Gender differences in trajectories of depressive symptoms across childhood and adolescence: A multi-group growth mixture model

Background This study sought to identify depression trajectories across childhood and to model a range of child and family predictors of whether a child may be on an increasing trajectory towards depressive disorder in adolescence. Methods Multi-group growth mixture modelling (MGMM) was used on a sample of 4983 children from the Longitudinal Study of Australia Children (LSAC). Depressive symptoms of these children were assessed over 10-years with six time-points, administered every second year commencing at 4 years via the parent report version of the Strength and Difficulties Questionnaire. Predictors of class membership were also examined. Results Four trajectories were found to be the best fitting model characterising low-stable (75%); decreasing (11%); increasing (9%); high and rising (6%) groups. Females were more likely to be in a trajectory of increasing depressive symptoms between 4 and 14 years of age than males. Reactive temperament and maternal depression at four and six years of age were consistent predictors of increasing and high trajectories while persistent temperament acts as a protective factor for females. Limitations The findings should be interpreted in the light of limitations due to common-method variance and the absence of diagnostic indicators of depressive disorder. Conclusions We conclude that there are gender differences in patterns of depressive symptoms from childhood to adolescence and meaningful predictors of these early developmental trajectories. Preventative interventions in childhood targeting parents with depression and children with temperamental difficulties may be indicated

Adolescent depressive symptoms in India, Australia and USA: Exploratory Structural Equation Modelling of cross-national invariance and predictions by gender and age

Background The present study compares depressive symptoms in adolescents from three countries: Mumbai, India; Seattle, United States; and Melbourne, Australia measured using the Short Moods and Feelings Questionnaire (SMFQ). The study cross nationally compares SMFQ depressive symptom responses by age and gender. Methods Data from a cross-nationally matched survey were used to compare factorial and measurement characteristics from samples of students from Grade 7 and 9 in Mumbai, India (n=3268) with the equivalent cohorts in the Washington State, USA (n=1907) and Victoria, Australia (n=1900). Exploratory Structural Equation Modelling (ESEM) was used to cross-nationally examine factor structure and measurement invariance. Results A number of reports suggesting that SMFQ is uni-dimensional were not supported in findings from any country. A model with two factors was a better fit and suggested a first factor clustering symptoms that were affective and physiologically based symptoms and a second factor of self-critical, cognitive symptoms. The two-factor model showed convincing cross national configural invariance and acceptable measurement invariance. The present findings revealed that adolescents in Mumbai, India, reported substantially higher depressive symptoms in both factors, but particularly for the self-critical dimension, as compared to their peers in Australia and the USA and that males in Mumbai report high levels of depressive symptoms than females in Mumbai. Limitations the cross sectional study collected data for adolescents in Melbourne and Seattle in 2002 and the data for adolescents in Mumbai was obtained in 2010–2011 Conclusions These findings suggest that previous findings in developed nations of higher depressive symptoms amongst females compared to males may have an important cultural component and cannot be generalised as a universal feature of adolescent development

The genetic architecture of the human cerebral cortex

INTRODUCTION The cerebral cortex underlies our complex cognitive capabilities. Variations in human cortical surface area and thickness are associated with neurological, psychological, and behavioral traits and can be measured in vivo by magnetic resonance imaging (MRI). Studies in model organisms have identified genes that influence cortical structure, but little is known about common genetic variants that affect human cortical structure. RATIONALE To identify genetic variants associated with human cortical structure at both global and regional levels, we conducted a genome-wide association meta-analysis of brain MRI data from 51,665 individuals across 60 cohorts. We analyzed the surface area and average thickness of the whole cortex and 34 cortical regions with known functional specializations. RESULTS We identified 306 nominally genome-wide significant loci (P < 5 × 10−8) associated with cortical structure in a discovery sample of 33,992 participants of European ancestry. Of the 299 loci for which replication data were available, 241 loci influencing surface area and 14 influencing thickness remained significant after replication, with 199 loci passing multiple testing correction (P < 8.3 × 10−10; 187 influencing surface area and 12 influencing thickness). Common genetic variants explained 34% (SE = 3%) of the variation in total surface area and 26% (SE = 2%) in average thickness; surface area and thickness showed a negative genetic correlation (rG = −0.32, SE = 0.05, P = 6.5 × 10−12), which suggests that genetic influences have opposing effects on surface area and thickness. Bioinformatic analyses showed that total surface area is influenced by genetic variants that alter gene regulatory activity in neural progenitor cells during fetal development. By contrast, average thickness is influenced by active regulatory elements in adult brain samples, which may reflect processes that occur after mid-fetal development, such as myelination, branching, or pruning. When considered together, these results support the radial unit hypothesis that different developmental mechanisms promote surface area expansion and increases in thickness. To identify specific genetic influences on individual cortical regions, we controlled for global measures (total surface area or average thickness) in the regional analyses. After multiple testing correction, we identified 175 loci that influence regional surface area and 10 that influence regional thickness. Loci that affect regional surface area cluster near genes involved in the Wnt signaling pathway, which is known to influence areal identity. We observed significant positive genetic correlations and evidence of bidirectional causation of total surface area with both general cognitive functioning and educational attainment. We found additional positive genetic correlations between total surface area and Parkinson’s disease but did not find evidence of causation. Negative genetic correlations were evident between total surface area and insomnia, attention deficit hyperactivity disorder, depressive symptoms, major depressive disorder, and neuroticism. CONCLUSION This large-scale collaborative work enhances our understanding of the genetic architecture of the human cerebral cortex and its regional patterning. The highly polygenic architecture of the cortex suggests that distinct genes are involved in the development of specific cortical areas. Moreover, we find evidence that brain structure is a key phenotype along the causal pathway that leads from genetic variation to differences in general cognitive function

Large-scale phenotyping of patients with long COVID post-hospitalization reveals mechanistic subtypes of disease

One in ten severe acute respiratory syndrome coronavirus 2 infections result in prolonged symptoms termed long coronavirus disease (COVID), yet disease phenotypes and mechanisms are poorly understood1. Here we profiled 368 plasma proteins in 657 participants ≥3 months following hospitalization. Of these, 426 had at least one long COVID symptom and 233 had fully recovered. Elevated markers of myeloid inflammation and complement activation were associated with long COVID. IL-1R2, MATN2 and COLEC12 were associated with cardiorespiratory symptoms, fatigue and anxiety/depression; MATN2, CSF3 and C1QA were elevated in gastrointestinal symptoms and C1QA was elevated in cognitive impairment. Additional markers of alterations in nerve tissue repair (SPON-1 and NFASC) were elevated in those with cognitive impairment and SCG3, suggestive of brain–gut axis disturbance, was elevated in gastrointestinal symptoms. Severe acute respiratory syndrome coronavirus 2-specific immunoglobulin G (IgG) was persistently elevated in some individuals with long COVID, but virus was not detected in sputum. Analysis of inflammatory markers in nasal fluids showed no association with symptoms. Our study aimed to understand inflammatory processes that underlie long COVID and was not designed for biomarker discovery. Our findings suggest that specific inflammatory pathways related to tissue damage are implicated in subtypes of long COVID, which might be targeted in future therapeutic trials

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2,3,4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease