Genetically predicted serum metabolites mediate the association between inflammatory proteins and polycystic ovary syndrome: a Mendelian randomization study

Abstract

Objective: To investigate the association between polycystic ovary syndrome (PCOS) and inflammatory proteins, and to identify and quantify the role of serum metabolites as potential mediators.

Methods: Utilizing summary-level data from a genome-wide association study (GWAS), we conducted a two-sample Mendelian Randomization (MR) analysis, a genetic approach that uses genetic variants as instrumental variables to assess the causal relationships between risk factors and outcomes. This analysis involved genetically predicted PCOS (1,639 cases and 218,970 controls) and inflammatory proteins (14,824 participants of primarily European descent). Additionally, a two-step MR analysis was performed to quantify the proportion of the effect of serum metabolites-mediated inflammatory proteins on PCOS. The Inverse Variance Weighted (IVW) method, a statistical technique used within MR to combine data from multiple genetic variants, was used to estimate the causal effects.

Results: The IVW method revealed that the inflammatory proteins IFN-γ (p-value = 0.037, OR = 1.396, 95% CI = 1.020-1.910) and CCL7 (p-value = 0.033, OR = 1.294, 95% CI = 1.021-1.641) were associated with an increased risk of PCOS, while IL-6 (p-value = 0.015, OR = 0.678, 95% CI = 0.495-0.929) and MMP-10 (p-value = 0.025, OR = 0.753, 95% CI = 0.587-0.967) were associated with a decreased risk. No significant evidence suggested an effect of genetically predicted PCOS on inflammatory proteins. The serum metabolite X-11444 was found to mediate 5.44% (95% CI: 10.8-0.0383%) of the effect of MMP-10 on PCOS.

Conclusion: This study not only introduces novel causal associations between inflammatory proteins and PCOS but also highlights the mediating role of serum metabolites in these associations. By applying MR, we were able to minimize confounding and reverse causality, offering robust insights into the biological mechanisms underlying PCOS. These findings advance the understanding of PCOS pathogenesis, particularly in relation to inflammatory pathways and serum metabolite interactions, and suggest potential therapeutic targets that could inform future clinical interventions aimed at mitigating inflammation-related PCOS risks.

Keywords: Chemokine C-C Motif Ligand 7; Interferon gamma; Interleukin-6; Matrix metalloproteinase-10; Mendelian randomization; inflammatory proteins; polycystic ovary syndrome; serum metabolites.

Figure 1

Diagrams illustrating the associations examined in this study. (A) Total effect between inflammatory proteins and PCOS. Parameter c represents the total effect of genetically predicted inflammatory proteins on PCOS, while d represents the total effect of genetically predicted PCOS on inflammatory proteins. (B) The total effect is decomposed into: (i) the indirect effect, assessed via a two-step approach, where a denotes the effect of inflammatory proteins on serum metabolites, and b denotes the effect of serum metabolites on PCOS, with the product method (a × b) capturing this indirect pathway; and (ii) the direct effect, calculated as c′ = c – a × b. The proportion mediated was determined by dividing the indirect effect by the total effect.

Figure 2

Associations between inflammatory proteins and PCOS in discovery cohorts. IVW, Inverse Variance Weighted; IFN-γ, Interferon gamma; CCL7, Chemokine C-C Motif Ligand 7; IL-6, Interleukin-6; MMP-10, Matrix metalloproteinase-10; PCOS, Polycystic Ovary Syndrome; FDR, False Discovery Rate.

Figure 3

Associations between serum metabolites and PCOS. IVW, Inverse Variance Weighted; FDR, False Discovery Rate; PCOS, Polycystic Ovary Syndrome.

Figure 4

MR results of inflammatory factors and PCOS, serum metabolites and PCOS, inflammatory factors and serum metabolites. MMP-10, Matrix metalloproteinase-10; PCOS, Polycystic Ovary Syndrome; X-11444, Serum metabolite identifier; IVW, Inverse Variance Weighted; FDR, False Discovery Rate.