{"id":2856,"date":"2026-01-20T10:43:08","date_gmt":"2026-01-20T09:43:08","guid":{"rendered":"https:\/\/nutrifizz.fr\/a-database-on-the-glycemic-index-a-tool-for-objectively-assessing-and-optimizing-the-carbohydrate-quality-of-foods\/"},"modified":"2026-02-16T13:03:56","modified_gmt":"2026-02-16T12:03:56","slug":"a-database-on-the-glycemic-index-a-tool-for-objectively-assessing-and-optimizing-the-carbohydrate-quality-of-foods","status":"publish","type":"post","link":"https:\/\/nutrifizz.fr\/en\/a-database-on-the-glycemic-index-a-tool-for-objectively-assessing-and-optimizing-the-carbohydrate-quality-of-foods\/","title":{"rendered":"A database on the glycemic index: a tool for objectively assessing and optimizing the carbohydrate quality of foods"},"content":{"rendered":"\n
\"Sugar<\/figure>\n\n

Introduction<\/h4>\n\n

Assessing the quality of carbohydrates in the diet is now a major public health issue. With the continued rise in metabolic diseases (type 2 diabetes, obesity, metabolic syndrome), indicators such as the glycemic index<\/strong> (GI) and glycemic load (GL) play a central role in the nutritional analysis of diets. <\/p>\n\n

However, until now, no national database had made it possible to link these indicators to the actual dietary habits of a population. A publication<\/a> in The American Journal of Clinical Nutrition<\/em> fills this gap, providing access to a unique GI\/GL database built from NHANES data and the international GI table. <\/p>\n\n

In this article, we explore the value of this resource for players in the agri-food and health nutrition sectors, as well as the opportunities it offers in terms of formulation, communication, and regulatory strategy.<\/p>\n\n

<\/div>\n\n

Glycemic index and glycemic load: What are the definitions?<\/strong><\/h4>\n\n

The glycemic index (GI) measures the ability of a carbohydrate-containing food to raise blood sugar levels after ingestion, on a scale of 0 to 100. It ranks foods according to their hyperglycemic effect compared to a reference (glucose or white bread). <\/p>\n\n

The glycemic load<\/strong> (GL), on the other hand, takes into account both the GI and the amount of carbohydrates consumed. It provides an estimate of the overall glycemic impact of a food portion or meal. <\/p>\n\n

These two indicators are essential for better understanding the influence of diet on blood sugar regulation, insulin secretion, and, in the long term, the risk of chronic diseases.<\/p>\n\n

<\/div>\n\n

A scientific innovation driven by artificial intelligence<\/strong><\/strong><\/h4>\n\n

The database published in 2024 marks a major advance in the field of nutritional monitoring. By cross-referencing data from the National Health and Nutrition Examination Survey<\/a> (NHANES) from 1999 to 2018 with the international GI\/GL table, researchers used artificial intelligence algorithms to match foods consumed with glycemic values. <\/p>\n\n

The result<\/strong> is a robust database covering more than 7,900 food codes, representing 99.9% of the carbohydrate intake of NHANES respondents. It serves as a reference tool for analyzing the carbohydrate quality of diets according to various criteria, including demographic groups, income levels, consumption habits, and changes over time. <\/p>\n\n

<\/div>\n\n

Scientific objectification of low GI or CG products<\/strong><\/h4>\n\n

This database now enables nutrition manufacturers to position their products in relation to foods that contribute most to a high glycemic load. It provides a framework for objectively assessing the benefits of a low GI food or ingredient in a typical diet. <\/p>\n\n

For example, a product containing soluble fiber<\/strong>, resistant starch<\/strong>, or a sugar substitute<\/strong> can now be compared to the average GC of products in the same market segment.<\/p>\n\n

<\/div>\n\n

Support for nutritional formulation and reformulation<\/strong><\/h4>\n\n

This foundation opens up new possibilities for:<\/p>\n\n