Parkin characteristics and blood biomarkers of Parkinson's disease in WPBLC study.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:40078640", "kind": "science-record", "revision": 3, "content_hash": "sha256:2ed86cdd7a796892d73b8a0d617735515ee7726286b972deff7b793d45046f77", "created_at": "2026-11-01T20:30:17Z", "updated_at": "2026-11-01T22:13:45Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Parkin characteristics and blood biomarkers of Parkinson's disease in WPBLC study.", "doi": "10.3389/fnagi.2025.1511272", "pmid": "40078640", "authors": ["Haijun He", "Xi Xiong", "Yi Zheng", "Jialong Hou", "Tao Jiang", "Weiwei Quan", "Jiani Huang", "Jiaxue Xu", "Keke Chen", "Jingjing Qian", "Jinlai Cai", "Yao Lu", "Mengjia Lian", "Chenglong Xie", "Ji Luo"], "journal": "Frontiers in aging neuroscience", "year": 2025, "abstract": "Background: The exact mechanisms of PD are unclear, but Parkin-mediated mitophagy dysfunction is believed to play a key role. We investigated whether blood levels of Parkin and other biomarkers are linked to the risk of developing PD.\nMethods: Baseline blood measures of Parkin and other biomarkers, including Homocysteine, carcinoembryonic antigen, Urea, total proteins, total cholesterol, creatine kinase, and albumin, were collected from 197 clinically diagnosed Parkinson's disease participants and 107 age-matched healthy controls in Wenzhou Parkinson's Biomarkers and Living Characteristics study. We conducted bioinformatics analysis using three datasets from the GEO database: GSE90514 (Cohort 1: PD = 4, HC = 4), GSE7621 (Cohort 2: PD = 16, HC = 9), and GSE205450 (Cohort 3: PD = 69, HC = 81).\nResults: Using a bioinformatic approach, we identified dysregulated biological processes in PD patients with PRKN mutations. Compared to controls, significant abnormalities were observed in blood levels of Parkin, Hcy, total proteins, urea, albumin, and CEA in PD patients. A model incorporating Parkin, Hcy, total proteins, and urea effectively distinguished PD from healthy controls, achieving a higher accuracy (AUC 0.841) than other biomarker combinations. Gene set enrichment analysis suggested that pathways such as PINK1-Parkin-mediated mitophagy, urea cycle, cysteine degradation, and riboflavin metabolism may be involved in PRKN mutation. Additionally, the link between Parkin and PD was partially mediated by CEA and albumin, not by Hcy, total proteins, or urea.\nConclusion: Our findings indicate that blood Parkin levels may be a minimally invasive biomarker for PD diagnosis. The model, which included Parkin, Hcy, total proteins, and urea, effectively distinguished PD from HC with greater accuracy.", "claims": [{"id": "claim-method", "statement": "The Random Forest (RF) classifier and Least Absolute Shrinkage And Selection Operator (LASSO) regression were employed to identify effective blood biomarkers for distinguishing PD from HC participants.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40078640", "locator": "sections[6] \"Statistical analyses\" paragraph 2", "quote": "The Random Forest (RF) classifier and Least Absolute Shrinkage And Selection Operator (LASSO) regression were employed to identify effective blood biomarkers for distinguishing PD from HC participants."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The data that support the findings of this study are available from the corresponding author upon reasonable request. Transcriptomic RNAseq datasets analysed in this study are available on NCBI GEO (Accession number: GSE90514, GSE7621 and GSE205450).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40078640", "locator": "sections[25] \"Data availability statement\" paragraph 2", "quote": "The data that support the findings of this study are available from the corresponding author upon reasonable request. Transcriptomic RNAseq datasets analysed in this study are available on NCBI GEO (Accession number: GSE90514, GSE7621 and GSE205450)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The model, which included Parkin, Hcy, total proteins, and urea, effectively distinguished PD from HC with greater accuracy.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:40078640", "locator": "sections[0] \"Abstract\" paragraph 4", "quote": "The model, which included Parkin, Hcy, total proteins, and urea, effectively distinguished PD from HC with greater accuracy."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "The model, which included Parkin, Hcy, total proteins, and urea, effectively distinguished PD from HC with greater accuracy.", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:40078640"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE205450"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "blocked", "blocked_on": "method:machine_learning_random_forest", "description": "Apply the paper's statistical method to the resolved series and compare the result with the published target.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE205450"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": ["step:run_analysis blocked on method:machine_learning_random_forest"]}, "data": {"state": "partial", "assets": [{"id": "source:pmid:40078640", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fnagi.2025.1511272", "sha256": "sha256:18ad336724b05de958ca63ab44007ea7d1302c2c748929ca5c88250334fb0765", "bytes": 48115, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fnagi.2025.1511272", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC11897490", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE205450", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE205450", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE205450", "version": null, "note": "The data that support the findings of this study are available from the corresponding author upon reasonable request. 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