Li, Debiao

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.

ABSTRACT Background The brain–heart axis is central to vascular health, yet no imaging biomarkers capture integrated dysfunction across neural and coronary microvascular networks. Although coronary microvascular dysfunction links to cognitive decline, neural correlates connecting cerebral efficiency with coronary physiology remain unclear. Objectives To determine whether the Unified Structural and Functional Connectivity (USFC)—a multimodal magnetic resonance imaging (MRI) “traffic map” of brain network efficiency—predicts coronary endothelial function and cognition in women with ischemia and no obstructive coronary artery disease (INOCA). Methods Thirty‐three women with suspected INOCA from the Women's Ischemia Syndrome Evaluation (WISE) study (NCT03876223) underwent invasive coronary function testing, cardiac MRI, cognitive evaluation, and multimodal brain MRI. USFC, structural connectivity (SC), and functional connectivity (FC) were computed for predefined 10 backbone pathways. Support vector regression and logistic classification assessed predictive performance. Results USFC explained 16%–20% more variance in coronary endothelial function, myocardial perfusion reserve, and cognition than SC or FC alone ( p < 0.05). Connectivity between the left caudate–superior medial orbital gyrus and right calcarine–inferior occipital gyrus emerged as robust predictors of crystallized cognition ( r = –0.78, p FDR < 0.05) and coronary endothelial function ( r = 0.70, p FDR < 0.05), respectively. USFC also best discriminated low versus high coronary blood flow (area under the ROC curve [AUC]: USFC 0.622 vs. SC 0.349 and FC 0.425; p < 0.05). Conclusions USFC identifies neuro–cardiac pathways linking cerebral efficiency with coronary endothelial function. These results introduce a sensitive biomarker of systemic vulnerability, highlighting occipital and frontostriatal pathways as shared substrates of dysfunction. USFC offers a mechanistic framework for detecting vascular risk across metabolically demanding tissues. Trial Registration ClinicalTrials.gov identifier: NCT03876223

AbstractBackgroundWomen with suspected coronary microvascular dysfunction (CMD) may be at higher risk of experiencing cognitive decline due to cerebral small vessel disease, a known contributor to Alzheimer’s disease and related dementias (ADRD). A potential underlying mechanism that could accelerate this cognitive decline is the accumulation of brain tissue iron, which has been previously linked to changes in brain function potentially caused by oxidative stress and cell death. Therefore, we aim to elucidate whether a similar mechanism could affect women with suspected CMD by investigating the potential role of iron deposition on the brain’s functional organization and its effect on cognition using advanced magnetic resonance imaging (MRI).Material and MethodsTwenty‐seven women with suspected CMD [Age; median (range) = 54 (29‐76)], drawn from ongoing cohorts (3R01HL146158‐04S1,3U54AG065141‐04S1), underwent a 3T MRI protocol, including submillimeter T2* 3‐dimensional echo‐planar‐imaging for assessing iron deposition with high‐resolution quantitative susceptibility mapping (QSM) and resting‐state fMRI (rs‐fMRI). Iron content was quantified by total‐generalized‐variation based QSM analysis. Functional integrity was determined via graph theoretical approach (i.e., nodal degree). Cognitive assessment was also performed using the NIH Toolbox. Mediation analysis was conducted using Python Statsmodels.ResultsMost of the women with suspected CMD showed lower performance in processing speed, working memory, executive function, and attention (Figure 1A). We found a significant association between elevated iron levels in paracentral gyrus and lower functional connectivity in left hippocampus (p=0.005, adjusted‐r2=0.19) (Figure 1B). Elevated iron level in the paracentral gyrus showed an impact on cognitive performance in the domains of executive function and attention (p<0.0001, coefficient=531.09) as well as language functions and crystalized abilities (p=0.036, coefficient=362.45), mediated by functional connectivity in the left hippocampus (indirect effect on executive function / language: p = 0.034 / 0.038, coefficient = ‐254.54 /‐ 213.06) (Figure 1C).ConclusionsOur results suggest that changes in hippocampal functional organization are associated with cortical iron deposition and mediate its impact on cognitive performances. These changes may increase the risk of cognitive decline/ADRD or in women with suspected CMD. Future research in a larger cohort with a longitudinal design is necessary to validate and expand upon these findings.

AbstractBackgroundWomen with suspected coronary microvascular dysfunction (CMD) may be at higher risk of experiencing cognitive decline due to cerebral small vessel disease, a known contributor to Alzheimer’s disease and related dementias (ADRD). A potential underlying mechanism that could accelerate this cognitive decline is the accumulation of brain tissue iron, which has been previously linked to changes in brain function potentially caused by oxidative stress and cell death. Therefore, we aim to elucidate whether a similar mechanism could affect women with suspected CMD by investigating the potential role of iron deposition on the brain’s functional organization and its effect on cognition using advanced magnetic resonance imaging (MRI).Material and MethodsTwenty‐seven women with suspected CMD [Age; median (range) = 54 (29‐76)], drawn from ongoing cohorts (3R01HL146158‐04S1,3U54AG065141‐04S1), underwent a 3T MRI protocol, including submillimeter T2* 3‐dimensional echo‐planar‐imaging for assessing iron deposition with high‐resolution quantitative susceptibility mapping (QSM) and resting‐state fMRI (rs‐fMRI). Iron content was quantified by total‐generalized‐variation based QSM analysis. Functional integrity was determined via graph theoretical approach (i.e., nodal degree). Cognitive assessment was also performed using the NIH Toolbox. Mediation analysis was conducted using Python Statsmodels.ResultsMost of the women with suspected CMD showed lower performance in processing speed, working memory, executive function, and attention (Figure‐1A). We found a significant association between elevated iron levels in paracentral gyrus and lower functional connectivity in left hippocampus (p = 0.005, adjusted‐r2 = 0.19) (Figure‐1B). Elevated iron level in the paracentral gyrus showed an impact on cognitive performance in the domains of executive function and attention (p<0.0001, coefficient = 531.09) as well as language functions and crystalized abilities (p = 0.036, coefficient = 362.45), mediated by functional connectivity in the left hippocampus (indirect effect on executive function / language: p = 0.034 / 0.038, coefficient = ‐254.54 /‐ 213.06) (Figure‐1C).ConclusionsOur results suggest that changes in hippocampal functional organization are associated with cortical iron deposition and mediate its impact on cognitive performances. These changes may increase the risk of cognitive decline/ADRD or in women with suspected CMD. Future research in a larger cohort with a longitudinal design is necessary to validate and expand upon these findings.

Background: Preclinical studies and pilot patient studies have shown that chronic infarctions can be detected and characterized from cardiac magnetic resonance without gadolinium-based contrast agents using native-T1 maps at 3T. We aimed to investigate the diagnostic capacity of this approach for characterizing chronic myocardial infarctions (MIs) in a multi-center setting. Methods: Patients with a prior MI (n=105) were recruited at 3 different medical centers and were imaged with native-T1 mapping and late gadolinium enhancement (LGE) at 3T. Infarct location, size, and transmurality were determined from native-T1 maps and LGE. Sensitivity, specificity, receiver-operating characteristic metrics, and inter- and intraobserver variabilities were assessed relative to LGE. Results: Across all subjects, T1 of MI territory was 1621±110 ms, and remote territory was 1225±75 ms. Sensitivity, specificity, and area under curve for detecting MI location based on native-T1 mapping relative to LGE were 88%, 92%, and 0.93, respectively. Native-T1 maps were not different for measuring infarct size (native-T1 maps: 12.1±7.5%; LGE: 11.8±7.2%, P =0.82) and were in agreement with LGE ( R 2 =0.92, bias, 0.09±2.6%). Corresponding inter- and intraobserver assessments were also highly correlated (interobserver: R 2 =0.90, bias, 0.18±2.4%; and intraobserver: R 2 =0.91, bias, 0.28±2.1%). Native T1 maps were not different for measuring MI transmurality (native-T1 maps: 49.1±15.8%; LGE: 47.2±19.0%, P =0.56) and showed agreement ( R 2 =0.71; bias, 1.32±10.2%). Corresponding inter- and intraobserver assessments were also in agreement (interobserver: R 2 =0.81, bias, 0.1±9.4%; and intraobserver: R 2 =0.91, bias, 0.28±2.1%, respectively). While the overall accuracy for detecting MI with native-T1 maps at 3T was high, logistic regression analysis showed that MI location was a prominent confounder. Conclusions: Native-T1 mapping can be used to image chronic MI with high degree of accuracy, and as such, it is a viable alternative for scar imaging in patients with chronic MI who are contraindicated for LGE. Technical advancements may be needed to overcome the imaging confounders that currently limit native-T1 mapping from reaching equivalent detection levels as LGE.

Abstract Background Recent genome wide association studies (GWAS) of Hodgkin lymphoma (HL) have identified several associations at both HLA and non-HLA loci. However, much of HL heritability remains unexplained. Methods To identify novel risk loci, we performed a meta-analysis of 3 HL GWAS including a total of 1,810 cases and 7,879 controls. Results were replicated in an independent set of 1,163 cases and 2,580 controls, for a total of 3,097 and 11,097 cases and controls combined, respectively. participants in discovery and replication stages were of European descent. quality control and imputation we conducted a meta-analysis addressing 1,004,829 variants (λ= 1.10, λ1000= 1.03). Associations between SNP genotypes and HL risk were evaluated under a log-additive model of inheritance adjusting for sex, study center and significant principal components to control for population stratification. We performed an analysis with all HL cases and then conducted stratified analyses by histological subtype (classical, nodular sclerosis and mixed cellularity), age at diagnosis (nodular sclerosis among those diagnosed at 15- 35 years in all studies, and those diagnosed at 35 and older years in the European Study only) and EBV tumor status (negative and positive). We then used a bioinformatic approach (FunciSNP) to identify potential functional variants associated with HD risk correlated with risk loci of interest. We extracted publically available ENCODE data on biofeatures to identify potential functional motifs associated with the index SNP or correlated SNPs. Finally, we measured expression levels of the two alternative mRNA transcripts in lymphoblastoid cell lines (LCLs) from 49 post-therapy HL patients and 25 unaffected controls. RT-PCR was carried out in triplicate. Relative expression levels were calculated relative to TBP as housekeeping gene. Linear models were used to assess correlation between genotype and TCF3expression levels. Results The meta-analysis identified a novel susceptibility variant at chromosome 19p13.3 (rs1860661) associated with HL risk (Odds Ratio [OR]= 0.78, P=2.0*10-8, I2=0%). variant is located in intron 2 of TCF3 (also known as E2A), a regulator of B- and T-cell lineage commitment. was also significantly associated with HL (OR= 0.85, P=0.002) in the replication series of 1,281 cases and 3,218 controls. the combined analysis consisting of the discovery and replication sets, rs1860661was strongly associated with HL (OR=0.81,=3.5*10-10), with no evidence of heterogeneity between contributing studies (Phom=0.41, I2=0%). The number of G alleles defined by rs1860661 was significantly associated with a reduced risk of each HL subtype except EBV positive HL. rs1860661 and two correlated SNPs, rs10413888 (r2=0.90) and rs8103453 (r2=0.89) identified by FunciSNP analysis map in or near marks of open chromatin and in DNAse hypersensitivity sites in TCF3 in CD20+ B cell lines., the protective minor alleles of these SNPs as defined by the G-G-G haplotype map to the binding sites for ZBTB7a (rs10413888 and rs1860661) and (rs8103453) transcription factors, likely improving the binding efficiency to the sites which may result in increased transcription rates of TCF3. TCF3 is encoded by two alternative transcripts (E12 and E47). Higher expression levels of TCF3-E47, whose transcription start site is located close to rs1860661, was associated with the rs1860661-G allele in controls (P=0.02), but not in HL patients (P=0.22). Conclusion/Discussion TCF3 is essential for the commitment of lymphoid progenitors to both B-cell and T-cell lineage development. A molecular and phenotypic hallmark of classical HL is the loss of the B-cell phenotype in HRS cells, including lack of demonstrable B-cell receptor and most B-cell specific markers such as CD19 or CD20. HRS cells have a low level of TCF3, particularly homodimers of the isoform E47, due to expression of the ABF-1 and ID2 inhibitors that bind to TCF3. Thus, higher TCF3 levels in HRS precursor cells may lead to enhanced retention of the B cell phenotype, thereby conferring a protective effect. These data suggest a link between the 19p13.3 locus including TCF3 and HL risk, indicating that TCF3 could be relevant to HL etiology and pathogenesis. Disclosures: Link: Genentech: Consultancy; Millenium: Consultancy; Pharmacyclics: Consultancy; Spectrum: Consultancy.