Zhu-Zhu Wang, Qin Xu, Yu-Han Zhang, Rong-Rong Wu, Jun-Ling Cui, Ji Zhou, Jing-Fang Hong
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引用次数: 0
Abstract
Background: Sarcopenia and obesity, two prevalent health conditions, often coexist and exacerbate each other's impact, increasing the risk of chronic diseases and mortality. This dual condition is termed "sarcopenic obesity." The correlation between oxidative stress (OS) and sarcopenia or obesity was established, and the oxidative balance score (OBS) can serve as an indicator of overall dietary or lifestyle-related OS exposure within an individual. Prior reports have not addressed the relationship between OBS and sarcopenia or sarcopenic obesity in adults under 60. This study endeavors to explore these associations and to identify potential dietary and lifestyle risk factors.
Methods: We performed a cross-sectional analysis utilizing data from 4,241 participants in the National Health and Nutrition Examination Survey (NHANES) from 2011 to 2018. OBS is a cumulative score derived from 16 dietary components and 4 lifestyle components, where higher scores indicate greater exposure to antioxidants and lower exposure to pro-oxidant factors, reflecting a reduced oxidative stress burden. Weighted multivariate logistic regression was employed to investigate the association of OBS and sarcopenia and sarcopenic obesity. Further subgroup analyses was conducted to examine interactions with various covariates. The least absolute shrinkage and selection operator (LASSO) regression was applied to identify significant components of OBS associated with sarcopenia and sarcopenic obesity, which were subsequently integrated into a risk prediction nomogram model. The model's predictive accuracy was evaluated using the receiver operating characteristic (ROC) curve.
Results: After adjusting for potential confounders, the weighted logistic regression analyses demonstrated a significant negative association between OBS and the prevalence of sarcopenia (odds ratio [OR] = 0.954, 95% confidence interval [CI] = 0.925-0.984, P = 0.004) and sarcopenic obesity (OR = 0.948, 95% CI = 0.918-0.980, P = 0.002). The nomogram models, informed by key OBS components identified through LASSO regression, exhibited considerable predictive value for sarcopenia (area under the ROC curve [AUC] = 0.813, 95% CI = 0.792-0.833) and sarcopenic obesity (AUC = 0.894, 95% CI = 0.879-0.909).
Conclusion: This study reveals a robust inverse correlation between OBS and both sarcopenia and sarcopenic obesity in adults aged 20-59. These results suggest that an antioxidant-rich diet and healthy lifestyle practices, including low-fat diets, adequate vitamin B intake, regular physical activity, and weight management, may help mitigate the risk of sarcopenia and sarcopenic obesity. Further research is warranted to confirm these associations and determine causality.
期刊介绍:
Nutrition & Metabolism publishes studies with a clear focus on nutrition and metabolism with applications ranging from nutrition needs, exercise physiology, clinical and population studies, as well as the underlying mechanisms in these aspects.
The areas of interest for Nutrition & Metabolism encompass studies in molecular nutrition in the context of obesity, diabetes, lipedemias, metabolic syndrome and exercise physiology. Manuscripts related to molecular, cellular and human metabolism, nutrient sensing and nutrient–gene interactions are also in interest, as are submissions that have employed new and innovative strategies like metabolomics/lipidomics or other omic-based biomarkers to predict nutritional status and metabolic diseases.
Key areas we wish to encourage submissions from include:
-how diet and specific nutrients interact with genes, proteins or metabolites to influence metabolic phenotypes and disease outcomes;
-the role of epigenetic factors and the microbiome in the pathogenesis of metabolic diseases and their influence on metabolic responses to diet and food components;
-how diet and other environmental factors affect epigenetics and microbiota; the extent to which genetic and nongenetic factors modify personal metabolic responses to diet and food compositions and the mechanisms involved;
-how specific biologic networks and nutrient sensing mechanisms attribute to metabolic variability.