Utilizing nomograms to predict prevalent vertebral fracture risk: An analysis of dysmobility syndrome in a community-dwelling population

Establishment and validation of a nomogram for predicting the risk of hip fracture in patients with stroke: A multicenter retrospective study

PURPOSE

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.

PATIENTS AND 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.

The association of body composition and fat distribution with dysmobility syndrome in community-dwelling older adults: Bushehr Elderly Health (BEH) program

BACKGROUND AND OBJECTIVE

Dysmobility Syndrome (DS) is characterized as an accumulation of clinical risk factors for functional disability, such as osteoporosis, sarcopenia, and obesity. Neurological disorders that affect the motor and sensory systems can also contribute to the condition, resulting in gait and muscle strength disturbances, as well as a history of falls and fractures. The study aimed to determine the association between fat distribution in different body areas and the odds of older adults developing DS, as there is still uncertainty about the accumulation of fat in which area is most closely linked to the condition.

METHODS

This cross-sectional study was conducted according to the data from the second phase of the Bushehr Elderly Health Cohort (BEH). Dysmobility Syndrome was defined based on the co-occurrence of at least three outcomes of its criteria. Body composition was measured using dual-energy X-ray absorptiometry (DXA) and anthropometric studies. For evaluating the relationship, multivariate logistic regression and adjusted univariate linear regression were used.

RESULTS

Of 2,359 who were recruited in the study, 1,277 participants (54.13%) had DS. According to the final logistic regression model in the limb region, FM and FM to FFM ratios were significantly associated with DS [OR (95%CI) = 1.04 (1.02 to 1.05), and 3.42 (1.95 to 5.99), respectively]. Also, In the trunk region, the FM and FM to FFM ratio were significantly related to the odds of DS, although this relationship was weaker than in the limbs region [OR (95%CI) = 1.02 (1.00 to 1.03), and 2.45 (1.36 to 4.39), respectively].

CONCLUSION

Our findings indicate that a higher regional and whole-body amount of fat mass rather than fat-free mass is closely linked to an increased risk of DS, particularly in the elderly population. Notably, higher fat mass in the limbs (especially in the legs) is associated with greater odds of DS, while a higher android-to-gynoid fat mass ratio is associated with lower DS risk. Screening fat mass distribution in older individuals can be a valuable strategy for promptly diagnosing DS, implementing interventions to prevent disabilities, and improving their quality of life.

Causal effects of homocysteine levels on the components of sarcopenia: A two-sample mendelian randomization study

Background: Currently, it is unclear whether there is a causal association between genetically predicted plasma homocysteine (Hcy) levels and the risk of sarcopenia. We performed a Mendelian randomization (MR) study to assess the association between circulating Hcy levels and the components [grip strength, walking pace, and appendicular lean mass (ALM)] of sarcopenia. Methods: Independent single nucleotide polymorphisms (SNPs) significantly associated with plasma Hcy levels served as instrumental variables. Summary-level data regarding the components of sarcopenia. Were obtained from the UK Biobank. Inverse variance weighted (IVW) as the primary method was used for Mendelian randomization (MR) analysis. We also use four models, weighted median, MR-Egger regression, Maximum likelihood, and Penalised weighted median, as supplementary methods to IVW. The MR-Egger intercept test, Cochran's Q test, and "leave-one-out" sensitivity analysis were performed to evaluate the horizontal pleiotropy, heterogeneities, and stability of the causal association between Hcy levels and the components of sarcopenia. Results: The IVW-MR analysis suggested significant negative associations of increased plasma Hcy levels with grip strength (right: effect = -0.036, SE = 0.032, p = 5.53E-4; left: effect = -0.045, SE = 0.010, p = 1.45E-5), walking pace (effect = -0.038, SE = 0.011, p = 3.18E-4), and ALM (effect = -0.058, 0.013, p = 1.03E-5). However, there were no significant associations of decreased plasma Hcy levels with grip strength (right: effect = 0.005, SE = 0.021, p = 0.82; left: effect = -0.006, SE = 0.014, p = 0.64), walking pace (effect = 0.01, 0.020, p = 0.61), or ALM (effect = -0.034, SE = 0.018, p = 0.06).The accuracy and robustness of these findings were confirmed by sensitivity tests. Conclusion: Increased circulating Hcy levels were associated with lower grip strength, slower walking pace, and decreased ALM.