Prediction of incident hypertension and arterial stiffness using the non-insulin-based metabolic score for insulin resistance (METS-IR) index.

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.

Development and validation of a predictive model for incident type 2 diabetes in middle-aged Mexican adults: the metabolic syndrome cohort.

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 (Dxy = 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.

Genome-wide association study of colorectal cancer in Hispanics.

Genome-wide association studies (GWAS) have identified 58 susceptibility alleles across 37 regions associated with the risk of colorectal cancer (CRC) with P < 5×10(-8) Most studies have been conducted in non-Hispanic whites and East Asians; however, the generalizability of these findings and the potential for ethnic-specific risk variation in Hispanic and Latino (HL) individuals have been largely understudied. We describe the first GWAS of common genetic variation contributing to CRC risk in HL (1611 CRC cases and 4330 controls). We also examine known susceptibility alleles and implement imputation-based fine-mapping to identify potential ethnicity-specific association signals in known risk regions. We discovered 17 variants across 4 independent regions that merit further investigation due to suggestive CRC associations (P < 1×10(-6)) at 1p34.3 (rs7528276; Odds Ratio (OR) = 1.86 [95% confidence interval (CI): 1.47-2.36); P = 2.5×10(-7)], 2q23.3 (rs1367374; OR = 1.37 (95% CI: 1.21-1.55); P = 4.0×10(-7)), 14q24.2 (rs143046984; OR = 1.65 (95% CI: 1.36-2.01); P = 4.1×10(-7)) and 16q12.2 [rs142319636; OR = 1.69 (95% CI: 1.37-2.08); P=7.8×10(-7)]. Among the 57 previously published CRC susceptibility alleles with minor allele frequency ≥1%, 76.5% of SNPs had a consistent direction of effect and 19 (33.3%) were nominally statistically significant (P < 0.05). Further, rs185423955 and rs60892987 were identified as novel secondary susceptibility variants at 3q26.2 (P = 5.3×10(-5)) and 11q12.2 (P = 6.8×10(-5)), respectively. Our findings demonstrate the importance of fine mapping in HL. These results are informative for variant prioritization in functional studies and future risk prediction modeling in minority populations.