BACKGROUND: We examined the relations of misfolded alpha synuclein (α-synuclein) with Alzheimer\'s disease (AD) biomarkers in two large independent cohorts.
METHODS: We included Biomarkers for Identifying Neurodegenerative Disorders Early and Reliably Two (BioFINDER-2) and Alzheimer\'s Disease Neuroimaging Initiative (ADNI) participants (n = 2315, cognitively unimpaired, mild cognitive impairment, AD dementia) who had cross-sectional cerebrospinal fluid (CSF) α-synuclein measurement from seed-amplification assay as well as cross-sectional and longitudinal amyloid beta (Aβ) and tau levels (measured in CSF and/or by positron emission tomography). All analyses were adjusted for age, sex, and cognitive status.
RESULTS: Across cohorts, the main biomarker associated with α-synuclein positivity at baseline was higher levels of Aβ pathology (all p values ≤ 0.02), but not tau. Looking at longitudinal measures of AD biomarkers, α-synuclein -positive participants had a statistically significant faster increase of Aβ load, although of modest magnitude (1.11 Centiloid/year, p = 0.02), compared to α-synuclein -negative participants in BioFINDER-2 but not in ADNI.
CONCLUSIONS: We showed associations between concurrent misfolded α-synuclein and Aβ levels, providing in vivo evidence of links between these two molecular disease pathways in humans.
CONCLUSIONS: Amyloid beta (Aβ), but not tau, was associated with alpha-synuclein (α-synuclein) positivity. Such association was consistent across two cohorts, beyond the effect of age, sex, and cognitive status. α-synuclein-positive participants had a small, statistically significant faster increase in Aβ positron emission tomography levels in one of the two cohorts.

This narrative review takes a personal approach in detailing the progression of cognitive decline in a loved one, and the measures taken to care for the patient. The author provides suggestions for a compassionate care plan and advice for clinicians helpful to both patient and caregiver.

Lewy body disorders are heterogeneous neurological conditions defined by intracellular inclusions composed of misshapen α-synuclein protein aggregates. Although α-synuclein aggregates are only one component of inclusions and not strictly coupled to neurodegeneration, evidence suggests they seed the propagation of Lewy pathology within and across cells. Genetic mutations, genomic multiplications, and sequence polymorphisms of the gene encoding α-synuclein are also causally linked to Lewy body disease. In nonfamilial cases of Lewy body disease, the disease trigger remains unidentified but may range from industrial/agricultural toxicants and natural sources of poisons to microbial pathogens. Perhaps due to these peripheral exposures, Lewy inclusions appear at early disease stages in brain regions connected with cranial nerves I and X, which interface with inhaled and ingested environmental elements in the nasal or gastrointestinal cavities. Irrespective of its identity, a stealthy disease trigger most likely shifts soluble α-synuclein (directly or indirectly) into insoluble, cross-β-sheet aggregates. Indeed, β-sheet-rich self-replicating α-synuclein multimers reside in patient plasma, cerebrospinal fluid, and other tissues, and can be subjected to α-synuclein seed amplification assays. Thus, clinicians should be able to capitalize on α-synuclein seed amplification assays to stratify patients into potential responders versus non-responders in future clinical trials of α-synuclein targeted therapies. Here, we briefly review the current understanding of α-synuclein in Lewy body disease and speculate on pathophysiological processes underlying the potential transmission of α-synucleinopathy across the neuraxis.

The cognitive impairment known as dementia affects millions of individuals throughout the globe. The use of machine learning (ML) and deep learning (DL) algorithms has shown great promise as a means of early identification and treatment of dementia. Dementias such as Alzheimer\'s Dementia, frontotemporal dementia, Lewy body dementia, and vascular dementia are all discussed in this article, along with a literature review on using ML algorithms in their diagnosis. Different ML algorithms, such as support vector machines, artificial neural networks, decision trees, and random forests, are compared and contrasted, along with their benefits and drawbacks. As discussed in this article, accurate ML models may be achieved by carefully considering feature selection and data preparation. We also discuss how ML algorithms can predict disease progression and patient responses to therapy. However, overreliance on ML and DL technologies should be avoided without further proof. It\'s important to note that these technologies are meant to assist in diagnosis but should not be used as the sole criteria for a final diagnosis. The research implies that ML algorithms may help increase the precision with which dementia is diagnosed, especially in its early stages. The efficacy of ML and DL algorithms in clinical contexts must be verified, and ethical issues around the use of personal data must be addressed, but this requires more study.

The heterogeneity of protein-rich inclusions and its significance in neurodegeneration is poorly understood. Standard patient-derived iPSC models develop inclusions neither reproducibly nor in a reasonable time frame. Here, we developed screenable iPSC \"inclusionopathy\" models utilizing piggyBac or targeted transgenes to rapidly induce CNS cells that express aggregation-prone proteins at brain-like levels. Inclusions and their effects on cell survival were trackable at single-inclusion resolution. Exemplar cortical neuron α-synuclein inclusionopathy models were engineered through transgenic expression of α-synuclein mutant forms or exogenous seeding with fibrils. We identified multiple inclusion classes, including neuroprotective p62-positive inclusions versus dynamic and neurotoxic lipid-rich inclusions, both identified in patient brains. Fusion events between these inclusion subtypes altered neuronal survival. Proteome-scale α-synuclein genetic- and physical-interaction screens pinpointed candidate RNA-processing and actin-cytoskeleton-modulator proteins like RhoA whose sequestration into inclusions could enhance toxicity. These tractable CNS models should prove useful in functional genomic analysis and drug development for proteinopathies.

Leucine-rich repeat kinase 2 (LRRK2) is the most common gene responsible for familial Parkinson\'s disease (PD). The gene product of LRRK2 contains multiple protein domains, including armadillo repeat, ankyrin repeat, leucine-rich repeat (LRR), Ras-of-complex (ROC), C-terminal of ROC (COR), kinase, and WD40 domains. In this study, we performed genetic screening of LRRK2 in our PD cohort, detecting sixteen LRRK2 rare variants. Among them, we selected seven variants that are likely to be familial and characterized them in terms of LRRK2 protein function, along with clinical information and one pathological analysis. The seven variants were S1120P and N1221K in the LRR domain; I1339M, S1403R, and V1447M in the ROC domain; and I1658F and D1873H in the COR domain. The kinase activity of the LRRK2 variants N1221K, S1403R, V1447M, and I1658F toward Rab10, a well-known phosphorylation substrate, was higher than that of wild-type LRRK2. LRRK2 D1873H showed enhanced self-association activity, whereas LRRK2 S1403R and D1873H showed reduced microtubule-binding activity. Pathological analysis of a patient with the LRRK2 V1447M variant was also performed, which revealed Lewy pathology in the brainstem. No functional alterations in terms of kinase activity, self-association activity, and microtubule-binding activity were detected in LRRK2 S1120P and I1339M variants. However, the patient with PD carrying LRRK2 S1120P variant also had a heterozygous Glucosylceramidase beta 1 (GBA1) L444P variant. In conclusion, we characterized seven LRRK2 variants potentially associated with PD. Five of the seven variants in different LRRK2 domains exhibited altered properties in kinase activity, self-association, and microtubule-binding activity, suggesting that each domain variant may contribute to disease progression in different ways.

BACKGROUND: Dementia is caused by neurodegenerative conditions and characterized by cognitive decline. Diagnostic accuracy for dementia subtypes, such as Alzheimer\'s Disease (AD), Dementia with Lewy Bodies (DLB) and Parkinson\'s Disease with dementia (PDD), remains challenging.
METHODS: Here, different methods of quantitative electroencephalography (qEEG) analyses were employed to assess their effectiveness in distinguishing dementia subtypes from healthy controls under eyes closed (EC) and eyes open (EO) conditions.
RESULTS: Classic Fast-Fourier Transform (FFT) and autoregressive (AR) power analyses differentiated between all conditions for the 4-8 Hz theta range. Only individuals with dementia with Lewy Bodies (DLB) differed from healthy subjects for the wider 4-15 Hz frequency range, encompassing the actual dominant frequency of all individuals. FFT results for this range yielded wider significant discriminators vs AR, also detecting differences between AD and DLB. Analyses of the inclusive dominant / peak frequency range (4-15 Hz) indicated slowing and reduced variability, also discriminating between synucleinopathies vs controls and AD. Dissociation of periodic oscillations and aperiodic components of AR spectra using Fitting-Oscillations-&-One-Over-F (FOOOF) modelling delivered a reliable and subtype-specific slowing of brain oscillatory peaks during EC and EO for all groups. Distinct and robust differences were particularly strong for aperiodic parameters, suggesting their potential diagnostic power in detecting specific changes resulting from age and cognitive status.
CONCLUSIONS: Our findings indicate that qEEG methods can reliably detect dementia subtypes. Spectral analyses comprising an integrated, multi-parameter EEG approach discriminating between periodic and aperiodic components under EC and EO conditions may enhance diagnostic accuracy in the future.

UNASSIGNED: Quality of life (QOL) and treatment needs of patients with dementia with Lewy bodies (DLB) and their caregivers are important factors to consider when developing treatment strategies.
UNASSIGNED: To investigate factors associated with QOL in patients with DLB, and to examine factors associated with activities of daily living (ADL) if ADL was associated with QOL.
UNASSIGNED: We previously conducted a questionnaire survey study to investigate the treatment needs of patients with DLB and their caregivers. This pre-specified additional analysis evaluated the Physical Component Score (PCS) and Mental Component Score (MCS) of the Short Form-8 for QOL, and the Movement Disorder Society-Unified Parkinson\'s Disease Rating Scale (MDS-UPDRS) Part II total score for ADL.
UNASSIGNED: In total, 231 patient- caregiver pairs and 38 physicians were included. Multivariable analysis of QOL showed that the MDS-UPDRS Part II total score (standard regression coefficient [β], - 0.432) was associated with the PCS, and presence of depression (β, - 0.330) was associated with the MCS. The severity of postural instability/gait disorder (PIGD) (β, 0.337) and rigidity (β, 0.266), presence of hallucinations (β, 0.165), male sex (β, 0.157), and use of \"short stay\" or \"small-scale, multifunctional home care\" (β, 0.156) were associated with worsened ADL.
UNASSIGNED: In patients with DLB, QOL was negatively impacted by severity of ADL disability and depression, and ADL was negatively impacted by severity of PIGD and rigidity, hallucinations, male sex, and use of \"short stay\" or \"small-scale, multifunctional home care.\"

α-Synuclein (αS) is an abundant presynaptic protein that regulates neurotransmission. It is also a key protein implicated in a broad class of neurodegenerative disorders termed synucleinopathies, including Parkinson\'s disease (PD) and Lewy body dementia (LBD). Pathological αS deposits in these diseases, Lewy bodies (LBs)/neurites (LNs), contain about 90% of αS in its phospho-serine129 (pS129) form. Therefore, pS129 is widely used as a surrogate marker of pathology. However, recent findings demonstrate that pS129 is also physiologically triggered by neuronal activity to positively regulate synaptic transmission. In this opinion article, we contrast the literature on pathological and physiological pS129, with a special focus on the latter. We emphasize that pS129 is ambiguous and knowledge about the context is necessary to correctly interpret changes in pS129.

Alzheimer\'s disease (AD) pathology is defined by β-amyloid (Aβ) plaques and neurofibrillary tau, but Lewy bodies (LBs; 𝛼-synuclein aggregates) are a common co-pathology for which effective biomarkers are needed.
A validated α-synuclein Seed Amplification Assay (SAA) was used on recent cerebrospinal fluid (CSF) samples from 1638 Alzheimer\'s Disease Neuroimaging Initiative (ADNI) participants, 78 with LB-pathology confirmation at autopsy. We compared SAA outcomes with neuropathology, Aβ and tau biomarkers, risk-factors, genetics, and cognitive trajectories.
SAA showed 79% sensitivity and 97% specificity for LB pathology, with superior performance in identifying neocortical (100%) compared to limbic (57%) and amygdala-predominant (60%) LB-pathology. SAA+ rate was 22%, increasing with disease stage and age. Higher Aβ burden but lower CSF p-tau181 associated with higher SAA+ rates, especially in dementia. SAA+ affected cognitive impairment in MCI and Early-AD who were already AD biomarker positive.
SAA is a sensitive, specific marker for LB-pathology. Its increase in prevalence with age and AD stages, and its association with AD biomarkers, highlights the clinical importance of α-synuclein co-pathology in understanding AD\'s nature and progression.
SAA shows 79% sensitivity, 97% specificity for LB-pathology detection in AD. SAA positivity prevalence increases with disease stage and age. Higher Aβ burden, lower CSF p-tau181 linked with higher SAA+ rates in dementia. SAA+ impacts cognitive impairment in early disease stages. Study underpins need for wider LB-pathology screening in AD treatment.