Detection of sudomotor alterations evaluated by Sudoscan in patients with recently diagnosed type 2 diabetes

Introduction Diabetic peripheral neuropathy (DPN) causes morbidity and affects the quality of life. Before diabetes diagnosis, neuropathic damage may be present. Sudoscan provides accurate measurement of the sudomotor function. This study aimed to assess the abnormalities detected by Sudoscan, offered estimates of DPN prevalence, and investigated the relationship between metabolic and clinical parameters. Additionally, we evaluated the diagnostic accuracy of the Sudoscan compared with monofilament and tuning fork tests for detecting DPN.Research design and methods Cross-sectional descriptive study including patients with type 2 diabetes for <5 years since diagnosis. We investigated the presence of DPN using a 128 Hz tuning fork test, the 10 g monofilament, and the sudomotor dysfunction in feet using Sudoscan. We compared patients with and without alterations in the Sudoscan. A logistic regression model analyzed variables independently associated with sudomotor dysfunction.Results From 2013 to 2020, 2243 patients were included, 55.1% women, age 51.8 years, and 17.1% with normal weight. Monofilament tests and/or tuning fork examination were abnormal in 29% (95% CI 0.23% to 0.27%) and 619 patients (27.6%, 0.25% to 0.29%) had sudomotor alterations. In logistic regression analysis, age (β=1.01, 0.005–1.02), diastolic blood pressure (β=0.98, 0.96–0.99), heart rate (β=1.01, 1.00–1.02), glucose (β=1.00, 1.00–1.03), albuminuria (β=1.001, 1.000–1.001), beta-blockers=1.98, 1.21–3.24) and fibrate use=0.61, 0.43–0.87) were associated with sudomotor dysfunction. The AUC (area under the curve) for Sudoscan was 0.495 (0.469–0.522), with sensitivity and specificity of 24% and 71%, respectively.Conclusion The Sudoscan identified an important proportion of patients with dysfunction, allowing prompt intervention to decrease the risk for complications.Trial registration number NCT02836808

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