Unveiling risk factors and predicting osteoporosis through bone density based aging model

Unveiling risk factors and predicting osteoporosis through bone density based aging model | 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 Unveiling risk factors and predicting osteoporosis through bone density based aging model Jinhong Tan, Jijun Zhu, Yongtao He, Zhaohao Fan, Fuqiang Cai, and 9 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7330727/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 14 Nov, 2025 Read the published version in BMC Musculoskeletal Disorders → Version 1 posted 10 You are reading this latest preprint version Abstract Background Osteoporosis is a progressive skeletal disorder influenced by multiple clinical and lifestyle factors. Early identification of individuals at high risk is essential for prevention and personalized management. This study aimed to identify key determinants of osteoporosis and to establish a bone density–based aging model to evaluate accelerated skeletal aging. Methods We analyzed data from a large cohort with dual-energy X-ray absorptiometry (DXA) measurements of the lumbar spine and proximal femur. Univariate and multivariate regression models were applied to assess the associations between clinical and lifestyle factors and osteoporosis risk. A bone density aging model was developed using support vector regression to estimate bone density age, and bone density age acceleration (BDAA) was calculated as the residual from chronological age. Results The bone density aging model showed good predictive performance (mean absolute error = 5.716 years, R² = 0.145). Higher BDAA was positively correlated with osteoporosis risk across bone health categories, including individuals without clinical diagnosis. In addition, BDAA differed significantly by exercise level, dietary pattern, body mass index, blood pressure, and metabolic comorbidities, providing insights into skeletal aging beyond chronological age. Conclusions Bone density based biological aging models can improve early identification and personalized risk stratification of osteoporosis. This approach may facilitate and support the development of precision medicine strategies in osteoporosis prevention and management. Trial registration Not applicable. Figures Figure 1 Figure 2 Figure 3 Background Osteoporosis is a prevalent metabolic bone disease marked by decreased bone density and an elevated risk of fractures, primarily affecting older adults and post-menopausal women 1 – 3 . As the global population continues to age, the incidence of osteoporosis is rising, presenting a significant public health challenge. This condition leads to considerable morbidity, mortality, and economic burden worldwide. Despite its widespread prevalence and severe consequences, osteoporosis is frequently underdiagnosed and undertreated, highlighting a pressing need for more effective screening and diagnostic tools 4 . Numerous studies have identified key risk factors for osteoporosis, including age, hormonal changes 5 , body mass index (BMI) 6 , physical activity 7 , dietary habits 8 , smoking status 8 , 9 , lipid metabolism 10 , and alcohol consumption 8 . However, existing approaches to osteoporosis risk prediction, such as the Osteoporosis Self-Assessment Tool for Asians, often fail to integrate these multifactorial risks, even including the presence of comorbidities such as stroke 11 , hypertension 12 , HIV infection 13 , chronic kidney disease 14 and diabetes 15 , and others 16 . The interactions among these variables and their collective impact on osteoporosis risk are poorly understood, especially when considering the differing risk profiles between men and women. Additionally, traditional methods do not account for the biological concept of bone density age or the accelerated aging of bone density. Bone density age, much like biological age, can provide a more accurate representation of bone health by quantifying the extent to which an individual’s bone density deviates from age-matched norms. By focusing on bone density age acceleration, where bone density declines more rapidly than expected for chronological age, researchers can identify individuals who may be at heightened risk for osteoporosis, even before clinical symptoms or significant bone loss occur. With the advent of machine learning, predictive modeling for osteoporosis risk has seen significant advancements. Recent studies have explored the application of machine learning algorithms for osteoporosis risk prediction, demonstrating superior accuracy in identifying individuals at high risk for fractures and bone density loss 17 – 19 . These data driven approaches have been validated in large cohorts, providing a robust foundation for developing decision support systems and preclinical screening tools for osteoporosis 20 , 21 . Despite significant advancements in identifying risk factors for osteoporosis, there remains a gap in accurately characterizing how high-risk individuals and patients deviate from their normal bone physiological age. This concept, akin to accelerated bone density aging, could provide a more precise representation of bone health by capturing the cumulative impact of various risk factors over time. However, the current literature lacks a robust framework to measure this "bone density age" deviation and its implications for osteoporosis risk. Additionally, the sex-specific nature of this phenomenon and the influence of lifestyle factors on accelerated bone aging remain largely unexplored. To address these gaps, there is a critical need for novel predictive models that accurately identify individuals at high risk for osteoporosis. The primary objectives of this study are threefold: to analyze the risk factors associated with osteoporosis, to construct and validate a bone density aging model, and to examine the correlations between bone density age acceleration and osteoporosis risk across different demographic and lifestyle factors. Methods Clinical Study Subjects We conducted a retrospective observational study including 4,286 patients treated for osteoporosis at Lunjiao Hospital, Foshan, China, from July 2019 to July 2022. Based on the BMD T-scores of the L3 and L4 vertebrae, patients were divided into different bone density status groups: reduced bone mass (n = 1,385), reduced bone density (n = 343), normal bone density (NOP, n = 684), and osteoporosis (OP, n = 1,874). For the univariate and multivariate analyses of osteoporosis risk factors, only the osteoporosis group (OP) and the normal bone density group (NOP) were considered to specifically compare patients with a clear diagnosis of osteoporosis against those with normal bone density. The diagnostic criteria for BMD were as follows: normal (T-score≥-1.0), low bone mass/osteopenia (T-score between − 1.0 and − 2.5), osteoporosis (T-score≤-2.5), and severe osteoporosis (T-score≤-2.5 accompanied by fragility fractures) 22 . BMD was measured using dual-energy X-ray absorptiometry (DXA) with the XR-800 system. The study was approved by the Lunjiao Hospital Research Ethics Committee, and informed consent was obtained from all participants. Clinical trial number: not applicable. Statistical analysis Data analysis was performed using R software. Normally distributed quantitative data were expressed as Mean ± SD and compared using independent sample t-tests, while non-normally distributed data were expressed as median (M) and compared using non-parametric tests. Categorical data were compared using Chi-square tests. OP risk factors were analyzed using binary logistic regression. A P-value < 0.05 was considered statistically significant. Osteoporosis risk scoring model In this study, we developed and rigorously evaluated several machine learning models to predict osteoporosis risk, leveraging bone density measurements from the L2, L3, L4 vertebrae, femoral neck (Tem.Neck), and trochanter (Troch) regions as input features. A total of 23 different machine learning algorithms were applied, including logistic regression, naive Bayes, k-nearest neighbors, decision trees, random forests, and various other advanced methods. Each model underwent five-fold cross-validation, repeated 50 times to ensure robustness and reliability. The model performances were assessed using two key metrics: accuracy and area under the receiver operating characteristic curve (AUC). The AUC was used to measure the models' ability to distinguish between patients with osteoporosis and those with normal bone density. Bone density based aging model We constructed a bone density aging model using bone density data from the L2, L3, L4, Tem.Neck, and Troch regions. This model was developed using a dataset of 500 individuals with normal bone density and 500 osteoporosis patients, matched by age and gender. Various regression models, including linear regression, support vector machines for regression (SVR), and random forests, were applied. Five-fold cross-validation was performed, with 50 repetitions to ensure consistency in the model's performance. The bone density aging model was evaluated using mean absolute error (MAE), R-squared (R 2 ), and the correlation coefficient between predicted bone age and chronological age. This model is intended to provide a more nuanced understanding of bone health deterioration, beyond conventional risk classification. Results Sample Information Statistics The distribution of both categorical and continuous features is detailed in Supplementary Table 1, while continuous features are shown in Supplementary Table 2. Figure 1 visualizes the correlations among features in the normal bone density group. Significant correlations due to feature definitions include: smoke age with smoking status, systolic blood pressure (SBP) with diastolic blood pressure (DBP), BMI with Osteoporosis Self-assessment Tool for Asians (OSTA), and age with OSTA, all showing strong positive correlations. Therefore, in subsequent multivariate regression analyses, age and OSTA, BMI and OSTA, smoke age and smoking status, and SBP and DBP were not included simultaneously. Significant negative correlations between smoking status, drinking frequency, and gender indicates gender differences; thus, osteoporosis risk factors were analyzed separately by gender. Univariate Analysis Supplementary Fig. 1A present the univariate analysis of osteoporosis risk factors across the entire sample using t-tests. Significant factors include OSTA, BMI, gender, SBP, history of hypertension or diabetes, age, and smoking status. Supplementary Fig. 1B show univariate analysis for males, identifying significant factors as OSTA, BMI, drinking frequency, exercise frequency, SBP, and age. Supplementary Fig. 1C show univariate analysis for females, highlighting significant factors as OSTA, BMI, drinking frequency, smoking status and smoke age, SBP, history of hypertension or diabetes, post-menopausal years, and age. Multivariate Analysis of Osteoporosis Risk Factors In the multivariate analysis for osteoporosis risk factors, features were divided into two groups to avoid strong correlations affecting model fitting. For the male population, Group 1 (Fig. 2 A) included age, SBP, BMI, exercise frequency, diet, smoking status, drinking frequency, and history of hypertension and diabetes. Group 2 (Fig. 2 B) included SBP, OSTA, exercise frequency, diet, smoking status, drinking frequency, and history of hypertension and diabetes. The analysis indicated that age, reduced exercise frequency, lower BMI, and lower OSTA were significant risk factors for osteoporosis in males. Similarly, for the female population, Group 1 (Fig. 2 C) included age, SBP, BMI, exercise frequency, diet, smoking status, drinking frequency, post-menopausal years, and history of hypertension and diabetes. Group 2 (Fig. 2 D) included SBP, OSTA, exercise frequency, diet, smoking status, drinking frequency, post-menopausal years, and history of hypertension and diabetes. The analysis showed that age, post-menopausal years, lower BMI, and lower OSTA were significant risk factors for osteoporosis in females. SBP significantly impacted osteoporosis risk in the first group due to its correlation with age but showed no significant impact in other combinations. For the entire population, the multivariate analysis, detailed in Supplementary Table 5, focused on gender and other features without significant gender correlation. The results indicated that females had a higher risk of osteoporosis than males. Additionally, age, reduced exercise frequency, lower BMI, and lower OSTA were significant risk factors across the entire population. Supplementary Fig. 2 and Supplementary Table 3 compare the fitting effects of five classifiers using five-fold cross-validation. Based on these results, logistic regression was selected for multivariate analysis of osteoporosis risk factors, which were analyzed separately by gender due to significant correlations. Osteoporosis Risk Scoring Model Prediction Performance Among the machine learning models evaluated, the random forest classifier demonstrated the best cross-validation performance, with an accuracy of 98.29% (SD = 0.15%) and an AUC of 0.9976 (SD = 0.0003) over 50 repetitions (Supplementary Table 6). When tested on an independent dataset, the random forest model achieved an accuracy of 97.5% and an AUC of 0.998, confirming its high predictive power for identifying osteoporosis risk. Bone Density Aging Model Prediction Performance In the regression analysis for the bone density aging model, the support vector machine (SVM) regression model exhibited the strongest performance during cross-validation, with a correlation coefficient of 0.3016 (SD = 0.0159), an R 2 of 0.0913 (SD = 0.0098), and an MAE of 6.0259 years (SD = 0.0538) (Supplementary Table 7). On the independent test set, the SVM model further improved its performance, with a correlation coefficient of 0.381, an R 2 of 0.145, and an MAE of 5.716 years, demonstrating its effectiveness in predicting bone density age. Bone Density Age Acceleration Analysis Using normal bone density samples, bone density age was predicted by the bone density aging model. Residuals from linear regression models were used to calculate bone density age acceleration, representing deviations from predicted values. The osteoporosis risk for all samples was predicted by the bone density classifier. Linear regression models analyzed the correlation between bone density age acceleration and osteoporosis risk probability, showing a significant positive correlation. Figure 3 presents the results for four groups: A. Reduced Bone Mass, B. Reduced Bone Density, C. Normal Bone Density, and D. Osteoporosis. The significant positive correlation observed across all samples is further detailed in Supplementary Fig. 3 (Beta = 0.073, p-value < 2e-16). In both male and female populations, osteoporosis patients exhibited significant bone density age acceleration compared to normal bone density controls and those with reduced bone mass and density. In females, those with reduced bone mass and density also showed significant acceleration compared to normal controls. Correlation analysis between bone density age acceleration and factors such as exercise frequency, BMI, diet, smoking frequency, history of hypertension and diabetes, drinking frequency, and blood pressure level was conducted. The results are illustrated in Table 1 . Males with occasional or daily exercise showed significant bone density age acceleration in osteoporosis patients compared to other groups. Males with normal or overweight BMI had significant bone density age acceleration in osteoporosis patients, with variations in other BMI categories. Balanced diet males showed significant acceleration in osteoporosis patients compared to other groups. Smoking status and history showed significant correlations with bone density age acceleration. Hypertension and diabetes history significantly influenced bone density age acceleration. Drinking frequency and blood pressure levels showed varying impacts on bone density age acceleration. Correlation analysis was similarly conducted for the female population. The results are illustrated in Table 2 . Females with different exercise frequencies showed significant variations in bone density age acceleration among osteoporosis patients. BMI levels showed significant variations in bone density age acceleration among osteoporosis patients. Dietary habits significantly influenced bone density age acceleration. Smoking status and history showed significant correlations with bone density age acceleration. Hypertension and diabetes history significantly impacted bone density age acceleration. Drinking frequency and blood pressure levels had varying effects on bone density age acceleration. Table 1 Bone Density Age Acceleration Analysis in Male Populations Lifestyle and Health Factors BDAA in Bone Density Category Normal Reduced Bone Density Reduced Bone Mass Osteoporosis Exercise Everyday 1.63(4.19) 1.98(3.93) 1.79(4.27) 5.00(3.39)* No 3.95(2.71)† 3.08(3.64)† 1.17(3.84)* 4.31(3.78) Sometimes 0.918(4.07) 0.847(3.96) 1.56(3.30) 5.01(3.52)* BMI Underweight NA 0.643(6.43) 2.57(4.17) 4.56(3.10) Normal 1.26(4.23) 2.15(3.86) 1.64(3.56) 4.93(3.53)* Overweight 1.66(4.03) 2.28(3.51) 2.24(3.82) 4.81(3.68)* Obesity 5.48(0.971)*† 0.214(4.97) 1.46(2.59) 5.75(2.83)* Diet Balanced 1.57(4.17) 1.93(3.92) 1.89(3.63) 4.88(3.53)* Meat 1.27(2.76) 4.91(3.07) 1.97(5.28) 5.24(3.32)* Vegetarian NA 1.55(1.43) -1.54(NA) 6.35(NA) Smoke Current 2.12(4.66) 1.47(4.18) 2.33(3.40) 5.83(3.25)*† No 1.40(3.76) 2.07(3.85) 1.73(3.75) 4.63(3.58)* Quit 1.16(4.87) 4.88(2.99) 1.49(3.65) 3.97(3.34)* Disease History Diabetes 1.73(4.02) -1.09(2.75)† 2.45(3.04) 5.15(3.97)* Hypertension 1.86(4.23) 2.00(4.20) 1.90(3.67) 4.78(3.37)* No 1.37(4.16) 2.46(3.59) 1.82(3.70) 4.94(3.56)* Drink Frequent 0.32(3.98) -1.19(3.91) 1.59(3.24) 4.84(3.26)* No 1.42(4.07) 2.23(3.74) 1.83(3.71) 4.93(3.50)* Occasional 2.42(4.29) 2.28(4.23) 2.31(3.69) 4.95(3.70)* Quit NA -0.274(1.32) 0.956(4.09) 4.86(3.09) Blood Pressure Elevated 1.17(3.92) 1.69(3.91) 2.37(3.63) 5.68(3.03)* Hypertension Stage 1 1.82(4.04) 2.04(3.98) 1.46(3.70) 4.58(3.65)* Hypertension Stage 2 2.82(5.28) 1.94(3.60) 2.31(3.41) 4.88(3.13) Normal 0.33(3.99) 3.55(4.50) 2.34(3.56) 5.06(3.79)* Data are presented as Mean (SD),*indicates a statistically significant difference within the same factor level (p < 0.05) and †indicates a statistically significant difference within the same bone density category (p < 0.05). Table 2 Bone Density Age Acceleration Analysis in Female Populations Lifestyle and Health Factors BDAA in Bone Density Category Normal Reduced Bone Density Reduced Bone Mass Osteoporosis Exercise Everyday -0.436(2.95)* 3.72(3.57) 3.81(3.81) 6.19(1.69)* No 0.350(3.51)* 3.85(3.32) 4.20(3.56) 6.12(2.09)* Sometimes -0.339(3.04)* 4.42(3.21) 4.09(3.69) 6.18(1.83)* BMI Underweight -0.598(2.68)* 6.42(0.893) 4.41(3.35) 5.57(2.11)† Normal -0.458(2.99)* 4.13(3.44) 4.27(3.62) 6.10(1.86)* Overweight -0.134(3.01)* 3.09(3.49) 3.63(3.65) 6.20(1.92)* Obesity -0.376(2.95) 4.66(2.84) 3.58(3.29) 6.65(1.09) Diet Balanced -0.384(2.97)* 3.98(3.42) 4.08(3.61) 6.09(1.89)* Meat 0.821(3.14)* 6.63(0.0532) 4.51(4.11) 6.12(1.87) Vegetarian -0.729(0.258) 0.899(3.25)† 4.05(3.04) 5.80(1.68) Smoke Current NA NA 6.76(3.21) 6.47(2.17) No -0.352(2.98)* 3.87(3.46) 4.07(3.61) 6.09(1.89)* Quit NA NA NA 5.83(NA) Disease History Diabetes -0.136(2.43)* 3.92(3.27) 4.11(3.74) 6.45(1.46)* Hypertension -0.124(3.38)* 3.40(3.46) 3.69(3.43) 5.63(1.90)*† No -0.406(2.88)* 4.08(3.47) 4.25(3.69) 6.32(1.89)* Drink Frequent -2.45(1.62)* 5.23(1.40) 4.32(3.36) 5.53(1.76) No -0.344(2.96)* 3.85(3.48) 4.08(3.61) 6.09(1.89)* Occasional 0.136(3.61) 2.95(NA) 3.67(3.96) 6.36(1.52) Quit NA NA NA NA Blood Pressure Elevated -0.526(3.01)* 3.93(3.70) 4.12(3.62) 6.25(1.86)* Hypertension Stage 1 -0.165(3.14)* 3.75(3.44) 3.91(3.68) 5.96(1.84)* Hypertension Stage 2 -0.885(2.18)* 5.02(2.59) 3.94(3.35) 5.48(1.99)† Normal -0.466(2.71)* 3.83(3.55) 4.45(3.57) 6.47(1.92)* Data are presented as Mean (SD), *indicates a statistically significant difference within the same factor level (p < 0.05) and †indicates a statistically significant difference within the same bone density category (p < 0.05). Discussion This study highlights critical risk factors for osteoporosis, such as age, BMI, and physical activity, while introducing bone density based age acceleration as a novel predictor of osteoporosis risk. Our univariate and multivariate analyses revealed that both men and women share common risk factors, with post-menopausal years being a notable additional risk for women. Moreover, bone density based age acceleration showed a consistent and strong correlation with osteoporosis risk across the study population, positioning it as a valuable biomarker for early detection. The significant association between age, BMI, and reduced exercise frequency with osteoporosis risk underscores well-established knowledge in the field. Age-related bone loss is a major factor driving osteoporosis, while maintaining a healthy BMI and regular physical activity are protective against this condition. Our results also highlighted gender-specific differences: post-menopausal years were a significant factor in females, aligning with the well-known role of estrogen in maintaining bone density. These findings reaffirm previous studies linking hormonal changes in women to osteoporosis 5 and strengthen the argument for targeted interventions during and after menopause. Chronic stress also plays a significant role, as recent studies suggest that stress-induced bone loss is mediated by a GABAergic neural circuit in the ventromedial hypothalamus 23 . This study makes a novel contribution by introducing bone density age acceleration as a predictor of osteoporosis risk. Previous research has shown that telomere shortening, a widely recognized indicator of biological aging, is associated with osteoporosis 24 ,and cellular senescence has been implicated in bone aging and osteoporosis development, suggesting that senescence markers may help predict bone health deterioration 25 . However, our analysis extends this understanding by demonstrating that individuals with osteoporosis show significantly accelerated bone density aging, even compared to those with reduced bone mass or density. This observation suggests that bone density age, as modeled by machine learning algorithms, may be a more sensitive and specific early biomarker for osteoporosis, allowing for earlier identification of at-risk individuals before clinical symptoms manifest. Moreover, the use of residuals from linear regression models to quantify bone density age acceleration introduces a new tool for assessing the deviation of biological aging from chronological aging, similar to the widely recognized epigenetic-based age acceleration 26 , which could be crucial for precision medicine approaches to osteoporosis prevention. The observed correlation between bone density age acceleration and osteoporosis risk likely reflects the cumulative effects of various lifestyle, genetic, and physiological factors influencing bone health. For instance, excessive caffeine intake, which has been shown to increase the risk of osteoporosis by affecting calcium clearance 27 . Aging, reduced physical activity, and low BMI all contribute to decreased bone remodeling and bone mineral density, leading to accelerated bone aging. In females, post-menopausal hormonal changes further exacerbate bone density loss, accounting for the significant acceleration observed in this group. These findings align with the bone remodeling theory, which suggests that the balance between bone formation and resorption shifts with age and hormonal changes, leading to osteoporosis 5 . Our data suggest that bone density age acceleration captures this shift, potentially offering a mechanistic link between lifestyle factors and biological aging of bones. The gender differences observed in smoking status, drinking frequency, and hypertension or diabetes history offer further insights into potential mechanisms. Females exhibited stronger correlations between these lifestyle factors and bone density age acceleration, which may indicate that hormonal fluctuations during menopause make women more susceptible to lifestyle-related bone deterioration. In contrast, males with lower physical activity and unbalanced diets showed significant bone density age acceleration, suggesting that lifestyle interventions targeting physical fitness and nutrition could have a meaningful impact on preventing osteoporosis in men. Recent genetic studies provide additional insights that could further enhance the predictive power of bone density age acceleration models. For example, research has identified the POLR2A gene as having a protective effect against post-menopausal osteoporosis 28 . Similarly, the discovery of the Men1 gene as a key regulator of age-related osteoporosis, along with the development of a new animal model for studying the disease 29 , underscores the potential for integrating genetic data into future models. Emerging therapeutic strategies also offer exciting opportunities to complement the predictive power of bone density age acceleration. For instance, synthetic biology-based therapies, such as bacterial extracellular vesicles displaying BMP-2 and CXCR4, have shown potential in ameliorating bone degeneration 30 . Additionally, extracellular vesicles from apoptotic bone marrow mesenchymal stem cells (ApoEVs) have been shown to carry regenerative signals that can mitigate osteoporosis 31 , opening new avenues for treatment. These therapies, alongside interventions targeting mitochondrial homeostasis in bone cells 32 and senolytic therapies aimed at reducing the load of senescent cells in post-menopausal women 33 , could complement early detection efforts by targeting the underlying biological processes that drive bone aging and osteoporosis. Furthermore, targeted therapies that modulate immune cell recruitment to bone matrix, such as those involving CXCL9 and CXCR3 34 , offer targeted approaches to reducing excessive bone resorption while preserving normal turnover. These strategies align well with the concept of bone density age acceleration, capturing early biological shifts and enabling timely interventions to prevent bone loss and reduce fracture risk. While our findings strongly support the role of bone density age acceleration as a predictor of osteoporosis, alternative explanations should also be considered. Genetic factors 35 , which were not accounted for in this study, may influence both bone density age and osteoporosis risk, and environmental factors such as calcium and vitamin D intake were not directly assessed 36 . Future studies should aim to incorporate genetic and dietary data to refine the predictive power of bone density age models and understand their interaction with lifestyle factors. Additionally, while this study demonstrated strong predictive performance for the bone density age model, the cross-sectional nature of our data limits the ability to make definitive conclusions about causality. Longitudinal studies are needed to confirm whether bone density age acceleration can serve as a reliable early marker for predicting future osteoporosis risk. Furthermore, the characteristics of the specific population under investigation may influence bone density outcomes, as highlighted by previous research 37 . If validated, bone density age acceleration could be integrated into clinical screening protocols, allowing for more personalized and timely interventions to prevent or slow the progression of osteoporosis. Conclusions This study systematically identified key clinical and lifestyle factors associated with osteoporosis and demonstrated the utility of a bone density based aging model in evaluating skeletal aging. Age, reduced exercise frequency, lower body mass index, and lower OSTA scores were consistently significant risk factors across both genders, with post-menopausal years further contributing to risk in females. Females exhibited a higher overall risk of osteoporosis than males. The bone density aging model effectively predicted bone density age and revealed that osteoporosis patients showed significant bone density age acceleration compared to controls, with correlations observed for exercise, BMI, diet, smoking, hypertension, diabetes, drinking habits, and blood pressure. These findings highlight the potential of bone density based biological age assessment for early identification of high risk individuals and personalized intervention strategies, providing a framework to enhance precision medicine approaches in osteoporosis prevention and management. Abbreviations DXA Dual-energy X-ray absorptiometry BDAA Bone density age acceleration BMI Body mass index OSTA Osteoporosis Self-Assessment Tool for Asians HIV Human Immunodeficiency Virus BMD Bone Mineral Density OP Osteoporosis group NOP Normal bone density group Tem.Neck Femoral neck Troch Trochanter AUC Area Under the Curve SVR Support vector machines for regression MAE Mean absolute error SBP Systolic blood pressure DBP Diastolic blood pressure Declarations Ethics approval and consent to participate The study was approved by the Lunjiao Hospital Research Ethics Committee. All participants provided written informed consent prior to participation. All procedures involving human participants were conducted in accordance with the principles of the Declaration of Helsinki. Consent for publication Not applicable Availability of data and materials The data that support the findings of this study were collected from participants at Lunjiao Hospital and are not publicly available due to privacy and ethical restrictions. Data may be made available from the corresponding authors upon reasonable request and with permission from the hospital's ethics committee. Competing Interests Jinhong Tan, Jijun Zhu, Yongtao He, Zhaohao Fan, Fuqiang Cai, Haowen Zhuang, Kangyan Liu, Xiangjie Chen, Qiancheng Li, Guangbiao Liu, Bo Feng, Yushi Guo, Manying Lin, Gan Li, Bin Wang and Junfang Chen declare that they have no conflict of interest. Funding This research was supported by a startup grant from the Greater Bay Area Institute of Precision Medicine (Guangzhou) to J.C. (Grant No: I0007), startup grant from the Greater Bay Area Institute of Precision Medicine (Guangzhou) to B.W. (Grant No: I0028), the National Natural Science Foundation (Grant No: 32370639), Natural Science Foundation of Guangdong Province (Grant No: 2024A1515012116) Authors' contributions Jinhong Tan and Jijun Zhu contributed equally to this work and share first authorship. Bin Wang and Junfang Chen share corresponding authorship. Jinhong Tan and Jijun Zhu contributed to study conception, research design, data interpretation, and manuscript drafting. Fuqiang Cai, Haowen Zhuang, and Yanhua Hu were responsible for data preprocessing and preliminary statistical analyses. Yongtao He, Zhaohao Fan, Kangyan Liu, Qiancheng Li, Bo Feng, Yushi Guo, and Gan Li contributed to data acquisition and provided study resources. All authors read and approved the final manuscript. 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Kaito, T., Ukon, Y., Hirai, H., Kitahara, T., Bun, M., Kodama, J., Tateiwa, D., Nakagawa, S., Ikuta, M., and Furuichi, T. (2023). Cellular senescence by loss of Men1 in osteoblasts is critical for age-related osteoporosis. Liu, H., Song, P., Zhang, H., Zhou, F., Ji, N., Wang, M., Zhou, G., Han, R., Liu, X., and Weng, W. (2024). Synthetic biology‐based bacterial extracellular vesicles displaying BMP‐2 and CXCR4 to ameliorate osteoporosis. Journal of Extracellular Vesicles 13 , e12429. Li, M., Tang, Q., Liao, C., Wang, Z., Zhang, S., Liang, Q., Liang, C., Liu, X., Zhang, J., and Tian, W. (2024). Extracellular vesicles from apoptotic BMSCs ameliorate osteoporosis via transporting regenerative signals. Theranostics 14 , 3583. Li, M., Yu, Y., Xue, K., Li, J., Son, G., Wang, J., Qian, W., Wang, S., Zheng, J., and Yang, C. (2023). Genistein mitigates senescence of bone marrow mesenchymal stem cells via ERRα-mediated mitochondrial biogenesis and mitophagy in ovariectomized rats. Redox Biology 61 , 102649. Farr, J.N., Atkinson, E.J., Achenbach, S.J., Volkman, T.L., Tweed, A.J., Vos, S.J., Ruan, M., Sfeir, J., Drake, M.T., and Saul, D. (2024). Effects of intermittent senolytic therapy on bone metabolism in postmenopausal women: a phase 2 randomized controlled trial. Nature Medicine, 1-8. Phan, Q.T., Tan, W.H., Liu, R., Sundaram, S., Buettner, A., Kneitz, S., Cheong, B., Vyas, H., Mathavan, S., and Schartl, M. (2020). Cxcl9l and Cxcr3. 2 regulate recruitment of osteoclast progenitors to bone matrix in a medaka osteoporosis model. Proceedings of the National Academy of Sciences 117 , 19276-19286. Guo, B., Wang, C., Zhu, Y., Liu, Z., Long, H., Ruan, Z., Lin, Z., Fan, Z., Li, Y., and Zhao, S. (2023). Causal associations of brain structure with bone mineral density: a large-scale genetic correlation study. Bone Research 11 , 37. Dai, Z., McKenzie, J.E., McDonald, S., Baram, L., Page, M.J., Allman-Farinelli, M., Raubenheimer, D., and Bero, L.A. (2021). Assessment of the methods used to develop vitamin D and calcium recommendations—a systematic review of bone health guidelines. Nutrients 13 , 2423. Lo, J.C., Chandra, M., Lee, C., Darbinian, J.A., Ramaswamy, M., and Ettinger, B. (2020). Bone mineral density in older US Filipino, Chinese, Japanese, and White women. Journal of the American Geriatrics Society 68 , 2656-2661. Additional Declarations No competing interests reported. Supplementary Files ostRiskSupplementary.docx Cite Share Download PDF Status: Published Journal Publication published 14 Nov, 2025 Read the published version in BMC Musculoskeletal Disorders → Version 1 posted Editorial decision: Revision requested 10 Sep, 2025 Reviews received at journal 08 Sep, 2025 Reviews received at journal 07 Sep, 2025 Reviewers agreed at journal 27 Aug, 2025 Reviewers agreed at journal 27 Aug, 2025 Reviewers invited by journal 27 Aug, 2025 Editor assigned by journal 25 Aug, 2025 Editor invited by journal 19 Aug, 2025 Submission checks completed at journal 18 Aug, 2025 First submitted to journal 18 Aug, 2025 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Fudan University","correspondingAuthor":false,"prefix":"","firstName":"Jijun","middleName":"","lastName":"Zhu","suffix":""},{"id":508354023,"identity":"67d07b2b-e057-46a0-8469-d9a5f07396a0","order_by":2,"name":"Yongtao He","email":"","orcid":"","institution":"Lunjiao Hospital of Shunde District","correspondingAuthor":false,"prefix":"","firstName":"Yongtao","middleName":"","lastName":"He","suffix":""},{"id":508354024,"identity":"84ee0d1a-5b24-44e3-92f1-706de43f80ba","order_by":3,"name":"Zhaohao Fan","email":"","orcid":"","institution":"Lunjiao Hospital of Shunde District","correspondingAuthor":false,"prefix":"","firstName":"Zhaohao","middleName":"","lastName":"Fan","suffix":""},{"id":508354025,"identity":"629f4d47-afdf-4f25-8632-afdc8ba6c3c1","order_by":4,"name":"Fuqiang Cai","email":"","orcid":"","institution":"Greater Bay Area Institute of Precision Medicine (Guangzhou), Fudan 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District","correspondingAuthor":false,"prefix":"","firstName":"Yushi","middleName":"","lastName":"Guo","suffix":""},{"id":508354032,"identity":"9ebaa964-7b6f-4086-bd2c-23f1cdf3656a","order_by":11,"name":"Gan Li","email":"","orcid":"","institution":"Southern Medical University","correspondingAuthor":false,"prefix":"","firstName":"Gan","middleName":"","lastName":"Li","suffix":""},{"id":508354033,"identity":"31d0f4e9-8723-4ff5-b10c-2b891fe000d1","order_by":12,"name":"Bin Wang","email":"","orcid":"","institution":"Greater Bay Area Institute of Precision Medicine (Guangzhou), Fudan University","correspondingAuthor":false,"prefix":"","firstName":"Bin","middleName":"","lastName":"Wang","suffix":""},{"id":508354034,"identity":"a80e6d99-e427-4b46-acef-5392810355ef","order_by":13,"name":"Junfang Chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABEElEQVRIiWNgGAWjYBACxgYGhg9glgQQf7ABCzAw8ODXwjgDpoVxRhoRWkC64FqYeYjRwjwj92DDzx21DPyzm489tkk4LNs/u4Hxwds2BnlznFbkJTb2njnOIHHnWLpxTsJh4xl3DjAbzm1jMNzZgEtLjvkD3rZjDAYSOWbSuT8OJzbcSGCT5m1jSDA4gFOLYeNfsJb8b9IWCYcT599IYP9NSEszb1sNyBY2aQaglg1AW5jxaul5Y9gs23aAQeJGmplkT0K68cYbic2Sc85JGG7AocWwHeiwt211DPwzkp9J/Eiwlp13I/nghzdlNvK4bDGEBMvh+gYkm0FsCezqgUAeQtXhVDAKRsEoGAWjgAEAAwlfnFMw3QMAAAAASUVORK5CYII=","orcid":"","institution":"Greater Bay Area Institute of Precision Medicine (Guangzhou), Fudan University","correspondingAuthor":true,"prefix":"","firstName":"Junfang","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2025-08-09 01:53:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7330727/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7330727/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12891-025-09298-0","type":"published","date":"2025-11-14T15:57:29+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":90494966,"identity":"7dce6061-bc5b-4ab5-9587-3d80460d2f07","added_by":"auto","created_at":"2025-09-03 10:24:03","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":43926,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelations among features in the normal bone density group\u003c/strong\u003e. Orange indicates positive correlations between features, blue indicates negative correlations, and asterisks denote significance levels: p \u0026lt; 0.05 (*), p \u0026lt; 0.01 (**), and p \u0026lt; 0.001 (***).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7330727/v1/0fe379a08b8ab41d1641589f.png"},{"id":90494976,"identity":"31f3df9a-d820-4e88-ae3b-9b87769e9296","added_by":"auto","created_at":"2025-09-03 10:24:04","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":52753,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMultivariate Analysis of Osteoporosis Risk Factors.\u003c/strong\u003eThe forest plots display logistic regression results for osteoporosis risk factors, separated by gender. \u003cstrong\u003ePanels A and B\u003c/strong\u003e show risk factors for males, with significant factors highlighted in blue and non-significant ones in gray. \u003cstrong\u003ePanels C and D\u003c/strong\u003e present results for females with the same color scheme. The red dashed vertical line indicates no effect (coefficient = 0), providing a clear view of the significance and effect sizes across genders.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7330727/v1/1e840543c16b49f86b91ad21.png"},{"id":90494963,"identity":"1b644448-efe6-4738-bad3-84477feccbd4","added_by":"auto","created_at":"2025-09-03 10:24:02","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":114107,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAssociation between BDAA and Osteoporosis Risk by Bone Density Status. \u003c/strong\u003eThis figure presents scatter plots showing the relationship between Bone Density Age Acceleration (BDAA) and the predicted risk of osteoporosis across different bone density status groups. Each panel corresponds to a specific bone density category: A. Reduced Bone Mass, B. Reduced Bone Density, C. Normal Bone Density, and D. Osteoporosis. The x-axis represents BDAA, while the y-axis indicates the predicted risk of osteoporosis. Annotations within each panel display the regression slope (Beta), the p-value, and the coefficient of determination (R²), reflecting the strength and significance of the association.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7330727/v1/6053dfa93c75ca1bef8edaa4.png"},{"id":96105013,"identity":"684ff76d-dfaa-47cd-b5e1-2e7753788f4a","added_by":"auto","created_at":"2025-11-17 16:07:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1131411,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7330727/v1/64f9123e-763c-410c-b62f-d0e0b0210c4c.pdf"},{"id":90494956,"identity":"75adcc46-1665-40ac-9b52-5b7f4bc4f24d","added_by":"auto","created_at":"2025-09-03 10:23:59","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":1686417,"visible":true,"origin":"","legend":"","description":"","filename":"ostRiskSupplementary.docx","url":"https://assets-eu.researchsquare.com/files/rs-7330727/v1/c5663b4de8f0cf7b36ec51e3.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Unveiling risk factors and predicting osteoporosis through bone density based aging model","fulltext":[{"header":"Background","content":"\u003cp\u003eOsteoporosis is a prevalent metabolic bone disease marked by decreased bone density and an elevated risk of fractures, primarily affecting older adults and post-menopausal women \u003csup\u003e\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. As the global population continues to age, the incidence of osteoporosis is rising, presenting a significant public health challenge. This condition leads to considerable morbidity, mortality, and economic burden worldwide. Despite its widespread prevalence and severe consequences, osteoporosis is frequently underdiagnosed and undertreated, highlighting a pressing need for more effective screening and diagnostic tools \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eNumerous studies have identified key risk factors for osteoporosis, including age, hormonal changes \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e, body mass index (BMI) \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e, physical activity \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e, dietary habits \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e, smoking status \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e, lipid metabolism \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e, and alcohol consumption \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. However, existing approaches to osteoporosis risk prediction, such as the Osteoporosis Self-Assessment Tool for Asians, often fail to integrate these multifactorial risks, even including the presence of comorbidities such as stroke \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e, hypertension \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e, HIV infection \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e, chronic kidney disease \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e and diabetes \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e, and others \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. The interactions among these variables and their collective impact on osteoporosis risk are poorly understood, especially when considering the differing risk profiles between men and women. Additionally, traditional methods do not account for the biological concept of bone density age or the accelerated aging of bone density. Bone density age, much like biological age, can provide a more accurate representation of bone health by quantifying the extent to which an individual\u0026rsquo;s bone density deviates from age-matched norms. By focusing on bone density age acceleration, where bone density declines more rapidly than expected for chronological age, researchers can identify individuals who may be at heightened risk for osteoporosis, even before clinical symptoms or significant bone loss occur.\u003c/p\u003e\u003cp\u003eWith the advent of machine learning, predictive modeling for osteoporosis risk has seen significant advancements. Recent studies have explored the application of machine learning algorithms for osteoporosis risk prediction, demonstrating superior accuracy in identifying individuals at high risk for fractures and bone density loss \u003csup\u003e\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. These data driven approaches have been validated in large cohorts, providing a robust foundation for developing decision support systems and preclinical screening tools for osteoporosis \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Despite significant advancements in identifying risk factors for osteoporosis, there remains a gap in accurately characterizing how high-risk individuals and patients deviate from their normal bone physiological age. This concept, akin to accelerated bone density aging, could provide a more precise representation of bone health by capturing the cumulative impact of various risk factors over time. However, the current literature lacks a robust framework to measure this \"bone density age\" deviation and its implications for osteoporosis risk. Additionally, the sex-specific nature of this phenomenon and the influence of lifestyle factors on accelerated bone aging remain largely unexplored.\u003c/p\u003e\u003cp\u003eTo address these gaps, there is a critical need for novel predictive models that accurately identify individuals at high risk for osteoporosis. The primary objectives of this study are threefold: to analyze the risk factors associated with osteoporosis, to construct and validate a bone density aging model, and to examine the correlations between bone density age acceleration and osteoporosis risk across different demographic and lifestyle factors.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eClinical Study Subjects\u003c/h2\u003e\u003cp\u003eWe conducted a retrospective observational study including 4,286 patients treated for osteoporosis at Lunjiao Hospital, Foshan, China, from July 2019 to July 2022. Based on the BMD T-scores of the L3 and L4 vertebrae, patients were divided into different bone density status groups: reduced bone mass (n\u0026thinsp;=\u0026thinsp;1,385), reduced bone density (n\u0026thinsp;=\u0026thinsp;343), normal bone density (NOP, n\u0026thinsp;=\u0026thinsp;684), and osteoporosis (OP, n\u0026thinsp;=\u0026thinsp;1,874). For the univariate and multivariate analyses of osteoporosis risk factors, only the osteoporosis group (OP) and the normal bone density group (NOP) were considered to specifically compare patients with a clear diagnosis of osteoporosis against those with normal bone density. The diagnostic criteria for BMD were as follows: normal (T-score\u0026ge;-1.0), low bone mass/osteopenia (T-score between \u0026minus;\u0026thinsp;1.0 and \u0026minus;\u0026thinsp;2.5), osteoporosis (T-score\u0026le;-2.5), and severe osteoporosis (T-score\u0026le;-2.5 accompanied by fragility fractures) \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. BMD was measured using dual-energy X-ray absorptiometry (DXA) with the XR-800 system. The study was approved by the Lunjiao Hospital Research Ethics Committee, and informed consent was obtained from all participants. Clinical trial number: not applicable.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eData analysis was performed using R software. Normally distributed quantitative data were expressed as Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD and compared using independent sample t-tests, while non-normally distributed data were expressed as median (M) and compared using non-parametric tests. Categorical data were compared using Chi-square tests. OP risk factors were analyzed using binary logistic regression. A P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eOsteoporosis risk scoring model\u003c/h3\u003e\n\u003cp\u003eIn this study, we developed and rigorously evaluated several machine learning models to predict osteoporosis risk, leveraging bone density measurements from the L2, L3, L4 vertebrae, femoral neck (Tem.Neck), and trochanter (Troch) regions as input features. A total of 23 different machine learning algorithms were applied, including logistic regression, naive Bayes, k-nearest neighbors, decision trees, random forests, and various other advanced methods. Each model underwent five-fold cross-validation, repeated 50 times to ensure robustness and reliability. The model performances were assessed using two key metrics: accuracy and area under the receiver operating characteristic curve (AUC). The AUC was used to measure the models' ability to distinguish between patients with osteoporosis and those with normal bone density.\u003c/p\u003e\n\u003ch3\u003eBone density based aging model\u003c/h3\u003e\n\u003cp\u003eWe constructed a bone density aging model using bone density data from the L2, L3, L4, Tem.Neck, and Troch regions. This model was developed using a dataset of 500 individuals with normal bone density and 500 osteoporosis patients, matched by age and gender. Various regression models, including linear regression, support vector machines for regression (SVR), and random forests, were applied. Five-fold cross-validation was performed, with 50 repetitions to ensure consistency in the model's performance. The bone density aging model was evaluated using mean absolute error (MAE), R-squared (R\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e), and the correlation coefficient between predicted bone age and chronological age. This model is intended to provide a more nuanced understanding of bone health deterioration, beyond conventional risk classification.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eSample Information Statistics\u003c/h2\u003e\u003cp\u003eThe distribution of both categorical and continuous features is detailed in Supplementary Table\u0026nbsp;1, while continuous features are shown in Supplementary Table\u0026nbsp;2. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e visualizes the correlations among features in the normal bone density group. Significant correlations due to feature definitions include: smoke age with smoking status, systolic blood pressure (SBP) with diastolic blood pressure (DBP), BMI with Osteoporosis Self-assessment Tool for Asians (OSTA), and age with OSTA, all showing strong positive correlations. Therefore, in subsequent multivariate regression analyses, age and OSTA, BMI and OSTA, smoke age and smoking status, and SBP and DBP were not included simultaneously. Significant negative correlations between smoking status, drinking frequency, and gender indicates gender differences; thus, osteoporosis risk factors were analyzed separately by gender.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eUnivariate Analysis\u003c/h3\u003e\n\u003cp\u003eSupplementary Fig.\u0026nbsp;1A present the univariate analysis of osteoporosis risk factors across the entire sample using t-tests. Significant factors include OSTA, BMI, gender, SBP, history of hypertension or diabetes, age, and smoking status. Supplementary Fig.\u0026nbsp;1B show univariate analysis for males, identifying significant factors as OSTA, BMI, drinking frequency, exercise frequency, SBP, and age. Supplementary Fig.\u0026nbsp;1C show univariate analysis for females, highlighting significant factors as OSTA, BMI, drinking frequency, smoking status and smoke age, SBP, history of hypertension or diabetes, post-menopausal years, and age.\u003c/p\u003e\n\u003ch3\u003eMultivariate Analysis of Osteoporosis Risk Factors\u003c/h3\u003e\n\u003cp\u003eIn the multivariate analysis for osteoporosis risk factors, features were divided into two groups to avoid strong correlations affecting model fitting. For the male population, Group 1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA) included age, SBP, BMI, exercise frequency, diet, smoking status, drinking frequency, and history of hypertension and diabetes. Group 2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB) included SBP, OSTA, exercise frequency, diet, smoking status, drinking frequency, and history of hypertension and diabetes. The analysis indicated that age, reduced exercise frequency, lower BMI, and lower OSTA were significant risk factors for osteoporosis in males. Similarly, for the female population, Group 1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC) included age, SBP, BMI, exercise frequency, diet, smoking status, drinking frequency, post-menopausal years, and history of hypertension and diabetes. Group 2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD) included SBP, OSTA, exercise frequency, diet, smoking status, drinking frequency, post-menopausal years, and history of hypertension and diabetes. The analysis showed that age, post-menopausal years, lower BMI, and lower OSTA were significant risk factors for osteoporosis in females. SBP significantly impacted osteoporosis risk in the first group due to its correlation with age but showed no significant impact in other combinations. For the entire population, the multivariate analysis, detailed in Supplementary Table\u0026nbsp;5, focused on gender and other features without significant gender correlation. The results indicated that females had a higher risk of osteoporosis than males. Additionally, age, reduced exercise frequency, lower BMI, and lower OSTA were significant risk factors across the entire population.\u003c/p\u003e\u003cp\u003eSupplementary Fig.\u0026nbsp;2 and Supplementary Table\u0026nbsp;3 compare the fitting effects of five classifiers using five-fold cross-validation. Based on these results, logistic regression was selected for multivariate analysis of osteoporosis risk factors, which were analyzed separately by gender due to significant correlations.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eOsteoporosis Risk Scoring Model Prediction Performance\u003c/h2\u003e\u003cp\u003eAmong the machine learning models evaluated, the random forest classifier demonstrated the best cross-validation performance, with an accuracy of 98.29% (SD\u0026thinsp;=\u0026thinsp;0.15%) and an AUC of 0.9976 (SD\u0026thinsp;=\u0026thinsp;0.0003) over 50 repetitions (Supplementary Table\u0026nbsp;6). When tested on an independent dataset, the random forest model achieved an accuracy of 97.5% and an AUC of 0.998, confirming its high predictive power for identifying osteoporosis risk.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eBone Density Aging Model Prediction Performance\u003c/h2\u003e\u003cp\u003eIn the regression analysis for the bone density aging model, the support vector machine (SVM) regression model exhibited the strongest performance during cross-validation, with a correlation coefficient of 0.3016 (SD\u0026thinsp;=\u0026thinsp;0.0159), an R\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e of 0.0913 (SD\u0026thinsp;=\u0026thinsp;0.0098), and an MAE of 6.0259 years (SD\u0026thinsp;=\u0026thinsp;0.0538) (Supplementary Table\u0026nbsp;7). On the independent test set, the SVM model further improved its performance, with a correlation coefficient of 0.381, an R\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e of 0.145, and an MAE of 5.716 years, demonstrating its effectiveness in predicting bone density age.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eBone Density Age Acceleration Analysis\u003c/h2\u003e\u003cp\u003eUsing normal bone density samples, bone density age was predicted by the bone density aging model. Residuals from linear regression models were used to calculate bone density age acceleration, representing deviations from predicted values. The osteoporosis risk for all samples was predicted by the bone density classifier. Linear regression models analyzed the correlation between bone density age acceleration and osteoporosis risk probability, showing a significant positive correlation. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents the results for four groups: A. Reduced Bone Mass, B. Reduced Bone Density, C. Normal Bone Density, and D. Osteoporosis. The significant positive correlation observed across all samples is further detailed in Supplementary Fig.\u0026nbsp;3 (Beta\u0026thinsp;=\u0026thinsp;0.073, p-value\u0026thinsp;\u0026lt;\u0026thinsp;2e-16).\u003c/p\u003e\u003cp\u003eIn both male and female populations, osteoporosis patients exhibited significant bone density age acceleration compared to normal bone density controls and those with reduced bone mass and density. In females, those with reduced bone mass and density also showed significant acceleration compared to normal controls. Correlation analysis between bone density age acceleration and factors such as exercise frequency, BMI, diet, smoking frequency, history of hypertension and diabetes, drinking frequency, and blood pressure level was conducted. The results are illustrated in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Males with occasional or daily exercise showed significant bone density age acceleration in osteoporosis patients compared to other groups. Males with normal or overweight BMI had significant bone density age acceleration in osteoporosis patients, with variations in other BMI categories. Balanced diet males showed significant acceleration in osteoporosis patients compared to other groups. Smoking status and history showed significant correlations with bone density age acceleration. Hypertension and diabetes history significantly influenced bone density age acceleration. Drinking frequency and blood pressure levels showed varying impacts on bone density age acceleration. Correlation analysis was similarly conducted for the female population. The results are illustrated in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Females with different exercise frequencies showed significant variations in bone density age acceleration among osteoporosis patients. BMI levels showed significant variations in bone density age acceleration among osteoporosis patients. Dietary habits significantly influenced bone density age acceleration. Smoking status and history showed significant correlations with bone density age acceleration. Hypertension and diabetes history significantly impacted bone density age acceleration. Drinking frequency and blood pressure levels had varying effects on bone density age acceleration.\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\u003eBone Density Age Acceleration Analysis in Male Populations\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=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e\u003cp\u003eLifestyle and Health Factors\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e\u003cp\u003eBDAA in Bone Density Category\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNormal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eReduced Bone Density\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eReduced Bone Mass\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eOsteoporosis\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eExercise\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEveryday\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.63(4.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.98(3.93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.79(4.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5.00(3.39)*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.95(2.71)\u0026dagger;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.08(3.64)\u0026dagger;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.17(3.84)*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.31(3.78)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSometimes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.918(4.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.847(3.96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.56(3.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5.01(3.52)*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eBMI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUnderweight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.643(6.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.57(4.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.56(3.10)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNormal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.26(4.23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.15(3.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.64(3.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.93(3.53)*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOverweight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.66(4.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.28(3.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.24(3.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.81(3.68)*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eObesity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.48(0.971)*\u0026dagger;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.214(4.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.46(2.59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5.75(2.83)*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eDiet\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBalanced\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.57(4.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.93(3.92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.89(3.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.88(3.53)*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMeat\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.27(2.76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.91(3.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.97(5.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5.24(3.32)*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVegetarian\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.55(1.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-1.54(NA)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6.35(NA)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eSmoke\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCurrent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.12(4.66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.47(4.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.33(3.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5.83(3.25)*\u0026dagger;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.40(3.76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.07(3.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.73(3.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.63(3.58)*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQuit\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.16(4.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.88(2.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.49(3.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.97(3.34)*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eDisease History\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDiabetes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.73(4.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-1.09(2.75)\u0026dagger;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.45(3.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5.15(3.97)*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHypertension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.86(4.23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.00(4.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.90(3.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.78(3.37)*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.37(4.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.46(3.59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.82(3.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.94(3.56)*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eDrink\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFrequent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.32(3.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-1.19(3.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.59(3.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.84(3.26)*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.42(4.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.23(3.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.83(3.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.93(3.50)*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOccasional\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.42(4.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.28(4.23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.31(3.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.95(3.70)*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQuit\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.274(1.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.956(4.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.86(3.09)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eBlood Pressure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eElevated\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.17(3.92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.69(3.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.37(3.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5.68(3.03)*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHypertension Stage 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.82(4.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.04(3.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.46(3.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.58(3.65)*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHypertension Stage 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.82(5.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.94(3.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.31(3.41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.88(3.13)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNormal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.33(3.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.55(4.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.34(3.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5.06(3.79)*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eData are presented as Mean (SD),*indicates a statistically significant difference within the same factor level (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and \u0026dagger;indicates a statistically significant difference within the same bone density category (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBone Density Age Acceleration Analysis in Female Populations\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=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e\u003cp\u003eLifestyle and Health Factors\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e\u003cp\u003eBDAA in Bone Density Category\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNormal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eReduced Bone Density\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eReduced Bone Mass\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eOsteoporosis\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eExercise\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEveryday\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.436(2.95)*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.72(3.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.81(3.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6.19(1.69)*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.350(3.51)*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.85(3.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.20(3.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6.12(2.09)*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSometimes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.339(3.04)*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.42(3.21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.09(3.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6.18(1.83)*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eBMI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUnderweight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.598(2.68)*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.42(0.893)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.41(3.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5.57(2.11)\u0026dagger;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNormal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.458(2.99)*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.13(3.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.27(3.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6.10(1.86)*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOverweight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.134(3.01)*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.09(3.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.63(3.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6.20(1.92)*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eObesity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.376(2.95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.66(2.84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.58(3.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6.65(1.09)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eDiet\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBalanced\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.384(2.97)*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.98(3.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.08(3.61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6.09(1.89)*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMeat\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.821(3.14)*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.63(0.0532)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.51(4.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6.12(1.87)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVegetarian\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.729(0.258)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.899(3.25)\u0026dagger;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.05(3.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5.80(1.68)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eSmoke\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCurrent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.76(3.21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6.47(2.17)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.352(2.98)*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.87(3.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.07(3.61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6.09(1.89)*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQuit\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5.83(NA)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eDisease History\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDiabetes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.136(2.43)*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.92(3.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.11(3.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6.45(1.46)*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHypertension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.124(3.38)*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.40(3.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.69(3.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5.63(1.90)*\u0026dagger;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.406(2.88)*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.08(3.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.25(3.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6.32(1.89)*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eDrink\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFrequent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-2.45(1.62)*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.23(1.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.32(3.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5.53(1.76)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.344(2.96)*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.85(3.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.08(3.61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6.09(1.89)*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOccasional\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.136(3.61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.95(NA)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.67(3.96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6.36(1.52)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQuit\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eBlood Pressure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eElevated\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.526(3.01)*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.93(3.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.12(3.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6.25(1.86)*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHypertension Stage 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.165(3.14)*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.75(3.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.91(3.68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5.96(1.84)*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHypertension Stage 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.885(2.18)*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.02(2.59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.94(3.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5.48(1.99)\u0026dagger;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNormal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.466(2.71)*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.83(3.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.45(3.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6.47(1.92)*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eData are presented as Mean (SD), *indicates a statistically significant difference within the same factor level (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and \u0026dagger;indicates a statistically significant difference within the same bone density category (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study highlights critical risk factors for osteoporosis, such as age, BMI, and physical activity, while introducing bone density based age acceleration as a novel predictor of osteoporosis risk. Our univariate and multivariate analyses revealed that both men and women share common risk factors, with post-menopausal years being a notable additional risk for women. Moreover, bone density based age acceleration showed a consistent and strong correlation with osteoporosis risk across the study population, positioning it as a valuable biomarker for early detection.\u003c/p\u003e\u003cp\u003eThe significant association between age, BMI, and reduced exercise frequency with osteoporosis risk underscores well-established knowledge in the field. Age-related bone loss is a major factor driving osteoporosis, while maintaining a healthy BMI and regular physical activity are protective against this condition. Our results also highlighted gender-specific differences: post-menopausal years were a significant factor in females, aligning with the well-known role of estrogen in maintaining bone density. These findings reaffirm previous studies linking hormonal changes in women to osteoporosis \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e and strengthen the argument for targeted interventions during and after menopause. Chronic stress also plays a significant role, as recent studies suggest that stress-induced bone loss is mediated by a GABAergic neural circuit in the ventromedial hypothalamus \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThis study makes a novel contribution by introducing bone density age acceleration as a predictor of osteoporosis risk. Previous research has shown that telomere shortening, a widely recognized indicator of biological aging, is associated with osteoporosis \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e,and cellular senescence has been implicated in bone aging and osteoporosis development, suggesting that senescence markers may help predict bone health deterioration \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. However, our analysis extends this understanding by demonstrating that individuals with osteoporosis show significantly accelerated bone density aging, even compared to those with reduced bone mass or density. This observation suggests that bone density age, as modeled by machine learning algorithms, may be a more sensitive and specific early biomarker for osteoporosis, allowing for earlier identification of at-risk individuals before clinical symptoms manifest. Moreover, the use of residuals from linear regression models to quantify bone density age acceleration introduces a new tool for assessing the deviation of biological aging from chronological aging, similar to the widely recognized epigenetic-based age acceleration \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e, which could be crucial for precision medicine approaches to osteoporosis prevention.\u003c/p\u003e\u003cp\u003eThe observed correlation between bone density age acceleration and osteoporosis risk likely reflects the cumulative effects of various lifestyle, genetic, and physiological factors influencing bone health. For instance, excessive caffeine intake, which has been shown to increase the risk of osteoporosis by affecting calcium clearance \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Aging, reduced physical activity, and low BMI all contribute to decreased bone remodeling and bone mineral density, leading to accelerated bone aging. In females, post-menopausal hormonal changes further exacerbate bone density loss, accounting for the significant acceleration observed in this group. These findings align with the bone remodeling theory, which suggests that the balance between bone formation and resorption shifts with age and hormonal changes, leading to osteoporosis \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Our data suggest that bone density age acceleration captures this shift, potentially offering a mechanistic link between lifestyle factors and biological aging of bones.\u003c/p\u003e\u003cp\u003eThe gender differences observed in smoking status, drinking frequency, and hypertension or diabetes history offer further insights into potential mechanisms. Females exhibited stronger correlations between these lifestyle factors and bone density age acceleration, which may indicate that hormonal fluctuations during menopause make women more susceptible to lifestyle-related bone deterioration. In contrast, males with lower physical activity and unbalanced diets showed significant bone density age acceleration, suggesting that lifestyle interventions targeting physical fitness and nutrition could have a meaningful impact on preventing osteoporosis in men.\u003c/p\u003e\u003cp\u003eRecent genetic studies provide additional insights that could further enhance the predictive power of bone density age acceleration models. For example, research has identified the POLR2A gene as having a protective effect against post-menopausal osteoporosis \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Similarly, the discovery of the Men1 gene as a key regulator of age-related osteoporosis, along with the development of a new animal model for studying the disease \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e, underscores the potential for integrating genetic data into future models.\u003c/p\u003e\u003cp\u003eEmerging therapeutic strategies also offer exciting opportunities to complement the predictive power of bone density age acceleration. For instance, synthetic biology-based therapies, such as bacterial extracellular vesicles displaying BMP-2 and CXCR4, have shown potential in ameliorating bone degeneration \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Additionally, extracellular vesicles from apoptotic bone marrow mesenchymal stem cells (ApoEVs) have been shown to carry regenerative signals that can mitigate osteoporosis \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e, opening new avenues for treatment. These therapies, alongside interventions targeting mitochondrial homeostasis in bone cells \u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e and senolytic therapies aimed at reducing the load of senescent cells in post-menopausal women\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e, could complement early detection efforts by targeting the underlying biological processes that drive bone aging and osteoporosis. Furthermore, targeted therapies that modulate immune cell recruitment to bone matrix, such as those involving CXCL9 and CXCR3 \u003csup\u003e34\u003c/sup\u003e, offer targeted approaches to reducing excessive bone resorption while preserving normal turnover. These strategies align well with the concept of bone density age acceleration, capturing early biological shifts and enabling timely interventions to prevent bone loss and reduce fracture risk.\u003c/p\u003e\u003cp\u003eWhile our findings strongly support the role of bone density age acceleration as a predictor of osteoporosis, alternative explanations should also be considered. Genetic factors \u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e, which were not accounted for in this study, may influence both bone density age and osteoporosis risk, and environmental factors such as calcium and vitamin D intake were not directly assessed \u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. Future studies should aim to incorporate genetic and dietary data to refine the predictive power of bone density age models and understand their interaction with lifestyle factors. Additionally, while this study demonstrated strong predictive performance for the bone density age model, the cross-sectional nature of our data limits the ability to make definitive conclusions about causality. Longitudinal studies are needed to confirm whether bone density age acceleration can serve as a reliable early marker for predicting future osteoporosis risk. Furthermore, the characteristics of the specific population under investigation may influence bone density outcomes, as highlighted by previous research \u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. If validated, bone density age acceleration could be integrated into clinical screening protocols, allowing for more personalized and timely interventions to prevent or slow the progression of osteoporosis.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study systematically identified key clinical and lifestyle factors associated with osteoporosis and demonstrated the utility of a bone density based aging model in evaluating skeletal aging. Age, reduced exercise frequency, lower body mass index, and lower OSTA scores were consistently significant risk factors across both genders, with post-menopausal years further contributing to risk in females. Females exhibited a higher overall risk of osteoporosis than males. The bone density aging model effectively predicted bone density age and revealed that osteoporosis patients showed significant bone density age acceleration compared to controls, with correlations observed for exercise, BMI, diet, smoking, hypertension, diabetes, drinking habits, and blood pressure. These findings highlight the potential of bone density based biological age assessment for early identification of high risk individuals and personalized intervention strategies, providing a framework to enhance precision medicine approaches in osteoporosis prevention and management.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv align=\"Left\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"566\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eDXA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eDual-energy X-ray absorptiometry\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBDAA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eBone density age acceleration\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eBody mass index\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eOSTA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eOsteoporosis Self-Assessment Tool for Asians\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHIV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eHuman Immunodeficiency Virus\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBMD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eBone Mineral Density\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eOP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eOsteoporosis group\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNOP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNormal bone density group\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTem.Neck\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eFemoral neck\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTroch\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTrochanter\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAUC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eArea Under the Curve\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSVR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSupport vector machines for regression\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMAE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMean absolute error\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSystolic blood pressure\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eDBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eDiastolic blood pressure\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was approved by the Lunjiao Hospital Research Ethics Committee. All participants provided written informed consent prior to participation. All procedures involving human participants were conducted in accordance with the principles of the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study were collected from participants at Lunjiao Hospital and are not publicly available due to privacy and ethical restrictions. Data may be made available from the corresponding authors upon reasonable request and with permission from the hospital's ethics committee.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJinhong Tan, Jijun Zhu, Yongtao He, Zhaohao Fan, Fuqiang Cai, Haowen Zhuang, Kangyan Liu, Xiangjie Chen, Qiancheng Li, Guangbiao Liu, Bo Feng, Yushi Guo, Manying Lin, Gan Li, Bin Wang and Junfang Chen declare that they have no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by a startup grant from the Greater Bay Area Institute of Precision Medicine (Guangzhou) to J.C. (Grant No: I0007), startup grant from the Greater Bay Area Institute of Precision Medicine (Guangzhou) to B.W. (Grant No: I0028), the National Natural Science Foundation (Grant No: 32370639), Natural Science Foundation of Guangdong Province (Grant No: 2024A1515012116)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJinhong Tan and Jijun Zhu contributed equally to this work and share first authorship.\u003c/p\u003e\n\u003cp\u003eBin Wang and Junfang Chen share corresponding authorship.\u003c/p\u003e\n\u003cp\u003eJinhong Tan and Jijun Zhu contributed to study conception, research design, data interpretation, and manuscript drafting. Fuqiang Cai, Haowen Zhuang, and Yanhua Hu were responsible for data preprocessing and preliminary statistical analyses. Yongtao He, Zhaohao Fan, Kangyan Liu, Qiancheng Li, Bo Feng, Yushi Guo, and Gan Li contributed to data acquisition and provided study resources. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank all study participants and the staff at Lunjiao Hospital for their support and assistance during data collection.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eChoi, M.H., Yang, J.H., Seo, J.S., Kim, Y.-j., and Kang, S.-W. (2021). Prevalence and diagnosis experience of osteoporosis in postmenopausal women over 50: Focusing on socioeconomic factors. Plos one \u003cem\u003e16\u003c/em\u003e, e0248020.\u003c/li\u003e\n \u003cli\u003ede Villiers, T.J. (2009). Bone health and osteoporosis in postmenopausal women. Best Practice \u0026amp; Research Clinical Obstetrics \u0026amp; Gynaecology \u003cem\u003e23\u003c/em\u003e, 73-85.\u003c/li\u003e\n \u003cli\u003eBouvard, B., Annweiler, C., and Legrand, E. (2021). Osteoporosis in older adults. 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Journal of the American Geriatrics Society \u003cem\u003e68\u003c/em\u003e, 2656-2661.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-musculoskeletal-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmsd","sideBox":"Learn more about [BMC Musculoskeletal Disorders](http://bmcmusculoskeletdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://author-welcome.nature.com/12891","title":"BMC Musculoskeletal Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7330727/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7330727/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003cbr\u003e\nOsteoporosis is a progressive skeletal disorder influenced by multiple clinical and lifestyle factors. Early identification of individuals at high risk is essential for prevention and personalized management. This study aimed to identify key determinants of osteoporosis and to establish a bone density–based aging model to evaluate accelerated skeletal aging.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003cbr\u003e\nWe analyzed data from a large cohort with dual-energy X-ray absorptiometry (DXA) measurements of the lumbar spine and proximal femur. Univariate and multivariate regression models were applied to assess the associations between clinical and lifestyle factors and osteoporosis risk. A bone density aging model was developed using support vector regression to estimate bone density age, and bone density age acceleration (BDAA) was calculated as the residual from chronological age.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003cbr\u003e\nThe bone density aging model showed good predictive performance (mean absolute error = 5.716 years, R² = 0.145). Higher BDAA was positively correlated with osteoporosis risk across bone health categories, including individuals without clinical diagnosis. In addition, BDAA differed significantly by exercise level, dietary pattern, body mass index, blood pressure, and metabolic comorbidities, providing insights into skeletal aging beyond chronological age.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003cbr\u003e\nBone density based biological aging models can improve early identification and personalized risk stratification of osteoporosis. This approach may facilitate and support the development of precision medicine strategies in osteoporosis prevention and management.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTrial registration\u003c/strong\u003e\u003cbr\u003e\nNot applicable.\u003c/p\u003e","manuscriptTitle":"Unveiling risk factors and predicting osteoporosis through bone density based aging model","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-03 10:23:40","doi":"10.21203/rs.3.rs-7330727/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-10T13:28:59+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-08T22:08:22+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-07T07:39:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"187426176117073931016658091766750720797","date":"2025-08-27T10:51:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"163259511120355162996770590358196800482","date":"2025-08-27T07:52:44+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-27T07:05:42+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-25T08:25:16+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-08-19T06:44:13+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-18T13:57:40+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Musculoskeletal Disorders","date":"2025-08-18T13:47:49+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-musculoskeletal-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmsd","sideBox":"Learn more about [BMC Musculoskeletal Disorders](http://bmcmusculoskeletdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://author-welcome.nature.com/12891","title":"BMC Musculoskeletal Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4d93b26a-e2dc-4340-ae52-37e2a2807f75","owner":[],"postedDate":"September 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-11-17T16:00:43+00:00","versionOfRecord":{"articleIdentity":"rs-7330727","link":"https://doi.org/10.1186/s12891-025-09298-0","journal":{"identity":"bmc-musculoskeletal-disorders","isVorOnly":false,"title":"BMC Musculoskeletal Disorders"},"publishedOn":"2025-11-14 15:57:29","publishedOnDateReadable":"November 14th, 2025"},"versionCreatedAt":"2025-09-03 10:23:40","video":"","vorDoi":"10.1186/s12891-025-09298-0","vorDoiUrl":"https://doi.org/10.1186/s12891-025-09298-0","workflowStages":[]},"version":"v1","identity":"rs-7330727","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7330727","identity":"rs-7330727","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}