Methodology for Adding Glycemic Index to the National Health and Nutrition Examination Survey Nutrient Database

Abstract

Generating valid estimates of dietary glycemic index (GI) and glycemic load (GL) has been a challenge in nutritional epidemiology. The methodologic issues may have contributed to the wide variation of GI/GL associations with health outcomes observed in existing literature. We describe a standardized methodology for assigning GI values to items in the National Health and Nutrition Examination Survey (NHANES) nutrient database using the new International Tables to develop research-driven, systematic procedures and strategies to estimate dietary GI/GL exposures of a nationally representative population sample. Nutrient databases for NHANES 2003-2006 contain information on 3,155 unique foods derived from the US Department of Agriculture National Nutrient Database for Standard Reference versions 18 and 20. Assignment of GI values were made to a subset of 2,078 carbohydrate-containing foods using systematic food item matching procedures applied to 2008 international GI tables and online data sources. Matching protocols indicated that 45.4% of foods had identical matches with existing data sources, 31.9% had similar matches, 2.5% derived GI values calculated with the formula for combination foods, 13.6% were assigned a default GI value based on low carbohydrate content, and 6.7% of GI values were based on data extrapolation. Most GI values were derived from international sources; 36.1% were from North American product information. To confirm data assignments, dietary GI and GL intakes of the NHANES 2003-2006 adult participants were estimated from two 24-hour recalls and compared with published studies. Among the 3,689 men and 4,112 women studied, mean dietary GI was 56.2 (men 56.9, women 55.5), mean dietary GL was 138.1 (men 162.1, women 116.4); the distribution of dietary GI was approximately normal. Estimates of population GI and GL compare favorably with other published literature. This methodology of adding GI values to an existing population nutrient database utilized systematic matching protocols and the latest comprehensive data sources on food composition. The database can be applied in clinical and survey research settings where there is interest in estimating individual and population dietary exposures and relating them to health outcomes.