Beyond Traditional Body Composition Metrics: Load-Capacity Indices Emerge as Predictors of Cardiometabolic Outcomes—A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
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).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.003 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".