Exploring the impact of interleukins on sarcopenia development: A systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: The role of interleukins in sarcopenia development has been acknowledged, yet the specifics of their involvement remain to be fully understood. This study aimed to explore alterations in interleukin levels among sarcopenia patients. METHODS: Searches were conducted in Embase, Medline, and the Cochrane Library for literature published up to May 2023. Eligible observational studies with a diagnosis of sarcopenia were included. The Newcastle-Ottawa Scale was utilized for quality assessment. For data synthesis, a random-effects model was used, and the Mantel-Haenszel method was used for pooled estimates. RESULTS: Of the 7685 articles screened, 37 met the inclusion criteria. Statistically significant differences in the levels of IL-1β, IL-6 and IL-10 were detected in sarcopenia patients. Specifically, IL-1β (95 % CI: 0.33 [0.12, 0.54], P < 0.05), IL-6 (95 % CI: 0.91 [0.59, 1.24], P < 0.05), and IL-10 (95 % CI: 0.11 [0.07,0.15], P < 0.05) were detected. However, no significant associations were found between serum IL-4 (95 % CI: 0.36 [-0.18, 0.42], P = 0.44), IL-8 (95 % CI: -1.05 [-3.06, 0.95], P = 0.3), IL-12 (95 % CI: -3.92 [-8.32,0.48], P = 0.08) or IL-17 (95 % CI: 0.22 [-2.43, 2.88], P = 0.87) and sarcopenia. Subgroup analysis showed no significant difference in IL-6 (95 % CI: -0.03 [-0.72, 0.66], P = 0.93) and IL-10 (95 % CI: 0.1 [-0.44, 0.64], P = 0.72) among patients with European standard sarcopenia. CONCLUSIONS: Inflammation plays a role in sarcopenia, and the serum levels of IL-1β, IL-6, and IL-10 are associated with sarcopenia. Further research is needed to clarify these associations. CLINICAL TRIALS REGISTRATION NUMBER: CRD42024506656.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.008 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".