Calf Circumference as a Predictive Biomarker for Falls in older Adults: A Retrospective Case– Control Study

Calf Circumference as a Predictive Biomarker for Falls in older Adults: A Retrospective Case– Control Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Calf Circumference as a Predictive Biomarker for Falls in older Adults: A Retrospective Case– Control Study Fatemeh Zahra Nezamdoust, Davoud Tanbakouchi, Sahar Heydari, Faezeh Sadeghzadeh, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7839117/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Falls are a significant cause of injury, loss of independence, and mortality among older adults. While muscle weakness and balance impairment are well-established modifiable risk factors, simple anthropometric measures such as calf circumference (CC) have not been thoroughly evaluated for their predictive value regarding fall risk in community settings. Objective This study aims to determine whether CC independently predicts fall risk in community-dwelling older adults and to compare its predictive performance with other sociodemographic, clinical, functional, and lifestyle factors. Methods In this retrospective case–control study, we analyzed electronic health records from 1,064 adults aged 60 years and older registered in the Sina health information system between March 2017 and December 2023 (266 fallers and 798 non-fallers). CC was measured at the widest point of the relaxed dominant calf (using the mean of four readings) and dichotomized into < 31 cm versus ≥ 31 cm. Covariates included age, sex, body mass index (BMI), education, marital and employment status, regular physical activity, limb muscle strength, cerebellar signs, foot examination findings, comorbidities (including diabetes, hypertension, dyslipidemia, cardiovascular disease, and obesity), and the use of statins or aspirin. Univariable and multiple logistic regression models were employed to estimate odds ratios (ORs) with 95% confidence intervals (CIs), with significance set at p ≤ 0.05. Results We analyzed 1,064 older adults (266 fallers; 25.0%). Fallers were older and had lower BMI than non-fallers (both p < 0.001). In multipule logistic regression, larger calf circumference (CC ≥ 31 cm) was independently associated with reduced odds of falling (adjusted OR = 0.54, 95% CI: 0.33–0.90, p = 0.01), whereas physical inactivity increased the odds (adjusted OR = 3.57, 95% CI: 2.19–5.83, p < 0.001). Cerebellar dysfunction showed a borderline association with higher fall risk (adjusted OR = 4.16, 95% CI: 0.98–17.71, p = 0.05). Other demographic and clinical factors were not independently associated after adjustment (all p > 0.05). Conclusions Calf circumference < 31 cm is independently associated with increased odds of falls in older adults. Routine measurement of CC using a simple tape in primary care or community settings offers a low-cost, feasible screening tool for the early identification of high-risk individuals and may inform targeted interventions such as resistance and balance training. Prospective cohort studies and randomized trials are necessary to validate sex- and age-specific CC thresholds and to assess whether interventions aimed at increasing calf muscle mass or function can reduce the incidence of falls. Introduction The global population aged 65 years and older is projected to increase from 727 million in 2020 to 1.5 billion by 2050 ( 1 ). In the United States, nearly one in four adults aged 65 years and older experienced at least one fall annually in 2018, with fallers facing a two- to threefold higher risk of subsequent fractures, hospitalization, and mortality ( 3 , 4 ). The direct medical cost of a single fall in U.S. healthcare settings was estimated at USD 30,000 in 2019, and fall-related expenses account for approximately 0.85% of total healthcare spending in high-income countries ( 5 ). Falls initiate a cascade of functional decline: fear of falling leads to reduced activity, progressive muscle atrophy, social isolation, and depression, perpetuating a cycle of frailty ( 6 ). Sarcopenia—defined by the revised EWGSOP2 criteria as age-related loss of skeletal muscle mass and strength—is a significant, modifiable risk factor for falls ( 7 ). Gold-standard assessments of muscle mass (e.g., DXA, BIA) remain impractical in many primary care and community settings due to cost, equipment requirements, and necessary training ( 8 ). Calf circumference (CC) measurement serves as a simple, noninvasive proxy for lower-limb muscle mass and function. CC correlates strongly with appendicular lean mass, gait speed, and timed up-and-go performance ( 11 , 15 ). Recent studies have linked smaller CC values to increased fall risk: Rodrigues et al. (2022) found that CC below sex- and age-specific cut-offs predicted falls in older hemodialysis patients ( 13 ), and Barbosa-Silva et al. (2023) demonstrated that incorporating CC into the SARC-F questionnaire enhanced the sensitivity of sarcopenia detection in community-dwelling older adults ( 14 ). However, standardized CC thresholds for fall-risk stratification and measurement protocols (e.g., seated vs. standing posture) vary across studies, and the influence of adipose tissue distribution on CC remains unresolved ( 16 ). Most existing studies are cross-sectional; few longitudinal or case-control investigations have examined CC’s independent predictive power for future falls after adjusting for confounding variables such as age, body mass index (BMI), comorbidities, and baseline functional status. Without high-quality data from rigorous study designs, the clinical application of CC measurement for fall-risk screening is limited ( 17 ). We aimed to determine whether calf circumference measurement serves as a reliable, practical indicator for detecting fall risk in older adults by assessing its validity against established muscle-mass and functional markers, its predictive power for future falls, and the optimal threshold values and measurement protocols for routine geriatric screening. Methods Study Design and Setting : This retrospective case–control study utilized data from the Sina health information system between March 2017 and December 2023 ( 18 ). Community-dwelling adults aged 60 years and older, registered at primary care health centers, urban health posts, and 'House of Behvarz' clinics affiliated with Mashhad University of Medical Sciences, were eligible for inclusion. Out of 1,200 potentially eligible individuals, 136 (11.3%) were excluded due to missing calf circumference data (n = 82) or key covariates (n = 54), resulting in 266 cases and 798 controls (1:3 ratio) for analysis. Participants and Sampling : Cases (n = 266) included individuals aged 60 years and older with at least one documented fall, defined according to WHO criteria, within six months of calf circumference (CC) measurement. Controls (n = 798) were selected through simple random sampling from age-stratified (decade) fall-free individuals at a 1:3 case-to-control ratio. Inclusion and Exclusion Criteria : Inclusion criteria consisted of: age 60 years or older; registration in the Sina system; complete anthropometric and clinical records; and written informed consent. Exclusion criteria included neurological disorders affecting muscle (e.g., stroke, Parkinson’s), acute illness at the time of measurement, or incomplete consent. Measurement Protocols Calf circumference (CC) was measured on the dominant leg at its widest point using a non-stretchable tape, with participants seated, knees at 90 degrees, and feet flat. Two trained raters performed two measurements each; the mean of all four readings was analyzed. Inter- and intra-rater reliability (ICC) both exceeded 0.90. Height and weight were measured following standard procedures to compute BMI (kg/m²). Additional Variables Demographic and lifestyle variables included age, sex, marital status, education level (≤ high school, undergraduate, ≥postgraduate), employment status, smoking status (current vs. never/former), and self-reported physical activity (Yes/No). Functional assessments included limb muscle strength (Yes/No), cerebellar function (Yes/No), and foot examination (Yes/No). Clinical history (diabetes, hypertension, dyslipidemia, cardiovascular disease) and medication use (statins, aspirin) were extracted from electronic medical records and verified through chart review. Outcome Definition Falls were defined as any event leading to an unintended rest on the ground or lower level and were recorded in the 'Elderly Care' form for the six months preceding CC measurement. Sample Size and Power Calculation Based on pilot data indicating a 30% prevalence of low CC (< 31 cm) among controls and an expected odds ratio (OR) of 1.5 for falls, we calculated that at least 250 cases and 750 controls would provide 80% power at α = 0.05. Therefore, 266 cases and 798 controls were included in the study. Statistical Analysis Continuous variables were assessed for normality using the Shapiro–Wilk test. Normally distributed variables are presented as mean ± SD and compared using independent-samples t-tests; non-normally distributed variables are presented as median (IQR) and compared using Mann–Whitney U tests. Categorical variables are presented as counts (n, %) and compared using χ² or Fisher’s exact test, as appropriate (Table 1 ). Univariable logistic regression analyses estimated unadjusted odds ratios (ORs) and 95% confidence intervals (CIs) for each covariate (Table 2 ). Variables with p < 0.20 in univariable analyses or selected a priori based on directed acyclic graph (DAG) theory were included in multiple logistic regression analyses. Forward stepwise selection retained variables with p ≤ 0.05, and multicollinearity was assessed using variance inflation factors (VIF < 2). Adjusted ORs and 95% CIs are reported (Table 3 ). Ethical Considerations The study protocol was approved by the Ethics Committee of Mashhad University of Medical Sciences (IR.MUMS.FHMPM.REC.1403.188). Written informed consent was obtained from all participants. Data were anonymized and securely stored on encrypted servers accessible only to study investigators. Results Participant characteristics We analyzed 1,064 community-dwelling older adults (266 fallers, 798 non-fallers) after exclusions described in Methods. The mean age of fallers was higher than non-fallers (79.30 ± 9.54 vs. 72.70 ± 8.34 years; p < 0.001). Body mass index (BMI) was lower in fallers (23.80 ± 4.80 vs. 24.96 ± 4.50 kg/m²; p < 0.001). Women constituted a larger share of fallers than non-fallers (64.29% vs. 54.76%; p < 0.001). Several functional and clinical findings differed between groups. Limb muscle weakness (9.74% vs. 3.42%), cerebellar dysfunction (7.80% vs. 0.86%), and abnormal foot findings (10.71% vs. 2.26%) were each more frequent among fallers than non-fallers (all p < 0.001). Hypertension was also more common in fallers (76.3% vs. 65.5%; p 0.05). Sociodemographic distributions also varied. Compared with non-fallers, fallers more often had ≤ high-school education (75.94% vs. 58.02%; p < 0.001) and differed by marital status (p < 0.001). Employment status showed a nonsignificant trend (p = 0.06). The distribution of calf circumference (CC) categories also differed between groups (p < 0.001). Baseline characteristics of cases and controls are summarized in Table 1 . Table 1 Demographic and Clinical Characteristics of Case (Fallers) and Control (Non-Fallers) Group Variable Fallers (n = 266) Non-Fallers (n = 798) p-value Age (years, mean ± SD) 79.30 ± 9.54 72.70 ± 8.34 < 0.001 Gender < 0.001 Male 95 (35.71%) 361 (45.24%) Female 171 (64.29%) 437 (54.76%) Marital Status < 0.001 Married 124 (46.62%) 228 (28.57%) Unmarried 142 (53.38%) 570 (71.43%) Employed 0.06 Yes 187 (89.47%) 446 (84.15%) No 22 (10.53%) 84 (15.85%) Education < 0.001 High school and less 202 (75.94%) 463 (58.02%) Undergraduate 44 (16.54%) 249 (31.20%) Postgraduate 20 (7.52%) 86 (10.78%) Calf Circumference < 0.001 ≥ 31 cm 106 (39.85%) 200 (25.06%) < 31 cm 160 (60.15%) 598 (74.94%) BMI (kg/m², mean ± SD) 23.80 ± 4.80 24.96 ± 4.50 < 0.001 Limb muscle strength < 0.001 Yes 15 (9.74%) 21 (3.42%) No 139 (90.26%) 593 (96.58%) Cerebellar function < 0.001 Yes 11 (7.80%) 5 (0.86%) No 130 (92.20%) 574 (99.14%) Foot examination < 0.001 Yes 15 (10.71%) 13 (2.26%) No 125 (89.29%) 562 (97.74%) Statin Use < 0.001 Yes 150 (69.44%) 422 (58.94%) No 66 (30.56%) 294 (41.06%) Aspirin Use < 0.001 Yes 133 (62.74%) 363 (50.77%) No 79 (37.26%) 352 (49.23%) Smoking 0.73 Current 249 (94.32%) 748 (93.73%) Never/Former 15 (5.68%) 50 (6.27%) Physical Activation < 0.001 Yes 148 (65.78%) 291 (40.64%) No 77 (34.22%) 425 (59.36%) Diabetes 0.90 Yes 71 (26.69%) 210 (26.32%) No 195 (73.31%) 588 (73.68%) Hypertension < 0.001 Yes 203 (76.3%) 523 (65.5%) No 63 (23.7%) 275 (34.5%) Dyslipidemia 0.20 Yes 22 (8.3%) 88 (11.0%) No 244 (91.7%) 710 (89.0%) CVD 0.56 Yes 15 (5.6%) 53 (6.6%) No 251 (94.4%) 745 (93.4%) Obesity 0.47 Yes 16 (6.0%) 39 (4.9%) No 250 (94.0%) 759 (95.1%) Univariable associations with falls : In univariable logistic regression, older age was associated with greater odds of falling (OR per year = 1.08; 95% CI: 1.06–1.10; p < 0.001). Higher BMI was inversely associated with falls (OR per kg/m² = 0.94; 95% CI: 0.91–0.97; p < 0.001). Relative to CC < 31 cm, CC ≥ 31 cm was associated with lower odds of falls (OR = 0.50; 95% CI: 0.37–0.67; p < 0.001). Female sex (OR = 1.48; 95% CI: 1.11–1.98; p < 0.001), unmarried status (OR = 2.18; 95% CI: 1.64–2.90; p < 0.001), limb muscle weakness (OR = 3.04; 95% CI: 1.53–6.06; p < 0.001), cerebellar dysfunction (OR = 9.71; 95% CI: 3.31–28.43; p < 0.001), and abnormal foot findings (OR = 5.18; 95% CI: 2.40–11.17; p < 0.001) were each associated with higher odds of falls. Statin use (OR = 1.58; 95% CI: 1.14–2.19; p < 0.001), aspirin use (OR = 1.63; 95% CI: 1.19–2.23; p < 0.001), physical inactivity (OR = 2.80; 95% CI: 2.05–3.84; p < 0.001), and hypertension (OR = 1.69; 95% CI: 1.23–2.32; p < 0.001) were also significant risk factors. Employment status showed a borderline association (OR = 1.60; 95% CI: 0.97–2.63; p = 0.06). Smoking, diabetes, dyslipidemia, cardiovascular disease, and obesity were not significant (all p > 0.05). Univariable associations between potential predictors and falls are presented in Table 2 . Table 2 Logistic Regression Analysis of Variables Associated with Fall Risk Variable Unadjusted OR (95% CI) p-value Calf Circumference (≥ 31 cm vs. <31 cm) 0.50 (0.37–0.67) < 0.001 Age (per year) 1.08 (1.06–1.10) < 0.001 BMI (per kg/m²) 0.94 (0.91–0.97) < 0.001 Gender (Female vs. Male) 1.48 (1.11–1.98) < 0.0010 Marital Status (Married vs. Unmarried) 2.18 (1.64–2.90) < 0.001 Employed (Yes vs. No) 1.60 (0.97–2.63) 0.06 Education Level (Undergraduate and less vs. High school) 0.40 (0.28–0.58) < 0.001 Education Level (postgraduate and less vs. High school) 0.53(0.31–0.89) < 0.001 Limb muscle strength (Yes vs. No) 3.04 (1.53–6.06) < 0.001 Cerebellar function (Yes vs. No) 9.71(3.31–28.43) < 0.001 Foot examination (Yes vs. No) 5.18(2.40–11.17) < 0.001 Statin Use (Yes vs. No) 1.58(1.14–2.19) < 0.001 Aspirin Use (Yes vs. No) 1.63(1.192–2.23) < 0.001 Smoking (Current vs. Never/Former) 1.11(0.612–2.01) 0.73 Physical Activation (Yes vs. No) 2.80(2.05–3.84) < 0.001 Diabetes (Yes vs. No) 1.01(0.74–1.39) 0.90 Hypertension (Yes vs. No) 1.69(1.23–2.32) < 0.001 Dyslipidemia (Yes vs. No) 0.72(0.44–1.18) 0.20 CVD (Yes vs. No) 0.84(0.46–1.51) 0.56 Obesity (Yes vs. No) 1.24(0.68–2.26) 0.47 Multiple logistic regression : In the final multivariable model (n = 1,019) (Table 3 ), three variables remained independently associated with falls. First, CC ≥ 31 cm was associated with lower odds of falling (adjusted OR = 0.54; 95% CI: 0.33–0.90; p = 0.01). Second, physical inactivity was associated with higher odds of falling (adjusted OR = 3.57; 95% CI: 2.19–5.83; p < 0.001). Third, cerebellar dysfunction showed a borderline association (adjusted OR = 4.16; 95% CI: 0.98–17.71; p = 0.05). Age, sex, limb muscle strength, abnormal foot findings, statin use, aspirin use, smoking, diabetes, hypertension, dyslipidemia, cardiovascular disease, and obesity were not independently associated with falls (all p > 0.05). Table 3 Multiple Logistic Regression Analysis for Fall Risk Variable adjusted OR (95% CI) p-value Calf Circumference (≥ 31 cm vs. <31 cm) 0.54(0.33–0.90) 0.01 Age (per year) 1.10(1.05–1.11) 0.10 Gender (Female vs. Male) 1.44(0.87–2.37) 0.15 Limb muscle strength (Yes vs. No) 1.94(0.74–5.04) 0.17 Cerebellar function (Yes vs. No) 4.16(0.98–17.71) 0.05 Foot examination (Yes vs. No) 2.12(0.69–6.52) 0.18 Statin Use (Yes vs. No) 0.83(0.43–1.62) 0.59 Aspirin Use (Yes vs. No) 1.34(0.70–2.58) 0.36 Smoking (Current vs. Never/Former) 0.87(0.32–2.39) 0.79 Physical Activation (Yes vs. No) 3.57(2.19–5.83) < 0.001 Diabetes (Yes vs. No) 1.01(0.59–1.71) 0.96 Hypertension (Yes vs. No) 1.46(0.64–3.30) 0.36 Dyslipidemia (Yes vs. No) 0.69(0.21–2.31) 0.55 CVD (Yes vs. No) 0.84(0.33–2.12) 0.72 Obesity (Yes vs. No) 1.74(0.65–4.66) 0.26 Summary of key findings Overall, older age and lower BMI characterized fallers at the descriptive level, and a broad set of functional impairments and cardiovascular risk factors were more prevalent among fallers. In regression analyses, larger calf circumference (≥ 31 cm) and regular physical activity emerged as protective , whereas cerebellar dysfunction signaled higher risk after adjustment. These findings support calf circumference as a practical, low-cost biomarker for fall-risk stratification in older adults, independent of common demographic and clinical covariates. Discussion In this retrospective case-control study of 1,064 community-dwelling adults aged 60 years and older, three variables—calf circumference, physical inactivity, and cerebellar dysfunction—were identified as independent predictors of falls after multiple adjustment (n = 1,019; Table 3 ). Specifically, a calf circumference of 31 cm or greater was associated with a 46% reduction in the odds of falling (adjusted OR 0.54; 95% CI 0.33–0.90; p = 0.01). In contrast, a lack of regular physical activity was linked to a 3.6-fold increased risk of falls (adjusted OR 3.57; 95% CI 2.19–5.83; p < 0.001), while cerebellar dysfunction exhibited a borderline independent association (adjusted OR 4.16; 95% CI 0.98–17.71; p = 0.05). Our findings regarding calf circumference are consistent with previous research in diverse older populations. Rodrigues et al. (2022) found that calf circumference below sex- and age-specific thresholds predicted falls in older hemodialysis patients, with hazard ratios approaching eightfold higher risk ( 13 ). Additionally, Barbosa-Silva et al. (2023) demonstrated that enhancing the SARC-F tool with calf circumference improved the sensitivity of sarcopenia detection among community seniors ( 14 ). Meta-analyses further validate strong correlations between calf circumference, appendicular lean mass, and functional performance measures such as gait speed and chair-stand tests ( 11 , 15 ). These consistent results highlight calf circumference as a simple, low-cost proxy for lower-limb muscle mass and a significant marker of fall risk. Physiologically, reduced calf circumference likely indicates a decrease in the cross-sectional area of type II muscle fibers and diminished peak power generation, which impairs both reactive corrective responses and anticipatory postural adjustments when balance is disturbed. Physical inactivity exacerbates these deficits by accelerating muscle atrophy, diminishing proprioceptive acuity, and prolonging neuromuscular reaction times. Cerebellar dysfunction interferes with the internal models that regulate coordinated movement and timing, further jeopardizing postural control and stability. From a clinical perspective, measuring calf circumference is a rapid, noninvasive procedure that requires minimal training, making it particularly suitable for primary care, community clinics, and resource-limited settings where dual-energy X-ray absorptiometry (DXA) or bioelectrical impedance analysis (BIA) are unavailable. A threshold of less than 31 cm could serve as an actionable indicator to prompt targeted interventions such as resistance training, balance exercises, or referral to physiotherapy. Incorporating calf circumference into routine geriatric screening protocols may facilitate the early identification of individuals at high risk of falls, thereby enabling preventive strategies prior to more advanced diagnostic testing. Strengths and limitations. The strengths of this study include its large, well-characterized sample, a rigorous measurement protocol with high inter-rater reliability (ICC > 0.90), and adjustment for a comprehensive array of sociodemographic, clinical, and functional covariates in multiple models. However, several limitations warrant consideration. First, the retrospective design may introduce recall and selection biases, and falls were ascertained through medical records and self-report, potentially underestimating true incidence. Second—and most importantly for interpretation—temporal ordering between exposure and outcome is imperfect: falls were defined as events occurring in the six months preceding the study visit, whereas calf circumference was measured at the index visit. This opens the possibility of reverse causation: a prior fall may lead to acute or subacute reductions in mobility, immobilization (e.g., casting, bracing), pain avoidance, or hospitalization, which in turn can precipitate disuse atrophy and smaller calf circumference. Consequently, part of the observed association could reflect the effect of recent falls on calf size rather than the effect of low calf circumference on subsequent falls. The likely direction of bias would be away from the null, potentially exaggerating the protective association observed for larger calf circumference. (Less commonly, post-injury edema could transiently enlarge the calf and bias estimates toward the null, but sustained disuse atrophy is the more plausible net effect in older adults.) Third, our single-system cohort may limit generalizability to other ethnic or clinical populations. Fourth, we did not assess important fall determinants such as environmental hazards, visual impairment, detailed polypharmacy (e.g., sedatives, antihypertensives by class and dose), or validate calf circumference against imaging-based muscle quantification in this sample. Future prospective studies should ensure forward temporal sequencing—for example, measuring calf circumference at baseline and tracking incident falls thereafter, or repeating calf measurements longitudinally—to mitigate reverse causation. Additional work should aim to validate optimal sex- and age-specific thresholds, evaluate the predictive utility of serial changes in calf circumference, and test calf circumference–guided prevention strategies in randomized trials. Integration of this straightforward anthropometric measure with digital gait analysis, wearable sensors, and home-monitoring technologies may further enhance precision in fall-risk stratification and enable personalized prevention. Conclusion This retrospective case–control study involving 1,064 community-dwelling older adults establishes that calf circumference (CC), measured using a simple non-stretch tape, is an effective biomarker for stratifying fall risk. In a multiple analysis (n = 1,019), a CC of ≥ 31 cm was independently associated with a 46% reduction in the odds of falling compared to a CC of < 31 cm (adjusted OR 0.54; 95% CI 0.33–0.90; p = 0.01). The incorporation of CC measurement into routine geriatric assessments—particularly in primary care or community settings that lack advanced imaging—could enable the early identification of at-risk individuals and facilitate targeted preventive interventions, such as resistance and balance training. Future research should utilize prospective cohort designs to validate optimal CC thresholds stratified by sex, age, and clinical subgroups, as well as randomized controlled trials to determine whether interventions aimed at increasing calf muscle mass or function can reduce fall incidence among individuals with low CC. Furthermore, integrating CC measurement with digital gait analysis, wearable sensors, or home-monitoring technologies may enhance fall-risk prediction and support personalized prevention strategies. Declarations Acknowledgements: This study was conducted at Mashhad University of Medical Sciences (MUMS), Mashhad, Iran. We sincerely thank the Vice-Chancellor for Health and the affiliated primary care health centers, urban health posts, and House of Behvarz clinics for granting access to the Sina health information system and for their collaboration. We are grateful to the staff of the School of Health (Departments of Epidemiology and Biostatistics) for methodological and statistical advice, and to the Elderly Health Program nurses and health workers for their diligent data recording. We also thank all participants whose records made this research possible. Consent to Participate declaration: Not applicable. Consent for publication: Not applicable. Clinical trial number: Not applicable. Availability of data and materials : The datasets used and analyzed during the current study are available from the corresponding author on reasonable request. Funding: This work was supported by the Vice-Chancellor for Research of Mashhad University of Medical Sciences (Grant No: 4031260 ). The funder had no role in the study design; data collection, management, analysis, or interpretation; manuscript preparation; or the decision to submit the article for publication. 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Bolaji OF, Santos LP, Gonzalez MC, Barbosa-Silva TG, Demarco FF, Bielemann RM. Longitudinal changes in calf circumference and health outcomes among community-dwelling older adults in Southern Brazil. Nutrition. 2025;139:112849. 10.1016/j.nut.2025.112849 . Epub ahead of print. PMID: 40554929. Norouzi M, Amiri Z, Farkhani EM, Hoseini SJ, Asl TK. Advancing healthcare infrastructure: the features of Iran’s Sina electronic health record system. Epidemiol Health Syst J. 2024;11(2):48–54. Additional Declarations No competing interests reported. Supplementary Files Table1DemographicandClinicalCharacteristics.docx Table2LogisticRegressionAnalysisofVariablesAssociatedwithFallRisk.docx Table3MultipleLogisticRegressionAnalysisforFallRisk.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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08:14:00","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":15063,"visible":true,"origin":"","legend":"","description":"","filename":"Table2LogisticRegressionAnalysisofVariablesAssociatedwithFallRisk.docx","url":"https://assets-eu.researchsquare.com/files/rs-7839117/v1/40ffa3b94e091cba2efdbb0f.docx"},{"id":96699770,"identity":"aff50519-1b3d-464f-9ab0-4f4e3aaeb14e","added_by":"auto","created_at":"2025-11-25 08:14:00","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":14532,"visible":true,"origin":"","legend":"","description":"","filename":"Table3MultipleLogisticRegressionAnalysisforFallRisk.docx","url":"https://assets-eu.researchsquare.com/files/rs-7839117/v1/bb5b3a3ccc45de830e98bed6.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Calf Circumference as a Predictive Biomarker for Falls in older Adults: A Retrospective Case– Control Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe global population aged 65 years and older is projected to increase from 727\u0026nbsp;million in 2020 to 1.5\u0026nbsp;billion by 2050 (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). In the United States, nearly one in four adults aged 65 years and older experienced at least one fall annually in 2018, with fallers facing a two- to threefold higher risk of subsequent fractures, hospitalization, and mortality (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). The direct medical cost of a single fall in U.S. healthcare settings was estimated at USD 30,000 in 2019, and fall-related expenses account for approximately 0.85% of total healthcare spending in high-income countries (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFalls initiate a cascade of functional decline: fear of falling leads to reduced activity, progressive muscle atrophy, social isolation, and depression, perpetuating a cycle of frailty (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Sarcopenia\u0026mdash;defined by the revised EWGSOP2 criteria as age-related loss of skeletal muscle mass and strength\u0026mdash;is a significant, modifiable risk factor for falls (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Gold-standard assessments of muscle mass (e.g., DXA, BIA) remain impractical in many primary care and community settings due to cost, equipment requirements, and necessary training (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eCalf circumference (CC) measurement serves as a simple, noninvasive proxy for lower-limb muscle mass and function. CC correlates strongly with appendicular lean mass, gait speed, and timed up-and-go performance (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Recent studies have linked smaller CC values to increased fall risk: Rodrigues et al. (2022) found that CC below sex- and age-specific cut-offs predicted falls in older hemodialysis patients (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e), and Barbosa-Silva et al. (2023) demonstrated that incorporating CC into the SARC-F questionnaire enhanced the sensitivity of sarcopenia detection in community-dwelling older adults (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). However, standardized CC thresholds for fall-risk stratification and measurement protocols (e.g., seated vs. standing posture) vary across studies, and the influence of adipose tissue distribution on CC remains unresolved (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eMost existing studies are cross-sectional; few longitudinal or case-control investigations have examined CC\u0026rsquo;s independent predictive power for future falls after adjusting for confounding variables such as age, body mass index (BMI), comorbidities, and baseline functional status. Without high-quality data from rigorous study designs, the clinical application of CC measurement for fall-risk screening is limited (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWe aimed to determine whether calf circumference measurement serves as a reliable, practical indicator for detecting fall risk in older adults by assessing its validity against established muscle-mass and functional markers, its predictive power for future falls, and the optimal threshold values and measurement protocols for routine geriatric screening.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cb\u003eStudy Design and Setting\u003c/b\u003e: This retrospective case\u0026ndash;control study utilized data from the Sina health information system between March 2017 and December 2023 (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Community-dwelling adults aged 60 years and older, registered at primary care health centers, urban health posts, and 'House of Behvarz' clinics affiliated with Mashhad University of Medical Sciences, were eligible for inclusion. Out of 1,200 potentially eligible individuals, 136 (11.3%) were excluded due to missing calf circumference data (n\u0026thinsp;=\u0026thinsp;82) or key covariates (n\u0026thinsp;=\u0026thinsp;54), resulting in 266 cases and 798 controls (1:3 ratio) for analysis.\u003c/p\u003e\u003cp\u003e\u003cb\u003eParticipants and Sampling\u003c/b\u003e: Cases (n\u0026thinsp;=\u0026thinsp;266) included individuals aged 60 years and older with at least one documented fall, defined according to WHO criteria, within six months of calf circumference (CC) measurement. Controls (n\u0026thinsp;=\u0026thinsp;798) were selected through simple random sampling from age-stratified (decade) fall-free individuals at a 1:3 case-to-control ratio.\u003c/p\u003e\u003cp\u003e\u003cb\u003eInclusion and Exclusion Criteria\u003c/b\u003e: Inclusion criteria consisted of: age 60 years or older; registration in the Sina system; complete anthropometric and clinical records; and written informed consent. Exclusion criteria included neurological disorders affecting muscle (e.g., stroke, Parkinson\u0026rsquo;s), acute illness at the time of measurement, or incomplete consent.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eMeasurement Protocols\u003c/strong\u003e\u003cp\u003eCalf circumference (CC) was measured on the dominant leg at its widest point using a non-stretchable tape, with participants seated, knees at 90 degrees, and feet flat. Two trained raters performed two measurements each; the mean of all four readings was analyzed. Inter- and intra-rater reliability (ICC) both exceeded 0.90. Height and weight were measured following standard procedures to compute BMI (kg/m\u0026sup2;).\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eAdditional Variables\u003c/strong\u003e\u003cp\u003eDemographic and lifestyle variables included age, sex, marital status, education level (\u0026le;\u0026thinsp;high school, undergraduate, \u0026ge;postgraduate), employment status, smoking status (current vs. never/former), and self-reported physical activity (Yes/No). Functional assessments included limb muscle strength (Yes/No), cerebellar function (Yes/No), and foot examination (Yes/No). Clinical history (diabetes, hypertension, dyslipidemia, cardiovascular disease) and medication use (statins, aspirin) were extracted from electronic medical records and verified through chart review.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eOutcome Definition\u003c/strong\u003e\u003cp\u003eFalls were defined as any event leading to an unintended rest on the ground or lower level and were recorded in the 'Elderly Care' form for the six months preceding CC measurement.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eSample Size and Power Calculation\u003c/strong\u003e\u003cp\u003eBased on pilot data indicating a 30% prevalence of low CC (\u0026lt;\u0026thinsp;31 cm) among controls and an expected odds ratio (OR) of 1.5 for falls, we calculated that at least 250 cases and 750 controls would provide 80% power at α\u0026thinsp;=\u0026thinsp;0.05. Therefore, 266 cases and 798 controls were included in the study.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eStatistical Analysis\u003c/strong\u003e\u003cp\u003eContinuous variables were assessed for normality using the Shapiro\u0026ndash;Wilk test. Normally distributed variables are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD and compared using independent-samples t-tests; non-normally distributed variables are presented as median (IQR) and compared using Mann\u0026ndash;Whitney U tests. Categorical variables are presented as counts (n, %) and compared using χ\u0026sup2; or Fisher\u0026rsquo;s exact test, as appropriate (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003c/p\u003e\u003cp\u003eUnivariable logistic regression analyses estimated unadjusted odds ratios (ORs) and 95% confidence intervals (CIs) for each covariate (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Variables with p\u0026thinsp;\u0026lt;\u0026thinsp;0.20 in univariable analyses or selected a priori based on directed acyclic graph (DAG) theory were included in multiple logistic regression analyses. Forward stepwise selection retained variables with p\u0026thinsp;\u0026le;\u0026thinsp;0.05, and multicollinearity was assessed using variance inflation factors (VIF\u0026thinsp;\u0026lt;\u0026thinsp;2). Adjusted ORs and 95% CIs are reported (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eEthical Considerations\u003c/strong\u003e\u003cp\u003eThe study protocol was approved by the Ethics Committee of Mashhad University of Medical Sciences (IR.MUMS.FHMPM.REC.1403.188). Written informed consent was obtained from all participants. Data were anonymized and securely stored on encrypted servers accessible only to study investigators.\u003c/p\u003e\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eParticipant characteristics\u003c/strong\u003e\u003cp\u003eWe analyzed 1,064 community-dwelling older adults (266 fallers, 798 non-fallers) after exclusions described in Methods. The mean age of fallers was higher than non-fallers (79.30\u0026thinsp;\u0026plusmn;\u0026thinsp;9.54 vs. 72.70\u0026thinsp;\u0026plusmn;\u0026thinsp;8.34 years; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Body mass index (BMI) was lower in fallers (23.80\u0026thinsp;\u0026plusmn;\u0026thinsp;4.80 vs. 24.96\u0026thinsp;\u0026plusmn;\u0026thinsp;4.50 kg/m\u0026sup2;; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Women constituted a larger share of fallers than non-fallers (64.29% vs. 54.76%; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003c/p\u003e\u003cp\u003eSeveral functional and clinical findings differed between groups. Limb muscle weakness (9.74% vs. 3.42%), cerebellar dysfunction (7.80% vs. 0.86%), and abnormal foot findings (10.71% vs. 2.26%) were each more frequent among fallers than non-fallers (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Hypertension was also more common in fallers (76.3% vs. 65.5%; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Diabetes, dyslipidemia, cardiovascular disease, obesity, and smoking status did not differ significantly (all p\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e\u003cp\u003eSociodemographic distributions also varied. Compared with non-fallers, fallers more often had\u0026thinsp;\u0026le;\u0026thinsp;high-school education (75.94% vs. 58.02%; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and differed by marital status (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Employment status showed a nonsignificant trend (p\u0026thinsp;=\u0026thinsp;0.06). The distribution of calf circumference (CC) categories also differed between groups (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Baseline characteristics of cases and controls are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDemographic and Clinical Characteristics of Case (Fallers) and Control (Non-Fallers) Group\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFallers\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;266)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNon-Fallers\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;798)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\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\"\u003e\u003cp\u003e\u003cb\u003eAge (years, mean\u0026nbsp;\u0026plusmn;\u0026nbsp;SD)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e79.30\u0026thinsp;\u0026plusmn;\u0026thinsp;9.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e72.70\u0026thinsp;\u0026plusmn;\u0026thinsp;8.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\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\u003e\u003cb\u003eGender\u003c/b\u003e\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=\"char\" char=\".\" colname=\"c4\"\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\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e95 (35.71%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e361 (45.24%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e171 (64.29%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e437 (54.76%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMarital Status\u003c/b\u003e\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=\"char\" char=\".\" colname=\"c4\"\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\u003eMarried\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e124 (46.62%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e228 (28.57%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnmarried\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e142 (53.38%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e570 (71.43%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEmployed\u003c/b\u003e\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=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e187 (89.47%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e446 (84.15%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e22 (10.53%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e84 (15.85%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEducation\u003c/b\u003e\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=\"char\" char=\".\" colname=\"c4\"\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\u003eHigh school and less\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e202 (75.94%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e463 (58.02%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUndergraduate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e44 (16.54%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e249 (31.20%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePostgraduate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20 (7.52%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e86 (10.78%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCalf Circumference\u003c/b\u003e\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=\"char\" char=\".\" colname=\"c4\"\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\u003e\u0026ge;\u0026nbsp;31\u0026nbsp;cm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e106 (39.85%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e200 (25.06%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026nbsp;31\u0026nbsp;cm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e160 (60.15%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e598 (74.94%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBMI (kg/m\u0026sup2;, mean\u0026nbsp;\u0026plusmn;\u0026nbsp;SD)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23.80\u0026thinsp;\u0026plusmn;\u0026thinsp;4.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24.96\u0026thinsp;\u0026plusmn;\u0026thinsp;4.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\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\u003e\u003cb\u003eLimb muscle strength\u003c/b\u003e\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=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15 (9.74%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21 (3.42%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e139 (90.26%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e593 (96.58%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCerebellar function\u003c/b\u003e\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=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11 (7.80%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5 (0.86%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e130 (92.20%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e574 (99.14%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFoot examination\u003c/b\u003e\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=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15 (10.71%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13 (2.26%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e125 (89.29%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e562 (97.74%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eStatin Use\u003c/b\u003e\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=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e150 (69.44%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e422 (58.94%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e66 (30.56%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e294 (41.06%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAspirin Use\u003c/b\u003e\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=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e133 (62.74%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e363 (50.77%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e79 (37.26%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e352 (49.23%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSmoking\u003c/b\u003e\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=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.73\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCurrent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e249 (94.32%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e748 (93.73%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNever/Former\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15 (5.68%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e50 (6.27%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePhysical Activation\u003c/b\u003e\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=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e148 (65.78%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e291 (40.64%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e77 (34.22%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e425 (59.36%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDiabetes\u003c/b\u003e\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=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.90\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e71 (26.69%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e210 (26.32%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e195 (73.31%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e588 (73.68%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHypertension\u003c/b\u003e\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=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e203 (76.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e523 (65.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e63 (23.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e275 (34.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDyslipidemia\u003c/b\u003e\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=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.20\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e22 (8.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e88 (11.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e244 (91.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e710 (89.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCVD\u003c/b\u003e\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=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.56\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15 (5.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e53 (6.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e251 (94.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e745 (93.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eObesity\u003c/b\u003e\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=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.47\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e16 (6.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e39 (4.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e250 (94.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e759 (95.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eUnivariable associations with falls\u003c/b\u003e: In univariable logistic regression, older age was associated with greater odds of falling (OR per year\u0026thinsp;=\u0026thinsp;1.08; 95% CI: 1.06\u0026ndash;1.10; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Higher BMI was inversely associated with falls (OR per kg/m\u0026sup2; = 0.94; 95% CI: 0.91\u0026ndash;0.97; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Relative to CC\u0026thinsp;\u0026lt;\u0026thinsp;31 cm, CC\u0026thinsp;\u0026ge;\u0026thinsp;31 cm was associated with lower odds of falls (OR\u0026thinsp;=\u0026thinsp;0.50; 95% CI: 0.37\u0026ndash;0.67; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003eFemale sex (OR\u0026thinsp;=\u0026thinsp;1.48; 95% CI: 1.11\u0026ndash;1.98; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), unmarried status (OR\u0026thinsp;=\u0026thinsp;2.18; 95% CI: 1.64\u0026ndash;2.90; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), limb muscle weakness (OR\u0026thinsp;=\u0026thinsp;3.04; 95% CI: 1.53\u0026ndash;6.06; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), cerebellar dysfunction (OR\u0026thinsp;=\u0026thinsp;9.71; 95% CI: 3.31\u0026ndash;28.43; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and abnormal foot findings (OR\u0026thinsp;=\u0026thinsp;5.18; 95% CI: 2.40\u0026ndash;11.17; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were each associated with higher odds of falls. Statin use (OR\u0026thinsp;=\u0026thinsp;1.58; 95% CI: 1.14\u0026ndash;2.19; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), aspirin use (OR\u0026thinsp;=\u0026thinsp;1.63; 95% CI: 1.19\u0026ndash;2.23; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), physical inactivity (OR\u0026thinsp;=\u0026thinsp;2.80; 95% CI: 2.05\u0026ndash;3.84; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and hypertension (OR\u0026thinsp;=\u0026thinsp;1.69; 95% CI: 1.23\u0026ndash;2.32; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were also significant risk factors. Employment status showed a borderline association (OR\u0026thinsp;=\u0026thinsp;1.60; 95% CI: 0.97\u0026ndash;2.63; p\u0026thinsp;=\u0026thinsp;0.06). Smoking, diabetes, dyslipidemia, cardiovascular disease, and obesity were not significant (all p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Univariable associations between potential predictors and falls are presented in 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\u003eLogistic Regression Analysis of Variables Associated with Fall Risk\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUnadjusted OR (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\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\"\u003e\u003cp\u003eCalf Circumference (\u0026ge;\u0026thinsp;31 cm vs. \u0026lt;31 cm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.50 (0.37\u0026ndash;0.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\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\u003eAge (per year)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.08 (1.06\u0026ndash;1.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\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\u003eBMI (per kg/m\u0026sup2;)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.94 (0.91\u0026ndash;0.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\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\u003eGender (Female vs. Male)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.48 (1.11\u0026ndash;1.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0010\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarital Status (Married vs. Unmarried)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.18 (1.64\u0026ndash;2.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\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\u003eEmployed (Yes vs. No)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.60 (0.97\u0026ndash;2.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducation Level (Undergraduate and less vs. High school)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.40 (0.28\u0026ndash;0.58)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\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\u003eEducation Level (postgraduate and less vs. High school)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.53(0.31\u0026ndash;0.89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\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\u003eLimb muscle strength (Yes vs. No)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.04 (1.53\u0026ndash;6.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\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\u003eCerebellar function (Yes vs. No)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9.71(3.31\u0026ndash;28.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\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\u003eFoot examination (Yes vs. No)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5.18(2.40\u0026ndash;11.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\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\u003eStatin Use (Yes vs. No)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.58(1.14\u0026ndash;2.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\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\u003eAspirin Use (Yes vs. No)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.63(1.192\u0026ndash;2.23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\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\u003eSmoking (Current vs. Never/Former)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.11(0.612\u0026ndash;2.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.73\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePhysical Activation (Yes vs. No)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.80(2.05\u0026ndash;3.84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\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\u003eDiabetes (Yes vs. No)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.01(0.74\u0026ndash;1.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.90\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypertension (Yes vs. No)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.69(1.23\u0026ndash;2.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\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\u003eDyslipidemia (Yes vs. No)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.72(0.44\u0026ndash;1.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.20\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCVD (Yes vs. No)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.84(0.46\u0026ndash;1.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.56\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObesity (Yes vs. No)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.24(0.68\u0026ndash;2.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.47\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\u003e\u003cb\u003eMultiple logistic regression\u003c/b\u003e: In the final multivariable model (n\u0026thinsp;=\u0026thinsp;1,019) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), three variables remained independently associated with falls. First, CC\u0026thinsp;\u0026ge;\u0026thinsp;31 cm was associated with lower odds of falling (adjusted OR\u0026thinsp;=\u0026thinsp;0.54; 95% CI: 0.33\u0026ndash;0.90; p\u0026thinsp;=\u0026thinsp;0.01). Second, physical inactivity was associated with higher odds of falling (adjusted OR\u0026thinsp;=\u0026thinsp;3.57; 95% CI: 2.19\u0026ndash;5.83; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Third, cerebellar dysfunction showed a borderline association (adjusted OR\u0026thinsp;=\u0026thinsp;4.16; 95% CI: 0.98\u0026ndash;17.71; p\u0026thinsp;=\u0026thinsp;0.05). Age, sex, limb muscle strength, abnormal foot findings, statin use, aspirin use, smoking, diabetes, hypertension, dyslipidemia, cardiovascular disease, and obesity were not independently associated with falls (all p\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\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\u003eMultiple Logistic Regression Analysis for Fall Risk\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eadjusted OR (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\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\"\u003e\u003cp\u003eCalf Circumference (\u0026ge;\u0026thinsp;31 cm vs. \u0026lt;31 cm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.54(0.33\u0026ndash;0.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (per year)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.10(1.05\u0026ndash;1.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender (Female vs. Male)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.44(0.87\u0026ndash;2.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLimb muscle strength (Yes vs. No)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.94(0.74\u0026ndash;5.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.17\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCerebellar function (Yes vs. No)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.16(0.98\u0026ndash;17.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFoot examination (Yes vs. No)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.12(0.69\u0026ndash;6.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStatin Use (Yes vs. No)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.83(0.43\u0026ndash;1.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.59\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAspirin Use (Yes vs. No)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.34(0.70\u0026ndash;2.58)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.36\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoking (Current vs. Never/Former)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.87(0.32\u0026ndash;2.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.79\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePhysical Activation (Yes vs. No)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.57(2.19\u0026ndash;5.83)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\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\u003eDiabetes (Yes vs. No)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.01(0.59\u0026ndash;1.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.96\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypertension (Yes vs. No)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.46(0.64\u0026ndash;3.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.36\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDyslipidemia (Yes vs. No)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.69(0.21\u0026ndash;2.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.55\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCVD (Yes vs. No)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.84(0.33\u0026ndash;2.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.72\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObesity (Yes vs. No)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.74(0.65\u0026ndash;4.66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.26\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\u003e\u003cstrong\u003eSummary of key findings\u003c/strong\u003e\u003cp\u003eOverall, older age and lower BMI characterized fallers at the descriptive level, and a broad set of functional impairments and cardiovascular risk factors were more prevalent among fallers. In regression analyses, \u003cb\u003elarger calf circumference (\u0026ge;\u0026thinsp;31 cm) and regular physical activity emerged as protective\u003c/b\u003e, whereas \u003cb\u003ecerebellar dysfunction\u003c/b\u003e signaled higher risk after adjustment. These findings support \u003cb\u003ecalf circumference as a practical, low-cost biomarker\u003c/b\u003e for fall-risk stratification in older adults, independent of common demographic and clinical covariates.\u003c/p\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this retrospective case-control study of 1,064 community-dwelling adults aged 60 years and older, three variables\u0026mdash;calf circumference, physical inactivity, and cerebellar dysfunction\u0026mdash;were identified as independent predictors of falls after multiple adjustment (n\u0026thinsp;=\u0026thinsp;1,019; Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Specifically, a calf circumference of 31 cm or greater was associated with a 46% reduction in the odds of falling (adjusted OR 0.54; 95% CI 0.33\u0026ndash;0.90; p\u0026thinsp;=\u0026thinsp;0.01). In contrast, a lack of regular physical activity was linked to a 3.6-fold increased risk of falls (adjusted OR 3.57; 95% CI 2.19\u0026ndash;5.83; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while cerebellar dysfunction exhibited a borderline independent association (adjusted OR 4.16; 95% CI 0.98\u0026ndash;17.71; p\u0026thinsp;=\u0026thinsp;0.05).\u003c/p\u003e\u003cp\u003eOur findings regarding calf circumference are consistent with previous research in diverse older populations. Rodrigues et al. (2022) found that calf circumference below sex- and age-specific thresholds predicted falls in older hemodialysis patients, with hazard ratios approaching eightfold higher risk (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Additionally, Barbosa-Silva et al. (2023) demonstrated that enhancing the SARC-F tool with calf circumference improved the sensitivity of sarcopenia detection among community seniors (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Meta-analyses further validate strong correlations between calf circumference, appendicular lean mass, and functional performance measures such as gait speed and chair-stand tests (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). These consistent results highlight calf circumference as a simple, low-cost proxy for lower-limb muscle mass and a significant marker of fall risk.\u003c/p\u003e\u003cp\u003ePhysiologically, reduced calf circumference likely indicates a decrease in the cross-sectional area of type II muscle fibers and diminished peak power generation, which impairs both reactive corrective responses and anticipatory postural adjustments when balance is disturbed. Physical inactivity exacerbates these deficits by accelerating muscle atrophy, diminishing proprioceptive acuity, and prolonging neuromuscular reaction times. Cerebellar dysfunction interferes with the internal models that regulate coordinated movement and timing, further jeopardizing postural control and stability.\u003c/p\u003e\u003cp\u003eFrom a clinical perspective, measuring calf circumference is a rapid, noninvasive procedure that requires minimal training, making it particularly suitable for primary care, community clinics, and resource-limited settings where dual-energy X-ray absorptiometry (DXA) or bioelectrical impedance analysis (BIA) are unavailable. A threshold of less than 31 cm could serve as an actionable indicator to prompt targeted interventions such as resistance training, balance exercises, or referral to physiotherapy. Incorporating calf circumference into routine geriatric screening protocols may facilitate the early identification of individuals at high risk of falls, thereby enabling preventive strategies prior to more advanced diagnostic testing.\u003c/p\u003e\u003cp\u003eStrengths and limitations. The strengths of this study include its large, well-characterized sample, a rigorous measurement protocol with high inter-rater reliability (ICC\u0026thinsp;\u0026gt;\u0026thinsp;0.90), and adjustment for a comprehensive array of sociodemographic, clinical, and functional covariates in multiple models. However, several limitations warrant consideration. First, the retrospective design may introduce recall and selection biases, and falls were ascertained through medical records and self-report, potentially underestimating true incidence. Second\u0026mdash;and most importantly for interpretation\u0026mdash;temporal ordering between exposure and outcome is imperfect: falls were defined as events occurring in the six months preceding the study visit, whereas calf circumference was measured at the index visit. This opens the possibility of reverse causation: a prior fall may lead to acute or subacute reductions in mobility, immobilization (e.g., casting, bracing), pain avoidance, or hospitalization, which in turn can precipitate disuse atrophy and smaller calf circumference. Consequently, part of the observed association could reflect the effect of recent falls on calf size rather than the effect of low calf circumference on subsequent falls. The likely direction of bias would be away from the null, potentially exaggerating the protective association observed for larger calf circumference. (Less commonly, post-injury edema could transiently enlarge the calf and bias estimates toward the null, but sustained disuse atrophy is the more plausible net effect in older adults.) Third, our single-system cohort may limit generalizability to other ethnic or clinical populations. Fourth, we did not assess important fall determinants such as environmental hazards, visual impairment, detailed polypharmacy (e.g., sedatives, antihypertensives by class and dose), or validate calf circumference against imaging-based muscle quantification in this sample.\u003c/p\u003e\u003cp\u003eFuture prospective studies should ensure forward temporal sequencing\u0026mdash;for example, measuring calf circumference at baseline and tracking incident falls thereafter, or repeating calf measurements longitudinally\u0026mdash;to mitigate reverse causation. Additional work should aim to validate optimal sex- and age-specific thresholds, evaluate the predictive utility of serial changes in calf circumference, and test calf circumference\u0026ndash;guided prevention strategies in randomized trials. Integration of this straightforward anthropometric measure with digital gait analysis, wearable sensors, and home-monitoring technologies may further enhance precision in fall-risk stratification and enable personalized prevention.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis retrospective case\u0026ndash;control study involving 1,064 community-dwelling older adults establishes that calf circumference (CC), measured using a simple non-stretch tape, is an effective biomarker for stratifying fall risk. In a multiple analysis (n\u0026thinsp;=\u0026thinsp;1,019), a CC of \u0026ge;\u0026thinsp;31 cm was independently associated with a 46% reduction in the odds of falling compared to a CC of \u0026lt;\u0026thinsp;31 cm (adjusted OR 0.54; 95% CI 0.33\u0026ndash;0.90; p\u0026thinsp;=\u0026thinsp;0.01). The incorporation of CC measurement into routine geriatric assessments\u0026mdash;particularly in primary care or community settings that lack advanced imaging\u0026mdash;could enable the early identification of at-risk individuals and facilitate targeted preventive interventions, such as resistance and balance training.\u003c/p\u003e\u003cp\u003eFuture research should utilize prospective cohort designs to validate optimal CC thresholds stratified by sex, age, and clinical subgroups, as well as randomized controlled trials to determine whether interventions aimed at increasing calf muscle mass or function can reduce fall incidence among individuals with low CC. Furthermore, integrating CC measurement with digital gait analysis, wearable sensors, or home-monitoring technologies may enhance fall-risk prediction and support personalized prevention strategies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted at Mashhad University of Medical Sciences (MUMS), Mashhad, Iran. We sincerely thank the Vice-Chancellor for Health and the affiliated primary care health centers, urban health posts, and \u003cem\u003eHouse of Behvarz\u003c/em\u003e clinics for granting access to the Sina health information system and for their collaboration. We are grateful to the staff of the School of Health (Departments of Epidemiology and Biostatistics) for methodological and statistical advice, and to the Elderly Health Program nurses and health workers for their diligent data recording. We also thank all participants whose records made this research possible.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate declaration:\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number:\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e: The datasets used and analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Vice-Chancellor for Research of Mashhad University of Medical Sciences (Grant No:\u003cstrong\u003e4031260\u003c/strong\u003e). The funder had no role in the study design; data collection, management, analysis, or interpretation; manuscript preparation; or the decision to submit the article for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;The authors declare that they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eUnited Nations, Department of Economic and Social Affairs, Population Division. World Population Ageing 2020 Highlights. \u003cem\u003eST\u003c/em\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e/ESA/SER.A/451\u003c/span\u003e\u003cspan address=\"http:///ESA/SER.A/451\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. 2020.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWorld Health Organization. WHO Global Report on Falls Prevention in Older Age. Geneva: WHO; 2018.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFlorence CS, Bergen G, Atherly A, Burns E, Stevens J, Drake C. Medical Costs of Fatal and Nonfatal Falls in Older Adults. J Am Geriatr Soc. 2018;66(4):693\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCenters for Disease Control and Prevention. Falls Among Older Adults: An Overview. MMWR Morb Mortal Wkly Rep. 2021;70(1):1\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eStevens JA, Mahoney JE, Thomas K. Costs of Falls Among Older Adults. J Am Geriatr Soc. 2019;67(7):143\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKojima G. Frailty as a Predictor of Hospitalization and Low Quality of Life: Systematic Review and Meta-Analysis. J Am Med Dir Assoc. 2018;19(4):323\u0026ndash;32.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCruz-Jentoft AJ, Bahat G, Bauer J, et al. Sarcopenia: Revised European Consensus on Definition and Diagnosis. Age Ageing. 2019;48(1):16\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChen LK, Woo J, Assantachai P, et al. Asian Working Group for Sarcopenia: 2019 Consensus Update on Sarcopenia Diagnosis and Treatment. J Am Med Dir Assoc. 2020;21(3):300\u0026ndash;e3072.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMontero-Odasso M, Speechley M, P\u0026eacute;rez-Zepeda MU. Gait and Falls: A Call to Action. J Am Geriatr Soc. 2021;69(10):2740\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHuang L, Lin T, Wang H, Xie D, Ge N, Yue J. Association Between Low Calf Circumference and Incident Sarcopenia in Community-Dwelling Older Adults: A 3-Year Prospective Cohort Study. J Am Med Dir Assoc. 2025 Jul 14:105741. doi: 10.1016/j.jamda.2025.105741. Epub ahead of print. PMID: 40675192.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBerlin-Piodena-Aportadera MR, Lau S, Chew J, Lim JP. Calf Circumference Measurement Protocols for Sarcopenia Screening: Differences in Agreement, Convergent Validity and Diagnostic Performance. Ann Geriatr Med Res. 2022;26(3):215\u0026ndash;24. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.4235/agmr.22.0057\u003c/span\u003e\u003cspan address=\"10.4235/agmr.22.0057\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCharoenpanich S, Chantarasuwan K, Sukonthasarn A, et al. Calf Circumference as a Screening Tool for Low Skeletal Muscle Mass: Cut-Off Values in Independent Thai Older Adults. BMC Geriatr. 2023;23:454.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRodrigues RG, Dalboni MAP, Correia MA. Calf Circumference Predicts Falls in Older Adults on Hemodialysis. J Ren Nutr. 2022;32(6):470\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBarbosa-Silva TG, Menezes AMB, Bielemann RM, Malmstrom TK, Gonzalez MC. Enhancing SARC-F: Performance of SARC-CalF for Screening Sarcopenia in Community-Dwelling Older Adults. Sci Rep. 2023;13:39002.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSato R, Sawaya Y, Hirose T, Shiba T, Yin L, Tsuji S, Ishizaka M, Urano T. Measurement of the Calf Muscle Circumference is Useful for Diagnosing Sarcopenia in Older Adults Requiring Long-Term Care. Ann Geriatr Med Res. 2025;29(1):58\u0026ndash;65. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.4235/agmr.24.0126\u003c/span\u003e\u003cspan address=\"10.4235/agmr.24.0126\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Epub 2024 Oct 7. PMID: 40195843; PMCID: PMC12010732.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYoshimura N, Wakabayashi H, Yamada M, Kim H, Harita N. Influence of Adipose Tissue on Calf Circumference Measurement in Older Women. Geriatr Gerontol Int. 2022;22(5):452\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBolaji OF, Santos LP, Gonzalez MC, Barbosa-Silva TG, Demarco FF, Bielemann RM. Longitudinal changes in calf circumference and health outcomes among community-dwelling older adults in Southern Brazil. Nutrition. 2025;139:112849. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.nut.2025.112849\u003c/span\u003e\u003cspan address=\"10.1016/j.nut.2025.112849\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Epub ahead of print. PMID: 40554929.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNorouzi M, Amiri Z, Farkhani EM, Hoseini SJ, Asl TK. Advancing healthcare infrastructure: the features of Iran\u0026rsquo;s Sina electronic health record system. Epidemiol Health Syst J. 2024;11(2):48\u0026ndash;54.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7839117/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7839117/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFalls are a significant cause of injury, loss of independence, and mortality among older adults. While muscle weakness and balance impairment are well-established modifiable risk factors, simple anthropometric measures such as calf circumference (CC) have not been thoroughly evaluated for their predictive value regarding fall risk in community settings.\u003c/p\u003e\u003cp\u003e\u003cb\u003eObjective\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis study aims to determine whether CC independently predicts fall risk in community-dwelling older adults and to compare its predictive performance with other sociodemographic, clinical, functional, and lifestyle factors.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIn this retrospective case\u0026ndash;control study, we analyzed electronic health records from 1,064 adults aged 60 years and older registered in the Sina health information system between March 2017 and December 2023 (266 fallers and 798 non-fallers). CC was measured at the widest point of the relaxed dominant calf (using the mean of four readings) and dichotomized into \u0026lt;\u0026thinsp;31 cm versus \u0026ge;\u0026thinsp;31 cm. Covariates included age, sex, body mass index (BMI), education, marital and employment status, regular physical activity, limb muscle strength, cerebellar signs, foot examination findings, comorbidities (including diabetes, hypertension, dyslipidemia, cardiovascular disease, and obesity), and the use of statins or aspirin. Univariable and multiple logistic regression models were employed to estimate odds ratios (ORs) with 95% confidence intervals (CIs), with significance set at p\u0026thinsp;\u0026le;\u0026thinsp;0.05.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe analyzed 1,064 older adults (266 fallers; 25.0%). Fallers were older and had lower BMI than non-fallers (both p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In multipule logistic regression, larger calf circumference (CC\u0026thinsp;\u0026ge;\u0026thinsp;31 cm) was independently associated with reduced odds of falling (adjusted OR\u0026thinsp;=\u0026thinsp;0.54, 95% CI: 0.33\u0026ndash;0.90, p\u0026thinsp;=\u0026thinsp;0.01), whereas physical inactivity increased the odds (adjusted OR\u0026thinsp;=\u0026thinsp;3.57, 95% CI: 2.19\u0026ndash;5.83, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Cerebellar dysfunction showed a borderline association with higher fall risk (adjusted OR\u0026thinsp;=\u0026thinsp;4.16, 95% CI: 0.98\u0026ndash;17.71, p\u0026thinsp;=\u0026thinsp;0.05). Other demographic and clinical factors were not independently associated after adjustment (all p\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusions\u003c/b\u003e\u003c/p\u003e\u003cp\u003eCalf circumference\u0026thinsp;\u0026lt;\u0026thinsp;31 cm is independently associated with increased odds of falls in older adults. Routine measurement of CC using a simple tape in primary care or community settings offers a low-cost, feasible screening tool for the early identification of high-risk individuals and may inform targeted interventions such as resistance and balance training. Prospective cohort studies and randomized trials are necessary to validate sex- and age-specific CC thresholds and to assess whether interventions aimed at increasing calf muscle mass or function can reduce the incidence of falls.\u003c/p\u003e","manuscriptTitle":"Calf Circumference as a Predictive Biomarker for Falls in older Adults: A Retrospective Case– Control Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-25 08:13:55","doi":"10.21203/rs.3.rs-7839117/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a9f7a38b-4259-4022-b1cb-72aa8b23fc7f","owner":[],"postedDate":"November 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-06T08:40:58+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-25 08:13:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7839117","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7839117","identity":"rs-7839117","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}