Identifying postmenopausal women at risk for cognitive decline within a healthy cohort using a panel of clinical metabolic indicators: potential for detecting an at-Alzheimer's risk metabolic phenotype.

TitleIdentifying postmenopausal women at risk for cognitive decline within a healthy cohort using a panel of clinical metabolic indicators: potential for detecting an at-Alzheimer's risk metabolic phenotype.
Publication TypeJournal Article
Year of Publication2016
AuthorsRettberg JR, Dang H, Hodis HN, Henderson VW, St John JA, Mack WJ, Brinton RDiaz
JournalNeurobiol Aging
Volume40
Pagination155-63
Date Published2016 Apr
ISSN1558-1497
KeywordsAged, Alzheimer Disease, Biomarkers, Cognition, Cognitive Dysfunction, Cohort Studies, Estradiol, Executive Function, Female, Humans, Memory, Middle Aged, Phenotype, Postmenopause, Randomized Controlled Trials as Topic, Risk
Abstract

Detecting at-risk individuals within a healthy population is critical for preventing or delaying Alzheimer's disease. Systems biology integration of brain and body metabolism enables peripheral metabolic biomarkers to serve as reporters of brain bioenergetic status. Using clinical metabolic data derived from healthy postmenopausal women in the Early versus Late Intervention Trial with Estradiol (ELITE), we conducted principal components and k-means clustering analyses of 9 biomarkers to define metabolic phenotypes. Metabolic clusters were correlated with cognitive performance and analyzed for change over 5 years. Metabolic biomarkers at baseline generated 3 clusters, representing women with healthy, high blood pressure, and poor metabolic phenotypes. Compared with healthy women, poor metabolic women had significantly lower executive, global and memory cognitive performance. Hormone therapy provided metabolic benefit to women in high blood pressure and poor metabolic phenotypes. This panel of well-established clinical peripheral biomarkers represents an initial step toward developing an affordable, rapidly deployable, and clinically relevant strategy to detect an at-risk phenotype of late-onset Alzheimer's disease.

DOI10.1016/j.neurobiolaging.2016.01.011
Alternate JournalNeurobiol. Aging
PubMed ID26973115
PubMed Central IDPMC4921204
Grant ListR01 AG033288 / AG / NIA NIH HHS / United States
F31 AG044997 / AG / NIA NIH HHS / United States
R01AG024154 / AG / NIA NIH HHS / United States
R01AG033288 / AG / NIA NIH HHS / United States
P01AG026572 / AG / NIA NIH HHS / United States
R01AG032236 / AG / NIA NIH HHS / United States
R01 AG032236 / AG / NIA NIH HHS / United States
TL1RR031992 / RR / NCRR NIH HHS / United States
TL1 RR031992 / RR / NCRR NIH HHS / United States
P50 AG047366 / AG / NIA NIH HHS / United States
R01 AG024154 / AG / NIA NIH HHS / United States
P01 AG026572 / AG / NIA NIH HHS / United States
F31AG044997 / AG / NIA NIH HHS / United States
Faculty Member Reference: 
Roberta Diaz Brinton, Ph.D