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Background: In dementia with Lewy bodies (DLB), Alzheimer co‐pathology is common, and associated with an unfavorable prognosis. For targeted treatment development it is crucial to understand the biological underpinnings of DLB with Alzheimer co‐pathology, and how it differs from Alzheimer disease (AD). We performed a cerebrospinal fluid (CSF) proteome profiling to biologically characterize DLB with and without AD co‐pathology in vivo. Method: Patients from Amsterdam Dementia Cohort, Twin60+, and UPENN, we selected individuals with DLB (n=109), and subcategorized them in CSF Aβ42 (A‐/A+) and pTau‐181 (T‐/T+) biomarker groups (A‐T‐ n=29; A‐T+ n=16; A+T‐ n=29; A+T+ n=36), and patients with AD dementia (A+T+ n=230) and cognitively normal controls (A‐T‐ n=246). CSF levels of 810 proteins were measured with the Olink proximity extension assay. We compared protein profiles from the DLB subgroups and AD dementia with controls with ANCOVA adjusted for age and sex. Proteins with significant FDR‐controlled p‐values were used to perform functional enrichment analyses in KEGG and GO Biology and Cellular components using STRING‐DB. Result: DLB A‐T‐ exhibited least abnormalities compared to controls, with 6 proteins dysregulated (Figure 1). DLB A‐T+ had 12 upregulated proteins. DLB A+T‐ exhibited the most distinct protein dysregulation, with 16 upregulated proteins, and 312 downregulated proteins. DLB A+T+ had 3 downregulated and 18 upregulated proteins. Of the DLB groups, DLB A+T+ had with 15 proteins the largest overlap in dysregulated proteins with AD dementia patients (Figure 1). In functional enrichment analysis for DLB A‐T‐ no enrichment was present. DLB A‐T+ and DLB A+T+ groups were enriched for proteins involved cytokine‐cytokine interactions. The DLB A+T‐ group had top enrichment for basement membrane components and phenylalaline metabolism upregulation, and cell adhesion molecules, VEGF, and axon guidance and morphogenesis downregulation. Conclusion: The CSF proteome in DLB varied depending on the CSF AD profile. The overlap in CSF proteome between AD dementia and DLB with signs of AD seems to cover mostly immune‐mediated processes. The biological abnormalities in DLB with isolated abnormal CSF Aβ42 were most distinct, possibly pointing to a unique subtype. Using biomarkers and proteomics for understanding the biology behind AD co‐pathology could guide treatment development for DLB. [ABSTRACT FROM AUTHOR]

Introduction: Our aim was to study whether systemic metabolites are associated with magnetic resonance imaging (MRI) measures of brain and hippocampal atrophy and white matter hyperintensities (WMH). Methods: We studied associations of 143 plasma‐based metabolites with MRI measures of brain and hippocampal atrophy and WMH in three independent cohorts (n = 3962). We meta‐analyzed the results of linear regression analyses to determine the association of metabolites with MRI measures. Results: Higher glucose levels and lower levels of three small high density lipoprotein (HDL) particles were associated with brain atrophy. Higher glucose levels were associated with WMH. Discussion: Glucose levels were associated with brain atrophy and WMH, and small HDL particle levels were associated with brain atrophy. Circulating metabolites may aid in developing future intervention trials. [ABSTRACT FROM AUTHOR]

Background: Frontotemporal dementia (FTD) is caused by frontotemporal lobar degeneration (FTLD) and the most common forms are characterized by either tau (FTLD‐Tau) or TDP43 (FTLD‐TDP) brain aggregates. However, FTD‐specific fluid biomarkers are lacking. Furthermore, the pathological subtypes are not distinct in their presentation, hampering accurate subtyping at clinical diagnosis. Therefore, there is a strong need to identify fluid biomarkers that could aid in FTD diagnosis and to discriminate the pathological subtypes. Method: We employed an antibody‐based proteomic technology to analyze >600 proteins in a large multicenter cohort including cerebrospinal fluid (CSF) samples from FTD (n = 189), AD (n = 235) and cognitively unimpaired individuals (n = 196). For a subset of cases the underlying neuropathology was known or could be predicted (FTLD‐Tau = 85 and FTLD‐TDP = 57). Differences in protein expression profiles were analyzed by nested linear models. Penalized generalized linear modeling was used to identify classification protein panels. Protein panels were then validated in independent clinical cohorts (cohort 1: n = 157; cohort 2: n = 165) and a neuropathology cohort (n = 100) using customized assays. Result: We observed 65 differentially regulated proteins in FTD versus controls and AD patients, associated with axonogenesis, synapse assembly, or locomotory behavior pathways. We identified panels of 14 and 13 proteins that could discriminate FTD from controls (AUC = 0.96, 95%CI:0.91‐0.99) and AD patients (AUC = 0.91, 95%CI:0.85‐0.96), respectively. Most of these proteins (21 out of 27) were translated into customized panels, which discriminated between groups with high accuracy for all three cohorts (FTDvsCon: AUCs > 0.96, FTDvsAD: AUCs > 0.88). When comparing the FTLD‐Tau and FTLD‐TDP subtypes, we observed that 86 proteins were increased in FTLD‐Tau, and associated with developmental and cellular processes and locomotion pathways. A panel of 8 proteins could discriminate between the pathological subtypes (AUC = 0.82, 95%CI:0.66‐0.95), which was however not stable during cross‐validation. Conclusion: We identified and validated CSF panels to discriminate FTD from controls and AD with high accuracy. The identification of a biomarker panel to discriminate between the FTLD pathological subtypes likely requires larger and more homogeneous groups. The panels developed within this study might be useful for diagnosis and trial inclusion of FTD patients. [ABSTRACT FROM AUTHOR]

Background: Understanding the earliest biochemical changes related to Alzheimer´s disease (AD) pathology is essential for the development of biomarkers and disease‐modifying therapies. Here, we aimed to map the cerebrospinal fluid (CSF) proteomic changes associated with amyloid ß (Aß) pathology in cognitively unimpaired individuals and identify a panel of CSF biomarkers detecting amyloid positivity as well as clinical progression to dementia. Method: Proximity extension‐based multiplex immunoassays were used to measure 614 proteins in 297 CSF samples from cognitively unimpaired controls with and without amyloid pathology (232 CON/Aβ‐ (age: 68±8; sex: 37% female) and 65 CON/Aβ+ (age: 65±7; sex: 49% female) from the Amsterdam Dementia cohort. A total of 39 cases (13%) progressed to mild cognitive impairment (MCI; n = 19) or dementia (n = 20). An additional cognitively unimpaired control cohort from the EMIF‐twin60+ study (103 CON/Aβ‐ and 19 CON/Aβ+) was used to validate the panel. Data was analysed using nested linear models adjusted for multiple testing and penalized generalized linear modelling. Result: We identified 110 CSF proteins with different levels in CON/Aβ+ compared to Aβ‐ (q<0.05). Proteins were enriched in processes related to proteolysis, enzyme activation and immunity. Classification modelling revealed a panel of 12‐CSF markers that detect amyloid positivity with high accuracy in both the discovery (AUC: 0.93) and validation (AUC: 0.89) cohorts. This panel also predicted clinical progression to MCI and dementia with high accuracy (AUC: 0.83). Chow´s test analysis revealed a structural change in the relation between a subset of these proteins and CSF Aβ42 before the amyloid positivity threshold. Conclusion: Overall, this study provides novel pathophysiological leads associated to distinct biological processes in individuals at risk of developing AD‐dementia and provides potential biomarker tools for clinical settings or trials. [ABSTRACT FROM AUTHOR]

Background: In dementia with Lewy bodies (DLB), amyloid co‐pathology is common and associated with an unfavorable prognosis. For targeted treatment development for DLB, we need to establish 1) whether the presence of amyloid marks co‐occurring Alzheimer disease (AD), and 2) how DLB with amyloid differs from DLB without amyloid. We employ proteomics in cerebrospinal fluid (CSF), an established method to assess biological dysregulation in vivo, for characterization of DLB with amyloid pathology. Method: We selected individuals from the ADC and UPENN cohorts, with DLB diagnosis with abnormal amyloid based on CSF (n=65), DLB with normal amyloid (n=45), AD dementia (n=235), and cognitively healthy controls with normal amyloid (n=252, Table 1). CSF protein levels (n=810) were measured using the Olink proximity extension assay. We compared protein levels between groups using ANCOVA's adjusted for age and sex, and FDR‐controlled p‐values. To aid identification of proteins that contribute to disease processes, we developed a neural network model prototype. We trained the model to learn simultaneously a low dimensional representation of the data and a dementia classifier. The assumption is that biologically important proteins show the strongest influence on the low network dimension, because these proteins contribute to classification and/or share information with multiple proteins. Result: The DLB‐amyloid group showed mostly decreased CSF protein levels, in particular compared to AD dementia (68%, Table 2). Compared to DLB‐pure, DLB‐amyloid has 132 decreased and 3 increased protein levels, which were most significantly enriched for KEGG pathways: cytokine‐cytokine receptor interaction and axon guidance; and for GO‐biological terms related to positive regulation of neurogenesis, axonogenesis, and neuronal differentiation. In DLB‐amyloid, most AD‐related proteins were normal or decreased, and multiple additional proteins were specifically decreased (Figure 1) These DLB‐amyloid associated proteins were enriched for GO‐biological terms related to chemotaxis and VEGF‐A induced cell proliferation. Conclusion: The CSF proteome in DLB with amyloid pathology differed from AD dementia and DLB pure, and the changes may be linked to disturbed axonal maintenance and growth. This data indicate that amyloid pathology in DLB is not an independent co‐pathology. Complementary to pathology studies, we have started to disentangle mechanisms of mixed pathology in vivo. [ABSTRACT FROM AUTHOR]

Background: Diagnosis of dementia with Lewy bodies (DLB) remains challenging and biomarkers discriminating DLB from Alzheimer's disease (AD) are highly needed. We aimed to establish the specific cerebrospinal fluid (CSF) proteomic changes that underlie DLB and to identify translatable diagnostic biomarkers. Methods: Proximity extension‐based multiplex immunoassays were used to measure 665 proteins in 534 CSF samples from patients with Dementia with Lewy bodies (n=109), AD‐dementia (n=235) and cognitively‐unimpaired controls (CON, n=190) from the Amsterdam Dementia cohort (ADC) and Penn University. An additional multicenter cohort (n=307) from ADC and SPIN was used for validation of custom assays. A positive/negative AD CSF profile supported the diagnosis of AD and CON respectively. Results: Nested linear models identified 97 CSF proteins with altered abundance in DLB compared to controls (p<0.05). After comparison with the AD CSF proteome, we observed that 52 of these proteins (54%) were especially associated to DLB (e.g. DDC, GH, FCER2, MMP1), while 15 proteins (16%) showed opposite changes to those detected in AD patients (CRH, MMP3). The protein with the highest fold‐changed observed in DLB was L‐amino acid decarboxylase (DDC; >1.5 fold‐change vs.CN or AD; q<1E‐16), an enzyme involved in dopamine biosynthesis. DDC could optimally discriminate DLB from controls and AD patients (AUC: 0.91 and 0.81 respectively). Using penalized generalized linear modelling we identified a panel of 7‐CSF markers including DDC that could discriminate DLB from AD patients with high accuracy (AUC: 0.93, 95%CI: 0.86‐0.98), which have been successfully translated into customized multiplex assays (correlations > 0.84 with discovery measurements). Conclusions: We unveil CSF changes specifically related to DLB and identified a panel of 7‐CSF markers associated to several aspects of DLB pathophysiology that enables discrimination of these dementia types with high accuracy. Multiplex custom assays containing these markers are currently being clinically validated in independent cohorts for their potential use in diagnostic settings or clinical trials. [ABSTRACT FROM AUTHOR]

Background: The development of disease‐modifying therapies against Alzheimer's disease (AD) requires biomarker panels that reflect the diverse pathological pathways specifically involved in AD. Here we aimed to identify and validate panels of cerebrospinal fluid (CSF) proteins covering different molecular pathways for early and specific diagnosis of AD. Method: We measured 665 proteins in 797 CSF samples from patients with mild cognitive impairment with abnormal amyloid (MCI(Aβ+): n=50), AD‐dementia (n=230), non‐AD dementias (n=322; 123 DLB and 199 FTD) and cognitively‐unimpaired controls (n=195; classical AD CSF biomarkers negative) using proximity ligation‐based multiplex immunoassays. Nested and penalized linear modeling were used to identify protein differences (q<0,05) and translatable classification signatures. Result: We detected highly dysregulated CSF proteins in MCI(Aβ+) or AD compared to controls (112 and 288 proteins respectively, lowest q:1‐15 and 1‐23), as well as between AD and non‐AD dementias (469 proteins; lowest q:1‐29). We confirmed previous findings (e.g., DDAH1, ENO2, PARK7), but we also identified novel proteins especially associated to the prodromal and/or dementia AD stages (e.g., ABL1, SCD4, ENTPD5). Proteins dysregulated in MCI(Aβ+) were primarily related to oxidative stress and energy metabolism, while those specifically dysregulated in later stages of AD dementia were related to cell remodeling, vascular function and immune system. Using penalised generalised linear modeling we identified the minimal number of markers with maximal power to discriminate clinical groups: for MCI(Aβ+) vs. controls: 10‐CSF proteins, AUC:0.99 (95%CI:0.97‐1); for AD vs. controls: 8‐CSF proteins, AUC:0.95 (95%CI:0.92‐0.99) and for AD vs. non‐AD dementias: 9‐CSF proteins, AUC:0.87 (95%CI:0.81‐0.93). The CSF panel discriminating AD from cognitively unimpaired controls was validated in an independent external cohort (n=62, AUC:0.94). Only 3 proteins overlapped across panels, suggesting that the panels with the strongest discriminative power are specific for disease stage and clinical group. Conclusion: This unprecedented large and protein‐rich CSF study indicates that CSF proteome follows a highly dynamic trajectory with distinct CSF profiles associated to different biological processes along the AD continuum. We unveil optimal CSF biomarker panels reflecting the specific multifactorial nature of AD, which can be now translated into customised assays for widespread validation and potential use in heterogeneous diagnostic settings or clinical trials. [ABSTRACT FROM AUTHOR]

Background: Frontotemporal dementia (FTD) is caused by frontotemporal lobar degeneration (FTLD) and is characterized mainly by the presence of aggregates of the proteins tau (FTLD‐Tau) or TDP43 (FTLD‐ TDP) in the brain, which likely require distinct pharmacological therapy. However, the clinical presentation of the pathological subtypes is heterogeneous and overlapping, making this diagnostic subtyping at present virtually impossible. Thus, there is a strong unmet need to identify body‐fluid biomarkers to discriminate FTD and its pathological subtypes. Most biomarker studies performed to date have analyzed pathologically heterogeneous populations. The few studies analyzing antemortem cerebrospinal fluid (CSF) with known underlying neuropathology have revealed several candidate biomarkers (e.g. p/tTau ratio). Despite these promising results, their specificity is still not optimal, most of the identified markers are awaiting further validation and their diagnostic accuracy remains to be evaluated. Method: We have recently taken advantage of a novel high‐multiplex and sensitive antibody‐based proteomic technology to analyze >600 proteins in a large multicenter cohort providing CSF samples from controls (n=195) and FTD patients (n=199). For a subset of FTD cases, the underlying specific pathology was known (FTLD‐Tau=34: 18 autopsy‐confirmed and 27 MAPT mutation carriers; FTLD‐ FTLD‐TDP=54: 27 autopsy‐confirmed and 18 C9orf72 and 9 GRN mutation carriers). Differences in the protein expression profile were analyzed by GlobalTesting. Generalized linear modeling was used to identified classification protein signatures. Result: We identified CSF protein biomarker signatures for specific diagnosis of FTD (8‐9 proteins, AUC>0.85). The proteins identified were related to mechanisms involved in immune system, extracellular remodeling and cell killing. There were no between‐group differences in the CSF protein profiles of the FTLD groups. However, differences in the absolute levels of some proteins were detected when autopsy‐confirmed cases and mutation carriers were analyzed separately. Conclusion: The similarity of the CSF profiles between FTLD groups suggests shared pathological mechanisms. Alternatively, each subtype may be heterogeneous, which possibly results from analyzing together familial and autopsy‐confirmed cases. Taken together, the FTLD studies performed to date indicate a highly complex disorder challenging the development of efficient diagnostic and therapeutic markers. [ABSTRACT FROM AUTHOR]

Background: Distinct pathologies accumulate in multiple brain regions (BR) and shape the heterogeneous clinical presentation of frontotemporal dementia (FTD). It is unknown how regional pathological burden links to symptoms, what the role of co‐occurring pathologies is, and whether the localization of pathology might be a bigger contributor to symptoms than the type of pathology. Our aim is to investigate how early FTD symptoms correlate to the burden of multiple pathologies throughout the brain. Method: Post‐mortem brain tissue of frontotemporal lobar degeneration (FTLD) donors from the Netherlands brain bank was dissected into twenty standard BR and stained for TAR DNA‐binding protein 43 (TDP‐43), tau, fused‐in‐sarcoma (FUS), amyloid‐beta (Aβ), and alpha‐synuclein. The burden of each pathological protein in each BR was quantified. All clinical records were reviewed to assess psychiatric, behavioral, language, and motor symptoms in the first three years from disease onset. Whole‐brain clinico‐pathological partial correlations were assessed using the heterogeneous correlation function (R 3.6.1). The local false discovery rate threshold was set at 0.01. Result: Eighty‐eight FTLD brain donors were studied, including 46 TDP‐43, 35 tau, and 7 FUS. Significant positive partial correlations (p < 0.01) were found between hippocampal TDP‐43 pathology and hallucinations (R = 0.23), perseverative‐compulsive behavior (R = 0.25), depression (R = 0.28), and mania (R = 0.32). Tau pathology in the substantia nigra and locus coeruleus was linked to depression (R = 0.25, R = 0.24). Both TDP‐43 and Aβ in the subthalamus were associated with severe disinhibition (R = 0.23, R = 0.25), while apathy correlated with both TDP‐43 and tau burden in the parietal lobe (R = 0.27, R = 0.24). Parkinsonism was linked to TDP‐43 burden in the substantia nigra (R = 0.33). Conclusion: Neuropsychiatric symptoms of FTD are linked to pathology burden in BR beyond the frontal lobes, including subcortical structures such as the hippocampus, the substantia nigra and locus coeruleus. Co‐occurring pathologies are not simple bystanders, but could play a role in configuring FTD clinical phenotype. Different pathologies in the same BR correlate with the same symptoms. [ABSTRACT FROM AUTHOR]