Serum parathyroid hormone and risk of sarcopenia: a meta-analysis
Bibliographic record
Abstract
BACKGROUND: Parathyroid hormone plays a key role in muscle metabolism and function, yet its precise association with sarcopenia remains controversial. This meta-analysis evaluated the relationship between serum parathyroid hormone levels and the risk of sarcopenia. METHODS: We systematically searched PubMed, Embase, and Web of Science until April 2025 for observational studies on the link between parathyroid hormone levels and sarcopenia. Using random-effects models, we derived pooled odds ratios with 95% confidence intervals and conducted subgroup analyses. Sensitivity analyses were performed to ensure robustness by excluding small or low-quality studies. Study quality was assessed with modified Newcastle-Ottawa scales, and publication bias was checked using funnel plot symmetry. RESULTS: This meta-analysis included 11 studies involving 4,759 participants, with mean ages ranging from 57.5 to 76.4 years and 50.37% of participants being female. Our meta-analysis showed a positive association between serum parathyroid hormone levels and the risk of sarcopenia (odds ratios = 1.10, 95% confidence intervals 1.03-1.17, P < 0.001). Subgroup analyses indicated a consistently positive association across diagnostic definitions and study settings, although the effect size was greater in studies using alternative diagnostic criteria (OR = 1.94, 95% confidence intervals 1.21-3.13) and in hospital-based populations (OR = 2.19, 95% confidence intervals 1.27-3.77). Sensitivity analysis confirmed the stability of these findings, with no publication bias detected. CONCLUSIONS: This meta-analysis suggests a potential positive association between elevated parathyroid hormone levels and sarcopenia risk. However, given the substantial heterogeneity and the observational nature of the included studies, these findings should be interpreted with caution. Further large-scale, prospective investigations are warranted to clarify the causal relationship and to explore whether targeting parathyroid hormone could contribute to sarcopenia prevention or management.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.026 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.070 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".