Characterization of Vascular Disease Risk in Postmenopausal Women and Its Association with Cognitive Performance

N. Maritza Dowling, Carey E. Gleason, JoAnn A E Manson, Howard N. Hodis, Virginia M Miller, Eliot A. Brinton, Genevieve Neal-Perry, M. Nanette Santoro, Marcelle Cedars, Rogerio Lobo, George R. Merriam, Whitney Wharton, Frederick Naftolin, Hugh Taylor, S. Mitchell Harman, Sanjay Asthana

Research output: Contribution to journalArticle

9 Citations (Scopus)

Abstract

Objectives:While global measures of cardiovascular (CV) risk are used to guide prevention and treatment decisions, these estimates fail to account for the considerable interindividual variability in pre-clinical risk status. This study investigated heterogeneity in CV risk factor profiles and its association with demographic, genetic, and cognitive variables.Methods:A latent profile analysis was applied to data from 727 recently postmenopausal women enrolled in the Kronos Early Estrogen Prevention Study (KEEPS). Women were cognitively healthy, within three years of their last menstrual period, and free of current or past CV disease. Education level, apolipoprotein E ε4 allele (APOE4), ethnicity, and age were modeled as predictors of latent class membership. The association between class membership, characterizing CV risk profiles, and performance on five cognitive factors was examined. A supervised random forest algorithm with a 10-fold cross-validation estimator was used to test accuracy of CV risk classification.Results:The best-fitting model generated two distinct phenotypic classes of CV risk 62% of women were "low-risk" and 38% "high-risk". Women classified as low-risk outperformed high-risk women on language and mental flexibility tasks (p = 0.008) and a global measure of cognition (p = 0.029). Women with a college degree or above were more likely to be in the low-risk class (OR = 1.595, p = 0.044). Older age and a Hispanic ethnicity increased the probability of being at high-risk (OR = 1.140, p = 0.002; OR = 2.622, p = 0.012; respectively). The prevalence rate of APOE-ε4 was higher in the high-risk class compared with rates in the low-risk class.Conclusion:Among recently menopausal women, significant heterogeneity in CV risk is associated with education level, age, ethnicity, and genetic indicators. The model-based latent classes were also associated with cognitive function. These differences may point to phenotypes for CV disease risk. Evaluating the evolution of phenotypes could in turn clarify preclinical disease, and screening and preventive strategies.ClinicalTrials.gov NCT00154180.

Original languageEnglish (US)
Article numbere68741
JournalPLoS One
Volume8
Issue number7
DOIs
StatePublished - Jul 17 2013

Fingerprint

vascular diseases
Vascular Diseases
cognition
nationalities and ethnic groups
educational status
cardiovascular diseases
Cognition
academic degrees
Cardiovascular Diseases
risk profile
Education
phenotype
menopause
apolipoprotein E
Apolipoprotein E4
Phenotype
Hispanic Americans
estrogens

ASJC Scopus subject areas

  • Agricultural and Biological Sciences(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Medicine(all)

Cite this

Dowling, N. M., Gleason, C. E., Manson, J. A. E., Hodis, H. N., Miller, V. M., Brinton, E. A., ... Asthana, S. (2013). Characterization of Vascular Disease Risk in Postmenopausal Women and Its Association with Cognitive Performance. PLoS One, 8(7), [e68741]. https://doi.org/10.1371/journal.pone.0068741

Characterization of Vascular Disease Risk in Postmenopausal Women and Its Association with Cognitive Performance. / Dowling, N. Maritza; Gleason, Carey E.; Manson, JoAnn A E; Hodis, Howard N.; Miller, Virginia M; Brinton, Eliot A.; Neal-Perry, Genevieve; Santoro, M. Nanette; Cedars, Marcelle; Lobo, Rogerio; Merriam, George R.; Wharton, Whitney; Naftolin, Frederick; Taylor, Hugh; Harman, S. Mitchell; Asthana, Sanjay.

In: PLoS One, Vol. 8, No. 7, e68741, 17.07.2013.

Research output: Contribution to journalArticle

Dowling, NM, Gleason, CE, Manson, JAE, Hodis, HN, Miller, VM, Brinton, EA, Neal-Perry, G, Santoro, MN, Cedars, M, Lobo, R, Merriam, GR, Wharton, W, Naftolin, F, Taylor, H, Harman, SM & Asthana, S 2013, 'Characterization of Vascular Disease Risk in Postmenopausal Women and Its Association with Cognitive Performance', PLoS One, vol. 8, no. 7, e68741. https://doi.org/10.1371/journal.pone.0068741
Dowling, N. Maritza ; Gleason, Carey E. ; Manson, JoAnn A E ; Hodis, Howard N. ; Miller, Virginia M ; Brinton, Eliot A. ; Neal-Perry, Genevieve ; Santoro, M. Nanette ; Cedars, Marcelle ; Lobo, Rogerio ; Merriam, George R. ; Wharton, Whitney ; Naftolin, Frederick ; Taylor, Hugh ; Harman, S. Mitchell ; Asthana, Sanjay. / Characterization of Vascular Disease Risk in Postmenopausal Women and Its Association with Cognitive Performance. In: PLoS One. 2013 ; Vol. 8, No. 7.
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AU - Dowling, N. Maritza

AU - Gleason, Carey E.

AU - Manson, JoAnn A E

AU - Hodis, Howard N.

AU - Miller, Virginia M

AU - Brinton, Eliot A.

AU - Neal-Perry, Genevieve

AU - Santoro, M. Nanette

AU - Cedars, Marcelle

AU - Lobo, Rogerio

AU - Merriam, George R.

AU - Wharton, Whitney

AU - Naftolin, Frederick

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N2 - Objectives:While global measures of cardiovascular (CV) risk are used to guide prevention and treatment decisions, these estimates fail to account for the considerable interindividual variability in pre-clinical risk status. This study investigated heterogeneity in CV risk factor profiles and its association with demographic, genetic, and cognitive variables.Methods:A latent profile analysis was applied to data from 727 recently postmenopausal women enrolled in the Kronos Early Estrogen Prevention Study (KEEPS). Women were cognitively healthy, within three years of their last menstrual period, and free of current or past CV disease. Education level, apolipoprotein E ε4 allele (APOE4), ethnicity, and age were modeled as predictors of latent class membership. The association between class membership, characterizing CV risk profiles, and performance on five cognitive factors was examined. A supervised random forest algorithm with a 10-fold cross-validation estimator was used to test accuracy of CV risk classification.Results:The best-fitting model generated two distinct phenotypic classes of CV risk 62% of women were "low-risk" and 38% "high-risk". Women classified as low-risk outperformed high-risk women on language and mental flexibility tasks (p = 0.008) and a global measure of cognition (p = 0.029). Women with a college degree or above were more likely to be in the low-risk class (OR = 1.595, p = 0.044). Older age and a Hispanic ethnicity increased the probability of being at high-risk (OR = 1.140, p = 0.002; OR = 2.622, p = 0.012; respectively). The prevalence rate of APOE-ε4 was higher in the high-risk class compared with rates in the low-risk class.Conclusion:Among recently menopausal women, significant heterogeneity in CV risk is associated with education level, age, ethnicity, and genetic indicators. The model-based latent classes were also associated with cognitive function. These differences may point to phenotypes for CV disease risk. Evaluating the evolution of phenotypes could in turn clarify preclinical disease, and screening and preventive strategies.ClinicalTrials.gov NCT00154180.

AB - Objectives:While global measures of cardiovascular (CV) risk are used to guide prevention and treatment decisions, these estimates fail to account for the considerable interindividual variability in pre-clinical risk status. This study investigated heterogeneity in CV risk factor profiles and its association with demographic, genetic, and cognitive variables.Methods:A latent profile analysis was applied to data from 727 recently postmenopausal women enrolled in the Kronos Early Estrogen Prevention Study (KEEPS). Women were cognitively healthy, within three years of their last menstrual period, and free of current or past CV disease. Education level, apolipoprotein E ε4 allele (APOE4), ethnicity, and age were modeled as predictors of latent class membership. The association between class membership, characterizing CV risk profiles, and performance on five cognitive factors was examined. A supervised random forest algorithm with a 10-fold cross-validation estimator was used to test accuracy of CV risk classification.Results:The best-fitting model generated two distinct phenotypic classes of CV risk 62% of women were "low-risk" and 38% "high-risk". Women classified as low-risk outperformed high-risk women on language and mental flexibility tasks (p = 0.008) and a global measure of cognition (p = 0.029). Women with a college degree or above were more likely to be in the low-risk class (OR = 1.595, p = 0.044). Older age and a Hispanic ethnicity increased the probability of being at high-risk (OR = 1.140, p = 0.002; OR = 2.622, p = 0.012; respectively). The prevalence rate of APOE-ε4 was higher in the high-risk class compared with rates in the low-risk class.Conclusion:Among recently menopausal women, significant heterogeneity in CV risk is associated with education level, age, ethnicity, and genetic indicators. The model-based latent classes were also associated with cognitive function. These differences may point to phenotypes for CV disease risk. Evaluating the evolution of phenotypes could in turn clarify preclinical disease, and screening and preventive strategies.ClinicalTrials.gov NCT00154180.

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