Predicting cognitive function from clinical measures of physical function and health status in older adults.
BACKGROUND
Current research suggests that the neuropathology of dementia-including brain changes leading to memory impairment and cognitive decline-is evident years before the onset of this disease. Older adults with cognitive decline have reduced functional independence and quality of life, and are at greater risk for developing dementia. Therefore, identifying biomarkers that can be easily assessed within the clinical setting and predict cognitive decline is important. Early recognition of cognitive decline could promote timely implementation of preventive strategies.


METHODS
We included 89 community-dwelling adults aged 70 years and older in our study, and collected 32 measures of physical function, health status and cognitive function at baseline. We utilized an L1-L2 regularized regression model (elastic net) to identify which of the 32 baseline measures were strongly predictive of cognitive function after one year. We built three linear regression models: 1) based on baseline cognitive function, 2) based on variables consistently selected in every cross-validation loop, and 3) a full model based on all the 32 variables. Each of these models was carefully tested with nested cross-validation.


RESULTS
Our model with the six variables consistently selected in every cross-validation loop had a mean squared prediction error of 7.47. This number was smaller than that of the full model (115.33) and the model with baseline cognitive function (7.98). Our model explained 47% of the variance in cognitive function after one year.


CONCLUSIONS
We built a parsimonious model based on a selected set of six physical function and health status measures strongly predictive of cognitive function after one year. In addition to reducing the complexity of the model without changing the model significantly, our model with the top variables improved the mean prediction error and R-squared. These six physical function and health status measures can be easily implemented in a clinical setting.
Experimental Medicine Program, University of British Columbia, Vancouver, British Columbia, Canada; Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, British Columbia, Canada; Centre for Hip Health and Mobility, Vancouver Coastal Health Research Institute, Vancouver, British Columbia, Canada; Aging, Mobility, and Cognitive Neuroscience Laboratory, Department of Physical Therapy, University of British Columbia, Vancouver, British Columbia, Canada.
Niousha
N
Bolandzadeh
Rehabilitation Institute of Chicago, Northwestern University, Chicago, Illinois, United States of America.
Konrad
K
Kording
Department of Biomedical Engineering, Marquette University, Milwaukee, Wisconsin, United States of America.
Nicole
N
Salowitz
Aging, Mobility, and Cognitive Neuroscience Laboratory, Department of Physical Therapy, University of British Columbia, Vancouver, British Columbia, Canada; School of Population and Public Health, University of British Columbia, Vancouver, British Columbia, Canada; Centre for Clinical Epidemiology and Evaluation, Vancouver, British Columbia, Canada.
Jennifer C
JC
Davis
Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, British Columbia, Canada; Centre for Hip Health and Mobility, Vancouver Coastal Health Research Institute, Vancouver, British Columbia, Canada; Aging, Mobility, and Cognitive Neuroscience Laboratory, Department of Physical Therapy, University of British Columbia, Vancouver, British Columbia, Canada.
Liang
L
Hsu
Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, British Columbia, Canada; Centre for Hip Health and Mobility, Vancouver Coastal Health Research Institute, Vancouver, British Columbia, Canada; Aging, Mobility, and Cognitive Neuroscience Laboratory, Department of Physical Therapy, University of British Columbia, Vancouver, British Columbia, Canada.
Alison
A
Chan
Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, British Columbia, Canada; Centre for Hip Health and Mobility, Vancouver Coastal Health Research Institute, Vancouver, British Columbia, Canada; Aging, Mobility, and Cognitive Neuroscience Laboratory, Department of Physical Therapy, University of British Columbia, Vancouver, British Columbia, Canada.
Devika
D
Sharma
Department of Biomedical and Molecular Sciences, Queen's University, Kingstone, Ontario, Canada.
Gunnar
G
Blohm
Experimental Medicine Program, University of British Columbia, Vancouver, British Columbia, Canada; Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, British Columbia, Canada; Centre for Hip Health and Mobility, Vancouver Coastal Health Research Institute, Vancouver, British Columbia, Canada; Aging, Mobility, and Cognitive Neuroscience Laboratory, Department of Physical Therapy, University of British Columbia, Vancouver, British Columbia, Canada.
Teresa
T
Liu-Ambrose
D000203:Activities of Daily Living / Q000523:psychology; D000368:Aged; D000369:Aged, 80 and over; D015415:Biomarkers / Q000032:analysis; D003071:Cognition / Q000502:physiology; D003704:Dementia / Q000175:diagnosis* / Q000503:physiopathology / Q000523:psychology; D005260:Female; D015577:Geriatric Assessment / Q000379:methods / Q000706:statistics & numerical data*; D006304:Health Status; D006801:Humans; D057187:Independent Living; D008297:Male; D015233:Models, Statistical; D011788:Quality of Life / Q000523:psychology; D012044:Regression Analysis; D012307:Risk Factors
D015415:Biomarkers