Search Results (354)

Background/Objectives: Precision nutrition is moving beyond population-based guidance and isolated gene–diet interactions toward integrative models of dietary response. However, current approaches remain fragmented across nutrigenomics, microbiome research, multi-omics profiling, digital health, and machine learning. This review proposes the Nutri-Exposome Intelligence Framework as a conceptual, data science-driven model for integrating cumulative dietary, environmental, microbial, molecular, clinical, and digital exposures for precision chronic disease prevention. Methods: This conceptual review synthesizes the literature on precision nutrition, nutrigenetics, nutrigenomics, exposomics, gut microbiome research, multi-omics integration, wearable and biomarker-based monitoring, and machine learning in nutrition studies. Evidence was organized into a framework linking exposure assessment, host susceptibility, microbiome-mediated biotransformation, molecular response profiling, computational modelling, personalized intervention, and longitudinal feedback. Results: The proposed framework consists of seven interconnected layers: diet, environment, and lifestyle exposures; host genome and microbiome; multi-omics molecular responses; machine learning-based integration; risk prediction and responder stratification; personalized dietary intervention; and wearable and biomarker-based feedback. It positions the nutri-exposome as a cumulative exposure–response system and highlights how machine learning can support data harmonization, feature engineering, predictive modelling, responder classification, explainable interpretation, and adaptive refinement of dietary recommendations. Key applications include obesity, type 2 diabetes, cardiovascular disease, metabolic dysfunction-associated steatotic liver disease, cardiovascular–kidney–metabolic syndrome, and broader cardiometabolic prevention. Conclusions: Nutri-exposome intelligence offers a structured pathway for transforming complex nutrition data into predictive, explainable, and adaptive precision nutrition strategies. Implementation will require longitudinal and multi-ethnic cohorts, standardized metadata, causal validation, interpretable machine learning, ethical governance, and equitable access to support responsible clinical and public health translation globally. Full article

Emerging research highlights the complex interplay between the gut microbiome, skin health, and environmental exposures, forming what is now recognized as the gut–skin–exposome axis. This narrative review explores the role of gut microbiome dysbiosis—a disruption in the balance of intestinal microorganisms—in the pathogenesis and progression of various non-communicable inflammatory skin diseases, including acne, atopic dermatitis, psoriasis, rosacea, systemic lupus erythematosus, chronic spontaneous urticaria, hidradenitis suppurativa, and alopecia areata. This review synthesizes mechanistic studies, clinical trials, and Mendelian randomization data to elucidate how altered gut microbial composition contributes to systemic and cutaneous inflammation. Key modifiable factors, such as diet, antibiotics, stress, and sleep, as well as interventions like probiotics, prebiotics, synbiotics, and fecal microbiota transplantation, are discussed for their potential therapeutic value. By integrating clinical insights with microbiome science, this review underscores the importance of a holistic, systems-based approach in managing inflammatory skin diseases, offering clinicians evidence-based strategies to improve patient outcomes through gut microbiome modulation. Full article

Environmental toxicants may affect the height of children and adolescents. However, studies on the toxicological effects based on extensive internal exposure omics are still lacking. This study aimed to identify key toxicants associated with height and assess the mediating role of sex steroid hormones. To this end 1660 participants aged 6–19 years from subsample A in the National Health and Nutrition Examination Survey (NHANES) were included. Exposome was characterized by 58 toxicants within 12 families. After assessment by the exposome-wide association analysis and mixture models, we identified 17 toxicants inversely associated with height-for-age Z-scores (HAZ), predominantly metals and volatile organic compound (VOC) metabolites. Tin exhibited the strongest inverse association (β = −0.261), followed by lead (β = −0.230). The primary contributors to reduced height included tin, lead, the VOC metabolite 2-ATCA, ethylene oxide, and nitrate. Notably, males and younger children were the more susceptible subgroups. Furthermore, mediation analysis revealed that sex steroid hormones, particularly total testosterone and estradiol, mediated 8% to 37% of the associations. These findings suggest that endocrine-related pathways may link toxicant exposure to impaired linear growth, highlighting the necessity of reducing exposure during childhood. Full article

Microbiome dysbiosis has become recognized as an important interface connecting environmental exposures to chronic inflammatory and degenerative diseases. Although prior research has largely considered heavy metals as biomarkers of exposure and toxicity, their function as ecological modulators of host-associated microbial communities remains underexplored. The oral cavity is a distinct exposome–microbiome interface where environmental, behavioral, and intraoral metal sources converge and interact with structured biofilms and mucosal immunity. This review adopts an ecological systems perspective, interpreting chronic low-dose exposure to metals such as cadmium, lead, mercury, nickel, chromium, arsenic, and aluminum as a sustained selective force on oral microbial networks. A resilience–threshold model is proposed in which cumulative metal pressure progressively diminishes microbial community stability, alters network topology, and drives transitions toward persistent dysbiosis. These modifications are further reinforced by oxidative–inflammatory feedback loops at the host–microbiome interface, facilitating a self-sustaining ecological imbalance. Sketching on insights from microbial ecology, environmental toxicology, and host response biology, this review presents a framework that links metallomic patterns to microbial restructuring, redox imbalance, immune activation, and regulatory adaptation. The analysis emphasizes ecological perturbations from stable dysbiotic states and identifies key methodological limitations that currently restrict causal inference. By conceptualizing heavy metals as active ecological drivers rather than passive exposure indicators, this work establishes a foundation for understanding microbiome-mediated disease susceptibility within an exposome-informed systems biology framework. Full article

Dilated cardiomyopathy (DCM) is a leading cause of heart failure, but up to 50% of cases have no definitive etiology. Genetic susceptibility alone does not account for phenotypic inconsistency, so a ‘two-hit’ model has been proposed to explore the spectrum of gene-environment interactions. Certain triggers, such as alcohol, chemotherapy agents, and viral myocarditis, are well-established second hits in the pathogenesis of DCM. The exposome, which encompasses environmental and social exposures across the lifespan, provides a more comprehensive framework to understand these interactions. In patients with DCM, air pollution and heavy metals have already been associated with higher rates of mortality and heart failure hospitalization. Microplastics and nanoplastics (MNPs) are novel components of the exposome. They form from the degradation of plastics and enter the circulatory system primarily through ingestion and inhalation. They have recently been found in human cardiovascular tissue, including atherosclerotic plaques and the myocardium. In vivo and in vitro models consistently demonstrate that MNPs induce oxidative stress, mitochondrial dysfunction, and calcium dysregulation. These pathways are shared with established cardiotoxins and converge on cardiomyocyte death, fibrosis, and eccentric ventricular remodeling, which is consistent with the pathogenesis and phenotype of DCM. In genetically susceptible individuals, MNP exposure may therefore contribute to the progression from subclinical myocardial injury to overt systolic dysfunction. This narrative review synthesizes preclinical mechanistic evidence linking MNP exposure to myocardial injury, compares the underlying mechanisms with those of other environmental pollutants and cardiovascular toxins, and integrates these findings within the proposed ‘two-hit’ model of DCM. Whether MNP exposure contributes to DCM in humans remains to be established, but understanding the potential consequences of MNPs has important implications for prevention, therapeutic development and health policy. Standardization of detection methods, chronic low-dose exposure models, and prospective human studies using functional cardiac assessment are needed before translating these experimental findings into clinical practice. Full article

Micro- and nanoplastics (MNPs) are widespread environmental pollutants, with increasing evidence of human exposure through multiple routes. Their detection in human tissues, including the lungs, raises concerns about their potential impact on respiratory health, including lung cancer (LC). This review synthesizes current evidence on the biological effects of MNP exposure, with a focus on mechanisms potentially relevant to LC. In particular, extracellular vesicles (EVs) are discussed as mediators potentially linking environmental exposure to cellular responses. Experimental studies suggest that MNPs may induce oxidative stress, inflammation, and genotoxicity, and may alter EV biogenesis and cargo, thereby influencing pathways involved in epithelial–mesenchymal transition, angiogenesis, and immune modulation. We also explore the potential contribution of the gut–lung axis, where MNP-induced dysbiosis and intestinal barrier disruption may promote systemic inflammatory responses, with bacterial EVs acting as additional mediators. However, evidence directly linking MNP exposure, EV-mediated signaling, and LC is limited and largely derived from experimental models. Key challenges include the lack of standardized detection methods, insufficient dose–response data, and scarce epidemiological evidence. Integrating exposomic and multi-omic approaches, including EV-omics, lipidomics, and metabolomics, is needed to clarify the relevance of these mechanisms and support the identification of potential biomarkers in human disease. Full article

The exposome concept combines classic risk factors for cardiovascular disease with new, non-classical factors. One of the main non-classical factors is environmental pollution, including water pollution. This pollution is widespread worldwide. Based on government reports on water pollution in Poland and Uzbekistan and the available literature, the authors point to health problems affecting residents of both countries. The presented tabulation of cardiovascular disease incidence rates indicates an upward trend. It was found that the components of water pollution and the mechanisms by which this pollution affects the cardiovascular system are similar in both countries. These include heavy metals, arsenic, cadmium, lead, mercury, as well as nitrogen compounds from soil and microplastics. This article is an observational report and represents an important step towards understanding the relationship between water pollution and the cardiovascular system. Due to the lack of comprehensive knowledge, particularly regarding the impact of microplastics, nitrates, and nitrites found in water on the cardiovascular system, further research in this area is necessary. Full article

Although hantaviruses have traditionally been considered geographically restricted rodent-borne pathogens, globalization, climate change, ecosystem disruption, and environmental contamination may collectively favor novel transmission scenarios and altered epidemiological patterns. The experience gained during the SARS-CoV-2 pandemic showed the importance of environmental determinants, airborne exposure, and host susceptibility factors in emerging viral diseases. In this context, increasing but still indirect evidence suggests that environmental toxicants and the exposome may modulate susceptibility to hantavirus infection and influence disease severity. The proposed mechanisms include oxidative stress, endothelial dysfunction, pulmonary inflammation, and immune dysregulation, rather than direct causal effects of toxicants on infection itself. This article discusses current knowledge regarding interactions among toxic environmental exposures, climate change, and hantavirus disease, with special emphasis on Andes orthohantavirus (ANDV), the principal hantavirus known to exhibit person-to-person transmission. The article integrates recent evidence within the One Health framework and highlights future research priorities linking environmental toxicology, zoonotic disease ecology, and global environmental change. Full article

The interplay between the environmental exposome and the cancer genome remains a critical gap in precision oncology. While somatic mutational signatures—genomic fossils imprinted by exposures such as ultraviolet radiation; tobacco smoke; and industrial pollutants—are well characterised for their etiological significance; their functional impact on therapeutic efficacy remains largely unexplored. We hypothesised that these environmental genomic scars induce distinct pharmacogenomic vulnerabilities and resistance mechanisms that vary by geographical exposure patterns. This study employs two complementary analytical frameworks. First, a linear regression-based pharmacogenomic screen across four datasets (GDSC1, GDSC2, CTRP, CCLE; 1001 cell lines, 31 cancer types) identified 608 statistically significant (p < 0.01) mutational signature–drug interactions, revealing that UV-associated signature SBS7a is associated with broad-spectrum therapeutic resistance, including to BRAF inhibitors (PLX-4720, p < 10⁻⁴), while pollution-driven oxidative stress (SBS18) is associated with sensitivity to p38 MAPK inhibition (VX-702, r = −0.45, p < 10⁻⁹). Second, an XGBoost predictive model trained exclusively on 33,679 GDSC2 records using a 1265-feature matrix integrating 40 SBS signatures, drug chemistry descriptors, proteomic features, and two satellite-derived environmental variables (NASA PM_2.5 and UV)—achieved R² = 0.7973 on a 20% holdout set (grouped cross-validation R² = 0.7296). SHAP analysis revealed that satellite-derived PM_2.5 (Zone_PM25) ranked 7th of 1265 features, exceeding all 40 individual SBS mutational signatures. Synthesising these findings with satellite-derived atmospheric data, we constructed an exploratory spatially interpolated risk surface spanning 122 nations, generating the hypothesis that uniform drug efficacy assumptions may not apply globally. These findings suggest that a patient’s environmental exposure history may constitute a measurable pharmacogenomic variable. This exploratory framework warrants validation in independent datasets and with individual-level geographic data before clinical application. Full article

Physiological and metabolic adaptations during pregnancy may increase susceptibility to harmful environmental chemicals. High maternal BMI that perturbs the metabolic, inflammatory, and hormonal milieus could modulate toxicant effects on pregnancy outcomes. First-trimester targeted urinary exposomes of 119 women (BMI ≥ 25 = 55; BMI < 25 = 64) from the Michigan Mother–Infant Pair cohort were profiled to assess how BMI influences urinary exposomes and related biological responses. A validated approach measured 96 chemicals and biomarkers of oxidative stress and glucocorticoids. Women in both groups reported similar lifestyles and consumer product use. Women with high BMI had lower urinary concentrations of several chemicals than women with low BMI. Phytoestrogens, polycyclic aromatic hydrocarbons, and insect repellants showed a higher magnitude of positive associations with other markers of exposure, oxidative and glucocorticoid stress in the high-BMI group, while UV filters, flame retardants, and phthalates showed a higher magnitude of positive associations with oxidative stress markers in the low-BMI group. The patterns of associations of exposure markers with stress markers and pregnancy outcomes (gestational age and birthweight) differed by maternal BMI. This highlights the importance of incorporating maternal BMI into the evaluation of exposure burden and its effects, as a factor that may actively shape biological responses. Full article

Life expectancy in the United States varies significantly by region, a gap often explained by socioeconomic factors like income and education. However, the relative contribution of atmospheric exposures is less understood. We identify formaldehyde exposure and wet-bulb temperature as leading predictors of county-level life expectancy. Our analysis of 22,540 county-year observations (2012–2019) shows that formaldehyde ranked as the second-strongest predictor, surpassed only by educational attainment. Wet-bulb temperature, a physiological measure of heat stress, ranked sixth and was the leading meteorological predictor. We identified these patterns using XGBoost with SHAP analysis, integrating atmospheric exposures, livestock density, socioeconomic conditions, and smoking prevalence within an external exposome framework. These results suggest that air pollutants and heat stress provide predictive information beyond traditional socioeconomic indicators. Full article

Metabolomics has emerged as a powerful analytical approach for comprehensive chemical profiling in complex biological and environmental systems. The increasing volume, dimensionality, and complexity of metabolomics data have driven the adoption of machine learning (ML) techniques to enhance chemical detection, classification, and interpretation. This narrative review critically discusses the integration of metabolomics and machine learning for advanced chemical detection, with particular emphasis on analytical workflows, data preprocessing strategies, supervised and unsupervised learning models, and validation approaches. In this context, advanced chemical detection refers to the data-driven identification, classification, and quantification of chemical signatures in complex matrices with improved sensitivity, selectivity, robustness, and interpretability. Current applications across food science, environmental monitoring, clinical diagnostics, and exposomics are discussed, along with key challenges related to data quality, interpretability, and reproducibility. Finally, future perspectives on explainable AI, multimodal data integration, and standardized pipelines are highlighted. Full article

Background: Exposure to anthropogenic and/or natural (e.g., herbicides or mycotoxins) endocrine-disrupting chemicals (EDCs) has been linked to several reproductive disorders. Glyphosate (GLY), a common agricultural agent, is a potential element of the exposome that bioaccumulates and has potential endocrine and oxidative stress-related effects. However, data on its presence in the human ovarian microenvironment remain limited. Our study examined GLY levels in follicular fluid (ff) and serum and their relationships with oxidative stress markers, reproductive hormones, and stress hormones in women undergoing in vitro fertilization (IVF). Methods: 50 women undergoing controlled ovarian stimulation participated. Serum and ff samples were routinely collected during oocyte retrieval. GLY, related hormones (e.g., cortisol, estradiol-E2, anti-Müllerian hormone-AMH, and melatonin-MT), an oxidative stress marker malondialdehyde (MDA), antioxidant enzyme activities, total antioxidant capacity, and co-occurring natural pollutant mycotoxin levels were measured. Relationships between GLY levels and these mediators were assessed using correlation and regression analyses. Results: GLY was detected in both serum and ff at similar concentrations (0.038 ± 0.006 ng/mL vs. 0.045 ± 0.006 ng/mL; p = 0.414). Follicular GLY levels showed a positive association with MDA (Spearman’s r = 0.4487, p < 0.001), explaining 28.6% of the variability in follicular MDA. Serum GLY was positively associated with serum (β = 40.26, p = 0.0058) and follicular E2 (r = 0.29, p = 0.042). Serum GLY levels were negatively correlated with cortisol (β = −0.0188, p = 0.020). A slight correlation between follicular GLY and MT was observed (p = 0.03). No associations were found between GLY levels and age, body mass index, AMH, the recombinant gonadotropin dose used, antioxidant enzyme activities, follicle count, oocyte yield, or embryo viability. Conclusions: This study might be the first to demonstrate the presence of GLY of exposome in human ff, indicating that environmental exposure to GLY may reach the oocyte microenvironment. The correlation with lipid peroxidation suggests GLY could contribute to follicular oxidative stress. The associations with E2 and cortisol point to potential endocrine-disrupting effects. While no direct impact on IVF outcomes was observed, findings suggest low-level exposure to GLY could influence ovarian physiology through specific biochemical mechanisms. Full article

Acne is a prevalent dermatological condition occurring globally and influenced by a variety of endogenous and exogenous factors. The microbiome and its contribution to skin disease have been increasingly explored, along with the influence of the exposome and host immune responses on this complex microbial system. Nine experts from different countries in Africa, America, Asia, and Europe gathered to harmonise definitions, identify key pathogenic and protective microbial strains, and prioritise the factors that most significantly impact the skin’s microbiome in the context of acne. Opportunity areas on the role of the microbiome in the prevention, treatment, recurrence, and sequelae avoidance in acne were identified. The relationships between current treatments and the diversity of the microbiome were described. Current microbiome-targeted strategies were assessed, including practical considerations of innovative future perspectives. The panel discussions emphasise the urgent need for universally adaptable guidelines encompassing alternatives to oral antibiotic therapies, in light of increasing antimicrobial resistance and the significant burden of treatment-related adverse events. Full article

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. Full article