Metabolites

Background. Plasma and serum metabolomic studies of myalgic encephalomyelitis / chronic fatigue syndrome (ME/CFS) have repeatedly implicated hypometabolic, lipid, mitochondrial, redox and tryptophan-kynurenine pathways, but prior cohorts have been modest in size and have used heterogeneous case definitions. Whether similar pathway-level signals are detectable at scale in dried blood spots (DBS), across questionnaire-derived fatigue constructs and across orthogonal LC gradients in the same individuals remains unresolved. Methods. We profiled DBS extracts from 1,784 community-cohort adults by reverse-phase LC-MS using paired 5 min and 15 min gradients. Six questionnaire-derived endpoints captured a pragmatic self-reported PEM-like phenotype, a DSQ-derived PEM-like construct, high or review clinical status, temporal fatigue state, comorbid fatigue and self-reported chronic fatigue. The locked primary endpoint for Phase 1 was pragmatic_fatigue_pem with 226 cases and 914 controls after excluding major metabolic comorbidity. We tested a biology-first panel comprising 22 literature-curated metabolites represented by four participant-level descriptors each, and evaluated three discovery extensions: a targeted m/z search of additional literature candidates, a hypothesis-free univariate screen across 4,553 5 min and 5,625 15 min consensus features, and pairwise z-difference ratios. Endpoint-specific Ridge classifiers were evaluated by five-fold out-of-fold AUC with bootstrap stability filtering. Cross-gradient agreement was assessed by per-metabolite AUC concordance between paired 5 min and 15 min profiles. Severity was modelled as an ordinal grade derived from the number of fatigue criteria met and chronic-fatigue-form status. Results. The biology-first DBS panel achieved out-of-fold AUC 0.81 for the pragmatic self-reported PEM-like endpoint (226 cases / 914 controls). The DSQ-derived PEM-like construct reached AUC 0.60 (57 cases / 201 controls) on the un-filtered set and AUC 0.778 (SD 0.013, twenty seeds) in a post-hoc signature-decomposition follow-up restricted to participants without a self-declared major-metabolic-history tag (29 cases / 230 controls); both are treated as construct-validity anchors rather than as provoked or clinically adjudicated PEM. An optimised operationalisation of the same construct (panel-self normalisation, restriction to non-comorbid participants and demographic covariates) reached AUC 0.71 (95 % CI 0.55 to 0.76), and an exploratory age-stratified signature decomposition suggested age-dependent pathway composition that requires confirmation given small per-stratum case counts. Stable contributors mapped to carnitine-shuttle, TCA-cycle, redox-thiol and tryptophan-kynurenine pathways. Cross-gradient analysis of 22 matched metabolites yielded Pearson r = 0.62 for signed univariate effects (p = 0.002; 68 % directional agreement). The metabolomic score increased with severity grade (Spearman rho = 0.45, p = 4 x 10^-91; median scores 0.24, 0.51 and 0.75 across grades 0, 1 and 2). Sensitivity analyses on the covariate-complete subset (n = 565; 138 cases / 427 controls) showed that the DBS signal was robust to adjustment for age, sex, BMI and medication burden (DBS-only AUC 0.76, DBS plus covariates 0.78, covariates only 0.64), and produced a metabolomic-specific lift of approximately 0.13 AUC over the strongest anti-leak declarative cross-form questionnaire baseline (AUC 0.63). DBS-only AUC was stable across sex, age and BMI subgroups, and a 1:4 nearest-neighbour matched analysis on age, sex and BMI yielded AUC 0.72 (95 % CI 0.67 to 0.77). The observed pattern supported pathway-level convergence with prior ME/CFS metabolomics literature, including carnitine shuttle, fatty-acid beta-oxidation, TCA cycle, redox-thiol, urea cycle, glycerophospholipid and tryptophan-kynurenine axes. In contrast, the hypothesis-free 15 min screen produced high-AUC features that mapped predominantly to environmental or technical signals, including pesticide, industrial-amine and mobile-phase artifact annotations; only one of eight top leads, a truncated oxidised phospholipid, was biologically plausible, and none had tandem-MS support. Conclusions. In this large community cohort, a literature-curated DBS metabolomic panel captured pathway-level biology associated with a questionnaire-derived PEM-like fatigue phenotype, showed directional concordance across LC gradients, scaled with symptom severity and remained robust to key demographic, anthropometric and anti-leak questionnaire baselines. The findings converge with several metabolic axes previously reported in ME/CFS plasma and serum studies, including carnitine-shuttle, TCA-cycle, redox-thiol, urea-cycle, glycerophospholipid and tryptophan-kynurenine pathways. They should not be interpreted as clinical validation of a diagnostic test, screening tool or objective provoked-PEM biomarker. Rather, they support at-home-compatible DBS metabolomics as a biologically grounded platform for future clinically adjudicated validation, decision-support development and longitudinal monitoring in fatigue and PEM-like syndromes. Because DBS contains cellular and plasma-derived components, matrix effects must be considered when comparing individual metabolites with venous plasma or serum studies, and hypothesis-free screening at this scale can preferentially surface exposome or technical variance unless molecular identification is enforced before biological interpretation.

Background The European Association for the Study of the Liver (ESAL) - Steatotic Liver Disease (SLD) screening algorithm involves two steps; initial screening with FIB-4 followed by referral for vibration-controlled transient elastography (VCTE) in patients likely to have significant fibrosis (SF). However, VCTE is not widely available in resource-limited settings. Aim To optimise the EASL SLD screening algorithm for resource-poor settings using machine learning (ML). Methods We analysed data from 964 adults aged [≥]35 years who underwent VCTE at a tertiary referral centre in Sri Lanka between November 2024 and 2025. Multiple ML models using different methods and variable combinations were trained on 80% of the dataset and tested on the remaining 20%. Best models were selected based on performance and externally validated using data from 430 patients who underwent VCTE before November 2024. Model performance was compared with the FIB-4 using confusion matrices. Results A Random Forest model incorporating age, AST, ALT, and platelet count separately, rather than using FIB-4, outperformed. The all-variable ML model showed the best predictive performance for SF, with accuracy of 77.2%, recall of 0.762, precision of 0.778, and AUC-ROC of 0.818. The variables used in the model, in descending order of feature importance, were AST, platelet count, BMI, ALT, age, diabetes mellitus, hypertension, dyslipidaemia, sex, family history, hypothyroidism, diabetes complication and smoking. External validation demonstrated 75.1% accuracy and an AUC of 0.779. When used as the first step of the SLD screening algorithm, the all-variable ML model identified 37 (17.1%) additional true positives and reduced false-negative diagnoses by 50% compared with FIB-4. Conclusions ML-based models were more effective than the FIB-4 score as the first-line screening tool for VCTE referral, substantially improving the identification of patients with significant fibrosis in this South Asian cohort.

Background and Aims: MASLD has become the most prevalent chronic liver disease globally. Although MVPA and plasma fatty acids have been individually studied in relation to metabolic health, their independent and combined associations with MASLD incidence remain unclear. We aimed to investigate these associations. Methods: This study included 51,717 UK Biobank participants free of liver disease at baseline, with MVPA measured using wrist-worn accelerometers and plasma fatty acids quantified via NMR. Multivariable-adjusted Cox models and restricted cubic splines were used. Results: Over a median follow-up of 7.8 years, 472 incident cases were identified. In fully adjusted models, meeting recommended MVPA levels together with higher n-6 PUFA concentrations was associated with a 71% lower risk (HR 0.29, 95% CI 0.18-0.45). The MVPA-MASLD association was nonlinear, with risk reduction plateauing at approximately 189 minutes per week. Higher n-6 PUFA was associated with reduced risk, whereas n-3 PUFA showed no significant association. Conclusions: These findings suggest that behavioral and metabolic factors may jointly influence MASLD risk. Further studies in diverse populations are needed to confirm these associations.

Background: Obesity is a global health crisis, contributing to chronic diseases such as diabetes, cardiovascular disease, and metabolic syndrome. Traditional Chinese Medicine (TCM) has been used in East Asia to manage obesity, but evidence on its efficacy and safety remains limited. This systematic review and meta-analysis assess clinical evidence from randomized controlled trials (RCTs) on TCM for obesity treatment. Methods: We systematically searched PubMed, EMBASE, Cochrane Library, and Web of Science from inception to April 2026. Eligible RCTs compared TCM interventions with placebo or conventional treatments in obese patients. Two reviewers independently conducted screening, data extraction, and quality assessment. Meta-analysis was conducted using a random-effects model to calculate pooled weighted mean differences (WMD) and odds ratios (OR) for body weight, BMI, waist-to-hip ratio (WHR), lipid profiles, and adverse events. Results: A total of 33 randomized controlled trials (RCTs) involving 3,053 participants were included in the analysis. TCM significantly reduced body weight (WMD = -5.86 kg, 95% CI: -7.51 to -4.21), BMI (WMD = -2.82 kg/m{superscript 2}, 95% CI: -3.38 to -2.25), and WHR (WMD = -0.04, 95% CI: -0.06 to -0.02). Lipid profiles improved, with reductions in total cholesterol (WMD = -0.82 mmol/L), triglycerides (WMD = -0.65 mmol/L), LDL-C (WMD = -0.39 mmol/L), and increased HDL-C (WMD = 0.29 mmol/L) (all p < 0.001). Adverse events were infrequent, with no significant difference observed between TCM and control groups (OR = 0.51, 95% CI: 0.24 to 1.08). Funnel plots indicated no publication bias. Conclusion: TCM appears effective in reducing body weight and improving lipid profiles in obese patients, with a low incidence of adverse events. It may serve as a complementary treatment for obesity, though further high-quality RCTs are needed to confirm these findings and assess long-term outcomes.

Metabolic dysfunction-associated steatotic liver disease (MASLD) is rapidly increasing worldwide, yet effective targeted therapies remain limited. To better understand the molecular mechanisms underlying MASLD, we performed an integrated proteogenomic analysis of human liver tissue. Using mass spectrometry, we quantified 2,744 proteins in 504 liver biopsies from the Quebec Obesity Biobank and examined changes across disease stages. To investigate causality, we integrated liver proteomics with RNA sequencing and genome-wide genotyping to map thousands of protein quantitative trait loci (pQTLs) and expression quantitative trait loci (eQTLs). These molecular data were combined with summary statistics from a meta-analysis of genome-wide association studies including 16,532 MASLD cases and 1,240,188 controls. Mendelian randomization and genetic colocalization analyses revealed that most proteins differentially expressed across MASLD stages were not causally implicated in disease risk, whereas several genetically predicted liver proteins showed evidence of causal effects. Among these, higher hepatic levels of the MTARC1 protein were causally associated with MASLD and hepatic fat accumulation. Phenome-wide analyses suggested that MTARC1 inhibition may reduce the risk of cirrhosis, hepatocellular carcinoma, and cholelithiasis while improving lipid profiles. Notably, the causal MTARC1 variant influenced liver protein levels but not gene expression. Genetic analyses also identified ERLIN1 and HSD17B13 as potential therapeutic targets. In contrast, eQTLs and pQTLs at other loci such as GCKR showed opposite effects on MASLD risk. These findings highlight the importance of integrating tissue proteomics with human genetics to distinguish biomarkers from causal drivers and to identify promising therapeutic targets for MASLD.

Background: Histological examination of mucosal tissue in inflammatory bowel diseases (IBD) is a sensitive tool to measure disease activity, and histological remission is emerging as a potentially important treatment target. There are several existing histopathological indices, but they often encompass caveats such as not primarily having been designed to measure the degree of inflammation, encompassing subjective components with poor intra- and interindividual reproducibility, and requiring expert pathologists who are scarce, thus resulting in extended response times. Aim: To construct a new computerized, automated index to objectively measure histological disease activity in the ileal and colonic mucosa, applicable to both Crohn's disease (CD) and ulcerative colitis (UC). Materials and methods: Ileocolonic biopsies were collected from control subjects and patients with CD or UC. A group of CD patients was sampled before and after 12 weeks of anti-TNF therapy. Another group of CD and UC patients functioned as a small validation cohort. Epithelial cells, neutrophils, macrophages, and T cells were immunohistochemically stained, followed by digitalization of the color signal and computerized delineation of the epithelial and lamina propria compartments. The various immune cell types within the epithelium and the lamina propria, respectively, were enumerated, and the numbers were compared between control subjects and patients with CD or UC. Results: The numbers of neutrophils and macrophages in the epithelium, and neutrophils in the lamina propria, showed the highest sensitivity and specificity for distinguishing control-subject tissues from CD and UC tissues. These three parameters were thus chosen to construct a new index, named QiC3 1.0, that could separate tissues from control subjects and patients with CD or UC with high precision. It performed equally well in a small validation cohort of patients. The QiC3 index correlated well with previously described histopathological indices, fecal calprotectin, and endoscopic scores in UC, but showed worse correlation with endoscopic scores in CD and symptomatic scores. When applying the new index to tissues from CD patients before and after therapy, it showed good responsiveness, demonstrating a distinct amelioration in the microscopic inflammatory status that corresponded well to improvements in histopathological scores. Conclusion: We describe a new quantitative, computerized, automated, non-subjective, and response-sensitive immunohistological index (QiC3) for measuring disease activity in ileal and colonic mucosal biopsies, suitable for both CD and UC.

Background and Aims: Fatigue is a prevalent and disabling symptom in inflammatory bowel disease (IBD), yet its underlying biological mechanisms remain poorly understood. We aimed to characterize fatigue-associated molecular signatures in IBD patients by integrating DNA methylation and mRNA expression analyses. Methods: Peripheral blood was collected from 40 patients with Crohn's disease (CD), 29 with ulcerative colitis (UC), and 10 healthy controls. Fatigue severity was assessed continuously using the Multidimensional Fatigue Inventory (MFI). Epigenome-wide DNA methylation profiling and mRNA sequencing were performed, identifying differentially methylated regions (DMRs) and differentially expressed genes (DEGs) for active and quiescent CD and UC, adjusting for age, sex, and smoking status. Pathway enrichment analysis was performed on genes with differential methylation and expression. Results: In active CD, more severe fatigue was associated with transcriptional suppression of immune and metabolic pathways (246 DMRs; 1,090 DEGs), versus upregulation of mitochondrial and metabolic processes in quiescent CD (200 DMRs; 1,619 DEGs). In active UC, fatigue was associated with anabolic pathway upregulation and epigenetic silencing of neuroactive pathways (6,927 DMRs; 343 DEGs; 56 concordant genes). Quiescent UC showed transcriptional changes without significant epigenetic pathway enrichment (1,710 DMRs; 3,224 DEGs). Healthy controls exhibited a distinct profile spanning metabolic, immune, and neuronal pathways (8,621 DMRs; 395 DEGs). Fatigue-associated signatures were largely non-overlapping across all five groups. Conclusions: Fatigue-associated molecular profiles differed substantially by disease subtype and activity state, highlighting the biological heterogeneity of IBD-related fatigue and laying the foundation for multi-omics approaches to identify biomarkers and potential therapeutic targets.

Aims: Sarcopenia and sarcopenic obesity are associated with increased risks of cardiovascular (CV) disease and mortality. This study examined the associations of body composition and daily physical activity with mortality, CV events and cancer in patients with diabetes. Methods: This prospective cohort study included patients with diabetes treated at a specialised clinic in Japan between January 2018 and March 2023. Body composition, including visceral adipose tissue (VAT), was assessed by bioelectrical impedance analysis. Daily physical activity was evaluated using the non-exercise activity thermogenesis (NEAT) questionnaire, and handgrip strength (HGS) was measured by dynamometry. Cox proportional hazards models were used to assess associations with mortality, CV events, and cancer. Results: Among 2,024 patients (mean age 63.0 years, BMI 24.6 kg/m^2, HbA1c 7.8%), NEAT, HGS, and VAT were not independently associated with all-cause mortality. Higher VAT was associated with increased cancer risk (HR 1.485; 95% CI 1.101-2.003; p = 0.009). Higher HGS was inversely associated with CV event risk (HR 0.951; 95% CI 0.919-0.984; p = 0.004). NEAT was not associated with any outcome. Conclusions: Higher VAT was associated with increased cancer risk, whereas higher HGS was protective against CV events. Incorporating body composition and HGS assessments into clinical practice may improve risk stratification and management in patients with diabetes.

Background: Unstructured data represent about 80% of total electronic health records (EHR) data. Structuring this free text is essential for advancing clinical research, including cohort selection for trials, retrospective studies, and the development of disease registries. While manual chart review (MCR) remains the gold standard for extracting this clinical data, the process is inherently slow, resource-intensive, and susceptible to errors from human fatigue. We evaluated the extraction accuracy, safety, and efficiency of the HeLIX (Hepatology Logic-Integrated Extraction) framework, a Large Language Model (LLM) protocol using Google Gemini 3 Pro, compared to a gold-standard Manual Chart Review (MCR). Methods: A prospective validation study was conducted using 50 high-complexity, simulated hepatology discharge summaries designed to replicate the real-world heterogeneity of EHRs. The HeLIX framework employed a Zero-Shot, Structured Chain-of-Thought (CoT) prompting strategy enforced by a three-layer architecture: Clinical Reasoning Trace, Schema Enforcement, and Evidence Verification. The model extracted 45 distinct clinical variables. Performance was benchmarked against a consensus MCR. Results: Across 2,250 evaluated data points, the model achieved an overall Extraction Accuracy of 99.24% (95% CI: 98.8%-99.5%), with perfect concordance in 35/45 (77.8%) variables. For binary diagnostic variables, the model demonstrated an overall F1-score of 0.98, Recall of 0.99 and substantial inter-rater reliability (Cohens {kappa} = 0.97). Hallucinations were exceptionally rare (2/2250; 0.08%). Critical errors affecting clinical management occurred in only 2 instances (<0.1% of total data), both involving etiological misattribution in complex multifactorial diagnoses. The AI workflow was 13.4-fold faster and 95.1% more cost-effective than manual extraction. Conclusion: The HeLIX framework demonstrates physician-level accuracy and reliability in extracting complex hepatology data. It offers a scalable, efficient, and economical alternative to manual chart review. Such frameworks could accelerate clinical research, enabling healthcare systems globally to build comprehensive patient registries for a fraction of the traditional cost.

The human small intestine (SI) plays a central role in nutrient processing, host-microbe interactions, and immune regulation, yet remains poorly characterized due to the lack of minimally disruptive sampling methods. Here, we present a protocol for deploying, recovering, and analyzing samples collected using an ingestible device that enables multi-region, lumen-targeted SI sampling during normal digestion. The device incorporates a ~30-cm collapsible tube wound into pH- or time-responsive layers that sequentially unfurl in situ, typically capturing three spatially ordered samples with high yield and reliable retrieval. This protocol outlines study design, participant handling, device recovery, contamination control, and standardized workflows for analyses, including cell quantification, culturomics, sequencing, and metabolomics. We further describe benchmarking approaches for evaluating spatial resolution and strategies for assay prioritization when sample volume is limiting. By reducing participant burden and facilitating integration with stool, saliva, and clinical metadata, this approach enables longitudinal and large-cohort studies linking SI microbial ecology and host physiology to human health.

Background Rheumatoid arthritis (RA) is a chronic systemic inflammatory disease which can lead to progressive disability and damage to multiple organs. Obesity is associated with higher disease activity in RA and inadequate long-term outcomes, so better understanding of mechanisms linking adiposity to immune dysregulation might help to refine optimal treatments. Monocytes are important contributors to immune activation in RA through antigen presentation and costimulatory signaling. We hypothesized that adiposity enhances monocyte costimulatory programming in RA, thereby promoting adaptive immune activation. Methods Single-cell RNA sequencing was performed using the 10x Genomics Flex platform on purified circulating monocytes from 31 donors (16 RA participants fulfilling 2010 ACR/EULAR classification criteria and 15 non-RA controls) generating transcriptomic profiles for approximately 135,599 monocytes. Donor-level pathway enrichment scores were calculated for predefined immune activation pathways including antigen processing and presentation, interferon signaling, and regulation of T-cell costimulation. Analyses were performed at the donor level to avoid cell-level pseudoreplication. Associations with disease status and body mass index were evaluated using factorial linear models and Spearman correlation analyses. Results Single-cell transcriptomic profiling identified classical, intermediate-like, non-classical, and interferon-responsive monocyte populations. RA was associated with enrichment of antigen processing and presentation programs in circulating monocytes (p=0.0106), indicating a primed antigen-presenting state. In contrast, regulation of T-cell costimulation pathway enrichment did not differ by RA status alone. However, within RA participants, higher BMI was associated with increased enrichment of monocyte T-cell costimulatory pathways (Spearman {rho}=0.56, p=0.0248), unlike in non-RA controls. Gene-level analyses demonstrated strong baseline expression of CD86, while ICOSLG and TNFSF4 transcripts were expressed at low levels overall, consistent with inducible costimulatory signaling programs. Conclusions These findings support a model in which metabolic dysregulation amplifies monocyte-mediated immune activation and may contribute to worsened disease outcomes in RA.

Background: Pancreatic ductal adenocarcinoma (PDAC) is characterized by extensive molecular complexity, profound stromal remodeling, and limited responsiveness to systemic therapies. Although gemcitabine-based regimens remain widely utilized, the molecular pathways that influence treatment-associated biological variation are incompletely understood. The TGF{beta} and JAK/STAT signaling networks are recognized regulators of tumor progression, immune modulation, and therapeutic resistance; however, their genomic architecture in clinically stratified PDAC populations remains poorly defined. Methods: We employed a conversational artificial intelligence-driven analytical framework to investigate TGF{beta} and JAK/STAT pathway alterations in a cohort of 184 PDAC patients. Clinical and molecular data were integrated to generate age- and treatment-stratified cohorts, enabling pathway-level and gene-level analyses according to gemcitabine exposure. Findings generated through AI-assisted interrogation were subsequently evaluated using conventional statistical approaches. Results: TGF{beta} pathway alterations were identified in approximately one-quarter to one-third of tumors across clinical subgroups and demonstrated relatively stable frequencies regardless of age at diagnosis or gemcitabine treatment status. Gene-level analyses revealed that pathway disruption was predominantly driven by recurrent alterations in SMAD4, with additional low-frequency events involving TGFBR1 and TGFBR2. Notably, TGFBR2 mutations were significantly more frequent among late-onset PDAC patients receiving gemcitabine compared with untreated late-onset patients (8.8% vs. 1.4%; p = 0.04), suggesting a potential treatment-associated enrichment. In contrast, JAK/STAT pathway alterations were rare throughout the cohort, with only isolated mutations observed in pathway components including JAK1, JAK2, JAK3, STAT1, STAT3, and related regulatory genes. No significant differences in JAK/STAT alteration frequencies were identified according to age or treatment exposure. Conclusions: TGF{beta} and JAK/STAT pathways exhibit distinct genomic architectures in PDAC. TGF{beta} pathway disruption represents a recurrent feature of disease biology, largely driven by SMAD4 alterations, while TGFBR2 enrichment in gemcitabine-treated late-onset tumors suggests a potential context-specific association worthy of further investigation. Conversely, genomic alterations within the JAK/STAT pathway are uncommon, indicating that pathway activity may be regulated predominantly through non-genomic mechanisms. These findings demonstrate the utility of conversational artificial intelligence agents for rapid, scalable, and clinically contextualized pathway interrogation and support future studies integrating multi-omic data to refine precision medicine strategies in PDAC.

The purpose of this work is to analyze the 2-year overall survival (OS2y) of limited-stage small cell lung cancer (LS-SCLC) treated with chemoradiotherapy (CRT), aiming at characterizing the response of LS-SCLC, and in particular the /{beta} value and proliferation parameters. Through a systematic analysis of the literature, we collated a dataset containing 57 entries (3363 patients) of response of LS-SCLC treated with CRT. Radiotherapy schedules ranged from hyper- to hypofractionation. Four radiobiological models to describe the OS2y were investigated, with progressive levels of complexity including the effect of radiotherapy, chemotherapy, treatment year and toxicity. The Akaike Information Criterion (AIC) was used to compare models, and the profile likelihood methodology to compute confidence intervals. Model 4, which includes the effect of radiotherapy, chemotherapy, treatment year and dose-dependent toxicity, provided the best fits of the experimental data (lowest AIC value). While being the best model, model 4 still fails to provide a good prediction of the OS2y, in particular failing to predict the survival of the schedules achieving the lower/higher survivals. The radiobiological analysis of the dose-response of LS-SCLC to CRT does not allow to narrowly constrain the value of response parameters. We attribute this limitation to the large heterogeneity of this disease. Nonetheless, our analysis shows a large /{beta} value (>9 Gy, 95% CI), which implies a low fractionation effect in the radiotherapy of LS-SCLC. and an accelerated proliferation of tumor cells, {lambda}' > 1.6 Gy/day (95% CI), after a kick-off time of ~4-5 weeks, which supports the use of accelerated protocols to avoid the effect of tumor proliferation on the clinical outcome.

Background. Type 2 diabetes mellitus (T2D) is defined by progressive pancreatic {beta}-cell dysfunction whose molecular underpinnings remain incompletely understood. Single-cohort transcriptomic analyses of donor islets have yielded heterogeneous gene lists of limited cross-study reproducibility, constraining both mechanistic interpretation and biomarker development. Methods. We combined two complementary analytical strategies applied to four public human islet transcriptomic cohorts (GSE25724, GSE20966, GSE38642, and GSE164416; n = 7-57 donors per contrast). For the integrative arm, three microarray datasets and one bulk RNA-seq dataset were processed independently and unified through gene-level random-effects meta-analysis, hallmark pathway scoring (GSVA/MSigDB), and iterative module refinement, yielding a two-axis disease framework. For the diagnostic arm, a consensus multi-method machine learning pipeline, combining LASSO penalized logistic regression, Support Vector Machine Recursive Feature Elimination (SVM-RFE), and Random Forest importance scoring, was applied to 184 differentially expressed genes from the RNA-seq cohort, with all normalization steps performed within leave-one-out cross-validation (LOOCV) folds to prevent data leakage. Machine learning classification of the RNA-seq cohort was additionally subjected to external transportability testing in the independent bulk human islet RNA-seq cohort GSE50244 using an overlap-restricted reduced score and a threshold fixed in the discovery cohort. Results. Meta-analysis across all four cohorts identified 337 high-confidence T2D-associated genes (96.1% directional concordance in beta-cell-enriched tissue). These were distilled into two refined 14-gene modules: ImmuneStress (MICB, HLA-DRA, HLA-DPA1, IL1R2, and others) and BetaCellIdentitySecretion (RASGRP1, PPP1R1A, SLC2A2, and others), whose composite IsletDysfunctionScore provided the most stable cross-platform separation of non-diabetic from T2D islets (Hedges' g = 1.80, p = 9.83 x $10^-17$, $\text{I}^2$= 0%). Consistent with progressive disease, IsletDysfunctionScore increased monotonically from non-diabetic to impaired glucose tolerance to T2D. Separately, the machine learning pipeline derived a 10-gene diagnostic panel: GABRA2, SLC2A2, ARG2, DKK3, PRIMA1, TAFA4, HHATL, PARVG, RNU1-70P, and the novel lncRNA ENSG00000284653, that achieved perfect discrimination in LOOCV (AUC = 1.000, sensitivity = 1.000, specificity = 1.000, zero misclassifications across all 57 donors). A leakage-verification experiment confirmed that this performance reflected genuine biological signal: global quantile normalization prior to cross-validation collapsed AUC to 0.380. External testing showed that 8 of the 10 panel genes were measurable in GSE50244. The frozen 8-gene reduced score retained strong discrimination (external AUC = 0.907), with 6 of 8 genes preserving directional concordance, but the discovery-derived threshold did not transfer because the external score distribution was shifted upward and compressed, yielding complete sensitivity but zero specificity at the frozen cutoff Conclusions. Integrating pathway-level meta-analysis with machine learning classification, we present a coherent two-axis model: immune/stress activation and loss of beta-cell identity/secretory competence, together with a compact, biologically interpretable 10-gene diagnostic signature. Panel genes converge on GABA signaling, glucose transport, arginine metabolism, WNT pathway inhibition, and a novel lncRNA, providing both mechanistic hypotheses and high-priority targets for external validation. These findings offer a reproducible transcriptomic scaffold for future mechanistic, biomarker, and clinical translation studies of human islet dysfunction. They also support external transportability of the core biological signal, while indicating that absolute operating thresholds are cohort-dependent and would require recalibration before deployment in independent datasets.

NAT2 is an important pharmacogene which encodes the N-acetyltransferase 2 enzyme that is involved in the metabolism of multiple medications, and variants in this gene can affect patient response to these medications. CPIC has published a clinical guideline for prescribing hydralazine using NAT2 genotypes. Just prior to the guideline, updated NAT2 star allele numbering and definitions were released, differing somewhat from the historical nomenclature. Clinical pharmacogenomic testing panels often test for the most common star alleles, so knowledge of the most common updated NAT2 star alleles is critical for the implementation of the CPIC NAT2/hydralazine guideline. We first determine NAT2 diplotype frequencies from UK Biobank (UKBB) 200k phased genomes, then analyzed allele, diplotype, and phenotype population frequencies from the All of Us Research program, PennMedicine BioBank (PMBB) and UKBB 500k datasets. We found that analyzing NAT2 diplotypes from phased data provides critical information for algorithms designed to predict diplotypes from unphased data. We observed that NAT2*5, *6, and *4 were the most common star alleles in that order, and the top 11 most frequent NAT2 star alleles were the same across all biobanks. However, differences in star allele frequencies across biogeographical populations were observed. The largest difference led to a higher frequency of NAT2 poor metabolizer phenotypes as compared to rapid and intermediate metabolizer phenotypes in all global populations except in the EAS population, where NAT2 poor metabolizers were in the minority.

Background: Pathologic aldosteronism induces oxidative stress, tissue injury, and increases in hemoglobin. Conversely, aldosterone antagonist therapy decreases hemoglobin. Whether these effects are attributable to aldosterone-mediated changes in iron and oxygen metabolism is unknown. Methods: The plasma proteome of participants with overt primary aldosteronism (PA) (n=50) was compared with participants without overt PA (n=61). To isolate aldosterone-dependent effects, participants without overt PA underwent oral sodium suppression testing to quantify the magnitude of renin-independent aldosterone production, enabling monotonic dose-response analyses across the continuum of renin-independent aldosteronism (subclinical to overt PA). Differential abundance testing was performed using empirical Bayes linear modeling, followed by Reactome pathway enrichment analysis and covariate-adjusted sensitivity analyses. To validate clinical relevance, aldosterone dose-response trends with blood count parameters were examined in this cohort, and an independent population-based cohort of 5,713 people with hypertension. Results: 903 proteins in the peripheral circulation were differentially abundant in overt PA versus participants without PA. The most significantly increased protein in overt PA was CYBRD1, involved in iron reduction and absorption. Pathway enrichment identified 16 iron- and heme-related pathways, including erythropoietin signaling, heme biosynthesis and mitochondrial iron-sulfur cluster biogenesis, with increases in heme and erythroid proteins and decreases in mitochondrial iron-sulfur proteins. Linear aldosterone dose-dependent trend analyses across the PA continuum further supported this signature, identifying progressive increases in hemoglobin subunits (HBA1/HBB), heme-related proteins (HMBS, UROS, AMBP, HPX, GLO1) and erythrocyte oxygen handling enzymes (CA1/CA3), alongside progressive reductions in mitochondrial electron transport chain subunits (CYCS, ETFA). These proteomic changes corresponded with aldosterone dose-dependent increases in red blood cell count, hemoglobin, and hematocrit, in this cohort and another population-based cohort. Conclusion: The continuum of PA is characterized by a progressive shift away from mitochondrial oxidative phosphorylation and toward increased intestinal iron absorption, preferential iron transport over storage, and enhanced heme synthesis and recycling, possibly reflecting cellular pseudohypoxia and systemic adaptations to increase oxygen delivery. These findings provide a novel mechanistic basis for aldosterone-mediated tissue injury and the benefits of aldosterone-directed therapy.

Background. Blood-based biomarkers are increasingly proposed for identifying high-risk individuals before clinical disease and for making prevention-oriented trials more efficient. Prognostic enrichment can increase event rates, but trial efficiency also depends on whether the intervention effect is preserved in the enriched population. Methods. Using the UK Biobank Pharma Proteomics Project, we trained disease-specific proteomic risk scores (ProRS) from 2,916 plasma proteins with elastic-net Cox models. We compared ProRS, polygenic risk scores (PRS), and combined PRS--ProRS scores across ten incident diseases. We estimated cumulative incidence and theoretical two-arm time-to-event trial sample sizes across risk strata. To evaluate effect preservation, we examined six intervention-analogue exposure--outcome pairs spanning genetic (PCSK9/coronary artery disease, APOE/Alzheimer's disease, PPARG/type 2 diabetes, IL23R/Crohn's disease), behavioural (physical activity/all-cause mortality), and pharmacological (RAAS inhibitors versus calcium channel blockers/coronary artery disease) examples. Results. ProRS outperformed PRS for 9 of 10 diseases (median C-index 0.75 versus 0.61). ProRS and PRS were weakly correlated (median Pearson |r| = 0.04), and joint PRS--ProRS stratification identified groups with higher observed incidence than either score alone for several endpoints. In the top risk quartile, combined-score enrichment reduced theoretical required sample sizes by 32--74\% under a fixed 20\% relative hazard reduction. These gains were not always preserved when stratum-specific intervention-analogue effects were used. Effects were broadly preserved for APOE/Alzheimer's disease and physical activity/mortality. The PPARG/type 2 diabetes effect attenuated toward the null under all three score types, showing that event-rate enrichment does not guarantee effect preservation. For IL23R/Crohn's disease and the antihypertensive comparison, point estimates differed across score types -- preserved under polygenic but attenuated under proteomic enrichment -- but confidence intervals were wide and overlapping. Conclusions. Proteomic risk scores can identify high-event-rate populations for prevention-oriented trials, but event-rate enrichment alone is insufficient for trial design. Biomarker-guided enrichment should evaluate mechanism-specific effect preservation and may be preferable as a stratification or adaptive-design variable rather than as a restrictive eligibility criterion.

Abstract Background Rheumatoid arthritis (RA) is an autoimmune inflammatory disease with complex and incompletely understood molecular mechanisms. Understanding circulating proteins associated with RA may improve understanding of disease biology and clarify its pathological links with cardiometabolic comorbidities. Methods A proteome-wide two-sample Mendelian randomisation (MR) drug target analysis was conducted using plasma proteins measured in 54,219 participants from the UK Biobank Pharma Proteomics Project as exposures and RA and cardiometabolic diseases as the outcomes. Summary statistics for RA included 53,663 cases and 1,070,200 controls. Colocalisation analysis was performed to confirm shared single causal variants and prioritise RA proteins supported by both MR and colocalisation. The prioritised proteins were then evaluated in the Accelerating Medicines Partnership RA Phase II synovial single-cell dataset for cell-type expression patterns. Druggability was then assessed followed by analysis of genetic overlap between RA-associated proteins and cardiometabolic diseases. Results 37 plasma proteins had a causal effect on RA risk, supported by combined evidence from MR and conditional colocalisation. In synovial tissue, TPPP3, RARRES2, AKAP12, and GGT5 were predominantly expressed in stromal and endothelial cell clusters. Druggability assessment identified IFNGR2, IL6R, CD40, and FCGR2B as Tier 1 targets. However, several biologically relevant proteins, including RARRES2, AKAP12, TPPP3, and SNX2, had limited available druggability data. Genetic overlap analysis demonstrated shared protein signals between RA and cardiovascular diseases, including overlap of RARRES2 and TPPP3 with coronary artery disease (CAD) and FCGR2B with atrial fibrillation (AF). To approximate the therapeutic effect of target inhibition, the direction of effect estimates for proteins showing overlap between RA-CAD and RA-AF was reversed. Conclusion This study identified circulating proteins involved in RA pathogenesis and reveals shared mechanisms between RA and cardiovascular diseases. While some proteins showed clear translational potential targets, several prioritised proteins had limited available druggability information and could not be confidently classified. Addressing these gaps may help identify new targets relevant to RA management. Future work should also use phenome-wide MR studies to evaluate potential on-target adverse effects of protein inhibition across RA-CAD and RA-AF.

Bone remodeling is a tightly regulated physiological process that maintains bone health through coordinated action of bone-resorbing osteoclasts and bone-forming osteoblasts. Disruption of this balance, such as the one induced by estrogen decline after menopause, results in bone loss and osteoporosis. Genetic factors play an important role in determining bone mineral density (BMD) loss over time. However, translating genetic associations into individualized risk prediction remains challenging due to small effect size of individuals variants and non-linear interactions within the bone remodeling unit. Here, we present a bone cell population dynamics model that includes major regulatory pathways, such as the RANK/RANKL/OPG axis, Wnt signaling, and hormonal regulation by estrogen, parathyroid hormone, and TGF-{beta}. We calibrate the model on clinical data from healthy postmenopausal women, and women with reduced BMD undergoing anti-osteoporotic therapy. The calibrated model captures healthy BMD decline in postmenopausal women and therapeutic response to anti-osteoporotic medications. We mechanistically incorporate the effect of 22 variants across 8 genes involved in bone remodeling and simulate BMD trajectories in 1,000 virtual subjects differing by ancestry and genetic makeup. The median predicted 5-year BMD loss was 3.57% (95% prediction interval: 1.31-5.24), consistent with the values reported in the literature. The virtual individuals with African ancestry were predicted to experience the highest average 5-year BMD loss. The strongest genetic risk factors for bone loss were predicted to be CYP19A1 rs727479 and OPG rs3102735, while LRP5 rs11228240 emerged as a protective factor that could partially counteract the detrimental effects of other variants. Several epistatic effects were observed in the genetic interaction analysis. Mechanistically, our model suggested that estrogen exerts its effect on bone remodeling primarily by modulating osteoclast apoptosis. Overall, this framework demonstrates a proof-of-concept for integration of genetic risk factors into mechanistic models of disease and can be extended to other conditions with polygenic inheritance.

Functional ex vivo assays using live tumor tissues have demonstrated strong predictive accuracy for response to immune checkpoint inhibitors (ICIs) but are not scalable, requiring manual processing of large resections collected at academic centers. Here, an ex vivo live tumor fragment (LTF) platform was developed using standard-of-care biopsies from 228 patients with suspected malignancy collected across prospective, multicenter observational trials and biobanks. Hierarchical clustering of ICI-mediated changes in cytokine production identified two groups: responders and nonresponders. A binary classifier (elive index) using 8 cytokines achieved an AUC of 0.99 for cluster prediction. elive index correctly predicted clinical benefit in 93% (26/28) of patients (P = 3.2x10-5) and accurately identified 83% (10/12) of objective responders. Critically, elive responders were identified among biomarker-negative patients, highlighting the platform as a scalable approach that complements existing companion diagnostics and expands the population of patients identified to benefit from ICI therapy.