Abstract
Context
Serum 25-hydroxyvitamin D (25(OH)D) is the current marker of vitamin D adequacy, but its relationship with bone health has been inconsistent. The ratio of 24,25-dihydroxyvitamin D3 to 25(OH)D3 (vitamin D metabolite ratio or VMR) is a marker of vitamin D that has been associated with longitudinal changes in bone mineral density (BMD) and fracture risk.
Objective
High-resolution peripheral quantitative computed tomography (HR-pQCT) provides information on bone health beyond standard dual-energy x-ray absorptiometry, in that it measures volumetric BMD (vBMD) as well bone strength. The relationship of the VMR with vBMD and bone strength remains unknown.
Methods
We evaluated the associations of the VMR and 25(OH)D3 with vBMD and bone strength in the distal radius and tibia, assessed by HR-pQCT in 545 older men participating in the Osteoporotic Fractures in Men (MrOS) Study. Primary outcomes were vBMD and estimated failure load (EFL, a marker of bone strength) at the distal radius and tibia.
Results
The mean age was 84 ± 4 years, 88.3% were White, and 32% had an estimated glomerular filtration rate <60 mL/min/1.73 m2. In adjusted models, each twofold higher VMR was associated with a 9% (3%, 16%) higher total vBMD and a 13% (5%, 21%) higher EFL at the distal radius. Results were similar at the distal tibia. 25(OH)D3 concentrations were not associated with any of the studied outcomes.
Conclusion
Among older men, a higher VMR was associated with greater vBMD and bone strength while 25(OH)D3 was not. The VMR may serve as a valuable marker of skeletal health in older men.
Keywords: DXA, vit osteoporosis, fracture prevention, HR-pQCT
Vitamin D deficiency, as defined by low 25-hydroxyvitamin D (25(OH)D) concentrations, is common in the United States (1) and severe vitamin D deficiency is known to lead to osteomalacia and rickets, which lead to fractures (2, 3). However, 25(OH)D is a relatively inactive vitamin D metabolite, and recent observational studies and randomized trials suggest that 25(OH)D may be a poor biomarker of vitamin D status and bone health, although these studies often focused on persons who were not considered vitamin D deficient based on standard criteria (4-7). Thus, novel markers of vitamin D status are needed for persons at risk of bone disease.
Vitamin D catabolism from 25(OH)D to 24,25-dihydroxyvitamin D (24,25(OH)2D) is stimulated by the active vitamin D metabolite, 1,25-dihydroxyvitamin D (1,25(OH)2D), binding to the vitamin D receptor (VDR) and preventing tissue level vitamin D toxicity (8). Thus, by virtue of this feedback system, 24,25(OH)2D has been suggested as a surrogate biomarker of VDR activity. Unfortunately, accurate interpretation of 24,25(OH)2D and 25(OH)D requires knowledge of vitamin D binding protein (VDBP) concentrations, as 85% of all vitamin D metabolites are bound to VDBP (9, 10). Free 25(OH)D represents a potential option as a vitamin D biomarker, although it is difficult to measure due to low circulating concentrations, and its relationship with bone and cardiovascular outcomes remain unclear (11, 12). The ratio of 24,25(OH)2D3 to 25(OH)D3 (vitamin D metabolite ratio or VMR) is a potential biomarker of vitamin D status that is theoretically increased by 1,25(OH)2D binding to the VDR leading to increased VDR activity. We recently demonstrated that the VMR is not associated with VDBP concentrations, and thus is independent of variability in VDBP (10, 13). We have also demonstrated that the VMR, but not 25(OH)D, is associated with incident hip fracture, longitudinal changes in areal bone mineral density (BMD), and death in older adults (4, 7, 14). However, the relationship of the VMR with volumetric bone mineral density (vBMD) as well as bone microarchitecture has not been studied. Additionally, the association of the VMR with bone metrics has not been well studied in men over the age of 80 years.
Most fractures in older adults occur in persons without osteoporosis determined by areal BMD (15). This may be in part due to areal BMD providing limited information, as it captures only bone density but not volume and does not provide information on bone microarchitecture in the cortical vs trabecular space (16). High-resolution peripheral quantitative computed-tomography (HR-pQCT) allows for bone imaging that addresses both of these deficiencies. Additionally, it can provide information on the estimated force needed to cause fracture (estimated failure load [EFL]). Here, we evaluate the relationship between vitamin D metabolites (including 25(OH)D3, 24,25(OH)2D, 1,25(OH)2D) and the VMR with volumetric bone density measurements performed with HR-pQCT among a subcohort of community-living older men participating in the Osteoporotic Fractures in Men (MrOS) Study. Based on our previous studies, a priori, we hypothesized that a higher VMR would be associated with higher vBMD, EFL, and bone microarchitecture in older men, while 25(OH)D3 would not be associated with these measures.
Materials and Methods
Study Population
The MrOS study is a longitudinal cohort designed to evaluate risk factors for osteoporosis and fractures in older men, as reported previously (17, 18). Between March 2000 and April 2002, 5994 men aged 65 or older were recruited from 6 centers across the United States (Birmingham, Alabama; Minneapolis, Minnesota; Palo Alto, California; Monongahela Valley near Pittsburgh, Pennsylvania; Portland, Oregon; and San Diego, California). The study was approved by the central and/or local institutional review boards and informed consent was obtained from all MrOS participants. In 2014-2016, 2786 survivors were contacted to participate in the “Visit 4” (Year 14) clinic visit. Of these, 362 refused participation, 583 completed questionnaires only, and 1841 completed questionnaires and at least part of the clinic visit. Of these 1841, 40 did not have a HR-pQCT scan, and 12 had a scan performed but had unusable data for both the distal radius and distal tibia sites, leaving 1789 with a useable HR-pQCT scan and data for 1 or both of the distal sites. Of these participants, 545 comprised a random subcohort of participants that participated in a larger stool microbiome study that also had vitamin D metabolites measured on stored serum samples, including 25(OH)D, 24,25(OH)2D, and 1,25(OH)2D, as described previously (Fig. 1) (19). These 545 participants constituted the analytic sample for this study.
Figure 1.
Study flowchart.
Exposure Variables
Fasting blood samples were collected at Visit 4 and were stored at −80 °C until measurement of vitamin D metabolites in 2017. Our primary exposure variables for this analysis were the VMR, 25(OH)D3, 24,25(OH)2D3, total 1,25(OH)2D (1,25(OH)2D2 plus 1,25(OH)2D3), and the vitamin D activation ratio. The VMR was calculated by dividing serum 24,25(OH)2D3 by serum 25(OH)D3 and then multiplying by 100. The vitamin D activation ratio was calculated by dividing 1,25(OH)2D by 25(OH)D (4, 19). The concentration of each metabolite was quantified using liquid chromatography coupled to tandem mass spectrometry at University Hospital Leuven/Katholieke Universiteit, Leuven, Belgium, as described previously (19). The coefficient of variability for 25(OH)D3 was 6.7% to 11.2%, for 24,25(OH)2D3 it was 4.9%, for 1,25(OH)2D2 it was 10.2%, and for 1,25(OH)2D3 it was 6.4%. As we were not able to detect spectrometric evidence of 24,25(OH)2D2, this metabolite was not included in the analyses.
Outcome Variables
The primary outcome variables for this analysis was total (both cortical and trabecular) vBMD (mg/cm3) of the distal radius and tibia assessed by HR-pQCT (XtremeCT II scanners, Scanco Medical AG, Brüttisellen, Switzerland). Details on methodology of HR-pQCT measurements have been described previously (16). Nondominant sides were scanned unless the participants had a history of fracture, implant, or nonweight-bearing on that side for >6 weeks (6.7% of distal tibia and 7.6% of distal radius scans were nondominant). Interscanner variability was <0.6% for vBMD measurements. HR-pQCT scans were obtained on the same day as the serum samples in 95% of the participants.
We additionally evaluated several other HR-pQCT measurements including a finite analysis EFL (calculation described previously) (16, 20). Briefly, this calculation depends on the distribution of mineralized tissue and is estimated by calculating the reaction force at which 7.5% of the elements exceed a local effective strain of 0.7%. Thus, EFL provides an estimate of the force needed to cause a bone to fracture and thus adds a clinically meaningful component to interpretation of fracture risk. Precision error for EFL was <3.5%. We also evaluated cortical area and thickness, as well as measures of microarchitecture (trabecular area and thickness, cortical area and thickness, cortical porosity) as secondary outcomes. All measurements were evaluated at both the distal tibia and distal radius. Staff involved in the processing of the HR-pQCT data were blinded to results of the vitamin D assays.
Other Measurements
All participants self-reported data on age, race, and smoking status. The clinical site and the season of blood draw were both recorded, as these parameters may influence vitamin D levels due to differing sun exposure. Blood pressure was measured at the study visit. Participant body mass index (BMI) was calculated in kg/m2. The prevalence of diabetes was based on participant self-report. Serum creatinine measurements were performed at the University of Minnesota using standard assays. We estimated glomerular filtration rate (eGFR) using the 2021 chronic kidney disease (CKD) epidemiology collaboration creatinine equation (21). CKD was defined as eGFR < 60 mL/min/1.73 m2 . Physical activity was measured using the Physical Activity Scale for the Elderly (22). All prescription and nonprescription medications used within the preceding 30 days were entered into an electronic database; each medication was matched to its ingredient(s) based on the Iowa Drug Information Service Drug Vocabulary (College of Pharmacy, University of Iowa, Iowa City, IA) (23).
Statistical Methods
The baseline characteristics within the subcohort of participants with vitamin D and HR-pQCT were evaluated across quartiles of the VMR using χ2 tests for homogeneity for categorical data, analysis of variance (normally distributed) or Kruskal–Wallis tests (skewed data) for continuous variables. We then used multivariable linear regression to assess the associations of the VMR, 25(OH)D3, 24,25(OH)2D3, 1,25(OH)2D, and the activation ratio with the HR-pQCT outcomes. To facilitate comparisons, we log transformed all exposure and outcome variables such that beta coefficients are interpretable as percentage higher outcome that is associated with a 100% higher (double) vitamin D metabolite. In companion analyses, we evaluated regression models using quartiles of each predictor variable to assess the functional form of relationships. We developed a sequence of models. Model 1 was adjusted for demographics: age, race (white vs nonwhite), season of blood sampling, and study site. Model 2 served as our primary analytic model and was additionally adjusted for physical activity, BMI, smoking status, self-reported history of diabetes mellitus, systolic blood pressure, diastolic blood pressure, and eGFR. We created multiplicative interaction terms of the predictor variables with race (white vs nonwhite) and eGFR (as a continuous variable) and interactions were tested in fully adjusted models. Identical nested models were utilized for all secondary outcomes listed above. We conducted all of our analyses using SAS version 9.4 (SAS Institute Inc, Cary, NC). P < .05 was considered to be statistically significant for all analyses including multiplicative interaction terms.
Results
The mean age of the 545 individuals was 84 years, 88% were Caucasian, 3% were Black, and 32% had eGFR < 60 mL/min/1.73 m2. The mean ± SD VMR was 9.5 ± 3.0 (ng/mL)/(ng/mL), 25(OH)D3 was 35 ± 13 ng/mL, 24,25(OH)2D3 was 3.4 ± 1.8 ng/mL, 1,25(OH)2D was 58 ± 20 pg./mL, and vitamin D activation ratio was, 1.7 ± 0.7 pg/ng. Baseline characteristics across quartiles of VMR are shown in Table 1. Compared with persons in the lowest VMR quartile, those with higher VMR were on average younger, more active, had a lower BMI, a higher diastolic blood pressure, were more likely to report taking a vitamin D supplement, and were less likely to have diabetes or CKD, and less likely to be prescribed antihypertensives. The VMR was similar across seasons.
Table 1.
Baseline characteristics by vitamin D metabolite ratio quartiles among 545 older men in the MrOS study
| Quartile 1 (n = 136) | Quartile 2 (n = 136) | Quartile 3 (n = 136) | Quartile 4 (n = 137) | P value | |
|---|---|---|---|---|---|
| Range (ng/mL/ng/mL) | 1.8-7.4 | 7.4-9.3 | 9.3-11.3 | 11.3-19.8 | |
| Age (years) ± SD | 85.1 ± 4.0 | 84.5 ± 4.3 | 83.8 ± 4.1 | 83.7 ± 3.8 | .02 |
| Race, non-White, n (%) | 19 (14.0) | 15 (11.0) | 15 (11.0) | 15 (10.9) | .83 |
| Black, n (%) | 9 (6.6) | 2 (1.5) | 3 (2.2) | 5 (3.7) | .10 |
| Site, n (%) | .13 | ||||
| Birmingham | 16 (11.8) | 17 (12.5) | 21 (15.4) | 10 (7.3) | |
| Minneapolis | 23 (16.9) | 19 (14.0) | 16 (11.8) | 26 (19.0) | |
| Palo Alto | 14 (10.3) | 18 (13.2) | 17 (12.5) | 19 (13.9) | |
| Pittsburgh | 32 (23.5) | 26 (19.1) | 17 (12.5) | 15 (10.9) | |
| Portland | 20 (14.7) | 29 (21.3) | 35 (25.7) | 32 (23.4) | |
| San Diego | 31 (22.8) | 27 (19.9) | 30 (22.1) | 35 (25.5) | |
| Season of blood measurement, n (%) | .41 | ||||
| Winter | 3 (2.2) | 1 (0.7) | 1 (0.7) | 1 (0.7) | |
| Spring | 51 (37.5) | 59 (43.4) | 47 (34.6) | 50 (36.5) | |
| Summer | 51 (37.5) | 52 (38.2) | 56 (41.2) | 45 (32.8) | |
| Fall | 31 (22.8) | 24 (17.6) | 32 (23.5) | 41 (29.9) | |
| BMI (kg/m2) ± SD | 27.7 ± 4.0 | 27.1 ± 3.8 | 27.3 ± 3.9 | 25.9 ± 3.1 | <.001 |
| Smoking status, n (%) | .28 | ||||
| Never smoked | 46 (33.8) | 52 (38.2) | 47 (34.6) | 64 (46.7) | |
| Past or current smoker | 84 (61.8) | 77 (56.6) | 80 (58.8) | 69 (50.4) | |
| Missing smoking status data | 6 (4.4) | 7 (5.1) | 9 (6.6) | 4 (2.9) | |
| Diabetesa, n (%) | 28 (20.6) | 26 (19.1) | 15 (11.0) | 11 (8.0) | <.01 |
| Use of any antihypertensivesb, n (%) | 110 (80.9) | 95 (69.9) | 106 (77.9) | 89 (65.0) | .01 |
| eGFR < 60 mL/min/1.73 m2, n (%) | 74 (55.2) | 49 (36.8) | 33 (24.3) | 17 (12.4) | <.001 |
| Reported taking a vitamin D supplement, n (%) | 47 (36.2) | 60 (46.2) | 65 (50.0) | 87 (64.4) | <.001 |
| eGFR (mL/min/1.73 m2, median (IQR) | 57.5 (45.0-69.4) | 68.3 (54.7-79.4) | 69.5 (60.2-80.2) | 77.0 (65.5-85.2) | <.001 |
| PASE physical activity score ± SD | 110.6 ± 65.0 | 115.9 ± 69.9 | 133.9 ± 66.6 | 131.7 ± 62.5 | <.01 |
| Systolic blood pressure (mmHg) ± SD | 126.3 ± 18.9 | 128.9 ± 19.0 | 127.4 ± 16.6 | 128.8 ± 19.0 | .60 |
| Diastolic blood pressure (mmHg) ± SD | 70.0 ± 10.7 | 72.7 ± 11.6 | 70.9 ± 10.4 | 73.6 ± 9.9 | .02 |
Abbreviations: BMI, body mass index; eGFR, estimated glomerular filtration rate; IQR, interquartile range; PASE, Physical Activity Scale for the Elderly.
a Self-reported history of diabetes.
b Beta blockers, ace inhibitors, angiotensin receptor blockers, calcium channel blockers, alpha blockers.
The mean ± SD total vBMD at the distal radius and tibia were 277 ± 62 and 282.1 ± 57 mg/cm3, respectively. In our demographics-adjusted model, a twofold higher VMR was associated with a 7% (95% CI 1%, 13%) higher total vBMD at the distal radius. In the fully adjusted, model the magnitude of association increased to 9% (3%, 16%). Similarly, a twofold higher VMR was associated with a 6% (0.4%, 11%) higher total vBMD at the distal tibia. Again, this association increased in magnitude in our fully adjusted model to 9% (3%, 15%) (Tables 2 and 3). Consistent with these results, a higher VMR was also associated with higher cortical and trabecular vBMD at the distal radius. Although point estimates were similar for the association of VMR with cortical and trabecular vBMD at the distal tibia, these associations approached but did not reach statistical significance. In contrast, we found no association of 25(OH)D3, 24,25(OH)2D3 or the activation ratio with vBMD at either anatomic site. In fully adjusted models, 1,25(OH)2D was inversely associated with distal radius total (−7% [−14%, −1%]) and trabecular vBMD (−8% [−15%, −2%]), respectively. Analyses by quartiles of vitamin D metabolites produced similar results. There were no significant interactions between 25(OH)D or the VMR with eGFR or race, on any of the vBMD parameters (P interaction > 0.10 for all).
Table 2.
Association of vitamin D metabolites and the VMR with distal radius vBMD among 545 participants of the MrOS study
| Total vBMD | Cortical vBMD | Trabecular vBMD | ||||
|---|---|---|---|---|---|---|
| Percent higher vBMD per twofold higher (95% CI) | P | Percent higher vBMD per twofold higher (95% CI) | P | Percent higher vBMD per twofold higher (95% CI) | P | |
| VMR | ||||||
| Model 1 | 7% (1%, 13%) | .030 | 1% (−1%, 4%) | .231 | 4% (−2%, 11%) | .180 |
| Model 2 | 9% (3%, 16%) | .005 | 2% (0.0%, 5%) | .049 | 7% (0.0%, 14%) | .049 |
| 25(OH)D3 | ||||||
| Model 1 | −2% (−8%, 4%) | .498 | 1% (−1%, 3%) | .405 | −6% (−12%, 0.3%) | .063 |
| Model 2 | −2% (−7%, 4%) | .578 | 1% (−1%, 3%) | .494 | −5% (−12%, 1%) | .080 |
| 24,25(OH)2D3 | ||||||
| Model 1 | 2% (−2%, 5%) | .305 | 1% (−0.3%, 2%) | .134 | −1% (−5%, 3%) | .720 |
| Model 2 | 3% (−1%, 6%) | .165 | 1% (−0.1%, 3%) | .070 | 0.0% (−4%, 4%) | .987 |
| 1,25(OH)2D | ||||||
| Model 1 | −7% (−13%, −1%) | .018 | −1% (−3%, 1%) | .318 | −9% (−15%, −3%) | .006 |
| Model 2 | −7% (−14%, −1%) | .029 | −1% (−3%, 1%) | .424 | −8% (−15%, −2%) | .017 |
| Activation ratio | ||||||
| Model 1 | −4% (−9%, 1%) | .115 | −2% (−4%, 0.1%) | .064 | −2% (−8%, 3%) | .436 |
| Model 2 | −4% (−10%, 2%) | .148 | −2% (−4%, 0.4%) | .111 | −1% (−7%, 5%) | .735 |
Model 1 Adjusted for age, race (white vs nonwhite), clinic, and season of blood draw.
Model 2 Additionally adjusted for physical activity (Physical Activity Scale for the Elderly), body mass index, smoking status (missing, ever, ref = never), self-reported history of diabetes mellitus, systolic blood pressure, diastolic blood pressure, estimated glomerular filtration rate.
Abbreviations: VMR, vitamin D metabolite ratio; vBMD, volumetric bone mineral density.
Table 3.
Association of vitamin D metabolites and the VMR with distal tibia vBMD among 545 participants of the MrOS study
| Total vBMD | Cortical vBMD | Trabecular vBMD | ||||
|---|---|---|---|---|---|---|
| Percent higher vBMD per twofold higher (95% CI) | P | Percent higher vBMD per twofold higher (95% CI) | P | Percent higher vBMD per twofold higher (95% CI) | P | |
| VMR | ||||||
| Model 1 | 6% (0.4%, 11%) | .037 | 2% (−1%, 4%) | .264 | 3% (−2%, 9%) | .217 |
| Model 2 | 9% (3%, 15%) | .002 | 3% (−0.1%, 6%) | .055 | 6% (0.0%, 12%) | .050 |
| 25(OH)D3 | ||||||
| Model 1 | −2% (−7%, 4%) | .500 | 1% (−2%, 3%) | .621 | −4% (−9%, 1%) | .146 |
| Model 2 | −1% (−6%, 4%) | .669 | 0.4% (−2%, 3%) | .724 | −3% (−8%, 3%) | .319 |
| 24,25(OH)2D3 | ||||||
| Model 1 | 1% (−2%, 5%) | .383 | 1% (−1%, 3%) | .234 | −0.3% (−4%, 3%) | .851 |
| Model 2 | 3% (−1%, 6%) | .134 | 1% (−1%, 3%) | .125 | 0.8% (−4%, 3%) | .646 |
| 1,25(OH)2D | ||||||
| Model 1 | −4% (−9%, 1%) | .122 | 1% (−2%, 3%) | .702 | −7% (−12%, −2%) | .013 |
| Model 2 | −3% (−8%, 3%) | .369 | 1% (−2%, 4%) | .486 | −5% (−11%, 1%) | .074 |
| Activation ratio | ||||||
| Model 1 | −1% (−6%, 3%) | .580 | −0.0% (−2%, 2%) | .969 | −2% (−6%, 3%) | .474 |
| Model 2 | −0.2% (−5%, 5%) | .933 | 1% (−2%, 0.3%) | .711 | −1% (−6%, 5%) | .749 |
Model 1 adjusted for age, race (white vs nonwhite), clinic, and season of blood draw.
Model 2 additionally adjusted for physical activity (Physical Activity Scale for the Elderly), body mass index, smoking status (missing, ever, ref = never), self-reported history of diabetes mellitus, systolic blood pressure, diastolic blood pressure, estimated glomerular filtration rate.
Abbreviations: VMR, vitamin D metabolite ratio; vBMD, volumetric bone mineral density.
The mean ± SD EFLs at the distal radius and tibia were 4935 ± 1370 and 13 651 ± 2987 mg/cm3, respectively. In the demographics-adjusted model, a twofold higher VMR was associated with an 8% (1%, 15%) and 6% (1%, 12%) higher EFL at the distal radius and tibia, respectively. This association increased in magnitude in our fully adjusted model to 13% (5%, 21%) and 10% (4%, 16%) at the distal radius and tibia, respectively (Table 4). We found no association of 25(OH)D3, 24,25(OH)2D3 or the activation ratio with EFL at either anatomic site, although the association of 24,25(OH)2D3 with EFL at the distal tibia approached statistical significance (P = .050). In fully adjusted models, 1,25(OH)2D was inversely associated with EFL at the distal tibia (−10% [−17%, −2%] per twofold higher 1,25(OH)2D). Analyses by quartiles of vitamin D metabolites produced similar results (Fig. 2). There were no significant interactions between 25(OH)D or the VMR with eGFR or race for the EFL outcome (P interaction > .06 for all).
Table 4.
Association of vitamin D metabolites and the VMR estimated failure load at the distal radius and tibia among 545 participants of the MrOS study
| Distal Radius EFL | Distal Tibia EFL | |||
|---|---|---|---|---|
| Percent higher EFL per twofold higher (95% CI) | P | Percent higher EFL per twofold higher (95% CI) | P | |
| VMR | ||||
| Model 1 | 8% (1%, 15%) | .031 | 6% (1%, 12%) | .024 |
| Model 2 | 13% (5%, 21%) | .001 | 10% (4%, 16%) | .001 |
| 25(OH)D3 | ||||
| Model 1 | −1% (−8%, 6%) | .813 | −1% (−6%, 4%) | .661 |
| Model 2 | −1% (−8%, 6%) | .855 | 0.3% (−5%, 6%) | .910 |
| 24,25(OH)2D3 | ||||
| Model 1 | 3% (−2%, 7%) | .237 | 2% (−2%, 5%) | .276 |
| Model 2 | 4% (−0.4%, 9%) | .078 | 3% (0%, 7%) | .050 |
| 1,25(OH)2D | ||||
| Model 1 | −10% (−17%, −3%) | .007 | −7% (−12%, −1%) | .018 |
| Model 2 | −10% (−17%, −2%) | .017 | −5% (−11%, 1%) | .079 |
| Activation ratio | ||||
| Model 1 | −6% (−12%, 1%) | .074 | −3% (−8%, 1%) | .171 |
| Model 2 | −5% (−12%, 2%) | .135 | −4% (−9%, 2%) | .197 |
Model 1 adjusted for age, race (white vs nonwhite), clinic, and season of blood draw.
Model 2 additionally adjusted for physical activity (Physical Activity Scale for the Elderly), body mass index, smoking status (missing, ever, ref = never), self-reported history of diabetes mellitus, systolic blood pressure, diastolic blood pressure, estimated glomerular filtration rate.
Abbreviations: EFL, estimated failure load; VMR, vitamin D metabolite ratio.
Figure 2.
Association of quartiles of the VMR and 25(OH)D with estimated failure load at the distal radius and tibia among 545 participants of the MrOS study. (A) Quartiles of the VMR and 25(OH)D and estimated failure load at the distal radius. (B) Quartiles of the VMR and 25(OH)D and estimated failure load at the distal tibia.
In secondary analyses, we examined other HR-pQCT measurements including trabecular and cortical area, cortical thickness, and cortical porosity (Tables 5 and 6). Notably, in fully adjusted models, a higher VMR was associated with a higher cortical thickness and area at both the distal radius and tibia. In contrast, 25(OH)D was not significantly associated with these parameters.
Table 5.
Association of vitamin D metabolites and the VMR with trabecular microarchitecture among 545 participants of the MrOS studya
| Trabecular area | Trabecular thickness | |||
|---|---|---|---|---|
| Percent higher per twofold higher (95% CI) | P | Percent higher per twofold higher (95% CI) | P | |
| VMR | ||||
| Radius | −1% (−7%, 5%) | .707 | 1% (−1%, 3%) | .318 |
| Tibia | −3% (−9%, 2%) | .235 | 2% (−1%, 4%) | .130 |
| 25(OH)D3 | ||||
| Radius | −2% (−3%, 7%) | .382 | −2% (−3%, 0.1%) | .071 |
| Tibia | 2% (−3%, 7%) | .405 | −1% (−3%, 1%) | .243 |
| 24,25(OH)2D3 | ||||
| Radius | 0.2% (−3%, 3%) | .869 | −0.3% (−1%, 1%) | .620 |
| Tibia | −0.3% (−3%, 3%) | .867 | 0.0% (−1%, 1%) | .999 |
| 1,25(OH)2D | ||||
| Radius | 1% (−5%, 6%) | .798 | −2% (−4%, 0.3%) | .098 |
| Tibia | −2% (−7%, 4%) | .516 | −1% (−3%, 2%) | .491 |
| Activation ratio | ||||
| Radius | 0.2% (−5%, 5%) | .940 | 0.2% (−2%, 2%) | .820 |
| Tibia | −4% (−8%, 1%) | .154 | 0.4% (−2%, 2%) | .671 |
a Adjusted for age, race (white vs nonwhite), clinic, season of blood draw, physical activity (Physical Activity Scale for the Elderly), body mass index, smoking status (missing, ever, ref = never), self-reported history of diabetes mellitus, systolic blood pressure, diastolic blood pressure, estimated glomerular filtration rate.
Abbreviation: VMR, vitamin D metabolite ratio.
Table 6.
Association of vitamin D metabolites and the VMR with cortical microarchitecture among 545 participants of the MrOS studya
| Cortical area | Cortical thickness | Cortical porosity | ||||
|---|---|---|---|---|---|---|
| Percent higher per twofold higher (95% CI) | P | Percent higher per twofold higher (95% CI) | P | Percent higher per twofold higher (95% CI) | P | |
| VMR | ||||||
| Radius | 11% (6%, 17%) | <.01 | 10% (4%, 17%) | <.01 | 6% (−9%, 21%) | .432 |
| Tibia | 12% (6%, 18%) | <.01 | 11% (4%, 17%) | <.01 | −4% (−15%, 7%) | .480 |
| 25D | ||||||
| Radius | 3% (−3%, 8%) | .314 | 0.4% (−6%, 6%) | .892 | −6% (−19%, 7%) | .342 |
| Tibia | 2% (−4%, 8%) | .482 | 0.3% (−5%, 6%) | .918 | −2% (−12%, 8%) | .690 |
| 24,25D | ||||||
| Radius | 5% (2%, 8%) | <.01 | 4% (−0.0%, 8%) | .051 | −1% (−10%, 7%) | .797 |
| Tibia | 5% (1%, 8%) | <.01 | 4% (−0.1%, 7%) | .057 | −2% (−9%, 4%) | .463 |
| 1,25D | ||||||
| Radius | −7% (−13%, −1%) | .017 | −8% (−14%, −1%) | .027 | −11% (−25%, 4%) | .160 |
| Tibia | −2% (−9%, −4%) | .441 | −2% (−8%, 5%) | .638 | −10% (−21%, 1%) | .063 |
| Activation ratio | ||||||
| Radius | −7% (−12%, −2%) | <.01 | −7% (−13%, −1%) | .030 | −1% (−14%, 12%) | .865 |
| Tibia | −3% (−8%, 3%) | .343 | −1% (−6%, 5%) | .876 | −5% (−15%, 5%) | .295 |
a Adjusted for age, race (white vs nonwhite), clinic, season of blood draw, physical activity (Physical Activity Scale for the Elderly), body mass index, smoking status (missing, ever, ref = never), self-reported history of diabetes mellitus, systolic blood pressure, diastolic blood pressure, estimated glomerular filtration rate.
Abbreviation: VMR, vitamin D metabolite ratio.
Discussion
In this analysis, we assessed the relationship of vitamin D metabolites and the VMR with HR-pQCT measurements of vBMD and EFL, as well as metrics of bone microarchitecture, in older men. Our results indicate that a higher VMR was strongly associated with higher vBMD and EFL at the distal radius and tibia whereas no significant associations were observed with 25(OH)D3. Additionally, a higher VMR was associated with higher cortical and trabecular vBMD whereas 25(OH)D3 was not. Our results appeared similar irrespective of eGFR.
We previously evaluated the association of vitamin D metabolites and the VMR with BMD using a single areal BMD measurement via dual-energy X-ray absorptiometry, as well as serial areal BMD measurements (4, 24). We found no association between 25(OH)D3 and the VMR with baseline total hip areal BMD in older adults in the Cardiovascular Health Study. In contrast, in the Health Aging and Body Composition Study, we found that a higher VMR was associated with better preservation of areal BMD at multiple anatomic sites, over 10 years. In the present work, we found that the VMR was crossectionally associated with a higher vBMD at the radius and tibia, while 25(OH)D3 was not. Notably, the lack of association between 25(OH)D3 and vBMD stands in contrast to a prior study in MrOS, in which 25(OH)D was positively associated with femoral neck vBMD (25). However, that analysis had several important differences from the present one, including evaluation of a different anatomic site (femur vs radius and tibia), a different study visit (participants were over 10 years younger in the femoral neck study), and the lack of adjustment for kidney function in the prior study.
Studies assessing associations of vitamin D metabolites and EFL have been limited to 25(OH)D. Bobillier et al evaluated the relationship of 25(OH)D, measured by radioimmunoassay with longitudinal changes in EFL at the distal tibia and radius in older men in the Structure of Aging Men's Bones (STRAMBO) study (26). They found that higher 25(OH)D was associated with greater preservation of EFL at the distal tibia and radius over 8 years of follow-up. This study did not evaluate other vitamin D metabolites or the VMR, and to date we are unaware of any other studies evaluating the relationship of the VMR with EFL. Although our cross-sectional study did not find an association between 25(OH)D and EFL, we are the first to evaluate and demonstrate an association of the VMR and EFL. Longitudinal studies evaluating vitamin D metabolites and the VMR with changes in EFL would be of value as they would validate our results and strengthen an argument for possible causality.
This study has several strengths. First, we evaluated a cohort of older men, largely in the ninth and tenth decades of life, in which the risk of fracture is higher and in whom the VMR is not well studied. Second, we used a novel method to evaluate bone health, HR-pQCT, which provides information not only on volumetric bone density, but also on bone strength and microarchitecture. This is the first study to evaluate the association of VMR with the cortical and trabecular bone compartments. Finally, this study evaluated an extensive set of vitamin D measurements performed with liquid chromatography coupled to tandem mass spectrometry, including 25(OH)D3, 24,25(OH)2D, and 1,25(OH)2D.
This study also has important limitations. First, the analysis is cross-sectional. Future studies are needed to assess if the VMR is associated with longitudinal changes in vBMD and EFL, and whether such changes occur in the trabecular or cortical compartments. Second, while HR-pQCT provides information beyond that of traditional dual-energy X-ray absorptiometry, we did not evaluate clinical outcomes like fracture. Nonetheless, this work is consistent with prior studies in context, which show that lower VMR levels are associated with fracture risk and longitudinal changes in BMD, in a unique cohort of persons at high risk of fracture (4, 24). Third, if the associations observed here are to be clinically meaningful, the effect size must be interpreted in the context of a per doubling scale of the VMR, which may be difficult to achieve for persons with a higher baseline VMR. Finally, this study is observational and susceptible to residual and unmeasured confounding. This limitation is important to consider in light of randomized trials of vitamin D3 supplements that have demonstrated no effect of supplementation on fracture risk (5). Our results suggest that interventions that influence VMR levels (like vitamin D supplementation) (27) and 24,25(OH)2D levels (like vitamin D receptor activator supplementation) (28) may have causal effects on vBMD and EFL, but this must be tested in a randomized setting. Finally, all of these analyses were prespecified, but multiple comparisons were performed and therefore some of our findings may be due to chance.
In summary, we demonstrate that a higher VMR is associated with a higher vBMD and EFL in the distal tibia and radius in older men. 25(OH)D3 was not associated with vBMD or EFL. These findings, in concert with prior studies showing stronger associations with fracture risk of VMR than with 25(OH)D3, suggest that the VMR may be a more reliable indicator of vitamin D adequacy and bone health than 25(OH)D3 concentrations in older men. Clinical trials are needed to determine whether the VMR can be used as a biomarker of bone health as well as an intermediate therapeutic target to guide trials of vitamin D therapy in older men.
Acknowledgments
The authors acknowledge the services of the MrOS, contributing research centers, and all study participants.
Abbreviations
- 1,25(OH)2D
1,25-dihydroxyvitamin D
- 24,25(OH)2D
24,25-dihydroxyvitamin D
- 25(OH)D
25-hydroxyvitamin D
- BMD
bone mineral density
- BMI
body mass index
- CKD
chronic kidney disease
- EFL
estimated failure load
- eGFR
estimated glomerular filtration rate
- HR-pQCT
high-resolution peripheral quantitative computed tomography
- MrOS
Osteoporotic Fractures in Men
- vBMD
volumetric BMD
- VDBP
vitamin D binding protein
- VDR
vitamin D receptor
- VMR
vitamin D metabolite ratio
Contributor Information
Charles Ginsberg, Division of Nephrology-Hypertension, University of California, San Diego, CA 92037, USA.
Terri Blackwell, California Pacific Medical Center Research Institute, Department of Epidemiology, University of California, SanFrancisco, San Francisco, CA 94107, USA.
Jonathan H Cheng, Division of Nephrology-Hypertension, University of California, San Diego, CA 92037, USA; Nephrology Section, Veterans Affairs San Diego Healthcare System, San Diego, CA 92161, USA.
O Alison Potok, Division of Nephrology-Hypertension, University of California, San Diego, CA 92037, USA; Nephrology Section, Veterans Affairs San Diego Healthcare System, San Diego, CA 92161, USA.
Jane A Cauley, Department of Epidemiology, School of Public Health, University of Pittsburgh, Pittsburgh, PA 15261, USA.
Kristine E Ensrud, Department of Medicine and Division of Epidemiology and Community Health, University of Minnesota, Minneapolis, MN 55455, USA; Center for Care Delivery and Outcomes Research, Minneapolis Veterans Affairs Healthcare System, Minneapolis, MN 55417, USA.
Simon Hsu, Division of Nephrology and Kidney Research Institute, Department of Medicine, University of Washington, Seattle, WA 98195, USA.
Deborah M Kado, Department of Medicine, Stanford University, Palo Alto, CA 94304, USA.
Eric Orwoll, Division of Endocrinology, Metabolism and Clinical Nutrition, Department of Medicine, Oregon Health and Sciences University, Portland, OR 97239, USA.
Peggy M Cawthon, California Pacific Medical Center Research Institute, Department of Epidemiology, University of California, SanFrancisco, San Francisco, CA 94107, USA.
Joachim H Ix, Division of Nephrology-Hypertension, University of California, San Diego, CA 92037, USA; Nephrology Section, Veterans Affairs San Diego Healthcare System, San Diego, CA 92161, USA.
Funding
This study was supported by grants from the National Heart, Lung and Blood Institute, National Institute of Diabetes and Digestive and Kidney Diseases K23DK118197 and Loan Repayment Program L30DK110882 (Dr. Ginsberg), R01DK101720 and K24 DK110427 (Dr. Ix). Dr. Ix was additionally supported by an Established Investigator Award from the American Heart Association (AHA) (14EIA18560026). The Osteoporotic Fractures in Men (MrOS) Study is supported by National Institutes of Health funding. The following institutes provide support: the National Institute on Aging (NIA), the National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS), the National Center for Advancing Translational Sciences (NCATS), and NIH Roadmap for Medical Research under the following grant numbers: U01 AG027810, U01 AG042124, U01 AG042139, U01 AG042140, U01 AG042143, U01 AG042145, U01 AG042168, U01 AR066160, R01 AG066671, and UL1 TR002369.
Primary Funding Source
The National Institute of Diabetes, Digestive, and Kidney Diseases, the National Heart, Lung and Blood Institute, the National Institute on Aging, and the American Heart Association.
Author Contributions
Conceptualization: C.G., J.H.I., E.O., D.M.K., P.M.C. Data Curation: T.B., D.M.K., C.G. Formal Analysis: T.B., C.G. Funding Acquisition: C.G., J.H.I., E.O., J.A.C., K.E., D.M.K., P.M.C. Investigation: All authors. Methodology: All authors. Supervision: C.G., E.O., J.H.I., D.M.C., P.M.C. Writing Original Draft: C.G. Writing Review and Editing: All Authors.
Disclosures
All authors report no conflicts of interest for this work.
Data Availability
Data available upon request from the MrOS steering committee (https://mrosonline.ucsf.edu).
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
Data available upon request from the MrOS steering committee (https://mrosonline.ucsf.edu).