literature review of diabetes

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• Review Article
• Published: 06 June 2022

The burden and risks of emerging complications of diabetes mellitus

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

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

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• Diabetes complications
• Type 1 diabetes
• Type 2 diabetes

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

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

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

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

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

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

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

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Introduction.

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

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

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

Diabetes mellitus and cancer

The burden of cancer mortality.

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

Gastrointestinal cancers

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

Hepatocellular carcinoma

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

Pancreatic cancer

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

Colorectal cancer

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

Female-specific cancers

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

Endometrial cancer

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

Breast cancer

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

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

Ovarian cancer

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

Male-specific cancers: prostate cancer

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

An assessment of cancer associations

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

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

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

Potential aetiological mechanisms

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

Hyperinsulinaemia

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

Hyperglycaemia and inflammation

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

Cellular signalling mechanisms

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

Diabetes mellitus and infections

Infection-related complications.

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

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

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

Post-operative infection

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

Respiratory infections

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

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

Pneumonia, MERS, SARS and H1N1 influenza

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

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

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

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

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

Diabetes mellitus and liver disease

Nonalcoholic fatty liver disease.

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

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

NASH and fibrosis

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

Bidirectional relationship between diabetes mellitus and liver disease

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

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

Insulin resistance

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

Ectopic adipose deposition

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

Diabetes mellitus and affective disorders

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

Anxiety and eating disorders

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

Broader psychological impacts

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

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

Diabetes mellitus and sleep disturbance

Obstructive sleep apnoea.

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

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

Diabetes mellitus and cognitive disability

Dementia and cognitive impairment.

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

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

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

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

Diabetes mellitus and disability

Functional disability.

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

Workplace productivity

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

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

Box 1 Diabetes mellitus and skeletal muscle atrophy

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

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

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

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

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

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

See refs 195 , 196 , 197 , 198 .

Diabetes management and control

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

Medications

Anti-diabetes mellitus medications and cancer.

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

Anti-diabetes mellitus medications and other emerging complications

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

Glucose control

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

Conclusions

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

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Acknowledgements

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

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

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

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

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Trends in incidence of total or type 2 diabetes: systematic review

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Showing the turning point in diabetes incidence in 61 populations

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Trends in type 2 diabetes

• Related content
• Peer review
• Rakibul M Islam , postdoctoral research fellow 1 2 ,
• Elizabeth L M Barr , postdoctoral research fellow 1 ,
• Edward W Gregg , chair in diabetes and cardiovascular disease epidemiology 3 4 ,
• Meda E Pavkov , physician scientist 3 ,
• Jessica L Harding , research fellow 3 ,
• Maryam Tabesh , research study coordinator 1 2 ,
• Digsu N Koye , postdoctoral research fellow 1 2 ,
• Jonathan E Shaw , deputy director of Baker Heart and Diabetes Institute 1 2
• 1 Baker Heart and Diabetes Institute, Melbourne, VIC 3004, Australia
• 2 School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia
• 3 Centres for Diseases Control and Prevention, Division of Diabetes Translation, Atlanta, GA, USA
• 4 School of Public Health, Epidemiology and Biostatistics, Imperial College London, London, UK
• Correspondence to: D J Magliano dianna.magliano{at}baker.edu.au
• Accepted 16 July 2019

Objective To assess what proportions of studies reported increasing, stable, or declining trends in the incidence of diagnosed diabetes.

Design Systematic review of studies reporting trends of diabetes incidence in adults from 1980 to 2017 according to PRISMA guidelines.

Data sources Medline, Embase, CINAHL, and reference lists of relevant publications.

Eligibility criteria Studies of open population based cohorts, diabetes registries, and administrative and health insurance databases on secular trends in the incidence of total diabetes or type 2 diabetes in adults were included. Poisson regression was used to model data by age group and year.

Results Among the 22 833 screened abstracts, 47 studies were included, providing data on 121 separate sex specific or ethnicity specific populations; 42 (89%) of the included studies reported on diagnosed diabetes. In 1960-89, 36% (8/22) of the populations studied had increasing trends in incidence of diabetes, 55% (12/22) had stable trends, and 9% (2/22) had decreasing trends. In 1990-2005, diabetes incidence increased in 66% (33/50) of populations, was stable in 32% (16/50), and decreased in 2% (1/50). In 2006-14, increasing trends were reported in only 33% (11/33) of populations, whereas 30% (10/33) and 36% (12/33) had stable or declining incidence, respectively.

Conclusions The incidence of clinically diagnosed diabetes has continued to rise in only a minority of populations studied since 2006, with over a third of populations having a fall in incidence in this time period. Preventive strategies could have contributed to the fall in diabetes incidence in recent years. Data are limited in low and middle income countries, where trends in diabetes incidence could be different.

Systematic review registration Prospero CRD42018092287.

Introduction

Over the past few decades, the prevalence of diabetes in developed and developing countries has risen substantially, making diabetes a key health priority globally. 1 Examination of trends in total burden of diabetes is an essential part of the monitoring of this health priority area, but, to date, it has consisted primarily of studies looking at diabetes prevalence. 1 2 3 4 5 Prevalence estimates suggest that the diabetes burden is still rising in most countries, and this is often interpreted as evidence of increasing risk in the population. However, selective incidence studies 6 7 and some accompanying risk factor data 8 suggest otherwise. Prevalence can be a crude and misleading metric of the trajectory of an epidemic, because increasing prevalence of a disease might be due to either increasing incidence or to improved survival. Furthermore, prevalence cannot be reliably used to study the effects of changes in population risk factors, because their effects are detected earlier with incidence trends than with prevalence trends, and incidence is not affected by changes in survival.

Incidence measures the proportion of people who develop diabetes over a period of time among the population at risk. It is the appropriate measure of population risk, and a valuable way of assessing whether public health campaigns for diabetes prevention are succeeding. While prevalence can rise simply because mortality falls, incidence of diagnosed diabetes is affected only by the risk of the population and the amount of screening undertaken. Changes in prevalence might be an inadequate guide to the effects of prevention activities, and could lead to the inappropriate rejection of effective interventions. It is only by measuring both incidence and prevalence that a better understanding of the extent of diabetes can be achieved.

Among existing diabetes incidence data, a few studies suggest that diabetes incidence could be falling despite rising or stable prevalence, 6 7 9 but not all data are consistently showing the same trends. For example, studies from England and Wales (1994-98), 10 Portugal (1992-2015), 11 and Canada (1995-2007) 12 are reporting increases in diabetes incidence. To understand what is happening at a global level over time, a systematic approach to review all incidence trend data should be undertaken to study patterns and distributions of incidence trends by time, age, and sex. So far, no systematic reviews have reported on trends in the incidence of diabetes. Therefore, we conducted a systematic review of the literature reporting diabetes incidence trends.

Data sources and searches

We conducted a systematic review in accordance with PRISMA guidelines. 13 We searched Medline, Embase, and CINAHL from January 1980 to December 2017 without language restrictions. The full search strategy is available in supplementary table 1.

Study selection

Inclusion and exclusion criteria.

Eligible studies needed to report diabetes incidence in two or more time periods. Study populations derived from open, population based cohort studies (that is, with ongoing recruitment over time), diabetes registries, or administrative or health insurance databases based mainly or wholly in primary care (electronic medical records, health insurance databases, or health maintenance organisations). We also included serial, cross sectional, population based studies where incidence was defined as a person reporting the development of diabetes in the 12 months before the survey. Studies were required to report on the incidence of either total diabetes or type 2 diabetes. We excluded studies reporting incidence restricted to select groups (eg, people with heart failure) and studies reporting only on children or youth.

Each title and abstract was screened by at least two authors (DJM, JES, DNK, JLH, and MT) and discrepancies were resolved by discussion. We aimed to avoid overlap of populations between studies. Therefore, if national data and regional data were available from the same country over the same time period, we only included the national data. If multiple publications used the same data source, over the same time period, we chose the publication that covered the longest time period.

Outcome measure

Our outcome was diabetes incidence using various methods of diabetes ascertainment including: blood glucose, glycated haemoglobin (HbA1c), linkage to drug treatment or reimbursement registries, clinical diagnosis by physicians, administrative data (ICD codes (international classification of diseases)), or self report. Several studies developed algorithms based on several of these elements to define diabetes. We categorised the definition of diabetes into one of five groups: clinical diagnosis, diabetes treatment, algorithm derived, glycaemia defined (blood glucose or HbA1c, with or without treatment), and self report.

Data extraction and quality of studies

We extracted crude and standardised incidence by year (including counts and denominators) and the reported pattern of the trends (increasing, decreasing, or stable, (that is, no statistically significant change)) in each time period as well as study and population characteristics. Age specific data were also extracted if available. Data reported only in graphs were extracted by DigitizeIt software (European Organisation for Nuclear Research, Germany). We assessed study quality using a modified Newcastle-Ottawa scale for assessing the risk of bias of cohort studies 14 (supplementary material).

Statistical methods

Data were reported as incidence density (per person year) or yearly rates (percentage per year). From every study, we extracted data from every subpopulation reported, such that a study reporting incidence in men and women separately contributed two populations to this analysis. If studies reported two different trends over different time periods, we considered these as two populations. Further, if the study was over 10 years in duration, we treated these as two separate time periods. To avoid double counting, when the data were reported in the total population as well as by sex and ethnic groups, we only included data once and prioritised ethnicity specific data over sex specific data.

We extracted the age specific incidence data reported for every individual calendar year. These data were then categorised into four age bands (<40, 40-54, 55-69, and ≥70), and were plotted against calendar year. In studies where counts and denominators were reported by smaller age groups than we used, we recalculated incidence across our specified larger age groups. If we found multiple age groups within any of our broader age groups, but with insufficient information to combine the data into a new category, only data from one age group were used. To limit overcrowding on plots, if data were available for men, women, and the total population, only total population data were plotted. Data from populations with high diabetes incidence such as Mauritians 15 and First Nation populations from Canada 16 were plotted separately to allow the examination of most of the data more easily on a common scale (supplementary material). Furthermore, studies reporting data before 1991 or populations with fewer than three data points were not plotted. We also categorised studies into European and non-European populations on the basis of the predominant ethnicity of the population in which they were conducted. Studies conducted in Israel, Canada, and the United States were assigned to the European category.

We took two approaches to analyse trends of diabetes incidence over time. Firstly, we allocated the reported trend (increasing, decreasing, or stable (that is, no statistically significant change)) of each population to the mid-point of each study’s observational period, and then assigned this trend into one of five time periods (1960-79, 1980-89, 1990-99, 2000-05, and 2006-14). Where a test of significance of trends was not reported or when a time period was longer than 10 years, we performed Joinpoint trend analyses 17 18 to observe any significant trends in the data (assuming a constant standard deviation). Joinpoint Trend Analysis Software (version 4.5.0.1) uses permutation tests to identify points where linear trends change significantly in direction or in magnitude, and calculates an annual percentage change for each time period identified. In sensitivity analyses we also tested different cut points in the last two time periods.

The second approach was used to more accurately allocate trends to the prespecified time periods. Among the studies that reported raw counts of diabetes cases and denominators, we examined the association between calendar year and incidence, using Poisson models with the log person years as offset. The midpoints of age and calendar period were used as continuous covariates, and the effects of these were taken as linear functions. We analysed each study separately by prespecified time periods, and reported annual percentage change when the number of data points in the time period was at least four. For studies that did not provide raw data but did report a sufficient number of points, we analysed the relation between year and incidence using Joinpoint regression across the time periods specified above and reported annual percentage change. Analyses were conducted with Stata software version 14.0 (Stata Corporation, College Station, TX, USA), and Joinpoint (Joinpoint Desktop Software Version 4.5.0.1). 17 18

Patient and public involvement

No patients or members of the public were involved in setting the research question or the outcome measures for this study. No patients were asked to advise on interpretation or writing up of results. We intend to disseminate this research through press releases and at research meetings.

We found 22 833 unique abstracts from 1 January 1980 to the end of 2017. Among these, 80 described trends of diabetes incidence, of which 47 met all inclusion criteria. Articles describing trends were excluded for the following reasons: duplicated data (n=21), closed cohorts (n=5), populations included youth only (n=1), occupational cohorts (n=2), or no usable data presented (n=4; fig 1 ).

Flowchart of study selection

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Table 1 and supplementary material table 2 describe the characteristics of the included studies. Only 19% (9/47) of studies were from predominantly non-Europid populations and 4% (2/47) of studies were from low or middle income countries (China 25 and Mauritius 15 ). Administrative datasets, health insurance data, registry data, survey data, and cohort studies accounted for 38% (n=18), 21% (n=10), 19% (n=9), 11% (n=5), and 11% (n=5) of the 47 data sources, respectively. Among the 47 studies, diabetes was defined by a clinical diagnosis, diabetes treatment (via linkage to drug treatment registers), an algorithm, blood glucose, and self report in 28% (n=13), 9% (n=4), 47% (n=22), 11% (n=5), and 6% (n=3) of studies, respectively. Sample sizes of the populations were greater than 10 000 in every year in 85% (n=40) of the studies, and greater than 130 000 per year in 70% (n=33) of the studies. A total of 62% (n=29) of the 47 included studies exclusively reported on type 2 diabetes, and 38% (n=18) reported on total diabetes.

Characteristics of 47 included studies reporting on diabetes incidence trends, by country

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Summary of patterns of diabetes incidence trends based on analyses reported in publications in 1960-99

Trends of diabetes incidence

Among the 47 studies, 16 provided information on incidence by age group. Of these 16 studies, 14 were plotted in figure 2 , with those from high incidence countries plotted in supplementary figure 1. In these figures, incidence in most studies increased progressively until the mid-2000s in all age groups. Thereafter, most studies showed a stable or decreasing trend, apart from studies in Denmark 26 27 and Germany 31 and in a US health insurance population 9 where the incidence inflected upwards in the later years for some age groups.

Incidence of diabetes over time for populations aged under 40, 40-54, 55-69, and 70 or more, among studies reporting age specific data. Only populations with at least three points were plotted. NHIS=National Health Interview Survey

Using the first approach to analyse trends of diabetes incidence over time, we separated the data into populations based on sex and ethnicity, and allocated a time period to each population, generating 105 populations for analysis. Seventy four and 31 populations were predominantly Europid and non-Europid, respectively. Table 2 and table 3 show the reported trend for each population. Table 4 summarises the findings in table 2 and table 3 , and shows that the proportion of populations reporting increasing trends peaked in 1990-99 and fell progressively in the two later time periods. Between 1960 and 1989, 36% (8/22) of the populations studied had increasing trends in incidence of diabetes, 55% (12/22) had stable trends, and 9% (2/22) had decreasing trends. In 1990-2005, diabetes incidence increased in 66% (33/50) of populations, was stable in 32% (16/50), and decreased in 2% (1/50). In 2006-14, increasing trends were reported in 33% (11/33) of populations, whereas 30% (10/33) and 36% (12/33) had stable or declining incidence, respectively.

Summary of patterns of diabetes incidence trends based on analyses reported in publications in 2000-14

Summary of incidence trends over time of total or type 2 diabetes

Populations that reported a decrease in incidence after 2005 came from the US, 6 9 Israel, 34 Switzerland, 46 Hong Kong, 32 Sweden, 43 and Korea. 36 Populations reporting increasing incidence after 2005 included Portugal, 11 Denmark, 26 27 and Germany, 31 while populations from Canada, 19 Italy, 35 Scotland, 40 Norway, 39 US (non-Hispanic white), 56 and the United Kingdom 50 showed stable incidence. For two studies (16 populations), 16 29 we could not determine a direction of a trend (increasing, decreasing, or stable), because they showed three phases of change with the trend of the middle phase differing from the trend of the first and last phase. Across the total time period, we observed a higher proportion of populations reporting stable or decreasing trends in predominantly Europid than in non-Europid populations (52% v 41%).

Using the second approach to analyse trends of diabetes incidence over time, we modelled 21 studies (62 populations) that reported diabetes counts and denominators specifically within each time period ( table 5 ). The percentage of populations with a decreased or stable incidence was highest in 1980-89 (88%; 7/8), but this proportion was based on only eight populations in three studies. From 1990 onwards, the percentage with decreasing or stable incidence increased progressively, reaching 83% (19/23) of populations in 2006-14. Eight studies (21 populations) that were analysed by Joinpoint had no data on counts or denominators (supplementary table 3). When these data were considered with the data in table 5 , the percentage of populations in 2006-14 with decreasing or stable incidence fell to 70% (19/27), but this proportion was still the highest of all the time periods, whereas the percentage for 1990-99 remained the lowest at 31% (5/16).

Annual percentage change in diabetes incidence in men (M), women (W), or total population (T) among studies that provided counts and denominators, by time period

In a sensitivity analysis, we tested whether our selection of time periods was driving our results. When we defined the final time periods to be 2000-07 and 2008-14, our results were not altered, with 66% (21/32) of the populations in the last time period showing decreasing or stable trends. We also repeated the analysis in table 4 and excluded cohort studies and surveys, and found that the results were not materially altered, with 65% (20/31) of populations in the last time period (from 2006 onwards) showing decreasing or stable incidence of diabetes.

Quality of studies

The median score for study quality was 10 (interquartile range 8-11; supplementary table 4). We repeated the analyses reported in table 4 after excluding studies that had quality scores in the lowest quarter, and observed similar results to the main findings. For example, in 1960-89, 67% (10/15) of populations reported stable or decreasing incidence, while in the final time period, 67% (18/27) of populations reported stable or decreasing incidence of diagnosed diabetes.

Principal findings

In this systematic review of population based studies on diabetes incidence, we show evidence that the incidence of diagnosed diabetes increased in most populations from the 1960s to the early 2000s, after which a pattern emerged of levelling trends in 30% and declining trends in 36% of the reported populations. Although the lack of data for non-Europid populations leaves global trends in incidence unclear, these findings suggest that trends in the diabetes epidemic in some high income countries have turned in a more encouraging direction compared with previous decades. It is important to note that these results apply predominantly to type 2 diabetes, as even though many studies did not accurately define diabetes type, the incidence of type 2 diabetes in adults is an order of magnitude greater than that of type 1 diabetes.

The countries that showed stable or decreasing trends in the last time period were from Europe and east Asia, with no obvious clustering or commonalities. For the countries showing decreasing or stable diabetes trends, if the prevalence data were used to understand the diabetes epidemic in that country, a different message would be obtained. For example, national data from Korea showed that the prevalence of diabetes increased from 2000 to 2010. 59 Similarly in Sweden, the prevalence of pharmacologically treated diabetes increased moderately from 2006 to 2014. 43 In the US, the prevalence of diabetes reached a plateau when incidence began to decrease. However, we lacked incidence data from many areas of the world where the most steady and substantial increases in prevalence have been reported, including the Pacific Islands, Middle East, and south Asia. Large increases in incidence could still be occurring in these areas. The lack of incidence data for much of the world, combined with the common observation of discordance between incidence and prevalence rates where such data exist, both underscore the importance of using incidence data to understand the direction of the diabetes epidemic.

Incidence could be starting to fall for several reasons. Firstly, we might be starting to benefit from prevention activities of type 2 diabetes, including increased awareness, education, and risk factor modification. These activities have involved both targeted prevention among high risk individuals, similar to that conducted in the Diabetes Prevention study 60 and Diabetes Prevention Programme 61 62 in many countries, 63 and less intensive interventions with broader reach such as telephone counselling in the general community. 64 65 67 Secondly, health awareness and education programmes have also been implemented in schools and work places, and many changes to the physical environment, such as the introduction of bike tracks and exercise parks, have occurred. 68 Thirdly, favourable trends in selected risk factors of type 2 diabetes in some countries provide indirect evidence of positive changes to reduce diabetes incidence. Finally, in the US, there is some evidence in recent years of improved diets and related behaviours, which include reductions in intake of sugar sweetened beverages 69 and fat, 70 small declines in overall energy intake, and declines in some food purchases. 8 71

Similar reduction in consumptions of sugar sweetened beverages have occurred in Norway 72 and Australia 73 and fast food intake has decreased in Korea. 74 Some of these changes could be linked to a fall in diabetes incidence. Some places such as Scotland 75 have also had a plateauing of obesity prevalence, but this is not universal. In the US, despite earlier studies suggesting that the rate of increase in obesity might be slowing down, 76 77 more recent data show a small increase. 78 79 While some evidence supports the hypothesis that these prevention activities for type 2 diabetes and an improved environment could trigger sufficient behaviour change to have an effect on diabetes incidence, other data, such as the continuing rising obesity prevalence in the US, 79 casts some doubt over the explanations underpinning our findings on diabetes incidence trends.

Other factors might have also influenced reported diabetes incidence. Only 11% (n=5) of the studies reported here screened for undiagnosed diabetes, and therefore trends could have been influenced by secular changes in diagnostic behaviour. In 1997, the threshold for fasting plasma glucose for diagnosis of diabetes was reduced from 7.8 to 7.0 mmol/L, which could increase diagnosis of new cases of type 2 diabetes. In 2009-10, HbA1c was then introduced as an alternative way to diagnose diabetes. 80 Evidence from some studies suggests that the HbA1c diagnostic threshold detects fewer people with diabetes than do the thresholds for fasting plasma blood glucose, 80 81 potentially leading to a lowering of incidence estimates. However, across multiple studies, prevalence estimates based on fasting plasma glucose only versus HbA1c definitions are similar. 82 Furthermore, because HbA1c can be measured in the non-fasting state (unlike the fasting blood glucose or oral glucose tolerance test), the number of people who actually undergo diagnostic testing could be higher with HbA1c. Nichols and colleagues 56 reported that among seven million insured US adults, despite a shift towards HbA1c as the diagnostic test in 2010, the incidence of diabetes did not change from 2010 to 2011.

Another potential explanation for declining or stable diabetes incidence after the mid-2000s is a reduction in the pool of undiagnosed diabetes 83 through the intensification of diagnostic and screening activities 83 84 and changing diagnostic criteria during the previous decade. 80 Data from Read and colleagues provide some evidence to support this notion. 41

Among the included studies, two studies specifically examined clinical screening patterns in parallel with incidence trends. These studies reported that the proportion of the population screened for diabetes increased over time, and the incidence of diabetes remained stable 56 or fell. 34 While the Karpati study 34 combined data for glucose testing with HbA1c testing, the study by Nichols and colleagues 56 separated the two, and showed that both glucose testing and HbA1c testing increased over time. A third study, in Korea, 36 also noted that the incidence of diabetes decreased in the setting of an increase in the uptake of the national health screening programme. Despite the introduction of HbA1c for diagnosis of diabetes by the World Health Organization, this practice has not been adopted everywhere. For example, neither Scotland nor Hong Kong have introduced the use of HbA1c for screening or diagnosis of diabetes, and studies in these areas showed a levelling of diabetes incidence trends and decreasing trends, respectively.

Our findings appear to contrast with data showing increasing global prevalence of diabetes. 1 3 However, increasing prevalence could be influenced by improved survival of people with diabetes, because this increases the length of time that each individual remains within the diabetes population. As is shown in several studies in this review, 23 41 mortality from diabetes and incidence of diabetes might both be falling but as long as mortality is lower than incidence, prevalence will rise. Therefore, we argue that prevalence alone is an insufficient measure to track the epidemic of diabetes and other non-communicable diseases.

Strengths and weaknesses of this study

A key strength of this work was the systematic approach and robust methodology to describe trends in diagnosed diabetes incidence. We also presented the reported trends allocated to approximate time periods, as well as conducting our own regression within exact time periods. The following limitations should also be considered. Firstly, we did not formally search the grey literature, because a preliminary grey literature search revealed only low quality studies, with inadequate methodological detail to provide confidence in any observed incidence trends, and thus review could be subject to publication bias. Secondly, we were not able to source age or sex specific data on all populations. Thirdly, it was not possible to adjust for different methods of diabetes diagnosis or ascertain trends by different definitions of diabetes. Fourthly, most data sources reported only on clinically diagnosed diabetes and so were subject to influence from diagnostic behaviour and coding practices. Fifthly, study type changed over time, with large administrative datasets becoming more common and cohort studies becoming less common over time. Nevertheless, the size and absence of volunteer bias in administrative datasets likely make them less biased. Finally, data were limited in low and middle income countries.

Conclusions and unanswered questions

This systematic review shows that in most countries for which data are available, the incidence of diagnosed diabetes was rising from the 1990s to the mid-2000s, but has been stable or falling since. Preventive strategies and public health education and awareness campaigns could have contributed to this recent trend. Data are limited in low and middle income countries where trends in diabetes incidence might be different. Improvement of the collection, availability, and analysis of incidence data will be important to effectively monitor the epidemic and guide prevention efforts into the future.

What is already known on this topic

Monitoring of the diabetes epidemic has mainly focused on reporting diabetes prevalence, which continues to rise; however, increasing prevalence is partly driven by improved medical treatment and declining mortality

Studies on diabetes incidence are scarce, but among those that exist, some report a fall or stabilisation of diabetes incidence;

Whether the proportion of studies reporting falling incidence has changed over time is not known

What this study adds

This systematic review of published data reporting diabetes incidence trends over time shows that in most countries with available data, incidence of diabetes (mainly diagnosed diabetes) increased from the 1990s to the mid-2000s, and has been stable or falling since

Preventive strategies and public health education and awareness campaigns could have contributed to this flattening of rates, suggesting that worldwide efforts to curb the diabetes epidemic over the past decade might have been effective

Published data were very limited in low and middle income countries, where trends in diabetes incidence might be different

Acknowledgments

The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the US Centers for Disease Control and Prevention (CDC).

Contributors: MT, DNK, JLH, and RMI are postdoctoral fellows who screened abstracts for selection into the systematic review. JES and DJM also screened abstracts. ELMB applied the quality criteria to the selected articles. RMI extracted data, applied quality criteria to selected articles, and contributed to preparing the manuscript. DJM conceived the project, screened abstracts, extracted the data, analysed the data, and wrote the manuscript. JES, MEP, and EWG conceived the project, edited the manuscript, and provided intellectual input throughout the process. The funder of the study (CDC) was part of the study group and contributed to data collection, data analysis, data interpretation, and writing of the report. The corresponding author had full access to all the data in the study and had final responsibility for the decision to submit for publication. DJM is guarantor. The corresponding author attests that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted.

Funding: Funded by the CDC. The researchers were independent from the funders.

Competing interests: All authors have completed the ICMJE uniform disclosure form at www.icmje.org/coi_disclosure.pdf and declare: support from the CDC for the submitted work; no financial relationships with any organisations that might have an interest in the submitted work in the previous three years; no other relationships or activities that could appear to have influenced the submitted work.

Ethical approval: Not required because this work was a systematic review.

Data sharing: Data are available from the corresponding author ([email protected]).

The lead author affirms that the manuscript is an honest, accurate, and transparent account of the study being reported; that no important aspects of the study have been omitted; and that any discrepancies from the study as planned (and, if relevant, registered) have been explained.

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

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Lifestyle Modification

Bariatric surgery, pharmacological agents, thiazolidinediones, vascular outcomes, translation and cost-effectiveness of diabetes prevention, who should be targeted for diabetes prevention, conclusions, type 2 diabetes prevention: a review.

Leena A. Ahmad, MD, is a fellow in the Department of Medicine, Division of Endocrinology, and Jill P. Crandall, MD, is an associate professor of clinical medicine and director of the Diabetes Clinical Trials Unit at Albert Einstein College of Medicine in Bronx, N.Y.

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Leena A. Ahmad , Jill P. Crandall; Type 2 Diabetes Prevention: A Review. Clin Diabetes 1 January 2010; 28 (2): 53–59. https://doi.org/10.2337/diaclin.28.2.53

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This review offers a discussion of various strategies for the prevention of type 2 diabetes. It includes results from recent clinical trials targeting patients who are at highest risk for the development of diabetes, with a particular emphasis on lifestyle modification strategies and the implementation of such programs in community-based settings.

T ype 2 diabetes has increased dramatically in the past two decades, with 1.6 million cases diagnosed each year in the United States. 1   Diabetes prevalence is highest among the elderly and in certain ethnic groups, especially African Americans, Hispanic Americans, and Native Americans. People with diabetes have a two- to fourfold increased risk of developing cardiovascular disease, peripheral vascular disease, and stroke. These complications account for 65% of mortality from diabetes and, as of 2006, have made diabetes the seventh leading cause of death in the United States. 1 , 2  

Unfortunately, diabetes is often diagnosed relatively late in the course of the disease, at a point when many patients have already developed complications. In addition, management efforts are labor intensive and challenging for both patients and physicians. Furthermore, the economic burden associated with diabetes is substantial, with U.S. costs estimated at $174 billion in 2007 and one of every five health care dollars spent on caring for someone diagnosed with diabetes. 2   The impact of diabetes on individuals' health and its economic burden to society have made its prevention a major goal of the current era.

In the past decade, major advances have been made in our understanding of the prevention of type 2 diabetes. Interventions that can reverse impaired glucose regulation early in its course may be the key to primary prevention of the long-term complications of diabetes.

Type 2 diabetes is a heterogeneous disorder characterized by two interrelated metabolic defects: insulin resistance coupled with impaired insulin secretion by β-cells in the pancreas. 3   Therefore, strategies that target these two mechanisms by improving insulin sensitivity and protecting β-cell function have become the focus of prevention efforts. Weight loss and physical activity, as well as some medications, are thought to improve both insulin sensitivity and secretion. The results of major clinical diabetes prevention trials will be reviewed here.

In the past decade, several randomized, controlled clinical trials have examined the role of diet and exercise in the prevention of type 2 diabetes. 4   One of the earliest studies was conducted in a Chinese community among 577 men and women with impaired glucose tolerance who were randomized to a program of diet, exercise, or both. 5   Dietary intervention focused on increased amounts of vegetables and reduced consumption of alcohol and simple sugars; overweight individuals (those with a BMI > 25 kg/m 2 ) were encouraged to lose weight. The exercise group was instructed to increase their daily activity by the equivalent of 20 minutes of moderate activity, such as brisk walking, and the diet-plus-exercise group was asked to do both exercise and dietary modification.

After 6 years of follow-up, all three interventions were similarly effective, with risk reductions of 31–46% compared to an untreated control group. During long-term follow-up of this cohort, most participants had progressed to diabetes, although diabetes prevalence was still lower in the former intervention groups (80% compared to 93% in the placebo group). 6  

More recently, the Finnish Diabetes Prevention Study (DPS) 7   randomized 522 overweight (average BMI 31 kg/m 2 ) middle-aged individuals to either intensive lifestyle modification or a control group. The former entailed both specific dietary recommendations and exercise guidelines, including a weight-loss goal of 5% of total body weight and at least 30 minutes per day of combined aerobic activity and resistance training.

This study demonstrated a clinically significant impact of intensive lifestyle changes in the reduction of diabetes. At the 3-year follow-up, the group reduced their cumulative risk by 58% compared to the control subjects. During the first year, the intervention group lost an average of 4.2 kg, which appeared to be the primary mediator of diabetes risk reduction. Further analysis demonstrated the impact of exercise on the risk reduction of diabetes: moderate to vigorous activity of at least 2.5 hours per week reduced the incidence of diabetes by 63–69%. In the extended follow-up (3 years after the active intervention was completed), the intensive lifestyle group maintained a 36% relative reduction in diabetes incidence, suggesting that these benefits could be maintained outside of a structured clinical trial setting. 8  

The largest clinical trial to date to study lifestyle intervention for the prevention of diabetes was the Diabetes Prevention Program (DPP). 9   The DPP randomized 3,234 overweight participants with impaired glucose tolerance and elevated fasting glucose from 22 sites in the United States to one of three interventions: intensive lifestyle intervention (ILS), metformin, or placebo. The participants were mostly middle aged and had an average BMI of 34 kg/m 2 . Forty-five percent were from ethnic and racial minority groups known to be at high risk for diabetes. The ILS group was instructed to follow a low-calorie, low-fat diet, with a weight-loss target of 7% of baseline body weight and an exercise goal of at least 150 minutes per week of moderate-intensity physical activity. The ILS group participated in a 16-week core curriculum focused on behavior modification, diet, and exercise education during the first 24 weeks, followed by at least monthly reinforcement.

After an average follow-up of 2.8 years, the ILS group achieved a mean weight loss of 7%, and three-fourths of the participants met the exercise targets during the first 6 months of the study. The ILS group had a 58% reduction in the development of diabetes compared to the placebo group. Weight loss was the predominant predictor of reduced diabetes incidence, with a 16% reduction of developing diabetes for each kilogram of weight lost. However, participants who did not achieve their weight-loss targets but were able to achieve the exercise goal also benefited (44% risk reduction compared to placebo). The effectiveness of the ILS intervention was similar in men and women and among racial and ethnic groups. The greatest risk reduction was in participants older than 60 years of age, most likely because they achieved the biggest weight loss and the greatest increase in physical activity. 10  

After completion of the initial masked phase of the DPP, all participants were offered the ILS program in a group session format and then were enrolled in the DPP Outcome Study (DPPOS), which aimed to examine whether the diabetes prevention was sustainable over time. During DPPOS, all participants were provided with quarterly lifestyle sessions, and the original ILS subjects received additional group classes.

Results from an additional 6.8 years of follow-up in DPPOS were recently published. 11   After a median total follow-up of 10 years, the ILS group, which had initially lost ~7 kg in the first year of the DPP, weighed 2 kg less on average than at DPP randomization. During DPPOS, diabetes incidence rates in the metformin and former placebo groups fell to equal those in the former ILS group, but the cumulative incidence remained lowest in the ILS group (34% risk reduction compared with placebo).

These results demonstrate that prevention or delay of diabetes achieved through lifestyle change can persist for at least 10 years. Furthermore, the decrease in diabetes incidence rates among former metformin and placebo groups suggests that lifestyle intervention provided in a group format is an effective approach.

Studies conducted in Japanese and Indian populations have also demonstrated the effectiveness of lifestyle modification in the prevention of diabetes. 12 , 13  

Bariatric surgery as a means of achieving weight loss has proven to be successful in diabetes prevention. In one prospective trial of > 2,000 patients who underwent a variety of surgical procedures (most commonly, vertical banded gastroplasty) and a matched standard-care control group, the risk of diabetes in the surgical group was reduced by 86% at 2 years and 75% at 10 years of follow-up. None of those who lost at least 12% of their baseline weight developed diabetes, compared to 7% of those with 2% weight loss and 9% of those who gained weight. 14 , 15  

Bariatric surgery has also been reported to induce remission of existing diabetes. In a randomized, controlled trial of gastric banding versus conventional diet therapy, 73% of surgical patients achieved a remission compared to 13% of control subjects. 16   Gastric banding procedures improve glycemic control in patients with established diabetes, further supporting the potential benefit in diabetes prevention for appropriately selected patients. 17  

Although moderate-intensity exercise and weight loss clearly have been shown to be effective in reducing diabetes risk, not all patients are able to achieve these lifestyle goals. For these patients and those who progress despite successful weight loss, additional therapeutic options are needed. Several pharmacological agents have been studied in clinical diabetes prevention trials.

Metformin is the most widely studied drug for diabetes prevention. In the DPP, participants randomized to metformin (850 mg, twice daily) achieved a 31% reduction in diabetes compared to placebo. 9   Metformin was most effective in more obese participants (baseline BMI > 35 kg/m 2 ), who experienced a 53% reduction of diabetes incidence, and in participants < 45 years of age, who saw a 44% reduction. Metformin had little benefit for older individuals who were 60–85 years of age at baseline. The effectiveness of metformin was attributed in part to weight loss, which averaged 1.7 kg and accounted for 64% of the beneficial effect of metformin. 9   Importantly, after an average of 10 years of follow-up, the metformin group had maintained an average weight loss of 2.5 kg, and diabetes risk was reduced by 18% compared to the former placebo group. 11   Smaller studies conducted in India and China reported similar reductions in diabetes risk. 13 , 18  

In general, metformin is widely available, inexpensive, and relatively well tolerated. These studies suggest that this medication is an appropriate treatment approach in appropriately selected patients, especially those who are younger and overweight.

The α-glucosidase inhibitor acarbose was studied in the Study to Prevent Non-Insulin-Dependent Diabetes (STOP-NIDDM) trial, which randomized 1,429 participants with impaired glucose tolerance to either acarbose, 100 mg, or placebo three times daily for a mean of 3.3 years. 19   In this study, subjects in the acarbose treatment arm had a 25% reduction in the incidence of diabetes. However, almost one-third of the acarbose group was unable to complete the study because of gastrointestinal side effects, which makes the results of the study difficult to interpret and the applicability to clinical care unclear.

The thiazolidinediones (TZDs) have also been studied as potential agents for diabetes prevention. In the first year of the DPP, diabetes incidence was reduced by 75% in the troglitazone arm before it was discontinued because of evidence of hepatotoxicity. 20   Troglitazone was also studied in a cohort of women with recent gestational diabetes and reduced diabetes by ~50% compared to untreated controls. 21   Rosiglitazone was studied in the Diabetes Reduction Assessment with Ramipril and Rosiglitazone Medication (DREAM) trial, 22   a large, international study that randomized high-risk patients (impaired fasting glucose, impaired glucose tolerance, or both) to rosiglitazone, 8 mg daily, or placebo. After an average follow-up of 3 years, the incidence of diabetes in the rosiglitazone group was reduced by 62% compared to placebo. Glucose intolerance was normalized in 50% of the rosiglitazone group compared to only 30% in the placebo group.

However, rosiglitazone does have well-known side effects, such as weight gain and peripheral edema; in the DREAM trial, the TZD group gained 2.2 kg more weight than the placebo group. Additional concerns include the controversy surrounding the potential cardiotoxicity of rosiglitazone and a report of increased fractures in women taking this medication, both of which have diminished enthusiasm for its routine use in diabetes prevention. 23 , 24  

Although delay of the diagnosis of diabetes is the primary outcome in all diabetes prevention studies, the critical clinical issue is the prevention of the micro- and macrovascular complications of diabetes. Indeed, these complications account for the morbidity and mortality of the disease, and the ultimate goal of diabetes prevention is to avoid these devastating outcomes.

Investigators from the STOP-NIDDM trial reported a 49% reduction in cardiovascular events in the acarbose-treated group during the 3.3 years of follow-up, but the number of events was small, and this finding remains to be confirmed. 25   Cardiovascular disease risk markers were improved in the ILS group in the DPP, including lipoproteins, C-reactive protein, and fibrinogen. 26   During long-term follow-up, this group continued to show improvements in both lipids and blood pressure measurements, despite the fact that they were receiving less drug treatment for these conditions. 11   Longer-term follow-up of the DPP cohort may provide more definitive data on cardiovascular and microvascular outcomes.

The protocols employed in most lifestyle intervention trials are labor intensive and require dedicated staff and resources, raising issues about the economics of implementing these programs. Analyses of the costs of various strategies are conflicting, and two fundamental questions have emerged. First, if we elect to treat prediabetes, which of the strategies is the most cost-effective? Second, is it more economically prudent to start such a program in patients who are at high risk for diabetes, or should treatment be initiated only after diabetes has developed?

The DPP investigators analyzed the cost per quality-adjusted life year (QALY), comparing the lifestyle and metformin interventions to placebo. 27   The cost per QALY for the ILS intervention was ~ $1,100 compared to $31,300 for the metformin intervention. This led investigators to conclude that, compared to placebo, the ILS intervention was not only the most effective treatment for diabetes prevention, but also the most cost-efficient. Furthermore, when compared to other well-accepted interventions, they concluded that both DPP interventions would be cost-effective from societal and health system perspectives.

However, another analysis concluded that such programs are too expensive for widespread implementation and suggested that it may be preferable to delay intervention until diabetes is diagnosed. 28   Much of the discrepancy between these analyses derives from varying assumptions about rates of progression to diabetes and its complications and differences in analytic approach. However, cost-benefit analyses have been reported from other diabetes prevention trials with generally favorable results. 29 , 30  

Resources for Implementing Lifestyle Modification

Lifestyle intervention has been conclusively proven effective in reducing diabetes risk, but for such an approach to be broadly implemented, it must be translated into community-based settings that are both accessible and affordable. Although such translation efforts are in their infancy, a number of significant efforts have been initiated ( Table 1 ).

Finnish investigators have developed a community-based model for intensive lifestyle intervention called Good Ageing in Lahti (GOAL). 31   This program identified high-risk participants from Finnish primary care settings and enrolled them in six 2-hour group counseling sessions that were based on a social-cognitive health behavior model and led by public health nurses. 32 , 33   Although the results of the GOAL trial were not as robust as the DPS in terms of meeting weight-loss and physical-activity targets (12 versus 43% and 65 versus 86%, respectively), this primary care–based program demonstrated a significant reduction in weight and BMI in high-risk individuals. Of the participants who had impaired glucose tolerance at baseline, 12% went on to develop type 2 diabetes at 3 years, and 43% returned to normal glucose tolerance.

Marrero and Ackermann developed a community-based program closely modeled after the DPP ILS for implementation at local YMCAs. 34   This program included a three-step approach: a 16-week core curriculum, a 4-week “training and refinement” phase, and a long-term maintenance phase. The core curriculum included weekly small-group sessions focused on mapping out explicit exercise plans and building problem-solving skills. In the second phase, participants met twice weekly with either a training partner or as a group to exercise. In the maintenance phase, monthly meetings included participants and their family members and addressed common barriers to weight loss and exercise (e.g., holidays and restaurant meals) and used many of the same tools as the original DPP.

High-risk individuals randomized to the group lifestyle program achieved a mean weight loss of 6% compared to only a 2% weight loss in a control group, which was sustained at 12 months. 35   Furthermore, the intervention group had a significantly reduced estimated 10-year risk of coronary heart disease (based on blood pressure, lipid levels, and A1C), supporting the potential for this community-based program to delay or prevent not only the onset of diabetes, but also the associated cardiovascular complications. 36   The cost per person to implement this type of community lifestyle intervention program was estimated at between $275 and $325 annually compared to the original DPP ILS intervention cost of $1,400 per participant for the first year. 37 , 38   This provides strong evidence that dissemination of the DPP lifestyle intervention in a well-established community organization is feasible and can be cost-effective.

There are similar group-based lifestyle intervention programs underway in communities throughout the United States. A recent review examined several such programs that were implemented in a wide variety of environments, including a rural Southern church community and an inner-city urban population in the Northeast. 39   Although the programs varied in length and target population, all reported significant weight loss and increased physical activity.

One of the larger translation efforts was reported by the Montana Diabetes Control Program, which collaborated with four health care facilities (urban and rural) to implement a group-based lifestyle program based on the DPP. This effort produced weight-loss results comparable to the DPP (mean weight loss 6.7 kg at 6 months), and most participants also achieved physical-activity goals. 40  

Such results reinforce the feasibility of effective community-based lifestyle intervention strategies for diabetes prevention in diverse populations and in varied settings. However, much remains to be done to gain commitment from insurers and health care systems to ensure broad implementation for high-risk populations.

Recommendations for Screening for Pre-Diabetes and Diabetes 41  

The first step in diabetes prevention is identifying patients who are at highest risk. This group includes individuals of any age who are overweight and obese (BMI > 25 kg/m 2 ) with at least one risk factor (such as high-risk ethnic group, first-degree relative with diabetes, personal history of gestational diabetes, or sedentary lifestyle). The American Diabetes Association (ADA) recommends that these patients should be screened every 3 years ( Table 2 ). All other patients should begin screening at the age of 45 years. 41  

The laboratory diagnosis of “at risk” has traditionally been determined by the presence of impaired fasting glucose or impaired glucose tolerance. However, the current ADA clinical practice recommendations recommend that A1C measurement may be used as a screening tool, with levels between 5.7 and 6.4% defining those at highest risk for diabetes. 41   This simple blood test is readily available in most primary care settings, can be performed regardless of fasting status, and has the potential to more easily identify patients who would benefit from diabetes prevention measures. Validation of this approach remains to be completed, however.

Recent clinical trials have convincingly shown that lifestyle modification is the most effective tool in the prevention or delay of type 2 diabetes. For overweight and obese patients, a modest weight-loss goal of 5–10% (often < 20 lb) can substantially reduce the risk of diabetes. Moderate-intensity physical activity such as brisk walking for at least 150 minutes per week also plays an important role in reducing diabetes risk, even in the absence of weight loss ( Table 3 ).

Recommendations and Resources for Lifestyle Modification for Diabetes Prevention

For patients who are unable to achieve these lifestyle goals or those who progress despite exercising and losing weight, metformin has also been proven effective, especially in younger obese patients. Acarbose, when tolerated at the maximum effective dose, may also confer a moderate risk reduction. Data regarding thiazolidinediones are conflicting, and the reports of cardiovascular and fracture risk make this option less attractive as a prevention strategy. However, none of these medications are as robust in diabetes prevention as the lifestyle intervention strategies, and cost-effectiveness analyses suggest that pharmacotherapy may have greater financial costs.

Perhaps the most pressing clinical question remaining is whether these prevention strategies will reduce the vascular complications of diabetes that are the cause of the greatest financial burden and personal suffering in patients with diabetes. Prevention of diabetes is our most powerful intervention, and successful implementation of these proven strategies should be the focus of our efforts.

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• Introduction
• Conclusions
• Article Information

Squares indicate IRRs, with horizontal lines indicating 95% CIs and the size of the squares representing weight; diamonds indicate pooled estimates, with outer points of the diamonds indicating 95% CIs. DKA indicates diabetic ketoacidosis; NA, not applicable; T1D, type 1 diabetes.

eTable 1. Subject Database and Gray Literature Search Strategies

eTable 2. Risk-of-Bias Evaluation Criteria Domains

eTable 3. Risk-of-Bias Assessments for Included Studies, Using the ROBINS-E Tool

eFigure. Rate Ratios Reported in the Meta-analysis by Rahmati et al, by Length of the Pandemic Observation Period

Data Sharing Statement

• Investigating the Increase in Childhood Type 1 Diabetes During COVID-19 JAMA Network Open Invited Commentary June 30, 2023 Clemens Kamrath, MD; Reinhard W. Holl, MD; Joachim Rosenbauer, MD

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D’Souza D , Empringham J , Pechlivanoglou P , Uleryk EM , Cohen E , Shulman R. Incidence of Diabetes in Children and Adolescents During the COVID-19 Pandemic : A Systematic Review and Meta-Analysis . JAMA Netw Open. 2023;6(6):e2321281. doi:10.1001/jamanetworkopen.2023.21281

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Incidence of Diabetes in Children and Adolescents During the COVID-19 Pandemic : A Systematic Review and Meta-Analysis

• 1 Child Health Evaluative Sciences, SickKids Research Institute, Toronto, Ontario, Canada
• 2 Department of Pediatrics, University of Toronto, Toronto, Ontario, Canada
• 3 Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Ontario, Canada
• 4 E.M. Uleryk Consulting, Mississauga, Ontario, Canada
• 5 Edwin S.H. Leong Centre for Healthy Children, University of Toronto, Toronto, Ontario, Canada
• 6 Division of Endocrinology, Hospital for Sick Children, Toronto, Ontario, Canada
• Invited Commentary Investigating the Increase in Childhood Type 1 Diabetes During COVID-19 Clemens Kamrath, MD; Reinhard W. Holl, MD; Joachim Rosenbauer, MD JAMA Network Open

Question   Was there a change in the incidence of diabetes in children and adolescents after the onset of the COVID-19 pandemic?

Findings   In this systematic review and meta-analysis of 42 studies including 102 984 youths, the incidence of type 1 diabetes was higher during the COVID-19 pandemic compared with before the pandemic.

Meaning   The findings suggest the need to elucidate possible underlying mechanisms to explain temporal changes and increased resources and support for the growing number of children and adolescents with diabetes.

Importance   There are reports of increasing incidence of pediatric diabetes since the onset of the COVID-19 pandemic. Given the limitations of individual studies that examine this association, it is important to synthesize estimates of changes in incidence rates.

Objective   To compare the incidence rates of pediatric diabetes during and before the COVID-19 pandemic.

Data Sources   In this systematic review and meta-analysis, electronic databases, including Medline, Embase, the Cochrane database, Scopus, and Web of Science, and the gray literature were searched between January 1, 2020, and March 28, 2023, using subject headings and text word terms related to COVID-19, diabetes, and diabetic ketoacidosis (DKA).

Study Selection   Studies were independently assessed by 2 reviewers and included if they reported differences in incident diabetes cases during vs before the pandemic in youths younger than 19 years, had a minimum observation period of 12 months during and 12 months before the pandemic, and were published in English.

Data Extraction and Synthesis   From records that underwent full-text review, 2 reviewers independently abstracted data and assessed the risk of bias. The Meta-analysis of Observational Studies in Epidemiology ( MOOSE ) reporting guideline was followed. Eligible studies were included in the meta-analysis and analyzed with a common and random-effects analysis. Studies not included in the meta-analysis were summarized descriptively.

Main Outcomes and Measures   The primary outcome was change in the incidence rate of pediatric diabetes during vs before the COVID-19 pandemic. The secondary outcome was change in the incidence rate of DKA among youths with new-onset diabetes during the pandemic.

Results   Forty-two studies including 102 984 incident diabetes cases were included in the systematic review. The meta-analysis of type 1 diabetes incidence rates included 17 studies of 38 149 youths and showed a higher incidence rate during the first year of the pandemic compared with the prepandemic period (incidence rate ratio [IRR], 1.14; 95% CI, 1.08-1.21). There was an increased incidence of diabetes during months 13 to 24 of the pandemic compared with the prepandemic period (IRR, 1.27; 95% CI, 1.18-1.37). Ten studies (23.8%) reported incident type 2 diabetes cases in both periods. These studies did not report incidence rates, so results were not pooled. Fifteen studies (35.7%) reported DKA incidence and found a higher rate during the pandemic compared with before the pandemic (IRR, 1.26; 95% CI, 1.17-1.36).

Conclusions and Relevance   This study found that incidence rates of type 1 diabetes and DKA at diabetes onset in children and adolescents were higher after the start of the COVID-19 pandemic than before the pandemic. Increased resources and support may be needed for the growing number of children and adolescents with diabetes. Future studies are needed to assess whether this trend persists and may help elucidate possible underlying mechanisms to explain temporal changes.

Diabetes is a common chronic disease in children. 1 , 2 Several studies have reported an increased incidence of types 1 and 2 diabetes in children since the COVID-19 pandemic. 3 , 4 Some studies reported an association between SARS-CoV-2 infection and new-onset diabetes. 5 , 6 However, given the challenges of ascertaining a SARS-CoV-2 infection, there are concerns about the validity of these studies. Furthermore, there is no clear mechanism by which COVID-19 could directly or indirectly lead to new-onset type 1 or 2 diabetes. 7 The pathophysiology of types 1 and 2 diabetes are distinct, as are the theoretical pathways by which COVID-19 might cause them 8 ; therefore, it is important to determine whether there has been an increased incidence rate of 1 or both types of diabetes.

The examination of diabetes incidence rates during the pandemic is nuanced because there was a preexisting increase of 3% to 4% in the annual incidence rate of type 1 diabetes reported in European countries, 9 seasonality to diabetes incidence, 10 , 11 and variability in the reported incidence rates between early and later months during the pandemic. 12 , 13 It is important to establish whether the reported increased incidence rates of new-onset diabetes in children are overall higher and sustained or a result of a catch-up effect from a lower incidence rate early in the pandemic likely due to delays in diagnoses. 7 , 14

A recent review and meta-analysis 4 that pooled results of 8 studies reported that the incidence rate of type 1 diabetes was higher during the pandemic in 2020 (32.39 per 100 000 children) compared with the same period prior to the pandemic in 2019 (19.73 per 100 000 children). An important limitation of that meta-analysis is that it only included studies conducted during the first wave of the pandemic. There may have been a lower incidence rate early in the pandemic and a higher incidence rate later in the pandemic due, in part, to the absence of an expected seasonal decline in summer months. 12 Importantly, the meta-analysis 4 only examined the incidence rate of type 1 diabetes in children. It is plausible that the increase in sedentary behavior observed during the COVID-19 pandemic due to school closures and lockdown measures was associated with the increased prevalence of childhood obesity, a known risk factor for type 2 diabetes. 15 , 16 In addition to reports of an increased incidence rate of diabetes, there have also been consistent reports of an increased risk of diabetic ketoacidosis (DKA), a preventable and life-threatening condition, at diabetes onset in children during the pandemic. 4 , 17 , 18

It is critical to know whether there was a sustained change in the incidence rates of both type 1 and type 2 diabetes in children because there are important implications for health resource planning for pediatric diabetes care, COVID-19–related and future pandemic-related public health measures, and immunization strategies. The primary objective of this systematic review and meta-analysis was to investigate whether there was a change in the incidence rate of types 1 and 2 diabetes in children and adolescents during the COVID-19 pandemic compared with before the pandemic. The secondary objective was to assess whether there was a change in the incidence rate of DKA among youths with new-onset diabetes during the COVID-19 pandemic.

We prospectively registered this systematic review and meta-analysis on the PROSPERO database. The study followed the Meta-analysis of Observational Studies in Epidemiology ( MOOSE ) reporting guideline. 19

We searched Medline (all segments), Embase, the Cochrane database, Scopus, and Web of Science for studies published from January 1, 2020, to March 28, 2023, in English. Our search strategy included subject headings and text word terms for COVID-19 and ( diabetes type 1 or 2 or diabetic ketoacidosis ) and incidence (eTable 1 in Supplement 1 ). We also conducted a gray literature search to identify studies published on government websites by searching for a combination of COVID and diabetes and statistical terms. We hand-searched the reference lists of all included studies and relevant systematic reviews.

Studies were included if they (1) reported the number of incident cases of type 1 or 2 diabetes during the COVID-19 pandemic and before the pandemic in children and adolescents younger than 19 years, (2) had a minimum study period of 12 months prior to and during the COVID-19 pandemic, and (3) were published in English. Two reviewers (D.D., J.E.) used Covidence software 20 to determine study eligibility. Conflicts were resolved by consensus or, if needed, in discussion with a third reviewer (R.S.). Interrater agreement at the screening and full-text stages was 95% and 90%, respectively.

We extracted the number of incident types 1 and 2 diabetes cases, study population size, and incidence rates of types 1 and 2 diabetes and DKA at diabetes diagnosis in the prepandemic and pandemic periods. The start of the pandemic period was defined according to the definition in each study. Two independent reviewers (D.D., J.E) extracted the data. Conflicts were resolved by consensus. Intercoder agreement was greater than 95%.

We used the Risk of Bias in Non-randomized Studies of Exposure 21 tool to assess the risk of bias in 7 domains (eTable 2 in Supplement 1 ). Two independent reviewers (D.D., J.E.) assessed the risk of bias for each of the included studies; conflicts were resolved by consensus or by a third reviewer (R.S).

We included studies in the meta-analysis if they reported the number of incident diabetes cases and the size of the study population for a minimum 12-month prepandemic period and a 12-month pandemic period. If those data were not reported, we contacted the corresponding author, requesting for them to share the data. If the study did not report the denominator (ie, study population) and we were unable to obtain it from the corresponding author, we included the study in a descriptive summary but excluded it from the meta-analysis because studies with missing denominators are likely to be of lower quality and, therefore, are not missing at random. 22 Also, we wanted to focus the meta-analysis on the highest-quality studies. Studies with both pediatric and adult participants but no subgroup analysis for individuals younger than 19 years were included in the descriptive summary.

The number of incident cases and the size of the study population during the 12 months preceding the start of the pandemic period and the first 12 months following the start of the pandemic period were used to calculate the incidence rate ratio (IRR), the pooled IRR, and the corresponding 95% CIs. We conducted a meta-analysis of IRRs using common and random-effects approaches. Statistical heterogeneity was measured using the I 2 statistic, and we assessed the statistical significance of between-study variation using a 2-sided P value of <.05.

Although some studies reported diabetes incidence for longer than 12 months in the prepandemic period, we included only data from 12 months preceding the start of the pandemic period in the meta-analysis because prepandemic diabetes incidence is known to have followed a seasonal pattern. 23 , 24 Studies that had pandemic periods longer than 12 months are described in the narrative summary. Because seasonality changed during the pandemic, 23 , 24 we conducted a post hoc additional analysis including only studies that reported more than 12 months of pandemic data, in which we compared incidence in the 12 months before the pandemic vs the first 12 months of the pandemic vs the second 12 months of the pandemic or the end of follow-up, whichever came first. We used the meta package in R, version 4.2.2 (R Project for Statistical Computing) for data analysis. 25 , 26

We identified 10 757 records, of which 4353 were duplicates ( Figure 1 ). After the abstract review, we retrieved 81 full-text articles to determine eligibility. Forty-two records met the full inclusion criteria. 3 , 13 , 23 , 24 , 27 - 64 The manual search of the included studies’ reference lists did not yield additional studies.

Among the 42 included studies, there were 102 984 incident diabetes cases across both the prepandemic and the pandemic periods ( Table 1 ). Twenty-four studies (57.1%) reported DKA incidence at diagnosis. 3 , 5 , 6 , 8 , 9 , 12 , 14 , 18 , 19 , 22 , 25 , 27 - 31 , 34 , 35 , 37 , 38 , 40 , 42 , 48 , 49 Incident cases of type 1 and type 2 diabetes were reported in 36 studies (85.7%) 12 , 23 , 24 , 27 - 33 , 35 - 42 , 44 - 56 , 58 - 61 , 63 and 9 studies (21.4%), 3 , 33 , 34 , 39 , 43 , 45 , 46 , 55 , 62 respectively. Two studies (4.8%) did not distinguish between diabetes types. 13 , 57 Thirty-two studies (76.2%) included children only, 3 , 12 , 13 , 23 , 24 , 27 - 32 , 35 - 42 , 44 , 48 - 53 , 56 - 58 , 60 , 61 , 63 while the rest (10 [23.8%]) included both children and adults. 33 , 34 , 43 , 45 - 47 , 54 , 55 , 59 , 62 Twenty-one studies (50.0%) were from Europe, 12 , 23 , 30 - 32 , 35 , 41 , 42 , 44 , 47 - 53 , 56 , 58 , 59 , 61 , 63 12 (28.6%) from North America, 3 , 13 , 33 , 34 , 38 , 39 , 43 , 45 , 46 , 54 , 57 , 62 7 (16.7%) from Asia, 27 - 29 , 36 , 37 , 40 , 60 and 1 (2.4%) from Australia 55 ; 1 study (2.4%) 24 included data from multiple countries across different continents. Nine studies (21.4%) reported either the race or ethnicity of the study population, 3 , 34 , 39 , 43 , 45 - 47 , 54 , 62 and 1 study reported socioeconomic status. 29 All included studies were assessed to have an overall risk of bias rating of “some” (eTable 3 in Supplement 1 ).

In a random-effects meta-analysis of pooled data from 17 studies (40.5%) including 38 149 children and adolescents with newly diagnosed type 1 diabetes, there was a higher incidence rate of type 1 diabetes during the first year of the pandemic period compared with the prepandemic period (IRR, 1.14; 95% CI, 1.08-1.21) ( Figure 2 A). 13 , 23 , 24 , 27 , 31 , 32 , 38 , 39 , 49 - 51 , 53 , 55 , 58 - 60 , 63 We excluded 2 studies 12 , 65 from the meta-analysis because they contained overlapping data with more recent studies included in the meta-analysis. The data used to calculate the IRRs are available in Table 2 . The unadjusted pooled IRR comparing the first year of the pandemic with the prepandemic period was 1.13 (95% CI, 1.11-1.16). Between-study heterogeneity was moderate ( I 2  = 66%). 22 In our post hoc additional analysis, among studies that reported more than 12 months after pandemic onset, there was an increased incidence of diabetes during months 13 to 24 of the pandemic compared with the prepandemic period (IRR, 1.27; 95% CI, 1.18-1.37) ( Figure 2 B). 23 , 32 , 49 , 52 , 53 , 58 - 60 , 63 The results of the remaining 20 studies, which reported the number of incident type 1 diabetes cases but were not included in the meta-analysis because they did not report the size of the study population, are summarized in Table 3 . 24 , 28 - 30 , 33 , 35 - 37 , 40 - 42 , 44 - 48 , 54 , 57 , 61 , 64 Of these, 15 (75.0%) reported an increase in the number of incident cases of type 1 diabetes during the first 12 months of the pandemic compared with during the 12 months before the pandemic. 33 , 35 - 37 , 40 - 42 , 44 - 48 , 54 , 61 , 64

Ten of 42 studies (23.8%) reported the number of incident type 2 diabetes cases 3 , 33 , 34 , 39 , 43 , 45 , 46 , 55 , 57 , 62 ; however, only 1 of those (10.0%) reported the size of the study populations. 55 Therefore, we were unable to conduct a meta-analysis comparing the incidence rate of type 2 diabetes between periods. We summarize the results of these studies in Table 3 . Eight studies (80.0%) reported an increase in the number of incident cases of type 2 diabetes during the first 12 months of the pandemic compared with during the 12 months before the pandemic. 3 , 33 , 34 , 39 , 43 , 45 , 46 , 62

In a random-effects meta-analysis of pooled data from 15 studies (35.7%) including a total of 4324 children and adolescents with DKA, the incidence rate of DKA was higher during the pandemic period compared with the prepandemic period (IRR, 1.26; 95% CI, 1.17-1.36) ( Figure 2 C). 27 , 29 , 36 , 37 , 40 - 42 , 44 - 47 , 49 , 50 , 54 , 65 Between-study heterogeneity was minimal ( I 2  = 0%).

In this systematic review and meta-analysis, in 17 studies including 38 149 children and adolescents with newly diagnosed type 1 diabetes, 13 , 23 , 24 , 27 , 31 , 32 , 38 , 39 , 49 - 51 , 53 , 55 , 58 - 60 , 63 we found that the incidence rate of type 1 diabetes was 1.14 times higher in the first year and 1.27 times higher in the second year after the onset of the COVID-19 pandemic compared with before the pandemic. In 15 studies including a total of 4324 children and adolescents with DKA, 27 , 29 , 36 , 37 , 40 - 42 , 44 - 47 , 49 , 50 , 54 , 65 we also found that the incidence rate of DKA at diagnosis was 1.26 times higher in the first year after the onset of the COVID-19 pandemic compared with before the pandemic. The magnitude of increase in the incidence rate of type 1 diabetes that we observed after the onset of the pandemic was greater than the expected 3% to 4% annual increase in the incidence rate based on prepandemic temporal trends in Europe. 9

Our findings are similar to those of another recent meta-analysis by Rahmati et al 4 that examined the incidence rate of type 1 diabetes and ketoacidosis in children during the COVID-19 pandemic in 2020 and during the same period in 2019. We compared the rate ratios reported in that meta-analysis by the length of their pandemic observation period. We found that studies with a pandemic period of 6 months or less had a lower estimated incidence rate compared with studies with a pandemic period of 12 months or greater (eFigure in Supplement 1 ). Our systematic review adds important new information because it included studies that examined the incidence of both types 1 and 2 diabetes in children and adolescents, included additional data from later in the pandemic, and required at least 12 months of observation in both the pandemic and the prepandemic periods to account for the prepandemic seasonality of diabetes incidence and changes in seasonality during the pandemic that differed between Europe and North America. 23 , 24

We found substantial heterogeneity in the meta-analysis of diabetes incidence but not in the meta-analysis of DKA incidence. It is presumptive to assume why this occurred; however, some potential explanations include that higher within-study variation in the DKA meta-analysis may have resulted in a lower I 2 value, 66 and other demographic, geographical, and methodologic factors may have led to increased heterogeneity between studies in the diabetes incidence meta-analysis.

Purported direct mechanisms to explain the association between new-onset diabetes and prior SARS-CoV-2 infection include evidence that the SARS-CoV-2 entry receptor ACE2 is expressed on insulin-producing β cells, SARS-CoV-2 infection contributes to dysregulation of glucose metabolism, and individuals who have an increased susceptibility to diabetes are especially vulnerable following SARS-CoV-2 infection because dysregulated glucose metabolism and direct viral damage to β cells impairs their compensatory mechanisms, leading to β-cell exhaustion. 7 However, there is no clear underlying mechanism explaining the association between SARS-CoV-2 infection and subsequent increased risk of incident diabetes. 7 , 8 While there are reports of an association between SARS-CoV-2 infection and subsequent increased risk of incident type 1 diabetes in children using routinely collected health record data, 5 , 6 , 67 there are concerns about the validity of such studies because the data sets used did not capture asymptomatic SARS-CoV-2 infections in children. Population-based studies that reported an increased incidence rate of type 1 diabetes in children and adolescents during the pandemic did not find an increase in the frequency of autoantibody-negative type 1 diabetes 12 , 23 , 68 ; this suggests that the increase in incidence may be due to an immune-mediated mechanism.

Proposed indirect effects of the COVID-19 pandemic and containment measures that may be associated with diabetes incidence include changes in lifestyle, change in the pattern of pediatric non–COVID-19 infections, and increased stress and social isolation. 12 , 69 - 71 It has been proposed that frequent respiratory or enteric infections in children are potential triggers for islet autoimmunity, promote progression to overt type 1 diabetes, or are precipitating stressors. 72 Pandemic containment measures were associated with a decrease in viral respiratory and gastrointestinal tract infections among children. 69 Given this finding, the observed increased incidence rate of type 1 diabetes during the pandemic is contrary to what would be expected based on the decrease in viral infections among children during the pandemic.

There may have initially been a catch-up effect caused by lower incidence rates of pediatric diabetes early in the pandemic, possibly due to delays in diagnoses associated with hesitancy to seek care or barriers to access care. 12 - 14 However, the reported incidence of diabetes remained increased in studies that included data from beyond the first year of the pandemic. 23 , 32 , 49 , 52 , 53 , 58 - 60 , 63 Furthermore, there appears to have been a disruption to the historic seasonal pattern of autoantibody-positive diabetes incidence in children. 23 , 24 The reasons for this remain uncertain but may be related to the effects of COVID-19 containment strategies, such as lockdowns, both at the beginning of the pandemic and at subsequent times in different countries. 73

There are limited data about the change in the incidence rate of pediatric type 2 diabetes during the COVID-19 pandemic. The studies included in this systematic review and meta-analysis described an increase in the number of incident type 2 diabetes cases between periods but had insufficient data reported to assess whether there was also an increase in the incidence rate of childhood type 2 diabetes after the onset of the pandemic. Population-based studies that can measure the size of the study population (denominator) and therefore determine whether there has been a change in the incidence rate of type 2 diabetes in children and adolescents since the onset of the COVID-19 pandemic are needed.

We found an increased incidence rate of DKA at diabetes diagnosis among children and adolescents during the pandemic. This is concerning because DKA is preventable and an important cause of morbidity and mortality and is associated with long-term poor glycemic management. 74 , 75 An international study that used data from 13 pediatric diabetes registries reported a prevalence of DKA at diagnosis in 2020 and 2021 that was higher than the predicted prevalence based on prepandemic years 2006 to 2019. 76 A population-based study in Germany 77 found that the regional incidence of COVID-19 cases and deaths was associated with an increased risk of DKA at diagnosis, suggesting that the local severity of the pandemic, rather than the pandemic containment measures, may have led to delayed health care use and diagnosis. In Ontario, Canada, there was a higher DKA rate among those who had no precedent primary care visits and a pattern of fewer emergency department visits during the pandemic, 14 suggesting that delays in diagnosis of diabetes resulting in DKA may reflect hesitancy to seek care or barriers to access emergency care. Individuals living in areas with high COVID-19 positivity reported more hesitancy to seek emergency care for children. 78 Therefore, hesitancy to seek care may be an important factor in the observed increased risk of DKA during the pandemic.

There is concern about widespread negative consequences of the COVID-19 pandemic for child and adolescent health inequities. 79 However, relatively few studies examining changes in the incidence rate of pediatric diabetes since the onset of the COVID-19 pandemic have reported the socioeconomic status, race, or ethnicity of the study population. Such information would elucidate whether health disparities in the incidence rates of diabetes and DKA widened during the pandemic. 80 , 81

The results of our systematic review and meta-analysis demonstrated an increased incidence in childhood diabetes after the onset of the COVID-19 pandemic. The increased incidence rate of type 1 diabetes appeared to persist beyond the first year of the pandemic; this has important resource implications given the limited personnel resources in pediatric diabetes care to provide initial diabetes education at diagnosis and for long-term care. Future studies examining longer-term trends of incident types 1 and 2 diabetes may assess whether the increased incidence rate of type 1 diabetes continued and whether there was an increased incidence rate of pediatric type 2 diabetes. A better understanding of the possible direct effects of SARS-CoV-2 infection and the indirect effects of pandemic-related containment measures on incident diabetes in children is needed.

The increased prevalence of DKA at the time of diabetes diagnosis brings to light the need to identify the gaps in the pathway from the time when children develop signs of diabetes to subsequent diagnosis with DKA. This knowledge is needed to inform the development and implementation of effective strategies to prevent DKA at diagnosis in children. These may include public and health care professional–facing awareness campaigns and addressing hesitancy to seek emergency care. 78 , 82

This study has limitations. Our search was restricted to studies published in English, and the included studies did not represent all regions of the world, limiting the generalizability of our findings worldwide. We included only studies that reported the incidence of DKA at diabetes diagnosis among studies that met our eligibility criteria, which required reporting incident diabetes cases in both study periods. Some studies included in our systematic review did not measure diabetes autoantibodies to confirm whether an individual had type 1 or another type of diabetes; thus, there may be a risk of misclassification of diabetes type.

This systematic review and meta-analysis found increased incidence rates of type 1 diabetes and DKA in children and adolescents during vs before the COVID-19 pandemic. Our findings underscore the need to dedicate resources to supporting an acute increased need for pediatric and ultimately young adult diabetes care and strategies to prevent DKA in patients with new-onset diabetes. Although prospective data examining whether this trend has persisted are needed, our findings suggest the need to elucidate possible underlying direct and indirect mechanisms to explain this increase. Furthermore, there is a paucity of data about socioeconomic, racial, and ethnic disparities in the incidence rate of diabetes during the COVID-19 pandemic; this gap must be filled to inform equitable strategies for intervention.

Accepted for Publication: May 15, 2023.

Published: June 30, 2023. doi:10.1001/jamanetworkopen.2023.21281

Open Access: This is an open access article distributed under the terms of the CC-BY License . © 2023 D’Souza D et al. JAMA Network Open .

Corresponding Author: Rayzel Shulman, MD, PhD, Division of Endocrinology, Hospital for Sick Children, 555 University Ave, Toronto, ON M5G 1X8, Canada ( [email protected] ).

Author Contributions: Drs Pechlivanoglou and Shulman had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Pechlivanoglou, Cohen, Shulman.

Acquisition, analysis, or interpretation of data: D’Souza, Empringham, Pechlivanoglou, Uleryk, Shulman.

Drafting of the manuscript: D’Souza, Empringham, Uleryk.

Critical revision of the manuscript for important intellectual content: Empringham, Pechlivanoglou, Uleryk, Cohen, Shulman.

Statistical analysis: D’Souza, Pechlivanoglou.

Obtained funding: Shulman.

Administrative, technical, or material support: Empringham.

Supervision: Shulman.

Conflict of Interest Disclosures: Dr Cohen reported receiving grants from the Canadian Institutes of Health Research outside the submitted work and being a member of the Committee to Evaluate Drugs, which provides advice on public drug policy to Ontario’s Ministry of Health. Dr Shulman reported receiving grants from The Hospital for Sick Children’s Department of Paediatrics during the conduct of the study and receiving grants from the Canadian Institutes of Health Research and speaking fees from Dexcom outside the submitted work. No other disclosures were reported.

Funding/Support: This study was supported, in part, by a grant from the Department of Paediatrics, The Hospital for Sick Children (Drs Empringham, Pechlivanoglou, and Cohen).

Role of the Funder/Sponsor: The sponsor had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Data Sharing Statement: See Supplement 2 .

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Association between per- and polyfluoroalkyl substances exposure and risk of diabetes: a systematic review and meta-analysis

• Qiao, Jian-Chao
• Li, Ze-Lian
• Chen, Yue-Nan
• Jiang, Zheng-Xuan
• Hu, Cheng-Yang

Emerging evidence suggests that per- and polyfluoroalkyl substances (PFAS) are endocrine disruptors and may contribute to the etiology of diabetes. This study aimed to systematically review the epidemiological evidence on the associations of PFAS with mortality and morbidity of diabetes and to quantitatively evaluate the summary effect estimates of the existing literature. We searched three electronic databases for epidemiological studies concerning PFAS and diabetes published before April 1, 2022. Summary odds ratio (OR), hazard ratio (HR), or β and their 95% confidence intervals (CIs) were respectively calculated to evaluate the association between PFAS and diabetes using random-effects model by the exposure type, and dose-response meta-analyses were also performed when possible. We also assessed the risk of bias of the studies included and the confidence in the body of evidence. An initial literature search identified 1969 studies, of which 22 studies were eventually included. The meta-analyses indicated that the observed statistically significant PFAS-T2DM associations were consistent in cohort studies, while the associations were almost non-significant in case-control and cross-sectional studies. Dose-response meta-analysis showed a "parabolic-shaped" association between perfluorooctanoate acid (PFOA) exposure and T2DM risk. Available evidence was rated with "low" risk of bias, and the level of evidence for PFAS and incident T2DM was considered "moderate". Our findings suggest that PFAS exposure may increase the risk of incident T2DM, and that PFOA may exert non-monotonic dose-response effect on T2DM risk. Considering the widespread exposure, persistence, and potential for adverse health effects of PFAS, further cohort studies with improvements in expanding the sample size, adjusting the covariates, and considering different types of PFAS exposure at various doses, are needed to elucidate the putative causal associations and potential mode of action of different PFAS on diabetes. A growing body of evidence suggests that per- and polyfluoroalkyl substances (PFAS) are endocrine disruptors and may contribute to the development of diabetes. However, epidemiological evidence on the associations of PFAS and diabetes is inconsistent. We performed this comprehensive systematic review and meta-analysis to quantitatively synthesize the evidence. The findings of this study suggest that exposure to PFAS may increase diabetes risk among the general population. Reduced exposure to these "forever and everywhere chemicals" may be an important preventative approach to reducing the risk of diabetes across the population.

• Per- and polyfluoroalkyl substances;
• Diabetes mellitus;
• Systematic review;
• Meta-analysis

• Open access
• Published: 08 August 2024

Risk of incident type 2 diabetes in male NAFLD and NAFLD-free smokers: a 7-year post-cessation study

• Jiarong Xie 1 , 2 , 3 ,
• Pengyao Lin 1 ,
• Linxiao Hou 2 , 3 ,
• Min Miao 4 ,
• Zhongwei Zhu 4 ,
• Youming Li 2 , 3 ,
• Chaohui Yu 2 , 3 ,
• Chengfu Xu 2 , 3 &
• Lei Xu 1 , 2 , 3  

Diabetology & Metabolic Syndrome volume  16 , Article number:  192 ( 2024 ) Cite this article

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Metrics details

We aimed to investigate the post-cessation T2DM risk in male NAFLD and NAFLD-free smokers in a 7-year cohort study.

The study population was male adults who underwent annual health checkups in a 7-year cohort study. Recent quitters were categorized into four groups based on their weight gain during follow-up: < 0 kg, 0–1.9 kg, 2.0–3.9 kg, and ≥ 4.0 kg. Cox proportional hazard models, adjusted for various variables, were used to estimate hazard ratios (HRs) for the association between post-cessation weight gain and incident T2DM in NAFLD and NAFLD-free individuals.

At baseline, we included 1,409 NAFLD and 5150 NAFLD-free individuals. During a total of 39,259 person-years of follow-up, 222 (15.8%) NAFLD patients and 621 (12.1%) NAFLD-free participants quit smoking, with the corresponding means (standard deviations) of post-cessation weight gain being 2.24 (3.26) kg and 1.15 (3.51) kg, respectively. Among NAFLD individuals, compared to current smokers, the fully adjusted HRs (95% CI) for incident T2DM were 0.41 (0.06–3.01), 2.39 (1.21–4.70), 4.48 (2.63–7.63), and 6.42 (3.68–11.23) for quitters with weight gains < 0 kg, 0.0–1.9 kg, 2.0–3.9 kg, and ≥ 4.0 kg, respectively. For NAFLD-free individuals, we only observed a significant association between post-cessation weight gain ≥ 4.0 kg and the risk of incident T2DM ( P  < 0.001). Further analysis revealed that the impact of post-cessation weight gain on T2DM risk was not affected by alcohol consumption or obesity status at baseline.

Conclusions

Mild post-cessation weight gain significantly increased the risk of T2DM in male NAFLD patients but not in male NAFLD-free individuals. Therefore, it is recommended that individuals with NAFLD manage their weight after quitting smoking.

Introduction

Nonalcoholic fatty liver disease (NAFLD) is rapidly becoming the most common chronic liver disease, with a global prevalence of 38% [ 1 ]. NAFLD often precedes type 2 diabetes mellitus (T2DM) [ 2 ], which is one of the most significant extrahepatic complications [ 3 , 4 ]. Considering that there are no approved pharmacological treatments, lifestyle modification is necessary but challenging to reduce the risk of T2DM in patients with NAFLD [ 5 ].

Cigarette smoking has a significant negative impact on public health, causing over 480,000 deaths each year [ 6 , 7 ]. Smoking has been reported as a risk factor for NAFLD and may accelerate the progression of liver disease [ 8 , 9 , 10 ]. Therefore, smoking cessation is recommended for NAFLD patients [ 11 , 12 ]. However, smoking cessation can be complicated by weight gain [ 13 ], which typically occurs within 5–7 years after quitting smoking [ 14 , 15 ]. Post-cessation weight gain poses a potential health concern, especially in NAFLD patients [ 16 ]. Weight gain is an important risk factor for the development of T2DM in NAFLD patients [ 17 , 18 ]. To date, clinical evidence about the impact of post-cessation weight gain on NAFLD and its comorbidities remains limited [ 19 ]. Furthermore, post-cessation weight gain limits the willingness of patients and reduces the success of cessation attempts. It has been reported to be a significant cause of relapse [ 20 ]. Therefore, it is important to assess the impact of weight change after smoking cessation on patients with NAFLD. Appropriate management of post-cessation weight could maximize its health benefits.

In this large-scale cohort study, we aimed to assess the effects of smoking cessation and subsequent weight change on the risk of incident T2DM in NAFLD and NAFLD-free smokers. These findings will facilitate the provision of smoking cessation advice to patients with NAFLD in clinical practice.

Study population

The study population was composed of adults aged ≥ 18 years who attended annual health checkups at Zhenhai Lianhua Hospital in 2007. Due to the low proportion of female smokers in China, we included only males in this study. We excluded the following participants: (i) female subjects (ii) those with missing information on hepatic ultrasound, smoking status, or body weight and (iii) those with excess alcohol intake or a history of chronic liver disease at baseline, including viral hepatitis (such as hepatitis B and hepatitis C), drug-induced liver disease, autoimmune liver disease, and genetic metabolic liver diseases (such as Wilson's disease). During the follow-up, we further excluded the following participants: (i) those who were diagnosed with T2DM at baseline and (ii) those who were lost to follow-up. After the exclusions, a total of 6,559 participants were included for follow-up until December 2014 (Supplementary Figure S1). Previous studies have shown that weight gain from smoking cessation usually occurs within 5–7 years after smoking cessation [ 14 , 15 ]. Longer follow-up periods may increase the potential bias for weight gain due to other causes. Thus, we set the follow-up endpoint at the end of 2014 to minimize potential bias in weight gain due to other factors.

The study was conducted in accordance with the Declaration of Helsinki and was approved by the Ethics Committee of the First Affiliated Hospital of Ningbo University (Approval Number: 2022RS127). The data utilized in this study were obtained from previous medical records. The waiver of informed consent was approved by the Hospital Ethics Committee, as it was determined that this waiver would not adversely affect the rights and health of the subjects. The study was registered with ClinicalTrials.gov (Registration Number: NCT05550688).

Assessment of smoking status and weight change

For each year of follow-up, we identified participants who reported being smokers in the previous year but non-smokers in the current year as recent quitters and considered the current year as the start of smoking cessation.

We calculated the duration of smoking cessation from the start of quitting to the time of relapse, the onset of T2DM, or the end of follow-up. Participants were divided into four groups based on their body weight changes during follow-up: < 0 kg, 0–1.9 kg, 2.0–3.9 kg, and ≥ 4.0 kg. Participants were divided into four groups according to the relative percentage of weight change: < 0%, 0.0–2.9%, 3.0–5.9%, and ≥ 6.0%, respectively, in NAFLD patients. We further assessed the association between the relative percentage of weight change after quitting smoking and the risk of incident T2DM. Additionally, transient quitters were defined as those who reported being non-smokers in the current year but were current smokers in the previous year and next year.

Assessment of demographic and clinical characteristics

Blood samples were measured using an autoanalyzer [ 21 ]. Standing height without shoes and body weight in light clothing were measured according to our previous procedure [ 21 ]. Blood pressure was determined by an automatic sphygmomanometer while participants were in a resting state. All participants were examined in the morning after fasting overnight.

Diagnosis of NAFLD and T2DM

We diagnosed NAFLD based on abdominal ultrasonography according to the Chinese Liver Disease Association [ 22 ]. Abdominal ultrasonography was performed by experienced ultrasonographers [ 21 ]. T2DM was defined as individuals with fasting plasma glucose ≥ 126 mg/dl, an HbA1c ≥ 6.5%, or self-reported clinician-diagnosed T2DM [ 23 ].

Statistical analysis

Continuous variables are expressed as the mean ± standard deviation and were analyzed by one-way ANOVA or Student’s t test. Categorical variables are expressed as percentages (numbers) and were analyzed using Pearson's chi-squared test.

Cox proportional hazards models were applied to assess the associations of weight change during smoking cessation with the risk of T2DM. The proportional hazards assumption was tested using Schoenfeld residuals. No evidence of violation of the proportional hazards assumption was found. In multivariable models, we constructed three nested models for analysis: (i) Model 1 adjusted for age and body mass index (ii) Model 2, Model 1 plus drinking status and (iii) Model 3, Model 2 plus aspartate aminotransferase, total cholesterol, triacylglycerol, creatinine, albumin, and systolic blood pressure. In the fully adjusted model, we further explored the interaction effect of NAFLD status on the association between smoking cessation and T2DM by fitting an interaction term on a multiplicative scale.

To test the robustness of our findings, we performed three sensitivity analyses: (i) considering the possible synergistic effect of obesity and NAFLD, we further explored the relationship between weight gain after smoking cessation and the risk of T2DM incidence in non-obese individuals at baseline (ii) to reduce the bias of relapse after cessation, we repeated the analysis after removing 376 individuals who relapsed into smoking cigarettes during follow-up and (iii) previous studies and our results evaluated the trajectory of weight change after quitting smoking, with weight gain occurring primarily in the first 2 years after quitting smoking [ 14 ]. Thus, we further evaluated the relationship between weight change in the first and second years after smoking cessation and the incidence of T2DM.

Statistical analyses were performed using R version 3.5.1 (The R Foundation for Statistical Computing). P values were two-tailed, and statistical significance was set at P  < 0.05.

Clinical characteristics of the study subjects

A total of 6,559 male participants (5150 NAFLD-free individuals and 1,409 NAFLD patients) were included in the cohort analysis at baseline (Supplementary Figure S1). Participants were divided into current smokers and non-smokers according to their baseline smoking status. The clinical characteristics are summarized according to NAFLD status and smoking status in Table  1 . Smokers had a greater body mass index, waist circumference, and serum levels of total cholesterol and triglycerides in both NAFLD and NAFLD-free individuals (all P  < 0.05), indicating that smoking may be associated with more unfavorable metabolic profiles than non-smokers.

Follow-up outcomes

During a total of 39,259 person-years of follow-up, 222 (15.8%) NAFLD patients and 621 (12.1%) NAFLD-free participants quit smoking. The clinical characteristics are summarized according to smoking status changes during follow-up in Supplementary Table S1. Compared with current smokers, recent quitters were older in both NAFLD and NAFLD-free individuals (both with P  < 0.05).

During the follow-up, 363 cases of T2DM were identified (Supplementary Table S2). Compared with participants who did not develop T2DM, those who developed T2DM were older and had greater body mass index and serum triglyceride levels (all with P  < 0.05). Notably, smoking was significantly associated with an increased risk of incident T2DM in both NAFLD and NAFLD-free populations (both with P  < 0.001 Supplementary Table S3).

Smoking cessation and risk of incident T2DM

The significant association between smoking and an elevated risk of incident T2DM prompted us to investigate whether quitting smoking decreases this risk. Table 2 shows the association between smoking cessation and the risk of incident T2DM among the whole study population. Surprisingly, we found that smoking cessation was significantly associated with an increased risk of incident T2DM compared with current smokers, after adjusting for age, body mass index, drinking status, aspartate aminotransferase, total cholesterol, triacylglycerol, creatinine, albumin, and systolic blood pressure (Fig.  1 ). The fully adjusted hazard ratio (HR) (95% confidence interval [CI]) for incident T2DM was 2.16 (95% CI 1.67–2.80).

The interaction effect of NAFLD status on the association between smoking cessation and T2DM. CI confidence interval, HR hazard ratio, NAFLD nonalcoholic fatty liver disease, T2DM type 2 diabetes. The Cox proportional hazards model was adjusted for age, body mass index, drinking status, aspartate aminotransferase, total cholesterol, triacylglycerol, creatinine, albumin, and systolic blood pressure. ** P  < 0.01, *** P  < 0.001

Because NAFLD is a major risk factor for T2DM, we separately analyzed the association between smoking cessation and the risk of incident T2DM in NAFLD and NAFLD-free participants (Fig.  1 ). We found that smoking cessation was significantly associated with an increased risk of incident T2DM in NAFLD patients but not in NAFLD-free individuals. The fully adjusted HRs (95% CIs) were 3.23 (2.21–4.74) and 1.30 (0.89–1.90) in NAFLD- and NAFLD-free individuals, respectively. Although the explanation remains unclear, the above findings clearly illustrated that NAFLD patients and NAFLD-free individuals had different risks of incident T2DM after smoking cessation ( P interaction  < 0.001).

Post-cessation weight gain and risk of incident T2DM

Since 58.0% of recent quitters had less than 5 years of follow-up, we assessed the mean cumulative weight gain by smoking cessation duration in the first 4 years. As shown in Supplementary Figure S2, recent quitters experienced a mean weight gain of 0.74 kg in the first year and 1.21 kg in the second year after quitting smoking. Compared with those in current smokers and never-smokers or ex-smokers, weight changes were greatest in recent quitters during the first and second years of follow-up ( P  < 0.001). These results indicate that individuals who quit smoking showed significantly greater weight gain than did current smokers, never-smokers, or former smokers. The detailed weight changes during the first and second years of follow-up are illustrated in the Sankey diagram (Supplementary Figure S3).

Furthermore, we found that participants with NAFLD experienced greater weight gain after smoking cessation than did those without NAFLD during the follow-up period (2.28 [3.35] kg versus 1.19 [3.37] kg, P  < 0.001). As shown in Table  2 , compared to current smokers, recent quitters with weight gains of 0–1.9 kg, 2.0–3.9 kg, and ≥ 4.0 kg had a significantly greater risk of incident T2DM among individuals with NAFLD, and the corresponding fully adjusted HRs (95% CIs) were 0.41 (0.06–3.01), 2.39 (1.21–4.70), 4.48 (2.63–7.63), and 6.42 (3.68–11.23), respectively. This indicates that even mild weight gain after smoking cessation significantly increases the risk of T2DM in NAFLD patients.

In addition, we noted a significantly greater risk of T2DM among transient quitters than among current smokers in the fully adjusted model ( P  < 0.001). This suggests that short periods of smoking cessation may still increase the risk of developing diabetes. Short-term quitters gained more weight after quitting than did continued smokers (0.29 [2.74] versus 0.19 [2.82] in the first year). This further suggested the importance of weight control after smoking cessation.

For NAFLD-free individuals, the fully adjusted HRs (95% CI) for the risk of T2DM were 0.63 (0.27–1.45), 0.87 (0.38–2.01), and 1.54 (0.66–3.56) among recent quitters with weight changes < 0 kg, 0.0–1.9 kg and 2.0–3.9 kg, respectively. We only observed a significant association between ≥ 4.0 kg weight gain after smoking cessation and the risk of T2DM, with a fully adjusted HR (95% CI) of 2.80 (1.54–5.07) (Table  2 ). This result suggested that mild post-cessation weight gain does not significantly increase the risk of T2DM in NAFLD-free participants.

We further assessed the association between the relative percentage of weight change after quitting smoking and the risk of incident T2DM (Fig.  2 ). Compared with those of current smokers, the fully adjusted HRs (95% CIs) for incident T2DM were 0.41 (0.06–3.00), 2.23 (1.16–4.28), 5.32 (3.23–8.78), and 6.47 (3.43–12.24) among recent quitters with weight gain < 0%, 0.0–2.9%, 3.0–5.9%, and ≥ 6.0%, respectively, among NAFLD patients. Among NAFLD-free individuals, we only observed a significant association between ≥ 6% weight gain after smoking cessation and the risk of T2DM ( P  = 0.010). This further confirmed that mild post-cessation weight gain was associated with an increased risk of incident T2DM in NAFLD patients but not in NAFLD-free participants.

Association between the relative percentage of weight change after quitting smoking and the risk of T2DM in the fully adjusted multivariable model. CI confidence interval, HR hazard ratio, NAFLD nonalcoholic fatty liver disease, T2DM type 2 diabetes. The Cox proportional hazards model was adjusted for age, body mass index, drinking status, aspartate aminotransferase, total cholesterol, triacylglycerol, creatinine, albumin, and systolic blood pressure. * P  < 0.05, ** P  < 0.01, *** P  < 0.001

Sensitivity analyses

To assess the robustness of our findings, we conducted three sensitivity analyses:

First, we explored the association between post-cessation weight gain and the risk of T2DM in non-drinkers and non-obese individuals and observed that the impact of weight gain after smoking cessation was not modified by alcohol consumption or obesity status (Fig.  3 A, B).

Sensitivity analyses of the association between smoking cessation and the incidence of T2DM by smoking status in a fully adjusted model. A Among individuals who reported no alcohol consumption at baseline. B Among individuals with a baseline BMI < 28 kg/m 2 . CI confidence interval, NAFLD nonalcoholic fatty liver disease. The Cox proportional hazards model was adjusted for age, body mass index, drinking status, aspartate aminotransferase, total cholesterol, triacylglycerol, creatinine, albumin, and systolic blood pressure. * P  < 0.05, ** P  < 0.01, *** P  < 0.001

Second, to minimize the potential impact of smoking relapse, we removed participants who relapsed smoking during follow-up (Supplementary Table S4). Compared with those of current smokers, the fully adjusted HRs (95% CIs) for incident T2DM were 1.00 (0.14–7.30), 3.23 (1.55–6.75), 5.04 (2.69–9.44), and 7.05 (4.04–12.30) for recent quitters with weight gains < 0 kg, 0–1.9 kg, 2.0–3.9 kg, and ≥ 4.0 kg, respectively, among individuals with NAFLD. For NAFLD-free individuals, the corresponding HRs (95% CI) were 0.82 (0.30–2.27), 1.53 (0.61–3.80), 2.50 (0.91–6.88), and 3.60 (1.72–7.55).

Third, we evaluated the relationship between weight change in the first and second years after quitting smoking and the incidence of T2DM (Supplementary Figure S4). We obtained similar results regarding the effect of weight change during the first year after smoking cessation on NAFLD individuals. Compared with those of current smokers, we observed fully adjusted HRs (95% CIs) for incident T2DM of 1.27 (0.57–2.87), 3.98 (2.26–7.02), 4.77 (2.64–8.60), and 7.16 (3.69–13.91) for recent quitters with weight gains < 0 kg, 0–1.9 kg, 2.0–3.9 kg, and ≥ 4.0 kg, respectively, among NAFLD patients. We found a significant relationship between weight gain ≥ 4.0 kg after smoking cessation and T2DM risk ( P  < 0.001) among NAFLD-free individuals. Similar associations were observed after removing 86 individuals with only 1 year of follow-up (Supplementary Figure S4).

Overall, these sensitivity analyses confirm the robustness of our findings and provide further evidence for the significant association between post-cessation weight gain and T2DM risk in NAFLD patients but not in NAFLD-free individuals.

This cohort study provides novel insight into the differential impact of post-cessation weight gain on the risk of incident T2DM in individuals with and without NAFLD. Our findings revealed that even mild weight gain following smoking cessation is significantly associated with an increased risk of T2DM in individuals with NAFLD. In NAFLD-free individuals, this risk becomes apparent only when weight gain exceeds 4.0 kg. These results underscore the unique vulnerability of NAFLD patients to weight gain-related metabolic disturbances after smoking cessation. Furthermore, the sensitivity analyses confirmed the robustness of these findings in subgroups of non-drinkers and non-obese individuals. These observations highlight the critical importance of managing weight gain post-cessation, particularly in NAFLD patients, to mitigate the elevated risk of T2DM and optimize health outcomes.

Smoking is an independent risk factor for NAFLD progression [ 8 , 9 ]. Jung et al . performed a cohort study of 199,468 Korean adults [ 9 ] and reported that current smoking status, pack-years, and urinary cotinine levels were positively associated with the risk of incident NAFLD. Therefore, it is advised that patients with NAFLD quit smoking. Smoking cessation is a common clinical problem. As part of the “Healthy China 2030” initiative, the Chinese government aims to reduce smoking prevalence from 27.7 to 20% by 2030 [ 24 ], which means a net reduction of nearly 100 million smokers [ 25 ]. However, smoking cessation is often accompanied by weight gain [ 26 ], which could be a potential risk factor for NAFLD progression. The magnitude of economic and medical costs associated with incident T2DM in individuals with NAFLD is significant. A better understanding of the impact of smoking cessation on NAFLD may provide new insights into NAFLD pathogenesis and develop preventive strategies.

Previous studies have shown that weight gain after smoking cessation is considered acceptable since it is not associated with an increased risk of cardiovascular disease or chronic diseases [ 27 , 28 ]. However, our study provides novel and critical insight into the impact of weight gain on the risk of developing T2DM in both NAFLD and NAFLD-free populations. Specifically, we found that even mild weight gain during follow-up after smoking cessation significantly increased the risk of T2DM in individuals with NAFLD. In contrast, for NAFLD-free individuals, an increased risk of T2DM was observed only if they weighed more than 4.0 kg. This critical finding underscores the importance of managing weight gain after smoking cessation, particularly in NAFLD patients, to mitigate the heightened risk of T2DM.

Relapse to smoking after quitting smoking is common among people who have quit smoking [ 29 ]. Therefore, we excluded the relapsed smoking population from the sensitivity analysis and again assessed the correlation. In this analysis, we likewise observed a similar phenomenon. These robust results suggest that patients with NAFLD need strict weight control after smoking cessation to reduce the risk of developing T2DM.

Two strengths of this study are the large sample size and its cohort design. Additionally, our study included repeated measures of smoking status during follow-up instead of depending on the “point prevalence” of smoking cessation, which could reflect dynamic changes in smoking status. We further excluded the relapsed smoking population from our sensitivity analysis as mentioned above. The results of these analyses provide evidence for further refining cessation management.

There are some limitations that should be acknowledged. First, our study focused on the Chinese population that regularly underwent health examinations. This may limit the generalizability of our results to other ethnic populations. Second, some additional factors, such as socioeconomic status, physical activity levels, and dietary habits, were not fully adjusted due to the lack of corresponding data. This may have led to residual confounders. Third, female participants were not included in our analysis since female smokers represent a very small minority in Chinese society [ 30 ]. The current smoking prevalence was only 2.1% in 2018 [ 30 ]. Future studies could include female participants to provide a more comprehensive understanding of the relationship between smoking cessation and type 2 diabetes risk. Fourth, fatty liver was diagnosed by ultrasonographic methods in our study. Although liver biopsy is the gold standard, it is difficult and impractical to perform in community-based studies. The ultrasound definition of steatosis is used in the current guidelines in China [ 22 ]. Additionally, since this study was conducted before the introduction of the MASLD definition, we adopted the NAFLD definition for fatty liver. However, according to recent literature, the differences between MASLD and NAFLD are minimal, suggesting that findings based on the NAFLD definition remain applicable and valid under the new MASLD framework [ 31 ]. Finally, reliance on self-reported smoking status may introduce bias or misclassification, despite the use of trained nurses to conduct professional and consistent interviews.

In conclusion, this large cohort study indicated that even mild weight gain during follow-up after smoking cessation is significantly associated with an increased risk of T2DM in male individuals with NAFLD. This finding emphasized the importance of weight control after smoking cessation to reduce the potential risk of T2DM, especially in individuals with NAFLD.

Availability of data and materials

The data that support the findings of this study are available from the corresponding authors, Dr. Lei Xu and Dr. Chengfu Xu, upon reasonable request.

Abbreviations

Confidence interval

Hazard ratio

• Nonalcoholic fatty liver disease
• Type 2 diabetes

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Acknowledgements

The authors thank all the staff and participants of this study for their important contributions.

This work was supported by the National Natural Science Foundation of China (82300654, 82270602, 82270597, 82070585, and U20A20347), the Key Research and Development Program of Zhejiang Province (2024C03153), the Ningbo Top Medical and Health Research Program (No. 2023020612), the project of Ningbo leading Medical & Health Discipline (2022-S04), and the Medical Health Science and Technology Project of Zhejiang Provincial Health Commission (2023KY257).

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Study concept and design: L. Xu, C. Xu, C. Yu, and J. Xie; acquisition of data: P. Lin, L. Hou, and M. Miao; analysis and interpretation of the data: P. Lin, L. Hou, Z. Zhu, and Y. Li; drafting of the manuscript: J. Xie and P. Lin; critical revision of the manuscript: L. Xu, C. Xu, and C. Yu; obtained funding; C. Xu, L. Xu, and J. Xie; study supervision: L. Xu, C. Xu, C. Yu, and Y. Li.

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Xie, J., Lin, P., Hou, L. et al. Risk of incident type 2 diabetes in male NAFLD and NAFLD-free smokers: a 7-year post-cessation study. Diabetol Metab Syndr 16 , 192 (2024). https://doi.org/10.1186/s13098-024-01435-4

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Effectiveness of diabetes education and awareness of diabetes mellitus in combating diabetes in the United Kigdom; a literature review

Chaudhary muhammad junaid nazar.

1 Department of Nephrology, Shifa International Hospital, Islamabad, Pakistan

Micheal Mauton Bojerenu

2 Department of Internal Medicine, Sickle Cell Unit, Harvard University Hospital, Washington DC, USA

Muhammad Safdar

Jibran marwat.

Diabetes mellitus is a metabolic disorder that is characterized by high blood glucose level, and body cannot produce enough insulin, or does not respond to the produced insulin. In spite of the diabetes education campaigns and programmes, a large number of people in the United Kingdom are living with diabetes. The main objective of the study is to evaluate the role of knowledge and awareness of diabetes in fighting against diabetes and to interpret to which extent is diabetes education successful. The systematic review to be carried out will include literature from 2001 to 2011 in the United Kingdom regarding awareness of diabetes among UK population and effectiveness of diabetes education. Literature will be accessed using search database, British medical journals, and library. Good quality papers will be used for the systematic review. Previous studies about diabetes education will consulted and assessed. This study is going to summarize the efficacy of diabetes education campaigns and programmes which are promising to enhance the awareness The outcome of the review will be the guideline for the government, education centres, researchers, and campaigns to implement more diabetic education programmes and easily accessible diabetes services and education interventions to increase the awareness of risk factors and complications of diabetes to overcome the increasing epidemic of diabetes in the United Kingdom.

Implication for health policy/practice/research/medical education:

Diabetes mellitus is a metabolic disorder, in which there is high blood glucose level, and body cannot produce enough insulin, or the body does not respond to the insulin produced. In spite of the diabetes education campaigns and programmes, a large number of people in the United Kingdom are living with diabetes. The main objective of the study is to evaluate the role of knowledge and awareness of diabetes in fighting against diabetes and to interpret to which extent is diabetes education successful. The outcome of the review will be the guideline for the government, education centres, researchers, and campaigns to implement more diabetic education programmes and easily accessible diabetes services and education interventions to increase the awareness of risk factors and complications of diabetes to overcome the increasing epidemic of diabetes in in the United Kingdom.

Introduction

Diabetes is a serious and life-threatening disease, however it can be managed very well through proper treatment and controlling. Diabetes self-management training and education plays a vital role in the management of diabetes ( 1 ). It is crucial for diabetic patients to be aware of nature, treatment, risk factors and complication of disease due to providing suitable modality to attenuate following complications. In a study to detect the relation between health literacy, complication awareness and diabetic control among patients with type 2 diabetes mellitus, it was concluded that patient awareness scores and health literacy was negatively related to diabetes control ( 2 ). This study was 6 months study, carried out from September 2005 to February 2006 with about 150 Chinese patients.

Materials and Methods

For this review, we used a variety of sources by searching through PubMed, EMBASE, Scopus and directory of open access journals (DOAJ). The search was performed by using combinations of the following key words and or their equivalents; Prevalence of diabetes mellitus, awareness and knowledge about diabetes and its management, diabetes education programmes, effectiveness of diabetes education.

Looking at the study carried out to explore the total prevalence of diabetes mellitus in 2001 in England to support delivery of healthcare services it was estimated that in 2001 the prevalence of diabetes (diagnosed as well as undiagnosed) in England was about 4.5%, affecting more than 2 million persons ( 3 ). It was found that the prevalence of type 2 diabetes was 92% affecting 2000000 persons and the prevalence of type 1 diabetes was nearly 8% affecting 160000 persons. The prevalence of diabetes was estimated to be more in women (5.2%) than men (3.6%). It was also estimated that the prevalence of diabetes was higher in the people from ethnic minority groups than the white people. The estimated prevalence rates are 4.3 for white people, 5.7 for black African/Caribbean, and 6.6 for South Asians and 2.1% for other groups. The prevalence of diabetes was found to be increased rapidly with age as the prevalence was found to be 0.3 in people aged 0–29, 3.3 in those 30–59 and 14% in people over 60 years age.

According to Diabetes UK (2010) in 2009, the prevalence of diabetes in adults over 17 years old is estimated to be 5.1% in England affecting 2213138, 4.5% in Northern Ireland affecting 65066, 4.6% in Wales affecting 146173 and 3.9% in Scotland affecting 209886 people. The total average prevalence of diabetes in 2009 in the United Kingdom is estimated to be 4.26%.

A systematic review was conducted to estimate the age- and sex-specific diabetes prevalence worldwide for years 2010 and 2030 ( 4 ). Studies from 91 countries were selected and it was found from the review findings that the incidence of diabetes among people aged 20–80 years will be 6.5% in 2010 and 286 million adults will be affected in 2010. The prevalence of diabetes will increase to 7.8%, and nearly 440 million adults will be affected by 2030. It was suggested that there will be a 70% increase in the prevalence of diabetes in adults of developing countries and about 21% rise in developed countries. By looking at CHASE study, a cross-sectional survey carried out involving nearly 4800 children aged 9-10 years old recruited from London, Birmingham and Leicester, it is found that South Asians adults, residents of UK are 3 times more prone to develop type 2 diabetes than white Europeans ( 5 , 6 ). These people have higher blood levels of glycated haemoglobin (HbA1c), higher level of C-reactive proteins in the blood, lower level of High-density lipoprotein -cholesterol (HDL-C) and high triglyceride levels than white people. Black African-Caribbean adults residing in the United Kingdom have also most of these diabetic risk factors but these people have high HDL-C levels and low triglyceride levels.

Better diabetic education and knowledge to control and treat diabetes at right time can minimize the chances to develop complications of diabetes and thus reduce morbidity and mortality in diabetics ( 7 , 8 ). It suggests that as the rising figures of people diagnosed with diabetes is becoming a challenge in the United Kingdom so a randomised clinical trial will be run by independent research teams to interpret effective delivery and cost effectiveness of CASCADE (Child and Adolescent Structured Competencies Approach to Diabetes Education) for children and young people involved in this trial. As we know that if diabetes is diagnosed in childhood and bitterly controlled, the chances to develop long-term complications become less. The CASCADE is a multi-centre randomised control trial involving 26 clinics randomly selected as control/intervention groups, including 572 children and young people ( 7 ). Despite of the advanced medications and their delivery systems there is less improvement in control of diabetes in children and young people in the United Kingdom in last decade ( 8 ). So new health delivery systems are needed for children and young people to improve and control the diabetes.

With regards to this, in 2010, fifth national survey was carried out to assess the delivery of UK diabetes services to children and young people and identified changes in service delivery systems since 2002 ( 9 ). One hundred twenty-nine services took part in the survey involving 220 clinics. Ninety-eight percent of paediatric consultants were found having special interest in diabetes whereas in 2002 about 89% of consultants were interested in diabetes. In 88% of services, the diabetes specialist nurse worked alone in paediatric diabetes compared to 53% of the services in 2002. So overall it was concluded that there is much improvement in diabetes services for children providing high quality care, but serious deficiencies still remains.

According to Diabetes UK (2010) most of the people with diabetes type 2 in the United Kingdom are over 60; their level of diabetes knowledge tends to be poorer. According to Diabetes UK (2010) report, the residents of care homes fail to receive diabetes education and screening. A care home resident gets admitted to the hospital for screening and diagnosis of diabetes due to the lack of screening facilities and lack of diabetes education. There are diabetic residents in 6 out of 10 care homes that cannot provide special education ( 10 ).

UK prospective diabetes study has shown that adapting the effective therapy to reduce high blood pressure and high blood glucose level will result in reducing the diabetes complications ( 11 ). Diabetes UK invested more than 2 million on this study ( 11 ). The UK Prospective Diabetes Study, the 20-year study involving 5000 patients with diabetes in the United Kingdom, has revealed that intensive blood glucose level control and adopting better treatment methods can reduce the risk of diabetic retinopathy by a quarter and early renal damage by a third ( 11 ). Intensive management and control of blood pressure in hypertensive patients can reduce the risk of death resulting from life threatening long-term complications of diabetes by a third, vision loss by more than a third and cardiovascular disease by more than a third ( 10 ).

By looking at the data collected between 1st April 2008 and 31st March 2010 from 1421 weight reducing operations carried out, it is found that before surgery 379 of these 1421 patients were having type 2 diabetes ( 11 ). After 1 year of surgery it was found that this number of diabetic patients was decreased to 188 from 379 ( 11 ). Therefore by providing knowledge of advance treatment methods to people helps in controlling the diabetes as educating people about the weight loss surgeries (gastric bypass and gastric bands) can tackle type 2 diabetes as seen in this study.

Diabetes education can improve the quality of life of diabetic patients and can also prevent the costs of long-term complications of diabetes in the patients ( 10 ). As amputation of lower limb in a diabetic patient, a long-term complication of diabetes is a costly intervention, the diabetes education can help in reducing the amputation rate that can lead to large cost savings ( 10 ). Diabetic foot ulcers can develop in patients having diabetes both in type 1 and type 2 diabetes ( 11 ). It has been found, 10% of diabetic individuals may suffer from foot ulcer during their lifetime. Foot ulcer often occurs in the people who develop peripheral diabetic neuropathy and also by wearing tight shoes, by walking on tread mill, having cuts, blisters and also having narrowed arteries; atherosclerotic peripheral arterial disease. The diabetic foot ulcers should not be avoided and diabetic foot needs a special care, otherwise the diabetic foot ulcer can result in the amputation of the foot even the whole lower limb ( 11 ). The risk of lower limb amputation in diabetic patients is 15 to 45 times more than in people with no diabetes ( 10 ). About 25% of hospital admissions of diabetic people in United States and Great Britain are due to diabetic foot complications ( 10 ). The annual incidence of diabetic foot ulcers and amputation are 2.5% to 10.7% and 0.25% to 1.8%, respectively ( 12 ).

In the United States an estimated more than 130 billion dollars in 2002 is the cost of diabetes ( 13 ). Because of these devastating numbers, the cost-efficacy of preventing and treating diabetes, and the cost-effectiveness of diabetes self-management training and medical nutrition therapy to treat diabetes are receiving much attention ( 13 ). While in the United Kingdom, the cost of diabetes to the National Health Service (NHS) stands at approximately £1 million per hour, and is increasing rapidly. Diabetes accounts for approximately a tenth of NHS budget each year, a total exceeding £9 billion ( 11 ). With regards to this a systematic review was carried out involving 26 articles including randomized controlled trials, retrospective database analyses, meta-analysis, prospective, quasi-experimental and, to evaluate the cost-effectiveness of diabetes education. The results of more than half of the studies reviewed were indicated positive association between diabetes education and decreased cost. The findings of these studies indicate that diabetes self-management education (DSME) has more benefits in reducing the costs associated with diabetes intervention. Study agreed with this finding by conducting a 12-month study involving primary care trusts in the United Kingdom to assess the long-term clinical and cost-effectiveness of the diabetes education and self-management for ongoing and newly diagnosed (DESMOND) intervention ( 14 ). The cost-utility analysis was undertaken using data from a 12-month, multicentre, cluster randomised controlled trial and the study resulted that the DESMOND intervention is considered to be cost effective compared with usual care, especially with respect to the real world cost of the intervention to primary care trusts, with reductions in Cardiovascular disease (CVD) risk especially reduction in weight and smoking ( 14 ).

According to a cohort study, conducted in 2005 by Diabetes UK, The cancer risk and mortality is progressively elevating in insulin treated diabetic individuals ( 15 ). This study involved 28900 UK resident patients with insulin-treated diabetes who were less than 50 years old at the diagnosis of diabetes. However, the results showed, risks of some cancers such as liver, pancreatic, endometrial, renal and colorectal cancer slightly are raising in patients with prime type 2 diabetes but some cancer incidence including gall bladder, breast cancers and non-Hodgkin lymphoma (NHL) have not changed or prostate cancer risk has been reduced ( 15 ).

Celiac disease, as a chronic immune mediated disorder, is triggered by gluten intake in predisposed patients ( 16 ). Type 1 diabetes is one of the diseases associated with celiac disease ( 18 ). Both diseases have a common genetic predisposition. In one Turkish study involving 100 diabetic patients (51 female, 49 male, mean age 26 ±9 years, and 80 control subjects - 40 female, 40 male, mean age 27 ± 8 years), it was estimated that the prevalence of celiac disease is more in diabetic patients than the general people and celiac disease in diabetic patients can only be diagnosed by screening tests for celiac disease as CD is mostly seen as asymptomatic in these patients. The most sensitive and specific test for the diagnosis of CD is the anti-endomysial IgA antibody (IgA-EMA) test with a sensitivity of more than 90% and a specificity about 100%. This is a screening method in patients at high risk for CD. Anti-endomysium IgA was tested by indirect immunofluorescence using sections of human umbilical cord for screening. Some investigators predicted that the complications of diabetes are increased in the presence of celiac disease and worsens the metabolic control in these diabetic patients ( 17 ).

High blood glucose level can lead to microvascular and macrovascular complications ( 18 ). For examining this, a prospective observational study (UKPDS 35) was conducted by Stratton et al ( 18 ). To report positive correlation between hyperglycaemia and macro/micro-vascular insults in type 2 diabetic patients. This study involved 23 hospital-based clinics in England, Scotland and Northern Ireland. About 4600 patients including white, Asian Indian and African-Caribbean patients were participated in incidence rates analysis. Risk factors related macro-vascular complication were noticed in about 3600 of the total patient. The results of the study indicated that there is a direct relation between hyperglycemia, micro-vascular and macro-vascular complications ( 18 ). This is also clear by examining a cohort study, conducted by Fuller et al to assess cardiovascular disease associated risk in type 1 diabetic patients in the United Kingdom ( 19 ). This study consisted of group of 7500 patients with type 1 diabetes and 5 age- and gender-matched controls per non-diabetic individuals comparison group (nearly 38200) selected from the General Practice Research Database (GPRD). The cardiovascular events in these two groups were apprehended between1992-1999. These high CVD risks were seen for strokes, acute coronary disorders, and for coronary revascularizations. Results showed that women having type 1 diabetes continue to experience greater relative risks of cardiovascular disease than men compared with those without diabetes ( 19 ). Hence, there is increased absolute and relative risk of mortality due to CVD in patients with type 1diabetes compared with those without diabetes in the United Kingdom ( 19 ).

Blood glucose awareness training and cognitive behavioural therapy have been able to balance blood glucose level in type 1 diabetic patients ( 20 ). To support this evidence, a systematic review was completed ( 20 ) in Oxford to assess fear of hypoglycaemia in the patients having diabetes. About 36 papers were reviewed. And it was implicated from the review that fear of hypoglycaemia can have negative impact on diabetes management and awareness training is needed to reduce this fear of hypoglycaemia. This was further supported by a randomised control trial, carried out ( 21 ) on 650 randomly selected diabetic patients from Bournemouth Diabetes and Endocrine Centre’s diabetes register to determine the relationship between numeracy skills and glycaemic control in type 1 diabetes. Out of 650 patients 112 patients completed the study. Forty-seven percent were the male patients and it was found that low numeracy skills were badly associated with glycaemic control in diabetes and literacy was also badly associated with glycaemic control in diabetes and also relationship between literacy and glycaemic control was found to be independent of the duration of diabetes and socio-economic status of the patients.

Diabetic patients can develop hyperglycaemia and hypoglycaemia in the critical care setting while hospitalized due to various factors including infection, poor diet, and drugs ( 22 ). Hospitalized patients can develop hyperglycaemia even in the absence of family history of diabetes ( 22 ). The blood glucose level range of 100–200 mg/dl is the target of glycaemic control in the hospitalized patients. Insulin infusion is done in hospitalized patients having type 1 diabetes and in type 2 diabetic patients, oral drugs are stopped and insulin is started for glycaemic control ( 22 ).

Educational and psychosocial interventions are able to approximately improve diabetes management. ( 23 , 24 ). A systematic review was completed by Hampson et al ( 23 ) to investigate the educational and psychosocial intervention efficacy on improvement of diabetes management in adolescents type 1 diabetes patients. About 60 articles were reviewed. This systematic review gave the result that educational and psychosocial interventions have beneficial impacts on various diabetes management consequences. Similarly a systematic review was conducted by Norris et al ( 24 ) to assess the effectiveness of self-management education on glycosylated hemoglobin in adults having type 2 diabetes. Total 31 articles on randomized control trials were reviewed and it was found that DSME improves glycated hemoglobin levels at immediate follow-up by 0.76%, that long-lasting interventions may be needed to maintain the improved glycaemic control brought about by DSME programs as the more contact time between patient and educator enhances the efficacy of the result and that the improvement in glycosylated hemoglobin level drops 1–3 months after the intervention ceases ( 24 ). Further supporting this, another systematic review was conducted by Hawthorne et al ( 25 ) to determine the efficacy of various diabetic diet advice on balancing blood glucose level and weight in type 2 diabetic individuals. Only randomized controlled trials of 6 months or longer, were selected for the review and total 36 articles were reviewed. In this review study, some parameters such as weight, mortality, maximal exercise capacity and compliance various lipoproteins levels and blood pressure were measured. The review indicated that dietary advice is effective in the glycaemic control in type 2 diabetes mellitus ( 25 ) further supported all these reviews by conducting a systematic review to assess the effectiveness of culturally appropriate diabetes health education on type 2 diabetes mellitus as prevalence of type 2 diabetes mellitus is higher in ethnic minorities in the developed countries like the United Kingdom ( 25 ). Eleven randomised control trials of culturally appropriate diabetes health education on people having type 2 diabetes over 15 years from defined ethnic minority groups of developed countries were reviewed. The trials indicated both glycaemic control as well as improvement in knowledge after culturally appropriate diabetes education interventions. It was suggested from the review that culturally appropriate diabetes health education is effective in glycaemic control in type 2 diabetes and improving the knowledge score and changing the lifestyles and attitudes of the people.

Various diabetes education courses are being carried out in the United Kingdom, including DAFNE, DESMOND and X-PERT in order to increase awareness and knowledge of diabetes among people. These diabetes courses are designed to empower diabetic patients to manage their own condition effectively. Various factors like cost, distance, shortage of enough educators or centres, lack of appropriate services affect many people with diabetes to get access to diabetes knowledge. Educating the patients regarding diabetes have a key role in encouraging and supporting them to assume active responsibility for the day to day control of their situation. The review depicts that illiteracy and lack of knowledge poses a great challenge to effective health education. The review demonstrates that south Asian patients face problems regarding diet aspect and show poor level of knowledge about diabetes and also are discouraged to join educational sessions. The review indicates that impaired awareness of the diabetes increases the chances to develop complications of diabetes as the severe hypoglycaemia is becoming more common in insulin treated type 2 diabetes than previously recognized and with increased duration of insulin therapy may increase to meet that observed in type 1 diabetes. The risk of severe hypoglycaemia increases with having impaired awareness of hypoglycaemia. The authors has concluded that diabetes associated complications and psychological insults is usual in diabetic individuals. The study indicates that many providers involved in the study are aware of the diabetes related psychological problems but lack confidence in their ability to evaluate these problems and to support these patients. So, there is a need for manipulating models of care that provide essential psychosocial services. There is also need of integrating mental health professionals into the diabetes care team. This study will help the government to implement the diabetes education programmes that are cost effective and attractive to the public, easy to get access. Any diabetes service should provide highly structured diabetes education programme. In spite of the advanced medications and their delivery systems there is less improvement in control of diabetes in children and young people in UK in last decade. Better diabetic education and knowledge to control and treat diabetes at right time can reduce the risk factors and minimize the chances to develop complications of diabetes and thus reduce morbidity and mortality in diabetics.

Authors’ contribution

CMJN completed the article, MS and MMB reviewed the article, and JM completed the draft.

Conflicts of interest

The authors declared no competing interests.

Ethical considerations

Ethical issues (including plagiarism, data fabrication, double publication) have been completely observed by the authors.

Funding/Support

Please cite this paper as: Nazar CMJ, Bojerenu MM, Safdar M, Marwat J. Effectiveness of diabetes education and awareness of diabetes mellitus in combating diabetes in the United Kigdom; a literature review. J Nephropharmacol. 2016;5(2):110-115.

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The effect of cinnamon supplementation on glycemic control in patients with type 2 diabetes mellitus: An updated systematic review and dose-response meta-analysis of randomized controlled trials

Affiliations.

• 1 Student Research Committee, Tabriz University of Medical Sciences, Tabriz, Iran.
• 2 School of Nutrition and Food Sciences, Tabriz University of Medical Sciences, Tabriz, Iran.
• 3 Department of Medicine, Tehran University of Medical Sciences, Tehran, Iran.
• 4 Tehran University of Medical Sciences, Tehran, Iran.
• 5 Maternal and Childhood Obesity Research Center, Urmia University of Medical Sciences, Urmia, Iran.
• PMID: 37818728
• DOI: 10.1002/ptr.8026

Although many randomized controlled trials (RCTs) have revealed the benefits of cinnamon on type 2 diabetes mellitus (T2DM), the effects of cinnamon supplementation on glycemic control in patients with T2DM are inconclusive. Therefore, the aim of this meta-analysis of RCTs was to assess the effects of cinnamon supplementation in managing glycemic control in patients with T2DM. Scientific international databases including Scopus, Web of Sciences, PubMed, Embase, and the Cochrane Library were searched till December 2022. For net changes in glycemic control, standard mean differences (SMDs) were calculated using random-effects models. Findings from 24 RCTs revealed that cinnamon supplementation had a statistically significant reduction in fasting blood sugar (SMD: -1.32; 95% CI: -1.77, -0.87, p < 0.001), Homeostatic Model Assessment for Insulin Resistance (SMD: -1.32; 95% CI: -1.77, -0.87, p < 0.001), and hemoglobin A1C (SMD: -0.67; 95% CI: -1.18, -0.15, p = 0.011) compared with the control group in patients with T2DM. Additionally, cinnamon did not change the serum levels of insulin (SMD: -0.17; 95% CI: -0.34, 0.01, p = 0.058) significantly. Our analysis indicated that glycemic control indicators are significantly decreased by cinnamon supplementation. Together, these findings support the notion that cinnamon supplementation might have clinical potential as an adjunct therapy for managing T2DM.

Keywords: cinnamon; glycemic control; meta-analysis; systematic review; type 2 diabetes.

© 2023 John Wiley & Sons Ltd.

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