Background Granulomatous-Lymphocytic Interstitial Lung Disease (GLILD) is a lung disease first described in people affected by common variable immunodeficiency disorders (CVID). Despite growing recognition of GLILD, there is no accepted diagnostic and management guideline, and current practice varies significantly across centres and countries. Objective This clinical practice guideline provides evidence- and consensus-based recommendations on the screening and diagnosis of GLILD in patients with CVID. Methods A panel representing multiple interdisciplinary perspectives convened with methodologists to prioritize clinical questions, review and assess the evidence using GRADE (Grading of Recommendations Assessment, Development and Evaluation) methodology. Evidence-to-Decision frameworks were used to decide on the direction and strength of recommendations. Results Screening for GLILD is recommended in all adult CVID patients, preferably using high resolution computed tomography. Evaluation should be performed by a multidisciplinary team. Routine lung biopsies are not mandatory but necessary for atypical presentations. As part of the diagnostic process, pulmonary infections should be excluded and lymphocytic alveolitis sought after by means of bronchoalveolar lavage. The severity of lung function impairment should be evaluated using pulmonary function tests including gas transfer assessment. Most of the recommendations are graded as conditional because of low certainty in the evidence regarding health effects. Conclusions This guideline allows for international homogeneity in the diagnosis of GLILD, thereby paving the way for improved comparability between centers, improving equity in health care for those affected by GLILD and facilitating multicenter research collaborations for future studies.

Studies linking mitochondrial DNA (mtDNA) with complex traits are often limited by small sample sizes or focused on specific phenotypes in clinically selected cohorts. Here, we use data from >600,000 participants in the Million Veteran Program (MVP) to perform a multi-ancestry analysis of mitochondrial DNA (mtDNA) variation and cardiometabolic phenotypes across European (EUR), African (AFR), Admixed American (AMR), and East Asian (EAS) populations. After validating 248 mtDNA loci, we identify 10 ancestry-stratified single-variant associations (8 EUR, 2 AFR) and 23 additional signals in sex- and type 2 diabetes (T2D)-stratified analyses. Four variants tagging haplogroup J, D-loop MT228G > A, MT-ND3 MT10398A > G (p.Thr114Ala), MT-ND5 MT13708G > A (p.Ala458Thr), and MT-CYB MT14798T > C (p.Phe18Leu), are associated with hypothyroidism in EUR and replicated in UK Biobank (UKBB) with concordant effects. In EUR females, MT-RNR1 MT1555A > G increases the risk of carditis and heart failure phenotypes, supporting prior reports of maternally inherited cardiomyopathy. Gene-based rare-variant tests (minor allele frequency ≤2%) yield 26 associations (12 EUR, 10 AFR, 2 AMR, 2 EAS), including mitochondrial tRNA burdens linked to primary cardiomyopathy (females) and exophthalmos (males). Twenty-three of the 33 single-variant signals map to the endocrine/metabolic category, indicating significant enrichment (Fisher's exact P = 0.003). These results define ancestry- and context-specific contributions of mtDNA to cardiometabolic disease, with a notable concentration in endocrine traits, and provide a framework for mtDNA analysis across diverse biobank cohorts.

BACKGROUND:Preexisting multiple (two or more) long-term conditions (MLTCs) may negatively affect recovery after COVID-19. We investigated how preexisting MLTCs, including different categorization and patterns of MLTCs, affect 1-year health outcomes after severe COVID-19. METHODS:Adults post-hospitalization after COVID-19 were recruited during 2020-2021. We compared recovery at 1 year after discharge using adjusted multivariable logistic regression in 1:1 propensity-matched adults (for age, sex, ethnicity, social deprivation, obesity, and smoking history) with and without preexisting MLTCs. In adults with MLTCs, different categorization such as number of conditions, number and types of body systems involved (e.g. respiratory, cardiovascular), and latent class analysis-derived patterns of condition co-occurrence were assessed for their association with recovery at 1 year. RESULTS:A total of 647 adults with MLTCs were matched with 647 adults without MLTCs (n = 1294; 61.9% male, 79.6% of White ethnicity, median age 59 [interquartile range 52-67] years). The presence of MLTCs was associated with lower odds of feeling fully recovered (odds ratio 0.66 [95% confidence interval 0.51-0.85], P = 0.001). In those with MLTCs, recovery was negatively affected by number and type of body systems involved (e.g. respiratory [odds ratio 0.49 (95% confidence interval 0.34-0.69), P <0.001]) but not by the number of conditions (P >0.1). Four latent classes of MLTC co-occurrence were estimated with different risks of recovery (P <0.01). CONCLUSION:Adults with preexisting MLTCs were 34% less likely to feel fully recovered at 1 year after COVID-19 hospitalization than adults without MLTCs. We describe prognostic classifications of MLTCs, with future work needed to understand whether they have prognostication in broader post-acute infection sequalae.

Heart failure (HF) affects 6.7 million people in the US and includes two major subtypes, HF with reduced ejection fraction (HFrEF) and HF with preserved ejection fraction (HFpEF), with distinct genetic architectures. We meta-analyze genome-wide association studies (GWAS) of 38,781 HFrEF cases, 38,163 HFpEF cases, and 526,135 controls across European, African, Hispanic, and Asian ancestries using the Million Veteran Program and Vanderbilt University DNA Databank (BioVU). We identify 46 genome-wide significant loci for HFrEF (9 novel) and 3 loci for HFpEF (1 novel). Four HFrEF loci are detected in African ancestry participants near CD36, SPI1, TRIM48, and SPNS3, with lead SNPs showing low risk-allele frequencies in European populations. In the all-cause HF meta-analysis (200,070 cases, 2,076,466 controls), we identify 136 loci (12 novel). Gene-based tests, tissue enrichment, transcriptome-wide association, and fine-mapping implicate vascular, metabolic, and TGF-β/Smad signaling pathways and nominate candidate causal genes, clarifying shared and subtype-specific risk across ancestries. This study maps the genetic basis of major heart failure subtypes across diverse populations, identifying shared and subtype-specific risk variants that may inform biology, risk prediction and future therapies.

Background Physical inactivity is a risk factor for severe COVID-19 and often worsens after hospitalisation. Clinicians need quick, accurate assessments to target interventions. We aimed to assess the validity of the General Practice Physical Activity Questionnaire (GPPAQ) in adults recovering one year after COVID-19 hospitalisation . Methods Post-hospitalisation for COVID-19, adults attended a one-year-visit and completed the GPPAQ Physical Activity Index (PAI-4 active), 14-day wrist-worn accelerometry (Moderate-Vigorous PA [MVPA]- active), and other health outcomes. Validity was examined via : (i) internal consistency (factor analysis); (ii) measurement invariance across sex, age, and ethnicity using differential item functioning (DIF); (iii) convergent validity (GPPAQ sensitivity/specificity versus accelerometry); and (iv) construct validity (correlations with health outcomes). Results 752 participants had GPPAQ and accelerometry (265 female, mean± sd age 60.9±11.6 years, MVPA 18.75 min·day −1 (IQR 7.55, 36.11), PAI-1 46.8%, PAI-2 15.6%, PAI-3 19.4%, PAI-4 18.2%. Factor analyses supported good internal consistency with two factors (daily activities, physical exercise). Confirmatory factor Index (CFI) showed excellent fit (CFI 0.965). DIF indicated moderate variability by sex and age. GPPAQ-PAI showed limited sensitivity (26.3%) for correctly classifying physically active individuals, but higher specificity (88.4%) for classifying physical inactivity, and weak-to-moderate correlations with health outcomes. Conclusions GPPAQ demonstrates internal consistency, with construct and convergent validity in adults 1-year post-COVID-19 hospitalisation. GPPAQ effectively identifies inactive individuals to support clinical care; however, its sensitivity suggests underestimation of activity relative to accelerometry. Future pathways should combine GPPAQ with device-based assessment to optimise evaluation of physical activity to guide pulmonary rehabilitation and targeted interventions.

Background: While clinical risk factors (CRF) have been used to predict heart failure (HF), an end-stage syndrome with high morbidity and mortality, the role of polygenic risk score (PRS) in identifying HF risk prediction remains unclear. Method: Using genome-wide summary statistics, we constructed 52 PRSs, including 1 for HF, 43 for echocardiographic(echo) traits, and 8 for CRF. Associations with incident HF were evaluated in 31,650 trans-ancestry participants (55% non-White; mean follow-up 16 years) from seven cohorts from the Trans-Omics for Precision Medicine (TOPMed) program, randomly divided into training (80%) and testing (20%) sets. Cox regression models adjusted for demographic and CRF were used to relate PRSs and to incident HF. Least absolute shrinkage and selection operator (LASSO) feature selection was performed on HF-related PRSs to build a multi-trait PRS (mPRS) in the training set. Associations of mPRS with incident HF, HF with preserved and reduced left ventricular (LV) ejection fraction (HFpEF and HFrEF, respectively), were examined in the testing set, with predictive performance evaluated by the C-statistic. Replication was conducted in 510,074 participants from the Million Veteran Program (MVP). Results: In the training set (n=25,320, HF cases=3,380), 17 out of 52 PRSs were associated with incident HF, where PRSs for HF and LV ejection fraction showed the largest effects (Figure 1a, HR HF-PRS : 2.02,95% CI 1.97-2.08, HR LVEF-PRS :0.93, 95% CI 0.89-0.97, p-value <0.05). Nine HF-related PRSs (1 HF, 6 echo, 2 CRF) were selected by LASSO to construct a multi-trait PRS (mPRS). In the testing set (n=6330, HF cases=856), per SD increase of mPRS was associated with about twofold increase in the risk of incident HF and its subtypes (Figure 1b, HR HF : 1.81, 95% CI 1.68–1.96; HR HFpEF : 2.08, 95% CI 1.78-2.43; HR HFrEF :1.97, 95% CI 1.68-2.31), and the associations were stronger in White vs Non-white participants. The associations of mPRS with incident HF and its subtypes were replicated in MVP (Figure 1b, HR HF : 1.13, 95% CI 1.11–1.15; HR HFpEF : 1.09, 95% CI 1.07-1.11; HR HFrEF :1.17, 95% CI 1.15-1.19). Adding mPRS over CRF modestly improved HF and HFrEF prediction in the trans-ancestral and white group (Figure 1c, HF trans-ancestral : estimated C: 0.89, delta C=1%, all p-values<0.05). Conclusion: The component PRS and mPRS were associated with HF risk, and mPRS can improve the prediction of incident HF, highlighting the potential for identifying at-risk populations.

Abstract Heart failure (HF) is a leading cause of morbidity and mortality. We conducted multi-ancestry genome-wide association studies of 345,687 HF cases (4,468,166 individuals), and 47,192 and 46,934 cases of HF with preserved (HFpEF) and reduced ejection fraction (HFrEF), respectively, integrating plasma proteomics and multi-tissue transcriptomics to identify druggable targets. Across HF, HFrEF, and HFpEF, we identified 383 loci (166 novel) and 568 genes (375 novel). Eleven novel genes are targets of approved or investigational cardiovascular therapies, supporting indication expansion of aldosterone synthase inhibitors ( CYP11B2 ) and type-II activin receptor antagonists ( ACVR2A ) to HF. Six cardiomyopathy genes were novel for HF and associated with cardiac structure and function. We identified nearly 100 genes involved in food intake and energy expenditure; metabolism of fatty acids, glucose, and branched-chain amino acids; and mitochondrial proteome, sustaining myocardial energy production. Our findings highlight the primordial role of metabolic pathways and adipokines as therapeutic targets for HF management.

BACKGROUND:Low-dose computed tomography (LDCT) employed in lung cancer screening (LCS) programmes is increasing in uptake worldwide. LCS programmes herald a generational opportunity to simultaneously detect cancer and non-cancer-related early-stage lung disease, yet these efforts are hampered by a shortage of radiologists to interpret scans at scale. Here, we present TANGERINE, a computationally frugal, open-source vision foundation model for volumetric LDCT analysis. METHODS:Designed for broad accessibility and rapid adaptation, TANGERINE can be fine-tuned off the shelf for a wide range of disease-specific tasks with limited computational resources and training data. The model is pretrained using self-supervised learning on more than 98,000 thoracic LDCT scans, including the United Kingdom's largest LCS initiative to date and 27 public datasets. By extending a masked autoencoder framework to three-dimensional imaging, TANGERINE provides a scalable solution for LDCT analysis, combining architectural simplicity, public availability, and modest computational requirements. RESULTS:TANGERINE demonstrates superior computational and data efficiency in a retrospective multi-dataset analysis: it converges rapidly during fine-tuning, requiring significantly fewer graphics processing unit hours than models trained from scratch, and achieves comparable or superior performance using only a fraction of the fine-tuning data. The model achieves strong performance across 14 disease classification tasks, including lung cancer and multiple respiratory diseases, and generalises robustly across diverse clinical centres. CONCLUSIONS:TANGERINE's accessible, open-source, lightweight design lays the foundation for rapid integration into next-generation medical imaging tools, enabling lung cancer screening programmes to pivot from a singular focus on lung cancer detection toward comprehensive respiratory disease management in high-risk populations.

Lung biopsy has traditionally played a fundamental role in addressing diagnostic uncertainty and guiding management of fibrotic interstitial lung disease (fILD). Multiple lung biopsy procedures are available for evaluating fILD, including bronchoscopic techniques such as transbronchial biopsy and transbronchial lung cryobiopsy, as well as surgical lung biopsy, which is now almost exclusively conducted using minimally invasive video-assisted thoracoscopic surgery. The various evolving -considerations for and against lung biopsy have led to substantial ambiguity regarding the optimal timing and choice of biopsy method. The rationale for performing a lung biopsy in fILD is multifaceted, shaped by its potential to enhance diagnostic confidence and inform therapeutic decisions, weighed against procedural risks, and further nuanced by patient values and preferences. The objective of this state-of-the-art document from a multidisciplinary group of experts and patient representatives is to summarize the rationale for and against lung biopsy in the evaluation and management of fILD. Amid ongoing technological innovations, we further emphasize that future research should prioritize the development and validation of minimally invasive and noninvasive modalities that may serve as either alternatives or adjuncts to biopsy in the diagnostic evaluation of fILD.

Background: Small airway disease (SAD) is a defining feature of chronic obstructive pulmonary disease (COPD), but its functional consequences across the whole bronchial tree remain incompletely quantified. We determined how progressive SAD alters lung airflow dynamics during inspiration and expiration, and whether simplified models reproduce the results of anatomically realistic 3D simulations. Methods: Three lung explants, healthy control, moderate (GOLD II), and end-stage (GOLD IV) COPD, were imaged by micro-CT and segmented from the main-bronchus to the small airways. Inspiratory and expiratory computational fluid dynamics (CFD) simulations used matched main-bronchus-flow and terminal-pressure scenarios. A linear Poiseuille model on the same anatomy benchmarked simplified airway representations. Findings: Airways (0·5–2·5 mm) decreased by 22% in GOLD II and 71% in GOLD IV relative to control. At matched inspiratory flow, whole-lung resistance rose from 0·33 (control) to 0·58 (GOLD II) and 2·15 cm H₂O·s/L (GOLD IV). Expiratory resistance exceeded inspiratory resistance by 19% (control), 38% (GOLD II), and 79% (GOLD IV), an asymmetry that widened with severity. Wall shear stress in GOLD IV rose 8-fold during inspiration and 5-fold during expiration vs control. Simplified linear models underestimated CFD-derived driving pressure 3·8- to 6·8-fold and overestimated expiratory outflow 12·6- to 14·3-fold. Interpretation: Whole-lung CFD revealed a severity-dependent airflow burden in COPD that is amplified during expiration and strongly underestimated by linear flow models and simplified airway geometries. These findings provide a quantitative bridge between distal airway pathology and expiratory flow limitation, and underline the need for anatomy-based 3D modelling when assessing SAD.

BACKGROUND:Phase-based X-ray microtomography is a powerful technique capable of quantitative volumetric imaging of lung tissue in health and disease. The maximum sample size is however limited by the fixed sizes of detectors and optical elements. Thus while high-resolution imaging can offer valuable microscale insights, it can be difficult to interpret without the context of the surrounding tissue. We propose a multi-contrast and multi-scale approach, combined with an offset geometry to extend the field-of-view (FOV). PURPOSE:FOV limitations make it a challenge to simultaneously achieve high spatial-resolution and image large samples. Our method doubles the possible FOV achievable for a given spatial-resolution, in a way compatible with multiple scales and imaging systems. METHODS:Multi-contrast whole sample volumetric images are acquired using a beam-tracking X-ray phase-contrast imaging(XPCI) system. Following this, a section of the same sample is imaged at higher resolution using an X-ray microscope with propagation-based imaging. The FOV of both methods is doubled using an offset center-of-rotation geometry, followed by weighted analytical reconstruction. RESULTS:We present exemplary multi-contrast reconstructions of resected human lung tissue at 10.5 μ m $\umu{\rm m}$ voxel size across a 4.3 cm horizontal FOV, and at 450 nm voxel size for a 2.7 mm section of the same sample. This enables the visualization of a range of features, from the macro to the cellular scale. CONCLUSIONS:We demonstrate a versatile method to image large samples without sacrificing spatial-resolution. This method is directly compatible with complementary implementations of XPCI, and is easily adapted to a range of other systems.

Pleuroparenchymal fibroelastosis (PPFE) is a progressive interstitial lung disease (ILD) with defining histology of intra-alveolar fibrosis with septal elastosis (AFE), suggesting unique cellular disease processes. Here, we present a binational single-nucleus RNA sequencing atlas of PPFE, based on explanted lungs from 40 patients. Immunofluorescence microscopy, RNA in situ hybridization, micro-computed tomography (CT), and hierarchical phase-contrast (HiP) synchrotron CT provided spatial context. We identify PPFE-associated adventitial and elastofibrotic fibroblasts as key drivers of elastotic remodeling within an inflammatory microenvironment, maintained by immune cells forming tertiary lymphoid structures. Spatial mapping reveals an intriguing zonation of AFE, maintained by intercellular circuits between PPFE-associated cell types. Comparative analysis with idiopathic pulmonary fibrosis highlights CTHRC1+ fibrotic fibroblasts and aberrant basaloid cells as conserved profibrotic cellular machinery mediating collagen deposition across ILDs. This integrative atlas defines the cellular landscape of PPFE and dissects elastotic from fibrotic remodeling, providing a molecular rationale for niche-specific therapeutic strategies.

BACKGROUND:It is unclear how lung function may recover in patients with residual lung abnormalities (RLAs) following COVID-19 pneumonia. PURPOSE:To evaluate lung function trends over time in patients with RLAs following hospitalization due to COVID-19. STUDY TYPE:Prospective, multicenter longitudinal cohort study. POPULATION:Twenty-four participants hospitalized due to COVID-19 with RLAs identified on CT ≥ 3 months postdischarge (median [IQR] age 69 (15) years; 3 female) underwent at least one MRI at 6 months (n = 16), 1 year (n = 19), or 2 years (n = 14). FIELD STRENGTH/SEQUENCE:1.5 T. Dynamic contrast enhanced (DCE) 3D spoiled gradient echo, 129Xe steady state free precession (ventilation), 129Xe 3D spoiled gradient echo multiple b-value (diffusion-weighted), 129Xe 4-echo flyback 3D radial (dissolved phase). ASSESSMENT:Pulmonary blood flow, volume, and mean transit time (MTT) were calculated from DCE MRI. The fraction of 129Xe signal in the red blood cells to membrane (RBC:M) was calculated from the dissolved phase 129Xe acquisition. Ventilation defect percentage (VDP) was calculated from the 129Xe ventilation acquisition. Mean diffusive length scale (LmD) was calculated from the 129Xe diffusion-weighted acquisition. STATISTICAL TESTS:Changes in metrics with time and associations between metrics were assessed using mixed-effect linear regression. Correlations were tested using Spearman's correlation coefficient. Regional differences were assessed using a Friedman's test with a Bonferroni adjustment. p < 0.05 was considered significant. RESULTS:Pulmonary blood flow and MTT improved significantly over time (MTT: 6 months, 15.3 (IQR, 2.0); 1 year, 15.6 (1.4); 2 years, 15.0 (5.3); pulmonary blood flow: 6 months, 75.4 (IQR, 22.0); 1 year, 83.2 (47.4); 2 years, 107.3 (51.1)). RBC:M z-score was low at all three visits (6 months, -2.85 (0.98); 1 year, -2.44 (1.34); 2 years, -2.60 (1.39)), with no improvement with time (p = 0.993). VDP and LmD did not significantly change with time (VDP: p = 0.100; LmD: p = 0.166). DATA CONCLUSION:Improvements in lung perfusion were measured; however, there was no corresponding enhancement in RBC:M. EVIDENCE LEVEL:Level 2. TECHNICAL EFFICACY:Stage 3.

Background Lung cancer screening (LCS) participants have a high competing risk of non-lung cancer related death, which limits screening benefit. We hypothesized that imaging-based biological age can identify individuals at higher risk of non-lung cancer mortality. Methods Low dose computed tomography scans from a large LCS trial were retrospectively analyzed. Bone density loss, muscular fat infiltration, vascular calcification, and visceral fat mass were calculated using segmentations from a nnU-Net based deep learning segmentation model. Each participant’s age gap, defined as the difference between their estimated biological age and chronological age, was estimated with disease course mapping using a Bayesian mixed-effects model. Disease course map construction used all available longitudinal scan data, whilst individual biological age prediction used baseline scan data only. The performance of age gap as a parameter in competing risk models for non-lung cancer related death versus lung cancer diagnosis was evaluated, following the TRIPOD + AI reporting guideline. Findings : We included 29,745 scans from 12,478 participants in the final analysis. Median follow-up was 5 years, representing 58,284 person-years at risk. Multivariable survival models of competing risks were constructed, including the estimated age gap. The subdistribution hazard ratio (sHR) for each decade of age gap for non-lung cancer related death was higher (sHR = 2.0, 95% CI 1.8–2.3), compared to lung cancer diagnosis (sHR = 1.3, 95% CI 1.2–1.5). Addition of age gap improved model concordance of non-lung cancer death compared to a model with clinical variables alone (pooled C-index = 0.69 vs 0.73, difference + 0.04, 95% CI 0.03–0.04, p < 0.001). Interpretation : Biological age estimation using an imaging-based disease progression model improves prediction of non-lung cancer related death in lung cancer screening.

RATIONALE:Lung cancer screening regularly identifies participants with interstitial lung abnormalities (ILA). Existing classification methods may underestimate the prevalence of clinically relevant ILA phenotypes. OBJECTIVES:Can a classification system for ILAs developed in a lung cancer screening setting identify clinically relevant phenotypes? METHODS:Classification criteria based on the presence and lobar extent of traction bronchiolectasis (TBe) were developed internally by expert consensus. Categories included: no ILA, non-fibrotic ILA (NF-ILA), fibrotic ILA (F-ILA), and undiagnosed fibrotic ILD (U-ILD). Interobserver agreement was calculated between two readers. Clinical characteristics, respiratory hospitalizations, and survival were compared between participants of different ILA grades. MEASUREMENTS AND MAIN RESULTS:Eight thousand, one hundred sixty-nine participants were included in the final analysis. TBe showed improved interobserver agreement compared to the American Thoracic Society (ATS) classification, identifying 344 participants (4%) with U-ILD, 86% more than the ATS classification. An additional 405 had F-ILA (5%) and 667 had NF-ILA (8%). Compared to participants without ILA, participants with U-ILD had a higher rate of respiratory hospitalization (IRR = 4.4, 95% CI 2.7-7.5, P < .001) and increased risk of death (aHR = 2.4, 95% CI 1.9-3.0, P < .001). Increasing ILA grade was associated with higher modified Medical Research Council dyspnea scores (OR = 1.1, 95% CI 1.0-1.1, P = .02). CONCLUSIONS:In a lung cancer screening setting, an ILA scoring system focused on lobar TBe identifies more high-risk participants and demonstrates improved interobserver concordance than the ATS classification. TBe identifies participants with a respiratory phenotype who may warrant further investigation and follow-up.