Journal of Human Reproductive Sciences (Sep 2024)

Influence of Interleukin Polymorphisms on the Risk of Recurrent Pregnancy Loss: A Systematic Review and Meta-analysis

  • Praveen Kumar Chandra Sekar,
  • Ramakrishnan Veerabathiran

DOI
https://doi.org/10.4103/jhrs.jhrs_110_24
Journal volume & issue
Vol. 17, no. 3
pp. 142 – 157

Abstract

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Background: Interleukin (IL) genes are essential for regulating the immune and inflammatory processes. Epidemiological studies suggest that polymorphisms in IL genes are associated with a higher risk of recurrent pregnancy loss (RPL). Objective: This study aimed to examine the association between RPL risk and IL gene polymorphisms. Materials and Methods: A comprehensive search of the literature was conducted using PubMed, Google Scholar and Embase to identify relevant studies published until May 30, 2024. A total of 58 studies involving 13,696 participants (both RPL cases and controls) were included. Pooled odds ratios and 95% confidence intervals were calculated using fixed- or random-effects models, as appropriate, with ReviewManager version 5.4, Cochrane RoB Tool 2 and G*Power 3.1 software. Results: The meta-analysis revealed significant correlations between RPL risk and genetic variations in IL-1β (-511T > C), IL-6 (-174 G > C) and IL-10 (-1082 A > G and-592 C > A), with statistical significance set at P C) and IL-10 (-592 C > A) variations were strongly associated with RPL risk in Asian populations. Interpretation: Our research suggests that genetic variations in IL-1β (-511T > C), IL-6 (-174 G > C) and IL-10 (-1082 A > G and-592 C > A) are associated with susceptibility to RPL. Limitation: Our meta-analysis concentrated exclusively on single-factor studies and did not consider potential confounding variables such as age, environmental influences and lifestyle, which could affect susceptibility to RPL. In addition, the lack of individual raw data prevented us from investigating gene–environment and gene–gene interactions or the influence of other polymorphisms and cytokines in our analysis. Protocol Registration: http://www.osf.io/qhbva.

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