Type 2 diabetes (T2D) affects more than 415 million people worldwide, and its costs to the health care system continue to rise. To identify common or rare genetic variation with potential therapeutic implications for T2D, we analyzed and replicated genome-wide protein coding variation in a total of 8,227 individuals with T2D and 12,966 individuals without T2D of Latino descent. We identified a novel genetic variant in the IGF2 gene associated with ∼20% reduced risk for T2D. This variant, which has an allele frequency of 17% in the Mexican population but is rare in Europe, prevents splicing between IGF2 exons 1 and 2. We show in vitro and in human liver and adipose tissue that the variant is associated with a specific, allele-dosage–dependent reduction in the expression of IGF2 isoform 2. In individuals who do not carry the protective allele, expression of IGF2 isoform 2 in adipose is positively correlated with both incidence of T2D and increased plasma glycated hemoglobin in individuals without T2D, providing support that the protective effects are mediated by reductions in IGF2 isoform 2. Broad phenotypic examination of carriers of the protective variant revealed no association with other disease states or impaired reproductive health. These findings suggest that reducing IGF2 isoform 2 expression in relevant tissues has potential as a new therapeutic strategy for T2D, even beyond the Latin American population, with no major adverse effects on health or reproduction.

Reverse causality has made it difficult to establish the causal directions between obesity and prediabetes and obesity and insulin resistance. To disentangle whether obesity causally drives prediabetes and insulin resistance already in non-diabetic individuals, we utilized the UK Biobank and METSIM cohort to perform a Mendelian randomization (MR) analyses in the non-diabetic individuals. Our results suggest that both prediabetes and systemic insulin resistance are caused by obesity (p = 1.2×10 −3 and p = 3.1×10 −24). As obesity reflects the amount of body fat, we next studied how adipose tissue affects insulin resistance. We performed both bulk RNA-sequencing and single nucleus RNA sequencing on frozen human subcutaneous adipose biopsies to assess adipose cell-type heterogeneity and mitochondrial (MT) gene expression in insulin resistance. We discovered that the adipose MT gene expression and body fat percent are both independently associated with insulin resistance (p�0.05 for each) when adjusting for the decomposed adipose cell-type proportions. Next, we showed that these 3 factors, adipose MT gene expression, body fat percent, and adipose

Hypertension is associated with insulin resistance (IR), metabolic syndrome (MS), and arterial stiffness. Non–insulin‐based IR indexes were developed as tools for metabolic screening. Here, we aimed to evaluate the novel non–insulin‐based Metabolic Score for IR (METS‐IR) index for the prediction of incident hypertension and arterial stiffness evaluated using pulse wave velocity (PWV) analysis, compared with other non–insulin‐based IR indexes. We evaluated two populations, a cross‐sectional evaluation of high‐risk individuals (n = 305) with a wide range of metabolic comorbidities and dyslipidemia in whom PWV measurement was performed and a 3‐year prospective cohort of normotensive individuals (N = 6850). We observed a positive correlation between METS‐IR and PWV in the cross‐sectional cohort, which was higher compared with other non–insulin‐based fasting IR indexes; furthermore, PWV values >75th percentile were associated with the upper tercile of METS‐IR values. In the prospective cohort, we observed an increased risk for incident hypertension for the upper METS‐IR tercile (METS‐IR ≥ 46.42; HR: 1.81, 95% CI: 1.41‐2.34), adjusted for known cardiovascular risk factors, and observed that METS‐IR had greater increases in the predictive capacity for hypertension along with SBP and the Framingham Hypertension Risk Prediction Model compared with other non–insulin‐based IR indexes. Therefore, METS‐IR is a novel non–insulin‐based IR index which correlates with arterial stiffness and is a predictor of incident hypertension, complementary to previously validated risk prediction models.

Background Type 2 diabetes mellitus (T2D) is a leading cause of morbidity and mortality in Mexico. Here, we aimed to report incidence rates (IR) of type 2 diabetes in middle-aged apparently-healthy Mexican adults, identify risk factors associated to ID and develop a predictive model for ID in a high-risk population. Methods Prospective 3-year observational cohort, comprised of apparently-healthy adults from urban settings of central Mexico in whom demographic, anthropometric and biochemical data was collected. We evaluated risk factors for ID using Cox proportional hazard regression and developed predictive models for ID. Results We included 7636 participants of whom 6144 completed follow-up. We observed 331 ID cases (IR: 21.9 per 1000 person-years, 95%CI 21.37–22.47). Risk factors for ID included family history of diabetes, age, abdominal obesity, waist-height ratio, impaired fasting glucose (IFG), HOMA2-IR and metabolic syndrome. Early-onset ID was also high (IR 14.77 per 1000 person-years, 95%CI 14.21–15.35), and risk factors included HOMA-IR and IFG. Our ID predictive model included age, hypertriglyceridemia, IFG, hypertension and abdominal obesity as predictors (D xy = 0.487, c-statistic = 0.741) and had higher predictive accuracy compared to FINDRISC and Cambridge risk scores. Conclusions ID in apparently healthy middle-aged Mexican adults is currently at an alarming rate. The constructed models can be implemented to predict diabetes risk and represent the largest prospective effort for the study metabolic diseases in Latin-American population. Electronic supplementary material The online version of this article (10.1186/s12902-019-0361-8) contains supplementary material, which is available to authorized users.