OBJECTIVE: There are currently no models for predicting hip fractures after stroke. This study wanted to investigate the risk factors leading to hip fracture in stroke patients and to establish a risk prediction model to visualize this risk.
METHODS: We reviewed 439 stroke patients with or without hip fractures admitted to the Affiliated Hospital of Xuzhou Medical University from June 2014 to June 2017 as the training set, and collected 83 patients of the same type from the First Affiliated Hospital of Harbin Medical University and the Affiliated Hospital of Xuzhou Medical University from June 2020 to June 2023 as the testing set. Patients were divided into fracture group and non-fracture group based on the presence of hip fractures. Multivariate logistic regression analysis was used to screen for meaningful factors. Nomogram predicting the risk of hip fracture occurrence were created based on the multifactor analysis, and performance was evaluated using receiver operating characteristic curve (ROC), calibration curves, and decision curve analysis (DCA). A web calculator was created to facilitate a more convenient interactive experience for clinicians.
RESULTS: In training set, there were 35 cases (7.9 %) of hip fractures after stroke, while in testing set, this data was 13 cases (15.6 %). In training set, univariate analysis showed significant differences between the two groups in the number of falls, smoking, hypertension, glucocorticoids, number of strokes, Mini-Mental State Examination (MMSE), visual acuity level, National Institute of Health stroke scale (NIHSS), Berg Balance Scale (BBS), and Stop Walking When Talking (SWWT) (P<0.05). Multivariate analysis showed that number of falls [OR=17.104, 95 % CI (3.727-78.489), P = 0.000], NIHSS [OR=1.565, 95 % CI (1.193-2.052), P = 0.001], SWWT [OR=12.080, 95 % CI (2.398-60.851), P = 0.003] were independent risk factors positively associated with new fractures. BMD [OR = 0.155, 95 % CI (0.044-0.546), P = 0.012] and BBS [OR = 0.840, 95 % CI (0.739-0.954), P = 0.007] were negatively associated with new fractures. The area under the curve (AUC) of nomogram were 0.939 (95 % CI: 0.748-0.943) and 0.980 (95 % CI: 0.886-1.000) in training and testing sets, respectively, and the calibration curves showed a high agreement between predicted and actual status with an area under the decision curve of 0.034 and 0.109, respectively.
CONCLUSIONS: The number of falls, fracture history, low BBS score, high NIHSS score, and positive SWWT are risk factors for hip fracture after stroke. Based on this, a nomogram with high accuracy was developed and a web calculator (https://stroke.shinyapps.io/DynNomapp/) was created.

UNASSIGNED: The issue of falls poses a significant threat to the health of the elderly population. Although statins can cause myopathy, which implies that they may cause balance problems and increase the risk of falling, this has not been tested. Our objective was to assess whether the use of statins is linked to a higher risk of falls.
UNASSIGNED: A cross-sectional survey study and Mendelian randomization (MR) study were conducted to examine whether the use of statins was associated with an increased risk of falling and balance problems. The cross-sectional study included 2,656 participants from the US population (NHANES) who reported information on balance and falling problems in the past year and their use of statins. Univariate and multivariate logistic regression models were used to investigate the association between statin use and the likelihood of falling or experiencing balance problems. The MR study identified five Single Nucleotide Polymorphisms (SNPs) that predict statin use across five ancestry groups: Admixed African or African, East Asian, European, Hispanic, and South Asian. Additionally, SNPs predicting the risk of falls were acquired from the UK Biobank population. A two-sample MR analysis was performed to examine whether genetically predicted statin use increased the risk of falls.
UNASSIGNED: The use of statins was found to be associated with an increased likelihood of balance and falling problems (balance problem, OR 1.25, 95%CI 1.02 to 1.55; falling problem, OR 1.27, 95%CI 1.03-1.27). Subgroup analysis revealed that patients under the age of 65 were more susceptible to these issues when taking statins (balance problem, OR 3.42, 95%CI 1.40 to 9.30; falling problem, OR 5.58, 95%CI 2.04-15.40). The MR analysis indicated that the use of statins, as genetically proxied, resulted in an increased risk of falling problems (OR 1.21, 95% CI 1.1-1.33).
UNASSIGNED: Our study found an association between the use of statins and an increased risk of balance problems and falls in adults over 40 years old, and the MR study result suggested statin use increased risk of falls. The risk was higher in participants under 65 years old compared to those over 65 years old.

OBJECTIVE: The ESPEN and the EASO recently developed consensus criteria for sarcopenic obesity (SO), employing the skeletal muscle mass to weight (SMM/W) ratio. Emerging evidence suggests that adjusting skeletal muscle mass for body mass index (SMM/BMI) could enhance the predictive accuracy for health outcomes. We aimed to validate the ESPEN/EASO criteria and explore the potential benefits of the SMM/BMI adjustment in predicting falls among older adults in Western China.
METHODS: We conducted a multicenter, cross-sectional study and included community-dwelling older adults. The diagnosis of SO was determined using the standard ESPEN/EASO consensus criteria (SOESPEN) and a modified version adjusting SMM/BMI (SOESPEN-M). The associations of SOESPEN, SOESPEN-M, and their components with falls were analyzed.
RESULTS: Among the 1353 participants, the prevalence of SO was 13.2 % (SOESPEN) and 11.4 % (SOESPEN-M), which increased with age and higher BMI levels. Within participants with a normal BMI, 4.2 % and 6.2 % were found to have SOESPEN and SOESPEN-M, respectively. SMM/W and SMM/BMI negatively correlated with fall risk (p=0.042 and p=0.021, respectively). Upon adjusting for confounders, only SOESPEN was significantly associated with falls (odds ratios [OR] 1.61, 95 % confidence interval [CI] 1.08 to 2.40), whereas the association for SOESPEN-M did not achieve significance (OR 1.55, 95 % CI 0.99 to 2.43).
CONCLUSIONS: This research validated the ESPEN/EASO criteria (SOESPEN) and their modified version (SOESPEN-M) among community-dwelling older adults in Western China. The SMM/BMI adjustment appears to offer a lower estimate of SO prevalence, with only SOESPEN showing a significant association with falls.

BACKGROUND: As a geriatric syndrome, sarcopenia may exacerbate static postural control and increase fall risk among older adults. The Romberg test, a simple method to assess static postural control, has the potential to predict fall, but has rarely been used to assess static postural control and fall risk in sarcopenic older adults.
OBJECTIVE: How does sarcopenia increase fall risk by affecting static postural control?
METHODS: Forty-four older adults performed the Romberg test and were included for analyses. Romberg parameters, including Center of Pressure (CoP), Center of Mass (CoM) and Displacement Angle (DA), were collected under eyes-open/eyes-closed conditions. Sarcopenia was defined according to the Asian Working Group for Sarcopenia 2019 criteria. Fall risk was assessed using the Morse Elderly Fall Risk Assessment Scale (MFS), and fear of falling was evaluated using the Falls Efficacy Scale-International (FES-I). Multivariate linear regression models were conducted to examine the associations of sarcopenia with Romberg test parameters, fear of falling, and fall risk.
RESULTS: Sarcopenic older adults had higher scores of both fear of falling and fall risk (P<0.001 and =0.006, respectively), and worse static postural control parameters (P values ranging from <0.001-0.043) than healthy controls, demonstrated by the multivariate linear regression models. Most of the Romberg test parameters were significantly associated with fear of falling score, especially under eyes-closed condition, and fear of falling was further associated with higher fall risk score (β=0.90, P=0.001). Meanwhile, the presence of sarcopenia also significantly increased fall risk score (β=10.0, P<0.001).
CONCLUSIONS: Sarcopenia may increase fall risk in older adults via worsen static postural control ability and increase fear of falling. Paying attention and making efforts to prevent sarcopenia may help to alleviate postural control dysfunction, decrease fear of falling, so as to reduce fall risk and prevent severe injuries caused by fall accidents.

BACKGROUND: With the advancement of global aging, there has been an increase in patients with dysmobility syndrome (DS), often accompanied by osteoporosis, sarcopenia, and sarcopenic obesity. The objective of this study was to evaluate the application value of the body mass frequency index (BMFI) in older patients with DS by comprehensively analyzing the differences in BMFI between community-dwelling older subjects using medical and engineering methods.
METHODS: A cross-sectional study was conducted to recruit community-dwelling older subjects aged 60-90 years. Various assessments and measurements were performed, including basic information collection, gait analysis, bone mineral density (BMD) and body composition measurement, fall and fracture risk et al. Gait analysis and body mass index (BMI) are in the established model to calculate BMFI. Analysis of BMFI was performed in community-dwelling older subjects, and the specificity and threshold of BMFI in predicting dysmobility syndrome (DS) were further analyzed.
RESULTS: Significant differences in BMFI were observed between older adults with DS and those without DS. BMFI in older people was associated with bone quality, fracture risk, body fat percentage, appendicular skeletal muscle mass index (ASMI), grip strength, and speed. The odds ratio (OR) and 95 % confidence interval (CI) for BMFI in the non-DS and DS groups were 0.823 (0.743-0.901), respectively. Receiver operating characteristic (ROC) analysis demonstrated that BMFI had predictive value in distinguishing non-DS from DS (AUC = 0.669) (P < 0.05). The optimal threshold for predicting non-DS and DS was found to be 16.04 (sensitivities = 0.483, specificities = 0.774).
CONCLUSIONS: The measurement of BMFI has demonstrated disparities in musculoskeletal status among older adults with and without DS. Notably, BMFI exhibits a unique predictive capacity for DS among the elderly population.

暂无摘要。

BACKGROUND: Population aging is accelerating, particularly in Asian countries. Falls are the leading cause of unintentional injuries in the elderly over 60 years old in China. Hence, it is crucial to anticipate the risk factors associated with fall risk. We aimed to explore whether oral frailty and fall risk were reciprocally related and whether nutrition mediated their association.
METHODS: From October 2022 to March 2023, a total of 409 elderly individuals from the Yangzhou community were selected using the convenience sampling method. Cross-sectional data on older adults\' oral frailty, nutrition, and fall risk were collected using questionnaires. Data analysis was performed using SPSS 27.0 and PROCESS macro.
RESULTS: The fall risk score was 1.0 (ranging from 0 to 4.0), with 107 cases (26.2%) identified as being at risk of falling. Spearman correlation analysis revealed a positive correlation between oral frailty and the risk of falls (rs = 0.430, P < 0.01). Nutrition was found to have a negative correlation with both oral frailty and fall risk (rs=-0.519、-0.457, P < 0.01). When controlling for covariates, it was observed that nutrition mediated the relationship between oral frailty and falls. The mediating effect value accounted for 48.8% of the total effect (P < 0.01).
CONCLUSIONS: Oral frailty was significantly associated with fall risk, and nutrition might be a mediating factor for adverse effects of oral frailty and fall risk. Enhancing the nutrition of older individuals is a vital approach to mitigating fall risk among those with oral frailty.

OBJECTIVE: Hip fracture and falls are significant health concerns. Handgrip strength (HGS) is closely associated with overall muscle strength and physical health. However, the longitudinal relationship between HGS and the risk of hip fractures and falls remains unclear, particularly regarding gender differences. This longitudinal study aimed to investigate the association between HGS and the risk of hip fracture and falls in individuals aged 45 years and above, considering gender-specific differences over a 4-year period.
METHODS: This study included 10,092 participants (4471 men and 5621 women) aged 45 years and above from the China Health and Retirement Longitudinal Study (CHARLS). Incidents of hip fractures and falls were recorded during a 4-year follow-up, along with various demographic and clinical factors. Participants were categorized into five groups based on their HGS quintiles. Logistic regression models were employed to estimate adjusted odds ratios (ORs) and 95% confidence intervals (CIs) to assess the relationship between HGS and hip fracture/fall risk.
RESULTS: During the 4-year follow-up period, 223 cases of hip fracture (2.2%) and 1831 cases of falls (18.1%) were documented. Notably, higher HGS demonstrated a strong inverse association with the risk of hip fracture in both males and females (p < 0.05). In comparison to the lowest HGS quintile, the adjusted odds ratios (ORs) for hip fracture were 0.46 (0.27-0.78) for the total population, 0.4 (0.19-0.81) for males and 0.48 (0.23-0.98) for females in the highest HGS quintile. Furthermore, a profound and statistically significant negative correlation between HGS and falls was detected (p < 0.05). The adjusted ORs for falls in the highest HGS quintile, compared to the lowest quintile, were 0.62 (0.51-0.76) in the overall population, 0.59 (0.44-0.78) in males, and 0.78 (0.62-0.99) in females.
CONCLUSIONS: Our findings highlight the significant inverse association between HGS and the risk of hip fracture and falls in both males and females aged 45 years and above. Assessing handgrip strength may serve as a valuable tool for predicting fracture and fall risk.

UNASSIGNED: To develop and internally validate a prediction model for identifying patients with hematologic diseases of fall risk.
UNASSIGNED: This is a prospective cohort study from a prospective collection of data for 6 months. We recruited 412 patients with hematologic diseases in medical institutions and home environment of China. The outcome of the prediction model was fall or not. These variables were filtered via univariable logistic analysis, LASSO, and multivariable logistic analysis. We adopt an internal validation method of K-fold cross validation. The area under the ROC curve and the H-L test were used to evaluate the discrimination and calibration of the model.
UNASSIGNED: Five influencing factors were identified multivariable logistic regression analysis. The established model equation is as follows: the H-L goodness-of-fit test of the model p > 0.05. The area under the ROC curve of train is 0.957 (95% CI: 0.936 ~ 0.978), and the area under the ROC curve of test is 0.962 (95% CI: 0.884 ~ 1), so the model calibration and discriminant validity are good.
UNASSIGNED: Our equation has good sensitivity and specificity in predicting the fall risk of patients with hematologic diseases, and has certain positive significance for clinical assessment of their fall risk.
UNASSIGNED: ChiCTR2200063940.

UNASSIGNED: Frailty and hypertension often coexist in older adults, which may lead to fall risks. This study aimed to examine the relationship between frailty status, hypertension, and fall risk.
UNASSIGNED: In this cross-sectional study, a total of 401 older adults were conveniently recruited from communities in Wuxi, China, between September 2022 and November 2022. The fall risk self-assessment checklist from the Stopping Elderly Accidents, Deaths & Injuries (STEADI) Toolkit was used to evaluate their fall risks. The FRAIL scale questionnaire was used to assess frailty status. Participants\' demographic information and comorbidities were collected. Multivariate logistic regression, generalized additive model, and smooth curve fitting were used to analyze the association between frailty, hypertension, and fall risk.
UNASSIGNED: Frailty had a strong association with increased prevalence of fall risk among the participants (OR 8.52, 95% CI 3.21-22.57; P < 0.001). Hypertension significantly increased the fall risk among older adults (OR 1.87, 95% CI 1.11-3.13; P = 0.019). The group with hypertension and frailty had the highest prevalence of fall risk (OR 12.24, 95% CI 3.51-42.65). Smooth curve fitting showed a nonlinear association between frailty and fall risk in hypertension status. In the progress of pre-frailty to frailty status, a higher tendency to fall was found among older adults with hypertension.
UNASSIGNED: Frailty status and hypertension independently and jointly influenced the increased prevalence of fall risk. Enhanced frailty and hypertension management may help decrease fall risk among this population.