Ischaemic heart disease: risk stratification and intervention. Risk of progression of coronary artery disease.
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Adult↗
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Results for “Risk Stratification”
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Risk stratification in aortic stenosis: exercise haemodynamics to refine risk in early cardiac damage stages.
AIMS: To describe exercise haemodynamics across cardiac damage stages and evaluate the incremental prognostic impact of cardiac damage stage and exercise-induced pulmonary hypertension (exPHT) in patients with symptomatic moderate aortic stenosis (AS) and asymptomatic severe AS. METHODS AND RESULTS: A total of 436 consecutive patients with ≥ moderate AS (74 ± 10 years, 32% women, 56% severe AS) underwent cardiopulmonary exercise testing with echocardiography. The primary endpoint was heart failure (HF) death and HF hospitalizations. Cardiac damage stage was 0 in 93 patients, 1 (LV damage) in 135, 2 (LA/mitral damage) in 135, and 3-4 (pulmonary vasculature/tricuspid or RV damage) in 73. Higher stages were associated with worse exercise capacity and haemodynamics. Over a median follow-up of 37 months, 65 patients met the primary endpoint. After adjustment for age, AS severity, and aortic valve replacement, cardiac damage stage and exPHT were independently associated with HF outcomes [HR per stage increase 1.51 (1.26-1.82); P < 0.001; exPHT HR 2.36 (1.10-5.07); P = 0.03]. exPHT improved risk stratification in early-stage disease (stages 1-2), conferring an approximately five-fold higher risk of HF events in patients with exPHT [HR 4.45 (1.58-12.59); P < 0.01]. CONCLUSION: In patients with ≥ moderate AS and discordant symptoms, cardiac damage stage and exPHT independently refined HF risk stratification. ExPHT provides incremental prognostic value in early damage stages (1-2), representing over half of the cohort, supporting a stepwise approach of routine damage staging with selective with exPHT assessment with exercise echocardiography in this subgroup to guide more personalized management and potentially optimize AVR timing.
Humans↗
Identifying gene expression signatures for risk stratification of postoperative adjuvant chemotherapy in colorectal cancer.
Clinical risk stratification for postoperative recurrence in patients with pathological stage II (pStage II) colorectal cancer (CRC) is essential for guiding the use of postoperative adjuvant chemotherapy (ACT). In this study, we identified novel prognostic gene expression biomarkers in patients with pStage II CRC and developed a new risk stratification framework for ACT decision-making. First, genome-wide biomarker discovery was conducted to identify prognostic gene expression biomarkers associated with recurrence risk in pStage II CRC. This analysis identified 10 differentially expressed genes as potential biomarkers for recurrence. The efficacy of these biomarkers was then tested using 188 clinical surgical specimens obtained from patients with pStage II CRC. A predictive panel was developed using qRT-PCR and used to assess 93 clinical specimens with an area under the curve (AUC) of 0.82, and its performance was further validated in an independent cohort (n = 95). By incorporating key clinicopathological features, a Gene expression-based Prediction of Recurrence in pStage II CRC (GPRSC) signature was developed, which robustly predicted postoperative recurrence (AUC: 0.80). Finally, combining the GPRSC signature, microsatellite instability status, and conventional criteria, we developed a novel risk stratification system for postoperative ACT decision-making in pStage II CRC. Overall, we identified novel gene expression biomarkers and developed a prognostic signature that informs clinical decision-making regarding postoperative ACT in patients with pStage II CRC.
Humans↗
Research progress and application prospects of multi-omics integration strategies in precision risk stratification of type 1 diabetes mellitus.
Type 1 diabetes (T1D) is a chronic metabolic disease mediated by autoimmunity. Its pathogenesis involves complex interactions between genetic susceptibility and environmental factors. Conventional T1D risk stratification primarily relies on genetic markers, islet autoantibodies, and glycemic indicators. Although these biomarkers remain indispensable in current clinical practice, they are often insufficient when used alone to accurately identify ultra-early high-risk individuals, predict disease progression rates, or support individualized preventive strategies. Consequently, more comprehensive molecular approaches are needed to improve precision risk stratification. In recent years, the rapid development of multi-omics technologies has provided new strategies for precise risk stratification of T1D. This narrative review critically evaluates how multi-omics integration strategies can improve precision risk stratification throughout the T1D disease continuum by integrating complementary molecular information from genomics, transcriptomics, proteomics, metabolomics, epigenomics, and the microbiome. Particular emphasis is placed on stage-specific biomarker discovery, multi-omics data integration frameworks, artificial intelligence-assisted prediction models, biomarker validation, and the opportunities and challenges associated with clinical translation. Current evidence suggests that integrated multi-omics approaches have the potential to improve risk prediction accuracy, distinguish heterogeneous disease trajectories, identify individuals at imminent risk of progression, and provide biologically informed targets for precision intervention. However, important challenges remain, including data harmonization, external validation, model interpretability, cost-effectiveness, and integration into routine clinical screening programs. Future research should prioritize prospective multicenter cohorts, standardized analytical pipelines, externally validated prediction models, and clinically interpretable multi-omics frameworks to facilitate the translation of precision risk stratification into routine T1D prevention and management.
Humans↗
Breast Cancer Risk Stratification in Black Women: Current Status and Potential Solutions to Improve Accuracy.
Breast cancer risk stratification models identify individuals at increased risk, allowing earlier screening than for those at average risk and potentially improving health outcomes. Due to the increasing rates of breast cancer in individuals aged <40 years, especially among Black females, the American College of Radiology now recommends all females initiate breast cancer risk assessment by age 25 years. Several breast cancer risk prediction models are readily available, including the Gail Model, Breast Cancer Surveillance Consortium Risk Calculator, BOADICEA, and Tyrer-Cuzick Model. However, because these models were primarily developed using data from White women of European ancestry, they may underestimate risk in Black women. Indeed, current evidence suggests that these models underpredict breast cancer risk among Black women, particularly those of African ancestry. Although cancer risk prediction models typically incorporate personal characteristics, family history of cancer, and hormonal and lifestyle factors, inherited breast cancer genes can also increase risk for breast cancer. Beyond monogenic inherited breast cancer genes that increase breast cancer risk, emerging data suggest that single nucleotide polymorphisms identified through genome-wide association studies (GWAS) may be used to generate polygenic risk scores, which may further refine breast cancer risk. However, GWAS data are also primarily gathered from European ancestry females, further reducing the ability to accurately stratify breast cancer risk in non-European ancestry populations. Current data highlight the importance of ensuring representation from all populations in developing cancer risk prediction models, conducting genomics research, and designing effective implementation strategies to enhance the use of these models in routine clinical care. Although new analytic methods and models are being developed to improve breast cancer risk stratification across populations, it remains critical to assess the utility and calibration of existing and new models to ensure applicability across non-European ancestry populations.
Humans↗
International Liver Cancer Association (ILCA) white paper on hepatocellular carcinoma risk stratification and surveillance.
Major research efforts in liver cancer have been devoted to increasing the efficacy and effectiveness of surveillance for hepatocellular carcinoma (HCC). As with other cancers, surveillance programmes aim to detect tumours at an early stage, facilitate curative-intent treatment, and reduce cancer-related mortality. HCC surveillance is supported by a large randomised-controlled trial in patients with chronic HBV infection and several cohort studies in cirrhosis; however, effectiveness in clinical practice is limited by several barriers, including inadequate risk stratification, underuse of surveillance, and suboptimal accuracy of screening tests. There are several proposed strategies to address these limitations, including risk stratification algorithms and biomarkers to better identity at-risk individuals, interventions to increase surveillance, and emerging imaging- and blood-based surveillance tests with improved sensitivity and specificity for early HCC detection. Beyond clinical validation, data are needed to establish clinical utility, i.e. increased early tumour detection and reduced HCC-related mortality. If successful, these data could facilitate a precision screening paradigm in which surveillance strategies are tailored to individual HCC risk to maximise overall surveillance value. However, practical and logistical considerations must be considered when designing and implementing these validation efforts. To address these issues, ILCA (the International Liver Cancer Association) adjourned a single topic workshop on HCC risk stratification and surveillance in June 2022. Herein, we present a white paper on these topics, including the status of the field, ongoing research efforts, and barriers to the translation of emerging strategies.
Humans↗
Artificial Intelligence for Diagnosis, Risk Stratification, and Prognosis of Neuroblastoma - A Systematic Review and Meta-Analysis.
PURPOSE: To synthesizes evidence on artificial intelligence (AI) performance in neuroblastoma (NB) diagnosis, risk stratification, prognosis, and genomic characterization. MATERIALS AND METHODS: A systematic review and meta-analysis was conducted following PRISMA 2020 guidelines (PROSPERO: CRD42024539475) across five databases. Meta-analyses used random-effects models with logit-transformed Area Under the Curve (AUCs) and cluster-robust standard errors. AI models were classified as Machine Learning Models (MLM) or Hybrid Nomograms (HN) based on their construction methodology. RESULTS: Of 3,742 articles identified, 53 were included. MLMs demonstrated higher point estimates than radiologists in differential diagnosis (AUC: 0.87 vs. 0.83), though this difference was not statistically significant and carried substantial uncertainty. HNs achieved stronger performance in risk stratification (AUC: 0.87). AI-derived nomograms (AUC: 0.9) and gene signatures (AUC: 0.8) outperformed conventional prognostic markers descriptively. Chemotherapy response prediction remained below clinical utility thresholds across all model types. Only 33.9% of models reported calibration and 24.5% underwent external validation. CONCLUSIONS: AI demonstrates proof-of-concept across multiple NB clinical domains. However, clinical adoption remains premature given persistent gaps in external validation, calibration, dataset size, and pediatric-specific model development. Future studies should test these models prospectively in multicenter pediatric cohorts, ideally through COG or SIOPEN, using shared definitions for diagnosis, risk group, treatment response, and survival outcomes.
Humans↗
Circulating IGF2BP3 enables risk stratification and predicts treatment response in Ewing sarcoma.
Ewing sarcoma (EWS), the second most common pediatric bone tumor, presents with a markedly heterogeneous clinical spectrum and optimal risk stratification is therefore crucial for improving treatment outcomes. The RNA-binding protein IGF2BP3 is a critical oncogenic driver of EWS malignancy. This study evaluates the clinical utility of circulating IGF2BP3 as a biomarker to predict treatment response and risk of disease progression in patients with EWS. Plasma samples from 60 patients with EWS diagnosed and treated at the IRCCS Rizzoli Orthopedic Institute (Bologna, Italy) were collected at diagnosis before treatment initiation and/or after induction chemotherapy. For 51 of these patients, blood was collected at diagnosis, prior to any treatments. For 25 patients, blood samples were available at diagnosis and before surgical intervention, allowing longitudinal analysis in the same patient. For 9 patients, blood was collected only after preoperative chemotherapy, before surgical intervention. Circulating IGF2BP3 levels were quantified using a highly specific and sensitive ELISA assay. Plasma samples from healthy donors served as controls. IGF2BP3 plasma levels were correlated with IGF2BP3 tumor tissue expression, established clinical risk factors, and cumulative incidence of relapse using univariable and multivariable analyses. Plasma IGF2BP3 levels were significantly elevated in patients with EWS compared with healthy controls, with a subset of patients (22/51, 43.2%) exhibiting clinically relevant concentrations. Circulating IGF2BP3 levels reflected tumor expression of the molecule and provided additional prognostic information beyond standard clinicopathologic features. The prognostic impact of circulating IGF2BP3 was primarily observed in patients with localized disease, in whom elevated levels were identified as a significant adverse prognostic factor for disease-specific survival (hazard ratio, 10.63; 95% CI, 1.27-88.62; P = 0.029). Longitudinal monitoring demonstrated that persistence of IGF2BP3 in plasma after induction chemotherapy was a strong predictor of poor clinical outcomes. Circulating IGF2BP3 represents a valuable biomarker for early risk stratification in EWS, particularly in patients with localized disease. Although this is single-marker assay, the expression of the molecule may impact on the fate of many mRNAs. We present an accurate, simple, cost-effective and easy clinical applicable tool to support risk-adapted therapeutic interventions. The limited number of employed patients warrants the need of larger cohorts for validation.
Humans↗
A three-metabolite microbiota-associated signature for early risk stratification of gestational diabetes mellitus.
BACKGROUND: Gestational diabetes mellitus (GDM) is associated with adverse pregnancy outcomes and long-term metabolic and cardiovascular risk. However, oral glucose tolerance testing at 24-28 gestational weeks limits early risk stratification. Gut microbiota-associated metabolites may reflect early metabolic abnormalities, including those relevant to cardiometabolic health, but robust early-pregnancy biomarkers remain limited. METHODS: We conducted a multicenter nested case-control and prospective study involving 2,693 pregnant women. Untargeted metabolomics and metagenomics were integrated to identify GDM-associated metabolites and gut microbial alterations. Three consistently dysregulated metabolites, 3-hydroxydecanoic acid, γ-Glu-Leu, and propionic acid, were quantified by targeted LC-MS/MS. Candidate algorithms were compared using repeated 10-fold cross-validation, and a final generalized linear model was externally and prospectively validated. RESULTS: Women who later developed GDM showed an adverse early-pregnancy metabolic profile, including higher BMI, triglycerides, and platelet count. Untargeted metabolomics identified 14 persistently altered metabolites enriched in energy, oxidative stress, and amino acid metabolism pathways. Metagenomics revealed taxonomic restructuring and coordinated microbiota-metabolite associations. The three-metabolite model achieved AUCs of 0.838 (95% CI, 0.791-0.885) in training, 0.840 (95% CI, 0.769-0.911) in internal validation, 0.955 (95% CI, 0.925-0.985) and 0.917 (95% CI, 0.875-0.958) in two external cohorts, and 0.969 (95% CI, 0.937-1.000) in the prospective cohort. CONCLUSION: Early microbiota-associated metabolic dysregulation is detectable before routine GDM diagnosis. This compact three-metabolite panel may support early GDM risk stratification and provides metabolic evidence relevant to broader cardiometabolic risk assessment in pregnancy.
Humans↗
Genome-wide association, polygenic risk scores, and machine learning for chronic post-surgical pain risk stratification: A UK biobank study.
Chronic post-surgical pain is a prevalent and debilitating complication following surgery, representing a clinical challenge. Despite the established heritability of pain phenotypes, large-scale genetic studies remain limited. This study aimed to identify genetic variants associated with chronic post-surgical pain, develop polygenic risk scores, and integrate these with clinical features for risk prediction. UK Biobank data from 47,836 participants (2490 cases and 45,346 controls) were split into training (80%; n = 38,268) and validation (20%; n = 9568) sets prior to analysis. A genome-wide association study was conducted on the training set only, across 19 million variants, and polygenic risk scores were constructed and integrated with clinical features in a logistic regression framework. Two close, rare, imputed signals crossed the genome-wide significance threshold but lacked local linkage-disequilibrium support, while 220 variants crossed the suggestive threshold. In the held-out validation set, cases had higher mean polygenic risk scores than controls (0.138 vs. -0.021; Cohen's d = 0.16, p < 0.001). A logistic regression model integrating clinical features and polygenic risk scores achieved an area under the curve of 0.639 (95% CI: 0.583-0.693), higher than models using either feature set alone. The polygenic risk score for chronic post-surgical pain was among the most important predictors. Risk stratification revealed the top quartile had 3.84-fold higher odds of chronic post-surgical pain than the bottom quartile (95% CI: 2.00-7.37). These findings suggest a possible modest genetic contribution to chronic post-surgical pain. Polygenic risk scores may complement clinical factors in surgical risk stratification. PERSPECTIVE: Chronic post-surgical pain may have a modest genetic contribution. This UK Biobank study identified over 220 variants at suggestive significance and constructed a polygenic risk score that was significantly elevated in cases. A combined clinical-genomic model achieved a 3.84-fold difference in odds across predicted-risk quartiles.
Chronic post-surgical pain↗
Deep learning-based cross-attention fusion of multimodal MRI for survival prediction and risk stratification in IDH-wildtype glioblastoma: a multicenter study.
BACKGROUND: Glioblastoma (GBM) exhibits profound molecular and spatial heterogeneity, complicating prognostic evaluations. While multiparametric MRI provides crucial multidimensional biological information, conventional end-to-end deep learning integration strategies, such as early or late fusion, often fail to capture complex nonlinear cross-modal interactions. We aimed to systematically evaluate a cross-attention fusion (CAF) architecture for GBM survival prediction and quantify its incremental prognostic value relative to existing clinical tools. METHODS: In this multicenter retrospective study, 386 adults with IDH-wildtype, WHO grade 4 GBM were assembled from an institutional cohort (n = 226), the Chinese Glioma Genome Atlas (CGGA, n = 62), and The Cancer Genome Atlas (TCGA, n = 98). Using a unified 3D ResNet-18 backbone, we compared single-modality models, early fusion, late fusion, and CAF on preoperative T1-weighted, contrast-enhanced T1-weighted (T1CE), and T2-weighted MRI, and integrated the resulting deep learning risk score with routine clinical variables through multivariable Cox regression. Performance was assessed using Harrell's C-index, time-dependent AUC, and decision curve analysis. RESULTS: CAF showed numerically higher, more consistent C-index trends than early fusion, late fusion, and single-modality models (pooled C-index 0.629, 95% CI 0.594-0.664), although pairwise differences in time-dependent AUC were not statistically significant. Integrating clinical variables raised the pooled C-index to 0.691 (95% CI 0.660-0.721) in the treatment-era model, with comparable performance across the three cohorts (Local 0.688; CGGA 0.716; TCGA 0.689); a pre-treatment configuration excluding adjuvant therapy yielded a pooled C-index of 0.642. Under leave-one-cohort-out external validation, the combined model retained significant risk stratification in all held-out cohorts (C-index 0.63-0.71; all log-rank P < 0.01), albeit with attenuated discrimination. The deep learning risk score remained independent after multivariable adjustment (HR 1.41 per SD, 95% CI 1.26-1.57; P < 0.001). Kaplan-Meier analysis confirmed significant high- versus low-risk separation in all cohorts, and decision curve analysis showed greater net benefit than clinical-only and deep-learning-only models. CONCLUSION: The CAF-derived risk score offers prognostic information complementary to routine clinical variables, representing a promising noninvasive tool for individualized risk stratification when molecular profiling is incomplete or unavailable; these findings warrant prospective external validation before clinical use.
cross-attention fusion↗
Diagnostic performance of machine learning models versus established risk stratification for intracranial aneurysm rupture: a systematic review and bivariate meta-analysis.
BACKGROUND: Machine learning (ML) models have been proposed to improve the discrimination of intracranial aneurysm rupture status beyond established clinical risk stratification tools. However, reported performance is heterogeneous and the relative contribution of model architecture and feature dominance remains unclear. METHODS: We performed a Preferred Reporting Items for Systematic Reviews and Meta-Analyses-diagnostic test accuracy systematic review and diagnostic meta-analysis of studies evaluating ML models for intracranial aneurysm rupture discrimination. PubMed, Embase and CENTRAL were searched to February 2026. Sensitivity and specificity were pooled using a bivariate random-effects model, with summary receiver operating characteristic curves generated across training, internal testing and external validation datasets. Models were compared with regression-based approaches and Population, Hypertension, Age, Size of aneurysm, Earlier subarachnoid haemorrhage, Site of aneurysm (PHASES) scores. Subgroup and meta-regression analyses explored associations between algorithm family and feature domain. RESULTS: Sixty-two retrospective cohorts (29 709 patients 209 models) met the inclusion criteria. In training datasets, pooled sensitivity and specificity for ML were 0.81 (95% CI 0.75 to 0.85) and 0.83 (0.80-0.86), with an area under the curve (AUC) of 0.878, exceeding PHASES (AUC 0.667). In testing datasets, ML retained higher discrimination (AUC 0.837) than regression models (0.806) and PHASES (0.646). In external validation, sensitivity was preserved (0.82), but specificity declined (0.66). Deep learning demonstrated the highest AUCs (training and testing). Incorporation of haemodynamic or radiomic features improved pooled discrimination relative to morphology alone. Evidence of small-study effects and mostly unclear Prediction Model Risk Of Bias Assessment Tool ratings were observed. CONCLUSIONS: ML approaches demonstrate higher pooled discrimination for aneurysm rupture status than conventional risk scores in retrospective datasets, but reduced external validation specificity and heterogeneity limit confidence for clinical translation. Prospective, externally validated, calibrated models are required before integration into routine cerebrovascular risk stratification.
Humans↗
Integrated multi-omic profiling enables recurrence risk stratification beyond pathological stage in resected EGFR-mutant lung adenocarcinoma.
BACKGROUND: Early-stage EGFR-mutant lung adenocarcinoma (LUAD) demonstrates heterogeneous outcomes after curative surgery, yet adjuvant treatment decisions are guided by pathological stage alone. Following the ADAURA trial, adjuvant osimertinib is the standard of care for resected stage IB-IIIA EGFR-mutant LUAD; however, real-world data demonstrate that up to 40% of patients remain disease-free at five years without adjuvant osimertinib, underscoring the need for improved risk stratification. PATIENTS AND METHODS: We performed integrated clinical, genomic and transcriptomic profiling of 400 patients with resected stage IA-IIIA EGFR-mutant LUAD. EGFR-mutant recurrence risk models integrating clinical, genomic and transcriptomic data were developed and validated across one internal and three external cohorts. RESULTS: Genomic instability, including TP53 co-mutations, copy number alterations and APOBEC-associated mutational signatures, increased with pathological stage. RBM10 co-mutations were enriched in tumours with L858R mutations and correlated with upregulation of WNT signalling and epithelial-mesenchymal transition. Transcriptomic features outperformed clinical or genomic variables alone in predicting recurrence risk, and a multi-omic model demonstrated superior and reproducible performance, achieving a median concordance index of 75.4% across four independent validation cohorts. The multi-omic model stratified recurrence risk within individual pathological stages, including stage I disease, and identified patients most likely to benefit from adjuvant EGFR TKI. CONCLUSIONS: These findings define the molecular heterogeneity of early-stage EGFR-mutant LUAD and support multi-omic risk stratification to inform adjuvant EGFR TKI decisions beyond pathological stage. Prospective validation in larger cohorts will be required to confirm these findings.
Journal Article↗
Graph neural network-based risk stratification of prostate cancer using gene expression and SHAP interpretability.
Accurate risk stratification is essential for guiding treatment decisions and preventing over treatment of prostate cancer, which remains one of the most prevalent cancers among adult men. While the Gleason score, obtained from prostate biopsies, is routinely used to assess tumor aggressiveness, the biopsy procedure carries risks such as pain, infection, and, in some cases, serious complications such as sepsis. In this study, we proposed an artificial intelligence-based framework that integrates mRNA expression profiles with functional interaction networks to classify prostate cancer patients into low-, medium-, and high-risk groups defined by Gleason scores. The pipeline comprised five steps: (1) data collection from The Cancer Genome Atlas (TCGA), (2) preprocessing of gene expression data, (3) two-stage feature selection to identify informative biomarkers, (4) risk classification using a dual-branch graph neural network (GNN) that combines gene-gene interaction graphs with sample-level expression features, and (5) model interpretation using SHAP to quantify feature contributions. Differentially expressed genes were identified in the High (ASPN, GMNN, PEBP4, C2, KNCK17), Medium (C2, IGSF1, ASPN, CDKN3, AMH), and Low (TNMD, VWA5B2, ST6GALNAC5, CYP3A5, PHGR1) risk groups, underscoring the molecular heterogeneity of disease progression. On an independent held-out test set, the model achieved AUCs of 0.86, 0.88, and 0.95 for the low-, medium-, and high-risk groups, respectively, with an overall accuracy of 80%. These results suggest that combining GNN-based modeling with explainable AI can capture both global and local molecular patterns relevant to tumor aggressiveness. However, as the model was developed and evaluated solely on the TCGA cohort, the findings should be regarded as exploratory, and external validation will be required to establish generalizability. Within these limitations, the proposed framework highlights the potential of molecular profiling and graph-based deep learning to support more precise, potentially less invasive, risk assessment and individualized treatment planning in prostate cancer.
Prostatic Neoplasms↗
Familial short stature: genetic architecture, risk stratification, and precision management.
BACKGROUND: Familial short stature (FSS) has traditionally been considered a benign growth pattern characterized by short stature clustering within families and has often been regarded as a normal variant of growth. However, recent advances in genomic technologies have demonstrated that a subset of children presenting with an FSS phenotype harbor identifiable monogenic variants, particularly in genes involved in growth plate development and skeletal growth. These findings challenge the traditional phenotype-based understanding of FSS and support an etiology-oriented diagnostic framework. OBJECTIVE: To summarize current knowledge regarding the genetic architecture of FSS, review existing clinical risk stratification frameworks for genetic evaluation, and evaluate available evidence regarding treatment outcomes across different genetic etiologies. METHODS: A literature search was performed in PubMed, Embase, and Web of Science from inception to May 2026, using keywords including "familial short stature," "familial idiopathic short stature," "genetic testing," "ACAN," "SHOX," and "NPR2". Relevant original studies and review articles addressing genotype-phenotype correlations, diagnostic yield of genetic testing, or responses to recombinant human growth hormone (rhGH) therapy were considered. RESULTS: Emerging evidence indicates that monogenic variants can be identified in a subset of children with an FSS phenotype, especially among those with more severe short stature and autosomal dominant inheritance patterns. Variants affecting growth plate biology represent some of the most frequently reported genetic causes of FSS, with ACAN, SHOX, and NPR2 being the most frequently implicated genes. Existing clinical frameworks based on parental height patterns and inheritance characteristics may help stratify patients with FSS according to the likelihood of monogenic etiology and guide selection of individuals who may benefit from genetic testing. Available evidence suggests that rhGH therapy may improve growth outcomes in several monogenic forms of FSS, although treatment responses vary according to genetic etiology. CONCLUSIONS: FSS should be regarded as a heterogeneous clinical phenotype rather than a single diagnostic entity. Integration of existing clinical risk stratification approaches with molecular diagnosis may enable more precise identification of underlying genetic causes and facilitate individualized therapeutic decision-making. Future advances in FSS management will likely depend on precision medicine approaches linking phenotype, genotype, and treatment response.
Humans↗
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↗
Risk stratification after acute myocardial infarction by means of echocardiographic wall motion scoring and Killip classification.
In order to perform risk stratification, 195 consecutive, unselected patients with acute myocardial infarction (AMI) underwent independent echocardiographic and clinical evaluation of their left ventricular function by means of the wall motion index (WMI) and Killip classification 5 days after AMI. The patients were prospectively allocated to a low, medium or high risk class depending on WMI alone, and the 1-year mortality in these classes was 2, 34 and 37%, respectively (p < 0.0001). The 1-year mortality of the patients in Killip class I, II, or III and IV was 6, 26 and 48%, respectively (p < 0.00001). The number of patients allocated to the low risk group by means of WMI was 87, and the number of patients in Killip class I was 86. Since these groups were not identical, a total of 103 patients, i.e. 53% of the study population, could be identified as low risk patients regarding 1-year mortality 5 days after AMI, when WMI and Killip classification were used in combination. We conclude that the combination of echocardiographic and clinical evaluation of left ventricular function after AMI provides a strong and yet very simple procedure to identify low risk patients, which could be easily implemented in the routine work of coronary care units.
Adult↗
Harnessing Polygenic Risk Scores to Refine Venous Thromboembolism Risk Stratification.
BACKGROUND: Venous thromboembolism (VTE) is a major cause of morbidity in patients of all ages. Despite growing interest in polygenic risk scores (PRS) for VTE, their utility remains understudied. Our objective was to evaluate the independent impact of a PRS on VTE susceptibility in adults and children. METHODS: We completed a retrospective, case-control study of two separate cohorts with evaluation of three VTE PRS models, with the primary analysis focused on a 293 single nucleotide polymorphism (SNP) PRS. The adult cohort included 597 VTE cases and 31 998 controls, and the pediatric cohort included 109 cases and 448 controls, both obtained from a de-identified databank with linked genetic data. Separate adult and pediatric multivariable logistic regressions were performed to measure the association of risk factors with VTE. RESULTS: Higher PRS in adults was significantly associated with increased odds of VTE, with each 1-standard deviation increase in PRS conferring an adjusted odds ratio of 1.25 (OR = 1.25, 95% CI 1.15-1.36, p < 0.001). Leading risk factors for adults were cancer (OR = 2.43, 95% CI: 2.04-2.89, p < 0.001) and recent surgery (OR = 2.16, 95% CI: 1.83-2.54, p < 0.001). The standardized PRS also exhibited increased risk for VTE in children (OR = 1.38, 95% CI 1.10-1.74, p = 0.003). Central venous catheterization (OR = 5.65, 95% CI 3.40-9.50, p < 0.001) was the foremost risk factor for pediatric VTE. CONCLUSION: VTE in adults and children is multifactorial, with clinical and genome-wide risk factors contributing. PRS may serve as a valuable adjunct to clinical risk factors for VTE risk stratification.
Humans↗