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Polymorphism of neurodegeneration-related genes associated with Parkinson’s disease risk

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Abstract

Background

Neurodegenerative genes are critical in neuronal loss in Parkinson’s disease (PD). We performed a systematic meta-analysis including all the studies published on PD risk related to genes encoding enzymes vital for dopamine metabolism and neuron survival.

Methods

We included neurodegeneration-related genes which were divided into four groups according to their functions: main enzymes in dopamine metabolism, receptors and transporters for dopamine or other metabolites, neuroprotective factors for dopaminergic neurons, and genes associated with dopaminergic neurons survival reported in other neurological diseases. We collected original articles from PubMed, Embase, and Web of Science databases. Revman 5.3 software was used to analyze data. The allele model (AM) was used to test the effect size of the effect allele between the case group and the control group and secondary analysis using the dominant model (DM) and recessive model (RM) to analyze the contributions from heterozygote and homozygote to the allele risk. Odds ratio (OR) and 95% confidence interval (CI) were used to present the pooled results.

Results

We included 31 variants in 20 genes for the final pooled analysis. Consequently, SLC6A4/5-HTT HTTLPR, BDNF rs56164415, FGF20 rs1721100, PARK16 rs823128, rs823156, rs947211, APOE e2, A2M rs669, RIT2 rs12456492, MAPT intron 9 H1H2, and STH rs62063857 variants were statistically associated with PD risk while researched variants in COMT, DBH, MAO, DAT/SLC6A3, DRD2, GRIN2B, GSK3β, ATP13A2, LINGO1, PICALM, and GRN were not related to PD risk.

Conclusion

Several variants from neurodegeneration-related genes are associated with PD risk, which may help deepen the understanding of PD pathogenesis and improve clinical treatment strategies.

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References

  1. Lew M (2007) Overview of Parkinson’s disease. Pharmacotherapy 27(12 Pt 2):155s–160s

    Article  CAS  Google Scholar 

  2. Chinta SJ, Andersen JK (2005) Dopaminergic neurons. Int J Biochem Cell Biol 37(5):942–946

    Article  CAS  Google Scholar 

  3. Miller DB, O’Callaghan JP (2015) Biomarkers of Parkinson’s disease: present and future. Metabolism 64(3 Suppl 1):S40–S46

    Article  CAS  Google Scholar 

  4. Costa P et al (1997) Association of a polymorphism in intron 13 of the monoamine oxidase B gene with Parkinson disease. Am J Med Genet 74(2):154–156

    Article  CAS  Google Scholar 

  5. Rush RA, Geffen LB (1980) Dopamine beta-hydroxylase in health and disease. Crit Rev Clin Lab Sci 12(3):241–277

    Article  CAS  Google Scholar 

  6. Yenisetti SC (2018) Parkinson’s disease - understanding pathophysiology and developing therapeutic strategies || effects of genetic variability in dopaminergic pathway on treatment response in Parkinson’s disease. https://doi.org/10.5772/intechopen.70111(Chapter 3).

  7. Liu Y et al (2021) FGF, mechanism of action, role in Parkinson’s disease, and therapeutics. Front Pharmacol 12:675725

    Article  CAS  Google Scholar 

  8. Paslawski W et al (2019) α-synuclein-lipoprotein interactions and elevated ApoE level in cerebrospinal fluid from Parkinson’s disease patients. Proc Natl Acad Sci U S A 116(30):15226–15235

    Article  CAS  Google Scholar 

  9. Zhou ZD, Sathiyamoorthy S, Tan EK (2012) LINGO-1 and neurodegeneration: pathophysiologic clues for essential tremor. Tremor Other Hyperkinet Mov (N Y), 2.

  10. Hughes AJ et al (1992) Accuracy of clinical diagnosis of idiopathic Parkinson’s disease: a clinico-pathological study of 100 cases. J Neurol Neurosurg Psychiatry 55(3):181–184

    Article  CAS  Google Scholar 

  11. Postuma RB et al (2015) MDS clinical diagnostic criteria for Parkinson’s disease. Mov Disord 30(12):1591–1601

    Article  Google Scholar 

  12. Lo CK, Mertz D, Loeb M (2014) Newcastle-Ottawa Scale: comparing reviewers’ to authors’ assessments. BMC Med Res Methodol 14:45

    Article  Google Scholar 

  13. Zhao F et al (2016) Genetic model. J Cell Mol Med 20(4):765

    Article  Google Scholar 

  14. Cacabelos R (2017) Parkinson’s disease: from pathogenesis to pharmacogenomics. Int J Mol Sci 18(3).

  15. Wang YC et al (2019) COMT Val158Met polymorphism and Parkinson’s disease risk: a pooled analysis in different populations. Neurol Res 41(4):319–325

    Article  Google Scholar 

  16. Zhang Y et al (2016) A meta-analysis on relationship of MAOB intron 13 polymorphisms, interactions with smoking/COMT H158L polymorphisms with the risk of PD. Int J Neurosci 126(5):400–407

    Article  CAS  Google Scholar 

  17. Yin Y et al (2021) Association of COMT rs4680 and MAO-B rs1799836 polymorphisms with levodopa-induced dyskinesia in Parkinson’s disease-a meta-analysis. Neurol Sci 42(10):4085–4094

    Article  Google Scholar 

  18. Tang S et al (2018) Association of dopamine beta-hydroxylase polymorphisms with Alzheimer’s disease, Parkinson’s disease and Schizophrenia: evidence based on currently available loci. Cell Physiol Biochem 51(1):411–428

    Article  CAS  Google Scholar 

  19. Kang S et al (2018) Association of the rs1611115 polymorphism in DBH gene with Parkinson’s disease: a meta-analysis. Neurol Sci 39(12):2085–2089

    Article  Google Scholar 

  20. Ghosh A et al (2019) Dopamine β Hydroxylase (DBH) is a potential modifier gene associated with Parkinson’s disease in Eastern India. Neurosci Lett 706:75–80

    Article  CAS  Google Scholar 

  21. Politis M, Loane C (2011) Serotonergic dysfunction in Parkinson’s disease and its relevance to disability. ScientificWorldJournal 11:1726–1734

    Article  CAS  Google Scholar 

  22. Cheng P et al (2021) 5-HTTLPR polymorphism and depression risk in Parkinson’s disease: an updated meta-analysis. Acta Neurol Belg 121(4):933–940

    Article  Google Scholar 

  23. McDonell KE et al (2018) Taq1A polymorphism and medication effects on inhibitory action control in Parkinson disease. Brain Behav 8(7):e01008

    Article  Google Scholar 

  24. Lee JY et al (2009) Association of DRD3 and GRIN2B with impulse control and related behaviors in Parkinson’s disease. Mov Disord 24(12):1803–1810

    Article  Google Scholar 

  25. Zainal Abidin S et al (2015) DRD and GRIN2B polymorphisms and their association with the development of impulse control behaviour among Malaysian Parkinson’s disease patients. BMC Neurol 15:59

    Article  Google Scholar 

  26. MacLeod DA et al (2013) RAB7L1 interacts with LRRK2 to modify intraneuronal protein sorting and Parkinson’s disease risk. Neuron 77(3):425–439

    Article  CAS  Google Scholar 

  27. He T et al (2017) Association between PARK16 and Parkinson’s disease: a meta-analysis. Neurosci Lett 657:179–188

    Article  CAS  Google Scholar 

  28. Gopalai AA et al (2016) PARK16 is associated with PD in the Malaysian population. Am J Med Genet B Neuropsychiatr Genet 171(6):839–847

    Article  CAS  Google Scholar 

  29. Miller KM, Mercado NM, Sortwell CE (2021) Synucleinopathy-associated pathogenesis in Parkinson’s disease and the potential for brain-derived neurotrophic factor. NPJ Parkinsons Dis 7(1):35

    Article  Google Scholar 

  30. Pal P et al (2019) Role of apolipoprotein E, cathepsin D, and brain-derived neurotrophic factor in Parkinson’s disease: a study from Eastern India. Neuromolecular Med 21(3):287–294

    Article  CAS  Google Scholar 

  31. Itoh N, Ohta H (2013) Roles of FGF20 in dopaminergic neurons and Parkinson’s disease. Front Mol Neurosci 6:15

    Article  CAS  Google Scholar 

  32. Ma ZG, Xu J, Liu TW (2015) Quantitative assessment of the association between fibroblast growth factor 20 rs1721100 C/G polymorphism and the risk of sporadic Parkinson’s diseases: a meta-analysis. Neurol Sci 36(1):47–51

    Article  Google Scholar 

  33. Fyfe I (2020) APOE(*)ε4 promotes synucleinopathy. Nat Rev Neurol 16(4):185

    Article  Google Scholar 

  34. Huang X, Chen PC, Poole C (2004) APOE-[epsilon]2 allele associated with higher prevalence of sporadic Parkinson disease. Neurology 62(12):2198–2202

    Article  CAS  Google Scholar 

  35. Paul KC et al (2016) APOE, MAPT, and COMT and Parkinson’s disease susceptibility and cognitive symptom progression. J Parkinsons Dis 6(2):349–359

    Article  CAS  Google Scholar 

  36. Robakis D et al (2016) The effect of MAPT haplotype on neocortical Lewy body pathology in Parkinson disease. J Neural Transm (Vienna) 123(6):583–588

    Article  CAS  Google Scholar 

  37. Millard SP et al (2014) Association of cerebrospinal fluid Aβ42 with A2M gene in cognitively normal subjects. Neurobiol Aging 35(2):357–364

    Article  CAS  Google Scholar 

  38. Guo X et al (2016) Association between two alpha-2-macroglobulin gene polymorphisms and Parkinson’s disease: a meta-analysis. Int J Neurosci 126(3):193–198

    Article  CAS  Google Scholar 

  39. Daneshmandpour Y, Darvish H, Emamalizadeh B (2018) RIT2: responsible and susceptible gene for neurological and psychiatric disorders. Mol Genet Genomics 293(4):785–792

    Article  CAS  Google Scholar 

  40. Lu Y et al (2015) Genetic association of RIT2 rs12456492 polymorphism and Parkinson’s disease susceptibility in Asian populations: a meta-analysis. Sci Rep 5:13805

    Article  Google Scholar 

Download references

Funding

This research was supported by the National Natural Science Foundation of China (No. 82001357), the Hunan Provincial Natural Science Foundation of China (No. 2020JJ5951, No. 2021JJ80079), the Youth Science Foundation of Xiangya Hospital (No. 2019Q17), the Degree & Postgraduate Education Reform Project of Central South University (No. 2021YJSKSA10), the Undergraduate Education Reform Project of Central South University (No. 2021CG065, No. 2021CG068) and the Research Project of Laboratory Construction and Management of Central South University (No. 202120).

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Authors

Contributions

Study design: M. H. Y. and Y. Z.; data collection: M. H. Y., J. X. L., B. B. L., S. J. Y., L. S., Y. Z., Z. N. H., and Y. Z.; data analysis: M. H. Y., J. X. L., B. B. L., S. J. Y., and Y. Z.; writing: M. H. Y., J. X. L., B. B. L., S. J. Y., L. S., and Y. Z.; funding: M. H. Y. and Y. Z.; administration: M. H. Y. and Y. Z.

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Correspondence to Yuan Zhang.

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Li, J., Yi, M., Li, B. et al. Polymorphism of neurodegeneration-related genes associated with Parkinson’s disease risk. Neurol Sci 43, 5301–5312 (2022). https://doi.org/10.1007/s10072-022-06192-8

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  • DOI: https://doi.org/10.1007/s10072-022-06192-8

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