eBioMedicine

Haemophagocytic lymphohistiocytosis (HLH) is a rare, life-threatening hyperinflammatory syndrome characterised by uncontrolled immune activation. Reduced high- and low-density lipoprotein cholesterol and hypertriglyceridaemia are reported in HLH, suggesting lipid metabolism disturbances although in-depth serum metabolomic analysis is lacking in HLH. Here a lipid-focused NMR spectroscopy platform was used to define the serum metabolomic landscape of adults hospitalised with HLH compared to adults with sepsis (HLH-mimic) and rheumatic disease (potential HLH drivers/triggers), following surgical resection of solid organ cancer (non-infectious acute inflammation controls) and healthy controls (HCs). Serum metabolites distinguished HLH from HCs with high accuracy (>91.36%) using multiple machine learning models. The top classifying features included elevated apolipoprotein-B (ApoB)-containing low, intermediate, and very low-density lipoprotein particles; and lipoprotein remodelling towards triglyceride enrichment and cholesterol depletion. Differentially abundant metabolites in HLH compared to all control groups were enriched in pathways related to lipid metabolism including: 'Lipid particles composition', 'Plasma lipoprotein clearance', 'Plasma lipoprotein remodelling', 'Glucose homeostasis' and 'Amino acid metabolism'. Metabolomic results were validated using matched whole blood RNA-sequencing which identified differentially expressed genes enriched in metabolic modules associated with lipid, amino acid, and glucose metabolism, supporting a coordinated metabolic dysregulation in HLH from a transcriptomic to metabolomic level. Finally, twenty-seven metabolites including ApoB-containing, triglyceride-rich lipoproteins and saturated fatty acids distinguished HLH from all disease controls (AUC>0.70) either alone or combined as a metabolomic signature. Elevated ApoB and ApoB:ApoA1 ratio in HLH vs sepsis and HCs were validated by ELISA, supporting their utility as biomarkers to distinguish HLH from other hyperinflammatory syndromes.

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.

ObjectivePostoperative pain outcomes following spinal fusion surgery remain difficult to predict, as structural and surgical indicators alone offer limited insight into who will experience meaningful relief. A substantial proportion of patients continue to report persistent pain after surgery, underscoring the need for objective markers that can help identify those at risk of poor recovery. Peak alpha frequency (PAF) has emerged as a promising trait-like neural signature of pain sensitivity in experimental models, where individuals with slower PAF tend to exhibit heightened pain sensitivity. Yet despite this link, its ability to forecast longer-term postoperative pain trajectories remains unclear. MethodsSeventeen adults undergoing cervical or lumbar fusion surgery were included. Resting-state, eyes-closed EEG was recorded preoperatively and at multiple visits after surgery. PAF was extracted from central electrodes using the centre-of-mass method. Pain intensity was assessed longitudinally on standardised self-report pain scales. Associations between PAF measures and postoperative pain change were examined using correlation analyses, and receiver operating characteristic (ROC) analyses evaluated discrimination of pain responders ([≥]50% improvement). ResultsPreoperative peak alpha frequency (PAF) was positively associated with longer-term pain reduction at the 3-month follow-up, but showed no consistent relationship with early postoperative pain. Across pain measures, a consistent pattern emerged across the Brief Pain Inventory (BPI), visual analogue scale (VAS), and numerical rating scale (NRS), but not the verbal rating scale (VRS) or Short-Form McGill (SF-MPQ). At the 3-month follow-up, associations reached statistical significance for BPI-Worst ({rho} = 0.67, p = 0.017), and BPI-Average Pain ({rho} = 0.62, p = 0.033). VAS and NRS showed moderate-to-strong effects that approached significance in non-parametric analyses and were significant for VAS when treated as an approximately interval measure (Pearson r = 0.63, p = 0.022). ROC analyses using BPI-Worst pain improvement demonstrated good discriminative ability of preoperative PAF for identifying treatment responders at 3 months (AUC = 0.84; 95% CI: 0.61-1.00), with high specificity and moderate sensitivity at the Youden-optimal threshold of 10.11 Hz. By contrast, changes in PAF over time were not reliably related to changes in pain scores, suggesting that PAF functions more as a stable, trait-like predictor than a dynamic biomarker in this context. ConclusionThis study demonstrates the feasibility and potential clinical value of preoperative EEG for characterising individual differences in postoperative pain recovery following spinal fusion. The results identify faster preoperative PAF as a stable neural signal that captures meaningful variability in longer-term pain reduction, with convergent support across multiple patient-reported measures. While replication in a larger cohort is required, these findings establish a clear foundation for evaluating PAF as a candidate neurophysiological marker to inform preoperative risk profiling and potentially personalised perioperative pain-management strategies in spinal fusion patients.

PurposeMaas et al. recently showed that a microenvironment-determined risk continuum, driven by the shift from microglia-like to myeloid-derived macrophage-like tumor-associated macrophages (TAMs), independently predicts meningioma progression beyond WHO grade. Whether this gradient is recoverable from bulk RNA-seq has not been tested. MethodsWe computed a microglia-to-macrophage ssGSEA ratio using expanded gene sets (15 microglia, 17 macrophage) anchored to Maas core markers across 968 meningiomas from 5 GEO datasets, validated it against pseudo-bulk profiles from the Maas snRNA-seq cohort (n=25), and tested recurrence-free survival (RFS) association by Cox regression in a 101-patient subset (73 events, median follow-up 110.2 months). ResultsThe ratio correlated with single-cell microglia proportion (overlap-controlled r=0.70, 95% CI 0.42-0.86) and discriminated WHO grades and transcriptomic clusters, confirming biological recoverability. The ratio did not predict RFS (HR 0.92, 95% CI 0.72-1.16, p=0.46). A quantitative attenuation analysis predicts the Maas IHC HR of 2.00 attenuates to HR 1.24-1.40 after proxy measurement error (r2=0.22-0.49) and NF2-wildtype dilution (30-45%), yielding only 15-40% power at 73 events. An exploratory NF2-expression proxy subgroup (uncorrected p=0.056) showed a trend in NF2-low tumors (HR 0.68, 95% CI 0.46-1.01) absent in NF2-high tumors (HR 0.98, p=0.89). The Chen 34-gene tumor-intrinsic panel also reached near-chance discrimination (C-index 0.552). ConclusionSingle-cell-anchored ssGSEA recovers the Maas gradient in bulk RNA-seq but attenuates it below detectability in moderate-sized, NF2-unselected cohorts. The prognostic component is bounded by power and NF2 stratification, not an intrinsic modality failure; NF2-annotated cohorts with approximately 480 events are required for definitive testing.

Cardiac surgery patients experience rapidly evolving hemodynamics in early post-operative period requiring intensive support. Identifying hemodynamic subphenotypes from these data can inform personalized management. Using 24-hour high-resolution physiologic and treatment data from 6,630 MIMIC-IV and 1,963 SICdb patients, we trained a transformer encoder with a reconstruction-contrastive objective to derive patient-level embeddings capturing multivariate temporal dynamics within first 24h of ICU stay and compared them against those generated by dynamic time warping (DTW). Spectral clustering uncovered three reproducible hemodynamic subphenotypes. Compared with subphenotype 1, subphenotype 3 received more IV fluids, vasopressors, inotropes, and exhibited higher in-hospital mortality (OR 5.85, 95 % CI 2.43-14.13), longer ICU stay (7.12 days, 95% CI: 5.52-8.73) and hospitalization (8.86 days, 95% CI: 6.57-11.16). DTW derived subphenotypes had weaker prognostic separation. Thus, contrastive-transformer framework identified more clinically meaningful temporal hemodynamic subphenotypes that may optimize post-operative risk stratification and inform personalized management.

Background: The gluteus maximus (GM) is a major hip extensor essential for mobility and metabolic health. Most MRI studies rely on global measures, such as muscle volume or fat fraction, which can overlook spatially localised remodelling. Here, we integrate conventional volumetric and fat fraction metrics with 3D mesh-based shape phenotypes to provide a spatially resolved characterisation of GM morphology in relation to anthropometric, lifestyle, and cardiometabolic factors, with a focus on type 2 diabetes (T2D) and sex-specific effects. Methods: We analysed T1 Dixon MRI from UK Biobank participants to quantify GM muscle volume, fat fraction, and regional surface morphology using 3D meshes. Statistical parametric mapping was used to assess regional associations with anthropometric, lifestyle, and clinical variables Bi-directional causal mediation analyses were performed using GM volumetric and principal components (PCs) of shape variation. PCs were also tested for associations with prevalent and incident disease. Longitudinal changes in GM composition were evaluated in participants with repeated imaging evaluations. Results: GM muscle volume and fat fraction were strongly associated with age, adiposity, and physical activity. Shape analysis revealed spatially localised remodelling patterns not captured by global measures, with region-specific surface shrinkage linked to age, BMI, alcohol intake, grip strength, physical activity, frailty, osteoporosis, and cardiometabolic disease. T2D showed marked sex-differences, with regional shrinkage in men and relative expansion in women. PCA reduced high-dimensional shape variation into interpretable components. Mediation analyses indicated that T2D-related differences in GM morphology partly mediated increases in fat fraction, suggesting that disease effects manifest through spatially patterned shape changes rather than overall muscle size. PCs capturing variations in the central-upper posterior and anterior GM, differentiated between T2D cases from controls, and were associated with incident T2D risk (Men: PC6 HR per SD: 0.81 [0.70-0.95], false discovery rate (FDR)-adjusted p = 0.038, in left GM; 0.76 [0.65-0.88], p = 0.002, in right GM; women; PC5 HR = 1.32, [1.08-1.61], p = 0.032, in right GM). Conclusions: Integrated 3D quantification of GM composition and morphology provides spatially resolved biomarkers that go beyond muscle volume and fat fraction. By capturing region-specific GM remodelling, linked to anthropometric, lifestyle and cardiometabolic factors, this approach offers a more nuanced characterisation of muscle-fat phenotypes and enhances mechanistic insight and risk stratification in population-based imaging studies.

BackgroundRecent multiomic analyses identify metformin-inducible genes, such as DDIT4, as predictors of delayed HIV rebound, suggesting a "cell-extrinsic" role for low systemic inflammation in viral control. We sought to validate this inflammatory "cooling" in a large-scale human population. MethodsUsing Olink proteomics from the UK Biobank (N = 502,493), we evaluated associations between metformin use and inflammatory markers (IL-6, CXCL10, GZMB) in a diabetic cohort (N = 18,548). Stepwise regression was used to adjust for co-medications (statins) and glycemic control (HbA1c). ResultsMetformin use was associated with significantly lower levels of IL-6 (p = 0.0049) and CXCL10 (p < 0.001) after adjusting for age, BMI, and statin use. In exploratory analyses of participants with HIV (N = 61), metformin users showed directionally lower mean IL-6 and CXCL10. However, associations in the primary cohort were attenuated upon adjustment for HbA1c, indicating that metformins systemic anti-inflammatory effect is largely mediated by its metabolic efficacy. ConclusionsMetformin use is associated with a reduction in master regulators of inflammation, independent of common co-medications. These findings suggest that metformin promotes the systemic "cell-extrinsic" environment required for HIV control via its metabolic effects, providing a population-level substrate for the "block-and-lock" mechanisms observed in clinical cohorts.

Histopathological evaluation of skeletal muscle biopsies relies on subjective, semi-quantitative assessment with no standardized grading system. We developed a four-tissue deep learning segmentation pipeline using Cellpose-SAM for myofiber instance segmentation, a pixel classifier for fat infiltration, and watershed detection for nuclei. We applied this pipeline to 478 H&E whole-slide images from two independent cohorts: HuashanMuscle (n = 79; China; myotonic dystrophy type 1 [DM1], n = 28; limb-girdle muscular dystrophy type R1 [LGMDR1, calpainopathy], n = 12; type R2 [LGMDR2, dysferlinopathy], n = 22; controls, n = 17) and GTEx (n = 399; United States; three-level myopathy spectrum). Thirty-seven unique morphometric features were extracted per sample. Nuclear centralization index (NCI) and fiber size variability coefficient (fiber CV) discriminated myopathy from controls (p = 1.3E-05, rank-biserial r = 0.69; and p = 2.9E-04, r = 0.58, respectively). DM1 showed the highest NCI (median 0.121), consistent with its centronuclear pathology, and NCI correlated with CTG repeat count (Spearman rho = 0.46, p = 0.042, n = 20). In the GTEx cohort, both biomarkers exhibited significant dose-response trends across the myopathy spectrum (Jonckheere-Terpstra p < E-04). The MyoPath Score, a logistic regression composite of seven pathology indicators trained on GTEx, achieved AUC = 0.788 (LOO-CV 0.735) and transferred to the independent HuashanMuscle cohort with AUC = 0.873 without retraining. Segmentation achieved Dice coefficients of 0.92 (myofiber), 0.95 (fat), 0.87 (nucleus), and 0.88 (connective tissue), with intraclass correlation coefficients exceeding 0.88. NCI and fiber CV provide objective, reproducible quantitative biomarkers for skeletal muscle pathology severity assessment with potential as standardized grading criteria and clinical trial endpoints.

Accessible screening for type 2 diabetes (T2D) is critical, with millions of cases remaining undiagnosed globally. Here, we present the largest known real-world validation study for a speech-based T2D prediction model, trained on speech data from over 21,000 individuals, that works on features extracted from 20-second speech recordings. The model was evaluated in two stages: 1) Against self-reported diagnoses in 7,319 English-speaking participants using AUC, and 2) Against HbA1c blood tests in a subset of 801 participants drawn from the full cohort. Performance was also compared against QDiabetes and in the presence of key confounding variables. The model demonstrated clinically useful predictive capacity on self-reported data (AUC = 0.80 {+/-} 0.03), approaching QDiabetes (AUC = 0.86 {+/-} 0.03). It was robust to most demographic confounds (e.g., age and sex) and medication use, with reduced performance in the presence of comorbidities (e.g., cardiovascular disease and hypertension). At diabetes threshold of HbA1c [&ge;]48 mmol/mol, the model achieved an AUC of 0.75 ({+/-}0.07). This biomarker-validated speech-based tool demonstrates potential to complement existing methods through accessible, scalable screening requiring only a 20-second speech sample.

Lipoprotein composition is altered in sepsis, and supplementation with high-density lipoproteins has been reported to improve outcomes in experimental settings. In this study, we aimed to investigate the nature and inter-individual variability in the lipoprotein proteome to inform risk stratification and opportunities for precision medicine approaches. In a large proteomic dataset including 1134 patients (1781 samples) with sepsis and 149 healthy volunteers, we analysed 18 protein components of lipoproteins. We characterise heterogeneity of the lipoprotein proteome, defining three step-wise sub-phenotypes associated with increasing disease severity, one close to health, then an early phase patient group showing increased abundance of proteins that integrate HDL under inflammatory conditions (SAA1 and SAA2), then a group with decreased abundance of proteins that are components of HDL under healthy conditions that was associated with higher organ failure intensity (SOFA score) and increased mortality. We developed and externally validated a quantitative score reflective of lipoproteins alterations in sepsis, and machine learning predictive models to predict the LP class, advancing future individualised lipoproteins-based therapeutics in sepsis.

BackgroundThe discontinuation of Fasiglifam (TAK-875), a GPR40/FFAR1 full agonist, during Phase 3 clinical trials due to hepatotoxicity led to widespread abandonment of GPR40 as a viable therapeutic target for type 2 diabetes mellitus (T2DM). However, mechanistic evidence suggests that Fasiglifams hepatotoxicity arises from mitochondrial liability driven by high lipophilicity (aLogP = 5.31), rather than from on-target GPR40 signaling. We hypothesized that target-level failure was incorrectly inferred from compound-level safety concerns, and that superior candidates exist within publicly available databases. MethodsWe queried ChEMBL Release 36 (28 GB SQLite, 74 tables) for all compounds with documented GPR40/FFAR1 activity (UniProt: O14842). Compounds were filtered by EC50 [&le;] 10 nM in nM units with standard relation "=". Drug-likeness was assessed using Lipinskis Rule of Five (Ro5), aLogP, molecular weight (MW), hydrogen bond donors/acceptors (HBD/HBA), and polar surface area (PSA). A parallel analysis of Therapeutic Target Database (TTD v10.1.01, 4,298 targets) provided clinical context. A real-world evidence (RWE) patient stratification framework was constructed using EMR data from tens of millions of patients with >10 years of longitudinal follow-up. ResultsOf 2,637 GPR40-active compounds in ChEMBL 36, 526 (19.9%) demonstrated EC50 < 100 nM and 102 (3.9%) demonstrated EC50 < 10 nM. Eight compounds met stringent drug-likeness criteria (Ro5 violations = 0, aLogP < 5.0, EC50 [&le;] 1 nM). The lead compound (CHEMBL4859651) exhibited EC50 = 0.04 nM (8.75-fold more potent than Fasiglifam), MW = 297 Da (43% lower), and aLogP = 4.30 (19% lower), with zero Ro5 violations. Mean MW of the eight candidates was 317 {+/-} 28 Da versus 524 Da for Fasiglifam. A parallel GCK analysis identified a protein-protein interaction target (CHEMBL3885579, GCK-GKRP interface) harboring 40 exclusive compounds as an orthogonal strategy for partial GCK activation. ConclusionsSystematic cheminformatic analysis reveals that compounds with substantially superior activity and drug-likeness profiles relative to Fasiglifam exist within ChEMBL 36. Fasiglifams hepatotoxicity is attributable to compound-specific physicochemical properties, not GPR40-mediated toxicity. RWE patient stratification may further mitigate hepatotoxicity risk for next-generation GPR40 agonists. These findings argue for systematic reappraisal of GPR40 as a viable therapeutic target for T2DM.

Tuberculosis (TB) continues to pose a significant global public health challenge with substantial patient morbidity and mortality. Current TB patient biomarkers lack sufficient resolution to inform treatment response and patient stratification. This necessitates the development of sensitive and reliable host biomarkers. We previously demonstrated the efficacy of TruCulture whole blood stimulation for differentiating asymptomatic TB from active pulmonary TB disease patients in endemic regions. Our systems immunology study expands upon this previous work by evaluating the potential of TruCulture to monitor longitudinal responses to TB treatment in patients from the Predict-TB trial before, during, and after 6 months of antibiotic therapy. We stimulated whole blood from TB patients (n=40) using TruCulture under four conditions (Null, Mycobacterium tuberculosis-antigen, LPS, and IL-1{beta}) at baseline (week 0), during treatment (weeks 16 and 24), and one-year follow-up post- treatment (week 72). 20/25 measured cytokines exhibited significant changes throughout treatment, with several continuing to evolve during post-therapy follow-up. Machine learning based analysis identified Mtb-Ag-induced IL-1RA (AUC = 0.90, 0.92, 0.95 at weeks 16, 24, 72) and LPS-induced NLRP3 (AUC = 0.94 at week 16) as the best protein and transcriptional biomarkers for distinguishing treated from untreated patients, strongly implicating the inflammasome response. Combining these results with the extent of lung disease assessed by FDG PET/CT scans, we showed direct disease relevance for these blood-based biomarkers. The identified biomarker profiles hold promise for improving TB patient care through early prediction of treatment responses, real-time therapy monitoring, and informed development of host-directed therapeutic strategies for clinical decision-making. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=146 HEIGHT=200 SRC="FIGDIR/small/723467v1_ufig1.gif" ALT="Figure 1"> View larger version (45K): org.highwire.dtl.DTLVardef@14a32eforg.highwire.dtl.DTLVardef@55f3d4org.highwire.dtl.DTLVardef@fb0137org.highwire.dtl.DTLVardef@10cf39e_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical abstractC_FLOATNO Predict-TB clinical study overview and summary of TB-specific biomarkers identified from TruCulture whole blood stimulation system. C_FIG

Stressful life events impact individual's functionality and contribute to disease outcomes, yet the biological pathways underlying life stress remain unclear. We characterized the metabolomic profiles of stressful life events using data from 3,264 participants (5,163 observations) of the Dutch NESDA cohort. 98 metabolites were identified, with upregulated metabolites overrepresented in phosphatidylethanolamine and downregulated metabolites overrepresented in fatty acid metabolism. 92 of these metabolites were available in the Dutch NEO cohort (N=599): 11 were significantly replicated including six lipids (e.g., three bile acids (glycochenodeoxycholate 3-sulfate)), one carbohydrate, and one xenobiotic. 21 overlapping metabolites were additionally available in the Chinese GBCS cohort (N=200): 10-undecenoate (11:1n1) (fatty acid) and glycochenodeoxycholate 3-sulfate (bile acid) showed consistent associations across both Dutch and Chinese cohorts. Stressful life events are associated with metabolic dysregulation, particularly involving fatty acid and bile acid pathways, highlighting promising biological targets to reduce the impact of stress on mental and somatic health.

Disposition index (DI) is an informative measure of {beta}-cell function adjusted for insulin resistance, but its assessment is procedurally demanding, requiring dynamic testing with timed sampling and insulin or C-peptide-based estimation of insulin sensitivity and secretion. A simple glucose-only metric derived from the oral glucose tolerance test (OGTT) could provide a practical approach to estimating DI. We developed the Recovery-Burden Index (RBI), a glucose-only geometric metric that quantifies post-peak glucose recovery relative to total glucose excursion during OGTT. Using densely sampled venous OGTT profiles with measured DI, RBI was evaluated for prediction of continuous DI by leave-one-out (LOO) cross-validated R2 and for discrimination of DI-defined {beta}-cell dysfunction by AUROC. Performance was compared with conventional glycemic metrics. RBI predicted continuous DI more accurately than conventional glycemic metrics, with LOO R2 of 0.43, Pearson r = 0.70, and Spearman{rho} = 0.75. RBI30-180 performed similarly, with cross-validated R2 of 0.42, Pearson r = 0.72, and Spearman{rho} = 0.75. RBI also discriminated DI-defined {beta}-cell dysfunction, with AUROC values of 0.90 for RBI and 0.91 for RBI30-180. Reduced sampling schedules preserved much of the RBI signal, whereas truncation at 120 min attenuated continuous DI prediction, supporting the contribution of late recovery-phase information. RBI extracts {beta}-cell-relevant information from the OGTT glucose profile using a single transparent glucose-only index. These findings highlight post-peak recovery as a key feature for estimating DI-associated {beta}-cell compensation and support further validation of RBI in extended or CGM-augmented OGTT settings.

Background Acute respiratory distress syndrome (ARDS) is characterised by substantial physiological heterogeneity, which contribute to a very variable clinical outcomes and therefore inconsistent responses to ventilatory strategies. We aimed to externally validate physiological ARDS subphenotypes previously identified using routine ventilatory and gas-exchange variables, assess their prognostic relevance across independent cohorts, and examine heterogeneity of treatment effect according to PEEP strategy. Methods Unsupervised Gaussian Mixture Modelling was used to identify physiological subphenotypes based on ventilatory mechanics and gas-exchange parameters. Labels were subsequently used to train and validate supervised classifiers using XGBoost. Prognostic relevance was assessed across three independent cohorts, including two randomised controlled trials (ALVEOLI and LOVS). Predictive enrichment for PEEP strategy was evaluated using individual patient data from ALVEOLI and LOVS (n = 1,532) using intention-to-treat analyses, applying both one-stage and two-stage fixed-effects IPD meta-analytic approaches to test for interaction between physiological subphenotype and PEEP strategy. Results Two distinct physiological subphenotypes, termed Efficient and Restrictive, were replicated across independent cohorts. Across each cohort, patients classified as Restrictive consistently exhibited higher all-cause 28-day mortality compared to Efficient patients. When pooled across studies, the Restrictive subphenotype was associated with a significantly increased risk of death (pooled odds ratio 1.75, 95% CI 1.36-2.24), with no evidence of between-study heterogeneity. Predictive analyses showed a statistically significant interaction between physiological subphenotype and PEEP strategy in the one-stage IPD model (p for interaction = 0.037), with concordant findings in the two-stage fixed-effects IPD meta-analysis (interaction OR 1.91, 95% CI 1.00-3.66; I2 = 0%). Higher PEEP was associated with increased mortality in Efficient patients and reduced mortality in Restrictive patients, indicating effect modification by physiological subphenotype. Interpretation Physiological ARDS subphenotypes derived from routinely collected bedside data provide robust and externally validated prognostic stratification across observational and randomised trial cohorts. The observed interaction with PEEP strategy suggests that underlying physiological profiles may influence treatment response, supporting the concept that physiology-based be a starting point for personalized medicine and therefore better ventilatory strategies in future clinical trials.

Background: Host genetics alone explains limited susceptibility to tuberculosis (TB), particularly in people with HIV (PWH). Protein quantitative trait loci (pQTLs), genetic variants that regulate plasma protein levels, may bridge genetic and immunological mechanisms underlying TB progression. Methods: We conducted cis-pQTL mapping in 60 PWH who progressed to active TB and 194 matched controls from the Swiss HIV Cohort Study. Plasma proteomes were quantified via high-resolution mass spectrometry (dia-PASEF), and genotype-protein associations were analyzed separately in TB and control groups. Results: TB progressors harbored 26 cis-pQTLs linked to 12 proteins uniquely enriched in immune pathways (antigen presentation, complement activation, phagocytosis, and T-cell regulation). Controls showed 107 cis-pQTLs linked to 14 targets. Gene Ontology enrichment revealed 46 immune biological processes in TB versus only 1 in controls, with HLA-C, C4B, and CHIT1 as key TB-specific proteins. Conclusions: Integrating proteomics with genomics suggests differential regulation of immune proteins associated with TB progression in PWH. hese genetically anchored protein candidates support follow-up studies and future biomarker evaluation for TB risk prediction.

IntroductionSex hormones shape biological sex differences and alter the onset and severity of sleep and metabolic diseases in a sex-specific manner. To better understand relationships and underlying mechanisms, we develop summary proteomics and metabolomics scores for sex hormones and investigate their associations with sleep and metabolic disorders. MethodsWe used proteome-(n= 3680) and metabolome-wide (n= 1649) data from the baseline exam of the Multi-Ethnic Study of Atherosclerosis (MESA) cohort to develop female- and male-specific omics scores for sex hormones including total (Total T), bioavailable (Bio T), and free (Free T) testosterone, estradiol (E2) and sex hormone binding protein (SHBG). Each omics dataset was randomly split assigning 80% of participants to a training dataset and the remaining 20% to a test dataset. We applied linear regression with bootstrap standard errors, adjusting for age, BMI, self-reported race/ethnicity and study site, to identify sex hormone-associated proteins and metabolites (i.e FDR< .05). Lasso penalized regression was then used to select independent features, from which weighted protein (ProtS) and metabolite scores (MetS) were constructed as weighted sums, and examined in the validation dataset. Subsequently, we conducted sex-stratified association analysis of the validated omics scores using data from MESA baseline, exams 4 (proteomics) and 5 (proteomics, metabolomics) with sleep and metabolic phenotypes, timepoints where sex hormones were not measured. ResultsAll constructed omics scores were significantly associated with their corresponding hormones in the test dataset. Higher omics scores of SHBG and lower omics scores of Free T were associated with lower diabetes risk in both sexes; and higher E2 scores with higher incident hypertension risk only in men. In males, Total T had protective diabetes associations, whereas in females they were linked to greater risk. Similarly, higher ProtS-Free T and lower ProtS-SHBG were associated with increased risk for OSA in both sexes. Finally, higher E2 scores were associated with higher risk of insomnia only in males. ConclusionsSummary omics-based scores reveal sex-specific cross-sectional associations with sleep and incident metabolic disorders. These findings highlight the potential of these omics proxies to improve risk stratification and generate insights into mechanisms linking sex hormones with disease.

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.

Type 2 diabetes mellitus (T2DM) is a progressive metabolic disorder characterized by persistent hyperglycemia, insulin resistance, and chronic low-grade inflammation. Despite the widespread use of established therapies such as metformin, long-term glycemic control remains suboptimal, and disease progression is often not adequately prevented. This highlights the need for novel therapeutic strategies that address both metabolic dysfunction and the underlying immunometabolic components of the disease. In this study, GLX10 (GLXM100) was evaluated as a novel immune modulator in a high-fat diet (HFD) and low-dose streptozotocin (STZ)-induced rat model of T2DM over a 91-day period. Glycemic outcomes were assessed using terminal random blood glucose and oral glucose tolerance testing (OGTT), with glucose exposure quantified by area under the curve (AUC 0-120). Complementary in vitro investigations were performed in hepatic and macrophage cell models to assess cytocompatibility, nitric oxide production, and modulation of pro-inflammatory cytokines, including IL-6 and TNF-. GLX10 treatment resulted in a significant reduction in random blood glucose levels and a marked improvement in glucose tolerance compared to diabetic control animals. Importantly, GLX10 demonstrated greater improvement in OGTT AUC compared to metformin under the same experimental conditions, indicating enhanced dynamic glucose regulation. In vitro, GLX10 maintained viability in normal hepatic cells while significantly suppressing nitric oxide production and inflammatory cytokine outputs in macrophages, supporting a favorable safety and immune profile. Collectively, these findings demonstrate that GLX10 exerts robust antidiabetic activity through a dual mechanism involving metabolic regulation and suppression of inflammatory signaling. The integration of in vivo efficacy with supportive in vitro safety and mechanistic data provides a strong preclinical foundation and supports the further development of GLX10 as a promising therapeutic candidate for T2DM.

BackgroundSub-Saharan Africa carries a disproportionate burden of HIV-associated malignancies, yet the contemporary tumour spectrum in the dolutegravir (DTG) era and the immunovirological context at cancer diagnosis remain incompletely characterised in East African routine HIV-care settings. We sought to describe the spectrum, malignant fraction, and immunovirological determinants of neoplasms diagnosed at a specialised HIV centre in Kampala, Uganda. MethodsWe conducted a retrospective cohort analysis of 220 tumour records from 217 unique people living with HIV (PLHIV) registered at Mildmay Hospital between November 2017 and January 2026. Tumours were classified by ICD-10 disease group and malignancy status (benign/malignant). Bivariate associations with malignancy status were tested using {chi}2 or Fisher exact tests for categorical variables and Mann-Whitney U tests for continuous variables ( = 0.05). Crude odds ratios (OR) with 95% confidence intervals (CI) were computed for selected dichotomous comparisons. ResultsThe cohort had a median age of 47 years (IQR 39-54) and was 58.2% female. Overall, 138/220 tumours (62.7%) were malignant. Kaposi sarcoma (KS) was the most frequent malignancy (n=65; 47.1% of all malignant cases), followed by haematopoietic malignancies (n=32; 23.2%) and malignant gynaecological tumours (n=10; 7.2%). Among 156 patients with viral load data, 134 (85.9%) were virally suppressed at tumour diagnosis, yet 70/134 (52.2%) of those suppressed patients had a malignant tumour. Advanced WHO clinical stage (III/IV) at ART initiation was strongly associated with malignant diagnosis (39/86 [45.3%] versus 9/79 [11.4%]; OR 3.53, 95% CI 1.59-7.84; p=0.002). Median CD4 at ART initiation was markedly lower in malignant compared to benign tumour patients (147 vs 476 cells/{micro}L; p<0.001), and median ART duration was substantially shorter (37 vs 102 months; p<0.001). KS patients had significantly lower CD4 (136 vs 269 cells/{micro}L; p=0.002) and shorter ART duration (4 vs 86 months; p<0.001) than patients with other malignancies. Malignancy did not differ across DTG-, EFV-, and PI-based regimens (p=0.992). ConclusionsHIV-associated tumours in this Ugandan cohort remain predominantly malignant and dominated by KS, even against a backdrop of high viral suppression rates. The clustering of malignancy among patients with low pre-ART CD4 counts and advanced WHO stage points to incomplete immune reconstitution, persistent oncoviral co-infection, and residual inflammation as mechanistic targets. These data establish the scientific platform for a prospective HIV-oncology cohort at Mildmay Hospital and Mildmay Research Centre and inform context-appropriate cancer screening and surveillance priorities.