The University of Southampton
University of Southampton Institutional Repository

Evaluation and interpretation of latent class modelling strategies to characterise dietary trajectories across early life: a longitudinal study from the Southampton Women's Survey

Evaluation and interpretation of latent class modelling strategies to characterise dietary trajectories across early life: a longitudinal study from the Southampton Women's Survey
Evaluation and interpretation of latent class modelling strategies to characterise dietary trajectories across early life: a longitudinal study from the Southampton Women's Survey

There is increasing interest in modelling longitudinal dietary data and classifying individuals into subgroups (latent classes) who follow similar trajectories over time. These trajectories could identify population groups and time points amenable to dietary interventions. This paper aimed to provide a comparison and overview of two latent class methods: group-based trajectory modelling (GBTM) and growth mixture modelling (GMM). Data from 2963 mother-child dyads from the longitudinal Southampton Women's Survey were analysed. Continuous diet quality indices (DQI) were derived using principal component analysis from interviewer-administered FFQ collected in mothers pre-pregnancy, at 11- and 34-week gestation, and in offspring at 6 and 12 months and 3, 6-7 and 8-9 years. A forward modelling approach from 1 to 6 classes was used to identify the optimal number of DQI latent classes. Models were assessed using the Akaike and Bayesian information criteria, probability of class assignment, ratio of the odds of correct classification, group membership and entropy. Both methods suggested that five classes were optimal, with a strong correlation (Spearman's = 0·98) between class assignment for the two methods. The dietary trajectories were categorised as stable with horizontal lines and were defined as poor (GMM = 4 % and GBTM = 5 %), poor-medium (23 %, 23 %), medium (39 %, 39 %), medium-better (27 %, 28 %) and best (7 %, 6 %). Both GBTM and GMM are suitable for identifying dietary trajectories. GBTM is recommended as it is computationally less intensive, but results could be confirmed using GMM. The stability of the diet quality trajectories from pre-pregnancy underlines the importance of promotion of dietary improvements from preconception onwards.

Diet quality, Group-based trajectory modelling, Growth mixture models, Lifecourse epidemiology, Trajectory modelling
0007-1145
1945-1954
Dalrymple, Kathryn V.
8ef94198-4e90-44a9-b77d-19d35d013cde
Vogel, Christina
768f1dcd-2697-4aae-95cc-ee2f6d63dff5
Godfrey, Keith M.
0931701e-fe2c-44b5-8f0d-ec5c7477a6fd
Baird, Janis
f4bf2039-6118-436f-ab69-df8b4d17f824
Hanson, Mark
1952fad1-abc7-4284-a0bc-a7eb31f70a3f
Cooper, Cyrus
e05f5612-b493-4273-9b71-9e0ce32bdad6
Inskip, Hazel
5fb4470a-9379-49b2-a533-9da8e61058b7
Crozier, Sarah
9c3595ce-45b0-44fa-8c4c-4c555e628a03
Dalrymple, Kathryn V.
8ef94198-4e90-44a9-b77d-19d35d013cde
Vogel, Christina
768f1dcd-2697-4aae-95cc-ee2f6d63dff5
Godfrey, Keith M.
0931701e-fe2c-44b5-8f0d-ec5c7477a6fd
Baird, Janis
f4bf2039-6118-436f-ab69-df8b4d17f824
Hanson, Mark
1952fad1-abc7-4284-a0bc-a7eb31f70a3f
Cooper, Cyrus
e05f5612-b493-4273-9b71-9e0ce32bdad6
Inskip, Hazel
5fb4470a-9379-49b2-a533-9da8e61058b7
Crozier, Sarah
9c3595ce-45b0-44fa-8c4c-4c555e628a03

Dalrymple, Kathryn V., Vogel, Christina, Godfrey, Keith M., Baird, Janis, Hanson, Mark, Cooper, Cyrus, Inskip, Hazel and Crozier, Sarah (2023) Evaluation and interpretation of latent class modelling strategies to characterise dietary trajectories across early life: a longitudinal study from the Southampton Women's Survey. British Journal of Nutrition, 129 (11), 1945-1954. (doi:10.1017/S000711452200263X).

Record type: Article

Abstract

There is increasing interest in modelling longitudinal dietary data and classifying individuals into subgroups (latent classes) who follow similar trajectories over time. These trajectories could identify population groups and time points amenable to dietary interventions. This paper aimed to provide a comparison and overview of two latent class methods: group-based trajectory modelling (GBTM) and growth mixture modelling (GMM). Data from 2963 mother-child dyads from the longitudinal Southampton Women's Survey were analysed. Continuous diet quality indices (DQI) were derived using principal component analysis from interviewer-administered FFQ collected in mothers pre-pregnancy, at 11- and 34-week gestation, and in offspring at 6 and 12 months and 3, 6-7 and 8-9 years. A forward modelling approach from 1 to 6 classes was used to identify the optimal number of DQI latent classes. Models were assessed using the Akaike and Bayesian information criteria, probability of class assignment, ratio of the odds of correct classification, group membership and entropy. Both methods suggested that five classes were optimal, with a strong correlation (Spearman's = 0·98) between class assignment for the two methods. The dietary trajectories were categorised as stable with horizontal lines and were defined as poor (GMM = 4 % and GBTM = 5 %), poor-medium (23 %, 23 %), medium (39 %, 39 %), medium-better (27 %, 28 %) and best (7 %, 6 %). Both GBTM and GMM are suitable for identifying dietary trajectories. GBTM is recommended as it is computationally less intensive, but results could be confirmed using GMM. The stability of the diet quality trajectories from pre-pregnancy underlines the importance of promotion of dietary improvements from preconception onwards.

Text
evaluation_and_interpretation_of_latent_class_modelling_strategies_to_characterise_dietary_trajectories_across_early_life_a_longitudinal_study_from_the_southampton_womens_survey - Accepted Manuscript
Available under License Creative Commons Attribution.
Download (694kB)

More information

Accepted/In Press date: 1 August 2022
e-pub ahead of print date: 15 August 2022
Published date: 14 June 2023
Additional Information: Publisher Copyright: © The Author(s), 2022. Published by Cambridge University Press on behalf of The Nutrition Society.
Keywords: Diet quality, Group-based trajectory modelling, Growth mixture models, Lifecourse epidemiology, Trajectory modelling

Identifiers

Local EPrints ID: 469523
URI: http://eprints.soton.ac.uk/id/eprint/469523
ISSN: 0007-1145
PURE UUID: a92b82a8-5ea1-40ff-8629-e5f9e486b48c
ORCID for Christina Vogel: ORCID iD orcid.org/0000-0002-3897-3786
ORCID for Keith M. Godfrey: ORCID iD orcid.org/0000-0002-4643-0618
ORCID for Janis Baird: ORCID iD orcid.org/0000-0002-4039-4361
ORCID for Mark Hanson: ORCID iD orcid.org/0000-0002-6907-613X
ORCID for Cyrus Cooper: ORCID iD orcid.org/0000-0003-3510-0709
ORCID for Hazel Inskip: ORCID iD orcid.org/0000-0001-8897-1749
ORCID for Sarah Crozier: ORCID iD orcid.org/0000-0002-9524-1127

Catalogue record

Date deposited: 16 Sep 2022 16:46
Last modified: 18 Mar 2024 03:15

Export record

Altmetrics

Contributors

Author: Kathryn V. Dalrymple
Author: Christina Vogel ORCID iD
Author: Janis Baird ORCID iD
Author: Mark Hanson ORCID iD
Author: Cyrus Cooper ORCID iD
Author: Hazel Inskip ORCID iD
Author: Sarah Crozier ORCID iD

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×