Genetically Low Vitamin D Levels, Bone Mineral Density, and Bone Metabolism Markers: a Mendelian Randomisation Study
Genetically Low Vitamin D Levels, Bone Mineral Density, and Bone Metabolism Markers: a Mendelian Randomisation Study
OPEN Low serum 25-hydroxyvitamin D (25OHD) is associated with osteoporosis and osteoporotic fracture, but it remains uncertain whether these associations are causal. We conducted a Mendelian randomization (MR) study of 1,824 postmenopausal Chinese women to examine whether the detected associations between serum 25OHD and bone mineral density (BMD) and bone metabolism markers were causal. In observational analyses, total serum 25OHD was positively associated with BMD at lumbar spine (P = 0.003), femoral neck (P = 0.006) and total hip (P = 0.005), and was inversely associated with intact parathyroid hormone (PTH) (P = 8.18E-09) and procollagen type 1 N-terminal propeptide (P1NP) (P = 0.020). By contract, the associations of bioavailable and free 25OHD with all tested outcomes were negligible (all P > 0.05). The use of four single nucleotide polymorphisms, GC-rs2282679, NADSYN1-rs12785878, CYP2R1-rs10741657 and CYP24A1-rs6013897, as candidate instrumental variables in MR analyses showed that none of the two stage least squares models provided evidence for associations between serum 25OHD and either BMD or bone metabolism markers (all P > 0.05). We suggest that after controlling for unidentified confounding factors in MR analyses, the associations between genetically low serum 25OHD and BMD and bone metabolism markers are unlikely to be causal.
Vitamin D insufficiency is an increasingly prevalent public health issue worldwide and is regarded as one of the
foremost modifiable risk factors for a several common diseases and conditions, including osteoporosis and
osteoporotic fracture1?3. Serum 25-hydroxyvitamin D (25OHD) levels are the established clinical marker of vitamin
D status. The findings from most observational studies have suggested associations between low serum 25OHD
levels and secondary hyperparathyroidism, elevated levels of bone turnover markers and excessive bone loss1,3.
Moreover, bone mineral density (BMD) and bone turnover markers are among the robust predictors of
osteoporotic fracture risk4,5. However, meta-analyses of randomized controlled trials have provided little evidence that
vitamin D supplements alone provide benefits of BMD improvement or fracture prevention6?8, thus suggesting
vitamin D may not have a causal effect on bone health9,10. Notably, these findings require cautious interpretation.
Observational investigations are susceptible to potential confounding factors and reverse causality11,12, whereas
the dosage of vitamin D supplements, research duration, baseline vitamin D levels of the study population, and
genetic factors inevitably affect the intervention response7,8,13. Therefore, whether these associations are causal
An alternative approach to a causality study is a Mendelian randomization (MR) analysis, an increasingly
used method that draws a causal inference from the observational data. MR analyses assess genetic variants
predicted risk factors to screen their causal effects on the outcomes of interest12,14. Because the genetic variants are
randomly assorted at conception, similarly to randomized trials, the MR approach is largely free of both
residual confounding factors and reverse causation. Moreover, the MR approach has successfully clarified the causal
effect of low 25OHD levels on the increased risk of type 2 diabetes15 and of high urate levels on a reduced rate
of Parkinson disease progression16, and has confirmed that genetically low urate levels and decreased BMD are
not causally related17. Our previous study of 2,897 healthy Chinese subjects has suggested that the GC, CYP2R1
and NADSYN1 polymorphisms within the vitamin D metabolic pathway are genetic determinants of variations
in serum 25OHD levels18, and these findings have provided a basis for our present MR analyses in a Chinese
In this study, we sought to verify whether serum 25OHD levels had a strong causal effect on bone health by
using the MR approach. First, we examined the observational associations between the total serum 25OHD and
BMD and bone metabolism markers [i.e., intact parathyroid hormone (PTH), Beta-CrossLaps of type I collagen
containing cross-linked C-telopeptide (Beta-CTX) and procollagen type 1 N-terminal propeptide (P1NP)] using
ordinary least squares (OLS) models. Second, we calculated the free and bioavailable (free + albumin bound)
25OHD levels by using Vermeulen formulas based on directly measured values of the total serum 25OHD,
vitamin D binding protein (DBP) and albumin levels19, and determined whether the bioavailable and free 25OHD
levels were more closely associated with BMD and bone metabolism markers. Then, we determined whether the
verified observational associations were causal by using different two stage least squares (TSLS) models based on
the assumptions of the MR analyses12,14,20.
General characteristics of the study population. Of the 2,013 participants, 158 subjects (7.9%) were
excluded because they had diseases or took medications that might affect bone or vitamin D metabolism. We
excluded an additional 31 subjects (1.5%) with abnormal plasma glucose, serum calcium, or phosphorus levels
and those with abnormal renal or liver function. 1,824 participants were included in our study, and their general
characteristics are summarized in Table?1. The average age was 65.5 (SD 8.9) years, the average BMI was 23.5
(SD 3.3) kg/m2, and the median (25th and 75th percentiles) total serum 25OHD level was 18.3 (13.3, 23.8) ng/mL.
Observational relationships between serum 25OHD and clinical traits. OLS regression analyses
provided strong evidence of observational associations between the total serum 25OHD levels and BMD at
different sites (i.e., lumbar spine at L1-L4, femoral neck, and total hip); these associations remained robust after
adjusting for age, season and BMI (Table?2). The absolute changes in BMD at L1-L4, femoral neck, and total hip
were 0.047 g/cm2 (P = 0.003), 0.031 g/cm2 (P = 0.006) and 0.034 g/cm2 (P = 0.005) per unit increase in adjusted
total serum 25OHD, respectively. Subjects with lower total 25OHD levels were shown to have significantly higher
PTH values (Beta = ?0.103, P = 8.18E-09) and P1NP values (Beta = ?0.088, P = 0.020) independent of age,
season and BMI. However, no significant associations were observed between the serum levels of bioavailable or free
25OHD and BMD, PTH, Beta-CTX or P1NP (all P > 0.05) after controlling for age, season, and BMI in the OLS
regression analyses (Supplementary Table 1). We conducted ANOVA tests for normal data and Kruskal-Wallis
tests for non-normal data in the subsequent analyses to address possible bias resulting from the multicollinearity
of the present analyses. However, we still did not identify any significant associations between the serum levels of
bioavailable or free 25OHD and the tested outcomes (Supplementary Tables 2 and 3).
Associations between SNPs with total serum 25OHD levels, clinical traits and potential
confounding factors. Table?3 presents the basic information pertaining to the SNPs. The minor allele
frequencies (MAFs) of the 10 SNPs selected in the present study were similar to the HapMap-CHB reference data,
and none of the SNPs failed the quality control checks. Four SNPs (GC-rs2282679, NADSYN1-rs12785878,
CYP2R1-rs10741657 and CYP24A1-rs6013897) were considered as candidate instrumental variables (IVs)
by default on the basis of their repeatable genome-wide significant associations with the 25OHD levels from
genome-wide association study (GWAS) data21. Because high linkage disequilibrium was identified in our study
between GC-rs4588, NADSYN1-rs2276360 and CYP2R1-rs2060793 and candidate IVs GC-rs2282679 (r2 = 0.95),
NADSYN1-rs12785878 (r2 = 0.99) and CYP2R1-rs10741657 (r2 = 0.99), respectively, we selected the last 3 SNPs
as proxies in the subsequent MR analyses. For the remaining 3 SNPs, the minor alleles of both GC-rs1155563
and CYP2R1-rs10766197 were significantly related to decreased total serum 25OHD levels after adjusting for
age, BMI and season of blood draw, but these fell below the significance threshold after False Discovery Rate
(FDR) correction (Table?4). Therefore, these 3 SNPs were excluded from the subsequent analyses. We then
examined whether the 4 selected IVs were associated with tested outcomes (i.e., BMD, PTH and P1NP) or potential
confounding factors (i.e., age, BMI, Ca, P, Cr and BUN), and no significant evidence of associations was
identified after FDR correction (Supplementary Tables 4 and 5). Therefore, 4 SNPs, including rs2282679, rs12785878,
rs10741657 and rs6013897, were considered to be the genetic IVs in the subsequent MR analyses.
Evaluation of causal associations between 25OHD levels and clinical outcomes. We used both
single instrument model and multiple instruments model in the present study. According to previous GWAS
data21, GC-rs2282679 exhibited the strongest association with the 25OHD levels and was used as the IV in the
single instrument model. The multiple instruments models, including unweighted allele scores model and weighted
allele scores model, were performed on the basis of the sum of the number of effect alleles of the 4 candidate IVs.
The variability in the log-transformed total serum 25OHD levels explained by each IV ranged from 0.7% to 1.1%
(Table?5). The lowest relative TSLS/OLS bias ratio was observed in both the single instrument model and the
weighted allele scores model. The F-statistic values, which indicated the instrument strength of the MR analyses,
are shown in Table?5. As a rule of thumb, all 3 MR analyses models were considered to have strong instruments for
the F-statistic values obtained from first stage regression analyses with values more than 10; thus, all 3 MR models
were used to assess the causal association.
As presented in Table?6, using the SNP (rs2282679) for the single instrument analyses, we found no significant
evidence of a causal effect of the total serum 25OHD levels on BMD, PTH or P1NP (all P > 0.05), with SE values
ranging from 0.056 to 0.139. The Hausman Test indicated significant differences in the IV estimates compared
with the OLS estimates of the effect of the 25OHD levels on the 4 tested outcomes after FDR correction (all
P < 0.05). We then performed MR analyses based on the unweighted and weighted allele scores models, and the
IV estimates from the weighted allele scores models were similar to the estimates from the OLS models and single
instrument models. Notably, the inclusion of the weighted allele score as a genetic instrument in the MR analyses
led to a considerable decrease in the SE values compared with those of the single instrument model, which
corresponded to considerably increased statistical power14. However, using multiple instruments models described
above, we still were unable to verify that the total serum 25OHD levels were causally associated with either BMD
or bone metabolism markers. Consistently with the results from the single instrument models, the estimates of
the effect of the total serum 25OHD levels on BMD at all sites and PTH from both the unweighted allele scores
models and the weighted allele scores models were significantly different from the estimates from OLS models
even after FDR correction (all P < 0.05).
In this study of 1,824 postmenopausal Chinese women, our three key findings were as follows: 1) the total serum
25OHD levels were positively associated with the BMD at L1-L4, femoral neck, and total hip and were inversely
associated with the serum PTH and P1NP levels; 2) the serum levels of bioavailable or free 25OHD were not
associated with any of the tested BMD sites or bone metabolism markers; and 3) the MR analyses showed that
genetically low serum 25OHD levels were not associated with decreased BMD or with elevated serum PTH or
P1NP levels. These results suggested that the total serum 25OHD levels were unlikely to have a robust causal effect
on BMD or bone metabolism markers, but could serve as a marker thereof.
Our observational analyses showed strong associations between the total serum 25OHD levels and BMD,
serum PTH and P1NP; these associations were independent of age, season and BMI. These results were consistent
with the results of several22,23, but not all24?26, previous studies. A significant relationship between the 25OHD
levels and the total hip BMD has also been observed in the NHANESIII study, which included 13,432 subjects22.
Saliba and colleagues have revealed an inverse correlation between the serum 25OHD levels and the serum PTH
levels23. Moreover, the Peking Vertebral Fracture study conducted by Zhao et al. has shown that serum 25OHD
levels are negatively correlated with P1NP levels in postmenopausal Chinese women, but the authors were unable
to identify any associations between the 25OHD levels and BMD at any sites24. Garnero et al. have observed only
a modest correlation between the serum 25OHD levels and intact PTH levels, but not the total hip BMD or bone
turnover markers, in home-dwelling, healthy postmenopausal women25. Additionally, Nimitphong et al. have
found that only subjects with certain DBP genotypes show a positive association between serum 25OHD levels
with BMD26. According to the free hormone hypothesis, which states that only hormones released from binding
proteins (i.e., DBP) are able to act on target cells to exert biological effect27, the bioavailable and free 25OHD levels
are thought to represent the serum 25OHD levels that are available for biological activity. This hypothesis might
Lumar 1?4 BMD (g/cm2)
partially explain the conflicting findings drawn from different association analyses of the total serum 25OHD
levels and bone health. In a cross-sectional study including 49 healthy, young subjects, Powe and colleagues have
established a closer relationship between the free and bioavailable 25OHD levels and BMD than between the
total 25OHD levels and BMD28. In addition, the finding from a study of 265 postmenopausal women has also
indicated that the serum levels of free and bioavailable 25OHD might provide more information about vitamin
D status in relation to BMD29. In a community-based study of African and Caucasian Americans, both groups
have been found to show similar PTH levels, owing to their similar levels of bioavailable 25OHD, though African
Americans commonly have lower total 25OHD levels30. These studies suggest that, like other serum hormone
carrier proteins, circulating DBP might play an important regulatory role in the biologic action of human vitamin D.
However, Dastani and colleagues have discovered that the biological effect of vitamin D on PTH levels is mainly
independent of DBP in a large vitamin D-sufficient cohort31. Moreover, an 8-week randomized controlled trial
has also indicated that for individuals with total 25OHD levels <20 ng/mL, the serum DBP levels do not influence
the effect of vitamin D supplements on either serum PTH or calcium levels32. Although the levels of bioavailable
25OHD in both the treatment and placebo groups showed significant associations with the PTH levels, these
relationships were weaker than the relationship between PTH and total 25OHD32. However, in our study, we did
not identify any significant observational associations between bioavailable or free 25OHD and either BMD or
bone metabolism markers. These conflicting findings might be due to the different study populations, which had
various ages, genders, genetic differences, and vitamin D nutrition. Our study enrolled postmenopausal women,
whereas Powe et al. studied young males and females28. The study population recruited by Johnsen et al. was
limited to postmenopausal women with low BMD, and the significant associations between the 25OHD levels
and BMD were not fully consistent at all sites29. Thus, further investigation in a larger study population may be
required. Additionally, different DBP kits might also lead to different results. The DBP kit used in our study was
identical to the kit used by most of the studies mentioned above28,29,32. In addition, it has been confirmed that
the serum levels of bioavailable and free 25OHD calculated using the estimated formulas are highly consistent
with the measured values30. Therefore, the differences in the findings might not primarily result from the DBP
detection method or the formulas used to calculate the 25OHD levels. Alternatively, it was possible that the
nominally significant relationships between total serum 25OHD and either BMD or bone metabolism markers might
largely be ascribed to some unidentified confounding factors in the observational analyses. Moreover, the serum
25OHD levels might have only mild associations with BMD and with bone metabolism markers in
postmenopausal Chinese women25.
Our MR analyses provided no evidence of a causal role of genetically low serum 25OHD levels in either
decreased BMD or elevated serum PTH or P1NP, though our study might not have had sufficient power to detect
very small effects. The Hausman Test revealed significant differences between the OLS estimates and TSLS
estimates of the 25OHD levels in relation to BMD and PTH in the three different MR models, thus strongly
suggesting the existence of unmeasured confounding factors in the observational analyses and providing further support
for the conclusion drawn from the MR analyses12,20. Our negative results for a causal effect of the serum 25OHD
levels on BMD were in accord with the findings derived from meta-analyses of the validity of using vitamin D
supplements alone to improve bone health6,8. Reid and co-workers have revealed only a small benefit at the
femoral neck from vitamin D supplements, with heterogeneity among trials, and have ascribed this localized effect to
chance after ruling out the possibility of a cortical-specific effect8. Furthermore, the pooled data from vitamin D
fracture trials in the US and Europe have indicated that the administration of a daily dose of 400?800 IU vitamin
D alone is ineffective at preventing fracture6. Our present findings, as well as the conclusions drawn from the
intervention trials described above, indicated the possibility of a lack of causal evidence showing that vitamin D
alone can improve BMD or prevent fracture. According to previous studies, the maximum suppression of serum
PTH has been used to define an optimal serum 25OHD levels33,34. In addition, it has been verified that a persistent
25OHD deficiency would maximally stimulate the parathyroid glands and thus lead to secondary
hyperparathyroidism, but two-thirds of study participants lacked relevant changes35. The precise mechanism underlying the
seemingly impaired PTH response is not readily apparent. PTH is among the major hormones responsible for
serum calcium and phosphorus homeostasis1,3. Parathyroid glands are abundant in vitamin D receptors (VDRs)
and are thus established as target tissues for vitamin D action36. 1,25-dihydroxyvitamin D [1,25(OH)2D], which
is derived from 25OHD by 1-hydroxylation in the kidney, is the metabolically active form on behalf of vitamin
D activity37. The inhibitory effect of 1,25(OH)2D on PTH gene transcription and parathyroid cell hyperplasia has
been clarified both in vitro and in vivo studies36,38. Moreover, it has been shown that decreased calcium
absorption, which accounts for elevated serum PTH levels, is precisely regulated by 1,25(OH)2D37,39. Notably, serum
25OHD levels do not exhibit a direct causal effect on calcium absorption, but might exert a permissive action
for regulating absorption through 1,25(OH)2D, thus regulating the PTH levels34,37. These findings may provide
possible explanation for the lack of a causal association between serum 25OHD and PTH in the MR analyses.
To our knowledge, this is the first study to investigate the causal relationships between the serum 25OHD
and BMD and bone metabolism markers by using the MR approach while gaining insights into the associations
of bioavailable and free 25OHD with the clinical outcomes listed above. The MR analyses assessed the effects of
the genetic variants predicted lifelong low serum 25OHD levels on the clinical outcomes of interest. Because the
genetic variants were randomly distributed at meiosis and remained unchanged throughout life, the MR analyses
were free of confounding factors and reverse causality, thus strengthening the causal conclusions. We included
tagging SNPs as potential genetic IVs in this study, thus maximizing the amount of 25OHD variability explained
by each SNP11,14 and increasing the statistical power to evaluate the causal associations between genetically low
serum 25OHD levels and the clinical outcomes. Because the estimates obtained from the TSLS models and the
OLS models were comparable, there was little possibility of violating the assumptions of the MR analyses12.
Notably, among the TSLS models, the weighted allele scores model using external weights for multiple genetic
variants showed the lowest SE values and corresponded to the greatest power. Although no recognized formulas
for calculating sample sizes were available for MR analyses using multiple genetic variants, the results showed that
a 20% decrease in SE would lead to a 56% increase in the practicable sample size14. However, we acknowledge that
the study has several potential limitations. Although the lack of causal associations between the serum 25OHD
levels and the tested clinical outcomes were confirmed in three different MR analyses models, the models had
a limited ability to exclude very small effects. In addition, it was possible that the null findings were caused by
biological adaptations of the genetic variants; in other words, the phenotypic influence of these SNPs might have
been buffered during development40. An MR analysis depends on the assumptions that the genetic variants are
specially associated with the risk factor (i.e., the serum 25OHD levels) and then act on the outcomes. However, a
violation of these assumptions occurs when the genetic variants are pleiotropic or are in linkage disequilibrium
with unknown functional variants related to the tested outcomes12,14. In addition, because the MR analyses were
based on assumed linear relationships between the intermediate variables and the outcomes, it was not possible
to estimate the different causal effects arising from the different physiological ranges of the serum 25OHD levels.
Because the association analyses for the serum levels of bioavailable and free 25OHD were based on values
calculated with established formulas, a confirmation of our findings using directly measured values would be ideal.
Finally, our study population was limited to postmenopausal Chinese women, and the findings drawn from this
study might not be appropriate for other population groups of different ages, sexes, or ethnic backgrounds.
In conclusion, our study affirmed the observational associations between the total serum 25OHD levels with BMD
and serum PTH and P1NP levels, but provided no evidence of relationships between the serum levels of bioavailable
or free 25OHD and the above clinical traits. The MR analyses indicated the existence of unrecognized confounding
factors in the observational analyses and showed that genetically low serum 25OHD levels were not casually
associated with any of the tested outcomes in postmenopausal Chinese women. We suggest that vitamin D, like other
micronutrients, is necessary for and serve as a good marker of bone health, but it might not be a direct causative factor.
Study subjects. This cross-sectional study involved 2,013 genetically unrelated Han Chinese women who
had been postmenopausal for more than 1 year, were living in Shanghai, and were recruited from the Department
of Osteoporosis and Bone Diseases Outpatient Clinic of Shanghai Jiao Tong University Affiliated Sixth People?s
Hospital. A standardized questionnaire was used to collect information about the date of birth, age at menarche
and amenorrhea, life style, medical history and medication use. Participants with any disease or medication
treatment known to affect bone or vitamin D metabolism were excluded. The selected subjects then attended a
medical examination and provided blood after overnight fasting to determine the blood counts, fasting plasma
glucose levels, serum calcium levels, serum phosphorus levels, serum albumin levels, and liver and renal
function. Subjects with normal results for the physical and biochemical examinations were considered eligible for
our study. The study was approved by the Ethics Committee of the Shanghai Jiao Tong University Affiliated Sixth
People?s Hospital, and written informed consent was obtained from each participant. All experiments were
performed in accordance with the approved guidelines and regulations.
BMD measurements. BMD values (g/cm2) of the spine at L1-L4, the left femoral neck, and the total hip
were measured using dual-energy X-ray absorptiometry (DXA) on a Lunar Prodigy GE densitometer (Lunar
Corp, Madison, WI, USA). Subjects with a history of left femur fracture or surgery received a right femoral neck
measurement instead. We calibrated the machine daily through triplicate measurements of the same 15
individuals; the coefficient of variability (CV) values of the BMD at L1-L4, femoral neck, and total hip were 1.39%, 2.22%
and 0.70%, respectively41. The long-term reproducibility of the DXA data during the study was 99.55%, which was
determined by weekly repeated phantom measurements. BMI (kg/m2) was calculated as the weight divided by the
height squared. All measurements were conducted by the same well-trained technicians throughout the study.
Biochemical assays. Serum samples were collected between 08:00 and 10:00 AM after overnight fasting of
at least 12 hours and then stored at ?80 ?C. The total serum 25OHD and bone metabolism markers (i.e., intact
PTH, Beta-CTX and P1NP) levels were measured using an automated Roche electro-chemiluminescence system
(E170; Roche Diagnostic GmbH, Mannheim, Germany) according to the manufacturer?s protocol and specialized
assay laboratory quality control procedures. The intra- and interassay CVs were 5.7% and 7.3% for 25OHD, 1.4%
and 2.9% for PTH, 2.5% and 3.5% for Beta-CTX, 2.9% and 3.8% for P1NP, respectively41.
Calculation of the serum levels of bioavailable and free 25OHD. The serum DBP levels were
determined using a commercial enzyme-linked immunosorbent assay (Catalogue Number DVDBP0, R&D Systems)
according to the manufacturer?s instructions. The intraassay CV was 5?7% and the interassay CV was 5?8%.
The detectable range could be expanded with an appropriate sample dilution. The bioavailable (non-DBP
fraction) and free 25OHD levels were calculated by using Vermeulen equations20 on the basis of the measured total
serum 25OHD, DBP, and albumin levels and the respective binding affinity constants between DBP, albumin, and
25OHD. The calculated serum 25OHD levels were validated to correlate well with the values that were directly
measured with a competitive radioligand binding assay in several previous studies28,30,31.
SNP selection and genotyping. On the basis on 25OHD GWAS data and previous association studies
in Chinese populations18,21,42, we selected 10 SNPs within the vitamin D metabolic pathway (i.e., GC-rs4588,
GC-rs7041, GC-rs2282679, GC-rs1155563, NADSYN1-rs2276360, NADSYN1-rs12785878, CYP2R1-rs2060793,
CYP2R1-rs10741657, CYP2R1-rs10766197 and CYP24A1-rs6013897) as potential IVs for the MR analyses. Blood
samples were collected from all of the participants, and genomic DNA was extracted and purified from peripheral
blood leukocytes by using a QuickGene DNA whole blood kit L by Nucleic Acid Isolation System
(QuickGene610L, FUJI FILM, Japan). Genotyping was performed with an ABI PRISM SNaPshot multiplex kit (Applied
Biosystems), an Mx3000p real-time PCR system (Stratagene), and GeneMapper 4.0 (Applied Biosystems).
Genotype frequencies were estimated on the basis of Hardy-Weinberg Equilibrium (HWE) with a chi-square test
to detect genotyping errors.
Statistical analyses. We assessed the distribution of all continuous variables and excluded the extreme
values [>3.5 standard deviations (SD) from the mean; <1% of all data points]. Descriptive statistics were reported as
the means ? SD for normally distributed data and as medians (25th and 75th percentiles) for non-normally
distributed data. Natural logarithmic transformation was used for the skewed variables to approximate normality for the
subsequent data analyses. The statistical analyses were performed using SPSS version 13.0 (SPSS Inc., Chicago, IL,
USA), PLINK (http://pngu.mgh.harvard.edu/purcell/plink/) was used for SNP quality control filtering and
association tests, and the linkage disequilibrium analyses among selected SNPs were conducted with Haploview 4.2
(http://www.broadinstitute.org/scientific-community/science/programs/medical-and-population-genetics/haploview/haploview). The null hypothesis was tested using alpha= 0.05 (two-sided). The FDR method was applied
to control the family-wise error rate when multiple tests were performed.
First, OLS regression models were performed to examine the observational associations between the serum
levels of total, bioavailable and free 25OHD with the tested clinical traits after adjusting for age, BMI and
season, and to obtain the estimates of the effect of the serum 25OHD levels on the tested variables in each model
(Fig.?1A). Because of the possibility of existence of multicollinearity in linear regression models that may have
obscured the significant associations, we divided the data into four groups according to the quartiles of the serum
25OHD levels and assessed the differences in the clinical variables with an ANOVA test or a Kruskal-Wallis test,
as appropriate. The verified observational associations were further analysed using TSLS models to determine
whether the genetically predicted 25OHD levels were casually associated with the tested clinical traits.
Second, as components of the MR analyses, we examined the following IV assumptions14: 1) the genetic
variants were associated with exposure; 2) the genetic variants were independent of unmeasured confounding
factors; and 3) the genetic variants were related to the outcomes only via their associations with the intermediate
phenotype (i.e., 25OHD levels) (Fig.?1B). SNPs that passed the quality control checks (P > 0.05 for HWE test;
genotyping rate >90%) were included in the subsequent analyses. The first-stage regression F-statistic and
coefficient of determination, R2, were used to assess the strength of the SNPs as possible IVs. The relative bias of the
TSLS regression estimator compared with the OLS regression estimator was calculated by using verified
equations based on the F-statistic values14. We performed linear regression analyses to evaluate the associations of
the potential IVs with the underlying confounding factors (i.e., age, BMI, Ca, P, Cr and BUN) and to exclude the
direct associations of the IVs with the clinical outcomes (i.e., BMD and bone metabolism markers).
Third, we constructed different MR models based on selected IVs, including single instrument model,
unweighted allele scores model, and weighted allele scores model, to examine the effects of the different genetic
markers determined serum 25OHD levels on the tested traits (Fig.?1C). These models were intended to provide
a more promising method for our causal studies while addressing possible issues arising from the violated IV
assumptions. In the MR analyses, genotypes were coded as 0, 1, and 2 across the number of effect alleles, namely
vitamin D lowering alleles. The allele score was calculated by counting the number of candidate IVs effect alleles.
The analyses of the associations of the genetically determined serum 25OHD levels with the tested outcomes
were estimated by using two stage regression models. The first stage generated the genetically predicted 25OHD
values by using a linear regression model of the adjusted 25OHD levels, based on the estimates for the genetic
IVs. The second stage was performed in a multivariable linear regression model of the clinical outcomes versus
the predicted 25OHD values after controlling for the age, BMI, season, and estimated residuals. For the weighted
allele scores model, the weights were external to our present study and were taken from the overall evidence from
previous studies to decrease possible bias11,42. The Hausman Test was used to check for the endogeneity by
comparing the TSLS estimates with the OLS estimates14.
The study was supported by the National Basic Research Program of China (2014CB942903), the National
Natural Science Foundation of China (81170803 and 81370978), the Frontier Technology Joint Research Program
of the Shanghai municipal hospitals (SHDC 12013115), Shanghai Leading Talents Award (051), the Science and
Technology Commission of Chongqing municipality (CSTC2013jcyjC00009), the Science and Technology
Commission of Shanghai municipality (14JC1405000). We thank all of the participants for their cooperation.
Meanwhile, we also thank the statistical help from Yi Wang, PHD (Fudan University, Shanghai China).
Z.-L.Z. was involved in research design and study supervision, S.-S.L., L.-H.G., J.-W.H. and W.-Z.F. carried out
experiments, L.-H.G., X.-Y.Z., Y.-J.L. and Y.-Q.H. performed data collection, S.-S.L. drafted the manuscript,
Z.-L.Z. and S.-S.L. revised the manuscript and took responsibility for the integrity of the data interpretation. All
authors reviewed the manuscript.
Supplementary information accompanies this paper at http://www.nature.com/srep
Competing financial interests: The authors declare no competing financial interests.
How to cite this article: Li, S.-S. et al. Genetically Low Vitamin D Levels, Bone Mineral Density, and Bone
Metabolism Markers: a Mendelian Randomisation Study. Sci. Rep. 6, 33202; doi: 10.1038/srep33202 (2016).
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