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Summary Background Body composition is recognized as a major determinant of health outcomes, but its multidimensional nature makes clinical adoption challenging. We sought to develop and validate a body composition index (BCI) for all-cause mortality risk assessment, integrating variables of six body composition tissues. Methods We analyzed 28509 consecutive patients undergoing myocardial perfusion imaging with routine low-dose chest CT attenuation correction (CTAC) scans acquired during myocardial perfusion imaging (MPI) at 12 centers across four countries. An artificial intelligence-based BCI was developed in a cohort of 15037 patients’ CTACs by integrating the CT-derived metrics of bone, skeletal muscle, and four adipose tissue compartments, coronary artery calcium score, and basic demographic variables (age, sex, BMI). The performance of BCI for mortality prediction was validated in an internal cohort of 6444 patients and an external cohort of 7028 patients by prognosis, calibration, net benefit, and explainability. Model-based simulation of tissue metrics modification was performed to evaluate estimated mortality risk reduction. Findings During a median of 3.5 (IQR [1.9, 5.1]) years, 4697 (16%) patients died. In the external testing cohort, the BCI demonstrated excellent discrimination for mortality (area under receiver operating characteristic curve 0.78 (95% CI [0.76, 0.79]) and Harrell’s concordance index 0.75 [0.73, 0.76]), calibration, and net benefit overall and across pre-specified subgroups stratified by patient characteristics and imaging protocols. Visceral adipose tissue attenuation was the most influential body composition measure, followed by skeletal muscle volume. Simulated improvement in body composition was associated with significant mortality risk reduction. Interpretation An index combining six body composition measures obtained opportunistically from routine chest CT provides robust mortality risk stratification. By converting complex body composition information into a single interpretable score, the BCI can facilitate clinical implementation of opportunistic CT biomarkers and guide individualized preventive strategies. Funding National Institute of Health Research in context Evidence before this study We searched PubMed and Google Scholar on July 7, 2026 for English language studies using the terms (“mortality prediction” OR “death prediction”) AND (“integrated body composition analysis” OR “body composition metrics integration”) AND (“predictive model” OR “model development”), and identified 41 publications describing predictive models incorporating body composition metrics. Recent advances in artificial intelligence (AI) have enabled increasingly granular quantification of body composition from CT, with multiple tissues and tissue-specific metrics showing independent prognostic value for all-cause mortality (ACM) prediction. However, reported associations of body composition metrics with mortality risk were highly inconsistent or even contradictory across studies. This heterogeneity likely reflected the differences in study design or the inherent complexity of body composition analysis, such as the interaction among variables derived from multiple tissues. Thus, it is difficult to interpret body composition results reliably in clinical practice. We hypothesized that integrating multiple body composition metrics could achieve comprehensive and reliable prediction of mortality across patient populations. Added value of this study In this large international, multicenter longitudinal cohort study of 28509 consecutive patients with suspected or known coronary artery disease (CAD) undergoing myocardial perfusion imaging across 12 sites in four countries, we developed and externally validated an artificial intelligence-based body composition index (BCI) for all-cause mortality prediction. The BCI integrated chest CT-derived metrics of six body composition tissue compartments – bone, skeletal muscle (SM), subcutaneous adipose tissue (SAT), intramuscular adipose tissue (IMAT), visceral adipose tissue (VAT), and epicardial adipose tissue (EAT), together with demographics (age, sex, BMI) and CT-derived coronary artery calcium (CAC) score, all derived automatically from routine low-dose attenuation correction CT scans without additional imaging or laboratory tests. In an external validation cohort of 7028 patients, the BCI achieved robust discrimination for mortality prediction, substantially outperforming a baseline model using demographics and calcium score alone, and exceeded the discriminative performance of prior body composition models. Performance was consistent across subgroups defined by sex, age, body mass index, cardiometabolic risk factors, imaging modality, and acquisition protocol, supporting generalizability across clinical settings. The BCI demonstrated excellent calibration and net clinical benefit. Explainability analysis identified visceral adipose tissue attenuation and skeletal muscle volume as the dominant contributors to mortality risk, offering tissue-specific therapeutic targets that complement generic weight-based metrics. Model-based simulations suggested that improvements in these specific compartments could reduce estimated mortality risk more effectively than equivalent changes in BMI alone, pointing toward precision therapeutic targets beyond weight reduction. Our study improved over previous literatures on body composition-based predictive models in one or more of the following ways: (1) we considered six types of body composition tissues while existing studies considered only three or less tissues, thus significantly higher granularity and improved ability to capture refined tissue interactions; (2) our model was rigorously validated in external sites, showing cross-site generalizability while most previous work lacked external validation; (3) our model achieved significant prediction performance improvement over previous body composition predictive models, offering better clinical outcomes prediction and facilitating the suitability for widespread clinical adoption; (4) our study was conducted with a large multi-center cohort across multiple countries, achieving statistical power and demonstrating reliability towards patients characteristics and imaging acquisition protocols while previous studies involved small single-center cohorts; (5) our model required only CT scans and basic demographics which were widely available in clinical routine while the existing models required dedicated laboratory tests or specialized imaging modalities, enhancing the applicability in routine clinical practice. Implications of all the available evidence BCI offers an explainable and clinically deployable tool for improved prediction of mortality risk from existing chest CT scans. Its use could inform updates to existing risk models, guide precision preventive strategies, and support development of therapeutics targeting specific body composition phenotypes.

Purpose: Depression is prevalent yet frequently underdiagnosed. Although speech-based detection methods show promise, most prior studies have emphasized binary classification in nontonal languages. This study examined whether acoustic features of spontaneous Mandarin speech reflect depression as a categorical condition or a continuous, dimensional construct. Method: A validated emotional Mandarin speech corpus paired with self-reported depression severity scores was analyzed. Acoustic features were extracted using the extended Geneva Minimalistic Acoustic Parameter Set. A multistage analytic framework was applied, including random forest classification to distinguish depressed from nondepressed clips, linear mixed-effects modeling to examine group and severity effects, and unsupervised clustering to examine latent structure in the acoustic feature space. Results: The random forest classifier achieved an accuracy of 72.3% and an area under the receiver operating characteristic curve of 0.826 on the held-out test set. Model performance favored sensitivity over precision in identifying depressed clips. SHapley Additive exPlanations analysis identified features across multiple acoustic domains, including spectral, pitch-related, voice quality, cepstral, and intensity measures, as important contributors to model predictions. Among the Top 10 features, no acoustic features showed statistically significant group differences. In contrast, median and mean fundamental frequencies were significantly positively associated with depression severity. Clustering analysis revealed overlapping group structures and a continuous distribution of severity scores across clusters. Conclusions: These findings provide preliminary support for a dimensional conceptualization of depression in spontaneous Mandarin speech. While classification models can distinguish depressed from nondepressed speech with moderate accuracy, acoustic features appear to vary more consistently with symptom severity than with diagnostic group. Speech-based measures may therefore have potential for continuous monitoring of depression, although this possibility requires further longitudinal verification.

DNA double-strand breaks (DSBs) that occur during mitosis are primarily repaired by Polθ-mediated microhomology-mediated end joining (MMEJ). The CIP2A–TOPBP1 complex has been shown to tether broken chromatin at mitotic DSBs, but whether it coordinates with or functions independently of Polθ remains unknown. Here, we show that the CIP2A–TOPBP1 complex is recruited to mitotic DSB sites, where it directly interacts with Polθ and corecruits Polθ to chromatin. CIP2A sustains Polθ phosphorylation by inhibiting PP2A, thereby prolonging Polθ chromatin retention and enabling efficient repair. Upon repair completion, the CIP2A–TOPBP1 complex dissociates, PP2A dephosphorylates Polθ, and Polθ is released from chromatin. Loss of CIP2A impairs tumor growth, while disruption of CIP2A–Polθ binding leads to persistent DNA damage, micronucleus formation, and synthetic lethality in BRCA1/2-deficient cells. These findings uncover a cooperative mechanism by which CIP2A–TOPBP1 dynamically regulates Polθ in mitotic DSB repair and provide insights into the synthetic lethal interaction of CIP2A with BRCA1/2 under radiotherapy.

ABSTRACT Introduction Composite indolent B‐cell lymphomas may remain unrecognized when one component dominates the clinical presentation. Apparent relapse after therapy may, therefore, represent the emergence of a biologically distinct lymphoma rather than the recurrence of the original disease. Case Presentation An 82‐year‐old woman presented with systemic low‐grade B‐cell lymphoma that was most consistent with marginal zone lymphoma (MZL). Staging demonstrated peripheral blood involvement and multifocal FDG‐avid disease. Treatment with the CD20×CD3‐bispecific antibody mosunetuzumab resulted in complete metabolic remission. During surveillance, a new, isolated cervical lymph node developed despite sustained systemic response. Biopsy revealed follicular lymphoma (Grade 1–2) with a germinal center phenotype and a BCL2 rearrangement, findings discordant with recurrence of MZL. Retrospective review of the original specimen identified in situ follicular neoplasia within the initial biopsy. The disease course was, therefore, reinterpreted as a synchronous composite lymphoma in which treatment of the dominant MZL unmasked a previously clinically silent B‐cell neoplasm with follicular lineage. Local radiotherapy achieved remission of the follicular component. Conclusions New disease following treatment of an indolent lymphoma should not automatically be interpreted as relapse. Discordant clinical behavior, particularly focal progression after systemic response, warrants repeat biopsy and integrated pathologic reassessment. This case illustrates how therapy may reveal preexisting clonal heterogeneity and emphasizes the importance of temporality in distinguishing relapse from composite lymphoma. Trial Registration The authors have confirmed clinical trial registration is not needed for this submission

Importance Elevated low-density lipoprotein cholesterol (LDL-C) is a modifiable risk factor for cardiovascular disease, the leading cause of premature death worldwide. Assessing the LDL-C–related burden is critical for guiding prevention and treatment strategies. Objectives To estimate the global, regional, and national burden of ischemic heart disease and ischemic stroke attributable to elevated LDL-C (relative to 35-54 mg/dL) from 1990 to 2023 and to quantify the contributions of population growth, aging, risk-deleted burden, and exposure changes to burden trends. Design, Setting, and Population This comparative risk assessment, part of the Global Burden of Disease Study 2023, estimated population-level LDL-C exposure and associated health loss in 204 countries and territories. Mean LDL-C levels were estimated using spatiotemporal gaussian process regression based on 806 studies across 161 countries. Relative risks were derived from meta-analyses of 38 randomized clinical trials. Population-attributable fractions for deaths and disability-adjusted life-years (DALYs) were estimated by age and sex for adults aged 25 years or older from 1990 to 2023, with 95% uncertainty intervals. Exposure Population-level LDL-C concentrations. Main Outcomes and Measures Population-attributable fractions, counts, and rates (all ages and age standardized per 100 000) of LDL-C–attributable deaths and DALYs from ischemic heart disease and ischemic stroke, with uncertainty intervals. Results In 2023, elevated LDL-C accounted for 3.6 million deaths (95% uncertainty interval, 2.2-5.4 million; 6.0% of global mortality) and 90.7 million DALYs (95% uncertainty interval, 58.9-123.3 million; 3.2% of DALYs). Although global all-ages rates remained stable, age-standardized death and DALY rates decreased by 45.6% and 39.5%, respectively, since 1990. In 2023, age-standardized LDL-C–attributable DALY rates were highest in Eastern Europe and lowest in high-income Asia-Pacific. One-third of the global LDL-C burden occurred in India and China. Population growth and aging drove the increasing burden, with notable regional disparities in LDL-C exposure and risk-deleted DALY rates shifting toward middle-sociodemographic settings. Conclusions and Relevance Despite declining age-standardized rates, the absolute LDL-C burden has increased since 1990 due to demographic changes and has shifted toward middle-sociodemographic countries. Measurement and surveillance gaps persist. Strengthened prevention, diagnosis, and treatment access strategies are essential to mitigate the health burden of LDL-C.

Background: Noninvasive tests are well validated in metabolic dysfunction–associated steatotic liver disease, but evidence in metabolic dysfunction and alcohol-associated liver disease (MetALD) and alcohol-associated liver disease (ALD) is limited. We evaluated noninvasive test–based clinical care pathways for fibrosis risk stratification in MetALD and ALD. Methods: This is a multinational, multicenter, cross-sectional study across 35 centers in 16 countries (2013–2025), including adults with MetALD or ALD. Fibrosis was assessed by liver biopsy and/or vibration-controlled transient elastography (VCTE). In biopsy-proven participants, we analyzed the sequential pathway (Fibrosis-4 [FIB-4] first, followed by VCTE for indeterminate FIB-4) to identify advanced fibrosis (≥F3). Diagnostic accuracy was assessed using the AUC. Results: Among 893 participants (41.3% MetALD and 58.7% ALD), the median age was 52 [43.0–61.0] years, and 79.4% were male. The estimated prevalence of advanced fibrosis was 45% (32.2% in MetALD and 54.0% in ALD). Participants with MetALD had a more dysmetabolic profile but lower fibrosis stages than ALD. Among biopsy-proven participants, FIB-4 AUC was 0.720, and VCTE AUC was 0.833. FIB-4 performed better in MetALD than ALD (AUC 0.773 vs. 0.540), while VCTE accuracy was similar across groups. In the sequential pathways, the false-negative rate for advanced fibrosis (misclassified as low risk) was 6.8% (4.9% MetALD and 11.3% ALD). Conclusions: In biopsy-proven patients, FIB-4 performance was acceptable in MetALD but substantially weaker in ALD. The standard 2-step pathway performed adequately in MetALD but missed a clinically meaningful proportion of advanced fibrosis in ALD.

Abstract Purpose: Homozygous deletion of the 9p21 locus, containing CDKN2A/B and MTAP [CDKN2A/MTAP deletion (CMdel)], underlies emerging therapeutic strategies, including PRMT5/MAT2A inhibition. The clinicogenomic context and treatment outcome associations of CMdel in non–small cell lung cancer (NSCLC) remain incompletely defined. Experimental Design: We analyzed Tempus Lens records with paired DNA (xT) and RNA (xR) sequencing. After quality filtering (purity ≥40%), 16,947 patients were included in the primary dataset, and 10,996 were included in the validation dataset and stratified by deletion status [CMdel vs. CM wild-type (CMwt)]. IFN status was defined by IFN-kappa (IFNK) expression [log2(TPM + 1)] using the 25th percentile of CMwt as the threshold. Immune composition was inferred by quanTIseq. First-line regimens were grouped as immune checkpoint inhibitor (ICI) monotherapy, ICI chemotherapy, chemotherapy, or tyrosine kinase inhibitors (TKI). Best response and time to next treatment (TTNT) with risk-set adjustment and 18-month truncation were assessed. Results: CMdel occurred in 11.7% (n = 1,991) and 15% (n = 1,676), respectively, and was enriched in mucinous histology, stage IV disease, and never smokers. CMdel was associated with EGFR, TERT, and SMARCA4 alterations, whereas CMwt was associated with RB1, SETD2, and ARID2. More than 50% of CMdel tumors harbored targetable coalterations, enriched in EGFR/ALK/RET and relatively depleted in KRAS. CMdel tumors exhibited lower PD-L1, mutation burden, and immune infiltration. TTNT did not differ by CM status for ICI monotherapy or TKI but was shorter in CMdel for ICI chemotherapy and chemotherapy. Discordant and broad deletions were less common and had weaker TTNT association. IFNK-low expression marked immune-excluded subgroups with worse treatment outcomes. Conclusions: CDKN2A/MTAP loss defines a prevalent, genomically distinct NSCLC subset enriched in actionable drivers. CMdel associates with worse outcomes to ICI/chemotherapy-containing regimens. These findings highlight convergent biology at 9p21 and support biomarker-guided development of PRMT5/MAT2A inhibitors in MTAP-deleted NSCLC.

Automated diagnostic report generation lies at the core of clinical diagnosis and can alleviate clinician shortages. Existing diagnostic report generation methods have two major limitations: they rely on unimodal inputs (e.g., images), ignoring textual biomarkers like medical history, and lack proactive dialogue capabilities to elicit personalized clinical information. To address those issues, we propose a ProActive Multimodal Agentic (PAMA) system, which performs comprehensive disease analysis by examining biomarkers in diverse sources, including multi-view medical images, medical histories, and diagnostic conversations. Built upon a knowledge graph and recommendation-based dialogue architecture, PAMA actively initiates adaptive, multi-turn conversations with patients, which is integrated with visual data for robust and reliable report generation. Specifically, PAMA actively generates adaptive multi-turn questions to collect clinically relevant background information, and then fuses the resulting dialogue context with visual representations for robust diagnostic report generation. We validate our approach on two real-world benchmark datasets, MIMIC-CXR and IU-Xray, through extensive quantitative evaluations and comparisons with state-of-the-art baselines. Furthermore, we conduct user studies to assess the realism and clinical quality of the generated reports. Finally, we present real-world case studies to examine the performance of our system across diverse scenarios, demonstrating its robustness on scalability, complexity, and data variability.

ABSTRACT Angioimmunoblastic T‐cell lymphoma (AITL) is an aggressive peripheral T‐cell lymphoma with poor outcomes, and the role of autologous stem cell transplantation (ASCT) as consolidation after frontline therapy remains controversial. We conducted a retrospective cohort study using the National Cancer Database, including adults diagnosed with AITL between 2004 and 2020 who received frontline systemic therapy. Patients were categorized as chemotherapy alone (Chemo) or chemotherapy followed by ASCT (Chemo + ASCT). To mitigate immortal time bias, prespecified landmark analyses were performed, with a 6‐month landmark analysis as the primary approach and a 9‐month landmark analysis as a sensitivity analysis. Multivariable Cox regression and propensity score weighting using inverse probability of treatment weighting for the average treatment effect were used to address measured confounding. Among 3996 patients receiving systemic therapy, 686 (17.2%) underwent ASCT. In the 6‐month landmark analysis, ASCT was associated with improved overall survival (HR, 0.53; 95% CI, 0.45–0.62; p < 0.001). Findings were consistent in the 9‐month landmark analysis (HR, 0.58; 95% CI, 0.49–0.69; p < 0.001) and IPTW‐adjusted model (HR, 0.51; 95% CI, 0.44–0.60; p < 0.001). In this large real‐world cohort, ASCT consolidation was consistently associated with improved survival in patients with AITL. Although treatment‐response data were unavailable and residual confounding cannot be excluded, these findings provide supportive real‐world evidence and warrant prospective studies with treatment‐timing and response data to better define patient selection.