NeuroImage: Clinical 27 (2020) 102333 Contents lists available at ScienceDirect NeuroImage: Clinical journal homepage: www.elsevier.com/locate/ynicl The combined effect of amyloid-β and tau biomarkers on brain atrophy in T dementia with Lewy bodies ⁎ Carla Abdelnoura,b, , Daniel Ferreirac, Ketil Oppedald,e,f, Lena Cavalling, Olivier Bousigesh, Lars Olof Wahlundc, Jakub Horti,j, Zuzana Nedelskai,j, Alessandro Padovanik, Andrea Pilottok, Laura Bonannil, Milica G. Krambergerm, Mercè Boadaa, Eric Westmanc,n, Javier Pagonabarragao, Jaime Kulisevskyo, Frédéric Blancp, Dag Aarslandd,q a Research Center and Memory Clinic, Fundació ACE, Institut Català de Neurociències Aplicades, Universitat Internacional de Catalunya-Barcelona, Centro de Investigación en Red-Enfermedades Neurodegenerativas (CIBERNED), Barcelona, Spain b Department of Medicine of the Universitat Autònoma de Barcelona, Barcelona, Spain c Division of Clinical Geriatrics, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden d Centre for Age-Related Medicine, Stavanger University Hospital, Stavanger, Norway e Department of Radiology, Stavanger University Hospital, Stavanger, Norway f Department of Electrical Engineering and Computer Science, University of Stavanger, Stavanger, Norway g Department of Neuroscience, Karolinska Institutet and Department of Radiology Karolinska University Hospital, Stockholm, Sweden h Hôpitaux Universitaire de Strasbourg, Laboratoire de Biochimie et Biologie Moléculaire, et CNRS, Laboratoire de Neurosciences Cognitives et Adaptatives (LNCA), UMR7364, Strasbourg, France i Department of Neurology, Charles University, 2nd Medical Faculty and Motol University Hospital, Prague, Czech Republic j International Clinical Research Centre, Brno, Czech Republic k Neurology Unit, Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy l Department of Neuroscience Imaging and Clinical Sciences and CESI, University G d'Annunzio of Chieti-Pescara, Chieti, Italy m Department of Neurology, University Medical Centre Ljubljana, Medical faculty, University of Ljubljana, Ljubljana, Slovenia n Department of Neuroimaging, Centre for Neuroimaging Sciences, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK o Movement Disorders Unit, Neurology Department, Hospital de la Santa Creu i Sant Pau. Biomedical Research Institute (IIB-Sant Pau), Centro de Investigación en Red- Enfermedades Neurodegenerativas (CIBERNED), Barcelona, Spain p Hôpitaux Universitaire de Strasbourg, CMRR (Centre Mémoire de Ressource et de Recherche), Hôpital de jour, pôle de Gériatrie, et CNRS, laboratoire ICube UMR 7357 et FMTS (Fédération de MédecineTranslationnelle de Strasbourg), équipe IMIS/Neurocrypto, Strasbourg, France q Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK A R T I C LE I N FO A B S T R A C T Keywords: Background: Alzheimer’s disease (AD)-related pathology is frequently found in patients with dementia with Lewy body disease Lewy bodies (DLB). However, it is unknown how amyloid-β and tau-related pathologies influence neurode- Biomarkers generation in DLB. Understanding the mechanisms underlying brain atrophy in DLB can improve our knowledge Atrophy about disease progression, differential diagnosis, drug development and testing of anti-amyloid and anti-tau Alzheimer disease therapies in DLB. Neuroimaging Objectives: We aimed at investigating the combined effect of CSF amyloid-β42, phosphorylated tau and total tau on regional brain atrophy in DLB in the European DLB (E-DLB) cohort. Methods: 86 probable DLB patients from the E-DLB cohort with CSF and MRI data were included. Random forest was used to analyze the association of CSF biomarkers (predictors) with visual rating scales for medial temporal lobe atrophy (MTA), posterior atrophy (PA) and global cortical atrophy scale-frontal subscale (GCA-F) (out- comes), including age, sex, education and disease duration as extra predictors. Results: DLB patients with abnormal MTA scores had abnormal CSF Aβ42, shorter disease duration and older age. DLB patients with abnormal PA scores had abnormal levels of CSF Aβ42 and p-tau, older age, lower edu- cation and shorter disease duration. Abnormal GCA-F scores were associated with lower education, male sex, and older age, but not with any AD-related CSF biomarker. Conclusions: This study shows preliminary data on the potential combined effect of amyloid-β and tau-related ⁎ Corresponding author at: Research Center and Memory Clinic, Fundació ACE, Institut Català de Neurociències Aplicades, Universitat Internacional de Catalunya- Barcelona, Centro de Investigación en Red-Enfermedades Neurodegenerativas (CIBERNED). Department of Medicine of the Universitat Autònoma de Barcelona, Barcelona, Spain. E-mail address: cabdelnour@fundacioace.org (C. Abdelnour). https://doi.org/10.1016/j.nicl.2020.102333 Received 1 March 2020; Received in revised form 5 June 2020; Accepted 26 June 2020 Available online 02 July 2020 2213-1582/ © 2020 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/BY-NC-ND/4.0/). C. Abdelnour, et al. NeuroImage: Clinical 27 (2020) 102333 pathologies on the integrity of posterior brain cortices in DLB patients, whereas only amyloid-β seems to be related to MTA. Future availability of α-synuclein biomarkers will help us to understand the effect of α-synuclein and AD-related pathologies on brain integrity in DLB. 1. Introduction (Donaghy et al., 2018). However, associations between regional atrophy and tau-related pathology have not been investigated yet. Im- Neuropathological studies have shown that many patients diag- portantly, in vitro studies have found cross-seeding of alpha-synuclein, nosed with dementia with Lewy bodies (DLB) often have Alzheimer’s amyloid and tau proteins (Spires-Jones et al., 2017), thus the combi- disease (AD)-related pathology (Gomez-Isla et al., 1999; Schneider nation of proteinopathies may have additional or even synergistic et al., 2009; Halliday et al., 2011; Dugger et al., 2014; Sierra et al., contributions to neurodegeneration. Hence, we aimed to explore this 2016) In these mixed cases, it has been found that the degree of AD- question by investigating the combined effect of CSF Aβ42, T-tau and p- related pathology is moderate or severe in more than 70% of patients tau on regional brain atrophy in the E-DLB cohort, a large multi-center (Kosaka, 1990; Marui et al., 2004; Barker et al., 2002; Irwin et al., study involving 19 centers from Europe (Oppedal et al., 2019). Our 2017). The combination of these proteinopathies have implications in hypothesis was that DLB patients with abnormal levels of CSF Aβ42, T- the clinical phenotype. Thus, postmortem studies have shown that the tau and p-tau would have a higher level of brain atrophy, in particular, coexistence of amyloid-β and tau-related pathologies in addition to the in the medial temporal lobes and posterior cortices, delineating the defining alpha-synuclein pathology usually results in a less typical typical pattern of brain atrophy in AD (Oppedal et al., 2019). presentation of DLB core features: a lower frequency of recurrent visual hallucinations, parkinsonism, REM sleep behavior disorder (RBD) and fluctuating cognition (Merdes et al., 2003; Del Ser et al., 2001; 2. Materials and methods Tiraboschi et al., 2015; Compta et al., 2013; Murray et al., 2013), and a more severe disease course (Irwin et al., 2017; Howlett et al., 2015; 2.1. Participants population Kraybill et al., 2005; Williams et al., 2006). Similar findings have been obtained in vivo (Di Censo et al., 2020), A total of 86 DLB patients were selected from 6 centers of the E-DLB which is more relevant for the earlier disease stages compared to the cohort. Inclusion criteria to enter the E-DLB cohort are reported in end-stage diseases assessed at autopsy. In this regard, cerebrospinal previous publications (Kramberger et al., 2017). For the current study, fluid (CSF) and neuroimaging studies have shown concomitant AD-re- selection criteria were: 1) a diagnosis of probable DLB; 2) availability of lated biomarkers in a significant proportion of DLB patients who often CSF data; and 3) availability of MRI data. Detailed information about are older, have shorter disease duration and worse cognitive perfor- the centers that contributed to the current study is shown in mance (Van Steenoven et al., 2016; Gomperts et al., 2016; Abdelnour Supplementary tables. et al., 2016; Lemstra et al., 2017), mainly in orientation and memory (Andersson et al., 2011; Tagawa et al., 2015). Nevertheless, how AD-related pathology influences the neurode- 2.2. Diagnostic and clinical examination generative process in DLB is less studied. In AD, amyloid-β and tau- related pathologies are hypothesized to lead to neuronal injury (Hyman Because the E-DLB cohort was assembled retrospectively, many et al., 2012; Braak et al., 2006). DLB patients with concomitant AD- patients had been diagnosed before 2017. Hence, The DLB diagnosis related pathology have shown a faster rate of brain atrophy over time was made according to McKeith 2005 criteria (McKeith et al., 2005), measured with MRI, mainly in the medial temporal lobe, when com- but we were able to confirm the diagnosis of probable DLB according to pared with DLB patients without concomitant AD-related pathology McKeith 2017 criteria (McKeith et al., 2017) in 83 out of 86 patients. (Nedelska et al., 2015; Sarro et al., 2016; Blanc et al., 2017; Nelson Diagnosis was made by the treating physician, a group of at least two et al., 2009). This finding suggests that AD pathology may contribute to expert clinicians, or a multidisciplinary team at a consensus diagnostic medial temporal lobe atrophy (MTA) in DLB (Elder et al., 2017; Sarro meeting on the basis of all available clinical and diagnostic test data as et al., 2016). Similarly, when amyloid is present the patterns of de- previously reported (Oppedal et al., 2019; Kramberger et al., 2017). position and atrophy resembles that observed in AD (Shimada et al., Clinicians interviewed both patients and caregivers, recorded de- 2013; Donaghy et al., 2015; Irwin and Hurtig, 2018; Mak et al., mographic information as well as medical and drug history. All centers 2019ab) Understanding the underlying mechanisms of regional brain included a detailed medical history, aside from physical, neurological, atrophy that could reflect distinct pathologies could have treatment and psychiatric examinations using standardized scales such as the implications. For example, a typical AD pattern of brain atrophy in- motor subscale of the Unified Parkinson’s Disease rating scale (Fahn volving the medial temporal lobes and posterior cortices was associated et al., 1987) and the Neuropsychiatric Inventory (Cummings et al., with poorer response to acetylcholinesterase inhibitors in DLB patients 1994). Based on the clinical examination and/or the aforementioned (Graff-Radford et al., 2012). Also, amyloid or tau-targeted therapies scales the core diagnostic features fluctuating cognition, parkinsonism, might be effective in a subgroup of patients with DLB, i.e. it is im- and recurrent visual hallucinations were recorded as present or absent. portant to improve our understanding in what degree AD pathologies Neuropsychological evaluation and complementary tests to rule out may contribute towards personalized medicine approaches and im- secondary causes of dementia (routine blood tests and brain imaging) prove differential diagnosis, disease prognosis, and treatment response were performed. 26 out of the total sample of 86 subjects had available in DLB. DAT SPECT, 25 (96.15%) of which were abnormal. Combining CSF biomarkers and structural MRI may inform about the mechanisms underlying regional atrophy, but few studies have been performed in DLB and results are inconsistent. A recent study reported 2.3. Ethics an association between abnormal levels of CSF Aβ42 and MTA in DLB, as well as an association between abnormal levels of CSF total tau (T- Local ethics committees at the individual centres approved the tau) and global brain atrophy (van der Zande et al., 2018). By contrast, study. The patients gave their written consent to use the anonymised another study reported no differences between amyloid PET positive results of their clinical, instrumental and laboratory investigations for and negative DLB patients in hippocampal or gray matter volume research purposes. 2 C. Abdelnour, et al. NeuroImage: Clinical 27 (2020) 102333 2.4. CSF procedures prediction of the outcome), random forest investigates combined effects (the predictors do not compete with each other but “cooperate” in the CSF was obtained at all centers with the following procedures: 1) prediction of the outcome) (Machado et al., 2018) Combined effects are lumbar puncture at the L3-4 or L4-5 interspace; 2) collection in poly- closer to what we hypothesized in this study, i.e., amyloid-β (CSF Aβ42) propylene tubes and centrifuged for 10 min at 4 °C; and 3) storage in and tau-related (CSF p-tau) pathologies have a synergistic deleterious aliquots of 0.5 mL at −80 °C or −70 °C until further analysis. Further effect on brain integrity. When CSF Aβ42 and CSF p-tau as predictors details are summarized in Supplementary Table 1. CSF analyses were show a contribution to the prediction of brain atrophy, we can conclude performed locally according to standard routines. INNOTEST enzyme- that both pathologies have a combined effect on brain integrity, which linked immunosorbent assays (ELISA) were used to analyze T-tau and p- may reflect their synergy at the pathological level (i.e. the “coopera- tau (missing for 1 patient) in all samples and Aβ42 in 80 samples tion” between Aβ42 and CSF p-tau contributes to the prediction of brain (Fujirebio, Ghent, Belgium). The remaining 6 samples were analyzed atrophy). Further, random forest performs very similarly to other ma- for Aβ42 using ELISA kits from Biosource Europe S.A. CSF values were chine learning algorithms (Machado et al., 2018) but it was preferred in dichotomized as normal or pathological based on well-established our current study due to the nature of our variables. We performed center-specific cut-off values for each biomarker as previously de- three random forest models: one for each atrophy scale (MTA, PA, and scribed (Abdelnour et al., 2016) (Supplementary Table A.1). GCA-F) as the outcome variable. The random forest models were comprised of 5000 trees, providing an accurate estimation of the vari- 2.5. MRI analysis ables importance without introducing too much noise in the models due to the addition of redundant trees. Each of the trees was trained on Different neuroimaging acquisition protocols and MRI scanners randomly picked 70% of the data and subsequently tested on the un- were used as detailed in Supplementary Table A.2. The interval be- seen 30% of the data. Classification models (normal vs. abnormal) tween MRI and CSF collection ranged from 0 to 3 months in the ma- (Liaw and Wiener, 2002) were conducted, accounting for the fact that jority of the cases (73 out 86, which corresponds to 84.88%). In 13 the outcome variable may present with an unbalanced amount of cases patients the interval ranged from 3 to 12 moths (15.12%). We used in its two levels (e.g. normal MTA n = 53, abnormal MTA n = 34). The visual rating of MRI scans, which is more feasible for clinical use than classification error is reported as a measure of goodness of the model automated analysis, and is not influenced by between-center differences (out-of-the-bag estimated error rate, OOB-EER) (Breiman, 2001). When in acquisition protocols and MRI scanners. Ratings of all scans were outcome variables are dichotomous, as it is our case, the error by performed by one expert radiologist (L. C.) as previously described chance is 50%. Therefore, a classification below 50% is better than (Ferreira et al., 2017), who has excellent intra-rater reliability - chance, with values closest to 0% denoting better classification per- weighted κ of 0.94 and 0.89 for MTA in left and right hemispheres formance, hence good reliability of the model. We also report the im- correspondingly, 0.88 for PA, and 0.83 for GCA-F in 120 random cases- portance (Imp) of the predictors as a measure of their contribution to- (Ferreira et al., 2017) blinded to clinical data. T1-weigthed images were wards the prediction of the outcome variable (regional brain atrophy). used to investigate regional brain atrophy by using three visual rating Higher Imp values denote stronger contribution to the prediction. The scales: the medial temporal lobe atrophy scale (MTA) (Scheltens et al., random forest results were further complemented with the Pearson 1992), the posterior atrophy scale (PA) (Koedam et al., 2011) and the correlation coefficient to easily represent the magnitude and direction global cortical atrophy scale-frontal subscale (GCA-F) (Ferreira et al., of the association between variables (bivariate association). P-values of 2016). Detailed information regarding the visual rating scales is pro- Pearson correlation are reported for completeness of information. vided elsewhere (Ferreira et al., 2017). The visual rating scores were dichotomized into normal and abnormal values in accordance with previously proposed cut-offs (Ferreira et al., 2015) 3. Results 2.6. Statistics 3.1. Sample features The statistical analyses were done using R (www.R-project.org) Clinical and demographic features of the sample are reported in version 3.2.4 and IBM SPSS version 26. Descriptive results are shown as Table 1. Of the 86 patients, the number of patients with pathological mean ± SD for normally distributed continuous variables, and number CSF values is 28 (32.56%) for Aβ42, 17 (19.77%) for Total-Tau and 24 and percentage for categorical variables. (27.91%) for p-Tau. The number of patients with abnormal scores in the The aim of this study was to investigate the combined effect of visual rating scales was: MTA: 33 (38.37%), GCA-F: 34 (39.53%) and Aβ42, T-tau and p-tau (predictors) on regional brain atrophy as mea- PA: 45 (52.33%). Fig. 1 shows 3 examples of different combinations for sured with visual rating scales (outcome variables). All these measures CSF Aβ42, CSF p-Tau CSF and the visual rating scales. are dichotomous (0 normal, 1 abnormal). We also wanted to model the effects of age, sex, education and disease duration to investigate their possible added effect to the association between CSF biomarkers and regional brain atrophy. Age, education and disease duration are con- tinuous variables while sex is dichotomous (0 males 1 females). Further, our interest was to investigate the predictive power of all these vari- Table 1 Demographic and clinical data of the participants. ables in combination as predictors of regional brain atrophy, rather than investigating their partial effects. Random forest (classification) Features Mean (SD) Range (Breiman, 2001) was thus chosen given our aim, the nature of the Age at diagnosis 69.36 (8.85) 49–88 variables, the number of predictors and the sample size. Random forest Sex: Male N (%) 49 (56.98) is an ensemble method in machine learning that involves growing of Years of education 11.24 (4.08) 5–22 multiple decision trees via bootstrap aggregation (bagging). Each tree Disease duration (years) 4.04 (3.10) 0.5–14 predicts a classification independently and votes for the corresponding MMSE 24.85 (3.72) 15–30 Parkinsonism (%) 82.6 (N = 71) class. The best model for each outcome variable is chosen from the Visual hallucinations (%) 58.1 (N = 50) majority of votes (Machado et al., 2018). Importantly, contrarily to Fluctuating cognition (%) 75.6 (N = 65) other predictive methods such as multiple linear or logistic regression that investigate partial effects (competition among predictors in the N: number. MMSE: Minimental State Examination. 3 C. Abdelnour, et al. NeuroImage: Clinical 27 (2020) 102333 Fig. 1. Normal and pathological CSF values of Aβ42 and p-Tau combined with visual rating scales. CSF levels of Aβ42 and p-Tau were dichotomized according the cut-offs of each center into normal or pathological values. MTA, PA and GCA-F visual rating scales were used to measure regional atrophy based on T1-weigthed images. A+= pathological CSF Aβ42; A− = normal CSF Aβ42; T+= pathological CSF p-Tau; T− = normal CSF p-Tau; MTA = medial temporal atrophy scale; PA = posterior atrophy scale; GCA-F = global cortical atrophy scale – frontal subscale; A = anterior part of the brain; P = posterior part of the brain; R = right; L = left. 3.2. Association between AD CSF biomarkers and visual rating scores Classification performance in the three random forest models was measured with MRI better than chance: MTA, OOB-EER = 32.56%; PA, OOB- EER = 44.83%, GCA-F, OOB-EER = 24.14% (Table 3). The classifi- The distribution of abnormal scores in the visual rating scales in cation error for normal MTA was 24.52% and for abnormal MTA it was relation to normal or pathological CSF Aβ42, T-tau and p-tau is pre- 45.45%. The classification error for normal PA was 46.34% and for sented in Table 2. abnormal PA it was 43.48%. The classification error for normal GCA-F Table 2 Distribution of abnormal visual rating scores between normal and pathological AD CSF biomarkers groups. CSF Aβ42 CSF T-tau CSF p-tau Visual rating scales Normal Abnormal Normal Abnormal Normal Abnormal Abnormal MTA N (%) 18 (54.55) 15 (45.45) 28 (84.85) 5 (15.15) 26 (78.79) 7 (21.21) Age (mean and SD) 69.78 (8.37) 73.93 (6.68) 70.86 (8.01) 76.20 (5.07) 70.27 (8.13) 76.86 (3.34) Sex (Male N and %) 11 (61.11) 6 (40) 18 (64.29) 2 (40) 17 (65.38) 3 (42.86) Disease duration (mean and SD) 3.64 (2.91) 2.43 (2.35) 3.11 (2.80) 3.00 (2.35) 2.87 (2.54) 3.93 (3.32) Abnormal PA N (%) 25 (55.56) 20 (44.44) 33 (73.33) 12 (26.67) 29 (64.44) 16 (35.56) Age (mean and SD) 67.24 (9.40) 75.70 (6.78) 70.24 (8.22) 73.08 (11.88) 69.17 (8.07) 74.31 (10.62) Sex (Male N and %) 15 (60) 12 (60) 20 (60.61) 7 (58.33) 18 (62.07) 9 (56.25) Disease duration (mean and SD) 3.98 (2.69) 2.48 (1.57) 3.26 (2.24) 3.46 (2.78) 2.91 (1.91) 4.03 (2.96) Abnormal GCA-F N (%) 19 (55.88) 15 (44.12) 27 (79.41) 7 (20.59) 20 (58.82) 14 (41.18) Age (mean and SD) 70.68 (9) 75.93 (7.06) 71.30 (8.57) 79.57 (6.05) 70.95 (8.57) 75.93 (8.36) Sex (Male N and %) 14 (73.68) 11 (73.33) 20 (74.07) 5 (71.43) 15 (75) 10 (71.43) Disease duration (mean and SD) 4.61 (3.08) 2.77 (2.15) 3.63 (2.74) 4.43 (3.31) 3.18 (2.20) 4.68 (3.44) N: number. CSF: cerebrospinal fluid. Aβ42: Amyloid-β42. T tau: Total tau. P tau: phosphorylated tau at threonine 181. MTA: medial temporal lobe atrophy. PA: posterior atrophy. GCA-F: global cortical atrophy scale-frontal subscale. 4 C. Abdelnour, et al. NeuroImage: Clinical 27 (2020) 102333 Table 3 frontal cortex was not associated with AD-related pathology, and no Association between AD CSF biomarkers and visual rating scales (random forest associations were found between regional brain atrophy and global models). unspecific neurodegeneration (CSF T-tau). Visual rating Variables contribution Pearson P value Our results are consistent with previous findings on the association scales correlation between amyloid β-related pathology and atrophy in the medial tem- poral lobe in DLB (Sarro et al., 2016; van der Zande et al., 2018; MTA Overall model: OOB-EER = 32.56% Shimada et al., 2013; Mak et al., 2019b; Kantarci et al., 2012a, 2012b). ‐ Classification error normal MTA = 24.53% Although MTA is less frequent in DLB when compared with AD ‐ Classification error abnormal (Shimada et al., 2013; Barber et al. Mar, 2000; Ballmaier et al., 2004), MTA = 45.45% previous studies suggest that when MTA is present, it could reflect Predictors retained in the model: −0.244 0.024 concomitant AD-related pathology (Sarro et al., 2016; van der Zande Disease duration, Imp = 63.53 0.217 0.045 CSF Aβ42, Imp = 41.71 0.207 0.056 et al., 2018). Both MTA and abnormal CSF Aβ42 levels are associated Age, Imp = 36.30 with more rapid cognitive decline in DLB and Parkinson’s disease PA Overall model: OOB-EER = 44.83% (Howlett et al., 2015; Siderowf et al., 2010; Stav et al., 2016; Caspell- ‐ Classification error normal Garcia et al., 2017). An interesting result of our study s that the clas- PA = 46.34% sification error was higher for the prediction of patients with abnormal ‐ Classification error abnormal PA = 43.48% MTA scores as compared with the prediction of patients with normal Predictors retained in the model: 0.072 MTA scores. This suggests that while in the absence of amyloid-beta Age, Imp = 24.27 0.195 0.126 pathology is unlikely to find MTA in probable DLB, the presence of MTA Education, Imp = 8.76 −0.166 0.100 is not always associated with amyloid-beta pathology. Other factors CSF p-tau, Imp = 8.23 0.179 0.013 CSF Aβ42, Imp = 8.615 0.266 0.021 than pathological CSF levels of Aβ42 may be involved in MTA in DLB. Disease duration, Imp = 7.02 −0.248 Our study shows that older age and shorter disease duration are asso- GCA-F Overall model: OOB-EER = 24.14% ciated with abnormal MTA scores. Future studies should also consider ‐ Classification error normal other pathologies potentially contributing to MTA, such as TDP-43 or GCA-F = 19.23% hippocampal sclerosis. ‐ Classification error abnormal GCA-F = 31.43% Furthermore, we found that posterior brain atrophy was associated Predictors retained in the model: 0.270 0.012 with both amyloid-β and tau-related pathologies. We acknowledge Sex, Imp = 54.20 −0.231 0.033 however that this finding should be considered as preliminary given the Education, Imp = 52.18 0.334 0.002 high classification error in our model (still under the threshold of error Age, Imp = 46.23 by chance). PET biomarkers are needed to confirm the collocation of N: number. CSF: cerebrospinal fluid. Aβ42: Amyloid-β42. T tau: Total tau. P tau: amyloid and tau-related pathologies and neurodegeneration in the phosphorylated tau at threonine 181. MTA: medial temporal lobe atrophy. PA: posterior cortex in DLB. Sarro et al showed that amyloid-β deposition is posterior atrophy. GCA-F: global cortical atrophy scale-frontal subscale. associated with greater atrophy rates in the posterior cingulate gyrus OOB-EER: out-of-the-bag estimated error rate (below 50% denotes good clas- sification performance). Imp: importance (the contribution of a given variable in the random forest, with higher values indicating stronger contribution to the prediction). Pearson correlation indicates the direction of the association. scores was 19.23% while it was 31.43% for patients with abnormal values. Table 3 shows that the best predictors of MTA were disease duration, CSF Aβ42 and age, ordered by importance. We found a combined effect of CSF Aβ42 and CSF p-tau on PA. Age, education and disease duration also contributed to the prediction of PA. Finally, the best predictors of GCA-F were sex, education and age. AD CSF bio- markers did not contribute to the prediction of GCA-F. The same pat- tern of results was observed when adding the center as a predictor in the models (data not shown), thus suggesting that variability across- centers does not seem to affect our findings. Pearson correlation coefficients show that abnormal scores in MTA were related to abnormal CSF Aβ42 levels, whereas abnormal values of PA were associated with both abnormal CSF Aβ42 and p-tau levels. Regarding the effect of age, sex, education and disease duration, ab- normal scores in MTA were related to shorter disease duration and older age. Abnormal scores in PA were related to older age, less edu- cation and shorter disease duration. Abnormal scores in GCA-F were related to less education, male sex and older age (Table 3). Fig. 2 shows the correlation matrix between visual ratings and CSF biomarkers, as well as among all predictors in our random forest models (Breiman, 2001). 4. Discussion We found that both amyloid-β and tau-related pathologies con- Fig. 2. Correlation matrix between visual ratings, CSF biomarkers, and pre- tribute in combination to atrophy in posterior brain cortex of probable dictors in the random forest models. Asterisk symbols (*) denote p-values < DLB patients. In addition, amyloid β-related pathology was associated 0.05. MTA = medial temporal atrophy scale; PA = posterior atrophy scale; with atrophy in the medial temporal lobe. In contrast, atrophy in the GCA-F = global cortical atrophy scale – frontal subscale. 5 C. Abdelnour, et al. NeuroImage: Clinical 27 (2020) 102333 and the occipital lobe in addition to the temporal lobe (Sarro et al., models. Another interesting finding is that the Pearson correlation be- 2016). Investigation of tau-related pathology with 18F-AV-1451 have tween CSF p-tau and PA was not significant while CSF p-tau contributed found that DLB patients display increased uptake in the posterior and to the prediction of PA in the multivariate random forest model. This inferior temporoparietal, occipital (Kantarci et al., 2017) and parietal suggests that the effect of tau-related pathology on the posterior cortex lobes (Smith et al., 2018). These findings indicate that tau-related pa- is not direct and instead emerges in combination with the effect of thology in DLB does not seem to follow AD Braak neurofibrillary tangle amyloid-β pathology. Hence, this dissociation between the results from (NFT) distribution with the typical involvement of the medial temporal our random forest and correlation analysis supports the potential sy- lobe (Braak and Braak, 1991), whereas amyloidosis presents with a nergistic effect of amyloid-β and tau-related pathologies when it comes diffuse cortical pattern similar to AD (Coughlin et al., 2019). Hence, the to predict brain atrophy in the posterior cortex. Since age, education coexistence of DLB and AD pathologies could result in a distinctive and disease duration were contributing variables in the random forest pattern of regional brain atrophy in DLB. The novelty of our study is for PA, this suggests that AD-concomitant pathology in DLB may have a that we show that both amyloid-β and tau-related pathologies seem to stronger impact on the posterior cortex in patients with lower education be associated with level of atrophy in posterior cortex, while solely and older age, hence perhaps accelerating disease progression (i.e. amyloid-β pathology appears to be related to atrophy in medial tem- shorter disease duration). poral lobes. This could be explained by a potential link between amy- This study has some limitations. Firstly, we discuss on the observed loid-β and tau-related pathologies in posterior brain areas, where tau association between CSF biomarkers and regional brain atrophy but our pathology is primarily deposited in DLB (Kantarci et al., 2017). analyses are cross-sectional and we cannot assume causality. Still, we Although both CSF T-tau and brain atrophy are considered markers believe our findings may inform on potential underlying mechanisms of of neurodegeneration, we did not find any association between the two. neurodegeneration in DLB, which need to be substantiated in future A possible explanation is that CSF T-tau is a marker of global unspecific longitudinal studies. Secondly, the E-DLB cohort was assembled retro- neurodegeneration, while visual rating scales are markers of regional spectively using common registered variables and procedures across the (local) neurodegeneration. AD studies show that the agreement be- participating centers. However, all variables and procedures are stan- tween CSF T-tau and brain atrophy is limited (Alexopoulos et al., 2014). dard for clinical practice across centers and we carefully inspected the Moreover, we cannot exclude that this negative result is also explained combinability of the data in order to exclude non-harmonized measures by the small number of subjects with abnormal levels of CSF T-tau in and cases. All centers have extensive clinical experience in the diagnosis our sample. Prior studies also observed that abnormal levels of CSF T- of neurodegenerative diseases and align with international consensus tau are less frequent than abnormal levels of CSF Aβ42 and p-tau in DLB diagnostic criteria, which we believe has contributed to minimize po- patients (Mukaetova-Ladinska et al., 2010). Nevertheless, only one tential differences among centers. Further, we followed two more previous study analyzed CSF T-tau levels and regional brain atrophy in strategies to minimize methodological differences across-centers. We DLB, finding a correlation with posterior and global brain atrophy (van used cut-offs that were established at their respective center to di- der Zande et al., 2018). Therefore, more studies are needed to de- chotomize CSF biomarker results into normal or abnormal values, termine the possible association between CSF T-tau -currently con- which is preferred in multi-center studies instead of continuous values. sidered as a biomarker of neuronal damage- and brain atrophy in Lewy Similarly, due to the variability in the MRI protocols across centers, we body dementia. used visual rating scales to investigate brain atrophy instead of more Similarly to what previously reported in AD, neither CSF Aβ42, T- fine-grained automated methods or quantitative measures. Nonetheless, tau nor p-tau were associated with atrophy in the frontal cortex in DLB the use of visual rating scales substantially increases the clinical ap- (Ferreira et al., 2016). Previous research has demonstrated increased plicability of our current findings (Ferreira et al., 2017, 2015, 2020). amyloid-β burden (Growdon et al., 2012) but not tau deposition On another hand, and connected to the retrospective nature of the co- (Kantarci et al., 2017) in frontal areas in patients with DLB. However, hort, it is worth mentioning that the interval between MRI and CSF amyloid-β deposition in the frontal lobes has not been associated with collection was long in 15.11% of cases (ranged from 3 to 12 moths). grey matter atrophy in this region (Sarro et al., 2016). Frontal atrophy Thirdly, there is no reliable in vivo biomarker of Lewy body pathology in DLB might be related to Lewy body pathology only, but more in- at present for diagnosis or analysis of the contribution of this pathology vestigations are needed to elucidate the pathological mechanisms un- towards the neurodegenerative process. Thus, the diagnosis of probable derlying the neurodegeneration of these areas. DLB was based on clinical grounds with its known limitations (Rizzo Regarding the effect of age, sex, education and disease duration, we et al., 2018; Huang and Halliday, 2013), although around one third of did not control for their effects but investigated to what extent they the cohort had a dopamine transporter SPECT scan. These limitations contribute to regional brain atrophy together with the CSF biomarkers. will be overcome in the prospective stage of E-DLB – we are currently The decision to do so is because it is currently unknown whether these collecting harmonized longitudinal data across many centers in Europe variables should be treated as confounding or contributing variables to (Oppedal et al., 2019). Finally, although random forest is able to handle regional brain atrophy in neurodegenerative disorders (Ferreira et al., multicollinearity to some degree, it might lead to an underestimation of 2020). For example, tau-related pathology is associated with increasing the contribution of multicollinear variables. The association among the age, and it is currently unknown whether tau deposition in DLB is re- predictors of the random forest models can be appreciated in Fig. 2. Our lated to AD- or aging-, or both. Thus, including age, sex, education and study has some important strengths. The study is a multicenter effort, disease duration in our models enabled us to investigate their combined which makes the generalization of the findings plausible through dif- effect together with the CSF biomarkers. We found that atrophy in ferent clinical centers. Also, the use of modern multivariate models medial temporal lobes and posterior cortices was associated with allowed us to investigate the combined effect of CSF biomarkers for the shorter disease duration and older age, whereas atrophy in the frontal first time, in contrast to previous reports which investigated partial cortex was associated with older age, male sex and less education. effects and could not investigate CSF p-tau in linear regression models Further, our multivariate analyses exposed the effect of variables such due to collinearity (van der Zande et al., 2018). as disease duration on the integrity of the brain, which traditional bi- variate correlations could not capture in our study. In fact, multivariate 5. Conclusions models can capture effects of relevant variables masked by the effect of third variables that cannot be captured in univariate or bivariate This study shows preliminary data on the potential combined effect models, and that are artificially removed in models testing for partial of amyloid-β and tau-related pathologies on posterior brain cortices in effects (Machado et al., 2018). This is therefore a strength of our patients with DLB. Future research should confirm our current findings multivariate statistical approach using random forest classification with more fine-grained automated methods for brain atrophy and, 6 C. Abdelnour, et al. NeuroImage: Clinical 27 (2020) 102333 ideally, with amyloid and tau PET biomarkers in order to verify the editing. Eric Westman: Conceptualization, Resources, Supervision, potential collocation of these pathologies with neurodegeneration in Validation, Visualization, Writing - original draft, Writing - review & posterior brain cortices. Likewise, future studies should also include editing. Javier Pagonabarraga: Validation, Visualization, Writing - alpha-synuclein biomarkers when available in order to advance our review & editing. Jaime Kulisevsky: Validation, Visualization, Writing current understanding of the neurodegeneration process in DLB. - review & editing. Frédéric Blanc: Data curation, Investigation, Resources, Supervision, Validation, Visualization, Writing - review & Funding sources of the study editing. Dag Aarsland: Conceptualization, Project administration, Supervision, Validation, Visualization, Writing - original draft, Writing - This research did not receive any specific grant from funding review & editing. agencies in the public, commercial or not-for-profit sectors. Acknowledgements Declaration of interest The authors would like to express their thanks to all the members of 1. Carla Abdelnour: Carla Abdelnour has received honoraria from the E-DLB consortium: Evelien Lemstra, Alzheimer Centre, Dept Neuro, Zambon and Schwabe. VU Med Centre, Amsterdam, The Netherlands; Inger van Steenoven, 2. Daniel Ferreira: none. Alzheimer Centre, Dept Neuro, VU Med Centre, Amsterdam, The 3. Ketil Oppedal: none. Netherlands; Jan Booij, Amsterdam University, The Netherlands; 4. Lena Cavallin: none. Sevasti Bostantjopoulou, 3rd Department of Neurology, Medical School, 5. Frédéric Blanc: none. Aristotle University of Thessaloniki, Greece; Zoe Katsarou, 3rd 6. Oliver Bousiges: none. Department of Neurology, Medical School, Aristotle University of 7. Lars Olof Wahlund: none. Thessaloniki, Greece; Irena Rektorova, Brain and mind research 8. Jakub Hort: none. Program, Central EU Institute of technology, Masaryk Uni. Masaryk 9. Zuzana Nedelska: Dr. Nedelska has been supported by IBRO-ISN University, Brno Czech Republic; Angelo Antonini, Center for fellowship 2018. Parkinson's disease and Movement Disorder Venice-Lido, Italy; Roberta 10. Alessandro Padovani: none. Biundo; Center for Parkinson's disease and Movement Disorder Venice- 11. Andrea Pilotto: none. Lido, Italy; Elisabet Londos, Clinical Memory Research Unit, Dept. of 12. Laura Bonanni: none. Clinical Sciences, Lund University Malmo Sweden; Flavio Nobili, 13. Milica Kramberger: none. Clinical Neurology, Dept. of Neuroscience (DINOGMI) University of 14. Mercè Boada: none. Genoa, Genova Italy; Josep Garre, ReDeGi, University of Girona, Spain; 15. Eric Westman: none. Cristian Falup-Pecurariu, Transilvania University of Brasov, Romania; 16. Javier Pagonabarraga: none. Arvid Rongve, Haugesund Hospital, Norway; John O'Brien, Department 17. Jaime Kulisevsky: none. of Psychiatry, University of Cambridge School of Clinical Medicine, UK; 18. Dag Aarsland: Dr Aarsland has received research support and/or Dan Weintraub, Departments of Psychiatry and Neurology, Perelman honoraria from Astra-Zeneca, H. Lundbeck, Novartis School of Medicine at the University of Pennsylvania, Philadelphia, Pharmaceuticals and GE Health, and served as paid consultant for Pennsylvania, USA; Tormod Fladby, Dept of Neurology, Akershus H. Lundbeck, Eisai, Heptares, Mentis Cura. Dag Aarsland is a Royal University Hospital, Oslo, Norway; Richard Dodel, Dept of Neurology, Society Wolfson Research Merit Award Holder and would like to University Hospital of Giessen and Marburg, Germany; Zuzana Walker, thank the Wolfson Foundation and the Royal Society for their Division of Psychiatry, University College London and North Essex support. Partnership University NHS Foundation Trust, UK; Frank Jan de Jong, Erasmus MC Rotterdam, The Netherlands; Clive Ballard, Exeter CRediT authorship contribution statement University UK; Henrik Zetterberg, Institute of Neuroscience and Physiology, Department of Psychiatry and Neurochemistry,The Carla Abdelnour: Data curation, Formal analysis, Investigation, Sahlgrenska Academy at the University of Gothenburg, Sweden; John Methodology, Resources, Validation, Visualization, Writing - original Paul Taylor, Institute of Neuroscience, Campus for Aging and vitality, draft, Writing - review & editing. Daniel Ferreira: Data curation, Newcastle University, UK; Ian McKeith, Institute of Neuroscience, Formal analysis, Methodology, Resources, Software, Validation, Campus for Aging and vitality, Newcastle University, UK; Erika Visualization, Writing - original draft, Writing - review & editing. Ketil Stefanova, Intitute of Neurology, CCS, School of Medicine, University of Oppedal: Data curation, Investigation, Methodology, Resources, Belgrade, Serbia; Per Svenningsson, Karolinska Institute Stockholm, Software, Validation, Visualization, Writing - original draft, Writing - Sweden; Sara Garcia-Pacek, Karolinska Insitutet Stockholm, Sweden, review & editing. Lena Cavallin: Data curation, Investigation, Bengt Winblad, Karolinska Insitutet Stockholm, Sweden; Richard Methodology, Resources, Software, Validation, Visualization, Writing - Brown, Kings College of London, UK; Ray Choudhury, Kings College of review & editing. Olivier Bousiges: Data curation, Investigation, London, UK; Oskar Hansson, Lund University Malmo, Sweden; Brad Resources, Validation, Visualization, Writing - review & editing. Lars Boeve, Mayo Clinic Rochester, USA; Brit Mollenhauer, Department of Olof Wahlund: Data curation, Investigation, Resources, Validation, Neurology, University Medicine Göttingen, Göttingen, Germany and Visualization, Writing - review & editing. Jakub Hort: Data curation, Paracelsus-Elena Klinik, Center for Parkinsonism and Movement Investigation, Resources, Validation, Visualization, Writing - review & Disorders, Kassel, Germany; Jon Snaedal, Landspitali University editing. Zuzana Nedelska: Data curation, Investigation, Resources, Hospital, Reykjavik, Iceland; Gert J. Geurtsen, Department of Medical Validation, Visualization, Writing - review & editing. Alessandro Psychology, Amsterdam University Medical Centers location Academic Padovani: Data curation, Investigation, Resources, Validation, Medical Center, Amsterdam, The Netherlands; Maria Petrova, Visualization, Writing - review & editing. Andrea Pilotto: Data cura- University Hospital “Alexandrovska”, Department of Neurology, Sofia, tion, Investigation, Resources, Validation, Visualization, Writing - re- Bulgaria; Latchezar Traykov, University Hospital “Alexandrovska”, view & editing. Laura Bonanni: Data curation, Investigation, Department of Neurology, Sofia, Bulgaria; Leonidas Stefanis, University Resources, Validation, Visualization, Writing - review & editing. Milica of Athens Medical School Athens, Greece; E. Kapaki, University of G. Kramberger: Data curation, Investigation, Resources, Validation, Athens Medical School Athens, Greece; Carlo Delena, Department of Visualization, Writing - review & editing. Mercè Boada: Data curation, Physiology and Pharmacology “Vittorio Erspamer”, Sapienza University Investigation, Resources, Validation, Visualization, Writing - review & of Rome, Rome, Italy; Claudio Babiloni, Department of Physiology and 7 C. Abdelnour, et al. 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