Multiple Exposures Research Articles

Impact of major depressive disorder on breast cancer outcomes: a national retrospective cohort study

Abstract Background Establishing whether women with major depressive disorder (MDD) who develop breast cancer (BC) have poor outcomes is key to optimizing care for this population. To address this, we examined associations between MDD and BC recurrence and mortality. Methods Using medical record data from the Veterans Affairs Healthcare System, we established a retrospective cohort of women with local or regional stage invasive BC between 2010 and 2019 and followed through 2022. We used a two-year window to identify women diagnosed with MDD prior to BC diagnosis. We used multivariable Cox-proportional hazards regression to estimate associations between MDD and BC recurrence and mortality while accounting for competing-risks and adjusting for sociodemographic, clinical, lifestyle, and tumor characteristics. Results We identified 6,051 women with BC, of whom 1,754 (29%) had MDD. The mean age at BC diagnosis was 57 years (standard deviation = 11). In multivariable analyses, women with MDD had a 37% (hazard ratio (HR)=1.37; 95% confidence interval (CI): 1.19-1.57) higher risk of recurrence and a 30% (HR = 1.30; 95% CI: 1.02-1.64) higher risk of BC mortality. The association between MDD and recurrence was stronger among women with estrogen receptor-positive BC. In secondary analyses, there were significant interactions between MDD and multiple exposures with respect to recurrence, including current smoking, substance abuse, and non-receipt of screening mammography. Conclusions Women with MDD had inferior BC outcomes compared to women without a history of MDD. Research is needed to investigate underlying mechanisms linking depression to BC progression and evaluate interventions to improve outcomes in this high-risk population.

The Use of Artificial Intelligence to Analyze the Exposome in the Development of Chronic Diseases: A Review of the Current Literature

The “Exposome” is a concept that indicates the set of exposures to which a human is subjected during their lifetime. These factors influence the health state of individuals and can drive the development of Noncommunicable Diseases (NCDs). Artificial Intelligence (AI) allows one to analyze large amounts of data in a short time. As such, several authors have used AI to study the relationship between exposome and chronic diseases. Under such premises, this study reviews the use of AI in analyzing the exposome to understand its role in the development of chronic diseases, focusing on how AI can identify patterns in exposure-related data and support prevention strategies. To achieve this, we carried out a search on multiple databases, including PubMed, ScienceDirect, and SCOPUS, from 1 January 2019 to 31 May 2023, using the MeSH terms (exposome) and (‘Artificial Intelligence’ OR ‘Machine Learning’ OR ‘Deep Learning’) to identify relevant studies on this topic. After completing the identification, screening, and eligibility assessment, a total of 18 studies were included in this literature review. According to the search, most authors used supervised or unsupervised machine learning models to study multiple exposure factors’ role in the risk of developing cardiovascular, metabolic, and chronic respiratory diseases. In some more recent studies, authors also used deep learning. Furthermore, the exposome analysis is useful to study the risk of developing neuropsychiatric disorders or evaluating pregnancy outcomes and child growth. Understanding the role of the exposome is pivotal to overcome the classic concept of a single exposure/disease. The application of AI allows one to analyze multiple environmental risks and their combined effects on health conditions. In the future, AI could be helpful in the prevention of chronic diseases, providing new diagnostic, therapeutic, and follow-up strategies.

Photocatalytic degradation of microcystins from a field-collected cyanobacterial assemblage by 3D printed TiO2 structures using artificial versus solar irradiation

Microcystins (MCs) from freshwater cyanobacteria cause adverse effects to humans and ecological receptors through multiple exposure routes requiring adaptable and diverse treatment technologies. Photocatalysis of MCs using TiO2 is a promising technology; however, TiO2 photocatalysts as unbound nanoparticles in suspension are impractical to deploy. 3D Printing (3DP) provides a means to immobilize TiO2, producing deployable photocatalyst structures with extensive geometric freedom. The objective of this proof-of-concept experiment was to incrementally increase the environmental complexity (e.g., broad-spectrum fluorescent lamps and outdoor solar; filtered cell lysate and algal assemblage as the source of MCs) while comparing photocatalysis rates of MCs by 3DP TiO2 structures using polylactic acid (PLA) as the binder. Degradation half-lives of MCs were shorter in TiO2 embedded in 3DP PLA relative to PLA-only controls with differences in half-lives ranging from 3.6 to 10h. The one exception was the outdoor solar and an algal assemblage, where significant differences could not be discerned due to the already rapid photolysis rates. Ultimately, photocatalysis rates (half-life = 1.9–11.6h) were comparable to those previously published for TiO2 3DP structures in a laboratory environment and TiO2 fixed-films (half-life = 2–13h) demonstrating feasibility of 3DP to immobilize TiO2 photocatalysts under a range of conditions. This is the first time that MC concentrations from a field-collected HAB were photocatalytically degraded in both solar simulated light and sunlight using a custom-made advanced photocatalytic nano-composite with enhanced performance through high surface area design enabled by 3D printing. These data inform future development of scalable, retrievable, and operationally flexible structures with immobilized TiO2.

Prediction of causal genes at GWAS loci with pleiotropic gene regulatory effects using sets of correlated instrumental variables

Multivariate Mendelian randomization (MVMR) is a statistical technique that uses sets of genetic instruments to estimate the direct causal effects of multiple exposures on an outcome of interest. At genomic loci with pleiotropic gene regulatory effects, that is, loci where the same genetic variants are associated to multiple nearby genes, MVMR can potentially be used to predict candidate causal genes. However, consensus in the field dictates that the genetic instruments in MVMR must be independent (not in linkage disequilibrium), which is usually not possible when considering a group of candidate genes from the same locus. Here we used causal inference theory to show that MVMR with correlated instruments satisfies the instrumental set condition. This is a classical result by Brito and Pearl (2002) for structural equation models that guarantees the identifiability of individual causal effects in situations where multiple exposures collectively, but not individually, separate a set of instrumental variables from an outcome variable. Extensive simulations confirmed the validity and usefulness of these theoretical results. Importantly, the causal effect estimates remained unbiased and their variance small even when instruments are highly correlated, while bias introduced by horizontal pleiotropy or LD matrix sampling error was comparable to standard MR. We applied MVMR with correlated instrumental variable sets at genome-wide significant loci for coronary artery disease (CAD) risk using expression Quantitative Trait Loci (eQTL) data from seven vascular and metabolic tissues in the STARNET study. Our method predicts causal genes at twelve loci, each associated with multiple colocated genes in multiple tissues. We confirm causal roles for PHACTR1 and ADAMTS7 in arterial tissues, among others. However, the extensive degree of regulatory pleiotropy across tissues and the limited number of causal variants in each locus still require that MVMR is run on a tissue-by-tissue basis, and testing all gene-tissue pairs with cis-eQTL associations at a given locus in a single model to predict causal gene-tissue combinations remains infeasible. Our results show that within tissues, MVMR with dependent, as opposed to independent, sets of instrumental variables significantly expands the scope for predicting causal genes in disease risk loci with pleiotropic regulatory effects. However, considering risk loci with regulatory pleiotropy that also spans across tissues remains an unsolved problem.

Neurodevelopmental Outcomes After Multiple General Anaesthetic Exposure Before Five Years Of Age – A Cohort Study

Background The GAS trial demonstrated evidence that most neurodevelopmental outcomes at 2 years and 5 years of age in infants who received a single general anaesthetic (GA) for elective inguinal herniorrhaphy were clinically equivalent when compared to infants who did not receive GA. More than 20% of the children in the trial had at least one subsequent anaesthetic exposure after their initial surgery. Using the GAS database, this study aimed to address whether multiple (2 or more) GA exposures compared to one or no GA exposure in early childhood were associated with worse neurodevelopmental outcomes at 5 years. Methods Children with multiple GA exposures and children with one or no GA exposure were identified from the GAS database. The primary outcome was the full-scale intelligence quotient (FSIQ) on the Wechsler Preschool and Primary Scale of Intelligence third edition (WPPSI-III) at 5 years of age. Secondary outcomes included neurocognitive tests addressing all major developmental domains and caregiver-reported questionnaires assessing emotional and behavioural problems. Results Complete assessment was available from a total of 90 children in the multiple GA group and 141 children in the 0 or 1 GA group. Compared with children with a single or no GA exposure, multiply exposed children scored on average almost 6 points lower (mean: -5.8, 95% CI: -10.2 to -1.4, p= 0.011) in WPPSI-III FSIQ. They also demonstrated lower verbal and performance IQ scores and more emotional, behavioural, and executive function difficulties. However, significant residual confounding cannot be excluded from the results due to the observational nature of this study. Conclusions Multiple GA exposure before 5 years of age was associated with reduced performance in general intelligence score and some domains of neurodevelopmental assessments. The clinical significance of our results must be cautiously interpreted in light of several sources of limitations including small sample size and unadjusted residual confounding. This study illustrates the limitations of trial data sets that may not be fit for the purpose for the secondary analysis.

Current trend in air pollution exposure and stroke.

Stroke is the second leading cause of death worldwide, and exposure to particulate air pollution is now recognized as one of the major modifiable risk factors. However, air pollution can vary in terms of physicochemical composition and exposition specificities. Therefore, its relationships with stroke outcomes remain under intense investigation. This review highlights, alongside particles, that short-term and long-term exposure to nitrogen dioxide (NO2) and ozone is likely to be also linked to stroke-related morbidity and mortality. Moreover, air pollution may increase the risk of transitioning from a healthy status to incident stroke and morbimortality after stroke. Additionally, relationships may vary depending on the air pollution mixture (e.g., particle-related components, pollutant interactions), pollutant sources (e.g., traffic-related or not), stroke etiology (ischemic or hemorrhagic), or exposed individual's characteristics (e.g., age, sex, genetic predisposition, weight status). Nonlinear dose-response functions and short-term effect lags have been reported, but these features need further refinement. The relationship between stroke and air pollution is now well established. Nonetheless, future research should further consider the physicochemical properties of air pollutants, multiple exposures, and individual vulnerabilities. Moreover, advanced statistical methods should be more commonly used to better describe the relationship shapes.

Lessons learned from the experiences of patients with long-term conditions: recommendations for enhancing the undergraduate medical curriculum

ABSTRACT The importance of involving patients in medical education is widely accepted, but their contribution to medical curriculum design is not well documented. Patients have the potential to bring a unique perspective and more refined experience to curriculum development. This applies particularly to those with long-term conditions (LTCs) who have multiple exposures to doctors throughout their healthcare journey. The aim of this study is to explore what views patients with LTCs have on the attributes medical students require to have, in order to provide them with high-quality care. Ten patients with LTCs, encompassing both primary and secondary care over a broad set of demographics, were interviewed by telephone using a semi-structured questionnaire. The interviews were recorded using a digital voice recorder and manually transcribed verbatim onto a Microsoft Word document for thematic analysis using NVIVO software and following Braun and Clarke’s six-phase framework. Five themes have emerged highlighting where the care of LTC patients could be improved: ‘advocacy’; ‘compassion’; ‘the desire to be acknowledged as an individual’”; acknowledgement of their expertise”; and ‘benefits of continuity of care’. The findings make a major contribution to undergraduate and postgraduate clinical training development. They reinforce themes in healthcare which need more emphasis in the medical curriculum, whilst simultaneously highlighting that even well-recognised concepts such as patient-centred holistic care, are not well implemented in the context of actual practice.

Epigenetic training of human bronchial epithelium cells by repeated rhinovirus infections.

Humans are subjected to various environmental stressors (bacteria, viruses, pollution) throughout life. As such, an inherent relationship exists between the effect of these exposures with age. The impact of these environmental stressors can manifest through DNA methylation (DNAm). However, whether these epigenetic effects selectively target genes, pathways, and biological regulatory mechanisms remains unclear. Due to the frequency of human rhinovirus (HRV) infections throughout life (particularly in early development), we propose the use of HRV under controlled conditions can model the effect of multiple exposures to environmental stressors. We generated a prediction model by combining transcriptome and DNAm datasets from human epithelial cells after repeated HRV infections. We applied a novel experimental statistical design and method to systematically explore the multifaceted experimental space (number of infections, multiplicity of infections and duration). Our model included 35 samples, each characterized by the three parameters defining their infection status. Trainable genes were defined by a consistent linear directionality in DNAm and gene expression changes with successive infections. We identified 77 trainable genes which could be further explored in future studies. The identified methylation sites were tracked within a pediatric cohort to determine the relative changes in candidate-trained sites with disease status and age. Repeated viral infections induce an immune training response in bronchial epithelial cells. Training-sensitive DNAm sites indicate alternate divergent associations in asthma compared to healthy individuals. Our novel model presents a robust tool for identifying trainable genes, providing a foundation for future studies.

Color‐Accurate Camera Capture with Multispectral Illumination and Multiple Exposures

AbstractCameras cannot capture the same colors as those seen by the human eye because the eye and the cameras' sensors differ in their spectral sensitivity. To obtain a plausible approximation of perceived colors, the camera's Image Signal Processor (ISP) employs a color correction step. However, even advanced color correction methods cannot solve this underdetermined problem, and visible color inaccuracies are always present. Here, we explore an approach in which we can capture accurate colors with a regular camera by optimizing the spectral composition of the illuminant and capturing one or more exposures. We jointly optimize for the signal‐to‐noise ratio and for the color accuracy irrespective of the spectral composition of the scene. One or more images captured under controlled multispectral illuminants are then converted into a color‐accurate image as seen under the standard illuminant of D65. Our optimization allows us to reduce the color error by 20–60% (in terms of CIEDE 2000), depending on the number of exposures and camera type. The method can be used in applications in which illumination can be controlled, and high colour accuracy is required, such as product photography or with a multispectral camera flash. The code is available at https://github.com/gfxdisp/multispectral_color_correction.

Association between the oxidative balance score with metabolic syndrome traits in US adults

ObjectiveTo explore the association between the Oxidative Balance Score (OBS), which represents the balance of multiple oxidative stress-related dietary and lifestyle exposures, and the risk of metabolic syndrome (MetS).MethodsA population-based cross-sectional study design was adopted and 16,850 participants in NHANES database were included in the statistics analysis stage. The OBS was constructed by combining information from 20 a priori selected pro- and antioxidant factors. Weighted logistic regression and restricted cubic splines (RCS) were used to estimate the association between OBS and MetS.ResultsParticipants in the highest OBS quartile, indicating low oxidative stress (OS) levels, exhibited a significantly lower risk of MetS (odds Ratio [OR] = 0.55, 95% confidence Interval [CI]: 0.47–0.64) compared to the lowest quartile. Specifically, higher OBS was inversely associated with abdominal obesity (OR = 0.61, 95% CI: 0.54–0.69), hypertension (OR = 0.69, 95% CI: 0.58–0.83), elevated triglycerides (OR = 0.68, 95% CI: 0.57–0.82), low high-density lipoprotein cholesterol (HDL-C) levels (OR = 0.60, 95% CI: 0.50–0.70) and fasting blood glucose (FBG) levels (OR = 0.74, 95% CI: 0.62–0.88). The observed inverse association between OBS and hypertension or FBG levels appeared to primarily influenced by BMI. The association between dietary OBS intervals and elevated FBG levels was not statistically significant in men, whereas the risk was lower by 25% in women.ConclusionsA higher OBS, representing a balance of multiple oxidative stress-related dietary and lifestyle exposures, is associated with a lower risk of MetS. Therefore, adhering to an antioxidant diet and lifestyle may help prevent the occurrence of metabolic disorders.

Effect of wild-type vaccine doses on BA.5 hybrid immunity, disease severity, and XBB reinfection risk.

Vaccination against the wild-type (WT) severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus did not produce detectable levels of neutralizing antibodies (NAbs) against the BA.5 strain before it emerged. However, coronavirus disease-2019 (COVID-19) severity varied highly between unvaccinated, partially vaccinated, and fully vaccinated individuals, for unknown reasons. We assessed the severity of BA.5 infection and the risk of XBB strain reinfection and measured serum levels of NAbs against WT, BA.5, and XBB.1.9.1 SARS-CoV-2 strains at varying time points in 1,373 individuals who received zero, one, two, or three WT vaccine doses. We found that two to three WT doses significantly increased WT and BA.5 NAb levels and reduced the incidence of COVID-19-associated pneumonia upon BA.5 strain infection compared to zero to one dose. Regarding XBB reinfection, those who received two to three doses and were infected with the BA.5 variant exhibited a significantly lower reinfection risk compared to those who received zero to one dose. RNA analysis revealed that the differentially expressed genes between the two to three dose and unvaccinated groups were enriched in B cell activation, cytokine-cytokine receptor interaction, complement, and monocyte activation functions-indicating that vaccination increased the antibody response and reduced inflammation. Our results suggest that multiple antigen exposures to either matched or unmatched SARS-COV-2 variants, through vaccination or infection, may be necessary to achieve significant immune imprinting.IMPORTANCEThe administration of coronavirus disease-2019 (COVID-19) vaccines that do not perfectly match the viral strains that individuals become infected with has been found to impact the resultant illness severity-although the precise mechanism underlying this phenomenon remains unclear. We assessed viral clearance, as well as serum levels of inflammatory cytokines and neutralizing antibodies (NAbs) against wild-type, BA.5, and XBB.1.9.1 variants of the severe acute respiratory syndrome coronavirus 2 among individuals who received varying doses of such strain-mismatched vaccines. Notably, vaccination with ≥2 doses of strain-mismatched COVID-19 vaccines appeared to stimulate the production of specific NAbs during infection with new variants, as well as attenuate the inflammatory response and enhance viral clearance. Such vaccination regimens can also reduce the risk of reinfection. These findings may be important for guiding the development of future COVID-19 vaccination strategies that target both matched and mismatched viral variants.

Quantitative summary on the human pharmacokinetic properties of cannabidiol to accelerate scientific clinical application of cannabis.

Cannabidiol (CBD) is a non-psychoactive substance that exerts numerous pharmacological benefits, including anti-inflammatory and antioxidant properties. It has received attention as a useful substance for the treatment of intractable pain, seizures, and anxiety, and related clinical trials have continued. However, the CBD pharmacokinetic results between reports are highly variable, making it difficult to clearly identify the pharmacokinetic properties of CBD. The main purpose of this study was to identify CBD clinical pharmacokinetic properties through meta-analysis. In particular, we sought to derive valid, interpretable independent variables and interpret their pharmacokinetic parameter correlations in relation to the large inter-individual and inter-study variability in CBD pharmacokinetics. For this study, CBD-related clinical trial reports were extensively screened and intercomparisons were performed between internal data sets through systematic classification and extraction of pharmacokinetic parameter values. The candidate independent variables associated with interpretation of CBD pharmacokinetic diversity established and explored in this study were as follows: diet, tetrahydrocannabinol (THC) combination, sample matrix type, liver and renal function, exposure route, dosage form, CBD exposure dose, cannabis smoking frequency, multiple exposure. The results of this study showed that CBD pharmacokinetics were influenced (increased plasma exposure by approximately 2-5 times) by diet immediately before or during CBD exposure, and that THC was not expected to have an antagonistic effect on the CBD absorption. The influence of changes in liver function would be significant in CBD pharmacokinetic diversity. Due to decreased liver function, the plasma exposure of CBD increased 2.57-5.15 times compared to healthy adults, and the half-life and clearance showed a 2.58-fold increase and a 5.15-fold decrease, respectively. CBD can be rapidly absorbed into the body (time to reach maximum concentration within 3.18h) by oral, transdermal, and inhalation exposures, and lipid emulsification and nanoformulation of CBD will greatly improve CBD bioavailability (up to approximately 2 times). The pharmacokinetics of CBD generally follow linear kinetic characteristics. The importance of this study is that it suggests key factors that should be considered in terms of pharmacokinetics in further clinical trials and formulations of CBD in the future.

Interplay and long-lasting effects of maternal low-level Pb, Hg, and Cd exposures on offspring cognition

Lead (Pb), mercury (Hg), and cadmium (Cd) are prevalent and persistent environmental contaminants, causing detrimental effects on millions of individuals worldwide. Our previous research demonstrated that early-life exposure to low-level Pb, Hg, and Cd mixtures may lead to cognitive impairments. However, the association and interaction among low levels of Pb, Hg, or Cd exposure remains unclear. In this study, a two-level full factorial design (5.481, 0.036, and 2.132 mg/L for Pb, Hg, and Cd respectively) was conducted to assess the interplay among maternal Pb, Hg, and Cd exposure on offspring cognition. Following exposure during pregnancy and lactation, a competitive absorption among Pb, Hg, and Cd was observed. Maternal exposure to each metal alone resulted in higher blood and brain concentrations of Pb, Hg, and Cd in offspring compared to co-exposure at equivalent levels. However, behavioral experiments conducted in the Morris water maze and novel object recognition test revealed maternal Pb, Hg, and Cd exposure synergistically impaired offspring's spatial cognition and recognition memory. Importantly, this dysfunction persisted into middle age even without exposure after adulthood. Moreover, the open field test and elevated plus maze indicated maternal low-level Pb, Hg, and Cd co-exposure triggered risk-taking behavior in weaning offspring, with a significant main effect for Pb exposure. No long-lasting effect on risk-taking behavior was detected in middle-aged offspring. Further investigation into molecular mechanisms showed that the dysregulation of corticosterone reaction and immune response might be the potential mechanism underlying Pb, Hg, and Cd co-exposure-induced cognitive impairments. Our study highlights the synergistic and long-lasting effects of multiple heavy metal exposures,underscoring the urgency to prevent exposure to metal mixtures among children and women of childbearing age.

Evaluating the impact of residual low chlorine concentration on phytoplankton communities by flow cytometry

Chlorination is widely used to prevent biological fouling in power station cooling water systems. It may impact non-target organisms both within the cooling system and after discharge (primary and secondary entrainment). However, there is a lack of data on the impacts of the low chlorine concentrations that occur in the discharged plume on marine phytoplankton community structure and function.We examine the impacts on natural phytoplankton communities of single and multiple exposures to chlorination at concentrations between 0.02 and 0.1 mg/L total residual oxidants (TRO). Low-level chlorination causes limited changes in diversity and has no impact on total biomass. However, changes in size structure and functional diversity quantified using flow cytometry do show a reduction in smaller cells, particularly eukaryote picophytoplankton.These impacts are not detectable using chlorophyll a concentration alone, so flow cytometry provides important additional information over more standard ecotoxicological methods.The effects are likely to be localised in the vicinity of the discharges (mixing zone) where the environmental quality standard (EQS) of 10 μg/L for chlorine is exceeded, but impacts on coastal food webs and biogeochemical cycles should be further evaluated.

Repeated high-dose esketamine in early postnatal rats leads to behavioural deficits with long-term modifications in white matter microstructural integrity

Esketamine is commonly used for sedation or general anaesthesia in infants and young children. However, repeated esketamine administration during periods of rapid brain growth and development may result in various pathophysiological and cognitive changes. Therefore, this study aimed to investigate the influence of recurrent esketamine exposure on long-term behavioural and white matter consequences. Seven-day-old (P7) male rats were allocated to control, high-, and low-dose groups. Behavioural paradigm assessment was conducted at P25–29. Diffusion tensor imaging revealed long-term effects on water diffusivity in the splenium and cingulum white matter of the corpus callosum at P30. Subsequent two-dimensional structure-tensor analysis of brain tissue sections stained with Luxol fast blue (LFB) showed marked changes in the white matter microstructure in rats after multiple exposures to varying esketamine doses. High-dose esketamine significantly reduced activity time and total distance in the open-field experiment. High-dose esketamine exposure might lead to impaired short-term memory in rats. Additionally, the high-dose group showed prolonged immobility time during the forced swimming test. On the balance beam, the high-dose group displayed more right turns and right-foot slips and lower time spent on the rotating bar, indicating motor defects, than did the other groups. Diffusion tensor imaging demonstrated a decreased water molecule diffusion ability in the corpus callosum in the high-dose group. LFB staining indicated microstructural differences in the white matter of animals in the high-dose group. These findings suggest that behavioural deficits in high-dose esketamine-treated rats are at least partially attributed to changes in the white matter microstructure.

Overexpression of Nfs1 Cysteine Desulphurase Relieves Sevoflurane-Induced Neurotoxicity and Cognitive Dysfunction in Neonatal Mice Via Suppressing Oxidative Stress and Ferroptosis.

Clinical evidence suggests that multiple exposures to sevoflurane in young people may be detrimental to cognitive development. Iron accumulation in the hippocampus is associated with sevoflurane-induced neurotoxicity and cognitive deficits. The cysteine desulphurase, Nfs1, the rate-limiting enzyme for the biosynthesis of iron-sulphur clusters, plays a role in cellular iron homeostasis. However, the impact of Nfs1-mediated ferroptosis on sevoflurane-induced neurotoxicity and cognitive impairments in neonatal mice remains undetermined. Neonatal mice at postnatal Day 6 received 3% sevoflurane daily for 3 consecutive days. Cognitive function was assessed using the Morris water maze test, and neurotoxicity was evaluated through terminal deoxynucleotidyl transferase dUTP nick end labeling and immunofluorescence staining. Here, HT22 hippocampal neurons were employed for in-vitro experiments, and Fe2+ accumulation was measured. Ferroptosis-related genes, including glutathione peroxidase 4 (GPX4), transferrin receptor 1 (TFR1) and ferritin, in the hippocampus and HT22 cells were observed, along with oxidative stress-related indicators such as reactive oxygen species (ROS), methionine adenosyltransferase (MAT), glutathione (GSH) and lipid peroxidation (LPO). Transmission electron microscopy was utilized to examine the mitochondrial microstructure. Sevoflurane exposure significantly decreased Nfs1 expression in the hippocampus of mice and HT22 cells. This exposure resulted in cognitive impairments and neuronal damage in the hippocampus, which were alleviated by overexpression of Nfs1. Intracellular and mitochondrial iron accumulation occurred in HT22 cells following sevoflurane treatment. Sevoflurane exposure also significantly reduced GSH levels and increased levels of malondialdehyde, ROS and LPO in the hippocampus or HT22 cells. Additionally, sevoflurane exposure decreased GPX4 expression but increased TFR1 and ferritin expression in the hippocampus or HT22 cells. Overexpression of Nfs1 reversed the sevoflurane-induced alterations in ferroptosis-related genes and oxidative stress-related indicators. Furthermore, overexpression of Nfs1 alleviated sevoflurane-induced mitochondrial dysfunction. However, Nfs1 knockdown alone did not result in cognitive impairments, ferroptosis or oxidative stress. The overexpression of Nfs1 mitigated sevoflurane-induced neurotoxicity and cognitive impairment by modulating oxidative stress and ferroptosis through the regulation of iron metabolism and transport.

Causal Mediation Analysis for Integrating Exposure, Genomic, and Phenotype Data

Causal mediation analysis provides an attractive framework for integrating diverse types of exposure, genomic, and phenotype data. Recently, this field has seen a surge of interest, largely driven by the increasing need for causal mediation analyses in health and social sciences. This article aims to provide a review of recent developments in mediation analysis, encompassing mediation analysis of a single mediator and a large number of mediators, as well as mediation analysis with multiple exposures and mediators. Our review focuses on the recent advancements in statistical inference for causal mediation analysis, especially in the context of high-dimensional mediation analysis. We delve into the complexities of testing mediation effects, especially addressing the challenge of testing a large number of composite null hypotheses. Through extensive simulation studies, we compare the existing methods across a range of scenarios. We also include an analysis of data from the Normative Aging Study, which examines DNA methylation CpG sites as potential mediators of the effect of smoking status on lung function. We discuss the pros and cons of these methods and future research directions.

Cross-Cohort Mixture Analysis: A Data Integration Approach With Applications on Gestational Age and DNA-Methylation-Derived Gestational Age Acceleration Metrics.

Data integration of multiple studies can provide enhanced exposure contrast and statistical power to examine associations between environmental exposure mixtures and health outcomes. Extant research has combined populations and identified an overall mixture-outcome association, without accounting for differences across studies. We extended the Bayesian Weighted Quantile Sum (BWQS) regression to a hierarchical framework to analyze mixtures across cohorts. The hierarchical BWQS (HBWQS) approach aggregates sample size of multiple cohorts to calculate an overall mixture index, thereby identifying the most harmful exposure(s) across cohorts; and provides cohort-specific associations between the overall mixture index and the outcome. We showed results from 10 simulated scenarios including four mixture components in three, eight, and ten populations, and two real-case examples on the association between prenatal metal mixture exposure-comprising arsenic, cadmium, and lead-and both gestational age and epigenetic-derived gestational age acceleration metrics. Simulated scenarios showed good empirical coverage and little bias for all HBWQS-estimated parameters. The Watanabe-Akaike information criterion showed a better average performance for the HBWQS regression than the BWQS across scenarios. HBWQS results incorporating cohorts within the national Environmental influences on Child Health Outcomes (ECHO) program from three different sites showed that the environmental mixture was negatively associated with gestational age in a single site. The HBWQS approach facilitates the combination of multiple cohorts and accounts for individual cohort differences in mixture analyses. HBWQS findings can be used to develop regulations, policies, and interventions regarding multiple co-occurring environmental exposures and it will maximize the use of extant publicly available data.

Risk Scores for Five Early Life Domains and Odds of Obesity and Hypertension Comorbidity

Abstract Background For outcomes such as obesity and hypertension, determinants are likely to be complex and multidimensional. Therefore, to design realistic interventions, epidemiological research should incorporate information from multiple risk exposure domains to assess the effect on health. In this paper we explore risk scores for five early life domains and odds of obesity and hypertension comorbidity. Methods We used data from 17,196 participants in the 1970 British Cohort Study. The outcome was obesity (BMI of ≥ 30) and hypertension (blood pressure>140/90mm Hg or self-reported doctor’s diagnosis) comorbidity at age 46. Early life domains included: ‘prenatal, antenatal, neonatal and birth’, ‘developmental attributes and behaviour’, ‘child education and academic ability’, ‘socioeconomic factors’ and ‘parental and family environment’. Stepwise backward elimination selected variables for inclusion for each domain. Predicted risk scores of obesity and hypertension for each cohort member within each domain were calculated. Logistic regression investigated the association between domain-specific risk scores and odds of obesity-hypertension, controlling for demographic factors and other domains. Results In unadjusted models, higher domain-specific risk scores were associated with increased odds of obesity-hypertension comorbidity. In adjusted analyses, higher domain-specific risk scores remained associated with increased odds of obesity-hypertension comorbidity, with the strongest associations to the parental and family environment domain (OR1.11 95%CI 1.05-1.18) and the socioeconomic factors domain (OR1.11 95%CI 1.05-1.17). Conclusions Appropriate modelling choices of combined effects of early-life risk factors need to be explored and tested. Targeted prevention interventions aimed at population groups with shared early-life characteristics could have an impact on cardiovascular risk in adulthood. Key messages • Targeted prevention interventions aimed at population groups with shared early-life characteristics could have an impact on obesity-hypertension prevalence. • Appropriate modelling choices of combined effects of early-life risk factors need to be explored and tested.

Urban burden of disease estimation for policy making

Abstract Introduction The Urban Burden of Disease Estimation for Policy Making (UBDPolicy) project aims to estimate the health impacts resulting from urban and transport planning related exposures in nearly 1000 European cities. So far, quantitative burden of disease assessments have estimated premature mortality attributable to the environmental stressors of air pollution, noise, urban heat islands and lack of green space for the year 2015. Regular reporting and trend monitoring of the health impacts associated with urban environments is crucial for assessing the wider impacts of urban and transport planning. Methods For almost 1000 European cities, quantitative Health Impact Assessments (HIAs) will be conducted that employ the comparative risk assessment methodology. Baseline exposure levels of air pollution, noise, urban heat islands and green space will be estimated and preventable health impacts modelled using counterfactual exposure scenarios. Analysis will comprise two components: firstly, the 2018 HIA will incorporate multiple environmental exposures for that year, while the second component will monitor trends at three-yearly reporting intervals covering the years of 2015, 2018, 2021 and 2024. Results Results are intended to shape evidence-informed urban planning and policies throughout Europe. By ranking cities according to environmental exposures and health impacts, cities are encouraged to address challenges and learn from successful urban design and planning policies in other cities. Discussion HIAs serve as an effective tool for identifying the health impacts of current and future urban policies. The provision of burden of disease estimates will provide policy-makers and practitioners robust data for informed decision-making, while estimates over time will allow tracking of changes from implemented policies. To fully harness the potential of cities as key drivers of sustainable and healthy living, robust evidence must be at the forefront of this change. Key messages • A multitude of urban and transport planning pathways contribute toward the health of urban populations. • Compact city designs often promote health, however there are exceptions.