Machine learning to assist risk of bias assessments in systematic reviews

Background: Risk-of-bias assessments are now a standard component of systematic reviews. At present, reviewers need to manually identify relevant parts of research articles for a set of methodological elements that affect the risk of bias, in order to make a risk-of-bias judgement for each of these elements. We investigate the use of text mining methods to automate risk-of-bias assessments in systematic reviews. We aim to identify relevant sentences within the text of included articles, to rank articles by risk of bias and to reduce the number of risk-of-bias assessments that the reviewers need to perform by hand. Methods: We use supervised machine learning to train two types of models, for each of the three risk-of-bias properties of sequence generation, allocation concealment and blinding. The first model predicts whether a sentence in a research article contains relevant information. The second model predicts a risk-of-bias value for each research article. We use logistic regression, where each independent variable is the frequency of a word in a sentence or article, respectively. Results: We found that sentences can be successfully ranked by relevance with area under the receiver operating characteristic (ROC) curve (AUC) > 0.98. Articles can be ranked by risk of bias with AUC > 0.72. We estimate that more than 33% of articles can be assessed by just one reviewer, where two reviewers are normally required. Conclusions: We show that text mining can be used to assist risk-of-bias assessments

MR-PheWAS:Hypothesis prioritization among potential causal effects of body mass index on many outcomes, using Mendelian randomization

Observational cohort studies can provide rich datasets with a diverse range of phenotypic variables. However, hypothesis-driven epidemiological analyses by definition only test particular hypotheses chosen by researchers. Furthermore, observational analyses may not provide robust evidence of causality, as they are susceptible to confounding, reverse causation and measurement error. Using body mass index (BMI) as an exemplar, we demonstrate a novel extension to the phenome-wide association study (pheWAS) approach, using automated screening with genotypic instruments to screen for causal associations amongst any number of phenotypic outcomes. We used a sample of 8,121 children from the ALSPAC dataset, and tested the linear association of a BMI-associated allele score with 172 phenotypic outcomes (with variable sample sizes). We also performed an instrumental variable analysis to estimate the causal effect of BMI on each phenotype. We found 21 of the 172 outcomes were associated with the allele score at an unadjusted p < 0.05 threshold, and use Bonferroni corrections, permutation testing and estimates of the false discovery rate to consider the strength of results given the number of tests performed. The most strongly associated outcomes included leptin, lipid profile, and blood pressure. We also found novel evidence of effects of BMI on a global self-worth score

A cross-disorder MR-pheWAS of 5 major psychiatric disorders in UK Biobank

Psychiatric disorders are highly heritable and associated with a wide variety of social adversity and physical health problems. Using genetic liability (rather than phenotypic measures of disease) as a proxy for psychiatric disease risk can be a useful alternative for research questions that would traditionally require large cohort studies with long-term follow up. Here we conducted a hypothesis-free phenome-wide association study in about 300,000 participants from the UK Biobank to examine associations of polygenic risk scores (PRS) for five psychiatric disorders (major depression (MDD), bipolar disorder (BP), schizophrenia (SCZ), attention-deficit/ hyperactivity disorder (ADHD) and autism spectrum disorder (ASD)) with 23,004 outcomes in UK Biobank, using the open-source PHESANT software package. There was evidence after multiple testing (p<2.55×10−06) for associations of PRSs with 226 outcomes, most of them attributed to associations of PRSMDD (n=120) with mental health factors and PRSADHD (n=77) with socio-demographic factors. Among others, we found strong evidence of associations between a 1 standard deviation increase in PRSADHD with 1.1 months younger age at first sexual intercourse [95% confidence interval [CI]: −1.26,−0.94]; PRSASD with 0.01% reduced lower erythrocyte distribution width [95%CI: −0.013,-0.007]; PRSSCZ with 0.98 odds of playing computer games [95%CI:0.976,0.989]; PRSMDD with a 0.11 points higher neuroticism score [95%CI:0.094,0.118] and PRSBP with 1.04 higher odds of having a university degree [95%CI:1.033,1.048]. We were able to show that genetic liabilities for five major psychiatric disorders associate with long-term aspects of adult life, including socio-demographic factors, mental and physical health. This is evident even in individuals from the general population who do not necessarily present with a psychiatric disorder diagnosis

Identifying potential causal effects of age at menarche: A Mendelian randomization phenome-wide association study

Background: Age at menarche has been associated with various health outcomes. We aimed to identify potential causal effects of age at menarche on health-related traits in a hypothesis-free manner. Methods: We conducted a Mendelian randomization phenome-wide association study (MR-pheWAS) of age at menarche with 17,893 health-related traits in UK Biobank (n = 181,318) using PHESANT. The exposure of interest was the genetic risk score for age at menarche. We conducted a second MR-pheWAS after excluding SNPs associated with BMI from the genetic risk score, to examine whether results might be due to the genetic overlap between age at menarche and BMI. We followed up a subset of health-related traits to investigate MR assumptions and seek replication in independent study populations. Results: Of the 17,893 tests performed in our MR-pheWAS, we identified 619 associations with the genetic risk score for age at menarche at a 5% false discovery rate threshold, of which 295 were below a Bonferroni-corrected P value threshold. These included potential effects of younger age at menarche on lower lung function, higher heel bone-mineral density, greater burden of psychosocial/mental health problems, younger age at first birth, higher risk of childhood sexual abuse, poorer cardiometabolic health, and lower physical activity. After exclusion of variants associated with BMI, the genetic risk score for age at menarche was related to 37 traits at a 5% false discovery rate, of which 29 were below a Bonferroni-corrected P value threshold. We attempted to replicate findings for bone-mineral density, lung function, neuroticism, and childhood sexual abuse using 5 independent cohorts/consortia. While estimates for lung function, higher bone-mineral density, neuroticism, and childhood sexual abuse in replication cohorts were consistent with UK Biobank estimates, confidence intervals were wide and often included the null. Conclusions: The genetic risk score for age at menarche was related to a broad range of health-related traits. Follow-up analyses indicated imprecise evidence of an effect of younger age at menarche on greater bone-mineral density, lower lung function, higher neuroticism score, and greater risk of childhood sexual abuse in the smaller replication samples available; hence, these findings need further exploration when larger independent samples become available

Psychosocial impact of undergoing prostate cancer screening for men with BRCA1 or BRCA2 mutations.

OBJECTIVES: To report the baseline results of a longitudinal psychosocial study that forms part of the IMPACT study, a multi-national investigation of targeted prostate cancer (PCa) screening among men with a known pathogenic germline mutation in the BRCA1 or BRCA2 genes. PARTICPANTS AND METHODS: Men enrolled in the IMPACT study were invited to complete a questionnaire at collaborating sites prior to each annual screening visit. The questionnaire included sociodemographic characteristics and the following measures: the Hospital Anxiety and Depression Scale (HADS), Impact of Event Scale (IES), 36-item short-form health survey (SF-36), Memorial Anxiety Scale for Prostate Cancer, Cancer Worry Scale-Revised, risk perception and knowledge. The results of the baseline questionnaire are presented. RESULTS: A total of 432 men completed questionnaires: 98 and 160 had mutations in BRCA1 and BRCA2 genes, respectively, and 174 were controls (familial mutation negative). Participants' perception of PCa risk was influenced by genetic status. Knowledge levels were high and unrelated to genetic status. Mean scores for the HADS and SF-36 were within reported general population norms and mean IES scores were within normal range. IES mean intrusion and avoidance scores were significantly higher in BRCA1/BRCA2 carriers than in controls and were higher in men with increased PCa risk perception. At the multivariate level, risk perception contributed more significantly to variance in IES scores than genetic status. CONCLUSION: This is the first study to report the psychosocial profile of men with BRCA1/BRCA2 mutations undergoing PCa screening. No clinically concerning levels of general or cancer-specific distress or poor quality of life were detected in the cohort as a whole. A small subset of participants reported higher levels of distress, suggesting the need for healthcare professionals offering PCa screening to identify these risk factors and offer additional information and support to men seeking PCa screening