A meta-analysis of VDR polymorphisms and postmenopausal osteoporosis

in Endocrine Connections
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  • 1 Department of Laboratory, Changyi People’s Hospital of Shandong Province, Changyi, Shandong, China
  • 2 Department of Laboratory, Changyi Maternal and Child Health Hospital of Shandong Province, Changyi, Shandong, China
  • 3 Department of Obstetrics, Changyi Maternal and Child Health Hospital of Shandong Province, Changyi, Shandong, China
  • 4 Department of Laboratory, Zhuji Affiliated Hospital of Shaoxing University, Zhuji, Zhejiang, China

Correspondence should be addressed to Q Si: sldc06@163.com

Background:

Whether polymorphisms in VDR gene affect the risk of postmenopausal osteoporosis or not remain unclear. Thus, the authors performed a meta-analysis to more robustly assess associations between polymorphisms in VDR gene and the risk of postmenopausal osteoporosis by integrating the results of previous literature.

Methods:

Medline, Embase, Wanfang, VIP and CNKI were searched comprehensively for eligible literature, and 67 genetic association studies were finally selected to be included in this meta-analysis.

Results:

We found that ApaI rs7975232 (dominant comparison: OR = 0.77, P = 0.007; allele comparison: OR = 0.81, P = 0.04), BsmI rs1544410 (dominant comparison: OR = 0.69, P = 0.002; allele comparison: OR = 0.78, P = 0.008) and TaqI rs731236 (recessive comparison: OR = 1.32 , P = 0.01) polymorphisms were significantly associated with the risk of postmenopausal osteoporosis in Caucasians, whereas FokI rs10735810 polymorphism was significantly associated with the risk of postmenopausal osteoporosis in Asians (dominant comparison: OR = 0.61, P = 0.0001; recessive comparison: OR = 2.02, P = 0.001; allele comparison: OR = 0.68, P = 0.002).

Conclusions:

This meta-analysis shows that ApaI rs7975232, BsmI rs1544410 and TaqI rs731236 polymorphisms may affect the risk of postmenopausal osteoporosis in Caucasians, while BsmI rs1544410 polymorphism may affect the risk of postmenopausal osteoporosis in Asians.

Abstract

Background:

Whether polymorphisms in VDR gene affect the risk of postmenopausal osteoporosis or not remain unclear. Thus, the authors performed a meta-analysis to more robustly assess associations between polymorphisms in VDR gene and the risk of postmenopausal osteoporosis by integrating the results of previous literature.

Methods:

Medline, Embase, Wanfang, VIP and CNKI were searched comprehensively for eligible literature, and 67 genetic association studies were finally selected to be included in this meta-analysis.

Results:

We found that ApaI rs7975232 (dominant comparison: OR = 0.77, P = 0.007; allele comparison: OR = 0.81, P = 0.04), BsmI rs1544410 (dominant comparison: OR = 0.69, P = 0.002; allele comparison: OR = 0.78, P = 0.008) and TaqI rs731236 (recessive comparison: OR = 1.32 , P = 0.01) polymorphisms were significantly associated with the risk of postmenopausal osteoporosis in Caucasians, whereas FokI rs10735810 polymorphism was significantly associated with the risk of postmenopausal osteoporosis in Asians (dominant comparison: OR = 0.61, P = 0.0001; recessive comparison: OR = 2.02, P = 0.001; allele comparison: OR = 0.68, P = 0.002).

Conclusions:

This meta-analysis shows that ApaI rs7975232, BsmI rs1544410 and TaqI rs731236 polymorphisms may affect the risk of postmenopausal osteoporosis in Caucasians, while BsmI rs1544410 polymorphism may affect the risk of postmenopausal osteoporosis in Asians.

Introduction

Postmenopausal osteoporosis (PMOP) is featured by a decreased bone mineral density and an increased risk of bone fractures in postmenopausal women (1, 2). According to a recent epidemiological research, postmenopausal osteoporosis currently affects nearly 50% of elderly women over 60 years old, and with more and more countries entering the aging society, the incidence of osteoporosis in postmenopausal women is still rapidly increasing, making it the most common disorder of bone metabolism for elderly women across the world (3, 4, 5).

The pathogenesis mechanisms of postmenopausal osteoporosis are still unclear despite previous investigations, but substantial evidence supports that vitamin D deficiency is definitely an important contributing factor to the development of postmenopausal osteoporosis (6, 7). Considering that the action of vitamin D, one of the most crucial modulating factor of bone metabolism, is mediated by the vitamin D receptor (VDR), it is thought that polymorphisms of VDR gene may also affect the risk of postmenopausal osteoporosis (8, 9, 10). Over the last decade, investigators across the world have repeatedly attempted to assess the associations between polymorphisms in VDR gene and the risk of postmenopausal osteoporosis, yet the relationships between these polymorphisms and the risk of postmenopausal osteoporosis are still inconclusive. So a meta-analysis was performed to robustly assess the associations between polymorphisms in VDR gene and the risk of postmenopausal osteoporosis by integrating the results of previous literature.

Materials and methods

This meta-analysis was conducted in accordance with the PRISMA guideline (11).

Literature search and inclusion criteria

Medline, Embase, Wanfang, VIP and CNKI were comprehensively searched by the authors using the below keywords: (vitamin D receptor OR VDR) AND (polymorphism OR polymorphic OR variation OR variant OR mutant OR mutation OR SNP OR genotypic OR genotype OR allelic OR allele) AND (postmenopausal OR postmenopause) AND (osteoporosis OR bone loss). Moreover, we also manually screened the references of retrieved literature to make up for the potential incompleteness of literature searching from databases.

Selection criteria of this meta-analysis were listed below: (1) studies of case–control or cohort design; (2) give genotypic frequencies of VDR polymorphisms in cases with postmenopausal osteoporosis and population-based controls; (3) the full manuscript with detailed genotypic frequencies of VDR polymorphisms is retrievable or buyable. Articles would be excluded if one of the following three criteria is satisfied: (1) studies without complete genotypic data of VDR polymorphisms in cases with postmenopausal osteoporosis and population-based controls; (2) narrative or systematic reviews, meta-analysis or comments; (3) case series of subjects with postmenopausal osteoporosis only. If duplicate reports are retrieved, we would only include the most complete one for integrated analyses.

Data extraction and quality assessment

The authors extracted the following data items from eligible studies: (1) last name of the leading author; (2) year of publication; (3) country and ethnicity of study population; (4) the number of cases with postmenopausal osteoporosis and population-based controls; (5) genotypic frequencies of VDR polymorphisms in cases with postmenopausal osteoporosis and population-based controls. We also examined Hardy–Weinberg equilibrium (HWE) by comparing the actual genotypic frequencies of investigated VDR polymorphisms to their expected distributions using the chi-square test. The significance threshold of HWE was set at 0.05, if P value > 0.05, then we considered that the genotypic distribution of the investigated polymorphism was in agreement with HWE. The quality of eligible literature was assessed by the Newcastle–Ottawa scale (NOS) (12), and these with a score of 7–9 were considered to be literature of good quality. Two authors extracted data and assessed quality of eligible literature in parallel. A thorough discussion until a consensus is reached would be endorsed in case of any discrepancy between two authors.

Statistical analyses

All statistical analyses in this meta-analysis were performed with the Cochrane Review Manager software version 5.3 (The Cochrane Collaboration, Software Update, Oxford, United Kingdom). Associations between VDR gene polymorphisms and the risk of postmenopausal osteoporosis were explored by using odds ratio and its 95 % CI. The statistically significant P value was set at 0.05. All investigated VDR polymorphisms have a major allele (M) and a minor allele (m), the dominant comparison was defined as MM vs Mm + mm, the recessive comparison was defined as mm vs MM + Mm, the over-dominant comparison was defined as Mm vs MM + mm, and the allele comparison was defined as M vs m. The authors used I2 statistics to estimate heterogeneities among included studies. The authors would use DerSimonian–Laird method, which is also known as the random effect model, to integrate the results of eligible studies if I2 is larger than 50%. Otherwise, the authors would use Mantel–Haenszel method, which is also known as the fixed effect model, to integrate the results of eligible studies. Meanwhile, the authors also conduct subgroup analyses by ethnic groups. Stabilities of integrated results were tested by deleting studies that violated HWE, and then integrating the results of the rest of eligible studies. Publication biases were evaluated by assessing symmetry of funnel plots.

Results

Characteristics of included studies

Five hundred and seven papers were retrieved by the authors by using our searching strategy. One hundred and thirty-three papers were then selected to screen for eligibility after omitting unrelated and repeated items. Thirty-eight reviews and 13 case series were further excluded, and another 15 papers without complete genotypic data were further excluded by the authors. Totally 67 studies met the inclusion criteria, and were finally enrolled for integrated analyses (Fig. 1). Data extracted from eligible studies were summarized in Table 1.

Figure 1
Figure 1

Flowchart of study selection for this meta-analysis.

Citation: Endocrine Connections 9, 9; 10.1530/EC-20-0296

Table 1

The characteristics of included studies in current meta-analysis.

First author, year

Country

Ethnicity

Sample size

Genotypes (wtwt/wtmt/mtmt)

P-value for HWE

NOS score

Cases

Controls

ApaI rs7975232
 Ahmad 2018IndiaMixed254/25462/140/5275/134/450.2647
 Castelán-Martínez 2015MexicoMixed387/147141/160/8646/75/260.6317
 Chen 2007ChinaAsian155/113108/40/760/41/120.2237
 Dabirnia 2016IranMixed50/5024/25/130/18/20.7297
 Douroudis 2003Hellenic RepublicCaucasian35/4411/14/1017/26/10.0167
 Duman 2004TurkeyCaucasian75/6613/56/615/45/60.0027
 Dundar 2009TurkeyCaucasian112/2426/61/258/14/20.2317
 Ge 2009ChinaAsian353/208160/157/36102/84/220.4538
 González-Mercado 2013MexicoMixed232/8779/118/3529/41/170.7157
 Gu 2010ChinaAsian186/14879/86/2174/61/130.9327
 Iván 2008ChileCaucasian67/5925/31/1118/27/140.5367
 Kim 2015KoreaAsian153/4797/53/324/19/40.9317
 Langdahl 2000DenmarkCaucasian78/7422/44/1225/32/170.2837
 Liang 2002ChinaAsian30/3020/6/427/2/10.0117
 Luan 2011ChinaAsian140/8871/56/1344/34/100.3907
 Marozik 2013BelarusCaucasian54/777/24/2329/34/140.4727
 Marozik 2018LithuaniaCaucasian149/17227/67/5560/74/380.1057
 Meng 2018ChinaAsian90/24660/25/5161/69/160.0288
 Mitra 2006IndiaMixed119/9750/44/2534/33/300.0027
 Mosaad 2014EgyptMixed30/15013/15/269/71/100.1427
 Riggs 1995USAMixed30/12812/19/938/59/310.3947
 Sassi 2015TunisiaMixed335/231130/143/6290/115/260.2337
 Seremak-Mrozikiewicz 2009PolandCaucasian163/6335/82/4612/32/190.8217
 Tanriover 2010TurkeyCaucasian50/5015/23/1222/15/130.0078
 Uysal 2008TurkeyCaucasian100/14635/50/1546/79/210.1657
 Vandevyver 1997BelgiumCaucasian87/69920/45/22197/375/1270.0278
 Wu 2016ChinaAsian79/23443/27/9105/111/180.1237
 Wu 2019ChinaAsian610/616331/218/61366/207/430.0708
 Xie 2005ChinaAsian295/56240/43/1234/16/60.0757
 Yoldemir 2011TurkeyCaucasian130/13034/60/3631/73/260.1557
 Zajickova 2002Czech RepublicCaucasian65/3323/33/910/17/60.7937
BsmI rs1544410
 Ahmad 2018IndiaMixed254/25454/137/6354/152/480.0027
 Berg 1996NorwayCaucasian19/304/8/78/11/110.1567
 Boroń 2015PolandCaucasian278/292101/121/56128/113/510.0047
 Cheishvili 2017IsraelMixed37/3713/11/1315/12/100.0397
 Chen 2003ChinaAsian78/8165/13/069/12/00.4727
 Douroudis 2003Hellenic RepublicCaucasian35/4420/12/329/10/50.0197
 Duman 2004KuwaitMixed75/6654/18/342/17/70.0217
 Efesoy 2011TurkeyCaucasian40/3012/23/510/15/50.8767
 Ge 2009ChinaAsian353/208314/33/6192/12/4<0.0018
 Gennari 1998ItalyCaucasian155/13623/92/4049/76/110.0137
 González-Mercado 2013MexicoMixed232/88143/76/1346/38/40.2677
 Houston 1996UKCaucasian44/4417/19/816/19/90.4507
 Huang 2000ChinaAsian14/2713/1/026/1/00.9227
 Hussien 2013EgyptMixed150/5050/57/4319/21/100.3517
 Iván 2008ChileCaucasian67/5910/46/1113/37/90.0467
 Kim 2015KoreaAsian153/47142/11/042/5/00.7007
 Langdahl 2000DenmarkCaucasian80/8023/38/1925/34/210.1867
 Li 2000ChinaAsian96/4254/36/620/21/10.0957
 Liang 2002ChinaAsian30/3028/1/130/0/0NA7
 Lim 1995KoreaAsian72/7061/9/260/9/10.3497
 Liu 2005ChinaAsian56/8950/6/076/11/20.0607
 Marozik 2013BelarusCaucasian54/7711/31/1240/26/110.0627
 Marozik 2018LithuaniaCaucasian149/17232/64/5364/73/350.0987
 Melhus 1994SwedenCaucasian70/7614/29/2734/35/70.6378
 Mencej-Bedrac 2009SloveniaCaucasian240/228103/110/2788/100/400.2158
 Meng 2017ChinaAsian90/24674/12/4216/24/6<0.0017
 Mitra 2006IndiaMixed119/9751/46/2240/38/190.0807
 Mosaad 2014EgyptMixed30/1502/19/936/74/400.8777
 Musumeci 2009IranMixed50/2027/15/817/2/10.0477
 Perez 2008ArgentinaMixed64/6817/35/1220/32/160.6497
 Pollak 2001IsraelMixed75/14324/38/1360/67/160.6757
 Pouresmaeili 2013IranMixed64/8217/33/1436/33/130.2527
 Riggs 1995USAMixed40/1299/20/1120/61/480.9327
 Seremak-Mrozikiewicz 2009PolandCaucasian163/6370/66/2726/27/100.5067
 Tanriover 2010TurkeyCaucasian50/5016/19/1524/19/70.3208
 Techapatiphandee 2018ThailandAsian105/13285/19/1103/25/40.1237
 Uysal 2008TurkeyCaucasian100/14618/48/3424/78/440.2837
 Vandevyver 1997BelgiumCaucasian86/69824/50/12203/368/1270.0768
 Wang 2007ChinaAsian50/4843/7/039/9/00.4747
 Yanagi 1996JapanAsian66/6622/12/1257/7/20.0137
 Yoldemir 2011TurkeyCaucasian130/13035/73/2243/65/220.7607
 Zajickova 2002Czech RepublicCaucasian65/3320/24/2110/13/100.2237
 Zhang 1998ChinaAsian17/16414/3/0148/16/00.5118
 Zhang 2000ChinaAsian77/3538/33/614/18/30.4037
 Zhu 2004ChinaAsian40/15826/8/6105/46/70.5007
FokI rs10735810
 Ahmad 2018IndiaMixed254/254148/92/14169/80/50.207
 Castelán-Martínez 2015MexicoMixed232/8861/118/5324/45/190.8077
 Choi 2000KoreaAsian48/6512/23/1326/33/60.3277
 González-Mercado 2013MexicoMixed88/8825/48/1524/45/190.8077
 Gu 2010ChinaAsian186/14846/100/4040/84/240.0717
 Iván 2008ChileCaucasian67/5929/27/1127/25/70.7447
 Kanan 2013JordanMixed120/9040/62/1829/48/130.3367
 Kim 2015KoreaAsian153/4750/83/2014/25/80.5777
 Langdahl 2000DenmarkCaucasian30/12812/19/938/59/310.3947
 Li 2019ChinaAsian224/15566/103/5558/68/290.2597
 Lisker 2003MexicoMixed65/5727/29/920/29/80.6257
 Lucotte 1999FranceCaucasian124/10545/69/1040/52/130.5357
 Mamolini 2017ItalyCaucasian170/7397/60/1340/25/80.1947
 Mansour 2010IranMixed50/2034/9/720/0/0NA7
 Mencej-Bedrac 2009SloveniaCaucasian240/22888/108/44105/97/260.6188
 Mitra 2006IndiaMixed119/9738/42/3946/33/180.0117
 Mohammadi 2015IranMixed96/35652/36/8198/128/300.1587
 Mosaad 2014EgyptMixed30/15023/6/193/55/20.0497
 Pérez 2008ArgentinaMixed64/6822/32/1022/36/100.4447
 Tanriover 2010TurkeyCaucasian50/5027/22/129/18/30.9268
 Techapatiphandee 2018ThailandAsian105/13231/46/2841/73/180.1067
 Wu 2019ChinaAsian610/616296/235/79404/186/260.4368
 Xing 2011ChinaAsian32/707/14/1127/35/80.5067
 Yasovanthi 2011IndiaMixed247/254104/119/24122/124/8<0.0018
 Yoldemir 2011TurkeyCaucasian130/13066/55/962/55/130.8767
 Zajickova 2002Czech RepublicCaucasian78/7422/44/1225/32/170.2837
TaqI rs731236
 Ahmad 2018IndiaMixed254/254124/96/3489/123/420.9647
 Dabirnia 2016IranMixed50/5020/24/616/29/50.1217
 Duman 2004KuwaitMixed75/6610/42/2315/28/230.2597
 Gennari 1998ItalyCaucasian160/14433/87/4062/71/110.1267
 González-Mercado 2013MexicoMixed232/88142/77/1346/36/60.7697
 Iván 2008ChileCaucasian67/5926/31/1017/34/80.1677
 Kim 2015KoreaAsian153/47140/12/142/5/00.7007
 Langdahl 2000DenmarkCaucasian46/28411/30/591/159/340.0057
 Larin 2015UkraineCaucasian44/3020/18/614/12/40.5847
 Marozik 2013BelarusCaucasian54/7717/26/1139/24/140.0087
 Marozik 2018LithuaniaCaucasian149/17238/62/4958/74/400.0887
 Masi 1998ItalyCaucasian90/11141/36/1338/64/90.0137
 Mitra 2006IndiaMixed119/9734/42/4344/31/220.0017
 Mosaad 2014EgyptMixed30/1509/19/239/74/370.8727
 Riggs 1995USAMixed31/13011/23/753/57/200.4757
 Sassi 2015TunisiaMixed335/231165/128/42103/95/330.1527
 Seremak-Mrozikiewicz 2009PolandCaucasian163/6378/59/2622/29/120.6597
 Tanriover 2010TurkeyCaucasian50/5015/29/625/17/80.1028
 Techapatiphandee 2018ThailandAsian105/13297/6/2116/15/10.5137
 Uysal 2008TurkeyCaucasian100/14640/46/1454/75/170.2377
 Vandevyver 1997BelgiumCaucasian46/28411/30/591/159/340.0058
 Wang 2013ChinaAsian92/9847/48/748/40/100.6987
 Yoldemir 2011TurkeyCaucasian130/13051/59/2049/59/220.5587
 Zajickova 2002Czech RepublicCaucasian65/3311/31/238/14/110.4077
 Ziablitsev 1994UkraineCaucasian44/3020/18/614/12/40.5847

HWE, Hardy–Weinberg equilibrium; mt, Mutant type; NA, not available; NOS, Newcastle-Ottawa scale; wt, Wild type;.

ApaI rs7975232 polymorphism and the risk of postmenopausal osteoporosis

Thirty papers assessed relationship between ApaI rs7975232 polymorphism and the risk of postmenopausal osteoporosis. The integrated analyses demonstrated that ApaI rs7975232 polymorphism was significantly associated with the risk of postmenopausal osteoporosis in overall population (recessive comparison: OR = 1.20, P = 0.004) and Caucasians (dominant comparison: OR = 0.77, P = 0.007; allele comparison: OR = 0.81, P = 0.04), but not in Asians (Table 2).

Table 2

Integrated analyses results of the current meta-analysis.

VariablesSample sizeDominant comparisonRecessive comparisonOver-dominant comparisonAllele comparison
P-valueOR (95% CI)P-valueOR (95% CI)P-valueOR (95% CI)P-valueOR (95% CI)
ApaI rs7975232
 Overall4693/45670.640.96 (0.83–1.12)0.0041.20 (1.06–1.37)0.590.98 (0.89–1.07)0.530.96 (0.85–1.09)
 Caucasian1165/16370.0070.77 (0.64–0.93)0.111.31 (0.94–1.82)0.850.98 (0.83–1.16)0.040.81 (0.67–0.99)
 Asian2091/17860.391.14 (0.85–1.52)0.590.90 (0.61–1.32)0.400.91 (0.72–1.14)0.381.12 (0.87–1.45)
BsmI rs1544410
 Overall4312/50150.0020.77 (0.65–0.91)0.00011.28 (1.13–1.45)0.171.07 (0.97–1.18)0.0020.80 (0.70–0.92)
 Caucasian1825/23880.0020.69 (0.55–0.87)0.081.29 (0.97–1.71)0.051.14 (1.00–1.30)0.0080.78 (0.65–0.94)
 Asian1297/14430.300.81 (0.54–1.21)0.061.76 (0.98–3.17)0.991.00 (0.79–1.27)0.170.74 (0.48–1.14)
FokI rs10735810
 Overall3612/3602<0.00010.76 (0.69–0.84)0.0051.40 (1.11–1.78)0.071.10 (0.99–1.21)0.040.86 (0.75–0.99)
 Caucasian889/8470.300.90 (0.74–1.10)0.891.02 (0.76–1.37)0.081.19 (0.98–1.45)0.711.04 (0.83–1.31)
 Asian1358/12330.00010.61 (0.52–0.72)0.0012.02 (1.32–3.08)0.181.12 (0.95–1.31)0.0020.68 (0.54–0.87)
TaqI rs731236
 Overall2684/29560.570.94 (0.76–1.16)0.131.13 (0.96–1.32)0.671.04 (0.87–1.24)0.930.99 (0.86–1.15)
 Caucasian1208/16130.200.83 (0.62–1.10)0.011.32 (1.06–1.63)0.811.02 (0.87–1.20)0.160.87 (0.73–1.05)
 Asian350/2770.331.24 (0.80–1.93)0.790.89 (0.37–2.14)0.770.89 (0.40–1.96)0.061.42 (0.98–2.06)

The values in bold represent there is statistically significant differences between cases and controls.

NA, not available; OR, odds ratio.

BsmI rs1544410 polymorphism and the risk of postmenopausal osteoporosis

Forty-five papers assessed relationship between BsmI rs1544410 polymorphism and the risk of postmenopausal osteoporosis. The integrated analyses demonstrated that BsmI rs1544410 polymorphism was significantly associated with the risk of postmenopausal osteoporosis in overall population (dominant comparison: OR = 0.77, P = 0.002; recessive comparison: OR = 1.28, P = 0.0001; allele comparison: OR = 0.80, P = 0.002) and Caucasians (dominant comparison: OR = 0.69, P = 0.002; allele com­parison: OR = 0.78, P = 0.008), but not in Asians (Table 2).

FokI rs10735810 polymorphism and the risk of postmenopausal osteoporosis

Twenty-six papers assessed relationship between FokI rs10735810 polymorphism and the risk of postmenopausal osteoporosis. The integrated analyses demonstrated that FokI rs10735810 polymorphism was significantly associated with the risk of osteoporosis in overall population (dominant comparison: OR = 0.76, P < 0.0001; recessive comparison: OR = 1.40, P = 0.005; allele comparison: OR = 0.86, P = 0.04) and Asians (dominant comparison: OR = 0.61, P = 0.0001; recessive comparison: OR = 2.02, P = 0.001; allele comparison: OR = 0.68, P = 0.002), but not in Caucasians (Table 2).

TaqI rs731236 polymorphism and the risk of postmenopausal osteoporosis

Twenty-five papers assessed relationship between TaqI rs731236 polymorphism and the risk of postmenopausal osteoporosis. The integrated analyses demonstrated that TaqI rs731236 polymorphism was significantly associated with the risk of postmenopausal osteoporosis in Caucasians (recessive comparison: OR = 1.32, P = 0.01), but not in Asians (Table 2).

Sensitivity analyses

The authors examined stabilities of integrated analyses results by deleting studies that violated HEW, and then integrating the results of the rest of studies. The trends of associations were not significantly altered in sensitivity analyses, which indicated that from statistical perspective, our integrated analyses results were reliable and stable.

Publication biases

The authors examined potential publication biases in this meta-analysis by assessing symmetry of funnel plots. Funnel plots were found to be generally symmetrical, which indicated that our integrated analyses results were not likely to be severely deteriorated by publication biases (Supplementary Fig. 1, see section on supplementary materials given at the end of this article).

Discussion

This meta-analysis, robustly assessed associations between gene polymorphisms in VDR and the risk of postmenopausal osteoporosis. The integrated analyses results showed that ApaI rs7975232, BsmI rs1544410 and TaqI rs731236 polymorphisms were significantly associated with the risk of postmenopausal osteoporosis in Caucasians, whereas FokI rs10735810 polymorphism was significantly associated with the risk of postmenopausal osteoporosis in Asians.

The following points should be considered when interpreting our integrated findings. First, based on the findings of previous observational studies, we speculated that these investigated VDR polymorphisms may alter mRNA expression level or protein function of VDR, impact vitamin D metabolism, and then affect the risk of postmenopausal osteoporosis (13, 14). Nevertheless, further experimental studies are still warranted to figure out the exact mechanisms underlying the observed positive associations between VDR gene polymorphisms and the risk of postmenopausal osteoporosis in the current meta-analysis. Second, we want to study all polymorphic loci of VDR gene initially. Nevertheless, our comprehensive literature searching did not reveal sufficient eligible literature to support integrated analyses for other polymorphic loci of VDR gene, so we only explored associations with the risk of postmenopausal osteoporosis for four most commonly investigated polymorphisms of VDR gene in this meta-analysis. Third, it is worth noting that previously, Zhang et al. (15) also tried to investigate associations between VDR gene polymorphisms and postmenopausal osteoporosis through a meta-analysis. Nevertheless, this previous meta-analysis only covered relevant genetic association studies that were published before 2015. Since our literature searching revealed that many related studies were published after 2015, an updated meta-analysis like ours is of course warranted to get more reliable findings. Consistent with the previous meta-analysis, similar significant findings for ApaI rs7975232, FokI rs10735810 and TaqI rs731236 polymorphisms were observed in our integrated analyses. Additionally, we also found that BsmI rs1544410 polymorphism was significantly associated with the risk of postmenopausal osteoporosis in overall population and Caucasians, which was failed to be detected by the previous meta-analysis. Considering that our integrated analyses were derived from more eligible studies, our observations should be considered as a valuable supplement to pre-existing literature.

The major limitations of our integrated analyses were listed below. First, our integrated analyses results were derived from unadjusted pooling of previous literature. Without access to raw data of eligible studies, we can only assess associations between VDR gene polymorphisms and the risk of postmenopausal osteoporosis based on recalculations of raw genotypic frequencies provided by eligible literature, and we need to admit that lack of further adjustment for baseline characteristics may possibly influence reliability of our findings (16). Secondly, environmental factors such as food intake, sunshine exposure or exercise levels may also influence associations between polymorphisms in VDR gene and the risk of postmenopausal osteoporosis. However, most of the authors only paid attention to genetic associations in their publications, so it is impossible for us to explore genetic–environmental interactions in a meta-analysis based on these previous literature (17). Thirdly, we did not select gray literature for integrated analyses because this literature is generally considered to be incomplete and it is almost impossible for us to extract all necessary data items, or assess their quality through the NOS scale. Nevertheless, since we did not select gray literature for integrated analyses, despite that funnel plots were found to be overall symmetrical, it should be acknowledged that publication biases still may influence reliability of our integrated analyses results (18).

In conclusion, this meta-analysis shows that ApaI rs7975232, BsmI rs1544410 and TaqI rs731236 polymorphisms may affect the risk of postmenopausal osteoporosis in Caucasians, while FokI rs10735810 polymorphism may affect the risk of postmenopausal osteoporosis in Asians. Further studies with larger sample sizes are still needed to confirm our findings. In addition, scholars should also try to reveal the exact underlying mechanisms of the positive associations observed between aforementioned VDR polymorphisms and the risk of postmenopausal osteoporosis in the future.

Supplementary materials

This is linked to the online version of the paper at https://doi.org/10.1530/EC-20-0296.

Declaration of interest

The authors declare that there is no conflict of interest that could be perceived as prejudicing the impartiality of the research reported.

Funding

This work did not receive any specific grant from any funding agency in the public, commercial, or not-for-profit sector.

Author contribution statement

Lijuan Fu and Qijun Si conceived and designed this meta-analysis. Lijuan Fu and Jinhuan Ma searched literature. Sumei Yan analyzed data. Lijuan Fu and Qijun Si wrote the manuscript. All authors have approved the final manuscript as submitted.

References

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    Sidlauskas KM, Sutton EE, Biddle MA. Osteoporosis in men: epidemiology and treatment with denosumab. Clinical Interventions in Aging 2014 9 593601. (https://doi.org/10.2147/CIA.S51940)

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    Stang A. Critical evaluation of the Newcastle-Ottawa scale for the assessment of the quality of nonrandomized studies in meta-analyses. European Journal of Epidemiology 2010 25 603605. (https://doi.org/10.1007/s10654-010-9491-z)

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    Uitterlinden AG, Fang Y, Van Meurs JB, Pols HA, Van Leeuwen JP. Genetics and biology of vitamin D receptor polymorphisms. Gene 2004 338 143156. (https://doi.org/10.1016/j.gene.2004.05.014)

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    Valdivielso JM, Fernandez E. Vitamin D receptor polymorphisms and diseases. Clinica Chimica Acta: International Journal of Clinical Chemistry 2006 371 112. (https://doi.org/10.1016/j.cca.2006.02.016)

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    Zhang L, Yin X, Wang J, Xu D, Wang Y, Yang J, Tao Y, Zhang S, Feng X, Yan C. Associations between VDR gene polymorphisms and osteoporosis risk and bone mineral density in postmenopausal women: a systematic review and meta-analysis. Scientific Reports 2018 8 981. (https://doi.org/10.1038/s41598-017-18670-7)

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    Zhang YJ, Zhang L, Chen SY, Yang GJ, Huang XL, Duan Y, Yang LJ, Ye DQ, Wang J. Association between VDR polymorphisms and multiple sclerosis: systematic review and updated meta-analysis of case-control studies. Neurological Sciences 2018 39 225234. (https://doi.org/10.1007/s10072-017-3175-3)

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    Zhang JZ, Wang M, Ding Y, Gao F, Feng YY, Yakeya B, Wang P, Wu XJ, Hu FX, Xian J, Vitamin D receptor gene polymorphism, serum 25-hydroxyvitamin D levels, and risk of vitiligo: a meta-analysis. Medicine 2018 97 e11506. (https://doi.org/10.1097/MD.0000000000011506)

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    Mashhadiabbas F, Neamatzadeh H, Nasiri R, Foroughi E, Farahnak S, Piroozmand P, Mazaheri M, Zare-Shehneh M. Association of vitamin D receptor BsmI, TaqI, FokI, and ApaI polymorphisms with susceptibility of chronic periodontitis: a systematic review and meta-analysis based on 38 case -control studies. Dental Research Journal 2018 15 155165.

    • Search Google Scholar
    • Export Citation

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  • 1

    Aspray TJ, Hill TR. Osteoporosis and the ageing skeleton. Sub-Cellular Biochemistry 2019 91 453476. (https://doi.org/10.1007/978-981-13-3681-2_16)

    • Search Google Scholar
    • Export Citation
  • 2

    Lane NE. Epidemiology, etiology, and diagnosis of osteoporosis. American Journal of Obstetrics and Gynecology 2006 194 (Supplement) S3S11. (https://doi.org/10.1016/j.ajog.2005.08.047)

    • Search Google Scholar
    • Export Citation
  • 3

    Thambiah SC, Yeap SS. Osteoporosis in South-East Asian countries. Clinical Biochemist: Reviews 2020 41 2940. (https://doi.org/10.33176/AACB-19-00034)

    • Search Google Scholar
    • Export Citation
  • 4

    Sidlauskas KM, Sutton EE, Biddle MA. Osteoporosis in men: epidemiology and treatment with denosumab. Clinical Interventions in Aging 2014 9 593601. (https://doi.org/10.2147/CIA.S51940)

    • Search Google Scholar
    • Export Citation
  • 5

    Harvey N, Dennison E, Cooper C. Osteoporosis: impact on health and economics. Nature Reviews: Rheumatology 2010 6 99105. (https://doi.org/10.1038/nrrheum.2009.260)

    • Search Google Scholar
    • Export Citation
  • 6

    Armas LA, Recker RR. Pathophysiology of osteoporosis: new mechanistic insights. Endocrinology and Metabolism Clinics of North America 2012 41 475486. (https://doi.org/10.1016/j.ecl.2012.04.006)

    • Search Google Scholar
    • Export Citation
  • 7

    Wimalawansa SJ. Vitamin D deficiency: effects on oxidative stress, epigenetics, gene regulation, and aging. Biology 2019 8 E30. (https://doi.org/10.3390/biology8020030)

    • Search Google Scholar
    • Export Citation
  • 8

    Sassi F, Tamone C, D’Amelio P. Vitamin D: nutrient, hormone, and immunomodulator. Nutrients 2018 10 E1656. (https://doi.org/10.3390/nu10111656)

    • Search Google Scholar
    • Export Citation
  • 9

    Haussler MR, Whitfield GK, Kaneko I, Haussler CA, Hsieh D, Hsieh JC, Jurutka PW. Molecular mechanisms of vitamin D action. Calcified Tissue International 2013 92 7798. (https://doi.org/10.1007/s00223-012-9619-0)

    • Search Google Scholar
    • Export Citation
  • 10

    Goltzman D. Functions of vitamin D in bone. Histochemistry and Cell Biology 2018 149 305312. (https://doi.org/10.1007/s00418-018-1648-y)

    • Search Google Scholar
    • Export Citation
  • 11

    Moher D, Liberati A, Tetzlaff J, Altman DG & PRISMA Group. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. Annals of Internal Medicine 2009 151 264269. (https://doi.org/10.7326/0003-4819-151-4-200908180-00135)

    • Search Google Scholar
    • Export Citation
  • 12

    Stang A. Critical evaluation of the Newcastle-Ottawa scale for the assessment of the quality of nonrandomized studies in meta-analyses. European Journal of Epidemiology 2010 25 603605. (https://doi.org/10.1007/s10654-010-9491-z)

    • Search Google Scholar
    • Export Citation
  • 13

    Uitterlinden AG, Fang Y, Van Meurs JB, Pols HA, Van Leeuwen JP. Genetics and biology of vitamin D receptor polymorphisms. Gene 2004 338 143156. (https://doi.org/10.1016/j.gene.2004.05.014)

    • Search Google Scholar
    • Export Citation
  • 14

    Valdivielso JM, Fernandez E. Vitamin D receptor polymorphisms and diseases. Clinica Chimica Acta: International Journal of Clinical Chemistry 2006 371 112. (https://doi.org/10.1016/j.cca.2006.02.016)

    • Search Google Scholar
    • Export Citation
  • 15

    Zhang L, Yin X, Wang J, Xu D, Wang Y, Yang J, Tao Y, Zhang S, Feng X, Yan C. Associations between VDR gene polymorphisms and osteoporosis risk and bone mineral density in postmenopausal women: a systematic review and meta-analysis. Scientific Reports 2018 8 981. (https://doi.org/10.1038/s41598-017-18670-7)

    • Search Google Scholar
    • Export Citation
  • 16

    Zhang YJ, Zhang L, Chen SY, Yang GJ, Huang XL, Duan Y, Yang LJ, Ye DQ, Wang J. Association between VDR polymorphisms and multiple sclerosis: systematic review and updated meta-analysis of case-control studies. Neurological Sciences 2018 39 225234. (https://doi.org/10.1007/s10072-017-3175-3)

    • Search Google Scholar
    • Export Citation
  • 17

    Zhang JZ, Wang M, Ding Y, Gao F, Feng YY, Yakeya B, Wang P, Wu XJ, Hu FX, Xian J, Vitamin D receptor gene polymorphism, serum 25-hydroxyvitamin D levels, and risk of vitiligo: a meta-analysis. Medicine 2018 97 e11506. (https://doi.org/10.1097/MD.0000000000011506)

    • Search Google Scholar
    • Export Citation
  • 18

    Mashhadiabbas F, Neamatzadeh H, Nasiri R, Foroughi E, Farahnak S, Piroozmand P, Mazaheri M, Zare-Shehneh M. Association of vitamin D receptor BsmI, TaqI, FokI, and ApaI polymorphisms with susceptibility of chronic periodontitis: a systematic review and meta-analysis based on 38 case -control studies. Dental Research Journal 2018 15 155165.

    • Search Google Scholar
    • Export Citation