极低热量生酮干预后纵向身体成分变化的表型驱动变异性:机器学习聚类方法。

IF 3 3区 医学 Q2 HEALTH CARE SCIENCES & SERVICES
Victor de la O, Begoña de Cuevillas, Miksa Henkrich, Barbara Vizmanos, Maitane Nuñez-Garcia, Ignacio Sajoux, Daniel de Luis, J Alfredo Martínez
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引用次数: 0

摘要

背景:肥胖是一个主要的全球公共卫生问题,没有完全令人满意的解决方案。大多数营养干预依赖于热量限制,取得了不同程度的成功。极低热量生酮饮食(VLCKD)通过脂肪分解诱导酮体,减少食欲,在保持代谢健康的同时保持瘦体重,证明了快速和持续的体重减轻。方法:一项前瞻性临床研究分析了7775名患者的社会人口学、人体测量学和依从性数据,这些患者接受了基于商业减肥计划的多学科营养单臂干预。该方法使用具有特定均衡营养特征的蛋白质制剂,旨在确定减肥成功的关键预测因素,并对具有共同基线特征和减肥模式的人群表型进行分类,以优化治疗个性化。结果:统计和机器学习分析显示,男性(-9.2 kg对-5.9 kg)和较高的初始体重(-8.9 kg对-4.0 kg)强烈预测VLCKD患者的体重减轻幅度较大,而年龄的影响较小。在年龄、性别、随访时间和就诊情况上,出现了两种不同的人群群,显示出独特的减肥成功模式。这些集群有助于定义个性化策略以优化结果。结论:这些发现在翻译上支持多学科VLCK减肥计划的有效性,并强调了成功的预测因素。认识到性别、年龄和初始体重等变量,可以提高肥胖管理中精确方法的潜力,为不同的患者提供更量身定制和有效的治疗,并开出个性化的减肥建议。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Phenotype-Driven Variability in Longitudinal Body Composition Changes After a Very Low-Calorie Ketogenic Intervention: A Machine Learning Cluster Approach.

Background: Obesity is a major global public health issue with no fully satisfactory solutions. Most nutritional interventions rely on caloric restriction, with varying degrees of success. Very low-calorie ketogenic diets (VLCKD) have demonstrated rapid and sustained weight loss by inducing ketone bodies through lipolysis, reducing appetite, and preserving lean mass while maintaining metabolic health. Methods: A prospective clinical study analyzed sociodemographic, anthropometric, and adherence data from 7775 patients undergoing a multidisciplinary nutritional single-arm intervention based on a commercial weight-loss program. This method, using protein preparations with a specific balanced nutritional profile, aimed to identify key predictors of weight-loss success and classify population phenotypes with shared baseline characteristics and weight-loss patterns to optimize treatment personalization. Results: Statistical and machine learning analyses revealed that male gender (-9.2 kg vs. -5.9 kg) and higher initial body weight (-8.9 kg vs. -4.0 kg) strongly predict greater weight loss on a VLCKD, while age has a lesser impact. Two distinct population clusters emerged, differing in age, sex, follow-up duration, and medical visits, demonstrating unique weight-loss success patterns. These clusters help define individualized strategies for optimizing outcomes. Conclusions: These findings translationally support associations with the efficacy of a multidisciplinary VLCK weight-loss program and highlight predictors of success. Recognizing variables such as sex, age, and initial weight enhances the potential for a precision-based approach in obesity management, enabling more tailored and effective treatments for diverse patient profiles and prescribe weight loss personalized recommendations.

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来源期刊
Journal of Personalized Medicine
Journal of Personalized Medicine Medicine-Medicine (miscellaneous)
CiteScore
4.10
自引率
0.00%
发文量
1878
审稿时长
11 weeks
期刊介绍: Journal of Personalized Medicine (JPM; ISSN 2075-4426) is an international, open access journal aimed at bringing all aspects of personalized medicine to one platform. JPM publishes cutting edge, innovative preclinical and translational scientific research and technologies related to personalized medicine (e.g., pharmacogenomics/proteomics, systems biology). JPM recognizes that personalized medicine—the assessment of genetic, environmental and host factors that cause variability of individuals—is a challenging, transdisciplinary topic that requires discussions from a range of experts. For a comprehensive perspective of personalized medicine, JPM aims to integrate expertise from the molecular and translational sciences, therapeutics and diagnostics, as well as discussions of regulatory, social, ethical and policy aspects. We provide a forum to bring together academic and clinical researchers, biotechnology, diagnostic and pharmaceutical companies, health professionals, regulatory and ethical experts, and government and regulatory authorities.
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