Evaluation of Reliability of the Recomputed Nutrient Intake Data in the National Heart, Lung, and Blood Institute Twin Study

Abstract

The nutrient intake dataset is crucial in epidemiological studies. The latest version of the food composition database includes more types of nutrients than previous ones and can be used to obtain data on nutrient intake that could not be estimated before. Usual food consumption data were collected among 910 twins between 1969 and 1973 through dietary history interviews, and then used to calculate intake of eight types of nutrients (energy intake, carbohydrate, protein, cholesterol, total fat, and saturated, monounsaturated, and polyunsaturated fatty acids) in the National Heart, Lung, and Blood Institute Twin Study. We recalculated intakes using the food composition database updated in 2008. Several different statistical methods were used to evaluate the validity and the reliability of the recalculated intake data. Intra-class correlation coefficients between recalculated and original intake values were above 0.99 for all nutrients. R2 values for regression models were above 0.90 for all nutrients except polyunsaturated fatty acids (R2 = 0.63). In Bland–Altman plots, the percentage of scattering points that outlay the mean plus or minus two standard deviations lines was less than 5% for all nutrients. The arithmetic mean percentage of quintile agreement was 78.5% and that of the extreme quintile disagreement was 0.1% for all nutrients between the two datasets. Recalculated nutrient intake data is in strong agreement with the original one, supporting the reliability of the recalculated data. It is also implied that recalculation is a cost-efficient approach to obtain the intake of nutrients unavailable at baseline.