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

Abstract

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.

Keywords: ethnic groups; insulin resistance; statistical models; type 2 diabetes mellitus.

Conflict of interest statement

Competing interests: JPB-L, AV-V and NEA-V are enrolled at the PECEM program of the Faculty of Medicine at UNAM. JPB-L and AV-V are supported by CONACyT.

© Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.

Figures

Figure 1

(A) Diabetes subgroup distribution in NHANES III used for model training, NHANES 1999–2004 used for model validation and ENSANUT 2016 used for model testing, demonstrating relevant differences in diabetes distribution. (B) Distribution of type 2 diabetes clusters according to ADO, HOMA2-β, HOMA2-IR, BMI, HbA1c and fasting plasma glucose in the combined NHANES cohorts. ADO, age at diabetes onset; BMI, body mass index; HbA1c, glycated hemoglobin; HOMA, homeostasis model assessment; IR, insulin resistance; MARD, mild age-related diabetes; MOD, mild obesity-related diabetes; NHANES, National Health and Nutrition Examination Survey; SIDD, severe insulin-deficient diabetes; SIRD, severe insulin-resistant diabetes.

Figure 2

Sankey plot of transitions of diabetes subtypes after 3 months (A, n=1680), 1 year (B, n=852) and 2 years (C, n=476) of an intensive multidisciplinary intervention with variables collected at baseline and after 3 months, 1 and 2 years of follow-up. MARD, mild age-related diabetes; MOD, mild obesity-related diabetes; SIDD, severe insulin-deficient diabetes; SIRD, severe insulin-resistant diabetes.