Juan Lantero-Rodriguez

Agathe Vrillon

Aida Fernández-Lebrero

Paula Ortiz-Romero

Anniina Snellman

Laia Montoliu-Gaya

Wagner S Brum

Emmanuel Cognat

Julien Dumurgier

Albert Puig-Pijoan

Irene Navalpotro-Gómez

Greta García-Escobar

Thomas K Karikari

Eugeen Vanmechelen

Nicholas J Ashton

Henrik Zetterberg

Marc Suárez-Calvet

Claire Paquet

Kaj Blennow

Juan Lantero

Alzheimer's Research & Therapy Clinical performance and head-to-head comparison of CSF p-tau235 with p-tau181, p-tau217 and p-tau231 in two memory clinic cohorts

Keywords: Alzheimer's disease, CSF, Biomarkers, p-tau235, p-tau181, p-tau217, p-tau231, Memory clinic

Background Cerebrospinal fluid (CSF) p-tau235 is a novel biomarker highly specific of Alzheimer's disease (AD). However, CSF p-tau235 has only been studied in well-characterized research cohorts, which do not fully reflect the patient landscape found in clinical settings. Therefore, in this multicentre study, we investigated the performance of CSF p-tau235 to detect symptomatic AD in clinical settings and compared it with CSF p-tau181, p-tau217 and p-tau231.

Methods

CSF p-tau235 was measured using an in-house single molecule array (Simoa) assay in two independent memory clinic cohorts: Paris cohort (Lariboisière Fernand-Widal University Hospital Paris, France; n=212) and BIODEG-MAR cohort (Hospital del Mar, Barcelona, Spain; n=175). Patients were classified by the syndromic diagnosis (cognitively unimpaired [CU], mild cognitive impairment [MCI] or dementia) and their biological diagnosis (amyloid-beta [Aβ]+ or Aβ -). Both cohorts included detailed cognitive assessments and CSF biomarker measurements (clinically validated core AD biomarkers [Lumipulse CSF Aβ 1-42/40 ratio, p-tau181 and t-tau] and in-house developed Simoa CSF p-tau181, p-tau217 and p-tau231).

Results

High CSF p-tau235 levels were strongly associated with CSF amyloidosis regardless of the clinical diagnosis, being significantly increased in MCI Aβ+ and dementia Aβ+ when compared with all other Aβ-groups (Paris cohort: P ˂0.0001 for all; BIODEGMAR cohort: P ˂0.05 for all). CSF p-tau235 was pronouncedly increased in the A+T+ profile group compared with A-T-and A+T-groups (P ˂0.0001 for all). Moreover, CSF p-tau235 demonstrated high diagnostic accuracies identifying CSF amyloidosis in symptomatic cases (AUCs=0.86 to 0.96) and discriminating AT groups (AUCs=0.79 to 0.98). Overall, CSF p-tau235 showed similar performances to CSF p-tau181 and CSF p-tau231 †

Background

Alzheimer's disease (AD) is characterized by the accumulation of aggregated amyloid-beta (Aβ) and hyperphosphorylated tau protein into extracellular Aβ plaques and intracellular neurofibrillary tangles (NFTs), respectively [START_REF] Braak | Neuropathological stageing of Alzheimer-related changes[END_REF][START_REF] Thal | Phases of A beta-deposition in the human brain and its relevance for the development of AD[END_REF]. These two immunohistochemical findings, together with gross atrophy and synaptic and neuronal loss, represent the hallmarks of AD and post-mortem examination demonstrating the presence of these proteinopathies is required for definitive diagnosis [START_REF] Jack Cr | NIA-AA research framework: toward a biological definition of Alzheimer's disease[END_REF]. Validated imaging and fluid biomarkers increasingly support the clinical assessment and diagnosis of AD during life. Imaging biomarkers include positron emission tomography (PET) using radio-ligands capable of specifically binding Aβ and tau aggregates, whereas widely validated fluid biomarkers currently include Aβ 1-42 (or Aβ 1-42/40 ratio), total tau (t-tau) and phosphorylated tau at threonine 181 (p-tau181), all measured in cerebrospinal fluid (CSF) [START_REF] Blennow | CSF total tau, Abeta42 and phosphorylated tau protein as biomarkers for Alzheimer's disease[END_REF][START_REF] Ashton | Update on biomarkers for amyloid pathology in Alzheimer's disease[END_REF][START_REF] Molinuevo | Current state of Alzheimer's fluid biomarkers[END_REF][START_REF] Scholl | Biomarkers for tau pathology[END_REF] often by full-automated instruments.

Recently, the field of fluid biomarkers in neurodegeneration has undergone a remarkable expansion, particularly with regard to biomarkers measuring different variants of tau protein. This has resulted in a wide range of novel p-tau biomarkers, measured with different methods and platforms (fundamentally immunoassays and mass spectrometry) [START_REF] Barthelemy | Tau phosphorylation rates measured by mass spectrometry differ in the intracellular brain vs. extracellular cerebrospinal fluid compartments and are differentially affected by Alzheimer's disease[END_REF][START_REF] Karikari | Blood phosphorylated tau 181 as a biomarker for Alzheimer's disease: a diagnostic performance and prediction modelling study using data from four prospective cohorts[END_REF][START_REF] Palmqvist | Discriminative accuracy of plasma phospho-tau217 for Alzheimer disease vs other neurodegenerative disorders[END_REF][START_REF] Ashton | Plasma p-tau231: a new biomarker for incipient Alzheimer's disease pathology[END_REF]. Different p-tau residues may change in different stages of the Alzheimer's continuum. For example, p-tau231 is the first p-tau residue increasing in preclinical AD, confirming the earliest underlying AD processes [START_REF] Ashton | Plasma p-tau231: a new biomarker for incipient Alzheimer's disease pathology[END_REF][START_REF] Ashton | Cerebrospinal fluid p-tau231 as an early indicator of emerging pathology in Alzheimer's disease[END_REF] whereas p-tau217, with its pronounced fold changes and strong association with AD pathological hallmarks, appears to be the most suitable p-tau biomarker for AD diagnosis and patient monitoring [START_REF] Janelidze | Cerebrospinal fluid p-tau217 performs better than p-tau181 as a biomarker of Alzheimer's disease[END_REF][START_REF] Ashton | Differential roles of Abeta42/40, p-tau231 and p-tau217 for Alzheimer's trial selection and disease monitoring[END_REF]. Moreover, different p-tau epitopes may provide different information about the disease and some of them be more suited as a state, stage, or prognostic biomarkers. The most recent p-tau biomarker to demonstrate potential utility in relation to AD is p-tau235 [START_REF] Lantero-Rodriguez | P-tau235: a novel biomarker for staging preclinical Alzheimer's disease[END_REF], a phosphorylation site that has been found to be a prominent feature in paired helical filaments (PHFs) [START_REF] Hasegawa | Protein sequence and mass spectrometric analyses of tau in the Alzheimer's disease brain[END_REF][START_REF] Hoffmann | Unique Alzheimer's disease paired helical filament specific epitopes involve double phosphorylation at specific sites[END_REF][START_REF] Singer | Neighbored phosphorylation sites as PHF-tau specific markers in Alzheimer's disease[END_REF][START_REF] Hanger | Novel phosphorylation sites in tau from Alzheimer brain support a role for casein kinase 1 in disease pathogenesis[END_REF]. Recently, using a targeted mass spectrometry method, our group has demonstrated that p-tau235 is highly increased in neuropathologically confirmed AD brain when compared with control cases [START_REF] Lantero-Rodriguez | P-tau235: a novel biomarker for staging preclinical Alzheimer's disease[END_REF]. These findings align with previous studies, indicating that phosphorylation at serine 235 hampers tubulin assembly, compromising normal microtubules dynamics [START_REF] Sengupta | Phosphorylation of tau at both Thr 231 and Ser 262 is required for maximal inhibition of its binding to microtubules[END_REF][START_REF] Cho | Primed phosphorylation of tau at Thr231 by glycogen synthase kinase 3beta (GSK3beta) plays a critical role in regulating tau's ability to bind and stabilize microtubules[END_REF]. We developed a single molecule array (Simoa) assay capable of measuring p-tau235 in CSF, demonstrating that this novel biomarker is highly specific for AD and that it increases during preclinical stages, when only subtle abnormalities in CSF Aβ 1-42/40 can be detected [START_REF] Lantero-Rodriguez | P-tau235: a novel biomarker for staging preclinical Alzheimer's disease[END_REF]. Interestingly, when examining postmortem brain samples, phosphorylation at serine 235 appears to occur only when preceded by nearby phosphorylation at threonine 231, in what appears to be a sequential phosphorylation event. Due to the translatability of this sequential phosphorylation event from the brain into CSF, p-tau235 has been previously proposed as a potentially useful staging biomarker of preclinical AD (CSF p-tau235 positivity being indicative of late asymptomatic AD stage) [START_REF] Lantero-Rodriguez | P-tau235: a novel biomarker for staging preclinical Alzheimer's disease[END_REF]. With clinical trials increasingly focusing their intervention at the earliest stages of AD, CSF p-tau235 may be useful to discriminate early from late preclinical AD cases and to evaluate if novel compounds effectively prevent disease progression.

In order to further characterize this novel p-tau biomarker and contextualize its performance with other CSF p-tau species, we previously compared CSF p-tau235 with CSF p-tau217 and p-tau231 in a wellcharacterized research cohort including the AD continuum and a preclinical AD cohort. We demonstrated that CSF p-tau235 displays a statistically equal performance to that of CSF p-tau217 and p-tau231 when discriminating cognitively impaired CSF Aβ-positive cases from cognitively impaired CSF Aβ-negative cases. However, the cases enrolled in that study belonged to a well-characterized research cohort, which does not fully reflect the patients from daily clinical practice including atypical AD, other dementia and comorbidities. Therefore, evaluating the performance of CSF p-tau235 in the routine practice of a memory clinic is much needed, since in these settings patients present with a higher heterogeneity in demographics, comorbidities and disease presentations [START_REF] Van Der Mussele | Behavioral syndromes in mild cognitive impairment and Alzheimer's disease[END_REF][START_REF] Valero | Interaction of neuropsychiatric symptoms with APOE epsilon4 and conversion to dementia in MCI patients in a Memory Clinic[END_REF][START_REF] Xiong | Complex interactions underlie racial disparity in the risk of developing Alzheimer's disease dementia[END_REF][START_REF] Santiago | The impact of disease comorbidities in Alzheimer's disease[END_REF]. As a secondary aim, we tested whether CSF p-tau235 was associated with cognitive performance.

Paris cohort enrolled a total of 212 patients who had undergone CSF analysis at the Centre of Cognitive Neurology at Lariboisière Fernand-Widal University Hospital between March 2014 and December 2019, including participants with subjective cognitive decline (SCD, n=21), non-AD mild cognitive impairment (non-AD MCI, n=45), AD-MCI (n=40), AD dementia (n=75) and non-AD dementia (n=31). Non-AD dementia patients encompassed patients with dementia with Lewy bodies (DLB, n=12), frontotemporal dementia (FTD, n=15), vascular cognitive impairment and dementia (VCID, n=3) and Creutzfeldt Jakob disease (n=1). Patients underwent a thorough clinical examination involving personal medical and family histories, treatment, neurological examination, extensive neuropsychological assessment, APOE genotyping, brain magnetic resonance imaging (MRI) extensive neuropsychological evaluation, MRI, APOE genotyping, blood and CSF analysis and fluid sampling for collection (blood and CSF). The diagnosis for each patient was made during multidisciplinary consensus meetings (including neurologists, neuropsychologists, gerontologists, neuroradiologist and biochemists) considering results of validated CSF biomarkers and according to clinical diagnostic criteria for AD dementia [START_REF] Jack Cr | NIA-AA research framework: toward a biological definition of Alzheimer's disease[END_REF], MCI due to AD (AD-MCI) [START_REF] Petersen | Mild cognitive impairment criteria in Alzheimer's disease neuroimaging initiative: meeting biological expectations[END_REF], DLB [START_REF] Mckeith | Diagnosis and management of dementia with Lewy bodies: Fourth consensus report of the DLB Consortium[END_REF] and FTD [START_REF] Rascovsky | Sensitivity of revised diagnostic criteria for the behavioural variant of frontotemporal dementia[END_REF]. AD patients displayed CSF biomarkers on the AD continuum [START_REF] Jack Cr | NIA-AA research framework: toward a biological definition of Alzheimer's disease[END_REF]. MCI of other causes (non-AD MCI) included patients with psychiatric disorder, sleep apnea, or systemic disease. Non-AD MCI presented with normal CSF biomarkers or suspected non-Alzheimer pathophysiology (normal Aβ 1-42/40 , high p-tau and/or high t-tau). Included SCD participants were individuals with several years of clinical follow-up for a clinical complaint, presenting with normal cognitive testing and no abnormalities at imaging and CSF examinations [START_REF] Molinuevo | Implementation of subjective cognitive decline criteria in research studies[END_REF][START_REF] Jessen | The characterisation of subjective cognitive decline[END_REF].

BIODEGMAR cohort

The BIODEGMAR cohort is an observational longitudinal study that enrolls patients with neurodegenerative diseases visiting the Cognitive Decline and Movement Disorders Unit of Hospital del Mar (Barcelona, Spain). The procedures of the BIODEGMAR study include extensive neuropsychological evaluation, MRI, APOE ε4 genotyping, lumbar puncture for CSF collection and blood sampling. Clinical evaluation was performed by a neurologist, including anamnesis, physical examination and clinical diagnosis. Neuropsychological evaluation was performed by a neuropsychologist and consisted of a series of standardized cognitive tests and functional scales. A comprehensive description of the BIODEG-MAR cohort has been previously published, with further details on the inclusion and exclusion criteria and on core AD CSF biomarker procedures, including assay details and cutoff determination [START_REF] Puig-Pijoan | The CORCOBIA study: cut-off points of Alzheimer's disease CSF biomarkers in a clinical cohort[END_REF][START_REF] Ashton | Plasma and CSF biomarkers in a memory clinic: headto-head comparison of phosphorylated tau immunoassays[END_REF]. In this study, 175 patients of the BIODEGMAR cohort were included from 27 April 2017 to 24 July 2020. The clinical diagnoses included SCD (n=18, similar diagnostic criteria as for Paris cohort), MCI (n=74) and, for subjects with moderate to severe cognitive impairment, possible AD dementia (n=11), probable AD dementia (n=43), LBD (n=2), extrapyramidal syndrome (n=2), vascular cognitive impairment and dementia (VCID; n=4), progressive supranuclear palsy (PSP; n=2), corticobasal syndrome (n=4), behavioral variant of FTD (bvFTD; n=4), primary progressive aphasia (PPA; n=8) and cerebral amyloid angiopathy (CAA; n=3).

Cohort stratification

Patient data from the Paris cohort and the BIODEGMAR cohort was examined using two stratification criteria: (i) according to clinical syndrome (cognitively unimpaired [CU], MCI, or dementia) and CSF amyloid status (Aβ-/ Aβ+, as defined by Lumipulse CSF Aβ 1-42/40 , Supplementary Table 3), resulting in six groups: CU Aβ-, CU Aβ+, MCI Aβ-, MCI Aβ+, dementia Aβ-and dementia Aβ+ (clinical diagnosis included in each group are available in Supplementary Tables 1 and2); (ii) based on the Aβ (A) and tau (T) status defined using CSF Aβ 1-42/40 and p-tau181, respectively (Lumipulse ® ) into A-T-, A+Tand A+T+ (the A-T+ group considered suspected non-AD pathology [SNAP] was not included in the statistical analysis, but is depicted in the boxplots). The clinical diagnoses included in each group are available in Supplementary Tables 5 and6). Additionally, CSF p-tau235 levels across clinical diagnostic groups for both cohorts are available in Supplementary Figure 1.

Neuropsychological assessment

All included participants were evaluated using the Mini-Mental State Examination (MMSE). Detailed cognitive assessment was available for a subsample of participants in both the Paris cohort (total n=135: CU [n=14], MCI patients [n=64], dementia patients [n=57]) and the BIODEGMAR cohort (total n=139: CU [n=18], MCI [n=59], dementia [n=62]). In the Paris cohort, memory domain scores were evaluated using total immediate and delayed recall of Free and Cued Selective Reminding Test (FCSRT), and the delayed matching-to-samples 48 test (DMS 48) for visual memory testing. Executive function was assessed using forward and backward digit span, frontal assessment battery and letter and animal fluencies. The language domain was evaluated using the Dénomination Orale 80 test, a naming test. In BIODEG-MAR cohort, memory domain was evaluated using total immediate and delayed recall of FCSRT and the Memory Impairment Screen; executive functions, with backward digit span, TMT A and B; language domain, with the Boston naming test. Z-scores were computed from the control group scores as a reference. Domain scores were obtained by averaging z-scores of the individual tests available results within that domain and the global cognition score by averaging the 3 domains' z-scores.

Biomarker measurements

In the Paris cohort, core AD CSF biomarkers (Aβ 1-42/40 ratio, p-tau181 and t-tau) were measured using the clinically validated Lumipulse ® G1200 assay (Fujirebio) [START_REF] Leitao | Clinical validation of the Lumipulse G cerebrospinal fluid assays for routine diagnosis of Alzheimer's disease[END_REF]. Biomarker measurements for CSF p-tau181, p-tau217 and p-tau231 were performed using in-house Simoa assays. CSF p-tau181 and p-tau217 measurements were previously reported [START_REF] Karikari | Head-to-head comparison of clinical performance of CSF phospho-tau T181 and T217 biomarkers for Alzheimer's disease diagnosis[END_REF] and were included here in order to allow the comparison with novel CSF p-tau235. In the BIODEGMAR cohort, core AD CSF biomarkers (Aβ 1- 42/40 ratio, p-tau181 and t-tau) were measured at Laboratori de Referència de Catalunya (LRC) with Lumipulse G600II (Fujirebio). Cutoff values for biomarkers and their ratios (Aβ 1-42/40 , p-tau181, t-tau) were previously defined in the CORCOBIA study [START_REF] Puig-Pijoan | The CORCOBIA study: cut-off points of Alzheimer's disease CSF biomarkers in a clinical cohort[END_REF]. Biomarker measurements for CSF p-tau181 and p-tau231 were performed using inhouse Simoa assays and were previously reported [START_REF] Ashton | Plasma and CSF biomarkers in a memory clinic: headto-head comparison of phosphorylated tau immunoassays[END_REF].

CSF p-tau235 measurements

CSF p-tau235 measurements were performed at the Clinical Neurochemistry Laboratory at the University of Gothenburg using a Simoa HD-X instrument (Quanterix), and all samples were blinded. Prior to the analysis, samples were allowed to thaw at room temperature for 45 min. Thawed samples were subsequently vortexed for 15 s, after which they were plated and diluted 1:2 using Tau2.0 sample assay diluent (Quanterix). Due to sample volume availability, CSF samples from the Paris cohort were run in singlicates, while BIODEGMAR cohort samples were run in duplicates. An eight-point calibration curve was generated using recombinant full-length GSK-3β-phosphorylated tau-441 (SignalChem) and run in duplicates. Two internal quality controls (iQC, low and high) were run at the beginning and end of each plate, also in duplicates. Repeatability (%CV r ) and intermediate precision (%CV Rw ) values for both cohorts were ˂15%.

Further details on assay specifications and validation can be found elsewhere [START_REF] Lantero-Rodriguez | P-tau235: a novel biomarker for staging preclinical Alzheimer's disease[END_REF].

Statistical analysis

Statistics were performed using SPSS (v26, IBM, Armonk, NY, USA) and Scistat (Ostend, Belgium). Graphs were generated using GraphPad PRISM (v7.03, San Diego, CA, USA) and R version 4.1 (https:// www.r-proje ct. org/). Boxplots show the median and interquartile range (IQR), with individual data points for all participants always shown. Statistical analyses were performed using parametric or non-parametric methods when appropriate, based on the normality or not of the data. Group comparisons between two categories were performed using a t-test and Mann-Whitney U test. All comparisons of multiple groups were tested with a one-way ANOVA adjusted for age and sex followed by Bonferroni-corrected post-hoc pairwise comparisons. Adjusted P-values for covariates are indicated as P ADJ . For each of the CSF p-tau biomarkers, accuracy for binary outcomes was determined using receiver operating characteristics (ROC) analysis and area under the curve (AUC). AUC values are accompanied by the 95% confidence interval, abbreviated as CI 95% . Performance comparison between different p-tau biomarkers was analysed using DeLong test (MedCalc). Spearman's rank correlation (r S ) was used to determine associations between biomarkers. Association of CSF p-tau biomarkers with global and domain-specific cognitive z-scores was studied with linear regression models adjusted for age, sex and level of education.

Results

Cohort characteristics

A total number of 387 participants were included in this study, which belong to two independent clinical cohorts (Paris cohort [n=212]; BIODEGMAR cohort [n=175]). Demographics and stratification in syndromic groups (CU Aβ-/Aβ+, MCI Aβ-/Aβ+ and dementia Aβ-/Aβ+) are presented in Table 1 for both cohorts. In the Paris cohort, MCI Aβ+ and dementia Aβ+ were older than CU Aβ-group (P<0.0001). There was a weak yet significant association with age in both cohorts (Paris cohort: r S =0.32, P <0.0001; BIODE-GMAR cohort: r S =0.17, P <0.05). There were no sex differences between groups (P ≥0.05; χ 2 test). In both cohorts, CSF p-tau levels were significantly higher in APOE ε4 carriers (overall P <0.0001). As expected, there were significant differences in MMSE scores across groups in both cohorts (P <0.0001), which, as compared with CU, gradually decreased in MCI and further in patients with dementia.

Table 1 Demographics and biomarkers levels

CSF p-tau235 across syndromic and CSF Aβ-positive and CSF Aβ-negative groups

All comparisons of multiple groups were adjusted for age and sex. In the Paris cohort, CSF p-tau235 was significantly higher in MCI Aβ+ and dementia Aβ+ when compared with CU Aβ-, MCI Aβ-and dementia Aβ-groups (all P ADJ <0.0001) (Fig. 1a). Similarly, in the BIODEGMAR cohort, CSF p-tau235 was significantly increased in MCI Aβ+ and dementia Aβ+ when compared with CU Aβ-, MCI Aβ-and dementia Aβ-(all P ADJ <0.001) (Fig. 1b). CSF p-tau235 was also significantly higher in dementia Aβ+ participants when compared with CU Aβ+ (despite this group being only comprised by seven subjects, P ADJ =0.038), but no statistical differences were found between MCI Aβ+ and CU Aβ+. No significant differences were found between CU Aβ-and CU Aβ+, likely due to small sample sizes (eleven and seven cases, respectively), although higher CSF p-tau235 levels in CU Aβ+ were noticeable (Fig. 1b).

CSF p-tau235 discriminatory accuracy identifying CSF amyloidosis in syndromic groups

We then investigated the discriminatory accuracies of CSF p-tau235 to detect CSF amyloidosis between each of the syndromic groups and compared its performance with other novel N-terminal directed CSF p-tau biomarkers, specifically p-tau181, p-tau217 and p-tau231 (Fig. 2).

In the Paris cohort, CSF p-tau235 displayed high accuracies when discriminating dementia Aβ-from dementia Aβ+ (AUC Dementia Aβ-vs Aβ+ =0.96, CI 95% =0.90-0.99) and MCI Aβ+ from MCI Aβ-(AUC MCI Aβ-vs Aβ+ =0.90, CI 95% =0.82-0.96) (Fig. 2a). In these two scenarios, CSF p-tau235 performance showed no statistical differences when compared with CSF p-tau181 while matched CSF p-tau231 (AUC MCI Aβ-vs Aβ+ =0.88, CI 95% =0.78-0.94) (Fig. 2b).

CSF p-tau235 across AT groups

All comparisons of multiple groups were adjusted for age and sex. In the Paris cohort, CSF p-tau235 levels progressively increased across the AD continuum, that is, from A-T-to A+T-and from A+T-to A+T+ (Fig. 3a). A borderline significant increase in CSF p-tau235 levels was observed between A-T-and A+T-(P ADJ =0.054). This was followed by a prominent increase between A+T-and A+T+ (P ADJ <0.0001). CSF p-tau235 concentration was also significantly higher in A+T+ when compared with A-T-(P ADJ <0.0001) (Fig. 3a). In the BIODEGMAR cohort, CSF p-tau235 also followed an increasing trajectory across the AD continuum, although no significant differences were found between A-T-and A+T-subjects when performing multiple comparisons (Fig. 3b). On the other hand, the levels of CSF p-tau235 were significantly higher in A+T+ when compared with A-Tand A+T-(P ADJ <0.0001 for both) (Fig. 3b). Finally, we stratified patients based on exclusively CSF Aβ, which resulted in CSF p-tau235 being highly increased in A+ cases when compared with A-in both cohorts (P<0.0001 for all) (Supplementary Figure 2). 4). Comparisons of AUC values between CSF p-tau235 and other available CSF p-tau biomarkers were determined using DeLong test (significance is indicated in bold)

CSF p-tau235 discriminatory accuracy identifying CSF amyloidosis in AT groups

We then examined the performance of CSF p-tau235 for discriminating the AT groups which comprise the AD continuum, that is A-T-, A+T-and A+T+ (Fig. 4). 4a).

In the Paris cohort, CSF p-tau235 displayed high accuracy discriminating A-T-from A+T-(AUC

In the BIODEGMAR cohort (Fig. 4b), CSF p-tau235 displayed equal accuracies when discriminating A-T-from A+T-(AUC A-T-vs A+T-=0.79, CI 95% =0.68-0.87) as CSF p-tau181 (AUC A-T-vs A+T-=0.80, CI 95% =0.69-0.88) and CSF p-tau231 (AUC A-T-vs A+T-=0.80, CI 95% =0.70-0.88). The accuracy of CSF p-tau235 when discriminating A+T-and A+T+ (AUC A+T-vs A+T+ =0.95, CI 95% =0.88-0.98) statistically matched that of CSF p-tau181 (AUC A+T-vs A+T+ =0.94, CI 95% =0.88-0.98) and slightly outperformed that of CSF p-tau231 (AUC A+T-vs A+T+ =0.86, CI 95% =0.79-0.92, P=0.0448). Finally, CSF p-tau235 showed a nearly perfect accuracy discriminating A-Tand A+T+ (AUC A-T-vs A+T+ =0.97, CI 95% =0.93-0.99), same as that observed for CSF p-tau181 (AUC A-T-vs A+T+ =0.98, CI 95% =0.94-1.00) and CSF p-tau231 (AUC A-T-vs A+T+ =0.95, CI 95% =0.90-0.98) (Fig. 4b).

Finally, we evaluated the performance of CSF p-tau235 when discriminating Aβ+ from Aβ-individuals (Supplementary Figure 3, Supplementary Table 8). In the Paris cohort, CSF p-tau235 showed high accuracy distinguishing these two groups (AUC Aβ-vs Aβ+ = 0.94, CI 95% =0.90-0.97). When compared with other p-tau species (Supplementary Table 8), CSF p-tau235 performance was significantly lower than CSF p-tau181 (AUC Aβ-vs Aβ + =0.97, CI 95% =0.93-0.99, P˂0.05) and CSF p-tau 217 (AUC Aβ-vs Aβ + =0.98, CI 95% =0.95-0.99, P˂0.01), but equal to CSF p-tau231 (AUC Aβ-vs Aβ+ =0.96, CI 95% =0.92-0.98) (Supplementary Figure 3a). In the BIODEGMAR cohort, CSF p-tau235 (AUC Aβ-vs Aβ+ =0.89, CI 95% =0.83-0.93) matched the performance of both CSF p-tau181 (AUC Aβ-vs Aβ+ =0.91, CI 95% =0.85-0.94) and CSF p-tau231 (AUC Aβ-vs Aβ+ =0.88, CI 95% =0.82-0.92) (Supplementary Figure 3b).

CSF p-tau235 association with cognition

In both cohorts, MMSE score was associated with CSF p-tau235 after adjustment on age, sex and level of education (Paris: β=-0.304, P ADJ <0.0001; BIODEGMAR: β=-0.247, P ADJ =0.0009). The association remained significant after further adjustment on Aβ 1-42/40 ratio (Paris Cohort: β=-0.243, P ADJ =0.0012; BIODEGMAR cohort: β=-0.225, P ADJ =0.004).

Neuropsychological assessment was available for a subset of patients in the Paris (n=136) and BIODEG-MAR cohort (n=139). The cross-sectional associations of CSF p-tau235 levels and other CSF p-tau measurements with global and domain-specific cognition are shown in Fig. 5, after adjustment on age, sex and level of education. CSF p-tau 235 associated with global cognition in both cohorts (Paris β=-0.202, P ADJ =0.046; BIODEGMAR β=-0.363, P ADJ <0.0001), similar to all CSF p-tau measurements but CSF p-tau217. Regarding specific cognitive domains, the strongest association was found with memory impairment, both for CSF p-tau235 and other p-tau species (CSF p-235: Paris β= -0.255, P ADJ =0.0031; BIO-DEGMAR: β= -0.454, P ADJ <0.0001). A weak association was also found with executive domain function but only in BIODEGMAR (β= -0.229, P ADJ =0.0051). 7). Comparisons of AUC values between CSF p-tau235 and other available CSF p-tau biomarkers were determined using DeLong test (significance is indicated in bold)

Discussion

In this study, we investigated the novel CSF biomarker p-tau235 in two independent memory clinic cohorts and compared its diagnostic performance with other relevant p-tau species, specifically CSF p-tau181, p-tau217 and p-tau231. We found that (i) CSF p-tau235 is strongly associated with CSF amyloidosis, regardless of clinical presentation; (ii) high levels of CSF p-tau235 in cognitively impaired patients are highly indicative of MCI or dementia of the AD type; (iii) the main increase in CSF p-tau235 levels occurs between A+T-and A+T+, irrespective of the presence of symptoms or not; (iv) CSF p-tau235 displayed overall the same diagnostic performance as CSF p-tau181 and p-tau231, but a slightly lower diagnostic performance compared with CSF p-tau217; and (v) CSF p-tau235 levels associate with global and specific-domain cognitive decline.

In a previous study, we described CSF p-tau235 across the AD continuum using two research cohorts, demonstrating its high specificity for AD and its comparable performance to other CSF p-tau species [START_REF] Lantero-Rodriguez | P-tau235: a novel biomarker for staging preclinical Alzheimer's disease[END_REF]. However, these findings cannot be directly translated into the real-world of memory clinics, which is characterized by higher heterogeneity in population (both in socialeconomic and ethnic terms), clinical presentations and co-pathologies, vastly contrasting with the stringent inclusion/exclusion criteria and detailed pathophysiological characterizations in research cohorts. Thus, the main goal of the present study was to investigate the translatability of previous CSF p-tau235 results into the routine practice of memory clinic. Consistent with our previous findings in selected research cohorts (similarly stratified using Aβ 1-42/40 ) [START_REF] Lantero-Rodriguez | P-tau235: a novel biomarker for staging preclinical Alzheimer's disease[END_REF], CSF p-tau235 was elevated across CSF Aβ-positive groups in both clinical cohorts. CSF p-tau235 was increased in MCI Aβ+ and dementia Aβ+ showing similar levels between the two groups. This aligns well with previous results in a preclinical AD cohort in which CSF p-tau235 measurements seemingly plateau between MCI Aβ+ and AD Aβ+ [START_REF] Lantero-Rodriguez | P-tau235: a novel biomarker for staging preclinical Alzheimer's disease[END_REF]. Moreover, in the BIODEGMAR cohort, CSF p-tau235 was noticeably higher in CU Aβ+ when compared with CU Aβ-, and despite lacking sufficient statistical power (eleven and seven cases, respectively), it approached significance. Hence, these results from clinical practice are concordant with our previous results in research cohorts, where CSF p-tau235 was increased in Aβ+ subjects both in preclinical and symptomatic AD [START_REF] Lantero-Rodriguez | P-tau235: a novel biomarker for staging preclinical Alzheimer's disease[END_REF]. Furthermore, these results indicate that high levels of CSF p-tau235 are strongly associated with CSF amyloidosis and highly indicative of MCI or dementia of an AD type. In terms of diagnostic accuracy identifying Aβ+ in MCI and dementia, CSF p-tau235 showed equal performance to that of CSF p-tau181 and p-tau231, but slightly lower than CSF p-tau217. Because these differences were very marginal, with almost fully overlapping 95% confidence intervals, and together with the lack of CSF p-tau217 measurements in the BIODEGMAR cohort to corroborate these results, this should be cautiously interpreted. CSF p-tau235 followed an increasing trajectory across AT groups, which was characterized by a minor increment between A-T-and A+T-, followed by a prominent increase from A+T-to A+T+. These differences across AT groups are similar to those previously reported in preclinical AD [START_REF] Lantero-Rodriguez | P-tau235: a novel biomarker for staging preclinical Alzheimer's disease[END_REF], despite the fact that the cohorts analysed here are memory clinic-based, and thus included proportionally more symptomatic cases. Therefore, this suggests that the increase in CSF p-tau235 is more prominent when abnormalities in CSF Aβ and p-tau are established (i.e. A+T+ cases), regardless of these cases being symptomatic, as shown here, or asymptomatic, like previously reported [START_REF] Lantero-Rodriguez | P-tau235: a novel biomarker for staging preclinical Alzheimer's disease[END_REF]. CSF p-tau235 showed high performance discriminating all three AT groups and was, for the most part, comparable to other CSF p-tau biomarkers. When compared with CSF p-tau181, CSF p-tau235 was outperformed when discriminating A-Tfrom A+T-, but only in the Paris cohort. This may suggest that N-terminal p-tau181 species emerge earlier in response to Aβ pathology than N-terminal p-tau235 species (which in turn start increasing earlier than midregion p-tau181) [START_REF] Lantero-Rodriguez | P-tau235: a novel biomarker for staging preclinical Alzheimer's disease[END_REF][START_REF] Snellman | N-terminal and mid-region tau fragments as fluid biomarkers in neurological diseases[END_REF]. CSF p-tau217 also outperformed CSF p-tau235 in the same scenario (A-T-vs A+T-) in the Paris cohort, which aligns well with previous results indicating that CSF p-tau217 emerges prior to CSF p-tau235 [START_REF] Lantero-Rodriguez | P-tau235: a novel biomarker for staging preclinical Alzheimer's disease[END_REF]. On the other hand, no differences in performance could be found between CSF p-tau235 and CSF p-tau231, except when discriminating A+T-and A+T+ in the BIODEGMAR cohort, where CSF p-tau235 slightly outperformed CSF p-tau231. The marginal difference observed is likely due to the fact that CSF p-tau231 emerges very early in preclinical stages, and tends to plateau later on the continuum AD, thus rendering a lower discrimination between A+T-and A+T+ groups [START_REF] Ashton | Cerebrospinal fluid p-tau231 as an early indicator of emerging pathology in Alzheimer's disease[END_REF]. Finally, CSF p-tau235 showed a high diagnostic accuracy discriminating Aβ-and Aβ+, however, slightly lower than CSF p-tau181 and clearly lower than CSF p-tau217. Despite this, CSF p-tau235 exhibited the same performance as CSF p-tau231 in both the Paris and BIODEGMAR cohorts. In our previous publication, CSF p-tau235 started to increase in A+T-cases; however, the magnitude or extent of this increase was smaller than for other CSF p-tau species, whereas the increase was more pronounced between A+T-and A+T+ [START_REF] Lantero-Rodriguez | P-tau235: a novel biomarker for staging preclinical Alzheimer's disease[END_REF]. The same was observed here in this study, and the resulting overlap between the levels of CSF p-tau235 in A-Tand A+T-groups rendered the lower performance of this novel biomarker species identifying CSF amyloidosis. Thus, despite its high performance, other p-tau biomarkers emerge earlier than CSF p-tau235 in response to CSF amyloidosis and, therefore, provide a superior performance discriminating A-T-from A+T-and Aβfrom Aβ+.

Higher CSF p-tau235 levels were associated with lower global cognition and memory function in both cohorts, which had not been previously investigated. CSF p-tau181 and CSF p-tau217 have been reported to associate with global cognition and memory impairment crosssectional and longitudinally, with p-tau217 showing stronger associations with cognitive decline [START_REF] Mielke | Comparison of CSF phosphorylated tau 181 and 217 for cognitive decline[END_REF]. In our study, CSF p-tau235 performed overall similarly to other p-tau species. We observed modest differences between assays; however, we cannot determine if this reflects differences in the analytical performances of the assays or differences in the biology underlying tau phosphorylation association with cognition.

Limitations/strengths

The strengths of this study include the use of two independent memory clinic-based cohorts, allowing us to validate and confirm our findings in two different clinical settings. Secondly, both cohorts had clinically validated biomarker measurements available, which enabled the detailed stratification of participants and the subsequent investigation of CSF p-tau235 in syndromic or AT groups. Moreover, the studied cohorts included measurements of other novel p-tau species, specifically N-terminal directed CSF p-tau181, p-tau217 and p-tau231 (all measured in the same analytical platform), making possible a head-to-head comparison between different p-tau residues. However, this study does not go without limitations. CSF p-tau217 measurements were not available in the BIODEGMAR cohort, and therefore, the biomarker comparison with CSF p-tau235 could only be explored in the Paris cohort. Another limitation is that CU Aβ+ cases were limited to the BIODEGMAR cohort, and their sample size was rather small. Considering that CSF p-tau235 increases late during preclinical AD [START_REF] Lantero-Rodriguez | P-tau235: a novel biomarker for staging preclinical Alzheimer's disease[END_REF], it is expected that in cohorts richer in CU cases (especially A+T-), other CSF p-tau biomarkers that abnormally emerge earlier in the AD continuum (such as p-tau231 and p-tau217) [START_REF] Lantero-Rodriguez | P-tau235: a novel biomarker for staging preclinical Alzheimer's disease[END_REF] would provide a superior performance identifying CSF amyloidosis in asymptomatic individuals. This would subsequently affect the A-T-vs A+T-analysis, where CSF p-tau235 discriminatory accuracy would likely be lower than that of other CSF p-tau biomarkers. A limited number of patients in non-AD dementia group also warranted further studies.

Conclusions

In conclusion, our memory-clinic-based study including two well-characterized cohorts brings further evidence that p-tau235 is a novel and specific CSF biomarker for AD diagnosis, both at MCI and dementia stages. Comparison with other CSF p-tau species, including p-tau181, p-tau217 and p-tau231, supports that CSF p-tau235 should be suitable for use in clinical settings. In addition, significant association with cognitive decline adds an argument to its relevance as a clinical tool to identify and monitor CSF amyloidosis along the whole AD continuum. Future studies will aim to elucidate whether CSF p-tau235 can predict cognitive decline in longitudinal samples and attempt to measure p-tau235 in blood samples.

Fig. 1

 1 Fig. 1 CSF levels of p-tau235 across syndromic groups stratified by CSF amyloidosis. a In the Paris cohort, CSF p-tau235 was significantly increased in both dementia Aβ+ and MCI Aβ+ when compared with CU Aβ-, MCI Aβ-and dementia Aβ-. b In the BIODEGMAR cohort, CSF p-tau235 was significantly higher in both dementia Aβ+ and MCI Aβ+ when compared with CU Aβ-, MCI Aβ-and dementia Aβ-. Additionally, CSF p-tau235 was significantly increased in dementia Aβ+ when compared with CU Aβ+. Data information: Boxplots show the median, IQR, and all the participants colour-coded based on the presence (red) or absence (blue) of CSF amyloidosis measured with Lumipulse CSF Aβ Aβ 1-42/40 . P-values were determined using one-way ANOVA adjusted by age and sex, followed by Bonferroni-corrected post hoc comparison (*P <0.05, **P <0.01, ***P <0.001, ****P <0.0001)

Fig. 2

 2 Fig. 2 Diagnostic performance of CSF p-tau235 when discriminating syndromic groups stratified by CSF amyloidosis. Both in a the Paris cohort and b the BIODEGMAR cohort CSF p-tau235 displayed high accuracies differentiating dementia Aβ+ from dementia Aβ-and MCI Aβ+ from MCI Aβ-, similar to CSF p-tau181 and p-tau231, and lower than CSF p-tau217. Data information: forest plots showing AUC values from ROC analysis (CI 95% available in Supplementary Table4). Comparisons of AUC values between CSF p-tau235 and other available CSF p-tau biomarkers were determined using DeLong test (significance is indicated in bold)

Fig. 3

 3 Fig. 3 CSF levels of p-tau235 across AT groups. In both a the Paris cohort and b the BIODEGMAR cohort, CSF p-tau235 followed an increasing trajectory across the AT continuum. CSF p-tau235 was significantly increased in A+T+ when compared with A-T-and A+T-, and approached significance between A-T-and A+T-in the Paris cohort. Data information: Boxplots show the median, IQR, and all the participants colour-coded based on the presence (red) or absence (blue) of CSF amyloidosis measured with Lumipulse CSF Aβ 1-42/40 . P-values were determined using one-way ANOVA adjusted by age and sex, followed by Bonferroni-corrected post hoc comparison (*P <0.05, **P <0.01, ***P <0.001, ****P <0.0001)

Fig. 4

 4 Fig.[START_REF] Blennow | CSF total tau, Abeta42 and phosphorylated tau protein as biomarkers for Alzheimer's disease[END_REF] Diagnostic performance of CSF p-tau235 when discriminating AT groups. a In the Paris cohort, CSF p-tau235 showed high accuracies in all three scenarios. CSF p-tau235 performance discriminating A-T-from A+T-was lower than CSF p-tau181 and p-tau217, but matched that of CSF p-tau231. When differentiating A+T-from A+T+ and A-T-from A+T+, CSF p-tau235 matched the performance of CSF p-tau181, p-tau217 and p-tau231. b In the BIODEGMAR cohort, CSF p-tau235 displayed high accuracies in all three scenarios, equal to those of CSF p-tau181 and p-tau231, slightly outperforming the latter when discriminating A+T-from A+T+. Data information: Forest plots showing AUC values from ROC analysis (CI 95% available in Supplementary Table7). Comparisons of AUC values between CSF p-tau235 and other available CSF p-tau biomarkers were determined using DeLong test (significance is indicated in bold)

Fig. 5

 5 Fig. 5 CSF p-tau 235 association with global cognition and specific cognitive domains. Comparison of CSF p-tau235 and other p-tau markers (p-tau181, p-tau217 and p-tau231) for global and domain-specific cognition in a the Paris and b the BIODEGMAR cohort. Data information: Linear regressions were adjusted for age, sex and level of education. Estimates are presented standardized to allow for comparison in between the different p-tau biomarkers. (*P <0.05, **P <0.01, ***P <0.001, ****P <0.0001)

Table 1 (

 1 continued)

	0.009		0.141		< 0.02		0.0267			< 0.0001		< 0.0001		< 0.0001		< 0.0001		< 0.0001														< 0.0001
	73.5	(4.5)	25	(39%)	39/62 (61%) a		7.7 (3.6)			20.4	(4.8) a	522	(153) a	0.043	(0.008) a	121.4 (57.9) a		759	(342) a		0 (0%)		64 (100%)		3	(5%)	61	(95%)	0 (0%)			871	(343) a
	74.4	(4.8)	19	(41%)	24/38 (63%) a		8.4 (4.6)			23.3	(3.9) a	556	(178) a	0.043	(0.009) a	112	(59.6) a	669	(323) a		0 (0%)		46 (100%)		11	(24%)	35	(76%)	0 (0%)			771	(257) a
	70.0 70.1	(6.1) (4.5)	6 2	(32%) (29%)	3/18 3/7	(17%) (43%)	7.75 (4.7) 11.4 (4.0)			22.2 28.7	(5.8) a (1.5)	986 634	(449) (206) a	0.091 (0.017) 0.049	(0.008) a	41.0 59.1	(24.4) (14.3)	311 407	(166) (107)		17 (89%) 0 (0%)		0 (0%) 7 (100%)		/ 6 (86%)		/ 1	(14%)	2 (11%) 0 (0%)			426 494	(168) (129)
	< 0.0001 70.4 71.4	(5.8) (6.1)	0.338 3 16	(27%) (57%)	< 0.001 1/10 4/24	(10%) (17%)	0.299 10.4 (4.9) 8.5 (4.6)			< 0.0001 28.4 25.6	(2.3) (2.7)	< 0.0001 1186 1164	(357) (549)	< 0.0001 0.091 (0.017) 0.0879	(0.012)	< 0.0001 56.8 50.2	(31.4) (30.9)	< 0.0001 307 357	(141) (230)		9 (82%%) 24 (86%)		0 (0%) 0 (0%)		/ /		/ /		2 (18%) 4 (14%)			< 0.0001 427 410	(110) (164)
	71.90 (8.61) a		30	(39%)	51/76	(67%) a	10.6 (2.4)			19.09 (5.74) a		497.88	(155.27) a	0.042	(0.009) a	113.53	(60.02) a	722.01	(393.87) a		0 (0%)		77 (100%)		10 (13%)		67 (87%)		0	(0%)		1223.94	(802.66) a
	64.6 66.30 (9.58) 66.04 (7.73) 72.40 (7.96) a	(9.42)	Male (% 8 16 15 16	male) (38%) (34%) (56%) (40%)	APOEε4 car-7/21 5/47 7/26 22/40	riers (33%) (11%) a (27%) (55%)	Level of 11.5 (2.4) 10.7 (2.2) 11.0 (2.1) 10.0 (2.5)	education,	years	MMSE score 27.14 (2.45) 24.00 (4.01) a 23.85 (4.87) a 23.82 (3.84) a	Core biomarkers Lumipulse	CSF 1060.95 1090.66 1027.78 553.00	Aβ 1-42 (245.23) (468.75) (354.02) (209.36) a	CSF 0.093 0.088 (0.012) 0.09 0.044	Aβ 1-42/40 (0.010) (0.01) (0.009) a	CSF pTau, 32.25 40.97 (20.81) 33.59 94.80	pg/mL (8.16) (10.50) (47.31) a	CSF t-tau, 236.62 333,085 362.26 594.83	pg/mL (63.69) (172.630) (341.90) (285.45) a	Biomarker profile	Normal 21 (100%) 39 (83%) 25 (92%) 0 (0%)	profile	AD con-0 (0%) 0 (0%) 0 (0%) 40 (100%)	tinuum	A+T-/ / / 7 (18%)		A+T+ / / / 33 (82%)		SNAP 0 8 (17%) 2 (8%) 0	(0%) (0%)	CSF p-tau biomarkers	CSF 224.72 300.708 242.23 965.91	N-term (66.33) (245.52) (110.58) (628.08) a	p-tau181,	pg/mL

  Data is shown as mean (SD) or n (%), as appropriate. We used Kruskal-Wallis test to compare age between groups and Pearson's chi-square to compare sex and APOE ε4 frequencies between groups. Fluid biomarkers levels and MMSE were compared with a one-way ANOVA adjusted by age and sex.

	N/A		< 0.0001				< 0.0001
	N/A		893	(469) a			41.6	(20.0) a
	N/A		788	(399) a			34.8	(12.9) a
	N/A		541	(144)			23.1	(3.65)
	N/A		331	(236)			17.4	(7.23)
	N/A		348	(201)			18.5	(11.3)
	< 0.0001 N/A		< 0.0001 313	(211)			< 0.0001 17.6	(8.10)
	16.62 (9.74) a		800.58	(640.95) a			28.03	(11.52) a
	2.14 13.88 (9.48) a	(1.67)	286.62 657.64	(114.72) (205.53) a			11.56 (4.30) 24.75 (8.11) a
	2.90	(2.50)	325.05	(148.44)			12.74 (5.67)
	1.84	(0.94)	271.93	(64.74)			11.19	(2.94)
			CSF	N-term	p-tau231,	pg/mL	CSF	N-term	p-tau235,	pg/mL

a P < 0.0001 versus controls. a P < 0.05 versus CU Aβ-Aβ 1-42 β-amyloid 42, Aβ 1-40 β-amyloid 40, CSF Cerebrospinal fluid, CU Cognitively unimpaired, MCI Mild cognitive impairment, MMSE Mini-Mental State Examination, N-term N-terminal, p-tau181 Tau phosphorylated at threonine 181, p-tau217 Tau phosphorylated at threonine 217, p-tau231 Tau phosphorylated at threonine 231, p-tau235 Tau phosphorylated at threonine 181, t-tau Total tau

Funding

Open access funding provided by University of Gothenburg. This study was funded by the Swedish Research Council (#2017-00915 and #2022-00732), Lantero-Rodriguez et al. Alzheimer's Research & Therapy (2023) 15:48 funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (Grant agreement No. 948677), Project "PI19/00155", funded by Instituto de Salud Carlos III (ISCIII) and co-funded by the European Union, and from a fellowship from "la Caixa" Foundation (ID 100010434) and from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 847648 (LCF/BQ/PR21/11840004).

Abbreviations

Supplementary Information

The online version contains supplementary material available at https:// doi. org/ 10. 1186/ s13195-023-01201-0.

Additional file 1: Supplementary Figure

Availability of data and materials

Bulk anonymized data can be shared by request from qualified investigators, providing data transfer is in agreement with EU legislation and decisions by the institutional review board of each participating center.

Declarations Ethics approval and consent to participate

All patients or their legal relatives in case of severe dementia gave written informed consent to their participation in this study. Collection and analysis of samples were approved by the ethic committee of Bichat University, Paris, France (CEERB GHU Nord n°10-037) for Paris cohort and by the local ethics committee (CEIC PSMAR, project code 2018/7805I) for the BIODEGMAR cohort. • support for research data, including large and complex data types • gold Open Access which fosters wider collaboration and increased citations maximum visibility for your research: over 100M website views per year

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Youri Peskine
email: youri.peskine@eurecom.fr

Raphaël Troncy
email: raphael.troncy@eurecom.fr

Analyzing COVID-Related Social Discourse on Twitter using Emotion, Sentiment, Political Bias, Stance, Veracity and Conspiracy Theories

Keywords: CCS CONCEPTS, Computing methodologies → Natural language processing Natural Language Processing, Transformers, BERT, COVID-datasets, Misinformation

Online misinformation has become a major concern in recent years, and it has been further emphasized during the COVID-19 pandemic. Social media platforms, such as Twitter, can be serious vectors of misinformation online. In order to better understand the spread of these fake-news, lies, deceptions, and rumours, we analyze the correlations between the following textual features in tweets: emotion, sentiment, political bias, stance, veracity and conspiracy theories. We train several transformer-based classifiers from multiple datasets to detect these textual features and identify potential correlations using conditional distributions of the labels. Our results show that the online discourse regarding some topics, such as COVID-19 regulations or conspiracy theories, is highly controversial and reflects the actual U.S. political landscape.

INTRODUCTION

As the amount of information shared online increase 1 , we are prone to face more misinformation on the web. Events such as the 2016 U.S. Presidential Elections [START_REF] Allcott | Social media and fake news in the 2016 election[END_REF] or the Brexit [START_REF] Hänska | Tweeting for Brexit: how social media influenced the referendum[END_REF] are prime examples of strongly discussed topics with large amount of false information shared online. Social media websites can have strong influence on shaping the beliefs of one individual, and can have consequences on real life topics such as politics [START_REF] Allcott | Social media and fake news in the 2016 election[END_REF][START_REF] Hänska | Tweeting for Brexit: how social media influenced the referendum[END_REF], science [START_REF] Kathie M D'i Treen | Online misinformation about climate change[END_REF], economics 2or health [START_REF] Mian | Coronavirus: the spread of misinformation[END_REF]. Considering that 'fake-news' tends to spread faster and wider than the truth [START_REF] Vosoughi | The spread of true and false news online[END_REF], researchers have started to help factcheckers scale-up their ability to verify information [START_REF] Nakov | Automated Fact-Checking for Assisting Human Fact-Checkers[END_REF]. The need of such technology has been even more evident with the recent COVID-19 pandemic, with misinformation shared profusely online, and the World Health Organization (WHO) describing it as an infodemic 3 . According to [START_REF] J S Brennen | Types, sources, and claims of COVID-19 misinformation[END_REF], the number of fact-check reports rose by more than 900% between January and March 2020, reflecting the large amount of misinformation shared about COVID- [START_REF] Naseem | COVIDSenti: A large-scale benchmark Twitter data set for COVID-19 sentiment analysis[END_REF].

While some research is focused on detecting misinformation, in this work, we focus on better understanding the online discourse around COVID-19 on dimensions that go beyond misinformation classification. We explore the relationships among emotion, sentiment, political bias, stance, veracity and conspiracy theories, by leveraging a dataset for each textual feature. We use three datasets for training models that detect sentiment, emotion and political bias, and we use those models on the other datasets to study in detail their interactions. We then compute the conditional distribution of the labels between those features to analyze and share some insights about their relationships. Notable results show that political bias plays a role in the stance toward COVID-19 regulations and conspiracy theories or that emotion and sentiment are used by people who share potentially misleading content. Our results can be reproduced using the code available at https://github.com/D2KLab/covid-twitter-discourse-analysis.

RELATED WORK

Online misinformation during the COVID-19 pandemic has urged researchers to study its prevalence in social media websites, such as Twitter. Many datasets have been built around annotating textual features in tweets during the pandemic. In this work, we selected three different core datasets, each one allowing the training of a model for the detection of one textual feature: COVID LTSE Attributes (Emotion) [START_REF] Gupta | COVID-19 Twitter Dataset with Latent Topics, Sentiments and Emotions Attributes[END_REF], COVIDSenti (Sentiment) [START_REF] Naseem | COVIDSenti: A large-scale benchmark Twitter data set for COVID-19 sentiment analysis[END_REF] and Russian Troll (Political Bias)4 [START_REF] Linvill | Troll Factories: Manufacturing Specialized Disinformation on Twitter[END_REF]. We also selected three additional external datasets, which will only be used for the evaluation of the correlation: COVID 19 Stance (Stance) [START_REF] Glandt | Stance Detection in COVID-19 Tweets[END_REF], Birdwatch (Veracity) [START_REF] Saeed | Crowdsourced Fact-Checking at Twitter: How Does the Crowd Compare With Experts?[END_REF] and MediaEval-FND (Conspiracy theories) [START_REF] Pogorelov | Combining Tweets and Connections Graph for FakeNews Detection at MediaEval[END_REF]. The three core datasets are also used for the evaluation of the correlation.

Datasets

2.1.1 [Core] COVID LTSE Attributes. The COVID LTSE Attributes dataset [START_REF] Gupta | COVID-19 Twitter Dataset with Latent Topics, Sentiments and Emotions Attributes[END_REF] contains 252 million of tweets from January 2020 to June 2021. The data was searched using keywords, such as 'wuhan' or 'corona'. This dataset is labeled with 17 attributes, such as topics or emotions. In this work, we focus on the emotion attributes, which has been labeled using the CrystalFeel5 pre-trained machine learning algorithm. [START_REF] Naseem | COVIDSenti: A large-scale benchmark Twitter data set for COVID-19 sentiment analysis[END_REF] is a dataset with labels for sentiment in COVID-related tweets. The tweets have been crawled from February 2020 to March 2020, using keywords such as 'coronavirus' or 'Corona Outbreak'. The data is annotated with TextBlob6 using the methodology described in [START_REF] Bandi | Socio-Analyzer: A Sentiment Analysis Using Social Media Data[END_REF] resulting in 90,000 annotations.

2.1.2 [Core] COVIDSenti. COVIDSenti

[Core] Russian

Troll. The Russian Troll dataset [START_REF] Linvill | Troll Factories: Manufacturing Specialized Disinformation on Twitter[END_REF] contains 2.9 million of tweets from February 2012 to May 2018. It is the only dataset with no COVID-related data. The tweets are from accounts associated with the Internet Research Agency, which interfered during the U.S. 2016 presidential elections. A detailed analysis of the disinformation tactics used by this group of people is available in [START_REF] Linvill | THE RUSSIANS ARE HACKING MY BRAIN!" investigating Russia's internet research agency twitter tactics during the 2016 United States presidential campaign[END_REF]. The data is labeled at the account level using five main categories ('Right troll', 'Left troll', 'Fearmonger', 'HashtagGamer' and 'NewsFeed'). In this work, we focus on 'Right Troll' and 'Left Troll' while the remaining labels are labeled as 'Other'.

[External] COVID 19

Stance. In the COVID 19 Stance dataset [START_REF] Glandt | Stance Detection in COVID-19 Tweets[END_REF], tweets are labeled with a stance towards a topic related to the pandemic. The data was crawled from February 2020 to August 2020 using keywords ('coronavirus', 'covid-19', etc.) or hashtags ('#lockdown', '#washhands', '#socialdistancing', etc.). The topics are 'Anthony S. Fauci', 'keeping schools closed', 'stay at home orders' and 'wearing a face mask', and the annotation was done with Amazon Mechanical Turk. Since the release of this dataset, numerous tweets have been deleted or removed, and we were only able to retrieve 3,616 tweets.

[External]

Birdwatch. Birdwatch7 is a crowdsourced factchecking program that allow Twitter users to identify potentially misleading tweets. The dataset used in [START_REF] Saeed | Crowdsourced Fact-Checking at Twitter: How Does the Crowd Compare With Experts?[END_REF] contains a total of 9,851 tweets that have been labeled by Birdwatch users with 'Misleading' or 'Not Misleading', from January 2021 to September 2021. As tweets can have multiple notes from multiple Birdwatch users, with different labels, we performed majority-voting to assign a single label to the tweet. If majority cannot be reached, the tweet is discarded.

[External]

MediaEval -FND. The MediaEval -FND dataset [START_REF] Pogorelov | Combining Tweets and Connections Graph for FakeNews Detection at MediaEval[END_REF] contains 1,912 tweets about COVID-19-related conspiracy theories. The tweets are labeled as 'Discussing', 'Promoting' or not related to a given conspiracy theory. In the original dataset, each tweet is annotated for nine different named conspiracy 8 . In this work, we merged the labels into three broad categories: 'Not related to any conspiracy', 'Discussing at least one conspiracy', 'Promoting at least one conspiracy'.

Classification Models

Transformer-based models [START_REF] Vaswani | Attention is All you Need[END_REF] have largely contributed to progress in many Natural Language Processing (NLP) tasks, including machine translation [START_REF] Dabre | A Survey of Multilingual Neural Machine Translation[END_REF][START_REF] Stahlberg | Neural machine translation: A review[END_REF], question answering [START_REF] Mccann | The Natural Language Decathlon: Multitask Learning as Question Answering[END_REF]30] and text classification [START_REF] Elnagar | Arabic text classification using deep learning models[END_REF][START_REF] Minaee | Deep Learning-Based Text Classification: A Comprehensive Review[END_REF]. Most notably, BERT [START_REF] Devlin | BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding[END_REF] has outperformed other methods, such as TF-IDF or Recurrent Neural Networks, while providing a pre-trained model that can be fine-tuned for specific tasks [START_REF] Sun | How to Fine-Tune BERT for Text Classification?[END_REF]. For example, Covid-Twitter-BERT (CT-BERT) [START_REF] Müller | COVID-Twitter-BERT: A Natural Language Processing Model to Analyse COVID-19 Content on Twitter[END_REF] has been trained on textual data from Twitter during the COVID-19 pandemic, which improves results on domain-specific datasets.

METHODOLOGY

In order to detect correlations, we build three text-classification models on the core datasets mentioned in Section 2.1, for sentiment, emotion and political bias. In this section, we will discuss the methodology to train the different models on their respective data, and explain how we analyze potential correlations between the textual features.

Model Training

Models are trained to perform text classification using supervised learning. As some datasets are very large, we sample 25,000 tweets from the COVID LTSE Attributes dataset and 42,000 from the Russian Troll dataset. We first apply some basic pre-processing on the text, by removing links and special characters. We then split datasets into a train set and a validation set with a 80/20 stratified split ratio. Models have pre-trained CT-BERT weights and a classification layer depending on the number of output classes. They are trained using Adam [START_REF] Diederik | Adam: A Method for Stochastic Optimization[END_REF] optimizer, with a weight decay of 1.10 -2 and a learning rate of 1.10 -5 for 25 epochs. We use a Cross Entropy loss with weights proportional to the inverse of the class distribution. We monitor the performance of the model with the F1 score on the validation set, and save the best performing model. We use the pytorch [START_REF] Paszke | PyTorch: An Imperative Style, High-Performance Deep Learning Library[END_REF] and huggingface-transformers [START_REF] Wolf | Transformers: State-of-the-Art Natural Language Processing[END_REF] libraries to implement our code. Results for the models during training are available in Section 4.1.

Correlation

In order to find possible correlations between the textual features, we used the different models to predict features on the other datasets. Core datasets are used for the training of the models and the prediction of other features, while external datasets are only used for prediction. For example, the model trained on COVIDSenti is used on the COVID LTSE Attributes dataset, the Russian Troll dataset, the COVDI-19-Stance dataset, the Birdwatch dataset and the MediaEval-FND dataset. This way, we can analyze the conditional distribution of the predicted labels given the ground truth labels. The results and analysis are presented in Section 4.2.

RESULTS

In this section, we discuss the results of the models during the training (4.1), and the analysis of the possible correlations between the features (4.2). Some examples of tweets in the dataset are available in Table 2, highlighting a particular predicted label.

Model performance

As discussed in Section 3.1, we split all core datasets into training and validation sets with a 80/20 stratified split ratio. Table 1 shows the performance of the models on the validation set.

The model based on the COVIDSenti dataset obtains the best score on its own evaluation set. This might be expected as sentiment detection is arguably the easiest task of the three. The overall performance of the models are fair, given the noise in the datasets, as COVID LTSE Attributes and COVIDSenti have been automatically annotated, and the Russian Troll dataset has been labelled at the user level, resulting in some generic tweets having annotations towards political bias.

Correlation Analysis

In order to detect some correlations between the studied textual features, we compute the matrix of frequency of the labels of two textual features in the data. The y-axis label 9 is the ground truth of the corresponding dataset, while the x-axis label represents the prediction of the model. Rows have been normalized to represent the conditional distribution of the predicted label given a groundtruth label. 4.2.1 Sentiment feature. Figure 1 shows the correlation between the sentiment feature and the other features. We can see in Figure 1a 1b 1c 1d that people against the mentioned topics tend to share a more negative sentiment. This is especially true for the topic 'Face masks', and less apparent for the topic 'School closures'. People in favor of the 'Face masks' and 'School closures' topics tend to use more negative sentiment as well. However, people use more positive sentiment when supporting 'Stay at home' orders.

Figure 1e shows that all emotions except happiness tend to be more negative than positive, which is expected. It also shows that anger is the emotion where negative sentiment is the most prevalent. Figure 1f shows that tweets that have a political bias use more sentiment (positive and negative) than other tweets. However the distribution of sentiment is the same for both Left and Right bias.

Figure 1g shows that tweets that share potentially misleading content tend to use slightly more negative sentiment than the nonmisleading tweets. Very few tweets share positive sentiment in this dataset overall, suggesting that people on Birdwatch are more Figure 2e shows that a majority of tweets from the COVIDSenti dataset use the fear emotion, even in positive tweets. This seems counter-intuitive and may be due to having numerous tweets about wishing people to stay safe, in fear of covid. However, positive tweets also use happiness a lot, which is to be expected. Political biased tweets are more likely to have an emotion than not, as shown in Figure 2f. Tweets from users tagged as having left bias tend to contain more happiness, while tweets from users having right bias tend to contain more anger.

Regarding veracity, in Figure 2g, anger is dominating the tweets sharing potentially misleading information, while emotion are slightly more even on not misleading tweets. In Figure 2h, we can see that emotion and conspiracy theories are not heavily correlated. We notice a slight decrease in anger in non-conspiracist tweets.

4.2.3

Political bias feature. Lastly, we analyze correlation between political bias and other textual features, highlighted in Figure 3. We again notice that some topics are controversial, for example, 'Face masks' and 'Stay at home'. In those topics, we see that people against (face masks) have more right political bias and people in favor have more left political bias. This reflects the U.S. political landscape during the pandemic, as Democrats governors had generally more strict mandates towards wearing face masks than their Republican counterparts [START_REF] Gelfand | Persuading republicans and democrats to comply with mask wearing: An intervention tournament[END_REF]. The topic of school closures and re-opening was also highly controversial, with Republicans leaning toward having more in-person classes and Democrats toward having more onlineclasses 10 . Figures 3e 3f show that specific sentiment and emotion are not strongly correlated to one political bias or the other. However, Figure 3g shows that potentially misleading tweets are not tied to one political bias, while non-misleading tweets are more likely to be shared with left political bias. Moreover, Figure 3h shows that tweets discussing conspiracy theories are more likely to have left political bias, while it is the opposite for tweets promoting conspiracy theories. This supports the findings in [START_REF] Rao | Political partisanship and antiscience attitudes in online discussions about COVID-19: Twitter content analysis[END_REF], which states that conservatives tend to share more anti-science information than pro-science, thus being more inclined towards conspiracy theories.

CONCLUSION

In this paper, we analyzed the correlations between five textual features: emotion, sentiment, political bias, stance and veracity. We leveraged relevant datasets to train three models to predict emotion, sentiment and political bias on COVID-19 related tweets. These models allowed us to analyze the conditional distribution of the different labels to better understand the online discourse. Main findings include that COVID-19-related regulations topics, such as 'Face masks', 'School closures' or 'Stay at home orders' are highly controversial, generating a lot of negative sentiment and anger emotion in the Twitter discourse. The users' political bias on those topics also outlined the stance of US politicians in the debate. Similarly, conspiracy theories are usually promoted with negative sentiment and right political bias, which might reflect the inclination of conservatives towards anti-science information.

Figure 1 :

 1 Figure 1: Distribution of the labels for the sentiment feature

Figure 2 :

 2 Figure 2: Distribution of the labels for the emotion feature

Figure 3 :

 3 Figure 3: Distribution of the labels for the political bias feature

Table 1 :

 1 F1-Score of the models on a validation set

	Core Dataset	F1-score
	COVID LTSE Attributes	0.622
	COVIDSenti	0.769
	Russian Troll	0.636

Table 2 :

 2 Examples of tweets from all the datasets. Ground Truth indicates the label of the tweet in its original dataset while Predicted Label is the output of one of the trained models I had such a good day with the students at @UNCG and @ncatsuaggies discussing activism, social justice, & organizing. They were incredible. Take sports away and Social Interaction in schools. Your kids will have a great immune system! Way to teach your kids your saving them from the coronavirus.Bill gates and all Ted Talk technocrats have wanted online learning for years. Wearing a mask and social distancing doesn't mean you are "living in fear. " It's like wearing a seat belt or using your headlights in the rain, it's for your safety and the safety of others. #WearADamnMask @Athens108 @realDonaldTrump It may have worked for an old Coronavirus that is a different virus from COVID19. All the studies coming out says chloroquine does not work for this virus. Dr. Fauci is always on the frontlines for all viruses. 87% of the deaths were caused by democrat leadership. Things like forcing nfected patents into nursing homes by executive order and banning HCQ. We now know that HCQ could have easily saved over 100000 lives over 20000 in NY alone. Trump was right. Democrats own the pandemic Zhou Yu, Jun Yu, Yuhao Cui, Dacheng Tao, and Qi Tian. 2019. Deep Modular Co-Attention Networks for Visual Question Answering. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

		Tweets	Ground Truth	Predicted Label
	(a)	Idc what you say, you're selfish if you refuse to wear a mask. This shouldn't be	Favor 'Face masks'	Negative sentiment
		political. #MaskUp #MaskMoaners
	(b)	@kylegriffin1 Close the damn schools until there is a vaccine. #NotMyChild	Favor 'School closures'	Negative sentiment
	(c)	If grocery stores can be open and people can risk their lives working there, then so	Against 'School closures'	Neutral sentiment
		can the schools and teachers. #OpenSchools
	(d)	Corona virus Day 4 diary entry: I have now been social distancing for the past 26	Sadness	Neutral sentiment
		years.
	(e)	Italy Declares State of Emergency Over Wuhan Coronavirus	Fear	Neutral sentiment
	(f)	Biden blames rise of COVID-19 cases on the unvaccinated: "This is a pandemic of	Not Misleading	Neutral sentiment
		the unvaccinated. "
	(g)	@saraecook Fauci is such a hypocrite! He knew back during the SARS outbreak	Against 'Fauci'	Anger emotion
		most people who died was due largely to cytokine storm. Much the same with
		Coronavirus. He had no problem with Hydroxychloroquine being used then. #Fau-
		ciFraud
	(h)	Trump and the White House are straight up publicly attacking the country's	Favor 'Fauci'	Anger emotion
		leading infectious disease expert during a #pandemic that has already killed nearly
		140,000 Americans. Yup, that tracks. #COVID19 #DrFauci
	(i)	The policymakers need to consider the fact that schools can't run without fees	Against 'School closures'	Sadness emotion
		and teachers can't survive without salary. #SaveOurSchools
	(j)
			Positive sentiment	Fear emotion
	(l)		Left political bias	Happiness emotion
	(m) THE ATTACK ON FREEDOM OF SPEECH CONTINUES! #CrookedHillary will	Right political bias	Anger emotion
		destroy the 1st Amendment Right of her Opposition!
	(n) Wake up! #OpenSchools	Against 'School closures'	Right political bias
	(o)		Favor 'Face masks'	Left political bias
	(p) #FauciIsAHero	Favor 'Fauci'	Right political bias
	(q) Promote conspiracy	Right political bias
	[30]

@GrandadJohn5 Good news that County cricket is starting up but no news on recreational cricket the cut off point for our league is August 8th after that only friendlies or right off the season #StaySafeStayHome No stance towards 'Stay at home' Happiness emotion (k) Coronavirus: is it safe to travel and should children be kept home?

https://www.forbes.com/sites/kenrapoza/2017/02/26/can-fake-news-impact-thestock-market/

3 https://www.who.int/health-topics/

infodemic 4 https://fivethirtyeight.com/features/why-were-sharing-3-million-russian-trolltweets/

https://socialanalyticsplus.net/crystalfeel/

https://textblob.readthedocs.io/en/dev/

Recently renamed Community Notes https://help.twitter.com/en/using-twitter/ community-notes

List of all the conspiracy theories: Suppressed Cures, Behaviour and Mind Control, Antivax, Fake Virus, Intentional Pandemic, Harmful Radiation/Influence, Population Reduction/Control, New World Order, Satanism

The labels 'N', 'H', 'A', 'S', 'F' represent the following emotions: 'None', 'Happiness', 'Anger', 'Sadness', 'Fear'.

https://www.pewresearch.org/fact-tank/2020/08/05/republicans-democrats-differover-factors-k-12-schools-should-consider-in-deciding-whether-to-reopen/

ACKNOWLEDGMENTS

This work has been partially supported by CHIST-ERA within the CIMPLE project (CHIST-ERA-19-XAI-003).