Genome-Wide Gene-Environment Interaction Analyses to Understand the Relationship between Red Meat and Processed Meat Intake and Colorectal Cancer Risk.

BACKGROUND: High red meat and/or processed meat consumption are established colorectal cancer risk factors. We conducted a genome-wide gene-environment (GxE) interaction analysis to identify genetic variants that may modify these associations. METHODS: A pooled sample of 29,842 colorectal cancer cases and 39,635 controls of European ancestry from 27 studies were included. Quantiles for red meat and processed meat intake were constructed from harmonized questionnaire data. Genotyping arrays were imputed to the Haplotype Reference Consortium. Two-step EDGE and joint tests of GxE interaction were utilized in our genome-wide scan. RESULTS: Meta-analyses confirmed positive associations between increased consumption of red meat and processed meat with colorectal cancer risk [per quartile red meat OR = 1.30; 95% confidence interval (CI) = 1.21-1.41; processed meat OR = 1.40; 95% CI = 1.20-1.63]. Two significant genome-wide GxE interactions for red meat consumption were found. Joint GxE tests revealed the rs4871179 SNP in chromosome 8 (downstream of HAS2); greater than median of consumption ORs = 1.38 (95% CI = 1.29-1.46), 1.20 (95% CI = 1.12-1.27), and 1.07 (95% CI = 0.95-1.19) for CC, CG, and GG, respectively. The two-step EDGE method identified the rs35352860 SNP in chromosome 18 (SMAD7 intron); greater than median of consumption ORs = 1.18 (95% CI = 1.11-1.24), 1.35 (95% CI = 1.26-1.44), and 1.46 (95% CI = 1.26-1.69) for CC, CT, and TT, respectively. CONCLUSIONS: We propose two novel biomarkers that support the role of meat consumption with an increased risk of colorectal cancer. IMPACT: The reported GxE interactions may explain the increased risk of colorectal cancer in certain population subgroups.

Modeling the longitudinal transitions of electronic cigarettes and conventional cigarettes with time-dependent covariates among adolescents.

E-cigarettes may help combustible cigarette smokers switch to a less harmful alternative, or may increase the risk of subsequent initiation of cigarettes among non-smokers. Among youth, it is not clear whether both pathways occur equally, or whether one direction is more likely than the other. We used data from a prospective cohort study of youth in Southern California followed twice annually from Fall 2013 (9th grade) to Fall 2015 (11th grade) (N = 1977). A polytomous logistic regression model was used to simultaneously estimate transition rates for initiation of and abstention from e-cigarettes and cigarettes. Use of e-cigarettes was positively associated with initiation of cigarettes (OR = 7.57; 95%CI:[5.32, 10.8]) and negatively associated with cigarette abstention (OR = 0.58; 95%CI:[0.33, 0.99]) in adjusted models; cigarette use was positively associated with e-cigarette initiation (OR = 2.54; 95%CI:[1.45, 4.47]) and negatively associated with e-cigarette abstention (OR = 0.31; 95%CI:[0.17,0.57]). Uni-directional transition from e-cigarettes only to cigarettes only occurred less frequently than expected under independence (OR = 0.33; 95% CI [0.20, 0.55]), whereas simultaneously initiating both products (OR = 9.79; 95%CI:[7.22, 13.3]) and simultaneously abstaining (OR = 2.84; 95%CI:[1.50, 5.37]) were more frequent than expected. E-cigarettes were more strongly associated with subsequent cigarette initiation than the reverse, though both models indicated that use of either product seems to encourage use of the other. Models also indicated that use of either e-cigarettes or cigarettes resulted in reduced abstention of the other product. Findings suggest that prevention efforts for that continue to focus on both e-cigarettes and cigarettes are needed.

Ambient Air Pollutant Exposures and COVID-19 Severity and Mortality in a Cohort of Patients with COVID-19 in Southern California.

Rationale: Ecological studies have shown air pollution associations with coronavirus disease (COVID-19) outcomes. However, few cohort studies have been conducted. Objectives: To conduct a cohort study investigating the association between air pollution and COVID-19 severity using individual-level data from the electronic medical record. Methods: This cohort included all individuals who received diagnoses of COVID-19 from Kaiser Permanente Southern California between March 1 and August 31, 2020. One-year and 1-month averaged ambient air pollutant (particulate matter ⩽2.5 µm in aerodynamic diameter [PM2.5], NO2, and O3) exposures before COVID-19 diagnosis were estimated on the basis of residential address history. Outcomes included COVID-19-related hospitalizations, intensive respiratory support (IRS), and ICU admissions within 30 days and mortality within 60 days after COVID-19 diagnosis. Covariates included socioeconomic characteristics and comorbidities. Measurements and Main Results: Among 74,915 individuals (mean age, 42.5 years; 54% women; 66% Hispanic), rates of hospitalization, IRS, ICU admission, and mortality were 6.3%, 2.4%, 1.5%, and 1.5%, respectively. Using multipollutant models adjusted for covariates, 1-year PM2.5 and 1-month NO2 average exposures were associated with COVID-19 severity. The odds ratios associated with a 1-SD increase in 1-year PM2.5 (SD, 1.5 µg/m3) were 1.24 (95% confidence interval [CI], 1.16-1.32) for COVID-19-related hospitalization, 1.33 (95% CI, 1.20-1.47) for IRS, and 1.32 (95% CI, 1.16-1.51) for ICU admission; the corresponding odds ratios associated with 1-month NO2 (SD, 3.3 ppb) were 1.12 (95% CI, 1.06-1.17) for hospitalization, 1.18 (95% CI, 1.10-1.27) for IRS, and 1.21 (95% CI, 1.11-1.33) for ICU admission. The hazard ratios for mortality were 1.14 (95% CI, 1.02-1.27) for 1-year PM2.5 and 1.07 (95% CI, 0.98-1.16) for 1-month NO2. No significant interactions with age, sex or ethnicity were observed. Conclusions: Ambient PM2.5 and NO2 exposures may affect COVID-19 severity and mortality.

Transcriptomic and metabolomic associations with exposures to air pollutants among young adults with childhood asthma history.

Ambient air pollutants are well-known risk factors for childhood asthma and asthma exacerbation. It is unknown whether different air pollutants individually or jointly affect pathophysiological mechanisms of asthma. In this study, we aim to integrate transcriptome and untargeted metabolome to identify dysregulated genetic and metabolic pathways that are associated with exposures to a mixture of ambient and traffic-related air pollutants among adults with asthma history. In this cross-sectional study, 102 young adults with childhood asthma history were enrolled from southern California in 2012. Whole blood transcriptome was measured with 20,869 expression signatures, and serum untargeted metabolomics including 937 metabolites were analyzed by Metabolon, Inc. Participants' exposures to regional air pollutants (NO2, O3, PM10, PM2.5) and near-roadway air pollutants averaged at one month and one year before study visit were estimated based on residential addresses. xMWAS network analysis and joint-pathway analysis were performed to identify subnetworks and genetic and metabolic pathways that were associated with exposure to air pollutants adjusted for socio-characteristic covariates. Network analysis found that exposures to air pollutants mixture were connected to 357 gene markers and 92 metabolites. One-year and one-month averaged PM2.5 and NO2 were associated with several amino acids related to serine, glycine, and beta-alanine metabolism. Lower serum levels of carnosine and aspartate, which are involved in the beta-alanine metabolic pathway, as well as choline were also associated with worse asthma control (p < 0.05). One-year and one-month averaged PM10 and one-month averaged O3 were associated with higher gene expression levels of HSPA5, LGMN, CTSL and HLA-DPB1, which are involved in antigen processing and presentation. These results indicate that exposures to various air pollutants are associated with altered genetic and metabolic pathways that affect anti-oxidative capacity and immune response and can potentially contribute to asthma-related pathophysiology.

Ambient air pollution and COVID-19 incidence during four 2020-2021 case surges.

BACKGROUND: Air pollution exposure may make people more vulnerable to COVID-19 infection. However, previous studies in this area mostly focused on infection before May 2020 and long-term exposure. OBJECTIVE: To assess both long-term and short-term exposure to air pollution and COVID-19 incidence across four case surges from 03/1/2020 to 02/28/2021. METHODS: The cohort included 4.6 million members from a large integrated health care system in southern California with comprehensive electronic medical records (EMR). COVID-19 cases were identified from EMR. Incidence of COVID-19 was computed at the census tract-level among members. Prior 1-month and 1-year averaged air pollutant levels (PM2.5, NO2, and O3) at the census tract-level were estimated based on hourly and daily air quality data. Data analyses were conducted by each wave: 3/1/2020-5/31/2020, 6/1/202-9/30/2020, 10/1/2020-12/31/2020, and 1/1/2021-2/28/2021 and pooled across waves using meta-analysis. Generalized linear mixed effects models with Poisson distribution and spatial autocorrelation were used with adjustment for meteorological factors and census tract-level social and health characteristics. Results were expressed as relative risk (RR) per 1 standard deviation. RESULTS: The cohort included 446,440 COVID-19 cases covering 4609 census tracts. The pooled RRs (95% CI) of COVID-19 incidence associated with 1-year exposures to PM2.5, NO2, and O3 were 1.11 (1.04, 1.18) per 2.3 µg/m3,1.09 (1.02, 1.17) per 3.2 ppb, and 1.06 (1.00, 1.12) per 5.5 ppb respectively. The corresponding RRs (95% CI) associated with prior 1-month exposures were 1.11 (1.03, 1.20) per 5.2 µg/m3 for PM2.5, 1.09 (1.01, 1.17) per 6.0 ppb for NO2 and 0.96 (0.85, 1.08) per 12.0 ppb for O3. CONCLUSION: Long-term PM2.5 and NO2 exposures were associated with increased risk of COVID-19 incidence across all case surges before February 2021. Short-term PM2.5 and NO2 exposures were also associated. Our findings suggest that air pollution may play a role in increasing the risk of COVID-19 infection.

Near-roadway air pollution associated with COVID-19 severity and mortality - Multiethnic cohort study in Southern California.

BACKGROUND: Air pollution exposure has been associated with increased risk of COVID-19 incidence and mortality by ecological analyses. Few studies have investigated the specific effect of traffic-related air pollution on COVID-19 severity. OBJECTIVE: To investigate the associations of near-roadway air pollution (NRAP) exposure with COVID-19 severity and mortality using individual-level exposure and outcome data. METHODS: The retrospective cohort includes 75,010 individuals (mean age 42.5 years, 54% female, 66% Hispanic) diagnosed with COVID-19 at Kaiser Permanente Southern California between 3/1/2020-8/31/2020. NRAP exposures from both freeways and non-freeways during 1-year prior to the COVID-19 diagnosis date were estimated based on residential address history using the CALINE4 line source dispersion model. Primary outcomes include COVID-19 severity defined as COVID-19-related hospitalizations, intensive respiratory support (IRS), intensive care unit (ICU) admissions within 30 days, and mortality within 60 days after COVID-19 diagnosis. Covariates including socio-characteristics and comorbidities were adjusted for in the analysis. RESULT: One standard deviation (SD) increase in 1-year-averaged non-freeway NRAP (0.5 ppb NOx) was associated with increased odds of COVID-19-related IRS and ICU admission [OR (95% CI): 1.07 (1.01, 1.13) and 1.11 (1.04, 1.19) respectively] and increased risk of mortality (HR = 1.10, 95% CI = 1.03, 1.18). The associations of non-freeway NRAP with COVID-19 outcomes were largely independent of the effect of regional fine particulate matter and nitrogen dioxide exposures. These associations were generally consistent across age, sex, and race/ethnicity subgroups. The associations of freeway and total NRAP with COVID-19 severity and mortality were not statistically significant. CONCLUSIONS: Data from this multiethnic cohort suggested that NRAP, particularly non-freeway exposure in Southern California, may be associated with increased risk of COVID-19 severity and mortality among COVID-19 infected patients. Future studies are needed to assess the impact of emerging COVID-19 variants and chemical components from freeway and non-freeway NRAP.

Asthma Disease Status, COPD, and COVID-19 Severity in a Large Multiethnic Population.

BACKGROUND: Current studies of asthma history on coronavirus disease 2019 (COVID-19) outcomes are limited and lack consideration of disease status. OBJECTIVE: To conduct a population-based study to assess asthma disease status and chronic obstructive pulmonary disease (COPD) in relation to COVID-19 severity. METHODS: Patients diagnosed with COVID-19 (n = 61,338) in a large, diverse integrated health care system were identified. Asthma/COPD history, medication use, and covariates were extracted from electronic medical records. Asthma patients were categorized into those with and without clinical visits for asthma 12 or fewer months prior to COVID-19 diagnosis and labeled as active and inactive asthma, respectively. Primary outcomes included COVID-19-related hospitalizations, intensive respiratory support (IRS), and intensive care unit admissions within 30 days, and mortality within 60 days after COVID-19 diagnosis. Logistic and Cox regression were used to relate COVID-19 outcomes to asthma/COPD history. RESULTS: The cohort was 53.9% female and 66% Hispanic and had a mean age of 43.9 years. Patients with active asthma had increased odds of hospitalization, IRS, and intensive care unit admission (odds ratio 1.47-1.66; P < .05) compared with patients without asthma or COPD. No increased risks were observed for patients with inactive asthma. Chronic obstructive pulmonary disease was associated with increased risks of hospitalization, IRS, and mortality (odds ratio and hazard ratio 1.27-1.67; P < .05). Among active asthma patients, those using asthma medications had greater than 25% lower odds for COVID-19 outcomes than those without medication. CONCLUSIONS: Patients with asthma who required clinical care 12 or fewer months prior to COVID-19 or individuals with COPD history are at increased risk for severe COVID-19 outcomes. Proper medication treatment for asthma may lower this risk.

Bayesian parameter estimation for automatic annotation of gene functions using observational data and phylogenetic trees.

Gene function annotation is important for a variety of downstream analyses of genetic data. But experimental characterization of function remains costly and slow, making computational prediction an important endeavor. Phylogenetic approaches to prediction have been developed, but implementation of a practical Bayesian framework for parameter estimation remains an outstanding challenge. We have developed a computationally efficient model of evolution of gene annotations using phylogenies based on a Bayesian framework using Markov Chain Monte Carlo for parameter estimation. Unlike previous approaches, our method is able to estimate parameters over many different phylogenetic trees and functions. The resulting parameters agree with biological intuition, such as the increased probability of function change following gene duplication. The method performs well on leave-one-out cross-validation, and we further validated some of the predictions in the experimental scientific literature.

Dysregulated lipid and fatty acid metabolism link perfluoroalkyl substances exposure and impaired glucose metabolism in young adults.

BACKGROUND: Per- and polyfluoroalkyl substances (PFASs) exposure is ubiquitous among the US population and has been linked to adverse health outcomes including cardiometabolic diseases, immune dysregulation and endocrine disruption. However, the metabolic mechanism underlying the adverse health effect of PFASs exposure is unknown. OBJECTIVE: The aim of this project is to investigate the association between PFASs exposure and altered metabolic pathways linked to increased cardiometabolic risk in young adults. METHODS: A total of 102 young adults with 82% overweight or obese participants were enrolled from Southern California between 2014 and 2017. Cardiometabolic outcomes were assessed including oral glucose tolerance test (OGTT) measures, body fat and lipid profiles. High-resolution metabolomics was used to quantify plasma exposure levels of three PFAS congeners and intensity profiles of the untargeted metabolome. Fasting concentrations of 45 targeted metabolites involved in fatty acid and lipid metabolism were used to verify untargeted metabolomics findings. Bayesian Kernel Machine Regression (BKMR) was used to examine the associations between PFAS exposure mixture and cardiometabolic outcomes adjusting for covariates. Mummichog pathway enrichment analysis was used to explore PFAS-associated metabolic pathways. Moreover, the effect of PFAS exposure on the metabolic network, including metabolomic profiles and cardiometabolic outcomes, was investigated. RESULTS: Higher exposure to perfluorooctanoic acid (PFOA) was associated with higher 30-minute glucose levels and glucose area under the curve (AUC) during the OGTT (p < 0.001). PFAS exposure was also associated with altered lipid pathways, which contributed to the metabolic network connecting PFOA and higher glucose levels following the OGTT. Targeted metabolomics analysis indicated that higher PFOA exposure was associated with higher levels of glycerol (p = 0.006), which itself was associated with higher 30-minute glucose (p = 0.006). CONCLUSIONS: Increased lipolysis and fatty acid oxidation could contribute to the biological mechanisms linking PFAS exposure and impaired glucose metabolism among young adults. Findings of this study warrants future experimental studies and epidemiological studies with larger sample size to replicate.

Radiation Treatment, ATM, BRCA1/2, and CHEK2*1100delC Pathogenic Variants and Risk of Contralateral Breast Cancer.

Whether radiation therapy (RT) affects contralateral breast cancer (CBC) risk in women with pathogenic germline variants in moderate- to high-penetrance breast cancer-associated genes is unknown. In a population-based case-control study, we examined the association between RT; variants in ATM, BRCA1/2, or CHEK2*1100delC; and CBC risk. We analyzed 708 cases of women with CBC and 1399 controls with unilateral breast cancer, all diagnosed with first invasive breast cancer between 1985 and 2000 and aged younger than 55 years at diagnosis and screened for variants in breast cancer-associated genes. Rate ratios (RR) and 95% confidence intervals (CIs) were estimated using multivariable conditional logistic regression. RT did not modify the association between known pathogenic variants and CBC risk (eg, BRCA1/2 pathogenic variant carriers without RT: RR = 3.52, 95% CI = 1.76 to 7.01; BRCA1/2 pathogenic variant carriers with RT: RR = 4.46, 95% CI = 2.96 to 6.71), suggesting that modifying RT plans for young women with breast cancer is unwarranted. Rare ATM missense variants, not currently identified as pathogenic, were associated with increased risk of RT-associated CBC (carriers of ATM rare missense variants of uncertain significance without RT: RR = 0.38, 95% CI = 0.09 to 1.55; carriers of ATM rare missense variants of uncertain significance with RT: RR = 2.98, 95% CI = 1.31 to 6.80). Further mechanistic studies will aid clinical decision-making related to RT.

A latent unknown clustering integrating multi-omics data (LUCID) with phenotypic traits.

MOTIVATION: Epidemiologic, clinical and translational studies are increasingly generating multiplatform omics data. Methods that can integrate across multiple high-dimensional data types while accounting for differential patterns are critical for uncovering novel associations and underlying relevant subgroups. RESULTS: We propose an integrative model to estimate latent unknown clusters (LUCID) aiming to both distinguish unique genomic, exposure and informative biomarkers/omic effects while jointly estimating subgroups relevant to the outcome of interest. Simulation studies indicate that we can obtain consistent estimates reflective of the true simulated values, accurately estimate subgroups and recapitulate subgroup-specific effects. We also demonstrate the use of the integrated model for future prediction of risk subgroups and phenotypes. We apply this approach to two real data applications to highlight the integration of genomic, exposure and metabolomic data. AVAILABILITY AND IMPLEMENTATION: The LUCID method is implemented through the LUCIDus R package available on CRAN (https://CRAN.R-project.org/package=LUCIDus). SUPPLEMENTARY INFORMATION: Supplementary materials are available at Bioinformatics online.

Association of a Pathway-Specific Genetic Risk Score With Risk of Radiation-Associated Contralateral Breast Cancer.

Importance: Radiation therapy for breast cancer is associated with increased risk of a second primary contralateral breast cancer, but the genetic factors modifying this association are not well understood. Objective: To determine whether a genetic risk score comprising single nucleotide polymorphisms in the nonhomologous end-joining DNA repair pathway is associated with radiation-associated contralateral breast cancer. Design, Setting, and Participants: This case-control study included a case group of women with contralateral breast cancer that was diagnosed at least 1 year after a first primary breast cancer who were individually matched to a control group of women with unilateral breast cancer. Inclusion criteria were receiving a first invasive breast cancer diagnosis prior to age 55 years between 1985 and 2008. Women were recruited through 8 population-based cancer registries in the United States, Canada, and Denmark as part of the Women's Environment, Cancer, and Radiation Epidemiology Studies I (November 2000 to August 2004) and II (March 2010 to December 2012). Data analysis was conducted from July 2017 to August 2019. Exposures: Stray radiation dose to the contralateral breast during radiation therapy for the first breast cancer. A novel genetic risk score comprised of genetic variants in the nonhomologous end-joining DNA repair pathway was considered the potential effect modifier, dichotomized as high risk if the score was above the median of 74 and low risk if the score was at or below the median. Main Outcomes and Measures: The main outcome was risk of contralateral breast cancer associated with stray radiation dose stratified by genetic risk score, age, and latency. Results: A total of 5953 women were approached for study participation, and 3732 women (62.7%) agreed to participate. The median (range) age at first diagnosis was 46 (23-54) years. After 5 years of latency or more, among women who received the first diagnosis when they were younger than 40 years, exposure to 1.0 Gy (to convert to rad, multiply by 100) or more of stray radiation was associated with a 2-fold increased risk of contralateral breast cancer compared with women who were not exposed (rate ratio, 2.0 [95% CI, 1.1-3.6]). The risk was higher among women with a genetic risk score above the median (rate ratio, 3.0 [95% CI, 1.1-8.1]), and there was no association among women with a genetic risk score below the median (rate ratio, 1.3 [95% CI, 0.5-3.7]). Among younger women with a high genetic risk score, the attributable increased risk for contralateral breast cancer associated with stray radiation dose was 28%. Conclusions and Relevance: This study found an increased risk of contralateral breast cancer that was attributable to stray radiation exposure among women with a high genetic risk score and who received a first breast cancer diagnosis when they were younger than 40 years after 5 years or more of latency. This genetic risk score may help guide treatment and surveillance for women with breast cancer.

Near-roadway air pollution exposure and altered fatty acid oxidation among adolescents and young adults - The interplay with obesity.

BACKGROUND: Air pollution exposure has been shown to increase the risk of obesity and metabolic dysfunction in animal models and human studies. However, the metabolic pathways altered by air pollution exposure are unclear, especially in adolescents and young adults who are at a critical period in the development of cardio-metabolic diseases. OBJECTIVES: The aim of this study was to examine the associations between air pollution exposure and indices of fatty acid and amino acid metabolism. METHODS: A total of 173 young adults (18-23 years) from eight Children's Health Study (CHS) Southern California communities were examined from 2014 to 2018. Near-roadway air pollution (NRAP) exposure (freeway and non-freeway) and regional air pollution exposure (nitrogen dioxide, ozone and particulate matter) during one year before the study visit were estimated based on participants' residential addresses. Serum concentrations of 64 targeted metabolites including amino acids, acylcarnitines, non-esterified fatty acid (NEFA) and glycerol were measured in fasting serum samples. Principal component analysis of metabolites was performed to identify metabolite clusters that represent key metabolic pathways. Mixed effects models were used to analyze the associations of air pollution exposure with metabolomic principal component (PC) scores and individual metabolite concentrations adjusting for potential confounders. RESULTS: Higher lagged one-year averaged non-freeway NRAP exposure was associated with higher concentrations of NEFA oxidation byproducts and higher NEFA-related PC score (all p's ≤ 0.038). The effect sizes were larger among obese individuals (interaction p = 0.047). Among females, higher freeway NRAP exposure was also associated with a higher NEFA-related PC score (p = 0.042). Among all participants, higher freeway NRAP exposure was associated with a lower PC score for lower concentrations of short- and median-chain acylcarnitines (p = 0.044). CONCLUSIONS: Results of this study indicate that NRAP exposure is associated with altered fatty acid metabolism, which could contribute to the metabolic perturbation in obese youth.

A Unified Model for the Analysis of Gene-Environment Interaction.

Gene-environment (G × E) interaction is important for many complex traits. In a case-control study of a disease trait, logistic regression is the standard approach used to model disease as a function of a gene (G), an environmental factor (E), G × E interaction, and adjustment covariates. We propose an alternative model with G as the outcome and show how it provides a unified framework for obtaining results from all of the common G × E tests. These include the 1-degree-of-freedom (df) test of G × E interaction, the 2-df joint test of G and G × E, the case-only and empirical Bayes tests, and several 2-step tests. In the context of this unified model, we propose a novel 3-df test and demonstrate that it provides robust power across a wide range of underlying G × E interaction models. We demonstrate the 3-df test in a genome-wide scan of G × sex interaction for childhood asthma using data from the Children's Health Study (Southern California, 1993-2001). This scan identified a strong G × sex interaction at the phosphodiesterase gene 4D locus (PDE4D), a known asthma-related locus, with a strong effect in males (per-allele odds ratio = 1.70; P = 3.8 × 10-8) and virtually no effect in females. We describe a software program, G×EScan (University of Southern California, Los Angeles, California), which can be used to fit standard and unified models for genome-wide G × E studies.

Metabolic signature of healthy lifestyle and its relation with risk of hepatocellular carcinoma in a large European cohort.

Background: Studies using metabolomic data have identified metabolites from several compound classes that are associated with disease-related lifestyle factors. Objective: In this study, we identified metabolic signatures reflecting lifestyle patterns and related them to the risk of hepatocellular carcinoma (HCC) in the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort. Design: Within a nested case-control study of 147 incident HCC cases and 147 matched controls, partial least squares (PLS) analysis related 7 modified healthy lifestyle index (HLI) variables (diet, BMI, physical activity, lifetime alcohol, smoking, diabetes, and hepatitis) to 132 targeted serum-measured metabolites and a liver function score. The association between the resulting PLS scores and HCC risk was examined in multivariable conditional logistic regression models, where ORs and 95% CIs were computed. Results: The lifestyle component's PLS score was negatively associated with lifetime alcohol, BMI, smoking, and diabetes, and positively associated with physical activity. Its metabolic counterpart was positively related to the metabolites sphingomyelin (SM) (OH) C14:1, C16:1, and C22:2, and negatively related to glutamate, hexoses, and the diacyl-phosphatidylcholine PC aaC32:1. The lifestyle and metabolomics components were inversely associated with HCC risk, with the ORs for a 1-SD increase in scores equal to 0.53 (95% CI: 0.38, 0.74) and 0.28 (0.18, 0.43), and the associated AUCs equal to 0.64 (0.57, 0.70) and 0.74 (0.69, 0.80), respectively. Conclusions: This study identified a metabolic signature reflecting a healthy lifestyle pattern which was inversely associated with HCC risk. The metabolic profile displayed a stronger association with HCC than did the modified HLI derived from questionnaire data. Measuring a specific panel of metabolites may identify strata of the population at higher risk for HCC and can add substantial discrimination compared with questionnaire data. This trial was registered at clinicaltrials.gov as NCT03356535.

Breast Cancer Family History and Contralateral Breast Cancer Risk in Young Women: An Update From the Women's Environmental Cancer and Radiation Epidemiology Study.

Purpose The Women's Environmental Cancer and Radiation Epidemiology (WECARE) study demonstrated the importance of breast cancer family history on contralateral breast cancer (CBC) risk, even for noncarriers of deleterious BRCA1/2 mutations. With the completion of WECARE II, updated risk estimates are reported. Additional analyses that exclude women negative for deleterious mutations in ATM, CHEK2*1100delC, and PALB2 were performed. Patients and Methods The WECARE Study is a population-based case-control study that compared 1,521 CBC cases with 2,212 individually matched unilateral breast cancer (UBC) controls. Participants were younger than age 55 years when diagnosed with a first invasive breast cancer between 1985 and 2008. Women were interviewed about breast cancer risk factors, including family history. A subset of women was screened for deleterious mutations in BRCA1/2, ATM, CHEK2*1100delC, and PALB2. Rate ratios (RRs) were estimated using multivariable conditional logistic regression. Cumulative absolute risks (ARs) were estimated by combining RRs from the WECARE Study and population-based SEER*Stat cancer incidence data. Results Women with any first-degree relative with breast cancer had a 10-year AR of 8.1% for CBC (95% CI, 6.7% to 9.8%). Risks also were increased if the relative was diagnosed at an age younger than 40 years (10-year AR, 13.5%; 95% CI, 8.8% to 20.8%) or with CBC (10-year AR, 14.1%; 95% CI, 9.5% to 20.7%). These risks are comparable with those seen in BRCA1/2 deleterious mutation carriers (10-year AR, 18.4%; 95% CI, 16.0% to 21.3%). In the subset of women who tested negative for deleterious mutations in BRCA1/2, ATM, CHEK2*1100delC, and PALB2, estimates were unchanged. Adjustment for known breast cancer single-nucleotide polymorphisms did not affect estimates. Conclusion Breast cancer family history confers a high CBC risk, even after excluding women with deleterious mutations. Clinicians are urged to use detailed family histories to guide treatment and future screening decisions for young women with breast cancer.

Are Metabolic Signatures Mediating the Relationship between Lifestyle Factors and Hepatocellular Carcinoma Risk? Results from a Nested Case-Control Study in EPIC.

Background: The "meeting-in-the-middle" (MITM) is a principle to identify exposure biomarkers that are also predictors of disease. The MITM statistical framework was applied in a nested case-control study of hepatocellular carcinoma (HCC) within European Prospective Investigation into Cancer and Nutrition (EPIC), where healthy lifestyle index (HLI) variables were related to targeted serum metabolites.Methods: Lifestyle and targeted metabolomic data were available from 147 incident HCC cases and 147 matched controls. Partial least squares analysis related 7 lifestyle variables from a modified HLI to a set of 132 serum-measured metabolites and a liver function score. Mediation analysis evaluated whether metabolic profiles mediated the relationship between each lifestyle exposure and HCC risk.Results: Exposure-related metabolic signatures were identified. Particularly, the body mass index (BMI)-associated metabolic component was positively related to glutamic acid, tyrosine, PC aaC38:3, and liver function score and negatively to lysoPC aC17:0 and aC18:2. The lifetime alcohol-specific signature had negative loadings on sphingomyelins (SM C16:1, C18:1, SM(OH) C14:1, C16:1 and C22:2). Both exposures were associated with increased HCC with total effects (TE) = 1.23 (95% confidence interval = 0.93-1.62) and 1.40 (1.14-1.72), respectively, for BMI and alcohol consumption. Both metabolic signatures mediated the association between BMI and lifetime alcohol consumption and HCC with natural indirect effects, respectively, equal to 1.56 (1.24-1.96) and 1.09 (1.03-1.15), accounting for a proportion mediated of 100% and 24%.Conclusions: In a refined MITM framework, relevant metabolic signatures were identified as mediators in the relationship between lifestyle exposures and HCC risk.Impact: The understanding of the biological basis for the relationship between modifiable exposures and cancer would pave avenues for clinical and public health interventions on metabolic mediators. Cancer Epidemiol Biomarkers Prev; 27(5); 531-40. ©2018 AACR.

Determining Risk of Colorectal Cancer and Starting Age of Screening Based on Lifestyle, Environmental, and Genetic Factors.

BACKGROUND & AIMS: Guidelines for initiating colorectal cancer (CRC) screening are based on family history but do not consider lifestyle, environmental, or genetic risk factors. We developed models to determine risk of CRC, based on lifestyle and environmental factors and genetic variants, and to identify an optimal age to begin screening. METHODS: We collected data from 9748 CRC cases and 10,590 controls in the Genetics and Epidemiology of Colorectal Cancer Consortium and the Colorectal Transdisciplinary study, from 1992 through 2005. Half of the participants were used to develop the risk determination model and the other half were used to evaluate the discriminatory accuracy (validation set). Models of CRC risk were created based on family history, 19 lifestyle and environmental factors (E-score), and 63 CRC-associated single-nucleotide polymorphisms identified in genome-wide association studies (G-score). We evaluated the discriminatory accuracy of the models by calculating area under the receiver operating characteristic curve values, adjusting for study, age, and endoscopy history for the validation set. We used the models to project the 10-year absolute risk of CRC for a given risk profile and recommend ages to begin screening in comparison to CRC risk for an average individual at 50 years of age, using external population incidence rates for non-Hispanic whites from the Surveillance, Epidemiology, and End Results program registry. RESULTS: In our models, E-score and G-score each determined risk of CRC with greater accuracy than family history. A model that combined both scores and family history estimated CRC risk with an area under the receiver operating characteristic curve value of 0.63 (95% confidence interval, 0.62-0.64) for men and 0.62 (95% confidence interval, 0.61-0.63) for women; area under the receiver operating characteristic curve values based on only family history ranged from 0.53 to 0.54 and those based only E-score or G-score ranged from 0.59 to 0.60. Although screening is recommended to begin at age 50 years for individuals with no family history of CRC, starting ages calculated based on combined E-score and G-score differed by 12 years for men and 14 for women, for individuals with the highest vs the lowest 10% of risk. CONCLUSIONS: We used data from 2 large international consortia to develop CRC risk calculation models that included genetic and environmental factors along with family history. These determine risk of CRC and starting ages for screening with greater accuracy than the family history only model, which is based on the current screening guideline. These scoring systems might serve as a first step toward developing individualized CRC prevention strategies.

Incorporation of Biological Knowledge Into the Study of Gene-Environment Interactions.

A growing knowledge base of genetic and environmental information has greatly enabled the study of disease risk factors. However, the computational complexity and statistical burden of testing all variants by all environments has required novel study designs and hypothesis-driven approaches. We discuss how incorporating biological knowledge from model organisms, functional genomics, and integrative approaches can empower the discovery of novel gene-environment interactions and discuss specific methodological considerations with each approach. We consider specific examples where the application of these approaches has uncovered effects of gene-environment interactions relevant to drug response and immunity, and we highlight how such improvements enable a greater understanding of the pathogenesis of disease and the realization of precision medicine.