scholarly journals Nocturia, Insomnia Symptoms and Mortality among Older Men: The Health, Aging and Body Composition Study

2016 ◽  
Vol 12 (06) ◽  
pp. 789-796 ◽  
Author(s):  
Yohannes W. Endeshaw ◽  
Ann V. Schwartz ◽  
Katie Stone ◽  
Paolo Caserotti ◽  
Tamara Harris ◽  
...  
Keyword(s):  
Body Composition ◽  
Older Men ◽  
Body Composition Study ◽  
Health Aging ◽  
Composition Study ◽  
Insomnia Symptoms

Inflammatory Markers and Incident Fracture Risk in Older Men and Women: The Health Aging and Body Composition Study

2007 ◽  
Vol 22 (7) ◽  
pp. 1088-1095 ◽  
Author(s):  
Jane A Cauley ◽  
Michelle E Danielson ◽  
Robert M Boudreau ◽  
Kimberly Y Z Forrest ◽  
Joseph M Zmuda ◽  
...  
Keyword(s):  
Body Composition ◽  
Fracture Risk ◽  
Inflammatory Markers ◽  
Older Men ◽  
Men And Women ◽  
Body Composition Study ◽  
Incident Fracture ◽  
Health Aging ◽  
Composition Study ◽  
Older Men And Women

scholarly journals All-cause Dementia Prediction by Machine Learning: The Health, Aging, and Body Composition Study

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 487-487
Author(s):  
Chenkai Wu ◽  
Xurui Jin
Keyword(s):  
Machine Learning ◽  
Body Composition ◽  
Risk Prediction ◽  
Risk Score ◽  
Body Composition Study ◽  
C Statistic ◽  
Dementia Risk ◽  
Health Aging ◽  
Composition Study ◽  
Traditional Approaches

Abstract There are several shortcomings of the currently available risk prediction models for dementia. We developed a risk prediction model for dementia using machine-learning approach and compared its performance with traditional approaches. Data were from the Health, Aging, and Body Composition Study, comprising 3,075 older adults (at least 70 years). Dementia was defined as (1) use of a prescribed dementia medication, (2) adjudicated dementia diagnosis, or (3) a race-stratified cognitive decline>1.5 SDs from the baseline mean. We selected 275 predictors collected from questionnaires, imaging data, performance testing, and biospecimen. We used random survival forest (RSF) to build the full model and rank the importance of predictors. Subsequently, we built parsimonious models with top-20 predictors using RSF and Cox regression. A dementia risk score was developed using top-ranked variables. We used the C-statistic for performance evaluation. Over a median of 11.4 years of follow-up, 659 dementias (21.4%) occurred. The RSF model (both including all and top-20 variables) showed a higher C-statistic than the regression model. Digit symbol score, physical performance battery, finger tapping score, weight change since age 50, serum adiponectin, and APOE genotype were the top-6 variables. We created a dementia risk score (0-10) using the top-6 variables. A 1-unit increase in the risk score was associated with an 8% higher risk of dementia. The risk score demonstrated good discrimination (C-statistic=0.75). Machine learning methods offered improvement over traditional approaches in predicting dementia. The risk prediction score derived from a parsimonious model had good prediction performance.

scholarly journals Glycemic Status and Incident Heart Failure in Elderly Without History of Diabetes Mellitus: The Health, Aging, and Body Composition Study

2009 ◽  
Vol 15 (7) ◽  
pp. 593-599 ◽  
Author(s):  
Andreas Kalogeropoulos ◽  
Vasiliki Georgiopoulou ◽  
Tamara B. Harris ◽  
Stephen B. Kritchevsky ◽  
Douglas C. Bauer ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Heart Failure ◽  
Body Composition ◽  
Glycemic Status ◽  
Body Composition Study ◽  
Health Aging ◽  
History Of ◽  
Incident Heart Failure ◽  
Composition Study
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