Katrine Andersen is a postdoctoral researcher specializing in Parkinson’s disease, with expertise in biological subtyping, clinical testing, multimodal biomarker integration, digital biomarkers and machine learning. Her research focuses on understanding disease heterogeneity across the Parkinson’s disease continuum, from prodromal stages such as isolated REM sleep behaviour disorder (iRBD) to clinically manifest disease.
Her work integrates PET imaging, MRI, polysomnography, autonomic biomarkers, neuropsychological assessments and smartphone-based digital biomarkers. She has works with machine learning approaches, to identify biologically meaningful disease subtypes (brain/body first Parkinson's) and disease progression trajectories. In addition, she currently leads analyses of longitudinal smartphone-based biomarkers within the PACE programme.
Parkinson's disease comprises biologically distinct subtypes that may differ in disease progression and prognosis. This project investigates whether smartphone-based digital biomarkers can identify biological subtypes, including body-first and brain-first Parkinson's disease, and monitor disease progression over time. Using the deeply phenotyped PACE cohort, digital biomarkers will be validated against PET imaging, MIBG scintigraphy, neuromelanin MRI, polysomnography, and comprehensive clinical assessments. The findings may enable more precise, patient-centred disease monitoring and earlier identification of patients at risk of rapid disease progression.
