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  • Review Article
  • Published: 06 June 2022

The burden and risks of emerging complications of diabetes mellitus

  • Dunya Tomic   ORCID: 1 , 2 ,
  • Jonathan E. Shaw   ORCID: 1 , 2   na1 &
  • Dianna J. Magliano   ORCID: 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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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 .

figure 1

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.


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.


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.


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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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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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).

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Diabetes affects an estimated 37.3 million people in the United States and is the eighth leading cause of death.  Diabetes is characterized by the body’s inability to produce and/or respond appropriately to insulin.  These defects result in persistent elevation of blood glucose levels and other metabolic abnormalities, which in turn lead to the development of disease complications, such as heart disease and stroke, blindness, kidney failure, and lower limb amputation. In addition to increasing the risk for these complications, diabetes also doubles the risk for many forms of cancer, some forms of dementia, hearing loss, erectile dysfunction, urinary incontinence, and many other common diseases.

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  • Prediabetes affects an estimated 96 million adults in the United States. Individuals with prediabetes are at high risk of developing type 2 diabetes.
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“Research at the ADA is the engine that drives clinical advances by catapulting them into practice. 2023 has brought many prominent achievements. We are incredibly proud of our legacy of highlighting science and eager to build on this research to move even closer to a world free of diabetes and all its burdens,” said Charles “Chuck” Henderson, the ADA’s chief executive officer.

The report highlights include:

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In addition, the report provides an update on the Pathway to Stop Diabetes® (Pathway) program, which pairs talented early-career scientists with mentorship from world-renowned diabetes scientists to drive research innovation free from traditional project constraints. This year, through the Pathway program, ADA dedicated over $4.8 million dollars in new grant funding to support breakthroughs in translation and clinical science, technology, care, and potential cures in the field of diabetes.

To learn more about the ADA’s research findings and ongoing areas of study, visit .

About the American Diabetes Association The American Diabetes Association (ADA) is the nation’s leading voluntary health organization fighting to bend the curve on the diabetes epidemic and help people living with diabetes thrive. For 83 years, the ADA has driven discovery and research to treat, manage, and prevent diabetes while working relentlessly for a cure. Through advocacy, program development, and education we aim to improve the quality of life for the over 136 million Americans living with diabetes or prediabetes. Diabetes has brought us together. What we do next will make us Connected for Life ® . To learn more or to get involved, visit us at  or call 1-800-DIABETES (1-800-342-2383). Join the fight with us on Facebook ( American Diabetes Association ), Spanish Facebook ( Asociación Americana de la Diabetes ), LinkedIn ( American Diabetes Association ), Twitter ( @AmDiabetesAssn ), and Instagram ( @AmDiabetesAssn ). 

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Diabetes mellitus is taken from the Greek word diabetes , meaning siphon - to pass through and the Latin word mellitus meaning sweet. A review of the history shows that the term "diabetes" was first used by Apollonius of Memphis around 250 to 300 BC. Ancient Greek, Indian, and Egyptian civilizations discovered the sweet nature of urine in this condition, and hence the propagation of the word Diabetes Mellitus came into being. Mering and Minkowski, in 1889, discovered the role of the pancreas in the pathogenesis of diabetes. In 1922 Banting, Best, and Collip purified the hormone insulin from the pancreas of cows at the University of Toronto, leading to the availability of an effective treatment for diabetes in 1922. Over the years, exceptional work has taken place, and multiple discoveries, as well as management strategies, have been created to tackle this growing problem. Unfortunately, even today, diabetes is one of the most common chronic diseases in the country and worldwide. In the US, it remains as the seventh leading cause of death.

Diabetes mellitus (DM) is a metabolic disease, involving inappropriately elevated blood glucose levels. DM has several categories, including type 1, type 2, maturity-onset diabetes of the young (MODY), gestational diabetes, neonatal diabetes, and secondary causes due to endocrinopathies, steroid use, etc. The main subtypes of DM are Type 1 diabetes mellitus (T1DM) and Type 2 diabetes mellitus (T2DM), which classically result from defective insulin secretion (T1DM) and/or action (T2DM). T1DM presents in children or adolescents, while T2DM is thought to affect middle-aged and older adults who have prolonged hyperglycemia due to poor lifestyle and dietary choices. The pathogenesis for T1DM and T2DM is drastically different, and therefore each type has various etiologies, presentations, and treatments.

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Article information, using community engagement methods to guide study protocol decisions for school-aged children with type 1 diabetes.

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Fayo Abadula , Lori C. Jordan , Lauren LeStourgeon , Sarah S. Jaser; Using Community Engagement Methods to Guide Study Protocol Decisions for School-Aged Children With Type 1 Diabetes. Diabetes Spectr 15 February 2024; 37 (1): 95–99.

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Many challenges exist in developing multisite protocols for newly diagnosed children with type 1 diabetes. Our research team engaged community members to increase the likelihood of study success during a planning grant for a longitudinal study aimed at understanding risk and protective factors for neurocognitive function in school-aged children newly diagnosed with type 1 diabetes.

Two methods were used to obtain caregiver input into study protocol decisions. The first was a survey given to caregivers of children with diabetes ( n = 21) about which aspects of the study protocol would make families more or less likely to participate. The second was a Community Engagement (CE) Studio to obtain recommendations from a diverse group of caregivers of children with diabetes ( n = 7) on key aspects of recruitment and enrollment.

Results from both the survey and the CE Studio indicated that caregivers were interested and willing to participate in a longitudinal study of this nature. Both methods resulted in similar preferences for the type and amount of compensation, convenient study visits, flexible scheduling options, and receipt of neurocognitive test results. Recommendations from the CE Studio included additional strategies to minimize participant burden and enhance communication around study participation.

Both the feasibility survey and the CE Studio were useful mechanisms to obtain caregiver input during the study’s planning and design phase. Uniquely, the CE Studio approach offers researchers the ability to gain valuable community member input with minimal staff effort.

Researchers increasingly acknowledge the need to involve community members in study design to ensure that research studies are meaningful to the study population and to anticipate and address barriers to participation ( 1 , 2 ). For example, the National Institutes of Health (NIH) All of Us study ( 3 ), which aims to enroll >1 million participants to ensure representative samples in medical research, has a dedicated group “to develop novel approaches to educate communities and support enduring relationships with program participants.” Community engagement in research exists on a continuum, ranging from brief surveys of a patient population to focus groups or Community Engagement (CE) Studios, to including community members as active partners or leaders on the research team ( 4 ).

It is particularly important to seek input from populations who have been historically underrepresented in research in the planning stage to ensure that the study addresses outcomes that are meaningful to them and to identify potential barriers to and facilitators of their participation ( 1 ). Given known disparities in diabetes treatment and outcomes among children with diabetes ( 5 ) and the lack of interventions for type 1 diabetes that involve racially and ethnically minoritized youth ( 6 ), community engagement in pediatric diabetes research is crucial.

Longitudinal cohort studies are needed in new-onset pediatric type 1 diabetes to better understand the risk and protective factors for neurocognitive complications ( 7 ). Yet, enrolling school-aged children with diabetes and their families in research studies may be especially challenging, particularly at the time of diagnosis, given the unexpected nature of the diagnosis and the burden of type 1 diabetes management on caregivers. In addition, differences in approaches for newly diagnosed children across medical centers (i.e., some centers admit all newly diagnosed patients and conduct diabetes education in the hospital, whereas others only admit children in diabetic ketoacidosis and conduct diabetes education in the outpatient setting) create challenges in developing protocols for multisite studies. Youth with type 1 diabetes from racially and ethnically minoritized groups are less likely to participate in research ( 6 ), and it is therefore crucial to engage these communities early in the process.

As part of a planning grant (NIH U34 mechanism), we aimed to explore potential barriers to and facilitators of participation in a longitudinal study of recently diagnosed children with type 1 diabetes. Although even young children assent to research participation, caregivers must consent to research participation as well as associated risks and time commitments. We used more traditional methods to survey caregiver preferences, as well as the innovative CES approach ( 8 ).

These methods were used in preparation for a longitudinal study to understand the risk and protective factors for neurocognitive complications of type 1 diabetes. The longitudinal study protocol is expected to include cognitive testing and MRI studies of the brain in children aged 6–11 years, along with parent questionnaires and review of glucose data. In addition, we anticipate collecting real-time information using ecological momentary assessment (EMA) via a smartphone app to measure children’s outcomes, including sleep quality, physical activity, diet, mood, and behavior.

To identify potential problems with feasibility, we developed a survey for parents/caregivers to understand which aspects of the potential study protocol would make families more or less willing to participate. The feasibility survey consisted of 33 items, including questions about basic demographic information (child age, sex, race/ethnicity, and diabetes duration; parental education; and family income) and parent attitudes about compensation, scheduling, study-related blood work, receipt of information about the child’s MRI and cognitive testing results, and overall likelihood of study participation ( Supplementary Material ).

Families were eligible to participate in the feasibility survey if they had a child aged 6–11 years who had type 1 diabetes and was followed by the Vanderbilt Pediatric Diabetes Program. After obtaining informed e-consent, questions were asked over the phone (by a member of the research team and entered into a REDCap [Research Electronic Data Capture] database) or in person after a clinic appointment (completed by the parent on a digital tablet and entered directly into REDCap). On average, the survey took 5–10 minutes to complete, and parents received a $10 gift card upon completion of the survey.

We also conducted a CE Studio with caregivers of children with type 1 diabetes to obtain diverse perspectives on barriers to and facilitators of participation. Specifically, we sought their input for study outcomes and recruitment and retention strategies. The Studio was conducted by the Community Engaged Research Core, which is part of the Vanderbilt Institute for Clinical and Translational Research. The Studio was implemented by a team that included faculty researchers, a community navigator, and a skilled facilitator.

The CE Studio is an approved model for the NIH All of Us project. In this model, the Studio facilitator creates a neutral environment that allows for open and frank discussion and guides the conversation between the researchers and community experts (i.e., caregivers for children with type 1 diabetes). The Studio facilitator has experience working with diverse populations and possesses the ability to balance the power differential that may naturally occur when researchers and community members come together ( 9 ).

Before the Studio, the community navigator conducted a planning meeting with the research team. The CE Studio team invited individuals from a database of past community engagement experts, in addition to reaching out to community-based organizations working with children with type 1 diabetes and social media groups for parents/caregivers of children with type 1 diabetes. All interested caregivers completed an online form, which collected basic information, including demographics, educational background, and life experience (e.g., age of the child and diabetes duration).

The CE Studio team then selected a group to invite to the Studio to ensure a diverse representation of experiences. The community experts attended an orientation meeting before the Studio, during which they reviewed an orientation guide. The orientation guide included frequently asked questions and a glossary of common research terms. The guide also outlined the purpose of the CE Studio, the roles and expectations of participants, and the CE Studio process.

The navigator scheduled the Studio at a time that was convenient for most people. At the start of the CE Studio, the researchers (L.C.J. and S.S.J.) gave a brief presentation about the project and posed specific questions to the community experts. The facilitator guided the discussion and kept the meeting length to 1.5 hours. The navigator captured feedback by taking notes. The feedback included major themes from the discussion, as well as suggestions from individual community experts. Community experts were compensated $50 for their time. Because the CE Studio has a nonresearch designation by institutional review boards (IRBs), informed consent was not needed ( 9 ).

Feasibility Survey Summary

A total of 21 parents completed the survey over 4 months; 30% reported an annual family income <$50,000, and the median parental level of education was a college degree. The average child age was 8.7 ± 1.7 years, 90% of participants identified as non-Hispanic White, 10% identified as Black or African American, and 57% were male. Of the 34 families approached by phone, only five completed the feasibility survey (15%), but 16 of the 20 families approached in person completed the survey (80%). Missing data were minimal; one parent did not answer the question about family income.

Compensation and scheduling

The majority of parents (57%) preferred a gift card as compensation, 14% preferred a check, and 29% preferred a check plus a small gift for the child. The majority of parents (86%) found $40–50 to be a reasonable compensation for the MRI portion of the study, which takes ∼60 minutes, and 90% supported $50 as reasonable compensation for the cognitive testing, which takes ∼90 minutes. Most parents indicated that travel reimbursement was either somewhat important (43%) or very important (43%). Scheduling study visits and clinic visits on the same day was very important to most parents (67%), as well as having weekend and evening scheduling options available (67%).

Barriers and facilitators to participation

Overall, 69% of parents indicated that they would participate in a study like this. Most parents (62%) said needing a blood draw from their child would not make a difference in their decision to participate, but 76% supported having numbing cream available for their child during the blood draw. If a child were to object to having blood drawn, 90% of parents indicated that they would allow the laboratory to draw extra blood (about two extra tubes) at the time of their child’s next routine blood testing for diabetes care.

When asked if they could think of a child the same age who might be interested in participating in a study as a control participant (i.e., a neighborhood control subject without diabetes), parents either said no (38%) or that they were not sure (38%). In addition, 38% of parents reported that having a friend participate along with their child would not make their child more or less likely to participate.

The majority of parents (67%) indicated that they would prefer to receive information on their child’s cognitive testing portion of the study. Parents deemed that provision of this information would be somewhat important (43%) or very important (43%) in their decision to participate. Most parents (76%) agreed that receiving a picture of their child’s brain after the MRI would make their child more interested in participating.

CE Studio Summary

The CE Studio hosted a diverse sample of caregivers ( n = 7), termed “community experts,” of whom four identified as African American or Black, two as White, and one as other race. All of the caregivers were aged 30–55 years; one had a child <5 years of age, three had children aged 5–12 years, and three had children ≥12 years of age. In terms of diabetes duration, three children were diagnosed <3 years before the Studio, 1 had been diagnosed 3–5 years before the Studio, and two had been diagnosed ≥5 years before the Studio. Community expert feedback from the session is presented in Table 1 .

CE Studio Recommendations

Recruitment recommendations

In the discussion around the timing of recruitment and enrollment, a two-part consent method was preferred for enrolling families with newly diagnosed children. Given concerns around the stressful nature of the diagnosis, community experts recommended that consent be obtained at the time of diagnosis for an initial blood draw and baseline data collection from the medical record only. Then, several weeks later, but within 3–6 months after diagnosis, these families could be approached to consent for participation in the full, longitudinal study protocol, including an MRI of the brain and cognitive assessments with the child, as well as parent surveys and EMA at multiple time points.

Scheduling preferences

Community experts expressed a preference for pairing study visits with diabetes clinic visits to minimize burden. They also recommended offering travel-related compensation and childcare, when possible. As a strategy to enhance retention in the longitudinal study, community experts expressed interest in receiving retention items with the study logo (e.g., water bottles and small toys).

Study-related information

When asked about recruitment approaches, community experts noted the need for clear communication about the study protocol. For example, they wanted an explanation of the research being conducted in pediatric type 1 diabetes focused on cognition and risk and protective factors. Given concerns about children’s comfort level with the MRI machine, community experts suggested that a curated playlist of videos related to MRI sounds and procedures could be offered to comfort the child before participation. Community experts also requested feedback on their child’s cognitive testing results after study participation. They also expressed interest in receiving study updates and reminders via emailed newsletters, videos, and text messages.

The current study describes methods to engage community members in protocol decisions to enhance study feasibility. Caregivers for children with type 1 diabetes identified strategies to optimize recruitment and retention, especially among underrepresented populations. The feasibility survey was helpful in determining which aspects of the study design potential participants considered important when deciding whether to consent to study participation. The CE Studio offered more detailed suggestions for the recruitment and enrollment process. Findings from both the feasibility survey and the CE Studio indicated that most families were willing to participate in a study design that offered fair compensation, flexible scheduling, and a summary of their child’s cognitive functioning. Uniquely, the CE Studio allowed for dialogue and rich details that the feasibility survey could not capture.

Findings from both the feasibility survey and the CE Studio indicated high levels of support for a research study of this nature. Community experts expressed interest in learning about risk and protective factors for cognitive function in youth with type 1 diabetes but noted that participating in a research study at the time of diagnosis could be challenging, given that it is a time of high stress and confusion for caregivers. The community experts advised that deciding to enroll in a longitudinal research study at the time of diagnosis would be overwhelming; however, they supported a two-step consent plan to facilitate participation.

Findings from both the feasibility survey and the CE Studio supported aligning study visits with clinic visits, and community experts offered additional suggestions to make scheduling study visits more convenient, such as travel accommodations and meal vouchers, in line with strategies found to be successful in recruiting and retaining parents of newly diagnosed children ( 10 ). In general, the community experts provided context and solutions to potential barriers to study participation ( Table 1 ).

Strengths and Limitations

The efficiency of the CE Studio model and the group dialogue with community experts were strengths of this approach. However, it is important to note that most of the caregivers who completed the feasibility survey and took part in the Studio had a child diagnosed with diabetes for >3 years and were answering based on their recollection of their mindset after their child’s diagnosis. In addition, the majority of the caregivers who completed the feasibility survey identified as non-Hispanic White, and these findings may not be generalizable to other populations. Finally, we did not have information on sociodemographic characteristics for families who were invited to complete the survey or take part in the Studio, so we cannot determine the representativeness of our sample.

We found the CE Studio process to be a helpful and efficient mechanism to obtain caregiver input during the study planning and design phase. Based on discussions with the community experts, we developed a study protocol that incorporated their concerns and suggestions around recruitment, scheduling, and receipt of study-related information. We believe their recommendations will translate into better study recruitment and retention and wanted to share this mechanism with other research teams. Before enrolling participants for a longitudinal cohort study, we plan to conduct an additional CE Studio at each study site and to invite community experts to be paid members of the research team (i.e., consultants).

The CE Studio approach is a consultative model ( 4 , 8 ) used to engage people with lived experience to inform aspects of study selection, design, conduct, or dissemination; it is typically exempt from IRB approval and provides rich information in a relatively short amount of time ( 9 ). Feasibility surveys offer information from a larger pool of potential participants but require IRB approval and substantial time by the research team for recruitment and enrollment (e.g., reviewing clinic schedules and meeting families in clinic before or after their diabetes visit). The alignment of themes and recommendations from these two approaches supports the potential benefit of the CE Studio as an efficient model for obtaining valuable information from community members when planning a study.

This article contains supplementary material online at .

This study was sponsored by the NIH’s National Institute of Diabetes and Digestive and Kidney Diseases (U34 DK123895-01).

Duality of Interest

No potential conflicts of interest relevant to this article were reported.

Author Contributions

F.A. collected data and wrote the manuscript. L.C.J. and S.S.J. conceptualized the study, secured funding, and reviewed and edited the manuscript. L.L. developed the database and reviewed and edited the manuscript. S.S.J. is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

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  • Published: 16 February 2024

Evaluating the coding accuracy of type 2 diabetes mellitus among patients with non-alcoholic fatty liver disease

  • Seungwon Lee 1 , 2 , 3 , 4 ,
  • Abdel Aziz Shaheen 1 , 2 , 5 ,
  • David J. T. Campbell 1 , 5 , 7 ,
  • Christopher Naugler 1 , 6 ,
  • Jason Jiang 2 , 3 ,
  • Robin L. Walker 1 , 2 ,
  • Hude Quan 1 , 2 &
  • Joon Lee 1 , 2 , 4 , 7  

BMC Health Services Research volume  24 , Article number:  218 ( 2024 ) Cite this article

Metrics details

Non-alcoholic fatty liver disease (NAFLD) describes a spectrum of chronic fattening of liver that can lead to fibrosis and cirrhosis. Diabetes has been identified as a major comorbidity that contributes to NAFLD progression. Health systems around the world make use of administrative data to conduct population-based prevalence studies. To that end, we sought to assess the accuracy of diabetes International Classification of Diseases (ICD) coding in administrative databases among a cohort of confirmed NAFLD patients in Calgary, Alberta, Canada.

The Calgary NAFLD Pathway Database was linked to the following databases: Physician Claims, Discharge Abstract Database, National Ambulatory Care Reporting System, Pharmaceutical Information Network database, Laboratory, and Electronic Medical Records. Hemoglobin A1c and diabetes medication details were used to classify diabetes groups into absent, prediabetes, meeting glycemic targets, and not meeting glycemic targets. The performance of ICD codes among these groups was compared to this standard. Within each group, the total numbers of true positives, false positives, false negatives, and true negatives were calculated. Descriptive statistics and bivariate analysis were conducted on identified covariates, including demographics and types of interacted physicians.

A total of 12,012 NAFLD patients were registered through the Calgary NAFLD Pathway Database and 100% were successfully linked to the administrative databases. Overall, diabetes coding showed a sensitivity of 0.81 and a positive predictive value of 0.87. False negative rates in the absent and not meeting glycemic control groups were 4.5% and 6.4%, respectively, whereas the meeting glycemic control group had a 42.2% coding error. Visits to primary and outpatient services were associated with most encounters.

Diabetes ICD coding in administrative databases can accurately detect true diabetic cases. However, patients with diabetes who meets glycemic control targets are less likely to be coded in administrative databases. A detailed understanding of the clinical context will require additional data linkage from primary care settings.

Peer Review reports


Health systems routinely use digital databases to store and code health information. The International Classification of Diseases (ICD) was developed by the World Health Organisation and is used to translate extensive details from electronic medical records (EMR) into standardised codes. ICD codes have been utilized for decades, with ICD code-driven algorithms being routinely employed for identifying chronic conditions, such as the Charlson comorbidity index [ 1 ] and the Elixhauser index [ 2 ].

Much like healthcare systems worldwide, Canada has multiple administrative health databases that are widely employed in health research. These databases, underpinned by ICD coding, encompass the Discharge Abstract Database (DAD) which contains inpatient data, National Ambulatory Care Reporting System (NACRS) which collects outpatient and emergency department visit details, and Physician Claims which collects details in inpatient and outpatient (e.g., primary care) settings.

ICD codes serve as a common tool for chronic disease and comorbidity surveillance in the populations of both Canada and various other countries [ 3 , 4 ]. In Canada, national agencies like the Canadian Institute for Health Information have issued directives for coding specifics conditions inclusive of diabetes, leading to the establishment of the National Diabetes Surveillance System [ 5 ]. Notably, Type 2 diabetes is strongly associated with non-alcoholic fatty liver disease (NAFLD) [ 6 , 7 ] and is considered requiring close monitoring for NAFLD [ 7 ].

NAFLD, the most common liver disease worldwide [ 6 ], is a progressive disease that advances from a non-alcoholic fatty liver to non-alcoholic steatohepatitis (NASH) to NASH with fibrosis [ 8 , 9 ]. This progression can eventually lead to end stage liver disease or hepatocellular carcinoma [ 8 , 9 ]. Accurate identification of comorbidity information, such as diabetes, in electronic databases is crucial in this patient population to ensure timely intervention. In Calgary, Canada, a prospective cohort of NAFLD patients from primary care settings has been evaluated for liver fibrosis. Primary Care Providers (PCP) in Calgary are equipped to promptly assess NAFLD patients without a referral to tertiary care [ 10 ]. They are also well-informed about the association between diabetes mellitus and NAFLD, and that it is a criterion for NAFLD evaluation (or assessment) [ 11 ]. Several studies to date [ 12 , 13 ] have assessed the accuracy of ICD codes for diabetes diagnoses, but these were related to the general population. Designing a surveillance program by integrating laboratory data and administrative data could inform PCPs on comorbidities such as diabetes for NAFLD, but validation of the diabetes-related ICD codes in a NAFLD population is required.

To that end, we designed this study with the focus on detecting and reporting diabetes in patients with NAFLD. Our primary objective was to assess the accuracy of diabetes ICD coding in administrative databases among a cohort of confirmed NAFLD patients. Our secondary objectives were to assess inpatient EMR data, visit data, geographical data, and BMI, and to assess how they could be used to peripherally confirm the accuracy of diabetes codes.

Cohort selection: Calgary NAFLD population and data linkage

The Calgary NAFLD Pathway Database (CNPD) was established in 2016 to identify primary care patients with incident NAFLD in the Calgary metropolitan area [ 10 ]. NAFLD suspected patients with initial abnormal alanine aminotransferase levels, diabetes mellitus or metabolic syndrome undergo stepped clinical protocols (Additional File 9 ). Patients’ medications and lifestyle are reviewed by physicians while laboratory tests are initiated to rule out other causes of liver diseases. Only patients formally diagnosed with NAFLD are recorded in the CNPD database. CNPD collects and records demographics and administrative details at the time of shear wave elastography (SWE) testing, and SWE diagnosis information [ 10 ]. SWE is a non-invasive imaging technique employed by clinicians to diagnose liver tissue stiffness and identify NAFLD stages [ 14 ]. Patients enter CNPD at differing stages of NAFLD based on initial clinician assessment. There were approximately 12,012 patients enrolled in this database at the end of the CNPD study (April 2022). SWE results contained in CNPD were validated and confirmed NAFLD status and stage.

We deterministically linked the CNPD cohort to the following administrative health databases and inpatient EMR using a previously established process [ 15 ]: physician claims, NACRS, DAD, pharmaceutical information network (PIN), laboratory database, and Sunrise Clinical Manager (SCM) EMR. Alberta has a unique lifetime identifier known as the Personal Health Number (PHN) which can be used to trace the healthcare utilization of individuals. Inpatient administrative databases have assigned codes which points to EMR encounter records. PHN, dates of visits, and these access codes were used to access and pull required sub-tables. Data from the five-year period prior to SWE and NAFLD diagnosis were linked and extracted from these databases. These databases are under the jurisdiction of Alberta Health and Alberta Health Services. Brief descriptions of these databases are provided below.

Physician claims: collects all physician-submitted ICD-9 billing codes from outpatient and inpatient care.

DAD: collects all ICD-10-CA codes from inpatient care.

NACRS: collects all emergency and outpatient ICD-10-CA codes.

PIN: collects all pharmacy dispensed medications details in community settings.

Lab: collects all laboratory test results from outpatient and inpatient care.

SCM EMR data: inpatient EMR records. Specifically, information tables on intake, discharge, and laboratory data were extracted.

Other data such as visits, geographical data, and BMI were extracted and presented in our work for future comparisons of our cohort to other cohorts in future studies.

Defining outcome and predictor/feature variables

Our outcome of interest was diabetes coding within the NAFLD population. We defined diabetes categories following the Diabetes Canada Clinical Practice Guidelines [ 16 ] by using laboratory hemoglobin A1c [ 17 , 18 , 19 , 20 ] and supplemented this phenotyping algorithm with diabetes medication data (Additional file 1 ). It should be noted that different jurisdictions may have different laboratory thresholds for defining diabetes. Specifically, absence of diabetes was defined as the highest HbA1c laboratory result below 6.1% [ 18 , 19 ] with no evidence of prescribed and fulfilled medications. Prediabetes was defined as HbA1c between 6.1 and 6.4% or an oral glucose tolerance test or random plasma glucose test or fasting plasma glucose test exceeding the thresholds listed in the Diabetes Canada Guidelines [ 16 ]. Diabetes category of meeting glycemic target was defined as (a) HbA1c between 6.4 and 7.0%, if no evidence of medication, and (b) HbA1c values < 7.0%, with evidence of prescribed and fulfilled medications. Diabetes category of not meeting glycemic target was defined as the highest HbA1c laboratory result above 7.0% [ 20 ]. The presence of fast plasma glucose ≥ 7.0 mmol/L or 2-hour plasma glucose in a 75 g oral glucose tolerance test ≥ 11.1 mmol/L or Random plasma glucose ≥ 11.1 mmol/L gave indication of diabetes in addition to HbA1c values. Intensified therapies such as (a) GLP1RA if obese or having cerebrovascular disease or stroke, and (b) SGLT2 if chronic kidney disease or albuminuria or cerebrovascular disease, were included as a part of the algorithm. This was achieved by applying Quan’s [ 21 ] ICD algorithm on cerebrovascular disease, stroke, chronic kidney disease, and cerebrovascular disease to define the sub-cohorts, and then checked for the presence of those medications.

Anatomical therapeutic classification (ATC) and drug identification numbers (DIN) were extracted from PIN to identify diabetes medications. These medication groups included insulin, oral hypoglycemic drugs, biguanides, Glucagon like peptide-1 receptor agonists, dipeptidyl peptidase-4 inhibitors, sulfonylureas, and thiazolidinediones, and sodium-glucose transporter-2 inhibitors (Additional File 1 ). Dates were checked to precede NAFLD diagnosis date. The list of diabetes medications was developed and assessed by physician authors of this study. Specific categorical variables were created for patients meeting HbA1c values but not receiving medications for later analytical steps. The list was validated against Canada’s drug product database [ 22 ] based on the active ingredients and their activity status was confirmed.

The presence of diabetes ICD codes, as defined by Quan et al. [ 21 ] utilizing the standard of 2 outpatient physician claims or 1 hospital discharge diagnosis [ 23 ], determined whether a patient had diabetes, based on ICD-10 codes E10.0 to 14.7 [ 21 , 23 ]. We also introduced a basic algorithm requiring either one physician claim or one hospital discharge diagnosis to enhance the verification of our findings. We abstracted from the physician claims database the number of visits to inpatient and outpatient care providers by each patient within five years prior to NAFLD assessment. Five years was considered clinically sound taking into the account the conditions onset [ 24 ] which typically takes 3–7 years to fully manifest. This timeframe also allowed for the identification of diabetes using a well-established validation algorithm (2 physician claims or 1 hospitalization within a 2-year period). Geographical data from DAD and physician claims were used to define rural/urban status of patients. Continuous body mass index (BMI) was calculated from weight in kg/height in m 2 data available from the CNPD database. Hospitalization record details (intake, progress of care, and discharge status) were extracted from SCM EMR which validated records in administrative databases. Sex was coded as male or female. Postal code from physician claims and DAD/NACRS were converted to identify geographic location (urban/rural status). We determined continuous age at the time of registry entry by subtracting the date of birth from the recorded NAFLD confirmation date. Physician claims data contained the type of physician responsible for billing and their practice settings (community, emergency, inpatient, and diagnostic settings). Laboratory data from inpatient EMR was also evaluated and compared against the laboratory database for data completeness. PIN data contained ATC and DIN codes for all fulfilled community dispensed medications. We used Additional file 1 to identify patients who received these drugs and created a variable representing the treatment status.

Descriptive statistics were calculated for the four diabetes cohorts. Demographic and other basic patient characteristics such as age, sex, and Charlson comorbidities were reported. The total numbers of visits with distinct types of physicians were calculated. The DIN codes of the medications listed in Additional file 1 were compared against the PIN database to assess whether patients were being treated for diabetes.

The presence of ICD codes for diabetes was compared against the reference diabetes severity established above. Performance measures, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated. We assessed the accuracy of diabetes codes within various categories, including medication (treated), medication (untreated), and two sub-cohorts: oHbA1c between 6.4 and 7.0%, and HbA1c above 7.0%.

The total numbers of true positives, true negatives, false positives, and false negatives were identified for these four diabetes groups. These categories were compared using appropriate statistical techniques, such as the t-test and chi-square test for respective data types. A p -value cut-off point of 0.05 was used for bivariate analysis. Non-parametric tests such as the Mann Whitney U test were used in cases where data was not normally distributed. Additionally, we used a time-series analysis to assess for diabetes remission status within each cohort as defined by the Diabetes Canada Clinical Practice Guidelines [ 25 ]. These individuals may have had a HbA1C > 6.5% on one occasion but then their HbA1c dropped below this threshold and was maintained there without any antihyperglycemic agents. The latest interpretation closest to the NAFLD diagnosis date per each patient was reported.

The Conjoint Health Research Ethics Board at the University of Calgary approved this study (REB-20-1127). All methods were performed in accordance with the Declaration of Helsinki. Python version 3.1.1 (Python Software Foundation, ) and R [ 26 ] was used for data extraction, cleaning, and parts of the analyses. Appropriate R packages (e.g., rpy2) were imported into Python for statistical analyses.

Data linkage

The CNPD database recorded a total of 12,012 patients diagnosed with NAFLD. All patients were linked successfully to the administrative databases. Data linkage to SCM EMR data linked a total of 3,545 patients (29.5%) accounting for 8,425 admissions. These inpatient visits ( n  = 8,425) accounted for an exceedingly small proportion of the total 1.63 million healthcare visits. Table  1 provides the demographics and comorbidities of the patients with and without coded diabetes. Laboratory data retrieved from SCM was matched with inpatient laboratory records from the lab database, achieving a 100% match rate. Extracted information accounted for: 1.6 million records from PIN, 16.6 million records from Physician claims, 9 million records from laboratory data, and a total of 7,268 hospitalization records. This informed us empirically that NAFLD was a dominantly outpatient managed disease. The performance of the standard diabetes algorithm (2 outpatient claims or 1 hospital discharge code) and the minimal code (1 outpatient claim or 1 hospital discharge) prevalence did not differ.

The patients with coded diabetes were older than those with the absence of diabetes codes (mean 57.4 vs. 51.2). Both groups predominately resided in urban areas (92.5 and 93.3%. respectively) which reflects the cohort selection process of the CNPD database. Additionally, individuals with coded diabetes exhibited a higher prevalence of Charlson comorbidities in comparison to those without diabetes codes.

The performance of diabetes ICD codes, as defined by Quan et al. [ 21 ], was assessed and are shown in Table  2 . Diabetes coding performance showed a sensitivity of 0.81 and a PPV of 0.87. Among patients who met glycemic control, a sensitivity of 0.58 and a PPV of 1.0 was found. The diabetes cohort not meeting glycemic control showed a sensitivity of 0.98 and a PPV of 1.0.

Error rates within severity sub-cohorts

Among those with the absence of diabetes, 6,789 were true negative cases and 323 were false positive cases, representing a diabetes coding error rate of 4.5% over the 5-year period. Patients with HbA1c values above 7.0% had a total of 31 false negative cases and 1426 true positive cases, representing an error rate of 2.2% in the same period. The diabetic meeting glycemic control group (HbA1c between 6.4 and 7.0%) had a total of 736 false negatives and 1,008 true positives, resulting in a 42.2% coding error rate. Upon further investigations it was discovered that a total of 536 among 736 false negatives had received diabetic medications and met glycemic targets.

Tables  3 and 4 presents a comparison of comorbidities and healthcare utilization among patients who achieved glycemic targets (HbA1c group of between 6.4 and 7.0%). Specific comparisons for this HbA1c subgroup are shown in supplementary materials (Additional files 2 to 7 ). Notably, the number of emergency GP visits were statistically significant and ambulatory specialist visits approached the p -value threshold of 0.05 ( p  = 0.07). Slightly different visitation patterns were observed in the HbA1c greater than 7.0% groups. Among those with HbA1c greater than 7.0%, the false negative groups had fewer visits to community GPs (mean 57.5 vs. 70.8), and fewer to community specialists (mean 31.5 vs. 59.6) then true positives cases. Additional File 8 provides a detailed description of the diabetic remissions status, as outlined in the methods section. (Additional File 8 ).

The remission status on diabetes closest to the NAFLD diagnosis date is reported in Additional File 8 and indicates that most individuals remained in their diabetic categories at the time of NAFLD diagnosis.

In this study, we examined the accuracy of diabetes severity coding in the NAFLD population by linking the NAFLD registry to multiple administrative and EMR databases. The primary aim was to identify predictive factors associated with error within the diabetes cohorts. In this study cohort it was observed that diabetes coding accuracy was not dependent on whether a patient received treatment with community-dispensed medications. The coding error among patients with clear indications of diabetes (HbA1c greater than 7.0%) was 6.4% (31/1,426), whereas among those without diabetes (HbA1c less than 6.1%) the error rate was 4.5% (323/7112) over a five-year period. In contrast, not meeting glycemic control group exhibited a considerably higher coding error rate of 42.2%.

In Canadian health systems, primary care physicians may submit up to three ICD codes as part of physician billing, which are compiled into claims databases [ 27 ]. Furthermore, physicians are only required to submit one code representing the commonly completed diagnoses during the patient encounter. Nearly all physician visits related to diabetes during the 5-year period took place in primary care settings, accounting for 99.9% of visits (1.629 million out of 1.630 million visits). However, it is noteworthy that 45.9% of the study cohort experienced at least one inpatient admission. Consequently, approximately half of the cohort had other primary medical conditions that were being managed and diabetes might have been considered as a comorbidity. It is hypothesized that the underreporting of diabetes codes in the glycemic control group may be due to this factor, contributing to the observed coding inaccuracies. The identification of 536 out of 736 false negatives within the cohort meeting glycemic control criteria, who also had documented prescriptions for diabetes medications and maintained a HbA1c control, further supports this observation. Despite linking to impatient EMRs and other administrative data in the 5-year period leading up to the NAFLD diagnosis, no specific details for the rationale for coding were obtained.

The list of ICD codes originally developed for defining diabetes was for identifying comorbidities for calculating the risk of mortality as part of the Charlson algorithm [ 1 ] undergone multiple revisions [ 21 , 28 ]. These refined ICD code-based algorithms are used for syndromic public health surveillance of chronic conditions [ 3 , 4 , 29 ]. Primarily, diabetes codes are employed in prevalence studies to determine the presence of the disease within the population [ 30 , 31 , 32 , 33 ]. These prevalence studies play a pivotal role in informing health systems and guiding the planning for control strategies. Therefore, understanding coding errors is essential for evaluating and refining existing health programs and keeping health databases up to date.

It is worth noting that, from our current understanding, diabetes cohorts have not been adequately considered in existing literature when assessing ICD code accuracy. This study indicated that the cohorts at the extremes (i.e., the highest and lowest A1c groups) demonstrated relatively precise ICD coding accuracy. However, the diabetes cohort in the glycemic control group encountered challenges, likely stemming from the structure of the ICD code submission system and, possibly a lack of coding, as diabetes is often presumed to be a well-managed comorbidity. To address these issues, we propose a few solutions to mitigate this for the boundary group. Currently, the DAD allows the submission of up to twenty-five diagnostic codes for acute facility admission encounters, regardless of payment status [ 34 ]. Expanding the scope of physician claims beyond three codes may provide a more comprehensive understanding of patient profiles. However, this may not be easy to achieve given the complexities and barriers involved in processes for creating administrative data [ 35 , 36 ]. Linkage to inpatient EMR confirmed the quality of extracted laboratory data and provided limited clinical context associated with lack of diabetes coding justification in this patient cohort. Connecting existing data systems with primary care EMRs and directly phenotyping diabetes from clinical notes may offer additional clinical context and contribute to enhancing the accuracy of ICD codes collected within administrative databases.

This study has several limitations. First, the claims database uses ICD-9 codes while DAD and NACRS use ICD-10 codes, and coding standards between the two could be different. Second, obtaining a comprehensive clinical context behind coding rationale can be challenging, as detailed data on patients, providers and context may not always be available. Third, our reference standard may not be perfect and there is a possibility that some diabetes cases were not phenotyped properly. Nevertheless, we followed clinical care practice guideline, and our observations align with clinical expectations. Lastly, the clinical and administrative data utilized in this study are specific to one city in a single Canadian province, and thus may not be generalizable to other settings. Additional external validation in diverse contexts is warranted.

Despite these limitations, this study offers a detailed assessment on coding accuracy for diabetes severity groups. Similar analyses could be conducted on other chronic conditions, contributing to the improvement of chronic disease surveillance programs. Furthermore, there is potential to enhance surveillance through ongoing research activities, including the incorporation of patient-reported outcomes and the artificial intelligence. The integration of self-reported diabetes data from patients [ 37 , 38 ] into existing health system infrastructure, coupled with the development and deployment of self-reported tools via recommender systems [ 39 ], can complement the quality of administrative databases and address these limitations.


In summary, ICD codes demonstrate strong performance in identifying individuals without diabetes and those who do not meet glycemic control within the NAFLD population. However, the codes did not perform well for accurately identifying diabetes cases meeting glycemic control. Patients with false negative diabetes-related ICD-codes often exhibit evidence of glycemic control and receiving medications, highlighting the need for a more comprehensive clinical context, which may require additional data linkage from primary care settings. Our study provides insights on accuracy of diabetes coding among NAFLD population, and similar methodologies can be employed on to assess other chronic conditions.

Data availability

The data that support the findings of this study are available from Alberta Health Services and Alberta Health, but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of Alberta Health Services and Alberta Health.


Anatomical therapeutic classification

Calgary NAFLD pathway database

Discharge abstract database

Drug identification number

Electronic Medical Records

General practitioner

Hemoglobin A1c

International Classification of Diseases

National ambulatory care reporting system

  • Non-alcoholic fatty liver disease

Non-alcoholic steatohepatitis

Negative predictive value

Primary care providers

Pharmaceutical information network

Positive predictive value

Allscripts sunrise clinical manager

Shear wave elastography

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S. Lee, A. Shaheen, C. Naugler, H. Quan, and J. Lee were involved in conceptualization of the study. A. Shaheen, and D. Campbell reviewed medications. A.Shaheen, J.Lee, D. Campbell, H. Quan, and C. Naugler provided mentorship to S.Lee. J.Jiang and S. Lee conducted data extractions. R. Walker provided expertise surrounding primary care settings. S. Lee conducted analysis. All authors reviewed the manuscript and approved submission to the journal.

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Type 2 diabetes alters the behavior of discs in the vertebral column

In a rat model, the discs became stiffer and changed shape earlier than normal.

Type 2 diabetes alters the behavior of discs in the vertebral column, making them stiffer, and also causes the discs to change shape earlier than normal. As a result, the disc's ability to withstand pressure is compromised. This is one of the findings of a new study in rodents from a team of engineers and physicians from the University of California San Diego, UC Davis, UCSF and the University of Utah.

Low back pain is a major cause of disability, often associated with intervertebral disc degeneration. People with Type 2 diabetes face a higher risk of low back pain and disc-related issues. Yet the precise mechanisms of disc degeneration remain unclear.

Investigating the biomechanical properties of the intervertebral disc is crucial for understanding the disease and developing effective strategies for managing low back pain. The research team was co-led by Claire Acevedo, a faculty member in the Department of Mechanical and Aerospace Engineering at the University of California San Diego, and Aaron Fields, faculty in the Department of Orthopaedic Surgery at UC San Francisco.

"These findings provide novel insight into the potential mechanisms underlying diabetes-related disc tissue damage and may inform the development of preventative and therapeutic strategies for this debilitating condition," the researchers write.

The study emphasizes that nanoscale deformation mechanisms of collagen fibrils accommodate compressive loading of the intervertebral disc. In the context of type 2 diabetes, these mechanisms are compromised, resulting in collagen embrittlement. These findings provide novel insight into the potential mechanisms underlying diabetes-related disc tissue damage and may inform the development of preventative and therapeutic strategies for this debilitating condition.

Researchers employed synchrotron small-angle x-ray scattering (SAXS), an experimental technique that looks at collagen fibril deformation and orientation at the nanoscale. They wanted to explore how alterations in collagen behavior contribute to changes in the disc's ability to withstand compression.

They compared discs from healthy rats to those from rats with Type 2 diabetes (UC Davis rat model). The healthy rats showed that collagen fibrils rotate and stretch when discs are compressed, allowing the disc to dissipate energy effectively.

"In diabetic rats, the way vertebral discs dissipate energy under compression is significantly impaired: diabetes reduces the rotation and stretching of collagen fibrils, indicating a compromised ability to handle pressure," the researchers write.

Further analysis showed that the discs from diabetic rats exhibited a stiffening of collagen fibrils, with a higher concentration of non-enzymatic cross-links. This increase in collagen cross-linking, induced by hyperglycemia, limited plastic deformations via fibrillar sliding. These findings highlight that fibril reorientation, straightening, stretching, and sliding are crucial mechanisms facilitating whole-disc compression. Type 2 diabetes disrupts these efficient deformation mechanisms, leading to altered whole-disc biomechanics and a more brittle (low-energy) behavior.

The team published their findings in the December 2023 issue of PNAS Nexus .

This research was supported by the Research Allocation Committee at UCSF (A.J.F.), the Core Center for Musculoskeletal Biology and Medicine at UCSF (A.J.F.), the University of California Office of the President (P.J.H.), the National Institutes of Health (R01 DK095980, R01 HL107256, R01 HL121324, P30 AR066262, R01 AR070198), the University of Utah (J.L.R.), and the Advanced Light Source (ALS07392; T.N.A., C.A.).

  • Bone and Spine
  • Back and Neck Pain
  • Personalized Medicine
  • Workplace Health
  • Chronic Illness
  • Wounds and Healing
  • Diabetes mellitus type 1
  • Diabetes mellitus type 2
  • Personalized medicine
  • Sympathetic nervous system
  • Chronic pain
  • Encephalopathy

Story Source:

Materials provided by University of California - San Diego . Original written by Ioana Patringenaru. Note: Content may be edited for style and length.

Journal Reference :

  • James L Rosenberg, Eric Schaible, Alan Bostrom, Ann A Lazar, James L Graham, Kimber L Stanhope, Robert O Ritchie, Tamara N Alliston, Jeffrey C Lotz, Peter J Havel, Claire Acevedo, Aaron J Fields. Type 2 diabetes impairs annulus fibrosus fiber deformation and rotation under disc compression in the University of California Davis type 2 diabetes mellitus (UCD-T2DM) rat model . PNAS Nexus , 2023; 2 (12) DOI: 10.1093/pnasnexus/pgad363

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Diabetes can be easily reversed (and prevented). A doctor explains how

Recent research has revealed that Type 2 diabetes is not as permanent as once thought.

Photo credit: Getty

Dr Jason Fung

Imagine your body as a big sugar bowl. At birth, the bowl is empty. Over several decades, you eat sugar and refined carbohydrates and the bowl gradually fills up. And when you next eat, sugar comes in and spills over the sides of the bowl because the bowl is already full.

The same situation exists in your body. When you eat sugar, your body secretes the hormone insulin to help move the sugar into your cells, where it’s used for energy. If you don’t burn off that sugar sufficiently, then over decades your cells become completely filled and cannot handle anymore.

The next time you eat sugar, insulin cannot force any more of it into your overflowing cells, so it spills out into the blood. Sugar travels in your blood in a form called glucose and having too much of it – known as high blood glucose – is a primary symptom of type 2 diabetes .

When there’s too much glucose in the blood, insulin does not appear to be doing its usual job of moving the sugar into the cells. We then say that the body has become insulin resistant, but it’s not truly insulin’s fault. The primary problem is that the cells are overflowing with glucose.

The high blood glucose is only part of the issue. Not only is there too much glucose in the blood, there’s too much glucose in all of the cells. Type 2 diabetes is simply an overflow phenomenon that occurs when there is too much glucose in the entire body.

In response to excess glucose in the blood, the body secretes even more insulin to overcome this resistance. This forces more glucose into the overflowing cells to keep blood levels normal.

This works, but the effect is only temporary because it has not addressed the problem of excess sugar; it has only moved the excess from the blood to the cells, making insulin resistance worse. At some point, even with more insulin, the body cannot force any more glucose into the cells.

  • Is there any hope of curing diabetes?
  • Can eating a lot of sugar really lead to diabetes?
  • Eating ultra-processed foods could increase risk of type 2 diabetes, study finds

What happens in the body if we do not remove the excess glucose? First, the body keeps increasing the amount of insulin it produces to try to force more glucose into the cells. But this only creates more insulin resistance, in what then becomes a vicious cycle.

When the insulin levels can no longer keep pace with rising resistance, blood glucose spikes. That’s when your doctor is likely to diagnose type 2 diabetes.

Your doctor may prescribe a medication such as insulin injections, or perhaps a drug called metformin, to lower blood glucose, but these drugs do not rid the body of excess glucose. Instead, they simply continue to take the glucose out of the blood and ram it back into the body.

It then gets shipped out to other organs, such as the kidneys, the nerves, the eyes, and the heart, where it can eventually create other problems. The underlying problem, of course, is unchanged.

Remember the bowl that was overflowing with sugar? It still is. Insulin has simply moved the glucose from the blood, where you could see it, into the body, where you cannot. So, the very next time you eat, sugar spills out into the blood again and you inject insulin to cram it into your body.

The more glucose you force your body to accept, the more insulin your body needs to overcome the resistance to it. But this insulin only creates more resistance as the cells become more and more distended.

Once you’ve exceeded what your body can produce naturally, medications can take over. At first, you need only a single medication, but eventually it becomes two and then three, and the doses become larger.

And here’s the thing: if you are taking more and more medications to keep your blood glucose at the same level, your diabetes is actually getting worse.

Type 2 diabetes is reversible and preventable...without medications

Once we understand that type 2 diabetes is simply too much sugar in the body, the solution becomes obvious. Get rid of the sugar. Don’t hide it away. Get rid of it. There are really only two ways to accomplish this.

  • Put less sugar in.
  • Burn off remaining sugar.

That’s it. That’s all we need to do. The best part? It’s all natural and completely free. No drugs. No surgery. No cost.

Step 1: Put less sugar in

The first step is to eliminate all sugar and refined carbohydrates from your diet. Added sugars have no nutritional value and you can safely withhold them. Complex carbohydrates, which are simply long chains of sugars, and highly refined carbohydrates, such as flour, are quickly digested into glucose.

The optimum strategy is to limit or eliminate breads and pastas made from white flour, as well as white rice and potatoes.

You should maintain a moderate, not high, intake of protein. When it is digested, dietary protein, such as meat, breaks down into amino acids. Adequate protein is required for good health, but excess amino acids cannot be stored in the body and so the liver converts them into glucose. Therefore, eating too much protein adds sugar to the body. So you should avoid highly processed, concentrated protein sources such as protein shakes, protein bars, and protein powders.

What about dietary fat ? Natural fats, such as those found in avocados, nuts, and olive oil – major components of the Mediterranean diet – have a minimal effect on blood glucose or insulin and are well known to have healthy effects on both heart disease and diabetes. Eggs and butter are also excellent sources of natural fats.

Dietary cholesterol, which is often associated with these foods, has been shown to have no harmful effect on the human body. Eating dietary fat does not lead to type 2 diabetes or heart disease. In fact, it is beneficial because it helps you feel full without adding sugar to the body.

 To put less sugar into your body, stick to whole, natural, unprocessed foods. Eat a diet low in refined carbohydrates, moderate in protein, and high in natural fats.

Step 2: Burn off the remaining sugar

Exercise – both resistance and aerobic training – can have a beneficial effect on type 2 diabetes, but it is far less powerful at reversing the disease than dietary interventions. And fasting is the simplest and surest method to force your body to burn sugar.

Fasting is merely the flip side of eating: if you are not eating, you are fasting. When you eat, your body stores food energy; when you fast, your body burns food energy. And glucose is the most easily accessible source of food energy. Therefore, if you lengthen your periods of fasting, you can burn off the stored sugar.

While it may sound severe, fasting is literally the oldest dietary therapy known and has been practised throughout human history without problems. If you are taking prescription medications, you should seek the advice of a physician.

But the bottom line is this: If you don’t eat, will your blood glucose come down? Of course. If you don’t eat, will you lose weight? Of course. So, what’s the problem? None that I can see.

To burn off sugar, a popular strategy is to fast for 24 hours, two to three times per week. Another popular approach is to fast for 16 hours, five to six times per week. The secret to reversing type 2 diabetes now lies within our grasp.

All it requires is having an open mind to accept a new paradigm and the courage to challenge conventional wisdom.

  • Diabetes is now an epidemic in the UK. Here’s how we should fight it

This text was extracted from The Diabetes Code: Prevent and Reverse Type 2 Diabetes Naturally by Dr Jason Fung , out now (£14.99, Greystone Books).

Buy from Amazon , Foyles or Waterstones

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How a Specific Gut Bacterium May Cause Type 1 Diabetes

A bacterium that produces an insulin-like peptide can give mice type 1 diabetes, and infection with the microbe seems to predict the onset of the disease in humans, a study finds..

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Dan is a News Editor at The Scientist . He writes and edits for the news desk and oversees the “The Literature” and “Modus Operandi” sections of the monthly TS Digest and quarterly print magazine. He has a background in neuroscience and earned his master's in science journalism at New York University.

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D iabetes, the broad term for a handful of diseases that prevent the body from properly regulating blood sugar levels, was first documented over 3,500 years ago in ancient Egypt—yet experts still aren’t sure exactly how it develops, although scientists are almost certain that there’s no single trigger. Indeed, two primary forms of the condition are already known: types 1 and 2. Type 1 diabetes , which tends to have a more sudden onset, has proven particularly enigmatic, as people can develop the condition at different ages, and unlike type 2, it seems to be more closely linked to genetic and other predispositions than to diet and lifestyle.

Now, research published July 25 in PNAS may have revealed a key piece of the puzzle. The presence of the bacterium Parabacteroides distasonis in the gut microbiome causes type 1 diabetes in a mouse model and seems to predict the onset of the disease in humans. This is likely because the microbe produces a peptide similar enough to part of an insulin molecule that it can lead to the production of insulin-targeted antibodies, priming the immune system to launch an attack against insulin and the cells that produce it. Thus, the researchers have identified a microbial culprit for doctors to examine as they look for new ways to screen for and perhaps eventually prevent the disease.

Stanley Hazen , a Cleveland Clinic researcher who studies how the gut microbiome influences various diseases, applauds the study’s authors for going beyond merely identifying an association between a gut microbe and disease and actually probing the underlying mechanisms—adding that the common failure to do so makes many similar studies “junk.”

“Most investigations into the microbiome simply look at the types of microbes in the intestine or feces and show that the composition is associated with the prevalence of the disease,” Hazen says. “That’s just association, and . . . you cannot tell from that kind of analysis what’s the chicken and what’s the egg.”

See “ Immune Response to Gut Microbes Linked to Diabetes Risk ”

Boston College biologist Emrah Altindis and his colleagues pieced together the behavior and functional role of P. distasonis  bit by bit. It’s well established that the immune systems of people with type 1 diabetes attack insulin and the pancreatic cells that produce it. The team hypothesized that this autoimmune response may actually be an attempt to assail a foreign entity that’s structurally similar to insulin, which then goes awry. So they screened existing databases for sequences of peptides known to be produced by gut bacteria, keeping an eye out for structural similarities to insulin. After that screen identified over 50 candidates, Altindis explains, the team gradually narrowed the list based on the peptides’ degree of similarity to insulin and ability to activate insulin-attacking CD8+ T cells taken from a human patient with diabetes.

The team then moved to a mouse model, testing their short list of candidates by injecting mice with one of the peptides or with insulin and measuring their immune cells’ response. Out of all the possible peptides, only one, called hprt4-18 (which had already shown to be produced by P. distasonis ),   activated an immune response from CD8+ T cells in mice, Altindis says. The team then began another experiment in which they fed the bacterium to mice, seeding their gut microbiomes, in order to see how it affected disease progression. The specific mouse model used is fated to eventually develop type 1 diabetes, Hazen notes, but not as quickly as they did in this experiment. By the time they were 12 weeks old, mice colonized with P. distasonis showed clear signs of type 1 diabetes while controls, who were otherwise identical, did not. “We were able to accelerate the disease onset by just giving this vector,” Altindis says.

The goal is to help patients get treated, or to hopefully prevent new cases. —Emrah Altindis, Boston College

Further investigation revealed that the newly diabetic mice had increased counts of CD8+ T cells and other immune cells implicated in type 1 diabetes such as dendritic cells and macrophages. Meanwhile, they had fewer of the CD4+ T cells that reduce inflammation. As Altindis phrased it, “the good cells are decreasing and the bad cells are increasing,” indicating that P. distasonis and its production of hprt4-18   had indeed triggered the kind of autoimmune attack that ultimately leads to type 1 diabetes.

“It is unclear what triggers the immune system to take that initial wrong turn,” University of Virginia diabetologist Heather Ferris , who like Hazen didn’t work on the study, tells The Scientist  over email. “This microbiome-derived insulin-like peptide, which is close to self but not quite the same, could be that first trigger,” she says. “Once one antibody starts causing damage to the pancreas, other proteins that the immune system shouldn’t normally see are released from the pancreas and trigger more antibodies. So if you can stop the trigger then you could potentially stop the whole cascade from taking place.”

However, Hazen adds: “What this paper does not get at is ‘How big of a contribution does this mechanism [make] to type 1 diabetes in humans in general?’”

In a first step towards probing the human relevance of their findings, the researchers looked for the same trend in diabetes patients. They turned to the DIABIMMUNE project , a database that contains medical records for infants from Estonia, Finland, and Russia alongside demographic information and other potentially immune disease–relevant data, including sequencing results from microbiome samples taken at various ages. Among the 222 records examined, infants younger than three years old who had P. distasonis  in their gut microbiomes had a greater risk of developing type 1 diabetes later in life (in the Russian and Estonian cohorts, 100 percent of the infants who were eventually diagnosed with type 1 diabetes had signs of P. distasonis  in their gut), which Altindis says indicates that a person’s gut microbiome composition can serve as a powerful predictor of type 1 diabetes risk, though he stresses that the disease’s development is likely more complex, with other factors playing into it. The work, he adds, does not establish a causal link in humans, only the potential for one.

See “ Three Studies Track People’s Microbiomes Through Health and Disease ”

“We are never going to purposefully infect someone with this [bacterium] and see if they develop type 1 diabetes, so I am not sure that there is a definitive study to be done [to demonstrate causality],” says Ferris. “At this point, the mouse data is very good, but we have cured mice of type 1 diabetes 100 times over and it has never translated to humans.”

Still, “it is very exciting,” Ferris adds. One big caveat she notes is that the children in DIABIMMUNE are particularly homogenous from a genomics standpoint. “It will be interesting to see if this association holds up in more genetically diverse populations and in older patients, as the DIABIMMUNE cohort was 0 to 3 years of age and while this is a time interval during which many are diagnosed, the majority of patients are diagnosed after the age of 3.”

“I think the most important thing to make this data more convincing for its relevance to humans is replication in other patient cohorts,” Ferris says.

To that end, Altindis says that his team is analyzing other datasets to see if the association between P. distasonis  and type 1 diabetes holds. “Then we will feel a bit more confident,” he says, though he notes that most of the data available for such analyses comes from the US and northern Europe.

Altindis, Hazen, and Ferris all say that it’s far too soon to talk about any therapeutic or clinical applications to the study, but that the research lays an important foundation for future work that may eventually reach that point, whether it be in the form of better screening for risk factors that may eventually lead to diabetes, uncovering novel treatments, or perhaps even a vaccine of sorts against P. distasonis that could be given to children genetically susceptible to the disease.

“Anything we can do to help people not have this difficult life. . . . The goal is to help patients get treated, or to hopefully prevent new cases,” Altindis says.

Related immunology microbiology Research Resources

Building Broader B Cell Diversity for Better Monoclonal Antibody Discovery<br ><br>

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New Strategies in the Battle Against Infectious Diseases


Beyond Cytotoxicity: The Importance of T Cell Memory

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Effectiveness of diabetes self-management education (DSME) in type 2 diabetes mellitus (T2DM) patients: Systematic literature review

Diabetes mellitus is a chronic disease characterized by high glucose levels (hyperglycemia) due to metabolic disorders that prevent patients from producing sufficient amounts of insulin. This research aims to test the effectiveness of implementing diabetes self-management education in patients with Type 2 diabetes mellitus. The search for relevant articles was carried out through Google Scholar, PubMed, ProQuest, and Science Direct using the keywords diabetes mellitus, management education, self-care, diabetes self-management education, DSME, T2DM. The articles were then selected based on inclusion and exclusion criteria. Furthermore, the data were extracted, grouped, and concluded. Based on 15 articles, diabetes self-management education intervention provides significant effectiveness to lifestyle changes and the self-care of T2DM patients. In conclusion, diabetes self-management education intervention has been shown to be effective in dealing with type 2 diabetes mellitus. Furthermore, DSME has a positive effect on lifestyle changes and the self-care of T2DM patients.

Significance for public health

Globally, there are various pillars of diabetes mellitus management. One of the important pillars for the prevention and management is education. When properly carried out, it provides benefits to people with diabetes mellitus. Furthermore, the Association of Diabetes Care and Education (AADE) has guidelines for diabetes self-management education (DSME). In reality, there are many health workers that provide education without paying attention to these guidelines. Therefore, this study on the effectiveness of diabetes self-management education (DSME) would provide information regarding the importance of using these guidelines.


Diabetes mellitus (DM) is a chronic disease characterized by high glucose levels (hyperglycemia) due to metabolic disorders that prevent the patient from producing sufficient amounts of insulin. The disease can be prevented and controlled by engaging in certain behaviors and lifestyles such as regular exercise, healthy eating patterns, avoiding smoking, and controlling fat and glucose in the blood. 1 The World Health Organization stated that the number of people living with diabetes mellitus (DM) worldwide reached 422 million, and every year 1.6 million deaths are recorded. 2 The prevalence of the disease in the world is estimated to reach 642 million people by 2040. In 2019, the countries with the highest number of DM sufferers were China, India, the United States, Pakistan, Brazil, Mexico, and Indonesia, with an estimated number of 10 million patients. 3 The number of people living with diabetes could be much greater than the prevalence described, because most sufferers only seek medical help after complications occur. The rising prevalence of diabetes mellitus is due to several factors, such as unhealthy behavior. 1 This behavior is still rampart in Indonesian society, and is evidenced by the results of the Basic Health Research 2018, 1 where 13.6% of the residents were overweight, 21.8% had obesity, and 31% central obesity. Other unhealthy habits include the use of tobacco by men (62.9%) and smoking by adolescents (10-18 years) (23.91%). 4 There are seven major behaviors related to diabetes self-care management, they include diet, physical activity, monitoring blood glucose levels, adherence to proper medication consumption, good problem solving, coping skills, and risk reduction behavior. 5 Continuous selfcare will reduce the incidence of DM complications. However, most DM sufferers do not practice adequate self-care techniques such as controlling fasting blood glucose levels. 6

DM management focuses on several aspects, namely education, meal planning, changes in lifestyle, physical activity, habits. 7 One study explained that educational interventions influence knowledge, physical activity, food intake, self-efficacy, and health literacy. 8 Diabetes self-management education (DSME) plays a key role in empowering people with diabetes to engage and sustain lifestyle changes, which have been shown to improve health outcomes. DSME is the process of facilitating the knowledge, attitudes, and abilities necessary for self-management. 9 In addition to this, DSME play an important role in influencing the self-care practices of patients with diabetes mellitus. Based on this phenomenon, a literature review was prepared to highlight effectiveness of DSME on T2DM.

Design and Methods

The collection and review of articles was carried during the month of October 2020. Furthermore, published articles were obtained through several electronic databases, such as Google Scholar, PubMed, ProQuest, and ScienceDirect using the keywords diabetes mellitus, self-care, diabetes self-management education, and DSME. The articles obtained from these databases were then selected based on the inclusion and exclusion criteria in order to obtain relevant articles. In addition to this, articles designs were selected using cross-sectional, randomized controlled trials (RCT), systematic reviews, and quasi-experimental studies. Subsequently, the data was then extracted, grouped, and concluded; 137 articles were obtained through the selection process (inclusion and exclusion criteria) ( Table 1 ). These articles were then assessed for criticism and 15 were found to be relevant to the criteria.

Results and discussions

The effectiveness of DSME in T2DM is the main focus of this literature review. The heterogeneity of DSME implementation were seen based on the number of sessions, the time span, and the methods used. The study presented 6 articles with homogeneous results showing that DSME has a good effect on T2DM patients ( Table 2 ).

Inclusion and exclusion criteria.

Article review result.

The DSME intervention given to T2DM patients in Ethiopia had a positive impact, such as an increase in knowledge and adherence to diet therapy, exercise, glucose monitoring, and wound care. 10 In line with that, another study explained that DSME significantly improved medication adherence, self-management behavior, knowledge, self-efficacy, and quality of life. 11-13 Several studies show that DSME interventions improve the quality of life. 14-17 Through these interventions, bad behavior such as smoking and alcohol consumption can also be avoided or reduced. 10

Several interventions are also able to influence lifestyle changes such as increasing the duration of exercises (cycling, walking, aerobics), reducing smoking habits, and increasing the consumption of fruits and vegetables. 18 Lifestyle changes caused by DSME interventions are expected to improve the clinical and health status of T2DM patients. One study proved this showing DSME’s effectiveness in controlling fasting blood glucose, random blood glucose, total cholesterol, and triglycerides. 11 In line with that, other studies also showed that DSME can influence glycemic control, body weight and BMI control. 11 , 19-21 Apart from data homogeneity in the article, another difference was found regarding the effect of DSME on HbA1c. Cunningham 14 states that DSME does not significantly affect HbA1c. This is in contrast with other studies which explain that this intervention can significantly affect HbA1c. 21-26 After reviewing several studies, it is proven that DSME has a positive effect on the lifestyle and clinical or health status of T2DM patients. However, the implementation process could be influenced by several factors, namely: i) limited resources, ii) culture, iii) relationship with diabetes, and iv) relationship with clinic.

This systematic review focuses on the effectiveness of DSME on T2DM disease progression. It is known that the DSME intervention provides benefits to the development of T2DM disease. The demonstrated benefits point to efforts to increase T2DM development through lifestyle changes and self-care for T2DM patients. Lifestyle changes such as exercising diligently, increasing consumption of fruits and vegetables, and avoiding smoking can improve the patient’s clinical condition and the patient’s health status. 18 The clinical condition can be seen from the levels of blood glucose and HbA1c.

DSME has a positive effect on T2DM patients to improve their knowledge, behavior, self-efficacy, and clinical conditions of patients such as blood glucose levels, HbA1c, lipid profiles. 10 , 11 , 19-21 However, there were differences in results in studies involving HbA1c levels. The difference that lies in the presence or absence of this effect on HbA1c can be a concern in future studies to consider the determining factors that can influence it. Several studies in this review show that the effectiveness of DSME is influenced by education providers and support systems. 17 , 22 , 25 , 26


Based on the 15 articles reviewed, it was found that DSME intervention provides significant effectiveness to lifestyle changes and the self-care of T2DM patients. Therefore, it improves the clinical or health status of T2DM patients.


The authors are grateful to the co-authors and reviewers for this research.

Funding Statement

Funding: This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.


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