1. Andrew Smith Describing childhood diet with cluster analysis 6th September 2012

2. Describing diet with cluster analysis Kate Northstone Pauline Emmett PK Newby World Cancer Research Fund MRC, Wellcome Trust, University of Bristol 2

3. Describing diet with cluster analysis Diet Meals Food Nutrients 3

4. Outline Introductions ALSPAC Food frequency questionnaires / diet diaries Dietary patterns Cluster analysis k-means cluster analysis Results 4 cluster solution Associations with socio-demographic variables 4

5. ALSPAC Avon Longitudinal Study of Parents and Children Birth cohort study 14,541 pregnant women and their children www.bris.ac.uk/alspac 5

6. Food frequency questionnaires 6

7. Diet diaries Records all food and drink consumed over 3 day period 2 weekdays and 1 weekend day Parent completes age 7 Child completes age 10 and 13 7

8. Dietary patterns Examine diet as a whole Start with many variables (food group intakes) Express as a small number of variables Image: Paul / FreeDigitalPhotos.net 8

9. Principal components analysis (PCA) Examine diet as a whole Start with many variables Use correlations between foods Express as a small number of components Image: Paul / FreeDigitalPhotos.net 9

10. Cluster analysis Examine diet as a whole Start with many variables Use similarities between people Express as a small number of clusters Image: Paul / FreeDigitalPhotos.net 10

11. Cluster analysis Separate subjects into non-overlapping groups Based on ‘distances’ between individuals Unsupervised learning 11 Image: Boaz Yiftach / FreeDigitalPhotos.net

12. k-means cluster analysis Most widely used for dietary patterns Number of clusters, k, is specified beforehand Minimises –Distance from each subject to his/her cluster mean –Summed over all subjects in that cluster –Summed over all clusters 12

13. k-means cluster analysis 13

14. Problems with the standard algorithm The algorithm for k-means cluster analysis is: Short-sighted Tends to find solutions that are at a local minimum –So run algorithm 100 times and choose solution that is minimum out of all minima 14

15. Standardising the input variables 15

16. Reliability of the cluster solution Split sample in half Perform separate analyses on each half See how many children change clusters Repeat 5 times –32 out of 8,279 children changed cluster (0.4%) 16

17. Results Food frequency questionnaire (FFQ) data –Age 7 –3 clusters Diet diary data –Age 7, 10 and 13 –4 clusters 17

18. Processed 30.2% of children 18 Image: Suat Eman, artemisphoto, -Marcus- / FreeDigitalPhotos.net

19. 27.8% of children Plant-based (Healthy) 19 Image: Suat Eman, Paul, Rob Wiltshire, Simon Howden, winnond / FreeDigitalPhotos.net

20. Traditional British 21.3% of children 20 Image: Suat Eman, Maggie Smith, Simon Howden / FreeDigitalPhotos.net

21. Packed Lunch 20.6% of children 21 Image: Grant Cochrane, luigi diamanti, Rawich, Master Isolated Images / FreeDigitalPhotos.net

22. Associations with socio-demographic vars Processed Plant-based Traditional British Processed Girls3,115111 Boys2,9410.82 (0.72, 0.93) 1.03 (0.89, 1.20) 1.18 (1.04, 1.34) 22

23. Associations with socio-demographic vars Maternal age Processed Plant-based Traditional British Processed < 21130111 21-259940.59 (0.33, 1.07) 1.07 (0.56, 2.05) 1.57 (1.02, 2.43) 26-302,6440.52 (0.29, 0.92) 1.20 (0.64, 2.28) 1.60 (1.04, 2.46) 31+2,2880.37 (0.21, 0.67) 1.50 (0.79, 2.88) 1.77 (1.13, 2.76) 23

24. Associations with socio-demographic vars Maternal education Processed Plant-based Traditional British Processed CSE740111 Vocational5040.84 (0.60, 1.17) 1.19 (0.82, 1.72) 1.01 (0.76, 1.32) O level2,1630.65 (0.51, 0.83) 1.46 (1.10, 1.94) 1.05 (0.86, 1.30) A level1,6040.42 (0.33, 0.55) 2.01 (1.50, 2.69) 1.18 (0.95, 1.48) Degree1,0450.30 (0.23, 0.39) 2.75 (2.00, 3.76) 1.22 (0.94, 1.57) 24

25. Associations with socio-demographic vars Siblings Processed Plant-based Traditional British Processed 0 older2,755111 1 older2,3171.21 (1.03, 1.42) 1.12 (0.94, 1.36) 0.73 (0.62, 0.86) 2+ older9841.58 (1.28, 1.97) 0.99 (0.76, 1.27) 0.64 (0.52, 0.80) 25

26. Associations with socio-demographic vars Siblings Processed Plant-based Traditional British Processed 0 younger2,946111 1 younger2,4901.01 (0.86, 1.19) 0.58 (0.48, 0.71) 1.69 (1.44, 1.99) 2+ younger6201.21 (0.92, 1.57) 0.43 (0.33, 0.58) 1.90 (2.50, 2.40) 26

27. Summary Multivariate methods to compress dietary data into dietary patterns k-means cluster analysis is widespread but must be applied carefully 3 clusters in FFQ data (Processed, Plant-based and Traditional British) 4 clusters in diet diary data ( + Packed Lunch) 27

28. References Northstone, AS et al. (2012) ‘Longitudinal comparisons of dietary patterns derived by cluster analysis in 7 to 13 year old children’ British Journal of Nutrition to appear. AS et al. (2011) ‘A comparison of dietary patterns derived by cluster and principal components analysis in a UK cohort of children.’ European Journal of Clinical Nutrition 65, p1102-9. 28