Association of serum osteocalcin with bone microarchitecture and muscle mass in Beijing community-dwelling postmenopausal women.

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Shuying Liu, Qianqian Pang, Wenmin Guan, Fan Yu, Ou Wang, Mei Li, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3299818/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 11 Jan, 2024 Read the published version in Endocrine → Version 1 posted 4 You are reading this latest preprint version Abstract Background Osteoporosis is a systemic skeletal disease with increasing bone fragility and prone to fracture. Osteocalcin (OC), as the most abundant non collagen in bone matrix, has been extensively used in clinic as a biochemical marker of osteogenesis. Two forms of OC were stated on circulation, including carboxylated osteocalcin (cOC) and undercarboxylated osteocalcin (ucOC). OC was not only involved in bone mineralization, but also in the regulation of muscle function. Objective This study explored the relationship between serum OC, cOC, ucOC levels and bone mineral density (BMD), bone microarchitecture, muscle mass and physical activity in Chinese postmenopausal women. Method 216 community-dwelling postmenopausal women were randomized enrolled. All subjects completed biochemical measurements, including serum β-isomer of C-terminal telopeptides of type I collagen (β-CTX), N-terminal propeptide of type 1 procollagen (P1NP), alkaline phosphatase (ALP), OC, cOC and ucOC. They completed X-ray absorptiometry (DXA) scan to measure BMD, appendicular lean mass (ALM) and trabecular bone score (TBS). They completed high resolution peripheral quantitative CT (HR-pQCT) to assess peripheral bone microarchitectures. Results Serum OC, cOC and ucOC were elevated in osteoporosis postmenopausal women. In bone geometry, serum ucOC was positively related with total bone area (Tt.Ar) and trabecular area(Tb.Ar). In bone volumetric density, serum OC and ucOC were negatively associated with total volume bone mineral density (Tt.vBMD) and trabecular volume bone mineral density (Tb.vBMD). In bone microarchitecture, serum OC and ucOC were negatively correlative with Tb.N and Tb.BV/TV, and were positively correlated with Tb.Sp. Serum OC and ucOC were positively associated with Tb.1/N.SD. Serum OC was negatively related with Tb.Th. Serum ucOC was positively associated with ALM. The high level of serum OC was the risk factor of osteoporosis. ALM was the protective factor for osteoporosis. Conclusion All forms of serum OC were negatively associated with BMD. Serum OC and ucOC mainly influenced microstructure of trabecular bone in peripheral skeletons. Serum ucOC participated in modulating both bone microstructure and muscle mass. osteocalcin undercarboxylated osteocalcin bone microarchitecture muscle mass postmenopausal women Figures Figure 1 Background Osteoporosis is a systemic skeletal disease described as low bone mass, impaired bone microarchitecture, increased bone fragility and prone to fracture.[ 1 ] Osteoporosis is a silent disease that can occur at any age, but it is more common in postmenopausal women.[ 2 ] Fragile fracture is a grave consequence related to osteoporosis which is one of the main causes of disability and death in postmenopausal women. The latest large scale epidemiological survey of osteoporosis in China demonstrated that the prevalence of osteoporosis in population over 50 years old was 19.2%, including 32.1% in women, and the ratio of people with low bone mass was 46.4%, with 45.9% in women.[ 3 ] It indicated that osteoporosis became a noticeable health problem in China, especially in postmenopausal women who were in urgent need to pay attention to bone health to early diagnosis and treatment of osteoporosis. Osteocalcin (OC) was regarded as an osteogenesis biomarker in clinical that could be served as a bone formation marker in postmenopausal women with osteoporosis. [ 4 ] OC was also known as bone γ- carboxyglutamic acid (Gla) protein which was synthesized by osteoblasts and then secreted in the bone extracellular matrix (ECM), becoming the most abundant non collagen protein in ECM.[ 5 ] OC contains two forms in circulation, including undercarboxylated osteocalcin (ucOC) and carboxylated osteocalcin (cOC). Through the γ-carboxylation of three glutamic acids at positions 17, 21, and 24, ucOC convert to cOC, which could obtain a high affinity for calcium and activate its ability to combine with mineral hydroxyapatite.[ 6 , 7 ] There were numerous studies exploring the relationship between serum OC and bone, including the relationship between serum OC and BMD. Some studies shown a negative correlation between serum OC and BMD, while some studies shown no association between serum OC and BMD. [ 8 – 11 ] However, there was relatively few research on the relationship between serum OC and bone microstructure. Moreover, occurrence of osteoporotic fracture was not only due to the defect in bone, but also related to deterioration in muscle. Many studies have shown that patients with osteoporosis accompanied with reduced muscle mass. [ 12 – 14 ] Low muscle mass increased the risk of fall and fracture. [ 15 – 17 ] Studies showed serum OC was involved in the regulation of muscle functions during exercise.[ 18 , 19 ] However, whether serum OC could be a mediator of bone and muscle communication needs to be explored. Therefore, in this study, we aimed to investigate the relationship among different forms of serum OC, BMD, bone microarchitecture, muscle mass and physical activity in Chinese postmenopausal women to identify the effects of serum OC on bone and muscle. Method Subjects The study population was a subgroup from a nationwide, observational, cross-sectional study to investigate the prevalence of VFs in Chinese urban-dwelling postmenopausal women aged over 50 years old (ChiVOS).[ 20 ] Menopause was regarded as having no menstrual cycle for more than one year. In strict accordance with the principle of age stratification and random sampling, a survey of postmenopausal women living in community was conducted in Dongcheng District, Beijing. 274 postmenopausal women were enrolled in this study. 55 subjects who did not complete entire examination were precluded. In final, 216 subjects were included in the study. All subjects completed informed consent forms. This study was approved by the PUMCH ethics committee. Clinical Data Collection All subjects performed the ChiVOS questionnaire, which collected the information of self-reported demographic, lifestyle, smoking history, alcohol history, physical activities, supplement of calcium, supplement of vitamin D and medical history of anti-osteoporosis drugs. The questionnaire interview was conducted by one designated investigator to minimize the variability in data collection. Anthropometric measurements including height and weight were measured in accordance with standards. Body mass index (BMI) was calculated as dividing weight (kg) by the square of height (m). Biochemical examinations Blood sample were harvested in the morning after at least 8 hours fasting. Serum was gathered after centrifugation at 3,000 r/min for 10 minutes. Serum ucOC were assayed by ELISA (MK118, Takara Bio Inc, Japan), with the intra assay coefficient of variation of 4.58%-6.66% and the inter assay coefficient of variation of 5.67%-9.87%. Serum cOC were measured by ELISA (MK111, Takara Bio Inc, Japan), with the intra assay coefficient of variation of 3.3%-4.8%, the inter assay coefficient of variation of 0.7%-2.4%. Serum calcium (Ca), phosphorus (Pi), and alkaline phosphatase (ALP) measurements were performed by a Beckman Automatic Biochemical Analyzer (AU5800, Beckman Coulter, Indianapolis, IN, USA). Serum OC, β-isomer of C-terminal telopeptides of type I collagen (β-CTX), N-terminal prepeptide of type 1 procollagen (P1NP) were measured by electrochemiluminescence immunoassay (Roche Cobas e601, Mannheim, Germany). Parathyroid hormone (PTH) was measured using chemiluminescence (Siemens ADVIA Centaur, Munich, Germany). 25-hydroxyvitamin D (25OHD) was measured by electrochemiluminescence immunoassay (Roche Cobas e601, Mannheim, Germany). Creatinine (Cr) was measured by an automated Roche electrochemiluminescence system (E170; Roche Diagnostics, Basel, Switzerland). All measurements were completed by central laboratory of PUMCH. BMD, TBS, and Appendicular Lean Mass Measurements Dual Energy X-ray Absorptiometry (DXA) was implemented by certified technicians through GE-Lunar scanners (GE Healthcare, Madison, WI, USA) to measure BMD at the lumbar spine (L1-L4), femur neck, and total hip with BMD absolute values and T-score were recorded. Based on BMD results according to WHO criteria for the classification of osteopenia and osteoporosis, T-score ≥ -1.0 was defined as normal group, while − 2.5༜T-score༜-1.0 as osteopenia group, and T-score ≤-2.5 as osteoporosis group. [ 21 ] The trabecular bone score (TBS) was calculated through TBS iNsight v2.1 software (Medimaps) by analysing the same region of interest (L1-L4) of DXA scan. The trunk and limbs were segmented automatically through DXA. Appendicular lean mass (ALM) was obtained by software. The calculation of skeletal muscle index (SMI) was using ALM divided by the square of height (m). High-resolution peripheral quantitative CT (HR-pQCT) Non-dominant distal radius and distal tibia were measured by HR-pQCT (XtremeCT II; Scanco Medical, Zurich, Switzerland) to assess bone microarchitecture using a standard protocol.[ 22 ] Isotropic resolution of the image was 61 mm. The first slice of region of interest (ROI) of every measurement including radius and tibia was to 9.5 mm and 22.5 mm away from the mid-endplate, respectively. Combining 168 parallel CT slices for 3D image reconstruction of non-dominant radius and tibia. The contour between the cortex and trabecula was automatically identified by the manufacturer's standard software. The relevant bone microarchitecture parameters were measured as following:(1) Bone geometry structure including total area (Tt.Ar, mm 2 ), cortical area (Ct.Ar, mm 2 ) and trabecular area (Tb.Ar, mm 2 ) .(2) Volumetric bone mineral density (vBMD) containing total volumetric BMD (Tt.vBMD, mg HA/ccm), cortical volumetric BMD (Ct.vBMD, mg HA/ccm) and trabecular volumetric BMD (Tb.vBMD, mg HA/ccm).(3) Bone microarchitecture including cortical parameters as cortical thickness (Ct.Th, mm) and cortical porosity (Ct.Po, %) and trabecular parameters as trabecular bone volume fraction (Tb.BV/TV), trabecular number (Tb.N, 1/mm), trabecular thickness (Tb.Th, mm), trabecular separation (Tb.Sp, mm), inhomogeneity of network (Tb.1/N.SD, mm). Muscle strength and physical performance measurement Hand grip strength as an assessment of muscle strength was assessed by electronic hand dynamometer, measuring the grip strength of dominant hand three times, with rest for at least 60 seconds, and take the maximum value to record. Short-Physical Performance Battery (SPPB) score was measured and calculated according to the standard, including standing balance, walking speed, and the ability to stand up from a chair.[ 23 ] Statistics One way analysis of variance (ANOVA) analysis was used to compare differences of variables in multiple groups. Least significant difference (LSD) analysis was used to compare difference of variables between two groups. Partial correlation analysis was conducted after adjusting for age, BMI, and 25OHD. Standardize the data and logistic regression were used to calculate the odds ratio (OR) with 95% CIs for prevalence of osteoporosis. All data were analyzed by SPSS software (version 22.0, SPSS Inc. of IBM, USA). Using two-tailed statistical measurements, p < 0.05 was considered statistically significant. Results Clinical characteristics and biochemical markers of the postmenopausal women The clinical characteristics and biochemical markers of 216 postmenopausal women were summarized in Table 1 . The average age was 67.92 ± 8.62 years old. The average BMI was 26.06 ± 4.16 kg/m 2 . The mean serum OC concentration was 17.50 ± 8.74 ng/mL, while serum cOC and ucOC concentration was 4.18 ± 2.96 ng/mL and 2.21 ± 1.88 ng/mL, respectively. The average ALM was 15.34 ± 2.28 kg, and the average SMI was 6.37 ± 0.78 kg/m 2 . Three groups were divided based on age as group aged 50–59 years, group 60–69 years and group aged over 70 years. There was no difference among 3 groups in serum OC and ucOC. Serum cOC had a trend of increasing with age that serum cOC was highest in group over 70 years. There was a tendency that ALM decreased with age that ALM reduced significantly in group over 70 years. SMI was declined in group over 70 years than group 60–69 years. Table 1 Clinical characteristics and biochemical markers of postmenopausal women(N = 216). Variables Total (n = 216) 50–59 years (n = 56) 60–69 years (n = 83) Over 70 years (n = 77) p p1 p2 p3 Age (year) 67.92 ± 8.62 57.11 ± 2.47 66.20 ± 2.31 77.64 ± 3.88 / / / / Height (cm) 155.05 ± 6.38 157.21 ± 6.60 155.68 ± 6.04 152.79 ± 5.93 < 0.01 0.152 < 0.01 < 0.01 Weight (kg) 62.66 ± 10.75 63.60 ± 11.78 64.85 ± 10.51 59.67 ± 9.60 < 0.01 0.460 < 0.05 < 0.01 BMI (kg/m 2 ) 26.06 ± 4.16 25.70 ± 4.55 26.79 ± 4.22 25.54 ± 3.71 0.124 0.132 0.817 0.057 Smoking history, n (%) 13(6.02%) 6 (10.71%) 4 (4.82%) 3 (3.90%) < 0.01 0.070 0.084 0.947 Alcohol history, n (%) 12(5.56%) 5 (8.93%) 5 (6.02%) 2 (2.60%) < 0.01 0.353 0.105 0.431 More than one hour of moderate intensity physical activities/day, n (%) 182(4.26%) 45 (80.36%) 71(85.54%) 66(85.71%) 0.651 0.414 0.406 0.976 Calcium supplements, n (%) 65(30.09%) 15(26.79%) 23(27.71%) 27(35.06%) < 0.01 0.248 0.068 0.444 Vitamin D supplements, n (%) 41(18.98%) 12(21.43%) 14(16.87%) 15(19.48%) 0.389 0.999 0.647 0.610 Anti-osteoporosis drugs (≥ 3 months), n (%) 5(2.31%) 5(8.93%) 0 0 / / / / OC (ng/mL) 17.50 ± 8.74 16.65 ± 4.82 17.50 ± 9.96 18.13 ± 9.56 0.630 0.577 0.337 0.648 cOC (ng/mL) 4.18 ± 2.96 3.51 ± 2.89 3.98 ± 2.97 4.90 ± 2.90 < 0.05 0.350 < 0.01 < 0.05 ucOC (ng/mL) 2.21 ± 1.88 2.10 ± 1.63 2.33 ± 2.08 2.16 ± 1.83 0.738 0.475 0.865 0.554 Ca (mmol/L) 2.32 ± 0.07 2.34 ± 0.07 2.29 ± 0.07 2.33 ± 0.07 < 0.01 < 0.01 0.132 < 0.01 Pi (mmol/L) 1.21 ± 0.14 1.24 ± 0.12 1.18 ± 0.14 1.21 ± 0.14 0.070 < 0.05 0.308 0.175 β-CTX (ng/mL) 0.37 ± 0.17 0.36 ± 0.12 0.37 ± 0.18 0.38 ± 0.19 0.774 0.849 0.504 0.593 P1NP (ng/mL) 55.05 ± 20.97 54.11 ± 17.09 55.89 ± 21.12 54.81 ± 23.46 0.881 0.625 0.850 0.746 ALP (U/L) 83.60 ± 22.42 89.75 ± 25.08 83.75 ± 20.17 78.96 ± 21.88 < 0.05 0.118 < 0.01 0.173 PTH (pg/mL) 42.13 ± 22.49 40.11 ± 11.40 44.15 ± 29.84 41.42 ± 19.19 0.552 0.301 0.740 0.445 25-OHD (ng/mL) 17.17 ± 6.72 17.35 ± 6.43 15.30 ± 5.61 19.05 ± 7.50 < 0.05 0.071 0.141 < 0.01 Cr (mmol/L) 65.98 ± 17.18 60.75 ± 9.11 60.80 ± 9.93 75.36 ± 23.02 < 0.01 0.987 < 0.01 < 0.01 ALM (kg) 15.34 ± 2.28 15.93 ± 2.64 15.76 ± 2.12 14.46 ± 1.90 < 0.01 0.661 < 0.01 < 0.01 SMI (kg/m 2 ) 6.37 ± 0.78 6.44 ± 0.97 6.50 ± 0.74 6.18 ± 0.62 < 0.05 0.654 0.059 < 0.05 Hand grip (kg) 22.12 ± 5.15 24.75 ± 4.76 22.83 ± 5.09 19.43 ± 4.21 < 0.01 < 0.05 < 0.01 < 0.01 SPPB score 11.15 ± 1.65 11.98 ± 0.13 11.48 ± 1.12 10.19 ± 2.19 < 0.01 0.279 < 0.01 < 0.01 BMI, body mass index; OC, osteocalcin; cOC, carboxylated osteocalcin; ucOC, undercarboxylated osteocalcin; Ca, calcium; Pi, phosphate; β-CTX, β-isomer of C-terminal telopeptides of type I collagen; P1NP, N-terminal propeptide of type 1 procollagen; ALP, alkaline phosphatase; PTH, parathyroid hormone; 25OHD, 25-hydroxyvitamin D; Cr, creatinine; ALM, appendicular lean mass; SMI, skeletal muscle index [SMI = ALM (kg) / height 2 (m)]; SPPB, short physical performance battery. p, difference among 3 groups; p1, groups between 50–59 years group and 60–69 years group; p2, groups between 50–59 years group and over 70 years group; p3, groups between 60–69 years group and over 70 years group. Serum OC, cOC and ucOC levels in normal, osteopenia and osteoporosis status The difference of serum OC, cOC and ucOC among normal group, osteopenia group and osteoporosis group were shown in Fig. 1. The number of postmenopausal women in normal, osteopenia and osteoporosis group were 71, 104 and 41, respectively. Serum OC, cOC and ucOC were significantly elevated in osteoporosis group than normal group. Serum OC and cOC were increased significantly in osteoporosis group than osteopenia group. Relationship between serum OC, cOC and ucOC with BMD, TBS and peripheral bone microstructure Partial correlation analysis after adjusted with age, BMI, 25OHD between serum OC, cOC, ucOC with BMD, TBS and bone microarchitecture were shown in Table 2 . Serum OC represented inverse correlation with lumbar spine, femoral neck and total hip BMD. Serum OC and cOC were negatively associated with femoral neck and total hip BMD. There was no association between serum OC, cOC and ucOC with TBS. In peripheral bone microstructure bone geometry analysis, serum ucOC was positively correlated with Tt.Ar of tibia and Tb.Ar of radius and tibia. In bone volumetric density analysis, serum OC and ucOC had inversely correlations with Tt.vBMD and Tb.vBMD at both radius and tibia. In bone microarchitecture analysis, serum OC and ucOC were negatively correlative with Tb.N and Tb.BV/TV, and were positively correlated with Tb.Sp of both radius and tibia. Serum OC and ucOC had positively correlation with Tb.1/N.SD. Serum OC had inversely correlation with Tb.Th of radius. Serum cOC was negatively associated with Tb.N and was positively related with Tb.Sp of radius. Table 2 Correlations between OC, cOC, ucOC with BMD and bone microarchitecture. (N = 216) OC cOC ucOC r p r p r p DXA Lumbar spine BMD -0.161 < 0.05 -0.092 0.183 -0.122 0.075 Femoral neck BMD -0.238 < 0.01 -0.168 < 0.05 -0.163 < 0.05 Total hip BMD -0.222 < 0.01 -0.177 < 0.01 -0.200 < 0.01 TBS 0.006 0.932 0.059 0.388 0.013 0.852 Geometry Tt.Ar (mm 2 ) R 0.039 0.567 0.048 0.482 0.130 0.059 T 0.067 0.329 0.060 0.384 0.179 < 0.01 Ct.Ar (mm 2 ) R -0.110 0.109 -0.058 0.403 0.005 0.942 T -0.083 0.230 -0.068 0.323 -0.024 0.726 Tb.Ar (mm 2 ) R 0.067 0.329 0.057 0.411 0.135 < 0.05 T 0.068 0.325 0.049 0.479 0.172 < 0.05 Volumetric density Tt.vBMD (mg HA/cm 3 ) R -0.180 < 0.01 -0.114 0.097 -0.142 < 0.05 T -0.208 < 0.01 -0.117 0.088 -0.223 < 0.01 Ct.vBMD (mg HA/cm 3 ) R -0.099 0.151 -0.041 0.552 -0.086 0.214 T -0.111 0.106 -0.018 0.793 -0.102 0.138 Tb.vBMD (mg HA/cm 3 ) R -0.213 < 0.01 -0.134 0.051 -0.166 < 0.05 T -0.214 < 0.01 -0.108 0.116 -0.222 < 0.01 Microarchitecture Ct.Th (mm) R -0.133 0.053 -0.077 0.264 -0.048 0.490 T -0.100 0.144 -0.097 0.159 -0.101 0.143 Ct.Po (%) R -0.052 0.448 0.067 0.332 0.030 0.658 T -0.037 0.587 -0.036 0.598 -0.089 0.196 Tb.N(1/mm) R -0.201 < 0.01 -0.139 < 0.05 -0.226 < 0.01 T -0.152 < 0.05 -0.075 0.277 -0.163 < 0.05 Tb.Th(mm) R -0.136 < 0.05 0.068 0.323 0.058 0.399 T -0.075 0.274 -0.027 0.695 -0.083 0.227 Tb.Sp (mm) R 0.191 < 0.01 0.145 < 0.05 0.283 < 0.01 T 0.148 < 0.05 0.064 0.355 0.152 < 0.05 Tb.BV/TV (%) R -0.213 < 0.01 -0.128 0.062 -0.149 < 0.05 T -0.213 < 0.01 -0.106 0.123 -0.213 < 0.01 Tb.1/N.SD (mm) R 0.161 < 0.05 0.121 0.077 0.277 < 0.01 T 0.113 0.102 0.034 0.620 0.106 0.122 Adjusted for age, BMI, 25OHD. BMD, bone mineral density; R, radius; T, Tibia; Tt.Ar, total bone area; Ct.Ar, cortical area; Tb.Ar, trabecular area; Tt.vBMD, total volume bone mineral density; Ct.vBMD, cortical volume bone mineral density; Tb.vBMD, trabecular volume bone mineral density; Ct.Th, cortical thickness; Ct.Po, cortical porosity; Ct.Pm, cortical perimeter; Tb.N, trabecular number; Tb.Th, trabecular thickness; Tb.Sp, trabecular separation; Tb.BV/TV, trabecular bone volume fraction; Tb.1/N.SD, inhomogeneity of network. Correlation between serum OC, cOC, ucOC with BTMs, muscle mass and physical activity Adjusted for age, BMI and 25OHD, partial correlation between serum OC, cOC, ucOC with BTMs, muscle mass and physical activity were shown in Table 3 . Serum OC, cOC and ucOC were positive associated with β-CTX, P1NP and PTH. Serum OC was also positive related with ALP. Serum ucOC but not OC or cOC was positively associated with ALM. There was no relationship between any form of serum OC (including cOC and ucOC) with hand grip and SPPB score. Table 3 Correlations between OC, cOC, ucOC with BTMs, muscle mass and physical activity. (N = 216) cOC ucOC β-CTX P1NP ALP PTH ALM SMI Hand grip SPPB OC 0.401 ** 0.380 ** 0.764 ** 0.733 ** 0.194 ** 0.599 ** -0.029 -0.044 -0.009 0.075 cOC - 0.319 ** 0.313 ** 0.280 ** -0.002 0.165 * 0.012 0.031 0.082 -0.003 ucOC - - 0.327 ** 0.319 ** 0.116 0.171 ** 0.147 * 0.117 0.059 0.034 Adjusted for age, BMI and 25OHD. OC, osteocalcin; cOC, carboxylated osteocalcin; ucOC, undercarboxylated osteocalcin; β-CTX, β-isomer of C-terminal telopeptides of type I collagen; P1NP, N-terminal propeptide of type 1 procollagen; ALP, alkaline phosphatase; PTH, parathyroid hormone; ALM, appendicular lean mass; SMI, skeletal muscle index [SMI = ASM (kg) / height 2 (m)]; SPPB, short physical performance battery. * , p < 0.05; ** , p < 0.01. The association of serum OC, cOC, ucOC, BTMs and muscle mass with prevalence of osteoporosis According to WHO criteria for the classification of osteoporosis, the odds ratio in logistic regression of prevalent osteoporosis was shown in Table 4 . 1-SD increase in serum OC was correlated with an OR with 1.491 for osteoporosis (95%CI = 1.071–2.076, p = 0.018). After adjusted age, BMI and 25OHD, 1-SD increase in serum OC was related with an OR with 1.414 (95%CI = 1.028–1.944, p = 0.033) for osteoporosis. Neither serum cOC or ucOC was associated with osteoporosis after adjusted. With or without adjustment, P1NP and β-CTX were risk factors of osteoporosis. ALM and SMI were protective factors of osteoporosis. 1-SD increase in ALM was correlated with an OR with 0.330 for osteoporosis (95%CI = 0.180–0.604, p = 0.000) after adjusted. 1-SD increase in SMI was associated with an OR with 0.513 for osteoporosis (95%CI = 0.266–0.989, p = 0.046) after adjusted. Table 4 Odds ratio in logistic regression of prevalent osteoporosis according to a 1-SD increase in OC, cOC, ucOC, BTMs, ALM and SMI. Variables Unadjusted Adjusted* OR (95% CI) p OR (95% CI) p OC 1.491 (1.071–2.076) 0.018 1.414 (1.028–1.944) 0.033 cOC 1.484 (1.094–2.012) 0.011 1.333 (0.946–1.879) 0.100 ucOC 1.297 (0.957–1.758) 0.093 1.311 (0.936–1.838) 0.115 P1NP 1.669 (1.199–2.325) 0.002 1.797 (1.258–2.567) 0.001 β-CTX 1.742 (1.249–2.430) 0.001 1.766 (1.244–2.506) 0.001 ALM 0.272 (0.161–0.459) 0.000 0.330 (0.180–0.604) 0.000 SMI 0.438 (0.283–0.678) 0.000 0.513 (0.266–0.989) 0.046 *Adjusted for age, BMI,25OHD. OC, osteocalcin; cOC, carboxylated osteocalcin; ucOC, undercarboxylated osteocalcin; P1NP, N-terminal propeptide of type 1 procollagen; β-CTX, β-isomer of C-terminal telopeptides of type I collagen; ALM, appendicular lean mass; SMI, skeletal muscle index [SMI = ALM (kg) / height 2 (m)] Discussion To our knowledge, this study firstly fully explored the relationship of serum OC and its different forms between BMD, bone microarchitecture, muscle mass and physical activity in Chinese postmenopausal women. All forms of serum OC were negatively associated with BMD. Elevated levels of serum OC and ucOC were related with trabecular bone deterioration. In addition, serum ucOC was positively associated with muscle mass. In our study, we found that serum OC and cOC showed a seemingly increasing trend with age but without statistical significance, which was in accord with Japanese female study.[ 24 ] While in another study, serum OC was negatively associated with age after 50 years old in Danish women.[ 25 ] Serum ucOC elevated in group 60–69 years and decreased in group of over 70 years, which was consistent with a study in Chinese women.[ 26 ] While in a study of Korea women, serum ucOC showed a trend that decreased with age after 50 years old.[ 27 ] The differences in studies may be due to differences in race and population. Our study found the negative correlation between serum OC and BMD which was in line with the results of other studies in postmenopausal women. [ 28 – 30 ] Elevated serum OC was sensitive to monitor the accelerated bone turnover after the onset of menopause.[ 31 ] In addition, negative associations between serum ucOC and femoral neck, total hip BMD were found in our study, which were consistent with another study involving elderly women.[ 30 ] In accordance with our research, there was no association between serum ucOC and lumbar BMD in a study of early postmenopausal women.[ 32 ] While in another study of Chinese women, serum ucOC was negative related with BMD at all sites.[ 26 ] Moreover, Serum ucOC could be used to predict hip fracture risk independently of femoral neck BMD in elderly women that serum ucOC reflected some bone metabolism in related to increased fragility.[ 33 , 34 ] For the bone microstructure, we firstly demonstrated no correlation between all forms of serum OC and TBS in axial skeletons. On the other hand, serum OC and ucOC influenced microstructure of trabecular bone in peripheral skeletons. The elevation of serum OC and ucOC were associated with decreasing in trabecular bone mass and impaired microstructure, manifested as the decrease in Tb.N and Tb.BV/TV, and the increase in Tb.Sp. There were a few studies on the correlation between serum OC and the changes of bone microstructure in postmenopausal women. In a study involving women aged 40–61 years, serum OC was negatively associated with trabecular BS/TV.[ 35 ] Another study in postmenopausal women indicated that the high level of serum ucOC was associated with destruction of tibial bone microstructure.[ 36 ] For the muscle mass and function, there was no relationship between muscle function (hand grip and SPPB score) and either forms of OC. In different forms of serum OC, only serum ucOC was positively associated with ALM. In a study of postmenopausal osteoporotic women, serum ucOC was positively correlated with legs lean mass.[ 37 ] In animal models, serum ucOC played an essential role in maintaining muscle mass.[ 38 , 39 ] During the process of high bone turnover, the acidic bone microenvironment caused the decarboxylation of OC that ucOC was easier to escape from the bone.[ 40 ] Serum ucOC could promote the protein synthesis in myotubes and increase the cross-sectional area of muscle fibers.[ 39 ] Serum ucOC also promoted glucose uptake and enhanced insulin sensitivity in muscle.[ 41 ] Therefore, ucOC may involve in the regulation of muscle mass in postmenopausal women.[ 42 ] Serum OC was positively correlated with serum CTX and P1NP, which were consistent with other studies.[ 26 , 43 ] In logistic regression analysis, high levels of serum OC, CTX and P1NP were risk factors for osteoporosis while serum ucOC was not associated with osteoporosis. Since ALM and SMI were the protective factors for osteoporosis and serum ucOC was positively associated with ALM, it indicated that serum ucOC may influence bone health through modulating muscle mass. There were several shortages in this study. First, the sample size was limited. Second, the level of Vitamin K was not measured. Third, this was a cross-sectional study that failed to observe the individual change. Therefore, further study needs to enlarge the sample size, complete all relevant measurements, and conduct prospective follow-up. Conclusion All forms of serum OC were negatively associated with BMD. The serum OC and ucOC mainly influenced microstructure of trabecular bone in peripheral skeletons. Serum ucOC participated in modulating both bone microstructure and muscle mass. Declarations Acknowledgements We are grateful to all of the participants in this study. Special thanks to the generous support from Merck Sharp& Dohme China, Shanghai, China. This study was supported by the National High Level Hospital Clinical Research Funding (2022-PUMCH-B-014, 2022-PUMCH-D-004), CAMS Innovation Fund for Medical Sciences (CIFMS)2021-I2M-1-002, National Key R&D Program of China (2021YFC2501700), Bethune Charitable Foundation Funding (G-X-2019-1107-1), Beijing Natural Science Foundation (7232120). References No author listed, Consensus development conference: diagnosis, prophylaxis, and treatment of osteoporosis. Am J Med. 94, 646–650, (1993) http://doi.org/10.1016/0002-9343(93)90218-e N.E. Lane, Epidemiology, etiology, and diagnosis of osteoporosis. Am. J. Obstet. Gynecol. 194 , 3–11 (2006). http://doi.org/10.1016/j.ajog.2005.08.047 C.S. Research, .M. Epidemiological survey of osteoporosis in China and release of the results of the special action of healthy bones. CHIN. J. OSTEOPOROS. BONE MINER RES　4, 317–318 (2019) P.D. Delmas, E.R. Garnero, P. Seibel, M.J. 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Delmas, Serum undercarboxylated osteocalcin is a marker of the risk of hip fracture in elderly women. J. Clin. Invest. 91 , 1769–1774 (1993) Ã. Bjørnerem, G.-Z.A. Bui, M. Wang, X. Rantzau, C. Nguyen, T.V. Hopper, J.L. Zebaze, R. Seeman, Remodeling markers are associated with larger intracortical surface area but smaller trabecular surface area: A twin study. Bone. 49 , 1125–1130 (2011). http://doi.org/10.1016/j.bone.2011.08.009 S.H. Rønn, H.T. Pedersen, S.B. Langdahl, Vitamin K2 (menaquinone-7) prevents age-related deterioration of trabecular bone microarchitecture at the tibia in postmenopausal women. Eur. J. Endocrinol. 175 , 541–549 (2016). http://doi.org/10.1530/EJE-16-0498 J.A. Vitale, S.V. Faraldi, M. Messina, C. Verdelli, C. Lombardi, G. Corbetta, Circulating Carboxylated Osteocalcin Correlates With Skeletal Muscle Mass and Risk of Fall in Postmenopausal Osteoporotic Women. Front. Endocrinol. (Lausanne). 12 , 669704 (2021). http://doi.org/10.3389/fendo.2021.669704 X. Lin, S.C. Moreno-Asso, A. Zarekookandeh, N. Brennan-Speranza, T.C. Duque, G. Hayes, A. Levinger, Undercarboxylated osteocalcin and ibandronate combination ameliorates hindlimb immobilization-induced muscle wasting. J. Physiol. 601 , 1851–1867 (2023). http://doi.org/10.1113/JP283990 P. Mera, L.K. Wei, J. Berger, J.M. Karsenty, Osteocalcin is necessary and sufficient to maintain muscle mass in older mice. Mol. Metab. 5 , 1042–1047 (2016). http://doi.org/10.1016/j.molmet.2016.07.002 M. Ferron, W.J. Yoshizawa, T. Fattore, A. DePinho, R. Teti, A. Ducy, P. Karsenty, Insulin signaling in osteoblasts integrates bone remodeling and energy metabolism. Cell. 142 , 296–308 (2010). http://doi.org/10.1016/j.cell.2010.06.003 X. Lin, P.L. McLennan, E. Hayes, A. McConell, G. Brennan-Speranza, T.C. Levinger, Undercarboxylated Osteocalcin Improves Insulin-Stimulated Glucose Uptake in Muscles of Corticosterone-Treated Mice. J. Bone Miner Res. 34 , 1517–1530 (2019). http://doi.org/10.1002/jbmr.3731 D. Hiam, L.S. Jacques, M. Voisin, S. Alvarez-Romero, J. Byrnes, E. Chubb, P. Levinger, I. Eynon, Osteocalcin and its forms respond similarly to exercise in males and females. Bone. 144 , 115818 (2021). http://doi.org/10.1016/j.bone.2020.115818 J. Fan, L.N. Gong, X. He, Serum 25-hydroxyvitamin D, bone turnover markers and bone mineral density in postmenopausal women with hip fractures. Clin. Chim. Acta. 477 , 135–140 (2018). http://doi.org/10.1016/j.cca.2017.12.015 Cite Share Download PDF Status: Published Journal Publication published 11 Jan, 2024 Read the published version in Endocrine → Version 1 posted Reviewers agreed at journal 20 Sep, 2023 Reviewers invited by journal 28 Aug, 2023 Editor assigned by journal 27 Aug, 2023 First submitted to journal 26 Aug, 2023 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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07:10:52","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3299818/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3299818/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s12020-023-03668-1","type":"published","date":"2024-01-11T15:01:42+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":42431321,"identity":"28dc6f35-3d74-4abc-b55f-fdae3bd5e840","added_by":"auto","created_at":"2023-08-31 14:36:03","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":131197,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3299818/v1/a44950e4b1656c076c5e6265.jpg"},{"id":49629275,"identity":"d8547fcb-2fe4-4b13-a939-c7023af58bea","added_by":"auto","created_at":"2024-01-15 15:10:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":612820,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3299818/v1/b30159df-4b76-4b0e-a1bc-07036ee9d7dd.pdf"}],"financialInterests":"","formattedTitle":"Association of serum osteocalcin with bone microarchitecture and muscle mass in Beijing community-dwelling postmenopausal women.","fulltext":[{"header":"Background","content":"\u003cp\u003eOsteoporosis is a systemic skeletal disease described as low bone mass, impaired bone microarchitecture, increased bone fragility and prone to fracture.[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] Osteoporosis is a silent disease that can occur at any age, but it is more common in postmenopausal women.[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] Fragile fracture is a grave consequence related to osteoporosis which is one of the main causes of disability and death in postmenopausal women. The latest large scale epidemiological survey of osteoporosis in China demonstrated that the prevalence of osteoporosis in population over 50 years old was 19.2%, including 32.1% in women, and the ratio of people with low bone mass was 46.4%, with 45.9% in women.[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] It indicated that osteoporosis became a noticeable health problem in China, especially in postmenopausal women who were in urgent need to pay attention to bone health to early diagnosis and treatment of osteoporosis.\u003c/p\u003e \u003cp\u003eOsteocalcin (OC) was regarded as an osteogenesis biomarker in clinical that could be served as a bone formation marker in postmenopausal women with osteoporosis. [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] OC was also known as bone γ- carboxyglutamic acid (Gla) protein which was synthesized by osteoblasts and then secreted in the bone extracellular matrix (ECM), becoming the most abundant non collagen protein in ECM.[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] OC contains two forms in circulation, including undercarboxylated osteocalcin (ucOC) and carboxylated osteocalcin (cOC). Through the γ-carboxylation of three glutamic acids at positions 17, 21, and 24, ucOC convert to cOC, which could obtain a high affinity for calcium and activate its ability to combine with mineral hydroxyapatite.[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eThere were numerous studies exploring the relationship between serum OC and bone, including the relationship between serum OC and BMD. Some studies shown a negative correlation between serum OC and BMD, while some studies shown no association between serum OC and BMD. [\u003cspan additionalcitationids=\"CR9 CR10\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] However, there was relatively few research on the relationship between serum OC and bone microstructure.\u003c/p\u003e \u003cp\u003eMoreover, occurrence of osteoporotic fracture was not only due to the defect in bone, but also related to deterioration in muscle. Many studies have shown that patients with osteoporosis accompanied with reduced muscle mass. [\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] Low muscle mass increased the risk of fall and fracture. [\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] Studies showed serum OC was involved in the regulation of muscle functions during exercise.[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] However, whether serum OC could be a mediator of bone and muscle communication needs to be explored.\u003c/p\u003e \u003cp\u003eTherefore, in this study, we aimed to investigate the relationship among different forms of serum OC, BMD, bone microarchitecture, muscle mass and physical activity in Chinese postmenopausal women to identify the effects of serum OC on bone and muscle.\u003c/p\u003e"},{"header":"Method","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSubjects\u003c/h2\u003e \u003cp\u003eThe study population was a subgroup from a nationwide, observational, cross-sectional study to investigate the prevalence of VFs in Chinese urban-dwelling postmenopausal women aged over 50 years old (ChiVOS).[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] Menopause was regarded as having no menstrual cycle for more than one year. In strict accordance with the principle of age stratification and random sampling, a survey of postmenopausal women living in community was conducted in Dongcheng District, Beijing. 274 postmenopausal women were enrolled in this study. 55 subjects who did not complete entire examination were precluded. In final, 216 subjects were included in the study. All subjects completed informed consent forms. This study was approved by the PUMCH ethics committee.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eClinical Data Collection\u003c/h2\u003e \u003cp\u003eAll subjects performed the ChiVOS questionnaire, which collected the information of self-reported demographic, lifestyle, smoking history, alcohol history, physical activities, supplement of calcium, supplement of vitamin D and medical history of anti-osteoporosis drugs. The questionnaire interview was conducted by one designated investigator to minimize the variability in data collection. Anthropometric measurements including height and weight were measured in accordance with standards. Body mass index (BMI) was calculated as dividing weight (kg) by the square of height (m).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eBiochemical examinations\u003c/h2\u003e \u003cp\u003eBlood sample were harvested in the morning after at least 8 hours fasting. Serum was gathered after centrifugation at 3,000 r/min for 10 minutes. Serum ucOC were assayed by ELISA (MK118, Takara Bio Inc, Japan), with the intra assay coefficient of variation of 4.58%-6.66% and the inter assay coefficient of variation of 5.67%-9.87%. Serum cOC were measured by ELISA (MK111, Takara Bio Inc, Japan), with the intra assay coefficient of variation of 3.3%-4.8%, the inter assay coefficient of variation of 0.7%-2.4%.\u003c/p\u003e \u003cp\u003eSerum calcium (Ca), phosphorus (Pi), and alkaline phosphatase (ALP) measurements were performed by a Beckman Automatic Biochemical Analyzer (AU5800, Beckman Coulter, Indianapolis, IN, USA). Serum OC, β-isomer of C-terminal telopeptides of type I collagen (β-CTX), N-terminal prepeptide of type 1 procollagen (P1NP) were measured by electrochemiluminescence immunoassay (Roche Cobas e601, Mannheim, Germany). Parathyroid hormone (PTH) was measured using chemiluminescence (Siemens ADVIA Centaur, Munich, Germany). 25-hydroxyvitamin D (25OHD) was measured by electrochemiluminescence immunoassay (Roche Cobas e601, Mannheim, Germany). Creatinine (Cr) was measured by an automated Roche electrochemiluminescence system (E170; Roche Diagnostics, Basel, Switzerland). All measurements were completed by central laboratory of PUMCH.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eBMD, TBS, and Appendicular Lean Mass Measurements\u003c/h2\u003e \u003cp\u003eDual Energy X-ray Absorptiometry (DXA) was implemented by certified technicians through GE-Lunar scanners (GE Healthcare, Madison, WI, USA) to measure BMD at the lumbar spine (L1-L4), femur neck, and total hip with BMD absolute values and T-score were recorded. Based on BMD results according to WHO criteria for the classification of osteopenia and osteoporosis, T-score \u0026ge; -1.0 was defined as normal group, while \u0026minus;\u0026thinsp;2.5༜T-score༜-1.0 as osteopenia group, and T-score \u0026le;-2.5 as osteoporosis group. [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eThe trabecular bone score (TBS) was calculated through TBS iNsight v2.1 software (Medimaps) by analysing the same region of interest (L1-L4) of DXA scan. The trunk and limbs were segmented automatically through DXA. Appendicular lean mass (ALM) was obtained by software. The calculation of skeletal muscle index (SMI) was using ALM divided by the square of height (m).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eHigh-resolution peripheral quantitative CT (HR-pQCT)\u003c/h2\u003e \u003cp\u003eNon-dominant distal radius and distal tibia were measured by HR-pQCT (XtremeCT II; Scanco Medical, Zurich, Switzerland) to assess bone microarchitecture using a standard protocol.[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] Isotropic resolution of the image was 61 mm. The first slice of region of interest (ROI) of every measurement including radius and tibia was to 9.5 mm and 22.5 mm away from the mid-endplate, respectively. Combining 168 parallel CT slices for 3D image reconstruction of non-dominant radius and tibia.\u003c/p\u003e \u003cp\u003eThe contour between the cortex and trabecula was automatically identified by the manufacturer's standard software. The relevant bone microarchitecture parameters were measured as following:(1) Bone geometry structure including total area (Tt.Ar, mm\u003csup\u003e2\u003c/sup\u003e), cortical area (Ct.Ar, mm\u003csup\u003e2\u003c/sup\u003e) and trabecular area (Tb.Ar, mm\u003csup\u003e2\u003c/sup\u003e) .(2) Volumetric bone mineral density (vBMD) containing total volumetric BMD (Tt.vBMD, mg HA/ccm), cortical volumetric BMD (Ct.vBMD, mg HA/ccm) and trabecular volumetric BMD (Tb.vBMD, mg HA/ccm).(3) Bone microarchitecture including cortical parameters as cortical thickness (Ct.Th, mm) and cortical porosity (Ct.Po, %) and trabecular parameters as trabecular bone volume fraction (Tb.BV/TV), trabecular number (Tb.N, 1/mm), trabecular thickness (Tb.Th, mm), trabecular separation (Tb.Sp, mm), inhomogeneity of network (Tb.1/N.SD, mm).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eMuscle strength and physical performance measurement\u003c/h2\u003e \u003cp\u003eHand grip strength as an assessment of muscle strength was assessed by electronic hand dynamometer, measuring the grip strength of dominant hand three times, with rest for at least 60 seconds, and take the maximum value to record. Short-Physical Performance Battery (SPPB) score was measured and calculated according to the standard, including standing balance, walking speed, and the ability to stand up from a chair.[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStatistics\u003c/h2\u003e \u003cp\u003eOne way analysis of variance (ANOVA) analysis was used to compare differences of variables in multiple groups. Least significant difference (LSD) analysis was used to compare difference of variables between two groups. Partial correlation analysis was conducted after adjusting for age, BMI, and 25OHD. Standardize the data and logistic regression were used to calculate the odds ratio (OR) with 95% CIs for prevalence of osteoporosis. All data were analyzed by SPSS software (version 22.0, SPSS Inc. of IBM, USA). Using two-tailed statistical measurements, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eClinical characteristics and biochemical markers of the postmenopausal women\u003c/h2\u003e \u003cp\u003eThe clinical characteristics and biochemical markers of 216 postmenopausal women were summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The average age was 67.92\u0026thinsp;\u0026plusmn;\u0026thinsp;8.62 years old. The average BMI was 26.06\u0026thinsp;\u0026plusmn;\u0026thinsp;4.16 kg/m\u003csup\u003e2\u003c/sup\u003e. The mean serum OC concentration was 17.50\u0026thinsp;\u0026plusmn;\u0026thinsp;8.74 ng/mL, while serum cOC and ucOC concentration was 4.18\u0026thinsp;\u0026plusmn;\u0026thinsp;2.96 ng/mL and 2.21\u0026thinsp;\u0026plusmn;\u0026thinsp;1.88 ng/mL, respectively. The average ALM was 15.34\u0026thinsp;\u0026plusmn;\u0026thinsp;2.28 kg, and the average SMI was 6.37\u0026thinsp;\u0026plusmn;\u0026thinsp;0.78 kg/m\u003csup\u003e2\u003c/sup\u003e. Three groups were divided based on age as group aged 50\u0026ndash;59 years, group 60\u0026ndash;69 years and group aged over 70 years. There was no difference among 3 groups in serum OC and ucOC. Serum cOC had a trend of increasing with age that serum cOC was highest in group over 70 years. There was a tendency that ALM decreased with age that ALM reduced significantly in group over 70 years. SMI was declined in group over 70 years than group 60\u0026ndash;69 years.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClinical characteristics and biochemical markers of postmenopausal women(N\u0026thinsp;=\u0026thinsp;216).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal (n\u0026thinsp;=\u0026thinsp;216)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50\u0026ndash;59 years (n\u0026thinsp;=\u0026thinsp;56)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e60\u0026ndash;69 years (n\u0026thinsp;=\u0026thinsp;83)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOver 70 years\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;77)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ep2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003ep3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (year)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e67.92\u0026thinsp;\u0026plusmn;\u0026thinsp;8.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57.11\u0026thinsp;\u0026plusmn;\u0026thinsp;2.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e66.20\u0026thinsp;\u0026plusmn;\u0026thinsp;2.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e77.64\u0026thinsp;\u0026plusmn;\u0026thinsp;3.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeight (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e155.05\u0026thinsp;\u0026plusmn;\u0026thinsp;6.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e157.21\u0026thinsp;\u0026plusmn;\u0026thinsp;6.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e155.68\u0026thinsp;\u0026plusmn;\u0026thinsp;6.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e152.79\u0026thinsp;\u0026plusmn;\u0026thinsp;5.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e62.66\u0026thinsp;\u0026plusmn;\u0026thinsp;10.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63.60\u0026thinsp;\u0026plusmn;\u0026thinsp;11.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e64.85\u0026thinsp;\u0026plusmn;\u0026thinsp;10.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e59.67\u0026thinsp;\u0026plusmn;\u0026thinsp;9.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.460\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.05\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26.06\u0026thinsp;\u0026plusmn;\u0026thinsp;4.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.70\u0026thinsp;\u0026plusmn;\u0026thinsp;4.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26.79\u0026thinsp;\u0026plusmn;\u0026thinsp;4.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25.54\u0026thinsp;\u0026plusmn;\u0026thinsp;3.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.817\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.057\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking history, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13(6.02%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (10.71%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (4.82%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3 (3.90%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.947\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol history, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12(5.56%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (8.93%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (6.02%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2 (2.60%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.353\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.431\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMore than one hour of moderate intensity physical activities/day, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e182(4.26%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45 (80.36%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e71(85.54%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e66(85.71%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.651\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.414\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.406\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.976\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCalcium supplements, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65(30.09%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15(26.79%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23(27.71%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27(35.06%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.444\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVitamin D supplements, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41(18.98%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12(21.43%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14(16.87%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15(19.48%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.389\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.647\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.610\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnti-osteoporosis drugs (\u0026ge;\u0026thinsp;3 months), n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5(2.31%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5(8.93%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOC (ng/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17.50\u0026thinsp;\u0026plusmn;\u0026thinsp;8.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.65\u0026thinsp;\u0026plusmn;\u0026thinsp;4.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.50\u0026thinsp;\u0026plusmn;\u0026thinsp;9.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18.13\u0026thinsp;\u0026plusmn;\u0026thinsp;9.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.630\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.577\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.337\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.648\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecOC (ng/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.18\u0026thinsp;\u0026plusmn;\u0026thinsp;2.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.51\u0026thinsp;\u0026plusmn;\u0026thinsp;2.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.98\u0026thinsp;\u0026plusmn;\u0026thinsp;2.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.90\u0026thinsp;\u0026plusmn;\u0026thinsp;2.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.05\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.05\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eucOC (ng/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.21\u0026thinsp;\u0026plusmn;\u0026thinsp;1.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.10\u0026thinsp;\u0026plusmn;\u0026thinsp;1.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.33\u0026thinsp;\u0026plusmn;\u0026thinsp;2.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.16\u0026thinsp;\u0026plusmn;\u0026thinsp;1.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.738\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.475\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.865\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.554\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCa (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.32\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.34\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.29\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePi (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.05\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.308\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.175\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eβ-CTX (ng/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.37\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.36\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.37\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.38\u0026thinsp;\u0026plusmn;\u0026thinsp;0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.774\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.849\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.504\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.593\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP1NP (ng/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55.05\u0026thinsp;\u0026plusmn;\u0026thinsp;20.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54.11\u0026thinsp;\u0026plusmn;\u0026thinsp;17.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e55.89\u0026thinsp;\u0026plusmn;\u0026thinsp;21.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e54.81\u0026thinsp;\u0026plusmn;\u0026thinsp;23.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.881\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.625\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.850\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.746\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALP (U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e83.60\u0026thinsp;\u0026plusmn;\u0026thinsp;22.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e89.75\u0026thinsp;\u0026plusmn;\u0026thinsp;25.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e83.75\u0026thinsp;\u0026plusmn;\u0026thinsp;20.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e78.96\u0026thinsp;\u0026plusmn;\u0026thinsp;21.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.05\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.173\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePTH (pg/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42.13\u0026thinsp;\u0026plusmn;\u0026thinsp;22.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40.11\u0026thinsp;\u0026plusmn;\u0026thinsp;11.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44.15\u0026thinsp;\u0026plusmn;\u0026thinsp;29.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e41.42\u0026thinsp;\u0026plusmn;\u0026thinsp;19.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.552\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.301\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.740\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.445\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25-OHD (ng/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17.17\u0026thinsp;\u0026plusmn;\u0026thinsp;6.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.35\u0026thinsp;\u0026plusmn;\u0026thinsp;6.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.30\u0026thinsp;\u0026plusmn;\u0026thinsp;5.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19.05\u0026thinsp;\u0026plusmn;\u0026thinsp;7.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.05\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCr (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65.98\u0026thinsp;\u0026plusmn;\u0026thinsp;17.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60.75\u0026thinsp;\u0026plusmn;\u0026thinsp;9.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e60.80\u0026thinsp;\u0026plusmn;\u0026thinsp;9.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e75.36\u0026thinsp;\u0026plusmn;\u0026thinsp;23.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.987\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALM (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.34\u0026thinsp;\u0026plusmn;\u0026thinsp;2.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.93\u0026thinsp;\u0026plusmn;\u0026thinsp;2.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.76\u0026thinsp;\u0026plusmn;\u0026thinsp;2.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.46\u0026thinsp;\u0026plusmn;\u0026thinsp;1.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.661\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.37\u0026thinsp;\u0026plusmn;\u0026thinsp;0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.44\u0026thinsp;\u0026plusmn;\u0026thinsp;0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.50\u0026thinsp;\u0026plusmn;\u0026thinsp;0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.05\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.654\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.05\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHand grip (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.12\u0026thinsp;\u0026plusmn;\u0026thinsp;5.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.75\u0026thinsp;\u0026plusmn;\u0026thinsp;4.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22.83\u0026thinsp;\u0026plusmn;\u0026thinsp;5.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19.43\u0026thinsp;\u0026plusmn;\u0026thinsp;4.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.05\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSPPB score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.15\u0026thinsp;\u0026plusmn;\u0026thinsp;1.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.98\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.48\u0026thinsp;\u0026plusmn;\u0026thinsp;1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.19\u0026thinsp;\u0026plusmn;\u0026thinsp;2.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.279\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eBMI, body mass index; OC, osteocalcin; cOC, carboxylated osteocalcin; ucOC, undercarboxylated osteocalcin; Ca, calcium; Pi, phosphate; β-CTX, β-isomer of C-terminal telopeptides of type I collagen; P1NP, N-terminal propeptide of type 1 procollagen; ALP, alkaline phosphatase; PTH, parathyroid hormone; 25OHD, 25-hydroxyvitamin D; Cr, creatinine; ALM, appendicular lean mass; SMI, skeletal muscle index [SMI\u0026thinsp;=\u0026thinsp;ALM (kg) / height\u003csup\u003e2\u003c/sup\u003e (m)]; SPPB, short physical performance battery.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003ep, difference among 3 groups; p1, groups between 50\u0026ndash;59 years group and 60\u0026ndash;69 years group; p2, groups between 50\u0026ndash;59 years group and over 70 years group; p3, groups between 60\u0026ndash;69 years group and over 70 years group.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eSerum OC, cOC and ucOC levels in normal, osteopenia and osteoporosis status\u003c/h2\u003e \u003cp\u003eThe difference of serum OC, cOC and ucOC among normal group, osteopenia group and osteoporosis group were shown in Fig.\u0026nbsp;1. The number of postmenopausal women in normal, osteopenia and osteoporosis group were 71, 104 and 41, respectively. Serum OC, cOC and ucOC were significantly elevated in osteoporosis group than normal group. Serum OC and cOC were increased significantly in osteoporosis group than osteopenia group.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eRelationship between serum OC, cOC and ucOC with BMD, TBS and peripheral bone microstructure\u003c/h2\u003e \u003cp\u003ePartial correlation analysis after adjusted with age, BMI, 25OHD between serum OC, cOC, ucOC with BMD, TBS and bone microarchitecture were shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Serum OC represented inverse correlation with lumbar spine, femoral neck and total hip BMD. Serum OC and cOC were negatively associated with femoral neck and total hip BMD. There was no association between serum OC, cOC and ucOC with TBS. In peripheral bone microstructure bone geometry analysis, serum ucOC was positively correlated with Tt.Ar of tibia and Tb.Ar of radius and tibia. In bone volumetric density analysis, serum OC and ucOC had inversely correlations with Tt.vBMD and Tb.vBMD at both radius and tibia. In bone microarchitecture analysis, serum OC and ucOC were negatively correlative with Tb.N and Tb.BV/TV, and were positively correlated with Tb.Sp of both radius and tibia. Serum OC and ucOC had positively correlation with Tb.1/N.SD. Serum OC had inversely correlation with Tb.Th of radius. Serum cOC was negatively associated with Tb.N and was positively related with Tb.Sp of radius.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCorrelations between OC, cOC, ucOC with BMD and bone microarchitecture. (N\u0026thinsp;=\u0026thinsp;216)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eOC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003ecOC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eucOC\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003er\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003er\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003er\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDXA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLumbar spine BMD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.05\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.092\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.075\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemoral neck BMD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.238\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.05\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.05\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal hip BMD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTBS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.932\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.388\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.852\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGeometry\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTt.Ar (mm\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.567\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.482\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.059\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.329\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.384\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCt.Ar (mm\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.403\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.942\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.230\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.323\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.726\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTb.Ar (mm\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.329\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.411\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.05\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.325\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.479\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.05\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVolumetric density\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTt.vBMD (mg HA/cm\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.05\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.088\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCt.vBMD (mg HA/cm\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.552\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.214\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.793\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.138\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTb.vBMD (mg HA/cm\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.05\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMicroarchitecture\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCt.Th (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.264\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.490\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.143\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCt.Po (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.448\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.332\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.658\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.587\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.598\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.196\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTb.N(1/mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.05\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.226\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.05\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.277\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.05\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTb.Th(mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.05\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.323\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.399\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.274\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.695\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.227\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTb.Sp (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.191\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.05\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.05\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.05\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTb.BV/TV (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.05\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTb.1/N.SD (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.05\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.277\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.620\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.122\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eAdjusted for age, BMI, 25OHD.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eBMD, bone mineral density; R, radius; T, Tibia; Tt.Ar, total bone area; Ct.Ar, cortical area; Tb.Ar, trabecular area; Tt.vBMD, total volume bone mineral density; Ct.vBMD, cortical volume bone mineral density; Tb.vBMD, trabecular volume bone mineral density; Ct.Th, cortical thickness; Ct.Po, cortical porosity; Ct.Pm, cortical perimeter; Tb.N, trabecular number; Tb.Th, trabecular thickness; Tb.Sp, trabecular separation; Tb.BV/TV, trabecular bone volume fraction; Tb.1/N.SD, inhomogeneity of network.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation between serum OC, cOC, ucOC with BTMs, muscle mass and physical activity\u003c/h2\u003e \u003cp\u003eAdjusted for age, BMI and 25OHD, partial correlation between serum OC, cOC, ucOC with BTMs, muscle mass and physical activity were shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Serum OC, cOC and ucOC were positive associated with β-CTX, P1NP and PTH. Serum OC was also positive related with ALP. Serum ucOC but not OC or cOC was positively associated with ALM. There was no relationship between any form of serum OC (including cOC and ucOC) with hand grip and SPPB score.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCorrelations between OC, cOC, ucOC with BTMs, muscle mass and physical activity. (N\u0026thinsp;=\u0026thinsp;216)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecOC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eucOC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eβ-CTX\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP1NP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eALP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePTH\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eALM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSMI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eHand grip\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eSPPB\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.401\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.380\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.764\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.733\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.194\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.599\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.075\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecOC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.319\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.313\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.280\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.165\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eucOC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.327\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.319\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.171\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.147\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003eAdjusted for age, BMI and 25OHD.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003eOC, osteocalcin; cOC, carboxylated osteocalcin; ucOC, undercarboxylated osteocalcin; β-CTX, β-isomer of C-terminal telopeptides of type I collagen; P1NP, N-terminal propeptide of type 1 procollagen; ALP, alkaline phosphatase; PTH, parathyroid hormone; ALM, appendicular lean mass; SMI, skeletal muscle index [SMI\u0026thinsp;=\u0026thinsp;ASM (kg) / height\u003csup\u003e2\u003c/sup\u003e (m)]; SPPB, short physical performance battery.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003e\u003csup\u003e*\u003c/sup\u003e, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; \u003csup\u003e**\u003c/sup\u003e, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eThe association of serum OC, cOC, ucOC, BTMs and muscle mass with prevalence of osteoporosis\u003c/h2\u003e \u003cp\u003eAccording to WHO criteria for the classification of osteoporosis, the odds ratio in logistic regression of prevalent osteoporosis was shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. 1-SD increase in serum OC was correlated with an OR with 1.491 for osteoporosis (95%CI\u0026thinsp;=\u0026thinsp;1.071\u0026ndash;2.076, p\u0026thinsp;=\u0026thinsp;0.018). After adjusted age, BMI and 25OHD, 1-SD increase in serum OC was related with an OR with 1.414 (95%CI\u0026thinsp;=\u0026thinsp;1.028\u0026ndash;1.944, p\u0026thinsp;=\u0026thinsp;0.033) for osteoporosis. Neither serum cOC or ucOC was associated with osteoporosis after adjusted. With or without adjustment, P1NP and β-CTX were risk factors of osteoporosis. ALM and SMI were protective factors of osteoporosis. 1-SD increase in ALM was correlated with an OR with 0.330 for osteoporosis (95%CI\u0026thinsp;=\u0026thinsp;0.180\u0026ndash;0.604, p\u0026thinsp;=\u0026thinsp;0.000) after adjusted. 1-SD increase in SMI was associated with an OR with 0.513 for osteoporosis (95%CI\u0026thinsp;=\u0026thinsp;0.266\u0026ndash;0.989, p\u0026thinsp;=\u0026thinsp;0.046) after adjusted.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOdds ratio in logistic regression of prevalent osteoporosis according to a 1-SD increase in OC, cOC, ucOC, BTMs, ALM and SMI.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnadjusted\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAdjusted*\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.491 (1.071\u0026ndash;2.076)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.018\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.414 (1.028\u0026ndash;1.944)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.033\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecOC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.484 (1.094\u0026ndash;2.012)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.011\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.333 (0.946\u0026ndash;1.879)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eucOC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.297 (0.957\u0026ndash;1.758)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.311 (0.936\u0026ndash;1.838)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.115\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP1NP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.669 (1.199\u0026ndash;2.325)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.797 (1.258\u0026ndash;2.567)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eβ-CTX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.742 (1.249\u0026ndash;2.430)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.766 (1.244\u0026ndash;2.506)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.272 (0.161\u0026ndash;0.459)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.330 (0.180\u0026ndash;0.604)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.438 (0.283\u0026ndash;0.678)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.513 (0.266\u0026ndash;0.989)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.046\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e*Adjusted for age, BMI,25OHD.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eOC, osteocalcin; cOC, carboxylated osteocalcin; ucOC, undercarboxylated osteocalcin; P1NP, N-terminal propeptide of type 1 procollagen; β-CTX, β-isomer of C-terminal telopeptides of type I collagen; ALM, appendicular lean mass; SMI, skeletal muscle index [SMI\u0026thinsp;=\u0026thinsp;ALM (kg) / height\u003csup\u003e2\u003c/sup\u003e (m)]\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eTo our knowledge, this study firstly fully explored the relationship of serum OC and its different forms between BMD, bone microarchitecture, muscle mass and physical activity in Chinese postmenopausal women. All forms of serum OC were negatively associated with BMD. Elevated levels of serum OC and ucOC were related with trabecular bone deterioration. In addition, serum ucOC was positively associated with muscle mass.\u003c/p\u003e \u003cp\u003eIn our study, we found that serum OC and cOC showed a seemingly increasing trend with age but without statistical significance, which was in accord with Japanese female study.[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] While in another study, serum OC was negatively associated with age after 50 years old in Danish women.[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] Serum ucOC elevated in group 60\u0026ndash;69 years and decreased in group of over 70 years, which was consistent with a study in Chinese women.[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] While in a study of Korea women, serum ucOC showed a trend that decreased with age after 50 years old.[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] The differences in studies may be due to differences in race and population.\u003c/p\u003e \u003cp\u003eOur study found the negative correlation between serum OC and BMD which was in line with the results of other studies in postmenopausal women. [\u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] Elevated serum OC was sensitive to monitor the accelerated bone turnover after the onset of menopause.[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] In addition, negative associations between serum ucOC and femoral neck, total hip BMD were found in our study, which were consistent with another study involving elderly women.[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] In accordance with our research, there was no association between serum ucOC and lumbar BMD in a study of early postmenopausal women.[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] While in another study of Chinese women, serum ucOC was negative related with BMD at all sites.[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] Moreover, Serum ucOC could be used to predict hip fracture risk independently of femoral neck BMD in elderly women that serum ucOC reflected some bone metabolism in related to increased fragility.[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eFor the bone microstructure, we firstly demonstrated no correlation between all forms of serum OC and TBS in axial skeletons. On the other hand, serum OC and ucOC influenced microstructure of trabecular bone in peripheral skeletons. The elevation of serum OC and ucOC were associated with decreasing in trabecular bone mass and impaired microstructure, manifested as the decrease in Tb.N and Tb.BV/TV, and the increase in Tb.Sp. There were a few studies on the correlation between serum OC and the changes of bone microstructure in postmenopausal women. In a study involving women aged 40\u0026ndash;61 years, serum OC was negatively associated with trabecular BS/TV.[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] Another study in postmenopausal women indicated that the high level of serum ucOC was associated with destruction of tibial bone microstructure.[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eFor the muscle mass and function, there was no relationship between muscle function (hand grip and SPPB score) and either forms of OC. In different forms of serum OC, only serum ucOC was positively associated with ALM. In a study of postmenopausal osteoporotic women, serum ucOC was positively correlated with legs lean mass.[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] In animal models, serum ucOC played an essential role in maintaining muscle mass.[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] During the process of high bone turnover, the acidic bone microenvironment caused the decarboxylation of OC that ucOC was easier to escape from the bone.[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] Serum ucOC could promote the protein synthesis in myotubes and increase the cross-sectional area of muscle fibers.[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] Serum ucOC also promoted glucose uptake and enhanced insulin sensitivity in muscle.[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e] Therefore, ucOC may involve in the regulation of muscle mass in postmenopausal women.[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eSerum OC was positively correlated with serum CTX and P1NP, which were consistent with other studies.[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e] In logistic regression analysis, high levels of serum OC, CTX and P1NP were risk factors for osteoporosis while serum ucOC was not associated with osteoporosis. Since ALM and SMI were the protective factors for osteoporosis and serum ucOC was positively associated with ALM, it indicated that serum ucOC may influence bone health through modulating muscle mass.\u003c/p\u003e \u003cp\u003eThere were several shortages in this study. First, the sample size was limited. Second, the level of Vitamin K was not measured. Third, this was a cross-sectional study that failed to observe the individual change. Therefore, further study needs to enlarge the sample size, complete all relevant measurements, and conduct prospective follow-up.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eAll forms of serum OC were negatively associated with BMD. The serum OC and ucOC mainly influenced microstructure of trabecular bone in peripheral skeletons. Serum ucOC participated in modulating both bone microstructure and muscle mass.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are grateful to all of the participants in this study. Special thanks to the generous support from Merck Sharp\u0026amp; Dohme China, Shanghai, China.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study was supported by the National High Level Hospital Clinical Research Funding (2022-PUMCH-B-014, 2022-PUMCH-D-004), CAMS Innovation Fund for Medical Sciences (CIFMS)2021-I2M-1-002, National Key R\u0026amp;D Program of China (2021YFC2501700), Bethune Charitable Foundation Funding (G-X-2019-1107-1), Beijing Natural Science Foundation (7232120).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eNo author listed, Consensus development conference: diagnosis, prophylaxis, and treatment of osteoporosis. 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Acta. \u003cb\u003e477\u003c/b\u003e, 135\u0026ndash;140 (2018). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://doi.org/10.1016/j.cca.2017.12.015\u003c/span\u003e\u003cspan address=\"10.1016/j.cca.2017.12.015\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"endocrine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"endo","sideBox":"Learn more about [Endocrine](https://www.springer.com/journal/12020)","snPcode":"12020","submissionUrl":"https://submission.nature.com/new-submission/12020/3","title":"Endocrine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"osteocalcin, undercarboxylated osteocalcin, bone microarchitecture, muscle mass, postmenopausal women","lastPublishedDoi":"10.21203/rs.3.rs-3299818/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3299818/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eOsteoporosis is a systemic skeletal disease with increasing bone fragility and prone to fracture. Osteocalcin (OC), as the most abundant non collagen in bone matrix, has been extensively used in clinic as a biochemical marker of osteogenesis. Two forms of OC were stated on circulation, including carboxylated osteocalcin (cOC) and undercarboxylated osteocalcin (ucOC). OC was not only involved in bone mineralization, but also in the regulation of muscle function.\u003c/p\u003e\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eThis study explored the relationship between serum OC, cOC, ucOC levels and bone mineral density (BMD), bone microarchitecture, muscle mass and physical activity in Chinese postmenopausal women.\u003c/p\u003e\u003ch2\u003eMethod\u003c/h2\u003e \u003cp\u003e216 community-dwelling postmenopausal women were randomized enrolled. All subjects completed biochemical measurements, including serum β-isomer of C-terminal telopeptides of type I collagen (β-CTX), N-terminal propeptide of type 1 procollagen (P1NP), alkaline phosphatase (ALP), OC, cOC and ucOC. They completed X-ray absorptiometry (DXA) scan to measure BMD, appendicular lean mass (ALM) and trabecular bone score (TBS). They completed high resolution peripheral quantitative CT (HR-pQCT) to assess peripheral bone microarchitectures.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eSerum OC, cOC and ucOC were elevated in osteoporosis postmenopausal women. In bone geometry, serum ucOC was positively related with total bone area (Tt.Ar) and trabecular area(Tb.Ar). In bone volumetric density, serum OC and ucOC were negatively associated with total volume bone mineral density (Tt.vBMD) and trabecular volume bone mineral density (Tb.vBMD). In bone microarchitecture, serum OC and ucOC were negatively correlative with Tb.N and Tb.BV/TV, and were positively correlated with Tb.Sp. Serum OC and ucOC were positively associated with Tb.1/N.SD. Serum OC was negatively related with Tb.Th. Serum ucOC was positively associated with ALM. The high level of serum OC was the risk factor of osteoporosis. ALM was the protective factor for osteoporosis.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eAll forms of serum OC were negatively associated with BMD. Serum OC and ucOC mainly influenced microstructure of trabecular bone in peripheral skeletons. Serum ucOC participated in modulating both bone microstructure and muscle mass.\u003c/p\u003e","manuscriptTitle":"Association of serum osteocalcin with bone microarchitecture and muscle mass in Beijing community-dwelling postmenopausal women.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-08-31 14:35:58","doi":"10.21203/rs.3.rs-3299818/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2023-09-20T05:21:35+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2023-08-28T06:54:18+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2023-08-28T02:36:10+00:00","index":"","fulltext":""},{"type":"submitted","content":"Endocrine","date":"2023-08-27T03:10:46+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"endocrine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"endo","sideBox":"Learn more about [Endocrine](https://www.springer.com/journal/12020)","snPcode":"12020","submissionUrl":"https://submission.nature.com/new-submission/12020/3","title":"Endocrine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"f98461f6-28e4-4803-b75e-bb908d1f94a1","owner":[],"postedDate":"August 31st, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-01-15T15:09:45+00:00","versionOfRecord":{"articleIdentity":"rs-3299818","link":"https://doi.org/10.1007/s12020-023-03668-1","journal":{"identity":"endocrine","isVorOnly":false,"title":"Endocrine"},"publishedOn":"2024-01-11 15:01:42","publishedOnDateReadable":"January 11th, 2024"},"versionCreatedAt":"2023-08-31 14:35:58","video":"","vorDoi":"10.1007/s12020-023-03668-1","vorDoiUrl":"https://doi.org/10.1007/s12020-023-03668-1","workflowStages":[]},"version":"v1","identity":"rs-3299818","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3299818","identity":"rs-3299818","version":["v1"]},"buildId":"rHA-KDH7Qsr4HCuvH75dn","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}