JBMR
ORIGINAL ARTICLE
Relationship Between Body Mass Index and Fracture Risk Is Mediated by Bone Mineral Density Mei Y Chan,1,2 Steve A Frost,1,3 Jacqueline R Center,1,2,4,5 John A Eisman,1,2,4,5,6 and Tuan V Nguyen1,3,4,7 1
Division of Musculoskeletal Diseases, Garvan Institute of Medical Research, Sydney, Australia School of Medicine, University of New South Wales, Sydney, Australia 3 School of Public Health and Community Medicine, University of New South Wales, Sydney, Australia 4 St Vincent’s Clinical School, St Vincent’s Hospital, Sydney, Australia 5 Department of Endocrinology, St Vincent’s Hospital, Sydney, Australia 6 School of Medicine, University of Notre Dame, Sydney, Australia 7 Centre of Health and Technologies, University of Technology, Sydney, Australia 2
ABSTRACT The relationship between body mass index (BMI) and fracture risk is controversial. We sought to investigate the effect of collinearity between BMI and bone mineral density (BMD) on fracture risk, and to estimate the direct and indirect effect of BMI on fracture with BMD being the mediator. The study involved 2199 women and 1351 men aged 60 years or older. BMI was derived from baseline weight and height. Femoral neck BMD was measured by dual‐energy X‐ray absorptiometry (DXA; GE‐LUNAR, Madison, WI, USA). The incidence of fragility fracture was ascertained by X‐ray reports from 1991 through 2012. Causal mediation analysis was used to assess the mediated effect of BMD on the BMI‐fracture relationship. Overall, 774 women (35% of total women) and 258 men (19%) had sustained a fracture. Approximately 21% of women and 20% of men were considered obese (BMI 30). In univariate analysis, greater BMI was associated with reduced fracture risk in women (hazard ratio [HR] 0.92; 95% confidence interval [CI], 0.85 to 0.99) and in men (HR 0.77; 95% CI, 0.67 to 0.88). After adjusting for femoral neck BMD, higher BMI was associated with greater risk of fracture in women (HR 1.21; 95% CI, 1.11 to 1.31) but not in men (HR 0.96; 95% CI, 0.83 to 1.11). Collinearity had minimal impact on the BMD‐adjusted results (variance inflation factor [VIF] ¼ 1.2 for men and women). However, in mediation analysis, it was found that the majority of BMI effect on fracture risk was mediated by femoral neck BMD. The overall mediated effect estimates were 0.048 (95% CI, 0.059 to 0.036; p < 0.001) in women and 0.030 (95% CI, 0.042 to 0.018; p < 0.001) in men. These analyses suggest that there is no significant direct effect of BMI on fracture, and that the observed association between BMI and fracture risk is mediated by femoral neck BMD in both men and women. © 2014 American Society for Bone and Mineral Research. KEY WORDS: BMI; OBESITY; BMD; FRACTURE; MULTICOLLINEARITY; MEDIATION ANALYSIS
Introduction
T
here are currently conflicting views on the relationship between obesity and fragility fracture. Traditionally, obesity is believed to be protective against fragility fracture; this view is supported by many earlier studies.(1–3) For instance, hip fracture risk in women was increased by 7.4% for each unit decrease in body mass index (BMI).(1) In men, lower BMI was also found to be a significant risk factor for hip fracture, with a reduction of 32% in relative risk per unit change in BMI.(3) In contrast, more recent findings suggest that obesity may not be beneficial to bone health. Some studies even showed that higher body mass is a significant risk factor for fragility fracture, particularly for those occurring at sites other than hip.(4–7) It is notable that most of the findings supporting an adverse effect of body mass on fracture risk were derived from bone mineral density (BMD)‐adjusted data. In those studies, it was
shown that after controlling for BMD, individuals with higher BMI had greater risk of fracture than those with low BMI.(5,8,9) For example, in a prospective study on elderly women, lower BMI was found to be associated with greater risk of hip fracture before adjustment for BMD, but the association was reversed after BMD was included in the statistical model.(8) Because BMI is known to be correlated with BMD,(10,11) a model including both BMI and BMD could be confounded by the presence of collinearity, a statistical phenomenon in which two independent variables in a regression model are linearly related and provide redundant information about the response. As a consequence, change in one variable affects the estimate of the other, resulting in inaccurate regression estimation, and sometimes incorrect sign for the estimated coefficients.(12,13) However, a significant change in the estimated coefficient of a predictor with inclusion of a second variable could also be an indication of a mediation effect. Mediation is defined as “the
Received in original form November 25, 2013; revised form May 7, 2014; accepted May 20, 2014. Accepted manuscript online May 24, 2014. Address correspondence to: Tuan V Nguyen, PhD, Division of Musculoskeletal Diseases, Garvan Institute of Medical Research, 384 Victoria Street, Sydney, NSW 2010, Australia. E‐mail: [email protected] For commentary on this article, please see Lix (J Bone Miner Res. 2014;29:2323–2326. DOI: 10.1002/jbmr.2369) Journal of Bone and Mineral Research, Vol. 29, No. 11, November 2014, pp 2327–2335 DOI: 10.1002/jbmr.2288 © 2014 American Society for Bone and Mineral Research
2327
generative mechanism through which the focal independent variable is able to influence the dependent variable of interest.”(14) Thus, according to the definition of mediation,(15) BMD is likely a mediator in the relationship between BMI and fracture risk because (1) the variations in BMD significantly account for variation in BMI(16,17); (2) variations in BMD significantly account for fracture risk(18,19); and (3) when BMD is controlled for, the effect of BMI on fracture is no longer significant or partially diminished. In case of inconsistent mediation, the sign of the mediated effect of BMI on fracture could be opposite to that of the total effect without controlling for BMD.(20) The causal structure of the relationship between BMI, BMD, and fracture has not been investigated. The present study sought to examine the effect of collinearity between BMI and BMD on fracture risk, and to estimate the direct and indirect effect of BMI on fracture with BMD being the mediator.
Subjects and Methods Participants Participants in this study were drawn from the Dubbo Osteoporosis Epidemiology Study (DOES), which commenced in 1989 and has been ongoing since. The setting is the city of Dubbo, with a population of 32,000, situated 400 km northwest of Sydney, Australia. DOES is an ongoing community‐based prospective study on risk factors for fracture and chronic diseases. Details of the study design and population have been described.(21) Briefly, the initial study population included 1581 men and 2095 women aged 60 years or above, of whom 98.6% was Caucasian and 1.4% was indigenous Aboriginal. Recruitment has since been extended to the younger community and the DOES study population has expanded to 20,000 people by December 2012. The present study consisted of 3550 participants (2199 women and 1,351 men) aged 60 years, who were recruited between 1989 and 2011. The median follow‐up period was 6 (range, 0.1 to 22) years. This study was approved by the St Vincent’s Hospital Ethics committee and written informed consent was obtained from all participants.
Measurements Anthropometric and clinical history data were ascertained by a structured questionnaire at baseline and subsequent visits. The interview was conducted by registered nurses to collect information on clinical risk factors such as smoking, history of falls in the last 12 months, prior fracture after age 50 years, use of hormonal replacement therapy (HRT) and glucocorticoid. At baseline, body weight (with heavy clothing and shoes removed) was obtained on an electronic scale, which was calibrated prior each measurement. Height was measured without shoes with a wall‐mounted stadiometer. BMI was calculated as weight in kilograms divided by the square of height in meters (kg/m2). Based on the WHO definition, individuals with a BMI 10 is considered as an indicative of serious collinearity. We also used the ridge regression(27) to detect collinearity in the data. Ridge regression is a statistical technique that is often used as an alternative to overcome collinearity by introducing a small amount of bias into the estimate so as to minimize the variance and control the instability of the estimators.(28) However, the technique can also be used to assess the existence of collinearity by examining the ridge coefficients with incremental bias or constant (k). If the regression coefficients remained stable with additional bias (k), then collinearity, even if it did exist, would not pose significant impact on the parameter estimates.(29) In the present study, we examined the variation in the regression coefficients of BMI and femoral neck BMD for 0 < k < 1, using the Cox ridge method proposed by Xue and colleagues.(27) To determine whether the relation between BMI and fracture risk was mediated by BMD, causal mediation analysis was carried out based on the following statistical models:
Journal of Bone and Mineral Research
Model I : Fracture risk ¼ a1 þ b1 ðBMIÞ þ e1 Model II : BMD ¼ a2 þ b2 ðBMIÞ þ e2 Model III : Fracture risk ¼ a3 þ b3 ðBMIÞþ b4 ðBMDÞ þ e3 where a, b, and e represent the intercepts, regression coefficients, and residuals, respectively.(15,20) The indirect or mediated effect was estimated as the product of the regression of femoral neck BMD on BMI (b2) and fracture risk on femoral neck BMD (b4). The direct effect is the net association of BMI and fracture risk without the mediation effect (b1). All models were adjusted for covariates, which included age, falls, prior fracture, smoking, physical activity level, glucocorticoid use, and (in women) HRT use. Because the outcome variable is dichotomous, the logistical regression with weighted least squares estimation method was used to calculate the estimated coefficients and the mediation effects were estimated from the standardized regression coefficients.(20) One of the key criteria to define a variable as mediator is that the mediator must precede the outcome. Given the prospective design of the study, the temporal precedence between the femoral neck BMD and fracture risk was readily established.(30) The statistical significance of the mediated effect was assessed by using the Sobel test.(31) Bias‐corrected bootstrapping technique(32) with 1000 replications was used to determine the CIs and confirmed the statistical significance of the proposed mediational pathway. To assess whether prediction of fracture risk from BMI differs across levels of femoral neck BMD (ie, femoral BMD as a moderator), an interaction term of BMI and femoral neck BMD was included in the model as follows:
Fracture risk ¼ a4 þ b5 ðBMIÞ þ b6 ðBMDÞ þ b7 ðBMI BMDÞ þ e4 where b7 is the regression coefficient for the interaction term. The moderation effect is indicated if the regression estimate of the interaction term is statistically different from zero.(33) In order to quantify the percentage of variance that can be explained by an individual predictor and the degree of improvement in fit of the models with different variables, we used Nagelkerke’s method(34) to calculate the pseudo‐R2 for each factor. Furthermore, net reclassification analysis was performed to quantify the level of improvement in discrimination between fracture and nonfracture cases when BMI was added to the base model with femoral neck BMD. In this analysis, the study population was divided into lower, middle, and upper risk groups based on their 10‐year absolute risk of fracture for the model with and without BMI included. To ensure a comparable sample size for each risk group, the cutoff values were chosen according to the distribution of fracture incidence in the study population (ie, lower tertile, middle tertile, and upper tertile). The difference in proportion of those with and those without fracture moving up or down a risk category was calculated as proposed by Pencina and colleagues.(35) All statistical analyses were performed using the R statistical environment, version 3.0.2 for Windows.(36)
Results Baseline characteristics During the 22‐year follow‐up period, 774 women (35%) and 258 men (19%) had sustained at least one low‐trauma fracture.
Journal of Bone and Mineral Research
Compared with those without fracture, individuals with fracture were older, lighter, and had lower femoral neck BMD. They were more likely to be glucocorticoid users, had prior fracture after age 50 years, and a higher incidence of falls in the preceding 12 months. Women with fracture also tended to be current or past smokers and were less likely to have received HRT compared to their nonfracture counterparts (Table 1). The prevalence of obesity (ie, BMI 30) was 21% in women and 20% in men. Women and men with greater BMI had lower risk of fracture (Fig. 1). In the unadjusted bivariate analysis (Table 2), greater BMI was associated with reduced risk of fragility fracture in women (HR 0.92; 95% CI, 0.85 to 0.99) and men (HR 0.77; 95% CI, 0.67 to 0.88). This association was also observed for hip fracture and vertebral fracture. After adjusting for femoral neck BMD, greater BMI was associated with greater risk of fracture in women (HR 1.21; 95% CI, 1.11 to 1.31), but not in men (HR 0.96; 95% CI, 0.83 to 1.11). Further adjustment for other covariates including age, falls, prior fracture, smoking, glucocorticoid use, and (in women) HRT use did not significantly alter the results.
Collinearity analysis BMI was positively and significantly correlated with femoral neck BMD. The coefficient of correlation was 0.41 (95% CI, 0.37 to 0.44; p < 0.001) for women and 0.37 (95% CI, 0.32 to 0.41; p < 0.001) for men. In women, the VIFs were 1.26 for BMI, 1.50 for femoral neck BMD, and 1.37 for age. Similar results were observed in men (VIF for BMI ¼ 1.20, femoral neck BMD ¼ 1.23, and age ¼ 1.17). In both men and women, the ridge regression coefficients of BMI and femoral neck BMD were relatively stable when incrementing bias was introduced into the models (Table 3), suggesting that collinearity did not contribute to the sudden change in the regression estimate of BMI on fracture risk. Table 1. Baseline Characteristics of 2199 Women and 1351 Men
Women Age (years) Weight (kg) Height (cm) BMI (kg/m2) FNBMD (g/cm2) Falls (%) Prior fracture (%) Glucocorticoid use (%) History of smoking (%) HRT (%) Men Age (years) Weight (kg) Height (cm) BMI (kg/m2) FNBMD (g/cm2) Falls (%) Prior fracture (%) Glucocorticoid use (%) History of smoking (%)
Nonfracture
Fracture
n ¼ 1425 68.5 6.4 69.0 13.5 160 5.9 27.8 5.1 0.83 0.13 32.40 9.70 6.80 28.60 10.70 n ¼ 1093 68.9 5.7 81.9 13.8 173 6.7 27.1 4.0 0.94 0.14 21.60 7.50 6.40 61.80
n ¼ 774 71 7.3 65 13.1 159 6.7 26 4.9 0.76 0.12 42.90 10.90 10.10 34.60 7.60 n ¼ 258 71.9 6.8 76.7 13.1 172 6.9 25.8 3.9 0.86 0.16 37.90 12.00 10.90 63.60
pa
ORIGINAL ARTICLE
Relationship Between Body Mass Index and Fracture Risk Is Mediated by Bone Mineral Density Mei Y Chan,1,2 Steve A Frost,1,3 Jacqueline R Center,1,2,4,5 John A Eisman,1,2,4,5,6 and Tuan V Nguyen1,3,4,7 1
Division of Musculoskeletal Diseases, Garvan Institute of Medical Research, Sydney, Australia School of Medicine, University of New South Wales, Sydney, Australia 3 School of Public Health and Community Medicine, University of New South Wales, Sydney, Australia 4 St Vincent’s Clinical School, St Vincent’s Hospital, Sydney, Australia 5 Department of Endocrinology, St Vincent’s Hospital, Sydney, Australia 6 School of Medicine, University of Notre Dame, Sydney, Australia 7 Centre of Health and Technologies, University of Technology, Sydney, Australia 2
ABSTRACT The relationship between body mass index (BMI) and fracture risk is controversial. We sought to investigate the effect of collinearity between BMI and bone mineral density (BMD) on fracture risk, and to estimate the direct and indirect effect of BMI on fracture with BMD being the mediator. The study involved 2199 women and 1351 men aged 60 years or older. BMI was derived from baseline weight and height. Femoral neck BMD was measured by dual‐energy X‐ray absorptiometry (DXA; GE‐LUNAR, Madison, WI, USA). The incidence of fragility fracture was ascertained by X‐ray reports from 1991 through 2012. Causal mediation analysis was used to assess the mediated effect of BMD on the BMI‐fracture relationship. Overall, 774 women (35% of total women) and 258 men (19%) had sustained a fracture. Approximately 21% of women and 20% of men were considered obese (BMI 30). In univariate analysis, greater BMI was associated with reduced fracture risk in women (hazard ratio [HR] 0.92; 95% confidence interval [CI], 0.85 to 0.99) and in men (HR 0.77; 95% CI, 0.67 to 0.88). After adjusting for femoral neck BMD, higher BMI was associated with greater risk of fracture in women (HR 1.21; 95% CI, 1.11 to 1.31) but not in men (HR 0.96; 95% CI, 0.83 to 1.11). Collinearity had minimal impact on the BMD‐adjusted results (variance inflation factor [VIF] ¼ 1.2 for men and women). However, in mediation analysis, it was found that the majority of BMI effect on fracture risk was mediated by femoral neck BMD. The overall mediated effect estimates were 0.048 (95% CI, 0.059 to 0.036; p < 0.001) in women and 0.030 (95% CI, 0.042 to 0.018; p < 0.001) in men. These analyses suggest that there is no significant direct effect of BMI on fracture, and that the observed association between BMI and fracture risk is mediated by femoral neck BMD in both men and women. © 2014 American Society for Bone and Mineral Research. KEY WORDS: BMI; OBESITY; BMD; FRACTURE; MULTICOLLINEARITY; MEDIATION ANALYSIS
Introduction
T
here are currently conflicting views on the relationship between obesity and fragility fracture. Traditionally, obesity is believed to be protective against fragility fracture; this view is supported by many earlier studies.(1–3) For instance, hip fracture risk in women was increased by 7.4% for each unit decrease in body mass index (BMI).(1) In men, lower BMI was also found to be a significant risk factor for hip fracture, with a reduction of 32% in relative risk per unit change in BMI.(3) In contrast, more recent findings suggest that obesity may not be beneficial to bone health. Some studies even showed that higher body mass is a significant risk factor for fragility fracture, particularly for those occurring at sites other than hip.(4–7) It is notable that most of the findings supporting an adverse effect of body mass on fracture risk were derived from bone mineral density (BMD)‐adjusted data. In those studies, it was
shown that after controlling for BMD, individuals with higher BMI had greater risk of fracture than those with low BMI.(5,8,9) For example, in a prospective study on elderly women, lower BMI was found to be associated with greater risk of hip fracture before adjustment for BMD, but the association was reversed after BMD was included in the statistical model.(8) Because BMI is known to be correlated with BMD,(10,11) a model including both BMI and BMD could be confounded by the presence of collinearity, a statistical phenomenon in which two independent variables in a regression model are linearly related and provide redundant information about the response. As a consequence, change in one variable affects the estimate of the other, resulting in inaccurate regression estimation, and sometimes incorrect sign for the estimated coefficients.(12,13) However, a significant change in the estimated coefficient of a predictor with inclusion of a second variable could also be an indication of a mediation effect. Mediation is defined as “the
Received in original form November 25, 2013; revised form May 7, 2014; accepted May 20, 2014. Accepted manuscript online May 24, 2014. Address correspondence to: Tuan V Nguyen, PhD, Division of Musculoskeletal Diseases, Garvan Institute of Medical Research, 384 Victoria Street, Sydney, NSW 2010, Australia. E‐mail: [email protected] For commentary on this article, please see Lix (J Bone Miner Res. 2014;29:2323–2326. DOI: 10.1002/jbmr.2369) Journal of Bone and Mineral Research, Vol. 29, No. 11, November 2014, pp 2327–2335 DOI: 10.1002/jbmr.2288 © 2014 American Society for Bone and Mineral Research
2327
generative mechanism through which the focal independent variable is able to influence the dependent variable of interest.”(14) Thus, according to the definition of mediation,(15) BMD is likely a mediator in the relationship between BMI and fracture risk because (1) the variations in BMD significantly account for variation in BMI(16,17); (2) variations in BMD significantly account for fracture risk(18,19); and (3) when BMD is controlled for, the effect of BMI on fracture is no longer significant or partially diminished. In case of inconsistent mediation, the sign of the mediated effect of BMI on fracture could be opposite to that of the total effect without controlling for BMD.(20) The causal structure of the relationship between BMI, BMD, and fracture has not been investigated. The present study sought to examine the effect of collinearity between BMI and BMD on fracture risk, and to estimate the direct and indirect effect of BMI on fracture with BMD being the mediator.
Subjects and Methods Participants Participants in this study were drawn from the Dubbo Osteoporosis Epidemiology Study (DOES), which commenced in 1989 and has been ongoing since. The setting is the city of Dubbo, with a population of 32,000, situated 400 km northwest of Sydney, Australia. DOES is an ongoing community‐based prospective study on risk factors for fracture and chronic diseases. Details of the study design and population have been described.(21) Briefly, the initial study population included 1581 men and 2095 women aged 60 years or above, of whom 98.6% was Caucasian and 1.4% was indigenous Aboriginal. Recruitment has since been extended to the younger community and the DOES study population has expanded to 20,000 people by December 2012. The present study consisted of 3550 participants (2199 women and 1,351 men) aged 60 years, who were recruited between 1989 and 2011. The median follow‐up period was 6 (range, 0.1 to 22) years. This study was approved by the St Vincent’s Hospital Ethics committee and written informed consent was obtained from all participants.
Measurements Anthropometric and clinical history data were ascertained by a structured questionnaire at baseline and subsequent visits. The interview was conducted by registered nurses to collect information on clinical risk factors such as smoking, history of falls in the last 12 months, prior fracture after age 50 years, use of hormonal replacement therapy (HRT) and glucocorticoid. At baseline, body weight (with heavy clothing and shoes removed) was obtained on an electronic scale, which was calibrated prior each measurement. Height was measured without shoes with a wall‐mounted stadiometer. BMI was calculated as weight in kilograms divided by the square of height in meters (kg/m2). Based on the WHO definition, individuals with a BMI 10 is considered as an indicative of serious collinearity. We also used the ridge regression(27) to detect collinearity in the data. Ridge regression is a statistical technique that is often used as an alternative to overcome collinearity by introducing a small amount of bias into the estimate so as to minimize the variance and control the instability of the estimators.(28) However, the technique can also be used to assess the existence of collinearity by examining the ridge coefficients with incremental bias or constant (k). If the regression coefficients remained stable with additional bias (k), then collinearity, even if it did exist, would not pose significant impact on the parameter estimates.(29) In the present study, we examined the variation in the regression coefficients of BMI and femoral neck BMD for 0 < k < 1, using the Cox ridge method proposed by Xue and colleagues.(27) To determine whether the relation between BMI and fracture risk was mediated by BMD, causal mediation analysis was carried out based on the following statistical models:
Journal of Bone and Mineral Research
Model I : Fracture risk ¼ a1 þ b1 ðBMIÞ þ e1 Model II : BMD ¼ a2 þ b2 ðBMIÞ þ e2 Model III : Fracture risk ¼ a3 þ b3 ðBMIÞþ b4 ðBMDÞ þ e3 where a, b, and e represent the intercepts, regression coefficients, and residuals, respectively.(15,20) The indirect or mediated effect was estimated as the product of the regression of femoral neck BMD on BMI (b2) and fracture risk on femoral neck BMD (b4). The direct effect is the net association of BMI and fracture risk without the mediation effect (b1). All models were adjusted for covariates, which included age, falls, prior fracture, smoking, physical activity level, glucocorticoid use, and (in women) HRT use. Because the outcome variable is dichotomous, the logistical regression with weighted least squares estimation method was used to calculate the estimated coefficients and the mediation effects were estimated from the standardized regression coefficients.(20) One of the key criteria to define a variable as mediator is that the mediator must precede the outcome. Given the prospective design of the study, the temporal precedence between the femoral neck BMD and fracture risk was readily established.(30) The statistical significance of the mediated effect was assessed by using the Sobel test.(31) Bias‐corrected bootstrapping technique(32) with 1000 replications was used to determine the CIs and confirmed the statistical significance of the proposed mediational pathway. To assess whether prediction of fracture risk from BMI differs across levels of femoral neck BMD (ie, femoral BMD as a moderator), an interaction term of BMI and femoral neck BMD was included in the model as follows:
Fracture risk ¼ a4 þ b5 ðBMIÞ þ b6 ðBMDÞ þ b7 ðBMI BMDÞ þ e4 where b7 is the regression coefficient for the interaction term. The moderation effect is indicated if the regression estimate of the interaction term is statistically different from zero.(33) In order to quantify the percentage of variance that can be explained by an individual predictor and the degree of improvement in fit of the models with different variables, we used Nagelkerke’s method(34) to calculate the pseudo‐R2 for each factor. Furthermore, net reclassification analysis was performed to quantify the level of improvement in discrimination between fracture and nonfracture cases when BMI was added to the base model with femoral neck BMD. In this analysis, the study population was divided into lower, middle, and upper risk groups based on their 10‐year absolute risk of fracture for the model with and without BMI included. To ensure a comparable sample size for each risk group, the cutoff values were chosen according to the distribution of fracture incidence in the study population (ie, lower tertile, middle tertile, and upper tertile). The difference in proportion of those with and those without fracture moving up or down a risk category was calculated as proposed by Pencina and colleagues.(35) All statistical analyses were performed using the R statistical environment, version 3.0.2 for Windows.(36)
Results Baseline characteristics During the 22‐year follow‐up period, 774 women (35%) and 258 men (19%) had sustained at least one low‐trauma fracture.
Journal of Bone and Mineral Research
Compared with those without fracture, individuals with fracture were older, lighter, and had lower femoral neck BMD. They were more likely to be glucocorticoid users, had prior fracture after age 50 years, and a higher incidence of falls in the preceding 12 months. Women with fracture also tended to be current or past smokers and were less likely to have received HRT compared to their nonfracture counterparts (Table 1). The prevalence of obesity (ie, BMI 30) was 21% in women and 20% in men. Women and men with greater BMI had lower risk of fracture (Fig. 1). In the unadjusted bivariate analysis (Table 2), greater BMI was associated with reduced risk of fragility fracture in women (HR 0.92; 95% CI, 0.85 to 0.99) and men (HR 0.77; 95% CI, 0.67 to 0.88). This association was also observed for hip fracture and vertebral fracture. After adjusting for femoral neck BMD, greater BMI was associated with greater risk of fracture in women (HR 1.21; 95% CI, 1.11 to 1.31), but not in men (HR 0.96; 95% CI, 0.83 to 1.11). Further adjustment for other covariates including age, falls, prior fracture, smoking, glucocorticoid use, and (in women) HRT use did not significantly alter the results.
Collinearity analysis BMI was positively and significantly correlated with femoral neck BMD. The coefficient of correlation was 0.41 (95% CI, 0.37 to 0.44; p < 0.001) for women and 0.37 (95% CI, 0.32 to 0.41; p < 0.001) for men. In women, the VIFs were 1.26 for BMI, 1.50 for femoral neck BMD, and 1.37 for age. Similar results were observed in men (VIF for BMI ¼ 1.20, femoral neck BMD ¼ 1.23, and age ¼ 1.17). In both men and women, the ridge regression coefficients of BMI and femoral neck BMD were relatively stable when incrementing bias was introduced into the models (Table 3), suggesting that collinearity did not contribute to the sudden change in the regression estimate of BMI on fracture risk. Table 1. Baseline Characteristics of 2199 Women and 1351 Men
Women Age (years) Weight (kg) Height (cm) BMI (kg/m2) FNBMD (g/cm2) Falls (%) Prior fracture (%) Glucocorticoid use (%) History of smoking (%) HRT (%) Men Age (years) Weight (kg) Height (cm) BMI (kg/m2) FNBMD (g/cm2) Falls (%) Prior fracture (%) Glucocorticoid use (%) History of smoking (%)
Nonfracture
Fracture
n ¼ 1425 68.5 6.4 69.0 13.5 160 5.9 27.8 5.1 0.83 0.13 32.40 9.70 6.80 28.60 10.70 n ¼ 1093 68.9 5.7 81.9 13.8 173 6.7 27.1 4.0 0.94 0.14 21.60 7.50 6.40 61.80
n ¼ 774 71 7.3 65 13.1 159 6.7 26 4.9 0.76 0.12 42.90 10.90 10.10 34.60 7.60 n ¼ 258 71.9 6.8 76.7 13.1 172 6.9 25.8 3.9 0.86 0.16 37.90 12.00 10.90 63.60
pa
