Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2023 Nov 1:14:1265711.
doi: 10.3389/fendo.2023.1265711. eCollection 2023.

Correlation between diabetic retinopathy and diabetic nephropathy: a two-sample Mendelian randomization study

Jiaxi Fang #  1   2 , Chuxuan Luo #  3 , Di Zhang  1 , Qiang He  4 , Lin Liu  1   5
Affiliations

Correlation between diabetic retinopathy and diabetic nephropathy: a two-sample Mendelian randomization study

Jiaxi Fang et al. Front Endocrinol (Lausanne). .

Abstract

Rationale & objective: A causal relationship concerning diabetic retinopathy (DR) and diabetic nephropathy (DN) has been studied in many epidemiological observational studies. We conducted a two-sample mendelian randomization study from the perspective of genetics to assess these associations.

Methods: 20 independent single nucleotide polymorphisms (SNPs) associated with diabetic retinopathy were selected from the FinnGen consortium. Summary-level data for diabetic nephropathy were obtained from the publicly available genome-wide association studies (GWAS) database, FinnGen and CKDGen consortium. Inverse variance weighted (IVW) was selected as the primary analysis. MR-Egger, weighted median (WM), simple mode and weighted mode were used as complementary methods to examine causality. Additionally, sensitivity analyses including Cochran's Q test, MR-Egger, MR-Pleiotropy Residual Sum and Outlier (MR-PRESSO), and leave-one-out analyses were conducted to guarantee the accuracy and robustness of our MR analysis.

Results: Our current study demonstrated positive associations of genetically predicted diabetic retinopathy with diabetic nephropathy (OR=1.32; P=3.72E-11), type 1 diabetes with renal complications (OR=1.96; P= 7.11E-11), and type 2 diabetes with renal complications (OR=1.26, P=3.58E-04). Further subtype analysis and multivariate mendelian randomization (MVMR) also reached the same conclusion. A significant casualty with DN was demonstrated both in non-proliferative DR (OR=1.07, P=0.000396) and proliferative DR (OR=1.67, P=3.699068E-14). All the findings were robust across several sensitivity analyses.

Conclusion: Consistent with previous clinical studies, our findings revealed a positive correlation between DR and DN, providing genetic evidence for the non-invasive nature of DR in predicting DN.

Keywords: Mendelian randomization; causality; diabetic nephropathy; diabetic retinopathy; proliferative diabetic retinopathy.

PubMed Disclaimer

Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
The framework of the Mendelian randomization analysis.
Figure 2
Figure 2
Forest plot of Mendelian randomization analyses showing the effect of diabetic retinopathy on the risk of diabetic nephropathy.
Figure 3
Figure 3
The scatter plot for MR analyses of causal associations between each diabetic retinopathy related SNP and Diabetic nephropathy (A), type 1 diabetes with renal complications (B), type2 diabetes with renal complications (C), glomerular filtration rate in diabetics (D), Glomerular filtration rate Urinary albumin−to−creatinine ratio (E).
Figure 4
Figure 4
Leave-one-out sensitivity analyses of each diabetic retinopathy related SNP and Diabetic nephropathy (A), type 1 diabetes with renal complications (B), type2 diabetes with renal complications (C).
Figure 5
Figure 5
An overview of primary results from the two-sample MR studies showing the effect of non-proliferative DR and proliferative DR on the risk of diabetic nephropathy. Full data for all results depicted in the figure can be found in the Supplementary Table S5. IVM, inverse variance weighted method; WM, weighted median estimator; ME, MR Egger; SM, Simple mode; MO, Weighted mode.
See this image and copyright information in PMC

References

    1. Bikbov B, Purcell CA, Levey AS, Smith M, Abdoli A, Abebe M, et al. . Global, regional, and national burden of chronic kidney disease 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet (London England) (2020) 395:709–33. doi: 10.1016/S0140-6736(20)30045-3 - DOI - PMC - PubMed
    1. Xiong Y, Zhou L. The signaling of cellular senescence in diabetic nephropathy. Oxid Med Cell Longevity (2019) 2019:7495629. doi: 10.1155/2019/7495629 - DOI - PMC - PubMed
    1. Galardi A, Colletti M, Di Paolo V, Vitullo P, Antonetti L, Russo I, et al. . Exosomal miRNAs in pediatric cancers. Int J Mol Sci (2019) 20(18):1–22. doi: 10.3390/ijms20184600 - DOI - PMC - PubMed
    1. Li H, Shen Y, Yu Z, Huang Y, He T, Xiao T, et al. . Potential role of the renal arterial resistance index in the differential diagnosis of diabetic kidney disease. Front Endocrinol (2021) 12:731187. doi: 10.3389/fendo.2021.731187 - DOI - PMC - PubMed
    1. Wu L, Gao L, Cao Y, Chen F, Sun T, Liu Y. Analysis of the protective mechanism of liraglutide on retinopathy based on diabetic mouse model. Saudi J Biol Sci (2019) 26:2096–101. doi: 10.1016/j.sjbs.2019.09.032 - DOI - PMC - PubMed

Publication types

MeSH terms