Chronic non-communicable diseases (NCDs) account for 2/3 of global deaths annually, primarily due to an aging population and external risk factors such as air/water/soil pollution, traffic noise, mental stress, and climate change emanating from the environment. These factors contribute to premature deaths and loss of healthy life years, as reflected by disability-adjusted life years. The exposome concept was proposed 16 years ago as a new research field to investigate environment-health associations, also by considering the underlying pathophysiological pathways. The exposome describes lifelong environmental exposures, besides pollutants also socioeconomic and lifestyle factors, aiming to explain the associated diseases and deaths. The exposome can be divided into the specific and general external environment and further subcategories such as organ-specific exposomes as well as spatially and temporally restricted pollutomes. The exposome also shows considerable interaction with genetic predisposition and pre-established chronic diseases, characteristics of the vulnerable groups. The present overview provides background information on the impact of the environment on health and disease by considering recent data of the Global Burden of Disease Study. We also explain the exposome concept with the help of selected studies, briefly describe how the exposome is measured, and discuss biomarkers identified by exposomic research and their impact on the development and progression of atherosclerosis. Major pathophysiological pathways comprise exacerbated stress hormone signaling, oxidative stress, inflammation and circadian rhythm dysregulation promoting impairment of cardiometabolic function. The present overview highlights the relevance of the exposome for future health research and preventive medicine, especially concerning cardiovascular diseases and therapy.

Genetic predisposition accounts for less than 20% of the global disease burden, highlighting the substantial role of environmental factors in health outcomes. In chronic kidney disease (CKD), a growing global prevalence, understanding the interplay between genes and the environment is crucial. Emerging research in the exposome and genome underscores how environmental exposures interact with genetic variants to influence the development and progression of CKD. The term "exposome" encompasses a variety of factors, including personal behaviors like smoking, a sedentary lifestyle, and making specific dietary choices (such as consuming ultra-processed foods, sugar, or fat). It also includes broader determinants such as pesticides, air, water, and soil pollution, nanoplastics, global warming, stressful life events, and socioeconomic status. Research on the exposome significantly increases our understanding of toxicological processes and individual variations in susceptibility to environmental stressors. This narrative review aims to explore the exposome associated with CKD, highlight key environmental exposures in its development, and discuss potential preventive and therapeutic strategies informed by these exposure-related factors.

In sub-Saharan Africa, sickle cell disease (SCD) remains a significant public health challenge. Despite the discovery of SCD over a century ago, progress in developing and accessing effective treatments has been limited. Hydroxyurea is the primary drug used for managing SCD and associated with improving clinical outcomes. However, up to 30% of patients do not respond to hydroxyurea, likely due to genetic factors. This study involved 148 individuals with SCD investigated the association of hydroxyurea response with genetic variants across 13 loci associated with HbF synthesis and drug metabolism, focusing on MYB, HBB, HBG1, HBG2, BCL11A, KLF10, HAO2, NOS1, ARG2, SAR1A, CYP2C9, and CYP2E1. Significant associations with hydroxyurea response were identified in CYP2C9, CYP2E1, KLF10, BCL11A, ARG2, HBG1, SAR1A, MYB, and NOS1 loci. Furthermore, pathway enrichment and gene-gene interaction analyses provide deeper insights into the genetic mechanisms underlying hydroxyurea treatment response, highlighting potential avenues for personalized therapy in SCD management.

Pregnancy provokes a heightened amino acid requirement, especially in the third trimester. Alterations to late pregnancy amino acid metabolism have been associated with environmental breast carcinogen exposures, including DDT and PFAS. This project examined whether maternal serum amino acids in late pregnancy are associated with subsequent breast cancer risk. Archival third-trimester serum samples from 172 women who were later diagnosed with breast cancer were compared to samples from 351 women without known breast cancer. A prospective metabolome-wide association study (MWAS) for breast cancer cases showed that associated amino acid pathways included lysine, arginine, proline, aspartate, asparagine, alanine, tyrosine, tryptophan, histidine and branched-chain amino acids. Lower mean concentrations of individual amino acids, including histidine, threonine, lysine, and proline, were associated with an increased risk of breast cancer, and network analyses showed that these amino acids were negatively associated with protective breast cancer risk factors. Prospective MWAS for breast cancer cases diagnosed within 15 years of sample collection showed pathway associations for tryptophan, histidine, lysine methionine, and cysteine metabolism. Nutrient stresses caused by low amino acid levels impair immunosurveillance and activate oncogenic mechanisms of cell survival, thereby providing mechanisms by which environmental exposures in late pregnancy can contribute to breast cancer risk.

Exposome represents one of the most pressing issues in the environmental science research field. However, a comprehensive summary of worldwide human exposome research is lacking. We aimed to explore the bibliometric characteristics of scientific publications on the human exposome. A bibliometric analysis of human exposome publications from 2005 to December 2024 was conducted using the Web of Science in accordance with PRISMA guidelines. Trends/hotspots were investigated with keyword frequency, co-occurrence, and thematic map. Sex disparities in terms of publications and citations were examined. From 2005 to 2024, 931 publications were published in 363 journals and written by 4529 authors from 72 countries. The number of publications tripled during the last 5 years. Publications written by females (51% as first authors and 34% as last authors) were cited fewer times (13,674) than publications written by males (22,361). Human exposome studies mainly focused on air pollution, metabolomics, chemicals (e.g., per- and polyfluoroalkyl substances (PFAS), endocrine-disrupting chemicals, pesticides), early-life exposure, biomarkers, microbiome, omics, cancer, and reproductive disorders. Social and built environment factors, occupational exposure, multi-exposure, digital exposure (e.g., screen use), climate change, and late-life exposure received less attention. Our results uncovered high-impact countries, institutions, journals, references, authors, and key human exposome research trends/hotspots. The use of digital exposome technologies (e.g., sensors, and wearables) and data science (e.g., artificial intelligence) has blossomed to overcome challenges and could provide valuable knowledge toward precision prevention. Exposome risk scores represent a promising research avenue.

The study of the adverse effects of chemical substances on living organisms is an old and intense field of research. However, toxicological and environmental health sciences have long been dominated by descriptive approaches that enable associations or correlations but relatively few robust causal links and molecular mechanisms. Recent achievements have shown that structural biology approaches can bring this added value to the field. By providing atomic-level information, structural biology is a powerful tool to decipher the mechanisms by which toxicants bind to and alter the normal function of essential cell components, causing adverse effects. Here, using endocrine-disrupting chemicals as illustrative examples, we describe recent advances in the structure-based understanding of their modes of action and how this knowledge can be exploited to develop computational tools aimed at predicting properties of large collections of compounds.

There seems to be an interdependency of superficial structures on deeper layers, so that aging-related changes in 1 layer may lead to changes to the adjacent layers. Following the same rationale, treatment of 1 area may influence other neighboring aesthetic units. A more holistic approach would encompass soft-tissue repositioning and regenerative biostimulation, aiming for improvement of skin quality by increasing skin's collagen content.

To describe the use of calcium hydroxylapatite (CaHA) in different presentations for soft-tissue repositioning and improvement of skin quality in the same session.

Males or females between 40 and 60 years of age, with normal BMI, mild facial laxity, underwent supraperiosteal injection of undiluted CaHA for focal biostimulation along the zygomatic arch, in the mandible angle and in the prejowl area, followed by treatment of diluted CaHA in the posterior temporal area, and the remainder in the premasseteric area in the same session, with follow-up pf at least 90 days. Investigator assessment was evaluated using the Global Aesthetic Improvement Scale.

Out of 6 treated patients (median age of 44.5 years), 66% were deemed as improved (Grade 3) for the treatment of upper third of the face, whereas 83% of the patients were assessed as having at least improved for the mid and lower thirds of the face. Only mild adverse events were reported.

The technique described in this pilot study provides a full-face approach with CaHA based on the current concepts of the line of ligaments and facial biomechanics. Further studies are needed to validate the results.

Tobacco smoke contains several electrophilic constituents which are capable of forming adducts with nucleophilic sites in DNA and proteins like hemoglobin (Hb) and albumin. New nicotine and tobacco products are discussed as less harmful forms of tobacco use compared to smoking combustible cigarettes (CC) due to reduced exposure to harmful constituents. Hence, the adduct profile in users of various tobacco/nicotine products is expected to differ characteristically. In this article, we present a novel nontargeted screening strategy using GC-MS/MS for Hb adducts based on the analysis of the respective derivatized N-terminal valine adducts after modified Edman degradation. We analyzed blood samples from a clinical study with habitual users of CCs, electronic cigarettes, heated tobacco products (HTPs), oral tobacco, nicotine replacement therapy products and nonusers of any tobacco/nicotine products. Our nontargeted approach revealed significant differences in the Hb adduct profiles of the investigated tobacco/nicotine product user groups. Adduct identification was performed by means of an internal database, retention time estimations based on the theoretical boiling points, as well as in-house synthesized reference compounds. Several chemicals that form adducts with Hb could be identified: methylating and ethylating agents, ethylene oxide, acrylonitrile, acrylamide, glycidamide and 4-hydroxybenzaldehyde. Levels were elevated in smokers compared to all other groups for Hb adducts from methylating agents, ethylene oxide, acrylonitrile, acrylamide and glycidamide. Our approach revealed higher concentrations of Hb adducts formed by ethylation, acrylamide and glycidamide in users of HTPs compared to nonusers. However, concentrations for the latter two were still lower than in smokers. Due to their long half-lives, Hb adducts related to acrylonitrile, acrylamide (glycidamide), and ethylene oxide exposure may be useful for the biochemical verification of subjects̀ compliance in longitudinal and cross-sectional studies with respect to smoking and HTP use/abstinence.

Exposure to environmental pollutants or contaminants is correlated with detrimental effects on human health, such as neurodegenerative diseases. Adopting hair as a biological matrix for biomonitoring is a significant innovation, since it can reflect the long-term chemical exposome, spanning months to years. However, only a limited number of studies have developed analytical strategies for profiling the chemical exposome in this heterogeneous biological matrix. In this study, a systematic investigation of the chemical extraction procedure from human hair was conducted, using a design of experiments and a high-resolution mass spectrometry (HRMS)-based suspect screening approach. The PlackettBurman (PB) design was applied to identify the significant variables influencing the number of detected features. Then, a central composite design was implemented to optimize the levels of each identified significant variable. Under the optimal conditions-15-minute pulverization, 25 mg of hair weight, 40 min of sonication, and a sonication temperature of 35 °C-approximately 32,000 and 15,000 aligned features were detected in positive and negative ion modes, respectively. This optimized analytical procedure was applied to hair samples from patients with Alzheimer's disease (AD) and individuals with normal cognitive function. Overall, 307 chemicals were identified using the suspect screening approach, with 37 chemicals differentiating patients with AD from controls. This study not only optimized an analytical procedure for characterizing the long-term chemical exposome in human hair but also explored the associations between AD and environmental factors.

We present a case of acne successfully treated with a topical spray containing live bacteria. The live bacteria used in the spray contain CRISPR, and adaptive immune system in the bacteria that are used to disable viral replication. Because acne skin contains bacteria in the microbiome where a shift toward non-CRISPR bacteria occurs, these bacteria are susceptible to bacteriophage infection and lysogeny. Normalizing the bacterial microbiome to one containing more CRISPR-containing bacteria renormalizes the microbiome by killing inflammation-causing bacteriophage infecting the non-CRISPR bacteria associated with acne.

Exposures to hazardous chemicals have been linked to many detrimental health effects and it is therefore critical to have effective biomonitoring methods to better evaluate key environmental exposures that increase the risk of chronic disease and death. Traditional biomonitoring utilizing blood and urine is limited due to the specialized skills and invasiveness of collecting these fluid samples. This systematic review focuses on tear fluid, which is largely under-researched, as a promising complementary matrix to the traditional fluids used for biomonitoring. The objective is to evaluate the practicability of using human tear fluid for biomonitoring environmental exposures, highlighting potential pitfalls and opportunities.

Tear fluid biomonitoring represents a promising method for assessing exposures because it can be collected with minimal invasiveness and tears contain exposure markers from both the external and internal environments. Tear fluid uniquely interfaces with the external environment at the air-tear interface, providing a surface for airborne chemicals to diffuse into the ocular environment and interact with biomolecules. Tear fluid also contains molecules from the internal environment that have travelled from the blood to tears by crossing the blood-tear barrier. This review demonstrates that tear fluid can be used to identify hazardous chemicals from the external environment and differentiate exposure groups.

Humans are exposed to a cocktail of food-related and environmental contaminants, potentially contributing to the etiology of chronic diseases. Better characterizing the "exposome" is a challenging task and requires broad human biomonitoring (HBM). Veterinary drugs (VDs)/antibiotics, widely used and regulated in food and animal production, however, are typically not yet included in exposomics workflows. Therefore, in this work, a previously established multianalyte liquid chromatography-tandem mass spectrometry (LC-MS/MS) method covering >80 diverse xenobiotics was expanded by >40 VDs/antibiotics and pesticides. It was investigated if the generic workflow allowed for the successful integration of a high number of new analytes in a proof-of-principle study. The expanded method was successfully in-house validated and specificity, matrix effects, linearity, intra- and inter-day precision, accuracy, limits of quantification, and detection were evaluated. The optimized method demonstrated satisfactory recovery (81-120%) for most of the added analytes with acceptable RSDs (<20%) at three spiking levels. The majority of VDs/antibiotics and pesticides (69%) showed matrix effects within a range of 50-140%. Moreover, sensitivity was excellent with median LODs and LOQs of 0.10 ng/mL and 0.31 ng/mL, respectively. In total, the expanded method can be used to detect and quantify more than 120 highly diverse analytes in a single analytical run. To the best of the authors' knowledge, this work represents the first targeted biomonitoring method integrating VDs with various other classes of pollutants including plasticizers, PFAS, bisphenols, mycotoxins, and personal care products. It demonstrates the potential to expand targeted multianalyte methods towards additional groups of potentially toxic chemicals.

Chemical pollution poses a significant threat to human health, with detrimental effects on various physiological systems, including the respiratory, cardiovascular, mental, and perinatal domains. While the impact of pollution on these systems has been extensively studied, the intricate relationship between chemical pollution and immunity remains a critical area of investigation. The focus of this study is to elucidate the relationship between chemical pollution and human immunity. To accomplish this task, this study presents a comprehensive review that encompasses in vitro, ex vivo, and in vivo studies, shedding light on the ways in which chemical pollution can modulate human immunity. Our aim is to unveil the complex mechanisms by which environmental contaminants compromise the delicate balance of the body's defense systems going beyond the well-established associations with defense systems and delving into the less-explored link between chemical exposure and various immune disorders, adding urgency to our understanding of the underlying mechanisms and their implications for public health.

Excess health and safety risks of commercial drivers are largely determined by, embedded in, or operate as complex, dynamic, and randomly determined systems with interacting parts. Yet, prevailing epidemiology is entrenched in narrow, deterministic, and static exposure-response frameworks along with ensuing inadequate data and limiting methods, thereby perpetuating an incomplete understanding of commercial drivers' health and safety risks. This paper is grounded in our ongoing research that conceptualizes health and safety challenges of working people as multilayered "wholes" of interacting work and nonwork factors, exemplified by complex-systems epistemologies. Building upon and expanding these assumptions, herein we: (a) discuss how insights from integrative exposome and network-science-based frameworks can enhance our understanding of commercial drivers' chronic disease and injury burden; (b) introduce the "working life exposome of commercial driving" (WLE-CD)-an array of multifactorial and interdependent work and nonwork exposures and associated biological responses that concurrently or sequentially impact commercial drivers' health and safety during and beyond their work tenure; (c) conceptualize commercial drivers' health and safety risks as multilayered networks centered on the WLE-CD and network relational patterns and topological properties-that is, arrangement, connections, and relationships among network components-that largely govern risk dynamics; and (d) elucidate how integrative exposome and network-science-based innovations can contribute to a more comprehensive understanding of commercial drivers' chronic disease and injury risk dynamics. Development, validation, and proliferation of this emerging discourse can move commercial driving epidemiology to the frontier of science with implications for policy, action, other working populations, and population health at large.

Obesity, a significant public health concern, disproportionately affects people with lower socioeconomic status (SES). Food environments have been identified as part of the causal chain of this disparity. This study investigated variations in the food environment across groups with different SES profiles residing in peri-urban municipal settings. In addition, it examined the association of the perceived and objective food environments with eating behaviour and assessed if these associations were moderated by SES. Utilizing GIS and survey data (n = 497, aged 25-65), results showed differences in the objective and perceived food environments based on SES. Respondents with higher SES perceived their food environments as better but resided farther from all food outlets compared to respondents with lower SES. However, there was no difference in outlet density or mRFEI between SES groups. SES moderated associations between the objective and perceived food environments and most eating behavior outcomes except fast food consumption frequency. For fruits and vegetables, SES moderated the association between neighborhood availability and consumption frequency (β0.23,CI0.03;0.49). Stratified analysis revealed a positive association for both lower (β0.15, CI0.03;0.27) and higher (β0.37, CI 0.12;0.63) SES groups. For snack foods, SES moderated the association between healthy outlet density and consumption frequency (β-0.60, CI-0.94; -0.23), showing statistical significance only for respondents with higher SES (β0.36,CI 0.18;0.55). Similarly, for sugar-sweetened beverages, a statistically significant interaction was observed between unhealthy outlet density in the 1000m buffer and consumption frequency (β 0.06, CI 0.02; 0.11). However, this association was only statistically significant for respondents with higher SES (β-0.02,CI -0.05;-0.0002). These results emphasize the significance of SES as a crucial element in comprehending the connection between the food environment and eating behaviour. Indicating the need for policymakers to take SES into account when implementing food environment interventions, particularly when focusing on the neighborhood food environment without considering residents' SES and their perceptions.

Genetic susceptibility to chemicals is incompletely characterized. However, nervous system disease development following pesticide exposure can vary in a population, implying some individuals may have higher genetic susceptibility to pesticide-induced nervous system disease.

We aimed to build a computational approach to characterize single-nucleotide polymorphisms (SNPs) implicated in chemically induced adverse outcomes and used this framework to assess the link between differential population susceptibility to pesticides and human nervous system disease.

We integrated publicly available datasets of Chemical-Gene, Gene-Pathway, and SNP-Disease associations to build Chemical-Pathway-Gene-SNP-Disease linkages for humans. As a case study, we integrated these linkages with spatialized pesticide application data for the US from 1992 to 2018 and spatialized nervous system disease rates for 2018. Through this, we characterized SNPs that may be important in states with high disease occurrence based on the pesticides used there.

We found that the number of SNP hits per pesticide in US states positively correlated with disease incidence and prevalence for Alzheimer's disease, Parkinson disease, and multiple sclerosis. We performed frequent itemset mining to differentiate pesticides used over time in states with high and low disease occurrence and found that only 19% of pesticide sets overlapped between 10 states with high disease occurrence and 10 states with low disease occurrence rates, and more SNPs were implicated in pathways in high disease occurrence states. Through a cross-validation of subsets of five high and low disease occurrence states, we characterized SNPs, genes, pathways, and pesticides more frequently implicated in high disease occurrence states.

Our findings support that pesticides contribute to nervous system disease, and we developed priority lists of SNPs, pesticides, and pathways for further study. This data-driven approach can be adapted to other chemicals, diseases, and locations to characterize differential population susceptibility to chemical exposures. https://doi.org/10.1289/EHP14108.

Over the past few decades, numerous environmental chemicals from solvents to pesticides have been suggested to be involved in the development and progression of neurodegenerative diseases. Most of the evidence has accumulated from occupational or cohort studies in humans or laboratory research in animal models, with a range of chemicals being implicated. What has been missing is a systematic approach analogous to genome-wide association studies, which have identified dozens of genes involved in Alzheimer's disease, Parkinson's disease and other neurodegenerative diseases. Fortunately, it is now possible to study hundreds to thousands of chemical features under the exposome framework. This Perspective explores how advances in mass spectrometry make it possible to generate exposomic data to complement genomic data and thereby better understand neurodegenerative diseases.

Aim: The current work was designed to demonstrate the application of the exposome framework in examining associations between exposures and children's long-term neurodevelopmental and behavioral outcomes. Methods: Longitudinal data were collected from birth through age 6 from 402 preterm infants. Three statistical methods were utilized to demonstrate the exposome framework: exposome-wide association study, cumulative exposure and machine learning models, with and without epigenetic data. Results: Each statistical approach answered a distinct research question regarding the impact of exposures on longitudinal child outcomes. Findings highlight associations between exposures, epigenetics and executive function. Conclusion: Findings demonstrate how an exposome-based approach can be utilized to understand relationships between internal (e.g., DNA methylation) and external (e.g., prenatal risk) exposures and long-term developmental outcomes in preterm children.

The thesis of this paper is that health and safety challenges of working people can only be fully understood by examining them as wholes with interacting parts. This paper unravels this indispensable whole by introducing the working life exposome and elucidating how associated epistemologies and methodologies can enhance empirical research.

Network and population health scientists have initiated an ongoing discourse on the state of empirical work-health-safety-well-being research.

Empirical research has not fully captured the totality and complexity of multiple and interacting work and nonwork factors defining the health of working people over their life course. We challenge the prevailing paradigm by proposing to expand it from narrow work-related exposures and associated monocausal frameworks to the holistic study of work and population health grounded in complexity and exposome sciences. Health challenges of working people are determined by, embedded in, and/or operate as complex systems comprised of multilayered and interdependent components. One can identify many potentially causal factors as sufficient and component causes where removal of one or more of these can impact disease progression. We, therefore, cannot effectively study them by an a priori determination of a set of components and/or properties to be examined separately and then recombine partial approaches, attempting to form a picture of the whole. Instead, we must examine these challenges as wholes from the start, with an emphasis on interactions among their multifactorial components and their emergent properties. Despite various challenges, working-life-exposome-grounded frameworks and associated innovations have the potential to accomplish that.

This emerging paradigm shift can move empirical work-health-safety-well-being research to cutting-edge science and enable more impactful policies and actions.

A genome-wide association study has recognized C6orf10-BTNL2 polymorphism in coronary artery disease. The goal of this study was to explore the potential correlation of nine missense TSBP1 variants with coronary heart disease (CHD) risk in the Chinese Han population.

Nine TSBP1 missense single nucleotide polymorphisms (SNPs) were selected for genotyping by the Agena MassARRAY platform. Odds ratios (ORs) with 95% confidence intervals (CIs) were calculated to analyze the contribution of TSBP1 SNPs to CHD predisposition by logistic regression models adjusted by age, sex, drinking, and smoking. The correlation of TSBP1 variants with clinical data in CHD patients was examined by Kruskal-Wallis test.

rs9268368-C (p = 0.039, OR = 1.18, 95 % CI: 1.01-1.38) was related to an increased risk of CHD, while rs3749966-C (p = 0.032, OR = 0.49, 95 % CI: 0.25-0.96) and rs3129941-A (p = 0.011, OR = 0.74, 95 % CI: 0.59-0.93) might be protective factors against CHD occurrence in the Chinese Han population. We also observed the effects of demographic characteristics (age, sex, alcohol consumption, and smoking) and complications (hypertension and diabetes) on the interactive association of TSBP1 polymorphisms with CHD susceptibility. rs139993810 was related to the levels of high-density lipoprotein cholesterol (HDL-C, p = 0.030).

Our findings determined the association of TSBP1 rs9268368, rs3749966, and rs3129941 with CHD occurrence in the Chinese Han population, and highlighted the influence of demographic characteristics and complications on the interactive association of TSBP1 polymorphisms with CHD risk.

This study aimed to determine the impact of various fast-interrupting shakes on markers of glycemic control including glucose, β-hydroxybutyrate (BHB), insulin, glucagon, GLP-1, and GIP. Twenty-seven sedentary adults (twelve female, fifteen male) with overweight or obesity completed this study. One condition consisted of a 38-h water-only fast, and the other two conditions repeated this, but the fasts were interrupted at 24 h by either a high carbohydrate/low fat (HC/LF) shake or an isovolumetric and isocaloric low carbohydrate/high fat (LC/HF) shake. The water-only fast resulted in 135.3% more BHB compared to the HC/LF condition (p < 0.01) and 69.6% more compared to the LC/HF condition (p < 0.01). The LC/HF condition exhibited a 38.8% higher BHB level than the HC/LF condition (p < 0.01). The area under the curve for glucose was 14.2% higher in the HC/LF condition than in the water condition (p < 0.01) and 6.9% higher compared to the LC/HF condition (p < 0.01), with the LC/HF condition yielding 7.8% more glucose than the water condition (p < 0.01). At the 25-h mark, insulin and glucose-dependent insulinotropic polypeptide (GIP) were significantly elevated in the HC/LF condition compared to the LC/HF condition (p < 0.01 and p = 0.02, respectively) and compared to the water condition (p < 0.01). Furthermore, insulin, GLP-1, and GIP were increased in the LC/HF condition compared to the water condition at 25 h (p < 0.01, p = 0.015, and p < 0.01, respectively). By the 38-h time point, no differences were observed among the conditions for any of the analyzed hormones. While a LC/HF shake does not mimic a fast completely, it does preserve some of the metabolic changes including elevated BHB and glucagon, and decreased glucose and insulin compared to a HC/LF shake, implying a potential for improved metabolic health.

Epigenetic modifications, particularly DNA methylation, have emerged as regulators of gene expression and are implicated in various biological processes and disease states. Understanding the factors influencing the epigenome is essential for unraveling its complexity. In this study, we aimed to identify how the methylome of buccal epithelial cells, a noninvasive and easily accessible tissue, is associated with demographic and health-related variables commonly used in clinical settings, such as age, sex, blood immune composition, hemoglobin levels, and others. We developed a model to assess the association of multiple factors with the human methylome and identify the genomic loci significantly impacted by each trait. We demonstrated that DNA methylation variation is accurately modeled by several factors. We confirmed the well-known impact of age and sex and unveiled novel clinical factors associated with DNA methylation, such as blood neutrophils, hemoglobin, red blood cell distribution width, high-density lipoprotein cholesterol, and urea. Genomic regions significantly associated with these traits were enriched in relevant transcription factors, drugs, and diseases. Among our findings, we showed that neutrophil-impacted loci were involved in neutrophil functionality and maturation. Similarly, hemoglobin-influenced sites were associated with several diseases, including aplastic anemia, and the genomic loci affected by urea were related to congenital anomalies of the kidney and urinary tract. Our findings contribute to a better understanding of the human methylome plasticity and provide insights into novel factors shaping DNA methylation patterns, highlighting their potential clinical implications as biomarkers and the importance of considering these physiological traits in future medical epigenomic investigations.NEW & NOTEWORTHY We have developed a quantitative model to assess how the human methylome is associated with several factors and to identify the genomic loci significantly impacted by each trait. We reported novel health-related factors driving DNA methylation patterns and new site-specific regulations that further elucidate methylome dynamics. Our study contributes to a better understanding of the plasticity of the human methylome and unveils novel physiological traits with a potential role in future medical epigenomic investigations.

The protection and management of water resources continues to be challenged by multiple and ongoing factors such as shifts in demographic, social, economic, and public health requirements. Physical limitations placed on access to potable supplies include natural and human-caused factors such as aquifer depletion, aging infrastructure, saltwater intrusion, floods, and drought. These factors, although varying in magnitude, spatial extent, and timing, can exacerbate the potential for contaminants of concern (CECs) to be present in sources of drinking water, infrastructure, premise plumbing and associated tap water. This monograph examines how current and emerging scientific efforts and technologies increase our understanding of the range of CECs and drinking water issues facing current and future populations. It is not intended to be read in one sitting, but is instead a starting point for scientists wanting to learn more about the issues surrounding CECs. This text discusses the topical evolution CECs over time (Section 1), improvements in measuring chemical and microbial CECs, through both analysis of concentration and toxicity (Section 2) and modeling CEC exposure and fate (Section 3), forms of treatment effective at removing chemical and microbial CECs (Section 4), and potential for human health impacts from exposure to CECs (Section 5). The paper concludes with how changes to water quantity, both scarcity and surpluses, could affect water quality (Section 6). Taken together, these sections document the past 25 years of CEC research and the regulatory response to these contaminants, the current work to identify and monitor CECs and mitigate exposure, and the challenges facing the future.

Compared with the rapid advances in genomics leading to broad understanding of human disease, the linkage between chemical exposome and diseases is still under investigation. High-resolution mass spectrometry (HRMS) is expected to accelerate the process via relatively accurate and precise biomonitoring of human exposome. This review covers recent advancements in biomonitoring of exposed environmental chemicals (chemical exposome) using HRMS described in the 124 articles that resulted from a systematic literature search on Medline and Web of Science databases. The analytical strategic aspects, including the selection of specimens, sample preparation, instrumentation, untargeted versus targeted analysis, and workflows for MS-based biomonitoring to explore the environmental chemical space of human exposome, are deliberated. Applications of HRMS in human exposome investigation are presented by biomonitoring (1) exposed chemical compounds and their biotransformation products; (2) DNA/protein adducts; and (3) endogenous compound perturbations. Challenges and future perspectives are also discussed.

Large epidemiological and health impact assessment studies at the global scale, such as the Global Burden of Disease project, indicate that chronic non-communicable diseases, such as atherosclerosis and diabetes mellitus, caused almost two-thirds of the annual global deaths in 2020. By 2030, 77% of all deaths are expected to be caused by non-communicable diseases. Although this increase is mainly due to the ageing of the general population in Western societies, other reasons include the increasing effects of soil, water, air and noise pollution on health, together with the effects of other environmental risk factors such as climate change, unhealthy city designs (including lack of green spaces), unhealthy lifestyle habits and psychosocial stress. The exposome concept was established in 2005 as a new strategy to study the effect of the environment on health. The exposome describes the harmful biochemical and metabolic changes that occur in our body owing to the totality of different environmental exposures throughout the life course, which ultimately lead to adverse health effects and premature deaths. In this Review, we describe the exposome concept with a focus on environmental physical and chemical exposures and their effects on the burden of cardiovascular disease. We discuss selected exposome studies and highlight the relevance of the exposome concept for future health research as well as preventive medicine. We also discuss the challenges and limitations of exposome studies.

Graphical abstracts may enhance dissemination of scientific and medical research but are also prone to reductionism and bias. We conducted a systematic content analysis of the Journal of Internal Medicine (JIM) Graphical Abstract Gallery to assess for evidence of bias.

We analyzed 140 graphical abstracts published by JIM between February 2019 and May 2020. Using a combination of inductive and deductive approaches, we developed a set of codes and code definitions for thematic, mixed-methods analysis.

We found that JIM graphical abstracts disproportionately emphasized male (59.5%) and light-skinned (91.3%) bodies, stigmatized large body size, and overstated genetic and behavioral causes of disease, even relative to the articles they purportedly represented. Whereas 50.7% of the graphical surface area was coded as representing genetic factors, just 0.4% represented the social environment.

Our analysis suggests evidence of bias and reductionism promoting normative white male bodies, linking large bodies with disease and death, conflating race with genetics, and overrepresenting genes while underrepresenting the environment as a driver of health and illness. These findings suggest that uncritical use of graphical abstracts may distort rather than enhance our understanding of disease; harm patients who are minoritized by race, gender, or body size; and direct attention away from dismantling the structural barriers to health equity.

We recommend that journals develop standards for mitigating bias in the publication of graphical abstracts that (1) ensure diverse skin tone and gender representation, (2) mitigate weight bias, (3) avoid racial or ethnic essentialism, and (4) attend to sociostructural contributors to disease.

Exposure science is evolving from its traditional "after the fact" and "one chemical at a time" approach to forecasting chemical exposures rapidly enough to keep pace with the constantly expanding landscape of chemicals and exposures. In this article, we provide an overview of the approaches, accomplishments, and plans for advancing computational exposure science within the U.S. Environmental Protection Agency's Office of Research and Development (EPA/ORD). First, to characterize the universe of chemicals in commerce and the environment, a carefully curated, web-accessible chemical resource has been created. This DSSTox database unambiguously identifies >1.2 million unique substances reflecting potential environmental and human exposures and includes computationally accessible links to each compound's corresponding data resources. Next, EPA is developing, applying, and evaluating predictive exposure models. These models increasingly rely on data, computational tools like quantitative structure activity relationship (QSAR) models, and machine learning/artificial intelligence to provide timely and efficient prediction of chemical exposure (and associated uncertainty) for thousands of chemicals at a time. Integral to this modeling effort, EPA is developing data resources across the exposure continuum that includes application of high-resolution mass spectrometry (HRMS) non-targeted analysis (NTA) methods providing measurement capability at scale with the number of chemicals in commerce. These research efforts are integrated and well-tailored to support population exposure assessment to prioritize chemicals for exposure as a critical input to risk management. In addition, the exposure forecasts will allow a wide variety of stakeholders to explore sustainable initiatives like green chemistry to achieve economic, social, and environmental prosperity and protection of future generations.

To assess whether associations of cardiopulmonary conditions and markers with glaucoma differ by background genetic risk for primary open angle glaucoma (POAG).

We constructed a POAG polygenic risk score (PRS) using genome-wide association study summary statistics from a large cross-ancestry meta-analysis. History of glaucoma (including self-report and codes for POAG, 'other glaucoma' or unspecified glaucoma), history of common cardiopulmonary conditions and cardiopulmonary measures were assessed in the UK Biobank. Stratifying by PRS decile 1 (lowest risk) versus decile 10 (highest risk), separate multivariable models were estimated to assess the associations of cardiopulmonary diseases or factors with glaucoma, adjusting for age, sex, smoking and medication use. A Bonferroni correction was used to adjust p values for multiple comparisons.

Individuals in POAG PRS decile 1 (417 cases, 44 458 controls; mean age 56.8 years) and decile 10 (2135 cases, 42 413 controls; mean age 56.7 years) were included. Within decile 1, glaucoma cases had significantly higher glycated haemoglobin (38.5 vs 35.9 mmol/mol) and higher prevalence of diabetes (17.5% vs 6.5%), dyslipidaemia (31.2% vs 18.3%) and chronic kidney disease (CKD) (6.7% vs 2.0%) than controls (adjusted p<0.0013 for each). Within decile 10, glaucoma was associated with higher prevalence of dyslipidaemia (27.7% vs 17.3%, p=6.9E-05). The magnitude of association between glaucoma and diabetes, CKD and glycated haemoglobin differed between deciles 1 and 10 (contrast test p value for difference <0.05).

The relations between systemic conditions and glaucoma vary by underlying genetic predisposition to POAG, with larger associations among those who developed glaucoma despite low genetic risk.

An accurate assessment of current trends in cardiovascular risks could inform public health policy. This study aims to determine 20-year trends in the prevalence of elevated cardiovascular risk and its risk factors' control among US adults.

In this serial cross-sectional analysis of 23,594 adults, aged 40-79 years, without clinical atherosclerotic cardiovascular disease (ASCVD) in the National Health and Nutrition Examination Survey from 2001 to 2020, we calculated the prevalence of elevated cardiovascular risk (10-year ASCVD risk ≥ 7.5%) for all participants and subgroups with their risk factors controlled for diabetes, hypertension, or dyslipidemia.

The age- and sex-adjusted prevalence of elevated cardiovascular risk slightly decreased from 41.5% (95% CI, 39.7-43.3%) in 2001-2004 to 38.6% (95% CI, 36.1-41.1%) in 2017-2020 (P for trend = 0.169) while the respective sex-adjusted prevalence significantly increased from 34.4% (95% CI, 32.8-36.0%) to 39.5% (95% CI, 37.0-42.0%; P for trend <0.001). Sex and race continued to show disparities in cardiovascular risk. Furthermore, a worsening disparity in age- and sex-adjusted prevalence of elevated cardiovascular risk between young and old and a narrowing gap among different education and poverty index levels (all P trend for interaction <0.05). Differential decomposition analysis found that demographic changes (primarily population aging) led to an 8.8% increase in the prevalence of elevated cardiovascular risk from 2001 to 2004 to 2017-2020, while risk factor control led to a 3.8% decrease. The rate of individuals receiving treatment for diabetes, hypertension, or dyslipidemia increased significantly between 2001 and 2020 (all P for trend <0.05). The rate of participants with hypertension who achieved blood pressure under 130/80 mmHg and those with dyslipidemia who achieved a non-high-density lipoprotein cholesterol level under 130 mg/dl increased significantly (all P for trend <0.001).

There is a slight reduction in the prevalence of age- and sex-adjusted elevated cardiovascular risk among US adults without clinical ASCVD between 2001 and 2020, while the sex-adjusted prevalence significantly increased. The decrease in elevated cardiovascular risk prevalence was mainly attributed to risk factor control, while demographic changes contributed to an increase.

Among medical specialties, laboratory medicine is the largest producer of structured data and must play a crucial role for the efficient and safe implementation of big data and artificial intelligence in healthcare. The area of personalized therapies and precision medicine has now arrived, with huge data sets not only used for experimental and research approaches, but also in the "real world". Analysis of real world data requires development of legal, procedural and technical infrastructure. The integration of all clinical data sets for any given patient is important and necessary in order to develop a patient-centered treatment approach. Data-driven research comes with its own challenges and solutions. The Findability, Accessibility, Interoperability, and Reusability (FAIR) Guiding Principles provide guidelines to make data findable, accessible, interoperable and reusable to the research community. Federated learning, standards and ontologies are useful to improve robustness of artificial intelligence algorithms working on big data and to increase trust in these algorithms. When dealing with big data, the univariate statistical approach changes to multivariate statistical methods significantly shifting the potential of big data. Combining multiple omics gives previously unsuspected information and provides understanding of scientific questions, an approach which is also called the systems biology approach. Big data and artificial intelligence also offer opportunities for laboratories and the In Vitro Diagnostic industry to optimize the productivity of the laboratory, the quality of laboratory results and ultimately patient outcomes, through tools such as predictive maintenance and "moving average" based on the aggregate of patient results.

Multiple Sclerosis (MS) is characterized by chronic deterioration of the nervous system, mainly the brain and the spinal cord. An individual with MS develops the condition when the immune system begins attacking nerve fibers and the myelin sheathing that covers them, affecting the communication between the brain and the rest of the body and eventually causing permanent damage to the nerve. Patients with MS (pwMS) might experience different symptoms depending on which nerve was damaged and how much damage it has sustained. Currently, there is no cure for MS; however, there are clinical guidelines that help control the disease and its accompanying symptoms. Additionally, no specific laboratory biomarker can precisely identify the presence of MS, leaving specialists with a differential diagnosis that relies on ruling out other possible diseases with similar symptoms. Since the emergence of Machine Learning (ML) in the healthcare industry, it has become an effective tool for uncovering hidden patterns that aid in diagnosing several ailments. Several studies have been conducted to diagnose MS using ML and Deep Learning (DL) models trained using MRI images, achieving promising results. However, complex and expensive diagnostic tools are needed to collect and examine imaging data. Thus, the intention of this study is to implement a cost-effective, clinical data-driven model that is capable of diagnosing pwMS. The dataset was obtained from King Fahad Specialty Hospital (KFSH) in Dammam, Saudi Arabia. Several ML algorithms were compared, namely Support Vector Machine (SVM), Decision Tree (DT), Logistic Regression (LR), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Adaptive Boosting (AdaBoost), and Extra Trees (ET). The results indicated that the ET model outpaced the rest with an accuracy of 94.74%, recall of 97.26%, and precision of 94.67%.

The authors report here the development of a high-throughput, automated, inexpensive and clinically validated saliva metatranscriptome test that requires less than 100 μl of saliva. RNA is preserved at the time of sample collection, allowing for ambient-temperature transportation and storage for up to 28 days. Critically, the RNA preservative is also able to inactivate pathogenic microorganisms, rendering the samples noninfectious and allowing for safe and easy shipping. Given the unique set of convenience, low cost, safety and technical performance, this saliva metatranscriptomic test can be integrated into longitudinal, global-scale systems biology studies that will lead to an accelerated development of precision medicine, diagnostic and therapeutic tools.

Cancer is the second leading worldwide disease that depends on oncogenic mutations and non-mutated genes for survival. Recent advancements in next-generation sequencing (NGS) have transformed the health care sector with big data and machine learning (ML) approaches. NGS data are able to detect the abnormalities and mutations in the oncogenes. These multi-omics analyses are used for risk prediction, early diagnosis, accurate prognosis, and identification of biomarkers in cancer patients. The availability of these cancer data and their analysis may provide insights into the biology of the disease, which can be used for the personalized treatment of cancer patients. Bioinformatics tools are delivering this promise by managing, integrating, and analyzing these complex datasets. The clinical outcomes of cancer patients are improved by the use of various innovative methods implicated particularly for diagnosis and therapeutics. ML-based artificial intelligence (AI) applications are solving these issues to a great extent. AI techniques are used to update the patients on a personalized basis about their treatment procedures, progress, recovery, therapies used, dietary changes in lifestyles patterns along with the survival summary of previously recovered cancer patients. In this way, the patients are becoming more aware of their diseases and the entire clinical treatment procedures. Though the technology has its own advantages and disadvantages, we hope that the day is not so far when AI techniques will provide personalized treatment to cancer patients tailored to their needs in much quicker ways.

Analytical methods and tools for the characterization of the human exposome by untargeted mass spectrometry approaches are advancing rapidly. Adductomics methods have been developed for untargeted screening of short-lived electrophiles, in the form of adducts to proteins or DNA, in vivo. The identification of an adduct and its precursor electrophile in the blood is more complex than that of stable chemicals. The present work aims to illustrate procedures for the identification of an adduct to N-terminal valine in hemoglobin detected with adductomics, and pathways for the tracing of its precursor and possible exposure sources. Identification of the adduct proceeded via preparation and characterization of standards of adduct analytes. Possible precursor(s) and exposure sources were investigated by measurements in blood of adduct formation by precursors in vitro and adduct levels in vivo. The adduct was identified as hydroxypropanoic acid valine (HPA-Val) by verification with a synthesized reference. The HPA-Val was measured together with other adducts (from acrylamide, glycidamide, glycidol, and acrylic acid) in human blood (n = 51, schoolchildren). The HPA-Val levels ranged between 6 and 76 pmol/g hemoglobin. The analysis of reference samples from humans and rodents showed that the HPA-Val adduct was observed in all studied samples. No correlation of the HPA-Val level with the other studied adducts was observed in humans, nor was an increase in tobacco smokers observed. A small increase was observed in rodents exposed to glycidol. The formation of the HPA-Val adduct upon incubation of blood with glycidic acid (an epoxide) was shown. The relatively high adduct levels observed in vivo in relation to the measured reactivity of the epoxide, and the fact that the epoxide is not described as naturally occurring, suggest that glycidic acid is not the only precursor of the HPA-Val adduct identified in vivo. Another endogenous electrophile is suspected to contribute to the in vivo HPA-Val adduct level.

Significance: Risk factors in the environment such as air pollution and traffic noise contribute to the development of chronic noncommunicable diseases. Recent Advances: Epidemiological data suggest that air pollution and traffic noise are associated with a higher risk for cardiovascular, metabolic, and mental disease, including hypertension, heart failure, myocardial infarction, diabetes, arrhythmia, stroke, neurodegeneration, depression, and anxiety disorders, mainly by activation of stress hormone signaling, inflammation, and oxidative stress. Critical Issues: We here provide an in-depth review on the impact of the environmental risk factors air pollution and traffic noise exposure (components of the external exposome) on cardiovascular health, with special emphasis on the role of environmentally triggered oxidative stress and dysregulation of the circadian clock. Also, a general introduction on the contribution of circadian rhythms to cardiovascular health and disease as well as a detailed mechanistic discussion of redox regulatory pathways of the circadian clock system is provided. Future Directions: Finally, we discuss the potential of preventive strategies or "chrono" therapy for cardioprotection. Antioxid. Redox Signal. 37, 679-703.

Omics-based technologies have enabled comprehensive characterization of our exposure to environmental chemicals (chemical exposome) as well as assessment of the corresponding biological responses at the molecular level (eg, metabolome, lipidome, proteome, and genome). By systematically measuring personal exposures and linking these stimuli to biological perturbations, researchers can determine specific chemical exposures of concern, identify mechanisms and biomarkers of toxicity, and design interventions to reduce exposures. However, further advancement of metabolomics and exposomics approaches is limited by a lack of standardization and approaches for assigning confidence to chemical annotations. While a wealth of chemical data is generated by gas chromatography high-resolution mass spectrometry (GC-HRMS), incorporating GC-HRMS data into an annotation framework and communicating confidence in these assignments is challenging. It is essential to be able to compare chemical data for exposomics studies across platforms to build upon prior knowledge and advance the technology. Here, we discuss the major pieces of evidence provided by common GC-HRMS workflows, including retention time and retention index, electron ionization, positive chemical ionization, electron capture negative ionization, and atmospheric pressure chemical ionization spectral matching, molecular ion, accurate mass, isotopic patterns, database occurrence, and occurrence in blanks. We then provide a qualitative framework for incorporating these various lines of evidence for communicating confidence in GC-HRMS data by adapting the Schymanski scoring schema developed for reporting confidence levels by liquid chromatography HRMS (LC-HRMS). Validation of our framework is presented using standards spiked in plasma, and confident annotations in outdoor and indoor air samples, showing a false-positive rate of 12% for suspect screening for chemical identifications assigned as Level 2 (when structurally similar isomers are not considered false positives). This framework is easily adaptable to various workflows and provides a concise means to communicate confidence in annotations. Further validation, refinements, and adoption of this framework will ideally lead to harmonization across the field, helping to improve the quality and interpretability of compound annotations obtained in GC-HRMS.