Citation: Xiaoxiao Wang, Bolun Wang, Fenfen Ji, Jie Yan, Jiacheng Fang, Doudou Zhang, Ji Xu, Jing Ji, Xinran Hao, Hemi Luan, Yanjun Hong, Shulan Qiu, Min Li, Zhu Yang, Wenlan Liu, Xiaodong Cai, Zongwei Cai. Discovery of plasma biomarkers for Parkinson’s disease diagnoses based on metabolomics and lipidomics[J]. Chinese Chemical Letters, ;2024, 35(11): 109653. doi: 10.1016/j.cclet.2024.109653 shu

Discovery of plasma biomarkers for Parkinson’s disease diagnoses based on metabolomics and lipidomics

Xiaoxiao Wang ^{a, 1} ,
Bolun Wang ^{a, 1} ,
Fenfen Ji ^a ,
Jie Yan ^b ,
Jiacheng Fang ^a ,
Doudou Zhang ^b ,
Ji Xu ^c ,
Jing Ji ^c ,
Xinran Hao ^a ,
Hemi Luan ^a ,
Yanjun Hong ^a ,
Shulan Qiu ^a ,
Min Li ^d ,
Zhu Yang ^{a, *,} ,
Wenlan Liu ^{c, *,} ,
Xiaodong Cai ^{b, *,} ,
Zongwei Cai ^{a, *,}

a.
State Key Laboratory of Environmental and Biological Analysis, Department of Chemistry, Hong Kong Baptist University, Hong Kong, China
b.
Department of Neurosurgery, Shenzhen Key Laboratory of Neurosurgery, the First Affiliated Hospital of Shenzhen University, Shenzhen Second People’s Hospital, Shenzhen 518035, China
c.
The Central Laboratory, the First Affiliated Hospital of Shenzhen University, Shenzhen Second People’s Hospital, Shenzhen 518035, China
d.
Mr. and Mrs. Ko Chi Ming Centre for Parkinson’s Disease Research, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong, China

Received Date: 16 October 2023
Revised Date: 4 February 2024
Accepted Date: 14 February 2024
Available Online: 20 February 2024

Figures(4)

Parkinson’s disease (PD) is an aging-associated neurodegenerative movement disorder with increasing morbidity and mortality rates. The current gold standard for diagnosing PD is clinical evaluation, which is often challenging and inaccurate. Metabolomics and lipidomics approaches have been extensively applied because of their potential in discovering valuable biomarkers for medical diagnostics. Here, we used comprehensive untargeted metabolomics and lipidomics methodologies based on liquid chromatography-mass spectrometry to evaluate metabolic abnormalities linked with PD. Two well-characterized cohorts of 288 plasma samples (143 PD patients and 145 control subjects in total) were used to examine metabolic alterations and identify diagnostic biomarkers. Unbiased multivariate and univariate studies were combined to identify the promising metabolic signatures, based on which the discriminant models for PD were established by integrating multiple machine learning algorithms. A 6-biomarker predictive model was constructed based on the omics profile in the discovery cohort, and the discriminant performance of the biomarker panel was evaluated with an accuracy over 81.6% both in the discovery cohort and validation cohort. The results indicated that PC (40:7), eicosatrienoic acid were negatively correlated with severity of PD, and pentalenic acid, PC (40:6p) and aspartic acid were positively correlated with severity of PD. In summary, we developed a multi-metabolite predictive model which can diagnose PD with over 81.6% accuracy based on this unique metabolic signature. Future clinical diagnosis of PD may benefit from the biomarker panel reported in this study.
- Parkinson’s disease,
- Metabolomics,
- Lipidomics,
- Machine learning,
- Biomarker
1. [1]
  D. Aarsland, L. Batzu, G.M. Halliday, G.J. Geurtsen, et al., Nat. Rev. Dis. Primers 7 (2021) 1–21. doi: 10.1038/s41572-020-00234-1
2. [2]
  K.R. Chaudhuri, N. Titova, Eur. Neurol. Rev. 14 (2019) 28–35. doi: 10.17925/ENR.2019.14.1.28
3. [3]
  D.W. Dickson, Cold Spring Harb. Perspect. Med. 2 (2012).
4. [4]
  G. DeMaagd, A. Philip, Clin. Pharmacol. Ther. 40 (2015) 504. doi: 10.1111/jcpt.12304
5. [5]
  Y. Shao, W. Le, Mol. Neurodegener. 14 (2019) 1–12. doi: 10.1186/s13024-018-0301-5
6. [6]
  L. Parnetti, L. Gaetani, et al., Lancet Neurol. 18 (2019) 573–586. doi: 10.1016/S1474-4422(19)30024-9
7. [7]
  G.A. Nagana Gowda, S. Zhang, H. Gu, et al., Expert Rev. Mol. Diagn. 8 (2008) 617–633. doi: 10.1586/14737159.8.5.617
8. [8]
  S. Gao, X. Zhou, M. Yue, S. Zhu, Q. Liu, X.E. Zhao, Trac-Trend Anal. Chem. 162 (2023) 117022. doi: 10.1016/j.trac.2023.117022
9. [9]
  S.S. Ahmed, W. Santosh, S. Kumar, et al., J. Biomed. Sci. 16 (2009) 63. doi: 10.1186/1423-0127-16-63
10. [10]
  H. Boecker, A.O. Ceballos-Baumann, D. Volk, B. Conrad, H. Forstl, P. Haussermann, Arch. Neurol. 64 (2007) 984–988. doi: 10.1001/archneur.64.7.984
11. [11]
  A. Anandhan, M.S. Jacome, S. Lei, P. Hernandez-Franco, et al., Brain Res. Bull. 133 (2017) 12–30. doi: 10.1016/j.brainresbull.2017.03.009
12. [12]
  E. Socha, M. Koba, P. Kośliński, Amino Acids 51 (2019) 367–371. doi: 10.1007/s00726-019-02705-6
13. [13]
  M. Figura, K. Kuśmierska, E. Bucior, et al., PLoS One 13 (2018) e0191670. doi: 10.1371/journal.pone.0191670
14. [14]
  S.M. Grant, S. Demorrow, Int. J. Mol. Sci. 21 (2020) 1–25.
15. [15]
  P. Li, B.A. Killinger, E. Ensink, et al., Metabolites 11 (2021) 29. doi: 10.3390/metabo11010029
16. [16]
  Y. Hasuike, T. Endo, M. Koroyasu, M. Matsui, et al., Med. Hypotheses 134 (2020) 109436. doi: 10.1016/j.mehy.2019.109436
17. [17]
  Y. Shao, T. Li, Z. Liu, et al., Mol. Neurodegener. 16 (2021) 4. doi: 10.1186/s13024-021-00425-8
18. [18]
  S. Fanning, A. Haque, T. Imberdis, V. Baru, et al., Mol. Cell 73 (2019) 1001–1014.e8. doi: 10.1016/j.molcel.2018.11.028
19. [19]
  H. Avisar, C. Guardia-Laguarta, E. Area-Gomez, et al., J. Parkinsons Dis. 11 (2021) 1141–1155. doi: 10.3233/JPD-202476
20. [20]
  A. Zardini Buzatto, J. Tatlay, B. Bajwa, et al., J. Proteome Res. 20 (2021) 4053–4067. doi: 10.1021/acs.jproteome.1c00374
21. [21]
  E. Sinclair, D.K. Trivedi, D. Sarkar, C. Walton-Doyle, et al., Nat. Commun. 12 (2021) 1–9. doi: 10.1038/s41467-020-20314-w
22. [22]
  F. Moisan, S. Kab, F. Mohamed, M. Canonico, et al., J. Neurol. Neurosurg. Psychiatry 87 (2016) 952–957. doi: 10.1136/jnnp-2015-312283
23. [23]
  E.R. Dorsey, A. Elbaz, E. Nichols, F. Abd-Allah, et al., Lancet Neurol. 17 (2018) 939–953. doi: 10.1016/S1474-4422(18)30295-3
24. [24]
  S. Cui, L. Li, Y. Zhang, et al., Adv. Sci. 8 (2021) 2003893. doi: 10.1002/advs.202003893
25. [25]
  P.S. Gromski, H. Muhamadali, et al., Anal. Chim. Acta 879 (2015) 10–23. doi: 10.1016/j.aca.2015.02.012
26. [26]
  X. Wang, X. Hao, J. Yan, et al., Chin. Chem. Lett. 34 (2023) 108230. doi: 10.1016/j.cclet.2023.108230
27. [27]
  K. Su, J. Wu, D. Gu, S. Yang, et al., Diagnostics 11 (2021) 960944.
28. [28]
  T. Chen, Y. Cao, Y. Zhang, et al., Evid. Based Complementary Altern. Med. 2013 (2013) 298183.
29. [29]
  R. Bujak, E. Daghir-Wojtkowiak, R. Kaliszan, M.J. Markuszewski, Front. Mol. Biosci. 3 (2016) 35.
30. [30]
  S.H. Law, M.L. Chan, G.K. Marathe, et al., Int. J. Mol. Sci. 20 (2019) 1149. doi: 10.3390/ijms20051149
31. [31]
  H.E. Moon, S.H. Paek, Exp. Neurobiol. 24 (2015) 103–116. doi: 10.5607/en.2015.24.2.103
32. [32]
  K.W. Chung, Cells 10 (2021) 880. doi: 10.3390/cells10040880
33. [33]
  S. Fanning, D. Selkoe, U. Dettmer, NPJ Parkinsons Dis. 6 (2020) 1–9. doi: 10.1038/s41531-019-0104-6
34. [34]
  M. Fais, A. Dore, M. Galioto, et al., Int. J. Mol. Sci. 22 (2021) 7630. doi: 10.3390/ijms22147630
35. [35]
  S.T. Tan, T. Ramesh, X.R. Toh, L.N. Nguyen, Prog. Lipid Res. 80 (2020) 101068. doi: 10.1016/j.plipres.2020.101068
36. [36]
  B. Fuchs, J. Schiller, U. Wagner, H. Häntzschel, K. Arnold, Clin. Biochem. 38 (2005) 925–933. doi: 10.1016/j.clinbiochem.2005.06.006
37. [37]
  A. Goonesinghe, E.S. Mundy, M. Smith, et al., Biochem. J. 387 (2005) 109–118. doi: 10.1042/BJ20041389
38. [38]
  J. Miletic Vukajlović, D. Drakulić, S. Pejić, et al., Rapid Commun. Mass Spectrom. 34 (2020) e8595. doi: 10.1002/rcm.8595
39. [39]
  D. Ruiz-Perez, H. Guan, P. Madhivanan, K. Mathee, G. Narasimhan, BMC Bioinform. 21 (2020) 1–10. doi: 10.1186/s12859-019-3325-0
40. [40]
  D. Grissa, M. Pétéra, M. Brandolini, et al., Front. Mol. Biosci. 3 (2016) 30.
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