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Validation of a dietary pattern approach for evaluating nutritional risk: the Framingham Nutrition Studies.
J Am Diet Assoc. 2001 Feb; 101(2):187-94.JA

Abstract

OBJECTIVE

To validate the use of cluster analysis for characterizing population dietary patterns.

DESIGN

Cluster analysis was applied to a food frequency questionnaire to define dietary patterns. Independent estimates of nutrient intake were derived from 3-day food records. Heart disease risk factors were assessed using standardized protocols in a clinic setting.

SETTING

Adult women (n = 1,828) participating in the Framingham Offspring-Spouse study.

STATISTICAL ANALYSES

Age-adjusted mean nutrient intakes were determined for each cluster. Analysis of covariance was used to evaluate pairwise differences in intake across clusters. Compliance with published recommendations was determined for selected heart disease risk factors. Differences in age-adjusted compliance across clusters were evaluated using logistic regression.

RESULTS

Cluster analysis identified 5 distinct dietary patterns characterized by unique food behaviors and significantly different nutrient intake profiles. Patterns rich in fruits, vegetables, grains, low-fat dairy, and lean protein foods resulted in higher nutrient density. Patterns rich in fatty foods, added fats, desserts, and sweets were less nutrient-dense. Women who consumed an Empty Calorie pattern were less likely to achieve compliance with clinical risk factor guidelines in contrast to most other groups of women.

CONCLUSIONS

Cluster analysis is a valid tool for evaluating nutrition risk by considering overall patterns and food behaviors. This is important because dietary patterns appear to be linked with other health-related behaviors that confer risk for chronic disease. Therefore, insight into dietary behaviors of distinct clusters within a population can help to design intervention strategies for prevention and management of chronic health conditions including obesity and cardiovascular disease.

Authors+Show Affiliations

Schools of Medicine and Public Health, Boston University, Boston, Mass., USA.No affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info available

Pub Type(s)

Journal Article
Research Support, U.S. Gov't, P.H.S.
Validation Study

Language

eng

PubMed ID

11271691

Citation

Millen, B E., et al. "Validation of a Dietary Pattern Approach for Evaluating Nutritional Risk: the Framingham Nutrition Studies." Journal of the American Dietetic Association, vol. 101, no. 2, 2001, pp. 187-94.
Millen BE, Quatromoni PA, Copenhafer DL, et al. Validation of a dietary pattern approach for evaluating nutritional risk: the Framingham Nutrition Studies. J Am Diet Assoc. 2001;101(2):187-94.
Millen, B. E., Quatromoni, P. A., Copenhafer, D. L., Demissie, S., O'Horo, C. E., & D'Agostino, R. B. (2001). Validation of a dietary pattern approach for evaluating nutritional risk: the Framingham Nutrition Studies. Journal of the American Dietetic Association, 101(2), 187-94.
Millen BE, et al. Validation of a Dietary Pattern Approach for Evaluating Nutritional Risk: the Framingham Nutrition Studies. J Am Diet Assoc. 2001;101(2):187-94. PubMed PMID: 11271691.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Validation of a dietary pattern approach for evaluating nutritional risk: the Framingham Nutrition Studies. AU - Millen,B E, AU - Quatromoni,P A, AU - Copenhafer,D L, AU - Demissie,S, AU - O'Horo,C E, AU - D'Agostino,R B, PY - 2001/3/29/pubmed PY - 2001/4/3/medline PY - 2001/3/29/entrez SP - 187 EP - 94 JF - Journal of the American Dietetic Association JO - J Am Diet Assoc VL - 101 IS - 2 N2 - OBJECTIVE: To validate the use of cluster analysis for characterizing population dietary patterns. DESIGN: Cluster analysis was applied to a food frequency questionnaire to define dietary patterns. Independent estimates of nutrient intake were derived from 3-day food records. Heart disease risk factors were assessed using standardized protocols in a clinic setting. SETTING: Adult women (n = 1,828) participating in the Framingham Offspring-Spouse study. STATISTICAL ANALYSES: Age-adjusted mean nutrient intakes were determined for each cluster. Analysis of covariance was used to evaluate pairwise differences in intake across clusters. Compliance with published recommendations was determined for selected heart disease risk factors. Differences in age-adjusted compliance across clusters were evaluated using logistic regression. RESULTS: Cluster analysis identified 5 distinct dietary patterns characterized by unique food behaviors and significantly different nutrient intake profiles. Patterns rich in fruits, vegetables, grains, low-fat dairy, and lean protein foods resulted in higher nutrient density. Patterns rich in fatty foods, added fats, desserts, and sweets were less nutrient-dense. Women who consumed an Empty Calorie pattern were less likely to achieve compliance with clinical risk factor guidelines in contrast to most other groups of women. CONCLUSIONS: Cluster analysis is a valid tool for evaluating nutrition risk by considering overall patterns and food behaviors. This is important because dietary patterns appear to be linked with other health-related behaviors that confer risk for chronic disease. Therefore, insight into dietary behaviors of distinct clusters within a population can help to design intervention strategies for prevention and management of chronic health conditions including obesity and cardiovascular disease. SN - 0002-8223 UR - https://www.unboundmedicine.com/medline/citation/11271691/Validation_of_a_dietary_pattern_approach_for_evaluating_nutritional_risk:_the_Framingham_Nutrition_Studies_ L2 - https://linkinghub.elsevier.com/retrieve/pii/S0002-8223(01)00051-7 DB - PRIME DP - Unbound Medicine ER -