Insulin index

The insulin index (II) is a dietary metric that quantifies the postprandial insulin response elicited by consuming a 1000 kJ (approximately 240 kcal) portion of a food, expressed relative to white bread, which is assigned a reference value of 100.^[1] Developed in 1997 by researchers Susanne Holt, Janette Brand Miller, and Peter Petocz, it provides a standardized way to assess how various foods—beyond just carbohydrates—stimulate insulin secretion in healthy individuals.^[1] The methodology involves measuring plasma insulin concentrations at regular intervals (typically every 15 minutes) over 120 minutes following food consumption, with the insulin score calculated as the incremental area under the insulin response curve (iAUC) compared to the reference food.^[1] In the original study, 38 common foods from six categories (fruits, bakery products, snacks, carbohydrate-rich foods, protein-rich foods, and breakfast cereals) were tested on 11–13 healthy subjects, revealing substantial variability in insulin responses both within and across categories. For example, high insulin indices were observed for carbohydrate-rich foods such as potatoes (121), jellybeans (160), baked beans (120), and Mars Bar (122), whereas lower indices were found for protein-rich foods such as peanuts (20), eggs (31), cheese (45), and fish (59).^[1] Unlike the glycemic index (GI), which focuses solely on blood glucose elevation primarily from carbohydrates, the II accounts for the insulinogenic effects of proteins and fats, showing that protein-rich foods (e.g., eggs, fish) and certain bakery items (e.g., croissants) can provoke insulin responses disproportionate to their glycemic impact. Notably, dairy products such as milk have been shown in subsequent studies to elicit high insulin responses (insulin index 90–148) comparable to or exceeding that of white bread, despite low glycemic indices (15–42) and lower carbohydrate content compared to many breads, sugars, and starches; this effect is largely attributed to the insulinotropic amino acids in milk proteins, particularly whey, and may be an important consideration for individuals following low-carbohydrate or ketogenic diets.^[1][2][3] The II correlates moderately with the GI (r = 0.70, P < 0.001), but carbohydrate content (r = 0.39, P < 0.05) and sugar (r = 0.36, P < 0.05) positively influence insulin scores, while fat and protein show inverse but non-significant trends.^[1] Subsequent research has expanded the II into the food insulin index (FII), an algorithm applicable to mixed meals and broader dietary patterns, enabling calculations of dietary insulin index (DII) and dietary insulin load (DIL) to predict overall insulin demand. High DII and DIL have been associated with increased risks of insulin resistance, metabolic syndrome, type 2 diabetes, and cardiometabolic disturbances in cohort studies from diverse populations, including Iranian adults. For instance, observational data link elevated insulinemic potential to greater inflammation markers and body weight gain, underscoring the II's relevance beyond carbohydrate-focused metrics. In clinical applications, the FII aids in personalized nutrition for diabetes management, outperforming traditional carbohydrate counting for predicting postprandial insulin excursions and optimizing insulin dosing in type 1 diabetes. It supports dietary strategies to mitigate hyperinsulinemia, such as favoring low-II foods like cheese or lentils over high-II options like potatoes, white rice, or milk. These strategies underpin low-insulin diets, a dietary approach aimed at minimizing blood insulin levels by reducing foods that trigger strong insulin responses, primarily carbohydrates (especially refined carbs, starches, and sugars) and sometimes certain proteins like non-fermented dairy. It emphasizes non-starchy vegetables, healthy fats, nuts, seeds, lean proteins, and low-glycemic foods to promote fat burning, improve insulin sensitivity, support weight loss, and manage conditions like insulin resistance, PCOS, and prediabetes. It often overlaps with low-carbohydrate or low-glycemic diets and may include meal timing to allow insulin to drop between meals, potentially reducing long-term complications like cardiovascular disease.^[4] Ongoing studies emphasize the need for larger, diverse trials to refine II databases and explore gene-diet interactions, but its integration into glycemic control protocols highlights its growing utility in preventive nutrition.

Background

Definition

The insulin index (II) is a metric that quantifies the postprandial insulin response elicited by consuming a food, specifically measuring the area under the curve (AUC) of blood insulin concentration over a 2-hour period following ingestion of an isoenergetic portion equivalent to 1000 kJ (approximately 240 kcal).^[5] This value is then expressed as a percentage relative to the insulin response from an equivalent portion of white bread, which is assigned an II of 100 as the reference standard.^[5] The primary purpose of the II is to evaluate how various foods stimulate insulin secretion independently of their carbohydrate content, thereby capturing the contributions of proteins, fats, and other macronutrients to overall insulin demand.^[5] Unlike the glycemic index, which focuses on blood glucose excursions, the II emphasizes insulinemic effects, revealing that certain protein-rich foods can provoke substantial insulin release with minimal impact on glycemia.^[5] Pure fats, such as butter or oils, typically elicit very low insulin responses (insulin index near 0-20) because they contain no carbohydrates and minimal protein to stimulate insulin secretion. For instance, butter's negligible carbohydrate and protein content results in minimal postprandial insulin rise when consumed alone, distinguishing it from higher-II dairy products like milk (90-148) that contain insulinotropic proteins. This focus on insulin demand makes the II particularly relevant for understanding metabolic responses in conditions such as insulin resistance and hyperinsulinemia, where excessive insulin secretion may contribute to disease progression, and for informing dietary strategies in non-insulin-dependent diabetes mellitus management.^[5]

History

The insulin index (II) was developed in the 1990s by Susanne Holt, Jennie Brand-Miller, and Peter Petocz at the University of Sydney's Human Nutrition Unit, as a metric to quantify the postprandial insulin response to foods beyond carbohydrate content alone.^[1] This work built on earlier glycemic index research to address the insulinogenic effects of proteins and fats. The concept was formally introduced in a seminal 1997 study published in the American Journal of Clinical Nutrition, where the researchers tested isoenergetic 1000-kJ portions of 38 common foods in healthy subjects, establishing II values relative to white bread, which was assigned a reference value of 100.^[1] This publication provided the foundational methodology and demonstrated that foods like dairy and proteins elicited unexpectedly high insulin responses, highlighting II's potential for nutritional assessment.^[1] In the following decades, the II evolved from single-food evaluations to applications for mixed meals and expanded databases. A key advancement came in 2009 with a study validating the food insulin index for predicting insulin demand from composite meals, showing that II-based calculations accurately estimated responses in real-world eating scenarios better than carbohydrate-focused metrics.^[6] During the 2010s, researchers broadened II testing to include more diverse foods, incorporating it into dietary load calculations (e.g., dietary insulin load) to evaluate overall meal and daily insulin demands.^[7] By the 2020s, comprehensive compilations emerged, such as a 2023 collectanea aggregating II values for 629 food and beverage items from 80 studies, facilitating clinical use in personalized nutrition planning.^[8] Post-2000, II gained prominence in nutritional research on diabetes and obesity, with studies linking high dietary insulin indices to increased risk of insulin resistance, weight gain, and metabolic disorders.^[7] For instance, analyses of dietary insulin load using II data showed associations with biomarkers of obesity and type 2 diabetes in population cohorts.^[9] Recent investigations have further explored II variations across protein sources, revealing differences in insulin responses to plant-based versus animal-based proteins, which inform strategies for managing glycemic control and metabolic health; for example, a 2025 clinical trial demonstrated that animal-based proteins result in higher energy expenditure and carbohydrate oxidation compared to plant-based proteins.^[10][11]

Measurement

Methodology

The methodology for determining the insulin index involves controlled human feeding studies designed to measure postprandial insulin responses to specific foods. In the seminal protocol developed at the University of Sydney, healthy, non-diabetic volunteers—typically 10 to 13 young adults per food category, with normal body mass index (mean 22.7 kg/m²)—undergo testing after a 10-hour overnight fast to ensure baseline insulin levels are standardized.^[1] On separate test days, participants consume an isoenergetic portion of the test food equivalent to 1000 kJ (approximately 240 kcal), accompanied by 220 mL of water, while remaining seated to minimize physical activity influences.^[1] Blood samples are collected via finger-prick at baseline and at 15-minute intervals up to 120 minutes post-consumption, with plasma insulin concentrations measured using radioimmunoassay techniques, such as the Coat-A-Count kit, which offers low coefficients of variation (within-assay 5%, between-assay 7%).^[1] Subsequent studies have adopted similar protocols but may employ enzyme-linked immunosorbent assay (ELISA) for insulin quantification to enhance sensitivity and reduce radioactivity concerns.^[12] To ensure reproducibility and accuracy, foods are prepared in bulk to precise energy content based on nutritional databases or manufacturer data, served in standardized portions (e.g., sliced or reheated as needed), and presented under controlled conditions, such as an opaque hood where feasible, to limit anticipatory cephalic-phase insulin release.^[1] The reference food, typically white bread, is tested on alternate days in a randomized order across sessions, allowing each participant to serve as their own control within food groups.^[1] Pre-testing standardization includes instructions for participants to maintain consistent physical activity, avoid alcohol and legumes the previous evening, and consume similar low-fat meals the night before, with all tests conducted at the same time of day to account for circadian variations.^[1] Variability in insulin responses is addressed by averaging individual area-under-the-curve values across multiple subjects and repeating tests as needed for reliability, with statistical analyses like two-way ANOVA used to quantify interindividual differences.^[1] These studies are conducted in controlled clinical or laboratory settings, with protocols approved by institutional ethics committees, such as the Human Research Ethics Committee of the University of Sydney, ensuring informed consent and participant safety.^[13] Early investigations noted limitations in subject diversity, primarily involving young university students of similar demographics, which may influence generalizability to broader populations.^[1]

Calculation

The insulin index (II) is computed as a percentage relative to a reference food, quantifying the insulin response elicited by a test food compared to the reference. The formula is:

$$\text{II} = \left( \frac{\text{AUC}_{\text{insulin, test food}}}{\text{AUC}_{\text{insulin, reference food}}} \right) \times 100$$

where $\text{AUC}$ denotes the incremental area under the 120-minute insulin concentration-time curve above the fasting baseline. This approach normalizes the insulin demand of isocaloric portions (typically 1000 kJ) across foods, with white bread assigned an II of 100 by definition to serve as the standard for comparability. The AUC is estimated using the trapezoidal rule, integrating insulin concentrations measured over time while subtracting the preprandial fasting level to focus on the postprandial increment; any negative excursions below baseline are truncated to zero to avoid underestimation. Individual responses from multiple subjects (typically 11–13 per food) are averaged to yield the mean II, with standard errors reported to indicate variability and account for inter-subject differences in insulin sensitivity. For example, if the AUC for a test food is 50% of that for the white bread reference, the resulting II is 50, signifying a moderate insulinogenic effect compared to the standard.

Food Insulin Index Values

Protein-Rich Foods

Protein-rich foods generate notable insulin responses despite containing minimal carbohydrates, a phenomenon captured by the insulin index (II), which quantifies the postprandial insulin secretion relative to an equal-energy portion of white bread. In the foundational 1997 study by Holt et al., protein sources were shown to stimulate insulin primarily through specific amino acids, such as leucine and other branched-chain amino acids, which directly promote beta-cell secretion in the pancreas independent of blood glucose elevation.^[1] This mechanism explains why the mean II for protein-rich foods was 61, higher than anticipated based on glycemic effects alone.^[14] Representative II values from this study illustrate the variability among protein sources. Beef elicited an II of 51, white fish 59, eggs 31, and cheese 45, demonstrating moderate to substantial insulin demand even without significant carbohydrate content. Dairy proteins exhibited the highest responses in the cohort, with yogurt reaching an II of 115, attributed to its rapid digestion and amino acid profile. Milk and other milk products also elicit substantial insulin responses despite low glycemic indexes (15-30), with insulinemic indexes of 90-98 relative to white bread (100) in a 2001 study, comparable to the insulin response of white bread but with much lower glycemic impact. This insulinotropic effect is attributed to proteins such as whey and casein.^[2][1] Comparisons between animal and plant proteins reveal distinct patterns, with animal sources often producing stronger insulinogenic effects due to higher concentrations of branched-chain amino acids. A 2023 review of postprandial responses confirmed that animal proteins, particularly dairy-derived ones like whey, yield higher insulin excursions than plant counterparts such as soy or pea protein.^[15] For example, soy-based products have been measured with lower II values, reflecting their differing amino acid composition and slower absorption. This disparity persists across low-carbohydrate contexts, where protein-induced insulin elevation remains prominent.^[15] Carnivore protein powders, typically beef protein isolate, stimulate insulin secretion primarily through amino acids such as leucine, which trigger insulin release from pancreatic beta cells. The response is moderate, similar to whole beef (II ≈ 51), and lower than that of fast-absorbing whey protein. In low-carbohydrate or carnivore diets lacking significant carbohydrates, such protein sources do not typically cause large blood glucose spikes, although excessive protein intake can contribute to gluconeogenesis and potential glucose production from amino acids.^[1] The following table compiles II values for selected protein-rich foods, drawn primarily from the 1997 Holt study and supplemented by subsequent measurements for broader representation: These empirical data highlight the elevated and sustained insulin response to proteins, particularly from dairy and animal sources, informing nutritional strategies focused on insulin management.^[1]

Carbohydrate-Rich Foods

Carbohydrate-rich foods exhibit a wide range of insulin index (II) values, reflecting variations in starch structure, fiber content, and processing that influence postprandial insulin secretion beyond simple carbohydrate content. In the seminal study by Holt et al., isoenergetic 1000-kJ portions of 38 common foods were tested, establishing white bread as the reference with an II of 100. Among carbohydrates, starchy foods like potatoes elicited a notably high II of 121 ± 11, exceeding the reference due to rapid digestion and absorption, while pasta showed a surprisingly low II of 40 ± 5 for both white and brown varieties, attributed to slower gastric emptying and lower glycemic impact.^[1] Patterns in II for carbohydrate-rich foods highlight that highly processed or low-fiber starches often provoke stronger insulin responses, sometimes surpassing expectations from glycemic index alone. For instance, boiled potatoes' high II contrasts with the moderate responses from fruits such as bananas (II = 81 ± 5) and oranges (II = 60 ± 3), where natural sugars and fiber moderate insulin demand. Legumes like lentils (II = 58 ± 12) further demonstrate lower II values, influenced by high fiber and protein content that slows carbohydrate breakdown. These deviations underscore how II integrates insulinogenic effects not fully captured by glucose excursions, providing a broader view of metabolic impact.^[1] Subsequent analyses building on this foundational work confirm these patterns, with II values for carbohydrate-rich foods generally ranging from 40 to 120, emphasizing the role of food matrix in insulin regulation. For example, expansions in glycemic and insulinemic response studies reinforce that fiber-rich or minimally processed carbs like legumes and whole grains consistently yield II below 70, aiding in dietary strategies for metabolic health.^[1][16]

Ranked Foods by Insulin Index (High to Low)

Foods are ranked by their insulin index (II), which measures postprandial insulin response to isoenergetic portions (typically 1000 kJ/240 kcal) relative to white bread (II = 100). The seminal study by Holt et al. (1997) tested 38 common foods and found that insulin response is influenced by carbohydrates, proteins, and their interactions, not just carbohydrates. Protein-rich foods can elicit significant insulin release despite low carbohydrate content.^[1] Approximate II values from aggregated sources and the seminal study (with variations possible across reports due to differences in food preparation, portions, and methodologies) are grouped as follows: High insulin response (high II):

• Jellybeans (~160)
• Potatoes (~121-141)
• Baked beans (~120)
• Lentils (~133 in some reports)
• Mars Bar (~122)
• Yogurt (~115)

Moderate insulin response:

• Breads (~97-100)
• Cereals (various)

Low insulin response (low II):

• Eggs (~31-42)
• Cheese (~45)
• Peanuts (~20)
• Fish (~59)
• Fats and oils (very low)

Comprehensive lists of 140+ foods compiled from various studies are available from aggregated sources.

Comparisons

With Glycemic Index

The insulin index (II) and glycemic index (GI) share methodological similarities, both assessing postprandial responses over a 2-hour period by calculating the area under the curve (AUC) relative to a reference food scaled to 100 (white bread in the original II study and typically glucose or white bread for GI).^[1] Portions for II are normalized to equal energy content (1000 kJ), whereas GI uses equal available carbohydrate amounts (usually 50 g), yet both aim to quantify physiological impacts of foods on blood responses.^[1] High-GI foods, such as potatoes, often elicit high II values, reflecting their shared sensitivity to rapidly digestible carbohydrates.^[1] Studies indicate a moderate positive correlation between II and GI (r = 0.70, P < 0.001).^[1] This relationship is particularly evident for carbohydrate-dominant foods like starchy and sugary items. Key discrepancies arise because II accounts for insulin secretion triggered by proteins and fats, independent of carbohydrates, whereas GI focuses solely on blood glucose rises from carbs and assigns near-zero values to non-carbohydrate foods.^[1] Consequently, protein-rich foods like eggs exhibit low GI (due to negligible carbs) but a moderate II from amino acid stimulation, and baked beans show low-to-moderate GI yet substantially higher II owing to their protein and fat content amplifying insulin demand.^[1] The following table illustrates these alignments and divergences using data from the seminal 1997 study, with published GI values for comparison (note: values can vary slightly by preparation and testing conditions):

With Other Nutritional Metrics

The insulin load (IL) extends the insulin index (II) by accounting for portion size to estimate the total insulin demand elicited by a serving of food, calculated as the product of the food's II and its energy content relative to the standardized 1000 kJ reference portion used in II measurements.^[17] Specifically, for a given serving, IL = II × (energy content of serving in kJ / 1000), providing a practical metric for predicting postprandial insulin secretion from realistic meal quantities rather than isoenergetic portions alone.^[17] This derivation allows IL to capture the cumulative insulinogenic effect of foods, particularly useful for mixed meals where non-carbohydrate components contribute significantly.^[18] The II and IL complement caloric metrics such as glycemic load (GL) by incorporating insulin responses to proteins and fats, which GL largely overlooks since it is based solely on carbohydrate availability.^[17] For instance, fats exhibit low II values, such as approximately 2 for butter, reflecting minimal insulin stimulation despite their caloric density, whereas GL for such foods is effectively zero.^[19] This distinction highlights how II addresses broader macronutrient influences on insulin dynamics, enabling more comprehensive assessments of dietary insulin burden beyond carbohydrate-focused tools like GL.^[20] II also integrates with the satiety index, where foods eliciting high insulin responses, such as potatoes (II ≈ 121), often promote greater fullness due to associated nutrient density and volume effects.^[20] In contrast, II contrasts with caloric density, as low-energy-density foods like potatoes (≈0.8 kcal/g) can generate disproportionately high insulin demand relative to their caloric contribution, influencing appetite regulation and energy intake. For mixed meals, overall II is estimated via energy-weighted averages of component IIs, summing the proportional insulin demands to approximate total response (e.g., predicted demand = Σ (II_i × energy fraction_i)).^[17]

Applications

In Dietary Management

A low-insulin diet is a dietary approach aimed at minimizing blood insulin levels by reducing foods that trigger strong insulin responses, primarily carbohydrates (especially refined carbs, starches, and sugars) and sometimes certain proteins like non-fermented dairy. It emphasizes non-starchy vegetables, healthy fats, nuts, seeds, lean proteins, and low-glycemic foods to promote fat burning, improve insulin sensitivity, support weight loss, and manage conditions like insulin resistance, PCOS, and prediabetes. It often overlaps with low-carbohydrate or low-glycemic diets and may include meal timing to allow insulin to drop between meals.^[4] The insulin index (II) plays a key role in designing low-II diets aimed at minimizing postprandial insulin spikes to support metabolic health, particularly for weight loss and managing insulin resistance (IR) in conditions such as polycystic ovary syndrome (PCOS).^[21] These diets prioritize foods with lower II values, such as walnuts (II=5) and other nuts (typically II<20), which elicit modest insulin responses while providing satiety and nutritional density.^[22] Oatmeal, with an II around 40, serves as a representative moderate-II option that can be incorporated to stabilize insulin levels without excessive spikes, making it suitable for sustained energy in weight management plans.^[23] Studies indicate that such low-II approaches reduce early-stage insulin responses by up to 56% in obese adolescents with IR, facilitating fat oxidation and appetite control for effective weight loss.^[21] In meal planning, the II enables calculation of composite insulin demand for mixed meals by weighting individual food II values based on their energy contributions, offering a more precise tool than carbohydrate counting alone.^[6] For instance, adding protein sources to a carbohydrate base can elevate the overall II of the meal due to synergistic macronutrient effects, but low-II combinations—like pairing nuts with vegetables—help maintain balanced responses.^[22] Recent databases, such as the 2023 collectanea compiling II values for over 600 foods and beverages, support practical application by providing comprehensive data for dietitians to formulate personalized plans.^[22] Clinically, II guidance enhances low-carbohydrate or ketogenic diets, including very low- or zero-carbohydrate approaches such as carnivore diets, by identifying high-satiety proteins with moderate II, such as fish, eggs, beef, and beef protein isolates (commonly referred to as carnivore protein powders), which promote fullness despite potential insulinogenic effects from proteins, thereby improving long-term adherence without compromising ketosis.^[21] These proteins trigger moderate insulin secretion primarily due to amino acids such as leucine, which stimulate insulin release from pancreatic beta cells. The insulin response is generally lower than that from fast-absorbing proteins like whey and similar to whole meats (e.g., beef has an insulin index of approximately 51). In the absence of carbohydrates, they do not typically cause large blood glucose spikes, though excess protein can contribute to gluconeogenesis.^[1][24] This selection strategy counters the misconception that all proteins are low-II, allowing for optimized macronutrient ratios that sustain energy and reduce cravings in IR patients. Evidence from controlled trials demonstrates that II-guided dietary choices lead to reduced hunger sensations by approximately 26% in the late postprandial phase and enhance adherence, with participants showing better compliance and fewer hypoglycemic events compared to standard approaches.^[21] These outcomes underscore the utility of II in fostering sustainable dietary behaviors for metabolic improvement.

In Health Research

Research on the insulin index (II) has elucidated its associations with chronic diseases, particularly through its influence on insulin demand and metabolic pathways. High dietary II has been linked to an increased risk of type 2 diabetes, as evidenced by prospective cohort studies showing that diets with elevated II and insulin load predict higher incidence rates, independent of glycemic index. Similarly, elevated II correlates with greater odds of obesity, where adherence to high-II diets promotes hyperinsulinemia and fat storage. For colorectal cancer, higher post-diagnostic II is associated with increased mortality risk, with hazard ratios of 1.32 (32% elevation) for overall mortality and 1.66 (66% elevation) for cancer-specific outcomes. A seminal 2011 study from the Nurses' Health Study and Health Professionals Follow-up Study examined dietary II and load in relation to biomarkers of inflammation and endothelial dysfunction in nondiabetic participants, revealing positive associations with triglycerides (26% higher in highest vs. lowest quintile) and inverse associations with HDL cholesterol in obese individuals.^{[25][26][27][7]} Key investigations have further explored II in specific disease contexts. A 2021 case-control study found that high food II was associated with 1.4-fold greater odds of non-alcoholic fatty liver disease (NAFLD), highlighting its role in hepatic insulin resistance among adults. Recent analyses have shown that animal-based proteins elicit higher insulin responses compared to plant-based ones; for instance, whey protein induces greater insulin secretion than soy equivalents, linking to differential metabolic outcomes in obesity and diabetes risk. These findings underscore II's utility in dissecting dietary impacts beyond macronutrient composition.^[28][29] In cohort studies, II serves as a predictive metric for hyperinsulinemia, with higher dietary scores correlating to elevated fasting insulin levels and reduced insulin sensitivity over time, as observed in large-scale analyses of metabolic syndrome components. Integration of II with microbiome research has revealed synergies with inflammation; for example, high-II diets alter gut microbiota composition, promoting pro-inflammatory taxa that amplify systemic insulin resistance.^[30][31] Emerging evidence indicates that diets maintaining an average II below 50 are associated with improved insulin sensitivity, as measured by lower HOMA-IR indices in intervention trials, potentially mitigating hyperinsulinemia risks. Adherence studies show that tailoring diets using II alongside glycemic metrics enhances glycemic control in type 2 diabetes patients. As of 2025, II is increasingly integrated into digital health apps for real-time meal planning in diabetes management. These applications position II as a valuable tool in precision health research for optimizing metabolic outcomes.^[32]

Limitations and Future Directions

Key Limitations

The insulin index database remains limited in scope, with the seminal 1997 study testing only 38 common foods and subsequent expansions, such as a 2016 compilation reaching 127 items, primarily focusing on Western staples like breads, proteins, and dairy. Even a 2023 systematic collection aggregates data for 629 food and beverage items from 80 studies, but significant gaps persist in ultra-processed products, ethnic cuisines, and region-specific foods, hindering broad applicability across diverse diets.^[1][33][34] Individual variability poses a major constraint, as postprandial insulin responses differ markedly by factors including age, sex, body mass index (BMI), and genetics, with insulin-resistant individuals exhibiting exaggerated responses to maintain euglycemia. The index was derived from small cohorts of healthy, lean young adults (typically 11-13 participants per food category), excluding broader populations such as older adults, those with metabolic conditions, or diverse ethnic groups, thus lacking standardization for real-world heterogeneity.^[14][35][36] Methodological scope issues further limit utility, including the reliance on a 2-hour incremental area under the curve (iAUC) for insulin measurement, which captures acute responses but overlooks prolonged effects from proteins and fats that may extend beyond this window. The index focuses solely on insulin secretion, ignoring contributions from gut hormones like glucagon-like peptide-1 (GLP-1), which modulate overall metabolic and satiety responses. Additionally, testing isoenergetic 1000 kJ portions does not mimic ad libitum eating behaviors, where portion sizes and meal compositions vary widely. Early validation relied on small-scale experiments (n<15 per test), potentially amplifying insulin's role without fully integrating glucose dynamics, as some high-insulin foods elicit minimal glycemia.^[1][37][14]

Ongoing Developments

In 2023, researchers compiled a comprehensive collectanea of food insulinaemic index (II) values, cataloging data for 629 food and beverage items drawn from 80 distinct articles across 32 countries, marking a nearly five-fold expansion from the prior 2011 database of approximately 134 entries. This update organizes II values into 25 food categories, with scores ranging from 1 for low-insulinogenic items like acacia fiber and gin to 209 for high-response options such as soy milk-based infant formulas, enhancing the tool's utility for dietary analysis.^[22] Emerging 2025 research initiatives are investigating AI-driven models to predict II values directly from foods' nutrient profiles, leveraging machine learning to analyze macronutrient compositions and forecast postprandial insulinaemia without direct testing, building on algorithms that integrate dietary data for personalized metabolic predictions.^[38][39] Technological advancements include the integration of continuous glucose monitors (CGMs) with AI analytics to indirectly track and model insulin dynamics, as seen in wearable systems that use real-time glucose data alongside dietary inputs to estimate insulin excursions and optimize meal planning for diabetes management. Additionally, genetic testing is enabling personalized II assessments by identifying polymorphisms, such as those in CETP and BDNF genes, that influence individual insulin responses to foods, allowing for tailored dietary strategies.^[40][41] In emerging applications, II research is extending to gut health, where studies examine interactions between dietary fiber and insulin responses; for instance, high-fiber intakes exceeding 25 g/day in women and 38 g/day in men have been associated with 20-30% reduced risk of insulin resistance through microbiota-mediated short-chain fatty acid production. Global standardization efforts are underway to harmonize II testing protocols, including consistent reference foods like glucose and standardized portion sizes, to support incorporation into clinical guidelines.^[42][22] Future directions emphasize recruiting larger, more diverse cohorts to address current database gaps and validate II across populations, alongside explorations of its role in food labeling to guide consumer choices for metabolic health. Ongoing investigations are also linking chronic high-II diets to insulin resistance pathways implicated in longevity and neurodegeneration, such as Alzheimer's disease modeled as "type 3 diabetes," prompting calls for II-informed interventions to mitigate brain aging risks.^[41][43]