Association Between Body Mass Index and Fall Risk in Older Adults: A Cross-Sectional Study

Association Between Body Mass Index and Fall Risk in Older Adults: A Cross-Sectional Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Association Between Body Mass Index and Fall Risk in Older Adults: A Cross-Sectional Study Yuexin Luo, Siqi Liu, Yue Chen, Sitong Shen, Yi Yang, Yuan Gao, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9087979/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Background Falls are a common health problem among older adults, with an incidence as high as 30%–50%. They can lead to physical injuries, functional decline, and even death, and have become a major public health concern. Body mass index (BMI) is a key indicator for assessing nutritional status. Previous studies have suggested that BMI is associated with the risk of falls; however, the dose–response relationship and potential nonlinear association between BMI and fall risk remain unclear. Therefore, this study aimed to examine the association between BMI and fall risk in older adults and to determine whether a U-shaped relationship exists. Methods This study employed a cross-sectional design and included 8,956 adults aged 60 years and older who were assessed using the Elderly Fall Risk Assessment System at a fall clinic of a tertiary hospital in Beijing between 2021 and 2024. Demographic characteristics, health status, and fall risk data were collected through structured questionnaires. Binary logistic regression models were used to examine the association between BMI and fall risk. Restricted cubic spline functions were applied to explore potential nonlinear relationships, and sex-stratified analyses were also conducted. Results BMI was positively associated with fall risk (OR = 1.02, 95% CI: 1.01–1.03). Restricted cubic spline analysis revealed a U-shaped relationship between BMI and fall risk: the lowest risk was observed at a BMI of 18.5–24 kg/m², while BMI values below or above this range were associated with increased risk. Subgroup analysis showed that high BMI (> 24 kg/m²) was significantly associated with an increased risk of falls (OR = 1.07, 95% CI: 1.03–1.11), whereas the association for low BMI (< 18.5 kg/m²) was not statistically significant. Sex-stratified analyses indicated that both high and low BMI were significantly associated with fall risk in women ( P < 0.05), while no significant association was observed in men. Conclusions These findings suggest that weight management should be incorporated into fall-prevention strategies for older adults, and that individualized interventions should be developed for populations with different BMI levels and by sex. Trial registration No trial registration.(This study was a crosssectional study and did not involve any intervention.) Older adults Body mass index Falls Fall risk Association analysis Dose–response relationship Figures Figure 1 Figure 2 1 Background Globally, population structures are undergoing an irreversible transition toward deep population aging. In 2020, individuals aged 65 years and older accounted for 10% of the global population (approximately 830 million people), and this proportion is projected to double to 20% (approximately 1.7 billion people) by 2050[ 1 ].According to the bulletin of the Seventh National Population Census of China, individuals aged 60 years and above account for 18.7% of the total population. Compared with the previous national census data, the proportion of older adults has increased by 5.44%[ 2 ], indicating that China has entered a deeply aging society.With the ongoing acceleration of population aging and the decline in physical function among older adults, falls have evolved from a common accidental event into a serious public health problem characterized by high incidence, high disability and mortality rates, and substantial economic burden.Relevant studies have shown that the incidence of falls among older adults ranges from 30% to 50%[ 3 ], with a fall-related mortality rate of 39.2 per 100,000 population and 1,238.9 disability-adjusted life years (DALYs) per 100,000 attributable to falls[ 4 ].In addition, studies have shown that more than half of individuals who experience a fall develop a severe fear of falling[ 5 ], which may lead them to voluntarily restrict their activities, thereby accelerating functional decline and creating a vicious cycle.A study by Edward J. Goetzl et al. showed that after a fall, 40% of older adults are unable to return to their pre-fall level of independent mobility [ 6 ], requiring long-term care and imposing a substantial economic burden on families and society. Therefore, preventing falls among older adults has become an urgent need for improving their quality of life and alleviating social burdens.At present, various fall-prevention intervention strategies have been developed internationally and have been shown to effectively reduce the incidence of falls [ 7 ]. However, most existing strategies are generalized approaches, with insufficient attention paid to the refined stratification and targeted intervention of intrinsic, quantifiable core physiological risk factors at the individual level. Body mass index (BMI), a key and readily obtainable indicator reflecting nutritional status and the risk of chronic diseases, has attracted considerable attention regarding its association with fall risk. BMI may influence the risk of falls through multiple pathways, including biomechanical, metabolic, and physiological mechanisms. A study conducted in Japan reported that overweight may contribute to recurrent falls among community-dwelling older adults [ 8 ]. Overweight increases mechanical load in older adults, thereby elevating their risk of falls[ 9 ].Another study similarly reported that individuals with obesity have a higher risk of falls and fall-related fractures than those with normal weight[ 10 ].Although adipose tissue may exert a protective effect on the skeleton, individuals with obesity are often accompanied by metabolic disorders such as diabetes, which markedly increase the risk of falls[ 11 ].The above two studies suggest that an excessively high BMI increases the risk of falls. However, some studies have also indicated that a low BMI may likewise increase the risk of falls in older adults[ 12 ].A low BMI is often associated with frailty and malnutrition, both of which are important risk factors for falls[ 13 , 14 ].Although the mechanisms by which BMI influences fall risk are relatively clear, epidemiological studies have generally reported only a single direction of association and have not provided insights into the potential nonlinear relationship between BMI and fall risk in older adults. Many studies have reported an association between BMI and fall risk; however, most have been limited by relatively small sample sizes and have not examined the potential nonlinear relationship or threshold effects between BMI and fall risk, making it difficult to simultaneously explain the effects of both low and high BMI on fall risk.Therefore, this study aimed to examine the dose–response relationship between BMI and the risk of falls among older adults, explore the potential threshold effects of this relationship, and conduct sex-stratified analyses to reveal heterogeneity in the association across different subgroups, thereby providing evidence for BMI-based precision management strategies for future fall-prevention interventions. 2 Method 2.1 Study design and sample This cross-sectional study included 8,956 older adults who were registered and assessed using the Elderly Fall Risk Assessment System database at a fall clinic of a tertiary hospital in Beijing from 2021 to 2024.A multivariable modeling approach was used to examine factors associated with fall risk among older adults, and seven variables were included in the analysis. According to the commonly used rule of thumb for multivariable analyses, the required sample size should be at least 10 times the number of variables, with a minimum of 100 participants. Therefore, at least 100 participants were required. A total of 8,956 participants were ultimately included in this study, which satisfied the sample size requirement.Participants were included if they were aged ≥ 60 years, were able to walk independently or with assistance, and had clear consciousness with no communication barriers. Individuals were excluded if they had psychiatric disorders, were experiencing an acute episode of physical illness, or had significant cognitive impairment. The study was approved by the Ethics Committee of the Chinese PLA General Hospital (S2018-048-01), and written informed consent was obtained from all participants or their family members. 2.2 Data collection Data on older adults were collected using the Elderly Fall Risk Assessment System. All nurses responsible for fall risk data collection using this system underwent standardized training and competency assessment and were certified as Fall Risk Management Specialists by the Chinese Geriatrics Society.During the development of the Elderly Fall Risk Assessment System, constraints on value ranges and data length were implemented for different data-entry fields to minimize manual input errors. This design helps ensure standardized assessments and improves the accuracy and reliability of the results.The system was designed and developed based on a literature review and expert consultation. It includes structured data on general demographic characteristics, diagnoses or symptoms related to falls, and other relevant information. A total of 9,568 records were extracted from the system database. After excluding 612 records with duplicate assessments or participants aged < 60 years, 8,956 participants were ultimately included in the analysis. 2.3 Measures 2.3.1 Demographic and fall-related questionnaire Demographic and fall-related data were collected using a demographic and fall-related questionnaire adapted from Su et al.[ 15 ], including age, gender, education, height, weight, body mass index (BMI), household status, diabetes, and arthropathy. 2.3.2 Fall risk Fall risk was assessed using the Self-Rated Fall Risk Questionnaire. The Self-Rated Fall Risk Questionnaire was originally developed and revised by Rubenstein et al.[ 16 ] and later translated and culturally adapted into Chinese by Chinese scholars. The Chinese version demonstrated good reliability, with a test–retest reliability of 0.957 and a Cronbach’s α coefficient of 0.724. The questionnaire consists of 12 items, with a total score ranging from 0 to 14. A total score ≥ 4 indicates a risk of falls. 2.4 Data analysis Statistical analyses were performed using SPSS version 27.0 and R software. The Kolmogorov–Smirnov test was used to assess the normality of the variables, and all variables included in this study were non-normally distributed. Continuous variables with non-normal distributions were presented as the median and interquartile range [M (Q 1 , Q 3 )], while categorical variables were expressed as frequencies and percentages (%). Differences between two groups for non-normally distributed continuous variables were analyzed using the Mann–Whitney U test, and categorical variables were compared using the χ² test. Variance inflation factors (VIFs) were calculated to assess multicollinearity among variables. Binary logistic regression was used to examine the association between BMI and fall risk perception among older adults. Restricted cubic spline functions were applied to model the potential nonlinear relationship between BMI and fall risk perception. Further stratified analyses were conducted by sex, and interactions were tested using the likelihood ratio test. All tests were two-sided, with a significance level of α = 0.05. 3 Results A total of 8,956 older adults were ultimately included in this study. The median age of the participants was 72 years [M (Q 1 , Q 3 ): 72 (67, 79)], and the median BMI was 23.12 (21.00, 25.60) kg/m². Females accounted for 55.84% (n = 5,001) of the participants, and 30.65% (n = 2,745) were identified as being at risk of falls. The participant selection process is shown in Fig. 1 . Significant differences were observed between the non-risk and risk groups in terms of gender, age, education, household, diabetes, arthropathy, and BMI ( Table 1 ) . No multicollinearity was detected among the independent variables (all VIF values < 5). Table 1 General data statistical description and difference analysis Variables Fall risk absent( n = 6211) Fall risk present( n = 2745) Total( n = 8956) c 2 / z P Gender, n (%) Male 2816(45.34) 1139(41.49) 3955(44.16) 11.41 <0.001 Female 3395(54.66) 1606(58.51) 5001(55.84) Age, M(Q 1 ,Q 3 ) 71(67,76) 77(70,84) 72(67,79) -26.09 <0.001 BMI, M(Q 1 ,Q 3 ) 23.05(20.96,25.16) 23.31(21.13,25.53) 23.12(21.00,25.60) -2.94 0.003 Education, n (%) Primary or below 4250(68.43) 1537(55.99) 5787(64.62) 155.63 <0.001 Junior high school 683(11.00) 448(16.32) 1131(12.63) Senior high school 822(13.23) 408(14.86) 1230(13.73) Graduate or above 456(7.34) 352(12.82) 808(9.02) Household, n (%) live alone 350(5.64) 270(9.84) 620(6.92) 152.44 <0.001 Live with family 5726(92.19) 2306(84.01) 8032(89.68) Live with the caregiver 134(2.16) 166(6.05) 300(3.35) Live in a nursing home 1(0.02) 3(0.11) 4(0.04) Diabetes, n (%) No 5036(81.08) 2008(73.15) 7044(78.65) 71.31 <0.001 Yes 1175(18.92) 737(26.85) 1912(21.35) Arthropathy, n (%) No 5499(88.54) 1867(68.01) 7366(82.25) 549.07 <0.001 Yes 712(11.46) 878(31.99) 1590(17.75) Binary logistic regression analysis showed a positive association between BMI and fall risk among older adults ( P = 0.016; OR = 1.02, 95% CI: 1.01–1.03). After adjusting for potential confounders, including gender, age, education, household, diabetes, and arthropathy, the positive association remained statistically significant ( P = 0.029; OR = 1.02, 95% CI: 1.01–1.03). These results indicate that for every 1 kg/m² increase in BMI, the risk of falls increased by 2% ( Table 2 ) . Table 2 Multivariate regression analysis of BMI and fall risk in the elderly Variables Model1 Model2 OR(95% CI) P OR(95% CI) P BMI 1.02(1.01ཞ1.03) 0.016 1.02(1.01ཞ1.03) 0.029 Abbreviation: OR: Odds Ratio. CI: Confidence Interval. Model 1: No adjusted. Model 2: Adjust gender,age, education, residence status,diabetes,arthropathy. BMI was included as a continuous variable in a binary logistic regression model fitted with restricted cubic splines. After adjusting for gender, age, education, household, diabetes, and arthropathy, the results showed a U-shaped nonlinear relationship between BMI and fall risk among older adults. Specifically, fall risk was lowest when BMI ranged from 18.5 to 24 kg/m². When BMI was 24 kg/m², the risk of falls increased with increasing BMI ( Fig. 2 ) . Based on the results of the restricted cubic spline analysis, BMI was categorized into three groups: low ( 24 kg/m²). These categories were entered into a binary logistic regression model, with the normal BMI group (18.5–24 kg/m²) used as the reference. After adjusting for gender, age, education, household, diabetes, and arthropathy, the analysis showed that low BMI was not significantly associated with fall risk ( P = 0.079; OR = 0.88, 95% CI: 0.76–1.02), whereas high BMI was positively associated with fall risk ( P = 0.001; OR = 1.07, 95% CI: 1.03–1.11). These findings indicate that when BMI is > 24 kg/m², each 1 kg/m² increase in BMI is associated with a 7% higher risk of falls ( Table 3 ) .Further sex-stratified analyses showed that in older men, neither high nor low BMI was significantly associated with fall risk (both P > 0.05). In contrast, among older women, high BMI was associated with an increased risk of falls ( P < 0.001; OR = 1.05, 95% CI: 1.08–1.13), and low BMI was also associated with an increased risk of falls ( P = 0.035; OR = 0.82, 95% CI: 0.68–0.99) ( P for interaction < 0.001) ( Table 4 ) . Table 3 Piecewise regression analysis of BMI and fall risk BMI(kg/m 2 ) OR(95% CI) P 18.5ཞ24 1 - 24 1.07(1.03ཞ1.11) 0.001 Note: OR: Odds Ratio. CI: Confidence Interval, The reference group was set at 18.5kg/m 2 to 24kg/m 2 . The model was adjusted for gender, age, education, household, diabetes, and arthropathy. Table 4 Multivariate logistic regression analysis of BMI and fall risk in elderly people of different genders BMI(kg/m 2 ) Male Female OR(95% CI ) P OR(95% CI ) P 18.5ཞ24 1 1 24 1.03(0.97ཞ1.09) 0.288 1.08(1.04ཞ1.13) <0.001 Note: OR: Odds Ratio. CI: Confidence Interval, The reference group was set at 18.5kg/m2 to 24kg/m2 The model was adjusted for gender, age, education, household, diabetes, and arthropathy 4 Discussion Our study found that 30.65% of older adults were at risk of falls, which is higher than the 11.9% reported by Haofeng Xu et al. [ 17 ]. This discrepancy may be explained by the fact that their study population consisted of community-dwelling older adults, who generally have better physical function and therefore a lower risk of falls [ 18 ]. Nevertheless, the overall risk of falls among older adults remains relatively high, highlighting the need for effective interventions to prevent falls [ 19 ]. In addition, our results showed that older women had a higher risk of falls than older men, suggesting that greater attention should be given to older women in future fall-prevention interventions. A previous international study also reported that older women are more likely to experience falls than men [ 20 ], which is consistent with our findings. Studies have shown that older women generally have poorer muscle strength and joint function than men, and that depression and anxiety are more prevalent among older women. These risk factors further increase the likelihood of falls in this population [ 21 , 22 ]. These findings suggest that muscle-strengthening exercises, joint function training, and psychological interventions should be incorporated into fall-prevention strategies for older women in the future. Our findings indicate that fall risk among older adults differs by age. Compared with older adults without fall risk, those at risk of falls were older. Several previous studies have also shown that the risk of falls is higher among the oldest-old population [ 7 , 23 , 24 ]. This may be explained by the age-related decline in muscle strength, joint function, and sensory function, which leads to impaired balance in older adults[ 7 ]. In addition, the oldest-old are more likely to have multiple chronic conditions [ 25 ], and these factors may further exacerbate their risk of falls [ 7 , 25 , 26 ]. When developing fall-prevention strategies, differences in fall risk between the oldest-old and the younger-old should be considered, and individualized intervention programs should be designed accordingly. The results of this study showed that fall risk differed among older adults with different educational levels. This finding is consistent with a previous international study, which reported that older adults with lower educational attainment had a higher risk of falls and a higher incidence of falls [ 27 ]. One possible explanation is that older adults with higher educational levels may have better access to health-related information and greater awareness of potential health risks compared with those with lower educational levels [ 28 , 29 ]. These findings suggest that future fall-prevention interventions should place greater emphasis on providing fall-prevention health education for older adults with lower educational levels in order to improve their awareness of fall risk. Our study found that fall risk differed among older adults with different living arrangements. Previous research has shown that the incidence of falls among community-dwelling older adults is approximately 33%, whereas the fall rate among older adults residing in long-term care institutions (such as nursing homes) can be as high as 60% [ 30 ]. This indicates that fall risk varies across different living settings, which is consistent with our findings. These results suggest that different levels of fall-prevention interventions should be implemented for older adults with different living arrangements. For community-dwelling older adults, fall risk may be reduced by assessing potential fall hazards in the home environment and implementing home safety modifications [ 31 ]. For older adults living in long-term care facilities, providing caregivers with more training in fall-prevention knowledge may help improve the quality of care and reduce fall risk[ 32 ]. This study found that fall risk among older adults differed according to diabetes status. Previous research has shown that older adults with diabetes have a significantly higher risk of falls compared with those without diabetes[ 33 ], which is consistent with our findings. Diabetes increases fall risk through multiple pathological mechanisms, such as neuropathy and impaired balance function[ 34 , 35 ]. Studies have also demonstrated that diabetes-related complications, including peripheral neuropathy, retinopathy, and episodic hypoglycemia, are significantly associated with fall occurrence[ 36 – 39 ]. These findings suggest that diabetes prevention should be integrated into fall-prevention interventions for older adults, and that fall-prevention strategies should be specifically tailored for older adults with diabetes. In addition, fall risk among older adults differed according to the presence of joint disorders. Several studies have also reported that older adults with osteoarthritis have a significantly higher risk of falls [ 40 ]. Joint disorders increase fall risk through multiple mechanisms. Compared with older adults without joint disorders, those with joint disorders are more likely to experience impaired proprioception, unstable gait, and balance deficits, all of which contribute to an elevated risk of falls[ 41 , 42 ]. These findings suggest that healthcare providers should pay greater attention to older adults with joint disorders and develop targeted fall-prevention strategies for this population. Our findings indicate that fall risk varies among older adults with different BMI levels, which is consistent with several previous studies conducted both domestically and internationally. Some studies have reported that low BMI in older adults is associated with a higher risk of falls, possibly due to decreased muscle strength caused by sarcopenia and malnutrition[ 43 ]. Other studies have identified obesity as a strong predictor of falls[ 44 ], with older adults who are obese exhibiting a significantly higher incidence of falls[ 45 ]. Obesity can impair balance and reduce basic activities of daily living, thereby increasing fall risk among older adults[ 45 ]. These findings suggest that future fall-prevention interventions should be tailored according to BMI in older adults[ 46 ]. Our study demonstrated a positive association between BMI and fall risk among older adults, which is consistent with the findings of several previous studies [ 42 , 44 , 45 , 47 , 48 ]. Obesity increases fall risk by affecting physical function, including balance and muscle strength, with abdominal obesity being particularly associated with a higher incidence of falls[ 45 ]. These findings suggest that weight management should be incorporated into fall-prevention strategies for older adults.Furthermore, our study revealed a U-shaped nonlinear relationship between BMI and fall risk. Fall risk gradually increased when BMI was below 18.5 kg/m² or above 24 kg/m², while it was lowest at a BMI of 18.5–24 kg/m². This may be explained by the fact that low BMI is often accompanied by malnutrition and reduced muscle strength, making older adults more susceptible to falls[ 12 , 49 ]. Conversely, obesity can impair balance and result in relatively low muscle mass (sarcopenic obesity), thereby increasing the incidence of falls[ 50 ]. These findings indicate that future fall-prevention strategies should aim to maintain BMI within the normal range, combined with balance and muscle-strengthening exercises, to reduce fall risk among older adults. Subgroup analyses showed that older adults with low BMI did not have a significantly different fall risk compared with those with normal BMI. This may be because the BMI values of underweight participants in this study were mostly close to the normal range, resulting in minimal differences between groups. However, previous research has reported that older adults with low BMI have a 32.7% higher risk of falls than those with normal BMI[ 51 ], suggesting that clinicians should still pay attention to underweight older adults and help them improve muscle strength to reduce fall risk.In addition, older adults with high BMI were generally at higher risk of falls compared with those with normal BMI. High BMI may alter gait patterns—such as increased step width, reduced step length, decreased knee flexion, and anterior pelvic tilt—which increase the difficulty of dynamic balance control and, consequently, fall risk[ 50 , 52 ]. Furthermore, obesity accompanied by sarcopenia or uneven fat distribution (e.g., abdominal obesity) may further impair physical function [ 48 ]. These findings indicate that reducing body weight in obese older adults may help lower fall risk. Tailored fall-prevention interventions should also be designed for different types of obesity. For individuals with abdominal obesity, aerobic and resistance training can reduce visceral fat, improve metabolic indicators, and enhance core muscle strength[ 53 , 54 ]. For those with general obesity, interventions should primarily focus on weight management to control BMI[ 10 ].Stratified analyses also revealed that the association between BMI and fall risk was more pronounced in older women than in older men. A study by Huang Lei et al.[ 46 ]reported similar results, showing a sex-specific effect of BMI on fall risk. These findings highlight the need to pay particular attention to fall risk among both underweight and obese older women. This study has several limitations. First, as a cross-sectional study based on an existing database, some potential confounding variables, such as muscle strength and nutritional status, were not included. Future longitudinal studies are recommended to control for these variables and further investigate the relationship between BMI and fall risk in older adults, as well as to examine potential interactions between BMI, muscle strength, and nutritional status, in order to develop more precise fall-prevention strategies. Second, the findings of this study are only applicable to Chinese adults aged 60 years and older, which limits the generalizability of the results regarding the impact of BMI on fall risk in other populations. Future research should include more comprehensive variables and conduct in-depth analyses across different cultural contexts. 6 Conclusion High BMI is a risk factor for falls among older adults. Our study found a U-shaped nonlinear relationship between BMI and fall risk, indicating that both underweight and obesity increase the likelihood of falls, with the effect of BMI on fall risk being more pronounced in women. These findings provide a theoretical basis for fall-prevention measures in older adults and highlight the importance of developing tailored fall-prevention and intervention strategies for different populations. Declarations Ethics approval and consent to participate The Ethics Committee of the General Hospital of the Chinese PLA approved this study. All procedures for this study were conducted by the Declaration of Helsinki. Written informed consent was obtained from each participant before participation in the study. Consent for publication Not applicable. Competing interests The authors declare that there are no conflicts of interest related to the design, conduct, analysis, or reporting of this study. No financial or non-financial relationships, funding, or other interests influenced the results or conclusions of this research. Funding This study was supported by the National Key Research and Development Program of China (2023YFC3603905) . Author Contribution YL contributed to the conceptualization and design of the paper, as well as the collection, analysis, and interpretation of data. YL also had a role in drafting and revising the manuscript. SL, YC, SS, YY and YG participated in the process of gathering and analyzing data. YL played a role in the design, planning, coordination, and revision of the text. *QS and HP contributed to the research paper investigation, methodology, project management, supervision, and editing. The article has been reviewed and endorsed by all authors. Acknowledgement We express our gratitude to all the senior volunteers who took part in our study, as well as the clinic nurses who provided us with assistance. Data Availability The corresponding authors can provide the data supporting the outcomes of this study upon a reasonable request. References Benny P, Kuerec AH, Yu J, Lee J, Yang Q, Maier AB, Huang Z. Biomarkers of female reproductive aging in gerotherapeutic clinical trials. 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Relationship between proprioception and balance control among Chinese senior older adults. Front Physiol. 2022;13:1078087. Sotoudeh GR, Mohammadi R, Mosallanezhad Z, Viitasara E, Soares JJF. A population study on factors associated with unintentional falls among Iranian older adults. BMC Geriatr. 2023;23(1):860. Yang Y, Ye Q, Yao M, Yang Y, Lin T. Development of the Home-Based Fall Prevention Knowledge (HFPK) questionnaire to assess home-based fall prevention knowledge levels among older adults in China. BMC Public Health. 2022;22(1):2071. Zhang T, Yang C, Shu G, Gao C, Ma H, Zou L, Zuo J, Liu S, Yan J, Hu Y. The direct and mediating effects of cognitive impairment on the occurrence of falls: a cohort study based on community-dwelling old adults. Front Med (Lausanne). 2023;10:1190831. Freiberger E. [The Complexity in Fall Prevention and Mobility in Older Persons]. Ther Umsch. 2023;80(5):227–33. Clemson L, Stark S, Pighills AC, Fairhall NJ, Lamb SE, Ali J, Sherrington C. Environmental interventions for preventing falls in older people living in the community. Cochrane Database Syst Rev. 2023;3(3):Cd013258. Leighton PA, Darby J, Allen F, Cook M, Evley R, Fox C, Godfrey M, Gordon A, Gladman J, Horne J et al. A realist evaluation of a multifactorial falls prevention programme in care homes. Age Ageing 2022, 51(12). Freire LB, Brasil-Neto JP, da Silva ML, Miranda MGC, de Mattos Cruz L, Martins WR, da Silva Paz LP. Risk factors for falls in older adults with diabetes mellitus: systematic review and meta-analysis. BMC Geriatr. 2024;24(1):201. Hu XX, Yang XG, Wang X, Ma X, Geng X. The influence of diabetes and age-related degeneration on body balance control during static standing: a study based on plantar center-of-pressure trajectories and principal component analysis. J Orthop Surg Res. 2023;18(1):740. Alasmari RS, Hassani HA, Almalky NA, Bokhari AF, Al Zahrani A, Hafez AA. Risk factors for fall among the elderly with diabetes mellitus type 2 in Jeddah, Saudi Arabia, 2022: a cross-sectional study. Ann Med Surg (Lond). 2023;85(3):412–7. Gupta G, Arun Maiya G, Bhat NS, Manjunatha Hande H. Multifactorial balance rehabilitation in diabetic neuropathy elders: Randomized controlled trial. J Bodyw Mov Ther. 2025;42:736–44. Sharma S, Kalia V. Effect of tibial nerve mobilization on balance & gait functions in subjects with subclinical diabetic neuropathy: A randomized clinical trial. J Diabetes Metab Disord. 2023;22(2):1283–90. Bayrak M, Kaşali K, Güner M, Cadirci K, Kılıç AF, Binici DN. Risk factors influencing fall risk in geriatric patients with type 2 diabetes: a comprehensive analysis. Aging Male. 2025;28(1):2469614. Cheng LY, Leung SY, Leung MKW. The association of glycemic control and fall risk in diabetic elderly: a cross-sectional study in Hong Kong. BMC Prim Care. 2022;23(1):192. Alenazi AM, Alhowimel AS, Alshehri MM, Alqahtani BA, Alhwoaimel NA, Waitman LR, Kluding PM. Generalized and localized osteoarthritis and risk of fall among older adults: the role of chronic diseases and medications using real world data from a single center. Eur Rev Med Pharmacol Sci. 2023;27(9):3957–66. Bishnoi A, Hu Y, Hernandez ME. Impact of sensory organization tasks on prefrontal cortex activity in older women: a comparative fNIRS study of osteoarthritis and healthy aging. Front Aging Neurosci. 2025;17:1583447. Yan Y, Ou J, Shi H, Sun C, Shen L, Song Z, Shu L, Chen Z. Plantar pressure and falling risk in older individuals: a cross-sectional study. J Foot Ankle Res. 2023;16(1):14. Nowak MM, Niemczyk M, Gołębiewski S, Pączek L. Impact of Body Mass Index on All-Cause Mortality in Adults: A Systematic Review and Meta-Analysis. J Clin Med 2024, 13(8). Oseni TIA, Ibharokhonre AO, Olawumi AL, Iyalomhe ES, Adebayo CU, Adewuyi BO, Fuh FN. Association between obesity, physical activity and falls among elderly patients attending the family medicine clinics of a teaching hospital in Southern Nigeria. BMC Geriatr. 2025;25(1):93. Monteiro ELF, Ikegami ÉM, Oliveira NGN, Reis ECD, Virtuoso Júnior JS. Use of structural models to elucidate the occurrence of falls among older adults according to abdominal obesity: a cross-sectional study. Sao Paulo Med J. 2023;141(1):51–9. Huang L, Shen X, Zou Y, Wang Y. Effects of BMI and grip strength on older adults' falls-A longitudinal study based on CHARLS. Front Public Health. 2024;12:1415360. Shim GY, Yoo MC, Soh Y, Chon J, Won CW. Obesity, Physical Performance, Balance Confidence, and Falls in Community-Dwelling Older Adults: Results from the Korean Frailty and Aging Cohort Study. Nutrients 2024, 16(5). Li R, Chen X, Tang H, Luo S, Lian R, Zhang W, Zhang X, Hu X, Yang M. Sarcopenic obesity and falls in older adults: A validation study of ESPEN/EASO criteria and modifications in Western China communities. Arch Gerontol Geriatr. 2024;127:105557. Berbel-Arcobé L, Benavent D, Valencia-Muntalà L, Gómez-Vaquero C, Juanola X, Nolla JM. Assessing Sarcopenia, Presarcopenia, and Malnutrition in Axial Spondyloarthritis: Insights from a Spanish Cohort. Nutrients 2025, 17(6). Ferhi H, Maktouf W. The impact of obesity on static and proactive balance and gait patterns in sarcopenic older adults: an analytical cross-sectional investigation. PeerJ. 2023;11:e16428. Shinta Wahyusari RRM. Rizka Yunita,: Hubungan Indeks Massa Tubuh (IMT) Dengan Resiko Jatuh Pada Lansia Di Desa Tegalsiwalan Kecamatan Tegalsiwalan Kabupaten Probolinggo. J Health Med Sci 2023. Wang X, Cao J, Zhao Q, Chen M, Luo J, Wang H, Yu L, Tsui KL, Zhao Y. Identifying sensors-based parameters associated with fall risk in community-dwelling older adults: an investigation and interpretation of discriminatory parameters. BMC Geriatr. 2024;24(1):125. de Oliveira Máximo R, de Oliveira DC, Ramírez PC, Luiz MM, de Souza AF, Delinocente MLB, Steptoe A, de Oliveira C, da Silva Alexandre T. Dynapenia, abdominal obesity or both: which accelerates the gait speed decline most? Age Ageing. 2021;50(5):1616–25. Zhang L, Liu S, Wang W, Sun M, Tian H, Wei L, Wu Y. Dynapenic abdominal obesity and the effect on long-term gait speed and falls in older adults. Clin Nutr. 2022;41(1):91–6. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 22 Apr, 2026 Reviewers agreed at journal 22 Apr, 2026 Reviewers agreed at journal 14 Apr, 2026 Reviewers invited by journal 07 Apr, 2026 Editor assigned by journal 06 Apr, 2026 Editor invited by journal 16 Mar, 2026 Submission checks completed at journal 13 Mar, 2026 First submitted to journal 13 Mar, 2026 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. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9087979","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":620580521,"identity":"03d18442-a190-4492-9ce4-17f6433bfd27","order_by":0,"name":"Yuexin Luo","email":"","orcid":"","institution":"Medical School of Chinese PLA","correspondingAuthor":false,"prefix":"","firstName":"Yuexin","middleName":"","lastName":"Luo","suffix":""},{"id":620580524,"identity":"dc2a2a9a-031f-4952-aa08-e9b8be0e8613","order_by":1,"name":"Siqi Liu","email":"","orcid":"","institution":"Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders \u0026 National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Siqi","middleName":"","lastName":"Liu","suffix":""},{"id":620580526,"identity":"42b39e30-278b-48e6-a7f4-073e52a8e623","order_by":2,"name":"Yue Chen","email":"","orcid":"","institution":"First Medical Center, PLA General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yue","middleName":"","lastName":"Chen","suffix":""},{"id":620580527,"identity":"8111ab7d-396f-4bb4-914c-29d19040c7fe","order_by":3,"name":"Sitong Shen","email":"","orcid":"","institution":"Medical School of Chinese PLA","correspondingAuthor":false,"prefix":"","firstName":"Sitong","middleName":"","lastName":"Shen","suffix":""},{"id":620580528,"identity":"8aed7bb1-86df-4eed-a636-a029e0f5f30f","order_by":4,"name":"Yi Yang","email":"","orcid":"","institution":"Medical School of Chinese PLA","correspondingAuthor":false,"prefix":"","firstName":"Yi","middleName":"","lastName":"Yang","suffix":""},{"id":620580530,"identity":"03b5468f-3721-460b-bd63-dc333b0c63dd","order_by":5,"name":"Yuan Gao","email":"","orcid":"","institution":"Department of Nursing, The First Medical Center, Chinese PLA General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yuan","middleName":"","lastName":"Gao","suffix":""},{"id":620580532,"identity":"b899e4e2-2434-474c-856f-0f7404f0c4e3","order_by":6,"name":"Hongying Pi","email":"","orcid":"","institution":"Medical Service Training Center, Chinese PLA General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Hongying","middleName":"","lastName":"Pi","suffix":""},{"id":620580534,"identity":"5b6c6c6f-df95-4c88-8831-10e33422b9d1","order_by":7,"name":"Qingqing Su","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+klEQVRIie3PsWrDMBCA4TOCTme02hDwK1ynEBKSV1Ex2EsGjxkFAXcJnZW5TxAKnWW0mno1dOmUOaZLaT30KLRblYyF6gct4j5OAgiF/miWD/IpP0ZaoJT6cvKS4KaYpMZevCxi0roFaeWfy0x+cNX7fDKV94rSukMCG52G9e+E+qJy+7sSZ+ao1HX9jFOhRbp/9JBkTS7eOaS+VfaGyUzbKxF7SGZ+SDfopn5CsspPoGeCb0w6nUe6tecJtcfKxbrkLVAI2OSYmmbr/Ut2mz+84jhfUWeZ0HIl5bY5Db6H8SKIagGQqO+LSHvnvwiMTKQ9NxgKhUL/tU8NDFkFx5+nfwAAAABJRU5ErkJggg==","orcid":"","institution":"Department of Nursing, The First Medical Center, Chinese PLA General Hospital","correspondingAuthor":true,"prefix":"","firstName":"Qingqing","middleName":"","lastName":"Su","suffix":""}],"badges":[],"createdAt":"2026-03-10 22:23:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9087979/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9087979/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106960870,"identity":"7d826774-aa96-42f5-be49-50cbcd9dafe8","added_by":"auto","created_at":"2026-04-15 09:23:28","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":31247,"visible":true,"origin":"","legend":"\u003cp\u003eThe screening process of participants\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9087979/v1/843634daf127feb36fb164b7.png"},{"id":106915819,"identity":"9f9d6e10-55b9-456b-ae62-a2fa588ccfda","added_by":"auto","created_at":"2026-04-14 18:01:48","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":14523,"visible":true,"origin":"","legend":"\u003cp\u003eThe nonlinear relationship between BMI and the risk of falls\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-9087979/v1/93c89d3e6e3cb672daaae263.png"},{"id":106963304,"identity":"6a34442d-882e-4b72-89ee-2bf390a51243","added_by":"auto","created_at":"2026-04-15 09:43:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":751651,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9087979/v1/946e0ad6-95e1-4b23-aac6-207241f355d9.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association Between Body Mass Index and Fall Risk in Older Adults: A Cross-Sectional Study","fulltext":[{"header":"1 Background","content":"\u003cp\u003eGlobally, population structures are undergoing an irreversible transition toward deep population aging. In 2020, individuals aged 65 years and older accounted for 10% of the global population (approximately 830\u0026nbsp;million people), and this proportion is projected to double to 20% (approximately 1.7\u0026nbsp;billion people) by 2050[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].According to the bulletin of the Seventh National Population Census of China, individuals aged 60 years and above account for 18.7% of the total population. Compared with the previous national census data, the proportion of older adults has increased by 5.44%[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], indicating that China has entered a deeply aging society.With the ongoing acceleration of population aging and the decline in physical function among older adults, falls have evolved from a common accidental event into a serious public health problem characterized by high incidence, high disability and mortality rates, and substantial economic burden.Relevant studies have shown that the incidence of falls among older adults ranges from 30% to 50%[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], with a fall-related mortality rate of 39.2 per 100,000 population and 1,238.9 disability-adjusted life years (DALYs) per 100,000 attributable to falls[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].In addition, studies have shown that more than half of individuals who experience a fall develop a severe fear of falling[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], which may lead them to voluntarily restrict their activities, thereby accelerating functional decline and creating a vicious cycle.A study by Edward J. Goetzl et al. showed that after a fall, 40% of older adults are unable to return to their pre-fall level of independent mobility [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], requiring long-term care and imposing a substantial economic burden on families and society. Therefore, preventing falls among older adults has become an urgent need for improving their quality of life and alleviating social burdens.At present, various fall-prevention intervention strategies have been developed internationally and have been shown to effectively reduce the incidence of falls [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. However, most existing strategies are generalized approaches, with insufficient attention paid to the refined stratification and targeted intervention of intrinsic, quantifiable core physiological risk factors at the individual level.\u003c/p\u003e \u003cp\u003eBody mass index (BMI), a key and readily obtainable indicator reflecting nutritional status and the risk of chronic diseases, has attracted considerable attention regarding its association with fall risk. BMI may influence the risk of falls through multiple pathways, including biomechanical, metabolic, and physiological mechanisms. A study conducted in Japan reported that overweight may contribute to recurrent falls among community-dwelling older adults [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Overweight increases mechanical load in older adults, thereby elevating their risk of falls[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].Another study similarly reported that individuals with obesity have a higher risk of falls and fall-related fractures than those with normal weight[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].Although adipose tissue may exert a protective effect on the skeleton, individuals with obesity are often accompanied by metabolic disorders such as diabetes, which markedly increase the risk of falls[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].The above two studies suggest that an excessively high BMI increases the risk of falls. However, some studies have also indicated that a low BMI may likewise increase the risk of falls in older adults[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].A low BMI is often associated with frailty and malnutrition, both of which are important risk factors for falls[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].Although the mechanisms by which BMI influences fall risk are relatively clear, epidemiological studies have generally reported only a single direction of association and have not provided insights into the potential nonlinear relationship between BMI and fall risk in older adults.\u003c/p\u003e \u003cp\u003eMany studies have reported an association between BMI and fall risk; however, most have been limited by relatively small sample sizes and have not examined the potential nonlinear relationship or threshold effects between BMI and fall risk, making it difficult to simultaneously explain the effects of both low and high BMI on fall risk.Therefore, this study aimed to examine the dose\u0026ndash;response relationship between BMI and the risk of falls among older adults, explore the potential threshold effects of this relationship, and conduct sex-stratified analyses to reveal heterogeneity in the association across different subgroups, thereby providing evidence for BMI-based precision management strategies for future fall-prevention interventions.\u003c/p\u003e"},{"header":"2 Method","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study design and sample\u003c/h2\u003e \u003cp\u003eThis cross-sectional study included 8,956 older adults who were registered and assessed using the Elderly Fall Risk Assessment System database at a fall clinic of a tertiary hospital in Beijing from 2021 to 2024.A multivariable modeling approach was used to examine factors associated with fall risk among older adults, and seven variables were included in the analysis. According to the commonly used rule of thumb for multivariable analyses, the required sample size should be at least 10 times the number of variables, with a minimum of 100 participants. Therefore, at least 100 participants were required. A total of 8,956 participants were ultimately included in this study, which satisfied the sample size requirement.Participants were included if they were aged\u0026thinsp;\u0026ge;\u0026thinsp;60 years, were able to walk independently or with assistance, and had clear consciousness with no communication barriers. Individuals were excluded if they had psychiatric disorders, were experiencing an acute episode of physical illness, or had significant cognitive impairment.\u003c/p\u003e \u003cp\u003e The study was approved by the Ethics Committee of the Chinese PLA General Hospital (S2018-048-01), and written informed consent was obtained from all participants or their family members.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Data collection\u003c/h2\u003e \u003cp\u003eData on older adults were collected using the Elderly Fall Risk Assessment System. All nurses responsible for fall risk data collection using this system underwent standardized training and competency assessment and were certified as Fall Risk Management Specialists by the Chinese Geriatrics Society.During the development of the Elderly Fall Risk Assessment System, constraints on value ranges and data length were implemented for different data-entry fields to minimize manual input errors. This design helps ensure standardized assessments and improves the accuracy and reliability of the results.The system was designed and developed based on a literature review and expert consultation. It includes structured data on general demographic characteristics, diagnoses or symptoms related to falls, and other relevant information. A total of 9,568 records were extracted from the system database. After excluding 612 records with duplicate assessments or participants aged\u0026thinsp;\u0026lt;\u0026thinsp;60 years, 8,956 participants were ultimately included in the analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Measures\u003c/h2\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.3.1 Demographic and fall-related questionnaire\u003c/h2\u003e \u003cp\u003eDemographic and fall-related data were collected using a demographic and fall-related questionnaire adapted from Su et al.[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], including age, gender, education, height, weight, body mass index (BMI), household status, diabetes, and arthropathy.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.3.2 Fall risk\u003c/h2\u003e \u003cp\u003eFall risk was assessed using the Self-Rated Fall Risk Questionnaire. The Self-Rated Fall Risk Questionnaire was originally developed and revised by Rubenstein et al.[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] and later translated and culturally adapted into Chinese by Chinese scholars. The Chinese version demonstrated good reliability, with a test\u0026ndash;retest reliability of 0.957 and a Cronbach\u0026rsquo;s α coefficient of 0.724. The questionnaire consists of 12 items, with a total score ranging from 0 to 14. A total score\u0026thinsp;\u0026ge;\u0026thinsp;4 indicates a risk of falls.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Data analysis\u003c/h2\u003e \u003cp\u003eStatistical analyses were performed using SPSS version 27.0 and R software. The Kolmogorov\u0026ndash;Smirnov test was used to assess the normality of the variables, and all variables included in this study were non-normally distributed. Continuous variables with non-normal distributions were presented as the median and interquartile range [M (Q\u003csub\u003e1\u003c/sub\u003e, Q\u003csub\u003e3\u003c/sub\u003e)], while categorical variables were expressed as frequencies and percentages (%). Differences between two groups for non-normally distributed continuous variables were analyzed using the Mann\u0026ndash;Whitney U test, and categorical variables were compared using the χ\u0026sup2; test. Variance inflation factors (VIFs) were calculated to assess multicollinearity among variables. Binary logistic regression was used to examine the association between BMI and fall risk perception among older adults. Restricted cubic spline functions were applied to model the potential nonlinear relationship between BMI and fall risk perception. Further stratified analyses were conducted by sex, and interactions were tested using the likelihood ratio test. All tests were two-sided, with a significance level of α\u0026thinsp;=\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cp\u003eA total of 8,956 older adults were ultimately included in this study. The median age of the participants was 72 years [M (Q\u003csub\u003e1\u003c/sub\u003e, Q\u003csub\u003e3\u003c/sub\u003e): 72 (67, 79)], and the median BMI was 23.12 (21.00, 25.60) kg/m\u0026sup2;. Females accounted for 55.84% (n\u0026thinsp;=\u0026thinsp;5,001) of the participants, and 30.65% (n\u0026thinsp;=\u0026thinsp;2,745) were identified as being at risk of falls. The participant selection process is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Significant differences were observed between the non-risk and risk groups in terms of gender, age, education, household, diabetes, arthropathy, and BMI \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. No multicollinearity was detected among the independent variables (all VIF values\u0026thinsp;\u0026lt;\u0026thinsp;5).\u003c/p\u003e \u003cp\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\u003eGeneral data statistical description and difference analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFall risk absent(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;6211)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFall risk present(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2745)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTotal(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;8956)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ec\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e/\u003cem\u003ez\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender, \u003cem\u003en\u003c/em\u003e(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2816(45.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1139(41.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3955(44.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3395(54.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1606(58.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5001(55.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, M(Q\u003csub\u003e1\u003c/sub\u003e,Q\u003csub\u003e3\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71(67,76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e77(70,84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e72(67,79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-26.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI, M(Q\u003csub\u003e1\u003c/sub\u003e,Q\u003csub\u003e3\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.05(20.96,25.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.31(21.13,25.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23.12(21.00,25.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-2.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation, \u003cem\u003en\u003c/em\u003e(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrimary or below\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4250(68.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1537(55.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5787(64.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e155.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJunior high school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e683(11.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e448(16.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1131(12.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSenior high school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e822(13.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e408(14.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1230(13.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGraduate or above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e456(7.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e352(12.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e808(9.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousehold, \u003cem\u003en\u003c/em\u003e(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003elive alone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e350(5.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e270(9.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e620(6.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e152.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLive with family\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5726(92.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2306(84.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8032(89.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLive with the caregiver\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e134(2.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e166(6.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e300(3.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLive in a nursing home\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1(0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3(0.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4(0.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes, \u003cem\u003en\u003c/em\u003e(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5036(81.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2008(73.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7044(78.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e71.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1175(18.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e737(26.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1912(21.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArthropathy, \u003cem\u003en\u003c/em\u003e(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5499(88.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1867(68.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7366(82.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e549.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e712(11.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e878(31.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1590(17.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eBinary logistic regression analysis showed a positive association between BMI and fall risk among older adults (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.016; OR\u0026thinsp;=\u0026thinsp;1.02, 95% CI: 1.01\u0026ndash;1.03). After adjusting for potential confounders, including gender, age, education, household, diabetes, and arthropathy, the positive association remained statistically significant (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.029; OR\u0026thinsp;=\u0026thinsp;1.02, 95% CI: 1.01\u0026ndash;1.03). These results indicate that for every 1 kg/m\u0026sup2; increase in BMI, the risk of falls increased by 2%\u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e)\u003c/b\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\u003eMultivariate regression analysis of BMI and fall risk in the elderly\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eModel1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eModel2\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR(95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOR(95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.02(1.01ཞ1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.02(1.01ཞ1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eAbbreviation: OR: Odds Ratio. CI: Confidence Interval.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eModel 1: No adjusted.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eModel 2: Adjust gender,age, education, residence status,diabetes,arthropathy.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eBMI was included as a continuous variable in a binary logistic regression model fitted with restricted cubic splines. After adjusting for gender, age, education, household, diabetes, and arthropathy, the results showed a U-shaped nonlinear relationship between BMI and fall risk among older adults. Specifically, fall risk was lowest when BMI ranged from 18.5 to 24 kg/m\u0026sup2;. When BMI was \u0026lt;\u0026thinsp;18.5 kg/m\u0026sup2;, the risk of falls increased as BMI decreased, whereas when BMI was \u0026gt;\u0026thinsp;24 kg/m\u0026sup2;, the risk of falls increased with increasing BMI\u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBased on the results of the restricted cubic spline analysis, BMI was categorized into three groups: low (\u0026lt;\u0026thinsp;18.5 kg/m\u0026sup2;), normal (18.5\u0026ndash;24 kg/m\u0026sup2;), and high (\u0026gt;\u0026thinsp;24 kg/m\u0026sup2;). These categories were entered into a binary logistic regression model, with the normal BMI group (18.5\u0026ndash;24 kg/m\u0026sup2;) used as the reference. After adjusting for gender, age, education, household, diabetes, and arthropathy, the analysis showed that low BMI was not significantly associated with fall risk (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.079; OR\u0026thinsp;=\u0026thinsp;0.88, 95% CI: 0.76\u0026ndash;1.02), whereas high BMI was positively associated with fall risk (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001; OR\u0026thinsp;=\u0026thinsp;1.07, 95% CI: 1.03\u0026ndash;1.11). These findings indicate that when BMI is \u0026gt;\u0026thinsp;24 kg/m\u0026sup2;, each 1 kg/m\u0026sup2; increase in BMI is associated with a 7% higher risk of falls \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e.Further sex-stratified analyses showed that in older men, neither high nor low BMI was significantly associated with fall risk (both \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). In contrast, among older women, high BMI was associated with an increased risk of falls (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; OR\u0026thinsp;=\u0026thinsp;1.05, 95% CI: 1.08\u0026ndash;1.13), and low BMI was also associated with an increased risk of falls (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.035; OR\u0026thinsp;=\u0026thinsp;0.82, 95% CI: 0.68\u0026ndash;0.99) (\u003cem\u003eP\u003c/em\u003e for interaction\u0026thinsp;\u0026lt;\u0026thinsp;0.001) \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePiecewise regression analysis of BMI and 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=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI(kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR(95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18.5ཞ24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;18.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.88(0.76ཞ1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.079\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.07(1.03ཞ1.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eNote: OR: Odds Ratio. CI: Confidence Interval, The reference group was set at 18.5kg/m\u003csup\u003e2\u003c/sup\u003e to 24kg/m\u003csup\u003e2\u003c/sup\u003e.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eThe model was adjusted for gender, age, education, household, diabetes, and arthropathy.\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 \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariate logistic regression analysis of BMI and fall risk in elderly people of different genders\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBMI(kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR(95%\u003cem\u003eCI\u003c/em\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOR(95%\u003cem\u003eCI\u003c/em\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18.5ཞ24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;18.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.92(0.76ཞ1.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.392\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.82(0.68ཞ0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.03(0.97ཞ1.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.08(1.04ཞ1.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eNote: OR: Odds Ratio. CI: Confidence Interval, The reference group was set at 18.5kg/m2 to 24kg/m2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eThe model was adjusted for gender, age, education, household, diabetes, and arthropathy\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eOur study found that 30.65% of older adults were at risk of falls, which is higher than the 11.9% reported by Haofeng Xu et al. [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. This discrepancy may be explained by the fact that their study population consisted of community-dwelling older adults, who generally have better physical function and therefore a lower risk of falls [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Nevertheless, the overall risk of falls among older adults remains relatively high, highlighting the need for effective interventions to prevent falls [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn addition, our results showed that older women had a higher risk of falls than older men, suggesting that greater attention should be given to older women in future fall-prevention interventions. A previous international study also reported that older women are more likely to experience falls than men [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], which is consistent with our findings. Studies have shown that older women generally have poorer muscle strength and joint function than men, and that depression and anxiety are more prevalent among older women. These risk factors further increase the likelihood of falls in this population [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. These findings suggest that muscle-strengthening exercises, joint function training, and psychological interventions should be incorporated into fall-prevention strategies for older women in the future.\u003c/p\u003e \u003cp\u003eOur findings indicate that fall risk among older adults differs by age. Compared with older adults without fall risk, those at risk of falls were older. Several previous studies have also shown that the risk of falls is higher among the oldest-old population [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. This may be explained by the age-related decline in muscle strength, joint function, and sensory function, which leads to impaired balance in older adults[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. In addition, the oldest-old are more likely to have multiple chronic conditions [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], and these factors may further exacerbate their risk of falls [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. When developing fall-prevention strategies, differences in fall risk between the oldest-old and the younger-old should be considered, and individualized intervention programs should be designed accordingly.\u003c/p\u003e \u003cp\u003eThe results of this study showed that fall risk differed among older adults with different educational levels. This finding is consistent with a previous international study, which reported that older adults with lower educational attainment had a higher risk of falls and a higher incidence of falls [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. One possible explanation is that older adults with higher educational levels may have better access to health-related information and greater awareness of potential health risks compared with those with lower educational levels [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. These findings suggest that future fall-prevention interventions should place greater emphasis on providing fall-prevention health education for older adults with lower educational levels in order to improve their awareness of fall risk.\u003c/p\u003e \u003cp\u003eOur study found that fall risk differed among older adults with different living arrangements. Previous research has shown that the incidence of falls among community-dwelling older adults is approximately 33%, whereas the fall rate among older adults residing in long-term care institutions (such as nursing homes) can be as high as 60% [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. This indicates that fall risk varies across different living settings, which is consistent with our findings. These results suggest that different levels of fall-prevention interventions should be implemented for older adults with different living arrangements. For community-dwelling older adults, fall risk may be reduced by assessing potential fall hazards in the home environment and implementing home safety modifications [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. For older adults living in long-term care facilities, providing caregivers with more training in fall-prevention knowledge may help improve the quality of care and reduce fall risk[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study found that fall risk among older adults differed according to diabetes status. Previous research has shown that older adults with diabetes have a significantly higher risk of falls compared with those without diabetes[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], which is consistent with our findings. Diabetes increases fall risk through multiple pathological mechanisms, such as neuropathy and impaired balance function[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Studies have also demonstrated that diabetes-related complications, including peripheral neuropathy, retinopathy, and episodic hypoglycemia, are significantly associated with fall occurrence[\u003cspan additionalcitationids=\"CR37 CR38\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. These findings suggest that diabetes prevention should be integrated into fall-prevention interventions for older adults, and that fall-prevention strategies should be specifically tailored for older adults with diabetes.\u003c/p\u003e \u003cp\u003eIn addition, fall risk among older adults differed according to the presence of joint disorders. Several studies have also reported that older adults with osteoarthritis have a significantly higher risk of falls [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Joint disorders increase fall risk through multiple mechanisms. Compared with older adults without joint disorders, those with joint disorders are more likely to experience impaired proprioception, unstable gait, and balance deficits, all of which contribute to an elevated risk of falls[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. These findings suggest that healthcare providers should pay greater attention to older adults with joint disorders and develop targeted fall-prevention strategies for this population.\u003c/p\u003e \u003cp\u003eOur findings indicate that fall risk varies among older adults with different BMI levels, which is consistent with several previous studies conducted both domestically and internationally. Some studies have reported that low BMI in older adults is associated with a higher risk of falls, possibly due to decreased muscle strength caused by sarcopenia and malnutrition[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Other studies have identified obesity as a strong predictor of falls[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e], with older adults who are obese exhibiting a significantly higher incidence of falls[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Obesity can impair balance and reduce basic activities of daily living, thereby increasing fall risk among older adults[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. These findings suggest that future fall-prevention interventions should be tailored according to BMI in older adults[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur study demonstrated a positive association between BMI and fall risk among older adults, which is consistent with the findings of several previous studies [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Obesity increases fall risk by affecting physical function, including balance and muscle strength, with abdominal obesity being particularly associated with a higher incidence of falls[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. These findings suggest that weight management should be incorporated into fall-prevention strategies for older adults.Furthermore, our study revealed a U-shaped nonlinear relationship between BMI and fall risk. Fall risk gradually increased when BMI was below 18.5 kg/m\u0026sup2; or above 24 kg/m\u0026sup2;, while it was lowest at a BMI of 18.5\u0026ndash;24 kg/m\u0026sup2;. This may be explained by the fact that low BMI is often accompanied by malnutrition and reduced muscle strength, making older adults more susceptible to falls[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Conversely, obesity can impair balance and result in relatively low muscle mass (sarcopenic obesity), thereby increasing the incidence of falls[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. These findings indicate that future fall-prevention strategies should aim to maintain BMI within the normal range, combined with balance and muscle-strengthening exercises, to reduce fall risk among older adults.\u003c/p\u003e \u003cp\u003eSubgroup analyses showed that older adults with low BMI did not have a significantly different fall risk compared with those with normal BMI. This may be because the BMI values of underweight participants in this study were mostly close to the normal range, resulting in minimal differences between groups. However, previous research has reported that older adults with low BMI have a 32.7% higher risk of falls than those with normal BMI[\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e], suggesting that clinicians should still pay attention to underweight older adults and help them improve muscle strength to reduce fall risk.In addition, older adults with high BMI were generally at higher risk of falls compared with those with normal BMI. High BMI may alter gait patterns\u0026mdash;such as increased step width, reduced step length, decreased knee flexion, and anterior pelvic tilt\u0026mdash;which increase the difficulty of dynamic balance control and, consequently, fall risk[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. Furthermore, obesity accompanied by sarcopenia or uneven fat distribution (e.g., abdominal obesity) may further impair physical function [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. These findings indicate that reducing body weight in obese older adults may help lower fall risk. Tailored fall-prevention interventions should also be designed for different types of obesity. For individuals with abdominal obesity, aerobic and resistance training can reduce visceral fat, improve metabolic indicators, and enhance core muscle strength[\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. For those with general obesity, interventions should primarily focus on weight management to control BMI[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].Stratified analyses also revealed that the association between BMI and fall risk was more pronounced in older women than in older men. A study by Huang Lei et al.[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]reported similar results, showing a sex-specific effect of BMI on fall risk. These findings highlight the need to pay particular attention to fall risk among both underweight and obese older women.\u003c/p\u003e \u003cp\u003eThis study has several limitations. First, as a cross-sectional study based on an existing database, some potential confounding variables, such as muscle strength and nutritional status, were not included. Future longitudinal studies are recommended to control for these variables and further investigate the relationship between BMI and fall risk in older adults, as well as to examine potential interactions between BMI, muscle strength, and nutritional status, in order to develop more precise fall-prevention strategies. Second, the findings of this study are only applicable to Chinese adults aged 60 years and older, which limits the generalizability of the results regarding the impact of BMI on fall risk in other populations. Future research should include more comprehensive variables and conduct in-depth analyses across different cultural contexts.\u003c/p\u003e"},{"header":"6 Conclusion","content":"\u003cp\u003eHigh BMI is a risk factor for falls among older adults. Our study found a U-shaped nonlinear relationship between BMI and fall risk, indicating that both underweight and obesity increase the likelihood of falls, with the effect of BMI on fall risk being more pronounced in women. These findings provide a theoretical basis for fall-prevention measures in older adults and highlight the importance of developing tailored fall-prevention and intervention strategies for different populations.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003cp\u003e The Ethics Committee of the General Hospital of the Chinese PLA approved this study. All procedures for this study were conducted by the Declaration of Helsinki. Written informed consent was obtained from each participant before participation in the study.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare that there are no conflicts of interest related to the design, conduct, analysis, or reporting of this study. No financial or non-financial relationships, funding, or other interests influenced the results or conclusions of this research.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis study was supported by the National Key Research and Development Program of China (2023YFC3603905) .\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eYL contributed to the conceptualization and design of the paper, as well as the collection, analysis, and interpretation of data. YL also had a role in drafting and revising the manuscript. SL, YC, SS, YY and YG participated in the process of gathering and analyzing data. YL played a role in the design, planning, coordination, and revision of the text. *QS and HP contributed to the research paper investigation, methodology, project management, supervision, and editing. The article has been reviewed and endorsed by all authors.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe express our gratitude to all the senior volunteers who took part in our study, as well as the clinic nurses who provided us with assistance.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe corresponding authors can provide the data supporting the outcomes of this study upon a reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBenny P, Kuerec AH, Yu J, Lee J, Yang Q, Maier AB, Huang Z. Biomarkers of female reproductive aging in gerotherapeutic clinical trials. Ageing Res Rev. 2025;112:102901.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang J, Chen Z, Song Y. Falls in aged people of the Chinese mainland: epidemiology, risk factors and clinical strategies. 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Identifying sensors-based parameters associated with fall risk in community-dwelling older adults: an investigation and interpretation of discriminatory parameters. BMC Geriatr. 2024;24(1):125.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ede Oliveira M\u0026aacute;ximo R, de Oliveira DC, Ram\u0026iacute;rez PC, Luiz MM, de Souza AF, Delinocente MLB, Steptoe A, de Oliveira C, da Silva Alexandre T. Dynapenia, abdominal obesity or both: which accelerates the gait speed decline most? Age Ageing. 2021;50(5):1616\u0026ndash;25.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang L, Liu S, Wang W, Sun M, Tian H, Wei L, Wu Y. Dynapenic abdominal obesity and the effect on long-term gait speed and falls in older adults. Clin Nutr. 2022;41(1):91\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-geriatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bgtc","sideBox":"Learn more about [BMC Geriatrics](http://bmcgeriatr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bgtc/default.aspx","title":"BMC Geriatrics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Older adults, Body mass index, Falls, Fall risk, Association analysis, Dose–response relationship","lastPublishedDoi":"10.21203/rs.3.rs-9087979/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9087979/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eFalls are a common health problem among older adults, with an incidence as high as 30%\u0026ndash;50%. They can lead to physical injuries, functional decline, and even death, and have become a major public health concern. Body mass index (BMI) is a key indicator for assessing nutritional status. Previous studies have suggested that BMI is associated with the risk of falls; however, the dose\u0026ndash;response relationship and potential nonlinear association between BMI and fall risk remain unclear. Therefore, this study aimed to examine the association between BMI and fall risk in older adults and to determine whether a U-shaped relationship exists.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis study employed a cross-sectional design and included 8,956 adults aged 60 years and older who were assessed using the Elderly Fall Risk Assessment System at a fall clinic of a tertiary hospital in Beijing between 2021 and 2024. Demographic characteristics, health status, and fall risk data were collected through structured questionnaires. Binary logistic regression models were used to examine the association between BMI and fall risk. Restricted cubic spline functions were applied to explore potential nonlinear relationships, and sex-stratified analyses were also conducted.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eBMI was positively associated with fall risk (OR\u0026thinsp;=\u0026thinsp;1.02, 95% CI: 1.01\u0026ndash;1.03). Restricted cubic spline analysis revealed a U-shaped relationship between BMI and fall risk: the lowest risk was observed at a BMI of 18.5\u0026ndash;24 kg/m\u0026sup2;, while BMI values below or above this range were associated with increased risk. Subgroup analysis showed that high BMI (\u0026gt;\u0026thinsp;24 kg/m\u0026sup2;) was significantly associated with an increased risk of falls (OR\u0026thinsp;=\u0026thinsp;1.07, 95% CI: 1.03\u0026ndash;1.11), whereas the association for low BMI (\u0026lt;\u0026thinsp;18.5 kg/m\u0026sup2;) was not statistically significant. Sex-stratified analyses indicated that both high and low BMI were significantly associated with fall risk in women (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), while no significant association was observed in men.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThese findings suggest that weight management should be incorporated into fall-prevention strategies for older adults, and that individualized interventions should be developed for populations with different BMI levels and by sex.\u003c/p\u003e\u003ch2\u003eTrial registration\u003c/h2\u003e \u003cp\u003eNo trial registration.(This study was a crosssectional study and did not involve any intervention.)\u003c/p\u003e","manuscriptTitle":"Association Between Body Mass Index and Fall Risk in Older Adults: A Cross-Sectional Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-14 18:01:45","doi":"10.21203/rs.3.rs-9087979/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-04-22T17:17:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"213547921638550087757580943212292631850","date":"2026-04-22T12:22:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"316849180862580735546845729629369898564","date":"2026-04-14T18:55:09+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-07T16:59:44+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-06T08:33:41+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-16T09:23:43+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-13T23:07:35+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Geriatrics","date":"2026-03-13T14:01:51+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-geriatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bgtc","sideBox":"Learn more about [BMC Geriatrics](http://bmcgeriatr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bgtc/default.aspx","title":"BMC Geriatrics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"67fd610c-8c31-4690-b6b4-bbc26bfbfee3","owner":[],"postedDate":"April 14th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-14T18:01:45+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-14 18:01:45","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9087979","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9087979","identity":"rs-9087979","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}