ORIGINAL
ARTICLE
Bone Turnover Markers Predict Bone Mass Development in Young Adult Men: A Five-Year Longitudinal Study Anna Darelid, Martin Nilsson, Jenny M. Kindblom, Dan Mellström, Claes Ohlsson, and Mattias Lorentzon Geriatric Medicine, Department of Internal Medicine and Clinical Nutrition (A.D., M.N., D.M., M.L.), and Center for Bone Research (A.D., M.N., J.M.K., D.M., C.O., M.L.) at the Sahlgrenska Academy, Institute of Medicine, University of Gothenburg, 413 45 Gothenburg, Sweden
Context: Peak bone mass is an important factor for the lifetime risk of developing osteoporosis. Ways to predict bone development in young adulthood are lacking. Objective and Main Outcome Measures: The aim of this study was to investigate whether baseline measurements of bone turnover markers could predict bone development in early adulthood in men. Design, Setting, and Participants: In total, 817 men (age at baseline, 18.9 ⫾ 0.6 y; mean ⫾ SD) from the population-based Gothenburg Osteoporosis and Obesity Determinants Study were included in this 5-year longitudinal study. Areal bone mineral density (aBMD) and bone mineral content (BMC) were measured using dual-energy x-ray absorptiometry, and volumetric BMD (vBMD) and cortical bone size were measured using peripheral quantitative computed tomography. Blood samples were collected at the baseline visit, and levels of osteocalcin (OC) and N-terminal telopeptide of type I collagen were analyzed. Results: OC was a positive predictor of the increase in aBMD and BMC of the total body (R2: aBMD, 6.6%; BMC, 4.9%), lumbar spine (R2: aBMD, 5.4%; BMC, 5.7%), and radius (R2: aBMD, 14.8%; BMC, 12.8%) between 19 and 24 years (P ⬍ .001). Men in the highest OC quartile at baseline (35.2 ⫾ 4.4 ng/mL; mean ⫾ SD) gained markedly more in radius cortical cross-sectional area (4.0 ⫾ 4.3 vs 1.9 ⫾ 2.9 mm2) and trabecular vBMD (11 ⫾ 7 vs 3 ⫾ 12 mg/mm3) than men in the lowest OC quartile at baseline (17.7 ⫾ 2.3 ng/mL; mean ⫾ SD) (P ⬍ .001). Conclusion: A high OC level at the age of 19 predicts a favorable development in BMD, BMC, and bone size between 19 and 24 years of age. (J Clin Endocrinol Metab 100: 1460 –1468, 2015)
ne of the major risk factors for osteoporotic fractures is low bone mineral density (BMD). It has been demonstrated that every SD decrease in BMD is associated with an increase in the age-adjusted risk of hip fracture that is about 2-fold in postmenopausal women and 3-fold in elderly men (1–3). Peak bone mass (PBM), defined as the amount of bone present in the skeleton at the end of its maturation process (4), has been demonstrated to account for up to half of the variation in BMD at age 65, indicating
O
that achievement of PBM has an important role in the risk of developing osteoporosis (5, 6). The time of achievement of PBM differs depending on skeletal site (7–11) and gender (7, 8, 12, 13). We reported earlier that in men, the timing of puberty is of importance for BMD in young adulthood (14) and is also an important determinant of the development of BMD and bone mineral content (BMC) between 19 and 24 years of age (15). In the present study, our aim was to investigate whether baseline measurements
ISSN Print 0021-972X ISSN Online 1945-7197 Printed in U.S.A. Copyright © 2015 by the Endocrine Society Received October 29, 2014. Accepted January 6, 2015. First Published Online January 16, 2015
Abbreviations: aBMD, areal BMD; BMC, bone mineral content; BMD, bone mineral density; BTM, bone turnover marker; CSA, cross-sectional area; CV, coefficient of variation; DXA, dual-energy x-ray absorptiometry; EC, endosteal circumference; NTX, N-terminal telopeptide of type I collagen; OC, osteocalcin; PBM, peak bone mass; PC, periosteal circumference; PHV, peak height velocity; pQCT, peripheral quantitative computerized tomography; sNTX, serum NTX; vBMD, volumetric BMD.
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of bone turnover markers (BTMs) could predict the development of BMC, BMD, and bone geometry in young men around the time of PBM. Changes of bone turnover during growth can be described during four periods: infancy, prepubertal period, puberty, and the postpubertal period, with corresponding changes in the levels of BTM (16). A Spanish study demonstrated that both formation and resorption markers were higher in early puberty compared to advanced puberty (17). Similar results were found in a recent study of 101 girls (10 –19 y of age), where the authors concluded that the more mature the participants were, the lower their bone biomarker concentrations were (18). In another study, the peak level of BTMs corresponded to the period of the most rapid growth velocity, but not to the peak in bone mass gain (19). The use of BTMs in the clinical setting has been widely debated over the last few years, with conflicting views of their usefulness (20 –23). A growing consensus is forming around the use of BTMs in the monitoring of osteoporosis treatment (24 –26). Less is known about the role of BTM around the time of PBM in young adulthood. In the present study, we aimed to determine whether baseline measurements of BTMs in serum (osteocalcin [OC] and Nterminal telopeptide of type I collagen [NTX]) could predict the development of BMC, BMD, and bone geometry between ages 19 and 24 years in men.
Subjects and Methods Subjects The population-based Gothenburg Osteoporosis and Obesity Determinants (GOOD) study was initiated with the aim to determine environmental and genetic factors involved in the regulation of bone mass and fat mass. The GOOD cohort (n ⫽ 1068) was found to be representative of the general young male population in Gothenburg (10). At the baseline visit, the time of the visit was recorded, and blood samples were drawn from all subjects. Subjects were not fasting at the time of blood sampling. Serum markers of bone turnover were analyzed. In total, 1061 subjects had results on BTMs (OC and NTX) and a recorded time of the visit. Five years later, the study participants were contacted by letter and telephone and invited to participate in the 5-year follow-up study, as previously described (27). Of the 1061 subjects, 817 men (age, 24.1 ⫾ 0.6 y) completed the measurements at the follow-up and were included in the present study. A standardized self-administered questionnaire was used at baseline and follow-up to collect information about smoking (yes/no), present physical activity (h/wk), and nutritional intake. Calcium intake was estimated from dairy product intake. No significant differences were seen between the included (n ⫽ 817) and not included (n ⫽ 244) subjects in age, height, weight, calcium intake, or amount of present physical activity at baseline, using an independent samples t test (data not shown). A lower percentage of smokers was found among the included men than among the not included men (7.3% [60 of 817] vs 13.5% [33 of 244]; P ⫽
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.003). For a subset of the population (n ⫽ 500), growth and weight charts from birth until 19 years of age were obtained to calculate age at peak height velocity (PHV), as previously described (14). This subset was used in the subanalyses including age at PHV. The follow-up period was 61.2 ⫾ 2.3 months (mean ⫾ SD), range 55–70 months. The regional ethical review board at the University of Gothenburg approved the study. Written and oral consent was obtained from all study participants.
Anthropometrical measurements Height was measured using a wall-mounted stadiometer, and weight was measured to the nearest 0.1 kg. The coefficient of variation (CV) was below 1% for these measurements.
Dual-energy x-ray absorptiometry (DXA) Areal BMD (aBMD; g/cm2) of the whole body, femoral neck, total hip (of the left leg), lumbar spine, and the left and right radius were assessed using Lunar Prodigy DXA (GE Lunar Corp). The CVs for the aBMD measurements ranged from 0.4 to 2.5% at baseline and 0.5 to 3% at follow-up, depending on site. The Lunar Prodigy DXA used at the follow-up visit was not the same specimen as the one used at the baseline visit. Cross-calibration between the two Lunar Prodigy DXA machines was performed at the time of follow-up, as previously described (15, 27).
Peripheral quantitative computerized tomography (pQCT) A pQCT device (XCT-2000; Stratec Medizintechnik GmbH) was used to scan the distal arm (radius) and the distal leg (tibia) of the nondominant arm and leg at both the baseline and follow-up visits. A 2-mm-thick single tomographic slice was scanned, with a voxel size of 0.50 mm. The cortical volumetric BMD (vBMD; mg/cm3), cortical cross-sectional area (CSA; mm2), endosteal and periosteal circumference (EC and PC), and cortical thickness (mm) were measured using a scan through the diaphysis (at 25% of the bone length in the proximal direction of the distal end of the bone) of the radius and tibia. The threshold for cortical bone was 711. Trabecular vBMD (mg/cm3) was measured using a scan through the metaphysis (at 4% of the bone length in the proximal direction of the distal end of the bone). Trabecular vBMD was assessed using the inner 45%. The CVs were less than 1% for all pQCT measurements.
Analyses of serum BTMs At the baseline visit, blood samples were drawn, serum was separated, and within 1 hour, the samples were frozen and stored at ⫺80°C. During transport, the samples were kept on dry ice, and they were not thawed until ready for analysis at TECO Medical, Bünde, Germany. Serum OC, a bone formation marker, was analyzed with an EDI Osteocalcin Specific ELISA kit (TECO Medical, Germany) with an intra-assay precision with CV 4.7–5.0%, and an interassay precision with CV 5.7– 8.3%. Serum NTX (sNTX), a bone resorption marker, was analyzed with Osteomark NTx Serum kit (TECO Medical, Germany) with an intra-assay precision with CV 4.6% and an interassay precision with CV 6.9%.
Estimation of age at PHV For a subset of the population (n ⫽ 500), detailed growth and weight charts from birth until 19 years of age were used for
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estimation of age at PHV according to the infancy-childhoodpuberty model (28), as previously described (14). Age at PHV was defined as the age at maximum growth velocity during puberty and was estimated by the algorithm. The average number of measurements between birth and 19 years of age was 21. PHV is generally believed to be reached within 2 years after pubertal onset (28, 29).
Statistical analysis Changes over 5 years in different bone parameters were calculated and adjusted for follow-up time, as previously described (15, 27). To determine whether BTMs were independent predictors of the change over 5 years in different bone variables, stepwise multiple linear regression analyses were performed. Stepwise linear regression analyses were performed, first, including baseline values of the respective bone variable, age, height, log weight, smoking, calcium intake, physical activity, log OC, and log sNTX as independent variables; then, in addition to the above-mentioned variables, also including age at PHV as an independent variable to determine whether BTM could predict bone mass development between 19 and 24 years and whether this prediction was independent of age at PHV or not. The percentage of the variation of change in each bone parameter explained (R2) by all covariates, log OC, log sNTX, and age at PHV, was calculated using the linear regression model. The stepwise selection process criterion for entry into the model was a P value ⱕ .05, and the criterion for removal from the model was a P value ⱖ .10. All P values in the stepwise linear regression
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analyses and Pearson’s correlation were adjusted for multiple comparisons by multiplication of the P value by the number of bone variables analyzed (24). Weight, OC, and NTX were not normally distributed and were therefore log-transformed before being entered into the regression analysis. OC was correlated to the time of the visit and was therefore adjusted for the time of the visit before being entered into statistical analyses. To investigate correlations between change in bone variables and log OC and log sNTX, respectively, as well as between log OC and age at PHV, Pearson’s correlation was used. Study subjects were divided into quartiles according to the baseline value of OC. Differences in baseline anthropometrics and bone variables, as well as change in bone variables between the different quartiles of OC were investigated using one-way ANOVA with Bonferroni correction for multiple comparisons. Pearson 2 test was used to determine whether or not the distribution of smokers differed between included and not included study subjects, and between men in different quartiles of OC. To study the linearity of the association between OC and change in bone variables in more detail, a spline regression model was fitted using knots at the 10th, 50th, and 90th percentiles of serum OC. The splines were second-order functions between the breakpoints and linear functions at the tails resulting in a smooth curve. In general, a P value ⬍ .05 was considered significant. Data were analyzed using SPSS software, version 20.0 (IBM SPSS, Armonk, NY: IBM Corp.).
Table 1. Anthropometrics and Bone Variables at Baseline (18 –20 y) of the Whole Cohort and the Cohort Divided Into Quartiles According to Level of OC at Baseline
n OC, ng/mL Age, y Height, cm Weight, kg Smoking, % Physical activity, h/wk Calcium intake, mg/d PHV (n ⫽ 500) NTX, nM BCE Lumbar spine L2-L4 BMC, g Total hip BMC, g Radius BMC, g Lumbar spine L2-L4 aBMD, g/cm2 Total hip aBMD, g/cm2 Radius aBMD, g/cm2 Tibia cortical vBMD, mg/cm3 Tibia cortical CSA, mm Tibia trabecular vBMD, mg/cm3 Radius cortical vBMD, mg/cm3 Radius cortical CSA, mm Radius trabecular vBMD, mg/cm3
Whole Cohort
OC 1
OC 2
OC 3
OC 4
P (ANOVA)
817 25.9 ⫾ 6.9 18.9 ⫾ 0.6 181.5 ⫾ 6.7 73.6 ⫾ 11.3 7.3 4.3 ⫾ 5.1 1103 ⫾ 697 13.5 ⫾ 1.0 18.6 ⫾ 9.1 61.2 ⫾ 11 43.0 ⫾ 7 10.1 ⫾ 2 1.23 ⫾ 0.1 1.17 ⫾ 0.2 0.58 ⫾ 0.1 1156 ⫾ 20 269 ⫾ 34 266 ⫾ 34 1166 ⫾ 23 96 ⫾ 12 220 ⫾ 41
204 17.7 ⫾ 2.3 19.1 ⫾ 0.5 180.7 ⫾ 6.6 75.4 ⫾ 12.6 11.8 3.7 ⫾ 5.5 1058 ⫾ 645 13.1 ⫾ 0.9 15.1 ⫾ 6.3 61.2 ⫾ 10 43.2 ⫾ 7 10.2 ⫾ 2 1.24 ⫾ 0.1 1.18 ⫾ 0.2 0.60 ⫾ 0.1 1164 ⫾ 16 272 ⫾ 36 272 ⫾ 34 1176 ⫾ 19 96 ⫾ 12 228 ⫾ 41
204 23.2 ⫾ 1.5 19.0 ⫾ 0.6b 181.0 ⫾ 6.6 73.6 ⫾ 11.1 6.4 4.1 ⫾ 4.8 1124 ⫾ 742 13.3 ⫾ 0.9 16.5 ⫾ 7.8 61.3 ⫾ 11 42.7 ⫾ 7 10.2 ⫾ 2 1.24 ⫾ 0.1 1.16 ⫾ 0.2 0.59 ⫾ 0.05 1162 ⫾ 16 267 ⫾ 34 265 ⫾ 35 1171 ⫾ 19 96 ⫾ 11 220 ⫾ 40
205 27.6 ⫾ 1.5 18.8 ⫾ 0.5 a,d 182.0 ⫾ 6.5 73.2 ⫾ 10.9 6.8 4.3 ⫾ 4.7 1186 ⫾ 714 13.7 ⫾ 0.9a 19.3 ⫾ 8.6a,d 60.6 ⫾ 11 43.0 ⫾ 7 10.1 ⫾ 1 1.22 ⫾ 0.1 1.16 ⫾ 0.2 0.58 ⫾ 0.1a,d 1153 ⫾ 20a,c 268 ⫾ 30 264 ⫾ 33 1163 ⫾ 21a,c 96 ⫾ 12 218 ⫾ 42
204 35.2 ⫾ 4.4 18.8 ⫾ 0.5a,f 182.4 ⫾ 6.9 72.0 ⫾ 10.3b 4.4 5.0 ⫾ 5.2 1045 ⫾ 678 14.0 ⫾ 1.0a,c,f 23.6 ⫾ 10.7a,c,e 61.7 ⫾ 12 43.2 ⫾ 7 9.9 ⫾ 2 1.23 ⫾ 0.2 1.16 ⫾ 0.2 0.56 ⫾ 0.1a,c 1147 ⫾ 22a,c,f 269 ⫾ 36 263 ⫾ 33b 1153 ⫾ 25a,c,e 95 ⫾ 12 214 ⫾ 40b
N/A ⬍.001 .032 .026 .032 .070 .146 ⬍.001 ⬍.001 .794 .874 .078 .576 .459 ⬍.001 ⬍.001 .411 .028 ⬍.001 .805 .008.
Abbreviations: BCE, bone collagen equivalents; N/A, not applicable. Values are presented as mean ⫾ SD. The cohort was divided into quartiles according to baseline value of OC. Differences between groups were investigated by ANOVA followed by Bonferroni post hoc test. Differences in smoking were investigated with 2 test. For PHV, quartile 1, n ⫽ 121; quartile 2, n ⫽ 122; quartile 3, n ⫽ 123; and quartile 4, n ⫽ 134. a–f Significantly different from quartile 1 in Bonferroni post hoc test: a P ⬍ .001; b P ⬍ .05. Significantly different from quartile 2: c P ⬍ .001; d P ⬍ .05. Significantly different from quartile 3: e P ⬍ .001; f P ⬍ .05.
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Results Anthropometric characteristics and OC levels at baseline The level of OC at baseline was 25.9 ⫾ 6.9 (mean ⫾ SD) (range, 9.9 –56.6) ng/mL. Subjects were divided into quartiles according to level of OC (Table 1). Men with high OC levels at baseline (quartile 4) were significantly younger than men with low OC at baseline (quartile 1); they weighed less, had a lower percentage of smokers among them, and also had significantly higher levels of NTX at baseline. There were no significant differences in calcium intake or amount of physical activity at baseline, although there was a tendency toward a higher weekly amount of physical activity among the men in quartile 4 (Table 1). As for bone variables, no significant differences were seen between the groups in BMC or aBMD of the lumbar spine or total hip at baseline. Radius aBMD but not BMC was significantly lower in quartile 4 at baseline, due to significantly lower cortical and trabecular vBMD (Table 1). Characteristics of the whole cohort (n ⫽ 817) and of the cohort divided into quartiles are presented in Table 1. OC and NTX levels at baseline were correlated to bone development between 19 and 24 years OC levels at baseline were significantly correlated to the 5-year change in all BMC and aBMD bone parameters, and NTX levels at baseline were significantly correlated to 5-year change in all BMC and aBMD bone parameters except for total hip aBMD and BMC and femoral neck aBMD. The level of OC at baseline was most strongly correlated to 5-year change in lumbar spine and radius BMC and to 5-year change in total body and radius aBMD (Table 2). The level of NTX at baseline was most strongly correlated to 5-year change in BMC at the lumbar spine and radius and to 5-year change in aBMD of the total body and radius (Table 2). The level of OC at baseline was significantly correlated to 5-year change in cortical vBMD of the radius, but not to 5-year change in cortical vBMD of the tibia (Table 2). The level of OC at baseline was also correlated to 5-year change in trabecular vBMD of the radius and tibia, and to 5-year change in cortical CSA of the radius and tibia (Table 2). The level of NTX at baseline was significantly correlated to 5-year change in cortical, but not trabecular, vBMD of the radius and tibia (Table 2). OC levels at baseline independently predicted bone development between 19 and 24 years Changes in BMC and aBMD between 19 and 24 years of age were adjusted for follow-up time and entered into a linear regression model (as dependent variable) including calcium intake, height, log weight, smoking (yes/no), physical activity (h/wk), age, NTX, and OC, all at base-
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Table 2. Association Between BTMs and Change in BMC, BMD, and Bone Geometry Between Baseline (Age 18 –20 y) and Follow-Up (Age 23–25 y) in Men Change Over 5 Years DXA Total body BMC Lumbar spine L2-L4 BMC Total hip BMC Femoral neck BMC Radius nondominant BMC Total body aBMD Lumbar spine L2-L4 aBMD Total hip aBMD Femoral neck aBMD Radius nondominant aBMD pQCT Tibia cortical vBMD Tibia cortical CSA Tibia cortical thickness Tibia periosteal circumference Tibia endosteal circumference Tibia polar SSI Tibia trabecular vBMD Radius cortical vBMD Radius cortical CSA Radius cortical thickness Radius periosteal circumference Radius endosteal circumference Radius polar SSI Radius trabecular vBMD
Log OC
Log sNTX
0.22a 0.24a 0.21a 0.18a 0.36a 0.26a 0.23a 0.18a 0.18a 0.38a
0.14b 0.19a 0.09NS 0.13b 0.31a 0.18a 0.15a 0.09NS 0.10NS 0.30a
0.09NS 0.19a 0.02NS 0.11c ⫺0.01NS 0.14b 0.12c 0.32a 0.22a 0.16a 0.10NS ⫺0.06NS 0.21a 0.18a
0.19a 0.09NS 0.01NS 0.03NS ⫺0.02NS 0.11c 0.05NS 0.27a 0.14b 0.08NS 0.12c 0.01NS 0.15a 0.07NS
Abbreviations: NS, not significant, SSI, strength strain index. n ⫽ 817. Bivariate correlations were performed (Pearson’s correlation). R values are shown. a P ⬍ .001; b P ⬍ .01; c P ⬍ .05. All P values were adjusted for multiple analyses by multiplication of the P value by the number of bone variables analyzed (24).
line, as well as the baseline value of the corresponding bone variable. OC was found to be an independent positive predictor of the increase in total body, lumbar spine, and radius BMC, explaining between 4.9 and 12.8% of the variation of change in these bone variables (Table 3). Likewise, OC was found to be an independent positive predictor of the increase in total body, lumbar spine, and radius aBMD, explaining 6.6, 5.4, and 14.8% of the variation of change in these bone variables, respectively (Table 3). In a subanalysis including the 500 men with available data on age at PHV, changes in BMC and aBMD were entered in a linear regression model as described above, including age at PHV as an additional independent variable. In this model, the percentage of variation explained by OC was diminished, although the percentage of variation explained by the whole model was augmented. The correlation coefficient for age at PHV and log OC was 0.38 (P ⬍ .001). OC levels at baseline predict changes in bone geometry and vBMD between 19 and 24 years Changes in bone geometry and vBMD of the radius and tibia between 19 and 24 years of age were adjusted for
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Table 3. BTMs as Predictors of Change in BMC, BMD, and Bone Geometry Between Baseline (Age 18 –20 y) and Follow-Up (Age 23–25 y) in Men R2, I (n ⴝ 817) Change Over 5 Years BMC Total body Lumbar spine L2-L4 Total hip Femoral neck Radius nondominant aBMD Total body Lumbar spine L2-L4 Total hip Femoral neck Radius nondominant Tibia Cortical vBMD Cortical CSA Cortical thickness PC EC SSI Trabecular vBMD Radius Cortical vBMD Cortical CSA Cortical thickness PC EC SSI Trabecular vBMD
R2, II (Including PHV; n ⴝ 500)
OC, %
NTX, %
Whole Model, %
OC, %
NTX, %
Whole Model, %
4.9a 5.7a 4.2a 3.2b 12.8a
— — — — 3.6a
9.6a 9.7a 6.7a 6.7a 20.9a
— — 3.9c — —
— 1.5c — — 1.9b
11.0a 16.5a 6.8a 10.8a 32.9a
6.6a 5.4a 3.1a 2.9a 14.8a
— — — — 2.0a
13.2a 6.0c 4.8a 7.9a 28.3a
— — — — —
— — — — —
18.4a 14.8a 8.0a 11.0a 40.0a
— 4.5a 0.6b 1.4b — 3.2a —
— — — — — — —
18.2a 11.3a 59.8a 6.4a 4.8a 13.5a 2.9a
— 3.5c — — — — —
— — — — — — —
21.5a 11.6a 61.6a 6.6a 3.0b 12.8a 6.4a
— 4.8a 1.6c — — 4.6a 3.1a
— — — — — — —
46.7a 7.5a 9.2a 8.1a 9.8a 9.5a 4.4a
— — — — — — —
— — — — — — —
49.5a 14.9a 14.7a 11.7a 9.2a 14.7a 7.9a
Abbreviation: SSI, strength strain index. Dashes indicate no significant association. Stepwise linear regression analyses were performed. The stepwise model included baseline values of: I— calcium intake, height, log weight, smoking (yes/no), physical activity (h/wk), age, log OC, log sNTX, and the respective bone variable; and II— calcium intake, height, log weight, smoking (yes/no), physical activity (h/wk), age, log OC, log sNTX, the respective bone variable, and age at PHV. R2 of the whole model is presented, as well as the respective contribution of OC and NTX. a P ⬍ .001; b P ⬍ .01; c P ⬍ .05. All P values were adjusted for multiple analyses by multiplication of the P value by the number of bone variables analyzed (24).
follow-up time and entered into a linear regression model (as dependent variable) as described in the previous section. OC was found to be an independent positive predictor of the increase in cortical CSA of the radius and tibia, explaining 4.8 and 4.5% of the variation of change in cortical CSA of these bones, respectively (Table 3). Likewise, OC was found to be an independent positive predictor of the increase in trabecular vBMD, explaining 3.1% of the variation of change in trabecular vBMD of the radius (Table 3). In a subanalysis including the 500 men with calculated age at PHV, changes in bone geometry and vBMD were entered in a linear regression model as described above, including age at PHV as an additional independent variable. In this model, the percentage of variation explained by OC was diminished, although the percentage of variation explained by the whole model was augmented (Table 3).
Larger increases in BMC, aBMD, vBMD, and bone geometry in men with high OC levels at baseline Men with high OC at baseline (quartile 4) gained twice as much in aBMD of the lumbar spine as men with low OC at baseline (quartile 1) (0.08 ⫾ 0.07 vs 0.04 ⫾ 0.06 g/cm2; P ⬍ .001), and a similar pattern was seen for gain in lumbar spine BMC (5.2 ⫾ 4.4 vs 2.7 ⫾ 3.5 g) (Figure 1). At the total hip, men with high OC at baseline lost markedly less in aBMD than men with low OC at baseline (⫺0.003 ⫾ 0.07 vs ⫺0.04 ⫾ 0.06 g/cm2; P ⬍ .001), whereas for total hip BMC, a small gain was observed for men with high OC at baseline (0.4 ⫾ 2.4 vs ⫺1.2 ⫾ 2.1 g) (Figure 1). At the radius, the increase in cortical CSA was twice as high in men with high OC at baseline as in men with low OC at baseline (4.0 ⫾ 4.3 vs 1.9 ⫾ 2.9 mm2; P ⬍ .001) (Figure 2). A similar pattern, although less pronounced, was observed for the increase in CSA of the tibia (13.6 ⫾ 10.3 vs
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Figure 1. A–D, Change in BMC of the lumbar spine (A) and total hip (B) and change in aBMD of the lumbar spine (C) and total hip (D) in men between 19 and 24 years of age, divided into quartiles [1 (low OC)– 4 (high OC)] according to level of OC at baseline. E, Change in lumbar spine aBMD between 19 and 24 years of age according to level of OC at baseline. F, Change in total hip aBMD between 19 and 24 years of age according to level of OC at baseline.
9.2 ⫾ 8.0 mm2; P ⬍ .001) (Figure 2). The increase in trabecular vBMD of the radius was almost four times higher in men with high OC at baseline than men with low OC at baseline (11 ⫾ 7 vs 3 ⫾ 12 mg/mm3; P ⬍ .001), and the loss in trabecular vBMD of the tibia was six times greater in men with low OC at baseline than men with high OC at baseline (⫺6 ⫾ 14 vs ⫺1 ⫾ 14 mg/mm3; P ⬍ .001) (Figure 2). Increasing levels of OC were associated with a greater increase in lumbar spine aBMD (Figure 1E). For total hip aBMD, an OC level above 39 ng/mL at baseline was associated with an increase in total hip aBMD over 5 years, whereas an OC level below that was associated with a decrease in total hip aBMD (Figure 1F). Similarly, a level above 41 ng/mL at baseline was associated with an increase in trabecular vBMD of the tibia over 5 years, whereas an OC level below that was associated with a decrease in trabecular vBMD of the tibia (Figure 2E).
Discussion In the present study, we found that OC measured at baseline was a positive independent predictor of bone devel-
opment between 19 and 24 years of age in men. A high OC level at 19 years of age was associated with larger increases in lumbar spine aBMD and BMC, a lesser decrease in total hip aBMD and BMC, and larger increases in CSA and trabecular vBMD of the radius. Because PBM is an important factor for the risk of developing osteoporosis and consequently osteoporotic fractures later in life (5, 6), the possibility to predict bone development in a young individual is valuable. In this cohort, we have earlier reported that between the ages of 19 and 24, aBMD especially of the radius but also lumbar spine and total body increased, whereas a decrease in aBMD at the femoral neck and total hip was seen (27), which indicates that this period in life is of importance in maximizing PBM. The current findings suggest that measuring OC can be of value when evaluating bone development in men in early adulthood. We have earlier reported that age at PHV is a strong independent positive predictor of bone development between 19 and 24 years of age (15). Age at PHV is an objective measurement of pubertal timing and has been shown to be strongly correlated to age at menarche in females (30). In the clinical setting, data needed to calculate age at PHV are generally not available. If, as the pres-
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Figure 2. A–D, Change in cortical CSA and trabecular vBMD of the tibia (A, B) and of the radius (C, D) in men between 19 and 24 years of age, divided into quartiles [1 (low OC)– 4 (high OC)] according to level of OC at baseline. E, Change in tibia trabecular vBMD between 19 and 24 years of age according to level of OC at baseline.
ent study indicates, measurement of OC could help predict bone development in a similar manner, it could be a useful complement when information about age at PHV is lacking. In previous studies, markers of bone turnover have been shown to correlate well with age at PHV (31, 32). One small study (n ⫽ 100) of males aged 10 –17 years reported a very close correlation between OC levels and the pubertal growth spurt (33). In our study, when dividing the cohort into quartiles according to OC level at baseline, men with high OC at baseline were significantly younger, weighed less, and had experienced age at PHV at a later age compared with men with low OC at baseline. They also had significantly higher NTX values than men with low OC at baseline. We suggest that a high OC level at age 19 implies that the individual is at a lower maturational level and therefore may achieve more in BMD, BMC, and bone size in the following years than an individual with lower OC at this point in life. A large, crosssectional study of men 19 – 85 years of age (n ⫽ 934) established that average concentrations of bone formation markers (OC, bone alkaline phosphatase, procollagen type 1 N-terminal propeptide) were highest before 25 years of age and then decreased (34). In a Japanese study of young females (aged 12–30 y), the authors demon-
strated high levels of bone alkaline phosphatase and NTX at age 12, which thereafter decreased until the age of 18 and then remained almost constant (35). Neither of these studies was longitudinal and therefore could not link levels of BTM to longitudinal development of BMD. To the best of our knowledge, our study is the first longitudinal study linking measurements of BTMs to bone development of men in young adulthood. One earlier study reported that NTX (measured at follow-up) was negatively correlated with change in femoral neck aBMD over 5 years (r ⫽ ⫺0.21) in men 35– 69 years of age and could explain 3.8% of the variance of the change in femoral neck aBMD (22). In the present study, NTX, as well as OC, was positively correlated to change in BMC and aBMD at most measured sites (r ⫽ 0.11– 0.38). NTX was not able to explain any of the variance of the change in femoral neck aBMD over 5 years, whereas OC could explain 2.9%. In contrast to the men in the study described above, the men in our cohort were still increasing in bone mass and bone density at most sites, and measurements of OC seem to contribute more than measurements of NTX to predicting bone development in this age group. In the present study, OC was found to be an independent positive predictor of the increase in total body, lumbar spine, and radius aBMD and BMC, explaining up to
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12.8 and 14.8% of the variation in change in radius BMC and aBMD, respectively. When adding NTX and baseline measurement of bone variables and covariates, the model could explain up to 20.9 and 28.3%, respectively. OC was also associated with a lesser decrease in total hip and femoral neck aBMD and BMC, explaining 2.9 – 4.2% of the variation of change over 5 years in these bone variables. When age at PHV was added to the linear regression analyses, the percentage explained by OC was diminished, although the percentage explained by the whole model was augmented. This indicates that most of the ability of OC to predict bone development is mediated by age at PHV, but also that for some bone variables, OC and age at PHV independently provide information to explain the variation in bone development. There are some limitations with the present study. Primarily, although we adjusted measurements of OC for the time of the visit, ideally all blood samples should have been collected at the same time of day from fasting subjects. We cannot exclude that limitations in the sampling method may have contributed to the lack of findings concerning NTX. Also, it would be preferable to have several blood samples instead of a single sample. Limitations also include that the study population was primarily white and was constituted of men only; therefore, the conclusions are restricted to the male population, and the results cannot be directly transferred to other ethnicities. There were also strengths with the present study. It is a large, longitudinal study involving a well-characterized cohort measured with both DXA and pQCT. Pubertal timing was assessed with an objective method, minimizing the risk of inaccurate classification. In conclusion, our results demonstrate that high levels of OC at age 19 were associated with larger increases in BMC, BMD, and bone size in young adulthood, indicating that measuring OC could be of value in the evaluation of a young individual’s bone health.
Acknowledgments We thank statistician Helena Johansson for excellent assistance and guidance related to statistical analyses. Address all correspondence and requests for reprints to: Mattias Lorentzon, Professor, MD, PhD, Department Head, Geriatric Medicine, Building K, Sixth Floor, Sahlgrenska University Hospital, 431 80 Mölndal, Sweden; and Centre for Bone and Arthritis Research, Institute of Medicine, Vita Stråket 11, Sahlgrenska University Hospital, 413 45 Gothenburg, Sweden. E-mail: [email protected]. This study was supported by the Swedish Research Council, the Swedish Foundation for Strategic Research, the European Commission, the Lundberg Foundation, the Torsten and Ragnar Söderberg’s Foundation, Petrus and Augusta Hedlund’s Foundation, the Avtal för Läkarutbildning Och Forskning grant from
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the Sahlgrenska University Hospital, the Novo Nordisk Foundation, and the Gustav V and Queen Victoria Freemason Foundation. Authors’ Roles: study design, M.L. and C.O.; study conduct, M.L. and M.N.; data collection, M.L., M.N., J.M.K., A.D.; data analysis, A.D., M.L., J.M.K.; data interpretation, A.D. and M.L.; drafting manuscript, A.D. and M.L.; revising manuscript content, M.L., M.N., J.M.K., D.M., C.O.; approving final version of manuscript, M.L., M.N., J.M.K., D.M., C.O., and A.D. A.D. and M.L. take responsibility for the integrity of the data analysis. Disclosure Summary: All authors have no conflicts of interest.
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27. Ohlsson C, Darelid A, Nilsson M, Melin J, Mellström D, Lorentzon M. Cortical consolidation due to increased mineralization and endosteal contraction in young adult men: a five-year longitudinal study. J Clin Endocrinol Metab. 2011;96:2262–2269. 28. Karlberg J. On the modelling of human growth. Stat Med. 1987;6: 185–192. 29. Liu YX, Wikland KA, Karlberg J. New reference for the age at childhood onset of growth and secular trend in the timing of puberty in Swedish. Acta Paediatr. 2000;89:637– 643. 30. Sherar LB, Baxter-Jones AD, Mirwald RL. Limitations to the use of secondary sex characteristics for gender comparisons. Ann Hum Biol. 2004;31:586 –593. 31. Johansen JS, Giwercman A, Hartwell D, et al. Serum bone Glaprotein as a marker of bone growth in children and adolescents: correlation with age, height, serum insulin-like growth factor I, and serum testosterone. J Clin Endocrinol Metab. 1988;67:273–278. 32. van Coeverden SC, Netelenbos JC, de Ridder CM, Roos JC, PoppSnijders C, Delemarre-van de Waal HA. Bone metabolism markers and bone mass in healthy pubertal boys and girls. Clin Endocrinol (Oxf). 2002;57:107–116. 33. Kanbur NO, Derman O, Sen TA, Kinik E. Osteocalcin. A biochemical marker of bone turnover during puberty. Int J Adolesc Med Health. 2002;14:235–244. 34. Szulc P, Garnero P, Munoz F, Marchand F, Delmas PD. Crosssectional evaluation of bone metabolism in men. J Bone Miner Res. 2001;16:1642–1650. 35. Orito S, Kuroda T, Onoe Y, Sato Y, Ohta H. Age-related distribution of bone and skeletal parameters in 1,322 Japanese young women. J Bone Miner Metab. 2009;27:698 –704.
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ARTICLE
Bone Turnover Markers Predict Bone Mass Development in Young Adult Men: A Five-Year Longitudinal Study Anna Darelid, Martin Nilsson, Jenny M. Kindblom, Dan Mellström, Claes Ohlsson, and Mattias Lorentzon Geriatric Medicine, Department of Internal Medicine and Clinical Nutrition (A.D., M.N., D.M., M.L.), and Center for Bone Research (A.D., M.N., J.M.K., D.M., C.O., M.L.) at the Sahlgrenska Academy, Institute of Medicine, University of Gothenburg, 413 45 Gothenburg, Sweden
Context: Peak bone mass is an important factor for the lifetime risk of developing osteoporosis. Ways to predict bone development in young adulthood are lacking. Objective and Main Outcome Measures: The aim of this study was to investigate whether baseline measurements of bone turnover markers could predict bone development in early adulthood in men. Design, Setting, and Participants: In total, 817 men (age at baseline, 18.9 ⫾ 0.6 y; mean ⫾ SD) from the population-based Gothenburg Osteoporosis and Obesity Determinants Study were included in this 5-year longitudinal study. Areal bone mineral density (aBMD) and bone mineral content (BMC) were measured using dual-energy x-ray absorptiometry, and volumetric BMD (vBMD) and cortical bone size were measured using peripheral quantitative computed tomography. Blood samples were collected at the baseline visit, and levels of osteocalcin (OC) and N-terminal telopeptide of type I collagen were analyzed. Results: OC was a positive predictor of the increase in aBMD and BMC of the total body (R2: aBMD, 6.6%; BMC, 4.9%), lumbar spine (R2: aBMD, 5.4%; BMC, 5.7%), and radius (R2: aBMD, 14.8%; BMC, 12.8%) between 19 and 24 years (P ⬍ .001). Men in the highest OC quartile at baseline (35.2 ⫾ 4.4 ng/mL; mean ⫾ SD) gained markedly more in radius cortical cross-sectional area (4.0 ⫾ 4.3 vs 1.9 ⫾ 2.9 mm2) and trabecular vBMD (11 ⫾ 7 vs 3 ⫾ 12 mg/mm3) than men in the lowest OC quartile at baseline (17.7 ⫾ 2.3 ng/mL; mean ⫾ SD) (P ⬍ .001). Conclusion: A high OC level at the age of 19 predicts a favorable development in BMD, BMC, and bone size between 19 and 24 years of age. (J Clin Endocrinol Metab 100: 1460 –1468, 2015)
ne of the major risk factors for osteoporotic fractures is low bone mineral density (BMD). It has been demonstrated that every SD decrease in BMD is associated with an increase in the age-adjusted risk of hip fracture that is about 2-fold in postmenopausal women and 3-fold in elderly men (1–3). Peak bone mass (PBM), defined as the amount of bone present in the skeleton at the end of its maturation process (4), has been demonstrated to account for up to half of the variation in BMD at age 65, indicating
O
that achievement of PBM has an important role in the risk of developing osteoporosis (5, 6). The time of achievement of PBM differs depending on skeletal site (7–11) and gender (7, 8, 12, 13). We reported earlier that in men, the timing of puberty is of importance for BMD in young adulthood (14) and is also an important determinant of the development of BMD and bone mineral content (BMC) between 19 and 24 years of age (15). In the present study, our aim was to investigate whether baseline measurements
ISSN Print 0021-972X ISSN Online 1945-7197 Printed in U.S.A. Copyright © 2015 by the Endocrine Society Received October 29, 2014. Accepted January 6, 2015. First Published Online January 16, 2015
Abbreviations: aBMD, areal BMD; BMC, bone mineral content; BMD, bone mineral density; BTM, bone turnover marker; CSA, cross-sectional area; CV, coefficient of variation; DXA, dual-energy x-ray absorptiometry; EC, endosteal circumference; NTX, N-terminal telopeptide of type I collagen; OC, osteocalcin; PBM, peak bone mass; PC, periosteal circumference; PHV, peak height velocity; pQCT, peripheral quantitative computerized tomography; sNTX, serum NTX; vBMD, volumetric BMD.
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of bone turnover markers (BTMs) could predict the development of BMC, BMD, and bone geometry in young men around the time of PBM. Changes of bone turnover during growth can be described during four periods: infancy, prepubertal period, puberty, and the postpubertal period, with corresponding changes in the levels of BTM (16). A Spanish study demonstrated that both formation and resorption markers were higher in early puberty compared to advanced puberty (17). Similar results were found in a recent study of 101 girls (10 –19 y of age), where the authors concluded that the more mature the participants were, the lower their bone biomarker concentrations were (18). In another study, the peak level of BTMs corresponded to the period of the most rapid growth velocity, but not to the peak in bone mass gain (19). The use of BTMs in the clinical setting has been widely debated over the last few years, with conflicting views of their usefulness (20 –23). A growing consensus is forming around the use of BTMs in the monitoring of osteoporosis treatment (24 –26). Less is known about the role of BTM around the time of PBM in young adulthood. In the present study, we aimed to determine whether baseline measurements of BTMs in serum (osteocalcin [OC] and Nterminal telopeptide of type I collagen [NTX]) could predict the development of BMC, BMD, and bone geometry between ages 19 and 24 years in men.
Subjects and Methods Subjects The population-based Gothenburg Osteoporosis and Obesity Determinants (GOOD) study was initiated with the aim to determine environmental and genetic factors involved in the regulation of bone mass and fat mass. The GOOD cohort (n ⫽ 1068) was found to be representative of the general young male population in Gothenburg (10). At the baseline visit, the time of the visit was recorded, and blood samples were drawn from all subjects. Subjects were not fasting at the time of blood sampling. Serum markers of bone turnover were analyzed. In total, 1061 subjects had results on BTMs (OC and NTX) and a recorded time of the visit. Five years later, the study participants were contacted by letter and telephone and invited to participate in the 5-year follow-up study, as previously described (27). Of the 1061 subjects, 817 men (age, 24.1 ⫾ 0.6 y) completed the measurements at the follow-up and were included in the present study. A standardized self-administered questionnaire was used at baseline and follow-up to collect information about smoking (yes/no), present physical activity (h/wk), and nutritional intake. Calcium intake was estimated from dairy product intake. No significant differences were seen between the included (n ⫽ 817) and not included (n ⫽ 244) subjects in age, height, weight, calcium intake, or amount of present physical activity at baseline, using an independent samples t test (data not shown). A lower percentage of smokers was found among the included men than among the not included men (7.3% [60 of 817] vs 13.5% [33 of 244]; P ⫽
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.003). For a subset of the population (n ⫽ 500), growth and weight charts from birth until 19 years of age were obtained to calculate age at peak height velocity (PHV), as previously described (14). This subset was used in the subanalyses including age at PHV. The follow-up period was 61.2 ⫾ 2.3 months (mean ⫾ SD), range 55–70 months. The regional ethical review board at the University of Gothenburg approved the study. Written and oral consent was obtained from all study participants.
Anthropometrical measurements Height was measured using a wall-mounted stadiometer, and weight was measured to the nearest 0.1 kg. The coefficient of variation (CV) was below 1% for these measurements.
Dual-energy x-ray absorptiometry (DXA) Areal BMD (aBMD; g/cm2) of the whole body, femoral neck, total hip (of the left leg), lumbar spine, and the left and right radius were assessed using Lunar Prodigy DXA (GE Lunar Corp). The CVs for the aBMD measurements ranged from 0.4 to 2.5% at baseline and 0.5 to 3% at follow-up, depending on site. The Lunar Prodigy DXA used at the follow-up visit was not the same specimen as the one used at the baseline visit. Cross-calibration between the two Lunar Prodigy DXA machines was performed at the time of follow-up, as previously described (15, 27).
Peripheral quantitative computerized tomography (pQCT) A pQCT device (XCT-2000; Stratec Medizintechnik GmbH) was used to scan the distal arm (radius) and the distal leg (tibia) of the nondominant arm and leg at both the baseline and follow-up visits. A 2-mm-thick single tomographic slice was scanned, with a voxel size of 0.50 mm. The cortical volumetric BMD (vBMD; mg/cm3), cortical cross-sectional area (CSA; mm2), endosteal and periosteal circumference (EC and PC), and cortical thickness (mm) were measured using a scan through the diaphysis (at 25% of the bone length in the proximal direction of the distal end of the bone) of the radius and tibia. The threshold for cortical bone was 711. Trabecular vBMD (mg/cm3) was measured using a scan through the metaphysis (at 4% of the bone length in the proximal direction of the distal end of the bone). Trabecular vBMD was assessed using the inner 45%. The CVs were less than 1% for all pQCT measurements.
Analyses of serum BTMs At the baseline visit, blood samples were drawn, serum was separated, and within 1 hour, the samples were frozen and stored at ⫺80°C. During transport, the samples were kept on dry ice, and they were not thawed until ready for analysis at TECO Medical, Bünde, Germany. Serum OC, a bone formation marker, was analyzed with an EDI Osteocalcin Specific ELISA kit (TECO Medical, Germany) with an intra-assay precision with CV 4.7–5.0%, and an interassay precision with CV 5.7– 8.3%. Serum NTX (sNTX), a bone resorption marker, was analyzed with Osteomark NTx Serum kit (TECO Medical, Germany) with an intra-assay precision with CV 4.6% and an interassay precision with CV 6.9%.
Estimation of age at PHV For a subset of the population (n ⫽ 500), detailed growth and weight charts from birth until 19 years of age were used for
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estimation of age at PHV according to the infancy-childhoodpuberty model (28), as previously described (14). Age at PHV was defined as the age at maximum growth velocity during puberty and was estimated by the algorithm. The average number of measurements between birth and 19 years of age was 21. PHV is generally believed to be reached within 2 years after pubertal onset (28, 29).
Statistical analysis Changes over 5 years in different bone parameters were calculated and adjusted for follow-up time, as previously described (15, 27). To determine whether BTMs were independent predictors of the change over 5 years in different bone variables, stepwise multiple linear regression analyses were performed. Stepwise linear regression analyses were performed, first, including baseline values of the respective bone variable, age, height, log weight, smoking, calcium intake, physical activity, log OC, and log sNTX as independent variables; then, in addition to the above-mentioned variables, also including age at PHV as an independent variable to determine whether BTM could predict bone mass development between 19 and 24 years and whether this prediction was independent of age at PHV or not. The percentage of the variation of change in each bone parameter explained (R2) by all covariates, log OC, log sNTX, and age at PHV, was calculated using the linear regression model. The stepwise selection process criterion for entry into the model was a P value ⱕ .05, and the criterion for removal from the model was a P value ⱖ .10. All P values in the stepwise linear regression
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analyses and Pearson’s correlation were adjusted for multiple comparisons by multiplication of the P value by the number of bone variables analyzed (24). Weight, OC, and NTX were not normally distributed and were therefore log-transformed before being entered into the regression analysis. OC was correlated to the time of the visit and was therefore adjusted for the time of the visit before being entered into statistical analyses. To investigate correlations between change in bone variables and log OC and log sNTX, respectively, as well as between log OC and age at PHV, Pearson’s correlation was used. Study subjects were divided into quartiles according to the baseline value of OC. Differences in baseline anthropometrics and bone variables, as well as change in bone variables between the different quartiles of OC were investigated using one-way ANOVA with Bonferroni correction for multiple comparisons. Pearson 2 test was used to determine whether or not the distribution of smokers differed between included and not included study subjects, and between men in different quartiles of OC. To study the linearity of the association between OC and change in bone variables in more detail, a spline regression model was fitted using knots at the 10th, 50th, and 90th percentiles of serum OC. The splines were second-order functions between the breakpoints and linear functions at the tails resulting in a smooth curve. In general, a P value ⬍ .05 was considered significant. Data were analyzed using SPSS software, version 20.0 (IBM SPSS, Armonk, NY: IBM Corp.).
Table 1. Anthropometrics and Bone Variables at Baseline (18 –20 y) of the Whole Cohort and the Cohort Divided Into Quartiles According to Level of OC at Baseline
n OC, ng/mL Age, y Height, cm Weight, kg Smoking, % Physical activity, h/wk Calcium intake, mg/d PHV (n ⫽ 500) NTX, nM BCE Lumbar spine L2-L4 BMC, g Total hip BMC, g Radius BMC, g Lumbar spine L2-L4 aBMD, g/cm2 Total hip aBMD, g/cm2 Radius aBMD, g/cm2 Tibia cortical vBMD, mg/cm3 Tibia cortical CSA, mm Tibia trabecular vBMD, mg/cm3 Radius cortical vBMD, mg/cm3 Radius cortical CSA, mm Radius trabecular vBMD, mg/cm3
Whole Cohort
OC 1
OC 2
OC 3
OC 4
P (ANOVA)
817 25.9 ⫾ 6.9 18.9 ⫾ 0.6 181.5 ⫾ 6.7 73.6 ⫾ 11.3 7.3 4.3 ⫾ 5.1 1103 ⫾ 697 13.5 ⫾ 1.0 18.6 ⫾ 9.1 61.2 ⫾ 11 43.0 ⫾ 7 10.1 ⫾ 2 1.23 ⫾ 0.1 1.17 ⫾ 0.2 0.58 ⫾ 0.1 1156 ⫾ 20 269 ⫾ 34 266 ⫾ 34 1166 ⫾ 23 96 ⫾ 12 220 ⫾ 41
204 17.7 ⫾ 2.3 19.1 ⫾ 0.5 180.7 ⫾ 6.6 75.4 ⫾ 12.6 11.8 3.7 ⫾ 5.5 1058 ⫾ 645 13.1 ⫾ 0.9 15.1 ⫾ 6.3 61.2 ⫾ 10 43.2 ⫾ 7 10.2 ⫾ 2 1.24 ⫾ 0.1 1.18 ⫾ 0.2 0.60 ⫾ 0.1 1164 ⫾ 16 272 ⫾ 36 272 ⫾ 34 1176 ⫾ 19 96 ⫾ 12 228 ⫾ 41
204 23.2 ⫾ 1.5 19.0 ⫾ 0.6b 181.0 ⫾ 6.6 73.6 ⫾ 11.1 6.4 4.1 ⫾ 4.8 1124 ⫾ 742 13.3 ⫾ 0.9 16.5 ⫾ 7.8 61.3 ⫾ 11 42.7 ⫾ 7 10.2 ⫾ 2 1.24 ⫾ 0.1 1.16 ⫾ 0.2 0.59 ⫾ 0.05 1162 ⫾ 16 267 ⫾ 34 265 ⫾ 35 1171 ⫾ 19 96 ⫾ 11 220 ⫾ 40
205 27.6 ⫾ 1.5 18.8 ⫾ 0.5 a,d 182.0 ⫾ 6.5 73.2 ⫾ 10.9 6.8 4.3 ⫾ 4.7 1186 ⫾ 714 13.7 ⫾ 0.9a 19.3 ⫾ 8.6a,d 60.6 ⫾ 11 43.0 ⫾ 7 10.1 ⫾ 1 1.22 ⫾ 0.1 1.16 ⫾ 0.2 0.58 ⫾ 0.1a,d 1153 ⫾ 20a,c 268 ⫾ 30 264 ⫾ 33 1163 ⫾ 21a,c 96 ⫾ 12 218 ⫾ 42
204 35.2 ⫾ 4.4 18.8 ⫾ 0.5a,f 182.4 ⫾ 6.9 72.0 ⫾ 10.3b 4.4 5.0 ⫾ 5.2 1045 ⫾ 678 14.0 ⫾ 1.0a,c,f 23.6 ⫾ 10.7a,c,e 61.7 ⫾ 12 43.2 ⫾ 7 9.9 ⫾ 2 1.23 ⫾ 0.2 1.16 ⫾ 0.2 0.56 ⫾ 0.1a,c 1147 ⫾ 22a,c,f 269 ⫾ 36 263 ⫾ 33b 1153 ⫾ 25a,c,e 95 ⫾ 12 214 ⫾ 40b
N/A ⬍.001 .032 .026 .032 .070 .146 ⬍.001 ⬍.001 .794 .874 .078 .576 .459 ⬍.001 ⬍.001 .411 .028 ⬍.001 .805 .008.
Abbreviations: BCE, bone collagen equivalents; N/A, not applicable. Values are presented as mean ⫾ SD. The cohort was divided into quartiles according to baseline value of OC. Differences between groups were investigated by ANOVA followed by Bonferroni post hoc test. Differences in smoking were investigated with 2 test. For PHV, quartile 1, n ⫽ 121; quartile 2, n ⫽ 122; quartile 3, n ⫽ 123; and quartile 4, n ⫽ 134. a–f Significantly different from quartile 1 in Bonferroni post hoc test: a P ⬍ .001; b P ⬍ .05. Significantly different from quartile 2: c P ⬍ .001; d P ⬍ .05. Significantly different from quartile 3: e P ⬍ .001; f P ⬍ .05.
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Results Anthropometric characteristics and OC levels at baseline The level of OC at baseline was 25.9 ⫾ 6.9 (mean ⫾ SD) (range, 9.9 –56.6) ng/mL. Subjects were divided into quartiles according to level of OC (Table 1). Men with high OC levels at baseline (quartile 4) were significantly younger than men with low OC at baseline (quartile 1); they weighed less, had a lower percentage of smokers among them, and also had significantly higher levels of NTX at baseline. There were no significant differences in calcium intake or amount of physical activity at baseline, although there was a tendency toward a higher weekly amount of physical activity among the men in quartile 4 (Table 1). As for bone variables, no significant differences were seen between the groups in BMC or aBMD of the lumbar spine or total hip at baseline. Radius aBMD but not BMC was significantly lower in quartile 4 at baseline, due to significantly lower cortical and trabecular vBMD (Table 1). Characteristics of the whole cohort (n ⫽ 817) and of the cohort divided into quartiles are presented in Table 1. OC and NTX levels at baseline were correlated to bone development between 19 and 24 years OC levels at baseline were significantly correlated to the 5-year change in all BMC and aBMD bone parameters, and NTX levels at baseline were significantly correlated to 5-year change in all BMC and aBMD bone parameters except for total hip aBMD and BMC and femoral neck aBMD. The level of OC at baseline was most strongly correlated to 5-year change in lumbar spine and radius BMC and to 5-year change in total body and radius aBMD (Table 2). The level of NTX at baseline was most strongly correlated to 5-year change in BMC at the lumbar spine and radius and to 5-year change in aBMD of the total body and radius (Table 2). The level of OC at baseline was significantly correlated to 5-year change in cortical vBMD of the radius, but not to 5-year change in cortical vBMD of the tibia (Table 2). The level of OC at baseline was also correlated to 5-year change in trabecular vBMD of the radius and tibia, and to 5-year change in cortical CSA of the radius and tibia (Table 2). The level of NTX at baseline was significantly correlated to 5-year change in cortical, but not trabecular, vBMD of the radius and tibia (Table 2). OC levels at baseline independently predicted bone development between 19 and 24 years Changes in BMC and aBMD between 19 and 24 years of age were adjusted for follow-up time and entered into a linear regression model (as dependent variable) including calcium intake, height, log weight, smoking (yes/no), physical activity (h/wk), age, NTX, and OC, all at base-
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Table 2. Association Between BTMs and Change in BMC, BMD, and Bone Geometry Between Baseline (Age 18 –20 y) and Follow-Up (Age 23–25 y) in Men Change Over 5 Years DXA Total body BMC Lumbar spine L2-L4 BMC Total hip BMC Femoral neck BMC Radius nondominant BMC Total body aBMD Lumbar spine L2-L4 aBMD Total hip aBMD Femoral neck aBMD Radius nondominant aBMD pQCT Tibia cortical vBMD Tibia cortical CSA Tibia cortical thickness Tibia periosteal circumference Tibia endosteal circumference Tibia polar SSI Tibia trabecular vBMD Radius cortical vBMD Radius cortical CSA Radius cortical thickness Radius periosteal circumference Radius endosteal circumference Radius polar SSI Radius trabecular vBMD
Log OC
Log sNTX
0.22a 0.24a 0.21a 0.18a 0.36a 0.26a 0.23a 0.18a 0.18a 0.38a
0.14b 0.19a 0.09NS 0.13b 0.31a 0.18a 0.15a 0.09NS 0.10NS 0.30a
0.09NS 0.19a 0.02NS 0.11c ⫺0.01NS 0.14b 0.12c 0.32a 0.22a 0.16a 0.10NS ⫺0.06NS 0.21a 0.18a
0.19a 0.09NS 0.01NS 0.03NS ⫺0.02NS 0.11c 0.05NS 0.27a 0.14b 0.08NS 0.12c 0.01NS 0.15a 0.07NS
Abbreviations: NS, not significant, SSI, strength strain index. n ⫽ 817. Bivariate correlations were performed (Pearson’s correlation). R values are shown. a P ⬍ .001; b P ⬍ .01; c P ⬍ .05. All P values were adjusted for multiple analyses by multiplication of the P value by the number of bone variables analyzed (24).
line, as well as the baseline value of the corresponding bone variable. OC was found to be an independent positive predictor of the increase in total body, lumbar spine, and radius BMC, explaining between 4.9 and 12.8% of the variation of change in these bone variables (Table 3). Likewise, OC was found to be an independent positive predictor of the increase in total body, lumbar spine, and radius aBMD, explaining 6.6, 5.4, and 14.8% of the variation of change in these bone variables, respectively (Table 3). In a subanalysis including the 500 men with available data on age at PHV, changes in BMC and aBMD were entered in a linear regression model as described above, including age at PHV as an additional independent variable. In this model, the percentage of variation explained by OC was diminished, although the percentage of variation explained by the whole model was augmented. The correlation coefficient for age at PHV and log OC was 0.38 (P ⬍ .001). OC levels at baseline predict changes in bone geometry and vBMD between 19 and 24 years Changes in bone geometry and vBMD of the radius and tibia between 19 and 24 years of age were adjusted for
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Table 3. BTMs as Predictors of Change in BMC, BMD, and Bone Geometry Between Baseline (Age 18 –20 y) and Follow-Up (Age 23–25 y) in Men R2, I (n ⴝ 817) Change Over 5 Years BMC Total body Lumbar spine L2-L4 Total hip Femoral neck Radius nondominant aBMD Total body Lumbar spine L2-L4 Total hip Femoral neck Radius nondominant Tibia Cortical vBMD Cortical CSA Cortical thickness PC EC SSI Trabecular vBMD Radius Cortical vBMD Cortical CSA Cortical thickness PC EC SSI Trabecular vBMD
R2, II (Including PHV; n ⴝ 500)
OC, %
NTX, %
Whole Model, %
OC, %
NTX, %
Whole Model, %
4.9a 5.7a 4.2a 3.2b 12.8a
— — — — 3.6a
9.6a 9.7a 6.7a 6.7a 20.9a
— — 3.9c — —
— 1.5c — — 1.9b
11.0a 16.5a 6.8a 10.8a 32.9a
6.6a 5.4a 3.1a 2.9a 14.8a
— — — — 2.0a
13.2a 6.0c 4.8a 7.9a 28.3a
— — — — —
— — — — —
18.4a 14.8a 8.0a 11.0a 40.0a
— 4.5a 0.6b 1.4b — 3.2a —
— — — — — — —
18.2a 11.3a 59.8a 6.4a 4.8a 13.5a 2.9a
— 3.5c — — — — —
— — — — — — —
21.5a 11.6a 61.6a 6.6a 3.0b 12.8a 6.4a
— 4.8a 1.6c — — 4.6a 3.1a
— — — — — — —
46.7a 7.5a 9.2a 8.1a 9.8a 9.5a 4.4a
— — — — — — —
— — — — — — —
49.5a 14.9a 14.7a 11.7a 9.2a 14.7a 7.9a
Abbreviation: SSI, strength strain index. Dashes indicate no significant association. Stepwise linear regression analyses were performed. The stepwise model included baseline values of: I— calcium intake, height, log weight, smoking (yes/no), physical activity (h/wk), age, log OC, log sNTX, and the respective bone variable; and II— calcium intake, height, log weight, smoking (yes/no), physical activity (h/wk), age, log OC, log sNTX, the respective bone variable, and age at PHV. R2 of the whole model is presented, as well as the respective contribution of OC and NTX. a P ⬍ .001; b P ⬍ .01; c P ⬍ .05. All P values were adjusted for multiple analyses by multiplication of the P value by the number of bone variables analyzed (24).
follow-up time and entered into a linear regression model (as dependent variable) as described in the previous section. OC was found to be an independent positive predictor of the increase in cortical CSA of the radius and tibia, explaining 4.8 and 4.5% of the variation of change in cortical CSA of these bones, respectively (Table 3). Likewise, OC was found to be an independent positive predictor of the increase in trabecular vBMD, explaining 3.1% of the variation of change in trabecular vBMD of the radius (Table 3). In a subanalysis including the 500 men with calculated age at PHV, changes in bone geometry and vBMD were entered in a linear regression model as described above, including age at PHV as an additional independent variable. In this model, the percentage of variation explained by OC was diminished, although the percentage of variation explained by the whole model was augmented (Table 3).
Larger increases in BMC, aBMD, vBMD, and bone geometry in men with high OC levels at baseline Men with high OC at baseline (quartile 4) gained twice as much in aBMD of the lumbar spine as men with low OC at baseline (quartile 1) (0.08 ⫾ 0.07 vs 0.04 ⫾ 0.06 g/cm2; P ⬍ .001), and a similar pattern was seen for gain in lumbar spine BMC (5.2 ⫾ 4.4 vs 2.7 ⫾ 3.5 g) (Figure 1). At the total hip, men with high OC at baseline lost markedly less in aBMD than men with low OC at baseline (⫺0.003 ⫾ 0.07 vs ⫺0.04 ⫾ 0.06 g/cm2; P ⬍ .001), whereas for total hip BMC, a small gain was observed for men with high OC at baseline (0.4 ⫾ 2.4 vs ⫺1.2 ⫾ 2.1 g) (Figure 1). At the radius, the increase in cortical CSA was twice as high in men with high OC at baseline as in men with low OC at baseline (4.0 ⫾ 4.3 vs 1.9 ⫾ 2.9 mm2; P ⬍ .001) (Figure 2). A similar pattern, although less pronounced, was observed for the increase in CSA of the tibia (13.6 ⫾ 10.3 vs
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Figure 1. A–D, Change in BMC of the lumbar spine (A) and total hip (B) and change in aBMD of the lumbar spine (C) and total hip (D) in men between 19 and 24 years of age, divided into quartiles [1 (low OC)– 4 (high OC)] according to level of OC at baseline. E, Change in lumbar spine aBMD between 19 and 24 years of age according to level of OC at baseline. F, Change in total hip aBMD between 19 and 24 years of age according to level of OC at baseline.
9.2 ⫾ 8.0 mm2; P ⬍ .001) (Figure 2). The increase in trabecular vBMD of the radius was almost four times higher in men with high OC at baseline than men with low OC at baseline (11 ⫾ 7 vs 3 ⫾ 12 mg/mm3; P ⬍ .001), and the loss in trabecular vBMD of the tibia was six times greater in men with low OC at baseline than men with high OC at baseline (⫺6 ⫾ 14 vs ⫺1 ⫾ 14 mg/mm3; P ⬍ .001) (Figure 2). Increasing levels of OC were associated with a greater increase in lumbar spine aBMD (Figure 1E). For total hip aBMD, an OC level above 39 ng/mL at baseline was associated with an increase in total hip aBMD over 5 years, whereas an OC level below that was associated with a decrease in total hip aBMD (Figure 1F). Similarly, a level above 41 ng/mL at baseline was associated with an increase in trabecular vBMD of the tibia over 5 years, whereas an OC level below that was associated with a decrease in trabecular vBMD of the tibia (Figure 2E).
Discussion In the present study, we found that OC measured at baseline was a positive independent predictor of bone devel-
opment between 19 and 24 years of age in men. A high OC level at 19 years of age was associated with larger increases in lumbar spine aBMD and BMC, a lesser decrease in total hip aBMD and BMC, and larger increases in CSA and trabecular vBMD of the radius. Because PBM is an important factor for the risk of developing osteoporosis and consequently osteoporotic fractures later in life (5, 6), the possibility to predict bone development in a young individual is valuable. In this cohort, we have earlier reported that between the ages of 19 and 24, aBMD especially of the radius but also lumbar spine and total body increased, whereas a decrease in aBMD at the femoral neck and total hip was seen (27), which indicates that this period in life is of importance in maximizing PBM. The current findings suggest that measuring OC can be of value when evaluating bone development in men in early adulthood. We have earlier reported that age at PHV is a strong independent positive predictor of bone development between 19 and 24 years of age (15). Age at PHV is an objective measurement of pubertal timing and has been shown to be strongly correlated to age at menarche in females (30). In the clinical setting, data needed to calculate age at PHV are generally not available. If, as the pres-
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Figure 2. A–D, Change in cortical CSA and trabecular vBMD of the tibia (A, B) and of the radius (C, D) in men between 19 and 24 years of age, divided into quartiles [1 (low OC)– 4 (high OC)] according to level of OC at baseline. E, Change in tibia trabecular vBMD between 19 and 24 years of age according to level of OC at baseline.
ent study indicates, measurement of OC could help predict bone development in a similar manner, it could be a useful complement when information about age at PHV is lacking. In previous studies, markers of bone turnover have been shown to correlate well with age at PHV (31, 32). One small study (n ⫽ 100) of males aged 10 –17 years reported a very close correlation between OC levels and the pubertal growth spurt (33). In our study, when dividing the cohort into quartiles according to OC level at baseline, men with high OC at baseline were significantly younger, weighed less, and had experienced age at PHV at a later age compared with men with low OC at baseline. They also had significantly higher NTX values than men with low OC at baseline. We suggest that a high OC level at age 19 implies that the individual is at a lower maturational level and therefore may achieve more in BMD, BMC, and bone size in the following years than an individual with lower OC at this point in life. A large, crosssectional study of men 19 – 85 years of age (n ⫽ 934) established that average concentrations of bone formation markers (OC, bone alkaline phosphatase, procollagen type 1 N-terminal propeptide) were highest before 25 years of age and then decreased (34). In a Japanese study of young females (aged 12–30 y), the authors demon-
strated high levels of bone alkaline phosphatase and NTX at age 12, which thereafter decreased until the age of 18 and then remained almost constant (35). Neither of these studies was longitudinal and therefore could not link levels of BTM to longitudinal development of BMD. To the best of our knowledge, our study is the first longitudinal study linking measurements of BTMs to bone development of men in young adulthood. One earlier study reported that NTX (measured at follow-up) was negatively correlated with change in femoral neck aBMD over 5 years (r ⫽ ⫺0.21) in men 35– 69 years of age and could explain 3.8% of the variance of the change in femoral neck aBMD (22). In the present study, NTX, as well as OC, was positively correlated to change in BMC and aBMD at most measured sites (r ⫽ 0.11– 0.38). NTX was not able to explain any of the variance of the change in femoral neck aBMD over 5 years, whereas OC could explain 2.9%. In contrast to the men in the study described above, the men in our cohort were still increasing in bone mass and bone density at most sites, and measurements of OC seem to contribute more than measurements of NTX to predicting bone development in this age group. In the present study, OC was found to be an independent positive predictor of the increase in total body, lumbar spine, and radius aBMD and BMC, explaining up to
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12.8 and 14.8% of the variation in change in radius BMC and aBMD, respectively. When adding NTX and baseline measurement of bone variables and covariates, the model could explain up to 20.9 and 28.3%, respectively. OC was also associated with a lesser decrease in total hip and femoral neck aBMD and BMC, explaining 2.9 – 4.2% of the variation of change over 5 years in these bone variables. When age at PHV was added to the linear regression analyses, the percentage explained by OC was diminished, although the percentage explained by the whole model was augmented. This indicates that most of the ability of OC to predict bone development is mediated by age at PHV, but also that for some bone variables, OC and age at PHV independently provide information to explain the variation in bone development. There are some limitations with the present study. Primarily, although we adjusted measurements of OC for the time of the visit, ideally all blood samples should have been collected at the same time of day from fasting subjects. We cannot exclude that limitations in the sampling method may have contributed to the lack of findings concerning NTX. Also, it would be preferable to have several blood samples instead of a single sample. Limitations also include that the study population was primarily white and was constituted of men only; therefore, the conclusions are restricted to the male population, and the results cannot be directly transferred to other ethnicities. There were also strengths with the present study. It is a large, longitudinal study involving a well-characterized cohort measured with both DXA and pQCT. Pubertal timing was assessed with an objective method, minimizing the risk of inaccurate classification. In conclusion, our results demonstrate that high levels of OC at age 19 were associated with larger increases in BMC, BMD, and bone size in young adulthood, indicating that measuring OC could be of value in the evaluation of a young individual’s bone health.
Acknowledgments We thank statistician Helena Johansson for excellent assistance and guidance related to statistical analyses. Address all correspondence and requests for reprints to: Mattias Lorentzon, Professor, MD, PhD, Department Head, Geriatric Medicine, Building K, Sixth Floor, Sahlgrenska University Hospital, 431 80 Mölndal, Sweden; and Centre for Bone and Arthritis Research, Institute of Medicine, Vita Stråket 11, Sahlgrenska University Hospital, 413 45 Gothenburg, Sweden. E-mail: [email protected]. This study was supported by the Swedish Research Council, the Swedish Foundation for Strategic Research, the European Commission, the Lundberg Foundation, the Torsten and Ragnar Söderberg’s Foundation, Petrus and Augusta Hedlund’s Foundation, the Avtal för Läkarutbildning Och Forskning grant from
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the Sahlgrenska University Hospital, the Novo Nordisk Foundation, and the Gustav V and Queen Victoria Freemason Foundation. Authors’ Roles: study design, M.L. and C.O.; study conduct, M.L. and M.N.; data collection, M.L., M.N., J.M.K., A.D.; data analysis, A.D., M.L., J.M.K.; data interpretation, A.D. and M.L.; drafting manuscript, A.D. and M.L.; revising manuscript content, M.L., M.N., J.M.K., D.M., C.O.; approving final version of manuscript, M.L., M.N., J.M.K., D.M., C.O., and A.D. A.D. and M.L. take responsibility for the integrity of the data analysis. Disclosure Summary: All authors have no conflicts of interest.
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