Mis-reporting, previous health status and health status of family may seriously bias the association between food patterns and disease

• Published in: Nutrition Journal
• Main Authors: Weinehall Lars, Hallmans Göran, Winkvist Anna, Hörnell Agneta, Johansson Ingegerd
• Format: Article in Journal/Newspaper
• Language: English
• Published: BMC 2010
• Subjects: Nutrition. Foods and food supply; TX341-641; Nutritional diseases. Deficiency diseases; RC620-627; Northern Sweden
• Online Access: https://doi.org/10.1186/1475-2891-9-48; https://doaj.org/article/7b24e6ef968d4c27b30a0de0316a45c7

Abstract 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.