Obesity is a risk factor for chronic diseases. The prevalence of obesity is rapidly increase and a significant social inequality exists. This study aimed to analyse obesity prevalence and complications in different regions and occupations in China. Multicentre cross-sectional study. Data from 62,893 adults in 10 Chinese provinces were collected in 2022. Measures included body mass index (BMI), metabolic status and complications. Analysis was stratified by gender, age, region and occupation, with results adjusted for age and gender. The average BMI was 23.81 ± 3.42 kg/m², with overweight and obesity prevalence at 34.29 % and 11.24 %, respectively. North China had the highest obesity rate (27.93 %), followed by West (26.64 %), South (25.37 %) and East China (20.06 %). Functionaries (e.g. as civil servants, corporate executives) had higher BMI than employees or intellectual professionals, while workers had higher BMI than farmers. Intellectual professionals had the lowest rates of metabolically healthy obesity (MHO) at 4.90 % and metabolically unhealthy obesity (MUO) at 3.47 %, followed by employees (MHO 6.63 %, MUO 4.45 %) and functionaries (MHO 6.74 %, MUO 4.94 %). Workers had the highest MHO prevalence rate at 8.51 %, while farmers had the highest MUO rate at 6.16 %. Obesity-associated complications were 1.5-3 times more common among obese individuals than those in the normal BMI category, with inequality observed across different occupational groups. In China, a large number of adults with obesity have poor metabolic health, highlighting the importance of considering metabolic status when addressing obesity-related chronic diseases. Addressing the inequality in obesity rates can inform public health strategies to tackle the increasing problem of obesity and its associated complications in China.

This retrospective study investigated circulating immune cell alteration in patients with myopic retinopathy. Blood test results and demographic and ocular information of 392 myopic patients and 129 emmetropia controls who attended Changsha Aier Eye Hospital from May 2017 to April 2022 were used in this study. Compared with emmetropia, the percentages of neutrophils and basophils and neutrophil/lymphocyte ratio were significantly higher in myopic patients, whereas the percentages of monocytes and lymphocytes and the counts of lymphocytes and eosinophils were significantly lower in myopic patients. After adjusting for age and hypertension/diabetes, the difference remained. Interestingly, the platelet counts were significantly lower in myopic patients after the adjustments. Further subgroup analysis using multivariable linear regression showed that higher levels of neutrophils, neutrophil/lymphocyte ratio, and platelet/lymphocyte ratio, lower levels of monocytes, eosinophils, lymphocytes, and platelets, were related to myopic peripheral retinal degeneration (mPRD) and posterior staphyloma (PS). A higher level of basophils was linked to myopic choroidal neovascularization (mCNV). Our results suggest that higher levels of circulating neutrophils and neutrophil/lymphocyte ratio, lower monocytes, eosinophils, lymphocytes, and platelets are related to mild myopic retinopathy. A higher level of circulating basophils is related to the severe form of myopic retinopathy, such as mCNV.

Background: Insulin resistance (IR), a precursor to type 2 diabetes and a major risk factor for various chronic diseases, is becoming increasingly prevalent in China due to population aging and unhealthy lifestyles. Current methods like the gold-standard hyperinsulinemic-euglycemic clamp has limitations in practical application. The development of more convenient and efficient methods to predict and manage IR in nondiabetic populations will have prevention and control value. Objective: This study aimed to develop and validate a machine learning prediction model for IR in a nondiabetic population, using low-cost diagnostic indicators and questionnaire surveys. Methods: A cross-sectional study was conducted for model development, and a retrospective cohort study was used for validation. Data from 17,287 adults with normal fasting blood glucose who underwent physical exams and completed surveys at the Health Management Center of Xiangya Third Hospital, Central South University, from January 2018 to August 2022, were analyzed. IR was assessed using the Homeostasis Model Assessment (HOMA-IR) method. The dataset was split into 80% (13,128/16,411) training and 20% (32,83/16,411) testing. A total of 5 machine learning algorithms, namely random forest, Light Gradient Boosting Machine (LightGBM), Extreme Gradient Boosting, Gradient Boosting Machine, and CatBoost were used. Model optimization included resampling, feature selection, and hyperparameter tuning. Performance was evaluated using F ₁ -score, accuracy, sensitivity, specificity, area under the curve (AUC), and Kappa value. Shapley Additive Explanations analysis was used to assess feature importance. For clinical implication investigation, a different retrospective cohort of 20,369 nondiabetic participants (from the Xiangya Third Hospital database between January 2017 and January 2019) was used for time-to-event analysis with Kaplan-Meier survival curves. Results: Data from 16,411 nondiabetic individuals were analyzed. We randomly selected 13,128 participants for the training group, and 3283 participants for the validation group. The final model included 34 lifestyle-related questionnaire features and 17 biochemical markers. In the validation group, their AUC were all greater than 0.90. In the test group, all AUC were also greater than 0.80. The LightGBM model showed the best IR prediction performance with an accuracy of 0.7542, sensitivity of 0.6639, specificity of 0.7642, F ₁ -score of 0.6748, Kappa value of 0.3741, and AUC of 0.8456. Top 10 features included BMI, fasting blood glucose, high-density lipoprotein cholesterol, triglycerides, creatinine, alanine aminotransferase, sex, total bilirubin, age, and albumin/globulin ratio. In the validation queue, all participants were separated into the high-risk IR group and the low-risk IR group according to the LightGBM algorithm. Out of 5101 high-risk IR participants, 235 (4.6%) developed diabetes, while 137 (0.9%) of 15,268 low-risk IR participants did. This resulted in a hazard ratio of 5.1, indicating a significantly higher risk for the high-risk IR group. Conclusion: By leveraging low-cost laboratory indicators and questionnaire data, the LightGBM model effectively predicts IR status in nondiabetic individuals, aiding in large-scale IR screening and diabetes prevention, and it may potentially become an efficient and practical tool for insulin sensitivity assessment in these settings.

Background: Metabolic dysfunction–associated fatty liver disease and steatotic liver disease (SLD) have emerged as one of the fastest-growing liver complications over recent decades; this study evaluated trends in SLD prevalence among Chinese adults and its variation across demographic groups. Methods: A multicenter, retrospective study analyzed health examination data from 600,536 Chinese adults across 10 provinces between 2015 and 2023. Participants (≥18 y) with complete data on age, sex, weight, waist circumference, ultrasound, and biochemical tests were included. SLD was defined as hepatic steatosis with at least 1 metabolic disorder. Age-adjusted SLD prevalence was calculated using direct standardization, and trends were assessed through Joinpoint regression. Logistic regression models examined associations between SLD and demographic/metabolic factors. Results: Of 501,017 participants (mean age: 43.39±12.89 y, 54.8% male), the age-standardized SLD prevalence rose from 36.24% in 2015 to 48.11% in 2023 (estimated annual percentage change=3.38, p=0.002). Metabolic dysfunction–associated SLD (MASLD) prevalence also increased from 30.01% to 43.17% (estimated annual percentage change=4.48, p<0.001). Subgroup analysis revealed significant increases in individuals aged 18–40, and higher prevalence in older adults, with the highest risk in those aged 41–64. Males had a higher risk than females (53.0% vs. 18.6%, adjusted prevalence ratio=1.97). Participants from North China had a higher risk of both SLD and MASLD compared with South China. Laborers had a lower risk of SLD compared with farmers, while administrative personnel had a higher risk of MASLD. Conclusions: The prevalence of SLD and MASLD significantly increased from 2015 to 2023, with notable variations across demographic groups, highlighting the need for targeted public health strategies to address the growing burden of SLD in China.

Background The present study aims to use structural equation modelling (SEM) with multiple regression pathways to examine direct and indirect links from diet and metabolic traits to microvascular endothelial dysfunction (ED) among middle-aged Chinese males. Methods The study was conducted in middle-aged Chinese males, who underwent a health check-up between 2018 and 2019. Data on lifestyle behaviour factors (physical activity, diet pattern, sleep quality and diet data underwent factor analysis in advance) and metabolic risk factors referring to metabolic traits were introduced into the SEM to examine inter-relationship among these factors and their association with ED, as evaluated by the reactive hyperaemia index (RHI). Results Both exploratory factor analysis and confirmatory factor analysis identified two major dietary patterns: ‘prudent pattern’ and ‘western pattern’. The univariate test suggested that only triglycerides (TGs) and prudent dietary pattern were directly associated with RHI. Furthermore, prudent dietary pattern had an indirect association with RHI via TG (prudent diet→TG: β=−0.15, p<0.05; TG→RHI: β=−0.17, p<0.001). As to confirming the hypothesised association between variables apart, physical activity frequency was correlated to the decrease in TG (β=−0.29, p<0.001), but had no direct correlation to RHI. Conclusion The network of direct and indirect associations among diet pattern and cardiometabolic risk factors with RHI measured ED among middle-aged males. The most significant modifiable factors identified were TG and prudent diet pattern, which needs to be targeted as preventive strategies for early microvascular impairment.

Background: Depression affects over 40% of middle-aged and older Chinese adults living with both hypertension and diabetes, amplifying cardiovascular risk, functional decline, and mortality. Existing screening instruments—such as the 10-item Center for Epidemiologic Studies Depression Scale—focus narrowly on mood symptoms and are rarely feasible in busy primary care consultations. They also omit routine functional, cognitive, and social data that may jointly drive depressive states in cardiometabolic populations. Objective: This study aimed to develop and validate a concise, clinically actionable nomogram that quantifies individual depression risk using readily available information in Chinese adults aged ≥45 years who have diagnosed hypertension and type 2 diabetes. Methods: We analyzed anonymized wave 5 China Health and Retirement Longitudinal Study data collected between July 2020 and August 2020. Of 1504 eligible participants, 635 (42.2%) met the Center for Epidemiologic Studies Depression Scale cutoff score of >10 for probable depression. A total of 42 candidate predictors spanning demographics, laboratory values, comorbidities, functional status, and socioenvironmental factors were screened. Least absolute shrinkage and selection operator regression with 10-fold cross-validation identified the most parsimonious set. A multivariable logistic model was built on a 70% training set (n=1052) and evaluated on a 30% testing set (n=452). Performance was assessed using the area under the receiver operating characteristic curve (AUC), calibration plots, decision curve analysis, and Shapley additive explanations for interpretability. Multiple imputation was used to handle <20% missingness. Results: Nine nonredundant predictors entered the final nomogram: activity of daily living score, memory impairment, number of pain sites, sleep duration, life satisfaction score, self-rated health score, social activity engagement score, retirement status, and memory test score. The model achieved excellent discrimination (training AUC=0.819; testing AUC=0.825) and calibration (mean absolute error ≤0.018). Decision curves demonstrated positive net clinical benefit across clinically relevant threshold probabilities. Shapley additive explanations analysis revealed a 3-fold increase in depression odds per 1-point increase in activity of daily living score, whereas retirement conferred substantial protection (prevalence of depression: 103/635, 16.2% in the retired group vs 269/869, 31.0% in the nonretired group; P <.001), mediated by greater social participation. Conclusions: The 9-item nomogram enables <3-minute depression risk stratification in resource-limited primary care settings for adults with hypertension and diabetes. Functional decline, affective-cognitive burden, and socioeconomic disengagement constitute the dominant causal pathway. Prospective trials should examine whether interventions targeting postretirement social engagement and functional rehabilitation can reduce incident depression in this high-risk population.