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STABILITY-BASED
VALIDATION OF DIETARY
PATTERNS OBTAINED BY
CLUSTER ANALYSIS
INTRODUCTION
• Cluster analysis is a data-driven method used to create clusters of individuals sharing similar
dietary habits. However, this method requires specific choices from the user which have an
influence on the results. Therefore, there is a need of an objective methodology helping
researchers in their decisions during cluster analysis.
• The objective of this study was to use such a methodology based on stability of clustering
solutions to select the most appropriate clustering method and number of clusters for
describing dietary patterns in the NESCAV study (Nutrition, Environment and
Cardiovascular Health), a large population-based cross-sectional study in the Greater Region
(N = 2298).
METHODS
• Transformation of the data.
• Formalization of cluster analysis.
• The measure of stability.
COMPARISON OF DIETARY PATTERNS
OBTAINED WITH PCA AND K-MEANS
RESULTS AND FUTURE OUTCOMES
• Distributions were described with box-plots and average values computed on
20 repetitions of the algorithm. Regardless of stability indices and number of
clusters, more stable solutions were obtained with K-means. In addition, the
most stable solution was obtained with 3 clusters. Therefore, dietary patterns
were computed with K-means algorithm and a predefined number of clusters
equal to three.
CONCLUSION
• In summary, we used an objective methodology based on the stability of
clustering solutions allowing selection of the most appropriate clustering
method and number of cluster for describing dietary patterns in a population.
Three main dietary patterns were identified in the Greater region. A
“Convenient” and a “Non-Prudent” pattern associated with a higher
cardiovascular risk and a “Prudent” pattern associated with a decreased
cardiovascular risk.
REFERENCES
• 1. Kim J, et al. Dietary patterns derived by cluster analysis are associated with cognitive function among Korean
older adults. Nutrients. 2015;7(6):4154–69. doi: 10.3390/nu7064154.
• 2. Newby PK, Tucker KL. Empirically derived eating patterns using factor or cluster analysis: a review. Nutr
Rev. 2004;62(5):177–203. doi: 10.1111/j.1753-4887.2004.tb00040.x.
• 3. Lo Siou G, et al. Exploring statistical approaches to diminish subjectivity of cluster analysis to derive dietary
patterns: the tomorrow project. Am J Epidemiol. 2011;173(8):956–67. doi: 10.1093/aje/kwq458.
• 4. Berg CM, et al. Food patterns and cardiovascular disease risk factors: the Swedish INTERGENE research
program. Am J Clin Nutr. 2008;88(2):289–97.
• 5. Anderson AL, et al. Relationships of dietary patterns with body composition in older adults differ by gender and
PPAR-gamma Pro12Ala genotype. Eur J Nutr. 2010;49(7):385–94. doi: 10.1007/s00394-010-0096-9.

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john krisinger-the science and history of the alcoholic beverage.pptx
john krisinger-the science and history of the alcoholic beverage.pptxjohn krisinger-the science and history of the alcoholic beverage.pptx
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Stability based validation of dietary patterns obtained by cluster (1)

  • 1. STABILITY-BASED VALIDATION OF DIETARY PATTERNS OBTAINED BY CLUSTER ANALYSIS
  • 2. INTRODUCTION • Cluster analysis is a data-driven method used to create clusters of individuals sharing similar dietary habits. However, this method requires specific choices from the user which have an influence on the results. Therefore, there is a need of an objective methodology helping researchers in their decisions during cluster analysis. • The objective of this study was to use such a methodology based on stability of clustering solutions to select the most appropriate clustering method and number of clusters for describing dietary patterns in the NESCAV study (Nutrition, Environment and Cardiovascular Health), a large population-based cross-sectional study in the Greater Region (N = 2298).
  • 3. METHODS • Transformation of the data. • Formalization of cluster analysis. • The measure of stability.
  • 4. COMPARISON OF DIETARY PATTERNS OBTAINED WITH PCA AND K-MEANS
  • 5. RESULTS AND FUTURE OUTCOMES • Distributions were described with box-plots and average values computed on 20 repetitions of the algorithm. Regardless of stability indices and number of clusters, more stable solutions were obtained with K-means. In addition, the most stable solution was obtained with 3 clusters. Therefore, dietary patterns were computed with K-means algorithm and a predefined number of clusters equal to three.
  • 6. CONCLUSION • In summary, we used an objective methodology based on the stability of clustering solutions allowing selection of the most appropriate clustering method and number of cluster for describing dietary patterns in a population. Three main dietary patterns were identified in the Greater region. A “Convenient” and a “Non-Prudent” pattern associated with a higher cardiovascular risk and a “Prudent” pattern associated with a decreased cardiovascular risk.
  • 7. REFERENCES • 1. Kim J, et al. Dietary patterns derived by cluster analysis are associated with cognitive function among Korean older adults. Nutrients. 2015;7(6):4154–69. doi: 10.3390/nu7064154. • 2. Newby PK, Tucker KL. Empirically derived eating patterns using factor or cluster analysis: a review. Nutr Rev. 2004;62(5):177–203. doi: 10.1111/j.1753-4887.2004.tb00040.x. • 3. Lo Siou G, et al. Exploring statistical approaches to diminish subjectivity of cluster analysis to derive dietary patterns: the tomorrow project. Am J Epidemiol. 2011;173(8):956–67. doi: 10.1093/aje/kwq458. • 4. Berg CM, et al. Food patterns and cardiovascular disease risk factors: the Swedish INTERGENE research program. Am J Clin Nutr. 2008;88(2):289–97. • 5. Anderson AL, et al. Relationships of dietary patterns with body composition in older adults differ by gender and PPAR-gamma Pro12Ala genotype. Eur J Nutr. 2010;49(7):385–94. doi: 10.1007/s00394-010-0096-9.