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Tu Q, Liu G, Liu X, Zhang J, Xiao W, Lv L, Zhao B. Perspective on using non-human primates in Exposome research. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2024; 286:117199. [PMID: 39426107 DOI: 10.1016/j.ecoenv.2024.117199] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/18/2024] [Revised: 08/02/2024] [Accepted: 10/13/2024] [Indexed: 10/21/2024]
Abstract
The physiological and pathological changes in the human body caused by environmental pressures are collectively referred to as the Exposome. Human society is facing escalating environmental pollution, leading to a rising prevalence of associated diseases, including respiratory diseases, cardiovascular diseases, neurological disorders, reproductive development disorders, among others. Vulnerable populations to the pathogenic effects of environmental pollution include those in the prenatal, infancy, and elderly stages of life. Conducting Exposome mechanistic research and proposing effective health interventions are urgent in addressing the current severe environmental pollution. In this review, we address the core issues and bottlenecks faced by current Exposome research, specifically focusing on the most toxic ultrafine nanoparticles. We summarize multiple research models being used in Exposome research. Especially, we discuss the limitations of rodent animal models in mimicking human physiopathological phenotypes, and prospect advantages and necessity of non-human primates in Exposome research based on their evolutionary relatedness, anatomical and physiological similarities to human. Finally, we declare the initiation of NHPE (Non-Human Primate Exposome) project for conducting Exposome research using non-human primates and provide insights into its feasibility and key areas of focus. SYNOPSIS: Non-human primate models hold unique advantages in human Exposome research.
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Affiliation(s)
- Qiu Tu
- Key Laboratory of Genetic Evolution & Animal Models, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan 650223, China; Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences & Yunnan Province, and KIZ-CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, Kunming, Yunnan 650223, China
| | - Gaojing Liu
- Key Laboratory of Genetic Evolution & Animal Models, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan 650223, China; Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences & Yunnan Province, and KIZ-CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, Kunming, Yunnan 650223, China; University of Chinese Academy of Sciences, Beijing 101408, China
| | - Xiuyun Liu
- Key Laboratory of Genetic Evolution & Animal Models, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan 650223, China; Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences & Yunnan Province, and KIZ-CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, Kunming, Yunnan 650223, China; University of Chinese Academy of Sciences, Beijing 101408, China
| | - Jiao Zhang
- Key Laboratory of Genetic Evolution & Animal Models, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan 650223, China; Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences & Yunnan Province, and KIZ-CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, Kunming, Yunnan 650223, China
| | - Wenxian Xiao
- Key Laboratory of Genetic Evolution & Animal Models, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan 650223, China; Primate Facility, National Research Facility for Phenotypic & Genetic Analysis of Model Animals, and National Resource Center for Non-Human Primates, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan 650223, China; National Resource Center for Non-Human Primates, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan 650107, China
| | - Longbao Lv
- Key Laboratory of Genetic Evolution & Animal Models, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan 650223, China; Primate Facility, National Research Facility for Phenotypic & Genetic Analysis of Model Animals, and National Resource Center for Non-Human Primates, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan 650223, China; National Resource Center for Non-Human Primates, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan 650107, China.
| | - Bo Zhao
- Key Laboratory of Genetic Evolution & Animal Models, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan 650223, China; Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences & Yunnan Province, and KIZ-CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, Kunming, Yunnan 650223, China; Primate Facility, National Research Facility for Phenotypic & Genetic Analysis of Model Animals, and National Resource Center for Non-Human Primates, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan 650223, China.
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Brassington L, Arner AM, Watowich MM, Damstedt J, Ng KS, Lim YAL, Venkataraman VV, Wallace IJ, Kraft TS, Lea AJ. Integrating the Thrifty Genotype and Evolutionary Mismatch Hypotheses to understand variation in cardiometabolic disease risk. Evol Med Public Health 2024; 12:214-226. [PMID: 39484023 PMCID: PMC11525211 DOI: 10.1093/emph/eoae014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Revised: 06/18/2024] [Indexed: 11/03/2024] Open
Abstract
More than 60 years ago, James Neel proposed the Thrifty Genotype Hypothesis to explain the widespread prevalence of type 2 diabetes in Western, industrial contexts. This hypothesis posits that variants linked to conservative energy usage and increased fat deposition would have been favored throughout human evolution due to the advantages they could provide during periods of resource limitation. However, in industrial environments, these variants instead produce an increased risk of obesity, metabolic syndrome, type 2 diabetes, and related health issues. This hypothesis has been popular and impactful, with thousands of citations, many ongoing debates, and several spin-off theories in biomedicine, evolutionary biology, and anthropology. However, despite great attention, the applicability and utility of the Thrifty Genotype Hypothesis (TGH) to modern human health remains, in our opinion, unresolved. To move research in this area forward, we first discuss the original formulation of the TGH and its critiques. Second, we trace the TGH to updated hypotheses that are currently at the forefront of the evolutionary medicine literature-namely, the Evolutionary Mismatch Hypothesis. Third, we lay out empirical predictions for updated hypotheses and evaluate them against the current literature. Finally, we discuss study designs that could be fruitful for filling current knowledge gaps; here, we focus on partnerships with subsistence-level groups undergoing lifestyle transitions, and we present data from an ongoing study with the Orang Asli of Malaysia to illustrate this point. Overall, we hope this synthesis will guide new empirical research aimed at understanding how the human evolutionary past interacts with our modern environments to influence cardiometabolic health.
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Affiliation(s)
- Layla Brassington
- Department of Biological Sciences, Vanderbilt University, Nashville, TN, USA
| | - Audrey M Arner
- Department of Biological Sciences, Vanderbilt University, Nashville, TN, USA
| | - Marina M Watowich
- Department of Biological Sciences, Vanderbilt University, Nashville, TN, USA
| | - Jane Damstedt
- Department of Anthropology, University of Utah, Salt Lake City, Utah, USA
| | - Kee Seong Ng
- Department of Medicine, Faculty of Medicine, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Yvonne A L Lim
- Department of Parasitology, Faculty of Medicine, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Vivek V Venkataraman
- Department of Anthropology and Archaeology, University of Calgary, Calgary, Alberta, Canada
| | - Ian J Wallace
- Department of Anthropology, University of New Mexico, Albuquerque, New Mexico, USA
| | - Thomas S Kraft
- Department of Anthropology, University of Utah, Salt Lake City, Utah, USA
| | - Amanda J Lea
- Department of Biological Sciences, Vanderbilt University, Nashville, TN, USA
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Di Scipio M, Khan M, Mao S, Chong M, Judge C, Pathan N, Perrot N, Nelson W, Lali R, Di S, Morton R, Petch J, Paré G. A versatile, fast and unbiased method for estimation of gene-by-environment interaction effects on biobank-scale datasets. Nat Commun 2023; 14:5196. [PMID: 37626057 PMCID: PMC10457310 DOI: 10.1038/s41467-023-40913-7] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Accepted: 08/16/2023] [Indexed: 08/27/2023] Open
Abstract
Identification of gene-by-environment interactions (GxE) is crucial to understand the interplay of environmental effects on complex traits. However, current methods evaluating GxE on biobank-scale datasets have limitations. We introduce MonsterLM, a multiple linear regression method that does not rely on model specification and provides unbiased estimates of variance explained by GxE. We demonstrate robustness of MonsterLM through comprehensive genome-wide simulations using real genetic data from 325,989 individuals. We estimate GxE using waist-to-hip-ratio, smoking, and exercise as the environmental variables on 13 outcomes (N = 297,529-325,989) in the UK Biobank. GxE variance is significant for 8 environment-outcome pairs, ranging from 0.009 - 0.071. The majority of GxE variance involves SNPs without strong marginal or interaction associations. We observe modest improvements in polygenic score prediction when incorporating GxE. Our results imply a significant contribution of GxE to complex trait variance and we show MonsterLM to be well-purposed to handle this with biobank-scale data.
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Affiliation(s)
- Matteo Di Scipio
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, ON, Canada
- Department of Medicine, Faculty of Health Sciences, McMaster University, Hamilton, ON, Canada
| | - Mohammad Khan
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, ON, Canada
- Department of Medicine, Faculty of Health Sciences, McMaster University, Hamilton, ON, Canada
| | - Shihong Mao
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, ON, Canada
| | - Michael Chong
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, ON, Canada
- Thrombosis and Atherosclerosis Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton, ON, Canada
- Department of Pathology and Molecular Medicine, McMaster University, Michael G. DeGroote School of Medicine, Hamilton, ON, Canada
| | - Conor Judge
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, ON, Canada
| | - Nazia Pathan
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, ON, Canada
- Department of Medicine, Faculty of Health Sciences, McMaster University, Hamilton, ON, Canada
| | - Nicolas Perrot
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, ON, Canada
| | - Walter Nelson
- Centre for Data Science and Digital Health, Hamilton Health Sciences, Hamilton, ON, Canada
- Department of Statistical Sciences, University of Toronto, Toronto, ON, Canada
| | - Ricky Lali
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, ON, Canada
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON, Canada
| | - Shuang Di
- Centre for Data Science and Digital Health, Hamilton Health Sciences, Hamilton, ON, Canada
- Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
| | - Robert Morton
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, ON, Canada
- Department of Pathology and Molecular Medicine, McMaster University, Michael G. DeGroote School of Medicine, Hamilton, ON, Canada
| | - Jeremy Petch
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, ON, Canada
- Department of Medicine, Faculty of Health Sciences, McMaster University, Hamilton, ON, Canada
- Centre for Data Science and Digital Health, Hamilton Health Sciences, Hamilton, ON, Canada
- Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada
| | - Guillaume Paré
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, ON, Canada.
- Thrombosis and Atherosclerosis Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton, ON, Canada.
- Department of Pathology and Molecular Medicine, McMaster University, Michael G. DeGroote School of Medicine, Hamilton, ON, Canada.
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON, Canada.
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Wuni R, Ventura EF, Curi-Quinto K, Murray C, Nunes R, Lovegrove JA, Penny M, Favara M, Sanchez A, Vimaleswaran KS. Interactions between genetic and lifestyle factors on cardiometabolic disease-related outcomes in Latin American and Caribbean populations: A systematic review. Front Nutr 2023; 10:1067033. [PMID: 36776603 PMCID: PMC9909204 DOI: 10.3389/fnut.2023.1067033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Accepted: 01/09/2023] [Indexed: 01/27/2023] Open
Abstract
Introduction The prevalence of cardiometabolic diseases has increased in Latin American and the Caribbean populations (LACP). To identify gene-lifestyle interactions that modify the risk of cardiometabolic diseases in LACP, a systematic search using 11 search engines was conducted up to May 2022. Methods Eligible studies were observational and interventional studies in either English, Spanish, or Portuguese. A total of 26,171 publications were screened for title and abstract; of these, 101 potential studies were evaluated for eligibility, and 74 articles were included in this study following full-text screening and risk of bias assessment. The Appraisal tool for Cross-Sectional Studies (AXIS) and the Risk Of Bias In Non-Randomized Studies-of Interventions (ROBINS-I) assessment tool were used to assess the methodological quality and risk of bias of the included studies. Results We identified 122 significant interactions between genetic and lifestyle factors on cardiometabolic traits and the vast majority of studies come from Brazil (29), Mexico (15) and Costa Rica (12) with FTO, APOE, and TCF7L2 being the most studied genes. The results of the gene-lifestyle interactions suggest effects which are population-, gender-, and ethnic-specific. Most of the gene-lifestyle interactions were conducted once, necessitating replication to reinforce these results. Discussion The findings of this review indicate that 27 out of 33 LACP have not conducted gene-lifestyle interaction studies and only five studies have been undertaken in low-socioeconomic settings. Most of the studies were cross-sectional, indicating a need for longitudinal/prospective studies. Future gene-lifestyle interaction studies will need to replicate primary research of already studied genetic variants to enable comparison, and to explore the interactions between genetic and other lifestyle factors such as those conditioned by socioeconomic factors and the built environment. The protocol has been registered on PROSPERO, number CRD42022308488. Systematic review registration https://clinicaltrials.gov, identifier CRD420223 08488.
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Affiliation(s)
- Ramatu Wuni
- Hugh Sinclair Unit of Human Nutrition, Department of Food and Nutritional Sciences and Institute for Cardiovascular and Metabolic Research (ICMR), University of Reading, Reading, United Kingdom
| | - Eduard F. Ventura
- Hugh Sinclair Unit of Human Nutrition, Department of Food and Nutritional Sciences and Institute for Cardiovascular and Metabolic Research (ICMR), University of Reading, Reading, United Kingdom
| | | | - Claudia Murray
- Department of Real Estate and Planning, University of Reading, Reading, United Kingdom
| | - Richard Nunes
- Department of Real Estate and Planning, University of Reading, Reading, United Kingdom
| | - Julie A. Lovegrove
- Hugh Sinclair Unit of Human Nutrition, Department of Food and Nutritional Sciences and Institute for Cardiovascular and Metabolic Research (ICMR), University of Reading, Reading, United Kingdom
| | - Mary Penny
- Instituto de Investigación Nutricional, Lima, Peru
| | - Marta Favara
- Oxford Department of International Development, University of Oxford, Oxford, United Kingdom
| | - Alan Sanchez
- Grupo de Análisis para el Desarrollo (GRADE), Lima, Peru
| | - Karani Santhanakrishnan Vimaleswaran
- Hugh Sinclair Unit of Human Nutrition, Department of Food and Nutritional Sciences and Institute for Cardiovascular and Metabolic Research (ICMR), University of Reading, Reading, United Kingdom
- Institute for Food, Nutrition and Health (IFNH), University of Reading, Reading, United Kingdom
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Ding E, Wang Y, Liu J, Tang S, Shi X. A review on the application of the exposome paradigm to unveil the environmental determinants of age-related diseases. Hum Genomics 2022; 16:54. [DOI: 10.1186/s40246-022-00428-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Accepted: 10/29/2022] [Indexed: 11/11/2022] Open
Abstract
AbstractAge-related diseases account for almost half of all diseases among adults worldwide, and their incidence is substantially affected by the exposome, which is the sum of all exogenous and endogenous environmental exposures and the human body’s response to these exposures throughout the entire lifespan. Herein, we perform a comprehensive review of the epidemiological literature to determine the key elements of the exposome that affect the development of age-related diseases and the roles of aging hallmarks in this process. We find that most exposure assessments in previous aging studies have used a reductionist approach, whereby the effect of only a single environmental factor or a specific class of environmental factors on the development of age-related diseases has been examined. As such, there is a lack of a holistic and unbiased understanding of the effect of multiple environmental factors on the development of age-related diseases. To address this, we propose several research strategies based on an exposomic framework that could advance our understanding—in particular, from a mechanistic perspective—of how environmental factors affect the development of age-related diseases. We discuss the statistical methods and other methods that have been used in exposome-wide association studies, with a particular focus on multiomics technologies. We also address future challenges and opportunities in the realm of multidisciplinary approaches and genome–exposome epidemiology. Furthermore, we provide perspectives on precise public health services for vulnerable populations, public communications, the integration of risk exposure information, and the bench-to-bedside translation of research on age-related diseases.
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Mphasha MH, Skaal L, Mothiba TM. Prevalence of overweight and obesity amongst patients with diabetes and their non-diabetic family members in Senwabarwana, Limpopo province, South Africa. S Afr Fam Pract (2004) 2022; 64:e1-e7. [PMID: 35695450 PMCID: PMC9210144 DOI: 10.4102/safp.v64i1.5409] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Revised: 12/22/2021] [Accepted: 12/22/2021] [Indexed: 12/02/2022] Open
Abstract
Background Diabetes remains a public health concern and the second cause of mortality in South Africa. Family history of diabetes increases risk of developing diabetes. Obesity amongst patients is associated with comorbidity, whilst amongst non-diabetic family members it is associated with developing diabetes. This study aimed at determining prevalence of overweight and obesity amongst patients with diabetes and non-diabetic family members. Methods A quantitative, cross-sectional descriptive study was conducted on 200 patients and 200 non-diabetic family members were selected using systematic random sampling from rural clinics of Senwabarwana. Data were collected using close-ended questionnaires and anthropometric measurements. Body mass index (BMI) and waist circumference were measured and interpreted according to World Health Organization guidelines. Data were analysed using Statistical Package for Social Sciences, using both descriptive and inferential statistics. Chi-square test was used to calculate associations at 95% confidence interval where a p-value of < 0.05 was considered statistically significant. Results Most patients (75.5%) had comorbidities and hypertension was most prevalent (89.0%). Over half of the patients (57.0%) and 38.0% of family members were obese. Most patients (75.0%) and 58.0% of family members had abdominal obesity. Conclusion Patients with diabetes suffer from comorbidities are overweight and obese whilst evidence from various studies suggest that non-diabetic family members are at added risk of developing diabetes because of higher BMI and abdominal obesity. There is an urgent need to create a conducive environment that discourages sedentary behaviours through lifestyle modifications using the family centred approach, and involve family members in the care of patients.
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Affiliation(s)
- Mabitsela H Mphasha
- Department of Public Health, Faculty of Healthcare Sciences, University of Limpopo, Polokwane.
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Tang S, Li T, Fang J, Chen R, Cha Y, Wang Y, Zhu M, Zhang Y, Chen Y, Du Y, Yu T, Thompson DC, Godri Pollitt KJ, Vasiliou V, Ji JS, Kan H, Zhang JJ, Shi X. The exposome in practice: an exploratory panel study of biomarkers of air pollutant exposure in Chinese people aged 60-69 years (China BAPE Study). ENVIRONMENT INTERNATIONAL 2021; 157:106866. [PMID: 34525388 DOI: 10.1016/j.envint.2021.106866] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Revised: 08/11/2021] [Accepted: 09/05/2021] [Indexed: 05/05/2023]
Abstract
The exposome overhauls conventional environmental health impact research paradigms and provides a novel methodological framework that comprehensively addresses the complex, highly dynamic interplays of exogenous exposures, endogenous exposures, and modifiable factors in humans. Holistic assessments of the adverse health effects and systematic elucidation of the mechanisms underlying environmental exposures are major scientific challenges with widespread societal implications. However, to date, few studies have comprehensively and simultaneously measured airborne pollutant exposures and explored the associated biomarkers in susceptible healthy elderly subjects, potentially resulting in the suboptimal assessment and management of health risks. To demonstrate the exposome paradigm, we describe the rationale and design of a comprehensive biomarker and biomonitoring panel study to systematically explore the association between individual airborne exposure and adverse health outcomes. We used a combination of personal monitoring for airborne pollutants, extensive human biomonitoring, advanced omics analysis, confounding information, and statistical methods. We established an exploratory panel study of Biomarkers of Air Pollutant Exposure in Chinese people aged 60-69 years (China BAPE), which included 76 healthy residents from a representative community in Jinan City, Shandong Province. During the period between September 2018 and January 2019, we conducted prospective longitudinal monitoring with a 3-day assessment every month. This project: (1) leveraged advanced tools for personal airborne exposure monitoring (external exposures); (2) comprehensively characterized biological samples for exogenous and endogenous compounds (e.g., targeted and untargeted monitoring) and multi-omics scale measurements to explore potential biomarkers and putative toxicity pathways; and (3) systematically evaluated the relationships between personal exposure to air pollutants, and novel biomarkers of exposures and effects using exposome-wide association study approaches. These findings will contribute to our understanding of the mechanisms underlying the adverse health impacts of air pollution exposures and identify potential adverse clinical outcomes that can facilitate the development of effective prevention and targeted intervention techniques.
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Affiliation(s)
- Song Tang
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China; Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu 211166, China
| | - Tiantian Li
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China; Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu 211166, China
| | - Jianlong Fang
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Renjie Chen
- School of Public Health, Key Lab of Public Health Safety of the Ministry of Education and NHC Key Laboratory of Health Technology Assessment, Fudan University, Shanghai 200032, China
| | - Yu'e Cha
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Yanwen Wang
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Mu Zhu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Yi Zhang
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Yuanyuan Chen
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Yanjun Du
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Tianwei Yu
- Institute for Data and Decision Analytics, The Chinese University of Hong Kong, Shenzhen, Guangdong 518172, China
| | - David C Thompson
- Department of Clinical Pharmacy, School of Pharmacy, University of Colorado, Aurora, CO 80045, USA
| | - Krystal J Godri Pollitt
- Department of Environmental Health Sciences, Yale School of Public Health, New Haven, CT 06520, USA
| | - Vasilis Vasiliou
- Department of Environmental Health Sciences, Yale School of Public Health, New Haven, CT 06520, USA
| | - John S Ji
- Environmental Research Center, Duke Kunshan University, Kunshan, Jiangsu 215316, China; Global Health Institute & Nicholas School of the Environment, Duke University, Durham, NC 27708, USA
| | - Haidong Kan
- School of Public Health, Key Lab of Public Health Safety of the Ministry of Education and NHC Key Laboratory of Health Technology Assessment, Fudan University, Shanghai 200032, China
| | - Junfeng Jim Zhang
- Environmental Research Center, Duke Kunshan University, Kunshan, Jiangsu 215316, China; Global Health Institute & Nicholas School of the Environment, Duke University, Durham, NC 27708, USA
| | - Xiaoming Shi
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China; Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu 211166, China.
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Capparelli R, Iannelli D. Role of Epigenetics in Type 2 Diabetes and Obesity. Biomedicines 2021; 9:977. [PMID: 34440181 PMCID: PMC8393970 DOI: 10.3390/biomedicines9080977] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2021] [Revised: 07/30/2021] [Accepted: 08/06/2021] [Indexed: 12/23/2022] Open
Abstract
Epigenetic marks the genome by DNA methylation, histone modification or non-coding RNAs. Epigenetic marks instruct cells to respond reversibly to environmental cues and keep the specific gene expression stable throughout life. In this review, we concentrate on DNA methylation, the mechanism often associated with transgenerational persistence and for this reason frequently used in the clinic. A large study that included data from 10,000 blood samples detected 187 methylated sites associated with body mass index (BMI). The same study demonstrates that altered methylation results from obesity (OB). In another study the combined genetic and epigenetic analysis allowed us to understand the mechanism associating hepatic insulin resistance and non-alcoholic disease in Type 2 Diabetes (T2D) patients. The study underlines the therapeutic potential of epigenetic studies. We also account for seemingly contradictory results associated with epigenetics.
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Affiliation(s)
- Rosanna Capparelli
- Department of Agriculture Sciences, University of Naples “Federico II”, Via Università, 100-Portici, 80055 Naples, Italy
| | - Domenico Iannelli
- Department of Agriculture Sciences, University of Naples “Federico II”, Via Università, 100-Portici, 80055 Naples, Italy
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李 慧, 李 文, 吴 辉, 李 海, 王 永, 安 珍, 姜 静, 吴 卫. [Dietary Patterns and Their Association with Diabetes Mellitus in Middle-Aged and Older Rural Population in Xinxiang County, Henan Province]. SICHUAN DA XUE XUE BAO. YI XUE BAN = JOURNAL OF SICHUAN UNIVERSITY. MEDICAL SCIENCE EDITION 2021; 52:662-670. [PMID: 34323047 PMCID: PMC10409393 DOI: 10.12182/20210760106] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 05/27/2020] [Indexed: 11/23/2022]
Abstract
OBJECTIVE To analyze the prevalence of diabetes mellitus among middle-aged and older rural adults of Xinxiang county, Henan Province and its correlation with dietary patterns. METHODS The study was done based on the data collected from a cross-sectional survey of Xinxiang County, which was part of the Prospective Cohort Study on the Common Chronic Non-Communicable Diseases in Rural areas of Henan Province. Randomized cluster sampling was used to select adult respondents (≥18 years old) from among the residents of 17 villages in Xinxiang county. The respondents completed questionnaires, and underwent physical examinations and laboratory tests between April, 2017 and June, 2017. A total of 7604 individuals aged between 45 and 79 were included in our study. Dietary patterns were established through factor analysis and the dietary pattern factor scores were divided into quartiles (Q1-Q4). The relationship between dietary patterns and diabetes mellitus was analyzed with multivariate logistic regression model. RESULTS Out of the total of 7604 middle-aged and older rural adults in Xinxiang County, Henan Province, 1604 had diabetes mellitus, suggesting a 21.1% prevalence of diabetes mellitus. Factor analysis was used to establish four dietary patterns, namely animal-based diet, vegetable-egg diet, mixed diet and traditional diet. Subjects of these four dietary patterns displayed different demographic characteristics. There were no statistical difference in anthropometricor clinical indicators between the quartile with the lowest dietary pattern factor score (Q1) and the quartile with the highest dietary pattern factor score (Q4) for subjects with animal-based diet ( P>0.05). Compared with those in the Q1 quartile of vegetable-egg diet, subjects in the Q4 quartile of vegetable-egg diet showed lower levels of high-density lipoprotein cholesterol (HDL-C), along with different distribution of fasting blood glucose (FBG), showing statistically significant difference ( P<0.05). In comparison to subjects in Q1 quartile of mixed diet, those in Q4 quartile showed lower levels of systolic blood pressure (SBP), the difference being statistically significant ( P<0.05). In the traditional diet group, subjects in the Q4 quartile had lower waist circumference (WC), but higher levels of HDL-C than those of subjects in Q1 quartile. In addition, the distribution of glycated-hemoglobin (HbA1c) and FBG were different, the difference being statistically significant ( P<0.05). The results of multivariate logistic regression analysis demonstrated that traditional diet could be a protective factor of diabetes mellitus (odds ratio [ OR]=0.810, 95% CI: 0.690-0.952, P trend<0.05) after adjusting for multiple confounding factors. CONCLUSION The prevalence of diabetes in middle-aged and older rural residents is relatively high in Xinxiang County, Henan Province, and there may be a protective relationship between traditional diet and diabetes mellitus.
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Affiliation(s)
- 慧君 李
- 新乡医学院公共卫生学院 河南省空气污染健康效应与干预国际联合实验室 （新乡 453003）School of Public Health, Xinxiang Medical University/Henan International Collaborative Laboratory for Health Effects and Intervention of Air Pollution, Xinxiang 453003, China
| | - 文龙 李
- 新乡医学院公共卫生学院 河南省空气污染健康效应与干预国际联合实验室 （新乡 453003）School of Public Health, Xinxiang Medical University/Henan International Collaborative Laboratory for Health Effects and Intervention of Air Pollution, Xinxiang 453003, China
| | - 辉 吴
- 新乡医学院公共卫生学院 河南省空气污染健康效应与干预国际联合实验室 （新乡 453003）School of Public Health, Xinxiang Medical University/Henan International Collaborative Laboratory for Health Effects and Intervention of Air Pollution, Xinxiang 453003, China
| | - 海斌 李
- 新乡医学院公共卫生学院 河南省空气污染健康效应与干预国际联合实验室 （新乡 453003）School of Public Health, Xinxiang Medical University/Henan International Collaborative Laboratory for Health Effects and Intervention of Air Pollution, Xinxiang 453003, China
| | - 永斌 王
- 新乡医学院公共卫生学院 河南省空气污染健康效应与干预国际联合实验室 （新乡 453003）School of Public Health, Xinxiang Medical University/Henan International Collaborative Laboratory for Health Effects and Intervention of Air Pollution, Xinxiang 453003, China
| | - 珍 安
- 新乡医学院公共卫生学院 河南省空气污染健康效应与干预国际联合实验室 （新乡 453003）School of Public Health, Xinxiang Medical University/Henan International Collaborative Laboratory for Health Effects and Intervention of Air Pollution, Xinxiang 453003, China
| | - 静 姜
- 新乡医学院公共卫生学院 河南省空气污染健康效应与干预国际联合实验室 （新乡 453003）School of Public Health, Xinxiang Medical University/Henan International Collaborative Laboratory for Health Effects and Intervention of Air Pollution, Xinxiang 453003, China
| | - 卫东 吴
- 新乡医学院公共卫生学院 河南省空气污染健康效应与干预国际联合实验室 （新乡 453003）School of Public Health, Xinxiang Medical University/Henan International Collaborative Laboratory for Health Effects and Intervention of Air Pollution, Xinxiang 453003, China
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10
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Abstract
Disease classification, or nosology, was historically driven by careful examination of clinical features of patients. As technologies to measure and understand human phenotypes advanced, so too did classifications of disease, and the advent of genetic data has led to a surge in genetic subtyping in the past decades. Although the fundamental process of refining disease definitions and subtypes is shared across diverse fields, each field is driven by its own goals and technological expertise, leading to inconsistent and conflicting definitions of disease subtypes. Here, we review several classical and recent subtypes and subtyping approaches and provide concrete definitions to delineate subtypes. In particular, we focus on subtypes with distinct causal disease biology, which are of primary interest to scientists, and subtypes with pragmatic medical benefits, which are of primary interest to physicians. We propose genetic heterogeneity as a gold standard for establishing biologically distinct subtypes of complex polygenic disease. We focus especially on methods to find and validate genetic subtypes, emphasizing common pitfalls and how to avoid them.
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Affiliation(s)
- Andy Dahl
- Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, Illinois 60637, USA; .,Department of Neurology, University of California, Los Angeles, California 90024, USA; .,Department of Computational Medicine, University of California, Los Angeles, California 90095, USA
| | - Noah Zaitlen
- Department of Neurology, University of California, Los Angeles, California 90024, USA; .,Department of Computational Medicine, University of California, Los Angeles, California 90095, USA
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11
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He Y, Lakhani CM, Rasooly D, Manrai AK, Tzoulaki I, Patel CJ. Comparisons of Polyexposure, Polygenic, and Clinical Risk Scores in Risk Prediction of Type 2 Diabetes. Diabetes Care 2021; 44:935-943. [PMID: 33563654 PMCID: PMC7985424 DOI: 10.2337/dc20-2049] [Citation(s) in RCA: 53] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/18/2020] [Accepted: 01/13/2021] [Indexed: 02/03/2023]
Abstract
OBJECTIVE To establish a polyexposure score (PXS) for type 2 diabetes (T2D) incorporating 12 nongenetic exposures and examine whether a PXS and/or a polygenic risk score (PGS) improves diabetes prediction beyond traditional clinical risk factors. RESEARCH DESIGN AND METHODS We identified 356,621 unrelated individuals from the UK Biobank of White British ancestry with no prior diagnosis of T2D and normal HbA1c levels. Using self-reported and hospital admission information, we deployed a machine learning procedure to select the most predictive and robust factors out of 111 nongenetically ascertained exposure and lifestyle variables for the PXS in prospective T2D. We computed the clinical risk score (CRS) and PGS by taking a weighted sum of eight established clinical risk factors and >6 million single nucleotide polymorphisms, respectively. RESULTS In the study population, 7,513 had incident T2D. The C-statistics for the PGS, PXS, and CRS models were 0.709, 0.762, and 0.839, respectively. Individuals in the top 10% of PGS, PXS, and CRS had 2.00-, 5.90-, and 9.97-fold greater risk, respectively, compared to the remaining population. Addition of PGS and PXS to CRS improved T2D classification accuracy, with a continuous net reclassification index of 15.2% and 30.1% for cases, respectively, and 7.3% and 16.9% for controls, respectively. CONCLUSIONS For T2D, the PXS provides modest incremental predictive value over established clinical risk factors. However, the concept of PXS merits further consideration in T2D risk stratification and is likely to be useful in other chronic disease risk prediction models.
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Affiliation(s)
- Yixuan He
- Program in Bioinformatics and Integrative Genomics, Harvard Medical School, Boston, MA.,Department of Biomedical Informatics, Harvard Medical School, Boston, MA
| | - Chirag M Lakhani
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA
| | - Danielle Rasooly
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA.,Computational Health Informatics Program, Boston Children's Hospital, Boston, MA
| | - Arjun K Manrai
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA.,Computational Health Informatics Program, Boston Children's Hospital, Boston, MA
| | - Ioanna Tzoulaki
- Department of Epidemiology and Biostatistics, Imperial College London School of Public Health, London, U.K.,Department of Hygiene and Epidemiology, University of Ioannina Medical School, Ioannina, Greece
| | - Chirag J Patel
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA
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12
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Nguyen LM, Chon JJ, Kim EE, Cheng JC, Ebersole JL. Biological Aging and Periodontal Disease: Analysis of NHANES (2001-2002). JDR Clin Trans Res 2021; 7:145-153. [PMID: 33605165 DOI: 10.1177/2380084421995812] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
INTRODUCTION Periodontitis is a chronic inflammatory disease caused by multiple potential contributing factors such as bacterial biofilm infection of the tissues surrounding the teeth and environmental determinants and a dysregulated host response for modifying and resolving the inflammation. Because periodontal disease is a major public health concern with substantial increases in the prevalence and severity in aging populations, previous studies of periodontitis tended to approach the disease as an age-associated outcome across the life span. However, few investigations have considered that, as a chronic noncommunicable disease, periodontitis may not simply be a disease that increases with age but may contribute to more rapid biologic aging. OBJECTIVES Increasing population data supports the potential disconnect between chronological aging and biologic aging, which would contribute to the heterogeneity of aging phenotypes within chronologic ages across populations. Thus, our aim was to test whether periodontal disease affects biological aging across the life span. METHODS The prevalence of periodontitis in the adult US population is a portion of the assessment of the National Health and Nutrition Examination Survey (NHANES), which has been ongoing since 1971 through 2-y cycles sampling populations across the country. We used NHANES 2001-2002 to test the hypothesis that the presence/severity of periodontal disease as an exposure variable would negatively affect telomere length, a measure of biological aging, and that this relationship is modified by factors that also affect the progression of periodontitis, such as sex, race/ethnicity, and smoking. RESULTS The data demonstrated a significant impact of periodontitis on decreasing telomere lengths across the life span. These differences were modulated by age, sex, race/ethnicity, and smoking within the population. CONCLUSION The findings lay the groundwork for future studies documenting broader effects on biological aging parameters as well as potential intervention strategies for periodontitis in driving unhealthy aging processes. KNOWLEDGE TRANSFER STATEMENT Periodontitis is a chronic inflammatory disease and dysregulated host response. Shortening of telomeres is a reflection of biologic aging. Decreased telomere lengths with periodontitis are seemingly related to chronic infection and persistent local and systemic inflammation. These findings suggest that periodontitis is not simply a disease of aging but may also transmit chronic systemic signals that could affect more rapid biological aging. Clinicians can use this outcome to recognize the role of periodontitis in driving unhealthy aging processes in patients.
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Affiliation(s)
- L M Nguyen
- Department of Biomedical Sciences, University of Nevada, Las Vegas-School of Dental Medicine, Las Vegas, NV, USA
| | - J J Chon
- Department of Clinical Sciences, University of Nevada, Las Vegas-School of Dental Medicine, Las Vegas, NV, USA
| | - E E Kim
- Department of Clinical Sciences, University of Nevada, Las Vegas-School of Dental Medicine, Las Vegas, NV, USA
| | - J C Cheng
- Department of Clinical Sciences, University of Nevada, Las Vegas-School of Dental Medicine, Las Vegas, NV, USA
| | - J L Ebersole
- Department of Biomedical Sciences, University of Nevada, Las Vegas-School of Dental Medicine, Las Vegas, NV, USA
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13
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Kant R, Verma V, Patel S, Chandra R, Chaudhary R, Shuldiner AR, Munir KM. Effect of serum zinc and copper levels on insulin secretion, insulin resistance and pancreatic β cell dysfunction in US adults: Findings from the National Health and Nutrition Examination Survey (NHANES) 2011-2012. Diabetes Res Clin Pract 2021; 172:108627. [PMID: 33333205 DOI: 10.1016/j.diabres.2020.108627] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/04/2020] [Revised: 11/04/2020] [Accepted: 12/09/2020] [Indexed: 01/22/2023]
Abstract
AIM To compare zinc (Zn) and copper (Cu) levels in US adults with normoglycemia, prediabetes and diabetes, and study the association of serum Zn and Cu levels with pancreatic β cell insulin secretion, pancreatic dysfunction and insulin resistance in US adults with normoglycemia and prediabetes. METHOD Homeostatic Model Assessment (HOMA2) calculator was used to compute estimates of steady state β cell insulin secretion (HOMA2-B), peripheral insulin sensitivity (HOMA2-S), insulin resistance (HOMA-IR), and disposition index (HOMA-DI) in 804 adult individuals from the National Health and Nutrition Examination Survey (NHANES 2011-2012). RESULTS There was no significant difference between serum Zn and Cu levels among subjects with normoglycemia, prediabetes, and diabetes. After adjusting for multiple possible confounders, higher serum Zn concentrations were associated with lower β cell insulin secretion (HOMA2-B; p = 0.01) and lower insulin resistance (HOMA-IR; p = 0.04) in the prediabetic subjects. In normoglycemic group, higher serum Zn levels were associated with improved pancreatic function (HOMA-DI; P = 0.02). On the other hand, higher serum Cu levels were associated with increased β cell insulin secretion (HOMA2-B, P = 0.03) only in the subjects with prediabetes. CONCLUSION These findings support the need for further studies to investigate the role of trace elements in diabetes pathogenesis.
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Affiliation(s)
- Ravi Kant
- Division of Endocrinology, Diabetes and Nutrition, Medical University of South Carolina/AnMed Campus, Anderson, SC 29621, USA.
| | - Vipin Verma
- Department of Medicine, Medical University of South Carolina/AnMed Campus, Anderson, SC 29621, USA.
| | - Siddharth Patel
- Department of Medicine, Decatur Morgan Hospital Decatur Campus, Decatur, AL, USA
| | - Rashmi Chandra
- Department of Medicine, Medical University of South Carolina/AnMed Campus, Anderson, SC 29621, USA
| | | | - Alan R Shuldiner
- Division of Endocrinology, Diabetes and Nutrition, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Kashif M Munir
- Division of Endocrinology, Diabetes and Nutrition, University of Maryland School of Medicine, Baltimore, MD, USA
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14
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Kant R, Verma V, Patel S, Chandra R, Chaudhary R, Shuldiner AR, Munir KM. Effect of serum zinc and copper levels on insulin secretion, insulin resistance and pancreatic β cell dysfunction in US adults: Findings from the National Health and Nutrition Examination Survey (NHANES) 2011–2012. Diabetes Res Clin Pract 2021; 172:108627. [DOI: https:/doi.org/10.1016/j.diabres.2020.108627] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/11/2025]
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15
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Shi P, Yan H, Fan X, Xi S. A benchmark dose analysis for urinary cadmium and type 2 diabetes mellitus. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2021; 273:116519. [PMID: 33493762 DOI: 10.1016/j.envpol.2021.116519] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/31/2020] [Revised: 12/16/2020] [Accepted: 01/13/2021] [Indexed: 05/22/2023]
Abstract
Cadmium (Cd) is a heavy metal referred to as one of the environmental endocrine disruptors. The dose-dependent association between Cd and type 2 diabetes mellitus (T2DM) has been elucidated, but the corresponding threshold has not been established. To evaluate the urinary Cd levels associated with T2DM, we perform a benchmark dose (BMD) analysis based on data from the 1999-2006 National Health and Nutrition Examination Survey (NHANES). Weighted datasets were generated by the inverse probability of treatment weighting analysis to develop the robustness of our analysis. We inferred a strong positive association between urinary Cd and T2DM in unweighted and weighted populations. BMD and its low limit (BMDL) estimates for 5% benchmark responses (BMR) was 0.297 (0.198) and 0.190 (0.178) μg/g creatinine for each population, respectively. The sensitivity analysis by race, followed by weight of sum method showed similar estimates of urinary Cd level for the risk of developing T2DM, which are rather low and far less than those for the renal or bone disease development risk. This indicates that T2DM can be a sensitive outcome of Cd exposure and therefore should be taken into account in the development of standard regulatory limits for safe exposure to Cd.
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Affiliation(s)
- Peng Shi
- Department of Environmental and Occupational Health, School of Public Health, China Medical University, Shenyang, China
| | - Huanchang Yan
- Department of Epidemiology, School of Public Health, Southern Medical University, Guangzhou, China
| | - Xingjun Fan
- Department of Environmental and Occupational Health, School of Public Health, Mudanjiang Medical University, Mudanjiang, China
| | - Shuhua Xi
- Department of Environmental and Occupational Health, School of Public Health, China Medical University, Shenyang, China.
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Transcriptional Profiling and Biological Pathway(s) Analysis of Type 2 Diabetes Mellitus in a Pakistani Population. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2020; 17:ijerph17165866. [PMID: 32823525 PMCID: PMC7460550 DOI: 10.3390/ijerph17165866] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/25/2020] [Revised: 08/03/2020] [Accepted: 08/06/2020] [Indexed: 12/22/2022]
Abstract
The epidemic of type 2 diabetes mellitus (T2DM) is an important global health concern. Our earlier epidemiological investigation in Pakistan prompted us to conduct a molecular investigation to decipher the differential genetic pathways of this health condition in relation to non-diabetic controls. Our microarray studies of global gene expression were conducted on the Affymetrix platform using Human Genome U133 Plus 2.0 Array along with Ingenuity Pathway Analysis (IPA) to associate the affected genes with their canonical pathways. High-throughput qRT-PCR TaqMan Low Density Array (TLDA) was performed to validate the selected differentially expressed genes of our interest, viz., ARNT, LEPR, MYC, RRAD, CYP2D6, TP53, APOC1, APOC2, CYP1B1, SLC2A13, and SLC33A1 using a small population validation sample (n = 15 cases and their corresponding matched controls). Overall, our small pilot study revealed a discrete gene expression profile in cases compared to controls. The disease pathways included: Insulin Receptor Signaling, Type II Diabetes Mellitus Signaling, Apoptosis Signaling, Aryl Hydrocarbon Receptor Signaling, p53 Signaling, Mitochondrial Dysfunction, Chronic Myeloid Leukemia Signaling, Parkinson's Signaling, Molecular Mechanism of Cancer, and Cell Cycle G1/S Checkpoint Regulation, GABA Receptor Signaling, Neuroinflammation Signaling Pathway, Dopamine Receptor Signaling, Sirtuin Signaling Pathway, Oxidative Phosphorylation, LXR/RXR Activation, and Mitochondrial Dysfunction, strongly consistent with the evidence from epidemiological studies. These gene fingerprints could lead to the development of biomarkers for the identification of subgroups at high risk for future disease well ahead of time, before the actual disease becomes visible.
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17
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Wang D, Lu C, Yu J, Zhang M, Zhu W, Gu J. Chinese Medicine for Psoriasis Vulgaris Based on Syndrome Pattern: A Network Pharmacological Study. EVIDENCE-BASED COMPLEMENTARY AND ALTERNATIVE MEDICINE : ECAM 2020; 2020:5239854. [PMID: 32419809 PMCID: PMC7204377 DOI: 10.1155/2020/5239854] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/09/2019] [Revised: 01/13/2020] [Accepted: 01/23/2020] [Indexed: 02/07/2023]
Abstract
BACKGROUND The long-term use of conventional therapy for psoriasis vulgaris remains a challenge due to limited or no patient response and severe side effects. Complementary and alternative treatments such as traditional Chinese medicine (TCM) are widely used in East Asia. TCM treatment is based on individual syndrome types. Three TCM formulae, Compound Qingdai Pills (F1), Yujin Yinxie Tablets (F2), and Xiaoyin Tablets (F3), are used for blood heat, blood stasis, and blood dryness type of psoriasis vulgaris, respectively. OBJECTIVES To explore the mechanism of three TCM formulae for three syndrome types of psoriasis vulgaris. METHODS The compounds of the three TCM formulae were retrieved from the Psoriasis Database of Traditional Chinese Medicine (PDTCM). Their molecular properties of absorption, distribution, metabolism, excretion and toxicity (ADME/T), and drug-likeness were compared by analyzing the distribution of compounds in the chemical space. The cellular targets of the compounds were predicted by molecular docking. By constructing the compound-target network and analyzing network centrality, key targets and compounds for each formula were screened. Three syndrome types of psoriasis vulgaris related pathways and biological processes (BPs) were enriched by the Database for Annotation, Visualization, and Integrated Discovery (DAVID) v6.8. RESULTS The compounds of the three formulae exhibited structural diversity, good drug-like properties, and ADME/T properties. A total of 72, 97 and 85 targets were found to have interactions with compounds of F1, F2, and F3, respectively. The three formulae were all related to 53 targets, 8 pathways, 9 biological processes, and 10 molecular functions (MFs). In addition, each formula had unique targets and regulated different pathways and BPs. CONCLUSION The three TCM formulae exhibited common mechanisms to some extent. The differences at molecular and systems levels may contribute to their unique applications in individualized treatment.
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Affiliation(s)
- Dongmei Wang
- Dermatology Hospital of Southern Medical University, Guangzhou 510091, China
- Guangdong Provincial Academy of Chinese Medical Sciences, Guangzhou 510006, China
- The Second Clinical College, Guangzhou University of Chinese Medicine, Guangzhou 510006, China
| | - Chuanjian Lu
- Guangdong Provincial Academy of Chinese Medical Sciences, Guangzhou 510006, China
- The Second Clinical College, Guangzhou University of Chinese Medicine, Guangzhou 510006, China
| | - Jingjie Yu
- Guangdong Provincial Academy of Chinese Medical Sciences, Guangzhou 510006, China
- The Second Clinical College, Guangzhou University of Chinese Medicine, Guangzhou 510006, China
| | - Miaomiao Zhang
- Guangdong Provincial Academy of Chinese Medical Sciences, Guangzhou 510006, China
- The Second Clinical College, Guangzhou University of Chinese Medicine, Guangzhou 510006, China
| | - Wei Zhu
- Guangdong Provincial Academy of Chinese Medical Sciences, Guangzhou 510006, China
- The Second Clinical College, Guangzhou University of Chinese Medicine, Guangzhou 510006, China
| | - Jiangyong Gu
- Guangdong Provincial Academy of Chinese Medical Sciences, Guangzhou 510006, China
- The Second Clinical College, Guangzhou University of Chinese Medicine, Guangzhou 510006, China
- Department of Biochemistry, School of Basic Medical Science, Guangzhou University of Chinese Medicine, Guangzhou 510006, China
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Zheng Y, Chen Z, Pearson T, Zhao J, Hu H, Prosperi M. Design and methodology challenges of environment-wide association studies: A systematic review. ENVIRONMENTAL RESEARCH 2020; 183:109275. [PMID: 32105887 PMCID: PMC7346707 DOI: 10.1016/j.envres.2020.109275] [Citation(s) in RCA: 39] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/20/2019] [Revised: 02/18/2020] [Accepted: 02/18/2020] [Indexed: 05/09/2023]
Abstract
Environment-wide association studies (EWAS) are an untargeted, agnostic, and hypothesis-generating approach to exploring environmental factors associated with health outcomes, akin to genome-wide association studies (GWAS). While design, methodology, and replicability standards for GWAS are established, EWAS pose many challenges. We systematically reviewed published literature on EWAS to categorize scope, impact, types of analytical approaches, and open challenges in designs and methodologies. The Web of Science and PubMed databases were searched through multiple queries to identify EWAS articles between January 2010 and December 2018, and a systematic review was conducted following the Preferred Reporting Item for Systematic Reviews and Meta-Analyses (PRISMA) reporting standard. Twenty-three articles met our inclusion criteria and were included. For each study, we categorized the data sources, the definitions of study outcomes, the sets of environmental variables, and the data engineering/analytical approaches, e.g. neighborhood definition, variable standardization, handling of multiple hypothesis testing, model selection, and validation. We identified limited exploitation of data sources, high heterogeneity in analytical approaches, and lack of replication. Despite of the promising utility of EWAS, further development of EWAS will require improved data sources, standardization of study designs, and rigorous testing of methodologies.
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Affiliation(s)
- Yi Zheng
- Department of Epidemiology, College of Public Health and Health Professions and College of Medicine, University of Florida, Gainesville, FL, USA
| | - Zhaoyi Chen
- Department of Epidemiology, College of Public Health and Health Professions and College of Medicine, University of Florida, Gainesville, FL, USA
| | - Thomas Pearson
- Department of Epidemiology, College of Public Health and Health Professions and College of Medicine, University of Florida, Gainesville, FL, USA
| | - Jinying Zhao
- Department of Epidemiology, College of Public Health and Health Professions and College of Medicine, University of Florida, Gainesville, FL, USA
| | - Hui Hu
- Department of Epidemiology, College of Public Health and Health Professions and College of Medicine, University of Florida, Gainesville, FL, USA.
| | - Mattia Prosperi
- Department of Epidemiology, College of Public Health and Health Professions and College of Medicine, University of Florida, Gainesville, FL, USA.
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Mambiya M, Shang M, Wang Y, Li Q, Liu S, Yang L, Zhang Q, Zhang K, Liu M, Nie F, Zeng F, Liu W. The Play of Genes and Non-genetic Factors on Type 2 Diabetes. Front Public Health 2019; 7:349. [PMID: 31803711 PMCID: PMC6877736 DOI: 10.3389/fpubh.2019.00349] [Citation(s) in RCA: 53] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2019] [Accepted: 11/04/2019] [Indexed: 12/12/2022] Open
Abstract
Diabetes has been a disease of public health concern for a number of decades. It was in the 1930s when scientists made an interesting discovery that the disease is actually divided into two types as some patients were insensitive to insulin treatment then. Type 2 Diabetes which happens to be the non-insulin dependent one is the most common form of the disease and is caused by the interaction between genetic and non-genetic factors. Despite conflicting results, numerous studies have identified genetic and non-genetic factors associated with this common type of diabetes. This review has summarized literature on some genes and non-genetic factors which have been identified to be associated with Type 2 diabetes. It has sourced literature from PubMed, Web of Science and Medline without any limitation to regions, publication types, or languages. The paper has started with the introduction, the play of non-genetic factors, the impact of genes in general, and ended with the interaction between some genes and environmental factors.
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Affiliation(s)
- Michael Mambiya
- Department of Nutrition and Food Hygiene, School of Public Health, China Medical University, Shenyang, China
| | - Mengke Shang
- Department of Nutrition and Food Hygiene, School of Public Health, China Medical University, Shenyang, China
| | - Yue Wang
- Department of Nutrition and Food Hygiene, School of Public Health, China Medical University, Shenyang, China
| | - Qian Li
- Department of Nutrition and Food Hygiene, School of Public Health, China Medical University, Shenyang, China
| | - Shan Liu
- Department of Nutrition and Food Hygiene, School of Public Health, China Medical University, Shenyang, China
| | - Luping Yang
- Department of Nutrition and Food Hygiene, School of Public Health, China Medical University, Shenyang, China
| | - Qian Zhang
- Department of Nutrition and Food Hygiene, School of Public Health, China Medical University, Shenyang, China
| | - Kaili Zhang
- Department of Nutrition and Food Hygiene, School of Public Health, China Medical University, Shenyang, China
| | - Mengwei Liu
- Department of Nutrition and Food Hygiene, School of Public Health, China Medical University, Shenyang, China
| | - Fangfang Nie
- Department of Nutrition and Food Hygiene, School of Public Health, China Medical University, Shenyang, China
| | - Fanxin Zeng
- Department of Nutrition and Food Hygiene, School of Public Health, China Medical University, Shenyang, China
| | - Wanyang Liu
- Department of Nutrition and Food Hygiene, School of Public Health, China Medical University, Shenyang, China
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20
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Kosnik MB, Reif DM. Determination of chemical-disease risk values to prioritize connections between environmental factors, genetic variants, and human diseases. Toxicol Appl Pharmacol 2019; 379:114674. [PMID: 31323264 PMCID: PMC6708494 DOI: 10.1016/j.taap.2019.114674] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2019] [Revised: 07/05/2019] [Accepted: 07/15/2019] [Indexed: 12/18/2022]
Abstract
Traditional methods for chemical risk assessment are too time-consuming and resource-intensive to characterize either the diversity of chemicals to which humans are exposed or how that diversity may manifest in population susceptibility differences. The advent of novel toxicological data sources and their integration with bioinformatic databases affords opportunities for modern approaches that consider gene-environment (GxE) interactions in population risk assessment. Here, we present an approach that systematically links multiple data sources to relate chemical risk values to diseases and gene-disease variants. These data sources include high-throughput screening (HTS) results from Tox21/ToxCast, chemical-disease relationships from the Comparative Toxicogenomics Database (CTD), hazard data from resources like the Integrated Risk Information System, exposure data from the ExpoCast initiative, and gene-variant-disease information from the DisGeNET database. We use these integrated data to identify variants implicated in chemical-disease enrichments and develop a new value that estimates the risk of these associations toward differential population responses. Finally, we use this value to prioritize chemical-disease associations by exploring the genomic distribution of variants implicated in high-risk diseases. We offer this modular approach, termed DisQGOS (Disease Quotient Genetic Overview Score), for relating overall chemical-disease risk to potential for population variable responses, as a complement to methods aiming to modernize aspects of risk assessment.
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Affiliation(s)
- Marissa B Kosnik
- Toxicology Program, North Carolina State University, Raleigh, NC 27695-7617, United States of America; Bioinformatics Research Center, North Carolina State University, Raleigh, NC 27695-7617, United States of America; Department of Biological Sciences, North Carolina State University, Raleigh, NC 27695-7617, United States of America.
| | - David M Reif
- Toxicology Program, North Carolina State University, Raleigh, NC 27695-7617, United States of America; Bioinformatics Research Center, North Carolina State University, Raleigh, NC 27695-7617, United States of America; Department of Biological Sciences, North Carolina State University, Raleigh, NC 27695-7617, United States of America; Center for Human Health and the Environment, North Carolina State University, Raleigh, NC 27695-7617, United States of America.
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21
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Brankovic M, Kardys I, Steyerberg EW, Lemeshow S, Markovic M, Rizopoulos D, Boersma E. Understanding of interaction (subgroup) analysis in clinical trials. Eur J Clin Invest 2019; 49:e13145. [PMID: 31135965 DOI: 10.1111/eci.13145] [Citation(s) in RCA: 57] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/18/2018] [Revised: 05/14/2019] [Accepted: 05/26/2019] [Indexed: 12/26/2022]
Abstract
BACKGROUND When the treatment effect on the outcome of interest is influenced by a baseline/demographic factor, investigators say that an interaction is present. In randomized clinical trials (RCTs), this type of analysis is typically referred to as subgroup analysis. Although interaction (or subgroup) analyses are usually stated as a secondary study objective, it is not uncommon that these results lead to changes in treatment protocols or even modify public health policies. Nonetheless, recent reviews have indicated that their proper assessment, interpretation and reporting remain challenging. RESULTS Therefore, this article provides an overview of these challenges, to help investigators find the best strategy for application of interaction analyses on binary outcomes in RCTs. Specifically, we discuss the key points of formal interaction testing, including the estimation of both additive and multiplicative interaction effects. We also provide recommendations that, if adhered to, could increase the clarity and the completeness of reports of RCTs. CONCLUSION Altogether, this article provides a brief non-statistical guide for clinical investigators on how to perform, interpret and report interaction (subgroup) analyses in RCTs.
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Affiliation(s)
- Milos Brankovic
- Clinical Epidemiology Unit, Department of Cardiology, Erasmus Medical Center, Rotterdam, The Netherlands.,School of Medicine, University of Belgrade, Belgrade, Serbia
| | - Isabella Kardys
- Clinical Epidemiology Unit, Department of Cardiology, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Ewout W Steyerberg
- Department of Medical Statistics and Bioinformatics, Leiden University Medical Center, Leiden, The Netherlands
| | - Stanley Lemeshow
- Division of Biostatistics, College of Public Health, The Ohio State University, Columbus, Ohio
| | - Maja Markovic
- Department of Child and Adolescent Psychiatry, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Dimitris Rizopoulos
- Department of Biostatistics, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Eric Boersma
- Clinical Epidemiology Unit, Department of Cardiology, Erasmus Medical Center, Rotterdam, The Netherlands
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22
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Dhondalay GK, Rael E, Acharya S, Zhang W, Sampath V, Galli SJ, Tibshirani R, Boyd SD, Maecker H, Nadeau KC, Andorf S. Food allergy and omics. J Allergy Clin Immunol 2019; 141:20-29. [PMID: 29307411 DOI: 10.1016/j.jaci.2017.11.007] [Citation(s) in RCA: 51] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2017] [Revised: 11/09/2017] [Accepted: 11/14/2017] [Indexed: 01/06/2023]
Abstract
Food allergy (FA) prevalence has been increasing over the last few decades and is now a global health concern. Current diagnostic methods for FA result in a high number of false-positive results, and the standard of care is either allergen avoidance or use of epinephrine on accidental exposure, although currently with no other approved treatments. The increasing prevalence of FA, lack of robust biomarkers, and inadequate treatments warrants further research into the mechanism underlying food allergies. Recent technological advances have made it possible to move beyond traditional biological techniques to more sophisticated high-throughput approaches. These technologies have created the burgeoning field of omics sciences, which permit a more systematic investigation of biological problems. Omics sciences, such as genomics, epigenomics, transcriptomics, proteomics, metabolomics, microbiomics, and exposomics, have enabled the construction of regulatory networks and biological pathway models. Parallel advances in bioinformatics and computational techniques have enabled the integration, analysis, and interpretation of these exponentially growing data sets and opens the possibility of personalized or precision medicine for FA.
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Affiliation(s)
- Gopal Krishna Dhondalay
- Sean N. Parker Center for Allergy and Asthma Research, Stanford University School of Medicine, Stanford, Calif; Institute for Immunity, Transplantation, and Infection, Stanford University School of Medicine, Stanford, Calif
| | - Efren Rael
- Sean N. Parker Center for Allergy and Asthma Research, Stanford University School of Medicine, Stanford, Calif; Institute for Immunity, Transplantation, and Infection, Stanford University School of Medicine, Stanford, Calif
| | - Swati Acharya
- Sean N. Parker Center for Allergy and Asthma Research, Stanford University School of Medicine, Stanford, Calif; Institute for Immunity, Transplantation, and Infection, Stanford University School of Medicine, Stanford, Calif
| | - Wenming Zhang
- Sean N. Parker Center for Allergy and Asthma Research, Stanford University School of Medicine, Stanford, Calif; Institute for Immunity, Transplantation, and Infection, Stanford University School of Medicine, Stanford, Calif
| | - Vanitha Sampath
- Sean N. Parker Center for Allergy and Asthma Research, Stanford University School of Medicine, Stanford, Calif; Institute for Immunity, Transplantation, and Infection, Stanford University School of Medicine, Stanford, Calif
| | - Stephen J Galli
- Sean N. Parker Center for Allergy and Asthma Research, Stanford University School of Medicine, Stanford, Calif; Department of Pathology, Stanford University School of Medicine, Stanford, Calif; Department of Microbiology & Immunology, Stanford University School of Medicine, Stanford, Calif
| | - Robert Tibshirani
- Sean N. Parker Center for Allergy and Asthma Research, Stanford University School of Medicine, Stanford, Calif; Department of Biomedical Data Sciences, Stanford University School of Medicine, Stanford, Calif
| | - Scott D Boyd
- Sean N. Parker Center for Allergy and Asthma Research, Stanford University School of Medicine, Stanford, Calif; Department of Pathology, Stanford University School of Medicine, Stanford, Calif; Institute for Immunity, Transplantation, and Infection, Stanford University School of Medicine, Stanford, Calif
| | - Holden Maecker
- Sean N. Parker Center for Allergy and Asthma Research, Stanford University School of Medicine, Stanford, Calif; Department of Microbiology & Immunology, Stanford University School of Medicine, Stanford, Calif; Institute for Immunity, Transplantation, and Infection, Stanford University School of Medicine, Stanford, Calif
| | - Kari Christine Nadeau
- Sean N. Parker Center for Allergy and Asthma Research, Stanford University School of Medicine, Stanford, Calif; Institute for Immunity, Transplantation, and Infection, Stanford University School of Medicine, Stanford, Calif.
| | - Sandra Andorf
- Sean N. Parker Center for Allergy and Asthma Research, Stanford University School of Medicine, Stanford, Calif; Institute for Immunity, Transplantation, and Infection, Stanford University School of Medicine, Stanford, Calif
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23
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Glicksberg BS, Johnson KW, Dudley JT. The next generation of precision medicine: observational studies, electronic health records, biobanks and continuous monitoring. Hum Mol Genet 2019; 27:R56-R62. [PMID: 29659828 DOI: 10.1093/hmg/ddy114] [Citation(s) in RCA: 37] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2018] [Accepted: 03/27/2018] [Indexed: 02/06/2023] Open
Abstract
Precision medicine can utilize new techniques in order to more effectively translate research findings into clinical practice. In this article, we first explore the limitations of traditional study designs, which stem from (to name a few): massive cost for the assembly of large patient cohorts; non-representative patient data; and the astounding complexity of human biology. Second, we propose that harnessing electronic health records and mobile device biometrics coupled to longitudinal data may prove to be a solution to many of these problems by capturing a 'real world' phenotype. We envision that future biomedical research utilizing more precise approaches to patient care will utilize continuous and longitudinal data sources.
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Affiliation(s)
- Benjamin S Glicksberg
- Institute for Next Generation Healthcare Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, Mount Sinai Health System, New York City, NY 10029, USA.,Institute for Computational Health Sciences, University of California San Francisco, San Francisco, CA 94158, USA
| | - Kipp W Johnson
- Institute for Next Generation Healthcare Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, Mount Sinai Health System, New York City, NY 10029, USA
| | - Joel T Dudley
- Institute for Next Generation Healthcare Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, Mount Sinai Health System, New York City, NY 10029, USA
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24
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Dahl A, Cai N, Ko A, Laakso M, Pajukanta P, Flint J, Zaitlen N. Reverse GWAS: Using genetics to identify and model phenotypic subtypes. PLoS Genet 2019; 15:e1008009. [PMID: 30951530 PMCID: PMC6469799 DOI: 10.1371/journal.pgen.1008009] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2018] [Revised: 04/17/2019] [Accepted: 02/07/2019] [Indexed: 12/16/2022] Open
Abstract
Recent and classical work has revealed biologically and medically significant subtypes in complex diseases and traits. However, relevant subtypes are often unknown, unmeasured, or actively debated, making automated statistical approaches to subtype definition valuable. We propose reverse GWAS (RGWAS) to identify and validate subtypes using genetics and multiple traits: while GWAS seeks the genetic basis of a given trait, RGWAS seeks to define trait subtypes with distinct genetic bases. Unlike existing approaches relying on off-the-shelf clustering methods, RGWAS uses a novel decomposition, MFMR, to model covariates, binary traits, and population structure. We use extensive simulations to show that modelling these features can be crucial for power and calibration. We validate RGWAS in practice by recovering a recently discovered stress subtype in major depression. We then show the utility of RGWAS by identifying three novel subtypes of metabolic traits. We biologically validate these metabolic subtypes with SNP-level tests and a novel polygenic test: the former recover known metabolic GxE SNPs; the latter suggests subtypes may explain substantial missing heritability. Crucially, statins, which are widely prescribed and theorized to increase diabetes risk, have opposing effects on blood glucose across metabolic subtypes, suggesting the subtypes have potential translational value.
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Affiliation(s)
- Andy Dahl
- Department of Medicine, UCSF, San Francisco, California, United States of America
| | - Na Cai
- Wellcome Sanger Institute, Cambridge, United Kingdom
- European Bioinformatics Institute (EMBL-EBI), Cambridge, United Kingdom
| | - Arthur Ko
- Department of Human Genetics, David Geffen School of Medicine, UCLA, Los Angeles, California, United States of America
| | - Markku Laakso
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland, Kuopio, Finland
- Kuopio University Hospital, Kuopio, Finland
| | - Päivi Pajukanta
- Department of Human Genetics, David Geffen School of Medicine, UCLA, Los Angeles, California, United States of America
| | - Jonathan Flint
- Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, UCLA, Los Angeles, California, United States of America
| | - Noah Zaitlen
- Department of Medicine, UCSF, San Francisco, California, United States of America
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25
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Nieuwenhuijsen MJ, Agier L, Basagaña X, Urquiza J, Tamayo-Uria I, Giorgis-Allemand L, Robinson O, Siroux V, Maitre L, de Castro M, Valentin A, Donaire D, Dadvand P, Aasvang GM, Krog NH, Schwarze PE, Chatzi L, Grazuleviciene R, Andrusaityte S, Dedele A, McEachan R, Wright J, West J, Ibarluzea J, Ballester F, Vrijheid M, Slama R. Influence of the Urban Exposome on Birth Weight. ENVIRONMENTAL HEALTH PERSPECTIVES 2019; 127:47007. [PMID: 31009264 PMCID: PMC6785228 DOI: 10.1289/ehp3971] [Citation(s) in RCA: 71] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/30/2018] [Revised: 02/20/2019] [Accepted: 03/07/2019] [Indexed: 05/20/2023]
Abstract
BACKGROUND The exposome is defined as the totality of environmental exposures from conception onwards. It calls for providing a holistic view of environmental exposures and their effects on human health by evaluating multiple environmental exposures simultaneously during critical periods of life. OBJECTIVE We evaluated the association of the urban exposome with birth weight. METHODS We estimated exposure to the urban exposome, including the built environment, air pollution, road traffic noise, meteorology, natural space, and road traffic (corresponding to 24 environmental indicators and 60 exposures) for nearly 32,000 pregnant women from six European birth cohorts. To evaluate associations with either continuous birth weight or term low birth weight (TLBW) risk, we primarily relied on the Deletion-Substitution-Addition (DSA) algorithm, which is an extension of the stepwise variable selection method. Second, we used an exposure-by-exposure exposome-wide association studies (ExWAS) method accounting for multiple hypotheses testing to report associations not adjusted for coexposures. RESULTS The most consistent statistically significant associations were observed between increasing green space exposure estimated as Normalized Difference Vegetation Index (NDVI) and increased birth weight and decreased TLBW risk. Furthermore, we observed statistically significant associations among presence of public bus line, land use Shannon's Evenness Index, and traffic density and birth weight in our DSA analysis. CONCLUSION This investigation is the first large urban exposome study of birth weight that tests many environmental urban exposures. It confirmed previously reported associations for NDVI and generated new hypotheses for a number of built-environment exposures. https://doi.org/10.1289/EHP3971.
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Affiliation(s)
- Mark J. Nieuwenhuijsen
- ISGlobal (Institute for Global Health), Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Lydiane Agier
- Team of environmental epidemiology applied to reproduction and respiratory health, Institut national de la santé et de la recherche médicale (Inserm, National Institute of Health & Medical Research), Institute for Advanced Biosciences (IAB), CNRS, Université Grenoble Alpes, Grenoble, France
| | - Xavier Basagaña
- ISGlobal (Institute for Global Health), Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Jose Urquiza
- ISGlobal (Institute for Global Health), Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Ibon Tamayo-Uria
- Department of Statistics, Faculty of Arts and Sciences, Harvard University, Cambridge, Massachusetts, USA
| | - Lise Giorgis-Allemand
- Team of environmental epidemiology applied to reproduction and respiratory health, Institut national de la santé et de la recherche médicale (Inserm, National Institute of Health & Medical Research), Institute for Advanced Biosciences (IAB), CNRS, Université Grenoble Alpes, Grenoble, France
| | - Oliver Robinson
- MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
| | - Valérie Siroux
- Team of environmental epidemiology applied to reproduction and respiratory health, Institut national de la santé et de la recherche médicale (Inserm, National Institute of Health & Medical Research), Institute for Advanced Biosciences (IAB), CNRS, Université Grenoble Alpes, Grenoble, France
| | - Léa Maitre
- ISGlobal (Institute for Global Health), Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Montserrat de Castro
- ISGlobal (Institute for Global Health), Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Antonia Valentin
- ISGlobal (Institute for Global Health), Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - David Donaire
- ISGlobal (Institute for Global Health), Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Payam Dadvand
- ISGlobal (Institute for Global Health), Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | | | | | | | - Leda Chatzi
- MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, USA
- Department of Social Medicine, University of Crete, Greece
- Department of Genetics & Cell Biology, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, Netherlands
| | | | | | | | - Rosie McEachan
- Bradford Institute for Health Research Bradford, Bradford, UK
| | - John Wright
- Bradford Institute for Health Research Bradford, Bradford, UK
| | - Jane West
- Bradford Institute for Health Research Bradford, Bradford, UK
| | - Jesús Ibarluzea
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
- Faculty of Psychology, University of the Basque Country UPV/EHU, San Sebastian, Basque Country, Spain
- Health Research Institute, BIODONOSTIA, San Sebastian, Basque Country, Spain
- Sub-Directorate for Public Health of Gipuzkoa, Department of Health, Government of the Basque Country, San Sebastian, Basque Country, Spain
| | - Ferran Ballester
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
- Nursing School, Universitat de València, Valencia, Spain
- Joint Research Unit of Epidemiology and Environmental Health, FISABIO–Universitat Jaume I–Universitat de València, Valencia, Spain
| | - Martine Vrijheid
- ISGlobal (Institute for Global Health), Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Rémy Slama
- Team of environmental epidemiology applied to reproduction and respiratory health, Institut national de la santé et de la recherche médicale (Inserm, National Institute of Health & Medical Research), Institute for Advanced Biosciences (IAB), CNRS, Université Grenoble Alpes, Grenoble, France
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26
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Esposito G, Azhari A, Borelli JL. Gene × Environment Interaction in Developmental Disorders: Where Do We Stand and What's Next? Front Psychol 2018; 9:2036. [PMID: 30416467 PMCID: PMC6212589 DOI: 10.3389/fpsyg.2018.02036] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2018] [Accepted: 10/03/2018] [Indexed: 02/01/2023] Open
Abstract
Although the field of psychiatry has witnessed the proliferation of studies on Gene × Environment (G×E) interactions, still limited is the knowledge we possess of G×E interactions regarding developmental disorders. In this perspective paper, we discuss why G×E interaction studies are needed to broaden our knowledge of developmental disorders. We also discuss the different roles of hazardous versus self-generated environmental factors and how these types of factors may differentially engage with an individual's genetic background in predicting a resulting phenotype. Then, we present examplar studies that highlight the role of G×E in predicting atypical developmental trajectories as well as provide insight regarding treatment outcomes. Supported by these examples, we explore the need to move beyond merely examining statistical interactions between genes and the environment, and the motivation to investigate specific genetic susceptibility and environmental contexts that drive developmental disorders. We propose that further parsing of genetic and environmental components is required to fully understand the unique contribution of each factor to the etiology of developmental disorders. Finally, with a greater appreciation of the complexities of G×E interaction, this discussion will converge upon the potential implications for clinical and translational research.
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Affiliation(s)
- Gianluca Esposito
- Psychology Program, Nanyang Technological University, Singapore, Singapore
- Department of Psychology and Cognitive Science, University of Trento, Trento, Italy
| | - Atiqah Azhari
- Psychology Program, Nanyang Technological University, Singapore, Singapore
| | - Jessica L. Borelli
- Department of Psychological Science, University of California, Irvine, Irvine, CA, United States
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27
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Mirzaei M, Khajeh M, Askarishahi M, Azizi R. Behavioral and familial predictors of diabetes mellitus in adults aged 20-69 in Yazd, Iran during 2014-2015. Diabetes Metab Syndr 2018; 12:667-671. [PMID: 29678604 DOI: 10.1016/j.dsx.2018.04.002] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/07/2018] [Accepted: 04/09/2018] [Indexed: 12/29/2022]
Abstract
INTRODUCTION The present study aimed to assess the behavioral and familial predictors of diabetes mellitus as well as their interaction in the risk of diabetes mellitus type2. METHODS The present cross-sectional study was conducted using the Yazd health study (YaHS) data which was collected in 2013-14. Statistical population of this study consisted of all 9340 individuals aged between 20 and 69 in Yazd City. Logistic regression was used to determine behavioral factors and family history of diabetes and their interaction in the risk of diabetes. RESULTS In the present study, age, family history of type 2 diabetes, waist-to-hip ratio, BMI, educational level, physical activity and smoking were considered as the risk factors for type 2 diabetes.There was a significant interaction(negative interaction) between family history of diabetes and other risk factors only for BMI, so that the risk of developing type-2 diabetes was lower in the presence of two risk factors- family history of diabetes and BMI- than the risk of diabetes in the presence of each of these factors. CONCLUSION Results of the present study suggested that despite the consideration of family history as an independent risk factor for type 2 diabetes, if it was used as a tool to raise the awareness and sensitivity in people with type 2 diabetes, it would reduce the risk of developing this type of diabetes in people who had other risk factors for type 2 diabetes.
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Affiliation(s)
- Masoud Mirzaei
- Yazd Cardiovascular Research Centre, Shahid Sadoughi University, Yazd, Iran
| | | | - Mohsen Askarishahi
- Department of Biostatistics, school of Health, Shahid Sadoughi University of Medical Sciences, Yazd, Iran
| | - Reyhaneh Azizi
- Department of Endocrinology, Faculty of Medicine, Shahid Sadoughi University, Yazd, Iran
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28
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Triangular relationship between CYP2R1 gene polymorphism, serum 25(OH)D 3 levels and T2DM in a Chinese rural population. Gene 2018; 678:172-176. [PMID: 30081191 DOI: 10.1016/j.gene.2018.08.006] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2018] [Revised: 08/01/2018] [Accepted: 08/02/2018] [Indexed: 01/08/2023]
Abstract
BACKGROUND A low serum vitamin D concentration is associated with an increased risk of type 2 diabetes mellitus (T2DM). Recently, several single nucleotid polymorphisms (SNPs) have been identified which influence vitamin D levels. If a causal relationship exists between vitamin D concentrations and T2DM, one would expect a similar association between the newly identified SNPs and T2DM risk. Therefore, this study investigated the association between four SNPs of cytochrome P450 family 2, subfamily R, peptide 1 (CYP2R1) gene, serum 25(OH)D3 levels and T2DM. METHODS Three hundred and ninety-seven patients with confirmed T2DM, as well as 397 age- and gender-matched controls were enrolled in this case-control study. Genotyping was performed by TaqMan probe assays. Kruskal-Wallis one-way analysis and muitiple logistic regression analysis were performed to identify the possible risk genotype for vitamin D levels and T2DM, respectively. Generalized multifactor dimensionality reduction (GMDR) was used to analyze the gene-gene and gene-environment interactions. RESULTS The serum 25(OH)D3 levels were significant lower in the T2DM group. Significant differences were observed between patients and controls in terms of the genotype distributions of rs1993116 (P = 0.048) and rs10766197 (P = 0.024). Similarly, rs1993116 and rs10766197 polymorphisms were found to be significantly associated with T2DM risk. AG + GG genotype carriers of the rs1993116 and rs10766197 polymorphisms could have an increased risk of developing T2DM compared with AA carriers, the OR and 95% CI were 1.64 (1.09-2.46) and 1.76 (1.18-2.65), respectively. However, none of the tested SNPs were independently associated with serum 25(OH)D3 levels (P > 0.059). Gene-gene and gene-environment interaction analyses indicated that rs12794714-rs10766197 and rs12794714-vitamin D deficiency (VDD) models successfully predicted T2DM risk (P < 0.001). CONCLUSIONS Rs1993116 and rs10766197 polymorphisms of CYP2R1 gene may be novel genetic markers for T2DM in China. Given the lack of association between SNPs and serum 25(OH)D3 levels, well-designed future studies should be conducted with larger sample sizes in rural areas of China.
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29
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Shankar K, Pivik RT, Johnson SL, van Ommen B, Demmer E, Murray R. Environmental Forces that Shape Early Development: What We Know and Still Need to Know. Curr Dev Nutr 2018; 2:nzx002. [PMID: 30167570 PMCID: PMC6111237 DOI: 10.3945/cdn.117.001826] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2017] [Revised: 09/18/2017] [Accepted: 11/15/2017] [Indexed: 01/22/2023] Open
Abstract
Understanding health requires more than knowledge of the genome. Environmental factors regulate gene function through epigenetics. Collectively, environmental exposures have been called the "exposome." Caregivers are instrumental in shaping exposures in a child's initial years. Maternal dietary patterns, physical activity, degree of weight gain, and body composition while pregnant will influence not only fetal growth, but also the infant's metabolic response to nutrients and energy. Maternal over- or underweight, excess caloric intake, nutrient imbalances, glucose dysregulation, and presence of chronic inflammatory states have been shown to establish risk for many later chronic diseases. During the period from birth to age 3 y, when the infant's metabolic rate is high and synaptogenesis and myelination of the brain are occurring extremely rapidly, the infant is especially prone to damaging effects from nutrient imbalances. During this period, the infant changes from a purely milk-based diet to one including a wide variety of foods. The process, timing, quality, and ultimate dietary pattern acquired are a direct outcome of the caregiver-infant feeding relationship, with potentially lifelong consequences. More research on how meal time interactions shape food acceptance is needed to avoid eating patterns that augment existing disease risk. Traditional clinical trials in nutrition, meant to isolate single factors for study, are inadequate to study the highly interconnected realm of environment-gene interactions in early life. Novel technologies are being used to gather broad exposure data on disparate populations, employing pioneering statistical approaches and correlations applied specifically to the individual, based on their genetic make-up and unique environmental experiences.
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Affiliation(s)
- Kartik Shankar
- Arkansas Children's Nutrition Research Center and Department of Pediatrics, University of Arkansas for Medical Sciences, Little Rock, AR
| | - R T Pivik
- Arkansas Children's Nutrition Research Center and Department of Pediatrics, University of Arkansas for Medical Sciences, Little Rock, AR
| | - Susan L Johnson
- Department of Pediatrics, Section of Nutrition, University of Colorado, Anschutz Medical Campus, Aurora, CO
| | - Ben van Ommen
- Netherlands Organization of Applied Scientifc Research (TNO), Zeist, Netherlands
| | | | - Robert Murray
- Department of Human Nutrition, Ohio State University, Columbus, OH
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Zhuang X, Ni A, Liao L, Guo Y, Dai W, Jiang Y, Zhou H, Hu X, Du Z, Wang X, Liao X. Environment-wide association study to identify novel factors associated with peripheral arterial disease: Evidence from the National Health and Nutrition Examination Survey (1999–2004). Atherosclerosis 2018; 269:172-177. [DOI: 10.1016/j.atherosclerosis.2018.01.006] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/06/2017] [Revised: 12/08/2017] [Accepted: 01/11/2018] [Indexed: 12/11/2022]
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Sayahi M, Shirali S. The Antidiabetic and Antioxidant Effects of Carotenoids: A Review. ASIAN JOURNAL OF PHARMACEUTICAL RESEARCH AND HEALTH CARE 2017. [DOI: 10.18311/ajprhc/2017/7689] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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Gavery MR, Roberts SB. Epigenetic considerations in aquaculture. PeerJ 2017; 5:e4147. [PMID: 29230373 PMCID: PMC5723431 DOI: 10.7717/peerj.4147] [Citation(s) in RCA: 45] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2017] [Accepted: 11/17/2017] [Indexed: 12/14/2022] Open
Abstract
Epigenetics has attracted considerable attention with respect to its potential value in many areas of agricultural production, particularly under conditions where the environment can be manipulated or natural variation exists. Here we introduce key concepts and definitions of epigenetic mechanisms, including DNA methylation, histone modifications and non-coding RNA, review the current understanding of epigenetics in both fish and shellfish, and propose key areas of aquaculture where epigenetics could be applied. The first key area is environmental manipulation, where the intention is to induce an ‘epigenetic memory’ either within or between generations to produce a desired phenotype. The second key area is epigenetic selection, which, alone or combined with genetic selection, may increase the reliability of producing animals with desired phenotypes. Based on aspects of life history and husbandry practices in aquaculture species, the application of epigenetic knowledge could significantly affect the productivity and sustainability of aquaculture practices. Conversely, clarifying the role of epigenetic mechanisms in aquaculture species may upend traditional assumptions about selection practices. Ultimately, there are still many unanswered questions regarding how epigenetic mechanisms might be leveraged in aquaculture.
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Affiliation(s)
- Mackenzie R Gavery
- School of Aquatic & Fishery Sciences, University of Washington, Seattle, WA, USA
| | - Steven B Roberts
- School of Aquatic & Fishery Sciences, University of Washington, Seattle, WA, USA
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McHale CM, Osborne G, Morello-Frosch R, Salmon AG, Sandy MS, Solomon G, Zhang L, Smith MT, Zeise L. Assessing health risks from multiple environmental stressors: Moving from G×E to I×E. MUTATION RESEARCH-REVIEWS IN MUTATION RESEARCH 2017; 775:11-20. [PMID: 29555026 DOI: 10.1016/j.mrrev.2017.11.003] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/24/2017] [Revised: 11/21/2017] [Accepted: 11/22/2017] [Indexed: 02/06/2023]
Abstract
Research on disease causation often attempts to isolate the effects of individual factors, including individual genes or environmental factors. This reductionist approach has generated many discoveries, but misses important interactive and cumulative effects that may help explain the broad range of variability in disease occurrence observed across studies and individuals. A disease rarely results from a single factor, and instead results from a broader combination of factors, characterized here as intrinsic (I) and extrinsic (E) factors. Intrinsic vulnerability or resilience emanates from a variety of both fixed and shifting biological factors including genetic traits, while extrinsic factors comprise all biologically-relevant external stressors encountered across the lifespan. The I×E concept incorporates the multi-factorial and dynamic nature of health and disease and provides a unified, conceptual basis for integrating results from multiple areas of research, including genomics, G×E, developmental origins of health and disease, and the exposome. We describe the utility of the I×E concept to better understand and characterize the cumulative impact of multiple extrinsic and intrinsic factors on individual and population health. New research methods increasingly facilitate the measurement of multifactorial and interactive effects in epidemiological and toxicological studies. Tiered or indicator-based approaches can guide the selection of potentially relevant I and E factors for study and quantification, and exposomics methods may eventually produce results that can be used to generate a response function over the life course. Quantitative data on I×E interactive effects should generate a better understanding of the variability in human response to environmental factors. The proposed I×E concept highlights the role for broader study design in order to identify extrinsic and intrinsic factors amenable to interventions at the individual and population levels in order to enhance resilience, reduce vulnerability and improve health.
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Affiliation(s)
- Cliona M McHale
- Superfund Research Center, School of Public Health, University of California, Berkeley, CA 94720, USA.
| | - Gwendolyn Osborne
- Office of Environmental Health Hazard Assessment, California Environmental Protection Agency, Oakland, CA 94612, USA
| | - Rachel Morello-Frosch
- Superfund Research Center, School of Public Health, University of California, Berkeley, CA 94720, USA; Department of Environmental Science, Policy and Management, University of California, Berkeley, CA 94720, USA
| | - Andrew G Salmon
- Office of Environmental Health Hazard Assessment, California Environmental Protection Agency, Oakland, CA 94612, USA
| | - Martha S Sandy
- Office of Environmental Health Hazard Assessment, California Environmental Protection Agency, Oakland, CA 94612, USA
| | - Gina Solomon
- California Environmental Protection Agency, Sacramento, CA 95814, USA
| | - Luoping Zhang
- Superfund Research Center, School of Public Health, University of California, Berkeley, CA 94720, USA
| | - Martyn T Smith
- Superfund Research Center, School of Public Health, University of California, Berkeley, CA 94720, USA
| | - Lauren Zeise
- Office of Environmental Health Hazard Assessment, California Environmental Protection Agency, Oakland, CA 94612, USA
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Hall MA, Moore JH, Ritchie MD. Embracing Complex Associations in Common Traits: Critical Considerations for Precision Medicine. Trends Genet 2017; 32:470-484. [PMID: 27392675 DOI: 10.1016/j.tig.2016.06.001] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2016] [Revised: 06/01/2016] [Accepted: 06/02/2016] [Indexed: 10/21/2022]
Abstract
Genome-wide association studies (GWAS) have identified numerous loci associated with human phenotypes. This approach, however, does not consider the richly diverse and complex environment with which humans interact throughout the life course, nor does it allow for interrelationships between genetic loci and across traits. As we move toward making precision medicine a reality, whereby we make predictions about disease risk based on genomic profiles, we need to identify improved predictive models of the relationship between genome and phenome. Methods that embrace pleiotropy (the effect of one locus on more than one trait), and gene-environment (G×E) and gene-gene (G×G) interactions, will further unveil the impact of alterations in biological pathways and identify genes that are only involved with disease in the context of the environment. This valuable information can be used to assess personal risk and choose the most appropriate medical interventions based on the genotype and environment of an individual, the whole premise of precision medicine.
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Affiliation(s)
- Molly A Hall
- Institute for Biomedical Informatics, Departments of Genetics and Biostatistics and Epidemiology, Perelman School of Medicine, University of Pennsylvania, 3535 Market Street, Philadelphia, PA 19104, USA
| | - Jason H Moore
- Institute for Biomedical Informatics, Departments of Genetics and Biostatistics and Epidemiology, Perelman School of Medicine, University of Pennsylvania, 3535 Market Street, Philadelphia, PA 19104, USA
| | - Marylyn D Ritchie
- Biomedical and Translational Informatics, Geisinger Health System, Danville, PA, USA; Department of Biochemistry and Molecular Biology, Center for Systems Genomics, Eberly College of Science, The Pennsylvania State University, University Park, PA, USA.
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McAllister K, Mechanic LE, Amos C, Aschard H, Blair IA, Chatterjee N, Conti D, Gauderman WJ, Hsu L, Hutter CM, Jankowska MM, Kerr J, Kraft P, Montgomery SB, Mukherjee B, Papanicolaou GJ, Patel CJ, Ritchie MD, Ritz BR, Thomas DC, Wei P, Witte JS, on behalf of workshop participants. Current Challenges and New Opportunities for Gene-Environment Interaction Studies of Complex Diseases. Am J Epidemiol 2017; 186:753-761. [PMID: 28978193 PMCID: PMC5860428 DOI: 10.1093/aje/kwx227] [Citation(s) in RCA: 116] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2016] [Revised: 03/14/2017] [Accepted: 03/16/2017] [Indexed: 12/25/2022] Open
Abstract
Recently, many new approaches, study designs, and statistical and analytical methods have emerged for studying gene-environment interactions (G×Es) in large-scale studies of human populations. There are opportunities in this field, particularly with respect to the incorporation of -omics and next-generation sequencing data and continual improvement in measures of environmental exposures implicated in complex disease outcomes. In a workshop called "Current Challenges and New Opportunities for Gene-Environment Interaction Studies of Complex Diseases," held October 17-18, 2014, by the National Institute of Environmental Health Sciences and the National Cancer Institute in conjunction with the annual American Society of Human Genetics meeting, participants explored new approaches and tools that have been developed in recent years for G×E discovery. This paper highlights current and critical issues and themes in G×E research that need additional consideration, including the improved data analytical methods, environmental exposure assessment, and incorporation of functional data and annotations.
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Affiliation(s)
| | - Leah E. Mechanic
- Correspondence to Dr. Leah E. Mechanic, Genomic Epidemiology Branch, Epidemiology and Genomics Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute, 9609 Medical Center Drive, Room 4E104, MSC 9763, Bethesda, MD 20892 (e-mail: )
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Patel CJ, Kerr J, Thomas DC, Mukherjee B, Ritz B, Chatterjee N, Jankowska M, Madan J, Karagas MR, McAllister KA, Mechanic LE, Fallin MD, Ladd-Acosta C, Blair IA, Teitelbaum SL, Amos CI. Opportunities and Challenges for Environmental Exposure Assessment in Population-Based Studies. Cancer Epidemiol Biomarkers Prev 2017; 26:1370-1380. [PMID: 28710076 PMCID: PMC5581729 DOI: 10.1158/1055-9965.epi-17-0459] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2017] [Revised: 06/14/2017] [Accepted: 06/22/2017] [Indexed: 12/15/2022] Open
Abstract
A growing number and increasing diversity of factors are available for epidemiological studies. These measures provide new avenues for discovery and prevention, yet they also raise many challenges for adoption in epidemiological investigations. Here, we evaluate 1) designs to investigate diseases that consider heterogeneous and multidimensional indicators of exposure and behavior, 2) the implementation of numerous methods to capture indicators of exposure, and 3) the analytical methods required for discovery and validation. We find that case-control studies have provided insights into genetic susceptibility but are insufficient for characterizing complex effects of environmental factors on disease development. Prospective and two-phase designs are required but must balance extended data collection with follow-up of study participants. We discuss innovations in assessments including the microbiome; mass spectrometry and metabolomics; behavioral assessment; dietary, physical activity, and occupational exposure assessment; air pollution monitoring; and global positioning and individual sensors. We claim the the availability of extensive correlated data raises new challenges in disentangling specific exposures that influence cancer risk from among extensive and often correlated exposures. In conclusion, new high-dimensional exposure assessments offer many new opportunities for environmental assessment in cancer development. Cancer Epidemiol Biomarkers Prev; 26(9); 1370-80. ©2017 AACR.
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Affiliation(s)
- Chirag J Patel
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts.
| | - Jacqueline Kerr
- Department of Family Medicine and Public Health, University of California San Diego, La Jolla, California
| | - Duncan C Thomas
- Department of Preventive Medicine, University of Southern California, Los Angeles, California
| | - Bhramar Mukherjee
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, Michigan
| | - Beate Ritz
- Department of Epidemiology, Fielding School of Public Health, University of California Los Angeles, Los Angeles, California
| | - Nilanjan Chatterjee
- Department of Biostatistics and Department of Oncology, Johns Hopkins University, Baltimore, Maryland
| | - Marta Jankowska
- Department of Family Medicine and Public Health, University of California San Diego, La Jolla, California
| | - Juliette Madan
- Division of Neonatology, Department of Pediatrics, Dartmouth-Hitchcock Medical Center, Lebanon, New Hampshire
| | - Margaret R Karagas
- Department of Epidemiology, Geisel School of Medicine, Dartmouth College, Lebanon, New Hampshire
| | - Kimberly A McAllister
- Susceptibility and Population Health Branch, National Institute of Environmental Health Sciences, NIH, Research Triangle Park, North Carolina
| | - Leah E Mechanic
- Epidemiology and Genomics Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute, NIH, Bethesda, Maryland
| | - M Daniele Fallin
- Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland
| | | | - Ian A Blair
- Center of Excellence in Environmental Toxicology and Penn SRP Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
- Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Susan L Teitelbaum
- Department of Preventive Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Christopher I Amos
- Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth College, Lebanon, New Hampshire.
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Rani J, Mittal I, Pramanik A, Singh N, Dube N, Sharma S, Puniya BL, Raghunandanan MV, Mobeen A, Ramachandran S. T2DiACoD: A Gene Atlas of Type 2 Diabetes Mellitus Associated Complex Disorders. Sci Rep 2017; 7:6892. [PMID: 28761062 PMCID: PMC5537262 DOI: 10.1038/s41598-017-07238-0] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2016] [Accepted: 06/28/2017] [Indexed: 12/11/2022] Open
Abstract
We performed integrative analysis of genes associated with type 2 Diabetes Mellitus (T2DM) associated complications by automated text mining with manual curation and also gene expression analysis from Gene Expression Omnibus. They were analysed for pathogenic or protective role, trends, interaction with risk factors, Gene Ontology enrichment and tissue wise differential expression. The database T2DiACoD houses 650 genes, and 34 microRNAs associated with T2DM complications. Seven genes AGER, TNFRSF11B, CRK, PON1, ADIPOQ, CRP and NOS3 are associated with all 5 complications. Several genes are studied in multiple years in all complications with high proportion in cardiovascular (75.8%) and atherosclerosis (51.3%). T2DM Patients' skeletal muscle tissues showed high fold change in differentially expressed genes. Among the differentially expressed genes, VEGFA is associated with several complications of T2DM. A few genes ACE2, ADCYAP1, HDAC4, NCF1, NFE2L2, OSM, SMAD1, TGFB1, BDNF, SYVN1, TXNIP, CD36, CYP2J2, NLRP3 with details of protective role are catalogued. Obesity is clearly a dominant risk factor interacting with the genes of T2DM complications followed by inflammation, diet and stress to variable extents. This information emerging from the integrative approach used in this work could benefit further therapeutic approaches. The T2DiACoD is available at www.http://t2diacod.igib.res.in/ .
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Affiliation(s)
- Jyoti Rani
- G N Ramachandran Knowledge of Centre, Council of Scientific and Industrial Research - Institute of Genomics and Integrative Biology (CSIR-IGIB), Room No. 130, Mathura Road, New Delhi, 110025, India
| | - Inna Mittal
- G N Ramachandran Knowledge of Centre, Council of Scientific and Industrial Research - Institute of Genomics and Integrative Biology (CSIR-IGIB), Room No. 130, Mathura Road, New Delhi, 110025, India
| | - Atreyi Pramanik
- G N Ramachandran Knowledge of Centre, Council of Scientific and Industrial Research - Institute of Genomics and Integrative Biology (CSIR-IGIB), Room No. 130, Mathura Road, New Delhi, 110025, India
| | - Namita Singh
- G N Ramachandran Knowledge of Centre, Council of Scientific and Industrial Research - Institute of Genomics and Integrative Biology (CSIR-IGIB), Room No. 130, Mathura Road, New Delhi, 110025, India
| | - Namita Dube
- G N Ramachandran Knowledge of Centre, Council of Scientific and Industrial Research - Institute of Genomics and Integrative Biology (CSIR-IGIB), Room No. 130, Mathura Road, New Delhi, 110025, India
| | - Smriti Sharma
- G N Ramachandran Knowledge of Centre, Council of Scientific and Industrial Research - Institute of Genomics and Integrative Biology (CSIR-IGIB), Room No. 130, Mathura Road, New Delhi, 110025, India
| | - Bhanwar Lal Puniya
- G N Ramachandran Knowledge of Centre, Council of Scientific and Industrial Research - Institute of Genomics and Integrative Biology (CSIR-IGIB), Room No. 130, Mathura Road, New Delhi, 110025, India
| | - Muthukurussi Varieth Raghunandanan
- G N Ramachandran Knowledge of Centre, Council of Scientific and Industrial Research - Institute of Genomics and Integrative Biology (CSIR-IGIB), Room No. 130, Mathura Road, New Delhi, 110025, India
| | - Ahmed Mobeen
- G N Ramachandran Knowledge of Centre, Council of Scientific and Industrial Research - Institute of Genomics and Integrative Biology (CSIR-IGIB), Room No. 130, Mathura Road, New Delhi, 110025, India
- Academy of Scientific and Innovative Research, CSIR-IGIB South Campus, New Delhi, 110025, India
| | - Srinivasan Ramachandran
- G N Ramachandran Knowledge of Centre, Council of Scientific and Industrial Research - Institute of Genomics and Integrative Biology (CSIR-IGIB), Room No. 130, Mathura Road, New Delhi, 110025, India.
- Academy of Scientific and Innovative Research, CSIR-IGIB South Campus, New Delhi, 110025, India.
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Magni P, Bier DM, Pecorelli S, Agostoni C, Astrup A, Brighenti F, Cook R, Folco E, Fontana L, Gibson RA, Guerra R, Guyatt GH, Ioannidis JPA, Jackson AS, Klurfeld DM, Makrides M, Mathioudakis B, Monaco A, Patel CJ, Racagni G, Schünemann HJ, Shamir R, Zmora N, Peracino A. Perspective: Improving Nutritional Guidelines for Sustainable Health Policies: Current Status and Perspectives. Adv Nutr 2017; 8:532-545. [PMID: 28710141 PMCID: PMC5502870 DOI: 10.3945/an.116.014738] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
A large body of evidence supports the notion that incorrect or insufficient nutrition contributes to disease development. A pivotal goal is thus to understand what exactly is appropriate and what is inappropriate in food ingestion and the consequent nutritional status and health. The effective application of these concepts requires the translation of scientific information into practical approaches that have a tangible and measurable impact at both individual and population levels. The agenda for the future is expected to support available methodology in nutrition research to personalize guideline recommendations, properly grading the quality of the available evidence, promoting adherence to the well-established evidence hierarchy in nutrition, and enhancing strategies for appropriate vetting and transparent reporting that will solidify the recommendations for health promotion. The final goal is to build a constructive coalition among scientists, policy makers, and communication professionals for sustainable health and nutritional policies. Currently, a strong rationale and available data support a personalized dietary approach according to personal variables, including sex and age, circulating metabolic biomarkers, food quality and intake frequency, lifestyle variables such as physical activity, and environmental variables including one's microbiome profile. There is a strong and urgent need to develop a successful commitment among all the stakeholders to define novel and sustainable approaches toward the management of the health value of nutrition at individual and population levels. Moving forward requires adherence to well-established principles of evidence evaluation as well as identification of effective tools to obtain better quality evidence. Much remains to be done in the near future.
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Affiliation(s)
- Paolo Magni
- Department of Pharmacological and Biomolecular Sciences, and
| | - Dennis M Bier
- Children’s Nutrition Research Center, Baylor College of Medicine, Houston, TX
| | | | - Carlo Agostoni
- Fondazione IRCCS Cà Granda Ospedale Maggiore Policlinico, DISCCO, Università degli Studi di Milano, Milan, Italy
| | - Arne Astrup
- Department of Nutrition, Exercise and Sports, University of Copenhagen, Copenhagen, Denmark
| | - Furio Brighenti
- Department of Food Sciences, University of Parma, Parma, Italy
| | - Robert Cook
- Bazian, Economist Intelligence Unit Healthcare, London, United Kingdom
| | - Emanuela Folco
- Giovanni Lorenzini Medical Science Foundation, Milan, Italy
| | - Luigi Fontana
- Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy;,Department of Medicine, Washington University, St. Louis, MO
| | - Robert A Gibson
- School of Agriculture, Food and Wine, FOODplus Research Centre, University of Adelaide, Adelaide, Australia
| | - Ranieri Guerra
- Department of Preventive Health, Ministry of Health, Rome, Italy
| | - Gordon H Guyatt
- Department of Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, Ontario, Canada
| | - John PA Ioannidis
- Department of Health Policy and Research, Stanford University, Stanford, CA
| | - Ann S Jackson
- Giovanni Lorenzini Medical Science Foundation, Houston, TX
| | - David M Klurfeld
- Human Nutrition Program, USDA Agricultural Research Service, Beltsville, MD
| | - Maria Makrides
- Healthy Mothers, Babies and Children, South Australian Health and Medical Research Institute, Adelaide, Australia
| | | | | | - Chirag J Patel
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA
| | - Giorgio Racagni
- Department of Pharmacological and Biomolecular Sciences, and
| | - Holger J Schünemann
- Department of Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, Ontario, Canada
| | - Raanan Shamir
- Institute of Gastroenterology, Nutrition and Liver Diseases, Schneider Children’s Medical Center of Israel, Sackler Faculty of Medicine, University of Tel Aviv, Tel Aviv, Israel; and
| | - Niv Zmora
- Department of Immunology, Weizmann Institute of Science, Rehovot, Israel
| | - Andrea Peracino
- Giovanni Lorenzini Medical Science Foundation, Milan, Italy;
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Gene-Diet Interactions in Type 2 Diabetes: The Chicken and Egg Debate. Int J Mol Sci 2017; 18:ijms18061188. [PMID: 28574454 PMCID: PMC5486011 DOI: 10.3390/ijms18061188] [Citation(s) in RCA: 44] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2017] [Revised: 05/23/2017] [Accepted: 05/26/2017] [Indexed: 02/07/2023] Open
Abstract
Consistent evidence from both experimental and human studies indicates that Type 2 diabetes mellitus (T2DM) is a complex disease resulting from the interaction of genetic, epigenetic, environmental, and lifestyle factors. Nutrients and dietary patterns are important environmental factors to consider in the prevention, development and treatment of this disease. Nutritional genomics focuses on the interaction between bioactive food components and the genome and includes studies of nutrigenetics, nutrigenomics and epigenetic modifications caused by nutrients. There is evidence supporting the existence of nutrient-gene and T2DM interactions coming from animal studies and family-based intervention studies. Moreover, many case-control, cohort, cross-sectional cohort studies and clinical trials have identified relationships between individual genetic load, diet and T2DM. Some of these studies were on a large scale. In addition, studies with animal models and human observational studies, in different countries over periods of time, support a causative relationship between adverse nutritional conditions during in utero development, persistent epigenetic changes and T2DM. This review provides comprehensive information on the current state of nutrient-gene interactions and their role in T2DM pathogenesis, the relationship between individual genetic load and diet, and the importance of epigenetic factors in influencing gene expression and defining the individual risk of T2DM.
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Manrai AK, Cui Y, Bushel PR, Hall M, Karakitsios S, Mattingly CJ, Ritchie M, Schmitt C, Sarigiannis DA, Thomas DC, Wishart D, Balshaw DM, Patel CJ. Informatics and Data Analytics to Support Exposome-Based Discovery for Public Health. Annu Rev Public Health 2017; 38:279-294. [PMID: 28068484 PMCID: PMC5774331 DOI: 10.1146/annurev-publhealth-082516-012737] [Citation(s) in RCA: 69] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
The complexity of the human exposome-the totality of environmental exposures encountered from birth to death-motivates systematic, high-throughput approaches to discover new environmental determinants of disease. In this review, we describe the state of science in analyzing the human exposome and provide recommendations for the public health community to consider in dealing with analytic challenges of exposome-based biomedical research. We describe extant and novel analytic methods needed to associate the exposome with critical health outcomes and contextualize the data-centered challenges by drawing parallels to other research endeavors such as human genomics research. We discuss efforts for training scientists who can bridge public health, genomics, and biomedicine in informatics and statistics. If an exposome data ecosystem is brought to fruition, it will likely play a role as central as genomic science has had in molding the current and new generations of biomedical researchers, computational scientists, and public health research programs.
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Affiliation(s)
- Arjun K Manrai
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts 02115;
| | - Yuxia Cui
- National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, North Carolina 27709;
| | - Pierre R Bushel
- National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, North Carolina 27709;
| | - Molly Hall
- Center for Systems Genomics, The Pennsylvania State University, College Station, Pennsylvania 16802
| | - Spyros Karakitsios
- Environmental Engineering Laboratory, Department of Chemical Engineering, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece
| | - Carolyn J Mattingly
- Department of Biological Sciences, College of Sciences, North Carolina State University, Raleigh, North Carolina 27695
| | - Marylyn Ritchie
- Center for Systems Genomics, The Pennsylvania State University, College Station, Pennsylvania 16802
- Geisinger Health System, Danville, Pennsylvania 17821
| | - Charles Schmitt
- Renaissance Computing Institute, Chapel Hill, North Carolina 27517
| | - Denis A Sarigiannis
- Environmental Engineering Laboratory, Department of Chemical Engineering, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece
| | - Duncan C Thomas
- Division of Biostatistics, Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California 90089-9011
| | - David Wishart
- Departments of Biological Sciences and Computing Science, University of Alberta, Edmonton, Alberta T6G 2E8, Canada
| | - David M Balshaw
- National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, North Carolina 27709;
| | - Chirag J Patel
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts 02115;
- Center for Assessment Technology and Continuous Health, Massachusetts General Hospital, Boston, Massachusetts 02114
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Poveda A, Chen Y, Brändström A, Engberg E, Hallmans G, Johansson I, Renström F, Kurbasic A, Franks PW. The heritable basis of gene-environment interactions in cardiometabolic traits. Diabetologia 2017; 60:442-452. [PMID: 28004149 PMCID: PMC6518092 DOI: 10.1007/s00125-016-4184-0] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/28/2016] [Accepted: 11/24/2016] [Indexed: 11/17/2022]
Abstract
AIMS/HYPOTHESIS Little is known about the heritable basis of gene-environment interactions in humans. We therefore screened multiple cardiometabolic traits to assess the probability that they are influenced by genotype-environment interactions. METHODS Fourteen established environmental risk exposures and 11 cardiometabolic traits were analysed in the VIKING study, a cohort of 16,430 Swedish adults from 1682 extended pedigrees with available detailed genealogical, phenotypic and demographic information, using a maximum likelihood variance decomposition method in Sequential Oligogenic Linkage Analysis Routines software. RESULTS All cardiometabolic traits had statistically significant heritability estimates, with narrow-sense heritabilities (h 2) ranging from 24% to 47%. Genotype-environment interactions were detected for age and sex (for the majority of traits), physical activity (for triacylglycerols, 2 h glucose and diastolic BP), smoking (for weight), alcohol intake (for weight, BMI and 2 h glucose) and diet pattern (for weight, BMI, glycaemic traits and systolic BP). Genotype-age interactions for weight and systolic BP, genotype-sex interactions for BMI and triacylglycerols and genotype-alcohol intake interactions for weight remained significant after multiple test correction. CONCLUSIONS/INTERPRETATION Age, sex and alcohol intake are likely to be major modifiers of genetic effects for a range of cardiometabolic traits. This information may prove valuable for studies that seek to identify specific loci that modify the effects of lifestyle in cardiometabolic disease.
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Affiliation(s)
- Alaitz Poveda
- Genetic and Molecular Epidemiology Unit, Department of Clinical Sciences, Clinical Research Centre, Lund University, Jan Waldenströms gata 35, Building 91, Skåne University Hospital, SE-20502, Malmö, Sweden
- Department of Genetics, Physical Anthropology and Animal Physiology, Faculty of Science and Technology, University of the Basque Country (UPV/EHU), Bilbao, Spain
| | - Yan Chen
- Genetic and Molecular Epidemiology Unit, Department of Clinical Sciences, Clinical Research Centre, Lund University, Jan Waldenströms gata 35, Building 91, Skåne University Hospital, SE-20502, Malmö, Sweden
| | - Anders Brändström
- Centre for Demographic and Ageing Research, Umeå University, Umeå, Sweden
| | - Elisabeth Engberg
- Centre for Demographic and Ageing Research, Umeå University, Umeå, Sweden
| | - Göran Hallmans
- Department of Biobank Research, Umeå University, Umeå, Sweden
| | | | - Frida Renström
- Genetic and Molecular Epidemiology Unit, Department of Clinical Sciences, Clinical Research Centre, Lund University, Jan Waldenströms gata 35, Building 91, Skåne University Hospital, SE-20502, Malmö, Sweden
- Department of Biobank Research, Umeå University, Umeå, Sweden
| | - Azra Kurbasic
- Genetic and Molecular Epidemiology Unit, Department of Clinical Sciences, Clinical Research Centre, Lund University, Jan Waldenströms gata 35, Building 91, Skåne University Hospital, SE-20502, Malmö, Sweden
| | - Paul W Franks
- Genetic and Molecular Epidemiology Unit, Department of Clinical Sciences, Clinical Research Centre, Lund University, Jan Waldenströms gata 35, Building 91, Skåne University Hospital, SE-20502, Malmö, Sweden.
- Department of Public Health and Clinical Medicine, Section for Medicine, Umeå University, Umeå, Sweden.
- Department of Nutrition, Harvard T. H. Chan School of Public Health, Boston, MA, USA.
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Patel CJ. Analytic Complexity and Challenges in Identifying Mixtures of Exposures Associated with Phenotypes in the Exposome Era. CURR EPIDEMIOL REP 2017; 4:22-30. [PMID: 28251040 PMCID: PMC5306298 DOI: 10.1007/s40471-017-0100-5] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/30/2022]
Abstract
PURPOSE OF REVIEW Mixtures, or combinations and interactions between multiple environmental exposures, are hypothesized to be causally linked with disease and health-related phenotypes. Established and emerging molecular measurement technologies to assay the exposome, the comprehensive battery of exposures encountered from birth to death, promise a new way of identifying mixtures in disease in the epidemiological setting. In this opinion, we describe the analytic complexity and challenges in identifying mixtures associated with phenotype and disease. RECENT FINDINGS Existing and emerging machine-learning methods and data analytic approaches (e.g., "environment-wide association studies" [EWASs]), as well as large cohorts may enhance possibilities to identify mixtures of correlated exposures associated with phenotypes; however, the analytic complexity of identifying mixtures is immense. SUMMARY If the exposome concept is realized, new analytical methods and large sample sizes will be required to ascertain how mixtures are associated with disease. The author recommends documenting prevalent correlated exposures and replicated main effects prior to identifying mixtures.
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Affiliation(s)
- Chirag J. Patel
- Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck St, Boston, MA 02115 USA
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Li J, Sun C, Liu S, Li Y. Dietary Protein Intake and Type 2 Diabetes Among Women and Men in Northeast China. Sci Rep 2016; 6:37604. [PMID: 27897193 PMCID: PMC5126628 DOI: 10.1038/srep37604] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2016] [Accepted: 11/01/2016] [Indexed: 12/18/2022] Open
Abstract
We conducted a comprehensive and in-depth assessment of different dietary protein sources related to type 2 diabetes (T2D) and determined whether the association is mediated by insulin resistance (IR) and β-cell dysfunction in a population-based cross sectional study of 4,427 women and 2,394 men aged 20–74 years in northeast China. We observed that the intake of total protein, animal protein, and red meat protein was positively associated with T2D prevalence in women. Comparing the women in the highest quintile of protein intake with those in the lowest quintile, the multivariable-adjusted odds ratios of T2D were 2.13 [95% confidence interval (CI): 1.18–3.81] for total protein, 2.27 (95% CI: 1.18–4.35) for animal protein, and 1.75 (95% CI: 1.14–2.68) for red meat protein. Mediation analyses indicated that these associations were mediated mainly by the IR as measured by the homeostasis model (HOMA-IR). The proportions via the mediation of HOMA-IR were 29.0% (95% CI: 10.3%–55.5%), 35.0% (95% CI: 12.9%–83.3%), and 17.2% (95% CI: 5.2%–44.8%) for total protein-, animal protein-, and red meat protein–T2D associations, respectively. These findings support the notion that modifying the sources of dietary protein may be potentially applied to prevent T2D.
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Affiliation(s)
- Jie Li
- National Key Discipline, Department of Nutrition and Food Hygiene, School of Public Health, Harbin Medical University, Harbin, China.,Departments of Epidemiology and Medicine, Center for Global Cardiometabolic Health, Brown University, Providence, RI USA
| | - Changhao Sun
- National Key Discipline, Department of Nutrition and Food Hygiene, School of Public Health, Harbin Medical University, Harbin, China
| | - Simin Liu
- Departments of Epidemiology and Medicine, Center for Global Cardiometabolic Health, Brown University, Providence, RI USA
| | - Ying Li
- National Key Discipline, Department of Nutrition and Food Hygiene, School of Public Health, Harbin Medical University, Harbin, China
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Cote I, Andersen ME, Ankley GT, Barone S, Birnbaum LS, Boekelheide K, Bois FY, Burgoon LD, Chiu WA, Crawford-Brown D, Crofton KM, DeVito M, Devlin RB, Edwards SW, Guyton KZ, Hattis D, Judson RS, Knight D, Krewski D, Lambert J, Maull EA, Mendrick D, Paoli GM, Patel CJ, Perkins EJ, Poje G, Portier CJ, Rusyn I, Schulte PA, Simeonov A, Smith MT, Thayer KA, Thomas RS, Thomas R, Tice RR, Vandenberg JJ, Villeneuve DL, Wesselkamper S, Whelan M, Whittaker C, White R, Xia M, Yauk C, Zeise L, Zhao J, DeWoskin RS. The Next Generation of Risk Assessment Multi-Year Study-Highlights of Findings, Applications to Risk Assessment, and Future Directions. ENVIRONMENTAL HEALTH PERSPECTIVES 2016; 124:1671-1682. [PMID: 27091369 PMCID: PMC5089888 DOI: 10.1289/ehp233] [Citation(s) in RCA: 68] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/12/2015] [Revised: 10/30/2015] [Accepted: 03/29/2016] [Indexed: 05/19/2023]
Abstract
BACKGROUND The Next Generation (NexGen) of Risk Assessment effort is a multi-year collaboration among several organizations evaluating new, potentially more efficient molecular, computational, and systems biology approaches to risk assessment. This article summarizes our findings, suggests applications to risk assessment, and identifies strategic research directions. OBJECTIVE Our specific objectives were to test whether advanced biological data and methods could better inform our understanding of public health risks posed by environmental exposures. METHODS New data and methods were applied and evaluated for use in hazard identification and dose-response assessment. Biomarkers of exposure and effect, and risk characterization were also examined. Consideration was given to various decision contexts with increasing regulatory and public health impacts. Data types included transcriptomics, genomics, and proteomics. Methods included molecular epidemiology and clinical studies, bioinformatic knowledge mining, pathway and network analyses, short-duration in vivo and in vitro bioassays, and quantitative structure activity relationship modeling. DISCUSSION NexGen has advanced our ability to apply new science by more rapidly identifying chemicals and exposures of potential concern, helping characterize mechanisms of action that influence conclusions about causality, exposure-response relationships, susceptibility and cumulative risk, and by elucidating new biomarkers of exposure and effects. Additionally, NexGen has fostered extensive discussion among risk scientists and managers and improved confidence in interpreting and applying new data streams. CONCLUSIONS While considerable uncertainties remain, thoughtful application of new knowledge to risk assessment appears reasonable for augmenting major scope assessments, forming the basis for or augmenting limited scope assessments, and for prioritization and screening of very data limited chemicals. Citation: Cote I, Andersen ME, Ankley GT, Barone S, Birnbaum LS, Boekelheide K, Bois FY, Burgoon LD, Chiu WA, Crawford-Brown D, Crofton KM, DeVito M, Devlin RB, Edwards SW, Guyton KZ, Hattis D, Judson RS, Knight D, Krewski D, Lambert J, Maull EA, Mendrick D, Paoli GM, Patel CJ, Perkins EJ, Poje G, Portier CJ, Rusyn I, Schulte PA, Simeonov A, Smith MT, Thayer KA, Thomas RS, Thomas R, Tice RR, Vandenberg JJ, Villeneuve DL, Wesselkamper S, Whelan M, Whittaker C, White R, Xia M, Yauk C, Zeise L, Zhao J, DeWoskin RS. 2016. The Next Generation of Risk Assessment multiyear study-highlights of findings, applications to risk assessment, and future directions. Environ Health Perspect 124:1671-1682; http://dx.doi.org/10.1289/EHP233.
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Affiliation(s)
- Ila Cote
- National Center for Environmental Assessment, U.S. Environmental Protection Agency (EPA), Washington, District of Columbia, USA
- Address correspondence to I. Cote, U.S. Environmental Protection Agency, Region 8, Room 8152, 1595 Wynkoop St., Denver, CO 80202-1129 USA. Telephone: (202) 288-9539. E-mail:
| | | | - Gerald T. Ankley
- National Health and Environmental Effects Research Laboratory, U.S. EPA, Duluth, Minnesota, USA
| | - Stanley Barone
- Office of Chemical Safety and Pollution Prevention, U.S. EPA, Washington, District of Columbia, USA
| | - Linda S. Birnbaum
- National Institute of Environmental Health Sciences, and
- National Toxicology Program, National Institutes of Health (NIH), Department of Health and Human Services (DHHS), Research Triangle Park, North Carolina, USA
| | - Kim Boekelheide
- Department of Pathology and Laboratory Medicine, Brown University, Providence, Rhode Island, USA
| | - Frederic Y. Bois
- Unité Modèles pour l’Écotoxicologie et la Toxicologie, Institut National de l’Environnement Industriel et des Risques, Verneuil en Halatte, France
| | - Lyle D. Burgoon
- U.S. Army Engineer Research and Development Center, Research Triangle Park, North Carolina, USA
| | - Weihsueh A. Chiu
- Department of Veterinary Integrative Biosciences, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, Texas, USA
| | | | | | - Michael DeVito
- National Institute of Environmental Health Sciences, and
- National Toxicology Program, National Institutes of Health (NIH), Department of Health and Human Services (DHHS), Research Triangle Park, North Carolina, USA
| | - Robert B. Devlin
- National Health and Environmental Effects Research Laboratory, U.S. EPA, Research Triangle Park, North Carolina, USA
| | - Stephen W. Edwards
- National Health and Environmental Effects Research Laboratory, U.S. EPA, Research Triangle Park, North Carolina, USA
| | | | - Dale Hattis
- George Perkins Marsh Institute, Clark University, Worcester, Massachusetts, USA
| | | | - Derek Knight
- European Chemicals Agency, Annankatu, Helsinki, Finland
| | - Daniel Krewski
- McLaughlin Centre for Population Health Risk Assessment, University of Ottawa, Ottawa, Ontario, Canada
| | - Jason Lambert
- National Center for Environmental Assessment, U.S. EPA, Cincinnati, Ohio, USA
| | - Elizabeth Anne Maull
- National Institute of Environmental Health Sciences, and
- National Toxicology Program, National Institutes of Health (NIH), Department of Health and Human Services (DHHS), Research Triangle Park, North Carolina, USA
| | - Donna Mendrick
- National Center for Toxicological Research, Food and Drug Administration, Jefferson, Arkansas, USA
| | | | - Chirag Jagdish Patel
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
| | - Edward J. Perkins
- U.S. Army Engineer Research and Development Center, Vicksburg, Mississippi, USA
| | - Gerald Poje
- Grant Consulting Group, Washington, District of Columbia, USA
| | | | - Ivan Rusyn
- Department of Veterinary Integrative Biosciences, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, Texas, USA
| | - Paul A. Schulte
- Education and Information Division, National Institute for Occupational Safety and Health, Centers for Disease Control and Prevention, Cincinnati, Ohio, USA
| | - Anton Simeonov
- National Center for Advancing Translational Sciences, NIH, DHHS, Bethesda, Maryland, USA
| | - Martyn T. Smith
- Division of Environmental Health Sciences, School of Public Health, University of California, Berkeley, Berkeley, California, USA
| | - Kristina A. Thayer
- National Institute of Environmental Health Sciences, and
- National Toxicology Program, National Institutes of Health (NIH), Department of Health and Human Services (DHHS), Research Triangle Park, North Carolina, USA
| | | | - Reuben Thomas
- Gladstone Institutes, University of California, San Francisco, San Francisco, California, USA
| | - Raymond R. Tice
- National Institute of Environmental Health Sciences, and
- National Toxicology Program, National Institutes of Health (NIH), Department of Health and Human Services (DHHS), Research Triangle Park, North Carolina, USA
| | - John J. Vandenberg
- National Center for Environmental Assessment, U.S. Environmental Protection Agency (EPA), Washington, District of Columbia, USA
| | - Daniel L. Villeneuve
- National Health and Environmental Effects Research Laboratory, U.S. EPA, Duluth, Minnesota, USA
| | - Scott Wesselkamper
- National Center for Environmental Assessment, U.S. EPA, Cincinnati, Ohio, USA
| | - Maurice Whelan
- Systems Toxicology Unit, European Commission Joint Research Centre, Ispra, Italy
| | - Christine Whittaker
- Education and Information Division, National Institute for Occupational Safety and Health, Centers for Disease Control and Prevention, Cincinnati, Ohio, USA
| | - Ronald White
- Center for Effective Government, Washington, District of Columbia, USA
| | - Menghang Xia
- National Center for Advancing Translational Sciences, NIH, DHHS, Bethesda, Maryland, USA
| | - Carole Yauk
- Environmental Health Science and Research Bureau, Health Canada, Ottawa, Ontario, Canada
| | - Lauren Zeise
- Office of Environmental Health Hazard Assessment, California EPA, Oakland, California, USA
| | - Jay Zhao
- National Center for Environmental Assessment, U.S. EPA, Cincinnati, Ohio, USA
| | - Robert S. DeWoskin
- National Center for Environmental Assessment, U.S. Environmental Protection Agency (EPA), Washington, District of Columbia, USA
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A database of human exposomes and phenomes from the US National Health and Nutrition Examination Survey. Sci Data 2016; 3:160096. [PMID: 27779619 PMCID: PMC5079122 DOI: 10.1038/sdata.2016.96] [Citation(s) in RCA: 105] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2016] [Accepted: 09/26/2016] [Indexed: 01/02/2023] Open
Abstract
The National Health and Nutrition Examination Survey (NHANES) is a population survey implemented by the Centers for Disease Control and Prevention (CDC) to monitor the health of the United States whose data is publicly available in hundreds of files. This Data Descriptor describes a single unified and universally accessible data file, merging across 255 separate files and stitching data across 4 surveys, encompassing 41,474 individuals and 1,191 variables. The variables consist of phenotype and environmental exposure information on each individual, specifically (1) demographic information, physical exam results (e.g., height, body mass index), laboratory results (e.g., cholesterol, glucose, and environmental exposures), and (4) questionnaire items. Second, the data descriptor describes a dictionary to enable analysts find variables by category and human-readable description. The datasets are available on DataDryad and a hands-on analytics tutorial is available on GitHub. Through a new big data platform, BD2K Patient Centered Information Commons (http://pic-sure.org), we provide a new way to browse the dataset via a web browser (https://nhanes.hms.harvard.edu) and provide application programming interface for programmatic access.
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Dennis KK, Auerbach SS, Balshaw DM, Cui Y, Fallin MD, Smith MT, Spira A, Sumner S, Miller GW. The Importance of the Biological Impact of Exposure to the Concept of the Exposome. ENVIRONMENTAL HEALTH PERSPECTIVES 2016; 124:1504-1510. [PMID: 27258438 PMCID: PMC5047763 DOI: 10.1289/ehp140] [Citation(s) in RCA: 65] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 12/22/2015] [Revised: 02/23/2016] [Accepted: 05/12/2016] [Indexed: 05/11/2023]
Abstract
BACKGROUND The term "exposome" was originally coined in 2005 and defined as the totality of exposures throughout the lifetime. The exposome provides an excellent scientific framework for studying human health and disease. Recently, it has been suggested that how exposures affect our biology and how our bodies respond to such exposures should be part of the exposome. OBJECTIVES The authors describe the biological impact of the exposome and outline many of the targets and processes that can be assessed as part of a comprehensive analysis of the exposome. DISCUSSION The processes that occur downstream from the initial interactions with exogenous and endogenous compounds determine the biological impact of exposures. If the effects are not considered in the same context as the exposures, it will be difficult to determine cause and effect. The exposome and biology are interactive-changes in biology due to the environment change one's vulnerability to subsequent exposures. Additionally, highly resilient individuals are able to withstand environmental exposures with minimal effects to their health. We expect that the vast majority of exposures are transient, and chemicals underlying exposures that occurred weeks, months, or years ago are long gone from the body. However, these past chemical exposures often leave molecular fingerprints that may be able to provide information on these past exposures. CONCLUSIONS Through linking exposures to specific biological responses, exposome research could serve to improve understanding of the mechanistic connections between exposures and health to help mitigate adverse health outcomes across the lifespan. CITATION Dennis KK, Auerbach SS, Balshaw DM, Cui Y, Fallin MD, Smith MT, Spira A, Sumner S, Miller GW. 2016. The importance of the biological impact of exposure to the concept of the exposome. Environ Health Perspect 124:1504-1510; http://dx.doi.org/10.1289/EHP140.
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Affiliation(s)
- Kristine K. Dennis
- Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, Georgia, USA
| | - Scott S. Auerbach
- Biomolecular Screening Branch, Division of the National Toxicology Program, and
| | - David M. Balshaw
- Exposure, Response, and Technology Branch, Division of Extramural Research and Training, National Institute of Environmental Health Sciences, National Institutes of Health, Department of Health and Human Resources, Research Triangle Park, North Carolina, USA
| | - Yuxia Cui
- Exposure, Response, and Technology Branch, Division of Extramural Research and Training, National Institute of Environmental Health Sciences, National Institutes of Health, Department of Health and Human Resources, Research Triangle Park, North Carolina, USA
| | - Margaret Daniele Fallin
- Department of Mental Health, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, USA
| | - Martyn T. Smith
- Division of Environmental Health Sciences, School of Public Health, University of California, Berkeley, Berkeley, California, USA
| | - Avrum Spira
- Division of Computational Biomedicine, School of Medicine, Boston University, Boston, Massachusetts, USA
| | - Susan Sumner
- Discovery Sciences, RTI International, Research Triangle Park, North Carolina, USA
| | - Gary W. Miller
- Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, Georgia, USA
- Address correspondence to G.W. Miller, Department of Environmental Health, Rollins School of Public Health, Emory University, 1518 Clifton Rd. NE, Mailstop: 1518-002-8BB, Atlanta, GA 30322 USA. Telephone: (404) 712-8582. E-mail:
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DeBord DG, Carreón T, Lentz TJ, Middendorf PJ, Hoover MD, Schulte PA. Use of the "Exposome" in the Practice of Epidemiology: A Primer on -Omic Technologies. Am J Epidemiol 2016; 184:302-14. [PMID: 27519539 DOI: 10.1093/aje/kwv325] [Citation(s) in RCA: 87] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2015] [Accepted: 11/17/2015] [Indexed: 12/13/2022] Open
Abstract
The exposome has been defined as the totality of exposures individuals experience over the course of their lives and how those exposures affect health. Three domains of the exposome have been identified: internal, specific external, and general external. Internal factors are those that are unique to the individual, and specific external factors include occupational exposures and lifestyle factors. The general external domain includes sociodemographic factors such as educational level and financial status. Eliciting information on the exposome is daunting and not feasible at present; the undertaking may never be fully realized. A variety of tools have been identified to measure the exposome. Biomarker measurements will be one of the major tools in exposomic studies. However, exposure data can also be obtained from other sources such as sensors, geographic information systems, and conventional tools such as survey instruments. Proof-of-concept studies are being conducted that show the promise of exposomic investigation and the integration of different kinds of data. The inherent value of exposomic data in epidemiologic studies is that they can provide greater understanding of the relationships among a broad range of chemical and other risk factors and health conditions and ultimately lead to more effective and efficient disease prevention and control.
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Windhorst DA, Mileva-Seitz VR, Rippe RCA, Tiemeier H, Jaddoe VWV, Verhulst FC, van IJzendoorn MH, Bakermans-Kranenburg MJ. Beyond main effects of gene-sets: harsh parenting moderates the association between a dopamine gene-set and child externalizing behavior. Brain Behav 2016; 6:e00498. [PMID: 27547500 PMCID: PMC4980469 DOI: 10.1002/brb3.498] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/31/2015] [Revised: 11/13/2015] [Accepted: 04/21/2016] [Indexed: 01/15/2023] Open
Abstract
BACKGROUND In a longitudinal cohort study, we investigated the interplay of harsh parenting and genetic variation across a set of functionally related dopamine genes, in association with children's externalizing behavior. This is one of the first studies to employ gene-based and gene-set approaches in tests of Gene by Environment (G × E) effects on complex behavior. This approach can offer an important alternative or complement to candidate gene and genome-wide environmental interaction (GWEI) studies in the search for genetic variation underlying individual differences in behavior. METHODS Genetic variants in 12 autosomal dopaminergic genes were available in an ethnically homogenous part of a population-based cohort. Harsh parenting was assessed with maternal (n = 1881) and paternal (n = 1710) reports at age 3. Externalizing behavior was assessed with the Child Behavior Checklist (CBCL) at age 5 (71 ± 3.7 months). We conducted gene-set analyses of the association between variation in dopaminergic genes and externalizing behavior, stratified for harsh parenting. RESULTS The association was statistically significant or approached significance for children without harsh parenting experiences, but was absent in the group with harsh parenting. Similarly, significant associations between single genes and externalizing behavior were only found in the group without harsh parenting. Effect sizes in the groups with and without harsh parenting did not differ significantly. Gene-environment interaction tests were conducted for individual genetic variants, resulting in two significant interaction effects (rs1497023 and rs4922132) after correction for multiple testing. CONCLUSION Our findings are suggestive of G × E interplay, with associations between dopamine genes and externalizing behavior present in children without harsh parenting, but not in children with harsh parenting experiences. Harsh parenting may overrule the role of genetic factors in externalizing behavior. Gene-based and gene-set analyses offer promising new alternatives to analyses focusing on single candidate polymorphisms when examining the interplay between genetic and environmental factors.
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Affiliation(s)
- Dafna A Windhorst
- Centre for Child and Family Studies Leiden University Leiden The Netherlands; The Generation R Study Group Erasmus University Medical Center Rotterdam The Netherlands; Department of Child and Adolescent Psychiatry Erasmus University Medical Center-Sophia Children's Hospital Rotterdam The Netherlands
| | - Viara R Mileva-Seitz
- Centre for Child and Family Studies Leiden University Leiden The Netherlands; The Generation R Study Group Erasmus University Medical Center Rotterdam The Netherlands; Department of Child and Adolescent Psychiatry Erasmus University Medical Center-Sophia Children's Hospital Rotterdam The Netherlands
| | - Ralph C A Rippe
- Centre for Child and Family Studies Leiden University Leiden The Netherlands
| | - Henning Tiemeier
- Department of Child and Adolescent Psychiatry Erasmus University Medical Center-Sophia Children's Hospital Rotterdam The Netherlands; Department of Epidemiology Erasmus University Medical Center Rotterdam The Netherlands; Department of Psychiatry Erasmus University Medical Center Rotterdam The Netherlands
| | - Vincent W V Jaddoe
- The Generation R Study Group Erasmus University Medical Center Rotterdam The Netherlands; Department of Epidemiology Erasmus University Medical Center Rotterdam The Netherlands; Department of Pediatrics Erasmus University Medical Center Rotterdam The Netherlands
| | - Frank C Verhulst
- Department of Child and Adolescent Psychiatry Erasmus University Medical Center-Sophia Children's Hospital Rotterdam The Netherlands
| | - Marinus H van IJzendoorn
- Centre for Child and Family Studies Leiden University Leiden The Netherlands; School of Pedagogical and Educational Sciences Erasmus University Rotterdam The Netherlands
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Patel CJ. Analytical Complexity in Detection of Gene Variant-by-Environment Exposure Interactions in High-Throughput Genomic and Exposomic Research. Curr Environ Health Rep 2016; 3:64-72. [PMID: 26809563 PMCID: PMC4789192 DOI: 10.1007/s40572-016-0080-5] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
It seems intuitive that disease risk is influenced by the interaction between inherited genetic variants and environmental exposure factors; however, we have few documented interactions between variants and exposures. Advances in technology may enable the simultaneous measurement (i.e., on the same individuals in an epidemiological study) of millions of genome variants with thousands of environmental "exposome" factors, significantly increasing the number of possible factor pairs available for testing for the presence of interactions. The burden of analytic complexity, or sheer number of genetic and exposure factors measured, poses a considerable challenge for discovery of interactions in population-scale data. Advances in analytic approaches, large sample sizes, less conservative methods to mitigate multiple testing, and strong biological priors will be required to prune the search space to find reproducible and robust gene-by-environment interactions in observational data.
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Affiliation(s)
- Chirag J Patel
- Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck St., Boston, MA, 02215, USA.
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