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Association of TyG and TyG-BMI with Hypertension in an Elderly Population: A Retrospective Cohort Study

This retrospective cohort study of 3,792 elderly participants in Wuhan demonstrates that elevated TyG and TyG-BMI levels are independently associated with an increased risk of hypertension, with TyG-BMI showing superior predictive performance and significant temporal causal effects on blood pressure compared to TyG alone.

Original authors: Sheng Zhou, Jinqiao Nie, Changfeng Li

Published 2026-06-24

📖 5 min read🧠 Deep dive

CC BY 4.0

Original authors: Sheng Zhou, Jinqiao Nie, Changfeng Li

Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). ✨ This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

Imagine your body as a busy city. In this city, Insulin is the traffic controller trying to keep sugar (glucose) and fat (triglycerides) moving smoothly through the streets. When the traffic controller gets overwhelmed and the cars start piling up, the city gets clogged. This clogging is called Insulin Resistance, and it's a major reason why the city's pressure (your blood pressure) starts to rise, leading to Hypertension (high blood pressure).

For a long time, doctors needed a very expensive, complicated machine to check if the traffic controller was struggling. This new study, however, looked at two simpler "dashboard gauges" that anyone can check using a standard blood test and a scale:

The TyG Gauge: This measures the pile-up of sugar and fat in your blood.
The TyG-BMI Gauge: This is the TyG gauge plus a measurement of how much "cargo" (body weight) the city is carrying.

Here is what the researchers found by looking at the health records of nearly 4,000 elderly people in Wuhan, China, over a six-year period (from 2012 to 2018).

1. The "Heavy Cargo" Gauge is Better at Predicting Trouble

The researchers wanted to know: Which gauge is better at predicting if the city's pressure will get too high?

They compared the two gauges using a "prediction score" (called an AUC).

The TyG gauge (sugar + fat only) was okay, but not great. It missed a lot of people who were about to get high blood pressure.
The TyG-BMI gauge (sugar + fat + body weight) was the clear winner. It was much better at spotting who was at risk.

The Analogy: Think of TyG as checking if the roads are clogged. TyG-BMI is checking if the roads are clogged and if the trucks carrying the cargo are overloaded. The study found that knowing the trucks are overloaded (high BMI) combined with clogged roads gives you a much clearer warning that the city is about to break down.

2. More Clogging Means More Pressure (The Dose-Response)

The study looked at people in four groups, from "least clogged" to "most clogged."

The Finding: As the levels of these gauges went up, the risk of high blood pressure went up, too. It wasn't a sudden jump; it was a steady climb. The more clogged the system, the higher the pressure.
The "Heavy Cargo" Difference: People with the highest TyG-BMI scores were three times more likely to have high blood pressure compared to those with the lowest scores. People with the highest TyG scores were about 1.6 times more likely. This confirms that adding body weight to the mix makes the warning signal much stronger.

3. Men and Women React Differently

The researchers noticed that the "traffic patterns" looked different for men and women.

For Women: Both gauges showed a straight, steady line. As the numbers went up, the risk went up evenly.
For Men: The TyG gauge (sugar + fat) was a bit wobbly and unpredictable (non-linear). However, the TyG-BMI gauge (sugar + fat + weight) remained a straight, reliable line.
The Takeaway: For men, just looking at sugar and fat wasn't enough to predict the pressure; you had to include body weight to get a clear picture. For women, both worked, but the "Heavy Cargo" gauge was still the strongest predictor.

4. Who Caused What? (The Time Travel Test)

This is the most important part of the study. Many studies just take a snapshot in time, but this one looked at the same people six years apart. They used a special statistical tool to see which came first: the clogged roads or the high pressure.

The Result: The study found that high TyG and TyG-BMI levels in 2012 caused higher blood pressure in 2018.
The Reverse? High blood pressure in 2012 did not cause the TyG or TyG-BMI levels to get worse in 2018.

The Analogy: It's like seeing smoke and fire. The study proved that the smoke (Insulin Resistance/TyG) came first and caused the fire (High Blood Pressure). The fire didn't create the smoke. This suggests that if you fix the "clogged roads" early, you might prevent the "pressure" from rising later.

Summary

This study looked at elderly people and found that a simple combination of blood sugar, blood fat, and body weight (TyG-BMI) is a powerful, early warning system for high blood pressure.

It is better at predicting risk than looking at blood sugar and fat alone.
It works consistently over time, showing that these metabolic issues happen before blood pressure rises.
It is especially important for men, where body weight is a crucial part of the warning sign.

The researchers suggest that because this "dashboard gauge" is simple and cheap to calculate, it could be a great tool for doctors to spot elderly people who are at risk of high blood pressure before it becomes a serious problem.

Technical Summary: Association of TyG and TyG-BMI with Hypertension in an Elderly Population

Problem Statement
Hypertension remains a pervasive chronic non-communicable disease with a rising global prevalence, particularly among aging populations. Insulin resistance (IR) is a well-established pathophysiological mechanism contributing to hypertension, yet the "gold standard" for assessing IR (hyperinsulinemic-euglycemic clamp) is too complex and costly for large-scale epidemiological use. While the Triglyceride-Glucose index (TyG) and its derivative, the Triglyceride-Glucose-Body Mass Index (TyG-BMI), have emerged as simple surrogate markers for IR, existing evidence regarding their predictive performance and causal relationships with hypertension in the elderly is limited. Most prior studies are cross-sectional, preventing the inference of temporal causality, and few have simultaneously compared TyG and TyG-BMI while accounting for sex-specific differences in dose-response patterns.

Methodology
This study employed a retrospective cohort design utilizing health examination data from the "National Basic Public Health Service Project" in a district of Wuhan, China, spanning 2012 to 2018.

Population: The baseline cohort included 3,792 participants aged 65 and older (2012). Of the normotensive subgroup at baseline, 1,341 participants were successfully followed up in 2018.
Variables: TyG was calculated as $Ln[TG(mg/dL) \times FPG(mg/dL)/2]$ , and TyG-BMI was derived as $TyG \times BMI$ . Hypertension was defined according to the 2024 Chinese Guidelines (SBP ≥140 mmHg and/or DBP ≥90 mmHg, or self-reported history/medication use).
Statistical Analysis:
- Predictive Performance: Receiver Operating Characteristic (ROC) curves were used to compare the Area Under the Curve (AUC), sensitivity, and specificity of TyG and TyG-BMI.
- Risk Association: Multivariate logistic regression (adjusted for age, gender, education, marital status, exercise, smoking, and biochemical markers) analyzed the association between quartiles of TyG/TyG-BMI and hypertension risk.
- Dose-Response: Restricted cubic spline (RCS) models were fitted to examine linear and non-linear relationships, with stratification by sex.
- Causal Inference: Autoregressive cross-lagged models (ARCLM) were constructed using baseline (2012) and follow-up (2018) data to examine temporal causal directions between TyG/TyG-BMI and blood pressure (SBP/DBP).

Key Contributions

Comparative Predictive Efficacy: The study provides a direct comparison of TyG and TyG-BMI in an elderly cohort, demonstrating that the inclusion of BMI significantly enhances predictive capability.
Sex-Specific Dose-Response Patterns: It identifies distinct non-linear and linear dose-response relationships between these indices and hypertension, specifically highlighting a non-linear association for TyG in males versus linear associations in females.
Longitudinal Causal Evidence: By utilizing ARCLM, the study moves beyond cross-sectional correlations to provide temporal evidence suggesting that elevated TyG and TyG-BMI precede increases in blood pressure, rather than the reverse.

Results

Predictive Performance: TyG-BMI demonstrated superior predictive ability for hypertension compared to TyG. The AUC for TyG-BMI was 0.613 (Sensitivity: 0.617, Specificity: 0.560), compared to an AUC of 0.552 for TyG (Sensitivity: 0.409, Specificity: 0.699).
Risk Association: After adjusting for confounders, both indices showed a significant positive trend with hypertension risk ( $P_{trend} \le 0.001$ ). Compared to the lowest quartile (Q1), the highest quartile (Q4) of TyG was associated with an Odds Ratio (OR) of 1.602 (95% CI: 1.140–2.251), while TyG-BMI showed a stronger association with an OR of 3.148 (95% CI: 2.229–4.445).
Sex Stratification:
- Males: TyG-BMI showed a significant positive trend with hypertension risk. TyG showed a significant trend only in the Q3 group.
- Females: Both TyG and TyG-BMI showed significant positive trends across quartiles. The highest TyG-BMI quartile in females carried the highest risk (OR = 3.986).
Dose-Response Relationships:
- Overall: Linear relationships were observed for both TyG and TyG-BMI against hypertension.
- Males: A non-linear relationship was found between TyG and hypertension ( $P_{non-linear}=0.040$ ), while TyG-BMI remained linear.
- Females: Both indices exhibited linear dose-response relationships.
Temporal Causality: Cross-lagged analysis revealed that baseline TyG significantly influenced follow-up SBP ( $\beta=1.452, P=0.031$ ), and baseline TyG-BMI significantly influenced both follow-up SBP ( $\beta=0.049, P<0.001$ ) and DBP ( $\beta=0.043, P<0.001$ ). Baseline blood pressure did not significantly predict follow-up TyG or TyG-BMI.

Significance and Claims
The authors conclude that elevated TyG and TyG-BMI levels are independently associated with an increased risk of hypertension in the elderly, characterized by linear dose-response relationships overall, with specific non-linear nuances in males. The study claims that TyG-BMI offers better predictive performance than TyG alone and exhibits significant temporal causal effects on both systolic and diastolic blood pressure.

Based on these findings, the paper posits that TyG-BMI may serve as a simple, effective indicator for the early screening of hypertension in the elderly. It suggests that this index could provide a reference for primary public health practice, particularly in resource-limited settings where complex insulin resistance testing is not feasible, thereby aiding in the development of intervention strategies for this high-risk demographic. The authors acknowledge limitations regarding the single urban population source and the 6-year follow-up duration but maintain that their longitudinal approach provides robust epidemiological evidence for the field.