Liquid-biopsy proteomics combined with AI identifies cellular drivers of eye aging and disease in vivo.

Single-cell analysis in living humans is essential for understanding disease mechanisms, but it is impractical in non-regenerative organs, such as the eye and brain, because tissue biopsies would cause serious damage. We resolve this problem by integrating proteomics of liquid biopsies with single-cell transcriptomics from all known ocular cell types to trace the cellular origin of 5,953 proteins detected in the aqueous humor. We identified hundreds of cell-specific protein markers, including for individual retinal cell types. Surprisingly, our results reveal that retinal degeneration occurs in Parkinson's disease, and the cells driving diabetic retinopathy switch with disease stage. Finally, we developed artificial intelligence (AI) models to assess individual cellular aging and found that many eye diseases not associated with chronological age undergo accelerated molecular aging of disease-specific cell types. Our approach, which can be applied to other organ systems, has the potential to transform molecular diagnostics and prognostics while uncovering new cellular disease and aging mechanisms.

Partial coherence enhances parallelized photonic computing.

Advancements in optical coherence control1-5 have unlocked many cutting-edge applications, including long-haul communication, light detection and ranging (LiDAR) and optical coherence tomography6-8. Prevailing wisdom suggests that using more coherent light sources leads to enhanced system performance and device functionalities9-11. Our study introduces a photonic convolutional processing system that takes advantage of partially coherent light to boost computing parallelism without substantially sacrificing accuracy, potentially enabling larger-size photonic tensor cores. The reduction of the degree of coherence optimizes bandwidth use in the photonic convolutional processing system. This breakthrough challenges the traditional belief that coherence is essential or even advantageous in integrated photonic accelerators, thereby enabling the use of light sources with less rigorous feedback control and thermal-management requirements for high-throughput photonic computing. Here we demonstrate such a system in two photonic platforms for computing applications: a photonic tensor core using phase-change-material photonic memories that delivers parallel convolution operations to classify the gaits of ten patients with Parkinson's disease with 92.2% accuracy (92.7% theoretically) and a silicon photonic tensor core with embedded electro-absorption modulators (EAMs) to facilitate 0.108 tera operations per second (TOPS) convolutional processing for classifying the Modified National Institute of Standards and Technology (MNIST) handwritten digits dataset with 92.4% accuracy (95.0% theoretically).

Discriminating α-synuclein strains in Parkinson's disease and multiple system atrophy.

Synucleinopathies are neurodegenerative diseases that are associated with the misfolding and aggregation of α-synuclein, including Parkinson's disease, dementia with Lewy bodies and multiple system atrophy1. Clinically, it is challenging to differentiate Parkinson's disease and multiple system atrophy, especially at the early stages of disease2. Aggregates of α-synuclein in distinct synucleinopathies have been proposed to represent different conformational strains of α-synuclein that can self-propagate and spread from cell to cell3-6. Protein misfolding cyclic amplification (PMCA) is a technique that has previously been used to detect α-synuclein aggregates in samples of cerebrospinal fluid with high sensitivity and specificity7,8. Here we show that the α-synuclein-PMCA assay can discriminate between samples of cerebrospinal fluid from patients diagnosed with Parkinson's disease and samples from patients with multiple system atrophy, with an overall sensitivity of 95.4%. We used a combination of biochemical, biophysical and biological methods to analyse the product of α-synuclein-PMCA, and found that the characteristics of the α-synuclein aggregates in the cerebrospinal fluid could be used to readily distinguish between Parkinson's disease and multiple system atrophy. We also found that the properties of aggregates that were amplified from the cerebrospinal fluid were similar to those of aggregates that were amplified from the brain. These findings suggest that α-synuclein aggregates that are associated with Parkinson's disease and multiple system atrophy correspond to different conformational strains of α-synuclein, which can be amplified and detected by α-synuclein-PMCA. Our results may help to improve our understanding of the mechanism of α-synuclein misfolding and the structures of the aggregates that are implicated in different synucleinopathies, and may also enable the development of a biochemical assay to discriminate between Parkinson's disease and multiple system atrophy.

In pursuit of degenerative brain disease diagnosis: Dementia biomarkers detected by DNA aptamer-attached portable graphene biosensor.

Dementia is a brain disease which results in irreversible and progressive loss of cognition and motor activity. Despite global efforts, there is no simple and reliable diagnosis or treatment option. Current diagnosis involves indirect testing of commonly inaccessible biofluids and low-resolution brain imaging. We have developed a portable, wireless readout-based Graphene field-effect transistor (GFET) biosensor platform that can detect viruses, proteins, and small molecules with single-molecule sensitivity and specificity. We report the detection of three important amyloids, namely, Amyloid beta (Aß), Tau (τ), and α-Synuclein (αS) using DNA aptamer nanoprobes. These amyloids were isolated, purified, and characterized from the autopsied brain tissues of Alzheimer's Disease (AD) and Parkinson's Disease (PD) patients. The limit of detection (LoD) of the sensor is 10 fM, 1-10 pM, 10-100 fM for Aß, τ, and αS, respectively. Synthetic as well as autopsied brain-derived amyloids showed a statistically significant sensor response with respect to derived thresholds, confirming the ability to define diseased vs. nondiseased states. The detection of each amyloid was specific to their aptamers; Aß, τ, and αS peptides when tested, respectively, with aptamers nonspecific to them showed statistically insignificant cross-reactivity. Thus, the aptamer-based GFET biosensor has high sensitivity and precision across a range of epidemiologically significant AD and PD variants. This portable diagnostic system would allow at-home and POC testing for neurodegenerative diseases globally.

Retinal organoids from human-induced pluripotent stem cells: From studying retinal dystrophies to early diagnosis of Alzheimer's and Parkinson's disease.

Human-induced pluripotent stem cells (hiPSCs) have provided new methods to study neurodegenerative diseases. In addition to their wide application in neuronal disorders, hiPSCs technology can also encompass specific conditions, such as inherited retinal dystrophies. The possibility of evaluating alterations related to retinal disorders in 3D organoids increases the truthfulness of in vitro models. Moreover, both Alzheimer's (AD) and Parkinson's disease (PD) have been described as causing early retinal alterations, generating beta-amyloid protein accumulation, or affecting dopaminergic amacrine cells. This review addresses recent advances and future perspectives obtained from in vitro modeling of retinal diseases, focusing on retinitis pigmentosa (RP). Additionally, we depicted the possibility of evaluating changes related to AD and PD in retinal organoids obtained from potential patients long before the onset of the disease, constituting a valuable tool in early diagnosis. With this, we pointed out prospects in the study of retinal dystrophies and early diagnosis of AD and PD.

α-Synuclein seeding activity in duodenum biopsies from Parkinson's disease patients.

Abnormal deposition of α-synuclein is a key feature and biomarker of Parkinson's disease. α-Synuclein aggregates can propagate themselves by a prion-like seeding-based mechanism within and between tissues and are hypothesized to move between the intestine and brain. α-Synuclein RT-QuIC seed amplification assays have detected Parkinson's-associated α-synuclein in multiple biospecimens including post-mortem colon samples. Here we show intra vitam detection of seeds in duodenum biopsies from 22/23 Parkinson's patients, but not in 6 healthy controls by RT-QuICR. In contrast, no tau seeding activity was detected in any of the biopsies. Our seed amplifications provide evidence that the upper intestine contains a form(s) of α-synuclein with self-propagating activity. The diagnostic sensitivity and specificity for PD in this biopsy panel were 95.7% and 100% respectively. End-point dilution analysis indicated up to 106 SD50 seeding units per mg of tissue with positivity in two contemporaneous biopsies from individual patients suggesting widespread distribution within the superior and descending parts of duodenum. Our detection of α-synuclein seeding activity in duodenum biopsies of Parkinson's disease patients suggests not only that such analyses may be useful in ante-mortem diagnosis, but also that the duodenum may be a source or a destination for pathological, self-propagating α-synuclein assemblies.

Parkinson's Disease: Advances in Treatment and the Syntheses of Various Classes of Pharmaceutical Drug Substances.

An overview of Parkinson's disease (PD) prevalence, diagnosis, and currently available treatment options is provided. A comprehensive list of different classes of marketed pharmaceutical drug products and the syntheses of various drug substances are summarized based on published literature.

Parkinson's disease variant detection and disclosure: PD GENEration, a North American study.

Variants in seven genes (LRRK2, GBA1, PRKN, SNCA, PINK1, PARK7 and VPS35) have been formally adjudicated as causal contributors to Parkinson's disease; however, individuals with Parkinson's disease are often unaware of their genetic status since clinical testing is infrequently offered. As a result, genetic information is not incorporated into clinical care, and variant-targeted precision medicine trials struggle to enrol people with Parkinson's disease. Understanding the yield of genetic testing using an established gene panel in a large, geographically diverse North American population would help patients, clinicians, clinical researchers, laboratories and insurers better understand the importance of genetics in approaching Parkinson's disease. PD GENEration is an ongoing multi-centre, observational study (NCT04057794, NCT04994015) offering genetic testing with results disclosure and genetic counselling to those in the US (including Puerto Rico), Canada and the Dominican Republic, through local clinical sites or remotely through self-enrolment. DNA samples are analysed by next-generation sequencing including deletion/duplication analysis (Fulgent Genetics) with targeted testing of seven major Parkinson's disease-related genes. Variants classified as pathogenic/likely pathogenic/risk variants are disclosed to all tested participants by either neurologists or genetic counsellors. Demographic and clinical features are collected at baseline visits. Between September 2019 and June 2023, the study enrolled 10 510 participants across >85 centres, with 8301 having received results. Participants were: 59% male; 86% White, 2% Asian, 4% Black/African American, 9% Hispanic/Latino; mean age 67.4 ± 10.8 years. Reportable genetic variants were observed in 13% of all participants, including 18% of participants with one or more 'high risk factors' for a genetic aetiology: early onset (<50 years), high-risk ancestry (Ashkenazi Jewish/Basque/North African Berber), an affected first-degree relative; and, importantly, in 9.1% of people with none of these risk factors. Reportable variants in GBA1 were identified in 7.7% of all participants; 2.4% in LRRK2; 2.1% in PRKN; 0.1% in SNCA; and 0.2% in PINK1, PARK7 or VPS35 combined. Variants in more than one of the seven genes were identified in 0.4% of participants. Approximately 13% of study participants had a reportable genetic variant, with a 9% yield in people with no high-risk factors. This supports the promotion of universal access to genetic testing for Parkinson's disease, as well as therapeutic trials for GBA1 and LRRK2-related Parkinson's disease.

A top-down proteomic approach reveals a salivary protein profile able to classify Parkinson's disease with respect to Alzheimer's disease patients and to healthy controls.

Parkinson's disease (PD) is a complex neurodegenerative disease with motor and non-motor symptoms. Diagnosis is complicated by lack of reliable biomarkers. To individuate peptides and/or proteins with diagnostic potential for early diagnosis, severity and discrimination from similar pathologies, the salivary proteome in 36 PD patients was investigated in comparison with 36 healthy controls (HC) and 35 Alzheimer's disease (AD) patients. A top-down platform based on HPLC-ESI-IT-MS allowed characterizing and quantifying intact peptides, small proteins and their PTMs (overall 51). The three groups showed significantly different protein profiles, PD showed the highest levels of cystatin SA and antileukoproteinase and the lowest of cystatin SN and some statherin proteoforms. HC exhibited the lowest abundance of thymosin ß4, short S100A9, cystatin A, and dimeric cystatin B. AD patients showed the highest abundance of α-defensins and short oxidized S100A9. Moreover, different proteoforms of the same protein, as S-cysteinylated and S-glutathionylated cystatin B, showed opposite trends in the two pathological groups. Statherin, cystatins SA and SN classified accurately PD from HC and AD subjects. α-defensins, histatin 1, oxidized S100A9, and P-B fragments were the best classifying factors between PD and AD patients. Interestingly statherin and thymosin ß4 correlated with defective olfactory functions in PD patients. All these outcomes highlighted implications of specific proteoforms involved in the innate-immune response and inflammation regulation at oral and systemic level, suggesting a possible panel of molecular and clinical markers suitable to recognize subjects affected by PD.

Abnormally low sensorimotor α band nonlinearity serves as an effective EEG biomarker of Parkinson's disease.

Biomarkers obtained from the neurophysiological signals of patients with Parkinson's disease (PD) have objective value in assessing their motor condition for effective diagnosis, monitoring, and clinical intervention. Prominent cortical biomarkers of PD have typically been derived from various ß band wave features. This study approached the topic from an alternative perspective and attempted to estimate a recently suggested measure representing α band nonlinear autocorrelative memory from a publicly available EEG dataset that involves 15 patients with earlier-stage PD (dopaminergic medication OFF and ON states) and 16 age-matched healthy controls. The cortical nonlinearity was elevated for the PD ON state compared with the OFF state for bilateral sensorimotor channels C3 and C4 (n = 26; P = 0.003). A similar statistical difference was also identified between PD OFF state and healthy subjects (n = 26; P = 0.049). Analysis over all channels revealed that the α band nonlinearity induced upon medication was constrained to sensorimotor regions. The α nonlinearity measure was compared with a well-accepted cortical biomarker of ß-γ phase-amplitude coupling (PAC). They were in moderate negative correlation (r = -0.412; P = 0.036) for only healthy subjects, but not for the patients. The nonlinearity measure was found to be insusceptible to the nonstationary variations within the particular data. Our study provides further evidence that the α band nonlinearity measure can serve as a promising cortical biomarker of PD. The suggested measure can be estimated from a noninvasive low-resolution single scalp EEG channel of patients with relatively early-stage PD, who did not yet need to undergo deep brain stimulation operation.NEW & NOTEWORTHY This study suggests a nonlinearity measure that differentiates Parkinson's disease (PD) dopamine OFF-state scalp EEG data from those of dopamine ON-state patients and healthy subjects. Unlike typical PD cortical biomarkers based on ß band activity, this metric shows elevation upon dopaminergic medication in the α band. We provide evidence supporting its potential as an early-stage promising PD biomarker that can be estimated from noninvasive EEG recordings with low resolution and SNR.

How close are we to a breakthrough? The hunt for blood biomarkers in Parkinson's disease diagnosis.

Parkinson's disease (PD), being the second largest neurodegenerative disease, poses challenges in early detection, resulting in a lack of timely treatment options to effectively manage the disease. By the time clinical diagnosis becomes possible, more than 60% of dopamine neurons in the substantia nigra (SN) of patients have already degenerated. Therefore, early diagnosis or identification of warning signs is crucial for the prompt and timely beginning of the treatment. However, conducting invasive or complex diagnostic procedures on asymptomatic patients can be challenging, making routine blood tests a more feasible approach in such cases. Numerous studies have been conducted over an extended period to search for effective diagnostic biomarkers in blood samples. However, thus far, no highly effective biomarkers have been confirmed. Besides classical proteins like α-synuclein (α-syn), phosphorylated α-syn and oligomeric α-syn, other molecules involved in disease progression should also be given equal attention. In this review, we will not only discuss proposed biomarkers that are currently under investigation but also delve into the mechanisms underlying the disease, focusing on processes such as α-syn misfolding, intercellular transmission and the crossing of the blood-brain barrier (BBB). Our aim is to provide an updated overview of molecules based on these processes that may potentially serve as blood biomarkers.

Brain-trait-associated variants impact cell-type-specific gene regulation during neurogenesis.

Interpretation of the function of non-coding risk loci for neuropsychiatric disorders and brain-relevant traits via gene expression and alternative splicing quantitative trait locus (e/sQTL) analyses is generally performed in bulk post-mortem adult tissue. However, genetic risk loci are enriched in regulatory elements active during neocortical differentiation, and regulatory effects of risk variants may be masked by heterogeneity in bulk tissue. Here, we map e/sQTLs, and allele-specific expression in cultured cells representing two major developmental stages, primary human neural progenitors (n = 85) and their sorted neuronal progeny (n = 74), identifying numerous loci not detected in either bulk developing cortical wall or adult cortex. Using colocalization and genetic imputation via transcriptome-wide association, we uncover cell-type-specific regulatory mechanisms underlying risk for brain-relevant traits that are active during neocortical differentiation. Specifically, we identified a progenitor-specific eQTL for CENPW co-localized with common variant associations for cortical surface area and educational attainment.

Intelligent Identification of Cerebrospinal Fluid for the Diagnosis of Parkinson's Disease.

Cerebrospinal fluid (CSF) biomarkers are more sensitive than the Movement Disorder Society (MDS) criteria for detecting prodromal Parkinson's disease (PD). Early detection of PD provides the best chance for successful implementation of disease-modifying treatments, making it crucial to effectively identify CSF extracted from PD patients or normal individuals. In this study, an intelligent sensor array was built by using three metal-organic frameworks (MOFs) that exhibited varying catalytic kinetics after reacting with potential protein markers. Machine learning algorithms were used to process fingerprint response patterns, allowing for qualitative and quantitative assessment of the proteins. The results were robust and capable of discriminating between PD and non-PD patients via CSF detection. The k-nearest neighbor regression algorithm was used to predict MDS scores with a minimum mean square error of 38.88. The intelligent MOF sensor array is expected to promote the detection of CSF biomarkers due to its ability to identify multiple targets and could be used in conjunction with MDS criteria and other techniques to diagnose PD more sensitively and selectively.

Photodynamic-Assisted Electrochemiluminescence Enhancement toward Advanced BODIPY for Precision Diagnosis of Parkinson.

Nowadays, signal enhancement is imperative to increase sensitivity of advanced ECL devices for expediting their promising applications in clinic. In this work, photodynamic-assisted electrochemiluminescence (PDECL) device was constructed for precision diagnosis of Parkinson, where an advanced emitter was prepared by electrostatically linking 2,6-dimethyl-8-(3-carboxyphenyl)4,4'-difluoroboradiazene (BET) with 1-butyl-3-methylimidazole tetrafluoroborate ([BMIm][BF4]). Specifically, protoporphyrin IX (PPIX) can trigger the photodynamic reaction under light irradiation with a wavelength of 450 nm to generate lots of singlet oxygen (1O2), showing a 2.43-fold magnification in the ECL responses. Then, the aptamer (Apt) was assembled on the functional BET-[BMIm] for constructing a "signal off" ECL biosensor. Later on, the PPIX was embedded into the G-quadruplex (G4) of the Apt to magnify the ECL signals for bioanalysis of α-synuclein (α-syn) under light excitation. In the optimized surroundings, the resulting PDECL sensor has a broad linear range of 100.0 aM ∼ 10.0 fM and a low limit of detection (LOD) of 63 aM, coupled by differentiating Parkinson patients from normal individuals according to the receiver operating characteristic (ROC) curve analysis of actual blood samples. Such research holds great promise for synthesis of other advanced luminophores, combined with achieving an early clinical diagnosis.

A diagnostic model for Parkinson's disease based on circadian rhythm-related genes.

BACKGROUND: Circadian rhythm (CR) disturbance is intricately associated with Parkinson's disease (PD). However, the involvement of CR-related mechanisms in the pathogenesis and progression of PD remains elusive. METHODS: A total of 141 PD patients and 113 healthy participants completed CR-related clinical examinations in this study. To further investigate the CR-related mechanisms in PD, we obtained datasets (GSE7621, GSE20141, GSE20292) from the Gene Expression Omnibus database to identify differentially expressed genes between PD patients and healthy controls and further selected CR-related genes (CRRGs). Subsequently, the least absolute shrinkage and selection operator (LASSO) followed by logistic algorithms were employed to identify the hub genes and construct a diagnostic model. The predictive performance was evaluated by area under the curve (AUC), calibration curve, and decision curve analyses in the training set and external validation sets. Finally, RT‒qPCR and Western blotting were conducted to verify the expression of these hub genes in blood samples. In addition, Pearson correlation analysis was utilized to validate the association between expression of hub genes and circadian rhythm function. RESULTS: Our clinical observational study revealed that even early-stage PD patients exhibited a higher likelihood of experiencing sleep disturbances, nocturnal hypertension, reverse-dipper blood pressure, and reduced heart rate variability compared to healthy controls. Furthermore, 4 CR-related hub genes (AGTR1, CALR, BRM14, and XPA) were identified and subsequently incorporated as candidate biomarkers to construct a diagnostic model. The model showed satisfactory diagnostic performance in the training set (AUC = 0.941), an external validation set GSE20295 (AUC = 0.842), and our clinical centre set (AUC = 0.805). Additionally, the up-regulation of CALR, BRM14 and the down-regulation of AGTR1, XPA were associated with circadian rhythm disruption. CONCLUSION: CR disturbance seems to occur in the early stage of PD. The diagnostic model based on CR-related genes demonstrated robust diagnostic efficacy, offering novel insights for future clinical diagnosis of PD and providing a foundation for further exploration into the role of CR-related mechanisms in the progression of PD.

A Combined Panel of Salivary Biomarkers in de novo Parkinson's Disease.

OBJECTIVE: To investigate molecular biomarkers of a-synuclein and tau aggregation, autophagy, and inflammation in the saliva of de novo Parkinson's disease (PD) patients in comparison to healthy subjects (HS), and to correlate molecular data with clinical features of PD patients, in order to establish whether abnormalities of these parameters are associated with specific clusters of de novo PD patients, and their potential diagnostic power in differentiating PD patients from HS. METHODS: We measured total and oligomeric a-synuclein, total-tau and phosphorylated-tau, microtubule-associated protein light chain 3 beta (MAP-LC3beta), and tumor necrosis factor alpha (TNFalpha) in the saliva of 80 de novo PD patients and 62 HS, using quantitative enzyme-linked immunosorbent Assay analysis. RESULTS: Oligomeric a-synuclein, total-tau, MAP-LC3beta, and TNFalpha levels resulted significantly higher in patients with respect to HS, while no significant differences were detected for total a-synuclein or phosphorylated-tau. Phosphorylated-tau directly correlated with MAP-LC3beta, whereas it inversely correlated with TNFalpha in PD patients. An inverse correlation was detected between MAP-LC3beta and non-motor symptoms severity. Principal Component Analysis showed that molecular and clinical parameters were independent of each other in de novo PD patients. Receiver operating characteristic curve analysis reported an accurate diagnostic performance of oligomeric a-synuclein and MAP-LC3beta. The diagnostic accuracy of total a-synuclein increased when it was combined with other salivary biomarkers targeting different molecular pathways. INTERPRETATION: Our study proposes a novel biomarker panel using saliva, a non-invasive biofluid, in de novo PD patients, with implications in understanding the molecular pathways involved in PD pathogenesis and the relevance of different molecular pathways in determining clinical PD subtypes. ANN NEUROL 2023;93:446-459.

Actigraphy Enables Home Screening of Rapid Eye Movement Behavior Disorder in Parkinson's Disease.

OBJECTIVES: Rapid eye movement sleep behavior disorder (RBD) is a potentially harmful, often overlooked sleep disorder affecting up to 70% of Parkinson's disease patients. Current diagnosis relies on nocturnal video-polysomnography, which is an expensive and cumbersome examination requiring specific clinical expertise. Here, we explored the use of wrist actigraphy to enable automatic RBD diagnoses in home settings. METHODS: A total of 26 Parkinson's disease patients underwent 2-week home wrist actigraphy, followed by two in-laboratory evaluations. Patients were classified as RBD versus non-RBD based on dream enactment history and video-polysomnography. We comprehensively characterized patients' movement patterns during sleep using actigraphic signals. We then trained machine learning classification algorithms to discriminate patients with or without RBD using the most relevant features. Classification performance was quantified with respect to clinical diagnosis, separately for in-laboratory and at-home recordings. Performance was further validated in a control group of non-Parkinson's disease patients with other sleep conditions. RESULTS: To characterize RBD, actigraphic features extracted from both (1) individual movement episodes and (2) global nocturnal activity were critical. RBD patients were more active overall, and showed movements that were shorter, of higher magnitude, and more scattered in time. Using these features, our classification algorithms reached an accuracy of 92.9 ± 8.16% during in-clinic tests. When validated on home recordings in Parkinson's disease patients, accuracy reached 100% over a 2-week window, and was 94.4% in non-Parkinson's disease control patients. Features showed robustness across tests and conditions. INTERPRETATION: These results open new perspectives for faster, cheaper, and more regular screening of sleep disorders, both for routine clinical practice and clinical trials. ANN NEUROL 2023;93:317-329.

A Comparison Between Early Presentation of Dementia with Lewy Bodies, Alzheimer's Disease, and Parkinson's Disease: Evidence from Routine Primary Care and UK Biobank Data.

OBJECTIVE: The purpose of this study was to simultaneously contrast prediagnostic clinical characteristics of individuals with a final diagnosis of dementia with Lewy Bodies (DLB), Parkinson's disease (PD), and Alzheimer's disease (AD) compared with controls without neurodegenerative disorders. METHODS: Using the longitudinal THIN database in the United Kingdom, we tested the association of each neurodegenerative disorder with a selected list of symptoms and broad families of treatments, and compared the associations between disorders to detect disease-specific effects. We replicated the main findings in the UK Biobank. RESULTS: We used data of 28,222 patients with PD, 20,214 with AD, 4,682 with DLB, and 20,214 healthy controls. All neurodegenerative disorders were significantly associated with the presence of multiple clinical characteristics before their diagnosis, including sleep disorders, falls, psychiatric symptoms, and autonomic dysfunctions. When comparing patients with DLB with patients with PD and patients with AD patients, falls, psychiatric symptoms, and autonomic dysfunction were all more strongly associated with DLB in the 5 years preceding the first neurodegenerative diagnosis. The use of statins was lower in patients who developed PD and higher in patients who developed DLB compared to patients with AD. In patients with PD, the use of statins was associated with the development of dementia in the 5 years following PD diagnosis. INTERPRETATION: Prediagnostic presentations of falls, psychiatric symptoms, and autonomic dysfunctions were more strongly associated with DLB than PD and AD. This study also suggests that although several associations with medications are similar in neurodegenerative disorders, statin usage is negatively associated with PD but positively with DLB and AD as well as development of dementia in PD. ANN NEUROL 2023;94:259-270.

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.

Clinical advances in analytical profiling of signature lipids: implications for severe non-communicable and neurodegenerative diseases.

BACKGROUND: Lipids play key roles in numerous biological processes, including energy storage, cell membrane structure, signaling, immune responses, and homeostasis, making lipidomics a vital branch of metabolomics that analyzes and characterizes a wide range of lipid classes. Addressing the complex etiology, age-related risk, progression, inflammation, and research overlap in conditions like Alzheimer's Disease, Parkinson's Disease, Cardiovascular Diseases, and Cancer poses significant challenges in the quest for effective therapeutic targets, improved diagnostic markers, and advanced treatments. Mass spectrometry is an indispensable tool in clinical lipidomics, delivering quantitative and structural lipid data, and its integration with technologies like Liquid Chromatography (LC), Magnetic Resonance Imaging (MRI), and few emerging Matrix-Assisted Laser Desorption Ionization- Imaging Mass Spectrometry (MALDI-IMS) along with its incorporation into Tissue Microarray (TMA) represents current advances. These innovations enhance lipidomics assessment, bolster accuracy, and offer insights into lipid subcellular localization, dynamics, and functional roles in disease contexts. AIM OF THE REVIEW: The review article summarizes recent advancements in lipidomic methodologies from 2019 to 2023 for diagnosing major neurodegenerative diseases, Alzheimer's and Parkinson's, serious non-communicable cardiovascular diseases and cancer, emphasizing the role of lipid level variations, and highlighting the potential of lipidomics data integration with genomics and proteomics to improve disease understanding and innovative prognostic, diagnostic and therapeutic strategies. KEY SCIENTIFIC CONCEPTS OF REVIEW: Clinical lipidomic studies are a promising approach to track and analyze lipid profiles, revealing their crucial roles in various diseases. This lipid-focused research provides insights into disease mechanisms, biomarker identification, and potential therapeutic targets, advancing our understanding and management of conditions such as Alzheimer's Disease, Parkinson's Disease, Cardiovascular Diseases, and specific cancers.