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

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A statistical model to describe longitudinal and correlated metabolic risk factors : the Whitehall II prospective study. / Breeze, P.; Squires, H.; Chilcott, J.; Stride, C.; Diggle, Peter J.; Brunner, E.; Tabak, A.; Brennan, A.

In: Journal of Public Health, Vol. 38, No. 4, 02.12.2016, p. 679-687.

Research output: Contribution to journalJournal article

Harvard

Breeze, P, Squires, H, Chilcott, J, Stride, C, Diggle, PJ, Brunner, E, Tabak, A & Brennan, A 2016, 'A statistical model to describe longitudinal and correlated metabolic risk factors: the Whitehall II prospective study', Journal of Public Health, vol. 38, no. 4, pp. 679-687. https://doi.org/10.1093/pubmed/fdv160

APA

Breeze, P., Squires, H., Chilcott, J., Stride, C., Diggle, P. J., Brunner, E., Tabak, A., & Brennan, A. (2016). A statistical model to describe longitudinal and correlated metabolic risk factors: the Whitehall II prospective study. Journal of Public Health, 38(4), 679-687. https://doi.org/10.1093/pubmed/fdv160

Vancouver

Breeze P, Squires H, Chilcott J, Stride C, Diggle PJ, Brunner E et al. A statistical model to describe longitudinal and correlated metabolic risk factors: the Whitehall II prospective study. Journal of Public Health. 2016 Dec 2;38(4):679-687. https://doi.org/10.1093/pubmed/fdv160

Author

Breeze, P. ; Squires, H. ; Chilcott, J. ; Stride, C. ; Diggle, Peter J. ; Brunner, E. ; Tabak, A. ; Brennan, A. / A statistical model to describe longitudinal and correlated metabolic risk factors : the Whitehall II prospective study. In: Journal of Public Health. 2016 ; Vol. 38, No. 4. pp. 679-687.

Bibtex

@article{d936602e343e45aca1970fcc1d1ea18a,
title = "A statistical model to describe longitudinal and correlated metabolic risk factors: the Whitehall II prospective study",
abstract = "BackgroundNovel 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.MethodBMI, 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.ResultsThe 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.ConclusionsThis 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.",
keywords = "diabetes, epidemiology",
author = "P. Breeze and H. Squires and J. Chilcott and C. Stride and Diggle, {Peter J.} and E. Brunner and A. Tabak and A. Brennan",
year = "2016",
month = dec,
day = "2",
doi = "10.1093/pubmed/fdv160",
language = "English",
volume = "38",
pages = "679--687",
journal = "Journal of Public Health",
issn = "1741-3842",
publisher = "Oxford University Press",
number = "4",

}

RIS

TY - JOUR

T1 - A statistical model to describe longitudinal and correlated metabolic risk factors

T2 - the Whitehall II prospective study

AU - Breeze, P.

AU - Squires, H.

AU - Chilcott, J.

AU - Stride, C.

AU - Diggle, Peter J.

AU - Brunner, E.

AU - Tabak, A.

AU - Brennan, A.

PY - 2016/12/2

Y1 - 2016/12/2

N2 - BackgroundNovel 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.MethodBMI, 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.ResultsThe 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.ConclusionsThis 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.

AB - BackgroundNovel 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.MethodBMI, 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.ResultsThe 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.ConclusionsThis 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.

KW - diabetes

KW - epidemiology

U2 - 10.1093/pubmed/fdv160

DO - 10.1093/pubmed/fdv160

M3 - Journal article

C2 - 28158533

VL - 38

SP - 679

EP - 687

JO - Journal of Public Health

JF - Journal of Public Health

SN - 1741-3842

IS - 4

ER -