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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.

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Sex hormone binding globulin (SHBG) levels are reported to be inversely associated with diabetes risk. It is unknown whether diabetes prevention interventions increase SHBG and whether resultant changes in SHBG affect diabetes risk. The purpose of this analysis was to determine whether intensive lifestyle intervention (ILS) or metformin changed circulating SHBG and if resultant changes influenced diabetes risk in the Diabetes Prevention Program (DPP). This is a secondary analysis from the DPP (1996-2001), a randomized trial of ILS or metformin versus placebo on diabetes risk over a mean follow-up of 3.2 years. The DPP was conducted across 27 academic study centers in the USA. Men, premenopausal and postmenopausal women without hormone use in the DPP were evaluated. The DPP included overweight/obese persons with elevated fasting glucose and impaired glucose tolerance. Main outcomes measures were changes in SHBG levels at 1 year and risk of diabetes over 3 years. ILS resulted in significantly higher increases (postmenopausal women: p

Coronary artery disease (CAD) is more frequent among individuals with dysglycemia. Preventive interventions for diabetes can improve cardiometabolic risk factors (CRFs), but it is unclear whether the benefits on CRFs are similar for individuals at different genetic risk for CAD. We built a 201-variant polygenic risk score (PRS) for CAD and tested for interaction with diabetes prevention strategies on 1-year changes in CRFs in 2,658 Diabetes Prevention Program (DPP) participants. We also examined whether separate lifestyle behaviors interact with PRS and affect changes in CRFs in each intervention group. Participants in both the lifestyle and metformin interventions had greater improvement in the majority of recognized CRFs compared with placebo (P < 0.001) irrespective of CAD genetic risk (P interaction > 0.05). We detected nominal significant interactions between PRS and dietary quality and physical activity on 1-year change in BMI, fasting glucose, triglycerides, and HDL cholesterol in individuals randomized to metformin or placebo, but none of them achieved the multiple-testing correction for significance. This study confirms that diabetes preventive interventions improve CRFs regardless of CAD genetic risk and delivers hypothesis-generating data on the varying benefit of increasing physical activity and improving diet on intermediate cardiovascular risk factors depending on individual CAD genetic risk profile.

Many community-based translations of evidence-based interventions are designed as one-arm studies due to ethical and other considerations. Evaluating the impacts of such programs is challenging. Here, we examine the effectiveness of the lifestyle intervention implemented by the Special Diabetes Program for Indians Diabetes Prevention (SDPI-DP) demonstration project, a translational lifestyle intervention among American Indian and Alaska Native communities. Data from the landmark Diabetes Prevention Program placebo group was used as a historical control. We compared the use of propensity score (PS) and disease risk score (DRS) matching to adjust for potential confounder imbalance between groups. The unadjusted hazard ratio (HR) for diabetes risk was 0.35 for SDPI-DP lifestyle intervention vs. control. However, when relevant diabetes risk factors were considered, the adjusted HR estimates were attenuated toward 1, ranging from 0.56 (95% CI 0.44-0.71) to 0.69 (95% CI 0.56-0.96). The differences in estimated HRs using the PS and DRS approaches were relatively small but DRS matching resulted in more participants being matched and smaller standard errors of effect estimates. Carefully employed, publicly available randomized clinical trial data can be used as a historical control to evaluate the intervention effectiveness of one-arm community translational initiatives. It is critical to use a proper statistical method to balance the distributions of potential confounders between comparison groups in this kind of evaluations.

Self-injurious behaviors (SIB) have been reported in more than 30% of children with an autism spectrum disorder (ASD) in clinic-based studies. This study estimated the prevalence of SIB in a large population-based sample of children with ASD in the United States. A total of 8,065 children who met the surveillance case definition for ASD in the Autism and Developmental Disabilities Monitoring (ADDM) Network during the 2000, 2006, and 2008 surveillance years were included. The presence of SIB was reported from available health and/or educational records by an expert clinician in ADDM Network. SIB prevalence averaged 27.7% across all sites and surveillance years, with some variation between sites. Clinicians should inquire about SIB during assessments of children with ASD.

Importance: Changes in the prevalence of youth-onset diabetes have previously been observed.Objective: To estimate changes in prevalence of type 1 and type 2 diabetes in youths in the US from 2001 to 2017.Design, Setting, and Participants: In this cross-sectional observational study, individuals younger than 20 years with physician-diagnosed diabetes were enumerated from 6 areas in the US (4 geographic areas, 1 health plan, and select American Indian reservations) for 2001, 2009, and 2017.Exposures: Calendar year.Main Outcomes and Measures: Estimated prevalence of physician-diagnosed type 1 and type 2 diabetes overall and by race and ethnicity, age, and sex.Results: Among youths 19 years or younger, 4958 of 3.35 million had type 1 diabetes in 2001, 6672 of 3.46 million had type 1 diabetes in 2009, and 7759 of 3.61 million had type 1 diabetes in 2017; among those aged 10 to 19 years, 588 of 1.73 million had type 2 diabetes in 2001, 814 of 1.85 million had type 2 diabetes in 2009, and 1230 of 1.85 million had type 2 diabetes in 2017. The estimated type 1 diabetes prevalence per 1000 youths for those 19 years or younger increased significantly from 1.48 (95% CI, 1.44-1.52) in 2001 to 1.93 (95% CI, 1.88-1.98) in 2009 to 2.15 (95% CI, 2.10-2.20) in 2017, an absolute increase of 0.67 per 1000 youths (95%, CI, 0.64-0.70) and a 45.1% (95% CI, 40.0%-50.4%) relative increase over 16 years. The greatest absolute increases were observed among non-Hispanic White (0.93 per 1000 youths [95% CI, 0.88-0.98]) and non-Hispanic Black (0.89 per 1000 youths [95% CI, 0.88-0.98]) youths. The estimated type 2 diabetes prevalence per 1000 youths aged 10 to 19 years increased significantly from 0.34 (95% CI, 0.31-0.37) in 2001 to 0.46 (95% CI, 0.43-0.49) in 2009 to 0.67 (95% CI, 0.63-0.70) in 2017, an absolute increase of 0.32 per 1000 youths (95% CI, 0.30-0.35) and a 95.3% (95% CI, 77.0%-115.4%) relative increase over 16 years. The greatest absolute increases were observed among non-Hispanic Black (0.85 per 1000 youths [95% CI, 0.74-0.97]) and Hispanic (0.57 per 1000 youths [95% CI, 0.51-0.64]) youths.Conclusions and Relevance: In 6 areas of the US from 2001 to 2017, the estimated prevalence of diabetes among children and adolescents increased for both type 1 and type 2 diabetes. [ABSTRACT FROM AUTHOR]

Objective: To compare treatment regimens and glycosylated hemoglobin (A1c) levels in Type 1 (T1D) and Type 2 diabetes (T2D) using diabetes registries from two countries—U.S. SEARCH for Diabetes in Youth (SEARCH) and Indian Registry of youth onset diabetes in India (YDR). Methods: The SEARCH and YDR data were harmonized to the structure and terminology in the Observational Medical Outcomes Partnership Common Data Model. Data used were from T1D and T2D youth diagnosed <20 years between 2006‐2012 for YDR, and 2006, 2008, and 2012 for SEARCH. We compared treatment regimens and A1c levels across the two registries. Results: There were 4003 T1D (SEARCH = 1899; YDR = 2104) and 611 T2D (SEARCH = 384; YDR = 227) youth. The mean A1c was higher in YDR compared to SEARCH (T1D:11.0% ± 2.9% vs 7.8% ± 1.7%, P <.001; T2D:9.9% ± 2.8% vs 7.2% ± 2.1%, P <.001). Among T1D youth in SEARCH, 65.1% were on a basal/bolus regimen, whereas in YDR, 52.8% were on once/twice daily insulin regimen. Pumps were used by 16.2% of SEARCH and 1.5% of YDR youth with T1D. Among T2D youth, in SEARCH and YDR, a majority were on metformin only (43.0% vs 30.0%), followed by insulin + any oral hypoglycemic agents (26.3% vs 13.7%) and insulin only (12.8% vs 18.9%), respectively. Conclusion: We found significant differences between SEARCH and YDR in treatment patterns in T1D and T2D. A1c levels were higher in YDR than SEARCH youth, for both T1D and T2D, irrespective of the regimens used. Efforts to achieve better glycemic control for youth are urgently needed to reduce the risk of long‐term complications. [ABSTRACT FROM AUTHOR]

Objective: Incidence of youth‐onset diabetes in India has not been well described. Comparison of incidence, across diabetes registries, has the potential to inform hypotheses for risk factors. We sought to compare the incidence of diabetes in the U.S.‐based registry of youth onset diabetes (SEARCH) to the Registry of Diabetes with Young Age at Onset (YDR—Chennai and New Delhi regions) in India. Methods: We harmonized data from both SEARCH and YDR to the Observational Medical Outcomes Partnership (OMOP) Common Data Model. Data were from youth registered with incident diabetes (2006‐2012). Denominators were from census and membership data. We calculated diabetes incidence by averaging the total cases across the entire follow‐up period and dividing this by the estimated census population corresponding to the source population for case ascertainment. Incidence was calculated for each of the registries and compared by type and within age and sex categories using a 2‐sided, skew‐corrected inverted score test. Results: Incidence of type 1 was higher in SEARCH (21.2 cases/100 000 [95% CI: 19.9, 22.5]) than YDR (4.9 cases/100 000 [95% CI: 4.3, 5.6]). Incidence of type 2 diabetes was also higher in SEARCH (5.9 cases/100 000 [95% CI: 5.3, 6.6] in SEARCH vs 0.5/cases/100 000 [95% CI: 0.3, 0.7] in YDR). The age distribution of incident type 1 diabetes cases was similar across registries, whereas type 2 diabetes incidence was higher at an earlier age in SEARCH. Sex differences existed in SEARCH only, with a higher rate of type 2 diabetes among females. Conclusion: The incidence of youth‐onset type 1 and 2 diabetes was significantly different between registries. Additional data are needed to elucidate whether the differences observed represent diagnostic delay, differences in genetic susceptibility, or differences in distribution of risk factors. [ABSTRACT FROM AUTHOR]

Background: Over the last decades, diabetes in youth has increased in both India and the United States, along with the burden of long‐term complications and healthcare costs. However, there are limited standardized population‐based data in contemporary youth cohorts for comparison of clinical and demographic characteristics of diabetes for both type 1 (T1D) and type 2 (T2D). Methods: In partnership, we harmonized demographic and clinical data from the SEARCH for Diabetes in Youth (SEARCH) registry in the United States and the Registry of People with Diabetes with Youth Age at Onset (YDR) in India to the structure and terminology of the Observational Medical Outcomes Partnership Common Data Model. Data were from youth with T1D and T2D, aged <20 years and newly diagnosed between 2006 and 2010. We compared key characteristics across registries using χ2 tests and t‐tests. Results: In total, there were 9650 youth with T1D and 2406 youth with T2D from 2006 to 2012. SEARCH youth were diagnosed at younger ages than YDR youth for T1D and T2D (10.0 vs 10.5 years, P <.001 and 14.7 vs 16.1 years, P <.001, respectively). For T2D, SEARCH had a higher proportion of females and significantly lower proportion of youth of high socioeconomic status compared to YDR. For T1D and T2D, SEARCH youth had higher BMI, lower blood pressure, and lower A1c compared to YDR youth. Conclusions: These data offer insights into the demographic and clinical characteristics of diabetes in youth across the two countries. Further research is needed to better understand why these differences exist. [ABSTRACT FROM AUTHOR]

Background: There is significant global variation in the prevalence of diabetic ketoacidosis (DKA) at diagnosis among youth with type 1 diabetes (T1D). However, data for youth with type 2 diabetes (T2D) are limited, even in developed countries. We compared the prevalence of DKA at diagnosis among individuals with T1D and T2D from the SEARCH for Diabetes in Youth (SEARCH) and the Registry of Youth Onset Diabetes in India (YDR) registries. Methods: We harmonized the SEARCH and YDR registries to the structure and terminology in the Observational Medical Outcome Partnership Common Data Model. Data used were from youth with T1D and T2D diagnosed before 20 years and newly diagnosed between 2006 and 2012 in YDR and 2009 and 2012 in SEARCH. Results: There were 5366 US youth (4078 with T1D, 1288 with T2D) and 2335 Indian youth (2108 with T1D, 227 with T2D). More than one third of T1D youth enrolled in SEARCH had DKA at diagnosis which was significantly higher than in YDR (35.3% vs 28.7%, P <.0001). The burden of DKA in youth with T1D was significantly higher among younger age groups; this relationship was similar across registries (P =.4). The prevalence of DKA among T2D in SEARCH and YDR were 5.5% and 6.6% respectively (P =.4). Conclusions: There is significant burden of DKA at diagnosis with T1D among youth from United States and India, especially among the younger age groups. The reasons for this high prevalence are largely unknown but are critical to developing interventions to prevent DKA at diagnosis. [ABSTRACT FROM AUTHOR]

Background: Although surveillance for diabetes in youth relies on provider‐assigned diabetes type from medical records, its accuracy compared to an etiologic definition is unknown. Methods: Using the SEARCH for Diabetes in Youth Registry, we evaluated the validity and accuracy of provider‐assigned diabetes type abstracted from medical records against etiologic criteria that included the presence of diabetes autoantibodies (DAA) and insulin sensitivity. Youth who were incident for diabetes in 2002‐2006, 2008, or 2012 and had complete data on key analysis variables were included (n = 4001, 85% provider diagnosed type 1). The etiologic definition for type 1 diabetes was ≥1 positive DAA titer(s) or negative DAA titers in the presence of insulin sensitivity and for type 2 diabetes was negative DAA titers in the presence of insulin resistance. Results: Provider diagnosed diabetes type correctly agreed with the etiologic definition of type for 89.9% of cases. Provider diagnosed type 1 diabetes was 96.9% sensitive, 82.8% specific, had a positive predictive value (PPV) of 97.0% and a negative predictive value (NPV) of 82.7%. Provider diagnosed type 2 diabetes was 82.8% sensitive, 96.9% specific, had a PPV and NPV of 82.7% and 97.0%, respectively. Conclusion: Provider diagnosis of diabetes type agreed with etiologic criteria for 90% of the cases. While the sensitivity and PPV were high for youth with type 1 diabetes, the lower sensitivity and PPV for type 2 diabetes highlights the value of DAA testing and assessment of insulin sensitivity status to ensure estimates are not biased by misclassification. [ABSTRACT FROM AUTHOR]

Novel biomarkers of type 2 diabetes (T2D) and response to preventative treatment in individuals with similar clinical risk may highlight metabolic pathways that are important in disease development. We profiled 331 metabolites in 2,015 baseline plasma samples from the Diabetes Prevention Program (DPP). Cox models were used to determine associations between metabolites and incident T2D, as well as whether associations differed by treatment group (i.e., lifestyle [ILS], metformin [MET], or placebo [PLA]), over an average of 3.2 years of follow-up. We found 69 metabolites associated with incident T2D regardless of treatment randomization. In particular, cytosine was novel and associated with the lowest risk. In an exploratory analysis, 35 baseline metabolite associations with incident T2D differed across the treatment groups. Stratification by baseline levels of several of these metabolites, including specific phospholipids and AMP, modified the effect that ILS or MET had on diabetes development. Our findings highlight novel markers of diabetes risk and preventative treatment effect in individuals who are clinically at high risk and motivate further studies to validate these interactions. [ABSTRACT FROM AUTHOR]