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CSF protein panels reflecting multiple pathophysiological mechanisms for early and specific diagnosis of Alzheimer’s disease
Author(s) -
Campo Marta Del,
Peeters Carel F.W.,
Johnson Erik C.B.,
Vermunt Lisa,
HokAHin Yanaika S.,
van Nee Mirrelijn,
ChenPlotkin Alice,
Hu William T.,
Lah James J.,
Seyfried Nicholas T.,
Herradon Gonzalo,
Meeter Lieke H.H.,
van Swieten John C.,
Levey Allan I.,
Lemstra Afina W.,
Pijnenburg Yolande A.L.,
Visser Pieter Jelle,
Tijms Betty M.,
van der Flier Wiesje M,
Teunissen Charlotte E.
Publication year - 2021
Publication title -
alzheimer's and dementia
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.713
H-Index - 118
eISSN - 1552-5279
pISSN - 1552-5260
DOI - 10.1002/alz.053710
Subject(s) - biomarker , dementia , cerebrospinal fluid , disease , alzheimer's disease , pathological , pathophysiology , medicine , proteomics , cognitive impairment , neuroscience , bioinformatics , biology , biochemistry , gene
Background The development of disease‐modifying therapies against Alzheimer’s disease (AD) requires biomarker panels that reflect the diverse pathological pathways specifically involved in AD. Here we aimed to identify and validate panels of cerebrospinal fluid (CSF) proteins covering different molecular pathways for early and specific diagnosis of AD. Method We measured 665 proteins in 797 CSF samples from patients with mild cognitive impairment with abnormal amyloid (MCI(Aβ+): n=50), AD‐dementia (n=230), non‐AD dementias (n=322; 123 DLB and 199 FTD) and cognitively‐unimpaired controls (n=195; classical AD CSF biomarkers negative) using proximity ligation‐based multiplex immunoassays. Nested and penalized linear modeling were used to identify protein differences (q<0,05) and translatable classification signatures. Result We detected highly dysregulated CSF proteins in MCI(Aβ+) or AD compared to controls (112 and 288 proteins respectively, lowest q:1 ‐15 and 1 ‐23 ), as well as between AD and non‐AD dementias (469 proteins; lowest q:1 ‐29 ). We confirmed previous findings (e.g., DDAH1, ENO2, PARK7), but we also identified novel proteins especially associated to the prodromal and/or dementia AD stages (e.g., ABL1, SCD4, ENTPD5). Proteins dysregulated in MCI(Aβ+) were primarily related to oxidative stress and energy metabolism, while those specifically dysregulated in later stages of AD dementia were related to cell remodeling, vascular function and immune system. Using penalised generalised linear modeling we identified the minimal number of markers with maximal power to discriminate clinical groups: for MCI(Aβ+) vs. controls: 10‐CSF proteins, AUC:0.99 (95%CI:0.97‐1); for AD vs. controls: 8‐CSF proteins, AUC:0.95 (95%CI:0.92‐0.99) and for AD vs. non‐AD dementias: 9‐CSF proteins, AUC:0.87 (95%CI:0.81‐0.93). The CSF panel discriminating AD from cognitively unimpaired controls was validated in an independent external cohort (n=62, AUC:0.94). Only 3 proteins overlapped across panels, suggesting that the panels with the strongest discriminative power are specific for disease stage and clinical group. Conclusion This unprecedented large and protein‐rich CSF study indicates that CSF proteome follows a highly dynamic trajectory with distinct CSF profiles associated to different biological processes along the AD continuum. We unveil optimal CSF biomarker panels reflecting the specific multifactorial nature of AD, which can be now translated into customised assays for widespread validation and potential use in heterogeneous diagnostic settings or clinical trials.