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.

Major depressive disorder (MDD) was previously associated with negative affective biases. Evidence from larger population-based studies, however, is lacking, including whether biases normalise with remission. We investigated associations between affective bias measures and depressive symptom severity across a large community-based sample, followed by examining differences between remitted individuals and controls.

Participants from Generation Scotland (N = 1109) completed the: (i) Bristol Emotion Recognition Task (BERT), (ii) Face Affective Go/No-go (FAGN), and (iii) Cambridge Gambling Task (CGT). Individuals were classified as MDD-current (n = 43), MDD-remitted (n = 282), or controls (n = 784). Analyses included using affective bias summary measures (primary analyses), followed by detailed emotion/condition analyses of BERT and FAGN (secondary analyses).

For summary measures, the only significant finding was an association between greater symptoms and lower risk adjustment for CGT across the sample (individuals with greater symptoms were less likely to bet more, despite increasingly favourable conditions). This was no longer significant when controlling for non-affective cognition. No differences were found for remitted-MDD v. controls. Detailed analysis of BERT and FAGN indicated subtle negative biases across multiple measures of affective cognition with increasing symptom severity, that were independent of non-effective cognition [e.g. greater tendency to rate faces as angry (BERT), and lower accuracy for happy/neutral conditions (FAGN)]. Results for remitted-MDD were inconsistent.

This suggests the presence of subtle negative affective biases at the level of emotion/condition in association with depressive symptoms across the sample, over and above those accounted for by non-affective cognition, with no evidence for affective biases in remitted individuals.

Major depressive disorder (MDD) is a polygenic disorder associated with brain alterations but until recently, there have been no brain-based metrics to quantify individual-level variation in brain morphology. Here, we evaluated and compared the performance of a new brain-based ‘Regional Vulnerability Index’ (RVI) with polygenic risk scores (PRS), in the context of MDD. We assessed associations with syndromal MDD in an adult sample (N = 702, age = 59 ± 10) and with subclinical depressive symptoms in a longitudinal adolescent sample (baseline N = 3,825, age = 10 ± 1; 2-year follow-up N = 2,081, age = 12 ± 1).

MDD-RVIs quantify the correlation of the individual’s corresponding brain metric with the expected pattern for MDD derived in an independent sample. Using the same methodology across samples, subject-specific MDD-PRS and six MDD-RVIs based on different brain modalities (subcortical volume, cortical thickness, cortical surface area, mean diffusivity, fractional anisotropy, and multimodal) were computed.

In adults, MDD-RVIs (based on white matter and multimodal measures) were more strongly associated with MDD (β = 0.099–0.281, PFDR = 0.001–0.043) than MDD-PRS (β = 0.056–0.152, PFDR = 0.140–0.140). In adolescents, depressive symptoms were associated with MDD-PRS at baseline and follow-up (β = 0.084–0.086, p = 1.38 × 10−4−4.77 × 10−4) but not with any MDD-RVIs (β < 0.05, p > 0.05).

Our results potentially indicate the ability of brain-based risk scores to capture a broader range of risk exposures than genetic risk scores in adults and are also useful in helping us to understand the temporal origins of depression-related brain features. Longitudinal data, specific to the developmental period and on white matter measures, will be useful in informing risk for subsequent psychiatric illness.