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Association of Cumulative Exposure and Dynamic Trajectories of C-Reactive Protein-Triglyceride-Glucose Index with Incident Cardiovascular-Kidney-Metabolic Syndrome Stage 4: A Nationwide Cohort Study from CHARLS

This nationwide cohort study of Chinese middle-aged and older adults demonstrates that both higher cumulative exposure to and persistently high dynamic trajectories of the C-reactive protein-triglyceride-glucose index (CTI) are significantly associated with an increased risk of developing Cardiovascular-Kidney-Metabolic (CKM) syndrome stage 4.

Original authors: Wenting Zhao, Yaoweilai Zhao, Wenxin Li, Yayi Mao, Jiaxin Li, Feiyan Li, Nan Mao

Published 2026-07-15

📖 5 min read🧠 Deep dive

CC BY 4.0

Original authors: Wenting Zhao, Yaoweilai Zhao, Wenxin Li, Yayi Mao, Jiaxin Li, Feiyan Li, Nan Mao

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 is a bustling city. Sometimes, the roads get clogged with traffic (metabolic issues like high sugar and fat), and other times, the air gets smoggy with pollution (inflammation). Usually, we check the traffic and the smog on just one single day to see if the city is in trouble. But what if the real danger isn't just a bad day, but a whole decade of bad days?

That's exactly what a team of researchers from Chengdu Medical College investigated using data from over 4,000 middle-aged and older adults in China. They wanted to see if looking at the "long-term weather report" of your body's health could predict a specific, serious condition called CKM Stage 4. Think of CKM Stage 4 as the city's "Code Red" emergency: it means you have established heart disease or have already had a stroke.

The New "City Health Meter"

The scientists used a special tool called the C-reactive protein-triglyceride-glucose index, or CTI for short.

The Ingredients: They mixed three things: a measure of inflammation (CRP), a measure of fat in the blood (triglycerides), and a measure of sugar (glucose).
The Twist: Instead of just checking this meter once, they looked at two snapshots of people's health: one from 2011 and another from 2015.

They created two ways to read this data:

The Cumulative Score (CuCTI): Imagine adding up how much "smog and traffic" a person had over those four years. It's like calculating the total miles of pollution a city has endured, not just the pollution on a single Tuesday.
The Trajectory Map: They used a computer to group people into three "driving styles" based on how their CTI changed over time:
- The Low-Stable Drivers: People who kept their levels low and steady.
- The Moderate-Increasing Drivers: People whose levels started okay but slowly crept up.
- The High-Stable Drivers: People who started high and stayed high.

What They Found

The results were clear: The more pollution and traffic you endure over time, the higher your risk of hitting "Code Red."

The Cumulative Effect: When they looked at the total "smog miles" (CuCTI), they found a straight-line relationship. The higher the score, the higher the risk.
- People in the middle group had a 15% higher chance of developing CKM Stage 4 compared to the lowest group.
- People in the highest group had a 37% higher chance.
- The paper explicitly states this is a linear relationship, meaning the risk goes up steadily as the score goes up, with no "safe threshold" where the risk suddenly stops increasing.
The Trajectory Effect: The "driving style" mattered, too.
- Compared to the "Low-Stable Drivers," the "High-Stable Drivers" (those who stayed high) had a 44% higher risk.
- The "Moderate-Increasing Drivers" had a 19% higher risk.
- This suggests that staying in a high-risk zone is just as dangerous as slowly climbing into it.

Why This Matters (And What It's Not)

The researchers tested if this new "Cumulative Score" was better than just looking at the old, single-day checks (like just checking CRP or just checking the sugar/fat mix).

The Verdict: The cumulative score suggested it was better at predicting the future. It added a little bit of extra accuracy to the prediction models, helping to spot people who might be at risk even if their single-day check looked okay.
What It's Not: The paper does not claim this is a magic cure or a guaranteed crystal ball. The predictive power was "modest," meaning it's a helpful tool, but not a perfect one. It also does not prove that the pollution caused the heart trouble; it only shows they are strongly linked.

The "Who" and the "When"

The study looked at specific groups and found some interesting quirks:

Age: The link between the "smog score" and heart trouble was very strong for people aged 45 to 69. However, for people over 70, the link got weaker, and for those over 80, the study couldn't find a clear connection. The authors suggest this might be because the oldest group is so small, or because the very oldest people are "survivors" who are naturally tougher, making the pollution score less obvious as a predictor.
Education: Interestingly, the link seemed stronger for people with more education. The authors suspect this isn't because education makes you sicker, but because educated people might be better at reporting their health issues to doctors, so the "Code Red" events were caught more often.

The Bottom Line

This study suggests that for middle-aged and older adults, keeping an eye on your long-term "inflammation and metabolism" score is crucial. If your levels are high and stay high, or if they keep creeping up, your risk of reaching that serious "Code Red" stage of heart and kidney disease goes up significantly.

While the study didn't prove why this happens (that needs more lab work), it did show that looking at the whole journey of your health, rather than just a single snapshot, gives a clearer picture of who is at risk. It's a reminder that in the city of your body, it's not just the bad day that matters—it's the bad decade.

Technical Summary: Association of Cumulative Exposure and Dynamic Trajectories of C-Reactive Protein-Triglyceride-Glucose Index with Incident Cardiovascular-Kidney-Metabolic Syndrome Stage 4

Problem Statement
Cardiovascular-kidney-metabolic (CKM) syndrome represents a systemic clinical condition driven by the interplay of cardiovascular disease (CVD), chronic kidney disease (CKD), and metabolic risk factors. The American Heart Association (AHA) stratifies this syndrome into five stages, with Stage 4 representing established clinical CVD or stroke, the most severe form. While the C-reactive protein–triglyceride–glucose index (CTI) has emerged as a novel biomarker integrating insulin resistance (via the TyG index) and systemic inflammation (via CRP), existing research is limited. Most prior studies rely on single baseline measurements, failing to account for the long-term cumulative exposure or the dynamic temporal evolution of CTI levels. Consequently, the relationship between longitudinal CTI changes and the incidence of CKM Stage 4 remains unexplored, particularly in middle-aged and older Chinese populations.

Methodology
This study utilized a prospective, nationwide cohort design based on data from the China Health and Retirement Longitudinal Study (CHARLS).

Study Population: From an initial recruitment of 17,708 participants in 2011, the final analytic sample comprised 4,452 individuals aged ≥45 years. Exclusion criteria included missing biochemical data (fasting triglycerides, glucose, CRP) at Waves 1 (2011) and 3 (2015), pre-existing CKM Stage 4 at baseline, and non-fasting blood collection.
Exposure Assessment:
- CTI Calculation: Calculated as $Ln[TG \times Glucose / 2] + CRP$ (using specific unit conversions implied by the TyG formula context).
- Cumulative CTI (CuCTI): Defined as the product of the mean CTI value across 2011 and 2015 and the time interval (4 years): $CuCTI = (CTI_{2011} + CTI_{2015}) / 2 \times 4$ .
- Dynamic Trajectories: K-means clustering was applied to CTI measurements from 2011 and 2015 to categorize participants into three distinct trajectory groups: Cluster 1 (persistently high-stable), Cluster 2 (persistently low-stable), and Cluster 3 (moderate-increasing).
Outcome: The primary outcome was incident CKM Stage 4, defined as self-reported physician-diagnosed heart disease or stroke occurring between 2011 and 2020.
Statistical Analysis: Cox proportional hazards regression models (adjusted for age, sex, socioeconomic factors, and lifestyle) were used to estimate hazard ratios (HRs). Analyses included Kaplan–Meier survival curves, Restricted Cubic Splines (RCS) for dose-response relationships, and Receiver Operating Characteristic (ROC) curves with Net Reclassification Improvement (NRI) and Integrated Discrimination Improvement (IDI) indices to evaluate predictive performance. Sensitivity analyses included logistic regression and exclusion of 2020 follow-up data to mitigate pandemic-related bias.

Key Results

Cumulative Exposure: Over a 9-year follow-up, 1,183 participants (26.6%) progressed to CKM Stage 4. A significant positive association was observed between CuCTI levels and CKM Stage 4 risk. In the fully adjusted model, participants in the highest CuCTI tertile (Q3) had a 37% higher risk compared to the lowest tertile (Q1) (HR = 1.37, 95% CI: 1.24–1.51, $P < 0.001$ ). RCS analysis confirmed a linear dose-response relationship without a clear threshold.
Dynamic Trajectories: K-means clustering identified three distinct patterns. Compared to the persistently low-stable group (Cluster 2), the persistently high-stable group (Cluster 1) exhibited a 44% increased risk of CKM Stage 4 (HR = 1.44, 95% CI: 1.29–1.60, $P < 0.001$ ), while the moderate-increasing group (Cluster 3) showed a 19% increased risk (HR = 1.19, 95% CI: 1.08–1.30, $P = 0.001$ ).
Predictive Performance: CuCTI demonstrated superior predictive accuracy compared to single-time-point measurements of CRP, TyG, or baseline CTI. At 9 years, adding CuCTI to a base model yielded an NRI of 12.06% and an IDI of 0.60% ( $P < 0.001$ ), significantly outperforming the addition of single-time-point CTI.
Subgroup and Sensitivity: The association remained robust across most subgroups. However, significant heterogeneity was observed by age, education, and marital status. The association attenuated in participants aged ≥70 and lost significance in those ≥80, potentially due to "inflammaging," survivor bias, or reduced statistical power. Sensitivity analyses confirmed the stability of the findings.

Significance and Claims
The authors claim that this study provides the first evidence linking both cumulative exposure and dynamic trajectories of CTI to the risk of incident CKM Stage 4 in a large, nationally representative Chinese cohort.

Clinical Utility: The study posits that CuCTI, derived from routinely measured biochemical markers (CRP, TG, FPG), serves as a cost-effective and accessible tool for early risk stratification. It offers incremental prognostic value beyond single-time-point measurements, particularly for identifying individuals with persistently high inflammatory-metabolic burdens.
Methodological Contribution: By employing both cumulative exposure modeling and trajectory clustering, the study highlights that long-term exposure patterns (specifically persistently high levels) are critical determinants of disease progression, suggesting that static measurements may underestimate risk in individuals with sustained metabolic dysfunction.
Limitations and Cautions: The authors modestly acknowledge limitations, including the reliance on self-reported outcomes, the restriction of biomarker data to only two time points (limiting the resolution of trajectory analysis), and the observational nature of the study which precludes causal inference. They emphasize that the findings require external validation in diverse populations and that the attenuation of risk in the oldest age group warrants further investigation into mechanisms like inflammaging and survivor bias.

In conclusion, the paper asserts that CuCTI and its dynamic trajectories are independent predictors of CKM Stage 4, supporting their potential use in refining risk stratification strategies for middle-aged and older adults, particularly in resource-constrained settings.