Association between IL-17 and sarcopenia in older adults
Bibliographic record
Abstract
Background Chronic low-grade inflammation has been implicated as a potential contributor to sarcopenia, but the specific inflammatory mediators involved remain under investigation. This study explores the association between serum interleukin-17 levels and sarcopenia in older outpatients without pre-existing inflammatory or autoimmune diseases. Methods A cross-sectional study was conducted using data from the MiMiCS-FRAIL cohort. Sarcopenia was defined according to the European Working Group on Sarcopenia in Older People 2 criteria. IL-17 plasma levels were measured using enzyme-linked immunosorbent assay (ELISA). A multivariate binary logistic regression model was used to assess the association between sarcopenia and IL-17 levels. Results A total of 255 older adults aged ≥60 years (67.6% women) were included, with a mean age of 70.8 ± 7.3 years. The prevalence of sarcopenia was 16.9%. Advanced age (OR = 4.26; 95% CI: 1.75–10.41; p = 0.001) was significantly associated with sarcopenia. In the fully adjusted model, IL-17 (log-transformed) remained significantly associated with sarcopenia (OR = 1.74; 95% CI: 1.11–2.74; p = 0.017). Age (OR = 1.10; 95% CI: 1.03–1.17; p = 0.003) and BMI (OR = 0.69; 95% CI: 0.60–0.79; p < 0.001) were also associated. IL-6, TNF-α, number of medications, sex, and cognitive score were not statistically significant. Conclusions Elevated IL-17 levels were associated with higher odds of sarcopenia among older adults. These findings suggest that IL-17 may serve as a potential biomarker for sarcopenia, although further longitudinal studies are needed to elucidate its causal role.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 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".