Body weight abnormalities, including overweight, obesity, and underweight, have become a dual public health challenge in Chinese adults: overweight and obesity lead to a variety of chronic complications, while underweight increases the risks of malnutrition, sarcopenia, and organ dysfunction. To systematically address these issues, multidisciplinary experts in endocrinology, sports science, nutrition, and psychiatry from various regions have held multiple weight management seminars. Based on the latest epidemiological data and clinical evidence, they expanded the guideline to include assessment and intervention strategies for underweight, in addition to the core content of obesity management. This guideline outlines the etiological mechanisms, evaluation methods, and multidimensional management strategies for overweight and obesity, covering key areas such as diagnosis and assessment, medical nutrition therapy, exercise prescription, pharmacological intervention, and psychological support. It is intended to provide a scientific and standardized approach to weight management across the adult population, aiming to curb the rising prevalence of obesity, mitigate complications associated with abnormal body weight, and improve nutritional status and overall quality of life.

Body weight abnormalities, including overweight, obesity, and underweight, have become a dual public health challenge in Chinese adults: overweight and obesity lead to a variety of chronic complications, while underweight increases the risks of malnutrition, sarcopenia, and organ dysfunction. To systematically address these issues, multidisciplinary experts in endocrinology, sports science, nutrition, and psychiatry from various regions have held multiple weight management seminars. Based on the latest epidemiological data and clinical evidence, they expanded the guideline to include assessment and intervention strategies for underweight, in addition to the core content of obesity management. This guideline outlines the etiological mechanisms, evaluation methods, and multidimensional management strategies for overweight and obesity, covering key areas such as diagnosis and assessment, medical nutrition therapy, exercise prescription, pharmacological intervention, and psychological support. It is intended to provide a scientific and standardized approach to weight management across the adult population, aiming to curb the rising prevalence of obesity, mitigate complications associated with abnormal body weight, and improve nutritional status and overall quality of life.

Depression's high recurrence rate and severe consequences pose significant challenges to public health.To address this issue effectively,this review explores the innovative application of wearable devices in monitoring and intervening in depression,surpassing the limitations of traditional subjective assessments and patient self-reports.The paper systematically analyzes recent studies utilizing wearable devices to monitor physiological and behavioral indicators of depression,categorizing them by different technological types and evaluating their practical effectiveness in early diagnosis and intervention.The findings indicate that wearable devices can continuously monitor physiological indicators and behavioral patterns related to depression,potentially enabling early detection of depressive episodes and supporting timely interventions.Despite challenges such as data privacy and user acceptance,wearable technology holds immense potential in enhancing clinical outcomes in depression treatment.

Objective To establish a universally applicable logistic risk prediction model for diabetes mellitus type 2(T2DM)in the middle-aged and elderly populations based on the results of a Meta-analysis,and to validate and confirm the efficacy of the model using the follow-up data of medical check-ups of National Basic Public Health Service.Methods Cohort studies evaluating T2DM risks were identified in Chinese and English databases.The logistic model utilized Meta-combined effect values such as the odds ratio(OR)to derive β,the partial regression coefficient,of the logistic model.The Meta-combined incidence rate of T2DM was used to obtain the parameter α of the logistic model.Validation of the predictive performance of the model was conducted with the follow-up data of medical checkups of National Basic Public Health Service.The follow-up data came from a community health center in Chengdu and were collected between 2017 and 2022 from 7 602 individuals who did not have T2DM at their baseline medical checkups done at the community health center.This community health center was located in an urban-rural fringe area with a large population of middle-aged and elderly people.Results A total of 40 cohort studies were included and 10 items covered in the medical checkups of National Basic Public Health Service were identified in the Meta-analysis as statistically significant risk factors for T2DM,including age,central obesity,smoking,physical inactivity,impaired fasting glucose,a reduced level of high-density lipoprotein cholesterol(HDL-C),hypertension,body mass index(BMI),triglyceride glucose(TYG)index,and a family history of diabetes,with the OR values and 95% confidence interval(CI)being 1.04(1.03,1.05),1.55(1.29,1.88),1.36(1.11,1.66),1.26(1.07,1.49),3.93(2.94,5.24),1.14(1.06,1.23),1.47(1.34,1.61),1.11(1.05,1.18),2.15(1.75,2.62),and 1.66(1.55,1.78),respectively,and the combined β values being 0.039,0.438,0.307,0.231,1.369,0.131,0.385,0.104,0.765,and 0.507,respectively.A total of 37 studies reported the incidence rate,with the combined incidence being 0.08(0.07,0.09)and the parameter α being-2.442 for the logistic model.The logistic risk prediction model constructed based on Meta-analysis was externally validated with the data of 7 602 individuals who had medical checkups and were followed up for at least once.External validation results showed that the predictive model had an area under curve(AUC)of 0.794(0.771,0.816),accuracy of 74.5%,sensitivity of 71.0%,and specificity of 74.7% in the 7 602 individuals.Conclusion The T2DM risk prediction model based on Meta-analysis has good predictive performance and can be used as a practical tool for T2DM risk prediction in middle-aged and elderly populations.

Objective:To explore the association of different obesity measurement indexes on serum C-reactive protein (CRP) in Chinese adult women.Methods:The data were obtained from baseline and follow-up surveys of the urban Breast Cancer Screening Program in Shuangliu District, Chengdu. A total of 441 adult women were included in the study. A questionnaire survey, physical examination, and laboratory testing were conducted on the subjects. Multivariate logistic regression model, two-level mixed effects logistic regression model, and restricted cubic spline method were used to investigate the linear and nonlinear correlation between different obesity measurement indexes and serum CRP in adult women.Results:For every 1 unit increase in BMI, waist circumference (WC), and adiposity, the risk of elevated serum CRP or exacerbation of chronic low-grade inflammation in adult women increased by 16.5%, 5.0%, and 11.1% ( P<0.05), respectively. Both BMI and adiposity were nonlinear correlated with serum CRP. Using BMI=24.0 kg/m 2 as the reference point, serum CRP level increased with the increase of BMI when BMI >24.0 kg/m 2. Using adiposity=30% as the reference point, serum CRP level increased with the increase of adiposity when adiposity >30%. Conclusions:Overall, obesity reflected by BMI had the strongest association with serum CRP in adult women, followed by body fat content reflected by adiposity, and central obesity reflected by WC had the weakest association with CRP. Adult women with BMI >24.0 kg/m 2 or adiposity >30% are at high risk for obesity-related inflammatory manifestations.

Objective:To survey the status of illness and consultation within two weeks among residents in a community in Chengdu.Methods:A multi-stage stratified random sampling was used to carry out a questionnaire survey among the residents in a community in Chengdu during March to April 2019. The disease status, disease severity and consultation within two weeks were surveyed, and influencing factors of two-week illness were analyzed with logistic regression.Results:Among 2 714 participants, 715 had illness (900 times) within two weeks, with a two-week prevalence rate of 26.3%. The main symptoms were dizziness (6.4%, 58/900), cough (6.2%, 56/900), lumbago and leg pain (4.9%, 44/900), palpitations (3.3%, 30/900) and headache (3.1%, 28/900). The three most common diseases were hypertension (38.3%, 345/900), diabetes (19.3%, 174/900) and common cold (6.4%, 58/900). In this survey, the rate of two-week in bed was 20.6‰(56/2 714), and the rate of two-week off work was 4.8‰(13/2 714). Age, gender, education level and work status were the influencing factors of the illness in two weeks ( P<0.05). The choice of medical institution was also different among residents. Conclusion:The survey suggest that primary care should focus on the key groups of residents in the community such as elderly, women, low education level, retirement and unemployed; and it is necessary to strengthen the primary care institutions and two-way referral system of health care.