Dietary glycemic load and type 2 diabetes: modeling the glucose-raising potential of carbohydrates for prevention

In epidemiologic investigations of diet and health outcomes, food-frequencyquestionnaires(FFQs)areoftenusedinlargecohorts to characterize participants’ average food intake. Central to the application of FFQs in these studies are comprehensive foodcomposition databases that document the specific nutrient content of various foods. However, myriad other factors, such as physical form and particle size, are not typically included in foodcomposition databases but influence the in vivo biological effects of foods consumed (1). Thus, it is important to classify foods according to biological effects obtained directly from metabolic experiments to gain further insights beyond those determined by chemical analysis alone. For carbohydrate-containing foods, a large body of experimental evidence has now accumulated regarding their abilities to increase blood glucose (1, 2). In 1981, Jenkins et al (3) developed the concept of glycemic index (GI), which ranks how much blood glucose increases after ingestion of a particular carbohydrate-containing food relative to that of a standardized source (eg, pure glucose or white bread). Although the clinical utility of GI in the dietary management of diabetes (hyperglycemia and hyperlipidemia in particular) has been documented (4), concerns were repeatedly raised about the potential for rating foods ‘‘good’’ or ‘‘bad’’ solely on the basis of their GI values. Because the amount of carbohydrate in a food is the major determinant for blood glucose response, dietary glycemic load (GL) was subsequently introduced to quantify the total glucose-increasing potential of carbohydrate-containing foods (1). In 1997, Salmeron et al (5, 6) reported the first 2 prospective studies directly linking dietary GI and GL to increased diabetes risk in 2 cohorts of men and women followed for 6 y. Since then, more than a dozen prospective studies (7–17) have reported their findings, revealing a generally positive— albeit heterogeneous—trend relating dietary GI and GL to diabetes risk (Figure 1). In this issue of the Journal, Sluijs et al (18) contribute additional observations in support of this positive trend from 37,846 men and women aged 21–70 y followed for 10 y. Analyzing data from the Dutch component of the European Prospective Investigation into Cancer and Nutrition cohort (EPIC-NL), the authors reported that increased dietary GI and GL were significantly associated with increased diabetes risk. The findings for different effects of simple compared with complex types of carbohydrates on diabetes risk appeared to be mixed in the EPIC-NL. Interestingly, dietary carbohydrate and sugar intakes were inversely related to diabetes risk in the univariate analysis, but after further adjustment for energy intake and lifestyle factors, a positive association with diabetes risk was observed, especially for intake of starch. Sluijs et al further excluded those who underreported energy intake and observed that the GL-diabetes relation was strengthened in their sensitivity analysis. Measurement error can occur in dietary GL just as it can in any aspect of diet in free-living humans. Such errors in assessing diet GL, however, are likely to be unrelated to the outcomes of interest in a prospective setting. Thus, those previous studies with a small sample size and/or lack of rigor in dietary assessment and follow-up may have underestimated the underlying association between dietary GL and diabetes risk by reporting null results. Aside from errors due to dietary assessment, differences in specific statistical models used in different studies, although less well appreciated, may have also accounted for the heterogeneous findings in the literature. Dietary GL is a function of total carbohydrate intake and GI values for foods. It is, by definition, highly correlated with intake of carbohydrates and total energy. To illustrate the various adjustment for variations due to individual differences in total energy and carbohydrate intake, we herein describe Models A–C. In the formulas below, a pooled logistic model is used with emphasis on macronutrient composition to simplify discussion. Y represents diabetes/outcome status; Pr(Y 1⁄4 1|Xi) represents the probability of having diabetes, given Xi, the exposures of interest, and other covariates including alcohol and other dietary factors.