Development and validation of a screening score for prediabetes and undiagnosed diabetes

Objective. To develop and validate an easy-to-use risk score to detect prediabetes and undiagnosed diabetes in Mexican population. Materials and methods. Using information from the Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán (INCMNSZ)’s cohort study of 10 234, risk factors were identified and included in stratified by sex multiple logistic regression models. The beta coefficients of the final model were multiplied by 10, thus obtaining the weights of each variable in the score. Results. The proposed score correctly classifies 55.4% of women with undiagnosed diabetes and 57.2% of women with prediabetes or diabetes. While for men it correctly classifies them at 68.6% and 69.9%, respectively. Conclusions. We present the design and validation of a risk score stratified by sex, to determine if an adult could have prediabetes or diabetes, in which case laboratory studies should be performed to confirm or not the diagnosis

Identification of a threshold to discriminate fasting hypertriglyceridemia with postprandial values

Abstract Background Postprandial lipemia is an important cardiovascular risk factor. The assessment of postprandial lipid metabolism is a newly trend that several consortiums and countries have adopted. The aim of the study is to determine a postprandial triglyceride concentration cut-off point that accurately discriminate individuals with fasting normal triglyceride concentrations from those with fasting hypertriglyceridemia. Methods Cross sectional population-based study. A total of 212 subjects underwent an eight hours’ oral fat tolerance test. Samples were taken fasting, three, four, five, six and eight hours after the meal. The area under the receiver operating characteristic curve (c-statistic) was computed using postprandial triglycerides concentrations as independent predictor, and fasting hypertriglyceridemia as dependent variable. Results The best threshold of postprandial lipemia to discriminate fasting hypertriglyceridemia was 280 mg/dL at any hour area under the curve 0.816 (95% confidence interval 0.753–0.866), bootstrap-corrected c-statistic = 0.733 (95% confidence interval 0.68–0.86). The same value was compared with apolipoprotein B concentrations (>90th percentile) having a good performance: area under the curve 0.687 95% confidence interval 0.624–0.751). Likewise, subjects with high postprandial lipemia have higher Globo risk scores. Conclusion The 280 mg/dL cut-off point value of postprandial triglycerides concentration any time after a test meal discriminate subjects with fasting hypertriglyceridemia. This threshold has a good performance in a heterogeneous population and has a good concordance with cardiovascular risk surrogates

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 (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

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

Introduction Previous reports in European populations demonstrated the existence of five data-driven adult-onset diabetes subgroups. Here, we use self-normalizing neural networks (SNNN) to improve reproducibility of these data-driven diabetes subgroups in Mexican cohorts to extend its application to more diverse settings.Research design and methods We trained SNNN and compared it with k-means clustering to classify diabetes subgroups in a multiethnic and representative population-based National Health and Nutrition Examination Survey (NHANES) datasets with all available measures (training sample: NHANES-III, n=1132; validation sample: NHANES 1999–2006, n=626). SNNN models were then applied to four Mexican cohorts (SIGMA-UIEM, n=1521; Metabolic Syndrome cohort, n=6144; ENSANUT 2016, n=614 and CAIPaDi, n=1608) to characterize diabetes subgroups in Mexicans according to treatment response, risk for chronic complications and risk factors for the incidence of each subgroup.Results SNNN yielded four reproducible clinical profiles (obesity related, insulin deficient, insulin resistant, age related) in NHANES and Mexican cohorts even without C-peptide measurements. We observed in a population-based survey a high prevalence of the insulin-deficient form (41.25%, 95% CI 41.02% to 41.48%), followed by obesity-related (33.60%, 95% CI 33.40% to 33.79%), age-related (14.72%, 95% CI 14.63% to 14.82%) and severe insulin-resistant groups. A significant association was found between the SLC16A11 diabetes risk variant and the obesity-related subgroup (OR 1.42, 95% CI 1.10 to 1.83, p=0.008). Among incident cases, we observed a greater incidence of mild obesity-related diabetes (n=149, 45.0%). In a diabetes outpatient clinic cohort, we observed increased 1-year risk (HR 1.59, 95% CI 1.01 to 2.51) and 2-year risk (HR 1.94, 95% CI 1.13 to 3.31) for incident retinopathy in the insulin-deficient group and decreased 2-year diabetic retinopathy risk for the obesity-related subgroup (HR 0.49, 95% CI 0.27 to 0.89).Conclusions Diabetes subgroup phenotypes are reproducible using SNNN; our algorithm is available as web-based tool. Application of these models allowed for better characterization of diabetes subgroups and risk factors in Mexicans that could have clinical applications