Analysis and Evaluation of Glycemic Indices and Glycemic Loads of Frequently Consumed Carbohydrate-Rich Snacks according to Variety and Cooking Method

김도연,; 이한송이,; 최은영,; 임현정,

doi:10.3746/jkfn.2015.44.1.014

Cited by 7 publications

(2 citation statements)

References 23 publications

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“…(a) Cereal grains, (b) noodles and pasta, (c) bread and other processed grains and (d) starchy vegetables. *Reproduced by permission of the Korean Society of Food Science and Nutrition (30) .…”

Section: Resultsmentioning

confidence: 99%

Glycaemic indices and glycaemic loads of common Korean carbohydrate-rich foods

Kim

Lim

2018

Br J Nutr

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Glycaemic index (GI) and glycaemic load (GL) values of foods consumed in Asia remain poorly characterised despite the fact that Asian diets are high in carbohydrates. We evaluated the GI and GL of the most commonly consumed carbohydrate-rich foods, according to food type and cooking methods. GI and GL values were determined using protocols from the FAO/WHO and International Standards Organization recommendations. A total of 152 healthy subjects were enrolled in the study. In all, forty-nine carbohydrate-rich foods were categorised as cereal grains, noodles and pasta, breads and other processed grains and starchy vegetables, prepared using standard cooking methods and evaluated. Cereal grains had the widest range of GI values that the food made with white rice and barley had GI values of 51–93 and 35–70, respectively, according to cooking methods, and most cereal grains had high GL values. Noodles and pasta had low to medium GI values, but most foods had high GL values. Breads had medium to high GI and GL values, while other processed grains had low to medium GI and GL values. The GI values for food made with starchy vegetables (e.g. potatoes and sweet potatoes) varied widely for different cooking methods but tended to have low GL values. In conclusion, GI values for a single food type varied widely with the cooking method used. This study of GI and GL values for common carbohydrate-rich foods provides a valuable reference for consumers and health professionals to make informed food choices for glycaemic control.

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Section: Resultsmentioning

confidence: 99%

Glycaemic indices and glycaemic loads of common Korean carbohydrate-rich foods

Kim

Lim

2018

Br J Nutr

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“…The correlation coefficient between GLs and eGLs measured from the 24 processed foods was statistically significant at 0.712 ( P < 0.01), indicating a strong positive linear relationship. Kim et al ( 27 ) applied foods' nutrient contents and GL measurements to the GL to compare GL and eGL values. They found a correlation coefficient of 0.866 indicating a strong and statistically significant ( p < 0.01) positive correlation.…”

Section: Methodsmentioning

confidence: 99%

Validation Study of the Estimated Glycemic Load Model Using Commercially Available Fast Foods

et al. 2022

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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 x2 value of 11.607 and a p-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 (p < 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.

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