TyG index linked to obesity measures can predict hypertension risk in US adults

By Dr. Chinta SidharthanReviewed by Susha Cheriyedath, M.Sc.Jan 20 2025

Discover how a simple metabolic marker, the TyG index, combined with obesity measures, can help predict and manage hypertension risk in American adults—offering new hope for cardiovascular health.

Study: Association of the triglyceride glucose index with obesity indicators and hypertension in American adults based on NHANES 2013 to 2018. Image Credit: Naeblys / Shutterstock

In a recent study published in the journal Scientific Reports, a research team from China examined how the triglyceride-glucose (TyG) index, a marker of insulin resistance, relates to obesity measures and hypertension in American adults.

Using data from the National Health and Nutrition Examination Survey (NHANES) for the years 2013 to 2018, they explored the interplay between metabolic health and blood pressure and discussed potential tools for predicting cardiovascular risks and guiding hypertension prevention strategies.

Background

Hypertension is a leading global health concern, affecting over a billion people and contributing substantially to cardiovascular diseases such as heart attacks and strokes. Despite advances in healthcare, the early detection and effective management of hypertension remain challenging, with many cases undiagnosed or inadequately controlled.

Obesity, which is rapidly becoming a serious health concern worldwide, is closely linked to hypertension, as excess weight exacerbates metabolic dysfunction and insulin resistance. Moreover, traditional markers such as body mass index (BMI), waist circumference (WC), and waist-to-height ratio (WHtR) that are commonly used to assess obesity-related health risks have limited predictive power for hypertension. However, the TyG index, a surrogate marker for insulin resistance, has emerged as a promising tool for identifying individuals at risk of hypertension.

About the study

In the present cross-sectional study, the researchers analyzed data from 4,813 adults aged 18 and older who participated in the NHANES between 2013 and 2018. To ensure robust statistical analysis, participants with missing data on the TyG index, obesity indices, or relevant covariates were excluded.

The study calculated the TyG index using fasting glucose and triglyceride levels and combined it with obesity measures such as body mass index (TyG-BMI), waist circumference (TyG-WC), and waist-to-height ratio (TyG-WHtR). Hypertension was defined based on self-reports, a systolic blood pressure ≥ 140 mmHg, a diastolic blood pressure ≥ 90 mmHg, or the use of antihypertensive medications.

The researchers performed numerous statistical analyses on the data, including multivariate logistic regression models, to explore associations between the TyG indices and hypertension. Covariates such as age, sex, race, smoking status, alcohol consumption, cholesterol levels, and diabetes status were adjusted to account for confounding factors.

Additionally, subgroup analyses examined the consistency of the findings across demographic and health-related factors, such as gender and comorbidities. A threshold effect analysis was also performed using smooth curve fitting to identify potential non-linear relationships between the TyG indices and hypertension risk. The analysis identified threshold values, including TyG = 8.1, TyG-BMI = 247.94, TyG-WHtR = 5.32, and TyG-WC = 806.48.

The study also assessed the prognostic value of TyG indices for cardiovascular mortality, to establish the role of these markers in long-term hypertension risk assessment. The researchers took various measures to ensure data accuracy, including quality checks and adjustments for missing values, to comprehensively understand the TyG index's relevance to hypertension and related cardiovascular risks.

Key findings

The researchers observed that the TyG index and the combinations derived using obesity measures (TyG-BMI, TyG-WHtR, and TyG-WC) showed significant associations with hypertension risk. Higher quartiles of these indices were found to correlate with increased odds of hypertension, even after adjusting for demographic and clinical variables.

The participants in the highest quartile of the TyG index had a greater likelihood of hypertension compared to those in the lowest quartile. The relationships were more pronounced in specific subgroups, including women and individuals with pre-existing conditions such as coronary heart disease, heart failure, or heart failure.

However, the threshold effect analysis revealed a nonlinear association between the TyG index and hypertension. Below a threshold of 8.1, the TyG index showed a strong positive correlation with hypertension, but the relationship weakened or reversed beyond this point. Similar trends were observed for TyG-BMI, TyG-WHtR, and TyG-WC, indicating potential saturation effects at higher levels.

Furthermore, the Cox regression analysis highlighted the predictive value of the TyG index for cardiovascular mortality. For example, individuals in the second quartile of the TyG index demonstrated a significantly increased risk of cardiovascular death (HR = 4.93, 95% CI (1.29,18.80), P < 0.05).

These findings suggested that the TyG index and its related metrics are useful markers for identifying individuals at heightened risk of hypertension and cardiovascular events. In addition, the subgroup analyses highlighted the variations in these associations based on demographic and clinical characteristics, emphasizing the relevance of tailored risk assessment strategies.

Conclusions

Overall, the study demonstrated a strong association between the TyG index and its combinations with obesity measures and hypertension risk. These findings suggested that the TyG index serves as a valuable marker for identifying individuals at risk of hypertension and related cardiovascular outcomes.

Furthermore, the study emphasized the need for further research to explore causal relationships and the potential of TyG-based interventions in improving hypertension prevention and management strategies across diverse populations. However, the study acknowledged several limitations, such as its cross-sectional design, potential biases in self-reported data, and the underrepresentation of specific subpopulations.