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ObjectiveTo determine whether metformin or lifestyle modification can lower rates of all-cause and cause-specific mortality in the Diabetes Prevention Program and Diabetes Prevention Program Outcomes Study.Research design and methodsFrom 1996 to 1999, 3,234 adults at high risk for type 2 diabetes were randomized to an intensive lifestyle intervention, masked metformin, or placebo. Placebo and lifestyle interventions stopped in 2001, and a modified lifestyle program was offered to everyone, but unmasked study metformin continued in those originally randomized. Causes of deaths through 31 December 2018 were adjudicated by blinded reviews. All-cause and cause-specific mortality hazard ratios (HRs) were estimated from Cox proportional hazards regression models and Fine-Gray models, respectively.ResultsOver a median of 21 years (interquartile range 20-21), 453 participants died. Cancer was the leading cause of death (n = 170), followed by cardiovascular disease (n = 131). Compared with placebo, metformin did not influence mortality from all causes (HR 0.99 [95% CI 0.79, 1.25]), cancer (HR 1.04 [95% CI 0.72, 1.52]), or cardiovascular disease (HR 1.08 [95% CI 0.70, 1.66]). Similarly, lifestyle modification did not impact all-cause (HR 1.02 [95% CI 0.81, 1.28]), cancer (HR 1.07 [95% CI 0.74, 1.55]), or cardiovascular disease (HR 1.18 [95% CI 0.77, 1.81]) mortality. Analyses adjusted for diabetes status and duration, BMI, cumulative glycemic exposure, and cardiovascular risks yielded results similar to those for all-cause mortality.ConclusionsCancer was the leading cause of mortality among adults at high risk for type 2 diabetes. Although metformin and lifestyle modification prevented diabetes, neither strategy reduced all-cause, cancer, or cardiovascular mortality rates.

ObjectiveGenetic risk scores (GRS) have been developed that differentiate individuals with type 1 diabetes from those with other forms of diabetes and are starting to be used for population screening; however, most studies were conducted in European-ancestry populations. This study identifies novel genetic variants associated with type 1 diabetes risk in African-ancestry participants and develops an African-specific GRS.Research design and methodsWe generated single nucleotide polymorphism (SNP) data with the ImmunoChip on 1,021 African-ancestry participants with type 1 diabetes and 2,928 control participants. HLA class I and class II alleles were imputed using SNP2HLA. Logistic regression models were used to identify genome-wide significant (P < 5.0 × 10-8) SNPs associated with type 1 diabetes in the African-ancestry samples and validate SNPs associated with risk in known European-ancestry loci (P < 2.79 × 10-5).ResultsAfrican-specific (HLA-DQA1*03:01-HLA-DQB1*02:01) and known European-ancestry HLA haplotypes (HLA-DRB1*03:01-HLA-DQA1*05:01-HLA-DQB1*02:01, HLA-DRB1*04:01-HLA-DQA1*03:01-HLA-DQB1*03:02) were significantly associated with type 1 diabetes risk. Among European-ancestry defined non-HLA risk loci, six risk loci were significantly associated with type 1 diabetes in subjects of African ancestry. An African-specific GRS provided strong prediction of type 1 diabetes risk (area under the curve 0.871), performing significantly better than a European-based GRS and two polygenic risk scores in independent discovery and validation cohorts.ConclusionsGenetic risk of type 1 diabetes includes ancestry-specific, disease-associated variants. The GRS developed here provides improved prediction of type 1 diabetes in African-ancestry subjects and a means to identify groups of individuals who would benefit from immune monitoring for early detection of islet autoimmunity.

OBJECTIVE: To assess associations between distal symmetric polyneuropathy (DSPN) and Diabetes Prevention Program (DPP) treatment groups, diabetes status or duration, and cumulative glycemic exposure approximately 21 years after DPP randomization. RESEARCH DESIGN AND METHODS: In the DPP, 3,234 adults ≥25 years old at high risk for diabetes were randomized to an intensive lifestyle (ILS), metformin, or placebo intervention to prevent diabetes. After the DPP ended, 2,779 joined the Diabetes Prevention Program Outcomes Study (DPPOS). Open-label metformin was continued, placebo was discontinued, ILS was provided in the form of semiannual group-based classes, and all participants were offered quarterly lifestyle classes. Symptoms and signs of DSPN were assessed in 1,792 participants at DPPOS year 17. Multivariable logistic regression models were used to evaluate DSPN associations with treatment group, diabetes status/duration, and cumulative glycemic exposure. RESULTS: At 21 years after DPP randomization, 66% of subjects had diabetes. DSPN prevalence did not differ by initial DPP treatment assignment (ILS 21.5%, metformin 21.5%, and placebo 21.9%). There was a significant interaction between treatment assignment to ILS and age (P < 0.05) on DSPN. At DPPOS year 17, the odds ratio for DSPN in comparison with ILS with placebo was 17.4% (95% CI 3.0, 29.3) lower with increasing 5-year age intervals. DSPN prevalence was slightly lower for those at risk for diabetes (19.6%) versus those with diabetes (22.7%) and was associated with longer diabetes duration and time-weighted HbA1c (P values <0.001). CONCLUSIONS: The likelihood of DSPN was similar across DPP treatment groups but higher for those with diabetes, longer diabetes duration, and higher cumulative glycemic exposure. ILS may have long-term benefits on DSPN for older adults. [ABSTRACT FROM AUTHOR]

ObjectiveTo evaluate whether etiologic diabetes type is associated with the degree of albuminuria in children with diabetes. RESEARCH DESIGN AND METHODS SEARCH: is an observational, longitudinal study of children with diabetes. Youth with newly diagnosed diabetes were classified according to diabetes autoantibody (DAA) status and presence of insulin resistance. We defined insulin resistance as an insulin sensitivity score

OBJECTIVE: To examine the association between diabetes stigma, socioeconomic status, psychosocial variables, and substance use in adolescents and young adults (AYAs) with type 1 or type 2 diabetes. RESEARCH DESIGN AND METHODS: This is a cross-sectional analysis of AYAs from the SEARCH for Diabetes in Youth study who completed a survey on diabetes-related stigma, generating a total diabetes stigma score. Using multivariable modeling, stratified by diabetes type, we examined the relationship of diabetes stigma with variables of interest. RESULTS: Of the 1,608 AYAs who completed the diabetes-related stigma survey, 78% had type 1 diabetes, and the mean age was 21.7 years. Higher diabetes stigma scores were associated with food insecurity (P = 0.001), disordered eating (P < 0.0001), depressive symptoms (P < 0.0001), and decreased health-related (P < 0.0001) and diabetes-specific quality of life (P < 0.0001). CONCLUSIONS: Diabetes stigma is associated with food insecurity, disordered eating, and lower psychosocial well-being. [ABSTRACT FROM AUTHOR]

OBJECTIVE: To project the prevalence and number of youths with diabetes and trends in racial and ethnic disparities in the U.S. through 2060. RESEARCH DESIGN AND METHODS: Based on a mathematical model and data from the SEARCH for Diabetes in Youth study for calendar years 2002–2017, we projected the future prevalence of type 1 and type 2 diabetes among youth aged <20 years while considering different scenarios of future trends in incidence. RESULTS: The number of youths with diabetes will increase from 213,000 (95% CI 209,000; 218,000) (type 1 diabetes 185,000, type 2 diabetes 28,000) in 2017 to 239,000 (95% CI 209,000; 282,000) (type 1 diabetes 191,000, type 2 diabetes 48,000) in 2060 if the incidence remains constant as observed in 2017. Corresponding relative increases were 3% (95% CI −9%; 21%) for type 1 diabetes and 69% (95% CI 43%; 109%) for type 2 diabetes. Assuming that increasing trends in incidence observed between 2002 and 2017 continue, the projected number of youths with diabetes will be 526,000 (95% CI 335,000; 893,000) (type 1 diabetes 306,000, type 2 diabetes 220,000). Corresponding relative increases would be 65% (95% CI 12%; 158%) for type 1 diabetes and 673% (95% CI 362%; 1,341%) for type 2 diabetes. In both scenarios, substantial widening of racial and ethnic disparities in type 2 diabetes prevalence are expected, with the highest prevalence among non-Hispanic Black youth. CONCLUSIONS: The number of youths with diabetes in the U.S. is likely to substantially increase in future decades, which emphasizes the need for prevention to attenuate this trend. [ABSTRACT FROM AUTHOR]

OBJECTIVE: To assess the prevalence of household food insecurity (HFI) and Supplemental Nutrition Assistance Program (SNAP) participation among youth and young adults (YYA) with diabetes overall and by type, and sociodemographic characteristics. RESEARCH DESIGN AND METHODS: The study included participants with youth-onset type 1 diabetes and type 2 diabetes from the SEARCH for Diabetes in Youth study. HFI was assessed using the 18-item U.S. Household Food Security Survey Module (HFSSM) administered from 2016 to 2019; three or more affirmations on the HFSSM were considered indicative of HFI. Participants were asked about SNAP participation. We used χ2 tests to assess whether the prevalence of HFI and SNAP participation differed by diabetes type. Multivariable logistic regression models were used to examine differences in HFI by participant characteristics. RESULTS: Of 2,561 respondents (age range, 10–35 years; 79.6% ≤25 years), 2,177 had type 1 diabetes (mean age, 21.0 years; 71.8% non-Hispanic White, 11.8% non-Hispanic Black, 13.3% Hispanic, and 3.1% other) and 384 had type 2 diabetes (mean age, 24.7 years; 18.8% non-Hispanic White, 45.8% non-Hispanic Black, 23.7% Hispanic, and 18.7% other). The overall prevalence of HFI was 19.7% (95% CI 18.1, 21.2). HFI was more prevalent in type 2 diabetes than type 1 diabetes (30.7% vs. 17.7%; P < 0.01). In multivariable regression models, YYA receiving Medicaid or Medicare or without insurance, whose parents had lower levels of education, and with lower household income had greater odds of experiencing HFI. SNAP participation was 14.1% (95% CI 12.7, 15.5), with greater participation among those with type 2 diabetes compared with those with type 1 diabetes (34.8% vs. 10.7%; P < 0.001). CONCLUSIONS: Almost one in three YYA with type 2 diabetes and more than one in six with type 1 diabetes reported HFI in the past year—a significantly higher prevalence than in the general U.S. population. [ABSTRACT FROM AUTHOR]

Objective: To determine glycemic and nonglycemic risk factors that contribute to the presence of diabetic retinopathy (DR) before and after the onset of type 2 diabetes (T2D).Research Design and Methods: During the Diabetes Prevention Program (DPP) and DPP Outcome Study (DPPOS), we performed fundus photography over time in adults at high risk for developing T2D, including after they developed diabetes. Fundus photographs were graded using the Early Treatment Diabetic Retinopathy Study (ETDRS) grading system, with DR defined as typical lesions of DR (microaneurysms, exudates, hemorrhage, or worse) in either eye.Results: By DPPOS year 16 (∼20 years after random assignment into DPP), 24% of 1,614 participants who had developed T2D and 14% of 885 who remained without diabetes had DR. In univariate analyses, using results from across the entire duration of follow-up, American Indian race was associated with less frequent DR compared with non-Hispanic White (NHW) race, and higher HbA1c, fasting and 2-h plasma glucose levels during an oral glucose tolerance test, weight, and history of hypertension, dyslipidemia, and smoking, but not treatment group assignment, were associated with more frequent DR. On multivariate analysis, American Indian race was associated with less DR compared with NHW (odds ratio [OR] 0.36, 95% CI 0.20-0.66), and average HbA1c was associated with more DR (OR 1.92, 95% CI 1.46-1.74 per SD [0.7%] increase in HbA1c).Conclusions: DR may occur in adults with prediabetes and early in the course of T2D. HbA1c was an important risk factor for the development of DR across the entire glycemic range from prediabetes to T2D. [ABSTRACT FROM AUTHOR]