standards of medical care in diabetes—2023. Diabetes Care . 2023;46 (suppl 1).
• American Association of Clinical Endocrinologists and American College of Endocrinology guidelines for the management of dyslipidemia and prevention of cardiovascular disease. Endocr Pract . 2017;23(suppl 2).
• Centers for Disease Control and Prevention. CDC WONDER. About Underlying Cause of Death 1999–2020. Accessed April 25, 2023. https://wonder.cdc.gov/ucd-icd10.html
• Parker ED, Lin J, Mahoney T, Ume N, Yang G, Gabbay RA, ElSayed NA, Bannuru RR. Economic Costs of Diabetes in the U.S. in 2022. Diabetes Care. 2024;47(1):26–43. doi: 10.2337/dci23-0085. Online ahead of print.
• Centers for Disease Control and Prevention. National Diabetes Statistics Report website. https://www.cdc.gov/diabetes/php/data-research/index.html Accessed [date].

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Breakthrough within reach for diabetes scientist and patients nearest to his heart

Harvard Correspondent

100 years after discovery of insulin, replacement therapy represents ‘a new kind of medicine,’ says Stem Cell Institute co-director Douglas Melton, whose children inspired his research

When Vertex Pharmaceuticals announced last month that its investigational stem-cell-derived replacement therapy was, in conjunction with immunosuppressive therapy, helping the first patient in a Phase 1/2 clinical trial robustly reproduce his or her own fully differentiated pancreatic islet cells, the cells that produce insulin, the news was hailed as a potential breakthrough for the treatment of Type 1 diabetes. For Harvard Stem Cell Institute Co-Director and Xander University Professor Douglas Melton, whose lab pioneered the science behind the therapy, the trial marked the most recent turning point in a decades-long effort to understand and treat the disease. In a conversation with the Gazette, Melton discussed the science behind the advance, the challenges ahead, and the personal side of his research. The interview was edited for clarity and length.

Douglas Melton

GAZETTE: What is the significance of the Vertex trial?

MELTON: The first major change in the treatment of Type 1 diabetes was probably the discovery of insulin in 1920. Now it’s 100 years later and if this works, it’s going to change the medical treatment for people with diabetes. Instead of injecting insulin, patients will get cells that will be their own insulin factories. It’s a new kind of medicine.

GAZETTE: Would you walk us through the approach?

MELTON: Nearly two decades ago we had the idea that we could use embryonic stem cells to make functional pancreatic islets for diabetics. When we first started, we had to try to figure out how the islets in a person’s pancreas replenished. Blood, for example, is replenished routinely by a blood stem cell. So, if you go give blood at a blood drive, your body makes more blood. But we showed in mice that that is not true for the pancreatic islets. Once they’re removed or killed, the adult body has no capacity to make new ones.

So the first important “a-ha” moment was to demonstrate that there was no capacity in an adult to make new islets. That moved us to another source of new material: stem cells. The next important thing, after we overcame the political issues surrounding the use of embryonic stem cells, was to ask: Can we direct the differentiation of stem cells and make them become beta cells? That problem took much longer than I expected — I told my wife it would take five years, but it took closer to 15. The project benefited enormously from undergraduates, graduate students, and postdocs. None of them were here for 15 years of course, but they all worked on different steps.

GAZETTE: What role did the Harvard Stem Cell Institute play?

MELTON: This work absolutely could not have been done using conventional support from the National Institutes of Health. First of all, NIH grants came with severe restrictions and secondly, a long-term project like this doesn’t easily map to the initial grant support they give for a one- to three-year project. I am forever grateful and feel fortunate to have been at a private institution where philanthropy, through the HSCI, wasn’t just helpful, it made all the difference.

I am exceptionally grateful as well to former Harvard President Larry Summers and Steve Hyman, director of the Stanley Center for Psychiatric Research at the Broad Institute, who supported the creation of the HSCI, which was formed specifically with the idea to explore the potential of pluripotency stem cells for discovering questions about how development works, how cells are made in our body, and hopefully for finding new treatments or cures for disease. This may be one of the first examples where it’s come to fruition. At the time, the use of embryonic stem cells was quite controversial, and Steve and Larry said that this was precisely the kind of science they wanted to support.

GAZETTE: You were fundamental in starting the Department of Stem Cell and Regenerative Biology. Can you tell us about that?

MELTON: David Scadden and I helped start the department, which lives in two Schools: Harvard Medical School and the Faculty of Arts and Science. This speaks to the unusual formation and intention of the department. I’ve talked a lot about diabetes and islets, but think about all the other tissues and diseases that people suffer from. There are faculty and students in the department working on the heart, nerves, muscle, brain, and other tissues — on all aspects of how the development of a cell and a tissue affects who we are and the course of disease. The department is an exciting one because it’s exploring experimental questions such as: How do you regenerate a limb? The department was founded with the idea that not only should you ask and answer questions about nature, but that one can do so with the intention that the results lead to new treatments for disease. It is a kind of applied biology department.

GAZETTE: This pancreatic islet work was patented by Harvard and then licensed to your biotech company, Semma, which was acquired by Vertex. Can you explain how this reflects your personal connection to the research?

MELTON: Semma is named for my two children, Sam and Emma. Both are now adults, and both have Type 1 diabetes. My son was 6 months old when he was diagnosed. And that’s when I changed my research plan. And my daughter, who’s four years older than my son, became diabetic about 10 years later, when she was 14.

When my son was diagnosed, I knew nothing about diabetes and had been working on how frogs develop. I changed my research focus, thinking, as any parent would, “What am I going to do about this?” Again, I come back to the flexibility of Harvard. Nobody said, “Why are you changing your research plan?”

GAZETTE: What’s next?

MELTON: The stem-cell-derived replacement therapy cells that have been put into this first patient were provided with a class of drugs called immunosuppressants, which depress the patient’s immune system. They have to do this because these cells were not taken from that patient, and so they are not recognized as “self.” Without immunosuppressants, they would be rejected. We want to find a way to make cells by genetic engineering that are not recognized as foreign.

I think this is a solvable problem. Why? When a woman has a baby, that baby has two sets of genes. It has genes from the egg, from the mother, which would be recognized as “self,” but it also has genes from the father, which would be “non-self.” Why does the mother’s body not reject the fetus? If we can figure that out, it will help inform our thinking about what genes to change in our stem cell-derived islets so that they could go into any person. This would be relevant not just to diabetes, but to any cells you wanted to transplant for liver or even heart transplants. It could mean no longer having to worry about immunosuppression.

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• Review Article
• Published: 06 June 2022

The burden and risks of emerging complications of diabetes mellitus

• Dunya Tomic   ORCID: orcid.org/0000-0003-2471-2523 1 , 2 ,
• Jonathan E. Shaw   ORCID: orcid.org/0000-0002-6187-2203 1 , 2   na1 &
• Dianna J. Magliano   ORCID: orcid.org/0000-0002-9507-6096 1 , 2   na1  

Nature Reviews Endocrinology volume  18 ,  pages 525–539 ( 2022 ) Cite this article

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• Diabetes complications
• Type 1 diabetes
• Type 2 diabetes

The traditional complications of diabetes mellitus are well known and continue to pose a considerable burden on millions of people living with diabetes mellitus. However, advances in the management of diabetes mellitus and, consequently, longer life expectancies, have resulted in the emergence of evidence of the existence of a different set of lesser-acknowledged diabetes mellitus complications. With declining mortality from vascular disease, which once accounted for more than 50% of deaths amongst people with diabetes mellitus, cancer and dementia now comprise the leading causes of death in people with diabetes mellitus in some countries or regions. Additionally, studies have demonstrated notable links between diabetes mellitus and a broad range of comorbidities, including cognitive decline, functional disability, affective disorders, obstructive sleep apnoea and liver disease, and have refined our understanding of the association between diabetes mellitus and infection. However, no published review currently synthesizes this evidence to provide an in-depth discussion of the burden and risks of these emerging complications. This Review summarizes information from systematic reviews and major cohort studies regarding emerging complications of type 1 and type 2 diabetes mellitus to identify and quantify associations, highlight gaps and discrepancies in the evidence, and consider implications for the future management of diabetes mellitus.

With advances in the management of diabetes mellitus, evidence is emerging of an increased risk and burden of a different set of lesser-known complications of diabetes mellitus.

As mortality from vascular diseases has declined, cancer and dementia have become leading causes of death amongst people with diabetes mellitus.

Diabetes mellitus is associated with an increased risk of various cancers, especially gastrointestinal cancers and female-specific cancers.

Hospitalization and mortality from various infections, including COVID-19, pneumonia, foot and kidney infections, are increased in people with diabetes mellitus.

Cognitive and functional disability, nonalcoholic fatty liver disease, obstructive sleep apnoea and depression are also common in people with diabetes mellitus.

As new complications of diabetes mellitus continue to emerge, the management of this disorder should be viewed holistically, and screening guidelines should consider conditions such as cancer, liver disease and depression.

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Introduction.

Diabetes mellitus is a common, albeit potentially devastating, medical condition that has increased in prevalence over the past few decades to constitute a major public health challenge of the twenty-first century 1 . Complications that have traditionally been associated with diabetes mellitus include macrovascular conditions, such as coronary heart disease, stroke and peripheral arterial disease, and microvascular conditions, including diabetic kidney disease, retinopathy and peripheral neuropathy 2 (Fig.  1 ). Heart failure is also a common initial manifestation of cardiovascular disease in patients with type 2 diabetes mellitus (T2DM) 3 and confers a high risk of mortality in those with T1DM or T2DM 4 . Although a great burden of disease associated with these traditional complications of diabetes mellitus still exists, rates of these conditions are declining with improvements in the management of diabetes mellitus 5 . Instead, as people with diabetes mellitus are living longer, they are becoming susceptible to a different set of complications 6 . Population-based studies 7 , 8 , 9 show that vascular disease no longer accounts for most deaths among people with diabetes mellitus, as was previously the case 10 . Cancer is now the leading cause of death in people with diabetes mellitus in some countries or regions (hereafter ‘countries/regions’) 9 , and the proportion of deaths due to dementia has risen since the turn of the century 11 . In England, traditional complications accounted for more than 50% of hospitalizations in people with diabetes mellitus in 2003, but for only 30% in 2018, highlighting the shift in the nature of complications of this disorder over this corresponding period 12 .

The traditional complications of diabetes mellitus include stroke, coronary heart disease and heart failure, peripheral neuropathy, retinopathy, diabetic kidney disease and peripheral vascular disease, as represented on the left-hand side of the diagram. With advances in the management of diabetes mellitus, associations between diabetes mellitus and cancer, infections, functional and cognitive disability, liver disease and affective disorders are instead emerging, as depicted in the right-hand side of the diagram. This is not an exhaustive list of complications associated with diabetes mellitus.

Cohort studies have reported associations of diabetes mellitus with various cancers, functional and cognitive disability, liver disease, affective disorders and sleep disturbance, and have provided new insights into infection-related complications of diabetes mellitus 13 , 14 , 15 , 16 , 17 . Although emerging complications have been briefly acknowledged in reviews of diabetes mellitus morbidity and mortality 11 , 17 , no comprehensive review currently specifically provides an analysis of the evidence for the association of these complications with diabetes mellitus. In this Review, we synthesize information published since the year 2000 on the risks and burden of emerging complications associated with T1DM and T2DM.

Diabetes mellitus and cancer

The burden of cancer mortality.

With the rates of cardiovascular mortality declining amongst people with diabetes mellitus, cancer deaths now constitute a larger proportion of deaths among this population in some countries/regions 8 , 9 . Although the proportion of deaths due to cancer appears to be stable, at around 16–20%, in the population with diabetes mellitus in the USA 7 , in England it increased from 22% to 28% between 2001 and 2018 (ref. 9 ), with a similar increase reported in Australia 8 . Notably, in England, cancer has overtaken vascular disease as the leading cause of death in people with diabetes mellitus and it is the leading contributor to excess mortality in those with diabetes mellitus compared with those without 9 . These findings are likely to be due to a substantial decline in the proportion of deaths from vascular diseases, from 44% to 24% between 2001 and 2018, which is thought to reflect the targeting of prevention measures in people with diabetes mellitus 18 . Over the same time period, cancer mortality rates fell by much less in the population with diabetes mellitus than in that without diabetes 9 , suggesting that clinical approaches for diabetes mellitus might focus too narrowly on vascular complications and might require revision 19 . In addition, several studies have reported that female patients with diabetes mellitus receive less-aggressive treatment for breast cancer compared with patients without diabetes mellitus, particularly with regard to chemotherapy 20 , 21 , 22 , suggesting that this treatment approach might result in increased cancer mortality rates in women with diabetes mellitus compared with those without diabetes mellitus. Although substantial investigation of cancer mortality in people with diabetes mellitus has been undertaken in high-income countries/regions, there is a paucity of evidence from low-income and middle-income countries/regions. It is important to understand the potential effect of diabetes mellitus on cancer mortality in these countries/regions owing to the reduced capacity of health-care systems in these countries/regions to cope with the combination of a rising prevalence of diabetes mellitus and rising cancer mortality rates in those with diabetes mellitus. One study in Mauritius showed a significantly increased risk of all-cause cancer mortality in patients with T2DM 23 , but this study has yet to be replicated in other low-income and middle-income countries/regions.

Gastrointestinal cancers

Of the reported associations between diabetes mellitus and cancer (Table  1 ), some of the strongest have been demonstrated for gastrointestinal cancers.

Hepatocellular carcinoma

In the case of hepatocellular carcinoma, the most rigorous systematic review on the topic — comprising 18 cohort studies with a combined total of more than 3.5 million individuals — reported a summary relative risk (SRR) of 2.01 (95% confidence interval (CI) 1.61–2.51) for an association with diabetes mellitus 24 . This increased risk of hepatocellular carcinoma with diabetes mellitus is supported by the results of another systematic review that included case–control studies 25 . Another review also found that diabetes mellitus independently increased the risk of hepatocellular carcinoma in the setting of hepatitis C virus infection 26 .

Pancreatic cancer

The risk of pancreatic cancer appears to be approximately doubled in patients with T2DM compared with patients without T2DM. A meta-analysis of 36 studies found an adjusted odds ratio (OR) of 1.82 (95% CI 1.66–1.89) for pancreatic cancer among people with T2DM compared with patients without T2DM 27 (Table  1 ). However, it is possible that these findings are influenced by reverse causality — in this scenario, diabetes mellitus is triggered by undiagnosed pancreatic cancer 28 , with pancreatic cancer subsequently being clinically diagnosed only after the diagnosis of diabetes mellitus. Nevertheless, although the greatest risk (OR 2.05, 95% CI 1.87–2.25) of pancreatic cancer was seen in people diagnosed with T2DM 1–4 years previously compared with people without T2DM, those with a diagnosis of T2DM of more than 10 years remained at increased risk of pancreatic cancer (OR 1.51, 95% CI 1.16–1.96) 27 , suggesting that reverse causality can explain only part of the association between T2DM and pancreatic cancer. Although T2DM accounts for ~90% of all cases of diabetes mellitus 29 , a study incorporating data from five nationwide diabetes registries also reported an increased risk of pancreatic cancer amongst both male patients (HR 1.53, 95% CI 1.30–1.79) and female patients (HR 1.25, 95% CI 1.02–1.53) with T1DM 30 .

Colorectal cancer

For colorectal cancer, three systematic reviews have shown a consistent 20–30% increased risk associated with diabetes mellitus 31 , 32 , 33 . One systematic review, which included more than eight million people across 30 cohort studies, reported an incidence SRR of 1.27 (95% CI 1.21–1.34) of colorectal cancer 31 , independent of sex and family history (Table  1 ). Similar increases in colorectal cancer incidence in patients with diabetes mellitus were reported in a meta-analysis of randomized controlled trials (RCTs) and cohort studies 32 and in a systematic review that included cross-sectional studies 33 .

Female-specific cancers

Endometrial, breast and ovarian cancers all occur more frequently in women with diabetes mellitus than in women without diabetes mellitus.

Endometrial cancer

For endometrial cancer, one systematic review of 29 cohort studies and a combined total of 5,302,259 women reported a SRR of 1.89 (95% CI 1.46–2.45) and summary incidence rate ratio (IRR) of 1.61 (95% CI 1.51–1.71) 34 (Table  1 ). Similar increased risks were found in two systematic reviews incorporating cross-sectional studies 35 , 36 , one of which found a particularly strong association of T1DM (relative risk (RR) 3.15, 95% CI 1.07–9.29) with endometrial cancer.

Breast cancer

The best evidence for a link between diabetes mellitus and breast cancer comes from a systematic review of six prospective cohort studies and more than 150,000 women, in which the hazard ratio (HR) for the incidence of breast cancer in women with diabetes mellitus compared with women without diabetes mellitus was 1.23 (95% CI 1.12–1.34) 32 (Table  1 ). Two further systematic reviews have also shown this increased association 37 , 38 .

The association of diabetes mellitus with breast cancer appears to vary according to menopausal status. In a meta-analysis of studies of premenopausal women with diabetes mellitus, no significant association with breast cancer was found 39 , whereas in 11 studies that included only postmenopausal women, the SRR was 1.15 (95% CI 1.07–1.24). The difference in breast cancer risk between premenopausal and postmenopausal women with diabetes mellitus was statistically significant. The increased risk of breast cancer after menopause in women with diabetes mellitus compared with women without diabetes mellitus might result from the elevated concentrations and increased bioavailability of oestrogen that are associated with adiposity 40 , which is a common comorbidity in those with T2DM; oestrogen synthesis occurs in adipose tissue in postmenopausal women, while it is primarily gonadal in premenopausal women 41 . Notably, however, there is evidence that hormone-receptor-negative breast cancers, which typically carry a poor prognosis, occur more frequently in women with breast cancer and diabetes mellitus than in women with breast cancer and no diabetes mellitus 42 , indicating that non-hormonal mechanisms also occur.

Ovarian cancer

Diabetes mellitus also appears to increase the risk of ovarian cancer, with consistent results from across four systematic reviews. A pooled RR of 1.32 (95% CI 1.14–1.52) was reported across 15 cohort studies and a total of more than 2.3 million women 43 (Table  1 ). A SRR of 1.19 (95% CI 1.06–1.34) was found across 14 cohort studies and 3,708,313 women 44 . Similar risks were reported in meta-analyses that included cross-sectional studies 45 , 46 .

Male-specific cancers: prostate cancer

An inverse association between diabetes mellitus and prostate cancer has been observed in a systematic review (RR 0.91, 95% CI 0.86–0.96) 47 , and is probably due to reduced testosterone levels that occur secondary to the low levels of sex hormone-binding globulin that are commonly seen in men with T2DM and obesity 48 . Notably, however, the systematic review that showed the inverse association involved mostly white men (Table  1 ), whereas a systematic review of more than 1.7 million men from Taiwan, Japan, South Korea and India found that diabetes mellitus increased prostate cancer risk 49 , suggesting that ethnicity might be an effect modifier of the diabetes mellitus–prostate cancer relationship. The mechanisms behind this increased risk in men in regions of Asia such as Taiwan and Japan, where most study participants came from, remain unclear. Perhaps, as Asian men develop diabetes mellitus at lower levels of total adiposity than do white men 50 , the adiposity associated with diabetes mellitus in Asian men might have a lesser impact on sex hormone-binding globulin and testosterone than it does in white men. Despite the reported inverse association between diabetes mellitus and prostate cancer in white men, however, evidence suggests that prostate cancers that do develop in men with T2DM are typically more aggressive, conferring higher rates of disease-specific mortality than prostate cancers in men without diabetes mellitus 51 .

An assessment of cancer associations

As outlined above, a wealth of data has shown that diabetes mellitus is associated with an increased risk of various cancers. It has been argued, however, that some of these associations could be due to detection bias resulting from increased surveillance of people with diabetes mellitus in the immediate period after diagnosis 52 , or reverse causality, particularly in the case of pancreatic cancer 53 . However, neither phenomenon can account for the excess risks seen in the longer term. An Australian study exploring detection bias and reverse causality found that standardized mortality ratios (SMRs) for several cancer types in people with diabetes mellitus compared with the general population fell over time, but remained elevated beyond 2 years for pancreatic and liver cancers 54 , suggesting that diabetes mellitus is a genuine risk factor for these cancer types.

A limitation of the evidence that surrounds diabetes mellitus and cancer risk is high clinical and methodological heterogeneity across several of the large systematic reviews, which makes it difficult to be certain of the effect size in different demographic groups. Additionally, many of the studies exploring a potential association between diabetes mellitus and cancer were unable to adjust for BMI, which is a major confounder. However, a modelling study that accounted for BMI found that although 2.1% of cancers worldwide in 2012 were attributable to diabetes mellitus as an independent risk factor, twice as many cancers were attributable to high BMI 55 , so it is likely that effect sizes for cancer risk associated with diabetes mellitus would be attenuated after adjustment for BMI. Notably, however, low-income and middle-income countries/regions had the largest increase in the numbers of cases of cancer attributable to diabetes mellitus both alone and in combination with BMI 55 , highlighting the need for public health intervention, given that these countries/regions are less equipped than high-income countries/regions to manage a growing burden of cancer.

As well as the cancer types outlined above, diabetes mellitus has also been linked to various other types of cancer, including kidney cancer 56 , bladder cancer 57 and haematological malignancies; however, the evidence for these associations is not as strong as for the cancers discussed above 58 . Diabetes mellitus might also be associated with other cancer types such as small intestine cancer, but the rarity of some of these types makes it difficult to obtain sufficient statistical power in analyses of any potential association.

Potential aetiological mechanisms

Several aetiological mechanisms that might be involved in linking diabetes mellitus to cancer have been proposed, including hyperinsulinaemia, hyperglycaemia, inflammation and cellular signalling mechanisms.

Hyperinsulinaemia

Most cancer cells express insulin receptors, through which hyperinsulinaemia is thought to stimulate cancer cell proliferation and metastasis 59 . Hyperinsulinaemia might also promote carcinogenesis through increased local levels of insulin-like growth factor 1 (IGF1), which has potent mitogenic and anti-apoptotic activities 60 , owing to decreased levels of insulin-like growth factor binding proteins. As outlined above, people with diabetes mellitus show a strong risk of pancreatic and liver cancers; this increased risk might occur because insulin is produced by pancreatic β-cells and transported to the liver via the portal vein 61 , thereby exposing the liver and pancreas to high levels of endogenous insulin 59 .

Hyperglycaemia and inflammation

Hyperglycaemia can induce DNA damage 62 , increase the generation of reactive oxygen species 63 and downregulate antioxidant expression 64 , all of which are associated with cancer development. Inflammatory markers, including cytokines such as IL-6, appear to have an important role in the association between diabetes and cancer 65 .

Cellular signalling mechanisms

Several cellular signalling components are common to the pathogenesis of T2DM and cancer. These include the mechanistic target of rapamycin (mTOR), a central controller of cell growth and proliferation; AMP-activated protein kinase, a cellular energy sensor and signal transducer 66 ; and the phosphatidylinositol 3-kinase (PI3K)–AKT pathway, which transduces growth factor signals during organismal growth, glucose homeostasis and cell proliferation 67 . Dysregulation of any of these cellular signalling components or pathways could contribute to the development of cancer and metabolic disorders, including T2DM, and glucose-lowering drugs such as metformin have been associated with a reduction in cancer cell proliferation through effective inhibition of some of these components 68 .

Diabetes mellitus and infections

Infection-related complications.

Although infection has long been recognized as a complication of diabetes mellitus, an association between diabetes mellitus and infection has not been well documented in epidemiological studies 69 . Only in the past decade have major studies quantified the burden of infection-related complications in people with diabetes mellitus and explored the specific infections accounting for this burden. In a US cohort of 12,379 participants, diabetes mellitus conferred a significant risk of infection-related hospitalization, with an adjusted HR of 1.67 (95% CI 1.52–1.83) compared with people without diabetes mellitus 70 (Table  2 ). The association was most pronounced for foot infections (HR 5.99, 95% CI 4.38–8.19), with significant associations also observed for respiratory infection, urinary tract infection, sepsis and post-operative infection, but not for gastrointestinal infection, a category that included appendicitis and gastrointestinal abscesses but not viral or bacterial gastroenteritis. Interestingly, a report from Taiwan demonstrated an association between the use of metformin and a lower risk of appendicitis 71 .

In an analysis of the entire Hong Kong population over the period 2001–2016, rates of hospitalization for all types of infection remained consistently higher in people with diabetes mellitus than in those without diabetes mellitus 72 . The strongest association was seen for hospitalization due to kidney infections, for which the adjusted RR was 4.9 (95% CI 3.9–6.2) in men and 3.2 (95% CI 2.8–3.7) in women with diabetes mellitus compared with those without diabetes mellitus in 2016 (Table  2 ). Diabetes mellitus roughly doubled the risk of hospitalization from tuberculosis or sepsis. The most common cause of infection-related hospitalization was pneumonia, which accounted for 39% of infections across the study period, while no other single cause accounted for more than 25% of infections across the same period. Pneumonia-related hospitalization rates increased substantially from 2001 to 2005, probably as a result of the 2003 severe acute respiratory syndrome (SARS) epidemic and the decreased threshold for pneumonia hospitalization in the immediate post-epidemic period. Rates for hospitalization for influenza increased from 2002 to 2016, possibly because of changes in the virus and increased testing for influenza. Declining rates of hospitalization for tuberculosis, urinary tract infections, foot infections and sepsis could be due to improvements in the management of diabetes mellitus.

Infection-related mortality rates were found to be significantly elevated among 1,108,982 Australians with diabetes mellitus studied over the period 2000–2010 compared with rates in people without diabetes mellitus 73 . For overall infection-related mortality, SMRs were 4.42 (95% CI 3.68–5.34) for T1DM and 1.47 (95% CI 1.42–1.53) for people with T2DM compared with those without diabetes mellitus (Table  2 ). Substantially higher infection-related mortality rates were seen in people with T1DM compared with those with T2DM for all infection types, even after accounting for age. Hyperglycaemia is thought to be a driver of infection amongst people with diabetes mellitus (see below) 73 , which might explain the higher SMRs amongst people with T1DM, in whom hyperglycaemia is typically more severe, than in those with T2DM. The highest SMRs were seen for osteomyelitis, and SMRs for septicaemia and pneumonia were also greater than 1.0 for both types of diabetes mellitus compared with those without diabetes mellitus.

Post-operative infection

Post-operative infection is also an important complication of diabetes mellitus. In a meta-analysis, diabetes mellitus was found to be associated with an OR of 1.77 (95% CI 1.13–2.78) for surgical site infection across studies that adjusted for confounding factors 74 (Table  2 ). The effect size appears to be greatest after cardiac procedures, and one US study of patients undergoing coronary artery bypass grafting found diabetes mellitus to be an independent predictor of surgical site infection, with an OR of 4.71 (95% CI 2.39–9.28) compared with those without diabetes mellitus 75 . Risks of infection of more than threefold were reported in some studies of gynaecological 76 and spinal surgery 77 in people with diabetes mellitus compared with those without diabetes mellitus. Increased risks of infection among people with diabetes mellitus were also observed in studies of colorectal and breast surgery and arthroplasty, suggesting that the association between diabetes mellitus and post-operative infection is present across a wide range of types of surgery 74 .

Respiratory infections

The incidence of hospitalizations due to respiratory infections among people with diabetes mellitus was increasing substantially even before the onset of the coronavirus disease 2019 (COVID-19) pandemic, probably owing to increased life expectancy in these patients as well as an increased likelihood of them being hospitalized for conditions such as respiratory infections, which occur mostly in older age 12 . This rising burden of respiratory infection, in combination with the rising prevalence of diabetes mellitus, highlights the importance of addressing the emerging complications of diabetes mellitus to minimize impacts on health-care systems in current and future global epidemics.

Although diabetes mellitus does not appear to increase the risk of becoming infected with COVID-19 (ref. 78 ), various population-based studies have reported increased risks of COVID-19 complications among people with diabetes mellitus. In a study of the total Scottish population, people with diabetes mellitus were found to have an increased risk of fatal or critical care unit-treated COVID-19, with an adjusted OR of 1.40 (95% CI 1.30–1.50) compared with those without diabetes mellitus 79 (Table  2 ). The risk was particularly high for those with T1DM (OR 2.40, 95% CI 1.82–3.16) 79 . Both T1DM and T2DM have been linked to a more than twofold increased risk of hospitalization with COVID-19 in a large Swedish cohort study 80 . In South Korean studies, T2DM was linked to intensive care unit admission among patients with COVID-19 infection 81 , and diabetes mellitus (either T1DM or T2DM) was linked to a requirement for ventilation and oxygen therapy 82 in patients with COVID-19. Diabetes mellitus appears to be the primary predisposing factor for opportunistic infection with mucormycosis in individuals with COVID-19 (ref. 83 ). The evidence for diabetes mellitus as a risk factor for post-COVID-19 syndrome is inconclusive 84 , 85 . Interestingly, an increase in the incidence of T1DM during the COVID-19 pandemic has been reported in several countries/regions 86 , and some data suggest an increased risk of T1DM after COVID-19 infection 87 , but the evidence regarding a causal effect is inconclusive.

Pneumonia, MERS, SARS and H1N1 influenza

The data regarding diabetes mellitus and COVID-19 are consistent with the published literature regarding other respiratory infections, such as pneumonia, for which diabetes mellitus has been shown to increase the risk of hospitalization 88 and mortality 88 , with similar effect sizes to those seen for COVID-19, compared with no diabetes mellitus. Diabetes mellitus has also been also linked to adverse outcomes in people with Middle East respiratory syndrome (MERS), SARS and H1N1 influenza 89 , 90 , 91 , 92 , suggesting that mechanisms specific to COVID-19 are unlikely to be responsible for the relationship between diabetes mellitus and COVID-19. Unlike the case for COVID-19, there is evidence that people with diabetes mellitus are at increased risk of developing certain other respiratory infections, namely pneumonia 93 and possibly also MERS 94 .

The mechanisms that might link diabetes mellitus and infection include a reduced T cell response, reduced neutrophil function and disorders of humoral immunity.

Mononuclear cells and monocytes of individuals with diabetes mellitus secrete less IL-1 and IL-6 than the same cells from people without diabetes mellitus 95 . The release of IL-1 and IL-6 by T cells and other cell types in response to infection has been implicated in the response to several viral infections 96 . Thus, the reduced secretion of these cytokines in patients with diabetes mellitus might be associated with the poorer responses to infection observed among these patients compared with people without diabetes mellitus.

In the context of neutrophil function, hyperglycaemic states might give rise to reductions in the mobilization of polymorphonuclear leukocytes, phagocytic activity and chemotaxis 97 , resulting in a decreased immune response to infection. Additionally, increased levels of glucose in monocytes isolated from patients with obesity and/or diabetes mellitus have been found to promote viral replication in these cells, as well as to enhance the expression of several cytokines, including pro-inflammatory cytokines that are associated with the COVID-19 ‘cytokine storm’; furthermore, glycolysis was found to sustain the SARS coronavirus 2 (SARS-CoV-2)-induced monocyte response and viral replication 98 .

Elevated glucose levels in people with diabetes mellitus are also associated with an increase in glycation, which, by promoting a change in the structure and/or function of several proteins and lipids, is responsible for many of the complications of diabetes mellitus 99 . In people with diabetes mellitus, antibodies can become glycated, a process that is thought to impair their biological function 100 . Although the clinical relevance of this impairment is not clear, it could potentially explain the results of an Israeli study that reported reduced COVID-19 vaccine effectiveness among people with T2DM compared with those without T2DM 101 .

Diabetes mellitus and liver disease

Nonalcoholic fatty liver disease.

The consequences of nonalcoholic fatty liver disease (NAFLD) make it important to recognize the burden of this disease among people with diabetes mellitus. NAFLD and nonalcoholic steatohepatitis (NASH; an advanced form of NAFLD) are major causes of liver transplantation in the general population. In the USA, NASH accounted for 19% of liver transplantations in 2016 — second only to alcoholic liver disease, which was the cause of 24% of transplantations 102 . In Australia and New Zealand, NAFLD was the primary diagnosis in 9% of liver transplant recipients in 2019, only slightly below the figure for alcoholic cirrhosis of 13% 103 . In Europe, NASH increased as the reason for transplantations from 1% in 2002 to more than 8% in 2016, in parallel with the rising prevalence of diabetes mellitus 104 .

NAFLD is highly prevalent among people with T2DM. In a systematic review of 80 studies across 20 countries/regions, the prevalence of NAFLD among 49,419 people with T2DM was 56% 105 , while the global prevalence of NAFLD in the general population is estimated to be 25% 106 . In a Chinese cohort study of 512,891 adults, diabetes mellitus was associated with an adjusted HR of 1.76 (95% CI 1.47–2.16) for NAFLD compared with no diabetes mellitus 107 (Table  3 ). Another smaller longitudinal Chinese study also reported an increased risk of developing NAFLD among those with T2DM compared with those without T2DM 108 . However, most evidence regarding the association between NAFLD and diabetes mellitus is from cross-sectional studies 109 , 110 , 111 .

NASH and fibrosis

Diabetes mellitus appears to enhance the risk of NAFLD complications, including NASH and fibrosis. An analysis of 892 people with NAFLD and T2DM across 10 studies showed that the prevalence of NASH was 37% (ref. 105 ); figures for the prevalence of NASH in the general population with NAFLD vary greatly across different study populations, ranging from 16% to 68% 112 . Amongst 439 people with T2DM and NAFLD in seven studies, 17% had advanced fibrosis 105 . An analysis of 1,069 people with NAFLD in a US study found that diabetes mellitus was an independent predictor for NASH (OR 1.93, 95% CI 1.37–2.73) and fibrosis (3.31, 95% CI 2.26–4.85) 113 .

Bidirectional relationship between diabetes mellitus and liver disease

The relationship between diabetes mellitus and NAFLD is bidirectional, as NAFLD is associated with an increased risk of developing T2DM 114 . There is also a notable bidirectional relationship between diabetes mellitus and liver cirrhosis. The prevalence of diabetes mellitus in people with liver cirrhosis has been reported as 20–63%, depending on the severity of liver damage, aetiology and diagnostic criteria 115 . In an Italian study of 401 participants with cirrhosis, 63% of those with decompensated liver disease had diabetes mellitus compared with 10% of those with well-compensated liver disease 116 , suggesting that diabetes mellitus is more common in severe cases of liver damage. The association between diabetes mellitus and cirrhosis also varies according to the cause of liver disease. In a US study of 204 people with cirrhosis, the prevalence of diabetes mellitus was 25% among those with cirrhosis caused by hepatitis C virus, 19% among those with cirrhosis from alcoholic liver disease and only 1% among those with cirrhosis due to cholestatic liver disease 117 . Among the causes of cirrhosis, haemochromatosis has the strongest association with diabetes mellitus, with diabetes mellitus mainly resulting from the iron deposition that is characteristic of haemochromatosis 118 .

Several factors have been implicated in the aetiology of liver disease in people with diabetes mellitus, with insulin resistance being the most notable 119 .

Insulin resistance

Insulin resistance causes lipolysis, thereby increasing the circulating levels of free fatty acids, which are then taken up by the liver as an energy source 120 . These fatty acids overload the mitochondrial β-oxidation system in the liver, resulting in the accumulation of fatty acids and, consequently, NAFLD 121 . Of those individuals with NAFLD, 2–3% develop hepatic inflammation, necrosis and fibrosis, which are the hallmarks of NASH 122 . The exact mechanisms leading to steatohepatitis are unclear, although dysregulated peripheral lipid metabolism appears to be important 14 .

Ectopic adipose deposition

Excessive or ectopic deposition of adipose tissue around the viscera and in the liver might be an important mechanism underlying both T2DM and liver disease, particularly NAFLD 123 . Dysfunction of long-term adipose storage in white adipose tissue is known to lead to ectopic adipose deposition in the liver. In this state, increased levels of fatty acyl-coenzyme As, the activated form of fatty acids, might lead to organ dysfunction, including NAFLD 124 . Ectopic adipose deposition leading to organ-specific insulin resistance has emerged as a major hypothesis for the pathophysiological basis of T2DM, and ectopic adipose in the pancreas could contribute to β-cell dysfunction and, thus, the development of T2DM 125 .

Diabetes mellitus and affective disorders

The prevalence of depression appears to be high among people with diabetes mellitus. The strongest evidence for an association comes from a systematic review of 147 studies among people with T2DM, which revealed a mean prevalence of depression of 28% 126 , while the global prevalence of depression in the general population is estimated at around 13% 127 . For T1DM, a systematic review reported a pooled prevalence of depression of 12% compared with only 3% in those without T1DM 128 . The risk of depression among people with diabetes mellitus appears to be roughly 25% greater than the risk in the general population, with consistent findings across several meta-analyses (Table  4 ). A 2013 study found an adjusted RR of 1.25 (95% CI 1.10–1.44) for incident depression among people with diabetes mellitus compared with those without diabetes mellitus 129 . Another systematic review of people with T2DM reported a near identical effect size 130 .

Anxiety and eating disorders

Evidence exists for an association of diabetes mellitus with anxiety, and of T1DM with eating disorders. In a systematic review involving 2,584 individuals with diabetes mellitus, a prevalence of 14% was found for generalized anxiety disorder and 40% for anxiety symptoms, whereas the prevalence of generalized anxiety disorder in the general population is estimated as only 3–4% 131 . People with diabetes mellitus had an increased risk of anxiety disorders (OR 1.20, 95% CI 1.10–1.31) and anxiety symptoms (OR 1.48, 95% CI 1.02–1.93) compared with those without diabetes mellitus in a meta-analysis 132 (Table  4 ), although these findings were based on cross-sectional data. Across 13 studies, 7% of adolescents with T1DM were found to have eating disorders, compared with 3% of peers without diabetes mellitus 133 .

Broader psychological impacts

There is a substantial literature on a broad range of psychological impacts of diabetes mellitus. Social stigma 134 can have profound impacts on the quality of life of not only people with diabetes mellitus, but their families and carers, too 135 . In a systematic review, diabetes mellitus distress was found to affect around one-third of adolescents with T1DM, which was consistent with the results of studies of adults with diabetes mellitus 136 . Diabetes mellitus burnout appears to be a distinct concept, and is characterized by exhaustion and detachment, accompanied by the experience of a loss of control over diabetes mellitus 137 .

Diabetes mellitus and depression appear to have common biological origins. Activation of the innate immune system and acute-phase inflammation contribute to the pathogenesis of T2DM — increased levels of inflammatory cytokines predict the onset of T2DM 138 — and there is growing evidence implicating cytokine-mediated inflammation in people with depression in the absence of diabetes mellitus 139 . Dysregulation of the hypothalamic–pituitary–adrenal axis is another potential biological mechanism linking depression and diabetes mellitus 140 . There have been numerous reports of hippocampal atrophy, which might contribute to chronic activation of the hypothalamic–pituitary–adrenal axis, in individuals with T2DM as well as those with depression 141 , 142 . A meta-analysis found that, although hypertension modified global cerebral atrophy in those with T2DM, it had no effect on hippocampal atrophy 143 . This suggests that, although global cerebral atrophy in individuals with T2DM might be driven by atherosclerotic disease, hippocampal atrophy is an independent effect that provides a common neuropathological aetiology for the comorbidity of T2DM with depression. There is a lack of relevant information regarding the potential aetiological mechanisms that link diabetes to other affective disorders.

Diabetes mellitus and sleep disturbance

Obstructive sleep apnoea.

Obstructive sleep apnoea (OSA) is highly prevalent among people with diabetes mellitus. In a systematic review of 41 studies of adults with diabetes mellitus, the prevalence of OSA was found to be 60% 144 , whereas reports for OSA prevalence in the general population range from 9% to 38% 145 . In a UK study of 1,656,739 participants, T2DM was associated with an IRR for OSA of 1.48 (95% CI 1.42–1.55) compared with no T2DM 146 . A population-based US study reported a HR of 1.53 (95% CI 1.32–1.77) for OSA in people with T2DM compared with those without diabetes mellitus 147 . However, the association in this latter report was attenuated after adjustment for BMI and waist circumference (1.08, 95% CI 1.00–1.16), suggesting that the excess risk of OSA among people with diabetes mellitus might be mainly explained by the comorbidity of obesity. Although most studies on OSA have focused on T2DM, a meta-analysis of people with T1DM revealed a similar prevalence of 52% 148 ; however, this meta-analysis was limited to small studies. The association between T2DM and OSA is bidirectional: the severity of OSA was shown to be positively associated with the incidence of T2DM, independent of adiposity, in a large US cohort study 149 .

The mechanism by which T2DM might increase the risk of developing OSA is thought to involve dysregulation of the autonomic nervous system leading to sleep-disordered breathing 150 . Conversely, the specific mechanism behind OSA as a causative factor for T2DM remains poorly understood. It has been suggested that OSA is able to induce insulin resistance 151 , 152 and is a risk factor for the development of glucose intolerance 152 . However, once T2DM has developed, there is no clear evidence that OSA worsens glycaemic control, as an RCT of people with T2DM found that treating OSA had no effect on glycaemic control 153 .

Diabetes mellitus and cognitive disability

Dementia and cognitive impairment.

Dementia is emerging as a major cause of mortality in both individuals with diabetes mellitus and the general population, and is now the leading cause of death in some countries/regions 9 . However, compared with the general population, diabetes mellitus increases the risk of dementia, particularly vascular dementia. The association is supported by several systematic reviews, including one of eight population-based studies with more than 23,000 people, which found SRRs of 2.38 (95% CI 1.79–3.18) for vascular dementia and 1.39 (95% CI 1.16–1.66) for Alzheimer disease comparing people with diabetes mellitus with those without diabetes mellitus 154 (Table  4 ). Similar results, as well as a RR of 1.21 (95% CI 1.02–1.45) for mild cognitive impairment (MCI), were reported across 19 population-based studies of 44,714 people, 6,184 of whom had diabetes mellitus 155 . Two meta-analyses of prospective cohort studies have shown increased risks of all-cause dementia in people with diabetes mellitus compared with those without diabetes mellitus 156 , 157 , and T2DM has been shown to increase progression to dementia in people with MCI 158 .

The boundaries between Alzheimer disease and vascular dementia remain controversial, and these conditions are often difficult to differentiate clinically 159 . Consequently, vascular dementia might have been misdiagnosed as Alzheimer disease in some studies investigating diabetes mellitus and dementia, resulting in an overestimation of the effect size of the association between diabetes mellitus and Alzheimer disease. Although a cohort study found a significant association between diabetes mellitus and Alzheimer disease using imaging 160 , autopsy studies have failed to uncover an association between diabetes mellitus and Alzheimer disease pathology 161 , 162 , suggesting that vascular mechanisms are the key driver of cognitive decline in people with diabetes mellitus.

Another important finding is a 45% prevalence of MCI among people with T2DM in a meta-analysis, compared with a prevalence of 3–22% reported for the general population 163 . Notably, however, the prevalence of MCI in individuals with T2DM was similar in people younger than 60 years (46%) and those older than 60 years (44%), which is at odds with previous research suggesting that MCI is most common in older people, particularly those aged more than 65 years 164 However, another meta-analysis found cognitive decline in people with T2DM who are younger than 65 years 165 , suggesting that a burden of cognitive disease exists among younger people with diabetes mellitus.

Although there is solid evidence that links diabetes mellitus to cognitive disability, our understanding of the underlying mechanisms is incomplete. Mouse models suggest a strong association between hyperglycaemia, the advanced glycation end products glyoxal and methylglyoxal, enhanced blood–brain barrier (BBB) permeability and cognitive dysfunction in both T1DM and T2DM 166 . The BBB reduces the access of neurotoxic compounds and pathogens to the brain and sustains brain homeostasis, so disruption to the BBB can result in cognitive dysfunction through dysregulation of transport of molecules between the peripheral circulation and the brain 167 . There appears to be a continuous relationship between glycaemia and cognition, with associations found between even high-normal blood levels of glucose and cognitive decline 168 . Another hypothetical mechanism involves a key role for impaired insulin signalling in the pathogenesis of Alzheimer disease. Brain tissue obtained post mortem from individuals with Alzheimer disease showed extensive abnormalities in insulin and insulin-like growth factor signalling mechanisms compared with control brain tissue 169 . Although the synthesis of insulin-like growth factors occurred normally in people with Alzheimer disease, their expression levels were markedly reduced, which led to the subsequent proposal of the term ‘type 3 diabetes’ to characterize Alzheimer disease.

Diabetes mellitus and disability

Functional disability.

Disability (defined as a difficulty in functioning in one or more life domains as experienced by an individual with a health condition in interaction with contextual factors) 170 is highly prevalent in people with diabetes mellitus. In a systematic review, lower-body functional limitation was found to be the most prevalent disability (47–84%) among people with diabetes mellitus 171 The prevalence of difficulties with activities of daily living among people with diabetes mellitus ranged from 12% to 55%, although most studies were conducted exclusively in individuals aged 60 years and above, so the results are not generalizable to younger age groups. A systematic review showed a significant association between diabetes mellitus and falls in adults aged 60 years and above 172 . A 2013 meta-analysis 173 showed an increased risk of mobility disability, activities of daily living disability and independent activities of daily living disability among people with diabetes mellitus compared with those without diabetes mellitus (Table  4 ). Although this analysis included cross-sectional data, results were consistent across longitudinal and cross-sectional studies, suggesting little effect of reverse causality. However, people with functional disabilities that limit mobility (for example, people with osteoarthritis or who have had a stroke) might be more prone to developing diabetes mellitus owing to physical inactivity 174 .

Workplace productivity

Decreased productivity while at work, increased time off work and early dropout from the workforce 175 are all associated with diabetes mellitus, probably partly due to functional disability, and possibly also to comorbidities such as obesity and physical inactivity 176 . Given that young-onset diabetes is becoming more common, and most people with diabetes mellitus in middle-income countries/regions are less than 65 years old 177 , a pandemic of diabetes mellitus-related work disability among a middle-aged population does not bode well for the economies of these regions.

The mechanisms by which diabetes mellitus leads to functional disability remain unclear. One suggestion is that hyperglycaemia leads to systemic inflammation, which is one component of a multifactorial process that results in disability 154 . The rapid loss of skeletal muscle strength and quality seen among people with diabetes mellitus might be another cause of functional disability 178 (Box  1 ). In addition, complications of diabetes mellitus, including stroke, peripheral neuropathy and cardiac dysfunction, can obviously directly cause disability 179 .

Box 1 Diabetes mellitus and skeletal muscle atrophy

Individuals with diabetes mellitus exhibit skeletal muscle atrophy that is typically mild in middle age and becomes more substantial with increasing age.

This muscle loss leads to reduced strength and functional capacity and, ultimately, increased mortality.

Skeletal muscle atrophy results from a negative balance between the rate of synthesis and degradation of contractile proteins, which occurs in response to disuse, ageing and chronic diseases such as diabetes mellitus.

Degradation of muscle proteins is more rapid in diabetes mellitus, and muscle protein synthesis has also been reported to be decreased.

Proposed mechanisms underlying skeletal muscle atrophy include systemic inflammation (affecting both protein synthesis and degradation), dysregulation of muscle protein anabolism and lipotoxicity.

Mouse models have also revealed a key role for the WWP1/KLF15 pathway, mediated by hyperglycaemia, in the pathogenesis of muscle atrophy.

See refs 195 , 196 , 197 , 198 .

Diabetes management and control

Although a detailed discussion of the impacts of anti-diabetes mellitus medications and glucose control on emerging complications is beyond the scope of this Review, their potential effect on these complications must be acknowledged.

Medications

Anti-diabetes mellitus medications and cancer.

In the case of cancer as an emerging complication, the use of medications for diabetes mellitus was not controlled for in most studies of diabetes mellitus and cancer and might therefore be a confounding factor. People taking metformin have a lower cancer risk than those not taking metformin 180 . However, this association is mainly accounted for by other factors. For example, metformin is less likely to be administered to people with diabetes mellitus who have kidney disease 181 , who typically have longer duration diabetes mellitus, which increases cancer risk. A review of observational studies into the association between metformin and cancer found that many studies reporting significant reductions in cancer incidence or mortality associated with metformin were affected by immortal time bias and other time-related biases, casting doubt on the ability of metformin to reduce cancer mortality 182 . Notably, the use of insulin was associated with an increased risk of several cancers in a meta-analysis 183 . However, in an RCT of more than 12,000 people with dysglycaemia, randomization to insulin glargine (compared with standard care) did not increase cancer incidence 184 . Furthermore, cancer rates in people with T1DM and T2DM do not appear to vary greatly, despite substantial differences in insulin use between people with these types of diabetes mellitus.

Anti-diabetes mellitus medications and other emerging complications

Anti-diabetes medications appear to affect the onset and development of some other emerging complications of diabetes mellitus. Results from RCTs suggest that metformin might confer therapeutic effects against depression 185 , and its use was associated with reduced dementia incidence in a systematic review 186 . In an RCT investigating a potential association between metformin and NAFLD, no improvement in NAFLD histology was found among people using metformin compared with those given placebo 187 . An RCT reported benefits of treatment with the glucagon-like peptide 1 receptor agonist dulaglutide on cognitive function in a post hoc analysis 188 , suggesting that trials designed specifically to test the effects of dulaglutide on cognitive function should be undertaken.

Glucose control

Another important consideration is glycaemic control, which appears to have variable effects on emerging complications. A meta-analysis found no association of glycaemic control with cancer risk among those with diabetes mellitus 189 , and an RCT found no effect of intensive glucose lowering on cognitive function in people with T2DM 190 . However, glycaemic control has been associated with improved physical function 191 , decreased COVID-19 mortality 192 and a decreased risk of NAFLD 193 in observational studies of patients with diabetes mellitus; notably, no RCTs have yet confirmed these associations.

Conclusions

With advances in the management of diabetes mellitus and associated increased life expectancy, the face of diabetes mellitus complications is changing. As the management of glycaemia and traditional complications of diabetes mellitus is optimized, we are beginning instead to see deleterious effects of diabetes mellitus on the liver, brain and other organs. Given the substantial burden and risk of these emerging complications, future clinical and public health strategies should be updated accordingly. There is a need to increase the awareness of emerging complications among primary care physicians at the frontline of diabetes mellitus care, and a place for screening for conditions such as depression, liver disease and cancers in diabetes mellitus guidelines should be considered. Clinical care for older people with diabetes mellitus should target physical activity, particularly strength-based activity, to reduce the risk of functional disability in ageing populations. Ongoing high-quality surveillance of diabetes mellitus outcomes is imperative to ensure we know where the main burdens lie. Given the growing burden of these emerging complications, the traditional management of diabetes mellitus might need to broaden its horizons.

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Acknowledgements

D.T. is supported by an Australian Government Research Training Program (RTP) Scholarship and Monash Graduate Excellence Scholarship. J.E.S. is supported by a National Health and Medical Research Council Investigator Grant. D.J.M. is supported by a National Health and Medical Research Council Senior Research Fellowship.

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Dunya Tomic, Jonathan E. Shaw & Dianna J. Magliano

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D.T. researched data for the article and wrote the article. J.E.S and D.J.M. contributed substantially to discussion of the content. D.T., J.E.S. and D.J.M reviewed and/or edited the manuscript before submission.

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Tomic, D., Shaw, J.E. & Magliano, D.J. The burden and risks of emerging complications of diabetes mellitus. Nat Rev Endocrinol 18 , 525–539 (2022). https://doi.org/10.1038/s41574-022-00690-7

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September 11, 2024

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Time-restricted eating found to improve blood sugar control in adults at risk of type 2 diabetes

by Diabetologia

Restricting the eating window to eight hours a day significantly improves blood glucose control in adults at risk of type 2 diabetes irrespective of whether it is earlier or later in the day, according to a randomized crossover trial presented at the Annual Meeting of The European Association for the Study of Diabetes (EASD), held in Madrid (9–13 Sept).

"Our study found that restricting eating to a window of eight hours per day significantly improved the daily time spent in the normal blood glucose range and reduced fluctuations in blood glucose levels. However, altering the eight-hour restricted eating period to earlier or later in the day did not appear to offer additional benefits," said lead author Dr. Kelly Bowden Davies from Manchester Metropolitan University, UK.

She added, "Our findings, which can be attributed to the 16-hour fasting window rather than the time of eating or changes in energy intake, also highlight that the benefit of time-restricted eating can be seen within just three days.

"Although time-restricted eating is becoming increasingly popular, no other studies have examined tightly controlled diet and altered the clock time of an eight-hour eating window on glycemic control in people at risk of type 2 diabetes."

Previous studies indicate that TRE (which limits when, but not what, individuals eat) can improve insulin sensitivity (the body's ability to respond to insulin) and glycated hemoglobin (HbA 1c ; average blood sugar levels over a period of weeks and months) in people at risk of type 2 diabetes.

However, the effect on glycemic variability (fluctuations in blood glucose levels ) is not clear and previous studies have attributed the positive effects of TRE to reduced energy intake. This study sought to understand alterations in meal timing when participants were in energy balance ( energy intake was matched with energy expenditure).

To find out more, researchers investigated the impact of TRE in a eucaloric manner—with diets provided to match energy requirements (taking into account sex, age, weight, height, activity level—comparing an early (E TRE ; between 8:00 and 16:00 hours) versus a late (L TRE ; between 12:00 and 20:00 hours) eating window on glycemic control in overweight sedentary adults.

Fifteen sedentary individuals (nine female / six male; average age 52 years; BMI 28 m/kg 2 ; HbA 1c 37.9 mmol/mol) who habitually spread their eating period over more than 14 hours per day were assigned to two different eating patterns for three days at a time.

Researchers compared the E TRE regimen (eating only between 8:00 am and 4:00 pm) and L TRE regimen (eating only between midday and 8:00 pm) periods, and the habitual eating regimen (more than 14 hours/day). A eucaloric standardized diet [50% carbohydrates, 30% fat and 20% protein] was provided during the TRE periods but participants consumed their own diets during habitual (free) living conditions.

Continuous glucose monitoring was used to assess daily time spent in euglycemia (with a normal concentration of blood glucose of 3.9-7.8 mmol/l) and markers of glycemic variability, including mean absolute glucose (MAG), coefficient of variation (CV), and mean amplitude of glucose excursions (MAGE).

The analyses found that in comparison to habitual eating (more than 14 h/day), TRE (eight h/day) significantly increased time spent within the normal blood glucose range by on average 3.3%, and also reduced markers of glycemic variability—MAG by 0.6 mmol/l, CV by 2.6%, and MAGE by 0.4 mmol/l.

However, no significant differences in glycemic control were found between the E TRE and L TRE regimens.

"Many people find counting calories hard to stick to in the long term, but our study suggests that watching the clock may offer a simple way to improve blood sugar control in people at risk of type 2 diabetes, irrespective of when they have their eight-hour eating window, which warrants investigation in larger studies and over the longer term," said Dr. Bowden Davies.

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Australians with type 2 diabetes missing out on crucial medications

Photo: Getty Images

More Australians with type 2 diabetes should gain access to potentially lifesaving medications, a new analysis has found. 

UNSW medical researchers say more Australians with type 2 diabetes need to be offered add-on medications that would better protect them from heart and kidney disease – which are among the major causes of death for people with diabetes.

About one in three people with type 2 diabetes are receiving early treatment with additional medicines that protect their heart and kidneys, new research led by UNSW Sydney shows.

The study, published in The British Journal of Clinical Pharmacology, analysed dispensing records of a 10% random sample of adults on the Pharmaceutical Benefits Scheme (PBS). It found almost 39,000 people aged 40 and over started on metformin – the first-line treatment for type 2 diabetes – between 2018 and 2020. Only about a third received any add-on therapy within two years of starting their treatment. “More than 1 million Australians are living with type 2 diabetes, but it is heart and kidney disease that are major causes of death and poor quality of life for these people,” said lead study author Dr Tamara Milder, an endocrinologist with UNSW’s School of Population Health.

“Fortunately, there are diabetes medicines available that can also protect their hearts and kidneys, and potentially save lives, but many people are missing out.”

It can take time for clinicians to adopt newer medications, as they may be wary of potential side effects and comfortable with medications that they have known for longer time periods, the study notes.

Limited PBS access to these medications – restricted to those with high blood sugar or for whom other subsidised medication had failed – had potentially limited prescribing practices, the researchers added. The COVID-19 pandemic and international shortages of GLP-1 RA since 2022 may also have had some impact.

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For enquiries about this story and interview requests, please contact  Kate Burke , News & Content Coordinator, UNSW Medicine & Health.

Tel:  +61 2 9348 2538 Email:  [email protected]

Rapid changes made to diabetes management guidelines  

Considerable changes have been made to national and international type 2 diabetes management guidelines in recent years, due to the advent of new tablets and injectable medicines, like Ozempic, Dr Milder said.

“There has been a major shift in recent years in how we treat diabetes,” Dr Milder said. “It used to just be about managing people’s sugars, but we know that heart and kidney disease are major drives of death and poor quality of life for people with diabetes, and we have these two major drug classes that can help protect people with type 2 diabetes.”

These drug classes, known as SGLT2i (sodium-glucose co-transporter 2 inhibitors) and GLP-1 RA (glucagon-like peptide-1 receptor agonists), help manage and improve blood sugar, but also have additional benefits like weight loss, lowering blood pressure and reducing the risk of kidney and cardiovascular events. Semaglutide, best known by brand name Ozempic, is a GLP-1 RA.

Their benefits were first highlighted in the Australian Diabetes Society’s Type 2 Diabetes Management Algorithm in 2020, before being recommended in 2022 for dual therapy use for adults with type 2 diabetes and cardiovascular disease, multiple cardiovascular risk factors and/or kidney disease.  

While it is not known how many people in the sample group would fall into this cohort – or meet existing PBS criteria – people with diabetes are generally at high risk of cardiovascular events, heart and kidney failure, Dr Milder said.  

“We have these medications with heart and kidney benefits that we can see are not being heavily used in early treatment, that there are a lot of people who are not having add-on therapy,” Dr Milder said.

While the overall use of add-on therapy remained low, SGLT2i and GLP-1 RA were increasingly used as the first add-on therapy over the analysed period, respectively rising from 28.8% to 35%, and from 3% to 9.6%. They were among six anti-hyperglycaemic medicine classes which the researchers focused on.  

Of those people who were given add-on therapy, one third started it on the same day as they began metformin. The remainder waited a median of about eight months before they started on any additional medicine.

Senior report author Dr Michael Falster , also from UNSW’s School of Population Health, said the medications were being increasingly used in people with established cardiovascular disease but were generally being underutilised across the board.

“It's challenging, we have new medicines with fantastic capabilities in helping prevent progression for serious health outcomes, but a slow uptake,” Dr Falster said. 

Greater action at a policy level to increase prescribers’ knowledge and confidence to use these medicines was needed, as was more research to better understand gaps in eligible people using these medicines. 

The researchers also acknowledged the government was regularly reviewing whether it was cost-effective to expand subsidised prescriptions of these medicines, and noted there were also issues of medication shortages. 

“We must ensure there's equitable access to these medicines for the people for whom there's the greatest therapeutic benefit,” Dr Falster said.

Dr Milder added: “We must prioritise early access to these medicines for Australians with type 2 diabetes. By doing so, we can better protect their hearts and kidneys, improving both quality of life and overall health, and saving lives.”

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Understanding diabetes and oral health

Two undergraduates, supported by purm, worked on research projects this summer with the graves lab to contribute to the knowledge of diabetes’ impact on oral wound healing and periodontal disease..

Despite a robust amount of research about diabetes, much is still unknown about its effects on oral health. Changes in inflammation are understood to contribute to the impact of diabetes, but what is driving the increase is less clear.  

Through a study in the Graves Lab, led by Dana Graves , a professor of periodontics in the School of Dental Medicine , the goal has been to identify molecules that are modulated by diabetes to increase pathology.  

One project, co-led by research scholar Hamideh Afzali, used single-cell RNA sequencing, which is a sophisticated assay that measures the mRNA level of thousands of genes in each individual cell. The sequencing focused on white blood cells in contrast to many wound healing studies that focus on fibroblasts. That led the research team to identify the gene S100A11 that was found at high levels in diabetic wounds in a specific white blood cell, neutrophils.

“Not as many genes were obviously affected by diabetes as we expected," explains Graves. “There’s not a huge number that are different. … And this gene is the one that Hamideh thought was pretty important.”

The process was to examine the healing of diabetic wounds in mice and conduct bioinformatic analysis during the healing process when connective tissue starts to form. The analysis led to S100A11, but the question remained whether inhibiting this gene would improve healing. 

Helpful in this process was Sanan Gueyikian, a third-year neuroscience major with a minor in health care management in the College of Arts and Sciences , who measured histologic tissues to assess, quantitatively, whether healing improved. Gueyikian conducted this work through the Penn Undergraduate Research Mentoring Program (PURM), a 10-week opportunity from the Center for Undergraduate Research and Fellowships . The program provides rising second- and third-year students with a $5,000 award to work alongside Penn faculty.

“There’s a long history of diabetes in my family, and the lack of information regarding its cause or what it can trigger fascinates me,” Gueyikian says. “I am also interested in dental medicine and pursuing it in the future. So, Dr. Graves’ lab was the perfect place for me to explore these interests.”

Through the experience, she says, she learned a lot about signaling pathways and testing for genes. Gueyikian adds that, with the mentorship of Afzali, she learned to read research papers differently and understand them better. She says that this was her first experience with research and further cemented her interest in oral surgery.

“I came out with a much better understanding of the research process,” she says. 

A second research project in the Graves Lab, supported with a PURM student researcher, looked at periodontal disease and bone loss among diabetics. Su Ah Kim, a third-year student majoring in finance and healthcare management at the Wharton School with a minor in chemistry, worked with senior research investigator Min Liu to genotype and quantify bone loss in diabetic mice. Liu says Kim is “very smart and works very hard and learns very fast,” and analyzed the bone levels to help determine whether the Akt1 gene played a significant role in the increased periodontal disease caused by diabetes.   

Like Gueyikian, Kim says her family has a history of diabetes, which partly spurred her interest in the PURM project. She plans to pursue dentistry after graduating and has spent time shadowing at University Dental Associates. She hopes to combine her business and dentistry knowledge to one day own her own practice.

The research opportunity, Kim says, taught her how to be more self-sufficient, while also giving her a chance to interact with Penn Dental students who were happy to answer questions about dentistry as a career path.

“I learned in the 10 weeks that self-initiation and being proactive are fundamental for success in scientific research,” Kim says.

“It was my first time conducting research in a wet lab, so I experienced learning curves with performing procedures, including genotyping and histological staining,” she adds. “However, through several processes of trial and error and collaboration with other lab members, I developed a strong knowledge of each procedure, which I was able to carry out successfully on my own.” Graves is a big fan of the PURM program and has brought undergraduates into projects for many years, he says—always to great success.

“Both of them contributed a lot to their projects,” says Graves. “This was not a teaching exercise; this was an opportunity to participate as a researcher. They both will be coauthors on a paper and both provided valuable data, bringing a lot of enthusiasm, while learning quickly. They were a pleasure to work with.”

Participating PURM students, Sanan and Kim, he says, learned how important teamwork is in a research lab setting and how it is necessary to obtain successful results. He says both students embraced a spirit of cooperation, which is “a key factor” in lab work. 

“They became real researchers toward the end, both understanding their projects well and capable of carrying out the experimental assays,” he says.

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Voice analysis can screen people for type 2 diabetes with high accuracy, study finds

AI model can detect changes in a person’s voice to distinguish whether they have type 2 diabetes with 66% accuracy in women and 71% accuracy in men.

Diabetologia

New research to be presented at this year’s Annual Meeting of The European Association for the Study of Diabetes (EASD), Madrid (9-13 Sept), highlights the potential of using voice analysis to detect undiagnosed type 2 diabetes (T2D) cases.

The study used on average 25 seconds of people’s voices along with basic health data including age, sex, body mass index (BMI), and hypertension status, to develop an AI model that can distinguish whether an individual has T2D or not, with 66% accuracy in women and 71% accuracy in men.

“Most current methods of screening for type 2 diabetes require a lot of time and are invasive, lab-based, and costly,” explained lead author Abir Elbeji from the Luxembourg Institute of Health, Luxembourg. “Combining AI with voice technology has the potential to make testing more accessible by removing these obstacles. This study is the first step towards using voice analysis as a first-line, highly scalable type 2 diabetes screening strategy.”

Around half of adults with diabetes (around 240 million worldwide) are unaware that they have the condition because the symptoms can be general or non-existent—around 90% of these have T2D [1]. But early detection and treatment can help prevent serious complications. Reducing undiagnosed T2D cases worldwide is an urgent public health challenge.

The study set out to develop and assess the performance of a voice-based AI algorithm to detect whether adults have T2D. Researchers asked 607 adults from the Colive Voice study (diagnosed with and without T2D) to provide a voice recording of themselves reading a few sentences of a provided, directly from their smartphone or laptop.

Both females and males with T2D were older (average age females 49.5 vs 40.0 years and males 47.6 vs 41.6 years) and were more likely to be living with obesity (average BMI females 35.8 vs 28.0 kg/m² and males 32.8 vs 26.6 kg/m²) than those without T2D.

From a total of 607 recordings, the AI algorithm analysed various vocal features, such as changes in pitches, intensity, and tone, to identify differences between individuals with and without diabetes.

This was done using two advanced techniques: one that captured up to 6,000 detailed vocal characteristics, and a more sophisticated deep-learning approach that focused on a refined set of 1,024 key features.

The performance of the best models was grouped by several diabetes risk factors including age, BMI, and hypertension, and compared to the reliable American Diabetes Association (ADA) tool for T2D risk assessment [2].

The voice-based algorithms showed good overall predictive capacity, correctly identifying 71% of male and 66% of female T2D cases. The model performed even better in females aged 60 years or older and in individuals with hypertension.

Additionally, there was 93% agreement with the questionnaire-based ADA risk score, demonstrating equivalent performances between voice analysis and a widely accepted screening tool. “While our findings are promising, further research and validation are necessary before the approach has the potential to become a first-line diabetes screening strategy and help reduce the number of people with undiagnosed type 2 diabetes. Our next steps are to specifically target early-stage type 2 diabetes cases and pre-diabetes,” said co-author Dr Guy Fagherazzi from the Luxembourg Institute of Health, Luxembourg.

Article Publication Date

Coi statement.

The authors declare no conflicts of interest.

Disclaimer: AAAS and EurekAlert! are not responsible for the accuracy of news releases posted to EurekAlert! by contributing institutions or for the use of any information through the EurekAlert system.

Late breakfast and earlier evening meal could cut Type 2 diabetes risk, research suggests

Experts say restricting our eating window to an eight-hour period improved blood glucose control and could could boost all round health

• 00:01, 11 Sep 2024

Having a late breakfast and an early evening meal cuts our risk of Type 2 diabetes , new research suggests.

Restricting our eating window to an eight-hour period improved blood glucose control in adults at risk of developing the condition . Researchers were able to confirm this was not due to people eating less in total, but because of the extended hours of the day when they were not eating.

This longer break between periods when the body is busy digesting food is thought to help a number of metabolic functions and boost healthy gut bacteria. It comes as there is a growing trend towards periods of fasting to boost health.

Lead author Dr Kelly Bowden Davies, of Manchester Metropolitan University, said: “Many people find counting calories hard to stick to in the long term, but our study suggests that watching the clock may offer a simple way to improve blood sugar control in people at risk of type 2 diabetes. This warrants investigation in larger studies and over the longer term.”

The new study is the first to show that it does not matter whether that eating window was early, between 8am and 4pm, or late, between 12pm and 8pm. The trail enrolled 15 people and controlled what they ate to ensure any benefit discovered was not due to participants consuming less food. They were each allocated a “eucaloric” diet matched to their energy requirements and taking into account sex, age, weight, height and activity levels.

Continuous glucose monitoring showed that in comparison to “habitual eating” spread over 14 hours a day, those eating in an eight-hour window increased their time spent within the normal blood glucose range by on average 3.3%. They also had reduced markers of glycaemic variability - fluctuations in blood glucose levels - by 2.6%. The benefits started to be seen after three days of time restricted eating (TRE).

Dr Bowden Davies said: “Our study found that restricting eating to a window of eight hours per day significantly improved the daily time spent in the normal blood glucose range and reduced fluctuations in blood glucose levels. This can be attributed to the 16-hour fasting window rather than the time of eating or changes in energy intake. Although time-restricted eating is becoming increasingly popular, no other studies have examined tightly controlled diet and altered the clock time of an eight-hour eating window on glycaemic control in people at risk of type 2 diabetes.”

The findings are presented at the annual meeting of The European Association for the Study of Diabetes (EASD) in Madrid.

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The essential role of exercise in the management of type 2 diabetes

John p. kirwan.

Department of Pathobiology, Lerner Research Institute, Cleveland Clinic; Department of Physiology and Biophysics, Case Western Reserve University; Metabolic Translational Research Center, Endocrinology & Metabolism Institute, Cleveland Clinic, Cleveland, OH

JESSICA SACKS

Department of Pathobiology, Lerner Research Institute, Cleveland Clinic, Department of Physiology and Biophysics, Case Western Reserve University, Cleveland, OH

STEPHAN NIEUWOUDT

Department of Pathobiology, Lerner Research Institute, Cleveland Clinic; Department of Physiology and Biophysics, Case Western Reserve University, Cleveland, OH

Exercise is typically one of the first management strategies advised for patients newly diagnosed with type 2 diabetes. Together with diet and behavior modification, exercise is an essential component of all diabetes and obesity prevention and lifestyle intervention programs. Exercise training, whether aerobic or resistance training or a combination, facilitates improved glucose regulation. High-intensity interval training is also effective and has the added benefit of being very time-efficient. While the efficacy, scalability, and affordability of exercise for the prevention and management of type 2 diabetes are well established, sustainability of exercise recommendations for patients remains elusive.

Type 2 diabetes has emerged as a major public health and economic burden of the 21st century. Recent statistics from the Centers for Disease Control and Prevention suggest that diabetes affects 29.1 million people in the United States, 1 and the International Diabetes Federation estimates diabetes effects 366 million people worldwide. 2

As these shocking numbers continue to increase, the cost of caring for patients with diabetes is placing enormous strain on the economies of the US and other countries. In order to manage and treat a disease on the scale of diabetes, the approaches need to be efficacious, sustainable, scalable, and affordable.

Of all the treatment options available, including multiple new medications and bariatric surgery (for patients who meet the criteria, discussed elsewhere in this supplement), 3 – 5 exercise as part of a lifestyle approach 6 is a strategy that meets the majority of these criteria.

The health benefits of exercise have a long and storied history. Hippocrates, the father of scientific medicine, was the first physician on record to recognize the value of exercise for a patient with “consumption.” 7 Today, exercise is recommended as one of the first management strategies for patients newly diagnosed with type 2 diabetes and, together with diet and behavior modification, is a central component of all type 2 diabetes and obesity prevention programs.

The evidence base for the efficacy, scalability, and affordability of exercise includes multiple large randomized controlled trials; and these data were used to create the recently updated exercise guidelines for the prevention and treatment of type 2 diabetes, published by the American Diabetes Association (ADA), American College of Sports Medicine (ACSM), and other national organizations. 8 – 10

Herein, we highlight the literature surrounding the metabolic effects and clinical outcomes in patients with type 2 diabetes following exercise intervention, and point to future directions for translational research in the field of exercise and diabetes.

It is known that adults who maintain a physically active lifestyle can reduce their risk of developing impaired glucose tolerance, insulin resistance, and type 2 diabetes. 8 It has also been established that low cardiovascular fitness is a strong and independent predictor of all-cause mortality in patients with type 2 diabetes. 11 , 12 Indeed, patients with diabetes are 2 to 4 times more likely than healthy individuals to suffer from cardiovascular disease, due to the metabolic complexity and underlying comorbidities of type 2 diabetes including obesity, insulin resistance, dyslipidemia, hyperglycemia, and hypertension. 13 , 14

Additionally, elevated hemoglobin A1c (HbA1c) levels are predictive of vascular complications in patients with diabetes, and regular exercise has been shown to reduce HbA1c levels, both alone and in conjunction with dietary intervention. In a meta-analysis of 9 randomized trials comprising 266 adults with type 2 diabetes, patients randomized to 20 weeks of regular exercise at 50% to 75% of their maximal aerobic capacity (VO 2max ) demonstrated marked improvements in HbA1c and cardiorespiratory fitness. 11 Importantly, larger reductions in HbA1c were observed with more intense exercise, reflecting greater improvements in blood glucose control with increasing exercise intensity.

In addition to greater energy expenditure, which aids in reversing obesity-associated type 2 diabetes, exercise also boosts insulin action through short-term effects, mainly via insulin-independent glucose transport. For example, our laboratory and others have shown that as little as 7 days of vigorous aerobic exercise training in adults with type 2 diabetes results in improved glycemic control, without any effect on body weight. 15 , 16 Specifically, we observed decreased fasting plasma insulin, a 45% increase in insulin-stimulated glucose disposal, and suppressed hepatic glucose production (HGP) during carefully controlled euglycemic hyperinsulinemic clamps. 15

Although the metabolic benefits of exercise are striking, the effects are short-lived and begin to fade within 48 to 96 hours. 17 Therefore, an ongoing exercise program is required to maintain the favorable metabolic milieu that can be derived through exercise.

EXERCISE MODALITIES

Aerobic exercise.

The vast majority of the literature about the effects of exercise on glycemic parameters in type 2 diabetes has been centered on interventions involving aerobic exercise. Aerobic exercise consists of continuous, rhythmic movement of large muscle groups, such as in walking, jogging, and cycling. The most recent ADA guidelines state that individual sessions of aerobic activity should ideally last at least 30 minutes per day and be performed 3 to 7 days of the week ( Table 1 ). 18 Moderate to vigorous (65%–90% of maximum heart and rate) aerobic exercise training improves VO 2max cardiac output, which are associated with substantially reduced cardiovascular and overall mortality risk in patients with type 2 diabetes. 19

American Diabetes Association recommendations for exercise in type 2 diabetes

Notably, aerobic exercise is a well-established way to improve HbA1c, and strong evidence exists with regard to the effects of aerobic activity on weight loss and the enhanced regulation of lipid and lipoprotein metabolism. 8 For example, in a 2007 report, 6 months of aerobic exercise training in 60 adults with type 2 diabetes led to reductions in HbA1c (−0.63% ± 0.41 vs 0.31% ± 0.10, P < .001), fasting plasma glucose (−18.6 mg/dL ± 4.4 vs 4.28 mg/dL ± 2.57, P < .001), insulin resistance (−1.52 ± 0.6 vs 0.56 ± 0.44, P = .023; as measured by homeostatic model assessment), fasting insulin (−2.91 mU/L ± 0.4 vs 0.94 mU/L ± 0.21, P = .031), and systolic blood pressure (−6.9 mm Hg ± 5.19 vs 1.22 mm Hg ± 1.09, P = .010) compared with the control group. 14

Furthermore, meta-analyses reviewing the benefits of aerobic activity for patients with type 2 diabetes have repeatedly confirmed that compared with patients in sedentary control groups, aerobic exercise improves glycemic control, insulin sensitivity, oxidative capacity, and important related metabolic parameters. 11 Taken together, there is ample evidence that aerobic exercise is a tried-and-true exercise modality for managing and preventing type 2 diabetes.

Resistance training

During the last 2 decades, resistance training has gained considerable recognition as a viable exercise training option for patients with type 2 diabetes. Synonymous with strength training, resistance exercise involves movements utilizing free weights, weight machines, body weight exercises, or elastic resistance bands.

Primary outcomes in studies evaluating the effects of resistance training in type 2 diabetes have found improvements that range from 10% to 15% in strength, bone mineral density, blood pressure, lipid profiles, cardiovascular health, insulin sensitivity, and muscle mass. 18 , 20 Furthermore, because of the increased prevalence of type 2 diabetes with aging, coupled with age-related decline in muscle mass, known as sarcopenia, 21 resistance training can provide additional health benefits in older adults.

Dunstan et al 21 reported a threefold greater reduction in HbA1c in patients with type 2 diabetes ages 60 to 80 compared with nonexercising patients in a control group. They also noted an increase in lean body mass in the resistance-training group, while those in the nonexercising control group lost lean mass after 6 months. In a shorter, 8-week circuit weight training study performed by the same research group, patients with type 2 diabetes had improved glucose and insulin responses during an oral glucose tolerance test. 22

These findings support the use of resistance training as part of a diabetes management plan. In addition, key opinion leaders advocate that the resistance-training-induced increase in skeletal muscle mass and the associated reductions in HbA1c may indicate that skeletal muscle is a “sink” for glucose; thus, the improved glycemic control in response to resistance training may be at least in part the result of enhanced muscle glycogen storage. 21 , 23

Based on increasing evidence supporting the role of resistance training in glycemic control, the ADA and ACSM recently updated their exercise guidelines for treatment and prevention of type 2 diabetes to include resistance training. 9

Combining aerobic and resistance training

The combination of aerobic and resistance training, as recommended by current ADA guidelines, may be the most effective exercise modality for controlling glucose and lipids in type 2 diabetes.

Cuff et al 24 evaluated whether a combined training program could improve insulin sensitivity beyond that of aerobic exercise alone in 28 postmenopausal women with type 2 diabetes. Indeed, 16 weeks of combined training led to significantly increased insulin-mediated glucose uptake compared with a group performing only aerobic exercise, reflecting greater insulin sensitivity.

Balducci et al 25 demonstrated that combined aerobic and resistance training markedly improved HbA1c (from 8.31% ± 1.73 to 7.1% ± 1.16, P < .001) compared with the control group and globally improved risk factors for cardiovascular disease, supporting the notion that combined training for patients with type 2 diabetes may have additive benefits.

Of note, Snowling and Hopkins 26 performed a head-to-head meta-analysis of 27 controlled trials on the metabolic effects of aerobic, resistance, and combination training in a total of 1,003 patients with diabetes. All 3 exercise modes provided favorable effects on HbA1c, fasting and postprandial glucose levels, insulin sensitivity, and fasting insulin levels, and the differences between exercise modalities were trivial.

In contrast, Schwingshackl and colleagues 27 performed a systematic review of 14 randomized controlled trials for the same 3 exercise modalities in 915 adults with diabetes and reported that combined training produced a significantly greater reduction in HbA1c than aerobic or resistance training alone.

Future research is necessary to quantify the additive and synergistic clinical benefits of combined exercise compared with aerobic or resistance training regimens alone; however, evidence suggests that combination exercise may be the optimal strategy for managing diabetes.

High-intensity interval training

High-intensity interval training (HIIT) has emerged as one of the fastest growing exercise programs in recent years. HIIT consists of 4 to 6 repeated, short (30-second) bouts of maximal effort interspersed with brief periods (30 to 60 seconds) of rest or active recovery. Exercise is typically performed on a stationary bike, and a single session lasts about 10 minutes.

HIIT increases skeletal muscle oxidative capacity, glycemic control, and insulin sensitivity in adults with type 2 diabetes. 28 , 29 A recent meta-analysis that quantified the effects of HIIT programs on glucose regulation and insulin resistance reported superior effects for HIIT compared with aerobic training or no exercise as a control. 28 Specifically, in 50 trials with interventions lasting at least 2 weeks, participants in HIIT groups had a 0.19% decrease in HbA1c and a 1.3-kg decrease in body weight compared with control groups.

Alternative high-intensity exercise programs have also emerged in recent years such as CrossFit, which we evaluated in a group of 12 patients with type 2 diabetes. Our proof-of-concept study found that a 6-week CrossFit program reduced body fat, diastolic blood pressure, lipids, and metabolic syndrome Z-score, and increased insulin sensitivity to glucose, basal fat oxidation, VO 2max , and high-molecular-weight adiponectin. 30 HIIT appears to be another effective way to improve metabolic health; and for patients with type 2 diabetes who can tolerate HIIT, it may be a time-efficient, alternative approach to continuous aerobic exercise.

BENEFITS OF EXERCISE FOR SPECIFIC METABOLIC TISSUES

Within 5 years of the discovery of insulin by Banting and Best in 1921, the first report of exercise-induced improvements in insulin action was published, though the specific cellular and molecular mechanisms that underpin these effects remain unknown. 31

There is general agreement that the acute or short-term exercise effects are the result of insulin-dependent and insulin-independent mechanisms, while longer-term effects also involve “organ crosstalk,” such as from skeletal muscle to adipose tissue, the liver, and the pancreas, all of which mediate favorable systemic effects on HbA1c, blood glucose levels, blood pressure, and serum lipid profiles ( Figure 1 ).

Tissue-specific metabolic effects of exercise in patients with type 2 diabetes.

Skeletal muscle

Following a meal, skeletal muscle is the primary site for glucose disposal and uptake. Peripheral insulin resistance originating in skeletal muscle is a major driver for the development and progression of type 2 diabetes.

Exercise enhances skeletal muscle glucose uptake using both insulin-dependent and insulin-independent mechanisms, and regular exercise results in sustained improvements in insulin sensitivity and glucose disposal. 32

Of note, acute bouts of exercise can also temporarily enhance glucose uptake by the skeletal muscle up to fivefold via increased (insulin-independent) glucose transport. 33 As this transient effect fades, it is replaced by increased insulin sensitivity, and over time, these 2 adaptations to exercise result in improvements in both the insulin responsiveness and insulin sensitivity of skeletal muscle. 34

The fuel-sensing enzyme adenosine monophosphate-activated protein kinase (AMPK) is the major insulin-independent regulator of glucose uptake, and its activation in skeletal muscle by exercise induces glucose transport, lipid and protein synthesis, and nutrient metabolism. 35 AMPK remains transiently activated after exercise and regulates several downstream targets involved in mitochondrial biogenesis and function and oxidative capacity. 36

In this regard, aerobic training has been shown to increase skeletal muscle mitochondrial content and oxidative enzymes, resulting in dramatic improvements in glucose and fatty acid oxidation 10 and increased expression of proteins involved in insulin signaling. 37

Adipose tissue

Exercise confers numerous positive effects in adipose tissue, namely, reduced fat mass, enhanced insulin sensitivity, and decreased inflammation. Chronic low-grade inflammation has been integrally linked to type 2 diabetes and increases the risk of cardiovascular disease. 38

Several inflammatory adipokines have emerged as novel predictors for the development of atherosclerosis, 39 and fat-cell enlargement from excessive caloric intake leads to increased production of pro-inflammatory cytokines, altered adipokine secretion, increased circulating fatty acids, and lipotoxicity concomitant with insulin resistance. 40

It has been suggested that exercise may suppress cytokine production through reduced inflammatory cell infiltration and improved adipocyte function. 41 Levels of the key pro-inflammatory marker C-reactive protein is markedly reduced by exercise, 14 , 42 and normalization of adipokine signaling and related cytokine secretion has been validated for multiple exercise modalities. 42

Moreover, Ibañez et al 43 demonstrated that in addition to significant improvements in insulin sensitivity, resistance exercise training reduced visceral and subcutaneous fat mass in patients with type 2 diabetes.

The liver regulates fasting glucose through gluconeogenesis and glycogen storage. The liver is also the primary site of action for pancreatic hormones during the transition from pre- to postprandial states.

As with skeletal muscle and adipose tissue, insulin resistance is also present within the liver in patients with type 2 diabetes. Specifically, impaired suppression of HGP by insulin is a hallmark of type 2 diabetes, leading to sustained hyperglycemia. 44

Approaches using fasting measures of glucose and insulin do not distinguish between peripheral and hepatic insulin resistance. 45 Instead, hepatic insulin sensitivity and HGP are best assessed by the hyperinsulinemic-euglycemic clamp technique, along with isotopic glucose tracers. 15

Although more elaborate, magnetic resonance spectroscopy may also be used to assess intrahepatic lipid content, as its accumulation has been shown to drive hepatic insulin resistance. 46 Indirect measures of hepatic dysfunction may be made from increased levels of the circulating hepatic enzymes alkaline phosphatase, alanine transaminase, and aspartate transaminase. 16

From an exercise perspective, we have shown that 7 days of aerobic training, in the absence of weight loss, improves hepatic insulin sensitivity. 15 It has also been shown that hepatic AMPK is stimulated during exercise, suggesting that an AMPK-induced adaptive response to exercise may facilitate improved suppression of HGP. 47 We have also shown that a longer 12-week aerobic exercise intervention reduces hepatic insulin resistance, with and without restricted caloric intake. 48 Further, HGP correlated with reduced visceral fat, suggesting that this fat depot may play an important mechanistic role in improved hepatic function.

Insulin resistance in adipose tissue, muscle, or the liver places greater demand on insulin secretion from pancreatic beta cells. For many, this hypersecretory state is unsustainable, and the subsequent loss of beta-cell function marks the onset of type 2 diabetes. 49 Fasting plasma glucose, insulin, and glucagon levels are generally poor indicators of beta-cell function.

Clinical research studies typically use the oral glucose tolerance test and hyperglycemic clamp technique to more accurately measure the dynamic regulation of glucose homeostasis by the pancreas. 50 However, few studies have examined the effects of exercise on beta-cell function in type 2 diabetes.

Dela and colleagues 51 showed that 3 months of aerobic training improved beta-cell function in type 2 diabetes, but only in those who had some residual function and were less severely diabetic. We have shown that a 12-week aerobic exercise intervention improves beta-cell function in older obese adults and in patients with type 2 diabetes. 52 , 53 We have also found that improvements in glycemic control that occur with exercise are better predicted by changes in insulin secretion as opposed to peripheral insulin sensitivity. 54 It has also been shown that a relatively short (8-week) HIIT program improved beta-cell function in patients with type 2 diabetes. 55 And we recently found that a 6-week CrossFit training program improved beta-cell function in adults with type 2 diabetes. 30

SUMMARY, CONCLUSIONS, AND FUTURE DIRECTIONS

Regular exercise produces health benefits beyond improvements in cardiovascular fitness. These include enhanced glycemic control, insulin signaling, and blood lipids, as well as reduced low-grade inflammation, improved vascular function, and weight loss.

Both aerobic and resistance training programs promote healthier skeletal muscle, adipose tissue, liver, and pancreatic function. 18 Greater whole-body insulin sensitivity is seen immediately after exercise and persists for up to 96 hours. While a discrete bout of exercise provides substantial metabolic benefits in diabetic cohorts, maintenance of glucose control and insulin sensitivity are maximized by physiologic adaptations that only occur with weeks, months, and years of exercise training. 15 , 33

Exercise intensity, 11 volume, and frequency 56 are associated with reductions in HbA1c; however, a consensus has not been reached on whether one is a better determinant than the other.

The most important consideration when recommending exercise to patients with type 2 diabetes is that the intensity and volume be optimized for the greatest metabolic benefit while avoiding injury or cardiovascular risk. In general, the risk of exercise-induced adverse events is low, even in adults with type 2 diabetes, and there is no current evidence that screening procedures beyond usual diabetes care are needed to safely prescribe exercise in asymptomatic patients in this population. 18

Future clinical research in this area will provide a broader appreciation for the interactions (positive and negative) between exercise and diabetes medications, the synergy between exercise and bariatric surgery, and the potential to use exercise to reduce the health burden of diabetes complications, including nephropathy, retinopathy, neuropathy, and peripheral arterial disease.

Moreover, basic research will likely identify the detailed molecular defects that contribute to diabetes in insulin-targeted tissues. The emerging science surrounding cytokines, adipokines, myokines, and, most recently, exerkines is likely to deepen our understanding of the mechanistic links between exercise and diabetes management.

Finally, although we have ample evidence that exercise is an effective, scalable, and affordable approach to prevent and manage type 2 diabetes, we still need to overcome the challenge of discovering how to make exercise sustainable for patients.

• Exercise is often the first lifestyle recommendation made to patients newly diagnosed with type 2 diabetes.
• Together with diet and behavior modification, exercise is central to effective lifestyle prevention and management of type 2 diabetes.
• All exercise, whether aerobic or resistance training or a combination, facilitates improved glucose regulation.
• In addition to the cardiovascular benefits, long-term exercise promotes healthier skeletal muscle, adipose tissue, and liver and pancreas function.
• Exercise programs for patients with type 2 diabetes should be of sufficient intensity and volume to maximize the metabolic benefit while avoiding injury and cardiovascular risk.

Acknowledgments

Dr. Kirwan reported research grant support from NIH R01DK108089, NIH R01HD088061, NIH U34DK107917, NIH R21AR067477, and Metagenics Inc.

Jessica Sacks and Stephan Nieuwoudt reported no financial interests or relationships that pose a potential conflict of interest with this article.

Contributor Information

JOHN P. KIRWAN, Department of Pathobiology, Lerner Research Institute, Cleveland Clinic; Department of Physiology and Biophysics, Case Western Reserve University; Metabolic Translational Research Center, Endocrinology & Metabolism Institute, Cleveland Clinic, Cleveland, OH.

JESSICA SACKS, Department of Pathobiology, Lerner Research Institute, Cleveland Clinic, Department of Physiology and Biophysics, Case Western Reserve University, Cleveland, OH.

STEPHAN NIEUWOUDT, Department of Pathobiology, Lerner Research Institute, Cleveland Clinic; Department of Physiology and Biophysics, Case Western Reserve University, Cleveland, OH.