Visceral adipose dysfunction is a major cause of metabolic disorders. However, there is lack of a clinical index for prediction of visceral fat dysfunction in Asians. The present study aims to establish a visceral adiposity index for evaluation of metabolic health status in Chinese, the largest Asian ethnic group. 485 subjects were recruited from Lianqian Community, Xiamen and received abdominal computed tomography(CT) for visceral fat area. A Chinese visceral adiposity index (CVAI) was created using multivariate linear regression analyses, and was further validated in 6495 subjects recruited from Changfeng Community, Shanghai. CVAI was well associated with visceral obesity (r = 0.68, P < 0.001) and HOMA-IR (r = 0.60, P < 0.001). The AUROCs were 0.89(0.88–0.90), 0.72(0.71–0.73), 0.69(0.68–0.71) and 0.67(0.65–0.68) for determination of metabolic syndrome, hypertension, diabetes and prediabetes, respectively. CVAI was more valuable compared to BMI and waist circumference in evaluation of metabolic risks (all P < 0.001), even in subjects with metabolically unhealthy normal weight (MUNW) and metabolically healthy obese/overweight (MHO). This study demonstrates that CVAI is a reliable and applicable index for evaluation of visceral fat dysfunction in Chinese. It might be used to evaluate metabolic health status in Asians.

Objective: Type 2 diabetes mellitus is increasing in young adults, and greater adiposity is considered a major risk factor. However, whether there is an association between obesity and diabetes and how this might be impacted by age is not clear. Therefore, we investigated the association between body mass index (BMI) and diabetes across a wide range of age groups (20–30, 30–40, 40–50, 50–60, 60–70 and ≥70 years old).Design: We performed a retrospective cohort study using healthy screening programme data.Setting: A total of 211 833 adult Chinese persons >20 years old across 32 sites and 11 cities in China (Shanghai, Beijing, Nanjing, Suzhou, Shenzhen, Changzhou, Chengdu, Guangzhou, Hefei, Wuhan, Nantong) were selected for the study; these persons were free of diabetes at baseline.Primary and secondary outcome measures: Fasting plasma glucose levels were measured and information regarding the history of diabetes was collected at each visit. Diabetes was diagnosed as fasting plasma glucose ≥7.00 mmol/L and/or self-reported diabetes. Patients were censored at the date of diagnosis or the final visit, whichever came first.Results: With a median follow-up of 3.1 years, 4174 of the 211 833 participants developed diabetes, with an age-adjusted incidence rate of 7.35 per 1000 persons. The risk of incident diabetes increased proportionally with increasing baseline BMI values, with a 23% increased risk of incident diabetes with each kg/m²increase in BMI (95% CI 1.22 to 1.24). Across all age groups, there was a linear association between BMI and the risk of incident diabetes, although there was a stronger association between BMI and incident diabetes in the younger age groups (age×BMI interaction, p<0.0001).Conclusions: An increased BMI is also independently associated with a higher risk of developing diabetes in young adults and the effects of BMI on incident diabetes were accentuated in younger adults.

The personalized titration and optimization of insulin regimens for treatment of type 2 diabetes (T2D) are resource-demanding healthcare tasks. Here we propose a model-based reinforcement learning (RL) framework (called RL-DITR), which learns the optimal insulin regimen by analyzing glycemic state rewards through patient model interactions. When evaluated during the development phase for managing hospitalized patients with T2D, RL-DITR achieved superior insulin titration optimization (mean absolute error (MAE) of 1.10 ± 0.03 U) compared to other deep learning models and standard clinical methods. We performed a stepwise clinical validation of the artificial intelligence system from simulation to deployment, demonstrating better performance in glycemic control in inpatients compared to junior and intermediate-level physicians through quantitative (MAE of 1.18 ± 0.09 U) and qualitative metrics from a blinded review. Additionally, we conducted a single-arm, patient-blinded, proof-of-concept feasibility trial in 16 patients with T2D. The primary outcome was difference in mean daily capillary blood glucose during the trial, which decreased from 11.1 (±3.6) to 8.6 (±2.4) mmol L⁻¹ (P < 0.01), meeting the pre-specified endpoint. No episodes of severe hypoglycemia or hyperglycemia with ketosis occurred. These preliminary results warrant further investigation in larger, more diverse clinical studies. ClinicalTrials.gov registration: NCT05409391.