Skip to main content

A review on the application of the exposome paradigm to unveil the environmental determinants of age-related diseases


Age-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.


Due to the continuous advancement of biomedical technologies and the substantial improvement in living conditions worldwide, human lifespans have increased dramatically over the past century [1]. Nevertheless, the prevalence of age-related diseases remains considerable: 92 (31.4%) of the 293 diseases listed in the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2017 were identified as age-related, and these accounted for 51.3% of the worldwide burden of disease in adults [2]. In addition, age-related diseases account for two-thirds of human deaths globally and 90% of all deaths in industrialized nations [3]. Moreover, sociodemographic index (SDI) analysis revealed that the rates of age-related diseases ranged from 137.8 disability-adjusted life years (DALYs) per 1000 adults in high-SDI countries to 265.9 DALYs per 1,000 adults in low-SDI countries [2]. The most common age-related diseases are neurological degenerative diseases (e.g., Parkinson’s disease [PD] and Alzheimer’s disease [AD] or dementia), chronic obstructive pulmonary disease (COPD), coronary artery disease (CAD), stroke, type 2 diabetes mellitus (T2DM), and senile deafness [2, 4]. These diseases place an enormous economic and psychological burden on patients, their families, and societies worldwide [5].

In 2021, Science published a special issue entitled “125 Questions: Exploration and Discovery.” One of these 125 questions was “Can we stop ourselves from aging?” The U.S. National Institute on Aging (NIA) at the National Institutes of Health (NIH) states that “aging is associated with changes in dynamic biological, physiological, environmental, psychological, behavioral, and social processes.” Although geneticists and epidemiologists have long debated the relative importance of the role played by genotype or the environment in the development of age-related diseases, it is apparent that both can play substantial roles in this process [6, 7]. However, most etiological studies have concentrated on the role of genotype and have considered the environment to play a secondary role. Nevertheless, an analysis of GBD data showed that nearly 50% of deaths worldwide are attributable to environmental exposure, primarily exposure to airborne particulates (including household air pollution and occupational exposure; 14% of all deaths), smoking and secondhand smoke (13%), plasma sodium concentrations (6%), and alcohol consumption (5%) [8]. In contrast, a recent analysis of 28 chronic diseases in identical twins showed that the genetic-related risks of developing one of five age-related diseases were 33.3%, 10.6%, 36.3%, 19.5%, and 33.9% for AD, PD, CAD, COPD, and T2DM, respectively, with a mean of only 26% [9]. The results of over 400 genome-wide association studies (GWASs) have also elucidated that the heritability of degenerative diseases is only approximately 10% [10, 11]. Consequently, nongenetic drivers, such as environmental factors, are now recognized as major risk factors for age-related diseases. The contributions of environmental factors to the development of age-related diseases can be revealed by analyses of all of the factors to which individuals are exposed in their life and the relationships between these exposures and age-related diseases [12, 13].

The concept of the “exposome” was established by C.P. Wild in 2005 as an environmental analogy of the genome and represents the sum of all exposures of an individual throughout the life course [12]. Wild described three categories of the exposome that should be evaluated: (a) specific external exposures, (b) general external exposures, and (c) internal exposures [14]. In this review, we describe the environmental drivers of typical age-related chronic diseases, mainly focusing on AD, PD, CAD, COPD, T2DM, and senile deafness within an exposomic framework. We propose representative strategies for exposomic research on age-related diseases and describe the measurements and statistical approaches that could be used. In addition, we note the challenges of exposomic research and recommend guidelines for the implementation of exposomic concepts in research on age-related diseases.

Epidemiologic studies on the environmental causes of age-related diseases

Specific external exposures

Air pollution

Atmospheric air pollution is the leading form of specific external exposures, and its role in the development of age-related diseases has been extensively studied. For example, a cohort study in Ontario, Canada, evaluated the association between air pollution and incident PD, which showed that ambient 2.5-µm particulate matter (PM2.5) was significantly associated with a 4% increase in incident PD (95% confidence interval [CI] 1.01, 1.08) [15]. Similarly, high concentrations of nitrogen oxides have been found to be significantly associated with an increased risk of AD or dementia in cohort studies in Sweden (hazard ratio [HR]: 1.38; 95% CI 0.87, 2.19) and Taiwan (HR: 1.54; 95% CI 1.34, 1.77) [16, 17]. In the past decade, many studies have explored the relationships between air pollutant exposure and COPD and have determined that the main airborne pollutants affecting the respiratory system are PM2.5, 10-µm particulate matter (PM10), ozone, carbon monoxide (CO), and sulfur dioxide (SO2) [18, 19]. Moreover, a systematic review and meta-analysis showed that short-term exposure to CO (relative risk [RR]: 1.015; 95% CI 1.004, 1.026), SO2 (RR: 1.019; 95% CI 1.011, 1.027), nitrogen dioxide (NO2) (RR: 1.014; 95% CI 1.009, 1.019), PM2.5 (RR: 1.011; 95% CI 1.011, 1.012), and PM10 (RR: 1.002; 95% CI 1.000, 1.004) was significantly associated with hospitalization or death due to stroke [20]. Furthermore, multiple cohort studies have shown that exposure to high concentrations of NO2 and PM2.5 was associated with an increased risk of T2DM [21,22,23,24,25].

Chemical contaminants

Some toxic chemicals (e.g., pesticides and heavy metals) have been confirmed to be significantly associated with higher risks of age-related diseases. A meta-analysis of 10 cohort and case–control studies identified that 5 and 10 years of pesticide exposure were associated with a 5% (95% CI 1.02, 1.09) and 11% (95% CI 1.05–1.18) increase in the risk of PD, respectively [26]. A cohort study of 1500 older adult participants aged 65 years or older in southwest France found that occupational exposure to pesticides (e.g., fungicides) was significantly associated with AD in men (RR: 2.29; 95% CI 1.02, 5.63) [27]. Numerous other studies—such as the Agricultural Health Study, the Consortium for the Health Assessment of Great Lakes Sport Fish Consumption, and the Prospective Investigation of the Vasculature in Uppsala Seniors Study—have confirmed that there are associations between exposure to pesticides (especially organochlorine and organophosphate pesticides) and a high risk of T2DM [28,29,30]. Although it remains unclear whether exposure to heavy metals increases the risk of PD, the data on lead (Pb) exposure suggest that this heavy metal has such an effect. That is, two case–control studies have found a higher risk of PD in the highest Pb exposure quartile than in the lowest Pb exposure quartile (odds ratio [OR]: 2.27 [95% CI 1.13, 4.55] and 3.21 [95% CI 1.17, 8.83]) [31, 32]. Relatively little evidence has been found for an association between heavy metal exposure and AD risk, although there is some evidence that exposure to aluminum (Al) increases the risk of AD. The Personnes Agées QUID (PAQUID) study, a prospective cohort study of almost 4,000 older adults aged 65 years or over in southwest France, found that consumption of Al in drinking water in excess of 0.1 mg per day was associated with a threefold increase in the risk of AD (RR: 3.35; 95% CI 1.49, 7.52) [33]. However, there have been fewer studies of associations of age-related diseases with internal exposures to emerging organic contaminants, such as perfluoroalkyl substances and brominated flame retardants. In one example, the Nurses’ Health Study II determined that higher plasma concentrations of perfluorooctanesulfonic acid (PFOS) or perfluorooctanoic acid (PFOA) were associated with an increased risk of T2DM in women, corresponding to ORs of 1.62 (95% CI 1.09, 2.41) and 1.54 (95% CI 1.04, 2.28), respectively [34]. Similarly, the French E3N prospective cohort study of French women found a possible association between dietary exposure to hexabromocyclododecane (HBCD, HR: 1.47; 95% CI 1.29, 1.67) and polybromodiphenyl ethers (PBDEs, HR: 1.20; 95% CI 1.08, 1.34) and the risk of T2DM [35].

Physical factors

Electromagnetic fields and noise are the most common risk factors for age-related diseases, such as neurodegenerative, cardiovascular, and cerebrovascular diseases. A follow-up of 931 participants aged 75 years and older without dementia in Sweden found that long-term occupational exposure to a high electromagnetic field might increase the risk of AD in men (RR: 2.30; 95% CI 1.00, 5.30) [36]. Moreover, short-term exposure to traffic noise was found to be associated with hospitalization due to dementia (RR: 1.15; 95% CI 1.11, 1.20), indicating that noise aggravates the symptoms of dementia [37]. Similarly, a recent meta-analysis and a large Scandinavian population study concluded that road traffic noise increased the incidence of coronary heart disease and stroke by 8% (95% CI 1.01, 1.15) and 6% (95% CI 1.03, 1.08) per 10 A-weighted decibels [dB(A)], respectively [38, 39].

Lifestyle and diet

The lifestyle factors most significantly associated with the risk of age-related diseases are smoking, passive smoking, and drinking alcohol. The European Prospective Investigation into Cancer and Nutrition for Parkinson’s Disease, a prospective European population-based cohort study of 220,494 people aged 37 to 70 from 13 centers in 8 countries, found a causal relationship between smoking and PD (HR: 0.70; 95% CI 0.49, 0.99) [40]. A review summarized the existing evidence and confirmed smoking as the main risk factor for COPD [41]. Other environmental exposures, such as dietary exposures and secondhand smoke exposure during pregnancy or early childhood, may also be important risk factors for COPD. A recent cross-sectional analysis of 164,770 adults aged 40 to 69 from the U.K. Biobank found that smoking and passive smoking were associated with a risk of senile deafness (ORs: 1.15 [95% CI 1.09, 1.21] and 1.28 [95% CI 1.21, 1.35], respectively) [42]. Analogously, a recent study of 1,787 mid-to-late-aged adult participants concluded that added salt in the diet might increase at-risk individuals’ risk of AD, while consuming cheese and red wine on a daily basis and lamb on a weekly basis might improve long-term cognitive outcomes [43]. Patel et al. conducted the first exposome-wide association study (EWAS) on T2DM using data from National Health and Nutrition Examination Survey (NHANES) cohorts from 1999 to 2006 [44]. Logistic regression models with multiple comparisons were used, and significant associations for vitamin γ-tocopherol (OR: 1.5; 95% CI 1.3, 1.7) and β-carotenes (OR: 0.6; 95% CI 0.5, 0.7) were associated with T2DM.

Internal exposures


The use of antipsychotics, hormones, and anticholinergics has been found to significantly increase the risks of PD and AD. For example, it has been found that among older adults, the use of antipsychotics, such as phenothiazine (RR: 3.65; 95% CI 1.41, 9.45) or benzamide (RR: 2.59; 95% CI 1.23, 5.43), and particularly trichloroethylene (OR: 6.1, 95% CI 1.2, 33), may increase the risk of PD [45, 46]. Moreover, a prospective population-based cohort study in Seattle, Washington (USA), found that higher cumulative anticholinergic use was significantly associated with an increased risk of dementia (HR: 1.54; 95% CI 1.21, 1.96) [47].

Gut microbiota

Gut microbial dysbiosis has been found to be associated with many age-related diseases. For example, the fecal samples of PD patients were found to have a substantially lower abundance of Prevotellaceae than those of healthy individuals. Additionally, a direct correlation was found between the fecal abundance of Enterobacteriaceae and the severity of postural instabilities and gait difficulties [48]. Moreover, the fecal samples of 64 Italian patients with PD exhibited low abundances of gut microbiota (e.g., Lachnospiraceae) linked to anti-inflammatory/neuroprotective effects and abnormal concentrations of several classes of fecal metabolites (lipids, vitamins, amino acids, and other organic compounds) [49]. Vogt et al. characterized the bacterial taxonomic composition of fecal samples from participants with and without a diagnosis of dementia due to AD and revealed that the gut microbiome of AD participants had less microbial diversity and was compositionally distinct from that of control individuals. They also identified phylum- to genus-wide differences in bacterial abundance in the microbiome of AD participants, such as a decreased abundance of Firmicutes, an increased abundance of Bacteroidetes, and a decreased abundance of Bifidobacterium [50].

Inflammation and oxidative stress

Inflammation is the basis of aging and many age-related chronic diseases, which in turn increase the rate of aging. Walker et al. examined the association between systemic inflammation measured during midlife and 20-year cognitive decline within the Atherosclerosis Risk in Communities cohort study. An increase in the midlife inflammation composite score was associated with an additional 20-year decline of − 0.035 SDs (95% CI − 0.062, − 0.007) in the cognitive composite score [51]. Gong et al. followed three cohort studies in the USA and explored whether proinflammatory diets were associated with increased CVD risks. During 5,291,518 person-years of follow-up, higher dietary inflammatory pattern scores were predefined based on levels of 3 systemic inflammatory biomarkers, which were associated with an increased risk of CVD (HR: 1.38; 95% CI 1.31, 1.46) [52]. Accordingly, age-related diseases can be partly conceptualized as manifestations of accelerated inflammation or aging [53].


Hormone exposure has been associated with the risk of many age-related diseases, especially among postmenopausal women. Savolainen-Peltonen et al. investigated hormone exposure and AD among Finnish postmenopausal women using Finnish national population and drug register data (1999–2013). The results indicated that long-term use of hormone therapy might be associated with an increased risk of AD (OR: 1.17, 95% CI 1.13, 1.21) [54]. In addition, the pooled results of a systematic review and a time–response meta-analysis showed that there was a significant association between hormone therapy and the risk of AD (OR: 1.08; 95% CI 1.03, 1.14) in menopausal women. However, no comparable association was uncovered for PD [55].

General external exposures

Green space and urbanization

Less adequate evidence has been found for an association between green spaces and cognitive function. In the Cognitive Function and Ageing Studies in England, a higher risk of dementia was found among people who were in the highest quartile of neighborhood natural environment availability than among those in lower quartiles of neighborhood natural environment availability [56]. In contrast, a longitudinal study in the UK demonstrated that a higher level of surrounding greenness in residential areas was associated with a slower cognitive decline among residents over a 10-year follow-up (0.020; 95% CI 0.003–0.037 per interquartile increment) and that this was independent of air pollution exposure [57]. Similarly, a comparison of the effects of the indicators of the built environment on the risk of disease found that urbanization was associated with a higher incidence of COPD (1.05; 95% CI 1.01–1.08 per interquartile increment), while residential green space was associated with a lower incidence of COPD (0.89; 95% CI 0.84–0.93 per interquartile increment) [58].

Socioeconomic status and educational level

In a prospective cohort study, social integration was found to contribute to a lower risk of cardiovascular disease (HR: 0.67; 95% CI 0.53, 0.86). In addition, people with the highest social integration level had reduced mortality risks of 30%, 47%, and 53% for AD, COPD, and T2DM, respectively, compared with those with the lowest social integration level [59]. A systematic review and meta-analysis of long-term studies determined that a low social participation index was related to the risk of dementia in terms of social support (RR: 1.28; 95% CI 1.01, 1.62) and social networking (RR: 1.59; 95% CI 1.31, 1.96), while a high social participation index had a moderately protective effect (RR: 0.88; 95% CI 0.80, 0.96) [60]. A cross-sectional study of adults aged 60 or older in China indicated that fewer years of education (OR: 1.55; 95% CI 1.38, 1.73) and being widowed, divorced, or living alone (OR: 2.66; 95% CI 2.29, 3.10) were risk factors for dementia [61]. Another cross-sectional study of older adults with a mean age of 75 years in Denmark showed that compared with individuals with lower household incomes, those with a higher household income were less likely to receive a dementia diagnosis after referral (OR: 0.65; 95% CI 0.55, 0.78) [62]. The search strategies and main results of epidemiologic studies reporting the association between environmental risk factors and age-related diseases (Table S1) in this review are shown in the Additional file 1.

Potential hallmarks linking the exposome to age-related diseases

Age-related diseases are thought to arise from common underlying processes that precipitate molecular changes over time [63]. These shared mechanisms are grouped into the following hallmarks of aging: genomic instability, epigenetic alterations, telomeric attrition, loss of proteostasis, deregulated nutrient sensing, mitochondrial dysfunction, oxidative stress/damage, inflammation, cellular senescence, stem cell exhaustion, and altered intercellular communication [64, 65]. Some studies have reported the effects of environmental exposures on various hallmarks of aging. For example, exposure to heavy metals and organic pollutants has been associated with mitochondrial dysfunction and DNA methylation alteration in AD [66, 67], and early embryonic exposure to 2,3,7,8-tetrachlorodibenzo-p-dioxin may alter global DNA methylation patterns, increasing the risk of T2DM [68]. Bisphenol A concentrations were also found to be associated with increased senescence, inflammation, and decreased telomere lengths in patients with T2DM [69]. However, there have been few other consistent findings confirming that aging hallmarks link the exposome to risks of age-related diseases.

In particular, the alteration of gene expression through DNA methylation, histone modification, and noncoding RNA-associated gene silencing are primary targets of environmental insults considered to induce and sustain epigenetic changes that might result in age-related diseases [70]. In that regard, advances in technology have made it feasible to assay methylation status across the genome in a robust, high-throughput manner, allowing us to address this issue in a data-driven manner. To date, numerous studies have detailed the pattern by which global or site-specific DNA methylation is affected by environmental triggers [67]. For instance, Prado-Bert et al. analyzed the association between the early-life exposome and epigenetic age acceleration based on the Human Early-Life Exposome (HELIX) project. Indoor particulate matter absorbance (PMabs) and parental smoking were found to be positively associated with age acceleration, which was calculated based on Horvath’s skin and blood clock for 1173 children [71]. However, relatively few researchers are looking to understand the functional consequences of methylation marks by assessing gene, protein, and metabolite expression. Recently, Everson et al. identified 443 CpGs that were associated with maternal smoking exposure during pregnancy in seven American, Australian, and European studies. Subsequent expression quantitative trait methylation analyses testing the associations between maternal smoking exposure-associated CpGs and the expression of nearby mRNA showed that exposure-associated CpGs were enriched for environmental response, growth factor signaling, and inflammation [72].

Limitations of studies examining whether environmental factors contribute to the development of age-related diseases

Although the link between environmental exposure and age-related diseases has been extensively investigated, there are three limitations to previous studies in this area. (1) Most exposure assessments have examined only one chemical or class of chemicals via a traditional targeted measurement approach. However, real-world exposures do not occur in such a manner, and thus, “broad-spectrum” exposure assessments are needed, together with systematic screening of key exposomic elements as well as optimized consideration of the components of exposome domains that affect age-related diseases [73]. (2) Exposure–health association studies have not fully considered the dynamic changes in exposure or the effects of (mixed) exposures in early life stages or in other sensitive time windows on the risks of age-related diseases. (3) Studies have rarely considered chemical absorption, distribution, metabolism, and excretion processes or endogenous biomolecular responses, such as those involving hormones, metabolites, infections, inflammatory reactions, fat peroxidation, oxidative stress, and aging hallmarks.

Therefore, the link between environmental exposure and age-related diseases needs to be explored within an exposomic framework. Such an approach would systematically and substantially increase our understanding of the environmental drivers of age-related diseases, reveal how environmental exposure interacts with genetic susceptibility to age-related diseases, and elucidate the potential biological mechanisms of how these factors affect disease susceptibility, development, and progression.

Strategies for exposomic research of age-related diseases

Given the limitations of recent research and the evolution of the exposome concept, we propose the following three major strategies for exposomic research of age-related diseases.

  1. (1)

    Comprehensive determination of the chemical compositions of biological fluids and standardized assessment. Pioneers in exposomic research, such as Rappaport, Smith, and Miller, have emphasized the need to fully characterize the chemical composition of biological fluids (e.g., blood and urine) for precisely assessing individuals’ immediate environments and thus their risk of age-related diseases [74, 75]. In addition, standardization of the exposure/outcome assessment methods using conventional tools along with innovative technologies as well as standardized quality assessment tools may contribute to the utility of the exposome concept [73].

  2. (2)

    Consideration of the roles of biological responses and endogenous processes, especially aging biomarkers, at various omics levels (i.e., at the level of the epigenome (e.g., DNA methylation clock), the transcriptome, the proteome, the metabolome, and the microbiome) and with respect to extracellular RNA (including microRNA and long noncoding RNA) abundance and telomere length [76]. Alterations in biomarkers compromise cell and tissue functions and contribute to the incidence of age-related diseases, leading to loss of function and death [77]. In addition, the concentrations of aging biomarkers can reflect the physiological state of individuals and the underlying molecular mechanisms (e.g., genomic instability, epigenetic perturbation, telomeric attrition, loss of proteostasis, altered nutrient sensing, mitochondrial dysfunction, cellular senescence, and disrupted intercellular communications) related to the exposome throughout individuals’ lifespans [64, 78].

  3. (3)

    Performance of exposure and outcome measurements in critical time windows during the aging process. The exposome includes a time dimension, as it emphasizes the importance of assessing exposure throughout life, beginning at the moment of conception. However, a lifelong study of all environmental factors would be highly challenging to perform in real life [79]. Moreover, people age at dissimilar rates, even in terms of tissues and organs, which have a tissue-specific aging signature [80]. In addition, environmental exposure to harmful factors in different critical life windows of susceptibility may lead to distinct disease phenotypes [81, 82]. However, a recent study indicated that there appear to be distinct “waves” of aging over the course of a lifetime at the population level at three ages: 34, 60, and 78 years old [83]. This suggests that exposomic measurements in critical time windows may be useful for the study of the risk of age-related diseases.

In summary, systematic approaches are needed to integrate diverse information in exposome research (Fig. 1). The comprehensive evaluation of the relationships between exposures and associated biological responses, such as epigenetic modifications and metabolites, would yield valuable insights into the key elements of the exposome and how environmental factors and their interactions contribute to the risk and development of age-related diseases.

Fig. 1
figure 1

Exposomic analytical framework linking exposures to age-related diseases. The exposome attempts to measure, integrate, and interpret the complex exposures (specific external exposure, general external exposure, and internal exposure) associated with the risk of age-related diseases. The use of multi-omics tools to investigate the adverse effects of these exposures on biological processes can reveal the biological responses to sets of exposures, thereby improving understanding of the development of age-related diseases. Statistical methods can then be applied to identify key elements of the exposome linked to age-related diseases and to determine how these complex exposures affect our biological systems. (AD, Alzheimer’s disease; CAD, cardia-cerebrovascular diseases; COPD, chronic obstructive pulmonary disease; PD, Parkinson’s disease; T2DM, type 2 diabetes mellitus; EWAS, exposome-wide association study)

Exposomic measurement tools for use in age-related disease studies

Exposures can be measured at the population level using geographic information systems (GISs) and remote sensing technologies (such as satellites) or at the individual level using records and surveys, personal sensors, and biological samples. The exposomic information of environmental and biological samples can be measured using advanced analytical techniques, such as high-resolution mass spectrometry (HRMS) and omics platforms.

Population-level exposures


The use of GISs has changed environmental health research, as GISs integrate databases that connect various attribute data based on geographic location (e.g., residential address). Therefore, GISs can quantify the buffer distance between exposure sources and human receptors and can be used to describe the proximity of roads, factories, green spaces, water bodies, and other features that may result in exposures that negatively (e.g., farms, which may result in exposure to pesticides) [84] or positively (e.g., health food stores or entertainment venues) affect human health [85].

Remote sensing

Remote sensing is the science of obtaining information about objects or regions from a distance (usually from airplanes or satellites). Remote sensing technology has been used to estimate the levels of various environmental exposures, such as PM2.5 [86], NO2 [87], green space [88], temperature [89], building environment [90], outdoor light at night [91], and pesticides [92]. For example, global PM2.5 concentrations were estimated from information from three satellites in conjunction with a chemical transport model and ground-based sun photometer observations [93]. Remote sensing estimates have also been used to assess the relationships between PM2.5 exposure and cardiovascular diseases [25, 94, 95]. Moreover, recent studies have used a 1-km valuation of U.S. PM2.5 concentrations, thereby increasing the utility of such measurements in exposomic studies [96, 97].

Individual-level exposures

Portable/personal sampling and sensing devices

An increasing number of personal sensors are being developed to monitor heart rate, blood sugar concentrations, blood pressure, muscle activity, temperature, and sweat production [98]. Remarkable advances in novel algorithms in wearable technologies are allowing for accurate acquisition of personal temperature exposures, chemical and biological exposures, and clinical laboratory measurements to explore individual-level 24-h exposures in various microenvironments in exposome studies [99,100,101,102]. Koelmel et al. captured the changes in gas- and particulate-phase airborne chemicals to which individuals were exposed and determined which exposures posed the greatest risk to health. They used wearable exposure monitors, i.e., passive air-sampling wristbands, which were provided to 84 healthy participants (aged 60–69) as part of the Biomarkers for Air Pollution Exposure (China BAPE) study [103, 104]. Jiang et al. developed a sensitive method to monitor personal airborne biological and chemical exposures, which enabled them to follow the personal exposomes of 15 individuals for up to 890 days over 66 distinct geographical locations. They found that humans are exposed to thousands of microbial species with great intraspecies diversity and demonstrated that the human exposome is highly dynamic and influenced by spatial/lifestyle and seasonal variables [105, 106].

Records and surveys

Data from health records and surveys can be used to describe the exposure characteristics of individuals at a relatively low cost. For example, electronic health records provide an efficient way to estimate individual-level exposure to, for example, pharmaceutical drugs, alcohol, or cigarette smoke. Similarly, surveys performed as part of epidemiological studies can provide valuable information on peoples’ diet, socioeconomic status, education level, and psychosocial stress level.

Biological samples

The analysis of biological samples (e.g., blood, urine, saliva, feces, and hair) enables quantification of the burden of chemical exposures for an individual. In particular, abundant information on the biological response to an exposure can be obtained by quantifying small molecules in biological samples, such as inflammatory factors and metabolites.

Analytical platforms


Advancements in HRMS have provided new tools to identify and quantify multiple chemical substances in a wide range of media and have extended the analytical window beyond the targeted profiling of known metabolites and priority pollutants [107]. In particular, HRMS enables the simultaneous analysis of many exogenous and endogenous compounds and the characterization of the state of a biological system and its response to environmental factors [13, 108].

Nontargeted HRMS-based analysis can collect data on thousands of chemical features in a single analytical run and is thus suitable for mass screening, although most of these features remain unannotated [109]. However, targeted HRMS-based analysis involves performing multiple analyses of known and specific chemicals, including absolute qualitative and quantitative analyses of the substances to be measured using certified standards, and is therefore suitable for validation in exposomic studies. For example, as part of the China BAPE study [104], Guo et al. performed targeted HRMS-based analysis of 70 airborne compound exposures of 84 elderly people in Jinan, China, and thereby described their personal exposure to air pollutants in terms of concentration and distribution [110].

Multiomics technology

The broad diversity of omics biomarkers that have been used to assess biological responses provides new opportunities to understand the impact of the environment on the risk of age-related diseases. For example, the multiomics analysis and integration method produces a priority list of multiple sets of biomarkers, which together reflect the molecular responses of the exposome. Each of these data warrants integration into a biomarker panel to aid physicians in developing age-related disease diagnoses and prognoses [78].

By integrating the response measures of genomics, epigenomics, transcriptomics, proteomics, and metabolomics, it is possible to develop a systems biology-level understanding of how exposure affects key biochemical processes. Similarly, the aggregated biological response model, which combines toxicology and pharmacology with molecular and environmental epidemiology, represents a new paradigm for delineating the mechanisms of chemical toxicology [13]. The further development of statistical methods to determine the interactions between biological response networks [111, 112] and the application of multiomics methods in cohort studies to elucidate the characteristics of human exposure [113, 114] will facilitate exposomic discoveries in the years ahead. For instance, the EXPOsOMICS project, an integration of 14 different studies, applied multiomics technologies such as metabolomics and adductomics to quantify exposure levels and to measure downstream effects of environmental exposures by the epigenome, transcriptome, and proteome [114]. The PISCINA-II study (part of the EXPOsOMICS project) included 60 volunteers who swam for 40 min in a chlorinated pool, and metabolome-wide association showed that disinfectant byproducts were associated with 333 metabolic features, of which 13 metabolites were identified and enriched in the tryptophan metabolism pathway [115].

Recently, more research has incorporated multiomics technologies and personal sampling and sensing devices to reveal the dynamic molecular effects of precise personal exposure. For example, Jiang et al. developed a personal wearable device that monitored airborne biological and chemical exposures and followed the personal exposomes of 15 individuals for nearly 3 years [105]. Next-generation sequencing and liquid chromatography‒mass spectrometry (LC‒MS) technologies were then used to detect the biotic/chemical exposome and found that the human exposome was dynamic and influenced by various environmental and spatial/lifestyle variables. Gao et al. examined the effects of environmental exposures on well-dissected personal internal health changes based on longitudinal individual exposomes and internal multiomics (gut microbiome, proteome and metabolome) [101]. By investigating the correlations of exposomics and the exposome and clinical data, it was found that changes in external exposures could be associated with thousands of biomolecular changes in the body involved in immune, kidney, and liver functions.

Evolving statistical methods in exposome–health association studies of age-related diseases

Exposome-wide association study (EWAS)

An EWAS is a statistical method similar to a GWAS that is used to continuously and independently detect the association of many exposures with a set of results (with adjustment for potential confounding factors). Although nonideal, for practical purposes—because exposure studies examine many exposures—the same set of confounding factors is typically used for all exposures. Furthermore, multiple comparison corrections should be applied to minimize the number of false-positive results obtained. Bonferroni–Holm correction method (family-wise error rate [FWER] method) or the Benjamini–Hochberg correction method (false discovery rate [FDR] method) are among the most commonly used multiple comparison correction methods in epidemiological research. However, a limitation is that these methods assume that tests are independent, and if this assumption is violated, these methods may generate highly conservative results [116]. The Bonferroni correction method has been devised and involves dividing the significance level α by the test number M (αcorrected = α/M); this changes the value of M according to the number of valid checks, as determined by the relevant structure of the data [116, 117]. Methods such as stepwise multiple testing have been developed to capture the joint dependency structure of the test statistic, thereby improving the ability to detect false hypotheses [118]. However, there has been no development of multiple-detection correction methods to simultaneously deal with the diversity of exposure and the results of exposure environments. In a study that simulated the true correlation structure of the Infancia y Medio Ambiente (INMA, Spain) birth cohort exposure variables, EWAS exhibited a higher FDR than other methods but showed the greatest sensitivity [119].

Variable selection tools

The most popular variable selection techniques in association research include the deletion/substitution/addition (DSA) algorithm, the elastic net (ENET) algorithm, and the graphical unit evolutionary stochastic search (GUESS) algorithm. For example, the DSA algorithm was used in the recent HELIX project to explore the relationship between early-life exposure to environmental hazards and childhood lung function and the relationship between urban pregnancy exposure to environmental hazards and birth weight [120, 121]. In simulation research, the DSA method has shown higher sensitivity and a lower FDR than other methods and good performance in capturing interactive items [119, 122]. However, the DSA method has routinely been criticized because its estimations are inconsistent if the ratio of the sample size to the number of candidate predictions is small, and its confidence intervals are low if there is a large correlation between predictions [123].

The ENET algorithm has been used to study the relationship between various environmental pollutants and birth weight in three cohorts, one each in Greenland, Poland, and Ukraine, and to examine the relationship between concentrations of several persistent organic pollutants in breast milk samples and infant behavioral problems [124, 125]. In addition, the ENET method showed high sensitivity and a moderate FDR in a simulation scenario [119].

The GUESS algorithm is a Bayesian variable selection technique and is a search algorithm based on a multichain genetic algorithm. It was developed to explore complex genetic association models and maximize the detection of genetic variation [126]. Similar to the DSA algorithm, the GUESS algorithm showed high sensitivity and a low FDR in simulation research [119]. However, to explain confounding factors, the results must first be fitted to these confounding factors, and the GUESS model then fitted to the residuals.

Dimension reduction techniques

Dimensional reduction techniques such as factor analysis or principal component analysis (PCA) have been used to analyze the impact of multiple pollutants on the risk of age-related diseases [127,128,129]. PCA uses the eigenvalues and eigenvectors of the exposure correlation matrix, makes full use of data variability, and derives orthogonal components from the set of exposure variables. The resulting “feature exposure” can be used for subsequent analysis. However, the relationship PCA determines between exposure and response variables does not take into account the generation of principal components. An improved version of PCA, supervised principal component analysis, can solve this problem [130], as it eliminates the prescreening results of pollutants that are not related to the result and thereby generates an estimated effect with a smaller deviation than the corresponding PCA estimate. The PISCINA-II study (part of the EXPOsOMICS project) aimed to apply the partial least squares (PLS) regression method to investigate the effect of disinfectant product exposure on inflammation [115, 131]. Sparse PLS (sPLS) regression has previously been used in research, such as to identify multiple pollutant exposure profiles related to male reproductive function biomarkers [132]. This method constructs latent variables (linear combinations of predictors) in a supervised manner, that is, uses the results and then regresses the results on the latent variables. The sPLS component not only captures as much predictor variance as possible but also focuses on the variance associated with the results of interest. In a simulation setting, this method showed high sensitivity and a moderate FDR [119].

Interaction detection models

In a recent paper, Barrera-Gómez considered scenarios with statistical interactions and systematically compared methods recommended for search interactions [119]. The simulation used contained 237 exposures with real structures, and several statistical regression methods were used. These methods were the two-step full environment association study, the DSA algorithm, the least absolute shrinkage and selection operator (LASSO), and the group-LASSO interaction network (GLINTERNET), which is a three-step method based on a regression tree and an enhanced regression tree. The GLINTERNET and DSA methods performed equally well in terms of the ability to capture interactive items but exhibited an identical trade-off between sensitivity and FDR.

The Bayesian model average (BMA) method is another statistical method used for the construction of a health risk model that considers multiple pollutants and their interactions. The BMA method is effective for dealing with model uncertainty, as it provides robust estimates of parameters through model averaging. That is, compared with traditional modeling methods that ignore model uncertainties, the BMA method is attractive because it does not choose a single “best” model but instead makes average inferences within a range of possible models [133, 134]. In a simulation study by Sun et al., the BMA method proved useful for the selection of variables with moderately strong exposure–response associations [135].

Mixture approaches

Notably, most of the current chemical exposure–health effect analyses are based on the risk assessment of individual compounds rather than chemical mixtures. However, even exposure to a single pollutant or dose might not cause detrimental health effects, the simultaneous and cumulative exposure to a variety of pollutants may induce the adverse health outcomes. For mixture effect analysis, numerous statistical approaches, such as weighted quantile sum (WQS) regression, Bayesian kernel machine regression (BKMR), and quantile G-computation, have been developed to interpret the overall effect of chemical coexposure on health outcomes and the relative importance of each exposure [136,137,138]. Recently, Caporale et al. used data from the Swedish Environmental Longitudinal, Mother and child, Asthma and allergy (SELMA) pregnancy cohort and developed a mixture-centric risk assessment strategy integrating epidemiological and experimental data [139]. By applying WQS regression to establish associations between endocrine-disrupting chemical (EDC) mixture exposure and language delay in children, they found that exposure to EDC mixtures in early pregnancy was associated with language delay in offspring. This study highlights the need to consider mixtures in chemical testing and risk assessment processes and provides a comprehensive framework to guide risk assessment strategies.

Overall, the above studies have shown that systems approaches are needed to integrate diverse information in exposomic research (Fig. 1). The systematic evaluation of the relationship between exposures and associated biological responses, such as epigenetic modifications and metabolites, provides valuable insights into key elements of the exposome and delineates how environmental factors and their interactions contribute to the risk of age-related diseases. The statistical strategy in this framework usually contains (I) an EWAS model to explore the associations of exposure–health outcomes; (II) variable selection tools as well as dimension reduction techniques for exposure screening and feature dimension reduction; (III) interaction detection models as well as mixture approaches for assessing the mixture effect, relative contribution, and interactions of chemical coexposure; and (IV) a multiomics network linking exposure to disease phenotype by elucidating their underlying biological mechanisms.

Challenges and opportunities

Multidisciplinary approaches

Each of the subdisciplines of environmental health science can aid in the elucidation of the connection between the exposome and age-related diseases. Information and findings from geriatric populations (via geriatric epidemiology), accurate measurements of chemical species in the environment (via exposure science), exposome–health outcome association analysis (via biostatistics), and data on biological mechanisms and target pathways (via toxicology, molecular biology, and bioinformatics) will all be needed for exposomic research. The findings from these major subdisciplines will need to be integrated via computational methods, together with data from analytical chemistry, genomics, behavioral science, nutritional sciences, and many other fields.

Environmental health science is most powerful when it capitalizes on the transfer of knowledge between its three major subdisciplines: toxicology, geriatric epidemiology, and exposure science. Toxicologists rely on geriatric epidemiological studies and the analysis of exposures to determine which compounds linked to age-related diseases should be studied. In turn, geriatric epidemiologists rely on toxicologists to determine if their observed associations are in line with what is known about the biological and toxicological pathways involved in the aging process or age-related diseases. Toxicologists also rely on exposure scientists to determine what levels of exposure are relevant. If data from all three subdisciplines agree that a particular environmental exposure has an impact on the risk of age-related diseases, this is very strong scientific evidence that such an impact is real. A similar level of convergence among all components of environmental health sciences will be necessary for data from the exposome to have an impact on studies of the risks of age-related diseases.

Genome–exposome epidemiology

The gene–environment (G × E) interaction is a key driver of human health and age-related diseases. The genetic coding system that has evolved to allow individuals to adapt to the environment is supported by a memory system to improve survival and reproduction [140, 141]. Ultimately, genetic epidemiology and exposure epidemiology must fully incorporate this interaction to account for the complex accumulation of G × E interactions over an individual’s lifetime. Given that the exposome is the accumulation of environmental impacts and related biological responses throughout the entire lifespan [76], predictive models of health outcomes and age-related diseases must comprehensively understand the genetic memory system, as it enables individual genomes to learn from their exposure history and thereby improve their response to subsequent exposures.

Exposure epidemiology is a necessary condition to solve the G × E (∫G × E) integral, which is a function of E and is thus constantly changing throughout an individual’s life. However, when the number of interacting elements exceeds 1018 (20,000 genes × 20,000 splicing variant transcripts × 100,000 or more proteins × 20 or more posttranslational modifications × 10,000 or more metabolites), it is impractical to conduct a systematic investigation of all assumptions [142]. One solution would be to obtain an informative longitudinal analysis of personal genetic and exposure information, as although the initial power would be limited, this would provide a platform to achieve long-term goals and clarify the level of the population whose exposure affects the results [143].

When discussing the analytical complexity inherent to determining an underlying G × E interaction, it should be noted that a statistical interaction does not represent a biological interaction [144]. However, by using emerging and abundant biological data sources, other potential signals can be identified from the large space of potential interactions [145]. Recently, sophisticated methods have been developed for exploring large-scale G × E interactions, and these methods could help solve the problem of dealing with many variables [9, 146].

At present, a few studies have examined interactions between sequence variations in the whole genome and environmental exposure to identify novel genetic modulators for certain environmental factors in age-related diseases. For instance, Biernacka et al. performed the first genome-wide gene‒environment interaction analysis of pesticide exposure and risk of PD using data on > 700,000 single nucleotide polymorphisms (SNPs) in 364 discordant sibling pairs [147]. They applied a PCA and logistic regression model to detect SNP–pesticide interactions and found that the effect of pesticide exposure on PD risk may be modified by SNPs of the ERCC6L2 gene. Patel et al. used the NHANES and screened 18 SNPs and 5 serum-based environmental factors (results from previous GWASs and EWASs) for interactions in association with T2DM [148]. The results showed that the interaction between rs13266634 (SLC30A8) and trans-β-carotene withstood Bonferroni correction (P = 0.006), and the per risk allele effect sizes among subjects with low levels of trans-β-carotene were 40% greater than the marginal effect size (OR: 1.8; 95% CI 1.3–2.6). Nonetheless, understanding the complex interplay between genes and the environment based on whole genome-wide and whole exposome-wide designs and more sophisticated models that incorporate the complexity of exposure mixtures, multiple variants, and multiple types of genomic data (epigenetic, genetic, transcriptomic, etc.) will likely shed new insights on this issue.

Perspectives and conclusions

The development of the exposome concept and related technologies will have several impacts on precise public health research and delivery.

  1. (1)

    The first impact will concern vulnerable populations, as the exposome offers an overall view and integrates the source of such populations’ vulnerability with the other exposures that an individual may have and which can aggravate or cause a pathological condition. These vulnerabilities can relate to life stage (e.g., affect children or older adults), sex, genetic factors, dietary origin, pathologies, or particular socioeconomic conditions [149, 150]. For example, older adults are generally considered a susceptible population because of the gradual decline in physiological processes over time. In addition, dosimetric studies showed that there was reduced clearance of PM in all regions of the respiratory tract with increasing age beyond young adulthood [151].

  2. (2)

    The second impact will concern the communication and dissemination of public health messages. Such messages regarding age-related diseases can be contradictory, e.g., “eat more vegetables” but “avoid exposure to pesticides.” Thus, a more integrated approach would give more credibility to these messages.

  3. (3)

    The third impact will concern perspective data reflecting exposures, which can be incorporated into precision medicine to complement genetic information. At a large scale, a systems biology approach can integrate various levels of heterogeneous information. This could include both genetic data and exposomic data, including multiomics data, thereby facilitating a more comprehensive analysis of disease etiologies and specific vulnerabilities than has been provided by previous studies. The subsequent challenge will be to more broadly integrate various risk exposures to obtain a global and more realistic overview of exposure–genetic interactions [152,153,154].

  4. (4)

    The last impact will involve the bench-to-bedside translation of research. The increasing combination of exposomic data with multiomics data will enable researchers to readily identify interim biomarkers of exposure and response that will reveal subtle biological changes. These will facilitate early diagnosis and the identification of potential intervention targets for the prevention and treatment of age-related diseases. In addition, primary prevention to reduce key exposures would reduce the contribution of environmental stressors to the development of age-related diseases and minimize the adverse effects of chemical stressors on individuals as they age.


The explosion in exposome research related to aging and the risk of age-related diseases may provide an answer—from an environmental perspective—to the question “Can we stop ourselves from aging?”, as recently posed by Science magazine.

Availability of data and materials

Data sharing is not applicable to this article as no datasets were generated or analyzed during the current study.



Global Burden of Diseases, Injuries, and Risk Factors Study


Socio-demographic index


Disability-adjusted life years


Parkinson’s disease


Alzheimer’s disease


Chronic obstructive pulmonary disease


Coronary artery disease


Type 2 diabetes mellitus


Genome-wide association study


Hazard ratio


Odds ratio


Relative risk


Confidence interval

PM2.5 :

2.5-µM particulate matter

PM10 :

10-µM particulate matter


Carbon monoxide

SO2 :

Sulfur dioxide

NO2 :

Nitrogen dioxide






Per- and polyfluoroalkyl substances


A-weighted decibels


Geographic information system


High-resolution mass spectrometry


Exposome-wide association study


False discovery rate




Elastic net


Graphical unit evolutionary stochastic search


Principal component analysis


Partial least squares


Sparse PLS


Least absolute shrinkage and selection operator


Group-LASSO interaction network


Bayesian model average

G × E:

Gene–environment interaction


  1. Campisi J, Kapahi P, Lithgow GJ, Melov S, Newman JC, Verdin E. From discoveries in ageing research to therapeutics for healthy ageing. Nature. 2019;571(7764):183–92.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Chang AY, Skirbekk VF, Tyrovolas S, Kassebaum NJ, Dieleman JL. Measuring population ageing: an analysis of the Global Burden of Disease Study 2017. Lancet Public Health. 2019;4(3):e159–67.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Erikson GA, Bodian DL, Rueda M, Molparia B, Scott ER, Scott-Van Zeeland AA, Topol SE, Wineinger NE, Niederhuber JE, Topol EJ, et al. Whole-genome sequencing of a healthy aging cohort. Cell. 2016;165(4):1002–11.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Rossiello F, Jurk D, Passos JF, Adda di Fagagna F. Telomere dysfunction in ageing and age-related diseases. Nat Cell Biol. 2022;24(2):135–47.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. de Magalhaes JP, Stevens M, Thornton D. The business of anti-aging science. Trends Biotechnol. 2017;35(11):1062–73.

    Article  PubMed  Google Scholar 

  6. Chakravarti A, Little P. Nature, nurture and human disease. Nature. 2003;421(6921):412–4.

    Article  PubMed  Google Scholar 

  7. Hemminki K, Bermejo JL, Försti A. The balance between heritable and environmental aetiology of human disease. Nat Rev Genet. 2006;7(12):958–65.

    Article  CAS  PubMed  Google Scholar 

  8. Lim SS, Vos T, Flaxman AD, Danaei G, Shibuya K, Adair-Rohani H, Amann M, Anderson HR, Andrews KG, Aryee M, et al. A comparative risk assessment of burden of disease and injury attributable to 67 risk factors and risk factor clusters in 21 regions, 1990–2010: a systematic analysis for the Global Burden of Disease Study 2010. Lancet. 2012;380(9859):2224–60.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Rappaport SM. Genetic factors are not the major causes of chronic diseases. PLoS ONE. 2016;11(4):1–9.

    Article  Google Scholar 

  10. Manolio TA, Collins FS, Cox NJ, Goldstein DB, Hindorff LA, Hunter DJ, McCarthy MI, Ramos EM, Cardon LR, Chakravarti A, et al. Finding the missing heritability of complex diseases. Nature. 2009;461(7265):747–53.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Rappaport SM. Implications of the exposome for exposure science. J Expo Sci Environ Epidemiol. 2011;21(1):5–9.

    Article  CAS  PubMed  Google Scholar 

  12. Wild CP. Complementing the genome with an “exposome”: the outstanding challenge of environmental exposure measurement in molecular epidemiology. Cancer Epidemiol Biomark Prev. 2005;14(8):1847–50.

    Article  CAS  Google Scholar 

  13. Niedzwiecki MM, Walker DI, Vermeulen R, Chadeau-Hyam M, Jones DP, Miller GW. The exposome: molecules to populations. Annu Rev Pharmacol Toxicol. 2019;59:107–27.

    Article  CAS  PubMed  Google Scholar 

  14. Wild CP. The exposome: from concept to utility. Int J Epidemiol. 2012;41(1):24–32.

    Article  PubMed  Google Scholar 

  15. Shin S, Burnett RT, Kwong JC, Hystad P, van Donkelaar A, Brook JR, Copes R, Tu K, Goldberg MS, Villeneuve PJ, et al. Effects of ambient air pollution on incident Parkinson’s disease in Ontario, 2001 to 2013: a population-based cohort study. Int J Epidemiol. 2018;47(6):2038–48.

    Article  PubMed  Google Scholar 

  16. Oudin A, Forsberg B, Adolfsson AN, Lind N, Modig L, Nordin M, Nordin S, Adolfsson R, Nilsson LG. Traffic-related air pollution and dementia incidence in Northern Sweden: a longitudinal study. Environ Health Perspect. 2016;124(3):306–12.

    Article  CAS  PubMed  Google Scholar 

  17. Chang KH, Chang MY, Muo CH, Wu TN, Chen CY, Kao CH. Increased risk of dementia in patients exposed to nitrogen dioxide and carbon monoxide: a population-based retrospective cohort study. PLoS ONE. 2014;9(8):e103078.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Han C, Oh J, Lim YH, Kim S, Hong YC. Long-term exposure to fine particulate matter and development of chronic obstructive pulmonary disease in the elderly. Environ Int. 2020;143:105895.

    Article  CAS  PubMed  Google Scholar 

  19. Li J, Sun S, Tang R, Qiu H, Huang Q, Mason TG, Tian L. Major air pollutants and risk of COPD exacerbations: a systematic review and meta-analysis. Int J Chron Obstruct Pulmon Dis. 2016;11:3079–91.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Shah AS, Lee KK, McAllister DA, Hunter A, Nair H, Whiteley W, Langrish JP, Newby DE, Mills NL. Short term exposure to air pollution and stroke: systematic review and meta-analysis. BMJ. 2015;350:h1295.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Kramer U, Herder C, Sugiri D, Strassburger K, Schikowski T, Ranft U, Rathmann W. Traffic-related air pollution and incident type 2 diabetes: results from the SALIA cohort study. Environ Health Perspect. 2010;118(9):1273–9.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Coogan PF, White LF, Jerrett M, Brook RD, Su JG, Seto E, Burnett R, Palmer JR, Rosenberg L. Air pollution and incidence of hypertension and diabetes mellitus in black women living in Los Angeles. Circulation. 2012;125(6):767–72.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Andersen ZJ, Raaschou-Nielsen O, Ketzel M, Jensen SS, Hvidberg M, Loft S, Tjonneland A, Overvad K, Sorensen M. Diabetes incidence and long-term exposure to air pollution: a cohort study. Diabetes Care. 2012;35(1):92–8.

    Article  CAS  PubMed  Google Scholar 

  24. Hansen AB, Ravnskjaer L, Loft S, Andersen KK, Brauner EV, Baastrup R, Yao C, Ketzel M, Becker T, Brandt J, et al. Long-term exposure to fine particulate matter and incidence of diabetes in the Danish Nurse Cohort. Environ Int. 2016;91:243–50.

    Article  CAS  PubMed  Google Scholar 

  25. Chen H, Burnett RT, Kwong JC, Villeneuve PJ, Goldberg MS, Brook RD, van Donkelaar A, Jerrett M, Martin RV, Brook JR, et al. Risk of incident diabetes in relation to long-term exposure to fine particulate matter in Ontario, Canada. Environ Health Perspect. 2013;121(7):804–10.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Yan D, Zhang Y, Liu L, Shi N, Yan H. Pesticide exposure and risk of Parkinson’s disease: dose-response meta-analysis of observational studies. Regul Toxicol Pharmacol. 2018;96:57–63.

    Article  CAS  PubMed  Google Scholar 

  27. Baldi I, Lebailly P, Mohammed-Brahim B, Letenneur L, Dartigues JF, Brochard P. Neurodegenerative diseases and exposure to pesticides in the elderly. Am J Epidemiol. 2003;157(5):409–14.

    Article  PubMed  Google Scholar 

  28. Montgomery MP, Saldana TM, Sandler DP, Alavanja MC, Kamel F. Incident diabetes and pesticide exposure among licensed pesticide applicators: agricultural health study, 1993–2003. Am J Epidemiol. 2008;167(10):1235–46.

    Article  CAS  PubMed  Google Scholar 

  29. Turyk M, Anderson H, Knobeloch L, Imm P, Persky V. Organochlorine exposure and incidence of diabetes in a cohort of Great Lakes sport fish consumers. Environ Health Perspect. 2009;117(7):1076–82.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Lee DH, Lind PM, Jacobs DR Jr, Salihovic S, van Bavel B, Lind L. Polychlorinated biphenyls and organochlorine pesticides in plasma predict development of type 2 diabetes in the elderly: the prospective investigation of the vasculature in Uppsala Seniors (PIVUS) study. Diabetes Care. 2011;34(8):1778–84.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Coon S, Stark A, Peterson E, Gloi A, Kortsha G, Pounds J, Chettle D, Gorell J. Whole-body lifetime occupational lead exposure and risk of Parkinson’s disease. Environ Health Perspect. 2006;114(12):1872–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Weisskopf MG, Weuve J, Nie H, Saint-Hilaire MH, Sudarsky L, Simon DK, Hersh B, Schwartz J, Wright RO, Hu H. Association of cumulative lead exposure with Parkinson’s disease. Environ Health Perspect. 2010;118(11):1609–13.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Rondeau V, Jacqmin-Gadda H, Commenges D, Helmer C, Dartigues JF. Aluminum and silica in drinking water and the risk of Alzheimer’s disease or cognitive decline: findings from 15-year follow-up of the PAQUID cohort. Am J Epidemiol. 2009;169(4):489–96.

    Article  PubMed  Google Scholar 

  34. Sun Q, Zong G, Valvi D, Nielsen F, Coull B, Grandjean P. Plasma concentrations of perfluoroalkyl substances and risk of type 2 Diabetes: a prospective investigation among U.S. women. Environ Health Perspect. 2018;126(3):037001.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Ongono JS, Dow C, Gambaretti J, Severi G, Boutron-Ruault MC, Bonnet F, Fagherazzi G, Mancini FR. Dietary exposure to brominated flame retardants and risk of type 2 diabetes in the French E3N cohort. Environ Int. 2019;123:54–60.

    Article  CAS  PubMed  Google Scholar 

  36. Qiu C, Fratiglioni L, Karp A, Winblad B, Bellander T. Occupational exposure to electromagnetic fields and risk of Alzheimer’s disease. Epidemiology. 2004;15(6):687–94.

    Article  PubMed  Google Scholar 

  37. Linares C, Culqui D, Carmona R, Ortiz C, Diaz J. Short-term association between environmental factors and hospital admissions due to dementia in Madrid. Environ Res. 2017;152:214–20.

    Article  CAS  PubMed  Google Scholar 

  38. Kempen EV, Casas M, Pershagen G, Foraster M. WHO environmental noise guidelines for the European region: a systematic review on environmental noise and cardiovascular and metabolic effects: a summary. Int J Environ Res Public Health. 2018;15(2):379.

    Article  PubMed Central  Google Scholar 

  39. Roswall N, Pyko A, Ogren M, Oudin A, Rosengren A, Lager A, Poulsen AH, Eriksson C, Segersson D, Rizzuto D, et al. Long-term exposure to transportation noise and risk of incident stroke: a pooled study of nine Scandinavian cohorts. Environ Health Perspect. 2021;129(10):107002.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Gallo V, Vineis P, Cancellieri M, Chiodini P, Barker RA, Brayne C, Pearce N, Vermeulen R, Panico S, Bueno-de-Mesquita B, et al. Exploring causality of the association between smoking and Parkinson’s disease. Int J Epidemiol. 2019;48(3):912–25.

    PubMed  Google Scholar 

  41. Eisner MD, Anthonisen N, Coultas D, Kuenzli N, Perez-Padilla R, Postma D, Romieu I, Silverman EK, Balmes JR, Committee on Nonsmoking Copd E, et al. An official American Thoracic Society public policy statement: novel risk factors and the global burden of chronic obstructive pulmonary disease. Am J Respir Crit Care Med. 2010;182(5):693–718.

    Article  PubMed  Google Scholar 

  42. Dawes P, Cruickshanks KJ, Moore DR, Edmondson-Jones M, McCormack A, Fortnum H, Munro KJ. Cigarette smoking, passive smoking, alcohol consumption, and hearing loss. J Assoc Res Otolaryngol. 2014;15(4):663–74.

    Article  PubMed  PubMed Central  Google Scholar 

  43. Klinedinst BS, Le ST, Larsen B, Pappas C, Hoth NJ, Pollpeter A, Wang Q, Wang Y, Yu S, Wang L, et al. Genetic factors of Alzheimer’s disease modulate how diet is associated with long-term cognitive trajectories: a UK Biobank Study. J Alzheimers Dis. 2020;78(3):1245–57.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Patel CJ, Bhattacharya J, Butte AJ. An Environment-Wide Association Study (EWAS) on type 2 diabetes mellitus. PLoS ONE. 2010;5(5):e10746.

    Article  PubMed  PubMed Central  Google Scholar 

  45. Foubert-Samier A, Helmer C, Perez F, Le Goff M, Auriacombe S, Elbaz A, Dartigues JF, Tison F. Past exposure to neuroleptic drugs and risk of Parkinson disease in an elderly cohort. Neurology. 2012;79(15):1615–21.

    Article  CAS  PubMed  Google Scholar 

  46. Goldman SM, Quinlan PJ, Ross GW, Marras C, Meng C, Bhudhikanok GS, Comyns K, Korell M, Chade AR, Kasten M, et al. Solvent exposures and Parkinson disease risk in twins. Ann Neurol. 2012;71(6):776–84.

    Article  CAS  PubMed  Google Scholar 

  47. Gray SL, Anderson ML, Dublin S, Hanlon JT, Hubbard R, Walker R, Yu O, Crane PK, Larson EB. Cumulative use of strong anticholinergics and incident dementia: a prospective cohort study. JAMA Intern Med. 2015;175(3):401–7.

    Article  PubMed  PubMed Central  Google Scholar 

  48. Scheperjans F, Aho V, Pereira PA, Koskinen K, Paulin L, Pekkonen E, Haapaniemi E, Kaakkola S, Eerola-Rautio J, Pohja M, et al. Gut microbiota are related to Parkinson’s disease and clinical phenotype. Mov Disord. 2015;30(3):350–8.

    Article  PubMed  Google Scholar 

  49. Vascellari S, Palmas V, Melis M, Pisanu S, Cusano R, Uva P, Perra D, Madau V, Sarchioto M, Oppo V, et al. Gut microbiota and metabolome alterations associated with Parkinson’s disease. mSystems. 2020;5(5):e00561.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Vogt NM, Kerby RL, Dill-McFarland KA, Harding SJ, Merluzzi AP, Johnson SC, Carlsson CM, Asthana S, Zetterberg H, Blennow K, et al. Gut microbiome alterations in Alzheimer’s disease. Sci Rep. 2017;7(1):13537.

    Article  PubMed  PubMed Central  Google Scholar 

  51. Walker KA, Gottesman RF, Wu A, Knopman DS, Gross AL, Mosley TH Jr, Selvin E, Windham BG. Systemic inflammation during midlife and cognitive change over 20 years: the ARIC study. Neurology. 2019;92(11):e1256–67.

    CAS  PubMed  PubMed Central  Google Scholar 

  52. Li J, Lee DH, Hu J, Tabung FK, Li Y, Bhupathiraju SN, Rimm EB, Rexrode KM, Manson JE, Willett WC, et al. Dietary inflammatory potential and risk of cardiovascular disease among men and women in the U.S. J Am Coll Cardiol. 2020;76(19):2181–93.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Franceschi C, Garagnani P, Parini P, Giuliani C, Santoro A. Inflammaging: a new immune-metabolic viewpoint for age-related diseases. Nat Rev Endocrinol. 2018;14(10):576–90.

    Article  CAS  PubMed  Google Scholar 

  54. Savolainen-Peltonen H, Rahkola-Soisalo P, Hoti F, Vattulainen P, Gissler M, Ylikorkala O, Mikkola TS. Use of postmenopausal hormone therapy and risk of Alzheimer’s disease in Finland: nationwide case-control study. BMJ. 2019;364:l665.

    Article  PubMed  PubMed Central  Google Scholar 

  55. Wu M, Li M, Yuan J, Liang S, Chen Z, Ye M, Ryan PM, Clark C, Tan SC, Rahmani J, et al. Postmenopausal hormone therapy and Alzheimer’s disease, dementia, and Parkinson’s disease: a systematic review and time-response meta-analysis. Pharmacol Res. 2020;155:104693.

    Article  CAS  PubMed  Google Scholar 

  56. Wu YT, Prina AM, Jones AP, Barnes LE, Matthews FE, Brayne C, Medical Research Council Cognitive F, Ageing S. Community environment, cognitive impairment and dementia in later life: results from the cognitive function and ageing study. Age Ageing. 2015;44(6):1005–11.

    Article  PubMed  PubMed Central  Google Scholar 

  57. de Keijzer C, Tonne C, Basagana X, Valentin A, Singh-Manoux A, Alonso J, Anto JM, Nieuwenhuijsen MJ, Sunyer J, Dadvand P. Residential surrounding greenness and cognitive decline: a 10-year follow-up of the Whitehall II cohort. Environ Health Perspect. 2018;126(7):077003.

    Article  PubMed  PubMed Central  Google Scholar 

  58. Sarkar C, Zhang B, Ni M, Kumari S, Bauermeister S, Gallacher J, Webster C. Environmental correlates of chronic obstructive pulmonary disease in 96 779 participants from the UK Biobank: a cross-sectional, observational study. Lancet Planet Health. 2019;3(11):e478–90.

    Article  PubMed  Google Scholar 

  59. Tan J, Wang Y. Social integration, social support, and all-cause, cardiovascular disease and cause-specific mortality: a prospective cohort study. Int J Environ Res Public Health. 2019;16(9):1498.

    Article  PubMed Central  Google Scholar 

  60. Penninkilampi R, Casey AN, Singh MF, Brodaty H. The association between social engagement, loneliness, and risk of dementia: a systematic review and meta-analysis. J Alzheimers Dis. 2018;66(4):1619–33.

    Article  PubMed  Google Scholar 

  61. Jia L, Du Y, Chu L, Zhang Z, Li F, Lyu D, Li Y, Li Y, Zhu M, Jiao H, et al. Prevalence, risk factors, and management of dementia and mild cognitive impairment in adults aged 60 years or older in China: a cross-sectional study. Lancet Public Health. 2020;5(12):e661–71.

    Article  PubMed  Google Scholar 

  62. Petersen JD, Wehberg S, Packness A, Svensson NH, Hyldig N, Raunsgaard S, Andersen MK, Ryg J, Mercer SW, Sondergaard J, et al. Association of socioeconomic status with dementia diagnosis among older adults in Denmark. JAMA Netw Open. 2021;4(5):e2110432.

    Article  PubMed  PubMed Central  Google Scholar 

  63. Kennedy BK, Berger SL, Brunet A, Campisi J, Cuervo AM, Epel ES, Franceschi C, Lithgow GJ, Morimoto RI, Pessin JE, et al. Geroscience: linking aging to chronic disease. Cell. 2014;159(4):709–13.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Lopez-Otin C, Blasco MA, Partridge L, Serrano M, Kroemer G. The hallmarks of aging. Cell. 2013;153(6):1194–217.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Roy AL, Sierra F, Howcroft K, Singer DS, Sharpless N, Hodes RJ, Wilder EL, Anderson JM. A blueprint for characterizing senescence. Cell. 2020;183(5):1143–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Huat TJ, Camats-Perna J, Newcombe EA, Valmas N, Kitazawa M, Medeiros R. Metal toxicity links to Alzheimer’s disease and neuroinflammation. J Mol Biol. 2019;431(9):1843–68.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Martin EM, Fry RC. Environmental influences on the epigenome: exposure-associated DNA methylation in human populations. Annu Rev Public Health. 2018;39:309–33.

    Article  PubMed  Google Scholar 

  68. Kubi JA, Chen ACH, Fong SW, Lai KP, Wong CKC, Yeung WSB, Lee KF, Lee YL. Effects of 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) on the differentiation of embryonic stem cells towards pancreatic lineage and pancreatic beta cell function. Environ Int. 2019;130:104885.

    Article  CAS  PubMed  Google Scholar 

  69. Soundararajan A, Prabu P, Mohan V, Gibert Y, Balasubramanyam M. Novel insights of elevated systemic levels of bisphenol-A (BPA) linked to poor glycemic control, accelerated cellular senescence and insulin resistance in patients with type 2 diabetes. Mol Cell Biochem. 2019;458(1–2):171–83.

    Article  CAS  PubMed  Google Scholar 

  70. Cavalli G, Heard E. Advances in epigenetics link genetics to the environment and disease. Nature. 2019;571(7766):489–99.

    Article  CAS  PubMed  Google Scholar 

  71. de Prado-Bert P, Ruiz-Arenas C, Vives-Usano M, Andrusaityte S, Cadiou S, Carracedo A, Casas M, Chatzi L, Dadvand P, Gonzalez JR, et al. The early-life exposome and epigenetic age acceleration in children. Environ Int. 2021;155:106683.

    Article  PubMed  Google Scholar 

  72. Everson TM, Vives-Usano M, Seyve E, Cardenas A, Lacasana M, Craig JM, Lesseur C, Baker ER, Fernandez-Jimenez N, Heude B, et al. Placental DNA methylation signatures of maternal smoking during pregnancy and potential impacts on fetal growth. Nat Commun. 2021;12(1):5095.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Haddad N, Andrianou XD, Makris KC. A scoping review on the characteristics of human exposome studies. Curr Pollut Rep. 2019;5(4):378–93.

    Article  Google Scholar 

  74. Rappaport SM, Smith MT. Epidemiology, environment and disease risks. Science. 2010;330(6003):460–1.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. 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. Environ Health Perspect. 2016;124(10):1504–10.

    Article  PubMed  PubMed Central  Google Scholar 

  76. Miller GW, Jones DP. The nature of nurture: refining the definition of the exposome. Toxicol Sci. 2014;137(1):1–2.

    Article  CAS  PubMed  Google Scholar 

  77. Wagner KH, Cameron-Smith D, Wessner B, Franzke B. Biomarkers of aging: from function to molecular biology. Nutrients. 2016;8(6):338.

    Article  PubMed Central  Google Scholar 

  78. Rivero-Segura NA, Bello-Chavolla OY, Barrera-Vazquez OS, Gutierrez-Robledo LM, Gomez-Verjan JC. Promising biomarkers of human aging: In search of a multi-omics panel to understand the aging process from a multidimensional perspective. Ageing Res Rev. 2020;64:101164.

    Article  CAS  PubMed  Google Scholar 

  79. Fang M, Hu L, Chen D, Guo Y, Liu J, Lan C, Gong J, Wang B. Exposome in human health: Utopia or wonderland? Innovation (N Y). 2021;2(4):100172.

    CAS  Google Scholar 

  80. Carmona JJ, Michan S. Biology of healthy aging and longevity. Rev Investig Clin. 2016;68(1):7–16.

    CAS  Google Scholar 

  81. Makris KC. Desynchronized circadian clock and exposures to xenobiotics are associated with differentiated disease phenotypes: the interface of desynchronized circadian clock and exposures to xenobiotics would lead to adverse response and recovery. BioEssays. 2021;43(11):e2100159.

    Article  PubMed  Google Scholar 

  82. Curtin P, Austin C, Curtin A, Gennings C, Arora M, Tammimies K, Willfors C, Berggren S, Siper P, Rai D, et al. Dynamical features in fetal and postnatal zinc-copper metabolic cycles predict the emergence of autism spectrum disorder. Sci Adv. 2018;4(5):eaat1293.

    Article  PubMed  PubMed Central  Google Scholar 

  83. Lehallier B, Gate D, Schaum N, Nanasi T, Lee SE, Yousef H, Moran Losada P, Berdnik D, Keller A, Verghese J, et al. Undulating changes in human plasma proteome profiles across the lifespan. Nat Med. 2019;25(12):1843–50.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  84. Paul KC, Sinsheimer JS, Rhodes SL, Cockburn M, Bronstein J, Ritz B. Organophosphate pesticide exposures, nitric oxide synthase gene variants, and gene-pesticide interactions in a case-control study of Parkinson’s disease, California (USA). Environ Health Perspect. 2016;124(5):570–7.

    Article  CAS  PubMed  Google Scholar 

  85. Christine PJ, Auchincloss AH, Bertoni AG, Carnethon MR, Snchez BN, Moore K, Adar SD, Horwich TB, Watson KE, Roux AVD. Longitudinal associations between neighborhood physical and social environments and incident type 2 diabetes mellitus: the multi-ethnic study of atherosclerosis (MESA). JAMA Intern Med. 2015;175(8):1311–20.

    Article  PubMed  PubMed Central  Google Scholar 

  86. Turner MC, Nieuwenhuijsen M, Anderson K, Balshaw D, Cui Y, Dunton G, Hoppin JA, Koutrakis P, Jerrett M. Assessing the exposome with external measures: commentary on the state of the science and research recommendations. Annu Rev Public Health. 2017;38:215–39.

    Article  PubMed  PubMed Central  Google Scholar 

  87. Geddes JA, Martin RV, Boys BL, van Donkelaar A. Long-term trends worldwide in ambient NO2 concentrations inferred from satellite observations. Environ Health Perspect. 2016;124(3):281–9.

    Article  CAS  PubMed  Google Scholar 

  88. Nieuwenhuijsen MJ, Kruize H, Gidlow C, Andrusaityte S, Anto JM, Basagana X, Cirach M, Dadvand P, Danileviciute A, Donaire-Gonzalez D, et al. Positive health effects of the natural outdoor environment in typical populations in different regions in Europe (PHENOTYPE): a study programme protocol. BMJ Open. 2014;4(4):e004951.

    Article  PubMed  PubMed Central  Google Scholar 

  89. Dadvand P, Ostro B, Figueras F, Foraster M, Basagana X, Valentin A, Martinez D, Beelen R, Cirach M, Hoek G, et al. Residential proximity to major roads and term low birth weight: the roles of air pollution, heat, noise, and road-adjacent trees. Epidemiology. 2014;25(4):518–25.

    Article  PubMed  Google Scholar 

  90. Su JG, Apte JS, Lipsitt J, Garcia-Gonzales DA, Beckerman BS, de Nazelle A, Texcalac-Sangrador JL, Jerrett M. Populations potentially exposed to traffic-related air pollution in seven world cities. Environ Int. 2015;78:82–9.

    Article  CAS  PubMed  Google Scholar 

  91. Hurley S, Goldberg D, Nelson D, Hertz A, Horn-Ross PL, Bernstein L, Reynolds P. Light at night and breast cancer risk among California teachers. Epidemiology. 2014;25(5):697–706.

    Article  PubMed  PubMed Central  Google Scholar 

  92. Maxwell SK, Meliker JR, Goovaerts P. Use of land surface remotely sensed satellite and airborne data for environmental exposure assessment in cancer research. J Expo Sci Environ Epidemiol. 2010;20(2):176–85.

    Article  CAS  PubMed  Google Scholar 

  93. van Donkelaar A, Martin RV, Brauer M, Kahn R, Levy R, Verduzco C, Villeneuve PJ. Global estimates of ambient fine particulate matter concentrations from satellite-based aerosol optical depth: development and application. Environ Health Perspect. 2010;118(6):847–55.

    Article  PubMed  PubMed Central  Google Scholar 

  94. Brook RD, Cakmak S, Turner MC, Brook JR, Crouse DL, Peters PA, van Donkelaar A, Villeneuve PJ, Brion O, Jerrett M, et al. Long-term fine particulate matter exposure and mortality from diabetes in Canada. Diabetes Care. 2013;36(10):3313–20.

    Article  PubMed  PubMed Central  Google Scholar 

  95. Crouse DL, Peters PA, van Donkelaar A, Goldberg MS, Villeneuve PJ, Brion O, Khan S, Atari DO, Jerrett M, Pope CA, et al. Risk of nonaccidental and cardiovascular mortality in relation to long-term exposure to low concentrations of fine particulate matter: a Canadian national-level cohort study. Environ Health Perspect. 2012;120(5):708–14.

    Article  PubMed  PubMed Central  Google Scholar 

  96. Lee M, Kloog I, Chudnovsky A, Lyapustin A, Wang Y, Melly S, Coull B, Koutrakis P, Schwartz J. Spatiotemporal prediction of fine particulate matter using high-resolution satellite images in the Southeastern US 2003–2011(Review). J Expo Sci Environ Epidemiol. 2016;26(4):377–84.

    Article  CAS  PubMed  Google Scholar 

  97. van Donkelaar A, Martin RV, Spurr RJ, Burnett RT. High-resolution satellite-derived PM2.5 from optimal estimation and geographically weighted regression over North America. Environ Sci Technol. 2015;49(17):10482–91.

    Article  PubMed  Google Scholar 

  98. Smolders R, De Boever P. Perspectives for environment and health research in Horizon 2020: dark ages or golden era? Int J Hyg Environ Health. 2014;217(8):891–6.

    Article  PubMed  Google Scholar 

  99. Bailey E, Fuhrmann C, Runkle J, Stevens S, Brown M, Sugg M. Wearable sensors for personal temperature exposure assessments: a comparative study. Environ Res. 2020;180:108858.

    Article  CAS  PubMed  Google Scholar 

  100. Constantinou A, Oikonomou S, Konstantinou C, Makris KC. A randomized cross-over trial investigating differences in 24-h personal air and skin temperatures using wearable sensors between two climatologically contrasting settings. Sci Rep. 2021;11(1):22020.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  101. Gao P, Shen X, Zhang X, Jiang C, Zhang S, Zhou X, Schussler-Fiorenza Rose SM, Snyder M. Precision environmental health monitoring by longitudinal exposome and multi-omics profiling. Genome Res. 2022;32(6):1199–214.

    Article  PubMed  PubMed Central  Google Scholar 

  102. Dunn J, Kidzinski L, Runge R, Witt D, Hicks JL, Schussler-Fiorenza Rose SM, Li X, Bahmani A, Delp SL, Hastie T, et al. Wearable sensors enable personalized predictions of clinical laboratory measurements. Nat Med. 2021;27(6):1105–12.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  103. Koelmel JP, Lin EZ, Guo P, Zhou J, He J, Chen A, Gao Y, Deng F, Dong H, Liu Y, Cha Y, Fang J, Beecher C, Shi X, Tang S, Godri Pollitt KJ. Exploring the external exposome using wearable passive samplers—the China BAPE study. Environmental pollution (Barking, Essex: 1987). 2020;270:116228.

    Article  Google Scholar 

  104. Tang S, Li T, Fang J, Chen R, Cha Y, Wang Y, Zhu M, Zhang Y, Chen Y, Du Y, et al. The exposome in practice: an exploratory panel study of biomarkers of air pollutant exposure in Chinese people aged 60–69 years (China BAPE Study). Environ Int. 2021;157:106866.

    Article  CAS  PubMed  Google Scholar 

  105. Jiang C, Wang X, Li X, Inlora J, Wang T, Liu Q, Snyder M. Dynamic human environmental exposome revealed by longitudinal personal monitoring. Cell. 2018;175(1):277-291 e231.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  106. Jiang C, Zhang X, Gao P, Chen Q, Snyder M. Decoding personal biotic and abiotic airborne exposome. Nat Protoc. 2021;16(2):1129–51.

    Article  CAS  PubMed  Google Scholar 

  107. Schymanski EL, Singer HP, Longree P, Loos M, Ruff M, Stravs MA, Ripolles Vidal C, Hollender J. Strategies to characterize polar organic contamination in wastewater: exploring the capability of high resolution mass spectrometry. Environ Sci Technol. 2014;48(3):1811–8.

    Article  CAS  PubMed  Google Scholar 

  108. Sevin DC, Kuehne A, Zamboni N, Sauer U. Biological insights through nontargeted metabolomics. Curr Opin Biotechnol. 2015;34:1–8.

    Article  CAS  PubMed  Google Scholar 

  109. Vermeulen R, Schymanski EL, Barabasi AL, Miller GW. The exposome and health: where chemistry meets biology. Science. 2020;367(6476):392–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  110. Guo P, Lin EZ, Koelmel JP, Ding E, Gao Y, Deng F, Dong H, Liu Y, Cha Y, Fang J, et al. Exploring personal chemical exposures in China with wearable air pollutant monitors: a repeated-measure study in healthy older adults in Jinan, China. Environ Int. 2021;156:106709.

    Article  CAS  PubMed  Google Scholar 

  111. Uppal K, Ma C, Go YM, Jones DP, Wren J. xMWAS: a data-driven integration and differential network analysis tool. Bioinformatics. 2018;34(4):701–2.

    Article  CAS  PubMed  Google Scholar 

  112. Li S, Sullivan NL, Rouphael N, Yu T, Banton S, Maddur MS, McCausland M, Chiu C, Canniff J, Dubey S, et al. Metabolic phenotypes of response to vaccination in humans. Cell. 2017;169(5):862-877 e817.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  113. Vrijheid M, Slama R, Robinson O, Chatzi L, Coen M, van den Hazel P, Thomsen C, Wright J, Athersuch TJ, Avellana N, et al. The human early-life exposome (HELIX): project rationale and design. Environ Health Perspect. 2014;122(6):535–44.

    Article  PubMed  PubMed Central  Google Scholar 

  114. Vineis P, Chadeau-Hyam M, Gmuender H, Gulliver J, Herceg Z, Kleinjans J, Kogevinas M, Kyrtopoulos S, Nieuwenhuijsen M, Phillips DH, et al. The exposome in practice: design of the EXPOsOMICS project. Int J Hyg Environ Health. 2017;220(2 Pt A):142–51.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  115. van Veldhoven K, Keski-Rahkonen P, Barupal DK, Villanueva CM, Font-Ribera L, Scalbert A, Bodinier B, Grimalt JO, Zwiener C, Vlaanderen J, et al. Effects of exposure to water disinfection by-products in a swimming pool: a metabolome-wide association study. Environ Int. 2018;111:60–70.

    Article  PubMed  PubMed Central  Google Scholar 

  116. Gao X, Becker LC, Becker DM, Starmer JD, Province MA. Avoiding the high Bonferroni penalty in genome-wide association studies. Genet Epidemiol. 2010;34(1):100–5.

    PubMed  PubMed Central  Google Scholar 

  117. Li MX, Yeung JM, Cherny SS, Sham PC. Evaluating the effective numbers of independent tests and significant p-value thresholds in commercial genotyping arrays and public imputation reference datasets. Hum Genet. 2012;131(5):747–56.

    Article  CAS  PubMed  Google Scholar 

  118. Romano JP, Wolf M. Stepwise multiple testing as formalized data snooping. Econometrica. 2005;73(4):1237–82.

    Article  Google Scholar 

  119. Agier L, Portengen L, Chadeau-Hyam M, Basagana X, Giorgis-Allemand L, Siroux V, Robinson O, Vlaanderen J, Gonzalez JR, Nieuwenhuijsen MJ, et al. A systematic comparison of linear regression-based statistical methods to assess exposome-health associations. Environ Health Perspect. 2016;124(12):1848–56.

    Article  PubMed  PubMed Central  Google Scholar 

  120. Agier L, Basagana X, Maitre L, Granum B, Bird PK, Casas M, Oftedal B, Wright J, Andrusaityte S, de Castro M, et al. Early-life exposome and lung function in children in Europe: an analysis of data from the longitudinal, population-based HELIX cohort. Lancet Planet Health. 2019;3(2):e81–92.

    Article  PubMed  Google Scholar 

  121. Nieuwenhuijsen MJ, Agier L, Basagana X, Urquiza J, Tamayo-Uria I, Giorgis-Allemand L, Robinson O, Siroux V, Maitre L, de Castro M, et al. Influence of the urban exposome on birth weight. Environ Health Perspect. 2019;127(4):47007.

    Article  PubMed  Google Scholar 

  122. Barrera-Gomez J, Agier L, Portengen L, Chadeau-Hyam M, Giorgis-Allemand L, Siroux V, Robinson O, Vlaanderen J, Gonzalez JR, Nieuwenhuijsen M, et al. A systematic comparison of statistical methods to detect interactions in exposome-health associations. Environ Health. 2017;16(1):74.

    Article  PubMed  PubMed Central  Google Scholar 

  123. Dominici F, Wang C, Crainiceanu C, Parmigiani G. Model selection and health effect estimation in environmental epidemiology. Epidemiology. 2008;19(4):558–60.

    Article  PubMed  Google Scholar 

  124. Lenters V, Portengen L, Rignell-Hydbom A, Jonsson BA, Lindh CH, Piersma AH, Toft G, Bonde JP, Heederik D, Rylander L, et al. Prenatal phthalate, perfluoroalkyl acid, and organochlorine exposures and term birth weight in three birth cohorts: multi-pollutant models based on elastic net regression. Environ Health Perspect. 2016;124(3):365–72.

    Article  CAS  PubMed  Google Scholar 

  125. Forns J, Mandal S, Iszatt N, Polder A, Thomsen C, Lyche JL, Stigum H, Vermeulen R, Eggesbo M. Novel application of statistical methods for analysis of multiple toxicants identifies DDT as a risk factor for early child behavioral problems. Environ Res. 2016;151:91–100.

    Article  CAS  PubMed  Google Scholar 

  126. Bottolo L, Chadeau-Hyam M, Hastie DI, Zeller T, Liquet B, Newcombe P, Yengo L, Wild PS, Schillert A, Ziegler A, Nielsen SF, et al. GUESS-ing polygenic associations with multiple phenotypes using a GPU-based evolutionary stochastic search algorithm. PLOS Genet. 2013;9(8):e1003657.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  127. Burnett RT, Brook J, Dann T, Delocla C, Philips O, Cakmak S, Vincent R, Goldberg MS, Krewski D. Association between particulate- and gas-phase components of urban air pollution and daily mortality in eight Canadian cities. Inhal Toxicol. 2000;12(Suppl 4):15–39.

    Article  CAS  PubMed  Google Scholar 

  128. Qian Z, Zhang J, Korn LR, Wei F, Chapman RS. Factor analysis of household factors: are they associated with respiratory conditions in Chinese children? Int J Epidemiol. 2004;33(3):582–8.

    Article  PubMed  Google Scholar 

  129. Arif AA, Shah SM. Association between personal exposure to volatile organic compounds and asthma among US adult population. Int Arch Occup Environ Health. 2007;80(8):711–9.

    Article  CAS  PubMed  Google Scholar 

  130. Roberts S, Martin MA. Using supervised principal components analysis to assess multiple pollutant effects. Environ Health Perspect. 2006;114(12):1877–82.

    Article  PubMed  PubMed Central  Google Scholar 

  131. Jain P, Vineis P, Liquet B, Vlaanderen J, Bodinier B, van Veldhoven K, Kogevinas M, Athersuch TJ, Font-Ribera L, Villanueva CM, et al. A multivariate approach to investigate the combined biological effects of multiple exposures. J Epidemiol Community Health. 2018;72(7):564–71.

    Article  PubMed  Google Scholar 

  132. Lenters V, Portengen L, Smit LA, Jonsson BA, Giwercman A, Rylander L, Lindh CH, Spano M, Pedersen HS, Ludwicki JK, et al. Phthalates, perfluoroalkyl acids, metals and organochlorines and reproductive function: a multipollutant assessment in Greenlandic, Polish and Ukrainian men. Occup Environ Med. 2015;72(6):385–93.

    Article  PubMed  Google Scholar 

  133. Thomas DC, Jerrett M, Kuenzli N, Louis TA, Dominici F, Zeger S, Schwarz J, Burnett RT, Krewski D, Bates D. Bayesian model averaging in time-series studies of air pollution and mortality. J Toxicol Environ Health A. 2007;70(3–4):311–5.

    Article  CAS  PubMed  Google Scholar 

  134. Thomas DC, Witte JS, Greenland S. Dissecting effects of complex mixtures: who’s afraid of informative priors? Epidemiology. 2007;18(2):186–90.

    Article  PubMed  Google Scholar 

  135. Sun Z, Tao Y, Li S, Ferguson KK, Meeker JD, Park SK, Batterman SA, Mukherjee B. Statistical strategies for constructing health risk models with multiple pollutants and their interactions: possible choices and comparisons. Environ Health. 2013;12(1):85.

    Article  PubMed  PubMed Central  Google Scholar 

  136. Carrico C, Gennings C, Wheeler DC, Factor-Litvak P. Characterization of weighted quantile sum regression for highly correlated data in a risk analysis setting. J Agric Biol Environ Stat. 2015;20(1):100–20.

    Article  PubMed  Google Scholar 

  137. Bobb JF, Valeri L, Claus Henn B, Christiani DC, Wright RO, Mazumdar M, Godleski JJ, Coull BA. Bayesian kernel machine regression for estimating the health effects of multi-pollutant mixtures. Biostatistics. 2015;16(3):493–508.

    Article  PubMed  Google Scholar 

  138. Keil AP, Buckley JP, O’Brien KM, Ferguson KK, Zhao S, White AJ. A quantile-based g-computation approach to addressing the effects of exposure mixtures. Environ Health Perspect. 2020;128(4):47004.

    Article  PubMed  Google Scholar 

  139. Caporale N, Leemans M, Birgersson L, Germain PL, Cheroni C, Borbely G, Engdahl E, Lindh C, Bressan RB, Cavallo F, et al. From cohorts to molecules: adverse impacts of endocrine disrupting mixtures. Science. 2022;375(6582):eabe8244.

    Article  CAS  PubMed  Google Scholar 

  140. Jones DP, Sies H. The redox code. Antioxid Redox Signal. 2015;23(9):734–46.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  141. Go YM, Jones DP. Exposure memory and lung regeneration. Ann Am Thorac Soc. 2016;13(Suppl 2):S452–61.

    Article  PubMed  PubMed Central  Google Scholar 

  142. Go YM, Fernandes J, Hu X, Uppal K, Jones DP. Mitochondrial network responses in oxidative physiology and disease. Free Radic Biol Med. 2018;116:31–40.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  143. Jones DP. Sequencing the exposome: a call to action. Toxicol Rep. 2016;3:29–45.

    Article  CAS  PubMed  Google Scholar 

  144. Thomas D. Gene–environment-wide association studies: emerging approaches. Nat Rev Genet. 2010;11(4):259–72.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  145. 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(1):64–72.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  146. Westerman KE, Pham DT, Hong L, Chen Y, Sevilla-Gonzalez M, Sung YJ, Sun YV, Morrison AC, Chen H, Manning AK. GEM: scalable and flexible gene–environment interaction analysis in millions of samples. Bioinformatics. 2021;37(20):3514–20.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  147. Biernacka JM, Chung SJ, Armasu SM, Anderson KS, Lill CM, Bertram L, Ahlskog JE, Brighina L, Frigerio R, Maraganore DM. Genome-wide gene-environment interaction analysis of pesticide exposure and risk of Parkinson’s disease. Parkinsonism Relat Disord. 2016;32:25–30.

    Article  PubMed  PubMed Central  Google Scholar 

  148. Patel CJ, Chen R, Kodama K, Ioannidis JP, Butte AJ. Systematic identification of interaction effects between genome- and environment-wide associations in type 2 diabetes mellitus. Hum Genet. 2013;132(5):495–508.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  149. Barouki R, Gluckman PD, Grandjean P, Hanson M, Heindel JJ. Developmental origins of non-communicable disease: implications for research and public health. Environ Health. 2012;11:42.

    Article  PubMed  PubMed Central  Google Scholar 

  150. Balbus JM, Barouki R, Birnbaum LS, Etzel RA, Gluckman PD Sr, Grandjean P, Hancock C, Hanson MA, Heindel JJ, Hoffman K, et al. Early-life prevention of non-communicable diseases. Lancet. 2013;381(9860):3–4.

    Article  PubMed  Google Scholar 

  151. Sacks JD, Stanek LW, Luben TJ, Johns DO, Buckley BJ, Brown JS, Ross M. Particulate matter-induced health effects: who is susceptible? Environ Health Perspect. 2011;119(4):446–54.

    Article  PubMed  Google Scholar 

  152. Barouki R, Audouze K, Coumoul X, Demenais F, Gauguier D. Integration of the human exposome with the human genome to advance medicine. Biochimie. 2018;152:155–8.

    Article  CAS  PubMed  Google Scholar 

  153. Wishart DS. Emerging applications of metabolomics in drug discovery and precision medicine. Nat Rev Drug Discov. 2016;15(7):473–84.

    Article  CAS  PubMed  Google Scholar 

  154. Khoury MJ, Galea S. Will precision medicine improve population health? JAMA. 2016;316(13):1357–8.

    Article  PubMed  PubMed Central  Google Scholar 

Download references


Not applicable.


This study was financially supported by the National Research Program for Key Issues in Air Pollution Control of China (No. DQGG0401), the National Natural Science Foundation of China (Nos. 82025030, 81941023, and 92043301), and the National Key Research and Development Program of China (No. 2016YFC0206500) to Prof. Xiaoming Shi.

Author information

Authors and Affiliations



All authors participated in this work. EMD, YW, and JL were involved in search strategy and drafting. ST and XMS revised and edited the draft and also supervised the project. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Xiaoming Shi.

Ethics declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

None of the authors have any conflict of interest to declare.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1.

The search strategies and main results of epidemiologic studies reporting the association between environmentalrisk factors and age-related diseases.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Ding, E., Wang, Y., Liu, J. et al. A review on the application of the exposome paradigm to unveil the environmental determinants of age-related diseases. Hum Genomics 16, 54 (2022).

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI:


  • Exposomic framework
  • Aging
  • Statistical approach
  • Multiomics
  • Precise public health