Potential Tear Biomarkers for the Diagnosis of Parkinson’s Disease—A Pilot Study
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Cohort
2.2. Ophthalmological Examination
2.3. Neurological Examination
2.4. Tear-Sample Collection
2.5. Proteomics Analyses
2.6. Statistical Analyses
3. Results
3.1. Patients and Clinical Parameters
3.2. nLC MS/MS Data
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Variables, Units | iPD | E46K-SNCA | Control |
---|---|---|---|
n | 24 | 3 | 27 |
Age, years | 60.4 (9.21) | 45 (14.73) | 49.69 (12.17) |
Gender (F/M) (%) | 38/62 | 0/100 | 54/46 |
Disease duration, years | 9.36 (6.66) | 3.33 (3.51) | N/A |
Hoehn and Yahr score | 2 (0.82) | 2 (1) | N/A |
UPDRS score (%) | 32.39 (9.03) | 16.56 (11.12) | N/A |
Part I UPDRS score | 17.19 (12.6) | 6.25 (0.0) | N/A |
Part II UPDRS score | 27.52 (10.07) | 11.54 (8.38) | N/A |
Part III UPDRS score | 45.13 (15.09) | 25.00 (16.7) | N/A |
Part IV UPDRS score | 16.30 (9.46) | 10.14 (9.05) | N/A |
Blepharitis (%) | 40.74 | 0 | 0 |
Entry Name | Protein Name | p-Value | Fold |
---|---|---|---|
OLFM4 | Olfactomedin-4 | 7.08 × 10−5 | 4.25 |
SPB3 | Serpin B3 | 5.29 × 10−4 | 3.99 |
SPRR3 | Small proline-rich protein 3 | 1.77 × 10−2 | 3.31 |
MMP9 | Matrix metalloproteinase-9 | 7.33 × 10−3 | 3.08 |
CASPE | Caspase-14 | 4.88 × 10−2 | 2.67 |
PRTN3 | Myeloblastin | 1.76 × 10−2 | 2.63 |
GGCT | Gamma-glutamylcyclotransferase | 2.56 × 10−2 | 2.40 |
CATD | Cathepsin D | 1.24 × 10−3 | 2.31 |
CALL5 | Calmodulin-like protein 5 | 3.48 × 10−2 | 1.91 |
TIG1 | Retinoic acid receptor responder protein 1 | 3.87 × 10−2 | 1.90 |
MDHM | Malate dehydrogenase mitochondrial | 8.59 × 10−3 | 1.66 |
NGAL | Neutrophil gelatinase-associated lipocalin | 3.51 × 10−2 | 1.54 |
NQO1 | NAD(P)H dehydrogenase (quinone) 1 | 1.92 × 10−2 | 0.49 |
PLST | Plastin-3 | 1.15 × 10−2 | 0.40 |
MYH14 | Myosin-14 | 4.80 × 10−2 | 0.37 |
AMPL | Cytosol aminopeptidase | 3.78 × 10−2 | 0.36 |
AK1A1 | Alcohol dehydrogenase (NADP(+)) | 3.53 × 10−2 | 0.35 |
ADH1G | Alcohol dehydrogenase 1C | 2.64 × 10−3 | 0.35 |
PRDX5 | Peroxiredoxin-5 mitochondrial | 4.71 × 10−2 | 0.35 |
RINI | Ribonuclease inhibitor | 2.21 × 10−2 | 0.34 |
VATA | V-type proton ATPase catalytic subunit A | 1.49 × 10−4 | 0.25 |
Entry Name | Protein Name | Peptides | Unique Peptides | p-Value | Fold Change | AUC % |
---|---|---|---|---|---|---|
LMNA | Prelamin-A/C | 25 | 24 | 4.00 × 10−2 | 2.25 | 67.1 |
CATD | Cathepsin D | 19 | 19 | 1.48 × 10−3 | 1.82 | 75.4 |
ASAH1 | Acid ceramidase | 6 | 6 | 2.98 × 10−3 | 1.80 | 72.3 |
TERA | Transitional endoplasmic reticulum ATPase | 18 | 18 | 1.99 × 10−2 | 1.60 | 65 |
DYHC1 | Cytoplasmic dynein 1 heavy chain 1 | 9 | 9 | 7.50 × 10−3 | 1.32 | 70.2 |
TPP1 | Tripeptidyl-peptidase 1 | 11 | 11 | 4.13 × 10−2 | 0.64 | 69 |
Unique Variable Used in Model | Estimate | p-Value |
---|---|---|
Sex | 1.163 | 0.072 |
Age | 0.088 | 0.00 * |
LMNA | 0.351 | 0.055 |
CATD | 1.426 | 0.010 * |
ASAH1 | 0.990 | 0.020 * |
TERA | 0.801 | 0.040 * |
DYHC1 | 1.197 | 0.085 |
TPP1 | −0.527 | 0.147 |
Time with disease | 9.483 | 0.995 |
UPDRS score | 488.104 | 0.995 |
Part I UPDRS score | 311.136 | 0.994 |
Part II UPDRS score | 964.522 | 0.995 |
Part III UPDRS score | 313.201 | 0.995 |
Part IV UPDRS score | 398.234 | 0.995 |
HY Score | 20.013 | 0.995 |
Multivariate Model Variables | Estimate | p-Value |
---|---|---|
(Intercept) | −50.576 | 0.053 |
Age | 0.166 | 0.040 * |
LMNA | 0.495 | 0.181 |
CATD | 1.670 | 0.040 * |
ASAH1 | 0.141 | 0.912 |
TERA | 0.470 | 0.655 |
DYHC1 | 0.798 | 0.602 |
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Acera, A.; Gómez-Esteban, J.C.; Murueta-Goyena, A.; Galdos, M.; Azkargorta, M.; Elortza, F.; Ruzafa, N.; Ibarrondo, O.; Pereiro, X.; Vecino, E. Potential Tear Biomarkers for the Diagnosis of Parkinson’s Disease—A Pilot Study. Proteomes 2022, 10, 4. https://doi.org/10.3390/proteomes10010004
Acera A, Gómez-Esteban JC, Murueta-Goyena A, Galdos M, Azkargorta M, Elortza F, Ruzafa N, Ibarrondo O, Pereiro X, Vecino E. Potential Tear Biomarkers for the Diagnosis of Parkinson’s Disease—A Pilot Study. Proteomes. 2022; 10(1):4. https://doi.org/10.3390/proteomes10010004
Chicago/Turabian StyleAcera, Arantxa, Juan Carlos Gómez-Esteban, Ane Murueta-Goyena, Marta Galdos, Mikel Azkargorta, Felix Elortza, Noelia Ruzafa, Oliver Ibarrondo, Xandra Pereiro, and Elena Vecino. 2022. "Potential Tear Biomarkers for the Diagnosis of Parkinson’s Disease—A Pilot Study" Proteomes 10, no. 1: 4. https://doi.org/10.3390/proteomes10010004
APA StyleAcera, A., Gómez-Esteban, J. C., Murueta-Goyena, A., Galdos, M., Azkargorta, M., Elortza, F., Ruzafa, N., Ibarrondo, O., Pereiro, X., & Vecino, E. (2022). Potential Tear Biomarkers for the Diagnosis of Parkinson’s Disease—A Pilot Study. Proteomes, 10(1), 4. https://doi.org/10.3390/proteomes10010004