Impact of socioeconomic status on cardiometabolic multimorbidity progression trajectories: a multi-state model analysis based on three prospective cohort studies

This multi-cohort study utilizing multi-state models demonstrates that lower socioeconomic status significantly accelerates the progression of cardiometabolic multimorbidity from initial disease onset to mortality and shortens survival time across diverse populations, highlighting the critical need to integrate socioeconomic factors into clinical management strategies.

The Gist

The Big Picture: A Race Against Time

Imagine your health as a long journey down a winding road. Along this road, there are three major "potholes" you might fall into: Type 2 Diabetes, Heart Disease, and Stroke.

If you fall into just one pothole, it's bad, but you can usually keep driving. But if you fall into two or more of these potholes at the same time, you have what the doctors call Cardiometabolic Multimorbidity (CMM). This is like getting stuck in a deep, muddy swamp where it's very hard to get out, and your car (your body) is much more likely to break down completely (death).

This study asked a simple but powerful question: Does the size of your wallet and your level of education change how fast you fall into these potholes and how long you stay stuck?

The Cast of Characters

The researchers didn't just look at one group of people. They gathered data from three massive "travel logs" (cohorts) from different parts of the world:

1. UK Biobank: People from the United Kingdom.
2. SHARE: People from various European countries.
3. KLoSA: People from South Korea.

In total, they tracked nearly 420,000 people over many years (some for over 17 years!). They watched to see who stayed healthy, who got sick, who got sicker, and who passed away.

The "Social Ladder" (Socioeconomic Status)

The researchers didn't just look at income. They built a "Social Ladder" based on three rungs:

• Money: How much you earn.
• Schooling: How much education you have.
• Work: Whether you have a job.

They grouped people into three categories: High SES (Top of the ladder), Medium SES (Middle), and Low SES (Bottom of the ladder).

What They Found: The "Fast-Forward" Effect

The study found that being at the bottom of the Social Ladder acts like a fast-forward button for getting sick and dying. Here is the breakdown:

1. The First Step: Falling into the First Pothole

• The Finding: People with lower income and education were 33% more likely to get their first major disease (like diabetes or heart disease) compared to those at the top of the ladder.
• The Analogy: Imagine two runners. The wealthy runner is wearing comfortable, high-tech shoes. The poor runner is wearing heavy boots. The runner in boots trips and falls into the first pothole (getting sick) much sooner. Specifically, they fell into the first pothole about 6 months earlier than the wealthy runner.

2. The Second Step: The Muddy Swamp (Multimorbidity)

• The Finding: Once you have one disease, being poor makes it much harder to stop from getting a second one. The risk of going from "one disease" to "two diseases" was 33% higher for low-income people.
• The Analogy: Once you are stuck in the first pothole, the wealthy runner has a ladder and a rescue team to pull them out quickly. The poor runner, however, finds the walls of the pothole slippery and steep. They slide down into the "Muddy Swamp" (having two diseases) about 9 months faster than the wealthy runner.

3. The Final Stretch: The Cliff (Death)

• The Finding: This is where the gap gets widest. Once someone has two diseases (CMM), their chances of dying are significantly higher if they are poor.
  • Low-income people with two diseases lived 1.5 years less on average than wealthy people with the same diseases.
  • The risk of dying was nearly 70% higher for the poor group.
• The Analogy: Imagine both runners are now stuck in the swamp. The wealthy runner has a boat, a map, and a medic. They can navigate the swamp and survive. The poor runner is sinking deeper. The study found that for the poor, the "exit ramp" to survival is closed much earlier. They reach the "cliff" (death) significantly sooner.

The "Special" Potholes

The study noticed that the "Social Ladder" affected different diseases differently:

• Diabetes: The gap was widest here. Poor people got diabetes much faster than rich people.
• Stroke: If a poor person had a stroke, they were much more likely to quickly develop a second disease or die compared to a rich person who had a stroke. It's as if the "muddy swamp" is stickier for stroke victims with low income.

Why Does This Happen?

The researchers suggest a few reasons, like a "Perfect Storm":

1. Stress: Worrying about money creates chronic stress that hurts the body.
2. Access: Poorer people often can't afford healthy food, gym memberships, or the best doctors. They might delay seeing a doctor until it's too late.
3. Environment: They might live in areas with more pollution or less safe places to walk.

The One Weird Exception (South Korea)

Interestingly, in the South Korean data, the gap in death rates between rich and poor narrowed once people were already very sick. The researchers think this is because South Korea has a very strong national health insurance system that covers the cost of major diseases, acting like a "safety net" that catches everyone, rich or poor, when they are at their most vulnerable.

The Takeaway

This study is a loud alarm bell. It tells us that health isn't just about genetics or bad luck; it's heavily influenced by your bank account and your education.

Being poor doesn't just make life harder; it literally speeds up the clock on getting sick and dying. To fix this, we can't just tell people to "eat better." We need to fix the "Social Ladder" itself—improving wages, education, and healthcare access—so that everyone, regardless of their wallet, has a fair shot at a long, healthy life.

Technical Summary

1. Problem Statement

Cardiometabolic Multimorbidity (CMM), defined as the coexistence of two or more cardiometabolic diseases (Type 2 Diabetes [T2D], Coronary Artery Disease [CAD], and Stroke), is a growing global health challenge associated with multiplicative mortality risks and reduced life expectancy. While socioeconomic status (SES) is a known determinant of individual cardiovascular diseases, existing literature has significant gaps:

• Static Focus: Most studies examine SES associations with single diseases or broad multimorbidity rather than the dynamic progression trajectory of CMM.
• Methodological Limitations: Prior research often relies on single SES indicators (e.g., income only) or cross-sectional designs, failing to capture the synergistic effects of multiple socioeconomic dimensions or the temporal sequence of disease onset.
• Lack of Generalizability: Evidence is often limited to single-population cohorts, lacking cross-cultural validation across diverse genetic and socioeconomic backgrounds.

The study aims to elucidate how SES influences the entire developmental continuum of CMM—from healthy baseline to first-onset cardiometabolic disease (FCMD), progression to multimorbidity (CMM), and subsequent mortality—using a multi-state modeling approach across three large prospective cohorts.

2. Methodology

Study Populations:
The study harmonized data from three major prospective cohorts:

1. UK Biobank (UKB): 387,665 participants (UK).
2. Survey of Health, Ageing and Retirement in Europe (SHARE): 22,505 participants (Europe).
3. Korean Longitudinal Study of Aging (KLoSA): 8,357 participants (Korea).

• Total Sample: 418,527 individuals.
• Follow-up: Median durations of 13.4, 17.3, and 16.0 years, respectively.
• Inclusion/Exclusion: Participants with pre-existing T2D, CAD, or stroke at baseline were excluded.

Exposure Assessment (SES):

• Variables: Income, Education, and Employment status.
• Construction: A Latent Class Analysis (LCA) was employed to synthesize these three dimensions into a composite, mutually exclusive SES classification: High, Medium, and Low. This approach captures the multifaceted nature of socioeconomic disparity better than single indicators.

Outcome Definitions:

• FCMD: First diagnosis of T2D, CAD, or Stroke.
• CMM: Diagnosis of a second cardiometabolic disease.
• Death: All-cause mortality (absorbing state).

Statistical Analysis:

• Multi-State Models (MSM): Two transition models were utilized:
  • Model A: 5 transitions (Health $\to$ FCMD; FCMD $\to$ CMM; Health $\to$ Death; FCMD $\to$ Death; CMM $\to$ Death).
  • Model B: Subdivided FCMD into specific disease types (T2D, CAD, Stroke) for 11 total transitions.
• Estimation: Cox proportional hazards models were used to estimate Hazard Ratios (HR) with 95% Confidence Intervals (CI), adjusted for covariates (age, sex, BMI, smoking, alcohol, physical activity, sleep, hypertension).
• Meta-Analysis: Inverse-variance weighted meta-analysis combined results across the three cohorts. Heterogeneity was assessed via Cochran's Q and $I^2$.
• Restricted Mean Survival Time (RMST): Used to quantify the absolute difference in time spent in specific states (e.g., time to FCMD onset, survival time post-CMM) between SES groups, providing a clinically interpretable measure of "years lost."

3. Key Contributions

• Dynamic Trajectory Mapping: This is the first multi-cohort study to map the entire CMM progression trajectory (Health $\to$ FCMD $\to$ CMM $\to$ Death) stratified by SES, moving beyond static risk associations.
• Composite SES Metric: The use of LCA to create a composite SES index allows for a more nuanced understanding of how the interplay of income, education, and employment drives health outcomes compared to single-variable analyses.
• Cross-Population Validation: By integrating data from the UK, Europe, and Asia, the study confirms the universality of SES disparities in CMM while identifying specific cultural/policy-mediated variations (e.g., the Korean context).
• RMST Application: The application of RMST provides concrete estimates of "years lost" due to low SES, offering a tangible metric for public health planning.

4. Key Results

Cumulative Risk and Progression:

• Accelerated Onset: Low-SES individuals reached the equivalent 10-year cumulative risk of FCMD 0.47 years earlier and CMM 0.76 years earlier than high-SES counterparts.
• Risk Magnitude:
  • Low SES increased the risk of developing FCMD by 33.4% (HR=1.33).
  • Low SES increased the risk of progressing from FCMD to CMM by 33.3% (HR=1.33).
• Disease-Specific Disparities:
  • The transition from Health $\to$ T2D showed the strongest SES gradient (HR=1.45 for low SES).
  • The transition from Stroke $\to$ CMM and Stroke $\to$ Death showed the most pronounced SES effects among disease-specific pathways.

Mortality Risks:

• Post-FCMD: Low SES increased mortality risk after FCMD diagnosis by 41.2% (HR=1.41).
• Post-CMM: The disparity peaked here. Low SES increased mortality risk after CMM diagnosis by 71.7% (HR=1.72).
• Survival Time (RMST):
  • Low-SES patients lived 1.57 years less after a CMM diagnosis compared to high-SES patients.
  • In the SHARE and KLoSA cohorts, the survival time loss for low-SES CMM patients was even more severe (approx. 2.0+ years).

Subgroup and Sensitivity Findings:

• Age: The mortality disparity between low and high SES was significantly stronger in individuals under 60 years (HR=2.53) compared to those 60+ (HR=1.48).
• KLoSA Anomaly: While low SES accelerated disease onset in Korea, the association between SES and mortality in the terminal CMM stage was not statistically significant. The authors attribute this to Korea's expanded health insurance coverage, which may decouple economic status from mortality risk at the end-of-life stage.
• Behavioral Factors: Smoking, high alcohol consumption, and unhealthy sleep patterns exacerbated the SES gradient.

5. Significance and Implications

Clinical and Public Health Impact:

• Risk Stratification: SES should be integrated into routine clinical assessments for CMM risk prediction. Identifying low-SES patients early allows for targeted, stage-specific interventions.
• Policy Interventions: The findings support upstream structural interventions (income support, education, employment stability) to mitigate the "cumulative disadvantage" that accelerates disease progression.
• Tertiary Prevention: The critical window of vulnerability is the transition from CMM to death. Targeted resource allocation and enhanced tertiary prevention are essential for low-SES populations to close the survival gap.

Conclusion:
The study provides robust evidence that lower socioeconomic status acts as a powerful accelerator across the entire spectrum of cardiometabolic multimorbidity. It not only increases the likelihood of disease onset and progression but drastically shortens survival time, particularly after the onset of multimorbidity. These disparities are consistent across diverse populations, highlighting the urgent need for integrated socioeconomic and medical strategies to combat CMM.