Machine learning approaches based on fibroblast morphometry do not predict ALS.
Neurobiol Aging
; 130: 80-83, 2023 10.
Article
en En
| MEDLINE
| ID: mdl-37473581
ABSTRACT
Amyotrophic lateral sclerosis (ALS) is a devastating neuromuscular disease with limited therapeutic options. Biomarkers are needed for early disease detection, clinical trial design, and personalized medicine. Early evidence suggests that specific morphometric features in ALS primary skin fibroblasts may be used as biomarkers; however, this hypothesis has not been rigorously tested in conclusively large fibroblast populations. Here, we imaged ALS-relevant organelles (mitochondria, endoplasmic reticulum, lysosomes) and proteins (TAR DNA-binding protein 43, Ras GTPase-activating protein-binding protein 1, heat-shock protein 60) at baseline and under stress perturbations and tested their predictive power on a total set of 443 human fibroblast lines from ALS and healthy individuals. Machine learning approaches were able to confidently predict stress perturbation states (ROC-AUC â¼0.99) but not disease groups or clinical features (ROC-AUC 0.58-0.64). Our findings indicate that multivariate models using patient-derived fibroblast morphometry can accurately predict different stressors but are insufficient to develop viable ALS biomarkers.
Palabras clave
Texto completo:
1
Banco de datos:
MEDLINE
Asunto principal:
Esclerosis Amiotrófica Lateral
Tipo de estudio:
Diagnostic_studies
/
Prognostic_studies
/
Risk_factors_studies
Límite:
Humans
Idioma:
En
Revista:
Neurobiol Aging
Año:
2023
Tipo del documento:
Article
País de afiliación:
Estados Unidos