Add like
Add dislike
Add to saved papers

Association between IGF-1 polymorphisms and risk of osteoporosis in Chinese population: a meta-analysis.

BACKGROUND: Several studies looking into the association between insulin-like growth factor-1 (IGF-1) gene polymorphisms and osteoporosis predisposition have been conducted among Chinese population with conflicting outcomes. The present systematic review and meta-analysis was performed to appraise and synthesize the existing evidence, so as to provide a more precise and reliable association between polymorphisms in IGF-1 gene and osteoporosis.

METHODS: Five electronic databases including PubMed, EMBASE, ISI Web of Science, CNKI and Wanfang were systematically searched for potential studies. Summary odds ratio (OR) and corresponding 95% confidence interval (95% CI) were calculated to evaluate the association. The best-matching genetic model of inheritance was determined using a genetic-model free approach.

RESULTS: Six case-control studies comprising 2068 osteoporosis patients and 2071 healthy controls were obtained for the meta-analysis. Dominant model was confirmed to be the best-matching genetic model (TT + TC versus CC). The overall data suggested that rs35767 polymorphism was significantly associated with osteoporosis vulnerability (OR 1.21, 95% CI 1.07, 1.37; P = 0.002). When stratifying the participants and performing subgroup-analysis according to source of patients, the result suggested that rs35767 was significantly correlated to osteoporosis in post-menopausal women subgroup (OR 1.29, 95% CI 1.08, 1.54; P = 0.005), but the correlation was not established in the subgroup of both gender (OR 1.14, 95% CI 0.96, 1.35; P = 0.12).

CONCLUSION: Taken together, the findings of our current study suggested a significant association between rs35767 polymorphism and risk of osteoporosis in Chinese post-menopausal women.

Full text links

We have located links that may give you full text access.
Can't access the paper?
Try logging in through your university/institutional subscription. For a smoother one-click institutional access experience, please use our mobile app.

Related Resources

For the best experience, use the Read mobile app

Mobile app image

Get seemless 1-tap access through your institution/university

For the best experience, use the Read mobile app

All material on this website is protected by copyright, Copyright © 1994-2024 by WebMD LLC.
This website also contains material copyrighted by 3rd parties.

By using this service, you agree to our terms of use and privacy policy.

Your Privacy Choices Toggle icon

You can now claim free CME credits for this literature searchClaim now

Get seemless 1-tap access through your institution/university

For the best experience, use the Read mobile app