Assessing the causal relationships between human blood metabolites and the risk of NAFLD: A comprehensive mendelian randomization study

Nonalcoholic fatty liver disease (NAFLD) is a major cause of liver disease worldwide. We estimated the global prevalence, incidence, progression, and outcomes of NAFLD and nonalcoholic steatohepatitis (NASH). PubMed/MEDLINE were searched from 1989 to 2015 for terms involving epidemiology and progression of NAFLD. Exclusions included selected groups (studies that exclusively enrolled morbidly obese or diabetics or pediatric) and no data on alcohol consumption or other liver diseases. Incidence of hepatocellular carcinoma (HCC), cirrhosis, overall mortality, and liver-related mortality were determined. NASH required histological diagnosis. All studies were reviewed by three independent investigators. Analysis was stratified by region, diagnostic technique, biopsy indication, and study population. We used random-effects models to provide point estimates (95% confidence interval [CI]) of prevalence, incidence, mortality and incidence rate ratios, and metaregression with subgroup analysis to account for heterogeneity. Of 729 studies, 86 were included with a sample size of 8,515,431 from 22 countries. Global prevalence of NAFLD is 25.24% (95% CI: 22.10-28.65) with highest prevalence in the Middle East and South America and lowest in Africa. Metabolic comorbidities associated with NAFLD included obesity (51.34%; 95% CI: 41.38-61.20), type 2 diabetes (22.51%; 95% CI: 17.92-27.89), hyperlipidemia (69.16%; 95% CI: 49.91-83.46%), hypertension (39.34%; 95% CI: 33.15-45.88), and metabolic syndrome (42.54%; 95% CI: 30.06-56.05). Fibrosis progression proportion, and mean annual rate of progression in NASH were 40.76% (95% CI: 34.69-47.13) and 0.09 (95% CI: 0.06-0.12). HCC incidence among NAFLD patients was 0.44 per 1,000 person-years (range, 0.29-0.66). Liver-specific mortality and overall mortality among NAFLD and NASH were 0.77 per 1,000 (range, 0.33-1.77) and 11.77 per 1,000 person-years (range, 7.10-19.53) and 15.44 per 1,000 (range, 11.72-20.34) and 25.56 per 1,000 person-years (range, 6.29-103.80). Incidence risk ratios for liver-specific and overall mortality for NAFLD were 1.94 (range, 1.28-2.92) and 1.05 (range, 0.70-1.56).

Background: The number of Mendelian randomization analyses including large numbers of genetic variants is rapidly increasing. This is due to the proliferation of genome-wide association studies, and the desire to obtain more precise estimates of causal effects. However, some genetic variants may not be valid instrumental variables, in particular due to them having more than one proximal phenotypic correlate (pleiotropy). Methods: We view Mendelian randomization with multiple instruments as a meta-analysis, and show that bias caused by pleiotropy can be regarded as analogous to small study bias. Causal estimates using each instrument can be displayed visually by a funnel plot to assess potential asymmetry. Egger regression, a tool to detect small study bias in meta-analysis, can be adapted to test for bias from pleiotropy, and the slope coefficient from Egger regression provides an estimate of the causal effect. Under the assumption that the association of each genetic variant with the exposure is independent of the pleiotropic effect of the variant (not via the exposure), Egger’s test gives a valid test of the null causal hypothesis and a consistent causal effect estimate even when all the genetic variants are invalid instrumental variables. Results: We illustrate the use of this approach by re-analysing two published Mendelian randomization studies of the causal effect of height on lung function, and the causal effect of blood pressure on coronary artery disease risk. The conservative nature of this approach is illustrated with these examples. Conclusions: An adaption of Egger regression (which we call MR-Egger) can detect some violations of the standard instrumental variable assumptions, and provide an effect estimate which is not subject to these violations. The approach provides a sensitivity analysis for the robustness of the findings from a Mendelian randomization investigation.

ABSTRACT Developments in genome‐wide association studies and the increasing availability of summary genetic association data have made application of Mendelian randomization relatively straightforward. However, obtaining reliable results from a Mendelian randomization investigation remains problematic, as the conventional inverse‐variance weighted method only gives consistent estimates if all of the genetic variants in the analysis are valid instrumental variables. We present a novel weighted median estimator for combining data on multiple genetic variants into a single causal estimate. This estimator is consistent even when up to 50% of the information comes from invalid instrumental variables. In a simulation analysis, it is shown to have better finite‐sample Type 1 error rates than the inverse‐variance weighted method, and is complementary to the recently proposed MR‐Egger (Mendelian randomization‐Egger) regression method. In analyses of the causal effects of low‐density lipoprotein cholesterol and high‐density lipoprotein cholesterol on coronary artery disease risk, the inverse‐variance weighted method suggests a causal effect of both lipid fractions, whereas the weighted median and MR‐Egger regression methods suggest a null effect of high‐density lipoprotein cholesterol that corresponds with the experimental evidence. Both median‐based and MR‐Egger regression methods should be considered as sensitivity analyses for Mendelian randomization investigations with multiple genetic variants.