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.