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Background: Both frailty and elevated serum neurofilament light chain (sNfL) levels are linked to cognitive impairment. However, evidence of their relationship is lacking, and whether it was mediated by renal function was unknown. This study aimed to investigate the association between frailty and sNfL levels in a representative U.S. population, and to explore the potential mediating role of estimated glomerular filtration rate (eGFR) in this relationship. Methods: Data from 1,782 participants aged 20-75 years in the 2013-2014 National Health and Nutrition Examination Survey (NHANES) were analyzed. Frailty was assessed using a 49-item frailty index, and participants were categorized as non-frail, pre-frail, or frail. sNfL levels were measured using acoustic emission technology. Multivariable linear regression models and restricted cubic spline analyses were employed to examine the associations between frailty, eGFR, and sNfL levels. Mediation analysis was conducted to evaluate the role of eGFR in the frailty-sNfL relationship. Results: The prevalence of pre-frailty and frailty was 45.39 and 11.60%, respectively. A significant positive association was observed between frailty score and sNfL levels (adjusted β: 39.97, SE: 11.07, p = 0.003), with a linear relationship confirmed by restricted cubic spline analysis. Frail individuals had significantly higher sNfL levels compared to non-frail participants (adjusted β: 11.86, SE: 5.42, p = 0.04). eGFR was negatively associated with sNfL levels (adjusted β: -0.23, SE: 0.05, p < 0.001). Mediation analysis revealed that eGFR accounted for 12.52% of the total effect of frailty on sNfL levels (p < 0.0001). Conclusion: This study demonstrates a significant association between frailty and elevated sNfL levels in a representative U.S. population, with eGFR partially mediating this relationship. These findings suggest that sNfL may serve as a potential biomarker for frailty-related neuronal damage and highlight the importance of kidney function in this association. Further research is warranted to explore the clinical implications of these findings in frailty assessment and management strategies. [ABSTRACT FROM AUTHOR]

Many researchers have examined the impact of detailed soil spatial information on hydrological modelling due to the fact that such information serves as important input to hydrological modelling, yet is difficult and expensive to obtain. Most research has focused on the effects at single scales; however, the effects in the context of spatial aggregation across different scales are largely missing. This paper examines such effects by comparing the simulated runoffs across scales from watershed models based on two different levels of soil spatial information: the 10-m-resolution soil data derived from the Soil-Land Inference Model (SoLIM) and the 1:24000 scale Soil Survey Geographic (SSURGO) database in the United States. The study was conducted at three different spatial scales: two at different watershed size levels (referred to as full watershed and sub-basin, respectively) and one at the model minimum simulation unit level. A fully distributed hydrologic model (WetSpa) and a semi-distributed model (SWAT) were used to assess the effects. The results show that at the minimum simulation unit level the differences in simulated runoff are large, but the differences gradually decrease as the spatial scale of the simulation units increases. For sub-basins larger than 10 km2 in the study area, stream flows simulated by spatially detailed SoLIM soil data do not significantly vary from those by SSURGO. The effects of spatial scale are shown to correlate with aggregation effect of the watershed routing process. The unique findings of this paper provide an important and unified perspective on the different views reported in the literature concerning how spatial detail of soil data affects watershed modelling. Different views result from different scales at which those studies were conducted. In addition, the findings offer a potentially useful basis for selecting details of soil spatial information appropriate for watershed modelling at a given scale. Copyright © 2011 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Background: Periodontitis can be avoided with a healthy lifestyle. However, studies have only looked at one lifestyle, ignoring the connection between lifestyle patterns and periodontitis. The purpose of this study was to look at the association between modifiable lifestyle patterns and periodontitis., Methods: Data were obtained from the National Health and Nutrition Examination Survey in 2009-2010 and 2011-2012. Smoke, drink, exercise, sleep duration, oral exams, and self-rated oral health were all lifestyle factors. The CDC/AAP classification/case definition was used to evaluate periodontitis. Drawing upon latent class analysis, distinct patterns of lifestyle were identified, with each participant exclusively affiliated with a single pattern. The association between lifestyle classes and periodontitis was then examined using ordinal logistic regression., Results: 4686 (52%) of the total 9034 participants, with a mean age of 54.08, were women. Three lifestyle latent classes were found by fitting 2-10 models: "Class 1" (52%), " Class 2" (13%), and " Class 3" (35%). The "Class 1" displayed a prevalence of oral examination (75%), favorable self-rated oral health (92%), and engagement in physical activity (50%). The 'Class 2' exhibited the lowest alcohol consumption (64%) and smoking rates (73%) but the highest prevalence of physical inactivity (98%). The 'Class 3' showed a tendency for smoking (72%), alcohol consumption (78%), shorter sleep duration (50%), absence of oral examinations (75%), and suboptimal self-rated oral health (68%). The influencing variables for the latent classes of lifestyle were age, education, and poverty level. Periodontitis risk may rise by 24% for each additional unhealthy lifestyle practiced by participants (OR = 1.24, 95% CI: 1.18-1.31). The 'Class 3' (OR = 1.80, 95% CI: 1.52-2.13) had a greater risk of periodontitis compared to the 'Class 1'., Conclusions: Our analysis revealed that unhealthy lifestyle patterns are associated with periodontitis. These different lifestyle patterns need to be taken into account when developing public health interventions and clinical care., (© 2024. The Author(s).)