Comparing 3 Dietary Pattern Methods--Cluster Analysis, Factor Analysis, and Index Analysis--With Colorectal Cancer Risk: The NIH-AARP Diet and Health Study

Reedy, Jill; Wirfält, Elisabet; Flood, Andrew; Mitrou, Panagiota; Krebs‐Smith, Susan M.; Kipnis, Victor; Midthune, Douglas; Leitzmann, Michael F.; Hollenbeck, Albert R.; Schatzkin, Arthur; Subar, Amy F.

doi:10.1093/aje/kwp393

Cited by 195 publications

(217 citation statements)

References 28 publications

(36 reference statements)

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“…In our investigation, the high number of participants in the HP may be trying to adopt a healthier eating pattern to lose weight or prevent further weight gain (70) . These individuals might also implement healthier foods but may not adopt recommended portion sizes.…”

Section: Discussionmentioning

confidence: 99%

Changes of dietary patterns during participation in a web-based weight-reduction programme

Luger¹,

Aspalter²,

Luger³

et al. 2015

Public Health Nutr.

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Objective: To examine the weight-loss success associated with distinct dietary patterns and to determine changes of these dietary patterns during participation in a web-based weight-reduction programme. Design: Factor analysis was used to identify the dietary patterns of twenty-two food groups that were administered in 14 d dietary protocols at baseline and after 3 months. Successful weight loss (≥5 % of initial weight) and BMI were calculated. Logistic regression analyses were used to assess the rates of weight-loss success from each dietary pattern and changing or remaining in the initial dietary pattern. A generalised linear mixed model was used to estimate the effects of changing or staying in a dietary pattern on change in BMI. Subjects: Adults (n 1635) aged 18-81 years. Setting: Users of a web-based weight-reduction programme (2006)(2007)(2008)(2009)(2010)(2011)(2012). Results: Participants who aligned to a healthful dietary pattern at baseline (OR = 1·8; 95 % CI 1·5, 2·3) and after 3 months (OR = 1·5; 95 % CI 1·2, 1·9) had a greater chance of successfully losing weight. After adjusting for age, sex, initial dietary pattern and BMI, participants who started with or changed to the healthful dietary pattern had a greater chance of being successful (OR = 1·4; 95 % CI 1·1, 1·7) and a higher BMI reduction of 0·30 (95 % CI 0·2, 0·5) kg/m 2 compared with those who started with or changed to the energy-dense or high-carbohydrate dietary pattern. Conclusions: A favourable healthful dietary pattern at the beginning and after 3 months was positively associated with anthropometry. However, successful weight loss was feasible in each dietary pattern. Key words Dietary patternsWeight loss Factor analysis Online weight-reduction programme In Europe, more than 50 % of men and women are overweight (1) . A BMI above 30 kg/m 2 is present in 21 % of women and 22 % of men aged 20 years or older (2) . Moreover, worldwide, the prevalence of obesity has almost doubled between 1980 and 2008 (1) . Obesity is an important health risk for diseases such as diabetes mellitus type 2, hypertension, CVD and others (3) . Nevertheless, weight is adjustable, and a moderate weight reduction of 5-10 % is associated with enhanced health benefits (4) . Weight gain is the result of an imbalance between energy intake and expenditure; however, the effect of different dietary factors on weight gain or weight loss remains unclear (5)

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Section: Discussionmentioning

confidence: 99%

Changes of dietary patterns during participation in a web-based weight-reduction programme

Luger¹,

Aspalter²,

Luger³

et al. 2015

Public Health Nutr.

View full text Add to dashboard Cite

show abstract

“…Some studies (Costacou et al, 2003;Kant et al, 2004;Bamia et al, 2005;Crozier et al, 2006;Reedy et al, 2010) have directly compared the dietary patterns obtained via PCA and cluster analysis, being careful to use the same input variables for both methods. Associations between the two methods were most obvious in those studies with two or three clusters.…”

Section: Discussionmentioning

confidence: 99%

“…A further way to assess reliability is linear discriminant analysis. Some studies performed separate analysis on males and females (Pryer et al, 2000;Corrêa Leite et al, 2003;Samieri et al, 2008;James, 2009;Pryer and Rogers, 2009;Reedy et al, 2010) and this was also assessed.…”

Section: Methodsmentioning

confidence: 99%

“…Several studies Reedy et al, 2010) have used both methods to investigate associations between dietary patterns and other variables, whereas other studies (Costacou et al, 2003;Bamia et al, 2005;Crozier et al, 2006;Hearty and Gibney, 2009) have directly compared the two methods. These studies found some agreement between both methods: all found large differences in mean principal component scores between clusters, particularly for the principal component with the highest variance.…”

Section: Introductionmentioning

confidence: 99%

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A comparison of dietary patterns derived by cluster and principal components analysis in a UK cohort of children

et al. 2011

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Background/Objectives: The objective of this study was to identify dietary patterns in a cohort of 7-year-old children through cluster analysis, compare with patterns derived by principal components analysis (PCA), and investigate associations with sociodemographic variables. Subjects/Methods: The main caregivers in the Avon Longitudinal Study of Parents and Children (ALSPAC) recorded dietary intakes of their children (8279 subjects) using a 94-item food frequency questionnaire. Items were then collapsed into 57 food groups. Dietary patterns were identified using k-means cluster analysis and associations with sociodemographic variables examined using multinomial logistic regression. Clusters were compared with patterns previously derived using PCA. Results: Three distinct clusters were derived: Processed (4177 subjects), associated with higher consumption of processed foods and white bread, Plant-based (2065 subjects), characterized by higher consumption of fruit, vegetables and non-white bread, and Traditional British (2037 subjects), associated with higher consumption of meat, vegetables and full-fat milk. Membership of the Processed cluster was positively associated with girls, younger mothers, snacking and older siblings. Membership of the Plant-based cluster was associated with higher educated mothers and vegetarians. The Traditional British cluster was associated with council housing and younger siblings. The three clusters were similar to the three dietary patterns obtained through PCA; each principal component score being higher on average in the corresponding cluster. Conclusions: Both cluster analysis and PCA identified three dietary patterns very similar both in the foods associated with them and sociodemographic characteristics. Both methods are useful for deriving meaningful dietary patterns.

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“…A recent comparison of dietary patterns generated by three different methods: cluster analysis, factor analysis and index score analysis found similar beneficial health characteristics in those with the highest quintiles of factor and index scores, suggesting some similarities across the approaches (Reedy et al, 2010). However, no comparison was made across patterns with health outcomes.…”

Section: Discussionmentioning

confidence: 99%

Does the Mediterranean dietary pattern or the Healthy Diet Index influence the risk of breast cancer in a large British cohort of women?

et al. 2011

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Background/Objective: To assess the risk of developing breast cancer associated with consumption of two common dietary patterns: a Mediterranean dietary pattern and a dietary pattern, which conforms to the World Health Organization Healthy Diet Index (WHO HDI). Subjects/Methods: Dietary data from a 217-item food frequency questionnaire were used to generate two dietary patterns according to pre-defined criteria in women from the UK Women's Cohort Study. Survival analysis using Cox regression was used to estimate hazards ratios for risk of breast cancer adjusted for known confounders.Results: This analysis included 828 incident cases of breast cancer in 33 731 women with a mean follow-up of 9 years. There were no statistically significant associations between either the Mediterranean dietary pattern or the WHO HDI and risk of breast cancer. In premenopausal women, there was a nonsignificant trend suggesting that increasing compliance with the Mediterranean diet was associated with lower risk of breast cancer. Maximal adherence to the Mediterranean diet was associated with hazards ratio ¼ 0.65 (95% confidence interval: 0.42-1.02, P trend ¼ 0.09) compared with minimal adherence. In postmenopausal women, no clear trends were observed. Conclusions: In this study, no strong association between the risk of breast cancer and the consumption of either a Mediterranean-type diet or one characterized by adherence to the WHO HDI was observed. In premenopausal, but not postmenopausal women, there was a nonsignificant inverse association with increasing adherence to the Mediterranean diet pattern.

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Comparing 3 Dietary Pattern Methods--Cluster Analysis, Factor Analysis, and Index Analysis--With Colorectal Cancer Risk: The NIH-AARP Diet and Health Study

Cited by 195 publications

References 28 publications

Changes of dietary patterns during participation in a web-based weight-reduction programme

Changes of dietary patterns during participation in a web-based weight-reduction programme

A comparison of dietary patterns derived by cluster and principal components analysis in a UK cohort of children

Does the Mediterranean dietary pattern or the Healthy Diet Index influence the risk of breast cancer in a large British cohort of women?

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