The association of ultra-processed food intake on age-related muscle conditions: a systematic review and dose–response meta-analysis with meta-regression
Bibliographic record
Abstract
Chronic excessive intake of ultra-processed foods (UPFs) has been linked to various metabolic conditions; however, its impact on skeletal muscle mass and function in older adults remains unclear. Therefore, we conducted this study to examine the association between UPF intake and age-related muscle outcomes, including frailty, sarcopenia, low muscle mass (LMM), and/or low muscle strength (LMS). A systematic search was conducted in ISI Web of Science, LILACS, PubMed/MEDLINE, and Scopus without restrictions up to November 1, 2024. Relative risks (RRs) and 95% confidence intervals (CIs) were pooled using a random-effects model. Study quality and the presence of publication bias were assessed using the Newcastle–Ottawa Scale, Egger’s regression asymmetry test, and Begg’s rank correlation test. Data from 29 studies were included. Cohort studies showed that higher UPF intake was significantly associated with an increased risk of frailty (RR = 1.40; 95% CI 1.25–1.58; I 2 = 83.0%; p < 0.001; n = 11), but not with LMS. In contrast, cross-sectional studies indicated that higher UPF intake was significantly associated with an increased risk of LMS (RR = 1.13; 95% CI 1.06–1.20; I 2 = 0.0%; p < 0.001; n = 5), but not with frailty, sarcopenia, or LMM. Furthermore, a 100 g increase in UPF intake was associated with a 3% higher risk of frailty (RR = 1.03; 95% CI 1.01–1.06; I 2 = 85.1%; p = 0.016; n = 5). Non-linear dose–response analysis showed a positive linear association between UPF intake and frailty risk (P_non-linearity = 0.807; P_dose-response < 0.001; n = 5). Higher UPF intake was associated with an increased risk of frailty in cohort studies and with low muscle strength in cross-sectional studies. These findings suggest that regular consumption of UPFs may negatively affect muscle health, potentially impairing quality of life and independence 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 | 0.016 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.061 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".