Gene-environment interactions and their impact on human health

Ethical, Legal, and Social Implications of Gene-Environment Interaction Research

Many complex disorders are impacted by the interplay of genetic and environmental factors. In gene-environment interactions (GxE), an individual's genetic and epigenetic makeup impacts the response to environmental exposures. Understanding GxE can impact health at the individual, community, and population levels. The rapid expansion of GxE research in biomedical studies for complex diseases raises many unique ethical, legal, and social implications (ELSIs) that have not been extensively explored and addressed. This review article builds on discussions originating from a workshop held by the National Institute of Environmental Health Sciences (NIEHS) and the National Human Genome Research Institute (NHGRI) in January 2022, entitled: "Ethical, Legal, and Social Implications of Gene-Environment Interaction Research." We expand upon multiple key themes to inform broad recommendations and general guidance for addressing some of the most unique and challenging ELSI in GxE research. Key takeaways include strategies and approaches for establishing sustainable community partnerships, incorporating social determinants of health and environmental justice considerations into GxE research, effectively communicating and translating GxE findings, and addressing privacy and discrimination concerns in all GxE research going forward. Additional guidelines, resources, approaches, training, and capacity building are required to further support innovative GxE research and multidisciplinary GxE research teams.

Association of the Immunity Genes with Type 1 Diabetes Mellitus

Type 1 diabetes mellitus (T1D) is a complicated illness marked by the death of insulin- producing pancreatic beta cells, which ultimately leads to insulin insufficiency and hyperglycemia. T lymphocytes are considered to destroy pancreatic beta cells in the etiology of T1D as a result of hereditary and environmental factors. Although the latter factors are very important causes of T1D development, this disease is very genetically predisposed, so there is a significant genetic component to T1D susceptibility. Among the T1D-associated gene mutations, those that affect genes that encode the traditional Human Leukocyte Antigens (HLA) entail the highest risk of T1D development. Accordingly, the results of decades of genetic linkage and association studies clearly demonstrate that mutations in the HLA genes are the most associated mutations with T1D. They can, therefore, be used as biomarkers for prediction strategies and may even prove to be of value for personalized treatments. Other immunity-associated genetic loci are also associated with higher T1D risk. Indeed, T1D is considered an autoimmune disease. Its prevalence is rising globally, especially among children and young people. Given the global rise of, and thus interest in, autoimmune diseases, here we present a short overview of the link between immunity, especially HLA, genes and T1D.

After the Infection: A Survey of Pathogens and Non-communicable Human Disease

There are many well-established relationships between pathogens and human disease, but far fewer when focusing on non-communicable diseases (NCDs). We leverage data from The UK Biobank and TriNetX to perform a systematic survey across 20 pathogens and 426 diseases, primarily NCDs. To this end, we assess the association between disease status and infection history proxies. We identify 206 pathogen-disease pairs that replicate in both cohorts. We replicate many established relationships, including Helicobacter pylori with several gastroenterological diseases and connections between Epstein-Barr virus with multiple sclerosis and lupus. Overall, our approach identified evidence of association for 15 pathogens and 96 distinct diseases, including a currently controversial link between human cytomegalovirus (CMV) and ulcerative colitis (UC). We validate this connection through two orthogonal analyses, revealing increased CMV gene expression in UC patients and enrichment for UC genetic risk signal near human genes that have altered expression upon CMV infection. Collectively, these results form a foundation for future investigations into mechanistic roles played by pathogens in NCDs. All results are easily accessible on our website, https://tf.cchmc.org/pathogen-disease.

Awareness, Beliefs, and Psychological Impact of Patients with Alopecia Areata in Saudi Arabia: A Multi-Center Study

Purpose

Alopecia Areata (AA) is a complex autoimmune condition characterized by long-term inflammatory non-scarring patches of hair loss on the face, scalp, and body. Its development involves a combination of genetic, immunological, and environmental factors, making it challenging to understand and treat. This study aims to assess the awareness, beliefs, and psychological impact of patients with Alopecia Areata.

Patients and Methods

This cross-sectional study was conducted in Eastern and Central provinces of Saudi Arabia and utilized online questionnaires to evaluate knowledge, beliefs, and psychological effects of patients with Alopecia Areata. These questionnaires were distributed using social media platforms.

Results

This study enrolled 248 patients with Alopecia Areata (AA), of whom 38.7% were aged 31-40 years and 55.2% were males. The majority were Saudis (98.4%), married (54%), and residing in the Central Region (60.1%). Nearly half (48.8%) were employed in non-medical fields, and 30.6% were vocational school graduates. Healthcare providers were the main source of AA information (52.8%), with most patients aware of AA's seriousness, curability, and quality of life impact, though many had low knowledge and experienced negative psychological effects such as shyness, anxiety, and depression.

Conclusion

Despite low self-rated knowledge, most patients recognized AA as serious and requiring treatment. Psychological well-being was heavily impacted by AA severity, quality of life, disease duration, and treatment efficacy. Although many patients showed improved AA status and good medication compliance, adverse psychological effects persisted, highlighting the need for larger studies to better understand and address these issues.

Genetic Biomarkers in Heart Failure: From Gene Panels to Polygenic Risk Scores

PURPOSE OF REVIEW

This review aims to provide a comprehensive overview of the current understanding of genetic markers associated with heart failure (HF) and its underlying causative diseases, such as cardiomyopathies. It highlights the relevance of genetic biomarkers in diagnosing HF, predicting prognosis, potentially identifying its preclinical stages and identifying targets to enable the implementation of individualized medicine approaches.

RECENT FINDINGS

The prevalence of HF is increasing due to an aging population but with greater access to disease-modifying therapies. Advanced diagnostic tools such as cardiac magnetic resonance, nuclear imaging, and AI-enabled diagnostic testing are now being utilized to further characterize HF patients. Additionally, the importance of genetic testing in HF diagnosis and management is increasingly being recognized. Genetic biomarkers, including single nucleotide polymorphisms (SNPs) and rare genetic variants, are emerging as crucial tools for diagnosing HF substrates, determining prognosis and increasingly directing therapy. These genetic insights are key to optimizing HF management and delivering personalized treatment tailored to individual patients. HF is a complex syndrome affecting millions globally, characterized by high mortality and significant economic burden. Understanding the underlying etiologies of HF is essential for improving management and clinical outcomes. Recent advances highlight the use of multimodal assessments, including AI-enabled diagnostics and genetic testing, to better characterize and manage HF. Genetic biomarkers are particularly promising in identifying preclinical HF stages and providing personalized treatment options. The genetic contribution to HF is heterogeneous, with both monogenic and polygenic bases playing a role. These developments underscore the shift towards personalized medicine in HF management.

Combined exercise training decreases blood pressure in OLDER women with NOS3 polymorphism providing changes in differentially methylated regions (DMRs)

The mechanisms by which the ageing process is associated to an unhealthy lifestyle and how they play an essential role in the aetiology of systemic arterial hypertension have not yet been completely elucidated. Our objective is to investigate the influence of NOS3 polymorphisms [-786T > C and (Glu298Asp)] on systolic blood pressure (SBP) and diastolic blood pressure (DBP) response, differentially methylated regions (DMRs), and physical fitness of adult and older women after a 14-week combined training intervention. The combined training was carried out for 14 weeks, performed 3 times a week, totalling 180 minutes weekly. The genotyping experiment used Illumina Infinium Global Screening Array version 2.0 (GSA V2.0) and Illumina's EPIC Infinium Methylation BeadChip. The participants were separated into SNP rs2070744 in TT (59.7 ± 6.2 years) and TC + CC (60.0 ± 5.2 years), and SNP rs17999 in GluGlu (58.8 ± 5.7 years) and GluAsp + AspAsp (61.6 ± 4.9 years). We observed an effect of time for variables BP, physical capacities, and cholesterol. DMRs related to SBP and DBP were identified for the rs2070744 and rs17999 groups pre- and decreased numbers of DMRs post-training. When we analysed the effect of exercise training in pre- and post-comparisons, the GluGlu SNP (rs17999) showed 10 DMRs, and after enrichment, we identified several biological biases. The combined training improved the SBP and DBP values of the participants regardless of the SNPs. In addition, exercise training affected DNA methylation differently between the groups of NOS3 polymorphisms.

Genetic vulnerability and adverse mental health outcomes following mild traumatic brain injury: a meta-analysis of CENTER-TBI and TRACK-TBI cohorts

Background

Post-traumatic stress disorder (PTSD) and depression are common after mild traumatic brain injury (mTBI), but their biological drivers are uncertain. We therefore explored whether polygenic risk scores (PRS) derived for PTSD and major depressive disorder (MDD) are associated with the development of cognate TBI-related phenotypes.

Methods

Meta-analyses were conducted using data from two multicenter, prospective observational cohort studies of patients with mTBI: the CENTER-TBI study (ClinicalTrials.gov ID NCT02210221) in Europe (December 2014-December 2017) and the TRACK-TBI study in the US (March 2014-July 2018). In both cohorts, the most common causes of injury were road traffic accidents and falls. Primary outcomes, specifically probable PTSD and depression, were defined at 6 months post-injury using scores ≥33 on the PTSD Checklist-5 and ≥15 on the Patient Health Questionnaire-9, respectively. We calculated PTSD-PRS and MDD-PRS for patients aged ≥17 years who had a Glasgow Coma Scale score of 13-15 upon hospital arrival and assessed their association with PTSD and depression following TBI. We also evaluated the transferability of the findings in a cohort of African Americans.

Findings

Overall, 11.8% (219/1869) and 6.7% (124/1869) patients were classified as having probable PTSD and depression, respectively. The PTSD-PRS was significantly associated with higher adjusted odds of PTSD in both cohorts, with a pooled odds ratio (OR) of 1.55 [95% confidence interval (CI) 1.30-1.84, p < 0.001, I 2  = 20.8%]. Although the MDD-PRS increased the risk of depression after TBI, it did not reach significance in the individual cohorts. However, in a combined analysis, the risk was significantly elevated with a pooled OR of 1.26 [95% CI 1.03-1.53, p = 0.02, I 2  = 0%]. The addition of PRSs improved the proportion of outcome variance explained in the two study cohorts from 19.5% and 30.3% to 21.6% and 34.0% for PTSD; and from 11.0% and 22.5% to 12.8% and 22.6% for depression. Patients in the highest cognate PRS quintile had increased odds of 3.16 [95% CI 1.80-5.55] and 2.03 [95% CI 1.04-3.94] of developing PTSD or depression compared to the lowest quintile, respectively.

Interpretation

Associations of PRSs with PTSD and depression following TBI are not disorder-specific. However, the overlap between MDD-PRS and depression following TBI is less robust compared to PTSD-PRS and PTSD. PRSs could improve risk prediction, and permit enrichment for interventional trials.

Funding

This study was supported by funding by an FP7 grant from the European Union, Hannelore Kohl Stiftung, Integra LifeSciences Corporation, NeuroTrauma Sciences, US National Institutes of Health, US Department of Defense, National Football League Advisory Board, US Department of Energy, and One Mind.

Clinical correlates of lifetime and current comorbidity patterns in autoimmune and inflammatory diseases

BACKGROUND

Autoimmune and inflammatory diseases (AIDs) are a heterogeneous group of disorders with diverse etiopathogenic mechanisms. This study explores the potential utility of family history, together with present and past comorbidities, in identifying distinct etiopathogenic subgroups. This approach may facilitate more accurate diagnosis, prognosis and personalized therapy.

METHODS

We performed a multiple correspondence analysis on patients' comorbidities, followed by hierarchical principal component clustering of clinical data from 48 healthy volunteers and 327 patients with at least one of 19 selected AIDs included in the TRANSIMMUNOM cross-sectional study.

RESULTS

We identified three distinct clusters characterized by: 1) the absence of comorbidities, 2) polyautoimmunity, and 3) polyinflammation. These clusters were further distinguished by specific comorbidities and biological parameters. Autoantibodies, allergies, and viral infections characterized the polyautoimmunity cluster, while older age, BMI, depression, cancer, hypertension, periodontal disease, and dyslipidemia characterized the polyinflammation cluster. Rheumatoid arthritis patients were distributed across all three clusters. They had higher DAS28 and prevalence of extra-articular manifestations when belonging to the polyinflammation and polyautoimmunity clusters, and also lower ACPA and RF seropositivity and higher pain scores within the polyinflammation cluster. We developed a model allowing to classify AID patients into comorbidity clusters.

CONCLUSIONS

In this study, we have uncovered three distinct comorbidity profiles among AID patients. These profiles suggest the presence of distinct etiopathogenic mechanisms underlying these subgroups. Validation, longitudinal stability assessment, and exploration of their impact on therapy efficacy are needed for a comprehensive understanding of their potential role in personalized medicine.

Identification of Novel Genomic Variants in COVID-19 Patients Using Whole-Exome Sequencing: Exploring the Plausible Targets of Functional Genomics

Covid-19 caused by SARS-CoV-2 virus has emerged as an immense burden and an unparalleled global health challenge in recorded human history. The clinical characteristics and risk factors of COVID-19 exhibit considerable variability, leading to a spectrum of clinical severity. Moreover, the likelihood of exposure to the virus may differ based on comorbidity status as comorbid illnesses have mechanisms that can considerably increase mortality by reducing the body's ability to withstand injury. The mammalian target of rapamycin (mTOR) pathway is essential for orchestrating innate immune cell defense, including cytokine production and is dysregulated in severe Coronavirus Disease 2019 (COVID-19) individuals. Through genome-wide, association studies, numerous genetic variants in the human host have been identified that have a significant impact on the immune response to SARS-CoV-2. To identify potentially significant genetic variants in Covid-19 patients that could affect the risk, severity, and clinical outcome of the infection, this study has used whole-exome sequencing (WES) on the 16 COVID-19 patients with varying comorbidities and severity of the disease including fatal outcomes. Among them, 8 patients made a full recovery and were discharged, while 8 patients unfortunately did not survive due to the severity of the illness and majority of them were males. The study identified 10,204 variants in the patients. From 1120 variants, which were chosen for novel variant analysis using mutation, function prediction tools to identify deleterious variants that could affect normal gene function, 116 variants of 57 genes were found to be deleterious. These variants were further classified as likely pathogenic and variants of uncertain significance. The data showed that among the likely pathogenic variants five genes were identified in connection to immune response whereas two were related to respiratory system. The common variants associated with the covid-19 phenotype showed the top 10 significant genes identified in this study such as ERCC2, FBXO5, HTR3D, FAIM, DNAH17, MTOR, IGHMBP2, ZNF530, QSER1, and FOXRED2 with variant rs1057079 of the MTOR gene representing the highest odds ratio (1.7, p = 8.7e-04). The mammalian target of rapamycin (mTOR) pathway variant rs1057079 was reported with high odds ratio, may orchestrate innate immune cell defense, including cytokine production, and is dysregulated. This study concluded that the mTOR signaling gene variant (rs1057079) is associated with different degrees of covid-19 severity and is essential for orchestrating innate immune cell defense including cytokine production. Inhibiting mTOR and its corresponding deleterious immune responses with medicinal approaches may provide a novel avenue for treating severe COVID-19 illness. Besides the PPI network exhibited a significantly high local clustering coefficient of 0.424 (p = 0.000536), suggesting the presence of tightly knit functional modules. These findings enhance our comprehension of the intricate interactions between genetic factors and COVID-19 disease.

Genetic Diversity Landscape in African Population: A Review of Implications for Personalized and Precision Medicine

Introduction

Africa, a continent considered to be the cradle of human beings has the largest genetic diversity among its population than other continents. This review discusses the implications of this high African genetic diversity to the development of personalized and precision medicine.

Methodology

A comprehensive search across PubMed, Google Scholar, Science Direct, DOAJ, AJOL, and the Cochrane Library electronic databases and manual Google searches was conducted using key terms "genetics", "genetic diversity", "Africa", "precision medicine", and "personalized medicine". Updated original and review studies focusing on the implications of African high genetic diversity on personalized and precision medicine were included. Included studies were thematically synthesized to elucidate their positive or negative implications for personalized healthcare, aiming to foster informed clinical practice and scientific inquiry.

Results

African populations' high genetic diversity presents opportunities for personalized and precision medicine including improving pharmacogenomics, understanding gene interactions, discovering new variants, mapping disease genes, creating updated genomic reference panels, and validating biomarkers. However, challenges include underrepresentation in studies, scarcity of reference genomes, inaccuracy of genetic testing and interpretation, and ancestry misclassification. Addressing these requires the establishment of genomic research centers, increasing funding, creating biobanks and repositories, education, infrastructure, and international cooperation to enhance healthcare equity and outcomes through personalized and precision medicine.

Conclusion

High African genetic diversity presents both positive and negative implications for personalized and precision medicine. Deep further research is recommended to harness the challenges and use the opportunities to develop customized treatments.

Aryl-hydrocarbon receptor in smooth muscle cells protect against dioxin induced adverse remodeling of atherosclerosis

Introduction

Environmental exposure to dioxin has been linked to increased myocardial infarction. Smooth muscle cells (SMC) in the coronary vasculature play a critical role in atherosclerotic plaque remodeling due to their phenotypic plasticity, however, the detailed mechanism linking dioxin exposure to adverse SMC modulation is not well understood.

Methods

Single-cell RNA and ATAC sequencing and histological analyses were performed on the aorta from mouse models of atherosclerosis exposed to 2,3,7,8-Tetrachlorodibenzo-p-dioxin (TCDD) or control. Primary human coronary artery SMC (HCASMC) treated in culture with TCDD were used to perform RNA-Seq, ATAC-Seq, and functional phenotypic assays. ChIP-Seq was performed with antibodies against Aryl-hydrocarbon receptor (AHR) and TCF21, two of known SMC modulating transcription factors.

Results

Modulated SMC were the most transcriptionally responsive cell type to dioxin in the atherosclerotic aorta. Dioxin accelerated disease phenotype by promoting a modulated SMC phenotype early, resulting in increased lesion size, migration of SMC, and macrophage recruitment to the lesion. We found C3 expressing modulated SMCs to be likely contributing to the increased macrophage recruitment and inflammation. Analysis of the RNA-Seq data from HCASMC treated with TCDD showed differential enrichment of biological pathways related to cell migration, localization, and inflammation. Furthermore, ATAC-Seq data showed a significant activation for pathways regulating vascular development, cell migration, inflammation, and apoptosis. With TCDD treatment, there was also enrichment of AHR ChIP-Seq peaks, while the TCF21 enrichment decreased significantly. The SMC-specific Ahr knockout resulted in increased oxidative stress in SMC, increased lesion size and macrophage content, and loss of SMC lineage cells in the lesion cap when exposed to TCDD, consistent with a more vulnerable plaque phenotype.

Conclusion

Dioxin adversely remodels atherosclerotic plaque by accelerating the SMC- phenotypic modulation, and increasing inflammation and oxidative stress resulting in increased macrophage recruitment and lesion size. Dioxin may adversely affect the SMC phenotype and disease state by affecting the TCF21 occupancy in the open chromatin regions. Furthermore, we observed that SMC-specific deletion of Ahr in mice resulted in worsening of dioxin mediated SMC modulation and atherosclerosis, suggesting that Ahr in SMC confers protection against dioxin by promoting a stable plaque phenotype and reducing dioxin induced oxidative stress.

Summary

Exposure to dioxin, an environmental pollutant present in tobacco smoke and air pollution, accelerates smooth muscle cell modulation, and atherosclerosis.Dioxin exposure leads to inflammatory smooth muscle cell phenotype characterized by complement pathway activation and increased macrophage recruitment to plaqueAryl-hydrocarbon receptor in SMC protects against oxidative stress, and promotes a stable plaque phenotype.

Gene-environment interactions in human health

Gene-environment interactions (G × E), the interplay of genetic variation with environmental factors, have a pivotal impact on human complex traits and diseases. Statistically, G × E can be assessed by determining the deviation from expectation of predictive models based solely on the phenotypic effects of genetics or environmental exposures. Despite the unprecedented, widespread and diverse use of G × E analytical frameworks, heterogeneity in their application and reporting hinders their applicability in public health. In this Review, we discuss study design considerations as well as G × E analytical frameworks to assess polygenic liability dependent on the environment, to identify specific genetic variants exhibiting G × E, and to characterize environmental context for these dynamics. We conclude with recommendations to address the most common challenges and pitfalls in the conceptualization, methodology and reporting of G × E studies, as well as future directions.

Exploring the role of genetic variability and exposure to bisphenols and parabens on excess body weight in Spanish children

Gene-environment interaction studies are emerging as a promising tool to shed light on the reasons for the rapid increase in excess body weight (overweight and obesity). We aimed to investigate the influence of several polymorphisms on excess weight in Spanish children according to a short- and long-term exposure to bisphenols and parabens, combining individual approach with the joint effect of them. This case-control study included 144 controls and 98 cases children aged 3-12 years. Thirty SNPs in genes involved in obesity-related pathways, xenobiotic metabolism and hormone systems were genotyped using the GSA microchip technology and qPCRs with Taqman® probes. Levels of bisphenols and parabens in urine and hair were used to assess short- and long-term exposure, respectively, via UHPLC-MS/MS system. LEPR rs9436303 was identified as a relevant risk variant for excess weight (ORDom:AAvsAG+GG=2.65, p<0.001), and this effect persisted across exposure-stratified models. For long-term exposure, GPX1 rs1050450 was associated with increased excess weight at low single paraben exposure (ORGvsA=2.00, p=0.028, p-interaction=0.016), whereas LEPR rs1137101 exhibited a protective function at high co-exposure (ORDom:AAvsAG+GG=0.17, p=0.007, p-interaction=0.043). ESR2 rs3020450 (ORDom:GGvsAG+AA=5.17, p=0.020, p-interaction=0.028) and CYP2C19 rs4244285 (ORDom:GGvsAG+AA=3.54, p=0.039, p-interaction=0.285) were identified as predisposing variants at low and high co-exposure, respectively. In short-term exposure, higher odds were observed for INSIG2 rs7566605 at high bisphenol exposure (ORCvsG=2.97, p=0.035, p-interaction=0.017) and for GSTP1 rs1695 at low levels (ORDom:AAvsAG+GG=5.38, p=0.016, p-interaction=0.016). At low and medium co-exposure, SH2B1 rs7498665 (ORAvsG=0.17, p=0.015, p-interaction=0.085) and MC4R rs17782313 (ORAvsG=0.10, p=0.023, p-interaction=0.045) displayed a protective effect, whereas ESR2 rs3020450 maintained its contributing role (ORGvsA=3.12, p=0.030, p-interaction=0.010). Our findings demonstrate for the first time that understanding the genetic variation in excess weight and how the level of exposure to bisphenols and parabens might interact with it, is crucial for a more in-depth comprehension of the complex polygenic and multifactorial aetiology of overweight and obesity.

Transcriptional variation and RNA polymorphism among different Lentinula edodes (Berk.) Pegler strains

BACKGROUND

Lentinula edodes is a commercially important mushroom known for its nutritional and therapeutic values. However, the molecular mechanisms underlying the distinct nutritional and physiological attributes of various L. edodes strains are not well understood. This study focused on three Lentinula strains (DMRO-356, DMRO-623, and DMRO-388s) with different nutritional and productivity profiles. Illumina sequencing was used to perform a whole-transcriptome analysis, conducting 100-base pair paired-end sequencing of total messenger RNA (mRNA) in duplicate, resulting in 28-48 million sequencing reads per strain. After rigorous data filtering, over 99% of high-quality reads were retained, and more than 95% were aligned to the Lentinula genome.

RESULTS

Differential gene expression analyses identified 2210 differentially expressed genes between DMRO-356 and DMRO-623, 862 between DMRO-356 and DMRO-388s, and 2212 between DMRO-623 and DMRO-388s. Significant genetic variations were found among the strains, including 7753 single nucleotide polymorphisms (SNPs) in DMRO-356 versus DMRO-623 and 4080 SNPs in DMRO-356 versus DMRO-388s. Additionally, 349 insertions/deletions (InDels) were found in DMRO-356/DMRO-623 and 218 in DMRO-356/DMRO-388 s. Non-synonymous SNPs, which alter amino acid compositions, were analyzed, showing a preference for polar over charged amino acids.

CONCLUSION

These differentially expressed genes were associated with various nutritional and developmental processes, highlighting the importance of genetic variations in shaping amino acid composition and potentially affecting protein function. This study is the first comprehensive exploration of transcriptional differences among Lentinula strains available for its cultivation, providing valuable insights to enhance mushroom quality and productivity. © 2024 Society of Chemical Industry.

Exposure to air pollution, genetic susceptibility, and prevalence of sarcopenia in the UK

BACKGROUND

The role of environmental factors, particularly air pollutants, in the prevalence of sarcopenia remains unclear.

OBJECTIVES

This study explored the relationship between the prevalence of sarcopenia and prolonged exposure to air pollutants, and investigated potential interactions with genetic susceptibility and inflammation.

METHODS

Data from 408,117 people at baseline and 35,060 participants in the longitudinal analysis in the UK Biobank were used in this prospective cohort study. Utilizing land use regression models, air pollutants (nitrogen dioxide (NO2), nitric oxides (NOx), and particulate matter with aerodynamic diameters of ≤2.5 μm (PM2.5) and ≤10 μm (PM10) were estimated and classified into quartiles. Alterations in body composition were among the secondary results.

RESULTS

Lastly, 3353 people (0.8 %) developed sarcopenia. Higher levels of air pollutants were linked to an increased prevalence of sarcopenia after controlling for confounding variables (highest vs lowest quartile: NOx, OR, 1.21 [95 % CI, 1.16-1.26]; NO2, OR, 1.22 [95 % CI, 1.16-1.27]; PM2.5, OR, 1.17 [95 % CI, 1.12-1.22]; PM10, OR, 1.15 [95 % CI, 1.10-1.20]; all P<.001). Longitudinal analysis revealed that air pollutants had adverse changes in body composition, including increased muscle fat infiltration and decreased muscle mass. At baseline, the probability of sarcopenia was strongly correlated with NOx, NO2, PM2.5, and PM10, and increased with elevated PRSBMI or CRP levels in subgroup analyses.

CONCLUSION

Air pollutants may contribute to accelerated muscle aging and highlight the importance of environmental factors in sarcopenia development.

Genomic Insights into Dementia: Precision Medicine and the Impact of Gene-Environment Interaction

The diagnosis, treatment, and management of dementia provide significant challenges due to its chronic cognitive impairment. The complexity of this condition is further highlighted by the impact of gene-environment interactions. A recent strategy combines advanced genomics and precision medicine methods to explore the complex genetic foundations of dementia. Utilizing the most recent research in the field of neurogenetics, the importance of precise genetic data in explaining the variation seen in dementia patients can be investigated. Gene-environment interactions are important because they influence genetic susceptibilities and aid in the development and progression of dementia. Modified to each patient's genetic profile, precision medicine has the potential to detect groups at risk and make previously unheard-of predictions about the course of diseases. Precision medicine techniques have the potential to completely transform treatment and diagnosis methods. Targeted medications that target genetic abnormalities will probably appear, providing the possibility for more efficient and customized medical interventions. Investigating the relationship between genes and the environment may lead to preventive measures that would enable people to change their surroundings and minimize the risk of dementia, leading to the improved lifestyle of affected people. This paper provides a comprehensive overview of the genomic insights into dementia, emphasizing the pivotal role of precision medicine, and gene-environment interactions.

Large-scale genome-wide interaction analyses on multiple cardiometabolic risk factors to identify age-specific genetic risk factors

The genetic landscape of cardiometabolic risk factors has been explored extensively. However, insight in the effects of genetic variation on these risk factors over the life course is sparse. Here, we performed genome-wide interaction studies (GWIS) on different cardiometabolic risk factors to identify age-specific genetic risks. This study included 270,276 unrelated European-ancestry participants from the UK Biobank (54.2% women, a median age of 58 [interquartile range (IQR): 50, 63] years). GWIS models with interaction terms between genetic variants and age were performed on apolipoprotein B (ApoB), low-density lipoprotein-cholesterol (LDL-C), log-transformed triglycerides (TG), body mass index (BMI) and systolic blood pressure (SBP). Replication was subsequently performed in the Copenhagen General Population Study (CGPS) and the Estonian Biobank (EstBB). Multiple lead variants were identified to have genome-wide significant interactions with age (Pinteraction < 1e - 08). In detail, rs429358 (tagging APOE4) was identified for ApoB (Pinteraction = 9.0e - 14) and TG (Pinteraction = 5.4e - 16). Three additional lead variants were identified for ApoB: rs11591147 (R46L in PCSK9, Pinteraction = 3.9e - 09), rs34601365 (near APOB, Pinteraction = 8.4e - 09) and rs17248720 (near LDLR, Pinteraction = 2.0e - 09). Effect sizes of the identified lead variants were generally closer to the null with increasing age. No variant-age interactions were identified for LDL-C, SBP and BMI. The significant interactions of rs429358 with age on ApoB and TG were replicated in both CGPS and EstBB. The majority of genetic effects on cardiometabolic risk factors remain relatively constant over age, with the noted exceptions of specific genetic effects on ApoB and TG.

Dynamics of small molecule-enzyme interactions: Novel benzenesulfonamides as multi-target agents endowed with inhibitory effects against some metabolic enzymes

In contemporary medicinal chemistry, employing a singular small molecule to concurrently multi-target disparate molecular entities is emerging as a potent strategy in the ongoing battle against metabolic disease. In this study, we present the meticulous design, synthesis, and comprehensive biological evaluation of a novel series of 1,2,3-triazolylmethylthio-1,3,4-oxadiazolylbenzenesulfonamide derivatives (8a-m) as potential multi-target inhibitors against human carbonic anhydrase (EC.4.2.1.1, hCA I/II), α-glycosidase (EC.3.2.1.20, α-GLY), and α-amylase (EC.3.2.1.1, α-AMY). Each synthesized sulfonamide underwent rigorous assessment for inhibitory effects against four distinct enzymes, revealing varying degrees of hCA I/II, a-GLY, and a-AMY inhibition across the tested compounds. hCA I was notably susceptible to inhibition by all compounds, demonstrating remarkably low inhibition constants (KI) ranging from 42.20 ± 3.90 nM to 217.90 ± 11.81 nM compared to the reference standard AAZ (KI of 439.17 ± 9.30 nM). The evaluation against hCA II showed that most of the synthesized compounds exhibited potent inhibition effects with KI values spanning the nanomolar range 16.44 ± 1.53-70.82 ± 4.51 nM, while three specific compounds, namely 8a-b and 8d, showcased lower inhibitory potency than other derivatives that did not exceed that of the reference drug AAZ (with a KI of 98.28 ± 1.69 nM). Moreover, across the spectrum of synthesized compounds, potent inhibition profiles were observed against diabetes mellitus-associated α-GLY (KI values spanning from 0.54 ± 0.06 μM to 5.48 ± 0.50 μM), while significant inhibition effects were noted against α-AMY, with IC50 values ranging between 0.16 ± 0.04 μM and 7.81 ± 0.51 μM) compared to reference standard ACR (KI of 23.53 ± 2.72 μM and IC50 of 48.17 ± 2.34 μM, respectively). Subsequently, these inhibitors were evaluated for their DPPH· and ABTS+· radical scavenging activity. Moreover, molecular docking investigations were meticulously conducted within the active sites of hCA I/II, α-GLY, and α-AMY to provide comprehensive elucidation and rationale for the observed inhibitory outcomes.

The role of sex hormone binding globulin levels in the association of surgical and natural premature menopause with incident type 2 diabetes

OBJECTIVE

To examine associations of surgical and natural menopause before the age of 40 years with the risk of type 2 diabetes (T2D) in women.

METHODS

A total of 273,331 women from the United Kingdom were recruited between 2006 and 2010 in the UK Biobank (UKB) study, and 146,343 women aged 40 to 69 years who were postmenopausal at baseline were included in the analysis. Surgical menopause and natural premature menopause were defined as bilateral oophorectomy before the age of 40 and menopause before the age of 40 without oophorectomy, respectively. Multivariable Cox regression models were used to estimate the hazard ratios (HRs) and 95 % confidence intervals (CIs) for the association between premature menopause and the incidence of T2D.

RESULTS

During a median follow-up of 10.4 years, 47 women with surgical premature menopause, 244 women with natural premature menopause, and 4724 women without premature menopause developed T2D. Compared with women without premature menopause, both surgical premature menopause (adjusted HR = 1.46, 95 % CI: 1.09-1.95; P = 0.01) and natural premature menopause (adjusted HR = 1.20, 95 % CI: 1.06-1.37; P < 0.01) were associated with higher risks of incident T2D in the multivariable-adjusted models. Additionally, we observed a significant interaction between levels of sex hormone binding globulin (SHBG) (Pinteraction < 0.01) and the effects of premature menopause on incident T2D. The association between premature menopause and T2D risk appeared to be stronger in women with higher SHBG levels. Furthermore, a joint association was detected between premature menopause and the genetic risk score (GRS) of T2D, with a higher score indicating a higher risk of developingT2D. The highest risk of T2D was observed with higher T2D GRS and surgical premature menopause (adjusted HR = 2.61, 95 % CI: 1.65-4.12; P < 0.01).

CONCLUSIONS

Surgical menopause and natural menopause before the age of 40 years were associated with an increased risk of T2D among postmenopausal women. The findings also suggest potential interactions of premature menopause with SHBG levels, with the association appearing to be stronger in higher SHBG levels, as well as a joint association between menopause status and genetic risk factors on T2D incidence.

Decreased cold-inducible RNA-binding protein (CIRP) binding to GluRl on neuronal membranes mediates memory impairment resulting from prolonged hypobaric hypoxia exposure

AIM

To investigate the molecular mechanisms underlying memory impairment induced by high-altitude (HA) hypoxia, specifically focusing on the role of cold-inducible RNA-binding protein (CIRP) in regulating the AMPA receptor subunit GluR1 and its potential as a therapeutic target.

METHODS

A mouse model was exposed to 14 days of hypobaric hypoxia (HH), simulating conditions at an altitude of 6000 m. Behavioral tests were conducted to evaluate memory function. The expression, distribution, and interaction of CIRP with GluR1 in neuronal cells were analyzed. The binding of CIRP to GluR1 mRNA and its impact on GluR1 protein expression were examined. Additionally, the role of CIRP in GluR1 regulation was assessed using Cirp knockout mice. The efficacy of the Tat-C16 peptide, which consists of the Tat sequence combined with the CIRP 110-125 amino acid sequence, was also tested for its ability to mitigate HH-induced memory decline.

RESULTS

CIRP was primarily localized in neurons, with its expression significantly reduced following HH exposure. This reduction was associated with decreased GluR1 protein expression on the cell membrane and increased localization in the cytoplasm. The interaction between CIRP and GluR1 was diminished under HH conditions, leading to reduced GluR1 stability on the cell membrane and increased cytoplasmic relocation. These changes resulted in a decreased number of synapses and dendritic spines, impairing learning and memory functions. Administration of the Tat-C16 peptide effectively ameliorated these impairments by modulating GluR1 expression and distribution in HH-exposed mice.

CONCLUSION

CIRP plays a critical role in maintaining synaptic integrity under hypoxic conditions by regulating GluR1 expression and distribution. The Tat-C16 peptide shows promise as a therapeutic strategy for alleviating cognitive decline associated with HA hypoxia.

Brain stars take the lead during critical periods of early postnatal brain development: relevance of astrocytes in health and mental disorders

In the brain, astrocytes regulate shape and functions of the synaptic and vascular compartments through a variety of released factors and membrane-bound proteins. An imbalanced astrocyte activity can therefore have drastic negative impacts on brain development, leading to the onset of severe pathologies. Clinical and pre-clinical studies show alterations in astrocyte cell number, morphology, molecular makeup and astrocyte-dependent processes in different affected brain regions in neurodevelopmental (ND) and neuropsychiatric (NP) disorders. Astrocytes proliferate, differentiate and mature during the critical period of early postnatal brain development, a time window of elevated glia-dependent regulation of a proper balance between synapse formation/elimination, which is pivotal in refining synaptic connectivity. Therefore, any intrinsic and/or extrinsic factors altering these processes during the critical period may result in an aberrant synaptic remodeling and onset of mental disorders. The peculiar bridging position of astrocytes between synaptic and vascular compartments further allows them to "compute" the brain state and consequently secrete factors in the bloodstream, which may serve as diagnostic biomarkers of distinct healthy or disease conditions. Here, we collect recent advancements regarding astrogenesis and astrocyte-mediated regulation of neuronal network remodeling during early postnatal critical periods of brain development, focusing on synapse elimination. We then propose alternative hypotheses for an involvement of aberrancies in these processes in the onset of ND and NP disorders. In light of the well-known differential prevalence of certain brain disorders between males and females, we also discuss putative sex-dependent influences on these neurodevelopmental events. From a translational perspective, understanding age- and sex-dependent astrocyte-specific molecular and functional changes may help to identify biomarkers of distinct cellular (dys)functions in health and disease, favouring the development of diagnostic tools or the selection of tailored treatment options for male/female patients.

Epigenetic control of commensal induced Th2 Responses and Intestinal immunopathology

Understanding the initiation of T-helper (Th)-2 immunity is crucial for addressing allergic diseases that have been linked to the commensal microbiota. However, Th2 responses are notably absent from known host-microbiota intestinal immune circuits. Notably, the commensal protist Tritrichomonas induces a transient innate ILC2 circuit rather than a chronic Th2 circuit. Canonical Th2 responses rely on the induction of IL-4 production by innate cells. This study shows that the absence of Tet2 , a DNA demethylase, reprograms naïve T cells to autonomously produce IL-4 upon T cell receptor stimulation, bypassing the need for IL-4 from innate cells for Th2 differentiation. Loss of this checkpoint induces chronic Th2 responses to Tritrichomonas , associated with IL-25-dependent barrier dysfunction and increased susceptibility to allergic pathology in response to dietary antigens.

Sentence Summary

Regulation of cell autonomous IL-4 in T cells is critical to prevent dysregulated Th2 immunity to commensals and predisposition to allergy.

The causality between use of glucocorticoids and risk of pancreatitis: a Mendelian randomization study

Background and aim

To date, the association between glucocorticoid use and the risk of pancreatitis remains controversial. The aim of this study was the investigation of this possible relationship.

Methods

We carried out a two-sample Mendelian randomization (MR) analysis using GWAS data from European ancestry, East Asian descendants and the FinnGen Biobank Consortium to evaluate this potential causal relationship. Genetic variants associated with glucocorticoid use were selected based on genome-wide significance (p < 5×10-8).

Results

Our MR analysis of European ancestry data revealed no significant causal relationship between glucocorticoid use and AP (IVW: OR=1.084, 95% CI= 0.945-1.242, P=0.249; MR-Egger: OR=1.049, 95% CI= 0.686-1.603, P=0.828; weighted median: OR=1.026, 95% CI= 0.863-1.219, P=0.775) or CP (IVW: OR=1.027, 95% CI= 0.850-1.240, P=0.785; MR-Egger: OR= 1.625, 95% CI= 0.913-2.890, P= 0.111; weighted median: OR= 1.176, 95% CI= 0.909-1.523, P= 0.218). Sensitivity analyses, including MR-Egger and MR-PRESSO, indicated no evidence of pleiotropy or heterogeneity, confirming the robustness of our findings. Multivariable MR analysis adjusted for alcohol consumption, BMI, cholelithiasis and C-reactive protein levels supported these findings. Replicated analysis was performed on datasets from the FinnGen Biobank Consortium and East Asian descendants, and similar results were obtained.

Conclusions

This MR analysis suggests that there is no causal association between glucocorticoid use and the risk of pancreatitis.

Assessing genetic and environmental components for pterygium: a nationwide study in Taiwan

This study aims to estimate the familial risks of pterygium and assess its relative contributions to environmental and genetic factors using the 2000-2017 Taiwan National Health Insurance Research Database. The marginal Cox's model and the polygenic liability model were made. In Taiwan, the prevalence rate of pterygium in 2017 was 1.64% for individuals with affected first-degree relatives, higher than the general population (1.34%). The adjusted relative risk (RR) for pterygium was highest for twins of the same sex (15.54), followed by siblings of the same sex (4.69), offsprings (3.39), siblings of the different sex (2.88), spouse (2.12), parents (1.86), twins of the different sex (1.57), respectively. The phenotypic variance of pterygium was 21.6% from additive genetic variance, 24.3% from common environmental factors shared by family members, and 54.1% from non-shared environmental factors, respectively. Sensitivity analysis by restricting those with surgical pterygium reveals that aRRs and the three components were similar to those of the overall pterygium. In summary, the prevalence rate of pterygium was higher for individuals with affected first-degree relatives than for the general population. The non-shared environmental factors account for half of the phenotypic variance of pterygium; genetic and shared environmental factors explain the rest.

Genotype-by-Environment Interactions in Nonalcoholic Fatty Liver Disease and Chronic Illness among Mexican Americans: The Role of Acculturation Stress

This study examines the complex interplay of genetic and environmental interactions that shape chronic illness risk. Evidence is mounting for the role of genetic expression and the immune response in the pathogenesis of chronic disease. In the Rio Grande Valley of south Texas, where 90% of the population is Mexican American, chronic illnesses (including obesity, diabetes, nonalcoholic liver disease, and depression) are reaching epidemic proportions. This study leverages an ongoing family study of the genetic determinants of risk for obesity, diabetes, hypertension, hyperlipidemia, and depression in a Mexican American population. Data collected included blood pressure, BMI, hepatic transaminases, HbA1c, depression (BDI-II), acculturation/marginalization (ARSMA-II), and liver health as assessed by elastography. Heritability and genotype-by-environment (G×E) interactions were analyzed, focusing on the marginalization/separation measure of the ARSMA-II. Significant heritabilities were found for traits such as HbA1c (h2 = 0.52), marginalization (h2 = 0.30), AST (h2 = 0.25), ALT (h2 = 0.41), and BMI (h2 = 0.55). Genotype-by-environment interactions were significant for HbA1c, AST/ALT ratio, BDI-II, and CAP, indicating that genetic factors interact with marginalization to influence these traits. This study found that acculturation stress exacerbates the genetic response to chronic illness. These findings underscore the importance of considering G×E interactions in understanding disease susceptibility and may inform targeted interventions for at-risk populations. Further research is warranted to elucidate the underlying molecular pathways and replicate these findings in diverse populations.

Dual exposure-by-polygenic score interactions highlight disparities across social groups in the proportion needed to benefit

The transferability of polygenic scores across population groups is a major concern with respect to the equitable clinical implementation of genomic medicine. Since genetic associations are identified relative to the population mean, inevitably differences in disease or trait prevalence among social strata influence the relationship between PGS and risk. Here we quantify the magnitude of PGS-by-Exposure (PGSxE) interactions for seven human diseases (coronary artery disease, type 2 diabetes, obesity thresholded to body mass index and to waist-to-hip ratio, inflammatory bowel disease, chronic kidney disease, and asthma) and pairs of 75 exposures in the White-British subset of the UK Biobank study (n=408,801). Across 24,198 PGSxE models, 746 (3.1%) were significant by two criteria, at least three-fold more than expected by chance under each criterion. Predictive accuracy is significantly improved in the high-risk exposures and by including interaction terms with effects as large as those documented for low transferability of PGS across ancestries. The predominant mechanism for PGS×E interactions is shown to be amplification of genetic effects in the presence of adverse exposures such as low polyunsaturated fatty acids, mediators of obesity, and social determinants of ill health. We introduce the notion of the proportion needed to benefit (PNB) which is the cumulative number needed to treat across the range of the PGS and show that typically this is halved in the 70th to 80th percentile. These findings emphasize how individuals experiencing adverse exposures stand to preferentially benefit from interventions that may reduce risk, and highlight the need for more comprehensive sampling across socioeconomic groups in the performance of genome-wide association studies.

Denoising Search doubles the number of metabolite and exposome annotations in human plasma using an Orbitrap Astral mass spectrometer

Chemical exposures may impact human metabolism and contribute to the etiology of neurodegenerative disorders like Alzheimer's Disease (AD). Identifying these small metabolites involves matching experimental spectra to reference spectra in databases. However, environmental chemicals or physiologically active metabolites are usually present at low concentrations in human specimens. The presence of noise ions can significantly degrade spectral quality, leading to false negatives and reduced identification rates. In response to this challenge, the Spectral Denoising algorithm removes both chemical and electronic noise. Spectral Denoising outperformed alternative methods in benchmarking studies on 240 tested metabolites. It improved high confident compound identifications at an average 35-fold lower concentrations than previously achievable. Spectral Denoising proved highly robust against varying levels of both chemical and electronic noise even with >150-fold higher intensity of noise ions than true fragment ions. For human plasma samples of AD patients that were analyzed on the Orbitrap Astral mass spectrometer, Denoising Search detected 2.3-fold more annotated compounds compared to the Exploris 240 Orbitrap instrument, including drug metabolites, household and industrial chemicals, and pesticides. This combination of advanced instrumentation with a superior denoising algorithm opens the door for precision medicine in exposome research.

Sound as a bell: a deep learning approach for health status classification through speech acoustic biomarkers

BACKGROUND

Human health is a complex, dynamic concept encompassing a spectrum of states influenced by genetic, environmental, physiological, and psychological factors. Traditional Chinese Medicine categorizes health into nine body constitutional types, each reflecting unique balances or imbalances in vital energies, influencing physical, mental, and emotional states. Advances in machine learning models offer promising avenues for diagnosing conditions like Alzheimer's, dementia, and respiratory diseases by analyzing speech patterns, enabling complementary non-invasive disease diagnosis. The study aims to use speech audio to identify subhealth populations characterized by unbalanced constitution types.

METHODS

Participants, aged 18-45, were selected from the Acoustic Study of Health. Audio recordings were collected using ATR2500X-USB microphones and Praat software. Exclusion criteria included recent illness, dental issues, and specific medical histories. The audio data were preprocessed to Mel-frequency cepstral coefficients (MFCCs) for model training. Three deep learning models-1-Dimensional Convolution Network (Conv1D), 2-Dimensional Convolution Network (Conv2D), and Long Short-Term Memory (LSTM)-were implemented using Python to classify health status. Saliency maps were generated to provide model explainability.

RESULTS

The study used 1,378 recordings from balanced (healthy) and 1,413 from unbalanced (subhealth) types. The Conv1D model achieved a training accuracy of 91.91% and validation accuracy of 84.19%. The Conv2D model had 96.19% training accuracy and 84.93% validation accuracy. The LSTM model showed 92.79% training accuracy and 87.13% validation accuracy, with early signs of overfitting. AUC scores were 0.92 and 0.94 (Conv1D), 0.99 (Conv2D), and 0.97 (LSTM). All models demonstrated robust performance, with Conv2D excelling in discrimination accuracy.

CONCLUSIONS

The deep learning classification of human speech audio for health status using body constitution types showed promising results with Conv1D, Conv2D, and LSTM models. Analysis of ROC curves, training accuracy, and validation accuracy showed all models robustly distinguished between balanced and unbalanced constitution types. Conv2D excelled with good accuracy, while Conv1D and LSTM also performed well, affirming their reliability. The study integrates constitution theory and deep learning technologies to classify subhealth populations using noninvasive approach, thereby promoting personalized medicine and early intervention strategies.

A scalable and robust variance components method reveals insights into the architecture of gene-environment interactions underlying complex traits

Understanding the contribution of gene-environment interactions (GxE) to complex trait variation can provide insights into disease mechanisms, explain sources of heritability, and improve genetic risk prediction. While large biobanks with genetic and deep phenotypic data hold promise for obtaining novel insights into GxE, our understanding of GxE architecture in complex traits remains limited. We introduce a method to estimate the proportion of trait variance explained by GxE (GxE heritability) and additive genetic effects (additive heritability) across the genome and within specific genomic annotations. We show that our method is accurate in simulations and computationally efficient for biobank-scale datasets. We applied our method to common array SNPs (MAF ≥1%), fifty quantitative traits, and four environmental variables (smoking, sex, age, and statin usage) in unrelated white British individuals in the UK Biobank. We found 68 trait-E pairs with significant genome-wide GxE heritability (p<0.05/200) with a ratio of GxE to additive heritability of ≈6.8% on average. Analyzing ≈8 million imputed SNPs (MAF ≥0.1%), we documented an approximate 28% increase in genome-wide GxE heritability compared to array SNPs. We partitioned GxE heritability across minor allele frequency (MAF) and local linkage disequilibrium (LD) values, revealing that, like additive allelic effects, GxE allelic effects tend to increase with decreasing MAF and LD. Analyzing GxE heritability near genes highly expressed in specific tissues, we find significant brain-specific enrichment for body mass index (BMI) and basal metabolic rate in the context of smoking and adipose-specific enrichment for waist-hip ratio (WHR) in the context of sex.

Gene-environment interactions within a precision environmental health framework

Understanding the complex interplay of genetic and environmental factors in disease etiology and the role of gene-environment interactions (GEIs) across human development stages is important. We review the state of GEI research, including challenges in measuring environmental factors and advantages of GEI analysis in understanding disease mechanisms. We discuss the evolution of GEI studies from candidate gene-environment studies to genome-wide interaction studies (GWISs) and the role of multi-omics in mediating GEI effects. We review advancements in GEI analysis methods and the importance of large-scale datasets. We also address the translation of GEI findings into precision environmental health (PEH), showcasing real-world applications in healthcare and disease prevention. Additionally, we highlight societal considerations in GEI research, including environmental justice, the return of results to participants, and data privacy. Overall, we underscore the significance of GEI for disease prediction and prevention and advocate for integrating the exposome into PEH omics studies.

Gene-vegetarianism interactions in calcium, estimated glomerular filtration rate, and testosterone identified in genome-wide analysis across 30 biomarkers

We examined the associations of vegetarianism with metabolic biomarkers using traditional and genetic epidemiology. First, we addressed inconsistencies in self-reported vegetarianism among UK Biobank participants by utilizing data from two dietary surveys to find a cohort of strict European vegetarians (N = 2,312). Vegetarians were matched 1:4 with nonvegetarians for non-genetic association analyses, revealing significant effects of vegetarianism in 15 of 30 biomarkers. Cholesterol measures plus vitamin D were significantly lower in vegetarians, while triglycerides were higher. A genome-wide association study revealed no genome-wide significant (GWS; 5×10-8) associations with vegetarian behavior. We performed genome-wide gene-vegetarianism interaction analyses for the biomarkers, and detected a GWS interaction impacting calcium at rs72952628 (P = 4.47×10-8). rs72952628 is in MMAA, a B12 metabolic pathway gene; B12 has major deficiency potential in vegetarians. Gene-based interaction tests revealed two significant genes, RNF168 in testosterone (P = 1.45×10-6) and DOCK4 in estimated glomerular filtration rate (eGFR) (P = 6.76×10-7), which have previously been associated with testicular and renal traits, respectively. These nutrigenetic findings indicate genotype can modify the associations between vegetarianism and health outcomes.

Rethinking neurodegenerative diseases: neurometabolic concept linking lipid oxidation to diseases in the central nervous system

ABSTRACT

Currently, there is a lack of effective medicines capable of halting or reversing the progression of neurodegenerative disorders, including amyotrophic lateral sclerosis, Parkinson's disease, multiple sclerosis, or Alzheimer's disease. Given the unmet medical need, it is necessary to reevaluate the existing paradigms of how to target these diseases. When considering neurodegenerative diseases from a systemic neurometabolic perspective, it becomes possible to explain the shared pathological features. This innovative approach presented in this paper draws upon extensive research conducted by the authors and researchers worldwide. In this review, we highlight the importance of metabolic mitochondrial dysfunction in the context of neurodegenerative diseases. We provide an overview of the risk factors associated with developing neurodegenerative disorders, including genetic, epigenetic, and environmental factors. Additionally, we examine pathological mechanisms implicated in these diseases such as oxidative stress, accumulation of misfolded proteins, inflammation, demyelination, death of neurons, insulin resistance, dysbiosis, and neurotransmitter disturbances. Finally, we outline a proposal for the restoration of mitochondrial metabolism, a crucial aspect that may hold the key to facilitating curative therapeutic interventions for neurodegenerative disorders in forthcoming advancements.

Genetic and environmental interactions contribute to immune variation in rewilded mice

The relative and synergistic contributions of genetics and environment to interindividual immune response variation remain unclear, despite implications in evolutionary biology and medicine. Here we quantify interactive effects of genotype and environment on immune traits by investigating C57BL/6, 129S1 and PWK/PhJ inbred mice, rewilded in an outdoor enclosure and infected with the parasite Trichuris muris. Whereas cellular composition was shaped by interactions between genotype and environment, cytokine response heterogeneity including IFNγ concentrations was primarily driven by genotype with consequence on worm burden. In addition, we show that other traits, such as expression of CD44, were explained mostly by genetics on T cells, whereas expression of CD44 on B cells was explained more by environment across all strains. Notably, genetic differences under laboratory conditions were decreased following rewilding. These results indicate that nonheritable influences interact with genetic factors to shape immune variation and parasite burden.

Prenatal exposure to environmental contaminants and cord serum metabolite profiles in future immune-mediated diseases

BACKGROUND

Prenatal exposure to environmental contaminants is a significant health concern because it has the potential to interfere with host metabolism, leading to adverse health effects in early childhood and later in life. Growing evidence suggests that genetic and environmental factors, as well as their interactions, play a significant role in the development of autoimmune diseases.

OBJECTIVE

In this study, we hypothesized that prenatal exposure to environmental contaminants impacts cord serum metabolome and contributes to the development of autoimmune diseases.

METHODS

We selected cord serum samples from All Babies in Southeast Sweden (ABIS) general population cohort, from infants who later developed one or more autoimmune-mediated and inflammatory diseases: celiac disease (CD), Crohn's disease (IBD), hypothyroidism (HT), juvenile idiopathic arthritis (JIA), and type 1 diabetes (T1D) (all cases, N = 62), along with matched controls (N = 268). Using integrated exposomics and metabolomics mass spectrometry (MS) based platforms, we determined the levels of environmental contaminants and metabolites.

RESULTS

Differences in exposure levels were found between the controls and those who later developed various diseases. High contaminant exposure levels were associated with changes in metabolome, including amino acids and free fatty acids. Specifically, we identified marked associations between metabolite profiles and exposure levels of deoxynivalenol (DON), bisphenol S (BPS), and specific per- and polyfluorinated substances (PFAS).

IMPACT STATEMENT

Abnormal metabolism is a common feature preceding several autoimmune and inflammatory diseases. However, few studies compared common and specific metabolic patterns preceding these diseases. Here we hypothesized that exposure to environmental contaminants impacts cord serum metabolome, which may contribute to the development of autoimmune diseases. We found differences in exposure levels between the controls and those who later developed various diseases, and importantly, on the metabolic changes associated with the exposures. High contaminant exposure levels were associated with specific changes in metabolome. Our study suggests that prenatal exposure to specific environmental contaminants alters the cord serum metabolomes, which, in turn, might increase the risk of various immune-mediated diseases.

Role of Epigenetic Modification in the Intergeneration Transmission of War Trauma

War trauma has been linked to changes in the neuroendocrine and immunological systems and increases the risk of physical disorders. Traumatic events during the war may have long-term repercussions on psychological and biological parameters in future generations, implying that traumatic stress may have transgenerational consequences. This article addresses how epigenetic mechanisms, which are a key biological mechanism for dynamic adaptation to environmental stressors, may help explain the long-term and transgenerational consequences of trauma. In war survivors, epigenetic changes in genes mediating the hypothalamus-pituitary-adrenal axis, as well as the immune system, have been reported. These genetic modifications may cause long-term changes in the stress response as well as physical health risks. Also, the finding of biomarkers for diagnosing the possibility of psychiatric illnesses in people exposed to stressful conditions such as war necessitates extensive research. While epigenetic research has the potential to further our understanding of the effects of trauma, the findings must be interpreted with caution because epigenetic molecular mechanisms is only one piece of a complicated puzzle of interwoven biological and environmental components.

Genotype × environment interactions in gene regulation and complex traits

Genotype × environment interactions (GxE) have long been recognized as a key mechanism underlying human phenotypic variation. Technological developments over the past 15 years have dramatically expanded our appreciation of the role of GxE in both gene regulation and complex traits. The richness and complexity of these datasets also required parallel efforts to develop robust and sensitive statistical and computational approaches. Although our understanding of the genetic architecture of molecular and complex traits has been maturing, a large proportion of complex trait heritability remains unexplained. Furthermore, there are increasing efforts to characterize the effect of environmental exposure on human health. We therefore review GxE in human gene regulation and complex traits, advocating for a comprehensive approach that jointly considers genetic and environmental factors in human health and disease.

Unveiling challenges in Mendelian randomization for gene-environment interaction

Gene-environment (GxE) interactions play a crucial role in understanding the complex etiology of various traits, but assessing them using observational data can be challenging due to unmeasured confounders for lifestyle and environmental risk factors. Mendelian randomization (MR) has emerged as a valuable method for assessing causal relationships based on observational data. This approach utilizes genetic variants as instrumental variables (IVs) with the aim of providing a valid statistical test and estimation of causal effects in the presence of unmeasured confounders. MR has gained substantial popularity in recent years largely due to the success of genome-wide association studies. Many methods have been developed for MR; however, limited work has been done on evaluating GxE interaction. In this paper, we focus on two primary IV approaches: the two-stage predictor substitution and the two-stage residual inclusion, and extend them to accommodate GxE interaction under both the linear and logistic regression models for continuous and binary outcomes, respectively. Comprehensive simulation study and analytical derivations reveal that resolving the linear regression model is relatively straightforward. In contrast, the logistic regression model presents a considerably more intricate challenge, which demands additional effort.

A retrospective analysis of the incidence and risk factors of perioperative urinary tract infections after total hysterectomy

INTRODUCTION

Perioperative urinary tract infections (PUTIs) are common in the United States and are a significant contributor to high healthcare costs. There is a lack of large studies on the risk factors for PUTIs after total hysterectomy (TH).

METHODS

We conducted a retrospective study using a national inpatient sample (NIS) of 445,380 patients from 2010 to 2019 to analyze the risk factors and annual incidence of PUTIs associated with TH perioperatively.

RESULTS

PUTIs were found in 9087 patients overall, showing a 2.0% incidence. There were substantial differences in the incidence of PUTIs based on age group (P < 0.001). Between the two groups, there was consistently a significant difference in the type of insurance, hospital location, hospital bed size, and hospital type (P < 0.001). Patients with PUTIs exhibited a significantly higher number of comorbidities (P < 0.001). Unsurprisingly, patients with PUTIs had a longer median length of stay (5 days vs. 2 days; P < 0.001) and a higher in-hospital death rate (from 0.1 to 1.1%; P < 0.001). Thus, the overall hospitalization expenditures increased by $27,500 in the median ($60,426 vs. $32,926, P < 0.001) as PUTIs increased medical costs. Elective hospitalizations are less common in patients with PUTIs (66.8% vs. 87.6%; P < 0.001). According to multivariate logistic regression study, the following were risk variables for PUTIs following TH: over 45 years old; number of comorbidities (≥ 1); bed size of hospital (medium, large); teaching hospital; region of hospital(south, west); preoperative comorbidities (alcohol abuse, deficiency anemia, chronic blood loss anemia, congestive heart failure, diabetes, drug abuse, hypertension, hypothyroidism, lymphoma, fluid and electrolyte disorders, metastatic cancer, other neurological disorders, paralysis, peripheral vascular disorders, psychoses, pulmonary circulation disorders, renal failure, solid tumor without metastasis, valvular disease, weight loss); and complications (sepsis, acute myocardial infarction, deep vein thrombosis, gastrointestinal hemorrhage, pneumonia, stroke, wound infection, wound rupture, hemorrhage, pulmonary embolism, blood transfusion, postoperative delirium).

CONCLUSIONS

The findings suggest that identifying these risk factors can lead to improved preventive strategies and management of PUTIs in TH patients. Counseling should be done prior to surgery to reduce the incidence of PUTIs.

THE MANUSCRIPT ADDS TO CURRENT KNOWLEDGE

In medical practice, the identification of risk factors can lead to improved patient prevention and treatment strategies. We conducted a retrospective study using a national inpatient sample (NIS) of 445,380 patients from 2010 to 2019 to analyze the risk factors and annual incidence of PUTIs associated with TH perioperatively. PUTIs were found in 9087 patients overall, showing a 2.0% incidence. We found that noted increased length of hospital stay, medical cost, number of pre-existing comorbidities, size of the hospital, teaching hospitals, and region to also a play a role in the risk of UTI's.

CLINICAL TOPICS

Urogynecology.

LDER-GE estimates phenotypic variance component of gene-environment interactions in human complex traits accurately with GE interaction summary statistics and full LD information

Gene-environment (GE) interactions are essential in understanding human complex traits. Identifying these interactions is necessary for deciphering the biological basis of such traits. In this study, we review state-of-art methods for estimating the proportion of phenotypic variance explained by genome-wide GE interactions and introduce a novel statistical method Linkage-Disequilibrium Eigenvalue Regression for Gene-Environment interactions (LDER-GE). LDER-GE improves the accuracy of estimating the phenotypic variance component explained by genome-wide GE interactions using large-scale biobank association summary statistics. LDER-GE leverages the complete Linkage Disequilibrium (LD) matrix, as opposed to only the diagonal squared LD matrix utilized by LDSC (Linkage Disequilibrium Score)-based methods. Our extensive simulation studies demonstrate that LDER-GE performs better than LDSC-based approaches by enhancing statistical efficiency by ~23%. This improvement is equivalent to a sample size increase of around 51%. Additionally, LDER-GE effectively controls type-I error rate and produces unbiased results. We conducted an analysis using UK Biobank data, comprising 307 259 unrelated European-Ancestry subjects and 966 766 variants, across 217 environmental covariate-phenotype (E-Y) pairs. LDER-GE identified 34 significant E-Y pairs while LDSC-based method only identified 23 significant E-Y pairs with 22 overlapped with LDER-GE. Furthermore, we employed LDER-GE to estimate the aggregated variance component attributed to multiple GE interactions, leading to an increase in the explained phenotypic variance with GE interactions compared to considering main genetic effects only. Our results suggest the importance of impacts of GE interactions on human complex traits.

Piloting a Measure of Segregation at the Census Tract Level: Associations with Place and Racial/Ethnic Disparities in Life Expectancy

This study considers residential segregation as a critical driver of racial/ethnic health disparities and introduces a proxy measure of segregation that estimates the degree of segregation at the census tract level with a metric capturing the overrepresentation of a racialized/ethnic group in a census tract in relation to that group's representation at the city level. Using Dallas, Texas as a pilot city, the measure is used to investigate mean life expectancy at birth for relatively overrepresented Hispanic, non-Hispanic white, non-Hispanic Black, and Asian census tracts and examine for significant differences between mean life expectancy in relatively overrepresented census tracts and that group's mean life expectancy at the state level. Multivariable linear regression analysis was utilized to assess how segregation measured at the census tract level associates with life expectancy across different racialized/ethnic groups, controlling for socioeconomic disparities. This study aimed to expose the need to consider the possibility of neighborhood mechanisms beyond socioeconomic characteristics as an important determinant of health and draw attention to the importance of critically engaging the experience of place in examinations of racial and ethnic health disparities. Multivariable linear regression modeling resulted in significant findings for non-Hispanic Black, non-Hispanic white, and Asian groups, indicating increased census tract-level life expectancy for Black and white residents in highly segregated census tracts and decreased life expectancy for residents of tracts in which the Asian community is overrepresented when compared to state means. Unadjusted models demonstrated socioeconomic inequities between first and fourth quartile census tracts and pointed to the importance of mixed methods in health disparities research and the importance of including the voice of community members to account for places of daily lived experience and people's relationships with them.

A Comprehensive Look at the Development of Asthma in Children

Asthma, a prevalent chronic respiratory condition affecting millions of children globally, presents a significant health challenge. This review critically examines the developmental pathways of asthma in children, focusing on genetic, environmental, and early-life determinants. Specifically, we explore the impact of prenatal and postnatal factors such as maternal smoking, nutrition, respiratory infections, and allergen exposure on asthma development. Our analysis highlights the intricate interplay of these influences and their contribution to childhood asthma. Moreover, we emphasize targeted strategies and interventions to mitigate its burden, including genetic counseling for at-risk families, environmental modifications to reduce triggers, and early-life immunomodulation. By delving into these preventive measures and interventions, our review aims to provide actionable insights for healthcare professionals in developing tailored strategies to address the complexities of childhood asthma. In summary, this article offers a detailed examination of asthma development in children, aiming to enhance understanding and inform efforts to reduce its burden through targeted interventions.

Genetic polymorphisms associated with adverse pregnancy outcomes in nulliparas

Adverse pregnancy outcomes (APOs) affect a large proportion of pregnancies and represent an important cause of morbidity and mortality worldwide. Yet the pathophysiology of APOs is poorly understood, limiting our ability to prevent and treat these conditions. To search for genetic markers of maternal risk for four APOs, we performed multi-ancestry genome-wide association studies (GWAS) for pregnancy loss, gestational length, gestational diabetes, and preeclampsia. We clustered participants by their genetic ancestry and focused our analyses on three sub-cohorts with the largest sample sizes: European, African, and Admixed American. Association tests were carried out separately for each sub-cohort and then meta-analyzed together. Two novel loci were significantly associated with an increased risk of pregnancy loss: a cluster of SNPs located downstream of the TRMU gene (top SNP: rs142795512), and the SNP rs62021480 near RGMA. In the GWAS of gestational length we identified two new variants, rs2550487 and rs58548906 near WFDC1 and AC005052.1, respectively. Lastly, three new loci were significantly associated with gestational diabetes (top SNPs: rs72956265, rs10890563, rs79596863), located on or near ZBTB20, GUCY1A2, and RPL7P20, respectively. Fourteen loci previously correlated with preterm birth, gestational diabetes, and preeclampsia were found to be associated with these outcomes as well.

Shaping immunity: The influence of natural selection on population immune diversity

Humans exhibit considerable variability in their immune responses to the same immune challenges. Such variation is widespread and affects individual and population-level susceptibility to infectious diseases and immune disorders. Although the factors influencing immune response diversity are partially understood, what mechanisms lead to the wide range of immune traits in healthy individuals remain largely unexplained. Here, we discuss the role that natural selection has played in driving phenotypic differences in immune responses across populations and present-day susceptibility to immune-related disorders. Further, we touch on future directions in the field of immunogenomics, highlighting the value of expanding this work to human populations globally, the utility of modeling the immune response as a dynamic process, and the importance of considering the potential polygenic nature of natural selection. Identifying loci acted upon by evolution may further pinpoint variants critically involved in disease etiology, and designing studies to capture these effects will enrich our understanding of the genetic contributions to immunity and immune dysregulation.

Genetic and biological drivers of prostate cancer disparities in Black men

Black men with prostate cancer have historically had worse outcomes than white men with prostate cancer. The causes of this disparity in outcomes are multi-factorial, but a potential basis is that prostate cancers in Black men are biologically distinct from prostate cancers in white men. Evidence suggests that genetic and ancestral factors, molecular pathways involving androgen and non-androgen receptor signalling, inflammation, epigenetics, the tumour microenvironment and tumour metabolism are contributing factors to the racial disparities observed. Key genetic and molecular pathways linked to prostate cancer risk and aggressiveness have potential clinical relevance. Describing biological drivers of prostate cancer disparities could inform efforts to improve outcomes for Black men with prostate cancer.

Atopy and allergic diseases among college students at a Saudi Public University

Background

Despite its significant prevalence worldwide, atopy and allergic diseases continue to need more studies, with a need for published articles describing the prevalence of atopy and allergic diseases in Saudi society. The study aimed to determine the prevalence of atopic dermatitis diseases among college students at Imam Mohammad Ibn Saud Islamic University.

Methods

The Cross-sectional study was conducted in Saudi Arabia among college students at Imam Mohammed bin Saud Islamic University in Riyadh based on self-report Asthma and Allergies questionnaires (ISSAC questionnaire, atopic dermatitis part).

Results

Seven hundred ninety-seven (797) students from 11 colleges participated in the study. About half (47.6%) aged between 21 and 24, and 73.7% were females. More than one-third (34.8%) had atopic dermatitis during the last six months, while 30.7% of the participants reported having itchy rash in the past 12 months, and 33.8% were diagnosed with eczema in any life interval. Age is not one of the significant factors affecting the prevalence of atopic dermatitis. However, the prevalence of atopic dermatitis slightly increases with age (P = 0.062). Atopic dermatitis was significantly higher among female students (39.7%) than 21.0% among male students (P = 0.000). College level nor GPA had no significant impact on the prevalence of atopic dermatitis (P = 0.238 and 0.884, respectively).

Conclusion

Imam Mohammad Ibn Saud Islamic University students have a high prevalence of atopic dermatitis, which may indicate a higher prevalence of allergens. Females and older participants were more liable to reported atopic dermatitis.

Predicting the Activity of Unidentified Chemicals in Complementary Bioassays from the HRMS Data to Pinpoint Potential Endocrine Disruptors

The majority of chemicals detected via nontarget liquid chromatography high-resolution mass spectrometry (HRMS) in environmental samples remain unidentified, challenging the capability of existing machine learning models to pinpoint potential endocrine disruptors (EDs). Here, we predict the activity of unidentified chemicals across 12 bioassays related to EDs within the Tox21 10K dataset. Single- and multi-output models, utilizing various machine learning algorithms and molecular fingerprint features as an input, were trained for this purpose. To evaluate the models under near real-world conditions, Monte Carlo sampling was implemented for the first time. This technique enables the use of probabilistic fingerprint features derived from the experimental HRMS data with SIRIUS+CSI:FingerID as an input for models trained on true binary fingerprint features. Depending on the bioassay, the lowest false-positive rate at 90% recall ranged from 0.251 (sr.mmp, mitochondrial membrane potential) to 0.824 (nr.ar, androgen receptor), which is consistent with the trends observed in the models' performances submitted for the Tox21 Data Challenge. These findings underscore the informativeness of fingerprint features that can be compiled from HRMS in predicting the endocrine-disrupting activity. Moreover, an in-depth SHapley Additive exPlanations analysis unveiled the models' ability to pinpoint structural patterns linked to the modes of action of active chemicals. Despite the superior performance of the single-output models compared to that of the multi-output models, the latter's potential cannot be disregarded for similar tasks in the field of in silico toxicology. This study presents a significant advancement in identifying potentially toxic chemicals within complex mixtures without unambiguous identification and effectively reducing the workload for postprocessing by up to 75% in nontarget HRMS.

Modulation of Bone Mineral Density and Its Response to Menopausal Hormone Therapy according to the Apolipoprotein E Genotype in Postmenopausal Korean Women

OBJECTIVES

Genetic factors are a major cause of osteoporosis. The present study evaluated the association of the apolipoprotein E (ApoE) genotype with bone mineral density (BMD) and its response to menopausal hormone therapy (MHT) in postmenopausal Korean women.

METHODS

This retrospective cohort study included 172 postmenopausal women with no endocrine diseases, medications, or lifestyles that would affect bone metabolism and who were continuously treated with MHT for at least 2 years. BMDs were measured at baseline and periodically.

RESULTS

Linear regression analysis demonstrated similar baseline BMDs at the lumbar spine, but significantly lower at the femur neck and total hip in the ApoE ε4 carrier than in the noncarrier group, after controlling for age, body mass index, and history of MHT usage. Overall, the Wilcoxon signed rank test demonstrated that MHT increased the BMD percentage change at all three regions, and the Generalized Estimating Equation (GEE) demonstrated significant time trends at the lumbar spine and femur neck. ApoE ε4 noncarriers exhibited a significant time trend in BMD changes at the femur neck, whereas ε4 carriers exhibited a time trend at the lumbar spine. However, BMD changes at each time point were comparable at all regions between the groups. Notably, GEE adjusted for baseline characteristics and BMD revealed a significant interaction effect of time and ApoE ε4 allele in BMD changes at the femur neck.

CONCLUSIONS

Postmenopausal Korean women carrying the ApoE ε4 allele demonstrated a lower hip BMD compared with ε4 noncarriers. Furthermore, the ε4 allele may modulate hip BMD responses to MHT.

Glycan-specific molecularly imprinted polymers towards cancer diagnostics: merits, applications, and future perspectives

Aberrant glycans are a hallmark of cancer states. Notably, emerging evidence has demonstrated that the diagnosis of cancers with tumour-specific glycan patterns holds great potential to address unmet medical needs, especially in improving diagnostic sensitivity and selectivity. However, despite vast glycans having been identified as potent markers, glycan-based diagnostic methods remain largely limited in clinical practice. There are several reasons that prevent them from reaching the market, and the lack of anti-glycan antibodies is one of the most challenging hurdles. With the increasing need for accelerating the translational process, numerous efforts have been made to find antibody alternatives, such as lectins, boronic acids and aptamers. However, issues concerning affinity, selectivity, stability and versatility are yet to be fully addressed. Molecularly imprinted polymers (MIPs), synthetic antibody mimics with tailored cavities for target molecules, hold the potential to revolutionize this dismal progress. MIPs can bind a wide range of glycan markers, even those without specific antibodies. This capacity effectively broadens the clinical applicability of glycan-based diagnostics. Additionally, glycoform-resolved diagnosis can also be achieved through customization of MIPs, allowing for more precise diagnostic applications. In this review, we intent to introduce the current status of glycans as potential biomarkers and critically evaluate the challenges that hinder the development of in vitro diagnostic assays, with a particular focus on glycan-specific recognition entities. Moreover, we highlight the key role of MIPs in this area and provide examples of their successful use. Finally, we conclude the review with the remaining challenges, future outlook, and emerging opportunities.

Microbiota Implications in Endocrine-Related Diseases: From Development to Novel Therapeutic Approaches

This comprehensive review article delves into the critical role of the human microbiota in the development and management of endocrine-related diseases. We explore the complex interactions between the microbiota and the endocrine system, emphasizing the implications of microbiota dysbiosis for the onset and progression of various endocrine disorders. The review aims to synthesize current knowledge, highlighting recent advancements and the potential of novel therapeutic approaches targeting microbiota-endocrine interactions. Key topics include the impact of microbiota on hormone regulation, its role in endocrine pathologies, and the promising avenues of microbiota modulation through diet, probiotics, prebiotics, and fecal microbiota transplantation. We underscore the importance of this research in advancing personalized medicine, offering insights for more tailored and effective treatments for endocrine-related diseases.

Decreased DNA repair capacity caused by exposure to metal mixtures is modulated by the PARP1 rs1136410 variant in newborns from a polluted metropolitan area

BACKGROUND

DNA damage caused by exposure to metal mixtures and the potential modulating role of genes involved in DNA repair and the antioxidant response have not been evaluated in newborns.

AIM

The aim was to evaluate the association between prenatal exposure to metal mixtures and DNA repair capacity (DRC) in newborns from the Metropolitan Area of Mexico City (MAMC), a heavily polluted area, and the impact of variants in genes involved in DNA repair and the antioxidant response on this association.

METHODS

We analyzed cord blood samples obtained at delivery from 125 healthy newborns from the MAMC. Twenty-four elements were determined by inductively coupled plasma mass spectrometry (ICP‒MS), but only 12 (Cu, I, Se, Zn, As, Ba, Cs, Mn, Sb, Sr, Pb, and Ti) were quantified in most samples. DRC was assessed by the challenge-comet assay, and OGG1, PARP1, and NFE2L2 genotyping was performed with TaqMan probes. Metal mixtures were identified and analyzed using principal component analysis (PCA) and weighted quantile sum (WQS) regression. Independent adjusted linear regression models were used to evaluate the associations.

RESULTS

A null DRC was observed in 46% of newborns. The metals with the highest concentrations were Mn, Sr, Ti, and Pb. Essential elements showed normal levels. Only the mixture characterized by increased As, Cs, Cu, Se, and Zn levels was inversely associated with DRC. As was the principal contributor (37.8%) in the negative direction in the DRC followed by Ba and Sb, according to the WQS regression. Newborns carrying of the derived (G) allele of the PARP1 rs1136410 variant showed decreased DRC by exposure to some potentially toxic metals (PTMs) (As, Cs, and Ba).

CONCLUSION

Prenatal exposure to metal mixtures negatively affected DRC in newborns, and the PARP1 rs1136410 variant had a modulating role in this association.