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J Am Geriatr Soc. 2018 Feb;66(2):289-296. doi: 10.1111/jgs.15205.

Metabolic Clusters and Outcomes in Older Adults: The Cardiovascular Health Study.

Author information

1
Department of Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts.
2
Department of Medicine, University of Washington, Seattle, Washington.
3
New York Academy of Medicine, New York, New York.
4
Department of Epidemiology, University of Washington, Seattle, Washington.
5
Veterans Affairs San Diego Healthcare System, San Diego, California.
6
School of Medicine, University of California, San Diego, California.
7
Department of Medicine, Albert Einstein College of Medicine, New York, New York.
8
Department of Epidemiology and Population Health, Albert Einstein College of Medicine, New York, New York.
9
Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts.
10
Boston Veterans Affairs Healthcare System, Boston, Massachusetts.
11
Department of Global Health, University of Washington, Seattle, Washington.
12
Department of Pathology, College of Medicine, University of Vermont, Burlington, Vermont.
13
Department of Biochemistry, College of Medicine, University of Vermont, Burlington, Vermont.
14
Veterans Affairs Puget Sound Health Care System, Seattle, Washington.
15
Department of Biostatistics, University of Washington, Seattle, Washington.

Abstract

BACKGROUND/OBJECTIVES:

Few studies have the requisite phenotypic information to define metabolic patterns that may inform our understanding of the pathophysiology and consequences of diabetes in older adults. We sought to characterize clusters of older adults on the basis of shared metabolic features.

DESIGN:

Population-based prospective cohort study.

SETTING:

Four U.S. Cardiovascular Health Study field centers.

PARTICIPANTS:

Individuals aged 65 and older taking no glucose-lowering agents (N = 2,231).

MEASUREMENTS:

K-means cluster analysis of 11 metabolic parameters (fasting and postload serum glucose and plasma insulin, fasting C-peptide, body mass index, C-reactive protein (CRP), estimated glomerular filtration rate (eGFR), albuminuria, carboxymethyl lysine (an advanced glycation end-product), procollagen III N-terminal propeptide (a fibrotic marker)) and their associations with incident cardiovascular disease, diabetes, disability, and mortality over 8 to 14.5 years of follow-up and with measures of subclinical cardiovascular disease.

RESULTS:

A 6-cluster solution provided robust differentiation into distinct, identifiable clusters. Cluster A (n = 739) had the lowest glucose and insulin and highest eGFR and the lowest rates of all outcomes. Cluster B (n = 419) had high glucose and insulin and intermediate rates of most outcomes. Cluster C (n = 118) had the highest insulin. Cluster D (n = 129) had the highest glucose with much lower insulin. Cluster E (n = 314) had the lowest eGFR and highest albuminuria. Cluster F (n = 512) had the highest CRP. Rates of CVD, mortality, and subclinical atherosclerosis were highest in clusters C, D, and E and were similar to rates in participants with treated diabetes. Incidence of disability was highest in Cluster C.

CONCLUSION:

Clustering according to metabolic parameters identifies distinct phenotypes that are strongly associated with clinical and functional outcomes, even at advanced age.

KEYWORDS:

aging; cardiovascular disease; chronic kidney disease; diabetes; epidemiology; inflammation; insulin resistance

PMID:
29431855
PMCID:
PMC5813705
DOI:
10.1111/jgs.15205
[Indexed for MEDLINE]
Free PMC Article

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