Mendelian randomization study of circulating lipids and biliary tract cancer among East Asians

Biliary tract cancer (BtC) constitutes approximately 3% of gastrointestinal malignancies with poor prognosis and involves a spectrum of invasive adenocarcinomas, including cholangiocarcinoma (cancers arising in the intrahepatic, perihilar, or distal biliary tree), and gallbladder carcinoma [1, 2]. The incidence of BtC varies across the world: the highest incidence rate was observed in East Asia and Latin America [2, 3]. In developed countries, BtC was rarely diagnosed in clinical practice. The varied BtC incidences in different regions were due to different underlying risk factors. Previous studies have demonstrated that a set of hepatic conditions including hepatic inflammation, fibrosis, and cirrhosis are risk factors for intrahepatic cholangiocarcinoma [4]. On the other hand, chronic irritation or inflammation of the gallbladder and cholelithiasis are deemed to be associated with a higher risk of gallbladder carcinoma [5‐7]. Additionally, hyperlipidemia was also reported to associate with BtC development even after adjustment for body-mass index (BMI), diabetes, hypertension, and alcohol drinking [8, 9].

Hyperlipidemia is characterized by high serum levels of total cholesterol (CHL), triglycerides (TRG), low-density lipoprotein cholesterol (LDL), and low level of high-density lipoprotein cholesterol (HDL). Previous observational studies suggested a role for the circulating lipids in biliary carcinogenesis. For example, Andreotti et al. reported that participants in the lowest quintile of serum HDL level had a 16.8-fold risk of BtC [9]. Another case–control study from China also suggested that serum levels of lipids were significantly associated with BtC risk [10]. However, the findings from observational studies might be subject to the inherent defects of this type of study design, namely residual confounding and reverse causality. So far, there has been no randomized clinical trial to assess the effect of statin use on BtC development. In this case, Mendelian randomization (MR) analysis could serve as a good surrogate. MR leveraging genetic data is less susceptible to such biases due to the fact that alleles are randomly assigned during meiosis and germline genetic variants are unaffected by disease process [11]. So far, MR analysis has been widely used to infer the causality between exposures and outcomes [12‐14]. The findings of MR studies were of importance not only for the discovery of disease biomarkers, but also for the therapeutic and prophylactic strategies of diseases [15]. Nevertheless, the association of circulating lipids with BtC risk has not been determined by MR analysis. Herein, we conducted a two-sample MR analysis to address this need.

After the quality control processes, we included 26, 19, 23, 10 variants in MR analysis for HDL, LDL, CHL, and TRG, respectively (Supplementary Tables S1-4). The mean F statistics for every instrument-exposure association were greater than 10 in our study (F = 21.2, 13.4, 18.4, and 10.5 for HDL, LDL, CHL, and TRG, respectively), demonstrating the small possibility of weak instrumental variable bias. No significant horizontal pleiotropy was detected for all circulating lipids according to the MR-PRESSO global test (P = 0.458, 0.368, 0.522, and 0.587 for HDL, LDL, CHL, and TRG, respectively). The results of assessment of heterogeneity and directional pleiotropy are shown in Table 1. No significant evidence of heterogeneity and pleiotropy was detected by the Cochran’s Q test and MR-Egger regression, suggesting the variants that included in MR analysis are valid instruments.

The estimated effect sizes of the SNPs on both the exposures (HDL, LDL, CHL, and TRG) and outcome (BtC) are displayed in scatter plots (Fig. 2). The fitted lines denoting association between SNP effects on exposure and on outcome, based on different methods, were in the same direction, albeit the nuances of slopes. This concordance connotes the robustness of our MR estimates. MR estimates from IVW method suggested that one standard deviation (1-SD) increase of inverse-normal transformed HDL (OR = 1.38, 95% CI 0.98–1.94), LDL (OR = 1.46, 95% CI 0.96–2.23), and CHL (OR = 1.34, 95% CI 0.83–2.16) were not significantly associated with BtC risk (Table 1; Fig. 3). Whereas 1-SD increase of inverse-normal transformed TRG showed a significantly negative association with BtC risk (OR = 0.48, 95% CI 0.31–0.74). We have calculated 80% power in our MR studies to show an OR of 1.56 for HDL, 1.75 for LDL, 1.59 for CHL, and 0.74 for TRG respectively. As such, we are underpowered to study effects smaller than these ORs. The IVW-based MR estimates were further validated in other three methods. An exception was found for weighted median methods, in which 1-SD increase of inverse-normal transformed LDL and CHL levels were significantly associated with an increased risk of BtC. We observed a non-significant association between 1-SD increase of inverse-normal transformed TRG level and BtC risk according to MR-Egger method, although this association was significant according to other three methods (Table 1; Fig. 3). The forest plots of “leave-one-out” analyses were shown in Supplementary Figs. 1–4. No potentially influential SNP was found for HDL, CHL, and TRG. In contrast, we found that the association between 1-SD increase of inverse-normal transformed LDL level and BtC risk was statistically significant if removing a variant (rs10119). No significant association was detected in MR analysis when examining the BtC effect on levels of circulating lipids (F = 4.1, 5.2, 3.8, and 3.5 for HDL, LDL, CHL, and TRG, respectively; Supplementary Table S5). However, the results might be subject to weak instrument bias in this analysis due to the low F-statistics.

The overlap among genetic instruments of circulating lipids was shown in Fig. 4A. We observed that a total of 11 variants were shared between LDL and CHL, whereas for other pairs of lipids, the shared variants were less than 5. We conducted a MVMR analysis to further validate the association between genetically predicted levels of circulating lipids and BtC risk. MVMR analysis estimated that 1-SD increase of inverse-normal transformed LDL was significantly associated with elevated risk of BtC (OR = 1.32, 95% CI 1.04–2.01). On the contrary, 1-SD increase of inverse-normal transformed TRG was significantly associated with decreased risk of BtC (OR = 0.54, 95% CI 0.42–0.68) (Fig. 4B). We also performed pairwise MVMR analysis between TRG and other three lipids. In all of the three models, we found that TRG were consistently associated with a decreased BtC risk (Supplementary Figure S5). Likewise, we observed an inverse relationship between TRG level and BtC risk in MVMR analysis in which we further incorporated BMI (Supplementary Figure S6).

In this study, using several MR methods, we tested for a causal relationship between circulating lipid traits and BtC risk. Our results suggested that genetically elevated TRG concentration was associated with a decreased risk of BtC. Multivariable MR analysis revealed that genetically elevated LDL level was associated with an increased risk of BtC, although this result did not detect in conventional MR analysis. Our findings were deemed to be robust due to no pleiotropy and heterogeneity was detected and were highly consistent with that of sensitivity analyses.

Biliary tract system plays important roles in many metabolic processes that are critical for the maintenance of body homeostasis [27, 28]. For example, lipid metabolism was reported to closely associate with biliary tract (including gallbladder) [29]. Therefore, it is reasonable to assume that damage in this organ may have a reflection in blood lipids. In other word, alterations of circulating lipid levels may suggest an injury in biliary tract. Indeed, a set of epidemiological studies have reported association of circulating lipid levels with biliary diseases [9, 30, 31]. However, the reported associations were not consistent between studies. For instance, results from previous studies of total CHL and LDL with gallstones are conflicting, with some studies reporting inverse, positive, and null associations [31‐35]. Andreotti et al. reported that participants with the highest quintile of triglycerides (≥ 160 mg/dl) had a 40%, 90%, and 4.8-fold increase in the risk of biliary stones, gallbladder cancer, and bile duct cancer, respectively, compared to the reference group (third quintile: 90–124 mg/dl) [9]. By contrast, Borena et al. found that there was no significant association between serum triglyceride level and gallbladder cancer [36]. The inconsistences might be ascribed to several reasons: (i) different study design and study participants; (ii) lipid measurement methods; (iii) lipid levels varied with times even in the same person; and (iv) inadequate adjustment for confounders. Given the inherent limitations of observational study, results from studies using genetic information might be an optimal complement for observational studies. For instance, Andreotti et al. reported that genetic variants in the lipid metabolism pathway (e.g., T allele of LDLR rs1003723) contribute to the risk of biliary tract stones and cancers, particularly of the bile duct [37]. Xu et al. reported that variants in a lipid metabolism-related gene (ABCG8 rs11887534) was also associated with an increased risk of BtC [8]. However, these studies conventionally investigated effect of a single genetic variant on BtC risk alone. The additive effect of other variants was not taken into account.

In our study, we retrieved the GWAS summary statistics regarding blood lipid traits and BtC from East Asian populations owing to BtC were more commonly diagnosed in populations in East Asian countries and there was lack of large scale GWAS of BtC in other populations [2]. Herein, we tested associations between a total of four lipids and BtC risk leveraging MR analyses with a set of genetic variants as instruments. Paradoxical to the observational studies that reported high levels of serum triglycerides and low level of HDL were associated with risk of BtC [9, 38], we observed an inverse association between genetically determined level of triglyceride and BtC risk, whereas no significant association between HDL level and BtC risk was detected. Moreover, multivariable MR results suggested that genetically high LDL level was associated with an increased risk of BtC. This association, to our knowledge, was rarely reported in previous studies. In a cross-sectional study, the authors reported a putatively “U-shaped” association between LDL level and BtC risk [9].

Although some of our results are seemed to be contradictory with the generally accepted association, these findings were further validated in sensitivity analyses that with different assumptions. The genetic instruments that we used in the current study were free of weak instrumental variable bias and therefore could serve as strong indicators for circulating lipid levels. Furthermore, to ensure the robustness of results, we constructed a frame work of MR analysis to avoid the influences of heterogeneity and pleiotropy. The detected links between lipids and BtC risk are clues for future studies, although our study lacks ability to provide more explanations regarding the main findings.

The limitations of our study should be noted here. First, our results were based on genetic data from East Asian populations, which limited the possibility of extrapolation to other populations. Second, the exposure and outcome studies in two-sample MR analysis should not involve overlapping participants. The participants in BBJ and AGEN might to some extent overlapped. However, Japanese participants only accounted for approximate 7% of AGEN population. Third, the genetic data of BtC were derived from GWAS with limited number of cancer cases, which might introduce bias into GWAS results due to unbalanced case–control ratios. Larger GWAS will allow for more precision in the estimates of SNPs used as instruments in future MR. Fourth, our estimates might also subject to the inherent pitfalls of MR analysis such as selection bias [39]. Genetic variants which are related to specific phenotypes might also related to participation. For example, participants with high polygenic risk score for the circulating lipids might be more likely to drop-out in the cohort because they might be more susceptible to diseases such as chronic cardiovascular disease than those have low genetic risk of lipid traits. Moreover, the MR estimates might be confounded by other unobserved environmental factors [40]. For example, in our study, we cannot correct the effect of lipid-lowering medicine, and the circulating levels of lipids are susceptible to transitory fluctuations due to many reasons. These potential factors might bias the GWAS results of circulating lipids. Finally, all the results from IVW method were underpowered (< 80%), although we conducted a rigorous quality-control process. Further investigations with larger sample size on associations between circulating lipids and BtC risk are needed.

In conclusion, according to both univariate and multivariate MR estimates, genetically determined higher triglyceride level is associated with lower risk of BtC. On the contrary, genetically elevated LDL concentration is associated with higher risk of BtC according to multivariate MR estimate. Our findings suggest that circulating lipids may have roles in the development of BtC and have potentials to be prediagnostic biomarkers for BtC.