Combining Personality Traits with Traditional Risk Factors for Coronary Stenosis: An Artificial Neural Networks Solution in Patients with Computed Tomography Detected Coronary Artery Disease
Background. Coronary artery disease (CAD) is a complex, multifactorial disease in which personality seems to play a role but with no definition in combination with other risk factors. Objective. To explore the nonlinear and simultaneous pathways between traditional and personality traits risk factors and coronary stenosis by Artificial Neural Networks (ANN) data mining analysis. Method. Seventy-five subjects were examined for traditional cardiac risk factors and personality traits. Analyses were based on a new data mining method using a particular artificial adaptive system, the autocontractive map (AutoCM). Results. Several traditional Cardiovascular Risk Factors (CRF) present significant relations with coronary artery plaque (CAP) presence or severity. Moreover, anger turns out to be the main factor of personality for CAP in connection with numbers of traditional risk factors. Hidden connection map showed that anger, hostility, and the Type D personality subscale social inhibition are the core factors related to the traditional cardiovascular risk factors (CRF) specifically by hypertension. Discussion. This study shows a nonlinear and simultaneous pathway between traditional risk factors and personality traits associated with coronary stenosis in CAD patients without history of cardiovascular disease. In particular, anger seems to be the main personality factor for CAP in addition to traditional risk factors. 1. Introduction Coronary artery disease (CAD) is a complex, multifactorial disease in which genetic predisposition, lifestyle, and environmental risk factors play a key role, the combination of which is not known. Studies on traditional cardiac risk factors have shown that older age, higher body mass index, male gender, diabetes, hypertension, and dyslipidemia increase the likelihood of the coronary artery plaque (CAP) burden and, moreover, these risk factors are directly related to 10-year risk of fatal cardiovascular events (CVE) [1]. Framingham Heart Study models [2] and the SCORE model [3] are predictive risk charts and among the more extensively used ones in cardiovascular disease prevention. These models are based on logistic regression [4] or Cox proportional hazards [5] predictive algorithms. However, weaknesses in the calibration and discrimination have been acknowledged which affect the reliability of these risk prediction tools [6]. Limitations could be due to the existence of unknown risk factors, incidence rates of the disease, and factors that are difficult to reproduce in every day. Although algorithms for cardiovascular risk
References
[1]
F. F. Faletra, C. Klersy, I. D'Angeli et al., “Relation between coronary atherosclerotic plaques and traditional risk factors in people with no history of cardiovascular disease undergoing multi-detector computed coronary angiography,” Heart, vol. 95, no. 15, pp. 1265–1272, 2009.
[2]
W. B. Kannel, D. McGee, and T. Gordon, “A general cardiovascular risk profile: the Framingham study,” American Journal of Cardiology, vol. 38, no. 1, pp. 46–51, 1976.
[3]
R. M. Conroy, K. Py？r？l？, A. P. Fitzgerald et al., “Estimation of ten-year risk of fatal cardiovascular disease in Europe: the SCORE project,” European Heart Journal, vol. 24, no. 11, pp. 987–1003, 2003.
[4]
K. Rothman and S. Greenland, Modern Epidemiology, Lippincott Williams & Wilkins, New York, NY, USA, 1998.
[5]
D. Kleinbaum, Survival Analysis, Springer, New York, NY, USA, 1996.
[6]
D. B. Panagiotakos and V. Stavrinos, “Methodological issues in cardiovascular epidemiology: the risk of determining absolute risk through statistical models,” Vascular Health and Risk Management, vol. 2, no. 3, pp. 309–315, 2006.
[7]
E. Grossi, “How artificial intelligence tools can be used to assess individual patient risk in cardiovascular disease: problems with the current methods,” BMC Cardiovascular Disorders, vol. 6, p. 20, 2006.
[8]
R. Voss, P. Cullen, H. Schulte, and G. Assmann, “Prediction of risk of coronary events in middle-aged men in the Prospective Cardiovascular Münster Study (PROCAM) using neural networks,” International Journal of Epidemiology, vol. 31, no. 6, pp. 1253–1262, 2002.
[9]
P. E. Puddu and A. Menotti, “Artificial neural network versus multiple logistic function to predict 25-year coronary heart disease mortality in the Seven Countries Study,” European Journal of Cardiovascular Prevention and Rehabilitation, vol. 16, no. 5, pp. 583–591, 2009.
[10]
Y. Chida and A. Steptoe, “The association of anger and hostility with future coronary heart disease. A meta-analytic review of prospective evidence,” Journal of the American College of Cardiology, vol. 53, no. 11, pp. 936–946, 2009.
[11]
J. Denollet, J. Vaes, and D. L. Brutsaert, “Inadequate response to treatment in coronary heart disease: adverse effects of type D personality and younger age on 5-year prognosis and quality of life,” Circulation, vol. 102, no. 6, pp. 630–635, 2000.
[12]
P. Greenland, M. D. Knoll, J. Stamler et al., “Major risk factors as antecedents of fatal and nonfatal coronary heart disease events,” Journal of the American Medical Association, vol. 290, no. 7, pp. 891–897, 2003.
[13]
A. Compare, E. Germani, R. Proietti, and D. Janeway, “Clinical psychology and cardiovascular disease: an up-to-date clinical practice review for assessment and treatment of anxiety and depression,” Clinical Practice and Epidemiology in Mental Health, vol. 7, pp. 148–156, 2011.
[14]
A. Compare, “Personality traits, cardiac risk factors and coronary artery plaque in people with no history of cardiovascular disease,” Journal of Cardiovascular Medicine, 2013.
[15]
E. O'Brien, N. Atkins, G. Stergiou et al., “European society of hypertension international protocol revision 2010 for the validation of blood pressure measuring devices in adults,” Blood Pressure Monitoring, vol. 15, no. 1, pp. 23–38, 2010.
[16]
K. G. Alberti, R. H. Eckel, S. M. Grundy et al., “Harmonizing the metabolic syndrome: a joint interim statement of the international diabetes federation task force on epidemiology and prevention; national heart, lung, and blood institute; American heart association; world heart federation; international atherosclerosis society; and international association for the study of obesity,” Circulation, vol. 120, no. 16, pp. 1640–1645, 2009.
[17]
W. W. Cook and D. M. Medley, “Proposed hostility and Pharisaic-virtue scales for the MMPI,” Journal of Applied Psychology, vol. 38, no. 6, pp. 414–418, 1954.
[18]
J. Suls and C. K. Wan, “The relationship between trait hostility and cardiovascular reactivity: a quantitative review and analysis,” Psychophysiology, vol. 30, no. 6, pp. 615–626, 1993.
[19]
S. H. Boyle, J. E. Michalek, and E. C. Suarez, “Covariation of psychological attributes and incident coronary heart disease in U.S. Air Force veterans of the Vietnam war,” Psychosomatic Medicine, vol. 68, no. 6, pp. 844–850, 2006.
[20]
J. Butcher, J. R. Graham, Y. S. Ben-Porath, A. Tellegen, W. G. Dahlstrom, and B. Kaemmer, Minnesota Multiphasic Personality Inventory-2 (MMPI-2): Manual For Administration and Scoring, University of Minnesota Press, Minneapolis, Minn, USA, 1989.
[21]
J. Butcher, J. R. Graham, C. L. Williams, and Y. S. Ben-Porath, Development and Use of the MMPI-2 Content Scales, University of Minnesota Press, Minneapolis, Minn, USA, 1990.
[22]
A. L. Brophy, “MMPI and MMPI-2 scores on the Cook-Medley hostility scale,” Psychological Reports, vol. 80, no. 3, pp. 1087–1090, 1997.
[23]
L. D. Kubzansky, S. R. Cole, I. Kawachi, P. Vokonas, and D. Sparrow, “Shared and unique contributions of anger, anxiety, and depression to coronary heart disease: a prospective study in the normative aging study,” Annals of Behavioral Medicine, vol. 31, no. 1, pp. 21–29, 2006.
[24]
J. Denollet, “DS14: standard assessment of negative affectivity, social inhibition, and type D personality,” Psychosomatic Medicine, vol. 67, no. 1, pp. 89–97, 2005.
[25]
E. Ferguson, L. Williams, R. C. O’Connor, et al., “A taxometric analysis of type-D personality,” Psychosomatic Medicine, vol. 71, no. 9, pp. 981–986, 2009.
[26]
M. Buscema and E. Grossi, “The semantic connectivity map: an adapting self-organising knowledge discovery method in data bases. Experience in gastro-oesophageal reflux disease,” International Journal of Data Mining and Bioinformatics, vol. 2, no. 4, pp. 362–404, 2008.
[27]
F. Licastro, E. Porcellini, M. Chiappelli et al., “Multivariable network associated with cognitive decline and dementia,” Neurobiology of Aging, vol. 31, no. 2, pp. 257–269, 2010.
[28]
F. Licastro, M. Chiappelli, E. Porcellini et al., “Gene-gene and gene-clinical factors interaction in acute myocardial infarction: a new detailed risk chart,” Current Pharmaceutical Design, vol. 16, no. 7, pp. 783–788, 2010.
[29]
A. Rozanski, J. A. Blumenthal, and J. Kaplan, “Impact of psychological factors on the pathogenesis of cardiovascular disease and implications for therapy,” Circulation, vol. 99, no. 16, pp. 2192–2217, 1999.
[30]
T. W. Smith, K. Glazer, J. M. Ruiz, and L. C. Gallo, “Hostility, anger, aggressiveness, and coronary heart disease: an interpersonal perspective on personality, emotion, and health,” Journal of Personality, vol. 72, no. 6, pp. 1217–1270, 2004.
[31]
A. Compare, M. Manzoni, E. Molinari, and A. M？ller, “Personalità di tipo A e di tipo D, rabbia e rischio di recidiva cardiaca, in Mente e cuore,” in Clinica Psicologica Della Malattia Cardiaca, E. Molinari, A. Compare, and G. Parati, Eds., Springer, Milano, Italy, 2007.
[32]
I. Kawachi, D. Sparrow, A. Spiro, P. Vokonas, and S. T. Weiss, “A prospective study of anger and coronary heart disease: the normative aging study,” Circulation, vol. 94, no. 9, pp. 2090–2095, 1996.
[33]
R. L. Verrier and M. A. Mittleman, “Life-threatening cardiovascular consequences of anger in patients with coronary heart disease,” Cardiology Clinics, vol. 14, no. 2, pp. 289–308, 1996.
[34]
M. Al'Absi and D. K. Arnett, “Adrenocortical responses to psychological stress and risk for hypertension,” Biomedicine and Pharmacotherapy, vol. 54, no. 5, pp. 234–244, 2000.
[35]
B. S. McEwen and E. Stellar, “Stress and the individual: mechanisms leading to disease,” Archives of Internal Medicine, vol. 153, no. 18, pp. 2093–2101, 1993.
[36]
C. K. Whalen, L. D. Jamner, B. Henker, and R. J. Delfino, “Smoking and moods in adolescents with depressive and aggressive dispositions: evidence from surveys and electronic diaries,” Health Psychology, vol. 20, no. 2, pp. 99–111, 2001.
[37]
K. T. Brady and S. C. Sonne, “The role of stress in alcohol use, alcoholism treatment, and relapse,” Alcohol Research and Health, vol. 23, no. 4, pp. 263–271, 1999.
[38]
L. Sher, “Type D personality: the heart, stress and cortisol,” QJM, vol. 98, no. 5, pp. 323–329, 2005.
[39]
K. R. O'Dell, K. S. Masters, G. I. Spielmans, and S. A. Maisto, “Does type-D personality predict outcomes among patients with cardiovascular disease? A meta-analytic review,” Journal of Psychosomatic Research, vol. 71, no. 4, pp. 199–206, 2011.
[40]
S. S. Pedersen, J. Daemen, M. van de Sande et al., “Type-D personality exerts a stable, adverse effect on vital exhaustion in PCI patients treated with paclitaxel-eluting stents,” Journal of Psychosomatic Research, vol. 62, no. 4, pp. 447–453, 2007.
[41]
J. Denollet and V. M. Conraads, “Type D personality and vulnerability to adverse outcomes in heart disease,” Cleveland Clinic Journal of Medicine, vol. 78, supplement 1, pp. S13–S19, 2011.
[42]
J. J. Gross and R. W. Levenson, “Hiding feelings: the acute effects of inhibiting negative and positive emotion,” Journal of Abnormal Psychology, vol. 106, no. 1, pp. 95–103, 1997.
[43]
S. W. Cole, M. E. Kemeny, O. B. Weitzman, M. Schoen, and P. A. Anton, “Socially inhibited individuals show heightened DTH response during intense social engagement,” Brain, Behavior, and Immunity, vol. 13, no. 2, pp. 187–200, 1999.
[44]
M. E. Habra, W. Linden, J. C. Anderson, and J. Weinberg, “Type D personality is related to cardiovascular and neuroendocrine reactivity to acute stress,” Journal of Psychosomatic Research, vol. 55, no. 3, pp. 235–245, 2003.
[45]
C. Carpeggiani, M. Emdin, F. Bonaguidi et al., “Personality traits and heart rate variability predict long-term cardiac mortality after myocardial infarction,” European Heart Journal, vol. 26, no. 16, pp. 1612–1617, 2005.
[46]
M. Maes, C. Song, A. Lin et al., “The effects of psychological stress on humans: increased production of pro-inflammatory cytokines and a Th1-like response in stress-induced anxiety,” Cytokine, vol. 10, no. 4, pp. 313–318, 1998.
[47]
W. Gerin, T. G. Pickering, L. Glynn, et al., “An historical context for behavioral models of hypertension,” Journal of Psychosomatic Research, vol. 48, no. 4-5, pp. 369–377, 2000.
[48]
S. B. Manuck, A. L. Kasprowicz, and M. F. Muldoon, “Behaviorally-evoked cardiovascular reactivity and hypertension: conceptual issues and potential associations,” Annals of Behavioral Medicine, vol. 12, no. 1, pp. 17–29, 1990.
[49]
S. B. Manuck, “Cardiovascular reactivity in cardiovascular disease: ‘Once more unto the breach’,” International Journal of Behavioral Medicine, vol. 1, no. 1, pp. 4–31, 1994.
[50]
D. L. Roter and C. K. Ewart, “Emotional inhibition in essential hypertension: obstacle to communication during medical visits?” Health Psychology, vol. 11, no. 3, pp. 163–169, 1992.
[51]
T. van Strien, F. A. van de Laar, J. F. J. van Leeuwe et al., “The Dieting dilemma in patients with newly diagnosed type 2 diabetes: does dietary restraint predict weight gain 4 years after diagnosis?” Health Psychology, vol. 26, no. 1, pp. 105–112, 2007.
[52]
F. X. Pi-Sunyer, “The obesity epidemic: pathophysiology and consequences of obesity,” Obesity Research, vol. 10, supplement 2, pp. 97S–104S, 2002.
[53]
M. Buscema, E. Grossi, D. Snowdon, and P. Antuono, “Auto-contractive maps: an artificial adaptive system for data mining. An application to Alzheimer disease,” Current Alzheimer Research, vol. 5, no. 5, pp. 481–498, 2008.
[54]
A. Villani, G. Malfatto, F. Della Rosa et al., “Disease management for heart failure patients: role of wireless technologies for telemedicine. The ICAROS project,” Giornale Italiano di Cardiologia, vol. 8, no. 2, pp. 107–114, 2007.
[55]
G. M. Manzoni, V. Villa, A. Compare et al., “Short-term effects of a multi-disciplinary cardiac rehabilitation programme on psychological well-being, exercise capacity and weight in a sample of obese in-patients with coronary heart disease: a practice-level study,” Psychology, Health and Medicine, vol. 16, no. 2, pp. 178–189, 2011.
[56]
A. Compare, C. Zarbo, G. M. Manzoni, et al., “Social support, depression, and heart disease: a ten year literature review,” Frontiers in Psychology, vol. 4, p. 384, 2013.
[57]
A. Compare, R. Proietti, D. Del Forno et al., “Vulnerable personality and takotsubo cardiomyopathy consequent to emotional stressful events: a clinical case report,” Monaldi Archives for Chest Disease, vol. 76, no. 2, pp. 99–103, 2011.
