Objectives A haplotype in SLC16A11 is associated with decreased insulin action, and risk for type 2 diabetes (T2D) in Mexicans. We aim to determine the impact of the risk haplotype on SLC16A11 on early therapeutic responses in treatments to prevent T2D. Methods We recruited subjects with at least one prediabetes criteria according to the American Diabetes Association, and body mass index 25–45 kg/m2. Subjects were randomized in two groups: lifestyle intervention (LSI): hypocaloric diet, 25 kcal/kg of ideal weight, 45% of the total intake of carbohydrates, 30% lipids and 15% protein sources + physical activity (>150 min medium intensity per week), or LSI + metformin (750 mg prolonged release twice a day). Interventions were prescribed by standardized dietitians. The goal was to achieve >3% weight loss. We evaluated the early treatment response in a follow-up period of 12 weeks with intermediate visits each 3 weeks to reinforce knowledge and treatment goals. Evaluations (baseline and post-treatment) included an oral glucose tolerance test (OGTT), and dual-energy X-ray absorptiometry. Adherence to treatment was measured trough electronic recordings. Participants were genotyped for the risk allele rs13342232. Researchers remained blinded to the genotype results. The effects of the risk haplotype were evaluated with linear and logistic regressions adjusted by age, sex, and baseline body fat %. Results We evaluated 61 subjects, 30 carriers, and 31 non-carriers. Most of participants (57%) achieved ≥3% weight loss. The LSI + metformin treatment increased in carriers, 2 times OR 3 IC95% (1.07 – 8.6) (P = 0.04) the probability to reach the ≥3% weight loss goal compared with LSI and non-carriers. In the same treatment, carriers had a greater decrease in the total and incremental area under the curve of insulin in the OGTT IC95% (−1.75 −0.11) (P = 0.02) compared with non-carriers and LSI. Carriers also had higher decrease in postprandial glucose compared with non-carriers regardless of treatment −12.63 + 30.38 vs 0.71 30.24 (P = 0.02). Conclusions After 12 weeks of treatment, carriers with prediabetes showed a higher probability achieve weight loss and to improve insulin secretion with metformin. Regardless of the treatment, carriers were prone to improve postprandial glucose. Funding Sources Miguel Aleman Medical Research Award.
Background: Fasting insulin concentrations reflect insulin resistance, which increase is associated with subsequent risk of heterogeneous cardiometabolic traits, including coronary artery disease (CAD), blood pressure (BP), estimated glomerular filtration rate (eGFR), BMI and lipid traits (TG, HDL, and LDL). We clustered insulin-associated genetic variants to define heterogeneous insulin resistance domains and tested whether these were associated with different cardiometabolic traits. Methods: We studied subjects of European Ancestry with genetic and clinical data available (N=18,127) in the Partners Biobank. We applied a soft clustering to 56 genome-wide association study (GWAS) fasting insulin genetic variants and 25 fasting insulin-related traits. We generated weighted genetic risk scores (GRS) for each cluster (alleles were aligned in the direction of increased fasting insulin effect). Then we aimed to identify clinical consequences between the clusters and cardiometabolic outcomes including CAD, SBP, DBP, eGFR, BMI, TG, HDL, and LDL. We tested the hypothesis that different genetic insulin domains will be cross-sectionally associated with some or all of these cardiometabolic traits. Results: We obtained three novel clusters of genetic variants. Each cluster was represented by a set of traits and a set of loci. The first represented VF and SAT, with loci near DLG2, FAM169B and, RGS12 genes, and its GRS was positive associated with CAD (p=0.004). The second cluster represents insulin sensitivity and lipid traits with loci near IRS1, ARRDC4 and, SNTG1, and its GRS was associated with lower TG (p=0.00001), and higher HDL(p=0.007). The last cluster represents proinsulin with loci near FABP2 and, PCAT5. No associations were found with this cluster. Conclusion: These results reveal different genetic insulin domains indicating different mechanisms for cardiometabolic traits associated with fasting insulin. Disclosure M. Sevilla: None. M. Udler: None. H. Kim: None. S. Hsu: None. J.B. Meigs: Consultant; Self; Quest Diagnostics. A. Manning: None.
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