Florbetaben amyloid PET acquisition time: Influence on Centiloids and interpretation.

INTRODUCTION: Amyloid positron emission tomography (PET) acquisition timing impacts quantification. METHODS: In florbetaben (FBB) PET scans of 245 adults with and without cognitive impairment, we investigated the impact of post-injection acquisition time on Centiloids (CLs) across five reference regions. CL equations for FBB were derived using standard methods, using FBB data collected between 90 and 110 min with paired Pittsburgh compound B data. Linear mixed models and t-tests evaluated the impact of acquisition time on CL increases. RESULTS: CL values increased significantly over the scan using the whole cerebellum, cerebellar gray matter, and brainstem as reference regions, particularly in amyloid-positive individuals. In contrast, CLs based on white matter-containing reference regions decreased across the scan. DISCUSSION: The quantification of CLs in FBB PET imaging is influenced by both the overall scan acquisition time and the choice of reference region. Standardized acquisition protocols or the application of acquisition time-specific CL equations should be implemented in clinical protocols. HIGHLIGHTS: Acquisition timing affects florbetaben positron emission tomography (PET) scan quantification, especially in amyloid-positive participants. The impact of acquisition timing on quantification varies across common reference regions. Consistent acquisitions and/or appropriate post-injection adjustments are needed to ensure comparability of PET data.

AI-based differential diagnosis of dementia etiologies on multimodal data.

Differential diagnosis of dementia remains a challenge in neurology due to symptom overlap across etiologies, yet it is crucial for formulating early, personalized management strategies. Here, we present an artificial intelligence (AI) model that harnesses a broad array of data, including demographics, individual and family medical history, medication use, neuropsychological assessments, functional evaluations and multimodal neuroimaging, to identify the etiologies contributing to dementia in individuals. The study, drawing on 51,269 participants across 9 independent, geographically diverse datasets, facilitated the identification of 10 distinct dementia etiologies. It aligns diagnoses with similar management strategies, ensuring robust predictions even with incomplete data. Our model achieved a microaveraged area under the receiver operating characteristic curve (AUROC) of 0.94 in classifying individuals with normal cognition, mild cognitive impairment and dementia. Also, the microaveraged AUROC was 0.96 in differentiating the dementia etiologies. Our model demonstrated proficiency in addressing mixed dementia cases, with a mean AUROC of 0.78 for two co-occurring pathologies. In a randomly selected subset of 100 cases, the AUROC of neurologist assessments augmented by our AI model exceeded neurologist-only evaluations by 26.25%. Furthermore, our model predictions aligned with biomarker evidence and its associations with different proteinopathies were substantiated through postmortem findings. Our framework has the potential to be integrated as a screening tool for dementia in clinical settings and drug trials. Further prospective studies are needed to confirm its ability to improve patient care.

The Parkinson's Disease Composite of Executive Functioning: A Measure for Detecting Cognitive Decline in Clinical Trials.

BACKGROUND AND OBJECTIVES: Executive functioning is one of the first domains to be impaired in Parkinson disease (PD), and the majority of patients with PD eventually develop dementia. Thus, developing a cognitive endpoint measure specifically assessing executive functioning is critical for PD clinical trials. The objective of this study was to develop a cognitive composite measure that is sensitive to decline in executive functioning for use in PD clinical trials. METHODS: We used cross-sectional and longitudinal follow-up data from PD participants enrolled in the PD Cognitive Genetics Consortium, a multicenter setting focused on PD. All PD participants with Trail Making Test, Digit Symbol, Letter-Number Sequencing, Semantic Fluency, and Phonemic Fluency neuropsychological data collected from March 2010 to February 2020 were included. Baseline executive functioning data were used to create the Parkinson's Disease Composite of Executive Functioning (PaCEF) through confirmatory factor analysis. We examined the changes in the PaCEF over time, how well baseline PaCEF predicts time to cognitive progression, and the required sample size estimates for PD clinical trials. PaCEF results were compared with the Montreal Cognitive Assessment (MoCA), individual tests forming the PaCEF, and tests of visuospatial, language, and memory functioning. RESULTS: A total of 841 participants (251 no cognitive impairment [NCI], 480 mild cognitive impairment [MCI], and 110 dementia) with baseline data were included, of which the mean (SD) age was 67.1 (8.9) years and 270 were women (32%). Five hundred forty five PD participants had longitudinal neuropsychological data spanning 9 years (mean [SD] 4.5 [2.2] years) and were included in analyses examining cognitive decline. A 1-factor model of executive functioning with excellent fit (comparative fit index = 0.993, Tucker-Lewis index = 0.989, and root mean square error of approximation = 0.044) was used to calculate the PaCEF. The average annual change in PaCEF ranged from 0.246 points per year for PD-NCI participants who remained cognitively unimpaired to -0.821 points per year for PD-MCI participants who progressed to dementia. For PD-MCI, baseline PaCEF, but not baseline MoCA, significantly predicted time to dementia. Sample size estimates were 69%-73% smaller for PD-NCI trials and 16%-19% smaller for PD-MCI trials when using the PaCEF rather than MoCA as the endpoint. DISCUSSION: The PaCEF is a sensitive measure of executive functioning decline in PD and will be especially beneficial for PD clinical trials.

Longitudinal Network Changes and Phenoconversion Risk in Isolated REM Sleep Behavior Disorder.

Isolated rapid eye movement sleep behavior disorder (iRBD) is a prodromal syndrome for Parkinson's disease (PD) and related α-synucleinopathies. We conducted a longitudinal imaging study of network changes in iRBD and their relationship to phenoconversion. Expression levels for the PD-related motor and cognitive networks (PDRP and PDCP) were measured at baseline, 2 and 4 years, along with dopamine transporter (DAT) binding. PDRP and PDCP expression increased over time, with higher values in the former network. While abnormal functional connections were identified initially within the PDRP, others bridging the two networks appeared later. A model based on the rates of PDRP progression and putamen dopamine loss predicted phenoconversion within 1.2 years in individuals with iRBD. In aggregate, the data suggest that maladaptive reorganization of brain networks takes place in iRBD years before phenoconversion. Network expression and DAT binding measures can be used together to assess phenoconversion risk in these individuals.

Plasma pTau181 Reveals a Pathological Signature that Predicts Cognitive Outcomes in Lewy Body Disease.

OBJECTIVE: To determine whether plasma phosphorylated-Tau181 (pTau181) could be used as a diagnostic biomarker of concurrent Alzheimer's disease neuropathologic change (ADNC) or amyloidosis alone, as well as a prognostic, monitoring, and susceptibility/risk biomarker for clinical outcomes in Lewy body disease (LBD). METHODS: We studied 565 participants: 94 LBD with normal cognition, 83 LBD with abnormal cognition, 114 with Alzheimer's disease, and 274 cognitively normal. Plasma pTau181 levels were measured with the Lumipulse G platform. Diagnostic accuracy for concurrent ADNC and amyloidosis was assessed with Receiver Operating Characteristic curves in a subset of participants with CSF pTau181/Aß42, and CSF Aß42/Aß40 or amyloid-ß PET, respectively. Linear mixed effects models were used to examine the associations between baseline and longitudinal plasma pTau181 levels and clinical outcomes. RESULTS: Plasma pTau181 predicted concurrent ADNC and amyloidosis in LBD with abnormal cognition with 87% and 72% accuracy, respectively. In LBD patients with abnormal cognition, higher baseline plasma pTau181 was associated with worse baseline MoCA and CDR-SB, as well as accelerated decline in CDR-SB. Additionally, in this group, rapid increases in plasma pTau181 over 3 years predicted a faster decline in CDR-SB and memory. In LBD patients with normal cognition, there was no association between baseline or longitudinal plasma pTau181 levels and clinical outcomes; however, elevated pTau181 at baseline increased the risk of conversion to cognitive impairment. INTERPRETATION: Our findings suggest that plasma pTau181 is a promising biomarker for concurrent ADNC and amyloidosis in LBD. Furthermore, plasma pTau181 holds potential as a prognostic, monitoring, and susceptibility/risk biomarker, predicting disease progression in LBD. ANN NEUROL 2024.

Development of highly multiplex targeted proteomics assays in biofluids using the Stellar mass spectrometer.

The development of targeted assays that monitor biomedically relevant proteins is an important step in bridging discovery experiments to large scale clinical studies. Targeted assays are currently unable to scale to hundreds or thousands of targets. We demonstrate the generation of large-scale assays using a novel hybrid nominal mass instrument. The scale of these assays is achievable with the Stellar™ mass spectrometer through the accommodation of shifting retention times by real-time alignment, while being sensitive and fast enough to handle many concurrent targets. Assays were constructed using precursor information from gas-phase fractionated (GPF) data-independent acquisition (DIA). We demonstrate the ability to schedule methods from an orbitrap and linear ion trap acquired GPF DIA library and compare the quantification of a matrix-matched calibration curve from orbitrap DIA and linear ion trap parallel reaction monitoring (PRM). Two applications of these proposed workflows are shown with a cerebrospinal fluid (CSF) neurodegenerative disease protein PRM assay and with a Mag-Net enriched plasma extracellular vesicle (EV) protein survey PRM assay.

Data Independent Acquisition to Inform the Development of Targeted Proteomics Assays Using a Triple Quadrupole Mass Spectrometer.

Mass spectrometry based targeted proteomics methods provide sensitive and high-throughput analysis of selected proteins. To develop a targeted bottom-up proteomics assay, peptides must be evaluated as proxies for the measurement of a protein or proteoform in a biological matrix. Candidate peptide selection typically relies on predetermined biochemical properties, data from semi-stochastic sampling, or by empirical measurements. These strategies require extensive testing and method refinement due to the difficulties associated with prediction of peptide response in the biological matrix of interest. Gas-phase fractionated (GPF) narrow window data-independent acquisition (DIA) aids in the development of reproducible selected reaction monitoring (SRM) assays by providing matrix-specific information on peptide detectability and quantification by mass spectrometry. To demonstrate the suitability of DIA data for selecting peptide targets, we reimplement a portion of an existing assay to measure 98 Alzheimer's disease proteins in cerebrospinal fluid (CSF). Peptides were selected from GPF-DIA based on signal intensity and reproducibility. The resulting SRM assay exhibits similar quantitative precision to published data, despite the inclusion of different peptides between the assays. This workflow enables development of new assays without additional up-front data acquisition, demonstrated here through generation of a separate assay for an unrelated set of proteins in CSF from the same dataset.

Subthalamic nucleus-language network connectivity predicts dopaminergic modulation of speech function in Parkinson's disease.

Speech impediments are a prominent yet understudied symptom of Parkinson's disease (PD). While the subthalamic nucleus (STN) is an established clinical target for treating motor symptoms, these interventions can lead to further worsening of speech. The interplay between dopaminergic medication, STN circuitry, and their downstream effects on speech in PD is not yet fully understood. Here, we investigate the effect of dopaminergic medication on STN circuitry and probe its association with speech and cognitive functions in PD patients. We found that changes in intrinsic functional connectivity of the STN were associated with alterations in speech functions in PD. Interestingly, this relationship was characterized by altered functional connectivity of the dorsolateral and ventromedial subdivisions of the STN with the language network. Crucially, medication-induced changes in functional connectivity between the STN's dorsolateral subdivision and key regions in the language network, including the left inferior frontal cortex and the left superior temporal gyrus, correlated with alterations on a standardized neuropsychological test requiring oral responses. This relation was not observed in the written version of the same test. Furthermore, changes in functional connectivity between STN and language regions predicted the medication's downstream effects on speech-related cognitive performance. These findings reveal a previously unidentified brain mechanism through which dopaminergic medication influences speech function in PD. Our study sheds light into the subcortical-cortical circuit mechanisms underlying impaired speech control in PD. The insights gained here could inform treatment strategies aimed at mitigating speech deficits in PD and enhancing the quality of life for affected individuals.

Mag-Net: Rapid enrichment of membrane-bound particles enables high coverage quantitative analysis of the plasma proteome.

Membrane-bound particles in plasma are composed of exosomes, microvesicles, and apoptotic bodies and represent ~1-2% of the total protein composition. Proteomic interrogation of this subset of plasma proteins augments the representation of tissue-specific proteins, representing a "liquid biopsy," while enabling the detection of proteins that would otherwise be beyond the dynamic range of liquid chromatography-tandem mass spectrometry of unfractionated plasma. We have developed an enrichment strategy (Mag-Net) using hyper-porous strong-anion exchange magnetic microparticles to sieve membrane-bound particles from plasma. The Mag-Net method is robust, reproducible, inexpensive, and requires <100 µL plasma input. Coupled to a quantitative data-independent mass spectrometry analytical strategy, we demonstrate that we can collect results for >37,000 peptides from >4,000 plasma proteins with high precision. Using this analytical pipeline on a small cohort of patients with neurodegenerative disease and healthy age-matched controls, we discovered 204 proteins that differentiate (q-value < 0.05) patients with Alzheimer's disease dementia (ADD) from those without ADD. Our method also discovered 310 proteins that were different between Parkinson's disease and those with either ADD or healthy cognitively normal individuals. Using machine learning we were able to distinguish between ADD and not ADD with a mean ROC AUC = 0.98 ± 0.06.

AI-based differential diagnosis of dementia etiologies on multimodal data.

Differential diagnosis of dementia remains a challenge in neurology due to symptom overlap across etiologies, yet it is crucial for formulating early, personalized management strategies. Here, we present an AI model that harnesses a broad array of data, including demographics, individual and family medical history, medication use, neuropsychological assessments, functional evaluations, and multimodal neuroimaging, to identify the etiologies contributing to dementia in individuals. The study, drawing on 51,269 participants across 9 independent, geographically diverse datasets, facilitated the identification of 10 distinct dementia etiologies. It aligns diagnoses with similar management strategies, ensuring robust predictions even with incomplete data. Our model achieved a micro-averaged area under the receiver operating characteristic curve (AUROC) of 0.94 in classifying individuals with normal cognition, mild cognitive impairment and dementia. Also, the micro-averaged AUROC was 0.96 in differentiating the dementia etiologies. Our model demonstrated proficiency in addressing mixed dementia cases, with a mean AUROC of 0.78 for two co-occurring pathologies. In a randomly selected subset of 100 cases, the AUROC of neurologist assessments augmented by our AI model exceeded neurologist-only evaluations by 26.25%. Furthermore, our model predictions aligned with biomarker evidence and its associations with different proteinopathies were substantiated through postmortem findings. Our framework has the potential to be integrated as a screening tool for dementia in various clinical settings and drug trials, with promising implications for person-level management.

Comprehensive proteomics of CSF, plasma, and urine identify DDC and other biomarkers of early Parkinson's disease.

Parkinson's disease (PD) starts at the molecular and cellular level long before motor symptoms appear, yet there are no early-stage molecular biomarkers for diagnosis, prognosis prediction, or monitoring therapeutic response. This lack of biomarkers greatly impedes patient care and translational research-L-DOPA remains the standard of care more than 50 years after its introduction. Here, we performed a large-scale, multi-tissue, and multi-platform proteomics study to identify new biomarkers for early diagnosis and disease monitoring in PD. We analyzed 4877 cerebrospinal fluid, blood plasma, and urine samples from participants across seven cohorts using three orthogonal proteomics methods: Olink proximity extension assay, SomaScan aptamer precipitation assay, and liquid chromatography-mass spectrometry proteomics. We discovered that hundreds of proteins were upregulated in the CSF, blood, or urine of PD patients, prodromal PD patients with DAT deficit and REM sleep behavior disorder or anosmia, and non-manifesting genetic carriers of LRRK2 and GBA mutations. We nominate multiple novel hits across our analyses as promising markers of early PD, including DOPA decarboxylase (DDC), also known as L-aromatic acid decarboxylase (AADC), sulfatase-modifying factor 1 (SUMF1), dipeptidyl peptidase 2/7 (DPP7), glutamyl aminopeptidase (ENPEP), WAP four-disulfide core domain 2 (WFDC2), and others. DDC, which catalyzes the final step in dopamine synthesis, particularly stands out as a novel hit with a compelling mechanistic link to PD pathogenesis. DDC is consistently upregulated in the CSF and urine of treatment-naïve PD, prodromal PD, and GBA or LRRK2 carrier participants by all three proteomics methods. We show that CSF DDC levels correlate with clinical symptom severity in treatment-naïve PD patients and can be used to accurately diagnose PD and prodromal PD. This suggests that urine and CSF DDC could be a promising diagnostic and prognostic marker with utility in both clinical care and translational research.

Multiple biomarkers improve diagnostic accuracy across Lewy body and Alzheimer's disease spectra.

OBJECTIVE: More than half of neurodegenerative disease patients have multiple pathologies at autopsy; however, most receive one diagnosis during life. We used the α-synuclein seed amplification assay (αSyn-SAA) and CSF biomarkers for amyloidosis and Alzheimer's disease (AD) neuropathological change (ADNC) to determine the frequency of co-pathologies in participants clinically diagnosed with Lewy body (LB) disease or AD. METHODS: Using receiver operating characteristic analyses on retrospective CSF samples from 150 participants determined αSyn-SAA accuracy, sensitivity, and specificity for identifying clinically defined LB disease and predicting future change in clinical diagnosis. CSF biomarkers helped determine the frequency of concomitant Lewy body pathology, ADNC, and/or amyloidosis in participants with LB disease and AD, across clinical spectra. RESULTS: Following a decade-long follow-up, the clinically or autopsy-defined diagnosis changed for nine participants. αSyn-SAA demonstrated improved accuracy (91.3%), sensitivity (89.3%), and specificity (93.3%) for identifying LB disease compared to all non-LB disease, highlighting the limitations of clinical diagnosis alone. When examining biomarkers of co-pathology, amyloidosis was present in 18%, 48%, and 71% (χ2(2) = 13.56, p = 0.001) and AD biomarkers were present in 0%, 8.7%, and 42.9% (χ2(2) = 18.44, p < 0.001) of LB disease participants with different stages of cognitive impairment respectively. Co-occurring biomarkers for αSyn-SAA and amyloidosis were present in 12% and 14% of AD compared to 43% and 57% LB disease participants with different stages of cognitive impairment (χ2(3) = 13.87, p = 0.003). INTERPRETATION: Our study shows that using a combination of αSyn-SAA and AD biomarkers can identify people with αSyn, ADNC, and co-pathology better and earlier than traditional clinical diagnostic criteria alone.

Speech patterns during memory recall relates to early tau burden across adulthood.

INTRODUCTION: Early cognitive decline may manifest in subtle differences in speech. METHODS: We examined 238 cognitively unimpaired adults from the Framingham Heart Study (32-75 years) who completed amyloid and tau PET imaging. Speech patterns during delayed recall of a story memory task were quantified via five speech markers, and their associations with global amyloid status and regional tau signal were examined. RESULTS: Total utterance time, number of between-utterance pauses, speech rate, and percentage of unique words significantly correlated with delayed recall score although the shared variance was low (2%-15%). Delayed recall score was not significantly different between ß-amyoid-positive (Aß+) and -negative (Aß-) groups and was not associated with regional tau signal. However, longer and more between-utterance pauses, and slower speech rate were associated with increased tau signal across medial temporal and early neocortical regions. DISCUSSION: Subtle speech changes during memory recall may reflect cognitive impairment associated with early Alzheimer's disease pathology. HIGHLIGHTS: Speech during delayed memory recall relates to tau PET signal across adulthood. Delayed memory recall score was not associated with tau PET signal. Speech shows greater sensitivity to detecting subtle cognitive changes associated with early tau accumulation. Our cohort spans adulthood, while most PET imaging studies focus on older adults.

Impaired 24-h activity patterns are associated with an increased risk of Alzheimer's disease, Parkinson's disease, and cognitive decline.

BACKGROUND: Sleep-wake regulating circuits are affected during prodromal stages in the pathological progression of both Alzheimer's disease (AD) and Parkinson's disease (PD), and this disturbance can be measured passively using wearable devices. Our objective was to determine whether accelerometer-based measures of 24-h activity are associated with subsequent development of AD, PD, and cognitive decline. METHODS: This study obtained UK Biobank data from 82,829 individuals with wrist-worn accelerometer data aged 40 to 79 years with a mean (± SD) follow-up of 6.8 (± 0.9) years. Outcomes were accelerometer-derived measures of 24-h activity (derived by cosinor, nonparametric, and functional principal component methods), incident AD and PD diagnosis (obtained through hospitalization or primary care records), and prospective longitudinal cognitive testing. RESULTS: One hundred eighty-seven individuals progressed to AD and 265 to PD. Interdaily stability (a measure of regularity, hazard ratio [HR] per SD increase 1.25, 95% confidence interval [CI] 1.05-1.48), diurnal amplitude (HR 0.79, CI 0.65-0.96), mesor (mean activity; HR 0.77, CI 0.59-0.998), and activity during most active 10 h (HR 0.75, CI 0.61-0.94), were associated with risk of AD. Diurnal amplitude (HR 0.28, CI 0.23-0.34), mesor (HR 0.13, CI 0.10-0.16), activity during least active 5 h (HR 0.24, CI 0.08-0.69), and activity during most active 10 h (HR 0.20, CI 0.16-0.25) were associated with risk of PD. Several measures were additionally predictive of longitudinal cognitive test performance. CONCLUSIONS: In this community-based longitudinal study, accelerometer-derived metrics were associated with elevated risk of AD, PD, and accelerated cognitive decline. These findings suggest 24-h rhythm integrity, as measured by affordable, non-invasive wearable devices, may serve as a scalable early marker of neurodegenerative disease.

Evaluation of ATNPD Framework and Biofluid Markers to Predict Cognitive Decline in Early Parkinson Disease.

BACKGROUND AND OBJECTIVES: In Parkinson disease (PD), Alzheimer disease (AD) copathology is common and clinically relevant. However, the longitudinal progression of AD CSF biomarkers-ß-amyloid 1-42 (Aß42), phosphorylated tau 181 (p-tau181), and total tau (t-tau)-in PD is poorly understood and may be distinct from clinical AD. Moreover, it is unclear whether CSF p-tau181 and serum neurofilament light (NfL) have added prognostic utility in PD, when combined with CSF Aß42. First, we describe longitudinal trajectories of biofluid markers in PD. Second, we modified the AD ß-amyloid/tau/neurodegeneration (ATN) framework for application in PD (ATNPD) using CSF Aß42 (A), p-tau181 (T), and serum NfL (N) and tested ATNPD prediction of longitudinal cognitive decline in PD. METHODS: Participants were selected from the Parkinson's Progression Markers Initiative cohort, clinically diagnosed with sporadic PD or as controls, and followed up annually for 5 years. Linear mixed-effects models (LMEMs) tested the interaction of diagnosis with longitudinal trajectories of analytes (log transformed, false discovery rate [FDR] corrected). In patients with PD, LMEMs tested how baseline ATNPD status (AD [A+T+N±] vs not) predicted clinical outcomes, including Montreal Cognitive Assessment (MoCA; rank transformed, FDR corrected). RESULTS: Participants were 364 patients with PD and 168 controls, with comparable baseline mean (±SD) age (patients with PD = 62 ± 10 years; controls = 61 ± 11 years]; Mann-Whitney Wilcoxon: p = 0.4) and sex distribution (patients with PD = 231 male individuals [63%]; controls = 107 male individuals [64%]; χ2: p = 1). Patients with PD had overall lower CSF p-tau181 (ß = -0.16, 95% CI -0.23 to -0.092, p = 2.2e-05) and t-tau than controls (ß = -0.13, 95% CI -0.19 to -0.065, p = 4e-04), but not Aß42 (p = 0.061) or NfL (p = 0.32). Over time, patients with PD had greater increases in serum NfL than controls (ß = 0.035, 95% CI 0.022 to 0.048, p = 9.8e-07); slopes of patients with PD did not differ from those of controls for CSF Aß42 (p = 0.18), p-tau181 (p = 1), or t-tau (p = 0.96). Using ATNPD, PD classified as A+T+N± (n = 32; 9%) had worse cognitive decline on global MoCA (ß = -73, 95% CI -110 to -37, p = 0.00077) than all other ATNPD statuses including A+ alone (A+T-N-; n = 75; 21%). DISCUSSION: In patients with early PD, CSF p-tau181 and t-tau were low compared with those in controls and did not increase over 5 years of follow-up. Our study shows that classification using modified ATNPD (incorporating CSF Aß42, CSF p-tau181, and serum NfL) can identify biologically relevant subgroups of PD to improve prediction of cognitive decline in early PD.

A biological definition of neuronal α-synuclein disease: towards an integrated staging system for research.

Parkinson's disease and dementia with Lewy bodies are currently defined by their clinical features, with α-synuclein pathology as the gold standard to establish the definitive diagnosis. We propose that, given biomarker advances enabling accurate detection of pathological α-synuclein (ie, misfolded and aggregated) in CSF using the seed amplification assay, it is time to redefine Parkinson's disease and dementia with Lewy bodies as neuronal α-synuclein disease rather than as clinical syndromes. This major shift from a clinical to a biological definition of Parkinson's disease and dementia with Lewy bodies takes advantage of the availability of tools to assess the gold standard for diagnosis of neuronal α-synuclein (n-αsyn) in human beings during life. Neuronal α-synuclein disease is defined by the presence of pathological n-αsyn species detected in vivo (S; the first biological anchor) regardless of the presence of any specific clinical syndrome. On the basis of this definition, we propose that individuals with pathological n-αsyn aggregates are at risk for dopaminergic neuronal dysfunction (D; the second biological anchor). Our biological definition establishes a staging system, the neuronal α-synuclein disease integrated staging system (NSD-ISS), rooted in the biological anchors (S and D) and the degree of functional impairment caused by clinical signs or symptoms. Stages 0-1 occur without signs or symptoms and are defined by the presence of pathogenic variants in the SNCA gene (stage 0), S alone (stage 1A), or S and D (stage 1B). The presence of clinical manifestations marks the transition to stage 2 and beyond. Stage 2 is characterised by subtle signs or symptoms but without functional impairment. Stages 2B-6 require both S and D and stage-specific increases in functional impairment. A biological definition of neuronal α-synuclein disease and an NSD-ISS research framework are essential to enable interventional trials at early disease stages. The NSD-ISS will evolve to include the incorporation of data-driven definitions of stage-specific functional anchors and additional biomarkers as they emerge and are validated. Presently, the NSD-ISS is intended for research use only; its application in the clinical setting is premature and inappropriate.

Post-translational modifications linked to preclinical Alzheimer's disease-related pathological and cognitive changes.

INTRODUCTION: In this study, we leverage proteomic techniques to identify communities of proteins underlying Alzheimer's disease (AD) risk among clinically unimpaired (CU) older adults. METHODS: We constructed a protein co-expression network using 3869 cerebrospinal fluid (CSF) proteins quantified by SomaLogic, Inc., in a cohort of participants along the AD clinical spectrum. We then replicated this network in an independent cohort of CU older adults and related these modules to clinically-relevant outcomes. RESULTS: We discovered modules enriched for phosphorylation and ubiquitination that were associated with abnormal amyloid status, as well as p-tau181 (M4: ß = 2.44, p < 0.001, M7: ß = 2.57, p < 0.001) and executive function performance (M4: ß = -2.00, p = 0.005, M7: ß = -2.39, p < 0.001). DISCUSSION: In leveraging CSF proteomic data from individuals spanning the clinical spectrum of AD, we highlight the importance of post-translational modifications for early cognitive and pathological changes.

Longitudinal hippocampal subfields, CSF biomarkers, and cognition in patients with Parkinson disease.

Objective: Hippocampal atrophy is an indicator of emerging dementia in PD, though it is unclear whether cerebral spinal fluid (CSF) Abeta-42, t-tau, or alpha-syn predict hippocampal subfield atrophy in a de novo cohort of PD patients. To examine whether levels of CSF alpha-synuclein (alpha-syn), beta-amyloid 1-42 (Abeta-42), or total-tau (t-tau) are associated with hippocampal subfield volumes over time. Methods: We identified a subset of Parkinson's Progression Markers Initiative (PPMI) de novo PD patients with longitudinal T1-weighted imaging (baseline plus at least two additional visits across 12, 24, and 48 months) and CSF biomarkers available at baseline. We performed cross-sectional, regression, and linear mixed model analyses to evaluate the baseline and longitudinal CSF biomarkers, hippocampal subfields, and cognition. A false discovery rate (FDR) was used to correct for multiple comparisons. Results: 88 PD-CN and 21 PD-MCI had high quality longitudinal data. PD-MCI patients exhibited reduced bilateral CA1 volumes relative to PD-CN, though there were no significant differences in CSF biomarkers between these groups. Relationships between CSF biomarkers and hippocampal subfields changed over time, with a general pattern that lower CSF Abeta-42, higher t-tau and higher alpha-syn were associated with smaller hippocampal subfields, primarily in the right hemisphere. Conclusion: We replicated prior reports that demonstrated reduced CA1 volumes in PD-MCI in a de novo PD cohort. CSF biomarkers were associated with individual subfields, with evidence that the increased CSF t-tau was associated with smaller subiculum volumes at baseline and over time, though there was no clear indication that the subfields associated with cognition (CA1 and HATA) were associated with CSF biomarkers.

Quantitative estimate of cognitive resilience and its medical and genetic associations.

BACKGROUND: We have proposed that cognitive resilience (CR) counteracts brain damage from Alzheimer's disease (AD) or AD-related dementias such that older individuals who harbor neurodegenerative disease burden sufficient to cause dementia remain cognitively normal. However, CR traditionally is considered a binary trait, capturing only the most extreme examples, and is often inconsistently defined. METHODS: This study addressed existing discrepancies and shortcomings of the current CR definition by proposing a framework for defining CR as a continuous variable for each neuropsychological test. The linear equations clarified CR's relationship to closely related terms, including cognitive function, reserve, compensation, and damage. Primarily, resilience is defined as a function of cognitive performance and damage from neuropathologic damage. As such, the study utilized data from 844 individuals (age = 79 ± 12, 44% female) in the National Alzheimer's Coordinating Center cohort that met our inclusion criteria of comprehensive lesion rankings for 17 neuropathologic features and complete neuropsychological test results. Machine learning models and GWAS then were used to identify medical and genetic factors that are associated with CR. RESULTS: CR varied across five cognitive assessments and was greater in female participants, associated with longer survival, and weakly associated with educational attainment or APOE ε4 allele. In contrast, damage was strongly associated with APOE ε4 allele (P value < 0.0001). Major predictors of CR were cardiovascular health and social interactions, as well as the absence of behavioral symptoms. CONCLUSIONS: Our framework explicitly decoupled the effects of CR from neuropathologic damage. Characterizations and genetic association study of these two components suggest that the underlying CR mechanism has minimal overlap with the disease mechanism. Moreover, the identified medical features associated with CR suggest modifiable features to counteract clinical expression of damage and maintain cognitive function in older individuals.