Published online Jul 16, 2024. doi: 10.12998/wjcc.v12.i20.4272
Revised: May 10, 2024
Accepted: June 3, 2024
Published online: July 16, 2024
Processing time: 170 Days and 14.8 Hours
Education, cognition, and intelligence are associated with cholelithiasis occurrence, yet which one has a prominent effect on cholelithiasis and which cardiometabolic risk factors mediate the causal relationship remain unelucidated.
To explore the causal associations between education, cognition, and intelligence and cholelithiasis, and the cardiometabolic risk factors that mediate the associations.
Applying genome-wide association study summary statistics of primarily European individuals, we utilized two-sample multivariable Mendelian randomization to estimate the independent effects of education, intelligence, and cognition on cholelithiasis and cholecystitis (FinnGen study, 37041 and 11632 patients, respectively; n = 486484 participants) and performed two-step Mendelian randomization to evaluate 21 potential mediators and their mediating effects on the relationships between each exposure and cholelithiasis.
Inverse variance weighted Mendelian randomization results from the FinnGen consortium showed that genetically higher education, cognition, or intelligence were not independently associated with cholelithiasis and cholecystitis; when adjusted for cholelithiasis, higher education still presented an inverse effect on cholecystitis [odds ratio: 0.292 (95%CI: 0.171-0.501)], which could not be induced by cognition or intelligence. Five out of 21 cardiometabolic risk factors were perceived as mediators of the association between education and cholelithiasis, including body mass index (20.84%), body fat percentage (40.3%), waist circumference (44.4%), waist-to-hip ratio (32.9%), and time spent watching television (41.6%), while time spent watching television was also a mediator from cognition (20.4%) and intelligence to cholelithiasis (28.4%). All results were robust to sensitivity analyses.
Education, cognition, and intelligence all play crucial roles in the development of cholelithiasis, and several cardiometabolic mediators have been identified for prevention of cholelithiasis due to defects in each exposure.
Core Tip: In this study, we investigated the independent causal effects of education, cognition, and intelligence on cholelithiasis and cholecystitis. Subsequently, we estimated the independent association between each exposure and cholecystitis, after adjustment for cholelithiasis. Finally, the ultimate aims were to screen out the mediator(s) and clarify the mediating effects of several correlated risk factors in the pathogenesis of cholelithiasis to instrument clinical practice.
- Citation: Li CL, Liu YK, Lan YY, Wang ZS. Association of education with cholelithiasis and mediating effects of cardiometabolic factors: A Mendelian randomization study. World J Clin Cases 2024; 12(20): 4272-4288
- URL: https://www.wjgnet.com/2307-8960/full/v12/i20/4272.htm
- DOI: https://dx.doi.org/10.12998/wjcc.v12.i20.4272
Cholelithiasis (gallstone disease) harasses approximately 10%-20% of the global population and places burdens on their health and socioeconomic status[1,2]. Cholecystitis, cholangitis, and pancreatitis, as the most ubiquitous complications derived from cholelithiasis, markedly influence patients’ quality of life and physical condition extensively and severely[3,4]. Concurrently, laparoscopic cholecystectomy is the standard treatment for gallstone-related diseases that comply with guideline standards[5-7]. With the increasing occurrence of cholelithiasis and its broad complications, identifying their potential risk factors is of great significance. For instance, emerging epidemiological evidence shows that several cardiometabolic risk factors—metabolic traits (adiposity[1,2,8-10], glucose-related traits[11,12], and lipid levels[13,14]), dietary behaviors[15-17], physical activity[18-20], and socioeconomic factors[21] might be correlated with the development of gallstone disease and even the occurrence of cholecystitis. Nevertheless, these observational findings could not be used to determine the causal relationship, owing to the residual confounding and the reverse causality biasing the inference process. In addition, the genetic causality of possible potential factors in cholelithiasis and the mediating factors in the pathway, remain uncertain.
Educational attainment has been demonstrated to be adversely related to gallbladder diseases, notably cholelithiasis, in numerous observational and Mendelian randomization (MR) studies[1,22-24]. In addition, recent studies have demonstrated that education might correspond with cognitive performance and intelligence closely and inseparably, and mutually impact phenotypical and genetic landscapes, especially in MR studies[25-27]. To date, whether education, cognition, or intelligence exerts an independent causal effect on cholelithiasis and whether those cardiometabolic risk factors mediate the pathogenesis await to be elucidated. Comprehensively disentangling this unexplored question contributes to deepening the understanding of cholelithiasis etiology and informing intervention approaches to restrain cholelithiasis prevalence.
MR is a well-acknowledged method to infer causal relationships, by perceiving genetic variants as proxies for exposure(s), parallel to performing a classical randomized control trial, and simultaneously avoiding several confounding biases and reverse causality that occur in observational studies[28]. Multivariable Mendelian randomization (MVMR) is an extensive approach to estimating the independent effects of several relevant or interrelated exposures on one outcome by combining genetic variants of each exposure simultaneously[29]. Subsequently, a two-step MVMR study (mediation MR) is performed to determine whether one putative mediator has a significant mediating effect in the pathway from a certain exposure to an outcome, markedly eliminating confounding bias and measurement error existing between exposure, mediator, and outcome[30].
In this study, we investigated the independent causal effects of education, cognition, and intelligence on cholelithiasis and cholecystitis, using the two-sample MR and MVMR methods. Subsequently, we estimated the independent association between each exposure and cholecystitis, after adjustment for cholelithiasis. Finally, the ultimate aims were to screen out the mediator(s) and clarify the mediating effects of several correlated risk factors in the pathogenesis of cholelithiasis to instrument clinical practice.
This study is mainly composed of two-stage analyses (Figure 1). First, we estimated the causal relationship of education, cognition, and intelligence with cholelithiasis and cholecystitis utilizing UVMR and MVMR, by selecting single nucleotide polymorphisms (SNPs) as instrumental variables (IVs) to substitute each exposure. Our UVMR results indicated that education, cognition, and intelligence were causally inversely related to cholelithiasis, while only education was causally associated with cholecystitis. Furthermore, MVMR results showed that none of the three exposures played an independent causal role in cholelithiasis and cholecystitis. In addition, MVMR was also applied to estimate the independent causal relationship between education, cognition, or intelligence and cholecystitis, after adjusting for cholelithiasis. In the second stage, we critically singled out possible candidate mediators in the association between education, cognition, or intelligence and cholelithiasis using two-step MR. The methods used above were based on the Strengthening the Reporting of Observational Studies in Epidemiology Using Mendelian Randomization guidelines[31].
In this MR analysis, data of exposures, mediators, and outcomes were all extracted from summary-level data from genome-wide association studies (GWASs) performed primarily in individuals of European ancestry (Table 1).
| Phenotype | Unit | No. of participants | Ancestry | Consortium/cohort | Ref. | GWAS resources |
| Education | SD (4.2 years) | 1131881 | European | SSGAC | Lee et al[32], 2018 | |
| Cognition | SD (0.99 point) | 257841 | European | COGENT | Lee et al[32], 2018 | |
| Intelligence | SD | 269867 | European | Meta | Savage et al[25], 2018 | |
| Cholelithiasis | Event | 330903 | European | FinnGen | Not available | https://r9.finngen.fi/ |
| Cholecystitis | Event | 330903 | European | FinnGen | Not available | https://r9.finngen.fi/ |
| BMI | SD (4.7 kg/m2) | 681275 | European | GIANT | Yengo et al[34], 2018 | |
| BF% | SD (6.6%) | 65831 | European | Meta | Lu et al[35], 2016 | |
| WHR | SD (0.09) | 212244 | European | GIANT | Shungin et al[36], 2015 | |
| Waist circumference | SD (12.5 cm) | 231353 | European | GIANT | Shungin et al[36], 2015 | |
| Watching TV | SD (1.5 h) | 408815 | European | United Kingdom Biobank | Van de Vegte et al[44], 2020 | |
| Childhood obesity | Event | 13848 | European | EGG | Bradfield et al[37], 2012 | |
| Type 2 diabetes | Event | 70127 | European | Meta | Sílvia Bonàs-Guarch et al[38], 2018 | |
| Fasting insulin | SD (0.79 pmol/L) | 108557 | European | MAGIC | Scott et al[39], 2012 | |
| Fasting glucose | SD (0.73 mmol/L) | 133010 | European | MAGIC | Scott et al[39], 2012 | |
| LDL-C | SD (38.7 mg/dL) | 173082 | Mixed1 | GLGC | Willer et al[40], 2013 | |
| HDL-C | SD (15.5 mg/dL) | 187167 | Mixed1 | GLGC | Willer et al[40], 2013 | |
| Triglycerides | SD (90.7 mg/dL) | 177861 | Mixed1 | GLGC | Willer et al[40], 2013 | |
| Total cholesterol | SD (41.8 mg/dL) | 187365 | Mixed1 | GLGC | Willer et al[40], 2013 | |
| Smoking initiation | Event | 607291 | European | GSCAN | Liu et al[41], 2019 | |
| Smoking heaviness | SD (8 cigarettes) | 337334 | European | GSCAN | Liu et al[41], 2019 | |
| Alcohol drinking | SD (9 drinks/wk) | 335394 | European | GSCAN | Liu et al[41], 2019 | |
| Coffee intake | SD (1% change) | 375833 | European | United Kingdom Biobank | Zhong et al[42], 2019 | |
| Tea intake | SD (2.85 cups/d) | 447485 | European | United Kingdom Biobank | Zhong et al[42], 2019 | UKB-b-6066 |
| MVPA | SD (2084 MET-min/wk) | 377234 | European | United Kingdom Biobank | Klimentidis et al[43], 2018 | |
| Computer using | SD (1.2 h) | 408815 | European | United Kingdom Biobank | Van de Vegte et al[44], 2020 | |
| Total house income | SD | 397751 | European | United Kingdom Biobank | Hemani et al[45], 2018 |
Exposures: Genetic instruments for education were selected from the Social Science Genetic Association Consortium[32]. GWAS of education attainment (years of schooling) enrolled 1131881 individuals of European ancestry with summary data, and after excluding participants from 23 and Me, 766345 of these participants were made available for while data can only be exhibited for up to 10000 SNPs[32]. Genetic instruments for cognition (cognitive performance) were derived from a meta-analysis in 257841 individuals from the Cognitive Genomics Consortium and United Kingdom Biobank, by calculating broadband index (g) or verbal-numerical reasoning scores with less statistically significant values of heterogeneity in meta-analytic tests[32,33]. Genetic instruments for intelligence were extracted from a GWAS meta-analysis of 269867 European individuals via neurocognitive tests (primarily gauging fluid domains of cognitive functioning) to assess intelligence, without evidence of heterogeneity between cohorts in the genetic associations[25].
After conducting linkage disequilibrium analyses using related criteria (r2 < 0.001; distance threshold: 10000 kb), 393/1271, 132/225, and 165/242 independent genome-wide significant (P < 5 × 10-8) SNPs were singled out as the primary genetic instruments for education, cognition, and intelligence, respectively.
Mediators: Based on observational studies, we selected 21 candidate mediators of cholelithiasis-related risk factors (Table 1), which may exist in the pathway from education, cognition, or intelligence to cholelithiasis. All of the genetic instruments were availably derived from the GWAS database, including adiposity characteristics (body mass index (BMI)[34], body fat percentage (BF%)[35], waist circumference[36], waist-to-hip ratio (WHR)[36], and childhood obesity[37]), glucose-related traits (type 2 diabetes[38], fasting glucose[39], and fast insulin[39]), serum lipid traits (low-density lipoprotein cholesterol (LDL-C)[40], high-density lipoprotein cholesterol (HDL-C)[40], triglycerides[40], and total cholesterol (TC)[40]), dietary behaviors (smoking initiation[41], smoking frequency[41], alcohol drinking[41], coffee intake[42], and tea intake [from IEU OpenGWAS project (mrcieu.ac.uk); GWAS ID: ukb-b-6066]), physical activity (moderate to vigorous physical activity (MVPA)[43], watching TV, and computer use[44]), and socioeconomic factor (total household income[45]).
Subsequently, we screened out mediators of the independent association of education, cognition, and intelligence with cholelithiasis, according to the following rigorous criteria: (1) A causal association exists between education, cognition, or intelligence and the mediator, and the effect of education, cognition, or intelligence on the mediator should be unidirectional, to avoid possible bidirectionality influencing the mediation; (2) a causal relationship consistently exists between the mediator and cholelithiasis with or without adjustment for education, cognition, or intelligence; and (3) based on common scientific evidence, the association between education, cognition, or intelligence and the mediator and the association between the mediator and cholelithiasis should be in inverse directions. Ultimately, five, one, and one risk factor met all criteria and were included in the Mediation analyses to evaluate their mediating effects on the independent causal associations of education, cognition, and intelligence with cholelithiasis, respectively. During MVMR analyses, we selected genetic instruments by combining SNPs, which were of genome-wide significance in either the GWAS of education or the GWAS of mediator after clumping, based on linkage disequilibrium threshold r2 < 0.001 and window > 10000 kb.
Outcomes: Summary-level genetic data for cholelithiasis (gallstone disease, defined by the International Classification of Diseases 10th Revision code K80) and cholecystitis (defined by the International Classification of Diseases 10th Revision code K81) was obtained from the FinnGen consortium (https://r9.finngen.fi/). The FinnGen study included 37041 European individuals with cholelithiasis and 4299 individuals with cholecystitis among a total of 330903 participants.
All the above GWAS data have obtained ethical approval from the related institutional review boards, stringent quality control, and participant informed consent. All participants have given consent to each GWAS. In addition, the database used in our study is publicly available in Table 1 (Consortium/Cohort column).
UVMR and MVMR analyses: We conducted a two-sample UVMR to determine the total effect of education, cognition, or intelligence on cholelithiasis and cholecystitis. We performed MVMR to (1) Estimate the independent direct effects of education, cognition, and intelligence on cholelithiasis and cholecystitis by mutually adjusting to determine which exposure exhibited a causal association with cholelithiasis and cholecystitis, independent of the other two exposures; and (2) determine whether education, cognition, or intelligence had a causal effect on cholecystitis after adjusted for cholelithiasis. More importantly, all MR analyses should fulfill three critical assumptions as follows: (1) SNPs must be closely associated with the exposure in the UVMR procedure and should be closely associated with at least one of the several exposures in the MVMR procedure; (2) SNPs should not be relevant to confounders of the associations between instruments of each exposure and cholelithiasis and cholecystitis; and (3) the effects of genetic variants on cholelithiasis or cholecystitis must pass through each exposure[46]. In addition, we searched proxy SNPs in high linkage disequilibrium (r2 > 0.8) for SNPs that cannot be matched in GWAS data of the outcomes (detailed in https://Ldlink. nci.nih.gov/). We used the random inverse variance weighted (IVW) effect model as the main method in both UVMR and MVMR analyses, which utilized the Wald ratio values of each SNP into one certain causal estimate for each exposure[28].
Mediation MR analyses: When carefully filtering the mediator(s), we utilized GWAS data from the FinnGen study as the primary source for cholelithiasis, which had no or few overlapping samples with the mediator GWASs. Given that cholecystitis is mostly derived from cholelithiasis, we abnegated cholecystitis in the mediation MR analysis to reduce the confounding bias caused by cholelithiasis. A two-step MR approach was performed to determine whether a putative intermediate risk factor plays a mediating role between education, cognition, or intelligence and cholelithiasis[47]. The first step was conducted to calculate the causal effects of genetically determined education, cognition, and intelligence on the mediator (β1) using UVMR. Then, the second step was to calculate the causal effect of the mediator on cholelithiasis by applying GWASs from the FinnGen Study with adjustment for education, cognition, or intelligence (β2) using MVMR. Furthermore, the proportion of the total effect of education, cognition, or intelligence on cholelithiasis that was mediated by each mediator was calculated. Then, the indirect effect was estimated by multiplying the estimates from the two-step results (β1 × β2) by the total effect. The Delta method was applied to obtain SEs using effect estimates acquired from two-sample MR analyses[48].
MR sensitivity analyses: To guarantee the strength of the IVW results, we also performed MR Egger, Weighted Median, and MR pleiotropy residual sum and outlier methods in UVMR analyses, and applied the MVMR Egger method in MVMR analyses. The MR Egger method has been widely applied to estimate whether selected genetic variants obtained directional pleiotropic effects on the outcome, differing on average from zero and providing a similar inference of the causal effect, under the InSIDE (Instrument Strength Independent of Direct Effect) hypothesis[49]. The Weighted Median method is utilized to provide consistent estimates under the assumption that more than 50% of the content leading to the analysis comes from solid instrumental variables[50]. In addition, the MR pleiotropy residual sum and outlier method can detect whether outlying SNPs exist, which can potentially induce horizontal pleiotropy, and estimate whether the exclusion of outlying SNPs can influence the causal effects, under the assumption that the candidate instruments in the largest group of with similar estimates compose a robust and valid group of instrumental variables[51,52].
The intercepting estimate of MR Egger was applied to test for pleiotropy, indicating potential infiltrations of the IV assumptions during the two-sample MR process. In addition, we also calculated the Q′ heterogeneity statistic to estimate the heterogeneity between instruments. Conditional F-statistics were used to test instrumental validity, with an F < 10 indicating for low instrument validity.
Collectively, we considered IVW results as causal inferences only when they exhibited the same direction and statistical significance when conducting at least one sensitivity analysis, with no evidence of pleiotropy (P > 0.05). All effect sizes are presented as odds ratios (ORs), beta coefficients (βs), or proportions, with corresponding 95% confidence intervals (CIs). All MR analyses were performed using R packages “TwoSampleMR,” “MRPRESSO,” “MendelianRandomization,” “MVMR,” and “easyMR” in R software (version 4.2.3; the R Foundation for Statistical Computing, Vienna, Austria).
First, we conducted UVMR analysis to estimate the total effect of the three exposures—education, cognition, or intelligence on cholelithiasis and its secondary complication—cholecystitis. We applied the IVW method as the main criterion, as presented in Figure 2. Each 1-SD (4.2 years) increase in years of schooling [OR: 0.73 (95%CI: 0.65-0.81)], better cognitive performance [OR: 0.80 (95%CI: 0.71-0.89)], and higher intelligence [OR: 0.83 (95%CI: 0.74-0.92)] were all associated with a lower risk of cholelithiasis. Similarly, it was genetically predicted that each 1-SD increase for years of schooling [OR: 0.69 (95%CI: 0.54-0.88)] was also negatively associated with cholecystitis. However, neither better cognitive performance nor higher intelligence was associated with cholecystitis, distinct from cholelithiasis above. Several sensitivity analyses were robust in all MR results. MR Egger and weight median results were both consistent with those of the IVW method (Supplementary Table 1). Genetic instrumental variables of all three exposures displayed certain heterogeneity and no pleiotropy with those of cholelithiasis and cholecystitis (Supplementary Tables 2 and 3).
Subsequently, we performed MVMR to calculate the independent causal relationship after adjusting for other interrelated phenotypes. Nevertheless, no causal association was observed between education and cholelithiasis or cholecystitis after adjusting for cognition, intelligence, or both. Meanwhile, cognitive performance did not exert a genetically causal effect on cholelithiasis or cholecystitis when adjusted for education, intelligence, or both of them. Similarly, intelligence did not show significant causal inference on the two outcomes, except when adjusted for cognition (OR: 1.70 [95%CI: 1.05-2.75]) which exhibited a positive association with cholecystitis, although it presented an insignificant statistical estimate (Supplementary Table 4). In addition, all directions and most of the statistical significance of IVW results resembled MVMR Egger sensitivity analyses, demonstrating a minor risk of bias for less horizontal pleiotropy (Supplementary Table 4).
We conducted the MVMR method to dissect the independent effect between education, cognition, or intelligence and cholecystitis, adjusting for cholelithiasis. To eliminate the overlap between the populations of cholelithiasis and cholecystitis, GWAS from the FinnGen consortium and United Kingdom Biobank cohort were applied, individually. In the MVMR results, each SD (4.2-year) increase in years of schooling [IVW OR: 0.292; (95%CI: 0.171-0.501)] was inversely associated with the occurrence of cholecystitis after adjustment for cholelithiasis (Table 2). However, after adjustment for cholelithiasis, neither cognition [OR: 0.942 (95%CI: 0.600-1.667)] or intelligence [OR: 0.834 (0.506-1.374)] was significantly negatively correlated with cholecystitis. The instrument strength of the SNPs for all variables in the MVMR models was estimated to be sufficient by instrument validity test, with an F-statistic > 10 (Supplementary Tables 5 and 6).
| Exposure | Method | β (95%CI) | OR (95%CI) | P value |
| Education | MR MV_IVW | -0.534 (-0.767 to -0.300) | 0.292 (0.171-0.501) | 1.73E-43 |
| MR MV_Egger | -0.614 (-0.853 to -0.376) | 0.243 (0.140-0.421) | < 0.001 | |
| MR MV_Median | -0.608 (-0.942 to -0.275) | 0.247 (0.114-0.531) | < 0.001 | |
| MR MV_Lasso | -0.513 (-0.738 to -0.288) | 0.307 (0.183-0.515) | < 0.001 | |
| Cognition | MR MV_IVW | -0.026 (-0.222 to 0.222) | 0.942 (0.600-1.667) | 0.839 |
| MR MV_Egger | -0.894 (-1.590 to -0.197) | 0.128 (0.026-1.574) | 0.012 | |
| MR MV_Median | 0.094 (-0.234 to 0.421) | 1.242 (0.583-2.636) | 0.576 | |
| MR MV_Lasso | -0.025 (-0.247 to 0.196) | 0.944 (0.566-1.570) | 0.822 | |
| Intelligence | MR MV_IVW | -0.079 (-0.296 to 0.138) | 0.834 (0.506-1.374) | 0.475 |
| MR MV_Egger | -0.454 (-1.171 to 0.264) | 0.352 (0.067-1.837) | 0.215 | |
| MR MV_Median | -0.105 (-0.414 to 0.203) | 0.785 (0.385-1.596) | 0.504 | |
| MR MV_Lasso | -0.122 (-0.328 to 0.084) | 0.755 (0.470-1.213) | 0.246 |
Among all 21 candidate mediators, we screened five, one, and one mediating risk factor for the independent associations of education, cognition, and intelligence with cholelithiasis in mediation MR analyses, respectively, all of which met the selecting criteria. Then, we conducted UVMR to figure out the effect size between each exposure and mediators (Table 3). In UVMR analyses, each 1-SD longer years of schooling was associated with a lower BMI [IVW β: −0.36; (95%CI: −0.42 to −0.31)], lower BF% [−0.28 SD; (95%CI: −0.32 to −0.24)], lower waist circumference [−0.30 SD; (95%CI: −0.36 to −0.23)], lower WHR [−0.28 SD; (95%CI: −0.34 to −0.23)], and less time spent watching TV [−0.41 SD; (95%CI: −0.44 to −0.39)]. Similarly, better cognitive performance [−0.20 SD; (95%CI: −0.24 to −0.16)] and higher intelligence [−0.21 SD; (95%CI: −0.25 to −0.18)] were both robustly associated with less time spent watching TV. MR Egger, weighed Median, and MR PRESSO results were consistent with those of the IVW method (Table 3). In addition, genetic instrumental variables of education, cognition, and intelligence showed persistent heterogeneity and no pleiotropy with those of mediators (Supplementary Tables 7 and 8).
| Exposure | Mediator | Method | No. of SNPs | β (95%CI) | P value |
| Education | BMI | IVW | 332 | -0.36 (-0.42 to -0.31) | 9.57E-39 |
| MR Egger | 332 | -0.30 (-0.50 to -0.10) | 2.92E-03 | ||
| Weighted Median | 332 | -0.29 ( -0.33 to -0.25) | 1.56E-46 | ||
| MR PRESSO | 651 | -0.35 (-0.37 to -0.33) | 1.44E-60 | ||
| BF% | IVW | 332 | -0.28 (-0.32 to -0.24) | 1.10E-41 | |
| MR Egger | 332 | -0.24 (-0.38 to -0.10) | 1.11E-03 | ||
| Weighted Median | 332 | -0.24 (-0.28 to -0.21) | 7.71E-51 | ||
| MR PRESSO | 541 | -0.29 (-0.30 to -0.28) | 5.98E-66 | ||
| Waist circumference | IVW | 271 | -0.30 (-0.36 to -0.23) | 5.57E-20 | |
| MR Egger | 271 | -0.41 (-0.66 to -0.17) | 1.11E-03 | ||
| Weighted Median | 271 | -0.26 (-0.34 to -0.18) | 1.96E-10 | ||
| MR PRESSO | 91 | -0.27 (-0.30 to -0.24) | 3.59E-22 | ||
| WHR | IVW | 271 | -0.28 (-0.34 to -0.23) | 5.91E-26 | |
| MR Egger | 271 | -0.32 (-0.53 to -0.12) | 1.89E-03 | ||
| Weighted Median | 271 | -0.25 (-0.32 to -0.17) | 2.26E-10 | ||
| MR PRESSO | 21 | -0.28 (-0.31 to -0.25) | 1.31E-25 | ||
| Watching TV | IVW | 332 | -0.41 (-0.44 to -0.39) | 9.10E-226 | |
| MR Egger | 332 | -0.44 (-0.53 to -0.35) | 9.50E-19 | ||
| Weighted Median | 332 | -0.38 (-0.41 to -0.35) | 7.23E-145 | ||
| MR PRESSO | 121 | -0.42 (-0.44 to -0.40) | 2.22E-127 | ||
| Cognition | Watching TV | IVW | 106 | -0.20 (-0.24 to -0.16) | 5.11E-25 |
| MR Egger | 106 | -0.26 (-0.42 to -0.10) | 1.57E-03 | ||
| Weighted Median | 106 | -0.20 (-0.18 to -0.11) | 1.43E-20 | ||
| MR PRESSO | 171 | -0.18 (-0.20 to -0.16) | 1.78E-26 | ||
| Intelligence | Watching TV | IVW | 136 | -0.21(-0.25 to -0.18) | 1.77E-36 |
| MR Egger | 136 | -0.32(-0.47 to -0.16) | 9.16E-05 | ||
| Weighted Median | 136 | -0.19(-0.22 to -0.16) | 3.76E-42 | ||
| MR PRESSO | 211 | -0.20(-0.22 to -0.18) | 4.11E-32 |
We performed the MVMR method to uncover the independent effects of the mediators on cholelithiasis, adjusting for education, cognition, or intelligence. In the MVMR results, each 1-SD unit increase in BMI [IVW OR: 1.60; (95%CI:1.45-1.77)]; BF% [OR: 1.94; (95%CI: 1.69-2.24)]; waist circumference [OR: 1.56; (95%CI: 1.33-1.84)]; WHR [OR: 1.43; (95%CI: 1.14-1.81)], and time spent watching TV [OR: 1.45; (95%CI: 1.06-1.75)] was associated with a high risk of cholelithiasis after adjustment for education (Table 4). In addition, after adjustment for cognition [OR: 1.42; (95%CI: 1.07-1.90)] or intelligence [OR: 1.46; (95%CI: 1.05-2.03)], time spent watching TV was also associated with cholelithiasis. Instrument strength of SNPs for all variables in MVMR models was estimated to be sufficient by instrument validity test, with F-statistic > 10.
| Exposure | Mediator | β (95%CI) | OR (95%CI) | P value |
| Education | BMI | 0.469 (0.369-0.569) | 1.60 (1.45-1.77) | 4.97E-20 |
| BF% | 0.665 (0.525-0.805) | 1.94 (1.69-2.24) | 1.45E-20 | |
| Waist circumference | 0.447 (0.282-0.612) | 1.56 (1.33-1.84) | 1.13E-07 | |
| WHR | 0.361 (0.130-0.592) | 1.43 (1.14-1.81) | 2.19E-03 | |
| Time spent watching TV | 0.372 (0.173-0.571) | 1.45 (1.06-1.75) | 4.29E-02 | |
| Cognition | Time spent watching TV | 0.357 (0.071-0.643) | 1.42 (1.07-1.90) | 1.44E-02 |
| Intelligence | Time spent watching TV | 0.382 (0.056-0.708) | 1.46 (1.05-2.03) | 2.17E-02 |
Subsequently, we conducted a two-step MR analysis to estimate the mediating effects induced by the mediators. Five candidate mediators met the criteria and were thus selected as the final mediators in the process from education to cholelithiasis, while only one was selected as the final mediator from cognition or intelligence. The largest causal mediator from education to cholelithiasis was waist circumference [44.4%; (95%CI: 18.6%-70.3%)], followed by time spent watching TV [41.6%; (95%CI: 13.9%-70.2%)], BF% [40.3%; (95%CI: 20.3%-60.3%)], WHR [32.9%; (95%CI: 9.7%-56.2%)], and BMI [28.4%; (95%CI: 13.8%-42.9%)] (Figure 3). Coincidentally, time spent watching TV also mediated the causal role from both cognition to cholelithiasis [20.4%; (95%CI: 4.8%-36.0%)] and intelligence to cholelithiasis [28.4%; (95%CI: 6.5%-50.4%)].
In this MR study, we provided novel genetic evidence for the causal effect of education on cholelithiasis and cholecystitis, with each 4.2-year-increment of schooling attenuating an approximately 27% risk of cholelithiasis and 31% risk of cholecystitis. In addition, we demonstrated causal associations of cognition and intelligence with cholelithiasis and cholecystitis. Each increasing 0.99 point of cognitive performance and each higher SD of intelligence resulted in a decreased risk of cholelithiasis of approximately 20% and 17%, respectively, with neither being significantly less than the educational effect nor showing a causal association with cholecystitis, which was inconsistent with education. Nevertheless, none of the above causal effects persisted when the associations were adjusted for other one or two other exposures. These results show that each of the three exposures—education, cognition, and intelligence—plays a crucial role in the causal process of cholelithiasis and can be influenced by other factors. Thus, we explored the mediating factor from each exposure to cholelithiasis, and ultimately selected five, one, and one out of 21 factors, respectively, in the process from education, cognition, or intelligence to cholelithiasis.
In our MR study, three broadly concerned events—educational attainment, cognitive performance, and intelligence level—were perceived to be the exposures. These exposures are interrelated with others phenotypically and genetically and hard to separate, strongly based on a previous GWAS and an MR study, demonstrating the mutually bidirectional associations among them[25,27]. Emerging evidence from observational studies and MR studies has illustrated that higher educational attainment (more years of schooling) is a protective factor for cholelithiasis[22,24,53,54]. To date, this novel research area, which dissects the association between cognitive performance or intelligence level and cholelithiasis, has not yet been entirely elaborated. For the first time, we used genetic inference to reveal that either better cognitive performance or a higher intelligence level is a protective factor for cholelithiasis. These results are in tune with our general perspectives, for those who have higher cognition and intelligence may be less likely to enjoy high-calorie, high-carbohydrate, and low-fiber diets, or neglect physical activities, all of which prompt gallstone formation[55,56]. As recent epidemiological study showed that gallstone diseases mainly affected adult individuals, notably elderly individuals[1], whose education, cognitive performance, and intelligence level have already been determined. However, earlier educational attainment profoundly affects later life, including cognitive training, knowledge acquisition, and notably health promotion[57]. This study elucidates the importance of improving academic qualifications, cognitive performance, and intelligence level to reduce the risk of gallstone diseases as much as possible. Nonetheless, considering the difficulty and near-impossibility of altering one’s the living environment owing to poverty and other difficult obstacles, a better lifestyle and a healthier diet are strongly recommended for those who cannot obtain prioritized educational sources to prevent the onset of gallstone diseases.
Cholecystitis is the most ubiquitous complication of cholelithiasis and is typically ascribed to calculus in the gallbladder, disrupting patients’ quality of life and threatening their lives[3,58]. Due to the bile duct obstruction caused by gallstones or sludge or lithogenic bile, these individuals are subjected to suffer from cholecystitis-induced right upper quadrant mass, pain, tenderness, and fever[59]. Once diagnosed with acute cholecystitis, within-3-day laparoscopic cholecystectomy (to remove the inflammatory or diseased gallbladder) is the first-line therapy to prevent severe complications[5,6]. Due to the economic burden and postsurgical psychiatric disorders, prevention should receive more attention than the treatment procedure[60,61]. From a clinical perspective, among the general population with gallstone diseases, approximately 80% are asymptomatic[62]. Thus, patients might pay little attention to lifestyle improvement and dieting alteration, despite being diagnosed with cholelithiasis. Suboptimal daily lifestyle habits, especially high-calorie and low-fiber eating habits, predispose individuals to the process from gallstone formation to inflammation in the gallbladder or in the bile duct. Among the three exposures, only education attainment played a causal role in attenuating the prevalence of cholecystitis. Furthermore, after adjustment for cholelithiasis, education still acted as an independent protective factor (Table 2), while neither cognition nor intelligence could do the same. In tune with the general acknowledgment, our results convey that individuals obtaining high educational qualifications may have a lower risk of cholecystitis, due to their predominant awareness, optimal lifestyles, balanced diets, etc. Nonetheless, the Nationwide Inpatient Sample database has indicated that White patients have a significantly lower rate of emergent admission of acute cholecystitis compared with non-White patients, suggesting the disparities in health care[63]. Our results are therefore not suppressed to be suitable for other ethnic populations and non-White community dwellers.
Concurrently, massive cardiometabolic risk factors are considered in many syndromes, including cardiovascular diseases[64,65], hepatobiliary disorders[66,67], nervous system disruption[68,69], and reproductive disturbance[70]. For instance, metabolic traits[1,2,8,9], dietary behaviors[15-17], physical activity[18,19], and socioeconomic factors[21] have attracted ubiquitous attention, playing protective roles or serving as risk factors in the process of gallstones. Previous MR studies and observational research have demonstrated the inverse causal association between educational attainment and gallstone disease[24,54], while whether these putative mediators mediate in this process remains unclear. Cardiometabolic risk factors were divided into several categories: Metabolic traits, dietary behaviors, physical activities, and socioeconomic factors, all of which were perceived as candidate mediators. Only those which met the above criteria could be considered as actual mediators.
In this study, three major metabolic stratifications were incorporated into the candidate mediators: Adiposity, glucose-related traits, and serum lipid levels. Adiposity can be displayed in several hierarchies—BMI, BF%, waist circumference, WHR, childhood obesity, etc. The detrimental role of these phenotypes in gallstone disease has been emphasized in previous observational and MR studies[71,72]. Four of these factors—BMI, BF%, waist circumference, and WHR—are perceived as the mediators from education to gallstones, excluding childhood obesity, which might account for the cumulative formation process of lithiasis requiring tens of years. Moreover, after adjustment for education, each mediator remained to be a statistically significant risk factor for gallstones. Among them, waist circumference had the highest mediating proportion (44.4%), followed by BF% (40.9%), WHR (32.9%), and BMI (28.4%) (Table 3), which is equivalent to mediating the corresponding extent of the risk of gallstones ascribed to low educational attainment. Commonly, adiposity traits are easily able to reflect the defects of long-range lifestyles and dieting habits. In addition, obesity is closely associated with high-fat, high-carbohydrate, and low-fibre intakes and less movement. Notably, these obesity-relevant and interrelated indexes carry substantial public health implications, as they are prone to exist as comorbidities and have common biological mechanisms, including genetics, energy metabolism, neuroendocrine regulation, and immunoinflammatory activation[73,74]. Thus, to attenuate the prevalence of gallstones, none of these phenotypes should receive less attention, but instead, they deserve more consideration.
Glucose-related traits have been extensively discussed as risk factors for gallstone disease in previous studies[11,12]. Yuan et al[24] validated the causal association between type 2 diabetes and gallstone disease [OR: 1.13 (95%CI: 1.09-1.17)] through the two-sample MR method, demonstrating its facilitating effectiveness. Considering that the foremost presence of pancreatic impairment in type 2 diabetes is the abnormal serum sugar-relevant level[75], fasting glucose and fasting insulin alike were embodied in our candidate mediators. However, none of the putative mediators were estimated to be definite mediators, which does not indicate a lack of the unimportance or necessity for taking precautions against diabetes-induced factors, which might concurrently promote gallstone shaping.
In general, the major proportion of gallstones in the Western world are cholesterol gallstones, instead of pigment components. Surprisingly, serum lipid levels were excluded as mediators in our study, although hyperlipidemia has been illustrated to be involved in gallstone diseases, based on compelling observational studies[13,14,76,77]. To our knowledge, high LDL-C levels, high triglycerides levels, and high total cholesterol are the risk factors and high HDL-C is a protective factor against gallstones. Commonly, cholesterol gallstone formation is frequently the consequence of an imbalance of physical–chemical cholesterol solubility, contributing to a disturbance in biliary cholesterol homeostasis and promoting cholesterol crystallization and gallstone formation[78]. Despite lipid index not causally kicking in the education—gallstone pathway, approaches to maintain physiological-state serum lipid are steered to be emphasized, especially reducing high fat intake. More recently, smoking and alcohol consumption have been considered to accelerate gallstone formation, while coffee and tea intake might have adverse effects, but the precise mechanism has not been elucidated[15-17]. Moreover, socioeconomic factors are also involved in the development of gallstone disease, and notably, higher income individuals are less likely to suffer from gallstone disease[21]. Considering that the major proportion of GWAS statistics utilized in this study were extracted from European populations from high-education and high-income regions, the economic or educational disparity seems not too apparent, explaining to a certain extent why income did not play a mediating role.
Physical activity has been demonstrated to play a preventive role in the process of gallstone formation, based on recent observational and MR studies[20,79,80]. Generally, watching TV is an intriguing way to ease anxiety, obtain pleasure, and kill time. Nonetheless, a long-term sedentary lifestyle increases the risk of subhealth problems, notably obesity, nonalcoholic fatty liver diseases (NAFLD), and even cancer[81-84]. This study first illustrated that increasing TV watching time has a causal effect on the formation of gallstones, from a genetic perspective. Importantly, after adjustment for educational attainment, cognitive performance, or intelligence, long-term watching TV still causally increased the risk of gallstone disease. Moreover, it played a mediating role in the process from education to gallstone (mediated proportion: 41.6%), as well as from cognitive performance (20.4%) or intelligence level to the counterpart (28.4%). It has been acknowledged that those who have undergone many years of schooling are more able to perceive the detrimental effects of watching TV, and instead, they prefer other beneficial events. A putative inference of the hazardous pathogenesis can be explained as follows: When absorbed into the colorful scenes in TV series, individuals are more likely to stay motionless for several hours, inhibiting intestinal motility and restraining cholecystokinin-dependent gallbladder emptying[85,86]. Apart from the time spent watching TV, we simultaneously focused on two other candidate mediators: MVPA and computer use. Although neither factor was not perceived to be a mediator, they deserve to be considered for individuals at homes, who should perform certain physical activities frequently and compulsorily avoid long-term concentrated computer use.
In conclusion, this is the first MR study to reveal that the causal effects of education on gallstone disease are independent of cognition and intelligence, although either can play a causal role in gallstone pathogenesis. Second, we are also the first to illustrate that individuals with high-education attainment may be substantially less likely suffer from cholecystitis, after being diagnosed with gallstone disease. Third, we strictly screened out the causal mediator, meeting the set rigorous criteria, in the independent association of education, cognition, or intelligence with gallstones, and afterward estimated the mediated proportion individually.
Nevertheless, this study also has several limitations to be addressed. First, several prevalent and crucial cardiometabolic risk factors were incorporated into the potential mediators in disease development. Second, given that gallstone diseases might be affected by heritage factors[87], future MR studies are required to take within-family genetic data into account, not least their parent–offspring and sibling traits. Third, the heterogeneity of SNPs persisted throughout our MR analysis, leading to potential bias and influencing the inference robustness. Fourth, given that the utmost GWASs applied were performed in high-income European populations, signifying their superior lifestyles, dieting habits, and even the awareness to seek medical attention, its substantial generalizability to middle- and even low-income countries and residents is limited. Fifth, sample overlap between GWASs, though it accounted for a small proportion, might have biased MR estimates toward observational association estimates.
Education, cognition, and intelligence all play crucial roles in the development of cholelithiasis, and several cardiometabolic mediators have been identified for prevention of cholelithiasis due to defects in each exposure.
The authors thank all the studies and participants contributing the data used in this work.
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