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Background and objectives: In June 2022, the mRNA COVID-19 vaccination was recommended for young children. We examined clinical characteristics and factors associated with vaccination status among vaccine-eligible young children hospitalized for acute COVID-19. Methods: We enrolled inpatients 8 months to <5 years of age with acute community-acquired COVID-19 across 28 US pediatric hospitals from September 20, 2022 to May 31, 2023. We assessed demographic and clinical factors, including the highest level of respiratory support, and vaccination status defined as unvaccinated, incomplete, or complete primary series [at least 2 (Moderna) or 3 (Pfizer-BioNTech) mRNA vaccine doses ≥14 days before hospitalization]. Results: Among 597 children, 174 (29.1%) patients were admitted to the intensive care unit and 75 (12.6%) had a life-threatening illness, including 51 (8.5%) requiring invasive mechanical ventilation. Children with underlying respiratory and neurologic/neuromuscular conditions more frequently received higher respiratory support. Only 4.5% of children hospitalized for COVID-19 (n = 27) had completed their primary COVID-19 vaccination series and 7.0% (n = 42) of children initiated but did not complete their primary series. Among 528 unvaccinated children, nearly half (n = 251) were previously healthy, 3 of them required extracorporeal membrane oxygenation for acute COVID-19 and 1 died. Conclusions: Most young children hospitalized for acute COVID-19, including most children admitted to the intensive care unit and with life-threatening illness, had not initiated COVID-19 vaccination despite being eligible. Nearly half of these children had no underlying conditions. Of the small percentage of children who initiated a COVID-19 primary series, most had not completed it before hospitalization.

AbstractBackgroundThe U.S. Study to Protect Brain Health Through Lifestyle Intervention to Reduce Risk (U.S. POINTER) is a two‐year randomized controlled trial designed to evaluate the effects of lifestyle interventions on older adults aged 60‐79 years. The POINTER Imaging ancillary study employs arterial spin labeling (ASL) magnetic resonance imaging (MRI) with two inversion times, enabling the mapping of arterial transit time (ATT) and the investigation of its relationship with dementia risk factors. This study examines the effect of the Framingham Risk Score (FRS) on ATT, which provides hemodynamic information on the cerebral vasculature.MethodA subset of POINTER Imaging participants (n = 134; F/M = 81/53; age = 68.1±5.1; FRS = 11.3±10.4) from two imaging sites underwent two ASL MRI scans during the baseline session. Two 3D Pulsed ASL scans were obtained with TI1 of 790ms and TI2 values of 2000ms or 2750ms, providing delay times (between labeling and imaging) of 1210ms or 1960ms. ATT, the time required for blood to travel from the neck to the tissue, was estimated in the range of 1210ms and 1960ms using a kinetic model (Figure 1). ATT maps were normalized to a standard space, and a voxel‐wise linear regression (ATT∼age+sex+log(FRS)) was performed on ATT maps in gray matter to assess the effect of FRS on ATT. To control false positives, clusters smaller than 1.4ml were removed based on a simulation with α = 0.05.ResultFigure 2 depicts three clusters found in the occipital, precuneus and frontal gray matter regions, all of which demonstrate a positive relationship between ATT and log(FRS). Participants with higher FRS exhibit prolonged mean ATT values in the clusters as shown in Figure 3. After stratifying into sex groups, both groups show the same direction.ConclusionAlthough the detectable range of ATT with two ASL scans was limited, the analysis suggests that an increased risk of cardiovascular disease, as quantified by the Framingham Risk Score, can serve as an indicator of potential cerebrovascular disease leading to prolonged ATT, which is associated with arterial stiffness, and vascular resistance. The clusters demonstrating the association between FRS and ATT are the brain regions that may have heightened vulnerability to vascular risk factors, according to previous studies.

AbstractBackgroundWe examined neuropsychiatric symptoms (NPS) and psychotropic medication use at the midway point of data collection of the Longitudinal Early‐onset Alzheimer’s Disease Study (LEADS). We compared amyloid‐positive early‐onset Alzheimer’s disease (EOAD) participants and amyloid‐negative early‐onset non‐Alzheimer’s disease [EOnonAD]) participants across NPS domains and psychotropic medication categories.Method282 participants enrolled in LEADS were compared across diagnostic groups – amyloid‐positive EOAD (n = 212) and amyloid‐negative EOnonAD (n = 70). All participants were between ages 40 and 64 at baseline. NPS were measured with the Neuropsychiatric Inventory – Questionnaire (NPI‐Q) and Geriatric Depression Scale – Short Form (GDS‐SF). Medications were categorized into psychotropic category (e.g., antidepressants, antipsychotics, benzodiazepines). Groups were compared on NPI‐Q using binary logistic regression adjusting for sex and disease severity, on GDS‐SF using linear regression adjusting for sex and disease severity, and on psychotropic medication use using Chi square or Fisher’s exact test.ResultDemographic data are presented in Table 1. Average total NPI‐Q score was higher in EOnonAD than EOAD (p = .002, ŋp 2 = .03). Regardless of diagnostic group, informants endorsed Affective behaviors most frequently (76.2%). EOAD informants endorsed fewer Distress‐Tension behaviors (p = .02, OR = .498) and Impulse Control behaviors (p = .01, OR = .438). EOAD informants also endorsed apathy (p < .001, OR = .316), irritability (p = .02, OR = .498), and disinhibition (p = .04, OR = .476) at lower frequency than EOnonAD. GDS‐SF showed that the EOnonAD participants self‐reported more symptoms of depression than the EOAD group (p = .002, ŋp 2 = .03). (Table 2A). A minority of participants, regardless of diagnostic group, reported psychotropic medication use (35%). There was higher use in EOnonAD (38%) than EOAD (24%) (p = .03, Cohen W = .14) (Table 2B).ConclusionNPS burden and psychotropic medication use were higher in EOnonAD than EOAD participants, despite EOnonAD being less severely impaired. LEADS continues to collect data. Future studies will examine etiologies that may be larger drivers of NPS than AD neuropathology, mediators (e.g., race, ethnicity) in the association between NPS and diagnostic group classication, compare NPS in EOAD vs. LOAD, and examine longitudinal changes in neuropsychiatric profiles.

AbstractBackgroundDecreased image self‐similarity of repeated FDG‐PET scans has been suggested to indicate increased risk for disease progression. Here, we compared several self‐similarity measures based on conventionally used voxel‐ and ROI‐based approaches and those based on patterns of cortical metabolism.MethodFrom the MCSA/ADRC, we included 376 cognitively unimpaired (CU) individuals and those with a clinical diagnosis of MCI (n = 55) or AD (n = 20) who had two FDG‐PET images within 2‐3 years and clinical follow‐up available. We compared image self‐similarity for three approaches: voxel, ROI and “eigenbrain” (Fig.1). For the voxel approach, FDG‐PET images were normalized to template space, grey matter masked, and intensity normalized to the pons to create standard uptake value ratios (SUVR) for each voxel. From these images, mean SUVR were extracted for 122 ROIs (MCALT‐ADIR122) for the ROI approach. For the “eigenbrain” approach, the SUVR images were transformed to a latent space embedding composed of 253 components (“eigenbrains”) and reflecting metabolism patterns, that was generated in an independent sample of 7008 FDG‐PET images. For each approach, image self‐similarity between baseline and follow‐up values (voxel, ROI or eigenbrain) was determined using three metrics (Pearson correlation, cosine, Euclidean distance). We computed AUC to determine which approach and metric would best discriminate between groups. We used Cox regression analyses to assess whether the metrics could predict disease progression.ResultComparing each distance metric across approaches, AUC was highest in differentiating clinical diagnoses using self‐similarity measures based on eigenbrains, then voxels, then ROIs (e.g., Euclidean distance CU vs AD AUC eigenbrain: 0.86, voxel: 0.75, ROI: 0.71; Fig.1). Across all approaches, Euclidean distance always discriminated best between classes. Eigenbrain‐based Euclidean distance was associated with risk for progression to MCI in CU (HR [95%CI]: 1.25 [1.05, 1.49]), and eigenbrain‐based cosine and Euclidean distances were associated with risk for progression to AD in MCI (1.61 [1.1, 2.34] and 1.53 [1.01, 2.31], respectively), while we found no associations for voxel‐ or ROI‐based distance measures.ConclusionEigenbrain‐based measures outperformed other, more conventional approaches in discriminating clinical diagnoses, and were associated with risk for disease progression. Our results suggest that decreased self‐similarity in metabolism patterns informs on disease stage and prognosis.

AbstractBackgroundHearing loss is associated with greater cognitive decline and incident dementia. Whether hearing intervention could reduce cognitive decline in older adults with hearing loss is unknown.MethodThe ACHIEVE study is a randomized trial (NCT03243422) of 70‐84 year‐old adults with untreated hearing loss and free from substantial cognitive impairment that took place at four U.S. sites. Participants were recruited from two study populations: 1) a group of adults participating in a longstanding observational study of cardiovascular health (Atherosclerosis Risk in Communities [ARIC] study), and 2) a group of healthy de novo community volunteers. Participants were randomised (1:1) to hearing intervention (HI; audiological counseling and provision of hearing aids) or a successful aging health education control intervention (SA; sessions with a health educator on chronic disease prevention). The primary intention‐to‐treat endpoint was 3‐year change in a global cognition standardized factor score from a comprehensive neurocognitive battery.Result977 participants (238 ARIC, 739 de novo) underwent randomisation; 490 were assigned to HI and 487 to SA control. Participants from ARIC were older, had more risk factors for cognitive decline, and had lower baseline cognitive scores than the de novo group. In the primary analysis combining the ARIC and de novo groups, 3‐year cognitive change (in S.D. units) was not significantly different between HI and SA control (HI: ‐0·200 [95% CI: ‐0·256,‐0·144]; SA: ‐0·202 [‐0·258,‐0·145]; Difference 0·002 [‐0·077,0·081],p = 0·96). However, prespecified analyses demonstrated significant differences in the effect of HI on cognitive change between the ARIC and de novo group (p interaction = 0·010). In the ARIC group, HI was associated with a 48% reduction in 3‐year cognitive change compared to SA control (HI: ‐0·211 [‐0·349,‐0·073]; SA: ‐0·402 [‐0·536, ‐0·267]; Difference 0·191 [0·022,0·360,p = 0·027]). In the de novo group, cognitive change was not significantly different between HI and SA control (HI: ‐0·213 [‐0·277,‐0·148]; SA: ‐0·151 [‐0·215,‐0·087]; Difference ‐0·061 [‐0·151,0·028,p = 0·18]). The rate of cognitive change among control participants was observed to be 2.7‐fold faster in the ARIC versus de novo group.ConclusionHearing intervention may have a significant effect on reducing cognitive change over 3 years in populations of older adults at increased risk for cognitive decline.

AbstractBackgroundApproximately 10‐25% of amnestic early‐onset dementia are negative for amyloid (Early‐onset non‐Alzheimer’s Disease, EOnonAD). EOnonAD is a heterogenous group with various etiologies. In order to better understand the traits of EOnonAD, we clustered amnestic EOnonAD individuals according to cognitive patterns.MethodCognitive data of 62 EOnonAD individuals from the Longitudinal Early‐onset AD study (LEADS) were Z‐normalized to 91 intact individuals after adjusting for age, sex, and education. This led to domain‐averaged composite Z‐scores for memory, language, speed/attention, visuospatial, and executive function. Cluster analysis using the Ward method on the residuals for the five composites was performed. We used Scree plot using the pseudo T‐squared to determine the optimal number of clusters. We compared demographics, imaging biomarkers (amyloid and tau PET SUVR, hippocampal volume), plasma (pTau231, Aß40, Aß42, glial fibrillary acidic protein, and neurofilament light chain (NfL)), and cerebrospinal fluid (CSF) biomarkers (Aß42/40, NfL, Neurogranin, Visinin‐like protein, YKL‐40, and Synaptosome Associated Protein 25) across the clusters after adjusting for age, sex, and education. We compared gray matter density of each EOnonAD cluster to controls (n = 84) using voxel‐wise multiple linear regressions in SPM12, adjusting for age, sex, education, and intracranial volume.ResultScree plot using the pseudo‐T‐squared suggested three clusters. Cluster 1 (38/62, 61.3%) was characterized by having the mildest cognitive impairment in all domains. Cluster 2 (19/62, 30.6%) was characterized by more severe memory and language impairment. Cluster 3 (5/62, 8.1%) was characterized by more severe impairment in speed/attention, visuospatial, and executive function (Table 1). Cluster 3 showed significantly higher NfL for both plasma (Cluster 1 vs 3, p<0.001; Cluster 2 vs 3, p < 0.004) and CSF (Cluster 1 vs 3, p = 0.001). Other biomarkers did not show significant difference (Table 1). Only Cluster 3 showed significant atrophy in the bilateral fronto‐parietal areas at p<0.01, uncorrected (Figure 1).ConclusionWe identified heterogeneity in cognitive patterns among amnestic EOnonAD individuals. Cluster 1 had mildest cognitive impairment; Cluster 2 had predominant impairment in memory and language; and Cluster 3 had predominant impairment in speed/attention, visuospatial, and executive function, with significant fronto‐parietal atrophy and high NfL indicating active neurodegeneration.

AbstractBackgroundThe mapping between risk factors and biomarkers with cognitive performance and decline in the older adults is complex and varies significantly across different age and sex groups. Our goals were to (i) develop a machine learning (ML) model to predict baseline cognition and cognitive decline over a 5‐year follow‐up using only baseline assessments, and (ii) identify important modifiable and non‐modifiable predictors of cognitive decline across different age strata.MethodWe utilized longitudinal data from 2219 participants (mean age (SD): 70 (12) years, 49% male) from Mayo Clinic Study of Aging who had 43 features belonging to cardiovascular health, plasma, MRI (diffusion and structural), and lifestyle measures. Cognition was measured using a global cognition z‐score. We built ML models to predict baseline cognition and rate of cognitive decline over 5‐years (linear regression slopes over all individual followup measurements for each participant) using baseline assessments as inputs. We then built a model for each category separately and as a pooled model with all measures included, incorporating education and sex as demographic features in all models (Fig. 1). We applied a multi‐layer stack ensemble ML technique that utilized repeated k‐fold bagging to maximize accuracy. Importance of the top predictors were assessed in three age strata (55‐65 years (31%), 65‐80 years (46%), 80‐95 years (23%)).ResultThe pooled model explained the largest variance for prediction of baseline cognition and cognitive decline (Fig. 2). In individuals ≤80 years, lower NfL was most predictive of higher baseline cognition. A combination of imaging and plasma features predicted the 5‐year cognitive decline. Additionally, in individuals ≤65 years, diabetes and hypertension were important predictors of faster cognitive decline and in those >80, lower BMI and midlife physical/cognitive activity was an important predictor of faster cognitive decline (Fig. 3).ConclusionThe plasma model was best for baseline cognition prediction and imaging model was best for cognitive decline prediction. There was significant heterogeneity in important features across the age strata. In individuals >65 years, plasma GFAP was the most important predictor of cognitive decline. Our model allowed identification of key risk factors that predicted cognitive decline.

AbstractBackgroundThe implications of a positive tau (T) Positron Emission Tomography (PET) with a negative beta amyloid (A) PET are not well understood. We investigated cognitive performance in participants who were positive on tau PET but negative on beta amyloid PET.MethodWe evaluated 98 participants from the Mayo Clinic Study of Aging (MCSA) and the Mayo Clinic Alzheimer’s Disease Research Center (ADRC) who were positive for Flortaucipir (FTP) and negative for PiB PET. Participants were classified as A‐ and T+ based on Standardized Uptake Value Ratios (SUVr) of FTP and PiB PET scans, and were matched 2:1 to amyloid negative and tau negative cognitively unimpaired controls from the MCSA. Cognitive test scores were compared between the combined ADRC/MCSA group and MCSA only group. We also examined clinical diagnoses in our cohort. The most common were Alzheimer’s Disease, mild cognitive impairment (MCI), Dementia with Lewy Bodies (DLB) and Frontotemporal dementia (FTD) amongst others.ResultThere were no significant differences in demographic features or PiB SUVr between groups. There were significant differences in APOE genotype between groups. In the combined ADRC/MCSA group, 25% of A‐T‐ and 13% of A‐T+ were APOE e4 carriers (p = 0.016), and in the MCSA only group, 27% of A‐T‐and 10% of A‐T+ were APOE e4 carriers (p = 0.010). In the combined ADRC/MCSA group, the A‐T+ group demonstrated lower performance than the A‐T‐ group on the Mini Mental Status Exam (MMSE) (p<0.001), Wechsler Memory Scale‐Revised Logical Memory I (p<0.001) and Logical Memory II (p<0.001), Auditory verbal Learning Test Delayed Recall (AVLT) (p = 0.004), Category fluency animals (p = 0.005), Category fluency vegetables (p = 0.021), Trailmaking test A (p<0.001), Trail making test B (p<0.001), Wechsler Adult Intelligence Scale‐IV (WAIS‐IV) (p = 0.002), and Unified Parkinson’s Disease Rating Scale part III (UPDRS III) (p = 0.003). In the MCSA only group, the A‐T+ group had lower scores on Logical Memory II (p = 0.022), AVLT delated recall (p = 0.04), and WAIS‐R block design (p = 0.035) compared to the A‐T‐group.ConclusionPositive tau PET in the absence of beta‐amyloid positivity is associated with lower cognitive performance.

AbstractBackgroundThe balance between production, clearance, and toxicity of Ab peptides is central to AD pathobiology. Though highly variable in terms of age of symptom onset (AAO), hundreds of pathogenic variants in Presenilin‐1 (PSEN1) cause autosomal dominant forms of AD (ADAD). PSEN1 forms the catalytic core of the γ‐secretase complex and thereby directly mediates the production of longer, aggregation‐prone Aβ peptides relative to shorter, non‐aggregating peptides. We hypothesized that the broad AAO and biomarker heterogeneity seen across ADAD would be predictable based on mutation‐specific differences in γ‐secretase function, as measured by a ratio of production of shorter vs. longer Aβ species.MethodsAβ‐37, 38, 40, 42, and 43 production was quantified from 162 unique PSEN1 variants expressed in HEK293 cells engineered to lack endogenous wild‐type PSEN1/2 (Figure 1). Prediction of AAO was carried out in 107 PSEN1 variants (characterized by Liu et al, 2022) and then replicated with a set 55 unique PSEN1 variants represented in the Dominantly Inherited Alzheimer Network (DIAN; n = 190 corresponding variant carriers with detailed cognitive and biomarker data; Figure 2).ResultsMutation‐level variations in Aβ production, including a novel composite representing γ‐secretase function (lower score = less g‐processivity), from the cellular model were highly predictive of actual AAO across the 162 mutants examined (Non‐DIAN variants [N = 107]: r = 0.71, p <2.2e‐16; DIAN variants [N = 55]: r = 0.61, p = 6.10e‐07). Our cell‐based γ‐secretase function composite was strongly associated with cerebral PiB‐PET β‐amyloid burden (B[SE] = ‐0.03[0.01], p = 4.06e‐07), MRI gray matter volume (B[SE] = 44.22[6.4], p = 5.15e‐01), cerebrospinal fluid Aβ42/40 (B[SE] = 6.3e‐04[1.5e‐04], p = 4.46e‐05) and phosphorylated tau‐217 (B[SE] = ‐0.01[0.002], p = 1.98e‐05), Clinical Dementia Rating®‐ Sum of Boxes (B[SE] = ‐0.07[0.02], p = 4.86e‐05), and Mini‐Mental State Exam (B[SE] = 0.11[.03], p = 2.17e‐04).ConclusionsBiochemical variations in γ‐secretase function across PSEN1 pathogenic variants broadly predicted the cross‐sectional clinical, cognitive, and biomarker course of ADAD, including AAO. These findings elucidate the critical link between γ‐secretase function, Aβ production, and severity of AD. The novel approach designed here also represents a tool to account for heterogeneity in ADAD clinical trials and to assess the pathogenicity of PSEN1 variants of unknown significance or with limited family history.

AbstractBackgroundIn EOAD, the localization of neurodegeneration is often more prominent in posterior lateral temporal, lateral and medial parietal, frontal, or occipital cortex relative to the medial and ventral temporal cortical localization of typical LOAD. Convergently, neuropathological investigations have identified a “hippocampal‐sparing” form of AD that is often present in younger patients. For these reasons, imaging biomarkers of neurodegeneration in EOAD, such as Magnetic Resonance Imaging (MRI) measures of atrophy, likely need to be different than those of neurodegeneration in typical LOAD. Although prior neuroimaging work has advanced our understanding of anatomical abnormalities in EOAD, these studies have included small samples, most composed of amnestic EOAD dementia and none focused on developing an MRI biomarker specific to EOAD. Such a disease‐signature MRI biomarker has been established and validated in LOAD dementia and has proven to be powerful in predicting progressive decline in people with MCI or who were cognitively unimpaired and in predicting molecular biomarker status. An a priori‐defined regional atrophy signature may also be helpful in constraining the analysis of MRI data in studies of putative disease‐modifying therapies.MethodLEADS is an observational study which includes annual MRI. In this analysis, we used FreeSurfer to define the sporadic EOAD atrophy signature in a small sample of MGH EOAD patients, and investigated its reproducibility in LEADS, as well as the magnitude of atrophy and its relationship to clinical measures.ResultThe EOAD signature is robustly reproducible across the two independent patient cohorts, with prominent atrophy in caudal lateral temporal cortex, inferior parietal lobule, and posterior cingulate and precuneus cortex, with relative sparing of medial temporal lobe structures. Comparing patients to controls, Cohen’s d effect sizes within various regions ranged from 0.99 to 1.98.ConclusionThis study demonstrates the specific cortical regions with most prominent atrophy in patients with sporadic EOAD; this signature of regional atrophy will be used to examine longitudinal change.