Mini-review on insulin resistance assessment: Advances in surrogate indices and clinical applications

Abstract

Insulin resistance (IR) is widely recognized as a key contributor to metabolic disorders, and various surrogate indices have been developed to estimate IR in clinical and research settings. The hyperinsulinemic-euglycemic clamp is considered the gold standard method for assessing insulin resistance due to its precision; however, its complexity limits its widespread clinical application. Consequently, surrogate indices derived from fasting and post-load glucose and insulin levels have been developed to estimate IR, facilitating early detection and risk stratification in metabolic disorders. This mini-review discusses the clinical utility, strengths, and limitations of key IR indices, including the homeostasis model assessment of IR, quantitative insulin sensitivity check index, Matsuda index, and triglyceride-glucose index. Overall, the evidence presented to date suggests that these indices provide valuable estimates of IR in various populations. Yet, their applicability varies depending on ethnic background, disease status, and clinical setting. Integrating these indices into routine clinical practice and research could improve metabolic risk assessment and guide preventive interventions. Further investigations are necessary to refine their accuracy and determine optimal cut-off values for various populations.

Key Words: Insulin resistance; Homeostasis model assessment of insulin resistance; Quantitative insulin sensitivity check index; Matsuda index; Triglyceride-glucose index; Surrogate markers; Metabolic disorders; Diabetes; Cardiovascular disease; Risk assessment

Core Tip: Surrogate indices for insulin resistance (IR) are receiving growing validation in the international scientific literature. While gold standard methods remain the most accurate, their complexity limits clinical applicability. Indices such as the homeostasis model assessment of IR, the quantitative insulin sensitivity check index, and the Matsuda index offer valuable estimates of IR, while emerging indices like the triglyceride-glucose index are gaining attention. Further validation across diverse populations is required. Integrating these indices into routine practice may enhance metabolic risk assessment and preventive strategies.

INTRODUCTION

Insulin resistance (IR) is a pathological condition characterized by impaired insulin signaling, resulting in reduced glucose uptake in insulin-dependent tissues, such as skeletal muscle and adipose tissue, while paradoxically increasing hepatic glucose production[1,2]. This dysfunction results in compensatory hyperinsulinemia, and if persistent, contributes to the development of metabolic disorders such as type 2 diabetes mellitus (T2DM), metabolic syndrome (MetS), non-alcoholic fatty liver disease (NAFLD), and cardiovascular disease (CVD). Given its role in metabolic homeostasis, IR remains a significant focus in clinical research and public health[3].

An accurate assessment of IR is crucial for early detection, disease monitoring, and evaluating the efficacy of treatment. The gold standard methods, such as the hyperinsulinemic-euglycemic clamp (HEC) and the frequently sampled intravenous glucose tolerance test (FSIVGTT), provide precise measurements of insulin sensitivity; however, they are labor-intensive and impractical for routine clinical use. Consequently, numerous surrogate indices have been developed to estimate IR based on fasting plasma glucose (FPG) or dynamic glucose and insulin measurements, each with varying degrees of accuracy and applicability.

Recent advances in metabolic research have highlighted the role of adipokines and inflammatory mediators in the progression of IR[4]. Dysregulated secretion of adipokines, including leptin, adiponectin, and resistin, has been implicated in obesity-induced IR, suggesting potential therapeutic targets for metabolic disorders[5]. Moreover, emerging methodologies such as machine learning (ML)-driven models and continuous glucose monitoring -based indices promise to refine IR classification and risk prediction[6,7].

This review comprehensively compared IR indices, highlighting their strengths, limitations, and clinical applications. By examining the fasting-based index [e.g., homeostasis model assessment of insulin resistance (HOMA-IR), quantitative insulin sensitivity check index (QUICKI)], dynamic indices derived from oral glucose tolerance tests (e.g., Matsuda index, Stumvoll index), and lipid-based indices [e.g., triglyceride (TG)-glucose (TyG) index, TG-to-high density lipoprotein cholesterol (TG/HDL-C) ratio)], we elucidated the most appropriate methods for different clinical and research settings. Additionally, we discuss emerging methodologies, including novel biomarkers and omics-based approaches, which may enhance IR assessment in the future.

MECHANISMS OF IR

IR is a pathological condition characterized by a reduced response to insulin in skeletal muscle and adipose tissue, leading to impaired glucose uptake despite normal or elevated insulin levels. Additionally, hepatic IR fails to suppress hepatic glucose production, thereby increasing endogenous glucose output and contributing to hyperglycemia[1-3]. The pathophysiology of IR is complex, involving molecular, cellular, and systemic mechanisms. Understanding these mechanisms is essential for identifying potential therapeutic targets and refining diagnostic approaches[2,8].

Molecular basis of IR

At the cellular level, IR primarily results from defects in insulin receptor signaling. Under physiological conditions, insulin binds to its receptor on target cells, such as skeletal muscle, liver, and adipose tissue, activating the insulin receptor tyrosine kinase. This triggers downstream signaling pathways, particularly the phosphoinositide 3-kinase (PI3K)/Akt pathway, which facilitates glucose uptake by translocating glucose transporter 4 to the cell membrane and promotes glycogen synthesis. However, in insulin-resistant states, multiple molecular disruptions impair this signaling cascade[9,10].

One key mechanism involves the aberrant phosphorylation of insulin receptor substrate (IRS) proteins. Under normal conditions, an IRS undergoes tyrosine phosphorylation, which promotes proper signal transduction. However, in insulin-resistant states, serine phosphorylation of an IRS disrupts its interaction with PI3K, thereby impairing glucose uptake[2,3,11]. Furthermore, excessive activation of stress kinases, including c-Jun N-terminal kinase (JNK), inhibitor of nuclear factor kappa B kinase subunit beta, and protein kinase C (PKC), has been implicated in IR due to their inhibitory effects on IRS function and insulin receptor signaling[11,12].

Another major contributor to IR is lipid accumulation in non-adipose tissues, such as skeletal muscle and the liver. Increased diacylglycerol (DAG) and ceramide levels interfere with insulin signaling by activating specific PKC isoforms, which in turn inhibit insulin receptor function. This phenomenon, often referred to as lipotoxicity, is strongly linked to obesity-related IR and metabolic dysfunction[3,13]. Additionally, saturated fatty acids can stimulate Toll-like receptor 4, leading to increased ceramide synthesis, which in turn contributes to IR through inflammatory and oxidative stress pathways[13]. These mechanisms highlight the complex interplay between lipid metabolites, inflammation, and insulin signaling impairment in the pathogenesis of IR.

Systemic contributors to IR

Beyond molecular impairments, several systemic factors contribute to the development of IR. Obesity and increased visceral adiposity are central factors as they promote the release of free fatty acids (FFAs), inflammatory cytokines, and adipokines that disrupt insulin signaling. Elevated FFAs enhance hepatic gluconeogenesis and impair insulin-mediated glucose disposal in skeletal muscle. Additionally, adipose tissue in obese individuals secretes pro-inflammatory cytokines, such as tumor necrosis factor alpha (TNF-α) and interleukin 6 (IL-6), which activate stress kinases and disrupt insulin receptor function[12,14,15].

Dysregulated secretion of adipokines, such as resistin, leptin, and adiponectin, further contributes to impaired insulin action. Low adiponectin levels are particularly relevant as they are linked to reduced insulin sensitivity and increased inflammation[16,17]. Moreover, recent studies have highlighted the concept of metaflammation, a state of chronic low-grade inflammation driven by nutrient excess and obesity, which further exacerbates metabolic dysfunction[18].

These findings underscore the complex interplay between adipose tissue dysfunction, chronic inflammation, and IR, reinforcing the need for multifaceted approaches to assess and mitigate these effects in metabolic disorders. Chronic low-grade inflammation is another significant systemic factor that contributes to IR. Adipose tissue infiltration by macrophages and other immune cells promotes the secretion of pro-inflammatory mediators, including TNF-α, IL-6, and monocyte chemoattractant protein-1, thereby perpetuating local and systemic inflammation[19]. This inflammatory state disrupts insulin receptor signaling and is closely associated with obesity-related metabolic disorders, including T2DM and MetS. Moreover, recent findings highlight the concept of metaflammation, the chronic immune-metabolic inflammatory state driven by nutrient excess, which further exacerbates IR[18].

Mitochondrial dysfunction plays a crucial role in the pathogenesis of IR. Impaired oxidative phosphorylation reduces ATP production, decreasing energy availability for insulin signaling and glucose transport. Additionally, mitochondrial dysfunction contributes to lipid accumulation in non-adipose tissues, promoting the accumulation of DAG and ceramide, PKC isoforms, and impairing insulin receptor function. These mechanisms have been extensively documented in both human and animal models of IR[20-22].

Endoplasmic reticulum (ER) stress plays a crucial role in IR by activating the unfolded protein response, which in turn inhibits insulin receptor signaling. Dysregulated ER stress responses in the liver, muscle, and adipose tissue contribute to systemic metabolic dysfunction[23,24].

Finally, hepatic and pancreatic dysfunction exacerbate IR. The liver, a significant organ in glucose metabolism, exhibits increased hepatic glucose production due to inadequate suppression of gluconeogenesis in insulin-resistant states. Simultaneously, pancreatic β-cells attempt to compensate by increasing insulin secretion, but chronic hyperinsulinemia may lead to β-cell exhaustion, dedifferentiation, and eventual failure, further exacerbating hyperglycemia and metabolic deterioration[25,26].

Clinical implications and intervention strategies based on IR assessment

IR is a hallmark of several metabolic disorders, including T2DM, MetS, and metabolic dysfunction-associated steatotic liver disease (MASLD), previously referred to as NAFLD. It also plays a crucial role in the pathogenesis of CVD, as IR contributes to endothelial dysfunction, dyslipidemia, and hypertension[27,28]. Moreover, IR has been implicated in conditions such as polycystic ovary syndrome and neurodegenerative diseases, highlighting its broad clinical impact[29,30].

Understanding the molecular and systemic mechanisms of IR is crucial for developing effective therapeutic strategies, given its pivotal role in disease progression. Targeted interventions, including lifestyle modifications, pharmacological agents, and emerging metabolic therapies, hold promise in mitigating the adverse effects of IR and reducing the global burden of metabolic diseases[31,32].

Assessing IR using surrogate indices provides diagnostic insights and a foundation for guiding clinical interventions. Intervention strategies can be tailored based on the severity of IR as indicated by these indices. Lifestyle modifications remain the cornerstone of management for individuals with mild to moderate IR, as reflected by slightly elevated HOMA-IR, TyG index, or TG/HDL-C ratio values. These include weight loss through caloric restriction, increased physical activity (preferably aerobic and resistance training), and dietary changes emphasizing low glycemic foods and reduced saturated fat intake[33].

Pharmacological approaches may be considered when lifestyle interventions fail to achieve sufficient improvement or when surrogate indices reveal severe IR. Metformin remains the first-line therapy due to its insulin-sensitizing effects and favorable safety profile[34]. In selected cases, agents such as thiazolidinediones, GLP-1 receptor agonists, or SGLT2 inhibitors may be added to improve insulin sensitivity further and reduce cardiometabolic risk.

Follow-up assessments using the same IR indices are essential to evaluate the effectiveness of interventions. For instance, a reduction in the TyG index or HOMA-IR by approximately 10%-20% after several months of intervention may indicate clinically meaningful improvement in insulin sensitivity[35]. Such monitoring enables clinicians to adjust therapeutic strategies promptly and personalize patient management.

Incorporating IR assessment into routine clinical practice facilitates early identification of high-risk individuals and provides measurable targets for intervention. Ultimately, this aims to delay or prevent the onset of T2DM, CVD, and other IR-related disorders.

GOLD STANDARD METHODS

HEC

The HEC, described by DeFronzo et al[1], is the gold standard technique for quantifying insulin sensitivity. In this procedure, insulin is infused intravenously at a constant rate to achieve hyperinsulinemia. At the same time, a variable glucose infusion is used to maintain euglycemia, typically at a level of about 90-100 mg/dL. The amount of glucose required to maintain constant blood glucose levels reflects the whole-body tissue uptake of glucose under stimulated insulin conditions.

One of the key advantages of the HEC is its high accuracy and direct measurement of insulin sensitivity, making it invaluable for mechanistic research and calibrating other indices. However, its limitations include the invasive nature of the procedure, the time-consuming protocol (typically lasting several hours), the need for specialized equipment, and the requirement for skilled personnel to adjust the glucose infusion rate in real time[36]. Consequently, its use is confined mainly to specialized research settings.

FSIVGTT

Another well-established method for evaluating insulin sensitivity is the FSIVGTT, developed and refined by Bergman et al[37]. In a typical FSIVGTT protocol, a glucose bolus is administered intravenously, followed by frequent blood sampling over up to 3 hours. Insulin may also be infused later in some protocols (e.g., insulin-modified FSIVGTT) to enhance the test’s ability to quantify insulin action and β-cell function.

Data from the FSIVGTT are often analyzed using the minimal model approach, which estimates parameters such as the insulin sensitivity index and glucose effectiveness[38]. While this test is generally less labor-intensive and expensive than the HEC, its accuracy relies on proper sampling frequency and the validity of model assumptions. Moreover, interindividual variability and protocol differences can impact its reliability. Despite these challenges, the FSIVGTT remains a valuable tool in clinical research and specific clinical settings where an in-depth understanding of insulin-glucose dynamics is needed.

SURROGATE INDICES FROM FASTING MEASUREMENTS

Given the limitations of gold standard methods such as the HEC and FSIVGTT, several surrogate indices have been developed to assess IR using fasting blood samples. These indices, which primarily rely on FPG and FINS levels, offer a more practical alternative for large-scale epidemiological studies and routine clinical assessments. The HOMA-IR and QUICKI are the most commonly employed.

HOMA-IR

HOMA-IR is a widely used index that estimates IR based on FPG and FINS levels. Initially proposed by Matthews et al[39], this model assumes a steady-state balance between hepatic glucose output and insulin secretion, allowing for a simplified mathematical estimation of insulin sensitivity. The calculation is typically expressed as HOMA-IR = [FPG (mg/dL) × FINS (μU/mL)]/405 or in International System of Units: HOMA-IR = [FPG (mmol/L) × FINS (μU/mL)]/22.5; higher values indicate better IR. Despite its relative simplicity, HOMA-IR has been shown to correlate well with insulin sensitivity measured by the HEC, particularly in individuals without severe metabolic dysfunction[40]. Its widespread adoption in research and clinical practice is primarily attributed to its ease of use and applicability in large-scale studies where dynamic testing is impractical.

However, several limitations should be noted. Since HOMA-IR is derived solely from fasting measurements, it primarily reflects hepatic IR rather than peripheral insulin sensitivity[36]. The assumption of a constant β-cell function also limits its accuracy in individuals with significant pancreatic dysfunction, such as those with long-standing T2DM. Furthermore, variability in FINS measurements can introduce inconsistencies, necessitating careful interpretation of results.

Despite these limitations, HOMA-IR remains one of the most practical and widely utilized indices for assessing IR in clinical and epidemiological settings. Its utility extends beyond individual patient assessments, as it has been instrumental in identifying metabolic trends at the population level.

QUICKI

As an alternative to HOMA-IR, the QUICKI index was developed to provide a more sensitive estimate of IR using logarithmic transformation of FPG and FINS values. The formula for QUICKI is expressed as: QUICKI = 1/[log (FINS) + log (FPG)][41], where FINS is expressed as μU/mL and FPG is expressed as mg/dL. Unlike HOMA-IR, higher QUICKI values indicate greater insulin sensitivity, making it an inverse measure of IR. Several studies have reported a stronger correlation between QUICKI and insulin sensitivity assessed by the HEC, suggesting that this index may provide a more robust estimation of IR, particularly in individuals with obesity or MetS[36].

One of QUICKI's advantages is its improved sensitivity in detecting IR across a broader range of metabolic conditions. Unlike HOMA-IR, which may be confounded by declining β-cell function, QUICKI retains its predictive value even in patients with advanced metabolic disease. Additionally, its reliance on a logarithmic transformation helps to reduce variability in FINS measurements, potentially improving reproducibility[41].

Despite these advantages, QUICKI is not as widely used in clinical practice as HOMA-IR. One reason is the lack of standardized cut-off values, which makes it challenging to apply in individual patient assessments. Furthermore, like HOMA-IR, QUICKI is based exclusively on fasting values and does not capture postprandial insulin sensitivity, limiting its ability to provide a comprehensive picture of glucose metabolism.

While HOMA-IR and QUICKI are helpful surrogate markers of IR, each has strengths and limitations. HOMA-IR remains the most widely adopted index due to its simplicity and established clinical relevance, whereas QUICKI may offer better sensitivity in specific populations. However, neither fully replaces dynamic testing methods, and their application should always be interpreted in conjunction with other clinical and metabolic parameters.

Comparison of HOMA-IR and QUICKI

HOMA-IR remains the most widely used surrogate index due to its simplicity and established clinical relevance. By contrast, QUICKI may offer better accuracy in populations with obesity and MetS, as it exhibits a stronger correlation with the HEC. However, neither index fully replaces dynamic tests, such as the HEC or the FSIVGTT, and their application should be interpreted in conjunction with other clinical parameters, including β-cell function indices and lipid markers.

POST-LOAD AND DYNAMIC INDICES

In contrast to fasting-based indices, dynamic tests incorporating glucose and insulin measurements over time provide a more comprehensive assessment of insulin sensitivity and β-cell function. These indices are primarily derived from the oral glucose tolerance test (OGTT). They are instrumental in evaluating both hepatic and peripheral insulin sensitivity, as well as pancreatic β-cell compensation in response to IR. Among these, the Matsuda index, Stumvoll index, and disposition index (DI) have been widely utilized in clinical and research settings.

OGTT-based indices

The OGTT is widely used to assess glucose metabolism and diagnose diabetes. During the test, a standardized dose of glucose (typically 75 g) is ingested, and plasma glucose and insulin levels are measured at multiple time points, usually at 0 minutes, 30 minutes, 60 minutes, 90 minutes, and 120 minutes. The data obtained from the OGTT can be used to calculate several indices of insulin sensitivity and β-cell function.

Matsuda index: The Matsuda index, also known as the insulin sensitivity index composite, is a widely used dynamic measure of insulin sensitivity. It incorporates fasting and post-load glucose and insulin levels to estimate whole-body insulin sensitivity, reflecting both hepatic and peripheral insulin sensitivity. The index is calculated as follows: Where FPG stands for fasting plasma glucose, FINS for fasting insulin, and the mean OGTT values represent the average glucose and insulin levels during the test[42].

The Matsuda index has the advantage of assessing both hepatic and peripheral insulin sensitivity, whereas fasting-based indices, such as HOMA-IR, primarily reflect hepatic IR. Several studies have shown that the Matsuda index correlates well with insulin sensitivity measured by the HEC, making it a valuable tool in metabolic research[36]. However, it requires multiple blood samples during OGTT, which can be impractical in routine clinical settings.

The Matsuda index’s advantage lies in its ability to assess both hepatic and peripheral insulin sensitivity, whereas fasting-based indices, such as HOMA-IR, primarily reflect hepatic IR. Several studies have shown that the Matsuda index correlates well with insulin sensitivity measured by the HEC, making it a valuable tool in metabolic research[36]. However, it requires multiple blood samples during OGTT, which can be impractical in routine clinical settings.

Stumvoll index: The Stumvoll index is another OGTT-derived measure that estimates insulin sensitivity using mathematical equations based on glucose and insulin levels at various time points. Multiple versions of the Stumvoll index exist, but one commonly used formula is: Stumvoll insulin sensitivity index = 0.226 − 0.00328 ×body mass index (BMI) − 0.00057 × FPG − 0.00005 × insulin₁₂₀, where BMI represents body mass index, FPG represents fasting plasma glucose, and insulin₁₂₀ denotes the insulin concentration at 120 minutes post-OGTT[43].

Unlike the Matsuda index, the Stumvoll index emphasizes peripheral insulin sensitivity, making it particularly useful for assessing skeletal muscle insulin action. It has been validated against HEC-derived insulin sensitivity measurements and is frequently used in metabolic research. However, its reliance on specific OGTT time points (e.g., 120 minutes) makes it less adaptable to variable clinical protocols, limiting its use in routine medical practice.

DI: The DI is crucial in understanding the relationship between insulin secretion and sensitivity. It is based on the concept that β-cell function should compensate for IR to maintain normal glucose homeostasis. Mathematically, DI is calculated as: DI = insulin secretion × insulin sensitivity, where insulin secretion can be estimated using OGTT-derived parameters, such as the insulinogenic index (Δinsulin₃₀/Δglucose₃₀), which represents the incremental insulin response relative to glucose elevation in the first 30 minutes; an alternative approach is to use the acute insulin response to glucose obtained from the IVGTT. Insulin sensitivity is typically assessed using indices such as the Matsuda index or derived from minimal model analysis of IVGTT data[44].

The clinical significance of DI lies in its ability to predict the progression from standard glucose tolerance to diabetes. Individuals with IR but preserved β-cell function (i.e. a high DI) are less likely to develop diabetes. By contrast, those with both IR and impaired β-cell function (low DI) are at high risk of developing T2DM[45]. This makes DI an essential tool in longitudinal studies investigating the pathogenesis of diabetes.

Clinical relevance and limitations

Post-load and dynamic indices offer a more comprehensive assessment of insulin sensitivity and β-cell function compared to fasting-based indices. The Matsuda index is especially valuable as it reflects both hepatic and peripheral insulin sensitivity, whereas the Stumvoll index offers a simplified alternative that requires fewer blood samples. The DI remains a key predictor of diabetes risk by integrating insulin sensitivity with compensatory insulin secretion, offering insight into β-cell function relative to IR.

However, the primary limitation of these indices lies in the need for multiple blood draws during an OGTT, which reduces their feasibility in routine clinical settings. Furthermore, interindividual variability in glucose and insulin responses—due to factors such as gastric emptying rates, incretin hormone effects, and β-cell functional reserve—can affect their reproducibility and accuracy. Despite these challenges, OGTT-based indices are well-established in metabolic research and serve as valuable tools for identifying individuals at high risk of developing T2DM, thereby facilitating early intervention and targeted prevention strategies.

EMERGING AND ALTERNATIVE MARKERS

As the understanding of IR continues to evolve, alternative markers have been proposed to improve its assessment. Traditional indices such as HOMA-IR and the Matsuda index provide valuable insights; however, they have limitations regarding accessibility, accuracy, and applicability across diverse populations. Emerging markers, including the TyG index, lipid-based ratios, adipokine-related measures, and omics-based approaches, may provide additional tools for identifying IR in clinical and research settings.

TyG index

The TyG index, calculated using FPG and TG levels, is gaining attention as a reliable and accessible surrogate marker for IR. It is calculated using the formula: TyG = ln [FPG (mg/dL) × TG (mg/dL)/2].

The rationale behind the TyG index stems from established relationships among elevated TG, fasting hyperglycemia, and IR. Guerrero-Romero et al[46] first proposed the index and demonstrated its strong correlation with insulin sensitivity, as measured by the HEC.

Subsequent studies have demonstrated that the TyG index is comparable to HOMA-IR and correlates with IR-related cardiometabolic risks, including MetS and T2DM[35,47]. A recent meta-analysis confirmed the utility of the TyG index as a practical alternative for large-scale epidemiologic studies, especially when insulin measurements are unavailable[48]. However, as TG levels can be influenced by lifestyle and genetic factors, caution is warranted when interpreting TyG values across different populations[49].

Lipid-based indices

Lipid markers have been extensively studied in relation to IR, given the central role of dyslipidemia in MetS. Among these, the TG/HDL-C ratio has emerged as a simple and effective surrogate marker. This ratio is calculated as: TG/HDL-C ratio = TG (mg/dL)/HDL-C (mg/dL).

An elevated TG/HDL-C ratio has been shown to correlate with reduced insulin sensitivity, particularly in individuals with obesity and MetS[50]. Studies have indicated that this ratio is strongly associated with visceral fat accumulation, a key driver of IR[51]. Furthermore, it is reportedly a valuable predictor of cardiovascular risk and metabolic dysfunction[52]. In the Japanese population, the TG/HDL-C ratio is significantly associated with IR and the components of MetS[53,54].

In addition to commonly used lipid indices, some studies have reported associations among HDL subfractions (HDL2-C/HDL3-C ratio), fatty acid composition (e.g., oleic/stearic acid ratio), and estimated desaturase enzyme activities (e.g., elongation of very long-chain fatty acids elongase 6 and delta-5 desaturase) with IR. While these markers are considered less established, they show promise as potential indicators of metabolic dysfunction in specific populations[55-57].

Other lipid-based markers under investigation include the total cholesterol/HDL-C ratio, non-HDL cholesterol, and remnant lipoprotein cholesterol levels, which may further refine risk stratification for IR-related disorders[58]. However, the clinical application of these markers remains limited due to inconsistencies in their predictive value across different cohorts[58].

Adipokine-related measures

Adipose tissue functions as an endocrine organ, secreting adipokines that regulate insulin sensitivity, inflammation, and lipid metabolism. Among the key adipokines, adiponectin, leptin, and resistin have been investigated as potential biomarkers of IR. Adiponectin enhances insulin sensitivity by stimulating glucose uptake and fatty acid oxidation. Lower adiponectin levels are strongly associated with IR, MetS, and T2DM[59]. Leptin, a hormone involved in appetite regulation, is often elevated in obesity, leading to leptin resistance, which is linked to IR and impaired glucose metabolism[60]. Resistin was initially proposed as a link between obesity and IR, but its exact role remains controversial, as findings have been inconsistent across different populations[61]. Studies suggest that combining multiple adipokine measurements may improve metabolic risk prediction beyond traditional IR measures[62]. However, the clinical implementation of adipokine-based indices remains limited, as circulating levels can be influenced by factors such as inflammation, renal function, and assay variability[63].

Omics approaches

Advances in high-throughput technologies, such as metabolomics, lipidomics, and epigenetics, have opened new avenues for identifying novel biomarkers and gaining mechanistic insights into IR.

Metabolomics-based IR scores: Metabolomics studies have revealed distinct metabolic signatures associated with IR. In particular, individuals with IR have consistently observed elevated levels of branched-chain amino acids (BCAAs), acylcarnitines, and other lipid intermediates. These metabolites are believed to impair insulin signaling and mitochondrial function, thereby contributing to metabolic inflexibility and IR[64]. Furthermore, prospective studies have demonstrated that specific metabolite profiles, including elevated BCAAs and aromatic amino acids, predict the future development of T2DM, underscoring their potential as early biomarkers of IR[65].

Epigenetic markers and clinical translation: Epigenetic modifications—including DNA methylation, histone modifications, and microRNA regulation—also play a critical role in the development of IR. For instance, genome-wide DNA methylation analysis of pancreatic islets from individuals with and without T2DM has identified differentially methylated regions in genes involved in insulin secretion, such as glycine receptor alpha 1 subunit gene and solute carrier family 2 member 2, suggesting a regulatory role for epigenetics in β-cell function[66]. In addition, circulating microRNAs (miRs) have emerged as promising non-invasive biomarkers for IR and β-cell dysfunction. Some miRs, such as miR-375 and miR-29, have been linked to insulin signaling pathways and glucose metabolism[67].

Although omics-based biomarkers are still under validation and not yet widely implemented in clinical settings, they offer promising tools for stratifying metabolic risk and personalizing treatment approaches in the future.

Emerging biomarkers and omics approaches: Barriers and implementation strategies

Emerging biomarkers, including adipokine-related measures, lipid-based indices, and omics-derived markers, offer promising avenues for improving the assessment of IR. However, several barriers hinder their translation into routine clinical practice.

One major challenge is the cost associated with omics technologies such as metabolomics, lipidomics, and epigenetic profiling. High-throughput assays require specialized equipment, technical expertise, and extensive data processing, making them expensive and impractical for widespread clinical use[68]. In addition, standardization across laboratories remains a significant issue. Variations in sample collection, storage, processing, and analytic methods can lead to inconsistencies, limiting the reproducibility and comparability of results across different studies and populations[69].

Another critical barrier is the limited clinical validation of many emerging markers. While numerous studies have identified candidate biomarkers associated with IR, few have undergone rigorous validation in large, diverse cohorts[65]. Without robust external validation, the clinical utility of these biomarkers remains uncertain.

Furthermore, regulatory and ethical considerations surrounding genetic and omics data pose additional obstacles. Privacy concerns, data security, and informed consent requirements must be carefully addressed before such technologies can be widely adopted[70].

Several strategies can be pursued to overcome these barriers. First, multi-center collaborative studies are essential to validate emerging biomarkers across different ethnic groups, age ranges, and clinical conditions[71]. Second, cost-reduction efforts through technological innovation, such as developing simplified point-of-care testing platforms, could enhance accessibility. Third, establishing standardized protocols for sample handling and data analysis would improve reproducibility and facilitate clinical integration. Finally, combining traditional IR indices with selected omics-derived markers in multi-marker models may provide incremental predictive value without costly full-scale omics profiling in all patients[72].

Continued research focusing on these areas will be crucial to translating emerging biomarkers and omics-based approaches into practical tools for individualized IR risk stratification and management.

ML models for IR assessment

ML models have emerged as powerful tools for assessing IR in recent years. These models can integrate diverse data types, including clinical parameters, biochemical markers, imaging data, and omics profiles, to develop predictive algorithms that may surpass the performance of traditional surrogate indices.

Several studies have demonstrated that ML approaches, such as random forests, support vector machines, and neural networks, can accurately predict IR status or related outcomes like T2DM and MetS[73]. By analyzing complex interactions among multiple variables, ML models can capture non-linear relationships and identify novel patterns that conventional regression-based methods may miss.

However, significant limitations and challenges are associated with the use of ML for IR assessment. First, ML models often require large datasets with high-quality, well-annotated information to achieve robust performance and avoid overfitting[74]. Second, interpretability remains a significant concern. Many ML models, particularly deep learning models, operate as "black boxes," making it difficult to understand how predictions are generated[74]. This lack of transparency can limit clinical trust and hinder regulatory approval. Third, external validation is frequently lacking. Due to demographic, genetic, and environmental differences, ML models developed in one cohort may not generalize well to other populations[73].

Despite these challenges, ML holds considerable promise for advancing IR assessment. Future efforts should focus on developing interpretable models, conducting external validation across diverse cohorts, and integrating ML-derived scores with existing clinical indices to enhance prediction accuracy and facilitate practical application in routine healthcare settings.

Population-specific considerations

The performance and applicability of IR indices can vary considerably across different population groups. Understanding these variations is essential for optimizing the clinical use of surrogate markers in diverse clinical settings.

Conventional IR indices such as HOMA-IR and the Matsuda index have been evaluated for early risk stratification in pregnant women, particularly those at risk of gestational diabetes mellitus (GDM). Although these indices show reasonable predictive value for GDM development, physiological changes during pregnancy, including increased IR in the second and third trimesters, can affect their accuracy[75]. Therefore, trimester-specific reference ranges or dynamic assessments may be necessary to enhance their utility in this population.

Assessment of IR in children and adolescents is challenging due to physiological insulin sensitivity changes during growth and puberty. Studies have shown that the TyG index and TG/HDL-C ratio may be reliable and simple markers for identifying IR and cardiometabolic risk in pediatric populations[76]. However, age- and sex-specific cutoff values are critical as IR levels naturally fluctuate during different stages of development.

Traditional surrogate indices often fail to accurately reflect the degree of metabolic disturbance in individuals with rare genetic disorders such as familial partial lipodystrophy or other congenital syndromes associated with severe IR. In these populations, direct measurements of insulin sensitivity (e.g., by HEC) or more sophisticated models incorporating genetic, metabolic, and body composition data may be necessary for accurate assessment[77].

Tailoring the selection and interpretation of IR indices according to population-specific characteristics is crucial for improving IR assessment accuracy and clinical relevance across diverse patient groups.

Future perspectives and clinical integration

Emerging and alternative markers offer promising tools for improving the detection and characterization of IR. The TyG index and lipid-based ratios provide cost-effective and widely accessible alternatives, while adipokine profiling and metabolomics-based markers may enhance risk stratification. Epigenetic markers hold potential for personalized medicine, although their clinical application remains in the research phase.

Future studies should focus on standardizing cut-off values, validating these markers in diverse populations, and integrating them into multi-marker models to enhance predictive accuracy. Combining traditional and novel markers may provide a more comprehensive and individualized approach to diagnosing and managing IR.

COMPARISON AND CLINICAL RELEVANCE

Given the complexity of IR, numerous indices have been developed to assess it in various clinical and research contexts. These indices range from gold standard methods, such as the HEC and FSIVGTT, to more practical surrogate markers, including HOMA-IR, the Matsuda index, and the TyG index. The selection of an appropriate method depends on multiple factors, including accuracy, practicality, population-specific considerations, and predictive value for disease outcomes.

Accuracy vs practicality

One of the most important considerations when selecting an IR index is the balance between accuracy and practicality. The HEC remains the gold standard for measuring insulin sensitivity, providing direct quantification of glucose disposal rates with high precision[1]. However, it is time-consuming, labor-intensive, costly, and impractical for large-scale epidemiological studies or routine clinical use.

Similarly, the FSIVGTT provides a dynamic evaluation of insulin action when analyzed using the minimal model. Still, its reliance on frequent blood sampling and technical expertise limits its feasibility[37].

By contrast, surrogate indices such as HOMA-IR and the TyG index are widely used due to their simplicity and feasibility, as they require only fasting blood samples for glucose, insulin, or TG[36,39]. However, these indices primarily reflect hepatic IR and may not accurately capture peripheral insulin sensitivity, which is more relevant to muscle and cardiovascular outcomes[35].

The Matsuda index, derived from OGTT data, integrates both fasting and post-load glucose and insulin values, providing a more comprehensive reflection of whole-body insulin sensitivity compared to fasting-based indices[42]. While it is valuable in research and screening contexts, its requirement for multiple blood draws during OGTT can pose logistical challenges.

Ultimately, choosing an IR index should be guided by study objectives—whether for clinical screening, mechanistic investigation, or monitoring therapeutic response—balancing precision with feasibility.

Table 1 summarizes the key features of major IR indices, including their calculation formulas, strengths, limitations, typical clinical applications, and corresponding references.

Table 1 Summary of key insulin resistance indices: Formulas, strengths, limitations, clinical applications, and references.

Index	Formula	Strengths	Limitations	Typical clinical applications	Ref.
HOMA-IR	[FINS (μU/mL) × FPG (mg/dL)] ÷ 405	Simple, inexpensive, widely used; correlates with hepatic IR	Reflects mainly hepatic IR; influenced by insulin assay variability	Screening for IR; early detection of T2DM	[36,39,40]
QUICKI	1 ÷ {log [FINS (μU/mL)] + log [FPG (mg/dL)]}	Slightly more stable than HOMA-IR; correlates with HEC studies	Similar limitations to HOMA-IR; mainly reflects hepatic IR	Alternative simple index for IR estimation	[41]
Matsuda index	10000/square root of (FPG × FINS) × (Mean OGTT glucose × Mean OGTT insulin)	Assesses whole-body insulin sensitivity; captures both hepatic and peripheral IR	Requires OGTT; multiple blood samples needed	Research use; screening in high-risk populations	[42]
TyG index	ln [Fasting TG (mg/dL) × FPG (mg/dL) ÷ 2]	Easily available; useful for predicting metabolic risk	Influenced by TG variability, not a direct measure of insulin sensitivity	Prediction of MetS, T2DM, and CVD risk	[35,46-49]
TG/HDL-C ratio	Fasting TG (mg/dL) ÷ HDL-C [(mg/dL)	Simple; associated with atherogenic dyslipidemia and IR	Ethnic variability in cut-off points; limited sensitivity for early IR	CVD risk stratification; MetS screening	[50-54]

CVD: Cardiovascular disease; FINS: Fasting insulin; FPG: Fasting plasma glucose; HDL-C: High-density lipoprotein cholesterol; HEC: Hyperinsulinemic-euglycemic clamp; HOMA-IR: Homeostatic model assessment of insulin resistance; IR: Insulin resistance; MetS: Metabolic syndrome; OGTT: Oral glucose tolerance test; QUICKI: Quantitative insulin sensitivity check index; T2DM: Type 2 diabetes mellitus; TG: Triglycerides.

Population-specific considerations

IR indices may not perform equally well across different populations. Several factors, including ethnicity, BMI, age, and comorbid conditions, can affect the accuracy and interpretation of these results.

Ethnicity: Ethnic differences in insulin sensitivity and β-cell function have been well-documented. For example, East Asians tend to have lower insulin secretion despite similar degrees of IR compared to other populations. This may affect the applicability of indices such as HOMA-IR and the Matsuda index[78]. By contrast, South Asians show a higher prevalence of IR and MetS even at lower BMI levels, making indices such as the TyG index particularly useful in this group[79].

BMI and obesity: The performance of IR indices can vary significantly with body composition. HOMA-IR correlates well with IR in obese individuals; however, its predictive value may be lower in lean individuals with IR, such as lean patients with NAFLD[80]. In such cases, OGTT-derived indices, such as the Matsuda index, or dynamic tests, such as FSIVGTT, may be more informative[43].

Age: Insulin sensitivity declines with age, and the cut-off values for IR indices may need to be adjusted in older populations[81]. Older adults may exhibit higher FPG levels due to reduced β-cell compensation, making fasting-based indices less accurate in this demographic[81].

Comorbid conditions: The presence of liver disease (e.g., MASLD), chronic kidney disease, or inflammatory conditions can alter insulin metabolism and influence the reliability of surrogate indices. For example, HOMA-IR is less reliable in individuals with hepatic dysfunction, as impaired insulin clearance can lead to an overestimation of IR[40].

Predictive value for disease outcomes

A critical aspect of IR indices is their ability to predict future disease risk. Studies have evaluated the correlation between various indexes and the development of T2DM, CVD, and the progression of liver disease.

T2DM: Longitudinal studies have demonstrated that HOMA-IR, the TyG index, and OGTT-derived indices (e.g., Matsuda index, Stumvoll index) strongly predict future risk of T2DM[82]. The DI, which integrates insulin secretion and insulin sensitivity, provides one of the best predictors of diabetes progression as it reflects β-cell compensatory capacity[81].

CVD: IR is a significant contributor to the development of atherosclerosis and cardiovascular events. The TyG index and TG/HDL-C ratio have been associated with increased risk of myocardial infarction and stroke, likely due to their strong correlation with dyslipidemia and metabolic dysfunction[35,52]. HOMA-IR also predicts CVD risk; however, studies have suggested that lipid-based indices may have stronger associations with atherosclerotic burden[50].

Liver disease progression: IR plays a key role in the pathogenesis of MASLD and its progressive form, metabolic dysfunction-associated steatohepatitis. Among available indices, HOMA-IR and the TyG index correlate with liver fibrosis progression, with higher values indicating a greater risk of advanced fibrosis and cirrhosis[83,84]. Additionally, emerging research suggests that epigenetic modifications, such as DNA methylation of key metabolic genes, may enhance the prediction of MASLD-related complications. For example, differential DNA methylation in pancreatic islets has been linked to altered insulin secretion and metabolic dysfunction in patients with T2DM[66].

CHALLENGES IN CLINICAL TRANSLATION

While surrogate indices and emerging technologies offer promising avenues for assessing IR, several challenges must be addressed before they are widely adopted in clinical practice.

Cost-effectiveness remains a significant barrier. Although traditional surrogate indices, such as the HOMA-IR and TyG index, are relatively inexpensive and easily calculated from routine laboratory tests, more sophisticated approaches incur substantial costs, including omics-based profiling and advanced imaging techniques[79]. These expenses may limit accessibility, particularly in resource-limited healthcare settings.

Patient burden is another important consideration. Tests such as OGTT and HEC require prolonged patient engagement, are invasive, and may cause discomfort, which can reduce patient compliance, especially in vulnerable populations, including pregnant women, children, and the elderly[80]. Even for surrogate indices derived from fasting samples, repeated blood draws and fasting conditions can pose logistical challenges for some patients.

Reproducibility and interindividual variability further complicate clinical translation. Factors such as circadian rhythms, recent dietary intake, physical activity levels, stress, and acute illnesses can significantly influence metabolic markers, affecting the reliability of IR indices[79]. Moreover, differences in assay methods across laboratories can lead to variability in key measurements, including fasting insulin or triglyceride levels.

Addressing these challenges will be critical to improving the clinical utility of IR assessment tools. Future efforts should prioritize the development of standardized testing protocols, validating indices across diverse populations, and exploring simplified, non-invasive methods that strike a balance between accuracy, feasibility, and patient-centeredness.

CONCLUSION

Selecting an appropriate index to assess IR requires balancing accuracy, practicality, and population-specific considerations. Although gold standard methods such as the HEC offer the most precise measurement of insulin sensitivity, they are impractical for routine use. Surrogate indices like HOMA-IR, the TyG index, and the Matsuda index provide accessible and effective alternatives for clinical and epidemiological purposes.

The interpretation of these indices must consider factors such as ethnicity, BMI, age, and comorbidities, which can impact insulin dynamics and the performance of each index. Regarding disease prediction, lipid-based indices, such as the TyG index and the TG/HDL-C ratio, are particularly useful in assessing cardiovascular risk. By contrast, HOMA-IR and OGTT-derived indices more strongly predict the onset of T2DM and the progression of metabolic liver disease.

Emerging approaches, including metabolomics, epigenetic profiling, and ML, may enhance the precision of IR assessment and enable earlier intervention. Continued efforts to validate and refine these tools across diverse populations are essential for improving metabolic disease prevention and management outcomes.

Significant challenges remain, however, particularly regarding cost-effectiveness, reproducibility, and standardization of emerging technologies. Addressing these barriers will be critical to ensure the successful clinical translation of novel IR assessment tools.