A Lower Maternal Cortisol-to-Cortisone Ratio Precedes Clinical Diagnosis of Preterm and Term Preeclampsia by Many Weeks.

CONTEXT: Previous studies have shown reduced placental levels of 11-β-hydroxysteroid dehydrogenase type 2 (11βHSD2) in preeclampsia (PE). However, it is unknown if the maternal cortisol-to-cortisone ratio is predictive of placental complications of pregnancy. OBJECTIVE: To determine the relationship between the maternal serum cortisol-to-cortisone ratio at different stages of pregnancy and the risk of PE or fetal growth restriction (FGR). DESIGN: Women from the Pregnancy Outcome Prediction Study experiencing PE (n = 194) or FGR (n = 185), plus a random sample of healthy controls (n = 279), were studied. Steroids were measured at ∼12, ∼20, ∼28, and ∼36 weeks of gestational age (wkGA). Separate analyses were performed for outcomes with term or preterm delivery. Associations were modeled using logistic regression. RESULTS: At 28 wkGA, the cortisol-to-cortisone ratio was negatively associated (OR per 1 SD increase, 95% CI)] with preterm PE (OR 0.33, 95% CI 0.19 to 0.57), term PE (OR 0.61, 95% CI 0.49 to 0.76), and preterm FGR (OR 0.50, 95% CI 0.29 to 0.85). At 36 wkGA, the cortisol-to-cortisone ratio was negatively associated with term PE (OR 0.42, 95% CI 0.32 to 0.55) but not term FGR (OR 1.07, 95% CI 0.87 to 1.31). Associations were not materially affected by adjustment for maternal characteristics. CONCLUSIONS: A lower maternal serum cortisol-to-cortisone ratio precedes clinical manifestation of PE and preterm FGR by many weeks, despite previous reports of reduced levels of placental 11βHSD2 in these conditions. Our observations implicate enhanced maternal 11βHSD2 activity or reduced 11βHSD type 1 activity in the pathophysiology of PE.The POP study was funded by the National Institute for Health Research (NIHR) Cambridge Comprehensive Biomedical Research Centre (Women’s Health theme), and a project grant from the Medical Research Council (United Kingdom; G1100221). The study was also supported by GE Healthcare (donation of two Voluson i ultrasound systems for the POP study), and by the NIHR Cambridge Clinical Research Facility, where all research visits took place. A.E.H. was an Academic Clinical Fellow funded by NIHR

Systematic, comprehensive, evidence-based approach to identify neuroprotective interventions for motor neuron disease: using systematic reviews to inform expert consensus

Objectives: Motor neuron disease (MND) is an incurable progressive neurodegenerative disease with limited treatment options. There is a pressing need for innovation in identifying therapies to take to clinical trial. Here, we detail a systematic and structured evidence-based approach to inform consensus decision making to select the first two drugs for evaluation in Motor Neuron Disease-Systematic Multi-arm Adaptive Randomised Trial (MND-SMART: NCT04302870), an adaptive platform trial. We aim to identify and prioritise candidate drugs which have the best available evidence for efficacy, acceptable safety profiles and are feasible for evaluation within the trial protocol. Methods: We conducted a two-stage systematic review to identify potential neuroprotective interventions. First, we reviewed clinical studies in MND, Alzheimer’s disease, Huntington’s disease, Parkinson’s disease and multiple sclerosis, identifying drugs described in at least one MND publication or publications in two or more other diseases. We scored and ranked drugs using a metric evaluating safety, efficacy, study size and study quality. In stage two, we reviewed efficacy of drugs in MND animal models, multicellular eukaryotic models and human induced pluripotent stem cell (iPSC) studies. An expert panel reviewed candidate drugs over two shortlisting rounds and a final selection round, considering the systematic review findings, late breaking evidence, mechanistic plausibility, safety, tolerability and feasibility of evaluation in MND-SMART. Results: From the clinical review, we identified 595 interventions. 66 drugs met our drug/disease logic. Of these, 22 drugs with supportive clinical and preclinical evidence were shortlisted at round 1. Seven drugs proceeded to round 2. The panel reached a consensus to evaluate memantine and trazodone as the first two arms of MND-SMART. Discussion: For future drug selection, we will incorporate automation tools, text-mining and machine learning techniques to the systematic reviews and consider data generated from other domains, including high-throughput phenotypic screening of human iPSCs

Building a Systematic Online Living Evidence Summary of COVID-19 Research

Throughout the global coronavirus pandemic, we have seen an unprecedented volume of COVID-19 researchpublications. This vast body of evidence continues to grow, making it difficult for research users to keep up with the pace of evolving research findings. To enable the synthesis of this evidence for timely use by researchers, policymakers, and other stakeholders, we developed an automated workflow to collect, categorise, and visualise the evidence from primary COVID-19 research studies. We trained a crowd of volunteer reviewers to annotate studies by relevance to COVID-19, study objectives, and methodological approaches. Using these human decisions, we are training machine learning classifiers and applying text-mining tools to continually categorise the findings and evaluate the quality of COVID-19 evidence

Smoking, blood cells and myeloproliferative neoplasms:Meta-analysis and Mendelian randomization of 2·3 million people

Meta‐analyses and Mendelian randomization (MR) may clarify the associations of smoking, blood cells and myeloproliferative neoplasms (MPN). We investigated the association of smoking with blood cells in the Danish General Suburban Population Study (GESUS, n = 11 083), by meta‐analyses (including GESUS) of 92 studies (n = 531 741) and MR of smoking variant CHRNA3 (rs1051730[A]) in UK Biobank, and with MPN in a meta‐analysis of six studies (n (total/cases):1 425 529/2187), totalling 2 307 745 participants. In the meta‐analysis the random‐effects standardized mean difference (SMD) in current smokers versus non‐smokers was 0·82 (0·75–0·89, P = 2·0 * 10−108) for leukocytes, 0·09 (−0·02 to 0·21, P = 0·12) for erythrocytes, 0·53 (0·42–0·64, P = 8·0 * 10−22) for haematocrit, 0·42 (0·34–0·51, P = 7·1 * 10−21) for haemoglobin, 0·19 (0·08–0·31, P = 1·2 * 10−3) for mean corpuscular haemoglobin (MCH), 0·29 (0·19–0·39, P = 1·6 * 10−8) for mean corpuscular volume (MCV), and 0·04 (−0·04 to 0·13, P = 0·34) for platelets with trends for ever/ex‐/current smokers, light/heavy smokers and female/male smokers. Analyses presented high heterogeneity but low publication bias. Per allele in CHRNA3, cigarettes per day in current smokers was associated with increased blood cell counts (leukocytes, neutrophils), MCH, red cell distribution width (RDW) and MCV. The pooled fixed‐effects odds ratio for MPN was 1·44 [95% confidence interval (CI): 1·33–1·56; P = 1·8 * 10−19; I2 = 0%] in current smokers, 1·29 (1·15–1·44; P = 8·0 * 10−6; I2 = 0%) in ex‐smokers, 1·49 (1·26–1·77; P = 4·4 * 10−6; I2 = 0%) in light smokers and 2·04 (1·74–2·39, P = 2·3 * 10−18; I2 = 51%) in heavy smokers compared with non‐smokers. Smoking is observationally and genetically associated with increased leukocyte counts and red blood cell indices (MCH, MCV, RDW) and observationally with risk of MPN in current and ex‐smokers versus non/never‐smokers

A Systematic Approach to Identify Neuroprotective Interventions for Motor Neuron Disease

Funding Statement For the purpose of open access, the authors have applied a Creative Commons Attribution (CC BY) licence to any Author Accepted Manuscript version arising from this submission. MND-SMART is funded by grants from MND Scotland, My Name'5 Doddie Foundation (DOD/14/15) and specific donations to the Euan MacDonald Centre. The Chandran lab is supported by the UK Dementia Research Institute, which receives its funding from UK DRI Ltd, funded by the UK Medical Research Council, Alzheimer's Society and Alzheimer's Research UK. E.E is a clinical academic fellow jointly funded by MND Scotland (MNDS) and the Chief Scientist Office (CSO) (217ARF R45951). A.R.M. was a Lady Edith Wolfson Clinical Fellow, jointly funded by the Medical Research Council (MRC) and the Motor Neurone Disease Association (MR/R001162/1). A.Salzinger is funded by Marie Sklodowska-Curie actions Innovative Training Network (ITN). B.T.S is funded by Rowling fellowship.Preprin

Systematic, comprehensive, evidence-based approach to identify neuroprotective interventions for motor neuron disease : Using systematic reviews to inform expert consensus

Funding Information: For the purpose of open access, the authors have applied a Creative Commons Attribution (CC BY) licence to any Author Accepted Manuscript version arising from this submission. MND-SMART is funded by grants from MND Scotland, My Name’5 Doddie Foundation (DOD/14/15) and specific donations to the Euan MacDonald Centre. The Chandran lab is supported by the UK Dementia Research Institute, which receives its funding from UK DRI Ltd, funded by the UK Medical Research Council, Alzheimer’s Society and Alzheimer’s Research UK. EE is a clinical academic fellow jointly funded by MND Scotland (MNDS) and the Chief Scientist Office (CSO) (217ARF R45951). ARM was a Lady Edith Wolfson Clinical Fellow, jointly funded by the Medical Research Council (MRC) and the Motor Neurone Disease Association (MR/R001162/1). ASalzinger is funded by Marie Sklodowska-Curie actions Innovative Training Network (ITN). BTS is funded by Rowling fellowship.Peer reviewedPublisher PD

Building a Systematic Online Living Evidence Summary of COVID-19 Research

Throughout the global coronavirus pandemic, we have seen an unprecedented volume of COVID-19 researchpublications. This vast body of evidence continues to grow, making it difficult for research users to keep up with the pace of evolving research findings. To enable the synthesis of this evidence for timely use by researchers, policymakers, and other stakeholders, we developed an automated workflow to collect, categorise, and visualise the evidence from primary COVID-19 research studies. We trained a crowd of volunteer reviewers to annotate studies by relevance to COVID-19, study objectives, and methodological approaches. Using these human decisions, we are training machine learning classifiers and applying text-mining tools to continually categorise the findings and evaluate the quality of COVID-19 evidence