Amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD) form a recognised clinicopathological spectrum, yet most stratification pipelines remain fragmented by diagnosis or modality. This project will establish an electronic patient record (EPR)-native pipeline within CogStack, combining strict phenotyping, MedCAT- driven concept extraction, socioeconomic linkage, and survival analytics to enable reproducible, spectrum-wide analyses in real-world data. Building on this framework, we will integrate large-scale multi-omics datasets, including whole-genome sequencing, transcriptomics, methylation, proteomics, and ante-mortem blood biomarkers, with advanced statistical and machine learning methods to identify homogeneous patient subgroups and characterise their underlying molecular mechanisms.
The overall aim is to define clinically and biologically meaningful subgroups across the ALS–FTD spectrum, uncover molecular signatures with biomarker potential, and provide a stratification model applicable to clinical research and trial design. This work will generate a unique multimodal dataset and a scalable, transparent framework, advancing prognosis-aware care, biomarker discovery, and precision therapeutic development in ALS–FTD.