Obesity Epidemiology

108 STUDY DESIGNS AND MEASUREMENTSUsing the same regression calibration approach discussed earlier, the change in truedietary intakes (X 2− X 1) is estimated as a function of the change in surrogate intakes(Z 2− Z 1) derived from the validation study data:(X 2− X 1) =α+ (γ)(Z 2− Z 1) +ε (6.4)In a linear regression with the amount of weight or waist change as the outcome, thecorrected point estimate for the exposure measure (i.e., difference in observed dietaryintake over time) is:β* = β∕γwhere β is the estimated (or uncorrected) linear regression coefficient from the mainstudy, and γ is the estimated regression slope of changes in X on changes in Z from thevalidation studies.Using this method, Koh-Banerjee et al. 102 estimated that after error correction, thesubstitution of trans fats as 2% of energy for polyunsaturated fats was associated witha 2.7 cm increase in waist circumference over 9 years (P < .001) (as compared with a0.77 cm waist gain, uncorrected). An increase of 12 g fiber/day (r = .68 between FFQsand diet records) was associated with a 2.21 cm reduction in waist circumference aftererror correction (P < .001) (0.63 cm waist gain, uncorrected). The same method wasemployed by Liu et al. 103 to correct for measurement error in the analyses of changes indietary fiber intake and weight gain during 12 years of follow-up in the NHS. After furthercorrection for measurement errors in changes in dietary fiber intake, they estimatedthat an increase of 12 g in dietary fiber intake was associated with ≈3.5 kg (8 lb) lessweight gain in 12 years.Dietary Pattern AnalysesA growing interest in the study of overall dietary patterns in relation to obesity andchronic diseases 104 has been spurred, in part, by several conceptual and methodologicalchallenges associated with the traditional approach of examining individual nutrientsand foods. These include high levels of intercorrelations among nutrients and foods,lack of consideration of synergistic or cumulative effects of multiple nutrients, multiplecomparison problems, and confounding by other dietary components. Patterns are characterizedbased on similarity of habitual food use, which minimizes confounding byother foods or nutrient. Thus, in dietary pattern analysis, the collinearity of nutrientsand foods can be used to advantage. Classifying individuals according to their overalleating pattern (i.e., by considering how foods and nutrients are consumed in combination)can yield a larger contrast between exposure groups than analyses based on singlenutrients. Because overall patterns of dietary intake might be easy for the public tointerpret or translate into diets, research on dietary patterns could have important publichealth implications.Several methods have been commonly used to characterize dietary patterns usingcollected dietary information, including factor analysis, cluster analysis, and dietaryindices. Factor analysis, as a generic term, includes both principal component analysis(PCA) and common factor analysis. PCA is commonly used to define dietary patternsbecause the principal components are expressed by certain mathematical functions ofthe observed consumption of food items. 105 The method aggregates specific food itemsor food groups based on the degree to which food items in the data set are correlatedwith one another. A summary score for each pattern is then derived and can be used in