Trans-ethnic Mendelian-randomization Study Reveals Causal Relationships Between Cardiometabolic Factors And Chronic Kidney Disease Ⅲ
Nov 27, 2023
Discussion
In this trans-ethnic MR study, we comprehensively assessed the causality of 45 risk factors on CKD and eGFR in >1 million Europeans and 17 risk factors on CKD and eGFR in > 250,000 East Asians. Using MR approaches, including five two-sample MR methods and multivariable MR, we found reliable evidence for the causal effects of eight cardiometabolic-related risk factors [BMI, SBP, hypertension, T2D, nephrolithiasis, HDL-C, apolipoprotein A-I and lipoprotein(a)] on CKD. The remaining 37 risk factors, including smoking and serum uric acid, had weak evidence to support causal effects on CKD using the currently available data. These findings are consistent with previous MR studies that analyzed similar risk factors separately.40–44 In addition, the null finding of the serum uric acid agreed with the recent clinical trial investigating the effects of serum urate-lowering (using Allopurinol) on CKD progression.45,46 Notably, our extensive MR and follow-up analyses suggested the possibility of glucose-independent pathways linking T2D with CKD. Using nonlinear MR, we observed a threshold relationship between genetically predicted BMI and CKD risk, with increased CKD risk at a BMI of >25 kg/m2 .
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The causal patterns of 17 risk factors were compared across the two ancestries and we observed consistent effects of T2D, BMI, and nephrolithiasis on CKD in Europeans and East Asians. In contrast, distinguishable causal patterns between ancestries were observed when examining the effect of hypertension on CKD, with a strong causal estimate in Europeans that was not replicated in the analysis of East Asians. These findings indicate that careful consideration is needed before implementing interventions for CKD risk factors in participants of one ancestry based on evidence from another ancestry.
Among the prioritized risk factors, hypertension is one of the most common risk factors for kidney function decline in patients with or without CKD.47–49 A recent bidirectional MR study in Europeans supported the causal effects of higher kidney function on lower blood pressure using eGFR instruments controlled by blood urea nitrogen. However, the same study suggested inconclusive evidence of an effect of blood pressure on eGFR.50 In our MR analysis, we found evidence of positive bidirectional causal effects between hypertension and CKD in Europeans.
CKD, chronic kidney disease; BMI, body mass index; HDL, high-density lipoprotein; CETP, cholesteryl ester transfer protein; Lp(a), lipoprotein (a); CRP, C-reactive protein.
There are several potential explanations for the inconsistent MR findings across these studies. Yu et al. used genetic associations for blood pressure that were adjusted for BMI with genetic associations for eGFR and CKD that were not adjusted for BMI in their MR analysis. Given the causal role of BMI on both CKD and hypertension, only controlling for BMI in the exposure data may create unintended bias in the MR estimates, as described previously.51 An alternative explanation is the difference in CKD case ascertainment. Specifically, we used CKD cases that were clinically diagnosed, which may bring additional statistical power and provide more reliable evidence for the effect of blood pressure on CKD.
Given the difference in MR evidence across the ancestries that we observed, combined with previous evidence from the literature, it is possible that hypertension could have differential effects on CKD by ancestry. Ethnic disparities in relation to hypertension and CKD have previously been reported.52,53 For example, Chinese people with hypertension have a lower risk of CKD compared with European people with hypertension.52 Additionally, in 2019, hypertensive nephropathy accounted for 27% of the overall CKD cases in the USA but 20.8% of the overall CKD cases among Chinese.54,55 Further well-powered studies are needed to validate the causal effect of blood pressure on CKD across ancestries.
In addition, our MR analyses suggested substantial causal effects for BMI and nephrolithiasis on CKD. Previous observational studies have suggested that BMI is positively associated with CKD onset56 and end-stage renal disease57,58 and negatively associated with kidney function.59 The effect of weight loss on reducing the risk of diabetic nephropathy in patients with T2D60 and slowing kidney-function decline has also been reported.61 Using linear and non-linear MR approaches, we observed a threshold causal relationship between BMI and CKD. Moreover, nephrolithiasis is a common and serious health concern globally.62–64 There is increasing evidence to suggest that having kidney stones is a risk factor for CKD.62,65 For instance, people with kidney stones tend to have lower eGFR.63,66 A previous cohort study suggested that even a single kidney-stone episode was associated with an increased likelihood of adverse renal outcomes.67 A recent genetic study also suggested an inverse association between eGFR and kidney-stone formation.68 However, the causal relationship between nephrolithiasis and CKD had not been investigated previously. Our MR analysis supported the causal effect of increased nephrolithiasis risk on increasing CKD risk. This is of particular importance as obstructive nephropathy is among the leading causes of CKD in the general population. Specifically, it is the third leading cause of CKD among the Chinese population and has been estimated to be present in 15.6% of CKD cases.55
Notably, diabetic kidney disease is considered the most common type of CKD worldwide.69 A previous MR study of T2D on CKD in Chinese participants suggested a strong causal link between the two phenotypes,70 which aligned with our MR findings in both East Asians and Europeans. However, despite the reliable evidence for a causal effect of T2D on CKD, our linear and non-linear MR found limited evidence to support the causal effects of glucoseand insulin-related phenotypes on CKD. This is consistent with the findings from a previous MR study conducted in Europe.71 It has also been observed that with increasing use of glucose-lowering medications, the prevalence of CKD in diabetics has not reduced as much as expected.72 This is supported by a meta-analysis of RCTs that found intensive glucose control to have an inconclusive effect on reducing the risk of end-stage renal disease.73 These findings, together with our MR results, suggest that glucose-independent pathways could play a role in the relationship between diabetes and CKD. Furthermore, it has consistently been suggested that the beneficial effects of SGLT2 inhibitors (anti-diabetic medication) on renal outcomes may be mediated by glucose-independent pathways.74,75 One potential limitation of our analysis in relation to integrating this finding is that the glucose GWAS that we used was conducted in a general population whose fasting glucose levels are <7 mmol/L. Existing MR studies using these data have made the assumption that the glucose change in the general population is similar to that in diabetic patients (i.e. individuals whose fasting glucose levels are typically >7 mmol/L), which may not necessarily be true. Although our stratified MR analysis showed little difference between diabetic and non-diabetic patients, we believe that better genetic instruments derived from a diabetic patient population and well-designed clinical trials are needed to evaluate the effect of glucose-dependent and -independent mechanisms on CKD prevention.
Hyperlipidaemia and dyslipidemia have been widely documented to be associated with kidney disease.76,77 But the causal effects of lipid components on CKD are still unclear. A few recent MR studies have suggested a protective effect of higher HDL-C on CKD in Europeans,78 an adverse effect of higher triglycerides on CKD in Chinese79, and a nominal effect of lipoprotein(a) lowering on reducing CKD risk.80 In this study, we validated the HDL-C findings, confirmed the triglycerides effect in Biobank Japan, and strengthened the evidence of the lipoprotein(a) finding in completely independent samples. Besides confirming these existing findings, our study also established novel causal evidence for the apolipoprotein A-I and nonapolipoprotein A-I properties of HDL-C on CKD risk in Europeans. Furthermore, our study extended the findings from recent studies of HDL-C81 and cholesteryl ester transfer protein inhibitors,82 which support the causal effect of circulating cholesteryl ester transfer protein levels on CKD in Europeans. The observed causal effect of HDL-C and the effect of cholesteryl ester transfer protein levels on CKD raises the possibility that increasing the HDL-C concentration may offer a potential intervention strategy for CKD prevention. Moreover, our study demonstrated that the causal effect of lipoprotein(a) levels on CKD was independent of the apolipoprotein(a) size. This finding, together with previous observational evidence,83–85 implies the possibility of lipoprotein(a)-reduction therapies, such as Pelacarsen [also known as IONIS-APO(a)-LRx], on reducing CKD risk.86 Overall, our findings have highlighted the potential for several lipid-management strategies in reducing CKD risk.
Strengths and limitations
Our study has some strengths compared with previous studies in this setting. We used clinically diagnosed CKD (instead of only using eGFR < 60 ml/min/1.73 m2 to define CKD) in two European (UK Biobank and HUNT) and two East Asian (China Kadoorie Biobank and Japan-KidneyBiobank/ToMMo) studies. These four studies included participants with abnormal urine protein levels but with normal eGFR as CKD cases. This increased the robustness of the CKD definition. By comprehensively validating the MR findings in the six CKD studies, we also greatly enhanced the reliability of the causal atlas that we derived of risk factors for CKD.
Our study also has some potential limitations. First, we used the ICD 10 code to define CKD cases in three of the six studies. Such selection criteria excluded undiagnosed cases and diagnoses made in an outpatient setting. Considering the low disease awareness of CKD,87,88 such misclassification of the outcome may reduce the power of our study. However, as a trade-off, such an approach also excluded non-CKD samples from the case group (e.g. participants with a single eGFR measurement of <60 due to measurement error), which brought additional power to the statistical analysis. Second, we set up a stringent Bonferroni-corrected threshold together with other criteria (e.g. little evidence of pleiotropy) to select the top MR findings. Such a strategy could create some false-negative findings but minimize the possibility of identifying false-positive findings. With the aim of supporting the future clinical practice of CKD management, we decided to apply such a stringent strategy to provide the most reliable causal evidence using genetics. Second, in the MR analysis, genetic predictors for binary exposures (e.g. coronary artery disease) are not mimicking the exposure itself, but the pre-disposition to the exposure instead.89 Consequently, our results must be interpreted as the effect of removing the predisposition to binary exposure (rather than treatment of the exposure) to reduce CKD risk. In addition, due to the relative lack of GWAS samples in East Asians, we could only examine causal effects for 17 of the 45 risk factors for this ancestry. For the same reason, the number of instruments for each risk factor in the analyses differed between the two ancestries. For risk factors with different MR evidence across ancestries, we conducted a comprehensive set of sensitivity analyses to minimize the influence of differences in power and instrument strength across ancestries. Other limitations of the study are listed in Supplementary Note S7 (available as Supplementary data at IJE online).
Conclusions
By evaluating the causal evidence for 45 risk factors on CKD in >1 million individuals of European ancestry and 17 risk factors in >250 000 individuals of East Asian ancestry, we have shown that eight risk factors are reliably causal for CKD in Europeans and three of these are also causal in East Asians. These risk factors are predominantly related to cardiometabolic health, which supports the shared causal link between cardiometabolic health and kidney function. The different causal pattern between hypertension and CKD in Europeans compared with that in East Asians suggests that blood pressure might have an ancestry-specific role in CKD etiology. Ultimately, our findings may have important clinical implications in terms of informing primary prevention in ‘at-risk’ individuals with normal renal function, which may in turn help to reduce the burden of CKD globally.
Data availability
The genetic-association data of the selected risk factors are available in Supplementary Tables (available as Supplementary data at IJE online). The GWAS summary statistics for CKD and eGFR that were generated using UK Biobank and CKDGen data are available from the MRC-IEU OpenGWAS database (https://gwas.mrcieu.ac. uk/) and CKDGen website (http://ckdgen.imbi.uni-freiburg.de/), respectively. The GWAS results from HUNT, Biobank Japan, China Kadoorie Biobank and Japan-Kidney-Biobank/ ToMMo can be accessed by request to the data holders. The raw China Kadoorie Biobank data underlying this article can be accessed via a formal data request to ckbaccess@ndph.ox. ac.uk, following the institution’s data-access policies. Preliminary event adjudication data are not publicly available. The data underlying this article will be shared on reasonable request to the corresponding author.
Author contributions
J.Z. is the guarantor; J.Z., Y.M.Z. and H.R. performed the linear MR analysis; J.Z. and H.R. performed the nonlinear MR with support from S.F., Q.Y. and S.B.; J.Z., H.R. and L.F.T. performed the GWAS in UK Biobank and the HUNT Study; Y.S., M.Y. and N.K. conducted the GWAS in each cohort and performed the GWAS metaanalysis in the Japan-Kidney-Biobank/ToMMo study; M.A., M.K., K.M., Y.K. and Y.O. performed the GWAS in Biobank Japan; C.Q.Y. and J.C.L. conducted the GWAS in the China Kadoorie Biobank; J.Z., Y.M.Z. and B.E. performed the systematic review of CKD risk factors; R.E.W. performed the sensitivity analyses of smoking and CKD; P.C.H., A.H., J.R., B.M.B., L.F.T., K.H., S.H., A.K., C.P., M.W. and B.O.A. provided key data and supported the MR analyses; M.C.B., Y.C., R.C., S.H., N.F., A.P.M., G.D.S., S.B., C.Q.Y. and B.O.A. reviewed the paper and provided key comments; J.Z., Y.M.Z., H.R., V.W., Y.S., Y.L., G.D.S., S.B., B.O.A., H.Z. and T.R.G. wrote the manuscript; J.Z., Y.M.Z., H.Z. and T.R.G. conceived of and designed the study and oversaw all analyses.
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