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: 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.