{"doi":"10.21203/rs.3.rs-31269/v2","title":"Cerebrospinal fluid α-synuclein predicts neurodegeneration and clinical progression in non-demented elders","abstract":"Abstract Background: Accumulating reports suggest that α-synuclein is involved in Alzheimer disease (AD) pathogenesis. Cerebrospinal fluid (CSF) α-synuclein could be a potential biomarker of AD. We sought to test whether CSF α-synuclein is associated with other AD biomarkers and could predict neurodegeneration and clinical progression in non-demented elders. Methods: Associations were investigated between CSF α-synuclein and other AD biomarkers at baseline in non-demented Chinese elders. The predictive values of CSF α-synuclein in longitudinal neuroimaging change and conversion risk of non-demented elders were assessed using linear mixed effects models and multivariate Cox proportional hazard models, respectively, in Alzheimer's disease Neuroimaging Initiative (ADNI) database. Results: We detected that CSF α-synuclein levels correlated with AD-specific biomarkers CSF total tau and phosphorylated tau levels in 651 Chinese Han participants (training set). These positive correlations were replicated in ADNI database (validation set). Using a longitudinal cohort from ADNI, CSF α-synuclein concentrations increased with disease severity. CSF α-synuclein had high diagnostic accuracy for AD based on the “ATN” system (A+T+) vs controls (A-T-) (area under the receiver operating characteristic curve, 0.84). Moreover, CSF α-synuclein predicted longitudinal hippocampus atrophy and conversion from MCI to AD dementia. Conclusions: CSF α-synuclein is associated with CSF tau levels and could predict neurodegeneration and clinical progression in non-demented elders. This finding indicates CSF α-synuclein is a potentially useful, early biomarker for AD.","journal":"Research Square","year":2020,"id":133050,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9452,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2020-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":428229,"name":"Yanlin Bi","orcid":"0000-0001-8688-9733","position":1,"is_corresponding":false},{"id":291630,"name":"Xue‐Ning Shen","orcid":"0009-0001-2658-3799","position":2,"is_corresponding":false},{"id":327870,"name":"Hui-Fu Wang","orcid":null,"position":3,"is_corresponding":false},{"id":588950,"name":"Wei Xu","orcid":"0000-0002-3505-1428","position":4,"is_corresponding":false},{"id":589206,"name":"Chen-Chen Tan","orcid":null,"position":5,"is_corresponding":false},{"id":291632,"name":"Qiang Dong","orcid":"0000-0002-3874-0130","position":6,"is_corresponding":false},{"id":428230,"name":"Yan‐Jiang Wang","orcid":"0000-0002-6227-6112","position":7,"is_corresponding":false},{"id":291633,"name":"Lan Tan","orcid":"0000-0002-8759-7588","position":8,"is_corresponding":false},{"id":284702,"name":"Jin‐Tai Yu","orcid":"0000-0002-7686-0547","position":9,"is_corresponding":false},{"id":326924,"name":"Jieqiong Li","orcid":"0000-0001-8496-8614","position":0,"is_corresponding":true}],"reference_count":9,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-07-18T23:16:14.071209Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}