Abstract

Background: Many studies have reported the association between vitamin D receptor (VDR) polymorphism and osteoporosis risk. However, their results were conflicting. Six previous meta-analyses have been published to analyze VDR BsmI, FokI, and Cdx2 polymorphisms on osteoporosis risk. However, they did not evaluate the reliability of statistically significant associations. Furthermore, a lot of new articles have been published on these themes, and therefore an updated meta-analysis was performed to further explore these issues.

Objectives: To explore the association between VDR BsmI, FokI, and Cdx2 polymorphisms polymorphisms and osteoporosis risk.

Methods: The odds ratios (ORs) and 95% confidence intervals (95% CIs) were pooled to evaluate the association between VDR BsmI, FokI, and Cdx2 polymorphisms and osteoporosis risk. To evaluate the credibility of statistically significant associations, we applied the false-positive report probabilities (FPRPs) test and the Venice criteria.

Results: Overall, statistically significantly increased osteoporosis risk was found in Indians and women for VDR FokI polymorphism. Statistically significantly decreased osteoporosis risk was found in West Asians for VDR BsmI polymorphism. However, when we performed a sensitivity analysis after excluding low quality and Hardy–Weinberg Disequilibrium (HWD) studies, significantly decreased osteoporosis risk was only found in overall population for VDR BsmI polymorphism. Further, less-credible positive results were identified when we evaluated the credibility of positive results.

Conclusion: These positive findings should be interpreted with caution and indicate that significant association may most likely result from less-credible, rather than from true associations or biological factors on the VDR BsmI and FokI polymorphisms with osteoporosis risk.

Introduction

Osteoporosis is a systemic skeletal disease characterized by a systemic impairment of bone mass and microarchitecture that results in a high risk of fractures [1]. According to WHO, osteoporosis is the reduction in bone density below 2.5 standard deviation from the average for healthy and mature adults with similar ethnicity and age. It is one of the most common metabolic bone diseases in the world, affecting women over the age of 59 and men over the age of 74 [2]. It was reported that there were approximately 200 million osteoporosis patients in the world [3]. Therefore, it is very important to explore the potential pathogenic factors.

Multiple factors were reported to affect osteoporosis, including environmental factors such as exercise, smoking and alcohol consumption, metabolic syndrome, and genetic factors [4–6]. Among them, genes were a very important factor. The heritability of osteoporosis-related traits (such as bone mineral density) was reported to be up to 60–80% [7]. Up till now, tens of hundreds of risk genes have been identified for osteoporosis, including collagen type I α 1 gene (COL1A1), calcitonin receptor (CTR), estrogen receptor (ESR), vitamin D receptor (VDR), and so on [8–10]. Most of these genes are known to influence the reabsorption of bone by osteoclasts and the formation of bone by osteoblasts.

VDR was the most extensively reported, located on chromosome 12q13 [11], through mediating 1,25-dihydroxycholecalciferol (1,25(OH)2D3) to play a variety of biological effects [12]. In human monocytes, 1,25(OH)2D3 modulates chromatin accessibility at 8979 loci [13]. Therefore, VDR polymorphisms were associated with a variety of diseases, including bone mineral density and osteoporosis [14,15]. Morrison et al. [16] first investigated that variability in osteocalcin levels reflect allelic variation in the VDR gene. Since then, a large number of studies have reported that VDR gene mutations (such as FokI (rs10735810), BsmI (rs1544410) and Cdx2 (rs11568820) were related to osteoporosis risk. However, these results were inconsistent or even conflicting. For example, Ling et al. [15] found that VDR Cdx-2 A allele was associated with decreased bone mineral density (BMD) risk and increased fracture risk. On the contrary, A allele was found to have protective effect on osteoporotic fractures in some studies [14,17]. Similarly, they were also conflicting in different studies [18–23] on the associations between the VDR FokI and BsmI polymorphisms and osteoporosis risk. These different results may be caused by small sample size, different races, regions, and sampling methods. Although several related meta-analyses have reported the associations between VDR BsmI, FokI, and Cdx2 polymorphisms and the risk of osteoporosis [24–29]. However, their studies have some disadvantages. First, the results of these meta-analyses were inconsistent. For example, Jia et al. [27] found that the VDR BsmI polymorphism may have a protective effect on the development of osteoporosis. However, Gang et al. [28] concluded that there was no association between VDR BsmI polymorphism and osteoporosis risk. Second, literature quality assessments had not been performed in some studies [24,25,27–29]. In addition, they did not evaluate the credibility of statistically significant associations [24–29]. Furthermore, some new studies have been published on the VDR polymorphisms and osteoporosis risk. Therefore, we performed an updated meta-analysis to provide more reliable results on these issues.

Materials and methods

Search strategy

We performed the meta-analysis according to the guidelines of the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) group [30]. Databases including PubMed, Embase, and Chinese Wanfang Data Knowledge Service Platform were searched to investigate the association between VDR polymorphisms and osteoporosis risk. The following search strategy were used: (VDR OR vitamin D receptor OR BsmI OR FokI OR Cdx2) AND (polymorphism OR mutaion OR variant) AND (osteoporosis OR osteoporoses). The search deadline was November 2019.

Selection criteria

The inclusion criteria were as follows: (1) case–control or cohort studies; (2) describe the association among VDR BsmI, FokI, and Cdx2 polymorphisms and osteoporosis risk; (3) the case and control groups have sufficient genotype data in the selected literature.

The exclusion criteria were: (1) duplicated studies; (2) studies without available data; (3) case reports, reviews, letters, and meta-analyses.

Data extraction

The data extraction tables in the present study were prepared in advance. According to the established inclusion and exclusion criteria, the data were independently extracted and cross-checked; if there was any objection, the consensus can not be reached after discussion and negotiation. The third author was invited to extract the data again, and finally check and confirm. If the data are not detailed or in doubt, try to contact the original author, supplement and confirm the accuracy and integrity of the data. The extracted information was as follows: first author’s surname, publication year, country, ethnicity, age of cases and controls, the number of cases and controls, diagnostic criteria for osteoporosis, menopausal status, matching variables, site of BMD measurement, and number of genotype distributions in cases and controls.

Quality assessment

The quality of all eligible studies was independently assessed by the two authors. We designed quality assessment criteria on the basis of two previous meta-analyses [31,32]. Supplementary Table S1 lists the scale for quality assessment of molecular association studies of osteoporosis risk. The total score was 20 points, studies scoring above 12 were excellent, those scoring less than 9 were poor, and those scoring between 9 and 12 were moderate.

Statistical analysis

The odds ratios (ORs) and 95% confidence intervals (95% CIs) were pooled to evaluate the association strength, P<0.05 was considered as statistically significant. Five genetic model comparisons were used: (1) allele model; (2) additive model; (3) dominant model; (4) recessive model; (5) overdominant model. Heterogeneity test used Chi-square-based Q-test and I2 test. There was no obvious heterogeneity among studies when P>0.10 and/or I2 ≤ 50% [33] and the ORs were pooled to apply a fixed-effects model [34]. Otherwise, a random-effects model was selected [35]. Furthermore, a meta-regression analysis was applied to explore sources of heterogeneity. Subgroup analyses were performed according to ethnicity or gender. Sensitivity analysis was estimated by the following three methods: (1) a single study was removed each time; (2) exclude low quality and Hardy–Weinberg Disequilibrium (HWD) studies; (3) the studies met the following conditions: high-quality studies, Hardy–Weinberg Equilibrium (HWE), and matching studies. Chi-square goodness-of-fit test was applied to examine HWE, and it was considered as HWE in control groups if P>0.05. In addition, the false-positive report probabilities (FPRP) test [36] and the Venice criteria [37] were applied to assess the credibility of statistically significant associations. Begg’s funnel plot [38] and Egger’s test were used to evaluate the publication bias [39]. All statistical analyses were conducted using Stata 12.0 software.

Results

Description of included studies

We got 506 articles by searching, according to the inclusion and exclusion criteria, 43 studies met our requirements (involving 4680 osteoporosis cases and 5373 controls) [21,22,40–80], of which 34 studies explored the association between VDR BsmI and osteoporosis risk (involving 2973 osteoporosis cases and 3724 controls), 19 studies reported VDR FokI (involving 3694 osteoporosis cases and 2943 controls), and 4 studies explored VDR Cdx2 (involving 378 osteoporosis cases and 743 controls). In addition, 23, 11, 4, 3, 1, and 1 case–control studies were conducted to analyze Caucasians, East Asians, West Asians, Indians, Southeast Asians, and Africans, respectively. Among them, seven studies were performed to examine the association between men and osteoporosis risk, and 38 studies explored the association between women and osteoporosis risk. Thirty studies on postmenopausal women, two studies on premenopausal women, and nine studies did not describe menopause status. Finally, there were 9 high-quality studies, 20 medium-quality studies, and 5 low-quality studies on VDR BsmI; 7 high-quality studies, 10 medium-quality studies, and 2 low-quality studies on VDR FokI; and 3 medium-quality studies and 1 low-quality study on VDR Cdx2. The detailed characteristics and scoring of each study are displayed in Table 1. The literature selection and inclusion processes are shown in Figure 1. The genotype frequencies of VDR BsmI, FokI, and Cdx2 polymorphisms with osteoporosis risk and HWE test results were shown in Tables 2–4.

Flow diagram of the literature search

Figure 1
Flow diagram of the literature search
Figure 1
Flow diagram of the literature search
Table 1
Main characteristics and quality score of studies included
First author/yearCountryEthnicityGenderCasesControlsScore
nAge1MenopauseBMD siteDiagnosisMatchingnHealthyAge1MenopauseBMD site
Kow, 2019 British Caucasian Men 69 58.96 ± 12.78 Ne LS-fn WHO Age and Sex 121 Yes 64.98 ± 10.06 Ne LS-hip 15 
Techapatiphandee, 2018 Thai Southeast Asian Female 105 73.10 ± 8.90 PSM LS-hip WHO Sex 132 Yes 63.40 ± 8.70 PSM LS-hip 13 
Ahmad, 2018 India Indian Female 254 56.12 ± 7.00 PSM LS-hip-fn WHO Age and Sex 254 Yes 55.11 ± 5.66 PSM LS-hip 14 
Meng, 2017 China East Asian Female 90 67.20 ± 8.60 Ne LS-hip Ne Sex 246 Yes 55.90 ± 9.60 Female LS-hip 
Dehghan, 2016 Iran West Asian Men 130 46.10 ± 6.00 Ne LS-fn WHO Sex 70 Yes 46.10 ± 6.00 Men LS-hip 10 
Ziablitsev, 2015 Ukraine Caucasian Female 30 Ne PSM Ne Ne Sex 44 Yes Ne PSM Ne 
Mohammadi, 2015 Iran West Asian Female 142 58.10 ± 7.90 PSM LS-hip-fn WHO Age and Sex 31 Yes 58.10 ± 7.90 PSM LS-hip-fn 14 
Mohammadi, 2015 Iran West Asian Female 101 35.40 ± 9.00 Pre LS-hip-fn WHO Age and Sex 374 Yes 35.40 ± 9.00 Pre LS-hip-fn 15 
Mohammadi, 2015 Iran West Asian Men < 50 75 32.90 ± 8.60 Ne LS-hip-fn WHO Age and Sex 195 Yes 32.90 ± 8.60 Ne LS-hip-fn 15 
Mohammadi, 2015 Iran West Asian Men ≥ 50 112 61.20 ± 8.90 Ne LS-hip-fn WHO Age and Sex 24 Yes 61.20 ± 8.90 Ne LS-hip-fn 14 
Moran, 2015 Spanish Caucasian Female 150 60.24 ± 7.74 PSM LS-fn WHO Age and Sex 30 Yes 59.73 ± 9.28 PSM LS-fn 16 
Boroń, 2015 Poland Caucasian Female 278 Ne PSM LS Ne Age and Sex 292 Yes Ne PSM LS 13 
Marozik, 2013 Belarus Caucasian Female 54 58.30 ± 6.20 PSM LS-fn WHO Age and BMI 77 Yes 56.70 ± 7.40 PSM LS-fn 11 
González, 2013 Mexico Caucasian Female 88 57.65 ± 5.58 PSM LS-fn WHO Sex 88 Yes 56.34 ± 4.98 PSM LS-fn 11 
Pouresmaeili, 2013 Iran West Asian Female 64 53.53 ± 9.80 Ne LS-fn WHO Age and Sex 82 Yes 53.53 ± 9.80 Ne LS-fn 12 
Efesoy, 2011 Turkey Caucasian Female 40 65.75 ± 9.80 PSM LS-fn WHO Sex 30 Yes 62.40 ± 8.70 PSM LS-fn 11 
Yasovanthi, 2011 India Indian Female 247 57.70 ± 4.60 PSM LS WHO Age and Sex 254 Yes 57.70 ± 4.60 PSM LS 16 
Yasovanthi, 2011 India Indian Female 180 39.50 ± 4.40 Pre LS WHO Age and Sex 206 Yes 39.50 ± 4.40 Pre LS 15 
Xing, 2011 China East Asian Female 32 72.50 ± 6.40 Ne LS T-score < 2.0 Sex 70 Yes 70.50 ± 5.20 Female LS 
Mansour, 2010 Egypt African Female 50 54.40 ± 5.10 PSM LS-fn WHO Age and Sex 20 Yes 53.50 ± 5.40 PSM LS-fn 
Durusu, 2010 Turkey Caucasian Female 50 58.30 ± 6.50 PSM LS-hip-fn WHO Sex 50 Yes 57.30 ± 6.60 PSM LS-hip-fn 11 
Gu, 2010 China East Asian Female 33 58.40 ± 6.30 PSM Fn WHO Sex 148 Yes 58.40 ± 6.30 PSM Fn 11 
Gu, 2010 China East Asian Men 61.60 ± 7.00 Ne Fn WHO Sex 260 Yes 61.60 ± 7.00 Men Fn 12 
Mencej, 2009 Slovenia Caucasian Female 239 64.50 ± 8.20 PSM LS-hip-fn WHO Sex 228 Yes 61.50 ± 8.30 PSM LS-hip-fn 12 
Seremak, 2009 Poland Caucasian Female 163 64.27 ± 8.72 PSM LS WHO Sex 63 Yes 63.08 ± 7.24 PSM LS 10 
Uysal, 2008 Turkey Caucasian Female 100 Ne PSM LS-fn WHO Sex 146 Yes Ne PSM LS-fn 12 
Pérez, 2008 Argentina Caucasian Female 64 62.70 ± 0.86 PSM LS-fn WHO Sex 68 Yes 59.40 ± 0.85 PSM LS-fn 14 
Mitra, 2006 India Indian Female 119 54.2 ± 3.40 PSM LS-fn WHO Sex 97 Yes 54.20 ± 3.40 PSM LS-fn 11 
Zhang, 2006 China East Asian Men 26 70.5 ± 5.30 Ne LS T-score < 2.0 Sex 66 Yes 73.40 ± 4.30 Men LS 
Liu, 2005 China East Asian Men 89 Ne Ne LS-hip T-score < 2.0 Sex 56 Yes Ne Men LS-hip 10 
Zhu, 2004 China East Asian Female 40 Ne PSM LS-fn WHO Sex 158 Yes Ne PSM LS-fn 10 
Duman, 2004 Turkey Caucasian Female 75 53.16 ± 1.31 PSM LS-hip WHO Age and Sex 66 Yes 52.62 ± 1.69 PSM LS-hip 10 
Lisker, 2003 Mexico Caucasian Female 65 65.20 ± 6.80 PSM LS-fn WHO Sex 57 Yes 56.50 ± 6.00 PSM LS-fn 11 
Douroudis, 2003 Greece Caucasian Female 35 61.37 ± 0.96 PSM Forearm WHO Sex 44 Yes 58.68 ± 1.01 PSM Forearm 12 
Chen, 2003 China East Asian Female 78 54.72 ± 2.60 PSM Forearm T-Score < 2.0 Sex 81 Yes 53.68 ± 2.90 PSM Forearm 
Zajickova, 2002 Czech Caucasian Female 65 60.10 ± 10.30 PSM LS-hip WHO Sex 33 Yes 63.60 ± 7.80 PSM LS-hip 10 
Pollak, 2001 Israel West Asian Female 75 Ne Ne LS-fn WHO Sex 143 Yes Ne Ne LS-fn 13 
Langdahl, 2000 Aarhus Caucasian Men 30 55.70 ± 11.00 Ne LS-hip WHO Age and Sex 73 Yes 51.10 ± 15.70 Ne LS-hip 13 
Langdahl, 2000 Aarhus Caucasian Female 80 58.20 ± 6.40 Ne LS-hip WHO Age and Sex 80 Yes 56.20 ± 7.70 Ne LS-hip 13 
Fontova Garrofe, 2000 Spanish Caucasian Female 75 58.30 ± 5.00 PSM LS-hip WHO Sex 51 Yes 57.20 ± 4.50 PSM LS-hip 
Choi, 2000 Korea East Asian Female 48 55.10 ± 6.00 PSM LS-fn WHO Sex 65 Yes 55.10 ± 6.00 PSM LS-fn 11 
Zhang, 1998 China East Asian Female 17 56. 76 Ne LS Ne Sex 52 Yes 54.38 Female LS 
Lucotte, 1999 French Caucasian Female 124 63.00 ± 12.30 PSM LS-fn WHO Age and Sex 105 Yes 63.00 ± 12.30 PSM LS-fn 15 
Gennari, 1999 Italian Caucasian Female 164 57.70 ± 0.60 PSM LS WHO Sex 119 Yes 56.90 ± 0.60 PSM LS 12 
Gennari, 1998 Italian Caucasian Female 155 58.20 ± 0.60 PSM LS WHO Sex 136 Yes 57.10 ± 0.70 PSM LS 12 
Vandevyver, 1997 Belgium Caucasian Female 698 75.20 ± 4.70 PSM LS-fn Ne Sex 86 Yes 66.30 ± 8.40 PSM LS-fn 
Tamai, 1997 Japan East Asian Female 90 71.00 ± 10.00 Ne LS Ne Sex 92 Yes 43.00 ± 17.00 Female LS 
Yanagi, 1996 Japan East Asian Female 23 Ne Ne LS Ne Sex 66 Yes Ne Female LS 
Houston, 1996 U.K. Caucasian Female 44 66.00 ± 0.85 Ne LS-hip WHO Sex 44 Yes 65.30 ± 0.95 Female LS-hip 13 
First author/yearCountryEthnicityGenderCasesControlsScore
nAge1MenopauseBMD siteDiagnosisMatchingnHealthyAge1MenopauseBMD site
Kow, 2019 British Caucasian Men 69 58.96 ± 12.78 Ne LS-fn WHO Age and Sex 121 Yes 64.98 ± 10.06 Ne LS-hip 15 
Techapatiphandee, 2018 Thai Southeast Asian Female 105 73.10 ± 8.90 PSM LS-hip WHO Sex 132 Yes 63.40 ± 8.70 PSM LS-hip 13 
Ahmad, 2018 India Indian Female 254 56.12 ± 7.00 PSM LS-hip-fn WHO Age and Sex 254 Yes 55.11 ± 5.66 PSM LS-hip 14 
Meng, 2017 China East Asian Female 90 67.20 ± 8.60 Ne LS-hip Ne Sex 246 Yes 55.90 ± 9.60 Female LS-hip 
Dehghan, 2016 Iran West Asian Men 130 46.10 ± 6.00 Ne LS-fn WHO Sex 70 Yes 46.10 ± 6.00 Men LS-hip 10 
Ziablitsev, 2015 Ukraine Caucasian Female 30 Ne PSM Ne Ne Sex 44 Yes Ne PSM Ne 
Mohammadi, 2015 Iran West Asian Female 142 58.10 ± 7.90 PSM LS-hip-fn WHO Age and Sex 31 Yes 58.10 ± 7.90 PSM LS-hip-fn 14 
Mohammadi, 2015 Iran West Asian Female 101 35.40 ± 9.00 Pre LS-hip-fn WHO Age and Sex 374 Yes 35.40 ± 9.00 Pre LS-hip-fn 15 
Mohammadi, 2015 Iran West Asian Men < 50 75 32.90 ± 8.60 Ne LS-hip-fn WHO Age and Sex 195 Yes 32.90 ± 8.60 Ne LS-hip-fn 15 
Mohammadi, 2015 Iran West Asian Men ≥ 50 112 61.20 ± 8.90 Ne LS-hip-fn WHO Age and Sex 24 Yes 61.20 ± 8.90 Ne LS-hip-fn 14 
Moran, 2015 Spanish Caucasian Female 150 60.24 ± 7.74 PSM LS-fn WHO Age and Sex 30 Yes 59.73 ± 9.28 PSM LS-fn 16 
Boroń, 2015 Poland Caucasian Female 278 Ne PSM LS Ne Age and Sex 292 Yes Ne PSM LS 13 
Marozik, 2013 Belarus Caucasian Female 54 58.30 ± 6.20 PSM LS-fn WHO Age and BMI 77 Yes 56.70 ± 7.40 PSM LS-fn 11 
González, 2013 Mexico Caucasian Female 88 57.65 ± 5.58 PSM LS-fn WHO Sex 88 Yes 56.34 ± 4.98 PSM LS-fn 11 
Pouresmaeili, 2013 Iran West Asian Female 64 53.53 ± 9.80 Ne LS-fn WHO Age and Sex 82 Yes 53.53 ± 9.80 Ne LS-fn 12 
Efesoy, 2011 Turkey Caucasian Female 40 65.75 ± 9.80 PSM LS-fn WHO Sex 30 Yes 62.40 ± 8.70 PSM LS-fn 11 
Yasovanthi, 2011 India Indian Female 247 57.70 ± 4.60 PSM LS WHO Age and Sex 254 Yes 57.70 ± 4.60 PSM LS 16 
Yasovanthi, 2011 India Indian Female 180 39.50 ± 4.40 Pre LS WHO Age and Sex 206 Yes 39.50 ± 4.40 Pre LS 15 
Xing, 2011 China East Asian Female 32 72.50 ± 6.40 Ne LS T-score < 2.0 Sex 70 Yes 70.50 ± 5.20 Female LS 
Mansour, 2010 Egypt African Female 50 54.40 ± 5.10 PSM LS-fn WHO Age and Sex 20 Yes 53.50 ± 5.40 PSM LS-fn 
Durusu, 2010 Turkey Caucasian Female 50 58.30 ± 6.50 PSM LS-hip-fn WHO Sex 50 Yes 57.30 ± 6.60 PSM LS-hip-fn 11 
Gu, 2010 China East Asian Female 33 58.40 ± 6.30 PSM Fn WHO Sex 148 Yes 58.40 ± 6.30 PSM Fn 11 
Gu, 2010 China East Asian Men 61.60 ± 7.00 Ne Fn WHO Sex 260 Yes 61.60 ± 7.00 Men Fn 12 
Mencej, 2009 Slovenia Caucasian Female 239 64.50 ± 8.20 PSM LS-hip-fn WHO Sex 228 Yes 61.50 ± 8.30 PSM LS-hip-fn 12 
Seremak, 2009 Poland Caucasian Female 163 64.27 ± 8.72 PSM LS WHO Sex 63 Yes 63.08 ± 7.24 PSM LS 10 
Uysal, 2008 Turkey Caucasian Female 100 Ne PSM LS-fn WHO Sex 146 Yes Ne PSM LS-fn 12 
Pérez, 2008 Argentina Caucasian Female 64 62.70 ± 0.86 PSM LS-fn WHO Sex 68 Yes 59.40 ± 0.85 PSM LS-fn 14 
Mitra, 2006 India Indian Female 119 54.2 ± 3.40 PSM LS-fn WHO Sex 97 Yes 54.20 ± 3.40 PSM LS-fn 11 
Zhang, 2006 China East Asian Men 26 70.5 ± 5.30 Ne LS T-score < 2.0 Sex 66 Yes 73.40 ± 4.30 Men LS 
Liu, 2005 China East Asian Men 89 Ne Ne LS-hip T-score < 2.0 Sex 56 Yes Ne Men LS-hip 10 
Zhu, 2004 China East Asian Female 40 Ne PSM LS-fn WHO Sex 158 Yes Ne PSM LS-fn 10 
Duman, 2004 Turkey Caucasian Female 75 53.16 ± 1.31 PSM LS-hip WHO Age and Sex 66 Yes 52.62 ± 1.69 PSM LS-hip 10 
Lisker, 2003 Mexico Caucasian Female 65 65.20 ± 6.80 PSM LS-fn WHO Sex 57 Yes 56.50 ± 6.00 PSM LS-fn 11 
Douroudis, 2003 Greece Caucasian Female 35 61.37 ± 0.96 PSM Forearm WHO Sex 44 Yes 58.68 ± 1.01 PSM Forearm 12 
Chen, 2003 China East Asian Female 78 54.72 ± 2.60 PSM Forearm T-Score < 2.0 Sex 81 Yes 53.68 ± 2.90 PSM Forearm 
Zajickova, 2002 Czech Caucasian Female 65 60.10 ± 10.30 PSM LS-hip WHO Sex 33 Yes 63.60 ± 7.80 PSM LS-hip 10 
Pollak, 2001 Israel West Asian Female 75 Ne Ne LS-fn WHO Sex 143 Yes Ne Ne LS-fn 13 
Langdahl, 2000 Aarhus Caucasian Men 30 55.70 ± 11.00 Ne LS-hip WHO Age and Sex 73 Yes 51.10 ± 15.70 Ne LS-hip 13 
Langdahl, 2000 Aarhus Caucasian Female 80 58.20 ± 6.40 Ne LS-hip WHO Age and Sex 80 Yes 56.20 ± 7.70 Ne LS-hip 13 
Fontova Garrofe, 2000 Spanish Caucasian Female 75 58.30 ± 5.00 PSM LS-hip WHO Sex 51 Yes 57.20 ± 4.50 PSM LS-hip 
Choi, 2000 Korea East Asian Female 48 55.10 ± 6.00 PSM LS-fn WHO Sex 65 Yes 55.10 ± 6.00 PSM LS-fn 11 
Zhang, 1998 China East Asian Female 17 56. 76 Ne LS Ne Sex 52 Yes 54.38 Female LS 
Lucotte, 1999 French Caucasian Female 124 63.00 ± 12.30 PSM LS-fn WHO Age and Sex 105 Yes 63.00 ± 12.30 PSM LS-fn 15 
Gennari, 1999 Italian Caucasian Female 164 57.70 ± 0.60 PSM LS WHO Sex 119 Yes 56.90 ± 0.60 PSM LS 12 
Gennari, 1998 Italian Caucasian Female 155 58.20 ± 0.60 PSM LS WHO Sex 136 Yes 57.10 ± 0.70 PSM LS 12 
Vandevyver, 1997 Belgium Caucasian Female 698 75.20 ± 4.70 PSM LS-fn Ne Sex 86 Yes 66.30 ± 8.40 PSM LS-fn 
Tamai, 1997 Japan East Asian Female 90 71.00 ± 10.00 Ne LS Ne Sex 92 Yes 43.00 ± 17.00 Female LS 
Yanagi, 1996 Japan East Asian Female 23 Ne Ne LS Ne Sex 66 Yes Ne Female LS 
Houston, 1996 U.K. Caucasian Female 44 66.00 ± 0.85 Ne LS-hip WHO Sex 44 Yes 65.30 ± 0.95 Female LS-hip 13 

Abbreviations: Fn, femoral neck; LS, lumbar spine; N, not available; Pre, premenopause; PSM, postmenopausal.

1Mean ± SD years.

Table 2
Genotype frequencies of VDR BsmI polymorphism in studies included in this meta-analysis
First author/yearEthnicityGenderCaseControlHWE
BBBbbbBBBbbbChi-square testP
Kow, 2019 Caucasian Male 31 66 21 11 34 13 1.752 0.1856 
Techapatiphandee, 2018 Southeast Asian Female 85 19 103 25 2.377 0.1231 
Ahmad, 2018 Indian Female 54 137 63 54 152 48 9.909 0.0016 
Meng, 2017 East Asian Female 12 74 24 216 19.383 
Dehghan, 2016 West Asian Male 31 70 29 14 39 17 0.947 0.3304 
Moran, 2015 Caucasian Female 18 65 67 19 2.752 0.0972 
Boroń, 2015 Caucasian Female 101 121 56 128 113 51 8.26 0.0041 
Marozik, 2013 Caucasian Female 12 31 11 11 26 40 3.495 0.0616 
González-Mercado, 2013 Caucasian Female 28 54 38 46 1.234 0.2667 
Pouresmaeili, 2013 West Asian Female 14 33 17 13 33 36 1.31 0.2524 
Efesoy, 2011 Caucasian Female 23 12 15 10 0.024 0.8756 
Mansour, 2010 African Female 27 15 17 3.951 0.0469 
Mencej-Bedrac, 2009 Caucasian Female 27 110 103 40 100 88 1.538 0.2149 
Seremak, 2009 Caucasian Female 27 66 70 10 27 26 0.442 0.5062 
Durusu, 2010 Caucasian Female 15 19 16 19 24 25.717 
Uysal, 2008 Caucasian Female 18 48 34 24 78 44 1.155 0.2826 
Pérez, 2008 Caucasian Female 17 35 12 20 32 16 0.21 0.6469 
Mitra, 2006 Indian Female 51 46 22 19 38 40 3.072 0.0796 
Liu, 2005 East Asian Male 11 76 50 0.179 0.6719 
Zhu, 2004 East Asian Female 26 105 46 27.257 
Duman, 2004 Caucasian Female 18 54 24 72 25 
Lisker, 2003 Caucasian Female 15 17 34 13 38 7.133 0.0076 
Douroudis, 2003 Caucasian Female 12 20 10 29 4.95 0.0261 
Chen, 2003 East Asian Female 13 65 12 69 0.518 0.4715 
Zajickova, 2002 Caucasian Female 21 24 20 10 13 10 1.485 0.223 
Pollak, 2001 West Asian Female 18 50 32 11 47 42 0.16 0.6896 
Langdahl, 2000 Caucasian Male 16 15 28 30 2.893 0.089 
Langdahl, 2000 Caucasian Female 23 38 19 25 34 21 1.749 0.186 
Fontova, 2000 Caucasian Female 49 17 10 22 19 0.612 0.4341 
Zhang, 1998 East Asian Female 14 49 0.046 0.8304 
Gennari, 1998 Caucasian Female 40 92 23 11 76 49 6.129 0.0133 
Vandevyver, 1997 Caucasian Female 12 50 24 127 368 203 3.142 0.0763 
Tamai, 1997 East Asian Female 11 74 16 73 2.784 0.0952 
Yanagi, 1996 East Asian Female 57 11 2.767 0.0962 
Houston, 1996 Caucasian Female 19 17 19 16 0.571 0.4498 
First author/yearEthnicityGenderCaseControlHWE
BBBbbbBBBbbbChi-square testP
Kow, 2019 Caucasian Male 31 66 21 11 34 13 1.752 0.1856 
Techapatiphandee, 2018 Southeast Asian Female 85 19 103 25 2.377 0.1231 
Ahmad, 2018 Indian Female 54 137 63 54 152 48 9.909 0.0016 
Meng, 2017 East Asian Female 12 74 24 216 19.383 
Dehghan, 2016 West Asian Male 31 70 29 14 39 17 0.947 0.3304 
Moran, 2015 Caucasian Female 18 65 67 19 2.752 0.0972 
Boroń, 2015 Caucasian Female 101 121 56 128 113 51 8.26 0.0041 
Marozik, 2013 Caucasian Female 12 31 11 11 26 40 3.495 0.0616 
González-Mercado, 2013 Caucasian Female 28 54 38 46 1.234 0.2667 
Pouresmaeili, 2013 West Asian Female 14 33 17 13 33 36 1.31 0.2524 
Efesoy, 2011 Caucasian Female 23 12 15 10 0.024 0.8756 
Mansour, 2010 African Female 27 15 17 3.951 0.0469 
Mencej-Bedrac, 2009 Caucasian Female 27 110 103 40 100 88 1.538 0.2149 
Seremak, 2009 Caucasian Female 27 66 70 10 27 26 0.442 0.5062 
Durusu, 2010 Caucasian Female 15 19 16 19 24 25.717 
Uysal, 2008 Caucasian Female 18 48 34 24 78 44 1.155 0.2826 
Pérez, 2008 Caucasian Female 17 35 12 20 32 16 0.21 0.6469 
Mitra, 2006 Indian Female 51 46 22 19 38 40 3.072 0.0796 
Liu, 2005 East Asian Male 11 76 50 0.179 0.6719 
Zhu, 2004 East Asian Female 26 105 46 27.257 
Duman, 2004 Caucasian Female 18 54 24 72 25 
Lisker, 2003 Caucasian Female 15 17 34 13 38 7.133 0.0076 
Douroudis, 2003 Caucasian Female 12 20 10 29 4.95 0.0261 
Chen, 2003 East Asian Female 13 65 12 69 0.518 0.4715 
Zajickova, 2002 Caucasian Female 21 24 20 10 13 10 1.485 0.223 
Pollak, 2001 West Asian Female 18 50 32 11 47 42 0.16 0.6896 
Langdahl, 2000 Caucasian Male 16 15 28 30 2.893 0.089 
Langdahl, 2000 Caucasian Female 23 38 19 25 34 21 1.749 0.186 
Fontova, 2000 Caucasian Female 49 17 10 22 19 0.612 0.4341 
Zhang, 1998 East Asian Female 14 49 0.046 0.8304 
Gennari, 1998 Caucasian Female 40 92 23 11 76 49 6.129 0.0133 
Vandevyver, 1997 Caucasian Female 12 50 24 127 368 203 3.142 0.0763 
Tamai, 1997 East Asian Female 11 74 16 73 2.784 0.0952 
Yanagi, 1996 East Asian Female 57 11 2.767 0.0962 
Houston, 1996 Caucasian Female 19 17 19 16 0.571 0.4498 
Table 3
Genotype frequencies of VDR FokI polymorphism in studies included in this meta-analysis
First author/yearEthnicityGenderCaseControlHWE
FFFfffFFFfffChi-square testP
Techapatiphandee, 2018 Southeast Asian Female 31 46 28 41 73 18 2.613 0.106 
Ahmad, 2018 Indian Female 148 92 14 169 80 1.637 0.2008 
Mohammadi, 2015 West Asian Female 80 56 11 17 0.95 0.3298 
Mohammadi, 2015 West Asian Female 52 36 198 128 30 1.996 0.1577 
Mohammadi, 2015 West Asian Male 40 26 111 73 0.476 0.4903 
Mohammadi, 2015 West Asian Male 64 41 12 0.182 0.6698 
González, 2013 Caucasian Female 24 45 19 25 48 15 0.974 0.3238 
Yasovanthi, 2011 Indian Female 104 119 24 122 124 12.594 0.0004 
Yasovanthi, 2011 Indian Female 73 82 25 97 101 8.71 0.0032 
Xing, 2011 East Asian Female 11 14 35 27 0.443 0.5058 
Mansour, 2010 African Female 34 20 
Durusu, 2010 Caucasian Female 27 22 29 18 0.009 0.9259 
Gu, 2010 East Asian Female 18 40 84 24 3.266 0.0707 
Gu, 2010 East Asian Male 76 137 47 1.171 0.2791 
Mencej-Bedrac, 2009 Caucasian Female 88 108 44 105 97 26 0.249 0.6179 
Pérez, 2008 Caucasian Female 22 32 10 22 36 10 0.586 0.4438 
Mitra, 2006 Indian Female 38 42 39 46 33 18 6.444 0.0111 
Zhang, 2006 East Asian Male 13 28 28 10 0.458 0.4984 
Lisker, 2003 Caucasian Female 27 29 20 29 0.239 0.625 
Zajickova, 2002 Caucasian Female 26 28 11 21 2.54 0.111 
Langdahl, 2000 Caucasian Male 12 13 30 34 0.018 0.8943 
Langdahl, 2000 Caucasian Female 28 42 10 34 31 15 2.554 0.11 
Choi, 2000 East Asian Female 12 23 13 26 33 0.961 0.327 
Lucotte, 1999 Caucasian Female 45 69 10 40 52 13 0.386 0.5346 
Gennari, 1999 Caucasian Female 60 73 31 53 55 11 0.372 0.542 
First author/yearEthnicityGenderCaseControlHWE
FFFfffFFFfffChi-square testP
Techapatiphandee, 2018 Southeast Asian Female 31 46 28 41 73 18 2.613 0.106 
Ahmad, 2018 Indian Female 148 92 14 169 80 1.637 0.2008 
Mohammadi, 2015 West Asian Female 80 56 11 17 0.95 0.3298 
Mohammadi, 2015 West Asian Female 52 36 198 128 30 1.996 0.1577 
Mohammadi, 2015 West Asian Male 40 26 111 73 0.476 0.4903 
Mohammadi, 2015 West Asian Male 64 41 12 0.182 0.6698 
González, 2013 Caucasian Female 24 45 19 25 48 15 0.974 0.3238 
Yasovanthi, 2011 Indian Female 104 119 24 122 124 12.594 0.0004 
Yasovanthi, 2011 Indian Female 73 82 25 97 101 8.71 0.0032 
Xing, 2011 East Asian Female 11 14 35 27 0.443 0.5058 
Mansour, 2010 African Female 34 20 
Durusu, 2010 Caucasian Female 27 22 29 18 0.009 0.9259 
Gu, 2010 East Asian Female 18 40 84 24 3.266 0.0707 
Gu, 2010 East Asian Male 76 137 47 1.171 0.2791 
Mencej-Bedrac, 2009 Caucasian Female 88 108 44 105 97 26 0.249 0.6179 
Pérez, 2008 Caucasian Female 22 32 10 22 36 10 0.586 0.4438 
Mitra, 2006 Indian Female 38 42 39 46 33 18 6.444 0.0111 
Zhang, 2006 East Asian Male 13 28 28 10 0.458 0.4984 
Lisker, 2003 Caucasian Female 27 29 20 29 0.239 0.625 
Zajickova, 2002 Caucasian Female 26 28 11 21 2.54 0.111 
Langdahl, 2000 Caucasian Male 12 13 30 34 0.018 0.8943 
Langdahl, 2000 Caucasian Female 28 42 10 34 31 15 2.554 0.11 
Choi, 2000 East Asian Female 12 23 13 26 33 0.961 0.327 
Lucotte, 1999 Caucasian Female 45 69 10 40 52 13 0.386 0.5346 
Gennari, 1999 Caucasian Female 60 73 31 53 55 11 0.372 0.542 
Table 4
Genotype frequencies of VDR Cdx2 polymorphism in studies included in this meta-analysis
First author/yearEthnicityGenderCaseControlHWE
GGGAAAGGGAAAChi-square testP
Ziablitsev, 2015 Caucasian Female 16 20 12 16 0.015 0.9009 
Marozik, 2013 Caucasian Female 41 13 53 24 2.624 0.1052 
Gu, 2010 East Asian Female 12 16 38 72 38 0.108 0.7423 
Gu, 2010 East Asian Male 81 116 63 2.78 0.0955 
Mencej-Bedrac, 2009 Caucasian Female 155 75 172 48 3.709 0.0541 
First author/yearEthnicityGenderCaseControlHWE
GGGAAAGGGAAAChi-square testP
Ziablitsev, 2015 Caucasian Female 16 20 12 16 0.015 0.9009 
Marozik, 2013 Caucasian Female 41 13 53 24 2.624 0.1052 
Gu, 2010 East Asian Female 12 16 38 72 38 0.108 0.7423 
Gu, 2010 East Asian Male 81 116 63 2.78 0.0955 
Mencej-Bedrac, 2009 Caucasian Female 155 75 172 48 3.709 0.0541 

Meta-analysis results

Table 5 summarizes the assessment of the association between VDR BsmI polymorphism and osteoporosis risk. Overall, significantly increased the risk of osteoporosis was not found for VDR BsmI polymorphism (P>0.05 in all genetic models). However, subgroup analysis by ethnicity, we observed that the VDR b allele genotype increased the osteoporosis risk (OR = 1.36, 95% CI: 1.06–1.74) and bb genotype (additive model: OR = 0.55, 95% CI: 0.33–0.92; recessive model: OR = 0.65, 95% CI: 0.45–0.96) reduced the risk of osteoporosis in the West Asians, as shown in Figure 2.

VDR BsmI polymorphism and osteoporosis risk in different races

Figure 2
VDR BsmI polymorphism and osteoporosis risk in different races

The forest plots of all selected studies on the association between VDR BsmI polymorphism and osteoporosis risk in different races (A) allele model; (B) additive model; (C) recessive model.

Figure 2
VDR BsmI polymorphism and osteoporosis risk in different races

The forest plots of all selected studies on the association between VDR BsmI polymorphism and osteoporosis risk in different races (A) allele model; (B) additive model; (C) recessive model.

Table 5
Pooled estimates of association of VDR BsmI polymorphism and osteoporosis risk
Genetic modelVariableTest of associationTests for heterogeneityEgger’s test
OR (95% CI)PPhI2PE
B vs b Overall 1.11 (0.94–1.31) 0.22 <0.001 77.40% 0.34 
 Caucasian 0.99 (0.83–1.18) 0.87 <0.001 70.70%  
 East Asian 1.06 (0.59–1.91) 0.85 <0.001 76.40%  
 West Asian 1.36 (1.06–1.74) 0.02 0.49 0.00%  
 Indian 1.49 (0.53–4.19) 0.45 <0.001 95%  
 Female 1.09 (0.90–1.31) 0.39 <0.001 79.60%  
 Male 1.29 (0.99–1.67) 0.06 0.75 0.00%  
bb vs BB Overall 0.79 (0.57–1.09) 0.15 <0.001 70.70% 0.28 
 Caucasian 0.97 (0.68–1.39) 0.88 <0.001 65.20%  
 East Asian 0.77 (0.19–3.08) 0.71 0.01 72.40%  
 West Asian 0.55 (0.33–0.92) 0.02 0.63 0.00%  
 Indian 0.53 (0.09–3.26) 0.49 <0.001 93.70%  
 Female 0.82 (0.58–1.17) 0.28 <0.001 73.60%  
 Male 0.58 (0.33–1.02) 0.06 0.79 0.00%  
Bb+bb vs BB Overall 0.87 (0.70-1.07) 0.19 <0.001 53.00% 0.15 
 Caucasian 1.02 (0.83–1.27) 0.83 0.06 34.20%  
 East Asian 0.74 (0.22–2.46) 0.63 0.02 65.80%  
 West Asian 0.68 (0.44–1.07) 0.09 0.82 0.00%  
 Indian 0.58 (0.19–1.76) 0.34 <0.001 88.40%  
 Female 0.89 (0.70–1.12) 0.32 <0.001 57.70%  
 Male 0.71 (0.45–1.13) 0.15 0.94 0.00%  
bb vs BB+Bb Overall 0.86 (0.67–1.11) 0.24 <0.001 76.10% 0.44 
 Caucasian 0.99 (0.72–1.35) 0.94 <0.001 75.70%  
 East Asian 0.96 (0.53–1.75) 0.89 0.01 66.80%  
 West Asian 0.65 (0.45–0.96) 0.02 0.42 0.00%  
 Indian 0.69 (0.16–2.93) 0.61 <0.001 93.40%  
 Female 0.89 (0.67–1.17) 0.40 <0.001 78.30%  
 Male 0.70 (0.46–1.06) 0.09 0.53 0.00%  
BB+bb vs Bb Overall 0.98 (0.82–1.15) 0.76 <0.001 55.20% 0.84 
 Caucasian 0.98 (0.77–1.24) 0.85 <0.001 66.60%  
 East Asian 1.04 (0.68–1.59) 0.87 0.19 31.50%  
 West Asian 0.87 (0.61–1.22) 0.41 0.49 0.00%  
 Indian 1.19 (0.89–1.61) 0.24 0.51 0.00%  
 Female 0.98 (0.82–1.18) 0.86 <0.001 59.30%  
 Male 0.94 (0.65–1.35) 0.74 0.56 0.00%  
Genetic modelVariableTest of associationTests for heterogeneityEgger’s test
OR (95% CI)PPhI2PE
B vs b Overall 1.11 (0.94–1.31) 0.22 <0.001 77.40% 0.34 
 Caucasian 0.99 (0.83–1.18) 0.87 <0.001 70.70%  
 East Asian 1.06 (0.59–1.91) 0.85 <0.001 76.40%  
 West Asian 1.36 (1.06–1.74) 0.02 0.49 0.00%  
 Indian 1.49 (0.53–4.19) 0.45 <0.001 95%  
 Female 1.09 (0.90–1.31) 0.39 <0.001 79.60%  
 Male 1.29 (0.99–1.67) 0.06 0.75 0.00%  
bb vs BB Overall 0.79 (0.57–1.09) 0.15 <0.001 70.70% 0.28 
 Caucasian 0.97 (0.68–1.39) 0.88 <0.001 65.20%  
 East Asian 0.77 (0.19–3.08) 0.71 0.01 72.40%  
 West Asian 0.55 (0.33–0.92) 0.02 0.63 0.00%  
 Indian 0.53 (0.09–3.26) 0.49 <0.001 93.70%  
 Female 0.82 (0.58–1.17) 0.28 <0.001 73.60%  
 Male 0.58 (0.33–1.02) 0.06 0.79 0.00%  
Bb+bb vs BB Overall 0.87 (0.70-1.07) 0.19 <0.001 53.00% 0.15 
 Caucasian 1.02 (0.83–1.27) 0.83 0.06 34.20%  
 East Asian 0.74 (0.22–2.46) 0.63 0.02 65.80%  
 West Asian 0.68 (0.44–1.07) 0.09 0.82 0.00%  
 Indian 0.58 (0.19–1.76) 0.34 <0.001 88.40%  
 Female 0.89 (0.70–1.12) 0.32 <0.001 57.70%  
 Male 0.71 (0.45–1.13) 0.15 0.94 0.00%  
bb vs BB+Bb Overall 0.86 (0.67–1.11) 0.24 <0.001 76.10% 0.44 
 Caucasian 0.99 (0.72–1.35) 0.94 <0.001 75.70%  
 East Asian 0.96 (0.53–1.75) 0.89 0.01 66.80%  
 West Asian 0.65 (0.45–0.96) 0.02 0.42 0.00%  
 Indian 0.69 (0.16–2.93) 0.61 <0.001 93.40%  
 Female 0.89 (0.67–1.17) 0.40 <0.001 78.30%  
 Male 0.70 (0.46–1.06) 0.09 0.53 0.00%  
BB+bb vs Bb Overall 0.98 (0.82–1.15) 0.76 <0.001 55.20% 0.84 
 Caucasian 0.98 (0.77–1.24) 0.85 <0.001 66.60%  
 East Asian 1.04 (0.68–1.59) 0.87 0.19 31.50%  
 West Asian 0.87 (0.61–1.22) 0.41 0.49 0.00%  
 Indian 1.19 (0.89–1.61) 0.24 0.51 0.00%  
 Female 0.98 (0.82–1.18) 0.86 <0.001 59.30%  
 Male 0.94 (0.65–1.35) 0.74 0.56 0.00%  

VDR BsmI: allele model: B vs b, additive model: bb vs BB, dominant model: Bb + bb vs BB, recessive model: bb vs BB + Bb, overdominance model: BB + bb vs Bb.

At the overall analysis, significantly increased osteoporosis risk was found in VDR FokI ff genotype (additive model: OR = 1.49, 95% CI: 1.07–2.07; recessive model: OR = 1.47, 95% CI: 1.13–1.93). In addition, when stratified by ethnicity, the results showed that f allele and ff genotypes were significantly associated with risk of osteoporosis in Indians. We further performed subgroup analysis according to gender, significantly elevated osteoporosis risk was also observed in ff genotype. All the data are shown in Table 6, Figures 3 and 4.

VDR FokI polymorphism and osteoporosis risk in different races

Figure 3
VDR FokI polymorphism and osteoporosis risk in different races

The forest plots of all selected studies on the association between VDR FokI polymorphism and osteoporosis risk in different races (A) allele model; (B) additive model; (C) dominant model; (D) recessive model.

Figure 3
VDR FokI polymorphism and osteoporosis risk in different races

The forest plots of all selected studies on the association between VDR FokI polymorphism and osteoporosis risk in different races (A) allele model; (B) additive model; (C) dominant model; (D) recessive model.

VDR FokI polymorphism and osteoporosis risk between different gender

Figure 4
VDR FokI polymorphism and osteoporosis risk between different gender

The forest plots of all selected studies on the association between VDR FokI polymorphism and osteoporosis risk between different gender (A) additive model; (B) recessive model.

Figure 4
VDR FokI polymorphism and osteoporosis risk between different gender

The forest plots of all selected studies on the association between VDR FokI polymorphism and osteoporosis risk between different gender (A) additive model; (B) recessive model.

Table 6
Pooled estimates of association of VDR FokI polymorphism and osteoporosis risk
Genetic modelVariableTest of associationTests for heterogeneityEgger’s test
OR (95% CI)PPhI2PE
F vs f Overall 0.86 (0.74–0.98) 0.03 <0.001 55.80% 0.30 
 Caucasian 0.89 (0.77–1.03) 0.12 0.35 9.70%  
 East Asian 0.78 (0.42–1.45) 0.43 0.001 79.10%  
 West Asian 1.18 (0.85–1.63) 0.32 0.002 73.90%  
 Indian 0.68 (0.58–0.80) 0.63 0.00%  
 Female 0.86 (0.74–1.00) 0.05 <0.001 59.90%  
 Male 0.83 (0.56–1.23) 0.35 0.14 41.90%  
ff vs FF Overall 1.49 (1.07–2.07) 0.02 <0.001 57.10% 0.11 
 Caucasian 1.23 (0.87–1.73) 0.24 0.26 19.50%  
 East Asian 1.69 (0.44–6.58) 0.45 0.001 79.30%  
 West Asian 0.66 (0.29–1.54) 0.34 0.23 31.10%  
 Indian 3.25 (2.14–4.94) 0.87 0.00%  
 Female 1.46 (1.02–2.11) 0.04 <0.001 62.60%  
 Male 1.61 (0.71–3.66) 0.25 0.27 22.70%  
Ff+ff vs FF Overall 1.16 (0.98–1.37) 0.08 0.02 40.00% 0.42 
 Caucasian 1.16 (0.96–1.40) 0.12 0.45 0.00%  
 East Asian 1.33 (0.53–3.35) 0.55 0.01 73.00%  
 West Asian 0.85 (0.58–1.24) 0.40 0.23 30.70%  
 Indian 1.40 (1.14–1.71) 0.001 0.64 0.00%  
 Female 1.15 (0.96–1.38) 0.12 0.02 45.20%  
 Male 1.19 (0.74–1.90) 0.47 0.26 24.10%  
ff vs FF+Ff Overall 1.47 (1.13–1.93) 0.01 0.01 47.50% 0.13 
 Caucasian 1.21 (0.89–1.64) 0.24 0.28 17.70%  
 East Asian 1.55 (0.67–3.60) 0.31 0.02 64.70%  
 West Asian 0.77 (0.42–1.43) 0.41 0.41 0.00%  
 Indian 2.87 (1.93–4.26) 0.67 0.00%  
 Female 1.48 (1.09–2.00) 0.01 0.001 55.40%  
 Male 1.50 (0.81–2.79) 0.20 0.55 0.00%  
FF+ff vs Ff Overall 1.01 (0.90–1.13) 0.87 0.69 0.00% 0.96 
 Caucasian 0.97 (0.81–1.18) 0.78 0.41 3.60%  
 East Asian 1.02 (0.69–1.51) 0.91 0.88 0.00%  
 West Asian 1.06 (0.78–1.45) 0.71 0.53 0.00%  
 Indian 0.97 (0.80–1.19) 0.80 0.63 0.00%  
 Female 1.03 (0.90–1.15) 0.78 0.45 0.80%  
 Male 0.94 (0.65–1.37) 0.76 0.93 0.00%  
Genetic modelVariableTest of associationTests for heterogeneityEgger’s test
OR (95% CI)PPhI2PE
F vs f Overall 0.86 (0.74–0.98) 0.03 <0.001 55.80% 0.30 
 Caucasian 0.89 (0.77–1.03) 0.12 0.35 9.70%  
 East Asian 0.78 (0.42–1.45) 0.43 0.001 79.10%  
 West Asian 1.18 (0.85–1.63) 0.32 0.002 73.90%  
 Indian 0.68 (0.58–0.80) 0.63 0.00%  
 Female 0.86 (0.74–1.00) 0.05 <0.001 59.90%  
 Male 0.83 (0.56–1.23) 0.35 0.14 41.90%  
ff vs FF Overall 1.49 (1.07–2.07) 0.02 <0.001 57.10% 0.11 
 Caucasian 1.23 (0.87–1.73) 0.24 0.26 19.50%  
 East Asian 1.69 (0.44–6.58) 0.45 0.001 79.30%  
 West Asian 0.66 (0.29–1.54) 0.34 0.23 31.10%  
 Indian 3.25 (2.14–4.94) 0.87 0.00%  
 Female 1.46 (1.02–2.11) 0.04 <0.001 62.60%  
 Male 1.61 (0.71–3.66) 0.25 0.27 22.70%  
Ff+ff vs FF Overall 1.16 (0.98–1.37) 0.08 0.02 40.00% 0.42 
 Caucasian 1.16 (0.96–1.40) 0.12 0.45 0.00%  
 East Asian 1.33 (0.53–3.35) 0.55 0.01 73.00%  
 West Asian 0.85 (0.58–1.24) 0.40 0.23 30.70%  
 Indian 1.40 (1.14–1.71) 0.001 0.64 0.00%  
 Female 1.15 (0.96–1.38) 0.12 0.02 45.20%  
 Male 1.19 (0.74–1.90) 0.47 0.26 24.10%  
ff vs FF+Ff Overall 1.47 (1.13–1.93) 0.01 0.01 47.50% 0.13 
 Caucasian 1.21 (0.89–1.64) 0.24 0.28 17.70%  
 East Asian 1.55 (0.67–3.60) 0.31 0.02 64.70%  
 West Asian 0.77 (0.42–1.43) 0.41 0.41 0.00%  
 Indian 2.87 (1.93–4.26) 0.67 0.00%  
 Female 1.48 (1.09–2.00) 0.01 0.001 55.40%  
 Male 1.50 (0.81–2.79) 0.20 0.55 0.00%  
FF+ff vs Ff Overall 1.01 (0.90–1.13) 0.87 0.69 0.00% 0.96 
 Caucasian 0.97 (0.81–1.18) 0.78 0.41 3.60%  
 East Asian 1.02 (0.69–1.51) 0.91 0.88 0.00%  
 West Asian 1.06 (0.78–1.45) 0.71 0.53 0.00%  
 Indian 0.97 (0.80–1.19) 0.80 0.63 0.00%  
 Female 1.03 (0.90–1.15) 0.78 0.45 0.80%  
 Male 0.94 (0.65–1.37) 0.76 0.93 0.00%  

VDR FokI: allele model: F vs f, additive model: ff vs FF, dominant model: Ff+ff vs FF, recessive model: ff vs FF+Ff, overdominance model: FF+ff vs Ff.

No significant association was observed between VDR Cdx2 polymorphism and osteoporosis risk, as shown in Table 7.

Table 7
Pooled estimates of association of VDR Cdx2 polymorphism and osteoporosis risk
Genetic modelTest of associationTests for heterogeneityEgger’s test
OR (95% CI)PPhI2PE
G vs A 1.54 (0.80–2.97) 0.20 <0.001 82.40% 0.12 
AA VS GG 0.37 (0.11–1.28) 0.11 0.02 68.30% 0.29 
GA+AA VS GG 0.64 (0.29–0.39) 0.27 0.002 75.70% 0.01 
AA VS GG+GA 0.48 (0.22–1.07) 0.07 0.14 45.70% 0.85 
GG+AA VS GA 0.84 (0.58–1.22) 0.36 0.28 21.30% 0.12 
Genetic modelTest of associationTests for heterogeneityEgger’s test
OR (95% CI)PPhI2PE
G vs A 1.54 (0.80–2.97) 0.20 <0.001 82.40% 0.12 
AA VS GG 0.37 (0.11–1.28) 0.11 0.02 68.30% 0.29 
GA+AA VS GG 0.64 (0.29–0.39) 0.27 0.002 75.70% 0.01 
AA VS GG+GA 0.48 (0.22–1.07) 0.07 0.14 45.70% 0.85 
GG+AA VS GA 0.84 (0.58–1.22) 0.36 0.28 21.30% 0.12 

VDR Cdx2: allele model: G vs A, additive model: AA VS GG, dominant model: GA+AA VS GG, recessive model: AA VS GG+GA, overdominance model: GG+AA VS GA.

Heterogeneity and sensitivity analyses

Heterogeneity was observed in overall and several subgroup analyses. Some potential factors were considered as sources of heterogeneity, such as ethnicity, gender, HWE, and menopausal status. Then, we applied meta-regression analysis to explore sources of heterogeneity. The results suggested that the studies of HWD were source of heterogeneity in overall population (additive model: P=0.024). In addition, the studies of HWD was also the source of heterogeneity on the association between women and osteoporosis risk (additive model: P=0.029 and recessive model: P=0.025).

Sensitivity analysis was estimated by applying three methods in this meta-analysis. First, results did not change when removing a single study each time to appraise the robustness. However, when we excluded studies of low quality and HWD, significantly decreased osteoporosis risk was found in overall analysis for VDR BsmI bb genotype (additive model: OR = 0.74, 95% CI: 0.56–0.99; recessive model: OR = 0.79, 95% CI: 0.63–0.98). Further, when we restrained only including high-quality HWE, and matching studies, the corresponding pooled OR do not appear to be significantly affected. Therefore, the results of the sensitivity analysis are shown in Tables 8 and 9.

Table 8
Pooled estimates of association of VDR BsmI, FokI, Cdx2 polymorphism and osteoporosis risk, excluding low quality and HWD studies
Genetic modelTest of associationTests for heterogeneity
OR (95% CI)PPhI2
VDR BsmI     
B vs b 1.16 (1.00–1.35) 0.05 0.002 53.00% 
bb vs BB 0.74 (0.56–0.99) 0.04 0.021 42.50% 
Bb+bb vs BB 0.88 (0.72–1.08) 0.22 0.194 20.60% 
bb vs BB+Bb 0.79 (0.63–0.98) 0.04 0.004 50.70% 
BB+bb vs Bb 0.91 (0.79–1.06) 0.23 0.224 17.80% 
VDR FokI     
F vs f 0.93 (0.81–1.08) 0.33 0.009 48.00% 
ff VS FF 1.17 (0.83–1.66) 0.37 0.006 50.20% 
Ff+ff VS FF 1.07 (0.89–1.27) 0.47 0.080 32.60% 
ff VS FF+Ff 1.23 (0.93–1.63) 0.16 0.036 39.60% 
FF+ff VS Ff 1.01 (0.88–1.15) 0.90 0.596 0.00% 
VDR Cdx2     
G vs A 1.17 (0.68–2.00) 0.57 0.026 67.50% 
AA VS GG 0.68 (0.29–1.58) 0.37 0.269 23.80% 
GA+AA VS GG 0.86 (0.44–1.66) 0.65 0.030 66.40% 
AA VS GG+GA 0.72 (0.37–1.40) 0.34 0.531 0.00% 
GG+AA VS GA 0.89 (0.55–1.45) 0.64 0.166 41.00% 
Genetic modelTest of associationTests for heterogeneity
OR (95% CI)PPhI2
VDR BsmI     
B vs b 1.16 (1.00–1.35) 0.05 0.002 53.00% 
bb vs BB 0.74 (0.56–0.99) 0.04 0.021 42.50% 
Bb+bb vs BB 0.88 (0.72–1.08) 0.22 0.194 20.60% 
bb vs BB+Bb 0.79 (0.63–0.98) 0.04 0.004 50.70% 
BB+bb vs Bb 0.91 (0.79–1.06) 0.23 0.224 17.80% 
VDR FokI     
F vs f 0.93 (0.81–1.08) 0.33 0.009 48.00% 
ff VS FF 1.17 (0.83–1.66) 0.37 0.006 50.20% 
Ff+ff VS FF 1.07 (0.89–1.27) 0.47 0.080 32.60% 
ff VS FF+Ff 1.23 (0.93–1.63) 0.16 0.036 39.60% 
FF+ff VS Ff 1.01 (0.88–1.15) 0.90 0.596 0.00% 
VDR Cdx2     
G vs A 1.17 (0.68–2.00) 0.57 0.026 67.50% 
AA VS GG 0.68 (0.29–1.58) 0.37 0.269 23.80% 
GA+AA VS GG 0.86 (0.44–1.66) 0.65 0.030 66.40% 
AA VS GG+GA 0.72 (0.37–1.40) 0.34 0.531 0.00% 
GG+AA VS GA 0.89 (0.55–1.45) 0.64 0.166 41.00% 
Table 9
Pooled estimates of association of VDR BsmI, FokI polymorphism and osteoporosis risk, only studies with high-quality matching, and studies conforming to HWE
Genetic modelTest of associationTest for heterogeneity
OR (95% CI)PPhI2
VDR BsmI     
B vs b 1.14 (0.96–1.36) 0.14 0.469 0.00% 
bb VS BB 0.71 (0.48–1.03) 0.07 0.652 0.00% 
Bb+bb VS BB 0.86 (0.64–1.14) 0.28 0.870 0.00% 
bb VS BB+Bb 0.81 (0.61–1.08) 0.15 0.215 26.80% 
BB+bb VS Bb 0.96 (0.76–1.22) 0.74 0.410 2.60% 
VDR FokI     
F vs f 0.96 (0.81–1.14) 0.63 0.157 31.50% 
ff VS FF 1.17 (0.84–1.61) 0.36 0.120 36.00% 
Ff+ff VS FF 1.08 (0.91–1.30) 0.39 0.434 0.40% 
ff VS FF+Ff 1.16 (0.86–1.57) 0.35 0.069 43.30% 
FF+ff VS Ff 0.97 (0.81–1.15) 0.70 0.301 15.50% 
Genetic modelTest of associationTest for heterogeneity
OR (95% CI)PPhI2
VDR BsmI     
B vs b 1.14 (0.96–1.36) 0.14 0.469 0.00% 
bb VS BB 0.71 (0.48–1.03) 0.07 0.652 0.00% 
Bb+bb VS BB 0.86 (0.64–1.14) 0.28 0.870 0.00% 
bb VS BB+Bb 0.81 (0.61–1.08) 0.15 0.215 26.80% 
BB+bb VS Bb 0.96 (0.76–1.22) 0.74 0.410 2.60% 
VDR FokI     
F vs f 0.96 (0.81–1.14) 0.63 0.157 31.50% 
ff VS FF 1.17 (0.84–1.61) 0.36 0.120 36.00% 
Ff+ff VS FF 1.08 (0.91–1.30) 0.39 0.434 0.40% 
ff VS FF+Ff 1.16 (0.86–1.57) 0.35 0.069 43.30% 
FF+ff VS Ff 0.97 (0.81–1.15) 0.70 0.301 15.50% 

Publication bias

Publication bias was assessed in the overall publication by Begg’s funnel plot and Egger’s test, the shape of the funnel plots revealed no significant funnel asymmetry (Figure 5) in overall population. The Egger tests also indicated that there was no obvious evidence of publication bias (P>0.05 in all genetic models), as shown in Tables 57.

Begg’s funnel plot to assess publication bias

Figure 5
Begg’s funnel plot to assess publication bias
Figure 5
Begg’s funnel plot to assess publication bias

Credibility of the identified genetic associations

We classified statistically significant associations that met the following criteria as ‘positive results’ [81]: (1) the P-value of Z-test is less than 0.05 in at least two gene models; (2) at the P-value level of 0.05, the FPRP is less than 0.2; (3) statistical power > 0.8; (4) I2 < 50%. Considered as ‘less credible affirmation’ with lower threshold when the following conditions were met: (1) P-value <0.05 in at least one of the genetic models; (2) the statistical power was between 50 and 79% or FPRP > 0.2 or I2 > 50%. Otherwise, the association was classified as ‘null’ or ‘negative’. After credibility assessment, we identified ‘less-credible positive results’ for the statistically significant associations in the current meta-analysis. The detailed credibility assessment results are listed in Table 10.

Table 10
FPRP values for the statistically significant associations in current meta-analysis
VariablesOR (95% CI)I2 (%)Statistical powerPrior probability of 0.001
OR = 1.2OR = 1.5OR = 1.2OR = 1.5
Overall       
ff vs FF 1.49 (1.07–2.07) 57.10% 0.098 0.516 0.994 0.971 
ff vs FF+Ff 1.47 (1.13–1.93) 47.50% 0.072 0.558 0.987 0.909 
West Asian       
B vs b 1.36 (1.06–1.74) 0% 0.160 0.782 0.989 0.949 
bb vs BB 0.55 (0.33–0.92) 0% 0.057 0.232 0.998 0.990 
bb vs BB+Bb 0.65 (0.45–0.96) 0% 0.106 0.449 0.997 0.985 
Indian       
F vs f 0.68 (0.58–0.80) 0% 0.007 0.594 0.317 0.006 
ff vs FF 3.25 (2.14–4.94) 0% 0.957 0.189 
Ff+ff vs FF 1.40 (1.14–1.71) 0% 0.065 0.75 0.937 0.565 
ff vs FF+Ff 2.87 (1.93–4.26) 0% 0.001 0.957 0.207 
Female       
ff vs FF 1.46 (1.02–2.11) 62.60% 0.148 0.557 0.997 0.987 
ff vs FF+Ff 1.48 (1.09–2.00) 55.40% 0.086 0.535 0.992 0.952 
Exclude low quality and HWD studies       
Overall       
bb VS BB 0.74 (0.56–0.99) 42.50% 0.212 0.759 0.995 0.982 
bb VS BB+Bb 0.79 (0.63–0.98) 50.70% 0.314 0.939 0.99 0.972 
VariablesOR (95% CI)I2 (%)Statistical powerPrior probability of 0.001
OR = 1.2OR = 1.5OR = 1.2OR = 1.5
Overall       
ff vs FF 1.49 (1.07–2.07) 57.10% 0.098 0.516 0.994 0.971 
ff vs FF+Ff 1.47 (1.13–1.93) 47.50% 0.072 0.558 0.987 0.909 
West Asian       
B vs b 1.36 (1.06–1.74) 0% 0.160 0.782 0.989 0.949 
bb vs BB 0.55 (0.33–0.92) 0% 0.057 0.232 0.998 0.990 
bb vs BB+Bb 0.65 (0.45–0.96) 0% 0.106 0.449 0.997 0.985 
Indian       
F vs f 0.68 (0.58–0.80) 0% 0.007 0.594 0.317 0.006 
ff vs FF 3.25 (2.14–4.94) 0% 0.957 0.189 
Ff+ff vs FF 1.40 (1.14–1.71) 0% 0.065 0.75 0.937 0.565 
ff vs FF+Ff 2.87 (1.93–4.26) 0% 0.001 0.957 0.207 
Female       
ff vs FF 1.46 (1.02–2.11) 62.60% 0.148 0.557 0.997 0.987 
ff vs FF+Ff 1.48 (1.09–2.00) 55.40% 0.086 0.535 0.992 0.952 
Exclude low quality and HWD studies       
Overall       
bb VS BB 0.74 (0.56–0.99) 42.50% 0.212 0.759 0.995 0.982 
bb VS BB+Bb 0.79 (0.63–0.98) 50.70% 0.314 0.939 0.99 0.972 

Discussion

Osteoporosis is a multifactorial disease and is strongly related to heredity [7]. Genes are very important factors for the risk of osteoporosis. Osteoporosis is characterized by low BMD and microarchitectural deterioration of bone leading to increased bone fragility and a high risk of fracture. The VDR gene is considered as a candidate gene and has been widely studied due to it plays a key role in regulating bone resorption and metabolism [10]. And the VDR gene has also been implicated as a factor affecting bone mass [84]. Hence, it will be very important to investigate the association between VDR gene polymorphism and osteoporosis. Moreover, the VDR polymorphisms play an important role in the pathogenesis, prevention, diagnosis and treatment of osteoporosis and other disease such as acute ischemic stroke [85]. In addition, single nucleotide polymorphism (SNP) may affect the function of VDR and may be related with osteoporosis risk [82]. Although many studies attempted to explore the association between VDR polymorphisms and the risk of osteoporosis. However, it is regrettable that no solid evidence has been obtained, which may be due to different reasons, including small sample size, ethnic, and regional differences. In order to overcome these shortcomings, meta-analysis is effective alternative.

A total of six previous meta-analyses explored the association between VDR polymorphisms and osteoporosis risk. Wang et al. [24] and Yu et al. [26] explored the association between osteoporosis risk and VDR BsmI polymorphism in Chinese and Han Chinese population, respectively. Their results suggested that there was no significant association between VDR BsmI polymorphism and osteoporosis risk. In 2013, Jia et al. [27] examined 26 studies including 2274 cases and 3150 controls to show that the VDR BsmI polymorphism was associated with an decreased osteoporosis risk. However, the examination of 41 studies on VDR BsmI polymorphism (including 3080 cases and 4157 controls) by Gang et al. [28] indicated that the VDR BsmI polymorphism was not significantly associated with osteoporosis risk. In addition, the examination of 36 studies on VDR BsmI, 15 studies on VDR FokI, and three studies on VDR Cdx2 by Zhang et al. [25] indicated that the VDR BsmI and VDR FokI polymorphisms were associated with an increased the risk of developing osteoporosis in overall and Asians, while the VDR Cdx2 polymorphism may be not associated with osteoporosis risk. However, VDR BsmI and VDR FokI polymorphisms had not been found to increase the risk of osteoporosis by Zintzaras et al. [29]. Further, when we examined these meta-analyses carefully, we found some disadvantages. First, quality assessments of the eligible studies had not been performed in some studies [24,25,27–29], and low-quality literature may be included in these meta-analyses, resulting in deviation of the results. Second, HWE is absolutely necessary for a sound genetic association study. There may be selection bias or genotyping errors if the control group did not meet HWE. It can lead to misleading results. The distribution of genotypes in the control group was not tested by HWE [24,25]. Then, the statistical power was not calculated in some previous meta-analyses [24,26–29]. Finally, the FPRPs of statistically significant association was not evaluated in all previous meta-analyses [24–29]. Therefore, results of their meta-analyses may be not credible.

A total of 43 studies were included in the current meta-analysis, of which 34 studies explored the association between VDR BsmI and osteoporosis risk, 19 studies reported VDR FokI polymorphism, and four studies related to VDR Cdx2 polymorphism. Furthermore, five genetic models are compared separately. Overall, compared with the FF and Ff genotypes, statistically significant increased osteoporosis risk was found in the VDR FokI ff genotype. In the subgroup analysis, the VDR FokI ff genotype was significantly associated with increased osteoporosis risk in Indians and women population. However, significantly decreased the risk of osteoporosis were observed in the West Asians for VDR BsmI b allele and bb genotype. In addition, when we excluded studies of low quality and HWD, a significantly decreased the risk of osteoporosis was found in the overall analysis for the VDR BsmI bb genotype. Further, significant association did not observed when the pooled analysis was limited only involving high quality, HWE, and matching studies. Furthermore, the current meta-analysis was performed by applying multiple subgroups and different genetic models, at the cost of multiple comparisons, in which case the pooled P-value must be adjusted [83]. The Venice criteria, statistical power, and I2 value were very important criteria [37]. Hence, the FPRP test and Venice criteria were used to assess positive results. After credibility assessment, we identified ‘less-credible positive results’ for the statistically significant associations in the current meta-analysis. Heterogeneity has also been observed in the current meta-analysis. Results of meta-regression analysis suggested that studies of HWD were the source of heterogeneity. In addition, no obvious asymmetry was found in the study of VDR BsmI and FokI by the Begg’s funnel plots and Egger tests. Due to the limited number of studies, the Begg’s funnel plot was not performed to explored publication bias in the VDR Cdx2 study. Meantime, the Egger tests revealed that there was no clear statistical evidence of publication bias.

The current meta-analysis has the following advantages: (1) the quality of included studies was assessed; (2) the HWE test was performed in the control group; (3) we applied FPRP and Venice criteria to evaluate the significant association in current meta-analysis; (4) the sample size was much larger than the previous meta-analyses; (5) we explored sources of heterogeneity based on meta-regression analysis. However, there are still some limitations in the present study. First, we did not control confounding factors such as smoking, drinking, and variable study designs, were closely related to affect the results. Second, in the subgroup analyses, the number of studies were relatively small in Indians, and there was not enough statistical power to explore the real association. Moreover, due to the limited number of studies, we did not perform subgroup analyses in the pooled analysis of VDR Cdx2 polymorphism and osteoporosis risk. Therefore, the study with large sample size and large enough subgroup will help to verify our findings.

In conclusion, these positive findings should be interpreted with caution and indicate that significant association may most likely result from less-credible, rather than from true associations or biological factors on the VDR BsmI and FokI polymorphisms with osteoporosis risk.

Competing Interests

The authors declare that there are no competing interests associated with the manuscript.

Funding

The authors declare that there are no sources of funding to be acknowledged.

Author Contribution

Bin Chen: designed and performed the research, collected and analyzed the data, wrote the paper. Wang-fa Zhu: collected data. Yi-yang Mu and Biao Liu: checked the data. Hong-zhuo Li and Xiao-feng He: designed the research and revised the article.

Acknowledgements

We would like to acknowledge the authors of all the original studies included in this meta-analysis. Furthermore, we would like to thank Jiao Su for his help in modifying the grammar of this article.

Abbreviations

     
  • BMD

    bone mineral density

  •  
  • FPRP

    false-positive report probability

  •  
  • HWD

    Hardy–Weinberg disequilibrium

  •  
  • HWE

    Hardy–Weinberg equilibrium

  •  
  • LS

    lumbar spine

  •  
  • OR

    odds ratio

  •  
  • SNP

    single nucleotide polymorphism

  •  
  • VDR

    vitamin D receptor

  •  
  • 95% CI

    95% confidence interval

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