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Background and objectives <p>Metabolic dysfunction-associated steatotic liver disease (MASLD) poses a challenge for Predictive, Preventive, and Personalised Medicine (3PM) due to intrinsic heterogeneity. Current management is reactive. This study’s rationale is that one-size-fits-all labels fail to capture systemic effects and early metabolic shifts. We hypothesized that sex-specific metabolic architectures and transitional hubs can be identified using probabilistic modeling to shift MASLD management toward predictive diagnostics and targeted prevention.</p> Methods <p>We performed unsupervised computational phenotyping using GSEM-based Latent Class Analysis (LCA) across two cohorts: Bogalusa Heart Study (<i>n</i> = 1,392) and NHANES (<i>n</i> = 5,335). LCA disaggregated population heterogeneity into individualised patient profiles with shared cardiometabolic signatures. Partition-based Graph Abstraction (PAGA) was applied to map health-to-disease trajectories.</p> Results <p>LCA disaggregated MASLD into sex-specific cardiometabolic risk architectures. While aging increased fibrosis risk equally across sexes, evolutionary pathways were profoundly dimorphic. PAGA revealed that females follow interconnected, potentially reversible trajectories—suggesting unique bioenergetic flexibility—whereas males exhibit polarized, abrupt transitions. Clinical relevance varied: in females, a singular high-risk subtype drove mortality (HR = 6.05), whereas risk was broadly distributed in males. Findings were translated into a Python-based 3PM-Risk Calculator for real-time stratification available at <a href="https://applacmetsynmasld-i9m9ctxj5icdtyyvdv8tkz.streamlit.app">https://applacmetsynmasld-i9m9ctxj5icdtyyvdv8tkz.streamlit.app</a>.</p> Conclusion <p>Our framework enables holistic health risk assessment by identifying high-priority metabolic phenotypes that traditional diagnostics overlook. Pinpointing sex-specific transitional hubs provides a critical window for targeted prevention to intercept disease before irreversible damage occurs. This digital tool facilitates the translation of individualised profiles into clinical practice, fulfilling the 3PM mission to shift from reactive care to proactive, personalised healthcare strategies across the life-course.</p>

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Sex-specific cardiometabolic phenotypes in MASLD: 3PM-centric stratification and trajectory mapping for proactive risk assessment

  • 期刊论文

  • 发表日期：

    2026年4月2日

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  • Carlos José Pirola,
  • Luis Diambra,
  • Tomas Fernández Gianotti,
  • Silvia Sookoian

摘要

Background and objectives

Metabolic dysfunction-associated steatotic liver disease (MASLD) poses a challenge for Predictive, Preventive, and Personalised Medicine (3PM) due to intrinsic heterogeneity. Current management is reactive. This study’s rationale is that one-size-fits-all labels fail to capture systemic effects and early metabolic shifts. We hypothesized that sex-specific metabolic architectures and transitional hubs can be identified using probabilistic modeling to shift MASLD management toward predictive diagnostics and targeted prevention.

Methods

We performed unsupervised computational phenotyping using GSEM-based Latent Class Analysis (LCA) across two cohorts: Bogalusa Heart Study (n = 1,392) and NHANES (n = 5,335). LCA disaggregated population heterogeneity into individualised patient profiles with shared cardiometabolic signatures. Partition-based Graph Abstraction (PAGA) was applied to map health-to-disease trajectories.

Results

LCA disaggregated MASLD into sex-specific cardiometabolic risk architectures. While aging increased fibrosis risk equally across sexes, evolutionary pathways were profoundly dimorphic. PAGA revealed that females follow interconnected, potentially reversible trajectories—suggesting unique bioenergetic flexibility—whereas males exhibit polarized, abrupt transitions. Clinical relevance varied: in females, a singular high-risk subtype drove mortality (HR = 6.05), whereas risk was broadly distributed in males. Findings were translated into a Python-based 3PM-Risk Calculator for real-time stratification available at https://applacmetsynmasld-i9m9ctxj5icdtyyvdv8tkz.streamlit.app.

Conclusion

Our framework enables holistic health risk assessment by identifying high-priority metabolic phenotypes that traditional diagnostics overlook. Pinpointing sex-specific transitional hubs provides a critical window for targeted prevention to intercept disease before irreversible damage occurs. This digital tool facilitates the translation of individualised profiles into clinical practice, fulfilling the 3PM mission to shift from reactive care to proactive, personalised healthcare strategies across the life-course.