The concept of the exposome, the cumulative measure of environmental exposures an organism experiences across its lifespan, has gained traction in adult epidemiology, but its application to the newborn period carries a particular urgency. Very preterm birth fundamentally reshapes the developmental exposome by relocating a substantial portion of gestation outside the body. The fetus that would have been bathed in amniotic fluid, nourished continuously through the placenta, and shielded from most external chemicals instead receives parenteral nutrition through central lines, undergoes repeated blood transfusions, rests on plasticized medical devices, and breathes filtered, conditioned air. Each of these interventions is essential, and each introduces its own chemical signature. The commentary emphasizes that because survival has improved so dramatically, understanding how this prolonged hospital-based developmental setting influences long-term neurodevelopmental, respiratory, and cardiometabolic health has become a central question for children’s environmental health.

The central methodological obstacle has always been measurement. Collecting repeated biospecimens from medically fragile infants weighing sometimes less than a kilogram presents formidable practical and ethical challenges, and blood draws are limited by volume and clinical necessity. Bauer and Romano-Keeler highlight an elegant alternative that has been quietly maturing over the past decade: naturally shed deciduous teeth. Children lose their baby teeth in mid-childhood, and those teeth, it turns out, are meticulous biological recorders. As dentin mineralizes, elements circulating in the bloodstream are incorporated into the growing crystalline matrix in temporal sequence, layer by layer, much like tree rings. A distinctive microstructural feature called the neonatal line marks the moment of birth, giving researchers an exact chronological anchor from which to reconstruct week-by-week exposure histories stretching from fetal life through the entire NICU stay.

This approach offers something maternal biomarkers cannot. Measurements in maternal blood or hair primarily reflect maternal dose and may fail to capture placental transfer or fetal uptake, whereas prenatal tooth measurements derive from tissues formed in utero and therefore provide a more direct indicator of the fetal internal chemical environment. Postnatal metal exposure measured in teeth is increasingly investigated in cohort studies, but the exposures unique to the NICU have remained poorly characterized. The commentary accompanies a study in the same issue by Lieberman-Cribbin and colleagues that applies the technique to precisely this question, reconstructing weekly metal exposure histories using shed teeth collected from children across the United States who were born preterm.

The study drew participants from the Developmental Impact of NICU Exposures, or DINE, study, which enrolled children from multiple preterm cohorts participating in the Environmental influences on Child Health Outcomes, or ECHO, program. These infants spent a median of 85 days in the NICU, an extended hospitalization that allowed the researchers to examine metal exposure across a substantial portion of early development that would otherwise have occurred in utero. The findings were striking in their asymmetry. Several metals were higher on average during the prenatal period, while estimated average levels of lithium, rubidium, and strontium were higher postnatally, with the clearest difference observed for strontium. In other words, the chemical profile of development measurably shifts when the environment shifts from placenta to intensive care, and those shifts are written permanently into the mineralized tissue.

What could explain a postnatal rise in elements like strontium? The commentary lays out a set of plausible exposure pathways that the findings generate as hypotheses for future investigation. Infant feeding is an obvious candidate, since both human milk and formula carry distinct elemental profiles. Parenteral nutrition, the intravenous feeding that sustains the smallest infants for weeks, has a documented history of delivering trace metals, with earlier research identifying increased manganese deposition in the brains of infants receiving it and, in a landmark 1997 study, aluminum neurotoxicity in preterm infants fed intravenous solutions. Blood transfusions represent another under-recognized route, as red cell transfusions can carry toxic metals into a newborn circulation. Medical devices, including plasticized tubing and equipment, and other ambient features of the NICU environment round out the list. These pathways are especially relevant for extremely and very low birthweight infants, who receive the most prolonged and intensive nutritional and medical support precisely during a period of heightened developmental susceptibility.

Interpreting tooth biomarker data, however, demands careful attention to developmental biology, and the commentary is refreshingly candid about this complication. Temporal changes in elemental concentrations in dentin may reflect not only external exposures but also tissue growth, mineralization dynamics, shifts in nutrient requirements, and the maturation of metabolic and homeostatic pathways. A developing infant’s physiology is itself changing week by week, and those internal changes can alter which elements circulate and how they are deposited in mineralizing tissue. Distinguishing exposures arising from the external environment from endogenous developmental processes is therefore central to the enterprise. At the same time, the authors argue, the real value of these measurements lies in their ability to place both processes on a common developmental timescale, clarifying how the external environment and internal physiology jointly shape the chemical milieu of the developing child rather than pretending one can be isolated from the other.

The roadmap that emerges from the commentary is both practical and ambitious. Future studies that integrate tooth biomarkers with detailed clinical, nutritional, and environmental records will be essential for identifying the origins of NICU exposures: information on enteral and parenteral nutrition, medications, transfusions, respiratory support, medical devices, and treatment duration could help separate the chemical contributions of neonatal care from those of endogenous development. The authors also propose a structural innovation with lasting value: incorporating permission for future recontact and deciduous teeth collection into existing neonatal biorepositories, creating a resource that would enable longitudinal investigations of how early-life exposures influence health and disease for decades to come. Extending tooth-based methods beyond metals to plastic-associated chemicals and persistent organic pollutants, with potential applications to pharmaceuticals and other metabolites, would round out the picture of the NICU environment, building on emerging work that has already detected organic pollutants in primary teeth and linked fetal exposures to later outcomes.

The ultimate prize is causal clarity about long-term consequences. Preterm birth is associated with elevated risks of neurodevelopmental impairment, respiratory disease, and cardiometabolic dysfunction in adulthood, but the contribution of hospital-based exposures during the NICU course to those outcomes remains largely unmapped. Longitudinal follow-up of children whose teeth have been analyzed will be essential for determining whether exposures occurring during specific windows of the NICU stay are associated with later cognition, growth, respiratory health, or cardiometabolic function. If specific periods or specific care practices turn out to carry disproportionate chemical burden, the exposome map becomes a clinical instrument, pointing toward modifiable aspects of neonatal care, from nutritional formulations to device materials, that could be adjusted without compromising the lifesaving function of the unit itself.

What makes this research paradigm resonate beyond neonatology is its simplicity and its reversibility. Baby teeth that most families discard, or tuck into keepsake boxes, contain a week-by-week archive of one of the most chemically complex periods of human development, retrievable years later without a single invasive procedure. The NICU sustains life outside the intrauterine environment, and it does so brilliantly, but it is also a distinct setting in which clinical care, nutrition, environmental chemicals, and developmental physiology converge during a period of heightened vulnerability. Defining the developmental exposome of that setting, as Bauer and Romano-Keeler argue, may ultimately help design developmental environments that better support health across the entire life course, turning an underutilized biological resource into a bridge between the intensive care that saves infants and the lifelong health those survivors deserve.

Subject of Research: Reconstructing prenatal and NICU metal exposures in preterm infants using deciduous tooth biomarkers

Article Title: Defining the developmental exposome of the neonatal intensive care unit in preterm infants

Article References: Bauer, J. A., & Romano-Keeler, J. (2026). Defining the developmental exposome of the neonatal intensive care unit in preterm infants. Journal of Exposure Science & Environmental Epidemiology. https://doi.org/10.1038/s41370-026-00981-5

Image Credits: AI Generated

DOI: 10.1038/s41370-026-00981-5

Keywords: exposome, neonatal intensive care unit, preterm infants, deciduous teeth, metal exposure, biomonitoring, ECHO program, DINE study, parenteral nutrition, child environmental health, dentin biomarkers, strontium

The team, led by ophthalmology researchers at the Eye Institute of Fudan University in Shanghai, quantified systemic aging using three complementary instruments. The first was PhenoAge acceleration, a measure derived from clinical biomarkers that estimates how much faster a person’s physiology is aging compared with their chronological peers. The second was age acceleration calculated by the Klemera-Doubal method, another biomarker-based biological age algorithm with a long track record in aging research. The third was a frailty index, a cumulative score built from deficits across health domains that captures the loss of physiological reserve characteristic of advanced aging. Each of these measures tells a slightly different story about aging, and the researchers wanted to know whether all of them converged on the same retinal signature.

That signature turned out to be strikingly consistent. Across all three aging metrics, higher biological age acceleration and greater frailty were associated with reduced macular thickness, with the effect spanning every inner retinal subfield measured by optical coherence tomography. The standardized effect sizes ranged from −1.386 to −0.421, and all associations remained highly significant after correction for multiple comparisons, with false discovery rate adjusted P values below 0.001. In practical terms, people whose bodies were aging faster than their birthdays implied tended to have measurably thinner retinas, and the relationship held whether aging was defined by molecular biomarkers, composite algorithms, or the clinical syndrome of frailty.

Optical coherence tomography, the imaging technology behind these measurements, deserves a moment of explanation. Originally described in Science in 1991, OCT uses low-coherence interferometry to generate cross-sectional images of biological tissue with micrometer-scale resolution. In the UK Biobank, tens of thousands of participants underwent retinal OCT scanning, producing an enormous, standardized dataset of retinal layer thicknesses. Because the retina is embryologically part of the central nervous system and shares vascular and metabolic characteristics with the brain, changes in its structure have been proposed as noninvasive biomarkers for neurological and systemic disease. Previous work has linked retinal thinning to cardiovascular risk, Alzheimer disease, and early age-related macular degeneration, but the broader question of how whole-body aging states map onto retinal structure had remained poorly characterized.

The most technically ambitious part of the new study involved the plasma metabolome. Blood samples collected from participants between 2006 and 2010 had been profiled using nuclear magnetic resonance spectroscopy, a platform that quantifies hundreds of circulating metabolites, including lipoprotein subclasses, fatty acids, amino acids, and glycolysis-related markers. From this high-dimensional data, the researchers derived metabolic signatures of aging using elastic net regression, a machine learning technique that selects sparse, predictive combinations of variables while guarding against overfitting. The resulting metabolomic aging signatures were then tested as statistical mediators of the relationship between biological aging and retinal thinning.

The mediation results were the study’s headline finding. Metabolic signatures accounted for 81.14 percent of the association between PhenoAge acceleration and macular thickness, 19.92 percent of the association for Klemera-Doubal method age acceleration, and 32.27 percent of the association for the frailty index, with all mediation effects significant after FDR correction. These proportions are remarkable, particularly for PhenoAge, and they suggest that circulating metabolites are not merely passive bystanders in the aging process but plausible conduits through which systemic senescence reaches the neurosensory retina. The retina is among the most metabolically demanding tissues in the body, with photoreceptors and the retinal pigment epithelium locked in a tightly coupled metabolic ecosystem that depends on glucose, lactate shuttling, and mitochondrial oxidative metabolism. Disruption of systemic metabolic homeostasis, the authors argue, is therefore well positioned to leave structural fingerprints in retinal tissue.

The study also incorporated the exposome, the concept introduced by cancer epidemiologist Christopher Wild in 2012 to describe the totality of environmental exposures an individual experiences across a lifetime. The researchers assembled exposome factors from phenotypic data covering lifestyle, diet, air pollution, and mental health. Their integrative analyses showed that adverse exposome profiles, including tobacco exposure, poor diet, air pollution, and negative psychosocial states such as loneliness and depression, were each correlated with reduced macular thickness. More importantly, systemic aging and metabolic dysregulation emerged as significant statistical intermediaries within these multidimensional pathways, meaning that environmental burdens appear to translate into retinal change at least partly by accelerating biological aging and reshaping the circulating metabolome.

This framing has implications that extend well beyond ophthalmology. If the retina reflects the convergence of environmental stress, metabolic dysregulation, and biological aging, then a routine retinal scan could in principle serve as a rapid, noninvasive readout of an individual’s cumulative aging trajectory. The findings align with a growing body of work on retinal oculomics, including phenome-wide analyses of UK Biobank OCT images that have linked ocular measurements to systemic health, and epidemiological studies showing that ambient air pollution is associated with retinal thinning and age-related macular degeneration. The new study unifies these threads by proposing an explicit causal architecture in which exposures act on aging biology, aging biology acts on metabolism, and metabolism acts on the retina.

The authors are careful about what their statistics can and cannot show. Mediation analysis in observational data identifies statistical intermediaries, not proven causal mechanisms, and the cross-sectional design of the UK Biobank baseline assessments means that temporal ordering cannot be fully established. The metabolomic platform used, while comprehensive for lipids and small molecules, does not capture every biologically relevant compound, and the elastic net signatures are predictive composites rather than single causal metabolites. Residual confounding by socioeconomic factors, which shape both exposome and health outcomes, remains a persistent challenge in cohort studies of this kind. Nevertheless, the sheer scale of the cohort, the consistency of the associations across three independent aging metrics, and the rigorous multiple-comparison correction lend considerable weight to the central conclusion.

For the aging research community, the study adds the retina to the growing list of organs whose structural integrity tracks systemic biological age, and it elevates plasma metabolism to the status of a key correlate of neurosensory retinal health. For clinicians, it hints at a future in which retinal imaging, already fast and inexpensive, might help identify people whose bodies are aging faster than their years, potentially guiding earlier interventions on smoking, diet, air quality, and psychosocial wellbeing. And for the public, the message is a vivid one: the same exposures that wear down the heart, the brain, and the metabolism may also be quietly etched into the tissue that lets you read this page. The eye, it seems, does not only take in the world; it keeps a record of what the world has done to us.

Subject of Research: Associations between systemic biological aging, frailty, exposome factors, plasma metabolomics, and retinal structural changes in the UK Biobank

Article Title: Association of systemic aging and frailty with retinal alterations: insights from an integrated exposome and metabolome framework

Article References: Chen, T., Wang, D., Ma, Y., Ye, Y., Wang, X., Lei, Y., Zhou, X., & Zhao, J. (2026). Association of systemic aging and frailty with retinal alterations: insights from an integrated exposome and metabolome framework. GeroScience. https://doi.org/10.1007/s11357-026-02572-6

Image Credits: AI Generated

DOI: 10.1007/s11357-026-02572-6

Keywords: retinal oculomics, biological aging, frailty, plasma metabolomics, exposome, UK Biobank, macular thickness, optical coherence tomography, PhenoAge, mediation analysis, GeroScience, aging biomarkers

The exposome, a concept first articulated to complement the genome, encompasses the totality of environmental exposures an individual experiences across a lifetime. In principle, it is the environmental mirror of the genome: where genes catalog inherited potential, the exposome catalogs the accumulated external and internal pressures that regulate health and disease. In practice, however, measuring the exposome has proven far harder than sequencing DNA. Genomic and multiomic technologies now deliver extraordinarily deep characterizations of biological systems, but the ability to characterize the external environment with comparable depth and rigor remains comparatively limited. Questionnaires, the traditional workhorse of exposure assessment, capture only what participants remember, notice, and are willing to report, leaving vast swaths of the chemical, physical, and social environment unmeasured.

A new study by MacNell and colleagues, examined in the commentary by R. Keith Reeves of Duke University School of Medicine, demonstrates a practical route forward. The researchers systematically integrated multiple publicly available geospatial datasets within an exposome-wide association study, or ExWAS, framework. Their platform was the Personalized Environment and Genes Study, known as PEGS, a research cohort that has already proven unusually versatile. Using residential information from more than 7,500 participants, the team linked each participant’s location to environmental indicators derived from geospatial datasets and then evaluated associations between those indicators and a range of common disease phenotypes. The approach effectively turns a participant’s address into a dense, multidimensional exposure profile, one assembled at low cost from data that already exist.

The technical logic of the ExWAS framework deserves attention. Analogous to genome-wide association studies, which scan hundreds of thousands of genetic variants for statistical links to disease, an ExWAS scans a broad panel of environmental exposures for associations with health outcomes. The challenge has always been assembling a sufficiently rich panel of exposures. GIS-based linkage addresses this by drawing on satellite-derived air pollution estimates, proximity to hazardous sites, temperature surfaces, and other spatially referenced data layers, then assigning each participant values based on where they live. Because these datasets are public and standardized, the same exposure panel can be reconstructed for almost any cohort with residential addresses, enabling retrospective and prospective analyses across studies at relatively low cost.

One of the most significant findings highlighted in the commentary is that geospatial information does more than reproduce what questionnaires already capture. In the MacNell analysis, GIS-based measures identified associations that overlapped with prior PEGS findings, including relationships between environmental exposures and cardiovascular and metabolic phenotypes. Critically, the geospatial approach also surfaced potential associations with exposures that had not previously been captured by survey-based assessments. That combination, confirming known signals while revealing new ones, is exactly what a maturing exposure science should deliver, and it suggests that place-based data can genuinely complement rather than merely duplicate self-reported measures.

PEGS has become a proving ground for this kind of integrative work. Beyond the new GIS-linked ExWAS, the cohort has supported exposome-wide studies of common diseases, the development of polyexposure risk scores for type 2 diabetes, investigations of air-pollution mixtures and inflammatory skin disease, and studies that combine geographic exposures with genetic susceptibility to immune-mediated disease. A dedicated dataset resource now provides genomic, exposomic, and geospatial data together, allowing researchers to test how environmental and genetic risk factors interact. Earlier questionnaire-based ExWAS analyses in PEGS had already revealed both expected and novel risk factors associated with cardiovascular outcomes, establishing the statistical machinery that the geospatial extension now enriches.

The broader opportunity, as Reeves frames it, could extend well beyond any single association. A partly standardized geospatial exposome would provide an environmental data infrastructure applicable across cohorts, retrospective and prospective alike. Exposure questionnaires could be complemented and streamlined, while environmental measures could be updated dynamically as participants move or as new data layers become available. This is a subtle but important shift in study design: rather than freezing exposure history at enrollment, researchers could maintain living exposure profiles that track participants through time. In a mobile society, where people change neighborhoods, cities, and even countries, that flexibility addresses one of the most persistent weaknesses of static exposure assessment.

The scope of what can be measured geospatially is also expanding. Air pollution and hazardous waste sites were the natural first targets, since both have well-developed national datasets and established health links. But the commentary points toward a much wider canvas: extreme heat, water quality, healthcare access, and other environmental and social determinants of health could all be layered into a geospatial exposome. Each addition broadens the range of exposures captured and opens new hypotheses about how the places people live shape chronic disease. Heat exposure, for instance, intersects with cardiovascular strain; healthcare access intersects with disease management and outcomes; water quality intersects with a growing list of chemical exposures of concern. Integrating these layers transforms the address from a simple demographic variable into a rich exposure summary.

The end goal, according to the commentary, is for environmental exposure information to become a routine component of phenotyped cohorts, integrated alongside genomic, molecular, clinical, and social determinants of health data. In that vision, a participant in a large biobank would carry not only a genome sequence and a set of lab values but also a standardized, continuously updated environmental profile derived from where they have lived. Disease association studies could then interrogate genes and environment in a single unified framework, something that has been promised conceptually for years but has been difficult to deliver because environmental data lagged so far behind biological data in depth and standardization.

Challenges remain, of course. Geospatial datasets are proxies, not personal monitors, and they capture exposure at residential locations rather than wherever a person actually spends their day. Spatial resolution, temporal coverage, and the comparability of datasets across regions all impose limits on how finely the exposome can be resolved. Yet the direction of travel is clear. As public geospatial data grow in quality and coverage, and as frameworks like ExWAS provide disciplined ways to test thousands of exposures against thousands of health outcomes, the environment is moving from the margins of epidemiology toward its center. The commentary on the MacNell study makes the case that putting place into the exposome is no longer an aspiration but an operational reality, one that could reshape how scientists understand, and ultimately prevent, the diseases that define modern life.

Subject of Research: Integration of geographic information systems data into exposome-wide association studies for human health research

Article Title: Putting place into the exposome for human health. Comment on: ‘Applying geographic information systems data linkages for an exposome-wide association study in the Personalized Environment and Genes Study’

Article References: Reeves, R. K. (2026). Putting place into the exposome for human health. Comment on: ‘Applying geographic information systems data linkages for an exposome-wide association study in the Personalized Environment and Genes Study’. Journal of Exposure Science & Environmental Epidemiology. https://doi.org/10.1038/s41370-026-00980-6

Image Credits: AI Generated

DOI: 10.1038/s41370-026-00980-6

Keywords: exposome, GIS, ExWAS, PEGS study, environmental epidemiology, air pollution, geospatial data, cardiovascular disease, type 2 diabetes, exposure assessment, public health, genomics

The new effort, known as the NorthMom Study, was described in a study protocol published in BMC Public Health. It is an observational, population-based cohort nested within NorthPop, designed to follow women from their pregnancies into the age at which routine breast cancer screening begins. Rather than waiting decades to count cancer diagnoses, the investigators are focusing on intermediate markers—most importantly mammographic breast density—that are strongly associated with future breast cancer risk and can be measured at scale through Sweden’s national screening program.

The scientific rationale rests on a concept that cancer biologists call windows of susceptibility. Breast tissue does not respond uniformly to environmental insults throughout a woman’s life; instead, there are discrete periods—prenatal development, puberty, pregnancy, and menopause—when the mammary gland is undergoing rapid structural and hormonal change and is correspondingly more vulnerable. During pregnancy in particular, breast tissue undergoes dramatic maturation driven by surges of hormones, making it sensitive to compounds that can interact with hormone receptors, including endocrine-disrupting chemicals. An exposure that perturbs these processes, the hypothesis goes, could trigger biological effects such as alterations in breast density, shortened breastfeeding duration, or persistent changes in gene expression and metabolism that mediate the path from exposure to eventual disease.

Breast density itself—the proportion of fibroglandular tissue within the breast—is one of the strongest known independent risk factors for breast cancer, and it also complicates screening by masking underlying tumors on mammograms. Density is shaped by hormonal, genetic, and environmental influences, yet surprisingly few studies have examined how environmental exposures, particularly those occurring during windows of susceptibility, relate to density measured later in life. NorthMom is explicitly designed to fill this gap by linking prospectively collected pregnancy exposures to density measurements taken when participants turn 40 and attend their first screening mammograms.

The exposure data available to the researchers are unusually rich. During pregnancy, NorthPop collected detailed questionnaire information on diet, physical activity, medication use, mental health, and home and work environments. For all participants, the cohort holds geospatial estimates of ambient air pollution, traffic noise, and residential access to green spaces. For subsets of women, levels of persistent organic pollutants were measured in plasma samples banked during pregnancy, and the team plans eventually to perform comprehensive chemical exposomics analyses on plasma from all enrolled women. Because these data were collected before any outcomes existed, the study avoids the recall bias that plagues much of environmental epidemiology.

Recruitment into NorthMom follows a straightforward protocol. Every woman enrolled in NorthPop is invited to participate in the year after her 40th birthday, provided she still lives in Västerbotten, has not withdrawn from the parent cohort, and is not currently pregnant. Those who agree visit the Clinical Research Center at the University Hospital of Umeå, where a nurse draws a venous blood sample that is separated into plasma, buffy coat, and erythrocyte fractions and stored at minus 80 degrees Celsius in the regional biobank. Participants also undergo dual-energy X-ray absorptiometry, or DXA, a scan that quantifies body fat percentage and distribution far more precisely than body mass index, and they complete a short questionnaire on reproductive history and hormone therapy or contraceptive use.

Breast density is assessed two ways. Screening mammograms obtained through Sweden’s national program are classified by a consultant breast radiologist using both the fourth and fifth editions of the Breast Imaging Reporting and Data System, and independently analyzed with LIBRA, an open-source software tool that produces automated measures of percent density and dense area in square centimeters. Using both subjective and automated methods allows the team to cross-validate the software and to express density as a continuous variable, which boosts statistical power. For women with multiple screening occasions, the study will also track how density changes over time using mixed-effects models, testing whether pregnancy exposures are associated not just with density at a single point but with the rate at which density evolves through the menopausal transition.

The statistical planning is already in place. Based on pilot data showing a standard deviation of roughly 19.7 percentage points for breast density, the investigators calculate that 1,049 women would give the study 80 percent power to detect a 1.7-percentage-point difference in density per one-standard-deviation increase in exposure, at a two-sided significance level of 0.05. Analyses will employ linear regression for continuous density, ordinal and logistic regression for BI-RADS categories, and repeated-measures models for longitudinal trajectories. Early recruitment figures show 36.6 percent of invited women enrolling—442 of 1,207 in the first year—and ongoing funding is expected to carry recruitment at least through 2028, by which time more than 3,000 NorthPop mothers will have turned 40 and around 1,000 participants should be enrolled.

The study’s design offers several distinctive strengths. DXA scans capture adiposity with a granularity that BMI cannot match, addressing a persistent weakness in breast cancer epidemiology where crude proxies often obscure the true role of body fat. The link to NorthPop means exposure data and biobanked pregnancy samples already exist, dramatically reducing costs and enabling biomarker analyses of the internal exposome—proteomic, metabolomic, and epigenetic signatures that may reveal how environmental chemicals leave durable biological footprints. Sweden’s comprehensive national registers, including those covering cancer diagnoses, prescribed drugs, causes of death, and family relationships, allow deep longitudinal follow-up, and linkage with the long-running Northern Sweden Health and Disease Study could eventually provide exposure and blood sample data spanning pregnancy, perimenopause, menopause, and beyond for a substantial share of participants.

The investigators are candid about limitations. The cohort’s ultimate size, likely around a thousand women, is too small to use breast cancer diagnoses themselves as a primary outcome. But because studies of environmental effects on breast cancer intermediates such as density are scarce, the researchers argue that this unusual setting is precisely what makes new discoveries possible, with density serving as a defensible surrogate for cancer-specific effects. If the hypothesis holds—that chemical and physical exposures during pregnancy leave measurable imprints on breast tissue decades later—NorthMom will have helped expose one of the most overlooked chapters in the environmental history of breast cancer, and it will have done so by following the mothers of a birth cohort every bit as carefully as their children.

Subject of Research: A population-based cohort study examining how environmental exposures during pregnancy affect breast cancer risk factors, particularly mammographic breast density, in women over 40

Article Title: The NorthMom study: population-based follow-up of women in a birth cohort to assess pregnancy exposures and effects on breast cancer risk factors

Article References: Fredriksson, A., Lundberg-Ulfsdotter, R., Vinnars, M.-T., Oudin, A., Van Guelpen, B., West, C. E., Domellöf, M., Wu, W. Y.-Y., Dembrower, K., & Harlid, S. (2026). The NorthMom study: population-based follow-up of women in a birth cohort to assess pregnancy exposures and effects on breast cancer risk factors. BMC Public Health, 26(1), Article 2670. https://doi.org/10.1186/s12889-026-29622-0

Image Credits: AI Generated

DOI: 10.1186/s12889-026-29622-0

Keywords: breast cancer, breast density, pregnancy, environmental exposures, endocrine-disrupting chemicals, air pollution, birth cohort, mammography, epidemiology, exposome, women's health, PFAS

The starting point for the framework is a deceptively simple observation: the gut microbiome of social bees is remarkably small and specific compared with that of mammals. Honey bees and bumble bees harbour a core set of bacterial symbionts that have coevolved with their hosts and are transmitted socially, passing between nestmates through shared food, faecal contact and the communal environment of the hive. These microbes contribute to nutrition by aiding the digestion of pollen, to detoxification of harmful compounds, to immune priming against pathogens, and even to behaviour, with measurable consequences for colony performance and resilience. Yet despite the field’s rapid growth, most studies continue to analyse host genetics and environmental exposures separately, and many rely on simple correlations between the abundance of particular microbes and health traits such as parasite resistance or longevity.

That reliance on correlation is where the trouble begins, the authors argue. When researchers observe that bees with a certain microbiome profile are healthier, it is difficult to know what is driving what. The microbiome differences may reflect underlying host genetics, they may be a consequence of environmental exposure, or they may genuinely contribute mechanistically to the health outcome. Disentangling these possibilities is one of the central challenges in bee microbiome research, and the GEM–P framework is designed precisely to make this disentangling more systematic. It treats the genome, the exposome and the gut microbiome as interacting components that jointly shape phenotypes, while phenotypes themselves feed back to modify subsequent exposures and microbiome states, creating a dynamic, looping system rather than a one-directional chain of cause and effect.

Each component of the framework carries specific technical meaning. The genome encompasses host genetic variation, which can filter which microbes are able to colonise the gut, a process the authors describe as genetic filtering. The exposome captures the totality of lifetime exposures, including diet, pesticides, pathogens, temperature and landscape characteristics, all of which can reshape the microbial community or act directly on bee physiology. The microbiome refers to the gut symbionts themselves and their assembly dynamics and functions. The phenome covers the full suite of observable traits, from individual characteristics such as immune gene expression and nutrient processing to colony-level outcomes such as brood production, overwintering success and disease burden. Crucially, the framework operates at both scales simultaneously, recognising that colony phenotypes emerge from the actions and interactions of thousands of individuals.

Perhaps the most intellectually important contribution of the review is its treatment of context dependence. The authors highlight three major sources of it: scale, timing and social transmission. Similar microbiome shifts can be associated with different trait outcomes depending on the life stage and task of the bee, since nurse bees and foragers, or larvae and adults, have distinct physiologies and diets. Exposure history matters too, because a bee that has previously encountered a pesticide may respond differently to a microbial change than a naive individual. And the colony’s transmission structure, the way microbes are shared among nestmates, means that an individual’s microbiome is inseparable from its social environment. A microbial pattern that signals dysbiosis in one context may be an adaptive response in another, which helps explain why apparently contradictory findings recur across the literature.

To illustrate the framework’s utility, the authors use GEM–P to map existing evidence on genetic filtering, exposomal drivers, microbiome assembly and function, and phenotypic outcomes. This mapping exercise reveals where the evidence is strong and where it remains thin. For example, host genetics clearly constrain which symbionts can establish in the bee gut, and dietary exposures demonstrably alter the relative abundance of core taxa, but the mechanistic links connecting specific microbial functions to specific colony outcomes are far less well established. By laying the components out in a single organisational scheme, researchers can identify which pathway connecting genome, exposome, microbiome and phenotype is being invoked in any given hypothesis, and design sampling strategies that test that pathway specifically rather than relying on broad correlational surveys.

Importantly, the authors stress that generating pathway-specific hypotheses does not require comprehensive multi-omics approaches, which remain expensive and technically demanding for many laboratories. Instead, GEM–P supports relatively simple, targeted study designs. Sampling across life stages and castes, recording exposure histories, accounting for colony-level transmission, and measuring phenotypes at matched individual and colony scales can all help distinguish among alternative biological explanations for a microbiome–phenotype association without sequencing everything. The framework thus serves as a guide for experimental economy, pointing researchers toward the comparisons that are most informative for ruling out competing interpretations, such as whether a microbiome shift precedes or follows a change in host state, or whether it is shared across related bees in a way consistent with genetic filtering.

The implications extend beyond basic science into the practical world of beekeeping and pollination services. Social bees underpin agriculture through their pollination of crops and wild plants, and colony losses have economic and ecological consequences that are now felt worldwide. If microbiome-based interventions, such as probiotic supplements or management practices that support beneficial symbionts, are to be developed responsibly, they must rest on an understanding of when microbial change actually causes improvements in health and when it is merely a bystander. GEM–P offers a way to formulate those questions rigorously, linking interventions to specific points in the genome–exposome–microbiome–phenome network and specifying the outcomes and contexts in which effects should be expected. The work also contributes to the objectives of the BeeSustain project, an initiative on integrative modelling for enhanced beekeeping carrying capacity funded through Xjenza Malta’s Research Excellence Programme.

The review, published open access on 3 September 2026, arrives at a moment when the bee microbiome literature has grown rapidly but interpretively fragmented, with different subfields emphasising different components of the system and rarely integrating them. By providing a shared vocabulary and a common structural map, the authors hope the framework will help researchers interpret microbiome–phenotype associations across contexts, generate hypotheses that are explicitly pathway-specific, and design studies whose results genuinely advance mechanistic understanding. For a field in which correlation has often been mistaken for causation, and in which the same microbial signal can mean different things in a larva, a nurse bee or an overwintering colony, that kind of conceptual discipline may prove as valuable as any single experiment. The GEM–P framework does not claim to answer how social bee health is determined, but it tells the research community, with unusual clarity, exactly which questions to ask next.

Subject of Research: An integrative framework linking genome, exposome, gut microbiome and phenome in social bees

Article Title: The Genome Exposome Microbiome–Phenome (GEM–P): a conceptual framework for social bees

Article References: Cutajar, S., Alberoni, D., Di Gioia, D., & Mifsud, D. (2026). The Genome Exposome Microbiome–Phenome (GEM–P): a conceptual framework for social bees. Applied Microbiology and Biotechnology. https://doi.org/10.1007/s00253-026-14016-4

Image Credits: AI Generated

DOI: 10.1007/s00253-026-14016-4

Keywords: social bees, gut microbiome, exposome, genome, phenome, GEM–P framework, colony health, microbial transmission, symbiosis, bee resilience, host genetics, context dependence

The urgency of this shift is grounded in hard epidemiological data. Twin studies and large meta-analyses indicate that genetic factors explain only a modest fraction of chronic disease burden, with heritability estimates for most human traits far below what would be needed to account for the rapid global rise of immune-mediated disorders. Genes simply do not change fast enough to explain why inflammatory bowel disease has surged in industrialized nations and is now emerging in regions that historically had very low incidence. Population-based studies of immigrants tell a striking story: people who move from low-incidence to high-incidence countries acquire elevated disease risk within a single generation, and their children, born in the new environment, often reach incidence rates approaching those of the host population. That pattern points squarely at modifiable environmental exposures acting on genetically susceptible individuals.

Decades of research have already identified a substantial catalog of risk factors. Smoking increases the risk of Crohn’s disease while paradoxically appearing protective in ulcerative colitis, yet molecular work shows that smoking alters DNA methylation at inflammatory loci in ways that plausibly mediate gut inflammation. Air pollution has been linked to ulcerative colitis through epigenetic changes in the CXCR2 gene and the MHC class III region. Antibiotic use, particularly early in life and during pregnancy, disturbs the developing gut microbiome and raises offspring risk. Appendectomy, oral contraceptives, gastrointestinal infections with pathogens such as Salmonella, Campylobacter and Helicobacter species, and Epstein-Barr virus timing all leave measurable fingerprints on disease risk. Conversely, protective exposures have emerged too: early-life contact with agriculture, biodiversity and green space, a diverse diet rich in plant-based foods, and physical activity are consistently associated with lower incidence.

The Perspective distinguishes carefully between early-life and later-life exposures, a distinction the authors argue is essential for prevention science. The developmental origins of health and disease paradigm, rooted in David Barker’s work on fetal programming, holds that exposures during critical windows of immune and microbiome development can set long-term trajectories of disease susceptibility. In inflammatory bowel disease, the preclinical phase can last years, with altered gut microbiome composition, elevated fecal calprotectin and subtle immune changes detectable in at-risk first-degree relatives long before symptoms appear. Studies analyzing deciduous teeth have even reconstructed prenatal and postnatal metal exposure histories, linking them to later Crohn’s disease risk. Early exposures such as mode of delivery, breastfeeding, infant diet diversity and childhood antibiotic courses therefore represent unusually potent intervention targets, because they act before the disease process has gained momentum.

At the same time, the authors highlight exposures that traditional epidemiology has largely overlooked, and this is where the exposome concept becomes technically transformative. Food contact contaminants are a case in point. Plastic teabags release billions of microparticles and nanoparticles into hot tea, microwavable plastic containers shed both microplastics and intentionally and non-intentionally added chemical substances, and nonstick cookware can contaminate food with microplastics and PTFE. Perfluoroalkyl and polyfluoroalkyl substances, the so-called forever chemicals, migrate from food packaging into the diet, and elevated serum levels of these compounds have been associated with later occurrence of inflammatory bowel disease and with intestinal barrier defects in experimental systems. Pesticide residues on produce, antibiotic residues in animal products, and synthetic chemicals in processed food collectively form a chronic, low-dose chemical mixture that no single-exposure study can adequately capture.

Among emerging pollutants, microplastics and nanoplastics occupy a special place of concern. These particles have now been detected in human stool, blood, lung tissue, placenta, atherosclerotic plaques and even human brain tissue, with some evidence of bioaccumulation over time. In animal models, chronic exposure to polystyrene nanoplastics induces mechanical and immune barrier dysfunction in the intestine. In humans, one analysis of fecal samples found that microplastic concentrations correlated with inflammatory bowel disease status, although the authors of the new Perspective are careful to note that causality remains unproven and that more rigorous science, standardized definitions and better detection methods are urgently needed. Novel analytical tools, including stimulated Raman scattering microscopy capable of single-particle nanoplastic imaging and surface-enhanced Raman spectroscopy with nanogap arrays, are now making it possible to detect and quantify these particles at environmentally relevant concentrations.

Measurement technology is the second pillar of the exposome revolution. High-resolution mass spectrometry platforms can now screen thousands of chemicals in biological samples without needing to know in advance which ones to look for, an approach known as non-targeted analysis. Coupled with ion mobility separation and computational metabolomics, these platforms are expanding the observable chemical space of human exposure from a few hundred targeted biomarkers to tens of thousands of features. On the statistical side, new methods designed for real-world exposure complexity, including weighted quantile sum regression, quantile-based g-computation and Bayesian kernel machine regression, allow researchers to model the health effects of correlated chemical mixtures rather than isolated agents. Machine learning approaches have begun to identify synergistic interactions among pesticides, phthalates, phenols and trace metals, and longitudinal personal monitoring with wearable sensors has revealed how dynamically an individual’s chemical exposure profile shifts from day to day.

The framework’s most consequential contribution, however, may be its prevention architecture, which differentiates between primary and primordial prevention. Primary prevention, borrowed from cardiovascular medicine, means identifying individuals at elevated risk, such as first-degree relatives of patients with inflammatory bowel disease, stratifying them by genetic, microbiome and exposome risk scores, and intervening before disease onset. Recent trials in adjacent fields show this is feasible: teplizumab has delayed type 1 diabetes in at-risk relatives and abatacept has shown promise in preventing rheumatoid arthritis in high-risk individuals, providing templates for immunoprophylaxis in inflammatory bowel disease. Primordial prevention operates one level deeper, aiming to lower the population-wide baseline of risk before susceptibility even arises, through community and policy interventions. Crucially, the authors emphasize the actionable exposome: those exposures that individuals, clinicians or policymakers can realistically modify today, as opposed to the vast conceptual exposome that remains beyond immediate control.

Individual-level mitigation strategies already have an evidentiary basis. Lifestyle studies suggest that a substantial fraction of inflammatory bowel disease cases could be prevented through modifiable behaviors, including a prudent plant-rich diet, regular physical activity, smoking cessation and avoidance of e-cigarettes. Randomized trials have shown that a low-plastic diet reduces urinary levels of plastic-associated phthalates and bisphenols, while simple dietary choices, such as avoiding heating food in plastic containers, reduce microplastic intake. For persistent pollutants like PFAS, interventions including plasma donation and anion exchange resin treatment have demonstrated measurable reductions in body burden. Dietary components rich in anthocyanins and other antioxidants may counteract some pollutant effects. At the community level, the levers are broader: air quality regulation, pesticide policy, green urban planning, washing machine filtration to reduce microfiber emissions, point-of-use drinking water filters, and international treaties to end plastic pollution all shape the exposures that entire populations experience from conception onward.

The authors also confront the global inequities embedded in this agenda. Inflammatory bowel disease is progressing through four epidemiological stages worldwide, from nascent emergence to high prevalence, and the environmental drivers differ profoundly between a newly industrializing city in Asia and a saturated North American market. Industrialized nations that created the modern exposome bear responsibility for generating evidence and technology that low- and middle-income regions can adapt as they urbanize, ideally avoiding the worst exposures from the start. Whether the exposome framework can also illuminate other overlapping immune-mediated diseases, from multiple sclerosis to rheumatoid arthritis, remains an open and tantalizing question. What is clear from this Perspective is that the era of treating environmental risk as an afterthought to genetics is ending, and that a coordinated science of exposure measurement, risk prediction and two-tiered prevention offers the first realistic pathway toward a future in which inflammatory bowel disease is not merely treated but prevented.

Subject of Research: The role of lifespan environmental exposures, the exposome, in inflammatory bowel disease risk and prevention

Article Title: The exposome and inflammatory bowel disease: a framework for primary and primordial prevention

Article References: Agrawal, M., Midya, V., Vianello, A., Valvi, D., Kaplan, G. G., & Colombel, J.-F. (2026). The exposome and inflammatory bowel disease: a framework for primary and primordial prevention. Nature Reviews Gastroenterology & Hepatology. https://doi.org/10.1038/s41575-026-01260-2

Image Credits: AI Generated

DOI: 10.1038/s41575-026-01260-2

Keywords: exposome, inflammatory bowel disease, Crohn's disease, ulcerative colitis, microplastics, PFAS, prevention, environmental risk factors, gut microbiome, air pollution, early-life exposures, epigenetics