Lee Miran, Kang Haejin, Chung Sang-Jin, Nam Kisun, Park Yoo Kyoung
Department of Medical Nutrition, Graduate School of East-West Medical Science, Kyung Hee University, Yongin, South Korea.
Department of Medical Nutrition (AgeTech-Service Convergence Major), Kyung Hee University, Yongin, South Korea.
Front Nutr. 2022 May 10;9:892403. doi: 10.3389/fnut.2022.892403. eCollection 2022.
The recent popularization of low-glycemic foods has expanded interest in glycemic index (GI) not only among diabetic patients but also healthy people. The purpose of this study is to validate the estimated glycemic load model (eGL) developed in 2018. This study measured the glycemic load (GL) of 24 fast foods in the market in 20 subjects. Then, the transportability of the model was assessed, followed by an assessment of model calibration and discrimination based on model performance. The transportability assessment showed that the subjects at the time of model development are different from the subjects of this validation study. Therefore, the model can be described as transportable. As for the model's performance, the calibration assessment found an value of 11.607 and a -value of 0.160, which indicates that the prediction model fits the observations. The discrimination assessment found a discrimination accuracy exceeding 0.5 (57.1%), which confirms that the performance and stability of the prediction model can be discriminated across all classifications. The correlation coefficient between GLs and eGLs measured from the 24 fast foods was statistically significant at 0.712 ( < 0.01), indicating a strong positive linear relationship. The explanatory powers of GL and eGL was high at 50.7%. The findings of this study suggest that this prediction model will greatly contribute to healthy food choices because it allows for predicting blood glucose responses solely based on the nutrient content labeled on the fast foods.
近期低升糖指数食物的普及不仅扩大了糖尿病患者对血糖生成指数(GI)的兴趣,健康人群亦是如此。本研究的目的是验证2018年开发的估计血糖负荷模型(eGL)。本研究测量了20名受试者食用的市售24种快餐的血糖负荷(GL)。然后,评估了该模型的可迁移性,接着基于模型性能对模型校准和区分度进行了评估。可迁移性评估表明,模型开发时的受试者与本验证研究的受试者不同。因此,该模型可被描述为具有可迁移性。至于模型性能,校准评估得出卡方值为11.607,P值为0.160,这表明预测模型与观测值拟合良好。区分度评估发现区分准确率超过0.5(57.1%),这证实了预测模型的性能和稳定性在所有分类中均可区分。从24种快餐测得的GL与eGL之间的相关系数在0.712时具有统计学意义(P<0.01),表明存在强正线性关系。GL和eGL的解释力较高,为50.7%。本研究结果表明,该预测模型将极大地有助于健康食品选择,因为它能够仅根据快餐上标注的营养成分预测血糖反应。