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Causal effect of lipoprotein(a) level on chronic kidney disease of European ancestry: a two-sample Mendelian randomization study

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posted on 2024-07-31, 12:40 authored by Yunhui Zhu, Songzan Chen, Zhebin Chen, Yao Wang, Guosheng Fu, Wenbin Zhang

Chronic kidney disease is a growing health issue, and the options of prevention and therapy remain limited. Although a number of observational studies have linked higher Lp(a) [lipoprotein(a)] levels to the kidney impairment, the causal relationship remains to be determined. The purpose of this study was to assess the causal association between Lp(a) levels and CKD.

We selected eight single-nucleotide polymorphisms (SNPs) significantly associated with Lp(a) levels as instrumental variables. Genome-wide association study (GWAS) from CKDGen consortium yielded the summary data information for CKD. We designed the bidirectional two-sample Mendelian randomization (MR) analyses. The estimates were computed using inverse-variance weighted (IVW), simple median, weighted median, and maximum likelihood. MR-Egger regression was used to detect pleiotropy.

Fixed-effect IVW analysis indicated that genetically predicted Lp(a) levels were associated with CKD significantly (odds ratio, 1.039; 95% CI, 1.009–1.069; p = 0.010). The SNPs showed no pleiotropy according to result of MR-Egger test. Results from sensitivity analyses were consistent. In the inverse MR analysis, random-effect IVW method showed CKD had no causal effect on the elevated Lp(a) (odds ratio, 1.154; 95% CI, 0.845–1.576; p = 0.367).

In this bidirectional two-sample MR analysis, the causal deteriorating effects of genetically predicted plasma Lp(a) levels on the risk of CKD were identified. On the contrary, there is no evidence to support a causal effect of CKD on Lp(a) levels.

Funding

This work was supported by grants from the National Natural Science Foundation of China (82070408), the Medical Health Science and Technology Project of Zhejiang Provincial Health Commission (2021RC014), and the Traditional Chinese Medicine Science and Technology Project of Zhejiang Province (2021ZB172).

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