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

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Title: Metabolic Clusters and Outcomes in Older Adults: The Cardiovascular Health Study.
Authors: Mukamal, Kenneth J., Siscovick, David S., de Boer, Ian H., Ix, Joachim H., Kizer, Jorge R., Djoussé, Luc, Fitzpatrick, Annette L., Tracy, Russell P., Boyko, Edward J., Kahn, Steven E., Arnold, Alice M.
Source: Journal of the American Geriatrics Society. Feb2018, Vol. 66 Issue 2, p289-296. 8p. 3 Charts, 1 Graph.
Subjects: Metabolic regulation, Diabetes in old age, Drug resistance, Cardiovascular disease treatment, Health outcome assessment, Kidney disease treatments, Atherosclerosis, Cardiovascular diseases, Chronic kidney failure, Cluster analysis (Statistics), Diabetes, Insulin resistance, Longitudinal method, Metabolism, Research funding, Phenotypes, Population-based case control, Old age
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. [ABSTRACT FROM AUTHOR]
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Database: Psychology and Behavioral Sciences Collection
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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. [ABSTRACT FROM AUTHOR]
ISSN:00028614
DOI:10.1111/jgs.15205