Prevalence and risk factors of postmenopausal osteoporosis: a systematic review and meta-analysis based on...

This meta-analysis aimed to investigate the prevalence and risk factors for Postmenopausal Osteoporosis (PMO) to provide a basis for early clinical identification of high-risk populations.

We systematically searched English databases (PubMed, Cochrane Library, Embase, Web of Science) and Chinese databases (CNKI, Wanfang Data, VIP Database, Sinomed) up to May 2026 for observational studies using multivariate logistic regression to analyze risk factors for PMO. The meta-analysis was conducted following the PRISMA guidelines. After data extraction, pooled odds ratios (OR) with 95% confidence intervals (CI) were calculated using STATA 15.0.

A total of 37 studies involving 32,366 postmenopausal women were included. Among these, 36 studies reported the prevalence of osteoporosis, yielding an overall prevalence of 33.87%. The meta-analysis identified the following factors as significantly associated with an increased risk of PMO: Age (OR = 1.16); Postmenopausal duration (OR = 1.23); Postmenopausal duration > 10 years (OR = 2.81); Number of births (OR = 1.32); With ≥ 2 births (OR = 3.07); Smoking (OR = 1.55); Lack of exercise (OR = 1.66); History of previous fracture (OR = 1.86); Diabetes mellitus (OR = 2.24); Conversely, a higher body mass index (BMI) (OR = 0.80), older age at menopause (OR = 0.92), and estrogen replacement therapy (OR = 0.45) were identified as protective factors. No significant association was found for the following factors: Family history of osteoporosis; Regular exercise; Hypertension; Calcium supplementation.

Osteoporosis is highly prevalent among postmenopausal women, particularly within the East Asian cohorts that comprised the majority of this meta-analysis. Several independent risk factors were identified, including age, longer postmenopausal duration, multiparity, smoking, physical inactivity, previous fracture, and diabetes. Estrogen replacement therapy showed a strong protective effect. While high statistical heterogeneity for several factors was addressed using random-effects models and sensitivity analyses, these findings-derived predominantly from Chinese cohorts-should be interpreted with consideration of this variability. Nevertheless, they provide a robust framework for early risk stratification and targeted prevention strategies in this high-risk population.

https://www.crd.york.ac.uk/prospero/, identifier CRD420251274575.