Mendelian Randomization Studies in Atopic Dermatitis: A Systematic Review

Prior studies have found associations between atopic dermatitis (AD) and comorbidities, including depression, obesity, asthma, and allergic rhinitis. Although observational studies often cannot establish robust causality between potential risk factors and AD, Mendelian randomization minimizes confounding when exploring causality by relying on random allelic assortment at birth. In this study, we systematically reviewed 30 Mendelian randomization studies in AD. Body mass index, gut microbial flora, the IL-18 signaling pathway, and gastroesophageal reflux disease were among the causal factors for AD, whereas AD was causal for several medical conditions, including heart failure, rheumatoid arthritis, and conjunctivitis. These insights may improve preventive counseling in AD.

Sample size and power calculations for causal mediation analysis: A Tutorial and Shiny App

When designing a study for causal mediation analysis, it is crucial to conduct a power analysis to determine the sample size required to detect the causal mediation effects with sufficient power. However, the development of power analysis methods for causal mediation analysis has lagged far behind. To fill the knowledge gap, I proposed a simulation-based method and an easy-to-use web application ( https://xuqin.shinyapps.io/CausalMediationPowerAnalysis/ ) for power and sample size calculations for regression-based causal mediation analysis. By repeatedly drawing samples of a specific size from a population predefined with hypothesized models and parameter values, the method calculates the power to detect a causal mediation effect based on the proportion of the replications with a significant test result. The Monte Carlo confidence interval method is used for testing so that the sampling distributions of causal effect estimates are allowed to be asymmetric, and the power analysis runs faster than if the bootstrapping method is adopted. This also guarantees that the proposed power analysis tool is compatible with the widely used R package for causal mediation analysis, mediation, which is built upon the same estimation and inference method. In addition, users can determine the sample size required for achieving sufficient power based on power values calculated from a range of sample sizes. The method is applicable to a randomized or nonrandomized treatment, a mediator, and an outcome that can be either binary or continuous. I also provided sample size suggestions under various scenarios and a detailed guideline of app implementation to facilitate study designs.

Causal moderated mediation analysis: Methods and software

Research questions regarding how, for whom, and where a treatment achieves its effect on an outcome have become increasingly valued in substantive research. Such questions can be answered by causal moderated mediation analysis, which assesses the heterogeneity of the mediation mechanism underlying the treatment effect across individual and contextual characteristics. Various moderated mediation analysis methods have been developed under the traditional path analysis/structural equation modeling framework. One challenge is that the definitions of moderated mediation effects depend on statistical models of the mediator and the outcome, and no solutions have been provided when either the mediator or the outcome is binary, or when the mediator or outcome model is nonlinear. In addition, it remains unclear to empirical researchers how to make causal arguments of moderated mediation effects due to a lack of clarifications of the underlying assumptions and methods for assessing the sensitivity to violations of the assumptions. This article overcomes the limitations by developing general definition, identification, estimation, and sensitivity analysis for causal moderated mediation effects under the potential outcomes framework. We also developed a user-friendly R package moderate.mediation ( https://cran.r-project.org/web/packages/moderate.mediation/index.html ) that allows applied researchers to easily implement the proposed methods and visualize the initial analysis results and sensitivity analysis results. We illustrated the application of the proposed methods and the package implementation with a re-analysis of the National Evaluation of Welfare-to-Work Strategies (NEWWS) Riverside data.

Life meaning and feelings of ineffectiveness as transdiagnostic factors in eating disorder and comorbid internalizing symptomatology - A combined undirected and causal network approach

The field of eating disorders is facing problems ranging from a suboptimal classification system to low long-term success rates of treatments. There is evidence supporting a transdiagnostic approach to explain the development and maintenance of eating disorders. Meaning in life has been proposed as a promising key transdiagnostic factor that could potentially not only bridge between the different eating disorder subtypes but also explain frequent co-occurrence with symptoms of comorbid psychopathology, such as anxiety and depression. The present study used self-report data from 501 participants to construct networks of eating disorder and comorbid internalizing symptomatology, including factors related to meaning in life, i.e., presence of life meaning, perceived ineffectiveness, and satisfaction with basic psychological needs. In an undirected network model, it was found that ineffectiveness is a central node, also bridging between eating disorder and other psychological symptoms. A directed network model displayed evidence for a causal effect of presence of life meaning both on the core symptomatology of eating disorders and depressive symptoms via ineffectiveness. These results support the notion of meaning in life and feelings of ineffectiveness as transdiagnostic factors within eating disorder symptomatology in the general population.

Parental physical disease severity and severe documented physical child abuse: a prospective cohort study

Successful prevention of physical child abuse is dependent on improvements in risk assessment. The risk of abuse is assumed to increase when family stressors overcome resources. Severe physical disease can increase stress, and parental physical disease has been studied as a risk factor for physical child abuse, but with heterogeneous definitions. This study evaluated the relation between parental physical disease severity and severe documented physical child abuse. Models were based on data on children aged 0-17 years in Denmark between 1997 and 2018, and their parents. Severe documented physical child abuse was modeled as violence against a child registered by either health authorities in treatment or mortality registries, or police authorities in cases confirmed by the courts. Parental physical disease severity was modeled as the sum of Charlson Comorbidity Index scores for the child's parents. The causal connection was examined in two model types: a survival model comparing exposed with non-exposed children, adjusted for covariates at baseline, and a G-model, taking time-varying covariates, including income and parental psychiatric disease into account. Neither model showed an association between parental physical disease severity and severe documented physical child abuse, with RR 0.99 and 95% CI (0.93-1.05) for the survival model and RR 1.08 for the G-model (CI not calculated).  Conclusion: In the model studied, parental physical disease severity was not a risk factor for severe documented physical child abuse. Individual categories of physical disease remain to be examined.  Trial registration: The study was pre-registered on Open Science Framework, https://osf.io/fh2sr . What is Known: • Parental physical disease severity has been studied previously as a risk indicator of physical child abuse, but based on heterogeneous definitions. • Previous studies have not studied parental physical disease severity preceding physical child abuse. What is New: • Parental severe physical disease was not prospectively associated with severe documented physical child abuse in a survival model, a G-model and a number of sensitivity analyses, respectively. • Results should be replicated in samples from populations without universal health care, and using different categories of disease.

Unbalanced bidirectional causal association between thyroid cancer and ER-positive breast cancer: should we recommend screening for thyroid cancer in breast cancer patients?

BACKGROUND

The association between breast cancer (BC) and thyroid cancer (TC) has been studied in several epidemiological studies. However, the underlying causal relationship between them is not yet clear.

METHODS

The data from the latest large-sample genome-wide association studies (GWAS) of BC and TC were searched in the public GWAS database. The BC GWAS data included estrogen receptor (ER)-positive and negative subgroups. Two-way two-sample Mendelian Randomization (MR) was used to explore the potential causal relationship between BC and TC. Inverse variance weighting (IVW) and the MR-Egger method were used to combine the estimation of each single nucleotide variation (previous single nucleotide polymorphism). BC was taken as the result, and the effect of TC exposure was analyzed. Then, the effect of BC exposure on the result of TC was analyzed.

RESULTS

Both IVW and MR-Egger results indicated that gene-driven thyroid cancer does not cause estrogen receptor-positive breast cancer and is a protective factor (β = -1.203, SE = 4.663*10-4, P = 0.010). However, gene-driven estrogen receptor-positive breast cancer can lead to the development of thyroid cancer (β = 0.516, SE = 0.220, P = 0.019).

CONCLUSION

From the perspective of gene drive, people with TC are less likely to have ER-positive BC. In contrast, people with ER-positive BC are more likely to have TC. Therefore, it is recommended that patients with BC be screened regularly for TC.

Causal relationship between addictive behaviors and epilepsy risk: A mendelian randomization study

BACKGROUND

Previous studies have reported inconsistent results regarding the potential relationships between addictive behaviors and the risk of epilepsy.

OBJECTIVE

To assess whether genetically predicted addictive behaviors are causally associated with the risk of epilepsy outcomes.

METHODS

The causation between five addictive behaviors (including cigarettes per day, alcoholic drinks per week, tea intake, coffee intake, and lifetime cannabis use) and epilepsy was evaluated by using a two-sample Mendelian Randomization (MR) analysis. The inverse-variance weighted (IVW) method was used as the primary outcome. The other MR analysis methods (MR Egger, weighted median, simulation extrapolation corrected MR-Egger, and Mendelian Randomization Pleiotropy Residual Sum and Outlier (MR-PRESSO)) were performed to complement IVW. In addition, the robustness of the MR analysis results was assessed by leave-one-out analysis.

RESULTS

The IVW analysis method indicated an approximately 20% increased risk of epilepsy per standard deviation increase in lifetime cannabis use (odds ratio [OR], 1.20; 95% confidence interval [CI]), 1.02-1.42, P = 0.028). However, there is no causal association between the other four addictive behaviors and the risk of epilepsy (cigarettes per day: OR, 1.04; 95% CI, 0.92-1.18, P = 0.53; alcoholic drinks per week: OR, 1.31; 95% CI, 0.93-1.84, P = 0.13; tea intake: OR, 1.15; 95% CI, 0.84-1.56, P = 0.39; coffee intake: OR, 0.86; 95% CI, 0.59-1.23, P = 0.41). The other MR analysis methods and further leave-one-out sensitivity analysis suggested the results were robust.

CONCLUSION

This MR study indicated a potential genetically predicted causal association between lifetime cannabis use and higher risk of epilepsy. As for the other four addictive behaviors, no evidence of a causal relationship with the risk of epilepsy was found in this study.

Causal Effects of Circulating Lipids and Lipid-lowering Drugs on The Risk of Epilepsy: A Two-Sample Mendelian Randomization Study

BACKGROUND

Previous studies have reported inconsistent results on the association between circulating lipids and lipid-lowering drugs with the risk of epilepsy.

OBJECTIVE

To assess whether genetically predicted circulating lipids and lipid-lowering drugs are causally associated with the risk of epilepsy outcome.

METHODS

We performed a two-sample Mendelian Randomization (MR) analysis model to genetically predict the causal effects of circulating lipids (Apolipoprotein A [APOA] Apolipoprotein B [APOB], Cholesterol, High density lipoprotein cholesterol [HDL-C], Low density lipoprotein cholesterol [LDL-C], Lipoprotein A, Triglycerides) and lipid-lowering drugs (HMG-CoA reductase [HMGCR] and Proprotein Convertase Subtilisin/Kexin Type 9 [PCSK9] inhibitors) on epilepsy. Nine MR analysis methods were conducted to analyze the final results. The inverse-variance weighted (IVW) method was used as the primary outcome. The other MR analysis methods (simple mode, weighted mode, simple median, weighted median, penalized weighted median, MR Egger, and MR-Egger [bootstrap)) were conducted as the complement to IVW. In addition, the robustness of the MR analysis results was assessed by leave-one-out analysis.

RESULTS

The IVW analysis method demonstrated that there is no causal association between circulating lipids (APOA: odds ratio [OR], 0.958, 95% confidence interval (CI), 0.728-1.261, P=0.760; APOB: OR, 1.092; 95% CI, 0.979-1.219, P=0.115; Cholesterol: OR, 1.210; 95% CI, 0.981-1.494, P=0.077; HDL-C: OR, 0.964; 95% CI, 0.767-1.212, P=0.753; LDL-C: OR, 1.100; 95% CI, 0.970-1.248, P=0.137; Lipoprotein A: OR, 1.082; 95% CI, 0.849-1.379, P=0.528; Triglycerides: OR, 1.126; 95% CI, 0.932-1.360, P=0.221) and lipid-lowering drugs (HMGCR inhibitors: OR, 0.221; 95% CI, 0.006-8.408, P=0.878; PCSK9 inhibitors: OR, 1.112; 95% CI, 0.215-5.761, P=0.902) with risk of epilepsy. The other MR analysis methods and further leave-one-out sensitivity analysis confirmed the robustness of final results.

CONCLUSION

This MR study demonstrated that there was no genetically predicted causal relationships between circulating lipids and lipid-lowering drugs with the risk of epilepsy.

Investigating the causal relationships between excess adiposity and cardiometabolic health in men and women

AIMS/HYPOTHESIS

Excess adiposity is differentially associated with increased risk of cardiometabolic disease in men and women, according to observational studies. Causal inference studies largely assume a linear relationship between BMI and cardiometabolic outcomes, which may not be the case. In this study, we investigated the shapes of the causal relationships between BMI and cardiometabolic diseases and risk factors. We further investigated sex differences within the causal framework.

METHODS

To assess causal relationships between BMI and the outcomes, we used two-stage least-squares Mendelian randomisation (MR), with a polygenic risk score for BMI as the instrumental variable. To elucidate the shapes of the causal relationships, we used a non-linear MR fractional polynomial method, and used piecewise MR to investigate threshold relationships and confirm the shapes.

RESULTS

BMI was associated with type 2 diabetes (OR 3.10; 95% CI 2.73, 3.53), hypertension (OR 1.53; 95% CI 1.44, 1.62) and coronary artery disease (OR 1.20; 95% CI 1.08, 1.33), but not chronic kidney disease (OR 1.08; 95% CI 0.67, 1.72) or stroke (OR 1.08; 95% CI 0.92, 1.28). The data suggest that these relationships are non-linear. For cardiometabolic risk factors, BMI was positively associated with glucose, HbA1c, triacylglycerol levels and both systolic and diastolic BP. BMI had an inverse causal relationship with total cholesterol, LDL-cholesterol and HDL-cholesterol. The data suggest a non-linear causal relationship between BMI and BP and other biomarkers (p<0.001) except lipoprotein A. The piecewise MR results were consistent with the fractional polynomial results. The causal effect of BMI on coronary artery disease, total cholesterol and LDL-cholesterol was different in men and women, but this sex difference was only significant for LDL-cholesterol after controlling for multiple testing (p<0.001). Further, the causal effect of BMI on coronary artery disease varied by menopause status in women.

CONCLUSIONS/INTERPRETATION

We describe the shapes of causal effects of BMI on cardiometabolic diseases and risk factors, and report sex differences in the causal effects of BMI on LDL-cholesterol. We found evidence of non-linearity in the causal effect of BMI on diseases and risk factor biomarkers. Reducing excess adiposity is highly beneficial for health, but there is greater need to consider biological sex in the management of adiposity.

Distinctively mathematical explanation and the problem of directionality: A quasi-erotetic solution

The increasing preponderance of opinion that some natural phenomena can be explained mathematically has inspired a search for a viable account of distinctively mathematical explanation. Among the desiderata for an adequate account is that it should solve the problem of directionality -the reversals of distinctively mathematical explanations should not count as members among the explanatory fold but any solution must also avoid the exclusion of genuine explanations. In what follows, I introduce and defend what I refer to as a quasi-erotetic solution which provides a remedy to the problem in the form of an additional necessary condition on explanation.

Phenome-wide investigation of the causal associations between childhood BMI and adult trait outcomes: a two-sample Mendelian randomization study

Background

Childhood obesity is reported to be associated with the risk of many diseases in adulthood. However, observational studies cannot fully account for confounding factors. We aimed to systematically assess the causal associations between childhood body mass index (BMI) and various adult traits/diseases using two-sample Mendelian randomization (MR).

Methods

After data filtering, 263 adult traits genetically correlated with childhood BMI (P < 0.05) were subjected to MR analyses. Inverse-variance weighted, MR-Egger, weighted median, and weighted mode methods were used to estimate the causal effects. Multivariable MR analysis was performed to test whether the effects of childhood BMI on adult traits are independent from adult BMI.

Results

We identified potential causal effects of childhood obesity on 60 adult traits (27 disease-related traits, 27 lifestyle factors, and 6 other traits). Higher childhood BMI was associated with a reduced overall health rating (β = − 0.10, 95% CI − 0.13 to − 0.07, P = 6.26 × 10⁻¹¹). Specifically, higher childhood BMI was associated with increased odds of coronary artery disease (OR = 1.09, 95% CI 1.06 to 1.11, P = 4.28 × 10⁻¹¹), essential hypertension (OR = 1.12, 95% CI 1.08 to 1.16, P = 1.27 × 10⁻¹¹), type 2 diabetes (OR = 1.36, 95% CI 1.30 to 1.43, P = 1.57 × 10⁻³⁴), and arthrosis (OR = 1.09, 95% CI 1.06 to 1.12, P = 8.80 × 10⁻⁹). However, after accounting for adult BMI, the detrimental effects of childhood BMI on disease-related traits were no longer present (P > 0.05). For dietary habits, different from conventional understanding, we found that higher childhood BMI was associated with low calorie density food intake. However, this association might be specific to the UK Biobank population.

Conclusions

In summary, we provided a phenome-wide view of the effects of childhood BMI on adult traits. Multivariable MR analysis suggested that the associations between childhood BMI and increased risks of diseases in adulthood are likely attributed to individuals remaining obese in later life. Therefore, ensuring that childhood obesity does not persist into later life might be useful for reducing the detrimental effects of childhood obesity on adult diseases.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13073-021-00865-3.

A national difference in differences analysis of the effect of PM2.5 on annual death rates

Many studies have reported that PM2.5 was associated with mortality, but these were criticized for unmeasured confounding, not using causal modeling, and not focusing on changes in exposure and mortality rates. Recent studies have used propensity scores, a causal modeling approach that requires the assumption of no unmeasured confounders. We used differences in differences, a causal modeling approach that focuses on exposure changes, and controls for unmeasured confounders by design to analyze PM2.5 and mortality in the U.S. Medicare population, with 623, 036, 820 person-years of follow-up, and 29, 481, 444 deaths. We expanded the approach by clustering ZIP codes into 32 groups based on racial, behavioral and socioeconomic characteristics, and analyzing each cluster separately. We controlled for multiple time varying confounders within each cluster. A separate analysis examined participants whose exposure was always below 12 μg/m3. We found an increase of 1 μg/m3 in PM2.5 produced an increased risk of dying in that year of 3.85 × 10-4 (95% CI 1.95 × 10-4, 5.76 × 10-4). This corresponds to 14,000 early deaths per year per 1 μg/m3. When restricted to exposures below 12 μg/m3, the increased mortality risk was 4.26 × 10-4 (95% CI 1.43 × 10-4, 7.09 × 10-4). Using a causal modeling approach robust to omitted confounders, we found associations of PM2.5 with increased death rates, including below U.S. and E.U. standards.

Do natural experiments have an important future in the study of mental disorders?

There is an enormous interest in identifying the causes of psychiatric disorders but there are considerable challenges in identifying which risks are genuinely causal. Traditionally risk factors have been inferred from observational designs. However, association with psychiatric outcome does not equate to causation. There are a number of threats that clinicians and researchers face in making causal inferences from traditional observational designs because adversities or exposures are not randomly allocated to individuals. Natural experiments provide an alternative strategy to randomized controlled trials as they take advantage of situations whereby links between exposure and other variables are separated by naturally occurring events or situations. In this review, we describe a growing range of different types of natural experiment and highlight that there is a greater confidence about findings where there is a convergence of findings across different designs. For example, exposure to hostile parenting is consistently found to be associated with conduct problems using different natural experiment designs providing support for this being a causal risk factor. Different genetically informative designs have repeatedly found that exposure to negative life events and being bullied are linked to later depression. However, for exposure to prenatal cigarette smoking, while findings from natural experiment designs are consistent with a causal effect on offspring lower birth weight, they do not support the hypothesis that intra-uterine cigarette smoking has a causal effect on attention-deficit/hyperactivity disorder and conduct problems and emerging findings highlight caution about inferring causal effects on bipolar disorder and schizophrenia.

The relationship between ferritin and urate levels and risk of gout

Background

Ferritin positively associates with serum urate and an interventional study suggests that iron has a role in triggering gout flares. The objective of this study was to further explore the relationship between iron/ferritin and urate/gout.

Methods

European (100 cases, 60 controls) and Polynesian (100 cases, 60 controls) New Zealand (NZ) males and 189 US male cases and 60 male controls participated. The 10,727 participants without gout were from the Jackson Heart (JHS; African American = 1260) and NHANES III (European = 5112; African American = 4355) studies. Regression analyses were adjusted for age, sex, body mass index and C-reactive protein. To test for a causal relationship between ferritin and urate, bidirectional two-sample Mendelian randomization analysis was performed.

Results

Serum ferritin positively associated with gout in NZ Polynesian (OR (per 10 ng ml^− 1 increase) = 1.03, p = 1.8E–03) and US (OR = 1.11, p = 7.4E–06) data sets but not in NZ European (OR = 1.00, p = 0.84) data sets. Ferritin positively associated with urate in NZ Polynesian (β (mg dl^− 1) = 0.014, p = 2.5E–04), JHS (β = 0.009, p = 3.2E–05) and NHANES III (European β = 0.007, p = 5.1E–11; African American β = 0.011, p = 2.1E–16) data sets but not in NZ European (β = 0.009, p = 0.31) or US (β = 0.041, p = 0.15) gout data sets. Ferritin positively associated with the frequency of gout flares in two of the gout data sets. By Mendelian randomization analysis a one standard deviation unit increase in iron and ferritin was, respectively, associated with 0.11 (p = 8E–04) and 0.19 mg dl^− 1 (p = 2E–04) increases in serum urate. There was no evidence for a causal effect of urate on iron/ferritin.

Conclusions

These data replicate the association of ferritin with serum urate. Increased ferritin levels associated with gout and flare frequency. There was evidence of a causal effect of iron and ferritin on urate.

Electronic supplementary material

The online version of this article (10.1186/s13075-018-1668-y) contains supplementary material, which is available to authorized users.

Time series models of environmental exposures: Good predictions or good understanding

Time series data are popular in environmental epidemiology as they make use of the natural experiment of how changes in exposure over time might impact on disease. Many published time series papers have used parameter-heavy models that fully explained the second order patterns in disease to give residuals that have no short-term autocorrelation or seasonality. This is often achieved by including predictors of past disease counts (autoregression) or seasonal splines with many degrees of freedom. These approaches give great residuals, but add little to our understanding of cause and effect. We argue that modelling approaches should rely more on good epidemiology and less on statistical tests. This includes thinking about causal pathways, making potential confounders explicit, fitting a limited number of models, and not over-fitting at the cost of under-estimating the true association between exposure and disease.