npj Parkinson's Disease

Background: Parkinson's disease (PD) is the second most common progressive neurological disorder that is pathologically characterized by the loss of dopaminergic neurons within the substantia nigra (SN). However, disease progression probably involves coordinated changes across both neuronal and glial cell populations. Although single-nucleus RNA-seq resolved cell-type-specific transcriptional profiling, differential expression and regulatory interpretation are commonly reported separately; however, they may limit the mechanistic prioritization to uncover novel therapeutic targets. Methods: Here, we performed sample-aware pseudobulk framework analysis on single-nucleus transcriptomes obtained SN of PD and control donors. Cell-type-specific differential expression for PD vs. control was identified using edgeR quasi-likelihood modeling (FDR < 0.05; |log2FC| > 0.5). Further, to quantify disease-specific remodelling, we computed one-vs-rest cell-type specificity scores in each condition and defined delta-specificity as the PD-control shift. We further prioritized the gene-set for dopaminergic neurons and microglia based on edge R significance and delta-specificity shifts, followed by upstream regulatory assessment using transcription factor enrichment and subnetwork visualization using ChEA-KG. Moreover, we used Cellchat to identify altered cell-cell communication networks to infer differences between both conditions. Results: Dopaminergic neurons demonstrated upregulation of neuronal-state remodeling transcriptional programs related gene sets in PD group, including receptor signaling and contact/guidance pathways (e.g., CHRM3, ROBO1, PLXNA4, UNC5D, EFNA5), neuronal excitability homeostatsis, RNA components, cellular traffickings and proteostasis, suggesting coordinated remodeling in surviving neuronal population. Microglia exhibited a compact PD-associated signature enriched for regulatory and activation state-related genes. TF networks analysis revealed distinct regulatory subnetwork in each population,including BNC2-centered network in microglia and an NPAS3-centered network in dopaminergic neurons with embedded ZNF804A and chromatin-associated components. Conclusions: In summary, integrating pseudobulk, delta-specificity scoring and TF-network enrichment analysis provides coherent dopaminergic and microglial programs in PD substantia nigra. This framework prioritizes cell-type-specific potential candidate mechanisms for downstream validation. The inferred regulatory networks and interactions are hypothesis generating and need orthogonal validation, such as spatial or proteomics approaches and independent cohorts.

Objective To map national Parkinsons disease (PD) research capability to inform an inclusive delivery strategy for a large-scale clinical trial. Background Few people with PD participate in clinical trials, particularly from under-served populations. The Edmond J Safra Accelerating Clinical Trials in PD initiative (EJS ACT-PD) aims to deliver an inclusive multi-arm multi-stage (MAMS) disease modification PD trial. Methods A survey disseminated to National Health Service (NHS) hospitals assessed PD research capability regarding trial experience, rater expertise, trial facilities and specialist investigations. A process was developed to categorise sites into 3 tiers, with tier 1 having the least PD-research capability or experience, and tier 3 being experienced specialist centres. We mapped tiers to PD prevalence, social deprivation and ethnic diversity to identify infrastructure gaps. We developed trial delivery strategies to facilitate rapid and inclusive recruitment. Results Out of 97 survey responses, 43 sites were categorised as tier 1, 33 as tier 2 and 21 as tier 3. Diversity and social deprivation index were higher for tier 3 sites (predominantly urban). A greater proportion of tier 1 and 2 sites were situated in areas of higher PD prevalence (predominantly rural). Ninety one percent of sites reported experience with remote trial delivery methods. Our delivery strategy included: initial trial set-up at tier 3 sites to enable rapid and ethnically diverse recruitment; core funded staff within strategic sites to develop regional solutions for inclusive trial participation and to enable research opportunity provision in areas where currently very little exists, and a hybrid delivery model of in-person and remote study visits, ensuring maximal acceptability and deliverability. Conclusions The mapping of current PD research delivery capability has allowed us to develop a trial delivery strategy that will broaden the provision of research participation opportunity to under-served groups. It has also enabled existing infrastructure to be maximised while mitigating identified gaps.

Adapting ongoing gait patterns to environmental challenges is essential for safe navigation through the environment. Impairment of gait adaptation is common in many neurodegenerative disorders, such as Parkinson's disease (PD), where it hampers mobility and limits quality of life. The neural control of gait adaptation remains largely unclear, thereby limiting the development of targeted treatments, such as deep brain stimulation of the subthalamic nucleus (STN-DBS). We integrated clinical, kinematic, brain metabolic imaging, and electrophysiological data, obtained during a fully immersive virtual reality overground walking task, to characterize the neural underpinnings of gait adaptation performance during dynamic obstacle avoidance and its improvement with STN-DBS. Movement kinematics, brain oscillatory activity, and metabolic activation were simultaneously acquired in 12 patients with PD during rest and gait adaptation, under active or paused STN-DBS, using inertial measurement units, electroencephalography, and three separate [18F]fluorodeoxyglucose positron emission tomography scans. Eight age-matched healthy subjects completed the same task for comparative kinematic analyses. All patients showed significant clinical improvement with STN-DBS. During the gait adaptation task with paused stimulation, patients exhibited increased metabolic activity in the cerebellum and sensorimotor cortex. Active STN-DBS selectively enhanced thalamic and superior frontal gyrus (SFG) metabolism, while concomitantly reducing cerebellar uptake. Right-lateralized SFG metabolism correlated with gait adaptation performance, with DBS-driven shifts toward greater right SFG activity predicting the magnitude of gait adaptation improvement. This correlation was independent of baseline asymmetry in clinical impairment, electrode placement, or structural connectivity to the SFG. Of note, STN-DBS amplitude asymmetry emerged as an independent predictor of right-lateralization of SFG metabolism. EEG recordings confirmed this lateralized network modulation, with theta-band asymmetry paralleling PET findings. Our findings identify a lateralized thalamo-cortical network supporting gait adaptation in PD and highlight a distinctive role for the SFG. We further show that effective STN-DBS acts as a lateralized regulator, dynamically rebalancing cortico-thalamic circuits to support context-appropriate gait control. The observed right-hemispheric lateralization may foster novel image-guided programming strategies to enhance the consistency and effectiveness of gait control in PD.

As genetic testing becomes increasingly integrated into Parkinson disease (PD) research, including targeted testing for variants in LRRK2 and GBA1, the return of individual research results is becoming more common. However, limited qualitative data exists regarding how research participants experience genetic results disclosure and post-test genetic counseling in PD research settings. We conducted semi-structured qualitative interviews with participants (n=13) enrolled in the Parkinson Precision Medicine Initiative (formerly Parkinson Progression Markers Initiative; PPMI) who had received PD-related genetic test results and post-test genetic counseling. Interviews were conducted 1 to 3 weeks following result disclosure and analyzed using thematic analysis with a primarily deductive coding approach informed by study aims and inductive identification of emergent themes. Four primary themes were identified: (1) personal connection and motivations for participation, (2) centrality of result disclosure and information preferences, (3) emotional experiences and support needs, and (4) communication quality and alignment with participant needs. Overall, our findings underscore the importance of person-centered genetic counseling within PD research. As return of genetic and biomarker results in research and clinical trial contexts expand, thoughtful integration of relational, informational, and communication-focused practices will be essential to support participant engagement and trust.

Introduction: Variants in the polymerase gamma (POLG) gene are associated with a wide range of mitochondrial disorders. Emerging evidence suggests a potential link between POLG variants and Parkinson's disease (PD); yet, results remain inconclusive. Objectives: To investigate the genetic spectrum and prevalence of POLG variants in PD across diverse ancestries. Methods: We leveraged multi-ancestry genetic data from the Global Parkinson's Genetics Program (GP2), including genotyping data from 98,589 and short-read sequencing data from 36,022 individuals. We performed a POLG rare variant screen, case-control association, and gene-level burden analyses. Results: Five PD cases carried potentially biallelic rare pathogenic/likely pathogenic POLG variants. Additionally, 228 individuals (<1%; 161 PD cases, 28 individuals with other neurological disorders, and 39 controls) carried 34 distinct rare pathogenic/likely pathogenic heterozygous variants, with no significant frequency differences between cases and controls, except for the p.Ala467Thr variant in the European population. The co-inherited pathogenic variants p.Thr251Ile and p.Pro587Leu were present in <1% of both cases and controls, with no significant group differences. Burden and variant-level association analyses showed no association between rare POLG variant burden or common POLG variant enrichment and PD. Conclusions: POLG variants are overall rare in PD. The identification of rare pathogenic variants among PD cases suggests that POLG-related mitochondrial dysfunction may contribute to PD in isolated instances, particularly under recessive inheritance. Our findings support a role for POLG variants in select cases and underscore the need for larger-scale sequencing and functional studies.

Background: Cognitive impairment in Parkinson's disease (PD) is linked to degeneration of the cholinergic basal forebrain, particularly cholinergic nucleus 4 (Ch4) in the nucleus basalis of Meynert. Structural and diffusion MRI separately detect this degeneration, but few studies have combined these modalities across the PD cognitive spectrum. Methods: We analyzed 92 participants: 14 healthy controls (HC), 35 PD with normal cognition (PD-NC), 33 with mild cognitive impairment (PD-MCI), and 10 with dementia (PDD). For Ch4 and cholinergic nuclei 1, 2, and 3 (Ch1-3) in the medial septal/diagonal band complex, we determined TIV-normalized gray matter density (GMD) and free-water (FW) fraction. We evaluated group differences, cognitive correlations, adjusted multivariable regression, and exploratory ROC discrimination. Results: Ch4 GMD was significantly lower in PDD compared to PD-MCI (p=0.007), PD-NC (p<0.001), and HC (p<0.001). Ch4 GMD was also lower in PD-MCI versus HC (p=0.028); the PD-MCI versus PD-NC difference was not significant after correction (p=0.074). Ch1-3 GMD was lower in PDD versus PD-NC (p=0.008) and HC (p=0.009). Ch4 and Ch1-3 FW were elevated in PDD versus all other groups (all p<0.01). Among PD patients (n=78), MoCA was positively correlated with Ch4 GMD ({rho}=0.49) and Ch1-3 GMD ({rho}=0.42) and negatively correlated with Ch4 FW ({rho}=-0.51) and Ch1-3 FW ({rho}=-0.40; all p<0.001). In the full four-metric model, Ch4 GMD and Ch4 FW were the only independent basal forebrain predictors (Ch4 GMD {beta}=+2.04, p<0.001; Ch4 FW {beta}=-1.46, p=0.005) of MoCA score. The combined Ch4 GMD + Ch4 FW model showed high discrimination for PDD versus non-demented PD (AUC=0.934; optimism-corrected AUC=0.925). Conclusions: Structural and free-water diffusion MRI provide complementary information about Ch4 degeneration in PD. The combined Ch4 model showed promising exploratory discrimination of PDD; validation in larger independent samples is needed.

Background: Mono-allelic Dehydrodolichyl Diphosphate Synthase (DHDDS) variants are associated with juvenile Parkinsonism, developmental delay and seizures. Symptoms are progressive, and various mechanisms, such as defective glycosylation, lysosomal dysfunction and cholesterol accumulation have been hypothesized to underlie disease symptoms. There is no treatment for DHDDS-related disease. Methods: Patient-derived cortical forebrain organoids were created to elucidate disease mechanisms and evaluate potential treatments. In these neuronal models, glycosylation, lipidomics, proteomics, cholesterol/ganglioside accumulation, mitochondrial function and electrophysiological activity were assessed. Finally, we investigated the effects of nicotinamide mononucleotide (NMN), identified through a yeast-based drug screen, in neuronal cell models and in six patients in an off-label, N-of-1, observational series. Results: DHDDS-patient derived organoids showed visual signs of degeneration after four months of culturing. This was accompanied by significant cholesterol accumulation in astrocytes, decreased mitochondrial respiration and loss of deep-layer neurons. In addition, we identified glycosylation abnormalities, showing for the first time that glycosylation in human tissue is affected by monoallelic DHDDS variants. Proteomic analysis revealed altered protein expression of proteins involved in lipid metabolism, cytoskeletal organization and neuronal development. We found that oral Nicotinamide Mononucleotide supplementation led to significant improvement in mitochondrial respiration and electrophysiological parameters in organoids, concurring with clinical improvements in all of the treated patients, particularly regarding their ataxia and tremor. Conclusion: Our findings reveal a progressive phenotype in DHDDS-patient-derived brain organoids, with mitochondrial dysfunction and astrocyte-specific metabolic alterations contributing to disease pathology. Notably, NMN treatment led to clinical improvements in patients with heterozygous DHDDS variants, highlighting its potential as a therapeutic strategy.

Young onset Parkinson's disease may be caused by biallelic mutations in PRKN or other autosomal recessive Parkinson's disease genes, but the majority of patients do not carry known monogenic variants. Previous studies have found an increased cumulative burden of common genetic risk variants for Parkinson's disease in young onset patients, but the specific genetic architecture of non-monogenic young onset Parkinson's disease is not well characterized. We conducted a genome-wide association study of 1,528 Parkinson's disease patients with symptom onset between 18 and 40 years and 20,408 controls of European ancestry using data from The Global Parkinson's Genetic Program, the International Parkinson's Disease Genomics Consortium, and the NeuroGenetics Research Consortium. We performed meta-analyses of additive and recessive regression models and investigated associations between age at onset groups and different polygenic risk scores. An additive model meta-analysis identified six independent loci passing a genome-wide significance threshold, including three loci identified in previous genome-wide association studies (near SNCA, GBA1, and HIP1R) and two loci not previously associated with Parkinson's disease (rs74950462, P = 1.24e-8 and rs72848817, P = 4.89e-8). Furthermore, we identified a significant signal at the PRKN locus, prompting a follow-up analysis employing a recessive model. The recessive genome-wide association meta-analysis identified nine loci passing a genome-wide significance threshold, including SNCA, PRKN, and seven novel variants. Patients with onset between 18 and 40 years had significantly higher polygenic risk scores than later onset patients when the score was modelled specifically on genome-wide association statistics from independent young onset Parkinson's disease participants versus healthy controls. This increased polygenic burden was driven in part by loci harbouring mitochondrial pathway genes. Our results indicate that previously unidentified common and low-frequency variants contribute specifically to the young onset subgroup of Parkinson's disease. Association signals detected uniquely with a recessive model suggest that genetic susceptibility to young onset Parkinson's disease may be partially driven by homozygous variation, in line with previous reports of increased runs of homozygosity in this particular group of patients and may be consistent with a loss of function mechanism. The findings support the notion of young onset Parkinson's disease as a partly distinct subphenotype and highlight the mitochondrial pathway. These results may have implications for future precision medicine but should be interpreted with caution pending independent replication.

Synapse loss is an early feature of neurodegeneration and may provide sensitive biomarkers for experimental medicine. Positron emission tomography (PET) with the synaptic vesicle glycoprotein 2A radioligand [11C]UCB-J shows widespread signal reduction across dementias. However, it remains unclear which aspects of synaptic integrity [11C]UCB-J PET measures. We developed a histological-imaging pipeline to quantify structurally intact synapses in post-mortem brain tissue. We applied it to six donors with the tauopathy progressive supranuclear palsy (PSP) who had ante-mortem [11C]UCB-J-PET, alongside six controls across 11 brain regions. Synapse loss in PSP was widespread but region-specific across cortical, subcortical, and brainstem regions. Greater synapse loss was associated with higher tau burden and pathology, and cortical synaptic density correlated with ante-mortem cognition. Post-mortem synaptic density correlated with in vivo [11C]UCB-J-PET signal. This study provides validation of SV2A PET as a biomarker of synaptic density and supports integration of imaging with histopathology in neurodegenerative disease research.

High placebo response is an obstacle in developing drugs to treat agitation in Alzheimer's disease (AAD), a prevalent and burdensome symptom. However, it has proved challenging to develop actionable models of placebo response that 1) can be applied prospectively, requiring only information available at screening or baseline, 2) yield strategies for reducing placebo response without equally depressing drug response, and 3) show generalizability across trials. Here, we first investigated placebo response in AAD at the trial level using meta-regression applied to 23 clinical trials. Meta-regression identified several factors associated with increased placebo response, but most of these factors were non-specific such that they predicted improvements in drug response as well. We therefore turned to individual level clinical trial datasets and applied causal modeling to predict which participants would have high placebo response relative to predicted drug response. We successfully built and validated the causal model across two independent clinical trials of risperidone and haloperidol at the level of individual patients (ability to predict subsequent improvement on drug or placebo). Crucially, we also found efficacy improvements in the overall trial through in silico exclusion/screen failing of high placebo-predicted subjects. We further characterized features most associated with placebo response to improve explainability and, lastly, validated the effect of these features at the trial level in clinical trials of galantamine, an acetylcholinesterase inhibitor (hence in a different class of drugs than those in the other two trials used). Taken together, we have developed and applied a causal modeling framework for reducing placebo response and increasing trial-level efficacy in neuropsychiatry clinical trials using historical trial datasets.

Pathogenic variants in ATP13A2, which encodes an endolysosomal polyamine exporter, cause Kufor-Rakeb syndrome and are associated with early-onset parkinsonism and related neurodegenerative disorders, however, the mechanisms by which ATP13A2 dysfunction drives disease remain incompletely defined. In Atp13a2 knockout mice, we identified an early, transient reduction in brain polyamines that precedes overt gliosis and behavioural abnormalities. Pharmacological polyamine depletion exacerbates phenotypes, whereas oral supplementation of spermidine, but not spermine, rescues parkinsonian symptoms establishing metabolic polyamine deficiency as a pathogenic driver. Mechanistically, spermidine counteracts microglia lysosomal dysfunction in the brain and exerts mitochondrial antioxidant and anti-inflammatory effects in primary mouse microglia, thereby improving neuronal integrity. In the absence of Atp13a2, microglial spermidine import relies on the related polyamine transporter Atp13a3. Importantly, these findings translate to human systems, whereby spermidine attenuates inflammation in ATP13A2-deficient human differentiated microglia, while postmortem ATP13A2-deficient brain analysis confirms increased microglia reactivity. Spermidine also rescues motor deficits and dopaminergic neuron loss in ATP13A2-deficient Drosophila and other fly parkinsonism models. Together, these findings identify early polyamine dysregulation as a mechanistic contributor to ATP13A2-associated parkinsonism and nominate spermidine supplementation as a potential therapeutic strategy for ATP13A2-driven pathology and possibly a broader range of parkinsonian sub-types.

Background. Plasma and serum metabolomic studies of myalgic encephalomyelitis / chronic fatigue syndrome (ME/CFS) have repeatedly implicated hypometabolic, lipid, mitochondrial, redox and tryptophan-kynurenine pathways, but prior cohorts have been modest in size and have used heterogeneous case definitions. Whether similar pathway-level signals are detectable at scale in dried blood spots (DBS), across questionnaire-derived fatigue constructs and across orthogonal LC gradients in the same individuals remains unresolved. Methods. We profiled DBS extracts from 1,784 community-cohort adults by reverse-phase LC-MS using paired 5 min and 15 min gradients. Six questionnaire-derived endpoints captured a pragmatic self-reported PEM-like phenotype, a DSQ-derived PEM-like construct, high or review clinical status, temporal fatigue state, comorbid fatigue and self-reported chronic fatigue. The locked primary endpoint for Phase 1 was pragmatic_fatigue_pem with 226 cases and 914 controls after excluding major metabolic comorbidity. We tested a biology-first panel comprising 22 literature-curated metabolites represented by four participant-level descriptors each, and evaluated three discovery extensions: a targeted m/z search of additional literature candidates, a hypothesis-free univariate screen across 4,553 5 min and 5,625 15 min consensus features, and pairwise z-difference ratios. Endpoint-specific Ridge classifiers were evaluated by five-fold out-of-fold AUC with bootstrap stability filtering. Cross-gradient agreement was assessed by per-metabolite AUC concordance between paired 5 min and 15 min profiles. Severity was modelled as an ordinal grade derived from the number of fatigue criteria met and chronic-fatigue-form status. Results. The biology-first DBS panel achieved out-of-fold AUC 0.81 for the pragmatic self-reported PEM-like endpoint (226 cases / 914 controls). The DSQ-derived PEM-like construct reached AUC 0.60 (57 cases / 201 controls) on the un-filtered set and AUC 0.778 (SD 0.013, twenty seeds) in a post-hoc signature-decomposition follow-up restricted to participants without a self-declared major-metabolic-history tag (29 cases / 230 controls); both are treated as construct-validity anchors rather than as provoked or clinically adjudicated PEM. An optimised operationalisation of the same construct (panel-self normalisation, restriction to non-comorbid participants and demographic covariates) reached AUC 0.71 (95 % CI 0.55 to 0.76), and an exploratory age-stratified signature decomposition suggested age-dependent pathway composition that requires confirmation given small per-stratum case counts. Stable contributors mapped to carnitine-shuttle, TCA-cycle, redox-thiol and tryptophan-kynurenine pathways. Cross-gradient analysis of 22 matched metabolites yielded Pearson r = 0.62 for signed univariate effects (p = 0.002; 68 % directional agreement). The metabolomic score increased with severity grade (Spearman rho = 0.45, p = 4 x 10^-91; median scores 0.24, 0.51 and 0.75 across grades 0, 1 and 2). Sensitivity analyses on the covariate-complete subset (n = 565; 138 cases / 427 controls) showed that the DBS signal was robust to adjustment for age, sex, BMI and medication burden (DBS-only AUC 0.76, DBS plus covariates 0.78, covariates only 0.64), and produced a metabolomic-specific lift of approximately 0.13 AUC over the strongest anti-leak declarative cross-form questionnaire baseline (AUC 0.63). DBS-only AUC was stable across sex, age and BMI subgroups, and a 1:4 nearest-neighbour matched analysis on age, sex and BMI yielded AUC 0.72 (95 % CI 0.67 to 0.77). The observed pattern supported pathway-level convergence with prior ME/CFS metabolomics literature, including carnitine shuttle, fatty-acid beta-oxidation, TCA cycle, redox-thiol, urea cycle, glycerophospholipid and tryptophan-kynurenine axes. In contrast, the hypothesis-free 15 min screen produced high-AUC features that mapped predominantly to environmental or technical signals, including pesticide, industrial-amine and mobile-phase artifact annotations; only one of eight top leads, a truncated oxidised phospholipid, was biologically plausible, and none had tandem-MS support. Conclusions. In this large community cohort, a literature-curated DBS metabolomic panel captured pathway-level biology associated with a questionnaire-derived PEM-like fatigue phenotype, showed directional concordance across LC gradients, scaled with symptom severity and remained robust to key demographic, anthropometric and anti-leak questionnaire baselines. The findings converge with several metabolic axes previously reported in ME/CFS plasma and serum studies, including carnitine-shuttle, TCA-cycle, redox-thiol, urea-cycle, glycerophospholipid and tryptophan-kynurenine pathways. They should not be interpreted as clinical validation of a diagnostic test, screening tool or objective provoked-PEM biomarker. Rather, they support at-home-compatible DBS metabolomics as a biologically grounded platform for future clinically adjudicated validation, decision-support development and longitudinal monitoring in fatigue and PEM-like syndromes. Because DBS contains cellular and plasma-derived components, matrix effects must be considered when comparing individual metabolites with venous plasma or serum studies, and hypothesis-free screening at this scale can preferentially surface exposome or technical variance unless molecular identification is enforced before biological interpretation.

Age-related hearing loss is a leading modifiable risk factor for dementia and is increasingly recognized as a non-motor feature of Parkinson's disease (PD). The apolipoprotein E (APOE) E4 allele is the strongest genetic risk factor for Alzheimer's disease and is associated with cognitive decline in PD, yet its relationship to hearing loss remains unclear. Therefore, we examined the independent and interactive effects of PD status and APOE E4 carrier status on age-related hearing loss using a validated web-based speech-in-noise (SIN) assessment in 239,620 23andMe Research Institute participants without PD and 4,361 PD cases. Generalized additive models for location, scale, and shape (GAMLSS) showed that both PD and APOE E4 independently exacerbated age-related hearing decline, with speech reception thresholds (SRTs) worsening non-linearly with advancing age, but without evidence of synergistic interaction. However, longitudinal analyses in a subcohort completing at least two assessments (1,434 PD cases; 36,242 controls) using GAMLSS mixed models showed a significant three-way interaction between PD status, APOE E4, and age2, such that SIN hearing loss accelerated more steeply with age in APOE E4 carriers with PD. Males and individuals with lower educational attainment also exhibited worse SIN hearing loss. These results identify APOE E4 carriers with PD as a priority population for hearing screening and intervention, and support the integration of SIN assessments into routine PD care to detect hearing decline that may compound cognitive and communicative burden in aging.

Background: Chronic diseases and symptom syndromes often develop after prolonged biological changes that may precede formal diagnosis. RNA-based metatranscriptomics captures active microbial and human gene expression and may provide a functional layer for disease risk evaluation. To address this translational gap, we developed and validated a Disease Risk Score (DRS) framework that integrates metatranscriptome-derived pathway activity scores from stool, saliva, and blood samples, and evaluated its potential clinical utility as an adjunct risk-evaluation tool. Methods: DRS uses disease-specific sets of pathway activity scores derived from stool and saliva microbial functions, stool and saliva microbial taxa, and blood human gene expression. For each disease, 'not optimal' pathway scores are aggregated into a normalized cumulative odds ratio, or cOR, using score-level odds ratios, statistical significance, and literature-supported biological relevance derived from a Development Cohort of 22,369 individuals. A cOR [&ge;] 5 is defined as high risk. Performance is evaluated in an independent Validation Cohort of 15,908 individuals using self-reported diseases as the reference. Disease support requires both significant cOR separation between self-reported and not-reported (Cohen's d [&ge;] 0.2) and risk ratio enrichment of self-reported disease among individuals classified as high risk (95% CI of Risk Ratio > 1). Results: Of 20 initially evaluated diseases, 15 meet the prespecified validation criteria on the independent validation cohort: ADHD, anxiety, chronic fatigue syndrome, depression, GERD, hypertension, inflammatory bowel disease, IBS-C, IBS-D, insomnia, MASLD, obesity, obstructive sleep apnea, Sjogren's syndrome, and type 2 diabetes. Five selected clinical scenarios illustrate how DRS can support clinician-mediated decision making, including IBS subtype reclassification, improved diagnostic acceptance in IBS-D, personalized lifestyle counseling in MASLD and early type 2 diabetes, and diagnostic uncertainty in atypical GERD. Conclusions: DRS is a metatranscriptomics-based risk-stratification framework that aggregates active microbial and human pathway signals into interpretable disease-specific risk estimates across a wide range of disease conditions. Validation against self-reported disease labels in an independent cohort shows significant risk enrichment for each of 15 diseases. DRS is intended as an adjunct to clinical evaluation: a decision support tool in situations where routine care encounters uncertainty, delay, or low patient engagement. Future prospective studies using clinically adjudicated endpoints are needed to assess calibration and clinical outcomes.

Background and Objectives: Older adults with epilepsy are at increased risk for Alzheimer's disease (AD), yet the mechanisms underlying this association remain poorly understood. We applied a validated AD neuroimaging signature to older adults with epilepsy to examine 1) whether older adults with epilepsy mirror AD-related changes, 2) associations with clinical, cognitive, and plasma biomarker outcomes, and 3) utility for identifying subgroups at heightened risk for cognitive decline. Our multicenter, prospectively enrolled cohort allowed for direct examination of differences in AD signatures between those with early-onset and late-onset unexplained epilepsy. Methods: Participants included 449 older adults: 87 with focal epilepsy from the multicenter Brain Aging and Cognition in Epilepsy (BrACE) cohort (age=66.10 [SD=6.86], including early-onset (<55 years at seizure onset) and late-onset ([&ge;]55 years at seizure onset) epilepsy); 362 from the Alzheimer's Disease Neuroimaging Initiative (ADNI), including cognitively unimpaired (CU) healthy controls and individuals with mild cognitive impairment (MCI) or AD dementia. An AD signature was derived from regional cortical thickness and hippocampal volume weighted by their sensitivity to AD-related neurodegeneration in prior work. Associations between the AD signature, epilepsy characteristics, plasma biomarkers ({beta}-amyloid 42/40, phosphorylated tau [pTau217, pTau181], neurofilament light chain [NfL]), and cognition were evaluated in BrACE. Results: Participants with epilepsy demonstrated more AD-like signatures compared to ADNI CU controls ({beta}= -0.43, p<0.001), reflecting reduced thickness/volume in AD-vulnerable regions. This effect was stronger among early-onset ({beta}= -0.57) versus late-onset ({beta}= -0.26) epilepsy. In BrACE, the AD signature correlated with NfL ({beta}= -0.30, p=0.050), memory performance ({beta}= 0.30, p=0.006), and predicted greater odds of cognitive impairment specifically among those with early-onset, but not late-onset, epilepsy (interaction p=0.043). Further, among those with early-onset epilepsy, the AD signature significantly improved identification of cognitive impairment over and beyond the effects of plasma AD biomarkers (p=0.041). Findings were similar when examining the effects of epilepsy duration rather than epilepsy onset age. Discussion: AD neuroimaging signatures may help identify clinically meaningful subgroups among older adults with epilepsy, particularly when integrated with AD biomarkers. Findings support a multimodal framework for assessing AD-related risk in epilepsy and highlight interactive effects of epilepsy chronicity and AD-related processes that can influence cognitive outcomes.

Background: Sleep fragmentation and reduced sleep efficiency are markers of disrupted sleep architecture linked to cognitive and age-related decline. Current assessments rely on subjective reports prone to recall bias, limiting their effectiveness for longitudinal monitoring. Data-driven analysis of sleep using physiological signals such as EEG and EMG remains underutilised, particularly in mid-to-older adults. Objective: We present a deep learning pipeline for automated sleep staging and label-free abnormality scoring, with the primary objective of quantifying deviations in sleep architecture to capture progressive sleep disruption and longitudinal change. Methods: Temporal and attention-based models were benchmarked using datasets from the National Sleep Research Resource and PhysioBank. To improve class-specific performance, we introduce a stacking-based ensemble of sleep stage classifiers, each trained to specialise in a different stage. For longitudinal scoring, we develop a reconstruction loss-based abnormality metric using a temporal convolutional autoencoder trained on hypnograms generated by the sleep staging models. Results: Attention-based models, particularly AttnSleep, achieved the highest performance in both multimodal and single-channel settings (accuracy: 0.85 and 0.83; F1: 0.79 and 0.74, respectively). The encoder-decoder ensemble model improved overall classification accuracy by 3% compared to the best-performing biased base classifier, with a modest gain in N1-stage F1 score (0.444). The proposed abnormality score correlated with Pittsburgh Sleep Quality Index components and showed sensitivity to synthetic hypnogram degradation, highlighting its potential as a label-free indicator of sleep disruption. Conclusion: Automated classification and annotation-free scoring enable an end-to-end multimodal pipeline that supports scalable, objective sleep health monitoring, with relevance for future clinical deployment.

Brain-derived tau (BD-tau) is an emerging blood-based biomarker for neurodegeneration, yet there are currently limited well validated BD-tau assays available for research and clinical use. To enhance access to this vital biomarker for neurological disorders including traumatic brain injury (TBI), we developed a novel blood-based immunoassay for BD-tau on the ultra-sensitive Quanterix HD-X platform using Single Molecule Array technology. Analytical validation assessed dilution linearity, specificity, precision, detection limits, and spike recovery, each recording robust metrics in agreement with international expert recommendations. The assay demonstrated robust validation metrics, achieving between-run stability of 95% when analyzing aliquots from six independent plasma and serum samples across five analytical runs. It also showed strong dilution linearity when diluted four-fold and achieved over 90% recovery when spiked with cerebrospinal fluid. Next, we evaluated the clinical utility of the assay in cohorts of individuals with traumatic brain injury (TBI), where strong performances were recorded whether using the 2-step or 3-step assay formats ({rho}= 0.94; p < 0.0001). Furthermore, plasma BD-tau distinguished samples from TBI patients based on time from injury and severity (AUC=0.93). Plasma BD-tau differentiated between favorable and unfavorable functional outcomes in the acute-severe group. Our findings underscore the significant potential of the BD-tau assay as a biomarker for TBI in the severe phase.

The objective assessment of patients with disorders of consciousness (DOC) remains a significant clinical challenge. Behavioral scales like the Coma Recovery Scale-Revised (CRS-R) are susceptible to rater subjectivity and have difficulty in detecting patients with cognitive-motor dissociation (CMD), while existing electrophysiological paradigms typically evaluate isolated processing levels, especially in visual functions. To address these limitations, we developed a novel, hierarchical visual EEG framework that evaluates three progressive tiers of visual processing--sensory input, selective attention, and object discrimination--within a single, unified paradigm. This framework uses steady-state and event-related potentials, analyzed with statistical testing and machine learning, to provide objective detection. In a cohort of 85 participants, the framework demonstrated a robust alignment with behavioral CRS-R levels and successfully identified CMD patients missed by bedside behavioral examinations. Notably, model predictions derived from this framework showed a significant correlation with 3-month clinical outcomes. This prognostic utility generalized effectively and remained consistent across distinct EEG acquisition systems in an independent validation cohort of 17 patients. In summary, this work offers electrophysiological validation for the hierarchical design of the CRS-R and provides a practical tool for bedside objective assessment of DOC.

Background: Cognitive assessments are sparsely documented in electronic health records (EHRs), limiting scalable detection of cognitive worsening in real-world clinical settings. Methods: We applied a deep neural network optimized for identifying clinical event timing from sparsely labeled gold-standard data (label-efficient incident phenotyping from longitudinal EHR, LATTE) to predict time-to-first sustained cognitive worsening in AD patients from a large healthcare system (2011-2022) with linkage to an AD Research Center registry in a subset. Sustained cognitive worsening was defined as cognitive decline persisting over [&ge;]2 consecutive visits within 3 years. Separate LATTE models were trained with worsening labels from Clinical Dementia Rating (CDR), Mini-Mental Status Examination (MMSE), and Montreal Cognitive Assessment (MoCA) scores; semi-supervised learning scaled predictions to larger imputation cohorts lacking sufficient longitudinal scores. We evaluated model performance using average time-specific area under the receiver operating characteristic curve (AUC), area between curves (ABC), and Brier scores. To demonstrate clinical utility, we examined whether predicted time-to-worsening differentiated clinically meaningful patient subgroups using competing-risk Cox proportional hazards models accounting for death. Findings: The cohort comprised 27,614 AD patients (65% women, 91% non-Hispanic White, mean [SD] age at start of follow-up 78.76 [9.53] years). In gold-standard cohorts (n: CDR=632, MMSE=710, MoCA=752; remaining patients formed imputation cohorts), LATTE demonstrated robust predictive performance (average time-AUC: CDR 0.816, MMSE 0.694, MoCA 0.710; ABC: CDR 0.067, MMSE 0.293, MoCA 0.078; Brier score: CDR 0.252, MMSE 0.437, MoCA 0.295). APOE-{varepsilon}4 carriers had shorter predicted time-to-worsening compared to non-carriers across all assessments in the imputation cohorts (HRs 1.241-1.376, all p<0.025), and k-means derived patient clusters showed differential time-to-worsening in the overall and imputation cohorts (HRs 0.777-0.908, all p<.001). Interpretation: LATTE enables scalable prediction of sustained cognitive worsening timing, differentiating clinically meaningful patient subgroups. This approach could improve AD clinical monitoring and decision-making in routine care and support targeted clinical trial enrichment.

Introduction: Myalgic Encephalomyelitis (ME)/Chronic Fatigue Syndrome (CFS) is a debilitating condition characterised by severe fatigue and post-exertional malaise (PEM). Reported neuropsychophysiological abnormalities suggest ME/CFS is multifactorial, but current knowledge remains fragmented. This study protocol outlines a multimodal investigation designed to (1) compare neuropsychophysiological mechanisms between ME/CFS patients and healthy participants, (2) test an integrative model of ME/CFS, (3) identify neuropsychophysiological subgroups within the patient population, and (4) identify predictors of symptom response during rehabilitation. Methods and analysis: This study will enroll 115 ME/CFS patients and 55 healthy participants. Groups will be comparable in age, sex, and education level, with a larger patient sample enabling subgroup and longitudinal analyses. A cross-sectional assessment at baseline will be carried out in both groups. Patients will then be evaluated longitudinally throughout a standardized cognitive-behavioral therapy rehabilitation program delivered as routine care. Baseline measures include systemic inflammation and general health biomarkers, measures of autonomic and central nervous system function, neuroinflammation (magnetic resonance spectroscopy, [18F]DPA714 PET in a subsample), serum short-chain fatty acid levels, gut microbiota composition and function, and neuroendocrine and self-reported responses to psychosocial stress. Fatigue severity (physical and cognitive) and PEM will be assessed through validated questionnaires, ecological momentary assessment, and laboratory tasks. These will be re-evaluated during therapy, and all non-neuroimaging measures will be repeated after the rehabilitation program. Statistical analyses will comprise multivariate analysis of variance, general linear models, classification algorithms, structural equation models, least absolute shrinkage selection operator principal component regression (LASSO-PCR), cluster analysis and latent class growth analysis (LCGA).