CSF proteome profiling across the Alzheimer’s disease spectrum reflects the multifactorial nature of the disease and identifies specific biomarker panels

Milan, Marta Del Campo; Peeters, Carel F.W.; Johnson, Erik C.B.; Vermunt, Lisa; Hok‐A‐Hin, Yanaika S.; Nee, Mirrelijn M. van; Chen‐Plotkin, Alice; Irwin, David J.; Hu, William T.; Lah, James J.; Seyfried, Nicholas T.; Dammer, Eric B.; Herradón, Gonzalo; Meeter, Lieke H.H.; Swieten, John van; Alcolea, Daniel; Lleó, Alberto; Levey, Aĺlan I.; Lemstra, Afina W.; Pijnenburg, Yolande A.L.; Visser, Pieter Jelle; Tijms, Betty M.; Flier, Wiesje M. van der; Teunissen, Charlotte E.

doi:10.1038/s43587-022-00300-1

Cited by 34 publications

(95 citation statements)

References 75 publications

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“…The proteomics approach gives us insights into proteins that would never have been considered or even found before. While the number of proteins is still limited by the availability of antibodies, it is due to the antibody-based technology that translation to a panel or single assays could be feasible 13 . However, the validation of our findings, especially across different proteomics methods is challenging, since the measurements of protein abundance depend on the nature of the techniques 35 .…”

Section: Discussionmentioning

confidence: 99%

“…All CSF samples have been analyzed by Olink Proteomics ("Olink Proteomics," 2021) and harmonised between batches to account for possible batch effects as described in 13 . Briefly, 979 proteins were measured with 11 Olink Target 96 multiplex panels based on the Proximity Extension Assay (PEA) technology.…”

Section: Protein Measurementsmentioning

confidence: 99%

“…However, biofluid biomarkers for the prediction of the individual prognosis within the dementia stage are still lacking. With novel discovery approaches and ultrasensitive assays, it is now possible to measure low-abundant protein markers in biofluids [9][10][11][12][13] . Such robust analytical techniques, combined with interpretable machine learning models provide the opportunity to develop biomarker panels to address the unmet need for good prognostic information for patients with dementia.…”

Section: Introductionmentioning

confidence: 99%

“…Surrounding the brain, CSF reflects its biological state, therefore CSF measurements can help to gain new insights into the progression of dementia. The CSF proteome is a promising anchor for biomarker discovery and it is used to uncover disease-specific pathophysiology [13][14][15][16][17][18] . Proteomics studies often show subtle effects per marker, which combined contribute to a clear profile 9,19,20 .…”

Section: Introductionmentioning

confidence: 99%

“…Reported values for an individual protein abundance and its alterations largely depend on the specific measurement technique used, which is a challenge in biomarker development 12,13,23 . While studies using traditional mass spectrometry techniques offer insights into the disease biology, it is challenging to detect low-abundant proteins and proceed to translation to biomarkers that can be used in clinical practice and treatment trials 12,24 .…”

Section: Introductionmentioning

confidence: 99%

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Discovery of novel CSF biomarkers to predict progression in dementia using machine learning

Gogishvili

Vromen²,

Koppes

et al. 2022

Preprint

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Providing an accurate prognosis for individual dementia patients remains a challenge since they greatly differ in rates of cognitive decline. In this study, we used machine learning techniques to identify cerebrospinal fluid (CSF) biomarkers that predict the rate of cognitive decline. First, longitudinal follow-up data of 210 dementia patients were used to create fast and slow progression groups. Secondly, we trained random forest classifiers on CSF proteomic profiles and obtained a well-performing prediction model for the progression group (ROC-AUC = 0.82). As a third step, Shapley values and Gini feature importance measures were used to interpret the model performance and identify top biomarker candidates. Lastly, we explored the progression biomarker potential for each of the 20 top candidates in internal sensitivity analyses. TNFRSF4 and TGF β-1 emerged as the top markers, being lower in fast-progressing patients compared to slow-progressing patients. Proteins of which a low concentration was associated with fast progression were enriched for cell signalling and immune response pathways, which could indicate a lack of a protective response in these individuals. None of our top markers stood out as strong individual predictors of subsequent cognitive decline. This could be explained by small effect sizes per protein and biological heterogeneity among dementia patients. Taken together, this study presents a novel progression biomarker identification framework and protein leads for personalised prediction of cognitive decline in dementia.

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Section: Discussionmentioning

confidence: 99%

Section: Protein Measurementsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Discovery of novel CSF biomarkers to predict progression in dementia using machine learning

Gogishvili

Vromen²,

Koppes

et al. 2022

Preprint

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Plasma Biomarker Strategy for Selecting Patients With Alzheimer Disease for Antiamyloid Immunotherapies

Mattsson-Carlgren,

Collij,

Stomrud

et al. 2024

JAMA Neurol

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ImportanceAntiamyloid immunotherapies against Alzheimer disease (AD) are emerging. Scalable, cost-effective tools will be needed to identify amyloid β (Aβ)–positive patients without an advanced stage of tau pathology who are most likely to benefit from these therapies. Blood-based biomarkers might reduce the need to use cerebrospinal fluid (CSF) or positron emission tomography (PET) for this.ObjectiveTo evaluate plasma biomarkers for identifying Aβ positivity and stage of tau accumulation.Design, Setting, and ParticipantsThe cohort study (BioFINDER-2) was a prospective memory-clinic and population-based study. Participants with cognitive concerns were recruited from 2017 to 2022 and divided into a training set (80% of the data) and test set (20%).ExposureBaseline values for plasma phosphorylated tau 181 (p-tau181), p-tau217, p-tau231, N-terminal tau, glial fibrillary acidic protein, and neurofilament light chain.Main Outcomes and MeasuresPerformance to classify participants by Aβ status (defined by Aβ-PET or CSF Aβ42/40) and tau status (tau PET). Number of hypothetically saved PET scans in a plasma biomarker–guided workflow.ResultsOf a total 912 participants, there were 499 males (54.7%) and 413 females (45.3%), and the mean (SD) age was 71.1 (8.49) years. Among the biomarkers, plasma p-tau217 was most strongly associated with Aβ positivity (test-set area under the receiver operating characteristic curve [AUC] = 0.94; 95% CI, 0.90-0.97). A 2–cut-point procedure was evaluated, where only participants with ambiguous plasma p-tau217 values (17.1% of the participants in the test set) underwent CSF or PET to assign definitive Aβ status. This procedure had an overall sensitivity of 0.94 (95% CI, 0.90-0.98) and a specificity of 0.86 (95% CI, 0.77-0.95). Next, plasma biomarkers were used to differentiate low-intermediate vs high tau-PET load among Aβ-positive participants. Plasma p-tau217 again performed best, with the test AUC = 0.92 (95% CI, 0.86-0.97), without significant improvement when adding any of the other plasma biomarkers. At a false-negative rate less than 10%, the use of plasma p-tau217 could avoid 56.9% of tau-PET scans needed to identify high tau PET among Aβ-positive participants. The results were validated in an independent cohort (n = 118).Conclusions and RelevanceThis study found that algorithms using plasma p-tau217 can accurately identify most Aβ-positive individuals, including those likely to have a high tau load who would require confirmatory tau-PET imaging. Plasma p-tau217 measurements may substantially reduce the number of invasive and costly confirmatory tests required to identify individuals who would likely benefit from antiamyloid therapies.

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Protein disulfide isomerases as CSF biomarkers for the neuronal response to tau pathology

Wolzak

Vermunt

Campo

et al. 2023

Alzheimer's & Dementia

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INTRODUCTIONCerebrospinal fluid (CSF) biomarkers for specific cellular disease processes are lacking for tauopathies. In this translational study we aimed to identify CSF biomarkers reflecting early tau pathology‐associated unfolded protein response (UPR) activation.METHODSWe employed mass spectrometry proteomics and targeted immunoanalysis in a combination of biomarker discovery in primary mouse neurons in vitro and validation in patient CSF from two independent large multicentre cohorts (EMIF‐AD MBD, n = 310; PRIDE, n = 771).RESULTSFirst, we identify members of the protein disulfide isomerase (PDI) family in the neuronal UPR‐activated secretome and validate secretion upon tau aggregation in vitro. Next, we demonstrate that PDIA1 and PDIA3 levels correlate with total‐ and phosphorylated‐tau levels in CSF. PDIA1 levels are increased in CSF from AD patients compared to controls and patients with tau‐unrelated frontotemporal and Lewy body dementia (LBD).Highlights Neuronal unfolded protein response (UPR) activation induces the secretion of protein disulfide isomerases (PDIs) in vitro. PDIA1 is secreted upon tau aggregation in neurons in vitro. PDIA1 and PDIA3 levels correlate with total and phosphorylated tau levels in CSF. PDIA1 levels are increased in CSF from Alzheimer's disease (AD) patients compared to controls. PDIA1 levels are not increased in CSF from tau‐unrelated frontotemporal dementia (FTD) and Lewy body dementia (LBD) patients.

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CSF proteome profiling across the Alzheimer’s disease spectrum reflects the multifactorial nature of the disease and identifies specific biomarker panels

Cited by 34 publications

References 75 publications

Discovery of novel CSF biomarkers to predict progression in dementia using machine learning

Discovery of novel CSF biomarkers to predict progression in dementia using machine learning

Plasma Biomarker Strategy for Selecting Patients With Alzheimer Disease for Antiamyloid Immunotherapies

Protein disulfide isomerases as CSF biomarkers for the neuronal response to tau pathology

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