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Address for Correspondence: Michael V. Holmes, MD, PhD, Medical Research Council Population Health Research Unit at the University of Oxford, Nuffield Department of Population Health, Big Data Institute, Old Road Campus, Oxford, OX3 7LF, United Kingdom.
Medical Research Council Population Health Research Unit, University of Oxford, Oxford, United KingdomClinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United KingdomNational Institute for Health Research, Oxford Biomedical Research Centre, Oxford University Hospital, Oxford, United KingdomMedical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
Understanding the causal basis of disease is important so that we approach disease prevention and treatment using a valid etiologic framework. Blood lipids play an important role in the shuttling of nutrients (in the form of triglycerides and fatty acids) and cholesterol from the diet to the peripheral tissues. Certain types of blood lipids (eg, low-density lipoprotein cholesterol [LDL-C] and probably triglycerides [TG]) are atherogenic and lead to higher risks for coronary heart disease (CHD).
Cholesterol Treatment Trialists' Collaborators The effects of lowering LDL cholesterol with statin therapy in people at low risk of vascular disease: meta-analysis of individual data from 27 randomised trials.
Its central purported role is the reverse transport of cholesterol, which theoretically should lead to a net reduction in atheroma in the tunica intima of the arterial vasculature (with supposedly commensurate reductions in risk for vascular disease). This idea has come under scrutiny in recent years in response to accumulating scientific evidence
suggesting that increases in conventional measures of HDL-C may not lead to tangible benefits to CHD. However, this does not rule out a potentially important role of HDL-C in other diseases (including other vascular diseases, such as abdominal aortic aneurysm).
the authors aimed to dissect the nature of the relationship between blood lipid concentrations and chronic kidney disease (CKD). Although traditional observational data provide evidence that HDL-C concentration is inversely associated with risk of kidney disease,
such findings need to be interpreted with caution because the inherent limitations of observational research (namely, confounding and reverse causality) can distort findings. For example, the inverse association of HDL-C concentration with CHD seen in conventional observational studies
; similar confounding could be at play in the reported associations of HDL-C with other diseases, including CKD. Mendelian randomization (MR) is an alternative analytical approach that uses genetic variants that are inherited at random and are nonmodifiable to make causal inference that should be relatively free of confounding and reverse causality.
used genetic variants identified from genome-wide association analyses that are associated with different concentrations of the 3 main blood lipid fractions (namely LDL-C, HDL-C, and TG) reported by the Global Lipids Genetics Consortium (GLGC).
These genetic variants were used to gauge insight into the causal relationships of blood lipids with 3 markers of kidney function reported from genome-wide association studies by the CKD Genetics (CKDGen) consortium
: (1) estimated glomerular filtration rate (eGFR) as a continuous trait (percent difference), (2) dichotomized eGFR (odds ratio of eGFR < 60 mL/min/1.73 m2), and (3) albumin-creatinine ratio (ACR; percent difference). Using a 2-sample MR framework (in which the single-nucleotide polymorphism [SNP]-to-exposure and SNP-to-outcome estimates were obtained from predominantly nonoverlapping data sets, with the authors reporting that <10% of data overlapped between GLGC and CKDGen), Lanktree et al
provide evidence in support of blood lipid concentrations being linked to kidney function.
The authors identified that genetically elevated HDL-C concentrations were associated with better eGFRs (a higher percent difference in eGFR and lower risk for eGFR < 60 mL/min/1.73 m2) and lower ACR using genetic instruments for HDL-C. These associations remained robust to adjustment for the association of the genetic variants with LDL-C, TG, and hemoglobin A1c concentrations and blood pressure in so-called multivariable MR.
Such findings are commensurate with HDL-C having a potentially protective role in kidney function. However, the authors note that treatment trials of drugs that increase HDL-C concentrations (such as the Atherothrombosis Intervention in Metabolic Syndrome With Low HDL/High Triglycerides: Impact on Global Health Outcomes [AIM-HIGH]
trial of niacin) had no discernible effect on kidney function. This discrepancy between a genetic instrument for HDL-C versus a specific therapy may arise for various reasons. First, a trial of an individual drug (such as niacin) may have a separate effect on kidney function compared with that of the overall causal effect of HDL-C concentration, the latter being a broad biomarker that has a genetic architecture comprising multiple independent loci.
As an extension to the critical period effect, lipids could be a causal risk factor for kidney disease progression rather than disease onset; disease initiation and progression could have distinct causes, meaning that exposures causal for disease onset may not be necessarily causal for progression (and vice versa).
For LDL-C–related SNPs, a relationship of genetically elevated LDL-C concentration with a higher percentage difference in eGFR (ie, better kidney function) was identified when LDL-C SNPs were examined on their own. However, incorporating the relationship of the LDL-C SNPs with other lipids, hemoglobin A1c, and blood pressure, the association between genetically elevated LDL-C concentration with percentage difference in eGFR became less pronounced. For TG, the relationship of genetically elevated TG concentration with percentage difference in eGFR, while weak on its individual analysis, became more pronounced on adjustment for these other traits.
These relationships are nontrivial to tease apart. For example, adjusting the LDL-C SNPs for hemoglobin A1c concentration (a marker of dysglycemia) could adjust for a potential mediating effect of diabetes on CKD, thereby resulting in the attenuation of the relationship between LDL-C concentration and percentage difference in eGFR that the authors report. Prior MR studies have shown that higher LDL-C concentration is related to lower risk for type 2 diabetes mellitus,
meaning that a causal pathway could exist from higher LDL-C concentration to lower risk for type 2 diabetes mellitus and lower risk for CKD. Alternatively, the wider 95% confidence intervals and resultant attenuated effect on percentage difference in eGFR in multivariable adjustment could simply arise from the imprecision introduced by the multivariate model, meaning that a true relationship might exist.
The pattern of consistency of the association of the lipid traits with the 3 kidney traits (percent difference in eGFR, risk for eGFR < 60 mL/min/1.73 m2, and percent difference in ACR) is where the complexity becomes further apparent. In the case of HDL-C, there is, as one might expect, a directionally consistent relationship of HDL-C concentration with percent difference in eGFR, risk for low eGFR, and percent difference in ACR. The consistency across these traits (although 2 are essentially marking the same entity; ie, eGFR) for HDL-C lends weight to a potential protective role of HDL-C in CKD. The same is not the case for LDL-C or TG, for which both traits appear to associate with higher percent differences in both eGFR and ACR, potentially indicating a physiologic phenomenon for which there is higher filtration yet deteriorating function.
This study raises several questions about how to reliably interpret these various strands of evidence. Undoubtedly the main challenge in the wake of abundant genome-wide data and large-scale resources such as the UK Biobank is how to address the potential for genetic pleiotropy to confound the estimates derived from MR. At a June 2017 MR conference hosted by the Medical Research Council Integrative Epidemiology Unit in Bristol, United Kingdom, more than 30 MR methodologies were presented, the majority of which are new and have yet to be subjected to the same scrutiny as those that are becoming more established in the MR field.
Although exciting for those of us active in this field, it also poses major challenges; for example, which approaches should we use in our battery of tests when conducting MR, and what is the added value of the newer methodologies? This will no doubt be the subject of many narrative reviews to follow, but allow us to synthesize a few points below, based on those MR approaches that are now commonly used.
In the absence of genetic pleiotropy (the scenario in which ≥1 genetic variant used in a genetic instrument associates with >1 phenotype, described in detail in a recent review
), gross pleiotropy is unlikely to account for the findings. These updated methods, although invaluable for testing the assumptions implicit in instrumental variable analyses, are not a panacea, and critical issues arise in selecting appropriate variants for MR and interpreting their findings as described in a recent review.
Challenges in interpreting findings from multivariable MR analyses can arise, as exemplified by the following 2 scenarios. First, when a genetic instrument shows associations with another trait (Fig 1, scenario 1), which may be a causal intermediate, a well-conducted multivariable MR should in theory provide a “direct” (rather than “total”) effect of the exposure on risk for disease; the direct effect is that which is due only to the exposure of interest and not mediated by another trait, even when the latter trait may be influenced by the exposure of interest (eg, in the case of adjusting the relationship of age at menarche with cancer for adult body mass index, for which it is known that age at menarche influences adult body mass index
). In clinical and public health terms, the total (rather than direct) effect is, of course, what modifying the exposure would produce. In the multivariable MR framework, measurement error in the intermediate phenotypes can influence the findings and may not produce entirely reliable adjusted estimates. This leads to multivariable MR not being an optimal approach for mediation analysis. Two-step MR
(Fig 1, scenario 2). Given these (nonexhaustive) scenarios, multivariable MR is an approach that can generate more questions than it answers. A recent modification to multivariable MR in the form of multivariable MR-Egger
enhances the conventional multivariable MR in additionally adjusting for unbalanced genetic pleiotropy of the genetic instruments and can quantify the extent to which adjustment for other traits addresses unbalanced pleiotropy.
the findings provide genetic support for the hypothesis that HDL-C may be causally protective for kidney disease. How might these findings be potentially translated to impact on patient care? As described, MR of a complex phenotype (such as HDL-C) is distinct to MR of a drug target (eg, cholesteryl ester transfer protein [CETP]). Natural areas for further investigation should now include individual drug targets that alter HDL-C concentrations (such as CETP) for which genetic studies
Authors’ Full Names and Academic Degrees: Michael V. Holmes, MD, PhD, and George Davey Smith, MD, DSc.
Financial Disclosure: Dr Holmes works in a unit that receives funds from the University of Oxford and UK Medical Research Council . Dr Davey Smith works in a unit that receives funds from the University of Bristol and UK Medical Research Council ( MC_UU_12013/1 ).
Peer Review: Received September 20, 2017 in response to an invitation from the journal. Editorial input from an Associate Editor and a Deputy Editor. Accepted in revised form October 11, 2017.
High-density lipoprotein (HDL) cholesterol, low-density lipoprotein (LDL) cholesterol, and triglyceride concentrations are heritable risk factors for vascular disease, but their role in the progression of chronic kidney disease (CKD) is unclear.