Association between sarcopenic obesity and risk of frailty in older adults: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Age-related changes in body composition such as muscle loss can lead to sarcopenia, which is closely associated with frailty. However, the effect of body fat accumulation on frailty in old age remains unclear. In particular, the association between the combination of these two conditions, known as sarcopenic obesity, and frailty in older adults is unclear. OBJECTIVE: To synthesise the association between sarcopenic obesity and the risk of frailty and to investigate the role of obesity in the risk of frailty in old age. METHODS: Six databases were searched from inception to 29 September 2024. Two reviewers independently extracted the data and assessed the risk of bias for the included observational studies using the adapted Newcastle-Ottawa scale. The control groups consisted of robust, obese and sarcopenic individuals. Meta-analyses were performed to examine the risk of frailty due to sarcopenic obesity and the role of obesity in frailty amongst sarcopenic older adults. RESULTS: Sixteen eligible studies were included in meta-analyses from 1098 records. Compared to robust individuals, older adults with sarcopenic obesity were more vulnerable to frailty [odds ratio (OR), 3.76; 95% confidence interval (CI), 2.62 to 5.39; I2 = 79.3%; P < .0001]. Obesity was not associated with the risk of frailty (OR, 1.23; 95% CI, 0.99 to 1.53; I2 = 0.0%; P = .501) in sarcopenic older adults. CONCLUSIONS: Sarcopenic obesity is associated with a high risk of frailty. Sarcopenia and obesity may have synergistic effects on frailty in older adults.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.029 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".