Canonical Correlation for the Analysis of Lifestyle Behaviors versus Cardiovascular Risk Factors and the Prediction of Cardiovascular Mortality: A Population Study
Abstract
:1. Introduction
2. Material and Methods
2.1. Study Population and Measurements
2.2. Statistical Analysis
- (a)
- Cox models including variate X and variate Y with a continuous shape predicting mortality from CHD, STROKE and HDUEs separately;
- (b)
- Similar Cox models to above including variate X and variate Y in three tertile classes, using tertile 1 (lowest) as a reference;
- (c)
- A Cox model with CHD mortality only and the five behavioral characteristics expressed as variable X in the canonical analysis as covariates (plus age);
- (d)
- The same Cox model as above with the addition of variate Y (continuous shape);
- (e)
- A Cox model with CHD mortality only and risk factors expressed as Y variables in the canonical analysis as covariates;
- (f)
- The same Cox model as above with the addition of variate X (continuous shape).
3. Results
3.1. Canonical Analysis
3.2. Cox Models Prediction
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
- Technical Note: Canonical correlation is a little known and little used procedure, as is its relative complexity. Below is a short list of terms that are specific to this statistical approach:
- -
- X and Y variables: two groups of original variables that are treated as opposite;
- -
- Variate X and variate Y: the linear combinations of X and Y variables, respectively (weighted averages of the original variables), that maximize the correlation between variate X and variate Y;
- -
- Variate X and Y coefficients: the regression coefficients of each variable within variate X and variate Y;
- -
- Canonical correlation: the linear correlation between variate X and variate Y;
- -
- Variate X score and variate Y score: values obtained by applying the coefficients of the linear combinations of variate X and Y to each individual separately;
- -
- Canonical coefficients: coefficients of the linear combination of the original variables in the construction of variates. They are an indicator of the influence of variables in the construction of variates;
- -
- Canonical loadings: the correlation coefficients of variates with the original variables. They are another indicator of the influence of variables in the construction of variates.
References
- Prabhat, J.P. The hazards of smoking and the benefits of cessation: A critical summation of the epidemiological evidence in high-income countries. eLife 2020, 9, e49979. [Google Scholar]
- Physical Activity and Health. A Report of the Surgeon General; US Department of Health and Human Services, Center for Disease Control and Prevention: Atlanta, GA, USA, 1996; pp. 1–278.
- The Mediterranean Diet. An Evidence-Based Approach, 2nd ed.; Preedy, V.R., Watson, R.R., Eds.; Academic Press: London, UK, 2020; pp. 1–588. [Google Scholar]
- Menotti, A.; Lanti, M.; Maiani, G.; Kromhout, D. Forty-year mortality from cardiovascular diseases and their risk factors in men of the Italian rural areas of the Seven Countries Study. Acta Cardiol. 2005, 60, 521–531. [Google Scholar] [CrossRef] [PubMed]
- Menotti, A.; Lanti, M.; Maiani, G.; Kromhout, D. Determinants of longevity and all-cause mortality among middle-aged men. Role of 48 risk factors in a 40-year follow-up of Italian rural areas in the Seven Countries Study. Aging Clin. Exp. Res. 2006, 18, 394–406. [Google Scholar] [CrossRef] [PubMed]
- Puddu, P.E.; Menotti, A.; Tolonen, H.; Nedeljkovic, S.; Kafatos, A.G. Determinants of 40-year all-cause mortality in the European cohorts of the Seven Countries Study. Eur. J. Epidemiol. 2011, 26, 595–608. [Google Scholar] [CrossRef] [PubMed]
- Puddu, P.E.; Menotti, A. Artificial neural networks versus proportional hazards Cox models to predict 45-year all-cause mortality in the Italian rural areas of the Seven Countries Study. BMC Med. Res. Methodol. 2012, 12, 100. [Google Scholar] [CrossRef] [PubMed]
- Menotti, A.; Puddu, P.E.; Lanti, M.; Maiani, G.; Fidanza, F. Cardiovascular risk factors predict survival in middle-aged men during 50 years. Eur. J. Intern. Med. 2013, 24, 67–74. [Google Scholar] [CrossRef] [PubMed]
- Menotti, A.; Puddu, P.E.; Lanti, M.; Maiani, G.; Catasta, G.; Alberti Fidanza, A. Lifestyle habits and mortality from all and specific causes of death: 40-year follow-up in the Italian rural areas of the Seven Countries Study. J. Nutr. Health Aging 2014, 18, 314–321. [Google Scholar] [CrossRef]
- Menotti, A.; Puddu, P.E. Lifetime prediction of coronary heart disease and heart disease of uncertain etiology in a 50-year follow-up population study. Int. J. Cardiol. 2015, 196, 55–60. [Google Scholar] [CrossRef]
- Puddu, P.E.; Piras, P.; Menotti, A. Lifetime competing risks between coronary heart disease mortality and other causes of death during 50 years of follow-up. Int. J. Cardiol. 2017, 228, 359–363. [Google Scholar] [CrossRef]
- Menotti, A.; Puddu, P.E.; Maiani, G.; Catasta, G. Lifestyle behavior and lifetime incidence of heart diseases. Int. J. Cardiol. 2015, 201, 293–299. [Google Scholar] [CrossRef]
- Menotti, A.; Puddu, P.E.; Maiani, G.; Catasta, G. Age at death as a useful indicator of healthy aging at population level: A 50-year follow-up of the Italian rural areas of the Seven Countries Study. Aging Exp. Clin. Res. 2018, 30, 901–911. [Google Scholar] [CrossRef] [PubMed]
- Menotti, A.; Puddu, P.E. How the Seven Countries Study contributed to the launch and development of cardiovascular epidemiology in Italy. A historical perspective. Nutr. Metab. Cardiovasc. Dis. 2020, 30, 368–383. [Google Scholar] [CrossRef] [PubMed]
- Menotti, A.; Puddu, V. Ten-year mortality from coronary heart disease among 172,000 men classified by occupational physical activity. Scand. J. Work. Environ. Health 1979, 5, 100–108. [Google Scholar] [CrossRef] [PubMed]
- Alberti Fidanza, A.; Seccareccia, F.; Torsello, S.; Fidanza, F. Diet of two rural population groups of middle-aged men in Italy. Intern. J. Vit. Nutr. Res. 1988, 58, 442–451. [Google Scholar]
- Menotti, A.; Alberti-Fidanza, A.; Fidanza, F.; Lanti, M.; Fruttini, D. Factor analysis in the identification of dietary patterns and their predictive role in morbid and fatal events. Public Health Nutr. 2012, 15, 1232–1239. [Google Scholar] [CrossRef] [PubMed]
- Rose, G.; Blackburn, H. Cardiovasc. Survey Methods; World Health Organization: Geneva, Switzerland, 1968; pp. 1–188. [Google Scholar]
- Hemsfield, S.B.; MacManus, C.; Smith, J.; Stevens, V.; Nixon, D.W. Anthropometric measurement of muscle mass: Revised equations for calculating bone-free arm muscle area. Am. J. Clin. Nutr. 1982, 36, 680–690. [Google Scholar] [CrossRef] [PubMed]
- Anderson, J.T.; Keys, A. Cholesterol in serum and lipoprotein fractions: Its measurement and stability. Clin. Chem. 1956, 2, 145–159. [Google Scholar] [CrossRef]
- World Health Organization. International Classification of Diseases and Causes of Death, 8th ed.; World Health Organization: Geneva, Switzerland, 1965; pp. 1–671. [Google Scholar]
- Afifi, A.A.; Clark, V. Computer-Aided Multivariate Analysis, 2nd ed.; Van Nostrand Reinhold: New York, NY, USA, 1990; pp. 1–505. [Google Scholar]
- NCSS-11 Statistical Software, NCSS, LLC: Kaysville, UT, USA, 2016. Available online: https://www.ness.com/software/ncss(accessed on 8 November 2023).
- Prospective Studies Collaboration. Blood cholesterol and vascular mortality by age, sex and blood pressure: A meta-analysis of individual data from 61 prospective studies with 55,000 vascular deaths. Lancet 2007, 370, 1829–1839. [Google Scholar] [CrossRef]
- Van Oort, S.; Beulens, J.W.J.; van Ballengooijen, J.; Burgess, S.; Larsson, S.C. Cardiovascular risk factors and lifestyle behaviors in relation to longevity: A Mendelian randomization study. J. Intern. Med. 2021, 289, 232–242. [Google Scholar] [CrossRef]
- Alamian, A.; Paradis, G. Individual and social determinants of multiple chronic disease behavioral risk factors among youth. BMC Public Health 2012, 12, 224. [Google Scholar] [CrossRef]
- Honda, T.; Chen, S.; Kishimoto, H.; Narazaki, K.; Kumagai, S. Identifying associations between sedentary time and cardiometabolic risk factors in working adults using objective and subjective measures: A cross sectional analysis. BMC Public Health 2014, 14, 1307. [Google Scholar] [CrossRef] [PubMed]
- Crichton, G.; Alkerwi, A. Physical activity, sedentary behavior time and lipid levels in the observation of cardiovascular risk factors in Luxembourg study. Lipids Health Dis. 2015, 14, 87. [Google Scholar] [CrossRef] [PubMed]
- Rao, D.P.; Orpana, H.; Krewski, D. Physical activity and non-movement behaviors: Their independent and combined associations with metabolic syndrome. Int. J. Behav. Nutr. Phys. Act. 2016, 13, 26. [Google Scholar] [CrossRef] [PubMed]
- Sauvageot, N.; Leite, S.; Alkerwi, A.; Sisanni, L.; Zannad, F.; Saverio, S. Association of empirically derived dietary patterns with cardiovascular risk factors. A comparison of PCA with RRR methods. PLoS ONE 2016, 11, e0161298. [Google Scholar] [CrossRef] [PubMed]
- Silfee, V.; Lemon, S.; Lora, V.; Rosal, M. Sedentary behavior and cardiovascular disease risk factors among Latino adults. J. Health Care Poor Underserved 2017, 28, 798–811. [Google Scholar] [CrossRef] [PubMed]
- Jezewska-Zychowicz, M.; Gebski, J.; Guzek, D.; Swiatkowska, M.; Stangierska, D.; Pitchta, M. The association between dietary pattern and sedentary behaviors in Polish adults (Lifestyle Study). Nutrients 2018, 10, 1004. [Google Scholar] [CrossRef] [PubMed]
- Gubelmann, C.; Antiochos, P.; Vollerweider, P.; Marques-Vidal, P. Association of activity behaviors and patterns with cardiovascular risk factors in Swiss middle-age adults. The Colaus study. Prev. Med. Rep. 2018, 11, 31–36. [Google Scholar] [CrossRef]
- Dash, S.R.; Hoare, E.; Varsamis, P.; Jennings, G.L.R.; Kingwll, B.A. Sex-specific lifestyle and biomedical risk factors for chronic disease among early-middle, middle and older aged Australian adults. Int. J. Environ. Res. Public Health 2019, 16, 224. [Google Scholar] [CrossRef]
- Shiffman, D.; Louie, J.Z.; Devlin, J.J.; Rowlan, C.M.; Mora, S. Concordance of cardiovascular risk factor and behaviors in a multiethnic US nationwide cohorts of married couples and domestic partners. JAMA Netw. Open 2020, 3, e2022119. [Google Scholar] [CrossRef]
- Menotti, A.; Seccareccia, F. Risk factors and mortality patterns in the Seven Countries Study. In Lessons for Science from the Seven Countries Study; Toshima, H., Loga, Y., Blackburn, H., Keys, A., Eds.; Springer: Tokyo, Japan, 1994; pp. 17–33. [Google Scholar]
- Băldescu, R.; Macarie, E.; Schioiu-Costache, L.; Suciu, A. Canonical correlation analysis as a special method for the study of the structural relations of risk factors in cardiovascular diseases. Rom. J. Intern. Med. 1991, 29, 133–138. [Google Scholar]
- Lyu, L.C.; Shieh, M.J.; Bailey, G.E.; Carrasco, W.I.; Ordivas, J.M.; Lichtenstein, A.H.; Schaefer, J. Relationship of body fat distribution with cardiovascular risk factors in healthy Chinese. Ann. Epidemiol. 1994, 4, 434–444. [Google Scholar] [CrossRef] [PubMed]
- Reeder, B.A.; Senthilselvan, A.; Despres, J.P.; Angel, A.; Liu, L.; Wang, H.; Rabkin, S.W. The association of cardiovascular disease risk factors with abdominal obesity in Canada. Canadian Heart Health Surveys Research Group. CMAJ 1997, 157 (Suppl. S1), S39–S45. [Google Scholar] [PubMed]
- Waaijenborg, S.; Zwindermman, A.H. Associating multiple longitudinal traits with high-dimensional single-nucleotide polymorphism data: Application to the Framingham Heart Study. BMC Proc. 2009, 15 (Suppl. S7), S47. [Google Scholar] [CrossRef] [PubMed]
- Yu, N.; Zhang, Q.; Zhang, L.; He, T.; Liu, Q.; Zhang, S. Canonical correlation analysis (CCA) of anthropometric parameters and physical activities and blood lipids. Lipids Health Dis. 2017, 16, 236. [Google Scholar] [CrossRef]
- Adza, W.K.; Hursthouse, A.S.; Miller, J.; Boakye, D. Exploring the Joint Association of Road Traffic Noise and Air Quality with Hypertension Using QGIS. Int. J. Environ. Res. Public Health 2023, 20, 2238. [Google Scholar] [CrossRef]
Risk Factor | Definition and Details | Unit of Measurement | Mean (and SD) or Proportion (%) (and SE) | Type of Variable: X or Y | References | Notes |
---|---|---|---|---|---|---|
Cigarette smoking (*) | Derived from a questionnaire and classified as: | X | ||||
Smokers | Code 1 | 61.0 (1.2) | ||||
Ex-smokers | Code 2 | 13.6 (0.8) | ||||
Never smoked | Code 3 | 25.4 (1.1) | ||||
Physical activity (*) | Job-related; derived from questions matched with reported occupations. Classified as: | X | [15,16] | Validated through ergonometric measurements and energy intake | ||
Sedentary | Code 1 | 9.7 (0.7) | ||||
Moderate | Code 2 | 22.1 (1.0) | ||||
Vigorous | Code 3 | 68.2 (1.1) | ||||
Dietary habits (*) | Derived from dietary history. Classified as: | X | [16,17] | Factor score derived from factor analysis of 18 food groups | ||
Non-Mediterranean diet | Code 1 | 33.4 (1.1) | Arbitrary denomination and selection | |||
Intermediate diet | Code 2 | 33.2 (1.1) | Arbitrary denomination and selection | |||
Mediterranean diet | Code 3 | 33.4 (1.1) | X | Arbitrary denomination and selection | ||
Marital status (*) | Currently married | 0 = no 1 = yes | 90.5 (0.7) | X | From questionnaire | |
High socioeconomic status (*) | Professional, business, public administrators, foremen and high-ranking clerical workers | 0 = no 1 = yes | 7.8 (0.6) | X | From questionnaire | |
Body mass index | Weight/height squared | kg/m2 | 25.2 (3.7) | Y | [18] | |
Tricipital skinfold | Right arm | mm | 9.4 (5.4) | Y | [18] | |
Subscapular skinfold | Right side | mm | 11.8 (5.8) | Y | [18] | |
Midarm circumference | Right arm; Tricipital skinfold was mathematically subtracted from midarm circumference for estimating only the muscular mass | mm | 268.6 (23.6) | Y | [18,19] | |
Systolic blood pressure | Supine; average of two measurements | mmHg | 143.6 (21.0) | Y | [18] | |
Diastolic blood pressure | Supine; average of two measurements | mmHg | Y | [18] | Fifth phase | |
Heart rate | From ECG; average rate in lead I and V6 | beats/min | 71.3 (12.9) | Y | ||
Double product | Systolic blood pressure times heart rate | score | 10,328 (2858) | Y | Indicator of oxygen consumption | |
Vital capacity | Best of two tests; adjusted (divided by height2) | L/m2 | 1.65 (0.24) | Y | [18] | |
Forced expiratory volume in ¾ s | Best of two tests; adjusted (divided by height2) | L/m2 | 1.08 (0.24) | Y | [18] | |
Serum cholesterol | Abell–Kendall method modified by Anderson and Keys; casual blood sample | mg/dL | 201.6 (40.8) | Y | [20] | |
Urine protein (*) | Spot urine sample; semiquantitative method by stix; definite present | 0 = absent 1 = present | 7.8 (0.65) | Y | ||
Urine glucose (*) | Spot urine sample; semiquantitative method by stix; definite present. | 0 = absent 1 = present | 4.5 (0.50) | Y | ||
Corneal arcus (*) | Clinical judgement | 0 = no 1 = yes | 13.9 (0.83) | Y | ||
Xanthelasma (*) | Clinical judgement | 0 = no 1 = yes | 1.5 (0.30) | Y |
X Variables | Standardized Canonical Coefficients | Rank | Canonical Loadings | Rank |
---|---|---|---|---|
Dietary habits | 0.8378 | 1 | 0.8671 | 1 |
Physical activity | 0.2934 | 2 | 0.4499 | 2 |
Smoking habits | −0.2885 | 3 | −0.2497 | 4 |
High SES | −0.2016 | 4 | −0.3343 | 3 |
Marital status | 0.0338 | 5 | 0.0626 | 5 |
Y Variables | Standardized Canonical Coefficients | Rank | Canonical loadings | Rank |
Heart rate | −0.8253 | 1 | −0.5668 | 6 |
Double product | 0.7576 | 2 | −0.6833 | 4 |
Systolic blood pressure | −0.5960 | 3 | −0.5718 | 5 |
Tricipital skinfold | −0.3990 | 4 | −0.7928 | 1 |
Body mass index | −0.3220 | 5 | −0.7058 | 3 |
Arm circumference | 0.1107 | 6 | −0.1001 | 11 |
Subscapular skinfold | −0.1062 | 7 | −0.7558 | 2 |
Diastolic blood pressure | −0.1006 | 8 | −0.5593 | 7 |
Urine protein | −0.0957 | 9 | −0.1941 | 10 |
Corneal arcus | −0.0640 | 10 | −0.0765 | 15 |
Vital capacity | 0.0424 | 11 | 0.2744 | 8 |
Urine glucose | 0.0419 | 12 | −0.0877 | 13 |
Xanthelasma | −0.0373 | 13 | −0.0975 | 12 |
Forced expiratory volume | 0.0179 | 14 | 0.0859 | 14 |
Serum cholesterol | −0.0129 | 15 | −0.2721 | 9 |
Risk Factors | Tertile 1 | Tertile 2 | Tertile 3 | p of ANOVA | |||
---|---|---|---|---|---|---|---|
Mean | SD | Mean | SD | Mean | SD | ||
Body mass index | 26.7 | 3.96 | 25.19 | 3.57 | 23.69 | 2.83 | <0.0001 |
Tricipital skinfold | 12.0 | 6.0 | 9.4 | 4.9 | 6.9 | 3.8 | <0.0001 |
Subscapular skinfold | 14.4 | 6.4 | 11.7 | 5.5 | 9.3 | 4.0 | <0.0001 |
Arm circumference | 270.0 | 25.7 | 256.7 | 23.6 | 267.0 | 21.3 | 0.0814 |
Systolic blood pressure | 151.4 | 22.7 | 142.1 | 19.5 | 137.4 | 17.9 | <0.0001 |
Diastolic blood pressure | 89.6 | 11.9 | 84.6 | 10.4 | 82.3 | 10.0 | <0.0001 |
Heart rate | 75.7 | 14.0 | 71.0 | 11.9 | 67.2 | 11.1 | <0.0001 |
Double product | 11,556 | 3222 | 10,145 | 2529 | 9285 | 2265 | <0.0001 |
Vital capacity | 1.60 | 0.25 | 1.65 | 0.24 | 1.69 | 0.22 | <0.0001 |
Forced expiratory volume | 1.07 | 0.24 | 1.09 | 0.26 | 1.10 | 0.23 | 0.1787 |
Serum cholesterol | 206.9 | 43.5 | 202.4 | 39.7 | 195.5 | 38.3 | <0.0001 |
Urine protein * | 0.12 | 0.01 | 0.06 | 0.01 | 0.49 | 0.009 | <0.0001 |
Urine glucose * | 0.05 | 0.01 | 0.05 | 0.01 | 0.03 | 0.01 | 0.1498 |
Corneal arcus * | 0.15 | 0.02 | 0.15 | 0.02 | 0.12 | 0.01 | 0.1853 |
Xanthelasma * | 0.021 | 0.144 | 0.016 | 0.125 | 0.037 | 0.073 | 0.2346 |
Physical Activity | Arm Circumference mm | Forced Expiratory Volume L/m2 | Double Product SBP × HR | |||
---|---|---|---|---|---|---|
Mean | SD | Mean | SD | Mean | SD | |
Sedentary | 259 | 26.7 | 1.043 | 0.26 | 11,580 | 3307 |
Moderate | 268 | 25.6 | 1.062 | 0.25 | 10,828 | 3104 |
Vigorous | 270 | 22.1 | 1.097 | 0.24 | 9988 | 2627 |
p of ANOVA | <0.001 | 0.004 | <0.001 | |||
Diet Score | Systolic Blood Pressure mmHg | Body Mass Index Kg/m2 | Serum Cholesterol mg/dL | |||
Mean | SD | Mean | SD | Mean | SD | |
Non-Mediterranean | 152.2 | 23.0 | 26.6 | 26.7 | 206.1 | 40.6 |
Intermediate | 143.2 | 18.8 | 25.2 | 25.3 | 202.2 | 48.7 |
Mediterranean | 136.4 | 17.6 | 23.7 | 23.6 | 196.4 | 37.5 |
p of ANOVA | <0.001 | <0.001 | <0.001 |
Cox Model Predicting CHD Mortality with X and Y Variates in a Continuous Shape | ||||
---|---|---|---|---|
Covariates | Coefficient | Hazard Ratio | 95% CI | p of Coefficient |
X variate score continuous | −0.2573 | 0.77 | 0.67–0.89 | 0.0003 |
Y variate score continuous | −0.1582 | 0.85 | 0.74–0.98 | 0.0255 |
Cox Model Predicting CHD Mortality with X and Y Variates in a Discrete Shape | ||||
Covariates | Coefficient | Hazard Ratio | 95% CI | p of Coefficient |
Tertile 1 X variate score | Reference | — | — | — |
Tertile 2 X variate score | −0.2362 | 0.59 | 0.44–0.79 | 0.0004 |
Tertile 3 X variate score | −0.6980 | 0.68 | 0.49–0.94 | 0.0188 |
Tertile 1 Y variate score | Reference | — | — | — |
Tertile 2 Y variate score | −0.5321 | 0.79 | 0.59–1.05 | 0.1047 |
Tertile 3 Y variate score | −0.3871 | 0.50 | 0.36–0.70 | 0.0001 |
Cox Model Predicting STROKE Mortality with X and Y Variates in a Continuous Shape | ||||
---|---|---|---|---|
Covariates | Coefficient | Hazard Ratio | 95% CI | p of Coefficient |
X variate score continuous | −0.0386 | 0.96 | 0.82–1.13 | 0.6305 |
Y variate score continuous | −0.2185 | 0.80 | 0.69–0.94 | 0.0065 |
Cox Model Predicting STROKE Mortality with X and Y Variates in a Discrete Shape | ||||
Covariates | Coefficient | Hazard Ratio | 95% CI | p of Coefficient |
Tertile 1 X variate score | Reference | — | — | — |
Tertile 2 X variate score | −0.1454 | 0.86 | 0.62–1.21 | 0.3917 |
Tertile 3 X variate score | −0.2424 | 0.78 | 0.54–1.13 | 0.1991 |
Tertile 1 Y variate score | Reference | — | — | — |
Tertile 2 Y variate score | 0.0099 | 1.01 | 0.73–1.41 | 0.9530 |
Tertile 3 Y variate score | −0.2703 | 0.76 | 0.53–1.11 | 0.1551 |
Cox Model Predicting HDUE Mortality with X and Y Variates in a Continuous Shape | ||||
---|---|---|---|---|
Covariates | Coefficient | Hazard Ratio | 95% CI | p of Coefficient |
X variate score continuous | −0.1149 | 0.89 | 0.75–1.05 | 0.1799 |
Y variate score continuous | 0.0244 | 1.02 | 0.86–1.22 | 0.7845 |
Cox Model Predicting HDUE Mortality with X and Y Variates in a Discrete Shape | ||||
Covariates | Coefficient | Hazard Ratio | 95% CI | p of Coefficient |
Tertile 1 X variate score | Reference | — | — | — |
Tertile 2 X variate score | −0.4502 | 0.64 | 0.44–0.92 | 0.0147 |
Tertile 3 X variate score | −0.5327 | 0.59 | 0.40–0.86 | 0.0070 |
Tertile 1 Y variate score | Reference | — | — | — |
Tertile 2 Y variate score | 0.1216 | 1.13 | 0.77–1.65 | 0.5286 |
Tertile 3 Y variate score | 0.2913 | 1.33 | 0.90–1.98 | 0.1470 |
Model 1 Five Behaviors Plus Age | Model 2 Behaviors Plus Variate Y Score | Model 3 Eleven Risk Factors Plus Age | Model 4 Eleven Risk Factors Plus Variate X Score | |
---|---|---|---|---|
Loglikelihood | −1821 | −1812 | −1799 | −1796 |
Chi squared Informativeness | Model 1 versus model 2 17.8 (p < 0.0001) | Model 3 versus Model 4 5.2 (p = 0.0226) | ||
Akaike information criterion | −3.01 | −1.00 | 9.01 | 11.00 |
AUC | 0.524 | 0.547 | 0.552 | 0.557 |
p of difference | Model 1 versus Model 2 0.0477 | Model 3 versus Model 4 0.2206 |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Menotti, A.; Puddu, P.E. Canonical Correlation for the Analysis of Lifestyle Behaviors versus Cardiovascular Risk Factors and the Prediction of Cardiovascular Mortality: A Population Study. Hearts 2024, 5, 29-44. https://doi.org/10.3390/hearts5010003
Menotti A, Puddu PE. Canonical Correlation for the Analysis of Lifestyle Behaviors versus Cardiovascular Risk Factors and the Prediction of Cardiovascular Mortality: A Population Study. Hearts. 2024; 5(1):29-44. https://doi.org/10.3390/hearts5010003
Chicago/Turabian StyleMenotti, Alessandro, and Paolo Emilio Puddu. 2024. "Canonical Correlation for the Analysis of Lifestyle Behaviors versus Cardiovascular Risk Factors and the Prediction of Cardiovascular Mortality: A Population Study" Hearts 5, no. 1: 29-44. https://doi.org/10.3390/hearts5010003
APA StyleMenotti, A., & Puddu, P. E. (2024). Canonical Correlation for the Analysis of Lifestyle Behaviors versus Cardiovascular Risk Factors and the Prediction of Cardiovascular Mortality: A Population Study. Hearts, 5(1), 29-44. https://doi.org/10.3390/hearts5010003