Email updates

Keep up to date with the latest news and content from BMC Medical Research Methodology and BioMed Central.

Open Access Research article

Latent variables and structural equation models for longitudinal relationships: an illustration in nutritional epidemiology

Michel Chavance12*, Sylvie Escolano12, Monique Romon3, Arnaud Basdevant456, Blandine de Lauzon-Guillain27 and Marie Aline Charles27

Author Affiliations

1 Biostatistics, CESP Centre for research in Epidemiology and Population Health, U1018, Inserm; F94807, Villejuif, France

2 Université Paris Sud 11, UMRS 1018, F-94807, Villejuif, France

3 Nutrition Department, University Hospital, Lille F-59000, France

4 INSERM, Nutrinomique U 872, Paris, France

5 Université Pierre et Marie Curie - Paris 6, Faculté de Médecine, Paris, France

6 Endocrinology and Nutrition Department, Pitié-Salpêtrière Hospital, Paris, France

7 Epidemiology of diabetes, obesity and chronic kidney disease over the life course, CESP Centre for research in Epidemiology and Population Health, U1018, Inserm, F-94807, Villejuif, France

For all author emails, please log on.

BMC Medical Research Methodology 2010, 10:37  doi:10.1186/1471-2288-10-37

Published: 30 April 2010

Abstract

Background

The use of structural equation modeling and latent variables remains uncommon in epidemiology despite its potential usefulness. The latter was illustrated by studying cross-sectional and longitudinal relationships between eating behavior and adiposity, using four different indicators of fat mass.

Methods

Using data from a longitudinal community-based study, we fitted structural equation models including two latent variables (respectively baseline adiposity and adiposity change after 2 years of follow-up), each being defined, by the four following anthropometric measurement (respectively by their changes): body mass index, waist circumference, skinfold thickness and percent body fat. Latent adiposity variables were hypothesized to depend on a cognitive restraint score, calculated from answers to an eating-behavior questionnaire (TFEQ-18), either cross-sectionally or longitudinally.

Results

We found that high baseline adiposity was associated with a 2-year increase of the cognitive restraint score and no convincing relationship between baseline cognitive restraint and 2-year adiposity change could be established.

Conclusions

The latent variable modeling approach enabled presentation of synthetic results rather than separate regression models and detailed analysis of the causal effects of interest. In the general population, restrained eating appears to be an adaptive response of subjects prone to gaining weight more than as a risk factor for fat-mass increase.