MultiSC: a deep learning pipeline for analyzing multiomics single-cell data.

Single-cell technologies enable researchers to investigate cell functions at an individual cell level and study cellular processes with higher resolution. Several multi-omics single-cell sequencing techniques have been developed to explore various aspects of cellular behavior. Using NEAT-seq as an example, this method simultaneously obtains three kinds of omics data for each cell: gene expression, chromatin accessibility, and protein expression of transcription factors (TFs). Consequently, NEAT-seq offers a more comprehensive understanding of cellular activities in multiple modalities. However, there is a lack of tools available for effectively integrating the three types of omics data. To address this gap, we propose a novel pipeline called MultiSC for the analysis of MULTIomic Single-Cell data. Our pipeline leverages a multimodal constraint autoencoder (single-cell hierarchical constraint autoencoder) to integrate the multi-omics data during the clustering process and a matrix factorization-based model (scMF) to predict target genes regulated by a TF. Moreover, we utilize multivariate linear regression models to predict gene regulatory networks from the multi-omics data. Additional functionalities, including differential expression, mediation analysis, and causal inference, are also incorporated into the MultiSC pipeline. Extensive experiments were conducted to evaluate the performance of MultiSC. The results demonstrate that our pipeline enables researchers to gain a comprehensive view of cell activities and gene regulatory networks by fully leveraging the potential of multiomics single-cell data. By employing MultiSC, researchers can effectively integrate and analyze diverse omics data types, enhancing their understanding of cellular processes.

Sensitivity analysis for unmeasured confounding in estimating the difference in restricted mean survival time.

The difference in restricted mean survival time has been increasingly used as an alternative measure to the hazard ratio in survival analysis. Although some statistical methods have been developed for estimating the difference in restricted mean survival time adjusted for measured confounders in observational studies, the impact of unmeasured confounding on the estimate has rarely been assessed. We develop a novel sensitivity analysis for the estimate of the difference in restricted mean survival time with respect to unmeasured confounding. After formulating the sensitivity analysis problem as an optimization problem, we explain how to obtain the sensitivity range of the difference in restricted mean survival time efficiently and assess its uncertainty using the percentile bootstrap confidence interval. Analytic results are provided for some important survival settings. Simulation studies show that the proposed methods perform well in various settings. We illustrate the proposed sensitivity analysis method by analyzing data from the German Breast Cancer Study Group study.

Sexual Harassment, Sexual Violence, and Mental Health Outcomes: Causal Inference with Ambiguous Exposures.

Social exposures and their impact on mental health has proven hard to capture, partly owing to the complex and multifaceted nature of social reality. Sexual harassment and sexual violence (SHV) are no exceptions. SHV can be conceptualized as a continuum of negative sexual experiences whose severity vary depending on multiple determinants. Further, SHV can be conceptualized as either discrete events or as a generally hostile sexual environment represented by latent variables. With any of these conceptualizations, SHV constitutes a broad construct containing many kinds of negative experiences. This ambiguity poses challenges for determining the mental health consequences, as different forms of SHV may vary in terms of their mental health impact. We discuss different conceptualizations of SHV in relation to mental health outcomes through the lens of the potential outcomes framework, with a focus on the consistency condition. The multiple versions of treatment theory is presented to show how to provide formal interpretations of causal estimates under ambiguous exposures. Lastly, we provide suggestions on how the increase the clarity and interpretability of the effects of SHV on mental health, by increasing the precision of the causal questions and the use of more specific definitions of SHV.

Prognostic score-based model averaging approach for propensity score estimation.

BACKGROUND: Propensity scores (PS) are typically evaluated using balance metrics that focus on covariate balance, often without considering their predictive power for the outcome. This approach may not always result in optimal bias reduction in the treatment effect estimate. To address this issue, evaluating covariate balance through prognostic scores, which account for the relationship between covariates and the outcome, has been proposed. Similarly, using a typical model averaging approach for PS estimation that minimizes prediction error for treatment status and covariate imbalance does not necessarily optimize PS-based confounding adjustment. As an alternative approach, using the averaged PS model that minimizes inter-group differences in the prognostic score may further reduce bias in the treatment effect estimate. Moreover, since the prognostic score is also an estimated quantity, model averaging in the prognostic scores can help identify a better prognostic score model. Utilizing the model-averaged prognostic scores as the balance metric for constructing the averaged PS model can contribute to further decreasing bias in treatment effect estimates. This paper demonstrates the effectiveness of the PS model averaging approach based on prognostic score balance and proposes a method that uses the model-averaged prognostic score as a balance metric, evaluating its performance through simulations and empirical analysis. METHODS: We conduct a series of simulations alongside an analysis of empirical observational data to compare the performances of weighted treatment effect estimates using the proposed and existing approaches. In our examination, we separately provid four candidate estimates for the PS and prognostic score models using traditional regression and machine learning methods. The model averaging of PS based on these candidate estimators is performed to either maximize the prediction accuracy of the treatment or to minimize intergroup differences in covariate distributions or prognostic scores. We also utilize not only the prognostic scores from each candidate model but also an averaged score that best predicted the outcome, for the balance assessment. RESULTS: The simulation and empirical data analysis reveal that our proposed model-averaging approaches for PS estimation consistently yield lower bias and less variability in treatment effect estimates across various scenarios compared to existing methods. Specifically, using the optimally averaged prognostic scores as a balance metric significantly improves the robustness of the weighted treatment effect estimates. DISCUSSION: The prognostic score-based model averaging approach for estimating PS can outperform existing model averaging methods. In particular, the estimator using the model averaging prognostic score as a balance metric can produce more robust estimates. Since our results are obtained under relatively simple conditions, applying them to real data analysis requires adjustments to obtain accurate estimates according to the complexity and dimensionality of the data. CONCLUSIONS: Using the prognostic score as the balance metric for the PS model averaging enhances the performance of the treatment effect estimator, which can be recommended for a wide variety of situations. When applying the proposed method to real-world data, it is important to use it in conjunction with techniques that mitigate issues arising from the complexity and high dimensionality of the data.

The evolution of honest and dishonest signals of fighting ability.

Competition over resources is often decided via aggressive interactions, which may or may not escalate to all-out fights. Weapons and body size play important roles in such interactions, as they often provide reliable cues of an individual's fighting ability. In contrast, traits like nonfunctional display "weapons" may dishonestly exaggerate fighting ability in order to intimidate opponents into retreating. Signals used in the context of aggressive interactions potentially evolve via very different mechanisms than courtship signals, but have received far less theoretical attention. Here, we contrast the evolution of honest and dishonest signals of fighting ability using a game-theoretic model. Contests are assumed to consist of three discrete stages: display from a distance, low-intensity physical contact, and fighting. At each stage, contestants evaluate the fighting ability of their opponents in comparison to their own based on body size and an aggressive signal. After making this evaluation, contestants decide whether to escalate the interaction or cede to their opponent. Our model predicts that both honest and dishonest aggressive signals can exaggerate far beyond their ecological optima, but that exaggeration is more pronounced for honest signals. Equilibrium levels of aggressiveness-as measured by individuals' propensity to escalate aggressive interactions to the next stage-are independent of the honesty of signals. We additionally develop a novel approach, based on causal inference theory, to understand how changes in underlying parameters shape the coevolution of multiple traits. We use this approach to study how aggression coevolves with body and signal size in response to changes in the cost of losing a fight.

Causal inference in multi-cohort studies using the target trial framework to identify and minimize sources of bias.

Longitudinal cohort studies, which follow a group of individuals over time, provide the opportunity to examine causal effects of complex exposures on long-term health outcomes. Utilizing data from multiple cohorts has the potential to add further benefit by improving precision of estimates through data pooling and by allowing examination of effect heterogeneity through replication of analyses across cohorts. However, the interpretation of findings can be complicated by biases that may be compounded when pooling data, or, contribute to discrepant findings when analyses are replicated. The "target trial" is a powerful tool for guiding causal inference in single-cohort studies. Here we extend this conceptual framework to address the specific challenges that can arise in the multi-cohort setting. By representing a clear definition of the target estimand, the target trial provides a central point of reference against which biases arising in each cohort and from data pooling can be systematically assessed. Consequently, analyses can be designed to reduce these biases and the resulting findings appropriately interpreted in light of potential remaining biases. We use a case study to demonstrate the framework and its potential to strengthen causal inference in multi-cohort studies through improved analysis design and clarity in the interpretation of findings. Special Collection: N/A.

Evaluating Natural Course Performance in Parametric G-formula: Review of Current Practice and Illustration Based on the United Autoworkers-General Motors Cohort.

The parametric g-formula is a causal inference method that appropriately adjusts for time-varying confounding affected by prior exposure. Like all parametric methods, it assumes correct model specification, usually assessed by comparing the observed outcome with the simulated outcome under no intervention (natural course). However, it is unclear how to evaluate natural course performance and whether other variables should also be considered. We reviewed current practices for evaluating model misspecification in applications of parametric g-formula. To illustrate the pitfalls of current practices, we then applied the parametric g-formula to examine cardiovascular disease mortality in relation to occupational exposure in the United Autoworkers-General Motors cohort (UAW-GM), comparing 20 parametric model sets and qualitatively assessing natural course performance for all time-varying variables over follow-up. We found that current practices of evaluating model misspecification are often insufficient, increasing risk of bias and statistical cherry picking. Based on our motivational analyses of the UAW-GM cohort, good natural course performance of the outcome does not guarantee good simulations of other covariates; poor predictions of exposures and covariates may still exist. We recommend reporting natural course performance for all time-varying variables at all time-points. Objective criteria for evaluating model misspecification in parametric g-formula need to be developed.

Enhancing causal inference in population-based neuroimaging data in children and adolescents.

Recent years have seen the increasing availability of large, population-based, longitudinal neuroimaging datasets, providing unprecedented capacity to examine brain-behavior relationships in the neurodevelopmental context. However, the ability of these datasets to deliver causal insights into brain-behavior relationships relies on the application of purpose-built analysis methods to counter the biases that otherwise preclude causal inference from observational data. Here we introduce these approaches (i.e., propensity score-based methods, the 'G-methods', targeted maximum likelihood estimation, and causal mediation analysis) and conduct a review to determine the extent to which they have been applied thus far in the field of developmental cognitive neuroscience. We identify just eight relevant studies, most of which employ propensity score-based methods. Many approaches are entirely absent from the literature, particularly those that promote causal inference in settings with complex, multi-wave data and repeated neuroimaging assessments. Causality is central to an etiological understanding of the relationship between the brain and behavior, as well as for identifying targets for prevention and intervention. Careful application of methods for causal inference may help the field of developmental cognitive neuroscience approach these goals.

Econometric Causality: The Central Role of Thought Experiments.

This paper examines the econometric causal model and the interpretation of empirical evidence based on thought experiments that was developed by Ragnar Frisch and Trygve Haavelmo. We compare the econometric causal model with two currently popular causal frameworks: the Neyman-Rubin causal model and the Do-Calculus. The Neyman-Rubin causal model is based on the language of potential outcomes and was largely developed by statisticians. Instead of being based on thought experiments, it takes statistical experiments as its foundation. The Do-Calculus, developed by Judea Pearl and co-authors, relies on Directed Acyclic Graphs (DAGs) and is a popular causal framework in computer science and applied mathematics. We make the case that economists who uncritically use these frameworks often discard the substantial benefits of the econometric causal model to the detriment of more informative analyses. We illustrate the versatility and capabilities of the econometric framework using causal models developed in economics.

Micronutrients, Vitamin D, and Inflammatory Biomarkers in COVID-19: A Systematic Review and Meta-analysis of Causal Inference Studies.

CONTEXT: Experimental and observational studies suggest that circulating micronutrients, including vitamin D (VD), may increase COVID-19 risk and its associated outcomes. Mendelian randomization (MR) studies provide valuable insight into the causal relationship between an exposure and disease outcomes. OBJECTIVES: The aim was to conduct a systematic review and meta-analysis of causal inference studies that apply MR approaches to assess the role of these micronutrients, particularly VD, in COVID-19 risk, infection severity, and related inflammatory markers. DATA SOURCES: Searches (up to July 2023) were conducted in 4 databases. DATA EXTRACTION AND ANALYSIS: The quality of the studies was evaluated based on the MR-STROBE guidelines. Random-effects meta-analyses were conducted where possible. RESULTS: There were 28 studies (2 overlapped) including 12 on micronutrients (8 on VD) and COVID-19, 4 on micronutrients (all on VD) and inflammation, and 12 on inflammatory markers and COVID-19. Some of these studies reported significant causal associations between VD or other micronutrients (vitamin C, vitamin B6, iron, zinc, copper, selenium, and magnesium) and COVID-19 outcomes. Associations in terms of causality were also nonsignificant with regard to inflammation-related markers, except for VD levels below 25 nmol/L and C-reactive protein (CRP). Some studies reported causal associations between cytokines, angiotensin-converting enzyme 2 (ACE2), and other inflammatory markers and COVID-19. Pooled MR estimates showed that VD was not significantly associated with COVID-19 outcomes, whereas ACE2 increased COVID-19 risk (MR odds ratio = 1.10; 95% CI: 1.01-1.19) but did not affect hospitalization or severity of the disease. The methodological quality of the studies was high in 13 studies, despite the majority (n = 24) utilizing 2-sample MR and evaluated pleiotropy. CONCLUSION: MR studies exhibited diversity in their approaches but do not support a causal link between VD/micronutrients and COVID-19 outcomes. Whether inflammation mediates the VD-COVID-19 relationship remains uncertain, and highlights the need to address this aspect in future MR studies exploring micronutrient associations with COVID-19 outcomes. SYSTEMATIC REVIEW REGISTRATION: PROSPERO registration no. CRD42022328224.

Core Concepts in Pharmacoepidemiology: Time-To-Event Analysis Approaches in Pharmacoepidemiology.

AIM: This article provides an overview of time-to-event (TTE) analysis in pharmacoepidemiology. MATERIALS & METHODS: The key concept of censoring is reviewed, including right-, left-, interval- and informative censoring. Simple descriptive statistics are explained, including the nonparametric estimation of the TTE distribution as per Kaplan-Meier method, as well as more complex TTE regression approaches, including the parametric Accelerated Failure Time (AFT) model and the semi-parametric Cox Proportional Hazards and Restricted Mean Survival Time (RMST) models. Additional approaches and various TTE model extensions are presented as well. Finally, causal inference for TTE outcomes is addressed. RESULTS: A thorough review of the available concepts and methods outlines the immense variety of available and useful TTE models. DISCUSSION: There may be underused TTE concepts and methods, which are highlighted to raise awareness for researchers who aim to apply the most appropriate TTE approach for their study. CONCLUSION: This paper constitutes a modern summary of TTE analysis concepts and methods. A curated list of references is provided.

Cross Modality Bias in Visual Question Answering: A Causal View with Possible Worlds VQA.

To increase the generalization capability of VQA systems, many recent studies have tried to de-bias spurious language or vision associations that shortcut the question or image to the answer. Despite these efforts, the literature fails to address the confounding effect of vision and language simultaneously. As a result, when they reduce bias learned from one modality, they usually increase bias from the other. In this paper, we first model a confounding effect that causes language and vision bias simultaneously, then propose a counterfactual inference to remove the influence of this effect. The model trained in this strategy can concurrently and efficiently reduce vision and language bias. To the best of our knowledge, this is the first work to reduce biases resulting from confounding effects of vision and language in VQA, leveraging causal explain-away relations. We accompany our method with an explain-away strategy, pushing the accuracy of the questions with numerical answers results compared to existing methods that have been an open problem. The proposed method outperforms the state-of-the-art methods in VQA-CP v2 datasets. R2: Providing brief insights into the experimental setup and results would add valuable context for readers. In response to R2, we released the code and documentation for the implementation as follows. Our codes are available at https://github.com/ali-vosoughi/PW-VQA.

Causal Hierarchy in the Financial Market Network-Uncovered by the Helmholtz-Hodge-Kodaira Decomposition.

Granger causality can uncover the cause-and-effect relationships in financial networks. However, such networks can be convoluted and difficult to interpret, but the Helmholtz-Hodge-Kodaira decomposition can split them into rotational and gradient components which reveal the hierarchy of the Granger causality flow. Using Kenneth French's business sector return time series, it is revealed that during the COVID crisis, precious metals and pharmaceutical products were causal drivers of the financial network. Moreover, the estimated Granger causality network shows a high connectivity during the crisis, which means that the research presented here can be especially useful for understanding crises in the market better by revealing the dominant drivers of crisis dynamics.

The Causal Effect of Urate Level on Female Infertility: A Mendelian Randomization Study.

BACKGROUND/OBJECTIVE: This study aimed to investigate the causal relationship between urate level and female infertility using Mendelian randomization (MR) analysis. METHODS: To identify instrumental variables, we selected independent genetic loci associated with serum urate levels in individuals of European ancestry, utilizing data from large-scale genome-wide association studies (GWAS). The GWAS dataset included information on serum urate levels from 288,649 CKDGen participants. Female infertility data, including different etiologic classifications, consisted of 13,142 female infertility patients and 107,564 controls. We employed four MR methods, namely inverse variance weighted (IVW), MR-Egger, weighted median, and weighted model, to investigate the causal relationship between urate levels and female infertility. The Cochran Q-test was used to assess heterogeneity among single nucleotide polymorphisms (SNPs), and the MR-Egger intercept test was employed to evaluate the presence of horizontal pleiotropy. Additionally, a "leave-one-out" sensitivity analysis was conducted to examine the influence of individual SNPs on the MR study. RESULTS: The IVW analysis demonstrated that elevated serum urate levels increased the risk of female infertility (odds ratio [OR] = 1.18, 95% confidence interval [CI]: 1.07-1.33). Furthermore, serum urate levels were found to be associated with infertility due to cervical, vaginal, or other unknown causes (OR = 1.16, 95% CI: 1.06-1.26), also confirmed by other methods. Heterogeneity among instrumental variables was assessed using Cochran's Q-test (p < 0.05), so a random-effects IVW approach was employed in the effects model. The MR-Egger intercept test indicated no presence of horizontal pleiotropy. A "leave-one-out" sensitivity analysis was conducted, demonstrating that no individual SNP had a substantial impact on the overall findings. CONCLUSIONS: In the European population, the urate level is significantly and causally associated with an increased risk of female infertility.

Causal Metabolomic and Lipidomic Analysis of Circulating Plasma Metabolites in Autism: A Comprehensive Mendelian Randomization Study with Independent Cohort Validation.

BACKGROUND: The increasing prevalence of autism spectrum disorder (ASD) highlights the need for objective diagnostic markers and a better understanding of its pathogenesis. Metabolic differences have been observed between individuals with and without ASD, but their causal relevance remains unclear. METHODS: Bidirectional two-sample Mendelian randomization (MR) was used to assess causal associations between circulating plasma metabolites and ASD using large-scale genome-wide association study (GWAS) datasets-comprising 1091 metabolites, 309 ratios, and 179 lipids-and three European autism datasets (PGC 2015: n = 10,610 and 10,263; 2017: n = 46,351). Inverse-variance weighted (IVW) and weighted median methods were employed, along with robust sensitivity and power analyses followed by independent cohort validation. RESULTS: Higher genetically predicted levels of sphingomyelin (SM) (d17:1/16:0) (OR, 1.129; 95% CI, 1.024-1.245; p = 0.015) were causally linked to increased ASD risk. Additionally, ASD children had higher plasma creatine/carnitine ratios. These MR findings were validated in an independent US autism cohort using machine learning analysis. CONCLUSION: Utilizing large datasets, two MR approaches, robust sensitivity analyses, and independent validation, our novel findings provide evidence for the potential roles of metabolomics and circulating metabolites in ASD diagnosis and etiology.

Causality of immune cells and endometriosis: a bidirectional mendelian randomization study.

BACKGROUND: Endometriosis, a prevalent chronic condition, afflicts approximately 10% of women in their reproductive years. Emerging evidence implicates immune cells in the pathogenesis of endometriosis, particularly in angiogenesis, tissue proliferation, and lesion invasion. This investigation employs two-sample Mendelian Randomization (MR) to dissect the bidirectional causal relationships between immune cell profiles and endometriosis. METHODS: We leveraged publicly available genome-wide association study (GWAS) data to elucidate the causal interplay between immune cell traits and endometriosis. Utilizing GWAS summary statistics ranging from accession numbers GCST90001391 to GCST90002121 and endometriosis data from the FinnGen study GWAS (8,288 endometriosis cases and 68,969 controls), we adopted stringent criteria for instrumental variable selection. We applied MR-Egger, weighted median, inverse variance weighted (IVW), and weighted mode methods to derive causal estimates. To address potential heterogeneity and pleiotropy, Cochran's Q test, MR-Egger intercept, and leave-one-out analyses were executed. Reverse-direction MR and bidirectional MR analyses evaluated potential reciprocal causation and the influence of endometriosis on immune cell composition. RESULTS: Our analysis identified five immune phenotypes inversely associated with endometriosis risk. These phenotypes comprise: a percentage of CD11c + HLA-DR + + monocytes, CD25 expression on CD39 + CD4 + T cells, elevated CD25 on CD45RA + CD4 + non-regulatory T cells, HLA-DR intensity on HLA-DR + CD8 bright (CD8br) T cells, and the proportion of naïve double-negative (CD4 - CD8- %DN) T cells. In contrast, eleven phenotypes were positively correlated with endometriosis risk, including: CD127 expression on T cells, the proportion of CD24 + CD27 + B cells within lymphocytes, CD25 expression on CD28 + CD4 + T cells, CD28 expression on CD39 + activated regulatory T cells (activated Tregs), the frequency of bright CD33 HLA-DR + CD14 - cells within the CD33br HLA-DR + compartment, CD45 expression on lymphocytes and natural killer (NK) cells, activation status of central memory CD8 bright (CM CD8br) T cells, CX3CR1 expression on monocytes, and the percentage of HLA-DR + NK cells within the NK cell subset. Sensitivity assessments that excluded significant heterogeneity and pleiotropy confirmed the stability of these associations, thereby reinforcing the validity of our findings. CONCLUSION: This study provides novel evidence of the potential causal impact of specific immune cells on the risk of developing endometriosis. These findings enhance our understanding of endometriosis pathophysiology and may inform innovative approaches for its diagnosis and management. While our findings provide novel insights, limitations such as potential horizontal pleiotropy and reliance on European ancestry data should be considered. Future research should expand to diverse populations and incorporate individual-level data to refine these findings.

Tea intake and non-alcoholic fatty liver disease risk: A two-sample Mendelian randomization study.

Background: Non-alcoholic fatty liver disease (NAFLD) is a major global health problem due to its great disease and economic burdens. Tea is a popular beverage consumed by billions of people.globally owing to its health benefits. However, the evidence regarding the association between tea intake and NAFLD risk is inconsistent. Objective: To examine the genetically predicted causal association between tea intake and NAFLD risk using the two-sample Mendelian randomization (MR) method. Methods: Single-nucleotide polymorphisms (SNPs) strongly associated with tea intake were obtained from a large dataset (N = 447,485) in the UK biobank, and summary-level genetic data for NAFLD (2,275 cases and 375,002 controls) were collected from the FinnGen consortium. The two-sample MR method was used to investigate the causal association between tea intake and NAFLD risk. The random-effects inverse-variance weighted (IVW) was used as the primary approach for estimating the causal effect, and MR Egger, weighted median, simple mode, and weighted mode were used to verify the robustness of the primary results. Results: Twenty-four valid SNPs were selected as the instrumental variables for tea intake. The IVW results indicated that tea intake was not causally associated with NAFLD risk (Odds ratio: 1.48; 95 % confidence interval: 0.64, 3.43; p = 0.364); moreover, the results from other methods were consistent with this finding. A leave-one-out analysis further demonstrated the robustness of our results. No evidence of heterogeneity, outliers, or horizontal pleiotropy was found. Conclusion: Our results do not support tea intake being causally associated with a decreased risk of NAFLD.

Semiparametric sensitivity analysis: unmeasured confounding in observational studies.

Establishing cause-effect relationships from observational data often relies on untestable assumptions. It is crucial to know whether, and to what extent, the conclusions drawn from non-experimental studies are robust to potential unmeasured confounding. In this paper, we focus on the average causal effect (ACE) as our target of inference. We generalize the sensitivity analysis approach developed by Robins et al., Franks et al., and Zhou and Yao. We use semiparametric theory to derive the non-parametric efficient influence function of the ACE, for fixed sensitivity parameters. We use this influence function to construct a one-step, split sample, truncated estimator of the ACE. Our estimator depends on semiparametric models for the distribution of the observed data; importantly, these models do not impose any restrictions on the values of sensitivity analysis parameters. We establish sufficient conditions ensuring that our estimator has $\sqrt{n}$ asymptotics. We use our methodology to evaluate the causal effect of smoking during pregnancy on birth weight. We also evaluate the performance of estimation procedure in a simulation study.

Genetic insights into dietary patterns, liposome mediation, and osteoporosis risk: a Mendelian randomization study.

Background: This study examines the indirect causal relationships between dietary habits and osteoporosis, mediated through liposomes, utilizing a two-sample Mendelian randomization (MR) approach. The research leverages genetic variations as instrumental variables to explore the genetic influences on dietary habits, liposomes, and osteoporosis, aiming to unravel the complex interplay between diet, lipid metabolism, and bone health. Methods: The study utilized genome-wide association studies (GWAS) data for liposomes from Finnish individuals and osteoporosis-related data, alongside dietary factors from the OpenGWAS database. Instrumental variables were selected based on genetic variants associated with these factors, using a strict significance level and linkage disequilibrium threshold. Statistical analysis employed the Inverse Variance Weighted method, weighted median, and mode-based methods within the R environment, complemented by sensitivity analyses to ensure the robustness of the causal inferences. Results: Findings revealed significant causal relationships between specific dietary components (white rice, cereal, and non-oily fish) and osteoporosis risk, both directly and mediated through changes in liposome levels. Notably, white rice consumption was associated with an increased risk of osteoporosis, while cereal and non-oily fish intake showed protective effects. Further, certain liposomes were identified as mediators in these relationships, suggesting a link between diet, lipid profiles, and bone health. Conclusion: The study highlights the significant impact of dietary habits on osteoporosis risk, mediated through liposomes. These findings underscore the importance of considering lipidomic profiles in dietary guidance and suggest potential targets for preventing osteoporosis through nutritional interventions.

Examining the causal association between moderate alcohol consumption and cardiovascular risk factors in the Taiwan Biobank: a Mendelian randomization analysis.

Background: The Mendelian randomization approach uses genetic variants as instrumental variables to study the causal association between the risk factors and health outcomes of interest. We aimed to examine the relation between alcohol consumption and cardiovascular risk factors using two genetic variants as instrumental variables: alcohol dehydrogenase 1B (ADH1B) rs1229984 and aldehyde dehydrogenase 2 (ALDH2) rs671. Methods: Using data collected in the Taiwan Biobank-an ongoing, prospective, population-based cohort study-our analysis included 129,032 individuals (46,547 men and 82,485 women) with complete data on ADH1B rs1229984 and ALDH2 rs671 genotypes and alcohol drinking status. We conducted instrumental variables regression analysis to examine the relationship between alcohol drinking and body mass index (BMI), systolic blood pressure (SBP), diastolic blood pressure (DBP), fasting glucose, glycated hemoglobin (HbA1c), triglycerides, high-density lipoprotein cholesterol (HDLc), and low-density lipoprotein cholesterol (LDLc). Results: In the rs1229984-instrumented analysis, alcohol drinking was only associated with higher levels of SBP in men and lower levels of DBP in women. In the rs671-instrumented analysis, alcohol drinking was associated with higher levels of BMI, SBP, DBP, fasting glucose, triglycerides, HDLc and lower levels of LDLc in men; alcohol drinking was associated with higher levels of HDLc and lower levels of SBP, HbA1c, and triglycerides in women. Conclusion: Using Mendelian randomization analysis, some of our study results among men echoed findings from the previous systematic review, suggesting that alcohol drinking may be causally associated with higher levels of BMI, SBP, DBP, fasting glucose, triglycerides, HDLc, and lower levels of LDLc. Although alcohol drinking is beneficial to a few cardiovascular risk factors, it is detrimental to many others. The assumptions that underlie the Mendelian randomization approach should also be carefully examined when interpreting findings from such studies.