Lower circulating irisin in middle-aged and older adults with osteoporosis: a systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVE: Osteoporosis has imposed a heavy socioeconomic burden worldwide, especially in postmenopausal women. As a newly found protein, irisin has an important physiological role in bone metabolism. This meta-analysis intends to identify the association between circulating irisin levels and osteoporosis. METHODS: This meta-analysis was conducted following the Meta-analysis of Observational Studies in Epidemiology (MOOSE) guideline. A comprehensive search of five databases was performed from inception to January 2019. Studies with original date on middle-aged and older participants were included. Data were analyzed according to study characteristics and heterogeneity between studies. The quality of each study and the presence of publication bias were assessed by Newcastle-Ottawa score (NOS) and normal quantile plot. RESULTS: Seven studies, with a total of 1,018 participants, conducted in four countries, were included. Six of them were identified as high-quality research. Five studies included postmenopausal women, and two studies included both men and women. Possible publication bias was found in the analysis of irisin and osteoporosis. Pooled analysis indicated decreased irisin levels in osteoporotic participants (mean difference -87.91, 95% CI, -92.56 to -83.25). Subgroup analysis revealed an even lower level of irisin in postmenopausal women and in participants with a history of fractures. Analysis on associations between irisin and femoral neck or lumbar spine bone mineral density showed a weak positive correlation. CONCLUSIONS: The findings of this analysis suggested that circulating irisin levels were decreased in middle-aged and older participants with osteoporosis. Irisin was positively correlated with bone mineral density.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.030 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".