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
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 dat...
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| Veröffentlicht in: | Neurobiology of aging Jg. 40; S. 155 - 163 |
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Elsevier Inc
01.04.2016
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| ISSN: | 0197-4580, 1558-1497, 1558-1497 |
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| 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. |
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| AbstractList | 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. 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.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. 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. Detecting at-risk individuals within a healthy population is critical for preventing or delaying Alzheimer’s disease. The 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 ELITE trial, we conducted principal components and k-means clustering analyses of nine biomarkers to define metabolic phenotypes. Metabolic clusters were correlated with cognitive performance and analyzed for change over five years. Metabolic biomarkers at baseline generated three clusters, representing women with healthy, high blood pressure, and poor metabolic phenotypes. Compared to healthy women, poor metabolic women had lower verbal memory performance at baseline. 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 towards developing an affordable, rapidly deployable, and clinically relevant strategy to detect an atrisk phenotype of late-onset Alzheimer’s disease. 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. |
| Author | Brinton, Roberta Diaz Dang, Ha Mack, Wendy J. St. John, Jan A. Hodis, Howard N. Rettberg, Jamaica R. Henderson, Victor W. |
| AuthorAffiliation | e Division of Epidemiology, Department of Health Research and Policy, Stanford University, Stanford, CA, 94305, USA g Department of Neurology, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90089, USA d Department of Pharmacology and Pharmaceutical Sciences, School of Pharmacy, University of Southern California, Los Angeles, CA, 90089, USA b Department of Preventive Medicine, University of Southern California, Los Angeles, CA, 90089, USA c Atherosclerosis Research Unit, Department of Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90089, USA a Neuroscience Graduate Program, University of Southern California, Los Angeles, CA, 90089, USA f Department of Neurology & Neurological Sciences, Stanford University, Stanford, CA, 94305, USA |
| AuthorAffiliation_xml | – name: b Department of Preventive Medicine, University of Southern California, Los Angeles, CA, 90089, USA – name: g Department of Neurology, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90089, USA – name: c Atherosclerosis Research Unit, Department of Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90089, USA – name: a Neuroscience Graduate Program, University of Southern California, Los Angeles, CA, 90089, USA – name: d Department of Pharmacology and Pharmaceutical Sciences, School of Pharmacy, University of Southern California, Los Angeles, CA, 90089, USA – name: e Division of Epidemiology, Department of Health Research and Policy, Stanford University, Stanford, CA, 94305, USA – name: f Department of Neurology & Neurological Sciences, Stanford University, Stanford, CA, 94305, USA |
| Author_xml | – sequence: 1 givenname: Jamaica R. surname: Rettberg fullname: Rettberg, Jamaica R. organization: Neuroscience Department, University of Southern California, Los Angeles, CA, USA – sequence: 2 givenname: Ha surname: Dang fullname: Dang, Ha organization: Department of Preventive Medicine, University of Southern California, Los Angeles, CA, USA – sequence: 3 givenname: Howard N. surname: Hodis fullname: Hodis, Howard N. organization: Department of Preventive Medicine, University of Southern California, Los Angeles, CA, USA – sequence: 4 givenname: Victor W. surname: Henderson fullname: Henderson, Victor W. organization: Division of Epidemiology, Department of Health Research and Policy, Stanford University, Stanford, CA, USA – sequence: 5 givenname: Jan A. surname: St. John fullname: St. John, Jan A. organization: Department of Preventive Medicine, University of Southern California, Los Angeles, CA, USA – sequence: 6 givenname: Wendy J. surname: Mack fullname: Mack, Wendy J. organization: Department of Preventive Medicine, University of Southern California, Los Angeles, CA, USA – sequence: 7 givenname: Roberta Diaz surname: Brinton fullname: Brinton, Roberta Diaz email: rbrinton@usc.edu organization: Neuroscience Department, University of Southern California, Los Angeles, CA, USA |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/26973115$$D View this record in MEDLINE/PubMed |
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| Keywords | Biomarker Cognitive aging Hormone therapy Metabolism Alzheimer's disease Menopause |
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| Title | 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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