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.

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: To investigate the association between post-coronavirus disease 2019 (COVID-19) syndrome (PCS) and chronic low-grade inflammation (LGI) in blood cells in 4108 mild COVID-19 patients. Methods: The data were collected within 12–24 weeks before and 24 weeks after COVID-19 infection. The χ2 test was utilized to analyse the differences between two groups. The relationship between PCS symptoms and inflammatory cells was analysed by logistic regression analysis. Results: The most common PCS symptom was cough, reported as ‘occasionally’ by 33.47% and ‘frequently’ by 5.92%. Women exhibited more severe symptoms than men. Among healthcare workers, palpitations were more pronounced (0.76% versus 0.35%), while the prevalence of other PCS symptoms was lower compared with non-healthcare workers. After adjusting for confounding factors, women showed a higher risk of five types of PCS, and healthcare workers had a greater risk of fatigue and dizziness compared with the non-healthcare worker. Fatigue was associated with lymphocytes, monocytes and the neutrophil:lymphocyte ratio (NLR). Cough correlated with high-sensitivity C-reactive protein (hs-CRP), NLR and the platelet:lymphocyte ratio (PLR). Dizziness was linked to hs-CRP, NLR and PLR. Palpitations correlated with hs-CRP. Conclusions: Our findings suggest that PCS symptoms may be associated with LGI in peripheral blood cells, gender and occupational inequalities, warranting further investigation.

Autoimmune thyroiditis (AIT), typified by Hashimoto’s thyroiditis, represents a prototypical organ-specific autoimmune disorder marked by lymphocytic infiltration, autoantibody production, and progressive thyroid dysfunction. Conventional hormone replacement alleviates hypothyroidism but fails to correct the underlying immune dysregulation. Preclinical models of experimental autoimmune thyroiditis (EAT) consistently demonstrate that these cell-based approaches mitigate inflammatory responses, correct Th17/Treg imbalance, and prevent follicular destruction. Moreover, emerging data on extracellular vesicle–mediated mechanisms and antigen-specific dendritic targeting further underscore the potential for durable immunological reprogramming. This review summarizes recent advances in tolerogenic cellular therapies aimed at restoring immune homeostasis in AIT. Mesenchymal stem cells (MSCs), regulatory T cells (Tregs), and tolerogenic dendritic cells (tolDCs) exert multifaceted immunomodulatory effects via cytokine secretion, metabolic reprogramming, and induction of antigen-specific tolerance, offering a promising immunotherapeutic strategy to modify AIT progression, moving beyond symptomatic relief toward long-term immune tolerance.

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.