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

Jamaica R Rettberg¹, Ha Dang², Howard N Hodis³, Victor W Henderson⁴, Jan A St John⁵, Wendy J Mack⁵, Roberta Diaz Brinton⁶

Affiliations

¹ Neuroscience Department, University of Southern California, Los Angeles, CA, USA.
² Department of Preventive Medicine, University of Southern California, Los Angeles, CA, USA.
³ Department of Preventive Medicine, University of Southern California, Los Angeles, CA, USA; Department of Medicine, Atherosclerosis Research Unit, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; Department of Pharmacology and Pharmaceutical Sciences, School of Pharmacy, University of Southern California, Los Angeles, CA, USA.
⁴ Division of Epidemiology, Department of Health Research and Policy, Stanford University, Stanford, CA, USA; Department of Neurology & Neurological Sciences, Stanford University, Stanford, CA, USA.
⁵ Department of Preventive Medicine, University of Southern California, Los Angeles, CA, USA; Department of Medicine, Atherosclerosis Research Unit, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
⁶ Neuroscience Department, University of Southern California, Los Angeles, CA, USA; Department of Pharmacology and Pharmaceutical Sciences, School of Pharmacy, University of Southern California, Los Angeles, CA, USA; Department of Neurology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA. Electronic address: rbrinton@usc.edu.

PMID: 26973115
PMCID: PMC4921204
DOI: 10.1016/j.neurobiolaging.2016.01.011

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

Jamaica R Rettberg et al. Neurobiol Aging. 2016 Apr.

. 2016 Apr:40:155-163.

doi: 10.1016/j.neurobiolaging.2016.01.011. Epub 2016 Jan 29.

Authors

Jamaica R Rettberg¹, Ha Dang², Howard N Hodis³, Victor W Henderson⁴, Jan A St John⁵, Wendy J Mack⁵, Roberta Diaz Brinton⁶

Affiliations

¹ Neuroscience Department, University of Southern California, Los Angeles, CA, USA.
² Department of Preventive Medicine, University of Southern California, Los Angeles, CA, USA.
³ Department of Preventive Medicine, University of Southern California, Los Angeles, CA, USA; Department of Medicine, Atherosclerosis Research Unit, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; Department of Pharmacology and Pharmaceutical Sciences, School of Pharmacy, University of Southern California, Los Angeles, CA, USA.
⁴ Division of Epidemiology, Department of Health Research and Policy, Stanford University, Stanford, CA, USA; Department of Neurology & Neurological Sciences, Stanford University, Stanford, CA, USA.
⁵ Department of Preventive Medicine, University of Southern California, Los Angeles, CA, USA; Department of Medicine, Atherosclerosis Research Unit, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
⁶ Neuroscience Department, University of Southern California, Los Angeles, CA, USA; Department of Pharmacology and Pharmaceutical Sciences, School of Pharmacy, University of Southern California, Los Angeles, CA, USA; Department of Neurology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA. Electronic address: rbrinton@usc.edu.

PMID: 26973115
PMCID: PMC4921204
DOI: 10.1016/j.neurobiolaging.2016.01.011

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.

Keywords: Alzheimer's disease; Biomarker; Cognitive aging; Hormone therapy; Menopause; Metabolism.

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Conflict of interest statement

DISCLOSURE

None of the authors have a conflict of interest to disclose.

Figures

**Figure 1. Cluster development**
Women in the Healthy Metabolic cluster are shown in green. Women in the High Blood Pressure cluster are shown in red. Women in the Poor Metabolic cluster are shown in blue. Can1, the first canonical variable: the linear combination of the clustering variables that best explains cluster group differences (i.e., is most correlated with cluster group). Can2, the second canonical variable: the linear combination of clustering variables that is most correlated with the cluster groups but is uncorrelated with Can1.

**Figure 2. Comparison of cognitive composite scores between phenotypes at baseline**
Significant differences between phenotypes on the three cognitive composite scores: global cognition, verbal memory, and executive functions. A. Results after adjusting for menopause cohort and random intervention assignment. B. Results after adding an adjustment for education. *p < 0.05. Error bars represent SEM. The Tukey-Kramer method was used to adjust for multiple comparisons.

See this image and copyright information in PMC

References

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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

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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

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