Saliva Neurofilament Light Chain Is Not a Diagnostic Biomarker for Neurodegeneration in a Mixed Memory Clinic Population

Neurodegeneration and axonal injury result in an increasing release of neurofilament light chain (NfL) into bodily fluids, including cerebrospinal fluid (CSF) and blood. Numerous studies have shown that NfL levels in CSF and blood are increased in neurodegenerative disorders and monitor neurodegeneration. Saliva is an easily accessible biofluid that could be utilized as a biofluid measurement of Alzheimer’s disease (AD) biomarkers. In this study, for the first time, salivary NfL was measured and compared to plasma NfL in a consecutive cohort of patients referred to cognitive assessments. In two mixed memory clinic cohorts, saliva samples were taken from 152 patients, AD (n = 49), mild cognitive impairment (MCI) (n = 47), non-AD (n = 56), and also 17 healthy controls. In addition, 135 also had a matching plasma sample. All saliva and plasma samples were analyzed for NfL, and the association between saliva and plasma NfL and CSF levels of total tau (t-tau), phosphorylated tau (p-tau), and beta amyloid 1–42 (Ab42) were investigated. In total, 162/169 had quantifiable levels of salivary NfL by single molecule array (Simoa). No statistically significant differences were found in salivary NfL concentration across the diagnostic groups, but as expected, significant increases were found for plasma NfL in dementia cases (P < 0.0001). There was no association between saliva and plasma NfL levels. Furthermore, saliva NfL did not correlate with CSF Ab42, p-tau, or tau concentrations. In conclusion, NfL is detectable in saliva but does not reflect neurodegeneration in the brain

A Parallel Reaction Monitoring Mass Spectrometric Method for Analysis of Potential CSF Biomarkers for Alzheimer's Disease

Scope: The aim of this study was to develop and evaluate a parallel reactionmonitoring mass spectrometry (PRM-MS) assay consisting of a panel ofpotential protein biomarkers in cerebrospinal fluid (CSF).Experimental design: Thirteen proteins were selected based on theirassociation with neurodegenerative diseases and involvement in synapticfunction, secretory vesicle function, or innate immune system. CSF sampleswere digested and two to three peptides per protein were quantified usingstable isotope-labeled peptide standards.Results: Coefficients of variation were generally below 15%. Clinicalevaluation was performed on a cohort of 10 patients with Alzheimer’s disease(AD) and 15 healthy subjects. Investigated proteins of the granin familyexhibited the largest difference between the patient groups. Secretogranin-2(p<0.005) and neurosecretory protein VGF (p<0.001) concentrations werelowered in AD. For chromogranin A, two of three peptides had significantlylowered AD concentrations (p<0.01). The concentrations of the synapticproteins neurexin-1 and neuronal pentraxin-1, as well as neurofascin werealso significantly lowered in AD (p<0.05). The other investigated proteins,β2-microglobulin, cystatin C, amyloid precursor protein, lysozyme C,neurexin-2, neurexin-3, and neurocan core protein, were not significantlyaltered.Conclusion and clinical relevance: PRM-MS of protein panels is a valuabletool to evaluate biomarker candidates for neurodegenerative disorders

Pittsburgh compound B imaging and cerebrospinal fluid amyloid-β in a multicentre European memory clinic study

The aim of this study was to assess the agreement between data on cerebral amyloidosis, derived using Pittsburgh compound B positron emission tomography and (i) multi-laboratory INNOTEST enzyme linked immunosorbent assay derived cerebrospinal fluid concentrations of amyloid-β 42 ; (ii) centrally measured cerebrospinal fluid amyloid-β 42 using a Meso Scale Discovery enzyme linked immunosorbent assay; and (iii) cerebrospinal fluid amyloid-β 42 centrally measured using an antibody-independent mass spectrometry-based reference method. Moreover, we examined the hypothesis that discordance between amyloid biomarker measurements may be due to interindividual differences in total amyloid-β production, by using the ratio of amyloid-β 42 to amyloid-β 40 . Our study population consisted of 243 subjects from seven centres belonging to the Biomarkers for Alzheimer’s and Parkinson’s Disease Initiative, and included subjects with normal cognition and patients with mild cognitive impairment, Alzheimer’s disease dementia, frontotemporal dementia, and vascular dementia. All had Pittsburgh compound B positron emission tomography data, cerebrospinal fluid INNOTEST amyloid-β 42 values, and cerebrospinal fluid samples available for reanalysis. Cerebrospinal fluid samples were reanalysed (amyloid-β 42 and amyloid-β 40 ) using Meso Scale Discovery electrochemiluminescence enzyme linked immunosorbent assay technology, and a novel, antibody-independent, mass spectrometry reference method. Pittsburgh compound B standardized uptake value ratio results were scaled using the Centiloid method. Concordance between Meso Scale Discovery/mass spectrometry reference measurement procedure findings and Pittsburgh compound B was high in subjects with mild cognitive impairment and Alzheimer’s disease, while more variable results were observed for cognitively normal and non-Alzheimer’s disease groups. Agreement between Pittsburgh compound B classification and Meso Scale Discovery/mass spectrometry reference measurement procedure findings was further improved when using amyloid-β 42/40 . Agreement between Pittsburgh compound B visual ratings and Centiloids was near complete. Despite improved agreement between Pittsburgh compound B and centrally analysed cerebrospinal fluid, a minority of subjects showed discordant findings. While future studies are needed, our results suggest that amyloid biomarker results may not be interchangeable in some individuals

Teaching and learning about dementia in UK medical schools: a national survey

Background: Dementia is an increasingly common condition and all doctors, in both primary and secondary care environments, must be prepared to competently manage patients with this condition. It is unclear whether medical education about dementia is currently fit for purpose. This project surveys and evaluates the nature of teaching and learning about dementia for medical students in the UK. Methods: Electronic questionnaire sent to UK medical schools. Results: 23/31 medical schools responded. All provided some dementia-specific teaching but this focussed more on knowledge and skills than behaviours and attitudes. Only 80% of schools described formal assessment of dementia-specific learning outcomes. There was a widespread failure to adequately engage the multidisciplinary team, patients and carers in teaching, presenting students with a narrow view of the condition. However, some innovative approaches were also highlighted. Conclusions: Although all schools taught about dementia, the deficiencies identified represent a failure to sufficiently equip medical students to care for patients with dementia which, given the prevalence of the condition, does not adequately prepare them for work as doctors. Recommendations for improving undergraduate medical education about dementia are outline

Detecting frontotemporal dementia syndromes using MRI biomarkers

BACKGROUND: Diagnosing frontotemporal dementia may be challenging. New methods for analysis of regional brain atrophy patterns on magnetic resonance imaging (MRI) could add to the diagnostic assessment. Therefore, we aimed to develop automated imaging biomarkers for differentiating frontotemporal dementia subtypes from other diagnostic groups, and from one another. METHODS: In this retrospective multicenter cohort study, we included 1213 patients (age 67 ± 9, 48% females) from two memory clinic cohorts: 116 frontotemporal dementia, 341 Alzheimer's disease, 66 Dementia with Lewy bodies, 40 vascular dementia, 104 other dementias, 229 mild cognitive impairment, and 317 subjective cognitive decline. Three MRI atrophy biomarkers were derived from the normalized volumes of automatically segmented cortical regions: 1) the anterior vs. posterior index, 2) the asymmetry index, and 3) the temporal pole left index. We used the following performance metrics: area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. To account for the low prevalence of frontotemporal dementia we pursued a high specificity of 95%. Cross-validation was used in assessing the performance. The generalizability was assessed in an independent cohort (n = 200). RESULTS: The anterior vs. posterior index performed with an AUC of 83% for differentiation of frontotemporal dementia from all other diagnostic groups (Sensitivity = 59%, Specificity = 95%, positive likelihood ratio = 11.8, negative likelihood ratio = 0.4). The asymmetry index showed highest performance for separation of primary progressive aphasia and behavioral variant frontotemporal dementia (AUC = 85%, Sensitivity = 79%, Specificity = 92%, positive likelihood ratio = 9.9, negative likelihood ratio = 0.2), whereas the temporal pole left index was specific for detection of semantic variant primary progressive aphasia (AUC = 85%, Sensitivity = 82%, Specificity = 80%, positive likelihood ratio = 4.1, negative likelihood ratio = 0.2). The validation cohort provided corresponding results for the anterior vs. posterior index and temporal pole left index. CONCLUSION: This study presents three quantitative MRI biomarkers, which could provide additional information to the diagnostic assessment and assist clinicians in diagnosing frontotemporal dementia

Evaluating combinations of diagnostic tests to discriminate different dementia types

INTRODUCTION: We studied, using a data-driven approach, how different combinations of diagnostic tests contribute to the differential diagnosis of dementia. METHODS: In this multicenter study, we included 356 patients with Alzheimer's disease, 87 frontotemporal dementia, 61 dementia with Lewy bodies, 38 vascular dementia, and 302 controls. We used a classifier to assess accuracy for individual performance and combinations of cognitive tests, cerebrospinal fluid biomarkers, and automated magnetic resonance imaging features for pairwise differentiation between dementia types. RESULTS: Cognitive tests had good performance in separating any type of dementia from controls. Cerebrospinal fluid optimally contributed to identifying Alzheimer's disease, whereas magnetic resonance imaging features aided in separating vascular dementia, dementia with Lewy bodies, and frontotemporal dementia. Combining diagnostic tests increased the accuracy, with balanced accuracies ranging from 78% to 97%. DISCUSSION: Different diagnostic tests have their distinct roles in differential diagnostics of dementias. Our results indicate that combining different diagnostic tests may increase the accuracy further

Data driven diagnostic classification in Alzheimer's disease based on different reference regions for normalization of PiB-PET images and correlation with CSF concentrations of Aβ species

Positron emission tomography (PET) neuroimaging with the Pittsburgh Compound_B (PiB) is widely used to assess amyloid plaque burden. Standard quantification approaches normalize PiB-PET by mean cerebellar gray matter uptake. Previous studies suggested similar pons and white-matter uptake in Alzheimer's disease (AD) and healthy controls (HC), but lack exhaustive comparison of normalization across the three regions, with data-driven diagnostic classification. We aimed to compare the impact of distinct reference regions in normalization, measured by data-driven statistical analysis, and correlation with cerebrospinal fluid (CSF) amyloid β (Aβ) species concentrations. 243 individuals with clinical diagnosis of AD, HC, mild cognitive impairment (MCI) and other dementias, from the Biomarkers for Alzheimer's/Parkinson's Disease (BIOMARKAPD) initiative were included. PiB-PET images and CSF concentrations of Aβ38, Aβ40 and Aβ42 were submitted to classification using support vector machines. Voxel-wise group differences and correlations between normalized PiB-PET images and CSF Aβ concentrations were calculated. Normalization by cerebellar gray matter and pons yielded identical classification accuracy of AD (accuracy-96%, sensitivity-96%, specificity-95%), and significantly higher than Aβ concentrations (best accuracy 91%). Normalization by the white-matter showed decreased extent of statistically significant multivoxel patterns and was the only method not outperforming CSF biomarkers, suggesting statistical inferiority. Aβ38 and Aβ40 correlated negatively with PiB-PET images normalized by the white-matter, corroborating previous observations of correlations with non-AD-specific subcortical changes in white-matter. In general, when using the pons as reference region, higher voxel-wise group differences and stronger correlation with Aβ42, the Aβ42/Aβ40 or Aβ42/Aβ38 ratios were found compared to normalization based on cerebellar gray matter