Background/Aims: Patients with concurrent type 2 diabetes mellitus (T2DM) and metabolic dysfunction-associated steatotic liver disease (MASLD) face elevated cardiovascular risks. However, optimal oral antidiabetic drug (OAD) selection for this population remains unclear. Methods: Using the Korean National Health Information Database, we conducted a target trial emulation comparing cardiovascular outcomes among patients with T2DM and MASLD (defined by fatty liver index ≥30) who initiated sodium-glucose cotransporter 2 (SGLT2) inhibitors, thiazolidinediones, dipeptidyl peptidase-4 (DPP-4) inhibitors, or sulfonylureas with metformin. The primary outcome was major adverse cardiovascular events (MACE), including cardiovascular mortality, nonfatal myocardial infarction, and nonfatal stroke. Results: Among 71,071 patients (331,726 person-years), SGLT2 inhibitor users experienced a significantly lower MACE risk compared to sulfonylurea users (adjusted subdistribution hazard ratio [aSHR], 0.44; 95% confidence interval [CI], 0.31–0.62). SGLT2 inhibitors also demonstrated a lower MACE risk compared to thiazolidinediones (aSHR, 0.61; 95% CI, 0.39–0.96) and DPP-4 inhibitors (aSHR, 0.59; 95% CI, 0.42–0.96). Cardiovascular mortality risk was notably reduced with SGLT2 inhibitors compared to sulfonylureas (aSHR, 0.13; 95% CI, 0.03–0.50), thiazolidinediones (aSHR, 0.19; 95% CI, 0.04–0.86), and DPP-4 inhibitors (aSHR, 0.22; 95% CI, 0.06–0.84). Mediation analysis revealed that MASLD regression accounted for 8.7% of the total cardiovascular benefit when comparing SGLT2 inhibitors to sulfonylureas. Conclusions In patients with concurrent T2DM and MASLD, SGLT2 inhibitors demonstrated better cardiovascular outcomes compared to other OADs. These findings suggest that SGLT2 inhibitors may be the preferred OAD choice for cardiovascular risk reduction in this high-risk population.

Mendelian randomization (MR) is a statistical method that uses genetic variants as instrumental variables to estimate the causal effect of exposure on an outcome in the presence of unmeasured confounding. In this review, we argue that it is crucial to acknowledge the instrumental variable assumptions in MR analysis. We describe widely used MR methods, using an example from obesity-related metabolic disorders. We describe situations in which instrumental variable assumptions are violated and explain how to evaluate these violations and employ robust methods for accommodating such violations.

Mendelian randomization (MR) is a statistical method that uses genetic variants as instrumental variables to estimate the causal effect of exposure on an outcome in the presence of unmeasured confounding. In this review, we argue that it is crucial to acknowledge the instrumental variable assumptions in MR analysis. We describe widely used MR methods, using an example from obesity-related metabolic disorders. We describe situations in which instrumental variable assumptions are violated and explain how to evaluate these violations and employ robust methods for accommodating such violations.

Mendelian randomization (MR) is a statistical method that uses genetic variants as instrumental variables to estimate the causal effect of exposure on an outcome in the presence of unmeasured confounding. In this review, we argue that it is crucial to acknowledge the instrumental variable assumptions in MR analysis. We describe widely used MR methods, using an example from obesity-related metabolic disorders. We describe situations in which instrumental variable assumptions are violated and explain how to evaluate these violations and employ robust methods for accommodating such violations.

Directed acyclic graphs (DAGs) are useful tools for visualizing the hypothesized causal structures in an intuitive way and selecting relevant confounders in causal inference. However, in spite of their increasing use in clinical and surgical research, the causal graphs might also be misused by a lack of understanding of the central principles. In this article, we aim to introduce the basic terminology and fundamental rules of DAGs, and DAGitty, a user-friendly program that easily displays DAGs. Specifically, we describe how to determine variables that should or should not be adjusted based on the backdoor criterion with examples. In addition, the occurrence of the various types of biases is discussed with caveats, including the problem caused by the traditional approach using p-values for confounder selection. Moreover, a detailed guide to DAGitty is provided with practical examples regarding minimally invasive surgery. Essentially, the primary benefit of DAGs is to aid researchers in clarifying the research questions and the corresponding designs based on the domain knowledge. With these strengths, we propose that the use of DAGs may contribute to rigorous research designs, and lead to transparency and reproducibility in research on minimally invasive surgery.

Traditional mediation analysis, which relies on linear regression models, has faced criticism due to its limited suitability for cases involving different types of variables and complex covariates, such as interactions. This can result in unclear definitions of direct and indirect effects. As an alternative, causal mediation analysis using the counterfactual framework has been introduced to provide clearer definitions of direct and indirect effects while allowing for more flexible modeling methods. However, the conceptual understanding of this approach based on the counterfactual framework remains challenging for applied researchers. To address this issue, the present article was written to highlight and illustrate the definitions of causal estimands, including controlled direct effect, natural direct effect, and natural indirect effect, based on the key concept of nested counterfactuals. Furthermore, we recommend using 2 R packages, ‘medflex’ and ‘mediation’, to perform causal mediation analysis and provide public health examples. The article also offers caveats and guidelines for accurate interpretation of the results.

Background: This study aimed to evaluate the association between exposure to occupational hazards and the metabolic syndrome. A secondary objective was to analyze the additive and multiplicative effects of exposure to risk factors. Methods: This retrospective cohort was based on 31,615 health examinees at the Pusan National University Yangsan Hospital in Republic of Korea from 2012–2021. Demographic and behavior-related risk factors were treated as confounding factors, whereas three physical factors, 19 organic solvents and aerosols, and 13 metals and dust were considered occupational risk factors. Time-dependent Cox regression analysis was used to calculate hazard ratios. Results: The risk of metabolic syndrome was significantly higher in night shift workers (hazard ratio = 1.45: 95% confidence interval = 1.36–1.54) and workers who were exposed to noise (1.15:1.07–1.24). Exposure to some other risk factors was also significantly associated with a higher risk of metabolic syndrome. They were dimethylformamide, acetonitrile, trichloroethylene, xylene, styrene, toluene, dichloromethane, copper, antimony, lead, copper, iron, welding fume, and manganese. Among the 28 significant pairs, 19 exhibited both positive additive and multiplicative effects. Conclusions Exposure to single or combined occupational risk factors may increase the risk of developing metabolic syndrome. Working conditions should be monitored and improved to reduce exposure to occupational hazards and prevent the development of the metabolic syndrome.

Epidemiological studies typically examine the causal effect of exposure on a health outcome. Standardization is one of the most straightforward methods for estimating causal estimands. However, compared to inverse probability weighting, there is a lack of user-centric explanations for implementing standardization to estimate causal estimands. This paper explains the standardization method using basic R functions only and how it is linked to the R package stdReg, which can be used to implement the same procedure. We provide a step-by-step tutorial for estimating causal risk differences, causal risk ratios, and causal odds ratios based on standardization. We also discuss how to carry out subgroup analysis in detail.

Background/Aims: Biofeedback therapy is widely used to treat patients with chronic constipation, especially those with dyssynergic defecation. Yet, the utility of high-resolution manometry with novel parameters in the prediction of biofeedback response has not been reported. Thus, we constructed a model for predicting biofeedback therapy responders by applying the concept of integrated pressurized volume in patients undergoing high-resolution anorectal manometry. Methods: Seventy-one female patients (age: 48-68 years) with dyssynergic defecation who underwent initial high-resolution anorectal manometry and subsequent biofeedback therapy were enrolled. The manometry profiles were used to calculate the 3-dimensional integrated pressurized volumes by multiplying the distance, time, and amplitude during simulated evacuation. Partial least squares regression was performed to generate a predictive model for responders to biofeedback therapy by using the integrated pressurized volume parameters. Results: Fifty-five (77.5%) patients responded to biofeedback therapy. The responders and non-responders did not show significant differences in the conventional manometric parameters. The partial least squares regression model used a linear combination of eight integrated pressurized volume parameters and generated an area under the curve of 0.84 (95% confidence interval: 0.76-0.95, P < 0.01), with 85.5% sensitivity and 62.1% specificity. Conclusions Integrated pressurized volume parameters were better than conventional parameters in predicting the responsiveness to biofeedback therapy, and the combination of these parameters and partial least squares regression was particularly promising. Integrated pressurized volume parameters can more effectively explain the physiology of the anorectal canal compared with conventional parameters.