Globally, cardiovascular disease (CVD) stands as a primary contributor to adult morbidity and mortality (1). In 2020, approximately 928,741 Americans succumbed to CVD, marking the highest annual death toll in recent years (2). In the US, the financial cost of CVD is enormous, totaling hundreds of billions of dollars annually (2).

Insulin resistance is a pathological condition characterized by reduced sensitivity and responsiveness to insulin’s actions, primarily affecting the metabolism of sugar, fat, and energy (IR) (3–5). The triglyceride glucose body mass index (TyG-BMI) and the hyperinsulinemic-euglycemic clamp technique have comparable specificity and sensitivity, according to recent research (6, 7), providing a simpler and more cost-effective means of assessing IR (8). The TyG-BMI index and diabetes and hypertension have a high relationship in several studies (9–11), especially noteworthy, a substantial relationship with the risk of heart failure in those with diabetes or pre-diabetes (12). Moreover, cohort studies including patients receiving peritoneal dialysis have demonstrated a separate association between elevated rates of CVD morbidity and all-cause death and the TyG-BMI index (13). Despite these results, the connection between CVD in general individuals and the TyG-BMI index is unknown.

Therefore, our study aimed to investigate the association between TyG-BMI and CVD in US adults using data from the 2011 to 2020 NHANES database. The potential of the TyG-BMI index as a novel biomarker for predicting cardiovascular disease in the general population warrants in-depth assessment. This could provide more precise information for the early prevention of cardiovascular disease.

To the best of our knowledge, this study is the first to explore the relationship between the prevalence of cardiovascular disease and the novel biomarker TyG-BMI. Using data from 11,016 participants in the NHANES database from 2011 to 2020, we found that TyG-BMI levels were significantly associated with an increased prevalence of cardiovascular disease, both as a continuous and categorical variable (P<0.05). This association persisted even after adjusting for basic demographic characteristics, lifestyle factors, and prior medical history. Furthermore, the robustness of our findings was confirmed through sensitivity analyses, which yielded results consistent with the weighted analyses (P<0.05).The TyG-BMI value has been validated by earlier studies as a trustworthy proxy indicator for IR (22–24). Given the public health implications of IR and CVD (25), recent investigations have linked IR to inflammation, oxidative stress, altered microRNA expression, disruptions in insulin metabolism signaling pathways, and mitochondrial dysfunction. These factors contribute to various cardiovascular risks, including lipid disorders, coronary artery disease, hypertension, vascular calcification, heart failure, and cardiomyopathy (26–29). The study by Yamazoe independently links the prevalence of coronary artery calcification and IR (29). In contrast, a sizable prospective cohort research which followed up for 5.5 years, shows that in 17.9% of participants with diabetes, it is linked to an increase peripheral in artery disease and heart failure (30). Moreover, a well-established link exists between IR and atherosclerosis (31), a key CVD risk factor, with the TyG-BMI index as an effective CVD predictor. A study by Yi Hu indicates an L-shaped association between TyG-BMI and all-cause mortality in patients with atrial fibrillation admitted to the ICU (15). According to surveys, individuals with heart failure can also utilize the TyG-BMI index to predict their one-year all-cause mortality (32). Additionally, a higher TyG-BMI index has been linked to prehypertension in Chinese adults (33). In our research, according to the fully adjusted model of our analysis, compared to individuals in the lowest tertile, those in the greatest TyG-BMI tertile exhibited a substantially greater likelihood of cardiovascular disease (OR=1.380; 95%CI=1.080,1.763). The adverse effects of IR on cardiovascular health are highlighted by this research. Moreover, interaction studies indicate that the positive association between the TyG-BMI value and CVD was not substantially affected by gender, race, education level, PIR, smoking, hypertension, hyperlipidemia, and diabetes (all P for interaction > 0.05). The ROC curves showed that the model with the addition of TyG-BMI had an incremental effect on predicting the prevalence of cardiovascular disease. However, A retrospective study indicated that a high TyG-BMI was positively associated with the incidence of major adverse cardiac and cerebrovascular events (MACCEs). it did not enhance the predictive power for MACCEs in elderly patients and female patients (34). The discrepancy between the results of the two studies may arise from differences in the populations and outcome variables. Our study focused on the general U.S. adult participants, and CVD defined using data from the NHANES database questionnaire. In contrast, the retrospective study examined patients in Henan Province, China, who underwent Percutaneous coronary intervention (PCI) and were treated with drug-eluting stent (DES), with the outcome defined as a composite of MACCEs at follow-up.

However, in our subgroup analysis of this study, age, and hyperlipidemia significantly influence the association between TyG-BMI and the prevalence of cardiovascular disease (P for interaction <0.05). In younger individuals, the TyG-BMI may better reflect cardiovascular risk due to their heightened sensitivity to metabolic changes (35). Conversely, in older adults, the presence of more significant cardiovascular risk factors may attenuate the impact of TyG-BMI. Hyperlipidemia, an independent risk factor for CVD, is closely associated with IR and metabolic syndrome (36), thereby enhancing the predictive power of TyG-BMI. Therefore, additional studies are required to validate these associations and investigate the underlying mechanisms. The advantages of our study include the use of the NHANES database, known for its large sample size and representativeness, and adjustments for some confounding covariates, enhancing the reliability of our results. We also performed subgroup analyses and sensitivity analyses to further investigate the robustness of the association between TyG-BMI index and cardiovascular disease (CVD). Nevertheless, there are limitations to acknowledge. Firstly, Because of our cross-sectional methodology, it is not feasible to establish a causal association between the TyG-BMI values and the prevalence of cardiovascular disease. Secondly, participants with missing TyG-BMI data were excluded to maintain data completeness. The Supplementary Table presents a comparison of baseline characteristics between the included and excluded groups. Additionally, weighted and sensitivity analyses were conducted to mitigate potential bias. Despite these efforts, the exclusion of these participants may still impact the generalizability of our findings. Thirdly, residual confounding cannot be ruled out despite adjusting for multiple covariates, including potential medication effects or other comorbidities. Finally, certain variables potentially relevant to CVD were unavailable in the NHANES dataset, possibly impacting result accuracy. Therefore, to support and build on our findings, further extensive prospective studies involving several centers should be conducted.

The TyG-BMI provides a practical approach to assessing cardiovascular disease risk in adults. Its simplicity and accessibility highlight its potential clinical value. However, prospective studies are necessary to validate its predictive reliability and explore broader applications.

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/ Supplementary Material .

The studies involving humans were approved by NCHS Ethics Review Board, the ERB is affiliated with the National Center for Health Statistics. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study. Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.

RW: Software, Methodology, Investigation, Data curation, Conceptualization, Writing – review & editing, Writing – original draft. XC: Writing – review & editing, Supervision, Methodology, Funding acquisition. WT: Writing – review & editing, Supervision, Methodology.