Locus coeruleus integrity and neuropsychiatric symptoms in a cohort of early- and late-onset Alzheimer's disease.

INTRODUCTION: Early-onset Alzheimer's disease (EOAD) shows a higher burden of neuropsychiatric symptoms than late-onset Alzheimer's disease (LOAD). We aim to determine the differences in the severity of neuropsychiatric symptoms and locus coeruleus (LC) integrity between EOAD and LOAD accounting for disease stage. METHODS: One hundred four subjects with AD diagnosis and 32 healthy controls were included. Participants underwent magnetic resonance imaging (MRI) to measure LC integrity, measures of noradrenaline levels in cerebrospinal fluid (CSF) and Neuropsychiatric Inventory (NPI). We analyzed LC-noradrenaline measurements and clinical and Alzheimer's disease (AD) biomarker associations. RESULTS: EOAD showed higher NPI scores, lower LC integrity, and similar levels of CSF noradrenaline compared to LOAD. Notably, EOAD exhibited lower LC integrity independently of disease stage. LC integrity negatively correlated with neuropsychiatric symptoms. Noradrenaline levels were increased in AD correlating with AD biomarkers. DISCUSSION: Decreased LC integrity negatively contributes to neuropsychiatric symptoms. The higher LC degeneration in EOAD compared to LOAD could explain the more severe neuropsychiatric symptoms in EOAD. HIGHLIGHTS: LC degeneration is greater in early-onset AD (EOAD) compared to late-onset AD. Tau-derived LC degeneration drives a higher severity of neuropsychiatric symptoms. EOAD harbors a more profound selective vulnerability of the LC system. LC degeneration is associated with an increase of cerebrospinal fluid noradrenaline levels in AD.

Cognitive profile, neuroimaging and fluid biomarkers in post-acute COVID-19 syndrome.

We aimed to characterize the cognitive profile of post-acute COVID-19 syndrome (PACS) patients with cognitive complaints, exploring the influence of biological and psychological factors. Participants with confirmed SARS-CoV-2 infection and cognitive complaints ≥ 8 weeks post-acute phase were included. A comprehensive neuropsychological battery (NPS) and health questionnaires were administered at inclusion and at 1, 3 and 6 months. Blood samples were collected at each visit, MRI scan at baseline and at 6 months, and, optionally, cerebrospinal fluid. Cognitive features were analyzed in relation to clinical, neuroimaging, and biochemical markers at inclusion and follow-up. Forty-nine participants, with a mean time from symptom onset of 10.4 months, showed attention-executive function (69%) and verbal memory (39%) impairment. Apathy (64%), moderate-severe anxiety (57%), and severe fatigue (35%) were prevalent. Visual memory (8%) correlated with total gray matter (GM) and subcortical GM volume. Neuronal damage and inflammation markers were within normal limits. Over time, cognitive test scores, depression, apathy, anxiety scores, MRI indexes, and fluid biomarkers remained stable, although fewer participants (50% vs. 75.5%; p = 0.012) exhibited abnormal cognitive evaluations at follow-up. Altered attention/executive and verbal memory, common in PACS, persisted in most subjects without association with structural abnormalities, elevated cytokines, or neuronal damage markers.

Towards early diagnosis of Alzheimer's disease: advances in immune-related blood biomarkers and computational approaches.

Alzheimer's disease has an increasing prevalence in the population world-wide, yet current diagnostic methods based on recommended biomarkers are only available in specialized clinics. Due to these circumstances, Alzheimer's disease is usually diagnosed late, which contrasts with the currently available treatment options that are only effective for patients at an early stage. Blood-based biomarkers could fill in the gap of easily accessible and low-cost methods for early diagnosis of the disease. In particular, immune-based blood-biomarkers might be a promising option, given the recently discovered cross-talk of immune cells of the central nervous system with those in the peripheral immune system. Here, we give a background on recent advances in research on brain-immune system cross-talk in Alzheimer's disease and review machine learning approaches, which can combine multiple biomarkers with further information (e.g. age, sex, APOE genotype) into predictive models supporting an earlier diagnosis. In addition, mechanistic modeling approaches, such as agent-based modeling open the possibility to model and analyze cell dynamics over time. This review aims to provide an overview of the current state of immune-system related blood-based biomarkers and their potential for the early diagnosis of Alzheimer's disease.

Evidence for a novel neuronal mechanism driving Alzheimer's disease, upstream of amyloid.

This perspective offers an alternative to the amyloid hypothesis in the etiology of Alzheimer's disease (AD). We review evidence for a novel signaling mechanism based on a little-known peptide, T14. T14 could drive neurodegeneration as an aberrantly activated process of plasticity selective to interconnecting subcortical nuclei, the isodendritic core, where cell loss starts at the pre-symptomatic stages of the disease. Each of these cell groups has the capacity to form T14, which can stimulate production of p-Tau and ß-amyloid, suggestive of an upstream driver of neurodegeneration. Moreover, results in an animal AD model show that antagonism of T14 with a cyclated variant, NBP14, prevents formation of ß-amyloid, and restores cognitive function to that of wild-type counterparts. Any diagnostic and/or therapeutic strategy based on T14-NBP14 awaits validation in clinical trials. However, an understanding of this novel signaling system could bring much-needed fresh insights into the progression of cell loss underlying AD. HIGHLIGHTS: The possible primary mechanism of neurodegeneration upstream of amyloid. Primary involvement of selectively vulnerable subcortical nuclei, isodendritic core. Bioactive peptide T14 trophic in development but toxic in context of mature brain. Potential for early-stage biomarker to detect Alzheimer's disease. Effective therapeutic halting neurodegeneration, validated already in 5XFAD mice.

Utility of visual rating scales in primary progressive aphasia.

INTRODUCTION: Differential diagnosis among subjects with Primary Progressive Aphasia (PPA) can be challenging. Structural MRI can support the clinical profile. Visual rating scales are a simple and reliable tool to assess brain atrophy in the clinical setting. The aims of the study were to establish to what extent the visual rating scales could be useful in the differential diagnosis of PPA, to compare the clinical diagnostic impressions derived from routine MRI interpretations with those obtained using the visual rating scale and to correlate results of the scales in a voxel-based morphometry (VBM) analysis. METHOD: Patients diagnosed with primary progressive aphasia (PPA) according to current criteria from two centers-Ospedale Maggiore Policlinico of Milan and Hospital Clínic de Barcelona-were included in the study. Two blinded clinicians evaluated the subjects MRIs for cortical atrophy and white matter hyperintensities using two protocols: routine readings and the visual rating scale. The diagnostic accuracy between patients and controls and within PPA subgroups were compared between the two protocols. RESULTS: One hundred fifty Subjects were studied. All the scales showed a good to excellent intra and inter-rater agreement. The left anterior temporal scale could differentiate between semantic PPA and all other variants. The rater impression after the protocol can increase the accuracy just for the logopenic PPA. In the VBM analysis, the scores of visual rating scales correlate with the corresponding area of brain atrophy. CONCLUSION: The Left anterior temporal rating scale can distinguish semantic PPA from other variants. The rater impression after structured view improved the diagnostic accuracy of logopenic PPA compared to normal readings. The unstructured view of the MRI was reliable for identifying semantic PPA and controls. Neither the structured nor the unstructured view could identify the nonfluent and undetermined variants.

Impact of demographics and comorbid conditions on plasma biomarkers concentrations and their diagnostic accuracy in a memory clinic cohort.

Plasma biomarkers have emerged as promising tools for identifying amyloid beta (Aß) pathology. Before implementation in routine clinical practice, confounding factors modifying their concentration beyond neurodegenerative diseases should be identified. We studied the association of a comprehensive list of demographics, comorbidities, medication and laboratory parameters with plasma p-tau181, glial fibrillary acidic protein (GFAP) and neurofilament light chain (NfL) on a prospective memory clinic cohort and studied their impact on diagnostic accuracy for discriminating CSF/amyloid PET-defined Aß status. Three hundred sixty patients (mean age 66.5 years, 55% females, 53% Aß positive) were included. Sex, age and Aß status-adjusted models showed that only estimated glomerular filtration rate (eGFR, standardized ß -0.115 [-0.192 to -0.035], p = 0.005) was associated with p-tau181 levels, although with a much smaller effect than Aß status (0.685 [0.607-0.763], p < 0.001). Age, sex, body mass index (BMI), Charlson comorbidity index (CCI) and eGFR significantly modified GFAP concentration. Age, blood volume (BV) and eGFR were associated with NfL levels. p-tau181 predicted Aß status with 87% sensitivity and specificity with no relevant increase in diagnostic performance by adding any of the confounding factors. Using two cut-offs, plasma p-tau181 could have spared 62% of amyloid-PET/CSF testing. Excluding patients with chronic kidney disease did not change the proposed cut-offs nor the diagnostic performance. In conclusion, in a memory clinic cohort, age, sex, eGFR, BMI, BV and CCI slightly modified plasma p-tau181, GFAP and NfL concentrations but their impact on the diagnostic accuracy of plasma biomarkers for Aß status discrimination was minimal.