Latent Class Analysis for Clustering Iranian Individuals Based on Cardiovascular Risk Factors.

Q: What are the key findings of this study?

Latent class analysis identified five distinct cardiovascular risk profiles capturing sex-specific and metabolically clustered patterns not reflected in conventional IraPEN stratification.

Q: What is the clinical evidence from this study?

Study design: Cross-Sectional. Population: Cardiovascular risk (n=1281). Intervention: Cardiovascular risk profiles vs. Conventional IraPEN classification. Primary outcome: Distribution of latent classes of cardiovascular risk factors.

Hajar Nikbakht; Seyyed Amir Yasin Ahmadi; Abbas Aghaei; Mahdi Chinichian

doi:10.47176/mjiri.40.88

January 1, 2026

Latent Class Analysis for Clustering Iranian Individuals Based on Cardiovascular Risk Factors.

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Key result

Latent class analysis identifies five distinct cardiovascular risk profiles missed by conventional IraPEN stratification.

n=1,281

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Why the study?

Conventional cardiovascular risk-stratification tools like WHO/ISH (IraPEN) may not adequately capture the heterogeneity of risk profiles within the population.

Key Points

To identify underlying patterns and clustering of cardiovascular disease risk factors among Iranian employees undergoing routine occupational health assessments.
Conducted a cross-sectional secondary analysis of 1,281 adults aged 40–79 years attending a governmental occupational health clinic in Iran in 2024.
Assessed core risk variables (sex, diabetes, smoking, systolic blood pressure, and total cholesterol) using latent class analysis across models with three to six classes, selecting a five-class model based on fit indices.
Evaluated the association between class membership probability and atherosclerotic cardiovascular disease score using beta regression and compared groupings to the IraPEN classification.
Latent class analysis identified five distinct profiles: low-risk individuals (population share: 0.3011), high-risk males without DM (0.1824), hyperlipidemic non-smoking females (0.1487), high-risk males without hypertension (0.36), and high-risk males without hyperlipidemia (0.0079).
Beta regression linked higher atherosclerotic risk scores to Class 1 (OR 1.075, 95% CI 1.050–1.101, p < 0.001), Class 4 (OR 1.020, 95% CI 1.002–1.038, p = 0.031), and Class 5 (OR 1.016, 95% CI 1.001–1.031, p = 0.037), whereas Class 2 (OR 0.828, 95% CI 0.807–0.849, p < 0.001) and Class 3 (OR 0.784, 95% CI 0.762–0.806, p < 0.001) were low risk.
While the conventional IraPEN system classified 1,273 of 1,281 participants as low risk, the latent class model categorized approximately 45% into low-risk classes (Classes 2 and 3), capturing risk heterogeneity undetected by IraPEN.

Study Design

Type

Cross-Sectional (n=1,281)

Structured PICO

P

Population

1,281 adults aged 40-79 years undergoing routine occupational health assessments in Iran, evaluated for cardiovascular risk factor clustering.

E

Exposure

Latent class analysis (LCA) to identify underlying patterns of cardiovascular risk factors

C

Comparator

WHO/ISH (IraPEN) conventional risk-stratification tool

O

Outcome

Identification of distinct latent classes of cardiovascular risk factorssurrogate

Limitations

Reliance on existing secondary data restricts control over measurement accuracy and data collection procedures
Certain behavioral or social determinants of CVD could not be evaluated
Reliance on existing secondary data
Lack of control over measurement accuracy
Inability to evaluate certain behavioral or social determinants of CVD

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Implication

Latent class analysis reveals heterogeneous and clustered cardiovascular risk profiles that conventional risk tools like IraPEN may fail to accurately capture.

Abstract

Background: Cardiovascular disease (CVD) remains a major public health concern, and conventional risk-stratification tools, such as WHO/ISH (IraPEN), may not adequately capture the heterogeneity of risk profiles within the population. This study aimed to identify the underlying patterns of CVD risk factors among employees undergoing routine occupational health assessments in Iran. Methods: In this cross-sectional secondary analysis, data from 1,281 adults aged 40-79 years attending a governmental occupational health clinic in 2024 were analyzed. Core cardiovascular risk variables-including sex, diabetes, smoking, systolic blood pressure, and total cholesterol-were used. Latent class analysis (LCA) employing a five-class model was conducted. Results: Five latent classes of cardiovascular risk were identified, reflecting heterogeneous conditions. These classes included high-risk males without DM (population share: 0.1824), hyperlipidemic non-smoking females (population share: 0.1487), low-risk individuals (population share: 0.3011), high-risk males without hypertension (population share: 0.36), and high-risk males without hyperlipidemia (population share: 0.0079). Conclusion: The LCA-based classification identified five distinct and internally homogeneous cardiovascular risk profiles that captured sex-specific and metabolically clustered patterns of risk. This approach reveals heterogeneity not reflected in the conventional IraPEN five-tier risk groups.

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Cite This Study

Nikbakht et al. (2026) conducted a cross-sectional in Cardiovascular risk (n=1,281). Cardiovascular risk profiles vs. Conventional IraPEN classification was evaluated on Distribution of latent classes of cardiovascular risk factors. Latent class analysis identified five distinct cardiovascular risk profiles capturing sex-specific and metabolically clustered patterns not reflected in conventional IraPEN stratification.

synapsesocial.com/papers/6ad0b8f43983294737c0c4f4 https://doi.org/10.47176/mjiri.40.88

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Latent Class Analysis for Clustering Iranian Individuals Based on Cardiovascular Risk Factors.

Figures

Key Points

Study Design

Structured PICO

Limitations

Ask about this paper

Implication

Abstract

Discussion

Cite This Study

Latent Class Analysis for Clustering Iranian Individuals Based on Cardiovascular Risk Factors.

Figures

Key Points

Study Design

Structured PICO

Limitations

Ask about this paper

Implication

Abstract

Discussion

Cite This Study

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