Search Results (168)

Background/Objectives: Predicting how fast a patient with Alzheimer’s disease will decline over the next year remains a challenge. Existing blood transcriptomic studies have not established whether probe selection is reproducible, whether the signal is transcriptional or reflects immune cell shifts, or whether they generalise across platforms. Methods: We applied five steps to 96 ADNI-GO whole-blood microarray samples (Affymetrix HG-U219; 12-month MMSE change): PyImpetus Markov Blanket selection, Elastic Net with leave-one-out cross-validation (LOOCV), SHAP attribution, MCP-counter cell-type deconvolution, and cross-platform mapping into AddNeuroMed (GSE63060, n = 329, Illumina). Feature selection preceded cross-validation without constituting data leakage. Results: The same six probes emerged across four independent runs (Jaccard J = 0.214, p = 0.03): AQP7, RPS5, CHD2, SNX5, ASS1, and an uncharacterised chr12q15 transcript. The panel achieved LOOCV MAE = 1.388 and R² = 0.247, outperforming the full-probe baseline by 14.9%. All probes survived immune cell correction with signs intact. SNX5 replicated in AddNeuroMed (r = −0.170, p = 0.002). Conclusions: The exploratory six-probe blood RNA panel predicts 12-month cognitive decline (LOOCV R² = 0.247) with transcriptional origin confirmed by cell-type deconvolution and cross-platform evidence for SNX5. External testing in ADNI-2 (n = 91, R² = −0.222) showed that generalisation depends on visit-timepoint matching, indicating clinical utility cannot yet be claimed and defining conditions for prospective validation. Code and a research prototype tool are publicly available. Full article

This study aimed to integrate apoptosis-associated and proliferation–apoptosis-coupled transcriptomic signatures with explainable machine learning to construct an exploratory molecular classification model for psoriasis. Transcriptomic datasets GSE30999 and GSE53552 were merged as the skin-tissue training cohort, and GSE55201, a whole-blood transcriptomic dataset, was used as an independent cross-tissue external validation cohort. Differential expression analysis identified 3707 DEGs, and intersection with GeneCards apoptosis-related genes yielded 894 overlapping genes. After PPI-based hub gene selection, eight machine learning algorithms were exploratorily compared within a preselected 25-gene feature space. DALEX-based permutation feature importance analysis identified a five-gene apoptosis-associated and proliferation–apoptosis-coupled signature comprising CCNB1, KIF11, HDAC1, TPX2, and MELK. The five-gene model achieved an AUC of 0.966 in the training cohort and 0.811 in the external whole-blood validation cohort, indicating moderate cross-tissue generalizability. Calibration and decision-curve analyses were performed only in the training cohort and should be interpreted as exploratory analyses rather than evidence of clinical utility. Overall, this study provides an interpretable transcriptomic classification framework for distinguishing psoriasis from healthy controls, while its ability to differentiate psoriasis from clinically similar dermatoses remains to be validated in independent disease-control cohorts. Full article

Background: Autism spectrum disorder (ASD) is associated with immune dysregulation in a subset of individuals, though findings remain heterogeneous and poorly defined, particularly regarding immune subtypes and metabolic context. Methods: We analyzed whole-blood microarray data from GSE18123 (GPL570: ASD n = 46, controls n = 19; GPL6244: ASD n = 68, controls n = 21) using an integrated immunometabolic framework. CD4⁺ T-cell transcriptional programs were used to assign dominant immune phenotypes (TH1, TH2, TH17, Tfh, FOXP3⁺ Treg, Tr1-like). Metabolic demand was quantified via the τ-axis; execution capacity was assessed using cytosolic and mitochondrial energy compensation ratios (CECR, MECR). Induction–execution mismatch was captured by three Gap metrics (Cytosolic, Warburg, Global). Functional validation correlated these metrics with transcriptional signatures of folate transport, one-carbon metabolism, receptor-mediated micronutrient uptake (LRP2–CUBN–AMN), cobalamin processing, and vitamin D activation across both platforms. Results: Six immunometabolic CD4⁺ subtypes were identified within ASD. τ-axis discrimination was strongest for Tr1-like (AUC = 0.811) and Tfh (AUC = 0.825) states, while TH17 profiles were indistinguishable from controls. Despite variation in metabolic demand, CECR and MECR remained relatively preserved, indicating decoupling between induction and execution capacity. Global Gap values were most negative in Tfh and TH1 states and positive in TH17 and controls. Negative Gap states showed coordinated suppression of ATP-intensive micronutrient acquisition pathways, including folate transport (FOLR1/2, SLC19A1), megalin–cubilin-mediated uptake (r ≈ 0.77–0.79), and vitamin D activation (CYP27B1). Intracellular cobalamin processing was upregulated in proportion to metabolic demand (r > 0.9). Findings were directionally replicated across both datasets. Conclusions: These data demonstrate that ASD exhibits structured immunometabolic heterogeneity characterized by subtype-specific demand–capacity imbalance. The Global Gap framework provides transcriptomic evidence of energetic deficit in Tfh- and Tr1-like-dominant states. Future clinical studies should incorporate subtype-stratified assessment of micronutrient status and metabolic execution capacity. Full article

Asthma is a heterogeneous inflammatory disorder involving multiple immune pathways, frequently presenting alongside comorbidities such as chronic rhinosinusitis with nasal polyps (CRSwNP). Although biologic therapies such as dupilumab have shown clinical efficacy, the molecular mechanisms underlying variable treatment responses remain poorly understood. This study aimed to characterize transcriptomic patterns that distinguish asthmatic patients from healthy controls and to evaluate transcriptomic changes induced by dupilumab. Whole-blood RNA-seq was performed in 66 samples, 18 patients (G0) with severe asthma before and after 6 months of dupilumab treatment compared with 30 non-asthmatic controls. Differentially expressed genes (DEGs) were identified and validated by quantitative PCR (qPCR). Clinical responses were assessed using the FEV1, Exacerbations, Oral corticosteroids, Symptoms (FEOS) score and the Sino-Nasal Outcome Test-22 (SNOT-22). A total of 1124 DEGs were identified, distinguishing asthmatic patients from controls. Notably, ABCC1, CYP4F12, FBN1, IKZF2, and RAB44 were differentially expressed across all patients’ subgroups and are proposed as putative general disease biomarkers. Unsupervised machine learning analysis of pre- vs. post-dupilumab transcriptomic profiles identified two distinct patient subgroups within G0, here termed G1 and G2. When comparing baseline vs. post-treatment samples in the overall cohort (G0), only 12 DEGs were identified. In contrast, stratified analysis revealed 1288 DEGs in G1 and 354 DEGs in G2, suggesting divergent molecular response to treatment. Additionally, baseline expression of DIXDC1 was identified as a predictor of CRSwNP non-super-responders. By applying unsupervised machine learning to transcriptomic profiles, this exploratory study identifies two distinct endotypes with divergent molecular mechanisms of response to dupilumab, supporting a precision medicine approach to biologic therapy in severe asthma. Full article

Background/Objectives: Peripheral-blood transcriptomic profiling can capture molecular heterogeneity in systemic lupus erythematosus (SLE), but discovery-stage signatures often show limited transportability across cohorts and validation layers. This study aimed to establish a staged whole-blood transcriptomic framework and to derive a compact, biologically interpretable activity score. Methods: Public whole-blood bulk transcriptome cohorts were organised into discovery, public validation, and single-cell reference layers. Local orthogonal validation included a peripheral blood mononuclear cell (PBMC) reverse transcription quantitative PCR (RT-qPCR)/flow-cytometric cohort and an expanded whole-blood RT-qPCR validation set. Discovery-stage BloodGen3 profiling included 233 samples, comprising 170 SLE and 63 healthy controls, and endotype discovery was restricted to SLE samples. Candidate genes were compressed into two 6-gene panels, with final selection adjudicated through staged public validation. Results: Two working whole-blood endotypes were identified, characterised by lymphoid versus myeloid/neutrophil-inflammatory polarisation. Although pre6-any showed a marginal discovery-stage advantage, the predefined integrated public-stage adjudication favoured pre6-balanced (MMP9, MYL9, HAL, CTLA4, CD40LG, VPREB3), which was locked as the final panel. In the PBMC cohort, the locked score discriminated SLE from healthy controls (AUC 0.838) and high from low/moderate disease activity (AUC 0.719), with associations with SLEDAI, complement C3/C4, and monocyte subpopulations. In the expanded whole-blood validation set, the score showed SLE-versus-HC discrimination (AUC 0.888, 95% CI 0.821–0.954), high versus low/moderate activity discrimination (AUC 0.918, 95% CI 0.831–0.980), and association with SLEDAI (ρ = 0.819, p = 1.25 × 10⁻¹⁵). Conclusions: This staged framework yielded a compact myeloid–lymphoid activity score supported across public and local validation layers. The score should be interpreted as a research-grade relative activity score and warrants prospective evaluation in SLE. Full article

Polycythemia vera (PV) is a JAK2V617F-driven myeloproliferative neoplasm characterized by erythroid expansion, increased thrombotic risk, and heterogeneous clinical outcomes. Although prior studies have described key transcriptional abnormalities—including Janus kinase–signal transducer and activator of transcription (JAK–STAT) hyperactivation and chronic myeloinflammation—most have examined single hematopoietic compartments. A multi-compartment approach may reveal conserved and lineage-specific disease-associated transcriptional programs. Here, an integrated, multi-compartment transcriptomic analysis of publicly available microarray datasets was performed, spanning bone marrow (BM) CD34+ progenitors, peripheral blood (PB) CD34+ progenitors, and whole blood from PV patients and healthy controls, with independent validation in neutrophils. Differential gene expression, pathway enrichment, and protein–protein interaction network analyses were used to delineate conserved versus compartment-specific transcriptional programs and to evaluate persistence of progenitor-derived signatures into mature myeloid cells. Across compartments, PV demonstrated consistent enrichment of inflammatory, interferon, and JAK–STAT-associated pathways despite limited overlap at the individual gene level, indicating that core disease processes are maintained through lineage- and differentiation-stage-specific transcriptional reprogramming. Network analysis identified highly connected hub genes, which were used to derive a single-sample gene set enrichment (ssGSEA) signature. This signature showed strong diagnostic performance across cohorts; remained enriched in PV neutrophils; and correlated with platelet count, indolent disease status, and reduced levels in post-splenectomy patients. Together, these findings support a model in which PV is driven by stable, progenitor-derived inflammatory programs that persist across myeloid differentiation while incorporating compartment-specific adaptations, and highlight the value of multi-compartment, network-based approaches for translational biomarker development. Full article

We analyze parameter identifiability in a Marchuk-type immune-response model using longitudinal whole-blood transcriptomic signatures from the influenza challenge. Latent states are extracted from curated gene signatures derived from nine symptomatic and eight asymptomatic subjects. The governing delay differential equations are cast in a linear-in-parameters form; derivatives are estimated by smoothing splines, coefficients are fit by ridge regression, and the delay $\tau$ is selected by grid search. We find that the parameters governing viral and innate dynamics are consistently identifiable, with low relative error, and are highly determined, whereas adaptive-immunity and tissue-damage parameters are poorly constrained by transcriptomics alone. Introducing a small additive background term and tissue dependence markedly reduces residual variance and stabilizes estimates. Symptomatic patients exhibit a characteristic regulatory delay near 21 h. These results show that aggregated transcriptomic time series can reliably identify some subsystems of classical immune models, but that adaptive immunity and damage dynamics require explicit structural extensions or additional data modalities. The study provides a practical identification pipeline and concrete guidance on model extensions needed for transcriptomic-driven mechanistic inference. Full article

Background/Objectives: Tuberculosis (TB), caused by Mycobacterium tuberculosis (MTB), remains a major global health problem. Drug resistance and the limitations of sputum-based diagnostic methods highlight the need for additional host-response biomarkers. Protein tyrosine phosphatase receptor type O (PTPRO) has been implicated in inflammatory signaling and macrophage immune regulation, but its relationship with TB-related host transcriptional responses remains unclear. This study aimed to identify and preliminarily evaluate a PTPRO-related blood transcriptional signature with potential relevance to TB discrimination and treatment-response assessment. Methods: Genes correlated with PTPRO expression were first screened using TCGA-LUSC as a large human transcriptomic discovery resource. The resulting candidate genes were then filtered in TB-related whole-blood datasets by intersecting genes upregulated in TB compared with healthy controls, pneumonia, and lung cancer. This strategy yielded a five-gene PTPRO-related signature, termed PO5. The signature was evaluated in independent GEO cohorts and further explored by RT-qPCR in H37Ra-infected THP-1-derived macrophages and in a small clinical blood cohort. A PO5-derived TB risk score was calculated for each sample, and receiver operating characteristic analysis was used to assess discriminatory performance. Changes in TB risk scores during anti-TB treatment were also examined. Results: PTPRO expression was increased in TB whole-blood transcriptomic data and in H37Ra-infected macrophages. In public datasets, PO5 showed potential for distinguishing TB from healthy controls, latent TB, pneumonia, and lung cancer. PO5-derived TB risk scores also decreased after anti-TB treatment. In the exploratory clinical cohort, several PO5 genes showed expression changes in the same general direction as those observed in the public datasets, although the small sample size limited the strength of this evidence. Conclusions: PO5 represents a preliminary PTPRO-related blood transcriptional signature with potential relevance to TB discrimination and treatment-response assessment. These findings remain exploratory and require validation in larger prospective multicenter cohorts, together with further mechanistic studies. Full article

Background/Objectives: Understanding the regulatory mechanisms of intramuscular fat accumulation is crucial for maintaining skeletal muscle function and treating muscle-related diseases. It is known that cold exposure can lead to fat deposition in the muscles of mice and pigs. However, so far, there is very limited knowledge about the factors influencing its formation under cold exposure conditions. This study used piglets as an animal model to investigate intramuscular fat accumulation under cold exposure. Methods: Six piglets were exposed to 10 °C, and six piglets were exposed to 25 °C. A whole transcriptome joint analysis was performed on the longissimus dorsi muscle of three piglets randomly selected from each group. Results: No fever or cough symptoms were observed in all experimental groups, and the cold exposure vs. control groups’ RNA data were compared. The study identified 705 differentially expressed messenger RNAs, 87 long non-coding RNAs, 57 microRNAs, and 236 circular RNAs. CD36 Molecule (CD36 Blood Group) (CD36) was upregulated, while adiponectin (ADIPOQ) was downregulated. Conclusion: We established a competing endogenous RNA network centered around CD36, Protein Phosphatase 1 Regulatory Subunit 3G (PPP1R3G) and ADIPOQ for intramuscular fat accumulation by using a pig model exposed to a cold temperature. This study provides important references for further understanding the regulatory mechanism of intramuscular fat. Full article

Ankylosing spondylitis (AS) is a chronic immune-mediated inflammatory disease affecting the axial skeleton, characterized by progressive structural damage and functional impairment. Although biologic therapies targeting tumor necrosis factor and interleukin-17 have improved clinical outcomes, a substantial proportion of patients fail to achieve sustained disease control. Emerging evidence suggests that metabolic alterations may contribute to AS pathogenesis; however, systematic characterization of metabolism-related biomarkers and their regulatory networks remains limited, and the interplay between metabolic dysfunction and immune dysregulation in AS is poorly understood. Two whole-blood GEO datasets (GSE25101, GSE73754; n = 104) were integrated as the primary analytical cohort. A third dataset (GSE11886, n = 18; monocyte-derived macrophages) was included for exploratory cross-tissue analysis. Differential expression analysis identified 847 DEGs, which were refined to 16 metabolism-related genes through weighted gene co-expression network analysis (WGCNA) and GeneCards database filtering. Eleven machine learning algorithms with 5-fold cross-validation were applied to construct diagnostic models and identify hub genes. Validation analyses included immune cell infiltration estimation using CIBERSORT, metabolic pathway activity assessment via ssGSEA, single-cell transcriptomics from GSE268839, functional enrichment through GSEA/GSVA, and chromosomal localization analysis. A competing endogenous RNA (ceRNA) regulatory network was constructed to map post-transcriptional regulation. Natural compounds from 66 AS-treating traditional Chinese medicines were screened against hub genes using deep learning-based binding prediction. Multiple machine learning algorithms achieved comparable cross-validated performance (CV AUC range 0.741–0.836; top five models: 0.805–0.836) using the six hub genes (MFN2, SLC27A3, RHOB, SMG7, AKR1B1, LCOR) identified through SHAP-based feature importance analysis of the PLS model. Leave-one-dataset-out validation between the two whole-blood cohorts showed that all algorithms exceeded an AUC of 0.77 in Round 1 (validate: GSE73754, n = 72; best AUC 0.861), while Round 2 (validate: GSE25101, n = 32) yielded more modest performance (best AUC, 0.715) reflecting the smaller validation sample. Exploratory application to GSE11886 (macrophage-derived samples) showed near-chance performance, consistent with the tissue-source discrepancy. AS patients exhibited significant downregulation of oxidative phosphorylation, TCA cycle, and glycolysis pathways (p < 0.01), accompanied by elevated glutathione metabolism (p < 0.001). Immune cell deconvolution revealed reduced CD8+ T cell proportions correlating with MFN2 downregulation, and increased neutrophil frequencies correlating with SLC27A3 upregulation. Exploratory single-cell analysis indicated that RHOB expression was relatively enriched in border-associated macrophages and fibroblasts, while AKR1B1 was more prominently expressed in vascular endothelial cells and plasmacytoid dendritic cells. The ceRNA network identified 21 miRNAs and 65 lncRNAs forming 86 regulatory interactions, with four key regulatory axes (SATB1-AS1/miR-539-5p/LCOR, FAM95B1/miR-223-3p/RHOB, LINC01106/miR-106a-5p/MFN2, AATBC/miR-185-5p/SMG7) predicted to regulate hub gene expression. Compound screening identified betaine, pyruvic acid, citric acid, etc., as top-ranking candidates, with MFN2 showing the highest binding capacity among hub genes. This study provides an integrative framework linking metabolic reprogramming with immune dysfunction in AS. The six-gene diagnostic signature showed preliminary discriminatory ability in the available datasets, while the ceRNA regulatory network and natural compound screening results prioritize candidate regulatory pathways and compounds for future validation. These findings advance our understanding of AS pathogenesis and may guide future biomarker development and targeted intervention strategies. Full article

Venom-mediated systemic toxicity is not fully understood. This study explored the dose-dependent effects of Vespa mandarinia venom (VMV) on mice via integrated transcriptomic and metabolomic analyses. Subcutaneous VMV injection induced dose-dependent hypothermia: 80 μg caused severe transient hypothermia and partial mortality, while 40/60 μg led to reversible hypothermia within 24 h. Whole-blood sequencing identified 2400–3281 differentially expressed genes (DEGs) per group, including 1764 shared DEGs. Immune-related pathways were significantly activated, with hub genes validated by qRT-PCR. Serum metabolomics revealed alterations in organic acids, alkaloids, and other metabolites. Integrative transcriptome–metabolome analysis predicted the potential involvement of various pathways in VMV-induced toxicity, including ferroptosis (shared in low-dose VMV groups) and apoptosis. Cumulatively, this study confirms that VMV induces immunometabolic reprogramming, providing a molecular framework for understanding venom-induced systemic toxicity. Full article

Background: Infective endocarditis (IE) affects both native and prosthetic heart valves, the endocardial surface, as well as cardiac implantable electronic devices. Identifying specific IE biomarkers for its early risk stratification remains crucial, particularly in cases with blood culture-negative endocarditis. Methods: Eleven native heart valves obtained from IE and calcific aortic valve disease (CAVD) patients were analyzed. Immunohistochemical analysis of a pan-leukocyte marker (CD45), macrophage marker (CD68), T-lymphocyte marker (CD3), B-lymphocyte marker (CD19), neutrophil myeloperoxidase (MPO), and marker of vascular endothelial cells (CD31) was performed. Differentially expressed genes (DEGs) were identified by whole-transcriptome sequencing; proteomic profiling was performed by dot-blotting. Results: The immunophenotyping demonstrates the infiltration of macrophages and neutrophils, as well as occasional T-lymphocytes in the IE-affected aortic valves, and the CAVD-affected heart valves were characterized by the absence of neutrophils. For the whole-transcriptome sequencing, 157 DEGs were identified: 124 DEGs were upregulated, and 33 genes were downregulated in the IE-affected heart valves compared to the CAVD-affected ones. According to the dot-blotting, 35 cytokines were identified in the studied heart valves, but only 21 molecules were expressed in both IE and CAVD-affected heart valves. Analysis of proteases and their inhibitors allowed the identification of 13 protease molecules and 18 enzyme inhibitor molecules in all examined heart valves. Conclusions: The results of the present study can help to improve our understanding of the IE pathogenesis. In addition, we identified the candidate cellular and molecular-genetic features of IE-affected native heart valves. Full article

Tuberculosis–diabetes mellitus (TB-DM) is increasingly recognized as a syndemic in which chronic metabolic dysregulation amplifies tuberculosis severity, delays treatment response, and increases relapse and mortality. However, conventional systemic correlates soluble cytokines and bulk whole-blood transcriptomic signatures often appear broadly similar between TB and TB-DM. This highlights a key gap: clinically meaningful immune dysfunction in TB-DM likely resides in specific lung and blood cell states that are poorly resolved by bulk assays. Small extracellular vesicles (EVs) in plasma and bronchoalveolar lavage (BAL) provide a tractable “liquid biopsy” layer because their RNA and protein cargo can integrate information from infected macrophages, neutrophils, and epithelial/endothelial compartments, and may also include pathogen-derived components. Yet most EV studies remain bulk and cell-agnostic, and interpretation is constrained by heterogeneous vesicle mixtures, selective cargo packaging, and co-isolated non-vesicular contaminants, issues that are especially problematic for nucleic-acid claims without rigorous controls. In this targeted narrative review (2010–2026), we argue that single-cell and multimodal immune reference atlases, including scRNA-seq/CITE-seq, provide a needed scaffold to link EV cargo patterns to specific immune cell states, pathways, and anatomic compartments in TB-DM, enabling prioritized candidates and testable hypotheses. We outline three complementary frameworks: reference-atlas anchoring to project EV cargo modules onto atlas-defined immune states; orthogonal triangulation combining computational inference with immunoaffinity enrichment, targeted validation, and functional assays; and cautious use of “droplet-era” extracellular signals as hypothesis-generating priors for EV-producing states. Implemented in longitudinal, clinically annotated cohorts with standardized EV workflows, atlas-guided EV profiling could yield cell-of-origin–resolved biomarkers of TB-DM immunopathology and treatment response, while prioritizing mechanistically plausible targets for host-directed intervention. Full article

Bronchial thermoplasty (BT) is a non-pharmacological treatment for severe asthma. The working mechanism and response determinants of BT remain partly unknown. This study aims to investigate whether a systemic transcriptomic response to BT can be detected and contextualized against a control cohort. Whole blood was collected at baseline and six months after BT from severe asthma patients (n = 31) and a control cohort (n = 126). RNA was isolated and sequenced. The following comparisons were made: before and after BT, responders and non-responders, and severe asthma (at baseline) versus controls. Differentially expressed genes were identified across 179 samples using DESeq2. Pathway enrichment was investigated using gene set enrichment and overrepresentation analyses. Following BT, pathways related to nervous system development, ion channel activity, muscle tissue development, and cilia function were downregulated. In responders specifically, gene sets involved in nervous system and muscle development were downregulated. Compared with the control cohort, pathways related to nervous system development and ion channel activity were upregulated in the severe asthma cohort at baseline. In conclusion, systemic blood-derived transcriptomic changes can be detected in severe asthma patients six months after BT and may provide insight into BT mechanisms and its responder profile. Full article

Background: The neutrophil-to-lymphocyte ratio (NLR) is a simple biomarker that reflects the balance between innate immune response and adaptive immunity. Currently, the genetic basis and clinical implications of NLR in relation to inflammatory gastrointestinal diseases have not been extensively explored. Methods: We carried out a genome-wide association study (GWAS) on European individuals from the UK Biobank to detect genetic variants related to NLR, followed by post-GWAS analyses including colocalization analysis, transcriptome-wide association studies (TWAS), and LD score regression. Logistic regression, Cox regression, and gene–environment interaction analysis were used to evaluate the impact of NLR polygenic risk scores (PRS) on inflammatory gastrointestinal disease risks. Results: GWAS of 395,442 Europeans identified 306 genomic regions (731 lead SNPs) associated with NLR, mapping to 1542 genes enriched for immune pathways. Colocalization revealed shared genetic signals with TWAS prioritization of 59, 19, 14, 22 and 28 genes in the whole blood, spleen, terminal ileum, transverse colon and sigmoid colon, respectively. LD-score regression showed significant positive genetic correlations with CD (rg = 0.132), coeliac disease (rg = 0.124), peptic ulcer (rg = 0.138) and duodenal ulcer (rg = 0.220). One-SD increase in NLR PRS predicted higher risk of IBD (OR = 1.05, 95% CI 1.03–1.08), Crohn’s disease (OR = 1.06, 1.02–1.10), ulcerative colitis (OR = 1.05, 1.02–1.08) and coeliac disease (OR = 1.07, 1.03–1.11). Restricted cubic splines demonstrated non-linear relationships of NLR PRS for IBD, CD and UC. Gene environment analyses showed smoking and diabetes amplified the risks, while cardioprotective diet, oily fish intake and polyunsaturated fatty acid level attenuated NLR PRS-associated risk in IBD (mainly CD). Conclusions: Our study delineates the polygenic basis of NLR and establishes its genetic correlation with inflammatory gastrointestinal diseases, offering a genetically informed indicator for disease risk stratification with potential utility in population-level prevention strategies. Full article