PERSPECTIVE article

Front. Nutr., 20 April 2022
Sec. Clinical Nutrition
https://doi.org/10.3389/fnut.2022.896333

Perspective: The Glycemic Index Falls Short as a Carbohydrate Food Quality Indicator to Improve Diet Quality

  • Food Context LLC, Indianapolis, IN, United States

This perspective examines the utility of the glycemic index (GI) as a carbohydrate quality indicator to improve Dietary Guidelines for Americans (DGA) adherence and diet quality. Achieving affordable, high-quality dietary patterns can address multiple nutrition and health priorities. Carbohydrate-containing foods make important energy, macronutrient, micronutrient, phytochemical, and bioactive contributions to dietary patterns, thus improving carbohydrate food quality may improve diet quality. Following DGA guidance helps meet nutrient needs, achieve good health, and reduce risk for diet-related non-communicable diseases in healthy people, yet adherence by Americans is low. A simple indicator that identifies high-quality carbohydrate foods and improves food choice may improve DGA adherence, but there is no consensus on a definition. The GI is a measure of the ability of the available carbohydrate in a food to increase blood glucose. The GI is well established in research literature and popular resources, and some have called for including the GI on food labels and in food-based dietary guidelines. The GI has increased understanding about physiological responses to carbohydrate-containing foods, yet its role in food-based dietary guidance and diet quality is unresolved. A one-dimensional indicator like the GI runs the risk of being interpreted to mean foods are “good” or “bad,” and it does not characterize the multiple contributions of carbohydrate-containing foods to diet quality, including nutrient density, a core concept in the DGA. New ways to define and communicate carbohydrate food quality shown to help improve adherence to high-quality dietary patterns such as described in the DGA would benefit public health.

Introduction

DGA recommendations are meant to help meet nutrient needs, achieve good health, and reduce risk for diet-related non-communicable diseases (NCDs) in healthy people or people at risk for diet-related chronic diseases; they are not intended for disease treatment (1). Healthy eating patterns can also be a part of lifestyle modifications to address NCDs such as Type 2 Diabetes Mellitus (T2DM) and cardiovascular disease (CVD) (2, 3). DGA Healthy Dietary Patterns (HDPs) comprise “nutrient-dense forms of foods and beverages across all food groups, in recommended amounts, and within calorie limits” (1). In the U.S., diet quality is defined in terms of the Healthy Eating Index, i.e., how well a diet reflects DGA recommendations (4). Together, foods and beverages habitually consumed in various quantities, combinations, and proportions over time yield dietary patterns (5). Because dietary patterns include all foods and beverages, they can account for interactions between dietary components that influence metabolism and health (6). The quality of dietary patterns depends on their component foods and beverages, macro- and micronutrients, essential trace elements, plant-based phytochemicals and phytonutrients, and bioactive compounds (5). On average, Americans under consume nutrient-dense fruits, vegetables, whole grains, beans, and dairy foods and over consume nutrient-poor foods that contribute excess added sugars, saturated fat, and sodium (1). DGA adherence and thus diet quality of Americans remains low, and more than 60% of American adults have one or more diet-related NCD (1). There is interest, therefore, in tools to improve DGA adherence.

The food-based DGA HDPs include carbohydrate-containing foods in all major food groups, and carbohydrates contribute ~50% of calorie intake in the U.S. DGA carbohydrate-related guidance includes meeting recommendations for under consumed nutrient-dense foods (e.g., fruits, vegetables, whole grains, milk, and legumes); limiting sugar-sweetened beverages; increasing foods high in dietary fiber; and meeting Acceptable Macronutrient Dietary Ranges for carbohydrate, fat, and protein (1). Carbohydrate-containing foods also contribute flavor and texture attributes to foods and beverages. The diversity of carbohydrate-containing foods and beverages adds complexity to defining carbohydrate food quality in a way that will complement DGA guidance and improve adherence.

Carbohydrate Food Quality

Carbohydrate-containing staple foods are important parts of HDPs, yet sometimes staples are also identified as foods to reduce or avoid in research literature and popular resources (711). Consumer confusion may result when foods that are affordable, accessible, and acceptable are not the foods recommended as part of HDPs. Of the 50% of dietary energy from carbohydrate-containing foods in the U.S., the main contributors are refined grains (16% of carbohydrates) and added sugars (14% of carbohydrates) (12). These general food categories may not be helpful for describing quality. Whole and refined (or enriched) staple grain foods can be part of HDPs as foods like tortillas, bread, or cereal, while more indulgent foods made from refined grains such as cookies and cakes are foods to limit (13).

Common indicators of carbohydrate food quality include dietary fiber, added sugars, nutrient density, and GI. The GI is a measure of the ability of the available carbohydrate in a food to increase blood glucose. It emerged in the 1980s to help people with Type 1 Diabetes modify their food choices to manage blood glucose and insulin (14), and it expanded the understanding of the physiological effects of carbohydrates beyond simple and complex designations. Low-GI foods (≤55) are digested and absorbed slowly, and high-GI foods (≥70) are digested and absorbed more quickly.

The GI is based on a food's available carbohydrate content and can be influenced by food processing and preparation methods, physical and chemical characteristics such as acidity or starch type and content (15), and the presence of protein, fat, and fiber (1520). Foods such as pasta, dairy, legumes, and some fruits have GI values in the low-GI category (14, 20). Staple high-carbohydrate foods such as bread, grains, and potatoes span from low- to high-GI values (20). The GI range for “rice products,” for example, is 19–116, with a mean GI for white rice of 73 and for brown rice of 65. The range for boiled potatoes is 38–103, with a mean GI of 73 (20). Differences for staples may be due to variety, cooking or processing methods, or storage temperature (20).

The GI has been associated with reduced risk for T2DM and CHD (21, 22), and combination indicators for carbohydrate quality have been linked with positive health outcomes. Carbohydrate-to-fiber ratios are associated with lower risk for T2DM (23) and CVD (24). The Carbohydrate Quality Index (CQI) accounts for dietary fiber, GI, whole grains-to-total grains ratio, and solid-to-liquid carbohydrates ratio (25). Higher CQI is associated with lower risk for obesity (26), CVD (27), and CVD risk factors (28). Comparisons between CQI and carbohydrate-to-fiber ratios associated with waist circumference change found carbohydrate-to-fiber and carbohydrate-to-cereal fiber ratios were better predictors than CQI (29). One carbohydrate indicator may not fit all situations.

Low-GI dietary patterns are not necessarily low-carbohydrate, though low-GI and low-carbohydrate concepts can overlap. Low-carbohydrate diets are not a core topic of this article, however, because they are not part of DGA recommendations. The 2020–2025 Dietary Guidelines Advisory Committee reviewed evidence about dietary patterns based on macronutrient distribution and found limited evidence for such diets reducing risk for CVD, and insufficient evidence to establish a relationship between these diets and T2DM; growth, size, body composition and risk of overweight or obesity; sarcopenia; and mortality (5).

Dietary Guidelines for Americans, GI, and Health Outcomes

Adherence to the DGA is associated with reduced risk for NCDs. The DGA HDPs reflect findings from extensive evidence reviews on dietary patterns and multiple health outcomes (5). Strong evidence links DGA HDPs with reduced risk for all-cause mortality and CVD, and moderate evidence links HDPs with improved growth, body composition and body weight; reduced risk for T2DM; and bone health (5). The 2015 and 2020–2025 DGA do not recommend the GI (5, 30), and the 2010 DGA found strong evidence that the GI and glycemic load (GL), the product of a food GI and the quantity of available carbohydrate in a serving of that food, “are not associated with body weight; thus, it is not necessary to consider these measures when selecting carbohydrate foods and beverages for weight management” (31).

Prolonged exposure to elevated blood glucose is a risk factor for T2DM (32) and glycemic control is an important factor in metabolic health, but the relevance of the GI to guide food choice for HDPs—the role of the DGA—is unresolved (3335). A series of systematic reviews and meta-analyses of prospective cohort studies using the carbohydrate quality indicators dietary fiber, whole grains, and the GI assessed the relationships between these indicators and several NCDs (36). Observational evidence indicated higher dietary fiber intakes were associated with a 15–30% decrease in all-cause and CVD mortality and incidence of CHD, stroke, and T2DM. The certainty of evidence was graded as moderate for dietary fiber, low to moderate for whole grains, and low to very low for dietary GI and GL (36). This evidence supports DGA guidance to consume foods high in dietary fiber and whole grains. The GI is not included by any national food agency in major food-based dietary guidelines (FBDGs) (37), though some countries allow GI labeling (38).

Dietary Guidelines for Americans, GI, and Nutrient Adequacy

The DGA recommendations help people meet nutrient needs by including nutrient-dense versions of foods from all food groups in HDPs that contribute specific groups of macro- and micronutrients to influence health (1). Together, foods are part of HDPs based on multiple attributes; no single food, macronutrient, or micronutrient can provide the attributes needed for good health.

Low-GI foods are not necessarily nutrient-dense, so adherence to the DGA is a more reliable way to ensure nutrient adequacy than consuming a low-GI diet. Over-reliance on GI values may lead to choices inconsistent with DGA recommendations. Some low-GI foods are energy-dense due to fat and/or sugar content and should be consumed in moderation (e.g., ice cream, cookies, and candy bars), while some high-GI foods are nutrient-dense and part of HDPs (e.g., carrots, potatoes, and grains).

Nutrient adequacy has been associated with carbohydrate food quality metrics including the GI (39, 40), carbohydrate ratios (4143), and CQI scores (25, 44). These relationships may be present because nutrients and other dietary components are correlated in foods (45). In a cohort consuming the Mediterranean dietary pattern, for example, the lowest probability of nutrient inadequacy was found in the highest quintile of both the a priori MeDiet score and the CQI (25), likely due to adherence to the Mediterranean dietary pattern.

More Clincial Trials About Diet Quality and GI are Needed

Many studies have examined chronic disease risk in terms of low- and high-GI diets, low- and high-GL diets, or low- and high-adherence to dietary patterns such as Mediterranean, Dietary Approaches to Stop Hypertension (DASH), or DGA. Few trials, however, have modified GI or GL of high-quality dietary patterns. Two well-known randomized controlled trials studied CVD biomarkers (46) and weight loss (47) in this context and found high-quality dietary patterns had unexpected impacts on outcomes (46, 47).

In the OmniCarb trial, the high-quality DASH diet was modified to test the effect of low- and high-GI foods in low- and high-carbohydrate dietary patterns on CVD risk factors in 163 overweight adults (46). Participants consumed each diet for 5 weeks. Contrary to their hypothesis, researchers concluded that a DASH-type diet containing low-GI foods compared with high-GI foods “does not improve CVD risk factors and may in fact reduce insulin sensitivity” (46).

The Diet Intervention Examining the Factors Interacting with Treatment Success (DIETFITS) study was a large, 12-month study of “healthy” diets that were either low-fat (51% carbohydrates; high-GL) or low-carbohydrate (27% carbohydrate; low-GL), compared for their effect on weight loss in 609 adults (47). All participants received guidance to choose high-quality, nutrient-dense foods including more vegetables; fewer added sugars, refined flours, and trans fats; and more minimally processed, nutrient-dense whole foods. The DIETFITS study found no difference in average weight loss between the diet groups, an outcome that differed from previous studies comparing low-fat and low-carbohydrate diets. The researchers indicated that the high quality of both diets, not simply their macronutrient differences, was a key contributor to findings (47).

More trials that shed light on the interactions between diet quality, nutrient-dense carbohydrate foods, and incidence of NCDs can aid understanding of diet quality impacts. Some evidence indicates that consuming vegan, vegetarian, DASH and DGA dietary patterns may improve glycemic control in people with T2DM compared to conventional diets for T2DM management (48). In prospective cohort studies, higher adherence to high-quality dietary patterns has been linked to less weight gain over time (49, 50), reduced risk for T2DM (51) and stroke mortality (52), and lower mortality from all causes and CVD (5355).

GI Values May Not Predict Meal Glycemic Responses

Because the GI model is based on individual foods analyzed under standard conditions, its application may be limited for characterizing health-promoting dietary patterns including foods consumed in real-world conditions. In studies that determined the GI of staple foods like rice, potatoes, or pasta alone and as part of mixed meals containing typical amounts of protein (chicken or egg), fiber (vegetables), and oil or sauce, the measured GIs of mixed meals were lower than predicted based on food GIs by 20–50% (5659). Different approaches to calculating mixed meal GIs have been proposed (60). Calculated meal GIs may not agree with measured meal GIs without accounting for protein, fat, and available carbohydrate (60), a practice unrealistic for general consumer use. The GI is considered a property of foods that does not change when eaten with other foods, but food GI values may not be sufficient information to predict post-meal GRs. Eating balanced meals or dietary patterns provides an avenue to modify GRs not apparent from GI values.

Another way to account for meal and diet GRs is GL, developed to evaluate diets in epidemiological studies (61, 62). The GL is the product of the food GI and the quantity of available carbohydrate in a serving of that food. Accurate GL population estimates depend on accurate food intake information and correct food GI values. Given the broad ranges in GIs of staple carbohydrate foods, and food frequency questionnaire inaccuracies, combining GL and food intake may exacerbate inaccuracies in prospective cohort study data about GL and health. Even assuming researchers calculate population GLs using accurate food intake and GI data, the approach would not account for mixed meal GRs described above.

The GI is a Property of Food

GIs are physiological responses measured in human subjects; thus, they are determined using a fundamentally different measurement approach than direct analysis of food components like nutrients or fiber. Its reliability has been questioned since its introduction, including critiques about methodology, the many things that can influence measurements, and the relationship between food GIs and post-meal GR (60). One unresolved topic is the role of within- and between-individual variation during GI determination (3335, 60, 63). GI methodology was developed to minimize within-person variation during measurement. The International Organization for Standardization method, for example, requires repeated testing in at least 10 subjects under highly-controlled conditions to minimize within-individual variation (64).

Another perspective about GRs has emerged from studies about personalized nutrition that collected detailed information about glycemic and other metabolic responses to foods and meals in hundreds of subjects (35, 65). The study by Zeevi et al. measured postprandial GR to meals in 800 subjects (65), and the study by Berry et al. measured postprandial glucose, triglyceride, and insulin responses to meals in 1,002 individuals (35), in a laboratory and at home. Both groups of researchers found metabolic responses to the same meals were highly variable between individuals, but they were very similar within individuals and predictable based on person-specific characteristics (emphasis added) (35, 65). Though this interpretation of GRs was challenged (66), the consistent findings of predictable individual GRs led authors to question the utility of identifying dietary ingredients as universally “good” or “bad” (65) or recommending universal nutrition recommendations (35) based on population averages.

Meal Context is a New Determinant of Postprandial Metabolism

Berry et al. examined sources of variance in individual postprandial metabolic responses. The variance attributable to genetic heritability explained 0% of the variance for triglyceride, and 48% for glucose, thus about half of the variance in GRs is not modifiable (35). Meal macronutrient composition (carbohydrates, sugar, fat, protein, and fiber) explained about 17% of variance, a similar percentage to “meal context,” which included non-food, meal-related factors such as meal timing, exercise, sleep, and circadian rhythm (35). Meal composition and context were “core determinants” of individual postprandial metabolism.

Personalized nutrition is not a reality for most people but studying metabolic responses to meals in real-world conditions may provide important insights about the interplay between dietary patterns and metabolic health beyond GI values or macronutrient content. The National Institutes of Health Precision Nutrition Program aims to develop algorithms to explain the “interactions between diet, genes, proteins, microbiome, metabolism, and other individual contextual factors” in response to different diets (67). The program made its first research awards in January 2022.

Discussion

The DGA and other FBDGs have been evolving and continue to adapt to new evidence and priorities. In 1990, one of the DGA's seven recommendations was to eat no more than 30% of calories from fat and 10% from saturated fat, and another was to eat more breads, cereals, pasta, and rice (68). Thirty years later, the 2020–2025 DGA are flexible food-based guidance organized by life stage, are informed by research on dietary patterns associated with positive health outcomes and can accommodate macronutrient ranges rather than require specific targets (1). Consuming DGA HDPs is associated with multiple positive health outcomes, but adherence is low. Because carbohydrates contribute about half of dietary calories in the U.S., tools to improve carbohydrate food quality may help improve overall diet quality of Americans.

FBDGs are also prioritizing access to affordable, high-quality dietary patterns for those who are food insecure (69) and to healthy diets from sustainable food systems (70). In populations consuming traditional diets with a high proportion of low-cost carbohydrate staple foods, improving diet quality may mean increasing intakes of nutrient-dense foods without disparaging affordable, accessible, culturally acceptable staples. In other populations, it may mean trading nutrient-poor choices high in added sugars, saturated fat, and sodium for more nutrient-dense choices while reducing energy intake. The search for sustainable dietary approaches to food and nutrition security may increase interest in traditional or innovative plant-based and carbohydrate-containing foods eaten in new contexts. These priorities have implications for defining carbohydrate food quality and communicating its role in diet quality and health.

Quality is difficult to define. Diet quality includes aspects of adequacy, moderation, and balance (71), but there is not a consensus about what constitutes high-quality carbohydrate foods and beverages. Single quality indicators fall short due to the diversity of carbohydrate foods. Saying high quality carbohydrate foods should all be high-fiber, sugar-free, or low-GI will not adequately characterize carbohydrate foods needed to build a healthy diet. Food quality indicators used within a dietary framework like the DGA can better guide food choice. The GI does not address nutrient density, it does not translate well to HDPs, and its singular focus on one dimension of carbohydrate-containing foods may divert public attention away from dietary patterns-based approaches to improving health. In addition, among common measures of carbohydrate food quality used in regulatory frameworks, the effects of carbohydrate-containing foods on postprandial glycemia are “the most contentious” (72).

Carbohydrate food quality is multi-dimensional. Hypotheses about carbohydrates in health and disease are changing in ways that support new, possibly composite, carbohydrate food quality indicators (35, 36, 47, 73, 74). Current efforts to improve diet quality would benefit from new approaches to define and communicate carbohydrate food quality that complement food-based guidance affordably, holistically, and sustainably.

Data Availability Statement

The original contributions presented in the perspective are included in the article; further inquiries can be directed to the corresponding author.

Author Contributions

The author confirms being the sole contributor of this work and has approved it for publication.

Funding

This perspective was supported by funds from the Alliance for Potato Research and Education. The funder was not involved in the interpretation of data, the writing of this article or the decision to submit it for publication.

Conflict of Interest

JN is the owner of Food Context, LLC, where she provides food and nutrition consulting services to the food and beverage industry.

Publisher's Note

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.

References

1. U.S. Department of Agriculture, U.S. Department of Health and Human Services. Dietary Guidelines for Americans, 2020-2025. 9th ed. (2020). Available online at: DietaryGuidelines.gov/ (accessed March 7, 2022).

Google Scholar

2. Lichtenstein AH, Appel LJ, Vadiveloo M, Hu FB, Kris-Etherton PM, Rebholz CM, et al. Dietary guidance to improve cardiovascular health: a scientific statement from the American Heart Association. Circulation. (2021) 144:e472–87. doi: 10.1161/CIR.0000000000001031

PubMed Abstract | CrossRef Full Text | Google Scholar

3. Davies MJ, D'Alessio DA, Fradkin J, Kernan WN, Mathieu C, Mingrone G, et al. A consensus report by the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD). Diabetes Care. (2018) 41:2669–701. doi: 10.2337/dci18-0033

PubMed Abstract | CrossRef Full Text | Google Scholar

4. Krebs-Smith SM, Pannucci TRE, Subar AF, Kirkpatrick SI, Lerman JL, Tooze JA, et al. Update of the healthy eating index: HEI-2015. J Acad Nutr Diet. (2018) 118:1591–602. doi: 10.1016/j.jand.2018.05.021

PubMed Abstract | CrossRef Full Text | Google Scholar

5. Dietary Guidelines Advisory Committee. Scientific Report of the 2020 Dietary Guidelines Advisory Committee: advisory report to the Secretary of Agriculture and Secretary of Human and Health Services. (2020). Available online at: https://www.dietaryguidelines.gov/2020-advisory-committee-report (accessed March 7, 2022).

6. Tapsell LC, Neale EP, Satija A, Hu FB. Foods, nutrients, and dietary patterns: Interconnections and implications for dietary guidelines. Adv Nutr. (2016) 7:445–54. doi: 10.3945/an.115.011718

PubMed Abstract | CrossRef Full Text | Google Scholar

7. Augustin LSA, Kendall CWC, Jenkins DJA, Willett WC, Astrup A, Barclay AW, et al. Glycemic index, glycemic load and glycemic response: an international scientific consensus summit from the international carbohydrate quality consortium (ICQC). Nutr Metab Cardiovasc Dis. (2015) 25:795–815. doi: 10.1016/j.numecd.2015.05.005

PubMed Abstract | CrossRef Full Text | Google Scholar

8. Ludwig DS, Ebbeling CB. The carbohydrate-insulin model of obesity: beyond “calories in, calories out”. JAMA Intern Med. (2018) 178:1098–103. doi: 10.1001/jamainternmed.2018.2933

PubMed Abstract | CrossRef Full Text | Google Scholar

9. Atkins RC. Atkins' New Diet Revolution. New York, NY: Harper Collins Publishers (2009). p. 560.

Google Scholar

10. Brand-Miller J, Wolever TMS, Foster-Powell K, Colagiuri S. The New Glucose Revolution: The Authoritative Guide to the Glycemic Index - the Dietary Solution for Lifelong Health. New York, NY: Hatchette Books (2006). p. 349.

11. Taubes G. The Case Against Sugar. New York, NY: Knopf Publishers (2016). p. 384.

Google Scholar

12. Shan Z, Rehm CD, Rogers G, Ruan M, Wang DD, Hu FB, et al. Trends in dietary carbohydrate, protein, and fat intake and diet quality among US adults, 1999-2016. JAMA. (2019) 322:1178–87. doi: 10.1001/jama.2019.13771

PubMed Abstract | CrossRef Full Text | Google Scholar

13. Gaesser GA. Perspective: refined grains and health: genuine risk, or guilt by association? Adv Nutr. (2019) 10:361–71. doi: 10.1093/advances/nmy104

PubMed Abstract | CrossRef Full Text | Google Scholar

14. Jenkins D, Wolever T, Taylor R, Barker H. Glycemic index of foods: a physiological basis for carbohydrate exchange. Am J Clin Nutr. (1981) 34:362–6. doi: 10.1093/ajcn/34.3.362

PubMed Abstract | CrossRef Full Text | Google Scholar

15. Brouns F, Bjorck I, Frayn KN, Gibbs AL, Lang V, Slama G, et al. MS Glycaemic index methodology. Nutr Res Rev. (2005) 18:145–71. doi: 10.1079/NRR2005100

PubMed Abstract | CrossRef Full Text | Google Scholar

16. Collier G, Mclean A, O'Dea K. Effect of co-ingestion of fat on the metabolic responses to slowly and rapidly absorbed carbohydrates. Diabetologia. (1984) 26:50–4. doi: 10.1007/BF00252263

PubMed Abstract | CrossRef Full Text | Google Scholar

17. Spiller GA, Jensen CD, Pattison TS, Chuck CS, Whittan JH, Scala J. Effect of protein dose on serum glucose and insulin response to sugars. Am J Clin Nutr. (1987) 46:474–80. doi: 10.1093/ajcn/46.3.474

PubMed Abstract | CrossRef Full Text | Google Scholar

18. Owen B, Wolever TM. Effect of fat on glycaemic responses in normal subjects: a dose-response study. Nutr Res. (2003) 23:1341–7. doi: 10.1016/S0271-5317(03)00149-0

CrossRef Full Text | Google Scholar

19. Henry CJK, Lightowler HJ, Newens KJ, Pata N. The influence of adding fats of varying saturation on the glycaemic response of white bread. Int J Food Sci Nutr. (2008) 59:61–9. doi: 10.1080/09637480701664183

PubMed Abstract | CrossRef Full Text | Google Scholar

20. Atkinson FS, Brand-Miller JC, Foster-Powell K, Buyken AE, Goletzke J. International tables of glycemic index and glycemic load values 2021: a systematic review. Am J Clin Nutr. (2021) 114:1625–32. doi: 10.1093/ajcn/nqab233

PubMed Abstract | CrossRef Full Text | Google Scholar

21. Livesey G, Taylor R, Livesey HF, Buyken AE, Jenkins DJA, Augustin LSA, et al. Dietary glycemic index and load and the risk of type 2 diabetes: a systematic review and updated meta-analyses of prospective cohort studies. Nutrients. (2019) 11:1280. doi: 10.3390/nu11061280

PubMed Abstract | CrossRef Full Text | Google Scholar

22. Livesey G, Livesey H. Coronary heart disease and dietary carbohydrate, glycemic index, and glycemic load: dose-response meta-analyses of prospective cohort studies. Mayo Clin Proc. (2019) 3:52–69. doi: 10.1016/j.mayocpiqo.2018.12.007

PubMed Abstract | CrossRef Full Text | Google Scholar

23. AlEssa HB, Bhupathiraju SN, Malik VS, Wedick NM, Campos H, Rosner B, et al. Carbohydrate quality and quantity and risk of type 2 diabetes in US women. Am J Clin Nutr. (2015) 102:1542–53. doi: 10.3945/ajcn.115.116558

PubMed Abstract | CrossRef Full Text | Google Scholar

24. AlEssa HB, Cohen R, Malik VS, Adebamowo SN, Rimm EB, Manson JAE, et al. Carbohydrate quality and quantity and risk of coronary heart disease among US women and men. Am J Clin Nutr. (2018) 107:257–67. doi: 10.1093/ajcn/nqx060

PubMed Abstract | CrossRef Full Text | Google Scholar

25. Zazpe I, Sánchez-Taínta A, Santiago S, De La Fuente-Arrillaga C, Bes-Rastrollo M, Alfredo Martínez J, et al. Association between dietary carbohydrate intake quality and micronutrient intake adequacy in a Mediterranean cohort: the SUN (Seguimiento Universidad de Navarra) Project. Br J Nutr. (2014) 111:2000–9. doi: 10.1017/S0007114513004364

PubMed Abstract | CrossRef Full Text | Google Scholar

26. Santiago S, Zazpe I, Bes-Rastrollo M, Sánchez-Tainta A, Sayón-Orea C, De La Fuente-Arrillaga C, et al. Carbohydrate quality, weight change and incident obesity in a Mediterranean cohort: the SUN Project. Eur J Clin Nutr. (2015) 69:297–302. doi: 10.1038/ejcn.2014.187

PubMed Abstract | CrossRef Full Text | Google Scholar

27. Zazpe I, Santiago S, Gea A, Ruiz-Canela M, Carlos S, Bes-Rastrollo M, et al. Association between a dietary carbohydrate index and cardiovascular disease in the SUN (Seguimiento Universidad de Navarra) project. Nutr Metab Cardiovasc Dis. (2016) 26:1048–56. doi: 10.1016/j.numecd.2016.07.002

PubMed Abstract | CrossRef Full Text | Google Scholar

28. Martínez-González M, Fernandez-Lazaro C, Toledo E, Díaz-López A, Corella D, Goday A, et al., Carbohydrate quality changes and concurrent changes in cardiovascular risk factors: a longitudinal analysis in the PREDIMED-Plus randomized trial. Am J Clin Nutr. (2020) 111:291–306. doi: 10.1093/ajcn/nqz298

PubMed Abstract | CrossRef Full Text | Google Scholar

29. Sawicki CM, Lichtenstein AH, Rogers GT, Jacques PF, Ma J, Saltzman E, et al. M. Comparison of indices of carbohydrate quality and food sources of dietary fiber on longitudinal changes in waist circumference in the framingham offspring cohort. Nutrients. (2021) 13:1–16. doi: 10.3390/nu13030997

PubMed Abstract | CrossRef Full Text | Google Scholar

30. U.S. Department of Health Human Services, U.S. Department of Agriculture. 2015 – 2020 Dietary Guidelines for Americans. 8th ed. (2015). Available online at: https://health.gov/our-work/food-nutrition/previous-dietary-guidelines/2015 (accessed March 7, 2022).

Google Scholar

31. U.S. Department of Agriculture, U.S. Department of Health and Human Services. Dietary Guidelines for Americans, 2010. 7th ed. (2010). Available online at: https://health.gov/sites/default/files/2020-01/DietaryGuidelines2010.pdf (accessed March 7, 2022).

Google Scholar

32. Richter B, Hemmingsen B, Metzendorf MI, Takwoingi Y. Development of type 2 diabetes mellitus in people with intermediate hyperglycaemia. Cochrane Database Syst Rev 2018. (2018) 10:CD012661. doi: 10.1002/14651858.CD012661.pub2

PubMed Abstract | CrossRef Full Text | Google Scholar

33. Meng H, Matthan NR, Ausman LM, Lichtenstein AH. Effect of macronutrients and fiber on postprandial glycemic responses and meal glycemic index and glycemic load value determinations. Am J Clin Nutr. (2017) 105:842–53. doi: 10.3945/ajcn.116.144162

PubMed Abstract | CrossRef Full Text | Google Scholar

34. Matthan NR, Ausman LM, Meng H, Tighiouart H, Lichtenstein AH, Mayer J. Estimating the reliability of glycemic index values and potential sources of methodological and biological variability. Am J Clin Nutr. (2016) 104:1004–17. doi: 10.3945/ajcn.116.137208

PubMed Abstract | CrossRef Full Text | Google Scholar

35. Berry SE, Valdes AM, Drew DA, Asnicar F, Mazidi M, Wolf J, et al, Human postprandial responses to food and potential for precision nutrition. Nat Med. (2020) 26:964–73. doi: 10.1038/s41591-020-0934-0

PubMed Abstract | CrossRef Full Text | Google Scholar

36. Reynolds A, Mann J, Cummings J, Winter N, Mete E, Te Morenga L. Carbohydrate quality and human health: a series of systematic reviews and meta-analyses. Lancet. (2019) 393:434–45. doi: 10.1016/S0140-6736(18)31809-9

PubMed Abstract | CrossRef Full Text | Google Scholar

37. Trumbo PR. Global evaluation of the use of glycaemic impact measurements to food or nutrient intake. Public Health Nutr. (2020) 24:3966–75. doi: 10.1017/S1368980021000616

PubMed Abstract | CrossRef Full Text | Google Scholar

38. Barclay AW, Augustin LSA, Brighenti F, Delport E, Henry CJ, Sievenpiper JL, et al. Dietary glycaemic index labelling: a global perspective. Nutrients. (2021) 13:3244. doi: 10.3390/nu13093244

PubMed Abstract | CrossRef Full Text | Google Scholar

39. Kwan DKY, Louie JCY. The association between carbohydrate quality and nutrient adequacy in Australian adults. Eur J Clin Nutr. (2020) 74:1594–602. doi: 10.1038/s41430-020-0620-9

PubMed Abstract | CrossRef Full Text | Google Scholar

40. Louie JCY, Buyken AE, Brand-Miller JC, Flood VM. The link between dietary glycemic index and nutrient adequacy. Am J Clin Nutr. (2012) 95:694–702. doi: 10.3945/ajcn.111.015271

PubMed Abstract | CrossRef Full Text | Google Scholar

41. Liu J, Rehm CD, Shi P, McKeown NM, Mozaffarian D, Micha R, et al. comparison of different practical indices for assessing carbohydrate quality among carbohydrate-rich processed products in the US. PLoS ONE. (2020) 15:1–16. doi: 10.1371/journal.pone.0231572

PubMed Abstract | CrossRef Full Text | Google Scholar

42. Blumfield M, McConnell A, Campos V, Lê K-A, Fayet-Moore F. Carbohydrate quality metrics and their association with population nutrient intakes and diet quality in Australia. Curr Dev Nutr. (2020) 4:510. doi: 10.1093/cdn/nzaa046_010

CrossRef Full Text | Google Scholar

43. Blumfield M, McConnell A, Cassettari T, Petocz P, Warner M, Campos V, et al. Balanced carbohydrate ratios are associated with improved diet quality in Australia: a nationally representative cross-sectional study. PLoS ONE. (2021) 16:e0253582. doi: 10.1371/journal.pone.0253582

PubMed Abstract | CrossRef Full Text | Google Scholar

44. Sánchez-Tainta A, Zazpe I, Bes-Rastrollo M, Salas-Salvadó J, Bullo M, Sorlí JV, et al. Nutritional adequacy according to carbohydrates and fat quality. Eur J Nutr Nutr. (2016) 55:93–106. doi: 10.1007/s00394-014-0828-3

PubMed Abstract | CrossRef Full Text | Google Scholar

45. Maki KC, Slavin JL, Rains TM, Kris-Etherton PM. Limitations of observational evidence: implications for evidence-based dietary recommendations. Adv Nutr. (2014) 5:7–15. doi: 10.3945/an.113.004929

PubMed Abstract | CrossRef Full Text | Google Scholar

46. Sacks FM, Carey VJ, Anderson CAM, Miller ER III, Copeland T, Charleston J, et al. Effects of high vs. low glycemic index of dietary carbohydrate on cardiovascular disease risk factors and insulin sensitivity: the OmniCarb randomized clinical trial. J Am Med Assoc. (2014) 312:2531–41. doi: 10.1001/jama.2014.16658

PubMed Abstract | CrossRef Full Text | Google Scholar

47. Gardner CD, Trepanowski JF, Del Gobbo LC, Hauser ME, Rigdon J, Ioannidis JPA, et al. Effect of low-fat vs. low-carbohydrate diet on 12-month weight loss in overweight adults and the association with genotype pattern or insulin secretion: the DIETFITS randomized clinical trial. J Am Med Assoc. (2018) 319:667–79. doi: 10.1001/jama.2018.0245

PubMed Abstract | CrossRef Full Text | Google Scholar

48. de Carvalho GB, Dias-Vasconcelos NL, Santos RKF, Brandão-Lima PN, da Silva DG, Pires LV. Effect of different dietary patterns on glycemic control in individuals with type 2 diabetes mellitus: a systematic review. Crit Rev Food Sci Nutr. (2019) 60:1999–2010. doi: 10.1080/10408398.2019.1624498

PubMed Abstract | CrossRef Full Text | Google Scholar

49. Fung TT, Pan A, Hou T, Chiuve SE, Tobias DK, Mozaffarian D, et al. Long-term change in diet quality is associated with body weight change in men and women. J Nutr. (2015) 145:1850–56. doi: 10.3945/jn.114.208785

PubMed Abstract | CrossRef Full Text | Google Scholar

50. Kang M, Boushey CJ, Shvetsov YB, Setiawan VW, Paik H-Y, Wilkens L, et al. Changes in diet quality and body weight over 10 years: the Multiethnic Cohort Study. Br J Nutr. (2021) 126:1389–97. doi: 10.1017/S000711452100012X

PubMed Abstract | CrossRef Full Text | Google Scholar

51. Jacobs S, Boushey CJ, Franke AA, Shvetsov YB, Monroe KR, Haiman CA, et al. A priori-defined diet quality indices, biomarkers and risk for type 2 diabetes in five ethnic groups: the multiethnic cohort. Br J Nutr. (2017) 118:312–20. doi: 10.1017/S0007114517002033

PubMed Abstract | CrossRef Full Text | Google Scholar

52. Aigner A, Becher H, Jacobs S, Wilkens LR, Boushey CJ, Le Marchand L, et al. Low diet quality and the risk of stroke mortality: the Multiethnic Cohort Study. Eur J Clin Nutr. (2018) 72:1035–45. doi: 10.1038/s41430-018-0103-4

PubMed Abstract | CrossRef Full Text | Google Scholar

53. Harmon BE, Boushey CJ, Shvetsov YB, Ettienne R, Reedy J, Wilkens LR, et al. Associations of key diet-quality indexes with mortality in the Multiethnic Cohort: the Dietary Patterns Methods Project. Am J Clin Nutr. (2015) 101:587–97. doi: 10.3945/ajcn.114.090688

PubMed Abstract | CrossRef Full Text | Google Scholar

54. Panizza CE, Shvetsov YB, Harmon BE, Wilkens LR, Le Marchand L, Haiman C, et al. Testing the predictive validity of the Healthy Eating Index-2015 in the Multiethnic Cohort: is the score associated with a reduced risk of all-cause and cause-specific mortality? Nutrients. (2018) 10:452. doi: 10.3390/nu10040452

PubMed Abstract | CrossRef Full Text | Google Scholar

55. Liese AD, Krebs-Smith SM, Subar AF, George SM, Harmon BE, Neuhouser ML, et al. The Dietary Patterns Methods Project: Synthesis of findings across cohorts and relevance to dietary guidance. J Nutr. (2015) 145:393–402. doi: 10.3945/jn.114.205336

PubMed Abstract | CrossRef Full Text | Google Scholar

56. Kim JS, Nam K, Chung SJ. Effect of nutrient composition in a mixed meal on the postprandial glycemic response in healthy people: a preliminary study. Nutr Res Pract. (2019) 13:126–33. doi: 10.4162/nrp.2019.13.2.126

PubMed Abstract | CrossRef Full Text | Google Scholar

57. Sun L, Ranawana DV, Leow MKS, Henry CJ. Effect of chicken, fat and vegetable on glycaemia and insulinaemia to a white rice-based meal in healthy adults. Eur J Nutr. (2014) 53:1719–26. doi: 10.1007/s00394-014-0678-z

PubMed Abstract | CrossRef Full Text | Google Scholar

58. Hätönen KA, Virtamo J, Eriksson JG, Sinkko HK, Sundvall JE, Valsta LM. Protein and fat modify the glycaemic and insulinaemic responses to a mashed potato-based meal. Br J Nutr. (2011) 106:248–53. doi: 10.1017/S0007114511000080

PubMed Abstract | CrossRef Full Text | Google Scholar

59. Dodd H, Williams S, Brown R, Venn B. Calculating meal glycemic index by using measured and published food values compared with directly measured meal glycemic index. Am J Clin Nutr. (2011) 94:992–6. doi: 10.3945/ajcn.111.012138

PubMed Abstract | CrossRef Full Text | Google Scholar

60. Wolever TM. Is glycaemic index (GI) a valid measure of carbohydrate quality? Eur J Clin Nutr. (2013) 67:522–31. doi: 10.1038/ejcn.2013.27

PubMed Abstract | CrossRef Full Text | Google Scholar

61. Salmerón J, Manson JE, Stampfer MJ, Colditz GA, Wing AL, Willett WC. Dietary fiber, glycemic load, and risk of non-insulin-dependent diabetes mellitus in women. J Am Med Assoc. (1997) 277:1455–61. doi: 10.1001/jama.1997.03540300040031

PubMed Abstract | CrossRef Full Text | Google Scholar

62. Salmerón J, Ascherio A, Rimm EB, Colditz GA, Spiegelman D, Jenkins DJ, et al. Dietary fiber, glycemic load, and risk of NIDDM in men. Diabetes Care. (1997) 20:545–50. doi: 10.2337/diacare.20.4.545

PubMed Abstract | CrossRef Full Text | Google Scholar

63. Whelan WJ, Hollar D, Agatston A, Dodson HJ, Tahal DS. The glycemic response is a personal attribute. IUBMB Life. (2010) 62:637–41. doi: 10.1002/iub.365

PubMed Abstract | CrossRef Full Text | Google Scholar

64. International Organization for Standardization (ISO) 26642-2010. Food Products–Determination of the Glycaemic Index (GI) and Recommendation for Food Classification. International Organization for Standardization, Geneva (2010).

Google Scholar

65. Zeevi D, Korem T, Zmora N, Israeli D, Rothschild D, Weinberger A, et al. Personalized nutrition by prediction of glycemic responses. Cell. (2015) 163:1079–94. doi: 10.1016/j.cell.2015.11.001

PubMed Abstract | CrossRef Full Text | Google Scholar

66. Wolever TMS. Personalized nutrition by prediction of glycaemic responses: fact or fantasy. Eur J Clin Nutr. (2016) 70:411–3. doi: 10.1038/ejcn.2016.31

PubMed Abstract | CrossRef Full Text | Google Scholar

67. NIH. Nutrition for Precision Health. National Institutes of Health Office of Strategic Coordination. Available online at: https://commonfund.nih.gov/nutritionforprecisionhealth (accessed March 1, 2022).

68. U.S. Department of Agriculture, U.S. Department of Health and Human Services. Dietary Guidelines for Americans, 1990. 3rd ed. (1990). Available online at: https://www.dietaryguidelines.gov/sites/default/files/2019-05/1990DietaryGuidelinesforAmericans.pdf (accessed March 7, 2022).

Google Scholar

69. FAO/IFAD/UNICEF/WFP/WHO. The State of Food Security and Nutrition in the World 2020. Transforming Food Systems for Affordable Healthy Diets. FAO/IFAD/UNICEF/WFP/WHO: Rome (2020). Available online at: https://www.fao.org/3/ca9692en/online/ca9692en.html (accessed March 7, 2022).

Google Scholar

70. FAO. Sustainable Healthy Diets Guiding Principles. (2019). Available online at: https://www.fao.org/3/ca6640en/ca6640en.pdf (accessed March 7, 2022).

Google Scholar

71. Kim S, Haines PS, Siega-Riz AM, Popking BM. The Diet Quality Index-International (DQI-I) provides an effective tool for cross-national comparison of diet quality as illustrated by China and the United States. J Nutr. (2003) 133:3476–84. doi: 10.1093/jn/133.11.3476

PubMed Abstract | CrossRef Full Text | Google Scholar

72. Marinangeli CPF, Harding SV, Glenn AJ, Chiavaroli L, Zurbau A, Jenkins DJA, et al. Destigmatizing carbohydrate with food labeling: the use of non-mandatory labelling to highlight quality carbohydrate foods. Nutrients. (2020) 12:1–29. doi: 10.3390/nu12061725

PubMed Abstract | CrossRef Full Text | Google Scholar

73. Speakman JR, Hall KD. Carbohydrates, insulin, and obesity. Science. (2021) 372:577–8. doi: 10.1126/science.aav0448

PubMed Abstract | CrossRef Full Text | Google Scholar

74. Comerford KB, Papanikolaou Y, Jones JM, Rodriguez J, Slavin J, Angadi S, et al. Toward an evidence-based definition and classification of carbohydrate food quality: an expert panel report. Nutrients. (2021) 13:2667. doi: 10.3390/nu13082667

PubMed Abstract | CrossRef Full Text | Google Scholar

Keywords: glycemic index, diet quality, dietary guidelines, dietary patterns, carbohydrate food quality, carbohydrates, nutrient-dense

Citation: Nicholls J (2022) Perspective: The Glycemic Index Falls Short as a Carbohydrate Food Quality Indicator to Improve Diet Quality. Front. Nutr. 9:896333. doi: 10.3389/fnut.2022.896333

Received: 14 March 2022; Accepted: 29 March 2022;
Published: 20 April 2022.

Edited by:

Nick Bellissimo, Ryerson University, Canada

Reviewed by:

Harvey Anderson, University of Toronto, Canada

Copyright © 2022 Nicholls. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Jill Nicholls, jill.nicholls20@gmail.com

Download