The Future of Obesity Management through Precision Nutrition: Putting the Individual at the Center.

IF 4.6 3区 医学 Q1 NUTRITION & DIETETICS
Current Nutrition Reports Pub Date : 2024-09-01 Epub Date: 2024-05-28 DOI:10.1007/s13668-024-00550-y
Hande Gül Ulusoy-Gezer, Neslişah Rakıcıoğlu
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引用次数: 0

Abstract

PURPOSE OF REVIEW: The prevalence of obesity continues to rise steadily. While obesity management typically relies on dietary and lifestyle modifications, individual responses to these interventions vary widely. Clinical guidelines for overweight and obesity stress the importance of personalized approaches to care. This review aims to underscore the role of precision nutrition in delivering tailored interventions for obesity management. RECENT FINDINGS: Recent technological strides have expanded our ability to detect obesity-related genetic polymorphisms, with machine learning algorithms proving pivotal in analyzing intricate genomic data. Machine learning algorithms can also predict postprandial glucose, triglyceride, and insulin levels, facilitating customized dietary interventions and ultimately leading to successful weight loss. Additionally, given that adherence to dietary recommendations is one of the key predictors of weight loss success, employing more objective methods for dietary assessment and monitoring can enhance sustained long-term compliance. Biomarkers of food intake hold promise for a more objective dietary assessment. Acknowledging the multifaceted nature of obesity, precision nutrition stands poised to transform obesity management by tailoring dietary interventions to individuals' genetic backgrounds, gut microbiota, metabolic profiles, and behavioral patterns. However, there is insufficient evidence demonstrating the superiority of precision nutrition over traditional dietary recommendations. The integration of precision nutrition into routine clinical practice requires further validation through randomized controlled trials and the accumulation of a larger body of evidence to strengthen its foundation.

通过精准营养管理肥胖症的未来:以人为本。
综述目的:肥胖症的发病率持续上升。虽然肥胖症的治疗通常依赖于饮食和生活方式的调整,但个人对这些干预措施的反应却千差万别。针对超重和肥胖症的临床指南强调了个性化护理方法的重要性。本综述旨在强调精准营养在提供量身定制的肥胖管理干预措施中的作用。最新发现:最近的技术进步扩大了我们检测肥胖相关基因多态性的能力,机器学习算法在分析复杂的基因组数据方面发挥了关键作用。机器学习算法还能预测餐后血糖、甘油三酯和胰岛素水平,从而促进定制化饮食干预,最终实现成功减肥。此外,鉴于坚持饮食建议是预测减肥成功与否的关键因素之一,采用更客观的方法进行饮食评估和监测可以提高长期持续的依从性。食物摄入的生物标志物有望实现更客观的饮食评估。认识到肥胖症的多面性,精准营养有望通过根据个人的遗传背景、肠道微生物群、代谢特征和行为模式定制膳食干预措施来改变肥胖症的管理。然而,目前还没有足够的证据证明精准营养优于传统的膳食建议。要将精准营养纳入常规临床实践,还需要通过随机对照试验进一步验证,并积累更多的证据来巩固其基础。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Current Nutrition Reports
Current Nutrition Reports Agricultural and Biological Sciences-Food Science
CiteScore
7.70
自引率
2.00%
发文量
59
期刊介绍: This journal aims to provide comprehensive review articles that emphasize significant developments in nutrition research emerging in recent publications. By presenting clear, insightful, balanced contributions by international experts, the journal intends to discuss the influence of nutrition on major health conditions such as diabetes, cardiovascular disease, cancer, and obesity, as well as the impact of nutrition on genetics, metabolic function, and public health. We accomplish this aim by appointing international authorities to serve as Section Editors in key subject areas across the field. Section Editors select topics for which leading experts contribute comprehensive review articles that emphasize new developments and recently published papers of major importance, highlighted by annotated reference lists. We also provide commentaries from well-known figures in the field, and an Editorial Board of more than 25 internationally diverse members reviews the annual table of contents, suggests topics of special importance to their country/region, and ensures that topics and current and include emerging research.
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