Blood-based protein biomarkers in definite frontotemporal dementia: a case-control study

doi:10.21203/rs.3.rs-1534214/v1

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Blood-based protein biomarkers in definite frontotemporal dementia: a case-control study

https://doi.org/10.21203/rs.3.rs-1534214/v1

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Background: Frontotemporal dementia (FTD) is caused by frontotemporal lobar degeneration (FTLD), characterized mainly by inclusions of Tau (FTLD-Tau) or TAR DNA binding43 (FTLD-TDP) proteins. Plasma biomarkers are strongly needed for specific diagnosis and potential treatment monitoring of FTD. We aimed to identify specific FTD plasma biomarker profiles discriminating FTD from AD and controls, and between FTD pathological subtypes. In addition, we compared plasma results with results in post-mortem frontal cortex of FTD cases to understand the underlying process.

Methods: Plasma proteins (n=1303) from pathologically and/or genetically confirmed FTD patients (n=56; FTLD-Tau n=16; age= 58.2±6.2; 44% female, FTLD-TDP n= 40; age= 59.8±7.9; 45% female), AD patients (n=57; age= 65.5±8.0; 39% female), and non-demented controls (n=148; 61.3±7.9; 41% female) were measured using an aptamer-based proteomic technology (SomaScan). In addition, tissue proteins of post-mortem frontal brain cortex of FTD (n=10; FTLD-Tau n=5; age= 56.2±6.9, 60% female, and FTLD-TDP n= 5; age= 64.0±7.7, 60% female) and non-demented controls (n=5; age= 61.3±8.1; 75% female) were measured. Differentially regulated plasma and tissue proteins were identified by global testing adjusting for demographic variables and multiple testing. Logistic lasso regression was used to identify plasma protein panels discriminating FTD from non-demented controls and AD, or FTLD-Tau from FTLD-TDP. Performance of the discriminatory plasma protein panels was based on predictions obtained from bootstrapping with 1000 resampled analysis.

Results: Overall plasma protein expression profiles differed between FTD, AD and controls (6 proteins; p=0.005). The overall tissue protein expression profile differed between FTD and controls (7-proteins; p=0.003). There was no difference in overall plasma or tissue expression profile between FTD subtypes. Regression analysis revealed a panel of 12-plasma proteins discriminating FTD from AD with high accuracy (AUC: 0.99). No plasma protein panels discriminating FTD from controls or FTD pathological subtypes were identified.

Conclusions: We identified a promising plasma protein panel as a minimally-invasive tool to aid in the differential diagnosis of FTD from AD, which were mainly associated to AD pathophysiology. The lack of plasma profiles specifically associated to FTD or its pathological subtypes might be explained by FTD heterogeneity, calling for FTD studies using large and well-characterize cohorts.

plasma biomarkers

frontotemporal dementia

FTD

Alzheimer’s disease

Somascan

Frontotemporal Dementia (FTD) is one of the most prevalent forms of young onset dementia (< 65 years) (1). The underlying pathological process is Frontotemporal Lobar Degeneration (FTLD), which can be mainly classified into two different pathological subtypes based on the typical protein aggregates present in brain tissue: the microtubule associated protein Tau (FTLD-Tau) or TAR DNA-binding protein 43 (FTLD-TDP) (2,3). Each pathological subtype will likely require distinct targeted drugs, and therefore, it is necessary to discriminate both subtypes in living patients. The poor correlation between the clinical presentation and underlying pathology (4) makes it hard to discriminate these pathological subtypes in sporadic FTD. However, in familial FTD cases (i.e. approximately 10–25% of cases (5)), the underlying genetic mutation is directly linked to these specific Tau or TDP pathologies. Genetic mutations in the microtubule-associated protein tau (MAPT) lead to FTLD-Tau pathology; while mutations in the progranulin (GRN), or chromosome 9 open reading frame 72 (C9ORF72) genes, lead to FTLD-TDP pathology (6).

Currently, there is no biomarker for the diagnosis and potential treatment response monitoring of FTD and its pathological subtypes. In addition, it is of particular importance to differentiate FTD from other dementia disorders, such as Alzheimer’s Disease (AD), or non-dementia disorders such as primary psychiatric disorders (PPD). Both PPD and AD can sometimes show similar clinical features as FTD, including language and executive function impairments (7,8) or behavioral changes [3, 4]. Previous studies have shown promising cerebrospinal fluid (CSF) or blood biomarker alterations in FTD compared to controls, in particular neurofilament light (NfL) levels or the CSF p/tTau ratio for the discrimination of FTD pathological subtypes (9–12). However, changes in these markers were either not specific for FTD as they were also changed in other types of dementia (9,10), or did not reach sufficiently high diagnostic accuracy (11,12). This warrants the identification of novel biomarker candidates for diagnosis and treatment monitoring of FTD and its pathological subtypes.

Most FTD biomarker studies performed to date have used CSF as the main source for biomarker discovery, due to its close proximity to the brain (9). However, as a lumbar puncture is often perceived as invasive, biomarkers in a more easily accessible body fluid such as blood is essential. The new high-throughput multiplex aptamer-based proteomic technology (SomaScan) (13–15), able to measure > 1000 proteins in a small volume of plasma, allows for the discovery of novel blood-based biomarkers, and has been used to identify novel candidate biomarkers for AD pathology (16–18). The multiplex feature of the aptamer-based proteomics technology is of importance as it is expected that a specific combination of proteins rather than a single biomarker will probably provide a more accurate profile of each specific dementia type, due to the complexity and heterogeneity of dementia pathologies (10).

In this study, we aimed to identify novel plasma protein profiles for the specific discrimination of FTD from AD and controls, as well as FTLD pathological subtypes using this innovative aptamer-based proteomic approach. To understand the possible relation of the different markers with the central nervous system, the plasma proteome differences were compared to those observed in post-mortem frontal cortex of FTD cases and controls.

Samples

Blood plasma

Human plasma samples from FTD subjects (n = 56) were obtained from two specialized memory centers in the Netherlands: Alzheimer Center Amsterdam (n = 96), and Erasmus Medical Center Rotterdam (n = 51) (19–21). All 56 FTD subjects had a definite diagnosis of FTD based on known FTD-causing mutations (i.e. GRN, MAPT or C9orf72) and/or autopsy-confirmation. Underlying FTLD-TDP pathology was present in 40 subjects (18 autopsy confirmed cases, 13 GRN [of whom 1 was autopsy confirmed], 9 C9orf72 [of whom 1 was autopsy confirmed]), and FTLD-Tau pathology in 16 subjects (3 autopsy-confirmed cases, 13 MAPT [of whom 2 were also autopsy confirmed]). AD plasma samples (n = 57) were selected from the Parelsnoer Initiative biobank, the neurodegeneration Parel, which collected samples from the eight Academic medical centers in the Netherlands, including Alzheimer Center Amsterdam and Erasmus Medical Center Rotterdam. AD subjects were selected based on clinical diagnosis using NINCDS-ADRDA criteria (22,23), with either CSF biomarker results concordant with AD or MTA score > = 2 in subjects aged < 75, and MTA score > = 3 in subjects aged > 75. Control plasma samples were (n = 148) were obtained from Alzheimer Center Amsterdam (n = 69), Erasmus Medical Center Rotterdam (n = 22), and the Parelsnoer Initiative biobank (n = 57). Controls were selected based on MMSE > 26, normal CSF biomarkers (available for all the Amsterdam and Rotterdam samples, and for 26% of the Parelsnoer samples) and if no CSF biomarker data were available, stable disease course over one year of follow-up. AD and control cases were not autopsy confirmed. Demographic information. Distribution of the samples per center is presented in supplementary Table 1.

Of note, patients and samples within the Parelsnoer initiative followed standardized clinical and biobanking protocols at time of diagnostic work-up (22), thereby minimizing potential center and biobanking effects. All samples were collected through venipuncture using Ethylenediaminetetraacetic acid (EDTA) collection tubes. Blood collection was followed by centrifugation at 1,800g. Plasma supernatant was collected, aliquoted and stored in 0.5ml polypropylene tubes at -80°C within 4 hours in each local biobank.

Post-mortem brain tissue

Post-mortem brain material was obtained from the Netherlands Brain Bank (Amsterdam, the Netherlands). We selected snap frozen medial frontal gyrus from FTD cases (FTLD-Tau n = 5; FTLD-TDP n = 5) and non-demented controls (n = 4). Four FTLD-TDP cases were familial (GRN n = 2, C9orf72 n = 2) and one was a sporadic case. Of the FTLD-Tau cases, all were familial and had an underlying MAPT mutation. Neuropathological evaluation and processing were performed as previously described (24). The distribution and the density of tau aggregates and TDP-43 inclusions were evaluated according to the criteria described by Lee, Cairns and MacKenzie (25–27). Post-mortem frontal cortex was homogenized using Tissue Protein Extraction Reagent (T-Per, 0.1g/ml, Thermo Scientific, Waltham, USA) containing EDTA-free Protease Inhibitor Cocktail (1:25, Roche, Basel, Germany), and left for 15 min at 4°C. Homogenates were subsequently centrifuged at 10,000g for 15 min at 4°C. Protein concentration was measured using Bio-Rad Protein Assay (Bio-Rad, Hercules, USA) and bovine serum albumin (BSA) (Thermo Scientific, Waltham, USA) following manufacturer’s recommendations. Samples were stored at -80ºC until further analysis.

Protein measures

Protein concentrations of 1303 human proteins in plasma of AD and FTD patients or controls, and brain tissue homogenates of FTD patients and controls were measured at the Neurochemistry Laboratory of Amsterdam UMC using SomaScan (SomaLogic, Inc. Boulder, Colorado, USA). Samples were diluted into three concentrations (i.e. 40%, 1%, and 0.005%) to enable the appropriate measurement range for all Somamers within one sample. The least concentrated sample is designed to detect the most abundant proteins, and the most concentrated sample is designed to detect the least abundant proteins. The precise SomaScan principle has been described in detail previously (13,16). Samples were randomly divided over the plates to ensure an even mix of diagnostic groups. Plasma samples were measured in 5 (AD vs CN) and 7 (FTD vs CN) runs, and tissue samples in 1 (FTD vs CN) run. Technicians trained and certified by SomaLogic conducted all analyses in a blinded manner. Both plasma datasets (i.e. FTD vs CN and AD vs CN) were run in two batches using different SOMAmer reagent master mixes and were standardized to a common reference using common calibrator control lots. In addition, all SomaScan data were normalized following a standard three step procedure (1. hybridization normalization, 2. plate scaling, 3. median signal normalization) to remove systematic biases in the raw assay data.

Statistical analysis

All statistical analyses were performed using R version 3.5.2. Demographics were compared between groups using analysis of variance (ANOVA) and Kruskal-Wallis tests where appropriate. First, we used the global test (28), which tests if the overall protein abundance profile is notably different between diagnoses. This test is suitable when there may be insufficient power to detect individual proteomic markers. We applied global testing corrected for age and sex to identify an overall difference in plasma and post-mortem protein expression profile between i) FTD, AD and controls, and ii) FTD pathological subtypes (FTLD-Tau vs FTLD-TDP). We also applied the global test in tissue to measure overall differences in protein expression profiles between FTD and controls. Multiplicity correction using the false discovery rate (FDR) was applied within each global test to the significant subtree that identifies those features to which the test result is attributable. Next, logistic lasso regression (LLR) with correction for age and sex was performed to select a panel of proteins that could discriminate between FTD vs controls, FTD vs AD and FTLD-Tau vs FTLD-TDP. Predictive performance was assessed by receiver operating characteristic (ROC) curves and the area under the ROC curves (AUCs). ROC curves and AUCs were produced by bootstrapping with 1000 resampling. 95% confidence interval around the resulting AUCs was calculated based on the resampling quantiles (percentile method).

Demographics

FTD patients and controls included in the plasma analyses were both younger than AD patients, and both dementia groups had lower MMSE scores than controls (p < 0.05, Table 1). FTLD-Tau and FTLD-TDP subtypes did not differ in age, sex or MMSE scores. In patients selected for the tissue analysis, no differences were observed in age and sex.

Plasma protein profile differs between FTD, AD and controls, but not between pathological subtypes

The overall plasma protein expression profile consisting of 1303 proteins was different between FTD, AD patients and controls (p = 0.005). We identified six proteins that attributed to this difference in expression profile (FN1.3, Fibronectin, FN1.4, VWF, ECM1 and ApoE; Table 2), which were all upregulated in AD compared to both FTD patients and controls (Table 2). There was no difference in overall plasma protein profiles between FTD–Tau and FTLD-TDP subtypes (p > 0.05).

Plasma protein profiles can discriminate FTD from AD

Next, we set out to identify panels of plasma proteins to discriminate between FTD vs Controls, FTD vs AD and FTD-Tau vs FTLD-TDP. No plasma proteomic signal that reliable discriminated FTD from controls or FTD pathological subtypes was detected (Fig. 1, AUC:0.61; 95% CI: CI:0.48–0.73). We however identified a panel of 12 plasma proteins that discriminated FTD from AD with very high accuracy (AUC: 0.99, 95% CI: CI:0.96-1) (Fig. 1; Table 3).

Tissue proteins levels differ between FTD subjects and controls, but not between pathological subtypes

Next, we exploratory analyzed post-mortem brain tissue of FTD cases versus controls, and FTD subtypes. The overall tissue protein expression profile was different between FTD and controls (p = 0.003). We identified seven proteins that attributed to this difference in expression profile, of which four were upregulated and three were downregulated in FTD (C4, WIF 1, Discoidin domain receptor 1, LRRT3, HO 2, Annexin I, Alpha-1-antichymotrypsin complex, Table 3). Similar to plasma results, no differences in overall brain protein profile was detected between FTD–Tau and FTD-TDP subtypes (p > 0.05).

In this plasma proteomics study, we measured 1303 proteins in over 260 human plasma samples to identify protein profiles for the specific diagnosis of FTD and its pathological subtypes. We found a difference in overall protein profile between FTD, AD and controls. Importantly, we identified a plasma protein panel that discriminated FTD from AD patients, but not FTD from controls. No plasma or tissue protein changes were detected between FTD pathological subtypes.

To our knowledge, we were the first to apply proteomics in blood plasma of genetically or pathologically confirmed FTLD patients (29). We found a difference in overall plasma protein profiles between FTD, AD patients and controls, which could be attributed to six proteins. All these proteins were upregulated in AD compared to FTD patients and controls, which suggests that none of the proteins identified are associated to FTD pathogenesis. In line with these findings, our bootstrap classification exercises identified a combination of 61 proteins demarcating FTD patients and non-demented controls with limited accuracy (AUC: 0.61) and large confident intervals, underpinning the insufficient diagnostic accuracy and further supporting the lack of specific plasma protein signals specifically associated to FTD. Considering that the number of FTD and AD cases analyzed were comparable, the lack of biomarker signals associated to FTD might be explained by the clinicopathological diversity of FTD. The different clinical, genetic and pathological phenotypes within the FTD spectrum may hurdle the identification of specific biomarkers, highlighting the need to include large cohorts in biomarker studies.

We identified a panel of 12 blood-based proteins discriminating FTD from AD with very high accuracy (AUC: 0.99). Three of these proteins (fibronectin fragments 3 and 4 and Von Willebrand Factor (vWF)), were among the proteins differentially regulated between AD, FTD and controls identified before. Our findings are supported by a previous AD aptamer-based study, where fibronectin fragment 4 and fibronectin were also selected in a panel of plasma proteins to discriminate AD patients from controls (16).The observed high diagnostic accuracy supports potential use of this blood-based biomarker panel for the differential dementia diagnosis. However, as an AUC of 0.99 is near to perfect, replication of these findings, preferably through external validation is needed. The four proteins that contributed most to the discriminatory panel (Fibronectin, Fibrinogen gamma chain, hnRNPK and vWF) based on the largest beta’s, will be discussed in more detail. The protein with the strongest beta was Fibronectin (FN), a glycoprotein that plays a role in tissue repair, and regulating cell attachment, motility, hemostasis and embryogenesis (30). Several studies reported higher amounts of high molecular FN forms in plasma, CSF and frontal and temporal cortex of AD patients compared to vascular dementia and controls (31–33), corroborating our results showing higher levels of fibronectin fragments 3 and 4 in AD patients compared to FTD patients and controls. Interestingly, increased expression of FN type III domain has shown to decrease Aβ secretion in a cellular model (34). These data together suggest an increase of fibronectin fragments in AD which might potentially convey a neuroprotective effect. The protein with the second highest beta was Fibrinogen gamma chain, a blood borne glycoprotein essential to form an insoluble fibrin matrix. It is associated to amyloid deposition (35) and brain atrophy (36). The lower levels of this protein in AD compared to FTD and controls (37) indicate that this marker is specifically associated to AD pathogenesis. Experimental and neuropathological studies indeed suggest that this protein may contribute to AD by altering thrombosis and fibrinolysis (38). hnRNP K is one of the major pre-mRNA-binding proteins, likely playing a role in the nuclear metabolism of hnRNAs and in the p53/TP53 response to DNA damage (39). A previous proteome study found an upregulation of this protein in frontal cortex of AD cases (40). Recent exciting evidence showed mislocalisation of hnRNA K in pyramidal neurons of the frontal cortex to be a novel neuropathological feature associated with both frontotemporal lobar degeneration and ageing (41,42). Future studies should therefore address the potential role of this protein in both FTD and AD to understand how it contributes to discriminate these disorders. The protein with the fourth highest beta was VWF, a glycoprotein with critical functions in hemostasis (43). It was identified by the global test and was also part of the protein panel discriminating AD and FTD. VWF has frequently been studied in AD since vascular damage plays a role in the pathogenesis of AD dementia. However, results of VWF levels in AD patients have been conflicting. One CSF proteomics study that aimed to discriminate AD from non-AD patients based, has shown discrepant results in CSF VWF levels between three independent cohorts (31). Other studies reported no difference in VWF levels in blood plasma, CSF or brain cells between AD and controls (44,45), and one large population study reported higher levels of VWF in blood plasma of AD patients (46). We recently observed increased levels of CSF VWF in our ongoing AD studies (Del Campo et al, in preparation). A possible speculative explanation for these discrepant findings could be that the cohorts that reported an increase in VWF levels, including ours, had more patients with mixed vascular and AD pathology, whereas other cohorts mostly included patients with pure AD pathology. It would be very relevant to investigate the markers identified here together with novel promising plasma biomarkers, such as plasma pTau levels, pTau levels, that show very good discrimination between AD and FTD patients, being specifically increased in AD (47–49).

We could not find differentially regulated proteins between Tau and TDP pathological subtypes in tissue or plasma, nor could we identify discriminatory plasma protein signatures between these subtypes. Throughout literature, it has been challenging to identify and validate protein alterations between both pathological subtypes. For CSF, two previous proteomic studies reported several differentially regulated CSF proteins (50) or a biomarker panel in CSF which enabled sensitive differentiation between TDP and Tau pathology (51), although independent multicenter validation and replication on different platforms is still needed. The lack of a biomarker (panel) for FTD subtypes with feasibility in clinical practice thus far, could have several possible explanations. First, a potential explanation is the heterogeneity within Tau and TDP pathological subtypes, such as the different isoforms of TDP and Tau pathology, which have not been accounted for in fluid biomarker studies so far (52,53). For instance, patients with the TDP-A isoform might have a different protein signature than patients with the TDP-C isoform. This heterogeneity will complicate the search for a single discriminatory protein panel for TDP vs Tau, and will require larger and more homogeneous sample sizes, which are scarce. An alternative explanation could be that both pathological subtypes might have similar downstream pathological pathways leading to FTD. For instance, local TDP and Tau pathology could potentially be initiating the same prominent cascades, represented in similar proteomic changes in body fluids, ultimately leading to the neurodegenerative changes seen in FTD. This could also explain why both pathological subtypes are seen across the clinical FTD spectrum (10). Lastly, in most FTD biomarker studies familial and sporadic cases are often grouped to achieve a large sample size. However, the question remains whether the familial form of FTD with GRN, C9orf72 or MAPT mutations is biologically similar to sporadic FTD patients with TDP or Tau proteins. Future studies where (plasma) protein profiles of familial and sporadic FTD subtypes are independently studied could provide more clarity.

Proteomics in body fluids such as blood plasma or CSF can provide valuable mechanistic information as to whether post-mortem pathological changes are also seen in earlier ante-mortem disease stages, or whether there are also systemic responses involved in CNS diseases. As suggested also by the Consensus report of The Reagan Working group in 1998 (54), comparison of biofluid results with the expression of those proteins in brain tissue would be the most direct proof for a relation with the brain pathology. Indeed, pathological correlates are the basis for the now widely used biomarkers in AD, such as amyloid beta and pTau. We observed in our exploratory analysis that the proteins differentially regulated in FTD brain tissue were not dysregulated in plasma, suggesting that the brain changes identified were not reflected in plasma. This might be explained by the redundancy of plasma proteins from the periphery, which may mask low concentration and subtle changes of CNS-derived proteins in plasma. It is important to note that biofluid based biomarker levels are dynamic and may change along the disease process (55). Thus, the different time point of collection (i.e. ante-mortem for plasma vs. post-mortem for tissue), may explain the lack of overlap. However, the small sample size of the tissue sections prohibits strong conclusions.

Among the limitations of our study is that despite the large number of plasma proteins analyzed, the aptamer-based proteomic platform is still a targeted analysis dependent on the protein library. Thus, we cannot exclude that the other relevant or powerful brain-disease related biomarkers are not present within the aptamer library (i.e. Somamer library (56)). Nevertheless, the hypothesis free approach allowed us to identify novel proteins in addition to previously described proteins. Another limitation is that there was some center bias, because especially AD and control samples were collected from several sites (five). However, the majority (two third) of the AD samples were from the two sites that provided also the FTD samples, and all centers collected their samples under the same standardized protocol. Another limitation is the lack of replication of our findings in an independent validation cohort, especially considering the high accuracy of our FTD vs AD discriminatory panel. Validation of the plasma panel is technically not feasible yet on the Somascan technology. Novel large proteomics studies using an independent platform (proximity extension assay) with versatility of building smaller panels in plasma of FTD patients are current underway in the course of the JPND bPRIDE project (neurodegenerationresearch.eu).

The strengths of our study are that all our FTD cases had confirmed diagnosis based on genetic and/or pathological confirmation. Because FTD is clinically heterogeneous and does not correlate strongly to its pathologic subtypes, cohorts with known pathologic subtypes are important to provide relevant insights into underlying disease mechanisms. Of note, some of the AD plasma samples analyzed in this study came from non-specialized memory clinics, and were diagnosed using clinical criteria without AD CSF biomarker confirmation.

In summary, we analyzed an unprecedented large number of proteins (1303) in plasma of FTD cases with confirmed underlying neuropathology together with AD and cognitively unimpaired controls. We observed that the plasma or tissue proteome were essentially similar between FTLD-Tau and FTLD-TDP. When the overall FTD group was analysed, we identified six plasma proteins differentially regulated between AD, FTD, and controls. However, these were mainly associated to AD dementia rather than FTD, underpinning the challenges to identify robust single markers associated to FTD. Classification exercises revealed a plasma protein panel discriminating FTD from AD with high accuracy, which needs to be validated in independent cohorts. The lack of plasma protein signals specifically associated to FTD-confirmed cases might be caused by the heterogeneity of this disorder, highlighting that the quest of FTD-specific biomarkers will likely require high-collaborative biomarker studies using large and well-characterized FTD cohorts.

Ethics approval and consent to participate

All subjects gave informed consent according to the declaration of Helsinki (1991), and protocols were approved by local ethical committees at each site participating in this study. All donors (or their next of kin) provided written informed consent for brain autopsy and use of tissue and medical records for research purposes.

Consent for publication

Not applicable

Availability of data and materials

The datasets generated and/or analysed during the current study are not publicly available, but are available from the corresponding author on reasonable request.

Competing interests

The authors declare that they have no competing interests.

Funding

Alzheimer Center Amsterdam is supported by Stichting Alzheimer Nederland and Stichting VUmc fonds. Research of Alzheimer Center Amsterdam is part of the neurodegeneration research program of Amsterdam Neuroscience. ZonMw Memorabel program project “PRODIA”, as part of the Deltaplan Dementie, and 2bike4alzheimer (Alzheimer Nederland) are acknowledged for funding this study. The VUmc Biobank is supported by VUmc. The work described in this study was carried out in the context of the Parelsnoer Institute (PSI). PSI was part of and funded by the Dutch Federation of University Medical Centers and has received initial funding from the Dutch Government (from 2007-2011). Since 2020, this work was carried out in the context of Parelsnoer clinical biobanks at Health-RI (https://www.health-ri.nl/initiatives/parelsnoer).

Authors' contributions

RBM and MCM interpreted the patient data, and were a major contributor in writing the manuscript. CFWP analyzed the patient data and was a contributor in writing the manuscript. LHHM and HS revised the manuscript for intellectual content. MKS performed the sample analysis and revised the manuscript for intellectual content. IHGBR, HAMM, PPDD, JAHRC, JCS, CB, JJMH, and PS revised the manuscript for intellectual content. WMF interpreted the data and revised the manuscript for intellectual content. YALP and CET interpreted the data, revised the manuscript for intellectual content, and were a contributor in writing the manuscript. All authors read and approved the final manuscript.

Acknowledgements

Wiesje van der Flier holds the Pasman chair.

Neary D, Snowden J, Mann D. Frontotemporal dementia. Lancet Neurol [Internet]. 2005 Nov 1 [cited 2019 Aug 16];4(11):771–80. Available from: https://www.sciencedirect.com/science/article/abs/pii/S1474442205702234.
Irwin DJ, Trojanowski JQ, Grossman M. Cerebrospinal fluid biomarkers for differentiation of frontotemporal lobar degeneration from Alzheimer’s disease. Vol. 5, Frontiers in Aging Neuroscience. 2013.
Sieben A, Van Langenhove T, Engelborghs S, Martin J-J, Boon P, Cras P, et al. The genetics and neuropathology of frontotemporal lobar degeneration. Acta Neuropathol [Internet]. 2012 Sep 14;124(3):353–72. Available from: http://link.springer.com/10.1007/s00401-012-1029-x.
Josephs KA, Hodges JR, Snowden JS, MacKenzie IR, Neumann M, Mann DM, et al. Neuropathological background of phenotypical variability in frontotemporal dementia. Acta Neuropathologica. 2011.
Seelaar H, Kamphorst W, Rosso SM, Azmani A, Masdjedi R, De Koning I, et al. Distinct genetic forms of frontotemporal dementia. Neurology. 2008.
Warren JD, Rohrer JD, Rossor MN. Frontotemporal dementia. BMJ [Internet]. 2013 Aug 20 [cited 2019 Oct 7];347(aug12 3):f4827–f4827. Available from: http://www.bmj.com/cgi/doi/10.1136/bmj.f4827.
Rabinovici GD, Miller BL. Frontotemporal lobar degeneration: epidemiology, pathophysiology, diagnosis and management. CNS Drugs [Internet]. 2010 May [cited 2019 Aug 16];24(5):375–98. Available from: http://www.ncbi.nlm.nih.gov/pubmed/20369906.
Pose M, Cetkovich M, Gleichgerrcht E, Ibáñez A, Torralva T, Manes F. The overlap of symptomatic dimensions between frontotemporal dementia and several psychiatric disorders that appear in late adulthood. Int Rev Psychiatry. 2013.
Zetterberg H, van Swieten JC, Boxer AL, Rohrer JD. Review. Fluid biomarkers for frontotemporal dementias. Neuropathol Appl Neurobiol [Internet]. 2019 Feb [cited 2019 Oct 7];45(1):81–7. Available from: http://www.ncbi.nlm.nih.gov/pubmed/30422329.
Meeter LH, Kaat LD, Rohrer JD, Van Swieten JC. Imaging and fluid biomarkers in frontotemporal dementia. Nature Reviews Neurology. 2017.
Meeter LHH, Vijverberg EG, Del Campo M, Rozemuller AJM, Donker Kaat L, de Jong FJ, et al. Clinical value of neurofilament and phospho-tau/tau ratio in the frontotemporal dementia spectrum. Neurology. 2018 Apr 3;90(14):e1231–9.
Pijnenburg YAL, Verwey NA, van der Flier WM, Scheltens P, Teunissen CE. Discriminative and prognostic potential of cerebrospinal fluid phosphoTau/tau ratio and neurofilaments for frontotemporal dementia subtypes. Assess Dis Monit: Alzheimer’s Dement Diagnosis; 2015.
Gold L, Ayers D, Bertino J, Bock C, Bock A, Brody EN, et al. Aptamer-based multiplexed proteomic technology for biomarker discovery. PLoS One. 2010 Jan;5(12):e15004.
Keefe AD, Pai S, Ellington A. Aptamers as therapeutics. Nat Rev Drug Discov. 2010;9(7):537–50.
Davies DR, Gelinas AD, Zhang C, Rohloff JC, Carter JD, O’Connell D, et al. Unique motifs and hydrophobic interactions shape the binding of modified DNA ligands to protein targets. Proc Natl Acad Sci U S A. 2012 Dec;109(49):19971–6.
Sattlecker M, Kiddle SJ, Newhouse S, Proitsi P, Nelson S, Williams S, et al. Alzheimer’s disease biomarker discovery using SOMAscan multiplexed protein technology. Alzheimer’s Dement. 2014.
Shi L, Westwood S, Baird AL, Winchester L, Dobricic V, Kilpert F, et al. Discovery and validation of plasma proteomic biomarkers relating to brain amyloid burden by SOMAscan assay. Alzheimer’s Dement [Internet]. 2019 Nov 1 [cited 2020 Aug 3];15(11):1478–88. Available from: https://pubmed.ncbi.nlm.nih.gov/31495601/.
Westwood S, Baird AL, Hye A, Ashton NJ, Nevado-Holgado AJ, Anand SN, et al. Plasma Protein Biomarkers for the Prediction of CSF Amyloid and Tau and [18F]-Flutemetamol PET Scan Result. Front Aging Neurosci [Internet]. 2018 Dec 11 [cited 2020 Aug 3];10. Available from: https://pubmed.ncbi.nlm.nih.gov/30618716/.
Rosso SM, Kaat LD, Baks T, Joosse M, De Koning I, Pijnenburg Y, et al. Frontotemporal dementia in The Netherlands: Patient characteristics and prevalence estimates from a population-based study. Brain. 2003.
van der Flier WM, Scheltens P. Amsterdam Dementia Cohort: Performing Research to Optimize Care. Perry G, Avila J, Tabaton M, Zhu X, editors. J Alzheimer’s Dis [Internet]. 2018 Mar 13 [cited 2018 Apr 16];62(3):1091–111. Available from: http://www.medra.org/servlet/aliasResolver?alias=iospress&doi=10.3233/JAD-170850.
Van Der Flier WM, Pijnenburg YAL, Prins N, Lemstra AW, Bouwman FH, Teunissen CE, et al. Optimizing patient care and research: The Amsterdam dementia cohort. J Alzheimer’s Dis. 2014;41(1):313–27.
Aalten P, Ramakers IHGB, Biessels GJ, de Deyn PP, Koek HL, OldeRikkert MGM, et al. The Dutch Parelsnoer Institute - Neurodegenerative diseases; methods, design and baseline results. BMC Neurol. 2014.
McKhann G, Drachman D, Folstein M, Katzman R, Price D, Stadlan EM. Clinical diagnosis of Alzheimer’s disease: Report of the NINCDS-ADRDA Work Group* under the auspices of Department of Health and Human Services Task Force on Alzheimer’s Disease. Neurology [Internet]. 1984 Jul 1;34(7):939–939. Available from: http://www.neurology.org/cgi/doi/10.1212/WNL.34.7.939.
Del Campo M, Hoozemans JJM, Dekkers L-L, Rozemuller AJ, Korth C, Müller-Schiffmann A, et al. BRI2-BRICHOS is increased in human amyloid plaques in early stages of Alzheimer’s disease. Neurobiol Aging [Internet]. 2014 Jul 1 [cited 2019 Aug 16];35(7):1596–604. Available from: https://www.sciencedirect.com/science/article/abs/pii/S0197458014000086?via%3Dihub.
Mackenzie IRA, Neumann M, Baborie A, Sampathu DM, Du Plessis D, Jaros E, et al. A harmonized classification system for FTLD-TDP pathology. Acta Neuropathologica. 2011.
Lee EB, Porta S, Michael Baer G, Xu Y, Suh ER, Kwong LK, et al. Expansion of the classification of FTLD-TDP: distinct pathology associated with rapidly progressive frontotemporal degeneration. Acta Neuropathol. 2017.
Cairns NJ, Bigio EH, Mackenzie IRA, Neumann M, Lee VMY, Hatanpaa KJ, et al. Neuropathologic diagnostic and nosologic criteria for frontotemporal lobar degeneration: Consensus of the Consortium for Frontotemporal Lobar Degeneration. Acta Neuropathol. 2007.
Goeman JJ, Van de Geer S, De Kort F, van Houwellingen HC. A global test for groups fo genes: Testing association with a clinical outcome. Bioinformatics. 2004.
Ashton NJ, Hye A, Rajkumar AP, Leuzy A, Snowden S, Suárez-Calvet M, et al. An update on blood-based biomarkers for non-Alzheimer neurodegenerative disorders. Nature Reviews Neurology. 2020.
Ouaissi MA, Capron A. Fibronectins. Structure and functions. Ann l’Institut Pasteur - Immunol. 1985 Jan;136(2)(1):169–85.
Bader JM, Geyer PE, Müller JB, Strauss MT, Koch M, Leypoldt F, et al. Proteome profiling in cerebrospinal fluid reveals novel biomarkers of Alzheimer’s disease. Mol Syst Biol [Internet]. 2020 Jun 2 [cited 2020 Aug 21];16(6). Available from: https://onlinelibrary.wiley.com/doi/abs/10.15252/msb.20199356.
Lemańska-Perek A, Leszek J, Krzyanowska-Gola̧b D, Radzik J, Ka̧tnik-Prastowska MI. Molecular status of plasma fibronectin as an additional biomarker for assessment of alzheimer’s dementia risk. Dement Geriatr Cogn Disord [Internet]. 2009 Nov [cited 2020 Aug 21];28(4):338–42. Available from: https://pubmed.ncbi.nlm.nih.gov/19864907/.
Lepelletier F-X, Mann DMA, Robinson AC, Pinteaux E, Boutin H. Early changes in extracellular matrix in Alzheimer’s disease. Neuropathol Appl Neurobiol [Internet]. 2017 Feb 1 [cited 2020 Aug 21];43(2):167–82. Available from: https://pubmed.ncbi.nlm.nih.gov/26544797/.
Noda Y, Kuzuya A, Tanigawa K, Araki M, Kawai R, Ma B, et al. Fibronectin type III domain-containing protein 5 interacts with APP and decreases amyloid β production in Alzheimer’s disease. Mol Brain [Internet]. 2018 Dec 24 [cited 2020 Aug 21];11(1):61. Available from: https://molecularbrain.biomedcentral.com/articles/10.1186/s13041-018-0401-8.
Westwood S, Leoni E, Hye A, Lynham S, Khondoker MR, Ashton NJ, et al. Blood-Based Biomarker Candidates of Cerebral Amyloid Using PiB PET in Non-Demented Elderly. J Alzheimers Dis [Internet]. 2016;52(2):561–72. Available from: http://www.ncbi.nlm.nih.gov/pubmed/27031486.
Thambisetty M, Simmons A, Hye A, Campbell J, Westman E, Zhang Y, et al. Plasma biomarkers of brain atrophy in Alzheimer’s disease. PLoS One. 2011.
Shi L, Buckley NJ, Bos I, Engelborghs S, Sleegers K, Frisoni GB, et al. Plasma Proteomic Biomarkers Relating to Alzheimer’s Disease: A Meta-Analysis Based on Our Own Studies. Front Aging Neurosci [Internet]. 2021 Jul 21;13. Available from: https://www.frontiersin.org/articles/10.3389/fnagi.2021.712545/full.
Cortes-Canteli M, Zamolodchikov D, Ahn HJ, Strickland S, Norris EH. Fibrinogen and Altered Hemostasis in Alzheimer’s Disease. de la Torre J, editor. J Alzheimer’s Dis [Internet]. 2012 Oct 29;32(3):599–608. Available from: https://www.medra.org/servlet/aliasResolver?alias=iospress&doi=10.3233/JAD-2012-120820.
Moumen A, Masterson P, O’Connor MJ, Jackson SP. hnRNP K: an HDM2 target and transcriptional coactivator of p53 in response to DNA damage. Cell [Internet]. 2005 Dec 16;123(6):1065–78. Available from: http://www.ncbi.nlm.nih.gov/pubmed/16360036.
Zhang Q, Ma C, Gearing M, Wang PG, Chin L-S, Li L. Integrated proteomics and network analysis identifies protein hubs and network alterations in Alzheimer’s disease. Acta Neuropathol Commun [Internet]. 2018;6(1):19. Available from: http://www.ncbi.nlm.nih.gov/pubmed/29490708.
Bampton A, Gatt A, Humphrey J, Cappelli S, Bhattacharya D, Foti S, et al. HnRNP K mislocalisation is a novel protein pathology of frontotemporal lobar degeneration and ageing and leads to cryptic splicing. Acta Neuropathol. 2021.
Moujalled D, Grubman A, Acevedo K, Yang S, Ke YD, Moujalled DM, et al. TDP-43 mutations causing amyotrophic lateral sclerosis are associated with altered expression of RNA-binding protein hnRNP K and affect the Nrf2 antioxidant pathway. Hum Mol Genet. 2017.
Peyvandi F, Garagiola I, Baronciani L. Role of von Willebrand factor in the haemostasis [Internet]. Vol. 9, Blood Transfusion. SIMTI Servizi; 2011 [cited 2021 Jan 18]. p. s3. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3159913/.
Yavuz BB, Dede DS, Yavuz B, Cankurtaran M, Halil M, Ulger Z, et al. Potential biomarkers for vascular damage in ALzheimer’s disease: Thrombomodulin and von Willebrand factor. J Nutr Health Aging [Internet]. 2010 Jun 5;14(6):439–41. Available from: http://link.springer.com/10.1007/s12603-010-0043-8.
Thomas T, Miners S, Love S. Post-mortem assessment of hypoperfusion of cerebral cortex in Alzheimer’s disease and vascular dementia. Brain. 2015.
Wolters FJ, Boender J, De Vries PS, Sonneveld MA, Koudstaal PJ, De Maat MP, et al. Von Willebrand factor and ADAMTS13 activity in relation to risk of dementia: A population-based study. Sci Rep [Internet]. 2018 Dec 1 [cited 2020 Jul 21];8(1). Available from: /pmc/articles/PMC5882924/?report = abstract.
Mielke MM, Aakre JA, Algeciras-Schimnich A, Proctor NK, Machulda MM, Eichenlaub U, et al. Comparison of CSF phosphorylated tau 181 and 217 for cognitive decline. Alzheimer’s Dement [Internet]. 2021 Jul 26; Available from: https://onlinelibrary.wiley.com/doi/10.1002/alz.12415.
Thijssen EH, La Joie R, Wolf A, Strom A, Wang P, Iaccarino L, et al. Diagnostic value of plasma phosphorylated tau181 in Alzheimer’s disease and frontotemporal lobar degeneration. Nat Med. 2020.
Janelidze S, Mattsson N, Palmqvist S, Smith R, Beach TG, Serrano GE, et al. Plasma P-tau181 in Alzheimer’s disease: relationship to other biomarkers, differential diagnosis, neuropathology and longitudinal progression to Alzheimer’s dementia. Nat Med. 2020.
Teunissen CE, Elias N, Koel-Simmelink MJA, Durieux-Lu S, Malekzadeh A, Pham TV, et al. Novel diagnostic cerebrospinal fluid biomarkers for pathologic subtypes of frontotemporal dementia identified by proteomics. Alzheimer’s Dement Diagnosis, Assess Dis Monit. 2016.
Hu WT, Chen-Plotkin A, Grossman M, Arnold SE, Clark CM, Shaw LM, et al. Novel CSF biomarkers for frontotemporal lobar degenerations. Neurology [Internet]. 2010 Dec 7;75(23):2079–86. Available from: http://www.neurology.org/cgi/doi/10.1212/WNL.0b013e318200d78d.
Mackenzie IRA, Neumann M, Bigio EH, Cairns NJ, Alafuzoff I, Kril J, et al. Nomenclature and nosology for neuropathologic subtypes of frontotemporal lobar degeneration: an update. Acta Neuropathol [Internet]. 2010 Jan 19 [cited 2020 Aug 21];119(1):1–4. Available from: https://pubmed.ncbi.nlm.nih.gov/19924424/.
Buée L, Bussière T, Buée-Scherrer V, Delacourte A, Hof PR. Tau protein isoforms, phosphorylation and role in neurodegenerative disorders11These authors contributed equally to this work. Brain Res Rev [Internet]. 2000 Aug [cited 2020 Aug 21];33(1):95–130. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0165017300000199.
Davies P, Resnick J, Resnick B, Gilman S, Growdon JH, Khachaturian ZS, et al. Consensus report of the Working Group on: “Molecular and Biochemical Markers of Alzheimer’s Disease”. The Ronald and Nancy Reagan Research Institute of the Alzheimer’s Association and the National Institute on Aging Working Group. Neurobiol Aging [Internet]. 1998;19(2):109–16. Available from: http://www.ncbi.nlm.nih.gov/pubmed/9558143.
Moscoso A, Grothe MJ, Ashton NJ, Karikari TK, Lantero Rodríguez J, Snellman A, et al. Longitudinal Associations of Blood Phosphorylated Tau181 and Neurofilament Light Chain With Neurodegeneration in Alzheimer Disease. JAMA Neurol [Internet]. 2021;78(4):396–406. Available from: http://www.ncbi.nlm.nih.gov/pubmed/33427873.
SomaLogic I. SOMAscan technical note.

Table 1. Demographics
	n (%)	Age, years mean (SD)^a	Sex, female, n (%)	MMSE, mean (SD)	Post-mortem delay mean (SD) in hours
Plasma
FTD	56 (22%)	59.4 (7.4)	25 (45%)	24 (5.2)
FTLD-TDP^d	40 (71%)	59.8 (7.9)	18 (45%)	24 (5.7)
FTLD-Tau^e	16 (29%)	58.2 (6.2)	7 (44%)	25 (3.6)
AD	57 (22%)	65.5 (8.0)^c	22 (39%)	23 (2.3)
Controls	148 (57%)	61.3 (7.9)	60 (41%)	29 (1.4)^b
Post-mortem frontal cortex
FTD	10 (67%)	60.1 (8.0)	6 (60%)	NA	6.5 (3.7)
FTLD-TDP	5 (50%)	64.0 (7.7)	3 (60%)	NA	6.8 (3.1)
FTLD-Tau	5 (50%)	56.2 (6.9)	3 (60%)	NA	5,3 (0.6)
Controls	5 (33%)	61.3 (8.1)	3 (75%)	NA	8.8 (2.5)

Analysis of variance (ANOVA) or Kruskal-Wallis test were used as appropriate. p<0.05 was considered significant. ^a age at inclusion in plasma samples and age at death in post-mortem tissue, ^bFTD patients and AD had significantly lower MMSE scores, ^c FTD patients and controls were significantly younger than AD patients, ^d FTLD-TDP pathology was present in 40 subjects (18 autopsy confirmed cases, 13 GRN [of whom 1 was autopsy confirmed], 9 C9orf72 [of whom 1 was autopsy confirmed]), and FTLD-Tau pathology in 16 subjects (3 autopsy-confirmed cases, 13 MAPT [of whom 2 were also autopsy confirmed]), ^e FTLD-TDP was present in 5 subjects (4 cases were familial [GRN n=2 and C9orf72 n=2] and 1 was a sporadic case), and FTLD-Tau was present in 5 cases (all 5 cases were familial [MAPT n=5]). Abbreviations: AD; Alzheimer’s Disease, TDP; TAR DNA Binding protein 43; FTD, Frontotemporal Lobar Degeneration; MMSE, Mini Mental State Examination. NA; Not Available.

Table 2. Plasma proteins from global test comparing FTD, AD and controls
Name	Associated with status	Statistic	Std. Dev	p-value
FN1.3	AD	8.72	0.406	2.26e-10
Fibronectin	AD	7.39	0.405	5.07e-09
FN1.4	AD	7.18	0.406	2.04e-08
VWF	AD	7.01	0.407	3.65e-08
ECM1	AD	4.23	0.398	1.94e-05
ApoE	AD	5.68	0.406	9.79e-07
Plasma proteins that attributed to a difference in expression profile between FTD, AD and controls after correction for multiple testing. All proteins were upregulated in AD compared to FTD and controls. Models were corrected for age and sex. Abbreviations: FN 1.3, Fibronectin Fragment 3; FN 1.4, Fibronectin Fragment 4; VWF, Von Willebrand Factor; ECM1, Extracellular matrix protein 1; ApoE, Apolipoprotein Epsilon. AD, Alzheimer’s Disease.

Table 3. Classification protein panel for FTD vs AD
Protein name	Beta^a
FN1.3	-3.390
Fibrinogen gamma chain dimer	-1.315
hnRNPK	-0.575
vWF	-0.500
IDS	-0.276
RSPO3	-0.168
STRATIFIN (14-3-3 protein sigma)	-0.098
TS	-0.077
IL24	-0.074
TRY3	-0.014
FN1.4	-0.006
TPSG1	0.003
Plasma proteins for the discrimination of FTD vs AD. Model did not select for age and sex. Inclusion. Of age and sex did not modify the outcomes. Reference is FTD. Abbreviations: FTD, Frontotemporal dementia; AD, Alzheimer’s Disease; RSPO3, R-Spondin 3; VWF, Von Willebrand Factor; IDS, Iduronate 2-Sulfatase; IL24, Interleukin 24; TPSG1, Tryptase Gamma 1; FN 1.3, Fibronectin Fragment 3; FN 1.4, Fibronectin Fragment 4; TRY3, serine protease 2; HNRNPK, Heterogeneous Nuclear Ribonucleoprotein K; TS, Thymidylate Synthetase.

Table 4. Tissue proteins from global test
Name	Associated with status	Statistic	Std. Dev	p-value
C4	FTD	85.4	10.5	2.30e-06
WIF 1	Control cortex	80.6	10.5	1.30e-05
Discoidin domain receptor 1	FTD	80.4	10.5	1.42e-05
LRRT3	Control cortex	78.0	10.5	2.84e-05
HO 2	Control cortex	71.9	10.5	1.29e-04
Annexin I	FTD	61.7	10.5	8.67e-04
Alpha-1-antichymotrypsin complex	FTD	65.7	10.5	4.39e-04
Tissue proteins that attributed to a difference in expression profile between FTD and control tissue after correction for multiple testing. Proteins were associated with either FTLD or control cortex. Abbreviations: C4; Complement C4, WIF-1; Wnt inhibitory factor 1, LRRT3; Leucine-rich repeat transmembrane neuronal protein 3, HO-2; Heme oxygenase 2, FTD; Frontotemporal Dementia.

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Blood-based protein biomarkers in definite frontotemporal dementia: a case-control study

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Blood plasma

Post-mortem brain tissue

Statistical analysis

Results

Demographics

Plasma protein profile differs between FTD, AD and controls, but not between pathological subtypes

Plasma protein profiles can discriminate FTD from AD

Tissue proteins levels differ between FTD subjects and controls, but not between pathological subtypes

Discussion

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