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Early-life exposure occurs during gestation through transfer to the fetus and later, during lactation. Recent monitoring data revealed that the Portuguese population is exposed to mycotoxins, including young children. This study aimed to develop a pilot study to assess the early-life exposure to mycotoxins through a mother–child cohort, and to identify the associated challenges. Participants were recruited during pregnancy (1st trimester) and followed-up in three moments of observation: 2nd trimester of pregnancy (mother), and 1st and 6th month of the child’s life (mother and child), with the collection of biological samples and sociodemographic and food consumption data. The earlyMYCO pilot study enrolled 19 mother–child pairs. The analysis of biological samples from participants revealed the presence of 4 out of 15 and 5 out of 18 mycotoxins’ biomarkers of exposure in urine and breast milk samples, respectively. The main aspects identified as contributors for the successful development of the cohort were the multidisciplinary and dedicated team members in healthcare units, reduced burden of participation, and the availability of healthcare units for the implementation of the fieldwork. Challenges faced, lessons learned, and suggestions were discussed as a contribution for the development of further studies in this area.

The interaction between the genome and the exposome is increasingly recognized as central to human health and disease. While exposome research has generally focused on adverse exposures such as pollutants and toxins, the concept of the beneficial exposome —positive environmental exposures that promote health—remains underexplored. Among the most promising beneficial exposures are plant-derived phytochemicals, a rich class of bioactive compounds with therapeutic potential. Phytoncides, a specific subset of volatile organic compounds released by plants, exemplify this beneficial potential through their antimicrobial, anti-inflammatory, antioxidant, and neuroprotective effects. Historically utilized in traditional medicine across cultures, plant-based remedies containing these compounds are now being examined through modern genomics, exposomics, and systems biology approaches to understand the specific contributions of phytoncides and other bioactive constituents. Emerging data suggest that phytochemicals modulate gene expression, immune function, and metabolic pathways across multiple organ systems, contributing to immune, neurological, endocrine, cardiovascular, respiratory, integumentary, and mental health improvements. However, the evidence base is predominantly preclinical, with limited human validation, considerable heterogeneity in plant-extract composition, and incompletely characterized molecular mechanisms. This review synthesizes current evidence on genome-exposome interactions (GxE) related to plant-derived compounds, highlighting recent mechanistic insights and exploring translational applications—including forest bathing, green space integration in urban design, and bioengineering approaches—while addressing the challenges of clinical translation. As environmental change accelerates, understanding beneficial GxE offers new opportunities for preventative and precision public health interventions and calls for integrating nature-based solutions into modern healthcare paradigms.

Despite extensive evidence showing that exposure to specific chemicals can lead to disease, current research approaches and regulatory policies fail to address the chemical complexity of our world. To safeguard current and future generations from the increasing number of chemicals polluting our environment, a systematic and agnostic approach is needed. The “exposome” concept strives to capture the diversity and range of exposures to synthetic chemicals, dietary constituents, psychosocial stressors, and physical factors, as well as their corresponding biological responses. Technological advances such as high-resolution mass spectrometry and network science have allowed us to take the first steps toward a comprehensive assessment of the exposome. Given the increased recognition of the dominant role that nongenetic factors play in disease, an effort to characterize the exposome at a scale comparable to that of the human genome is warranted.

In the last years, the issue of exposure assessment of airborne pollutants has been on the rise, both in the environmental and occupational fields. Increasingly severe national and international air quality standards, indoor air guidance values, and exposure limit values have been developed to protect the health of the general population and workers; this issue required a significant and continuous improvement in monitoring technologies to allow the execution of proper exposure assessment studies. One of the most interesting aspects in this field is the development of the “next-generation” of airborne pollutants monitors and sensors (NGMS). The principal aim of this review is to analyze and characterize the state of the art and of NGMS and their practical applications in exposure assessment studies. A systematic review of the literature was performed analyzing outcomes from three different databases (Scopus, PubMed, Isi Web of Knowledge); a total of 67 scientific papers were analyzed. The reviewing process was conducting systematically with the aim to extrapolate information about the specifications, technologies, and applicability of NGMSs in both environmental and occupational exposure assessment. The principal results of this review show that the use of NGMSs is becoming increasingly common in the scientific community for both environmental and occupational exposure assessment. The available studies outlined that NGMSs cannot be used as reference instrumentation in air monitoring for regulatory purposes, but at the same time, they can be easily adapted to more specific applications, improving exposure assessment studies in terms of spatiotemporal resolution, wearability, and adaptability to different types of projects and applications. Nevertheless, improvements needed to further enhance NGMSs performances and allow their wider use in the field of exposure assessment are also discussed.

Abstract Previously, we established an estimated exposome score for schizophrenia (ES-SCZ) as a cumulative measure of environmental liability for schizophrenia to use in gene–environment interaction studies and for risk stratification in population cohorts. Hereby, we examined the discriminative function of ES-SCZ for identifying individuals diagnosed with schizophrenia spectrum disorder in the general population by measuring the area under the receiver operating characteristic curve (AUC). Furthermore, we compared this ES-SCZ method to an environmental sum score (Esum-SCZ) and an aggregate environmental score weighted by the meta-analytical estimates (Emet-SCZ). We also estimated ORs and Nagelkerke’s R2 for ES-SCZ in association with psychiatric diagnoses and other medical outcomes. ES-SCZ showed a good discriminative function (AUC = 0.84) and statistically significantly performed better than both Esum-SCZ (AUC = 0.80) and Emet-SCZ (AUC = 0.80). At optimal cut point, ES-SCZ showed similar performance in ruling out (LR− = 0.20) and ruling in (LR+ = 3.86) schizophrenia. ES-SCZ at optimal cut point showed also a progressively greater magnitude of association with increasing psychosis risk strata. Among all clinical outcomes, ES-SCZ was associated with schizophrenia diagnosis with the highest OR (2.76, P < .001) and greatest explained variance (R2 = 14.03%), followed by bipolar disorder (OR = 2.61, P < .001, R2 = 13.01%) and suicide plan (OR = 2.44, P < .001, R2 = 12.44%). Our findings from an epidemiologically representative general population cohort demonstrate that an aggregate environmental exposure score for schizophrenia constructed using a predictive modeling approach—ES-SCZ—has the potential to improve risk prediction and stratification for research purposes and may help gain insight into the multicausal etiology of psychopathology.

Health is increasingly understood as a multidimensional phenomenon shaped by complex interactions among biological, psychosocial, environmental, and informational factors. Building on the human exposome and its extensions, this paper introduces the interpretive exposome, a conceptual framework that captures cumulative exposure to how health-related information is framed, recorded, interpreted, and communicated by clinicians, artificial intelligence (AI) mechanisms, and institutions across the life course. We argue that the interpretive process, including biased clinical health records, algorithmic decision-support outputs, and inequitable communication, operates as exposures that can accumulate and influence downstream health outcomes. We further describe how AI systems function as interpretive filters that may reproduce, alleviate, or amplify bias through training data and recursive deployment. While remaining conceptual in nature, this proposed framework outlines methodological pathways for operationalization using natural language processing (NLP), bias auditing, and multi-modal data integration. The interpretive exposome complements existing exposome models and offers a theoretical foundation for future empirical validation aimed at promoting equitable, transparent, and context-aware healthcare.

Rare earth elements (REEs) are metals including the 15 lanthanides together with Yttrium and Scandium. China is the leading country in their exploitation and production (∼90%). REEs are necessary for the production of several technological devices. This extended use of REEs has raised concerns about human health safety. In this review, we investigated the hazard of REEs to human health and the main gaps into the knowledge like as the need to develop further focused research activity. We categorized the research papers collected into eight main sections: environmental exposure, association of REEs with health problems, exposure to REEs due to lifestyle, REE exposure through the food chain, Gd contrast agents causing health problems, occupational REE exposure, and cytotoxicity studies of REEs. This review provided information about the exposome of REEs (the exposure of REEs to the human body), the existing research data, and the gaps that require attention and must be further investigated. More than one third of the literature about REE toxicity to human health concerns their cytotoxicity to human cell lines, while hair, blood serum and blood are the most studied matrices. The main results evidenced that REEs can enter human body via several routes, are associated with numerous diseases, can cause ROS production, DNA damage and cell death, and are more toxic to cancer cells than normal cells.

Understanding the interplay between genetic predisposition and environmental and lifestyle exposures is essential for advancing precision medicine and public health. The exposome, defined as the sum of all environmental exposures an individual encounters throughout their lifetime, complements genomic data by elucidating how external and internal exposure factors influence health outcomes. This treatise highlights the emerging discipline of translational exposomics that integrates exposomics and genomics, offering a comprehensive approach to decipher the complex relationships between environmental and lifestyle exposures, genetic variability, and disease phenotypes. We highlight cutting-edge methodologies, including multi-omics technologies, exposome-wide association studies (EWAS), physiology-based biokinetic modeling, and advanced bioinformatics approaches. These tools enable precise characterization of both the external and the internal exposome, facilitating the identification of biomarkers, exposure-response relationships, and disease prediction and mechanisms. We also consider the importance of addressing socio-economic, demographic, and gender disparities in environmental health research. We emphasize how exposome data can contextualize genomic variation and enhance causal inference, especially in studies of vulnerable populations and complex diseases. By showcasing concrete examples and proposing integrative platforms for translational exposomics, this work underscores the critical need to bridge genomics and exposomics to enable precision prevention, risk stratification, and public health decision-making. This integrative approach offers a new paradigm for understanding health and disease beyond genetics alone.

With the continuous increase of the elderly population, there is an urgency to understand and develop relevant treatments for Alzheimer’s disease and related dementias (ADRD). In tandem with this, the prevalence of health inequities continues to rise as disadvantaged communities fail to be included in mainstream research. The neural exposome poses as a relevant mechanistic approach and tool for investigating ADRD onset, progression, and pathology as it accounts for several different factors: exogenous, endogenous, and behavioral. Consequently, through the neural exposome, health inequities can be addressed in ADRD research. In this paper, we address how the neural exposome relates to ADRD by contributing to the discourse through defining how the neural exposome can be developed as a tool in accordance with machine learning. Through this, machine learning can allow for developing a greater insight into the application of transferring and making sense of experimental mouse models exposed to health inequities and potentially relate it to humans. The overall goal moving beyond this paper is to define a multitude of potential factors that can increase the risk of ADRD onset and integrate them to create an interdisciplinary approach to the study of ADRD and subsequently translate the findings to clinical research.

Digital technologies are reshaping human behavior, health care delivery, and population health; however, their cumulative effects across the lifespan remain underexplored. This viewpoint argues that exposures arising from interactions with digital technologies should be formally integrated into exposome science as a distinct, measurable component of the human environment. Our aims are to (1) redefine the digital component of the exposome (the digital exposome) within the broader exposome framework, (2) examine its life course implications for health and equity, and (3) outline a research and policy agenda to enable its systematic measurement and integration into clinical and public health practice. Digital technology–related exposures can confer benefits such as enhanced health monitoring, personalized interventions, improved access to care, and the promotion of healthy behaviors. However, they may also introduce potential risks, including mental health challenges, cognitive and circadian disruptions, sedentary lifestyles, exposure to misinformation, and widening inequities among vulnerable populations. Despite their ubiquity, digital technology–related exposures remain poorly integrated into clinical medicine, epidemiology, or public and global health policies. Drawing on interdisciplinary evidence from exposure science, epidemiology, and digital phenotyping research, we propose a refined conceptual definition of the digital exposome grounded in the classical exposome domains. We propose redefining the digital exposome as the full spectrum of exposures resulting from interactions or proximities with digital technologies and their combined influence on health across the lifespan. This framework conceptualizes digital technology–related exposures as a dynamic set of environmental influences operating through sociotechnical, behavioral, and biological pathways over the life course. To operationalize this framework, we discuss practical approaches using validated behavioral instruments, objective device use logs, ecological momentary assessments, smartphone-based digital phenotyping, and wearable sensing technologies. Systematic measurement, large-scale longitudinal studies, and harmonized exposure metrics are needed to characterize the cumulative health impacts of digital environments more accurately. Emerging tools such as digital markers or biomarkers and digital phenotypes offer promising opportunities to link real-world technology use with physiological and biological outcomes, thereby supporting precision medicine and population health strategies. Ethical governance, privacy safeguards, and equity considerations must be embedded from the start, drawing on emerging exposomethics frameworks. Recognizing the digital exposome as a modifiable determinant of health offers a foundation for evidence-based guidance, prevention strategies, and policy interventions suited to increasingly digital societies. By integrating digital technology–related exposures into exposome science, clinical practice, and public health research, this viewpoint seeks to foster interdisciplinary dialogue, guide future empirical work, and support the development of safer and more equitable digital environments across the lifespan.