Development of a DXA-Based Risk Score for Cardiovascular Outcomes Among Older Adults: The Health, Aging, and Body Composition Study

Development of a DXA-Based Risk Score for Cardiovascular Outcomes Among Older Adults: The Health, Aging, and Body Composition Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Development of a DXA-Based Risk Score for Cardiovascular Outcomes Among Older Adults: The Health, Aging, and Body Composition Study Lihui Chen, Xinran Wang, Tian-Ze Lin, Hao Xiang, Hua Liu, Shen Xu, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4203225/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 07 Oct, 2024 Read the published version in Scientific Reports → Version 1 posted 11 You are reading this latest preprint version Abstract Cardiovascular disease (CVD) is the leading cause of death worldwide and its risk factors have long been investigating in epidemiological studies. Although the Framingham Risk Score provided an estimate for cardiovascular risk, it did not utilize the body compositions biomarkers. Therefore, our study aims to develop a body composition-based score and incorporate the score into the FRS for better predicting cardiovascular disease among initially well-functioning older adults. 1,882 older adults in the Health, Aging and Body Composition (Health ABC) study were included in our study to screen body composition variables obtained from the Dual-energy X-ray absorptiometry (DXA). We developed the 4-DXA risk score model from the selected variables, refitted the Framingham Risk Score (FRS), incorporated the 4-DXA risk score into the FRS, and compared these developed prediction models. C-statistics were 0.58, 0.62, and 0.63 respectively. Compared to the refit FRS, the net reclassification index and the integrated discrimination index was 0.012 (95% CI: 0.0068–0.018) and 0.12 (95% CI = 0.021, 0.22) for the refit FRS plus the 4-DXA model. Inclusion of body composition indices slightly improved the model performance for predicting CVD. Further research is needed to validate the score accuracy in a higher-risk population. Health sciences/Risk factors Health sciences/Biomarkers/Predictive markers Health sciences/Diseases/Cardiovascular diseases Health sciences/Health care/Geriatrics Health sciences/Cardiology Cardiovascular Disease Body Composition Indices Dual-energy X-ray absorptiometry Framingham Risk Score Older Adults Figures Figure 1 Figure 2 Introduction Cardiovascular disease (CVD) is the leading cause of death worldwide, accounting for approximately 17.9 million deaths each year 1 . Understanding the natural history of CVD and identifying its risk factors has long been a focus in epidemiological studies 2 . Risk prediction models for CVD have been developed since the 1950s, mainly focusing on demographics, healthy lifestyles, and blood-based biomarkers 3 . The Framingham Risk Score (FRS) is one of the most widely used algorithms to evaluate cardiovascular risk. It was first developed using data from the Framingham Heart Study to predict the risk of incident coronary heart disease over ten years 4 . Seven predictors were included in the FRS: chronological age, gender total cholesterol, high-density lipoprotein cholesterol (HDL-C), systolic blood pressure (SBP), smoking status, and diabetes. Body composition is a multi-dimensional concept that refers to the amount of fat, muscle, bone, and water in the body. DXA (Dual-energy X-ray absorptiometry) was developed 30 years ago as a method for body composition measurements, including both body bone mass and soft tissue composition 5 . These measurements have been implemented a lot in detecting different diseases. For example, low bone density can increase the risk of osteoporosis and fractures 6 , and body fat, as a kind of soft tissue composition, is a risk factor for obesity, which can lead to a range of health problems such as type 2 diabetes, high blood pressure, and stroke 7 . However, there has not been a study that comprehensively investigates the association between those body composition measurements and the occurance of CVD. Prior studies have only identified the association between a subset of body composition measurements such as body mass index (BMI) 7–9 , waist circumferences (WC) 8,9 , and waist-hip ratio (WHR) 8,9 with CVD 10,11 . These measurements only represent a small fraction of body composition indices. Little is known about whether and how multiple body composition indices jointly contributed to the prediction of CVD. As a result, in this study, we include a pool of 87 body composition indices and try to find the association. The objectives of the present study were as follows: ( 1 ) to investigate a broader range of body composition variables and their association with CVD; ( 2 ) to create a body composition variable based model of biomarkers for prognostic stratification; ( 3 ) to determine whether incorporating the body composition variables in the traditional FRS improves risk prediction. Material and Methods Data and Study Participants The Health, Aging and Body Composition (Health ABC) Study is a longitudinal cohort designed to examine risk factors for aging-related changes in body composition and physical function among initially well-functioning older adults. From March 1997 to July 1998, 3,075 Black and White individuals aged 68–80 years were recruited from a list of Medicare beneficiaries provided by the Health Care Financing Administration at two study sites across the United States (Pittsburgh, Pennsylvania, and Memphis, Tennessee). The inclusion criteria of Health ABC study were (i) free of life-threatening illness, (ii) self-reported ability to walk a quarter of a mile, climb ten steps without resting, and perform basic activities of daily living without assistance, and (iii) no intention to move out of the current geographic area for at least three years. Details about the Health ABC study have been previously published elsewhere 12,13 . The study was approved by the Institutional Review Board of the University of California, San Francisco (H5254-12688-14), the University of Tennessee (95-05531-FB), and the University of Pittsburgh (#960212). All participants provided written informed consent, and all methods in the study were performed in accordance with the principles of the Declaration of Helsinki. The current analyses restricted the analytic sample to participants who (i) were free of CVD at baseline, (ii) had no missing data in all the DXA measures, FRS measures, and baseline characteristics. A total of 1,882 participants were included. The details of data processing, statistical modeling, and assessments of our established models are summarized in Fig. 1 . Body Composition Body composition indices (lean mass, fat mass, bone mineral content, and bone mineral density) were measured by DXA (QDR 4500A; Hologic Inc, Waltham, MA) using standard procedures 14 . The validity and reproducibility of the body composition data in the Health ABC Study were reported elsewhere 15 . In our study, we first manually removed 16 irrelevant variables (e.g. scan_id, scan_date) from the original DXA dataframe. Then, since the same measures from the left and right sides of the body are highly correlated (e.g., left arm fat free mass and right arm fat free mass), we took measures only from the side of non-dominant hand and disregard the variable from the other side for each individual in our study. We believed that measures from the non-dominant side were less influenced by non-aging related reasons such as exercise and trauma and therefore, their relationship with CVD could be more unperturbed and stable. Finally, a total of 87 DXA indices were selected and analyzed in the present study (Supplementary Table S1 ). Framingham Risk Score The FRS included seven components: age, gender, smoking status, diabetes, SBP, total cholesterol, and HDL-C. Age in years, gender (male or female), and smoking status (current smoker or not) were self-reported. SBP was calculated as the average of two measurements by a conventional mercury sphygmomanometer with an appropriately sized cuff, taken in the seated position after five minutes of quiet rest. Baseline blood samples were obtained at the clinic in the morning after overnight fasting of at least eight hours, frozen at − 70°C, and shipped to the core laboratory at the University of Vermont. Total cholesterol (mg/dL) and HDL-C (mg/dL) were measured on a Vitros 950 analyzer (Johnson & Johnson). Diabetes was assessed by self-report, medication use, or a positive diagnosis by fasting blood glucose level or oral glucose tolerance test. Outcomes Outcomes of interest included incident CVD and CVD mortality (N = 613). Incident CVD included the following events: acute myocardial infarction (death of part of the myocardium due to occlusion of a coronary artery from any cause, including spasm, embolus, thrombosis, or the rupture of a plaque), angina pectoris (symptoms, such as chest pain, chest tightness, or shortness of breath, produced by myocardial ischemia that do not result in infarction), or congestive heart failure (a constellation of symptoms and physical signs that occur in a participant whose cardiac output cannot match metabolic need despite adequate filling pressures). The event must result in at least one overnight hospitalization. CVD mortality refers to inpatient death due to CVD. Follow-up for outcomes occurred every six months, either by telephone or annual visits to clinical centers; participants were asked about hospitalizations and major outpatient procedures. CVD events were adjudicated based on interviews, reviews of all hospital records, death certificates, and other documents by a panel of experts. Deaths were ascertained by the review of local obituaries, by the report to the clinical centers by family members, or by semiannual telephone contacts. Diagnoses and causes of death were adjudicated based on interviews, reviews of all hospital records, death certificates, and other support documents by a panel of physicians. The follow-up time was calculated as the difference between the time from the baseline visit and the first CVD event date or date of death due to CVD, whichever came first. Participants were censored at the date of the last contact or by the end of the follow-up period (30 April 2010 for Memphis and 30 June 2010 for Pittsburgh), whichever came first. Covariates Covariates included study site (Pittsburgh or Memphis), race (Black or White), education (less than high school, high school or equivalent, or more than high school), and body mass index (BMI) calculated as body weight in kilograms divided by the square of standing height in meters. Statistical Analysis We applied the Cox proportional hazards model with the least absolute shrinkage and selection operator 16 (LASSO) to select body composition variables. Each body composition variable was standardized. The LASSO method adds an L-1 norm term to the ordinary least square loss function and minimizes it, leading to a shrinkage of some coefficients to zero. We chose the LASSO regularization level based on the minimum rule by cross-validation. The penalization coefficient was selected using a 5-fold cross-validation and grid search technique. We selected a penalization coefficient of 0.033, at which the model has achieved maximum prediction accuracy. Subsequently, we used the stepwise backward selection technique to further reduce the number of variables retained in the Cox model using the Bayesian information criterion stopping criteria. The final subset of body composition variables was then included in a fully parametric Accelerated Failure Time (AFT) model based on the Weibull distribution for a prediction. We estimated the 4000-day risk of CVD and CVD mortality based on this model. As a comparative reference for the body composition risk model, the variables from the FRS model 17 were refitted to the Health ABC cohort (referred to as refit FRS). The refit FRS combined with the variables from the body composition risk model was also fit (referred to refit FRS plus 4-DXA model). Model performance was assessed by discrimination and calibration. For discrimination, both the C-statistic and the discrimination slope were reported. The category-free net reclassification index (NRI > 0) and integrated discrimination index (IDI) were used to assess the performance of reclassification and the improvement in discrimination over the refit FRS. Calibration performance was assessed with a calibration plot and summarized the risk scores using the Hosmer-Lemeshow statistic. Calibration in the large was also reported as the difference between the observed 4000-day event frequency and the mean predicted risk score. Distribution-free (nonparametric) 95% CIs were reported for median values and bootstrapped intervals for point estimates of performance metrics when asymptotic intervals were unavailable. All analyses were performed using R and Python 3.9. Results Baseline Characteristics Among the 1,882 participants, the median age was 73.0 years; 45.4% were men (Table 1 ). The median follow-up time was 13.4 years (range: 0.02–15.9 years). A total of 613 incident CVD or cardiovascular mortality occurred (28.04 per 1,000 person-years). Of 87 variables included in the LASSO model, 42 were retained after selection. 4 variables, maximum sagittal diameter (mm), pelvic Bone Mineral Density (BMD) (g/cm2), lumbar spine Bone Mineral Content (BMC) (g), and thigh intermuscular fat density SD (Sectional Density) (HU), were left after further applying the stepwise backward selection technique. The characteristics of 4 DXA variables and variables included in the refit FRS model were summarized in Table 1 . Supplementary Table S2 also summarized these characteristics for those excluded by our study (N = 448), providing p-values from a two-sided t-test on the average values between the included and excluded samples. Risk Score The 4-DXA risk score reflected the probability of a cardiovascular event occurring within 4000-days and was given by: $$risk score=1-{e}^{{-e}^{\left(\frac{\text{log}\left(4000\right)-PI}{0.89}\right)}}$$ where the prognostic index combined the measurements of four DXA variables as follows: $$prognostic index =9.25-0.20*absag\_d-0.23*lspibmc+0.14*pelvbmd+0.15*thimfsd$$ ,where absag_d represents maximum sagittal diameter (mm), lspibmc represents lumbar spine bone mineral content, pelvbmd represents pelvic bone mineral density, and thimfsd represents Thigh Intermuscular Fat Density SD (Sectional Density) (HU). Table 2 provided the Cox proportional hazards model coefficients of the refit FRS model, with and without the addition of the prognostic index from the 4-DXA model. Total cholesterol was not a significant risk predictor with (P = 0.14) or without (P = 0.12) the prognostic index from the 4-DXA model in our study cohort. Gender was statistically significant in the refit FRS (P < 0.005) but became statistically insignificant (P = 0.076) when adding the prognostic index to the refit FRS. All other refit FRS variables were statistically significant, both with and without the prognostic index. Model Performance Table 3 showed the performance metrics for the refit FRS, the 4-DXA model, and the combination of both models. The C-statistic was 0.62 (95% Confidence Interval (CI): 0.59, 0.64) for the refit FRS model and 0.58 (95% CI: 0.55, 0.61, \({\Delta }\) C = -0.04, P = 0.017) for the 4-DXA model. The C-statistic slightly increased to 0.63 (95% CI = 0.60–0.66, \({\Delta }\) C = 0.014, P = 0.051) in the refit FRS plus 4-DXA model. The discrimination slope was 0.040 (95% CI = 0.021, 0.060) for the refit FRS, 0.023 (95% CI = 0, 0.045) for the 4-DXA model, and 0.052 (95% CI = 0.033, 0.076) for the Framingham plus 4-DXA model. Compared with the refit FRS model, the FRS plus 4-DXA model had an IDI of 0.012 (95% CI = 0.0068, 0.018), indicating an absolute increase of 1.2% in mean risk for participants with events compared with participants without events. The FRS plus 4-DXA model had an NRI > 0 of 0.12 (95% CI = 0.021, 0.22), with event-specific components of -0.0016 (95% CI = -0.083, 0.076) and no event-specific components of 0.12 (95% CI = 0.059, 0.18). Calibration performances of the FRS plus 4-DXA model are shown in Fig. 2 . It illustrates that the performance of the refit FRS plus 4-DXA model is generally better and especially in those high-risk population. The predicted median risk from the 5-th decile on is more consistent with the actual median risk compared to the Refit FRS. The height of bars representing actual median risk appear in an increasing mamner which also demonstrates a more plausible model for combining the FRS variables and the 4-DXA prognostic index. Discussion The present study aimed to select body composition variables predictive for CVD among a broader range of variables obtained through DXA. We also examined whether incorporating the selected features could improve the predictability of traditional FRS among older adults. We selected four features among over 87 body composition variables and established a 4-DXA risk prediction model for CVD. In addition, although the established 4-DXA model independently performs no better than the traditional FRS, combining the 4-DXA prognostic index into the FRS could offer a slight improvement in the performance. Four body composition indices, maximum sagittal diameter, pelvic bone mineral density, lumbar spine bone mineral content, and thigh intermuscular fat density SD (Sectional Density), were retained after variable selction in the DXA-based prognostic model. We found that the older adults with higher lumber spine bone mineral content, higher max abdominal sagittal diameter, lower pelvic bone mineral density, and lower thigh intramuscular fat sectional density had a higher risk of CVD outcomes. The relationship between each individual variable and CVD is as follow: Abdominal sagittal diameter measures the distance from the back to the upper abdomen and can be used to reflect visceral obesity. Sagittal abdominal diameter was suggested to be positively related to the risk of coronary heart disease in large prospective studies from the National Health Nutrition and Examination Survey 2011–2016 18 , Kaiser Permanente of Northern California subscribers 19 , and Risérus et al.’s survey in Sweden 20 . In addition, the negative association between Pelvic BMD and CVD found in the present study can also be verified in other research 21–25 . For instance, Trivedi and Khaw 24 found that BMD measured at the hip is inversely associated with all-cause mortality and cardiovascular disease mortality from the population of over one thousand older men in the Cambridge General Practice Health Study. There have also been new findings regarding the other two selected variables, lumbar spine BMC and thigh intramuscular fat sectional density. The present study showed a positive association between lumbar spine BMC with CVD incidence. Although Farhat and Cauley 26 , using the same study cohort, concluded a negative association between lumbar spine BMD with CVD outcomes, this relationship only presented in the white men and black women group, but was missing in the black men and white women group. Therefore, it is likely that the relationship becomes insubstantial when tested in the general population regardless of race and gender. Furthermore, in the systematic review by Khandkar et al. 27 , there was no significant association between lumbar spine BMD and CVD. As for intramuscular thigh muscle fat, its association with CVD outcomes was also conflicting. Some study shows that the thigh intramuscular fat density is positively correlated with CVD risk 28,29 . A study, which focuses on the same population as our study, shows intramuscular thigh muscle fat is independently associated with CVD risk 30 . Our study has several strengths. First, our study considered a broader range of body composition variables and investigated their potential association with CVD. We also conducted multiple comparisons between the DXA-based model, refit FRS model, and FRS plus DXA model. Second, the present study proposed a novel method that combines the lasso-penalized Cox PH model and backward elimination for variable selection. We addressed collinearity between body composition variables in the LASSO and preserved statistical significance when conducting stepwise backward elimination. Third, this study supplemented the traditional FRS by adding body composition variables, which led to a more accurate prediction of the risk of CVD. Nevertheless, the limitations of the present study also warrant mentioning. First, the Health ABC cohort population was sampled from well-functioning older adults who are free of life-threatening illness and possess good mobility. Therefore, the sample might have better indices in the examination compared with the older population that has limitation in functioning and mobility. A more balanced sample is needed to better represent the less-functioning population. Second, we were unable to determine whether the selected body composition variables were causally associated with CVD or whether there might be other confounders of CVD. A final causal relationship is still needed to address the importance of each selected variable and to contribute to better prevention and therapy of CVD. Conclusion In conclusion, among the older adults, the combination of four selected DXA variables slightly improved the performance of FRS in predicting cardiovascular endpoints, but the accuracy is still modest. Further study is needed to verify the effect of individual body composition variables on CVD and investigate more effective measures to improve the predictability of FRS. Declarations Additional information Competing Interests: The authors declare that they have no competing financial or non-financial interests in relation to the work described. Author Contribution C.W. designed the study. L.C., X.W., and T.L. carried out the study and conducted statistical analysis. H.X., H.L., S.X., and J.Y. interpreted the acquired results. L.C. and X.W. drafted the manuscript. All authors provided critical feedback for important intellectual content. Data Availability The dataset utilized in our research, known as the Health, Aging and Body Composition (Health ABC) Study, is a longitudinal cohort dataset designed to investigate the risk factors associated with aging-related changes in body composition and physical function in initially well-functioning older adults. This dataset was assembled through extensive field work and follow-ups by the National Institute on Aging (NIA) and the National Institute of Health (NIH). The datasets analysed during the current study are available in the National Institute on Aging's website https://www.nia.nih.gov/healthabc-study. References Mahmood, S. S., Levy, D., Vasan, R. S. & Wang, T. J. The Framingham Heart Study and the epidemiology of cardiovascular disease: a historical perspective. The Lancet 383, 999–1008 (2014). Ford, E. S. & Capewell, S. 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Median (Interquartile Range) FRS and DXA baseline (N = 1, 882) Age, years 73.0 (71.0, 76.0) Men (%) 854 (45.4%) Maximum sagittal diameter (mm) 234.0 (210.0, 257.0) Pelvic Bone Mineral Density (g/cm2) 1.1 (1.0, 1.3) Lumb spine Bone Mineral Content (g) 48.6 (38.0, 61.8) Thigh intermuscular fat density Sectional Density (HU) 25.5 (24.0, 27.1) Incidence (%) 613 (32.6%) Body Mass Index 26.8 (23.9, 29.9) Total cholesterol (mg/dL) 203.0 (179.0, 228.0) High-density Lipoprotein Cholesterol (mg/dL) 52.0 (43.0, 64.0) Diabetes (%) 233 (12.4%) Systolic blood pressure (mm Hg) 134.0 (122.0, 148.0) Current Smoker (%) 187 (9.9%) Table 2 Risk Prediction Models for the Primary Endpoint of Cardiovascular Diseases. All continuous variables were standardized. FRS variables were refit using a Cox proportional hazards model with and without the 4-DXA prognostic index. Refit FRS Refit FRS plus 4-DXA model Coefficients (95% CI) p-value Coefficients (95% CI) p-value Age, years 0.045 (0.017, 0.074) < 0.005 0.042 (0.014, 0.071) < 0.005 Men 0.27 (0.093, 0.44) < 0.005 0.16 (-0.017, 0.34) 0.076 Total cholesterol (mg/dL) 0.062 (-0.020, 0.14) 0.14 0.065 (-0.017, 0.15) 0.12 High-density Lipoprotein Cholesterol (mg/dL) -0.19 (-0.29, -0.095) < 0.001 -0.17 (-0.26, -0.071) < 0.001 Diabetes 0.49 (0.28, 0.70) < 0.001 0.43 (0.22, 0.64) < 0.001 Systolic blood pressure (mm Hg) 0.17 (0.090, 0.25) < 0.001 0.16 (0.080, 0.24) < 0.001 Current Smoker 0.26 (0.088, 0.60) < 0.01 0.38 (0.13, 0.64) < 0.005 4-DXA prognostic index - -0.21 (-0.30, -0.13) < 0.001 Table 3 Comparative Performance Metrics for the FRS Model, the 4-DXA Model, and the Refit FRS Plus 4-DXA Model. Change of C-statistics, Integrated Discrimination Index, Net Reclassification Index, Event and No-event Net Reclassification Index were calculated using the Refit FRS as the reference model. Refit FRS 4-DXA model Refit FRS plus 4-DXA model C-statistics 0.62 (0.59, 0.64) Reference 0.58 (0.55, 0.61) P = 0.017 0.63 (0.60, 0.66) P = 0.051 Discrimination Slope 0.04 (0.021, 0.06) 0.023 (0, 0.045) 0.052 (0.033, 0.076) Quintile 2.53 (1.81, 3.26) 2.02 (1.69, 2.34) 2.84 (2.14, 3.54) Hosmer-Lemeshow 17.07 P = 0.029 28.93 P < 0.001 11.20 P = 0.19 Integrated Discrimination Index 1 [Reference] -0.017 (-0.027, -0.0059) P = 0.002 0.012 (0.0068, 0.018) P < 0.001 Net Reclassifcation Index 1 [Reference] -0.13 (-0.22, -0.030); P = 0.010 0.12 (0.022, 0.21) P = 0.017 Event Net Reclassification Index 1 [Reference] -0.10 (-0.19, -0.022) -0.0016 (-0.083, 0.076) No-event Net Reclassification Index 1 [Reference] -0.023 (-0.079, 0.036) 0.12 (0.059, 0.18) Additional Declarations No competing interests reported. Supplementary Files SupplementaryInformation.docx Cite Share Download PDF Status: Published Journal Publication published 07 Oct, 2024 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 18 Jul, 2024 Reviews received at journal 18 Jun, 2024 Reviewers agreed at journal 04 Jun, 2024 Reviewers agreed at journal 03 Jun, 2024 Reviews received at journal 21 May, 2024 Reviewers agreed at journal 11 May, 2024 Reviewers invited by journal 18 Apr, 2024 Editor assigned by journal 17 Apr, 2024 Editor invited by journal 10 Apr, 2024 Submission checks completed at journal 10 Apr, 2024 First submitted to journal 01 Apr, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4203225","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":291003389,"identity":"e59c7f0e-0b42-46fa-974c-8ee65f867cb7","order_by":0,"name":"Lihui Chen","email":"","orcid":"","institution":"Duke Kunshan University","correspondingAuthor":false,"prefix":"","firstName":"Lihui","middleName":"","lastName":"Chen","suffix":""},{"id":291003390,"identity":"76bdda0e-1f6f-48b1-a9d0-c1525f67562f","order_by":1,"name":"Xinran Wang","email":"","orcid":"","institution":"Duke Kunshan University","correspondingAuthor":false,"prefix":"","firstName":"Xinran","middleName":"","lastName":"Wang","suffix":""},{"id":291003391,"identity":"07a948ad-bd7e-4566-815c-d8877419bf9e","order_by":2,"name":"Tian-Ze Lin","email":"","orcid":"","institution":"Duke Kunshan University","correspondingAuthor":false,"prefix":"","firstName":"Tian-Ze","middleName":"","lastName":"Lin","suffix":""},{"id":291003392,"identity":"06096743-6544-49af-badc-9a68c56d1c6d","order_by":3,"name":"Hao Xiang","email":"","orcid":"","institution":"Wuhan University","correspondingAuthor":false,"prefix":"","firstName":"Hao","middleName":"","lastName":"Xiang","suffix":""},{"id":291003393,"identity":"b1b2c021-edeb-4935-a621-698be350c737","order_by":4,"name":"Hua Liu","email":"","orcid":"","institution":"The Affiliated Kunshan Hospital of Jiangsu University","correspondingAuthor":false,"prefix":"","firstName":"Hua","middleName":"","lastName":"Liu","suffix":""},{"id":291003394,"identity":"59547bbd-95de-45d4-925a-25686b35eb51","order_by":5,"name":"Shen Xu","email":"","orcid":"","institution":"Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Shen","middleName":"","lastName":"Xu","suffix":""},{"id":291003395,"identity":"299e9cec-981c-4f79-924a-1dacd691250d","order_by":6,"name":"Jirong Yue","email":"","orcid":"","institution":"Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Jirong","middleName":"","lastName":"Yue","suffix":""},{"id":291003396,"identity":"77d4c826-aa98-49fd-a32d-86a721cb285e","order_by":7,"name":"Chenkai Wu","email":"data:image/png;base64,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","orcid":"","institution":"Duke Kunshan University","correspondingAuthor":true,"prefix":"","firstName":"Chenkai","middleName":"","lastName":"Wu","suffix":""}],"badges":[],"createdAt":"2024-04-02 01:59:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4203225/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4203225/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-024-74185-y","type":"published","date":"2024-10-07T15:57:50+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":54994541,"identity":"d2686217-fde3-47e7-8966-0fd6b582abd2","added_by":"auto","created_at":"2024-04-19 17:48:42","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":3097610,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of the Current Study on the Health ABC Population.\u003c/p\u003e","description":"","filename":"Figure1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4203225/v1/eec4578d9b3cd603d5581a63.jpeg"},{"id":54994542,"identity":"548006c4-9bca-4248-920c-7fed2e0c1c88","added_by":"auto","created_at":"2024-04-19 17:48:42","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":883187,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAgreement between Observed vs Predicted 4000-day Incidence of Cardiovascular Disease with the Refit FRS model, and the Refit FRS Plus 4-DXA Model\u003c/strong\u003e. Predicted 4000-day incidence for each decile is the median predicted risk in percentage. Error bars indicates the 95% prediction intervals.\u003c/p\u003e","description":"","filename":"Figure2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4203225/v1/050543a74a035e86ccf99502.jpeg"},{"id":66597391,"identity":"6dcf400f-1ce4-4240-8e5e-46a806667835","added_by":"auto","created_at":"2024-10-14 16:10:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4546695,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4203225/v1/ec4e44e1-476c-4368-b0ac-f99868e6e432.pdf"},{"id":54994543,"identity":"736d1025-f7b4-4019-a2f7-d29f5bb93ae7","added_by":"auto","created_at":"2024-04-19 17:48:42","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":25161,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-4203225/v1/71cce1a95c199a0e1ff9588b.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Development of a DXA-Based Risk Score for Cardiovascular Outcomes Among Older Adults: The Health, Aging, and Body Composition Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCardiovascular disease (CVD) is the leading cause of death worldwide, accounting for approximately 17.9\u0026nbsp;million deaths each year\u003csup\u003e1\u003c/sup\u003e. Understanding the natural history of CVD and identifying its risk factors has long been a focus in epidemiological studies\u003csup\u003e2\u003c/sup\u003e. Risk prediction models for CVD have been developed since the 1950s, mainly focusing on demographics, healthy lifestyles, and blood-based biomarkers\u003csup\u003e3\u003c/sup\u003e. The Framingham Risk Score (FRS) is one of the most widely used algorithms to evaluate cardiovascular risk. It was first developed using data from the Framingham Heart Study to predict the risk of incident coronary heart disease over ten years\u003csup\u003e4\u003c/sup\u003e. Seven predictors were included in the FRS: chronological age, gender total cholesterol, high-density lipoprotein cholesterol (HDL-C), systolic blood pressure (SBP), smoking status, and diabetes.\u003c/p\u003e \u003cp\u003eBody composition is a multi-dimensional concept that refers to the amount of fat, muscle, bone, and water in the body. DXA (Dual-energy X-ray absorptiometry) was developed 30 years ago as a method for body composition measurements, including both body bone mass and soft tissue composition\u003csup\u003e5\u003c/sup\u003e. These measurements have been implemented a lot in detecting different diseases. For example, low bone density can increase the risk of osteoporosis and fractures\u003csup\u003e6\u003c/sup\u003e, and body fat, as a kind of soft tissue composition, is a risk factor for obesity, which can lead to a range of health problems such as type 2 diabetes, high blood pressure, and stroke\u003csup\u003e7\u003c/sup\u003e. However, there has not been a study that comprehensively investigates the association between those body composition measurements and the occurance of CVD. Prior studies have only identified the association between a subset of body composition measurements such as body mass index (BMI)\u003csup\u003e7\u0026ndash;9\u003c/sup\u003e, waist circumferences (WC)\u003csup\u003e8,9\u003c/sup\u003e, and waist-hip ratio (WHR)\u003csup\u003e8,9\u003c/sup\u003e with CVD\u003csup\u003e10,11\u003c/sup\u003e. These measurements only represent a small fraction of body composition indices. Little is known about whether and how multiple body composition indices jointly contributed to the prediction of CVD. As a result, in this study, we include a pool of 87 body composition indices and try to find the association.\u003c/p\u003e \u003cp\u003eThe objectives of the present study were as follows: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) to investigate a broader range of body composition variables and their association with CVD; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) to create a body composition variable based model of biomarkers for prognostic stratification; (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) to determine whether incorporating the body composition variables in the traditional FRS improves risk prediction.\u003c/p\u003e"},{"header":"Material and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData and Study Participants\u003c/h2\u003e \u003cp\u003eThe Health, Aging and Body Composition (Health ABC) Study is a longitudinal cohort designed to examine risk factors for aging-related changes in body composition and physical function among initially well-functioning older adults. From March 1997 to July 1998, 3,075 Black and White individuals aged 68\u0026ndash;80 years were recruited from a list of Medicare beneficiaries provided by the Health Care Financing Administration at two study sites across the United States (Pittsburgh, Pennsylvania, and Memphis, Tennessee). The inclusion criteria of Health ABC study were (i) free of life-threatening illness, (ii) self-reported ability to walk a quarter of a mile, climb ten steps without resting, and perform basic activities of daily living without assistance, and (iii) no intention to move out of the current geographic area for at least three years. Details about the Health ABC study have been previously published elsewhere\u003csup\u003e12,13\u003c/sup\u003e. The study was approved by the Institutional Review Board of the University of California, San Francisco (H5254-12688-14), the University of Tennessee (95-05531-FB), and the University of Pittsburgh (#960212). All participants provided written informed consent, and all methods in the study were performed in accordance with the principles of the Declaration of Helsinki.\u003c/p\u003e \u003cp\u003eThe current analyses restricted the analytic sample to participants who (i) were free of CVD at baseline, (ii) had no missing data in all the DXA measures, FRS measures, and baseline characteristics. A total of 1,882 participants were included. The details of data processing, statistical modeling, and assessments of our established models are summarized in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eBody Composition\u003c/h2\u003e \u003cp\u003eBody composition indices (lean mass, fat mass, bone mineral content, and bone mineral density) were measured by DXA (QDR 4500A; Hologic Inc, Waltham, MA) using standard procedures\u003csup\u003e14\u003c/sup\u003e. The validity and reproducibility of the body composition data in the Health ABC Study were reported elsewhere\u003csup\u003e15\u003c/sup\u003e. In our study, we first manually removed 16 irrelevant variables (e.g. scan_id, scan_date) from the original DXA dataframe. Then, since the same measures from the left and right sides of the body are highly correlated (e.g., left arm fat free mass and right arm fat free mass), we took measures only from the side of non-dominant hand and disregard the variable from the other side for each individual in our study. We believed that measures from the non-dominant side were less influenced by non-aging related reasons such as exercise and trauma and therefore, their relationship with CVD could be more unperturbed and stable. Finally, a total of 87 DXA indices were selected and analyzed in the present study (Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eFramingham Risk Score\u003c/h2\u003e \u003cp\u003eThe FRS included seven components: age, gender, smoking status, diabetes, SBP, total cholesterol, and HDL-C. Age in years, gender (male or female), and smoking status (current smoker or not) were self-reported. SBP was calculated as the average of two measurements by a conventional mercury sphygmomanometer with an appropriately sized cuff, taken in the seated position after five minutes of quiet rest. Baseline blood samples were obtained at the clinic in the morning after overnight fasting of at least eight hours, frozen at \u0026minus;\u0026thinsp;70\u0026deg;C, and shipped to the core laboratory at the University of Vermont. Total cholesterol (mg/dL) and HDL-C (mg/dL) were measured on a Vitros 950 analyzer (Johnson \u0026amp; Johnson). Diabetes was assessed by self-report, medication use, or a positive diagnosis by fasting blood glucose level or oral glucose tolerance test.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eOutcomes\u003c/h2\u003e \u003cp\u003eOutcomes of interest included incident CVD and CVD mortality (N\u0026thinsp;=\u0026thinsp;613). Incident CVD included the following events: acute myocardial infarction (death of part of the myocardium due to occlusion of a coronary artery from any cause, including spasm, embolus, thrombosis, or the rupture of a plaque), angina pectoris (symptoms, such as chest pain, chest tightness, or shortness of breath, produced by myocardial ischemia that do not result in infarction), or congestive heart failure (a constellation of symptoms and physical signs that occur in a participant whose cardiac output cannot match metabolic need despite adequate filling pressures). The event must result in at least one overnight hospitalization. CVD mortality refers to inpatient death due to CVD.\u003c/p\u003e \u003cp\u003eFollow-up for outcomes occurred every six months, either by telephone or annual visits to clinical centers; participants were asked about hospitalizations and major outpatient procedures. CVD events were adjudicated based on interviews, reviews of all hospital records, death certificates, and other documents by a panel of experts. Deaths were ascertained by the review of local obituaries, by the report to the clinical centers by family members, or by semiannual telephone contacts. Diagnoses and causes of death were adjudicated based on interviews, reviews of all hospital records, death certificates, and other support documents by a panel of physicians. The follow-up time was calculated as the difference between the time from the baseline visit and the first CVD event date or date of death due to CVD, whichever came first. Participants were censored at the date of the last contact or by the end of the follow-up period (30 April 2010 for Memphis and 30 June 2010 for Pittsburgh), whichever came first.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eCovariates\u003c/h2\u003e \u003cp\u003eCovariates included study site (Pittsburgh or Memphis), race (Black or White), education (less than high school, high school or equivalent, or more than high school), and body mass index (BMI) calculated as body weight in kilograms divided by the square of standing height in meters.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eWe applied the Cox proportional hazards model with the least absolute shrinkage and selection operator\u003csup\u003e16\u003c/sup\u003e (LASSO) to select body composition variables. Each body composition variable was standardized. The LASSO method adds an L-1 norm term to the ordinary least square loss function and minimizes it, leading to a shrinkage of some coefficients to zero. We chose the LASSO regularization level based on the minimum rule by cross-validation. The penalization coefficient was selected using a 5-fold cross-validation and grid search technique. We selected a penalization coefficient of 0.033, at which the model has achieved maximum prediction accuracy. Subsequently, we used the stepwise backward selection technique to further reduce the number of variables retained in the Cox model using the Bayesian information criterion stopping criteria. The final subset of body composition variables was then included in a fully parametric Accelerated Failure Time (AFT) model based on the Weibull distribution for a prediction. We estimated the 4000-day risk of CVD and CVD mortality based on this model.\u003c/p\u003e \u003cp\u003eAs a comparative reference for the body composition risk model, the variables from the FRS model \u003csup\u003e17\u003c/sup\u003e were refitted to the Health ABC cohort (referred to as refit FRS). The refit FRS combined with the variables from the body composition risk model was also fit (referred to refit FRS plus 4-DXA model).\u003c/p\u003e \u003cp\u003eModel performance was assessed by discrimination and calibration. For discrimination, both the C-statistic and the discrimination slope were reported. The category-free net reclassification index (NRI\u0026thinsp;\u0026gt;\u0026thinsp;0) and integrated discrimination index (IDI) were used to assess the performance of reclassification and the improvement in discrimination over the refit FRS. Calibration performance was assessed with a calibration plot and summarized the risk scores using the Hosmer-Lemeshow statistic. Calibration in the large was also reported as the difference between the observed 4000-day event frequency and the mean predicted risk score. Distribution-free (nonparametric) 95% CIs were reported for median values and bootstrapped intervals for point estimates of performance metrics when asymptotic intervals were unavailable.\u003c/p\u003e \u003cp\u003eAll analyses were performed using R and Python 3.9.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eBaseline Characteristics\u003c/h2\u003e \u003cp\u003eAmong the 1,882 participants, the median age was 73.0 years; 45.4% were men (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The median follow-up time was 13.4 years (range: 0.02\u0026ndash;15.9 years). A total of 613 incident CVD or cardiovascular mortality occurred (28.04 per 1,000 person-years). Of 87 variables included in the LASSO model, 42 were retained after selection. 4 variables, maximum sagittal diameter (mm), pelvic Bone Mineral Density (BMD) (g/cm2), lumbar spine Bone Mineral Content (BMC) (g), and thigh intermuscular fat density SD (Sectional Density) (HU), were left after further applying the stepwise backward selection technique. The characteristics of 4 DXA variables and variables included in the refit FRS model were summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Supplementary Table S2 also summarized these characteristics for those excluded by our study (N\u0026thinsp;=\u0026thinsp;448), providing p-values from a two-sided t-test on the average values between the included and excluded samples.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eRisk Score\u003c/h2\u003e \u003cp\u003eThe 4-DXA risk score reflected the probability of a cardiovascular event occurring within 4000-days and was given by:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$risk score=1-{e}^{{-e}^{\\left(\\frac{\\text{log}\\left(4000\\right)-PI}{0.89}\\right)}}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere the prognostic index combined the measurements of four DXA variables as follows:\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$prognostic index =9.25-0.20*absag\\_d-0.23*lspibmc+0.14*pelvbmd+0.15*thimfsd$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e,where \u003cem\u003eabsag_d\u003c/em\u003e represents maximum sagittal diameter (mm), \u003cem\u003elspibmc\u003c/em\u003e represents lumbar spine bone mineral content, \u003cem\u003epelvbmd\u003c/em\u003e represents pelvic bone mineral density, and \u003cem\u003ethimfsd\u003c/em\u003e represents Thigh Intermuscular Fat Density SD (Sectional Density) (HU). Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e provided the Cox proportional hazards model coefficients of the refit FRS model, with and without the addition of the prognostic index from the 4-DXA model. Total cholesterol was not a significant risk predictor with (P\u0026thinsp;=\u0026thinsp;0.14) or without (P\u0026thinsp;=\u0026thinsp;0.12) the prognostic index from the 4-DXA model in our study cohort. Gender was statistically significant in the refit FRS (P\u0026thinsp;\u0026lt;\u0026thinsp;0.005) but became statistically insignificant (P\u0026thinsp;=\u0026thinsp;0.076) when adding the prognostic index to the refit FRS. All other refit FRS variables were statistically significant, both with and without the prognostic index.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eModel Performance\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e showed the performance metrics for the refit FRS, the 4-DXA model, and the combination of both models. The C-statistic was 0.62 (95% Confidence Interval (CI): 0.59, 0.64) for the refit FRS model and 0.58 (95% CI: 0.55, 0.61, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\Delta }\\)\u003c/span\u003e\u003c/span\u003eC = -0.04, P\u0026thinsp;=\u0026thinsp;0.017) for the 4-DXA model. The C-statistic slightly increased to 0.63 (95% CI\u0026thinsp;=\u0026thinsp;0.60\u0026ndash;0.66, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\Delta }\\)\u003c/span\u003e\u003c/span\u003eC\u0026thinsp;=\u0026thinsp;0.014, P\u0026thinsp;=\u0026thinsp;0.051) in the refit FRS plus 4-DXA model. The discrimination slope was 0.040 (95% CI\u0026thinsp;=\u0026thinsp;0.021, 0.060) for the refit FRS, 0.023 (95% CI\u0026thinsp;=\u0026thinsp;0, 0.045) for the 4-DXA model, and 0.052 (95% CI\u0026thinsp;=\u0026thinsp;0.033, 0.076) for the Framingham plus 4-DXA model. Compared with the refit FRS model, the FRS plus 4-DXA model had an IDI of 0.012 (95% CI\u0026thinsp;=\u0026thinsp;0.0068, 0.018), indicating an absolute increase of 1.2% in mean risk for participants with events compared with participants without events. The FRS plus 4-DXA model had an NRI\u0026thinsp;\u0026gt;\u0026thinsp;0 of 0.12 (95% CI\u0026thinsp;=\u0026thinsp;0.021, 0.22), with event-specific components of -0.0016 (95% CI = -0.083, 0.076) and no event-specific components of 0.12 (95% CI\u0026thinsp;=\u0026thinsp;0.059, 0.18). Calibration performances of the FRS plus 4-DXA model are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. It illustrates that the performance of the refit FRS plus 4-DXA model is generally better and especially in those high-risk population. The predicted median risk from the 5-th decile on is more consistent with the actual median risk compared to the Refit FRS. The height of bars representing actual median risk appear in an increasing mamner which also demonstrates a more plausible model for combining the FRS variables and the 4-DXA prognostic index.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe present study aimed to select body composition variables predictive for CVD among a broader range of variables obtained through DXA. We also examined whether incorporating the selected features could improve the predictability of traditional FRS among older adults. We selected four features among over 87 body composition variables and established a 4-DXA risk prediction model for CVD. In addition, although the established 4-DXA model independently performs no better than the traditional FRS, combining the 4-DXA prognostic index into the FRS could offer a slight improvement in the performance.\u003c/p\u003e \u003cp\u003eFour body composition indices, maximum sagittal diameter, pelvic bone mineral density, lumbar spine bone mineral content, and thigh intermuscular fat density SD (Sectional Density), were retained after variable selction in the DXA-based prognostic model. We found that the older adults with higher lumber spine bone mineral content, higher max abdominal sagittal diameter, lower pelvic bone mineral density, and lower thigh intramuscular fat sectional density had a higher risk of CVD outcomes. The relationship between each individual variable and CVD is as follow:\u003c/p\u003e \u003cp\u003eAbdominal sagittal diameter measures the distance from the back to the upper abdomen and can be used to reflect visceral obesity. Sagittal abdominal diameter was suggested to be positively related to the risk of coronary heart disease in large prospective studies from the National Health Nutrition and Examination Survey 2011\u0026ndash;2016\u003csup\u003e18\u003c/sup\u003e, Kaiser Permanente of Northern California subscribers\u003csup\u003e19\u003c/sup\u003e, and Ris\u0026eacute;rus et al.\u0026rsquo;s survey in Sweden\u003csup\u003e20\u003c/sup\u003e. In addition, the negative association between Pelvic BMD and CVD found in the present study can also be verified in other research \u003csup\u003e21\u0026ndash;25\u003c/sup\u003e. For instance, Trivedi and Khaw\u003csup\u003e24\u003c/sup\u003e found that BMD measured at the hip is inversely associated with all-cause mortality and cardiovascular disease mortality from the population of over one thousand older men in the Cambridge General Practice Health Study.\u003c/p\u003e \u003cp\u003eThere have also been new findings regarding the other two selected variables, lumbar spine BMC and thigh intramuscular fat sectional density. The present study showed a positive association between lumbar spine BMC with CVD incidence. Although Farhat and Cauley\u003csup\u003e26\u003c/sup\u003e, using the same study cohort, concluded a negative association between lumbar spine BMD with CVD outcomes, this relationship only presented in the white men and black women group, but was missing in the black men and white women group. Therefore, it is likely that the relationship becomes insubstantial when tested in the general population regardless of race and gender. Furthermore, in the systematic review by Khandkar et al.\u003csup\u003e27\u003c/sup\u003e, there was no significant association between lumbar spine BMD and CVD. As for intramuscular thigh muscle fat, its association with CVD outcomes was also conflicting. Some study shows that the thigh intramuscular fat density is positively correlated with CVD risk\u003csup\u003e28,29\u003c/sup\u003e. A study, which focuses on the same population as our study, shows intramuscular thigh muscle fat is independently associated with CVD risk\u003csup\u003e30\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOur study has several strengths. First, our study considered a broader range of body composition variables and investigated their potential association with CVD. We also conducted multiple comparisons between the DXA-based model, refit FRS model, and FRS plus DXA model. Second, the present study proposed a novel method that combines the lasso-penalized Cox PH model and backward elimination for variable selection. We addressed collinearity between body composition variables in the LASSO and preserved statistical significance when conducting stepwise backward elimination. Third, this study supplemented the traditional FRS by adding body composition variables, which led to a more accurate prediction of the risk of CVD.\u003c/p\u003e \u003cp\u003eNevertheless, the limitations of the present study also warrant mentioning. First, the Health ABC cohort population was sampled from well-functioning older adults who are free of life-threatening illness and possess good mobility. Therefore, the sample might have better indices in the examination compared with the older population that has limitation in functioning and mobility. A more balanced sample is needed to better represent the less-functioning population. Second, we were unable to determine whether the selected body composition variables were causally associated with CVD or whether there might be other confounders of CVD. A final causal relationship is still needed to address the importance of each selected variable and to contribute to better prevention and therapy of CVD.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, among the older adults, the combination of four selected DXA variables slightly improved the performance of FRS in predicting cardiovascular endpoints, but the accuracy is still modest. Further study is needed to verify the effect of individual body composition variables on CVD and investigate more effective measures to improve the predictability of FRS.\u003c/p\u003e "},{"header":"Declarations","content":"\u003ch2\u003e \u003cb\u003eAdditional information\u003c/b\u003e \u003c/h2\u003e \u003cp\u003e \u003cstrong\u003eCompeting Interests:\u003c/strong\u003e \u003cp\u003eThe authors declare that they have no competing financial or non-financial interests in relation to the work described.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eC.W. designed the study. L.C., X.W., and T.L. carried out the study and conducted statistical analysis. H.X., H.L., S.X., and J.Y. interpreted the acquired results. L.C. and X.W. drafted the manuscript. All authors provided critical feedback for important intellectual content.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe dataset utilized in our research, known as the Health, Aging and Body Composition (Health ABC) Study, is a longitudinal cohort dataset designed to investigate the risk factors associated with aging-related changes in body composition and physical function in initially well-functioning older adults. This dataset was assembled through extensive field work and follow-ups by the National Institute on Aging (NIA) and the National Institute of Health (NIH). The datasets analysed during the current study are available in the National Institute on Aging's website https://www.nia.nih.gov/healthabc-study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMahmood, S. S., Levy, D., Vasan, R. S. \u0026amp; Wang, T. J. 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P. \u0026amp; Khaw, K. T. Bone Mineral Density at the Hip Predicts Mortality in Elderly Men. Osteoporos. Int. 12, 259\u0026ndash;265 (2001).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWall\u0026eacute;n, E. F. \u003cem\u003eet al.\u003c/em\u003e High prevalence of cardio-metabolic risk factors among adolescents with intellectual disability. Acta Paediatr. 98, 853\u0026ndash;859 (2009).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFarhat, G. N. \u0026amp; Cauley, J. A. The link between osteoporosis and cardiovascular disease. Clin. Cases Miner. Bone Metab. Off. J. Ital. Soc. Osteoporos. Miner. Metab. Skelet. Dis. 5, 19\u0026ndash;34 (2008).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhandkar, C., Vaidya, K., Karimi Galougahi, K. \u0026amp; Patel, S. Low bone mineral density and coronary artery disease: A systematic review and meta-analysis. Int. J. Cardiol. Heart Vasc. 37, 100891 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHassler, E. M. \u003cem\u003eet al.\u003c/em\u003e Distribution of subcutaneous and intermuscular fatty tissue of the mid-thigh measured by MRI\u0026mdash;A putative indicator of serum adiponectin level and individual factors of cardio-metabolic risk. PLOS ONE 16, e0259952 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVan Pelt, R. E., Evans, E. M., Schechtman, K. B., Ehsani, A. A. \u0026amp; Kohrt, W. M. Contributions of total and regional fat mass to risk for cardiovascular disease in older women. Am. J. Physiol.-Endocrinol. Metab. 282, E1023\u0026ndash;E1028 (2002).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuynh, K. \u003cem\u003eet al.\u003c/em\u003e Association Between Thigh Muscle Fat Infiltration and Incident Heart Failure. JACC Heart Fail. 10, 485\u0026ndash;493 (2022).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"char\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eBaseline Characteristics of the Study Cohort.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMedian (Interquartile Range)\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eFRS and DXA baseline\u003c/p\u003e\n\u003cp\u003e(N\u0026thinsp;=\u0026thinsp;1, 882)\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAge, years\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e73.0 (71.0, 76.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMen (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e854 (45.4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMaximum sagittal diameter (mm)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e234.0 (210.0, 257.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePelvic Bone Mineral Density (g/cm2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.1 (1.0, 1.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLumb spine Bone Mineral Content (g)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e48.6 (38.0, 61.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eThigh intermuscular fat density Sectional Density (HU)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e25.5 (24.0, 27.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIncidence (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e613 (32.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBody Mass Index\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e26.8 (23.9, 29.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTotal cholesterol (mg/dL)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e203.0 (179.0, 228.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHigh-density Lipoprotein Cholesterol (mg/dL)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e52.0 (43.0, 64.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDiabetes (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e233 (12.4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSystolic blood pressure (mm Hg)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e134.0 (122.0, 148.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCurrent Smoker (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e187 (9.9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003e\u003cstrong\u003eRisk Prediction Models for the Primary Endpoint of Cardiovascular Diseases.\u003c/strong\u003e All continuous variables were standardized. FRS variables were refit using a Cox proportional hazards model with and without the 4-DXA prognostic index.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eRefit FRS\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eRefit FRS plus 4-DXA model\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCoefficients (95% CI)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ep-value\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCoefficients (95% CI)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ep-value\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAge, years\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.045 (0.017, 0.074)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.005\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.042 (0.014, 0.071)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.005\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMen\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.27 (0.093, 0.44)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.005\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.16 (-0.017, 0.34)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.076\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cspan class=\"SmallCaps\"\u003eTotal cholesterol (mg/dL)\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.062 (-0.020, 0.14)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.065 (-0.017, 0.15)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.12\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHigh-density Lipoprotein Cholesterol (mg/dL)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.19 (-0.29, -0.095)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.17 (-0.26, -0.071)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cspan class=\"SmallCaps\"\u003eDiabetes\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.49 (0.28, 0.70)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.43 (0.22, 0.64)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cspan class=\"SmallCaps\"\u003eSystolic blood pressure (mm Hg)\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.17 (0.090, 0.25)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.16 (0.080, 0.24)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cspan class=\"SmallCaps\"\u003eCurrent Smoker\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.26 (0.088, 0.60)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.38 (0.13, 0.64)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.005\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cspan class=\"SmallCaps\"\u003e4-DXA prognostic index\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.21 (-0.30, -0.13)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab3\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003e\u003cstrong\u003eComparative Performance Metrics for the FRS Model, the 4-DXA Model, and the Refit FRS Plus 4-DXA Model.\u003c/strong\u003e Change of C-statistics, Integrated Discrimination Index, Net Reclassification Index, Event and No-event Net Reclassification Index were calculated using the Refit FRS as the reference model.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eRefit FRS\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e4-DXA model\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eRefit FRS plus 4-DXA model\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eC-statistics\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.62 (0.59, 0.64)\u003c/p\u003e\n\u003cp\u003eReference\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.58 (0.55, 0.61)\u003c/p\u003e\n\u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.017\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.63 (0.60, 0.66)\u003c/p\u003e\n\u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.051\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDiscrimination Slope\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.04 (0.021, 0.06)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.023 (0, 0.045)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.052 (0.033, 0.076)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eQuintile\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.53 (1.81, 3.26)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.02 (1.69, 2.34)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.84 (2.14, 3.54)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHosmer-Lemeshow\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e17.07\u003c/p\u003e\n\u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.029\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e28.93\u003c/p\u003e\n\u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11.20\u003c/p\u003e\n\u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.19\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIntegrated Discrimination Index\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1 [Reference]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.017 (-0.027, -0.0059)\u003c/p\u003e\n\u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.002\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.012 (0.0068, 0.018)\u003c/p\u003e\n\u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNet Reclassifcation Index\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1 [Reference]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.13 (-0.22, -0.030);\u003c/p\u003e\n\u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.010\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.12 (0.022, 0.21)\u003c/p\u003e\n\u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.017\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEvent Net Reclassification Index\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1 [Reference]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.10 (-0.19, -0.022)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.0016 (-0.083, 0.076)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo-event Net Reclassification Index\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1 [Reference]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.023 (-0.079, 0.036)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.12 (0.059, 0.18)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u0026nbsp;\u003c/div\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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Cardiovascular Disease, Body Composition Indices, Dual-energy X-ray absorptiometry, Framingham Risk Score, Older Adults","lastPublishedDoi":"10.21203/rs.3.rs-4203225/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4203225/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCardiovascular disease (CVD) is the leading cause of death worldwide and its risk factors have long been investigating in epidemiological studies. Although the Framingham Risk Score provided an estimate for cardiovascular risk, it did not utilize the body compositions biomarkers. Therefore, our study aims to develop a body composition-based score and incorporate the score into the FRS for better predicting cardiovascular disease among initially well-functioning older adults. 1,882 older adults in the Health, Aging and Body Composition (Health ABC) study were included in our study to screen body composition variables obtained from the Dual-energy X-ray absorptiometry (DXA). We developed the 4-DXA risk score model from the selected variables, refitted the Framingham Risk Score (FRS), incorporated the 4-DXA risk score into the FRS, and compared these developed prediction models. C-statistics were 0.58, 0.62, and 0.63 respectively. Compared to the refit FRS, the net reclassification index and the integrated discrimination index was 0.012 (95% CI: 0.0068\u0026ndash;0.018) and 0.12 (95% CI\u0026thinsp;=\u0026thinsp;0.021, 0.22) for the refit FRS plus the 4-DXA model. Inclusion of body composition indices slightly improved the model performance for predicting CVD. Further research is needed to validate the score accuracy in a higher-risk population.\u003c/p\u003e","manuscriptTitle":"Development of a DXA-Based Risk Score for Cardiovascular Outcomes Among Older Adults: The Health, Aging, and Body Composition Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-19 17:48:37","doi":"10.21203/rs.3.rs-4203225/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-07-18T17:27:45+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-18T08:35:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"69908750446027096255960744802361631961","date":"2024-06-04T07:00:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"320272055824330274747228204309293400580","date":"2024-06-04T03:04:56+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-05-21T20:24:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"211206804911102963066628302541023868901","date":"2024-05-11T14:58:07+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-04-18T07:45:41+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-04-17T12:50:44+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-04-10T12:31:09+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-04-10T06:09:59+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-04-02T01:56:28+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0a4ad4dc-c083-4566-bc53-bca18b92deba","owner":[],"postedDate":"April 19th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":30652449,"name":"Health sciences/Risk factors"},{"id":30652450,"name":"Health sciences/Biomarkers/Predictive markers"},{"id":30652451,"name":"Health sciences/Diseases/Cardiovascular diseases"},{"id":30652452,"name":"Health sciences/Health care/Geriatrics"},{"id":30652453,"name":"Health sciences/Cardiology"}],"tags":[],"updatedAt":"2024-10-14T16:05:59+00:00","versionOfRecord":{"articleIdentity":"rs-4203225","link":"https://doi.org/10.1038/s41598-024-74185-y","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2024-10-07 15:57:50","publishedOnDateReadable":"October 7th, 2024"},"versionCreatedAt":"2024-04-19 17:48:37","video":"","vorDoi":"10.1038/s41598-024-74185-y","vorDoiUrl":"https://doi.org/10.1038/s41598-024-74185-y","workflowStages":[]},"version":"v1","identity":"rs-4203225","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4203225","identity":"rs-4203225","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}