¿Son fiables y aplicables los relojes de envejecimiento basados en indicadores clínicos rutinarios? Una revisión

Jiajia Zhang1, Jie Hu1, Yinyan Gao1

  • 1Xiangya School of Public health, Central South University, Changsha, Hunan, China.

Los relojes de envejecimiento basados en indicadores clínicos rutinarios han surgido como una herramienta rentable para evaluar la edad biológica. Esta revisión sistemática tiene como objetivo resumir las características y evaluar críticamente los relojes de envejecimiento disponibles. Se recuperaron estudios que desarrollaron relojes de envejecimiento para adultos (≥18 años) basados en indicadores clínicos rutinarios a partir de seis bases de datos (PubMed, EMBASE, Web of Science, CNKI, Wanfang Data y Sinomed) hasta el 18 de junio de 2024. Se utilizó la herramienta PROBAST+AI para evaluar la calidad metodológica, el riesgo de sesgo y la aplicabilidad de los relojes de envejecimiento incluidos. Todos los resultados se resumieron de forma narrativa. Se incluyeron cincuenta y nueve estudios que involucraban 81 relojes de envejecimiento, de los cuales 71 (87,7 %) se desarrollaron utilizando conjuntos de datos de un solo país, predominantemente de China, Estados Unidos, Corea y el Reino Unido. Cabe destacar que 31 relojes de envejecimiento (38,3 %) se desarrollaron sin validación interna ni externa. La mayoría de los relojes de envejecimiento fueron calificados con una preocupación elevada respecto a la calidad y un alto riesgo de sesgo, incluso aquellos publicados en revistas de alto impacto. Solo tres relojes de envejecimiento (3,7 %) de dos estudios fueron calificados con una preocupación baja respecto a la calidad y la aplicabilidad durante el desarrollo, y dos de estos (4,0 %) de un estudio demostraron además un bajo riesgo de sesgo y una preocupación baja por la aplicabilidad durante la evaluación del modelo. Las investigaciones futuras deberían priorizar la validación de los relojes de envejecimiento prometedores en las poblaciones objetivo en lugar de desarrollar otros nuevos, adherirse a las directrices PROBAST+AI y TRIPOD+AI para garantizar el rigor metodológico y la transparencia en la presentación de los informes, y proporcionar códigos y herramientas de modelos reproducibles y fáciles de usar.

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