The National Institutes of Health (NIH) Executive Abilities: Measures and Instruments for Neurobehavioral Evaluation and Research (EXAMINER) is a validated laptop-based battery of executive functioning tests. A modified tablet version of the EXAMINER was developed on the UCSF Tablet-based Cognitive Assessment Tool (TabCAT-EXAMINER). Here we describe the battery and investigate the reliability and validity of a composite score.

A diagnostically heterogeneous sample of 2135 individuals (mean age = 65.58, SD = 16.07), including controls and participants with a variety of neurodegenerative syndromes, completed the TabCAT-EXAMINER. A composite score was developed using confirmatory factor analysis and item response theory. Validity was evaluated via linear regressions that tested associations with neuropsychological tests, demographics, clinical diagnosis, and disease severity. Replicability of cross-sectional results was tested in a separate sample of participants (n = 342) recruited from a frontotemporal dementia study. As this separate sample also collected longitudinal TabCAT-EXAMINER measures, we additionally assessed test-retest reliability and associations between baseline disease severity and changes in TabCAT-EXAMINER scores.

The TabCAT-EXAMINER score was normally distributed, demonstrated high test-retest reliability, and was associated in the expected directions with independent tests of executive functioning, demographics, disease severity, and diagnosis. Greater baseline disease severity was associated with more rapid longitudinal TabCAT-EXAMINER decline.

The TabCAT-EXAMINER is a tablet-based executive functioning battery developed for observational research and clinical trials. Performance can be summarized as a single composite score, and results of this study support its reliability and validity in cognitive aging and neurodegenerative disease cohorts.

Travel distance is a key barrier for patients to participate in clinical trials or receive cancer care. The National Cancer Institute (NCI) is a major funder of cancer research infrastructure through grant programs like the NCI Cancer Center (NCICC) and NCI Community Oncology Research Program (NCORP); however, the majority of US sites that care for people with cancer do not directly receive this funding.

Through geospatial analysis we examined patient distance to NCI-funded sites and evaluated demographic subgroups to identify potential disparities in access to research opportunities. We assessed whether new NCI support to previously unfunded sites could address identified barriers in access.

NCI-funded sites tend to be in urban centers and are less accessible to low-income or rural patients. Nearly 17% of the US population over 35 years old would have to drive over 100 miles to obtain care at an NCI-funded site; only 1.6% would be beyond that distance when non-funded sites are added. For those below poverty level, the proportions are 20.2% and 1.9%, respectively. Several US regions, including the South and Appalachia, have particularly limited access to NCI-funded sites despite high cancer incidence, and much of the West and Great Plains are distant from any cancer facilities.

NCI could address travel distance as a major barrier to research participation by expanding the geographical footprint of its infrastructure funding using existing institutions in areas with identified gaps. Geospatial analysis at the census tract level is recommended and geospatial visualization can help identify strategic areas for interventions.

Posttraumatic stress disorder (PTSD) has been associated with advanced epigenetic age cross-sectionally, but the association between these variables over time is unclear. This study conducted meta-analyses to test whether new-onset PTSD diagnosis and changes in PTSD symptom severity over time were associated with changes in two metrics of epigenetic aging over two time points.

We conducted meta-analyses of the association between change in PTSD diagnosis and symptom severity and change in epigenetic age acceleration/deceleration (age-adjusted DNA methylation age residuals as per the Horvath and GrimAge metrics) using data from 7 military and civilian cohorts participating in the Psychiatric Genomics Consortium PTSD Epigenetics Workgroup (total N = 1,367).

Meta-analysis revealed that the interaction between Time 1 (T1) Horvath age residuals and new-onset PTSD over time was significantly associated with Horvath age residuals at T2 (meta β = 0.16, meta p = 0.02, p-adj = 0.03). The interaction between T1 Horvath age residuals and changes in PTSD symptom severity over time was significantly related to Horvath age residuals at T2 (meta β = 0.24, meta p = 0.05). No associations were observed for GrimAge residuals.

Results indicated that individuals who developed new-onset PTSD or showed increased PTSD symptom severity over time evidenced greater epigenetic age acceleration at follow-up than would be expected based on baseline age acceleration. This suggests that PTSD may accelerate biological aging over time and highlights the need for intervention studies to determine if PTSD treatment has a beneficial effect on the aging methylome.

Peripheral inflammatory markers, including serum interleukin 6 (IL-6), are associated with depression, but less is known about how these markers associate with depression at different stages of the life course.

We examined the associations between serum IL-6 levels at baseline and subsequent depression symptom trajectories in two longitudinal cohorts: ALSPAC (age 10–28 years; N = 4,835) and UK Biobank (39–86 years; N = 39,613) using multilevel growth curve modeling. Models were adjusted for sex, BMI, and socioeconomic factors. Depressive symptoms were measured using the Short Moods and Feelings Questionnaire in ALSPAC (max time points = 11) and the Patient Health Questionnaire-2 in UK Biobank (max time points = 8).

Higher baseline IL-6 was associated with worse depression symptom trajectories in both cohorts (largest effect size: 0.046 [ALSPAC, age 16 years]). These associations were stronger in the younger ALSPAC cohort, where additionally higher IL-6 levels at age 9 years was associated with worse depression symptoms trajectories in females compared to males. Weaker sex differences were observed in the older cohort, UK Biobank. However, statistically significant associations (pFDR <0.05) were of smaller effect sizes, typical of large cohort studies.

These findings suggest that systemic inflammation may influence the severity and course of depressive symptoms across the life course, which is apparent regardless of age and differences in measures and number of time points between these large, population-based cohorts.

Diagnostic criteria for major depressive disorder allow for heterogeneous symptom profiles but genetic analysis of major depressive symptoms has the potential to identify clinical and etiological subtypes. There are several challenges to integrating symptom data from genetically informative cohorts, such as sample size differences between clinical and community cohorts and various patterns of missing data.

We conducted genome-wide association studies of major depressive symptoms in three cohorts that were enriched for participants with a diagnosis of depression (Psychiatric Genomics Consortium, Australian Genetics of Depression Study, Generation Scotland) and three community cohorts who were not recruited on the basis of diagnosis (Avon Longitudinal Study of Parents and Children, Estonian Biobank, and UK Biobank). We fit a series of confirmatory factor models with factors that accounted for how symptom data was sampled and then compared alternative models with different symptom factors.

The best fitting model had a distinct factor for Appetite/Weight symptoms and an additional measurement factor that accounted for the skip-structure in community cohorts (use of Depression and Anhedonia as gating symptoms).

The results show the importance of assessing the directionality of symptoms (such as hypersomnia versus insomnia) and of accounting for study and measurement design when meta-analyzing genetic association data.

The brain can be represented as a network, with nodes as brain regions and edges as region-to-region connections. Nodes with the most connections (hubs) are central to efficient brain function. Current findings on structural differences in Major Depressive Disorder (MDD) identified using network approaches remain inconsistent, potentially due to small sample sizes. It is still uncertain at what level of the connectome hierarchy differences may exist, and whether they are concentrated in hubs, disrupting fundamental brain connectivity.

We utilized two large cohorts, UK Biobank (UKB, N = 5104) and Generation Scotland (GS, N = 725), to investigate MDD case–control differences in brain network properties. Network analysis was done across four hierarchical levels: (1) global, (2) tier (nodes grouped into four tiers based on degree) and rich club (between-hub connections), (3) nodal, and (4) connection.

In UKB, reductions in network efficiency were observed in MDD cases globally (d = −0.076, pFDR = 0.033), across all tiers (d = −0.069 to −0.079, pFDR = 0.020), and in hubs (d = −0.080 to −0.113, pFDR = 0.013–0.035). No differences in rich club organization and region-to-region connections were identified. The effect sizes and direction for these associations were generally consistent in GS, albeit not significant in our lower-N replication sample.

Our results suggest that the brain's fundamental rich club structure is similar in MDD cases and controls, but subtle topological differences exist across the brain. Consistent with recent large-scale neuroimaging findings, our findings offer a connectomic perspective on a similar scale and support the idea that minimal differences exist between MDD cases and controls.

The AD8 is a validated screening instrument for functional changes that may be caused by cognitive decline and dementia. It is frequently used in clinics and research studies because it is short and easy to administer, with a cut off score of 2 out of 8 items recommended to maximize sensitivity and specificity. This cutoff assumes that all 8 items provide equivalent “information” about everyday functioning. In this study, we used item response theory (IRT) to test this assumption. To determine the relevance of this measure of everyday functioning in men and women, and across race, ethnicity, and education, we conducted differential item functioning (DIF) analysis to test for item bias.

Data came from the 2021 follow up of the High School & Beyond cohort (N=8,690; mean age 57.5 ± 1.2; 55% women), a nationally representative, longitudinal study of Americans who were first surveyed in 1980 when they were in the 10th or 12th grade. Participants were asked AD8 questions about their own functioning via phone or internet survey. First, we estimated a one-parameter (i.e., differing difficulty, equal discrimination across items) and two-parameter IRT model (i.e., differing difficulty and differing discrimination across items). We compared model fit using a likelihood-ratio test. Second, we tested for uniform and non-uniform DIF on AD8 items by sex, race and ethnicity (non-Hispanic White, non-Hispanic Black, Hispanic), education level (high school or less, some college, BA degree or more), and survey mode (phone or internet). We examined DIF salience by comparing the difference between original and DIF-adjusted AD8 scores to the standard error of measurement of the original score.

The two-parameter IRT model fit the data significantly better than the one-parameter model, indicating that some items were more strongly related to underlying everyday functional ability than others. For example, the “problems with judgment” item had higher discrimination (more information) than the “less interest in hobbies/activities” item. There were significant differences in item endorsement by race/ethnicity, education, and survey mode. We found significant uniform and non-uniform DIF on several items across each of these groups. For example, for a given level of functional decline (theta) White participants were more likely to endorse “Daily problems with thinking/memory” than Black and Hispanic participants. The DIF was salient (i.e., caused AD8 scores to change by greater than the standard error of measurement for a large portion of respondents) for those with a college degree and phone respondents.

In a population representative sample of Americans ∼age 57, the items on the AD8 contributed differing levels of discrimination along the range of everyday functioning that is impacted by later life cognitive impairment. This suggests that a simple cut-off or summed score may not be appropriate since some items yield more information about the underlying construct than others. Furthermore, we observed significant and salient DIF on several items by education and survey mode, AD8 scores should not be compared across education groups and assessment modes without adjustment for this measurement bias.

The NKX2.5 gene is an important cardiac developmental transcription factor, and variants in this gene are most commonly associated with CHD. However, there is an increased need to recognise associations with conduction disease and potentially dangerous ventricular arrhythmias. There is an increased risk of arrhythmia and sudden cardiac death in patients with NKX2.5 variants, an association with relatively less attention in the literature.

We created a family pedigree and reconstructed familial relationships involving numerous relatives with CHD, conduction disease, and ventricular non-compaction following the sudden death of one family member. Two informative but distantly related family members had genetic testing to determine the cause of arrhythmias via arrhythmia/cardiomyopathy gene testing, and we identified obligate genetic-positive relatives based on family relationships and Mendelian inheritance pattern.

We identified a novel pathogenic variant in the NKX2.5 gene (c.437C > A; p. Ser146*), and segregation analysis allowed us to link family cardiac phenotypes including CHD, conduction disease, left ventricular non-compaction, and ventricular arrhythmias/sudden cardiac death.

We report a novel NKX2.5 gene variant linking a spectrum of familial heart disease, and we also encourage recognition of the association between NKX2.5 gene and potentially dangerous ventricular arrhythmias, which will inform clinical risk stratification, screening, and management.

North Dakota (ND) had the highest coronavirus disease 2019 (COVID-19) case and mortality rate in the United States for nearly 2 mo. This study aims to compare 3 metrics ND used to guide public health action across its 53 counties.

Daily COVID-19 case and death totals in North Dakota were evaluated using data from the COVID-tracker website provided by the North Department of Health (NDDoH). It was reported as: active cases per 10,000, tests administered per 10,000, and test positivity rate (the North Dakota health metric). The COVID-19 Response press conferences provided data for the Governor’s metric. The Harvard model used daily new cases per 100,000. A chi-squared test was used to compare differences in these 3 metrics on July 1, August 26, September 23, and November 13, 2020.

On July 1, no significant difference between the metrics was found. By September 23, Harvard’s health metric indicated critical risk while ND’s health metric was moderate risk, and the Governor’s metric was still low risk.

ND’s and the Governor’s metric underrepresented the risk of the COVID-19 outbreak in North Dakota. The Harvard metric reflected North Dakota’s increasing risk; it should be considered as a national standard in future pandemics.

Model-based predictors could guide policy-makers to effectively control spread of infectious disease; proactive models could reduce risk of disease as it progresses in vulnerable communities.