scholarly journals Educational attainment and late life telomere length in the Health, Aging and Body Composition Study

2013 ◽  
Vol 27 ◽  
pp. 15-21 ◽  
Author(s):  
Nancy Adler ◽  
Matthew S. Pantell ◽  
Aoife O’Donovan ◽  
Elizabeth Blackburn ◽  
Richard Cawthon ◽  
...  
Keyword(s):  
Body Composition ◽  
Educational Attainment ◽  
Telomere Length ◽  
Late Life ◽  
Body Composition Study ◽  
Health Aging ◽  
Composition Study

scholarly journals Cumulative Inflammatory Load Is Associated with Short Leukocyte Telomere Length in the Health, Aging and Body Composition Study

PLoS ONE ◽  
2011 ◽  
Vol 6 (5) ◽  
pp. e19687 ◽  
Author(s):  
Aoife O'Donovan ◽  
Matthew S. Pantell ◽  
Eli Puterman ◽  
Firdaus S. Dhabhar ◽  
Elizabeth H. Blackburn ◽  
...  
Keyword(s):  
Body Composition ◽  
Telomere Length ◽  
Leukocyte Telomere Length ◽  
Body Composition Study ◽  
Health Aging ◽  
Composition Study

P2-172: The impact of maintaining physical activity in late life on subsequent change in brain micro- and macro-structure: Findings from the health, aging, and body composition study

2015 ◽  
Vol 11 (7S_Part_12) ◽  
pp. P556-P557
Author(s):  
John R. Best ◽  
Caterina Rosano ◽  
Robert M. Boudreau ◽  
Hilsa N. Ayonayon ◽  
Suzanne Satterfield ◽  
...  
Keyword(s):  
Physical Activity ◽  
Body Composition ◽  
Late Life ◽  
Subsequent Change ◽  
Body Composition Study ◽  
Health Aging ◽  
Composition Study ◽  
The Impact

scholarly journals Race, Socioeconomic Resources, and Late-Life Mobility and Decline: Findings From the Health, Aging, and Body Composition Study

2011 ◽  
Vol 66A (10) ◽  
pp. 1114-1123 ◽  
Author(s):  
R. J. Thorpe ◽  
A. Koster ◽  
S. B. Kritchevsky ◽  
A. B. Newman ◽  
T. Harris ◽  
...  
Keyword(s):  
Body Composition ◽  
Late Life ◽  
Socioeconomic Resources ◽  
Body Composition Study ◽  
Health Aging ◽  
Composition Study

scholarly journals Personality and Reduced Incidence of Walking Limitation in Late Life: Findings From the Health, Aging, and Body Composition Study

2012 ◽  
Vol 67 (6) ◽  
pp. 712-719 ◽  
Author(s):  
M. I. Tolea ◽  
L. Ferrucci ◽  
P. T. Costa ◽  
K. Faulkner ◽  
C. Rosano ◽  
...  
Keyword(s):  
Body Composition ◽  
Late Life ◽  
Body Composition Study ◽  
Health Aging ◽  
Composition Study

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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