Background: Over time, there have been significant changes in the dietary patterns of the Chinese population with the emergence of ultra-processed foods (UPFs). Methods: To ascertain the changes in UPFs intake among adults in southern China, over the past two decades, the study included residents aged 18 years and older who participated in the China National Nutrition Surveys in Guangdong province in 2002, 2012, and 2022. Dietary intake data were collected via three-day 24 h dietary recalls and weighing household foods and condiments. The recorded dietary data were classified according to the NOVA classification system, and the percentage of total energy derived from each food group was calculated. Results: From 2002 to 2022, there was a notable increase in the consumption of UPFs among adults in Guangdong Province, with the percentage of UPFs intake rising from 0.88% to 8.52% (p-value < 0.001). This growth was especially pronounced among specific population groups, including the young, the better educated, and those living in highly urbanized areas. The largest increase in energy intake from UPFs was observed among students, from 0.42% to 17.26% (p-value < 0.001). The nutrient contents of UPFs in Guangdong in 2022 were found to contain a markedly higher calculated percentage of calories provided by carbohydrates in comparison to minimally processed foods (56.6% vs. 43.8%) as well as in sodium (749 mg/100 kcal vs. 29 mg/100 kcal). Conclusions: Given the increasing consumption of UPFs and the growing evidence linking these products to chronic diseases, it is important to promote healthy food intake and balanced diets through active nutritional education campaigns to prevent potential health risks that may arise.

Human papillomavirus (HPV) infection remains a critical public health challenge in China, particularly in Guangdong Province, where HPV-52, 16, and 58 genotypes predominate, and male infection rates exceed 40%. Despite the successful implementation of a government-funded school-based program that has achieved 88% HPV vaccine coverage among adolescent girls, several persistent barriers, including genotype mismatch (the free HPV vaccine covers < 50% of high-risk local strains), regional disparities (80% vs. 60% for first-dose coverage), and exclusion of males, thwart progress toward herd immunity. Financial sustainability risks pose an even more significant threat to the expansion of HPV vaccination programs, especially in Guangdong province where annual expenditures exceed CNY 200 million. This review delves into Guangdong’s pioneering efforts and proposes practical solutions: accelerating domestic multivalent HPV vaccine development, adopting gender-neutral vaccination policies, and leveraging mobile clinics for remote populations. These strategies not only provide a roadmap for China but also serve as valuable insight for other LMICs striving to overcome HPV-related inequalities.

Metabolic syndrome (MetS) is a cluster of risk factors for cardiovascular diseases and type 2 diabetes. Gut microbiota dysbiosis has been implicated in the pathogenesis of MetS, but the mechanisms remain poorly understood. This study investigates gut microbiota interaction networks in MetS and explores their potential role in host metabolic regulation. In this population-based cross-sectional study, 221 MetS patients and 382 healthy controls were analyzed. Co-abundance network analysis was used to examine microbial interactions across the study. Significant differences in microbial co-abundance patterns were observed between MetS and healthy participants. In MetS, the gut microbiota displayed fewer and generally weaker co-abundance correlations compared with healthy controls. These changes appear to be more strongly associated with the synergistic effects of microbial interactions than solely with the abundance of individual taxa investigated here. Specific microbiota combinations were found to influence key metabolic functions, contributing to MetS development. The findings suggest that microbial interactions, rather than the abundance of individual bacteria, are associated with MetS. This study provides new insights into the role of disrupted gut microbiota networks in MetS pathogenesis.

Background: Metabolic syndrome (MetS) is a rising public health concern in Southern China, with limited evidence available on dietary and metabolic factors. This cross-sectional study employed an interpretable machine learning (ML) approach to identify factors that could inform clinical and community interventions. Methods: Data were obtained from the Guangdong Nutrition Surveys conducted in 2015 and 2022, including sociodemographic information, lifestyle patterns, physical examinations, laboratory measurements and dietary intake information (collected via repeated 24-h dietary recalls). Potentially relevant variables were selected using the Least Absolute Shrinkage and Selection Operator (LASSO) regression and incorporated into seven ML models. Model performance was primarily assessed using the area under the receiver operating characteristic curve (AUC), and the contribution of identified features was interpreted through SHapley Additive exPlanations (SHAP). Results: This analysis included 5593 participants, of whom 1103 were classified as having MetS. After removing collinear features, the ML models retained 19 candidate variables, which were selected through LASSO regression. XGBoost achieved the best performance (AUC: 0.834; F1 score: 0.537) with a misclassification rate of 27.1%. SHAP analysis highlighted body mass index (BMI), age, and uric acid levels as major risk factors, while insoluble dietary fiber, carbohydrate and specific micronutrients exhibited protective associations. Conclusions: Machine learning identified key dietary and metabolic factors of MetS. Integrating these factors into clinical practice and public health initiatives may enhance early detection and support personalized prevention strategies for MetS in Southern China.

Abdominal obesity (AO) significantly contributes to cardiometabolic diseases and poses an increasing public health challenge. Composite lipid-derived indices have been proposed as simple indicators of atherogenic dyslipidemia, but their relative importance for identifying AO at the population level remains incompletely understood. This study aimed to evaluate the link between various atherogenic lipid indices and AO, identifying key lipid-related predictors through interpretable machine learning methods. This cross-sectional study analyzed 5,612 adults from a population-based survey in Guangdong Province, China. Atherogenic lipid indices encompass the atherogenic index of plasma (AIP), non-high-density lipoprotein cholesterol (non-HDL-C), atherogenic coefficient (AC), Castelli risk indices I and II (CRI-I and CRI-II), lipid composite index (LCI), and the triglyceride-to-high-density lipoprotein cholesterol ratio (TG/HDL-C). Feature selection was conducted using Boruta and Least Absolute Shrinkage and Selection Operator (LASSO) methods prior to machine learning modeling. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, and F1 score. SHapley Additive exPlanations (SHAP) analysis was applied to quantify feature importance. Individuals with AO were generally older and more likely to exhibit lower education levels, reduced physical activity, cardiometabolic comorbidities, and unfavorable metabolic profiles. Feature selection identified 15 key predictors. Logistic regression demonstrated the most stable performance (training AUC: 0.767; testing AUC: 0.768; accuracy: 0.712; F1 score: 0.667) with good calibration and clinical utility. SHAP analysis consistently identified AIP, sex, CRI-II, uric acid, and diastolic blood pressure as the most influential predictors of AO. Composite lipid indices, particularly AIP and CRI-II, are strongly associated with AO and may serve as practical indicators for identifying individuals at elevated metabolic risk. Because these indices are derived from routinely measured lipid parameters, they may support scalable approaches for metabolic risk screening and monitoring in both clinical and community health settings.

Background: China has experienced substantial dietary transitions over recent decades, accompanied by increasing burdens of overweight and obesity. However, long-term changes in dietary patterns and their associations with obesity among middle-aged and older adults remain insufficiently characterized. Methods: We analyzed data from 8502 adults aged ≥45 years participating in four waves (2002–2022) of the China National Nutrition Surveys in Guangdong Province. Dietary patterns were identified using k-means cluster analysis based on 23 food groups. Temporal changes in dietary pattern distributions were assessed using Rao–Scott adjusted chi-square tests and survey-weighted logistic regression models incorporating linear and quadratic terms for survey year. Multivariable logistic regression models were used to examine associations between dietary patterns and overweight, obesity, and central obesity. Results: Three dietary patterns were identified: a traditional cereal-based pattern, a transitional mixed pattern, and an animal-rich diverse pattern. The prevalence of the Traditional Cereal-based pattern decreased from 15.8% in 2002 to 0.4% in 2022, whereas the Animal-rich Diverse pattern increased from 19.2% to 47.0%. Trend analyses confirmed significant temporal changes in all dietary patterns after false discovery rate correction. Compared with the Traditional Cereal-based Pattern, the Transitional Mixed Pattern and Animal-rich Diverse Pattern was associated with higher odds of overweight/obesity, with adjusted ORs of 1.57 (95% CI: 1.29–1.90) and 1.42 (95% CI: 1.14–1.77), respectively. Similar associations were observed for central obesity, with corresponding adjusted ORs of 1.54 (95% CI: 1.26–1.89) and 1.35 (95% CI: 1.08–1.71), respectively. Conclusions: Significant dietary transitions occurred among middle-aged and older adults in Guangdong Province from 2002 to 2022. Dietary patterns characterized by higher intakes of animal-source and energy-dense foods were associated with greater odds of overweight/obesity and central obesity. These findings suggest that dietary diversity alone may not necessarily represent a healthier dietary pattern during nutrition transition and highlight the potential importance of considering overall dietary composition and energy balance in obesity prevention strategies.