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Exploración de una subpoblación de MASLD asociada con el nuevo inicio de ERC utilizando técnicas de agrupación
Itaru Hosaka1, Marenao Tanaka2, Tatsuya Sato2,3
1Division of Cardiovascular Surgery, Department of Surgery, Sapporo Medical University School of Medicine, Sapporo, Japan.
Resumen
El aprendizaje automático identificó subgrupos distintos de disfunción metabólica asociada con enfermedad del hígado graso (MASLD). Un subgrupo de MASLD obeso con lípidos altos y ácido úrico mostró el mayor riesgo de desarrollar enfermedad renal crónica (ERC).
Área de la Ciencia:
- Hepatología y Nefrología
- Investigación de Síndrome Metabólico
- Aprendizaje automático en medicina
Sus antecedentes:
- La enfermedad del hígado graso asociada a disfunción metabólica (MASLD) está relacionada con el síndrome cardiovascular-renal-metabólico, incluida la enfermedad renal crónica (ERC).
- La heterogeneidad de la MASLD y su impacto en el desarrollo de la ERC no se comprenden completamente.
- La estratificación precisa del riesgo de ERC en pacientes con MASLD es crucial.
Objetivo del estudio:
- Investigar la relación entre los subgrupos de MASLD y la ERC de nuevo inicio.
- Utilizar el aprendizaje automático para identificar fenotipos distintos de MASLD.
- Mejorar la estratificación del riesgo para el desarrollo de ERC en MASLD.
Principales métodos:
- Se empleó agrupación supervisada utilizando SHAP (SHapley Additive exPlanations) y UMAP (uniform manifold approximation and projection) en datos de enfermedad del hígado graso.
- Se analizaron datos de exámenes de salud de 12,168 sujetos japoneses durante un seguimiento de 10 años.
- Se utilizaron modelos de mezcla gaussiana para detectar subagrupaciones de MASLD y análisis de Kaplan-Meier para la incidencia de ERC.
Principales resultados:
- Se identificaron 10 subagrupaciones y 3 subgrupos distintos de MASLD utilizando aprendizaje automático.
- Se demostraron diferencias significativas en la incidencia acumulada de ERC entre los subgrupos de MASLD.
- Un subgrupo de MASLD obeso caracterizado por perfiles lipídicos aterogénicos, alto índice de hígado graso y niveles elevados de ácido úrico exhibió el mayor riesgo de desarrollo de ERC.
Conclusiones:
- La agrupación supervisada de MASLD revela subpoblaciones fenotípicamente distintas, mejorando la estratificación del riesgo de ERC.
- Un subgrupo de MASLD obeso con dislipidemia aterogénica e hiperuricemia se asocia con un riesgo significativamente mayor de ERC.
- Estos hallazgos ayudan en la evaluación y manejo personalizado del riesgo de pacientes con MASLD para prevenir la ERC.
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