scholarly journals Construction of a risk prediction model for Alzheimer’s disease in the elderly population

BMC Neurology ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Lingling Wang ◽  
Ping Li ◽  
Ming Hou ◽  
Xiumin Zhang ◽  
Xiaolin Cao ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Health Status ◽  
Prediction Model ◽  
Genetic Risk ◽  
Elderly Population ◽  
Economic Status ◽  
The Elderly ◽  
Predictor Variables ◽  
Lasso Regression ◽  
Validation Set

Abstract Background Dementia is one of the greatest global health and social care challenges of the twenty-first century. The etiology and pathogenesis of Alzheimer’s disease (AD) as the most common type of dementia remain unknown. In this study, a simple nomogram was drawn to predict the risk of AD in the elderly population. Methods Nine variables affecting the risk of AD were obtained from 1099 elderly people through clinical data and questionnaires. Least Absolute Shrinkage Selection Operator (LASSO) regression analysis was used to select the best predictor variables, and multivariate logistic regression analysis was used to construct the prediction model. In this study, a graphic tool including 9 predictor variables (nomogram-see precise definition in the text) was drawn to predict the risk of AD in the elderly population. In addition, calibration diagram, receiver operating characteristic (ROC) curve and decision curve analysis (DCA) were used to verify the model. Results Six predictors namely sex, age, economic status, health status, lifestyle and genetic risk were identified by LASSO regression analysis of nine variables (body mass index, marital status and education level were excluded). The area under the ROC curve in the training set was 0.822, while that in the validation set was 0.801, suggesting that the model built with these 6 predictors showed moderate predictive ability. The DCA curve indicated that a nomogram could be applied clinically if the risk threshold was between 30 and 40% (30 to 42% in the validation set). Conclusion The inclusion of sex, age, economic status, health status, lifestyle and genetic risk into the risk prediction nomogram could improve the ability of the prediction model to predict AD risk in the elderly patients.

The Relationship Between The Perception of Elderly’s Health Status and Health Behaviors in Nakhon Nayok Province, Thailand

2019 ◽  
Vol 12 (1) ◽  
pp. 420-423
Author(s):  
Prapada Watcharanat ◽  
Prasong Tanpichai ◽  
Ravee Sajjasophon
Keyword(s):  
Regression Analysis ◽  
Health Status ◽  
Health Behaviors ◽  
Multiple Regression Analysis ◽  
Multiple Regression ◽  
Statistical Significance ◽  
The Elderly ◽  
Cross Sectional ◽  
Congenital Diseases ◽  
The Relationship

Purpose: This research aims to study the relationship between perception of elderly’s health and health behaviors in Nakhon Nayok province, Thailand Methods: This research was a cross-sectional study. The questionnaire was used to collect the data. This research was conducted in Nakhon Nayok province. The sample size was 270 which applied Taro Yamane's formula at a significant level 0.05. The descriptive statistics was implemented to describe the variables by presenting the frequency, percentage, mean and standard deviation. Furthermore, multiple regression analysis was applied to analyze the relationship between perception of elderly’s health and health behaviors. The statistical significance was considered to reject Hypothesis-null at < 0.05. Results: From a total of 270 people, more than 58.22% of the elderly perceived that they had moderate health conditions. Most elderly had congenital diseases (62.2%). The multiple regression analysis results showed that health status perception and health status perception when compared to their cohort related significantly to health behavior. Conclusion: The government should support the elderly on participation, trust, engagement, and cultural concern of the people in the community, which can contribute to promoting the physical, mental and social condition of the elderly.

scholarly journals The landscape of gene co-expression modules correlating with prognostic genetic abnormalities in AML

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Chao Guo ◽  
Ya-yue Gao ◽  
Qian-qian Ju ◽  
Chun-xia Zhang ◽  
Ming Gong ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Prediction Model ◽  
Hox Genes ◽  
Expression Profiles ◽  
Penalized Regression ◽  
Diagnostic Utility ◽  
Lasso Regression ◽  
Hub Genes ◽  
Npm1 Mutation ◽  
Genetic Abnormalities

Abstract Background The heterogenous cytogenetic and molecular variations were harbored by AML patients, some of which are related with AML pathogenesis and clinical outcomes. We aimed to uncover the intrinsic expression profiles correlating with prognostic genetic abnormalities by WGCNA. Methods We downloaded the clinical and expression dataset from BeatAML, TCGA and GEO database. Using R (version 4.0.2) and ‘WGCNA’ package, the co-expression modules correlating with the ELN2017 prognostic markers were identified (R2 ≥ 0.4, p < 0.01). ORA detected the enriched pathways for the key co-expression modules. The patients in TCGA cohort were randomly assigned into the training set (50%) and testing set (50%). The LASSO penalized regression analysis was employed to build the prediction model, fitting OS to the expression level of hub genes by ‘glmnet’ package. Then the testing and 2 independent validation sets (GSE12417 and GSE37642) were used to validate the diagnostic utility and accuracy of the model. Results A total of 37 gene co-expression modules and 973 hub genes were identified for the BeatAML cohort. We found that 3 modules were significantly correlated with genetic markers (the ‘lightyellow’ module for NPM1 mutation, the ‘saddlebrown’ module for RUNX1 mutation, the ‘lightgreen’ module for TP53 mutation). ORA revealed that the ‘lightyellow’ module was mainly enriched in DNA-binding transcription factor activity and activation of HOX genes. The ‘saddlebrown’ module was enriched in immune response process. And the ‘lightgreen’ module was predominantly enriched in mitosis cell cycle process. The LASSO- regression analysis identified 6 genes (NFKB2, NEK9, HOXA7, APRC5L, FAM30A and LOC105371592) with non-zero coefficients. The risk score generated from the 6-gene model, was associated with ELN2017 risk stratification, relapsed disease, and prior MDS history. The 5-year AUC for the model was 0.822 and 0.824 in the training and testing sets, respectively. Moreover, the diagnostic utility of the model was robust when it was employed in 2 validation sets (5-year AUC 0.743–0.79). Conclusions We established the co-expression network signature correlated with the ELN2017 recommended prognostic genetic abnormalities in AML. The 6-gene prediction model for AML survival was developed and validated by multiple datasets.

scholarly journals Oral health status in a group of the elderly population residing at home

Gerodontology ◽  
2011 ◽  
Vol 29 (2) ◽  
pp. e761-e767 ◽  
Author(s):  
Haviye Erverdi Nazliel ◽  
Nur Hersek ◽  
Murat Ozbek ◽  
Ergun Karaagaoglu
Keyword(s):  
Health Status ◽  
Oral Health ◽  
Elderly Population ◽  
The Elderly ◽  
Oral Health Status ◽  
At Home

scholarly journals Development of a Survival Model Based on Autophagy-Associated Genes for Predicting Prognosis of Gastric Cancer

2020 ◽  
Author(s):  
Wanli Yang ◽  
Lili Duan ◽  
Xinhui Zhao ◽  
Liaoran Niu ◽  
Yiding Li ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Regression Analysis ◽  
Prediction Model ◽  
Roc Curve ◽  
Survival Model ◽  
Differentially Expressed ◽  
Lasso Regression ◽  
Prognosis Prediction ◽  
Cellular Processes ◽  
Model Based

Abstract Background: Gastric cancer (GC) is one of lethal diseases worldwide. Autophagy-associated genes play a crucial role in the cellular processes of GC. Our study aimed to investigate and identify the prognostic potential of autophagy-associated genes signature in GC. Methods: RNA-seq and clinical information of GC and normal controls were downloaded from The Cancer Genome Atlas (TCGA) database. Then, the Wilcoxon signed-rank test was used to pick out the differentially expressed autophagy-associated genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed to investigate the potential roles and mechanisms of autophagy-associated genes in GC. Cox proportional hazard regression analysis and Lasso regression analysis were carried out to identify the overall survival (OS) related autophagy-associated genes, which were then collected to construct a predictive model. Kaplan-Meier method and receiver operating characteristic (ROC) curve were utilized to validate the accuracy of this model. Finally, a clinical nomogram was established by combining the clinical factors and autophagy-associated genes signature. Results: A total of 28 differentially expressed autophagy-associated genes were identified. GO and KEGG analyses revealed that several important cellular processes and signaling pathways were correlated with these genes. Through Cox regression and Lasso regression analyses, we identified 4 OS-related autophagy-associated genes (GRID2, ATG4D, GABARAPL2, and CXCR4) and constructed a prognosis prediction model. GC Patients with high-risk had a worse OS than those in low-risk group (5-year OS, 27.7% vs 38.3%; P=9.524e-07). The area under the ROC curve (AUC) of the prediction model was 0.67. The nomogram was demonstrated to perform better for predicting 3-year and 5-year survival possibility for GC patients with a concordance index (C-index) of 0.70 (95% CI: 0.65-0.72). The calibration curves also presented good concordance between nomogram-predicted survival and actual survival. Conclusions: We constructed and evaluated a survival model based on the autophagy-associated genes for GC patients, which may improve the prognosis prediction in GC.

scholarly journals Predictors of Assessment of Spondyloarthritis International Society (ASAS) Health Index in Axial Spondyloarthritis and Comparison of ASAS Health Index between Ankylosing Spondylitis and Nonradiographic Axial Spondyloarthritis: Data from the Catholic Axial Spondyloarthritis COhort (CASCO)

2019 ◽  
Vol 8 (4) ◽  
pp. 467 ◽  
Author(s):  
Hong Ki Min ◽  
Jennifer Lee ◽  
Ji Hyeon Ju ◽  
Sung-Hwan Park ◽  
Seung-Ki Kwok
Keyword(s):  
Regression Analysis ◽  
Health Status ◽  
Ankylosing Spondylitis ◽  
Alcohol Consumption ◽  
International Society ◽  
Structural Damage ◽  
Axial Spondyloarthritis ◽  
Economic Status ◽  
Linear Regression Analysis ◽  
Health Index

The Assessment of Spondyloarthritis International Society (ASAS) health index (HI) is a novel tool for approaching disability, health, and functioning in spondyloarthritis (SpA). In the present study we compared ASAS HI between patients with ankylosing spondylitis (AS) and those with nonradiographic axial SpA (nr-axSpA). In addition, we identified predictors of ASAS HI. We designed this cross-sectional study using data from the Catholic Axial Spondyloarthritis COhort (CASCO), a prospective cohort from a single tertiary hospital. We compared baseline characteristics, including ASAS HI, between AS and nr-axSpA, and determined the frequency of each item constituting the ASAS HI. We used linear regression analysis to identify factors associated with ASAS HI. Total of 357 patients with axSpA—261 with AS and 96 with nr-axSpA—were included in analysis. AS patients were older and had higher ASAS HI than nr-axSpA. Among ASAS HI items, pain (item No. 1) and energy/drive (item No. 5) were the most common areas for which axSpA patients experienced discomfort. ASAS HI correlated with other SpA-related parameters such as BASDAI, ASDAS, and BASFI. Multivariable regression analysis of the axSpA group showed that high NSAID intake and mSASSS were positively associated with ASAS HI, whereas higher economic status and alcohol consumption were negatively associated with ASAS HI. Results were consistent in the AS group on subgroup analysis, whereas alcohol consumption was the only factor significantly associated with ASAS HI in the nr-axSpA group. In the present cohort study, patients with AS had poorer health status (higher ASAS HI) than those with nr-axSpA. Items proposed by AS patients (items No. 1 and 5) were the most frequently checked areas as axSpA patients feel discomfort, and this support that ASAS HI could practically assess actual discomfort of axSpA patient. ASAS HI was well correlated with known disease parameters, including activity, function, and quality of life; therefore, ASAS HI could be used in the future to represent the health status of SpA in a systematic way. Spinal structural damage (higher mSASSS), high NSAID intake, alcohol consumption, and economic status were predictors of ASAS HI in patients with axSpA, especially those with AS.

scholarly journals Self-Reported Food Insecurity and Depression among the Older Population in South Africa

Psych ◽  
2019 ◽  
Vol 2 (1) ◽  
pp. 34-43 ◽  
Author(s):  
Ghose Bishwajit ◽  
Komlan Kota ◽  
Amos Buh ◽  
Sanni Yaya
Keyword(s):  
Mental Health ◽  
South Africa ◽  
Regression Analysis ◽  
Food Security ◽  
Food Insecurity ◽  
Elderly Population ◽  
The Elderly ◽  
Cross Sectional ◽  
Explanatory Variables ◽  
Multivariable Regression

South Africa represents one of the most rapidly aging countries in sub-Saharan Africa with a rising burden of age-related psychological morbidities. Despite having one of the highest human development scores in the region, the country faces serious poverty and food insecurity related challenges. Previous studies have shown a positive association between food insecurity and poor mental health among the adult population, however there is no systematic evidence on this association among the elderly population in an African setting. In the present study, we aimed to address this research gap by analyzing cross-sectional data (n = 931) on the over-50 population (>50 years) from the SAGE (Study on global AGEing and adult health) Well-Being of Older People Study (WOPS) of the World Health Organization, conducted between 2010 and 2013. The outcome variable was perceived depression and the explanatory variables included several sociodemographic factors including self-reported food insecurity. The independent associations between the outcome and explanatory variables were measured using multivariable regression analysis. Results showed that close to a quarter of the population (22.6%, 95% CI = 21.4, 24.7) reported having depression in the last 12 months, with the percentage being markedly higher among women (71.4%). In the multivariable regression analysis, self-reported food insecurity was found to be the strongest predictor of depression among both sexes. For instance, severe food insecurity increased the odds of depression by 4.805 [3.325, 7.911] times among men and by 4.115 [2.030, 8.341] times among women. Based on the present findings, it is suggested that national food security programs focus on promoting food security among the elderly population in an effort to improve their mental health status. Nonetheless, the data were cross-sectional and the associations can’t imply causality.

Sleep Quality and Cognitive Function in the Elderly Population

2020 ◽  
Author(s):  
Seyed Valiollah Mousavi ◽  
Elham Montazar ◽  
Sajjad Rezaei ◽  
Shima Poorabolghasem Hosseini
Keyword(s):  
Regression Analysis ◽  
Cognitive Function ◽  
Sleep Quality ◽  
Sleep Disorders ◽  
Elderly Population ◽  
Sleep Latency ◽  
The Elderly ◽  
Poor Sleep ◽  
Subjective Sleep Quality ◽  
Subjective Sleep

Background and Objective: Physiological process of sleep is considered as one of the influential factors of human’s health and mental functions, especially in the elderly. This research aimed at studying the association between sleep quality and the cognitive functions in the elderly population. Materials and Methods: A total of 200 elderly people (65 years and older) who were the members of retirees associa-tion in Mashhad, Iran, participated in this cross-sectional study. The participants were asked to answer the questionnaire of Pittsburgh Sleep Quality Index (PSQI) and Montreal Cognitive Assessment (MoCA) test. Correlation between the total scores of PSQI and MoCA was evaluated by Pearson correlation coefficient. In order to predict the cognitive func-tion based on different aspects of PSQI, multiple regression analysis by hierarchical method was used after removing confounding variables. Results: A significant association was found between PSQI and MoCA (P < 0.001, r = -0.55) suggesting that the com-ponents of use of sleeping medication (P < 0.001, r = -0.47), sleep disorders (P < 0.001, r = -0.37), sleep latency (P < 0.001, r = -0.34), subjective sleep quality (P < 0.001, r = -0.32), sleep duration (P < 0.001, r = -0.27), sleep effi-ciency (P < 0.001, r = -0.26), and daytime dysfunction (P < 0.001, r = -0.15) had significant negative correlation with cognitive function, and the four components of subjective sleep quality (P = 0.010, β = -0.15), sleep latency (P = 0.040, β = -0.13), sleep disorders (P = 0.010, β = -0.26), and use of sleeping medication (P = 0.010, β = -0.26) played a role in prediction of cognitive function in regression analysis. Conclusion: Poor sleep quality, sleep latency, insomnia, sleep breathing disorder, and use of sleeping medication play a determining role in cognitive function of the elderly. Thus, taking care of the sleep health is necessary for the elderly.

Health status of the elderly population among four primitive tribes of Orissa, India: A clinico-epidemiological study

2009 ◽  
Vol 42 (1) ◽  
pp. 53-59 ◽  
Author(s):  
Anna S. Kerketta ◽  
Gandham Bulliyya ◽  
Bontha V. Babu ◽  
Surendra S. S. Mohapatra ◽  
Rabi N. Nayak
Keyword(s):  
Health Status ◽  
Epidemiological Study ◽  
Elderly Population ◽  
The Elderly

Rural versus Urban Differences in Health Dependence among the Elderly Population

1989 ◽  
Vol 28 (2) ◽  
pp. 141-156 ◽  
Author(s):  
John A. Krout
Keyword(s):  
New York ◽  
Regression Analysis ◽  
Multiple Regression Analysis ◽  
Elderly People ◽  
Elderly Population ◽  
Central City ◽  
The Elderly ◽  
Elderly Persons ◽  
Community Settings ◽  
Elderly Health

This article examines data on rural versus urban differences in health dependency for a random sample of 600 western New York elderly people residing in a range of community settings from farm areas to a metropolitan central city. Data were collected via personal interviews, and health dependency was operationalized as an index composed of nine criterion measures. The nonmetropolitan elderly population is found to be less health dependent as are elderly persons who are younger, white, married, and have higher incomes. However, the rural/urban variable is not a significant predictor of health dependency when included in a multiple-regression analysis. These findings do not support the rural elderly health disadvantage argument and serve to illustrate some of the shortcomings of existing research on this topic.

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