Unmeasured Confounders Research Articles

Mendelian randomization: causal inference leveraging genetic data.

Mendelian randomization (MR) leverages genetic information to examine the causal relationship between phenotypes allowing for the presence of unmeasured confounders. MR has been widely applied to unresolved questions in epidemiology, making use of summary statistics from genome-wide association studies on an increasing number of human traits. However, an understanding of essential concepts is necessary for the appropriate application and interpretation of MR. This review aims to provide a non-technical overview of MR and demonstrate its relevance to psychiatric research. We begin with the origins of MR and the reasons for its recent expansion, followed by an overview of its statistical methodology. We then describe the limitations of MR, and how these are being addressed by recent methodological advances. We showcase the practical use of MR in psychiatry through three illustrative examples - the connection between cannabis use and psychosis, the link between intelligence and schizophrenia, and the search for modifiable risk factors for depression. The review concludes with a discussion of the prospects of MR, focusing on the integration of multi-omics data and its extension to delineating complex causal networks.

Mild obesity does not affect perinatal outcome in gestational carrier cycles.

Does BMI of gestational carriers (GCs) affect perinatal outcomes after embryo transfer? Overweight and class I obesity in GCs does not affect the rate of good perinatal outcomes. The use of GCs is increasing, but uniform guidance regarding optimal BMI for GCs is lacking. Women with obesity who conceive without fertility treatment or through autologous or donor in vitro fertilization are at higher risk of adverse maternal and fetal outcomes, but data on obesity in GCs are very limited. We performed a retrospective cohort study of 1121 GC cycles from January 2015 to December 2020 at US Fertility, the largest national partnership of fertility practices in the USA. All GC cycles performed at a large network of fertility practices were reviewed. Same-sex partners undergoing co-IVF were excluded. The primary outcome was good perinatal outcome from the first embryo transfer, defined as a singleton live birth at ≥37 weeks of gestation with birth weight between 2500 and 4000 g. Secondary outcome measures included frequencies of live birth, clinical pregnancy, miscarriage, full-term birth, low birth weight, large for gestational age, and cesarean delivery. A generalized linear model (log-binomial) was used for each to compare outcomes across BMI groups using normal BMI (20-24.9 kg/m2) as the reference group. Risk ratios and 95% CIs were estimated for each category group relative to normal BMI. We identified 1121 cycles in which GCs underwent first embryo transfer, of which 263 (23.5%) were in GCs with BMI >30. Demographics and reproductive history for GCs did not differ by BMI groups. The age of intended parents, use of frozen eggs, and fresh embryo transfers were higher with increasing BMI group. There were no statistically significant associations between BMI and good perinatal outcomes, live birth, clinical pregnancy, biochemical, spontaneous abortion, or low birth weight. However, among live births, higher BMI was significantly associated with birth by cesarean (P = 0.015) and large for gestational age infants (P = 0.023). This was a retrospective study, and there may be unmeasured confounders. The number of patients with BMI <20 or ≥35 was small, limiting the power for these groups. We were not able to assess all maternal and fetal outcomes. In this study, we did not identify any significant impact of BMI on the chances of having a good perinatal outcome. Prior research studies have been inconsistent and this is the largest study to date. No external funding was received for this work. The authors do not have any conflicts of interest to declare. N/A.

Race-Conscious Research Using Extracorporeal Life Support Organization Registry Data: A Narrative Review.

Race-conscious research identifies health disparities with 1) rigorous and responsible data collection, 2) intentionality and considered analyses, and 3) interpretation of results that advance health equity. Individual registries must overcome specific challenges to promote race-conscious research, and this paper describes ways to achieve this with a focus on the international Extracorporeal Life Support Organization (ELSO) registry. This article reviews ELSO registry publications that studied race with outcomes to consider whether research outputs align with race-conscious concepts and describe the direction of associations reported. Studies were identified via secondary analysis of a comprehensive scoping review on ECMO disparities. Of 32 multicenter publications, two (6%) studied race as the primary objective. Statistical analyses, confounder adjustment, and inclusive, antibiased language were inconsistently used. Only two (6%) papers explicitly discussed mechanistic drivers of inequity such as structural racism, and five (16%) discussed race variable limitations or acknowledged unmeasured confounders. Extracorporeal Life Support Organization registry publications demonstrated more adverse ECMO outcomes for underrepresented/minoritized populations than non-ELSO studies. With the objective to promote race-conscious ELSO registry research outputs, we provide a comprehensive understanding of race variable limitations, suggest reasoned retrospective analytic approaches, offer ways to interpret results that advance health equity, and recommend practice modifications for data collection.

An Instrumental Variable Approach to Learning the Causal Exposure–Response Relationship in a Randomized Dose Comparison Trial

We consider the problem of estimating the causal effect of drug exposure on clinical response in a randomized dose comparison trial where each group receives a different dose. Because exposure is not randomized, the exposure–response relationship is subject to confounding in this setting. Conventional statistical methods for confounding adjustment with a continuous exposure typically assume that there are no unmeasured confounders. This article provides an instrumental variable approach that does not require the assumption of no unmeasured confounders. Specifically, we use randomized dose assignment as an instrumental variable and characterize the causal exposure–response relationship using a control variable under a mild monotonicity assumption on individual dose–exposure profiles. Based on this characterization, we derive partial identification bounds for the causal exposure–response relationship, which are model-free but may be too wide to be useful in practice. For practicality, we further develop a simple estimation method based on a regression model for the mean response conditional on exposure and the control variable. Simulation results show that, in the presence of unmeasured confounding, the model-based estimation method reduces estimation bias effectively at the expense of increased variability, as compared to existing methods. The method is illustrated with real data from a study of chimeric antigen receptor T cell therapy for treating chronic lymphocytic leukemia.

Sharp symbolic nonparametric bounds for measures of benefit in observational and imperfect randomized studies with ordinal outcomes

Abstract The probability of benefit can be a valuable and meaningful measure of treatment effect. Particularly for an ordinal outcome, it can have an intuitive interpretation. Unfortunately, this measure, and variations of it, are not identifiable even in randomized trials with perfect compliance. There is, for this reason, a long literature on nonparametric bounds for unidentifiable measures of benefit. These have primarily focused on perfect randomized trial settings and one or two specific estimands. We expand these bounds to observational settings with unmeasured confounders and imperfect randomized trials for all three estimands considered in the literature: the probability of benefit, the probability of no harm and the relative treatment effect.

Maternal asthma and asthma exacerbation during pregnancy and attention-deficit/hyperactivity disorder in offspring: a population-based cohort study.

The link between inflammatory disorders, such as asthma, and attention deficit hyperactivity disorder (ADHD) is attracting increasing attention but few studies have examined cross-generational associations. We sought to examine associations of maternal asthma and asthma exacerbation during pregnancy, as well as paternal asthma, with the risk of ADHD in children. This population-based cohort study used data from the Taiwan National Health Insurance Research Database from 2004 to 2017. Cox regression models compared the risk of ADHD in children of parents with and without asthma, adjusting for parental sociodemographic, physical, and mental health conditions, as well as the child's birth weight, and number of births. A sibling control approach was employed to compensate for unmeasured confounders of asthma exacerbation during pregnancy. In the fully adjusted models, maternal and paternal asthma were both significantly associated with an increased risk of ADHD in offspring, with hazard ratios (HRs) of 1.36 (1.31-1.40) and 1.10 (1.05-1.14), respectively. Acute asthma exacerbation during pregnancy was not associated with the risk of further offspring ADHD (adjusted HR 1.00, 95% CI: 0.75-1.34). Both maternal and paternal asthma are associated with an increased risk of ADHD in offspring. The risk was higher from maternal asthma. However, no such association was found with maternal asthma exacerbation during pregnancy of sibling comparison.

Evaluating photodynamic therapy versus brolucizumab as a second-line treatment for polypoidal choroidal vasculopathy.

To compare the one-year outcomes between intravitreal brolucizumab (IVBr) monotherapy and photodynamic therapy (PDT) as a second-line treatment in patients with polypoidal choroidal vasculopathy (PCV) who did not respond to first-line therapy. This case-control study included eyes with PCV that do not respond to aflibercept or ranibizumab. The patients were retrospectively registered. We compared outcomes, including best-corrected visual acuity (BCVA), anatomical results, and the need for additional treatments, between IVBr and a combination therapy using PDT as second-line treatments for refractory PCV, after adjusting for potential confounders. We analyzed E-values to evaluate the robustness of the results against unmeasured confounders. Twenty-two eyes received IVBr, and twenty-four underwent PDT. No apparent differences were observed in BCVA and central macular thickness (CMT) changes from baseline between the groups (IVBr vs. PDT: BCVA, 0.01 ± 0.47 logMAR vs. 0.04 ± 0.18 logMAR, P-value = 0.756; CMT: -36.3 ± 99.4μm vs. -114.7 ± 181.4μm, P-value = 0.146). Only in the PDT group, five eyes (20.8%) did not require additional treatment after the second-line treatment, the adjusted odds ratio indicating no further treatment needed was 11.98 (95% confidence interval: 1.42-2070.07, P-value = 0.019). The E-value for the adjusted odds ratio was 23.44. Both second-line treatments for PCV exhibited similar visual and anatomical outcomes. Only in the PDT-treated eyes were there some patients who did not require further treatment after second-line therapy.

Maternal diabetes and risk of attention-deficit/hyperactivity disorder in offspring in a multinational cohort of 3.6 million mother–child pairs

Previous studies report an association between maternal diabetes mellitus (MDM) and attention-deficit/hyperactivity disorder (ADHD), often overlooking unmeasured confounders such as shared genetics and environmental factors. We therefore conducted a multinational cohort study with linked mother–child pairs data in Hong Kong, New Zealand, Taiwan, Finland, Iceland, Norway and Sweden to evaluate associations between different MDM (any MDM, gestational diabetes mellitus (GDM) and pregestational diabetes mellitus (PGDM)) and ADHD using Cox proportional hazards regression. We included over 3.6 million mother–child pairs between 2001 and 2014 with follow-up until 2020. Children who were born to mothers with any type of diabetes during pregnancy had a higher risk of ADHD than unexposed children (pooled hazard ratio (HR) = 1.16, 95% confidence interval (CI) = 1.08-1.24). Higher risks of ADHD were also observed for both GDM (pooled HR = 1.10, 95% CI = 1.04-1.17) and PGDM (pooled HR = 1.39, 95% CI = 1.25-1.55). However, siblings with discordant exposure to GDM in pregnancy had similar risks of ADHD (pooled HR = 1.05, 95% CI = 0.94-1.17), suggesting potential confounding by unmeasured, shared familial factors. Our findings indicate that there is a small-to-moderate association between MDM and ADHD, whereas the association between GDM and ADHD is unlikely to be causal. This finding contrast with previous studies, which reported substantially higher risk estimates, and underscores the need to reevaluate the precise roles of hyperglycemia and genetic factors in the relationship between MDM and ADHD.

A Swedish population-based study to evaluate the usefulness of resting heart rate in the prediction of suicidal behavior among males.

Resting heart rate has been distinctly related to both internalizing (high pulse) and externalizing (low pulse) disorders. We aimed to explore the associations between resting heart rate and suicidal behavior (nonfatal suicide attempt [SA] and suicide death [SD]) and evaluate if such associations exist beyond the effects of internalizing/externalizing symptomatology. We used Cox proportional hazards models to evaluate the associations between resting heart rate (age 19) and later SA/SD in 357,290 Swedish men. Models were controlled for internalizing disorders, externalizing disorders, and resilience (the ability to deal with adversity). Co-relative analysis (comparing pairs of different genetic relatedness) was used to control for unmeasured family confounders and improve causal inference. In baseline models, low resting heart rate was associated with SA (HR = 0.96; 95% CI: 0.95,0.98) and high resting heart rate with SD (HR = 1.04; 95% CI: 1.002,1.07). The association with SA remained after adjustment for all confounders (HR = 0.98). However, the association with SD did not persist after controlling for covariates. Co-relative analysis did not support causal associations. Our findings raise interesting etiological questions for the understanding of suicidal behaviors but do not support the usefulness of resting heart rate in suicide prediction.

Assessment of the E-value in the presence of bias amplification: a simulation study

BackgroundThe E-value, a measure that has received recent attention in the comparative effectiveness literature, reports the minimum strength of association between an unmeasured confounder and the treatment and outcome that would explain away the estimated treatment effect. This study contributes to the literature on the applications and interpretations of E-values by examining how the E-value is impacted by data with varying levels of association of unobserved covariates with the treatment and outcome measure when covariate adjustment is applied. We calculate the E-value after using regression and propensity score methods (PSMs) to adjust for differences in observed covariates. Propensity score methods are a common observational research method used to balance observed covariates between treatment groups. In practice, researchers may assume propensity score methods that balance treatment groups across observed characteristics will extend to balance of unobserved characteristics. However, that assumption is not testable and has been shown to not hold in realistic data settings. We assess the E-value when covariate adjustment affects the imbalance in unobserved covariates.MethodsOur study uses Monte Carlo simulations to evaluate the impact of unobserved confounders on the treatment effect estimates and to evaluate the performance of the E-Value sensitivity test with the application of regression and propensity score methods under varying levels of unobserved confounding. Specifically, we compare observed and unobserved confounder balance, odds ratios of treatment vs. control, and E-Value sensitivity test statistics from generalized linear model (GLM) regression models, inverse-probability weighted models, and propensity score matching models, over correlations of increasing strength between observed and unobserved confounders.ResultsWe confirm previous findings that propensity score methods – matching or weighting – may increase the imbalance in unobserved confounders. The magnitude of the effect depends on the strength of correlation between the confounder, treatment, and outcomes. We find that E-values calculated after applying propensity score methods tend to be larger when unobserved confounders result in more biased treatment effect estimates.ConclusionsThe E-Value may misrepresent the size of the unobserved effect needed to change the magnitude of the association between treatment and outcome when propensity score methods are used. Thus, caution is warranted when interpreting the E-Value in the context of propensity score methods.

Case weighted power priors for hybrid control analyses with time-to-event data.

We develop a method for hybrid analyses that uses external controls to augment internal control arms in randomized controlled trials (RCTs) where the degree of borrowing is determined based on similarity between RCT and external control patients to account for systematic differences (e.g., unmeasured confounders). The method represents a novel extension of the power prior where discounting weights are computed separately for each external control based on compatibility with the randomized control data. The discounting weights are determined using the predictive distribution for the external controls derived via the posterior distribution for time-to-event parameters estimated from the RCT. This method is applied using a proportional hazards regression model with piecewise constant baseline hazard. A simulation study and a real-data example are presented based on a completed trial in non-small cell lung cancer. It is shown that the case weighted power prior provides robust inference under various forms of incompatibility between the external controls and RCT population.

Adverse childhood experiences and mental health disorder in China: A nationwide study from CHARLS

ObjectivesMental health disorder is highly prevalent worldwide, and factors attributable to its development have not been fully understood. We aimed to explore the association of adverse childhood experiences (ACEs) with mental health disorder using data from the China Health and Retirement Longitudinal Study (CHARLS). MethodsCHARLS is an ongoing nationwide survey enrolling 17,708 residents aged ≥45 years in China. Total 20 ACE indicators were extracted. Unmeasured confounding and potential mediation were assessed, and effect-size estimates are expressed as odds ratio (OR) with 95 % confidence interval (CI). ResultsOf 10,961 assessable participants with complete information, 3652 were reported to have mental health disorder. The risk for mental health disorder was increased by 20.7 % for participants with one to less than four events of ACEs (OR: 1.207, 95 % CI: 0.711 to 2.049), and this number reached 102.7 % for participants with four or more events (OR: 2.027, 95 % CI: 1.196 to 3.436) relative to those with no ACE. In subgroup analyses, between-group risk estimates differed significantly for residence, marital status, personal asset, life satisfaction, and living standard. The E-value of 2.35 for ACEs of 4 or more events indicated the low likelihood of unmeasured confounders. In mediation analyses, the proportion eliminated was 5.1 % for continuous ACEs and 6.1 % for categorical ACEs (both P < 0.001). DiscussionOur findings indicate that ACEs, especially with four or more events, can predict the significant risk of mental health disorder, underscoring the necessity of screening mental illness based on ACEs to reduce the resultant morbidity and mortality in China.

On the effect of confounding in linear regression models: an approach based on the theory of quadratic forms

In the last two decades, significant research efforts have been dedicated to addressing the issue of spatial confounding in linear regression models. Confounding occurs when the relationship between the covariate and the response variable is influenced by an unmeasured confounder associated with both. This results in biased estimators for the regression coefficients reduced efficiency, and misleading interpretations. This article aims to understand how confounding relates to the parameters of the data generating process. The sampling properties of the regression coefficient estimator are derived as ratios of dependent quadratic forms in Gaussian random variables: this allows us to obtain exact expressions for the marginal bias and variance of the estimator, that were not obtained in previous studies. Moreover, we provide an approximate measure of the marginal bias that gives insights of the main determinants of bias. Applications in the framework of geostatistical and areal data modeling are presented. Particular attention is devoted to the difference between smoothness and variability of random vectors involved in the data generating process. Results indicate that marginal covariance between the covariate and the confounder, along with marginal variability of the covariate, play the most relevant role in determining the magnitude of confounding, as measured by the bias.

Efficacy of cytoreductive radical cystectomy in metastatic urothelial bladder cancer based on site and number of metastases

BackgroundRecent studies have highlighted the overall survival (OS) benefit of cytoreductive radical cystectomy (CRC) in metastatic bladder cancer (mBCa). Cytoreductive surgery has been established in other urologic cancers. However, the efficacy of CRC and optimal criteria for patient selection in mBCa is unclear. This study investigated the oncologic efficacy of CRC, particularly emphasizing the location and number of metastasis sites as a predictor of survival and treatment response. MethodsA retrospective analysis of cT2-4N0-3M1 mBCa patients treated with multiagent chemotherapy between 2004 and 2019 was conducted using the National Cancer Database. Patients were classified by additional treatment with CRC or conservative local treatment (CLT), consisting of transurethral resection of bladder tumor, radiation, or no local treatment and propensity score (PS) matched. Kaplan-Meier analysis and multivariate Cox Proportional Hazards model assessed the effect of CRC or CLT on OS within the matched cohort and in four subgroups (1) patients with only distant lymph node (LN) metastasis vs. any organ metastasis, (2) patients with single metastasis vs. multiple metastases. Sensitivity analysis estimated the influence of unmeasured confounders on CRC OS benefit. ResultsPropensity matching yielded 247 and 251 patients treated with CRC and CLT, respectively. Median OS in patients who received CRC was greater than that of patients treated with CLT (20.4 months vs. 12.0 months, P < 0.001). CRC was associated with reduced mortality risk in patients with only distant LN metastases (HR = 0.545, P = 0.039), any organ metastasis (HR = 0.421, P < 0.001), and single visceral metastasis (HR = 0.483, P = 0.002). However, CRC did not significantly improve OS in patients with multiple metastases (HR = 0.501, P = 0.064). ConclusionThese findings demonstrate an OS benefit of CRC with multiagent chemotherapy and pinpoint multiple visceral metastases as a potential contraindication for CRC. Although limited by the influence of unmeasured confounders, these findings may inform future prospective investigations into CRC.

Using Negative Control Populations to Assess Unmeasured Confounding and Direct Effects.

Sometimes treatment effects are absent in a subgroup of the population. For example, penicillin has no effect on severe symptoms in individuals infected by resistant Staphylococcus aureus , and codeine has no effect on pain in individuals with certain polymorphisms in the CYP2D6 enzyme. Subgroups where a treatment is ineffective are often called negative control populations or placebo groups. They are leveraged to detect bias in different disciplines. Here we present formal criteria that justify the use of negative control populations to rule out unmeasured confounding and mechanistic (direct) causal effects. We further argue that negative control populations, satisfying our formal conditions, are available in many settings, spanning from clinical studies of infectious diseases to epidemiologic studies of public health interventions. Negative control populations can also be used to rule out placebo effects in unblinded randomized experiments. As a case study, we evaluate the effect of mobile stroke unit dispatches on functional outcomes at discharge in individuals with suspected stroke, using data from a large trial. Our analysis supports the hypothesis that mobile stroke units improve functional outcomes in these individuals.

Short-Term Exposure to Fine Particulate Matter and Nitrogen Dioxide and Mortality in 4 Countries

The association between short-term exposure to air pollution and mortality has been widely documented worldwide; however, few studies have applied causal modeling approaches to account for unmeasured confounders that vary across time and space. To estimate the association between short-term changes in fine particulate matter (PM2.5) and nitrogen dioxide (NO2) concentrations and changes in daily all-cause mortality rates using a causal modeling approach. This cross-sectional study used air pollution and mortality data from Jiangsu, China; California; central-southern Italy; and Germany with interactive fixed-effects models to control for both measured and unmeasured spatiotemporal confounders. A total of 8 963 352 deaths in these 4 regions from January 1, 2015, to December 31, 2019, were included in the study. Data were analyzed from June 1, 2021, to October 30, 2023. Day-to-day changes in county- or municipality-level mean PM2.5 and NO2 concentrations. Day-to-day changes in county- or municipality-level all-cause mortality rates. Among the 8 963 352 deaths in the 4 study regions, a 10-μg/m3 increase in daily PM2.5 concentration was associated with an increase in daily all-cause deaths per 100 000 people of 0.01 (95% CI, 0.001-0.01) in Jiangsu, 0.03 (95% CI, 0.004-0.05) in California, 0.10 (95% CI, 0.07-0.14) in central-southern Italy, and 0.04 (95% CI, 0.02- 0.05) in Germany. The corresponding increases in mortality rates for a 10-μg/m3 increase in NO2 concentration were 0.04 (95% CI, 0.03-0.05) in Jiangsu, 0.03 (95% CI, 0.01-0.04) in California, 0.10 (95% CI, 0.05-0.15) in central-southern Italy, and 0.05 (95% CI, 0.04-0.06) in Germany. Significant effect modifications by age were observed in all regions, by sex in Germany (eg, 0.05 [95% CI, 0.03-0.06] for females in the single-pollutant model of PM2.5), and by urbanicity in Jiangsu (0.07 [95% CI, 0.04-0.10] for rural counties in the 2-pollutant model of NO2). The findings of this cross-sectional study contribute to the growing body of evidence that increases in short-term exposures to PM2.5 and NO2 may be associated with increases in all-cause mortality rates. The interactive fixed-effects model, which controls for unmeasured spatial and temporal confounders, including unmeasured time-varying confounders in different spatial units, can be used to estimate associations between changes in short-term exposure to air pollution and changes in health outcomes.

A sonographic endometrial thickness

BackgroundThe prevalence of placenta accreta spectrum (PAS), a potentially life-threatening condition, has exhibited a significant global rise in recent decades. Effective screening methods and early identification strategies for PAS could enable early treatment and improved outcomes. Endometrial thickness (EMT) plays a crucial role in successful embryo implantation and favorable pregnancy outcomes. Extensive research has been conducted on the impact of EMT on assisted reproductive technology cycles, specifically in terms of pregnancy rates, live birth rates, and pregnancy loss rates. However, limited knowledge exists regarding the influence of EMT on PAS. ObjectiveThis study aims to evaluate association between pre-implantation EMT and the occurrence of PAS in women undergoing assisted reproductive technology cycles. Study DesignA total of 4637 women who had not undergone prior caesarean section, conceived by in vitro fertilization or intracytoplasmic sperm injection embryo transfer treatment and subsequently delivered at the Third Affiliated Hospital of Guangzhou Medical University between January 2008 to December 2020 were included in this study. To explore the relationship between EMT and PAS, we employed smooth curve fitting, threshold effect, and saturation effect analysis. Multivariate logistic regression analysis was performed to evaluate the independent association between EMT and PAS while adjusting for potential confounding factors. Propensity score matching (PSM) was performed to reduce the influence of bias and unmeasured confounders. Furthermore, we employed causal mediation effect analysis to investigate the mediating role of EMT in the relationship between gravidity and ovarian stimulation protocol and the occurrence of PAS. ResultsAmong the 4637 women included in this study, pregnancies with PAS (159, 3.4%) had significantly thinner EMT (non-PAS 10.08±2.04 vs PAS 8.88±2.21 mm，P<0.001) during the last ultrasound before embryo transfer. By smooth curve fitting, the saturated effect was found on EMT and PAS, with an inflection point at 10.9mm. Then the EMT was divided into four groups: ≤7mm, >7 to≤10.9mm, >10.9 to≤13mm, >13mm, and the absolute rates of PAS in each group was 11.91%, 3.73%, 1.35%, and 2.54%, respectively. When compared to women with an EMT ranging from between 10.9-13mm, the odds of PAS increased from adjusted odds ratio (aOR) of 2.27 (95% CI, 1.33-3.86) for EMT ranging from 7-10.9mm to aOR of 7.15 (95% CI, 3.73-13.71) for EMT <7mm after adjusting for potential confounding factors. Placenta previa remained as an independent risk factor for PAS (aOR, 11.80; 95% CI, 7.65-18.19). Moreover, EMT <7mm was still an independent risk factor for PAS (aOR, 3.91; 95% CI, 1.57-9.73) in the matched cohort after PSM. Causal mediation analysis revealed that approximately 63.9% of the total effect of gravidity and 18.6% of the total effect of ovarian stimulation protocol on PAS were mediated by the EMT. ConclusionsThe findings from our study indicate that thin EMT is an independent risk factor for PAS in women without prior caesarean section undergoing assisted reproductive technology treatment. The clinical significance of this risk factor is slightly lower than placenta previa. Furthermore, our results demonstrate that EMT plays a significant mediating role in the relationship between gravida or ovarian stimulation protocol and PAS.

Unveiling challenges in Mendelian randomization for gene-environment interaction.

Gene-environment (GxE) interactions play a crucial role in understanding the complex etiology of various traits, but assessing them using observational data can be challenging due to unmeasured confounders for lifestyle and environmental risk factors. Mendelian randomization (MR) has emerged as a valuable method for assessing causal relationships based on observational data. This approach utilizes genetic variants as instrumental variables (IVs) with the aim of providing a valid statistical test and estimation of causal effects in the presence of unmeasured confounders. MR has gained substantial popularity in recent years largely due to the success of genome-wide association studies. Many methods have been developed for MR; however, limited work has been done on evaluating GxE interaction. In this paper, we focus on two primary IV approaches: the two-stage predictor substitution and the two-stage residual inclusion, and extend them to accommodate GxE interaction under both the linear and logistic regression models for continuous and binary outcomes, respectively. Comprehensive simulation study and analytical derivations reveal that resolving the linear regression model is relatively straightforward. In contrast, the logistic regression model presents a considerably more intricate challenge, which demands additional effort.

Clarifying the relationship between physical injuries and risk for suicide attempt in a Swedish national sample.

The Interpersonal-Psychological Theory of Suicide proposes that capability for suicide is acquired through exposure to painful and provocative events (PPEs). Although there is robust evidence for a positive association between aggregate measures of PPEs and risk for suicidal behavior, little is known about the contributions of physical injuries. The present study investigated the relationship between injuries and risk of subsequent suicide attempt (SA). Data were from Swedish population-based registers. All individuals born in Sweden between 1970 and 1990 were included (N = 1,011,725 females and 1,067,709 males). We used Cox regression models to test associations between 10 types of injuries (eye injury; fracture; dislocation/sprain/strain; injury to nerves and spinal cord; injury to blood vessels; intracranial injury; crushing injury; internal injury; traumatic amputation; and other or unspecified injuries) and risk for later SA. Analyses were stratified by sex and adjusted for year of birth and parental education. Additional models tested for differences in the pattern of associations based on age group and genetic liability for SA. In co-relative models, we tested the association between each injury type and risk for SA in relative pairs of varying genetic relatedness to control for unmeasured familial confounders. All 10 injury types were associated with elevated risk for SA (hazard ratios [HRs] = 1.2-7.0). Associations were stronger in the first year following an injury (HRs = 1.8-7.0), but HRs remained above 1 more than 1 year after injury exposure (HRs = 1.2-2.6). The strength of associations varied across injury type, sex, age, and genetic liability for SA. For example, the magnitude of the association between crushing injury and risk for SA was larger in females than males, whereas other injuries showed a similar pattern of associations across sex. Moreover, there was evidence to support positive additive interaction effects between several injury types and aggregate genetic liability for SA (relative excess risk due to interaction [RERI] = 0.1-0.3), but the majority of these interactions became non-significant or changed direction after accounting for comorbid psychiatric and substance use disorders. In co-relative models, the pattern of associations differed by injury type, such that there was evidence to support a potential causal effect of eye injury, fracture, dislocation/sprain/strain, intracranial injury, and other and unspecified injuries on risk for SA. For the remaining injury types, HRs were not significantly different from 1 in monozygotic twins, which is consistent with confounding by familial factors. Injuries are associated with increased risk for subsequent SA, particularly in the first year following an injury. While genetic and familial environmental factors may partly explain these associations, there is also evidence to support a potential causal effect of several injury types on future risk for SA.

Arrhythmia and Time of Day in Maintenance Hemodialysis: Secondary Analysis of the Monitoring in Dialysis Study

Rationale & ObjectiveThe incidence of arrhythmia varies by time of day. How this impacts individuals on maintenance dialysis is uncertain. Our objective was to quantify the relationship of arrhythmia with the time of day and the timing of dialysis. Study DesignSecondary analysis of Monitoring in Dialysis study- a multicenter prospective cohort study. Settings & ParticipantsLoop recorders were implanted for continuous cardiac monitoring in 66 participants on maintenance dialysis with a follow up of 6 months. ExposureTime of day based on 6-hour intervals OutcomesEvent rates of clinically significant arrhythmia Analytical ApproachNegative binomial mixed effects regression models for repeated measures were used to evaluate data from the Monitoring in Dialysis study for differences in diurnal patterns of clinically significant arrhythmia among those with end stage kidney disease with heart failure and end stage kidney disease alone. We additionally analyzed rates according to presence of heart failure, time of dialysis shift, and dialysis vs. non dialysis day. ResultsRates of clinically significant arrhythmia peaked between 0:00 AM-<6:00 AM and were more than 1.5-fold as frequent during this interval compared with the rest of the day. In contrast, variations in atrial fibrillation peaked between 6:00 AM-<12: PM, but variations across the day were qualitatively small. Clinically significant arrhythmia occurred at numerically higher rate in individuals with end stage kidney disease and heart failure (5.9 events/month, 95% CI: 1.3-26.8) than those without heart failure (4.0 events/month, 95% CI: 0.9-17.9). Although differences in overall rate were not significant, their periodicity was significantly different (P<0.001) with a peak between 0:00 AM-6:00 AM with kidney failure alone and between 6:00 AM-<12:00 PM in those with heart failure. Although the overall clinically significant arrhythmia rate was similar with morning compared with evening dialysis shifts (P=0.43), their periodicity differed with a peak between 0:00 AM-<6:00 AM in those with AM dialysis and a later peak between 6:00 AM-<12:00 PM in those with PM shifts LimitationsPost hoc analysis, unable to account for unmeasured confounders ConclusionClinically significant arrhythmias showed strong diurnal patterns with a maximal peak between 0:00-<6:00 AM and noon. Although overall arrhythmia rates were similar the peak rate occurred overnight in individuals without heart failure and during the morning in individuals with heart failure. Further exploration of the influence of circadian rhythm on arrhythmia in the setting of hemodialysis is needed.