Antidepressants, social adversity and outcome of depression in general practice

Background: The role of current social risk factors in moderating the impact of antidepressant medication has not previously been explored.Method: In a RCT of SSRIs of general practice patients with mild to moderate depression (HDRS 12–19) two social indices of aversive experience were developed on the basis of prior research. First, the Life Events and Difficulties Schedule (LEDS) was used twice to document: i) recent stressful experience prior to baseline, and ii) after baseline and before follow up at 12 weeks both stressful and positive experiences, taking account of ‘fresh start’ and ‘difficulty-reduction’ events. Second, an index of unemployment-entrapment at baseline was developed for the current project. The HDRS was used to measure outcome as a continuous score and as a cut-point representing improvement below score 8.Results: Each social index (LEDS and Unemployment-entrapment) was associated with a lower chance of remission at 12 weeks and each was required to model remission along with treatment arm. However there was no interaction: the degree of increased remission for those randomised to SSRIs plus supportive care compared to that for those with supportive care alone was the same regardless of social context.Limitations: Dating of remission was not as thorough as in previous work with the LEDS. Detailed examination of positive experiences suggested the large majority were not the result of remitting symptoms, but it is impossible to rule this out altogether.Conclusions: Remission rates among patients in aversive social contexts are consistently much lower irrespective of treatment. There is thus a need to evaluate the efficacy of alternative more socially focussed interventions for depressive conditions likely to take a chronic course in general practice

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

Altres ajuts: Department of Health and Social Care (DHSC); Illumina; LifeArc; Medical Research Council (MRC); UKRI; Sepsis Research (the Fiona Elizabeth Agnew Trust); the Intensive Care Society, Wellcome Trust Senior Research Fellowship (223164/Z/21/Z); BBSRC Institute Program Support Grant to the Roslin Institute (BBS/E/D/20002172, BBS/E/D/10002070, BBS/E/D/30002275); UKRI grants (MC_PC_20004, MC_PC_19025, MC_PC_1905, MRNO2995X/1); UK Research and Innovation (MC_PC_20029); the Wellcome PhD training fellowship for clinicians (204979/Z/16/Z); the Edinburgh Clinical Academic Track (ECAT) programme; the National Institute for Health Research, the Wellcome Trust; the MRC; Cancer Research UK; the DHSC; NHS England; the Smilow family; the National Center for Advancing Translational Sciences of the National Institutes of Health (CTSA award number UL1TR001878); the Perelman School of Medicine at the University of Pennsylvania; National Institute on Aging (NIA U01AG009740); the National Institute on Aging (RC2 AG036495, RC4 AG039029); the Common Fund of the Office of the Director of the National Institutes of Health; NCI; NHGRI; NHLBI; NIDA; NIMH; NINDS.Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care or hospitalization 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