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PubMed2026

Association of time-averaged systemic immune-inflammation indices with in-hospital mortality after intracerebral hemorrhage: a retrospective study.

BACKGROUND: Systemic inflammation plays a central role in secondary brain injury following intracerebral hemorrhage (ICH). Although inflammatory indices such as the neutrophil-to-lymphocyte ratio (NLR), systemic immune-inflammation index (SII), and systemic inflammation response index (SIRI) are linked to poor outcomes, their associations with mortality are commonly assumed to be linear, potentially overlooking nonlinear patterns where mortality risk rises steeply at higher levels. METHODS: We conducted a retrospective study using the MIMIC-IV database, including 440 patients with non-traumatic ICH who were alive and remained in the ICU for at least 72 h after admission. Mean NLR, SII, and SIRI were calculated from measurements obtained during this period. Multivariable logistic regression and restricted cubic spline (RCS) analyses were applied to assess their independent and nonlinear associations with in-hospital mortality. Model discrimination and calibration were internally validated using 1,000 bootstrap resamples. RESULTS: The in-hospital mortality rate was 26.1%. After multivariable adjustment, NLR and SIRI remained independently associated with mortality. Patients in the highest SIRI quartile had the highest risk of death (aOR = 5.12; 95% CI: 2.57-12.24; p < 0.001). RCS analysis revealed a significant nonlinear association between SIRI and mortality (p-nonlinearity < 0.05), showing a steep risk increase at higher SIRI levels. Adding SIRI to the base model provided a modest improvement in discrimination (AUC 0.762 to 0.785, p = 0.045) and significantly improved risk reclassification (cNRI = 0.4778, p < 0.001; IDI = 0.0240, p = 0.0151). CONCLUSIONS: Among patients with ICH who met the 72-hour eligibility criterion, higher 72-hour average SIRI was independently associated with in-hospital mortality. As a time-averaged measure, SIRI should be interpreted as a dynamic marker integrating the initial inflammatory state and the early clinical course rather than as a purely baseline prognostic factor. Although adding SIRI to the base model modestly improved discrimination and risk reclassification, it should be considered a candidate prognostic marker requiring external validation before clinical application.

Humans↗

PubMed2026

Longitudinal variability of lipoprotein(a) in youth-onset type 1 diabetes: implications for cardiovascular risk stratification.

BACKGROUND: Lipoprotein(a) [Lp(a)] is a genetically determined and independent cardiovascular risk factor, traditionally considered stable across the lifespan, supporting a single lifetime measurement strategy. However, its longitudinal behaviour during childhood and adolescence remains poorly characterised, particularly in individuals with type 1 diabetes who face a markedly increased lifetime risk of coronary artery disease. We therefore aimed to characterise intra- and inter-individual trajectories of Lp(a) in a paediatric type 1 diabetes cohort and to assess the implications of Lp(a) variability for cardiovascular risk classification. METHODS: We conducted a retrospective single-centre cohort study of children and adolescents with type 1 diabetes attending Geneva University Hospitals between 2012 and 2023. Annual fasting Lp(a) concentrations were analysed longitudinally. Variability was assessed in participants with ≥ 2 measurements. Clinically relevant thresholds were used to evaluate cardiovascular risk reclassification. Paired Wilcoxon tests, Pearson and Kendall correlations, and Holm-adjusted p-values (P < 0.05) were applied. Analyses were conducted in R. RESULTS: A total of 286 participants contributed 1403 Lp(a) measurements, with observation periods varying across individuals (median 6.2 years, IQR 2.9-9.6) and between 1 and 13 measurements per participant. At baseline, 26% had elevated Lp(a) (≥ 300 mg/l). Among participants with serial measurements, 32% showed intraindividual fluctuations exceeding 50% of their individual maximum value. Reclassification across the 300 mg/l cardiovascular risk threshold occurred in 11.9% of participants. Lp(a) concentrations peaked between ages 10 and 13 years and declined thereafter. Modest seasonal variation was observed, with higher concentrations in autumn and winter (P < 0.05). CONCLUSIONS: In youth with type 1 diabetes, Lp(a) is not as stable as previously assumed, exhibiting clinically relevant variability over time. These findings challenge the current paradigm of a single lifetime Lp(a) measurement and suggest that repeated assessment, particularly during adolescence, may improve early cardiovascular risk stratification.

Humans↗

PubMed2026

Proteomic signatures for sudden cardiac death and related intermediate phenotypes.

BACKGROUND: Novel markers for sudden cardiac death (SCD) are needed. OBJECTIVE: This study aimed to explore whether a protein risk score derived from a large-scale proteomics dataset improves risk prediction of SCD in the general population. METHODS: A total of 52,705 individuals with 1459 unique plasma protein measurements were included from the UK Biobank Pharma Proteomics Project. A protein risk score was developed using lasso-penalized Cox regression on 40,722 participants enrolled at the English centers and validated on 11,983 participants enrolled at the remaining centers. RESULTS: The protein risk score formula developed from the derivation set comprised 64 unique plasma proteins including latent-transforming growth factor beta-binding protein 2, protein tyrosine phosphatase receptor sigma, and spondin-1. In the test set, a per standard deviation increase in protein risk score was associated with a hazard ratio of 2.60 (95% confidence interval [CI] 2.12-3.18) for SCD. Adding a protein risk score to SCD clinical risk factors resulted in a concordance index increase of 0.063 (95% CI 0.037-0.105) for SCD. For ventricular arrhythmia-mediated SCDs, an increase in concordance index when a protein risk score was added to SCD clinical risk factors was 0.070 (95% CI 0.010-0.188). A protein risk score added to SCD clinical risk factors resulted in a risk reclassification of 16.9% (95% CI 9.0-24.7) at a 10-year risk threshold of 5%. A protein risk score was significantly associated with intermediate phenotypes of SCD including corrected QT prolongation, an increase in left ventricular mean myocardial thickness, and a decrease in left ventricular global longitudinal strain. CONCLUSION: A protein risk score derived from a single plasma sample significantly improved risk prediction of SCD and related intermediate phenotypes.

Humans↗

PubMed2025

Automated Deep Learning-Based Detection of Early Atherosclerotic Plaques in Carotid Ultrasound Imaging.

BACKGROUND: Carotid plaque presence is associated with cardiovascular risk, even among asymptomatic individuals. While deep learning has shown promise for carotid plaque phenotyping in patients with advanced atherosclerosis, its application in population-based settings of asymptomatic individuals remains unexplored. METHODS: We developed a YOLOv8-based model for plaque detection using carotid ultrasound images from 19,499 participants of the population-based UK Biobank (UKB) and fine-tuned it for external validation in the BiDirect study (N = 2,105). Cox regression was used to estimate the impact of plaque presence and count on major cardiovascular events. To explore the genetic architecture of carotid atherosclerosis, we conducted a genome-wide association study (GWAS) meta-analysis of the UKB and CHARGE cohorts. Mendelian randomization (MR) assessed the effect of genetic predisposition to vascular risk factors on carotid atherosclerosis. RESULTS: Our model demonstrated high performance with accuracy, sensitivity, and specificity exceeding 85%, enabling identification of carotid plaques in 45% of the UKB population (aged 47-83 years). In the external BiDirect cohort, a fine-tuned model achieved 86% accuracy, 78% sensitivity, and 90% specificity. Plaque presence and count were associated with risk of major adverse cardiovascular events (MACE) over a follow-up of up to seven years, improving risk reclassification beyond the Pooled Cohort Equations. A GWAS meta-analysis of carotid plaques uncovered two novel genomic loci, with downstream analyses implicating targets of investigational drugs in advanced clinical development. Observational and MR analyses showed associations between smoking, LDL cholesterol, hypertension, and odds of carotid atherosclerosis. CONCLUSIONS: Our model offers a scalable solution for early carotid plaque detection, potentially enabling automated screening in asymptomatic individuals and improving plaque phenotyping in population-based cohorts. This approach could advance large-scale atherosclerosis research.

atherosclerosis↗

PubMed2025

Cardiac Troponins and Cardiovascular Disease Risk Prediction: An Individual-Participant-Data Meta-Analysis.

BACKGROUND: The extent to which high-sensitivity cardiac troponin can predict cardiovascular disease (CVD) is uncertain. OBJECTIVES: We aimed to quantify the potential advantage of adding information on cardiac troponins to conventional risk factors in the prevention of CVD. METHODS: We meta-analyzed individual-participant data from 15 cohorts, comprising 62,150 participants without prior CVD. We calculated HRs, measures of risk discrimination, and reclassification after adding cardiac troponin T (cTnT) or I (cTnI) to conventional risk factors. The primary outcome was first-onset CVD (ie, coronary heart disease or stroke). We then modeled the implications of initiating statin therapy using incidence rates from 2.1 million individuals from the United Kingdom. RESULTS: Among participants with cTnT or cTnI measurements, 8,133 and 3,749 incident CVD events occurred during a median follow-up of 11.8 and 9.8 years, respectively. HRs for CVD per 1-SD higher concentration were 1.31 (95% CI: 1.25-1.37) for cTnT and 1.26 (95% CI: 1.19-1.33) for cTnI. Addition of cTnT or cTnI to conventional risk factors was associated with C-index increases of 0.015 (95% CI: 0.012-0.018) and 0.012 (95% CI: 0.009-0.015) and continuous net reclassification improvements of 6% and 5% in cases and 22% and 17% in noncases. One additional CVD event would be prevented for every 408 and 473 individuals screened based on statin therapy in those whose CVD risk is reclassified from intermediate to high risk after cTnT or cTnI measurement, respectively. CONCLUSIONS: Measurement of cardiac troponin results in a modest improvement in the prediction of first-onset CVD that may translate into population health benefits if used at scale.

Humans↗

PubMed2025

A Prospective Validation of the Decipher Genomic Classifier in Men With Early Localized Prostate Cancer: The VANDAAM Study.

BACKGROUND: The emergence of genomic precision oncology has advanced personalized care for some patients with prostate cancer (PCa), while threatening to widen existing disparities due to the historically low recruitment of African American men (AAM), who have the highest disease burden. Here, we report the first prospective validation of a genomic classifier (GC) to predict rapid-onset biochemical recurrence (BCR) in AAM. METHODS: Between 2016 and 2021, this multicenter prospective validation study recruited 243 patients with low- or intermediate-risk PCa who received treatment for their disease. Patients were recruited on a 1:1 basis (AAM:White) and matched by CAPRA score. Patients who elected active surveillance were ineligible for participation. Decipher GC testing was ordered for all patients using their biopsy and/or radical prostatectomy (RP) tumor tissue. The primary outcome was to determine whether the GC could predict 2-year BCR rates-used as a surrogate for disease aggressiveness-following standard treatment. The secondary outcome evaluated the concordance between biopsy- and RP-derived GC risk scores for treatment recommendations. RESULTS: The final analytical cohort included 226 matched patients with genomic information, and 207 evaluable cases (104 AAM, 103 White) with both genomic and complete clinical outcome data. Overall, a high genomic-risk GC score was associated with a 5.25-fold increase in the odds of rapid-onset 2-year BCR compared with the low-risk group (odds ratio, 5.25 [95% CI, 1.27-21.66]; P=.021). In a subset of the surgical cohort (n=74), biopsy- and RP-derived GC scores exhibited a 77% concordance rate, defined as no reclassification in GC risk-based categories. CONCLUSIONS: This study represents the first prospective validation of GC performance in predicting early 2-year BCR in both AAM and White men. The findings provide strong evidence supporting the integration of the GC into clinical practice guidelines to improve risk stratification and management of AAM with early-stage PCa. CLINICALTRIALS: gov identifier: NCT02723734.

Aged↗

PubMed2025

Optical Genome Mapping in Myelodysplastic Syndromes: Clinical Value and Limitations Derived From a Cohort of 236 Patients.

Identification of cytogenetic abnormalities is critical for the classification and risk stratification of myelodysplastic syndromes (MDS). Optical genome mapping (OGM) is an emerging cytogenomic platform that enables high-resolution genome-wide cytogenetic analysis. We analyzed bone marrow specimens of 236 MDS patients, 149 newly diagnosed and 87 with relapsed/refractory disease, using OGM, conventional karyotyping, and next-generation sequencing analysis. OGM and karyotyping showed concordant results in 68% of cases, including 34% with normal findings by both assays. OGM provided additional information in 27% of patients. Common abnormalities detected exclusively by OGM included chromoanagenesis (n = 33), KMT2A partial tandem duplication (n = 7), and MECOM rearrangement (n = 4). These OGM findings led to disease reclassification and/or changes in risk stratification in 14 patients (9.4%) with newly diagnosed MDS. In contrast, OGM failed to detect small clones or subclones in 5% of patients, resulting in risk group changes in 2% of newly diagnosed MDS patients. We conclude that OGM enhances the cytogenetic assessment of MDS in approximately 25% of patients and leads to a change in disease classification and/or risk stratification in approximately 10% of patients. However, low sensitivity for detecting small clones or subclones remains a limitation of OGM.

Humans↗

PubMed2026

Additive value of polygenic risk and family history for coronary heart disease risk stratification in two diverse US cohorts.

Whether polygenic risk, monogenic familial hypercholesterolemia (FH), and family history (FamHx) are additively informative for coronary heart disease (CHD) risk prediction across self-identified race/ethnicity (SIRE) groups has not been established. In two diverse cohorts-Electronic Medical Records and Genomics (eMERGE) phase IV (eIV; n = 19,348) and All of Us (AoU; n = 239,645)-we quantified the associations of a polygenic risk score (PRSCHD), pathogenic/likely pathogenic variants in genes associated with FH, and FamHx with CHD and evaluated their incremental value when added to the pooled cohort equations (PCEs). CHD was defined as myocardial infarction, unstable angina, or coronary revascularization. We modeled associations with multivariable logistic regression (prevalent CHD in eIV) and Cox proportional hazards (incident CHD in AoU) and characterized predictive performance with the c-statistic and reclassification and decision-curve net benefits across actionable 10-year risk thresholds. The effects of PRSCHD and FamHx were independent and additive in both cohorts and consistent across White, Black, and Latino SIRE groups. In eIV, adding PRSCHD and FamHx to the PCE increased the c-statistic for prevalent CHD from 0.719 to 0.753 (p-diff = 9.1 × 10-3) and reclassified 18.8% of participants at the 7.5% 10-year threshold, yielding approximately 4 additional true-positive CHD identifications per 1,000 screened. Net benefit gains were observed between the 7.5% and 10% thresholds across all three SIRE groups. In conclusion, PRSCHD and FamHx were independently and additively associated with CHD across major SIRE groups in two diverse cohorts in the United States (US), motivating the addition of these factors to clinical risk algorithms.

Humans↗

PubMed2026

Proteomic Profiling Captures Residual Cardiovascular Risk Beyond the PREVENT Model in Individuals With Cardiovascular-Kidney-Metabolic Syndrome Stages 2-3.

BACKGROUND: Cardiovascular-kidney-metabolic (CKM) syndrome reflects complex pathobiological interactions among metabolic disorders, kidney injury, and cardiovascular disease (CVD). Stages 2 and 3 represent critical phases of disease progression characterised by high pathological heterogeneity. This study aimed to develop a CVD protein risk score (PRS) for this population and evaluate its incremental predictive value over the PREVENT model. METHODS: This study included 24 017 participants with CKM Stages 2-3 from the UK Biobank. Using 2923 plasma proteins measured via the Olink platform, a PRS was developed in a training set (n = 19 218) using the LASSO method. In the validation set (n = 4799), the incremental predictive performance of this score over the PREVENT model was assessed using Harrell's C-statistic, net reclassification improvement (NRI) and integrated discrimination improvement (IDI). RESULTS: A risk score comprising 63 proteins was constructed, primarily reflecting inflammation, kidney injury and matrix remodelling. Key proteins included growth differentiation factor 15 (GDF15), hepatitis A virus cellular receptor 1 (HAVCR1), matrix metallopeptidase 12 (MMP12) and NT-proBNP. In the validation set, after adjusting for PREVENT risk factors, individuals in the high PRS group had a 2.56-fold higher risk of CVD compared to those in the low score group (HR: 2.56, 95% CI: 1.96-3.37). Integrating the score into the PREVENT model improved the C-statistic by 0.034 (0.672-0.706) and achieved a 10-year NRI of 15.8% (95% CI: 9.5%-20.9%) and an IDI of 2.2% (95% CI: 1.3%-3.3%). CONCLUSION: Combining the PREVENT model with the PRS developed in this study enhances the prediction of future CVD events in the CKM Stages 2-3 population. This approach facilitates the capture of residual risk and supports precision risk stratification and management for this high-risk group.

Humans↗

PubMed2025

Proteomics-enabled learning machine algorithms enhance the prediction of cardiovascular diseases in patients with type 2 diabetes mellitus.

BACKGROUND AND AIMS: Estimating the risk of cardiovascular disease (CVD) complications in type 2 diabetes mellitus (T2DM) patients is critical in the medical decision-making process. This study aimed to use a machine learning technique combined with proteomics to develop personalized models for predicting CVD in patients with T2DM. METHODS AND RESULTS: In total, 874 patients with T2DM and 2,920 Olink proteins obtained from the UK Biobank were used in this study. Proteins were screened using Cox regression and LASSO regression. A basic model containing clinical features and a full model combining proteome and clinical features were constructed using the random survival forest algorithm. The area under the receiver operating characteristic (ROC) curve (AUC) was used to evaluate the predictive performance of the models and compare them with other CVD predictive models. Compared with the basic model, the full model performed better in predicting CVD, with time-dependent AUCs of 0.81 (3 years), 0.74 (5 years) and 0.74 (10 years) (0.77, 0.69 and 0.67). We calculated the risk scores of the Framingham, ASCVD and Score2-Diabetes models. The results revealed that the prediction performance of the full model was also better than that of the abovementioned models. In terms of differentiation accuracy, the results of the net reclassification improvement index and integrated discrimination improvement index showed that the full model can identify high-risk individuals more accurately (accuracy rate: 79% vs. 69%). CONCLUSIONS: Proteomics can be used to predict cardiovascular complications in diabetic patients. It is also necessary to consider the applicability of the model due to the limitations of the sample size and the constraints of proteomics in clinical applications.

Humans↗

PubMed1979

Mutational mosaicism and genetic counseling in retinoblastoma.

Mutations may arise throughout an organism's life cycle. Typically, sporadic meiotic mutations give rise to individuals with all their germinal and somatic cells bearing the mutant gene. These mutations may be amorphs (with full penetrance and expressivity) or hypomorphs (with reduced penetrance and expressivity). Mutational mosaicism, however, involves the origin of mutations occurring during mitosis, whether in the parent at some stage prior to reproductive maturity or in the offspring at some time following fertilization. The phenotypic expression and transmission of these new mutations are dependent on the proportion of cells bearing the mutant gene as well as the location of these cells in somatic and/or germinal tissues. Mutational mosaicism was used as a developmental model to analyze 1,500 sporadic and 179 familial cases of retinoblastoma from the world literature. This model provided an interpretation for the origin, onset, and transmissibility of the sporadic unilateral retinoblastoma cases, which represent over 60% of all retinoblastoma patients. The model also permits a reclassification of all transmissible types of retinoblastoma; based on this classification, more accurate risk figures for genetic counseling can be offered. In addition, mutational mosaicism can be extended as a model to other autosomal dominant and X-linked mutations.

Animals↗

PubMed2026

Comprehensive assessment of vasospastic angina using coronary computed tomography angiography: synergistic value of the presence of myocardial bridge, perivascular inflammation, and myocardial extracellular volume fraction.

AIMS: Coronary computed tomography angiography (CCTA) has evolved beyond anatomical assessment to include sophisticated tissue characterization. While an elevated perivascular fat attenuation index around the right coronary artery (FAI-RCA) is known to reflect coronary inflammation in vasospastic angina (VSA), recurrent vasospasms may also induce chronic subclinical myocardial injury and subsequent remodelling, potentially associated with an increased myocardial extracellular volume fraction (ECV). However, the diagnostic integration of ECV and FAI-RCA for identifying VSA in patients with angina with non-obstructive coronary arteries (ANOCA) remains to be elucidated. METHODS AND RESULTS: This study included consecutive ANOCA patients who underwent CCTA with a dedicated ECV protocol, followed by an invasive spasm provocation test. Comprehensive CCTA analysis quantified both FAI-RCA and the transmural ECV gradient (the difference between endocardial and epicardial ECV: ECVEndo - ECVEpi). Of the 100 patients analysed (mean age: 65.3 ± 11.8 years; 55% male), 27 were diagnosed with VSA. Multivariable logistic regression analysis identified transmural ECV gradient [odds ratio (OR): 1.12, 95% confidence interval (CI): 1.01-1.25], presence of myocardial bridging (MB) (OR: 3.49, 95% CI: 1.25-9.74), and high FAI-RCA (> -70.95 Hounsfield units [HU]) (OR: 5.79, 95% CI: 2.06-16.30) as significant independent predictors of VSA (all P < 0.05). Notably, the integration of transmural ECV gradient provided incremental diagnostic value beyond FAI-RCA and MB, as assessed by the Net Reclassification Improvement and Integrated Discrimination Improvement. CONCLUSION: A multi-parametric CCTA approach potentially identifies patients at high risk for VSA. The significant association of the transmural ECV gradient with VSA suggests that myocardial remodelling imaging provides a novel diagnostic window into the cumulative myocardial impact of vasospasm, independent of active adipose tissue inflammation and the presence of MB.

Humans↗

PubMed2026

Large-Scale Plasma Proteomics Enhances Prediction of Liver-Related Events Among Individuals With Prediabetes and Type 2 Diabetes: A Prospective Cohort Study in the UK Biobank.

OBJECTIVE: To develop a protein risk score (ProRS) for predicting liver-related events (LREs) in patients with diabetes and compare its predictive performance with the Fibrosis-4 Index (FIB-4) and an established polygenic risk score. RESEARCH DESIGN AND METHODS: This prospective cohort study included 13 516 individuals with prediabetes and type 2 diabetes (T2D) from the UK Biobank. Cox proportional hazards models and LASSO regression were applied to identify proteins associated with incident LREs and construct the ProRS. Predictive performance was assessed using Harrell's C-index, time-dependent area under the receiver operating characteristic curve, net reclassification improvement and integrated discrimination improvement. RESULTS: Over a median follow-up of 13.5 years, 171 (1.3%) incident LREs occurred. We identified 877 proteins associated with LRE risk, primarily enriched in inflammatory signalling, extracellular matrix remodelling and complement/coagulation cascades. In the training set, we developed a 24-protein ProRS (C-index, 0.842; 95% CI 0.797-0.884) that stratified individuals into low-, medium- and high-risk groups, with 10-year cumulative incidences of LREs of 0.2%, 1.2% and 14.2%, respectively. Compared with the low-risk group, the hazard ratio for LREs was 57.1 (95% CI 31.9-102) in the high-risk group. In the internal validation set, the ProRS model (C-index, 0.876; 95% CI 0.827-0.920) accurately predicted both short- and long-term LREs and outperformed FIB-4 index (C-index, 0.733; 95% CI 0.657-0.807) and polygenic risk score (C-index, 0.636; 95% CI 0.564-0.706). CONCLUSIONS: The protein risk score demonstrated superior performance compared with the FIB-4 index and the polygenic risk score in predicting incident LREs among individuals with prediabetes and T2D. The score allows stratification of individuals according to liver-related risk, though external validation in multi-ethnic cohorts is warranted.

Humans↗

PubMed2025

Contributions of Common, Rare, and Somatic Genetic Variants to Incidence of Atrial Fibrillation.

IMPORTANCE: Atrial fibrillation (AF) has a complex genetic architecture involving common, rare, and somatic variants. The association between these components requires further investigation. OBJECTIVE: To examine the individual and combined contributions of polygenic, monogenic, and somatic genetic variants to AF incidence, and develop an integrated genomic model (IGM-AF) for improved risk prediction. DESIGN, SETTING, AND PARTICIPANTS: This cohort study used whole-genome sequence data from participants of the UK Biobank, with follow-up for AF events through hospital records, death registries, and self-report. The UK Biobank recruited participants aged 40 to 69 years in the UK between 2006 and 2010. Study data were analyzed from August 2022 to November 2024. EXPOSURES: IGM-AF comprising an AF polygenic risk score (PRS), a composite rare variant gene set (AFgeneset), and somatic variants associated with clonal hematopoiesis of indeterminate potential (CHIP). Clinical AF risk was estimated using the Cohorts for Heart and Aging Research in Genomic Epidemiology AF (CHARGE-AF) score. MAIN OUTCOMES AND MEASURES: The primary outcome was hazard ratios (HRs) for 5-year incident AF attributable to PRS, AFgeneset, CHIP, and their interactions. The predictive performance of IGM-AF and its components was quantified using HRs, C statistics, and reclassification indices. RESULTS: A total of 416 085 individuals (mean [SD] age, 56.6 [8.0] years; 224 642 female [54.0%]) with 30 797 AF cases were included. The PRS (HR per 1 SD, 1.65; 95% CI, 1.63-1.67; P < 1 × 10-8), AFgeneset (HR, 1.63; 95% CI, 1.52-1.75; P = 1.46 × 10-42), and CHIP (HR, 1.26; 95% CI, 1.15-1.38; P = 1.41 × 10-6) were associated with incident AF. The 5-year cumulative incidence of AF was at least 2-fold among individuals having all 3 genetic drivers (common, rare, and somatic drivers) compared with those with only 1 driver. Integration of IGM-AF with a clinical risk model (CHARGE-AF) showed higher predictive performance (C statistic, 0.80; 95% CI, 0.80-0.80) compared with IGM-AF and CHARGE-AF alone. The classification of the at-risk population for AF was improved when IGM-AF was added to CHARGE-AF (net reclassification index, 0.08; 95% CI, 0.07-0.09). CONCLUSIONS AND RELEVANCE: Results of this cohort study demonstrated the complementary value of common, rare, and somatic variants in shaping genomic AF risk. Leveraging comprehensive genetic information may enhance screening and preventive interventions for AF.

Humans↗

PubMed2026

Advances in the diagnosis and classification of B-ALL: comparative insights from updated guidelines.

Accurate molecular classification is essential for diagnosis, risk stratification, and treatment selection in B-cell lymphoblastic leukemia (B-ALL). In this study, we performed a comprehensive, real-world reclassification of 1015 consecutively diagnosed B-ALL patients using the fifth edition of the World Health Organization Classification of Haematolymphoid Tumours (WHO-HAEM5) and the International Consensus Classification (ICC). An integrative genomic strategy that combined whole transcriptome sequencing, fusion detection, mutational analysis, and cytogenetics enabled reclassification according to both the WHO-HAEM5 and ICC frameworks, thereby substantially reducing the proportion of unclassifiable B-ALL from 41.9% (2016 WHO revision [WHO-HAEM4R]) to 15.9% (WHO-HAEM5) and 11.9% (ICC). Distinct clinical and prognostic features were identified across newly defined subtypes. Multivariable analysis confirmed that this genomic classification is a robust, independent predictor of survival after adjusting for age, minimal residual disease status, and transplant intervention. Specifically, HLF-rearranged and MEF2D-rearranged B-ALL conferred a persistently poor prognosis across all age groups despite allogeneic hematopoietic stem cell transplantation, highlighting an urgent need for novel therapeutic strategies. Gene expression profiling resolved cryptic subtypes, including ETV6::RUNX1-like, ZNF384-rearranged-like, and BCR::ABL1-like B-ALL, and uncovered diagnostic ambiguity in patients with concurrent lesions. In addition, we report emerging high-risk groups, including IDH1/2- and ZEB2 Q1072-mutated B-ALL, that may warrant recognition as distinct molecular entities. Our findings demonstrate the clinical use of integrative transcriptomic profiling in refining B-ALL taxonomy in guiding risk-adapted therapies and informing future revisions of diagnostic standards. This study supports the incorporation of high-throughput molecular diagnostics into routine leukemia classification and precision treatment planning.

Humans↗

PubMed2025

Extracting and calibrating evidence of variant pathogenicity from population biobank data.

Genomic medicine requires a robust evidence base of variant phenotypic impacts, which remains incomplete even in extensively studied genes with monogenic disease associations. Here, we evaluated the broad potential of using population cohort data to identify evidence that can be used in variant assessment. Across 41 genes related to 18 clinically actionable monogenic phenotypes, we calculated variant-level odds ratios of disease enrichment using data from 469,803 UK Biobank participants. We found significant differences in odds ratio values between ClinVar-labeled pathogenic and benign variants in 11 phenotypes, spanning both common and rare disorders. To facilitate clinical translation, we calibrated the strength of evidence provided by variant-level odds ratios to align with American College of Medical Genetics and Genomics and the Association for Molecular Pathology (ACMG/AMP) interpretation guidelines (PS4 criterion) and found that odds ratios may reach "moderate," "strong," or "very strong" evidence, varying by phenotype and gene. Overall, we found that 2.6% (N = 12,350) of participants harbor a rare variant of uncertain significance (VUS) with at least moderate evidence of pathogenicity-an indication of potentially unrecognized disease risk. Finally, by incorporating computational and functional data alongside population-based odds ratios, we identified variants that met the criteria for clinical reclassification. Notably, using this approach, we identified that 12.4% of rare VUSs in LDLR seen in participants meet diagnostic criteria to be classified as likely pathogenic, demonstrating its potential to scale the reclassification of VUSs.

Humans↗

PubMed2022

Abdominal aortic calcification on lateral spine images captured during bone density testing and late-life dementia risk in older women: A prospective cohort study.

BACKGROUND: Dementia after the age of 80 years (late-life) is increasingly common due to vascular and non-vascular risk factors. Identifying individuals at higher risk of late-life dementia remains a global priority. METHODS: In prospective study of 958 ambulant community-dwelling older women (≥70 years), lateral spine images (LSI) captured in 1998 (baseline) from a bone density machine were used to assess abdominal aortic calcification (AAC). AAC was classified into established categories (low, moderate and extensive). Cardiovascular risk factors and apolipoprotein E (APOE) genotyping were evaluated. Incident 14.5-year late-life dementia was identified from linked hospital and mortality records. FINDINGS: At baseline women were 75.0 ± 2.6 years, 44.7% had low AAC, 36.4% had moderate AAC and 18.9% had extensive AAC. Over 14.5- years, 150 (15.7%) women had a late-life dementia hospitalisation (n = 132) and/or death (n = 58). Compared to those with low AAC, women with moderate and extensive AAC were more likely to suffer late-life dementia hospitalisations (9.3%, 15.5%, 18.3%, respectively) and deaths (2.8%, 8.3%, 9.4%, respectively). After adjustment for cardiovascular risk factors and APOE, women with moderate and extensive AAC had twice the relative hazards of late-life dementia (moderate, aHR 2.03 95%CI 1.38-2.97; extensive, aHR 2.10 95%CI 1.33-3.32), compared to women with low AAC. INTERPRETATION: In community-dwelling older women, those with more advanced AAC had higher risk of late-life dementia, independent of cardiovascular risk factors and APOE genotype. Given the widespread use of bone density testing, simultaneously capturing AAC information may be a novel, non-invasive, scalable approach to identify older women at risk of late-life dementia. FUNDING: Kidney Health Australia, Healthway Health Promotion Foundation of Western Australia, Sir Charles Gairdner Hospital Research Advisory Committee Grant, National Health and Medical Research Council of Australia.

AAC, abdominal aortic calcification↗

PubMed2026

Large-scale multi-omics enhance risk prediction for type 2 diabetes.

BACKGROUND: Polygenic risk scores (PRS), metabolomics, and proteomics have each shown promise in improving type 2 diabetes risk prediction, but their combined utility beyond established clinical models remains unclear. We aimed to evaluate whether integrating multi-omics biomarkers enhances 10-year type 2 diabetes risk prediction beyond single-omics extensions and the clinical Cambridge Diabetes Risk Score (CDRS), which includes HbA1c measurements. METHODS: We analysed data from 42,840 UK Biobank participants without diagnosed diabetes at baseline. The study population was split into a derivation set (Phase 1 metabolomics release, N = 23,108) to fit models and an independent validation set (Phase 2 release, N = 19,732) to evaluate performance. Data for a PRS for type 2 diabetes, 11 metabolites, and 15 proteins were added to the CDRS to develop multi-omics prediction models. Model performance was evaluated using Harrell's C-index and the net reclassification index (NRI). RESULTS: During 10 years of follow-up, 1090 participants developed incident type 2 diabetes. Among individual omics layers, proteomics contributed the greatest improvement in predictive performance, increasing the C-index from 0.862 (clinical CDRS) to 0.884 (ΔC-index; + 0.022; P < 0.001), with a continuous NRI of 42.0%. The full multi-omics model further significantly increased the C-index compared to a model combining the clinical CDRS with proteomics data (C-index, 0.891; ΔC-index; + 0.007; P < 0.001). CONCLUSION: Integrating proteomics, metabolomics, and a diabetes-PRS into a clinical model substantially improves type 2 diabetes risk prediction beyond single-omics extensions. Several of the selected proteins and metabolites are on cardiovascular disease pathways, highlighting the link between diabetes and cardiovascular risk. However, the C-index difference between the proteomics extended and full multi-omics extended models is small, and the clinical models extended with proteomics data would be easier to translate into routine care because it needs only the measurement of 15 proteins. External validation and cost-effectiveness analyses are needed to support clinical adoption.

Humans↗