Guan Zhongyang, Minnetti Marianna, Heymsfield Steven B, Poggiogalle Eleonora, Prado Carla M, Sim Marc, Stephan Blossom Cm, Wells Jonathan Ck, Donini Lorenzo M, Siervo Mario
School of Population Health, Faculty of Health Science, Curtin University, Perth, WA, Australia.
Department of Experimental Medicine, Sapienza University, Rome, Italy.
Adv Nutr. 2025 Feb;16(2):100364. doi: 10.1016/j.advnut.2024.100364. Epub 2025 Jan 3.
The adaptive and independent interrelationships between different body composition components have been identified as crucial determinants of disease risk. On the basis of this concept, the load-capacity model of body composition, which utilizes measurements obtained through nonanthropometric techniques such as dual-energy X-ray absorptiometry, was proposed. This model is typically operationalized as the ratio of metabolic load (adipose mass) to metabolic capacity (lean mass). In recent years, a series of load-capacity indices (LCIs) have been utilized to identify abnormal body composition phenotypes such as sarcopenic obesity (SO) and to predict the risk of metabolic, cardiovascular, and cognitive disorders. In this review, we comprehensively review the characteristics of different LCIs used in previous studies, with a specific focus on their applications, especially in identifying SO and predicting cardiometabolic outcomes. A systematic literature search was performed using PubMed, MEDLINE, PsycINFO, Embase, and the Cochrane Library. Two meta-analyses were conducted to 1) estimate the overall prevalence of SO mapped by LCIs, and 2) assess the association of LCIs with cardiometabolic outcomes. A total of 48 studies (all observational) were included, comprising 22 different LCIs. Ten studies were included in the meta-analysis of SO prevalence, yielding a pooled prevalence of 14.5% [95% confidence interval (CI): 9.4%, 21.6%]. Seventeen studies were included in the meta-analysis of the association between LCIs and adverse cardiometabolic outcomes, which showed a significant association between higher LCI values and increased risk (odds ratio = 2.22; 95% CI: 1.81, 2.72) of cardiometabolic diseases (e.g. diabetes and metabolic syndrome). These findings suggest that the load-capacity model of body composition could be particularly useful in the identification of SO cases and prediction of cardiometabolic risk. Future longitudinal studies are needed to validate the association of LCIs with chronic cardiometabolic and neurodegenerative diseases. This systematic review and meta-analysis has been registered with PROSPERO (CRD42024457750).
不同身体成分之间的适应性和独立性相互关系已被确定为疾病风险的关键决定因素。基于这一概念，提出了身体成分的负荷能力模型，该模型利用通过双能X射线吸收法等非人体测量技术获得的测量数据。该模型通常通过代谢负荷（脂肪量）与代谢能力（瘦体重）的比值来运作。近年来，一系列负荷能力指数（LCI）已被用于识别诸如肌少症肥胖（SO）等异常身体成分表型，并预测代谢、心血管和认知障碍的风险。在本综述中，我们全面回顾了先前研究中使用的不同LCI的特征，特别关注它们的应用，尤其是在识别SO和预测心脏代谢结局方面。使用PubMed、MEDLINE、PsycINFO、Embase和Cochrane图书馆进行了系统的文献检索。进行了两项荟萃分析，以1）估计由LCI映射的SO的总体患病率，以及2）评估LCI与心脏代谢结局的关联。总共纳入了48项研究（均为观察性研究），包括22种不同的LCI。SO患病率的荟萃分析纳入了10项研究，汇总患病率为14.5%[95%置信区间（CI）：9.4%，21.6%]。LCI与不良心脏代谢结局之间关联的荟萃分析纳入了17项研究，结果显示较高的LCI值与心脏代谢疾病（如糖尿病和代谢综合征）风险增加之间存在显著关联（优势比=2.22；95%CI：1.81，2.72）。这些发现表明，身体成分的负荷能力模型在识别SO病例和预测心脏代谢风险方面可能特别有用。未来需要进行纵向研究来验证LCI与慢性心脏代谢和神经退行性疾病的关联。本系统综述和荟萃分析已在PROSPERO（CRD42024457750）注册。
