Osteosarcopenia, bone-muscle interactions, and Frailty risk: A Prospective Cohort Study of Community- Dwelling Older Adults

Osteosarcopenia, bone-muscle interactions, and Frailty risk: A Prospective Cohort Study of Community- Dwelling Older Adults | 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 Osteosarcopenia, bone-muscle interactions, and Frailty risk: A Prospective Cohort Study of Community- Dwelling Older Adults Mizhgan Fatima, Ben Kirk, Sara Vogrin, Jason Talevski, Sharon Brennan-Olsen, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6759229/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 10 Dec, 2025 Read the published version in Aging Clinical and Experimental Research → Version 1 posted 12 You are reading this latest preprint version Abstract Background Osteosarcopenia, defined as the coexistence of osteopenia/osteoporosis and sarcopenia, may influence frailty risk in older adults. However, the longitudinal association between osteosarcopenia and its components with frailty remains unclear. This study aimed to address this. Methods Data from a prospective cohort study of community-dwelling adults in Australia. Frailty was defined by the presence of three or more components based on Fried criteria: exhaustion, slow gait speed, low grip strength, unintentional weight loss, and low physical activity. Osteosarcopenia was defined by osteopenia/osteoporosis (WHO criteria) and sarcopenia (European Working Group on Sarcopenia in Older People [EWGSOP2] and Sarcopenia Definition and Outcome consortium [SDOC]). Multivariable logistic regression models evaluated the associations between osteosarcopenia or its components with frailty. Results Of 301 participants enrolled, 151 (mean age: 65.1 years, 59.6% women) completed a follow-up (median: 4.5 years). Osteosarcopenia was associated with frailty risk irrespective of the definition used: EWGSOP2: OR = 8.13, 95% CI 1.61–41.10; SDOC OR = 8.0, 95% CI 1.42–45.06). Among its components, low grip strength (OR = 0.92, 95% CI 0.84–1.00) and slow gait speed (OR = 0.67, 95% CI 0.53–0.85) were independently associated with frailty; bone mineral density/lean mass were not. An interaction between bone and muscle health and frailty risk was observed (p < 0.011), whereby higher values of grip strength were protective against frailty when bone density was lower (the opposite was also true). Conclusion In this prospective cohort study, osteosarcopenia increased the risk of frailty. Our interaction analysis suggests therapies targeting bone density and grip strength may mitigate against frailty. Aging osteosarcopenia sarcopenia osteoporosis frailty Figures Figure 1 Introduction Frailty is an age-related syndrome characterized by diminished physiological reserve and resilience, increasing susceptibility to stressors, and the risk of hospitalization, disability, and mortality ( 1 ). It results from senescence-related cellular and subcellular changes that disrupt key physiological systems, including the musculoskeletal system, hypothalamic-pituitary-adrenal (HPA) axis, and metabolic function. These disruptions give rise to the Fried frailty phenotype, defined when three or more of the following five criteria are present: unintentional weight loss, exhaustion, slow gait speed, reduced grip strength, and low physical activity ( 2 ). As the global population ages, frailty is becoming increasingly prevalent, with significant implications for individual health and healthcare systems ( 3 ). Osteosarcopenia is a recently defined clinical entity that describes the co-occurrence of osteopenia or osteoporosis (low bone mineral density) and sarcopenia (low muscle mass and strength) ( 4 ). Each of these conditions individually contributes to an increased risk of falls, fractures, and physical functional decline in older adults; however, their combined impact is synergistic and accelerates musculoskeletal deterioration in older people ( 5 ). The pathophysiological basis of osteosarcopenia stems from impaired bone-muscle crosstalk and shared mechanisms such as chronic inflammation, hormonal dysregulation, and environmental factors involving both bone and muscle ( 6 ). Through its compounded impact on bone and muscle, osteosarcopenia may contribute to the development of frailty. Several cross-sectional studies have reported an association between osteosarcopenia and frailty, suggesting a potential biological and clinical link ( 7 – 14 ). However, the inherent limitations of cross-sectional designs preclude conclusions about causality or temporal sequence. It remains uncertain whether osteosarcopenia precedes frailty or if both arise concurrently in response to common age-related mechanisms. To date, longitudinal data exploring osteosarcopenia as a potential predictor of frailty are limited. To address this gap, the present study aimed to: ( 1 ) determine whether osteosarcopenia at baseline predicts the onset of frailty over time; ( 2 ) investigate whether the individual components of osteosarcopenia—specifically, low bone mass and reduced muscle strength—independently predict frailty; and ( 3 ) explore whether bone-muscle interaction contributes to frailty development. We hypothesize that osteosarcopenia, due to its combined impact on bone and muscle, is an independent and significant predictor of frailty in older adults. Materials and Methods Study Design The Brimbank Ageing Well Study (BRAW) is a prospective cohort study that investigated the association between baseline osteosarcopenia and frailty among community-dwelling older adults aged ≥ 50 years in the Western suburbs of Melbourne, Australia. Participants were recruited through convenience sampling and assessed at two time points: baseline (2018–2020) and follow-up (2023–2024). Inclusion criteria were aged ≥ 50 years, living in the Western suburbs of Melbourne, and the ability to attend the study site for assessments. Exclusion criteria included inability to provide informed consent; severe cognitive impairment (dementia or dementia with Lewy body); medical conditions affecting balance or physical performance (e.g., Parkinson’s disease, progressive neurological disorders, multiple sclerosis, uncontrolled psychiatric disorders); severe arthritis, chronic lung disease requiring oxygen or renal disease requiring dialysis; history of stroke or recent major surgeries (e.g., knee replacement, spinal, or cardiac surgery) within the past 6 months; life expectancy 150 kg (a contraindication for DEXA scanning). Written informed consent was obtained from all participants. The study was approved by the Human Research Ethics Committee at Melbourne Health (HREC/45024/MH-2020). Study Assessments Participants attended an assessment session at Sunshine Hospital that lasted approximately 2 to 2.5 hours. They were provided with a self-administered questionnaire capturing sociodemographic information (age, sex, country of birth, living arrangements, income, education, quality of life scores), lifestyle factors (smoking and alcohol consumption history), and physical activity levels using the International Physical Activity Questionnaire (IPAQ) ( 15 ). Interpreters were available for non-English speaking participants. Subsequently, study personnel collected clinical data, including comorbidities (using the Charlson Comorbidity Index), medication use, mini nutritional assessment, and fracture history. Physical assessments included anthropometric measurements (height, weight, body mass index), handgrip strength (using a handheld dynamometer), sit-to-stand test, gait speed, Timed Up and Go (TUG) test, and the Short Physical Performance Battery (SPPB). A whole-body Dual-Energy X-ray Absorptiometry (DEXA) scan was performed to assess Bone Mineral Density (BMD), body composition, and Appendicular Lean Mass adjusted for height (ALM/height 2 ). Finally, participants underwent blood tests to assess serum vitamin D and C-Reactive Protein (CRP) levels. Blood tests were conducted only at baseline; all other assessments were conducted at both visits. Osteosarcopenia Osteosarcopenia was defined by the concomitant presence of osteopenia or osteoporosis and sarcopenia ( 5 ). Osteopenia and osteoporosis were defined using the World Health Organization (WHO) criteria based on BMD measured via DEXA. Osteoporosis was defined as a BMD T-score of ≤ -2.5, while osteopenia was classified as a BMD T-score between − 1.0 and − 2.5 ( 16 ). The lowest recorded BMD at three sites, lumbar spine, femoral neck, and distal 1/3rd of the radius, was used to define osteopenia or osteoporosis. Sarcopenia was defined using two criteria: the European Working Group on Sarcopenia in Older People, revised (EWGSOP2, 2019), and the Sarcopenia Definition and Outcomes Consortium (SDOC, 2020). The EWGSOP2 criteria were employed, as the majority of our study population was Australian-born Caucasian, and these criteria have recently been endorsed for use in Australia via the Delphi convention ( 17 ). SDOC criteria are recommended in Caucasians. According to EWGSOP2, probable sarcopenia was identified by either a low handgrip strength (< 27 kg in men and 15 seconds. Confirmed sarcopenia required the presence of probable sarcopenia along with DXA evidence of low ALM/height (< 7.0 kg/m² in men and < 5.5 kg/m² in women) ( 18 ). As per SDOC, sarcopenia was defined based on a low handgrip strength (< 35.5 kg in men and < 20 kg in women) and slow gait speed (< 0.8 m/s). A second SDOC definition was used where handgrip strength was adjusted for BMI ( 19 ). Frailty The validated criterion for frailty, the Fried criteria, was used, which classified individuals as frail if they met at least three out of five criteria, prefrail if they met two or one, and non-frail if they met none. The five criteria included: 1) Unintentional weight loss of > 4.5 kg in the past year or a BMI < 18 kg/m², modified as in previous studies ( 20 – 22 ) 2) Self-reported exhaustion (assessed using two questions from the CES-D depression scale, if the participants provided a positive answer to any of following two questions, i) I felt that everything I did was an effort, for ≥ 3 days in a week, ii) I could not get going, for ≥ 3 days in a week) 3) Low physical activity, defined based on the IPAQ score, categorized as low physical activity, using the gender-specific lowest quintile for the study population. 4) Slowness (gait speed < 1.0 m/s) 5) Weakness (grip strength in the lowest 20% by gender and BMI) ( 23 ) Co-variates Covariates were selected based on previous evidence and studies suggesting a potential influence on the exposure (osteosarcopenia) and outcome (frailty) variables ( 24 ). These included demographic factors (age, sex, living status), socioeconomic factors (education level, income status), and lifestyle behaviors (alcohol consumption, smoking status). Annual income was classified as binary, categorizing those earning less than AUD 40,000 and those earning more, based on an Australian couple’s full age pension of approximately AUD 39,640 per year ( 25 ). Clinical variables such as comorbidities, current medications, serum levels of vitamin D, C-reactive protein (CRP), and nutritional status were also considered. Statistical Analysis Descriptive statistics were used to summarize the baseline characteristics of study participants. Categorical variables (frequencies and percentages) were compared using chi-square tests, and continuous variables (means ± SD or medians ± IQR for skewed data) using one-way ANOVA. Participants were classified into two groups (non-frail and frail), and frailty transition was reported as a percentage. At baseline, participants were categorized into four mutually exclusive groups: ( 1 ) no condition (neither osteopenia/osteoporosis nor sarcopenia), ( 2 ) sarcopenia only, ( 3 ) osteopenia or osteoporosis only, and ( 4 ) osteosarcopenia. Logistic regression was used to estimate the odds of developing frailty at follow-up, comparing each condition group to the no-condition group. Due to the limited number of frailty cases for multivariable analyses, participants were stratified into two groups: those with osteosarcopenia and those without. To minimize overfitting, each confounder was adjusted individually in separate models, adhering to the recommended guideline of at least 10 events per variable in logistic regression analyses ( 26 ). Confounders were selected based on significant associations with frailty in univariate analyses and included demographic (age, sex), lifestyle (smoking, alcohol use, physical activity), clinical (polypharmacy, vitamin D, CRP), and socioeconomic (education, income) factors. Muscle parameters (grip strength, gait speed, five-times sit-to-stand test, SPPB, ALM, ALM/height²) and bone measures (BMD and T-scores at various sites) were analyzed as continuous variables using logistic regression to assess associations with frailty at follow-up. Models were adjusted for sex, physical activity, and income, based on initial findings. Interaction analysis models were employed to assess whether muscle parameters significantly interacted with bone measures in predicting frailty. If significant interactions were identified, indicating effect modification by value level, no further analysis was conducted. Otherwise, mutually adjusted models were used to assess the independent effects of muscle and bone measures. Results are presented as odds ratios (OR) with 95% confidence intervals (CI). A p-value < 0.05 was considered statistically significant, except for interactions where p < 0.06 was used, aligning with exploratory research practices that recognize the value of marginal trends ( 27 ). All analyses were conducted using Stata version 18 (StataCorp, College Station, TX, USA). Results Participants Characteristics Out of 300 participants at baseline, 151 completed follow-ups (reasons detailed in Supplementary Figure S1). The characteristics of participants who attended and those who did not attend are compared in Supplementary Table 1. Of those who completed follow-ups, 143 were non-frail and eight were frail at baseline assessment. Over the study follow-up, out of the non-frail, 13 (9.1%) became frail; of the frail, 4 (50%) improved to non-frail. The overall frailty incidence in this cohort was 6.6%. Table 1 shows the baseline characteristics of 151 participants categorized as robust, prefrail, or frail. Eight frail participants at baseline were excluded from follow-up analysis, leaving 143 non-frail individuals for the final analysis. Table 1 Summary of Participants’ Characteristics at Baseline, Categorized by Frailty (Three Groups) Fried Frailty categories Robust Pre-Frail Frail Total p -value Numbers 97 (64.2%) 46 (30.5%) 8 (5.3%) 151 (100.0%) Age at first visit 64.5 (5.9) 66.3 (7.1) 65.7 (6.7) 65.1 (6.3) 0.398 Women 56 (57.7%) 30 (65.2%) 4 (50.0%) 90 (59.6%) 0.592 Lives alone 19 (19.6%) 12 (26.1%) 3 (37.5%) 34 (22.5%) 0.398 Born in Australia 68 (70.1%) 30 (65.2%) 3 (37.5%) 101 (66.9%) 0.163 ATSI 0 (0.0%) 1 (2.2%) 0 (0.0%) 1 (0.7%) 0.317 Speak English at home 94 (96.9%) 44 (95.7%) 7 (87.5%) 145 (96.0%) 0.419 Education Post-secondary/tertiary qualification 82 (84.5%) 35 (76.1%) 5 (62.5%) 122 (80.8%) 0.196 High school or less 15 (15.5%) 11 (23.9%) 3 (37.5%) 29 (19.2%) Income Annual income > 40K AUD 77 (79.4%) 30 (65.2%) 2 (25.0%) 109 (72.2%) 0.002 Has private health insurance 79 (81.4%) 23 (50.0%) 2 (25.0%) 104 (68.9%) < 0.001 Health card holder 24 (24.7%) 21 (45.7%) 6 (75.0%) 51 (33.8%) 0.002 Smoking Smoking (past or present) 42 (43.3%) 21 (45.7%) 3 (37.5%) 66 (43.7%) 0.904 Smoking (current) No, not at all 41 (91.1%) 20 (95.2%) 3 (75.0%) 64 (91.4%) 0.614 Yes, but not daily 3 (6.7%) 1 (4.8%) 1 (25.0%) 5 (7.1%) Yes, daily 1 (2.2%) 0 (0.0%) 0 (0.0%) 1 (1.4%) Duration smoking (years) [3] 23.0 (12.8) [3] 23.3 (20.8) [1] 55.0 (.) [7] 27.7 (18.5) 0.319 Smoking (past) 38 (80.9%) 21 (91.3%) 2 (50.0%) 61 (82.4%) 0.120 Alcohol Alcohol (past or present) 94 (96.9%) 43 (93.5%) 6 (75.0%) 143 (94.7%) 0.026 Alcohol (last month) 79 (83.2%) 32 (74.4%) 5 (71.4%) 116 (80.0%) 0.417 Alcohol amount last year No days 10 (10.4%) 6 (14.0%) 1 (14.3%) 17 (11.6%) 0.567 Less than once a month 11 (11.5%) 9 (20.9%) 2 (28.6%) 22 (15.1%) One to three days per month 23 (24.0%) 12 (27.9%) 2 (28.6%) 37 (25.3%) One to four days per week 31 (32.3%) 12 (27.9%) 1 (14.3%) 44 (30.1%) Five or more days per week 21 (21.9%) 4 (9.3%) 1 (14.3%) 26 (17.8%) Alcohol (average) over 12 months [90] 2.2 (3.2) [40] 1.7 (1.0) [6] 2.8 (3.5) [136] 2.1 (2.7) 0.437 Comorbidities Any comorbidity 70 (72.2%) 38 (82.6%) 7 (87.5%) 115 (76.2%) 0.290 Arthritis 33 (34.0%) 17 (37.0%) 3 (37.5%) 53 (35.1%) 0.933 Chronic Back pain 26 (26.8%) 18 (39.1%) 2 (25.0%) 46 (30.5%) 0.308 Cardiovascular Disease 9 (9.3%) 12 (26.1%) 1 (12.5%) 22 (14.6%) 0.029 Chronic Lung disease 5 (5.2%) 7 (15.2%) 2 (25.0%) 14 (9.3%) 0.044 Cancer 4 (4.1%) 4 (8.7%) 2 (25.0%) 10 (6.6%) 0.059 Depression or Anxiety 7 (7.2%) 8 (17.4%) 2 (25.0%) 17 (11.3%) 0.089 Diabetes 4 (4.1%) 6 (13.0%) 1 (12.5%) 11 (7.3%) 0.134 Cerebrovascular disease 1 (1.0%) 2 (4.3%) 1 (12.5%) 4 (2.6%) 0.105 Other comorbidities 16 (16.5%) 9 (19.6%) 1 (12.5%) 26 (17.2%) 0.844 Previous fragility fractures Any fracture in last 5 years 13 (13.8%) 8 (17.8%) 2 (25.0%) 23 (15.6%) 0.631 Vertebral fracture 2 (2.1%) 2 (4.3%) 0 (0.0%) 4 (2.7%) 0.655 Non-vertebral fractures (exclude hip) 49 (50.5%) 23 (50.0%) 6 (75.0%) 78 (51.7%) 0.397 Hip fractures 2 (2.1%) 4 (8.7%) 0 (0.0%) 6 (4.0%) 0.143 Physical Activity Scale (IPAQ) Low 0 (0.0%) 22 (47.8%) 8 (100.0%) 30 (19.9%) < 0.001 Moderate 19 (19.6%) 5 (10.9%) 0 (0.0%) 24 (15.9%) High 78 (80.4%) 19 (41.3%) 0 (0.0%) 97 (64.2%) Nutrition (MNA) At risk of malnutrition 2 (8.0%) 4 (28.6%) 0 (0.0%) 6 (15.0%) 0.206 Medications Any medication (exclude nutritional supplements) 65 (67.0%) 31 (67.4%) 8 (100.0%) 104 (68.9%) 0.148 Polypharmacy ( > = 5 medications) 10 (10.3%) 9 (19.6%) 2 (25.0%) 21 (13.9%) 0.212 Psychological Distress Scale (Kessler K6+) No/low distress 95 (97.9%) 36 (80.0%) 4 (66.7%) 135 (91.2%) < 0.001 Moderate distress 2 (2.1%) 6 (13.3%) 2 (33.3%) 10 (6.8%) High distress 0 (0.0%) 3 (6.7%) 0 (0.0%) 3 (2.0%) Clinical & anthropometric data Systolic BP (mmHg) 135.9 (18.7) 133.5 (18.7) 136.1 (14.6) 135.2 (18.4) 0.792 Diastolic BP (mmHg) 85.3 (10.4) 84.5 (11.3) 91.9 (10.9) 85.4 (10.7) 0.211 Body Mass Index (Kg/m 2 ) 27.1 (4.1) 28.4 (6.6) 33.8 (6.3) 27.8 (5.3) 0.011 Hip circumference (cm) 104.7 (8.9) 102.9 (16.9) 120.6 (17.3) 105.0 (12.8) 0.020 Waist circumference (cm) 91.7 (14.6) 95.3 (18.9) 114.8 (17.3) 94.0 (16.8) 0.002 Pathology Vitamin D levels 67.9 (20.3) 59.6 (22.4) 83.1 (37.3) 66.2 (22.6) 0.127 Hs-CRP 1.5 (2.0) 3.0 (3.7) 6.0 (6.3) 2.1 (3.1) 0.004 Physical measurements Hand Grip Strength Max (Kg) 37.8 (8.1) 29.7 (10.0) 24.4 (8.3) 34.6 (9.8) < 0.001 Hand Grip Strength/BMI 1.4 (0.3) 1.1 (0.4) 0.8 (0.3) 1.3 (0.4) < 0.001 5x Sit to Stand (sec) 10.8 (7.8) 14.8 (13.0) 17.3 (5.1) 12.3 (9.8) < 0.001 Short Physical Performance battery 11.5 (0.9) 10.6 (2.0) 8.8 (2.0) 11.1 (1.5) < 0.001 Gait speed best (m/s) 1.4 (0.2) 1.2 (0.3) 0.7 (0.2) 1.3 (0.3) < 0.001 Muscle measurement on DXA ALM 20.9 (5.1) 19.5 (5.7) 21.0 (6.1) 20.5 (5.4) 0.181 ALM/Height 7.3 (1.2) 7.0 (1.4) 7.8 (1.7) 7.3 (1.3) 0.247 Subtotal bodyfat, %age 34.4 (8.3) 37.1 (9.0) 43.7 (11.9) 35.7 (9.0) 0.031 Whole body fat, %age 33.8 (7.8) 36.3 (8.6) 42.7 (11.5) 35.0 (8.5) 0.032 Whole body fat, kg 26.8 (8.7) 29.4 (11.2) 41.5 (16.7) 28.3 (10.5) 0.012 Bone measurement on DXA Lowest site BMD (g/cm2) [97] 0.5 (0.1) [46] 0.5 (0.1) [8] 0.5 (0.1) [151] 0.5 (0.1) 0.892 Lumbar spine BMD (g/cm2) [97] 1.0 (0.2) [46] 1.0 (0.2) [8] 1.1 (0.2) [151] 1.0 (0.2) 0.366 Femoral Neck BMD (g/cm2) [96] 0.8 (0.1) [46] 0.7 (0.1) [8] 0.8 (0.1) [150] 0.7 (0.1) 0.497 Hip total BMD (g/cm2) [96] 0.9 (0.1) [46] 0.9 (0.2) [8] 0.9 (0.1) [150] 0.9 (0.2) 0.687 Distal 1/3 radius BMD (g/cm2) [97] 0.7 (0.1) [45] 0.7 (0.1) [8] 0.7 (0.1) [150] 0.7 (0.1) 0.295 Lowest site T-score [97] -1.3 (1.0) [46] -1.5 (1.2) [8] -1.2 (1.0) [151] -1.3 (1.0) 0.589 Lumbar spine (T-score) [97] -0.6 (1.4) [46] -0.7 (1.7) [8] 0.1 (1.6) [151] -0.6 (1.5) 0.477 Femoral neck (T-score) [95] -1.1 (0.9) [46] -1.2 (1.0) [8] -1.1 (1.1) [149] -1.1 (0.9) 0.687 Distal 1/3 radius (T-score) [96] -0.6 (1.2) [46] -1.0 (1.3) [6] -1.3 (1.2) [148] -0.8 (1.2) 0.173 Fried criteria – individual measurements Exhaustion 0 (0.0%) 4 (8.7%) 2 (25.0%) 6 (4.0%) < 0.001 Weight loss 0 (0.0%) 7 (15.2%) 0 (0.0%) 7 (4.6%) < 0.001 Low physical activity 0 (0.0%) 22 (47.8%) 8 (100.0%) 30 (19.9%) < 0.001 Slowness 0 (0.0%) 12 (26.1%) 8 (100.0%) 20 (13.2%) < 0.001 Weakness 0 (0.0%) 17 (37.0%) 6 (75.0%) 23 (15.2%) < 0.001 Osteoporosis (WHO criteria) Normal 33 (34.0%) 17 (37.0%) 4 (50.0%) 54 (35.8%) 0.753 Osteopenia 54 (55.7%) 22 (47.8%) 3 (37.5%) 79 (52.3%) Osteoporosis 10 (10.3%) 7 (15.2%) 1 (12.5%) 18 (11.9%) Sarcopenia categorization Sarcopenia EWGSOP 8 (8.2%) 13 (28.3%) 7 (87.5%) 28 (18.5%) < 0.001 Sarcopenia EWGSOP (severe) 2 (2.1%) 7 (15.2%) 4 (50.0%) 13 (8.6%) < 0.001 Sarcopenia SDOC 1 0 (0.0%) 18 (39.1%) 8 (100.0%) 26 (17.2%) < 0.001 Sarcopenia SDOC 2 4 (4.1%) 17 (37.0%) 6 (75.0%) 27 (17.9%) < 0.001 Osteosarcopenia categorization Osteosarcopenia (EWGSOP2) mutually exclusive groups Healthy 28 (28.9%) 14 (30.4%) 0 (0.0%) 42 (27.8%) < 0.001 Osteopenia/osteoporosis 61 (62.9%) 19 (41.3%) 1 (12.5%) 81 (53.6%) Sarcopenia 5 (5.2%) 3 (6.5%) 4 (50.0%) 12 (7.9%) Osteosarcopenia 3 (3.1%) 10 (21.7%) 3 (37.5%) 16 (10.6%) Osteosarcopenia (SDOC 1) mutually exclusive groups Healthy 33 (34.0%) 11 (23.9%) 0 (0.0%) 44 (29.1%) < 0.001 Osteopenia/osteoporosis 64 (66.0%) 17 (37.0%) 0 (0.0%) 81 (53.6%) Sarcopenia 0 (0.0%) 6 (13.0%) 4 (50.0%) 10 (6.6%) Osteosarcopenia 0 (0.0%) 12 (26.1%) 4 (50.0%) 16 (10.6%) Osteosarcopenia (SDOC 2) mutually exclusive groups Healthy 30 (30.9%) 9 (19.6%) 1 (12.5%) 40 (26.5%) < 0.001 Osteopenia/osteoporosis 63 (64.9%) 20 (43.5%) 1 (12.5%) 84 (55.6%) Sarcopenia 3 (3.1%) 8 (17.4%) 3 (37.5%) 14 (9.3%) Osteosarcopenia 1 (1.0%) 9 (19.6%) 3 (37.5%) 13 (8.6%) Unless otherwise specified, mean (SD) for continuous variables and number (%age) for categorical variables. [] before reading shows number of observation. ALM Appendicular lean mass, ALM/height Appendicular lean mass adjusted for height, ATSI Aboriginal and Torres Strait Island, AUD Australian Dollar, BMD Bone Muscle Density, BP blood pressure, DXA Dual energy X-ray absorptiometry, EWGSOP2 European Working Group of Sarcopenia in Older People 2, Hs CRP High sensitivity C-Reactive Protein, IPAQ International Physical Activity Questionnaire, MNA Mini Nutritional Assessment, SDOC 1 Sarcopenia Definition and Outcome Consortium, SDOC 2 SDOC adjusted for BMI, WHO World Health Organization. The mean age of participants was 65.1 years (SD 6.3), with 59.6% female. No age or sex differences were found between frailty groups. Most participants lived with others (78%), were Australian-born (66.9%), English-speaking (96%), and had completed secondary school education or higher (> 80.8%). Socioeconomic differences were noted across frailty groups when classified by an annual income > 40K AUD (p = 0.002), private health insurance (p < 0.001), and health care card ownership (p = 0.002). Differences were also noted between groups in lifestyle factors; alcohol use (p = 0.026), physical activity (IPAQ, p < 0.001), and inflammatory markers, hs-CRP levels (p = 0.004). No differences were found for smoking, comorbidities, fractures, medications, polypharmacy, or vitamin D. While muscle function measures (grip strength, gait speed) differed between groups, muscle and bone mass (ALM, ALM/height², BMD, T-scores) did not. Supplementary Figure S2 shows the overlap of osteopenia/osteoporosis and sarcopenia at baseline. Each condition (osteosarcopenia, sarcopenia and osteopenia/osteoporosis) was analyzed as a distinct group to assess its association with frailty at follow-up. Association Between Osteosarcopenia and Frailty Using the EWGSOP2 criteria, osteosarcopenia was significantly associated with increased odds of developing frailty compared to individuals without osteopenia/osteoporosis or sarcopenia (OR = 8.13, 95% CI: 1.61–41.10, p = 0.011). Sarcopenia alone at baseline was associated with higher odds of frailty (OR = 1.86, 95% CI: 0.17–20.51, p = 0.613), though this was not statistically significant. Interestingly, baseline osteopenia or osteoporosis was associated with lower odds of frailty (OR = 0.68, 95% CI: 0.15–3.21, p = 0.63), although this result was also not significant. Similar patterns were observed using the SDOC definition, with osteosarcopenia again showing a significant association with frailty (OR = 8.00, 95% CI: 1.42–45.06, p = 0.018), while associations for sarcopenia and osteopenia/osteoporosis remained non-significant (Table 2 ). Table 2 Association of Baseline Osteosarcopenia (and its components) with Frailty Risk in a Longitudinal Cohort. Frailty vs non-frailty at follow-up Total number at follow up Non-Frail at follow up Frail at follow up Unadjusted – univariable analysis n = 143 n = 13 n = 13 OR 95% CI p-value European Working Group of Sarcopenia in older People – Revised (EWGSOP2) Definition Non-osteopenic/non-sarcopenic (control group) 42 39 3 Ref Osteopenia or osteoporosis only 80 76 4 0.68 [0.15,3.21] 0.63 Sarcopenia only 8 7 1 1.86 [0.17,20.51] 0.613 Osteosarcopenia 13 8 5 8.13 [1.61,41.10] 0.011 Sarcopenia Definition and Outcome Consortium (SDOC) Definition Non-osteopenic/non-sarcopenic (control group) 39 36 3 Ref Osteopenia or osteoporosis only 83 78 5 0.77 [0.17,3.40] 0.729 Sarcopenia only 11 10 1 1.2 [0.11,12.83] 0.88 Osteosarcopenia 10 6 4 8 [1.42,45.06] 0.018 In a multivariable analysis comparing two groups, osteosarcopenia to non-osteosarcopenia, osteosarcopenia (EWGSOP2 definition) was significantly associated with higher odds of frailty (OR = 9.53, 95% CI: 2.53–35.92, p = 0.001). Models were individually adjusted for age, sex, polypharmacy, smoking, alcohol use, education, income, physical activity, and CRP levels. Osteosarcopenia remained a consistent predictor of frailty across all models. Among the covariates, only an income greater than AUD 40,000/year was protective; other variables showed no significant associations (Table 3 ). Using the SDOC criteria (adjusted for BMI), osteosarcopenia was again significantly associated with increased odds of frailty (OR = 9.19, 95% CI: 2.19–38.56, p = 0.002). This association remained robust in multivariable models. Similar to the EWGSOP2 findings, an income above AUD 40,000/year was the only covariate significantly associated with a reduced risk of frailty (Table 3 ). Table 3 Association of Baseline Osteosarcopenia with Frailty Risk - Multivariable Analysis Osteosarcopenia vs non-osteosarcopenia Frail vs non-frail (dependent variable) Total n Non-Frail n Frail n Multivariable analysis adjusted for each variable 143 130 13 OR 95% CI p-value OR 95% CI p-value Osteosarcopenia (by EWGSOP2) 13 8 5 Unadjusted 9.53 [2.53,35.92] 0.001 Adjusted 8.28 [2.08,32.91] 0.003 Age at first visit 1.04 [0.94,1.14] 0.481 57 53 4 9.3 [2.46,35.20] 0.001 Sex - Men 0.72 [0.20,2.60] 0.612 19 16 3 9.16 [2.27,36.98] 0.002 Polypharmacy 1.16 [0.24,5.64] 0.858 63 60 3 8.87 [2.30,34.14] 0.002 Smoking 0.39 [0.10,1.56] 0.183 137 125 12 9.56 [2.45,37.31] 0.001 Alcohol 1.03 [0.08,12.40] 0.984 117 107 10 9.81 [2.49,38.70] 0.001 Post-secondary qualification 1.13 [0.25,5.19] 0.871 107 101 6 12.68 [2.91,55.15] 0.001 Income AUD > 40K/yr 0.19 [0.05,0.69] 0.012 143 93 4 11.91 [2.57,55.09] 0.002 High Physical activity 0.29 [0.06,1.49] 0.139 138 10.95 [2.63,45.50] 0.001 Hs-CRP 1.16 [0.98,1.37] 0.093 Osteosarcopenia (by SDOC2) 10 6 4 Unadjusted 9.19 [2.19,38.56] 0.002 Adjusted 8.52 [1.97,36.84] 0.004 Age at first visit 1.06 [0.96,1.16] 0.244 57 43 4 8.8 [2.06,37.65] 0.003 Sex - Men 0.8 [0.22,2.90] 0.738 19 16 3 8.92 [2.10,37.88] 0.003 Polypharmacy 1.98 [0.45,8.68] 0.367 63 60 3 8.52 [1.98,36.69] 0.004 Smoking 0.38 [0.10,1.52] 0.172 137 125 12 9.92 [2.32,42.43] 0.002 Alcohol 0.34 [0.03,3.23] 0.345 117 107 10 9.3 [2.12,40.80] 0.003 Post-secondary qualification 1.05 [0.23,4.74] 0.946 107 101 6 10.14 [2.20,46.86] 0.003 Income AUD > 40K/yr 0.23 [0.07,0.79] 0.020 143 93 4 11.45 [2.29,57.32] 0.003 High Physical activity 0.22 [0.04,1.07] 0.060 138 7.79 [1.55,39.05] 0.013 Hs-CRP 1.10 [0.92,1.31] 0.303 Association Between Bone and Muscle Measures and Frailty Clinical measures of muscle strength, handgrip strength (both unadjusted and BMI-adjusted), gait speed, and the SPPB were significantly associated with frailty at follow-up. In contrast, DEXA-derived measures of muscle (ALM and ALM/height 2 ) and bone (BMD and T-scores) showed no significant associations. The models were adjusted for sex, income, and physical activity, based on significant differences observed in the initial analysis (Table 4 ). Table 4 Association of Baseline Bone and Muscle Measures with Frailty Risk in a longitudinal cohort. Frailty vs non-frailty at follow-up (13 vs 130) Model 1 Model 2 Model 3 n OR 95% CI p-value OR 95% CI p-value OR 95% CI p-value Muscle Hand Grip Strength Max (Kg) 143 0.92 [0.84,1.00] 0.038 0.93 [0.87,1.00] 0.057 0.94 [0.87,1.01] 0.085 Hand Grip Strength/BMI 143 0.15 [0.03,0.90] 0.037 0.2 [0.04,0.97] 0.046 0.24 [0.04,1.28] 0.095 Gait speed best(m/s) [0.1] 143 0.67 [0.53,0.85] 0.001 0.69 [0.54,0.88] 0.003 0.64 [0.49,0.84] 0.001 5x Sit to Stand (sec) 143 1.04 [1.00,1.08] 0.077 1.04 [1.00,1.09] 0.043 1.03 [0.99,1.07] 0.152 Short Physical Performance Battery 142 0.58 [0.42,0.81] 0.001 0.59 [0.43,0.82] 0.001 0.6 [0.43,0.83] 0.002 ALM (kg) 143 0.94 [0.78,1.14] 0.545 0.94 [0.84,1.05] 0.301 0.95 [0.84,1.07] 0.361 ALM/Height (kg/m2) 143 0.75 [0.38,1.47] 0.396 0.75 [0.46,1.20] 0.23 0.75 [0.45,1.25] 0.268 Subtotal body fat, %age 143 1.05 [0.96,1.14] 0.308 1.05 [0.98,1.13] 0.134 1.02 [0.96,1.10] 0.489 Bone Lowest site BMD (g/cm2) [0.5] 143 1.22 [0.04,33.96] 0.906 0.48 [0.02,10.93] 0.648 0.47 [0.02,11.80] 0.645 Lumbar spine BMD (g/cm2) [0.5] 143 0.92 [0.15,5.56] 0.924 0.73 [0.14,3.93] 0.719 0.56 [0.09,3.30] 0.521 Femoral Neck BMD (g/cm2) [0.5] 142 0.14 [0.01,2.75] 0.195 0.11 [0.05,1.81] 0.122 0.08 [0.00,1.75] 0.108 Hip total BMD (g/cm2) [0.5] 142 0.31 [0.03,3.13] 0.319 0.27 [0.04,2.04] 0.206 0.14 [0.12,1.48] 0.102 Forearm distal 1/3rd BMD (g/cm2) [0.5] 142 10.83 [0.20,589.19] 0.243 1.01 [0.07,14.46] 0.995 1.59 [0.10,25.0] 0.742 Lowest site (T-score) 143 0.65 [0.35,1.21] 0.177 0.64 [0.35,1.16] 0.14 0.53 [0.27,1.04] 0.064 Lumbar Spine (T-score) 143 1.05 [0.71,1.55] 0.796 1.01 [0.69,1.46] 0.976 0.94 [0.63,1.40] 0.767 Femoral neck (T-score) 141 0.6 [0.29,1.24] 0.168 0.56 [0.27,1.16] 0.121 0.51 [0.23,1.11] 0.09 Distal 1/3rd forearm (T-score) 142 1.13 [0.69,1.83] 0.626 1.06 [0.64,1.76] 0.814 1.07 [0.65,1.75] 0.791 Model 1 Adjusted for sex Model 2 Adjusted for income Model 3 Adjusted for high physical activity score All analysis represents an increase by one unit unless specified in square brackets []. Gait speed analysis for an increase by 0.1m/s and BMD increase by 0.5g/cm 2 . A 1 kg increase in handgrip strength was associated with an 8% reduction in frailty odds (OR = 0.92, 95% CI: 0.84–1.00, p = 0.038). When adjusted for BMI, a 1-unit increase in handgrip strength corresponded to an 85% lower risk of frailty (OR = 0.15, 95% CI: 0.03–0.90, p = 0.037). This association remained significant after adjusting for income but was attenuated after further adjustment for physical activity. For gait speed, each 0.1 m/s increase was linked to a 33% reduction in frailty odds (OR = 0.67, 95% CI: 0.53–0.85, p = 0.001), with the association persisting across all models. Similarly, a 1-point improvement in SPPB score was associated with 42% lower odds of frailty (OR = 0.58, 95% CI: 0.42–0.81, p = 0.001), consistent across adjusted models (Table 4 ). Bone-Muscle Interaction and Frailty Bone–muscle interaction analyses were conducted to assess whether the effects of muscle function (handgrip strength, gait speed, sit-to-stand time) on frailty vary by bone strength (Table 5 ). Handgrip strength (adjusted for sex) showed significant interactions with lumbar spine BMD (p = 0.061), lumbar spine T-scores (p = 0.051), and the lowest site T-score (p = 0.011), the latter being clinically relevant for defining osteopenia and osteoporosis (WHO criteria). These findings suggest that the protective effect of handgrip strength on frailty is more pronounced at lower T-scores and BMD values (Fig. 1 a and 1 b). In contrast, gait speed showed no significant interaction with bone measures, indicating its association with frailty is independent of bone strength. Table 5 Interaction Analysis of Baseline Bone and Muscle Measures with Frailty Risk in a Longitudinal Cohort Muscle Parameter Bone Parameter P-value (Interaction) Main Effect (Muscle) OR 95% CI P-value Main Effect (Bone) OR 95% CI P-value Hand Grip Strength Max (Kg) adjusted for sex Hip total BMD (g/cm2) 0.101 Lumbar spine BMD (g/cm2) 0.061 Lowest site (T-score) 0.051 Femoral neck (T-score) 0.592 Lumbar spine (T-score) 0.011 Gait Speed (m/sec) Hip total BMD (g/cm2) 0.844 0.02 [0.00,0.20] 0.001 0.06 [0.00,5.78] 0.232 Lumbar spine BMD (g/cm2) 0.253 0.02 [0.00,0.19] 0.001 0.33 [0.01,12.27] 0.551 Lowest site (T-score) 0.406 0.02 [0.00,0.20] 0.001 0.6 [0.30,1.18] 0.141 Femoral neck (T-score) 0.109 0.02 [0.00,0.21] 0.001 0.59 [0.28,1.26] 0.174 Lumbar spine (T-score) 0.168 0.02 [0.00,0.19] 0.001 0.95 [0.64,1.42] 0.811 STS (sec) Hip total BMD (g/cm2) 0.983 1.04 [1.00,1.08] 0.051 0.06 [0.00,4.47] 0.202 Lumbar spine BMD (g/cm2) 0.243 1.04 [1.00,1.08] 0.051 0.39 [0.01,12.67] 0.598 Lowest site (T-score) 0.838 1.04 [1.00,1.09] 0.032 0.58 [0.31,1.08] 0.088 Femoral neck (T-score) 0.961 1.04 [1.00,1.09] 0.044 0.59 [0.29,1.24] 0.165 Lumbar spine (T-score) 0.243 1.04 [1.00,1.08] 0.055 0.97 [0.65,1.42] 0.859 ALM/height (Kg/m2) adjusted for sex Hip total BMD (g/cm2) 0.819 0.84 [0.40,1.80] 0.661 0.16 [0.00,27.71] 0.485 Lumbar spine BMD (g/cm2) 0.245 0.73 [0.36,1.48] 0.381 1.52 [0.04,61.62] 0.826 Lowest site (T-score) 0.500 0.91 [0.42,1.95] 0.809 0.68 [0.34,1.36] 0.275 Femoral neck (T-score) 0.982 0.88 [0.43,1.81] 0.729 0.63 [0.29,1.35] 0.235 Lumbar spine (T-score) 0.079 0.7 [0.34,1.42] 0.322 1.12 [0.76,1.67] 0.561 Multivariable logistic regression analyses were performed to assess whether bone and muscle measures independently predicted frailty. In models adjusted for both, gait speed remained significantly associated with frailty, independent of bone strength. Similarly, the sit-to-stand test retained a significant association after adjusting for bone parameters. In contrast, DEXA-derived muscle mass measures (ALM and ALM/height²), adjusted for sex and bone measurements (BMD and T-scores), were not significantly associated with frailty. Discussion In this longitudinal cohort study of older adults followed for an average of 4.5 years, we identified an association between osteosarcopenia and an increased risk of frailty. Participants with osteosarcopenia were compared to those without, and in a separate analysis, each condition (osteosarcopenia, sarcopenia, osteopenia/osteoporosis) was compared to individuals without any condition at baseline. In both analyses, osteosarcopenia was consistently linked to frailty risk. Although sarcopenia showed a trend of increased risk, this did not reach statistical significance, likely due to sample size limitations. Muscle strength measures, such as handgrip strength and gait speed, were stronger predictors of frailty than DXA-derived bone (BMD, T-score) and muscle parameters (ALM and ALM/Height 2 ), which did not predict frailty. Furthermore, an interaction between bone and muscle health and frailty risk was observed, whereby higher grip strength values were protective against frailty when bone density was lower (the opposite was also true). These findings support our hypothesis that osteosarcopenia contributes to frailty risk and emphasize the importance of addressing both bone and muscle health in frailty prevention. The temporal relationship between osteosarcopenia and frailty remains insufficiently understood due to limited longitudinal data. While cross-sectional studies have consistently demonstrated an association, longitudinal cohort studies are better suited to assess its predictive value over time. We previously reported findings from the I-Lan Longitudinal Study on Aging (ILAS) in Taiwan, where osteosarcopenia (osteopenia/osteoporosis by WHO definition and sarcopenia by AWGS 2019 criteria) was not associated with frailty (Fried criteria) among 998 participants followed over eight years ( 28 ). Two additional cohort studies have examined this relationship. A retrospective analysis of the prospective ROAD cohort in Japan found a significant association between osteosarcopenia (sarcopenia by AWGS) and frailty (Fried criteria) over four years (OR 5.80, 95% CI 1.38–24.4, p = 0.017) in community-dwelling adults aged 60 and above ( 29 ). In contrast, the Canadian Longitudinal Study on Aging (CLSA) reported no association between osteosarcopenia (sarcopenia by SDOC) and frailty (Rockwood deficit accumulation index) over three years in Caucasian adults aged 65 and older. However, osteosarcopenia was associated with an increased risk of falls, fractures, reduced quality of life, and impaired activities of daily living ( 30 ). Our findings match the ROAD study in supporting an association between osteosarcopenia and frailty risk, but differ from the CLSA and ILAS results. These inconsistencies likely reflect variations in cohort characteristics, geographic settings, study follow-up duration, and the definitions of frailty and sarcopenia employed. Cohort characteristics are critical to study outcomes, with variations in lifestyle factors (e.g., alcohol use, smoking, physical activity, health literacy, socioeconomic status, education) influencing bone and muscle health through their impact on diet, exercise behaviors, and lifestyle choices ( 31 , 32 ). Geographic differences in comorbidities, medication use, and genetic factors, such as regional variation in insulin resistance and metabolic syndrome, may further impact musculoskeletal outcomes uniquely ( 33 ). Inconsistencies in findings across studies may also stem from differing definitions of frailty and sarcopenia. The CLSA employed the Rockwood Frailty Index, based on a deficit accumulation model, while our study, along with the ROAD and ILAS cohorts, used the Fried Frailty Phenotype. Sarcopenia definitions likewise varied: the CLSA used the SDOC criteria, we applied both SDOC and EWGSOP2, and the ROAD and ILAS studies used the AWGS classification. These methodological differences may partly explain the divergent results. In addition to the differences in cohort characteristics and definitions, frailty and osteosarcopenia are complex, dynamic, and interact bidirectionally with multiple feedback loops, challenging the unidirectional assumptions of longitudinal cohort analyses. Long-term studies with multiple assessment points are needed to clarify this relationship. We propose that the biological basis of our results may be due to the shared pathways underlying bone–muscle interactions and frailty. Bone–muscle crosstalk, mediated by factors such as insulin-like growth factor and growth hormone implicated in frailty development ( 6 , 34 ), have been suggested. Additionally, nutrition, inflammation, lifestyle, and socioeconomic factors collectively influence musculoskeletal health and frailty risk ( 1 , 35 ). These interconnected mechanisms support a multifactorial basis for the association of osteosarcopenia and frailty risk. The association between osteosarcopenia and frailty risk in our cohort implies that osteosarcopenia may represent an early, pre-frail stage in the frailty continuum. Frailty is known to develop progressively, often through an intermediate ‘pre-frail phase’, and is associated with increased vulnerability and adverse outcomes ( 36 , 37 ). Osteosarcopenia is linked to adverse outcomes, such as falls, fractures, impaired ADLs, reduced quality of life, and increased mortality, outcomes that are also strongly linked to frailty ( 30 , 38 , 39 ). However, defining pre-frailty remains challenging when using the same criteria due to its clinical overlap with frailty. Unlike the pre-frailty ‘conceptual’ definition, osteosarcopenia offers objective measures of bone and muscle deficits, making it a promising marker for early risk identification. Recognizing it as a precursor could enable earlier, targeted interventions to prevent or delay frailty progression in both clinical and research contexts. Our study observed a non-significant trend toward a positive association between sarcopenia and frailty, and an inverse trend for osteoporosis. Although limited by sample size, a possible explanation is that osteoporosis patients were more likely to receive pharmacotherapy to improve BMD, which may enhance muscle strength, potentially mitigating frailty risk. Our secondary analysis examining the relationship between bone and muscle parameters and frailty demonstrated that muscle parameters, specifically handgrip strength, gait speed, the five-times sit-to-stand test, and the SPPB, were associated with frailty. In contrast, bone parameters, represented by BMD and T-scores obtained via DXA, did not show significant associations. One limitation of these analyses is that handgrip strength and gait speed are integral components of the Fried frailty phenotype, and their inclusion in the frailty definition may partly influence the observed associations. Moreover, the SPPB is a composite measure incorporating gait speed, handgrip strength, and the five-times sit-to-stand test, which complicates the interpretation of its independent association with frailty. Nevertheless, by analyzing these parameters as continuous variables, rather than dichotomizing them, as included in sarcopenia classification, we were able to more precisely assess the influence of handgrip strength and gait speed on frailty risk. These findings align with previous research, such as the Geelong Osteoporosis Study (GOS), which found that muscle parameters (lower limb strength and lean mass index) were better predictors of frailty than bone parameters ( 40 ). A previous 10-year longitudinal study (Osteoporosis Risk Assessment, OPRA) reported an association between low BMD and frailty. The discrepancy with our results is likely attributable to the shorter duration of follow-up in our study, as clinically significant changes in bone density typically occur over longer timeframes and would therefore take longer to impact frailty onset ( 41 ). Our findings also support the hypothesis that muscle function precedes and influences bone adaptation through mechanical loading and muscle–bone crosstalk, potentially initiating changes that contribute to frailty development. Another plausible explanation for the superior predictive value of muscle measures lies in the inherently dynamic and adaptable nature of muscle tissue, which better reflects early functional decline. In contrast, DXA-derived bone and muscle mass metrics represent more static structural measures that evolve gradually. Therefore, physical performance tests for muscle were better associated with frailty risk. The analysis of bone–muscle interaction in frailty risk revealed a noteworthy finding. This suggests a synergistic relationship between bone and muscle, wherein the combined decline in both parameters increases the likelihood of frailty. Interestingly, the interaction analysis demonstrated that higher handgrip strength attenuates the risk of frailty even in individuals with low BMD, and conversely, higher BMD mitigates frailty risk in those with reduced handgrip strength. These findings may emphasize the interdependence of bone and muscle in the pathophysiology of frailty and highlight the potential of targeting either organ to reduce frailty risk. As illustrated in Figs. 1 a and 1 b, the co-occurrence of low BMD and low handgrip strength markedly elevates frailty risk, whereas improvement in either parameter is associated with a meaningful reduction in frailty probability. A limitation of this interaction analysis is that it did not account for other contributing factors that may impact the relationship. For instance, individuals with low BMD may have experienced fragility fractures, which are independently associated with increased frailty risk. Likewise, participants with low grip strength may have been more vulnerable to falls, potentially leading to functional decline and frailty. These unmeasured clinical events may have influenced the observed associations and should be considered in future analyses. Despite these limitations, the key implication of our findings is that improving either bone or muscle strength may reduce the risk of frailty. This underscores the importance of integrated interventions that target both bone and muscle to preserve physical function and delay the onset of frailty. Our group has previously reported a synergistic effect of hip BMD and gait speed on fracture risk, reinforcing the notion that the interplay between bone and muscle health has critical implications for adverse functional outcomes ( 42 ). Strengths and Limitations One of the key strengths of our research is its longitudinal cohort design, which allows for the assessment of changes over time and supports causal inference. In addition to assessing the impact of osteosarcopenia on frailty, we conducted a detailed analysis of the individual contributions of bone and muscle parameters and their interaction, offering a comprehensive evaluation of bone–muscle interplay in predicting frailty risk. Validated tools and well-recognized criteria were used; Sarcopenia was defined using validated criteria from EWGSOP2 and SDOC, while bone and muscle mass were measured via DEXA, and frailty was measured using the Fried phenotype. Another strength of our study was the comprehensive data collection (demographic, socioeconomic, lifestyle, and clinical variables), allowing for robust adjustments for covariates, thus enhancing the reliability of our findings. However, our study has several limitations. First, although the sample size was adequately powered based on the expected transition to frailty, a higher-than-anticipated loss to follow-up (50% vs. 30%) may have reduced the number of transitions to frailty, thereby limiting the model’s ability to adjust for potential confounders. Second, although the follow-up period was adequate to assess a single transition to frailty, the complex and dynamic nature of frailty would be better captured through extended follow-up with repeated assessments. Third, the study population consisted of community-dwelling adults aged 50 and older, which may limit generalizability to broader older populations. Fourth, while the cohort included individuals from diverse backgrounds, with 70% born in Australia and 30% overseas, these demographic characteristics should be considered when interpreting and generalizing the findings. Fifth, as with many cohort studies involving older adults, there may be unmeasured bias due to the likely inclusion of a relatively health-conscious and motivated subgroup, potentially limiting external validity. Lastly, the use of the Fried frailty phenotype introduces overlap with sarcopenia through the inclusion of gait speed and handgrip strength, which may confound associations. Although alternative models, such as the Rockwood frailty index, could offer a broader perspective, the physical domain remains central to frailty assessment, particularly when investigating musculoskeletal contributions. We used validated definitions to align with research in this field. Conclusion This longitudinal study demonstrated that osteosarcopenia is associated with frailty risk. Clinical measures of muscle function—specifically handgrip strength and gait speed—were more strongly associated with frailty risk than DXA-derived bone measures. Notably, a synergistic interaction between bone density and muscle strength was observed, suggesting that improvements in either may mitigate frailty risk. Future research should incorporate larger study sizes, more extended follow-up periods, repeated assessments, and broader frailty definitions (particularly Rockwood definition) to explore this relationship further. Declarations The authors have no competing interests to declare relevant to this article's content. Funding Mizhgan Fatima acknowledges support from the Australian Government Research Training Program (RTP) Scholarship for the conduct of this research work. Author Contributions Mizhgan Fatima – Conceptualization, Methodology, Ethics approval, Project administration, Data curation, Formal analysis, Resources, Visualization, Writing – original draft, Writing – review & editing. Ben Kirk – Conceptualization, Resources, Supervision, Writing – review & editing. Sara Vogrin - Formal analysis, Writing – review & editing. Jason Talevski- Data curation, Writing – review & editing. Sharon Brennan-Olsen- Conceptualization, Methodology, Resources, Supervision, Writing – review & editing. Gustavo Duque – Conceptualization, Resources, Supervision, Writing – review & editing. Ethics Declarations Ethics Approval: All procedures performed involving human participants were in accordance with the ethical standards of the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. Written informed consent was obtained from all study participants. The study protocol was approved by the Melbourne Human Research Ethics Committee (HREC/17/MH/381). References Kim DH, Rockwood K (2024) Frailty in older adults. N Engl J Med 391(6):538–548 Fried LP, Cohen AA, Xue Q-L, Walston J, Bandeen-Roche K, Varadhan R (2021) The physical frailty syndrome as a transition from homeostatic symphony to cacophony. Nat aging 1(1):36–46 Hoogendijk EO, Afilalo J, Ensrud KE, Kowal P, Onder G, Fried LP (2019) Frailty: implications for clinical practice and public health. Lancet 394(10206):1365–1375 Duque G (2021) Osteosarcopenia: a geriatric giant of the XXI century. Springer, pp 716–719 Kirk B, Zanker J, Duque G (2020) Osteosarcopenia: epidemiology, diagnosis, and treatment—facts and numbers. Wiley Online Library, pp 609–618 Kirk B, Al Saedi A, Duque G (2019) Osteosarcopenia: A case of geroscience. Aging Med 2(3):147–156 Laskou F, Fuggle NR, Patel HP, Jameson K, Cooper C, Dennison E (2022) Associations of osteoporosis and sarcopenia with frailty and multimorbidity among participants of the Hertfordshire Cohort Study. J cachexia sarcopenia muscle 13(1):220–229 Saeki C, Kanai T, Nakano M, Oikawa T, Torisu Y, Abo M et al (2020) Relationship between osteosarcopenia and frailty in patients with chronic liver disease. J Clin Med 9(8):2381 Davenport A, Frailty (2022) appendicular lean mass, osteoporosis and osteosarcopenia in peritoneal dialysis patients. J Nephrol 35(9):2333–2340 Tembo MC, Mohebbi M, Holloway-Kew KL, Gaston J, Sui SX, Brennan-Olsen SL et al (2021) The contribution of musculoskeletal factors to physical frailty: a cross-sectional study. BMC Musculoskelet Disord 22(1):921 Yoshimura N, Muraki S, Iidaka T, Oka H, Horii C, Kawaguchi H et al (2019) Prevalence and co-existence of locomotive syndrome, sarcopenia, and frailty: the third survey of Research on Osteoarthritis/Osteoporosis Against Disability (ROAD) study. J Bone Min Metab 37(6):1058–1066 Heng MWY, Chan AW, Man RE, Fenwick EK, Chew ST, Tay L et al (2023) Individual and combined associations of sarcopenia, osteoporosis and obesity with frailty in a multi-ethnic asian older adult population. BMC Geriatr 23(1):802 Wang Y-J, Wang Y, Zhan J-K, Tang Z-Y, He J-Y, Tan P et al (2015) Sarco-osteoporosis: prevalence and association with frailty in Chinese community-dwelling older adults. Int J Endocrinol 2015:482940 Szlejf C, Parra-Rodríguez L, Rosas-Carrasco O (2017) Osteosarcopenic Obesity: Prevalence and Relation With Frailty and Physical Performance in Middle-Aged and Older Women. J Am Med Dir Assoc 18(8):733. .e1-.e5 Hagströmer M, Oja P, Sjöström M (2006) The International Physical Activity Questionnaire (IPAQ): a study of concurrent and construct validity. Public Health Nutr 9(6):755–762 Kanis JA, Melton LJ 3rd, Christiansen C, Johnston CC, Khaltaev N (1994) The diagnosis of osteoporosis. J bone mineral research: official J Am Soc Bone Mineral Res 9(8):1137–1141 Zanker J, Scott D, Reijnierse E, Brennan-Olsen S, Daly R, Girgis C et al (2018) Establishing an Operational Definition of Sarcopenia in Australia and New Zealand: Delphi Method Based Consensus Statement. The journal of nutrition, health & aging. :1–6 Cruz-Jentoft AJ, Bahat G, Bauer J, Boirie Y, Bruyère O, Cederholm T et al (2019) Sarcopenia: Revised European consensus on definition and diagnosis. Age Ageing 48(1):16–31 Bhasin S, Travison TG, Manini TM, Patel S, Pencina KM, Fielding RA et al (2020) Sarcopenia definition: the position statements of the sarcopenia definition and outcomes consortium. J Am Geriatr Soc 68(7):1410–1418 Bandeen-Roche K, Xue Q-L, Ferrucci L, Walston J, Guralnik JM, Chaves P et al (2006) Phenotype of frailty: characterization in the women's health and aging studies. Journals Gerontol Ser A: Biol Sci Med Sci 61(3):262–266 Woods NF, LaCroix AZ, Gray SL, Aragaki A, Cochrane BB, Brunner RL et al (2005) Frailty: emergence and consequences in women aged 65 and older in the Women's Health Initiative Observational Study. J Am Geriatr Soc 53(8):1321–1330 Espinoza SE, Woods RL, Ekram AS, Ernst ME, Polekhina G, Wolfe R et al (2022) The effect of low-dose aspirin on frailty phenotype and frailty index in community-dwelling older adults in the ASPirin in Reducing Events in the Elderly study. Journals Gerontology: Ser A 77(10):2007–2014 Fried LP, Tangen CM, Walston J, Newman AB, Hirsch C, Gottdiener J et al (2001) Frailty in older adults: evidence for a phenotype. The journals of gerontology Series A, Biological sciences and medical sciences. 56(3):M146–M156 Brennan-Olsen SL, Vogrin S, Balogun S, Wu F, Scott D, Jones G et al (2020) Education, occupation and operational measures of sarcopenia: Six years of Australian data. Australas J Ageing 39(4):e498–e505 ServicesAustralia [Available from: https://www.servicesaustralia.gov.au/how-much-carer-payment-you-can-get?context=21816 Vittinghoff E, McCulloch CE (2007) Relaxing the rule of ten events per variable in logistic and Cox regression. Am J Epidemiol 165(6):710–718 Goodman S (ed) (2008) editor A dirty dozen: twelve p-value misconceptions. Seminars in hematology. Elsevier Fatima M, Kirk B, Vogrin S, Lee W-J, Peng L-N, Chen L-K et al (2025) Osteosarcopenia and frailty risk in community-dwelling older adults: A follow-up of the I-Lan Longitudinal Aging Study. Arch Gerontol Geriatr 136:105888 Yoshimura N, Muraki S, Oka H, Iidaka T, Kodama R, Horii C et al (2018) Do sarcopenia and/or osteoporosis increase the risk of frailty? A 4-year observation of the second and third ROAD study surveys. Osteoporosis international: a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA. 29(10):2181–2190 Lee A, McArthur C, Ioannidis G, Duque G, Adachi JD, Griffith LE et al (2024) Associations between Osteosarcopenia and Falls, Fractures, and Frailty in Older Adults: Results From the Canadian Longitudinal Study on Aging (CLSA). J Am Med Dir Assoc 25(1):167–76e6 Stolz E, Mayerl H, Waxenegger A, Rásky é, Freidl W (2016) Impact of socioeconomic position on frailty trajectories in 10 European countries: Evidence from the Survey of Health, Ageing and Retirement in Europe (2004–2013). J Epidemiol Commun Health 71(1):73–80 Brennan SL, Winzenberg TM, Pasco JA, Wluka AE, Dobbins AG, Jones G (2013) Social disadvantage, bone mineral density and vertebral wedge deformities in the Tasmanian Older Adult Cohort. Osteoporosis international: a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA. 24(6):1909–1916 Gurka MJ, Filipp SL, DeBoer MD (2018) Geographical variation in the prevalence of obesity, metabolic syndrome, and diabetes among US adults. Nutr diabetes 8(1):14 Greco EA, Pietschmann P, Migliaccio S (2019) Osteoporosis and sarcopenia increase frailty syndrome in the elderly. Front Endocrinol 10:255 Kirk B, Miller S, Zanker J, Duque G (2020) A clinical guide to the pathophysiology, diagnosis and treatment of osteosarcopenia. Maturitas 140:27–33 Jenkins ND, Welstead M, Stirland L, Hoogendijk EO, Armstrong JJ, Robitaille A et al (2023) Frailty trajectories and associated factors in the years prior to death: evidence from 14 countries in the Survey of Health, Aging and Retirement in Europe. BMC Geriatr 23(1):49 Hoogendijk EO, Dent E (2022) Trajectories, transitions, and trends in frailty among older adults: a review. Annals geriatric Med Res 26(4):289 Paulin TK, Malmgren L, McGuigan FE, Akesson KE (2024) Osteosarcopenia: prevalence and 10-year fracture and mortality risk–a longitudinal, population-based study of 75-year-old women. Calcif Tissue Int 114(4):315–325 Balogun S, Winzenberg T, Wills K, Scott D, Callisaya M, Cicuttini F et al (2019) Prospective associations of osteosarcopenia and osteodynapenia with incident fracture and mortality over 10 years in community-dwelling older adults. Arch Gerontol Geriatr 82:67–73 Tembo MC, Mohebbi M, Holloway-Kew KL, Gaston J, Brennan-Olsen SL, Williams LJ et al (2021) The Predictability of Frailty Associated with Musculoskeletal Deficits: A Longitudinal Study. Calcif Tissue Int 109(5):525–533 Bartosch P, McGuigan FE, Akesson KE (2018) Progression of frailty and prevalence of osteoporosis in a community cohort of older women-a 10-year longitudinal study. Osteoporosis international: a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA. 29(10):2191–2199 Kirk B, Zhang S, Vogrin S, Harijanto C, Sales M, Duque G (2023) Comparing the fracture profile of osteosarcopenic older adults with osteopenia/osteoporosis alone. Calcif Tissue Int 112(3):297–307 Additional Declarations No competing interests reported. Supplementary Files SupplementaryFigureandTable.docx Cite Share Download PDF Status: Published Journal Publication published 10 Dec, 2025 Read the published version in Aging Clinical and Experimental Research → Version 1 posted Editorial decision: Revision requested 06 Aug, 2025 Reviews received at journal 05 Aug, 2025 Reviewers agreed at journal 16 Jul, 2025 Reviews received at journal 12 Jul, 2025 Reviewers agreed at journal 11 Jul, 2025 Reviewers agreed at journal 02 Jun, 2025 Reviews received at journal 02 Jun, 2025 Reviewers agreed at journal 02 Jun, 2025 Reviewers invited by journal 02 Jun, 2025 Editor assigned by journal 29 May, 2025 Submission checks completed at journal 27 May, 2025 First submitted to journal 27 May, 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. 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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-6759229","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":465823534,"identity":"100b30d5-1194-4ac2-a2c6-f2010bbfa2f2","order_by":0,"name":"Mizhgan Fatima","email":"","orcid":"","institution":"University of Melbourne and Western Health","correspondingAuthor":false,"prefix":"","firstName":"Mizhgan","middleName":"","lastName":"Fatima","suffix":""},{"id":465823535,"identity":"8d9916ab-3610-4b97-8ce5-07f67b93e43c","order_by":1,"name":"Ben Kirk","email":"","orcid":"","institution":"University of Melbourne and Western Health","correspondingAuthor":false,"prefix":"","firstName":"Ben","middleName":"","lastName":"Kirk","suffix":""},{"id":465823536,"identity":"8e2ebee7-719d-472f-ac37-d2e9d2a3f527","order_by":2,"name":"Sara Vogrin","email":"","orcid":"","institution":"University of Melbourne and Western Health","correspondingAuthor":false,"prefix":"","firstName":"Sara","middleName":"","lastName":"Vogrin","suffix":""},{"id":465823537,"identity":"a6d2154f-968d-4ffe-a343-89c9fca8c76d","order_by":3,"name":"Jason Talevski","email":"","orcid":"","institution":"Northern Health","correspondingAuthor":false,"prefix":"","firstName":"Jason","middleName":"","lastName":"Talevski","suffix":""},{"id":465823538,"identity":"9848f776-cc5c-4492-a243-80fbe4d43256","order_by":4,"name":"Sharon Brennan-Olsen","email":"","orcid":"","institution":"University of Melbourne and Western Health","correspondingAuthor":false,"prefix":"","firstName":"Sharon","middleName":"","lastName":"Brennan-Olsen","suffix":""},{"id":465823539,"identity":"1c3cbbcb-bf8b-4875-85dd-cd521167629e","order_by":5,"name":"Gustavo Duque","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8klEQVRIiWNgGAWjYFCDAyCiAoiZmRsPEFDL2IDQcgakhbGBBC2MbRABvFrkI9KfP/jZZsPAdyN5m8THedvk5d1BWmrscGoxvJFj2NjblsYgeSOtTHLmttuGGw+DtBxLxq1lRg5jA8+ZwwwGN3LMpHm33U4wbAZqYWxgxqMl/WHjH5iWv3PgWupx+0UCqIanAqqFseF2gjwzWMthnFoMeN4YzpapSOORPPOs2LLn2G3DDSAtCceO47alPf3BxzcGNnJ8x5M33vhRc1tevv/wwQcfaqpx23IAQvOA2AiRBJwagLY0IGnHEBkFo2AUjIJRAAIAzOxc3VVzgLkAAAAASUVORK5CYII=","orcid":"","institution":"McGill University","correspondingAuthor":true,"prefix":"","firstName":"Gustavo","middleName":"","lastName":"Duque","suffix":""}],"badges":[],"createdAt":"2025-05-27 12:08:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6759229/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6759229/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s40520-025-03242-x","type":"published","date":"2025-12-10T15:57:37+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":84186487,"identity":"2d08858c-50b0-4b05-aa0e-8e15a8a8b8f0","added_by":"auto","created_at":"2025-06-09 05:38:46","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":56853,"visible":true,"origin":"","legend":"\u003cp\u003e1a. The line graph illustrates the interaction between handgrip strength (kg) (adjusted for sex) and the DXA T-score at the lowest site with frailty as the outcome. Lower handgrip strength (blue) increases the likelihood of frailty at lower T-scores (\u0026lt;-2.5) compared to higher T-scores (\u0026gt;-1.0) at the lowest site.\u003c/p\u003e\n\u003cp\u003e1b. The line graph illustrates the interaction between handgrip strength (kg) (adjusted for sex) and DXA T-score at lumbar spine for frailty as outcome. Lower handgrip strength (blue) increases the probability of frailty at lower T-scores (\u0026lt;-2.5) compared to a higher T-scores (\u0026gt;-1.0) at lumbar spine.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6759229/v1/ca35ba857f218dd855d9ea7c.png"},{"id":98245203,"identity":"5bf23aea-5660-4c28-954a-0dc62537d0a8","added_by":"auto","created_at":"2025-12-15 16:17:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1796949,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6759229/v1/0a25758c-d02f-44fa-b41a-b411e8b83a77.pdf"},{"id":84186498,"identity":"7a01ea91-c882-4c13-b3dc-dfcf468c4a59","added_by":"auto","created_at":"2025-06-09 05:38:46","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":450457,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigureandTable.docx","url":"https://assets-eu.researchsquare.com/files/rs-6759229/v1/3b5b1ef305699c40da80116a.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Osteosarcopenia, bone-muscle interactions, and Frailty risk: A Prospective Cohort Study of Community- Dwelling Older Adults","fulltext":[{"header":"Introduction","content":"\u003cp\u003eFrailty is an age-related syndrome characterized by diminished physiological reserve and resilience, increasing susceptibility to stressors, and the risk of hospitalization, disability, and mortality (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). It results from senescence-related cellular and subcellular changes that disrupt key physiological systems, including the musculoskeletal system, hypothalamic-pituitary-adrenal (HPA) axis, and metabolic function. These disruptions give rise to the Fried frailty phenotype, defined when three or more of the following five criteria are present: unintentional weight loss, exhaustion, slow gait speed, reduced grip strength, and low physical activity (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). As the global population ages, frailty is becoming increasingly prevalent, with significant implications for individual health and healthcare systems (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOsteosarcopenia is a recently defined clinical entity that describes the co-occurrence of osteopenia or osteoporosis (low bone mineral density) and sarcopenia (low muscle mass and strength) (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Each of these conditions individually contributes to an increased risk of falls, fractures, and physical functional decline in older adults; however, their combined impact is synergistic and accelerates musculoskeletal deterioration in older people (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). The pathophysiological basis of osteosarcopenia stems from impaired bone-muscle crosstalk and shared mechanisms such as chronic inflammation, hormonal dysregulation, and environmental factors involving both bone and muscle (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Through its compounded impact on bone and muscle, osteosarcopenia may contribute to the development of frailty.\u003c/p\u003e \u003cp\u003eSeveral cross-sectional studies have reported an association between osteosarcopenia and frailty, suggesting a potential biological and clinical link (\u003cspan additionalcitationids=\"CR8 CR9 CR10 CR11 CR12 CR13\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). However, the inherent limitations of cross-sectional designs preclude conclusions about causality or temporal sequence. It remains uncertain whether osteosarcopenia precedes frailty or if both arise concurrently in response to common age-related mechanisms. To date, longitudinal data exploring osteosarcopenia as a potential predictor of frailty are limited.\u003c/p\u003e \u003cp\u003eTo address this gap, the present study aimed to: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) determine whether osteosarcopenia at baseline predicts the onset of frailty over time; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) investigate whether the individual components of osteosarcopenia\u0026mdash;specifically, low bone mass and reduced muscle strength\u0026mdash;independently predict frailty; and (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) explore whether bone-muscle interaction contributes to frailty development. We hypothesize that osteosarcopenia, due to its combined impact on bone and muscle, is an independent and significant predictor of frailty in older adults.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design\u003c/h2\u003e \u003cp\u003eThe Brimbank Ageing Well Study (BRAW) is a prospective cohort study that investigated the association between baseline osteosarcopenia and frailty among community-dwelling older adults aged\u0026thinsp;\u0026ge;\u0026thinsp;50 years in the Western suburbs of Melbourne, Australia. Participants were recruited through convenience sampling and assessed at two time points: baseline (2018\u0026ndash;2020) and follow-up (2023\u0026ndash;2024). Inclusion criteria were aged\u0026thinsp;\u0026ge;\u0026thinsp;50 years, living in the Western suburbs of Melbourne, and the ability to attend the study site for assessments. Exclusion criteria included inability to provide informed consent; severe cognitive impairment (dementia or dementia with Lewy body); medical conditions affecting balance or physical performance (e.g., Parkinson\u0026rsquo;s disease, progressive neurological disorders, multiple sclerosis, uncontrolled psychiatric disorders); severe arthritis, chronic lung disease requiring oxygen or renal disease requiring dialysis; history of stroke or recent major surgeries (e.g., knee replacement, spinal, or cardiac surgery) within the past 6 months; life expectancy\u0026thinsp;\u0026lt;\u0026thinsp;12 months; or body weight\u0026thinsp;\u0026gt;\u0026thinsp;150 kg (a contraindication for DEXA scanning). Written informed consent was obtained from all participants. The study was approved by the Human Research Ethics Committee at Melbourne Health (HREC/45024/MH-2020).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy Assessments\u003c/h3\u003e\n\u003cp\u003eParticipants attended an assessment session at Sunshine Hospital that lasted approximately 2 to 2.5 hours. They were provided with a self-administered questionnaire capturing sociodemographic information (age, sex, country of birth, living arrangements, income, education, quality of life scores), lifestyle factors (smoking and alcohol consumption history), and physical activity levels using the International Physical Activity Questionnaire (IPAQ) (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Interpreters were available for non-English speaking participants.\u003c/p\u003e \u003cp\u003eSubsequently, study personnel collected clinical data, including comorbidities (using the Charlson Comorbidity Index), medication use, mini nutritional assessment, and fracture history. Physical assessments included anthropometric measurements (height, weight, body mass index), handgrip strength (using a handheld dynamometer), sit-to-stand test, gait speed, Timed Up and Go (TUG) test, and the Short Physical Performance Battery (SPPB). A whole-body Dual-Energy X-ray Absorptiometry (DEXA) scan was performed to assess Bone Mineral Density (BMD), body composition, and Appendicular Lean Mass adjusted for height (ALM/height\u003csup\u003e2\u003c/sup\u003e). Finally, participants underwent blood tests to assess serum vitamin D and C-Reactive Protein (CRP) levels. Blood tests were conducted only at baseline; all other assessments were conducted at both visits.\u003c/p\u003e\n\u003ch3\u003eOsteosarcopenia\u003c/h3\u003e\n\u003cp\u003eOsteosarcopenia was defined by the concomitant presence of osteopenia or osteoporosis and sarcopenia (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Osteopenia and osteoporosis were defined using the World Health Organization (WHO) criteria based on BMD measured via DEXA. Osteoporosis was defined as a BMD T-score of \u0026le; -2.5, while osteopenia was classified as a BMD T-score between \u0026minus;\u0026thinsp;1.0 and \u0026minus;\u0026thinsp;2.5 (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). The lowest recorded BMD at three sites, lumbar spine, femoral neck, and distal 1/3rd of the radius, was used to define osteopenia or osteoporosis. Sarcopenia was defined using two criteria: the European Working Group on Sarcopenia in Older People, revised (EWGSOP2, 2019), and the Sarcopenia Definition and Outcomes Consortium (SDOC, 2020). The EWGSOP2 criteria were employed, as the majority of our study population was Australian-born Caucasian, and these criteria have recently been endorsed for use in Australia via the Delphi convention (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). SDOC criteria are recommended in Caucasians. According to EWGSOP2, probable sarcopenia was identified by either a low handgrip strength (\u0026lt;\u0026thinsp;27 kg in men and \u0026lt;\u0026thinsp;16 kg in women) or a five-times sit-to-stand time of \u0026gt;\u0026thinsp;15 seconds. Confirmed sarcopenia required the presence of probable sarcopenia along with DXA evidence of low ALM/height (\u0026lt;\u0026thinsp;7.0 kg/m\u0026sup2; in men and \u0026lt;\u0026thinsp;5.5 kg/m\u0026sup2; in women) (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). As per SDOC, sarcopenia was defined based on a low handgrip strength (\u0026lt;\u0026thinsp;35.5 kg in men and \u0026lt;\u0026thinsp;20 kg in women) and slow gait speed (\u0026lt;\u0026thinsp;0.8 m/s). A second SDOC definition was used where handgrip strength was adjusted for BMI (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eFrailty\u003c/h3\u003e\n\u003cp\u003eThe validated criterion for frailty, the Fried criteria, was used, which classified individuals as frail if they met at least three out of five criteria, prefrail if they met two or one, and non-frail if they met none. The five criteria included:\u003c/p\u003e \u003cp\u003e1) Unintentional weight loss of \u0026gt;\u0026thinsp;4.5 kg in the past year or a BMI\u0026thinsp;\u0026lt;\u0026thinsp;18 kg/m\u0026sup2;, modified as in previous studies (\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e2) Self-reported exhaustion (assessed using two questions from the CES-D depression scale, if the participants provided a positive answer to any of following two questions, i) I felt that everything I did was an effort, for \u0026ge;\u0026thinsp;3 days in a week, ii) I could not get going, for \u0026ge;\u0026thinsp;3 days in a week)\u003c/p\u003e \u003cp\u003e3) Low physical activity, defined based on the IPAQ score, categorized as low physical activity, using the gender-specific lowest quintile for the study population.\u003c/p\u003e \u003cp\u003e4) Slowness (gait speed\u0026thinsp;\u0026lt;\u0026thinsp;1.0 m/s)\u003c/p\u003e \u003cp\u003e5) Weakness (grip strength in the lowest 20% by gender and BMI) (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e)\u003c/p\u003e\n\u003ch3\u003eCo-variates\u003c/h3\u003e\n\u003cp\u003eCovariates were selected based on previous evidence and studies suggesting a potential influence on the exposure (osteosarcopenia) and outcome (frailty) variables (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). These included demographic factors (age, sex, living status), socioeconomic factors (education level, income status), and lifestyle behaviors (alcohol consumption, smoking status). Annual income was classified as binary, categorizing those earning less than AUD 40,000 and those earning more, based on an Australian couple\u0026rsquo;s full age pension of approximately AUD 39,640 per year (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Clinical variables such as comorbidities, current medications, serum levels of vitamin D, C-reactive protein (CRP), and nutritional status were also considered.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eDescriptive statistics were used to summarize the baseline characteristics of study participants. Categorical variables (frequencies and percentages) were compared using chi-square tests, and continuous variables (means\u0026thinsp;\u0026plusmn;\u0026thinsp;SD or medians\u0026thinsp;\u0026plusmn;\u0026thinsp;IQR for skewed data) using one-way ANOVA. Participants were classified into two groups (non-frail and frail), and frailty transition was reported as a percentage. At baseline, participants were categorized into four mutually exclusive groups: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) no condition (neither osteopenia/osteoporosis nor sarcopenia), (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) sarcopenia only, (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) osteopenia or osteoporosis only, and (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) osteosarcopenia.\u003c/p\u003e \u003cp\u003eLogistic regression was used to estimate the odds of developing frailty at follow-up, comparing each condition group to the no-condition group. Due to the limited number of frailty cases for multivariable analyses, participants were stratified into two groups: those with osteosarcopenia and those without. To minimize overfitting, each confounder was adjusted individually in separate models, adhering to the recommended guideline of at least 10 events per variable in logistic regression analyses (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Confounders were selected based on significant associations with frailty in univariate analyses and included demographic (age, sex), lifestyle (smoking, alcohol use, physical activity), clinical (polypharmacy, vitamin D, CRP), and socioeconomic (education, income) factors.\u003c/p\u003e \u003cp\u003eMuscle parameters (grip strength, gait speed, five-times sit-to-stand test, SPPB, ALM, ALM/height\u0026sup2;) and bone measures (BMD and T-scores at various sites) were analyzed as continuous variables using logistic regression to assess associations with frailty at follow-up. Models were adjusted for sex, physical activity, and income, based on initial findings.\u003c/p\u003e \u003cp\u003eInteraction analysis models were employed to assess whether muscle parameters significantly interacted with bone measures in predicting frailty. If significant interactions were identified, indicating effect modification by value level, no further analysis was conducted. Otherwise, mutually adjusted models were used to assess the independent effects of muscle and bone measures. Results are presented as odds ratios (OR) with 95% confidence intervals (CI). A p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant, except for interactions where p\u0026thinsp;\u0026lt;\u0026thinsp;0.06 was used, aligning with exploratory research practices that recognize the value of marginal trends (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). All analyses were conducted using Stata version 18 (StataCorp, College Station, TX, USA).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eParticipants Characteristics\u003c/h2\u003e \u003cp\u003eOut of 300 participants at baseline, 151 completed follow-ups (reasons detailed in Supplementary Figure S1). The characteristics of participants who attended and those who did not attend are compared in Supplementary Table\u0026nbsp;1. Of those who completed follow-ups, 143 were non-frail and eight were frail at baseline assessment. Over the study follow-up, out of the non-frail, 13 (9.1%) became frail; of the frail, 4 (50%) improved to non-frail. The overall frailty incidence in this cohort was 6.6%.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the baseline characteristics of 151 participants categorized as robust, prefrail, or frail. Eight frail participants at baseline were excluded from follow-up analysis, leaving 143 non-frail individuals for the final analysis.\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\u003eSummary of Participants\u0026rsquo; Characteristics at Baseline, Categorized by Frailty (Three Groups)\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003eFried Frailty categories\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRobust\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePre-Frail\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFrail\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumbers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e97 (64.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46 (30.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 (5.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e151 (100.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge at first visit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64.5 (5.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66.3 (7.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e65.7 (6.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e65.1 (6.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.398\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWomen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56 (57.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30 (65.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (50.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e90 (59.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.592\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLives alone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19 (19.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (26.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (37.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e34 (22.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.398\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBorn in Australia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68 (70.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30 (65.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (37.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e101 (66.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.163\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eATSI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (2.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (0.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.317\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpeak English at home\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e94 (96.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44 (95.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7 (87.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e145 (96.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.419\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePost-secondary/tertiary qualification\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e82 (84.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35 (76.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (62.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e122 (80.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.196\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school or less\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15 (15.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (23.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (37.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29 (19.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eIncome\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnnual income\u0026thinsp;\u0026gt;\u0026thinsp;40K AUD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e77 (79.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30 (65.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (25.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e109 (72.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHas private health insurance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e79 (81.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23 (50.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (25.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e104 (68.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealth card holder\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24 (24.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21 (45.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (75.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e51 (33.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking (past or present)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42 (43.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21 (45.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (37.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e66 (43.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.904\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking (current)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo, not at all\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41 (91.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20 (95.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (75.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e64 (91.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.614\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes, but not daily\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (6.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (4.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (25.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5 (7.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes, daily\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (2.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (1.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDuration smoking (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e[3] 23.0 (12.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[3] 23.3 (20.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[1] 55.0 (.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[7] 27.7 (18.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.319\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking (past)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38 (80.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21 (91.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (50.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e61 (82.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.120\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eAlcohol\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol (past or present)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e94 (96.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43 (93.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (75.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e143 (94.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol (last month)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e79 (83.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32 (74.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (71.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e116 (80.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.417\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol amount last year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (10.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (14.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (14.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17 (11.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.567\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLess than once a month\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (11.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (20.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (28.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22 (15.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOne to three days per month\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23 (24.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (27.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (28.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e37 (25.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOne to four days per week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31 (32.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (27.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (14.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e44 (30.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFive or more days per week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21 (21.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (9.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (14.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26 (17.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol (average) over 12 months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e[90] 2.2 (3.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[40] 1.7 (1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[6] 2.8 (3.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[136] 2.1 (2.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.437\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eComorbidities\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAny comorbidity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70 (72.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38 (82.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7 (87.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e115 (76.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.290\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArthritis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33 (34.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (37.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (37.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e53 (35.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.933\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic Back pain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26 (26.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (39.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (25.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e46 (30.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.308\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCardiovascular Disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 (9.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (26.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (12.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22 (14.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic Lung disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (5.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (15.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (25.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14 (9.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (4.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (8.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (25.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10 (6.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.059\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDepression or Anxiety\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (7.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (17.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (25.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17 (11.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.089\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (4.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (13.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (12.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11 (7.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.134\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCerebrovascular disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (1.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (4.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (12.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4 (2.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.105\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther comorbidities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16 (16.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (19.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (12.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26 (17.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.844\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003ePrevious fragility fractures\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAny fracture in last 5 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13 (13.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (17.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (25.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23 (15.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.631\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVertebral fracture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (2.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (4.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4 (2.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.655\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-vertebral fractures (exclude hip)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49 (50.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23 (50.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (75.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e78 (51.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.397\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHip fractures\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (2.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (8.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6 (4.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.143\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003ePhysical Activity Scale (IPAQ)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22 (47.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 (100.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30 (19.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19 (19.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (10.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24 (15.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e78 (80.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19 (41.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e97 (64.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eNutrition (MNA)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAt risk of malnutrition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (8.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (28.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6 (15.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.206\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eMedications\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAny medication (exclude nutritional supplements)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65 (67.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31 (67.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 (100.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e104 (68.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.148\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePolypharmacy (\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;5 medications)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (10.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (19.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (25.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21 (13.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.212\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003ePsychological Distress Scale (Kessler K6+)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo/low distress\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e95 (97.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36 (80.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (66.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e135 (91.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate distress\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (2.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (13.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (33.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10 (6.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh distress\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (6.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3 (2.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eClinical \u0026amp; anthropometric data\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSystolic BP (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e135.9 (18.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e133.5 (18.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e136.1 (14.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e135.2 (18.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.792\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiastolic BP (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e85.3 (10.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e84.5 (11.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e91.9 (10.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e85.4 (10.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.211\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody Mass Index (Kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27.1 (4.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.4 (6.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33.8 (6.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27.8 (5.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHip circumference (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e104.7 (8.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e102.9 (16.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e120.6 (17.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e105.0 (12.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWaist circumference (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e91.7 (14.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95.3 (18.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e114.8 (17.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e94.0 (16.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003ePathology\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVitamin D levels\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e67.9 (20.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59.6 (22.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e83.1 (37.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e66.2 (22.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.127\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHs-CRP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.5 (2.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.0 (3.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.0 (6.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.1 (3.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003ePhysical measurements\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHand Grip Strength Max (Kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37.8 (8.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29.7 (10.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.4 (8.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e34.6 (9.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHand Grip Strength/BMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.4 (0.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.1 (0.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.8 (0.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.3 (0.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5x Sit to Stand (sec)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.8 (7.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.8 (13.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.3 (5.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.3 (9.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShort Physical Performance battery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.5 (0.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.6 (2.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.8 (2.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.1 (1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGait speed best (m/s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.4 (0.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.2 (0.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.7 (0.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.3 (0.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eMuscle measurement on DXA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20.9 (5.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.5 (5.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.0 (6.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20.5 (5.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.181\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALM/Height\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.3 (1.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.0 (1.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.8 (1.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.3 (1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.247\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubtotal bodyfat, %age\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34.4 (8.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37.1 (9.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43.7 (11.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35.7 (9.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhole body fat, %age\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33.8 (7.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36.3 (8.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42.7 (11.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35.0 (8.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhole body fat, kg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26.8 (8.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29.4 (11.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41.5 (16.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28.3 (10.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eBone measurement on DXA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLowest site BMD (g/cm2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e[97] 0.5 (0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[46] 0.5 (0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[8] 0.5 (0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[151] 0.5 (0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.892\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLumbar spine BMD (g/cm2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e[97] 1.0 (0.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[46] 1.0 (0.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[8] 1.1 (0.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[151] 1.0 (0.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.366\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemoral Neck BMD (g/cm2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e[96] 0.8 (0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[46] 0.7 (0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[8] 0.8 (0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[150] 0.7 (0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.497\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHip total BMD (g/cm2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e[96] 0.9 (0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[46] 0.9 (0.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[8] 0.9 (0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[150] 0.9 (0.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.687\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistal 1/3 radius BMD (g/cm2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e[97] 0.7 (0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[45] 0.7 (0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[8] 0.7 (0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[150] 0.7 (0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.295\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLowest site T-score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e[97] -1.3 (1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[46] -1.5 (1.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[8] -1.2 (1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[151] -1.3 (1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.589\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLumbar spine (T-score)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e[97] -0.6 (1.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[46] -0.7 (1.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[8] 0.1 (1.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[151] -0.6 (1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.477\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemoral neck (T-score)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e[95] -1.1 (0.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[46] -1.2 (1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[8] -1.1 (1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[149] -1.1 (0.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.687\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistal 1/3 radius (T-score)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e[96] -0.6 (1.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[46] -1.0 (1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[6] -1.3 (1.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[148] -0.8 (1.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.173\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eFried criteria \u0026ndash; individual measurements\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExhaustion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (8.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (25.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6 (4.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight loss\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (15.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7 (4.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow physical activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22 (47.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 (100.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30 (19.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSlowness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (26.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 (100.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20 (13.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeakness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (37.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (75.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23 (15.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eOsteoporosis (WHO criteria)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33 (34.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (37.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (50.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e54 (35.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.753\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOsteopenia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54 (55.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22 (47.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (37.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e79 (52.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOsteoporosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (10.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (15.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (12.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18 (11.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eSarcopenia categorization\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSarcopenia EWGSOP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (8.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 (28.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7 (87.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28 (18.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSarcopenia EWGSOP (severe)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (2.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (15.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (50.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13 (8.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSarcopenia SDOC 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (39.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 (100.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26 (17.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSarcopenia SDOC 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (4.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (37.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (75.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27 (17.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eOsteosarcopenia categorization\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eOsteosarcopenia (EWGSOP2) mutually exclusive groups\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealthy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28 (28.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (30.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e42 (27.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOsteopenia/osteoporosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e61 (62.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19 (41.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (12.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e81 (53.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSarcopenia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (5.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (6.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (50.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12 (7.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOsteosarcopenia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (3.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (21.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (37.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16 (10.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eOsteosarcopenia (SDOC 1) mutually exclusive groups\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealthy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33 (34.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (23.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e44 (29.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOsteopenia/osteoporosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64 (66.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (37.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e81 (53.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSarcopenia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (13.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (50.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10 (6.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOsteosarcopenia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (26.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (50.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16 (10.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eOsteosarcopenia (SDOC 2) mutually exclusive groups\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealthy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30 (30.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (19.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (12.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40 (26.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOsteopenia/osteoporosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63 (64.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20 (43.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (12.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e84 (55.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSarcopenia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (3.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (17.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (37.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14 (9.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOsteosarcopenia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (1.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (19.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (37.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13 (8.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eUnless otherwise specified, mean (SD) for continuous variables and number (%age) for categorical variables. [] before reading shows number of observation.\u003c/p\u003e \u003cp\u003eALM Appendicular lean mass, ALM/height Appendicular lean mass adjusted for height, ATSI Aboriginal and Torres Strait Island, AUD Australian Dollar, BMD Bone Muscle Density, BP blood pressure, DXA Dual energy X-ray absorptiometry, EWGSOP2 European Working Group of Sarcopenia in Older People 2, Hs CRP High sensitivity C-Reactive Protein, IPAQ International Physical Activity Questionnaire, MNA Mini Nutritional Assessment, SDOC 1 Sarcopenia Definition and Outcome Consortium, SDOC 2 SDOC adjusted for BMI, WHO World Health Organization.\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\u003eThe mean age of participants was 65.1 years (SD 6.3), with 59.6% female. No age or sex differences were found between frailty groups. Most participants lived with others (78%), were Australian-born (66.9%), English-speaking (96%), and had completed secondary school education or higher (\u0026gt;\u0026thinsp;80.8%). Socioeconomic differences were noted across frailty groups when classified by an annual income\u0026thinsp;\u0026gt;\u0026thinsp;40K AUD (p\u0026thinsp;=\u0026thinsp;0.002), private health insurance (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and health care card ownership (p\u0026thinsp;=\u0026thinsp;0.002). Differences were also noted between groups in lifestyle factors; alcohol use (p\u0026thinsp;=\u0026thinsp;0.026), physical activity (IPAQ, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and inflammatory markers, hs-CRP levels (p\u0026thinsp;=\u0026thinsp;0.004). No differences were found for smoking, comorbidities, fractures, medications, polypharmacy, or vitamin D. While muscle function measures (grip strength, gait speed) differed between groups, muscle and bone mass (ALM, ALM/height\u0026sup2;, BMD, T-scores) did not. Supplementary Figure S2 shows the overlap of osteopenia/osteoporosis and sarcopenia at baseline. Each condition (osteosarcopenia, sarcopenia and osteopenia/osteoporosis) was analyzed as a distinct group to assess its association with frailty at follow-up.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eAssociation Between Osteosarcopenia and Frailty\u003c/h2\u003e \u003cp\u003eUsing the EWGSOP2 criteria, osteosarcopenia was significantly associated with increased odds of developing frailty compared to individuals without osteopenia/osteoporosis or sarcopenia (OR\u0026thinsp;=\u0026thinsp;8.13, 95% CI: 1.61\u0026ndash;41.10, p\u0026thinsp;=\u0026thinsp;0.011). Sarcopenia alone at baseline was associated with higher odds of frailty (OR\u0026thinsp;=\u0026thinsp;1.86, 95% CI: 0.17\u0026ndash;20.51, p\u0026thinsp;=\u0026thinsp;0.613), though this was not statistically significant. Interestingly, baseline osteopenia or osteoporosis was associated with lower odds of frailty (OR\u0026thinsp;=\u0026thinsp;0.68, 95% CI: 0.15\u0026ndash;3.21, p\u0026thinsp;=\u0026thinsp;0.63), although this result was also not significant. Similar patterns were observed using the SDOC definition, with osteosarcopenia again showing a significant association with frailty (OR\u0026thinsp;=\u0026thinsp;8.00, 95% CI: 1.42\u0026ndash;45.06, p\u0026thinsp;=\u0026thinsp;0.018), while associations for sarcopenia and osteopenia/osteoporosis remained non-significant (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\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\u003eAssociation of Baseline Osteosarcopenia (and its components) with Frailty Risk in a Longitudinal Cohort.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrailty vs non-frailty at follow-up\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal number at follow up\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-Frail at follow up\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFrail at follow up\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eUnadjusted \u0026ndash; univariable analysis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEuropean Working Group of Sarcopenia in older People \u0026ndash; Revised (EWGSOP2) Definition\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-osteopenic/non-sarcopenic (control group)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOsteopenia or osteoporosis only\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[0.15,3.21]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSarcopenia only\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[0.17,20.51]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.613\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOsteosarcopenia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[1.61,41.10]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSarcopenia Definition and Outcome Consortium (SDOC) Definition\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-osteopenic/non-sarcopenic (control group)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOsteopenia or osteoporosis only\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[0.17,3.40]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.729\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSarcopenia only\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[0.11,12.83]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOsteosarcopenia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[1.42,45.06]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.018\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\u003eIn a multivariable analysis comparing two groups, osteosarcopenia to non-osteosarcopenia, osteosarcopenia (EWGSOP2 definition) was significantly associated with higher odds of frailty (OR\u0026thinsp;=\u0026thinsp;9.53, 95% CI: 2.53\u0026ndash;35.92, p\u0026thinsp;=\u0026thinsp;0.001). Models were individually adjusted for age, sex, polypharmacy, smoking, alcohol use, education, income, physical activity, and CRP levels. Osteosarcopenia remained a consistent predictor of frailty across all models. Among the covariates, only an income greater than AUD 40,000/year was protective; other variables showed no significant associations (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Using the SDOC criteria (adjusted for BMI), osteosarcopenia was again significantly associated with increased odds of frailty (OR\u0026thinsp;=\u0026thinsp;9.19, 95% CI: 2.19\u0026ndash;38.56, p\u0026thinsp;=\u0026thinsp;0.002). This association remained robust in multivariable models. Similar to the EWGSOP2 findings, an income above AUD 40,000/year was the only covariate significantly associated with a reduced risk of frailty (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation of Baseline Osteosarcopenia with Frailty Risk - Multivariable Analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eOsteosarcopenia vs non-osteosarcopenia\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"7\" nameend=\"c12\" namest=\"c6\"\u003e \u003cp\u003eFrail vs non-frail (dependent variable)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal n\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-Frail\u003c/p\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFrail\u003c/p\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMultivariable analysis adjusted for each variable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eOR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e95% CI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003ep-value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003eOR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e95% CI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003ep-value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOsteosarcopenia\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(by EWGSOP2)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUnadjusted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[2.53,35.92]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"8\" rowspan=\"9\"\u003e \u003cp\u003eAdjusted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[2.08,32.91]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eAge at first visit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e[0.94,1.14]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.481\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[2.46,35.20]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSex - Men\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e[0.20,2.60]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.612\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[2.27,36.98]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003ePolypharmacy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e[0.24,5.64]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.858\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[2.30,34.14]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e[0.10,1.56]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.183\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[2.45,37.31]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eAlcohol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e[0.08,12.40]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.984\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[2.49,38.70]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003ePost-secondary qualification\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e[0.25,5.19]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.871\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[2.91,55.15]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eIncome AUD\u0026thinsp;\u0026gt;\u0026thinsp;40K/yr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e[0.05,0.69]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[2.57,55.09]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eHigh Physical activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e[0.06,1.49]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.139\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[2.63,45.50]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eHs-CRP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e[0.98,1.37]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.093\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOsteosarcopenia\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(by SDOC2)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUnadjusted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[2.19,38.56]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"8\" rowspan=\"9\"\u003e \u003cp\u003eAdjusted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[1.97,36.84]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eAge at first visit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e[0.96,1.16]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.244\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[2.06,37.65]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSex - Men\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e[0.22,2.90]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.738\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[2.10,37.88]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003ePolypharmacy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e[0.45,8.68]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.367\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[1.98,36.69]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e[0.10,1.52]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.172\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[2.32,42.43]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eAlcohol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e[0.03,3.23]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.345\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[2.12,40.80]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003ePost-secondary qualification\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e[0.23,4.74]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.946\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[2.20,46.86]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eIncome AUD\u0026thinsp;\u0026gt;\u0026thinsp;40K/yr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e[0.07,0.79]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[2.29,57.32]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eHigh Physical activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e[0.04,1.07]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[1.55,39.05]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eHs-CRP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e[0.92,1.31]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.303\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eAssociation Between Bone and Muscle Measures and Frailty\u003c/h2\u003e \u003cp\u003eClinical measures of muscle strength, handgrip strength (both unadjusted and BMI-adjusted), gait speed, and the SPPB were significantly associated with frailty at follow-up. In contrast, DEXA-derived measures of muscle (ALM and ALM/height\u003csup\u003e2\u003c/sup\u003e) and bone (BMD and T-scores) showed no significant associations. The models were adjusted for sex, income, and physical activity, based on significant differences observed in the initial analysis (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation of Baseline Bone and Muscle Measures with Frailty Risk in a longitudinal cohort.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFrailty vs non-frailty\u003c/p\u003e \u003cp\u003eat follow-up (13 vs 130)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003e\u003cb\u003eMuscle\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHand Grip Strength Max (Kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[0.84,1.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[0.87,1.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e[0.87,1.01]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.085\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHand Grip Strength/BMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[0.03,0.90]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[0.04,0.97]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e[0.04,1.28]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.095\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGait speed best(m/s) [0.1]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[0.53,0.85]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[0.54,0.88]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e[0.49,0.84]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5x Sit to Stand (sec)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[1.00,1.08]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[1.00,1.09]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e[0.99,1.07]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.152\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eShort Physical Performance Battery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[0.42,0.81]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[0.43,0.82]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e[0.43,0.83]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eALM (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[0.78,1.14]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.545\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[0.84,1.05]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.301\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e[0.84,1.07]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.361\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eALM/Height (kg/m2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[0.38,1.47]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.396\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[0.46,1.20]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e[0.45,1.25]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.268\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSubtotal body fat, %age\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[0.96,1.14]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.308\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[0.98,1.13]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e[0.96,1.10]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.489\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"8\" rowspan=\"9\"\u003e \u003cp\u003e\u003cb\u003eBone\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLowest site BMD (g/cm2) [0.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[0.04,33.96]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.906\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[0.02,10.93]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.648\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e[0.02,11.80]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.645\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLumbar spine BMD (g/cm2) [0.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[0.15,5.56]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.924\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[0.14,3.93]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.719\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e[0.09,3.30]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.521\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemoral Neck BMD (g/cm2) [0.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[0.01,2.75]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[0.05,1.81]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e[0.00,1.75]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.108\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHip total BMD (g/cm2) [0.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[0.03,3.13]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.319\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[0.04,2.04]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.206\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e[0.12,1.48]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.102\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForearm distal 1/3rd BMD (g/cm2) [0.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[0.20,589.19]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.243\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[0.07,14.46]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.995\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e[0.10,25.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.742\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLowest site (T-score)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[0.35,1.21]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[0.35,1.16]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e[0.27,1.04]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.064\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLumbar Spine (T-score)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[0.71,1.55]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.796\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[0.69,1.46]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.976\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e[0.63,1.40]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.767\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemoral neck (T-score)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[0.29,1.24]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[0.27,1.16]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e[0.23,1.11]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDistal 1/3rd forearm (T-score)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[0.69,1.83]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.626\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[0.64,1.76]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.814\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e[0.65,1.75]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.791\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"12\"\u003eModel 1 Adjusted for sex\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"12\"\u003eModel 2 Adjusted for income\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"12\"\u003eModel 3 Adjusted for high physical activity score\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"12\"\u003eAll analysis represents an increase by one unit unless specified in square brackets []. Gait speed analysis for an increase by 0.1m/s and BMD increase by 0.5g/cm\u003csup\u003e2\u003c/sup\u003e.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eA 1 kg increase in handgrip strength was associated with an 8% reduction in frailty odds (OR\u0026thinsp;=\u0026thinsp;0.92, 95% CI: 0.84\u0026ndash;1.00, p\u0026thinsp;=\u0026thinsp;0.038). When adjusted for BMI, a 1-unit increase in handgrip strength corresponded to an 85% lower risk of frailty (OR\u0026thinsp;=\u0026thinsp;0.15, 95% CI: 0.03\u0026ndash;0.90, p\u0026thinsp;=\u0026thinsp;0.037). This association remained significant after adjusting for income but was attenuated after further adjustment for physical activity. For gait speed, each 0.1 m/s increase was linked to a 33% reduction in frailty odds (OR\u0026thinsp;=\u0026thinsp;0.67, 95% CI: 0.53\u0026ndash;0.85, p\u0026thinsp;=\u0026thinsp;0.001), with the association persisting across all models. Similarly, a 1-point improvement in SPPB score was associated with 42% lower odds of frailty (OR\u0026thinsp;=\u0026thinsp;0.58, 95% CI: 0.42\u0026ndash;0.81, p\u0026thinsp;=\u0026thinsp;0.001), consistent across adjusted models (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eBone-Muscle Interaction and Frailty\u003c/h2\u003e \u003cp\u003eBone\u0026ndash;muscle interaction analyses were conducted to assess whether the effects of muscle function (handgrip strength, gait speed, sit-to-stand time) on frailty vary by bone strength (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Handgrip strength (adjusted for sex) showed significant interactions with lumbar spine BMD (p\u0026thinsp;=\u0026thinsp;0.061), lumbar spine T-scores (p\u0026thinsp;=\u0026thinsp;0.051), and the lowest site T-score (p\u0026thinsp;=\u0026thinsp;0.011), the latter being clinically relevant for defining osteopenia and osteoporosis (WHO criteria). These findings suggest that the protective effect of handgrip strength on frailty is more pronounced at lower T-scores and BMD values (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003ea and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). In contrast, gait speed showed no significant interaction with bone measures, indicating its association with frailty is independent of bone strength.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eInteraction Analysis of Baseline Bone and Muscle Measures with Frailty Risk in a Longitudinal Cohort\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMuscle Parameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBone Parameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP-value (Interaction)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMain Effect (Muscle) OR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMain Effect (Bone) OR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u003cb\u003eHand Grip Strength Max (Kg) adjusted for sex\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHip total BMD (g/cm2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLumbar spine BMD (g/cm2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.061\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLowest site (T-score)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.051\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemoral neck (T-score)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.592\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLumbar spine (T-score)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.011\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u003cb\u003eGait Speed (m/sec)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHip total BMD (g/cm2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.844\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[0.00,0.20]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[0.00,5.78]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.232\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLumbar spine BMD (g/cm2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.253\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[0.00,0.19]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[0.01,12.27]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.551\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLowest site (T-score)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.406\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[0.00,0.20]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[0.30,1.18]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.141\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemoral neck (T-score)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[0.00,0.21]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[0.28,1.26]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.174\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLumbar spine (T-score)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[0.00,0.19]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[0.64,1.42]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.811\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u003cb\u003eSTS (sec)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHip total BMD (g/cm2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.983\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[1.00,1.08]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[0.00,4.47]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.202\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLumbar spine BMD (g/cm2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.243\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[1.00,1.08]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[0.01,12.67]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.598\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLowest site (T-score)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.838\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[1.00,1.09]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[0.31,1.08]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.088\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemoral neck (T-score)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.961\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[1.00,1.09]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[0.29,1.24]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.165\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLumbar spine (T-score)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.243\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[1.00,1.08]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[0.65,1.42]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.859\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u003cb\u003eALM/height (Kg/m2) adjusted for sex\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHip total BMD (g/cm2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.819\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[0.40,1.80]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.661\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[0.00,27.71]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.485\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLumbar spine BMD (g/cm2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.245\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[0.36,1.48]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.381\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[0.04,61.62]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.826\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLowest site (T-score)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[0.42,1.95]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.809\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[0.34,1.36]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.275\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemoral neck (T-score)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.982\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[0.43,1.81]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.729\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[0.29,1.35]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.235\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLumbar spine (T-score)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[0.34,1.42]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.322\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[0.76,1.67]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.561\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\u003eMultivariable logistic regression analyses were performed to assess whether bone and muscle measures independently predicted frailty. In models adjusted for both, gait speed remained significantly associated with frailty, independent of bone strength. Similarly, the sit-to-stand test retained a significant association after adjusting for bone parameters. In contrast, DEXA-derived muscle mass measures (ALM and ALM/height\u0026sup2;), adjusted for sex and bone measurements (BMD and T-scores), were not significantly associated with frailty.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this longitudinal cohort study of older adults followed for an average of 4.5 years, we identified an association between osteosarcopenia and an increased risk of frailty. Participants with osteosarcopenia were compared to those without, and in a separate analysis, each condition (osteosarcopenia, sarcopenia, osteopenia/osteoporosis) was compared to individuals without any condition at baseline. In both analyses, osteosarcopenia was consistently linked to frailty risk. Although sarcopenia showed a trend of increased risk, this did not reach statistical significance, likely due to sample size limitations. Muscle strength measures, such as handgrip strength and gait speed, were stronger predictors of frailty than DXA-derived bone (BMD, T-score) and muscle parameters (ALM and ALM/Height\u003csup\u003e2\u003c/sup\u003e), which did not predict frailty. Furthermore, an interaction between bone and muscle health and frailty risk was observed, whereby higher grip strength values were protective against frailty when bone density was lower (the opposite was also true). These findings support our hypothesis that osteosarcopenia contributes to frailty risk and emphasize the importance of addressing both bone and muscle health in frailty prevention.\u003c/p\u003e \u003cp\u003eThe temporal relationship between osteosarcopenia and frailty remains insufficiently understood due to limited longitudinal data. While cross-sectional studies have consistently demonstrated an association, longitudinal cohort studies are better suited to assess its predictive value over time. We previously reported findings from the I-Lan Longitudinal Study on Aging (ILAS) in Taiwan, where osteosarcopenia (osteopenia/osteoporosis by WHO definition and sarcopenia by AWGS 2019 criteria) was not associated with frailty (Fried criteria) among 998 participants followed over eight years (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). Two additional cohort studies have examined this relationship. A retrospective analysis of the prospective ROAD cohort in Japan found a significant association between osteosarcopenia (sarcopenia by AWGS) and frailty (Fried criteria) over four years (OR 5.80, 95% CI 1.38\u0026ndash;24.4, p\u0026thinsp;=\u0026thinsp;0.017) in community-dwelling adults aged 60 and above (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). In contrast, the Canadian Longitudinal Study on Aging (CLSA) reported no association between osteosarcopenia (sarcopenia by SDOC) and frailty (Rockwood deficit accumulation index) over three years in Caucasian adults aged 65 and older. However, osteosarcopenia was associated with an increased risk of falls, fractures, reduced quality of life, and impaired activities of daily living (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). Our findings match the ROAD study in supporting an association between osteosarcopenia and frailty risk, but differ from the CLSA and ILAS results. These inconsistencies likely reflect variations in cohort characteristics, geographic settings, study follow-up duration, and the definitions of frailty and sarcopenia employed.\u003c/p\u003e \u003cp\u003eCohort characteristics are critical to study outcomes, with variations in lifestyle factors (e.g., alcohol use, smoking, physical activity, health literacy, socioeconomic status, education) influencing bone and muscle health through their impact on diet, exercise behaviors, and lifestyle choices (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). Geographic differences in comorbidities, medication use, and genetic factors, such as regional variation in insulin resistance and metabolic syndrome, may further impact musculoskeletal outcomes uniquely (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). Inconsistencies in findings across studies may also stem from differing definitions of frailty and sarcopenia. The CLSA employed the Rockwood Frailty Index, based on a deficit accumulation model, while our study, along with the ROAD and ILAS cohorts, used the Fried Frailty Phenotype. Sarcopenia definitions likewise varied: the CLSA used the SDOC criteria, we applied both SDOC and EWGSOP2, and the ROAD and ILAS studies used the AWGS classification. These methodological differences may partly explain the divergent results.\u003c/p\u003e \u003cp\u003eIn addition to the differences in cohort characteristics and definitions, frailty and osteosarcopenia are complex, dynamic, and interact bidirectionally with multiple feedback loops, challenging the unidirectional assumptions of longitudinal cohort analyses. Long-term studies with multiple assessment points are needed to clarify this relationship.\u003c/p\u003e \u003cp\u003eWe propose that the biological basis of our results may be due to the shared pathways underlying bone\u0026ndash;muscle interactions and frailty. Bone\u0026ndash;muscle crosstalk, mediated by factors such as insulin-like growth factor and growth hormone implicated in frailty development (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e), have been suggested. Additionally, nutrition, inflammation, lifestyle, and socioeconomic factors collectively influence musculoskeletal health and frailty risk (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). These interconnected mechanisms support a multifactorial basis for the association of osteosarcopenia and frailty risk.\u003c/p\u003e \u003cp\u003eThe association between osteosarcopenia and frailty risk in our cohort implies that osteosarcopenia may represent an early, pre-frail stage in the frailty continuum. Frailty is known to develop progressively, often through an intermediate \u0026lsquo;pre-frail phase\u0026rsquo;, and is associated with increased vulnerability and adverse outcomes (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). Osteosarcopenia is linked to adverse outcomes, such as falls, fractures, impaired ADLs, reduced quality of life, and increased mortality, outcomes that are also strongly linked to frailty (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). However, defining pre-frailty remains challenging when using the same criteria due to its clinical overlap with frailty. Unlike the pre-frailty \u0026lsquo;conceptual\u0026rsquo; definition, osteosarcopenia offers objective measures of bone and muscle deficits, making it a promising marker for early risk identification. Recognizing it as a precursor could enable earlier, targeted interventions to prevent or delay frailty progression in both clinical and research contexts.\u003c/p\u003e \u003cp\u003eOur study observed a non-significant trend toward a positive association between sarcopenia and frailty, and an inverse trend for osteoporosis. Although limited by sample size, a possible explanation is that osteoporosis patients were more likely to receive pharmacotherapy to improve BMD, which may enhance muscle strength, potentially mitigating frailty risk.\u003c/p\u003e \u003cp\u003eOur secondary analysis examining the relationship between bone and muscle parameters and frailty demonstrated that muscle parameters, specifically handgrip strength, gait speed, the five-times sit-to-stand test, and the SPPB, were associated with frailty. In contrast, bone parameters, represented by BMD and T-scores obtained via DXA, did not show significant associations. One limitation of these analyses is that handgrip strength and gait speed are integral components of the Fried frailty phenotype, and their inclusion in the frailty definition may partly influence the observed associations. Moreover, the SPPB is a composite measure incorporating gait speed, handgrip strength, and the five-times sit-to-stand test, which complicates the interpretation of its independent association with frailty. Nevertheless, by analyzing these parameters as continuous variables, rather than dichotomizing them, as included in sarcopenia classification, we were able to more precisely assess the influence of handgrip strength and gait speed on frailty risk.\u003c/p\u003e \u003cp\u003eThese findings align with previous research, such as the Geelong Osteoporosis Study (GOS), which found that muscle parameters (lower limb strength and lean mass index) were better predictors of frailty than bone parameters (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). A previous 10-year longitudinal study (Osteoporosis Risk Assessment, OPRA) reported an association between low BMD and frailty. The discrepancy with our results is likely attributable to the shorter duration of follow-up in our study, as clinically significant changes in bone density typically occur over longer timeframes and would therefore take longer to impact frailty onset (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). Our findings also support the hypothesis that muscle function precedes and influences bone adaptation through mechanical loading and muscle\u0026ndash;bone crosstalk, potentially initiating changes that contribute to frailty development. Another plausible explanation for the superior predictive value of muscle measures lies in the inherently dynamic and adaptable nature of muscle tissue, which better reflects early functional decline. In contrast, DXA-derived bone and muscle mass metrics represent more static structural measures that evolve gradually. Therefore, physical performance tests for muscle were better associated with frailty risk.\u003c/p\u003e \u003cp\u003eThe analysis of bone\u0026ndash;muscle interaction in frailty risk revealed a noteworthy finding. This suggests a synergistic relationship between bone and muscle, wherein the combined decline in both parameters increases the likelihood of frailty. Interestingly, the interaction analysis demonstrated that higher handgrip strength attenuates the risk of frailty even in individuals with low BMD, and conversely, higher BMD mitigates frailty risk in those with reduced handgrip strength. These findings may emphasize the interdependence of bone and muscle in the pathophysiology of frailty and highlight the potential of targeting either organ to reduce frailty risk. As illustrated in Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003ea and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003eb, the co-occurrence of low BMD and low handgrip strength markedly elevates frailty risk, whereas improvement in either parameter is associated with a meaningful reduction in frailty probability.\u003c/p\u003e \u003cp\u003eA limitation of this interaction analysis is that it did not account for other contributing factors that may impact the relationship. For instance, individuals with low BMD may have experienced fragility fractures, which are independently associated with increased frailty risk. Likewise, participants with low grip strength may have been more vulnerable to falls, potentially leading to functional decline and frailty. These unmeasured clinical events may have influenced the observed associations and should be considered in future analyses. Despite these limitations, the key implication of our findings is that improving either bone or muscle strength may reduce the risk of frailty. This underscores the importance of integrated interventions that target both bone and muscle to preserve physical function and delay the onset of frailty. Our group has previously reported a synergistic effect of hip BMD and gait speed on fracture risk, reinforcing the notion that the interplay between bone and muscle health has critical implications for adverse functional outcomes (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eStrengths and Limitations\u003c/h2\u003e \u003cp\u003eOne of the key strengths of our research is its longitudinal cohort design, which allows for the assessment of changes over time and supports causal inference. In addition to assessing the impact of osteosarcopenia on frailty, we conducted a detailed analysis of the individual contributions of bone and muscle parameters and their interaction, offering a comprehensive evaluation of bone\u0026ndash;muscle interplay in predicting frailty risk. Validated tools and well-recognized criteria were used; Sarcopenia was defined using validated criteria from EWGSOP2 and SDOC, while bone and muscle mass were measured via DEXA, and frailty was measured using the Fried phenotype. Another strength of our study was the comprehensive data collection (demographic, socioeconomic, lifestyle, and clinical variables), allowing for robust adjustments for covariates, thus enhancing the reliability of our findings.\u003c/p\u003e \u003cp\u003eHowever, our study has several limitations. First, although the sample size was adequately powered based on the expected transition to frailty, a higher-than-anticipated loss to follow-up (50% vs. 30%) may have reduced the number of transitions to frailty, thereby limiting the model\u0026rsquo;s ability to adjust for potential confounders. Second, although the follow-up period was adequate to assess a single transition to frailty, the complex and dynamic nature of frailty would be better captured through extended follow-up with repeated assessments. Third, the study population consisted of community-dwelling adults aged 50 and older, which may limit generalizability to broader older populations. Fourth, while the cohort included individuals from diverse backgrounds, with 70% born in Australia and 30% overseas, these demographic characteristics should be considered when interpreting and generalizing the findings. Fifth, as with many cohort studies involving older adults, there may be unmeasured bias due to the likely inclusion of a relatively health-conscious and motivated subgroup, potentially limiting external validity. Lastly, the use of the Fried frailty phenotype introduces overlap with sarcopenia through the inclusion of gait speed and handgrip strength, which may confound associations. Although alternative models, such as the Rockwood frailty index, could offer a broader perspective, the physical domain remains central to frailty assessment, particularly when investigating musculoskeletal contributions. We used validated definitions to align with research in this field.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis longitudinal study demonstrated that osteosarcopenia is associated with frailty risk. Clinical measures of muscle function\u0026mdash;specifically handgrip strength and gait speed\u0026mdash;were more strongly associated with frailty risk than DXA-derived bone measures. Notably, a synergistic interaction between bone density and muscle strength was observed, suggesting that improvements in either may mitigate frailty risk. Future research should incorporate larger study sizes, more extended follow-up periods, repeated assessments, and broader frailty definitions (particularly Rockwood definition) to explore this relationship further.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eThe authors have no competing interests to declare relevant to this article\u0026apos;s content.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eMizhgan Fatima acknowledges support from the Australian Government Research Training Program (RTP) Scholarship for the conduct of this research work.\u003c/p\u003e\n\u003ch2\u003eAuthor Contributions\u003c/h2\u003e\n\u003cp\u003eMizhgan Fatima \u0026ndash; Conceptualization, Methodology, Ethics approval, Project administration, Data curation, Formal analysis, Resources, Visualization, Writing \u0026ndash; original draft, Writing \u0026ndash; review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003eBen Kirk \u0026ndash; Conceptualization, Resources, Supervision, Writing \u0026ndash; review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003eSara Vogrin - Formal analysis, Writing \u0026ndash; review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003eJason Talevski- Data curation, Writing \u0026ndash; review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003eSharon Brennan-Olsen- Conceptualization, Methodology, Resources, Supervision, Writing \u0026ndash; review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003eGustavo Duque \u0026ndash; Conceptualization, Resources, Supervision, Writing \u0026ndash; review \u0026amp; editing.\u003c/p\u003e\n\u003ch2\u003eEthics Declarations\u003c/h2\u003e\n\u003cp\u003eEthics Approval: All procedures performed involving human participants were in accordance with the ethical standards of the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. Written informed consent was obtained from all study participants. The study protocol was approved by the Melbourne Human Research Ethics Committee (HREC/17/MH/381).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eKim DH, Rockwood K (2024) Frailty in older adults. N Engl J Med 391(6):538\u0026ndash;548\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFried LP, Cohen AA, Xue Q-L, Walston J, Bandeen-Roche K, Varadhan R (2021) The physical frailty syndrome as a transition from homeostatic symphony to cacophony. Nat aging 1(1):36\u0026ndash;46\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHoogendijk EO, Afilalo J, Ensrud KE, Kowal P, Onder G, Fried LP (2019) Frailty: implications for clinical practice and public health. Lancet 394(10206):1365\u0026ndash;1375\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDuque G (2021) Osteosarcopenia: a geriatric giant of the XXI century. Springer, pp 716\u0026ndash;719\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKirk B, Zanker J, Duque G (2020) Osteosarcopenia: epidemiology, diagnosis, and treatment\u0026mdash;facts and numbers. Wiley Online Library, pp 609\u0026ndash;618\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKirk B, Al Saedi A, Duque G (2019) Osteosarcopenia: A case of geroscience. Aging Med 2(3):147\u0026ndash;156\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLaskou F, Fuggle NR, Patel HP, Jameson K, Cooper C, Dennison E (2022) Associations of osteoporosis and sarcopenia with frailty and multimorbidity among participants of the Hertfordshire Cohort Study. J cachexia sarcopenia muscle 13(1):220\u0026ndash;229\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaeki C, Kanai T, Nakano M, Oikawa T, Torisu Y, Abo M et al (2020) Relationship between osteosarcopenia and frailty in patients with chronic liver disease. J Clin Med 9(8):2381\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDavenport A, Frailty (2022) appendicular lean mass, osteoporosis and osteosarcopenia in peritoneal dialysis patients. J Nephrol 35(9):2333\u0026ndash;2340\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTembo MC, Mohebbi M, Holloway-Kew KL, Gaston J, Sui SX, Brennan-Olsen SL et al (2021) The contribution of musculoskeletal factors to physical frailty: a cross-sectional study. BMC Musculoskelet Disord 22(1):921\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYoshimura N, Muraki S, Iidaka T, Oka H, Horii C, Kawaguchi H et al (2019) Prevalence and co-existence of locomotive syndrome, sarcopenia, and frailty: the third survey of Research on Osteoarthritis/Osteoporosis Against Disability (ROAD) study. J Bone Min Metab 37(6):1058\u0026ndash;1066\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHeng MWY, Chan AW, Man RE, Fenwick EK, Chew ST, Tay L et al (2023) Individual and combined associations of sarcopenia, osteoporosis and obesity with frailty in a multi-ethnic asian older adult population. BMC Geriatr 23(1):802\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang Y-J, Wang Y, Zhan J-K, Tang Z-Y, He J-Y, Tan P et al (2015) Sarco-osteoporosis: prevalence and association with frailty in Chinese community-dwelling older adults. Int J Endocrinol 2015:482940\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSzlejf C, Parra-Rodr\u0026iacute;guez L, Rosas-Carrasco O (2017) Osteosarcopenic Obesity: Prevalence and Relation With Frailty and Physical Performance in Middle-Aged and Older Women. J Am Med Dir Assoc 18(8):733. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e.e1-.e5\u003c/span\u003e\u003cspan address=\"http://.e1-.e5\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHagstr\u0026ouml;mer M, Oja P, Sj\u0026ouml;str\u0026ouml;m M (2006) The International Physical Activity Questionnaire (IPAQ): a study of concurrent and construct validity. Public Health Nutr 9(6):755\u0026ndash;762\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKanis JA, Melton LJ 3rd, Christiansen C, Johnston CC, Khaltaev N (1994) The diagnosis of osteoporosis. J bone mineral research: official J Am Soc Bone Mineral Res 9(8):1137\u0026ndash;1141\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZanker J, Scott D, Reijnierse E, Brennan-Olsen S, Daly R, Girgis C et al (2018) Establishing an Operational Definition of Sarcopenia in Australia and New Zealand: Delphi Method Based Consensus Statement. The journal of nutrition, health \u0026amp; aging. :1\u0026ndash;6\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCruz-Jentoft AJ, Bahat G, Bauer J, Boirie Y, Bruy\u0026egrave;re O, Cederholm T et al (2019) Sarcopenia: Revised European consensus on definition and diagnosis. Age Ageing 48(1):16\u0026ndash;31\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBhasin S, Travison TG, Manini TM, Patel S, Pencina KM, Fielding RA et al (2020) Sarcopenia definition: the position statements of the sarcopenia definition and outcomes consortium. J Am Geriatr Soc 68(7):1410\u0026ndash;1418\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBandeen-Roche K, Xue Q-L, Ferrucci L, Walston J, Guralnik JM, Chaves P et al (2006) Phenotype of frailty: characterization in the women's health and aging studies. Journals Gerontol Ser A: Biol Sci Med Sci 61(3):262\u0026ndash;266\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWoods NF, LaCroix AZ, Gray SL, Aragaki A, Cochrane BB, Brunner RL et al (2005) Frailty: emergence and consequences in women aged 65 and older in the Women's Health Initiative Observational Study. J Am Geriatr Soc 53(8):1321\u0026ndash;1330\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEspinoza SE, Woods RL, Ekram AS, Ernst ME, Polekhina G, Wolfe R et al (2022) The effect of low-dose aspirin on frailty phenotype and frailty index in community-dwelling older adults in the ASPirin in Reducing Events in the Elderly study. Journals Gerontology: Ser A 77(10):2007\u0026ndash;2014\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFried LP, Tangen CM, Walston J, Newman AB, Hirsch C, Gottdiener J et al (2001) Frailty in older adults: evidence for a phenotype. The journals of gerontology Series A, Biological sciences and medical sciences. 56(3):M146\u0026ndash;M156\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrennan-Olsen SL, Vogrin S, Balogun S, Wu F, Scott D, Jones G et al (2020) Education, occupation and operational measures of sarcopenia: Six years of Australian data. Australas J Ageing 39(4):e498\u0026ndash;e505\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eServicesAustralia [Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.servicesaustralia.gov.au/how-much-carer-payment-you-can-get?context=21816\u003c/span\u003e\u003cspan address=\"https://www.servicesaustralia.gov.au/how-much-carer-payment-you-can-get?context=21816\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVittinghoff E, McCulloch CE (2007) Relaxing the rule of ten events per variable in logistic and Cox regression. Am J Epidemiol 165(6):710\u0026ndash;718\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGoodman S (ed) (2008) editor A dirty dozen: twelve p-value misconceptions. Seminars in hematology. Elsevier\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFatima M, Kirk B, Vogrin S, Lee W-J, Peng L-N, Chen L-K et al (2025) Osteosarcopenia and frailty risk in community-dwelling older adults: A follow-up of the I-Lan Longitudinal Aging Study. Arch Gerontol Geriatr 136:105888\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYoshimura N, Muraki S, Oka H, Iidaka T, Kodama R, Horii C et al (2018) Do sarcopenia and/or osteoporosis increase the risk of frailty? A 4-year observation of the second and third ROAD study surveys. Osteoporosis international: a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA. 29(10):2181\u0026ndash;2190\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee A, McArthur C, Ioannidis G, Duque G, Adachi JD, Griffith LE et al (2024) Associations between Osteosarcopenia and Falls, Fractures, and Frailty in Older Adults: Results From the Canadian Longitudinal Study on Aging (CLSA). J Am Med Dir Assoc 25(1):167\u0026ndash;76e6\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStolz E, Mayerl H, Waxenegger A, R\u0026aacute;sky \u0026eacute;, Freidl W (2016) Impact of socioeconomic position on frailty trajectories in 10 European countries: Evidence from the Survey of Health, Ageing and Retirement in Europe (2004\u0026ndash;2013). J Epidemiol Commun Health 71(1):73\u0026ndash;80\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrennan SL, Winzenberg TM, Pasco JA, Wluka AE, Dobbins AG, Jones G (2013) Social disadvantage, bone mineral density and vertebral wedge deformities in the Tasmanian Older Adult Cohort. Osteoporosis international: a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA. 24(6):1909\u0026ndash;1916\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGurka MJ, Filipp SL, DeBoer MD (2018) Geographical variation in the prevalence of obesity, metabolic syndrome, and diabetes among US adults. Nutr diabetes 8(1):14\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGreco EA, Pietschmann P, Migliaccio S (2019) Osteoporosis and sarcopenia increase frailty syndrome in the elderly. Front Endocrinol 10:255\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKirk B, Miller S, Zanker J, Duque G (2020) A clinical guide to the pathophysiology, diagnosis and treatment of osteosarcopenia. Maturitas 140:27\u0026ndash;33\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJenkins ND, Welstead M, Stirland L, Hoogendijk EO, Armstrong JJ, Robitaille A et al (2023) Frailty trajectories and associated factors in the years prior to death: evidence from 14 countries in the Survey of Health, Aging and Retirement in Europe. BMC Geriatr 23(1):49\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHoogendijk EO, Dent E (2022) Trajectories, transitions, and trends in frailty among older adults: a review. Annals geriatric Med Res 26(4):289\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePaulin TK, Malmgren L, McGuigan FE, Akesson KE (2024) Osteosarcopenia: prevalence and 10-year fracture and mortality risk\u0026ndash;a longitudinal, population-based study of 75-year-old women. Calcif Tissue Int 114(4):315\u0026ndash;325\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBalogun S, Winzenberg T, Wills K, Scott D, Callisaya M, Cicuttini F et al (2019) Prospective associations of osteosarcopenia and osteodynapenia with incident fracture and mortality over 10 years in community-dwelling older adults. Arch Gerontol Geriatr 82:67\u0026ndash;73\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTembo MC, Mohebbi M, Holloway-Kew KL, Gaston J, Brennan-Olsen SL, Williams LJ et al (2021) The Predictability of Frailty Associated with Musculoskeletal Deficits: A Longitudinal Study. Calcif Tissue Int 109(5):525\u0026ndash;533\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBartosch P, McGuigan FE, Akesson KE (2018) Progression of frailty and prevalence of osteoporosis in a community cohort of older women-a 10-year longitudinal study. Osteoporosis international: a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA. 29(10):2191\u0026ndash;2199\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKirk B, Zhang S, Vogrin S, Harijanto C, Sales M, Duque G (2023) Comparing the fracture profile of osteosarcopenic older adults with osteopenia/osteoporosis alone. Calcif Tissue Int 112(3):297\u0026ndash;307\u003c/span\u003e\u003c/li\u003e\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":"aging-clinical-and-experimental-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"acer","sideBox":"Learn more about [Aging Clinical and Experimental Research](http://link.springer.com/journal/40520)","snPcode":"40520","submissionUrl":"https://submission.nature.com/new-submission/40520/3","title":"Aging Clinical and Experimental Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Aging, osteosarcopenia, sarcopenia, osteoporosis, frailty","lastPublishedDoi":"10.21203/rs.3.rs-6759229/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6759229/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eOsteosarcopenia, defined as the coexistence of osteopenia/osteoporosis and sarcopenia, may influence frailty risk in older adults. However, the longitudinal association between osteosarcopenia and its components with frailty remains unclear. This study aimed to address this.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eData from a prospective cohort study of community-dwelling adults in Australia. Frailty was defined by the presence of three or more components based on Fried criteria: exhaustion, slow gait speed, low grip strength, unintentional weight loss, and low physical activity. Osteosarcopenia was defined by osteopenia/osteoporosis (WHO criteria) and sarcopenia (European Working Group on Sarcopenia in Older People [EWGSOP2] and Sarcopenia Definition and Outcome consortium [SDOC]). Multivariable logistic regression models evaluated the associations between osteosarcopenia or its components with frailty.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eOf 301 participants enrolled, 151 (mean age: 65.1 years, 59.6% women) completed a follow-up (median: 4.5 years). Osteosarcopenia was associated with frailty risk irrespective of the definition used: EWGSOP2: OR\u0026thinsp;=\u0026thinsp;8.13, 95% CI 1.61\u0026ndash;41.10; SDOC OR\u0026thinsp;=\u0026thinsp;8.0, 95% CI 1.42\u0026ndash;45.06). Among its components, low grip strength (OR\u0026thinsp;=\u0026thinsp;0.92, 95% CI 0.84\u0026ndash;1.00) and slow gait speed (OR\u0026thinsp;=\u0026thinsp;0.67, 95% CI 0.53\u0026ndash;0.85) were independently associated with frailty; bone mineral density/lean mass were not. An interaction between bone and muscle health and frailty risk was observed (p\u0026thinsp;\u0026lt;\u0026thinsp;0.011), whereby higher values of grip strength were protective against frailty when bone density was lower (the opposite was also true).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eIn this prospective cohort study, osteosarcopenia increased the risk of frailty. Our interaction analysis suggests therapies targeting bone density and grip strength may mitigate against frailty.\u003c/p\u003e","manuscriptTitle":"Osteosarcopenia, bone-muscle interactions, and Frailty risk: A Prospective Cohort Study of Community- Dwelling Older Adults","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-09 05:38:15","doi":"10.21203/rs.3.rs-6759229/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-08-06T07:42:07+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-05T06:50:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"281829867212596168466651051085022007543","date":"2025-07-16T11:04:47+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-13T03:04:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"205056113146804807285007978303354835340","date":"2025-07-12T01:34:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"230842406133558134827727333923970969440","date":"2025-06-03T01:32:51+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-02T16:08:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"213515245781419940384429602243664943964","date":"2025-06-02T15:48:31+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-02T15:01:23+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-29T12:50:09+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-27T13:26:08+00:00","index":"","fulltext":""},{"type":"submitted","content":"Aging Clinical and Experimental Research","date":"2025-05-27T11:54:33+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"aging-clinical-and-experimental-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"acer","sideBox":"Learn more about [Aging Clinical and Experimental Research](http://link.springer.com/journal/40520)","snPcode":"40520","submissionUrl":"https://submission.nature.com/new-submission/40520/3","title":"Aging Clinical and Experimental Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"e4793529-48c8-48e9-aa1e-972be84a461b","owner":[],"postedDate":"June 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-12-15T16:12:32+00:00","versionOfRecord":{"articleIdentity":"rs-6759229","link":"https://doi.org/10.1007/s40520-025-03242-x","journal":{"identity":"aging-clinical-and-experimental-research","isVorOnly":false,"title":"Aging Clinical and Experimental Research"},"publishedOn":"2025-12-10 15:57:37","publishedOnDateReadable":"December 10th, 2025"},"versionCreatedAt":"2025-06-09 05:38:15","video":"","vorDoi":"10.1007/s40520-025-03242-x","vorDoiUrl":"https://doi.org/10.1007/s40520-025-03242-x","workflowStages":[]},"version":"v1","identity":"rs-6759229","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6759229","identity":"rs-6759229","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}