Department of Endocrinology, Medical University Sofia, Sofia, Bulgaria.
Department of Endocrinology, Medical University Sofia, Sofia, Bulgaria.
J Nutr. 2023 May;153(5):1427-1438. doi: 10.1016/j.tjnut.2023.03.007. Epub 2023 Mar 10.
Diurnal glucose fluctuations are increased in prediabetes and might be affected by specific dietary patterns.
The present study assessed the relationship between glycemic variability (GV) and dietary regimen in people with normal glucose tolerance (NGT) and impaired glucose tolerance (IGT).
Forty-one NGT (mean age: 45.0 ± 9.0 y, mean BMI: 32.0 ± 7.0 kg/m) and 53 IGT (mean age: 48.4 ± 11.2 y, mean BMI: 31.3 ± 5.9 kg/m) subjects were enrolled in this cross-sectional study. The FreeStyleLibre Pro sensor was used for 14 d, and several parameters of GV were calculated. The participants were provided with a diet diary to record all meals. ANOVA analysis, Pearson correlation, and stepwise forward regression were performed.
Despite no difference in diet patterns between the 2 groups, GV parameters were higher in IGT than in NGT. GV worsened with an increase in overall daily carbohydrate and refined grain consumption and improved with the increase in whole grain intake in IGT. GV parameters were positively related [r = 0.14-0.53; all P < 0.02 for SD, continuous overall net glycemic action 1 (CONGA1), J-index, lability index (LI), glycemic risk assessment diabetes equation, M-value, and mean absolute glucose (MAG)], and low blood glucose index (LBGI) inversely (r = -0.37, P = 0.006) related to the total percentage of carbohydrate, but not to the distribution of carbohydrate between the main meals in the IGT group. A negative relationship existed between total protein consumption and GV indices (r = -0.27 to -0.52; P < 0.05 for SD, CONGA1, J-index, LI, M-value, and MAG). The total EI was related to GV parameters (r = 0.27-0.32; P < 0.05 for CONGA1, J-index, LI, and M-value; and r = -0.30, P = 0.028 for LBGI).
The primary outcome results showed that insulin sensitivity, calories, and carbohydrate content are predictors of GV in individuals with IGT. Overall, the secondary analyses suggested that carbohydrate and daily consumption of refined grains might be associated with higher GV, whereas whole grains and daily protein intake were related to lower GV in people with IGT.
糖尿病前期患者的日间血糖波动增加,并且可能受到特定饮食模式的影响。
本研究评估了血糖变异性(GV)与糖耐量正常(NGT)和糖耐量受损(IGT)人群饮食方案之间的关系。
本横断面研究纳入了 41 名 NGT(平均年龄:45.0 ± 9.0 岁,平均 BMI:32.0 ± 7.0 kg/m)和 53 名 IGT(平均年龄:48.4 ± 11.2 岁,平均 BMI:31.3 ± 5.9 kg/m)受试者。使用 FreeStyleLibre Pro 传感器进行 14 天,并计算了多个 GV 参数。参与者需要记录所有饮食的饮食日记。进行方差分析、Pearson 相关性分析和逐步向前回归分析。
尽管两组的饮食模式没有差异,但 IGT 患者的 GV 参数高于 NGT 患者。IGT 患者的 GV 随着总日碳水化合物和精制谷物摄入量的增加而恶化,随着全谷物摄入量的增加而改善。IGT 患者的 GV 参数与总碳水化合物百分比呈正相关(r = 0.14-0.53;所有 P < 0.02,SD、连续总体净血糖作用 1(CONGA1)、J 指数、变异性指数(LI)、血糖风险评估糖尿病方程、M 值和平均绝对血糖(MAG)),而低血糖指数(LBGI)呈负相关(r = -0.37,P = 0.006)。IGT 组总蛋白质摄入量与 GV 指数呈负相关(r = -0.27 至 -0.52;P < 0.05,SD、CONGA1、J 指数、LI、M 值和 MAG)。总能量摄入与 GV 参数相关(r = 0.27-0.32;P < 0.05,CONGA1、J 指数、LI 和 M 值;r = -0.30,P = 0.028,LBGI)。
主要结果表明,胰岛素敏感性、热量和碳水化合物含量是 IGT 患者 GV 的预测因子。总体而言,二次分析表明,碳水化合物和精制谷物的日摄入量可能与更高的 GV 相关,而全谷物和蛋白质的日摄入量与 IGT 患者的较低 GV 相关。