Since it was developed in , awareness of the index has spread beyond clinicians. Many people now consider it to be a metric for healthy nutrition.

Key omissions include how foods are grown, manufactured, cooked and stored; serving size; and the combination of foods eaten as part of a meal 2. These problems have incentivized researchers to search for more comprehensive ways to evaluate carbohydrates and personalize glycemic control — while incorporating other nutritional indicators such as fibre content.

The goal is to help health-care professionals support their diabetes patients to choose the most appropriate foods. And these foods, Patterson adds, should be affordable, available, and respectful of cultural eating patterns so that recommendations work in the real world.

The GI, with its narrow focus on glucose, skews the health value of some carbohydrate foods, says Slavin. Fructose, she points out, ranks a healthy-sounding 19 on the GI, but its consumption is associated with diabetes and heart disease 3 — another oversight because the GI does not consider chronic disease risk.

A candy bar, which is loaded with sucrose but contains little glucose, gets a better GI score than a slice of whole-grain bread, she adds.

Furthermore, there are numerous rankings for the same food, making the GI confusing for physicians and patients. Patterson cites the example of rice, which has different entries for various brands and regional varieties, further sub-divided by cooking method 4.

The GI scores range from 37 for Chinese rice vermicelli, cooked for 8 min to Jasmine rice, Reindeer brand, made in a rice cooker.

These differences can be compounded by how the GI score is determined. When seven different labs assessed the same type of rice, GI scores ranged from 55 to 87 5.

Cooked rice, when eaten hot, causes a higher glycemic response than the same rice eaten cold 6. The same is true of other starchy foods like potatoes. One of the biggest issues with the index, says Patterson, is that it tests carbohydrates alone to derive a GI score, but these foods are rarely eaten in isolation.

Most are consumed as part of meals also containing fat, protein and fibre, which alter glucose absorption and blood-sugar levels. Attempts have been made to modify the GI to better reflect real life.

The glycemic load GL adjusts GI scores for portion sizes, but it creates some confusing scenarios. High-GI foods may have a low GL. Watermelon, for example, has a GI of 72, but the GL of a standard-sized portion is only 4.

Not only does the GL suffer from the same issues as the GI, it is impractical for real world situations. Ongoing research continues to highlight individualized glycemic responses to foods. Dietary habits, physical activity, body composition and gut microbiota were all important factors in response 7.

A more recent study assessed the postprandial metabolic responses of more than 1, healthy adults, eating identical meals over a 2-week period.

Continuous glucose monitors mapped blood sugar levels and revealed large inter-individual variability, even between identical twins — suggesting that genetics were not the main cause of the differences 8.

Such is the variability that even the same person can exhibit different responses to the same food eaten on consecutive days 9 or at different times of the day Altogether, such studies have found that many biological and behavioural factors influence glycemic response, including age, stress, health status, baseline insulin levels, alcohol consumption and sleep.

The upshot is that people with type 2 diabetes, and their physicians are in need of more holistic dietary tools. In , an expert panel report outlined ways to revamp carbohydrate assessment The authors — including Slavin — outlined the need for a more expansive, standardized, evidence-based way to evaluate carbohydrate quality.

The panel envisioned a single simple carbohydrate-food metric that melds multiple intrinsic properties, including total fibre content, nutrient density, food group designation and processing effects. By separating the intrinsic food values from the extrinsic effects, there is a clearer pathway to fully personalized nutrition, says Patterson.

Research can then build by better understanding the effects that carbohydrate quality has on human health. Many labs are already working towards developing such predictive models.

For more information about this issue, check out some of the latest science and resources at potatogoodness. United States Diabetes Surveillance System April Google Scholar. Naser, K. SRL Diabetes Metab.

Andres-Hernando, A. et al. Cell Metab. A study at Maastricht University Medical Centre in the Netherlands investigated the effect on liver fat content and substrate metabolism of both reducing GI and saturated fat intake but in practical, realistic amounts under isocaloric conditions.

Australians have used the glycemic index GI since ; however, there are no data on changes in carbohydrate quality over time.

The aim of the study was to compare average dietary GI and glycemic load GL , and contributing carbohydrate foods, in the two most recent national dietary surveys. Although the average dietary GI and GL declined between and, trends in specific carbohydrate foods suggest that Australians are avoiding potatoes and sugary beverages in favour of a greater variety of carbohydrate foods, particularly cereal products.

People with type 1 or type 2 diabetes who followed a low-glycemic diet had a small yet clinically meaningful reduction in blood glucose levels, as well as reductions in fasting glucose, body weight, low-density lipoprotein cholesterol and C-reactive protein levels, compared with those who followed a high-glycemic diet.

The findings published in The BMJ also showed that the positive effects of a low-GI diet among diabetes patients remained consistent over and above their existing drug or insulin treatments.

This study aimed to determine the major food groups contributing to dietary glycaemic load GL. The mean SD dietary GI and GL was 54 7 and 59 respectively and the top three contributors to dietary GL were breads There were small but significant differences in the GL contribution pattern between the sexes.

The findings indicate that the average dietary GI of Australian adults is similar to that of other population groups, with a large proportion of starchy and energy-dense nutrient-poor foods that contribute to a high GL. The aim of the study is to determine the impact of a high protein, low glycemic index diet in combination with physical activity on the incidence of type two diabetes in overweight children and adults.

Professor Jennie Brand-Miller is leading the work program responsible for communicating and exploiting the findings of PREVIEW to convert new knowledge into strategies to reduce the risk of diabetes, including web-based lifestyle interventions for the public and collaborations between food industry and health professionals on development of innovative products and ingredients.

At present, few countries regulate the inclusion of GI on food labels even though the information may assist consumers to manage blood glucose levels.

Inconsistencies in food regulation around the world undermine consumer and health professional confidence and call for harmonisation. This important paper was a global effort seeking input and direction from leading authorities in GI research, testing and labelling. A low GI diet could be beneficial for managing or preventing a number of health conditions.

This is evidenced by a wide body of existing and emerging research. See recent research supporting the growing scientific evidence in favour of a low GI lifestyle:. The International Carbohydrate Quality Consortium ICQC is a non-profit, worldwide organization whose mission is to support, summarize and disseminate the science around dietary carbohydrate and health with a focus on quality and to harmonize the work of scientists from academia, industry and government.

GI Science and Latest Emerging Research. New Report This new report summarises 20 years of published scientific evidence on the glycemic index and its role in the prevention and management of lifestyle related chronic disease. DOWNLOAD THE KEY FINDINGS. DOWNLOAD THE FULL REPORT. ON THIS PAGE. The science of GI.

First in low GI research. What is the food insulin index? Research Activities. International Carbohydrate Quality Consortium. Further reading. PRINT, DOWNLOAD, SHARE. The science of GI The glycemic index, or GI, was discovered in Canada in by Professor David Jenkins.

First in low GI research In The Sydney University Glycemic Index Research Service SUGiRS was established to provide a reliable commercial GI testing laboratory. Here are some tips to help people with diabetes achieve that: Balance meals with some carbs, lean protein and plenty of salad or vegetables as all foods stimulate some insulin response.

Opt for lower FII carbohydrates such as pasta and noodles over higher FII ones such as most white rices and regular couscous when it comes to putting starchy carbohydrates on the dinner plate.

Choose higher fibre, less processed breads and cereals, such as dense grainy bread and traditional porridge oats. Snack on fruit and non-starchy vegetables as they are low FII and packed with vitamins and minerals. Processed meats like bacon and sausages may have a lower FII, but their high saturated fat content makes them a much less healthy choice.

Supported Research Activities The Glycemic Index Foundation GIF is the world authority on the glycemic index GI , its health benefits and how it can be used to guide your food choices.

VIEW STUDY. Dietary glycaemic index and glycaemic load among Australian children and adolescents: results from the Australian Health Survey This study aimed to examine the dietary glycaemic index GI and glycaemic load GL of Australian children and adolescents, as well as the major food groups contributing to GL, in the recent Australian Health Survey.

Discovery of a lower glycemic index potato Potatoes are a popular source of dietary carbohydrate worldwide and are generally considered to be a high glycemic index GI food. Macronutrient quality improves liver fat and Postprandial Glycemia in just two weeks A study at Maastricht University Medical Centre in the Netherlands investigated the effect on liver fat content and substrate metabolism of both reducing GI and saturated fat intake but in practical, realistic amounts under isocaloric conditions.

Changes in dietary glycemic index and glycemic load in Australian adults from to Australians have used the glycemic index GI since ; however, there are no data on changes in carbohydrate quality over time.

Study: Low-GI diet lowers blood glucose in diabetes People with type 1 or type 2 diabetes who followed a low-glycemic diet had a small yet clinically meaningful reduction in blood glucose levels, as well as reductions in fasting glucose, body weight, low-density lipoprotein cholesterol and C-reactive protein levels, compared with those who followed a high-glycemic diet.

Dietary glycaemic index and glycaemic load among Australian adults — results from the Australian Health Survey This study aimed to determine the major food groups contributing to dietary glycaemic load GL. Prevention of Diabetes in Europe and Worldwide The aim of the study is to determine the impact of a high protein, low glycemic index diet in combination with physical activity on the incidence of type two diabetes in overweight children and adults.

Dietary Glycaemic Index Labelling: A Global Perspective At present, few countries regulate the inclusion of GI on food labels even though the information may assist consumers to manage blood glucose levels.

Research Activities A low GI diet could be beneficial for managing or preventing a number of health conditions. High glycemic index and glycemic load diets associated with increased risk of mortality The findings of previous studies investigating the association between dietary glycemic index, glycemic load, and the risk of mortality have been inconsistent.

A metaanalysis to evaluate this association was performed. VIEW PAPER. Study: Carbohydrate quality - not quantity - is more strongly associated with weight management This study comprehensively examined the associations between changes in carbohydrate intake and weight change at four year intervals.

International Carbohydrate Quality Consortium The International Carbohydrate Quality Consortium ICQC is a non-profit, worldwide organization whose mission is to support, summarize and disseminate the science around dietary carbohydrate and health with a focus on quality and to harmonize the work of scientists from academia, industry and government.

Willett W, Manson J, Liu S. Glycemic index, glycemic load, and risk of type 2 diabetes. Gross LS, Li L, Ford ES, Liu S. Increased consumption of refined carbohydrates and the epidemic of type 2 diabetes in the United States: an ecologic assessment.

Bhupathiraju SN, Tobias DK, Malik VS, et al. Glycemic index, glycemic load, and risk of type 2 diabetes: results from 3 large US cohorts and an updated meta-analysis. Mosdol A, Witte DR, Frost G, Marmot MG, Brunner EJ. Dietary glycemic index and glycemic load are associated with high-density-lipoprotein cholesterol at baseline but not with increased risk of diabetes in the Whitehall II study.

Sahyoun NR, Anderson AL, Tylavsky FA, et al. Dietary glycemic index and glycemic load and the risk of type 2 diabetes in older adults. Sakurai M, Nakamura K, Miura K, et al. Dietary glycemic index and risk of type 2 diabetes mellitus in middle-aged Japanese men.

Sluijs I, Beulens JW, van der Schouw YT, et al. Dietary glycemic index, glycemic load, and digestible carbohydrate intake are not associated with risk of type 2 diabetes in eight European countries. van Woudenbergh GJ, Kuijsten A, Sijbrands EJ, Hofman A, Witteman JC, Feskens EJ.

Glycemic index and glycemic load and their association with C-reactive protein and incident type 2 diabetes. J Nutr Metab. Villegas R, Liu S, Gao YT, et al. Prospective study of dietary carbohydrates, glycemic index, glycemic load, and incidence of type 2 diabetes mellitus in middle-aged Chinese women.

Arch Intern Med. Greenwood DC, Threapleton DE, Evans CE, et al. Glycemic index, glycemic load, carbohydrates, and type 2 diabetes: systematic review and dose-response meta-analysis of prospective studies.

Diabetes Care. Livesey G, Taylor R, Livesey H, Liu S. Is there a dose-response relation of dietary glycemic load to risk of type 2 diabetes? Meta-analysis of prospective cohort studies. Dyson PA, Kelly T, Deakin T, et al.

Diabetes UK evidence-based nutrition guidelines for the prevention and management of diabetes. Mann JI, De Leeuw I, Hermansen K, et al. Evidence-based nutritional approaches to the treatment and prevention of diabetes mellitus. American Diabetes Association.

Prevention or delay of type 2 diabetes. Ma XY, Liu JP, Song ZY. Glycemic load, glycemic index and risk of cardiovascular diseases: meta-analyses of prospective studies. Dong JY, Zhang YH, Wang P, Qin LQ. Meta-analysis of dietary glycemic load and glycemic index in relation to risk of coronary heart disease.

Am J Cardiol. Fan J, Song Y, Wang Y, Hui R, Zhang W. Dietary glycemic index, glycemic load, and risk of coronary heart disease, stroke, and stroke mortality: a systematic review with meta-analysis.

PLoS One. Mirrahimi A, de Souza RJ, Chiavaroli L, et al. Associations of glycemic index and load with coronary heart disease events: a systematic review and meta-analysis of prospective cohorts.

J Am Heart Assoc. Turati F, Dilis V, Rossi M, et al. Glycemic load and coronary heart disease in a Mediterranean population: the EPIC Greek cohort study. Liu S, Willett WC, Stampfer MJ, et al. A prospective study of dietary glycemic load, carbohydrate intake, and risk of coronary heart disease in US women.

Beulens JW, de Bruijne LM, Stolk RP, et al. High dietary glycemic load and glycemic index increase risk of cardiovascular disease among middle-aged women: a population-based follow-up study. J Am Coll Cardiol. Cai X, Wang C, Wang S, et al.

Carbohydrate intake, glycemic index, glycemic load, and stroke: a meta-analysis of prospective cohort studies. Asia Pac J Public Health. Rossi M, Turati F, Lagiou P, Trichopoulos D, La Vecchia C, Trichopoulou A. Relation of dietary glycemic load with ischemic and hemorrhagic stroke: a cohort study in Greece and a meta-analysis.

Eur J Nutr. Buscemi S, Cosentino L, Rosafio G, et al. Effects of hypocaloric diets with different glycemic indexes on endothelial function and glycemic variability in overweight and in obese adult patients at increased cardiovascular risk.

Clin Nutr. Bullo M, Casas R, Portillo MP, et al. Liu S, Manson JE, Buring JE, Stampfer MJ, Willett WC, Ridker PM. Relation between a diet with a high glycemic load and plasma concentrations of high-sensitivity C-reactive protein in middle-aged women.

Jones JL, Park Y, Lee J, Lerman RH, Fernandez ML. A Mediterranean-style, low-glycemic-load diet reduces the expression of 3-hydroxymethylglutaryl-coenzyme A reductase in mononuclear cells and plasma insulin in women with metabolic syndrome. Nutr Res. Turati F, Galeone C, Gandini S, et al.

High glycemic index and glycemic load are associated with moderately increased cancer risk. Mol Nutr Food Res. Aune D, Chan DS, Lau R, et al. Carbohydrates, glycemic index, glycemic load, and colorectal cancer risk: a systematic review and meta-analysis of cohort studies.

Cancer Causes Control. Choi Y, Giovannucci E, Lee JE. Glycaemic index and glycaemic load in relation to risk of diabetes-related cancers: a meta-analysis. Br J Nutr. Mulholland HG, Murray LJ, Cardwell CR, Cantwell MM. Glycemic index, glycemic load, and risk of digestive tract neoplasms: a systematic review and meta-analysis.

Mullie P, Koechlin A, Boniol M, Autier P, Boyle P. Relation between breast cancer and high glycemic index or glycemic load: a meta-analysis of prospective cohort studies.

Crit Rev Food Sci Nutr. Tsai CJ, Leitzmann MF, Willett WC, Giovannucci EL. Dietary carbohydrates and glycaemic load and the incidence of symptomatic gall stone disease in men.

Glycemic load, glycemic index, and carbohydrate intake in relation to risk of cholecystectomy in women. Wang Q, Xia W, Zhao Z, Zhang H. Effects comparison between low glycemic index diets and high glycemic index diets on HbA1c and fructosamine for patients with diabetes: A systematic review and meta-analysis.

Prim Care Diabetes. Evert AB, Boucher JL. New diabetes nutrition therapy recommendations: what you need to know. Diabetes Spectr. Evert AB, Boucher JL, Cypress M, et al. Nutrition therapy recommendations for the management of adults with diabetes.

Louie JC, Markovic TP, Perera N, et al. A randomized controlled trial investigating the effects of a low-glycemic index diet on pregnancy outcomes in gestational diabetes mellitus. Louie JC, Markovic TP, Ross GP, Foote D, Brand-Miller JC.

Effect of a low glycaemic index diet in gestational diabetes mellitus on post-natal outcomes after 3 months of birth: a pilot follow-up study. Matern Child Nutr. Markovic TP, Muirhead R, Overs S, et al.

Randomized controlled trial investigating the effects of a low-glycemic index diet on pregnancy outcomes in women at high risk of gestational diabetes mellitus: The GI Baby 3 Study. Flegal KM, Kit BK, Orpana H, Graubard BI. As for percent body fat, the AUROC was 0.

The AUROC was the highest among participants with standard or higher percent body fat, and lowest among male subjects at 0. Table 3 summarizes the findings on the GL-eGL correlation of the 24 fast foods.

The correlation coefficient was statistically significant at 0. Figure 2 indicates the simple regression analysis results for GL and eGL of the 24 processed foods.

The eGL was found to explain In other words, when eGL increases by 1, actual GL increases by 1. Table 3. Relationships between means of GL and eGL for available processed food.

Figure 2. Relationships between means of measured glycemic load GL and estimated glycemic load eGL for available processed food by simple linear regression. Values of parameter estimation. Table 4 lists the IAUC, GI, GL, and eGL calculated based on the dietary intake and nutrient contents of the processed foods used in the study and the blood glucose measurements.

The low-GI foods were: two bread products bulgogi croquette and sponge cake ; one hot dog product cheese and sausage hot dog ; three noodle products cream pasta, spicy noodle, and tomato pasta ; one salad product corn salad ; one shake product balance shake ; one soup product button mushroom soup ; and one tteokbokki product wheat noodle tteokbokki.

Bibimbap frozen , fried rice with shrimp frozen , and cereals were high-GI foods. Button mushroom soup, corn salad, spicy noodle, balance shake, cheese and sausage hot dog, and sponge cake were low-GL foods Table 4.

Table 4. Nutrient values, IAUC, GI, GL, and eGL values of fast foods used in this study. Table 5 summarizes the classifications based on GI and GL measurements from the foods selected for this study.

Corn salad, button mushroom soup, spicy noodle, balance shake, cheese and sausage hot dog, and sponge cake were classified as low-GI and low-GI foods. Beef and mushroom rice porridge and whole-grain cereal were classified as medium-GI and medium-GL products. Bibimbap frozen , fried rice with shrimp frozen , and cereals were high-GI and high GL foods.

Low-GI and high-GL foods included cream pasta, almond cookies, tomato pasta, fried rice with hamburger steak frozen , and wheat noodle tteokbokki. Medium-GI and high-GL foods were dried tofu snack, red bean porridge, dumplings with meat frozen , and tofu and lentil rice meal.

Table 5. Classification between measured GI and GL for one serving of provided food. This study was conducted to validate the eGL prediction model developed in a previous study 12 by commercially available fast foods with a more diverse nutrient content, assessing the model's prediction model and performance, and using correlation analysis.

The transportability assessment showed that the subjects at the time of model development had different characteristics from those of this validation study. As for the performance assessment of the eGL model across all subjects, the calibration assessment found the good fit of the model.

The discrimination of the prediction model was assessed at 0. Although it is not highly accurate, as the value exceeds 0. The correlation analysis between the observed GL and the eGL across the 24 fast foods used in this study found a correlation coefficient of 0.

The finding suggests that it is appropriate to use the eGL prediction model to predict GL. The transportability of the prediction model was analyzed based on the research data at the time of the model's development.

As widely recommended for transportability assessment, the characteristics of the validation subjects and those of the development subjects were directly compared The body measurement items of the validation subjects and the development subjects were not significantly different, which can be attributed to the fact that healthy subjects with less blood glucose response fluctuations were selected for both studies for higher accuracy, as typically recommended for blood glucose studies However, the available carbohydrate, protein, and fiber content were different between the development study and this validation study, which indicates the generalizability of the developed model.

The x 2 values from the Hosmer-Lemeshow test indicate the goodness-of-fit of the model, which shows the congruence between the actually observed dependent variables and the predictions from the model An x 2 value close to 0 indicates a higher level of goodness-of-fit.

The model is statistically significant if the significance probability is higher than a significance level of 0. The observed GLs were congruent to the eGLs under both classifications, however, men group had higher level of goodness-of-fit than women.

The same values were 7. As for percentage of body fat, the prediction models were found to be statistically significant in both groups.

However, Percent body fat above average group had higher goodness-of-fit in the prediction models than percent body fat average group according to x 2.

A higher AUROC value indicates a higher level of discrimination 21 , The discrimination accuracy was similar between the two genders: As for the classifications based on BMI, in both below 23 or above 23, discrimination accuracy was at As for percent body fat, the group with standard body fat reported a discrimination accuracy of The prediction model was found to be less accurate across all subjects, and consistent findings were observed across genders, BMI groups, and body fat groups.

The AUROC assessment found a discrimination accuracy exceeding 0. The correlation coefficient between GLs and eGLs measured from the 24 processed foods was statistically significant at 0. Kim et al. They found a correlation coefficient of 0. The findings suggest that GL measurements can be predicted by applying the nutrient contents from other previous studies to the eGL model.

Assuming that the classifications for eGL are the same as the GL classifications, balance shake and button mushroom soup were classified as low-GL foods.

They were also classified as low-GL foods in the estimated GL prediction model. These foods were also classified as medium-GL foods in the estimated GL prediction model. Tofu and lentil rice meal, cereal, bibimbap frozen , fried rice with hamburger steak frozen , fried rice with shrimp frozen cream pasta, tomato pasta, almond cookies, and wheat noodle tteokbokki were found to have high GL.

These foods were also classified as high-GL foods in the estimated GL prediction model. These findings are similar to those reported by Sun et al. The findings are also similar to those reported by Quek et al. High-GI and high-GL foods are digested and absorbed faster and create faster blood glucose response, resulting in a rapid increase in early blood glucose levels Therefore, caution is advised when consuming these foods.

The findings of this study confirmed a strong correlation between eGL values and GL measurements based on available carbohydrate, protein, fat, and fiber. This suggests that GL values can be predicted using the eGL prediction model and food nutrient contents, instead of repeatedly taking blood glucose measurements.

As such, the model is expected to contribute to facilitating GL measurement. The model is expected to be particularly helpful for Koreans by providing quantitative and qualitative information on carbohydrate intake. Our model provides accurate information on the GLs of the foods recently preferred by Koreans, who tend to eat out more and consume more fast foods.

The information will be useful for patients and people requiring weight control, and contribute to prevention and management of chronic diseases. As for the limitation of this study, first, although using finger-prick glucose test is a well-established procedure, used widely in hospitals as a standard practice, there is still a possibility of low accuracy by measuring blood sugar by self-blood glucose meter.

Second, the subject criteria for the validity verification study and the subject criteria at the time for the development of the prediction model were similar, which is not the best choice for an external feasibility study. To our knowledge this is the first study not only in Korea, but, also globally that suggest a simplified blood glucose prediction model.

This equation, especially, may serve as a convenient blood glucose management method for diabetic patients, people with impaired glucose tolerance, and people seeking prevention and management of chronic diseases.

We can assure that this model facilitates GL prediction, and promotes understanding of blood glucose control among people in need of, or interested in, blood glucose control, and helps people with their food choice and health management in general.

With the increase of consumers purchasing fast foods compared to home-cooked meals, the findings of this study suggest that this prediction model will greatly contribute to healthy food choices because it allows customers to predict blood glucose responses based on the readily available nutrient label.

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

The studies involving human participants were reviewed and approved by KyungHee University, Global Campus IRB. YP, S-JC, and KN conceptualized and designed the study. ML, KN, and S-JC selected the test foods and conducted the statistical analyses. ML and YP recruited participants, performed the experiment, and prepared the original draft.

HK Interpretation of the data and prepared the manuscript in English. ML, HK, and YP finalized the manuscript. All authors listed have made substantial, direct, intellectual contribution to the work and approved it for publication.

This research was partially funded by Pulmuone Inc, and BK21 plus program, AgeTech-service convergence major through the National Research Foundation NRF funded by the Ministry of Education of Korea The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

The funder was not involved in collection, analysis, interpretation, or decision to any of the process for publication. All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers.

Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. Seidelmann SB, Claggett B, Cheng S, Henglin M, Shah A, Steffen LM, et al.

Dietary carbohydrate intake and mortality: a prospective cohort study and meta-analysis. Lancet Public Health. doi: PubMed Abstract CrossRef Full Text Google Scholar. Lee M, Kim H.

A minireview on carbohydrate in weight management diet: the quantity and the quality. J Korean Med Obes Res. Sun F, Li C, Zhang Y, Wong SH, Wang L.

Effect of glycemic index of breakfast on energy intake at subsequent meal among healthy people: a meta-analysis. Kim IJ. The Glycemic Index Foundation will soon go one practical step further by launching a new profit — for — purpose venture, a bold and disruptive social enterprise whose aim is to provide funding for Diabetes prevention and community education programs.

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Weight loss and cardiovascular disease. Explore our nutrition research Charles Perkins Centre. Professor Jennie Brand-Miller. Our Microsoft partnership is taking quantum computing from the lab into reality.

Maryniuk; The 3 R's of Glycemic Index: Recommendations, Research, and the Real World. Clin Diabetes 1 October ; 29 4 : — T he ability to achieve optimal glycemic control in diabetes management is highly influenced by food intake.

The initial focus for nutrition education messages is to aim for consistency in both type and quantity of carbohydrates consumed. However, research in the past decade has acknowledged that not all carbohydrates effect blood glucose levels in the same way. One of the methods for evaluating this effect is known as the glycemic index GI.

Using the GI in meal planning can improve diabetes control and other health parameters. Understanding the benefits of the GI and how one can implement it into the diet, allows health care practitioners to educate patients about its use. This article will define GI; highlight key recommendations regarding the use of GI scales; summarize recent research related to the impact of choosing lower-GI foods on diabetes control, lipids, and weight; and conclude with some practical, real-world tips for using the GI when counseling patients.

The GI is a ranking system that indicates how quickly a carbohydrate food raises blood glucose. This is determined by measuring the area under the curve in the 2 hours after the consumption of a test food.

These values are then compared to the area under the curve 2 hours after the consumption of a similar weight of glucose or bread. Low-GI foods include many fruits and vegetables, legumes, whole grains, and dairy products. Foods with a ranking between 56 and 75 are considered to have a moderate GI.

High-GI foods, those with a ranking between 76 and , often include highly processed and refined carbohydrates such as instant oatmeal, white bread, and cornflakes Table 1. Although the GI ranking compares standard carbohydrate portions usually 25 or 50 g , the amount of food tested to derive the GI may not reflect usual amounts consumed.

Although popcorn has a relatively high GI 72 , because it is low in carbohydrate per serving 11 g per 1. Thus, popcorn would have an overall lower impact or glycemic load based on the portion size eaten. The patient information page handout provided on page of this issue of Clinical Diabetes does just that.

There are a variety of factors that can account for the GI of different foods. Some of these characteristics are naturally occurring, whereas others are affected in commercialization or home preparation.

Physical form: Generally, the more processed a food, the higher its GI. For example, instant oatmeal has a GI of 79, whereas steel cut rolled oats has a GI of Food combinations: When carbohydrate foods are eaten as part of a meal, the GI of the meal changes based on the average of all the GI values factored together.

Thus, a useful message for patients is to complement a high-GI food such as rice with low-GI foods such red beans or legumes. Cooking time: Longer cooking times may increase the glycemic impact of a food by breaking down the starch or carbohydrate and allowing it to pass through the body more quickly when consumed.

Pasta cooked al dente for 5—10 minutes has a slightly lower GI than pasta cooked longer. Acidity: The more acidic a food is e.

For example, sourdough bread, which uses a lactobacillus or lactic acid culture as part of the leavening process, has a lower GI than white bread. Physical entrapment: The fibrous coat around beans, seeds, and plant cell walls in whole grains acts as a physical barrier, slowing access of digestive enzymes to break down the carbohydrate.

Thus, many whole grains and legumes have a lower GI. For example, adding cheese to a slice of bread would decrease the GI. Soluble fiber: In general, the higher the food is in viscous or soluble fiber, the lower its GI will be.

By increasing the viscosity of the intestinal contents, the interaction between the starch and the digestive enzymes is slowed, resulting in slower and lower glycemic excursions. Beans are a great example of a food high in soluble fiber.

GI and GL Rankings At the Joslin Diabetes Center in Boston, Mass. In the center's guidelines for patients with type 2 diabetes who are overweight or obese, use of the GI is encouraged as another way to improve the quality of carbohydrates consumed.

There is increasing evidence that choosing foods with a low GI and GL has a favorable impact on glycemic response. The GI indicates the rate at which food enters the bloodstream. Therefore, foods with lower GI enter at a slower rate, which reduces both the glycemic response and the corresponding insulin release.

There is a positive correlation between consumption of high-GI foods and an increases incidence of type 2 diabetes. Eating a lower-GI diet may also decrease the risk for complications in those previously diagnosed with diabetes.

A Cochrane review of 11 randomized, controlled trials involving patients with diabetes revealed that low-GI diets significantly lowered A1C by 0. Following a low-GI diet was found to decrease hypoglycemia compared to either a high-GI diet or a diet based on Exchange lists. Overall improvements in glycemic variability and control have been shown to reduce the incidence and risk for microvascular complications associated with diabetes.

A diet composed of lower-GI foods may result in lower LDL cholesterol and improved HDL cholesterol. This may be the result of decreased gluconeogenesis after the consumption of low-GI foods that may suppress nonesterified fatty acid NEFA release and ultimately result in increased HDL levels.

The evidence for low-GI diets lowering LDL cholesterol is less conclusive. Seven out of 10 studies were able to demonstrate a positive change in LDL following a low-GI diet.

However, the results did not reach significance. is a year-old woman who was diagnosed with type 2 diabetes 5 years ago. Her most recent A1C level was 7. She has been trying to keep her carbohydrate intake consistent at meals and has lost 5 lb in the past 8 months because of caloric restriction and increased physical activity.

She asks about the elevated blood glucose levels after meals and complains of hunger after meals as well.

Review of her food logs reveals that she consistently eats about 45 g of carbohydrate at breakfast, 60 g at lunch, and 75 g at dinner.

Her diet history shows the following pattern:. Using the list of high-, medium-, and low-GI foods, Mrs. identified the foods in her diet that may be exacerbating higher postmeal glycemic responses. She decided to make the following substitutions and changes in her diet:. More whole fruits apples, oranges, and bananas and vegetables for snacks instead of refined carbohydrates.

It was also discussed that many of these foods are also higher in dietary fiber and may contribute to increasing her feeling of satiety. She reported increased satiety and had lost an additional 3 lb. Six of 13 studies showed improvement in triglycerides but were not statistically significant.

There is some evidence available that lowering the GI of the diet may be helpful in improving lipid profiles and reducing the risk for cardiac disease. More studies are needed to confirm this, but there are no data indicating any detrimental cardiac effects from consuming a lower-GI diet.

Emphasizing low-GI foods in the diet may facilitate weight loss and weight maintenance. Decreased caloric intake throughout the day has been shown after consumption of a low-GI meal. Lowering the GI in the diet increased cholecystokinin CCK release, ultimately resulting in greater satiety for minutes.

The rapid transit of high-GI foods has been shown to result in postprandial hyperglycemia immediately after a meal, with potential hypoglycemia subsequently.

This pattern of blood glucose response may result in increased hunger. Fat oxidation is also affected by the GI content of a meal. After a rise in hyperglycemia and insulinemia from a high-GI meal, increases in carbohydrate oxidation are also observed.

Decreased fatty acid oxidation is present in obese individuals. A low-GI, high-protein diet resulted in more successful weight maintenance in people after weight loss than high- or low-protein diets and high-protein, low-GI diets. Perhaps the biggest challenge in discussing the GI with patients is knowing where to turn for information about GI values.

For example, the tables list the GI for types of rice. Variations in the GI of Rice As illustrated in Table 2 , the GI can vary greatly within a category, making it difficult to put a single GI ranking on a particular food. Although a GI value is listed on many food labels in Australia, the values are not available for most food products in the United States.

Thus, general food tables, such as the one shown in this issue's Patient Information page p. The most helpful message for patients is to not focus on the numerical GI or GL values of foods, but rather to think about the overall glycemic impact of the food.

Keep in mind that many foods that are encouraged in a healthy meal plan such as broccoli, spinach, lettuce, and cucumbers have not been evaluated for a GI level. But because of their relatively low carbohydrate content, we know they have an overall low glycemic impact.

Also, when heart-healthy fats and lean proteins in the form of nut butters, low-fat cheeses, and lean meats are added to a meal or snack, the overall glycemic impact will be lower. Thus, it is most helpful to give patients general guidance to limit their intake of foods with a higher GI such as white, highly processed breads and grains and replace these with lower-GI foods such as most fruits and vegetables, dairy products, beans and legumes, and whole grains.

The following tips may be helpful discussion points when talking about glycemic impact with patients. This message is part of all healthy-eating guidelines, but it is especially true for people with diabetes, those needing to control blood pressure using the Dietary Approaches to Stop Hypertension, or DASH, diet , and those seeking to decrease weight.

Nearly all fruits and vegetables have a low GI value. PREVIEW complements and reinforces the findings of another world-first study — a clinical trial by Professor Brand-Miller and her team that investigated how people who already had type 2 diabetes would react to a low-GI diet.

The results showed that the diet improved average blood glucose levels, a key biomarker that predicts complications in type 2 diabetes. After the success of this trial the team began to investigate other applications of the GI and its effect on weight control and cardiovascular disease.

Recent research suggested month old infants of mothers who had consumed a high GI diet during pregnancy had higher risk of cardiovascular disease. Thus blood glucose levels in mothers were playing a role in atherosclerosis hardening of arteries in the second generation.

Her research in this field, which links high blood glucose levels with harmful effects in many different ways, is yet another of her game-changing discoveries that will have far-reaching implications for our diet, health and disease for generations to come.

The Glycemic Index Foundation will soon go one practical step further by launching a new profit — for — purpose venture, a bold and disruptive social enterprise whose aim is to provide funding for Diabetes prevention and community education programs.

Australian develops type 2 diabetes every 5 minutes. Home Research Research impact Technology Health and wellbeing Environment Society and culture Glycemic index. How the glycemic index has changed the meaning of healthy food. World-first findings on diet changes and lifestyle diseases.

Weight loss and cardiovascular disease. Explore our nutrition research Charles Perkins Centre. Professor Jennie Brand-Miller.