Association of Modified Cardiometabolic Index with Cardiovascular-Kidney-Metabolic Syndrome Staging, All-Cause Mortality, and Cardiovascular Mortality: A Population-Based Cohort Study

This population-based cohort study demonstrates that the modified cardiometabolic index (MCMI) is an independent, superior predictor of cardiovascular-kidney-metabolic syndrome progression and mortality compared to traditional indices, with a specific threshold of >3.5 indicating elevated risk and diabetes mediating nearly half of its association with cardiovascular death.

The Gist

The Big Picture: The "Triple Threat" and a New Warning Light

Imagine your body is a complex city. In this city, three major systems are constantly working together: the Heart (the power plant), the Kidneys (the water treatment plant), and the Metabolism (the fuel supply).

For a long time, doctors treated problems in these three areas separately. But recently, experts realized they are deeply connected. If the fuel supply gets messy (diabetes/obesity), it clogs the pipes (heart disease) and poisons the water treatment (kidney disease). The American Heart Association gave this messy connection a new name: CKM Syndrome (Cardiovascular-Kidney-Metabolic).

This study asks a simple question: "Can we build a single 'dashboard gauge' to tell us how badly this city is struggling?"

The researchers tested a new gauge called the Modified Cardiometabolic Index (MCMI). Think of MCMI as a "Super-Health Score" that combines your waist size, blood sugar, and cholesterol into one number. They wanted to see if this new score was better at predicting who might get sick or die than the old, standard scores doctors use today.

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The Experiment: A 20-Year Time Travel

The researchers didn't just look at one day; they looked at data from over 5,000 Americans tracked over 10+ years. They used a massive, national database (NHANES) that acts like a giant, high-resolution map of the country's health.

They sorted these people into "Stages" of the CKM syndrome, like levels in a video game:

• Level 0: Healthy city.
• Level 1: A few potholes (overweight, slightly high blood sugar).
• Level 2: Traffic jams (diabetes, high blood pressure).
• Level 3: Major gridlock (early heart or kidney damage).
• Level 4: City-wide blackout (clinical heart disease or heart failure).

They then watched to see who passed away from any cause, and specifically who passed away from heart or blood vessel issues.

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The Findings: What the "Super-Health Score" Revealed

1. The "Decelerating" Climb

As people moved up the "levels" of sickness (from Level 0 to Level 4), their MCMI score went up. However, it wasn't a straight line.

• The Analogy: Imagine climbing a hill. At the bottom, the hill is steep and easy to climb fast. As you get higher, the hill gets flatter and harder to climb. The MCMI score rose quickly at first as health declined, but then the rise slowed down at the very top stages. This tells us that MCMI is great at catching people early before they hit the worst stages.

2. The "Danger Zone" Threshold (The 3.5 Mark)

The study found a specific "tipping point" for the MCMI score.

• The Analogy: Think of the MCMI score like the water level in a bathtub.
  • Below 3.5: The water is safe. You are in the "Green Zone."
  • Above 3.5: The water hits the overflow drain. The risk of dying from any cause jumps significantly.
• The Result: If your MCMI score was higher than 3.5, your risk of death increased by about 41% compared to those with lower scores. This gives doctors a clear "Red Light" number to watch for.

3. The "Sugar" Connection (Mediation)

When looking specifically at heart-related deaths, the researchers found something fascinating. The MCMI score didn't kill people directly; it killed them through diabetes.

• The Analogy: Imagine MCMI is a match, and Diabetes is the fire. The match (MCMI) lights the fire (Diabetes), and the fire causes the damage (Heart Death).
• The Stat: About 45% of the heart deaths caused by a high MCMI score happened because the high score led to diabetes first. If you control the diabetes, you cut the path of the damage.

4. The "Hidden Danger" of Competing Risks

This is a tricky but important part. Sometimes, people with heart risks die from other things first (like cancer or accidents). If you don't account for this, you might think the heart risk is lower than it actually is.

• The Analogy: Imagine you are waiting for a bus (Heart Attack). But sometimes, a tree falls on you (Accident) before the bus arrives. If you only count the people who died waiting for the bus, you miss the people who would have died waiting if the tree hadn't fallen.
• The Result: When the researchers used a special math model to account for these "tree falls," the danger of the MCMI score skyrocketed. The risk of heart death for the highest group was 3.25 times higher than the lowest group. This means the danger was being underestimated by traditional methods.

5. The New Gauge vs. The Old Gauges

Finally, they compared the new MCMI score against two older, famous scores (TyG and CMI).

• The Verdict: The new MCMI score was the clear winner. It was more accurate at predicting who would die sooner. It's like upgrading from a 1990s GPS to a modern, real-time satellite navigation system.

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Why This Matters to You

1. It's a Better Early Warning System: The MCMI score combines your waistline, blood sugar, and cholesterol into one number. If that number is above 3.5, it's a loud siren telling you to take action.
2. Focus on Diabetes: If your score is high, the study suggests that preventing or managing diabetes is the most critical step to saving your heart.
3. It's Not Just About the Heart: This score helps predict the health of your whole "city" (heart, kidneys, and metabolism) together, not just one part.

The Bottom Line

This study introduces a new, sharper tool for doctors. Instead of looking at your blood sugar, your waist, and your cholesterol separately, the MCMI looks at them as a team. If that team's score is high (above 3.5), it's a strong signal that the "city" is in trouble, and immediate intervention is needed to prevent a total system failure.

Note: This study is a preprint, meaning it has been written by scientists but hasn't been fully checked by other experts yet. However, the methods are rigorous, and the findings offer a promising new direction for how we manage heart and metabolic health.

Technical Summary

1. Problem Statement

The study addresses the growing global burden of the interrelated conditions of Type 2 diabetes, cardiovascular disease (CVD), and chronic kidney disease (CKD). In October 2023, the American Heart Association (AHA) introduced the Cardiovascular-Kidney-Metabolic (CKM) Syndrome to describe the pathophysiological interplay of these conditions.

• The Gap: While the Modified Cardiometabolic Index (MCMI) has shown predictive value for single metabolic or cardiovascular endpoints, its prognostic significance in the complex, multimorbid population of patients with CKM syndrome remains unexplored.
• Objective: To investigate the association between MCMI and CKM syndrome staging, all-cause mortality, and cardiovascular-specific mortality, and to compare its predictive performance against traditional indices (CMI and TyG).

2. Methodology

Study Design & Population:

• Design: Prospective cohort study using data from the National Health and Nutrition Examination Survey (NHANES) cycles 1999–2018.
• Sample: 5,189 adults (aged 30–79) diagnosed with CKM syndrome (Stages 0–4).
• Follow-up: Median of 10.4 years (up to 20.75 years), with mortality data linked to the National Death Index (NDI) through December 31, 2019.
• Exclusions: Pregnant women, those with missing covariate data (e.g., statin use, poverty-to-income ratio), and those with missing MCMI components.

Key Variables & Definitions:

• Exposure: Modified Cardiometabolic Index (MCMI) calculated as:
  $$\ln\left[\frac{TG \times FPG}{HDL-C}\right] \times \frac{WC}{Height}$$
  (Where TG=Triglycerides, FPG=Fasting Plasma Glucose, HDL-C=High-Density Lipoprotein, WC=Waist Circumference).
• Outcome 1 (CKM Staging): Defined per AHA guidelines (Stage 0: No risk; Stage 1: Obesity/Adipose dysfunction; Stage 2: Metabolic risk/CKD; Stage 3: Subclinical CVD/High risk; Stage 4: Clinical CVD).
• Outcomes 2 & 3 (Mortality): All-cause mortality and Cardiovascular mortality (ICD-10 codes I00-I09, I11, I13, I20-I51, I60-I69).
• Covariates: Age, sex, race, poverty-to-income ratio (PIR), smoking, alcohol, statin/antihypertensive use, hypertension, diabetes, UACR, and eGFR.

Statistical Analysis:

• Associations: Polynomial/ordinal logistic regression for CKM staging; Weighted Cox proportional hazards models for mortality.
• Non-linearity: Restricted Cubic Spline (RCS) regression to identify dose-response relationships and thresholds.
• Mediation: Mediation analysis to quantify the role of diabetes in the MCMI-cardiovascular mortality pathway.
• Robustness:
  • Competing Risks: Fine-Gray subdistribution hazard models to account for non-cardiovascular death.
  • Sensitivity: Exclusion of specific populations (e.g., <1 year follow-up, cancer, severe renal insufficiency), subgroup analyses, and E-value calculation to assess unmeasured confounding.
  • Predictive Performance: Comparison of C-index and Area Under the Curve (AUC) between MCMI, CMI, and TyG.

3. Key Results

A. Association with CKM Staging:

• MCMI exhibited a "decelerating increase" non-linear positive association with CKM staging.
• Ordinal Logistic Regression: MCMI is an independent risk factor for higher CKM stages (Adjusted OR = 3.90, 95% CI: 3.38–4.50).
• Mean MCMI increased from Stage 0 (2.19) to Stage 3 (3.75) but slightly decreased in Stage 4 (3.56), suggesting a plateau or stabilization in advanced clinical CVD stages.

B. Association with Mortality:

• All-Cause Mortality:
  • Threshold Effect: A significant risk increase was observed when MCMI > 3.5.
  • Quartile Analysis: The highest quartile (Q4) had a 41.2% higher risk compared to Q1 (HR = 1.412, 95% CI: 1.046–1.907).
  • Continuous Analysis: Each 1-unit increase in MCMI was associated with a 22% increased risk (HR = 1.22, 95% CI: 1.05–1.40) after full adjustment.
• Cardiovascular Mortality:
  • Mediation by Diabetes: In fully adjusted models, the direct association was non-significant. However, diabetes mediated 45.5% of the total effect. When diabetes was excluded from covariates, MCMI showed a significant positive association (HR = 1.374).
  • Non-linear Pattern: RCS analysis revealed a fluctuating "rise-fall-rise" pattern.
    • Low-risk interval: MCMI 3.0–3.5.
    • High-risk intervals: MCMI < 2.5 or > 4.0.
• Competing Risks: The Fine-Gray model revealed that ignoring competing risks (non-CVD death) severely underestimates the true effect. The subdistribution hazard ratio (sHR) for Q4 vs. Q1 was 3.25 (P < 0.001), significantly higher than standard Cox estimates.

C. Predictive Performance:

• MCMI demonstrated superior predictive accuracy for both all-cause and cardiovascular mortality compared to the traditional Cardiometabolic Index (CMI) and Triglyceride-Glucose (TyG) index.
• The mean AUC difference between MCMI and TyG was 0.0243 in favor of MCMI.

D. Robustness:

• Results remained consistent across sensitivity analyses (excluding cancer, severe renal failure, short follow-up) and subgroups (age, sex, race).
• E-values (1.73 for point estimate) indicated that an unmeasured confounder would need a strong association (HR ≥ 1.73) with both MCMI and mortality to fully explain away the observed results, confirming the findings' robustness.

4. Key Contributions

1. First Application to CKM Syndrome: This is the first study to systematically evaluate MCMI across the entire spectrum of the newly defined CKM syndrome (Stages 0–4).
2. Identification of Clinical Thresholds: Established MCMI > 3.5 as a critical cut-off for all-cause mortality risk and identified specific risk intervals (3.0–3.5 as low risk) for cardiovascular mortality.
3. Elucidation of Mechanism: Quantified that diabetes mediates nearly half (45.5%) of the cardiovascular mortality risk associated with elevated MCMI, clarifying the "MCMI → Diabetes → CVD Death" pathway.
4. Methodological Rigor: Utilized Competing Risk Models to correct for the underestimation of CVD mortality risk, revealing a much stronger dose-response relationship (sHR = 3.25) than traditional Cox models suggested.
5. Superior Predictive Tool: Demonstrated that MCMI outperforms established metabolic indices (CMI, TyG) in risk stratification for CKM patients.

5. Significance and Clinical Implications

• Risk Stratification: MCMI serves as a robust, independent predictor for CKM progression and mortality. The identified threshold of 3.5 provides a potential clinical target for early intervention.
• Precision Medicine: The study suggests that managing metabolic parameters (reflected by MCMI) is crucial, particularly in preventing the transition to diabetes, which is a primary driver of cardiovascular death in this population.
• Clinical Utility: MCMI offers a simple, cost-effective composite marker (using routine labs and anthropometrics) that is superior to traditional indices for identifying high-risk CKM patients who require intensive management.
• Future Directions: The findings support the need for prospective interventional studies to determine if reducing MCMI levels directly improves CKM prognosis and to validate these thresholds in diverse global populations.

Limitations: Observational design limits causal inference; potential unmeasured confounders (e.g., detailed diet, genetics); and reliance on a specific US-based cohort (NHANES) may limit generalizability to other ethnicities.