Machine Learning-Assisted Analysis of Sublingual Microcirculatory Dysfunction for Early Cardiovascular Risk Evaluation and Cardiovascular-Kidney-Metabolic Syndrome Stage in Patients With Type 2 Diabetes Mellitus.

AIMS: To examine whether sublingual microcirculation can be used as an effective and noninvasive method for assessing cardiovascular, kidney, and metabolic risks in patients with type 2 diabetes mellitus (T2DM). MATERIALS AND METHODS: This cross-sectional observational study enrolled 186 patients with T2DM. All patients were evaluated using the Framingham General Cardiovascular Risk Score (FGCRS) and cardiovascular-kidney-metabolic (CKM) syndrome stage. Side-stream dark-field microscopy was used for sublingual microcirculation, including total and perfused vessel density (TVD and PVD). Multiple machine-learning prediction models have been developed for CKM risk and stage assessment in T2DM patients. Receiver operating characteristic (ROC) curves were generated to determine cutoff points. RESULTS: Compared to patients with T2DM, diabetic patients with subclinical atherosclerosis (SA) had a greater CV risk, as measured by the FGCRS, accompanied by markedly decreased microcirculation perfusion. Microcirculatory parameters (TVD and PVD), including carotid intima-media thickness (IMT), brachial-ankle pulse wave velocity (ba-PWV), and FGCRS, were closely associated with SA incidence. Microcirculatory parameters, Index (DMSA screen), and cut-off points were used to screen for SA in patients with T2DM. Furthermore, a new set of four factors identified through machine learning showed optimal sensitivity and specificity for detecting CKM risk in patients with T2DM. Decreased microcirculatory perfusion served as a useful early marker for CKM syndrome risk stratification in patients with T2DM without SA. CONCLUSIONS: Sublingual microcirculatory dysfunction is closely correlated with the risk of SA and CKM risk in T2DM patients. Sublingual microcirculation could be a novel tool for assessing the CKM syndrome stage in patients with T2DM.

Changes in thyroid hormones predict weight regain in patients with obesity who undergo metabolic surgery.

AIM: To investigate the relationship between thyroid function and weight regain in patients with obesity after metabolic surgery. METHODS: This retrospective study enrolled 162 patients who underwent metabolic surgery. Correlations between decreases in thyroid hormone levels and changes in weight, waist circumference (WC) and the Chinese visceral adiposity index (CVAI) were assessed. Binary logistic regression and receiver operating characteristic (ROC) curves were used to identify predictors and clinically useful cut-off values, respectively. RESULTS: The levels of thyroid-stimulating hormone (TSH) and free triiodothyronine (FT3) decreased markedly at 1 year after surgery, as did weight, body mass index (BMI), triglycerides, total cholesterol, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, WC and CVAI. Decreases in TSH and FT3 after metabolic surgery were associated with changes in weight, BMI and CVAI. Binary logistic regression and ROC curve analyses confirmed that decreases in TSH can predict good weight loss after metabolic surgery to some extent. Finally, binary logistic regression and ROC curve analyses confirmed that changes in TSH can predict weight regain after metabolic surgery. CONCLUSIONS: Changes in TSH and FT3 after metabolic surgery were correlated with changes in weight and CVAI. Changes in thyroid hormones can predict weight regain in patients with obesity who underwent metabolic surgery.