A critical evaluation of statistical approaches to examining the role of growth trajectories in the developmental origins of health and disease

The developmental origins of health and disease hypothesis suggests that small birth size in conjunction with rapid compensatory childhood growth might yield a greater risk of developing chronic diseases in later life. For example, there is evidence that people who developed coronary heart disease a...

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Bibliographic Details
Published in:International journal of epidemiology 2013-10, Vol.42 (5), p.1327-1339
Main Authors: TU, Yu-Kang, TILLING, Kate, STERNE, Jonathan A. C, GILTHORPE, Mark S
Format: Article
Language:English
Subjects:
Adolescent
Biological and medical sciences
Birth Weight - physiology
Blood Pressure - physiology
Body Weight - physiology
Child
Child Development - physiology
Child, Preschool
Cohort Studies
Female
Fetal Development - physiology
General aspects
Humans
Infant
Male
Medical sciences
Miscellaneous
Multilevel Analysis
Philippines
Pregnancy
Public health. Hygiene
Public health. Hygiene-occupational medicine
Statistics as Topic
Young Adult
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Summary:The developmental origins of health and disease hypothesis suggests that small birth size in conjunction with rapid compensatory childhood growth might yield a greater risk of developing chronic diseases in later life. For example, there is evidence that people who developed coronary heart disease and diabetes experienced different growth trajectories from those who did not develop these diseases. However, some of the methods used in these articles may have been flawed. We critically evaluate proposed approaches for identifying the growth trajectories distinctive to those developing later disease and identifying critical phases of growth during the early lifecourse. Among the approaches we examined (tracing the z-scores, lifecourse plots and models, lifecourse path analysis, conditional body size analysis, multilevel analysis, latent growth curve models and growth mixture models) conditional body size analysis, multilevel analysis, latent growth curve models and growth mixture models are least prone to collinearity problems caused by repeated measures. Multilevel analysis is more flexible when body size is not measured at the same age for all cohort members. Strengths and weaknesses of each approach are illustrated using real data. Demonstrating the influence of growth trajectories on later disease is complex and challenging; therefore, it is likely that a combination of approaches will be required to unravel the complexity in lifecourse research.
ISSN:0300-5771
1464-3685
DOI:10.1093/ije/dyt157