Autistic traits are associated with faster pace of aging: evidence from the Dunedin Study at age 45

Growing evidence indicates that the defining characteristics of Autism Spectrum Disorder (ASD) are distributed throughout the general population; hence, understanding correlates of aging in people with high autistic traits could shed light on ASD and aging. 915 members of the Dunedin longitudinal birth cohort completed a measure of autistic traits at age 45. A composite measure of the “pace of aging” was derived by tracking decline in 19 biomarkers across ages 26, 32, 38, and 45 years. Facial age was also assessed. Reports of perceived health were collected from participants themselves, informants, and interviewers. Higher self-reported autistic traits significantly correlated with a faster pace of aging, older facial age, and poorer self-, informant- and interviewer-rated health. After control for sex, SES and IQ, autistic traits were significantly associated with each variable: pace of aging (β=0.09), facial age (β=0.08), self- (β=-.15), informant (β=-.12), and interviewer-rated (β=-.17) health. Autistic traits measured at age 45 are associated with faster aging. Participants with high autistic traits appear to be more vulnerable to poor health outcomes, as previously reported for those clinically diagnosed with ASD. Therefore, autistic traits may have important health implications. Replicating these findings in samples of autistic people is needed to identify the mechanism of their effect on aging and physical health to improve outcomes for those with ASD diagnoses or high autistic traits

A Measurement of the Branching Fraction for the Inclusive B --> X(s) gamma Decays with the Belle Detector

We have measured the branching fraction of the inclusive radiative B meson decay B --> X(s) gamma to be Br(B->X(s)gamma)=(3.36 +/- 0.53(stat) +/- 0.42(sys) +0.50-0.54(th)) x 10^{-4}. The result is based on a sample of 6.07 x 10^6 BBbar events collected at the Upsilon(4S) resonance with the Belle detector at the KEKB asymmetric e^+e^- storage ring.Comment: 14 pages, 6 Postsript figures, uses elsart.cl

In search of disorders: internalizing symptom networks in a large clinical sample.

Background The co‐occurrence of internalizing disorders is a common form of psychiatric comorbidity, raising questions about the boundaries between these diagnostic categories. We employ network psychometrics in order to: (a) determine whether internalizing symptoms cluster in a manner reflecting DSM diagnostic criteria, (b) gauge how distinct these diagnostic clusters are and (c) examine whether this network structure changes from childhood to early and then late adolescence. Method Symptom‐level data were obtained for service users in publicly funded mental health services in England between 2011 and 2015 (N = 37,162). A symptom network (i.e. Gaussian graphical model) was estimated, and a community detection algorithm was used to explore the clustering of symptoms. Results The estimated network was densely connected and characterized by a multitude of weak associations between symptoms. Six communities of symptoms were identified; however, they were weakly demarcated. Two of these communities corresponded to social phobia and panic disorder, and four did not clearly correspond with DSM diagnostic categories. The network structure was largely consistent by sex and across three age groups (8–11, 12–14 and 15–18 years). Symptom connectivity in the two older age groups was significantly greater compared to the youngest group and there were differences in centrality across the age groups, highlighting the age‐specific relevance of certain symptoms. Conclusions These findings clearly demonstrate the interconnected nature of internalizing symptoms, challenging the view that such pathology takes the form of distinct disorders

Genomic and phenotypic insights from an atlas of genetic effects on DNA methylation

DNA methylation quantitative trait locus (mQTL) analyses on 32,851 participants identify genetic variants associated with DNA methylation at 420,509 sites in blood, resulting in a database of >270,000 independent mQTLs.Characterizing genetic influences on DNA methylation (DNAm) provides an opportunity to understand mechanisms underpinning gene regulation and disease. In the present study, we describe results of DNAm quantitative trait locus (mQTL) analyses on 32,851 participants, identifying genetic variants associated with DNAm at 420,509 DNAm sites in blood. We present a database of >270,000 independent mQTLs, of which 8.5% comprise long-range (trans) associations. Identified mQTL associations explain 15-17% of the additive genetic variance of DNAm. We show that the genetic architecture of DNAm levels is highly polygenic. Using shared genetic control between distal DNAm sites, we constructed networks, identifying 405 discrete genomic communities enriched for genomic annotations and complex traits. Shared genetic variants are associated with both DNAm levels and complex diseases, but only in a minority of cases do these associations reflect causal relationships from DNAm to trait or vice versa, indicating a more complex genotype-phenotype map than previously anticipated.Molecular Epidemiolog