SEARCH · PubMed Health

Results for “Structural Equation Modelling”

Explore indexed PubMed citations for clinical trials, systematic reviews and public health research. Read source abstracts and follow each citation to its original PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 19 recordsLinked to original sources

PubMed2026

Revealing the Shared Genetic Architecture of Metabolic Dysfunction-Associated Steatotic Liver Disease-Related Traits Through Genomic Structural Equation Modeling.

Although individual traits related to metabolic dysfunction-associated steatotic liver disease (MASLD) have been investigated through large-scale genome-wide association studies (GWASs), the shared genetic susceptibility across these traits remains unclear. We therefore conducted a multivariate GWAS of key MASLD-related traits to elucidate their common genetic architecture. We applied genomic structural equation modeling to model a latent genetic factor (MASLD-F) underlying genetically correlated MASLD-related traits, leveraging their GWAS-derived genetic correlations. We then performed functional annotations, including fine-mapping, transcriptome-wide association study, and cell- and tissue-type-specific enrichment analyses, and conducted Mendelian randomization analyses to identify modifiable risk factors. Our multivariate MASLD-F GWAS identified 50 independent variants across 48 genomic loci. Transcriptomic imputation identified several MASLD-F-associated genes, including ARNTL, NPC1, BTBD10, VDAC2, TSKU, SFMBT1, and ABHD17C. We observed significant enrichment of MASLD-F-related genetic signals predominantly in brain tissues, pancreatic islets, and the adrenal gland. Additionally, six modifiable risk factors and four modifiable protective factors for MASLD-F were identified. These findings reveal a complex shared genetic architecture underlying MASLD components, thereby expanding our understanding of disease pathogenesis and providing novel insights for precision medicine and public health interventions.

Humans↗

PubMed2026

Exploring the shared genetic architecture of sarcopenia using genomic structural equation modeling.

Sarcopenia is a common age-associated condition characterized by the progressive loss of skeletal muscle mass, strength, and physical functionality. While large-scale genome-wide association studies (GWAS) have previously addressed isolated traits of sarcopenia, the multifactorial genetic architecture underlying this condition remains largely undefined. To characterize the common genetic basis of sarcopenia-related traits, genomic structural equation modeling (Genomic-SEM) was implemented. Multiple post-GWAS analytic approaches were integrated to pinpoint susceptibility loci. These analyses encompassed identifying enriched genetic pathways and relevant genomic elements, as well as cell-type-specific enrichment in skeletal muscle satellite stem cells, mesenchymal stem cells, and skeletal muscle satellite cells in limb muscle. Furthermore, based on the integrated GWAS data of sarcopenia-related traits, polygenic risk score (PRS) analysis was conducted to evaluate risk associations at the chromosomal level. A well-fitted Genomic-SEM successfully integrated the GWAS data, revealing the shared genetic architecture of sarcopenia-related traits. We identified 110 single nucleotide polymorphisms (SNPs) reaching genome-wide significance (p < 5 × 10-8), of which 9 represent novel discoveries. Subsequent fine-mapping procedures and gene-set analyses identified 15 causal variants alongside 77 candidate susceptibility genes. This study provides a comprehensive genetic characterization of sarcopenia via Genomic-SEM, offering new insights into the etiological pathways underlying sarcopenia.

Sarcopenia↗

PubMed2026

Genomic Structural Equation Modeling Identifies a Shared Inflammatory Genetic Dimension Across Inflammatory Arthritis Phenotypes and Biomarkers.

BACKGROUND: Inflammatory arthritis (IA), including rheumatoid arthritis (RA), psoriatic arthritis (PsA) and gout, shares systemic inflammatory features indexed by C-reactive protein (CRP) and interleukin-6 (IL-6), yet the extent of their common genetic basis remains unclear. AIMS: We aimed to delineate the shared genetic architecture across IA phenotypes and inflammatory biomarkers. MATERIALS AND METHODS: We applied genomic structural equation modelling (Genomic SEM) to GWAS summary statistics for RA, PsA, gout, CRP and IL-6, fitted a single common factor, and performed multivariate GWAS followed by fine-mapping, transcriptome-wide association, gene-based analysis, pathway enrichment, and cell-type and spatial mapping. RESULTS: A single common factor was fitted (CFI = 0.990, SRMR = 0.045). The multivariate GWAS identified 56 genome-wide significant SNPs across 10 independent lead loci, including one novel signal. Fine-mapping prioritized high-confidence variants near PTPN22, the CRP gene cluster and a urate-associated locus. Gene-level analyses converged on DCLRE1B, PTPN22, IL6R, NLRP3 and HNF1A, with pathway enrichment implicating inflammasome assembly and metabolic-inflammatory overlap. Cell-type enrichment highlighted myeloid populations, and spatial mapping localized signals to lung, kidney, mucosal epithelium and gastrointestinal tissues. DISCUSSION: These results delineate a shared inflammatory genetic dimension across IA phenotypes and biomarkers, anchored in immune, inflammasome, cytokine-receptor and metabolic pathways. CONCLUSION: Together, these findings provide a valuable framework for prioritizing candidate genes and cellular contexts for future investigation.

TWAS↗

PubMed2025

Genomic structural equation modeling elucidates the shared genetic architecture of allergic disorders.

BACKGROUND: The intricate shared genetic architecture underlying allergic disorders-including allergic asthma, atopic dermatitis, contact dermatitis, allergic rhinitis, allergic conjunctivitis, allergic urticaria, anaphylaxis, and eosinophilic esophagitis-remains incompletely characterized. METHODS: Our study employed genomic structural equation modeling (Genomic SEM) to define the common factor representing the shared genetic architecture of allergic disorders. Coupled with diverse post-GWAS analytical methods, we aimed to discover susceptible loci and investigate genetic associations with external traits. Furthermore, we explored enriched genetic pathways, cellular layers, and genomic elements, and investigated putative plasma protein biomarkers. Polygenic risk score (PRS) analyses, leveraging our integrated GWAS data, were conducted to assess chromosomal-level risk associations for allergic disorders. RESULTS: A well-fitted genomic SEM integrated GWAS data, revealing the shared genetic architecture of allergic disorders. We identified a total of 2038 genome-wide significant SNP loci (p < 5e-8), including 31 previously unreported loci. Fine-mapping of variants and gene sets pinpointed 2 causal variants and 31 candidate susceptible genes. Genetic correlation analyses further illuminated the shared genetic architecture underlying multiple traits, notably psychiatric disorders. Preliminary findings identified four putative causal plasma protein biomarkers. CONCLUSION: Notably, this study presents the first comprehensive genetic characterization of allergic disorders through a GWAS analysis of an unmeasured composite phenotype, providing novel insights into shared etiological pathways across these conditions.

Humans↗

PubMed2026

Effect of a pharmacist-led mHealth app on adherence, quality of life, and glycaemic control in diabetes: A multicentre RCT.

AIMS: To evaluate whether CareAide®, a pharmacist-driven mHealth application, improves medication adherence, health-related quality of life (HRQoL), and glycaemic control in diabetes mellitus using structural equation modelling. METHODS: Pre-specified secondary analysis of the type 2 diabetes mellitus cohort from a 6-month multicentre open-label randomised controlled trial (N = 663) across three Malaysian hospitals. Adherence was assessed by MMAS-8 (subjective) and Proportion of Days Covered (PDC; pharmacy-verified). HRQoL was measured by AQoL-6D and EQ-5D-5 L. Structural equation modelling (SEM), Necessary Condition Analysis, and Importance-Performance Map Analysis (cIPMA) were applied. RESULTS: CareAide® produced large adherence gains (MMAS-8: 7.31 vs 5.55, d = 1.64; PDC ≥ 80%: 81.6% vs 33.0%; both p < 0.001). Early 3-month adherence was the strongest predictor of sustained 6-month adherence in both models (β std = 0.567 and 0.688; p < 0.001). AQoL-6D utility improved significantly (0.669 vs 0.618; d = 0.353, p < 0.001), driven by coping (d = 0.447) and relationships (d = 0.254) domains. HRQoL did not mediate adherence; gains were a direct independent benefit. The intervention effect on HbA1c was not statistically significant in the PDC-based SEM model (β =  - 0.333, p = 0.065); a group difference was, however, supported by baseline-adjusted ANCOVA (β =  - 0.41%, p = 0.002), and the complete-case comparison was non-significant (p = 0.153), so glycaemic findings warrant cautious interpretation. cIPMA identified the intervention as the primary optimisation target. CONCLUSIONS: CareAide® significantly improves medication adherence and psychosocial quality of life. Evidence for glycaemic benefit came from baseline-adjusted analysis (ANCOVA), though findings should be interpreted with caution given incomplete HbA1c data at one site. The first three months are the most critical period for pharmacist support. In this dataset, PDC appeared more sensitive than MMAS-8 to the HbA1c signal within 6 months, but this finding requires confirmation in longer studies with more complete HbA1c data. TRIAL REGISTRATION: ClinicalTrials.gov NCT06068309.

Aged↗

PubMed2026

Virtual reality physical education and adolescents' exercise interest and physical fitness: An explanatory sequential mixed-methods randomized trial with exploratory pathway analysis.

Traditional physical education (PE) faces declining student interest and limited fitness gains. Virtual reality (VR) offers immersive, gamified experiences, but evidence regarding its effectiveness and explanatory pathways remains limited. This explanatory sequential mixed-methods randomized trial assigned 360 adolescents (aged 13-16) from three middle schools to either VR-supported PE (n = 180) or conventional PE (n = 180) for 12 weeks, with a 4-week follow-up. Outcomes included exercise interest (validated scale), physical fitness (coordination via MABC-2, cardiorespiratory endurance via the 20-m shuttle run, explosive power via the standing long jump, and speed via the 10-m sprint), and accelerometer-measured physical activity. The qualitative component involved 38 unique students: 32 completed individual semi-structured interviews, and six additional students participated only in focus groups. Three-level linear mixed-effects models and exploratory structural equation modeling were used. The VR group showed significantly greater improvements in exercise interest (d = 0.78), coordination (d = 0.62), cardiorespiratory endurance (d = 0.55), and speed (d = 0.48) than the control group (all p < 0.001), but not in explosive power (d = 0.12, p = 0.148). Effects were partially retained at follow-up (interest d = 0.65, coordination d = 0.48, endurance d = 0.42, and speed d = 0.30), a pattern not fully consistent with a purely novelty-driven explanation. Exploratory mediation identified exercise interest as a statistically compatible explanatory pathway (indirect effect = 0.34, 95% CI [0.22, 0.46]), although the timing of measurement precludes causal interpretation. Qualitative findings contextualized these results by highlighting immersion, feedback, self-efficacy, and perceived transfer. VR-supported PE may enhance adolescents' exercise interest and selected fitness dimensions, but its limited effect on explosive power and possible novelty contribution indicate that it should complement, rather than replace, conventional PE. Longer-term studies are needed.

Humans↗

PubMed2026

Unraveling 'F' factor: towards a genetic-clinical framework for the musculoskeletal-heart crosstalk in metabolic aging.

BACKGROUND: The rising co-occurrence of cardiometabolic diseases and musculoskeletal degeneration poses a critical challenge to healthy aging, yet the shared biological mechanisms underlying this multimorbidity remain poorly defined. This study aimed to establish an integrative clinical-genetic framework to elucidate the common frailty factor, the 'F' factor, that captures the systemic vulnerability linking cardiometabolic multimorbidity (CMM) and musculoskeletal aging. METHODS: Utilizing the prospective China Health and Retirement Longitudinal Study (CHARLS) cohort, we developed and validated novel Frailty-Integrated Indices for CMM risk prediction, evaluated with machine learning models interpreted via SHapley Additive exPlanations (SHAP). Independently, we applied genomic structural equation modeling (Genomic-SEM) to integrate genome-wide association data from six traits-coronary artery disease, type 2 diabetes, hypertension, bone mineral density, frailty, and telomere length-to model a shared latent genetic factor ('F' factor). This was followed by multivariate GWAS, fine-mapping, transcriptome-wide association study (TWAS), gene-based analysis, and functional annotation to prioritize causal genes, pathways, and cell types. RESULTS: Clinically, several Frailty-Integrated Indices significantly improved CMM risk prediction, with the optimal model achieving an AUC of 0.727. Genetically, we modeled a significant shared latent genetic factor ('F' factor), pinpointing novel risk loci and implicating key genes such as APOE and SLC22A3. These genes were enriched in pathways including cellular senescence and cholesterol metabolism and showed specific expression patterns in developmental brain stages and across multi-organ endothelial cells. CONCLUSION: Our findings provide converging evidence for Musculoskeletal‑Heart crosstalk of metabolic aging and inferred the 'F' factor as a genetic correlate of a transdiagnostic state, which links genetic predisposition to metabolic dysregulation, and systemic functional decline. This work provides a multi-level biological characterization of multimorbidity liability, informing early-risk detection and preventive strategies for complex aging-related comorbidities.

Humans↗

PubMed2025

Coupling of spectroscopy and nitrogen-oxygen isotopes unveils the mechanisms of dissolved organic matter and nitrate pollution in lakes within the agro-pastoral transition zone.

Lakes in arid and semi-arid regions are subjected to severe ecological stress, such as organic pollution, eutrophication, and salinization, due to climate change and human activities. This study investigates Chagannur Lake, a typical arid-region lake that is representative and ecologically sensitive in Northern China's agro-pastoral ecotone, to uncover its pollution characteristics and mechanisms. We employed fluorescence spectroscopy and stable isotope analysis to trace dissolved organic matter (DOM) and nitrate sources. The DOM composition was dominated by microbial metabolic byproducts and protein-like substances, suggesting that microbial processes are key to organic matter transformation. Source apportionment revealed that pollutants primarily originated from livestock and poultry manure (37.6 %), agricultural fertilizers (35.6 %), and soil erosion (24.7 %), with agricultural fertilizers contributing most significantly in the Gogstai River (63.3 %). A structural equation model (SEM) coupling spectral and mass spectrometric data revealed that microbial transformation significantly impairs the lake's self-purification capacity, thereby promoting pollutant accumulation (path coefficient = 0.91,*p < 0.05). Moreover, microbial processes link endogenous and exogenous pollution, a mechanism effectively traced by isotopic and fluorescence indices (path coefficient = 0.55, ⁎⁎p < 0.01). These findings enhance the understanding of pollution sources and transformation mechanisms in arid-region lakes and offer foundational theoretical support for policymakers engaged in pollution control strategies.

Lakes↗

PubMed2026

Glymphatic dysfunction mediates inflammation-driven vascular burden and cognitive decline in cerebral small vessel disease.

BACKGROUND: Cerebral small vessel disease (CSVD) is increasingly recognized as a disorder involving microvascular dysfunction, impaired perivascular clearance, and inflammatory processes. However, how systemic inflammatory burden, neurovascular coupling (NVC), glymphatic MRI markers, vascular lesion burden, and cognition are interrelated remains unclear. MATERIALS AND METHODS: In this prospective study, 155 patients with CSVD and 70 healthy controls (HCs) underwent multimodal MRI. NVC was quantified using the cerebral blood flow/fractional amplitude of low-frequency fluctuations ratio. Glymphatic function was assessed via the diffusion tensor image analysis along the perivascular space (ALPS) index, choroid plexus volume (CPV), and perivascular space (PVS) fractions. Structural equation modeling (SEM) was employed to evaluate the direct and indirect effects of inflammatory markers on vascular burden and cognitive performance. RESULTS: Patients with CSVD exhibited significantly diminished NVC (specifically in the right median cingulate and left frontal gyri) and impaired glymphatic function (lower ALPS-index; higher CPV and PVS fractions) compared to HCs. SEM revealed that inflammatory biomarkers exerted both a direct effect on vascular burden and a substantial indirect effect (accounting for 66.3% of the total effect) mediated through two pathways: a single-mediation path via glymphatic function (42.8%) and a serial-mediation path via NVC and glymphatic function (23.5%). Increased vascular burden was significantly associated with poorer cognitive performance. CONCLUSION: Inflammation drives CSVD progression and cognitive decline primarily through the disruption of NVC and glymphatic clearance mechanisms. These findings highlight glymphatic dysfunction as a critical mediator of inflammation-related structural brain damage.

Humans↗

PubMed2026

Multi-omics revealed the effects of rumen to blood path on early lactation performance in transition dairy cows.

BACKGROUND: The transition period is vitally important to the life cycle of dairy cows. However, the function of the microbiota during both pre- and post-partum and their relationship with ruminal, plasma, and milk metabolites still require systematic investigation. To address this, the 7 highest- and 7 lowest-performing animals among a cohort of 100 dairy cows were selected based on their postpartum energy-corrected milk yield. Rumen fluid and plasma samples were collected during both pre- and post-partum periods, whereas milk samples were obtained postpartum. Shotgun metagenomics of rumen contents in addition to metabolomics of rumen, plasma, and milk samples were performed to evaluate the associations between ruminal microbes and early lactation performance in transition dairy cows. RESULTS: Compared with prepartum cows, postpartum high-yield cows had greater concentrations of ruminal volatile fatty acids and plasma total bile acid. Moreover, plasma urea nitrogen and most amino acids, peptides, and their derivatives in plasma and milk were increased in postpartum high-yield cows, relative to postpartum low-yield cows. Metagenomic analysis revealed that the relative abundances of several species within the Prevotella, Succinimonas, Succinatimonas, and Methanosphaera increased, while other bacteria belong to Alistipes and Bacteroides, and archaeal Methanobrevibacter species decreased in postpartum cows, particularly in postpartum high-yield cows. Co-occurrence network and correlation analysis suggested that Prevotella and Succinatimonas were negatively correlated to Alistipes, Bacteroides, and Methanobrevibacter, potentially contributing to the nutritionally efficient phenotype of postpartum high-yield cows. A metabolic pathway analysis of our metagenomic data revealed that postpartum high-yield cows possessed more microbial genes involved in starch utilization and amino acid synthesis, while a wide range of microbial genes involved in cellulose utilization, acetogenesis, and amino acid degradation were found in prepartum cows with low-yield in postpartum. A structural equation model analysis showed that the increased relative abundances of Prevotella tf.2-5 and Succinatimonas CAG_777 were related to greater concentrations of plasma chenodeoxycholic acid glycine conjugate, milk 5-Methoxytryptophan, and energy-corrected milk yield. Finally, pan-genomic analysis confirmed that Alistipes, Bacteroides, and Methanobrevibacter possess genetic conservation of both hydrogenases and dehydrogenases, which may contribute to energy loss in the rumen via hydrogen dissipation. CONCLUSION: In summary, our findings provide a fundamental understanding of how microbiome-dependent mechanisms contribute to early lactation performance in dairy cows during the transition period. The increased abundance of Prevotella, Succinimonas, and Succinatimonas in postpartum cows suggest that they are important microbes during the transition period and may help in coping with metabolic challenges, while improving nutrient utilization efficiency during this period. Our study underscores the importance of the ruminal microbiome during the transition period and highlights the need for rumen-based nutritional intervention strategies to improve production efficiency in ruminants. Video Abstract.

Animals↗

PubMed2026

Ruminosignatures associated with methane emissions and feed efficiency across geographies and cattle breeds.

The cattle rumen microbiota represents a complex and dynamic ecosystem whose organization and relationship to host phenotypes are important for food security and environmental sustainability. We analyzed rumen microbiota profiles from 2496 cattle representing five breeds and production systems across five countries, identifying microbial co-abundance groups termed Ruminosignatures. We detected 14 distinct Ruminosignatures, including 2 observed across all populations dominated by Prevotella and UBA2810. Additional Ruminosignatures showed breed- and diet-specific patterns and collectively explained 96%-99% of variance in rumen microbial composition. Integrative cross-country analysis confirmed 10 out of 14 Ruminosignatures identified in cohort-specific analyses. Several Ruminosignatures were associated with methane emissions and feed efficiency traits and were partially under host genetic control, with heritability estimates ranging from 0.09 to 0.58. Structural equation modeling revealed consistent negative genetic and phenotypic correlations between the UBA2810-dominated Ruminosignature (RS_UBA2) and methane emissions across cohorts (rg = -0.40 to -0.65), with structural coefficients concordant in sign across all populations, supporting the expected direction of phenotypic response to selection on RS_UBA2. Meta-analysis confirmed positive associations of RS_UBA2 with average daily gain and negative associations with methane-related traits and feed conversion ratio. Functional genome-based predictions suggested RS_UBA2 may reduce methanogenesis through alternative hydrogen utilization pathways competing with methanogenic archaea. Production system type influenced both Ruminosignature occurrence and relationships with host phenotypes, emphasizing the relevance of context-specific strategies for microbiome modulation. Our findings highlight the potential of the Ruminosignatures framework for microbiome-informed breeding programs aimed at improving feed efficiency while reducing the environmental impact of cattle production.

Animals↗

PubMed2026

Functional convergence of rTCA-related carbon-fixation potential and biochemical residue accumulation in seagrass sediments.

Seagrass meadows are globally significant blue carbon ecosystems, yet the microbial and biochemical mechanisms driving sediment organic carbon (SOC) accumulation remain poorly understood. To address this, we employed an integrated approach combining metagenomic sequencing, biochemical assays, and structural equation modeling to investigate carbon cycling in the seagrass and adjacent unvegetated sediments of Swan Lake, China. A total of 115,179 carbon fixation genes and 119,615 decomposition genes were identified, revealing distinct microbial community structures among the habitats. Seagrass sediments harbored more diverse carbon-fixing (CFMs) and decomposing microorganisms (CDMs), with 83 medium-to high-quality metagenome-assembled genomes (MAGs) recovered. While neutral community model analysis indicated that stochastic processes predominantly governed community assembly, functional analyses highlighted specific drivers of sequestration. The reductive tricarboxylic acid (rTCA) cycle emerged as the dominant carbon fixation pathway, with key genes (e.g., aclA, korA) showing strong positive correlations with SOC. Conversely, decomposition pathways for starch and lignin were negatively associated with SOC. Furthermore, seagrass sediments exhibited elevated concentrations of total amino sugars (TAS) and lignin phenols (TLP), which linked significantly to carbon fixation rather than decomposition. PLS-SEM revealed statistically significant associations among seagrass traits, environmental variables, microbial carbon-fixation potential, biochemical residue pools, and SOC, supporting a mechanistic pathway in which enhanced microbial functional potential drives the accumulation of recalcitrant biochemical residues, thereby facilitating long-term carbon retention in sediments. These findings emphasize the pivotal role of microbial anabolism and the accumulation of biosynthetic residues in sediment carbon storage, suggesting a functional convergence in seagrass-driven carbon sinks.

Metagenomics↗

PubMed2025

Nutritional modulation of host physiology, behavior, and gut microbiome in the captive rodent Octodon degus.

Diet is a key determinant of health by affecting nutrient metabolism, energy balance, body weight regulation, and mental health. The gut-brain axis is a critical pathway through which dietary factors influence cognitive function and behavior via microbial metabolites. While this relationship has been extensively studied in traditional laboratory models, diet-microbiome-cognition interactions remain largely unexplored in Octodon degus, an emerging model for aging, neurodegeneration, and cognitive research. Here, we compared two widely used rodent diets-LabDiet and Champion-to evaluate their effects on digestive efficiency, behavior, and gut microbiome composition. We also examined the relationships between these variables using piecewise structural equation modeling (pSEM). Our results indicated that LabDiet-fed degus exhibited enhanced nutrient absorption, higher fecal acetic acid levels, and a higher abundance of Actinobacteria (particularly Bifidobacterium), likely driven by its vitamin C supplementation. These animals also showed improved working memory and social motivation, but they displayed increased anxiety-like behavior. In contrast, Champion-fed degus, which consumed a more fiber-diverse, plant-based diet, showed lower anxiety traits and significantly greater gut microbial richness, with higher abundance of Bacteroidota and Tenericutes. Innate behaviors, such as burrowing and nesting, remained unaffected by the diet. SEM analysis revealed that diet explained most of the variance in microbial activity and identified a positive association between acetic acid levels and cognitive performance. This emphasizes a strong relationship among diet, microbiome, and brain function. Overall, our results suggest that dietary composition is a key factor influencing experimental outcomes in degus, with important implications for physiology, cognition, and microbial ecology. Standardizing dietary inputs is essential to ensure reproducibility in behavioral and biomedical studies using this model. Additionally, our results reinforce the microbiome's role as a mediator of diet-driven brain function via SCFAs, underscoring degus as a powerful system for investigating diet-microbiome-neurobehavioral interactions relevant to aging and mental health.

Animals↗

PubMed2026

Elevated water levels drive greenhouse gas mitigation in the riparian zone profile.

Wetlands are critical for climate regulation, with their hyporheic zone serving as sensitive interfaces for groundwater-soil-atmosphere exchange. These zones are active hotspots for carbon-nitrogen cycling and greenhouse gas (GHG) emissions (CO2, CH4, N2O), yet the impact of water level fluctuations on these emissions and their microbial drivers in freshwater wetlands remains poorly understood. This study investigated the spatiotemporal dynamics of GHG emissions and carbon-nitrogen coupling processes along riparian soil profiles of Baiyangdian Lake during water level fluctuations. Employing static chamber measurements, microcosms, quantitative PCR, Metagenome-Assembled genome (MAG) analyses, and Structural Equation Modeling (SEM), we observed that GHG emissions were significantly affected by water level fluctuations. Specifically, CO2 and N2O fluxes, as well as CO2 production potential were significantly lower at high-water-level conditions. Water level also emerged as a key driver of microbial community structure, with Methylococcaceae and Methanosarcinaceae as key regulators of CH4 emission, and Anaeromyxobacteraceae as central to N2O dynamics. A high-quality Methylomirabilales-like MAG, possessing the complete pathway for coupled nitrate reduction and methane oxidation, was identified. Its abundance negatively correlated with water level, suggesting that these C-N coupling bacteria contribute to reducing GHG emissions. This study provides crucial theoretical insights and identifies microbial targets for mitigating wetland GHG emission through hydrological management.

Greenhouse Gases↗

PubMed2026

Social disconnection integrates genetic and proteomic risks in suicidal ideation and depression.

Suicidal ideation (SI) and major depressive disorder (MDD) are complex psychiatric conditions arising from the interplay of genetic liability, molecular processes, and psychosocial factors. While these dimensions have been extensively studied in isolation, their joint contribution to SI and MDD remains unclear. This study integrates multi-modal data to elucidate these synergistic effects and develop robust models for individual-level risk stratification. Leveraging longitudinal multi-modal data from 13,085 UK Biobank participants, we integrated genomic, proteomic, and social connection profiles. We developed interpretable risk scores using a rigorous supervised machine learning framework encompassing diverse linear and ensemble classifiers. Permutation importance was employed to quantify feature contributions and derive transparent, weighted risk metrics across diverse classifiers. These scores were validated through association, interaction, and mediation analyses. Social connection-based risk scores significantly differentiated cases and controls across the two suicidal ideation phenotypes at 2017 and 2023 with cross-sectional analyses (AUCs: 0.70 - 0.73), outperforming proteomic-only models. Functional dimensions of social connection emerged as the most informative predictors. Longitudinal analyses revealed that social risk scores at baseline predicted suicidal ideation onset six years later, independent of demographic covariates. Interaction analyses demonstrated that polygenic risk for suicide attempt significantly interacted with both social and proteomic risk features in relation to depression. Structural equation models further confirmed that social disconnection acts as a key mediator linking genetic predisposition to MDD and SI. Social disconnection is a critical risk factor mediating the impact of genetic vulnerability on psychiatric outcomes. Integrating social, genetic, and molecular data supports a multilevel framework for risk stratification and highlights the potential of socially oriented interventions to mitigate biological risk.

Humans↗

PubMed2026

Unraveling causal links between chronic rhinosinusitis and peripheral artery diseases: insights from genetic correlations through genome-wide association studies.

OBJECTIVES: Chronic Rhinosinusitis (CRS) shares epidemiological links with Cardiovascular Diseases (CVDs), however, their shared genetic basis remains unclear. We hypothesized that pleiotropic genetic variants underlie CRS-CVDs links via distinct biological pathways. METHODS: Using large-scale GWAS data from European-ancestry individuals, we assessed global and local genetic correlations. We applied Genomic Structural Equation Modeling (Genomic SEM) to dissect shared genetic architecture, performed bidirectional Mendelian Randomization (MR) to infer causality, and conducted cis-eQTL colocalization to identify shared genetic signals. Finally, in vitro endothelial models (HUVECs) validated the functional dynamics of candidate genes under CRS-mimicking inflammatory stress. RESULTS: CRS showed significant genetic correlations with multiple CVDs. Genomic SEM revealed a latent factor structuring shared genetic risk through three pathways: artery diseases, myocardial diseases, and heart failure. Local genetic correlations identified significant local genetic correlations specifically between CRS and Peripheral Atherosclerosis (PAS)/Peripheral Artery Disease (PAD) specifically within the chr6: 31.57‒33.24 Mb locus. MR demonstrated causal effects of CRS on PAD (OR = 1.23, p = 0.022) and PAS (OR = 1.21, p = 0.011), but not vice versa. Genetically predicted HLA-DRB1, APOM, and COL11A2 expression conferred protection, while HLA-DQA2 increased risk. Crucially, in vitro validation corroborated these pathogenic trajectories, inflammatory stress significantly downregulated the protective APOM and upregulated the risk-associated HLA-DQA2 alongside pro-atherogenic VCAM-1, while HLA-DRB1 exhibited a compensatory upregulation (p < 0.05). CONCLUSION: CRS shares global genetic liability with CVDs, structured through three primary etiological pathways. Causal effects of CRS on peripheral artery diseases are mediated by immune and lipid-related genes within the chr6 locus, revealing divergent pleiotropic mechanisms. Our integrated genetic and in vitro evidence provides a mechanistic framework wherein chronic mucosal inflammation contributes to systemic endothelial vulnerability, thereby highlighting candidate targets for mechanism-directed therapy.

Humans↗

PubMed2026

Integrated bioinformatics and SEM analysis reveal GPAM as a key mediator of fibrosis in NAFLD with metabolic dysfunction.

Nonalcoholic fatty liver disease (NAFLD) is a complex condition influenced by metabolic and genetic factors, yet the shared genetic architecture underlying its progression remains poorly understood. The aim of this study was to employ genomic structural equation modeling (GSEM) to elucidate the genetic architecture linking NAFLD with key metabolic traits-including insulin resistance, body mass index (BMI), hemoglobin A1c (HbA1c), and liver fibrosis using summary statistics from large-scale genome-wide association studies. By harmonizing 2.18 million variants across five genome-wide association studies (GWAS) datasets, we identified 134 genome-wide significant loci that mapped to 24 genes. GSEM revealed a latent genetic structure composed of two distinct dimensions: a metabolic regulation factor primarily driven by insulin resistance, BMI, and HbA1c; and a structural pathology factor specifically associated with liver fibrosis. These factors explained 65.5% and 78.1% of the genetic variance in BMI and fibrosis, respectively, with minimal correlation (rg = 0:07), indicating their genetic distinctness. Additionally, integrating Mendelian randomization with liver transcriptome profiling, we characterized how the 24 genes contribute to disease and identified mitochondrial glycerol-3-phosphate acyltransferase (GPAM) as the key gene that causally links lipid metabolism to fibrogenesis. In conclusion, we present the first genetically grounded mechanism for the progression of NAFLD to fibrosis. This mechanism encompasssses genetic variants, dysregulated gene expression, metabolic disturbances, and the processes involved in fibrotic remodeling. This research establishes a genetic framework for understanding the pathogenesis of NAFLD and highlights novel therapeutic targets for intervention.

Non-alcoholic Fatty Liver Disease↗

PubMed2026

From fear to empowerment: the impact of employees AI awareness on workplace well-being - a new insight from the JD-R model.

PURPOSE: The primary purpose of the study was to explore the impact of health workers' awareness of artificial intelligence (AI) on their workplace well-being, addressing a critical gap in the literature. By examining this relationship through the lens of the Job demands-resources (JD-R) model, the study aimed to provide insights into how health workers' perceptions of AI integration in their jobs and careers could influence their informal learning behaviour and, consequently, their overall well-being in the workplace. The study's findings could inform strategies for supporting healthcare workers during technological transformations. DESIGN/METHODOLOGY/APPROACH: The study employed a quantitative research design using a survey methodology to collect data from 420 health workers across 10 hospitals in Ghana that have adopted AI technologies. The study was analysed using OLS and structural equation modelling. FINDINGS: The study findings revealed that health workers' AI awareness positively impacts their informal learning behaviour at the workplace. Again, informal learning behaviour positively impacts health workers' workplace well-being. Moreover, informal learning behaviour mediates the relationship between health workers' AI awareness and workplace wellbeing. Furthermore, employee learning orientation was found to strengthen the effect of AI awareness on informal learning behaviour. RESEARCH LIMITATIONS/IMPLICATIONS: While the study provides valuable insights, it is important to acknowledge its limitations. The study was conducted in a specific context (Ghanaian hospitals adopting AI), which may limit the generalizability of the findings to other healthcare settings or industries. Self-reported data from the questionnaires may be subject to response biases, and the study did not account for potential confounding factors that could influence the relationships between the variables. PRACTICAL IMPLICATIONS: The study offers practical implications for healthcare organizations navigating the digital transformation era. By understanding the positive impact of health workers' AI awareness on their informal learning behaviour and well-being, organizations can prioritize initiatives that foster a learning-oriented culture and provide opportunities for informal learning. This could include implementing mentorship programs, encouraging knowledge-sharing among employees and offering training and development resources to help workers adapt to AI-driven changes. Additionally, the findings highlight the importance of promoting employee learning orientation, which can enhance the effectiveness of such initiatives. ORIGINALITY/VALUE: The study contributes to the existing literature by addressing a relatively unexplored area - the impact of AI awareness on healthcare workers' well-being. While previous research has focused on the potential job displacement effects of AI, this study takes a unique perspective by examining how health workers' perceptions of AI integration can shape their informal learning behaviour and, subsequently, their workplace well-being. By drawing on the JD-R model and incorporating employee learning orientation as a moderator, the study offers a novel theoretical framework for understanding the implications of AI adoption in healthcare organizations.

Humans↗