AUTHOR=Zhang Qi , Yu Meihua , Huang Zheng , Shen Yimei , Zhu Xinfeng , Yun Jingyi , Ding Jingying , Chen Rong , Shi Lijie , Wang Lingyan 

TITLE=Associations between dietary patterns and sarcopenia in aging populations: a community study from eastern China’s Huzhou city

JOURNAL=Frontiers in Nutrition

VOLUME=Volume 12 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2025.1677335

DOI=10.3389/fnut.2025.1677335

ISSN=2296-861X

ABSTRACT=BackgroundAs the global population ages, sarcopenia has become a significant health issue. Although diet is a key factor, evidence linking specific dietary patterns to sarcopenia risk in older adults is inconsistent, especially in unique regional diets like China’s. The study aimed to identify main dietary patterns and explore their associations, including possible dose–response relationships, with sarcopenia risk among older adults in Huzhou, China.MethodsIn 2024, a convenience sample study in Huzhou collected fasting blood samples and administered food frequency questionnaires (FFQs). Principal component analysis (PCA) identified dietary patterns, which were divided into tertiles. Logistic regression (LR) and restricted cubic spline (RCS) models analyzed the link between these patterns and sarcopenia.ResultsOur study involving 1,030 participants aged 60 and above, sarcopenia prevalence was found to be 21.2%. PCA identified four distinct dietary patterns: Pattern 1 (plant-based whole grain-legume), Pattern 2 (traditional high meat-egg), Pattern 3 (vegetable-freshwater aquatic), and Pattern 4 (high dairy-low refined grain). Adjusted LR analysis demonstrated that greater adherence to Dietary Patterns 2 [T3 vs. T1: adjusted odds ratio (aOR) = 0.63, 95% confidence interval (CI): 0.40–0.98, p < 0.05] and 3 (T3 vs. T1: aOR = 0.48, 95% CI: 0.29–0.78, p < 0.01) was inversely associated with the occurrence of sarcopenia. Conversely, moderate adherence to Dietary Pattern 4 (T2 vs. T1: aOR = 1.73, 95% CI: 1.10–2.72, p < 0.05) showed a positive association with sarcopenia. No statistically significant association was identified for Dietary Pattern 1. RCS analysis revealed linear dose–response relationships for Dietary Pattern 3, which correlated with a decreased risk of sarcopenia, and for Dietary Pattern 4, which correlated with an increased risk (both p for overall association < 0.05; p for non-linearity > 0.05). Additionally, significant linear or non-linear associations were observed between Dietary Patterns 2–4 and sarcopenia diagnostic indicators, including physical performance, muscle mass, and grip strength.ConclusionDietary Patterns 2 and 3 were negatively associated with sarcopenia risk, suggesting potential benefits of these dietary habits. In contrast, moderate adherence to Dietary Pattern 4 was positively associated with risk. Future interventions should consider personalized dietary thresholds to optimize muscle health in aging populations.