Zhu-Zhu Wang, Qin Xu, Yu-Han Zhang, Rong-Rong Wu, Jun-Ling Cui, Ji Zhou, Jing-Fang Hong
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This study endeavors to explore these associations and to identify potential dietary and lifestyle risk factors.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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).</p><p><strong>Conclusion: </strong>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. 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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.</p><p><strong>Results: </strong>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). 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引用次数: 0
摘要
背景:骨骼肌减少症和肥胖是两种常见的健康状况,经常共存并加剧彼此的影响,增加慢性疾病和死亡率的风险。这种双重情况被称为“肌肉减少性肥胖”。氧化应激(OS)与肌肉减少症或肥胖之间存在相关性,氧化平衡评分(OBS)可以作为个体总体饮食或生活方式相关的氧化应激暴露的指标。先前的报道没有涉及60岁以下成人OBS与肌肉减少症或肌肉减少性肥胖之间的关系。这项研究试图探索这些关联,并确定潜在的饮食和生活方式风险因素。方法:我们利用2011年至2018年国家健康与营养检查调查(NHANES)的4241名参与者的数据进行了横断面分析。OBS是由16种饮食成分和4种生活方式组成的累积分数,分数越高表明抗氧化剂接触越多,促氧化因子接触越少,反映出氧化应激负担减轻。采用加权多因素logistic回归分析OBS与肌肉减少症和肌肉减少性肥胖的关系。进一步进行亚组分析以检查与各种协变量的相互作用。最小绝对收缩和选择算子(LASSO)回归用于识别与肌肉减少症和肌肉减少性肥胖相关的OBS的重要组成部分,随后将其整合到风险预测nomogram模型中。采用受试者工作特征(ROC)曲线评价模型的预测精度。结果:调整潜在混杂因素后,加权logistic回归分析显示,OBS与肌少症患病率(比值比[OR] = 0.954, 95%可信区间[CI] = 0.925-0.984, P = 0.004)和肌少症肥胖患病率(OR = 0.948, 95% CI = 0.918-0.980, P = 0.002)呈显著负相关。由LASSO回归确定的关键OBS成分组成的nomogram模型对肌肉减少症(ROC曲线下面积[AUC] = 0.813, 95% CI = 0.792-0.833)和肌肉减少性肥胖(AUC = 0.894, 95% CI = 0.879-0.909)具有相当大的预测价值。结论:本研究揭示了在20-59岁的成年人中,OBS与肌肉减少症和肌肉减少性肥胖之间存在显著的负相关。这些结果表明,富含抗氧化剂的饮食和健康的生活方式,包括低脂饮食、充足的维生素B摄入量、有规律的体育活动和体重管理,可能有助于减轻肌肉减少症和肌肉减少性肥胖的风险。需要进一步的研究来证实这些关联并确定因果关系。
Oxidative balance score is associated with increased risk of sarcopenia and sarcopenic obesity in non-elderly adults: results from NHANES 2011-2018.
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.