Causal relationship between circulating insulin-like growth factor-1 and Parkinson's disease: a two-sample Mendelian randomization study

Front Aging Neurosci. 2024 Apr 17:16:1333289. doi: 10.3389/fnagi.2024.1333289. eCollection 2024.

Abstract

Background: Linear associations between circulating insulin-like growth factor-1 (IGF-1) levels and Parkinson's disease (PD) have been evidenced in observational studies. Yet, the causal relationship between IGF-1 levels and PD remains obscure. We conducted Mendelian randomization to examine the correlation between genetically predicted IGF-1 levels and PD.

Methods: By reviewing genome-wide association studies (GWAS) that are publicly accessible, we uncovered SNPs linked to both serum concentrations of IGF-1 and PD. A two-sample Mendelian randomization (MR) analysis was carried out to evaluate the individual effect of IGF-1 on PD.

Results: In a primary causal effects model in MR analysis, employing the inverse-variance weighted (IVW) method, IGF-1 levels exhibited a notable association with the risk of PD (OR, 1.020, 95% CI, 1.003-1.038, p = 0.0215). Multiple evaluations revealed that horizontal pleiotropy was improbable to distort the main results (MR-Egger: P PD intercept =0.719), and no bias was detected by leave-one-out analysis.

Conclusion: This study unearthed evidence indicating that heightened IGF-1 levels might be causally correlated with an increased risk of PD.

Keywords: Mendelian randomization; Parkinson’s disease; causality; circulating insulin-like growth factor-1; genetics.

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. Our study was supported by National Natural Science Foundation of China (grant number 82273709), the Natural Science Foundation of Guangdong Province, China (grant number 2021A1515011038), Discipline construction project of Guangdong Medical University (grant number 4SG23001G), Non-funded Scientific and Technological Research Project in Zhanjiang (grant number 2023B01047).