{"title":"通过数据包络分析全面评估肠道微生物群:横断面研究","authors":"Taizo Matsuki , Sho Nakamura , Minami Nishiyama , Hiroto Narimatsu","doi":"10.1016/j.cdnut.2024.104469","DOIUrl":null,"url":null,"abstract":"<div><h3>Background</h3><div>The gut microbiome plays a crucial role in human health, but maintaining a healthy gut microbiome remains challenging. Current approaches often focus on individual components rather than providing a holistic assessment.</div></div><div><h3>Objectives</h3><div>To introduce and evaluate a novel approach using data envelopment analysis (DEA) for assessing gut microbiota efficiency and identifying potential targets for personalized interventions.</div></div><div><h3>Methods</h3><div>We conducted a cross-sectional analysis of 577 participants from the Kanagawa “ME-BYO” Prospective Cohort Study. Lifestyle factors and gut microbiota composition were assessed. DEA was employed to calculate an efficiency score for each participant, incorporating multiple inputs (lifestyle factors) and outputs (gut microbiotas). This score represents how efficiently an individual’s lifestyle factors contribute to their gut microbiota composition. Tobit regression analysis was used to assess associations between efficiency scores and demographic and health-related factors.</div></div><div><h3>Results</h3><div>The mean efficiency score was 0.86, with 14.2% of participants classified as efficient. Efficiency scores showed positive correlations with alcohol intake and Faith's phylogenetic diversity. Tobit regression analysis revealed significant associations between efficiency scores and sex, fat intake, and yogurt consumption. DEA identified specific targets for improving gut microbiota composition in inefficient individuals.</div></div><div><h3>Conclusions</h3><div>This study demonstrates the potential of DEA as a tool for evaluating gut microbiota efficiency and providing personalized recommendations for microbiota optimization. This approach could lead to more effective strategies for optimizing gut health across diverse populations.</div></div>","PeriodicalId":10756,"journal":{"name":"Current Developments in Nutrition","volume":null,"pages":null},"PeriodicalIF":3.8000,"publicationDate":"2024-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Holistic Evaluation of the Gut Microbiota through Data Envelopment Analysis: A Cross-Sectional Study\",\"authors\":\"Taizo Matsuki , Sho Nakamura , Minami Nishiyama , Hiroto Narimatsu\",\"doi\":\"10.1016/j.cdnut.2024.104469\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Background</h3><div>The gut microbiome plays a crucial role in human health, but maintaining a healthy gut microbiome remains challenging. Current approaches often focus on individual components rather than providing a holistic assessment.</div></div><div><h3>Objectives</h3><div>To introduce and evaluate a novel approach using data envelopment analysis (DEA) for assessing gut microbiota efficiency and identifying potential targets for personalized interventions.</div></div><div><h3>Methods</h3><div>We conducted a cross-sectional analysis of 577 participants from the Kanagawa “ME-BYO” Prospective Cohort Study. Lifestyle factors and gut microbiota composition were assessed. DEA was employed to calculate an efficiency score for each participant, incorporating multiple inputs (lifestyle factors) and outputs (gut microbiotas). This score represents how efficiently an individual’s lifestyle factors contribute to their gut microbiota composition. Tobit regression analysis was used to assess associations between efficiency scores and demographic and health-related factors.</div></div><div><h3>Results</h3><div>The mean efficiency score was 0.86, with 14.2% of participants classified as efficient. Efficiency scores showed positive correlations with alcohol intake and Faith's phylogenetic diversity. Tobit regression analysis revealed significant associations between efficiency scores and sex, fat intake, and yogurt consumption. DEA identified specific targets for improving gut microbiota composition in inefficient individuals.</div></div><div><h3>Conclusions</h3><div>This study demonstrates the potential of DEA as a tool for evaluating gut microbiota efficiency and providing personalized recommendations for microbiota optimization. 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引用次数: 0
摘要
背景肠道微生物群在人类健康中发挥着至关重要的作用,但保持健康的肠道微生物群仍然具有挑战性。方法 我们对神奈川 "ME-BYO "前瞻性队列研究的 577 名参与者进行了横断面分析。对生活方式因素和肠道微生物群组成进行了评估。采用 DEA 计算每位参与者的效率得分,其中包含多个输入(生活方式因素)和输出(肠道微生物群)。该得分代表了个人的生活方式因素对其肠道微生物群组成的贡献效率。结果平均效率得分为 0.86,14.2% 的参与者被归类为有效率。效率得分与酒精摄入量和信仰的系统发育多样性呈正相关。托比特回归分析显示,效率得分与性别、脂肪摄入量和酸奶消费量之间存在显著关联。结论这项研究证明了 DEA 作为一种评估肠道微生物群效率和提供个性化微生物群优化建议的工具的潜力。这种方法可以为优化不同人群的肠道健康提供更有效的策略。
Holistic Evaluation of the Gut Microbiota through Data Envelopment Analysis: A Cross-Sectional Study
Background
The gut microbiome plays a crucial role in human health, but maintaining a healthy gut microbiome remains challenging. Current approaches often focus on individual components rather than providing a holistic assessment.
Objectives
To introduce and evaluate a novel approach using data envelopment analysis (DEA) for assessing gut microbiota efficiency and identifying potential targets for personalized interventions.
Methods
We conducted a cross-sectional analysis of 577 participants from the Kanagawa “ME-BYO” Prospective Cohort Study. Lifestyle factors and gut microbiota composition were assessed. DEA was employed to calculate an efficiency score for each participant, incorporating multiple inputs (lifestyle factors) and outputs (gut microbiotas). This score represents how efficiently an individual’s lifestyle factors contribute to their gut microbiota composition. Tobit regression analysis was used to assess associations between efficiency scores and demographic and health-related factors.
Results
The mean efficiency score was 0.86, with 14.2% of participants classified as efficient. Efficiency scores showed positive correlations with alcohol intake and Faith's phylogenetic diversity. Tobit regression analysis revealed significant associations between efficiency scores and sex, fat intake, and yogurt consumption. DEA identified specific targets for improving gut microbiota composition in inefficient individuals.
Conclusions
This study demonstrates the potential of DEA as a tool for evaluating gut microbiota efficiency and providing personalized recommendations for microbiota optimization. This approach could lead to more effective strategies for optimizing gut health across diverse populations.