Medical diagnosis, prognosis and prediction of treatment using multiplexed signatures

Abstract

The present disclosure relates to diagnosis, prognosis, and prediction of treatment outcomes of neuropathological conditions.

Description

Docket No. 2960447-000048-W01Filed: January 9, 20261MEDICAL DIAGNOSIS, PROGNOSIS AND PREDICTION OF TREATMENT USING MULTIPLEXED SIGNATURESFIELD OF THE INVENTION

[0001] The present disclosure relates to diagnosis, prognosis, and prediction of treatment outcomes of neuropathological conditions.BACKGROUND OF THE INVENTION

[0002] In the following discussion certain articles and methods will be described for background and introductory purposes. Nothing contained herein is to be construed as an “admission” of prior art. Applicant expressly reserves the right to demonstrate, where appropriate, that the articles and methods referenced herein do not constitute prior art under the applicable statutory provisions.

[0003] Genomic and proteomic analyses have demonstrated substantial promise for clinical application in various medical fields. Although various analytic approaches have been used to advance the knowledge and treatment of various disorders, and much remains to be learned from the study of individual analytes within specific disease states, multi-omics data can provide a holistic understanding of the corresponding diseases compared with single-omics data. See, e.g., Calabrese C. et al., Nature. 2020;578:129-136; Sammut S.-J. et al., Nature. 2022;601:623-629. The combined analysis of multiple cell-free molecules in biological samples can enhance diagnostic efficacy. For example, the utilization of cfRNA and cfDNA facilitates the detection of EGFR mutations in plasma. Krug A.K. et al., Ann. Oncol. 2018;29:700-706. Similarly, a multi-analyte blood assay involving 61 DNA mutations and 8 proteins has demonstrated clinical value in cancer diagnostics. Cohen J.D. et al., Science.2018;359:926-930. However, there has been limited systematic exploration of multi-omics data in the context of neuropathological conditions over time.

[0004] Therefore, there is a need for utilizing cell-free biological indicator signatures to characterize these neuropathological conditions in diagnosis and prognosis of specific disorders, especially for difficult to diagnose progressive neuropathological disorders. The present invention addresses this need.SUMMARY OF THE INVENTIONDocket No. 2960447-000048-W01Filed: January 9, 20262

[0005] This Summary is provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description. This Summary is not intended to identify key or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter. Other features, details, utilities, and advantages of the claimed subject matter will be apparent from the following written Detailed Description including those aspects illustrated in the accompanying drawings and defined in the appended claims.

[0006] The present disclosure provides uses, methods and kits as disclosed herein.

[0007] In one aspect, the present disclosure provides the use of at least one gene selected from a group consisting of CNTFR, CXCL12, ITGB3:ITGAV, ADPGK, GFRA2, STC2, FBLN2, VCAM1, MAG, APAF1, DKK3, PRG4, LILRA4, BNIPL, SEZ6L, ESM1, STAR, TMX4, BCAN, SLITRK1, SOD3, C1S, SERPINA11, DPEP1, ITGA11, CXADR, FGFBP3, LTBP4, OIP5, FANCD2, EPHA2, EPHB6, GFAP, IL15RA, CCER2, ITGAV, APLP1, CD276, CDH3, CRIM1, PDGFRA, ROR1, SUSD5, CD300LG, CSPG4, QPCT, CALB2, IL7, CD209, MTAP, PRL, DDC, MCAT, LYN, STX4, PPIF, DECR1, PNPT1, MPIG6B, ANXA7, IVD, RAC2, PLEKHF2, GFRA2, DDX19B, DFFA, ARSA, BACE1, CD40LG, IL16, NEFL, TARDBP, TUBA1A, SUMF1, LAP3, OGDHL, CHMP2B, NEFL, OMD, CALB2, CD276, NCAM1, EDIL3, TGAV, QPCT, DDC, LSM1, PRL, GPT, GRP, IGHG4, PRR15, GLRX3, KCTD10, CCN5, ARPC5, CMPK1, SARS1, PPM1G, LTBP4, IVD, RAP1B, CORIN, LRRTM2, FKBP1B, AASDH, TPM4, HSPD1, IRF5, PTGR1, COPS5, PYGB, PYGL, PHGDH, MICU1, ARHGAP25, CCT8, KYAT1, LRRC45, GP1BB, SAMD9L, PTPN11, CRIM1, SMAD1, CXCL5, LATS1, NFATC1, METAP1D, DFFA, CST5, CCL5, NAP1L4, LAT2, TDRD3, ALDH5A1, CCER2, MTDH, FKBPL, PPP1CC, SNAP23, CEP85, RAB37, CSPG4, OGA, MAGED1, VAMP8, PRDX5, GMPR2, MPP7, ZNF649, RIMOC1, AAGAB, BACE1, LEG1, OXT, UBL5, GPNMB, KVAT1, GFAP, ANOS1, RPL30, TMX2, EIF1AX, SMYD3, VWF, SCUBE1, TNFRSFUb, NEFH, ARSA, BDNF, CD300LG, CPXM1, CXCL3, EPHA2, HARS1, MANF, NCAN, PDGFRA, ROR1, SERPINE1, SLITRK2 and SUSD5, and any combination thereof, as a biomarker for Parkinson’s Disease risk assessment in a subject, wherein the subject has not been diagnosed with Parkinson’s Disease and is not exhibiting any symptoms associated with Parkinson’s Disease.

[0008] In a particular embodiment, the at least one gene is selected from a group consisting of:Docket No. 2960447-000048-W01Filed: January 9, 20263- Prodromal genes, wherein the group of prodromal genes consists of CNTFR, CXCL12, ITGB3JTGAV, ADPGK, GFRA2, STC2, FBLN2, VCAM1, MAG, APAF1, DKK3, PRG4, LILRA4, BNIPL, SEZ6L, ESM1, STAR, TMX4, BCAN, SLITRK1, SOD3, C1S, SERPINA11, DPEP1, ITGA11, CXADR, FGFBP3, LTBP4, OIP5, FANCD2, EPHA2, EPHB6, GFAP, IL15RA, CCER2, ITGAV, APLP1, CD276, CDH3, CRIM1, PDGFRA, ROR1, SUSD5, CD300LG, CSPG4, QPCT, CALB2 and IL7, preferably wherein the prodromal genes show early detection of consistent differences up to 12 years prior to Parkinson’s Disease onset and are detectable long before clinical symptoms appear, or preferably wherein the prodromal genes exhibit detectable differences relative to a reference up to 12 years prior to onset of Parkinson’s Disease and before the appearance of clinical symptoms,- Progression genes, wherein the group of progression genes consists of CD209, MTAP, PRL and DDC, preferably wherein the progression genes show a significant difference in their rate of change more than 12 years before and following Parkinson’s Disease onset and may indicate early pathological alterations, or preferably wherein the progression genes exhibit a difference in a rate of change relative to a reference that starts more than 12 years prior to and following clinical onset,- Transient preclinical genes, wherein the group of transient preclinical genes consists of MCAT, LYN, STX4, PPIF, DECR1, PNPT1, MPIG6B, ANXA7, IVD, RAC2, PLEKHF2, GFRA2, DDX19B, DFFA, ARSA, BACE1, CD40LG, IL16, NEFL and TARDBP, preferably wherein transient preclinical genes exhibit distinct changes in their rate of progression between 12 and 5 years before onset but stabilize thereafter, or preferably wherein transient preclinical genes exhibit a detectable inflection in a rate of change relative to a reference during a preclinical phase occurring between 12 and 5 years prior to disease onset and do not exhibit the detectable change at or after disease onset,- Transitional genes, wherein the group of transitional genes consists of TLIBA1A, SLIMF1, LAP3, OGDHL and CHMP2B, preferably wherein transitional genes exhibit the most significant rate of change beginning 5 years prior to and following Parkinson’s Disease onset and may indicate transition to symptomatic Parkinson’s Disease, or preferably wherein the transitional genes exhibit a change in a rate of change relative to a reference beginning approximately 5 years prior to and following onset of Parkinson’s disease,- and any combination thereof.Docket No. 2960447-000048-W01Filed: January 9, 20264

[0009] In the context of the present invention, the term biomarker can refer to a gene-based biomarker or protein-based biomarker, wherein the gene-based biomarker is a nucleic acid sequence, its expression level, or the level of the polypeptide encoded by the gene, and wherein the protein-based biomarker is the corresponding protein or polypeptide, including its abundance, isoforms, post-translational modifications or activity. The gene-based biomarker and the corresponding protein-based biomarker may be used interchangeably or in combination, and each can be quantitatively measured and used as an indicator for diagnostic, prognostic, predictive, or therapeutic purposes, including disease risk assessment and response to treatment. Individual biomarkers may be combined to form biomarker signatures in various ways known to a person skilled in the art of the invention. Unless otherwise indicated, reference to a biomarker gene herein includes reference to the corresponding biomarker protein.

[0010] Thus, in a particular embodiment, the biomarker forms part of a biomarker signature comprising two or more biomarkers selected from a group consisting of CNTFR, CXCL12, ITGB3:ITGAV, ADPGK, GFRA2, STC2, FBLN2, VCAM1, MAG, APAF1, DKK3, PRG4, LILRA4, BNIPL, SEZ6L, ESM1, STAR, TMX4, BCAN, SLITRK1, SOD3, C1S, SERPINA11, DPEP1, ITGA11, CXADR, FGFBP3, LTBP4, OIP5, FANCD2, EPHA2, EPHB6, GFAP, IL15RA, CCER2, ITGAV, APLP1, CD276, CDH3, CRIM1, PDGFRA, ROR1, SUSD5, CD300LG, CSPG4, QPCT, CALB2, IL7, CD209, MTAP, PRL, DDC, MCAT, LYN, STX4, PPIF, DECR1, PNPT1, MPIG6B, ANXA7, IVD, RAC2, PLEKHF2, GFRA2, DDX19B, DFFA, ARSA, BACE1, CD40LG, IL16, NEFL, TARDBP, TUBA1A, SUMF1, LAP3, OGDHL, CHMP2B, NEFL, OMD, CALB2, CD276, NCAM1, EDIL3, TGAV, QPCT, DDC, LSM1, PRL, GPT, GRP, IGHG4, PRR15, GLRX3, KCTD10, CCN5, ARPC5, CMPK1, SARS1, PPM1G, LTBP4, IVD, RAP1B, CORIN, LRRTM2, FKBP1B, AASDH, TPM4, HSPD1, IRF5, PTGR1, COPS5, PYGB, PYGL, PHGDH, MICU1, ARHGAP25, CCT8, KYAT1, LRRC45, GP1BB, SAMD9L, PTPN11, CRIM1, SMAD1, CXCL5, LATS1, NFATC1, METAP1D, DFFA, CST5, CCL5, NAP1L4, LAT2, TDRD3, ALDH5A1, CCER2, MTDH, FKBPL, PPP1CC, SNAP23, CEP85, RAB37, CSPG4, OGA, MAGED1, VAMP8, PRDX5, GMPR2, MPP7, ZNF649, RIMOC1, AAGAB, BACE1, LEG1, OXT, UBL5, GPNMB, KVAT1, GFAP, ANOS1, RPL30, TMX2, EIF1AX, SMYD3, VWF, SCUBE1, TNFRSFUb, NEFH, ARSA, BDNF, CD300LG, CPXM1, CXCL3, EPHA2, HARS1, MANF, NCAN, PDGFRA, ROR1, SERPINE1, SLITRK2, SUSD5, and any combination thereof.

[0011] Any possible combination of the above biomarkers is contemplated within the scope of the invention. Accordingly, in particular embodiments the biomarkerDocket No. 2960447-000048-W01Filed: January 9, 20265signature comprises combinations of 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70,71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100,101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120,121, 122, 123, 124, 125, 126, 127, 128, 129, 130,131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143 or 144 (i.e., all) biomarkers.

[0012] In a particular embodiment, the biomarker signature comprises at least three of the biomarker genes MTAP, TUBA1A, GPT, SUMF1, OIP5, CNTFR, LSM1, QPCT, ITGB3, and ITGAV.

[0013] In another aspect, the present disclosure provides the use of at least one gene selected from a group consisting of CNTFR, CXCL12, ITGB3: ITGAV, ADPGK, GFRA2, STC2, FBLN2, VCAM1, MAG, APAF1, DKK3, PRG4, LILRA4, BNIPL, SEZ6L, ESM1, STAR, TMX4, BCAN, SLITRK1, SOD3, C1S, SERPINA11, DPEP1, ITGA11, CXADR, FGFBP3, LTBP4, OIP5, FANCD2, EPHA2, EPHB6, GFAP, IL15RA, CCER2, ITGAV, APLP1, CD276, CDH3, CRIM1, PDGFRA, ROR1, SUSD5, CD300LG, CSPG4, QPCT, CALB2, IL7, CD209, MTAP, PRL, DDC, MCAT, LYN, STX4, PPIF, DECR1, PNPT1, MPIG6B, ANXA7, IVD, RAC2, PLEKHF2, GFRA2, DDX19B, DFFA, ARSA, BACE1, CD40LG, IL16, NEFL, TARDBP, TUBA1A, SUMF1, LAP3, OGDHL, CHMP2B, NEFL, OMD, CALB2, CD276, NCAM1, EDIL3, TGAV, QPCT, DDC, LSM1, PRL, GPT, GRP, IGHG4, PRR15, GLRX3, KCTD10, CCN5, ARPC5, CMPK1, SARS1, PPM1G, LTBP4, IVD, RAP1B, CORIN, LRRTM2, FKBP1B, AASDH, TPM4, HSPD1, IRF5, PTGR1, COPS5, PYGB, PYGL, PHGDH, MICU1, ARHGAP25, CCT8, KYAT1, LRRC45, GP1BB, SAMD9L, PTPN11, CRIM1, SMAD1, CXCL5, LATS1, NFATC1, METAP1D, DFFA, CST5, CCL5, NAP1L4, LAT2, TDRD3, ALDH5A1, CCER2, MTDH, FKBPL, PPP1CC, SNAP23, CEP85, RAB37, CSPG4, OGA, MAGED1, VAMP8, PRDX5, GMPR2, MPP7, ZNF649, RIMOC1, AAGAB, BACE1, LEG1, OXT, UBL5, GPNMB, KVAT1, GFAP, ANOS1, RPL30, TMX2, EIF1AX, SMYD3, VWF, SCUBE1, TNFRSFUb, NEFH, ARSA, BDNF, CD300LG, CPXM1, CXCL3, EPHA2, HARS1, MANF, NCAN, PDGFRA, ROR1, SERPINE1, SLITRK2 and SUSD5, and any combination thereof, as a biomarker for Parkinson’s Disease diagnosis, for monitoring Parkinson’s Disease progression, and / or for predicting response to a therapy against Parkinson’s Disease.

[0014] In yet another aspect, the present disclosure provides the use of at leastDocket No. 2960447-000048-W01Filed: January 9, 20266one gene selected from a group consisting of:- Prodromal genes, wherein the group of prodromal genes consists of CNTFR, CXCL12, ITGB3:ITGAV, ADPGK, GFRA2, STC2, FBLN2, VCAM1, MAG, APAF1, DKK3, PRG4, LILRA4, BNIPL, SEZ6L, ESM1, STAR, TMX4, BCAN, SLITRK1, SOD3, C1S, SERPINA11, DPEP1, ITGA11, CXADR, FGFBP3, LTBP4, OIP5, FANCD2, EPHA2, EPHB6, GFAP, IL15RA, CCER2, ITGAV, APLP1, CD276, CDH3, CRIM1, PDGFRA, ROR1, SUSD5, CD300LG, CSPG4, QPCT, CALB2 and IL7,- Progression genes, wherein the group of progression genes consists of CD209, MTAP, PRL and DDC,- Transient preclinical genes, wherein the group of transient preclinical genes consists of MCAT, LYN, STX4, PPIF, DECR1, PNPT1, MPIG6B, ANXA7, IVD, RAC2, PLEKHF2, GFRA2, DDX19B, DFFA, ARSA, BACE1, CD40LG, IL16, NEFL and TARDBP,- Transitional genes, wherein the group of transitional genes consists of TLIBA1A, SUMF1, LAP3, OGDHL and CHMP2B,- Post-diagnostic genes, wherein the group of post-diagnostic genes consists of EPN1, H1-10, HS3ST4, STAT6, RGS18, CYLD, ESYT2, EBI3, IL12A, NAA10, SH3PXD2A, HNRNPAO, PLCD4, CSNK1G2, ZC3H12C, H2AC20, RIT1, CCDC80, HSP90AA1, ARID3C, TWSG1, H2AX, IST1, AASDH, ZNF354A, AP1B1, HSP90AB1, CIA01, RAB6A, PPP1CA, GPI, SCARF2, CSTF2, TFRC, ADA2, TRIM25, OIP5, PTGR1, CXCL13, COL14A1, PDGFRA, SM0C1, CD58, AAGAB and RNF5,- and any combination thereof,as a biomarker for assessing the risk of Parkinson’s Disease in a subject, for Parkinson’s Disease diagnosis, for monitoring Parkinson’s Disease progression, and / or for predicting response to a therapy against Parkinson’s Disease.

[0015] As indicated above, the biomarkers of the invention may form part of a biomarker signature of two or more biomarkers, wherein the biomarker signature comprises at least one prodromal gene, at least one progression gene, at least one transient preclinical gene, at least one transitional gene and / or at least one postdiagnostic gene.

[0016] As used herein, the term “biomarker signature” refers to a combination of two or more biomarkers, wherein each biomarker is evaluated individually or collectively to provide information relating to a disease state, disease risk, disease progression, orDocket No. 2960447-000048-W01Filed: January 9, 20267response to treatment.

[0017] In the context of the instant invention, the different biomarker genes are grouped into five different categories (prodromal genes, progression genes, transient preclinical genes, transitional genes and post-diagnostic genes) whose expression rates of change (reflected in increasing / decreasing protein levels) correlates strongly with distinct disease stages and biomarker performance. Thus, temporal variation in protein levels (i.e. , rate of change) can be tied to the diagnostic utility of biomarkers in distinguishing disease stages.

[0018] As used herein, the term “prodromal gene” refers to a gene that exhibits a detectable difference relative to a reference during a preclinical phase of a disease and prior to the appearance of clinical symptoms, wherein the detectable difference persists until or beyond disease onset. Prodromal biomarkers show significantly changed protein levels years before onset of symptoms of Parkinson’s Disease (PD), e.g., up to 12 years before onset of symptoms of Parkinson’s Disease (PD), or, e.g., 1-12 years before onset of symptoms of Parkinson’s Disease (PD). The biomarkers can stay increased or decreased after PD onset. They were found from pre-diagnostic samples or detected as risk biomarkers by joint modeling using samples before PD onset. Thus, prodromal gene biomarkers have significantly different expression levels before PD onset, and some may also have significantly different expression levels after PD onset.

[0019] As used herein, the term “progression gene” refers to a gene that exhibits a detectable change relative to a reference prior to and following disease onset and that correlates with a change in disease stage, severity, or rate of disease progression. Significant rate of change was detected for progression biomarker genes in the entire timeframe of analysis (12 years before PD onset to 9 years after PD onset). Thus, progression genes are actively changing from years before PD onset and after PD onset.

[0020] As used herein, the term “transient preclinical gene” refers to a gene that exhibits a detectable inflection in a rate of change relative to a reference during a preclinical phase of a disease and that does not exhibit the detectable inflection in the rate of change after disease onset. Transient preclinical biomarkers were identified PD biomarkers with significantly different rate of change in a first slope with the inflection point before PD onset or around PD onset from linear spline mixed model results. Significant rate of change can be detected 12 to 4-5 years before PD onset. Active changes in rate of change cannot be detected afterwards, even if transient preclinical biomarker gene protein expression levels can still be significantly different fromDocket No. 2960447-000048-W01Filed: January 9, 20268reference values after this period of change. Thus, active change for these markers may start many years before PD onset (i.e., at least 12 years before PD onset), but active change stopped mostly 4-5 years before PD onset.

[0021] As used herein, the term “transitional gene” refers to a gene that exhibits a detectable inflection in a rate of change relative to a reference during a transition period between a preclinical phase and a clinically manifest phase of a disease. Transitional biomarkers were identified PD biomarkers with significantly different rate of change in a second slope with inflection point before PD onset or around PD onset from linear spline mixed model results. Transitional biomarker genes show active change a few years before PD onset and forward (e.g., 5, 4, 3 years before PD onset and forward). Their active change is around PD onset and continued with PD progression.

[0022] As used herein, the term “post-diagnostic gene” refers to a gene that exhibits a detectable change relative to a reference after clinical diagnosis of a disease and that is associated with disease monitoring, prognosis, or therapeutic response.

[0023] Prodromal biomarkers and transient preclinical biomarkers were mainly found based on pre-diagnostic (preDx) samples. As such, they are considered to be more reliable as biomarkers of prediction of risk of developing a diagnosis of PD, future PD predictive biomarkers, or as PD diagnostic biomarkers. In contrast, transitional, progression and post-diagnostic (postDx) biomarkers were also partially informed by postDx samples. In these samples PD medications could have an effect in terms of protein expression, which may need to be considered. These transitional, progression and post-diagnostic biomarkers could be developed into PD prognostic biomarkers, and they could also be useful as PD diagnostic and prediction biomarkers.

[0024] The above biomarker genes may also be distinguished depending on the type of significant difference that is detected. In a first type (prodromal and postDX biomarkers) there were significant differences in protein levels between PD and control. In a second type (transitional preclinical biomarkers, transitional biomarkers and progression biomarkers) there were significant differences in rate of change, so the actively changing phase can be determined. These differences may need to be taken into account when developing medical tests. That is, in the first type, critical values (e.g., threshold values) may be set for distinguishing between normal vs abnormal values, whereas, in the second type, multiple tests may be needed, so that changes can be traced, as opposed to determining absolute values.

[0025] A given biomarker gene may belong to more than one category depending onDocket No. 2960447-000048-W01Filed: January 9, 20269the timing and nature of the detectable change.

[0026] Accordingly, the biomarker signature may comprise at least one prodromal gene and at least one progression gene; at least one prodromal gene and at least one transient preclinical gene; at least one prodromal gene and at least one transitional gene; at least one prodromal gene and at least one post-diagnostic gene; at least one progression gene and at least one transient preclinical gene; at least one progression gene and at least one transitional gene; at least one progression gene and at least one post-diagnostic gene; at least one transient preclinical gene and at least one transitional gene; at least one transient preclinical gene and at least one postdiagnostic gene; at least one transitional gene and at least one post-diagnostic gene; at least one prodromal gene, at least one progression gene and at least one transient preclinical gene; at least one prodromal gene, at least one progression gene and at least one transitional gene; at least one prodromal gene, at least one progression gene and at least one post-diagnostic gene; at least one prodromal gene, at least one transient preclinical gene and at least one transitional gene; at least one prodromal gene, at least one transient preclinical gene and at least one post-diagnostic gene; at least one prodromal gene, at least one transitional gene and at least one postdiagnostic gene; at least one progression gene, at least one transient preclinical gene and at least one transitional gene; at least one progression gene, at least one transient preclinical gene and at least one post-diagnostic gene; at least one progression gene, at least one transitional gene and at least one post-diagnostic gene; at least one transient preclinical gene, at least one transitional gene and at least one postdiagnostic gene; at least one prodromal gene, at least one progression gene, at least one transient preclinical gene and at least one transitional gene; at least one prodromal gene, at least one progression gene, at least one transient preclinical gene and at least one post-diagnostic gene; at least one prodromal gene, at least one progression gene, at least one transitional gene and at least one post-diagnostic gene; at least one prodromal gene, at least one transient preclinical gene, at least one transitional gene and at least one post-diagnostic gene; at least one progression gene, at least one transient preclinical gene, at least one transitional gene and at least one postdiagnostic gene; or at least one prodromal gene, at least one progression gene, at least one transient preclinical gene, at least one transitional gene and at least one post-diagnostic gene.

[0027] In particular embodiments, the biomarker forms part of a biomarker signature of two or more biomarkers, wherein the biomarker signature furtherDocket No. 2960447-000048-W01Filed: January 9, 202610comprises at least one of NEFL, OMD, CALB2, CD276, NCAM1, EDIL3, TGAV, QPCT, DDC, LSM1, PRL, GPT, GRP, IGHG4, PRR15, GLRX3, KCTD10, CCN5, ARPC5, CMPK1, SARS1, PPM1G, LTBP4, IVD, RAP1B, CORIN, LRRTM2, FKBP1B, AASDH, TPM4, HSPD1, IRF5, PTGR1, COPS5, PYGB, PYGL, PHGDH, MICU1, ARHGAP25, CCT8, KYAT1, LRRC45, GP1BB, SAMD9L, PTPN11, CRIM1, SMAD1, CXCL5, LATS1, NFATC1, METAP1D, DFFA, CST5, CCL5, NAP1L4, LAT2, TDRD3, ALDH5A1, CCER2, MTDH, FKBPL, PPP1CC, SNAP23, CEP85, RAB37, CSPG4, OGA, MAGED1, VAMP8, PRDX5, GMPR2, MPP7, ZNF649, RIMOC1, AAGAB, BACE1, LEG1, OXT, UBL5, GPNMB, KVAT1, GFAP, ANOS1, RPL30, TMX2, EIF1AX, SMYD3, VWF, SCUBE1, TNFRSFUb, NEFH, ARSA, BDNF, CD300LG, CPXM1, CXCL3, EPHA2, HARS1, MANF, NCAN, PDGFRA, ROR1, SERPINE1, SLITRK2, SUSD5.

[0028] In certain embodiments, the biomarker signature comprises at least three of the biomarker genes MTAP, TUBA1A, GPT, SUMF1, OIP5, CNTFR, LSM1, QPCT, ITGB3, and ITGAV. In an embodiment, the biomarker signature consists of the biomarker genes MTAP, TUBA1A, GPT, SUMF1, OIP5, CNTFR, LSM1, QPCT, ITGB3, and ITGAV.

[0029] In another aspect, the present disclosure provides an in vitro method for determining the risk that a subject develops Parkinson’s Disease, wherein the method comprises:a) determining, in a biological sample from the subject, the expression level of at least one gene selected from a group consisting of CNTFR, CXCL12, ITGB3JTGAV, ADPGK, GFRA2, STC2, FBLN2, VCAM1, MAG, APAF1, DKK3, PRG4, LILRA4, BNIPL, SEZ6L, ESM1, STAR, TMX4, BCAN, SLITRK1, SOD3, C1S, SERPINA11, DPEP1, ITGA11, CXADR, FGFBP3, LTBP4, OIP5, FANCD2, EPHA2, EPHB6, GFAP, IL15RA, CCER2, ITGAV, APLP1, CD276, CDH3, CRIM1, PDGFRA, ROR1, SUSD5, CD300LG, CSPG4, QPCT, CALB2, IL7, CD209, MTAP, PRL, DDC, MCAT, LYN, STX4, PPIF, DECR1, PNPT1, MPIG6B, ANXA7, IVD, RAC2, PLEKHF2, GFRA2, DDX19B, DFFA, ARSA, BACE1, CD40LG, IL16, NEFL, TARDBP, TUBA1A, SUMF1, LAP3, OGDHL, CHMP2B, NEFL, OMD, CALB2, CD276, NCAM1, EDIL3, TGAV, QPCT, DDC, LSM1, PRL, GPT, GRP, IGHG4, PRR15, GLRX3, KCTD10, CCN5, ARPC5, CMPK1, SARS1, PPM1G, LTBP4, IVD, RAP1B, CORIN, LRRTM2, FKBP1B, AASDH, TPM4, HSPD1, IRF5, PTGR1, COPS5, PYGB, PYGL, PHGDH, MICU1, ARHGAP25, CCT8, KYAT1, LRRC45, GP1BB, SAMD9L, PTPN11, CRIM1, SMAD1, CXCL5, LATS1, NFATC1, METAP1D, DFFA, CST5, CCL5, NAP1L4, LAT2, TDRD3, ALDH5A1, CCER2,Docket No. 2960447-000048-W01Filed: January 9, 202611MTDH, FKBPL, PPP1CC, SNAP23, CEP85, RAB37, CSPG4, OGA, MAGED1, VAMP8, PRDX5, GMPR2, MPP7, ZNF649, RIMOC1, AAGAB, BACE1, LEG1, OXT, UBL5, GPNMB, KVAT1, GFAP, ANOS1, RPL30, TMX2, EIF1AX, SMYD3, VWF, SCUBE1, TNFRSFUb, NEFH, ARSA, BDNF, CD300LG, CPXM1, CXCL3, EPHA2, HARS1, MANF, NCAN, PDGFRA, ROR1, SERPINE1, SLITRK2 and SLISD5, and any combination thereof,b) comparing the expression level of said at least one gene in said biological sample with a reference value for said gene; wherein an altered expression level of said at least one gene in the biological sample when compared to the reference value is indicative that the subject is at risk of developing Parkinson’s Disease.

[0030] In an embodiment of the method of this aspect of the invention, the subject has not been diagnosed with Parkinson’s Disease and is not exhibiting any symptoms associated with Parkinson’s Disease, preferably wherein the subject is not exhibiting any one of tremor, bradykinesia, rigidity, postural instability, loss of automatic movement, speech changes, micrographia, anosmia, nerve pain, mild cognitive impairment, or dementia.

[0031] In the context of the invention, the term “reference value” refers to a standardized value (e.g., a laboratory value) used for comparison with data from subject samples. It may be absolute, relative, a range, or based on averages, medians, means, or control / baseline values. It can derive from an individual sample (e.g., earlier time point or disease-free tissue) or from pooled samples, such as age-matched populations. Factors like age, weight, sex, and health influence its determination. Preferred embodiments include expression levels of a gene of interest from pooled populations (mixed risk / diagnosis / prognosis), or from patients with a positive or a negative diagnosis for comparison. Risk of disease, diagnosis or prognosis is inferred by comparing expression levels to the reference value: higher-than-poor reference suggests good outcomes; lower-than-good reference suggests poor outcomes. Reference values could be established per gene. Biomarker quantities may be assessed relative to the reference value (e.g., fold-change) without determining absolute amounts. For example, an increase in expression levels above the reference value of at least 1.1-fold, 1.5-fold, 5-fold, 10-fold, 20-fold, 30-fold, 40- fold, 50-fold, 60-fold, 70-fold, 80-fold, 90-fold, 100-fold or even more compared with the reference value is considered as "increased" expression level. On the other hand, a decrease in expression levels below the reference value of at least 0.9-fold, 0.75-fold, 0.2-fold, 0.1 -fold, 0.05-fold, 0.025-fold, 0.02-fold, 0.01 -fold, 0.005-fold or even less compared withDocket No. 2960447-000048-W01Filed: January 9, 202612reference value is considered as "decreased" expression level.

[0032] In the context of the present invention, an altered expression level can be taken to be an increased expression level or a decreased expression level:

[0033] An increased expression level of the prodromal CNTFR, CXCL12, ADPGK, GFRA2, STC2, FBLN2, VCAM1, MAG, DKK3, LILRA4, BNIPL, SEZ6L, ESM1, BCAN, SLITRK1, SOD3, SERPINA11, DPEP1, FGFBP3, LTBP4, OIP5, EPHA2, EPHB6, GFAP, IL15RA, CCER2, APLP1, CD276, CDH3, CRIM1, PDGFRA, ROR1, SUSD5, CD300LG, CSPG4, QPCT and CALB2 genes is indicative that the subject is at risk of developing Parkinson’s Disease or suffers from Parkinson’s Disease, whereas a decreased expression level of the prodromal ITGB3:ITGAV, APAF1, PRG4, STAR, TMX4, C1S, ITGA11, CXADR, FANCD2, ITGAV and IL7 genes is indicative that the subject is at risk of developing Parkinson’s Disease or suffers from Parkinson’s Disease.

[0034] An increased expression level of the progression MTAP and DDC genes is indicative that the subject is at risk of developing Parkinson’s Disease or suffers from Parkinson’s Disease, whereas a decreased expression level of the progression CD209 and PRL genes is indicative that the subject is at risk of developing Parkinson’s Disease or suffers from Parkinson’s Disease.

[0035] An increased expression level of the transient preclinical GFRA2, BACE1 and NEFL genes is indicative that the subject is at risk of developing Parkinson’s Disease or suffers from Parkinson’s Disease, whereas a decreased expression level of the transient preclinical MCAT, LYN, STX4, PPIF, DECR1, PNPT1, MPIG6B, ANXA7, IVD, RAC2, PLEKHF2, DDX19B, DFFA, ARSA, CD40LG, IL16 and TARDBP genes are indicative that the subject is at risk of developing Parkinson’s Disease or suffers from Parkinson’s Disease.

[0036] An increased expression level of the transitional TLIBA1A and SLIMF1 genes is indicative that the subject is at risk of developing Parkinson’s Disease or suffers from Parkinson’s Disease, whereas a decreased expression level of the transitional LAP3 gene is indicative that the subject is at risk of developing Parkinson’s Disease or suffers from Parkinson’s Disease.

[0037] An increased expression level of the post-diagnostic OIP5, EBI3, IL12A, CCDC80, TWSG1, SCARF2, TFRC, ADA2, CXCL13, COL14A1, PDGFRA, SMOC1 and CD58 genes is indicative that the subject is at risk of developing Parkinson’s Disease or suffers from Parkinson’s Disease, whereas a decreased expression level of the post-diagnostic EPN1, H1-10, HS3ST4, STAT6, RGS18, CYLD, ESYT2, NAA10,Docket No. 2960447-000048-W01Filed: January 9, 202613SH3PXD2A, HNRNPAO, PLCD4, CSNK1G2, ZC3H12C, H2AC20, RIT1, HSP90AA1, ARID3C, H2AX, IST1, AASDH, ZNF354A, AP1B1, HSP90AB1, CIAO1, RAB6A, PPP1CA, GPI, CSTF2, TRIM25, PTGR1, AAGAB and RNF5 genes is indicative that the subject is at risk of developing Parkinson’s Disease or suffers from Parkinson’s Disease.

[0038] In another aspect, the invention refers to an in vitro method for diagnosing Parkinson’s Disease in a subject, wherein the method comprises:a) determining, in a biological sample from the subject, the expression level of at least one gene selected from a group consisting of:- Prodromal genes, wherein the group of prodromal genes consists of CNTFR, CXCL12, ITGB3JTGAV, ADPGK, GFRA2, STC2, FBLN2, VCAM1, MAG, APAF1, DKK3, PRG4, LILRA4, BNIPL, SEZ6L, ESM1, STAR, TMX4, BCAN, SLITRK1, SOD3, C1S, SERPINA11, DPEP1, ITGA11, CXADR, FGFBP3, LTBP4, OIP5, FANCD2, EPHA2, EPHB6, GFAP, IL15RA, CCER2, ITGAV, APLP1, CD276, CDH3, CRIM1, PDGFRA, ROR1, SUSD5, CD300LG, CSPG4, QPCT, CALB2 and IL7,- Progression genes, wherein the group of progression genes consists of CD209, MTAP, PRL and DDC,- Transient preclinical genes, wherein the group of transient preclinical genes consists of MCAT, LYN, STX4, PPIF, DECR1, PNPT1, MPIG6B, ANXA7, IVD, RAC2, PLEKHF2, GFRA2, DDX19B, DFFA, ARSA, BACE1, CD40LG, IL16, NEFL and TARDBP,- Transitional genes, wherein the group of transitional genes consists of TUBA1A, SUMF1, LAP3, OGDHL and CHMP2B,- Post-diagnostic genes, wherein the group of post-diagnostic genes consists of EPN1, H1-10, HS3ST4, STAT6, RGS18, CYLD, ESYT2, EBI3, IL12A, NAA10, SH3PXD2A, HNRNPAO, PLCD4, CSNK1G2, ZC3H12C, H2AC20, RIT1, CCDC80, HSP90AA1, ARID3C, TWSG1, H2AX, IST1, AASDH, ZNF354A, AP1B1, HSP90AB1, CIAO1, RAB6A, PPP1CA, GPI, SCARF2, CSTF2, TFRC, ADA2, TRIM25, OIP5, PTGR1, CXCL13, COL14A1, PDGFRA, SMOC1, CD58, AAGAB and RNF5- and any combination thereof,b) comparing the expression level of said at least one gene in said biological sample with a reference value for said gene; wherein an altered expression level of said at least one gene in the biological sample when compared to the reference value isDocket No. 2960447-000048-W01Filed: January 9, 202614indicative that the subject is likely to suffer from Parkinson’s Disease.

[0039] In an embodiment, the method further comprises a step (c) of subjecting the subject to a procedure to confirm that the subject suffers from Parkinson’s Disease, wherein the procedure is selected from the group consisting of neurological examination, magnetic resonance imaging (MRI), brain ultrasound, positron emission tomography (PET) scan, dopamine transporter (DAT) scan, Alpha-synuclein test, and combinations thereof.

[0040] Diagnosing Parkinson’s disease can be followed by administering to the subject a treatment regimen suitable for treating Parkinson disease.

[0041] Suitably, the method may comprise the step of selecting subjects to undergo further investigation and / or selecting subjects for Parkinson disease treatment.

[0042] In another aspect, the invention refers to a method for diagnosing and treating a subject who suffers from Parkinson’s Disease, wherein the method comprises:a) determining, in a biological sample from the subject, the expression level of at least one gene selected from a group consisting of:- Prodromal genes, wherein the group of prodromal genes consists of CNTFR, CXCL12, ITGB3JTGAV, ADPGK, GFRA2, STC2, FBLN2, VCAM1, MAG, APAF1, DKK3, PRG4, LILRA4, BNIPL, SEZ6L, ESM1, STAR, TMX4, BCAN, SLITRK1, SOD3, C1S, SERPINA11, DPEP1, ITGA11, CXADR, FGFBP3, LTBP4, OIP5, FANCD2, EPHA2, EPHB6, GFAP, IL15RA, CCER2, ITGAV, APLP1, CD276, CDH3, CRIM1, PDGFRA, ROR1, SUSD5, CD300LG, CSPG4, QPCT, CALB2 and IL7- Progression genes, wherein the group of progression genes consists of CD209, MTAP, PRL and DDC- Transient preclinical genes, wherein the group of transient preclinical genes consists of MCAT, LYN, STX4, PPIF, DECR1, PNPT1, MPIG6B, ANXA7, IVD, RAC2, PLEKHF2, GFRA2, DDX19B, DFFA, ARSA, BACE1, CD40LG, IL16, NEFL and TARDBP- Transitional genes, wherein the group of transitional genes consists of TUBA1A, SUMF1, LAP3, OGDHL and CHMP2B,- Post-diagnostic genes, wherein the group of post-diagnostic genes consists of EPN1, H1-10, HS3ST4, STAT6, RGS18, CYLD, ESYT2, EBI3, IL12A, NAA10, SH3PXD2A, HNRNPA0, PLCD4, CSNK1G2, ZC3H12C, H2AC20, RIT1,Docket No. 2960447-000048-W01Filed: January 9, 202615CCDC80, HSP90AA1, ARID3C, TWSG1, H2AX, IST1, AASDH, ZNF354A, AP1B1, HSP90AB1, CIA01, RAB6A, PPP1CA, GPI, SCARF2, CSTF2, TFRC, ADA2, TRIM25, 0IP5, PTGR1, CXCL13, C0L14A1, PDGFRA, SM0C1, CD58, AAGAB and RNF5,- and any combination thereof,b) comparing the expression level of said at least one gene in said biological sample with a reference value for said gene; wherein an altered expression level of said at least one gene in the biological sample when compared to the reference value is indicative that the subject is likely to suffer from Parkinson’s Disease,c) administering to the subject a therapy selected from the group consisting of Levodopa, Carbidopa, Dopamine Agonists, MAO-B and COMT enzyme inhibitors, Amantadine, Anticholinergics and combinations thereof if the subject is diagnosed as suffering from Parkinson’s Disease, said subject benefiting from administering said therapy.

[0043] In particular embodiments, the methods of the invention for diagnosing and treating a subject may involve an active step of obtaining a plasma sample from the subject. Thus, in certain embodiments, the methods of the invention for diagnosing and treating a subject involve an active step of obtaining a plasma sample from the subject.

[0044] In another aspect, the invention discloses an in vitro method for predicting response to therapy of a subject suffering from Parkinson’s Disease, wherein the method comprises:a) determining, in a biological sample taken from the subject before and after administration of therapy, the expression level of at least one gene selected from a group consisting of:- Prodromal genes, wherein the group of prodromal genes consists of CNTFR, CXCL12, ITGB3JTGAV, ADPGK, GFRA2, STC2, FBLN2, VCAM1, MAG, APAF1, DKK3, PRG4, LILRA4, BNIPL, SEZ6L, ESM1, STAR, TMX4, BCAN, SLITRK1, SOD3, C1S, SERPINA11, DPEP1, ITGA11, CXADR, FGFBP3, LTBP4, OIP5, FANCD2, EPHA2, EPHB6, GFAP, IL15RA, CCER2, ITGAV, APLP1, CD276, CDH3, CRIM1, PDGFRA, ROR1, SUSD5, CD300LG, CSPG4, QPCT, CALB2 and IL7- Progression genes, wherein the group of progression genes consists of CD209, MTAP, PRL and DDC,- Transient preclinical genes, wherein the group of transient preclinical genes consists of MCAT, LYN, STX4, PPIF, DECR1, PNPT1, MPIG6B, ANXA7, IVD,Docket No. 2960447-000048-W01Filed: January 9, 202616RAC2, PLEKHF2, GFRA2, DDX19B, DFFA, ARSA, BACE1, CD40LG, IL16, NEFL and TARDBP- Transitional genes, wherein the group of transitional genes consists of TUBA1A, SUMF1, LAP3, OGDHL and CHMP2B,- Post-diagnostic genes, wherein the group of post-diagnostic genes consists of EPN1, H1-10, HS3ST4, STAT6, RGS18, CYLD, ESYT2, EBI3, IL12A, NAA10, SH3PXD2A, HNRNPAO, PLCD4, CSNK1G2, ZC3H12C, H2AC20, RIT1, CCDC80, HSP90AA1, ARID3C, TWSG1, H2AX, IST1, AASDH, ZNF354A, AP1B1, HSP90AB1, CIA01, RAB6A, PPP1CA, GPI, SCARF2, CSTF2, TFRC, ADA2, TRIM25, OIP5, PTGR1, CXCL13, COL14A1, PDGFRA, SM0C1, CD58, AAGAB and RNF5,- and any combination thereof,b) comparing the expression level of said at least one gene in said biological sample taken from the subject before therapy with the expression level of said at least one gene in said biological sample taken from the subject after therapy;- wherein a reduced altered expression level of said at least one gene in the biological sample taken from the subject after therapy when compared to the expression level of said at least one gene in the biological sample taken from the subject before therapy is indicative of a positive response of the patient to therapy, or- wherein an increased altered expression level of said at least one gene in the biological sample taken from the subject after therapy when compared to the expression level of said at least one gene in the biological sample taken from the subject before therapy is indicative of a negative response of the patient to therapy.

[0045] In an alternative embodiment of this last method of the invention, step (b) would require comparing the expression level of said at least one gene in said biological sample with a reference value for said gene; wherein an altered expression level of said at least one gene in the biological sample when compared to the reference value is indicative of a negative response of the patient to therapy.

[0046] In an embodiment, the patient is administered a different therapy, preferably wherein the different therapy is selected from the group consisting of Levodopa, Carbidopa, Dopamine Agonists, MAO-B and COMT enzyme inhibitors, Amantadine, Anticholinergics and combinations thereof.

[0047] In another aspect, the invention refers to an in vitro method for selecting aDocket No. 2960447-000048-W01Filed: January 9, 202617subject to participate in a clinical trial for Parkinson’s Disease, wherein the method comprises:a) determining, in a biological sample from the subject, the expression level of at least one gene selected from a group consisting of:- Prodromal genes, wherein the group of prodromal genes consists of CNTFR, CXCL12, ITGB3JTGAV, ADPGK, GFRA2, STC2, FBLN2, VCAM1, MAG, APAF1, DKK3, PRG4, LILRA4, BNIPL, SEZ6L, ESM1, STAR, TMX4, BCAN, SLITRK1, SOD3, C1S, SERPINA11, DPEP1, ITGA11, CXADR, FGFBP3, LTBP4, OIP5, FANCD2, EPHA2, EPHB6, GFAP, IL15RA, CCER2, ITGAV, APLP1, CD276, CDH3, CRIM1, PDGFRA, ROR1, SUSD5, CD300LG, CSPG4, QPCT, CALB2 and IL7,- Progression genes, wherein the group of progression genes consists of CD209, MTAP, PRL and DDC,- Transient preclinical genes, wherein the group of transient preclinical genes consists of MCAT, LYN, STX4, PPIF, DECR1, PNPT1, MPIG6B, ANXA7, IVD, RAC2, PLEKHF2, GFRA2, DDX19B, DFFA, ARSA, BACE1, CD40LG, IL16, NEFL and TARDBP,- Transitional genes, wherein the group of transitional genes consists of TUBA1A, SUMF1, LAP3, OGDHL and CHMP2B,- Post-diagnostic genes, wherein the group of post-diagnostic genes consists of EPN1, H1-10, HS3ST4, STAT6, RGS18, CYLD, ESYT2, EBI3, IL12A, NAA10, SH3PXD2A, HNRNPAO, PLCD4, CSNK1G2, ZC3H12C, H2AC20, RIT1, CCDC80, HSP90AA1, ARID3C, TWSG1, H2AX, IST1, AASDH, ZNF354A, AP1B1, HSP90AB1, CIA01, RAB6A, PPP1CA, GPI, SCARF2, CSTF2, TFRC, ADA2, TRIM25, OIP5, PTGR1, CXCL13, COL14A1, PDGFRA, SM0C1, CD58, AAGAB and RNF5- and any combination thereof,b) comparing the expression level of said at least one gene in said sample with a reference value for said gene; wherein an altered expression level of said at least one gene in the sample when compared to the reference value for said gene is indicative that the subject is at risk of developing Parkinson’s Disease,c) selecting the subject at risk of developing Parkinson’s Disease to participate in a clinical trial for Parkinson’s Disease.

[0048] In embodiments of the methods of the invention, the biomarker forms part of a biomarker signature of two or more biomarkers, wherein the biomarker signatureDocket No. 2960447-000048-W01Filed: January 9, 202618comprises at least one prodromal gene, at least one progression gene, at least one transient preclinical gene, at least one transitional gene and / or at least one postdiagnostic gene.

[0049] In particular embodiments, the biomarker forms part of a biomarker signature of two or more biomarkers, wherein the biomarker signature further comprises at least one of NEFL, OMD, CALB2, CD276, NCAM1, EDIL3, TGAV, QPCT, DDC, LSM1, PRL, GPT, GRP, IGHG4, PRR15, GLRX3, KCTD10, CCN5, ARPC5, CMPK1, SARS1, PPM1G, LTBP4, IVD, RAP1B, CORIN, LRRTM2, FKBP1B, AASDH, TPM4, HSPD1, IRF5, PTGR1, COPS5, PYGB, PYGL, PHGDH, MICU1, ARHGAP25, CCT8, KYAT1, LRRC45, GP1BB, SAMD9L, PTPN11, CRIM1, SMAD1, CXCL5, LATS1, NFATC1, METAP1D, DFFA, CST5, CCL5, NAP1L4, LAT2, TDRD3, ALDH5A1, CCER2, MTDH, FKBPL, PPP1CC, SNAP23, CEP85, RAB37, CSPG4, OGA, MAGED1, VAMP8, PRDX5, GMPR2, MPP7, ZNF649, RIMOC1, AAGAB, BACE1, LEG1, OXT, UBL5, GPNMB, KVAT1, GFAP, ANOS1, RPL30, TMX2, EIF1AX, SMYD3, VWF, SCUBE1, TNFRSFUb, NEFH, ARSA, BDNF, CD300LG, CPXM1, CXCL3, EPHA2, HARS1, MANF, NCAN, PDGFRA, ROR1, SERPINE1, SLITRK2 and SUSD5.

[0050] In another aspect, the invention refers to a kit or assay device comprising reagents for detecting and / or quantifying the expression level of at least one biomarker gene selected from a group consisting of CNTFR, CXCL12, ITGB3:ITGAV, ADPGK, GFRA2, STC2, FBLN2, VCAM1, MAG, APAF1, DKK3, PRG4, LILRA4, BNIPL, SEZ6L, ESM1, STAR, TMX4, BCAN, SLITRK1, SOD3, C1S, SERPINA11, DPEP1, ITGA11, CXADR, FGFBP3, LTBP4, OIP5, FANCD2, EPHA2, EPHB6, GFAP, IL15RA, CCER2, ITGAV, APLP1, CD276, CDH3, CRIM1, PDGFRA, ROR1, SUSD5, CD300LG, CSPG4, QPCT, CALB2, IL7, CD209, MTAP, PRL, DDC, MCAT, LYN, STX4, PPIF, DECR1, PNPT1, MPIG6B, ANXA7, IVD, RAC2, PLEKHF2, GFRA2, DDX19B, DFFA, ARSA, BACE1, CD40LG, IL16, NEFL, TARDBP, TUBA1A, SUMF1, LAP3, OGDHL, CHMP2B, EPN1, H1-10, HS3ST4, STAT6, RGS18, CYLD, ESYT2, EBI3, IL12A, NAA10, SH3PXD2A, HNRNPA0, PLCD4, CSNK1G2, ZC3H12C, H2AC20, RIT1, CCDC80, HSP90AA1, ARID3C, TWSG1, H2AX, IST1, AASDH, ZNF354A, AP1B1, HSP90AB1, CIAO1, RAB6A, PPP1CA, GPI, SCARF2, CSTF2, TFRC, ADA2, TRIM25, OIP5, PTGR1, CXCL13, COL14A1, PDGFRA, SMOC1, CD58, AAGAB, RNF5, NEFL, OMD, CALB2, CD276, NCAM1, EDIL3, TGAV, QPCT, DDC, LSM1, PRL, GPT, GRP, IGHG4, PRR15, GLRX3, KCTD10, CCN5, ARPC5, CMPK1, SARS1, PPM1G, LTBP4, IVD, RAP1B, CORIN, LRRTM2, FKBP1B, AASDH, TPM4, HSPD1, IRF5, PTGR1, COPS5, PYGB, PYGL, PHGDH, MICU1, ARHGAP25, CCT8, KYAT1, LRRC45, GP1BB,Docket No. 2960447-000048-W01Filed: January 9, 202619SAMD9L, PTPN11, CRIM1, SMAD1, CXCL5, LATS1, NFATC1, METAP1D, DFFA, CST5, CCL5, NAP1L4, LAT2, TDRD3, ALDH5A1, CCER2, MTDH, FKBPL, PPP1CC, SNAP23, CEP85, RAB37, CSPG4, OGA, MAGED1, VAMP8, PRDX5, GMPR2, MPP7, ZNF649, RIMOC1, AAGAB, BACE1, LEG1, OXT, UBL5, GPNMB, KVAT1, GFAP, ANOS1, RPL30, TMX2, EIF1AX, SMYD3, VWF, SCUBE1 , TNFRSF11b, NEFH, ARSA, BDNF, CD300LG, CPXM1, CXCL3, EPHA2, HARS1, MANF, NCAN, PDGFRA, ROR1, SERPINE1, SLITRK2 and SLISD5, and any combination thereof, wherein the reagents are capable of specifically binding to the polypeptides encoded by said genes and wherein said reagents comprise at least 10% of the total number of reagents present in the kit.

[0051] In particular embodiments, the reagents adequate for the determination of the expression levels of one or more genes comprise at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 90% or at least 100% of the total amount of reagents adequate for the determination of the expression levels of genes forming the kit.

[0052] In an embodiment, the at least one biomarker gene of the kit or assay device of the invention is selected from a group consisting of:- Prodromal genes, wherein the group of prodromal genes consists of CNTFR, CXCL12, ITGB3JTGAV, ADPGK, GFRA2, STC2, FBLN2, VCAM1, MAG, APAF1, DKK3, PRG4, LILRA4, BNIPL, SEZ6L, ESM1, STAR, TMX4, BCAN, SLITRK1, SOD3, C1S, SERPINA11, DPEP1, ITGA11, CXADR, FGFBP3, LTBP4, OIP5, FANCD2, EPHA2, EPHB6, GFAP, IL15RA, CCER2, ITGAV, APLP1, CD276, CDH3, CRIM1, PDGFRA, ROR1, SUSD5, CD300LG, CSPG4, QPCT, CALB2 and IL7,- Progression genes, wherein the group of progression genes consists of CD209, MTAP, PRL and DDC,- Transient preclinical genes, wherein the group of transient preclinical genes consists of MCAT, LYN, STX4, PPIF, DECR1, PNPT1, MPIG6B, ANXA7, IVD, RAC2, PLEKHF2, GFRA2, DDX19B, DFFA, ARSA, BACE1, CD40LG, IL16, NEFL and TARDBP,- Transitional genes, wherein the group of transitional genes consists of TLIBA1A, SUMF1, LAP3, OGDHL and CHMP2B,- Post-diagnostic genes, wherein the group of post-diagnostic genes consists of EPN1, H1-10, HS3ST4, STAT6, RGS18, CYLD, ESYT2, EBI3, IL12A, NAA10, SH3PXD2A, HNRNPA0, PLCD4, CSNK1G2, ZC3H12C, H2AC20, RIT1, CCDC80,Docket No. 2960447-000048-W01Filed: January 9, 202620HSP90AA1, ARID3C, TWSG1, H2AX, IST1, AASDH, ZNF354A, AP1B1, HSP90AB1, CIAO1, RAB6A, PPP1CA, GPI, SCARF2, CSTF2, TFRC, ADA2, TRIM25, OIP5, PTGR1, CXCL13, COL14A1, PDGFRA, SMOC1, CD58, AAGAB and RNF5- and any combination thereof.

[0053] In an embodiment, the at least one biomarker of the kit of the invention forms part of a biomarker signature comprising two or more biomarkers selected from a group consisting of CNTFR, CXCL12, ITGB3:ITGAV, ADPGK, GFRA2, STC2, FBLN2, VCAM1, MAG, APAF1, DKK3, PRG4, LILRA4, BNIPL, SEZ6L, ESM1, STAR, TMX4, BCAN, SLITRK1, SOD3, C1S, SERPINA11, DPEP1, ITGA11, CXADR, FGFBP3, LTBP4, OIP5, FANCD2, EPHA2, EPHB6, GFAP, IL15RA, CCER2, ITGAV, APLP1, CD276, CDH3, CRIM1, PDGFRA, ROR1, SUSD5, CD300LG, CSPG4, QPCT, CALB2, IL7, CD209, MTAP, PRL, DDC, MCAT, LYN, STX4, PPIF, DECR1, PNPT1, MPIG6B, ANXA7, IVD, RAC2, PLEKHF2, GFRA2, DDX19B, DFFA, ARSA, BACE1, CD40LG, IL16, NEFL, TARDBP, TUBA1A, SUMF1, LAP3, OGDHL, CHMP2B, EPN1, H1-10, HS3ST4, STAT6, RGS18, CYLD, ESYT2, EBI3, IL12A, NAA10, SH3PXD2A, HNRNPAO, PLCD4, CSNK1G2, ZC3H12C, H2AC20, RIT1, CCDC80, HSP90AA1, ARID3C, TWSG1, H2AX, IST1, AASDH, ZNF354A, AP1B1, HSP90AB1, CIAO1, RAB6A, PPP1CA, GPI, SCARF2, CSTF2, TFRC, ADA2, TRIM25, OIP5, PTGR1, CXCL13, COL14A1, PDGFRA, SMOC1, CD58, AAGAB, RNF5, NEFL, OMD, CALB2, CD276, NCAM1, EDIL3, TGAV, QPCT, DDC, LSM1, PRL, GPT, GRP, IGHG4, PRR15, GLRX3, KCTD10, CCN5, ARPC5, CMPK1, SARS1, PPM1G, LTBP4, IVD, RAP1B, CORIN, LRRTM2, FKBP1B, AASDH, TPM4, HSPD1, IRF5, PTGR1, COPS5, PYGB, PYGL, PHGDH, MICU1, ARHGAP25, CCT8, KYAT1, LRRC45, GP1BB, SAMD9L, PTPN11, CRIM1, SMAD1, CXCL5, LATS1, NFATC1, METAP1D, DFFA, CST5, CCL5, NAP1L4, LAT2, TDRD3, ALDH5A1, CCER2, MTDH, FKBPL, PPP1CC, SNAP23, CEP85, RAB37, CSPG4, OGA, MAGED1, VAMP8, PRDX5, GMPR2, MPP7, ZNF649, RIMOC1, AAGAB, BACE1, LEG1, OXT, UBL5, GPNMB, KVAT1, GFAP, ANOS1, RPL30, TMX2, EIF1AX, SMYD3, VWF, SCUBE1, TNFRSFUb, NEFH, ARSA, BDNF, CD300LG, CPXM1, CXCL3, EPHA2, HARS1, MANF, NCAN, PDGFRA, ROR1, SERPINE1, SLITRK2 and SUSD5.

[0054] In embodiments of the invention, the biomarker signature comprises at least three of the biomarker genes MTAP, TUBA1A, GPT, SUMF1, OIP5, CNTFR, LSM1, QPCT, ITGB3, and ITGAV. In a particular embodiment, the biomarker signature comprises all of the biomarker genes MTAP, TUBA1A, GPT, SUMF1, OIP5, CNTFR,Docket No. 2960447-000048-W01Filed: January 9, 202621LSM1, QPCT, ITGB3, and ITGAV.

[0055] A diagnostic kit comprising:a) at least one binding agent that specifically binds biomarker selected from a group consisting of CNTFR, CXCL12, ITGB3JTGAV, ADPGK, GFRA2, STC2, FBLN2, VCAM1, MAG, APAF1, DKK3, PRG4, LILRA4, BNIPL, SEZ6L, ESM1, STAR, TMX4, BCAN, SLITRK1, SOD3, C1S, SERPINA11, DPEP1, ITGA11, CXADR, FGFBP3, LTBP4, OIP5, FANCD2, EPHA2, EPHB6, GFAP, IL15RA, CCER2, ITGAV, APLP1, CD276, CDH3, CRIM1, PDGFRA, ROR1, SUSD5, CD300LG, CSPG4, QPCT, CALB2, IL7, CD209, MTAP, PRL, DDC, MCAT, LYN, STX4, PPIF, DECR1, PNPT1, MPIG6B, ANXA7, IVD, RAC2, PLEKHF2, GFRA2, DDX19B, DFFA, ARSA, BACE1, CD40LG, IL16, NEFL, TARDBP, TUBA1A, SUMF1, LAP3, OGDHL, CHMP2B, EPN1, H1-10, HS3ST4, STAT6, RGS18, CYLD, ESYT2, EBI3, IL12A, NAA10, SH3PXD2A, HNRNPAO, PLCD4, CSNK1G2, ZC3H12C, H2AC20, RIT1, CCDC80, HSP90AA1, ARID3C, TWSG1, H2AX, IST1, AASDH, ZNF354A, AP1B1, HSP90AB1, CIAO1, RAB6A, PPP1CA, GPI, SCARF2, CSTF2, TFRC, ADA2, TRIM25, OIP5, PTGR1, CXCL13, COL14A1, PDGFRA, SMOC1, CD58, AAGAB, RNF5, NEFL, OMD, CALB2, CD276, NCAM1, EDIL3, TGAV, QPCT, DDC, LSM1, PRL, GPT, GRP, IGHG4, PRR15, GLRX3, KCTD10, CCN5, ARPC5, CMPK1, SARS1, PPM1G, LTBP4, IVD, RAP1B, CORIN, LRRTM2, FKBP1B, AASDH, TPM4, HSPD1, IRF5, PTGR1, COPS5, PYGB, PYGL, PHGDH, MICU1, ARHGAP25, CCT8, KYAT1, LRRC45, GP1BB, SAMD9L, PTPN11, CRIM1, SMAD1, CXCL5, LATS1, NFATC1, METAP1D, DFFA, CST5, CCL5, NAP1L4, LAT2, TDRD3, ALDH5A1, CCER2, MTDH, FKBPL, PPP1CC, SNAP23, CEP85, RAB37, CSPG4, OGA, MAGED1, VAMP8, PRDX5, GMPR2, MPP7, ZNF649, RIMOC1, AAGAB, BACE1, LEG1, OXT, UBL5, GPNMB, KVAT1, GFAP, ANOS1, RPL30, TMX2, EIF1AX, SMYD3, VWF, SCUBE1, TNFRSFUb, NEFH, ARSA, BDNF, CD300LG, CPXM1, CXCL3, EPHA2, HARS1, MANF, NCAN, PDGFRA, ROR1, SERPINE1, SLITRK2 and SUSD5; andb) a detection reagent configured to generate a detectable signal indicative of the presence of said biomarker in a biological sample, preferably wherein the sample is a plasma sample.

[0056] A kit for assessing risk of Parkinson disease, comprising:a) at least one reagent configured to detect at least one biomarker selected from the group consisting of biomarkers CNTFR, CXCL12, ITGB3:ITGAV, ADPGK, GFRA2, STC2, FBLN2, VCAM1, MAG, APAF1, DKK3, PRG4, LILRA4, BNIPL, SEZ6L, ESM1, STAR, TMX4, BCAN, SLITRK1, SOD3, C1S, SERPINA11, DPEP1, ITGA11, CXADR,Docket No. 2960447-000048-W01Filed: January 9, 202622FGFBP3, LTBP4, 0IP5, FANCD2, EPHA2, EPHB6, GFAP, IL15RA, CCER2, ITGAV, APLP1, CD276, CDH3, CRIM1, PDGFRA, R0R1, SUSD5, CD300LG, CSPG4, QPCT, CALB2, IL7, CD209, MTAP, PRL, DDC, MCAT, LYN, STX4, PPIF, DECR1, PNPT1, MPIG6B, ANXA7, IVD, RAC2, PLEKHF2, GFRA2, DDX19B, DFFA, ARSA, BACE1, CD40LG, IL16, NEFL, TARDBP, TUBA1A, SUMF1, LAP3, OGDHL, CHMP2B, EPN1, H1-10, HS3ST4, STAT6, RGS18, CYLD, ESYT2, EBI3, IL12A, NAA10, SH3PXD2A, HNRNPAO, PLCD4, CSNK1G2, ZC3H12C, H2AC20, RIT1, CCDC80, HSP90AA1, ARID3C, TWSG1, H2AX, IST1, AASDH, ZNF354A, AP1B1, HSP90AB1, CIAO1, RAB6A, PPP1CA, GPI, SCARF2, CSTF2, TFRC, ADA2, TRIM25, OIP5, PTGR1, CXCL13, COL14A1, PDGFRA, SMOC1, CD58, AAGAB, RNF5, NEFL, OMD, CALB2, CD276, NCAM1, EDIL3, TGAV, QPCT, DDC, LSM1, PRL, GPT, GRP, IGHG4, PRR15, GLRX3, KCTD10, CCN5, ARPC5, CMPK1, SARS1, PPM1G, LTBP4, IVD, RAP1B, CORIN, LRRTM2, FKBP1B, AASDH, TPM4, HSPD1, IRF5, PTGR1, COPS5, PYGB, PYGL, PHGDH, MICU1, ARHGAP25, CCT8, KYAT1, LRRC45, GP1BB, SAMD9L, PTPN11, CRIM1, SMAD1, CXCL5, LATS1, NFATC1, METAP1D, DFFA, CST5, CCL5, NAP1L4, LAT2, TDRD3, ALDH5A1, CCER2, MTDH, FKBPL, PPP1CC, SNAP23, CEP85, RAB37, CSPG4, OGA, MAGED1, VAMP8, PRDX5, GMPR2, MPP7, ZNF649, RIMOC1, AAGAB, BACE1, LEG1, OXT, UBL5, GPNMB, KVAT1, GFAP, ANOS1, RPL30, TMX2, EIF1AX, SMYD3, VWF, SCUBE1, TNFRSFUb, NEFH, ARSA, BDNF, CD300LG, CPXM1, CXCL3, EPHA2, HARS1, MANF, NCAN, PDGFRA, ROR1, SERPINE1, SLITRK2 and SUSD5; andb) instructions for use in performing the methods of the present invention.

[0057] Other aspects and embodiments of the invention are also considered.

[0058] In one aspect, the present disclosure provides a system for determining a clinical risk score based on one or more cell-free, biological indicator signatures of a subject that indicates a subject’s risk of a neuropathological condition and / or risk that a clinical event will occur. In specific aspects, the risk score may indicate that a clinical event is likely to occur within a certain period of time.

[0059] This disclosure thus provides systems and methods for analyzing cell-free samples obtained from subjects for biological indicators associated with a chronic condition, e.g., a neuropathological condition; and determining a risk score for the neuropathological condition based on the presence, absence and / or detected levels of one or more biological indicators in the cell-free sample.

[0060] In specific aspects, the risk score is determined using a system and methods that compare an obtained biological indicator signature of a subject with aDocket No. 2960447-000048-W01Filed: January 9, 202623reference biological indicator signature generated from one or more subjects without the neuropathological condition being assessed, and determining a risk score that indicates a risk that a clinical event {e.g., onset or progression of a neuropathological condition) is likely to occur.

[0061] In specific aspects, the risk score is determined in a system or method by comparing the obtained biological indicator signature with a reference biological indicator signature from the same person at an earlier timepoint and determining a risk score that indicates a risk that a clinical event is likely to occur. Differences between the observed signature and the reference biological indicator signature reveal properties of the samples and can be correlated to a diagnosis of a neuropathological condition.

[0062] In specific aspects, the risk score is determined in a system or method by comparing the obtained signature with a reference biological indicator signature from a subject without the neuropathological condition being assessed. Disease-affected and healthy subject samples can be distinguished according to their biological indicator signature.

[0063] The biological indicator signatures may be determined, for example, by evaluating at least a portion of a probabilistic model, preferably a Bayesian network, representing the various biological indicators.

[0064] The sample(s) to be used in accordance with the present disclosure can be any cell-free bodily fluid. In some aspects, the cell-free bodily fluid is plasma. In some aspects, the cell-free bodily fluid is serum. The cells of which a sample is extracted may also be tumorous cells from hematologic malignancies (such as leukemia or lymphoma). In some aspects, the cell-free bodily fluid is obtained using suitable isolation techniques, such as apheresis {e.g., plasmapheresis) or conventional venous blood withdrawal. Aside from blood, the body fluid from which a sample is extracted may be plasma, serum, dried blood, cerebrospinal fluid, saliva, feces, and urine.

[0065] Further preferred is a method that additionally comprises combining the risk score and / or at least one of the inferred activities with one or more additional risk scores obtained from one or more additional prognostic tests to obtain a combined risk score, wherein the combined risk score indicates a risk that the clinical event will occur within a certain period of time.

[0066] In an aspect, the present disclosure provides a system for identifying the risk score for a disease in a subject, the method comprising: (a) providing a sample from a subject comprising cell-free biological indicators; (b) identifying a selected set ofDocket No. 2960447-000048-W01Filed: January 9, 202624biological indicators from the sample; (c) generating a subject biological indicator signature from the biological indicators identified in the sample; (d) comparing the subject biological indicator signature to a reference biological indicator signature; and (e) based on the comparison, determining a risk score for the disease in the subject.

[0067] In some examples, the biological indicator is a nucleic acid or protein sequence, and (d) comprises aligning the sequence of the biological indicator to a reference biological indicator sequence.

[0068] In some examples, the biological indicator is the measurement of a particular analyte, and (d) comprises comparing the levels of a subject’s analyte with the level of the reference biological indicator.

[0069] In another aspect, the systems of the invention comprise a non-transitory storage medium which stores instructions that are executable by a digital processing device to perform the system or method according to the disclosure as described herein. The non-transitory storage medium may be a computer-readable storage medium, such as a hard drive or other magnetic storage medium, an optical disk or other optical storage medium, a random access memory (RAM), read only memory (ROM), flash memory, or other electronic storage medium, a network server, or so forth. The digital processing device may be a handheld device (e.g., a smart watch or smart phone), a notebook computer, a desktop computer, a tablet computer or device, a remote network server, or so forth.

[0070] In another aspect, the methods of the invention utilize a computer program comprising program code means for causing a digital processing device to perform a method according to the disclosure as described herein. The digital processing device may be a handheld device (e.g., a smartwatch or smart phone), a notebook computer, a desktop computer, a tablet computer or device, a remote network server, or so forth.

[0071] In a specific aspect, the risk score is a risk that a clinical event will occur. In a specific aspect, the risk score is a risk that a clinical event will occur within a certain period of time.

[0072] The biological indicator signature produced from a subject’s samples can be used for various clinical purposes, including prognosis prediction, prediction of drug efficacy and / or safety, monitoring of drug efficacy and / or safety, drug development based in whole or in part on the biological indicator signatures, use in determining the applicability of investigational or existing drugs on the subject, and determining the stage of intervention for treatment, especially for treatment of chronic, progressive disorders.Docket No. 2960447-000048-W01Filed: January 9, 202625

[0073] Accordingly, in certain aspects the disclosure provides a system for diagnosis or prognosis of a neuropathological condition in a subject, comprising performing assays on cell-free samples from a subject at two or more timepoints to generate data on biological indicators associated with the neuropathological condition from the cell-free samples, comparing the generated data on the biological indicators to reference biological indicator data to determine a risk score for a subject for the neuropathological condition, wherein the risk score based on the presence, absence and / or detected levels of one or more biological indicators in the cell-free sample compared to the reference biological indicator data.

[0074] In certain aspects, the disclosure also provides a system for analyzing cell-free samples to determine a risk score for a neuropathological condition in a subject, comprising performing assays on cell-free samples from a subject at two or more timepoints to generate data on biological indicators associated with the neuropathological condition from the cell-free samples, generating a biological indicator signature for the subject from the data obtained from the cell-free samples, and comparing the generated biological indicator signature to a reference biological indicator signature to determine a risk score for a subject for the neuropathological condition.

[0075] In some aspects, the neuropathological condition is Parkinson’s disease. In specific aspects, the neuropathological disorder is Parkinson’s disease and at least one biological indicator is genetic alteration selected from the group consisting of GBA, LRRK2, SNCA, PINK1, PRKN, DJI. ATP13A2, MAPT, PARK7 and VPS35.

[0076] In some aspects, the disclosure provides a method for treating a subject suffering from or at risk of developing Parkinson’s disease (PD) comprising the steps of generating a biological indicator signature for a subject by providing two or more cell-free biological samples from a subject at two different timepoints and detecting two or more biological indicators associated with risk, onset or progression of PD in the cell-free biological samples, comparing the biological indicator signature from the subject to a reference biological indicator signature and treating the subject for PD based on the comparison of the subject’s biological indicator signature with a reference biological indicator signature.

[0077] In some aspects, the reference biological indicator signature is generated from one or more subjects without the neuropathological condition being assessed. In some aspects, the reference biological indicator signature is generated from the same subject at an earlier timepoint. In some aspects, the reference biological indicatorDocket No. 2960447-000048-W01Filed: January 9, 202626signature is generated from one or more subjects diagnosed with the neuropathological condition being assessed.

[0078] In some aspects, the biological indicator is a nucleic acid or proteomic indicator. In specific aspects, the proteomic indicator is selected from those listed in FIG. 20. In specific aspects, the proteomic indicator is selected from those listed in FIG. 21. In specific aspects, the proteomic indicator is MTAP, TLIBA1A, SLIMF1, GTP, IGHG4, OIP5, CD300LG, CNTFR, RHOD and / or DDC.

[0079] All the terms and embodiments described in this document are equally applicable to all aspects of the invention.

[0080] It should be noted that, as used in the specification and in the appended claims, the singular forms "a", "an", and "the" include their plural referents unless the context clearly indicates otherwise. Similarly, the term "comprises" or "comprising" as used herein also describes "consists of" or "consisting of" in accordance with generally accepted patent practice.

[0081] Further advantages will be apparent to those of ordinary skill in the art upon reading and understanding the attached figures, the following description and, in particular, upon reading the detailed examples provided herein.BRIEF DESCRIPTION OF THE FIGURES

[0082] It should be understood that the drawings are not necessarily to scale, and that like reference numbers refer to like features.

[0083] Figures 1A and 1B are a set of components plots illustrating detectable age-related changes in 247 cross-sectional samples from the plasmapheresis biobank.

[0084] Figures 2A and 2B show illustrative examples of the detection of longitudinal molecular changes reflecting life events.

[0085] Figure 3 is a visualization of plasma samples in the plasmapheresis biobank for 1,497 donors who developed PD.

[0086] Figure 4 shows a funnel graph with the initial criteria to define the PD cohort from the plasmapheresis biobank for further study.

[0087] Figure 5 is a visualization of the plasma samples for 348 donors selected from the plasmapheresis biobank using the criteria of Figure 4.

[0088] Figure 6 summarizes the characteristics of the PD and control samples used in the longitudinal study of Example 4.Docket No. 2960447-000048-W01Filed: January 9, 202627

[0089] Figures 7A and 7B show the samples collected with respect to clinical PD progression for PD patients and their respective control samples.

[0090] Figure 8 is a PLS-DA components plot for PD subjects and their matched control subjects following SomaScan™ analysis of plasmapheresis samples over a multi-year timeframe.

[0091] Figure 9 is a schematic summarizing certain exemplary proteomic changes identified using a SomaScan and Olink analysis of plasmapheresis samples over a multi-year timeframe.

[0092] Figure 10 is a plot graph showing the change in levels of the protein SLIMF1 in the plasma of PD and control samples.

[0093] Figure 11 is a plot graph showing the change in levels of the protein IGHG4 in the plasma of PD and control samples.

[0094] Figure 12 is a plot graph showing the change in levels of the protein TLIBA1A in the plasma of PD and control samples.

[0095] Figure 13 is a plot graph showing the change in levels of the protein MTAP in the plasma of PD and control samples.

[0096] Figure 14 is a plot graph showing the change in levels of the protein GTP in the plasma of PD and control samples.

[0097] Figure 15 is a plot graph showing the change in levels of the protein OIP5 in the plasma of PD and control samples.

[0098] Figure 16 is a plot graph showing the change in levels of the protein DDC in the plasma of PD and control samples.

[0099] Figure 17 is a plot graph showing the change in protein levels of the protein MTAP in plasma samples from one cohort of PD patients and one cohort of controls from the plasmapheresis biobank.

[0100] Figure 18 is a bar graph showing the difference in MTAP protein levels in the plasma of subjects before and after treatment with various medications for PD.

[0101] Figure 19 is a bar graph showing the difference in MTAP protein levels in the plasma of subjects before and after treatment with levodopa.

[0102] Figure 20 is a list of biological indicators of PD biological indicators identified using Somalogic SomaScan assay v5.

[0103] Figure 21 is a list of biological indicators of biological indicators of Parkinson’s Disease identified using Olink™ Explore HT (5k).

[0104] Figure 22 is a Principal Component Analysis highlighting a subpopulation ofDocket No. 2960447-000048-W01Filed: January 9, 202628patients with PD identified using a combination of biological indicators including MTAP, TUBA1A, GPT, SUMF1, OIP5, CNTFR, LSM1, QPCT, ITGB3, and ITGAV.

[0105] Figure 23 is a graph illustrating the raw data measurements of the SLIMF1 biological indicator in patients diagnosed with Parkinson’s disease (light dots) and matched controls (dark dots).

[0106] Figure 24 is a graph showing the changes in expression of SLIMF1 and individual trajectories of patients over a multi-year timeframe using modeling of the raw data shown in Figure 23.

[0107] Figure 25 is a graph showing the differences in SLIMF1 expression in the affected Parkinson’s population (dark line) and matched controls (light line) using modeling of the raw data shown in Figure 23.

[0108] Figure 26 is a graph illustrating the average inflection point in SLIMF1 expression in a population affected with Parkinson’s disease (dark line) and a matched control population (light line) using modeling of the raw data shown in Figure 23.

[0109] Figure 27 is a graph illustrating the estimated non-linear changes and velocity of SLIMF1 expression in a population affected with Parkinson’s disease (dark line) and a matched control population (light line) using modeling of the raw data shown in Figure 23.

[0110] Figure 28 is a graph illustrating differential trajectories within PD individuals (dark line) as compared to their matched controls (light line).

[0111] Figure 29 is a color summary of figures 23-28, wherein figure 29A corresponds to figure 23, figure 29B corresponds to figure 24, figure 29C corresponds to figure 25, figure 29D corresponds to figure 26, figure 29E corresponds to figure 27, figure 29F corresponds to Figure 28.

[0112] Figure 30 is a graph showing the plot of significant proteins identified in preParkinson’s patients as plotted against proteins identified in the complete dataset after a first evaluation of PD biomarkers.

[0113] Figure 31 is a graph showing the risk change for development of Parkinson’s disease as determined for various biological indicators after a first evaluation of PD biomarkers.

[0114] Figure 32 is a graph showing the significance of individual protein biological identifiers in Parkinson’s disease after a first evaluation of PD biomarkers.

[0115] Figure 33 is a graph showing the significance of individual protein biological identifiers in Parkinson’s disease, within the group of prodromal biomarkers after a first evaluation of PD biomarkers.Docket No. 2960447-000048-W01Filed: January 9, 202629

[0116] Figure 34 is a graph showing the significance of individual protein biological identifiers in Parkinson’s disease, within the group of progression biomarkers after a first evaluation of PD biomarkers.

[0117] Figure 35 is a graph showing the significance of individual protein biological identifiers in Parkinson’s disease, within the group of transient preclinical biomarkers after a first evaluation of PD biomarkers.

[0118] Figure 36 is a graph showing the significance of individual protein biological identifiers in Parkinson’s disease, within the group of transitional biomarkers after a first evaluation of PD biomarkers.

[0119] Figure 37 is a graph showing the significance of individual protein biological identifiers in Parkinson’s disease, within the group of post-diagnostic biomarkers after a first evaluation of PD biomarkers.

[0120] Figure 38 is a list of biological indicators of PD identified as prodromal biological indicators after a first evaluation of PD biomarkers.

[0121] Figure 39 is a list of biological indicators of PD identified as progression biological indicators after a first evaluation of PD biomarkers.

[0122] Figure 40 is a list of biological indicators of PD identified as transient preclinical biological indicators after a first evaluation of PD biomarkers.

[0123] Figure 41 is a list of biological indicators of PD identified as transitional biological indicators after a first evaluation of PD biomarkers.

[0124] Figure 42 is a list of biological indicators of PD identified as post-diagnostic biological indicators after a first evaluation of PD biomarkers.

[0125] Figure 43 is a graph showing the plot of significant proteins identified in preParkinson’s patients as plotted against proteins identified in the complete dataset after a subsequent evaluation of PD biomarkers.

[0126] Figure 44 is a graph showing the risk change for development of Parkinson’s disease as determined for various biological indicators after a subsequent evaluation of PD biomarkers.

[0127] Figure 45 is a graph showing the significance of individual protein biological identifiers in Parkinson’s disease after a subsequent evaluation of PD biomarkers.

[0128] Figure 46 is a graph showing the significance of individual protein biological identifiers in Parkinson’s disease, within the group of prodromal biomarkers, after a subsequent evaluation of PD biomarkers.

[0129] Figure 47 is a graph showing the significance of individual protein biological identifiers in Parkinson’s disease, within the group of progression biomarkers, after aDocket No. 2960447-000048-W01Filed: January 9, 202630subsequent evaluation of PD biomarkers.

[0130] Figure 48 is a graph showing the significance of individual protein biological identifiers in Parkinson’s disease, within the group of transient preclinical biomarkers, after a subsequent evaluation of PD biomarkers.

[0131] Figure 49 is a graph showing the significance of individual protein biological identifiers in Parkinson’s disease, within the group of transitional biomarkers, after a subsequent evaluation of PD biomarkers.

[0132] Figure 50 is a graph showing the significance of individual protein biological identifiers in Parkinson’s disease, within the group of post-diagnostic biomarkers, after a subsequent evaluation of PD biomarkers.

[0133] Figure 51 is a list of biological indicators of PD identified as prodromal biological indicators after a subsequent evaluation of PD biomarkers.

[0134] Figure 52 is a list of biological indicators of PD identified as progression biological indicators after a subsequent evaluation of PD biomarkers.

[0135] Figure 53 is a list of biological indicators of PD identified as transient preclinical biological indicators after a subsequent evaluation of PD biomarkers.

[0136] Figure 54 is a list of biological indicators of PD identified as transitional biological indicators after a subsequent evaluation of PD biomarkers.

[0137] Figure 55 is a list of biological indicators of PD identified as post-diagnostic biological indicators after a subsequent evaluation of PD biomarkers.DETAILED DESCRIPTION OF EMBODIMENTS

[0138] The following examples merely illustrate particularly preferred methods and selected aspects in connection therewith. The teaching provided therein may be used for constructing several tests and / or kits. The following examples are not to be construed as limiting the scope of the claims.

[0139] In the following description, numerous specific details are set forth to provide a more thorough understanding of the present disclosure. However, it will be apparent to one of skill in the art that the present disclosure may be practiced without one or more of these specific details. In other instances, features and procedures well known to those skilled in the art have not been described in order to avoid obscuring the invention. The terms used herein are intended to have the plain and ordinary meaning as understood by those of ordinary skill in the art.

[0140] All of the functionalities described in connection with one embodiment of theDocket No. 2960447-000048-W01Filed: January 9, 202631compositions and / or methods described herein are intended to be applicable to the additional embodiments of the compositions and / or methods except where expressly stated or where the feature or function is incompatible with the additional embodiments. For example, where a given feature or function is expressly described in connection with one embodiment but not expressly mentioned in connection with an alternative embodiment, it should be understood that the feature or function may be deployed, utilized, or implemented in connection with the alternative embodiment unless the feature or function is incompatible with the alternative embodiment.

[0141] Note that as used herein and in the appended claims, the singular forms "a," "an," and "the" include plural referents unless the context clearly dictates otherwise. Thus, for example, reference to “a cell” refers to one or more cells, and reference to “a system” includes reference to equivalent steps, methods and devices known to those skilled in the art, and so forth. Additionally, it is to be understood that terms such as "left," "right," "top," "bottom," "front," "rear," "side," "height," "length," "width," "upper," "lower," "interior," "exterior," "inner," "outer" that may be used herein merely describe points of reference and do not necessarily limit embodiments of the present disclosure to any particular orientation or configuration. Furthermore, terms such as "first," "second," "third," etc., merely identify one of a number of portions, components, steps, operations, functions, and / or points of reference as disclosed herein, and likewise do not necessarily limit embodiments of the present disclosure to any particular configuration or orientation.

[0142] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs. All publications mentioned herein are incorporated by reference for all purposes, including describing and disclosing devices, formulations and methodologies that may be used in connection with the presently described disclosure. Conventional methods are used for the procedures described herein, such as those provided in the art and demonstrated in the Examples and various general references. Unless otherwise stated, nucleic acid sequences described herein are given, when read from left to right, in the 5' to 3' direction. Nucleic acid sequences may be provided as DNA, as RNA, or a combination of DNA and RNA (e.g., a chimeric nucleic acid) and may contain non-natural nucleotides and / or bases. Unless otherwise stated, protein sequences described herein are given, when read from left to right, in the N-terminal to C-terminal direction. Protein sequences may contain non-natural amino acids and may contain chemical modifications at specified locations within theDocket No. 2960447-000048-W01Filed: January 9, 202632sequence and with various degrees of modification.

[0143] Where a range of values is provided, it is understood that each intervening value, between the upper and lower limit of that range and any other stated or intervening value in that stated range is encompassed within the disclosure. The upper and lower limits of these smaller ranges may independently be included in smaller ranges, and are also encompassed within the disclosure, subject to any specifically excluded limit in the stated range. Where the stated range includes one or both limits, ranges excluding either or both of those included limits are also included in the disclosure.

[0144] The term “and / or” where used herein is to be taken as specific disclosure of each of the multiple specified features or components with or without another. Thus, the term “and / or” as used in a phrase such as “A and / or B” herein is intended to include “A and B,” “A or B,” “A” (alone), and “B” (alone). Likewise, the term “and / or” as used in a phrase such as “A, B, and / or C” is intended to encompass each of the following embodiments: A, B, and C; A, B, or C; A or C; A or B; B or C; A and C; A and B; B and C; A (alone); B (alone); and C (alone).

[0145] As used herein, the term "about," as applied to one or more values of interest, refers to a value that falls within 10%, 9%, 8%, 7%, 6%, 5%, 4%, 3%, 2%, 1%, or less in either direction (greater than or less than) of a stated reference value, unless otherwise stated or otherwise evident from the context (except where such number would exceed 100% of a possible value).Definitions

[0146] The term “biological indicators” as used herein refers to the data output of various techniques that can be used to produce the biological indicator signatures from a subject’s outcomes of various analytes in a cell-free sample. Such techniques include various forms of analysis, including multi-omics assays including but not limited to genomic, epigenomic, proteomic and transcriptomic assays.

[0147] The term “biological indicator signature” as used herein refers to a set of two or more biological indicators obtained, analyzed, or derived from one or more cell-free samples of a subject that are indicative of a disease status in the subject.

[0148] The term “cell-free samples” can mean any sample from which cell-free analytes can be obtained and / or analyzed and that can provide the basis for detection of biological indicators using the systems and methods of the disclosure. Such samples include, but are not limited to, blood, plasma, serum, cerebrospinal fluid, saliva, feces,Docket No. 2960447-000048-W01Filed: January 9, 202633and urine. Preferably the cell-free sample is a fluid. In specific aspects, the cell-free sample is plasma, or a plasma product. In some aspects, the sample is obtained from blood or a dried blood spot.

[0149] The term “neurodegenerative disorder” and variations thereof as used herein refer to neuropathological disorders in which cells of the nervous system stop working or die. Neurodegenerative disorders are usually progressive and incurable. Examples of neurodegenerative disorders include Alzheimer's disease, Parkinson's disease., frontotemporal dementia, Huntington’s disease, and Lewy body dementia.

[0150] The term “neuropathological condition” as used herein refers to any nervous system condition, including but not limited to neuropsychiatric disorders and neurodegenerative disorders, which may impact a subject’s life span and / or quality of life. These conditions may directly affect neurons, or may affect other cells, structures or systems of the brain or nervous system. Neuropathological conditions include disorders of the central nervous system and / or peripheral nervous system. These include, but are not limited to, acute inflammatory demyelinating polyradiculoneuropathy (Al DP), acute spinal cord injury, Alzheimer’s disease, ataxia, Bell’s palsy, brain tumors (including glioblastoma), cerebral aneurysms or ischemic events, cluster headaches, epilepsy and other seizure disorders, frontotemporal dementia, Huntington’s disease, Lewy body dementia, migraine, multiple sclerosis, muscular dystrophy, myasthenia gravis, neurocutaneous syndromes, neuropsychiatric disorders, Parkinson’s disease, prion diseases, or other proteinopathies {e.g., synucleinopathies or tauopathies).

[0151] The term “neuropsychiatric disorder” and variations thereof as used herein refers to neuropathological disorders such as depression, anxiety, schizophrenia, substance abuse disorder, post-traumatic stress disorder, primary migraines, bipolar disorder, obsessive compulsive disorder, Tourette’s syndrome, and attention deficit hyperactivity disorder (ADHD). These disorders produce a wide spectrum of neurological symptoms that may vary widely between subjects.

[0152] The “plasmapheresis biobank” referred to herein is a collection of plasma samples that originates from 3M+ plasma donors and offers a unique opportunity to study the preclinical and clinical phases of diseases over a multi-year period. Presently, the Chronos Biobank contains more than 80 million samples dating back to 2010. This establishes the plasmapheresis biobank as an extensive collection of biospecimens from individuals providing a valuable tool for numerous medical uses including diagnostic, prognostic, therapeutic and drug development applications.Docket No. 2960447-000048-W01Filed: January 9, 202634

[0153] The term “reference biological indicator signature” as used herein refers to a biological indicator signature that serves as a comparator for the identification of the likelihood of a clinical event in a subject. Such reference biological indicator signature can include, but is not limited to, a biological indicator signature generated over time from other subjects that have undergone the clinical event, a biological indicator signature generated over time from other subjects that do not have or have not undergone the clinical event (and preferably but not necessarily are phenotypically and / or genotypically matched to the subject), a biological indicator signature generated from assay data obtained from the subject at other timepoints, a biological indicator signature that is based on the aggregate of data from other subjects that are known to have or have undergone the clinical event.

[0154] The term “temporal signature” as used herein refers to a sample signature that is obtained using biological indicator data from subject samples taken at two or more different time points.

[0155] A concise list of definitions with reference to the embodiments of the invention is included herein below:

[0156] As used herein, the term “biomarker” refers to a biological indicator that can be measured in a biological sample and used for diagnostic, prognostic, predictive, monitoring, or therapeutic purposes in relation to Parkinson’s disease. A biomarker may be a gene-based biomarker or a protein-based biomarker. A gene-based biomarker includes a nucleic acid sequence, a variant thereof, or its expression level. A protein-based biomarker includes the corresponding protein or polypeptide encoded by the gene, including its abundance, isoforms, post-translational modifications, or detectable activity. Unless otherwise indicated, reference to a biomarker gene includes reference to the corresponding biomarker protein, and vice versa.

[0157] The term “subject” refers to a mammal, preferably a human. The subject may be asymptomatic, suspected of having Parkinson’s disease, diagnosed with Parkinson’s disease, or at risk of developing Parkinson’s disease.

[0158] The term “biological sample” refers to any sample obtained from a subject that is suitable for biomarker analysis. Biological samples include blood, plasma, serum, whole blood, or fractions thereof. In preferred embodiments, the biological sample is plasma.

[0159] The term “expression level” or “amount” refers to a measurable level of a biomarker gene or biomarker protein in a biological sample and may be determined by any suitable molecular or immunological method, including nucleic acid-based assays,Docket No. 2960447-000048-W01Filed: January 9, 202635protein-based assays, or signal detection methods.

[0160] The term “reference value” refers to a predetermined value against which a biomarker level is compared. The reference value may be derived from one or more control subjects, population averages, historical samples, longitudinal samples from the same subject, or any combination thereof.

[0161] As used herein, the term “biomarker signature” refers to a combination of two or more biomarkers evaluated individually or collectively to provide information relating to disease risk, disease presence, disease progression, prognosis, therapeutic response, or clinical trial selection.

[0162] Biomarker Gene Categories:

[0163] In the context of the instant invention, the different biomarker genes are grouped into five different categories (prodromal genes, progression genes, transient preclinical genes, transitional genes and post-diagnostic genes) whose expression rates of change (reflected in increasing / decreasing protein levels) correlates strongly with distinct disease stages and biomarker performance. Thus, temporal variation in protein levels (i.e. , rate of change) can be tied to the diagnostic utility of biomarkers in distinguishing disease stages.

[0164] As used herein, the term “prodromal gene” refers to a gene that exhibits a detectable difference relative to a reference during a preclinical phase of a disease and prior to the appearance of clinical symptoms, wherein the detectable difference persists until or beyond disease onset. Prodromal biomarkers show significantly changed protein levels up to 12 years before onset of symptoms of Parkinson’s Disease (PD), e.g., 1-12 years before onset of symptoms of Parkinson’s Disease (PD). The biomarkers can stay increased or decreased after PD onset. They were found from pre-diagnostic samples or detected as risk biomarkers by joint modeling using samples before PD onset. Thus, prodromal gene biomarkers have significantly different expression levels before PD onset, and some may also have significantly different expression levels after PD onset.

[0165] As used herein, the term “progression gene” refers to a gene that exhibits a detectable change relative to a reference prior to and following disease onset and that correlates with a change in disease stage, severity, or rate of disease progression. Significant rate of change was detected for progression biomarker genes in the entire timeframe of analysis (12 years before PD onset to 9 years after PD onset). Thus, progression genes are actively changing from years before PD onset and after PD onset.Docket No. 2960447-000048-W01Filed: January 9, 202636

[0166] As used herein, the term “transient preclinical gene” refers to a gene that exhibits a detectable inflection in a rate of change relative to a reference during a preclinical phase of a disease and that does not exhibit the detectable inflection in the rate of change after disease onset. Transient preclinical biomarkers were identified PD biomarkers with significantly different rate of change in a first slope with the inflection point before PD onset or around PD onset from linear spline mixed model results. Significant rate of change can be detected 12 to 4-5 years before PD onset. Active changes in rate of change cannot be detected afterwards, even if transient preclinical biomarker gene protein expression levels can still be significantly different from reference values after this period of change. Thus, active change for these markers may start many years before PD onset (i.e., at least 12 years before PD onset), but active change stopped mostly 4-5 years before PD onset.

[0167] As used herein, the term “transitional gene” refers to a gene that exhibits a detectable inflection in a rate of change relative to a reference during a transition period between a preclinical phase and a clinically manifest phase of a disease. Transitional biomarkers were identified PD biomarkers with significantly different rate of change in a second slope with inflection point before PD onset or around PD onset from linear spline mixed model results. Transitional biomarker genes show active change a few years before PD onset and forward (e.g., 5, 4, 3 years before PD onset and forward). Their active change is around PD onset and continued with PD progression.

[0168] As used herein, the term “post-diagnostic gene” refers to a gene that exhibits a detectable change relative to a reference after clinical diagnosis of a disease and that is associated with disease monitoring, prognosis, or therapeutic response.

[0169] Prodromal biomarkers and transient preclinical biomarkers were mainly found based on pre-diagnostic (preDx) samples. As such, they are considered to be more reliable as biomarkers of prediction of risk of developing a diagnosis of PD, future PD predictive biomarkers, or as PD diagnostic biomarkers. In contrast, transitional, progression and post-diagnostic (postDx) biomarkers were also partially informed by postDx samples. In these samples PD medications could have an effect in terms of protein expression, which may need to be considered. These transitional, progression and post-diagnostic biomarkers could be developed into PD prognostic biomarkers, and they could also be useful as PD diagnostic and prediction biomarkers.

[0170] The above biomarker genes may also be distinguished depending on the type of significant difference that is detected. In a first type (prodromal and postDXDocket No. 2960447-000048-W01Filed: January 9, 202637biomarkers) there were significant differences in protein levels between PD and control. In a second type (transitional preclinical biomarkers, transitional biomarkers and progression biomarkers) there were significant differences in rate of change, so the actively changing phase can be determined. These differences may need to be taken into account when developing medical tests. That is, in the first type, critical values (e.g., threshold values) may be set for distinguishing between normal vs abnormal values, whereas, in the second type, multiple tests may be needed, so that changes can be traced, as opposed to determining absolute values.

[0171] A given biomarker gene may belong to more than one category depending on the timing and nature of the detectable change. Detection Methods and Assays:

[0172] As used herein, the terms “detecting,” “determining,” or “assessing” include qualitative or quantitative measurement of a biomarker gene or biomarker protein using any suitable assay known to the skilled person.

[0173] The term “protein amplification-based assay” refers to assays capable of amplifying misfolded or aggregated protein species to enable detection, including realtime quaking-induced conversion (RT-QuIC), protein misfolding cyclic amplification (PMCA), and functionally equivalent methods.

[0174] Treatment and Therapeutic Response

[0175] As used herein, the term “treatment” or “therapy” refers to any pharmacological or clinical intervention used to manage, alleviate, or modify Parkinson’s disease symptoms or progression, including symptomatic and diseasemodifying therapies.

[0176] General Interpretation:

[0177] Unless otherwise indicated, terms referring to biomarkers, genes, proteins, detection, assessment, diagnosis, risk determination, monitoring, treatment, kits, or assay devices include all technically equivalent methods, materials, reagents, and formats known to a person skilled in the art.Multi-omics analysis

[0178] The biological indicators that can be used to determine biological indicator signature(s) associated with a subject’s neuropathological status can be any molecule present in the cell-free samples used for analysis.

[0179] The selection of which “omic” assays to use will depend upon the nature of the biological indicator sought as well as the clinical goal of the system developed. InDocket No. 2960447-000048-W01Filed: January 9, 202638various examples, biological assays are performed on different portions of a biological sample to provide a data set corresponding to the biological assay for various biological analytes. Various assays are known to those of skill in the art and are useful to interrogate a biological sample.

[0180] As used herein the term “assay” includes known biological assays and may also include computational biology approaches for transforming biological information into useful inputs for systems analysis and modeling. Various preprocessing computational tools may be included with the assays described herein and the term “assay” is not intended to be limiting to solely physical aspects of the molecular techniques.

[0181] Examples of such assays include but are not limited to: whole-genome sequencing (WGS), whole-genome bisulfite sequencing (WGSB), small-RNA sequencing, quantitative immunoassay, enzyme-linked immunosorbent assay (ELISA), proximity extension assay (PEA), protein microarray, mass spectrometry, low-coverage Whole-Genome Sequencing (IcWGS); selective tagging 5mC sequencing (WO2019 / 051484), CNV calling; tumor fraction (TF) estimation; Whole Genome Bisulfite Sequencing; LINE-1 CpG methylation; 56 genes CpG methylation; cf-Protein Immuno-Quant ELISAs, SIMOA; and cf-miRNA sequencing, and cell type or cell phenotype mixture proportions derived from any of the above assays. The ability to analyze multiple analytes (such as but not limited to DNA, RNA, proteins, autoantibodies, metabolites, or combinations thereof) simultaneously from the same biological sample, or fractions thereof can increase the sensitivity and specificity of such bodily fluid diagnostic tests by exploiting independent information between signals.

[0182] In one example, cell-free DNA (cfDNA) content is assessed by low-coverage whole-genome sequencing IcWGS or targeted sequencing, or whole-genome bisulfite sequencing (WGBS) or whole-genome enzymatic methyl sequencing; cell-free microRNA (cf-miRNA) is assessed by small-RNA sequencing or PCR (digital droplet or quantitative); and levels of circulating proteins are measured by quantitative immunoassay. In one example, cell-free DNA (cfDNA) content is assessed by WGBS, proteins are measured by quantitative immunoassay (including ELISA or proximity extension assay), and autoantibodies are measured by protein microarrays. Extracellular nucleic acid molecules include cell-free DNA (cfDNA) and cell-free RNA (cfRNA). These molecules are typically fragmented but resist full degradation in plasma because of protection from extracellular vesicles or bindingDocket No. 2960447-000048-W01Filed: January 9, 202639proteins (e.g., nucleosomes for cfDNA and ribonucleoproteins for cfRNA).

[0183] Many cfDNA features (e.g., methylation pattern, mutation, copy number, fragment pattern, and nucleosome footprint) have been utilized for noninvasive assessments of disease diagnosis and prognosis. See, e.g., van der Pol Y. and Mouliere F. Cancer Cell. 2019; 36:350-368; Jamshidi A. et al., Cancer Cell.2022;40:1537-1549. e12. Shen S.Y. et al., Nature. 2018;563:579-583. Biological information may also include information regarding transcription start sites, transcription factor binding sites, nucleosomal positioning or occupancy, transposase-accessible chromatin using sequencing (ATAC-seq) data, histone marker data, DNAse hypersensitivity sites (DHSs), and the like. The plasma concentration of cfDNA may be assayed as a biological indicator that in various examples indicates the presence of a pathology.

[0184] Many cfRNA features can also serve as biological indicators; such features include the abundance of microRNAs (Zhou J. et al., J. Clin. Oncol.2011;29:4781-4788) and circular RNAs (Wang S. et al., Mol. Cancer.2021;20:13), fragment copies, and alternative splicing patterns of mRNAs and long noncoding RNAs. Zhu Y. et al., Theranostics. 2021;11:181-193; Larson M.H. et al., Nat. Common. 2021; 12:2357.

[0185] In various examples, assays that profile the characteristics of cfDNA and / or cfRNA are used to generate signatures useful in the computational applications. In one example, characteristics of cf-DNA are used in machine learning models and to generate classifiers to stratify individuals or detect disease as described herein. Exemplary signatures include but are not limited to those that provide biological information regarding gene expression, 3D chromatin, chromatin states, copy number variants, tissue of origin and cell composition in cfDNA samples. Metrics of cfDNA concentration that may be used as input signatures for machine learning methods and models may be obtained by methods that include but are not limited to methods that quantitate dsDNA within specified size ranges (e.g., Agilent TapeStation, Bioanalyzer, Fragment Analyzer), methods that quantitate all dsDNA using dsDNA-binding dyes (e.g., QuantiFluor, PicoGreen, SYBR Green), and methods quantify DNA fragments (either dsDNA or ssDNA) at or below specific sizes (e.g., short fragment qPCR, long fragment qPCR, and long / short qPCR ratio).

[0186] In some aspects, changes in gene expression are identified by measuring plasma cfDNA or cfRNA concentration levels and methods such as microarray analysis are used to assess changes in gene expression levels in a sample.Docket No. 2960447-000048-W01Filed: January 9, 202640Metrics of cfDNA or cfRNA concentration that may be used as input signatures for machine learning methods and models include but are not limited to Tape Station, short qPCR, long qPCR, and long / short qPCR ratio.

[0187] In some aspects, IcWGS can be used to sequence the cf-DNA in a sample and then interrogated for somatic mutations associated with a particular neuropathological condition. IcWGS, deep WGS, or targeted sequencing (by NGS or other techniques) may generate somatic mutation data which may be used as an input to biological indicator signatures or analyzed in conjunction with biological indicator signatures to identify the onset, diagnosis, prognosis, etc. of a neuropathological condition in a subject.

[0188] Somatic mutation analysis allows multiplexed detection of specific loci information in a single test. Such panel-based analysis can range in gene number from several to several hundred in a single assay. Other types of gene panels include whole-exon or whole-gene sequencing, or genomic analysis may be directed to a selected class of genes {e.g., kinome or transcription factor profiling) and offer the advantage of identifying novel mutations in a known gene set.

[0189] In other examples, assays are used to infer the three-dimensional structure of a genome using cell-free DNA (cfDNA). In particular, the present disclosure provides methods and systems for detecting chromatin abnormalities associated with diseases or conditions. For example, the abundance of a cfDNA fragment in a biological sample is believed to be predictive of the chromatin state of the gene from which the cfDNA fragment originated, and these states can change in certain pathological disorders. Identifying changes in the chromatin state of genes can thus serve as a method to identify the presence or progression of a particular disease in a subject. The chromatin state of genes can be predicted from the abundance and position of cfDNA fragments in biological samples using computer-aided techniques. The chromatin state may also be useful in inferring gene expression in a sample. Pliner H. A. et al., Molecular Cell 71 (5) (2018) 858-871. The expression of a gene can be controlled by controlling access of the cellular machinery to the transcription start site. Access to the transcription start site can be determined by the state of the chromatin on which the transcription start site is located. Chromatin state can be controlled through chromatin remodeling, which can condense (close) or loosen (open) transcription start site. A closed transcription start site results in decreased gene expression while an open transcription start site results in increased gene expression. Also, the length of cfDNA fragments may depend on chromatin state. ChromatinDocket No. 2960447-000048-W01Filed: January 9, 202641remodeling can occur through the modification of histone and other related proteins. Non-limiting examples of histone modifications that can control the state of chromatin and transcription start sites include, for example, methylation, acetylation, phosphorylation, and ubiquitination.

[0190] Expression of genes is also controlled by more distal elements such as enhancers, which interact with transcriptional machinery in the 3D space of the physical genome. ATAC-seq and DNAse-seq provide measurements of open chromatin, which correlate with the binding of these more distal elements which may not be obviously associated with a particular gene. For example, ATAC-seq data can be obtained for a multitude of cell types and states and be used to identify regions of the genome with open chromatin for a variety of underlying regions such as active transcription start sites or bound enhancers or repressors.

[0191] The half-life of cfDNA once released from cells can depend on chromatin remodeling states. Thus, the abundance of a cfDNA fragment in a biological sample can be indicative of the chromatin state of the gene from which the cfDNA fragment originated (referred to herein as a cfDNA's “position”). Chromatin states of genes can change in diseases. Identifying changes in the chromatin state of genes can serve as a method to identify the presence of a disease in a subject. When comparing expressed and unexpressed genes, there is a quantitative shift in both the number and positional distribution of cell-free DNA (cfDNA) fragments.

[0192] The position of cfDNA sequence reads within the genome can be determined by “mapping” the sequence to a reference genome. Mapping can be performed with the aid of computer algorithms including, for example, the Needleman-Wunsch algorithm, the BLAST algorithm, the Smith-Waterman algorithm, a Burrowswheeler alignment, a suffix tree, or a custom-developed algorithm.

[0193] DNA methylation, which refers to the addition of the methyl group to DNA, is one of the most extensively characterized epigenetic modification with important functional consequences. Typically, DNA methylation occurs at cytosine bases of nucleic acid sequences. Enzymatic methyl sequencing is especially useful since it uses a three-step conversion requiring lower volume of sample for analysis. Various methods of determining changes in methylation state of nucleic acids also can be used to identify biological indicators in the samples from the plasmapheresis biobank. Enzymatic methyl sequencing (“EMseq”) is capable of characterizing DNA methylation of nearly every nucleotide in the genome. In certain methods, the cfDNA is subjected to conditions sufficient to convert cytosine nucleobases into uracilDocket No. 2960447-000048-W01Filed: January 9, 202642nucleobases by performing bisulfite conversion, e.g., oxidizing the cfDNA. In some aspects, the bisulfite conversion comprises reduced representation bisulfite sequencing.

[0194] In other aspects, the assay that is used for methylation analysis is selected from mass spectrometry, methylation-Specific PCR (MSP), reduced representation bisulfite sequencing, (RRBS), HELP assay, GLAD-PCR assay, ChlP-on-chip assays, restriction landmark genomic scanning, methylated DNA immunoprecipitation (MeDIP), pyrosequencing of bisulfite treated DNA, molecular break light assay, methyl Sensitive Southern Blotting, High Resolution Melt Analysis (HRM or HRMA, ancient DNA methylation reconstruction, WGBS, or Methylation Sensitive Single Nucleotide Primer Extension Assay (msSNuPE).

[0195] In some aspects, the biological indicators are identified using cfRNA analysis. Methods for performing this include, but are not limited to, RNA sequencing, whole transcriptome shotgun sequencing, northern blot, in situ hybridization, hybridization array, serial analysis of gene expression (SAGE), reverse transcription PCR, real-time PCR, real-time reverse transcription PCR, quantitative PCR, digital droplet PCR, microarray, NanoString mRNA analysis, FISH assays or a combination thereof.

[0196] Methods of “quantitative” amplification are a variety of suitable methods. For example, quantitative PCR involves simultaneously co-amplifying a known quantity of a control sequence using the same primers. This provides an internal standard that may be used to calibrate the PCR reaction. Detailed protocols for quantitative PCR are provided in Innis, et al. (1990) PCR Protocols, A Guide to Methods and Applications (Academic Press, Inc. N.Y.). Measurement of DNA copy number at microsatellite loci using quantitative PCR analysis is described in Ginzonger, et al. (2000) Cancer Research 60:5405-5409. The known nucleic acid sequence for the genes is sufficient to enable one to routinely select primers to amplify any portion of the gene. Fluorogenic quantitative PCR may also be used in the methods of the disclosure. In fluorogenic quantitative PCR, quantitation is based on amount of fluorescence signals, e.g., TaqMan and SYBR green. Other suitable amplification methods include, but are not limited to, ligase chain reaction (LCR) (see Wu and Wallace (1989) Genomics 4: 560, Landegren, et al. (1988) Science 241:1077, and Barringer et al. (1990) Gene 89: 117), transcription amplification (Kwoh, et al. (1989) Proc. Natl. Acad. Sci. USA 86: 1173), self-sustained sequence replication (Guatelli, et al. (1990) Proc. Nat. Acad. Sci. USA 87: 1874), dot PCR, and linker adapter PCR, etc.Docket No. 2960447-000048-W01Filed: January 9, 202643

[0197] In various examples, proteins are assayed using immunoassay or mass spectrometry. For example, proteins may be measured by liquid chromatography-tandem mass spectrometry (LC-MS / MS).

[0198] In various examples, proteins are measured by affinity reagents or immunoassays such as protein arrays, SIMOA (Quanterix, Billerica, MA, USA), ELISA (Abeam, Cambridge, UK), the Olink™ (Explore HT, Upsalla, Sweden) or SomaScan (Somalogic, Boulder, CO, USA), Luminex and Meso Scale Discovery.

[0199] In other examples, proteins in the samples are assayed based on characteristics such as post-translational modifications, phosphorylation status, or other protein modifications that may impact their activity, localization, lifespan and the like.

[0200] The level of one or more biomarker in a biological fluid sample from the subject may be determined in any of the methods by contacting the biological fluid sample with an appropriate antibody specific to the biomarker of interest and detecting binding between the biomarker and the corresponding antibody (i.e. CNTFR binding to an anti CNTFR antibody, CXCL12 binding to an anti- CXCL12 antibody, etc.).

[0201] In some aspects, the protein data is normalized by a standard curve. In various examples, each protein is treated as an essentially unique immunoassay, each with a standard curve that can be calculated in various ways. The concentration relationship is typically non-linear. Then the sample may be run. and calculated based on the expected fluorescence concentration in the primary sample.Applications

[0202] Aspects of the disclosed embodiments provide a method of diagnosing a neuropathological condition. The condition may be any disease or disorder that involves or affects neurons including developmental and genetic disorders, neuropsychiatry conditions and neurodegenerative diseases. The present methods are minimally invasive and can be performed for subjects of any age. Since the methods described here can be performed at an early age to diagnose a neurodegenerative disease, a disease can be identified well before it has advanced significantly and caused substantial damage - and in some cases before the actual onset of symptoms that are associated with the disease.

[0203] An aspect of the systems and methods of the disclosure provide a comparison of a temporal signature from a subject’s samples with a reference biological indicator signature. A negative reference biological indicator signature mayDocket No. 2960447-000048-W01Filed: January 9, 202644be obtained by obtaining a temporal signature from one or more subjects suspected of not having the condition being assessed in a subject, or from a sample from the same subject taken at an earlier time point. This includes sample signatures derived from one or more individuals known not to have the condition nor to have genetic mutations associated with risk of the condition.

[0204] A positive control signal can be obtained using sample signatures from subjects who have been diagnosed with the neuropathological condition being assessed in a subject. In specific aspects, these “affected sample signatures” are matched with the subject’s genetic and / or phenotypic status, e.g., mutation state for germline alterations or diagnosis with other pathologies such as hypertension or diabetes mellitus. In embodiments where the reference biological indicator signature is associated with a specific neuropathological condition, a match between the observed signature and the affected sample signature corresponds to a positive diagnosis of the condition.

[0205] Systems and methods of the disclosure are suited to diagnosing conditions such as neuro-psychiatric conditions, neurodevelopmental disorders, and neurodegenerative diseases. Exemplary neurodegenerative diseases include but are not limited to Alzheimer's disease, frontotemporal dementia, Lewy body dementia, Huntington's disease, multiple sclerosis, Parkinson's disease, spinal and bulbar muscular atrophy, and amyotrophic lateral sclerosis. Exemplary neuro-psychiatric conditions include schizophrenia, autism, epilepsy, bipolar disorder, migraines, cluster headaches, multiple sclerosis, depression, anxiety and panic disorder, post-traumatic stress disorder, schizophrenia, attention deficit hyperactivity disorder, obsessive compulsive disorders, eating disorders e.g., anorexia nervosa or bulimia) and the like.

[0206] In certain aspects of the disclosure, the subject is identified as having one or more germline genetic alterations that increase their risk for developing a neuropathological condition. For example, a subject that is being assessed for amyotrophic lateral sclerosis (ALS) may have an identified mutation in a gene such as SOD1, TARDBP, FUS, LIBQL2, ALS2, or SETX. In another example, a subject that is being assessed for Parkinson’s disease may have an identified mutation in GBA, LRRK2, SNCA, PINK1, PRKN, DJI. ATP13A2, MAPT, PARK7 and VPS35. In yet another example, a subject that is being assessed for Alzheimer’s disease may have identified mutations in amyloid precursor protein (APP), PSEN1 or PSEN2, or the presence of a disease-associated allele such as apolipoprotein E4. In some aspects, a germline alteration can be detected separately from the risk determined by theDocket No. 2960447-000048-W01Filed: January 9, 202645temporal signatures — e.g., from SNP analysis or genome sequencing performed prior to or contemporaneously with the identification of the temporal signatures — and the data aggregated to provide a combined risk score, In other aspects, information on germline risk can be obtained from the sample used to identify the temporal signature — e.g., through identification of the alteration in cfDNA or cfRNA in the cell-free biological sample.

[0207] Methods and systems provided herein may perform predictive analytics using artificial intelligence-based approaches to analyze acquired data from a subject (subject) to generate an output of diagnosis of the subject suspected of having a neuropathological condition. For example, the application may apply a prediction algorithm to the acquired data to generate the diagnosis of the subject having the neuropathological condition. The prediction algorithm may comprise an artificial intelligence-based predictor, such as a machine learning-based predictor, configured to process the acquired data to generate the diagnosis of the subject having the neuropathological condition.

[0208] The machine learning predictor may be trained using datasets e.g., datasets generated by performing multiplexed biological assays of samples of individuals from one or more sets of cohorts of subjects having a neuropathological condition as inputs and known diagnosis outcomes of the subjects as outputs to the machine learning predictor.

[0209] Training datasets (e.g., datasets generated by performing multiplexed assays of biological samples of individuals) may be generated from, for example, one or more sets of subjects having common characteristics (features) and outcomes (labels). Training datasets may comprise a set of features and labels corresponding to the features relating to diagnosis. Features may comprise characteristics such as, for example, certain ranges or categories of cfDNA assay measurements, such as counts of cfDNA fragments in a biological sample obtained from healthy and diseased samples that overlap or fall within each of a set of bins (genomic windows) of a reference genome. For example, a set of features collected from a given subject at a given time point may collectively serve as a diagnostic signature, which may be indicative of an identified cancer of the subject at the given time point. Characteristics may also include labels indicating the subject's diagnostic outcome, such as for one or more neuropathological conditions.

[0210] Protein-based training sets may be generated in a similar fashion using characteristics such as, for example, assay measurements on protein levels in theDocket No. 2960447-000048-W01Filed: January 9, 202646sample, assays on protein activity and / or activation (e.g., ligand engagement, phosphorylation status, etc.), assays on post-translational modification, etc.

[0211] Training data may be selected randomly using a sample set of data corresponding to one or more sets of subjects (e.g., retrospective and / or prospective cohorts of subjects having or not having one or more neuropathological conditions). Alternatively, training datasets may be selected by proportionate sampling of a set of data corresponding to one or more sets of subjects (e.g., retrospective and / or prospective cohorts of subjects having or not having one or more neuropathological conditions). Training sets may be balanced across sets of data corresponding to one or more sets of subjects {e.g., subjects from different clinical sites or trials). The machine learning predictor may be trained until certain predetermined conditions for accuracy or performance are satisfied, such as having minimum desired values corresponding to diagnostic accuracy measures.

[0212] Examples of diagnostic accuracy measurements may include sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy, and area under the curve (AUC) of a Receiver Operating Characteristic (ROC) curve corresponding to the diagnostic accuracy of detecting or predicting the neuropathological condition.EXAMPLES

[0213] The following examples are put forth so as to provide those of ordinary skill in the art with a complete disclosure and description of how to make and use the present invention and are not intended to limit the scope of what the inventors regard as their invention, nor are they intended to represent or imply that the experiments below are all of or the only experiments performed. It will be appreciated by persons skilled in the art that numerous variations and / or modifications may be made to the invention as shown in the specific aspects without departing from the spirit or scope of the disclosure as broadly described. The present aspects are, therefore, to be considered in all respects as illustrative and not restrictive.Example 1: Samples from the Plasmapheresis Biobank for Identification of Biological Indicators in Parkinson’s Disease

[0214] Earlier stage diagnosis can be especially useful for chronic, progressive neurodegenerative diseases in which the symptoms follow the earlier pathology of theDocket No. 2960447-000048-W01Filed: January 9, 202647disease. For example, around 60-80% of dopaminergic neurons in the substantia nigra of a subject must die before noticeable Parkinson's symptoms appear, meaning a significant loss of these neurons is needed before motor symptoms become evident. See Lang AE et al., / V Engl J Med. 1998;339:1044-1053; Dauer W et al. Neuron.2003;39:889-909. Identification of such progressive neuropathological conditions through non-invasive assessment of biological indicators and temporal signatures will allow for earlier treatment and even intervention in the disease state before it progresses to the symptomatic stage.

[0215] Ongoing initiatives such as The Parkinson Progression Marker Initiative (PPMI)(Prog Neurobiol. 2011 Dec;95(4):629-35) have played a crucial role in identifying potential biological indicators associated with Parkinson's disease and to understand the development of the disease and identify progression markers. PPM I and research cohorts rely on a detailed longitudinal follow up of hundreds to several thousands of individuals with early symptoms or established PD diagnosis. In contrast, population-based investigations like those involving the UK Biobank (Sudlow C et al., PLoS Med. 2015 Mar 31;12(3):e1001779.) follow hundreds of thousands of individuals during the entire development of diseases but only a small fraction of them will develop rare diseases like PD. While tailored research cohorts like PPMI and population-based studies like those using the UK Biobank are complementary by nature, they both suffer from a major limitation: the number of biospecimens collected during the pre-clinical phases of diseases such as PD is by nature extremely limited. Indeed, research cohorts are mostly focused on the clinical phase of diseases while the collection of biospecimens in population-based cohorts primarily occurs at the baseline. This prevents a longitudinal understanding of the early phases of disease progression from a molecular perspective.

[0216] These limitations were overcome in the present studies by the use of a unique set of samples that provide the ability to perform retrospective analysis on a large cohort of subjects spanning years from prior to onset of a disease through diagnosis and progression of the disease. The identification of biological indicators for use in the systems and methods of the disclosure was empowered by a unique asset of plasmapheresis samples taken from a cohort of over 3,000,000 individuals over two decades, with as many as thirty samples taken from a single individual during this timeframe, allowing a unique opportunity for retrospective analysis of this population. Importantly, as the subjects donating these samples were phenotypically designated as “normal” at the time of donation, the retrospective analysis of these samples had theDocket No. 2960447-000048-W01Filed: January 9, 202648potential was able to provide molecular identification of disease onset and progression in an agnostic fashion, and potentially years prior to clinical identification of the disease state.Example 2: Confirmation of Robust Biological Signals in Samples from the Plasmapheresis Biobank

[0217] Prior to using samples from the plasmapheresis biobank described in Example 1 for the identification of such predictive biological indicators, the samples were first confirmed to be suitable for such analysis. Specifically, the feasibility of using the plasma samples from the plasmapheresis biobank for the analysis of neuropathological conditions was investigated using longitudinal profiling of age-related molecular changes in healthy donors. The samples used for the confirmation assays and analysis were stored for between 2 months to ~9 years. The samples used for analysis of sample integrity and subject aging process were selected using linear regression adjusted for gender, race, storage weeks and number of donations within 30 days. Correction for multiple comparisons was performed using Benjamini-Hochberg (BH) adjustment, a statistical method that controls the false discovery rate (FDR) in multiple comparisons. Benjamini, Y., & Hochberg, Y. (1995) Journal of the Royal Statistical Society: Series B (Methodological), 57(1), 289-300.

[0218] Briefly, the potential impact of age and storage conditions on the plasma proteome in the stored plasmapheresis samples was measured using two proteomics detection platforms: the Olink™ Explore HT (5k) (Olink, Uppsala, Sweden), a proteomics antibody-based detection platform that can measure over 5,400 proteins in a plasma sample and the SomaScan™ assay v5 (Somalogic™, Boulder, CO, USA), a proteomics aptamer-based detection platform capable of measuring 7288 human proteins in a plasma sample. Results are shown in Figures 1A and 1B.

[0219] Age-related proteomics changes were compared to those from the UK Biobank cohort (Sun, B. B. et al. Nature (2023) 622, 329-338) and a meta-analysis of four independent cohorts (Coenen, L. et al., Front. Aging 4, 112109 (2023)). Proteomic changes were connected to biological events associated with aging, such as menopause or cardiovascular events (respectively shown in Figures 2A and 2B). Plasma proteomics measurements were further performed using the Somalogic SomaScan™ platform (Somalogic, Boulder, CO, USA) in 504 samples from 40 donors. Lines in Figures 2A and 2B connect samples from the same donors. Observed temporal molecular changes can be associated with menopause (Follicle stimulatingDocket No. 2960447-000048-W01Filed: January 9, 202649hormone, Figure 2A) or cardiovascular event as identified by medical records (Natriuretic peptides B, Figure 2B).

[0220] These data demonstrate that the samples from the stored plasmapheresis biobank asset were robust and suitable for various multi-omic assays for identification of biological indicators for systems and method for improved drug development. In particular, the samples were uniquely positioned to allow identification of targets, pathways, and various other biological indicia that are altered over time in pathological conditions, and in assisting in the development of drug development systems, tools, and reagents.Example 3: Longitudinal Profiling of Biological Processes in for Parkinson’s Disease in Samples from the Plasmapheresis Biobank

[0221] The feasibility of using the samples from the plasmapheresis biobank for multi-omic analysis of neuropathological conditions was investigated using longitudinal profiling of all phases of Parkinson’s disease (“PD”) and by demonstrating the experimental feasibility of measuring longitudinal molecular changes in -700 samples from human plasma donors.

[0222] More than 58,000 samples from donors who developed PD were stored within the plasmapheresis biobank, establishing the plasmapheresis biobank as the most extensive collection of biospecimens from individuals undergoing PD development. This unique asset allowed the retrospective analysis and deep longitudinal molecular profiling of plasma samples from these individuals who developed PD and a unique opportunity to study all phases of PD development, from onset to progression, at a molecular level. Longitudinal samples were selected from a subset of plasma donors who were identified as being affected with PD and carefully matched control donors, who were then each assayed across multiple proteomics technologies to discover new pre-clinical biological indicators of PD and new candidate therapeutics targets and pathways for further drug development.

[0223] As illustrated in Figure 3, the plasmapheresis biobank was annotated with real-world data (RWD), and the individual members of the sample library (each designated by a black dot) was shown to cover all phases of PD development from fourteen years before clinical diagnosis to nine years after clinical diagnosis. Only donors with complete and consistent demographics from the plasmapheresis biobank were included in this visualization. PD diagnosis was established based on the presence of at least one G20 ICD10 code in their medical records (claims or electronicDocket No. 2960447-000048-W01Filed: January 9, 202650health record (“EHR”) data). Plasma samples were represented relative to the time of their first diagnosis of PD (G20 ICD10 code) and donors were ranked based on the time difference between their last donation and their first PD diagnosis.

[0224] The confirmed data from the proteomic changes were used to provide an initial definition of the PD cohort and to identify potential plasma donors with a high likelihood of having developed PD (Figure 4). The selection process involved the definition of specific filtering criteria that included demographics, medical history (G20 code, PD medication), and sample availability. This initial PD cohort comprised around 700 donors who had developed PD based on claims and EHR data. The identification of donors who developed PD were based on claims data only and the EHR data were used to provide additional information on donors with specific medical or proteomics patterns when needed.

[0225] Three main cohorts were defined based primarily on the claims data: a cohort of established PD; an expert reviewed PD cohort; and a non-PD controls cohort.

[0226] The data-driven cohort of established PD was identified by the following set of inclusion criteria: two appearance of inclusion ICD-10-CM codes (Table 1), two or more prescriptions for PD medications (Table 2), and at least three or more diagnosis claims. 125 donors with at least one plasma donation were included in the established PD cohort based on claims data.

[0227] In addition, the following criteria were used: exclusion ICD-10-CM codes (Table 3) and exclusion of medications that can induce symptoms similar to PD (Table 4).

[0228] Individuals considered for the expert reviewed cohort were selected using different approaches relying on presence / absence of ICD10 codes related to diagnosis and medication, and machine learning algorithms. The health journey of each of these individuals was examined by CNS scientists trained to identify PD based on medical health records. 223 donors were included in the expert-reviewed cohort.

[0229] The samples from these donors who developed PD covered all phases of PD development (Figure 5). Plasma samples were represented relative to the time of their first diagnosis of PD (G20 ICD10 code) and donors were ranked based on the time difference between their last donation and their first PD diagnosis.

[0230] Finally, the control cohort exclusion criteria are a lack of sufficient information and any ICD-10-CM codes from Tables 1 and 3 and all medications from Tables 2 and 4.

[0231] Table 1: Inclusion ICD-10-CM codes used for the selection of PD cohortsDocket No. 2960447-000048-W01Filed: January 9, 202651and for the exclusion of donors from the control cohort:

[0232] Table 2: Parkinson’s disease medications used for the selection of PD cohorts and for the exclusion of donors from the control cohort:Docket No. 2960447-000048-W01Filed: January 9, 202652Docket No. 2960447-000048-W01Filed: January 9, 202653

[0233] Table 3: Exclusion ICD-10-CM codes for the high confidence PD cohort and control cohort (list also referred as diseases like PD):Docket No. 2960447-000048-W01Filed: January 9, 202654

[0234] Table 4: Parkinson’s disease medications used for the exclusion of donors from the high confidence PD cohort and controls cohort (list also referred as antipsychotics)Docket No. 2960447-000048-W01Filed: January 9, 202655Docket No. 2960447-000048-W01Filed: January 9, 202656

[0235] Donor cohorts were assessed using in silica and manual approaches to confirm the relevance of individuals selected for the study. Due to the large number of medical information and the number of individuals considered for this study, an in silica framework was used to identify outliers in the donors-PD and controls cohorts for an in- depth evaluation of their medical information. The in silica approach leveraged network analysis of donor similarities of medical history based RWD. This analysis encompasses the evaluation of comorbidities and medication patterns, facilitating the identification and exclusion of mislabeled donors and instances of atypical PD. Furthermore, the health journey of each donor was reviewed manually by PD experts, leading to the final selection of donors who have developed PD.

[0236] Adhering to the concept of digital twins, a similar number of paired control samples were identified based on demographics and sample availability. CoDocket No. 2960447-000048-W01Filed: January 9, 202657diagnoses for major diseases such as diabetes and hypertension were matched and patients with all major central nervous system disorders were excluded from the control group (Table 3). In total, -2600 samples from -700 individuals were selected for deep molecular profiling, ensuring a comprehensive and representative dataset for advancing the understanding of PD.Example 4: In-depth characterization of PD development in subjects from the plasmapheresis biobank using real world data (RWD)

[0237] To uncover novel biological indicators of PD and identify potential therapeutic targets and pathways, it was imperative to gain a precise understanding of PD development and its associated co-morbidities. The limitations of smaller data sets available that had been available in conventional resources were overcome by analyzing RWD from an extensive cohort of samples from the plasmapheresis biobank in conjunction with claims data and EHR of approximately 1.3 million individuals that have developed PD, providing a unique opportunity to decipher the multifaceted nature of PD onset and progression.

[0238] Deep molecular profiling of 2609 samples from PD and control subjects was performed by deep proteomics profiling of individuals developing PD and their respective paired control samples (See Figure 6). The proteomics used for analysis of these samples included the previously used Olink Explore HT (5k) (Olink, Uppsala, Sweden), and SomaScan assay v5 (Somalogic, Boulder, CO, USA) platforms as well as Biognosys™ TrueDiscovery™, a platform that uses mass spectrometry to provide proteomics solutions for drug development (Biognosys, Newton, MA, USA). These different proteomics platforms have shown to provide complementary signals, and their combination allows the investigation of around 15k proteins. These molecular changes were be contextualized with the medical records from RWD to integrate the molecular and proteomic analysis with the clinical and phenotypic data available for the subjects.

[0239] Samples were identified at all phases of PD progression, including prior to identified clinical onset (Figures 7A and 7B). 348 PD donors with a total of 1323 samples were identified. 245 individuals (70%) associated with a total of 913 samples were identified with at least one sample donated prior to the first doctor’s visit associated with a G20 code. 135 individuals (39%) associated with a total of 410 samples were identified with at least one sample donated after the first doctor visit associated with a G20 code. 32 samples donated before and at least one sample donated after the first doctor visit were associated with a G20 code.Docket No. 2960447-000048-W01Filed: January 9, 202658

[0240] Partial least squares discriminant analysis (PLS-DA) was performed on both the PD and control samples. The PLS-DA showed proteomic variance in all samples over time but also showed particular variance of certain proteins in the PD samples as compared to their matched control samples (Figure 8). Variance of certain proteins at specific timepoints is also summarized in the table of Figure 9. These included changes in a subset of the proteins SLIMF (Uniprot Q8NBK3, Entrez Gene Id No:285362) (see Figure 10), IGHG4 (Uniprot P01861, Entrez Gene Id No:3503) (see Figure 11), TUBA1A (Uniprot Q71U36, Entrez Gene Id No: 11339) (see Figure 12), MTAP (Uniprot Q13126, Entrez Gene Id No:4507) (see Figure 13), GTP (Uniprot 24298, Entrez Gene Id No:2875) (see Figure 14), OIP5 (Uniprot 043482, Entrez Gene Id No:11339) (see Figure 15), and DDC (Uniprot P80041, Entrez Gene Id No:1644) (see Figure 16) in the plasma of PD and control samples. In each of these plot graphs, the lighter dots represent the level of the protein biological indicator in a sample from a patient identified as being in the PD cohort, while the darker spots represent the level of the protein biological indicator in a sample from a matched subject in the non-PD cohort.Example 5: Identification of PD Subtypes using Biological Indicator Level

[0241] The protein S-methyl-5’-thioadenosine phosphorylase (MTAP) is a metabolic enzyme which catalyzes the reversible phosphorylation of S-methyl-5'-thioadenosine (MTA) to adenine and 5-methylthioribose-1 -phosphate. MTAP is involved in the breakdown of MTA, a major by-product of polyamine biosynthesis. MTAP expression inhibits tumor-related phenotypes in various cancers, including fibrosarcoma, melanoma, lung cancer, and renal cell carcinoma, and this tumor suppressive function is often independent of its enzymatic activity. See, e.g., Tang B, et al., G3 (Bethesda). 2014 Nov 11 ;5(1 ):35-44. Limm K et al., PLoS One.2016 Aug 1;11(8):e0160348; MTAP deficiency leads to the accumulation of MTA, which inhibits protein arginine methyltransferase 5 (PRMT5). Chang Wen-Hsin et al., EMBO Rep. 2022 Aug 3;23(8):e54265. This inhibition affects the methylation of various proteins, including histones and vimentin, thereby influencing gene expression and cellular processes such as metastasis and invasion. Id.

[0242] The expression levels of MTAP within the PD cohort identified in the plasmapheresis biobank indicated two PD subtypes or different and distinct stages of the disease: the one with low MTAP levels and a small rate of change, which is more similar to controls, and another with high MTAP levels and increased rate of changeDocket No. 2960447-000048-W01Filed: January 9, 202659(see Figure 17). This demonstrated that certain biological indicators can be useful not just in the general indication of disease onset and / or progression, but also in the stratification of patient subtypes within neurological conditions, and also perhaps in the identification of patients within specific sub-contexts, e.g., responders versus nonresponders to specific therapies, those with differences on prognosis, etc.Example 6: Effect of Medications on Biological Indicator Levels in plasma from PD Patient Samples

[0243] The effect of PD medication on the levels of protein biological indicators was also examined using MTAP as an exemplary biological indicator. Initial analysis found that patients that had been treated with any medication for PD (Figure 18) or specifically with levodopa (Figure 19) showed lower levels of the biological indicator MTAP in their plasma than other patients in the PD cohorts or their matched control patients.Example 7: Deep Modeling of Protein Trajectories for Biological Indicators Indicative of Disease Inflection Points

[0244] Proteins associated with higher disease risk serve as biological indicators for early detection or intervention highlighting the importance of protein monitoring in disease risk prediction. To investigate the relationship between certain protein biological indicators and the progression of PD, various forms of modeling were used to investigate the relevance and progression of the biological indicators in the evolution of the disease state. In particular, models such as linear models, linear mixed models, linear spline models, natural cubic spline models and growth mixture models were applied to raw proteomic data to better elucidate the impact of specific biological indicators on the development and / or progression of PD.

[0245] Raw data was collected on protein levels over a period of time for a cohort of individuals that have been diagnosed with PD and their matched controls using Olink Explore HT and SomaScan technologies. These controls were matched for age as well as certain phenotypes and genotypes. The raw data of patients that have been diagnosed with PD and matched controls is shown in Figure 23.

[0246] A linear model is one that represents the relationship between two quantities and where the degree of the equation is 1. The most basic linear equation demonstrates the relationship between (x,y), which can then be used to create a table or a graph on the coordinate plane or to evaluate at a certain x or y value. Linear mixedDocket No. 2960447-000048-W01Filed: January 9, 202660models extend traditional linear models by incorporating both fixed effects (populationaverage effects) and random effects (subject-specific or group-level variations) to analyze clustered or longitudinal data where observations within groups are dependent. Using linear mixed modeling to study the raw data set forth in Figure 23 (see also Figure 29A), the trajectories in the change of SUMF1 expression of individuals (see Figure 24, also Figure 29B) and the average expression differences between affected and matched control individuals (Figure 25, see also Figure 29C) over a multi-year period were determined.

[0247] Linear spline models can be used to identify the progression of protein expression of a single biological indicator. Linear spline models are therefore useful in identifying the inflection point at which a specific biological indicator begins to change in expression level in the progression of a disease such as a neurodegenerative disease {e.g., PD). A linear spline model is a piecewise linear regression model where the data is divided into segments by one or more knots. It fits a separate linear model to each segment, with the segments joined together continuously at the knots, allowing for a flexible, non-linear relationship between variables that is easy to interpret.

[0248] Figure 26 (see also Figure 29D) demonstrates a graph using a linear spline model to determine the linear progression of the expression of the biological indicator SUMF1 in an affected Parkinson’s population and matched controls over a multi-year period. As demonstrated using the aggregated data from Figure 23, SUMF1 expression begins to increase approximately 6.5 years prior to the diagnosed onset of PD and continues increasing until at least seven years following diagnosis.

[0249] A natural cubic spline model is a piecewise cubic polynomial interpolation that is linear at the endpoints of the data. In natural cubic spline models, "the two additional boundary conditions () constrain the shape of the curve at the very beginning and end. This makes the spline linear outside the first and last interval, rather than being a cubic polynomial like a standard spline. This linearity at the ends is the defining feature and why it's called "natural". Natural cubic spline models can be used to estimate non-linear changes and velocity of change of biological indicator expression over time. Figure 27 (see also Figure 29E) is a graph showing the trajectories determined using natural cubic spline modeling for the biological indicator SUMF1 in patients affected with PD and matched controls over a multi-year period.

[0250] Growth mixture models can also be used to identify differentialDocket No. 2960447-000048-W01Filed: January 9, 202661trajectories of SLIMF1 expression within individuals affected with PD and their matched controls. Figure 28 (see also Figure 29F) is a graph illustrating estimated non-linear changes and velocity for expression of SLIMF1 in these PD samples over a multi-year period as compared to their non-PD matched controls.Example 8: Using Longitudinal Proteins Trajectories to Predict Risk Of Developing Parkinson’s Disease

[0251] The joint modeling of survival and longitudinal data combined survival analysis (modeling time to event) with longitudinal data (repeated protein measurements over time) to account for both the timing of disease development and the changes in biological indicators simultaneously. This analysis has demonstrated up to 20% increased risk identified with a change in specific biological indicators, especially those that displayed significance in both pre-Parkinson’s samples and in those obtained after diagnosis ( Figure 30 is a graph showing the plot of significant proteins identified in pre-Parkinson’s patients as plotted against proteins identified in the complete dataset. The analysis was repeated in a subsequent iteration, shown in Figure 43). Moreover, the underlying biology of proteins that predict PD risk supports their relevance as potential biomarkers for early disease processes, as discussed in more detail below.

[0252] This integrated approach combining survival and longitudinal data helped assess how protein level changes over time correlated with the risk of developing PD, improving prediction accuracy by considering both current and future biological indicators. Several proteins, including those showing up to a 20% increased risk, were identified as significant predictors for developing PD. The majority of proteins identified as significant exhibited a risk increase in the 10% range (Figure 31 is a graph showing the risk change for development of Parkinson’s disease as determined for various biological indicators. The analysis was repeated in a subsequent iteration, shown in Figure 44).

[0253] The top protein changes were then graphed to identify potential inflection points in the development of the disease. Various biological indicators were examined in at least two iterations of data analysis (Figure 32 and Figure 45). Based on linear mixed models, two non-linear mixed models and joint models, we were able to identify early (potential risk) biomarkers and later PD biomarkers and estimate their different trajectories. Five groups of PD biomarkers were proposed from this study: transient preclinical biomarkers, transitional biomarkers, progression biomarkers,Docket No. 2960447-000048-W01Filed: January 9, 202662prodromal biomarkers, and post-diagnosis biomarkers:• Transient preclinical biomarkers were identified PD biomarkers with significantly different rate of change in the first slope with the inflection point before PD onset or around PD onset from linear spline mixed model results.• Transitional biomarkers were identified PD biomarkers with significantly different rate of change in the second slope with inflection point before PD onset or around PD onset from linear spline mixed model results.• Progression biomarkers were identified PD biomarkers with significant rate of changes from whole sample mixed model results.• Prodromal biomarkers were significantly different between PD and control in whole sample linear mixed model from at least one platforms and were early PD biomarkers based on pre-diagnostic samples linear mixed models or candidate risk PD biomarkers based on joint modeling.• Finally, post-diagnosis biomarkers were the PD signals that were not in either of the above four groups but significantly different between PD and control in postDx samples or significantly different in the second slope with inflection point after PD onset from linear spline mixed model results.

[0254] Prodromal biomarkers (Figure 33, Figure 38) demonstrate early detection of changes - 12+ years before PD onset. 25 prodromal biomarkers including TMX4, CNTFR, CXCL12 and VCAM1 that are linked with CNS / inflammation, showed consistent group differences up to 12 years prior to PD onset. This suggested they could be early biomarkers for the disease, detectable long before clinical symptoms appear. This group includes CNTFR, CXCL12, ITGB3JTGAV, ADPGK, GFRA2, STC2, FBLN2, VCAM1, MAG, APAF1, DKK3, PRG4, LILRA4, BNIPL, SEZ6L, ESM1, STAR, TMX4, BCAN, SLITRK1, SOD3, C1S, SERPINA11, DPEP1 and ITGA11. A subsequent iteration (Figure 46, Figure 51) identified markers DKK3, CXADR, FGFBP3, LTBP4, CNTFR, VCAM1, ITGB3JTGAV, MAG, CXCL12, CNTFR, OIP5, TMX4, FANCD2, BCAN, DKK3, ESM1, EPHA2, EPHB6, SOD3, LILRA4, SERPINA11, GFAP, IL15RA, CCER2, DPEP1, ITGAV, SLITRK1, APLP1, CD276, CDH3, CRIM1, ESM1, PDGFRA, ROR1, SEZ6L, SUSD5, CD300LG, CSPG4, QPCT, CALB2, GFAP, IL7.

[0255] Progression biomarkers (Figure 34, Figure 39) also demonstrated a preonset rate of change - 12+ years before PD onset. Progression biomarkers included two proteins (MTAP, CD209) that showed significant difference in their rate of change more than 12 years before PD onset. These changes may indicate early pathologicalDocket No. 2960447-000048-W01Filed: January 9, 202663alterations related to PD. A subsequent iteration (Figure 47, Figure 52) identified markers CD209, MTAP, PRL, DDC.

[0256] Transient preclinical biomarkers (Figure 35, Figure 40) demonstrated changes 5+ years before onset. Seven transient preclinical biomarkers including DECR1, LYN, PPIF, STX4, MCAT, MPIG6B and PNPT1 exhibited distinct changes in their rate of progression between 12 and 5 years before onset but stabilized thereafter, suggesting their role as biomarkers of early PD progression. Four of these biomarkers (DECR1, MCAT, PNPT1, PPIF) were linked to mitochondrial function. A subsequent iteration (Figure 48, Figure 53) identified markers STX4, ANXA7, IVD, RAC2, PLEKHF2, GFRA2, MCAT, LYN, PPIF, DDX19B, DFFA, ARSA, BACE1, CD40LG, IL16, NEFL, TARDBP.

[0257] Transitional biomarkers (Figure 36, Figure 41) were identified that demonstrated rapid changes around PD onset - 5 years before and around PD onset. Five transitional biomarkers including SUMF1, TUBA1A, LAP3, OGDHL, CHMP2B that were linked to neurodevelopment disorders exhibited the most significant rate of change beginning five years prior to PD onset, peaking around the time of onset. These proteins serve as key indicators of the transition to symptomatic PD. A subsequent iteration (Figure 49, Figure 54) identified markers LAP3, TUBA1A, SUMF1.

[0258] Post-diagnostic biomarkers (Figure 37, Figure 42) demonstrated changes in expression after diagnosis of PD, which makes them useful for tracking disease progression and treatment efficacy. Proteins like PLCD4, AASDH, involved in cell signaling and lipid metabolism showed significant group difference only after PD onset, suggesting their involvement in later-stage PD pathology. This group includes EPN1, H1-10, HS3ST4, STAT6, RGS18, CYLD, ESYT2, EBI3, IL12A, NAA10, SH3PXD2A, HNRNPA0, PLCD4, CSNK1G2, ZC3H12C, H2AC20, RIT1, CCDC80, HSP90AA1, ARID3C, TWSG1, H2AX, IST1, AASDH, ZNF354A, AP1B1, HSP90AB1, CIAO1, RAB6A, PPP1CA, GPI, SCARF2, CSTF2, TFRC, ADA2, TRIM25 and OIP5. A subsequent iteration (Figure 50, Figure 55) identified markers EPN1, HS3ST4, H1-10, PTGR1, STAT6, CXCL13, RGS18, SH3PXD2A, IL12A, EBI3, COL14A1, CYLD, NAA10, HNRNPA0, ESYT2, PLCD4, CCDC80, CSNK1G2, ZC3H12C, RIT1, HSP90AA1, ZNF354A, H2AX, AASDH, CIAO1, PDGFRA, AP1B1, IST1, SMOC1, HSP90AB1, SCARF2, TFRC, CD58, AAGAB, RNF5.

[0259] Trajectories of all biomarkers were visualized based on the control-normalized marginal mean estimations in PD group in the grid of 0.1 years from 12 years before PD onset and 9 years after PD onset, a total of 21 years that Chronos PDDocket No. 2960447-000048-W01Filed: January 9, 202664phase 1 cohort samples covered. For transient preclinical biomarkers and transitional biomarkers, marginal mean values were first estimated through the predict function using linear spline mixed model results at the selected inflection points in PD group and control group, then estimated values for PD group minus those for control group as control-normalized marginal means; for progression biomarkers, prodromal biomarkers and postDx biomarkers, marginal means were first estimated through emmeans function from emmeans package based on linear mixed models, then difference between estimated values in the PD group and in the control group were calculated for control-normalized marginal means.

[0260] While this invention is satisfied by embodiments in many different forms, as described in detail in connection with preferred embodiments of the disclosure, it is understood that the present disclosure is to be considered as exemplary of the principles of the disclosure and is not intended to limit the invention to the specific embodiments illustrated and described herein. Numerous variations may be made by persons skilled in the art without departure from the spirit of the disclosure. The scope of the disclosure will be measured by the appended claims and their equivalents. The abstract and the title are not to be construed as limiting the scope of the present invention, as their purpose is to enable the appropriate authorities, as well as the general public, to quickly determine the general nature of the disclosure.Docket No. 2960447-000048-W01Filed: January 9, 202665

[0261] The following numbered embodiments illustrate specific implementations of the invention and are intended to provide explicit support for the subject matter defined in the claims. The embodiments are not to be construed as limiting the scope of the invention, and individual features described in connection with any one embodiment may be combined with features of other embodiments where technically feasible. The invention is defined by the claims and encompasses all such modifications, variations, and combinations as fall within their scope.Embodiment 1 : Use of at least one gene selected from a group consisting of - CNTFR, CXCL12, ITGB3:ITGAV, ADPGK, GFRA2, STC2, FBLN2, VCAM1, MAG, APAF1, DKK3, PRG4, LILRA4, BNIPL, SEZ6L, ESM1, STAR, TMX4, BCAN, SLITRK1, SOD3, C1S, SERPINA11, DPEP1, ITGA11, CXADR, FGFBP3, LTBP4, OIP5, FANCD2, EPHA2, EPHB6, GFAP, IL15RA, CCER2, ITGAV, APLP1, CD276, CDH3, CRIM1, PDGFRA, ROR1, SUSD5, CD300LG, CSPG4, QPCT, CALB2, IL7, CD209, MTAP, PRL, DDC, MCAT, LYN, STX4, PPIF, DECR1, PNPT1, MPIG6B, ANXA7, IVD, RAC2, PLEKHF2, GFRA2, DDX19B, DFFA, ARSA, BACE1, CD40LG, IL16, NEFL, TARDBP, TUBA1A, SUMF1, LAP3, OGDHL, CHMP2B, NEFL, OMD, CALB2, CD276, NCAM1, EDIL3, TGAV, QPCT, DDC, LSM1, PRL, GPT, GRP, IGHG4, PRR15, GLRX3, KCTD10, CCN5, ARPC5, CMPK1, SARS1, PPM1G, LTBP4, IVD, RAP1B, CORIN, LRRTM2, FKBP1B, AASDH, TPM4, HSPD1, IRF5, PTGR1, COPS5, PYGB, PYGL, PHGDH, MICU1, ARHGAP25, CCT8, KYAT1, LRRC45, GP1BB, SAMD9L, PTPN11, CRIM1, SMAD1, CXCL5, LATS1, NFATC1, METAP1D, DFFA, CST5, CCL5, NAP1L4, LAT2, TDRD3, ALDH5A1, CCER2, MTDH, FKBPL, PPP1CC, SNAP23, CEP85, RAB37, CSPG4, OGA, MAGED1, VAMP8, PRDX5, GMPR2, MPP7, ZNF649, RIMOC1, AAGAB, BACE1, LEG1, OXT, UBL5, GPNMB, KVAT1, GFAP, ANOS1, RPL30, TMX2, EIF1AX, SMYD3, VWF, SCUBE1, TNFRSFUb, NEFH, ARSA, BDNF, CD300LG, CPXM1, CXCL3, EPHA2, HARS1, MANF, NCAN, PDGFRA, ROR1, SERPINE1, SLITRK2 and SUSD5,- and any combination thereof,as a biomarker for assessing the risk of Parkinson’s Disease in a subject, wherein the subject has not been diagnosed with Parkinson’s Disease and does not exhibit clinical symptoms associated with Parkinson’s Disease.Docket No. 2960447-000048-W01Filed: January 9, 202666Embodiment 2: The use according to Embodiment 1, wherein said at least one gene is selected from a group consisting of:- Prodromal genes, wherein the group of prodromal genes consists of CNTFR, CXCL12, ITGB3:ITGAV, ADPGK, GFRA2, STC2, FBLN2, VCAM1, MAG, APAF1, DKK3, PRG4, LILRA4, BNIPL, SEZ6L, ESM1, STAR, TMX4, BCAN, SLITRK1, SOD3, C1S, SERPINA11, DPEP1, ITGA11, CXADR, FGFBP3, LTBP4, OIP5, FANCD2, EPHA2, EPHB6, GFAP, IL15RA, CCER2, ITGAV, APLP1, CD276, CDH3, CRIM1, PDGFRA, ROR1, SUSD5, CD300LG, CSPG4, QPCT, CALB2 and IL7,- Progression genes, wherein the group of progression genes consists of CD209, MTAP, PRL and DDC,- Transient preclinical genes, wherein the group of transient preclinical genes consists of MCAT, LYN, STX4, PPIF, DECR1, PNPT1, MPIG6B, ANXA7, IVD, RAC2, PLEKHF2, GFRA2, DDX19B, DFFA, ARSA, BACE1, CD40LG, IL16, NEFL and TARDBP,- Transitional genes, wherein the group of transitional genes consists of TUBA1A, SUMF1, LAP3, OGDHL and CHMP2B,- and any combination thereof.Embodiment 3: The use according to any one of Embodiments 1 or 2, wherein the biomarker forms part of a biomarker signature comprising two or more biomarkers selected from a group consisting of CNTFR, CXCL12, ITGB3: ITGAV, ADPGK, GFRA2, STC2, FBLN2, VCAM1, MAG, APAF1, DKK3, PRG4, LILRA4, BNIPL, SEZ6L, ESM1, STAR, TMX4, BCAN, SLITRK1, SOD3, C1S, SERPINA11, DPEP1, ITGA11, CXADR, FGFBP3, LTBP4, OIP5, FANCD2, EPHA2, EPHB6, GFAP, IL15RA, CCER2, ITGAV, APLP1, CD276, CDH3, CRIM1, PDGFRA, ROR1, SUSD5, CD300LG, CSPG4, QPCT, CALB2, IL7, CD209, MTAP, PRL, DDC, MCAT, LYN, STX4, PPIF, DECR1, PNPT1, MPIG6B, ANXA7, IVD, RAC2, PLEKHF2, GFRA2, DDX19B, DFFA, ARSA, BACE1, CD40LG, IL16, NEFL, TARDBP, TUBA1A, SUMF1, LAP3, OGDHL, CHMP2B, NEFL, OMD, CALB2, CD276, NCAM1, EDIL3, TGAV, QPCT, DDC, LSM1, PRL, GPT, GRP, IGHG4, PRR15, GLRX3, KCTD10, CCN5, ARPC5, CMPK1, SARS1, PPM1G, LTBP4, IVD, RAP1B, CORIN, LRRTM2, FKBP1B, AASDH, TPM4, HSPD1, IRF5, PTGR1, COPS5, PYGB, PYGL, PHGDH, MICU1, ARHGAP25, CCT8, KYAT1, LRRC45, GP1BB, SAMD9L, PTPN11, CRIM1, SMAD1, CXCL5, LATS1, NFATC1,Docket No. 2960447-000048-W01Filed: January 9, 202667METAP1D, DFFA, CST5, CCL5, NAP1L4, LAT2, TDRD3, ALDH5A1, CCER2, MTDH, FKBPL, PPP1CC, SNAP23, CEP85, RAB37, CSPG4, OGA, MAGED1, VAMP8, PRDX5, GMPR2, MPP7, ZNF649, RIMOC1, AAGAB, BACE1, LEG1, OXT, UBL5, GPNMB, KVAT1, GFAP, ANOS1, RPL30, TMX2, EIF1AX, SMYD3, VWF, SCUBE1, TNFRSFUb, NEFH, ARSA, BDNF, CD300LG, CPXM1, CXCL3, EPHA2, HARS1, MANF, NCAN, PDGFRA, ROR1, SERPINE1, SLITRK2, SUSD5, and any combination thereof.Embodiment 4: The use according to Embodiment 3, wherein the biomarker signature comprises at least three of the biomarker genes MTAP, TLIBA1A, GPT, SUMF1, OIP5, CNTFR, LSM1, QPCT, ITGB3, and ITGAV.Embodiment 5: An in vitro method for determining the risk that a subject develops Parkinson’s Disease, wherein the method comprises:a) determining, in a biological sample from the subject, the expression level of at least one gene selected from a group consisting of:- CNTFR, CXCL12, ITGB3:ITGAV, ADPGK, GFRA2, STC2, FBLN2, VCAM1, MAG, APAF1, DKK3, PRG4, LILRA4, BNIPL, SEZ6L, ESM1, STAR, TMX4, BCAN, SLITRK1, SOD3, C1S, SERPINA11, DPEP1, ITGA11, CXADR, FGFBP3, LTBP4, OIP5, FANCD2, EPHA2, EPHB6, GFAP, IL15RA, CCER2, ITGAV, APLP1, CD276, CDH3, CRIM1, PDGFRA, ROR1, SUSD5, CD300LG, CSPG4, QPCT, CALB2, IL7, CD209, MTAP, PRL, DDC, MCAT, LYN, STX4, PPIF, DECR1, PNPT1, MPIG6B, ANXA7, IVD, RAC2, PLEKHF2, GFRA2, DDX19B, DFFA, ARSA, BACE1, CD40LG, IL16, NEFL, TARDBP, TUBA1A, SUMF1, LAP3, OGDHL, CHMP2B, NEFL, OMD, CALB2, CD276, NCAM1, EDIL3, TGAV, QPCT, DDC, LSM1, PRL, GPT, GRP, IGHG4, PRR15, GLRX3, KCTD10, CCN5, ARPC5, CMPK1, SARS1, PPM1G, LTBP4, IVD, RAP1B, CORIN, LRRTM2, FKBP1B, AASDH, TPM4, HSPD1, IRF5, PTGR1, COPS5, PYGB, PYGL, PHGDH, MICU1, ARHGAP25, CCT8, KYAT1, LRRC45, GP1BB, SAMD9L, PTPN11, CRIM1, SMAD1, CXCL5, LATS1, NFATC1, METAP1D, DFFA, CST5, CCL5, NAP1L4, LAT2, TDRD3, ALDH5A1, CCER2, MTDH, FKBPL, PPP1CC, SNAP23, CEP85, RAB37, CSPG4, OGA, MAGED1, VAMP8, PRDX5, GMPR2, MPP7, ZNF649, RIMOC1, AAGAB, BACE1, LEG1, OXT, UBL5, GPNMB, KVAT1, GFAP, ANOS1, RPL30, TMX2, EIF1AX, SMYD3, VWF,Docket No. 2960447-000048-W01Filed: January 9, 202668SCUBE1, TNFRSFUb, NEFH, ARSA, BDNF, CD300LG, CPXM1, CXCL3, EPHA2, HARS1, MANF, NCAN, PDGFRA, R0R1, SERPINE1, SLITRK2 and SUSD5,- and any combination thereof,b) comparing the expression level of said at least one gene in said biological sample with a reference value for said gene; wherein an altered expression level of said at least one gene in the biological sample when compared to the reference value is indicative that the subject is at risk of developing Parkinson’s Disease.Embodiment 6: An in vitro method for determining the risk that a subject develops Parkinson’s Disease, wherein the method comprises:a) determining, in a biological sample from the subject, the expression level of at least one gene selected from a group consisting of:- Prodromal genes, wherein the group of prodromal genes consists of CNTFR, CXCL12, ITGB3:ITGAV, ADPGK, GFRA2, STC2, FBLN2, VCAM1, MAG, APAF1, DKK3, PRG4, LILRA4, BNIPL, SEZ6L, ESM1, STAR, TMX4, BCAN, SLITRK1, SOD3, C1S, SERPINA11, DPEP1, ITGA11, CXADR, FGFBP3, LTBP4, OIP5, FANCD2, EPHA2, EPHB6, GFAP, IL15RA, CCER2, ITGAV, APLP1, CD276, CDH3, CRIM1, PDGFRA, ROR1, SUSD5, CD300LG, CSPG4, QPCT, CALB2 and IL7,- Progression genes, wherein the group of progression genes consists of CD209, MTAP, PRL and DDC,- Transient preclinical genes, wherein the group of transient preclinical genes consists of MCAT, LYN, STX4, PPIF, DECR1, PNPT1, MPIG6B, ANXA7, IVD, RAC2, PLEKHF2, GFRA2, DDX19B, DFFA, ARSA, BACE1, CD40LG, IL16, NEFL and TARDBP,- Transitional genes, wherein the group of transitional genes consists of TUBA1A, SUMF1, LAP3, OGDHL and CHMP2B,- Post-diagnostic genes, wherein the group of post-diagnostic genes consists of EPN1, H1-10, HS3ST4, STAT6, RGS18, CYLD, ESYT2, EBI3, IL12A, NAA10, SH3PXD2A, HNRNPAO, PLCD4, CSNK1G2, ZC3H12C, H2AC20, RIT1, CCDC80, HSP90AA1, ARID3C, TWSG1, H2AX, IST1, AASDH, ZNF354A, AP1B1, HSP90AB1, CIA01, RAB6A, PPP1CA, GPI, SCARF2, CSTF2, TFRC, ADA2, TRIM25, OIP5, PTGR1, CXCL13, COL14A1,Docket No. 2960447-000048-W01Filed: January 9, 202669PDGFRA, SM0C1, CD58, AAGAB and RNF5,- and any combination thereof,b) comparing the expression level of said at least one gene in said biological sample with a reference value for said gene; wherein an altered expression level of said at least one gene in the biological sample when compared to the reference value is indicative that the subject is at risk of developing Parkinson’s Disease.Embodiment 7: The method according to any one of Embodiments 5 or 6, wherein the subject has not been diagnosed with Parkinson’s Disease and is not exhibiting any clinical symptoms associated with Parkinson’s Disease, preferably wherein the subject is not exhibiting any one of tremor, bradykinesia, rigidity, postural instability, loss of automatic movement, speech changes, micrographia, anosmia, nerve pain, mild cognitive impairment, or dementia.Embodiment 8: An in vitro method for diagnosing Parkinson’s Disease in a subject, wherein the method comprises:a) determining, in a biological sample from the subject, the expression level of at least one gene selected from a group consisting of:- Prodromal genes, wherein the group of prodromal genes consists of CNTFR, CXCL12, ITGB3:ITGAV, ADPGK, GFRA2, STC2, FBLN2, VCAM1, MAG, APAF1, DKK3, PRG4, LILRA4, BNIPL, SEZ6L, ESM1, STAR, TMX4, BCAN, SLITRK1, SOD3, C1S, SERPINA11, DPEP1, ITGA11, CXADR, FGFBP3, LTBP4, OIP5, FANCD2, EPHA2, EPHB6, GFAP, IL15RA, CCER2, ITGAV, APLP1, CD276, CDH3, CRIM1, PDGFRA, ROR1, SUSD5, CD300LG, CSPG4, QPCT, CALB2 and IL7,- Progression genes, wherein the group of progression genes consists of CD209, MTAP, PRL and DDC,- Transient preclinical genes, wherein the group of transient preclinical genes consists of MCAT, LYN, STX4, PPIF, DECR1, PNPT1, MPIG6B, ANXA7, IVD, RAC2, PLEKHF2, GFRA2, DDX19B, DFFA, ARSA, BACE1, CD40LG, IL16, NEFL and TARDBP,- Transitional genes, wherein the group of transitional genes consists of TUBA1A, SUMF1 , LAP3, OGDHL and CHMP2B,- Post-diagnostic genes, wherein the group of post-diagnostic genes consistsDocket No. 2960447-000048-W01Filed: January 9, 202670of EPN1, H1-10, HS3ST4, STAT6, RGS18, CYLD, ESYT2, EBI3, IL12A, NAA10, SH3PXD2A, HNRNPAO, PLCD4, CSNK1G2, ZC3H12C, H2AC20, RIT1, CCDC80, HSP90AA1, ARID3C, TWSG1, H2AX, IST1, AASDH, ZNF354A, AP1B1, HSP90AB1, CIAO1, RAB6A, PPP1CA, GPI, SCARF2, CSTF2, TFRC, ADA2, TRIM25, OIP5, PTGR1, CXCL13, COL14A1, PDGFRA, SMOC1, CD58, AAGAB and RNF5,- and any combination thereof,b) comparing the expression level of said at least one gene in said biological sample with a reference value for said gene; wherein an altered expression level of said at least one gene in the biological sample when compared to the reference value is indicative that the subject is likely to suffer from Parkinson’s Disease.Embodiment 9: The method according to Embodiment 8, wherein the method further comprises a step (c) of subjecting the subject to a procedure to confirm that the subject suffers from Parkinson’s Disease, wherein the procedure is selected from the group consisting of neurological examination, magnetic resonance imaging (MRI), brain ultrasound, positron emission tomography (PET) scan, dopamine transporter (DAT) scan, Alpha-synuclein test, and combinations thereof.Embodiment 10: A method for diagnosing and treating a subject who suffers from Parkinson’s Disease, wherein the method comprises:a) determining, in a biological sample from the subject, the expression level of at least one gene selected from a group consisting of:- Prodromal genes, wherein the group of prodromal genes consists of CNTFR, CXCL12, ITGB3JTGAV, ADPGK, GFRA2, STC2, FBLN2, VCAM1, MAG, APAF1, DKK3, PRG4, LILRA4, BNIPL, SEZ6L, ESM1, STAR, TMX4, BCAN, SLITRK1, SOD3, C1S, SERPINA11, DPEP1, ITGA11, CXADR, FGFBP3, LTBP4, OIP5, FANCD2, EPHA2, EPHB6, GFAP, IL15RA, CCER2, ITGAV, APLP1, CD276, CDH3, CRIM1, PDGFRA, ROR1, SUSD5, CD300LG, CSPG4, QPCT, CALB2 and IL7,- Progression genes, wherein the group of progression genes consists of CD209, MTAP, PRL and DDC,- Transient preclinical genes, wherein the group of transient preclinical genes consists of MCAT, LYN, STX4, PPIF, DECR1, PNPT1, MPIG6B, ANXA7,Docket No. 2960447-000048-W01Filed: January 9, 202671IVD, RAC2, PLEKHF2, GFRA2, DDX19B, DFFA, ARSA, BACE1, CD40LG, IL16, NEFL and TARDBP,- Transitional genes, wherein the group of transitional genes consists of TUBA1A, SUMF1 , LAP3, OGDHL and CHMP2B,- Post-diagnostic genes, wherein the group of post-diagnostic genes consists of EPN1, H1-10, HS3ST4, STAT6, RGS18, CYLD, ESYT2, EBI3, IL12A, NAA10, SH3PXD2A, HNRNPAO, PLCD4, CSNK1G2, ZC3H12C, H2AC20, RIT1, CCDC80, HSP90AA1, ARID3C, TWSG1, H2AX, IST1, AASDH, ZNF354A, AP1B1, HSP90AB1, CIA01, RAB6A, PPP1CA, GPI, SCARF2, CSTF2, TFRC, ADA2, TRIM25, OIP5, PTGR1, CXCL13, COL14A1, PDGFRA, SM0C1, CD58, AAGAB and RNF5,- and any combination thereof,b) comparing the expression level of said at least one gene in said biological sample with a reference value for said gene; wherein an altered expression level of said at least one gene in the biological sample when compared to the reference value is indicative that the subject is likely to suffer from Parkinson’s Disease,c) administering to the subject a therapy selected from the group consisting of Levodopa, Carbidopa, Dopamine Agonists, MAO-B and COMT enzyme inhibitors, Amantadine, Anticholinergics and combinations thereof if the subject is diagnosed as suffering from Parkinson’s Disease, said subject benefiting from administering said therapy.Embodiment 11: A method for predicting response to therapy of a subject suffering from Parkinson’s Disease, wherein the method comprises:a) determining, in a biological sample taken from the subject before and after administration of therapy, the expression level of at least one gene selected from a group consisting of:- Prodromal genes, wherein the group of prodromal genes consists of CNTFR, CXCL12, ITGB3JTGAV, ADPGK, GFRA2, STC2, FBLN2, VCAM1, MAG, APAF1, DKK3, PRG4, LILRA4, BNIPL, SEZ6L, ESM1, STAR, TMX4, BCAN, SLITRK1, SOD3, C1S, SERPINA11, DPEP1, ITGA11, CXADR, FGFBP3, LTBP4, OIP5, FANCD2, EPHA2, EPHB6, GFAP, IL15RA, CCER2, ITGAV, APLP1, CD276, CDH3, CRIM1, PDGFRA, ROR1, SUSD5, CD300LG, CSPG4, QPCT, CALB2 and IL7,Docket No. 2960447-000048-W01Filed: January 9, 202672- Progression genes, wherein the group of progression genes consists of CD209, MTAP, PRL and DDC,- Transient preclinical genes, wherein the group of transient preclinical genes consists of MCAT, LYN, STX4, PPIF, DECR1, PNPT1, MPIG6B, ANXA7, IVD, RAC2, PLEKHF2, GFRA2, DDX19B, DFFA, ARSA, BACE1, CD40LG, IL16, NEFL and TARDBP,- Transitional genes, wherein the group of transitional genes consists of TUBA1A, SUMF1 , LAP3, OGDHL and CHMP2B,- Post-diagnostic genes, wherein the group of post-diagnostic genes consists of EPN1, H1-10, HS3ST4, STAT6, RGS18, CYLD, ESYT2, EBI3, IL12A, NAA10, SH3PXD2A, HNRNPAO, PLCD4, CSNK1G2, ZC3H12C, H2AC20, RIT1, CCDC80, HSP90AA1, ARID3C, TWSG1, H2AX, IST1, AASDH, ZNF354A, AP1B1, HSP90AB1, CIA01, RAB6A, PPP1CA, GPI, SCARF2, CSTF2, TFRC, ADA2, TRIM25, OIP5, PTGR1, CXCL13, COL14A1, PDGFRA, SM0C1, CD58, AAGAB and RNF5,- and any combination thereof,b) comparing the expression level of said at least one gene in said biological sample taken from the subject before therapy with the expression level of said at least one gene in said biological sample taken from the subject after therapy; - wherein a reduced altered expression level of said at least one gene in the biological sample taken from the subject after therapy when compared to the expression level of said at least one gene in the biological sample taken from the subject before therapy is indicative of a positive response of the patient to therapy, or- wherein an increased altered expression level of said at least one gene in the biological sample taken from the subject after therapy when compared to the expression level of said at least one gene in the biological sample taken from the subject before therapy is indicative of a negative response of the patient to therapy.Embodiment 12: The method according to Embodiment 11, wherein the patient is administered a different therapy, preferably wherein the different therapy is selected from the group consisting of Levodopa, Carbidopa, Dopamine Agonists, MAO-B and COMT enzyme inhibitors, Amantadine, Anticholinergics and combinations thereof.Docket No. 2960447-000048-W01Filed: January 9, 202673Embodiment 13: An in vitro method for selecting a subject to participate in a clinical trial for Parkinson’s Disease, wherein the method comprisesa) determining, in a biological sample from the subject, the expression level of at least one gene selected from a group consisting of:- Prodromal genes, wherein the group of prodromal genes consists of CNTFR, CXCL12, ITGB3JTGAV, ADPGK, GFRA2, STC2, FBLN2, VCAM1, MAG, APAF1, DKK3, PRG4, LILRA4, BNIPL, SEZ6L, ESM1, STAR, TMX4, BCAN, SLITRK1, SOD3, C1S, SERPINA11, DPEP1, ITGA11, CXADR, FGFBP3, LTBP4, OIP5, FANCD2, EPHA2, EPHB6, GFAP, IL15RA, CCER2, ITGAV, APLP1, CD276, CDH3, CRIM1, PDGFRA, ROR1, SUSD5, CD300LG, CSPG4, QPCT, CALB2 and IL7,- Progression genes, wherein the group of progression genes consists of CD209, MTAP, PRL and DDC,- Transient preclinical genes, wherein the group of transient preclinical genes consists of MCAT, LYN, STX4, PPIF, DECR1, PNPT1, MPIG6B, ANXA7, IVD, RAC2, PLEKHF2, GFRA2, DDX19B, DFFA, ARSA, BACE1, CD40LG, IL16, NEFL and TARDBP,- Transitional genes, wherein the group of transitional genes consists of TUBA1A, SUMF1 , LAP3, OGDHL and CHMP2B,- Post-diagnostic genes, wherein the group of post-diagnostic genes consists of EPN1, H1-10, HS3ST4, STAT6, RGS18, CYLD, ESYT2, EBI3, IL12A, NAA10, SH3PXD2A, HNRNPAO, PLCD4, CSNK1G2, ZC3H12C, H2AC20, RIT1, CCDC80, HSP90AA1, ARID3C, TWSG1, H2AX, IST1, AASDH, ZNF354A, AP1B1, HSP90AB1, CIA01, RAB6A, PPP1CA, GPI, SCARF2, CSTF2, TFRC, ADA2, TRIM25, OIP5, PTGR1, CXCL13, COL14A1, PDGFRA, SM0C1, CD58, AAGAB and RNF5,- and any combination thereof,b) comparing the expression level of said at least one gene in said sample with a reference value for said gene; wherein an altered expression level of said at least one gene in the sample when compared to the reference value for said gene is indicative that the subject is at risk of developing Parkinson’s Disease, c) selecting the subject at risk of developing Parkinson’s Disease to participate in a clinical trial for Parkinson’s Disease.Docket No. 2960447-000048-W01Filed: January 9, 202674Embodiment 14: The method according to any one of Embodiments 6 to 13, wherein the biomarker forms part of a biomarker signature of two or more biomarkers, wherein the biomarker signature comprises at least one prodromal gene, at least one progression gene, at least one transient preclinical gene, at least one transitional gene and / or at least one post-diagnostic gene.Embodiment 15: The method according to any one of Embodiments 6 to 14, wherein the biomarker forms part of a biomarker signature of two or more biomarkers, wherein the biomarker signature further comprises at least one of: - NEFL, OMD, CALB2, CD276, NCAM1, EDIL3, TGAV, QPCT, DDC, LSM1, PRL, GPT, GRP, IGHG4, PRR15, GLRX3, KCTD10, CCN5, ARPC5, CMPK1, SARS1, PPM1G, LTBP4, IVD, RAP1B, CORIN, LRRTM2, FKBP1B, AASDH, TPM4, HSPD1, IRF5, PTGR1, COPS5, PYGB, PYGL, PHGDH, MICU1, ARHGAP25, CCT8, KYAT1, LRRC45, GP1BB, SAMD9L, PTPN11, CRIM1, SMAD1, CXCL5, LATS1, NFATC1, METAP1D, DFFA, CST5, CCL5, NAP1L4, LAT2, TDRD3, ALDH5A1, CCER2, MTDH, FKBPL, PPP1CC, SNAP23, CEP85, RAB37, CSPG4, OGA, MAGED1, VAMP8, PRDX5, GMPR2, MPP7, ZNF649, RIMOC1, AAGAB, BACE1, LEG1, OXT, UBL5, GPNMB, KVAT1, GFAP, ANOS1, RPL30, TMX2, EIF1AX, SMYD3, VWF, SCUBE1, TNFRSFUb, NEFH, ARSA, BDNF, CD300LG, CPXM1, CXCL3, EPHA2, HARS1, MANF, NCAN, PDGFRA, ROR1, SERPINE1, SLITRK2 and SUSD5.Embodiment 16: A kit or assay device comprising reagents for detecting and / or quantifying the expression level of at least one biomarker gene selected from a group consisting of:- CNTFR, CXCL12, ITGB3JTGAV, ADPGK, GFRA2, STC2, FBLN2, VCAM1, MAG, APAF1, DKK3, PRG4, LILRA4, BNIPL, SEZ6L, ESM1, STAR, TMX4, BCAN, SLITRK1, SOD3, C1S, SERPINA11, DPEP1, ITGA11, CXADR, FGFBP3, LTBP4, OIP5, FANCD2, EPHA2, EPHB6, GFAP, IL15RA, CCER2, ITGAV, APLP1, CD276, CDH3, CRIM1, PDGFRA, ROR1, SUSD5, CD300LG, CSPG4, QPCT, CALB2, IL7, CD209, MTAP, PRL, DDC, MCAT, LYN, STX4, PPIF, DECR1, PNPT1, MPIG6B, ANXA7, IVD, RAC2, PLEKHF2, GFRA2, DDX19B, DFFA, ARSA, BACE1, CD40LG, IL16, NEFL, TARDBP, TUBA1A, SUMF1, LAP3, OGDHL, CHMP2B, EPN1, H1-10, HS3ST4, STAT6, RGS18, CYLD, ESYT2, EBI3, IL12A, NAA10,Docket No. 2960447-000048-W01Filed: January 9, 202675SH3PXD2A, HNRNPAO, PLCD4, CSNK1G2, ZC3H12C, H2AC20, RIT1, CCDC80, HSP90AA1, ARID3C, TWSG1, H2AX, IST1, AASDH, ZNF354A, AP1B1, HSP90AB1, CIAO1, RAB6A, PPP1CA, GPI, SCARF2, CSTF2, TFRC, ADA2, TRIM25, OIP5, PTGR1, CXCL13, COL14A1, PDGFRA, SMOC1, CD58, AAGAB, RNF5, NEFL, OMD, CALB2, CD276, NCAM1, EDIL3, TGAV, QPCT, DDC, LSM1, PRL, GPT, GRP, IGHG4, PRR15, GLRX3, KCTD10, CCN5, ARPC5, CMPK1, SARS1, PPM1G, LTBP4, IVD, RAP1B, CORIN, LRRTM2, FKBP1B, AASDH, TPM4, HSPD1, IRF5, PTGR1, COPS5, PYGB, PYGL, PHGDH, MICU1, ARHGAP25, CCT8, KYAT1, LRRC45, GP1BB, SAMD9L, PTPN11, CRIM1, SMAD1, CXCL5, LATS1, NFATC1, METAP1D, DFFA, CST5, CCL5, NAP1L4, LAT2, TDRD3, ALDH5A1, CCER2, MTDH, FKBPL, PPP1CC, SNAP23, CEP85, RAB37, CSPG4, OGA, MAGED1, VAMP8, PRDX5, GMPR2, MPP7, ZNF649, RIMOC1, AAGAB, BACE1, LEG1, OXT, UBL5, GPNMB, KVAT1, GFAP, ANOS1, RPL30, TMX2, EIF1AX, SMYD3, VWF, SCUBE1, TNFRSFUb, NEFH, ARSA, BDNF, CD300LG, CPXM1, CXCL3, EPHA2, HARS1, MANF, NCAN, PDGFRA, ROR1, SERPINE1, SLITRK2 and SUSD5,- and any combination thereof,wherein the reagents are capable of specifically binding to the polypeptides encoded by said genes and wherein said reagents comprise at least 10% of the total number of reagents present in the kit.Embodiment 17: The kit or assay device according to Embodiment 16, wherein the at least one biomarker gene is selected from a group consisting of:- Prodromal genes, wherein the group of prodromal genes consists of CNTFR, CXCL12, ITGB3JTGAV, ADPGK, GFRA2, STC2, FBLN2, VCAM1, MAG, APAF1, DKK3, PRG4, LILRA4, BNIPL, SEZ6L, ESM1, STAR, TMX4, BCAN, SLITRK1, SOD3, C1S, SERPINA11, DPEP1, ITGA11, CXADR, FGFBP3, LTBP4, OIP5, FANCD2, EPHA2, EPHB6, GFAP, IL15RA, CCER2, ITGAV, APLP1, CD276, CDH3, CRIM1, PDGFRA, ROR1, SUSD5, CD300LG, CSPG4, QPCT, CALB2 and IL7,- Progression genes, wherein the group of progression genes consists of CD209, MTAP, PRL and DDC,- Transient preclinical genes, wherein the group of transient preclinical genesDocket No. 2960447-000048-W01Filed: January 9, 202676consists of MCAT, LYN, STX4, PPIF, DECR1, PNPT1, MPIG6B, ANXA7, IVD, RAC2, PLEKHF2, GFRA2, DDX19B, DFFA, ARSA, BACE1, CD40LG, IL16, NEFL and TARDBP,- Transitional genes, wherein the group of transitional genes consists of TUBA1A, SUMF1 , LAP3, OGDHL and CHMP2B,- Post-diagnostic genes, wherein the group of post-diagnostic genes consists of EPN1, H1-10, HS3ST4, STAT6, RGS18, CYLD, ESYT2, EBI3, IL12A, NAA10, SH3PXD2A, HNRNPAO, PLCD4, CSNK1G2, ZC3H12C, H2AC20, RIT1, CCDC80, HSP90AA1, ARID3C, TWSG1, H2AX, IST1, AASDH, ZNF354A, AP1B1, HSP90AB1, CIA01, RAB6A, PPP1CA, GPI, SCARF2, CSTF2, TFRC, ADA2, TRIM25, OIP5, PTGR1, CXCL13, COL14A1, PDGFRA, SM0C1, CD58, AAGAB and RNF5,- and any combination thereof.Embodiment 18: The kit or assay device according to any one of Embodiment 16 or 17, wherein the biomarker forms part of a biomarker signature comprising two or more biomarkers selected from a group consisting of CNTFR, CXCL12, ITGB3JTGAV, ADPGK, GFRA2, STC2, FBLN2, VCAM1, MAG, APAF1, DKK3, PRG4, LILRA4, BNIPL, SEZ6L, ESM1, STAR, TMX4, BCAN, SLITRK1, SOD3, C1S, SERPINA11, DPEP1, ITGA11, CXADR, FGFBP3, LTBP4, OIP5, FANCD2, EPHA2, EPHB6, GFAP, IL15RA, CCER2, ITGAV, APLP1, CD276, CDH3, CRIM1, PDGFRA, ROR1, SUSD5, CD300LG, CSPG4, QPCT, CALB2, IL7, CD209, MTAP, PRL, DDC, MCAT, LYN, STX4, PPIF, DECR1, PNPT1, MPIG6B, ANXA7, IVD, RAC2, PLEKHF2, GFRA2, DDX19B, DFFA, ARSA, BACE1, CD40LG, IL16, NEFL, TARDBP, TUBA1A, SUMF1, LAP3, OGDHL, CHMP2B, EPN1, H1-10, HS3ST4, STAT6, RGS18, CYLD, ESYT2, EBI3, IL12A, NAA10, SH3PXD2A, HNRNPAO, PLCD4, CSNK1G2, ZC3H12C, H2AC20, RIT1, CCDC80, HSP90AA1, ARID3C, TWSG1, H2AX, IST1, AASDH, ZNF354A, AP1B1, HSP90AB1, CIA01, RAB6A, PPP1CA, GPI, SCARF2, CSTF2, TFRC, ADA2, TRIM25, OIP5, PTGR1, CXCL13, COL14A1, PDGFRA, SM0C1, CD58, AAGAB, RNF5, NEFL, OMD, CALB2, CD276, NCAM1, EDIL3, TGAV, QPCT, DDC, LSM1, PRL, GPT, GRP, IGHG4, PRR15, GLRX3, KCTD10, CCN5, ARPC5, CMPK1, SARS1, PPM1G, LTBP4, IVD, RAP1B, CORIN, LRRTM2, FKBP1B, AASDH, TPM4, HSPD1, IRF5, PTGR1, COPS5, PYGB, PYGL, PHGDH, MICU1, ARHGAP25, CCT8, KYAT1, LRRC45, GP1BB, SAMD9L, PTPN11, CRIM1, SMAD1, CXCL5, LATS1, NFATC1,Docket No. 2960447-000048-W01Filed: January 9, 202677METAP1D, DFFA, CST5, CCL5, NAP1L4, LAT2, TDRD3, ALDH5A1, CCER2, MTDH, FKBPL, PPP1CC, SNAP23, CEP85, RAB37, CSPG4, OGA, MAGED1, VAMP8, PRDX5, GMPR2, MPP7, ZNF649, RIMOC1, AAGAB, BACE1, LEG1, OXT, UBL5, GPNMB, KVAT1, GFAP, ANOS1, RPL30, TMX2, EIF1AX, SMYD3, VWF, SCUBE1, TNFRSFUb, NEFH, ARSA, BDNF, CD300LG, CPXM1, CXCL3, EPHA2, HARS1, MANF, NCAN, PDGFRA, ROR1, SERPINE1, SLITRK2 and SUSD5.Embodiment 19: The kit or assay device according to Embodiment 18, wherein the biomarker signature comprises at least three of the biomarker genes MTAP, TUBA1A, GPT, SUMF1, OIP5, CNTFR, LSM1, QPCT, ITGB3, and ITGAV.Embodiment 20: An in vitro method of determining the presence of a biomarker protein in a subject, the method comprising:(a) providing a plasma sample from the subject; and(b) determining the presence of the biomarker protein selected from the group consisting of: CNTFR, CXCL12, ITGB3JTGAV, ADPGK, GFRA2, STC2, FBLN2, VCAM1, MAG, APAF1, DKK3, PRG4, LILRA4, BNIPL, SEZ6L, ESM1, STAR, TMX4, BCAN, SLITRK1, SOD3, C1S, SERPINA11, DPEP1, ITGA11, CXADR, FGFBP3, LTBP4, OIP5, FANCD2, EPHA2, EPHB6, GFAP, IL15RA, CCER2, ITGAV, APLP1, CD276, CDH3, CRIM1, PDGFRA, ROR1, SUSD5, CD300LG, CSPG4, QPCT, CALB2, IL7, CD209, MTAP, PRL, DDC, MCAT, LYN, STX4, PPIF, DECR1, PNPT1, MPIG6B, ANXA7, IVD, RAC2, PLEKHF2, GFRA2, DDX19B, DFFA, ARSA, BACE1, CD40LG, IL16, NEFL, TARDBP, TUBA1A, SUMF1, LAP3, OGDHL, CHMP2B, EPN1, H1-10, HS3ST4, STAT6, RGS18, CYLD, ESYT2, EBI3, IL12A, NAA10, SH3PXD2A, HNRNPAO, PLCD4, CSNK1G2, ZC3H12C, H2AC20, RIT1, CCDC80, HSP90AA1, ARID3C, TWSG1, H2AX, IST1, AASDH, ZNF354A, AP1B1, HSP90AB1, CIAO1, RAB6A, PPP1CA, GPI, SCARF2, CSTF2, TFRC, ADA2, TRIM25, OIP5, PTGR1, CXCL13, COL14A1, PDGFRA, SMOC1, CD58, AAGAB, RNF5, NEFL, OMD, CALB2, CD276, NCAM1, EDIL3, TGAV, QPCT, DDC, LSM1, PRL, GPT, GRP, IGHG4, PRR15, GLRX3, KCTD10, CCN5, ARPC5, CMPK1, SARS1, PPM1G, LTBP4, IVD, RAP1B, CORIN, LRRTM2, FKBP1B, AASDH, TPM4, HSPD1, IRF5, PTGR1, COPS5, PYGB, PYGL, PHGDH, MICU1, ARHGAP25, CCT8, KYAT1, LRRC45, GP1BB, SAMD9L, PTPN11, CRIM1, SMAD1, CXCL5, LATS1, NFATC1, METAP1D, DFFA, CST5, CCL5, NAP1L4, LAT2, TDRD3, ALDH5A1, CCER2, MTDH, FKBPL, PPP1CC, SNAP23,Docket No. 2960447-000048-W01Filed: January 9, 202678CEP85, RAB37, CSPG4, OGA, MAGED1, VAMP8, PRDX5, GMPR2, MPP7, ZNF649, RIMOC1, AAGAB, BACE1, LEG1, OXT, UBL5, GPNMB, KVAT1, GFAP, ANOS1, RPL30, TMX2, EIF1AX, SMYD3, VWF, SCUBE1, TNFRSFUb, NEFH, ARSA, BDNF, CD300LG, CPXM1, CXCL3, EPHA2, HARS1, MANF, NCAN, PDGFRA, ROR1, SERPINE1, SLITRK2 and SLISD5 in the plasma sample with a protein amplification based assay.Embodiment 21: The method of Embodiment 20, wherein the plasma sample is from a subject that is suspected of having Parkinson Disease or having an increased risk of developing Parkinson Disease.Embodiment 22: The method of Embodiment 20-21, wherein the subject has early stage Parkinson Disease.Embodiment 23: The method of Embodiments 20-22, wherein the a protein amplification based assay is real-time quaking-induced conversion (RT-QuIC), or protein misfolding cyclic amplification (PMCA).Embodiment 24: A method of identifying, monitoring, or treating Parkinson’s disease in a subject, comprising:(a) providing a plasma sample from the subject;(b) assessing the amount of at least one biomarker selected from the group consisting of CNTFR, CXCL12, ITGB3JTGAV, ADPGK, GFRA2, STC2, FBLN2, VCAM1, MAG, APAF1, DKK3, PRG4, LILRA4, BNIPL, SEZ6L, ESM1, STAR, TMX4, BCAN, SLITRK1, SOD3, C1S, SERPINA11, DPEP1, ITGA11, CXADR, FGFBP3, LTBP4, OIP5, FANCD2, EPHA2, EPHB6, GFAP, IL15RA, CCER2, ITGAV, APLP1, CD276, CDH3, CRIM1, PDGFRA, ROR1, SUSD5, CD300LG, CSPG4, QPCT, CALB2, IL7, CD209, MTAP, PRL, DDC, MCAT, LYN, STX4, PPIF, DECR1, PNPT1, MPIG6B, ANXA7, IVD, RAC2, PLEKHF2, GFRA2, DDX19B, DFFA, ARSA, BACE1, CD40LG, IL16, NEFL, TARDBP, TUBA1A, SUMF1, LAP3, OGDHL, CHMP2B, EPN1, H1-10, HS3ST4, STAT6, RGS18, CYLD, ESYT2, EBI3, IL12A, NAA10, SH3PXD2A, HNRNPAO, PLCD4, CSNK1G2, ZC3H12C, H2AC20, RIT1, CCDC80, HSP90AA1, ARID3C, TWSG1, H2AX, IST1, AASDH, ZNF354A, AP1B1, HSP90AB1, CIAO1, RAB6A, PPP1CA, GPI, SCARF2, CSTF2, TFRC, ADA2, TRIM25, OIP5, PTGR1, CXCL13, COL14A1, PDGFRA, SMOC1, CD58, AAGAB, RNF5, NEFL, OMD, CALB2, CD276,Docket No. 2960447-000048-W01Filed: January 9, 202679NCAM1, EDIL3, TGAV, QPCT, DDC, LSM1, PRL, GPT, GRP, IGHG4, PRR15, GLRX3, KCTD10, CCN5, ARPC5, CMPK1, SARS1, PPM1G, LTBP4, IVD, RAP1B, CORIN, LRRTM2, FKBP1B, AASDH, TPM4, HSPD1, IRF5, PTGR1, COPS5, PYGB, PYGL, PHGDH, MICU1, ARHGAP25, CCT8, KYAT1, LRRC45, GP1BB, SAMD9L, PTPN11, CRIM1, SMAD1, CXCL5, LATS1, NFATC1, METAP1D, DFFA, CST5, CCL5, NAP1L4, LAT2, TDRD3, ALDH5A1, CCER2, MTDH, FKBPL, PPP1CC, SNAP23, CEP85, RAB37, CSPG4, OGA, MAGED1, VAMP8, PRDX5, GMPR2, MPP7, ZNF649, RIMOC1, AAGAB, BACE1, LEG1, OXT, UBL5, GPNMB, KVAT1, GFAP, ANOS1, RPL30, TMX2, EIF1AX, SMYD3, VWF, SCUBE1, TNFRSFUb, NEFH, ARSA, BDNF, CD300LG, CPXM1, CXCL3, EPHA2, HARS1, MANF, NCAN, PDGFRA, ROR1, SERPINE1, SLITRK2 and SLISD5 in the plasma sample of the subject; and(c) comparing the assessed amount of the at least one biomarker with a reference value for the at least one biomarker;wherein an assessed amount of the at least one biomarker greater or less than the reference value for the at least one biomarker is indicative of Parkinson’s disease and (d) administering a treatment for Parkinson’s disease to the subject if the assessed amount of the at least one biomarker is greater or less than the reference value.Embodiment 25: A method of determining the risk of a subject developing Parkinson’s disease or treating a subject for Parkinson’s disease comprising:(a) providing a plasma sample from the subject;(b) assessing the amount of at least one biomarker selected from the group consisting of CNTFR, CXCL12, ITGB3:ITGAV, ADPGK, GFRA2, STC2, FBLN2, VCAM1, MAG, APAF1, DKK3, PRG4, LILRA4, BNIPL, SEZ6L, ESM1, STAR, TMX4, BCAN, SLITRK1, SOD3, C1S, SERPINA11, DPEP1, ITGA11, CXADR, FGFBP3, LTBP4, OIP5, FANCD2, EPHA2, EPHB6, GFAP, IL15RA, CCER2, ITGAV, APLP1, CD276, CDH3, CRIM1, PDGFRA, ROR1, SUSD5, CD300LG, CSPG4, QPCT, CALB2, IL7, CD209, MTAP, PRL, DDC, MCAT, LYN, STX4, PPIF, DECR1, PNPT1, MPIG6B, ANXA7, IVD, RAC2, PLEKHF2, GFRA2, DDX19B, DFFA, ARSA, BACE1, CD40LG, IL16, NEFL, TARDBP, TUBA1A, SUMF1, LAP3, OGDHL, CHMP2B, EPN1, H1-10, HS3ST4, STAT6, RGS18, CYLD, ESYT2, EBI3, IL12A, NAA10, SH3PXD2A, HNRNPAO, PLCD4, CSNK1G2, ZC3H12C, H2AC20, RIT1, CCDC80, HSP90AA1, ARID3C, TWSG1, H2AX, IST1, AASDH, ZNF354A, AP1B1, HSP90AB1, CIA01, RAB6A, PPP1CA, GPI, SCARF2, CSTF2, TFRC, ADA2, TRIM25, OIP5, PTGR1, CXCL13, COL14A1, PDGFRA, SMOC1, CD58, AAGAB, RNF5, NEFL, OMD, CALB2, CD276,Docket No. 2960447-000048-W01Filed: January 9, 202680NCAM1, EDIL3, TGAV, QPCT, DDC, LSM1, PRL, GPT, GRP, IGHG4, PRR15, GLRX3, KCTD10, CCN5, ARPC5, CMPK1, SARS1, PPM1G, LTBP4, IVD, RAP1B, CORIN, LRRTM2, FKBP1B, AASDH, TPM4, HSPD1, IRF5, PTGR1, COPS5, PYGB, PYGL, PHGDH, MICU1, ARHGAP25, CCT8, KYAT1, LRRC45, GP1BB, SAMD9L, PTPN11, CRIM1, SMAD1, CXCL5, LATS1, NFATC1, METAP1D, DFFA, CST5, CCL5, NAP1L4, LAT2, TDRD3, ALDH5A1, CCER2, MTDH, FKBPL, PPP1CC, SNAP23, CEP85, RAB37, CSPG4, OGA, MAGED1, VAMP8, PRDX5, GMPR2, MPP7, ZNF649, RIMOC1, AAGAB, BACE1, LEG1, OXT, UBL5, GPNMB, KVAT1, GFAP, ANOS1, RPL30, TMX2, EIF1AX, SMYD3, VWF, SCUBE1, TNFRSFUb, NEFH, ARSA, BDNF, CD300LG, CPXM1, CXCL3, EPHA2, HARS1, MANF, NCAN, PDGFRA, ROR1, SERPINE1, SLITRK2 and SLISD5 in the plasma sample of the subject; and(c) comparing the assessed amount of the at least one biomarker with a reference value for the at least one biomarker;wherein an assessed amount of the at least one biomarker greater or less than the reference value for the at least one biomarker is indicative of an increased risk of the subject developing Parkinson’s disease and(d) administering a treatment for Parkinson’s disease to the subject if the assessed amount of the at least one biomarker is greater or less than the reference value.Embodiment 26: An in vitro method of assessing the amount of at least one biomarker selected from the group consisting of CNTFR, CXCL12, ITGB3:ITGAV, ADPGK, GFRA2, STC2, FBLN2, VCAM1, MAG, APAF1, DKK3, PRG4, LILRA4, BNIPL, SEZ6L, ESM1, STAR, TMX4, BCAN, SLITRK1, SOD3, C1S, SERPINA11, DPEP1, ITGA11, CXADR, FGFBP3, LTBP4, OIP5, FANCD2, EPHA2, EPHB6, GFAP, IL15RA, CCER2, ITGAV, APLP1, CD276, CDH3, CRIM1, PDGFRA, ROR1, SUSD5, CD300LG, CSPG4, QPCT, CALB2, IL7, CD209, MTAP, PRL, DDC, MCAT, LYN, STX4, PPIF, DECR1, PNPT1, MPIG6B, ANXA7, IVD, RAC2, PLEKHF2, GFRA2, DDX19B, DFFA, ARSA, BACE1, CD40LG, IL16, NEFL, TARDBP, TUBA1A, SUMF1, LAP3, OGDHL, CHMP2B, EPN1, H1-10, HS3ST4, STAT6, RGS18, CYLD, ESYT2, EBI3, IL12A, NAA10, SH3PXD2A, HNRNPAO, PLCD4, CSNK1G2, ZC3H12C, H2AC20, RIT1, CCDC80, HSP90AA1, ARID3C, TWSG1, H2AX, IST1, AASDH, ZNF354A, AP1B1, HSP90AB1, CIAO1, RAB6A, PPP1CA, GPI, SCARF2, CSTF2, TFRC, ADA2, TRIM25, OIP5, PTGR1, CXCL13, COL14A1, PDGFRA, SMOC1, CD58, AAGAB, RNF5, NEFL, OMD, CALB2, CD276, NCAM1, EDIL3, TGAV, QPCT, DDC, LSM1, PRL, GPT, GRP, IGHG4, PRR15, GLRX3, KCTD10, CCN5, ARPC5, CMPK1, SARS1, PPM1G, LTBP4, IVD,Docket No. 2960447-000048-W01Filed: January 9, 202681RAP1B, CORIN, LRRTM2, FKBP1B, AASDH, TPM4, HSPD1, IRF5, PTGR1, COPS5, PYGB, PYGL, PHGDH, MICU1, ARHGAP25, CCT8, KYAT1, LRRC45, GP1BB, SAMD9L, PTPN11, CRIM1, SMAD1, CXCL5, LATS1, NFATC1, METAP1D, DFFA, CST5, CCL5, NAP1L4, LAT2, TDRD3, ALDH5A1, CCER2, MTDH, FKBPL, PPP1CC, SNAP23, CEP85, RAB37, CSPG4, OGA, MAGED1, VAMP8, PRDX5, GMPR2, MPP7, ZNF649, RIMOC1, AAGAB, BACE1, LEG1, OXT, UBL5, GPNMB, KVAT1, GFAP, ANOS1, RPL30, TMX2, EIF1AX, SMYD3, VWF, SCUBE1 , TNFRSF11b, NEFH, ARSA, BDNF, CD300LG, CPXM1, CXCL3, EPHA2, HARS1, MANF, NCAN, PDGFRA, ROR1, SERPINE1, SLITRK2 and SLISD5 in a subject, the method comprising:(a) providing plasma sample from the subject; and(b) assessing the amount of at least one biomarker selected from the group consisting of CNTFR, CXCL12, ITGB3JTGAV, ADPGK, GFRA2, STC2, FBLN2, VCAM1, MAG, APAF1, DKK3, PRG4, LILRA4, BNIPL, SEZ6L, ESM1, STAR, TMX4, BCAN, SLITRK1, SOD3, C1S, SERPINA11, DPEP1, ITGA11, CXADR, FGFBP3, LTBP4, OIP5, FANCD2, EPHA2, EPHB6, GFAP, IL15RA, CCER2, ITGAV, APLP1, CD276, CDH3, CRIM1, PDGFRA, ROR1, SUSD5, CD300LG, CSPG4, QPCT, CALB2, IL7, CD209, MTAP, PRL, DDC, MCAT, LYN, STX4, PPIF, DECR1, PNPT1, MPIG6B, ANXA7, IVD, RAC2, PLEKHF2, GFRA2, DDX19B, DFFA, ARSA, BACE1, CD40LG, IL16, NEFL, TARDBP, TUBA1A, SUMF1, LAP3, OGDHL, CHMP2B, EPN1, H1-10, HS3ST4, STAT6, RGS18, CYLD, ESYT2, EBI3, IL12A, NAA10, SH3PXD2A, HNRNPAO, PLCD4, CSNK1G2, ZC3H12C, H2AC20, RIT1, CCDC80, HSP90AA1, ARID3C, TWSG1, H2AX, IST1, AASDH, ZNF354A, AP1B1, HSP90AB1, CIAO1, RAB6A, PPP1CA, GPI, SCARF2, CSTF2, TFRC, ADA2, TRIM25, OIP5, PTGR1, CXCL13, COL14A1, PDGFRA, SMOC1, CD58, AAGAB, RNF5, NEFL, OMD, CALB2, CD276, NCAM1, EDIL3, TGAV, QPCT, DDC, LSM1, PRL, GPT, GRP, IGHG4, PRR15, GLRX3, KCTD10, CCN5, ARPC5, CMPK1, SARS1, PPM1G, LTBP4, IVD, RAP1B, CORIN, LRRTM2, FKBP1B, AASDH, TPM4, HSPD1, IRF5, PTGR1, COPS5, PYGB, PYGL, PHGDH, MICU1, ARHGAP25, CCT8, KYAT1, LRRC45, GP1BB, SAMD9L, PTPN11, CRIM1, SMAD1, CXCL5, LATS1, NFATC1, METAP1D, DFFA, CST5, CCL5, NAP1L4, LAT2, TDRD3, ALDH5A1, CCER2, MTDH, FKBPL, PPP1CC, SNAP23, CEP85, RAB37, CSPG4, OGA, MAGED1, VAMP8, PRDX5, GMPR2, MPP7, ZNF649, RIMOC1, AAGAB, BACE1, LEG1, OXT, UBL5, GPNMB, KVAT1, GFAP, ANOS1, RPL30, TMX2, EIF1AX, SMYD3, VWF, SCUBE1, TNFRSFUb, NEFH, ARSA, BDNF, CD300LG, CPXM1, CXCL3, EPHA2, HARS1, MANF, NCAN, PDGFRA, ROR1, SERPINE1, SLITRK2 and SUSD5 in the plasma sample.Docket No. 2960447-000048-W01Filed: January 9, 202682Embodiment 27: The method of Embodiment 26, wherein the subject has, or is suspected of having Parkinson disease or is suspected of having an increased risk of developing Parkinson’s disease.Embodiment 28: The method of Embodiment 27, wherein the subject is human.Embodiment 29: As assay device for diagnosing Parkinson disease or determining the risk of developing Parkinson disease in a subject, the device comprising a surface with at least one detectably labeled agent located thereon, wherein the at least one detectably labelled agent specifically binds to a biomarker protein selected from the group consisting of CNTFR, CXCL12, ITGB3:ITGAV, ADPGK, GFRA2, STC2, FBLN2, VCAM1, MAG, APAF1, DKK3, PRG4, LILRA4, BNIPL, SEZ6L, ESM1, STAR, TMX4, BCAN, SLITRK1, SOD3, C1S, SERPINA11, DPEP1, ITGA11, CXADR, FGFBP3, LTBP4, OIP5, FANCD2, EPHA2, EPHB6, GFAP, IL15RA, CCER2, ITGAV, APLP1, CD276, CDH3, CRIM1, PDGFRA, ROR1, SUSD5, CD300LG, CSPG4, QPCT, CALB2, IL7, CD209, MTAP, PRL, DDC, MCAT, LYN, STX4, PPIF, DECR1, PNPT1, MPIG6B, ANXA7, IVD, RAC2, PLEKHF2, GFRA2, DDX19B, DFFA, ARSA, BACE1, CD40LG, IL16, NEFL, TARDBP, TUBA1A, SUMF1, LAP3, OGDHL, CHMP2B, EPN1, H1-10, HS3ST4, STAT6, RGS18, CYLD, ESYT2, EBI3, IL12A, NAA10, SH3PXD2A, HNRNPAO, PLCD4, CSNK1G2, ZC3H12C, H2AC20, RIT1, CCDC80, HSP90AA1, ARID3C, TWSG1, H2AX, IST1, AASDH, ZNF354A, AP1B1, HSP90AB1, CIA01, RAB6A, PPP1CA, GPI, SCARF2, CSTF2, TFRC, ADA2, TRIM25, OIP5, PTGR1, CXCL13, COL14A1, PDGFRA, SM0C1, CD58, AAGAB, RNF5, NEFL, OMD, CALB2, CD276, NCAM1, EDIL3, TGAV, QPCT, DDC, LSM1, PRL, GPT, GRP, IGHG4, PRR15, GLRX3, KCTD10, CCN5, ARPC5, CMPK1, SARS1, PPM1G, LTBP4, IVD, RAP1B, CORIN, LRRTM2, FKBP1B, AASDH, TPM4, HSPD1, IRF5, PTGR1, COPS5, PYGB, PYGL, PHGDH, MICU1, ARHGAP25, CCT8, KYAT1, LRRC45, GP1BB, SAMD9L, PTPN11, CRIM1, SMAD1, CXCL5, LATS1, NFATC1, METAP1D, DFFA, CST5, CCL5, NAP1L4, LAT2, TDRD3, ALDH5A1, CCER2, MTDH, FKBPL, PPP1CC, SNAP23, CEP85, RAB37, CSPG4, OGA, MAGED1, VAMP8, PRDX5, GMPR2, MPP7, ZNF649, RIM0C1, AAGAB, BACE1, LEG1, OXT, UBL5, GPNMB, KVAT1, GFAP, ANOS1, RPL30, TMX2, EIF1AX, SMYD3, VWF, SCUBE1, TNFRSFUb, NEFH, ARSA, BDNF, CD300LG, CPXM1, CXCL3, EPHA2, HARS1, MANF, NCAN, PDGFRA, ROR1, SERPINE1, SLITRK2 and SUSD5.Docket No. 2960447-000048-W01Filed: January 9, 202683Embodiment 30: The assay device of Embodiment 29 wherein the device comprises at least two separate detectably labelled agents specifically binds to a biomarker protein selected from the group consisting of CNTFR, CXCL12, ITGB3:ITGAV, ADPGK, GFRA2, STC2, FBLN2, VCAM1, MAG, APAF1, DKK3, PRG4, LILRA4, BNIPL, SEZ6L, ESM1, STAR, TMX4, BCAN, SLITRK1, SOD3, C1S, SERPINA11, DPEP1, ITGA11, CXADR, FGFBP3, LTBP4, OIP5, FANCD2, EPHA2, EPHB6, GFAP, IL15RA, CCER2, ITGAV, APLP1, CD276, CDH3, CRIM1, PDGFRA, ROR1, SUSD5, CD300LG, CSPG4, QPCT, CALB2, IL7, CD209, MTAP, PRL, DDC, MCAT, LYN, STX4, PPIF, DECR1, PNPT1, MPIG6B, ANXA7, IVD, RAC2, PLEKHF2, GFRA2, DDX19B, DFFA, ARSA, BACE1, CD40LG, IL16, NEFL, TARDBP, TUBA1A, SUMF1, LAP3, OGDHL, CHMP2B, EPN1, H1-10, HS3ST4, STAT6, RGS18, CYLD, ESYT2, EBI3, IL12A, NAA10, SH3PXD2A, HNRNPAO, PLCD4, CSNK1G2, ZC3H12C, H2AC20, RIT1, CCDC80, HSP90AA1, ARID3C, TWSG1, H2AX, IST1, AASDH, ZNF354A, AP1B1, HSP90AB1, CIAO1, RAB6A, PPP1CA, GPI, SCARF2, CSTF2, TFRC, ADA2, TRIM25, OIP5, PTGR1, CXCL13, COL14A1, PDGFRA, SMOC1, CD58, AAGAB, RNF5, NEFL, OMD, CALB2, CD276, NCAM1, EDIL3, TGAV, QPCT, DDC, LSM1, PRL, GPT, GRP, IGHG4, PRR15, GLRX3, KCTD10, CCN5, ARPC5, CMPK1, SARS1, PPM1G, LTBP4, IVD, RAP1B, CORIN, LRRTM2, FKBP1B, AASDH, TPM4, HSPD1, IRF5, PTGR1, COPS5, PYGB, PYGL, PHGDH, MICU1, ARHGAP25, CCT8, KYAT1, LRRC45, GP1BB, SAMD9L, PTPN11, CRIM1, SMAD1, CXCL5, LATS1, NFATC1, METAP1D, DFFA, CST5, CCL5, NAP1L4, LAT2, TDRD3, ALDH5A1, CCER2, MTDH, FKBPL, PPP1CC, SNAP23, CEP85, RAB37, CSPG4, OGA, MAGED1, VAMP8, PRDX5, GMPR2, MPP7, ZNF649, RIMOC1, AAGAB, BACE1, LEG1, OXT, UBL5, GPNMB, KVAT1, GFAP, ANOS1, RPL30, TMX2, EIF1AX, SMYD3, VWF, SCUBE1 , TNFRSF11b, NEFH, ARSA, BDNF, CD300LG, CPXM1, CXCL3, EPHA2, HARS1, MANF, NCAN, PDGFRA, ROR1, SERPINE1, SLITRK2 and SUSD5.Embodiment 31: A method of detecting Parkinson Disease in a subject, the method comprising: measuring the level of one or more biomarker in a plasma sample from the subject, wherein the one or more biomarker is selected from the group consisting of: CNTFR, CXCL12, ITGB3JTGAV, ADPGK, GFRA2, STC2, FBLN2, VCAM1, MAG, APAF1, DKK3, PRG4, LILRA4, BNIPL, SEZ6L, ESM1, STAR, TMX4, BCAN, SLITRK1, SOD3, C1S, SERPINA11, DPEP1, ITGA11, CD209, MTAP, MCAT, LYN, STX4, PPIF, DECR1, PNPT1, MPIG6B, TUBA1A, SUMF1, LAP3, OGDHL, CHMP2B, NEFL, OMD,Docket No. 2960447-000048-W01Filed: January 9, 202684CALB2, CD276, NCAM1, EDIL3, TGAV, QPCT, DDC, LSM1, PRL, GPT, GRP, IGHG4, PRR15, GLRX3, KCTD10, CCN5, ARPC5, CMPK1, SARS1, PPM1G, LTBP4, IVD, RAP1B, CORIN, LRRTM2, FKBP1B, AASDH, TPM4, HSPD1, IRF5, PTGR1, COPS5, PYGB, PYGL, PHGDH, MICU1, ARHGAP25, CCT8, KYAT1, LRRC45, GP1BB, SAMD9L, PTPN11, CRIM1, SMAD1, CXCL5, LATS1, NFATC1, METAP1D, DFFA, CST5, CCL5, NAP1L4, LAT2, TDRD3, ALDH5A1, CCER2, MTDH, FKBPL, PPP1CC, SNAP23, CEP85, RAB37, CSPG4, OGA, MAGED1, VAMP8, PRDX5, GMPR2, MPP7, ZNF649, RIMOC1, AAGAB, BACE1, LEG1, OXT, UBL5, GPNMB, KVAT1, GFAP, ANOS1, RPL30, TMX2, EIF1AX, SMYD3, VWF, SCUBE1 , TNFRSF11b, NEFH, ARSA, BDNF, CD300LG, CPXM1, CXCL3, EPHA2, HARS1, MANF, NCAN, PDGFRA, ROR1, SERPINE1, SLITRK2 and SLISD5 by contacting the plasma sample with an appropriate antibody specific to the biomarker of interest and detecting binding between the biomarker and the corresponding antibody.

Claims

Docket No. 2960447-000048-W01Filed: January 9, 202685CLAIMS1. Use of at least one gene selected from a group consisting of- CNTFR, CXCL12, ITGB3:ITGAV, ADPGK, GFRA2, STC2, FBLN2, VCAM1, MAG, APAF1, DKK3, PRG4, LILRA4, BNIPL, SEZ6L, ESM1, STAR, TMX4, BCAN, SLITRK1, SOD3, C1S, SERPINA11, DPEP1, ITGA11, CXADR, FGFBP3, LTBP4, OIP5, FANCD2, EPHA2, EPHB6, GFAP, IL15RA, CCER2, ITGAV, APLP1, CD276, CDH3, CRIM1, PDGFRA, ROR1, SUSD5, CD300LG, CSPG4, QPCT, CALB2, IL7, CD209, MTAP, PRL, DDC, MCAT, LYN, STX4, PPIF, DECR1, PNPT1, MPIG6B, ANXA7, IVD, RAC2, PLEKHF2, GFRA2, DDX19B, DFFA, ARSA, BACE1, CD40LG, IL16, NEFL, TARDBP, TUBA1A, SUMF1, LAP3, OGDHL, CHMP2B, NEFL, OMD, CALB2, CD276, NCAM1, EDIL3, TGAV, QPCT, DDC, LSM1, PRL, GPT, GRP, IGHG4, PRR15, GLRX3, KCTD10, CCN5, ARPC5, CMPK1, SARS1, PPM1G, LTBP4, IVD, RAP1B, CORIN, LRRTM2, FKBP1B, AASDH, TPM4, HSPD1, IRF5, PTGR1, COPS5, PYGB, PYGL, PHGDH, MICU1, ARHGAP25, CCT8, KYAT1, LRRC45, GP1BB, SAMD9L, PTPN11, CRIM1, SMAD1, CXCL5, LATS1, NFATC1, METAP1D, DFFA, CST5, CCL5, NAP1L4, LAT2, TDRD3, ALDH5A1, CCER2, MTDH, FKBPL, PPP1CC, SNAP23, CEP85, RAB37, CSPG4, OGA, MAGED1, VAMP8, PRDX5, GMPR2, MPP7, ZNF649, RIMOC1, AAGAB, BACE1, LEG1, OXT, UBL5, GPNMB, KVAT1, GFAP, ANOS1, RPL30, TMX2, EIF1AX, SMYD3, VWF, SCUBE1, TNFRSFUb, NEFH, ARSA, BDNF, CD300LG, CPXM1, CXCL3, EPHA2, HARS1, MANF, NCAN, PDGFRA, ROR1, SERPINE1, SLITRK2 and SUSD5,- and any combination thereof,as a biomarker for assessing the risk of Parkinson’s Disease in a subject, wherein the subject has not been diagnosed with Parkinson’s Disease and does not exhibit clinical symptoms associated with Parkinson’s Disease.

2. The use according to claim 1, wherein said at least one gene is selected from a group consisting of:- Prodromal genes, wherein the group of prodromal genes consists of CNTFR, CXCL12, ITGB3:ITGAV, ADPGK, GFRA2, STC2, FBLN2, VCAM1, MAG, APAF1, DKK3, PRG4, LILRA4, BNIPL, SEZ6L, ESM1, STAR, TMX4, BCAN, SLITRK1, SOD3, C1S, SERPINA11, DPEP1, ITGA11, CXADR, FGFBP3,Docket No. 2960447-000048-W01Filed: January 9, 202686LTBP4, 0IP5, FANCD2, EPHA2, EPHB6, GFAP, IL15RA, CCER2, ITGAV, APLP1, CD276, CDH3, CRIM1, PDGFRA, R0R1, SUSD5, CD300LG, CSPG4, QPCT, CALB2 and IL7,- Progression genes, wherein the group of progression genes consists of CD209, MTAP, PRL and DDC,- Transient preclinical genes, wherein the group of transient preclinical genes consists of MCAT, LYN, STX4, PPIF, DECR1, PNPT1, MPIG6B, ANXA7, IVD, RAC2, PLEKHF2, GFRA2, DDX19B, DFFA, ARSA, BACE1, CD40LG, IL16, NEFL and TARDBP,- Transitional genes, wherein the group of transitional genes consists of TUBA1A, SUMF1, LAP3, OGDHL and CHMP2B,- and any combination thereof.

3. The use according to any one of claims 1 or 2, wherein the biomarker forms part of a biomarker signature comprising two or more biomarkers selected from a group consisting of CNTFR, CXCL12, ITGB3:ITGAV, ADPGK, GFRA2, STC2, FBLN2, VCAM1, MAG, APAF1, DKK3, PRG4, LILRA4, BNIPL, SEZ6L, ESM1, STAR, TMX4, BCAN, SLITRK1, SOD3, C1S, SERPINA11, DPEP1, ITGA11, CXADR, FGFBP3, LTBP4, OIP5, FANCD2, EPHA2, EPHB6, GFAP, IL15RA, CCER2, ITGAV, APLP1, CD276, CDH3, CRIM1, PDGFRA, ROR1, SUSD5, CD300LG, CSPG4, QPCT, CALB2, IL7, CD209, MTAP, PRL, DDC, MCAT, LYN, STX4, PPIF, DECR1, PNPT1, MPIG6B, ANXA7, IVD, RAC2, PLEKHF2, GFRA2, DDX19B, DFFA, ARSA, BACE1, CD40LG, IL16, NEFL, TARDBP, TUBA1A, SUMF1, LAP3, OGDHL, CHMP2B, NEFL, OMD, CALB2, CD276, NCAM1, EDIL3, TGAV, QPCT, DDC, LSM1, PRL, GPT, GRP, IGHG4, PRR15, GLRX3, KCTD10, CCN5, ARPC5, CMPK1, SARS1, PPM1G, LTBP4, IVD, RAP1B, CORIN, LRRTM2, FKBP1B, AASDH, TPM4, HSPD1, IRF5, PTGR1, COPS5, PYGB, PYGL, PHGDH, MICU1, ARHGAP25, CCT8, KYAT1, LRRC45, GP1BB, SAMD9L, PTPN11, CRIM1, SMAD1, CXCL5, LATS1, NFATC1, METAP1D, DFFA, CST5, CCL5, NAP1L4, LAT2, TDRD3, ALDH5A1, CCER2, MTDH, FKBPL, PPP1CC, SNAP23, CEP85, RAB37, CSPG4, OGA, MAGED1, VAMP8, PRDX5, GMPR2, MPP7, ZNF649, RIM0C1, AAGAB, BACE1, LEG1, OXT, UBL5, GPNMB, KVAT1, GFAP, ANOS1, RPL30, TMX2, EIF1AX, SMYD3, VWF, SCUBE1, TNFRSFUb, NEFH, ARSA, BDNF, CD300LG, CPXM1, CXCL3, EPHA2, HARS1, MANF, NCAN, PDGFRA, ROR1, SERPINE1, SLITRK2, SUSD5, and any combination thereof.Docket No. 2960447-000048-W01Filed: January 9, 2026874. The use according to claim 3, wherein the biomarker signature comprises at least three of the biomarker genes MTAP, TUBA1A, GPT, SUMF1, OIP5, CNTFR, LSM1, QPCT, ITGB3, and ITGAV.

5. An in vitro method for determining the risk that a subject develops Parkinson’s Disease, wherein the method comprises:c) determining, in a biological sample from the subject, the expression level of at least one gene selected from a group consisting of:- CNTFR, CXCL12, ITGB3:ITGAV, ADPGK, GFRA2, STC2, FBLN2, VCAM1, MAG, APAF1, DKK3, PRG4, LILRA4, BNIPL, SEZ6L, ESM1, STAR, TMX4, BCAN, SLITRK1, SOD3, C1S, SERPINA11, DPEP1, ITGA11, CXADR, FGFBP3, LTBP4, OIP5, FANCD2, EPHA2, EPHB6, GFAP, IL15RA, CCER2, ITGAV, APLP1, CD276, CDH3, CRIM1, PDGFRA, ROR1, SUSD5, CD300LG, CSPG4, QPCT, CALB2, IL7, CD209, MTAP, PRL, DDC, MCAT, LYN, STX4, PPIF, DECR1, PNPT1, MPIG6B, ANXA7, IVD, RAC2, PLEKHF2, GFRA2, DDX19B, DFFA, ARSA, BACE1, CD40LG, IL16, NEFL, TARDBP, TUBA1A, SUMF1, LAP3, OGDHL, CHMP2B, NEFL, OMD, CALB2, CD276, NCAM1, EDIL3, TGAV, QPCT, DDC, LSM1, PRL, GPT, GRP, IGHG4, PRR15, GLRX3, KCTD10, CCN5, ARPC5, CMPK1, SARS1, PPM1G, LTBP4, IVD, RAP1B, CORIN, LRRTM2, FKBP1B, AASDH, TPM4, HSPD1, IRF5, PTGR1, COPS5, PYGB, PYGL, PHGDH, MICU1, ARHGAP25, CCT8, KYAT1, LRRC45, GP1BB, SAMD9L, PTPN11, CRIM1, SMAD1, CXCL5, LATS1, NFATC1, METAP1D, DFFA, CST5, CCL5, NAP1L4, LAT2, TDRD3, ALDH5A1, CCER2, MTDH, FKBPL, PPP1CC, SNAP23, CEP85, RAB37, CSPG4, OGA, MAGED1, VAMP8, PRDX5, GMPR2, MPP7, ZNF649, RIMOC1, AAGAB, BACE1, LEG1, OXT, UBL5, GPNMB, KVAT1, GFAP, ANOS1, RPL30, TMX2, EIF1AX, SMYD3, VWF, SCUBE1, TNFRSFUb, NEFH, ARSA, BDNF, CD300LG, CPXM1, CXCL3, EPHA2, HARS1, MANF, NCAN, PDGFRA, ROR1, SERPINE1, SLITRK2 and SUSD5,- and any combination thereof,d) comparing the expression level of said at least one gene in said biological sample with a reference value for said gene; wherein an altered expression level of said at least one gene in the biological sample when compared to theDocket No. 2960447-000048-W01Filed: January 9, 202688reference value is indicative that the subject is at risk of developing Parkinson’s Disease.

6. An in vitro method for determining the risk that a subject develops Parkinson’s Disease, wherein the method comprises:d) determining, in a biological sample from the subject, the expression level of at least one gene selected from a group consisting of:- Prodromal genes, wherein the group of prodromal genes consists of CNTFR, CXCL12, ITGB3:ITGAV, ADPGK, GFRA2, STC2, FBLN2, VCAM1, MAG, APAF1, DKK3, PRG4, LILRA4, BNIPL, SEZ6L, ESM1, STAR, TMX4, BCAN, SLITRK1, SOD3, C1S, SERPINA11, DPEP1, ITGA11, CXADR, FGFBP3, LTBP4, OIP5, FANCD2, EPHA2, EPHB6, GFAP, IL15RA, CCER2, ITGAV, APLP1, CD276, CDH3, CRIM1, PDGFRA, ROR1, SUSD5, CD300LG, CSPG4, QPCT, CALB2 and IL7,- Progression genes, wherein the group of progression genes consists of CD209, MTAP, PRL and DDC,- Transient preclinical genes, wherein the group of transient preclinical genes consists of MCAT, LYN, STX4, PPIF, DECR1, PNPT1, MPIG6B, ANXA7, IVD, RAC2, PLEKHF2, GFRA2, DDX19B, DFFA, ARSA, BACE1, CD40LG, IL16, NEFL and TARDBP,- Transitional genes, wherein the group of transitional genes consists of TUBA1A, SUMF1 , LAP3, OGDHL and CHMP2B,- Post-diagnostic genes, wherein the group of post-diagnostic genes consists of EPN1, H1-10, HS3ST4, STAT6, RGS18, CYLD, ESYT2, EBI3, IL12A, NAA10, SH3PXD2A, HNRNPAO, PLCD4, CSNK1G2, ZC3H12C, H2AC20, RIT1, CCDC80, HSP90AA1, ARID3C, TWSG1, H2AX, IST1, AASDH, ZNF354A, AP1B1, HSP90AB1, CIA01, RAB6A, PPP1CA, GPI, SCARF2, CSTF2, TFRC, ADA2, TRIM25, OIP5, PTGR1, CXCL13, COL14A1, PDGFRA, SM0C1, CD58, AAGAB and RNF5,- and any combination thereof,e) comparing the expression level of said at least one gene in said biological sample with a reference value for said gene; wherein an altered expression level of said at least one gene in the biological sample when compared to the reference value is indicative that the subject is at risk of developing Parkinson’s Disease.Docket No. 2960447-000048-W01Filed: January 9, 2026897. The method according to any one of claims 5 or 6, wherein the subject has not been diagnosed with Parkinson’s Disease and is not exhibiting any clinical symptoms associated with Parkinson’s Disease, preferably wherein the subject is not exhibiting any one of tremor, bradykinesia, rigidity, postural instability, loss of automatic movement, speech changes, micrographia, anosmia, nerve pain, mild cognitive impairment, or dementia.

8. An in vitro method for diagnosing Parkinson’s Disease in a subject, wherein the method comprises:c) determining, in a biological sample from the subject, the expression level of at least one gene selected from a group consisting of:- Prodromal genes, wherein the group of prodromal genes consists of CNTFR, CXCL12, ITGB3:ITGAV, ADPGK, GFRA2, STC2, FBLN2, VCAM1, MAG, APAF1, DKK3, PRG4, LILRA4, BNIPL, SEZ6L, ESM1, STAR, TMX4, BCAN, SLITRK1, SOD3, C1S, SERPINA11, DPEP1, ITGA11, CXADR, FGFBP3, LTBP4, OIP5, FANCD2, EPHA2, EPHB6, GFAP, IL15RA, CCER2, ITGAV, APLP1, CD276, CDH3, CRIM1, PDGFRA, ROR1, SUSD5, CD300LG, CSPG4, QPCT, CALB2 and IL7,- Progression genes, wherein the group of progression genes consists of CD209, MTAP, PRL and DDC,- Transient preclinical genes, wherein the group of transient preclinical genes consists of MCAT, LYN, STX4, PPIF, DECR1, PNPT1, MPIG6B, ANXA7, IVD, RAC2, PLEKHF2, GFRA2, DDX19B, DFFA, ARSA, BACE1, CD40LG, IL16, NEFL and TARDBP,- Transitional genes, wherein the group of transitional genes consists of TUBA1A, SUMF1 , LAP3, OGDHL and CHMP2B,- Post-diagnostic genes, wherein the group of post-diagnostic genes consists of EPN1, H1-10, HS3ST4, STAT6, RGS18, CYLD, ESYT2, EBI3, IL12A, NAA10, SH3PXD2A, HNRNPAO, PLCD4, CSNK1G2, ZC3H12C, H2AC20, RIT1, CCDC80, HSP90AA1, ARID3C, TWSG1, H2AX, IST1, AASDH, ZNF354A, AP1B1, HSP90AB1, CIA01, RAB6A, PPP1CA, GPI, SCARF2, CSTF2, TFRC, ADA2, TRIM25, OIP5, PTGR1, CXCL13, COL14A1, PDGFRA, SM0C1, CD58, AAGAB and RNF5,- and any combination thereof,Docket No. 2960447-000048-W01Filed: January 9, 202690d) comparing the expression level of said at least one gene in said biological sample with a reference value for said gene; wherein an altered expression level of said at least one gene in the biological sample when compared to the reference value is indicative that the subject is likely to suffer from Parkinson’s Disease.

9. The method according to claim 8, wherein the method further comprises a step (c) of subjecting the subject to a procedure to confirm that the subject suffers from Parkinson’s Disease, wherein the procedure is selected from the group consisting of neurological examination, magnetic resonance imaging (MRI), brain ultrasound, positron emission tomography (PET) scan, dopamine transporter (DAT) scan, Alpha-synuclein test, and combinations thereof.

10. A method for diagnosing and treating a subject who suffers from Parkinson’s Disease, wherein the method comprises:d) determining, in a biological sample from the subject, the expression level of at least one gene selected from a group consisting of:- Prodromal genes, wherein the group of prodromal genes consists of CNTFR, CXCL12, ITGB3JTGAV, ADPGK, GFRA2, STC2, FBLN2, VCAM1, MAG, APAF1, DKK3, PRG4, LILRA4, BNIPL, SEZ6L, ESM1, STAR, TMX4, BCAN, SLITRK1, SOD3, C1S, SERPINA11, DPEP1, ITGA11, CXADR, FGFBP3, LTBP4, OIP5, FANCD2, EPHA2, EPHB6, GFAP, IL15RA, CCER2, ITGAV, APLP1, CD276, CDH3, CRIM1, PDGFRA, ROR1, SUSD5, CD300LG, CSPG4, QPCT, CALB2 and IL7,- Progression genes, wherein the group of progression genes consists of CD209, MTAP, PRL and DDC,- Transient preclinical genes, wherein the group of transient preclinical genes consists of MCAT, LYN, STX4, PPIF, DECR1, PNPT1, MPIG6B, ANXA7, IVD, RAC2, PLEKHF2, GFRA2, DDX19B, DFFA, ARSA, BACE1, CD40LG, IL16, NEFL and TARDBP,- Transitional genes, wherein the group of transitional genes consists of TUBA1A, SUMF1 , LAP3, OGDHL and CHMP2B,- Post-diagnostic genes, wherein the group of post-diagnostic genes consists of EPN1, H1-10, HS3ST4, STAT6, RGS18, CYLD, ESYT2, EBI3, IL12A, NAA10, SH3PXD2A, HNRNPAO, PLCD4, CSNK1G2, ZC3H12C, H2AC20,Docket No. 2960447-000048-W01Filed: January 9, 202691RIT1, CCDC80, HSP90AA1, ARID3C, TWSG1, H2AX, IST1, AASDH, ZNF354A, AP1B1, HSP90AB1, CIA01, RAB6A, PPP1CA, GPI, SCARF2, CSTF2, TFRC, ADA2, TRIM25, 0IP5, PTGR1, CXCL13, C0L14A1, PDGFRA, SM0C1, CD58, AAGAB and RNF5,- and any combination thereof,e) comparing the expression level of said at least one gene in said biological sample with a reference value for said gene; wherein an altered expression level of said at least one gene in the biological sample when compared to the reference value is indicative that the subject is likely to suffer from Parkinson’s Disease,f) administering to the subject a therapy selected from the group consisting of Levodopa, Carbidopa, Dopamine Agonists, MAO-B and COMT enzyme inhibitors, Amantadine, Anticholinergics and combinations thereof if the subject is diagnosed as suffering from Parkinson’s Disease, said subject benefiting from administering said therapy.

11. A method for predicting response to therapy of a subject suffering from Parkinson’s Disease, wherein the method comprises:c) determining, in a biological sample taken from the subject before and after administration of therapy, the expression level of at least one gene selected from a group consisting of:- Prodromal genes, wherein the group of prodromal genes consists of CNTFR, CXCL12, ITGB3JTGAV, ADPGK, GFRA2, STC2, FBLN2, VCAM1, MAG, APAF1, DKK3, PRG4, LILRA4, BNIPL, SEZ6L, ESM1, STAR, TMX4, BCAN, SLITRK1, SOD3, C1S, SERPINA11, DPEP1, ITGA11, CXADR, FGFBP3, LTBP4, OIP5, FANCD2, EPHA2, EPHB6, GFAP, IL15RA, CCER2, ITGAV, APLP1, CD276, CDH3, CRIM1, PDGFRA, ROR1, SUSD5, CD300LG, CSPG4, QPCT, CALB2 and IL7,- Progression genes, wherein the group of progression genes consists of CD209, MTAP, PRL and DDC,- Transient preclinical genes, wherein the group of transient preclinical genes consists of MCAT, LYN, STX4, PPIF, DECR1, PNPT1, MPIG6B, ANXA7, IVD, RAC2, PLEKHF2, GFRA2, DDX19B, DFFA, ARSA, BACE1, CD40LG, IL16, NEFL and TARDBP,- Transitional genes, wherein the group of transitional genes consists ofDocket No. 2960447-000048-W01Filed: January 9, 202692TUBA1A, SUMF1 , LAP3, OGDHL and CHMP2B,- Post-diagnostic genes, wherein the group of post-diagnostic genes consists of EPN1, H1-10, HS3ST4, STAT6, RGS18, CYLD, ESYT2, EBI3, IL12A, NAA10, SH3PXD2A, HNRNPAO, PLCD4, CSNK1G2, ZC3H12C, H2AC20, RIT1, CCDC80, HSP90AA1, ARID3C, TWSG1, H2AX, IST1, AASDH, ZNF354A, AP1B1, HSP90AB1, CIA01, RAB6A, PPP1CA, GPI, SCARF2, CSTF2, TFRC, ADA2, TRIM25, OIP5, PTGR1, CXCL13, COL14A1, PDGFRA, SM0C1, CD58, AAGAB and RNF5,- and any combination thereof,d) comparing the expression level of said at least one gene in said biological sample taken from the subject before therapy with the expression level of said at least one gene in said biological sample taken from the subject after therapy; - wherein a reduced altered expression level of said at least one gene in the biological sample taken from the subject after therapy when compared to the expression level of said at least one gene in the biological sample taken from the subject before therapy is indicative of a positive response of the patient to therapy, or- wherein an increased altered expression level of said at least one gene in the biological sample taken from the subject after therapy when compared to the expression level of said at least one gene in the biological sample taken from the subject before therapy is indicative of a negative response of the patient to therapy.

12. The method according to claim 11, wherein the patient is administered a different therapy, preferably wherein the different therapy is selected from the group consisting of Levodopa, Carbidopa, Dopamine Agonists, MAO-B and COMT enzyme inhibitors, Amantadine, Anticholinergics and combinations thereof.

13. An in vitro method for selecting a subject to participate in a clinical trial for Parkinson’s Disease, wherein the method comprisesc) determining, in a biological sample from the subject, the expression level of at least one gene selected from a group consisting of:- Prodromal genes, wherein the group of prodromal genes consists of CNTFR, CXCL12, ITGB3JTGAV, ADPGK, GFRA2, STC2, FBLN2, VCAM1, MAG, APAF1, DKK3, PRG4, LILRA4, BNIPL, SEZ6L, ESM1, STAR, TMX4,Docket No. 2960447-000048-W01Filed: January 9, 202693BCAN, SLITRK1, S0D3, C1S, SERPINA11, DPEP1, ITGA11, CXADR, FGFBP3, LTBP4, 0IP5, FANCD2, EPHA2, EPHB6, GFAP, IL15RA, CCER2, ITGAV, APLP1, CD276, CDH3, CRIM1, PDGFRA, R0R1, SUSD5, CD300LG, CSPG4, QPCT, CALB2 and IL7,- Progression genes, wherein the group of progression genes consists of CD209, MTAP, PRL and DDC,- Transient preclinical genes, wherein the group of transient preclinical genes consists of MCAT, LYN, STX4, PPIF, DECR1, PNPT1, MPIG6B, ANXA7, IVD, RAC2, PLEKHF2, GFRA2, DDX19B, DFFA, ARSA, BACE1, CD40LG, IL16, NEFL and TARDBP,- Transitional genes, wherein the group of transitional genes consists of TUBA1A, SUMF1 , LAP3, OGDHL and CHMP2B,- Post-diagnostic genes, wherein the group of post-diagnostic genes consists of EPN1, H1-10, HS3ST4, STAT6, RGS18, CYLD, ESYT2, EBI3, IL12A, NAA10, SH3PXD2A, HNRNPAO, PLCD4, CSNK1G2, ZC3H12C, H2AC20, RIT1, CCDC80, HSP90AA1, ARID3C, TWSG1, H2AX, IST1, AASDH, ZNF354A, AP1B1, HSP90AB1, CIA01, RAB6A, PPP1CA, GPI, SCARF2, CSTF2, TFRC, ADA2, TRIM25, OIP5, PTGR1, CXCL13, COL14A1, PDGFRA, SM0C1, CD58, AAGAB and RNF5,- and any combination thereof,d) comparing the expression level of said at least one gene in said sample with a reference value for said gene; wherein an altered expression level of said at least one gene in the sample when compared to the reference value for said gene is indicative that the subject is at risk of developing Parkinson’s Disease, f) selecting the subject at risk of developing Parkinson’s Disease to participate in a clinical trial for Parkinson’s Disease.

14. The method according to any one of claims 6 to 13, wherein the biomarker forms part of a biomarker signature of two or more biomarkers, wherein the biomarker signature comprises at least one prodromal gene, at least one progression gene, at least one transient preclinical gene, at least one transitional gene and / or at least one post-diagnostic gene.

15. The method according to any one of claims 6 to 14, wherein the biomarker forms part of a biomarker signature of two or more biomarkers, wherein the biomarkerDocket No. 2960447-000048-W01Filed: January 9, 202694signature further comprises at least one of:- NEFL, OMD, CALB2, CD276, NCAM1, EDIL3, TGAV, QPCT, DDC, LSM1, PRL, GPT, GRP, IGHG4, PRR15, GLRX3, KCTD10, CCN5, ARPC5, CMPK1, SARS1, PPM1G, LTBP4, IVD, RAP1B, CORIN, LRRTM2, FKBP1B, AASDH, TPM4, HSPD1, IRF5, PTGR1, COPS5, PYGB, PYGL, PHGDH, MICU1, ARHGAP25, CCT8, KYAT1, LRRC45, GP1BB, SAMD9L, PTPN11, CRIM1, SMAD1, CXCL5, LATS1, NFATC1, METAP1D, DFFA, CST5, CCL5, NAP1L4, LAT2, TDRD3, ALDH5A1, CCER2, MTDH, FKBPL, PPP1CC, SNAP23, CEP85, RAB37, CSPG4, OGA, MAGED1, VAMP8, PRDX5, GMPR2, MPP7, ZNF649, RIMOC1, AAGAB, BACE1, LEG1, OXT, UBL5, GPNMB, KVAT1, GFAP, ANOS1, RPL30, TMX2, EIF1AX, SMYD3, VWF, SCUBE1, TNFRSFUb, NEFH, ARSA, BDNF, CD300LG, CPXM1, CXCL3, EPHA2, HARS1, MANF, NCAN, PDGFRA, ROR1, SERPINE1, SLITRK2 and SUSD5.

16. A kit or assay device comprising reagents for detecting and / or quantifying the expression level of at least one biomarker gene selected from a group consisting of:- CNTFR, CXCL12, ITGB3JTGAV, ADPGK, GFRA2, STC2, FBLN2, VCAM1, MAG, APAF1, DKK3, PRG4, LILRA4, BNIPL, SEZ6L, ESM1, STAR, TMX4, BCAN, SLITRK1, SOD3, C1S, SERPINA11, DPEP1, ITGA11, CXADR, FGFBP3, LTBP4, OIP5, FANCD2, EPHA2, EPHB6, GFAP, IL15RA, CCER2, ITGAV, APLP1, CD276, CDH3, CRIM1, PDGFRA, ROR1, SUSD5, CD300LG, CSPG4, QPCT, CALB2, IL7, CD209, MTAP, PRL, DDC, MCAT, LYN, STX4, PPIF, DECR1, PNPT1, MPIG6B, ANXA7, IVD, RAC2, PLEKHF2, GFRA2, DDX19B, DFFA, ARSA, BACE1, CD40LG, IL16, NEFL, TARDBP, TUBA1A, SUMF1, LAP3, OGDHL, CHMP2B, EPN1, H1-10, HS3ST4, STAT6, RGS18, CYLD, ESYT2, EBI3, IL12A, NAA10, SH3PXD2A, HNRNPAO, PLCD4, CSNK1G2, ZC3H12C, H2AC20, RIT1, CCDC80, HSP90AA1, ARID3C, TWSG1, H2AX, IST1, AASDH, ZNF354A, AP1B1, HSP90AB1, CIAO1, RAB6A, PPP1CA, GPI, SCARF2, CSTF2, TFRC, ADA2, TRIM25, OIP5, PTGR1, CXCL13, COL14A1, PDGFRA, SMOC1, CD58, AAGAB, RNF5, NEFL, OMD, CALB2, CD276, NCAM1, EDIL3, TGAV, QPCT, DDC, LSM1, PRL, GPT, GRP, IGHG4, PRR15, GLRX3, KCTD10, CCN5, ARPC5, CMPK1, SARS1, PPM1G, LTBP4, IVD, RAP1B, CORIN, LRRTM2, FKBP1B, AASDH, TPM4, HSPD1, IRF5,Docket No. 2960447-000048-W01Filed: January 9, 202695PTGR1, C0PS5, PYGB, PYGL, PHGDH, MICU1, ARHGAP25, CCT8, KYAT1, LRRC45, GP1BB, SAMD9L, PTPN11, CRIM1, SMAD1, CXCL5, LATS1, NFATC1, METAP1D, DFFA, CST5, CCL5, NAP1L4, LAT2, TDRD3, ALDH5A1, CCER2, MTDH, FKBPL, PPP1CC, SNAP23, CEP85, RAB37, CSPG4, OGA, MAGED1, VAMP8, PRDX5, GMPR2, MPP7, ZNF649, RIMOC1, AAGAB, BACE1, LEG1, OXT, UBL5, GPNMB, KVAT1, GFAP, ANOS1, RPL30, TMX2, EIF1AX, SMYD3, VWF, SCUBE1, TNFRSFUb, NEFH, ARSA, BDNF, CD300LG, CPXM1, CXCL3, EPHA2, HARS1, MANF, NCAN, PDGFRA, ROR1, SERPINE1, SLITRK2 and SUSD5,- and any combination thereof,wherein the reagents are capable of specifically binding to the polypeptides encoded by said genes and wherein said reagents comprise at least 10% of the total number of reagents present in the kit.

17. The kit or assay device according to claim 16, wherein the at least one biomarker gene is selected from a group consisting of:- Prodromal genes, wherein the group of prodromal genes consists of CNTFR, CXCL12, ITGB3JTGAV, ADPGK, GFRA2, STC2, FBLN2, VCAM1, MAG, APAF1, DKK3, PRG4, LILRA4, BNIPL, SEZ6L, ESM1, STAR, TMX4, BCAN, SLITRK1, SOD3, C1S, SERPINA11, DPEP1, ITGA11, CXADR, FGFBP3, LTBP4, OIP5, FANCD2, EPHA2, EPHB6, GFAP, IL15RA, CCER2, ITGAV, APLP1, CD276, CDH3, CRIM1, PDGFRA, ROR1, SUSD5, CD300LG, CSPG4, QPCT, CALB2 and IL7,- Progression genes, wherein the group of progression genes consists of CD209, MTAP, PRL and DDC,- Transient preclinical genes, wherein the group of transient preclinical genes consists of MCAT, LYN, STX4, PPIF, DECR1, PNPT1, MPIG6B, ANXA7, IVD, RAC2, PLEKHF2, GFRA2, DDX19B, DFFA, ARSA, BACE1, CD40LG, IL16, NEFL and TARDBP,- Transitional genes, wherein the group of transitional genes consists of TUBA1A, SUMF1 , LAP3, OGDHL and CHMP2B,- Post-diagnostic genes, wherein the group of post-diagnostic genes consists of EPN1, H1-10, HS3ST4, STAT6, RGS18, CYLD, ESYT2, EBI3, IL12A, NAA10, SH3PXD2A, HNRNPAO, PLCD4, CSNK1G2, ZC3H12C, H2AC20,Docket No. 2960447-000048-W01Filed: January 9, 202696RIT1, CCDC80, HSP90AA1, ARID3C, TWSG1, H2AX, IST1, AASDH, ZNF354A, AP1B1, HSP90AB1, CIA01, RAB6A, PPP1CA, GPI, SCARF2, CSTF2, TFRC, ADA2, TRIM25, 0IP5, PTGR1, CXCL13, C0L14A1, PDGFRA, SM0C1, CD58, AAGAB and RNF5,- and any combination thereof.

18. The kit or assay device according to any one of claims 16 or 17, wherein the biomarker forms part of a biomarker signature comprising two or more biomarkers selected from a group consisting of CNTFR, CXCL12, ITGB3:ITGAV, ADPGK, GFRA2, STC2, FBLN2, VCAM1, MAG, APAF1, DKK3, PRG4, LILRA4, BNIPL, SEZ6L, ESM1, STAR, TMX4, BCAN, SLITRK1, SOD3, C1S, SERPINA11, DPEP1, ITGA11, CXADR, FGFBP3, LTBP4, OIP5, FANCD2, EPHA2, EPHB6, GFAP, IL15RA, CCER2, ITGAV, APLP1, CD276, CDH3, CRIM1, PDGFRA, ROR1, SUSD5, CD300LG, CSPG4, QPCT, CALB2, IL7, CD209, MTAP, PRL, DDC, MCAT, LYN, STX4, PPIF, DECR1, PNPT1, MPIG6B, ANXA7, IVD, RAC2, PLEKHF2, GFRA2, DDX19B, DFFA, ARSA, BACE1, CD40LG, IL16, NEFL, TARDBP, TUBA1A, SUMF1, LAP3, OGDHL, CHMP2B, EPN1, H1-10, HS3ST4, STAT6, RGS18, CYLD, ESYT2, EBI3, IL12A, NAA10, SH3PXD2A, HNRNPAO, PLCD4, CSNK1G2, ZC3H12C, H2AC20, RIT1, CCDC80, HSP90AA1, ARID3C, TWSG1, H2AX, IST1, AASDH, ZNF354A, AP1B1, HSP90AB1, CIAO1, RAB6A, PPP1CA, GPI, SCARF2, CSTF2, TFRC, ADA2, TRIM25, OIP5, PTGR1, CXCL13, COL14A1, PDGFRA, SMOC1, CD58, AAGAB, RNF5, NEFL, OMD, CALB2, CD276, NCAM1, EDIL3, TGAV, QPCT, DDC, LSM1, PRL, GPT, GRP, IGHG4, PRR15, GLRX3, KCTD10, CCN5, ARPC5, CMPK1, SARS1, PPM1G, LTBP4, IVD, RAP1B, CORIN, LRRTM2, FKBP1B, AASDH, TPM4, HSPD1, IRF5, PTGR1, COPS5, PYGB, PYGL, PHGDH, MICU1, ARHGAP25, CCT8, KYAT1, LRRC45, GP1BB, SAMD9L, PTPN11, CRIM1, SMAD1, CXCL5, LATS1, NFATC1, METAP1D, DFFA, CST5, CCL5, NAP1L4, LAT2, TDRD3, ALDH5A1, CCER2, MTDH, FKBPL, PPP1CC, SNAP23, CEP85, RAB37, CSPG4, OGA, MAGED1, VAMP8, PRDX5, GMPR2, MPP7, ZNF649, RIMOC1, AAGAB, BACE1, LEG1, OXT, UBL5, GPNMB, KVAT1, GFAP, ANOS1, RPL30, TMX2, EIF1AX, SMYD3, VWF, SCUBE1, TNFRSFUb, NEFH, ARSA, BDNF, CD300LG, CPXM1, CXCL3, EPHA2, HARS1, MANF, NCAN, PDGFRA, ROR1, SERPINE1, SLITRK2 and SUSD5.Docket No. 2960447-000048-W01Filed: January 9, 20269719. The kit or assay device according to claim 18, wherein the biomarker signature comprises at least three of the biomarker genes MTAP, TLIBA1A, GPT, SLIMF1, OIP5, CNTFR, LSM1, QPCT, ITGB3, and ITGAV.

20. An in vitro method of determining the presence of a biomarker protein in a subject, the method comprising:(a) providing a plasma sample from the subject; and(b) determining the presence of the biomarker protein selected from the group consisting of: CNTFR, CXCL12, ITGB3JTGAV, ADPGK, GFRA2, STC2, FBLN2, VCAM1, MAG, APAF1, DKK3, PRG4, LILRA4, BNIPL, SEZ6L, ESM1, STAR, TMX4, BCAN, SLITRK1, SOD3, C1S, SERPINA11, DPEP1, ITGA11, CXADR, FGFBP3, LTBP4, OIP5, FANCD2, EPHA2, EPHB6, GFAP, IL15RA, CCER2, ITGAV, APLP1, CD276, CDH3, CRIM1, PDGFRA, ROR1, SUSD5, CD300LG, CSPG4, QPCT, CALB2, IL7, CD209, MTAP, PRL, DDC, MCAT, LYN, STX4, PPIF, DECR1, PNPT1, MPIG6B, ANXA7, IVD, RAC2, PLEKHF2, GFRA2, DDX19B, DFFA, ARSA, BACE1, CD40LG, IL16, NEFL, TARDBP, TUBA1A, SUMF1, LAP3, OGDHL, CHMP2B, EPN1, H1-10, HS3ST4, STAT6, RGS18, CYLD, ESYT2, EBI3, IL12A, NAA10, SH3PXD2A, HNRNPAO, PLCD4, CSNK1G2, ZC3H12C, H2AC20, RIT1, CCDC80, HSP90AA1, ARID3C, TWSG1, H2AX, IST1, AASDH, ZNF354A, AP1B1, HSP90AB1, CIAO1, RAB6A, PPP1CA, GPI, SCARF2, CSTF2, TFRC, ADA2, TRIM25, OIP5, PTGR1, CXCL13, COL14A1, PDGFRA, SMOC1, CD58, AAGAB, RNF5, NEFL, OMD, CALB2, CD276, NCAM1, EDIL3, TGAV, QPCT, DDC, LSM1, PRL, GPT, GRP, IGHG4, PRR15, GLRX3, KCTD10, CCN5, ARPC5, CMPK1, SARS1, PPM1G, LTBP4, IVD, RAP1B, CORIN, LRRTM2, FKBP1B, AASDH, TPM4, HSPD1, IRF5, PTGR1, COPS5, PYGB, PYGL, PHGDH, MICU1, ARHGAP25, CCT8, KYAT1, LRRC45, GP1BB, SAMD9L, PTPN11, CRIM1, SMAD1, CXCL5, LATS1, NFATC1, METAP1D, DFFA, CST5, CCL5, NAP1L4, LAT2, TDRD3, ALDH5A1, CCER2, MTDH, FKBPL, PPP1CC, SNAP23, CEP85, RAB37, CSPG4, OGA, MAGED1, VAMP8, PRDX5, GMPR2, MPP7, ZNF649, RIMOC1, AAGAB, BACE1, LEG1, OXT, UBL5, GPNMB, KVAT1, GFAP, ANOS1, RPL30, TMX2, EIF1AX, SMYD3, VWF, SCUBE1, TNFRSFUb, NEFH, ARSA, BDNF, CD300LG, CPXM1, CXCL3, EPHA2, HARS1, MANF, NCAN, PDGFRA, ROR1, SERPINE1, SLITRK2 and SUSD5 in the plasma sample with a protein amplification based assay.

21. The method of claim 20, wherein the plasma sample is from a subject that isDocket No. 2960447-000048-W01Filed: January 9, 202698suspected of having Parkinson Disease or having an increased risk of developing Parkinson Disease.

22. The method of claims 20-21, wherein the subject has early stage Parkinson Disease.

23. The method of claims 20-22, wherein the a protein amplification based assay is real-time quaking-induced conversion (RT-QuIC), or protein misfolding cyclic amplification (PMCA).

24. A method of identifying, monitoring, or treating Parkinson’s disease in a subject, comprising:(a) providing a plasma sample from the subject;(b) assessing the amount of at least one biomarker selected from the group consisting of CNTFR, CXCL12, ITGB3JTGAV, ADPGK, GFRA2, STC2, FBLN2, VCAM1, MAG, APAF1, DKK3, PRG4, LILRA4, BNIPL, SEZ6L, ESM1, STAR, TMX4, BCAN, SLITRK1, SOD3, C1S, SERPINA11, DPEP1, ITGA11, CXADR, FGFBP3, LTBP4, OIP5, FANCD2, EPHA2, EPHB6, GFAP, IL15RA, CCER2, ITGAV, APLP1, CD276, CDH3, CRIM1, PDGFRA, ROR1, SUSD5, CD300LG, CSPG4, QPCT, CALB2, IL7, CD209, MTAP, PRL, DDC, MCAT, LYN, STX4, PPIF, DECR1, PNPT1, MPIG6B, ANXA7, IVD, RAC2, PLEKHF2, GFRA2, DDX19B, DFFA, ARSA, BACE1, CD40LG, IL16, NEFL, TARDBP, TUBA1A, SUMF1, LAP3, OGDHL, CHMP2B, EPN1, H1-10, HS3ST4, STAT6, RGS18, CYLD, ESYT2, EBI3, IL12A, NAA10, SH3PXD2A, HNRNPAO, PLCD4, CSNK1G2, ZC3H12C, H2AC20, RIT1, CCDC80, HSP90AA1, ARID3C, TWSG1, H2AX, IST1, AASDH, ZNF354A, AP1B1, HSP90AB1, CIAO1, RAB6A, PPP1CA, GPI, SCARF2, CSTF2, TFRC, ADA2, TRIM25, OIP5, PTGR1, CXCL13, COL14A1, PDGFRA, SMOC1, CD58, AAGAB, RNF5, NEFL, OMD, CALB2, CD276, NCAM1, EDIL3, TGAV, QPCT, DDC, LSM1, PRL, GPT, GRP, IGHG4, PRR15, GLRX3, KCTD10, CCN5, ARPC5, CMPK1, SARS1, PPM1G, LTBP4, IVD, RAP1B, CORIN, LRRTM2, FKBP1B, AASDH, TPM4, HSPD1, IRF5, PTGR1, COPS5, PYGB, PYGL, PHGDH, MICU1, ARHGAP25, CCT8, KYAT1, LRRC45, GP1BB, SAMD9L, PTPN11, CRIM1, SMAD1, CXCL5, LATS1, NFATC1, METAP1D, DFFA, CST5, CCL5, NAP1L4, LAT2, TDRD3, ALDH5A1, CCER2, MTDH, FKBPL, PPP1CC, SNAP23, CEP85, RAB37, CSPG4, OGA, MAGED1, VAMP8, PRDX5, GMPR2, MPP7, ZNF649, RIMOC1, AAGAB, BACE1, LEG1, OXT, UBL5, GPNMB, KVAT1, GFAP, ANOS1,Docket No. 2960447-000048-W01Filed: January 9, 202699RPL30, TMX2, EIF1AX, SMYD3, VWF, SCUBE1, TNFRSFUb, NEFH, ARSA, BDNF, CD300LG, CPXM1, CXCL3, EPHA2, HARS1, MANF, NCAN, PDGFRA, R0R1, SERPINE1, SLITRK2 and SLISD5 in the plasma sample of the subject; and(c) comparing the assessed amount of the at least one biomarker with a reference value for the at least one biomarker;wherein an assessed amount of the at least one biomarker greater or less than the reference value for the at least one biomarker is indicative of Parkinson’s disease and (d) administering a treatment for Parkinson’s disease to the subject if the assessed amount of the at least one biomarker is greater or less than the reference value.

25. A method of determining the risk of a subject developing Parkinson’s disease or treating a subject for Parkinson’s disease comprising:(a) providing a plasma sample from the subject;(b) assessing the amount of at least one biomarker selected from the group consisting of CNTFR, CXCL12, ITGB3:ITGAV, ADPGK, GFRA2, STC2, FBLN2, VCAM1, MAG, APAF1, DKK3, PRG4, LILRA4, BNIPL, SEZ6L, ESM1, STAR, TMX4, BCAN, SLITRK1, SOD3, C1S, SERPINA11, DPEP1, ITGA11, CXADR, FGFBP3, LTBP4, OIP5, FANCD2, EPHA2, EPHB6, GFAP, IL15RA, CCER2, ITGAV, APLP1, CD276, CDH3, CRIM1, PDGFRA, ROR1, SUSD5, CD300LG, CSPG4, QPCT, CALB2, IL7, CD209, MTAP, PRL, DDC, MCAT, LYN, STX4, PPIF, DECR1, PNPT1, MPIG6B, ANXA7, IVD, RAC2, PLEKHF2, GFRA2, DDX19B, DFFA, ARSA, BACE1, CD40LG, IL16, NEFL, TARDBP, TUBA1A, SUMF1, LAP3, OGDHL, CHMP2B, EPN1, H1-10, HS3ST4, STAT6, RGS18, CYLD, ESYT2, EBI3, IL12A, NAA10, SH3PXD2A, HNRNPAO, PLCD4, CSNK1G2, ZC3H12C, H2AC20, RIT1, CCDC80, HSP90AA1, ARID3C, TWSG1, H2AX, IST1, AASDH, ZNF354A, AP1B1, HSP90AB1, CIA01, RAB6A, PPP1CA, GPI, SCARF2, CSTF2, TFRC, ADA2, TRIM25, OIP5, PTGR1, CXCL13, COL14A1, PDGFRA, SM0C1, CD58, AAGAB, RNF5, NEFL, OMD, CALB2, CD276, NCAM1, EDIL3, TGAV, QPCT, DDC, LSM1, PRL, GPT, GRP, IGHG4, PRR15, GLRX3, KCTD10, CCN5, ARPC5, CMPK1, SARS1, PPM1G, LTBP4, IVD, RAP1B, CORIN, LRRTM2, FKBP1B, AASDH, TPM4, HSPD1, IRF5, PTGR1, COPS5, PYGB, PYGL, PHGDH, MICU1, ARHGAP25, CCT8, KYAT1, LRRC45, GP1BB, SAMD9L, PTPN11, CRIM1, SMAD1, CXCL5, LATS1, NFATC1, METAP1D, DFFA, CST5, CCL5, NAP1L4, LAT2, TDRD3, ALDH5A1, CCER2, MTDH, FKBPL, PPP1CC, SNAP23, CEP85, RAB37, CSPG4, OGA, MAGED1, VAMP8, PRDX5, GMPR2, MPP7, ZNF649, RIM0C1, AAGAB, BACE1, LEG1, OXT, UBL5, GPNMB, KVAT1, GFAP, ANOS1,Docket No. 2960447-000048-W01Filed: January 9, 2026100RPL30, TMX2, EIF1AX, SMYD3, VWF, SCUBE1, TNFRSFUb, NEFH, ARSA, BDNF, CD300LG, CPXM1, CXCL3, EPHA2, HARS1, MANF, NCAN, PDGFRA, ROR1, SERPINE1, SLITRK2 and SLISD5 in the plasma sample of the subject; and(c) comparing the assessed amount of the at least one biomarker with a reference value for the at least one biomarker;wherein an assessed amount of the at least one biomarker greater or less than the reference value for the at least one biomarker is indicative of an increased risk of the subject developing Parkinson’s disease and(d) administering a treatment for Parkinson’s disease to the subject if the assessed amount of the at least one biomarker is greater or less than the reference value.

26. An in vitro method of assessing the amount of at least one biomarker selected from the group consisting of CNTFR, CXCL12, ITGB3JTGAV, ADPGK, GFRA2, STC2, FBLN2, VCAM1, MAG, APAF1, DKK3, PRG4, LILRA4, BNIPL, SEZ6L, ESM1, STAR, TMX4, BCAN, SLITRK1, SOD3, C1S, SERPINA11, DPEP1, ITGA11, CXADR, FGFBP3, LTBP4, OIP5, FANCD2, EPHA2, EPHB6, GFAP, IL15RA, CCER2, ITGAV, APLP1, CD276, CDH3, CRIM1, PDGFRA, ROR1, SUSD5, CD300LG, CSPG4, QPCT, CALB2, IL7, CD209, MTAP, PRL, DDC, MCAT, LYN, STX4, PPIF, DECR1, PNPT1, MPIG6B, ANXA7, IVD, RAC2, PLEKHF2, GFRA2, DDX19B, DFFA, ARSA, BACE1, CD40LG, IL16, NEFL, TARDBP, TUBA1A, SUMF1, LAP3, OGDHL, CHMP2B, EPN1, H1-10, HS3ST4, STAT6, RGS18, CYLD, ESYT2, EBI3, IL12A, NAA10, SH3PXD2A, HNRNPAO, PLCD4, CSNK1G2, ZC3H12C, H2AC20, RIT1, CCDC80, HSP90AA1, ARID3C, TWSG1, H2AX, IST1, AASDH, ZNF354A, AP1B1, HSP90AB1, CIAO1, RAB6A, PPP1CA, GPI, SCARF2, CSTF2, TFRC, ADA2, TRIM25, OIP5, PTGR1, CXCL13, COL14A1, PDGFRA, SMOC1, CD58, AAGAB, RNF5, NEFL, OMD, CALB2, CD276, NCAM1, EDIL3, TGAV, QPCT, DDC, LSM1, PRL, GPT, GRP, IGHG4, PRR15, GLRX3, KCTD10, CCN5, ARPC5, CMPK1, SARS1, PPM1G, LTBP4, IVD, RAP1B, CORIN, LRRTM2, FKBP1B, AASDH, TPM4, HSPD1, IRF5, PTGR1, COPS5, PYGB, PYGL, PHGDH, MICU1, ARHGAP25, CCT8, KYAT1, LRRC45, GP1BB, SAMD9L, PTPN11, CRIM1, SMAD1, CXCL5, LATS1, NFATC1, METAP1D, DFFA, CST5, CCL5, NAP1L4, LAT2, TDRD3, ALDH5A1, CCER2, MTDH, FKBPL, PPP1CC, SNAP23, CEP85, RAB37, CSPG4, OGA, MAGED1, VAMP8, PRDX5, GMPR2, MPP7, ZNF649, RIMOC1, AAGAB, BACE1, LEG1, OXT, UBL5, GPNMB, KVAT1, GFAP, ANOS1, RPL30, TMX2, EIF1AX, SMYD3, VWF, SCUBE1, TNFRSFUb, NEFH, ARSA, BDNF, CD300LG, CPXM1, CXCL3, EPHA2, HARS1, MANF, NCAN, PDGFRA, ROR1,Docket No. 2960447-000048-W01Filed: January 9, 2026101SERPINE1, SLITRK2 and SLISD5 in a subject, the method comprising:(a) providing plasma sample from the subject; and(b) assessing the amount of at least one biomarker selected from the group consisting of CNTFR, CXCL12, ITGB3JTGAV, ADPGK, GFRA2, STC2, FBLN2, VCAM1, MAG, APAF1, DKK3, PRG4, LILRA4, BNIPL, SEZ6L, ESM1, STAR, TMX4, BCAN, SLITRK1, SOD3, C1S, SERPINA11, DPEP1, ITGA11, CXADR, FGFBP3, LTBP4, OIP5, FANCD2, EPHA2, EPHB6, GFAP, IL15RA, CCER2, ITGAV, APLP1, CD276, CDH3, CRIM1, PDGFRA, ROR1, SUSD5, CD300LG, CSPG4, QPCT, CALB2, IL7, CD209, MTAP, PRL, DDC, MCAT, LYN, STX4, PPIF, DECR1, PNPT1, MPIG6B, ANXA7, IVD, RAC2, PLEKHF2, GFRA2, DDX19B, DFFA, ARSA, BACE1, CD40LG, IL16, NEFL, TARDBP, TUBA1A, SUMF1, LAP3, OGDHL, CHMP2B, EPN1, H1-10, HS3ST4, STAT6, RGS18, CYLD, ESYT2, EBI3, IL12A, NAA10, SH3PXD2A, HNRNPAO, PLCD4, CSNK1G2, ZC3H12C, H2AC20, RIT1, CCDC80, HSP90AA1, ARID3C, TWSG1, H2AX, IST1, AASDH, ZNF354A, AP1B1, HSP90AB1, CIAO1, RAB6A, PPP1CA, GPI, SCARF2, CSTF2, TFRC, ADA2, TRIM25, OIP5, PTGR1, CXCL13, COL14A1, PDGFRA, SMOC1, CD58, AAGAB, RNF5, NEFL, OMD, CALB2, CD276, NCAM1, EDIL3, TGAV, QPCT, DDC, LSM1, PRL, GPT, GRP, IGHG4, PRR15, GLRX3, KCTD10, CCN5, ARPC5, CMPK1, SARS1, PPM1G, LTBP4, IVD, RAP1B, CORIN, LRRTM2, FKBP1B, AASDH, TPM4, HSPD1, IRF5, PTGR1, COPS5, PYGB, PYGL, PHGDH, MICU1, ARHGAP25, CCT8, KYAT1, LRRC45, GP1BB, SAMD9L, PTPN11, CRIM1, SMAD1, CXCL5, LATS1, NFATC1, METAP1D, DFFA, CST5, CCL5, NAP1L4, LAT2, TDRD3, ALDH5A1, CCER2, MTDH, FKBPL, PPP1CC, SNAP23, CEP85, RAB37, CSPG4, OGA, MAGED1, VAMP8, PRDX5, GMPR2, MPP7, ZNF649, RIMOC1, AAGAB, BACE1, LEG1, OXT, UBL5, GPNMB, KVAT1, GFAP, ANOS1, RPL30, TMX2, EIF1AX, SMYD3, VWF, SCUBE1, TNFRSFUb, NEFH, ARSA, BDNF, CD300LG, CPXM1, CXCL3, EPHA2, HARS1, MANF, NCAN, PDGFRA, ROR1, SERPINE1, SLITRK2 and SUSD5 in the plasma sample.

27. The method of claim 26, wherein the subject has, or is suspected of having Parkinson disease or is suspected of having an increased risk of developing Parkinson’s disease.

28. The method of claim 27, wherein the subject is human.

29. As assay device for diagnosing Parkinson disease or determining the risk ofDocket No. 2960447-000048-W01Filed: January 9, 2026102developing Parkinson disease in a subject, the device comprising a surface with at least one detectably labeled agent located thereon, wherein the at least one detectably labelled agent specifically binds to a biomarker protein selected from the group consisting of CNTFR, CXCL12, ITGB3JTGAV, ADPGK, GFRA2, STC2, FBLN2, VCAM1, MAG, APAF1, DKK3, PRG4, LILRA4, BNIPL, SEZ6L, ESM1, STAR, TMX4, BCAN, SLITRK1, SOD3, C1S, SERPINA11, DPEP1, ITGA11, CXADR, FGFBP3, LTBP4, OIP5, FANCD2, EPHA2, EPHB6, GFAP, IL15RA, CCER2, ITGAV, APLP1, CD276, CDH3, CRIM1, PDGFRA, ROR1, SUSD5, CD300LG, CSPG4, QPCT, CALB2, IL7, CD209, MTAP, PRL, DDC, MCAT, LYN, STX4, PPIF, DECR1, PNPT1, MPIG6B, ANXA7, IVD, RAC2, PLEKHF2, GFRA2, DDX19B, DFFA, ARSA, BACE1, CD40LG, IL16, NEFL, TARDBP, TUBA1A, SUMF1, LAP3, OGDHL, CHMP2B, EPN1, H1-10, HS3ST4, STAT6, RGS18, CYLD, ESYT2, EBI3, IL12A, NAA10, SH3PXD2A, HNRNPAO, PLCD4, CSNK1G2, ZC3H12C, H2AC20, RIT1, CCDC80, HSP90AA1, ARID3C, TWSG1, H2AX, IST1, AASDH, ZNF354A, AP1B1, HSP90AB1, CIAO1, RAB6A, PPP1CA, GPI, SCARF2, CSTF2, TFRC, ADA2, TRIM25, OIP5, PTGR1, CXCL13, COL14A1, PDGFRA, SMOC1, CD58, AAGAB, RNF5, NEFL, OMD, CALB2, CD276, NCAM1, EDIL3, TGAV, QPCT, DDC, LSM1, PRL, GPT, GRP, IGHG4, PRR15, GLRX3, KCTD10, CCN5, ARPC5, CMPK1, SARS1, PPM1G, LTBP4, IVD, RAP1B, CORIN, LRRTM2, FKBP1B, AASDH, TPM4, HSPD1, IRF5, PTGR1, COPS5, PYGB, PYGL, PHGDH, MICU1, ARHGAP25, CCT8, KYAT1, LRRC45, GP1BB, SAMD9L, PTPN11, CRIM1, SMAD1, CXCL5, LATS1, NFATC1, METAP1D, DFFA, CST5, CCL5, NAP1L4, LAT2, TDRD3, ALDH5A1, CCER2, MTDH, FKBPL, PPP1CC, SNAP23, CEP85, RAB37, CSPG4, OGA, MAGED1, VAMP8, PRDX5, GMPR2, MPP7, ZNF649, RIMOC1, AAGAB, BACE1, LEG1, OXT, UBL5, GPNMB, KVAT1, GFAP, ANOS1, RPL30, TMX2, EIF1AX, SMYD3, VWF, SCUBE1, TNFRSFUb, NEFH, ARSA, BDNF, CD300LG, CPXM1, CXCL3, EPHA2, HARS1, MANF, NCAN, PDGFRA, ROR1, SERPINE1, SLITRK2 and SUSD5.

30. The assay device of claim 29 wherein the device comprises at least two separate detectably labelled agents specifically binds to a biomarker protein selected from the group consisting of CNTFR, CXCL12, ITGB3JTGAV, ADPGK, GFRA2, STC2, FBLN2, VCAM1, MAG, APAF1, DKK3, PRG4, LILRA4, BNIPL, SEZ6L, ESM1, STAR, TMX4, BCAN, SLITRK1, SOD3, C1S, SERPINA11, DPEP1, ITGA11, CXADR, FGFBP3, LTBP4, OIP5, FANCD2, EPHA2, EPHB6, GFAP, IL15RA, CCER2, ITGAV, APLP1, CD276, CDH3, CRIM1, PDGFRA, ROR1, SUSD5, CD300LG, CSPG4, QPCT, CALB2,Docket No. 2960447-000048-W01Filed: January 9, 2026103IL7, CD209, MTAP, PRL, DDC, MCAT, LYN, STX4, PPIF, DECR1, PNPT1, MPIG6B, ANXA7, IVD, RAC2, PLEKHF2, GFRA2, DDX19B, DFFA, ARSA, BACE1, CD40LG, IL16, NEFL, TARDBP, TUBA1A, SUMF1, LAP3, OGDHL, CHMP2B, EPN1, H1-10, HS3ST4, STAT6, RGS18, CYLD, ESYT2, EBI3, IL12A, NAA10, SH3PXD2A, HNRNPAO, PLCD4, CSNK1G2, ZC3H12C, H2AC20, RIT1, CCDC80, HSP90AA1, ARID3C, TWSG1, H2AX, IST1, AASDH, ZNF354A, AP1B1, HSP90AB1, CIAO1, RAB6A, PPP1CA, GPI, SCARF2, CSTF2, TFRC, ADA2, TRIM25, OIP5, PTGR1, CXCL13, COL14A1, PDGFRA, SMOC1, CD58, AAGAB, RNF5, NEFL, OMD, CALB2, CD276, NCAM1, EDIL3, TGAV, QPCT, DDC, LSM1, PRL, GPT, GRP, IGHG4, PRR15, GLRX3, KCTD10, CCN5, ARPC5, CMPK1, SARS1, PPM1G, LTBP4, IVD, RAP1B, CORIN, LRRTM2, FKBP1B, AASDH, TPM4, HSPD1, IRF5, PTGR1, COPS5, PYGB, PYGL, PHGDH, MICU1, ARHGAP25, CCT8, KYAT1, LRRC45, GP1BB, SAMD9L, PTPN11, CRIM1, SMAD1, CXCL5, LATS1, NFATC1, METAP1D, DFFA, CST5, CCL5, NAP1L4, LAT2, TDRD3, ALDH5A1, CCER2, MTDH, FKBPL, PPP1CC, SNAP23, CEP85, RAB37, CSPG4, OGA, MAGED1, VAMP8, PRDX5, GMPR2, MPP7, ZNF649, RIMOC1, AAGAB, BACE1, LEG1, OXT, UBL5, GPNMB, KVAT1, GFAP, ANOS1, RPL30, TMX2, EIF1AX, SMYD3, VWF, SCUBE1, TNFRSFUb, NEFH, ARSA, BDNF, CD300LG, CPXM1, CXCL3, EPHA2, HARS1, MANF, NCAN, PDGFRA, ROR1, SERPINE1, SLITRK2 and SUSD5.

31. A method of detecting Parkinson Disease in a subject, the method comprising: measuring the level of one or more biomarker in a plasma sample from the subject, wherein the one or more biomarker is selected from the group consisting of: CNTFR, CXCL12, ITGB3JTGAV, ADPGK, GFRA2, STC2, FBLN2, VCAM1, MAG, APAF1, DKK3, PRG4, LILRA4, BNIPL, SEZ6L, ESM1, STAR, TMX4, BCAN, SLITRK1, SOD3, C1S, SERPINA11, DPEP1, ITGA11, CD209, MTAP, MCAT, LYN, STX4, PPIF, DECR1, PNPT1, MPIG6B, TUBA1A, SUMF1, LAP3, OGDHL, CHMP2B, NEFL, OMD, CALB2, CD276, NCAM1, EDIL3, TGAV, QPCT, DDC, LSM1, PRL, GPT, GRP, IGHG4, PRR15, GLRX3, KCTD10, CCN5, ARPC5, CMPK1, SARS1, PPM1G, LTBP4, IVD, RAP1B, CORIN, LRRTM2, FKBP1B, AASDH, TPM4, HSPD1, IRF5, PTGR1, COPS5, PYGB, PYGL, PHGDH, MICU1, ARHGAP25, CCT8, KYAT1, LRRC45, GP1BB, SAMD9L, PTPN11, CRIM1, SMAD1, CXCL5, LATS1, NFATC1, METAP1D, DFFA, CST5, CCL5, NAP1L4, LAT2, TDRD3, ALDH5A1, CCER2, MTDH, FKBPL, PPP1CC, SNAP23, CEP85, RAB37, CSPG4, OGA, MAGED1, VAMP8, PRDX5, GMPR2, MPP7, ZNF649, RIMOC1, AAGAB, BACE1, LEG1, OXT, UBL5, GPNMB, KVAT1, GFAP,Docket No. 2960447-000048-W01Filed: January 9, 2026104ANOS1, RPL30, TMX2, EIF1AX, SMYD3, VWF, SCUBE1 , TNFRSF11b, NEFH, ARSA, BDNF, CD300LG, CPXM1, CXCL3, EPHA2, HARS1, MANF, NCAN, PDGFRA, ROR1, SERPINE1, SLITRK2 and SLISD5 by contacting the plasma sample with an appropriate antibody specific to the biomarker of interest and detecting binding between the biomarker and the corresponding antibody.

Images