Triglyceride-glucose-waist circumference index: A powerful tool for metabolic dysfunction-associated steatotic liver disease

Abstract

The search for reliable biomarkers to predict metabolic dysfunction-associated steatotic liver disease (MASLD) remains a key research focus. Traditional anthropometric parameters, such as triglycerides, glucose, and waist circumference (WC), have proven to be robust tools for diagnosing, stratifying, and predicting health outcomes. These measures facilitate early detection, personalized treatment strategies, and long-term risk assessment in metabolic health. The triglyceride-glucose (TyG) index and related parameters, particularly the TyG-WC index, are gaining recognition as reliable biomarkers for MASLD, with consistently high diagnostic accuracy across diverse populations. The TyG-WC index is associated with MASLD and an increased likelihood of all-cause, cardiovascular, and diabetes-related mortality, highlighting its importance in stratification and patient management. This opinion review summarizes key findings on the TyG-WC index across different MASLD populations and provides nutritional recommendations aimed at reducing this index. The TyG-WC index stands out as a practical and scalable biomarker for identifying and stratifying the risk of MASLD, particularly in resource-limited environments where access to advanced diagnostic tools is restricted. However, before the TyG-WC index can be integrated into routine clinical practice, rigorous, longitudinal studies involving ethnically diverse cohorts must validate its prognostic performance. It should be viewed as a complementary tool within a comprehensive metabolic risk assessment framework, supporting preventive strategies while awaiting formal endorsement in clinical guidelines.

Key Words: Steatotic liver disease; Liver steatosis; Non-invasive; Biomarkers; Nutritional therapy; Cardiometabolic risk

Core Tip: The search for reliable biomarkers for metabolic dysfunction-associated steatotic liver disease (MASLD) remains a critical priority. Among non-invasive and accessible tools, the triglyceride-glucose-waist circumference index (TyG-WC) has emerged as a particularly robust marker, demonstrating superior diagnostic performance compared with other indices. Its utility extends beyond MASLD, offering valuable insights into cardiovascular risk and other extrahepatic manifestations. Given its simplicity and affordability, the TyG-WC index holds significant promise, particularly in low-resource settings. Nonetheless, longitudinal studies are needed to validate its predictive capacity and guide clinical implementation. Integrating this index within clinical and research frameworks, alongside investigations into the gut microbiota, multiomic profiling, and artificial intelligence, may unlock new pathways for improving MASLD diagnosis and management.

INTRODUCTION

In recent years, the terminology surrounding liver diseases has evolved, shifting from non-alcoholic fatty liver disease (NAFLD) to metabolic dysfunction-associated fatty liver disease (MAFLD)[1] and metabolic dysfunction-associated steatotic liver disease (MASLD)[2]. These newer terms emphasize the integration of liver steatosis within the broader context of metabolic syndrome.

MASLD is the leading cause of chronic liver disease worldwide[3], with projections that the number of cases will continue to rise over the next few years[4], representing a silent pandemic that imposes significant costs on healthcare systems, contributes to long-term complications, decreases quality of life, and carries considerable stigma[5,6].

Characterized by lipid accumulation in the liver, MASLD is a sex-dimorphic disease[7], that presents with distinct “metabotypes”, leading to significant heterogeneity in its clinical presentation and associated risks[8,9]. While liver biopsy remains the diagnostic gold standard, its invasive nature, inherent limitations, and associated risks underscore the urgent need for reliable non-invasive biomarkers[10].

Epidemiological studies indicate that up to 99% of individuals diagnosed with NAFLD meet the criteria for MASLD[11–13], suggesting that the clinical profiles are the same from NAFLD to MASLD[14]. However, ongoing debate surrounds the reclassification of MAFLD to MASLD.

MAFLD has been proposed as a more effective nomenclature for identifying individuals with a pro-inflammatory metabolic profile who are at an increased risk of disease progression and complications[15,16]. Compared with NAFLD and MASLD, MAFLD has been associated with a higher risk of cardiovascular disease[17], chronic kidney disease[18], and liver-related adverse outcomes[16,19]. However, this increased risk may be attributed to the broader inclusion criteria of MAFLD, which allow for the presence of other hepatic comorbidities and alcohol use[20].

Conversely, MASLD is a more inclusive classification regarding metabolic risk factors, facilitating earlier diagnosis and improving the recognition of lean individuals and those at heightened risk for diabetes[21]. On the other hand, from a nomenclatural perspective, the term “fatty” does not universally carry the same stigma and may be more comprehensible to the general public than “steatotic”[22], raising concerns about the global applicability of the revised terminology. Furthermore, MASLD could lead to overdiagnosis, underscoring the need for extensive educational campaigns targeting both healthcare professionals and the general population.

Steatosis indices have supported the diagnosis of early-stage NAFLD in patients with preserved liver function. However, these patients do not meet the criteria for inclusion in the MAFLD or MASLD frameworks, highlighting a gap in the current diagnostic systems[23].

A notable proposal is to change the “D” in “disease” to “disorders,” which would better reflect the spectrum of the condition, rather than a single disease entity[20]. Additionally, the term “combinatorial MASLD” has been proposed to encompass individuals with MASLD and other causes of liver disease, potentially improving overall clinical outcomes[24,25].

Moving forward, a critical aspect to consider is the number, type, and potential combinations of metabolic criteria, along with specific genetic polymorphisms[26], epigenetic changes[27,28], hormonal status[29], microbiota signatures[30,31], sex-specific microbial heterogeneity[32], social determinants of health[33,34], and endocrine conditions, such as polycystic ovary syndrome[35,36], gout[37] and hypothyroidism[38,39], as these factors may influence disease progression and clinical phenotypes. Given these considerations, the MASLD nomenclature is likely to continue evolving to better integrate multi-level determinants, potentially leading to distinct disease subtypes.

Some essential strategies for integrating the MASLD nomenclature into healthcare systems involve education and training, standardization of diagnostic protocols, incorporation into national and international coding frameworks, public health awareness initiatives, and tailored adaptations for resource-limited settings, as highlighted by Iruzubieta et al[20].

PATHOPHYSIOLOGY OF MASLD: A COMPLEX AND MULTIFACTORIAL PROCESS

The pathophysiology of MASLD is multifactorial and remains incompletely understood. It involves a complex and dynamic interplay of genetic, epigenetic, environmental, metabolic, immunological, and cellular processes. Genetic susceptibility plays a central role, with variants in PNPLA3 (I148M), TM6SF2 (E167K), MBOAT7, GCKR, HSD17B13, and CDKN1A associated with altered lipid metabolism, increased hepatic fat accumulation, and fibrogenesis[40–42]. Epigenetic influences, particularly abnormal DNA methylation and the dysregulation of non-coding RNAs, play a significant role in the pathogenesis of MASLD. Among non-coding RNAs, several microRNAs (miRNAs) have been implicated, including miR-21, miR-34a, miR-93[43], miR122, miR-193-5p, miR-214[44], miR-542-3p, miR-574-3p, miR-200a-3p[45], and miR-511-3p[46], among others.

Environmental factors are increasingly recognized as important contributors to MASLD. Chronic exposure to microplastics, heavy metals, pesticides, and air pollutants, such as particulate matter (PM) 2.5, PM10, nitrogen dioxide, and nitrogen oxides, is associated with systemic and hepatic stress[47,48]. Dietary influences, including high consumption of fructose, food additives, preservatives, emulsifiers, and artificial sweeteners, may further exacerbate hepatic steatosis and inflammation[49]. Disruption of the gut–liver axis, largely driven by intestinal microbiota dysbiosis, increases intestinal permeability and promotes endotoxemia. This facilitates the translocation of microbial products and enhances hepatic inflammation through mediators, such as neurotensin and advanced glycation end products[50].

Metabolic dysregulation is a hallmark of MASLD, encompassing dysregulated lipid metabolism[51], peripheral leptin resistance[52], adiposopathy[53], intrahepatic hypothyroidism[54], bile acid abnormalities[55,56], and altered systemic amino acid profiles[57]. At the cellular level, a variety of mechanisms, including changes in the antioxidant system[58], stellate cell dysfunction[59], cell senescence[60], mitochondrial and lysosomal dysfunction[61], ferroptosis[62] and apoptosis mediated by Bcl-2 family proteins[63], contribute to hepatocellular injury and fibrosis. The convergence of these pathways results in a maladaptive interplay between inflammatory and metabolic processes, which accelerates disease progression and increases clinical severity in MASLD.

The transition from MASLD to metabolic dysfunction-associated steatohepatitis is driven by insulin resistance in key metabolic tissues, such as adipose tissue, skeletal muscle, and the pancreas, culminating in hepatocyte injury (apoptosis, necrosis, pyroptosis), pro-inflammatory cytokine release, and fibrogenesis, with progression to cirrhosis and hepatocellular carcinoma[64]. Central to this pathophysiology is visceral adipose tissue (VAT), which amplifies metabolic dysfunction through dysregulated lipolysis and chronic inflammation. VAT releases excessive free fatty acids (FFAs) into the portal circulation, overwhelming hepatic β-oxidation capacity and driving re-esterification into triglycerides, thereby exacerbating hepatic steatosis[65]. Concurrently, VAT-derived inflammatory mediators (e.g., tumor necrosis factor-alpha, interleukin-6) and adipokines (e.g., reduced adiponectin) impair insulin signaling pathways, worsening systemic insulin resistance. This creates a vicious cycle: Hepatic de novo lipogenesis and gluconeogenesis are upregulated, while skeletal muscle glucose uptake is suppressed, further elevating circulating glucose and insulin levels[66].

The metabolic sequelae of these disruptions are profound. Hypertriglyceridemia, elevated FFA flux, and impaired glucose homeostasis reflect both hepatic and peripheral insulin resistance. Clinically, these disturbances correlate with visceral adiposity [elevated waist circumference (WC)] and hyperinsulinemia, hallmarks of heightened cardiometabolic risk. Prolonged insulin resistance and chronic inflammation activate hepatic stellate cells, promoting collagen deposition and fibrosis[67]. Over time, this maladaptive cascade perpetuates inflammation, dysbiosis, systemic hyperglycemia, compensatory hyperinsulinemia, and end-organ damage, creating a bidirectional link between hepatic and extrahepatic metabolic dysfunction.

Beyond hepatic pathology, MASLD has been associated with cerebrovascular changes[68] and cognitive impairment[69], likely driven by chronic inflammation and dysfunction of the microbiota-liver-brain axis[70]. Cognitive impairment in MASLD patients may further hinder adherence to treatment regimens, reduce physical activity engagement, and exacerbate fatigue, ultimately worsening metabolic dysfunction. Moreover, visceral fat (VF) may exacerbate brain dysfunction[71].

The possible combination of mechanisms and individual environmental exposure leads to significant heterogeneity in the clinical presentation of MASLD, making a “one-size-fits-all” approach to therapy impossible and highlighting the urgency of advancing precision medicine to tailor interventions based on individual pathophysiological profiles[72].

BIOMARKERS FOR MASLD

MASLD is diagnosed based on the presence of hepatic steatosis along with at least one metabolic criterion[2]. However, diagnosing steatosis presents challenges, as liver biopsy—the gold standard—has inherent limitations, and commonly used imaging techniques, such as ultrasound, magnetic resonance imaging, and controlled attenuation parameter, are not always accessible, particularly in developing countries. This underscores the need for non-invasive diagnostic methods, which offer significant opportunities across various clinical settings.

A wide range of non-invasive biomarkers has been developed for MASLD[73,74], including serum markers, scoring systems, genetic and epigenetic[75] biomarkers, and imaging-based tools. These biomarkers provide a promising alternative for diagnosis and disease monitoring, potentially reducing the reliance on liver biopsies, which are invasive and carry procedural risks.

While composite scoring systems, such as the fatty liver index[76], hepatic steatosis index[77], SteatoTest[78], NAFLD liver fat score[79], and triglyceride-glucose (TyG) index models, are widely recognized, emerging data suggest that the triglyceride-waist index variants: (1) TyG-body mass index; (2) TyG-waist-to-height ratio; (3) TyG-high-density lipoprotein; and particularly (4) the TyG-WC, are proving to be superior tools for diagnosing and assessing MASLD[80]. Table 1 summarizes various non-invasive biomarkers for MASLD[75-77,81-110], while Supplementary Table 1 shows the components of the Dallas Steatosis Index equation.

Table 1 Different biomarkers for metabolic dysfunction-associated steatotic liver disease assessment.

Index/biomarker	Abbreviation	Formula	Ref.
Body mass index	BMI	Weight (kg)/height² (m²)	Ma et al[83]
Dallas steatosis index	DSI		McHenry et al[84], McHenry et al[85]
Fatty liver index	FLI	[e0953 × ln (TG) + 0.139 × BMI + 0.718 × ln (GGT) + 0.053 × WC - 15.745]/[1 + e0953 × ln (TG) + 0.139 × BMI + 0.718 × ln (GGT) + 0.053 × WC - 15.745) × 100]	Bedogni et al[76]
Fibroblast growth 21	FGF-21	Molecular kits	Gallego-Durán et al[86], Keskin et al[87]
Framingham steatosis index	FSI	-7.981 + 0.011 × age - (0.146 × sex, female = 1, male = 0) + 0.173 × BMI + 0.007 × TG + (0.593 × hypertension, yes = 1, no = 0) + (0.789 × diabetes, yes = 1, no = 0) + (1.1 × ALT/AST ratio ≥ 1.33, yes = 1, no = 0)	Long et al[88]
Hepatic steatosis index	HSI	8 × ALT/AST ratio + BMI (+ 2, if diabetes mellitus; + 2, if woman)	Lee et al[77], Chung et al[89]
Homeostatic model assessment	HOMA	Fasting glucose × fasting insulin/405	Matthews et al[90]
Leukocyte cell-derived chemotaxin 2	LECT2	Molecular kits	Suzuki et al[91]
Lipid accumulation product	LAP	WC and fasting plasma TG: LAP = (WC – 65) × TG for men, and LAP = (WC – 58) × TG for women	Ebrahimi et al[92], Li et al[93]
Micro RNA	MiR-122-5p, miR-151a-3p, miR-126-5p and miR-21-5p	Molecular kits	Tobaruela-Resola et al[75]
N3 MASH for MASLD	N3-MASH	N3-MASH = 5.278 × log10(CK-18) + 4.423 × log10(CXCL10) + 1.833 × BMI – 21.652	Zhang et al[94]
N7 MASH for MASLD	N7-MASH	N7-MASH = 3.639 × log10(CK-18) + 4.811 × log10(CXCL10) + 2.911 × log10(squalene epoxidase) + 0.050 × ALT + 0.869 × glycated hemoglobin – 0.117 × AST + 1.193 × BMI – 33.023	Zhang et al[94]
Triglyceride-glucose index	TyG	Ln (TG × glucose/2)	Simental-Mendía et al[95], Zou et al[96]
TyG-BMI	TyG-BMI	BMI × TyG index	Khamseh et al[81], Zou et al[96]
TyG-HDL cholesterol	TyG-HDL-c	TyG/HDL cholesterol
TyG-WC	TyG-WC	TyG index × WC	Khamseh et al[81]
TyG-waist-to-height ratio	TyG-WHtr	TyG index × WC/height	Zhang et al[82]
TyG-weight-adjusted-waist index	TyG-WWI	TyG index × WC/weight	Zhang et al[82]
Uric acid	UA	Plasma/urine concentrations	Chang et al[97], Fukuda et al[98]
Uric acid to creatinine ratio	UACR	UA/creatinine	Choi et al[99], Wang et al[100]
Visceral adiposity index	VAI	WC/(39.68 + 1.88 × BMI) × (TG/1.03) × (1.31/HDL)	Amato et al[101], Liu et al[102]
Visceral fat	VF	Measured by bioelectrical impedance/magnetic resonance imaging	Lee et al[103]
Waist circumference	WC	Measuring tape	Pustjens et al[104]
Waist-to-height ratio	WHtr	WC/height	Sheng et al[105]
Waist to hip ratio	WHr	WC/hip circumference as measured by measuring tape	Song et al[106]
Weight-adjusted-waist index	WWI	WC/weight	Park et al[107], Lian et al[108]
Zhejiang university index	ZJU	BMI + fasting plasma glucose + TG + 3 × ALT/AST ratio (+ 2, if woman)	Wang et al[109], Ma et al[110]

ALT: Alanine aminotransferase; AST: Aspartate aminotransferase; BMI: Body mass index; CK-18: Cytokeratin-18; CXCL10: C-X-C motif ligand 10; GGT: Gamma-glutamyl transferase; HDL: High-density lipoprotein; LAP: Lipid accumulation product; MASH: Metabolic dysfunction-associated steatohepatitis; MASLD: Metabolic dysfunction-associated steatotic liver disease; MiR: Micro RNA; TG: Triglycerides; TyG: Triglyceride-Glucose; UA: Uric acid; WC: Waist circumference.

TYG-WC IN NAFLD/MAFLD/MASLD ACROSS DIVERSE POPULATIONS

The TyG-WC index has been widely recognized as a superior diagnostic biomarker for NAFLD, MAFLD, and MASLD across different populations. In Japan, Sheng et al[111] demonstrated its diagnostic strength with an area under the receiver operating characteristic (AUROC) curve of 0.88, a finding consistent with studies from South Korea, where Han et al[112] (AUROC = 0.94), Song et al[113] (AUROC = 0.83), and Kim et al[114] (AUROC = 0.86) reported similar accuracy. Research from Iran (Khamseh et al[81], AUROC = 0.69, and Forouzesh et al[115], AUROC = 0.77), Turkey (Demirci and Sezer[116], AUROC = 0.82) and China (Yu et al[117], AUROC = 0.87, and Xue et al[118], AUROC = 0.83) further supports its diagnostic utility. In the United States, Li et al[119] reported an AUROC of 0.80 in non-obese NAFLD individuals, whereas Wu et al[120] highlighted its broad applicability in the general population. Mexican studies by Mijangos-Trejo et al[80] and Priego-Parra et al[121] found AUROCs of 0.81 and 0.84, respectively, for detecting liver steatosis and MASLD.

He et al[122] and Zhang et al[94] identified non-linear associations between TyG-WC and MASLD, which suggests that minor fluctuations in TyG-WC values might not uniformly influence the prevalence or severity of MASLD across its entire spectrum. Instead, the findings imply that genetic predisposition, epigenetics, lifestyle factors, and comorbid conditions could serve as more critical determinants of disease progression or clinical manifestations. Yang et al[123] recently described the TyG-WC as the best indicator for MASLD screening in middle-aged and elderly Americans. These collective findings support the strong capacity of TyG-WC for MASLD detection.

Critically, the TyG-WC has shown associations with all-cause mortality, cardiovascular mortality, diabetes-related mortality[124], and the prediction of major adverse cardiovascular events in MASLD patients with heart failure[125]. Therefore, its values could be useful not only for diagnosing MASLD but also for identifying individuals at higher cardiovascular risk, potentially influencing staging and long-term prognosis.

NUTRITIONAL THERAPY FOR MASLD INDIVIDUALS

Nutritional therapy is the first-line treatment for MASLD. Caloric restriction between 500–1000 kcal/day is widely recognized as a primary approach for achieving weight loss of 5%-10% of total body weight and reducing fat mass, both of which significantly improve hepatic steatosis and fibrosis[126]. Moreover, the Mediterranean diet remains the most well-established dietary pattern for MASLD management, as it has consistently demonstrated beneficial effects on key disease-related outcomes[127,128].

While these general dietary patterns provide a solid foundation for improving metabolic health, a more targeted approach is needed to directly modulate the TyG-WC components and further optimize disease management. Although no clinical trials have yet evaluated diet-induced changes in TyG-WC as a primary outcome, the strong pathophysiological relationship between its components and dietary factors justifies targeted interventions.

DIETARY STRATEGIES FOR MODULATING TYG-WC COMPONENTS IN MASLD INDIVIDUALS

Carbohydrate quality directly influences TyG-WC modulation. Reducing the intake of refined carbohydrates from added sugars, refined grains, and fructose-rich foods is essential for improving triglyceride and glucose metabolism. Although carbohydrates should constitute 45%–55% of total energy intake, added sugars should be limited to 5%–10%[128]. In particular, liquid sugars, such as soft drinks, fruit juices, syrups, and sweetened yogurts, have been associated with higher glucose and triglyceride levels, increased WC, and metabolic dysfunction. Their rapid intestinal absorption facilitates hepatic fructose transport and fat storage. Moreover, they induce lower satiety than solid sugar sources, promoting excessive energy intake and impairing metabolic regulation[128]. These effects contribute to elevations in both fasting glucose and triglyceride levels, directly impacting two components of the TyG-WC.

Not all carbohydrate sources have the same metabolic impact. Carbohydrates from fruits, legumes, and whole grains are associated with reductions in WC and lower cardiometabolic risk, likely due to their higher fiber content, which slows glucose absorption and modulates metabolic responses. To better assess carbohydrate quality, a ≥ 1 g fiber per 10 g carbohydrate ratio has been recommended[129]. Despite the general recommendation of consuming at least 25 g of fiber per day, meeting this target is often challenging in clinical practice. To bridge this gap, fiber supplementation of 5–10 g/day may be beneficial. Soluble fiber, in particular, has been strongly associated with lower circulating lipid levels[130] with effects on cognition.

Protein plays a crucial role in MASLD, contributing 20%–25% of total energy expenditure or 1.2–1.5 g/kg/day, with higher intakes (1.4–1.5 g/kg/day) recommended for individuals following a hypocaloric diet or presenting with abdominal obesity[126,131]. High-protein diets (30%–35% of total energy intake) have shown benefits in glycemic and lipid control, particularly triglyceride levels. While some evidence suggests a potential reduction in WC, findings remain unclear[131]. To meet protein requirements and support metabolic outcomes, a balanced combination of plant-based and animal-based proteins is advised.

Conversely, diets providing < 1 g/kg/day are not recommended, as they have been linked to greater insulin resistance, MASLD progression, sarcopenic obesity, and an increased risk of cardiovascular disease and all-cause mortality[132]. A balanced intake of both plant-based and animal-based proteins is necessary. Plant proteins contribute vitamins, minerals, and fiber, whereas animal proteins provide all essential amino acids. When dietary intake is insufficient, protein modules can be considered as supplementation; however, formulas designed for body mass gain are not recommended in weight-loss strategies. In this regard, whey protein and casein exert greater effects on satiety than other protein sources, potentially supporting weight loss by enhancing appetite regulation and reducing overall energy intake[131].

Once carbohydrate and protein requirements are determined, the remaining total energy intake is covered by lipids, contributing approximately 30% of total energy intake, while saturated fat intake should not exceed 7%–10%[133]. This recommendation can be achieved by using cooking methods, such as boiling, roasting, steaming, or cooking with spray oil, while avoiding fried and ultra-processed foods containing more than 17 g of fat per 100 g. Limiting saturated fat intake and favoring unsaturated fat sources may improve triglyceride levels and reduce VF accumulation, thus supporting favorable changes in TyG-WC index parameters.

Monounsaturated fatty acids, found in olive oil and avocado, should contribute between 10%-20% of total energy expenditure, while polyunsaturated fatty acids (PUFAs), including omega-3, account for 6%-10%. Among PUFAs, omega-3 has shown beneficial effects on MASLD, as a daily intake of 2–4 g improves transaminases, hepatic steatosis, and hypertriglyceridemia, and reduces VF. However, exceeding 3 g/day may cause gastrointestinal discomfort and a perceived fishy taste.

Omega-3 supplements contain eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA). EPA has anti-inflammatory effects and improves cardiovascular function, while DHA is essential for the structure and function of cell membranes, particularly in the brain and liver. The EPA/DHA ratio varies among supplements, so dosage should consider individual tolerance, especially in MASLD patients treated with glucagon-like peptide-1, thyroid hormone receptor beta receptor agonists, or fibroblast growth factor analogs, who often experience gastrointestinal symptoms[134]. The proposed nutritional strategies, by targeting the individual components of the TyG-WC, may collectively contribute to its improvement.

PHYSICAL EXERCISE RECOMMENDATIONS FOR TYG-WC INDEX IN MASLD INDIVIDUALS

Beyond dietary modifications, physical exercise plays a key role in optimizing TyG-WC components and other surrogate markers[135], by improving insulin sensitivity, enhancing fatty acid utilization, reducing triglyceride levels, and promoting VF loss. Both aerobic and resistance training provide metabolic benefits, with evidence suggesting that their combination offers the greatest impact[136–138].

Aerobic exercise is recommended 3–5 times per week at moderate intensity, including activities such as swimming, walking, and running, as it enhances glucose metabolism and promote VF reduction[139]. Resistance training is crucial for individuals with altered TyG-WC parameters, given that skeletal muscle plays a key role in glucose disposal and lipid oxidation. Low muscle mass is linked to higher insulin resistance and triglyceride levels, worsening TyG-WC index scores[140]. To mitigate these effects, resistance training should be performed 2–3 times per week, with at least three sets of 12 repetitions targeting all major muscle groups, improving insulin sensitivity and metabolic function.

For individuals with low physical conditioning, walking is a safe and effective option, starting with 25 minutes, or as much as tolerated, progressively increasing duration and intensity to maximize its impact on TyG-WC parameters. In cases where mobility is limited, stationary cycling or seated movements may serve as viable alternatives.

Prolonged sedentary behavior negatively influences TyG-WC components by promoting insulin resistance and VF accumulation, both of which contribute to increased fasting glucose and WC. Although reducing sitting time alone has shown modest effects on insulin sensitivity, slight improvements in fasting insulin have been reported[141]. Encouraging regular movement breaks, such as standing up or walking every 30-60 minutes for 5 minutes[142,143] and incorporating daily activities like climbing stairs or performing light upper-body exercises can help improve metabolic health.

Hence, physical activity complements dietary strategies by targeting all three components of the TyG-WC index simultaneously. Figure 1 summarizes these key recommendations, highlighting the role of macronutrient distribution, fiber intake, and structured physical activity in improving triglyceride levels, glucose metabolism, and WC/VF.

Figure 1 Overview of dietary and physical exercise strategies for optimizing triglyceride-glucose-waist circumference index components. Each box highlights targeted interventions: Blue for triglycerides, light blue for glucose metabolism, and orange for waist circumference/visceral fat. Exercise contributes to improvements in all components. MUFA: Monounsaturated fatty acids; Med diet: Mediterranean diet; PUFA: Polyunsaturated fatty acids; SFA: Saturated fatty acids.

TYG-WC INDEX BEYOND METABOLIC PARAMETERS

TyG-WC index relevance extends beyond classic metabolic disorders, being associated with conditions, such as hyperuricemia[144], arthritis[145], periodontitis[146], osteoporosis[147], gallstone risk[148], renal lithiasis[149], psoriasis[150], lower extremity peripheral artery disease[151], serum creatinine levels and glomerular filtration rate[152], depressive symptoms[153,154], frailty[155], heart failure[156], and even exposure to fine PM[157]. This growing body of evidence opens new avenues for research in MASLD. However, it is important to note that while these associations are intriguing, further investigation is required to establish causality and better understand the role of the TyG-WC in these diverse conditions.

LIMITATIONS OF TYG-WC INDEX

It is crucial to recognize that the cut-off points and diagnostic accuracy of the TyG-WC index differ significantly between sexes[121]. A significant limitation of the current literature is the absence of longitudinal studies, which are crucial for establishing causal relationships and assessing the temporal validity of the TyG-WC as a predictive tool. The predominance of cross-sectional studies restricts the ability to evaluate the index’s predictive accuracy over time. Furthermore, heterogeneity in study designs—including variations in measurement techniques and the lack of standardized protocols—impedes the comparability and generalizability of findings across different populations.

A notable concern is the observed variability in diagnostic performance of the TyG-WC index across diverse populations. For instance, the reported AUROC score of 0.69 in a cohort from Iran suggests suboptimal performance in that population. Such a low AUROC may indicate reduced diagnostic accuracy, which could be attributed to several factors, including genetic differences, regional lifestyle variations, dietary habits, and environmental influences. This variability underscores the critical need for population-specific validation and the establishment of tailored cut-off values to improve the diagnostic accuracy of the TyG-WC index in different demographic and clinical settings.

To address these limitations, large-scale, multi-ethnic, longitudinal studies are essential to establish robust, population-specific thresholds and validate the clinical utility of the TyG and TyG-WC indices. Additionally, the impact of confounding factors—such as genetic predispositions, age, hormonal status (particularly in women), comorbid conditions, and medication use—requires further investigation to better understand their influence on the diagnostic accuracy of the index.

Given that triglyceride levels, fasting glucose, and WC are markers of underlying metabolic dysfunction and inflammatory processes, the TyG-WC index represents a promising, low-cost tool for identifying individuals at risk for MASLD. However, its clinical integration is contingent on further rigorous validation and refinement across diverse populations. This article provides an opinion-based review of the current evidence and should not be interpreted as a clinical guideline or definitive recommendation. High-quality, large-scale studies are needed to establish the TyG-WC index as a reliable tool for routine clinical practice.

CONCLUSION

The TyG-WC index holds considerable promise as a non-invasive, cost-effective marker for identifying individuals at risk of MASLD, particularly when integrated with advanced analytical approaches, such as multiomics and artificial intelligence. However, substantial inter-population variability and methodological limitations underscore the need for rigorous, longitudinal validation. Establishing standardized, population-specific cut-offs and addressing confounding variables will be critical to unlocking its full clinical potential. Current evidence is encouraging but insufficient to support widespread clinical adoption. Future research should prioritize large-scale, multi-ethnic studies and harmonized protocols to enhance comparability and reliability. Until such data are available, the TyG-WC index should be regarded as a complementary tool within a broader diagnostic framework, supporting but not substituting established approaches in the effort to improve MASLD detection and management.