Impact of Central Obesity on Bone Mineral Density Across Life Stages: A Genetic Epidemiology and Cross-Sectional Study

Impact of Central Obesity on Bone Mineral Density Across Life Stages: A Genetic Epidemiology and Cross-Sectional Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Impact of Central Obesity on Bone Mineral Density Across Life Stages: A Genetic Epidemiology and Cross-Sectional Study Camilo Alberto Pinzon Galvis, Yuhong Jiang, Xianhao Huang, Cui Wang, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5872489/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Introduction: This study aimed to investigate the association and causality between central obesity and bone mineral density (BMD). Methods We utilized linkage disequilibrium score regression (LDSC) and Mendelian randomization (MR) to assess genetic correlations and causal relationships between waist circumference adjusted for BMI (WCadjBMI) and total body less head BMD (TB-BMD). Additionally, a cross-sectional analysis of 7,452 participants evaluated the relationship between A body shape index (ABSI) and TB-BMD using weighted multivariable linear regression and smooth curve fitting. Results LDSC and MR analysis confirmed a negative relationship between WCadjBMI and TB-BMD (β=-0.16; 95% CI: -0.26, -0.07). The cross-sectional study indicated that an increase of 0.01 ABSI corresponded to a decrease of 0.035 TB-BMD (g/cm 2 ), with this negative effect being particularly pronounced in males and older adults. An inflection point was identified at ABSI = 0.076: below this threshold, ABSI positively correlated with pelvis BMD, whereas above it, the association became negative. Conclusions Central obesity is significantly negatively related to BMD. Maintaining ABSI within 0.058–0.078 is crucial for individuals in bone mass accrual (20–30 years) and stabilization (30–45 years) periods. In contrast, managing central obesity in people experiencing early bone loss (45–60 years) presents greater complexity and warrants further investigation. Obesity Osteoporosis A Body Shape Index Mendelian randomization NHANES Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Osteoporosis is a degenerative skeletal disorder characterized by reduced bone mineral density (BMD) and deterioration of bone microarchitecture, leading to pain, fractures, and significantly diminishing the quality of life [ 1 ]. Obesity, on the other hand, is defined as excessive fat accumulation that poses a health risk. Central obesity, primarily characterized by the accumulation of abdominal and visceral fat, is considered a more concerning high-risk type of obesity [ 2 ]. Owing to systemic metabolic and inflammatory changes, central obesity significantly increases the risk of various chronic diseases such as hypertension [ 3 ] and diabetes [ 4 ]. However, the relationship between central obesity and BMD remains controversial. Traditionally, it was believed that obesity might protect against bone mass loss owing to increased mechanical loading, which enhances bone formation [ 5 ]. Nevertheless, increasing evidence suggests that obesity may contribute to higher risks of bone mass loss [ 6 ]. The concept often referred to as the "obesity paradox" in research concerning obesity and disease risk emerges from findings seemingly contradicting each other [ 7 ]. One reason underlying these contradictions is the reliance on a singular metric such as body mass index (BMI) to assess obesity [ 8 , 9 ]. BMI does not distinguish between adipose and lean tissue mass, nor does it account for fat distribution, which can obscure the unique adverse effects of central obesity [ 10 ]. Waist circumference (WC) is a risk indicator supplement to BMI and is more closely related to abdominal and visceral fat [ 11 ]. It can better assess the mortality risk associated with central obesity [ 12 ]. However, due to the high correlation between WC and BMI, it is challenging to investigate the independent impact of central obesity on health beyond overall body weight. To address this issue, some new obesity indices have been proposed, such as A body shape index (ABSI) [ 13 ]. ABSI is considered to better reflect the hazards caused by visceral fat and is positively associated with the risk of many metabolic diseases [ 14 ]. Researchers proposed that the ABSI may more accurately predict diseases linked to central obesity than WC [ 15 ]. In Mendelian randomization studies, WC adjusted for BMI (WCadjBMI) has emerged as a commonly used indicator of central obesity, as it excludes genetic influences associated with BMI [ 16 ]. Despite existing research suggesting a negative correlation between central obesity indices and BMD, most studies have been cross-sectional and limited to specific age groups [ 17 , 18 ], lacking a comprehensive evidence chain from genomic epidemiological analysis to large-scale population validation. Consequently, there remains a significant gap in understanding the effects of central obesity on bone health across different life stages, including periods of rapid growth, attainment of peak bone mass, and age-related bone loss. In this study, we first applied linkage disequilibrium score regression (LDSC) and Mendelian randomization (MR) approaches to investigate the genetic correlation and potential causal relationship between WCadjBMI and total body less head bone mineral density (TB-BMD) across different age groups. Subsequently, we analyzed large-scale population data from the National Health and Nutrition Examination Survey (NHANES) spanning 2011 to 2018 with regression analysis to investigate the specific correlation patterns between ABSI and TB-BMD across various age groups. We also validated the accuracy of our findings with different BMD outcomes, including pelvis BMD (PE-BMD) and lumbar spine BMD (LS-BMD). We aimed to rigorously investigate whether central obesity differentially affects bone development at distinct stages of skeletal growth and to identify specific correlation patterns. Our objective is to provide insights that may enhance the management of central obesity across various age groups, ultimately reducing the risk of osteoporosis. Methods Genomic Epidemiological Analysis Data source The data utilized in this study were obtained from publicly available GWAS datasets [ 19 , 20 ] (Supplementary file 1: Table S1 ). WCadjBMI is a composite index adjusted for multiple factors, primarily designed to more accurately assess the impact of genetic influences on waist circumference without confounding from BMI [ 21 ]. The TB-BMD measurement was conducted using DXA. The procedure adheres to standardized operating protocols for measuring TB-BMD [ 20 ]. TB-BMD data was derived from distinct gene sets categorized by age groups: 60 years old (TB_60_or_more). A comprehensive dataset encompassing all age groups (TB) was also analyzed. Genetic correlation analysis LDSC assesses genetic correlations by regressing GWAS summary statistics on LD scores. Specifically, we utilized bivariate LDSC to investigate the pairwise genetic correlations between WCadjBMI and TB-BMD, with the estimated values ranging from − 1 to 1, where − 1 indicates complete negative genetic correlation and 1 indicates complete positive genetic correlation. Before the analysis, we excluded single nucleotide polymorphisms (SNPs) in the major histocompatibility complex region to reduce confounding factors. Additionally, given the potential issue of sample overlap between populations, we did not constrain the intercept term; instead, we used the intercept term to stratify and determine the presence of sample overlap. A false discovery rate (FDR) corrected P .adjust < 0.05 was considered statistically significant. MR analysis Various methods were employed to conduct an MR analysis to assess the causal relationship between WCadjBMI and TB-BMD. The primary analysis method used was inverse-variance weighting (IVW), which synthesizes the overall causal estimate by utilizing multiple genetic instrumental variables. IVW is known for its high statistical efficiency and robustness compared to other methods. To ensure consistency, we also conducted MR-Egger, weighted median, weighted mode, and simple mode analyses to compare and evaluate the results obtained from IVW. To ensure the accuracy of the MR results, sensitivity analyses were performed: (1) MR-Egger intercept test: Used to test for horizontal pleiotropy, with Cochran's Q statistic estimating heterogeneity. (2) Leave-one-out analysis: Applied to determine whether any single SNP drives the potential causal effect between the traits. (3) MR-Steiger directionality test: Employed to assess the correctness of the causal direction between the traits. (4) F-statistic: Used to evaluate the strength of the instrumental variables (IVs), wherein an F-statistic < 10 indicates potential bias from weak instruments. (5) FDR correction: The Benjamini–Hochberg method was used for the primary results, with a significance threshold of P .adjust < 0.05. Finally, we conducted a reverse causality analysis. The ethics committee of each institutional review board authorized written informed consent from all participants in the separate studies. No extra ethical approval or informed consent was required. The analysis was conducted using Two Sample MR packages in R (version 4.3.1). Cross-sectional study Study population from NHANES NHANES is a biennial, comprehensive assessment conducted by the Centers for Disease Control and Prevention to evaluate the health and nutritional status of the United States population ( https://www.cdc.gov/nchs/nhanes/index.htm ). The study received approval from the National Center for Health Statistics Ethics Review Board (NCHS ERB), and all participants provided written informed consent. Our study combined data from four NHANES cycles spanning 2011–2018, initially including a total of 39,156 participants. Following the exclusion of individuals lacking BMD and ABSI data, those under 20 years of age, and those with chronic or significant diseases that could potentially impact BMD, the analysis was performed on the remaining cohort of 7,452 subjects (Fig. 1 ). Variables assessment The ABSI was determined using the subject's WC (m), BMI (kg/m²), and height (m) with the following formula[ 13 ]: $$\:\text{A}\text{B}\text{S}\text{I}=\frac{WC}{{BMI}^{2/3}{Height}^{1/2}}$$ The TB-BMD, PE-BMD, and LS-BMD extracted in our study were assessed using dual-energy X-ray absorptiometry (DXA). Pregnant individuals, those with a history of radiographic contrast-material use within 7 days before testing, and individuals weighing over 450 pounds or taller than 6'5" were excluded. The methodology to calculate TB-BMD involves subtracting the head bone mineral content (BMC) from the total BMC (g) and dividing the result by the difference between the total area and head area (cm 2 ) [ 22 ]. Covariate selection To control for potential confounding effects, we adjusted for the following demographic characteristics: age, sex, race, educational level, and family income-poverty ratio (FIPR). These data were collected via personal demographic questionnaires. Diabetes status was determined based on self-reported medical history ("Has a doctor ever told you that you have diabetes?"). Data on lifestyle habits were collected using a questionnaire, which assessed alcohol consumption (defined as having at least 12 alcoholic drinks per year), smoking history (having smoked at least 100 cigarettes in one's lifetime), and engagement in moderate work activity. Serological tests were conducted to measure the levels of alkaline phosphatase (ALP), alanine aminotransferase (ALT), aspartate aminotransferase (AST), creatinine, high-density lipoprotein cholesterol (HDL-C), total cholesterol, serum phosphorus, serum calcium and vitamin D (25OHD2 + 25OHD3). The BMI, WC, and height measurements were recorded at the mobile examination center (Table 1 ). Table 1 The baseline characteristics of study participants from the NHANES 2011–2018 grouped by ABSI tertiles Characteristics Total(N = 7,452) Low(N = 2,484) Medium (N = 2,484) High (N = 2,489) p-value (0.058–0.099) (0.058–0.078) (0.078–0.082) (0.082–0.099) Age (years) 35.59 (35.34 35.84) 31.38 (31.02 31.75) 35.69 (35.28 36.11) 40.24 (39.79 40.68) < 0.001 20-<30 2257 (30.29%) 1139 (45.85%) 707 (28.46%) 411 (16.55%) ≥ 30-<45 3029 (40.65%) 947 (38.12%) 1101 (44.32%) 981 (39.49%) ≤ 45-<60 2166 (29.07%) 398 (16.02%) 676 (27.21%) 1092 (43.96%) Race < 0.001 Mexican American 1210 (16.24%) 296 (11.92%) 445 (17.91%) 469 (18.88%) Other Hispanic 775 (10.40%) 248 (9.98%) 262 (10.55%) 265 (10.67%) Non-Hispanic White 2348 (31.51%) 661 (26.61%) 811 (32.65%) 876 (35.27%) Non-Hispanic Black 1597 (21.43%) 812 (32.69%) 434 (17.47%) 351 (14.13%) Other Race 1522 (20.42%) 467 (18.80%) 532 (21.42%) 523 (21.05%) FIPR 1.88 (1.84 1.93) 1.85 (1.78 1.92) 1.96 (1.89 2.04) 1.84 (1.77 1.91) 0.046 Education level < 0.001 Below high school 1365 (18.32%) 360 (14.50%) 463 (18.64%) 542 (21.82%) High school 1652 (22.17%) 519 (20.91%) 549 (22.10%) 584 (23.51%) College or above 4433 (59.50%) 1603 (64.59%) 1472 (59.26%) 1358 (54.67%) Smoking < 0.001 Yes 2661 (35.73%) 756 (30.43%) 890 (35.86%) 1015 (40.89%) No 4787 (64.27%) 1728 (69.57%) 1592 (64.14%) 1467 (59.11%) Drinking < 0.001 Yes 820 (13.58%) 221 (10.77%) 274 (13.47%) 325 (16.64%) No 5219 (86.42%) 1831 (89.23%) 1760 (86.53%) 1628 (83.36%) Moderate activity 0.585 Yes 2987 (40.08%) 1011 (40.70%) 1000 (40.26%) 976 (39.29%) No 4465 (59.92%) 1473 (59.30%) 1484 (59.74%) 1508 (60.71%) Diabetes < 0.001 Yes 385 (5.25%) 58 (2.36%) 103 (4.21%) 224 (9.23%) No 6954 (94.75%) 2404 (97.64%) 2346 (95.79%) 2204 (90.77%) ALP (U/L) 63.75 (63.30 64.20) 60.25 (59.55 60.96) 63.22 (62.47 63.97) 68.00 (67.15 68.85) < 0.001 ALT (U/L) 22.17 (21.91 22.44) 20.04 (19.65 20.44) 22.79 (22.33 23.27) 23.85 (23.35 24.36) < 0.001 AST (U/L) 22.94 (22.75 23.12) 22.40 (22.10 22.71) 23.00 (22.69 23.30) 23.41 (23.06 23.78) < 0.001 Creatinine (mg/dl) 0.82 (0.81 0.82) 0.84 (0.84 0.85) 0.82 (0.81 0.82) 0.80 (0.79 0.80) < 0.001 Total cholesterol (mg/dl) 185.25 (184.36 186.14) 177.11 (175.67 178.56) 186.56 (185.06 188.07) 192.35 (190.75 193.96) < 0.001 HDL-C (mg/dL) 50.11 (49.78 50.45) 53.25 (52.66 53.85 49.48 (48.92 50.05) 47.78 (47.24 48.33) < 0.001 phosphorus (mg/dL) 3.67 (3.66 3.68) 3.69 (3.67 3.72) 3.66 (3.64 3.68) 3.66 (3.63 3.68) 0.038 calcium (mg/dL) 9.36 (9.35 9.37) 9.38 (9.37 9.40) 9.35 (9.34 9.37) 9.35 (9.34 9.36) 0.002 25(OH)D (nmol/L) 53.97 (53.45 54.50) 52.92 (51.99 53.87) 54.24 (53.36 55.14) 54.77 (53.87 55.69) 0.118 BMI (kg/m 2 ) 27.66 (27.52 27.79) 26.98 (26.75 27.22) 27.69 (27.46 27.92) 28.31 (28.07 28.55) < 0.001 Height (cm) 167.66 (167.44 167.88) 167.37 (167.00 167.74) 167.82 (167.44 168.20) 167.79 (167.41 168.18) 0.079 WC (cm) 94.47 (94.13 94.82) 87.51 (86.99 88.03) 94.80 (94.26 95.34) 101.64 (101.04 102.24) < 0.001 TB-BMD (g/cm 2 ) 0.97 (0.97 0.98) 1.00 (0.99 1.00) 0.97 (0.97 0.98) 0.95 (0.95 0.96) < 0.001 PE-BMD (g/cm 2 ) 1.24 (1.24 1.24) 1.26 (1.25 1.26) 1.24 (1.24 1.25) 1.22 (1.22 1.23) < 0.001 LS-BMD (g/cm 2 ) 1.02 (1.02 1.03) 1.07 (1.07 1.08) 1.02 (1.02 1.03) 0.98 (0.98 0.99) < 0.001 ABSI: A Body Shape Index; FIPR: Family income-poverty ratio; ALP: Alkaline phosphatase; ALT: Alanine aminotransferase; AST: Aspartate aminotransferase; HDL-C: High-density lipoprotein cholesterol; BMI: Body mass index; WC: Waist circumference; TB-BMD: Total body(less head) bone mineral density. PE-BMD: Pelvis bone mineral density; LS-BMD: Lumbar spine bone mineral density. Statistical analysis This study conducted all analyses following the NCHS analytical guidelines and utilized sample weights. Categorical variables were reported as frequencies and percentages, and differences between groups were assessed using weighted chi-square tests. Continuous variables were expressed as means with 95% confidence intervals. We employed survey-weighted linear regression or Kruskal-Wallis rank-sum tests as appropriate. To investigate correlations between ABSI and BMD, three distinct weighted multivariable linear regression models were developed: Model 1 included unmodified variables; Model 2 was adjusted for age, sex, and race; and Model 3 controlled for all covariates listed in Table 1 except WC, BMI, and height. The outcomes are reported as β coefficients with 95% confidence intervals, where β denotes the change in BMD corresponding to each unit increase in ABSI. ABSI was further categorized into tertiles, and regression analyses were performed to evaluate trends in BMD changes across ABSI categories ( p for trend < 0.05). To assess non-linear associations, we employed smooth curve fitting and threshold effect analysis. This involved using a recursive algorithm to identify inflection points and applying a two-segment linear regression model on either side of these points. Subgroup analyses were conducted using stratified multivariate linear regression models, incorporating predefined potential effect modifiers. These effect modifiers were identified by including interaction terms in the models and performing likelihood ratio tests to evaluate heterogeneity across subgroups ( p for interaction < 0.05). Statistical analysis was performed using R version 4.2.3 (R Project) supplemented by EmpowerStats software package tools. For all analyses, p < 0.05 was considered to indicate statistical significance. Results Genomic epidemiological analysis Genetic correlation analysis We employed LDSC to evaluate the global genetic correlation between traits. Using bivariate LDSC, we investigated the genetic correlation between WCadjBMI and TB-BMD across various age cohorts. Our results demonstrated significant genetic correlations between WCadjBMI and TB-BMD within distinct age groups: 60 years, as well as cumulatively across all ages ( p .adjust 0.05 confirmed no sample overlaps between the analyzed traits (Supplementary File 1: Table S2 ). MR analysis After establishing the genetic correlation between WCadjBMI and TB-BMD, we employed MR analysis to investigate the potential causal relationship between these two variables. We meticulously selected SNPs based on stringent criteria, including significance threshold (P < 5×10 − 8 ), correction (FDR 10), and LD (r 2 < 0.1), suggesting that our findings are unlikely to be influenced by weak instruments. Detailed information can be found in Supplementary File 1: Tables S5-S10. The IVW results from MR analyses suggested a negative causal correlation between WCadjBMI and TB-BMD, with a significant negative association observed across the three specific age groups (Figure. 2): TB (β=-0.16; 95% CI: -0.26, -0.07), TB_15_or_less (β=-0.26; 95% CI: -0.39, -0.12), TB_3045 (β=-0.19; 95% CI: -0.35, -0.03), and TB_60_or_more (β=-0.22; 95% CI: -0.35, -0.10). Specifically, an increase in WCadjBMI was inversely associated with TB-BMD across these age groups. The weighted median approach confirmed the findings from the IVW analysis (Supplementary File 1: Table S3 ). The details are illustrated in the funnel plots and forest plots provided in Supplementary File 2: Figure S1 - S2 Sensitivity analysis In our sensitivity analysis, we first used the leave-one-out method to evaluate the effects of WCadjBMI and TB-BMD across all age groups. No SNP was found to strongly influence the summary estimates (Supplementary File 2: Figure S3 ). Additionally, no evidence of horizontal pleiotropy was observed in funnel plots (Supplementary File 2: Figure S4). While some heterogeneity was present, we applied a random-effects IVW model to account for potential heterogeneity among SNPs. The Steiger directionality test further confirmed the causal direction from WCadjBMI to TB-BMD across different age groups. These results strongly support the robustness of the causal relationship between WCadjBMI and TB-BMD in various age groups (Supplementary File 1: Table S4). Last, our reverse MR analysis did not indicate any causal effect of TB-BMD across different age groups on WCadjBMI, excluding reverse causation's influence (Supplementary File 1: Table S11). Cross-sectional study Baseline characteristics of participants To further investigate the impact of central obesity on BMD in a large population, we analyzed data from the NHANES spanning 2011–2018, which enrolled 7,452 participants. We categorized the ABSI into three tertiles: low (0.058–0.078), medium (0.078–0.082), and high (0.082–0.099), to investigate the baseline characteristics across different ABSI levels (Table 1 ). The proportion of individuals with high ABSI increased with age, peaking in the 45–60 year age group. Participants with a college education or higher, those engaged in moderate physical activity at work, and individuals without diabetes predominantly maintained low ABSI levels. Conversely, individuals with an education level below college, those with smoking and alcohol consumption habits, and those lacking moderate physical activity were more likely to exhibit a high ABSI. Biochemical analyses revealed that individuals with a high ABSI exhibited elevated levels of ALP, ALT, AST, and total cholesterol, along with reduced levels of HDL-C, phosphorus, and calcium compared to those with a low ABSI. Additionally, individuals with high ABSI had higher BMI and WC and lower TB-BMD, PE-BMD, and LS-BMD than participants with low ABSI ( p < 0.05). Association between ABSI and BMD The results of distinct weighted multivariable linear regression models consistently demonstrate a negative association between ABSI and BMD. For clarity, we report here only the findings from Model 3, which adjusts for all covariates. After adjustment, a higher ABSI was inversely correlated with TB-BMD values. Specifically, an increase of 0.01 in ABSI corresponded to a decrease of 0.035 units in TB-BMD (β = -3.531; 95% CI: -4.146 to -2.915). Similarly, ABSI was robustly negatively associated with various BMD indices: for PE-BMD, β = -1.949 (95% CI: -4.146 to -2.915), and for LS-BMD, β = -6.364 (95% CI: -7.365 to -5.363). Subsequent sensitivity analyses categorized ABSI into tertiles, revealing stronger negative associations at higher tertile levels compared to the reference group (lowest tertile). Participants in the highest tertile exhibited significant decreases in BMD: TB-BMD decreased by 0.032 units (95% CI: -0.038 to -0.026), PE-BMD by 0.015 units (95% CI: -0.026 to -0.004), and TS-BMD by 0.055 units (95% CI: -0.066 to -0.045), with p for trend < 0.05 (Supplementary File 3: Tables S1-S3). Smooth curve fitting and threshold effect analysis To explore the potential non-linear relationship between ABSI and BMD across age-stratified populations, we employed smooth curve fitting and threshold effect analysis. The results indicate a complex non-linear relationship between ABSI and BMD across different age groups (Fig. 3 ). Notably, in the 45–60 age group, an inverted U-shaped curve was observed between ABSI and PE-BMD. A threshold effect analysis using a weighted two-segment linear regression model and a recursive algorithm revealed that ABSI was positively associated with PE-BMD (β = 9.202, 95% CI: 0.185 to 18.219, p = 0.0457) before reaching 0.076, and negatively correlated after that (β = -3.221, 95% CI: -5.419 to -1.023, p = 0.0041), with 0.076 serving as the inflection point (K). However, in the 20–30 and 30–45 age groups, 0.076 was not the peak but acted as an inflection point resembling an L-shaped curve. In the 30–45 age group, although the curve indicates a positive association between ABSI with both TB-BMD and PE-BMD beyond the inflection point of 0.086, this relationship is not statistically significant ( p > 0.05). For detailed information, refer to Supplementary File 3: Tables S4-S6. Subgroup analysis A subgroup analysis was conducted to evaluate whether the relationship between ABSI and TB-BMD was consistent across different population settings (Fig. 4 ). None of the stratifications, including sex, age, race, diabetes status, education level, smoking, drinking, and moderate activity, significantly altered the negative association between ABSI and TB-BMD. However, variations in the negative impact of high ABSI on TB-BMD were observed across different sexes, ages, and moderate activity levels ( p for interaction < 0.05). Specifically, an increase in ABSI was associated with a more pronounced decline in TB-BMD among males (β = -5.601; 95% CI: -6.589 to -4.614) than females (β = -1.624; 95% CI: -2.375 to -0.873). The negative impact of ABSI on TB-BMD was most marked in the 45–60 age group (β = -7.530; 95% CI: -9.246 to -5.814), p < 0.0001. Discussion This study utilized genomic epidemiological approaches, initially employing LDSC to uncover the genetic correlation between the central obesity index—WCadjBMI and TB-BMD. Subsequently, MR analysis confirmed a negative causal relationship between WCadjBMI and TB-BMD, with sensitivity analyses conducted to mitigate potential reverse causation. However, this causal relationship was inconsistent across all population subgroups, observed only in individuals aged 60 years. We then conducted a cross-sectional study from NHANES, supplementing TB-BMD with PE-BMD and LS-BMD to enhance the reliability of our findings. Given the age-related limitations of DXA measurements (restricted to individuals aged < 60), as well as missing lifestyle data (smoking and alcohol consumption) in participants under 20 years, our analysis was focused on the 20–60 age group. We employed age stratification similar to the genomic epidemiological study (20–30, 30–45, 45–60 years) to investigate the correlation patterns between another central obesity index—ABSI and BMD. Using weighted multivariate linear regression analysis, we identified a negative correlation between ABSI and TB-BMD, PE-BMD, and LS-BMD, with the relationship exhibiting a complex non-linear pattern across different age groups. Moreover, threshold effect analysis identified an inflection point at ABSI = 0.076: below this threshold, ABSI positively correlated with PE-BMD, whereas above it, the association became negative. Subgroup analysis revealed that an increase in ABSI was associated with a more pronounced decline in TB-BMD among males and in the 45–60 age group. To our knowledge, this is the first study to employ large-scale observational data, genetic correlation, and MR analysis of extensive genetic datasets to examine the relationship between central obesity and BMD comprehensively. The findings indicate a negative association between central obesity indices and BMD, aligning with prior cross-sectional research [ 17 , 23 ]. However, these studies have primarily focused on the impact of ABSI on BMD in older adults (> 60 years) and adolescents (< 15 years), which lack a comprehensive understanding of how central obesity affects BMD throughout the lifespan, particularly during critical periods of bone density change. Moreover, due to the cross-sectional design of these studies, it is challenging to eliminate the influence of reverse causality, which limits the conclusions. A preceding MR study also identified a negative causal relationship between WCadjBMI and BMD [ 24 ]. However, it solely relied on heel bone measurements as its outcome variable, which was not representative of overall skeletal health. In populations undergoing rapid skeletal changes due to growth spurts [ 25 ], local density measures such as those of the lumbar spine, femoral neck, and total hip are potentially limited in their effectiveness during these quick changes [ 25 ]. In geriatric assessments, degenerative spine alterations can erroneously increase BMD values, introducing measurement inaccuracies among older adults [ 26 ]. TB-BMD measurement is considered the most reliable method for an unbiased assessment of BMD across all ages, from childhood to old age [ 20 ]. Therefore, our study selected TB-BMD as the primary outcome measure. The observed negative correlation between central obesity and BMD in this study may primarily be attributed to the detrimental impacts of accumulating abdominal and visceral fat [ 11 , 16 ]. Metabolically distinct from subcutaneous fat, visceral fat has been shown to exert more detrimental effects on bone health [ 27 , 28 ]. Visceral adipose tissues secrete cytokines such as tumour necrosis factor-alpha (TNF-α) and interleukin-6 (IL-6), which are known to promote bone resorption [ 29 , 30 ] while inhibiting osteoblast proliferation.[ 31 ] Human bone mass steadily increases during childhood, accelerates in adolescence, and peaks between 25 and 30 years [ 32 ]. It then stabilizes before experiencing a more pronounced decline after 45–60 years [ 33 ]. Therefore, in our cross-sectional study, we stratified age into three groups: 20–30 years (pre-peak bone mass), 30–45 years (peak bone mass), and 45–60 years (post-peak bone mass), to assess the differential patterns of the negative correlation between central obesity and BMD across various stages of bone development. Our findings demonstrate that central obesity negatively affects BMD throughout the entire bone lifecycle. These findings are consistent with those reported by Yoo et al.[ 34 ], who noted similar trends among individuals ranging from 20 to 88 years old. Their study indicated that obesity impairs peak bone mass acquisition in women, potentially increasing their risk for postmenopausal osteoporosis [ 35 ]. Our research indicates that maintaining ABSI at a low level (0.058–0.078) is most beneficial for bone health in individuals aged 20 to 45. Our baseline data analysis across different ABSI levels suggests that maintaining healthy lifestyle habits—specifically reducing smoking and alcohol consumption and increasing moderate-intensity physical activity—may help maintain lower ABSI levels. Notably, we found that ABSI in individuals aged 45 to 60 exhibits an inverted U-shaped relationship with PE-BMD. A similar phenomenon was observed in previous studies, suggesting an inverted U-shaped correlation between the visceral adiposity index (VAI) and BMD in older adults [ 34 ]. This may be attributed to the estrogen produced by adipocytes, which could provide potential resistance to early bone loss [ 36 ]. However, it is essential to note that this inverted U-shaped relationship does not apply to all bone density metrics. Our study also demonstrates that the negative impact of central obesity on BMD is more pronounced in 45–60 adults compared to younger individuals. With advancing age, central obesity may increase the risk of falls and subsequent fractures[ 37 ]. Additionally, ageing is associated with increased bone marrow fat, which can alter the cellular composition and molecular interactions within the bone microenvironment[ 38 ], thereby posing a fracture risk beyond that associated with low BMD alone. Therefore, optimal management of central obesity in individuals experiencing age-related bone loss requires further investigation. Our research also indicates that the negative effect of central obesity on men is more significant than that on women. This disparity may stem from distinct fat distribution patterns and hormonal variations between the sexes. Men generally accumulate more visceral fat, whereas women are prone to higher subcutaneous fat levels [ 39 ]. Unlike subcutaneous fat, visceral adiposity produces numerous pro-inflammatory cytokines contributing to metabolic complications like insulin resistance[ 40 ]. Additionally, visceral fat influences osteoblast activity by modulating IGF-1 levels, limiting collagen degradation, and enhancing osteoblast recruitment—all factors that detrimentally impact BMD [ 41 , 42 ]. We acknowledge several limitations in our study. Firstly, due to the lack of GWAS data sources, we could not obtain data stratified by age for BMD at specific sites such as the lumbar spine and pelvis. Additionally, regarding central obesity indicators, the cross-sectional study utilized ABSI, while WCadjBMI was used in the genetic epidemiology analyses. Although both metrics adjust waist circumference for BMI, their different calculation methods may introduce heterogeneity in assessing central obesity. Finally, due to the limited and age-restricted data on osteoporosis and fractures in the NHANES database, these factors were not included in our analysis. It is important to note that although a strong correlation exists between BMD loss and increased fracture risk, the relationship between central obesity, osteoporosis, and fracture risk requires further investigation. Conclusions Our study revealed a significant negative correlation between central obesity indices and TB-BMD. This finding is pivotal for understanding the relationship between central obesity and BMD throughout the bone lifecycle (20–60), suggesting that ABSI could serve as a potential indicator for osteoporosis prevention. Maintaining ABSI within 0.058–0.078 is crucial for individuals in bone mass accrual (20–30 years) and stabilization (30–45 years) periods. In contrast, managing central obesity in people experiencing early bone loss (45–60 years) presents greater complexity and warrants further investigation. Abbreviations ABSI: a body shape index; BMD: bone mineral density; BMI: body mass index; LDSC: linkage disequilibrium score regression; LS-BMD: lumbar spine bone mineral density; MR: mendelian randomization; PE-BMD: pelvis bone mineral density; SNP: single nucleotide polymorphisms; TB-BMD: total body (less head) bone mineral density; WC: waist circumference; WCadjBMI: waist circumference adjusted for body mass index; NHANES: national health and nutrition examination survey; IVW: inverse-variance weighting. Declarations Author contributions Camilo Alberto Pinzon Galvis, Yuhong Jiang, and Xianhao Huang contributed equally to this work and shared the first authorship. Xianhao Huang and Yuhong Jiang led the study's design and were pivotal in drafting the manuscript. Camilo Alberto Pinzon Galvis, Cui Wang, and Jialu Wu focused on data collection. Data analysis and result interpretation were handled by Lingyun Lu, Li Tian, and Ning Li. Funding This work was supported by the National Natural Science Youth Fund (approval number 82204847) and the Key Research and Development Project of Science and Technology Department of Sichuan Province (2023YFS0332). Data Sharing Statement The data that support the findings of this study are available in NHANES at https://www.cdc.gov/nchs/nhanes/index.htm; GIANT Consortium at https://portals.broadinstitute.org/collaboration/giant/index.php/; GEFOS Consortium at http://www.gefos.org/. Ethics Statement This study followed the principles outlined in the Declaration of Helsinki and received approval from the NCHS. Disclosure The authors report no conflicts of interest in this work. References Zeng Q, Li N, Wang Q, et al (2019) The Prevalence of Osteoporosis in China, a Nationwide, Multicenter DXA Survey. J Bone Miner Res 34:1789–1797. https://doi.org/10.1002/jbmr.3757 Kuk JL, Katzmarzyk PT, Nichaman MZ, et al (2006) Visceral Fat Is an Independent Predictor of All‐cause Mortality in Men. Obesity 14:336–341. https://doi.org/10.1038/oby.2006.43 Cabral M, Bangdiwala SI, Severo M, et al (2019) Central and peripheral body fat distribution: Different associations with low-grade inflammation in young adults? Nutrition, Metabolism and Cardiovascular Diseases 29:931–938. https://doi.org/10.1016/j.numecd.2019.05.066 Huang T, Qi Q, Zheng Y, et al (2015) Genetic Predisposition to Central Obesity and Risk of Type 2 Diabetes: Two Independent Cohort Studies. Diabetes Care 38:1306–1311. https://doi.org/10.2337/dc14-3084 Asomaning K, Bertone-Johnson ER, Nasca PC, et al (2006) The Association between Body Mass Index and Osteoporosis in Patients Referred for a Bone Mineral Density Examination. Journal of Women’s Health 15:1028–1034. https://doi.org/10.1089/jwh.2006.15.1028 Rinonapoli G, Pace V, Ruggiero C, et al (2021) Obesity and Bone: A Complex Relationship. International Journal of Molecular Sciences 22:13662. https://doi.org/10.3390/ijms222413662 Fassio A, Idolazzi L, Rossini M, et al (2018) The obesity paradox and osteoporosis. Eat Weight Disord 23:293–302. https://doi.org/10.1007/s40519-018-0505-2 Ma W, Zhou X, Huang X, Xiong Y (2023) Causal relationship between body mass index, type 2 diabetes and bone mineral density: Mendelian randomization. PLoS ONE 18:e0290530. https://doi.org/10.1371/journal.pone.0290530 Wang G-X, Fang Z-B, Li H-L, et al (2022) Effect of obesity status on adolescent bone mineral density and saturation effect: A cross-sectional study. Front Endocrinol 13:994406. https://doi.org/10.3389/fendo.2022.994406 Heymsfield SB, Cefalu WT (2013) Does Body Mass Index Adequately Convey a Patient’s Mortality Risk? JAMA 309:87. https://doi.org/10.1001/jama.2012.185445 Oka R, Miura K, Sakurai M, et al (2009) Comparison of waist circumference with body mass index for predicting abdominal adipose tissue. Diabetes Research and Clinical Practice 83:100–105. https://doi.org/10.1016/j.diabres.2008.10.001 Pischon T, Boeing H, Hoffmann K, et al (2008) General and Abdominal Adiposity and Risk of Death in Europe. N Engl J Med 359:2105–2120. https://doi.org/10.1056/NEJMoa0801891 Krakauer NY, Krakauer JC (2012) A New Body Shape Index Predicts Mortality Hazard Independently of Body Mass Index. PLoS ONE 7:e39504. https://doi.org/10.1371/journal.pone.0039504 Wei J, Liu X, Xue H, et al (2019) Comparisons of Visceral Adiposity Index, Body Shape Index, Body Mass Index and Waist Circumference and Their Associations with Diabetes Mellitus in Adults. Nutrients 11:1580. https://doi.org/10.3390/nu11071580 Orsi E, Solini A, Penno G, et al (2022) Body mass index versus surrogate measures of central adiposity as independent predictors of mortality in type 2 diabetes. Cardiovasc Diabetol 21:266. https://doi.org/10.1186/s12933-022-01706-2 McCaffery JM, Jablonski KA, Pan Q, et al (2022) Genetic Predictors of Change in Waist Circumference and Waist-to-Hip Ratio With Lifestyle Intervention: The Trans-NIH Consortium for Genetics of Weight Loss Response to Lifestyle Intervention. Diabetes 71:669–676. https://doi.org/10.2337/db21-0741 Lin R, Tao Y, Li C, et al (2024) Central obesity may affect bone development in adolescents: association between abdominal obesity index ABSI and adolescent bone mineral density. BMC Endocr Disord 24:81. https://doi.org/10.1186/s12902-024-01600-w Wang X, Yang S, He G, Xie L (2023) The association between weight-adjusted-waist index and total bone mineral density in adolescents: NHANES 2011–2018. Front Endocrinol 14:1191501. https://doi.org/10.3389/fendo.2023.1191501 The ADIPOGen Consortium, The CARDIOGRAMplusC4D Consortium, The CKDGen Consortium, et al (2015) New genetic loci link adipose and insulin biology to body fat distribution. Nature 518:187–196. https://doi.org/10.1038/nature14132 Medina-Gomez C, Kemp JP, Trajanoska K, et al (2018) Life-Course Genome-wide Association Study Meta-analysis of Total Body BMD and Assessment of Age-Specific Effects. The American Journal of Human Genetics 102:88–102. https://doi.org/10.1016/j.ajhg.2017.12.005 Randall JC, Winkler TW, Kutalik Z, et al (2013) Sex-stratified Genome-wide Association Studies Including 270,000 Individuals Show Sexual Dimorphism in Genetic Loci for Anthropometric Traits. PLoS Genet 9:e1003500. https://doi.org/10.1371/journal.pgen.1003500 Sun Y, Wang Y-X, Liu C, et al (2023) Exposure to Trihalomethanes and Bone Mineral Density in US Adolescents: A Cross-Sectional Study (NHANES). Environ Sci Technol 57:21616–21626. https://doi.org/10.1021/acs.est.3c07214 Zhang M, Hou Y, Ren X, et al (2024) Association of a body shape index with femur bone mineral density among older adults: NHANES 2007–2018. Arch Osteoporos 19:63. https://doi.org/10.1007/s11657-024-01424-0 Du D, Jing Z, Zhang G, et al (2022) The relationship between central obesity and bone mineral density: a Mendelian randomization study. Diabetol Metab Syndr 14:63. https://doi.org/10.1186/s13098-022-00840-x Wren TAL, Kalkwarf HJ, Zemel BS, et al (2014) Longitudinal Tracking of Dual-Energy X-ray Absorptiometry Bone Measures Over 6 Years in Children and Adolescents: Persistence of Low Bone Mass to Maturity. The Journal of Pediatrics 164:1280-1285.e2. https://doi.org/10.1016/j.jpeds.2013.12.040 Rand Th, Seidl G, Kainberger F, et al (1997) Impact of Spinal Degenerative Changes on the Evaluation of Bone Mineral Density with Dual Energy X-Ray Absorptiometry (DXA). Calcif Tissue Int 60:430–433. https://doi.org/10.1007/s002239900258 Xu H-W, Chen H, Zhang S-B, et al (2022) Association Between Abdominal Obesity and Subsequent Vertebral Fracture Risk. Pain Physician Kershaw EE, Flier JS (2004) Adipose Tissue as an Endocrine Organ. The Journal of Clinical Endocrinology & Metabolism 89:2548–2556. https://doi.org/10.1210/jc.2004-0395 Fried SK, Bunkin DA, Greenberg AS (1998) Omental and Subcutaneous Adipose Tissues of Obese Subjects Release Interleukin-6: Depot Difference and Regulation by Glucocorticoid 1 . The Journal of Clinical Endocrinology & Metabolism 83:847–850. https://doi.org/10.1210/jcem.83.3.4660 Hotamisligil GS, Shargill NS, Spiegelman BM (1993) Adipose Expression of Tumor Necrosis Factor-α: Direct Role in Obesity-Linked Insulin Resistance. Science 259:87–91. https://doi.org/10.1126/science.7678183 Maurin AC, Chavassieux PM, Frappart L, et al (2000) Influence of mature adipocytes on osteoblast proliferation in human primary cocultures. Bone 26:485–489. https://doi.org/10.1016/S8756-3282(00)00252-0 Bailey DA, Mckay HA, Mirwald RL, et al (1999) A Six-Year Longitudinal Study of the Relationship of Physical Activity to Bone Mineral Accrual in Growing Children: The University of Saskatchewan Bone Mineral Accrual Study. Journal of Bone and Mineral Research 14:1672–1679. https://doi.org/10.1359/jbmr.1999.14.10.1672 Chevalley T, Rizzoli R (2022) Acquisition of peak bone mass. Best Practice & Research Clinical Endocrinology & Metabolism 36:101616. https://doi.org/10.1016/j.beem.2022.101616 Rokoff LB, Rifas-Shiman SL, Switkowski KM, et al (2019) Body composition and bone mineral density in childhood. Bone 121:9–15. https://doi.org/10.1016/j.bone.2018.12.009 Yoo HJ, Park MS, Yang SJ, et al (2012) The differential relationship between fat mass and bone mineral density by gender and menopausal status. J Bone Miner Metab 30:47–53. https://doi.org/10.1007/s00774-011-0283-7 Kuk JL, Saunders TJ, Davidson LE, Ross R (2009) Age-related changes in total and regional fat distribution. Ageing Research Reviews 8:339–348. https://doi.org/10.1016/j.arr.2009.06.001 Zhao X, Yu J, Hu F, et al (2022) Association of body mass index and waist circumference with falls in Chinese older adults. Geriatric Nursing 44:245–250. https://doi.org/10.1016/j.gerinurse.2022.02.020 Ali D, Tencerova M, Figeac F, et al (2022) The pathophysiology of osteoporosis in obesity and type 2 diabetes in aging women and men: The mechanisms and roles of increased bone marrow adiposity. Front Endocrinol 13:981487. https://doi.org/10.3389/fendo.2022.981487 Palmer BF, Clegg DJ (2015) The sexual dimorphism of obesity. Molecular and Cellular Endocrinology 402:113–119. https://doi.org/10.1016/j.mce.2014.11.029 Krotkiewski M, Björntorp P, Sjöström L, Smith U (1983) Impact of obesity on metabolism in men and women. Importance of regional adipose tissue distribution. J Clin Invest 72:1150–1162. https://doi.org/10.1172/JCI111040 Bredella MA, Torriani M, Ghomi RH, et al (2011) Determinants of bone mineral density in obese premenopausal women. Bone 48:748–754. https://doi.org/10.1016/j.bone.2010.12.011 Giustina A, Mazziotti G, Canalis E (2008) Growth Hormone, Insulin-Like Growth Factors, and the Skeleton. Endocrine Reviews 29:535–559. https://doi.org/10.1210/er.2007-0036 Additional Declarations No competing interests reported. Supplementary Files ObesityandBMDSupplementaryFile1.xlsx ObesityandBMDSupplementaryFile2.pdf ObesityandBMDSupplementaryFile3.docx Cite Share Download PDF Status: Posted Version 1 posted 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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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5872489","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":405302646,"identity":"abe36dd7-6a8f-474b-b29d-4d35096dfeca","order_by":0,"name":"Camilo Alberto Pinzon Galvis","email":"","orcid":"","institution":"Division of Internal Medicine, Institute of Integrated Traditional Chinese and Western Medicine, West China Hospital, Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Camilo","middleName":"Alberto 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Sichuan University","correspondingAuthor":true,"prefix":"","firstName":"Lingyun","middleName":"","lastName":"Lu","suffix":""}],"badges":[],"createdAt":"2025-01-21 10:08:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5872489/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5872489/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":74586340,"identity":"f9332e59-249f-4626-b604-11edb8600797","added_by":"auto","created_at":"2025-01-23 16:48:27","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":160191,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of the study\u003c/p\u003e","description":"","filename":"ObesityandBMDFigure1.png","url":"https://assets-eu.researchsquare.com/files/rs-5872489/v1/41459a0a699f5d5a95c1b92e.png"},{"id":74586342,"identity":"68f59b3a-3ad5-48b2-8f1d-e06fd4f2c23a","added_by":"auto","created_at":"2025-01-23 16:48:27","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":340285,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot from the Mendelian randomization study examining the impact of WCadjBMI on TB-BMD. WCadjBMI: Waist circumference adjusted for BMI; TB-BMD: Total body less head bone mineral density. TB-BMD data was derived from distinct gene sets categorized by age groups: \u0026lt;15 years old (TB_15_or_less), 30–45 years old (TB_3045), \u0026gt;60 years old (TB_60_or_more), and all age groups (TB).\u003c/p\u003e","description":"","filename":"ObesityandBMDFigure2.png","url":"https://assets-eu.researchsquare.com/files/rs-5872489/v1/7ef01879500f21c4da065c41.png"},{"id":74586348,"identity":"338d7c17-cabc-4d21-8dbd-29cc90102572","added_by":"auto","created_at":"2025-01-23 16:48:28","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":687433,"visible":true,"origin":"","legend":"\u003cp\u003eThe smooth curve fitting analysis of the association between ABSI and BMD across different age groups\u003c/p\u003e","description":"","filename":"ObesityandBMDFigure3.png","url":"https://assets-eu.researchsquare.com/files/rs-5872489/v1/9f34625b2bef4210c10910d2.png"},{"id":74586351,"identity":"18d13173-357f-4c0e-950e-2c46c241165a","added_by":"auto","created_at":"2025-01-23 16:48:28","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":275973,"visible":true,"origin":"","legend":"\u003cp\u003eThe subgroup analysis between ABSI and TB-BMD\u003c/p\u003e","description":"","filename":"ObesityandBMDFigure4.png","url":"https://assets-eu.researchsquare.com/files/rs-5872489/v1/beaed308b88f318105d5968c.png"},{"id":74588883,"identity":"36bad32c-5124-43d6-b497-1ac2d44a1a51","added_by":"auto","created_at":"2025-01-23 17:20:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2208277,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5872489/v1/79c9c310-94d6-401d-a8f9-8670db13400e.pdf"},{"id":74586343,"identity":"43531a75-0079-455d-a45e-fc8cbff774ed","added_by":"auto","created_at":"2025-01-23 16:48:27","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":65004,"visible":true,"origin":"","legend":"","description":"","filename":"ObesityandBMDSupplementaryFile1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5872489/v1/cb0b932847f1100ad0d05fa7.xlsx"},{"id":74586347,"identity":"a6b2f63a-263d-4373-b4dd-9d4c6dddd714","added_by":"auto","created_at":"2025-01-23 16:48:28","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":1372829,"visible":true,"origin":"","legend":"","description":"","filename":"ObesityandBMDSupplementaryFile2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5872489/v1/7673a9d9c20a0086b653117f.pdf"},{"id":74586349,"identity":"dc224139-381f-4755-a0d7-4de525817134","added_by":"auto","created_at":"2025-01-23 16:48:28","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":31821,"visible":true,"origin":"","legend":"","description":"","filename":"ObesityandBMDSupplementaryFile3.docx","url":"https://assets-eu.researchsquare.com/files/rs-5872489/v1/730ae66bccb956181df5af69.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Impact of Central Obesity on Bone Mineral Density Across Life Stages: A Genetic Epidemiology and Cross-Sectional Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eOsteoporosis is a degenerative skeletal disorder characterized by reduced bone mineral density (BMD) and deterioration of bone microarchitecture, leading to pain, fractures, and significantly diminishing the quality of life [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Obesity, on the other hand, is defined as excessive fat accumulation that poses a health risk. Central obesity, primarily characterized by the accumulation of abdominal and visceral fat, is considered a more concerning high-risk type of obesity [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Owing to systemic metabolic and inflammatory changes, central obesity significantly increases the risk of various chronic diseases such as hypertension [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] and diabetes [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. However, the relationship between central obesity and BMD remains controversial. Traditionally, it was believed that obesity might protect against bone mass loss owing to increased mechanical loading, which enhances bone formation [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Nevertheless, increasing evidence suggests that obesity may contribute to higher risks of bone mass loss [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe concept often referred to as the \"obesity paradox\" in research concerning obesity and disease risk emerges from findings seemingly contradicting each other [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. One reason underlying these contradictions is the reliance on a singular metric such as body mass index (BMI) to assess obesity [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. BMI does not distinguish between adipose and lean tissue mass, nor does it account for fat distribution, which can obscure the unique adverse effects of central obesity [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Waist circumference (WC) is a risk indicator supplement to BMI and is more closely related to abdominal and visceral fat [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. It can better assess the mortality risk associated with central obesity [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. However, due to the high correlation between WC and BMI, it is challenging to investigate the independent impact of central obesity on health beyond overall body weight. To address this issue, some new obesity indices have been proposed, such as A body shape index (ABSI) [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. ABSI is considered to better reflect the hazards caused by visceral fat and is positively associated with the risk of many metabolic diseases [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Researchers proposed that the ABSI may more accurately predict diseases linked to central obesity than WC [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. In Mendelian randomization studies, WC adjusted for BMI (WCadjBMI) has emerged as a commonly used indicator of central obesity, as it excludes genetic influences associated with BMI [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDespite existing research suggesting a negative correlation between central obesity indices and BMD, most studies have been cross-sectional and limited to specific age groups [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], lacking a comprehensive evidence chain from genomic epidemiological analysis to large-scale population validation. Consequently, there remains a significant gap in understanding the effects of central obesity on bone health across different life stages, including periods of rapid growth, attainment of peak bone mass, and age-related bone loss.\u003c/p\u003e \u003cp\u003eIn this study, we first applied linkage disequilibrium score regression (LDSC) and Mendelian randomization (MR) approaches to investigate the genetic correlation and potential causal relationship between WCadjBMI and total body less head bone mineral density (TB-BMD) across different age groups. Subsequently, we analyzed large-scale population data from the National Health and Nutrition Examination Survey (NHANES) spanning 2011 to 2018 with regression analysis to investigate the specific correlation patterns between ABSI and TB-BMD across various age groups. We also validated the accuracy of our findings with different BMD outcomes, including pelvis BMD (PE-BMD) and lumbar spine BMD (LS-BMD). We aimed to rigorously investigate whether central obesity differentially affects bone development at distinct stages of skeletal growth and to identify specific correlation patterns. Our objective is to provide insights that may enhance the management of central obesity across various age groups, ultimately reducing the risk of osteoporosis.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eGenomic Epidemiological Analysis\u003c/h2\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003eData source\u003c/h2\u003e \u003cp\u003eThe data utilized in this study were obtained from publicly available GWAS datasets [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] (Supplementary file 1: Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). WCadjBMI is a composite index adjusted for multiple factors, primarily designed to more accurately assess the impact of genetic influences on waist circumference without confounding from BMI [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The TB-BMD measurement was conducted using DXA. The procedure adheres to standardized operating protocols for measuring TB-BMD [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. TB-BMD data was derived from distinct gene sets categorized by age groups: \u0026lt;15 years old (abbreviated as TB_15_or_less), 15\u0026ndash;30 years old (TB_1530), 30\u0026ndash;45 years old (TB_3045), 45\u0026ndash;60 years old (TB_4560), and \u0026gt;\u0026thinsp;60 years old (TB_60_or_more). A comprehensive dataset encompassing all age groups (TB) was also analyzed.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e\n\u003ch3\u003eGenetic correlation analysis\u003c/h3\u003e\n\u003cp\u003eLDSC assesses genetic correlations by regressing GWAS summary statistics on LD scores. Specifically, we utilized bivariate LDSC to investigate the pairwise genetic correlations between WCadjBMI and TB-BMD, with the estimated values ranging from \u0026minus;\u0026thinsp;1 to 1, where \u0026minus;\u0026thinsp;1 indicates complete negative genetic correlation and 1 indicates complete positive genetic correlation. Before the analysis, we excluded single nucleotide polymorphisms (SNPs) in the major histocompatibility complex region to reduce confounding factors. Additionally, given the potential issue of sample overlap between populations, we did not constrain the intercept term; instead, we used the intercept term to stratify and determine the presence of sample overlap. A false discovery rate (FDR) corrected \u003cem\u003eP\u003c/em\u003e.adjust\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\n\u003ch3\u003eMR analysis\u003c/h3\u003e\n\u003cp\u003eVarious methods were employed to conduct an MR analysis to assess the causal relationship between WCadjBMI and TB-BMD. The primary analysis method used was inverse-variance weighting (IVW), which synthesizes the overall causal estimate by utilizing multiple genetic instrumental variables. IVW is known for its high statistical efficiency and robustness compared to other methods. To ensure consistency, we also conducted MR-Egger, weighted median, weighted mode, and simple mode analyses to compare and evaluate the results obtained from IVW. To ensure the accuracy of the MR results, sensitivity analyses were performed: (1) MR-Egger intercept test: Used to test for horizontal pleiotropy, with Cochran's Q statistic estimating heterogeneity. (2) Leave-one-out analysis: Applied to determine whether any single SNP drives the potential causal effect between the traits. (3) MR-Steiger directionality test: Employed to assess the correctness of the causal direction between the traits. (4) F-statistic: Used to evaluate the strength of the instrumental variables (IVs), wherein an F-statistic\u0026thinsp;\u0026lt;\u0026thinsp;10 indicates potential bias from weak instruments. (5) FDR correction: The Benjamini\u0026ndash;Hochberg method was used for the primary results, with a significance threshold of \u003cem\u003eP\u003c/em\u003e.adjust\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Finally, we conducted a reverse causality analysis. The ethics committee of each institutional review board authorized written informed consent from all participants in the separate studies. No extra ethical approval or informed consent was required. The analysis was conducted using Two Sample MR packages in R (version 4.3.1).\u003c/p\u003e\n\u003ch3\u003eCross-sectional study\u003c/h3\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStudy population from NHANES\u003c/h2\u003e \u003cp\u003eNHANES is a biennial, comprehensive assessment conducted by the Centers for Disease Control and Prevention to evaluate the health and nutritional status of the United States population (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cdc.gov/nchs/nhanes/index.htm\u003c/span\u003e\u003cspan address=\"https://www.cdc.gov/nchs/nhanes/index.htm\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The study received approval from the National Center for Health Statistics Ethics Review Board (NCHS ERB), and all participants provided written informed consent. Our study combined data from four NHANES cycles spanning 2011\u0026ndash;2018, initially including a total of 39,156 participants. Following the exclusion of individuals lacking BMD and ABSI data, those under 20 years of age, and those with chronic or significant diseases that could potentially impact BMD, the analysis was performed on the remaining cohort of 7,452 subjects (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eVariables assessment\u003c/h3\u003e\n\u003cp\u003eThe ABSI was determined using the subject's WC (m), BMI (kg/m\u0026sup2;), and height (m) with the following formula[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:\\text{A}\\text{B}\\text{S}\\text{I}=\\frac{WC}{{BMI}^{2/3}{Height}^{1/2}}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe TB-BMD, PE-BMD, and LS-BMD extracted in our study were assessed using dual-energy X-ray absorptiometry (DXA). Pregnant individuals, those with a history of radiographic contrast-material use within 7 days before testing, and individuals weighing over 450 pounds or taller than 6'5\" were excluded. The methodology to calculate TB-BMD involves subtracting the head bone mineral content (BMC) from the total BMC (g) and dividing the result by the difference between the total area and head area (cm\u003csup\u003e2\u003c/sup\u003e) [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eCovariate selection\u003c/h3\u003e\n\u003cp\u003eTo control for potential confounding effects, we adjusted for the following demographic characteristics: age, sex, race, educational level, and family income-poverty ratio (FIPR). These data were collected via personal demographic questionnaires. Diabetes status was determined based on self-reported medical history (\"Has a doctor ever told you that you have diabetes?\"). Data on lifestyle habits were collected using a questionnaire, which assessed alcohol consumption (defined as having at least 12 alcoholic drinks per year), smoking history (having smoked at least 100 cigarettes in one's lifetime), and engagement in moderate work activity. Serological tests were conducted to measure the levels of alkaline phosphatase (ALP), alanine aminotransferase (ALT), aspartate aminotransferase (AST), creatinine, high-density lipoprotein cholesterol (HDL-C), total cholesterol, serum phosphorus, serum calcium and vitamin D (25OHD2\u0026thinsp;+\u0026thinsp;25OHD3). The BMI, WC, and height measurements were recorded at the mobile examination center (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\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\u003eThe baseline characteristics of study participants from the NHANES 2011\u0026ndash;2018 grouped by ABSI tertiles\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal(N\u0026thinsp;=\u0026thinsp;7,452)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow(N\u0026thinsp;=\u0026thinsp;2,484)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMedium (N\u0026thinsp;=\u0026thinsp;2,484)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHigh (N\u0026thinsp;=\u0026thinsp;2,489)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ep-value\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.058\u0026ndash;0.099)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.058\u0026ndash;0.078)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.078\u0026ndash;0.082)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.082\u0026ndash;0.099)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e35.59 (35.34 35.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31.38 (31.02 31.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e35.69 (35.28 36.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e40.24 (39.79 40.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20-\u0026lt;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2257 (30.29%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1139 (45.85%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e707 (28.46%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e411 (16.55%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;30-\u0026lt;45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3029 (40.65%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e947 (38.12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1101 (44.32%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e981 (39.49%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;45-\u0026lt;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2166 (29.07%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e398 (16.02%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e676 (27.21%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1092 (43.96%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace\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=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMexican American\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1210 (16.24%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e296 (11.92%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e445 (17.91%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e469 (18.88%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther Hispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e775 (10.40%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e248 (9.98%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e262 (10.55%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e265 (10.67%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic White\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2348 (31.51%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e661 (26.61%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e811 (32.65%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e876 (35.27%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic Black\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1597 (21.43%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e812 (32.69%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e434 (17.47%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e351 (14.13%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther Race\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1522 (20.42%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e467 (18.80%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e532 (21.42%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e523 (21.05%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFIPR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.88 (1.84 1.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.85 (1.78 1.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.96 (1.89 2.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.84 (1.77 1.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation level\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=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBelow high school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1365 (18.32%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e360 (14.50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e463 (18.64%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e542 (21.82%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1652 (22.17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e519 (20.91%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e549 (22.10%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e584 (23.51%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCollege or above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4433 (59.50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1603 (64.59%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1472 (59.26%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1358 (54.67%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking\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=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2661 (35.73%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e756 (30.43%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e890 (35.86%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1015 (40.89%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4787 (64.27%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1728 (69.57%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1592 (64.14%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1467 (59.11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrinking\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=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e820 (13.58%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e221 (10.77%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e274 (13.47%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e325 (16.64%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5219 (86.42%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1831 (89.23%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1760 (86.53%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1628 (83.36%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate activity\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=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.585\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2987 (40.08%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1011 (40.70%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1000 (40.26%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e976 (39.29%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4465 (59.92%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1473 (59.30%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1484 (59.74%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1508 (60.71%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\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=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e385 (5.25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e58 (2.36%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e103 (4.21%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e224 (9.23%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6954 (94.75%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2404 (97.64%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2346 (95.79%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2204 (90.77%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e63.75 (63.30 64.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e60.25 (59.55 60.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e63.22 (62.47 63.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e68.00 (67.15 68.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALT (U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22.17 (21.91 22.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20.04 (19.65 20.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22.79 (22.33 23.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e23.85 (23.35 24.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAST (U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22.94 (22.75 23.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22.40 (22.10 22.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23.00 (22.69 23.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e23.41 (23.06 23.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine (mg/dl)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.82 (0.81 0.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.84 (0.84 0.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.82 (0.81 0.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.80 (0.79 0.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal cholesterol (mg/dl)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e185.25 (184.36 186.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e177.11 (175.67 178.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e186.56 (185.06 188.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e192.35 (190.75 193.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-C (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e50.11 (49.78 50.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e53.25 (52.66 53.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e49.48 (48.92 50.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e47.78 (47.24 48.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ephosphorus (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.67 (3.66 3.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.69 (3.67 3.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.66 (3.64 3.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.66 (3.63 3.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecalcium (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.36 (9.35 9.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.38 (9.37 9.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.35 (9.34 9.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.35 (9.34 9.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25(OH)D (nmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e53.97 (53.45 54.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e52.92 (51.99 53.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e54.24 (53.36 55.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e54.77 (53.87 55.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.118\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e27.66 (27.52 27.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26.98 (26.75 27.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27.69 (27.46 27.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e28.31 (28.07 28.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e167.66 (167.44 167.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e167.37 (167.00 167.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e167.82 (167.44 168.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e167.79 (167.41 168.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.079\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWC (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e94.47 (94.13 94.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e87.51 (86.99 88.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e94.80 (94.26 95.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e101.64 (101.04 102.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTB-BMD (g/cm\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.97 (0.97 0.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.00 (0.99 1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.97 (0.97 0.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.95 (0.95 0.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePE-BMD (g/cm\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.24 (1.24 1.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.26 (1.25 1.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.24 (1.24 1.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.22 (1.22 1.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLS-BMD (g/cm\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.02 (1.02 1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.07 (1.07 1.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.02 (1.02 1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.98 (0.98 0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eABSI: A Body Shape Index; FIPR: Family income-poverty ratio; ALP: Alkaline phosphatase; ALT: Alanine aminotransferase; AST: Aspartate aminotransferase; HDL-C: High-density lipoprotein cholesterol; BMI: Body mass index; WC: Waist circumference; TB-BMD: Total body(less head) bone mineral density. PE-BMD: Pelvis bone mineral density; LS-BMD: Lumbar spine bone mineral density.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003e This study conducted all analyses following the NCHS analytical guidelines and utilized sample weights. Categorical variables were reported as frequencies and percentages, and differences between groups were assessed using weighted chi-square tests. Continuous variables were expressed as means with 95% confidence intervals. We employed survey-weighted linear regression or Kruskal-Wallis rank-sum tests as appropriate. To investigate correlations between ABSI and BMD, three distinct weighted multivariable linear regression models were developed: Model 1 included unmodified variables; Model 2 was adjusted for age, sex, and race; and Model 3 controlled for all covariates listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e except WC, BMI, and height. The outcomes are reported as β coefficients with 95% confidence intervals, where β denotes the change in BMD corresponding to each unit increase in ABSI. ABSI was further categorized into tertiles, and regression analyses were performed to evaluate trends in BMD changes across ABSI categories (\u003cem\u003ep\u003c/em\u003e for trend\u0026thinsp;\u0026lt;\u0026thinsp;0.05). To assess non-linear associations, we employed smooth curve fitting and threshold effect analysis. This involved using a recursive algorithm to identify inflection points and applying a two-segment linear regression model on either side of these points. Subgroup analyses were conducted using stratified multivariate linear regression models, incorporating predefined potential effect modifiers. These effect modifiers were identified by including interaction terms in the models and performing likelihood ratio tests to evaluate heterogeneity across subgroups (\u003cem\u003ep\u003c/em\u003e for interaction\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Statistical analysis was performed using R version 4.2.3 (R Project) supplemented by EmpowerStats software package tools. For all analyses, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered to indicate statistical significance.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eGenomic epidemiological analysis\u003c/h2\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003eGenetic correlation analysis\u003c/h2\u003e \u003cp\u003eWe employed LDSC to evaluate the global genetic correlation between traits. Using bivariate LDSC, we investigated the genetic correlation between WCadjBMI and TB-BMD across various age cohorts. Our results demonstrated significant genetic correlations between WCadjBMI and TB-BMD within distinct age groups: \u0026lt;15 years, 30\u0026ndash;45 years, 45\u0026ndash;60 years, and \u0026gt;\u0026thinsp;60 years, as well as cumulatively across all ages (\u003cem\u003ep\u003c/em\u003e.adjust\u0026thinsp;\u0026lt;\u0026thinsp;0.05). This implies significant correlations across all groups except 15\u0026ndash;30 years. Moreover, an intercept yielding a \u003cem\u003ep\u003c/em\u003e.adjust\u0026thinsp;\u0026gt;\u0026thinsp;0.05 confirmed no sample overlaps between the analyzed traits (Supplementary File 1: Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eMR analysis\u003c/h2\u003e \u003cp\u003eAfter establishing the genetic correlation between WCadjBMI and TB-BMD, we employed MR analysis to investigate the potential causal relationship between these two variables. We meticulously selected SNPs based on stringent criteria, including significance threshold (P\u0026thinsp;\u0026lt;\u0026thinsp;5\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e), correction (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05), F-statistic (\u0026gt;\u0026thinsp;10), and LD (r\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.1), suggesting that our findings are unlikely to be influenced by weak instruments. Detailed information can be found in Supplementary File 1: Tables S5-S10. The IVW results from MR analyses suggested a negative causal correlation between WCadjBMI and TB-BMD, with a significant negative association observed across the three specific age groups (Figure. 2): TB (β=-0.16; 95% CI: -0.26, -0.07), TB_15_or_less (β=-0.26; 95% CI: -0.39, -0.12), TB_3045 (β=-0.19; 95% CI: -0.35, -0.03), and TB_60_or_more (β=-0.22; 95% CI: -0.35, -0.10). Specifically, an increase in WCadjBMI was inversely associated with TB-BMD across these age groups. The weighted median approach confirmed the findings from the IVW analysis (Supplementary File 1: Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e). The details are illustrated in the funnel plots and forest plots provided in Supplementary File 2: Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e-\u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eSensitivity analysis\u003c/h2\u003e \u003cp\u003eIn our sensitivity analysis, we first used the leave-one-out method to evaluate the effects of WCadjBMI and TB-BMD across all age groups. No SNP was found to strongly influence the summary estimates (Supplementary File 2: Figure \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e). Additionally, no evidence of horizontal pleiotropy was observed in funnel plots (Supplementary File 2: Figure S4). While some heterogeneity was present, we applied a random-effects IVW model to account for potential heterogeneity among SNPs. The Steiger directionality test further confirmed the causal direction from WCadjBMI to TB-BMD across different age groups. These results strongly support the robustness of the causal relationship between WCadjBMI and TB-BMD in various age groups (Supplementary File 1: Table S4). Last, our reverse MR analysis did not indicate any causal effect of TB-BMD across different age groups on WCadjBMI, excluding reverse causation's influence (Supplementary File 1: Table S11).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eCross-sectional study\u003c/h2\u003e \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e \u003ch2\u003eBaseline characteristics of participants\u003c/h2\u003e \u003cp\u003eTo further investigate the impact of central obesity on BMD in a large population, we analyzed data from the NHANES spanning 2011\u0026ndash;2018, which enrolled 7,452 participants. We categorized the ABSI into three tertiles: low (0.058\u0026ndash;0.078), medium (0.078\u0026ndash;0.082), and high (0.082\u0026ndash;0.099), to investigate the baseline characteristics across different ABSI levels (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The proportion of individuals with high ABSI increased with age, peaking in the 45\u0026ndash;60 year age group. Participants with a college education or higher, those engaged in moderate physical activity at work, and individuals without diabetes predominantly maintained low ABSI levels. Conversely, individuals with an education level below college, those with smoking and alcohol consumption habits, and those lacking moderate physical activity were more likely to exhibit a high ABSI. Biochemical analyses revealed that individuals with a high ABSI exhibited elevated levels of ALP, ALT, AST, and total cholesterol, along with reduced levels of HDL-C, phosphorus, and calcium compared to those with a low ABSI. Additionally, individuals with high ABSI had higher BMI and WC and lower TB-BMD, PE-BMD, and LS-BMD than participants with low ABSI (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eAssociation between ABSI and BMD\u003c/h2\u003e \u003cp\u003eThe results of distinct weighted multivariable linear regression models consistently demonstrate a negative association between ABSI and BMD. For clarity, we report here only the findings from Model 3, which adjusts for all covariates. After adjustment, a higher ABSI was inversely correlated with TB-BMD values. Specifically, an increase of 0.01 in ABSI corresponded to a decrease of 0.035 units in TB-BMD (β = -3.531; 95% CI: -4.146 to -2.915). Similarly, ABSI was robustly negatively associated with various BMD indices: for PE-BMD, β = -1.949 (95% CI: -4.146 to -2.915), and for LS-BMD, β = -6.364 (95% CI: -7.365 to -5.363). Subsequent sensitivity analyses categorized ABSI into tertiles, revealing stronger negative associations at higher tertile levels compared to the reference group (lowest tertile). Participants in the highest tertile exhibited significant decreases in BMD: TB-BMD decreased by 0.032 units (95% CI: -0.038 to -0.026), PE-BMD by 0.015 units (95% CI: -0.026 to -0.004), and TS-BMD by 0.055 units (95% CI: -0.066 to -0.045), with \u003cem\u003ep\u003c/em\u003e for trend\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (Supplementary File 3: Tables S1-S3).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eSmooth curve fitting and threshold effect analysis\u003c/h2\u003e \u003cp\u003eTo explore the potential non-linear relationship between ABSI and BMD across age-stratified populations, we employed smooth curve fitting and threshold effect analysis. The results indicate a complex non-linear relationship between ABSI and BMD across different age groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Notably, in the 45\u0026ndash;60 age group, an inverted U-shaped curve was observed between ABSI and PE-BMD. A threshold effect analysis using a weighted two-segment linear regression model and a recursive algorithm revealed that ABSI was positively associated with PE-BMD (β\u0026thinsp;=\u0026thinsp;9.202, 95% CI: 0.185 to 18.219, p\u0026thinsp;=\u0026thinsp;0.0457) before reaching 0.076, and negatively correlated after that (β = -3.221, 95% CI: -5.419 to -1.023, p\u0026thinsp;=\u0026thinsp;0.0041), with 0.076 serving as the inflection point (K). However, in the 20\u0026ndash;30 and 30\u0026ndash;45 age groups, 0.076 was not the peak but acted as an inflection point resembling an L-shaped curve. In the 30\u0026ndash;45 age group, although the curve indicates a positive association between ABSI with both TB-BMD and PE-BMD beyond the inflection point of 0.086, this relationship is not statistically significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). For detailed information, refer to Supplementary File 3: Tables S4-S6.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eSubgroup analysis\u003c/h2\u003e \u003cp\u003eA subgroup analysis was conducted to evaluate whether the relationship between ABSI and TB-BMD was consistent across different population settings (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). None of the stratifications, including sex, age, race, diabetes status, education level, smoking, drinking, and moderate activity, significantly altered the negative association between ABSI and TB-BMD. However, variations in the negative impact of high ABSI on TB-BMD were observed across different sexes, ages, and moderate activity levels (\u003cem\u003ep\u003c/em\u003e for interaction\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Specifically, an increase in ABSI was associated with a more pronounced decline in TB-BMD among males (β = -5.601; 95% CI: -6.589 to -4.614) than females (β = -1.624; 95% CI: -2.375 to -0.873). The negative impact of ABSI on TB-BMD was most marked in the 45\u0026ndash;60 age group (β = -7.530; 95% CI: -9.246 to -5.814), \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study utilized genomic epidemiological approaches, initially employing LDSC to uncover the genetic correlation between the central obesity index\u0026mdash;WCadjBMI and TB-BMD. Subsequently, MR analysis confirmed a negative causal relationship between WCadjBMI and TB-BMD, with sensitivity analyses conducted to mitigate potential reverse causation. However, this causal relationship was inconsistent across all population subgroups, observed only in individuals aged\u0026thinsp;\u0026lt;\u0026thinsp;15 years, 30\u0026ndash;45 years, and \u0026gt;\u0026thinsp;60 years. We then conducted a cross-sectional study from NHANES, supplementing TB-BMD with PE-BMD and LS-BMD to enhance the reliability of our findings. Given the age-related limitations of DXA measurements (restricted to individuals aged\u0026thinsp;\u0026lt;\u0026thinsp;60), as well as missing lifestyle data (smoking and alcohol consumption) in participants under 20 years, our analysis was focused on the 20\u0026ndash;60 age group. We employed age stratification similar to the genomic epidemiological study (20\u0026ndash;30, 30\u0026ndash;45, 45\u0026ndash;60 years) to investigate the correlation patterns between another central obesity index\u0026mdash;ABSI and BMD. Using weighted multivariate linear regression analysis, we identified a negative correlation between ABSI and TB-BMD, PE-BMD, and LS-BMD, with the relationship exhibiting a complex non-linear pattern across different age groups. Moreover, threshold effect analysis identified an inflection point at ABSI\u0026thinsp;=\u0026thinsp;0.076: below this threshold, ABSI positively correlated with PE-BMD, whereas above it, the association became negative. Subgroup analysis revealed that an increase in ABSI was associated with a more pronounced decline in TB-BMD among males and in the 45\u0026ndash;60 age group.\u003c/p\u003e \u003cp\u003eTo our knowledge, this is the first study to employ large-scale observational data, genetic correlation, and MR analysis of extensive genetic datasets to examine the relationship between central obesity and BMD comprehensively. The findings indicate a negative association between central obesity indices and BMD, aligning with prior cross-sectional research [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. However, these studies have primarily focused on the impact of ABSI on BMD in older adults (\u0026gt;\u0026thinsp;60 years) and adolescents (\u0026lt;\u0026thinsp;15 years), which lack a comprehensive understanding of how central obesity affects BMD throughout the lifespan, particularly during critical periods of bone density change. Moreover, due to the cross-sectional design of these studies, it is challenging to eliminate the influence of reverse causality, which limits the conclusions.\u003c/p\u003e \u003cp\u003eA preceding MR study also identified a negative causal relationship between WCadjBMI and BMD [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. However, it solely relied on heel bone measurements as its outcome variable, which was not representative of overall skeletal health. In populations undergoing rapid skeletal changes due to growth spurts [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], local density measures such as those of the lumbar spine, femoral neck, and total hip are potentially limited in their effectiveness during these quick changes [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. In geriatric assessments, degenerative spine alterations can erroneously increase BMD values, introducing measurement inaccuracies among older adults [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. TB-BMD measurement is considered the most reliable method for an unbiased assessment of BMD across all ages, from childhood to old age [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Therefore, our study selected TB-BMD as the primary outcome measure.\u003c/p\u003e \u003cp\u003eThe observed negative correlation between central obesity and BMD in this study may primarily be attributed to the detrimental impacts of accumulating abdominal and visceral fat [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Metabolically distinct from subcutaneous fat, visceral fat has been shown to exert more detrimental effects on bone health [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Visceral adipose tissues secrete cytokines such as tumour necrosis factor-alpha (TNF-α) and interleukin-6 (IL-6), which are known to promote bone resorption [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] while inhibiting osteoblast proliferation.[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eHuman bone mass steadily increases during childhood, accelerates in adolescence, and peaks between 25 and 30 years [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. It then stabilizes before experiencing a more pronounced decline after 45\u0026ndash;60 years [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Therefore, in our cross-sectional study, we stratified age into three groups: 20\u0026ndash;30 years (pre-peak bone mass), 30\u0026ndash;45 years (peak bone mass), and 45\u0026ndash;60 years (post-peak bone mass), to assess the differential patterns of the negative correlation between central obesity and BMD across various stages of bone development. Our findings demonstrate that central obesity negatively affects BMD throughout the entire bone lifecycle. These findings are consistent with those reported by Yoo et al.[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], who noted similar trends among individuals ranging from 20 to 88 years old. Their study indicated that obesity impairs peak bone mass acquisition in women, potentially increasing their risk for postmenopausal osteoporosis [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Our research indicates that maintaining ABSI at a low level (0.058\u0026ndash;0.078) is most beneficial for bone health in individuals aged 20 to 45. Our baseline data analysis across different ABSI levels suggests that maintaining healthy lifestyle habits\u0026mdash;specifically reducing smoking and alcohol consumption and increasing moderate-intensity physical activity\u0026mdash;may help maintain lower ABSI levels.\u003c/p\u003e \u003cp\u003eNotably, we found that ABSI in individuals aged 45 to 60 exhibits an inverted U-shaped relationship with PE-BMD. A similar phenomenon was observed in previous studies, suggesting an inverted U-shaped correlation between the visceral adiposity index (VAI) and BMD in older adults [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. This may be attributed to the estrogen produced by adipocytes, which could provide potential resistance to early bone loss [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. However, it is essential to note that this inverted U-shaped relationship does not apply to all bone density metrics. Our study also demonstrates that the negative impact of central obesity on BMD is more pronounced in 45\u0026ndash;60 adults compared to younger individuals. With advancing age, central obesity may increase the risk of falls and subsequent fractures[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Additionally, ageing is associated with increased bone marrow fat, which can alter the cellular composition and molecular interactions within the bone microenvironment[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], thereby posing a fracture risk beyond that associated with low BMD alone. Therefore, optimal management of central obesity in individuals experiencing age-related bone loss requires further investigation.\u003c/p\u003e \u003cp\u003eOur research also indicates that the negative effect of central obesity on men is more significant than that on women. This disparity may stem from distinct fat distribution patterns and hormonal variations between the sexes. Men generally accumulate more visceral fat, whereas women are prone to higher subcutaneous fat levels [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Unlike subcutaneous fat, visceral adiposity produces numerous pro-inflammatory cytokines contributing to metabolic complications like insulin resistance[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Additionally, visceral fat influences osteoblast activity by modulating IGF-1 levels, limiting collagen degradation, and enhancing osteoblast recruitment\u0026mdash;all factors that detrimentally impact BMD [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWe acknowledge several limitations in our study. Firstly, due to the lack of GWAS data sources, we could not obtain data stratified by age for BMD at specific sites such as the lumbar spine and pelvis. Additionally, regarding central obesity indicators, the cross-sectional study utilized ABSI, while WCadjBMI was used in the genetic epidemiology analyses. Although both metrics adjust waist circumference for BMI, their different calculation methods may introduce heterogeneity in assessing central obesity. Finally, due to the limited and age-restricted data on osteoporosis and fractures in the NHANES database, these factors were not included in our analysis. It is important to note that although a strong correlation exists between BMD loss and increased fracture risk, the relationship between central obesity, osteoporosis, and fracture risk requires further investigation.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eOur study revealed a significant negative correlation between central obesity indices and TB-BMD. This finding is pivotal for understanding the relationship between central obesity and BMD throughout the bone lifecycle (20\u0026ndash;60), suggesting that ABSI could serve as a potential indicator for osteoporosis prevention. Maintaining ABSI within 0.058\u0026ndash;0.078 is crucial for individuals in bone mass accrual (20\u0026ndash;30 years) and stabilization (30\u0026ndash;45 years) periods. In contrast, managing central obesity in people experiencing early bone loss (45\u0026ndash;60 years) presents greater complexity and warrants further investigation.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eABSI: a body shape index; BMD: bone mineral density; BMI: body mass index; LDSC: linkage disequilibrium score regression; LS-BMD: lumbar spine bone mineral density; MR: mendelian randomization; PE-BMD: pelvis bone mineral density; SNP: single nucleotide polymorphisms; TB-BMD: total body (less head) bone mineral density; WC: waist circumference; WCadjBMI: waist circumference adjusted for body mass index; NHANES: national health and nutrition examination survey; IVW: inverse-variance weighting.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCamilo Alberto Pinzon Galvis, Yuhong Jiang, and Xianhao Huang contributed equally to this work and shared the first authorship. Xianhao Huang and Yuhong Jiang led the study\u0026apos;s design and were pivotal in drafting the manuscript. Camilo Alberto Pinzon Galvis, Cui Wang, and Jialu Wu focused on data collection. Data analysis and result interpretation were handled by Lingyun Lu, Li Tian, and Ning Li.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Natural Science Youth Fund (approval number 82204847) and the Key Research and Development Project of Science and Technology Department of Sichuan Province (2023YFS0332).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Sharing Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available in NHANES at https://www.cdc.gov/nchs/nhanes/index.htm; GIANT Consortium at https://portals.broadinstitute.org/collaboration/giant/index.php/; GEFOS Consortium at http://www.gefos.org/.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study followed the principles outlined in the Declaration of Helsinki and received approval from the NCHS.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclosure\u0026nbsp;\u003c/strong\u003eThe authors report no conflicts of interest in this work.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eZeng Q, Li N, Wang Q, et al (2019) The Prevalence of Osteoporosis in China, a Nationwide, Multicenter DXA Survey. J Bone Miner Res 34:1789\u0026ndash;1797. https://doi.org/10.1002/jbmr.3757\u003c/li\u003e\n\u003cli\u003eKuk JL, Katzmarzyk PT, Nichaman MZ, et al (2006) Visceral Fat Is an Independent Predictor of All‐cause Mortality in Men. Obesity 14:336\u0026ndash;341. https://doi.org/10.1038/oby.2006.43\u003c/li\u003e\n\u003cli\u003eCabral M, Bangdiwala SI, Severo M, et al (2019) Central and peripheral body fat distribution: Different associations with low-grade inflammation in young adults? Nutrition, Metabolism and Cardiovascular Diseases 29:931\u0026ndash;938. https://doi.org/10.1016/j.numecd.2019.05.066\u003c/li\u003e\n\u003cli\u003eHuang T, Qi Q, Zheng Y, et al (2015) Genetic Predisposition to Central Obesity and Risk of Type 2 Diabetes: Two Independent Cohort Studies. Diabetes Care 38:1306\u0026ndash;1311. https://doi.org/10.2337/dc14-3084\u003c/li\u003e\n\u003cli\u003eAsomaning K, Bertone-Johnson ER, Nasca PC, et al (2006) The Association between Body Mass Index and Osteoporosis in Patients Referred for a Bone Mineral Density Examination. Journal of Women\u0026rsquo;s Health 15:1028\u0026ndash;1034. https://doi.org/10.1089/jwh.2006.15.1028\u003c/li\u003e\n\u003cli\u003eRinonapoli G, Pace V, Ruggiero C, et al (2021) Obesity and Bone: A Complex Relationship. International Journal of Molecular Sciences 22:13662. https://doi.org/10.3390/ijms222413662\u003c/li\u003e\n\u003cli\u003eFassio A, Idolazzi L, Rossini M, et al (2018) The obesity paradox and osteoporosis. Eat Weight Disord 23:293\u0026ndash;302. https://doi.org/10.1007/s40519-018-0505-2\u003c/li\u003e\n\u003cli\u003eMa W, Zhou X, Huang X, Xiong Y (2023) Causal relationship between body mass index, type 2 diabetes and bone mineral density: Mendelian randomization. PLoS ONE 18:e0290530. https://doi.org/10.1371/journal.pone.0290530\u003c/li\u003e\n\u003cli\u003eWang G-X, Fang Z-B, Li H-L, et al (2022) Effect of obesity status on adolescent bone mineral density and saturation effect: A cross-sectional study. Front Endocrinol 13:994406. https://doi.org/10.3389/fendo.2022.994406\u003c/li\u003e\n\u003cli\u003eHeymsfield SB, Cefalu WT (2013) Does Body Mass Index Adequately Convey a Patient\u0026rsquo;s Mortality Risk? JAMA 309:87. https://doi.org/10.1001/jama.2012.185445\u003c/li\u003e\n\u003cli\u003eOka R, Miura K, Sakurai M, et al (2009) Comparison of waist circumference with body mass index for predicting abdominal adipose tissue. Diabetes Research and Clinical Practice 83:100\u0026ndash;105. https://doi.org/10.1016/j.diabres.2008.10.001\u003c/li\u003e\n\u003cli\u003ePischon T, Boeing H, Hoffmann K, et al (2008) General and Abdominal Adiposity and Risk of Death in Europe. N Engl J Med 359:2105\u0026ndash;2120. https://doi.org/10.1056/NEJMoa0801891\u003c/li\u003e\n\u003cli\u003eKrakauer NY, Krakauer JC (2012) A New Body Shape Index Predicts Mortality Hazard Independently of Body Mass Index. PLoS ONE 7:e39504. https://doi.org/10.1371/journal.pone.0039504\u003c/li\u003e\n\u003cli\u003eWei J, Liu X, Xue H, et al (2019) Comparisons of Visceral Adiposity Index, Body Shape Index, Body Mass Index and Waist Circumference and Their Associations with Diabetes Mellitus in Adults. Nutrients 11:1580. https://doi.org/10.3390/nu11071580\u003c/li\u003e\n\u003cli\u003eOrsi E, Solini A, Penno G, et al (2022) Body mass index versus surrogate measures of central adiposity as independent predictors of mortality in type 2 diabetes. Cardiovasc Diabetol 21:266. https://doi.org/10.1186/s12933-022-01706-2\u003c/li\u003e\n\u003cli\u003eMcCaffery JM, Jablonski KA, Pan Q, et al (2022) Genetic Predictors of Change in Waist Circumference and Waist-to-Hip Ratio With Lifestyle Intervention: The Trans-NIH Consortium for Genetics of Weight Loss Response to Lifestyle Intervention. Diabetes 71:669\u0026ndash;676. https://doi.org/10.2337/db21-0741\u003c/li\u003e\n\u003cli\u003eLin R, Tao Y, Li C, et al (2024) Central obesity may affect bone development in adolescents: association between abdominal obesity index ABSI and adolescent bone mineral density. BMC Endocr Disord 24:81. https://doi.org/10.1186/s12902-024-01600-w\u003c/li\u003e\n\u003cli\u003eWang X, Yang S, He G, Xie L (2023) The association between weight-adjusted-waist index and total bone mineral density in adolescents: NHANES 2011\u0026ndash;2018. Front Endocrinol 14:1191501. https://doi.org/10.3389/fendo.2023.1191501\u003c/li\u003e\n\u003cli\u003eThe ADIPOGen Consortium, The CARDIOGRAMplusC4D Consortium, The CKDGen Consortium, et al (2015) New genetic loci link adipose and insulin biology to body fat distribution. Nature 518:187\u0026ndash;196. https://doi.org/10.1038/nature14132\u003c/li\u003e\n\u003cli\u003eMedina-Gomez C, Kemp JP, Trajanoska K, et al (2018) Life-Course Genome-wide Association Study Meta-analysis of Total Body BMD and Assessment of Age-Specific Effects. The American Journal of Human Genetics 102:88\u0026ndash;102. https://doi.org/10.1016/j.ajhg.2017.12.005\u003c/li\u003e\n\u003cli\u003eRandall JC, Winkler TW, Kutalik Z, et al (2013) Sex-stratified Genome-wide Association Studies Including 270,000 Individuals Show Sexual Dimorphism in Genetic Loci for Anthropometric Traits. PLoS Genet 9:e1003500. https://doi.org/10.1371/journal.pgen.1003500\u003c/li\u003e\n\u003cli\u003eSun Y, Wang Y-X, Liu C, et al (2023) Exposure to Trihalomethanes and Bone Mineral Density in US Adolescents: A Cross-Sectional Study (NHANES). Environ Sci Technol 57:21616\u0026ndash;21626. https://doi.org/10.1021/acs.est.3c07214\u003c/li\u003e\n\u003cli\u003eZhang M, Hou Y, Ren X, et al (2024) Association of a body shape index with femur bone mineral density among older adults: NHANES 2007\u0026ndash;2018. Arch Osteoporos 19:63. https://doi.org/10.1007/s11657-024-01424-0\u003c/li\u003e\n\u003cli\u003eDu D, Jing Z, Zhang G, et al (2022) The relationship between central obesity and bone mineral density: a Mendelian randomization study. Diabetol Metab Syndr 14:63. https://doi.org/10.1186/s13098-022-00840-x\u003c/li\u003e\n\u003cli\u003eWren TAL, Kalkwarf HJ, Zemel BS, et al (2014) Longitudinal Tracking of Dual-Energy X-ray Absorptiometry Bone Measures Over 6 Years in Children and Adolescents: Persistence of Low Bone Mass to Maturity. The Journal of Pediatrics 164:1280-1285.e2. https://doi.org/10.1016/j.jpeds.2013.12.040\u003c/li\u003e\n\u003cli\u003eRand Th, Seidl G, Kainberger F, et al (1997) Impact of Spinal Degenerative Changes on the Evaluation of Bone Mineral Density with Dual Energy X-Ray Absorptiometry (DXA). Calcif Tissue Int 60:430\u0026ndash;433. https://doi.org/10.1007/s002239900258\u003c/li\u003e\n\u003cli\u003eXu H-W, Chen H, Zhang S-B, et al (2022) Association Between Abdominal Obesity and Subsequent Vertebral Fracture Risk. Pain Physician\u003c/li\u003e\n\u003cli\u003eKershaw EE, Flier JS (2004) Adipose Tissue as an Endocrine Organ. The Journal of Clinical Endocrinology \u0026amp; Metabolism 89:2548\u0026ndash;2556. https://doi.org/10.1210/jc.2004-0395\u003c/li\u003e\n\u003cli\u003eFried SK, Bunkin DA, Greenberg AS (1998) Omental and Subcutaneous Adipose Tissues of Obese Subjects Release Interleukin-6: Depot Difference and Regulation by Glucocorticoid \u003csup\u003e1\u003c/sup\u003e. The Journal of Clinical Endocrinology \u0026amp; Metabolism 83:847\u0026ndash;850. https://doi.org/10.1210/jcem.83.3.4660\u003c/li\u003e\n\u003cli\u003eHotamisligil GS, Shargill NS, Spiegelman BM (1993) Adipose Expression of Tumor Necrosis Factor-\u0026alpha;: Direct Role in Obesity-Linked Insulin Resistance. Science 259:87\u0026ndash;91. https://doi.org/10.1126/science.7678183\u003c/li\u003e\n\u003cli\u003eMaurin AC, Chavassieux PM, Frappart L, et al (2000) Influence of mature adipocytes on osteoblast proliferation in human primary cocultures. Bone 26:485\u0026ndash;489. https://doi.org/10.1016/S8756-3282(00)00252-0\u003c/li\u003e\n\u003cli\u003eBailey DA, Mckay HA, Mirwald RL, et al (1999) A Six-Year Longitudinal Study of the Relationship of Physical Activity to Bone Mineral Accrual in Growing Children: The University of Saskatchewan Bone Mineral Accrual Study. Journal of Bone and Mineral Research 14:1672\u0026ndash;1679. https://doi.org/10.1359/jbmr.1999.14.10.1672\u003c/li\u003e\n\u003cli\u003eChevalley T, Rizzoli R (2022) Acquisition of peak bone mass. Best Practice \u0026amp; Research Clinical Endocrinology \u0026amp; Metabolism 36:101616. https://doi.org/10.1016/j.beem.2022.101616\u003c/li\u003e\n\u003cli\u003eRokoff LB, Rifas-Shiman SL, Switkowski KM, et al (2019) Body composition and bone mineral density in childhood. Bone 121:9\u0026ndash;15. https://doi.org/10.1016/j.bone.2018.12.009\u003c/li\u003e\n\u003cli\u003eYoo HJ, Park MS, Yang SJ, et al (2012) The differential relationship between fat mass and bone mineral density by gender and menopausal status. J Bone Miner Metab 30:47\u0026ndash;53. https://doi.org/10.1007/s00774-011-0283-7\u003c/li\u003e\n\u003cli\u003eKuk JL, Saunders TJ, Davidson LE, Ross R (2009) Age-related changes in total and regional fat distribution. Ageing Research Reviews 8:339\u0026ndash;348. https://doi.org/10.1016/j.arr.2009.06.001\u003c/li\u003e\n\u003cli\u003eZhao X, Yu J, Hu F, et al (2022) Association of body mass index and waist circumference with falls in Chinese older adults. Geriatric Nursing 44:245\u0026ndash;250. https://doi.org/10.1016/j.gerinurse.2022.02.020\u003c/li\u003e\n\u003cli\u003eAli D, Tencerova M, Figeac F, et al (2022) The pathophysiology of osteoporosis in obesity and type 2 diabetes in aging women and men: The mechanisms and roles of increased bone marrow adiposity. Front Endocrinol 13:981487. https://doi.org/10.3389/fendo.2022.981487\u003c/li\u003e\n\u003cli\u003ePalmer BF, Clegg DJ (2015) The sexual dimorphism of obesity. Molecular and Cellular Endocrinology 402:113\u0026ndash;119. https://doi.org/10.1016/j.mce.2014.11.029\u003c/li\u003e\n\u003cli\u003eKrotkiewski M, Bj\u0026ouml;rntorp P, Sj\u0026ouml;str\u0026ouml;m L, Smith U (1983) Impact of obesity on metabolism in men and women. Importance of regional adipose tissue distribution. J Clin Invest 72:1150\u0026ndash;1162. https://doi.org/10.1172/JCI111040\u003c/li\u003e\n\u003cli\u003eBredella MA, Torriani M, Ghomi RH, et al (2011) Determinants of bone mineral density in obese premenopausal women. Bone 48:748\u0026ndash;754. https://doi.org/10.1016/j.bone.2010.12.011\u003c/li\u003e\n\u003cli\u003eGiustina A, Mazziotti G, Canalis E (2008) Growth Hormone, Insulin-Like Growth Factors, and the Skeleton. Endocrine Reviews 29:535\u0026ndash;559. https://doi.org/10.1210/er.2007-0036\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Obesity, Osteoporosis, A Body Shape Index, Mendelian randomization, NHANES","lastPublishedDoi":"10.21203/rs.3.rs-5872489/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5872489/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eIntroduction:\u003c/h2\u003e \u003cp\u003eThis study aimed to investigate the association and causality between central obesity and bone mineral density (BMD).\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe utilized linkage disequilibrium score regression (LDSC) and Mendelian randomization (MR) to assess genetic correlations and causal relationships between waist circumference adjusted for BMI (WCadjBMI) and total body less head BMD (TB-BMD). Additionally, a cross-sectional analysis of 7,452 participants evaluated the relationship between A body shape index (ABSI) and TB-BMD using weighted multivariable linear regression and smooth curve fitting.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eLDSC and MR analysis confirmed a negative relationship between WCadjBMI and TB-BMD (β=-0.16; 95% CI: -0.26, -0.07). The cross-sectional study indicated that an increase of 0.01 ABSI corresponded to a decrease of 0.035 TB-BMD (g/cm\u003csup\u003e2\u003c/sup\u003e), with this negative effect being particularly pronounced in males and older adults. An inflection point was identified at ABSI\u0026thinsp;=\u0026thinsp;0.076: below this threshold, ABSI positively correlated with pelvis BMD, whereas above it, the association became negative.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eCentral obesity is significantly negatively related to BMD. Maintaining ABSI within 0.058\u0026ndash;0.078 is crucial for individuals in bone mass accrual (20\u0026ndash;30 years) and stabilization (30\u0026ndash;45 years) periods. In contrast, managing central obesity in people experiencing early bone loss (45\u0026ndash;60 years) presents greater complexity and warrants further investigation.\u003c/p\u003e","manuscriptTitle":"Impact of Central Obesity on Bone Mineral Density Across Life Stages: A Genetic Epidemiology and Cross-Sectional Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-23 16:48:23","doi":"10.21203/rs.3.rs-5872489/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"bf8c2805-2ea2-4d3c-9f0e-ad07978e41f7","owner":[],"postedDate":"January 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-01-23T16:48:25+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-23 16:48:23","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5872489","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5872489","identity":"rs-5872489","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}