Questionnaire-based exposome-wide association studies for common diseases in the Personalized Environment and Genes Study

Our plain-English summary of Questionnaire-based exposome-wide association studies for common diseases in the Personalized Environment and Genes Study, by D. Lloyd, J.S. House, F.S. Akhtari, et al (Exposome, 2024).

The study

• Authors: D. Lloyd, J.S. House, F.S. Akhtari, C.P. Schmitt, D.C. Fargo, E.H. Scholl, J. Phillips, S. Choksi, R. Shah, J.E. Hall, A.A. Motsinger-Reif
• Journal: Exposome
• Published: 2024;4(1):osae002
• Cited by: 12 articles, as counted by NIH iCite
• Read it: PubMed · Publisher · Free full text

Abstract

The exposome collectively refers to all exposures, beginning in utero and continuing throughout life, and comprises not only standard environmental exposures such as point source pollution and ozone levels but also exposures from diet, medication, lifestyle factors, stress, and occupation. The exposome interacts with individual genetic and epigenetic characteristics to affect human health and disease, but large-scale studies that characterize the exposome and its relationships with human disease are limited. To address this gap, we used extensive questionnaire data from the diverse North Carolina-based Personalized Environment and Genes Study (PEGS, n = 9, 429) to evaluate exposure associations in relation to common diseases. We performed an exposome-wide association study (ExWAS) to examine single exposure models and their associations with 11 common complex diseases, namely allergic rhinitis, asthma, bone loss, fibroids, high cholesterol, hypertension, iron-deficient anemia, ovarian cysts, lower GI polyps, migraines, and type 2 diabetes. Across diseases, we found associations with lifestyle factors and socioeconomic status as well as asbestos, various dust types, biohazardous material, and textile-related exposures. We also found disease-specific associations such as fishing with lead weights and migraines. To differentiate between a replicated result and a novel finding, we used an AI-based literature search and database tool that allowed us to examine the current literature. We found both replicated findings, especially for lifestyle factors such as sleep and smoking across diseases, and novel findings, especially for occupational exposures and multiple diseases.

Published by Oxford University Press 2024.

In plain English

This cross-sectional study used questionnaire answers from 9429 participants in the Personalized Environment and Genes Study, a North Carolina cohort. After screening out unrelated, redundant, free-text, nested and sparsely answered questions, the authors tested each remaining item separately against 11 diseases, adjusting for age, income, race/ethnicity and sex. Asbestos was one item in a list of work or daily-life exposures; no fiber type is named. Among the results the article describes, asbestos exposure was associated with higher odds of asthma (odds ratio 1.65, 95% confidence interval 1.28 to 2.11), iron-deficient anemia (2.12, 1.68 to 2.64), high cholesterol (1.55, 1.28 to 1.87), type 2 diabetes (1.53, 1.18 to 1.96), fibroids (1.51, 1.12 to 2.02), hypertension (1.38, 1.14 to 1.68) and lower GI polyps (1.33, 1.04 to 1.69). Dust, textile fibers and exhaust were also associated with many diseases. The authors called the asbestos association consistent across the diseases studied. They stated that causal relationships cannot be inferred from survey data. They noted possible recall bias, surveys sometimes completed after diagnosis, a cohort over 60% women, and the need for replication.

We wrote this plain-English summary from the full article with the help of AI, and a second AI model checked each sentence against the article. It is not the authors' wording; the abstract and the article are their own account.

What the study did and found

• Design: Cross-sectional, questionnaire-based exposome-wide association study (ExWAS). Questions were first filtered in quality control. Each remaining exposure was then tested separately with logistic regression, adjusted for age, income, self-reported race/ethnicity and sex. Significance was set by a Benjamini-Hochberg false discovery rate of 0.10. Multi-exposure models and an AI-based literature search (Causaly) were also used.
• Who was studied: 9429 participants in the Personalized Environment and Genes Study (PEGS), a North Carolina-based cohort, using data freeze 2. Fewer participants completed the External (3579) and Internal (3034) Exposome Surveys. Eleven diseases were studied: allergic rhinitis, asthma, bone loss, fibroids, iron-deficient anemia, lower GI polyps, migraines, ovarian cysts, type 2 diabetes, high cholesterol and hypertension.
• Exposure: Self-reported survey items covering occupational, hobby, residential, lifestyle, diet, sleep, socioeconomic and infectious-disease factors. After filtering, 281, 359 and 438 variables remained for the three surveys. Asbestos was one item in the Health and Exposure Survey list of exposures at work or in daily life. No fiber type, product or dose is given.
• Main results:
  • Asbestos exposure was associated with asthma: adjusted P less than 0.001, odds ratio 1.65 (95% CI 1.28 to 2.11).
  • Asbestos exposure was associated with fibroids: adjusted P 0.016, odds ratio 1.51 (95% CI 1.12 to 2.02).
  • Asbestos exposure was associated with high cholesterol: adjusted P less than 0.001, odds ratio 1.55 (95% CI 1.28 to 1.87).
  • Asbestos exposure was associated with hypertension: adjusted P 0.003, odds ratio 1.38 (95% CI 1.14 to 1.68).
  • Asbestos exposure was associated with iron-deficient anemia: adjusted P less than 0.001, odds ratio 2.12 (95% CI 1.68 to 2.64).
  • Asbestos exposure was associated with lower GI polyps: adjusted P 0.049, odds ratio 1.33 (95% CI 1.04 to 1.69).
  • Asbestos exposure was associated with type 2 diabetes: adjusted P 0.004, odds ratio 1.53 (95% CI 1.18 to 1.96).
  • Occupational exposures such as dust, biohazardous material, and diesel and gasoline exhaust were associated with diseases across the set studied.
  • Textile-related exposures were associated with most of the diseases considered.
• The authors' conclusion: The authors concluded that the results showed both novel and established associations of many exposures with multiple diseases. They described the association of asbestos with the diseases studied as consistent. They stated that causal relationships cannot be inferred from ExWAS of survey data.
• Limitations the authors state:
  • Some case groups were small, giving imprecise estimates with wide confidence intervals for groups with fewer than 10 people.
  • Exposure data were collected cross-sectionally, so differential recall may be an issue.
  • The cohort is over 60% women, and some exposures may be more or less common than in the wider population.
  • Exposure surveys were sometimes completed after disease diagnosis. This can cause problems of reverse causality.
  • Exposure data may have been misclassified. The authors assume this misclassification would bias results toward the null.
  • The associations need to be replicated in an independent sample.
  • The smaller sample sizes of the Internal and External Exposome Surveys may have caused associations to be missed for lack of power. Excluding questions that depended on answers to other questions added to this problem.

Topics

Medical terms in this study, explained in our Lexicon: Environmental Exposure, Occupational Exposure.

Related studies

• Genome-wide Gene-Asbestos Exposure Interaction Association Study Identifies a Common Susceptibility Variant on 22q13.31 Associated with Lung Cancer Risk — C.Y. Liu, I. Stücker, et al. (2015)
• Questionnaire-Based Polyexposure Assessment Outperforms Polygenic Scores for Classification of Type 2 Diabetes in a Multiancestry Cohort — F.S. Akhtari, D. Lloyd, et al. (2023)
• Genome-wide analysis of abdominal and pleural malignant mesothelioma with DNA arrays reveals both common and distinct regions of copy number alteration — A.C. Borczuk, J. Pei, et al. (2016)
• Common sense in the environment — (1991)
• BAP1 forms a trimer with HMGB1 and HDAC1 that modulates gene × environment interaction with asbestos — F. Novelli, A. Bononi, et al. (2021)
• Assessment of "gene-environment" interaction in cases of familial and sporadic pancreatic cancer — T.P. Yeo, R.H. Hruban, et al. (2009)

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