Metabolic dysfunction-associated steatotic liver disease (MASLD) represents the most prevalent chronic liver disorder globally (1–3), impacting approximately 32% of the world’s population (4). This condition is marked by excessive lipid deposits in the liver, which can progress to inflammation and liver injury. Without intervention, these changes can advance to liver cirrhosis and potentially hepatocellular carcinoma (3, 5).

MASLD is linked not only to elevated liver-related health issues and mortality rates but also to an increased likelihood of developing cardiovascular diseases, type 2 diabetes (T2D), and overall mortality (1, 6–8). Research indicates that MASLD may act as a precursor to or exacerbate the onset of T2D (1, 9). Recent epidemiological investigations reveal that individuals diagnosed with MASLD face a two-fold greater risk of developing diabetes compared to those without the disease (10). Consequently, it is crucial to comprehend the fundamental risk factors that lead to glucose dysregulation in patients with MASLD, as this knowledge could guide the formulation of effective preventive measures against the onset of diabetes.

The triglyceride glucose index (TyG-i) has emerged as a significant biomarker for evaluating insulin resistance and predicting diabetes risk (11, 12). This index is derived from fasting triglyceride and glucose levels, offering a straightforward yet effective measure of metabolic health. Numerous studies have established substantial correlations between TyG-i and various health outcomes. Recent research has identified associations between TyG-i and conditions such as MASLD, cardiovascular disease, gestational diabetes, prediabetes, T2D, and all-cause mortality (13–16). Despite the increasing evidence linking TyG-i to T2D risk within general populations, its specific relationship with T2D among individuals with MASLD remains inadequately explored. Given the shared pathophysiological mechanisms of insulin resistance and dyslipidemia that characterize both MASLD and T2D, investigating TyG-i within the context of MASLD presents a unique opportunity to clarify its role as an early predictor of diabetes onset. Consequently, this retrospective study aims to examine the longitudinal association between TyG-i and the development of T2D among individuals with MASLD.

In this retrospective cohort study involving 2,507 Japanese adults with MASLD, we identified a positive association between elevated TyG-i levels and the risk of T2D. Our findings further revealed a U-shaped relationship between TyG-i and an increased risk of T2D. Furthermore, sensitivity and subgroup analyses corroborated these results, reinforcing the robustness of our conclusions.

The TyG-i has been widely used as a surrogate for insulin resistance to predict the risk of metabolic diseases (26, 27). A meta-analysis that included 12 studies, including 105,365 participants, found that the TyG-i was positively associated with the risk of MASLD (OR: 2.84, 95%CI: 2.01-4.01) (28). In a longitudinal cohort study of 16,172 non-obese participants, individuals in the highest quartile of the baseline TyG-i had a 3.58-fold increased risk of developing MASLD relative to those in the lowest quartile (HR: 4.58, 95% CI: 3.48-6.02) (29). A meta-analysis encompassing 13 cohort studies with a total of 70,380 participants identified a significant and positive correlation between the TyG-i and T2D risk (HR: 2.44, 95% CI: 2.17-2.76) (30). In addition, a longitudinal cohort study that included 179541 Chinese adults found a positive nonlinear association between TyG-i and the risk of prediabetes and T2D after adjusting for confounders(HR: 1.67, 95%CI: 1.62-1.71, P< 0.001) (13). MASLD is a common chronic liver disease that is closely associated with metabolic syndrome (31). Past evidence has shown that the prevalence of diabetes is significantly increased in subjects with MASLD (8). However, there have been few studies discussing the relationship between TyG-i and T2D in the MASLD population. In our study, TyG-i was positively related to the risk of developing diabetes in people with MASLD when TyG-i > 7.94. Therefore, early intervention using the TyG-i may be effective in reducing the risk of diabetes in patients with MASLD.

Our research uncovered a U-shaped relationship between the TyG-i and T2D risk after controlling for confounders. Specifically, the analysis revealed that when TyG-i levels were below 7.94, there was a 79% decrease in the risk of T2D development for each one-unit increase in TyG-i. Conversely, a positive association was found between TyG-i and T2D risk when TyG-i levels exceeded 7.94. Understanding this U-shaped relationship is essential for identifying individuals exhibiting altered metabolic profiles across different TyG-i ranges. Those with values near the 7.94 inflection point may constitute a key population for targeted preventive interventions. Clinicians should closely monitor TyG-i as an early biomarker indicative of elevated T2D risk, particularly among patients with MASLD. Interventions aimed at sustaining TyG-i around the inflection point through lifestyle modifications—including dietary improvements, physical activity enhancement, and weight management—should be prioritized for individuals approaching this critical level. Such proactive measures could delay or prevent the progression from insulin resistance to overt T2D, thereby improving clinical outcomes. Additionally, the prospect of pharmacological strategies targeting the TyG-i warrants investigation. As the understanding of TyG-i’s metabolic implications advances, clinical trials designed to assess treatments that modulate TyG-i are necessary to expand therapeutic options for high-risk populations. From a public health perspective, our findings underscore the importance of recognizing TyG-i as a valuable marker in T2D risk stratification. Health professionals and policymakers should consider integrating TyG-i assessments into preventive care frameworks to optimize resource allocation and intervention efficacy. Furthermore, educational programs aimed at raising awareness of the significance of metabolic health and elevated TyG-i levels could encourage early evaluation and engagement in risk-reducing behaviors.

The precise mechanisms underlying the U-shaped relationship between the TyG-i and the risk of developing diabetes in individuals with MASLD are still not fully understood. There is a notable positive association between higher TyG-i values and diabetes, likely linked to insulin resistance. Persistently high TG levels intensify liver fat accumulation, causing increased hepatic triglyceride production and worsening insulin sensitivity (32). This metabolic disturbance enhances lipogenesis, which further reduces insulin’s effectiveness in managing glucose metabolism and increases liver lipid accumulation, eventually damaging pancreatic beta-cell functionality (33). The build-up of lipid droplets within pancreatic islets disrupts glucose-induced insulin release, leading to diabetes onset (34, 35). Moreover, low TyG-i levels are similarly linked to an increased risk of developing diabetes. Interestingly, Black individuals exhibit unexpectedly low TG levels despite high insulin resistance or risk factors for diabetes, a phenomenon potentially explained by the inhibition of insulin-sensitive lipase activity and the consequent reduction in free fatty acid release from fat tissue due to hyperinsulinemia (36–39). Additionally, those with the PNPLA3 148M allele have lower triglyceride levels, increased insulin resistance, and greater vulnerability to diabetes (40). Pancreatic α-cells are vital for maintaining glucose, amino acid, and lipid balance (41). Malfunctions in these α-cells can result in hypoglycemia, which may indicate α-cell dysregulation, a core pathogenic process in diabetes development (42).

Our study is limited to a Japanese cohort, which may constrain the generalizability of our findings. It is essential to consider how genetic, dietary, and healthcare system differences might influence the observed associations between the TyG-i and the risk of T2D. Genetic predispositions play a significant role in metabolic regulation and the pathogenesis of diabetes. Ethnic variations in genes related to lipid metabolism and insulin sensitivity could modulate the relationship between the TyG-i and diabetes risk. For example, certain genetic polymorphisms prevalent in Asian populations may impact triglyceride levels and glucose homeostasis, potentially yielding risk profiles distinct from those in other ethnic groups (43). The traditional Japanese diet—characterized by high consumption of rice, fish, and soy products—imposes unique metabolic effects (44). Dietary patterns may interact with genetic factors to influence TyG-i levels and their associations with diabetes risk. Notably, omega-3 fatty acids abundant in fish have been documented to improve insulin sensitivity, which could affect metabolic outcomes within our cohort (45). Recognizing dietary variations across populations is critical when interpreting our results, as these differences could inform culturally tailored dietary recommendations for T2D prevention. Moreover, the Japanese healthcare system, with its emphasis on universal coverage and preventive care, may significantly impact the management of metabolic diseases (46, 47). Routine health screenings and early interventions are commonplace in Japan, potentially facilitating better management of conditions associated with the TyG-i, such as MASLD. This proactive healthcare approach may alter the observed relationship between TyG-i and diabetes risk, underscoring the need for caution when extrapolating our findings to populations with differing healthcare infrastructures. In light of these considerations, we stress the importance of further research involving diverse populations to validate the U-shaped association between the TyG-i and T2D risk. Future investigations should include a broad range of ethnic groups to examine the consistency and applicability of these findings across varied demographic and clinical contexts.

This study offers several notable advantages. Firstly, we identified a U-shaped association, allowing us to pinpoint the optimal inflection point where the TyG-i affects T2D risk. Secondly, we applied rigorous statistical adjustments to our results to reduce confounding factors, thereby enhancing their validity. Lastly, we employed a diverse array of sensitivity analyses to bolster the validity and reliability of our results, thereby enhancing the overall methodological strength of the study.

Despite these strengths, several limitations warrant consideration. Primarily, the research focused on a Japanese cohort, which may restrict the applicability of the results to other ethnic and geographic populations. Additionally, the definition of T2D employed in this study did not incorporate oral glucose tolerance testing, potentially resulting in an underestimation of T2D incidence. Secondly, although we have controlled for known confounding variables, the possibility remains that unmeasured factors—such as certain lifestyle habits or genetic predispositions—may have influenced the observed relationship between the TyG-i and T2D. Nevertheless, the calculated E-value of 2.32 exceeds the relative risk of 1.78 associated with both TyG-i and potential unknown confounders, implying that the effect of such unmeasured variables on this association is likely minimal. In future prospective investigations, we will strive to systematically collect and incorporate comprehensive information on lifestyle and genetic factors to further validate and strengthen our results. Thirdly, the absence of repeated measurements of the TyG-i precluded the assessment of the impact of longitudinal dynamic changes in TyG-i on T2D risk. The TyG-i, like other metabolic markers, is subject to fluctuations influenced by various factors, including dietary habits, physical activity, weight changes, and underlying metabolic conditions. These dynamic changes may significantly impact an individual’s risk profile for T2D. Incorporating analyses of TyG-i variability over time could enhance our understanding of its relationship with diabetes risk. In light of these considerations, we plan to design future studies to investigate the relationship between changes in the TyG-i and diabetes prognosis.

This research revealed a U-shaped relationship between the TyG-i and the risk of T2D in adults with MASLD. These results underscore that early intervention using the TyG-i may effectively improve the risk of T2D in patients with MASLD.