Background: Food pattern analyses are popular tools in the study of associations between diet and health. However, there is a need for further evaluation of this methodology. The aim of the present cross-sectional study was to evaluate the relationship between food pattern groups (FPG) and existing health, and to identify factors influencing this relationship.

Methods: The inhabitants of Västerbotten County in northern Sweden are invited to health check-ups when they turn 30, 40, 50, and 60 years of age. The present study includes data collected from almost 60,000 individuals between 1992 and 2005. Associations between FPG (established using K-means cluster analyses) and health were analyzed separately in men and women.

Results: The health status of the participants and their close family and reporting accuracy differed significantly between men and women and among FPG. Crude regression analyses, with the high fat FPG as reference, showed increased risks for several health outcomes for all other FPGs in both sexes. However, when limiting analysis to individuals without previous ill-health and with adequate energy intake reports, most of the risks instead showed a trend towards protective effects.

Conclusions: Food pattern classifications reflect both eating habits and other own and family health related factors, a finding important to remember and to adjust for before singling out the diet as a primary cause for present and future health problems. Appropriate exclusions are suggested to avoid biases and attenuated associations in nutrition epidemiology.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2988699PMC
http://dx.doi.org/10.1186/1475-2891-9-48DOI Listing

Publication Analysis

Top Keywords

health status
12
food pattern
12
health
10
mis-reporting previous
4
previous health
4
status health
4
status family
4
family seriously
4
seriously bias
4
bias association
4

Similar Publications

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!