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ABSTRACTBackground and AimsChronic inflammatory demyelinating polyradiculoneuropathy (CIDP) is difficult to distinguish from mimicking disorders, with misdiagnosis resulting in IVIG overutilization. We evaluate a clinical‐electrophysiological model to facilitate CIDP versus mimic neuropathy prediction.MethodsUsing the European Academy of Neurology/Peripheral Nerve Society (EAN/PNS) 2021 CIDP guidelines we derived 26 clinical and 144 nerve conduction variables. The model was generated and validated utilizing total CIDP (n = 129) and mimics (n = 309); including (1) IgG4‐nodopathies; (2) POEMS (polyneuropathy–organomegaly–endocrinopathy–monoclonal protein‐skin changes); (3) anti‐myelin‐associated‐glycoprotein; (4) paraneoplastic; (5) Waldenström B‐cell lymphoma; (6) diabetic neuropathies; (7) amyloidosis; (8) Charcot–Marie–Tooth; (9) motor neuropathies/neuronopathies; and (10) idiopathic‐inflammatory‐myopathies.ResultsWe analyzed 9282 clinical and 51 408 electrophysiological data points. Univariate analysis identified 11 of 26 clinical variables with significant odds ratios. A multivariate regression model using four clinical and two electrophysiologic variables achieved 93% area‐under‐curve (95% CI 91–95): progression over 8 weeks (OR 40.66, 95% CI 5.31–311.36), absent autonomic involvement (OR 17.82, 95% CI 2.93–108.24), absent muscle atrophy (OR 16.65, 95% CI 3.27–84.73), proximal weakness (OR 3.63, 95% CI 1.58–8.33), ulnar motor conduction velocity slowing < 35.7 m/s (OR 5.21, 95% CI 2.13–12.76), and ulnar motor conduction block (OR 13.37, 95% CI 2.47–72.40). A web‐based probability calculator (https://news.mayocliniclabs.com/cidp‐calculator/) was developed, with 100% sensitivity and 68% specificity at a 92% probability threshold. Specificity improved to 93% when considering “red flags,” electrophysiologic criteria, and laboratory testing.InterpretationA probability calculator using clinical electrophysiological variables assists CIDP differentiation from mimics, with scores below 92% unlikely to have CIDP. The highest specificity is achieved by considering clinical “red flags,” electrophysiologic demyelination, and laboratory testing.

Objective The aim of this study was to determine whether pollutants such as fire smoke–related particulate matter <2.5 μm (PM 2.5 ) are associated with incident rheumatoid arthritis (RA) and RA‐associated interstitial lung disease (RA‐ILD). Methods This patient–control study used Veterans Affairs (VA) data from October 1, 2009, to December 31, 2018. We identified patients with incident RA and RA‐ILD using validated algorithms, matching each patient to ≤10 controls on age, sex, and VA enrollment year. We obtained pollutants including fire smoke PM 2.5 , carbon monoxide, nitrogen oxides (NOx), ozone, overall PM 2.5 , PM 10 , and sulfur dioxide (SO 2 ) at least one year before the index date. We fit conditional logistic regression models to estimate adjusted odds ratios (aORs) with 95% confidence intervals (CIs) for incident RA and RA‐ILD, adjusted for confounders. Results We identified 9,701 patients with incident RA (mean age 65 years, 86% male), including 531 patients with RA‐ILD (mean age 69 years, 91% male), and 68,852 matched controls. Fire smoke PM 2.5 was not associated with RA (aOR 1.07, 95% CI 0.92–1.23) but was associated with RA‐ILD (aOR 1.98, 95% CI 1.08–3.62, per 1 μg/m 3 ). Increased levels of NOx were associated with RA (aOR 1.16, 95% CI 1.06–1.27, highest vs lowest quartile). The highest quartiles of ozone (aOR 1.19, 95% CI 1.06–1.34) and PM 10 (aOR 1.25, 95% CI 1.10–1.43) were associated with seronegative RA. Carbon monoxide, overall PM 2.5 , and SO 2 were not, or negatively, associated with RA and RA‐ILD. Conclusion Increased fire smoke PM 2.5 was associated with RA‐ILD, whereas NOx, ozone, and PM 10 were associated with RA risk. Thus, air pollution may increase the risk of RA and RA‐ILD. image

Abstract There are ~100 genes or copy number variants (CNVs) used in genetic testing for Autism Spectrum Disorder (ASD, or autism). These genes are protein-coding, and the associated phenotypes often extend beyond socio-behavioral traits seen in autism including cognitive/medical complexities, epilepsy, and ADHD. Here, we characterize 27 males with ASD through whole genome sequencing (WGS), delineating X-chromosome microdeletions that implicate the long non-coding RNA (lncRNA) PTCHD1-AS as an ASD-susceptibility gene (OR=2.56, p=0.01). Two Ptchd1-as knockout (KO) murine models, created by removing the evolutionarily conserved exon-3, show ASD-like features in males, increasing repetitive behaviors and impairing typical social behavior and communication without overt cognitive comorbidities or ADHD-like behaviors. Hippocampus-dependent synaptic function, complex learning, and locomotor activity are unaffected in KO mice. Native nuclear-enriched mouse Ptchd1-as showed sustained expression from post-natal day-7 onward in the dorsal striatum, a predominantly GABAergic brain region implicated in ASD. Multi-omics revealed transcriptomic alterations in striatal oligodendrocyte, astrocyte and neurons impacting myelination and plasticity pathways. Disrupting Ptchd1-as led to reductions in conventional Protein Kinase-C, altered Src and GSK3α/β phosphorylation, and an enhancement of synaptic plasticity (long-term potentiation and long-term depression). Together, these findings implicate striatal molecular and circuit level dysregulation via Ptchd1-as in ASD etiology.

Abstract Background Despite advances in our understanding of the molecular underpinnings of meningioma progression and innovations in systemic and local treatments, recurrent meningiomas remain a substantial therapeutic challenge. The objective of this systematic review and meta-analysis is to provide a historical baseline, contemporary analysis, and propose a “rate of probable interest” to inform future clinical trial design and development on behalf of the Response Assessment in Neuro-Oncology meningioma group. Methods PubMed, ClinicalTrials.gov, and ASCOpubs databases were screened for clinical trials evaluating the activity of systemic therapies for adults with recurrent meningiomas. The pooled progression-free survival at 6-months and 1-year (PFS-6 and PFS-1 year) values were calculated using the random effects technique with I2 indices. Results The pooled PFS-6 and PFS-1 year rates for recurrent WHO grade 1 meningiomas were 43.6% (95% CI: 22.7-67.0%, I2 = 80%) and 21.7% (95% CI: 6.2-53.9%, I2 = 76%), and for grades 2-3 meningiomas, the PFS-6 was 38.0% (95% CI: 28.3-48.8%, I2 = 68%). In the targeted therapy group, PFS-6 and PFS-1 year rates stood at 62.0% (I2 = 58%) and 49.0% (I2 = 63%) for grade 1, while for grades 2-3 tumors, the PFS-6 rates with targeted therapy and immunotherapy were 42.1% (I² = 60%) and 46.0% (I² = 0%), respectively. The benchmarks were set at 67% and 54% for PFS-6 and PFS-1 year for grade 1 tumors, and PFS-6 of 49% for grades 2-3 tumors. Conclusions Several studies have reported outcomes in patients with recurrent meningiomas testing a variety of agents with modest, but variable and progressively increasing activity. In this context, we recommend new benchmarks for future trials to define efficacy of future investigational therapies.

AbstractBackgroundEarly‐onset Alzheimer’s disease (EOAD) occurs before age 65 and has more diverse disease presentations than late‐onset AD. To improve our understanding of phenotypic heterogeneity among EOAD individuals, we analyzed cognitive scores using data‐driven statistical analysis.MethodBaseline cognitive data from 286 sporadic EOAD individuals from the Longitudinal EOAD study (LEADS) were transformed to z‐scores using data from 95 cognitively normal (CN) individuals. Cognitive composites were generated for domains of memory, language, speed/attention, visuospatial, and executive function. Residuals from linear regression models on Z‐scores adjusted for age, sex, and education were obtained. Cluster analysis using the Ward method on the cognitive domain residuals was performed and scree plot using the pseudo T‐squared determined the optimal number of clusters for the EOAD sample. We also compared gray matter density (GMD) of each EOAD cluster to CN participants using voxel‐wise multiple linear regressions.ResultsThree clusters of cognitive performance were identified from the EOAD sample. Disease duration was not significantly different across clusters. Using a z‐score of ‐1.5 SD as the impairment threshold, all clusters were impaired across most domains (Table 1). Cluster‐3 was more impaired than cluster‐2 in all domains (Table 2; all p<.0001), and in all domains except episodic memory compared to cluster‐1 (all p<.01). Cluster‐1 (n = 71; 85.9% amnestic) was most impaired in executive function, visuospatial, and speed/attention. Cluster‐2 (n = 133; 88.7% amnestic) was most impaired in episodic memory. Cluster‐3 (n = 82; 69.5% amnestic) was most impaired in executive function, visuospatial, and speed/attention (Table 1). 3D‐comparisons showed all EOAD clusters had reduced GMD compared to CN. Cluster‐1 and cluster‐3 both showed widespread atrophy, with cluster‐3 being more severe. Cluster‐2 showed the most atrophy in the temporal and parietal lobes (Figure 1).ConclusionWe identified heterogeneity in cognitive patterns among sporadic EOAD individuals. Cluster‐3 appeared to reflect widespread impairment, and cluster‐2 represented an amnestic‐only presentation. Despite comparable disease duration, some EOAD patients progress faster, while some are more resilient. 3D‐comparisons showed neurodegenerative changes affecting brain regions responsible for respective impaired cognitive functions in each cluster (e.g., cluster‐2 is primarily amnestic‐impaired and has temporoparietal atrophy). Future work should explore amyloid‐PET and tau‐PET burden.

AbstractBackgroundDiagnosing sporadic early‐onset AD (EOAD, age‐at‐onset<65) is challenging: in the multi‐center Longitudinal Early‐onset Alzheimer’s Disease Study, ∼25% of patients with clinically diagnosed EOAD are amyloid‐PET‐negative. Here we used FDG‐PET to characterize the heterogeneity of hypometabolic profiles in these patients and better identify underlying etiologies.MethodSeventy‐four amyloid‐PET‐negative patients with clinical diagnosis of sporadic EOAD (MCI or mild dementia stage) underwent FDG‐PET. Patients were classified as having normal or hypometabolic FDG‐PET based on a data‐driven approach that compared each patient to a group of 61 age‐matched amyloid‐PET‐negative controls using 12 methodological combinations (3 reference regions, 2 voxel‐level thresholds, 2 outlier detection methods). We then assessed clinical and demographic differences between patients with normal versus hypometabolic FDG‐PET, and further compared groups using independent biomarkers of neurodegeneration (structural MRI and fluid biomarkers). Finally, we applied hierarchical clustering to hypometabolic FDG‐PET scans to identify patterns of hypometabolism.ResultThirty‐six amyloid‐negative patients (49%) had hypometabolic FDG‐PET scans. They were older and more severely impaired across most cognitive domains than patients with normal FDG‐PET (Table 1). They also had reduced hippocampal volumes and cortical thickness (Figure 1A), higher plasma and CSF neurofilament light chain (NfL) levels, and elevated plasma GFAP compared to patients with normal FDG‐PET (Figure 1B). In contrast, the latter, who had intermediate cognitive scores between hypometabolic patients and controls, had MRI and fluid biomarker levels in the range of controls (Figure 1). In hypometabolic patients, hierarchical clustering identified four profiles: i) anterior temporal extending to temporo‐parietal and frontal regions (n=5), ii) anterior temporal and orbitofrontal (n=11), iii) occipito‐parietal (n=6), and iv) lateral frontal and parietal (n=14) (Figure 2). Genetic testing identified two patients with Frontotemporal Lobar Degeneration (FTLD)‐associated pathogenic variants, both considered hypometabolic and assigned to the first (MAPT) and second (c9orf72) metabolic profiles.ConclusionFifty‐one percent of amyloid‐negative patients had normal FDG‐PET: they had milder clinical impairment, normal MRI measures, and normal NfL values, suggesting non‐neurodegenerative etiologies. Patients with abnormal FDG showed heterogeneous hypometabolic patterns suggestive of multiple etiologies including Lewy body disease, FTLD or corticobasal degeneration. Longitudinal follow‐up to autopsy will ultimately clarify the amyloid‐negative clinical mimics of sporadic EOAD.

AbstractBackgroundTraumatic encephalopathy syndrome (TES) is the proposed clinical syndrome of the neurodegenerative disease chronic traumatic encephalopathy (CTE). As part of the 2021 TES NINDS consensus diagnostic criteria, certainty levels of underlying CTE neuropathology can be determined (i.e., suggestive, possible, probable) partially based on the presence/absence of supportive psychiatric features including but not limited to depression, anxiety, and apathy. These are ‘supportive’ because of their non‐specificity and unclear biological correlates in this setting. We examined the association between CSF biomarkers of amyloid, tau, neurodegeneration, microglial activation and neuroinflammation and supportive psychiatric features of TES in former American football players.MethodThe sample included 180 male former football players (120 professional, 60 college) from the DIAGNOSE CTE Research Project. Participants completed a lumbar puncture, and CSF assays were conducted for biomarkers of amyloid (Aβ40, Aβ42), p‐tau (p‐tau181, p‐tau231, p‐tau217), neurodegeneration (total tau), axonal degeneration (NfL), microglial activation (strem2) and neuroinflammation (GFAP, IL‐6). Biomarkers were log‐transformed. The Apathy Evaluation Scale (AES), Beck Depression Inventory‐II (BDI‐II) and the Beck Anxiety Inventory (BAI) self‐report questionnaires were completed by participants. Multivariable regression models tested the association between each CSF biomarker and each scale, controlling for age, race, body mass index, revised Framingham stroke risk profile and Alcohol Use Disorders Identification Test. P‐values were adjusted for multiple comparisons using false discovery rate methods.Result140 participants had successful LPs and available CSF for assays. 33(22%) reported moderate/severe anxiety, 34(22%) reported moderate/severe depression, and 51(33%) had an elevated AES score. Higher CSF concentrations of IL‐6 were associated with higher scores on the BAI (unstandardized‐β=0.016, 95%CI [0.017, 0.026], padj=0.004), BDI‐II (unstandardized‐β=0.013, 95%CI [0.004, 0.021], padj=0.008) and AES (unstandardized‐β=0.014, 95%CI [0.006,0.022], padj=0.002). There were no significant relationships between other biomarkers with the BDI‐II, BAI or AES.ConclusionIn this sample of former American football players, there were significant associations between IL‐6 (a proinflammatory cytokine) and reported symptoms of depression, anxiety, and apathy. CSF biomarkers of amyloid, tau and neurodegeneration had no associations, highlighting the role of neuroinflammatory processes in the manifestation of psychiatric symptoms of TES. Continued research on the biological correlates of TES features will inform future iterations of the criteria.

AbstractBackgroundAs new anti‐amyloid immunotherapies emerge for Alzheimer’s disease (AD), it is clear that early diagnosis of AD pathology is crucial for treatment success. This can be challenging in atypical presentations of AD and, together with our reliance on CSF or PET scans, can, at times, lead to delayed diagnosis. Here, we further explore the possible role of plasma tau phosphorylated at threonine 217 (P‐tau217) for the detection of primary AD or AD co‐pathology when frontotemporal dementia spectrum disorders are the main clinical presentation.MethodParticipants were recruited through ALLFTD, a North American multisite research consortium for the longitudinal assessment of frontotemporal lobar degeneration. After excluding cases with known FTD‐causing mutations, 573 participants with sporadic disease, mild cognitive/behavioral impairment, or healthy familial controls evaluated between 2014 and 2023 were included in this analysis (45.8% female, median age 66 years). 39 of those cases underwent neuropathological evaluation. Plasma P‐tau217 was measured with the Eli Lilly electrochemiluminescence‐based assay. P‐tau217 concentrations were compared by phenotype, disease severity, genotype, and, when available, neuropathological diagnosis with non‐parametric tests. Its diagnostic performance was tested with ROC curves. Associations between plasma P‐tau217 concentrations and clinical scales of global cognition, memory, executive function, motor function, and social cognition with linear regressions and non‐parametric tests.ResultPlasma P‐tau217 concentrations were higher in logopenic variant primary progressive aphasia (lvPPA) and amnestic dementia (AmDem), compared to participants with normal cognition or other FTD phenotypes. P‐tau217 concentrations were higher in APOE e4 carriers, compared to non‐carriers, regardless of phenotype, but were not affected by disease severity. P‐tau217 concentrations showed associations with clinical measures of global cognition, memory, and executive function, but had no associations with measures of motor function or social cognition. Plasma P‐tau217 concentrations were elevated in high and intermediate ADNC scoring, or more advanced Braak stages, even when AD was not the primary pathology.ConclusionEven when FTD is suspected, high plasma p‐tau 217 concentrations are strongly associated with underlying AD as primary pathology or contributing co‐pathology. Plasma P‐tau217 could be a tool to support the development of disease‐modifying therapies for atypical Alzheimer’s disease presentations or for FTD cases with Alzheimer’s disease co‐pathology.

Abstract Background Diffusion tensor imaging along perivascular spaces index (DTI‐ALPS), which measures diffusivity increases in the perivascular spaces along the medullary veins, is being increasingly utilized as a surrogate marker of glymphatic clearance (Taoka et. al. Jpn J Radio 2017). Lower DTI‐ALPS index means higher measured diffusivity from the perivascular space, and so it is expected to decrease with age. However, correlation with aging and dementia markers have not been systematically evaluated. The goal of this work was to automatically estimate DTI‐ALPS in a large sample and evaluate the variance explained by various demographic and imaging markers. Methods We identified 2739 participants aged 30+ years (mean 69.6, 48% female, 88% CU) in the population‐based Mayo Clinic Study of Aging with diffusion MRI. We estimated DTI‐ALPS through automated placement of regions as published previously (Liu et al. AAIC 2023) and shown in Figure 1. We examined the associations of DTI‐ALPS with demographics (age, sex), vascular risk (cardiovascular and metabolic condition score), clinical data (diagnosis, global cognition) and imaging markers (white matter hyperintensity (WMH), global amyloid load from PIB‐PET, and the temporal meta‐ROI Tau‐PET SUVR) using partial Pearson correlations and ANCOVA with adjustments for age and sex. Results DTI‐ALPS was negatively correlated with age, higher in females, declined with vascular risk, and lower in MCI/dementia groups (Figure 2). Higher WMH and higher amyloid were associated with lower DTI‐ALPS whereas Tau‐PET SUVR was not associated with DTI‐ALPS. Conclusions DTI‐ALPS can be automatically computed in large samples. Vascular risk and WMH had stronger associations with the computed DTI‐ALPS compared to AD biomarkers (amyloid and tau) suggesting that the surrogate was more likely linked to vascular injury.

AbstractBackgroundThere is a strong link between tau and progression of Alzheimer’s disease (AD), necessitating an understanding of tau spreading mechanisms. Prior research, predominantly in typical AD, suggested that tau propagates from epicenters (regions with earliest tau) to functionally connected regions. However, given the constrained spatial heterogeneity of tau in typical AD, validating this connectivity‐based tau spreading model in AD variants with distinct tau deposition patterns is crucial.MethodWe included 269 amyloid‐β‐positive (PET/CSF) individuals with clinically diagnosed atypical AD (113 posterior cortical atrophy, PCA‐AD; 83 logopenic variant primary progressive aphasia, lvPPA‐AD; 33 behavioural variant AD, bvAD; 40 corticobasal syndrome, CBS‐AD) and 68 with typical AD from 12 international cohorts, who underwent tau‐PET (54% [18F]AV1451/[18F]flortaucipir/Tauvid, 27% [18F]MK6240, 19% [18F]PI2620). Using Gaussian mixture modeling including amyloid‐β‐negative controls, cross‐sectional tau‐PET standardized uptake value ratios within Schaefer‐200 atlas regions were transformed to tau positivity probabilities. Tau epicenters were defined as the 5% regions with highest tau positivity probabilities. For each variant, the association between functional connectivity‐based distance (using the 30% strongest positive region‐to‐region connections of a group‐average connectivity matrix from ADNI elderly controls) and tau‐PET covariance (group‐average correlation per region pair) was assessed through linear regression, adjusting for age, sex, site, and Euclidean distance. Regions were categorized based on functional proximity to the epicenter (quartiles 1‐4) and tau positivity probabilities were assessed accordingly.ResultTau positivity probabilities matched clinical variants, with a posterior pattern in PCA‐AD, left‐hemispheric dominant pattern in lvPPA‐AD, widespread pattern in bvAD, sensorimotor cortex involvement in CBS‐AD, and temporo‐parietal predominance in typical AD (Figure 1). In line with this, tau epicenters were highly heterogeneous across variants (Figure 1). In all variants, greater tau‐PET covariance was associated with shorter functional connectivity‐based distance (Figure 2). We observed that regions in closer functional proximity to the epicenter exhibited higher tau positivity probabilities than regions functionally further away (p<0.05, Figure 3).ConclusionThis multi‐center study shows that the brain’s functional architecture serves as a universal predictor of tau spreading in AD. Since tau is a key driver of neurodegeneration and cognitive decline in AD, this finding holds potential for personalized medicine and defining participant‐specific endpoints in clinical trials.