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A statistical model to describe longitudinal and correlated metabolic risk factors: the Whitehall II prospective study

Research output: Contribution to journalJournal article

Published
  • P. Breeze
  • H. Squires
  • J. Chilcott
  • C. Stride
  • Peter J. Diggle
  • E. Brunner
  • A. Tabak
  • A. Brennan
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<mark>Journal publication date</mark>2/12/2016
<mark>Journal</mark>Journal of Public Health
Issue number4
Volume38
Number of pages9
Pages (from-to)679-687
Publication statusPublished
Early online date6/11/15
Original languageEnglish

Abstract

Background
Novel epidemiology models are required to link correlated variables over time, especially haemoglobin A1c (HbA1c) and body mass index (BMI) for diabetes prevention policy analysis. This article develops an epidemiology model to correlate metabolic risk factor trajectories.
Method
BMI, fasting plasma glucose, 2-h glucose, HbA1c, systolic blood pressure, total cholesterol and high density lipoprotein (HDL) cholesterol were analysed over 16 years from 8150 participants of the Whitehall II prospective cohort study. Latent growth curve modelling was employed to simultaneously estimate trajectories for multiple metabolic risk factors allowing for variation between individuals. A simulation model compared simulated outcomes with the observed data.
Results
The model identified that the change in BMI was associated with changes in glycaemia, total cholesterol and systolic blood pressure. The statistical analysis quantified associations among the longitudinal risk factor trajectories. Growth in latent glycaemia was positively correlated with systolic blood pressure and negatively correlated with HDL cholesterol. The goodness-of-fit analysis indicates reasonable fit to the data.
Conclusions
This is the first statistical model that estimates trajectories of metabolic risk factors simultaneously for diabetes to predict joint correlated risk factor trajectories. This can inform comparisons of the effectiveness and cost-effectiveness of preventive interventions, which aim to modify metabolic risk factors.