Associations of empirically derived eating patterns with plasma lipid biomarkers: a comparison of factor and cluster analysis methods.
BACKGROUND
Despite the growing use of patterning methods in nutritional epidemiology, a direct comparison of factor and cluster analysis methods has not been performed.


OBJECTIVE
Our main objective was to compare patterns derived from the cluster and factor analysis procedures with measures of plasma lipids.


METHODS
This cross-sectional study included 459 healthy subjects who participated in the Baltimore Longitudinal Study of Aging and had measures of diet and plasma lipids. Eating patterns were derived by using both factor and cluster analysis methods.


RESULTS
In separate multivariate-adjusted regression models, subjects in the healthy cluster had lower plasma triacylglycerols than did those not in the healthy cluster (beta = -15.97; 95% CI: -29.51, -2.43; P < 0.05), and factor 1 (reduced-fat dairy products, fruit, and fiber) was inversely related to plasma triacylglycerols (beta = -7.02 mg/dL for a one-unit increase in z score; 95% CI: -12.92, -1.12; P < 0.05). Those in the alcohol cluster had higher total cholesterol concentrations than did those not in the alcohol cluster (beta = 12.81; 95% CI: 2.74, 22.88; P < 0.05), and factor 2 (protein and alcohol) was also directly associated with total cholesterol (beta = 1.59 for a one-unit increase in z score; 95% CI: 0.55, 2.63; P < 0.05). The multivariate model containing all of the clusters was not significantly different from the model containing all of the factors in predicting each lipid outcome.


CONCLUSIONS
Our study provides evidence of comparability between cluster and factor analysis methods in relation to plasma lipid biomarkers.
Jean Mayer USDA Human Nutrition Research Center on Aging, Tufts University, 711 Washington Street, Boston, MA 02111, USA. pknewby@post.harvard.edu
P K
PK
Newby
Denis
D
Muller
Katherine L
KL
Tucker
D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000375:Aging / Q000502:physiology; D015142:Baltimore; D015415:Biomarkers / Q000097:blood; D002784:Cholesterol / Q000097:blood*; D008076:Cholesterol, HDL / Q000097:blood; D008078:Cholesterol, LDL / Q000097:blood; D016000:Cluster Analysis; D015331:Cohort Studies; D003430:Cross-Sectional Studies; D004032:Diet / Q000706:statistics & numerical data*; D005163:Factor Analysis, Statistical; D005247:Feeding Behavior; D005260:Female; D006304:Health Status; D006801:Humans; D008137:Longitudinal Studies; D008297:Male; D008875:Middle Aged; D015999:Multivariate Analysis; D015596:Nutrition Assessment; D014280:Triglycerides / Q000097:blood*
D015415:Biomarkers; D008076:Cholesterol, HDL; D008078:Cholesterol, LDL; D014280:Triglycerides; D002784:Cholesterol