Longitudinal study investigating the influence of COMT gene polymorphism on cortical thickness changes in Parkinson's disease over four years.
COMT
Cortical Thickness
Genetics
Neuroimaging
PPMI
Parkinson’s disease
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
30 04 2024
30 04 2024
Historique:
received:
06
09
2023
accepted:
27
04
2024
medline:
1
5
2024
pubmed:
1
5
2024
entrez:
30
4
2024
Statut:
epublish
Résumé
Parkinson's disease (PD) is a progressive neurodegenerative disorder affecting over 3% of those over 65. It's caused by reduced dopaminergic neurons and Lewy bodies, leading to motor and non-motor symptoms. The relationship between COMT gene polymorphisms and PD is complex and not fully elucidated. Some studies have reported associations between certain COMT gene variants and PD risk, while others have not found significant associations. This study investigates how COMT gene variations impact cortical thickness changes in PD patients over time, aiming to link genetic factors, especially COMT gene variations, with PD progression. This study analyzed data from 44 PD patients with complete 4-year imaging follow-up from the Parkinson Progression Marker Initiative (PPMI) database. Magnetic resonance imaging (MRI) scans were acquired using consistent methods across 9 different MRI scanners. COMT single-nucleotide polymorphisms (SNPs) were assessed based on whole genome sequencing data. Longitudinal image analysis was conducted using FreeSurfer's processing pipeline. Linear mixed-effect models were employed to examine the interaction effect of genetic variations and time on cortical thickness, while controlling for covariates and subject-specific variations. The rs165599 SNP stands out as a potential contributor to alterations in cortical thickness, showing a significant reduction in overall mean cortical thickness in both hemispheres in homozygotes (Left: P = 0.023, Right: P = 0.028). The supramarginal, precentral, and superior frontal regions demonstrated significant bilateral alterations linked to rs165599. Our findings suggest that the rs165599 variant leads to earlier manifestation of cortical thinning during the course of the disease. However, it does not result in more severe cortical thinning outcomes over time. There is a need for larger cohorts and control groups to validate these findings and consider genetic variant interactions and clinical features to elucidate the specific mechanisms underlying COMT-related neurodegenerative processes in PD.
Identifiants
pubmed: 38689006
doi: 10.1038/s41598-024-60828-7
pii: 10.1038/s41598-024-60828-7
doi:
Substances chimiques
Catechol O-Methyltransferase
EC 2.1.1.6
COMT protein, human
EC 2.1.1.6
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Research Support, N.I.H., Extramural
Langues
eng
Sous-ensembles de citation
IM
Pagination
9920Informations de copyright
© 2024. The Author(s).
Références
Braak, H. & Del Tredici, K. Neuropathological staging of brain pathology in sporadic Parkinson’s disease: Separating the Wheat from the chaff. J. Parkinsons Dis. 7(s1), S71-s85 (2017).
doi: 10.3233/JPD-179001
pubmed: 28282810
pmcid: 5345633
Raza, C. & Anjum, R. Parkinson’s disease: Mechanisms, translational models and management strategies. Life Sci. 226, 77–90 (2019).
doi: 10.1016/j.lfs.2019.03.057
pubmed: 30980848
Coon, S. et al. Whole-body lifetime occupational lead exposure and risk of Parkinson’s disease. Environ. Health Perspect. 114(12), 1872–1876 (2006).
doi: 10.1289/ehp.9102
pubmed: 17185278
pmcid: 1764163
Maiti, P., Manna, J. & Dunbar, G. L. Current understanding of the molecular mechanisms in Parkinson’s disease: Targets for potential treatments. Transl. Neurodegeneration. 6(1), 28 (2017).
doi: 10.1186/s40035-017-0099-z
Zhou, C., Huang, Y. & Przedborski, S. Oxidative stress in Parkinson’s disease: a mechanism of pathogenic and therapeutic significance. Ann. N Y Acad. Sci. 1147, 93–104 (2008).
doi: 10.1196/annals.1427.023
pubmed: 19076434
pmcid: 2745097
Ryman, S. G. & Poston, K. L. MRI biomarkers of motor and non-motor symptoms in Parkinson’s disease. Parkinsonism Related Disorders. 73, 85–93 (2020).
doi: 10.1016/j.parkreldis.2019.10.002
pubmed: 31629653
DeMaagd, G. & Philip, A. Parkinson’s disease and its management: Part 1: Disease entity, risk factors, pathophysiology, clinical presentation, and diagnosis. P t. 40(8), 504–532 (2015).
pubmed: 26236139
pmcid: 4517533
Chen, J. et al. Functional analysis of genetic variation in catechol-O-methyltransferase (COMT): effects on mRNA, protein, and enzyme activity in postmortem human brain. Am. J. Hum. Genet. 75(5), 807–821 (2004).
doi: 10.1086/425589
pubmed: 15457404
pmcid: 1182110
Fabbri, M., Ferreira, J. J. & Rascol, O. COMT inhibitors in the management of Parkinson’s disease. CNS Drugs. 36(3), 261–282 (2022).
doi: 10.1007/s40263-021-00888-9
pubmed: 35217995
Jiménez-Jiménez, F. J., Alonso-Navarro, H., García-Martín, E. & Agúndez, J. A. COMT gene and risk for Parkinson’s disease: a systematic review and meta-analysis. Pharmacogenet. Genom. 24(7), 331–339 (2014).
doi: 10.1097/FPC.0000000000000056
Won, J. H., Kim, M., Youn, J. & Park, H. Prediction of age at onset in Parkinson’s disease using objective specific neuroimaging genetics based on a sparse canonical correlation analysis. Sci. Rep. 10(1), 11662 (2020).
doi: 10.1038/s41598-020-68301-x
pubmed: 32669683
pmcid: 7363828
Lee, A. & Qiu, A. Modulative effects of COMT haplotype on age-related associations with brain morphology. Hum. Brain Mapp. 37(6), 2068–2082 (2016).
doi: 10.1002/hbm.23161
pubmed: 26920810
pmcid: 6867428
Salat, D., Noyce, A. J., Schrag, A. & Tolosa, E. Challenges of modifying disease progression in prediagnostic Parkinson’s disease. Lancet Neurol. 15(6), 637–648 (2016).
doi: 10.1016/S1474-4422(16)00060-0
pubmed: 26993435
Marek, K. et al. The Parkinson progression marker initiative (PPMI). Progress in neurobiology. 95(4), 629–635 (2011).
doi: 10.1016/j.pneurobio.2011.09.005
pmcid: 9014725
Matloff W, Toga A. Allelic status of selected Parkinson disease-associated variants for PPMI subjects with available whole-genome sequencing data. (2011).
Slatkin, M. Linkage disequilibrium–understanding the evolutionary past and mapping the medical future. Nat. Rev. Genet. 9(6), 477–485 (2008).
doi: 10.1038/nrg2361
pubmed: 18427557
pmcid: 5124487
Rogers, A. R. & Huff, C. Linkage disequilibrium between loci with unknown phase. Genetics. 182(3), 839–844 (2009).
doi: 10.1534/genetics.108.093153
pubmed: 19433632
pmcid: 2710162
Miles A, Bot Pi, R M, Ralph P, Kelleher J, Schelker M, et al. cggh/scikit-allel: v1.3.7. v1.3.7 ed: Zenodo; (2023).
Tang, D. et al. SRplot: A free online platform for data visualization and graphing. PLoS One. 18(11), e0294236 (2023).
doi: 10.1371/journal.pone.0294236
pubmed: 37943830
pmcid: 10635526
Liao, B., Li, X., Cai, L., Cao, Z. & Chen, H. A hierarchical clustering method of selecting kernel SNP to unify informative SNP and tag SNP. IEEE/ACM Trans. Comput. Biol. Bioinf. 12(1), 113–122 (2015).
doi: 10.1109/TCBB.2014.2351797
Reuter, M., Schmansky, N. J., Rosas, H. D. & Fischl, B. Within-subject template estimation for unbiased longitudinal image analysis. NeuroImage. 61(4), 1402–1418 (2012).
doi: 10.1016/j.neuroimage.2012.02.084
pubmed: 22430496
Reuter, M. & Fischl, B. Avoiding asymmetry-induced bias in longitudinal image processing. NeuroImage. 57(1), 19–21 (2011).
doi: 10.1016/j.neuroimage.2011.02.076
pubmed: 21376812
Edwards, L. J., Muller, K. E., Wolfinger, R. D., Qaqish, B. F. & Schabenberger, O. An R2 statistic for fixed effects in the linear mixed model. Stat. Med. 27(29), 6137–6157 (2008).
doi: 10.1002/sim.3429
pubmed: 18816511
pmcid: 2587505
Nakagawa, S., Johnson, P. C. D. & Schielzeth, H. The coefficient of determination R(2) and intra-class correlation coefficient from generalized linear mixed-effects models revisited and expanded. J. R Soc. Interface. 14(134), 20170213 (2017).
doi: 10.1098/rsif.2017.0213
pubmed: 28904005
pmcid: 5636267
Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67(1), 1–48 (2015).
doi: 10.18637/jss.v067.i01
Brzyski, D. et al. Controlling the rate of GWAS false discoveries. Genetics. 205(1), 61–75 (2017).
doi: 10.1534/genetics.116.193987
pubmed: 27784720
Brinster, R., Köttgen, A., Tayo, B. O., Schumacher, M. & Sekula, P. Control procedures and estimators of the false discovery rate and their application in low-dimensional settings: an empirical investigation. BMC Bioinf. 19(1), 78 (2018).
doi: 10.1186/s12859-018-2081-x
Williams-Gray, C. H. et al. The distinct cognitive syndromes of Parkinson’s disease: 5 year follow-up of the CamPaIGN cohort. Brain. 132(Pt 11), 2958–2969 (2009).
doi: 10.1093/brain/awp245
pubmed: 19812213
Cheng, H. et al. The COMT (rs165599) gene polymorphism contributes to chemotherapy-induced cognitive impairment in breast cancer patients. Am. J. Transl. Res. 8(11), 5087–5097 (2016).
pubmed: 27904710
pmcid: 5126352
du Boisgueheneuc, F. et al. Functions of the left superior frontal gyrus in humans: a lesion study. Brain. 129(Pt 12), 3315–3328 (2006).
doi: 10.1093/brain/awl244
pubmed: 16984899
Deschamps, I., Baum, S. R. & Gracco, V. L. On the role of the supramarginal gyrus in phonological processing and verbal working memory: evidence from rTMS studies. Neuropsychologia. 53, 39–46 (2014).
doi: 10.1016/j.neuropsychologia.2013.10.015
pubmed: 24184438
Wada, S. et al. Volume of the right supramarginal gyrus is associated with a maintenance of emotion recognition ability. PLoS One. 16(7), e0254623 (2021).
doi: 10.1371/journal.pone.0254623
pubmed: 34293003
pmcid: 8297759
DiGuiseppi J, Tadi P. Neuroanatomy, Postcentral Gyrus. StatPearls. Treasure Island (FL) ineligible companies. Disclosure: Prasanna Tadi declares no relevant financial relationships with ineligible companies.: StatPearls Publishing Copyright © 2024, StatPearls Publishing LLC.; (2024).
Servaas, M. N. et al. Associations between genetic risk, functional brain network organization and neuroticism. Brain Imag. Behav. 11(6), 1581–1591 (2017).
doi: 10.1007/s11682-016-9626-2
Gozukara Bag, H. G. Association between COMT gene rs165599 SNP and schizophrenia: A meta-analysis of case-control studies. Mol. Genet. Genomic Med. 6(5), 845–854 (2018).
doi: 10.1002/mgg3.468
pubmed: 30165727
pmcid: 6160701
Jugurnauth, S. K. et al. A COMT gene haplotype associated with methamphetamine abuse. Pharmacogenet. Genom. 21(11), 731–740 (2011).
doi: 10.1097/FPC.0b013e32834a53f9
Matsuzaka, C. T. et al. Catechol-O-methyltransferase (COMT) polymorphisms modulate working memory in individuals with schizophrenia and healthy controls. Braz. J. Psychiatry. 39(4), 302–308 (2017).
doi: 10.1590/1516-4446-2016-1987
pubmed: 28273278
pmcid: 7111404