Systematic Review and Meta-Analysis on the Association Between Phthalates Exposure and Insulin Resistance Publisher Pubmed



Shoshtariyeganeh B¹ ; Zarean M¹ ; Mansourian M² ; Riahi R³ ; Poursafa P¹ ; Teiri H¹ ; Rafiei N¹ ; Dehdashti B¹ ; Kelishadi R³ Show Affiliations
Source: Environmental Science and Pollution Research Published:2019

Abstract

This study aims to provide an overview of human studies on the association of exposure to phthalates and insulin resistance. We systematically searched human studies available until 15 January 2018.We conducted a literature search in Scopus, ISI Web of Science, PubMed, Google Scholar, and Cochrane Collaboration. We used the following keywords to identify relevant articles: “phthalate”, “phthalate ester”, “metabolic syndrome”, “insulin resistance”, “glucose intolerance”, and “diabetes”. For analyzing data, we conducted meta-analysis using the Stata software. We appraised each study to examine the sources of heterogeneity, including difference in clinical outcomes and exposure measurements. To determine the robustness and whether some of the factors have the highest impact on the results of the present meta-analysis, several sensitivity analyses were conducted. Sensitivity analysis showed that by removing studies with the highest weight and age groups, no change was observed in heterogeneity. Moreover, with excluding the study conducted in Europe, the results remained unchanged and constant. In addition, the funnel plot and Egger’s tests were executed to access publication bias. Both the funnel plots and Egger’s test did not show any evidence of publication bias (P = 0.31). In the random effects meta-analysis of all studies (n = 8), the pooled correlation coefficient between phthalate exposure and HOMA-IR was 0.10 (95% CI; 0.07–0.12, P < 0.001), with significant heterogeneity (P < 0.001, I 2 = 85.5%). Our findings revealed positive association between exposure to phthalate metabolites and increased HOMA-IR; this association remained significant even after adjusting the analysis for multiple confounding variables. © 2019, Springer-Verlag GmbH Germany, part of Springer Nature.