Hearts (Jan 2024)

Canonical Correlation for the Analysis of Lifestyle Behaviors versus Cardiovascular Risk Factors and the Prediction of Cardiovascular Mortality: A Population Study

  • Alessandro Menotti,
  • Paolo Emilio Puddu

DOI
https://doi.org/10.3390/hearts5010003
Journal volume & issue
Vol. 5, no. 1
pp. 29 – 44

Abstract

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Objectives: To assess the overall association of lifestyle behaviors with multiple cardiovascular risk factors and mortality. Material and Methods: In the Italian Rural Areas of the Seven Countries Study, involving 1712 middle-aged men (40–59 years) enrolled in 1960, smoking habits, physical activity, dietary habits, marital status, and socioeconomic status (SES) were studied as possible determinants of 15 measurable risk factors (body mass index, tricipital and subscapular skinfold, arm circumference, systolic and diastolic blood pressure, heart rate, double product (systolic blood pressure × heart rate), vital capacity, forced expiratory volume, serum cholesterol, urine protein, urine glucose, corneal arcus and xanthelasma) using canonical correlation (CC). Results: The first CC had a value of 0.54 (R2 0.29, p < 0.0001). The role of marital status was marginal; that of a high SES was contrary to expectations. The strongest behaviors based on standardized CC coefficients were dietary habits and physical activity. The risk factors mostly associated with overall lifestyle behaviors were some anthropometric and cardiovascular measurements. The mean levels of risk factors distributed in tertile classes of the CC variate score of lifestyle behaviors were largely associated in a coherent and graded way with the expected relationship of behaviors versus risk factors. In a large series of Cox models, the CC variate scores were significantly associated with 50-year coronary heart disease (CHD) mortality and much less with stroke and other heart diseases of uncertain etiology. Conclusions: Lifestyle behaviors correlate well with cardiovascular risk factors associated with CHD mortality, and CC is a useful method of analysis to detect long-term impacting characteristics.

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