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Background: The mechanistic associations between obesity and risk of colorectal cancer (CRC) remain unclear. Here, using body mass index (BMI) as an obesity indicator, we decomposed the total effects of obesity on the risk of CRC into: (1) direct effects, which are possibly mediated by unmeasured or currently unknown factors; (2) indirect effects mediated by circulating leptin and adiponectin; and (3) indirect effects that are not mediated by circulating leptin and adiponectin but by hyperinsulinemia and chronic inflammation (assessed via circulating connecting peptide and C-reactive protein, respectively). Methods: We adopted a causal mediation framework, using data from a large prospective cohort study of 44,271 Japanese men. Results: BMI was not associated with the risk of CRC due to direct and indirect effects that were not mediated by circulating leptin and adiponectin. By contrast, individuals with BMIs of 25.0-27.4 kg/m2 (risk ratio 1.29; 95% confidence interval, 0.98-1.69) and ≥27.5 kg/m2 (risk ratio 1.28; 95% confidence interval, 0.98-1.68) had a higher risk of CRC due to indirect effects of circulating leptin and adiponectin. Conclusion: Our mediation analyses suggest that the association between BMI and CRC risk may be largely mediated by a pathway involving circulating leptin and adiponectin. [ABSTRACT FROM AUTHOR]

Abstract The purpose of this research was to investigate the association between leisure activity and cognitive function in Chinese older adults, and to explore the mediating role of individual evaluation, anxiety and depression. Using cross-sectional data from the 2018 Chinese Longitudinal Healthy Longevity Survey (CLHLS), Pearson correlation analyses were employed to examine the relationships among various indicators. A multiple mediation model was constructed to investigate the mediating pathways through which leisure activities impact cognitive function in elderly individuals. Leisure activities, individual evaluation, anxiety, and depression were significantly correlated with cognitive function (p

The purpose of this research was to investigate the association between leisure activity and cognitive function in Chinese older adults, and to explore the mediating role of individual evaluation, anxiety and depression. Using cross-sectional data from the 2018 Chinese Longitudinal Healthy Longevity Survey (CLHLS), Pearson correlation analyses were employed to examine the relationships among various indicators. A multiple mediation model was constructed to investigate the mediating pathways through which leisure activities impact cognitive function in elderly individuals. Leisure activities, individual evaluation, anxiety, and depression were significantly correlated with cognitive function (p < 0.01). Leisure activities not only directly positively impacted cognitive function in elderly individuals (effect = 0.3780; 95% CI = [0.3459, 0.4101]), but also exerted indirect effects through five pathways. Individual evaluation, anxiety, depression, they plays a mediating role in the association between leisure activity and cognitive function in older adults in China. Leisure activities can positively impact cognitive function in elderly individuals by enhancing individual evaluation and subsequently reducing anxiety and depression. [ABSTRACT FROM AUTHOR]

Background: Fatty liver, obesity, and dyslipidemia are associated with prediabetes or diabetes risk, and hyperuricemia co-exists. The present study evaluated the role of multiple mediators, namely, fatty liver, body mass index (BMI), and dyslipidemia, in the association between hyperuricemia and diabetes status. Methods: Baseline data from the ongoing Fuqing cohort (5,336 participants) were analyzed to investigate the association of hyperuricemia with diabetes status using a multinomial logistic regression model. Furthermore, causal mediation analysis with the weighting-based approach was performed to estimate hyperuricemia's total natural direct effect (tnde), total natural indirect effect (tnie), and total effect (te) on prediabetes and diabetes risk, mediating jointly via fatty liver, BMI, and dyslipidemia. Results: In multinomial analysis without considering mediators' effects, hyperuricemia was associated with a higher risk of prediabetes only (odds ratio: 1.25; 95% CI: 1.09-1.43; p < 0.001). When fatty liver, BMI, and dyslipidemia were considered as multiple mediators in the association, hyperuricemia was linked to both prediabetes [tnde: 1.11, 95% CI: 1.04-1.11; tnie: 1.07, 95% CI: 1.05-1.09; and overall proportion mediated (pm): 42%, 95% CI: 27%-73%] and diabetes risk (tnde: 0.96, 95% CI: 0.82-1.14; tnie: 1.25, 95% CI: 1.18-1.33; and pm: 100%, 95% CI: 57%-361%). Hyperuricemia showed significant tnde, te, and tnie, mediated by fatty liver jointly with dyslipidemia (pm = 17%) or BMI (pm = 35%), on prediabetes risk. Conclusion: Hyperuricemia could increase prediabetes or diabetes risk, partially mediated by fatty liver, BMI, and dyslipidemia. Fatty liver is the crucial mediator in the association between hyperuricemia and prediabetes. [ABSTRACT FROM AUTHOR]

BackgroundFatty liver, obesity, and dyslipidemia are associated with prediabetes or diabetes risk, and hyperuricemia co-exists. The present study evaluated the role of multiple mediators, namely, fatty liver, body mass index (BMI), and dyslipidemia, in the association between hyperuricemia and diabetes status.MethodsBaseline data from the ongoing Fuqing cohort (5,336 participants) were analyzed to investigate the association of hyperuricemia with diabetes status using a multinomial logistic regression model. Furthermore, causal mediation analysis with the weighting-based approach was performed to estimate hyperuricemia’s total natural direct effect (tnde), total natural indirect effect (tnie), and total effect (te) on prediabetes and diabetes risk, mediating jointly via fatty liver, BMI, and dyslipidemia.ResultsIn multinomial analysis without considering mediators’ effects, hyperuricemia was associated with a higher risk of prediabetes only (odds ratio: 1.25; 95% CI: 1.09–1.43; p < 0.001). When fatty liver, BMI, and dyslipidemia were considered as multiple mediators in the association, hyperuricemia was linked to both prediabetes [tnde: 1.11, 95% CI: 1.04–1.11; tnie: 1.07, 95% CI: 1.05–1.09; and overall proportion mediated (pm): 42%, 95% CI: 27%–73%] and diabetes risk (tnde: 0.96, 95% CI: 0.82–1.14; tnie: 1.25, 95% CI: 1.18–1.33; and pm: 100%, 95% CI: 57%–361%). Hyperuricemia showed significant tnde, te, and tnie, mediated by fatty liver jointly with dyslipidemia (pm = 17%) or BMI (pm = 35%), on prediabetes risk.ConclusionHyperuricemia could increase prediabetes or diabetes risk, partially mediated by fatty liver, BMI, and dyslipidemia. Fatty liver is the crucial mediator in the association between hyperuricemia and prediabetes.

Randomized experiments are often complicated because of treatment noncompliance. This challenge prevents researchers from identifying the mediated portion of the intention-to-treated (ITT) effect, which is the effect of the assigned treatment that is attributed to a mediator. One solution suggests identifying the mediated ITT effect on the basis of the average causal mediation effect among compliers when there is a single mediator. However, considering the complex nature of the mediating mechanisms, it is natural to assume that there are multiple variables that mediate through the causal path. Motivated by an empirical analysis of a data set collected in a randomized interventional study, we develop a method to estimate the mediated portion of the ITT effect when both multiple dependent mediators and treatment noncompliance exist. This enables researchers to make an informed decision on how to strengthen the intervention effect by identifying relevant mediators despite treatment noncompliance. We propose a nonparametric estimation procedure and provide a sensitivity analysis for key assumptions. We conduct a Monte Carlo simulation study to assess the finite sample performance of the proposed approach. The proposed method is illustrated by an empirical analysis of JOBS II data, in which a job training intervention was used to prevent mental health deterioration among unemployed individuals.

In many health studies, researchers are interested in estimating the treatment effects on the outcome around and through an intermediate variable. Such causal mediation analyses aim to understand the mechanisms that explain the treatment effect. Although multiple mediators are often involved in real studies, most of the literature considered mediation analyses with one mediator at a time. In this article, we consider mediation analyses when there are causally non-ordered multiple mediators. Even if the mediators do not affect each other, the sum of two indirect effects through the two mediators considered separately may diverge from the joint natural indirect effect when there are additive interactions between the effects of the two mediators on the outcome. Therefore, we derive an equation for the joint natural indirect effect based on the individual mediation effects and their interactive effect, which helps us understand how the mediation effect works through the two mediators and relative contributions of the mediators and their interaction. We also discuss an extension for three mediators. The proposed method is illustrated using data from a randomized trial on the prevention of dental caries.

Regression-based approaches are widely used in causal mediation analysis. The presence of multiple mediators, however, increases the complexity and difficulty of mediation analysis. In such cases, regression-based approaches cannot efficiently address estimation issues. Hence, a flexible approach to mediation analysis is needed. Therefore, we developed a method for using g-computation algorithm to conduct causal mediation analysis in the presence of multiple ordered mediators. Compared to regression-based approaches, the proposed simulation-based approach increases flexibility in the choice of models and increases the range of the outcome scale. The Taiwanese Cohort Study dataset was used to evaluate the efficacy of the proposed approach for investigating the mediating role of early and late HBV viral load in the effect of HCV infection on hepatocellular carcinoma (HCC) in HBV seropositive patients (n = 2,878; HCV carrier n = 123). Our results indicated that early HBV viral load had a negative mediating role in HCV-induced HCC. Additionally, early exposure to a low HBV viral load affected HCC through a lag effect on HCC incidence [OR = 0.873, 95% CI = (0.853, 0.893)], and the effect of early exposure to a low HBV viral load on HCC incidence was slightly larger than that of a persistently low viral load on HCC incidence [OR = 0.918, 95% CI = (0.896, 0.941)].

Neighbourhood safety represents an important topic of study to illustrate the reasons behind the increases in crime and mitigate its effects in neighbourhoods. This study examines how the social and environmental features of neighbourhoods may influence the social sustainability of residents based on the assumption that the perception of safety and social cohesion mediates the effects of neighbourhood environment on social sustainability. A quantitative method was employed to collect data from residents in a low-rise residential area in Penang, Malaysia. The results of structural equation modelling (SEM) indicated the positive and significant effect of neighbourhood accessibility on perceived disorder, whilst the effect of accessibility on social cohesion was negative. Disorders may comprise social and physical disorders, and may have a negative effect on perception of safety, but not on social cohesion. The relationship between disorders and social sustainability is serially mediated by the perception of safety and social cohesion. This implies that those who perceived high disorderliness in a neighbourhood environment reported a lower level of perception of safety, social cohesion and lower levels of social sustainability. Attempts need to be made to reduce neighbourhood disorderliness to pave the way for 2030 Agenda goals implementation. [ABSTRACT FROM AUTHOR]

Abstract Background Mapping expression quantitative trait loci (eQTLs) has provided insight into gene regulation. Compared to cis-eQTLs, the regulatory mechanisms of trans-eQTLs are less known. Previous studies suggest that trans-eQTLs may regulate expression of remote genes by altering the expression of nearby genes. Trans-association has been studied in the mediation analysis with a single mediator. However, prior applications with one mediator are prone to model misspecification due to correlations between genes. Motivated from the observation that trans-eQTLs are more likely to associate with more than one cis-gene than randomly selected SNPs in the GTEx dataset, we developed a computational method to identify trans-eQTLs that are mediated by multiple mediators. Results We proposed two hypothesis tests for testing the total mediation effect (TME) and the component-wise mediation effects (CME), respectively. We demonstrated in simulation studies that the type I error rates were controlled in both tests despite model misspecification. The TME test was more powerful than the CME test when the two mediation effects are in the same direction, while the CME test was more powerful than the TME test when the two mediation effects are in opposite direction. Multiple mediator analysis had increased power to detect mediated trans-eQTLs, especially in large samples. In the HapMap3 data, we identified 11 mediated trans-eQTLs that were not detected by the single mediator analysis in the combined samples of African populations. Moreover, the mediated trans-eQTLs in the HapMap3 samples are more likely to be trait-associated SNPs. In terms of computation, although there is no limit in the number of mediators in our model, analysis takes more time when adding additional mediators. In the analysis of the HapMap3 samples, we included at most 5 cis-gene mediators. Majority of the trios we considered have one or two mediators. Conclusions Trans-eQTLs are more likely to associate with multiple cis-genes than randomly selected SNPs. Mediation analysis with multiple mediators improves power of identification of mediated trans-eQTLs, especially in large samples.

Background: Socio-economic inequalities in mortality are well established, yet the contribution of intermediate risk factors that may underlie these relationships remains unclear. We evaluated the role of multiple modifiable intermediate risk factors underlying socio-economic-associated mortality and quantified the potential impact of reducing early all-cause mortality by hypothetically altering socio-economic risk factors.Methods: Data were from seven cohort studies participating in the LIFEPATH Consortium (total n = 179 090). Using both socio-economic position (SEP) (based on occupation) and education, we estimated the natural direct effect on all-cause mortality and the natural indirect effect via the joint mediating role of smoking, alcohol intake, dietary patterns, physical activity, body mass index, hypertension, diabetes and coronary artery disease. Hazard ratios (HRs) were estimated, using counterfactual natural effect models under different hypothetical actions of either lower or higher SEP or education.Results: Lower SEP and education were associated with an increase in all-cause mortality within an average follow-up time of 17.5 years. Mortality was reduced via modelled hypothetical actions of increasing SEP or education. Through higher education, the HR was 0.85 [95% confidence interval (CI) 0.84, 0.86] for women and 0.71 (95% CI 0.70, 0.74) for men, compared with lower education. In addition, 34% and 38% of the effect was jointly mediated for women and men, respectively. The benefits from altering SEP were slightly more modest.Conclusions: These observational findings support policies to reduce mortality both through improving socio-economic circumstances and increasing education, and by altering intermediaries, such as lifestyle behaviours and morbidities. [ABSTRACT FROM AUTHOR]

Neighbourhood safety represents an important topic of study to illustrate the reasons behind the increases in crime and mitigate its effects in neighbourhoods. This study examines how the social and environmental features of neighbourhoods may influence the social sustainability of residents based on the assumption that the perception of safety and social cohesion mediates the effects of neighbourhood environment on social sustainability. A quantitative method was employed to collect data from residents in a low-rise residential area in Penang, Malaysia. The results of structural equation modelling (SEM) indicated the positive and significant effect of neighbourhood accessibility on perceived disorder, whilst the effect of accessibility on social cohesion was negative. Disorders may comprise social and physical disorders, and may have a negative effect on perception of safety, but not on social cohesion. The relationship between disorders and social sustainability is serially mediated by the perception of safety and social cohesion. This implies that those who perceived high disorderliness in a neighbourhood environment reported a lower level of perception of safety, social cohesion and lower levels of social sustainability. Attempts need to be made to reduce neighbourhood disorderliness to pave the way for 2030 Agenda goals implementation.

Background: Mapping expression quantitative trait loci (eQTLs) has provided insight into gene regulation. Compared to cis-eQTLs, the regulatory mechanisms of trans-eQTLs are less known. Previous studies suggest that trans-eQTLs may regulate expression of remote genes by altering the expression of nearby genes. Trans-association has been studied in the mediation analysis with a single mediator. However, prior applications with one mediator are prone to model misspecification due to correlations between genes. Motivated from the observation that trans-eQTLs are more likely to associate with more than one cis-gene than randomly selected SNPs in the GTEx dataset, we developed a computational method to identify trans-eQTLs that are mediated by multiple mediators. Results: We proposed two hypothesis tests for testing the total mediation effect (TME) and the component-wise mediation effects (CME), respectively. We demonstrated in simulation studies that the type I error rates were controlled in both tests despite model misspecification. The TME test was more powerful than the CME test when the two mediation effects are in the same direction, while the CME test was more powerful than the TME test when the two mediation effects are in opposite direction. Multiple mediator analysis had increased power to detect mediated trans-eQTLs, especially in large samples. In the HapMap3 data, we identified 11 mediated trans-eQTLs that were not detected by the single mediator analysis in the combined samples of African populations. Moreover, the mediated trans-eQTLs in the HapMap3 samples are more likely to be trait-associated SNPs. In terms of computation, although there is no limit in the number of mediators in our model, analysis takes more time when adding additional mediators. In the analysis of the HapMap3 samples, we included at most 5 cis-gene mediators. Majority of the trios we considered have one or two mediators. Conclusions: Trans-eQTLs are more likely to associate with multiple cis-genes than randomly selected SNPs. Mediation analysis with multiple mediators improves power of identification of mediated trans-eQTLs, especially in large samples. [ABSTRACT FROM AUTHOR]

Mediation analysis explores the degree to which an exposure's effect on an outcome is diverted through a mediating variable. We describe a classical regression framework for conducting mediation analyses in which estimates of causal mediation effects and their variance are obtained from the fit of a single regression model. The vector of changes in exposure pathway coefficients, which we named the essential mediation components (EMCs), is used to estimate standard causal mediation effects. Because these effects are often simple functions of the EMCs, an analytical expression for their model-based variance follows directly. Given this formula, it is instructive to revisit the performance of routinely used variance approximations (e.g., delta method and resampling methods). Requiring the fit of only one model reduces the computation time required for complex mediation analyses and permits the use of a rich suite of regression tools that are not easily implemented on a system of three equations, as would be required in the Baron-Kenny framework. Using data from the BRAIN-ICU study, we provide examples to illustrate the advantages of this framework and compare it with the existing approaches. [ABSTRACT FROM AUTHOR]

Background: Approximately twice as many females as males are diagnosed with posttraumatic stress disorder (PTSD). However, little is known about why females report more PTSD symptoms than males. Prior studies have generally focused on few potential mediators at a time and have often used methods that were not ideally suited to test for mediation effects. Prior research has identified a number of individual risk factors that may contribute to sex differences in PTSD severity, although these cannot fully account for the increased symptom levels in females when examined individually. Objective: The present study is the first to systematically test the hypothesis that a combination of pre-, peri-, and posttraumatic risk factors more prevalent in females can account for sex differences in PTSD severity. Method: The study was a quasi-prospective questionnaire survey assessing PTSD and related variables in 73.3% of all Danish bank employees exposed to bank robbery during the period from April 2010 to April 2011. Participants filled out questionnaires 1 week (T1, N=450) and 6 months after the robbery (T2, N=368; 61.1% females). Mediation was examined using an analysis designed specifically to test a multiple mediator model. Results: Females reported more PTSD symptoms than males and higher levels of neuroticism, depression, physical anxiety sensitivity, peritraumatic fear, horror, and helplessness (the A2 criterion), tonic immobility, panic, dissociation, negative posttraumatic cognitions about self and the world, and feeling let down. These variables were included in the model as potential mediators. The combination of risk factors significantly mediated the association between sex and PTSD severity, accounting for 83% of the association. Conclusions: The findings suggest that females report more PTSD symptoms because they experience higher levels of associated risk factors. The results are relevant to other trauma populations and to other trauma-related psychiatric disorders more prevalent in females, such as depression and anxiety.

The advent of counterfactual-based mediation analysis has triggered enormous progress on how, and under what assumptions, one may disentangle path-specific effects upon combining arbitrary (possibly nonlinear) models for mediator and outcome. However, current developments have largely focused on single mediators because required identification assumptions prohibit simple extensions to settings with multiple mediators that may depend on one another. In this article, we propose a procedure for obtaining fine-grained decompositions that may still be recovered fromobserved data in such complex settings.We first show that existing analytical approaches target specific instances of a more general set of decompositions and may therefore fail to provide a comprehensive assessment of the processes that underpin cause-effect relationships between exposure and outcome. We then outline conditions for obtaining the remaining set of decompositions. Because the number of targeted decompositions increases rapidly with the number of mediators, we introduce natural effects models along with estimation methods that allow for flexible and parsimonious modeling. Our procedure can easily be implemented using off-the-shelf software and is illustrated using a reanalysis of theWorld Health Organization's Large Analysis and Review of European Housing and Health Status (WHO-LARES) study on the effect ofmold exposure onmental health (2002-2003). [ABSTRACT FROM AUTHOR]

Background: Approximately twice as many females as males are diagnosed with posttraumatic stress disorder (PTSD). However, little is known about why females report more PTSD symptoms than males. Prior studies have generally focused on few potential mediators at a time and have often used methods that were not ideally suited to test for mediation effects. Prior research has identified a number of individual risk factors that may contribute to sex differences in PTSD severity, although these cannot fully account for the increased symptom levels in females when examined individually. Objective: The present study is the first to systematically test the hypothesis that a combination of pre-, peri-, and posttraumatic risk factors more prevalent in females can account for sex differences in PTSD severity. Method: The study was a quasi-prospective questionnaire survey assessing PTSD and related variables in 73.3% of all Danish bank employees exposed to bank robbery during the period from April 2010 to April 2011. Participants filled out questionnaires 1 week (T1, N=450) and 6 months after the robbery (T2, N=368; 61.1% females). Mediation was examined using an analysis designed specifically to test a multiple mediator model. Results: Females reported more PTSD symptoms than males and higher levels of neuroticism, depression, physical anxiety sensitivity, peritraumatic fear, horror, and helplessness (the A2 criterion), tonic immobility, panic, dissociation, negative posttraumatic cognitions about self and the world, and feeling let down. These variables were included in the model as potential mediators. The combination of risk factors significantly mediated the association between sex and PTSD severity, accounting for 83% of the association. Conclusion: The findings suggest that females report more PTSD symptoms because they experience higher levels of associated risk factors. The results are relevant to other trauma populations and to other traumarelated psychiatric disorders more prevalent in females, such as depression and anxiety. [ABSTRACT FROM AUTHOR]

Within the fields of epidemiology, interventions research and social sciences researchers are often faced with the challenge of decomposing the effect of an exposure into different causal pathways working through defined mediator variables. The goal of such analyses is often to understand the mechanisms of the system or to suggest possible interventions. The case of a single mediator, thus implying only 2 causal pathways (direct and indirect) from exposure to outcome, has been extensively studied. By using the framework of counterfactual variables, researchers have established theoretical properties and developed powerful tools. However, in practical problems, it is not uncommon to have several distinct causal pathways from exposure to outcome operating through different mediators. In this article, we suggest a widely applicable approach to quantifying and ranking different causal pathways. The approach is an extension of the natural effect models proposed by Lange et al. (Am J Epidemiol. 2012;176(3):190–195). By allowing the analysis of distinct multiple pathways, the suggested approach adds to the capabilities of modern mediation techniques. Furthermore, the approach can be implemented using standard software, and we have included with this article implementation examples using R (R Foundation for Statistical Computing, Vienna, Austria) and Stata software (StataCorp LP, College Station, Texas). [ABSTRACT FROM PUBLISHER]

Since the positive psychology was emerged from 2000s, these positive constructs contribute to athlete's well-being received much attention in sport. However, gratitude is a topic rarely discussed in the sport psychology. Thus, the current study aims at investigated the relationship between gratitude and athlete well-being. Furthermore, the mechanisms underlie the relationship was also explored. We proposed that the perceived social support from coach and teammate would be mediators. Participants were 291 adolescent athletes. Structural equation modeling was used to examine the hypothesis. Results indicated that gratitude is a positive related to athlete's well-being. Besides, multiple-mediators analysis indicated that both perceived coach and teammate social support partially mediated the relationship between gratitude and athlete's well-being. This study contributes to the gratitude and well-being literature by unpacking the essential psychological process behind the relationship. Implication and application were discussed in term of gratitude theory and social support. [ABSTRACT FROM AUTHOR]