@article {9581, title = {Investigating predictors of cognitive decline using machine learning.}, journal = {Journals of Gerontology, Series B: Psychological Sciences \& Social Sciences}, year = {2020}, abstract = {

Objectives: Genetic risks for cognitive decline are not modifiable; however their relative importance compared to modifiable factors is unclear. We used machine learning to evaluate modifiable and genetic risk factors for Alzheimer{\textquoteright}s disease(AD), to predict cognitive decline.

Methods: Health and Retirement Study participants, aged 65-90, with DNA and >2 cognitive evaluations, were included (n=7,142). Predictors included age, body mass index, gender, education, APOE ε4, CVD, hypertension, diabetes, stroke, neighborhood socio-economic status(NSES), and AD risk genes. Latent class trajectory analyses of cognitive scores determined the form and number of classes. Random Forests (RF) classification investigated predictors of cognitive trajectories. Performance metrics (accuracy, sensitivity and specificity) were reported.

Results: Three classes were identified. Discriminating highest from lowest classes produced the best RF performance: accuracy=78\%(1.0\%), sensitivity=75\%(1.0\%) and specificity=81\%(1.0\%). Top ranked predictors were education, age, gender, stroke, NSES, and diabetes, APOE ε4 carrier status and BMI. When discriminating high from medium classes, top predictors were education, age, gender, stroke, diabetes, NSES and BMI. When discriminating medium from the low classes, education, NSES, age, diabetes and stroke were top predictors.

Discussion: The combination of latent trajectories and RF classification techniques suggested that non-genetic factors contribute more to cognitive decline than genetic factors. Education was the most relevant predictor for discrimination.

}, keywords = {Alzheimer{\textquoteright}s disease, Cognitive Ability, Machine learning}, issn = {1758-5368}, doi = {10.1093/geronb/gby054}, author = {Casanova, Ramon and Saldana, Santiago and Michael W Lutz and Brenda L Plassman and Kuchibhatla, Maragatha and Kathleen M Hayden} } @article {10148, title = {Analysis of pleiotropic genetic effects on cognitive impairment, systemic inflammation, and plasma lipids in the Health and Retirement Study.}, journal = {Neurobiology of Aging}, volume = {80}, year = {2019}, pages = {173-186}, abstract = {Variants associated with modulation of c-reactive protein (CRP) and plasma lipids have been investigated for polygenic overlap with Alzheimer{\textquoteright}s disease risk variants. We examined pleiotropic genetic effects on cognitive impairment conditioned on genetic variants (SNPs) associated with systemic inflammation as measured by CRP and with plasma lipids using data from the Health and Retirement Study. SNP enrichment was observed for cognitive impairment conditioned on the secondary phenotypes of plasma CRP and lipids. Fold enrichment of 100\%-800\% was observed for increasingly stringent p-value thresholds for SNPs associated with cognitive impairment conditional on plasma CRP, 80\%-800\% for low-density lipoprotein, and 80\%-600\% for total cholesterol. Significant associations (false discovery rate Q <= 0.05) between cognitive impairment, conditional with either CRP, low-density lipoprotein, or total cholesterol, were found for the locus on chromosome 19 that contains the APOE, TOMM40, APOC1, and PVRL2 genes. Relative numbers of significant SNPs in each of the genes differed by the conditional associations with the secondary phenotypes. Biological interpretation of both the genetic pleiotropy results and the individual genome-wide association results showed that the variants and proximal genes identified are involved in multiple pathological processes including cholesterol metabolism, inflammation, and mitochondrial transport. These findings are potentially important for Alzheimer{\textquoteright}s disease risk prediction and development of novel therapeutic approaches.}, keywords = {Cognitive Ability, Genetics, Inflammation}, issn = {1558-1497}, doi = {10.1016/j.neurobiolaging.2018.10.028}, author = {Michael W Lutz and Casanova, Ramon and Saldana, Santiago and Kuchibhatla, Maragatha and Brenda L Plassman and Kathleen M Hayden} } @article {10047, title = {The association between neighborhood socioeconomic status, cardiovascular and cerebrovascular risk factors, and cognitive decline in the Health and Retirement Study (HRS).}, journal = {Aging \& Mental Health}, year = {2019}, type = {Journal}, abstract = {

BACKGROUND: A small but growing body of evidence supports a relationship between neighborhood socioeconomic status (NSES) and cognitive decline. Additional work is needed to characterize this relationship controlling for risk factors such as cardiovascular, cerebrovascular, and genetic risk factors.

METHODS: Cognitive decline was assessed in association with NSES, and cardiovascular and cerebrovascular risk factors (heart disease, diabetes, hypertension, and stroke) in 8,198 individuals from the 1992-2010 waves of the Health and Retirement Study (HRS). Latent class trajectory analysis determined the number of cognitive trajectory classes that best fit the data, and a multinomial logistic regression model in the latent class framework assessed the risk for cognitive classes conferred by NSES index score and heart disease, diabetes, hypertension, and stroke across three trajectory classes of cognitive function. The analyses controlled for genetic risk for cognitive decline (including APOE genotype) and demographic variables, including education.

RESULTS: The HRS sample was 57.6\% female and 85.5\% White, with a mean age of 67.5(3.5) years at baseline. The three-quadratic-class model best fit the data, where higher classes represented better cognitive function. Those with better cognitive function were mainly younger white females. Those in the highest quartile of NSES had 57\% higher odds of being in the high cognitive function class. Heart disease, diabetes, hypertension, and stroke each increased the odds having of lower cognitive function.

CONCLUSIONS: In examining the relationship of cognitive status with various variables, neighborhood socioeconomic status, cardiovascular risk, and cerebrovascular risk persisted across the cognitive trajectory classes.

}, keywords = {Cardiovascular health, Cognition \& Reasoning, Risk Factors, Socioeconomic factors}, issn = {1364-6915}, doi = {10.1080/13607863.2019.1594169}, author = {Kuchibhatla, Maragatha and Jaimie C Hunter and Brenda L Plassman and Michael W Lutz and Casanova, Ramon and Saldana, Santiago and Kathleen M Hayden} } @article {9481, title = {Neighborhoods, sleep quality, and cognitive decline: Does where you live and how well you sleep matter?}, journal = {Alzheimer{\textquoteright}s \& Dementia}, volume = {14}, year = {2018}, pages = {454-461}, abstract = {Introduction We evaluated the association between neighborhood socioeconomic status (NSES) and sleep quality on cognitive decline in the Health and Retirement Study. Methods Health and Retirement Study participants (n = 8090), aged 65+ with DNA and multiple biennial cognitive observations (abbreviated Telephone Interview for Cognitive Status), were included. Participants were grouped into quartiles of NSES and sleep quality scores. We adjusted for apolipoprotein E ε4, demographic, and cardiovascular risk factors. Random effects modeling evaluated cognitive change over time. Results NSES and sleep were significantly associated with cognitive decline, and there was a significant interaction between them (P = .02). Significant differences between high/low NSES and high/low sleep quality (P < .0001) were found. Discussion Sleep and NSES were associated with cognitive decline; the association between sleep and cognition appeared stronger among those with low NSES. The association between low NSES, poor sleep quality, and cognitive decline was roughly equivalent to the association between apolipoprotein E ε4 and cognitive decline.}, keywords = {Cognitive Ability, Neighborhoods, Sleep, Socioeconomic factors}, issn = {15525260}, doi = {10.1016/j.jalz.2017.10.007}, url = {https://pubmed.ncbi.nlm.nih.gov/29396109/}, author = {Jaimie C Hunter and Elizabeth P. Handing and Casanova, Ramon and Kuchibhatla, Maragatha and Michael W Lutz and Saldana, Santiago and Brenda L Plassman and Kathleen M Hayden} }