The advent of whole genome sequencing has significantly improved our knowledges of role of genes in pathogenesis, yet genes themselves only account less than 10% of the diseases.¹ The exposome, on the other hand, play a bigger role in biological aging and the development of systemic diseases processes.^2,3 Specifically, the exposome refers to the integrated compilation of all physical, chemical, biological, and psychosocial influences—such as air pollution, microplastics, nutrition, psychosocial stress, socioeconomic status, and physical activity—that impact biology.^4-6 To capture the complexity of the exposome, researchers collect and analyze a wide range of biosamples, including serum, urine, saliva, hair, and other biological matrices, to monitor environmental exposures and their biological effects. While blood samples (serum or plasma) are widely used and particularly valuable for assessing persistent chemicals and circulating metabolites,⁷ they are less suitable for non-persistent compounds with short half-lives, where concentrations in blood may fall below detection limits with high-resolution mass spectrometers-based non-targeted analysis. In such cases, urine or hair can provide more appropriate matrices for capturing exposure profiles.⁸ Advances in mass spectrometry have expanded the ability to detect and annotate both exogenous chemicals and endogenous metabolites across these matrices, though the majority of data are semi-quantitative rather than strictly quantitative.⁹

In recent years, precision environmental health (PEH) has emerged as a new discipline that explores the complex interplay between the exposome and biological systems across the human life course.¹⁰ Building upon the exposome framework, PEH integrates exposomic data with genomic and molecular information for more personalized health insights. Omics-based biomarkers, including genomics, epigenomics, transcriptomics, proteomics, metabolomics, lipidomics, and microbiomics, provide critical insights into human physiology and pathophysiology, elucidating the molecular mechanisms through which environmental exposures influence human health and contribute to disease development.¹¹ Various omics-based biological clocks, such as epigenetic and proteomic clocks, have been proposed, offering tools to quantify biological aging.^12,13 While current multi-omics technologies offer powerful tools for deciphering molecular changes, these are often costly, complex and many are invasive, thus limiting the ability to scale the application of these technologies across larger populations in community settings.

Multi-modal ocular biomarkers approach may serve as a novel, cost-effective, and promising complement to the current omics-based biomarkers in PEH. National Institute of Health (NIH) BEST (Biomarkers, EndpointS, and other Tools) resource categorizes biomarkers into different purposes, including diagnostic, response, monitoring, and predictive functions, highlighting their versatility in addressing diverse clinical and research needs.¹⁴ Though ocular biomarkers have been conventionally used for diagnosing ocular diseases and monitoring treatment response, there is emerging evidence that they can be used to monitor the impact of the exposome on systemic health and predict systemic disease risk over time.^15-19 Based on synthesis of the emerging evidence, we proposes future directions that integrates ocular biomarker into next generation of PEH research.

The human eye holds great potential for biomarker discovery due to its unique anatomy and accessibility for non-invasive imaging. (Figure 1) Color fundus photography (CFP) captures high-resolution images of the retina, enabling the assessment of structural biomarkers such as vessel caliber and vessel tortuosity, which reflect vascular health. Optical coherence tomography (OCT) provides cross-sectional and three-dimensional views of the retina, enabling detailed evaluation of specific biomarkers such as retinal layer thickness, the presence of pathological fluid (eg, edema or subretinal fluid).²⁰ Optical coherence tomography angiography (OCTA) delivers functional insights into retinal and choroidal blood flow without requiring dye injections, making it particularly valuable for detecting microvascular abnormalities, such as changes in capillary density and foveal avascular zone (FAZ) area.²¹ Advances in artificial intelligence (AI) have enabled automated extraction of quantitative ocular biomarkers with remarkable accuracy and repeatability, making them a scalable and clinically relevant tool for large-scale population health studies.^22-24

Retinal imaging biomarkers are intricately connected to environmental exposures and psychosocial factors, providing valuable insights into how these exposures contribute to microvascular and neurodegenerative changes.^25-27 Genome-wide association studies (GWAS) suggest that genetic factors account for less than 20% of the variations in retinal vasculature, underscoring the substantial role of environmental and lifestyle factors in shaping retinal microvascular features.²⁸ Table 1 summarizes current studies on common environmental exposures and their associated ocular imaging biomarker changes. Notably, similar alterations—such as decreased central retinal artery equivalent (CRAE), increased central retinal vein equivalent (CRVE), and thinning of the retinal nerve fiber layer (RNFL) and ganglion cell inner plexiform layer (GCIPL)—have been observed across diverse exposures, including particulate matter (pm), smoking, and psychosocial stressors. This convergence of findings suggests shared biological pathways through which distinct exposures exert overlapping vascular and neurodegenerative effects.

Mechanistically, chronic exposure to environmental and psychosocial stressors can induce vascular changes through mechanisms like endothelial dysfunction and inflammation. Stress-related hormonal responses impair endothelial nitric oxide (NO) synthesis, a key regulator of vascular tone, and generate reactive oxygen species, which degrade NO and disrupt vascular homeostasis.^29,30 Inflammation further mediates these effects, with elevated pro-inflammatory cytokines (eg, IL-6, TNF-α) and C-reactive protein being strongly associated with retinal venular widening, even in children.^30,31 These vascular changes may also be indirectly exacerbated by stress-related lifestyle factors such as reduced physical activity and poor dietary habits.^32,33 Taken together, these mechanisms provide a biological basis for the observed imaging signatures, reinforcing the value of retinal biomarkers as sensitive indicators of systemic processes—including inflammation, oxidative stress, and microvascular dysfunction.

Retinal biomarkers not only reflect environmental exposures but are also closely linked to systemic diseases.^28,34 In 2020, the term oculomics was introduced to describe the study of the associations between ocular biomarkers and systemic health conditions, positioning the retina as an emerging critical component in precision health initiatives.³⁵ Table 2 summarizes current studies on ocular imaging biomarkers and systemic health outcomes. Notably, the same biomarkers implicated in environmental exposures—such as increased CRVE, decreased CRAE, and thinning of the RNFL and GCIPL—are also observed across chronic diseases including hypertension, hyperlipidemia, chronic kidney disease (CKD), coronary heart disease, and Alzheimer’s disease and related dementias (ADRD). Phenome-wide association studies have correlated retinal biomarkers with multiple clinical phenotypes and the Eye Biomarker Database consolidates 889 biomarkers across 26 ocular diseases and 939 biomarkers associated with 181 systemic diseases.³⁶ Recent advances in AI have enabled the prediction of multiple systemic disease from retinal images with remarkable accuracy.^19,37-40

The retina’s diagnostic potential lies in its unique ability to integrate and reflect systemic processes due to its shared embryological origin, vascular architecture, and neurovascular connections with other organs. As part of the microcirculation, the retinal vasculature mirrors systemic endothelial health, responding to circulating inflammatory mediators, oxidative stress, atherosclerosis, and hemodynamic changes.^41,42 These processes drive vascular remodeling, including arteriolar narrowing, venular widening, and altered vessel tortuosity, while also contributing to neurodegenerative changes, such as amyloid-beta accumulation and tauopathy seen in ADRD.^43,44 These pathways are central to the “common soil hypothesis,” implicating shared mechanisms—such as inflammation, oxidative stress, and vascular dysfunction—in multiple chronic diseases, including cardiometabolic diseases, neurodegenerative disorders, and CKD.⁴⁵ These interconnected processes influence retinal and choroidal thickness, vascular permeability, and optic nerve structure, further underscoring the retina’s value as a window into systemic health.

Beyond structural parameters, composite aging biomarkers derived from retinal images provide powerful, non-invasive indicators of systemic aging. The retinal age gap—defined as the difference between predicted retinal age from fundus photographs and chronological age—has been consistently associated with all-cause mortality, cause-specific mortality, and multiple systemic morbidities.^46-48 Building on this framework, fundus images have also been used to estimate PhenoAge, a validated composite of nine blood-based biomarkers of biological age, giving rise to the retina-derived RetiPhenoAge.^49,50 This biomarker has been shown to outperform conventional markers such as telomere length and grip strength in predicting morbidity and mortality, and has demonstrated strong associations with cognitive decline, dementia, cerebral small vessel disease, and proteomic signatures of aging.⁵¹ Together, these retinal imaging–based biological age markers complement structural parameters by providing scalable, non-invasive, and mechanistically informative indicators of systemic aging across diverse diseases.

Beyond imaging biomarkers, the eye’s liquid-filled chambers—tears, aqueous humor (AH), and vitreous humor (VH)—offer a rich reservoir of biomarkers for exposomic analysis. (Figure 1) These fluids are in constant exchange with both the external environment and systemic circulation, thereby capturing signals of external exposures and internal physiological responses. Compared with traditional matrices such as blood, tears can be collected in a minimally invasive manner, making them well suited for temporal monitoring of the exposome.⁵² AH and VH offer complementary information to blood, providing localized insights into ocular pathophysiology and capturing metabolic or inflammatory changes that may not be detectable systemically.

Tear, located at the air–tear interface, is particularly sensitive to airborne pollutants.⁵² Studies have shown that tears capture markers of PMs, volatile organic compounds, trace metals, and nicotine within minutes to hours of exposure, highlighting their value for short-term exposure assessment.⁵² More broadly, microplastics (MPs) have now been identified across multiple ocular compartments, including tears, AH, and VH, with their presence associated with alterations in key ocular biomarkers such as tear break-up time, Schirmer’s test scores, intraocular pressure, and vitreous opacities.^53-55 While analytical methods for micro- and nanoplastic detection are still being refined, converging evidence from these studies suggests that MPs are not confined to the ocular surface but can accumulate throughout different eye matrices, reinforcing the eye’s potential as a unique system for monitoring environmental exposures.

In addition to capturing external exposures, ocular fluids also reflect the body’s internal physiological responses. Tears and AH contain inflammatory and angiogenic mediators that change dynamically with pollutant exposure. Studies have shown significant increases in inflammatory markers like IL-6, IL-8, and vascular endothelial growth factor (VEGF) in tear with increased exposure to air pollution, highlighting its ability to reflect localized and systemic inflammatory effects.⁵⁶ Experimental and animal studies shows that ocular surface exposure to microplastics triggers oxidative stress, inflammation, reduced goblet cell density, and microbial dysbiosis.⁵⁷ Beyond these molecular changes, the ocular surface microbiome has emerged as a key internal exposome. Ambient air pollutants disrupt microbial homeostasis, reducing commensal diversity while enriching pathogenic taxa such as Staphylococcus, Bacteroidia, and Klebsiella, which correlate with goblet cell depletion and chronic ocular inflammation.⁵⁸

Systematic evidence now links tear protein and metabolite changes to a wide spectrum of systemic diseases, including neurodegenerative, autoimmune, and metabolic conditions.⁵⁹ For example, in ADRD, alterations in tear protein composition have been reported, including elevated lactoferrin, which is thought to reflect amyloid-related immune dysregulation and oxidative stress pathways.⁵⁹ In multiple sclerosis, tear proteomics has revealed increased immunoglobulins and pro-inflammatory cytokines, consistent with systemic immune activation and blood–brain barrier dysfunction mirrored at the ocular surface.⁵⁹ Similarly, in type 2 diabetes (T2DM), specific metabolites such as carnitine, tyrosine, and uric acid are elevated in tears, and anti-diabetic drugs like metformin can also be detected, illustrating how tear fluid integrates both disease-related metabolic perturbations and treatment exposure.⁶⁰ Building on this, tear-derived extracellular vesicles (EVs) represent a promising next-generation biomarker platform. EVs protect their protein and RNA cargo from degradation and can cross from the bloodstream into tears, offering a stable and information-rich signal.⁶¹ They have shown implicated in multiple autoimmune diseases such as Dry Eye Disease, Sjögren’s, graft-versus-host disease.⁶¹

In addition to tear-based biomarkers, AH and VH provide a complementary perspective, enabling deeper molecular understanding of ocular and systemic aging processes through advanced omic technologies. For instance, using a combination of liquid-biopsy proteomics and AI, researchers have developed proteomic clocks based on the AH and VH specimens that can assess cellular aging within non-regenerative tissues, such as the eye, in vivo.⁶² These tools reveal that diseases like ADRD not only alter molecular profiles but also accelerate cellular aging processes in the retina, highlighting the interplay between systemic diseases and ocular aging.⁶²

One of the most pressing challenges in the field of oculomics is the need for standardized “big” data. The power of oculomics studies largely stems from their reliance on vast datasets, often incorporating tens of thousands of retinal images. Similarly, ExWAS usually requires a large sample size to yield statistically significant results. However, the intersection of these two fields presents a significant hurdle: very few datasets currently exist that include both comprehensive exposomics data and detailed retinal imaging on a scale sufficient to conduct robust analyses. Table 3 summarizes current datasets current datasets with exposome and ocular imaging data. Notably, two major large scale cohorts for epidemiological research in the United States, All of Us, and Million Veteran Program currently both do not collect or provide retinal imaging data for research purpose.^87,88 UK Biobank is so far the only national biobank that incorporates multi-modal ocular imaging, in an organized and easily retrievable format.⁸⁹ Its rich modalities of data, comprehensive coverage of diseases, and large sample size have made it an essential part of most of the exposomics and oculomics studies nowadays. A few disease-specific cohorts, such as the Atherosclerosis Risk in Communities (ARIC) study and the Chronic Renal Insufficiency Cohort (CRIC), include ocular imaging data.^90,91 However, large exposome cohorts such Human Early Life Exposome (HELIX) and Personalized Environment and Genes Study (PEGS) do not collect retinal imaging as one of their data modalities.^64,92 The scarcity of such large, integrated datasets limits the ability to fully explore the potential connections between environmental exposures and retinal biomarkers, which in turn hinders our capacity to uncover novel insights into disease mechanisms. To overcome this limitation, multi-center collaborations and international consortium initiatives will be essential. The National Heart, Lung, and Blood Institute for example convened a workshop in 2022 that emphasized the importance of standardized retinal imaging protocols, interoperable data structures, and scalable analytics to advance the use of retinal biomarkers in cardiovascular and systemic disease research.⁴²

Another critical challenge is the lack of standardized exposomics profiling. Currently, even if researchers identify several cohorts with rich environmental exposome data, inconsistencies in biometrics, sample collection and storage protocols, and lab analytical platforms can make it difficult to integrate the omics data across cohorts. For instance, the composition of tears has high temporal variability, making it difficult to compare data across different studies.^93,94 To overcome these barriers, the establishment of internationally accepted guidelines for data normalization, harmonization, and sharing is urgently needed. The Banbury Exposomics Consortium has made infrastructure development and the establishment of data standards for harmonization one of top priorities for exposome research.⁹⁵ Beyond biospecimen analysis, questionnaire-based data collection also requires rigorous standardization. Although most cohorts incorporate questionnaires, these are rarely uniform and are often tailored for specific study objectives rather than capturing the full breadth of the exposome. A notable example of efforts to address this challenge is the PEGS, a large and demographically diverse North Carolina-based cohort.^64,96 PEGS collects exposome data through multiple surveys, including the Health and Exposure Survey, the Internal Exposome Survey, and the External Exposome Survey, which together capture over 1000 variables on lifestyle, occupational and residential exposures, medication use, diet, sleep, stress, and infectious diseases.^64,96

The collection and management of such extensive datasets also raise significant concerns about data privacy, particularly when data are gathered from multiple sites. These exposomics and retinal imaging data are highly sensitive personal information, making it crucial to implement robust data protection measures to ensure confidentiality. The use of AI algorithms to analyze retinal images may introduce biases if the training data is not representative of diverse populations.⁹⁷ Beyond population diversity, it is equally important to ensure that the data encompasses a wide range of systemic and ocular conditions, as these significantly influence retinal changes. A lack of representation in such conditions could hinder a comprehensive understanding of their effects and interactions. This gap may further exacerbate healthcare disparities, particularly for individuals with limited access to advanced ocular imaging technologies, who may be excluded from the analysis.⁹⁷ Addressing these ethical and logistical challenges is crucial to ensure that the integration of exposomics and oculomics is both scientifically robust and equitable.

One potential solution to address data privacy concerns is through approaches such as federated learning, a machine learning technique that enables multiple institutions or devices to collaboratively train a shared model without transferring raw data to a central repository.^98,99 Instead, the data remains local, and only model updates, such as gradients or weights, are shared and aggregated. This approach can enhance privacy while still enabling comprehensive analysis. In parallel with these privacy-preserving strategies, global collaborative efforts are equally important to tackle the pressing issue of diversity and representation in ocular imaging datasets.

The GlobalRetFound Consortium is one initiative designed to address the lack of diversity in ocular imaging data.¹⁰⁰ It aggregates contributions from over 100 countries and combines synthetic data generation with selective real data to build a more representative global dataset. By minimizing the need for direct raw data transfer, the consortium supports data privacy while expanding demographic and geographic diversity. Similar international consortia will be essential to integrate oculomics and exposomics, ensuring that future precision environmental health research is both inclusive and equitable.

Ocular biomarkers hold great potential to advance PEH research because they are non-invasive, scalable, and capable of capturing both structural and molecular signatures of systemic processes. Retinal imaging modalities such as CFP, OCT, and OCTA provide reproducible measures of vascular health, neurodegeneration, and biological aging, while ocular fluids capture molecular signatures of environmental exposures and systemic responses. Together, they offer a cost-effective and accessible complement to traditional omics-based biomarkers. Looking forward, novel research methodologies—including ExWAS, high dimensional mediation analysis, and multimodal foundation models—offer exciting opportunities to better integrate ocular biomarkers with environmental data. Nonetheless, significant challenges remain. The scarcity of large datasets linking exposome and ocular data, lack of standardized profiling methods, and issues of data privacy and equity limit progress. Addressing these gaps through international collaboration, harmonization of data collection, and equitable technology deployment will be crucial. By overcoming these barriers, ocular biomarkers can become a cornerstone of PEH, enabling earlier detection, targeted prevention, and healthier longevity.