To develop a prediction equation for 10-year risk of a combined endpoint (incident coronary heart disease, stroke, heart failure, chronic kidney disease, lower extremity hospitalizations) in people with diabetes, using demographic and clinical information, and a panel of traditional and non-traditional biomarkers.
The prevalence of type 2 diabetes is increasing in youth, particularly in certain ethnic groups, and studies have highlighted differences between youth- and adult-onset disease. However, its genetic determinants remain largely unexplored. To identify genetic variants predisposing to type 2 diabetes in youth, we formed ProDiGY, a multi-ethnic collaboration of the TODAY, SEARCH for Diabetes in Youth, and T2D-GENES studies, with 30young cases (mean age= 15.1 y ±2.8, 63% female, 22% white, 36% African American, 42% Hispanic) and 6061 diabetes-free adult controls (mean age = 54.2 y ±12.4; 57% female, 24% white, 18% African American, 58% Hispanic). After stratifying by self-reported and principal component-clustered ethnicity, we performed association analyses on 206,928 directly genotyped variants using a generalized linear mixed model using a genetic relationship matrix to account for population structure. We identified 5 genome-wide significant loci, including the novel locus rs13130484 in GNPDA2 (P= 1.4 ×10-8, odds ratio [OR]= 1.24), which has nominal association with type 2 diabetes in adults. Known loci identified in our analysis include rs4132670 in the well-known type 2 diabetes locus TCF7L2 (P= 4.2×10-13, OR 1.35) in high linkage disequilibrium with causal SNP rs7903146 (R2=0.83), rs11662368 near the obesity locus MC4R (P= 3.9×10-9, OR =1.31), rs11257655 in RN7SL232P (P= 5.7×10-12, OR= 1.33), and rs7075724 in CDC123 (P= 8.3×10-11, OR= 1.29); effect sizes are greater for these latter three loci than those found in adults (Pdiff= 8.2×10-5, 2.4×10-5, and 1.7×10-6, respectively). Secondary analysis with 856 diabetes-free youth controls demonstrated consistent direction of effect but no additional loci. In conclusion, we identified known and novel genetic loci predisposing to type 2 diabetes in youth. Our future analysis of ∼10 million variants holds the potential to uncover additional new type 2 diabetes susceptibility loci. Disclosure S. Srinivasan: None. J. Todd: None. L. Chen: None. J. Divers: None. S. Gidding: None. S. Chernausek: Advisory Panel; Self; Novo Nordisk Inc.. R. Gubitosi-Klug: None. M.M. Kelsey: Other Relationship; Self; Daiichi Sankyo Company, Limited, Merck Sharp & Dohme Corp.. R. Shah: None. M. Black: None. L.E. Wagenknecht: None. J.M. Mercader: None. A. Manning: None. J. Flannick: None. D. Dabelea: None. J.C. Florez: Consultant; Self; Intarcia Therapeutics, Inc.. Consultant; Spouse/Partner; Santen.
We sought to determine the rate of progression of carotid atherosclerosis in persons with normal glucose tolerance, impaired glucose tolerance, and undiagnosed and diagnosed type 2 diabetes.
OBJECTIVE Diabetes surveillance often requires manual medical chart reviews to confirm status and type. This project aimed to create an electronic health record (EHR)-based procedure for improving surveillance efficiency through automation of case identification. RESEARCH DESIGN AND METHODS Youth (<20 years old) with potential evidence of diabetes (N = 8,682) were identified from EHRs at three children’s hospitals participating in the SEARCH for Diabetes in Youth Study. True diabetes status/type was determined by manual chart reviews. Multinomial regression was compared with an ICD-10 rule-based algorithm in the ability to correctly identify diabetes status and type. Subsequently, the investigators evaluated a scenario of combining the rule-based algorithm with targeted chart reviews where the algorithm performed poorly. RESULTS The sample included 5,308 true cases (89.2% type 1 diabetes). The rule-based algorithm outperformed regression for overall accuracy (0.955 vs. 0.936). Type 1 diabetes was classified well by both methods: sensitivity (Se) (>0.95), specificity (Sp) (>0.96), and positive predictive value (PPV) (>0.97). In contrast, the PPVs for type 2 diabetes were 0.642 and 0.778 for the rule-based algorithm and the multinomial regression, respectively. Combination of the rule-based method with chart reviews (n = 695, 7.9%) of persons predicted to have non–type 1 diabetes resulted in perfect PPV for the cases reviewed while increasing overall accuracy (0.983). The Se, Sp, and PPV for type 2 diabetes using the combined method were ≥0.91. CONCLUSIONS An ICD-10 algorithm combined with targeted chart reviews accurately identified diabetes status/type and could be an attractive option for diabetes surveillance in youth.
OBJECTIVE: To examine short-term mortality and cause of death among youth and young adults (YYA) with youth-onset diabetes. <p>RESEARCH DESIGN AND METHODS: We included 19,717 YYA’s newly-diagnosed with diabetes before age 20 from 1/1/2002–12/31/2015 enrolled in the SEARCH for Diabetes in Youth Study. Of these, 14,721 had type 1; 4,141 type 2; 551 secondary and 304 other/unknown diabetes type. Cases were linked with the National Death Index through 12/31/2017. We calculated standardized mortality ratios (SMR) and 95% CIs based on age, sex, and race/ethnicity for state and county population areas and examined underlying causes of death.</p> <p>RESULTS: During 170,148 person-years (PY) (median follow-up=8.5 years), 283 individuals died: 133 with type 1 (103.0/100,000 PY), 55 with type 2 (161.5/100,000 PY), 87 with secondary (1,952/100,000 PY) and 8 with other/unknown diabetes type (312.3/100,000 PY). SMRs (95% CI) for the first three groups were 1.5 (1.2-1.8), 2.3 (1.7-3.0) and 28.0 (22.4-34.6), respectively. Diabetes was the underlying cause of death for 42.1%, 9.1% and 4.6% of deaths, respectively. The SMR was greater for type 2 than for type 1 diabetes (p<0.001). SMRs were significantly higher for ages <20 years, non-Hispanic White and Hispanic individuals and females with type 1 diabetes and for ages <25 years, all race/ethnic minority groups and both sexes with type 2 diabetes. </p> <p>CONCLUSION: Excess mortality was observed among YYA for each type of diabetes with differences in risk associated with diabetes type, age, race/ethnicity, and sex. The root causes of excess mortality among YYAs with diabetes merits further study. </p>
Introduction: The obesity epidemic in the United States accelerated in the late 1970’s and has lifelong effects. Longitudinal patterns of racial/ethnic and socioeconomic differences in weight gain across the lifespan are relatively uncharacterized. Hypothesis: Weight gain starts earlier and is larger in individuals who are black or have less education compared to those who are white or have higher education; weight loss after midlife shows similar patterns. Methods: We analyzed body mass index (BMI, kg/m2) changes in 14,291 ARIC participants (ages 45 - 64) followed from Visit 1 (1987 - 89) to Visit 5 (2011 - 13, Table 1). Participant BMI at age 25 was ascertained by self-report at the time of study enrollment. BMI change between these time points was categorized as stable (<+/-2 kg/m2, average of <+/-13 lbs.), increasing or decreasing (>+/-2 kg/m2) and analyzed using multinomial logistic regression. Results: Table 1 shows BMI change and its associations with race and education. BMI change from age 25 to visit 1 (median age: 54) increased in 71%, was stable in 25% and decreased in 4% of participants. In midlife (visit 1 [1987 - 89] to visit 4 [1996 - 98]), an increase in BMI was more common (33%) compared to a decrease in BMI (7%). However, at older age (visit 4 [1996 - 98] to visit 5 [2011 - 2013]), both an increase and decrease in BMI (23% and 21% respectively) were common. Multinomial logistic regression demonstrated that being black, and having lower education, was consistently associated with weight gain from age 25 to midlife (visit 1). Interestingly, weight loss was also more common among blacks than whites, particularly at older age. Conclusion: Increases in BMI over the lifespan are common; weight gain from young adulthood to midlife, and both further weight gain and weight loss are common at older ages. Compared to whites, blacks experience more weight gain to midlife and greater late life weight loss. The implications for cardiovascular risk for weight gain and weight loss (a subset of which may mark poor health) needs to be explored.
ABSTRACT Background & Aims A common genetic variant near MBOAT7 (rs641738C>T) has been previously associated with hepatic fat and advanced histology in non-alcoholic fatty liver disease (NAFLD), however, these findings have not been consistently replicated in the literature. We aimed to establish whether rs641738C>T is a risk factor across the spectrum of NAFLD and characterize its role in the regulation of related metabolic phenotypes through meta-analysis. Methods We performed meta-analysis of studies with data on the association between rs641738C>T genotype and: liver fat, NAFLD histology, and serum ALT, lipids, or insulin. These included directly genotyped studies and population-level data from genome-wide association studies (GWAS). We performed random effects meta-analysis using recessive, additive, and dominant genetic models. Results Data from 1,047,265 participants (8,303 with liver biopsies) across 42 studies was included in the meta-analysis. rs641738C>T was associated with higher liver fat on CT/MRI (+0.03 standard deviations [95% CI: 0.02 - 0.05]) and diagnosis of NAFLD (OR 1.22 [95% CI 1.08 - 1.39]) in Caucasian adults. The variant was also positively associated with presence of severe steatosis, NASH, and advanced fibrosis (OR: 1.32 [95% CI: 1.06 - 1.63]) in Caucasian adults using a recessive model of inheritance (CC+CT vs. TT). Meta-analysis of data from previous GWAS found the variant to be associated with higher ALT (P z =0.002) and lower serum triglycerides (P z =1.5×10 −4 ). rs641738C>T was not associated with fasting insulin and no effect was observed in children with NAFLD. Conclusion Our study validates rs641738C>T near MBOAT7 as a risk factor for the presence and severity of NAFLD in individuals of European descent.
Look AHEAD found no difference between intensive lifestyle intervention (ILI) for weight loss and diabetes support and education (DSE; control) in cardiovascular (CVD) outcomes among individuals with overweight or obesity and type 2 diabetes (T2DM). This secondary analysis evaluated the association between change in weight and waist circumference (WC), a possible proxy for visceral adiposity, and risk for CVD outcomes. We classified Look AHEAD participants (n=4590) into one of four categories based on change in weight and WC from baseline to Year 1 (increase/increase, etc.) to examine the association between the categories and primary (myocardial infarction, stroke, hospitalized angina, CVD death) and secondary (primary outcomes and CABG/PTCA, hospitalized congestive heart failure, carotid endarterectomy, PVD, and total mortality) CVD outcomes from Year 1 to end of active treatment (median of 9 years). Cox proportional-hazards regression models were used to 1) compare ILI participants in the four categories to DSE and 2) evaluate the four categories within treatment groups using the group that decreased WC and weight as the reference. Compared to DSE, individuals in ILI who increased WC and weight and individuals who increased WC but decreased weight both had greater risk of secondary CVD outcomes (HR[95%CI]: 1.95 [1.33, 2.86] and 1.35 [1.02, 1.80] respectively). In analyses stratified by randomization group, DSE participants did not differ in cardiovascular events across categories. Individuals in ILI who gained WC (regardless of weight loss or gain), had increased risk of primary and secondary CVD outcomes compared to individuals in ILI who decreased weight and WC. Increased WC during weight loss treatment, regardless of weight loss or gain, is associated with greater risk for CVD outcomes in individuals with T2DM. This emphasizes the importance of measuring WC in clinical practice and testing interventions focused on decreasing WC. Disclosure K.L. Olson: None. R.H. Neiberg: None. M. Espeland: Other Relationship; Self; Boehringer Ingelheim International GmbH, Ironwood Pharmaceuticals. K.C. Johnson: None. W.C. Knowler: None. A.E. Staiano: None. L.E. Wagenknecht: None. R.R. Wing: None. Funding National Institutes of Health
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