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

BMJ Open Diabetes Research & Care
Omar Yaxmehen Bello-ChavollaGroup of Study CAIPaDi

Abstract

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. 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. 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....Continue Reading

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Citations

Mar 25, 2021·Pharmacogenomics and Personalized Medicine·Ranjit UnnikrishnanViswanathan Mohan
Mar 18, 2021·The Journal of Clinical Endocrinology and Metabolism·Omar Yaxmehen Bello-ChavollaJessica Paola Bahena-López
Oct 6, 2021·Diabetes Technology & Therapeutics·Ranjit Mohan AnjanaViswanathan Mohan

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Methods Mentioned

BETA
X-ray

Software Mentioned

R
SNNN
fpc R package
shiny R
ENSANUT
MatchIt R

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