Association of triglyceride-glucose index with bone mineral density and fracture: a systematic review
Bibliographic record
Abstract
BACKGROUND AND AIM: Studies have found inconsistent results regarding triglyceride-glucose (TyG) index and bone health. This systematic review aims to synthesize the existing evidence on the association between the (TyG) index, bone mineral density (BMD), and bone fractures. METHOD: A comprehensive search of PubMed, Scopus, Web of Science, and Embase databases was performed for studies published up to December 26, 2024. Inclusion criteria encompassed human studies examining the TyG index in relation to BMD or fractures. The risk of bias was assessed using the Newcastle-Ottawa Scale (NOS). Data synthesis included both qualitative and descriptive statistical analyses. RESULTS: From 201 studies identified, 12 met the inclusion criteria comprising 817,242 participants. Most studies reported a significant association between TyG index and bone fractures. The studies reported inconsistent findings regarding the association between the TyG index and BMD. While some studies found no correlation between the TyG index and BMD in individuals aged ≥ 50 years, studies on the general population aged ≥ 18 years demonstrated a significant correlation between the TyG index and BMD. Variations in the age of study populations, the presence of diabetes, BMI, and adjustment factors likely contributed to these discrepancies. Further research is needed to clarify the role of the TyG index in bone health and its potential utility as a surrogate marker. CONCLUSION: The TyG index is associated with bone fractures and can serve as a surrogate marker for osteoporosis in the general populations rather than exclusively for the elderly.
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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 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".