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.

Objective:To explore the effectiveness of artificial intelligent (AI) quality control system on the standard mid-sagittal view of fetal face in 11-13 +6 weeks of gestation. Methods:The quality of the images was evaluated using online " Intelligent ultrasonic quality control system" for 1 063 sections of fetal Nuchal translucency ultrasound images in the Ultrasound Department of Shenzhen Maternity and Child Healthcare Hospital of Southern Medical University. The manual quality control results and time consuming of two experts were compared with AI. The gold standard was decided by experts group with higher seniority.Results:The overall standard rate, substandard rate and nonstandard rate of the image evaluated by the intelligent quality control system was 87.3%, 2.82%, and 9.88%. The overall accuracy of images was 96.6%. The coincidence rates between AI and the two experts were 96.1% and 96.3%, respectively, with strong consistency (Kappa were 0.835 and 0.845 respectively). The time required for intelligent quality control was significantly shorter than manual quality control (208 s vs 6 696 s/6 602 s). All the differences were statistically significant ( Z=-3.981, P<0.001). Conclusions:The intelligent quality control system could accurately and quickly evaluate whether the mid-sagittal view of fetal face in 11-13 +6 weeks of gestation is standard.

Objective:To develop an artificial intelligence (AI) quality control model of fetal heart in the first trimester and verify its effectiveness.Methods:A total of 18 694 images of the four-chamber view(4CV) and three-vessel and tracheal view(3VT) of fetal heart in the first trimester were selected from Shenzhen Maternal and Child Health Hospital Affiliated to Southern Medical University since January 2022 to December 2022. A total of 14 432 images were manually annotated. The one-stage target detection algorithm YOLO V5 was used to train the AI quality control model in the first trimester of fetal heart, and 4 262 images (golden standard set by expert group) were used to evaluate the application effectiveness of AI quality control model. Kappa consistency test was used to compare the results of section classification and standard degree judgment from AI quality control model, Doctor 1(D1) and Doctor 2(D2).Results:①Precision of the AI quality control model was 0.895, recall was 0.852, mean average precision (mAP 50) was 0.873.The average precision(AP) of the AI quality control model for section classification was 0.907 (4CV) and 0.989 (3VT), respectively. ②Compared with the gold standard, the overall coincidence rate and consistency of section classification of AI quality control model, D1 and D2 were 99.91% (Kappa=0.998), 100% (Kappa=1.000), 100% (Kappa=1.000), respectively. The coincidence rate and consistency of the plane standard degree evaluation from the AI quality control model, D1 and D2 were 97.46% (Weighted Kappa=0.932), 93.73% (Weighted Kappa=0.847), and 93.12% (Weighted Kappa=0.832), respectively. Strong consistency was displayed. Moreover, AI quality control model showed the highest coincidence rate and the strongest consistency in judging section standard degree, which was superior to manual quality control. The time-consuming of AI quality control (0.012 s/sheet) was significantly less than the way of manual quality control (4.76-6.11 s/sheet)( Z=-8.079, P<0.001). Conclusions:The use of artificial intelligent fetal heart quality control model in the first trimester can effectively and accurately control the image quality.

BACKGROUNDMany studies have suggested that C-reactive protein (CRP) and blood lipids are associated with hypertension and cardiovascular disease (CVD). However, few studies discussed the combined action of CRP and blood lipids on the risk of hypertension and prehypertension. This study aimed to investigate the combined action of CRP and lipid profiles on the risk of hypertension and prehypertension in Mongolian adults from Inner Mongolia, China.

METHODSThe systolic and diastolic blood pressure, height, weight and waist circumference were measured and factors such as smoking, alcohol intake, family history of hypertension, etc., were investigated and CRP, low-density lipoprotein cholesterol (LDL-C), triglycerides (TG) were tested for 2 534 Mongolian adults aged ≥ 20 years. The subjects were divided into four subgroups, namely CRP median subgroup, CRP >median and LDL-C (TG) median and LDL-C (TG) >median subgroup. The ORs (95% CIs) of hypertension and prehypertension for the subgroups were calculated by univariate and multivariate analysis.

RESULTSThe multivariate adjusted ORs (95%CIs) of hypertension/prehypertension were 1.389 (0.979-1.970)/1.151(0.865-1.531), 1.666 (1.159-2.394)/1.431 (1.060-1.930), 1.756 (1.242-2.484)/ 1.770 (1.321-2.372), for CRP median subgroup, CRP >median and LDL-C median and LDL-C >median subgroup, respectively, compared with CRP median subgroup, CRP >median and TG median and TG >median subgroup, respectively, compared with CRP median and LDL-C (TG) >median subgroup among the four subgroups.

CONCLUSIONSSubjects with both CRP >median and LDL-C (TG) >median had highest risks of hypertension and prehypertension among all subjects. This study appeared to indicate that the combined action of elevated CRP and elevated LDL-C (TG) further increase the risks of hypertension and prehypertension among Mongolian population.

Adult ; Body Height ; physiology ; Body Weight ; physiology ; C-Reactive Protein ; metabolism ; China ; Cross-Sectional Studies ; Female ; Humans ; Hypertension ; blood ; epidemiology ; metabolism ; Lipids ; blood ; Male ; Middle Aged ; Multivariate Analysis ; Waist Circumference ; physiology