Advancing therapy in people with suboptimally controlled basal insulin-treated type 2 diabetes: Subanalysis of the SoliMix trial in participants in Latin American countries

Aims: This subanalysis of the SoliMix trial assessed the efficacy and safety of advancing basal insulin (BI) therapy with iGlarLixi versus BIAsp 30 in people with type 2 diabetes (T2D) living in Latin American (LATAM) countries, i.e. Argentina and Mexico (N = 160). Materials and Methods: SoliMix (EudraCT: 2017-003370-13) was a 26-week, open-label, multicentre study, where adults with T2D suboptimally controlled with BI plus one or two oral glucose-lowering drugs and glycated haemoglobin (HbA1c) ≥7.5% to ≤10% were randomized to once-daily iGlarLixi or twice-daily BIAsp 30. Primary efficacy endpoints were non-inferiority in HbA1c reduction (margin 0.3%) or superiority in body weight change for iGlarLixi versus BIAsp 30. Results: Both primary efficacy endpoints were met in the LATAM region. After 26 weeks, HbA1c was reduced by 1.8% with iGlarLixi and 1.4% with BIAsp 30, meeting non-inferiority [least squares mean difference −0.47% (95% confidence interval: −0.82, −0.11); p <.001]. iGlarLixi was superior to BIAsp 30 for body weight change [least squares mean difference −1.27% (95% confidence interval: −2.41, −0.14); p =.028]. iGlarLixi was also superior to BIAsp 30 for HbA1c reduction (p =.010). A greater proportion of participants achieved HbA1c <7% without weight gain and HbA1c <7% without weight gain and without hypoglycaemia with iGlarLixi versus BIAsp 30. Incidence and rates of American Diabetes Association Level 1 and 2 hypoglycaemia were lower with iGlarLixi versus BIAsp 30. Conclusions: Once-daily iGlarLixi provided better glycaemic control with weight benefit and less hypoglycaemia than twice-daily premix BIAsp 30. iGlarLixi may be a favourable alternative to premix BIAsp 30 in people with suboptimally controlled T2D to advance BI therapy in the LATAM region.Fil: Frechtel, Gustavo Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Inmunología, Genética y Metabolismo. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Inmunología, Genética y Metabolismo; ArgentinaFil: Sauque Reyna, Leobardo. Instituto de Diabetes, Obesidad y Nutrición; MéxicoFil: Choza Romero, Ricardo. Centro Médico Ono; MéxicoFil: Anguiano, Luis. Sanofi Pasteur; MéxicoFil: Melas Melt, Lydie. Ividata Life Sciences; MéxicoFil: Sañudo Maury, María Elena. Sanofi Pasteur; Méxic

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