Andrew Smith

1. Andrew Smith Dietary patterns in the ALSPAC cohort: Cluster analysis EUCCONET International Workshop 18th October 2011

2. Andrew Smith Pauline EmmettP. K. NewbyKate Northstone World Cancer Research Fund Dietary patterns in the ALSPAC cohort: Cluster analysis

3. Dietary patterns • Examine diet as a whole • Start with many variables(e.g. FFQ, diet diary) • Express as a small number of variables Image: Paul / FreeDigitalPhotos.net

4. Principal components analysis (PCA) • Examine diet as a whole • Start with many variables(e.g. FFQ, diet diary) • Use correlations between foods • Express as a small number of components Image: Paul / FreeDigitalPhotos.net

5. Cluster analysis • Examine diet as a whole • Start with many variables(e.g. FFQ, diet diary) • Use similarities between people • Express as a small number of clusters Image: Paul / FreeDigitalPhotos.net

6. Dietary patterns studies in ALSPAC

7. Case study: PCA of FFQ age 7 Junk Northstone and Emmett, 2005 Traditional Health conscious Image: Suat Eman, winnond / FreeDigitalPhotos.net

8. Dietary patterns studies in ALSPAC

9. Cluster analysis • k-means is most widely-used method • Must avoid pitfalls • Standardization • Algorithm • Reliability Image: Boaz Yiftach / FreeDigitalPhotos.net

10. Dietary patterns studies in ALSPAC

11. Case study: cluster analysis of FFQ age 7 • FFQ administered to ALSPAC children at 81 months of age • Input variables are the same as PCA (Northstone, Emmett et al. 2005) • 8,279 children • 3 clusters • Smith et al. 2011

12. Case study: cluster analysis of FFQ age 7

13. Similarities with PCA

14. Cluster analysis of diet diary data • 7473 children aged 10 • Advantages • Similar 3-cluster solution(Processed, Plant-based, Traditional British) • Good separation between clusters • Disadvantages • Not robust to under-reporting • PCA shows better associations with fat mass

15. Dietary patterns studies in ALSPAC

16. Dietary patterns studies in ALSPAC