Dose-Response Association Between Bilirubin and Cardiovascular Disease: A Systematic Review and Meta-analysis

The association between bilirubin (BIL) and cardiovascular disease (CVD) remains controversial. We performed a meta-analysis of prospective studies to evaluate this association in the general population. We searched PubMed, EMBASE, Web of Science, Cochrane, and Scopus databases through to September 2021. The Newcastle-Ottawa Quality Assessment Scale was used to assess study quality. The pooled effect estimate was calculated by the fixed-effect model or random-effect model. We included 12 prospective studies (368 567 participants). The pooled risk ratio of CVD for the lowest vs highest groups of BIL levels was .75 (95% CI: .58-.97) with high heterogeneity (I2 = 87.5%, P < .001). Similar associations were observed for coronary heart disease and stroke. We further performed a “dose-response” meta-analysis, and a significant U-shaped relationship between circulating (most values were serum bilirubin, but a few were plasma bilirubin) BIL and CVD ( P < .01) was observed. The lowest risk of CVD events was observed in participants with a BIL of 17-20 µmol/L in serum and/or plasma. In conclusion, there was a U-shaped dose-response relationship between BIL and CVD, especially for men. Further studies are needed to confirm our findings and identify the mechanisms involved as well as any prognostic or therapeutic potential.

Hypothesis-free detection of gene-interaction effects on biomarker concentration in UK Biobank using variance prioritisation

Blood biomarkers include disease intervention targets that may interact with genetic and environmental factors resulting in subgroups of individuals who respond differently to treatment. Such interactions may be observed in genetic effects on trait variance. Variance prioritisation is an approach to identify genetic loci with interaction effects by estimating their association with trait variance, even where the modifier is unknown or unmeasured. Here, we develop and evaluate a regression-based Brown-Forsythe test and variance effect estimate to detect such interactions. We provide scalable open-source software (varGWAS) for genome-wide association analysis of SNP-variance effects (https://github.com/MRCIEU/varGWAS) and apply our software to 30 blood biomarkers in UK Biobank. We find 468 variance quantitative trait loci across 24 biomarkers and follow up findings to detect 82 gene-environment and six gene-gene interactions independent of strong scale or phantom effects. Our results replicate existing findings and identify novel epistatic effects of TREH rs12225548 x FUT2 rs281379 and TREH rs12225548 x ABO rs635634 on alkaline phosphatase and ZNF827 rs4835265 x NEDD4L rs4503880 on gamma glutamyltransferase. These data could be used to discover possible subgroup effects for a given biomarker during preclinical drug development.

Mean platelet volume and polycystic ovary syndrome: a systematic review and meta-analysis

Objective This meta-analysis evaluated the association between the mean platelet volume (MPV) and polycystic ovary syndrome (PCOS). Methods A systematic literature search using PubMed, EMBASE, and Web of Science databases until June 2021 was conducted. Pooled standardized mean differences (SMD) and 95% confidence intervals (CI) were determined using a random effects model. Results Ten studies involving 866 women with PCOS and 548 age- and body mass index-matched women without PCOS were included. The MPV was significantly increased in women with PCOS compared with non-PCOS women (SMD = 0.43, 95% CI = 0.13–0.72). Subgroup analyses showed that this trend was consistent in cross-sectional studies (SMD = 0.44, 95% CI = 0.03–0.86) and in Turkish women (SMD = 0.46, 95% CI = 0.13–0.79). Meta-regression analysis revealed a marginally positive correlation between the MPV and the homoeostasis model assessment of insulin resistance in women with PCOS. The sensitivity analysis showed that the effect estimate was robust and stable, and publication bias was not evidenced in the pooled analysis. Conclusions This meta-analysis revealed that women with PCOS have a significantly increased MPV than women without PCOS, which is probably associated with insulin resistance. INPLASY registration number: INPLASY2021100021.

The Accuracy of Confocal Laser Endomicroscopy in Diagnosing Bladder Cancer: A Systematic Review & Meta-Analyses

Background: Multiple advancements of endoscopic technology were designed to enhance the sensitivity and specificity of the diagnostic tools of bladder cancer; thus, we perform a meta-analysis to compare diagnostic performance between confocal laser endomicroscopy (CLE) and biopsy for detecting bladder cancer.Methods: We compared CLE’s accuracy in diagnosing bladder cancer reported by studies obtained from the electronic database MEDLINE, CENTRAL, and CINAHL, from May to June 2020. The pooled effect estimate was calculated employing the DerSimonian and Laird random-effects model. We only included moderate to high-quality studies, which had been assessed by the QUADAS-2 tool.Results: Eight studies were included in this review; five of those were good-quality studies. A total of 519 samples from 345 patients were included in the pooled effect estimate calculation. Pooled sensitivity and specificity of CLE in diagnosing bladder cancer were 90.2% (0.86, 0.93) and 78.1% (0.71, 0.85), respectively. The use of white-light cystoscopy (WLC) before CLE increased its specificity (56.8% versus 84.6%). Pooled sensitivity and specificity of CLE in predicting lowgrade lesion were 73% (0.66, 0.80) dan 83% (0.78, 0.87), respectively. Meanwhile, pooled sensitivity and specificity of CLE in predicting high-grade lesion were 73% (0.66, 0.78) and 79% (0.73, 0.83), respectively.Conclusions: CLE has good accuracy in distinguishing malignant and benign tumors. Grading tumors with this modality is also accurate. The use of probe CLE (pCLE), coupled with WLC, will increase its specificity.

Methodological Perspectives on the Study of the Health Effects of Unemployment – Reviewing the Mode of Unemployment, the Statistical Analysis Method and the Role of Confounding Factors

Introduction: Studying the relationship between unemployment and health raises many methodological challenges. In the current study, the aim was to evaluate how different ways of measuring unemployment and the choice of statistical model affects the effect estimate. Methods: The Northern Swedish cohort was used, and two follow-up surveys thereof from 1995 and 2007, as well as register data about unemployment. Self-reported current unemployment, self-reported accumulated unemployment and register-based accumulated unemployment were used to measure unemployment and its effect on self-reported health was evaluated. Analyses were conducted with G-computation, logistic regression and three estimators for the inverse probability weighting propensity scores, and 11 potentially confounding variables were part of the analyses. Results were presented with absolute differences in the proportion with poor self-reported health between unemployed and employed individuals for all estimators but logistic regression. Results: Of the initial 1083 pupils in the cohort, 488–693 individuals were defined as employed and 61–214 individuals were defined as unemployed in our different analyses. In the analyses, the deviation was large between the unemployment measures, with a difference of at least 2.5% in effect size when unemployed was compared with employed for the self-reported and register-based unemployment modes. The choice of statistical method only had a small influence on effect estimates and the deviation was in most cases lower than 1%. When models were compared based on the choice of potential confounders in the analytical model, the deviations were rarely above 0.6% when comparing models with 4 and 11 potential confounders. Our variable for health selection was the only one that strongly affected estimates when it was not part of the statistical model. Conclusions: Misspecifications of the statistical model or choice of analytical method might not matter much for effect estimates of the relationship between unemployment and health except for the inclusion of a variable measuring earlier health status before becoming unemployed. On the other hand, how unemployment is measured is highly important.

A three-wave longitudinal study on the relation between commuting strain and somatic symptoms in university students: exploring the role of learning-family conflicts

Background We examine the role of learning-family conflicts for the relation between commuting strain and health in a sample of medical university students. The first goal of the study was to investigate the mediating role of learning-family conflicts. The second goal was to extend the temporal view on relations between study variables. Therefore, we differentiated long-term systematic change among variables over a period of two-years from a dynamic perspective with repeated commuting events on the individual level of analyses. Methods We applied a multilevel research design and collected survey data from 128 medical students on three points in time (N = 339 measurement points). Participants informed about commuting strain, learning-family conflicts, somatic symptoms, as well as commuting distance and time. Results Bayesian multilevel analyses showed that results differed with regard to level of analysis: while learning-family conflicts mediated the relation between commuting strain and somatic symptoms on a systematic aggregation-level perspective of analysis (indirect effect estimatebetween = 0.13, SE = .05, 95% CI [0.05; ∞), Evidence Ratio = 250.57), this was not the case on the dynamic event perspective (indirect effect estimatewithin = 0.00, SE = 0.00, 95% CI [− 0.01; ∞), Evidence Ratio = 0.84). Conclusions We demonstrated that learning-family conflicts explain why commuting may have unfavorable effects on health for medical students. We also showed that it is the long-term commuting experience that is related to health complaints and not the single commuting event. This means that short-term deviations from general levels of commuting strain do not cause somatic symptoms, but general high levels of commuting strain do instead.

Oxygen systems strengthening as an intervention to prevent childhood deaths due to pneumonia in low-resource settings: systematic review, meta-analysis and cost-effectiveness

ObjectivesIncreasing access to oxygen services may improve outcomes among children with pneumonia living in low-resource settings. We conducted a systematic review to estimate the impact and cost-effectiveness of strengthening oxygen services in low-income and middle-income countries with the objective of including oxygen as an intervention in the Lives Saved Tool.DesignWe searched EMBASE and PubMed on 31 March 2021 using keywords and MeSH terms related to ‘oxygen’, ‘pneumonia’ and ‘child’ without restrictions on language or date. The risk of bias was assessed for all included studies using the quality assessment tool for quantitative studies, and we assessed the overall certainty of the evidence using Grading of Recommendations, Assessment, Development and Evaluations. Meta-analysis methods using random effects with inverse-variance weights was used to calculate a pooled OR and 95% CIs. Programme cost data were extracted from full study reports and correspondence with study authors, and we estimated cost-effectiveness in US dollar per disability-adjusted life-year (DALY) averted.ResultsOur search identified 665 studies. Four studies were included in the review involving 75 hospitals and 34 485 study participants. We calculated a pooled OR of 0.52 (95% CI 0.39 to 0.70) in favour of oxygen systems reducing childhood pneumonia mortality. The median cost-effectiveness of oxygen systems strengthening was $US62 per DALY averted (range: US$44–US$225). We graded the risk of bias as moderate and the overall certainty of the evidence as low due to the non-randomised design of the studies.ConclusionOur findings suggest that strengthening oxygen systems is likely to reduce hospital-based pneumonia mortality and may be cost-effective in low-resource settings. Additional implementation trials using more rigorous designs are needed to strengthen the certainty in the effect estimate.

Comparative effectiveness and safety of pharmaceuticals assessed in observational studies compared with randomized controlled trials

Background There have been ongoing efforts to understand when and how data from observational studies can be applied to clinical and regulatory decision making. The objective of this review was to assess the comparability of relative treatment effects of pharmaceuticals from observational studies and randomized controlled trials (RCTs). Methods We searched PubMed and Embase for systematic literature reviews published between January 1, 1990, and January 31, 2020, that reported relative treatment effects of pharmaceuticals from both observational studies and RCTs. We extracted pooled relative effect estimates from observational studies and RCTs for each outcome, intervention-comparator, or indication assessed in the reviews. We calculated the ratio of the relative effect estimate from observational studies over that from RCTs, along with the corresponding 95% confidence interval (CI) for each pair of pooled RCT and observational study estimates, and we evaluated the consistency in relative treatment effects. Results Thirty systematic reviews across 7 therapeutic areas were identified from the literature. We analyzed 74 pairs of pooled relative effect estimates from RCTs and observational studies from 29 reviews. There was no statistically significant difference (based on the 95% CI) in relative effect estimates between RCTs and observational studies in 79.7% of pairs. There was an extreme difference (ratio < 0.7 or > 1.43) in 43.2% of pairs, and, in 17.6% of pairs, there was a significant difference and the estimates pointed in opposite directions. Conclusions Overall, our review shows that while there is no significant difference in the relative risk ratios between the majority of RCTs and observational studies compared, there is significant variation in about 20% of comparisons. The source of this variation should be the subject of further inquiry to elucidate how much of the variation is due to differences in patient populations versus biased estimates arising from issues with study design or analytical/statistical methods.

Immunomodulation and Reduction of Thromboembolic Risk in Hospitalized COVID-19 Patients: Systematic Review and Meta-Analysis of Randomized Trials

Background: We aimed to investigate the potential beneficial effect of immunomodulation therapy on the thromboembolic risk in hospitalized COVID-19 patients. Methods: We searched PubMed and Scopus for randomized trials reporting the outcomes of venous thromboembolism (VTE), ischemic stroke or systemic embolism, myocardial infarction, any thromboembolic event, and all-cause mortality in COVID-19 patients treated with immunomodulatory agents. Odds ratios (OR) and 95% confidence intervals (CI) were calculated using the Mantel–Haenszel random effects method. Results: Among 8499 patients hospitalized with COVID-19, 4638 were treated with an immunomodulatory agent, 3861—with usual care only. Among the patients prescribed immunomodulatory agents, there were 1.77 VTEs per 100 patient-months compared to 2.30 among those treated with usual care (OR: 0.84, 95% CI: 0.61–1.16; I2: 0%). Among the patients who received an interleukin 6 (IL-6) antagonist, VTEs were reported in 12 among the 1075 patients compared to 20 among the 848 receiving the usual care (OR: 0.52, 95% CI: 0.22–1.20; I2: 6%). Immunomodulators as an add-on to usual care did not reduce the risk of stroke or systemic embolism (OR: 1.10, 95% CI: 0.50–2.40; I2: 0%) or of myocardial infarction (OR: 1.06, 95% CI: 0.47–2.39; I2: 0%) and there was a nonsignificant reduction in any thromboembolic event (OR: 0.86, 95% CI: 0.65–1.14; I2: 0%). Conclusions: We did not identify a statistically significant effect of immunomodulation on prevention of thromboembolic events in COVID-19. However, given the large effect estimate for VTE prevention, especially in the patients treated with IL-6 antagonists, we cannot exclude a potential effect of immunomodulation.