Thiazide Use and Fracture Risk: An updated Bayesian Meta-Analysis
Tesfaye Getachew Charkos1, Yawen Liu1, Lina Jin1
1Department of Epidemiology and Biostatistics, School of Public Health, Jilin University, Changchun, 130021, Jilin, China.
Scientific Reports
|December 26, 2019
Summary
This meta-analysis found thiazide use was linked to reduced fracture risk in case-control studies but not in cohort studies. The findings suggest thiazides may not be a protective factor against fractures due to potential biases.
Area of Science:
- Pharmacology and Epidemiology
- Gerontology and Bone Health
Background:
- The relationship between thiazide diuretic use and fracture risk remains debated.
- Existing evidence is conflicting, necessitating an updated comprehensive analysis.
Purpose of the Study:
- To conduct an updated meta-analysis assessing the association between thiazide use and overall fracture risk.
- To synthesize findings from both observational and experimental human studies.
Main Methods:
- Systematic literature search of PubMed, Embase, and Cochrane Library databases up to July 2019.
- Inclusion of 19 case-control and 21 cohort studies, encompassing over 4.8 million subjects.
- Application of a Bayesian hierarchical random effect model to calculate pooled relative risks (RRs) with 95% credible intervals (CrI).
Main Results:
- Case-control studies indicated a reduced fracture risk (RR: 0.87, 95% CrI: 0.70-0.99), with a 93% probability of benefit.
- Cohort studies showed no significant association (RR: 0.95, 95% CrI: 0.85-1.08), with a 72% probability of benefit.
- Significant heterogeneity was observed in both study types (I² > 75% for case-control, I² > 97% for cohort).
Conclusions:
- Thiazide use showed an association with reduced fracture risk in case-control studies, potentially due to recall and selection biases.
- The lack of association in cohort studies suggests thiazide use may not be a protective factor for fractures.
- Further high-quality research is needed to clarify the true relationship, considering potential biases in observational data.
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