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

Abstract

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.

Predicting Mortality Due to SARS-CoV-2: A Mechanistic Score Relating Obesity and Diabetes to COVID-19 Outcomes in Mexico

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Current State of Diabetes Mellitus Prevalence, Awareness, Treatment, and Control in Latin America: Challenges and Innovative Solutions to Improve Health Outcomes Across the Continent

“…Therefore, current estimates of the prevalence of diabetes across continents may not fully account for the necessary resources to provide adequate health care for Latin Americans with diabetes [7,8,190,191]. Considering the workforce and resources needed to screen the millions of persons across the region who are at risk of diabetes or have the disease and are not aware, experts have proposed diabetes predictive models requiring specific easily obtained clinical data points that could be readily used in primary care settings [192][193][194]. Also, the Finnish Diabetes Risk Score (FINDRISC) has been proposed, tested, or modified to screen and identify individuals at high risk of developing diabetes in Latin America [195][196][197][198][199].…”

Clinical characterization of data-driven diabetes subgroups in Mexicans using a reproducible machine learning approach

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