Universal clinical Parkinson’s disease axes identify a major influence of neuroinflammation

Abstract

Background There is large individual variation in both clinical presentation and progression between Parkinson’s disease patients. Generation of deeply and longitudinally phenotyped patient cohorts has enormous potential to identify disease subtypes for prognosis and therapeutic targeting. Methods Replicating across three large Parkinson’s cohorts (Oxford Discovery cohort (n = 842)/Tracking UK Parkinson’s study (n = 1807) and Parkinson’s Progression Markers Initiative (n = 472)) with clinical observational measures collected longitudinally over 5–10 years, we developed a Bayesian multiple phenotypes mixed model incorporating genetic relationships between individuals able to explain many diverse clinical measurements as a smaller number of continuous underlying factors (“phenotypic axes”). Results When applied to disease severity at diagnosis, the most influential of three phenotypic axes “Axis 1” was characterised by severe non-tremor motor phenotype, anxiety and depression at diagnosis, accompanied by faster progression in cognitive function measures. Axis 1 was associated with increased genetic risk of Alzheimer’s disease and reduced CSF Aβ1-42 levels. As observed previously for Alzheimer’s disease genetic risk, and in contrast to Parkinson’s disease genetic risk, the loci influencing Axis 1 were associated with microglia-expressed genes implicating neuroinflammation. When applied to measures of disease progression for each individual, integration of Alzheimer’s disease genetic loci haplotypes improved the accuracy of progression modelling, while integrating Parkinson’s disease genetics did not. Conclusions We identify universal axes of Parkinson’s disease phenotypic variation which reveal that Parkinson’s patients with high concomitant genetic risk for Alzheimer’s disease are more likely to present with severe motor and non-motor features at baseline and progress more rapidly to early dementia.

Alterations in RNA editing in skeletal muscle following exercise training in individuals with Parkinson’s disease

“…Moreover, inferences of protein functional outcomes due to nucleotide changes, made by computational tools such as SnpEff and SIFT, may also underestimate the extent of downstream consequences. Lastly, the wide range of PD phenotypes observed in this disease give credence to hypotheses suggesting a broad range of genetic and environmental factors that may contribute to subcategories of PD manifestation [83,84]; however, the number of samples prevents us from considering PD subcategories.…”

“…ADAR editing has been implicated in the development and progression of multiple neurological, neurodegenerative and psychiatric disorders such as epilepsy [61][62][63][64], autism [65,66], schizophrenia [67], Amyotrophic Lateral Sclerosis (ALS) [68][69][70][71], Huntington's Disease (HD) [67], Alzheimer's Disease (AD) [67,[72][73][74], schizophrenia [75][76][77][78][79][80], suicide [75,77,78,80], and depression [81,82]. Similar to other neurodegenerative diseases, PD is a complicated disease with multiple phenotypes that vary across individuals, as well as by age and sex [83,84]. Therefore, it is likely that multiple complicating factors such as the aging process, hormones, genetic and epigenetic factors, including dynamic ADAR editing, may play a role in PD pathology and/or response to exercise.…”

Toward Personalized Medicine Approaches for Parkinson Disease Using Digital Technologies

“…Based on these observed heterogeneities in clinical presentation and progressions, there are sustained efforts to track phenotyped cohorts longitudinally. A very recent study examined patients across the UK Tracking PD cohort (N=1807), the Oxford Discovery cohort (N=842), and PPMI cohort (N=472) using mixed modeling methods including genetic factors [ 19 ]. The Bayesian phenotypes developed (from longitudinal data spanning 5-10 years) were characterized in the form of three “axes,” the most influential of which (axis 1) was associated with an increased risk for AD and upregulation of microglia-expressed genes, which may imply involvement of neuroinflammatory pathways [ 19 ].…”

“…A very recent study examined patients across the UK Tracking PD cohort (N=1807), the Oxford Discovery cohort (N=842), and PPMI cohort (N=472) using mixed modeling methods including genetic factors [ 19 ]. The Bayesian phenotypes developed (from longitudinal data spanning 5-10 years) were characterized in the form of three “axes,” the most influential of which (axis 1) was associated with an increased risk for AD and upregulation of microglia-expressed genes, which may imply involvement of neuroinflammatory pathways [ 19 ]. This axis presents with lowered levels of CSF amyloid-β 1-42 , severe baseline motor and nonmotor features, and with a higher likelihood of progression to early dementia.…”

Epigenetic insights into neuropsychiatric and cognitive symptoms in Parkinson's disease: A DNA co-methylation network analysis

“…Although the occurrence of neuropsychiatric and cognitive symptoms in PD is much more common than in age-matched populations 9 , 10 , individual to individual level susceptibility to these secondary features is highly variable 17 , 18 . Genetic liability has been implicated, for example a recent genome-wide association study (GWAS) of cognitive progression in PD highlighted the contribution of risk genes such as GBA with worsening cognitive decline over time 19 and meta analyses of the gene have shown an association to the emergence of psychosis and depression symptoms 20 .…”