Characterization of vascular disease risk in postmenopausal women and its association with cognitive performance

N Maritza Dowling, Carey E Gleason, Joann 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, N Maritza Dowling, Carey E Gleason, Joann 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

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.

Conflict of interest statement

Competing Interests: Bayer HealthCare Pharmaceuticals, Inc. supplied the CLIMARA® estradiol and placebo patches and Abbott Laboratories (formerly Solvay Pharmaceuticals) provided the micronized progesterone (PROMETRIUM®) and placebo capsules. This does not alter the authors’ adherence to all the PLOS ONE policies on sharing data and materials.

Figures

Figure 1. Bi-factor model for the cognitive…
Figure 1. Bi-factor model for the cognitive baseline data.
Eighteen variables from nine tests were used to estimate the model with a global cognitive factor capturing covariation across all variables and four independent secondary factors explaining specific shared covariations beyond that shared with other variables.
Figure 2. Diagram illustrating the latent profile…
Figure 2. Diagram illustrating the latent profile model.
BMI = Body mass index; CIMT = Carotid artery intima-media thickness; LDL-C = Low density lipoprotein-cholesterol; HDL-C = High density lipoprotein-cholesterol; FSG = Fasting blood glucose.
Figure 3. Schematic diagram of the principal…
Figure 3. Schematic diagram of the principal steps in the analysis.
Models were estimated via full information (direct) maximum likelihood algorithms using all available data.
Figure 4. Estimated Mean Vascular Disease Risk…
Figure 4. Estimated Mean Vascular Disease Risk for Each Latent Group.
BMI = Body mass index; CIMT = Carotid artery intima-media thickness; LDL-C = Low density lipoprotein-cholesterol; HDL-C = High density lipoprotein-cholesterol; FSG = Fasting blood glucose.
Figure 5. Variable Importance Measures Estimated by…
Figure 5. Variable Importance Measures Estimated by Mean Decreased Accuracy.
BMI = Body mass index; CIMT = Carotid artery intima-media thickness; LDL-C = Low density lipoprotein-cholesterol; HDL-C = High density lipoprotein-cholesterol; FSG = Fasting blood glucose; FRAM = Framingham Point scores; TRIG = Triglycerides.

References

    1. Anderson KM, Odell PM, Wilson PWF, Kannel WB (1991) Cardiovascular disease risk profiles. Am Heart J 121: 293–298.
    1. Howard BV, Rodriguez BL, Bennett PH, Harris MI, Hamman R, et al. (2002) Prevention Conference VI: Diabetes and Cardiovascular disease: Writing Group I: epidemiology. Circulation 105: 132–137.
    1. McSweeney JC, Cody M, O’Sullivan P, Elberson K, Moser DK, et al. (2003) Women’s early warning symptoms of acute myocardial infarction. Circulation 108: 2619–2623.
    1. Pappolla MA, Bryant-Thomas TK, Herbert D, Pacheco J, Fabra-Garcia M, et al. (2003) Mild hypercholesterolemia is an early risk factor for the development of Alzheimer amyloid pathology. Neurology 61: 199–205.
    1. Sherwin BB (2003) Estrogen and cognitive functioning in women. Endocrine Reviews 24: 133–151.
    1. Mosca L, Benjamin EJ, Berra K, Bezanson JL, Dolor RJ, et al. (2011) Effectiveness-based guidelines for the prevention of cardiovascular disease in women–2011 update: a guideline from the American Heart Association. Circulation 123: 1243–1262.
    1. Mosca L, Mochari-Greenberger H, Dolor RJ, Newby LK, Robb KJ (2010) Twelve-year follow-up of American women’s awareness of cardiovascular disease risk and barriers to heart health. Circ Cardiovasc Qual Outcomes 3: 120–127.
    1. Kivipelto M, Helkala EL, Laakso MP, Hänninen T, Hallikainen M, et al. (2002) Apolipoprotein E epsilon4 allele, elevated midlife total cholesterol level, and high midlife systolic blood pressure are independent risk factors for late-life Alzheimer disease. Ann Intern Med 137: 149–155.
    1. Chan N, Colhoun H, Vallance P (2001) Cardiovascular risk factors as determinants of endothelium-dependent and endothelium-independent vascular reactivity in the general population. J Am Coll Cardiol 38: 1814–1820.
    1. Kuvin JT, Patel AR, Sidhu M, Rand WM, Sliney KA, et al. (2003) Relation between high-density lipoprotein cholesterol and peripheral vasomotor function. Am J Cardiol 92: 275–279.
    1. Schachinger V, Britten MB, Zeiher AM (2000) Prognostic impact of coronary vasodilator dysfunction on adverse long-term outcome of coronary heart disease. Circulation 101: 1899–1906.
    1. Biegelsen ES, Loscalzo J (1999) Endothelial function and atherosclerosis. Coron Artery Dis 10: 241–256.
    1. Hirata K, Miki N, Kuroda Y, Sakoda T, Kawashima S, et al. (1995) Low concentration of oxidized low-density lipoprotein and lysophosphatidylcholine upregulate constitutive nitric oxide synthasemRNA expression in bovine aortic endothelial cells. Circ Res 76: 958–962.
    1. O’Keefe JH, Carter MD, Lavie CJ (2009) Primary and secondary prevention of cardiovascular diseases: a practical evidence-based approach. Mayo Clin Proc 84: 741–757.
    1. Ferre R, Aragones G, Plana N, Merino J, Heras M, et al. (2011) High-density lipoprotein cholesterol and apolipoprotein A1 levels strongly influence the reactivity of small peripheral arteries. Atherosclerosis 216: 115–119.
    1. Taddei S, Virdis A, Ghiadoni L, Mattei P, Sudano I, et al. (1996) Menopause is associated with endothelial dysfunction in women. Hypertension 28: 576–582.
    1. Christian A. Mochari H, Mosca LJ (2005) Coronary Heart Disease in Ethnically Diverse Women: Risk Perception and Communication Mayo Clin Proc. 80: 1593–1599.
    1. Nyboe J, Jensen G, Appleyard M, Schnohr P (1989) Risk factors for acute myocardial infarction in Copenhagen. I: Hereditary, educational and socioeconomic factors. Copenhagen City Heart Study. Eur Heart J 10: 910–916.
    1. Corder EH, Saunders AM, Strittmatter WJ, Schmechel DE, Gaskell PC, et al. (1993) Gene dose of apolipoprotein E type 4 allele and the risk of Alzheimer’s disease in late onset families. Science 261: 921–923.
    1. Wolf PA, D’Agostino RB, Belanger AJ, Kannel WB (1991) Probability of stroke: a risk profile from the Framingham Study. Stroke 22: 312–318.
    1. Kahn R, Buse J, Ferrannini E, Stern M (2005) The metabolic syndrome: time for a critical appraisal: joint statement from the American Diabetes Association and the European Association for the Study of Diabetes. Diabetes Care 28: 2289–2304.
    1. Laakso M, Sarlund H, Mykkanen L (1990) Insulin resistance is associated with lipid and lipoprotein abnormalities in subjects with varying degrees of glucose tolerance. Arteriosclerosis 10: 223–231.
    1. Erdine S, Ari O (2006) ESH–ESC guidelines for the management of hypertension. Herz 31: 331–338.
    1. Rodondi N, Locatelli I, Aujesky D, Butler J, Vittinghoff E, et al. (2012) Framingham Risk Score and Alternatives for Prediction of Coronary Heart Disease in Older Adults. PLoS ONE 7: e34287.
    1. Liu J, Hong Y, D’Agostino RB Sr, Wu Z, Wang W, et al. (2004) Predictive value for the Chinese population of the Framingham CHD risk assessment tool compared with the Chinese Multi-provincial Cohort Study. JAMA 291: 2591–2599.
    1. McLachlan GJ, Peel D (2000). Finite Mixture Models. New York: John Wiley & Sons.
    1. Harman SM, Brinton EA, Cedars M, Lobo R, Manson JE, et al. (2005) KEEPS: The Kronos Early Estrogen Prevention Study. Climacteric 8: 3–12.
    1. Harman SM, Brinton EA (2009) Biphasic effects of hormone treatment on risk of cardiovascular disease: Resolving the paradox in postmenopausal women. Menopausal Medicine 17: S1–S10.
    1. Miller VM, Black DM, Brinton EA, Budoff MJ, Cedars MI, et al. (2009) Using basic science to design a clinical trial: Baseline characteristics of women enrolled in the Kronos Early Estrogen Prevention Study (KEEPS). J Cardiovasc Transl Res 2: 228–239.
    1. Small BJ, Rosnick CB, Fratiglioni L, Backman L (2004) Apolipoprotein E and cognitive performance: a meta-analysis. Psychology and Aging 19, 592–600.
    1. Deary IJ, Whiteman MC, Pattie A, Starr JM, Hayward C, et al. (2002) Cognitive change and the ApoE ε4 allele. Nature 418: 932.
    1. Kofler BM, Miles EA, Curtis P, Armah CK, Tricon S, et al. (2012) Apolipoprotein E genotype and the cardiovascular disease risk phenotype: impact of sex and adiposity (the FINGEN study).Atherosclerosis. 221: 467–470.
    1. Dupuy AM, Mas E, Ritchie K, Descomps B, Badiou S, et al. (2001) The relationship between apolipoprotein E4 and lipid metabolism is impaired in Alzheimer’s disease. Gerontology 47: 213–218.
    1. Farrer LA, Cupples LA, Haines JL, Hyman B, Kukull WA, et al. (1997) Effects of age, sex, and ethnicity on the association between apolipoprotein E genotype and Alzheimer disease. A meta-analysis. APOE and Alzheimer Disease Meta Analysis Consortium. JAMA 278: 1349–1356.
    1. Yaffe K, Cauley J, Sands L, Browner W (1997) Apolipoprotein E phenotype and cognitive decline in a prospective study of elderly community women. Archives of Neurology 54: 1110–1114.
    1. National Institute of Health. National Heart Lung and Blood Institute (1998) Clinical guidelines on the identification, evaluation and treatment of overweight and obesity in adults: the evidence report. Obesity Research 6: 51S–209S.
    1. Jensen J, Nilas L, Christiansen C (1990) Influence of menopause on serum lipids and lipoproteins. Maturitas 12: 321–331.
    1. Wilson PWF, D’Agostino RB, Levy D, Belanger AM, Silbershatz H, et al. (1998) Prediction of coronary heart disease using risk factor categories. Circulation 97: 1837–1847.
    1. Hodis HN, Mack WJ, Lobo RA, Shoupe D, Sevanian A, et al. (2001) Estrogen in the prevention of atherosclerosis. A randomized, double-blind, placebo-controlled trial.Ann Intern Med 135: 939–953.
    1. Jöreskog KG (1969) A general approach to confirmatory maximum likelihood factor analysis. Psychometrika 34: 183–202.
    1. Hu LT, Bentler PM (1999) Cutoff Criteria for Fit Indexes in Covariance Structure Analysis: Conventional Criteria Versus New Alternatives, Structural Equation Modeling. 6: 1–55.
    1. Steiger JH (1990) Structural model evaluation and modification, Multivariate Behavioral Research. 25: 214–212.
    1. Schwarz GE (1978) “Estimating the dimension of a model”. Ann Stat 6: 461–464.
    1. Rosseel Y (2012) Lavaan: An R Package for Structural Equation Modeling. J Stat Softw 48: 1–36.
    1. Gibbons RD, Hedeker DR (1992) Full-information item bi-factor analysis. Psychometrika 57: 423–436.
    1. Lazarsfeld PF, Henry NW (1968) Latent Structure Analysis, Boston: Houghton Mifflin.
    1. Magidson J, Vermunt JK (2001) Latent class factor and cluster models, bi-plots and related graphical displays. Sociological Methodology 31: 223–264.
    1. Bandeen-Roche K, Miglioretti DL, Zeger SL, Rathouz PJ (1997) Latent variable regression for multiple discrete outcomes. JASA 92: 1375–1386.
    1. Nylund KL, Asparouhov T, Muthén B (2007) Deciding on the number of classes in latent class analysis and growth mixture modeling. A Monte Carlo simulation study. Structural Equation Modeling 14: 535–569.
    1. Lo Y, Mendell NR, Rubin DB (2001) Testing the number of components in a normal mixture. Biometrika 88: 767–778.
    1. Muthén B, Muthén L (2010) Mplus User’s Guide. Los Angeles, CA: Muthén & Muthén.
    1. Dempster AP, Laird NM, Rubin DM (1977) Maximum likelihood from incomplete data via the EM algorithm. J R Stat Soc B 39: 1–38.
    1. Breiman L (2001) Random forests. Machine Learning 45: 5–32.
    1. Liaw A, Wiener M (2002) Classification and regression by randomForest. R News 2–3: 18–22.
    1. Breiman L, Cutler A, Liaw A, Wiener M (2012) Breiman and Cutler’s Random Forests for Classification and Regression. R package version 4.6–6.
    1. Mickanin J, Grossman M, Onishi K, Auriacombe S, Clark C (1994) Verbal and non-verbal fluency in patients with probable Alzheimer’s disease. Neuropsychology 8: 385–394.
    1. Weingartner HJ, Kawas C, Rawlings R, Shapiro M (1993) Changes in semantic memory in early stages of Alzheimer’s disease patients. The Gerontologist 33: 637–643.
    1. Carew TG, Lamar M, Cloud BS, Grossman M, Libon DJ (1997) Impairment in category fluency in ischemic vascular dementia. Neuropsychology 11: 400–412.
    1. Giovannetti T, Lamar M, Cloud BS, Swenson R, Fein D, et al. (2001) Different underlying mechanisms for deficits in concept formation in dementia. Archives of Clinical Neuropsychology 16: 547–560.
    1. Knopman D, Boland LL, Mosley T, Howard G, Liao D, et al. (2001) Atherosclerosis Risk in Communities (ARIC) Study Investigators. Cardiovascular risk factors and cognitive decline in middle-aged adults. Neurology 56: 42–48.
    1. Yaffe K, Barrett-Connor E, Lin F, Grady D (2002) Serum lipoprotein levels, statin use, and cognitive function in older women. Arch Neurol 59: 378–384.
    1. Kivipelto M, Helkala EL, Laakso MP, Hanninen T, Alhainen K, et al. (2001) Midlife vascular risk factors and Alzheimer’s disease in late life: a longitudinal, population based study. BMJ 2 322: 1447–1451.
    1. Kerwin DR, Zhang Y, Kotchen JM, Epeland MA, Van Horn, L, et al (2010) The cross-sectional relationship between body mass index, waist-hip ratio, and cognitive performance in postmenopausal women enrolled in the Women’s Health Initiative. J Am Geriatr Soc. 58: 1427–1432.
    1. Kerwin DR, Gaussoin SA, Chlebowski RT, Kuller LH, Vitolins M, et al. (2011) Interaction between body mass index and central adiposity and risk of incident cognitive impairment and dementia: results from the Women’s Health Initiative Memory Study. J Am Geriatr Soc. 59: 107–112.
    1. Grundy SM, Pasternak R, Greenland P, Smith S Jr, Fuster V (1999) AHA/ACC scientific statement: assessment of cardiovascular risk by use of multiple-risk-factor assessment equations: a statement for healthcare professionals from the American Heart Association and the American College of Cardiology. J Am Coll Cardiol. 34: 1348–1359.
    1. Naqvi TZ, Mendoza F, Rafii F, Gransar H, Guerra M, et al. (2010) High prevalence of ultrasound detected carotid atherosclerosis in subjects with low Framingham risk score: potential implications for screening for subclinical atherosclerosis. J Am Soc Echocardiogr 23: 809–815.
    1. Shetterly SM, Rewers M, Hamman RF, Marshall JA (1994) Patterns and predictors of hypertension incidence among Hispanics and non-Hispanic whites: the San Luis Valley Diabetes Study. J Hypertens 12: 1095–1102.
    1. Luepker RV, Rosamond WD, Murphy R, Sprafka JM, Folsom AR, et al. (1993) Socioeconomic status and coronary heart disease risk factor trends. The Minnesota Heart Study. Circulation 88: 2172–2179.
    1. Eaker ED (1989) Psychological factors in the epidemiology of coronary heart disease in women. Psychiatric Clinics of North America 12: 167–174.
    1. Tyroler HA (1989) Socioeconomic status in the epidemiology and treatment of hypertension. Hypertension 13: 194–197.
    1. Dannenberg AL, Garrison RJ, Kannel WB (1988) Incidence of hypertension in the Framingham Study. Am J Public Health 78: 676–679.
    1. Kraus JF, Borhani NO, Franti CE (1980) Socioeconomic status, ethnicity, and risk of coronary heart disease. Am J Epidemiol 111: 407–414.
    1. Derby CA, Wildman RP, McGinn AP, Green RR, Polotsky AJ, et al. (2010) Cardiovascular risk factor variation within a Hispanic cohort: SWAN, the Study of Women’s Health Across the Nation. Ethn Dis. 20: 396–402.
    1. Harris TB, Savage PJ, Tell GS, Haan M, Kumanyika S, et al. (1997) Carrying the burden of cardiovascular risk in old age: associations of weight and weight change with prevalent cardiovascular disease, risk factors, and health status in the Cardiovascular Health Study. Am J Clin Nutr 66: 837–844.
    1. Burchfiel CM, Hamman RF, Marshall JA, Baxter J, Kahn LB, et al. (1990) Cardiovascular risk factors and impaired glucose tolerance: The San Luis Valley Diabetes Study. Am J Epidemiol 131: 57–70.
    1. Mitchell BD, Stern MP, Haffner SM, Hazuda HP, Patterson JK (1990) Risk factors for cardiovascular mortality in Mexican Americans and non-Hispanic whites: San Antonio Heart Study. Am J Epidemiol 131: 423–433.
    1. Reilly M, Rader DJ (2006) Apolipoprotein E and coronary disease: a puzzling paradox. PLoS Med 3: e258.
    1. Wilson PW, Schaefer EJ, Larson MG, Ordovas JM (1996) Apolipoprotein E alleles and risk of coronary disease: a meta-analysis. Arterioscler Thromb Vasc Biol 16: 1250–1255.
    1. Schaefer EJ, Lamon-Fava S, Cohn SD, Schaefer MM, Ordovas JM, et al. (1994) Effects of age, gender, and menopausal status on plasma low density lipoprotein cholesterol and apolipoprotein B levels in the Framingham Offspring Study J Lipid Res. 35: 770–792.

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