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Despite efforts to predict suicide risk in children, the ability to reliably identify who will engage in suicide thoughts or behaviours has remained unsuccessful.
We apply a novel machine-learning approach and examine whether children with suicide thoughts or behaviours could be differentiated from children without suicide thoughts or behaviours based on a combination of traditional (sociodemographic, physical health, social–environmental, clinical psychiatric) risk factors, but also more novel risk factors (cognitive, neuroimaging and genetic characteristics).
The study included 5885 unrelated children (50% female, 67% White, 9–11 years of age) from the Adolescent Brain Cognitive Development (ABCD) study. We performed penalised logistic regression analysis to distinguish between: (a) children with current or past suicide thoughts or behaviours; (b) children with a mental illness but no suicide thoughts or behaviours (clinical controls); and (c) healthy control children (no suicide thoughts or behaviours and no history of mental illness). The model was subsequently validated with data from seven independent sites involved in the ABCD study (n = 1712).
Our results showed that we were able to distinguish the suicide thoughts or behaviours group from healthy controls (area under the receiver operating characteristics curve: 0.80 child-report, 0.81 for parent-report) and clinical controls (0.71 child-report and 0.76–0.77 parent-report). However, we could not distinguish children with suicidal ideation from those who attempted suicide (AUROC: 0.55–0.58 child-report; 0.49–0.53 parent-report). The factors that differentiated the suicide thoughts or behaviours group from the clinical control group included family conflict, prodromal psychosis symptoms, impulsivity, depression severity and history of mental health treatment.
This work highlights that mostly clinical psychiatric factors were able to distinguish children with suicide thoughts or behaviours from children without suicide thoughts or behaviours. Future research is needed to determine if these variables prospectively predict subsequent suicidal behaviour.
Pneumonia is a respiratory condition with complex etiology. Host genetic variation is thought to contribute to individual differences in susceptibility and symptom manifestation. Here, we analyze pneumonia data from the UK Biobank (14,780 cases and 439,096 controls) and FinnGen (9980 cases and 86,519 controls) and perform a genomewide association study meta-analysis. We use gene-based tests, colocalization, genetic correlation, latent causal variable (LCV) and polygenic prediction in an independent Australian sample (N = 5595) to draw insights into the etiology of pneumonia risk. We identify two independent loci on chromosome 15 (lead single-nucleotide polymorphisms rs2009746 and rs76474922) to be associated with pneumonia (p < 5e−8). Gene-based tests revealed 18 genes in chromosomes 15, 16 and 9, including IL127, PBX3, ApoB receptor (APOBR) and smoking related genes CHRNA3/5, statistically associated with pneumonia. We observed genetic correlations between pneumonia and cardiorespiratory, psychiatric and inflammatory related traits. LCV analysis suggests a strong genetic causal relationship with cardiovascular health phenotypes. Polygenic risk scores for pneumonia significantly predicted self-reported pneumonia in an independent sample, albeit with a small effect size (OR = 1.11 95% CI [1.04, 1.19], p < .05). Sensitivity analyses suggested the associations in chromosome 15 are mediated by smoking history, but the associations in chromosomes 16 and 9, and polygenic prediction were robust to adjustment for smoking. Altogether, our results highlight common genetic variants, genes and potential pathways that contribute to individual differences in susceptibility to pneumonia, and advance our understanding of the genetic factors underlying heterogeneity in respiratory medical outcomes.
Several neuroimaging studies have reported associations between brain white matter microstructure and chronotype. However, it is unclear whether those phenotypic relationships are causal or underlined by genetic factors. In the present study, we use genetic data to examine the genetic overlap and infer causal relationships between chronotype and diffusion tensor imaging (DTI) measures. We identify 29 significant pairwise genetic correlations, of which 13 also show evidence for a causal association. Genetic correlations were identified between chronotype and brain-wide mean, axial and radial diffusivities. When exploring individual tracts, 10 genetic correlations were observed with mean diffusivity, 10 with axial diffusivity, 4 with radial diffusivity and 2 with mode of anisotropy. We found evidence for a possible causal association of eveningness with white matter microstructure measures in individual tracts including the posterior limb and the retrolenticular part of the internal capsule; the genu and splenium of the corpus callosum and the posterior, superior and anterior regions of the corona radiata. Our findings contribute to the understanding of how genes influence circadian preference and brain white matter and provide a new avenue for investigating the role of chronotype in health and disease.
TwinsMX is a national twin registry in Mexico recently created with institutional support from the Universidad Nacional Autónoma de México. It aims to serve as a platform to advance epidemiological and genetic research in the country and to disentangle the genetic and environmental contributions to health and disease in the admixed Mexican population. Here, we describe our recruitment and data collection strategies and discuss both the progress to date and future directions. More information about the registry is available on our website: https://twinsmxofficial.unam.mx/ (content in Spanish).
The aim of the 25 and Up (25Up) study was to assess a wide range of psychological and behavioral risk factors behind mental illness in a large cohort of Australian twins and their non-twin siblings. Participants had already been studied longitudinally from the age of 12 and most recently in the 19Up study (mean age = 26.1 years, SD = 4.1, range = 20–39). This subsequent wave follows up these twins several years later in life (mean age = 29.7 years, SD = 2.2, range = 22–44). The resulting data set enables additional detailed investigations of genetic pathways underlying psychiatric illnesses in the Brisbane Longitudinal Twin Study (BLTS). Data were collected between 2016 and 2018 from 2540 twins and their non-twin siblings (59% female, including 341 monozygotic complete twin-pairs, 415 dizygotic complete pairs and 1028 non-twin siblings and singletons). Participants were from South-East Queensland, Australia, and the sample was of predominantly European ancestry. The 25Up study collected information on 20 different mental disorders, including depression, anxiety, substance use, psychosis, bipolar and attention-deficit hyper-activity disorder, as well as general demographic information such as occupation, education level, number of children, self-perceived IQ and household environment. In this article, we describe the prevalence, comorbidities and age of onset for all 20 examined disorders. The 25Up study also assessed general and physical health, including physical activity, sleep patterns, eating behaviors, baldness, acne, migraines and allergies, as well as psychosocial items such as suicidality, perceived stress, loneliness, aggression, sleep–wake cycle, sexual identity and preferences, technology and internet use, traumatic life events, gambling and cyberbullying. In addition, 25Up assessed female health traits such as morning sickness, breastfeeding and endometriosis. Furthermore, given that the 25Up study is an extension of previous BLTS studies, 86% of participants have already been genotyped. This rich resource will enable the assessment of epidemiological risk factors, as well as the heritability and genetic correlations of mental conditions.
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