We selected genetic variants associated with cardiovascular risk factors, including lipids, glycemic traits and blood pressure, and then extracted the corresponding effect sizes for CKD using the largest GWAS summary-level dataset (Dupuis et al., 2010; Soranzo et al., 2010; Willer et al., 2013; Ehret et al., 2016; Pattaro et al., 2016). No ethical approval was conducted in our study due to this being a re-analysis based on previous collected and published data. Lipids data of HDL cholesterol (n = 187, 167), LDL cholesterol (n = 173, 082), TC (n = 187, 365), and triglyceride (TG, n = 177, 861) were extracted from the GLGC consortium (Willer et al., 2013). We obtained glycemic traits data of fasting glucose (Dupuis et al., 2010) (n = 133, 010) and HbA1c (n = 46, 368) (Soranzo et al., 2010) from the MAGIC consortium. And blood pressure data (Ehret et al., 2016) of SBP (n = 317, 754) and DBP (n = 317, 756) were extracted from UK Biobank imputed genotype data¹. Additionally, people with eGFR based on serum creatinine (eGFRcrea) < 60 mL/min/1.73 m² were defined as CKD, and the dataset were acquired from CKDGen consortium (n = 117, 165) (Pattaro et al., 2016). All of the dataset were conducted on European populations from RCTs and population-based cohorts. Genomic control to each sample was applied to correct for inflated test statistics due to potential population stratification in our datasets. Age, sex, and body mass index were all adjusted in regression models of GLGC, MAGIC and UK Biobank, and age and sex were also corrected in CKDGen (Dupuis et al., 2010; Soranzo et al., 2010; Willer et al., 2013; Ehret et al., 2016; Pattaro et al., 2016) (Supplementary Table S1).

The genetic variants used as IVs in TSMR analysis must satisfy three assumptions as follows (Figure 1): (1) IVs are strongly associated with cardiovascular risk factors, including lipids, glycemic traits, and blood pressure. (2) The IVs are independent of any known confounders. (3) The selected IVs are conditionally independent of CKD, given cardiovascular risk factors and confounders. The second and third assumptions are known as independence from pleiotropy (Bowden et al., 2015). In this study, we used cardiovascular risk factors as the exposures, including HDL cholesterol, LDL cholesterol, TC and TG for lipids, HbA1c and fasting glucose for glycemic traits, SBP and DBP for blood pressure, and CKD as the outcome to perform the TSMR analysis.

Instrumental variables must be associated with cardiovascular risk factors, including HDL cholesterol, LDL cholesterol, TC, TG, HbA1c, fasting glucose, SBP, and DBP. To ensure the close relationship between IVs and cardiovascular risk factors, we selected variants with P < 5 × 10⁻⁸ in the corresponding GWAS summary-level dataset. In addition, pairwise-linkage disequilibrium (LD) was calculated by PLINK 1.90 (Purcell et al., 2007) to ensure the independence among selected IVs and SNPs with r²> 0.001 will be removed from our analysis.

Selected IVs should be independent of any known confounders and conditionally independent of CKD, given the related traits of cardiovascular risk factors. The two assumptions indicate that IVs must influence CKD only through cardiovascular risk factors rather than another pathway (Geng et al., 2018). Firstly, we obtained the corresponding effect estimates of these variables on CKD. For the SNPs that were not available in the CKD, we used proxy SNPs that were highly correlated (r² > 0.8) based on the SNP Annotation and Proxy (SNAP) search system2 (Johnson et al., 2008). Secondly, MR-Egger regression was performed to assess the horizontal pleiotropic (Bowden et al., 2015), which will be introduced in the following statistic analysis. Additionally, we excluded any palindromic SNPs that have minor allele frequency above 0.42 to ensure that the effects of the SNPs on the exposures correspond to the same allele as their effects on CKD (Davey Smith and Hemani, 2014). Moreover, to adjust for potential confounding, GWAS Catalog was used to check for the associations between selected IVs, smoking and type II diabetes (H1bAc and fasting glucose were excluded). Additionally, inspired by the idea of Palmer et al. (2012), we used the F statistic to investigate the association of selected IVs with the exposure on a web application³.

Mendelian randomization-Egger regression was performed to assess the horizontal pleiotropic pathway between IVs and CKD, independent of cardiovascular risk factors (Bowden et al., 2015). MR-Egger regression was developed from Egger regression, which has been used to examine the publication bias in meta-analysis (ref needed here). This approach is expressed as α_i = βγ_i +β₀, α_i represents the estimated effect between IVs and CKD; γ_i represents the estimated effect between IVs and cardiovascular risk factors, including HDL cholesterol, LDL cholesterol, TC, TG, HbA1c, fasting glucose, SBP, and DBP; slope β represents the estimated causal effect of cardiovascular risk factors on CKD; intercept β₀ could be explained as the estimated average value of horizontal pleiotropic. Intercept with P > 0.05 indicates no horizontal pleiotropic exists. Additionally, the slope estimate provides the pleiotropy-corrected causal effect. However, this estimate may be underpowered if the selected SNPs collectively fail to explain a large proportion of the variance in the exposure (Bowden et al., 2015).

In this study, IVW method was used for TSMR analysis to estimate the causal effect between cardiovascular risk factors and CKD (Burgess et al., 2013). The causal effect β was estimated as w_i(α_i/γ_i), where i refers to the ith IV, α_i defines as the association effect of IVs on CKD, γ_i represents the association effect of IVs on cardiovascular risk factors, and w_i means the weights of the causal effect of cardiovascular risk factors on CKD. MR Steiger test was also performed to infer the causal direction between exposures and CKD. It calculates the variance explained in the exposures and outcome by the instrumenting SNPs, and tests if the variance in the outcome is less than the exposures. Given the multiple testing situation, we used a conservative approach and applied a Bonferroni corrected significance level of 0.006 (0.05/8). 0.006 < P < 0.05 was considered as suggestive evidence for a potential association.

The TSMR might fail if the selected SNPs are weak instruments. Therefore, we carried out a recently proposed method called robust adjusted profile score (RAPS) (Zhao et al., 2019) which considers the measurement error in SNP-exposure effects and is unbiased even when there are many (e.g., hundreds of) weak instruments, and is robust to systematic and idiosyncratic pleiotropy. Detailed information about this method please refer to the original paper (Zhao et al., 2019).

Previous studies already reported established causal relationship between coronary artery disease (CAD) and CKD (Jansen et al., 2014), while there is little evidence indicating that cardiovascular risk factors are associated with myopia. To further demonstrate the validity of the selected IVs, we included coronary heart disease and myopia as positive and negative controls in our analysis. The summary statistics of them were respectively derived from CARDIoGRAMplusC4D consortium (Nikpay et al., 2015) and UK Biobank imputed genotype data¹, including 184, 305 and 335, 700 individuals from European population.

In the current study, weighted median and simple median methods were also applied as the follow-up sensitivity analysis (Geng et al., 2018). Compared with IVW, the weighted median and simple median methods have greater robustness to individual genetics with strongly outlying causal estimates and would generate a consistent estimate of the causal effect when valid IVs exceed 50% (Geng et al., 2018; Hemani et al., 2018). Furthermore, leave-one-out sensitivity analysis was performed to identify if the association was disproportionately influenced by a single SNP. The TSMR analysis is performed again but leaving out each SNP in turn and the overall analysis including all SNPs was shown for comparison (Mokry et al., 2016). All of the analysis was implemented by the “TwoSampleMR” package in R software environment.

We obtained 84, 66, 78, 54, 10, 31, 103, 111 LD-independent (r² < 0.001) IVs in total that achieved genome-wide significance (P < 5 × 10⁻⁸) from HDL cholesterol, LDL cholesterol, TC, TG, HbA1c, fasting glucose, SBP and DBP datasets, respectively. Not all of the SNPs were directly found in the CKD dataset; detailed information of all independent IVs in this TSMR analysis were shown in Supplementary Table S2. In addition, the intercept term, estimated for the exposures from MR-Egger regression, demonstrated that no horizontal pleiotropic exists in our TSMR analysis (Table 1). Moreover, we did not detect a direct association between selected IVs and smoking or type II diabetes (H1bAc and fasting glucose were excluded) in GWAS Catalog.

F statistics were presented to demonstrate the strength of relationship between IVs and exposures, and F statistics greater than 10 are often considered as strong enough to mitigate against any bias of the causal IV estimate. Our selected IVs showed strong strength with F statistics ranging between 802 and 9447 (Supplementary Table S3).

According to the IVW analysis results, the odds ratio (OR) and 95% confidence interval (CI) per 1-SD increase of DBP within CKD was 1.35 (1.10, 1.65) (P = 0.004). There was suggestive evidence for potential associations between genetically predicted higher HDL cholesterol [OR: 0.88, 95%CI (0.80, 0.98), P = 0.025] and lower adds of CKD, and between higher SBP [OR: 1.36, 95%CI (1.07, 1.73); P = 0.013] and higher adds of CKD. However, genetically predicted LDL cholesterol, TC, TG, HbA1c and fasting glucose were not associated with CKD (Figure 2).

The results turned out to be consistent with the TSMR results that increased HDL cholesterol decreases the risk of CKD [OR = 0.89, 95% CI (0.79, 0.98), P = 0.025], but increased SBP [OR = 1.37, 95% CI (1.17, 1.56), P = 0.002] and DBP [OR = 1.33, 95% CI (1.14, 1.51), P = 0.003] increases the risk of CKD (Figure 2).

The inferred causal direction between exposures (HDL cholesterol, LDL cholesterol, TC, TG, HbA1c, fasting glucose, DBP, SBP) and CKD were “TRUE” in our MR Steiger test (Supplementary Table S4).

In sensitivity analysis, there was suggestive evidence for potential associations between higher genetically predicted DBP [Weighted median: OR = 1.37, P = 0.013, 95% CI (1.07, 1.76); Simple median: OR = 1.32, P = 0.028, 95% CI (1.03, 1.70)] and SBP [Weighted median: OR = 1.30, P = 0.050, 95% CI (1.10, 1.70); Simple median: OR = 1.45, P = 0.007, 95% CI (1.11, 1.89)] and higher odds of CKD. However, genetically predicted HDL cholesterol, LDL cholesterol, TC, TG, HbA1c and fasting glucose were not associated with CKD (Figure 2). Furthermore, leave-one-out analysis showed a consistent significant causal effect of cardiovascular risk factors on CKD, supporting the robustness of our IVW analysis findings (Supplementary Table S5).

Consistent with the conclusion of previous published paper (Jansen et al., 2014), our analysis demonstrated a causal relationship between all exposures and the positive control (coronary artery disease). As for negative control (myopia), our analysis showed no causal relationship between all exposures and myopia (Supplementary Table S6). Independent IVs of CAD and myopia were listed in Supplementary Tables S7, S8, respectively. Both negative and positive control analysis results suggest that the selected IVs of CKD are appropriate.