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.

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.

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.