Exploiting gene-environment independence in family-based case-control studies: increased power for detecting associations, interactions and joint effects

Family-based case-control studies are popularly used to study the effect of genes and gene-environment interactions in the etiology of rare complex diseases. We consider methods for the analysis of such studies under the assumption that genetic susceptibility (G) and environmental exposures (E) are independently distributed of each other within families in the source population. Conditional logistic regression, the traditional method of analysis of the data, fails to exploit the independence assumption and hence can be inefficient. Alternatively, one can estimate the multiplicative interaction between G and E more efficiently using cases only, but the required population-based G-E independence assumption is very stringent. In this article, we propose a novel conditional likelihood framework for exploiting the within-family G-E independence assumption. This approach leads to a simple and yet highly efficient method of estimating interaction and various other risk parameters of scientific interest. Moreover, we show that the same paradigm also leads to a number of alternative and even more efficient methods for analysis of family-based case-control studies when parental genotype information is available on the case-control study participants. Based on these methods, we evaluate different family-based study designs by examining their relative efficiencies to each other and their efficiencies compared to a population-based case-control design of unrelated subjects. These comparisons reveal important design implications. Extensions of the methodologies for dealing with complex family studies are also discussed.     

Invited Commentary: Efficient Testing of Gene-Environment Interaction

Gene-environment-wide interaction studies of disease occurrencein human populations may be able to exploit the same agnosticapproach to interrogating the human genome used by genome-wideassociation studies. The authors discuss 2 methods for takingadvantage of possible independence between a single nucleotidepolymorphism they call G (a genetic factor) and an environmentalfactor they call E while maintaining nominal type I error instudying G-E interaction when information on many genes is available.The first method is a simple 2-step procedure for testing thenull hypothesis of no multiplicative interaction against thealternative hypothesis of a multiplicative interaction betweenan E and at least one of the markers genotyped in a genome-wideassociation study. The added power for the method derives froma clever work-around of a multiple testing procedure. The secondis an empirical-Bayes–style shrinkage estimation frameworkfor G-E interaction and the associated tests that can gain efficiencyand power when the G-E independence assumption is met for mostG's in the underlying population and yet, unlike the case-onlymethod, is resistant to increased type I error when the underlyingassumption of independence is violated. The development of newapproaches to testing for interaction is an example of methodologicalprogress leading to practical advantages. association; environment; genes; genetic markers; genetics; genome     

A weighting approach to causal effects and additive interaction in case-control studies: marginal structural linear odds models

Estimates of additive interaction from case-control data are often obtained by logistic regression; such models can also be used to adjust for covariates. This approach to estimating additive interaction has come under some criticism because of possible misspecification of the logistic model: If the underlying model is linear, the logistic model will be misspecified. The authors propose an inverse probability of treatment weighting approach to causal effects and additive interaction in case-control studies. Under the assumption of no unmeasured confounding, the approach amounts to fitting a marginal structural linear odds model. The approach allows for the estimation of measures of additive interaction between dichotomous exposures, such as the relative excess risk due to interaction, using case-control data without having to rely on modeling assumptions for the outcome conditional on the exposures and covariates. Rather than using conditional models for the outcome, models are instead specified for the exposures conditional on the covariates. The approach is illustrated by assessing additive interaction between genetic and environmental factors using data from a case-control study.     

Efficient designs of gene-environment interaction studies: implications of Hardy-Weinberg equilibrium and gene-environment independence

It is important to investigate whether genetic susceptibility variants exercise the same effects in populations that are differentially exposed to environmental risk factors. Here, we assess the power of four two-stage case-control design strategies for assessing multiplicative gene-environment (G-E) interactions or for assessing genetic or environmental effects in the presence of G-E interactions. We considered a di-allelic single nucleotide polymorphism G and a binary environmental variable E under the constraints of G-E independence and Hardy-Weinberg equilibrium and used the Wald statistic for all tests. We concluded that (i) for testing G-E interactions or genetic effects in the presence of G-E interactions when data for E are fully available, it is preferable to ascertain data for G in a subsample of cases with similar numbers of exposed and unexposed and a random subsample of controls; and (ii) for testing G-E interactions or environmental effects in the presence of G-E interactions when data for G are fully available, it is preferable to ascertain data for E in a subsample of cases that has similar numbers for each genotype and a random subsample of controls. In addition, supplementing external control data to an existing case-control sample leads to improved power for assessing effects of G or E in the presence of G-E interactions. Copyright ? 2012 John Wiley & Sons, Ltd.     

Interaction as departure from additivity in case-control studies: a cautionary note

It has been argued that assessment of interaction should be based on departures from additive rates or risks. The corresponding fundamental interaction parameter cannot generally be estimated from case-control studies. Thus, surrogate measures of interaction based on relative risks from logistic models have been proposed, such as the relative excess risk due to interaction (RERI), the attributable proportion due to interaction (AP), and the synergy index (S). In practice, it is usually necessary to include covariates such as age and gender to control for confounding. The author uncovers two problems associated with surrogate interaction measures in this case: First, RERI and AP vary across strata defined by the covariates, whereas the fundamental interaction parameter is unvarying. S does not vary across strata, which suggests that it is the measure of choice. Second, a misspecification problem implies that measures based on logistic regression only approximate the true measures. This problem can be rectified by using a linear odds model, which also enables investigators to test whether the fundamental interaction parameter is zero. A simulation study reveals that coverage is much improved by using the linear odds model, but bias may be a concern regardless of whether logistic regression or the linear odds model is used.     

The Case‐Only Test for Gene–Environment Interaction is Not Uniformly Powerful: An Empirical Example

The case‐only test has been proposed as a more powerful approach to detect gene–environment (G × E) interactions. This approach assumes that the genetic and environmental factors are independent. Although it is well known that Type I error rate will increase if this assumption is violated, it is less widely appreciated that G × E correlation can also lead to power loss. We illustrate this phenomenon by comparing the performance of the case‐only test to other approaches to detect G × E interactions in a genome‐wide association study (GWAS) of esophageal squamous‐cell carcinoma (ESCC) in Chinese populations. Some of these approaches do not use information on the correlation between exposure and genotype (standard logistic regression), whereas others seek to use this information in a robust fashion to boost power without increasing Type I error (two‐step, empirical Bayes, and cocktail methods). G × E interactions were identified involving drinking status and two regions containing genes in the alcohol metabolism pathway, 4q23 and 12q24. Although the case‐only test yielded the most significant tests of G × E interaction in the 4q23 region, the case‐only test failed to identify significant interactions in the 12q24 region which were readily identified using other approaches. The low power of the case‐only test in the 12q24 region is likely due to the strong inverse association between the single nucleotide polymorphism (SNPs) in this region and drinking status. This example underscores the need to consider multiple approaches to detect G × E interactions, as different tests are more or less sensitive to different alternative hypotheses and violations of the G × E independence assumption.     

Robust semiparametric gene-environment interaction analysis using sparse boosting

For the pathogenesis of complex diseases, gene-environment (G-E) interactions have been shown to have important implications. G-E interaction analysis can be challenging with the need to jointly analyze a large number of main effects and interactions and to respect the “main effects, interactions” hierarchical constraint. Extensive methodological developments on G-E interaction analysis have been conducted in recent literature. Despite considerable successes, most of the existing studies are still limited as they cannot accommodate long-tailed distributions/data contamination, make the restricted assumption of linear effects, and cannot effectively accommodate missingness in E variables. To directly tackle these problems, a semiparametric model is assumed to accommodate nonlinear effects, and the Huber loss function and Q_n estimator are adopted to accommodate long-tailed distributions/data contamination. A regression-based multiple imputation approach is developed to accommodate missingness in E variables. For model estimation and selection of relevant variables, we adopt an effective sparse boosting approach. The proposed approach is practically well motivated, has intuitive formulations, and can be effectively realized. In extensive simulations, it significantly outperforms multiple direct competitors. The analysis of The Cancer Genome Atlas data on stomach adenocarcinoma and cutaneous melanoma shows that the proposed approach makes sensible discoveries with satisfactory prediction and stability.     

Test for additive interaction in proportional hazards models

PURPOSE: We describe a method for testing and estimating a two-way additive interaction between two categorical variables, each of which has greater than or equal to two levels. METHODS: We test additive and multiplicative interactions in the same proportional hazards model and measure additivity by relative excess risk due to interaction (RERI), proportion of disease attributable to interaction (AP), and synergy index (S). A simulation study was used to compare the performance of these measures of additivity. Data from the Atherosclerosis Risk in Communities cohort study with a total of 15,792 subjects were used to exemplify the methods. RESULTS: The test and measures of departure from additivity depend neither on follow-up time nor on the covariates. The simulation study indicates that RERI is the best choice of measures of additivity using a proportional hazards model. The examples indicated that an interaction between two variables can be statistically significant on additive measure (RERI=1.14, p=0.04) but not on multiplicative measure (beta3=0.59, p=0.12) and that additive and multiplicative interactions can be in opposite directions (RERI=0.08, beta3=-0.08). CONCLUSIONS: The method has broader application for any regression models with a rate as the dependent variable. In the case that both additive and multiplicative interactions are statistically significant and in the opposite direction, the interpretation needs caution.     

Interaction analysis under misspecification of main effects: Some common mistakes and simple solutions

The statistical practice of modeling interaction with two linear main effects and a product term is ubiquitous in the statistical and epidemiological literature. Most data modelers are aware that the misspecification of main effects can potentially cause severe type I error inflation in tests for interactions, leading to spurious detection of interactions. However, modeling practice has not changed. In this article, we focus on the specific situation where the main effects in the model are misspecified as linear terms and characterize its impact on common tests for statistical interaction. We then propose some simple alternatives that fix the issue of potential type I error inflation in testing interaction due to main effect misspecification. We show that when using the sandwich variance estimator for a linear regression model with a quantitative outcome and two independent factors, both the Wald and score tests asymptotically maintain the correct type I error rate. However, if the independence assumption does not hold or the outcome is binary, using the sandwich estimator does not fix the problem. We further demonstrate that flexibly modeling the main effect under a generalized additive model can largely reduce or often remove bias in the estimates and maintain the correct type I error rate for both quantitative and binary outcomes regardless of the independence assumption. We show, under the independence assumption and for a continuous outcome, overfitting and flexibly modeling the main effects does not lead to power loss asymptotically relative to a correctly specified main effect model. Our simulation study further demonstrates the empirical fact that using flexible models for the main effects does not result in a significant loss of power for testing interaction in general. Our results provide an improved understanding of the strengths and limitations for tests of interaction in the presence of main effect misspecification. Using data from a large biobank study “The Michigan Genomics Initiative”, we present two examples of interaction analysis in support of our results.     

Gene-environment interactions in genome-wide association studies: a comparative study of tests applied to empirical studies of type 2 diabetes

The question of which statistical approach is the most effective for investigating gene-environment (G-E) interactions in the context of genome-wide association studies (GWAS) remains unresolved. By using 2 case-control GWAS (the Nurses' Health Study, 1976-2006, and the Health Professionals Follow-up Study, 1986-2006) of type 2 diabetes, the authors compared 5 tests for interactions: standard logistic regression-based case-control; case-only; semiparametric maximum-likelihood estimation of an empirical-Bayes shrinkage estimator; and 2-stage tests. The authors also compared 2 joint tests of genetic main effects and G-E interaction. Elevated body mass index was the exposure of interest and was modeled as a binary trait to avoid an inflated type I error rate that the authors observed when the main effect of continuous body mass index was misspecified. Although both the case-only and the semiparametric maximum-likelihood estimation approaches assume that the tested markers are independent of exposure in the general population, the authors did not observe any evidence of inflated type I error for these tests in their studies with 2,199 cases and 3,044 controls. Both joint tests detected markers with known marginal effects. Loci with the most significant G-E interactions using the standard, empirical-Bayes, and 2-stage tests were strongly correlated with the exposure among controls. Study findings suggest that methods exploiting G-E independence can be efficient and valid options for investigating G-E interactions in GWAS.     

Robust discovery of genetic associations incorporating gene-environment interaction and independence

This article considers the detection and evaluation of genetic effects incorporating gene-environment interaction and independence. Whereas ordinary logistic regression cannot exploit the assumption of gene-environment independence, the proposed approach makes explicit use of the independence assumption to improve estimation efficiency. This method, which uses both cases and controls, fits a constrained retrospective regression in which the genetic variant plays the role of the response variable, and the disease indicator and the environmental exposure are the independent variables. The regression model constrains the association of the environmental exposure with the genetic variant among the controls to be null, thus explicitly encoding the gene-environment independence assumption, which yields substantial gain in accuracy in the evaluation of genetic effects. The proposed retrospective regression approach has several advantages. It is easy to implement with standard software, and it readily accounts for multiple environmental exposures of a polytomous or of a continuous nature, while easily incorporating extraneous covariates. Unlike the profile likelihood approach of Chatterjee and Carroll (Biometrika. 2005;92:399-418), the proposed method does not require a model for the association of a polytomous or continuous exposure with the disease outcome, and, therefore, it is agnostic to the functional form of such a model and completely robust to its possible misspecification.     

Empirical Hierarchical Bayes Approach to Gene‐Environment Interactions: Development and Application to Genome‐Wide Association Studies of Lung Cancer in TRICL

The analysis of gene‐environment (G × E) interactions remains one of the greatest challenges in the postgenome‐wide association studies (GWASs) era. Recent methods constitute a compromise between the robust but underpowered case‐control and powerful case‐only methods. Inferences of the latter are biased when the assumption of gene‐environment (G‐E) independence in controls fails. We propose a novel empirical hierarchical Bayes approach to G × E interaction (EHB‐GE), which benefits from greater rank power while accounting for population‐based G‐E correlation. Building on Lewinger et al.'s ([2007] Genet Epidemiol 31:871–882) hierarchical Bayes prioritization approach, the method first obtains posterior G‐E correlation estimates in controls for each marker, borrowing strength from G‐E information across the genome. These posterior estimates are then subtracted from the corresponding case‐only G × E estimates. We compared EHB‐GE with rival methods using simulation. EHB‐GE has similar or greater rank power to detect G × E interactions in the presence of large numbers of G‐E correlations with weak to strong effects or only a low number of such correlations with large effect. When there are no or only a few weak G‐E correlations, Murcray et al.'s method ([2009] Am J Epidemiol 169:219–226) identifies markers with low G × E interaction effects better. We applied EHB‐GE and competing methods to four lung cancer case‐control GWAS from the Interdisciplinary Research in Cancer of the Lung/International Lung Cancer Consortium with smoking as environmental factor. A number of genes worth investigating were identified by the EHB‐GE approach.     

Powerful Set‐Based Gene‐Environment Interaction Testing Framework for Complex Diseases

Identification of gene‐environment interaction (G × E) is important in understanding the etiology of complex diseases. Based on our previously developed Set Based gene EnviRonment InterAction test (SBERIA), in this paper we propose a powerful framework for enhanced set‐based G × E testing (eSBERIA). The major challenge of signal aggregation within a set is how to tell signals from noise. eSBERIA tackles this challenge by adaptively aggregating the interaction signals within a set weighted by the strength of the marginal and correlation screening signals. eSBERIA then combines the screening‐informed aggregate test with a variance component test to account for the residual signals. Additionally, we develop a case‐only extension for eSBERIA (coSBERIA) and an existing set‐based method, which boosts the power not only by exploiting the G‐E independence assumption but also by avoiding the need to specify main effects for a large number of variants in the set. Through extensive simulation, we show that coSBERIA and eSBERIA are considerably more powerful than existing methods within the case‐only and the case‐control method categories across a wide range of scenarios. We conduct a genome‐wide G × E search by applying our methods to Illumina HumanExome Beadchip data of 10,446 colorectal cancer cases and 10,191 controls and identify two novel interactions between nonsteroidal anti‐inflammatory drugs (NSAIDs) and MINK1 and PTCHD3.     

Multivariate methods for binary longitudinal data with heterogeneous correlation over time.

Clustered binary data occur frequently in biostatistical work. One particular application is in binary longitudinal data, where several visits are available for the same individual. Several approaches have been proposed for the analysis of clustered binary data. In Rosner, a polychotomous logistic regression model was proposed which is a generalization of the beta-binomial distribution and allows for person- and visit-specific covariates, while controlling for clustering effects. One assumption of this model is that all pairs of visits within an individual are equally correlated, which may be inappropriate if several visits are available over a long follow-up period. In this paper, this approach is extended to allow for heterogeneous correlation over time. The total time period is divided into subintervals and a beta-binomial mixture model is introduced to estimate odds ratios relating outcomes for pairs of visits both within a subinterval as well as in different subintervals. To include covariates, an extension of the polychotomous logistic regression model is proposed, which allows one to estimate effects of person-, subinterval-, and visit-specific covariates, while controlling for clustering using the beta-binomial mixture model. This model is applied to the analysis of respiratory symptom data in children collected over a 14-year period in East Boston, MA, in relation to maternal and child smoking, where the unit is the child and symptom history is divided into early-adolescent and late-adolescent symptom experience.     

Mixture analysis of longitudinal binary data

The dependence of longitudinal binary outcomes on covariates and the covariation observed between them is often modelled by (multivariate) logistic and probit models, respectively, assuming specified association structure or random effects. Alternatively, latent class models may be used that capture the covariation by assuming heterogeneity of the observational units regarding their reaction tendencies while postulating independence within classes. In the presence of a few categorical covariates, the multi-group method of latent class analysis allows one to relate the class sizes and the class-specific response probabilities to these covariates. Wheeze data from the Harvard Six-Cities study on respiratory health are a typical example for such a situation: at four occasions, the wheeze status of 537 children was examined, 187 among them exposed to maternal smoking and 350 not exposed. Thus, there is a single binary covariate (maternal smoking versus no maternal smoking) making easily applicable the multi-group method of latent class analysis. Based on a series of unrestricted and restricted models having up to three classes for the exposed and not-exposed subgroup each, no statistically significant effect of maternal smoking on children's wheeze status could be substantiated. Moreover, it was not possible to show statistically significant difference at all between the two distributions of wheeze patterns collected from exposed and not-exposed children.     

SBERIA: Set‐Based Gene‐Environment Interaction Test for Rare and Common Variants in Complex Diseases

Identification of gene‐environment interaction (G × E) is important in understanding the etiology of complex diseases. However, partially due to the lack of power, there have been very few replicated G × E findings compared to the success in marginal association studies. The existing G × E testing methods mainly focus on improving the power for individual markers. In this paper, we took a different strategy and proposed a set‐based gene‐environment interaction test (SBERIA), which can improve the power by reducing the multiple testing burdens and aggregating signals within a set. The major challenge of the signal aggregation within a set is how to tell signals from noise and how to determine the direction of the signals. SBERIA takes advantage of the established correlation screening for G × E to guide the aggregation of genotypes within a marker set. The correlation screening has been shown to be an efficient way of selecting potential G × E candidate SNPs in case‐control studies for complex diseases. Importantly, the correlation screening in case‐control combined samples is independent of the interaction test. With this desirable feature, SBERIA maintains the correct type I error level and can be easily implemented in a regular logistic regression setting. We showed that SBERIA had higher power than benchmark methods in various simulation scenarios, both for common and rare variants. We also applied SBERIA to real genome‐wide association studies (GWAS) data of 10,729 colorectal cancer cases and 13,328 controls and found evidence of interaction between the set of known colorectal cancer susceptibility loci and smoking.     

The method of sib-pair linkage analysis in context of case-control design

We used sib-pair linkage analysis as part of an epidemiologic approach to solving Problem 2 of the GAW10 data set of nuclear families. We recoded the quantitative trait Q1 into a dichotomous trait using Q1 ≥ 40 as the cut-point. In a case-control design of sib-pair analysis, the affected siblings of the proband were the case subjects and the unaffected siblings were the control subjects. Case and control subjects were compared with respect to the number of alleles at one or more loci (0,1,2) that were identical-by-descent (IBD) with those of the proband. Odds ratios (Ors) and 95% confidence intervals (95% CI) were then computed with subjects sharing no alleles (share-0) serving as the reference group. Significantly high ORs were taken as indication of linkage between a marker locus and a suspected disease-susceptibility locus. The case-control sib-pair analysis identified marker D5G15 as associated with disease susceptibility (OR of sharing two alleles [share-2] = 7.7 [95% CI 2.5-23.9]). Our results were consistent with the results from Kruglyak and Lander's method of complete multipoint sib-pair analysis for linkage. For the marker (D5G15) identified through sib-pair analysis, we examined the effects of other covariates and evaluated gene-environment interaction using conditional logistic regression. © 1997 Wiley-Liss, Inc.     

Detecting treatment‐covariate interactions using permutation methods

The primary objective of a Randomized Clinical Trial usually is to investigate whether one treatment is better than its alternatives on average. However, treatment effects may vary across different patient subpopulations. In contrast to demonstrating one treatment is superior to another on the average sense, one is often more concerned with the question that, for a particular patient, or a group of patients with similar characteristics, which treatment strategy is most appropriate to achieve a desired outcome. Various interaction tests have been proposed to detect treatment effect heterogeneity; however, they typically examine covariates one at a time, do not offer an integrated approach that incorporates all available information, and can greatly increase the chance of a false positive finding when the number of covariates is large. We propose a new permutation test for the null hypothesis of no interaction effects for any covariate. The proposed test allows us to consider the interaction effects of many covariates simultaneously without having to group subjects into subsets based on pre‐specified criteria and applies generally to randomized clinical trials of multiple treatments. The test provides an attractive alternative to the standard likelihood ratio test, especially when the number of covariates is large. We illustrate the proposed methods using a dataset from the Treatment of Adolescents with Depression Study. Copyright © 2015 John Wiley & Sons, Ltd.     

Evidence of interaction between polychlorinated biphenyls and phthalates in relation to human sperm motility

Previously, we reported evidence of inverse associations between exposure to some polychlorinated biphenyls (PCBs) and some phthalate monoesters in relation to semen parameters, specifically sperm motility. Because humans are exposed to both phthalates and PCBs and because experimental studies suggest that PCBs may interact with glucuronidative enzymes that are responsible for phthalate metabolism, we explored the potential interaction between phthalates and PCBs in relation to human semen quality. We studied 303 men who were partners in subfertile couples seeking infertility diagnosis from the andrology laboratory at Massachusetts General Hospital. Semen parameters were dichotomized based on World Health Organization reference values, and phthalate and PCB levels were dichotomized at their respective medians. After adjusting for age and abstinence time, for below reference sperm motility there was a greater than additive interaction between monobenzyl phthalate and PCB-153 [relative excess risk due to interaction (RERI) = 1.40; 95% confidence interval (CI), 0.41-3.22], sum of PCBs (RERI = 1.24; 95% CI, 0.15-2.94), and cytochrome P450 (CYP450)-inducing PCBs (RERI = 1.30; 95% CI, 0.21-3.06). For below-reference sperm motility, there was also a greater than additive interaction between monobutyl phthalate (MBP) and PCB-153 (RERI = 1.42; 95% CI, 0.09-3.76) and CYP450-inducing PCBs (RERI = 1.87; 95% CI, 0.56-4.52) and a suggestive interaction between MBP and sum of PCBs (RERI = 1.35; 95% CI, -0.11 to 3.48). In conclusion, because there are important risk assessment and public health implications of interactions between these two ubiquitous classes of compounds, further studies need to be conducted to confirm these results and identify potential mechanisms of interactions.     

糖尿病与脂肪肝对绝经女性胆石症患病的交互作用

目的 探讨糖尿病与脂肪肝的交互作用对绝经女性胆石症患病的影响。方法 研究采用多阶段分层整群抽样方法抽取苗族、侗族绝经女性作为研究对象，纳入符合条件的研究对象共3 938人。采用SPSS25.0软件进行Mann - Whitney U检验、χ2检验、单因素和多因素logistic回归分析；同时运用相乘模型和相加模型探讨糖尿病与脂肪肝的交互作用对绝经女性胆石症患病的影响。结果 本次3 938名绝经女性中，平均年龄为59.51岁，胆石症检出率为17.01%，糖尿病检出率为9.09%，脂肪肝检出率为19.86%，糖尿病合并脂肪肝检出率为4.06%。多因素logistic回归结果显示，糖尿病(OR = 1.715, 95%CI:1.325～2.220)和脂肪肝(OR = 1.438, 95%CI:1.162～1.780)均与绝经女性胆石症的患病风险有关。交互作用分析结果显示，糖尿病与脂肪肝对绝经女性胆石症的患病不存在相乘交互作用(OR = 1.605,95%CI:0.951～2.707)，结果无统计学意义；但糖尿病与脂肪肝对绝经女性胆石症的患病存在相加交互作用，糖尿病合并脂肪肝者的患病风险高于无糖尿病且无脂肪肝者(OR = 2.905,95%CI:2.040～4.138)，其相加交互作用评价指标RERI(95%CI)、AP(95%CI)和SI(95%CI)分别为1.216(0.115～2.316)、0.418(0.148～0.688)和2.760(1.043～7.305)，结果有统计学意义。结论 在绝经女性中，糖尿病和脂肪肝均与胆石症存在关联，二者对增加胆石症患病风险可能存在协同作用。