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From the Program for Population Genetics, Harvard School of Public Health, Boston, MA (Y-HH, SAV, HAT, YF, and JDB); the Division of Preventive Medicine, Department of Medicine, Brigham and Women Hospital, Harvard Medical School, Boston, MA (TN); Anhui Medical University, Institute of Medicine, Anhui, China (ZL and XX); the Department of Biostatistics, Harvard School of Public Health, Boston, MA (NL); the San Francisco Coordinating Center, University of California, San Francisco, CA (SRC); the Beth Israel Deaconess Medical Center, Boston, MA (MLB); the Maine Center for Osteoporosis Research and Education, St Joseph Hospital, Bangor, ME (CJR); and the Center for Population Genetics, School of Public Health, University of Illinois at Chicago (XX)
2 Supported by National Institute of Arthritis and Musculoskeletal and Skin Diseases grant R01 AR045651. 3 Address reprint requests to X Xu, Center for Population Genetics, School of Public Health, University of Illinois at Chicago, 1603 West Taylor Street, Room 978A, Chicago, IL 60612. E-mail: xipingxu@uic.edu. E-mail: xipingxu{at}uic.edu.
ABSTRACT
Background: Higher fat mass may be an independent risk factor for osteoporosis and osteoporotic fractures.
Objective: We aimed to determine the independent contribution of fat mass to osteoporosis and to estimate the risk of osteoporotic fractures in relation to body weight, lean mass, and other confounders.
Design: This was a community-based, cross-sectional study of 7137 men, 4585 premenopausal women, and 2248 postmenopausal women aged 25–64 y. Total-body and hip bone mineral content (BMC) and bone mineral density (BMD) and body composition were measured by dual-energy X-ray absorptiometry. Serum lipids were measured. Sex- and menopause-specific multiple generalized linear models were applied.
Results: Across 5-kg strata of body weight, fat mass was significantly inversely associated with BMC in the whole body and total hip. When we compared the highest quartile with the lowest quartile of percentage fat mass in men, premenopausal women, and postmenopausal women, the adjusted odds ratios (95% CIs) of osteoporosis defined by hip BMD were 5.2 (2.1, 13.2), 5.0 (1.7, 15.1), and 6.9 (4.3, 11.2), respectively. Significant linear trends existed for higher risks of osteoporosis, osteopenia, and nonspine fractures with higher percentage fat mass. Significant negative relations were found between whole-body BMC and cholesterol, triacylglycerol, LDL, and the ratio of HDL to LDL in all groups.
Conclusions: Risks of osteoporosis, osteopenia, and nonspine fractures were significantly higher for subjects with higher percentage body fat independent of body weight, physical activity, and age. Thus, fat mass has a negative effect on bone mass in contrast with the positive effect of weight-bearing itself.
Key Words: Bone mineral density • osteoporosis • fracture • body composition • lipids
INTRODUCTION
Osteoporosis and its related fractures have become a major problem in elderly populations. Body weight is one of the strongest positive predictors of bone mass. A positive association between body weight and bone mass in subjects of all age groups has been shown (1–3). Higher body weight is thought to affect bone mass by increasing the mechanical stress mediated through muscle or by mass gravitational action through load placed on the skeleton, thereby increasing the stimulus for osteogenesis (4, 5). Lean, fat, and bone mass are the 3 components of body weight. Lean and fat mass together account for > 95% of body weight. Several epidemiologic studies have reported that both fat mass and lean mass may help to determine bone mass (6–13). An association between lean mass and bone mass may be due to mechanical load forces on bone. Because fat tissue is metabolically active, its effect on the skeleton may be influenced not only by the weight-bearing effect but also by other non-weight-bearing effects, including the hormonal metabolism of adipocytes. Because of strong collinearity between fat mass and body weight, most epidemiologic studies that had small sample sizes could not explore the effects of fat mass on bone mass independent of body weight.
In contrast with epidemiologic studies, animal and in vitro studies support a negative effect of fat mass on bone mass. A possible link between fat tissue and bone tissue is the common stromal cell origin of both osteoblasts and adipocytes (14, 15). Stromal cells in the marrow can differentiate into one of several mature forms, including osteoblasts and adipocytes. Under in vitro conditions, bone loss is associated with an expansion of adipose tissue in the marrow (16). A recent study showed that the peroxisome proliferator-activated receptor pathway, the dominant regulator of adipogenesis, not only determines adipocyte differentiation from mesenchymal progenitors, but also inhibits osteoblast differentiation (17). Other evidence for a link between fat and bone mass is the action of leptin. Leptin is an anorexigenic metabolic hormone that is secreted in proportion to fat mass (18). Leptin's anti-osteogenic action was demonstrated by intracerebroventricular injection of leptin into an ob/ob mouse, which decreased bone formation (19). A strong positive correlation between fat mass and serum lipid concentrations has been reported (20). Studies have shown that hyperlipidemia may contribute to osteoporosis by increasing osteoclastic bone resorption (21) and osteoclast viability (22). All of the above imply an inverse reciprocal relation between fat mass and bone mass.
In the present study, we investigated the association between bone mass and body fat composition for a given body weight and estimated the risk of higher percentage fat mass (%FM) on osteoporosis, osteopenia, and nonspine fractures by adjusting for body weight and other possible confounders in a large-scale cohort of men, premenopausal women, and postmenopausal women.
SUBJECTS AND METHODS
Study population
This study is part of an ongoing community-based osteoporosis study initiated in 2003 among residents of Anhui Province, China. Men and women aged 25–64 y were recruited. Participants with a history of the following conditions were excluded from the study: type 1 diabetes; renal failure; chronic infections, such as tuberculosis or other diseases; malignancy; rickets or other metabolic bone diseases; chronic glucocorticoid use; viral cirrhosis; and thyrotoxicosis.
This study was approved by the Human Subjects Committee (the institutional review board) of the Harvard School of Public Health and the Ethics Committee of Anhui Medical University. Written informed consent was obtained from each participant.
Measurement of bone mineral content, bone mineral density, and body composition
Dual-energy X-ray absorptiometry (GE-lunar Prodigy, Waukesha, WI) was used to measure soft-tissue body composition, bone mineral content (BMC, in g), and bone mineral density (BMD, in g/cm2) through whole-body and total-hip scans. Whole-body fat mass and lean mass were expressed in terms of weight (g) and as a percentage of body weight. We define osteoporosis as a total-hip BMD of >2.5 SDs below the average peak BMD of young, healthy Chinese in same study area between 25 and 30 y of age (T-score < –2.5). Osteopenia was defined as a total-hip BMD between 1 and 2.5 SDs below the peak BMD (–2.5 < T-score < –1).
Anthropometry
A general physical examination was conducted of each participant. Height (m) was measured to the nearest 0.1 cm on a portable stand meter, and weight was measured to the nearest 0.1 kg with the subject standing motionless in the center of the scale. Weight and height were measured without the subjects' wearing shoes. Body mass index (BMI) was calculated as weight/height2.
Serum lipid measurements
Fasting blood samples were collected and stored in aliquots at –80 °C. Serum cholesterol was measured enzymatically with a Cobas Integra Roche analyzer (Roche, Indianapolis, IN). Serum triacylglycerol was assayed by using the glyceryl dehydrogenase reaction after enzymatic hydrolysis of the glycerides on the Cobas Integra Roche analyzer. HDL cholesterol was measured after precipitation of LDL and VLDL with polyanions and phosphotungstic acid–magnesium chloride. The supernatant portion was assayed enzymatically on the Cobas Integra Roche analyzer.
Questionnaires
Comprehensive questionnaires were used to collect the participants' demographic, occupational, and lifestyle information; reproductive history; disease history; consumption of alcohol; cigarette smoking; physical activity; history of fractures; and daily diet. A fracture questionnaire was applied for those participants who self-reported their fracture history. Fracture sites, treatments, and the age of the participants when they had the fractures were recorded. For this analysis, a nonspine fracture case was defined as participants with fractures at nonspine sites that occurred within 2 y of the BMD measurements.
Statistical analyses
Participants were divided into men, premenopausal women, and postmenopausal women. We defined menopausal status by questionnaire. Because sex and menopausal status are 2 of the most important predictors of bone mass, osteoporosis, and fractures, we report analyses separately on the basis of sex and menopausal status. The SAS 8.2 software package (SAS Institute Inc, Cary, NC) was used to perform all statistical analyses.
Univariate analyses
Analysis of variance (ANOVA) tests and chi-square tests were used to compare the principal characteristics of the study subjects among sex and menopausal status groups. Tukey's test was also used to perform pairwise comparisons among groups when there was significance for an ANOVA or chi-square test. We then further divided %FM into quartiles. For covariates among quartiles of %FM, the generalized linear model was used to test for linear trend. Spearman's rank correlation coefficients were used to determine the strength of relations between fat mass (or lean mass) and other variables, such as weight, serum lipid profile, and physical activity. t Tests were used to test the significance of the correlations.
Multivariate analyses
To assess the effects of fat mass on BMC independently from the effects of body weight, quartiles of fat mass in 5-kg strata of weight were plotted against BMC. For maximum statistical power, only strata with 200 persons were included. Multiple linear regression models adjusted for differences in age, height, occupation, cigarette smoking (for men only), alcohol consumption (for men only), physical activity, and years since menopause (for postmenopausal women only) were used in each stratum to test for a linear trend in the relation of fat mass to BMC. Least-squares means and SDs of BMC were computed. Multivariate logistic regression models adjusted for age, physical activity, occupation, cigarette smoking, alcohol consumption, height, weight, and years since menopause (for postmenopausal women only) were used to estimate the independent risks of %FM on osteoporosis and osteopenia. For the risk of %FM on nonspine fractures, the adjusted variables were age, physical activity, occupation, whole-body BMD, and years since menopause (for postmenopausal women only). The log likelihood ratio test was used to test the interaction among covariates. Linear regression models adjusted for age, height, %FM, occupation, physical activity, cigarette smoking, alcohol consumption, and years since menopause (for postmenopausal women only) were applied to explore the magnitude of relations between serum lipid profiles (cholesterol, triacylglycerol, HDL, LDL, and the ratio of LDL to HDL) and bone mass (BMD and BMC). Generalized estimating equation models were used to adjust for intraclass correlation within family members.
RESULTS
Principal and clinical characteristics
The analyses included 13 970 subjects (7137 men, 4585 premenopausal women, and 2248 postmenopausal women). The principal characteristics of the study population, including the distributions of the serum lipids profile, body composition, BMD, and BMC, are shown in Table 1. Significant differences in all reported covariates were found among the 3 groups. Age, weight, height, BMI, BMD, BMC, body composition, cigarette smoking, and secondhand smoking status differed significantly among the 3 groups (P < 0.01). Men had significantly higher percentages of alcohol consumption and heavy physical activity and a lower percentage of occupation as a farmer than both premenopausal and postmenopausal women. No significant differences were found in alcohol consumption, physical activity, or occupation between premenopausal and postmenopausal women. As shown in the table, our study population was relatively lean, especially compared with populations in North America or Europe.
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TABLE 1. Principal characteristics of the study population stratified by sex and menopausal status1
Negative associations between bone mineral content and fat mass
We further divided the subjects into 5-kg strata of body weight. As shown in Figure 1, there were 7 weight strata (from 45 to 80 kg) in men and 6 weight strata (from 40 to 69 kg) in premenopausal and postmenopausal women. The least-squares means and SDs of total-hip BMC in each quartile of fat mass among weight strata were plotted. A significant fat mass x weight x group interaction for total-hip BMC (P < 0.0001) was found by log likelihood ratio test. Significant fat mass x weight interactions were also found in men (P < 0.0001), premenopausal women (P = 0.01), and postmenopausal women (P = 0.005). Fat mass explained 0.5% to 24% of BMC variation throughout body weight strata. Significant negative associations between fat mass and BMC at the total hip were found in all the weight strata. A significant fat mass x weight x group interaction for whole-body BMC (P < 0.0001) was found by log likelihood ratio tests. Significant fat mass x weight interactions were also found in men (P < 0.0001), premenopausal women (P = 0.001), and postmenopausal women (P = 0.01). We also found significant negative associations between fat mass and whole-body BMC (see Figure 1 under "Supplemental data" in the current issue at www.ajcn.org
).
FIGURE 1.. Least-squares mean (±SD) total-hip bone mineral content (BMC) stratified by fat mass in 5-kg strata of body weight in men, premenopausal women, and postmenopausal women. The bars from left to right are quartiles (Q) 1, 2, 3, and 4 of percentage fat mass in each body weight stratum. Multiple linear regression models adjusted for age, height, occupation, cigarette smoking (for men only), alcohol consumption (for men only), physical activity, and years since menopause (for postmenopausal women only) were used to test for linear trends in total-hip BMC by percentage fat mass quartiles within weight strata. *P < 0.05; **P < 0.01; ***P < 0.001 (Wald chi-square test). P < 0.0001 for fat mass x weight x group interaction; fat mass x weight interactions were also found in men (P < 0.0001), premenopausal women (P = 0.01), and postmenopausal women (P = 0.005).
Percentage fat mass and odds ratios of osteoporosis, osteopenia, and nonspine fracture
We then divided the subjects into quartiles of %FM. The average %FM in each quartile of %FM is shown in Table 2. Participants with higher %FM tended to have lower percentage lean mass (%LM); lower physical activity; higher body weight; higher cholesterol, triacylglycerol, and LDL concentrations; lower HDL concentrations; and a higher ratio of LDL to HDL. Significant interactions were found between quartiles of %FM and sex for %LM, weight, serum lipids, and physical activity by log likelihood ratio test (P values < 0.001).
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TABLE 2. Mean percentage fat mass (%FM) and other covariates by quartiles (Q) of %FM1
We defined 156 (2.2%) men, 82 (1.8%) premenopausal women, and 278 (12.4%) postmenopausal women as having osteoporosis. The numbers of osteoporosis, osteopenia, and nonspine fracture subjects in each quartile of %FM are shown in Table 3. Significant linear trends of higher ORs of osteoporosis, osteopenia, and nonspine fracture with higher %FM were found (P < 0.0001, P < 0.0001, and P = 0.002, respectively) in multiple logistic regression models that included all subjects and that were adjusted for body weight and other covariates. A significant group x quartile of %FM interaction for the OR of osteoporosis was found (P = 0.02), but not for the ORs of osteopenia or fracture (P = 0.3 and 0.2, respectively). As shown in Table 3, comparing the highest quartile (Q4) with the lowest quartile of %FM, the adjusted ORs (95% CI) of osteoporosis were 5.2 (2.1, 13.2), 5.0 (1.7, 15.1), and 6.9 (4.3, 11.2), in men, premenopausal women, and postmenopausal women, respectively.
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TABLE 3. . Adjusted odds ratios (ORs) and 95% CIs of osteoporosis, osteopenia, and nonspine fractures by quartile (Q) of percentage fat mass (%FM) in men, premenopausal women, and postmenopausal women1
A total of 1823 (25.6%) men, 1172 (25.6%) premenopausal women, and 1166 (51.9%) postmenopausal women were defined as having osteopenia. There were significant and independent linear trends for higher ORs of osteopenia with higher %FM (P values < 0.01 in all groups).
A total of 82 (1.1%) men, 54 (1.2%) premenopausal women, and 46 (2.0%) postmenopausal women self-reported at least one nonspine fracture. There were significant and independent linear trends for higher ORs of nonspine fractures in men and premenopausal women with higher %FM (P values < 0.05 in men and premenopausal women).
Relative odds by lean mass, physical activity, and years since menopause
We investigated the ORs of osteoporosis, osteopenia, and nonspine fracture by %LM, physical activity, and years since menopause. Weight was positively correlated with both fat mass and lean mass (see Table 1 under "Supplemental data" in the current issue at www.ajcn.org). Participants with higher weights tended to have a higher %FM and a lower %LM, and participants with lower weights had lower %FM and higher %LM. The crude and adjusted risks of %LM, heavy physical activity, and years since menopause for osteoporosis, osteopenia, and nonspine fractures for all subjects are shown in Table 4. These covariates have been reported as important risk factors that may influence BMD and cause osteoporosis. Heavy physical activity reduced the risk of osteoporosis and osteopenia but increased the risk of nonspine fractures after adjustment for other covariates. Years since menopause increased the odds of osteoporosis and osteopenia. We divided %LM into quartiles. The mean (±SD) values for Q1 to Q4 were 64.8 ± 3.53%, 72.9 ± 1.95%, 80.8 ± 2.61%, and 88.6 ± 1.74%, respectively. %LM slightly decreased the adjusted ORs of osteopenia and fractures, but significantly so only for osteopenia. We used the generalized linear model to test for linear trends. After adjustment for possible confounders, there were significant linear trends for osteopenia (P < 0.01) and nonspine fracture (P < 0.05), but not for osteoporosis (P = 0.8), with increasing %LM. There were neither significant interactions between groups and %LM (P = 0.2, 0.1, and 0.4, respectively, for osteoporosis, osteopenia, and fracture) nor between groups and physical activity (P = 0.8, 0.1, and 0.7, respectively, for osteoporosis, osteopenia, and fracture).
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TABLE 4. Odds ratios (ORs) and 95% CIs of osteoporosis, osteopenia, and nonspine fracture for percentage lean mass (%LM), physical activity, and years since menopause (postmenopausal women only)
Interaction of %FM and heavy physical activity
Because physical activity is negatively correlated with %FM (see Table 1 under "Supplemental data" in the current issue at www.ajcn.org), we estimated the interaction of %FM and heavy physical activity for the ORs of osteoporosis and osteopenia. A significant 3-way interaction (group x %FM x physical activity) for risk of osteoporosis was found (P < 0.001), but not for risk of osteopenia (P = 0.4). There was neither a significant interaction between %FM and physical activity for the odds of osteoporosis (P = 0.8, 0.08, and 0.3, respectively, for men, premenopausal women, and postmenopausal women) nor a significant interaction between %FM and physical activity for the odds of osteopenia (P = 0.3, 0.1, and 0.4, respectively, for men, premenopausal women, and postmenopausal women). As shown in Figure 2, a linear trend toward higher ORs of osteoporosis and osteopenia with higher %FM was observed with or without heavy physical activity. For osteoporosis, the adjusted ORs of Q4 and Q3 compared with the lowest quartile in premenopausal women who performed heavy physical activity are not shown because there were too few subjects (<5).
FIGURE 2.. Adjusted odds ratios (ORs) and 95% CIs of osteoporosis and osteopenia by percentage fat mass quartiles (Q) stratified by physical activity in men, premenopausal women, and postmenopausal women. Q1 in each stratum is the reference group. Multiple logistic regression models adjusted for age, percentage fat mass, occupation, cigarette smoking, alcohol consumption, weight, height, and years since menopause (in postmenopausal women only) were used to estimate the ORs and 95% CIs. The Wald chi-square test was used to perform the significance test. The adjusted ORs of Q4 and Q3 for premenopausal women in the heavy physical activity stratum are not shown because there were too few subjects (<5). A significant 3-way interaction (group x percentage fat mass x physical activity) for risk of osteoporosis was found (P < 0.001).
Associations between bone mass and the serum lipid profile
In addition to body weight and physical activity, serum lipids were significantly correlated with fat mass and lean mass. Cholesterol, triacylglycerol, and LDL concentrations and the ratio of LDL to HDL were positively correlated with fat mass, but HDL was negatively correlated with fat mass. The correlations between serum lipids and fat mass were obviously higher than the correlations between serum lipids and lean mass (see Table 1 under "Supplemental data" in the current issue at www.ajcn.org). Adjusted linear regression models were applied to explore the magnitude of relations between serum lipid profiles and whole-body BMC (Table 5). Significantly negative associations were found between whole-body BMC and cholesterol, triacylglycerol, and LDL concentrations and the ratio of HDL to LDL. No significant associations between whole-body BMC and HDL were found. The same patterns of relations were found between total-hip BMC and serum lipids, but the associations were not significant.
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TABLE 5. . Regression coefficients (ß) for serum lipids (mg/dL) to whole-body bone mineral content (BMC)1
DISCUSSION
Our study showed significantly higher ORs of osteoporosis, osteopenia defined by BMD, and nonspine fractures in subjects with higher %FM after adjustment for possible confounders. The higher ORs were associated with %FM independently of body weight, age, and physical activity. Furthermore, the large sample size allowed us to place the subjects into 5-kg strata of body weight and to still have enough statistical power to assess the effect of fat mass on bone mass independently of body weight. Fat mass was negatively associated with BMC in the whole body and total hip for a given body weight. The negative associations between fat mass and BMC were independent of age and physical activity. Thus, fat mass seems to affect BMD and BMC beyond the effect of weight bearing itself.
Lean mass and fat mass together account for 95% of body weight. The remaining 5% is bone mass. Lean mass has been reported as a predictor of bone mass through its mechanical pull on the skeleton (2). %FM is an estimate of the proportion of body weight that is fat tissue. This means that %FM can differentiate characteristics of the tissue types from the mass effect of the tissues. In our study, %FM and %LM had a reciprocal relation. It is reasonable to doubt that the risk of higher %FM on osteoporosis may be due to the effect of lower %LM. As shown in Table 4, the ORs of osteoporosis were lower in subjects with higher %LM after adjustment for body weight and other confounders. Because the correlations between body weight and %LM were negative, the effect of %LM on osteoporosis and osteopenia without adjustment for body weight seemed only to reflect the effect of body weight. However, it was difficult to differentiate the effect of %FM from that of %LM in our cross-sectional design.
%FM could be a surrogate marker for lifestyle factors that are themselves negatively associated with BMD. Intervention studies have shown that exercise is associated with higher bone density and lower fat mass (23, 24). However, when we examined the ORs of %FM on bone outcomes, whether or not we took physical activity into account did not appreciably change the results. In addition, we still observed positive associations between %FM and osteoporosis in subjects with or without heavy physical activity. Therefore, the risks of %FM on osteoporosis, osteopenia, and nonspine fractures were independent of physical activity. Sex, menopausal status, and other environmental factors may confound the associations between fat mass and bone mass. Our large sample size allowed us to analyze men, premenopausal women, and postmenopausal women separately. Because of the limited availability of public transportation, the similarity of lifestyle, and the lack of calcium supplements or hormone replacement therapy, the study population appeared to be relatively stable and fairly homogeneous. The potential for confounding in our study was at least significantly minimized.
Similar to previous studies, without control for body weight, we observed positive associations between fat mass and bone mass (6–13; see Table 2 under "Supplemental data" in the current issue at www.ajcn.org). However, we found significantly negative relations between fat mass and bone mass for a given body weight. Other studies have shown the same phenomenon of fat mass being negatively correlated with bone mass (8, 9, 11) and forearm fractures (25) after control for body weight. As the result of strong collinearity between fat mass and weight, most other studies with small sample sizes could not reliably explore the effect of fat mass on BMD independently of body weight.
Fat mass likely affects bone mass through both weight-bearing and non-weight-bearing effects. Like lean mass, the weight-bearing effect is a positive one and is likely related to cortical and periosteal modeling. On the other hand, the non-weight-bearing effect of higher fat mass may be negative, particularly for this cohort. An animal study of the peroxisome proliferator-activated receptor pathway showed that the regulation of adipocyte differentiation may be important for the regulation of bone homeostasis (17). That study observed that peroxisome proliferator-activated receptor haploinsufficiency was shown to enhance osteoblastogenesis in vitro and to increase bone mass in mice at both 8 and 52 wk of age in vivo. This effect was not mediated by insulin or leptin. A positive correlation between fat mass and serum leptin concentrations was found in humans (8, 11). Obese mouse models have shown that both leptin-deficient ob/ob mice and leptin-receptor-deficient db/db mice have higher rates of bone formation, despite their hypogonadism and hypercortisolism, which reduce bone mass (26). However, the results of observational studies in humans are somewhat controversial. Both negative (11, 13) and positive (8, 9, 27) associations between serum leptin concentrations and bone mass have been reported. The observed positive correlations between leptin concentrations and bone mass may be confounded by body weight (9, 27) or by menopausal status (8). The above evidence seems to explain the negative effect of fat on bone formation. However, in addition to these hypotheses, other unknown factors may exist that link both fat tissue and bone tissue. These unknown factors require further investigation.
Besides weight and lean mass, serum lipids are strongly correlated with fat mass. Epidemiologic evidence has linked osteoporosis and cardiovascular disease (28). In our study, after adjustment for weight, fat mass, and other confounders, significantly negative relations were found between whole-body BMC and cholesterol, triacylglycerol, and LDL concentrations. Similar patterns were also found between total-hip BMC and serum lipids, but these were not significant. Previous studies reported negative correlations between cholesterol, triacylglycerol, or LDL and bone mass at the spine and total body but not at the hip (29, 30). A prospective study showed increasing serum cholesterol concentrations with decreasing BMD at the spine but not at the hip, independent of the change in BMI during an 8-y follow-up (29). After adjustment for BMI and age, a study with 1303 postmenopausal women also reported a higher risk of osteopenia for participants with higher plasma LDL concentrations (31). Studies have shown that oxidized lipids inhibit osteoblastic differentiation from preosteoblasts in vitro and bone formation in vivo. Products of lipoprotein oxidation inhibit preosteoblast differentiation (16, 32) and result in reduced bone mineralization (33). Several studies indicated that statins, which are widely used as lipid-lowering agents, seem to provide benefits in the prevention of bone loss and fractures (34, 35). Another study showed that lipid-lowering therapy results in slight increases in osteocalcin without changes in collagen type I crosslinked carboxyl terminal peptide, which also suggests the interruption of serum lipids on osteoblast function (36). However, in our study, serum lipids may explain only a small part of the relation between fat mass and bone mass.
Compared with other studies, this population may represent a lean and underweight population. Because of the age inclusion criterion (25–64 y old), the prevalence of osteoporosis in our study may not represent the prevalence in the general Asian population. The results from our study may also not apply directly to populations other than Asians. Furthermore, our study, being observational in design, cannot prove the existence of a causal relation between fat mass and bone mass. Our cross-sectional design also cannot clarify whether fat mass or %FM was associated with peak bone density or with age-related bone loss. More studies are needed in other populations, particularly in those with higher BMIs, to explore this relation further.
In conclusion, the results of the present study show that body fat mass and serum lipids are negatively associated with bone mass for a given body weight in a relatively lean population. These results highlight the importance of %FM and serum lipid profiles as risk factors for osteoporosis and also provide a rationale for further exploration of the underlying mechanisms.
ACKNOWLEDGMENTS
We thank Melissa Veno for editing this manuscript.
Y-HH completed the data analyses and prepared the manuscript. SAV and MLB contributed to data analysis and manuscript preparation. HAT, CJR, SRC, and JDB participated in the study design and critically reviewed the manuscript. NL contributed to data analysis and critically reviewed the manuscript. YF, TN, ZL, and XX participated in the data collection. None of the authors had any financial or personal interest, including advisory board affiliations, in any company or organization sponsoring the research.
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