Minimal Effective Dose of Beans Required to Elicit a Significantly Lower Glycemic Response Than Commonly Consumed Starchy Foods: Predictions Based on In Vitro Digestion and Carbohydrate Analysis

引起血糖反应显著低于常见淀粉类食物的最低有效豆类剂量:基于体外消化和碳水化合物分析的预测

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Abstract

Beans elicit lower glycemic responses (GRs) than other starchy foods, but the minimum effective dose (MED) to reduce GR is unknown. We sought to determine the MED of beans compared to common starchy foods. Overnight-fasted healthy volunteers consumed ¼c (phase 1, n = 24) or ½c (phase 2, n = 18) of black, cranberry, great northern, kidney, navy and pinto beans and corn, rice, pasta and potato (controls), with blood glucose measured before and for 2 h after eating. GRs (incremental areas under the curves, iAUCs) after beans were consumed were compared to those of controls by ANOVA followed by Dunnett's test. To qualify for MED, beans had to elicit an effective reduction in GR, defined as a statistically significant reduction in iAUC of ≥20% (i.e., a relative glycemic response, RGR, ≤80). Outcomes from in vitro digestion were compared with in vivo RGR. Both doses of all six beans effectively reduced GR versus all four starchy controls, except for ¼c and ½c cranberry and pinto vs. corn, ¼c great northern and navy vs. corn and ¼c navy and pinto vs. potato. MED criteria were met for 18 comparisons of the ¼c servings, with four of the remaining six met by the ½c servings. The overall mean ± SEM RGR vs. controls was similar for the ¼c and ½c servings: 53 ± 4% and 56 ± 3%, respectively. By multiple regression analysis, RGR = 23.3 × RDS + 8.3 × SDS - 20.1 × RS + 39.5 × AS - 108.2 (rapidly digested starch, p < 0.001; slowly digested starch, p = 0.054; resistant starch, p = 0.18; available sugars, p = 0.005; model r = 0.98, p = 0.001). RGR correlated with in vitro glucose release (r = 0.92, p < 0.001). The MED of beans is ¼ cup. For n = 30 comparisons (n = 24 beans vs. controls, n = 6 controls vs. each other), an effective reduction in GR was predicted from in vitro carbohydrate analysis with 86% sensitivity and 100% specificity.

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