Exposure to autoimmune disorders increases Alzheimer's disease risk in a multi-site electronic health record analysis.

Molecular studies of Alzheimer's disease (AD) implicate potential links between autoimmunity and AD, but the underlying clinical relationships between these conditions remain poorly understood. Electronic health records (EHRs) provide an opportunity to determine the clinical risk relationship between autoimmune disorders and AD and understand whether specific disorders and disorder subtypes affect AD risk at the phenotypic level in human populations. We evaluated relationships between 26 autoimmune disorders and AD across retrospective observational case-control and cohort study designs in the EHR systems at UCSF and Stanford. We quantified overall and sex-specific AD risk effects that these autoimmune disorders confer. We identified significantly increased AD risk in autoimmune disorder patients in both study designs at UCSF and at Stanford. This pattern was driven by specific autoimmunity subtypes including endocrine, gastrointestinal, dermatologic, and musculoskeletal disorders. We also observed increased AD risk from autoimmunity in both women and men, but women with autoimmune disorders continued to have a higher AD prevalence than men, indicating persistent sex-specificity. This study identifies autoimmune disorders as strong risk factors for AD that validate across several study designs and EHR databases. It sets the foundation for exploring how underlying autoimmune mechanisms increase AD risk and contribute to AD pathogenesis.

Machine learning models identify predictive features of patient mortality across dementia types.

BACKGROUND: Dementia care is challenging due to the divergent trajectories in disease progression and outcomes. Predictive models are needed to flag patients at risk of near-term mortality and identify factors contributing to mortality risk across different dementia types. METHODS: Here, we developed machine-learning models predicting dementia patient mortality at four different survival thresholds using a dataset of 45,275 unique participants and 163,782 visit records from the U.S. National Alzheimer's Coordinating Center (NACC). We built multi-factorial XGBoost models using a small set of mortality predictors and conducted stratified analyses with dementiatype-specific models. RESULTS: Our models achieved an area under the receiver operating characteristic curve (AUC-ROC) of over 0.82 utilizing nine parsimonious features for all 1-, 3-, 5-, and 10-year thresholds. The trained models mainly consisted of dementia-related predictors such as specific neuropsychological tests and were minimally affected by other age-related causes of death, e.g., stroke and cardiovascular conditions. Notably, stratified analyses revealed shared and distinct predictors of mortality across eight dementia types. Unsupervised clustering of mortality predictors grouped vascular dementia with depression and Lewy body dementia with frontotemporal lobar dementia. CONCLUSIONS: This study demonstrates the feasibility of flagging dementia patients at risk of mortality for personalized clinical management. Parsimonious machine-learning models can be used to predict dementia patient mortality with a limited set of clinical features, and dementiatype-specific models can be applied to heterogeneous dementia patient populations.

Dementia has emerged as a major cause of death in societies with increasingly aging populations. However, predicting the exact timing of death in dementia cases is challenging, due to variations in the gradual process where cognitive decline interferes with the body's normal functions. In our study, we build machine-learning models to predict whether a patient diagnosed with dementia will survive or die within 1, 3, 5, or 10 years. We found that the prediction models can work well across patients from different parts of the US and across patients with different types of dementia. The key predictive factor was the information that is already used to diagnose and stage dementia, such as the results of memory tests. Interestingly, broader risk factors related to other causes of death, such as heart conditions, were less significant for predicting death in dementia patients. The ability of these models to identify dementia patients at a heightened risk of mortality could aid clinical practices, potentially allowing for earlier interventions and tailored treatment strategies to improve patient outcomes.

Discriminating nonfluent/agrammatic and logopenic PPA variants with automatically extracted morphosyntactic measures from connected speech.

Morphosyntactic assessments are important for characterizing individuals with nonfluent/agrammatic variant primary progressive aphasia (nfvPPA). Yet, standard tests are subject to examiner bias and often fail to differentiate between nfvPPA and logopenic variant PPA (lvPPA). Moreover, relevant neural signatures remain underexplored. Here, we leverage natural language processing tools to automatically capture morphosyntactic disturbances and their neuroanatomical correlates in 35 individuals with nfvPPA relative to 10 healthy controls (HC) and 26 individuals with lvPPA. Participants described a picture, and ensuing transcripts were analyzed via part-of-speech tagging to extract sentence-related features (e.g., subordinating and coordinating conjunctions), verbal-related features (e.g., tense markers), and nominal-related features (e.g., subjective and possessive pronouns). Gradient boosting machines were used to classify between groups using all features. We identified the most discriminant morphosyntactic marker via a feature importance algorithm and examined its neural correlates via voxel-based morphometry. Individuals with nfvPPA produced fewer morphosyntactic elements than the other two groups. Such features robustly discriminated them from both individuals with lvPPA and HCs with an AUC of .95 and .82, respectively. The most discriminatory feature corresponded to subordinating conjunctions was correlated with cortical atrophy within the left posterior inferior frontal gyrus across groups (pFWE < .05). Automated morphosyntactic analysis can efficiently differentiate nfvPPA from lvPPA. Also, the most sensitive morphosyntactic markers correlate with a core atrophy region of nfvPPA. Our approach, thus, can contribute to a key challenge in PPA diagnosis.

Leveraging electronic health records and knowledge networks for Alzheimer's disease prediction and sex-specific biological insights.

Identification of Alzheimer's disease (AD) onset risk can facilitate interventions before irreversible disease progression. We demonstrate that electronic health records from the University of California, San Francisco, followed by knowledge networks (for example, SPOKE) allow for (1) prediction of AD onset and (2) prioritization of biological hypotheses, and (3) contextualization of sex dimorphism. We trained random forest models and predicted AD onset on a cohort of 749 individuals with AD and 250,545 controls with a mean area under the receiver operating characteristic of 0.72 (7 years prior) to 0.81 (1 day prior). We further harnessed matched cohort models to identify conditions with predictive power before AD onset. Knowledge networks highlight shared genes between multiple top predictors and AD (for example, APOE, ACTB, IL6 and INS). Genetic colocalization analysis supports AD association with hyperlipidemia at the APOE locus, as well as a stronger female AD association with osteoporosis at a locus near MS4A6A. We therefore show how clinical data can be utilized for early AD prediction and identification of personalized biological hypotheses.

Optimal trade-off control in machine learning-based library design, with application to adeno-associated virus (AAV) for gene therapy.

Adeno-associated viruses (AAVs) hold tremendous promise as delivery vectors for gene therapies. AAVs have been successfully engineered-for instance, for more efficient and/or cell-specific delivery to numerous tissues-by creating large, diverse starting libraries and selecting for desired properties. However, these starting libraries often contain a high proportion of variants unable to assemble or package their genomes, a prerequisite for any gene delivery goal. Here, we present and showcase a machine learning (ML) method for designing AAV peptide insertion libraries that achieve fivefold higher packaging fitness than the standard NNK library with negligible reduction in diversity. To demonstrate our ML-designed library's utility for downstream engineering goals, we show that it yields approximately 10-fold more successful variants than the NNK library after selection for infection of human brain tissue, leading to a promising glial-specific variant. Moreover, our design approach can be applied to other types of libraries for AAV and beyond.

The Obesity Epidemic Is Not the Victims' Fault.

The Centers for Disease Control and Prevention recently issued two statements that 1) maintain that obesity causes diabetes and other expressions of the metabolic syndrome and 2) that imply obesity is the victim's fault. Both statements are incorrect and potentially harmful.

Demographic, clinical, biomarker, and neuropathological correlates of posterior cortical atrophy: an international cohort study and individual participant data meta-analysis.

BACKGROUND: Posterior cortical atrophy is a rare syndrome characterised by early, prominent, and progressive impairment in visuoperceptual and visuospatial processing. The disorder has been associated with underlying neuropathological features of Alzheimer's disease, but large-scale biomarker and neuropathological studies are scarce. We aimed to describe demographic, clinical, biomarker, and neuropathological correlates of posterior cortical atrophy in a large international cohort. METHODS: We searched PubMed between database inception and Aug 1, 2021, for all published research studies on posterior cortical atrophy and related terms. We identified research centres from these studies and requested deidentified, individual participant data (published and unpublished) that had been obtained at the first diagnostic visit from the corresponding authors of the studies or heads of the research centres. Inclusion criteria were a clinical diagnosis of posterior cortical atrophy as defined by the local centre and availability of Alzheimer's disease biomarkers (PET or CSF), or a diagnosis made at autopsy. Not all individuals with posterior cortical atrophy fulfilled consensus criteria, being diagnosed using centre-specific procedures or before development of consensus criteria. We obtained demographic, clinical, biofluid, neuroimaging, and neuropathological data. Mean values for continuous variables were combined using the inverse variance meta-analysis method; only research centres with more than one participant for a variable were included. Pooled proportions were calculated for binary variables using a restricted maximum likelihood model. Heterogeneity was quantified using I2. FINDINGS: We identified 55 research centres from 1353 papers, with 29 centres responding to our request. An additional seven centres were recruited by advertising via the Alzheimer's Association. We obtained data for 1092 individuals who were evaluated at 36 research centres in 16 countries, the other sites having not responded to our initial invitation to participate to the study. Mean age at symptom onset was 59·4 years (95% CI 58·9-59·8; I2=77%), 60% (56-64; I2=35%) were women, and 80% (72-89; I2=98%) presented with posterior cortical atrophy pure syndrome. Amyloid ß in CSF (536 participants from 28 centres) was positive in 81% (95% CI 75-87; I2=78%), whereas phosphorylated tau in CSF (503 participants from 29 centres) was positive in 65% (56-75; I2=87%). Amyloid-PET (299 participants from 24 centres) was positive in 94% (95% CI 90-97; I2=15%), whereas tau-PET (170 participants from 13 centres) was positive in 97% (93-100; I2=12%). At autopsy (145 participants from 13 centres), the most frequent neuropathological diagnosis was Alzheimer's disease (94%, 95% CI 90-97; I2=0%), with common co-pathologies of cerebral amyloid angiopathy (71%, 54-88; I2=89%), Lewy body disease (44%, 25-62; I2=77%), and cerebrovascular injury (42%, 24-60; I2=88%). INTERPRETATION: These data indicate that posterior cortical atrophy typically presents as a pure, young-onset dementia syndrome that is highly specific for underlying Alzheimer's disease pathology. Further work is needed to understand what drives cognitive vulnerability and progression rates by investigating the contribution of sex, genetics, premorbid cognitive strengths and weaknesses, and brain network integrity. FUNDING: None.

Coexistence of many species under a random competition-colonization trade-off.

The competition-colonization (CC) trade-off is a well-studied coexistence mechanism for metacommunities. In this setting, it is believed that the coexistence of all species requires their traits to satisfy restrictive conditions limiting their similarity. To investigate whether diverse metacommunities can assemble in a CC trade-off model, we study their assembly from a probabilistic perspective. From a pool of species with parameters (corresponding to traits) sampled at random, we compute the probability that any number of species coexist and characterize the set of species that emerges through assembly. Remarkably, almost exactly half of the species in a large pool typically coexist, with no saturation as the size of the pool grows, and with little dependence on the underlying distribution of traits. Through a mix of analytical results and simulations, we show that this unlimited niche packing emerges as assembly actively moves communities toward overdispersed configurations in niche space. Our findings also apply to a realistic assembly scenario where species invade one at a time from a fixed regional pool. When diversity arises de novo in the metacommunity, richness still grows without bound, but more slowly. Together, our results suggest that the CC trade-off can support the robust emergence of diverse communities, even when coexistence of the full species pool is exceedingly unlikely.

Neural basis of speech and grammar symptoms in non-fluent variant primary progressive aphasia spectrum.

The non-fluent/agrammatic variant of primary progressive aphasia (nfvPPA) is a neurodegenerative syndrome primarily defined by the presence of apraxia of speech (AoS) and/or expressive agrammatism. In addition, many patients exhibit dysarthria and/or receptive agrammatism. This leads to substantial phenotypic variation within the speech-language domain across individuals and time, in terms of both the specific combination of symptoms as well as their severity. How to resolve such phenotypic heterogeneity in nfvPPA is a matter of debate. 'Splitting' views propose separate clinical entities: 'primary progressive apraxia of speech' when AoS occurs in the absence of expressive agrammatism, 'progressive agrammatic aphasia' (PAA) in the opposite case, and 'AOS + PAA' when mixed motor speech and language symptoms are clearly present. While therapeutic interventions typically vary depending on the predominant symptom (e.g. AoS versus expressive agrammatism), the existence of behavioural, anatomical and pathological overlap across these phenotypes argues against drawing such clear-cut boundaries. In the current study, we contribute to this debate by mapping behaviour to brain in a large, prospective cohort of well characterized patients with nfvPPA (n = 104). We sought to advance scientific understanding of nfvPPA and the neural basis of speech-language by uncovering where in the brain the degree of MRI-based atrophy is associated with inter-patient variability in the presence and severity of AoS, dysarthria, expressive agrammatism or receptive agrammatism. Our cross-sectional examination of brain-behaviour relationships revealed three main observations. First, we found that the neural correlates of AoS and expressive agrammatism in nfvPPA lie side by side in the left posterior inferior frontal lobe, explaining their behavioural dissociation/association in previous reports. Second, we identified a 'left-right' and 'ventral-dorsal' neuroanatomical distinction between AoS versus dysarthria, highlighting (i) that dysarthria, but not AoS, is significantly influenced by tissue loss in right-hemisphere motor-speech regions; and (ii) that, within the left hemisphere, dysarthria and AoS map onto dorsally versus ventrally located motor-speech regions, respectively. Third, we confirmed that, within the large-scale grammar network, left frontal tissue loss is preferentially involved in expressive agrammatism and left temporal tissue loss in receptive agrammatism. Our findings thus contribute to define the function and location of the epicentres within the large-scale neural networks vulnerable to neurodegenerative changes in nfvPPA. We propose that nfvPPA be redefined as an umbrella term subsuming a spectrum of speech and/or language phenotypes that are closely linked by the underlying neuroanatomy and neuropathology.

Assessing processing speed and its neural correlates in the three variants of primary progressive aphasia with a non-verbal tablet-based task.

Prior research has revealed distinctive patterns of impaired language abilities across the three variants of Primary Progressive Aphasia (PPA): nonfluent/agrammatic (nfvPPA), logopenic (lvPPA) and semantic (svPPA). However, little is known about whether, and to what extent, non-verbal cognitive abilities, such as processing speed, are impacted in PPA patients. This is because neuropsychological tests typically contain linguistic stimuli and require spoken output, being therefore sensitive to verbal deficits in aphasic patients. The aim of this study is to investigate potential differences in processing speed between PPA patients and healthy controls, and among the three PPA variants, using a brief non-verbal tablet-based task (Match) modeled after the WAIS-III digit symbol coding test, and to determine its neural correlates. Here, we compared performance on the Match task between PPA patients (n = 61) and healthy controls (n = 59) and across the three PPA variants. We correlated performance on Match with voxelwise gray and white matter volumes. We found that lvPPA and nfvPPA patients performed significantly worse on Match than healthy controls and svPPA patients. Worse performance on Match across PPA patients was associated with reduced gray matter volume in specific parts of the left middle frontal gyrus, superior parietal lobule, and precuneus, and reduced white matter volume in the left parietal lobe. To conclude, our behavioral findings reveal that processing speed is differentially impacted across the three PPA variants and provide support for the potential clinical utility of a tabled-based task (Match) to assess non-verbal cognition. In addition, our neuroimaging findings confirm the importance of a set of fronto-parietal regions that previous research has associated with processing speed and executive control. Finally, our behavioral and neuroimaging findings combined indicate that differences in processing speed are largely explained by the unequal distribution of atrophy in these fronto-parietal regions across the three PPA variants.

Clinical dimensions along the non-fluent variant primary progressive aphasia spectrum.

It is debated whether primary progressive apraxia of speech (PPAOS) and progressive agrammatic aphasia (PAA) belong to the same clinical spectrum, traditionally termed non-fluent/agrammatic variant primary progressive aphasia (nfvPPA), or exist as two completely distinct syndromic entities with specific pathologic/prognostic correlates. We analysed speech, language and disease severity features in a comprehensive cohort of patients with progressive motor speech impairment and/or agrammatism to ascertain evidence of naturally occurring, clinically meaningful non-overlapping syndromic entities (e.g. PPAOS and PAA) in our data. We also assessed if data-driven latent clinical dimensions with aetiologic/prognostic value could be identified. We included 98 participants, 43 of whom had an autopsy-confirmed neuropathological diagnosis. Speech pathologists assessed motor speech features indicative of dysarthria and apraxia of speech (AOS). Quantitative expressive/receptive agrammatism measures were obtained and compared with healthy controls. Baseline and longitudinal disease severity was evaluated using the Clinical Dementia Rating Sum of Boxes (CDR-SB). We investigated the data's clustering tendency and cluster stability to form robust symptom clusters and employed principal component analysis to extract data-driven latent clinical dimensions (LCD). The longitudinal CDR-SB change was estimated using linear mixed-effects models. Of the participants included in this study, 93 conformed to previously reported clinical profiles (75 with AOS and agrammatism, 12 PPAOS and six PAA). The remaining five participants were characterized by non-fluent speech, executive dysfunction and dysarthria without apraxia of speech or frank agrammatism. No baseline clinical features differentiated between frontotemporal lobar degeneration neuropathological subgroups. The Hopkins statistic demonstrated a low cluster tendency in the entire sample (0.45 with values near 0.5 indicating random data). Cluster stability analyses showed that only two robust subgroups (differing in agrammatism, executive dysfunction and overall disease severity) could be identified. Three data-driven components accounted for 71% of the variance [(i) severity-agrammatism; (ii) prominent AOS; and (iii) prominent dysarthria]. None of these data-driven LCDs allowed an accurate prediction of neuropathology. The severity-agrammatism component was an independent predictor of a faster CDR-SB increase in all the participants. Higher dysarthria severity, reduced words per minute and expressive and receptive agrammatism severity at baseline independently predicted accelerated disease progression. Our findings indicate that PPAOS and PAA, rather than exist as completely distinct syndromic entities, constitute a clinical continuum. In our cohort, splitting the nfvPPA spectrum into separate clinical phenotypes did not improve clinical-pathological correlations, stressing the need for new biological markers and consensus regarding updated terminology and clinical classification.

Longitudinal clinical, cognitive and biomarker profiles in dominantly inherited versus sporadic early-onset Alzheimer's disease.

Approximately 5% of Alzheimer's disease cases have an early age at onset (<65 years), with 5-10% of these cases attributed to dominantly inherited mutations and the remainder considered as sporadic. The extent to which dominantly inherited and sporadic early-onset Alzheimer's disease overlap is unknown. In this study, we explored the clinical, cognitive and biomarker profiles of early-onset Alzheimer's disease, focusing on commonalities and distinctions between dominantly inherited and sporadic cases. Our analysis included 117 participants with dominantly inherited Alzheimer's disease enrolled in the Dominantly Inherited Alzheimer Network and 118 individuals with sporadic early-onset Alzheimer's disease enrolled at the University of California San Francisco Alzheimer's Disease Research Center. Baseline differences in clinical and biomarker profiles between both groups were compared using t-tests. Differences in the rates of decline were compared using linear mixed-effects models. Individuals with dominantly inherited Alzheimer's disease exhibited an earlier age-at-symptom onset compared with the sporadic group [43.4 (SD ± 8.5) years versus 54.8 (SD ± 5.0) years, respectively, P < 0.001]. Sporadic cases showed a higher frequency of atypical clinical presentations relative to dominantly inherited (56.8% versus 8.5%, respectively) and a higher frequency of APOE-ε4 (50.0% versus 28.2%, P = 0.001). Compared with sporadic early onset, motor manifestations were higher in the dominantly inherited cohort [32.5% versus 16.9% at baseline (P = 0.006) and 46.1% versus 25.4% at last visit (P = 0.001)]. At baseline, the sporadic early-onset group performed worse on category fluency (P < 0.001), Trail Making Test Part B (P < 0.001) and digit span (P < 0.001). Longitudinally, both groups demonstrated similar rates of cognitive and functional decline in the early stages. After 10 years from symptom onset, dominantly inherited participants experienced a greater decline as measured by Clinical Dementia Rating Sum of Boxes [3.63 versus 1.82 points (P = 0.035)]. CSF amyloid beta-42 levels were comparable [244 (SD ± 39.3) pg/ml dominantly inherited versus 296 (SD ± 24.8) pg/ml sporadic early onset, P = 0.06]. CSF phosphorylated tau at threonine 181 levels were higher in the dominantly inherited Alzheimer's disease cohort (87.3 versus 59.7 pg/ml, P = 0.005), but no significant differences were found for t-tau levels (P = 0.35). In summary, sporadic and inherited Alzheimer's disease differed in baseline profiles; sporadic early onset is best distinguished from dominantly inherited by later age at onset, high frequency of atypical clinical presentations and worse executive performance at baseline. Despite these differences, shared pathways in longitudinal clinical decline and CSF biomarkers suggest potential common therapeutic targets for both populations, offering valuable insights for future research and clinical trial design.

Time-Resolved 3D cardiopulmonary MRI reconstruction using spatial transformer network.

The accurate visualization and assessment of the complex cardiac and pulmonary structures in 3D is critical for the diagnosis and treatment of cardiovascular and respiratory disorders. Conventional 3D cardiac magnetic resonance imaging (MRI) techniques suffer from long acquisition times, motion artifacts, and limited spatiotemporal resolution. This study proposes a novel time-resolved 3D cardiopulmonary MRI reconstruction method based on spatial transformer networks (STNs) to reconstruct the 3D cardiopulmonary MRI acquired using 3D center-out radial ultra-short echo time (UTE) sequences. The proposed reconstruction method employed an STN-based deep learning framework, which used a combination of data-processing, grid generator, and sampler. The reconstructed 3D images were compared against the start-of-the-art time-resolved reconstruction method. The results showed that the proposed time-resolved 3D cardiopulmonary MRI reconstruction using STNs offers a robust and efficient approach to obtain high-quality images. This method effectively overcomes the limitations of conventional 3D cardiac MRI techniques and has the potential to improve the diagnosis and treatment planning of cardiopulmonary disorders.

Combined Multiharmonic Frequency Analysis for Improved Dynamic Energy Measurements and Accuracy in Charge Detection Mass Spectrometry.

The ability to determine ion energies in electrostatic ion-trap-based charge detection mass spectrometry (CDMS) experiments is important for the accurate measurement of individual ion m/z, charge, and mass. Dynamic energy measurements throughout the time an ion is trapped take advantage of the relationship between ion energy and the harmonic amplitude ratio (HAR) composed from the fundamental and second harmonic amplitudes in the Fourier transform of the ion signal. This method eliminates the need for energy-filtering optics in CDMS and makes it possible to measure energy lost in collisions and changes in ion masses due to dissociation. However, the accuracy of the energy measurement depends on the signal-to-noise ratio (S/N) of the amplitudes used to determine the HAR. Here, a major improvement to this HAR-based dynamic energy measurement method is achieved using HARs composed of higher-order harmonics in addition to the fundamental and second harmonic to determine ion energies. This combined harmonic amplitude ratios for precision energy refinement (CHARPER) method is applied to the analysis of a 103 nm polystyrene nanoparticle ion (359.7 MDa, m/z = 308,300) and the energy resolution (3140) and effective mass resolution (730) achieved are the best yet demonstrated in electrostatic ion-trap-based CDMS. The CHARPER method applied to an ensemble of several thousand adeno-associated virus ion signals also results in higher mass resolution compared to the basic HAR method, making it possible to resolve additional features in the composite mass histogram.

[18F]PI-2620 Binding Patterns in Patients with Suspected Alzheimer Disease and Frontotemporal Lobar Degeneration.

Tau PET has enabled the visualization of paired helical filaments of 3 or 4 C-terminal repeat tau in Alzheimer disease (AD), but its ability to detect aggregated tau in frontotemporal lobar degeneration (FTLD) spectrum disorders is uncertain. We investigated 2-(2-([18F]fluoro)pyridin-4-yl)-9H-pyrrolo[2,3-b:4,5c']dipyridine ([18F]PI-2620), a newer tracer with ex vivo evidence for binding to FTLD tau, in a convenience sample of patients with suspected FTLD and AD using a static acquisition protocol and parametric SUV ratio (SUVr) images. Methods: We analyzed [18F]PI-2620 PET data from 65 patients with clinical diagnoses associated with AD or FTLD neuropathology; most (60/65) also had amyloid-ß (Aß) PET. Scans were acquired 30-60 min after injection; SUVr maps (reference, inferior cerebellar cortex) were created for the full acquisition and for 10-min truncated sliding windows (30-40, 35-45,…50-60 min). Age- and sex-adjusted z score maps were computed for each patient, relative to 23 Aß-negative cognitively healthy controls (HC). Mean SUVr in the globus pallidus, substantia nigra, subthalamic nuclei, dentate nuclei, white matter, and temporal gray matter was extracted for the full and truncated windows. Results: Patients with suspected AD neuropathology (Aß-positive patients with mild cognitive impairment or AD dementia) showed high-intensity temporoparietal cortex-predominant [18F]PI-2620 binding. At the group level, patients with clinical diagnoses associated with FTLD (progressive supranuclear palsy with Richardson syndrome [PSP Richardson syndrome], corticobasal syndrome, and nonfluent-variant primary progressive aphasia) exhibited higher globus pallidus SUVr than did HCs; pallidal retention was highest in the PSP Richardson syndrome group, in whom SUVr was correlated with symptom severity (ρ = 0.53, P = 0.05). At the individual level, only half of PSP Richardson syndrome, corticobasal syndrome, and nonfluent-variant primary progressive aphasia patients had a pallidal SUVr above that of HCs. Temporal SUVr discriminated AD patients from HCs with high accuracy (area under the receiver operating characteristic curve, 0.94 [95% CI, 0.83-1.00]) for all time windows, whereas discrimination between patients with PSP Richardson syndrome and HCs using pallidal SUVr was fair regardless of time window (area under the receiver operating characteristic curve, 0.77 [95% CI, 0.61-0.92] at 30-40 min vs. 0.81 [95% CI, 0.66-0.96] at 50-60 min; P = 0.67). Conclusion: [18F]PI-2620 SUVr shows an intense and consistent signal in AD but lower-intensity, heterogeneous, and rapidly decreasing binding in patients with suspected FTLD. Further work is needed to delineate the substrate of [18F]PI-2620 binding and the usefulness of [18F]PI2620 SUVr quantification outside the AD continuum.

Early-onset Alzheimer's disease explained by polygenic risk of late-onset disease?

Early-onset Alzheimer's disease (AD) is highly heritable, yet only 10% of cases are associated with known pathogenic mutations. For early-onset AD patients without an identified autosomal dominant cause, we hypothesized that their early-onset disease reflects further enrichment of the common risk-conferring single nucleotide polymorphisms associated with late-onset AD. We applied a previously validated polygenic hazard score for late-onset AD to 193 consecutive patients diagnosed at our tertiary dementia referral center with symptomatic early-onset AD. For comparison, we included 179 participants with late-onset AD and 70 healthy controls. Polygenic hazard scores were similar in early- versus late-onset AD. The polygenic hazard score was not associated with age-of-onset or disease biomarkers within early-onset AD. Early-onset AD does not represent an extreme enrichment of the common single nucleotide polymorphisms associated with late-onset AD. Further exploration of novel genetic risk factors of this highly heritable disease is warranted.Highlights: There is a unique genetic architecture of early- versus late-onset Alzheimer's disease (AD).Late-onset AD polygenic risk is not an explanation for early-onset AD.Polygenic risk of late-onset AD does not predict early-onset AD biology.Unique genetic architecture of early- versus late-onset AD parallels AD heterogeneity.

Diminished baseline autonomic outflow in semantic dementia relates to left-lateralized insula atrophy.

In semantic dementia (SD), asymmetric degeneration of the anterior temporal lobes is associated with loss of semantic knowledge and alterations in socioemotional behavior. There are two clinical variants of SD: semantic variant primary progressive aphasia (svPPA), which is characterized by predominant atrophy in the anterior temporal lobe and insula in the left hemisphere, and semantic behavioral variant frontotemporal dementia (sbvFTD), which is characterized by predominant atrophy in those structures in the right hemisphere. Previous studies of behavioral variant frontotemporal dementia, an associated clinical syndrome that targets the frontal lobes and anterior insula, have found impairments in baseline autonomic nervous system activity that correlate with left-lateralized frontotemporal atrophy patterns and disruptions in socioemotional functioning. Here, we evaluated whether there are similar impairments in resting autonomic nervous system activity in SD that also reflect left-lateralized atrophy and relate to diminished affiliative behavior. A total of 82 participants including 33 people with SD (20 svPPA and 13 sbvFTD) and 49 healthy older controls completed a laboratory-based assessment of respiratory sinus arrhythmia (RSA; a parasympathetic measure) and skin conductance level (SCL; a sympathetic measure) during a two-minute resting baseline period. Participants also underwent structural magnetic resonance imaging, and informants rated their current affiliative behavior on the Interpersonal Adjective Scale. Results indicated that baseline RSA and SCL were lower in SD than in healthy controls, with significant impairments present in both svPPA and sbvFTD. Voxel-based morphometry analyses revealed left-greater-than-right atrophy related to diminished parasympathetic and sympathetic outflow in SD. While left-lateralized atrophy in the mid-to-posterior insula correlated with lower RSA, left-lateralized atrophy in the ventral anterior insula correlated with lower SCL. In SD, lower baseline RSA, but not lower SCL, was associated with lower gregariousness/extraversion. Neither autonomic measure related to warmth/agreeableness, however. Through the assessment of baseline autonomic nervous system physiology, the present study contributes to expanding conceptualizations of the biological basis of socioemotional alterations in svPPA and sbvFTD.

Metabolic impact of polyphenol-rich aronia fruit juice mediated by inflammation status of gut microbiome donors in humanized mouse model.

Background: The Aronia melanocarpa fruit is emerging as a health food owing to its high polyphenolic content and associated antioxidant activity. Antioxidant-rich foods, such as Aronia fruit, may counter inflammatory stimuli and positively modulate the gut microbiome. However, a comprehensive study characterizing the impact of Aronia fruit supplementation has not been completed. Therefore, we completed analyses measuring the metabolic, microbial, and inflammatory effects of a diet supplemented with Aronia fruit juice. Method: Humanized mice were generated by colonizing gnotobiotic mice with microbiomes from human donors presenting disparate inflammation levels. Blood and fecal samples were collected throughout the course of an 8-week dietary intervention with either Aronia juice or a carbohydrate-matched beverage alone (2 weeks) or in combination with a high-fat diet to induce inflammation (6 weeks). Samples were analyzed using 16S rRNA gene sequencing (stool) and liquid chromatography-mass spectrometry (serum). Results: We demonstrated transfer of microbiome composition and diversity and metabolic characteristics from humans with low and high inflammation levels to second-generation humanized mice. Aronia supplementation provided robust protection against high-fat diet induced metabolic and microbiome changes that were dependent in part on microbiome donor. Aronia induced increases in bacteria of the Eggerthellaceae genus (7-fold) which aligns with its known ability to metabolize (poly)phenols and in phosphatidylcholine metabolites which are consistent with improved gut barrier function. The gut microbiome from a low inflammation phenotype donor provided protection against high-fat diet induced loss of microbiome ß-diversity and global metabolomic shifts compared to that from the high inflammation donor. Conclusion: These metabolic changes elucidate pathway-specific drivers of reduced inflammation stemming from both Aronia and the gut microbiota.

Support for management actions to protect night sky quality: Insights from visitors to state and national park units in the U.S.

Light pollution is a global phenomenon where anthropogenic light sources continue to grow unabated, affecting both social and ecological systems. This is leaving parks and protected areas as some of the last vestiges of naturally dark environments for protecting views of the night sky. Yet, even parks and protected areas have outdoor lighting. Alternative lighting practices are needed to reduce or prevent light pollution from within parks. However, making parks darker may not be desirable for some visitors if they believe it will reduce navigability, safety, or restrict how they recreate (e.g., requiring the use of red-light flashlights after dark and before dawn). How visitors will respond to alternative lighting practices that park managers can implement is still unknown. We used an on-site intercept survey at nine state and national park units in Utah, U.S., to investigate nighttime visitors' support or opposition to management actions to protect night sky quality and their interest in learning about topics related to night skies. Further, this study also segmented visitors into two groups: those 'dependent' on the dark sky as a resource and those whose activities did not depend on a dark sky. Defining what a 'dark sky dependent' visitor is, which has yet to be done in the literature, is a fundamental step to furthering night sky research and management efforts. Across nine parks and protected areas, 62% of nighttime visitors participated in dark sky dependent activities. Findings indicate broad support for management actions designed to improve night sky quality, with between 74% and 89% of all visitors supporting seven different management actions. There was stronger support from dark sky dependent visitors for some elements of alternative lighting practices, but there was still strong support for those who do not participate in dark sky dependent outdoor recreation. Additionally, between 57% and 75% of visitors were interested in learning more about topics related to night skies. This research indicates most visitors would welcome actions to preserve the quality of the rapidly dwindling naturally dark experiences offered by parks and protected areas.