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PubMed2026

Multi-omic biomarkers in cardiovascular disease: Discovery to clinical translation.

Cardiovascular disease (CVD) remains the leading cause of mortality worldwide, necessitating improved risk stratification and early detection strategies. Multiomics approaches that integrate genomics, transcriptomics, proteomics, metabolomics, and epigenomics offer unprecedented opportunities for biomarker discovery and precision medicine in cardiovascular care. This narrative review examines the current landscape of multiomics biomarkers for CVD, tracing their evolution from discovery to clinical translation. We synthesize evidence from recent studies evaluating the clinical utility of integrated omics approaches across diverse cardiovascular conditions, including atherosclerotic cardiovascular disease, heart failure, and atrial fibrillation. High-throughput proteomics has identified novel protein signatures that enhance cardiovascular risk prediction beyond traditional risk factors. Metabolomics has revealed pathway-specific biomarkers, including trimethylamine N-oxide and lipid species, associated with atherogenesis. Polygenic risk scores derived from genomic data demonstrate incremental value when combined with clinical risk scores. Multiomics biomarkers represent a transformative approach to cardiovascular risk assessment and disease management.

Humans↗
PubMed2005

Beyond sepsis pathophysiology with cytokines: what is their value as biomarkers for disease severity?

Sepsis is a major challenge in medicine. It is a common and frequently fatal infectious condition. The incidence continues to increase, with unacceptably high mortality rates, despite the use of specific antibiotics, aggressive operative intervention, nutritional support, and anti-inflammatory therapies. Typically, septic patients exhibit a high degree of heterogeneity due to variables such as age, weight, gender, the presence of secondary disease, the state of the immune system, and the severity of the infection. We are at urgent need for biomarkers and reliable measurements that can be applied to risk stratification of septic patients and that would easily identify those patients at the highest risk of a poor outcome. Such markers would be of fundamental importance to decision making for early intervention therapy or for the design of septic clinical trials. In the present work, we will review current biomarkers for sepsis severity and especially the use of cytokines as biomarkers with important pathophysiological role.

Biomarkers↗
PubMed2026

Inclusion of Multi-Omic Biomarkers Improves Prediction Accuracy of Response, Relapse, and Overall Survival in Acute Myeloid Leukemia Patients Receiving High-Intensity Induction Chemotherapy.

BACKGROUND: Despite advancements in genetic markers for acute myeloid leukemia (AML) risk stratification, outcome prediction remains challenging due to disease heterogeneity and dynamic genetic changes, highlighting the need for reliable biomarkers to improve AML treatment strategies and patient outcomes. To refine outcome predictions, we investigated the use of microbial-derived biomarkers to predict composite complete remission (CRc), relapse, and survival for patients on high- and low-intensity regimens, and to integrate those variables into the widely clinically utilized European Leukemia Network (ELN-2022) genetic risk classification model for high-intensity-treated patients. METHODS: We first developed machine learning models that integrate baseline fecal metabolomics, 16S rRNA-based stool microbiome features, and clinical metadata (sex, antibiotic administration, AML somatic mutations, and cytogenetics) from two cohorts of AML patients (n = 83) undergoing remission induction chemotherapy. Univariate tests and sparse canonical correlation analysis were employed for variable selection and to explore fecal metabolite-microbe relationships. A robust machine learning approach using XGBoost was employed, with 100 stratified data splits (80% training, 20% testing) and coarse-to-fine hyperparameter optimization. Variable importance was aggregated across all models to select key predictors. RESULTS: For high-intensity-treated patients, XGBoost models achieved aggregated AUROC scores of 0.719, 0.729, and 0.65 for CRc, relapse, and overall survival, respectively. For low-intensity-treated patients, these models achieved aggregate AUROC scores of 0.945, 0.724, and 0.768 for these same outcomes, respectively. Integrating the biomarkers identified in the high-intensity machine-learning models with the current ELN-2022 AML risk stratification system effectively stratified patients into risk categories, which obtained higher concordance indices and likelihood ratios, demonstrating improved prognostic accuracy for each outcome compared to ELN-2022 alone. CONCLUSIONS: The inclusion of microbial-derived biomarkers serves as a robust prognostic tool to improve outcome prediction in AML patients, highlighting the potential of its integration into AML risk assessment and paving the way for personalized treatment strategies and improved patient outcomes.

Humans↗
PubMed2026

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↗
PubMed2024

Use of extracellular vesicle microRNA profiles in patients with acute myeloid leukemia for the identification of novel biomarkers.

OBJECTIVES: This study aimed to establish clinically significant microRNA (miRNA) sets using extracellular vesicles (EVs) from bone marrow (BM) aspirates of patients with acute myelogenous leukemia (AML), and to identify the genes that interact with these EV-derived miRNAs in AML. MATERIALS AND METHODS: BM aspirates were collected from 32 patients with AML at the time of AML diagnosis. EVs were isolated using size-exclusion chromatography. A total of 965 EV-derived miRNAs were identified in all the samples. RESULTS: We analyzed the expression levels of these EV-derived miRNAs of the favorable (n = 10) and non-favorable (n = 22) risk groups; we identified 32 differentially expressed EV-derived miRNAs in the non-favorable risk group. The correlation of these miRNAs with risk stratification and patient survival was analyzed using the information of patients with AML from The Cancer Genome Atlas (TCGA) database. Of the miRNAs with downregulated expression in the non-favorable risk group, hsa-miR-181b and hsa-miR-143 were correlated with non-favorable risk and short overall survival. Regarding the miRNAs with upregulated expression in the non-favorable risk group, hsa-miR-188 and hsa-miR-501 were correlated with non-favorable risk and could predict poor survival. Through EV-derived miRNAs-mRNA network analysis using TCGA database, we identified 21 mRNAs that could be potential poor prognosis biomarkers. CONCLUSIONS: Overall, our findings revealed that EV-derived miRNAs can serve as biomarkers for risk stratification and prognosis in AML. In addition, these EV-derived miRNA-based bioinformatic analyses could help efficiently identify mRNAs with biomarker potential, similar to the previous cell-based approach.

Humans↗
PubMed2006

Influence of imprecision on ROC curve analysis for cardiac markers.

BACKGROUND: There has been considerable debate regarding the impact of assay imprecision on the performance of cardiac biomarkers for diagnosis of acute coronary syndromes (ACS) and risk stratification for future adverse cardiac events. METHODS: Using existing data from 2 published clinical trials, we used a resampling method to statistically introduce 5%, 10%, and 20% imprecision to results for B-type natriuretic peptide (BNP) and cardiac troponin I (cTnI) and examined its impact on ROC curve analysis. RESULTS: Superimposition of artificial imprecision produced no significant difference in the area under the ROC curve observed for BNP for diagnosis of heart failure or for cTnI for 30-day risk stratification of patients with ACS. CONCLUSION: Assay imprecision does not appear to be a critical determinant in the interpretation of cardiac marker results for patients with heart disease.

Acute Disease↗
PubMed2006

Sepsis-associated myocardial dysfunction: diagnostic and prognostic impact of cardiac troponins and natriuretic peptides.

Myocardial dysfunction, which is characterized by transient biventricular impairment of intrinsic myocardial contractility, is a common complication in patients with sepsis. Left ventricular systolic dysfunction is reflected by a reduced left ventricular stroke work index or, less accurately, by an impaired left ventricular ejection fraction (LVEF). Early recognition of myocardial dysfunction is crucial for the administration of the most appropriate therapy. Cardiac troponins and natriuretic peptides are biomarkers that were previously introduced for diagnosis and risk stratification in patients with acute coronary syndrome and congestive heart failure, respectively. However, their prognostic and diagnostic impact in critically ill patients warrants definition. The elevation of cardiac troponin levels in patients with sepsis, severe sepsis, or septic shock has been shown to indicate left ventricular dysfunction and a poor prognosis. Troponin release in this population occurs in the absence of flow-limiting coronary artery disease, suggesting the presence of mechanisms other than thrombotic coronary artery occlusion, probably a transient loss in membrane integrity with subsequent troponin leakage or microvascular thrombotic injury. In contrast to the rather uniform results of studies dealing with cardiac troponins, the impact of raised B-type natriuretic peptide (BNP) levels in patients with sepsis is less clear. The relationship between BNP and both LVEF and left-sided filling pressures is weak, and data on the prognostic impact of high BNP levels in patients with sepsis are conflicting. Mechanisms other than left ventricular wall stress may contribute to BNP release, including right ventricular overload, catecholamine therapy, renal failure, diseases of the CNS, and cytokine up-regulation. Whereas cardiac troponins may be integrated into the monitoring of myocardial dysfunction in patients with severe sepsis or septic shock to identify those patients requiring early and aggressive supportive therapy, the routine use of BNP and other natriuretic peptides in this setting is discouraged at the moment.

Biomarkers↗
PubMed2001

Biomarkers in Barrett esophagus.

Barrett esophagus is a premalignant condition that may progress to adenocarcinoma. The risk of developing cancer has been estimated to be approximately 1 in 250 patient-years of observation; however, there appear to be subsets of patients at much higher risk. Risk stratification has previously been determined by histological identification of dysplasia. Several new biomarkers are being tested to help clinicians better determine the risk of cancer development. Although none of these biomarkers has been proven in a prospective study to predict the onset of cancer, they have been correlated with cancer development. Most of these are factors that have been associated with cancer development in other organs. These include assessment of cell proliferation, expression of cyclooxygenase 2, growth factors and oncogenes, secretory factors, cell cycle proteins, adhesion molecules, and aneuploidy and other genetic abnormalities. In addition to their role as potential cancer biomarkers, these factors have increasingly been reported as surrogate markers to monitor the effectiveness of conservative treatments for Barrett esophagus. In this article, biological markers are reviewed for their relevance in Barrett esophagus. Although most biological markers need to be evaluated further and, for most, prospective follow-up studies are lacking, at present abnormal ploidy status, P16 and P53 gene abnormalities, or allelic losses are the most extensively documented.

Barrett Esophagus↗
PubMed2026

Epigenetic Profiling for Early Detection and Treatment Response Monitoring in Non-Small Cell Lung Cancer: Protocol for a Prospective Translational Biomarker Study.

BACKGROUND: Non-small cell lung cancer (NSCLC) is the leading cause of cancer-related mortality worldwide and continues to have poor survival outcomes, with most patients diagnosed at advanced stages of disease. In New Zealand, NSCLC contributes substantially to cancer inequities, with Māori communities experiencing disproportionately high incidence and mortality rates. Although low-dose computed tomography screening can improve early detection, major limitations remain, including false-positive findings, overdiagnosis, high infrastructure costs, and limited accessibility for rural and underserved populations. Liquid biopsy approaches using circulating tumor DNA (ctDNA), particularly DNA methylation profiling, have emerged as promising, minimally invasive strategies for improving cancer detection, treatment monitoring, and precision oncology. OBJECTIVE: This study aims to establish integrated genomic and epigenomic predictive and prognostic biomarkers using ctDNA, tumor tissue, and transcriptomic profiling to improve early detection, risk stratification, treatment selection and response prediction, and longitudinal monitoring, with particular emphasis on identifying molecular mechanisms associated with treatment resistance and disease progression. METHODS: This prospective observational translational biomarker study is being conducted through the University of Otago and associated respiratory and oncology services in New Zealand. The study will recruit participants with NSCLC (including squamous and nonsquamous subtypes), individuals referred to fast-track lung nodule assessment clinics, and nonmalignant respiratory controls. Serial peripheral blood sampling will be performed in selected participants at predefined clinical follow-up time points to evaluate treatment response and disease progression. The availability of formalin-fixed paraffin-embedded archival tissues will be recorded, but will not be mandatory for enrollment. Genome-scale DNA methylation profiling will be performed using cell-free reduced representation bisulfite sequencing (cfRRBS), while targeted genomic profiling and transcriptomic analyses will be conducted using targeted sequencing panels and RNA sequencing. Integrative bioinformatic analyses will be used to identify molecular biomarkers associated with early-stage disease, advanced disease, treatment response, and therapeutic resistance. RESULTS: Ethics approval for the study has been obtained from the New Zealand Health and Disability Ethics Committee (2022 EXP 12566). This study commenced in 2022, and recruitment and biospecimen collection are ongoing. The study aims to recruit approximately 450 participants, including patients with NSCLC, individuals referred through respiratory diagnostic pathways, and nonmalignant controls. As of July 31, 2026, 205 participants have been recruited, with recruitment continuing until the target sample size is reached. Molecular and data analyses are ongoing, with additional publications expected as the cohort matures. CONCLUSIONS: This study will generate one of the first integrated genomic, epigenomic, and transcriptomic liquid biopsy datasets for NSCLC in New Zealand. The findings are expected to support the development of sensitive, accessible, and equitable blood-based biomarkers for NSCLC detection and treatment monitoring while also contributing to improved precision oncology approaches and reducing NSCLC inequities among Māori populations.

Humans↗
PubMed2006

Improved proteomic discovery by sample pre-fractionation using dual-column ion-exchange high performance liquid chromatography.

Clinically relevant biomarkers are urgently needed for improving patient diagnosis, risk stratification, prognosis and therapeutic treatments. There is a particularly compelling motivation for identifying protein-based indicators of early-stage disease for more effective interventions. Despite recent progress, the proteomic discovery process remains a daunting challenge due to the sheer heterogeneity and skewed protein abundances in biofluids. Even the most advanced mass spectrometry systems exhibit limiting overall dynamic ranges and sensitivities relative to the needs of modern biomedical applications. To this end, we report the development of a robust, rapid, and reproducible high performance ion-exchange liquid chromatography pre-fractionation method that allows for improved proteomic detection coverage of complex biological specimens using basic tandem mass spectrometry screening procedures. This form of sample simplification prior to global proteomic profiling, which we refer to collectively as 'fractionomics', increases the number and diversity of proteins that can be confidently identified in tissue and cell lysates as compared to the straight analysis of unfractionated crude extracts.

Animals↗
PubMed2005

Applications of the universal DNA microarray in molecular medicine.

Integration of molecular medicine into standard clinical practice will require the availability of diagnostics that are sensitive, rapid, and robust. The backbone technology underlying the diagnostic will likely serve double duty during clinical trials in order to first validate the biomarkers that contribute to both drug response and disease stratification. PCR/LDR/Universal DNA microarray is a promising technology to help drive the transition from the current paradigms of clinical decision making to the new era of personalized medicine. By uncoupling the mutation detection step from array hybridization, this technology becomes fully programmable. It exploits full use of the sensitivity that the ligase detection reaction can provide, while maintaining a rapid read out on a universal microarray. Thus, PCR/LDR/Universal DNA microarray is 50-fold more sensitive and 10-fold more rapid than conventional hybridization-only arrays. The intent of this article is to provide investigators with a perspective on current uses of this approach, as well as to serve as a practical guide to implementation.

Humans↗
PubMed2026

Epilepsy of infancy with migrating focal seizures: A scoping review of clinical features, diagnostic testing including genetics, long-term outcomes, mortality, and current and emerging therapeutic strategies.

BACKGROUND: Epilepsy of infancy with migrating focal seizures (EIMFS) is among the most severe developmental and epileptic encephalopathies (DEEs), marked by intractable multifocal seizures migrating across both hemispheres, profound developmental arrest, and high early mortality. Advances in next-generation sequencing have revealed a heterogeneous genetic architecture dominated by KCNT1 gain-of-function variants across more than 30 implicated genes, creating opportunities for precision therapeutics. OBJECTIVE: To systematically map published evidence on the clinical, electrophysiological, neuroimaging, genetic, and therapeutic landscape of EIMFS, and to delineate critical knowledge gaps and future research priorities. METHODS: A scoping review was conducted following the Arksey and O'Malley framework, searching PubMed, Ovid MEDLINE, Embase, Cochrane Library/CENTRAL, and ClinicalTrials.gov. RESULTS: Of 643 articles screened, 89 met inclusion criteria. Beyond confirmation of the canonical electroclinical phenotype, several gaps emerged: neonatal versus post-neonatal onset stratification by genetic etiology remains largely uncharacterized; genotype-specific EEG biomarkers are lacking except for a single small KCNT1 study; and the clinical significance of atypical EEG features-including burst suppression and hypsarrhythmia-is undefined. Neuroimaging literature documents progressive cerebral atrophy and myelination abnormalities without quantitative volumetry, diffusion tractography markers, or attribution to seizure burden, medication effects, or underlying etiology. Genetic diagnostic yield was 70-80%, with KCNT1 accounting for 30-50% of solved cases; however, genotype-outcome stratification is limited. Seizures were broadly refractory; potassium bromide, ketogenic diet, cannabidiol, and quinidine (in KCNT1-confirmed cases) showed partial efficacy. Emerging precision approaches include sodium channel blockers for SCN2A gain-of-function variants, novel small molecules, fluoxetine, antisense oligonucleotides, and divalent siRNA targeting KCNT1. Systemic-to-pulmonary collateral circulation causing severe cardiopulmonary complications was reported across multiple cases, yet no consensus screening protocol exists. CONCLUSIONS: EIMFS remains one of the most refractory epilepsy syndromes of infancy. Precision genetic diagnosis is essential to guide targeted therapy. International collaborative registries, standardized outcome measures, genotype-stratified biomarker studies, and rapid point-of-care genomic testing are urgently needed to advance evidence-based care for this highly vulnerable population.

Humans↗
PubMed2006

Daily dialyses decrease plasma levels of brain natriuretic peptide (BNP), a biomarker of left ventricular dysfunction.

Brain natriuretic peptide or B-type natriuretic peptide (BNP) is a sensitive marker of heart disease. Plasma levels of BNP increase in left ventricular failure and determination of plasma BNP has become a useful tool in the diagnosis of heart failure. Hemodialysis (HD) patients may have elevated plasma levels of BNP, particularly predialysis, that correlate with echocardiographic signs of left ventricular dysfunction. High BNP levels are also a strong predictor of mortality in both nonrenal and HD patients. We studied plasma BNP levels in patients who changed from conventional thrice-weekly dialysis to daily dialysis 6 times a week while maintaining a total weekly time on dialysis of 12 hr. Twelve HD patients, mean age 55 years, had 4 hr of conventional thrice-weekly treatment for 4 weeks. Predialysis and postdialysis blood samples were obtained at the last dialysis. Patients were then dialyzed for 2 hr, 6 times weekly, for 4 weeks (daily dialysis). Again, predialysis and postdialysis blood samples were collected at the last HD. Brain natriuretic peptide plasma concentrations were determined by immunoradiometric assay. Predialysis BNP levels decreased from 194+/-51 ng/L (68+/-19 pmol/L; mean+SE) during thrice-weekly HD to 113+/-45 ng/L (41+/-18 pmol/L; p = 0.001) after 4 weeks on daily dialysis. With thrice-weekly HD, predialysis BNP levels were higher than postdialysis levels: 120+/-26 ng/L (39+/-8 pmol/L; p = 0.059). With daily dialysis, predialysis BNP levels did not differ significantly from postdialysis levels. Elevated predialysis plasma levels of BNP, considered sensitive and early markers of left ventricular dysfunction, decreased when patients were changed from conventional thrice-weekly HD to daily dialysis maintaining total hours of dialysis per week constant. Given the accumulated evidence that BNP is a biomarker of left ventricular dysfunction and can be used for risk stratification and guidance in pharmacotherapy of heart failure, daily dialysis appears to lead to less cardiac distress.

Adult↗
PubMed2026

Multi-Omics Biomarker Signatures for Precision Diagnosis and Prognosis in Primary Liver Cancer: A Literature Review.

Primary liver cancer (PLC) is a biologically heterogeneous group of malignancies dominated by hepatocellular carcinoma (HCC), intrahepatic cholangiocarcinoma (iCCA), and a smaller subset of combined hepatocellular-cholangiocarcinoma (cHCC-CCA), and its clinical burden remains high because current diagnostic and prognostic tools do not adequately capture molecular diversity. Conventional imaging, serum markers, and histopathological assessment remain insufficient for precise early diagnosis, subtype-resolved classification, and outcome stratification, while tissue and liquid biopsy approaches have expanded the range of analytes available for clinical assessment. Recent studies have identified candidate biomarker signatures across genomic, epigenomic, transcriptomic, proteomic, metabolomic, and circulating layers, suggesting that integrated multi-omics profiling may better represent tumor lineage, clonal evolution, immune context, and therapeutic vulnerability than isolated molecular readouts. However, these layers are not equally mature for clinical use: genomic testing is closest to routine therapeutic application in iCCA, plasma methylation assays are advancing for HCC surveillance augmentation, and many proteomic or metabolomic panels remain validation-stage tools. Their clinical value remains constrained by sampling bias, biospecimen-dependent signal loss, assay standardization, cost, and the need for prospective validation across clinically diverse populations. This narrative review critically synthesizes current evidence on multi-omics biomarker signatures for precision diagnosis and prognosis in primary liver cancer and argues that clinically useful signatures should be question-specific, stage-aware, and specimen-aware rather than universal multi-analyte panels.

Humans↗
PubMed2005

Glutathione peroxidase-1 and homocysteine for cardiovascular risk prediction: results from the AtheroGene study.

OBJECTIVES: This prospective study was designed to evaluate the effect of joint determination of two important contrary biomarkers--homocysteine and glutathione peroxidase (GPx)-1--on cardiovascular risk stratification. BACKGROUND: Homocysteine plasma levels have been associated with cardiovascular risk. Experimental data suggest that antioxidative GPx-1 activity modulates cardiovascular risk associated with homocysteine. METHODS: In 643 patients with coronary artery disease, we performed a prospective study to assess the risk of homocysteine plasma levels and GPx-1 activity on long-term cardiovascular risk with a median follow-up of 7.1 years. RESULTS: Both homocysteine and GPx-1 were among the strongest univariate predictors of future cardiovascular risk, even after adjustment for cardiovascular confounders. Homocysteine levels were significantly elevated in individuals with future cardiovascular events (15.4 vs. 13.4 micromol/l; p < 0.0001); GPx-1 activity was lower (45.3 +/- 13.1 vs. 50.2 +/- 11.0 U/g hemoglobin; p < 0.0001). In patients with GPx-1 activity below the median value, homocysteine plasma levels above the median were associated with a 3.2-fold (95% confidence interval 1.8 to 5.6; p < 0.0001) increase in cardiovascular risk, whereas it lost its independent risk prediction in individuals with increased antioxidative capacity, as reflected by high GPx-1 activity. In contrast to single determination, combined assessment revealed a significant increase in the area under the curve of cardiovascular risk predictive models from 0.72, including traditional risk factors to 0.75 and also including homocysteine levels and GPx-1 activity. CONCLUSIONS: Plasma homocysteine levels and GPx-1 activity are complementary in identifying individuals at high cardiovascular risk. Joint determination of both biomarkers provides substantial information on top of classic risk factors in cardiovascular risk assessment.

Aged↗