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Purpose <p>As the global prevalence of metabolic dysfunction-associated steatotic liver disease (MASLD) continues to increase, its early detection is crucial. We investigated the combined effects of various factors on MASLD development to enhance the understanding of individuals at high risk of MASLD.</p> Methods <p>A total of 4670 participants from the Korean Genome and Epidemiology Study were included. Cluster analysis and Cox proportional hazards regression were conducted to identify distinct risk factor clusters and evaluate their association with MASLD incidence.</p> Results <p>Four clusters were identified based on lifestyle characteristics: Cluster 1 (LA)—low activity, Cluster 2 (HPF)—lowest carbohydrate intake with high protein and fat intake, Cluster 3 (HC)—highest carbohydrate intake with low protein and fat intake, and Cluster 4 (ASC)—highest alcohol consumption and smoking levels. Cluster 4 (ASC) exhibited the highest cumulative incidence of MASLD, followed by Cluster 3 (HC). Compared to Clusters 1, 2, and 3, Cluster 4 (ASC) had significantly higher hazard ratios (HRs) for MASLD incidence (HR: 1.35 [95% confidence interval {CI}: 1.19–1.53]; <i>p</i> &lt; 0.001, HR: 1.31 [95% CI: 1.14–1.50]; <i>p</i> &lt; 0.001, and HR: 1.22 [95% CI: 1.07–1.38]; <i>p</i> = 0.002, respectively).</p> Conclusion <p>Clusters exhibiting various risk factor phenotypes were identified, and smoking, alcohol consumption, and carbohydrate intake were significantly associated with a higher risk of MASLD. Our findings support the importance of early screening and personalized approaches for individuals at high risk of MASLD.</p>

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Cluster analysis of diet and lifestyle factors associated with metabolic dysfunction-associated steatotic liver disease: findings from Korean Genome and Epidemiology Study

  • 期刊论文

  • 发表日期：

    2025年3月1日

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  • Jong Hee Lee,
  • Ha-Eun Ryu,
  • Taehwa Han,
  • Byoungjin Park,
  • Seok-Jae Heo,
  • Yu-Jin Kwon

摘要

Purpose

As the global prevalence of metabolic dysfunction-associated steatotic liver disease (MASLD) continues to increase, its early detection is crucial. We investigated the combined effects of various factors on MASLD development to enhance the understanding of individuals at high risk of MASLD.

Methods

A total of 4670 participants from the Korean Genome and Epidemiology Study were included. Cluster analysis and Cox proportional hazards regression were conducted to identify distinct risk factor clusters and evaluate their association with MASLD incidence.

Results

Four clusters were identified based on lifestyle characteristics: Cluster 1 (LA)—low activity, Cluster 2 (HPF)—lowest carbohydrate intake with high protein and fat intake, Cluster 3 (HC)—highest carbohydrate intake with low protein and fat intake, and Cluster 4 (ASC)—highest alcohol consumption and smoking levels. Cluster 4 (ASC) exhibited the highest cumulative incidence of MASLD, followed by Cluster 3 (HC). Compared to Clusters 1, 2, and 3, Cluster 4 (ASC) had significantly higher hazard ratios (HRs) for MASLD incidence (HR: 1.35 [95% confidence interval {CI}: 1.19–1.53]; p < 0.001, HR: 1.31 [95% CI: 1.14–1.50]; p < 0.001, and HR: 1.22 [95% CI: 1.07–1.38]; p = 0.002, respectively).

Conclusion

Clusters exhibiting various risk factor phenotypes were identified, and smoking, alcohol consumption, and carbohydrate intake were significantly associated with a higher risk of MASLD. Our findings support the importance of early screening and personalized approaches for individuals at high risk of MASLD.