Abstract BACKGROUND A fundamental precept of the carbohydrate–insulin model of obesity is that insulin secretion drives weight gain. However, fasting hyperinsulinemia can also be driven by obesity-induced insulin resistance. We used genetic variation to isolate and estimate the potentially causal effect of insulin secretion on body weight. METHODS Genetic instruments of variation of insulin secretion [assessed as insulin concentration 30 min after oral glucose (insulin-30)] were used to estimate the causal relationship between increased insulin secretion and body mass index (BMI), using bidirectional Mendelian randomization analysis of genome-wide association studies. Data sources included summary results from the largest published metaanalyses of predominantly European ancestry for insulin secretion (n = 26037) and BMI (n = 322154), as well as individual-level data from the UK Biobank (n = 138541). Data from the Cardiology and Metabolic Patient Cohort study at Massachusetts General Hospital (n = 1675) were used to validate genetic associations with insulin secretion and to test the observational association of insulin secretion and BMI. RESULTS Higher genetically determined insulin-30 was strongly associated with higher BMI (β = 0.098, P = 2.2 × 10−21), consistent with a causal role in obesity. Similar positive associations were noted in sensitivity analyses using other genetic variants as instrumental variables. By contrast, higher genetically determined BMI was not associated with insulin-30. CONCLUSIONS Mendelian randomization analyses provide evidence for a causal relationship of glucose-stimulated insulin secretion on body weight, consistent with the carbohydrate–insulin model of obesity.
ABSTRACT Hypothesis The prevalence of type 2 diabetes is higher in Latino populations compared with other major ancestry groups. Not only has the Latino population been systematically underrepresented in large-scale genetic analyses, but previous studies relied on the imputation of ungenotyped variants based on the 1000 Genomes (1000G) imputation reference panel, which results in suboptimal capture of low-frequency or Latino-enriched variants. The NHLBI Trans-Omics for Precision Medicine (TOPMed) reference panel represents a unique opportunity to analyze rare genetic variations in the Latino population. Methods We evaluate the TOPMed imputation performance using genotyping array and whole-exome sequence data in 6 Latino cohorts. To evaluate the ability of TOPMed imputation of increasing the identified loci, we performed a Latino type 2 diabetes GWAS meta-analysis in 8,150 type 2 diabetes cases and 10,735 controls and replicated the results in 6 additional cohorts including whole-genome sequence data from the All of Us cohort. Results We show that, compared to imputation with 1000G, the TOPMed panel improves the identification of rare and low-frequency variants. We identified 26 distinct signals including a novel genome-wide significant variant (minor allele frequency 1.6%, OR=2.0, P=3.4×10 −9 ) near ORC5 . A Latino-tailored polygenic score constructed from our data and GWAS data from East Asian and European populations improves the prediction accuracy in a Latino target dataset, explaining up to 7.6% of the type 2 diabetes risk variance. Conclusions Our results demonstrate the utility of TOPMed imputation for identifying low-frequency variation in understudied populations, leading to the discovery of novel disease associations and the improvement of polygenic scores.
Background: Diabetes affects 37.3 million people, with a total estimated cost of $327 billion. Studies have shown significant knowledge gaps in resident education regarding diabetes management, resulting in medical errors and inappropriate care. Although studies show that hands-on diabetes education is beneficial, most curricula are delivered as online, self-paced, or case-based learning, without insight into the day-to-day management of diabetes. Description: Using personal experience of living with diabetes as a unique educational tool, we created an education session to share the patient experience of daily diabetes tasks, teach diabetes management skills, and foster peer teaching. The hour-long interactive session included an introduction about the presenter’s experience with diabetes and three 15-minute hands-on sessions, where participants counted their own carbohydrates for a meal, tested their blood sugar and/or tried on a Dexcom G7 sensor, and put on an insulin pump site. Methods: We presented the session to pediatric residents and clerkship students, and surveyed participants on their understanding of diabetes care before and after the session and their overall rating of the session. Results: A total of 17 participants answered the pre-session survey and 7 answered the post-session survey. Prior to the session, the proportion of participants indicating they somewhat or strongly agreed they knew what it felt like to check a blood glucose, give an insulin injection or pump insertion, or to perform the work of carbohydrate counting was 53%, 18%, and 29%; post-session these proportions increased to 100%, 100%, and 86% respectively. All the post-session respondents somewhat or strongly agreed they found the session valuable and would recommend to other residents. Discussion: The session increased participants’ understanding of daily tasks of diabetes management and was perceived as a valuable part of residency education. Future directions include expanding to internal medicine and family practice residents. Disclosure A. Jenkins: None. J.N. Todd: None.
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.
Abstract Aims/hypothesis The Latino population has been systematically underrepresented in large-scale genetic analyses, and previous studies have relied on the imputation of ungenotyped variants based on the 1000 Genomes (1000G) imputation panel, which results in suboptimal capture of low-frequency or Latino-enriched variants. The National Heart, Lung, and Blood Institute (NHLBI) Trans-Omics for Precision Medicine (TOPMed) released the largest multi-ancestry genotype reference panel representing a unique opportunity to analyse rare genetic variations in the Latino population. We hypothesise that a more comprehensive analysis of low/rare variation using the TOPMed panel would improve our knowledge of the genetics of type 2 diabetes in the Latino population. Methods We evaluated the TOPMed imputation performance using genotyping array and whole-exome sequence data in six Latino cohorts. To evaluate the ability of TOPMed imputation to increase the number of identified loci, we performed a Latino type 2 diabetes genome-wide association study (GWAS) meta-analysis in 8150 individuals with type 2 diabetes and 10,735 control individuals and replicated the results in six additional cohorts including whole-genome sequence data from the All of Us cohort. Results Compared with imputation with 1000G, the TOPMed panel improved the identification of rare and low-frequency variants. We identified 26 genome-wide significant signals including a novel variant (minor allele frequency 1.7%; OR 1.37, p =3.4 × 10 −9 ). A Latino-tailored polygenic score constructed from our data and GWAS data from East Asian and European populations improved the prediction accuracy in a Latino target dataset, explaining up to 7.6% of the type 2 diabetes risk variance. Conclusions/interpretation Our results demonstrate the utility of TOPMed imputation for identifying low-frequency variants in understudied populations, leading to the discovery of novel disease associations and the improvement of polygenic scores. Data availability Full summary statistics are available through the Common Metabolic Diseases Knowledge Portal ( https://t2d.hugeamp.org/downloads.html ) and through the GWAS catalog ( https://www.ebi.ac.uk/gwas/ , accession ID: GCST90255648). Polygenic score (PS) weights for each ancestry are available via the PGS catalog ( https://www.pgscatalog.org , publication ID: PGP000445, scores IDs: PGS003443, PGS003444 and PGS003445). Graphical abstract
There is a limited understanding of how genetic loci associated with glycemic traits and type 2 diabetes (T2D) influence the response to anti-diabetes medications. Polygenic scores provide increasing power to detect patterns of disease predisposition that might benefit from a targeted pharmacologic intervention. In the Study to Understand the Genetics of the Acute Response to Metformin and Glipizide in Humans (SUGAR-MGH), we constructed weighted polygenic scores using known genome-wide significant associations for T2D, fasting glucose (FG), and fasting insulin (FI), comprised of 65, 43, and 13 single nucleotide polymorphisms, respectively. Multiple linear regression tested for associations between scores and glycemic traits as well as pharmacodynamic endpoints, adjusting for age, sex, race, and body mass index (BMI). A higher T2D score was nominally associated with a shorter time to insulin peak, greater glucose area over the curve, shorter time to glucose trough, and steeper slope to glucose trough after glipizide. In replication, a higher T2D score was associated with a greater 1-year HbA1c reduction to sulfonylureas in the Genetics of Diabetes Audit and Research, Tayside and Scotland (GoDARTS) study (<i>p</i>=0.02). Our findings suggest that individuals with a higher genetic burden for T2D experience a greater acute and sustained response to sulfonylureas.<b> </b>
<p>Genome-wide significant loci for metformin response in type 2 diabetes reported elsewhere have not replicated in the Diabetes Prevention Program (DPP). To assess pharmacogenetic interactions in pre-diabetes, we conducted a genome-wide association study (GWAS) in the DPP. Cox proportional hazards models tested associations with diabetes incidence in metformin (MET, n=876) and placebo (PBO, n=887) arms. Multiple linear regression assessed association with one-year change in metformin-related quantitative traits, adjusted for baseline trait, age, sex, and 10 ancestry principal components. We tested for gene-by-treatment interaction. No significant associations emerged for diabetes incidence. We identified four genome-wide significant variants after correcting for correlated traits (<em>p</em><9×10-9). In MET, rs144322333 near <em>ENOSF1 </em>(minor allele frequency [MAF]AFR=0.07, MAFEUR=0.002) was associated with an increase in % glycated hemoglobin (per minor allele β=0.39 [95% CI 0.28, 0.50], <em>p</em>=2.8×10-12). Rs145591055 near <em>OMSR</em> (MAF=0.10 in American Indians), was associated with weight loss (kg) (per G allele β=-7.55 [95% CI -9.88, -5.22], <em>p</em>=3.2×10-10) in MET. Neither variant was significant in PBO; gene-by-treatment interaction was significant for both variants (<em>p(G×T)</em><1.0×10-4). Replication in individuals with diabetes did not yield significant findings. A GWAS for metformin response in pre-diabetes revealed novel ethnic-specific associations that require further investigation but may have implications for tailored therapy. <br> </p>
Obesity has been posited as an independent risk factor for diabetic kidney disease (DKD), but establishing causality from observational data is problematic. We aimed to test whether obesity is causally related to DKD using Mendelian randomization, which exploits the random assortment of genes during meiosis. In 6,049 subjects with type 1 diabetes, we used a weighted genetic risk score (GRS) comprised of 32 validated BMI loci as an instrument to test the relationship of BMI with macroalbuminuria, end-stage renal disease (ESRD), or DKD defined as presence of macroalbuminuria or ESRD. We compared these results with cross-sectional and longitudinal observational associations. Longitudinal analysis demonstrated a U-shaped relationship of BMI with development of macroalbuminuria, ESRD, or DKD over time. Cross-sectional observational analysis showed no association with overall DKD, higher odds of macroalbuminuria (for every 1 kg/m(2) higher BMI, odds ratio [OR] 1.05, 95% CI 1.03-1.07, P < 0.001), and lower odds of ESRD (OR 0.95, 95% CI 0.93-0.97, P < 0.001). Mendelian randomization analysis showed a 1 kg/m(2) higher BMI conferring an increased risk in macroalbuminuria (OR 1.28, 95% CI 1.11-1.45, P = 0.001), ESRD (OR 1.43, 95% CI 1.20-1.72, P < 0.001), and DKD (OR 1.33, 95% CI 1.17-1.51, P < 0.001). Our results provide genetic evidence for a causal link between obesity and DKD in type 1 diabetes. As obesity prevalence rises, this finding predicts an increase in DKD prevalence unless intervention should occur.
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