General and abdominal obesity operate differently as influencing factors of fracture risk in old adults

Summary To infer the causality between obesity and fracture and the difference between general and abdominal obesity, a prospective study was performed in 456,921 participants, and 10,142 participants developed an incident fracture with follow-up period of 7.96 years. A U-shape relationship was observed between BMI and fracture, with the lowest risk of fracture in overweight participants. The obesity individuals had higher fracture risk when BMD was adjusted, and the protective effect of moderate-high BMI on fracture was mostly mediated by bone mineral density (BMD). However, for abdominal obesity, the higher WCadjBMI (linear) and HCadjBMI (J-shape) were found to be related to higher fracture risk, and less than 30% of the effect was mediated by BMD. By leveraging genetic instrumental variables, it provided additional evidences to support the aforementioned findings. In conclusion, keeping moderate-high BMI might be of benefit to old people in terms of fracture risk, whereas abdominal adiposity might increase risk of fracture.

The higher WCadjBMI and HCadjBMI are found to be related to higher fracture risk

INTRODUCTION
Fractures in older adults are often the precursor of disability, loss of independence, and premature death, seriously affecting their quality of life (Svedbom et al., 2018). As a complex disease, fracture is influenced by both genetic and environmental factors, and dozens of susceptible loci have been identified by genomewide association studies (GWASs) (Zhu et al., 2021). Many environmental factors were reported to be related to the incidence of fracture, such as smoking (Wu et al., 2016), alcohol intake , physical activity (Cauley and Giangregorio, 2020), and dietary intakes (Mozaffari et al., 2018). Previous studies have suggested that the increased falling was one of the major risk factors for fracture among older people (Schwartz et al., 2005), and falls account for 87% of all fractures in the elderly (Fife and Barancik, 1985). Besides, low bone mineral density (BMD) was another major risk factor of fracture risk confirmed by mendelian randomization (MR) analyses (Trajanoska et al., 2018).
Obesity was previously deemed to be a protective factor for osteoporosis or brittle fractures because patients affected by obesity have more soft tissue to protect bone tissue (De Laet et al., 2005;Tang et al., 2013). However, recent studies suggested that obesity might increase the risk of certain fracture types (Cao and Picklo, 2015;Scott et al., 2016;Kim et al., 2018). Kim et al. found that overweight might be protective against hip fracture in Asian adults but not obesity, and lower body mass index (BMI) was a risk factor for hip fracture, whereas obesity was associated with an increased risk of hip fracture, particularly in women (Kim et al., 2018). In addition, because abdominal obesity is a surrogate of visceral fat with more endocrinological activities than subcutaneous fat, using different obesity indices would add information to differentiate the role of fat accumulation on bone health (Ibrahim, 2010). In epidemiologic studies, waist circumference (WC) and hip circumference (HC) are used as surrogate indices of abdominal adiposity (Yang et al., 2006;Ahmad et al., 2016). Associations between abdominal obesity indices and fracture were inconsistent; most of the studies found that abdominal obesity increased the risk of fracture (Sogaard et al., 2015;Li et al., 2017;Ofir et al., 2020), whereas few studies reported nonassociation (Benetou et al., 2011) and some studies reported opposite findings between males and females (Laslett et al., 2012;Luo and Lee, 2020).
Although there were some explanations for the controversial findings, leveraging genetic data to infer the causal relationship between exposure and outcome could be additional evidences for association (Xia et al., 2020;Qian et al., 2021). Therefore, in the present study, we firstly conducted a prospective observational study to investigate the relationship between general obesity index (BMI), abdominal obesity indices (waist circumference adjusted for BMI [WCadjBMI], and hip circumference adjusted for BMI [HCadjBMI]) and fracture risk using the UK Biobank dataset. We tried to explore the intermediate role of BMD or falls on the association between obesity indices and fracture. Furthermore, we performed genome-wide association analyses for BMI, WCadjBMI, and HCadjBMI, and tested the causal association between genetically determined obesity indices and fracture.

RESULTS
The association of general obesity index with fracture risk An overview of the study design was illustrated in Figure 1. The characteristics of UK Biobank participants included in this study were shown in Table S1. In this prospective study, there were 205,029 males and 241,750 females, and the mean age of participants was 56.75 years (range, 38-79 years (Table 1). Normal weight showed similar trends of effect as underweight. As for obesity, the risk effect of obesity on fracture was not significant in model 0 and model 1; however, when BMD was adjusted, the risk effect of obesity on fracture became In all these analyses, models were adjusted for risk factors for fracture, including age, sex, smoking statue, alcohol drinker status, physical activity and the use of glucocorticoid, socioeconomic status, and processed meat intake. Hazard ratios are indicated by solid lines and the 95% confidence intervals by shaded areas. iScience Article larger with significance (p = 0.0222 in model 2) (Table 1). These results suggested that BMD might play important role in the pathway between BMI and fracture. In fact, we observed a positive linear correlation between BMI and BMD in our data ( Figure S2A). We also performed observational analyses by excluding younger participants (i.e., <50 years old, N = 99,133) and found that the patterns of association in each model were similar to the aforementioned findings (Table S2). When stratified by gender, we observed that the effect of underweight/normal weight on fracture in females was smaller than in males (Table S3).
Further, we conducted a series of mediation analyses to assess the role of BMD and falls on the observed association between BMI and fracture. Here, a suppression effect (Mackinnon et al., 2000) was observed because the direct effect and mediated effect had opposite direction (Table 2). In the basic model (model 0), the total effect of BMI on fracture was protective. When including BMD as the intermediary factor, the average direct effect (ADE) of BMI on fracture turned to risk with nonsignificance (p = 0.64), and the average causal mediation effect (ACME) by BMD was larger than the total effect of BMI (Table 2). These results, together with the results from Cox regression in different models, suggested that the protective effect of BMI on fracture was mainly mediated by BMD. In addition, only 13.8% of the intermediary effect of BMI on fracture was mediated by falls ( Table 2).

The association of abdominal obesity indices with fracture
The restricted cubic spline analysis showed that there was a linear correlation between WCadjBMI and fracture risk (p = 0.2188 for nonlinearity) ( Figure 2B). We found that WCadjBMI could increase the fracture risk in pooled samples in model 0 (HR = 1.02, 95% CI 1.01 to 1.02, p = 9.71E-16) ( Table 1) and in both men and women (Table S3). The effect size of WCadjBMI on fracture did not change much when falls (model 1) and BMD (model 2) were further adjusted (Table 1). Based on the fully adjusted model (model 3), WCadjBMI was associated with incident fracture with a 1.0% higher risk (HR = 1.01, 95% CI 1.01-1.02, p = 2.09E-09). Besides, it was found that the higher WCadjBMI was related to lower BMD in our study (Figure S2B). We also observed similar findings between WCadjBMI and fracture risk when participants younger than 50 years were excluded (Table S2). The mediation analyses showed that 28.80% and 2.08% of the intermediary effect of WCadjBMI on fracture were mediated by BMD and falls (Table 2).
A J-shape association between HCadjBMI and fracture risk was observed in model 0 (p = 0.0178 for nonlinearity) ( Figure 2C). Compared with those with 95-105 cm of HCadjBMI, the participants with smaller HCadjBMI (<95 cm) would not increase risk of fracture in all models (all p > 0.05) ( Table 2), but the participants with larger HCadjBMI (R105 cm) had increased risk of fracture in model 0 (HR = 1.09, 95% CI 1.03 to iScience Article 1.14, p = 0.0011) ( Table 1). Further adjusting for falls (model 1) and BMD (model 2) did not really attenuate the estimated HR for the association between HCadjBMI and fracture risk (Table 1). And, it was observed that the participants with higher HCadjBMI had lower BMD in our data ( Figure S2C). Moreover, the relationship between HCadjBMI and fracture risk remained the same when participants younger than 50 years were excluded (Table S2). Similar trends were observed in the stratified analysis by sex (Table S3). For HCadjBMI, the intermediary effect by BMD and falls were 28.70% and 1.18%, respectively (Table 2).

Genome-wide association study and the association of genetically determined obesity indices with fracture
In order to test the association between genetically determined obesity indices and fracture, we performed genome-wide association analyses for BMI, HCadjBMI, and WCadjBMI in 377,635 UKB participants of European ancestry. A total of 4,105,386 SNPs with MAF >0.05 were tested in the GWAS analyses, and we identified 54,134; 47,918; and 27,257 genome-wide significance (GWS) variants for BMI, HCadjBMI, and WCadjBMI (p < 5.0E-08), respectively. The Manhattan plots and QQ-plots for these traits were presented in Figures S3-S5. Finally, 1,456; 1,391; and 1,331 independent loci were identified for BMI, HCadjBMI, and WCadjBMI at genome-wide significance. To calculate the weighted genetic risk score (wGRS), we used independent SNPs with a p value less than 5.0 3 E-06; therefore, we finally included 2,241 SNPs for BMI, 2,100 SNPs for HCadjBMI, and 1,510 SNPs for WCadjBMI in the wGRS calculation.
We generated the wGRS of BMI/HCadjBMI/WCadjBMI for each individual, then we divided the wGRS value into four quartiles in the population. As shown in Figure 3, the incident of fracture was at the lowest in the Q3 group of BMI wGRS (incidence:2.08%), where the incident of fracture was higher in other three quartiles (Q1 2.20%, Q2 2.18%, Q4 2.24%), with the highest in the Q4 group ( Figure 3); the difference between Q3 and Q4 was significant (p = 0.0304). As for WCadjBMI, the incident of fracture increased as the genetic risk score increased, with the lowest incident of fracture at Q1 (2.09%) and the highest incident of fracture at Q4 (2.31%), and the difference between them was significant (p = 0.0294). The lowest incident of fracture was observed in Q2 of HCadjBMI wGRS (2.09%), and we found statistically significant difference of fracture incident with the highest Q4 (2.31%) (p = 0.001) (Figure 3).
Finally, as the WCadjBMI had linear correlation with fracture risk, we also performed two-sample MR analysis to assess the causal effect of WCadjBMI (76 SNPs selected,  Figure 4 and Table S5).

DISCUSSION
In this prospective observational study, the lowest risk of fracture was observed within the overweight participants (25.0 kg/m 2 -29.9 kg/m 2 ), the obesity individuals had higher risk of fracture when BMD was adjusted, and the protective effect of moderate-high BMI on fracture was mostly mediated by high BMD. However, the higher WCadjBMI was found to be related to higher risk of fracture, and only 28.80% of the effect was mediated by BMD. In our study, we observed J-shape for HCadjBMI and fracture risk. The BMD had a larger intermediary effect than falls in both general and abdominal obesity indices. By leveraging the genetic instrumental variables, the wGRS analysis provided additional evidences to support the aforementioned findings.
The interaction of obesity with fracture is complex and not as yet fully elucidated, and the effect of fat on the skeleton was mediated by both mechanical and biochemical factors (Gkastaris et al., 2020). Earlier studies reported that obesity, as demonstrated by high BMI, was protective against fragility fracture (Joakimsen et al., 1998;Kanis et al., 1999). In our study, we observed that moderate-high BMI was a protective factor for fracture; this is consistent with a recent large-scale meta-analysis of observational studies (Zhang et al., 2021b). Bone formation is stimulated by the weight-bearing effect caused by increased mechanical loading and higher fat padding as a result of elevated fat mass (Imai et al., 2010;Zhang et al., 2016;Savvidis et al., 2018). In our study, a significant positive relationship was also observed between the BMI and BMD measurements, which might explain why overweight (moderate-high BMI) had positive effect on fracture risk in our study.
In obese individuals, it would increase the risk of fracture when BMD was adjusted, probably because the BMD benefit from obesity would be insufficient to compensate for other risk factors. Previous studies had found that inflammation, which was more prevalent in obesity, had deleterious effects on bone strength and fracture risk (Ishii et al., 2013). Another factor involved was vitamin D deficiency, a very common situation among obese individuals that might have significant implications for skeletal health. Serum 25(OH)D concentrations were approximately 20% lower in obese people compared with those of normal weight (Ardawi et al., 2011;Walsh et al., 2016). Increased bone marrow fat in obesity might also have deleterious effects on bone (Biver et al., 2011). In addition, the mediation analysis suggested that BMD had larger intermediary effect than falls between BMI and fracture risk. Interestingly, our previous study to investigate the relationship between insomnia and fracture suggested a larger intermediary effect by falls than BMD (Qian et al., 2021).
Unlike general obesity, our study found that abdominal obesity (higher waist circumference) was associated with fracture risk in linear model. This relation might be explained by the effects of iScience Article abdominal-obesity-related inflammation (Zhang et al., 2016;Gkastaris et al., 2020). Earlier studies demonstrated that inflammatory cytokines (including interleukin-1 [IL-1], IL-6, resistin, and tumor necrosis factor alpha [TNF-a]) that are released by visceral adipose tissue (VAT) would uncouple bone remodeling by suppressing bone formation and enhancing bone reabsorption (Kawai et al., 2012). These impulses decreased osteoblast differentiation and increased osteoclast recruitment, thereby uncoupling the bone remodeling unit (Rolland et al., 2012). It had been shown that abdominal obesity, compared with general obesity, was associated with higher levels of inflammatory markers; however, heavier weight that led to increased strain on bone could decrease the effect of inflammation on bone in individuals with general obesity (Pannacciulli et al., 2001, Stepanikova et al., 2017. In addition, it has been shown that higher high-sensitivity C-reactive protein levels were associated with a lower trabecular density, lower trabecular number, higher trabecular spacing, and more heterogeneous trabecular distribution (Kim et al., 2016). Abdominal obesity-relayed inflammation, therefore, could adversely influence trabecular bone score and bone quality index (Yamaguchi et al., 2009). In fact, we found that higher WCadjBMI was associated with lower BMD in our study. In addition, our findings suggested a J-shape relationship between HCadjBMI and fracture risk. In other words, the trend of the association between lower HCadjBMI and fracture risk might be mild, but higher HCadjBMI was significantly associated with fracture risk (p < 0.05 in all models). Previous study showed that hip circumference could represent adiposity of the hip region and larger hip circumference likely indicated greater subcutaneous fat accumulation, and finally leading to obesity-related inflammation (Hughes et al., 2004).
Furthermore, by using wGRS, it suggested that the incident of fracture was at the lowest in the Q3 group of BMI wGRS, where the incident of fracture was higher in other three quartiles, with the highest in the Q4 group. As for WCadjBMI, the incident of fracture increased, as the genetic risk score increased, with the lowest incident of fracture at Q1 and the highest incident of fracture at Q4. These findings were supportive to our observational results.
In summary, the observational and genetic evidence suggested that general and abdominal obesity operate differently as risk factors of fracture risk in old adults. A moderate-high BMI was a protective factor for fracture, and the BMD was the main intermediate factor. For abdominal obesity, higher WCadjBMI and HCadjBMI associated the higher risk of fracture, and BMD only mediated less than 30% of the effect, whereas falls had barely intermediate effect.
Keeping moderate-high BMI would be of benefit to old people in terms of fracture risk; however, abdominal adiposity might increase risk of fracture; this is a compensation between mechanical and biochemical factors.

Limitations of the study
Nonetheless, our study also has some limitations, some of which we have discussed. First, the participants in this study were of European descent; therefore, our findings might not apply to populations of other descents. Second, to enlarge the sample size and statistic power, we only evaluated the relationship between obesity-related indices and any-type fracture rather than fracture at specific anatomical site (such as hip and spine).
Zhu, X., Bai, W., and Zheng, H. (2021). Twelve years of gwas discoveries for osteoporosis and related traits: advances, challenges and applications. Bone Res. 9, 23. https://doi.org/10. 1038/s41413-021-00143-3. In the prospective study of fracture risk, follow-up time was calculated from the date of attending the UK Biobank to the diagnosis of fracture, death, or the censoring date (31 March 2017). We then investigated association between the obesity indices (BMI, WCadjBMI, and HCadjBMI) and the risk of fracture using Cox regression. Restricted cubic spline was used to model the potential non-linear association of obesityrelated traits with fracture risk. In the multivariable Cox regression, the basic model was adjusted for confounders, including age, sex, smoking status, alcohol status, physical activity, the use of glucorticoid, Socioeconomic Status (SES) and processed meat intake (model 0). Additional covariables such as falls and BMD were also included to set different models (model 1 = model 0 + falls, model 2 = model 0 + BMD and model 3 = model 0 + BMD + falls). Detailed information on these covariates is provided in Table S7.
Furthermore, we performed mediation analysis to explore whether the relationship between obesityrelated indices (exposure) and fracture (outcome) could be explained, at least partially, by an intermediate variable (mediator). Here we set the BMD and falls as the mediators from the prior knowledge (Trajanoska and Rivadeneira, 2019;Ganz and Latham, 2020). We applied the causal mediation analysis method to dissect the total effect of exposure into direct and indirect effect, and to examine the indirect effect which was transmitted via mediator to the outcome. The mediation analysis was performed using the R packages of 'mediation' and adjusting the age, sex, smoking status, alcohol consumption, physical activity and the use of glucorticoid, SES and processed meat intake.

Weighted genetic risk score (wGRS) analysis
We calculated the weighted genetic risk score (wGRS) using the independent SNPs with p value less than 5.0E-06 for BMI, HCadjBMI and WCadjBMI within the UK Biobank dataset, respectively. The wGRS method formula was:

Two-sample MR
We performed two-sample summary-level MR analyses for WCadjBMI. 76 independent genetic variants associated with WCadjBMI at genome-wide significance level (p < 5.0E-08) (Table S4) were chose from Shungin D et al. (Shungin et al., 2015). Summary-level data for the outcome (fracture) were available from the previous published GWAS ( Morris et al., 2019).

QUANTIFICATION AND STATISTICAL ANALYSIS
For Observational study, all these statistical analyses were conducted in R version 4.0.3 and STATA 14.1 software. A Bonferroni-corrected threshold of p < 0.05 was considered statistically significant.
The GWAS analysis of BMI, WCadjBMI and HCadjBMI was performed using PLINK software (http://www. coggenomics.org/plink2), and we used the option -clump-r2 and -clump -kb to obtain the independent locus.
The wGRS method was performed using PLINK software (http://www.coggenomics.org/plink2) with the command of -score sum to obtain the sum of valid per-allele scores (Chang et al., 2015). The asterisks indicate significant differences between groups (* = p < 0.05; ** = p < 0.01). The different letters above each of plots indicate significant differences according to Chi-Square test. The Chi square test for quartile groups was conducted in STATA 14.1 software.
For Two-sample MR, all statistical analyses were performed with R 4.0.3. The IVW, simple mode, weighted mode, weighted-median, and MR-Egger methods were performed using the ''MendelianRandomization'' package (Yavorska and Burgess, 2017). The MR-PRESSO approach was performed using the ''MR-PRESSO'' package (Verbanck et al., 2018). The two-sided p value of less than 0.05 was considered statistically significant.