4.6 Article

CSF complement 3 and factor H are staging biomarkers in Alzheimer's disease

期刊

出版社

BMC
DOI: 10.1186/s40478-016-0277-8

关键词

Amyloid beta; Diagnosis; C3; FH; Machine learning; Replication; Tau

资金

  1. CIHR Funding Source: Medline
  2. NIA NIH HHS [K23 AG016976, P50 AG025688, U01 AG042856, U01 AG024904, K23 AG042856, U01 AG016976] Funding Source: Medline
  3. NINDS NIH HHS [P30 NS055077, K08 NS087121] Funding Source: Medline

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Introduction: CSF levels of established Alzheimer's disease (AD) biomarkers remain stable despite disease progression, and non-amyloid non-tau biomarkers have the potential of informing disease stage and progression. We previously identified complement 3 (C3) to be decreased in AD dementia, but this change was not found by others in earlier AD stages. We hypothesized that levels of C3 and associated factor H (FH) can potentially distinguish between mild cognitive impairment (MCI) and dementia stages of AD, but we also found their levels to be influenced by age and disease status. Results: We developed a biochemical/bioinformatics pipeline to optimize the handling of complex interactions between variables in validating biochemical markers of disease. We used data from the Alzheimer's Disease Neuro-imaging Initiative (ADNI, n = 230) to build parallel machine learning models, and objectively tested the models in a test cohort (n = 73) of MCI and mild AD patients independently recruited from Emory University. Whereas models incorporating age, gender, APOE epsilon 4 status, and CSF amyloid and tau levels failed to reliably distinguish between MCI and mild AD in ADNI, introduction of CSF C3 and FH levels reproducibly improved the distinction between the two AD stages in ADNI (p < 0.05) and the Emory cohort (p = 0.014). Within each AD stage, the final model also distinguished between fast vs. slower decliners (p < 0.001 for MCI, p = 0.007 for mild AD), with lower C3 and FH levels associated with more advanced disease and faster progression. Conclusions: We propose that CSF C3 and FH alterations may reflect stage-associated biomarker changes in AD, and can complement clinician diagnosis in diagnosing and staging AD using the publically available ADNI database as reference.

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