eBioMedicine

Tuberculosis (TB) diagnosis remains challenging, particularly for extrapulmonary TB (EPTB), where invasive sampling, low bacillary burden, and suboptimal sensitivity of nucleic acid-based tests in peripheral specimens hinder timely detection. Here, we report an immunology-driven strategy for biomarker discovery and development of a peptide-based serological assay targeting Mycobacterium tuberculosis zinc metalloprotease-1 (Zmp1). Leveraging fundamental principles of adaptive immunity that antigenic regions containing overlapping B-cell and CD4 T-helper cell epitopes would preferentially generate high antibody titers through linked recognition and cognate T-cell help, we used an immunoinformatics pipeline to identify two nested immunodominant peptide regions within Zmp1 (Mtb-Zp-NT and Mtb-Zp-CT) enriched for overlapping B- and T-cell epitopes. The diagnostic potential of these peptides was evaluated through ELISA-based serological assays. A blinded pilot study (N=137) demonstrated a clear discrimination between active TB and TB-recovered individuals. The assay was subsequently validated in an expanded cohort (N=875) by screening 6,086 individuals, which identified 457 TB-positive cases. The cohort included pulmonary TB (PTB), EPTB, TB-recovered individuals, household contacts, non-specific infections, and healthy controls. Receiver operating characteristic analyses, supported by DeLong and bootstrap comparisons, revealed superior diagnostic performance of the peptide-based assays relative to full-length Zmp1. Mtb-Zp-CT exhibited the highest accuracy (AUC=0.93; specificity >90%), while Mtb-Zp-NT also demonstrated strong discriminatory power (AUC{approx}0.89). These findings establish that the immunologically optimized Zmp1 peptides are highly promising serological biomarkers for TB and EPTB. More broadly, they demonstrate how mechanistically informed epitope selection can accelerate translation of pathogen-specific immune signatures into sensitive, minimally invasive, and potentially point-of-care diagnostic platforms for resource-limited settings.

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.

Reduced nicotinamide mononucleotide (NMNH) is a reduced NAD precursor with reported NAD- augmenting activity in preclinical models; however, controlled human data remain limited. This was a randomized, double-blind, placebo-controlled, parallel-group phase I trial evaluating oral NMNH-Ca in healthy adults aged 40-65 years. Eighty participants received placebo or NMNH-Ca 125, 250, or 500 mg once daily for 90 days. The primary objective was safety and tolerability. Whole-blood NAD was assessed as the key pharmacodynamic endpoint, including a 24-hour post-dose substudy, with biomarker-derived blood phenotypic age, treadmill-based six-minute walk distance, body mass index, and SF-36 domains analyzed as exploratory outcomes. NMNH-Ca was well tolerated at all doses, with no serious adverse events, treatment-related adverse events, or discontinuations. In the acute substudy, whole-blood NAD increased after single-dose NMNH-Ca, with peak mean concentrations at 12 hours. Over 90 days, NAD increased in a dose-related pattern; Day 90 mean changes from baseline were 2.33 {+/-} 18.53 M with placebo and 8.22 {+/-} 10.25, 15.85 {+/-} 11.16, and 39.90 {+/-} 14.11 M with NMNH-Ca 125, 250, and 500 mg, respectively. Exploratory analyses showed hypothesis-generating favorable signals in blood phenotypic age, treadmill-based six-minute walk distance, and health-related quality of life, most consistently at 500 mg. Oral NMNH-Ca was safe and pharmacodynamically active over 90 days, supporting larger and longer confirmatory trials with prespecified geroscience endpoints and tissue-relevant NAD metabolomics.

Necrotizing enterocolitis (NEC), frequently resulting in sepsis, is among the leading causes of morbidity and mortality of pre-term newborns. However, diagnostic and therapeutic strategies for NEC and sepsis are still limited and controversial. In this context, there are ongoing debates regarding the application of human milk-based fortifiers (HMF) versus bovine milk-based fortifiers (BMF), but robust evidence is lacking. Systematic reviews and meta-analyses are expected to provide the highest level of evidence, but they are time-consuming and resource-intensive and are at risk of potential bias and subjectivity. The rapidly progressing large language model (LLM) artificial intelligence (AI) tools thus emerge as a promising complementary methodology for systematic review and meta-analysis. We conceptualized a cross-LLM AI platform meta-research and evidence synthesis workflow, leveraging 3 representative state-of-the-art platforms, ChatGPT, Claude and Manus AI. We analyzed 3371 PubMed-indexed publications. 3 platforms reported highly concordant findings. We found that prior systematic reviews and meta-analyses generally reported mixed findings comparing HMF versus BMF. Our LLM AI-assisted meta-research and evidence synthesis found non-inferiority of BMF to HMF for NEC and sepsis outcomes. Here, we present an unbiased direct head-to-head comparison between HMF and BMF in the context of NEC and sepsis. Our analyses also represent a proof-of-concept example for LLM AI-assisted meta-research and evidence synthesis, supporting the integration of LLM AI methodologies into evidence-based medicine and digital health.

Self-repairs, in-utterance revisions in which a speaker abandons and reformulates their speech, are a promising interpretable marker for speech-based cognitive screening. Detecting them automatically is difficult because a self-repair is defined by its relationship to surrounding speech rather than by fixed lexical cues. On the DementiaBank ADReSS corpus, we compared the capability of generative LLMs under a five-condition prompt ablation against a fine-tuned DistilBERT token classifier at detecting self-repairs. GPT-5 performed best (test F1 = 0.73) and was largely insensitive to prompt design, whereas the LLaMA (open-weight alternative) was both weaker and far more prompt-sensitive (test F1 = 0.47). DistilBERT, nearly 100 times smaller, matched the open-weight LLM at a fraction of the computational cost. These results suggest that a locally deployable encoder, given sufficient in-domain annotation, is a more plausible route to clinical self-repair detection than scaling model size or prompt complexity.

Tuberculosis (TB) remains a major public health challenge, exerting profound socio-economic burdens and causing debilitating illness in approximately 2.5 million individuals across Africa annually. Optimized large-scale treatment regimens, such as NAT2-genotype adjusted dosing, could improve patient outcomes and strengthen healthcare systems. However, fully addressing the complexity of multi-drug TB treatment responses requires consideration of the entire pharmacogenomic (PGx) landscape, particularly within African populations, which are both genetically diverse and critically understudied. In this study, we predict NAT2 genotypes and phenotypes in specific African populations, and we extend TB PGx research beyond well-established biomarkers. Current bioinformatic prediction tools were used to evaluate individual- and population-specific variation in genotype and next-generation sequencing data from 2,143 individuals across 20 African population groups, spanning ten PGx genes associated with multi-drug TB treatment and response. Most predicted functionally deleterious variants occurred at low frequencies (MAF < 0.01) and were observed in only one of the 20 populations. The Khomani and Nama populations had a distinctly higher proportion of NAT2 fast metabolizer phenotypes than other African populations, indicating a lower risk of INH overexposure and possibly different dosage requirements in these groups. These findings highlight both the potential and current limitations of functional prediction for absorption, distribution, metabolism and excretion (ADME) variants, and the transferability of their predictive value between African population groups. With the increasing accessibility of next-generation sequencing, alongside the development of comprehensive databases capturing African variation and advances in computational algorithms, the cumulative impact of genetic variation on TB drug response can be more accurately captured, thereby informing precision treatment strategies.

The Dietary Approaches to Stop Hypertension (DASH) diet reduces blood pressure and cholesterol. However, the mechanisms underlying these effects are unclear, and no randomized studies have evaluated the long-term benefits on health outcomes such as coronary artery disease (CAD) or type 2 diabetes (T2D). We performed a series of Mendelian randomization analyses of 71 serum proteins perturbed by the DASH diet in previous randomized controlled feeding studies to understand their potential mechanistic role on health outcomes. Four proteins (ANGPTL3, INHBC, PCOLCE, PLXNB2) had causal evidence of beneficial effects on risk factors that aligned with diet-induced changes in protein levels. Missense variants in INHBC and PLXNB were associated with lower risks of CAD and T2D respectively, and these effects are directionally concordant with DASH diet-induced changes in protein levels, providing causal evidence that these proteins influence disease risk. Combining molecular phenotyping in randomized interventional studies with human genetics evidence identified molecular regulators of the long-term cardioprotective effects of the DASH diet.

Background Lysosomal disorders (LDs) are a heterogeneous group of rare inherited disorders characterized by multi-system involvement and high comorbidity burden, which raises concerns about severe COVID-19 outcomes. Conversely, because SARS-CoV-2 relies on endolysosomal pathways for cellular entry and replication, certain LDs may exert a protective effect against viral pathogenesis. Prior clinical evidence investigating LDs and severe SARS-CoV-2 infection has been limited by small sample sizes and inconsistent findings. Therefore, to resolve these conflicting biological hypotheses and estimate population-level outcomes, we conducted a large-scale retrospective cohort study using nationwide U.S. harmonized electronic health record data from the National Clinical Cohort Collaborative (N3C). This design utilized longitudinal records starting January 1, 2018, to evaluate COVID-19 infections captured between January 1, 2020, and July 11, 2024. Results The study included 16,380 individuals, comprising 5,460 patients with lysosomal disorders and 10,920 matched controls. Patients with LDs had significantly higher odds of COVID-19 hospitalization compared with controls (OR = 1.86, 95% CI: 1.70-2.04). Elevated odds were observed across the evaluated categories, but varied substantially. Notably, neurodegenerative LDs such as neuronal ceroid lipofuscinosis (OR = 9.32) and metachromatic leukodystrophy (OR = 2.33) remained associated with hospitalization after adjustment for comorbidities. Contrarily, the elevated odds for Fabry disease and Gaucher disease were no longer significant after adjustment. Mortality among hospitalized patients with LDs was comparable to that of matched controls (one-year survival: 82.1% vs 82.0%), suggesting that LD status does not independently worsen survival once hospitalization occurs. Conclusions Patients with LDs were at an increased odds of COVID-19 hospitalization, driven by a combination of elevated comorbidity burden and disorder-specific effects, which vary significantly across LD categories. This study clarifies that excess risk is concentrated in the transition to hospitalization. These patients may thus require personalized clinical care to mitigate the negative consequences of COVID-19.

Preterm infants are acutely susceptible to neonatal sepsis, a syndrome characterized by systemic pro-inflammatory activity and life-threatening multi-organ dysfunction. However, the specific pulmonary pathological response to sepsis and the potential for metabolic interventions to mitigate lung injury remain poorly characterized. Herein, we evaluated the impact of varying parenteral glucose regimens on pulmonary outcomes during severe infection using a preterm piglet model. Genome-wide gene expression analysis was used to characterize lung transcriptome profiles. The relationships between gene expression and circulating biochemical and immune profiles were also investigated. Our findings demonstrate that significant pulmonary tissue damage is a hallmark of neonatal sepsis. A reduced-glucose regimen markedly attenuated pulmonary tissue damage while simultaneously alleviating systemic metabolic acidosis and hyperlactatemia. Mechanistically, lung transcriptome profiling revealed a profound activation of pathways associated with inflammatory signaling, programmed cell death, and the dysregulation of glucose, amino acid, and lipid metabolism. The low-glucose intervention effectively mitigated these widespread molecular and metabolic disturbances, suggesting a restorative effect on the pulmonary transcriptome landscape. To facilitate further mechanistic exploration and the identification of novel therapeutic targets, we developed the NeoSepPulmoExplorer (https://pharmaco-omicslab.shinyapps.io/NeoSepPulmoExplorer/), an interactive web-based toolkit for better mechanistic understanding and the identification of potential treatment targets. These results collectively underscore the importance of metabolic modulation in preserving organ function, though further translational studies are requisite to improve clinical outcomes in septic neonates.

Despite contributing substantially to clinician burnout, nursing documentation lacks empirical evidence distinguishing clinically essential from administratively driven documentation. Exploiting a COVID-19 documentation relaxation policy as a natural experiment, we analyzed 520,357 patient shifts from 36,321 patients in 54 inpatient units (2019 - 2022) using large language model-assisted flowsheet classification and structural equation modeling. When permitted, front-line nurses reliably distinguished two types of documentation: in acute care units, primary nurses reduced compliance-driven Cares & Safety documentation by 19% (106.4 to 86.2 entries, r = -0.19), while maintaining or increasing documentation directly relevant to respiratory management, with no impact on patient respiratory outcomes. Documentation intensity also co-varied with real-time patient deterioration, consistently across unit types (|{beta}| = 0.13 - 0.14). Together, these findings provide the first large-scale quantitative evidence distinguishing clinically essential documentation from compliance-driven documentation and demonstrate that targeted reduction of the latter is a viable strategy for alleviating documentation burden without compromising care quality for respiratory care management.

Tuberculosis remains the leading cause of death from a single infectious agent, with drug-resistant tuberculosis, particularly multidrug-resistant and extensively drug-resistant strains, posing major challenges for timely treatment. Whole-genome sequencing can accelerate resistance detection, but current genomic and machine-learning approaches typically predict resistance to individual drugs, do not directly infer regimen-relevant resistance profiles, and generalise poorly across regions or newly introduced drugs. We developed MuseAMR, a multimodal, multi-label deep-learning framework that predicts both individual-drug resistance and clinically actionable composite phenotypes from Mycobacterium tuberculosis genomes, with robust cross-regional performance and few-shot adaptation to emerging drugs. Trained on 10,886 isolates and externally validated on 18,334 isolates from six global regions, MuseAMR improved sensitivity for second-line drug resistance (0.857 versus 0.655) and MDR/pre-XDR profiles compared with WHO catalogue-based prediction while maintaining high specificity. It also showed robust cross-regional performance and few-shot adaptation to bedaquiline, delamanid and linezolid using 5-20 resistant isolates, with attribution analyses recovering established resistance loci. These results support its potential for regimen-level tuberculosis resistance profiling and surveillance.

The Systemic Lupus Erythematosus Disease Activity Index 2000 (SLEDAI-2K) is a crucial but labor-intensive tool for managing SLE. We developed a privacy-preserving, model-agnostic large language model (LLM) framework to automate SLEDAI-2K assessment from real-world electronic health records. The framework was developed on a specialist-verified ground truth of 658 clinical notes and externally validated on 56 MIMIC-IV discharge summaries. Seven open-source LLMs were evaluated using advanced prompting and ensemble strategies. The top-performing model, a two-layered GPT-OSS-120B + verifier, achieved a micro-F1 of 94.2% for descriptor classification and an 86% exact match for SLEDAI-2K scores on the internal set, with corresponding external validation performance of 87.7% and 64%, respectively. To demonstrate clinical utility, the LLMs were deployed on 2,576 serial notes from 108 SLE patients. Patients identified by the LLMs as achieving sustained low disease activity had a significantly lower incidence of stage 3 chronic kidney disease (log-rank p = 0.0053), the need for kidney replacement therapy (p = 0.044), and hospitalization (p = 0.021) over 18.3 years of follow-up. These findings demonstrate that privacy-preserving LLMs, when guided by a well-designed framework, can assist in specialist-level reasoning in autoimmune diseases, offering a scalable solution for clinical decision support and patient management.

While Photoplethysmography (PPG) is established as a noninvasive optical tool for monitoring heart rate and oxygen saturation, its high-resolution blood flow waveforms contain rich physiological data that extend far beyond conventional vital signs. We introduce PulseOx-FM, a foundation model, trained using self-supervised learning on 6,995,558 segments of pulse oximetry signals collected during 42,282 overnight sleep monitoring recordings of 10,704 participants in the Human Phenotype Project (HPP). Using chronological age as a global health benchmark, PulseOx-FM significantly outperformed existing open-source and proprietary feature extraction methods while demonstrating robust generalization in an external out-of-distribution cohort. PulseOx-FM representations predicted 64 phenotypic targets spanning cardiometabolic, and neuropsychiatric domains beyond demographic baselines, and prospectively identified two-year hypertension incidence in normotensive individuals. Nightly embeddings further tracked next-day glycemic, dietary and activity-based state within individuals, dissociating this signal from sleep architecture alone. This next-day glycemic signal was predominantly a direct physiological effect, not explained by next-day dietary intake. These findings suggest that PulseOx-FM provides a generalizable framework for encoding physiological patterns from sleep, offering a non-invasive tool for global health risk stratification and precision medicine.

Introduction. Distinct temperature patterns have long been recognized to correlate with fevers of differing etiologies. While the use of wearable sensors for high-frequency temperature monitoring (HFTM) on a near minute-by-minute basis has been shown to detect fevers earlier than standard-of-care nursing vital sign assessments in hospitalized patients, leveraging these high-resolution datasets to computationally identify unique digital signatures for real-time diagnosis of underlying fever etiology has not been widely explored. Diagnostic uncertainty is common in patients undergoing hematopoietic stem cell transplantation (HCT), with only 20-30% of febrile neutropenic episodes being microbiologically documented. We hypothesized that unique temperature patterns extracted from HFTM data collected during episodes of febrile neutropenia could be used to develop a supervised machine learning classifier capable of accurately predicting underlying fever etiology in HCT patients. Methods. We analyzed 68 clinically independent fever episodes recorded in HCT patients (n=90) outfitted with an FDA-cleared wireless temperature sensor (TempTraq(R), BlueSpark Technologies) that measured axillary temperature every 2 minutes throughout hospitalization. Time-series features were extracted from temperature traces spanning 1 hour before to 3 hours after fever onset and used to train a suite of machine-learning models to distinguish engraftment fevers from other fever etiologies. Model training and evaluation were performed using repeated stratified 5-fold patient-level cross-validation, yielding 100 train-test evaluations. Results. Among all classification models, the logistic regression classifier provided the best overall performance and interpretability, achieving 94% specificity (95% CI, 0.84-1.0) for identifying engraftment fevers with a mean AUROC of 0.88 {+/-} 0.10. Feature importance analysis demonstrated that both clinical variables and HFTM-derived temperature dynamics contributed to model performance, with a strong reliance on time-series features captured within the first 4 hours of fever onset. Conclusion. Our study provides a demonstration that continuous temperature data collected from patients outfitted with wearable sensors can be leveraged not only for early fever detection but also for machine learning-based diagnosis of fever etiology. These findings suggest that dynamic temperature patterns contain clinically meaningful physiologic information that with further studies could support real-time diagnostic decision-making and guide safe de-escalation of empiric antibiotics during febrile neutropenia in patients undergoing intensive cancer therapy.

Background: ERAS protocols reduce hospital stay by 1.88 days and complications by 29% globally, but their one-size-fits-all paradigm, validated at sea level, may fail at high altitude where chronic hypoxia and population-specific genetic adaptations remodel baseline physiology. No study has quantified ERAS effect weight shifts at high altitude or proposed a theoretical model to explain the gap. Objectives: To evaluate three dimensions of plateau ERAS remodeling: (i) risk factor weight shift, (ii) traditional marker failure, (iii) genetic background modification, and propose the PAERS (Plateau Adaptation-ERAS Remodeling Syndrome) risk stratification model tailored to altitude. Methods: Retrospective cohort of 612 adults undergoing elective laparoscopic cholecystectomy (2018-2023) at Qinghai Red Cross Hospital (2260 m). Three analytical tiers: (1) multivariable regression comparing risk factor coefficients against plain-altitude benchmarks; (2) restricted cubic spline and interaction modeling for Hb, SpO2, and LOS; (3) inferential genetic modifier analysis using population-level EPAS1 carrier rates. Primary outcomes: LOS and complication rate. Results: Three-dimensional shift was observed: (1) Weight Remodeling: BMI replaced sex as primary risk factor (OR = 1.86, P < .001), surgeon variability amplified (F = 6.33 vs plain benchmark 2-4, an ~58% increase in F-statistic ratio, P < .001); (2) Marker Failure: Hb showed J-type relationship with LOS (Hb x SpO2 interaction beta = -0.0095, P = .009), with effect reversal across SpO2 strata (Plateau Hemoglobin Paradox); (3) Genetic Modification (population-level inference): ~70% EPAS1 carrier rate (range 57-85% across studies) suggests HIF-2alpha pathway is a baseline modifier that must be accounted for. Three falsifiable predictions were proposed. Conclusions: High-altitude ERAS faces three challenges: effect weight remodeling, biomarker failure, and genetic background calibration. The PAERS hypothesis proposes an integrated risk stratification model, shifting from one-size-fits-all to altitude-aware, patient-specific protocols.

Objective. To develop an interpretable multimodal machine-learning model for risk stratification of the rapid pain progression phenotype in knee osteoarthritis and to evaluate its performance in the independent PROCOAC cohort. Methods. An elastic-net logistic regression model was trained using Osteoarthritis Initiative (OAI) data. Rapid pain progression was defined over overlapping 24-month windows using normalized WOMAC pain. Harmonized clinical, genetic and proteomic candidates were evaluated, with feature selection by permutation importance. The frozen algorithm was tested in an OAI hold-out set and externally evaluated in PROCOAC. Logistic recalibration corrected prevalence shifts. Clinical utility was assessed by decision curve analysis. Results. OAI comprised 2,934 individuals and 14,488 instances. Feature pruning reduced 159 candidates to a 19-variable clinical-genetic signature driven by Kellgren-Lawrence grade, localized knee pain, BMI and two genetic variants (rs73631790, rs9912678); no proteomic variable was retained. External testing in PROCOAC (582 individuals, 1609 instances) showed ROC-AUC 0.744 (95% CI 0.714 to 0.772) and PR-AUC 0.519. Following recalibration, the sensitive screening threshold yielded NPV 0.875 (95% CI 0.849 to 0.898) and sensitivity 0.804 (95% CI 0.760 to 0.844), whereas the high-specificity threshold achieved PPV 0.610 (95% CI 0.523 to 0.692) and specificity 0.941 (95% CI 0.924 to 0.954). Decision curve analysis showed positive net benefit at both thresholds, supporting a three-tier risk stratification framework. Conclusions. This externally evaluated model identified patients at risk of rapid pain progression using an MRI-free clinical-genetic signature. Recalibrated thresholds may support risk-adapted monitoring, advanced imaging prioritization and trial enrichment.

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.

Biological aging occurs heterogeneously across individuals and organs. However, current measures of biological age incompletely capture organ-specific differences in health and disease risk. Because chest CT visualizes multiple thoracic organs, it offers an opportunity to quantify structural aging across organ systems. Here, we developed MOSAIC-Age, a framework characterizing eight organ-specific aging clocks on chest CT. The clocks were developed and validated using 9,971 CT scans from CT-RATE and MIDRC, and subsequently locked and applied to two independent prospective cohorts with 35,293 participants from the National Lung Screening Trial and Genetic Epidemiology of COPD study. CT-derived biological age gaps (BAGs) were examined in relation to lifestyle and socioeconomic factors, prevalent comorbidities, incident chronic diseases, and all-cause and cause-specific mortality. Higher BAGs, indicating organs that appeared older on CT than expected for their chronological age, were broadly associated with adverse health characteristics, chronic disease burden, and increased mortality risk. Multiple disease outcomes were associated with aging across several organs, whereas in multivariable analyses including all eight organ-specific BAGs, the remaining associations were more organ specific. A greater number of markedly older-appearing organs and a faster pace of aging were each associated with higher mortality. Together, these findings demonstrate that routine chest CT captures both shared and organ-specific patterns of biological aging and establish CT-derived organ aging as a quantitative imaging biomarker for assessing multi-organ health and long-term disease risk.

External validation of clinical AI emphasizes discrimination, although deployment requires the endpoint, probability estimates and operating policy to transport. Here we show that these layers diverged in retrospective bidirectional evaluation of five model families across eICU and MIMIC-IV. Coarse-label AUROC fell from 0.87-0.92 internally to 0.66-0.83 during source-only transfer. For assessment-conditioned repeated monitoring of persistence or recurrence, external AUROC reached 0.76-0.94, but removing assessment history reduced it by 0.16-0.32; broader features did not help consistently. Transported scores concentrated future-positive ICU stays 2.4-6.9-fold in the top risk decile. Development-selected cutoffs alerted 0.3-2.0% of prediction rows and captured 9.2-11.0% of future-positive rows; after deduplication, 4.9-12.2% of stays were alerted, capturing 43.9-49.4% of future-positive stays. Thus, ranking can persist while probability and policy transport remain site dependent. Layered validation is a prerequisite for prospective evaluation, not evidence of clinical benefit.

Purpose: Whether host germline genetic variation determines tumor drug response remains underexplored. We evaluated whether EPAS1 (HIF-2) adaptive loss-of-function variants, enriched in high-altitude-adapted populations, predispose HCC to primary antiangiogenic TKI resistance through a HIF-2/STC2 signaling axis. Experimental Design: We integrated five independent data sources: the QHRCH-HCC retrospective cohort (n = 1,396), multi-ancestry iPSC-derived endothelial cell transcriptome data (GSE160906), TCGA pan-cancer data (LIHC, KIRC, LUAD, BRCA), GDSC2 pharmacogenomics (n = 951 cell lines; 11 antiangiogenic TKIs), and DepMap dependency data. The AESI_score integrated altitude, AFP-PIVKA-II inversion, platelet-altitude, and hemoglobin-altitude dimensions. Bayesian evidence integration employed the Effective Number of Independent Pieces of Evidence (ENIPE) method ({delta} = 0.504). Results: In QHRCH-HCC, altitude correlated positively with PIVKA-II ({rho} = +0.244, p = 0.0003) and with an altitude-adaptive genetic background score ({rho} = +0.517, p = 5.59x10-49). Under hypoxia, EPAS1 expression in high-altitude-adapted iPSC-ECs was reduced to 61.4% of controls (p = 0.0006), while STC2 remained relatively unaffected (89.2%, p = 0.180). In TCGA-LIHC, EPAS1[-&gt;]STC2 was weak ({rho} = 0.092) compared with HIF1A[-&gt;]STC2 ({rho} = 0.379, p = 2.21x10-14), establishing a negative control. Cross-cancer validation revealed strong EPAS1[-&gt;]STC2 in ccRCC ({rho} = 0.320, p = 3.47x10-14) but not in LUAD or BRCA. In GDSC2, EPAS1 correlated positively with IC50 of all 11 antiangiogenic TKIs (sign test p = 0.0005). Bayesian updating yielded posterior probability 0.970 (Log10BF = 1.99). Conclusions: EPAS1 LoF represents a germline determinant of TKI response, independent of tumor-acquired mutations. The AESI_score and HIF-2 inhibitor belzutifan constitute a predictive biomarker-therapeutic pair for genotype-stratified clinical validation. This hypothesis-generating study establishes a germline determinant framework for TKI resistance; definitive mechanistic validation will require prospective EPAS1 genotype-stratified cohorts (2023-ZJ-786).