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PubMed2026

A flexible framework for robust and efficient Mendelian randomization with debiasing.

Mendelian randomization (MR) has been widely used to infer causal relationships between exposures and outcomes in epidemiological studies. However, classical MR assumptions can be violated when genetic variants are associated with outcomes through pathways other than the exposure, leading to uncorrelated and/or correlated pleiotropy. Additionally, measurement error arising from the inherent uncertainty in summary statistics obtained from large-scale genome-wide association studies can introduce bias into the causal effect estimate. To address these issues, we develop a debiased mixture inverse variance weighting ($\mathsf{dmIVW}$) method with three major advantages. First, it is capable of simultaneously handling various types of pleiotropy and eliminating the bias caused by uncertainty. Second, it can guard against distortion caused by invalid genetic variants while effectively harnessing their information. Third, our unified framework facilitates a fair comparison and combination of a series of submodels, encompassing several popular MR methods as special cases. Through real data applications, the effectiveness and robustness of $\mathsf{dmIVW}$ in estimating the causal effects of risk factors on common diseases are demonstrated.

Mendelian Randomization Analysis↗

PubMed2025

Exploring the causal association between television viewing and meniscal injuries: A two-sample Mendelian randomization analysis.

The aim of this study was to assess whether there is a potential causal relationship between sedentary behavior and meniscal injuries based on the Mendelian randomization (MR) method. This study used a two-sample MR design to integrate pooled data from a large-scale genome-wide association studies (GWAS). Single nucleotide polymorphisms (SNPs) that were significantly associated with sedentary behavior (represented by daily TV-viewing time) and independent of each other were selected as instrumental variables, while focusing on data from populations of European ancestry. To ensure the robustness and reliability of the analyses, 3 mainstream MR analysis methods were combined in this study: inverse variance weighted (IVW), weighted median estimation (WME) and MR-Egger regression. Heterogeneity test, horizontal multivariate analysis, and leave-one-out sensitivity test were also conducted to further validate the stability of causal estimation. The results of the IVW method showed that sedentary behavior was significantly associated with the risk of meniscus injury, with an OR (95% CI) of 2.93 (1.89-4.52), and a P-value of < .001, suggesting that sedentary behavior may be an important risk factor for meniscus injury. No significant bias was found in the heterogeneity test and the assessment of multiple validity, and the sensitivity analysis showed that the effect of individual SNPs on the overall estimation was small, and the results had good robustness. This study provides genetic epidemiological evidence of a positive causal effect of sedentary behavior on meniscal injuries based on a causal inference approach with genetic instrumental variables. The results suggest that reducing sedentary time, especially prolonged TV watching behavior, may reduce the risk of meniscus injury to some extent.

Humans↗

PubMed2026

Fluoride as a Modifier of Metallome Homeostasis: A Systematic Review of Animal Studies.

Fluoride is widely used for caries prevention due to its effects on mineralized tissues, yet its potential role as a modifier of systemic metal homeostasis remains insufficiently explored. This systematic review synthesizes preclinical evidence on the association between fluoride exposure and changes in metal and semi-metal concentrations across biological matrices. A comprehensive search strategy was conducted across major databases without language or date restrictions, following SyRF, CAMARADES and PRISMA 2020 guidelines. Thirty-one animal studies were included, encompassing multiple species, exposure conditions and analytical approaches. Despite substantial methodological heterogeneity, consistent patterns emerged. Fluoride exposure was associated with element-specific redistribution of the metallome rather than uniform change. Essential elements were predominantly depleted, most consistently zinc, copper and manganese, whereas the toxic metals lead and cadmium tended to be retained. This contrast between homeostatically regulated essential elements that are lost and non-regulated toxic metals that accumulate supports the hypothesis that fluoride differentially modifies the distribution and retention of co-existing elements. The novelty of this review lies in integrating metallomic outcomes across experimental models, highlighting fluoride as a potential systemic modulator rather than a tissue-specific agent. Although variability in study design and risk of bias limits causal inference, the consistent directionality of findings across models reinforces their biological plausibility and translational relevance.

Animals↗

PubMed2026

Multi-omics uncovers the pleiotropic genetic mechanisms linking MASLD and cardiometabolic syndromes.

BACKGROUND: Metabolic dysfunction-associated steatotic liver disease (MASLD) and cardiovascular-kidney-metabolic (CKM) syndrome are interrelated conditions with shared pathophysiological features; however, the genetic architecture underlying their relationship has not been fully elucidated. Deciphering this shared genetic basis holds promise for advancing mechanistic insights and therapeutic discovery. METHODS: We performed an integrated genome-wide cross-trait analysis using GWAS summary statistics for MASLD and 38 CKM traits. Our analysis estimated genetic correlations, inferred causal relationships, and identified pleiotropic variants. Candidate causal genes and druggable targets were subsequently prioritized through integrating multi-omics data. RESULTS: MASLD exhibited significant genetic correlations with 16 CKM traits, especially metabolic and cardiovascular conditions. Bidirectional causal relationships were observed between MASLD and T2D, adiposity, and lipid traits. We discovered 116 pleiotropic loci, including 65 shared causal variants such as rs429358 near APOE, which exerted influence across multiple traits. Gene-based analyses prioritized 152 unique candidate pleiotropic genes, enriched in lipid and cholesterol metabolism, and highly expressed in the liver, adipose, and immune-related cell types, such as macrophages and endothelial cells. Multi-omics integration validated 131 genes using eQTL and pQTL data from multiple tissues and cohorts. Notably, FTO and APOE emerged as central pleiotropic hubs, and druggability evaluation highlighted APOE, LPL, PPARG, and GPBAR1 as established therapeutic targets for metabolic diseases. CONCLUSION: This study provides a comprehensive map of the shared genetic architecture between MASLD and CKM syndrome, reveals novel causal genes and repurposable drug targets, and offers insights into precision medicine approaches for cardiometabolic and liver diseases.

Humans↗

PubMed2025

Global burden of peripheral arterial disease (1990-2021), global burden trends and the impact of blood lead on peripheral arterial disease: a multidimensional analysis based on NHANES, GBD, and Mendelian randomization.

OBJECTIVE: Peripheral arterial disease (PAD) is a common cardiovascular disease that it is an important reason for the decline of patients' quality of life and the increase of family economic burden. To systematically evaluate the association between environmental lead exposure and peripheral arterial disease (PAD) and to characterize the global distribution of PAD disease burden, while exploring differences among regions with varying socioeconomic development. METHODS: Using data from the National Health and Nutrition Examination Survey (NHANES), the Global Burden of Disease (GBD) database, and genome-wide association studies (GWAS), we employed multivariable logistic regression to examine the link between lead exposure and PAD. Mendelian randomization (MR) was used to infer causality, and we analyzed PAD disease burden trends across countries of differing income levels. RESULTS: The burden on PAD patients worldwide shows a downward trend. In high SDI and high middle SDI countries, the burden of PAD gradually decreases, while in low middle SDI and low SDI countries, the burden of PAD gradually decreases. After adjusting for potential confounders, a significant dose-response relationship was observed between blood lead levels and PAD risk (OR = 1.04, 95% CI: 1.00-1.09). This association was more pronounced among males (OR = 1.07, 95% CI: 1.05-1.09), individuals with higher education (OR = 1.24, 95% CI: 1.16-1.32), and patients with hypertension (OR = 1.07, 95% CI: 1.05-1.09). MR analysis supported a causal link between lead exposure and PAD. Global trend analysis indicated that PAD burden is declining in high-income countries but rising in low-income regions, highlighting significant health inequities. CONCLUSION: Environmental lead exposure is significantly associated with increased PAD risk, with notable differences in population susceptibility. These findings underscore the necessity of environmental exposure control and tailored prevention strategies to enhance cardiovascular health worldwide.

Humans↗

PubMed2025

Advancing translational exposomics: bridging genome, exposome and personalized medicine.

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

Humans↗

PubMed2026

Arrhythmia and cardiomyopathy risk in Taiwan with complementary biobank evidence on thyroid genetic susceptibility: an integrative population-based framework.

BACKGROUND: Arrhythmia-induced cardiomyopathy (AiCM) is a potentially reversible cause of ventricular dysfunction; however, only a subset of patients with arrhythmia develop cardiomyopathy. Emerging evidence suggests that endocrine factors, particularly thyroid dysfunction with genetic susceptibility, may contribute to inter-individual variability in arrhythmia-related myocardial outcomes. METHODS: We performed a dual-cohort population-based study using the National Health Insurance Research Database (NHIRD, 2000-2015) and the Taiwan Biobank (TWB). In NHIRD, we examined the association between newly diagnosed arrhythmia and incident cardiomyopathy using Cox proportional hazards models. In TWB, genome-wide data, thyroid-stimulating hormone (TSH), polygenic risk scores (PRSs), lifestyle factors, and metabolic comorbidities were analyzed using multivariable regression and interaction models to assess determinants of thyroid dysfunction. RESULTS: In the NHIRD cohort, arrhythmia was associated with a significantly increased risk of incident cardiomyopathy (adjusted hazard ratio (aHR): 2.49, 95% CI: 1.94-2.96), with atrial fibrillation showing the strongest association among arrhythmia subtypes. In the TWB cohort, a higher thyroid polygenic risk score was strongly associated with thyroid dysfunction (adjusted odds ratio (aOR): 6.64, 95% CI: 5.86-7.52). The association between genetic susceptibility and thyroid dysfunction was further modified by metabolic and lifestyle factors, including diabetes, hyperlipidemia, and dietary patterns. Genome-wide analysis identified multiple loci associated with thyroid-stimulating hormone regulation, consistent with a polygenic architecture of thyroid endocrine traits. CONCLUSION: Arrhythmia was associated with an increased risk of cardiomyopathy in a nationwide cohort, while thyroid genetic susceptibility was strongly associated with thyroid dysfunction in a biobank cohort and modified by metabolic and lifestyle factors. These findings provide complementary population-level evidence of parallel cardiovascular and endocrine-genetic associations. Because the two cohorts were not individually linked, causal inference cannot be established. The results support a systems-level framework of endocrine-cardiac interaction and suggest that integrated clinical and genetic risk assessment may help identify individuals who warrant closer monitoring.

arrhythmia↗

PubMed1979

The effects of prenatal care upon the health of the newborn.

Data upon all births and infant deaths in New York City in 1968 are analyzed using methods for the analysis of multidimensional contingency tables. These methods provide estimates of the effect of variations in prenatal care upon the relative risks of low birth weight and neonatal and postneonatal mortality, controlling for a wide variety of factors which tend to "select" women into a program of prenatal care. Significant relationships between lack of prenatal care and infant mortality are estimated, but these occur mainly via the relationship of inadequate prenatal care to low birth weight. Furthermore, among white mothers who delivered on a private service, those receiving inadequate levels of prenatal care experienced only slightly increased risks of a low birth weight infant. In contrast, white mothers who delivered on a general service, and all black mothers, experienced substantially increased risks when receiving inadequate prenatal care. A variety of behavioral characteristics of mothers were not controlled in these analyses, and thus clear causal inferences concerning the efficacy of prenatal care cannot be drawn. These analyses do, however, identify a significant population of women at substantial risk.

Adult↗

PubMed2026

Novel Insights into Immune Cell Function in Type 2 Diabetes Mediated by Gut Microbiota: A Two-Sample Mendelian Randomization Study.

INTRODUCTION: The role of immune cells in type 2 diabetes mellitus (T2DM) development is well-studied, but their interactions with the gut microbiota and the mediating role in this process remain unclear. METHODS: We analyzed 731 immune cell phenotypes (3,757 Europeans), 473 gut microbiota traits (5,959 Finns), and T2DM data (over 400,000 Finns). Mendelian randomization (MR) was based on three assumptions: the instrumental variable (IV) is associated with exposure, IV is not influenced by confounding, and IV affects the outcome only through exposure. We selected single-nucleotide polymorphisms (SNPs) from genome-wide association studies as instrumental variables (IVs) to infer causal effects in two-sample MR analysis. RESULTS: We identified 36 immune cell phenotypes associated with T2DM, including 29 protective factors and seven risk factors, as well as 10 gut microbiota significantly linked to T2DM, with eight protective factors and two risk factors. MR revealed that five gut microbiota mediated the relationship between immune cells and T2DM. For example, the effects of CD3 on resting Treg (OR: 1.0136), CD3 on CM CD4+ (OR: 1.0180), and CD3 on naive CD4+ cells (OR: 1.0150) in T2DM were found to be partially mediated by the species Bacillus. AYThe corresponding mediation effect proportions were 8.99%, 11.8%, and 11.4%. DISCUSSION: MR analysis identified multiple gut microbiota mediators in the relationship between immune cells and T2DM, addressing previous observational evidence. Limitations included the European ancestry bias, among others. CONCLUSION: This study has highlighted the gut microbiota as a mediator between immune cells and T2DM, offering new insights for its early prevention and intervention.

Diabetes Mellitus, Type 2↗

PubMed2026

Gut Microbiota, Lipidome, and Metabolites Mediate Immune Dysregulation in Diabetic Microvascular Disease: A Two-sample Mendelian Randomization and Mediation Analysis.

INTRODUCTION: Diabetic microvascular disease (DMiVD) involves dysregulated immune cell function, but the precise pathogenic mechanisms remain unclear. MATERIALS AND METHODS: We conducted a two-sample Mendelian randomization (MR) study using comprehensive GWAS and FinnGen summary statistics, encompassing 731 immune cell phenotypes, 473 gut microbial taxa, 91 inflammatory proteins, 179 lipid types, 1,400 plasma metabolites, 20 micronutrients, and DMiVD cases. The analysis aimed to evaluate causal associations between these variables and DMiVD. We further explored potential mediating roles of gut microbiota, plasma lipidome, and metabolites using mediation analysis, with multiple sensitivity tests confirming the robustness of our findings. RESULTS: We identified 20 immune cell phenotypes, 33 gut microbial taxa, 31 lipid types, and 83 plasma metabolites with significant causal associations with DMiVD. Mediation analysis revealed that the risk effect of CD3+ resting Tregs on diabetic nephropathy was partly mediated by phosphatidylcholine (16:0_18:2) (10.7%). Additionally, the protective effect of CX3CR1 on monocytes against DMiVD was partly mediated by Unclassified Bacilli A (35%), Species CAG-177 sp003538135 (22.6%), and triacylglycerol (52:6) (25.5%). DISCUSSION: These findings advance understanding of DMiVD pathogenesis, highlighting that modulation of key metabolic pathways and immune regulatory nodes may represent promising therapeutic strategies. Further experimental studies are needed to validate these potential causal relationships. CONCLUSION: Using causal inference approaches, this study identifies immune cell-mediated mechanisms underlying DMiVD, involving gut microbiota, plasma lipids, and metabolites. The results suggest potential intervention targets for mechanistic studies and therapeutic development.

Mendelian Randomization Analysis↗

PubMed2026

Shielding the First 24 Postnatal Months of Life: A Proposal for a Prospective Cohort Study of Early-Life Electromagnetic Exposure and Autism Risk.

BACKGROUND: Autism Spectrum Disorder (ASD) involves Mirror Neuron System (MNS) dysfunction, driving core social and imitative impairments. Systemic physiological alterations such as autonomic dysregulation, mitochondrial dysfunction and neuroinflammation are known to impair synchronization and plasticity of neuronal clusters. A less-evident environmental cofactor, coinciding with rising ASD prevalence, is the considerable world-wide increase in electromagnetic radiation (EMR) overall exposure among children. Experimental evidence shows how low-intensity EMR influences cellular processes, via voltage-gated calcium channels (VGCCs), oxidative stress, and mitochondrial metabolism. The Resonant Convergence framework, allow to predict how chronic EMR exposure during the first 24 postnatal months of life can act as a factor in ASD pathogenesis. The best candidate mechanism is chronic Ion Cyclotron Resonance (ICR) detuning the Ca2+-calmodulin pathway, thus disrupting MNS synchronization. METHODS AND ANALYSIS: A prospective observational pilot cohort study (24-month follow-up) proposes to enroll 1000 full-term newborns into two arms: an EMR-reduced cohort (n = 500, rest and sleep-phase Faraday shielding) and a standard exposure cohort (n = 500). Exposure is quantified via radiofrequency (RF)/extremely low frequency(ELF) measurements, proximity analysis, device inventories and wearable dosimetry. The primary endpoint is a continuous neurodevelopmental trajectory score (joint attention, language, electroencephalogram (EEG) mu-rhythm); binary ASD diagnosis (Autism Diagnostic Observation Schedule, Second Edition (ADOS-2), Autism Diagnostic Interview-Revised (ADI-R)) is a secondary, exploratory endpoint. Moreover, an optional genomic screening will evaluate gene-environment interactions within extremely low-frequency electromagnetic field (ELF-EMF) vulnerable pathways, including ASD-associated genes upregulated by RF via bromodomain and extraterminal protein (BET)-mediated epigenetic mechanisms. Analyses will employ risk ratios, Fisher's exact tests and logistic regression adjusted for confounders; mixed-effects and Bayesian modeling will evaluate longitudinal outcomes and exposure reduction effects. Given a 2-3% baseline prevalence, approximately 20-30 ASD cases are expected. The study is therefore powered for exploratory signal detection rather than definitive causal inference, providing the critical baseline data required to justify and design future confirmatory trials. Sex-stratified modeling will address the 4:1 male-to-female prevalence ratio. ETHICS AND DISSEMINATION: Ethics committee approval is not yet sought; full protocol review and approval will be obtained prior to the study initiation, in strict accordance with the Declaration of Helsinki. Written parental informed consent will be mandatory for all participants prior to enrollment. Study findings and methodological milestones will be disseminated through peer-reviewed international scientific publications. This protocol provides a structured methodological framework for the first prospective investigation of sleep-phase EMR reduction as a potential modulator of ASD incidence during early neurodevelopment. Results will inform adequately powered confirmatory trials in electromagnetic neurodevelopmental epidemiology.

autism spectrum disorder↗

PubMed2025

BiomedSQL: Text-to-SQL for Scientific Reasoning on Biomedical Knowledge Bases.

Biomedical researchers increasingly rely on large-scale structured databases for complex analytical tasks. However, current text-to-SQL systems often struggle to map qualitative scientific questions into executable SQL, particularly when implicit domain reasoning is required. We introduce BiomedSQL, the first benchmark explicitly designed to evaluate scientific reasoning in text-to-SQL generation over a real-world biomedical knowledge base. BiomedSQL comprises 68,000 question/SQL query/answer triples generated from templates and grounded in a harmonized BigQuery knowledge base that integrates gene-disease associations, causal inference from omics data, and drug approval records. Each question requires models to infer domain-specific criteria, such as genome-wide significance thresholds, effect directionality, or trial phase filtering, rather than rely on syntactic translation alone. We evaluate a range of open- and closed-source LLMs across prompting strategies and interaction paradigms. Our results reveal a substantial performance gap: GPT-o3-mini achieves 59.0% execution accuracy, while our custom multi-step agent, BMSQL, reaches 62.6%, both well below the expert baseline of 90.0%. BiomedSQL provides a new foundation for advancing text-to-SQL systems capable of supporting scientific discovery through robust reasoning over structured biomedical knowledge bases. Our dataset is publicly available at https://huggingface.co/datasets/NIH-CARD/BiomedSQL, and our code is open-source at https://github.com/NIH-CARD/biomedsql.

Journal Article↗

PubMed2024

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.

causal inference↗

PubMed2024

Assessing the causal link between liver function and acute pancreatitis: A Mendelian randomisation study.

A correlation has been reported to exist between exposure factors (e.g. liver function) and acute pancreatitis. However, the specific causal relationship remains unclear. This study aimed to infer the causal relationship between liver function and acute pancreatitis using the Mendelian randomisation method. We employed summary data from a genome-wide association study involving individuals of European ancestry from the UK Biobank and FinnGen. Single-nucleotide polymorphisms (SCNPs), closely associated with liver function, served as instrumental variables. We used five regression models for causality assessment: MR-Egger regression, the random-effect inverse variance weighting method (IVW), the weighted median method (WME), the weighted model, and the simple model. We assessed the heterogeneity of the SNPs using Cochran's Q test. Multi-effect analysis was performed using the intercept term of the MR-Egger method and leave-one-out detection. Odds ratios (ORs) were used to evaluate the causal relationship between liver function and acute pancreatitis risk. A total of 641 SNPs were incorporated as instrumental variables. The MR-IVW method indicated a causal effect of gamma-glutamyltransferase (GGT) on acute pancreatitis (OR = 1.180, 95%CI [confidence interval]: 1.021-1.365, P = 0.025), suggesting that GGT may influence the incidence of acute pancreatitis. Conversely, the results for alkaline phosphatase (ALP) (OR = 0.997, 95%CI: 0.992-1.002, P = 0.197) and aspartate aminotransferase (AST) (OR = 0.939, 95%CI: 0.794-1.111, P = 0.464) did not show a causal effect on acute pancreatitis. Additionally, neither the intercept term nor the zero difference in the MR-Egger regression attained statistical significance (P = 0.257), and there were no observable gene effects. This study suggests that GGT levels are a potential risk factor for acute pancreatitis and may increase the associated risk. In contrast, ALP and AST levels did not affect the risk of acute pancreatitis.

Humans↗

PubMed2024

BICEP: Bayesian inference for rare genomic variant causality evaluation in pedigrees.

Next-generation sequencing is widely applied to the investigation of pedigree data for gene discovery. However, identifying plausible disease-causing variants within a robust statistical framework is challenging. Here, we introduce BICEP: a Bayesian inference tool for rare variant causality evaluation in pedigree-based cohorts. BICEP calculates the posterior odds that a genomic variant is causal for a phenotype based on the variant cosegregation as well as a priori evidence such as deleteriousness and functional consequence. BICEP can correctly identify causal variants for phenotypes with both Mendelian and complex genetic architectures, outperforming existing methodologies. Additionally, BICEP can correctly down-weight common variants that are unlikely to be involved in phenotypic liability in the context of a pedigree, even if they have reasonable cosegregation patterns. The output metrics from BICEP allow for the quantitative comparison of variant causality within and across pedigrees, which is not possible with existing approaches.

Pedigree↗

PubMed2026

Livestock Multi-Omics Integration: A Systematic Framework From Statistical Association to Causal Interpretation.

Livestock multi-omics integration is key to unraveling complex trait regulation, yet systematic, livestock-specific strategies remain scarce. This review traces the progression from single-omics accumulation to multi-dimensional integration, highlighting how large-scale genomic, epigenomic, and transcriptomic projects lay the foundation for functional dissection. We identify core impediments: extreme species diversity, marked data heterogeneity, limited sample sizes, and a pervasive reduction of multi-omics data to simplistic differential screens, resulting in low translational efficiency. We critically appraise four common pitfalls-overinterpreting correlation as causation, relegating proteomics to corroborating transcriptomics, incomplete microbiome-host integration lacking environmental context, and systematic neglect of metabolic fluxomics-and show how exposomics and fluxomics add necessary causal and dynamic dimensions. To address these, we propose a livestock-adapted three-tier analytical framework: (1) statistical association of cross-omics covariation patterns; (2) machine learning-driven feature mining and integrative modeling; and (3) causal interpretation encompassing Mendelian randomization, prior-knowledge-guided network inference, and physical causal evidence via fluxomics and metabolic control analysis. We further discuss how multimodal sequencing (single-cell, spatial, temporal) and generative AI can fundamentally mitigate heterogeneity and strengthen causal evidence. Finally, we outline future priorities in database standardization, livestock-specific benchmarking, and translational pipelines, charting a path from correlation-centric reporting to mechanistic causality and precision breeding.

Animals↗

PubMed2026

Accelerated Biological Aging Increases the Risk of Head and Neck Cancer: Insights From Genetic Instruments of Epigenetic Clocks.

Epigenetic clocks are robust biomarkers of biological aging and have been associated with cancer susceptibility. However, the relationship between genetically predicted epigenetic age acceleration and head and neck cancer risk remains unclear. Using a large case-control study of 2189 head and neck squamous cell carcinoma (HNSCC) cases and 2189 age- and sex-matched controls, we investigated the associations between polygenic scores (PGSs) for multiple epigenetic clocks and HNSCC risk, and evaluated their potential causal roles using two-sample Mendelian randomization (MR). Genome-wide association study (GWAS)-identified single nucleotide polymorphisms (SNPs) associated with four epigenetic clocks (HannumAge, HorvathAge, GrimAge, and PhenoAge) were used to construct clock-specific PGSs. Logistic regression models were applied to assess associations between PGSs and HNSCC risk, while MR analyses, including inverse-variance weighted (IVW), weighted median, and MR-Egger methods, were used to infer potential causal relationships. Among the 48 epigenetic clock-associated SNPs, 12 showed nominal associations with HNSCC risk, and one variant (rs2275558 in PBX1) remained significant after Bonferroni correction (OR = 0.67, 95% CI: 0.60-0.76). PGSs for all four epigenetic clocks were higher in cases than in controls. In logistic regression analyses, each standard deviation increase in HannumAge PGS was associated with a 25% higher risk of HNSCC (OR = 1.25, 95% CI: 1.10-1.41), whereas HorvathAge, GrimAge, and PhenoAge PGSs showed weaker positive associations (ORs ranging from 1.06 to 1.10). Individuals in the highest PGS quartile for all four epigenetic clocks exhibiting 14%-25% higher risk than those in the lower three quartiles. MR analyses supported potential causal effects of genetically predicted HannumAge (IVW OR = 1.24 per SD increase, 95% CI: 1.09-1.42) and GrimAge (IVW OR = 1.23 per SD increase, 95% CI: 0.98-1.56) on HNSCC risk, with consistent estimates in weighted median analyses. Our results highlight biological aging as a potential etiologic mechanism for HNSCC and suggest that epigenetic clock-related genetic profiles may improve HNSCC risk stratification.

Humans↗

PubMed2026

Genetic evidence that advanced COVID-19 accelerates longitudinal brain atrophy: A Mendelian randomization study.

Coronavirus disease 2019 (COVID-19) was reported to persist long-term in the brain and leave several long-term neurologic sequelae. However, the causal relationship between COVID-19 and brain aging is still unknown. The genome-wide association study (GWAS) data on COVID-19 phenotypes (susceptibility, hospitalization, and severity), involving a total of 5,779,391 participants, were collected from the COVID-19 Host Genetics Initiative. In addition, GWAS data on longitudinal changes in 15 brain structures, assessed via magnetic resonance imaging across the lifespan, were sourced from the ENIGMA Consortium and involved 15,640 participants. Two-sample Mendelian randomization was conducted to infer the causal relationship between COVID-19 and longitudinal brain changes. Multi-trait GWAS meta-analysis, colocalization, and fine-mapping analyses were performed to identify shared genetic etiologies. H3K27me3 ChIP-seq was used to evaluate the regulatory effect of colocalized loci. Two-step Mendelian randomization was applied to explore potential mediating mechanisms across multi-omics layers, including proteomics, metabolomics, and immunomics. Our results showed that COVID-19 hospitalization (β = -262.405, P = .041) and severity (β = -177.676, P = .049) were genetically associated with atrophied volume of total brain during longitudinal change. This suggests that individuals with advanced COVID-19 may be more susceptible to accelerated global brain aging. Caudate was genetically affected by all COVID-19 phenotypes. Seven variants were shared between advanced COVID-19 and global brain aging. rs117169628 was colocalized between advanced COVID-19 and global brain aging, and exerted an inhibitory effect on CDH15 expression, further strengthening the causality. Six metabolites, 1 protein, and 1 immune trait were identified as potential mediators. Our study indicates that advanced COVID-19 might be genetically associated with accelerated brain aging. Brain health should be paid more attention in long COVID-19.

Humans↗