Multiply robust estimation of natural indirect effects with multiple ordered mediators.

Multiple mediation analysis is a powerful methodology to assess causal effects in the presence of multiple mediators. Several methodologies, such as G-computation and inverse-probability-weighting, have been widely used to draw inferences about natural indirect effects (NIEs). However, a limitation of these methods is their potential for model misspecification. Although powerful semiparametric methods with high robustness and consistency have been developed for inferring average causal effects and for analyzing the effects of a single mediator, a comparably robust method for multiple mediation analysis is still lacking. Therefore, this theoretical study proposes a method of using multiply robust estimators of NIEs in the presence of multiple ordered mediators. We show that the proposed estimators not only enjoy the multiply robustness to model misspecification, they are also consistent and asymptotically normal under regular conditions. We also performed simulations for empirical comparisons of the finite-sample properties between our multiply robust estimators and existing methods. In an illustrative example, a dataset for liver disease patients in Taiwan is used to examine the mediating roles of liver damage and liver cancer in the pathway from hepatitis B/C virus infection to mortality. The model is implemented in the open-source R package "MedMR."

Sodium-Glucose Cotransporter 2 Inhibitors Reduce the Risk of Hospitalization for Heart Failure and Amputation Rate Compared With Incretin-Based Therapy in Patients With Diabetic Foot Disease: A Nationwide Population-Based Study.

OBJECTIVE: Major adverse cardiovascular event (MACE) outcomes associated with sodium-glucose cotransporter 2 inhibitor (SGLT2i) and glucagon-like peptide-1 receptor agonist (GLP-1 RA) therapies remain unclear in patients with type 2 diabetes and newly diagnosed diabetic foot complications (DFCs). This study examined the impact of SGLT2i and GLP-1 RA use on the rates of MACEs and amputations in patients with type 2 diabetes and without cardiovascular disease. METHODS: Data from the Taiwan National Health Insurance Research Database (2004-2017) were analyzed, focusing on patients with type 2 diabetes without previous MACE and newly diagnosed DFCs. The primary outcome was the first MACE occurrence, and the secondary outcomes included MACE components, all-cause mortality, and lower extremity amputation (LEA) rates. RESULTS: SGLT2i users showed a significant decrease in the MACE (hazard ratio [HR], 0.64; 95% confidence interval [CI], 0.46-0.88) and hospitalization for heart failure (HR, 0.54; 95% CI, 0.35-0.83) rates compared with dipeptidyl peptidase-4 inhibitor users. The amputation rates were also lower in SGLT2i users without LEA at the first DFC diagnosis (HR, 0.28; 95% CI, 0.10-0.75) and did not increase in those with a history of peripheral artery disease or LEA. No significant differences were observed between dipeptidyl peptidase-4 inhibitor and GLP-1 RA users in terms of the primary or secondary outcomes. CONCLUSION: In patients with type 2 diabetes initially diagnosed with DFC, SGLT2i are effective in significantly reducing the hospitalization for heart failure and MACE rates. SGLT2i lower the amputation rates, especially in patients who have not previously had a LEA, than the dipeptidyl peptidase-4 inhibitor therapy.

Causal Mediation Analysis with Multiple Time-varying Mediators.

In longitudinal studies with time-varying exposures and mediators, the mediational g-formula is an important method for the assessment of direct and indirect effects. However, current methodologies based on the mediational g-formula can deal with only one mediator. This limitation makes these methodologies inapplicable to many scenarios. Hence, we develop a novel methodology by extending the mediational g-formula to cover cases with multiple time-varying mediators. We formulate two variants of our approach that are each suited to a distinct set of assumptions and effect definitions and present nonparametric identification results of each variant. We further show how complex causal mechanisms (whose complexity derives from the presence of multiple time-varying mediators) can be untangled. We implemented a parametric method, along with a user-friendly algorithm, in R software. We illustrate our method by investigating the complex causal mechanism underlying the progression of chronic obstructive pulmonary disease. We found that the effects of lung function impairment mediated by dyspnea symptoms accounted for 14.6% of the total effect and that mediated by physical activity accounted for 11.9%. Our analyses thus illustrate the power of this approach, providing evidence for the mediating role of dyspnea and physical activity on the causal pathway from lung function impairment to health status. See video abstract at, http://links.lww.com/EDE/B988 .

On the Conventional Definition of Path-specific Effects: Fully Mediated Interaction With Multiple Ordered Mediators.

Path-specific effects are a critical measure for assessing mediation in the presence of multiple mediators. However, the conventional definition of path-specific effects has generated controversy because it often causes misinterpretation of the results of multiple mediator analysis. For in-depth analysis of this issue, we propose the concept of decomposing fully mediated interaction from the average causal effect. We show that misclassification of fully mediated interaction is the main cause of misinterpretation of path-specific effects. We propose two strategies for specifying fully mediated interaction: isolating and reclassifying fully mediated interaction. The choice of strategy depends on the objective. Isolating fully mediated interaction is the superior strategy when the main objective is elucidating the mediation mechanism, whereas reclassifying it is superior when the main objective is precisely interpreting the mediation analysis results. To compare performance, this study used the two proposed strategies and the conventional decomposition strategy to analyze the mediating roles of dyspnea and anxiety in the effect of impaired lung function on poor health status in a population of patients with chronic obstructive pulmonary disease. The estimation result showed that the conventional decomposition strategy underestimates the importance of dyspnea as a mechanism of this disease. Specifically, the strategy of reclassifying fully mediated interaction revealed that 50% of the average causal effect is attributable to mediating effects, particularly the mediating effect of dyspnea.

Genome-wide causal mediation analysis identifies genetic loci associated with uterine fibroids mediated by age at menarche.

STUDY QUESTION: Could the direct contribution of genetic variants to the pathophysiology of uterine fibroids and the contribution mediated by age at menarche be different? SUMMARY ANSWER: Age at menarche plays a mediation role in the genetic influence on uterine fibroids, and four causal genetic mechanisms underlying the age at menarche-mediated effects of common genetic loci on uterine fibroid development were identified. WHAT IS KNOWN ALREADY: Uterine fibroids are common benign tumors developing from uterine smooth muscle. Genome-wide association studies (GWASs) have identified over 30 genetic loci associated with uterine fibroids in different ethnic populations. Several genetic variations in or nearby these identified loci were also associated with early age at menarche, one of the major risk factors of uterine fibroids. Although the results of GWASs reveal how genetic variations affect uterine fibroids, the genetic mechanism of uterine fibroids mediated by age at menarche remains elusive. STUDY DESIGN, SIZE, DURATION: In this study, we conducted a genome-wide causal mediation analysis in two cohorts covering a total of 69 552 females of Han Chinese descent from the Taiwan Biobank (TWB). TWB is an ongoing community- and hospital-based cohort aiming to enroll 200 000 individuals from the general Taiwanese population between 30 and 70 years old. It has been enrolling Taiwanese study participants since 2012 and has extensive phenotypic data collected from 148 291 individuals as of May 2021. PARTICIPANTS/MATERIALS, SETTING, METHODS: We recruited individuals in two cohorts, with 13 899 females in TWB1 and 55 653 females in TWB2. The two sets of individuals are almost distinct, with only 730 individuals enrolled in both cohorts. Over 99% of the participants are Han Chinese. Approximately 21% of participants developed uterine fibroids. DNA samples from both cohorts were genotyped using two different customized chips (TWB1 and TWB2 arrays). After quality control and genotype imputation, 646 973 TWB1 single-nucleotide polymorphisms (SNPs) and 686 439 TWB2 SNPs were assessed in our analysis. There were 99 939 SNPs which overlapped between the TWB1 and TWB2 arrays, 547 034 TWB1 array-specific SNPs and 586 500 TWB2 array-specific SNPs. We performed GWASs for screening potential risk SNPs for age at menarche and for uterine fibroids. We subsequently identified causal mediation effects of risk SNPs on uterine fibroids mediated by age at menarche. MAIN RESULTS AND THE ROLE OF CHANCE: In addition to known loci at LIN28B associated with age at menarche and loci at WNT4 associated with uterine fibroids, we identified 162 SNPs in 77 transcripts that were associated with menarche-mediated causal effects on uterine fibroids via four different causal genetic mechanisms: a both-harmful group with 52 SNPs, a both-protective group with 34 SNPs, a mediator-harmful group with 22 SNPs and a mediator-protective group with 54 SNPs. Among these SNPs, rs809302 in SLK significantly increased the risk of developing uterine fibroids by 3.92% through a mechanism other than age at menarche (P < 10-10), and rs371721345 in HLA-DOB was associated with a 2.70% decreased risk (P < 10-10) in the occurrence of uterine fibroids, mediated by age at menarche. These findings provide insights into the mechanism underlying the effect of genetic loci on uterine fibroids mediated by age at menarche. LIMITATIONS, REASONS FOR CAUTION: A potential issue is that the present study relied upon self-reported age at menarche and uterine fibroid information. Due to the experimental design, the consistency between self-reports and medical records for uterine fibroids in Taiwan cannot be checked. Fortunately, the literature support that self-reporting even years later remains a practical means for collecting data on menarche and uterine fibroids. We found that the impact of under-reporting of uterine fibroids is less in our study. In addition, the rate of reporting a diagnosis of uterine fibroids was within the rates of medical diagnosis based on national health insurance data. Future work investigating the consistency between self-reports and medical records in Taiwan can remedy this issue. WIDER IMPLICATIONS OF THE FINDINGS: This study is the first to investigate whether and to what extent age at menarche mediates the causal effects of genetic variants on uterine fibroids by using genome-wide causal mediation analysis. By treating age at menarche as a mediator, this report provides an insight into the genetic risk factors for developing uterine fibroids. Thus, this article represents a step forward in deciphering the role of intermediated risk factors in the genetic mechanism of disease. STUDY FUNDING/COMPETING INTEREST(S): This work was supported by the China Medical University, Taiwan (CMU110-ASIA-13 and CMU107-Z-04), the Ministry of Science and Technology, Taiwan (MOST 110-2314-B-039-058) and the International Joint Usage/Research Center, the Institute of Medical Science, the University of Tokyo, Japan (K2104). The authors have no competing interests. TRIAL REGISTRATION NUMBER: N/A.

Identification and robust estimation of swapped direct and indirect effects: Mediation analysis with unmeasured mediator-outcome confounding and intermediate confounding.

Counterfactual-model-based mediation analysis can yield substantial insight into the causal mechanism through the assessment of natural direct effects (NDEs) and natural indirect effects (NIEs). However, the assumptions regarding unmeasured mediator-outcome confounding and intermediate mediator-outcome confounding that are required for the determination of NDEs and NIEs present practical challenges. To address this problem, we introduce an instrumental blocker, a novel quasi-instrumental variable, to relax both of these assumptions, and we define a swapped direct effect (SDE) and a swapped indirect effect (SIE) to assess the mediation. We show that the SDE and SIE are identical to the NDE and NIE, respectively, based on a causal interpretation. Moreover, the empirical expressions of the SDE and SIE are derived with and without an intermediate mediator-outcome confounder. Then, a multiply robust estimation method is derived to mitigate the model misspecification problem. We prove that the proposed estimator is consistent, asymptotically normal, and achieves the semiparametric efficiency bound. As an illustration, we apply the proposed method to genomic datasets of lung cancer to investigate the potential role of the epidermal growth factor receptor in the treatment of lung cancer.

Robust inference on effects attributable to mediators: A controlled-direct-effect-based approach for causal effect decomposition with multiple mediators.

Effect decomposition is a critical technique for mechanism investigation in settings with multiple causally ordered mediators. Causal mediation analysis is a standard method for effect decomposition, but the assumptions required for the identification process are extremely strong. Moreover, mediation analysis focuses on addressing mediating mechanisms rather than interacting mechanisms. Mediation and interaction for mediators both contribute to the occurrence of disease, and therefore unifying mediation and interaction in effect decomposition is important to causal mechanism investigation. By extending the framework of controlled direct effects, this study proposes the effect attributable to mediators (EAM) as a novel measure for effect decomposition. For policymaking, EAM represents how much an effect can be eliminated by setting mediators to certain values. From the perspective of mechanism investigation, EAM contains information about how much a particular mediator or set of mediators is involved in the causal mechanism through mediation, interaction, or both. EAM is more appropriate than the conventional path-specific effect for application in clinical or medical studies. The assumptions of EAM for identification are considerably weaker than those of causal mediation analysis. We develop a semiparametric estimator of EAM with robustness to model misspecification. The asymptotic property is fully realized. We applied EAM to assess the magnitude of the effect of hepatitis C virus infection on mortality, which was eliminated by controlling alanine aminotransferase and treating hepatocellular carcinoma.

Higher reinfection rate after two-stage revision arthroplasty in patients with refractory diabetes mellitus: a retrospective analysis with a minimum ten-year follow up.

BACKGROUND: Treatment protocols for two-stage revision arthroplasty with diabetes mellitus (DM) have not yet been established. The control of glycated hemoglobin (HbA1c) in two-stage revision arthroplasty is still debated. This study aimed to clarify the importance of preoperative HbA1c levels before each stage of revision arthroplasty and to analyze the risk factors for reinfection. METHODS: Five hundred eighty-eight patients suffered from first-time PJI and was treated in our institute from January 1994 to December 2010 were reviewed. The mean follow-up time was 13.8 (range, 10.2-24.8) years. Patients underwent two-stage revision arthroplasty with DM at presentation were included. The endpoint of the study was reinfection of the revision arthroplasty. Demographic, survivorship, and surgical variables were also analyzed. RESULTS: Eighty-eight patients were identified and grouped by HbA1c level before the first stage surgery: Groups 1 and 2 had HbA1c levels < 7% and ≥ 7%, respectively. Reinfection was identified in 4.55% (2/44) and 18.18% (8/44) of the patients in Groups 1 and 2, respectively. Survivorship analysis revealed correction of the HbA1c before the final stage of revision arthroplasty as an independent factor (p < 0.001). The identified risks for reinfection were HbA1c levels ≥ 7% before final-stage surgery, ≥ 3 stages of revision arthroplasty, and extended-spectrum beta-lactamase (ESBL)-Escherichia coli PJI. CONCLUSION: The HbA1c level before the final stage of revision arthroplasty could affect staged revision arthroplasty outcomes. Therefore, the necessity of postponing the elective final-stage revision arthroplasty procedure for HbA1c control should be further investigated in the future.

Integrated multiple mediation analysis: A robustness-specificity trade-off in causal structure.

Recent methodological developments in causal mediation analysis have addressed several issues regarding multiple mediators. However, these developed methods differ in their definitions of causal parameters, assumptions for identification, and interpretations of causal effects, making it unclear which method ought to be selected when investigating a given causal effect. Thus, in this study, we construct an integrated framework, which unifies all existing methodologies, as a standard for mediation analysis with multiple mediators. To clarify the relationship between existing methods, we propose four strategies for effect decomposition: two-way, partially forward, partially backward, and complete decompositions. This study reveals how the direct and indirect effects of each strategy are explicitly and correctly interpreted as path-specific effects under different causal mediation structures. In the integrated framework, we further verify the utility of the interventional analogues of direct and indirect effects, especially when natural direct and indirect effects cannot be identified or when crossworld exchangeability is invalid. Consequently, this study yields a robustness-specificity trade-off in the choice of strategies. Inverse probability weighting is considered for estimation. The four strategies are further applied to a simulation study for performance evaluation and for analyzing the Risk Evaluation of Viral Load Elevation and Associated Liver Disease/Cancer dataset from Taiwan to investigate the causal effect of hepatitis C virus infection on mortality.

Survival mediation analysis with the death-truncated mediator: The completeness of the survival mediation parameter.

In medical research, the development of mediation analysis with a survival outcome has facilitated investigation into causal mechanisms. However, studies have not discussed the death-truncation problem for mediators, the problem being that conventional mediation parameters cannot be well defined in the presence of a truncated mediator. In the present study, we systematically defined the completeness of causal effects to uncover the gap, in conventional causal definitions, between the survival and nonsurvival settings. We propose a novel approach to redefining natural direct and indirect effects, which are generalized forms of conventional causal effects for survival outcomes. Furthermore, we developed three statistical methods for the binary outcome of survival status and formulated a Cox model for survival time. We performed simulations to demonstrate that the proposed methods are unbiased and robust. We also applied the proposed method to explore the effect of hepatitis C virus infection on mortality, as mediated through hepatitis B viral load.

Residential greenness and birth outcomes: Evaluating the mediation and interaction effects of particulate air pollution.

BACKGROUND: The few studies that examined the association between residential greenness and birth outcomes have produced inconsistent results, and the underlying mechanisms of these associations remain unclear. OBJECTIVES: We examined the mediation and interaction effects of particulate matter (PM) air pollution on the relationship between greenness exposure during the first and third trimesters of pregnancy and birth outcomes, including preterm birth (PTB), term low birth weight (TLBW), small for gestational age (SGA), birth weight (BW), and head circumference (HC). METHODS: We conducted a retrospective cohort study on 16,184 singleton live births between 2010 and 2012 in Taiwan. Residential greenness was estimated based on the normalized difference vegetation index (NDVI), and the PM information during the first and third trimesters was estimated through hybrid kriging land use regression and ordinary kriging interpolation methods. Multiple regression analyses were performed to evaluate the associations between greenness exposure and birth outcomes. We estimated the mediating effects of PM associated with greenness exposure on birth outcomes through causal mediation analyses. We also examined the potential multiplicative and additive interactions between greenness exposure and PM and their effects on birth outcomes. RESULTS: The first trimester NDVI exposure was associated with reduced risks for PTB, TLBW, and SGA, which had an adjusted OR (aOR) of 0.93 (95% CI: 0.89-0.97), 0.91 (95% CI: 0.83-0.99), and 0.95 (95% CI: 0.91-1.00), respectively, per 0.1 unit increase in multi-pollutant models. The causal mediation analysis showed that PM mediated approximately 5-19% of the association between first and third trimester greenness and PTB and mediated approximately 15-37% of the association between greenness and SGA. We identified multiplicative interactions in log scale between first trimester PM10 and NDVI exposure for SGA (aORinteraction = 0.92, p = 0.03) and HC (estimateinteraction = 1.47, p = 0.04). CONCLUSIONS: This study revealed beneficial associations between residential greenness and birth outcomes, including PTB, TLBW, and SGA. The associations were partly mediated by a reduction in exposure to PM air pollution. SUMMARY: The beneficial effects of greenness on PTB and SGA are partly mediated by a reduction in exposure to PM air pollution.

Effects of COVID-19 Prevention Measures on Other Common Infections, Taiwan.

To determine whether policies to limit transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) hinder spread of other infectious diseases, we analyzed the National Health Insurance database in Taiwan. Rates of other infections were significantly lower after SARS-CoV-2 prevention measures were announced. This finding can be applied to cost-effectiveness of SARS-CoV-2 prevention.

Stochastic approach for mechanistic interaction under longitudinal studies with noninformative right censoring.

Characterizing the mechanistic interactions between exposures and diseases is one of the most critical issues in epidemiologic studies. Previous studies have proposed a stochastic sufficient component cause framework, under which each sufficient cause is treated as a stochastic process instead of a time-invariant random variable. However, different types of mechanistic interactions such as synergism and agonism cannot be further identified. In this study, we proposed a stochastic marginal sufficient component cause model to conceptualize and identify agonism and synergism by exploiting the additional information. We further provided six approaches to identify and estimate agonism and synergism based on an additive hazard model and a complementary log model. Researchers can easily adjust confounding factors by including appropriate covariates into a regression model. Simulations have proven that approaches under three models are all valid tests. The power of an additive hazard model increases as the total follow-up time increases and is higher than that of the other two models. We applied this method to a Taiwanese cohort data set to investigate the mechanistic interaction among hepatitis B and C viruses on the incidence of hepatocellular carcinoma. The hazard of people with agonistic interaction is 1.28×10-5 (95% CI: 6.97×10-6 , 1.87×10-5 ), and the cumulative hazard of those people is 7.41×10-2 (95% CI: 4.09×10-2 , 1.07×10-1 ), which is approximately 3.5 times stronger than that of synergistic interaction. The proposed method makes it possible to quantify different types of mechanistic interactions in longitudinal studies with censored data.

Six-way decomposition of causal effects: Unifying mediation and mechanistic interaction.

The sufficient component cause (SCC) model and counterfactual model are two common methods for causal inference, each with their own advantages: the SCC model allows the mechanistic interaction to be detailed, whereas the counterfactual model features a systemic framework for quantifying causal effects. Hence, integrating the SCC and counterfactual models may facilitate the conceptualization of causation. Based on the marginal SCC (mSCC) model, we propose a novel counterfactual mSCC framework that includes the steps of definition, identification, and estimation. We further propose a six-way effect decomposition for assessing mediation and the mechanistic interaction. The results demonstrate that when all variables are binary, the six-way decomposition is an extension of four-way decomposition and that without agonism, the six-way decomposition is reduced to four-way decomposition. To illustrate the utility of the proposed decomposition, we apply it to a Taiwanese cohort to examine the mechanism of hepatitis C virus (HCV)-induced hepatocellular carcinoma (HCC) with liver inflammation measured by alanine aminotransferase (ALT) as a mediator. Among the HCV-induced HCC cases, 62.27% are not explained by either mediation or interaction in relation to ALT; 9.32% are purely mediated by ALT; 16.53% are caused by the synergistic effect of HCV and ALT; and 9.31% are due to the mediated synergistic effect of HCV and ALT. In summary, we introduce an SCC model framework based on counterfactual theory and detail the required identification assumptions and estimation procedures; we also propose a six-way effect decomposition to unify mediation and mechanistic interaction analyses.

On identification of agonistic interaction: Hepatitis B and C interaction on hepatocellular carcinoma.

Agonistic interaction is one of the most important types of mechanistic interaction, which is difficult to be distinguished from synergistic interaction by empirical data. In this study, we propose four approaches that suffice to identify and estimate the agonistic interaction: (1) to make a strong assumption that synergism does not exist; (2) to exploit information from a third factor by assuming that this factor is a necessary component for the background condition of synergistic interaction but is not involved in other mechanisms; (3) to consider a third factor necessary for the background condition of agonistic interaction but not involved in other mechanisms; and (4) similar to (3) but to allow flexibility that the third factor may have a main effect on the outcome and/or a synergistic effect with the two risk factors of interest. We applied the proposed methods to quantify the agonism of Hepatitis B and C viruses (HBV and HCV) infections on liver cancer using a Taiwanese cohort study (n = 23 820; HBV carrier n = 4149 (17.44%), HCV carrier n = 1313 (5.52%)). The result demonstrated that agonistic interaction is more dominant compared with synergistic interaction, which explains the findings that the dual infected patients do not have a significantly higher risk of liver cancer than those with single infection. By exploiting an additional risk factor that satisfies certain assumptions, these approaches potentially fill the gap between mechanistic and causal interactions, contributing the comprehensive understanding of causal mechanisms.

Parametric Mediational g-Formula Approach to Mediation Analysis with Time-varying Exposures, Mediators, and Confounders.

The assessment of direct and indirect effects with time-varying mediators and confounders is a common but challenging problem, and standard mediation analysis approaches are generally not applicable in this context. The mediational g-formula was recently proposed to address this problem, paired with a semiparametric estimation approach to evaluate longitudinal mediation effects empirically. In this article, we develop a parametric estimation approach to the mediational g-formula, including a feasible algorithm implemented in a freely available SAS macro. In the Framingham Heart Study data, we apply this method to estimate the interventional analogues of natural direct and indirect effects of smoking behaviors sustained over a 10-year period on blood pressure when considering weight change as a time-varying mediator. Compared with not smoking, smoking 20 cigarettes per day for 10 years was estimated to increase blood pressure by 1.2 mm Hg (95% CI: -0.7, 2.7). The direct effect was estimated to increase blood pressure by 1.5 mm Hg (95% CI: -0.3, 2.9), and the indirect effect was -0.3 mm Hg (95% CI: -0.5, -0.1), which is negative because smoking which is associated with lower weight is associated in turn with lower blood pressure. These results provide evidence that weight change in fact partially conceals the detrimental effects of cigarette smoking on blood pressure. Our study represents, to our knowledge, the first application of the parametric mediational g-formula in an epidemiologic cohort study (see video abstract at, http://links.lww.com/EDE/B159.).

Mediation analysis for a survival outcome with time-varying exposures, mediators, and confounders.

We propose an approach to conduct mediation analysis for survival data with time-varying exposures, mediators, and confounders. We identify certain interventional direct and indirect effects through a survival mediational g-formula and describe the required assumptions. We also provide a feasible parametric approach along with an algorithm and software to estimate these effects. We apply this method to analyze the Framingham Heart Study data to investigate the causal mechanism of smoking on mortality through coronary artery disease. The estimated overall 10-year all-cause mortality risk difference comparing "always smoke 30 cigarettes per day" versus "never smoke" was 4.3 (95% CI = (1.37, 6.30)). Of the overall effect, we estimated 7.91% (95% CI: = 1.36%, 19.32%) was mediated by the incidence and timing of coronary artery disease. The survival mediational g-formula constitutes a powerful tool for conducting mediation analysis with longitudinal data.

Effectiveness of hyaluronic acid for treating diabetic foot: a systematic review and meta-analysis.

Diabetic foot ulceration is a major complication of diabetes mellitus. Hyaluronic acid (HA) is used in the treatment of diabetic foot. This meta-analysis was designed to evaluate if HA increased the complete healing rate of diabetic foot compared with controls. We searched Medline, Cochrane, EMBASE, Google Scholar (until January 31, 2014) databases for prospective randomized controlled trials that assessed the effectiveness of HA in treating foot ulcers resulting from diabetes. The primary outcome for the study was complete healing rate of the ulcer at 12 weeks. Three hundred twenty-eight patients were identified from four studies that evaluated the rate of healing of diabetic foot that were treated with HA or controls. Among the four studies, odd ratios (OR) ranged from 1.19 to 8.86, with the overall OR being 1.71 (p = 0.047; 95% confidence interval = 1.01 to 2.90). In summary, our meta-analysis strengthens the findings that HA is beneficial in treating diabetic foot by increasing the rate of wound healing. These findings support the use of HA in treating diabetic foot.