The focus on social determinants of health (SDOH) and their impact on health outcomes is evident in U.S. federal actions by Centers for Medicare & Medicaid Services and Office of National Coordinator for Health Information Technology. The disproportionate impact of COVID-19 on minorities and communities of color heightened awareness of health inequities and the need for more robust SDOH data collection. Four Clinical and Translational Science Award (CTSA) hubs comprising the Texas Regional CTSA Consortium (TRCC) undertook an inventory to understand what contextual-level SDOH datasets are offered centrally and which individual-level SDOH are collected in structured fields in each electronic health record (EHR) system potentially for all patients.

Hub teams identified American Community Survey (ACS) datasets available via their enterprise data warehouses for research. Each hub’s EHR analyst team identified structured fields available in their EHR for SDOH using a collection instrument based on a 2021 PCORnet survey and conducted an SDOH field completion rate analysis.

One hub offered ACS datasets centrally. All hubs collected eleven SDOH elements in structured EHR fields. Two collected Homeless and Veteran statuses. Completeness at four hubs was 80%–98%: Ethnicity, Race; < 10%: Education, Financial Strain, Food Insecurity, Housing Security/Stability, Interpersonal Violence, Social Isolation, Stress, Transportation.

Completeness levels for SDOH data in EHR at TRCC hubs varied and were low for most measures. Multiple system-level discussions may be necessary to increase standardized SDOH EHR-based data collection and harmonization to drive effective value-based care, health disparities research, translational interventions, and evidence-based policy.

Blood-based biomarkers represent a scalable and accessible approach for the detection and monitoring of Alzheimer’s disease (AD). Plasma phosphorylated tau (p-tau) and neurofilament light (NfL) are validated biomarkers for the detection of tau and neurodegenerative brain changes in AD, respectively. There is now emphasis to expand beyond these markers to detect and provide insight into the pathophysiological processes of AD. To this end, a reactive astrocytic marker, namely plasma glial fibrillary acidic protein (GFAP), has been of interest. Yet, little is known about the relationship between plasma GFAP and AD. Here, we examined the association between plasma GFAP, diagnostic status, and neuropsychological test performance. Diagnostic accuracy of plasma GFAP was compared with plasma measures of p-tau181 and NfL.

This sample included 567 participants from the Boston University (BU) Alzheimer’s Disease Research Center (ADRC) Longitudinal Clinical Core Registry, including individuals with normal cognition (n=234), mild cognitive impairment (MCI) (n=180), and AD dementia (n=153). The sample included all participants who had a blood draw. Participants completed a comprehensive neuropsychological battery (sample sizes across tests varied due to missingness). Diagnoses were adjudicated during multidisciplinary diagnostic consensus conferences. Plasma samples were analyzed using the Simoa platform. Binary logistic regression analyses tested the association between GFAP levels and diagnostic status (i.e., cognitively impaired due to AD versus unimpaired), controlling for age, sex, race, education, and APOE e4 status. Area under the curve (AUC) statistics from receiver operating characteristics (ROC) using predicted probabilities from binary logistic regression examined the ability of plasma GFAP to discriminate diagnostic groups compared with plasma p-tau181 and NfL. Linear regression models tested the association between plasma GFAP and neuropsychological test performance, accounting for the above covariates.

The mean (SD) age of the sample was 74.34 (7.54), 319 (56.3%) were female, 75 (13.2%) were Black, and 223 (39.3%) were APOE e4 carriers. Higher GFAP concentrations were associated with increased odds for having cognitive impairment (GFAP z-score transformed: OR=2.233, 95% CI [1.609, 3.099], p<0.001; non-z-transformed: OR=1.004, 95% CI [1.002, 1.006], p<0.001). ROC analyses, comprising of GFAP and the above covariates, showed plasma GFAP discriminated the cognitively impaired from unimpaired (AUC=0.75) and was similar, but slightly superior, to plasma p-tau181 (AUC=0.74) and plasma NfL (AUC=0.74). A joint panel of the plasma markers had greatest discrimination accuracy (AUC=0.76). Linear regression analyses showed that higher GFAP levels were associated with worse performance on neuropsychological tests assessing global cognition, attention, executive functioning, episodic memory, and language abilities (ps<0.001) as well as higher CDR Sum of Boxes (p<0.001).

Higher plasma GFAP levels differentiated participants with cognitive impairment from those with normal cognition and were associated with worse performance on all neuropsychological tests assessed. GFAP had similar accuracy in detecting those with cognitive impairment compared with p-tau181 and NfL, however, a panel of all three biomarkers was optimal. These results support the utility of plasma GFAP in AD detection and suggest the pathological processes it represents might play an integral role in the pathogenesis of AD.

It is unclear how agenesis of the corpus callosum (ACC), a congenital brain malformation defined by complete or partial absence of the corpus callosum, impacts language development. fMRI studies of middle childhood suggest that the corpus callosum plays a role in the interhemispheric language network (Bartha-Doering et al., 2020), and that reduced interhemispheric functional connectivity is correlated with worse language abilities in children with ACC (Bartha-Doering et al., 2021). Additionally, accumulating evidence suggests structural abnormalities of the corpus callosum play a role in neurodevelopmental disorders. While children who go on to receive an autism spectrum disorder (ASD) diagnosis may show early signs of altered word and gesture acquisition (Iverson et al., 2018), the same is not known about ACC. This study examined language development during the second year of life in children with ACC in comparison to neurotypical control participants, as well as other children at elevated risk of ASD.

The MacArthur-Bates Communicative Development Inventories (MCDI): Words and Gestures scales were administered to parents of 74 children with isolated ACC at 12, 18 and 24 months of age. Children whose first language was not English and children who were bilingual were excluded. Comparison groups consisted of individuals with a low familial likelihood of ASD (LL- n=140) and individuals with high familial likelihood of ASD who do and do not have a confirmed ASD diagnosis (HL+ n=68, HL- n=256).

Compared to LL controls, the ACC group produced fewer words at 18 and 24 months of age, and demonstrated fewer words understood at all three timepoints. Similarly, compared to the HL- group, the ACC group demonstrated fewer words produced and understood at 18 months of age, and fewer words produced at 24 months of age. The ACC and HL+ groups did not differ in words produced or words understood at any timepoint.

Overall, infants with ACC demonstrated delayed vocabulary expansion from 12 to 24 months of age. These findings illustrate the role of callosal connectivity in the development of language across the first 2 years of life, and highlight the need for support and interventions that target vocabulary production and comprehension.

White matter hyperintensity (WMH) burden is greater, has a frontal-temporal distribution, and is associated with proxies of exposure to repetitive head impacts (RHI) in former American football players. These findings suggest that in the context of RHI, WMH might have unique etiologies that extend beyond those of vascular risk factors and normal aging processes. The objective of this study was to evaluate the correlates of WMH in former elite American football players. We examined markers of amyloid, tau, neurodegeneration, inflammation, axonal injury, and vascular health and their relationships to WMH. A group of age-matched asymptomatic men without a history of RHI was included to determine the specificity of the relationships observed in the former football players.

240 male participants aged 45-74 (60 unexposed asymptomatic men, 60 male former college football players, 120 male former professional football players) underwent semi-structured clinical interviews, magnetic resonance imaging (structural T1, T2 FLAIR, and diffusion tensor imaging), and lumbar puncture to collect cerebrospinal fluid (CSF) biomarkers as part of the DIAGNOSE CTE Research Project. Total WMH lesion volumes (TLV) were estimated using the Lesion Prediction Algorithm from the Lesion Segmentation Toolbox. Structural equation modeling, using Full-Information Maximum Likelihood (FIML) to account for missing values, examined the associations between log-TLV and the following variables: total cortical thickness, whole-brain average fractional anisotropy (FA), CSF amyloid ß42, CSF p-tau181, CSF sTREM2 (a marker of microglial activation), CSF neurofilament light (NfL), and the modified Framingham stroke risk profile (rFSRP). Covariates included age, race, education, APOE z4 carrier status, and evaluation site. Bootstrapped 95% confidence intervals assessed statistical significance. Models were performed separately for football players (college and professional players pooled; n=180) and the unexposed men (n=60). Due to differences in sample size, estimates were compared and were considered different if the percent change in the estimates exceeded 10%.

In the former football players (mean age=57.2, 34% Black, 29% APOE e4 carrier), reduced cortical thickness (B=-0.25, 95% CI [0.45, -0.08]), lower average FA (B=-0.27, 95% CI [-0.41, -.12]), higher p-tau181 (B=0.17, 95% CI [0.02, 0.43]), and higher rFSRP score (B=0.27, 95% CI [0.08, 0.42]) were associated with greater log-TLV. Compared to the unexposed men, substantial differences in estimates were observed for rFSRP (Bcontrol=0.02, Bfootball=0.27, 994% difference), average FA (Bcontrol=-0.03, Bfootball=-0.27, 802% difference), and p-tau181 (Bcontrol=-0.31, Bfootball=0.17, -155% difference). In the former football players, rFSRP showed a stronger positive association and average FA showed a stronger negative association with WMH compared to unexposed men. The effect of WMH on cortical thickness was similar between the two groups (Bcontrol=-0.27, Bfootball=-0.25, 7% difference).

These results suggest that the risk factor and biological correlates of WMH differ between former American football players and asymptomatic individuals unexposed to RHI. In addition to vascular risk factors, white matter integrity on DTI showed a stronger relationship with WMH burden in the former football players. FLAIR WMH serves as a promising measure to further investigate the late multifactorial pathologies of RHI.

Blood-based biomarkers offer a more feasible alternative to Alzheimer’s disease (AD) detection, management, and study of disease mechanisms than current in vivo measures. Given their novelty, these plasma biomarkers must be assessed against postmortem neuropathological outcomes for validation. Research has shown utility in plasma markers of the proposed AT(N) framework, however recent studies have stressed the importance of expanding this framework to include other pathways. There is promising data supporting the usefulness of plasma glial fibrillary acidic protein (GFAP) in AD, but GFAP-to-autopsy studies are limited. Here, we tested the association between plasma GFAP and AD-related neuropathological outcomes in participants from the Boston University (BU) Alzheimer’s Disease Research Center (ADRC).

This sample included 45 participants from the BU ADRC who had a plasma sample within 5 years of death and donated their brain for neuropathological examination. Most recent plasma samples were analyzed using the Simoa platform. Neuropathological examinations followed the National Alzheimer’s Coordinating Center procedures and diagnostic criteria. The NIA-Reagan Institute criteria were used for the neuropathological diagnosis of AD. Measures of GFAP were log-transformed. Binary logistic regression analyses tested the association between GFAP and autopsy-confirmed AD status, as well as with semi-quantitative ratings of regional atrophy (none/mild versus moderate/severe) using binary logistic regression. Ordinal logistic regression analyses tested the association between plasma GFAP and Braak stage and CERAD neuritic plaque score. Area under the curve (AUC) statistics from receiver operating characteristics (ROC) using predicted probabilities from binary logistic regression examined the ability of plasma GFAP to discriminate autopsy-confirmed AD status. All analyses controlled for sex, age at death, years between last blood draw and death, and APOE e4 status.

Of the 45 brain donors, 29 (64.4%) had autopsy-confirmed AD. The mean (SD) age of the sample at the time of blood draw was 80.76 (8.58) and there were 2.80 (1.16) years between the last blood draw and death. The sample included 20 (44.4%) females, 41 (91.1%) were White, and 20 (44.4%) were APOE e4 carriers. Higher GFAP concentrations were associated with increased odds for having autopsy-confirmed AD (OR=14.12, 95% CI [2.00, 99.88], p=0.008). ROC analysis showed plasma GFAP accurately discriminated those with and without autopsy-confirmed AD on its own (AUC=0.75) and strengthened as the above covariates were added to the model (AUC=0.81). Increases in GFAP levels corresponded to increases in Braak stage (OR=2.39, 95% CI [0.71-4.07], p=0.005), but not CERAD ratings (OR=1.24, 95% CI [0.004, 2.49], p=0.051). Higher GFAP levels were associated with greater temporal lobe atrophy (OR=10.27, 95% CI [1.53,69.15], p=0.017), but this was not observed with any other regions.

The current results show that antemortem plasma GFAP is associated with non-specific AD neuropathological changes at autopsy. Plasma GFAP could be a useful and practical biomarker for assisting in the detection of AD-related changes, as well as for study of disease mechanisms.

Bilingualism has shown to have significant implications for neuropsychological assessment, namely, the Digit Span task. Moreover, bilingual individuals have been shown to exhibit both advantages and disadvantages on Digit Span; however, the relationship between bilingualism and performance on this subtest is poorly understood. This research aims to better understand how Hispanic Spanish-English bilinguals perform on this commonly administered working memory subtest.

Participants included 82 Hispanic Spanish-English bilinguals [Age: M=29.11 (SD=6.369); Education: M=15.68 (SD=2.255); 53.7% female]. The participants completed the Language and Social Background Questionnaire (LSBQ; composite factor scores) and the Wechsler Adult Intelligence Scale -Fourth Edition (WAIS-IV) Digit Span (raw scores) subtest via Zoom, an online video conferencing platform. A hierarchical multiple regression analysis was utilized to predict participants’ Digit Span performance based on their LSBQ composite factor scores. Hierarchical multiple regression analyses were conducted using SPSS Version 27.

LSBQ composite factor scores significantly predicted Digit Span Forward, F (3, 78) = 1.835, p < 0.43 (R2 = .030) and Longest Digit Span Forward, F (1, 78) = 4.02, p < 0.48 (R2 = .041) scores. LSBQ composite factor scores did not significantly predict Digit Span Backward, F (3, 78) = .344, p = .941, Digit Span Sequencing, F (3, 78) = .598, p = .731, Digit Span Total, F (3, 78) = .440, p = 0.296, Longest Digit Span Backward, F (3, 78) = .510, p = .666, or Longest Digit Span sequencing F (3, 78) = .200, p = .751 scores.

Results suggest that Hispanic Spanish-English bilinguals perform worse on Digit Span Forward and Longest Digit Span Forward as their bilingual experiences increase. However, bilingual experiences did not significantly predict Digit Span Backward, Digit Span Sequencing, Digit Span Total, Longest Digit Span Backward, or Longest Digit Span Sequencing scores. The contrasts in Digit Span performance may be attributed to the different ways in which each condition of the subtest is cognitively processed. Therefore, clinicians and researchers should use caution when interpreting test data for Digit Span with Hispanic Spanish-English bilinguals.

Differences in adaptive functioning present early in development for many children with monogenic (Down Syndrome, Fragile X) and neurodevelopmental disorders. At this time, it is unclear whether children with ACC present with early adaptive delays, or if difficulties emerge later as functional tasks become more complex. While potential delays in motor development are frequently reported, other domains such as communication, social and daily living skills are rarely described. We used a prospective, longitudinal design to examine adaptive behavior from 6-24 months in children with ACC and compared their trajectories to those with monogenic and neurodevelopmental conditions.

Our sample included children with primary ACC (n= 27-47 depending on time point) whose caregivers completed the Vineland Adaptive Behavior Scales-Interview 3rd Edition, via phone at 6, 12, 18 and 24 months. Comparison samples (using the Vineland-2) included children with Down Syndrome (DS; n = 15-56), Fragile X (FX; n = 15-20), children at high familial likelihood for autism (HL-; n=192-280), and low likelihood (LL; no family history of autism and no developmental/behavioral diagnosis; n = 111196). A subset of the HL children received an autism diagnosis (HL+; n = 48-74). The DS group did not have an 18-month Vineland.

A series of linear mixed model analyses (using maximum likelihood) for repeated measures was used to compare groups on three Vineland domains at 6, 12, 18 and 24 month timepoints). All fixed factors (diagnostic group, timepoint, and group X timepoint interaction) accounted for significant variance on all Vineland domains (p < .001). Post hoc comparisons with Bonferroni-correction examined ACC Vineland scores compared to the other diagnostic groups at each timepoint. At 6 months, parent-ratings indicated the ACC group had significantly weaker skills than the LL group in Communication and Motor domains. At 12, 18 and 24 months, ratings revealed weaker Communication, Daily Living and Motor skills in the ACC group compared to both the LL and HL- groups. Compared to the other clinical groups, the ACC group had stronger Socialization and Motor skills than Fragile X at 6 months, and at 24 months had stronger Communication and Socialization skills than both the DS and FX groups, as well as stronger Socialization than the HL+ group.

Compared to children with low likelihood of ASD, children with primary ACC reportedly have weaker Communication and Motor skills from 6 to 24 months, with weakness in Daily Living Skills appearing at 12 months and all differences increase with age. Compared to Fragile X, the ACC exhibited relative strengths in socialization and motor skills starting at 6 months. By 24 months, the ACC group was outperforming the monogenic groups on Socialization and Communication. In general, the ACC scores were consistent with the HL+ sample, except the ACC group had stronger Social skills at 18 and 24 months. The results clearly inform the need for early intervention in the domains of motor and language skills. Additionally, as we know that children with ACC are at increased risk for social difficulties, research is needed both using more fine-grained social-communication tools, and following children from infancy through middle childhood.

Identifying youths most at risk to COVID-19-related mental illness is essential for the development of effective targeted interventions.

To compare trajectories of mental health throughout the pandemic in youth with and without prior mental illness and identify those most at risk of COVID-19-related mental illness.

Data were collected from individuals aged 18–26 years (N = 669) from two existing cohorts: IMAGEN, a population-based cohort; and ESTRA/STRATIFY, clinical cohorts of individuals with pre-existing diagnoses of mental disorders. Repeated COVID-19 surveys and standardised mental health assessments were used to compare trajectories of mental health symptoms from before the pandemic through to the second lockdown.

Mental health trajectories differed significantly between cohorts. In the population cohort, depression and eating disorder symptoms increased by 33.9% (95% CI 31.78–36.57) and 15.6% (95% CI 15.39–15.68) during the pandemic, respectively. By contrast, these remained high over time in the clinical cohort. Conversely, trajectories of alcohol misuse were similar in both cohorts, decreasing continuously (a 15.2% decrease) during the pandemic. Pre-pandemic symptom severity predicted the observed mental health trajectories in the population cohort. Surprisingly, being relatively healthy predicted increases in depression and eating disorder symptoms and in body mass index. By contrast, those initially at higher risk for depression or eating disorders reported a lasting decrease.

Healthier young people may be at greater risk of developing depressive or eating disorder symptoms during the COVID-19 pandemic. Targeted mental health interventions considering prior diagnostic risk may be warranted to help young people cope with the challenges of psychosocial stress and reduce the associated healthcare burden.