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

Cynthia Sandor; Stephanie Millin; Andrew Dahl; Ann-Kathrin Schalkamp; Michael Lawton; Leon Hubbard; Nabila Rahman; Nigel Williams; Yoav Ben-Shlomo; Donald G Grosset; Michele T Hu; Jonathan Marchini; Caleb Webber

doi:10.1186/s13073-022-01132-9

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

Cynthia Sandor^*, Stephanie Millin, Andrew Dahl, Ann-Kathrin Schalkamp, Michael Lawton, Leon Hubbard, Nabila Rahman, Nigel Williams, Yoav Ben-Shlomo, Donald G Grosset, Michele T Hu, Jonathan Marchini, Caleb Webber^*

^*Corresponding author for this work

Research output: Contribution to journal › Article (Academic Journal) › peer-review

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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.

Original language	English
Article number	129
Number of pages	15
Journal	Genome Medicine
Volume	14
Issue number	1
DOIs	https://doi.org/10.1186/s13073-022-01132-9
Publication status	Published - 16 Nov 2022

Bibliographical note

Funding Information:
This work was supported by the Monument Trust Discovery Award from Parkinson’s UK, the Oxford Genomics Centre at the Wellcome Centre for Human Genetics, the Wellcome Trust (grant reference 090532/Z/09/Z) and an MRC Hub grant G0900747 91070). Samples and associated clinical data were supplied by the Oxford Parkinson's Disease Centre (OPDC) study, funded by the Monument Trust Discovery Award from Parkinson’s UK, with the support of the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (BRC). CW’s lab is supported by the UK Dementia Research Institute, which receives its funding from DRI Ltd, funded by the UK Medical Research Council, Alzheimer’s Society and Alzheimer’s Research. CW and CS are supported by Computational Science Program funded by Michael J. Fox Foundation. CW, NR and CS are supported by the UK Dementia Research Institute (UK DRI) funded by the Medical Research Council (MRC), Alzheimer’s Society and Alzheimer’s Research UK (AR-UK). CS and NR are supported by the Ser Cymru II programme which is part-funded by Cardiff University and the European Regional Development Fund through the Welsh Government. AS is supported by a PhD studentship funded by Heath and Cares Research Wales. JM acknowledges funding for this work from the European Research Council (ERC; grant 617306). We thank the Oxford Genomics Centre at the Wellcome Centre for Human Genetics, Oxford) for the generation of genotyping data.

Publisher Copyright:
© 2022, The Author(s).

Keywords

Humans
Parkinson Disease/genetics
Alzheimer Disease
Neuroinflammatory Diseases
Bayes Theorem
Cohort Studies

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@article{cd20a2b221cd4fa2b787302fc525204e,

title = "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{\textquoteright}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{\textquoteright}s cohorts (Oxford Discovery cohort (n = 842)/Tracking UK Parkinson{\textquoteright}s study (n = 1807) and Parkinson{\textquoteright}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{\textquoteright}s disease and reduced CSF Aβ1-42 levels. As observed previously for Alzheimer{\textquoteright}s disease genetic risk, and in contrast to Parkinson{\textquoteright}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{\textquoteright}s disease genetic loci haplotypes improved the accuracy of progression modelling, while integrating Parkinson{\textquoteright}s disease genetics did not. Conclusions We identify universal axes of Parkinson{\textquoteright}s disease phenotypic variation which reveal that Parkinson{\textquoteright}s patients with high concomitant genetic risk for Alzheimer{\textquoteright}s disease are more likely to present with severe motor and non-motor features at baseline and progress more rapidly to early dementia.",

keywords = "Humans, Parkinson Disease/genetics, Alzheimer Disease, Neuroinflammatory Diseases, Bayes Theorem, Cohort Studies",

author = "Cynthia Sandor and Stephanie Millin and Andrew Dahl and Ann-Kathrin Schalkamp and Michael Lawton and Leon Hubbard and Nabila Rahman and Nigel Williams and Yoav Ben-Shlomo and Grosset, \{Donald G\} and Hu, \{Michele T\} and Jonathan Marchini and Caleb Webber",

note = "Funding Information: This work was supported by the Monument Trust Discovery Award from Parkinson{\textquoteright}s UK, the Oxford Genomics Centre at the Wellcome Centre for Human Genetics, the Wellcome Trust (grant reference 090532/Z/09/Z) and an MRC Hub grant G0900747 91070). Samples and associated clinical data were supplied by the Oxford Parkinson's Disease Centre (OPDC) study, funded by the Monument Trust Discovery Award from Parkinson{\textquoteright}s UK, with the support of the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (BRC). CW{\textquoteright}s lab is supported by the UK Dementia Research Institute, which receives its funding from DRI Ltd, funded by the UK Medical Research Council, Alzheimer{\textquoteright}s Society and Alzheimer{\textquoteright}s Research. CW and CS are supported by Computational Science Program funded by Michael J. Fox Foundation. CW, NR and CS are supported by the UK Dementia Research Institute (UK DRI) funded by the Medical Research Council (MRC), Alzheimer{\textquoteright}s Society and Alzheimer{\textquoteright}s Research UK (AR-UK). CS and NR are supported by the Ser Cymru II programme which is part-funded by Cardiff University and the European Regional Development Fund through the Welsh Government. AS is supported by a PhD studentship funded by Heath and Cares Research Wales. JM acknowledges funding for this work from the European Research Council (ERC; grant 617306). We thank the Oxford Genomics Centre at the Wellcome Centre for Human Genetics, Oxford) for the generation of genotyping data. Publisher Copyright: {\textcopyright} 2022, The Author(s).",

year = "2022",

month = nov,

day = "16",

doi = "10.1186/s13073-022-01132-9",

language = "English",

volume = "14",

journal = "Genome Medicine",

issn = "1756-994X",

publisher = "BioMed Central Ltd",

number = "1",

}

TY - JOUR

T1 - Universal clinical Parkinson's disease axes identify a major influence of neuroinflammation

AU - Sandor, Cynthia

AU - Millin, Stephanie

AU - Dahl, Andrew

AU - Schalkamp, Ann-Kathrin

AU - Lawton, Michael

AU - Hubbard, Leon

AU - Rahman, Nabila

AU - Williams, Nigel

AU - Ben-Shlomo, Yoav

AU - Grosset, Donald G

AU - Hu, Michele T

AU - Marchini, Jonathan

AU - Webber, Caleb

N1 - Funding Information: This work was supported by the Monument Trust Discovery Award from Parkinson’s UK, the Oxford Genomics Centre at the Wellcome Centre for Human Genetics, the Wellcome Trust (grant reference 090532/Z/09/Z) and an MRC Hub grant G0900747 91070). Samples and associated clinical data were supplied by the Oxford Parkinson's Disease Centre (OPDC) study, funded by the Monument Trust Discovery Award from Parkinson’s UK, with the support of the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (BRC). CW’s lab is supported by the UK Dementia Research Institute, which receives its funding from DRI Ltd, funded by the UK Medical Research Council, Alzheimer’s Society and Alzheimer’s Research. CW and CS are supported by Computational Science Program funded by Michael J. Fox Foundation. CW, NR and CS are supported by the UK Dementia Research Institute (UK DRI) funded by the Medical Research Council (MRC), Alzheimer’s Society and Alzheimer’s Research UK (AR-UK). CS and NR are supported by the Ser Cymru II programme which is part-funded by Cardiff University and the European Regional Development Fund through the Welsh Government. AS is supported by a PhD studentship funded by Heath and Cares Research Wales. JM acknowledges funding for this work from the European Research Council (ERC; grant 617306). We thank the Oxford Genomics Centre at the Wellcome Centre for Human Genetics, Oxford) for the generation of genotyping data. Publisher Copyright: © 2022, The Author(s).

PY - 2022/11/16

Y1 - 2022/11/16

N2 - 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.

AB - 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.

KW - Humans

KW - Parkinson Disease/genetics

KW - Alzheimer Disease

KW - Neuroinflammatory Diseases

KW - Bayes Theorem

KW - Cohort Studies

U2 - 10.1186/s13073-022-01132-9

DO - 10.1186/s13073-022-01132-9

M3 - Article (Academic Journal)

C2 - 36384636

SN - 1756-994X

VL - 14

JO - Genome Medicine

JF - Genome Medicine

IS - 1

M1 - 129

ER -

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

Abstract

Bibliographical note

Keywords

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