What is new in the exposome? Vineis, Paolo; Robinson, Oliver; Chadeau-Hyam, Marc ...
Environment international,
October 2020, 2020-10-00, 2020-10-01, Volume:
143
Journal Article
Peer reviewed
Open access
The exposome concept refers to the totality of exposures from a variety of external and internal sources including chemical agents, biological agents, or radiation, from conception onward, over a ...complete lifetime. It encompasses also “psychosocial components” including the impact of social relations and socio-economic position on health. In this review we provide examples of recent contributions from exposome research, where we believe their application will be of the greatest value for moving forward. So far, environmental epidemiology has mainly focused on hard outcomes, such as mortality, disease exacerbation and hospitalizations. However, there are many subtle outcomes that can be related to environmental exposures, and investigations can be facilitated by an improved understanding of internal biomarkers of exposure and response, through the application of omic technologies. Second, though we have a wealth of studies on environmental pollutants, the assessment of causality is often difficult because of confounding, reverse causation and other uncertainties. Biomarkers and omic technologies may allow better causal attribution, for example using instrumental variables in triangulation, as we discuss here. Even more complex is the understanding of how social relationships (in particular socio-economic differences) influence health and imprint on the fundamental biology of the individual. The identification of molecular changes that are intermediate between social determinants and disease status is a way to fill the gap. Another field in which biomarkers and omics are relevant is the study of mixtures. Epidemiology often deals with complex mixtures (e.g. ambient air pollution, food, smoking) without fully disentangling the compositional complexity of the mixture, or with rudimentary approaches to reflect the overall effect of multiple exposures or components.
From the point of view of disease mechanisms, most models hypothesize that several stages need to be transitioned through health to the induction of disease, but very little is known about the characteristics and temporal sequence of such stages. Exposome models reinforce the idea of a biography-to-biology transition, in that everyone’s disease is the product of the individual history of exposures, superimposed on their underlying genetic susceptibilities. Finally, exposome research is facilitated by technological developments that complement traditional epidemiological study designs. We describe in depth one such new tools, adductomics. In general, the development of high-resolution and high-throughput technologies interrogating multiple -omics (such as epigenomics, transcriptomics, proteomics, adductomics and metabolomics) yields an unprecedented perspective into the impact of the environment in its widest sense on disease.
The world of the exposome is rapidly evolving, though a huge gap still needs to be filled between the original expectations and the concrete achievements. Perhaps the most urgent need is for the establishment of a new generation of cohort studies with appropriately specified biosample collection, improved questionnaire data (including social variables), and the deployment of novel technologies that allow better characterization of individual environmental exposures, ranging from personal monitoring to satellite based observations.
The incremental value of polygenic risk scores in addition to well-established risk prediction models for coronary artery disease (CAD) is uncertain.
To examine whether a polygenic risk score for CAD ...improves risk prediction beyond pooled cohort equations.
Observational study of UK Biobank participants enrolled from 2006 to 2010. A case-control sample of 15 947 prevalent CAD cases and equal number of age and sex frequency-matched controls was used to optimize the predictive performance of a polygenic risk score for CAD based on summary statistics from published genome-wide association studies. A separate cohort of 352 660 individuals (with follow-up to 2017) was used to evaluate the predictive accuracy of the polygenic risk score, pooled cohort equations, and both combined for incident CAD.
Polygenic risk score for CAD, pooled cohort equations, and both combined.
CAD (myocardial infarction and its related sequelae). Discrimination, calibration, and reclassification using a risk threshold of 7.5% were assessed.
In the cohort of 352 660 participants (mean age, 55.9 years; 205 297 women 58.2%) used to evaluate the predictive accuracy of the examined models, there were 6272 incident CAD events over a median of 8 years of follow-up. CAD discrimination for polygenic risk score, pooled cohort equations, and both combined resulted in C statistics of 0.61 (95% CI, 0.60 to 0.62), 0.76 (95% CI, 0.75 to 0.77), and 0.78 (95% CI, 0.77 to 0.79), respectively. The change in C statistic between the latter 2 models was 0.02 (95% CI, 0.01 to 0.03). Calibration of the models showed overestimation of risk by pooled cohort equations, which was corrected after recalibration. Using a risk threshold of 7.5%, addition of the polygenic risk score to pooled cohort equations resulted in a net reclassification improvement of 4.4% (95% CI, 3.5% to 5.3%) for cases and -0.4% (95% CI, -0.5% to -0.4%) for noncases (overall net reclassification improvement, 4.0% 95% CI, 3.1% to 4.9%).
The addition of a polygenic risk score for CAD to pooled cohort equations was associated with a statistically significant, yet modest, improvement in the predictive accuracy for incident CAD and improved risk stratification for only a small proportion of individuals. The use of genetic information over the pooled cohort equations model warrants further investigation before clinical implementation.
The impact of elevated systolic blood pressure (SBP) and low-density lipoprotein cholesterol (LDL-C) on the risk of coronary heart disease (CHD) at different stages of life is unclear. We aimed to ...investigate whether genetically mediated SBP/LDL-C is associated with the risk of CHD throughout life.
We conducted a three-sample Mendelian randomization analysis using data from the UK Biobank including 136,648 participants for LDL-C, 135,431 participants for SBP, and 24,052 cases for CHD to assess the effect of duration of exposure to the risk factors on risk of CHD. Analyses were stratified by age at enrolment. In univariable analyses, there was a consistent association between exposure to higher LDL-C and SBP with increased odds of incident CHD in individuals aged ≤55 years, ≤60 years, and ≤65 years (p-value for heterogeneity = 1.00 for LDL-C and 0.67 for SBP, respectively). In multivariable Mendelian randomization analyses, exposure to elevated LDL-C/SBP early in life (age ≤55 years) was associated with a higher risk of CHD independent of later life levels (age >55 years) (odds ratio 1.68, 95% CI 1.20-2.34 per 1 mmol/L LDL-C, and odds ratio 1.33, 95% CI 1.18-1.51 per 10 mmHg SBP).
Genetically predicted SBP and LDL-C increase the risk of CHD independent of age. Elevated SBP and LDL-C in early to middle life is associated with increased CHD risk independent of later-life SBP and LDL-C levels. These findings support the importance of lifelong risk factor control in young individuals, whose risk of CHD accumulates throughout life.
Brain structure in later life reflects both influences of intrinsic aging and those of lifestyle, environment and disease. We developed a deep neural network model trained on brain MRI scans of ...healthy people to predict "healthy" brain age. Brain regions most informative for the prediction included the cerebellum, hippocampus, amygdala and insular cortex. We then applied this model to data from an independent group of people not stratified for health. A phenome-wide association analysis of over 1,410 traits in the UK Biobank with differences between the predicted and chronological ages for the second group identified significant associations with over 40 traits including diseases (e.g., type I and type II diabetes), disease risk factors (e.g., increased diastolic blood pressure and body mass index), and poorer cognitive function. These observations highlight relationships between brain and systemic health and have implications for understanding contributions of the latter to late life dementia risk.
Limited evidence is available about the association between serum uric acid and sub-stages of the spectrum from normoglycaemia to type 2 diabetes mellitus. We aimed to investigate the association ...between serum uric acid and risk of prediabetes and type 2 diabetes mellitus.
Eligible participants of the Rotterdam Study (n = 8,367) were classified into mutually exclusive subgroups of normoglycaemia (n = 7,030) and prediabetes (n = 1,337) at baseline. These subgroups were followed up for incident prediabetes (n = 1,071) and incident type 2 diabetes mellitus (n = 407), respectively. We used Cox proportional hazard models to determine hazard ratios (HRs) for incident prediabetes among individuals with normoglycaemia and incident type 2 diabetes mellitus among individuals with prediabetes.
The mean duration of follow-up was 7.5 years for incident prediabetes and 7.2 years for incident type 2 diabetes mellitus. A standard deviation increment in serum uric acid was significantly associated with incident prediabetes among individuals with normoglycaemia (HR 1.10, 95% confidence interval (CI) 1.01; 1.18), but not with incident type 2 diabetes mellitus among individuals with prediabetes (HR 1.07, 95% CI 0.94; 1.21). Exclusion of individuals who used diuretics or individuals with hypertension did not change our results. Serum uric acid was significantly associated with incident prediabetes among normoglycaemic women (HR 1.13, 95% CI 1.02; 1.25) but not among normoglycaemic men (HR 1.08, 95% CI 0.96; 1.21). In contrast, serum uric acid was significantly associated with incident type 2 diabetes mellitus among prediabetic men (HR 1.23, 95% CI 1.01; 1.48) but not among prediabetic women (HR 1.00, 95% CI 0.84; 1.19).
Our findings agree with the notion that serum uric acid is more closely related to early-phase mechanisms in the development of type 2 diabetes mellitus than late-phase mechanisms.
Drug effects can be investigated through natural variation in the genes for their protein targets. The present study aimed to use this approach to explore the potential side effects and repurposing ...potential of antihypertensive drugs, which are among the most commonly used medications worldwide.
Genetic proxies for the effect of antihypertensive drug classes were identified as variants in the genes for the corresponding targets that associated with systolic blood pressure at genome-wide significance. Mendelian randomization estimates for drug effects on coronary heart disease and stroke risk were compared with randomized, controlled trial results. A phenome-wide association study in the UK Biobank was performed to identify potential side effects and repurposing opportunities, with findings investigated in the Vanderbilt University biobank (BioVU) and in observational analysis of the UK Biobank.
Suitable genetic proxies for angiotensin-converting enzyme inhibitors, β-blockers, and calcium channel blockers (CCBs) were identified. Mendelian randomization estimates for their effect on coronary heart disease and stroke risk, respectively, were comparable to results from randomized, controlled trials against placebo. A phenome-wide association study in the UK Biobank identified an association of the CCB standardized genetic risk score with increased risk of diverticulosis (odds ratio, 1.02 per standard deviation increase; 95% CI, 1.01-1.04), with a consistent estimate found in BioVU (odds ratio, 1.01; 95% CI, 1.00-1.02). Cox regression analysis of drug use in the UK Biobank suggested that this association was specific to nondihydropyridine CCBs (hazard ratio 1.49 considering thiazide diuretic agents as a comparator; 95% CI, 1.04-2.14) but not dihydropyridine CCBs (hazard ratio, 1.04; 95% CI, 0.83-1.32).
Genetic variants can be used to explore the efficacy and side effects of antihypertensive medications. The identified potential effect of nondihydropyridine CCBs on diverticulosis risk could have clinical implications and warrants further investigation.
Differences in cardiac and aortic structure and function are associated with cardiovascular diseases and a wide range of other types of disease. Here we analyzed cardiovascular magnetic resonance ...images from a population-based study, the UK Biobank, using an automated machine-learning-based analysis pipeline. We report a comprehensive range of structural and functional phenotypes for the heart and aorta across 26,893 participants, and explore how these phenotypes vary according to sex, age and major cardiovascular risk factors. We extended this analysis with a phenome-wide association study, in which we tested for correlations of a wide range of non-imaging phenotypes of the participants with imaging phenotypes. We further explored the associations of imaging phenotypes with early-life factors, mental health and cognitive function using both observational analysis and Mendelian randomization. Our study illustrates how population-based cardiac and aortic imaging phenotypes can be used to better define cardiovascular disease risks as well as heart-brain health interactions, highlighting new opportunities for studying disease mechanisms and developing image-based biomarkers.
Data are scarce for the lifetime risk of developing impaired glucose metabolism, including prediabetes, as are data for the risk of eventual progression from prediabetes to diabetes and for ...initiation of insulin treatment in previously untreated patients with diabetes. We aimed to calculate the lifetime risk of the full range of glucose impairments, from normoglycaemia to prediabetes, type 2 diabetes, and eventual insulin use.
In this prospective population-based cohort analysis, we used data from the population-based Rotterdam Study. We identified diagnostic events by use of general practitioners' records, hospital discharge letters, pharmacy dispensing data, and serum fasting glucose measurements taken at the study centre (Rotterdam, Netherlands) visits. Normoglycaemia, prediabetes, and diabetes were defined on the basis of WHO criteria for fasting glucose (normoglycaemia: ≤6·0 mmol/L; prediabetes: >6·0 mmol/L and <7·0 mmol/L; and diabetes ≥7·0 mmol/L or use of glucose-lowering drug). We calculated lifetime risk using a modified version of survival analysis adjusted for the competing risk of death. We also estimated the lifetime risk of progression from prediabetes to overt diabetes and from diabetes free of insulin treatment to insulin use. Additionally, we calculated years lived with healthy glucose metabolism.
We used data from 10 050 participants from the Rotterdam Study. During a follow-up of up to 14·7 years (between April 1, 1997, and Jan 1, 2012), 1148 participants developed prediabetes, 828 developed diabetes, and 237 started insulin treatment. At age 45 years, the remaining lifetime risk was 48·7% (95% CI 46·2-51·3) for prediabetes, 31·3% (29·3-33·3) for diabetes, and 9·1% (7·8-10·3) for insulin use. In individuals aged 45 years, the lifetime risk to progress from prediabetes to diabetes was 74·0% (95% CI 67·6-80·5), and 49·1% (38·2-60·0) of the individuals with overt diabetes at this age started insulin treatment. The lifetime risks attenuated with advancing age, but increased with increasing BMI and waist circumference. On average, individuals with severe obesity lived 10 fewer years without glucose impairment compared with normal-weight individuals.
Impaired glucose metabolism is a substantial burden on population health, and our findings emphasise the need for more effective prevention strategies, which should be implemented as soon in a person's life as possible. The substantial lifetime risk of prediabetes and diabetes in lean individuals also supports risk factor control in non-obese individuals.
Erasmus MC and Erasmus University Rotterdam; Netherlands Organisation for Scientific Research; Netherlands Organisation for Health Research and Development; Research Institute for Diseases in the Elderly; Netherlands Genomics Initiative; Netherlands Ministry of Education, Culture and Science; Netherlands Ministry of Health, Welfare and Sports; European Commission; and Municipality of Rotterdam.
Abstract Background Epigenetic modifications of the genome, such as DNA methylation and histone modifications, have been reported to play a role in processes underlying cardiovascular disease (CVD), ...including atherosclerosis, inflammation, hypertension and diabetes. Methods Eleven databases were searched for studies investigating the association between epigenetic marks (either global, site-specific or genome-wide methylation of DNA and histone modifications) and CVD. Results Of the 3459 searched references, 31 studies met our inclusion criteria (26 cross-sectional studies and 5 prospective studies). Overall, 12,648 individuals were included, with total of 4037 CVD events. The global DNA methylation assessed at long-interspersed nuclear element (LINE-1) was inversely associated with CVD, independent of established cardiovascular risk factors. Conversely, a higher degree of global DNA methylation measured at Alu repeats or by the LUMA method was associated with the presence of CVD. The studies reported epigenetic regulation of 34 metabolic genes (involved in fetal growth, glucose and lipid metabolism, inflammation, atherosclerosis and oxidative stress) in blood cells to be related with CVD. Among them, 5 loci were validated and methylation at F2RL3 was reported in two large prospective studies to predict cardiovascular disease beyond the traditional risk factors. Conclusions Current evidence supports an association between genomic DNA methylation and CVD. However, this review highlights important gaps in the existing evidences including lack of large-scale epigenetic investigations, needed to reliably identify genomic loci where DNA methylation is related to risk of CVD.
Context:
Although thyroid function is associated with several risk factors of nonalcoholic fatty liver disease (NAFLD), its role in NAFLD development remains unclear.
Objective:
We aimed to ...prospectively investigate the association between variations in thyroid function and NAFLD.
Design and Setting:
The Rotterdam Study, a large population-based, prospective cohort study.
Participants and Main Outcome Measures:
Participants with thyroid function measurements at baseline and NAFLD data (ie, at baseline fatty liver index/at follow-up ultrasound) were eligible. Transient elastography was performed to assess the presence of fibrosis in patients with NAFLD, using the liver stiffness measurements more than or equal to 8 kPa as cutoff for clinically relevant fibrosis. The association between thyroid parameters and incident NAFLD was explored by using logistic regression models.
Results:
A total of 9419 participants (mean age, 64.75 y) were included. The median follow-up time was 10.04 years (interquartile range, 5.70–10.88 y). After adjusting for age, sex, cohort, follow-up time, use of hypolipidemic drugs, and cardiovascular risk factors, higher free T4 levels were associated with a decreased risk of NAFLD (odds ratio, 0.42; 95% confidence interval CI, 0.28–0.63). In line, higher TSH levels were associated with an increased risk of having clinically relevant fibrosis in NAFLD (odds ratio, 1.49; CI, 1.04–2.15). Compared with euthyroidism, hypothyroidism was associated with a 1.24-fold higher NAFLD risk (CI, 1.01–1.53). Moreover, NAFLD risk decreased gradually from hypothyroidism to hyperthyroidism (P for trend = .003).
Conclusion:
Lower thyroid function is associated with an increased NAFLD risk. These findings may lead to new avenues regarding NAFLD prevention and treatment.
This study evaluated the effect of thyroid function on the subsequent risk of NAFLD and presence of clinically relevant fibrosis. Lower thyroid function was associated with an increased NAFLD risk.
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