The technique ‘joint and individual variance explained’ highlights persistent aspects of the diet using longitudinal food frequency data

• Published in: British journal of nutrition Vol. 128; no. 10; pp. 2054 - 2062
• Main Authors: Jones, M. Beatrix, Merchant, Amaan, Morales-Soto, Larisa, Thompson, John M. D., Wall, Clare R.
• Format: Journal Article
• Language: English
• Published: Cambridge, UK Cambridge University Press 28-11-2022
• Subjects: Age; Beverages; Birth weight; Blood pressure; Child, Preschool; Cluster analysis; Collaboration; Datasets; Diet; Feeding Behavior - physiology; Food; Gestational age; Humans; Innovative Techniques; Longitudinal studies; Meat; Nutrition research; Pattern analysis; Principal Component Analysis; Principal components analysis; Questionnaires; Surveys and Questionnaires; Vegetables; New Zealand; FFQ; Dimension reduction; Principal components analysis; Dietary patterns; Longitudinal data; Joint and individual variance explained
• ISSN: 0007-1145; 1475-2662
• DOI: 10.1017/S0007114521004955

Dietary pattern analysis is typically based on dimension reduction and summarises the diet with a small number of scores. We assess ‘joint and individual variance explained’ (JIVE) as a method for extracting dietary patterns from longitudinal data that highlights elements of the diet that are associated over time. The Auckland Birthweight Collaborative Study, in which participants completed an FFQ at ages 3·5 (n 549), 7 (n 591) and 11 (n 617), is used as an example. Data from each time point are projected onto the directions of shared variability produced by JIVE to yield dietary patterns and scores. We assess the ability of the scores to predict future BMI and blood pressure measurements of the participants and make a comparison with principal component analysis (PCA) performed separately at each time point. The diet could be summarised with three JIVE patterns. The patterns were interpretable, with the same interpretation across age groups: a vegetable and whole grain pattern, a sweets and meats pattern and a cereal v. sweet drinks pattern. The first two PCA-derived patterns were similar across age groups and similar to the first two JIVE patterns. The interpretation of the third PCA pattern changed across age groups. Scores produced by the two techniques were similarly effective in predicting future BMI and blood pressure. We conclude that when data from the same participants at multiple ages are available, JIVE provides an advantage over PCA by extracting patterns with a common interpretation across age groups.