Digital Cognitive Testing in Mitochondrial Disease: Validity and Challenges for Clinical Trial Use
Oksana Pogoryelova, Jan Smeitink, Herma Renkema, Rosabeth White, Frankie Barton, Rachel Lyon, Jane Newman, Aye Moe, Yi Shiau Ng, Grainne Siobhan GormanABSTRACT
Background
Primary mitochondrial disease is a group of genetic disorders caused by pathogenic variants in nuclear or mitochondrial DNA, often resulting in progressive neurodegeneration and cognitive decline. Current management is primarily supportive, though recent research offers hope for disease‐modifying treatments in the future. Selecting appropriate therapeutic outcomes for clinical trials in mitochondrial diseases is challenging due to limited sensitivity to changes, small sample sizes, and the burden of study related activities. This study aims to identify an efficient choice of cognitive endpoints for translational research.
Methods
This study compared digital cognitive assessments with traditional paper‐based tools. It included two cohorts: the Newcastle cohort of 45 patients recruited from the mitochondrial clinic Newcastle upon Tyne (UK) and the KHENERGYZE clinical trial cohort of 27 patients recruited from four European countries. Patients in the Newcastle cohort underwent two conventional cognitive assessments (Addenbrooke's Cognitive Examination and Montreal Cognitive Assessment), along with two computerized tests (Cogstate and Test of Attentional Performance). Potential confounding factors were also assessed.
Results
Both cohorts showed a high prevalence of moderate to severe perceived fatigue. Over 50% of patients showed reduced reaction times. Strong correlations were found between conventional and digital assessments. Several confounding factors such as education and employment were identified as influencing cognitive performance.
Conclusions
The findings support the understanding of mitochondrial disease as a slowly progressive condition, where impaired cognitive function is evident even in patients in the absence of devastating CNS manifestations such as stroke‐like episodes. Observed variability in cognitive performance may help detect meaningful changes over time.