REVIEW ARTICLE
Hippokratia 2020, 24(4): 147-156
Papadopoulos V1, Filippou D2, Mimidis K3
1Department of Internal Medicine, Xanthi General Hospital, Xanthi, 2Laboratory of Anatomy, Faculty of Medicine, National and Kapodistrian University of Athens, Athens, 3First Department of Internal Medicine, Faculty of Medicine, Democritus University of Thrace, Alexandroupolis, Greece
Abstract
Background: It has been claimed that smoking is linked with an increased risk for gallbladder disease (GBD); however, related issues need further consolidation and clarification. The present systematic review and meta-analysis aimed to further investigate the potent correlation between GBD and smoking.
Methods: We conducted a comprehensive literature review to identify every study published from January 1989 to December 2019, reporting risk estimates regarding GBD and smoking. The random-effect, generic inverse variance method, according to description by DerSimonian and Laird, was used to compute pooled estimates. We used the Newcastle-Ottawa quality assessment scale to appraise the included studies’ quality.
Results: Thirty published case-control, cross-sectional, and cohort studies including 4,623,435 individuals met the eligibility criteria and were considered for data synthesis. Compared to the non-smokers, ever smokers had 1.25 times higher odds of developing GBD [95 % confidence interval (CI): 1.09-1.44]; however, increased heterogeneity was observed (I2 =96 %, 95 % CI: 62-100 %, p <0.001). Publication bias was non-significant (Eggers’ regression p =0.072). The main sources of heterogeneity, as detected by meta-regression analyzing study characteristics, biases and confounders, were non-adjustment for family history (p =0.007) and alcohol (p =0.020), respectively. Subgroup analysis indicated a comparable risk for GBD as far as current, former and ever smokers are concerned (p =0.520). Quantitative analysis suggested a dose-effect for current smoking and GBD (p =0.010).
Conclusions: Non-smokers were demonstrated to be at a lower risk of presenting GBD when compared with ever smokers; all relevant risk estimates necessitate adjustment for family history and alcohol intake. HIPPOKRATIA 2020, 24(4): 147-156.
Keywords: Gallbladder diseases, cholelithiasis, Smoking, Review, Meta-Analysis
Corresponding author: Papadopoulos Vasileios, Head of the Department of Internal Medicine of Xanthi General Hospital, 2 Staliou str., 67132 Xanthi, Greece, tel: +302541068306, +306937172721, e-mail: vaspapmd@gmail.com
Introduction
Gallbladder disease (GBD), constituting gallstones, cholecystitis, and other causes, is a major public health determinant with significant morbidity and mortality worldwide1. Its frequency ranges from <5 % in Chinese, Japanese, and Thai to >60 % in Indians2. Risk for GBD rises with age while it is increased in females and individuals with family history or genetics (non-modifiable factors); however, a series of modifiable factors as obesity, rapid weight loss, and sedentary lifestyle have been recognized3-5.
Several studies investigated the possible relation of smoking and GBD, surprisingly with seemingly contradictory results. The related topic remained obscure as published results were based on studies that differ significantly concerning characteristics and methodology. A five-year-old meta-analysis, including ten published studies, suggested that there might be a positive correlation between smoking and GBD6. Since then, the effect of smoking in GBD is believed to be minor, if any7. A recently published study reports that lifetime smoking abstinence could contribute only a small portion of the multivariate and mutually adjusted partial population attributable risks for symptomatic cholelithiasis (1 % for women and 5 % for men)8.
To investigate the potent underlying pathophysiology between smoking and GBD, a sonographic study exhibited that the maximal emptying time of the gallbladder was larger in smokers compared to non-smokers; however, the result was not statistically significant. Thus, the study proposed that chronic smoking delays gallbladder contraction and leads to a significant decrease in gallbladder emptying volume, though it does not influence gallbladder refilling. As a result, bile stasis, a cause of most gallbladder disorders, could be attributed to smoking adverse effects9.
The present systematic review and meta-analysis aim to provide any additional evidence concerning the potential correlation between GBD and smoking by detecting all relevant studies and summarizing the results derived from them.
Materials and Methods
Literature search
We conducted a systematic review of the literature using the EMBASE, PubMed/Medline, and Cochrane Library databases and ClinicalTrials.gov from January 1989 to December 2019 to identify every study that reported risk estimates regarding GBD and smoking. We utilized Google Scholar as a secondary pool of published data; iterative search lasted until no additional publication could be traced. Lastly, we scavenged, wherever possible, unpublished dissertations and other unpublished work. The study protocol was submitted to the PROSPERO database on 24/7/2019 and revised on 28/10/2019 (ID: 144620).
Study selection
Study selection was independently performed by two authors (V.P. and D.F.) and included a search for the following terms: (cholelithiasis OR gallstones OR gallbladder disease OR cholecystitis OR cholecystectomy) AND (smoking OR tobacco); the third author (K.M.) closely observed the process and was responsible for dissolving any dispute. We did not use any software for the study retrieval process. Wherever possible, we traced every source of financial support. Eligible studies were considered to be all that i) were published in English; ii) were case-control, cross-sectional, or cohort ones; iii) reported a risk estimate in the form of an odds ratio (OR) or provided sufficient information for result conversion to OR format; iv) reported a measure of statistical significance; and v) were not duplicates.
Outcome measures
The study was carried out according to the PRISMA statement guidelines to pre-specify eligibility criteria based on the well-established PICO [P- for Populations/People/Patient/Problem: patients with GBD and controls, I- for Intervention(s): smoking, C- for Comparison: between ever smokers and never smokers (primary endpoint); between current smokers and never smokers; between ex-smokers and never smokers, O- for Outcome: cholelithiasis] worksheet and search strategy10. AMSTAR checklist was used to assess the quality of the present meta-analysis11.
Data extraction
A pre-specified structured form for data collection by means of an Excel worksheet was used for data extraction from each study. In detail, title of the study, first author’s name, publication year, country where the study was conducted, number of patients with cholelithiasis, number of healthy individuals, risk estimates in the form of an OR for current smokers, ex-smokers, and never smokers, adjustment for potent confounders (sex, age, alcohol intake, and family history) and quality assessment data. Two of the authors (V.P. and D.F.) independently performed data extraction, while K.M. closely observed the process and was responsible for cross-checking in case of any dispute.
Quality assessment of the studies
We used the Newcastle-Ottawa quality assessment scale (NOS) to estimate the quality of the included studies by means of three distinct grouping items, namely i) the selection item (referring to the identification and recruitment of participants), ii) the comparability item (referring to the comparability between the two groups), and iii) the exposure/outcome of interest item (referring to the ascertainment of either the exposure or the outcome of interest regarding case-control and cohort studies, respectively). We used a modified version of NOS12 for cross-sectional studies. In detail, the selection item was given a maximum of either four stars (in case of cohort / case-control studies) or five stars (in case of cross-sectional studies), comparability item a maximum of two stars, and exposure/outcome of interest a maximum of three stars.
The inter-rater agreement evaluation concerning the NOS assessment was performed using Kappa statistics.
Data synthesis
Data synthesis was performed using the Revman 5.3 software that is freely available from the Cochrane Collaboration13. As effect estimates, the natural logarithm of OR (LnOR) was used; wherever OR was not available, conversion from relative risk (RR) or hazard ratio (HR) was performed using the formulas OR =RR•(1-r)/(1–RR•r) and RR =[1-eHR•ln(1-r)]/r.
Conventional meta-analytic techniques assume that all effect size estimates derived from different studies are independent; however, this assumption might be violated if several estimates based on the same individuals are available, as is the case here. A commonly used methodology is simply ignoring that some of the effect size estimates might not be independent and thus use the same meta-analytic approaches as usual. Generally, this strategy inflates type I error rates as far as the significance of the moderators is concerned14; nevertheless, it may not be too misleading if the number of studies reporting more than one effect size is relatively small. Additionally, it may lead to conservative estimation of the difference between average effects of different types, which may, in fact, be sufficient for rough inferences15.
Statistical analysis
Given the OR and confidence intervals (CI) of each risk estimation, standard error (SE) was calculated; Furthermore, the random effects model was used to estimate overall OR and its CI; for that purpose, the Revman 5.3 software was preferred13.
We performed analysis of publication bias through several approaches, including Eggers’ regression, funnel plot accompanied by the relevant trim-and-fill analysis, Galbraith plot, normal quantile plot, standardized residual histogram, Rosenthal failsafe-N test as well as Gleser and Olkin number of unpublished studies using Meta-Essentials software16.
Heterogeneity was approached using Q test and I2 statistic as derived from Meta-Essentials (Q test p-value <0.10 and/or I2 >50 % was indicative of significant heterogeneity). CI of I2 statistics was computed using either the formula ±1.96•0.50•{[Ln(Q)-Ln(df)]/[(2Q)½-(2•df-1)½] for Q >df+1 or ±1.96/{[2•(df-1)•{1-{1/[3•(df-1)2]}}}½ for Q≤df+1, where df denotes degrees of freedom17.
Heterogeneity was quantitatively approached through three separate meta-regressions focusing separately on the study characteristics, quality assessment, and potential confounders; subgroup analyses followed in all cases, independently of the result of the multivariate analysis.
Quantitative analysis regarding the potential effect of current smoking was based on pooled data expressed as OR for every increment of ten cigarettes/day up to 30. Spearman’s r non-parametric correlation coefficients between medians of the above-mentioned increments and the relevant OR were computed. Regression was used to define the best fit curve that could approach the phenomenon.
The IBM SPSS Statistics for Windows, Version 20.0. (IBM Corp., Armonk, NY, USA) was used for all statistical tests.
Results
Study characteristics
Our concise literature search revealed 920 publications of interest in EMBASE (377), PubMed/Medline (448), Cochrane Library (89), and ClinicalTrials.gov (4). Two additional publications were scavenged through Google Scholar search. No unpublished data of interest was traced. No personal contact was performed.
After the initial exclusion of 397 duplicates, we reviewed the remaining 523 publications based on the title and abstract; during this procedure, we excluded 460 as being ineligible. Moreover, 31 failed to fulfill the eligibility criteria based on the type of article, measured outcomes, and risk estimates. We included the remaining 32 publications in the qualitative synthesis; two were excluded from meta-analysis as they reported unadjusted risk estimates.
Finally, 30 studies (four case-control, 12 cross-sectional, and 14 cohort studies), including 4,623,435 individuals, were considered for quantitative data synthesis (Figure 1). Based on these studies, 91 risk estimates (63 direct and 28 pooled) regarding current, ex-, or ever versus never smokers and GBD were collected.
Figure 1: Flow chart of the systematic review of the literature from January 1989 to December 2019 for studies reporting risk estimates regarding gallbladder disease and smoking.
All characteristics regarding leading author, year of publication, study design, origin, endpoint, outcome measures, sex representation, number of patients and controls, adjustment for potent confounders, and OR regarding smokers, ex-smokers and ever smokers vs non-smokers are analytically presented in Table 1.
Quality assessment items are analyzed in Table 2. The inter-rater agreement between the two authors who accomplished the quality assessment process was high (kappa =0.74).
Publication bias
There was cumulative evidence for absence of significant publication bias. In detail, Eggers’ regression was not significant (p =0.072), Rosenthal failsafe-N test failed to reject the ad hoc rule (Failsafe-N =70), and Gleser & Olkin number of unpublished studies yielded a null result. Moreover, no lack of symmetry was observed in the funnel plot, no imputed data points were produced in the relevant trim-and-fill analysis (Figure 2), and all studies were within the 95 % CI area of the Galbraith plot (Figure 3).
Figure 2: Funnel plot with trim-and-fill analysis indicating absence of significant publication bias as the plot is symmetrical and no imputed data points have been added.
Figure 3: Galbraith plot depicting all studies within its 95 % confidence intervals area.
Primary outcome
Compared to the non-smokers, ever smokers had 1.25 times higher odds of developing cholelithiasis (95 % CI: 1.09-1.44); however, increased heterogeneity was observed (I2 =96 %, 95 % CI: 62-100 %, p <0.001) (Figure 4).
Figure 4: Subgroup analysis according to study type (never versus ever smokers).
Meta-regression analysis
The main sources of heterogeneity, as detected by meta-regression analyzing potent confounders, were family history (p =0.007) and alcohol (p =0.020) non-adjustment (Table 3). Interestingly, sex was not considered as a major determinant of heterogeneity (p =0.330).
No statistically significant result was revealed from the meta-regression carried out regarding study characteristics and quality assessment.
Subgroup analysis
Pooled OR between case-control, cross-sectional, and cohort studies and GBD was 1.23 (95 % CI: 0.77-1.97), 1.20 (95 % CI: 1.02-1.42), and 1.27 (95 % CI: 1.04-1.55), respectively (Figure 4). No sources of heterogeneity were identified regarding the basic issues of smoking habits and type of study by subgroup analysis (p =0.920).
Moreover, pooled OR between current, former, and ever versus never smokers and GBD was computed to be 1.19 (95 % CI: 1.10-1.28), 1.15 (95 % CI: 1.10-1.19), and 1.24 (95 % CI: 1.05-1.47), respectively (Figure 5). Subgroup analysis indicated comparable risk as far as current, former, and ever smokers are concerned (p =0.520).
Figure 5: Subgroup analysis according to smoking habits.
Interestingly, a positive dose effect was observed for smoking, at least current; Spearman’s r =1.000 (p =0.010). The best-fit regression model was linear, as demonstrated after analysis of various alternatives. Linear regression analysis revealed a 0.011±0.002 increase in OR per cigarette per day (p =0.046). Analytical presentation of pooled ORs per ten cigarettes/day increments is available in Figure 6.
Figure 6: Quantitative (subgroup) analysis for different levels of smoking (from top to bottom: 0-9 cig/day, 10-19 cig/day, and 20-29 cig/day versus never smokers). Assuming that OR =1 for non-smokers, Spearman’s r =1.000 (p <0.01).
As far as quality assessment is concerned, studies with optimal comparability (two stars independently of the type of the study), when compared with studies with suboptimal comparability, were characterized by a more conservative positive correlation of smoking with GBD (p <0.001).
Lastly, potential confounders, as non-adjustment for age and alcohol intake (Q test P=0.05 and P=0.08, respectively), could lead to statistically significant heterogeneity and thus affect pooled effect estimates.
Sensitivity analysis
About one-third of increased heterogeneity was attributed to the study of Etminan (2011)40; excluding this study, I2 falls from 96 % to 60 %. In that case, compared to the non-smokers, smokers still had 1.17 times higher odds of developing GBD (95 % CI: 1.10-1.25).
Discussion
Whether smoking is associated with GBD remained disputable for a long period of time. Interestingly, some early publications proposed a prophylactic effect of smoking over symptomatic cholelithiasis or even the whole spectrum of GBD19,47,48. Two meta-analyses based on few studies suggested a positive correlation of smoking with GBD; however, the limited number of studies included could be considered potent drawbacks6,41.
The present meta-analysis, being the first to incorporate as many as 30 studies of different types, concludes that smoking is positively correlated with GBD and that this phenomenon is dose-dependent, at least as far as current smoking is concerned.
In particular, it is hereby clearly stated for the first time that there is a comparable risk between current, former, and ever smokers as indicated by subgroup analysis. Consequently, smokers, independently of being reported as current or ex-, could be considered in practice as a single group, as a comparable increase of risk for GBD was observed between relevant subgroups. Furthermore, our main result, namely the 25 % increased odds ratio of GBD among either current or former or ever smokers, although characterized by increased heterogeneity, is merely uniformly repeated in all analyzed subgroups. This finding considerably enhances the possibility that it indeed reflects a true statement.
Of interest, a clear-cut positive dose-dependent effect was observed for smoking, at least current, and GBD through quantitative analysis: every additional cigarette per day increases by 0.011±0.002 the OR for GBD. More complex methods as that proposed by Greenland and Longnecker49 were avoided during that process as all risk estimates used for pooling were adjusted for confounders. As we have observed that current and former smoking had comparable effects over GBD, it was reasonable to assume that the dose-dependent effect also referred to ex-smoking. Our results agree with those reported by Kato et al, who demonstrated a positive correlation between pack-years of cigarettes and GBD23; similar observations are reported by Figueiredo et al independently of current or former smoking status3.
Furthermore, the study performs publication bias analysis as well as subgroup analyses and meta-regression regarding the potent effect of publication period, study type, region of origin, sample size, outcomes (either the whole spectrum of GBD or cholelithiasis only), quality assessment (either optimal or suboptimal in every NOS grouping item), and potent confounders on pooled OR. Interestingly, no publication bias was detected, which could be because there had been no clear-cut pre-defined or pre-judged size or even direction of difference in the whole literature. However, several potential sources of heterogeneity were proposed.
Among the three quality assessment items, subgroup analysis suggested that comparability might contribute to the increased heterogeneity; retaining only the group of studies with optimal comparability diminishes heterogeneity from 96 % to 0 %. Furthermore, subgroups regarding adjustment to age and alcohol intake exhibit statistically significant differences. Using meta-regression, lack of adjustment for family history and alcohol intake is shown to be independently correlated with LnOR. All our findings align with existing knowledge and current literature: age, family history, and alcohol are known determinants for GBD50,51. Understanding heterogeneity sources might enable more careful data interpretation and more precise study design in the future.
Sensitivity analysis carried out for each study separately indicated that the study of Etminan et al explained 37.5 % of total heterogeneity, while no other study contributed more than 1 % itself40. Thus, despite this study being the largest one included in the present meta-analysis by contributing 2,721,014 women (58.9 % of the total number of individuals), its influence on the results needs special care due to the major drawback of lack of adjustment to any confounder. Additionally, a positive though not decisively significant correlation of oral contraceptives with cholelithiasis was reported, thus explaining at least a portion of substantially increased OR for female smokers and GBD in the study of Etminan et al51. Based on the above, the fact that the study of Etminan et al was included in the meta-analysis published by Aune et al might be disputable6. Nevertheless, the present study still included the vast study of Etminan et al, as all appropriate measures have been used to interpret heterogeneity, concluding to an overall OR that agrees with that reported by Aune at al.
Data combination from different kinds of studies, thus case-control, cross-sectional, and cohort ones might be considered the present study’s major limitation. However, neither subgroup analysis nor meta-regression revealed any statistically significant difference regarding overall OR. Therefore, our approach might be considered non-misleading. A second limitation is that we failed to incorporate unpublished data; despite that neither positive nor negative prejudiced correlation between smoking and GBD had been prevailed in the literature, the observed absence of any publication bias would be further strengthened in case of implementation of unpublished sources.
In the present systematic review and meta-analysis, we argue that smoking, either current or former, has an apparent positive effect on gallbladder disease. We had also demonstrated that this effect is dose-dependent, at least for current smoking. Additionally, we concluded that family history and alcohol intake could represent potential confounders; therefore, all risk estimates regarding smoking and GBD have to be appropriately adjusted for proper study design and performance in the future.
Conflict of interest
All authors declared no conflict of interest.
References
1. Shaffer EA. Gallstone disease: Epidemiology of gallbladder stone disease. Best Pract Res Clin Gastroenterol. 2006; 20: 981-996.
2. Stinton LM, Shaffer EA. Epidemiology of gallbladder disease: cholelithiasis and cancer. Gut Liver. 2012; 6: 172-187.
3. Figueiredo JC, Haiman C, Porcel J, Buxbaum J, Stram D, Tambe N, et al. Sex and ethnic/racial-specific risk factors for gallbladder disease. BMC Gastroenterol. 2017; 17: 153.
4. Aune D, Norat T, Vatten LJ. Body mass index, abdominal fatness and the risk of gallbladder disease. Eur J Epidemiol. 2015; 30: 1009-1019.
5. Aune D, Leitzmann M, Vatten LJ. Physical Activity and the Risk of Gallbladder Disease: A Systematic Review and Me-ta-Analysis of Cohort Studies. J Phys Act Health 2016; 13: 788-795.
6. Aune D, Vatten LJ, Boffetta P. Tobacco smoking and the risk of gallbladder disease. Eur J Epidemiol. 2016; 31: 643-653.
7. Shabanzadeh DM, Novovic S. Alcohol, smoking and benign hepato-biliary disease. Best Pract Res Clin Gastroenterol. 2017; 31: 519-527.
8. Wirth J, Joshi AD, Song M, Lee DH, Tabung FK, Fung TT, et al. A healthy lifestyle pattern and the risk of symptomatic gall-stone disease: results from 2 prospective cohort studies. Am J Clin Nutr. 2020; 112: 586-594.
9. Degirmenci B, Albayrak R, Haktanir A, Acar M, Yucel A. Acute effect of smoking on gallbladder emptying and refilling in chronic smokers and nonsmokers: a sonographic study. World J Gastroenterol. 2006; 12: 5540-5543.
10. Moher D, Liberati A, Tetzlaff J, Altman DG. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS Med. 2009; 6: e1000097.
11. Shea BJ, Reeves BC, Wells G, Thuku M, Hamel C, Moran J, et al. AMSTAR 2: a critical appraisal tool for systematic reviews that include randomised or non-randomised studies of healthcare interventions, or both. BMJ. 2017; 358: j4008.
12. Herzog R, Álvarez-Pasquin MJ, Díaz C Del Barrio JL, Estrada JM, Gil Á. Are healthcare workers’ intentions to vaccinate re-lated to their knowledge, beliefs and attitudes? A systematic review. BMC Public Health. 2013; 13: 154.
13. Review Manager Web (RevMan Web). The Cochrane Collaboration, 2020. Available at: revman.cochrane.org, date accessed: 12/01/2020.
14. López-López JA, Van den Noortgate W, Tanner-Smith EE, Wilson SJ, Lipsey MW. Assessing meta-regression methods for examining moderator relationships with dependent effect sizes: A Monte Carlo simulation. Res Synth Methods. 2017; 8: 435-450.
15. Hedges LV, Tipton E, Johnson MC. Robust variance estimation in meta-regression with dependent effect size estimates. Res Synth Methods. 2010; 1: 39-65.
16. Suurmond R, van Rhee H, Hak T. Introduction, comparison, and validation of Meta-Essentials: A free and simple tool for meta-analysis. Res Synth Methods. 2017; 8: 537-553.
17. Borenstein M, Hedges LV, Higgins JPT, Rothstein HR. Introduction to Meta-Analysis. John Wiley & Sons, New York, 2009, 107-125.
18. Jørgensen T. Gall stones in a Danish population. Relation to weight, physical activity, smoking, coffee consumption, and diabetes mellitus. Gut. 1989; 30: 528-534.
19. Pastides H, Tzonou A, Trichopoulos D, Katsouyanni K, Trichopoulou A, Kefalogiannis N, et al. A case-control study of the relationship between smoking, diet, and gallbladder disease. Arch Intern Med. 1990; 150: 1409-1412.
20. La Vecchia C, Negri E, D’Avanzo B, Franceschi S, Boyle P. Risk factors for gallstone disease requiring surgery. Int J Epi-demiol. 1991; 20: 209-215.
21. McMichael AJ, Baghurst PA, Scragg RK. A case-control study of smoking and gallbladder disease: importance of examining time relations. Epidemiology 1992; 3: 519-522.
22. Stampfer MJ, Maclure KM, Colditz GA, Manson JE, Willett WC. Risk of symptomatic gallstones in women with severe obe-sity. Am J Clin Nutr. 1992; 55: 652-658.
23. Kato I, Nomura A, Stemmermann GN, Chyou PH. Prospective study of clinical gallbladder disease and its association with obesity, physical activity, and other factors. Dig Dis Sci. 1992; 37: 784-790.
24. Murray FE, Logan RF, Hannaford PC, Kay CR. Cigarette smoking and parity as risk factors for the development of sympto-matic gall bladder disease in women: results of the Royal College of General Practitioners’ oral contraception study. Gut. 1994; 35: 107-111.
25. Grodstein F, Colditz GA, Hunter DJ, Manson JE, Willett WC, Stampfer MJ. A prospective study of symptomatic gallstones in women: relation with oral contraceptives and other risk factors. Obstet Gynecol. 1994; 84: 207-214.
26. Kono S, Shinchi K, Todoroki I, Honjo S, Sakurai Y, Wakabayashi K, et al. Gallstone disease among Japanese men in relation to obesity, glucose intolerance, exercise, alcohol use, and smoking. Scand J Gastroenterol. 1995; 30: 372-376.
27. Misciagna G, Leoci C, Guerra V, Chiloiro M, Elba S, Petruzzi J, et al. Epidemiology of cholelithiasis in southern Italy. Part II: Risk factors. Eur J Gastroenterol Hepatol. 1996; 8: 585-593.
28. Kratzer W, Kächele V, Mason RA, Muche R, Hay B, Wiesneth M, et al. Gallstone prevalence in relation to smoking, alcohol, coffee consumption, and nutrition. The Ulm Gallstone Study. Scand J Gastroenterol. 1997; 32: 953-958.
29. Martínez de Pancorbo C, Carballo F, Horcajo P, Aldeguer M, de la Villa I, Nieto E, et al. Prevalence and associated factors for gallstone disease: results of a population survey in Spain. J Clin Epidemiol. 1997; 50: 1347-1355.
30. Sahi T, Paffenbarger RS Jr, Hsieh CC, Lee IM. Body mass index, cigarette smoking, and other characteristics as predictors of self-reported, physician-diagnosed gallbladder disease in male college alumni. Am J Epidemiol. 1998; 147: 644-651.
31. Okamoto M, Yamagata Z, Takeda Y, Yoda Y, Kobayashi K, Fujino MA. The relationship between gallbladder disease and smoking and drinking habits in middle-aged Japanese. J Gastroenterol. 2002; 37: 455-462.
32. Völzke H, Baumeister SE, Alte D, Hoffmann W, Schwahn C, Simon P, et al. Independent risk factors for gallstone formation in a region with high cholelithiasis prevalence. Digestion 2005; 71: 97-105.
33. Yamada M, Wong FL, Fujiwara S, Tatsukawa Y, Suzuki G. Smoking and alcohol habits as risk factors for benign digestive diseases in a Japanese population: the radiation effects research foundation adult health study. Digestion 2005; 71: 231-237.
34. González-Pérez A, García Rodríguez LA. Gallbladder disease in the general population: association with cardiovascular mor-bidity and therapy. Pharmacoepidemiol Drug Saf. 2007; 16: 524-531.
35. Abu-Eshy SA, Mahfouz AA, Badr A, El Gamal MN, Al-Shehri MY, Salati MI, et al. Prevalence and risk factors of gallstone disease in a high altitude Saudi population. East Mediterr Health J. 2007; 13: 794-802.
36. Katsika D, Tuvblad C, Einarsson C, Lichtenstein P, Marschall HU. Body mass index, alcohol, tobacco and symptomatic gall-stone disease: a Swedish twin study. J Intern Med. 2007; 262: 581-587.
37. Panpimanmas S, Manmee C. Risk factors for gallstone disease in a Thai population. J Epidemiol. 2009; 19: 116-121.
38. Halldestam I, Kullman E, Borch K. Incidence of and potential risk factors for gallstone disease in a general population sam-ple. Br J Surg. 2009; 96: 1315-1322.
39. Liu B, Balkwill A, Roddam A, Brown A, Beral V. Separate and joint effects of alcohol and smoking on the risks of cirrhosis and gallbladder disease in middle-aged women. Am J Epidemiol. 2009; 169: 153-160.
40. Walcher T, Haenle MM, Mason RA, Koenig W, Imhof A, Kratzer W. The effect of alcohol, tobacco and caffeine consumption and vegetarian diet on gallstone prevalence. Eur J Gastroenterol Hepatol. 2010; 22: 1345-1351.
41. Etminan M, Delaney JA, Bressler B, Brophy JM. Oral contraceptives and the risk of gallbladder disease: a comparative safety study. CMAJ 2011; 183: 899-904.
42. Palermo M, Berkowski DE, Córdoba JP, Verde JM, Giménez ME. Prevalence of cholelithiasis in Buenos Aires, Argentina. Acta Gastroenterol Latinoam. 2013; 43: 98-105.
43. Shabanzadeh DM, Sørensen LT, Jørgensen T. Determinants for gallstone formation – a new data cohort study and a system-atic review with meta-analysis. Scand J Gastroenterol. 2016; 51: 1239-1248.
44. Kang Q, Kang G, Li R, Zhu X, Yu Y, Yu Q. Relationship of Gallbladder Diseases with Sociodemographic Characteristics, Lifestyle, and Chronic Diseases in Northeastern China. Int J Environ Res Public Health. 2018; 15: 2596.
45. Chang CM, Chiu THT, Chang CC, Lin MN, Lin CL. Plant-Based Diet, Cholesterol, and Risk of Gallstone Disease: A Prospec-tive Study. Nutrients. 2019; 11: 335.
46. Kim HS, Cho SK, Kim CS, Park JS. Big data and analysis of risk factors for gallbladder disease in the young generation of Korea. PLoS One. 2019; 14: e0211480.
47. Rhodes M, Venables CW. Symptomatic gallstones–a disease of non-smokers? Digestion. 1991; 49: 221-226.
48. Greenland S, Longnecker MP. Methods for trend estimation from summarized dose-response data, with applications to me-ta-analysis. Am J Epidemiol. 1992; 135: 1301-1309.
49. Shabanzadeh DM. New determinants for gallstone disease?. Dan Med J. 2018; 65: B5438.
50. Cha BH, Jang MJ, Lee SH. Alcohol Consumption Can Reduce the Risk of Gallstone Disease: A Systematic Review with a Dose-Response Meta-Analysis of Case-Control and Cohort Studies. Gut Liver. 2019; 13: 114-131.
51. Wang S, Wang Y, Xu J, Chen Y. Is the oral contraceptive or hormone replacement therapy a risk factor for cholelithiasis: A systematic review and meta-analysis. Medicine (Baltimore). 2017; 96: e6556.