Routine Biomarkers Research Articles

Non-lipid metabolomic biomarkers are associated with adverse cardiac remodelling and cardiovascular outcomes

Abstract Background Metabolomics is the detailed profiling of circulating metabolites including carbohydrates, lipids, amino and fatty acids, and ketones, all of which are substrates used in energy metabolism by cardiomyocytes. Purpose The role of cholesterol fractions with cardiovascular risk is well-established. This novel study examines the association between non-lipid circulating metabolites and markers of cardiac remodelling as assessed by cardiovascular magnetic resonance (CMR) imaging. Metabolites associated with adverse remodelling were taken forward to examine their impact on incident cardiovascular disease (CVD); ischaemic heart disease (IHD) and heart failure (HF). Methods This study used data from the UK Biobank. After exclusions (prevalent IHD, HF; outliers), there were a maximum of 34,727 individuals for CMR analyses and 262,536 individuals for incident CVD analysis. For CMR analyses, outcome variables were LV end-diastolic volume (EDV), LV mass, LV mass:volume ratio (MVR), LV global longitudinal strain (GLS) and native T1. Separate regression models were built for each of the 40 scaled metabolite biomarkers. Each model incorporated one metabolite at a time with additional covariates: age, sex, ethnicity, systolic blood pressure, cholesterol medication use, type 2 diabetes, dietary intake, smoking status, fasting time, and time between recruitment and CMR examination. A Bonferroni-corrected significance threshold of 2.4x10-4 was used. To account for correlation between metabolites, Lasso regression with 10 fold cross-validation to identify the most important predictors was performed for LV MVR, LV GLS and native T1 whereby all metabolites and above covariates were included in the model. Metabolite predictors of adverse remodelling were included in an adjusted logistic regression model with incident CVD as an outcome. Results Metabolites demonstrating significant (p < 2.4x10-4) associations with CMR parameters are presented in Fig 1. For higher LV MVR and more positive LV GLS, the metabolites with the largest effect were monounsaturated fatty acids and glycoprotein acetyls. Higher omega-6:omega-3 ratio was associated with higher T1 values. The Lasso model identified 8 important predictors for adverse cardiac remodelling. Of these, two were routine biomarkers (creatinine, glucose). Of the remainder, monounsaturated fatty acids, glycoprotein acetyls and apolipoprotein B:apolipoprotein A1 ratio were significantly associated with increased risk of incident CVD (Fig 2). Current evidence regarding monounsaturated fatty acids in CVD is mixed. Glycoprotein acetyls are implicated in vascular inflammation. Apolipoproteins are thought to be a potential therapeutic target for CVD risk reduction. Conclusion This study demonstrates a role of non-lipid metabolites on subclinical CVD as assessed by adverse remodelling with CMR as well as associations with incident CVD. We highlight potential novel biomarkers for identification of CVD.Metabolite association with LV MVR/GLSMetabolite association with incident CVD

Prediction of incident major adverse cardiac events in patients starting haemodialysis for end stage kidney disease, using clinical laboratory cardiac biomarkers - a real world analysis.

Abstract Background and Aims Despite its many advantages, haemodialysis (HD) is associated with increased cardiovascular mortality and morbidity, with some studies reporting 48% higher mortality within 2 years of starting HD. Cardiac biomarkers provide insight into cardiac structure and function, including myocyte stress, inflammation and fibrosis. We aimed to investigate the prognostic significance of routine cardiac biomarkers (troponin and natriuretic peptides) in the prediction of incident major adverse cardiac events (MACE) within 5 years if commencing HD. Methods A retrospective cohort study was performed using electronic medical records from a global federated research network from the US. The network was searched on 26th February 2023. The cohorts commenced HD post-diagnosis of end-stage kidney disease. Data censoring for MACE was invoked prior to the index event of HD. Cardiac biomarkers were the first reported result within 3 months of starting HD. Cohorts were grouped according to biomarker-specific thresholds and 1:1 propensity-score matched for age, gender and co-morbidities (hypertension, diabetes mellitus and smoking status). Logistical regression produced odds ratios with 95%CI for 5-year incident MACE. MACE was defined, a priori, as a composite of ischaemic heart disease, angina pectoris, acute myocardial infarction, heart failure, AF, stroke and all-cause mortality. All statistical analysis was performed on the online platform. Results The results are shown Tables 1(Troponin I and BNP alone) and 2 (Combined Troponin I and BNP). Results that reached statistical significance (p<0.05) are shown. Conclusion Routinely available cardiac biomarkers can predict MACE and cardiac outcomes in incident HD, although results differ between markers of myocyte injury (troponin) and stress (BNP). A combined approach may prove most beneficial. The results suggest the clinical need for CV mortality and morbidity risk profiling in incident dialysis using a combination of clinical and laboratory variables and the need for prospective validation.Table 1Table 2

Does PhenoAge acceleration impact adverse outcomes following major surgery (rehospitalization or death)? A genome-wide association study and mendelian randomisation

Abstract Background Biological aging reflects the state of an individual's cellular and molecular function, and is distinct from chronological age; which reflects the passage of time[1]. Validated measures of biological aging, such as Phenotypic aging (PhenoAge) estimated from nine routine clinical biomarkers, have been associated with increased risk of age-related diseases and mortality in cohort studies[2]. However, the potential causal relationship between them remains unclear. In this study, we aim to identify genetic variants associated with PhenoAge Acceleration (PhenoAgeAccl) and adverse outcome after surgery (rehospitalisation or death within 365-days] and assess the causal relationship between these two phenotypes using Mendelian Randomization framework. Objectives -Perform a genome-wide association study(GWAS) on adverse outcomes following major surgery. -Assess the potential causal relationship between PhenoAgeAccl and adverse outcomes following major surgery using Mendelian randomization analysis. Methods We conducted a GWAS via plink using participants from the UKBiobank (Application 77596) to generate summary statistics for 6509 participants who had undergone major surgery within 365 days of their baseline assessment date(3). For quality control, SNPs were excluded if they didn't meet the following criteria: (1) imputation information score <0.3 (2) minor allele frequency <1%, (3) Hardy–Weinberg equilibrium test p-value significant at the Bonferroni-corrected level. Summary statistics for PhenoAgeAccl was obtained from a previous GWAS of European descents[4]. Gene enrichment analysis was performed using Multi-marker Analysis of GenoMic Annotation (MAGMA) in Functional Mapping and Annotation (FUMA). Then, genetic instruments for PhenoAgeAccl and adverse outcomes following surgery were identified to conduct a summary-based Mendelian Randomisation [Assumptions are listed in Figure 1]. Results GWAS for adverse outcomes following major surgery identified 3 lead SNPs (rs1360618, rs11848297, rs7978) [Figure 2]. Gene-set analysis showed enrichment in the immune system, cell migration, fear response, and iron metabolism. For PhenoAge Acceleration, gene-set analysis showed enrichment in immune system, and cell signalling pathways. 17 SNPs were selected which were used for the Two-Sample MR. We found no causal relationship, as the beta coefficient for Inverse variance weighted was -0.308 [standard error - 0.075, p=0.68]. For every 5-year increase in PhenoAge compared to chronological age, the risk of emergency re-hospitalisation or death increased by 21% (HR 1.16 – 1.3, p < 0.001). Conclusion Gene-set analysis for adverse outcomes following major surgery showed enrichment in immune system, iron metabolism, and fear response which have all recently been associated with mortality post-surgery in cohort studies(4,5). No causal relationship between PhenoAgeAccel and mortality post-surgery was established.Mendelian Randomisation assumptionsManhattan plot adverse-outcomes post-sur

Assessment of experimental circulating biomarkers in volume and pressure overload: insights from a cohort study

Abstract Introduction Experimental biomarkers including Angiopoietin-2 (ANG2), bone morphogenetic protein 10 (BMP10), fibroblast growth factor 23 (FGF23), and insulin-like growth factor-binding protein 7 (IGFBP7) are hypothesized to reflect cardiac pathophysiological processes that are potentially present in adverse cardiac remodeling in volume or pressure overload. This study aims to elucidate the relationship between these circulating biomarkers and the presence of hemodynamically significant pressure or volume overload and adverse outcomes and compare it to N-terminal pro B-type natriuretic peptide (NT-proBNP). Methods We studied the association between the four experimental circulating biomarkers in patients with pressure or volume overload with reference to none/mild valvular disease in N=1302 of an observational outpatient cardiology cohort. The median age was 63.0 (25th/75th quartile 51.5, 71.4) years, 445 (34.2%) were women. Circulating biomarkers were quantified using a novel antibody-based method. Logistic regression models with cox regression plots were employed to assess the association of each biomarker with the severity of valve disease concerning volume or pressure overload, adjusted for age and sex. Results In the overall population, elevated levels of ANG2, BMP10, FGF23 and IGFB7 were positively associated with the presence of significant volume overload (ANG2: Odds ratio (OR) 1.26 (95% confidence interval (CI) 1.17-1.35), p<0.001; BMP10: OR 2.57 (CI 1.89-3.48), p<0.001; FGF23: OR 1.51 (CI 1.14-1.20), p=0.004; IGFBP7: OR 1.39 (CI 1.14-1.69), p=0.001, NT-proBNP: OR 1.69 (CI 1.47-1.95), p<0.001). No statistically significant association between circulating biomarkers and pressure overload was observed. Over a median follow-up of 4 years, 31 (20.1%) participants died in the group of volume overload and 9 (40.9%) individuals in the group of pressure overload. After multivariable-adjustment, elevated levels of BMP 10 showed a borderline association with an increased risk of all-cause mortality in individuals with volume overload compared to none/mild valvular disease: hazard ratio (HR) 1.51 (CI 0.96- 2.38), p=0.075. Higher concentrations of all biomarkers were predictive of all-cause mortality for both individuals with volume overload and those with none/mild valvular disease (ANG2: HR 3.72 (CI 1.67-8.33), p=0.001; BMP10: HR 1.65 (CI 1.20-2.26), p=0.002; FGF23: HR 2.08 (CI 1.65-2.62), p<0.001; IGFBP7: HR 1.55 (CI 1.25-1.91), p<0.001; NT-proBNP: HR 1.72 (CI 1.47-2.00), p<0.001). Conclusions This study demonstrates that circulating biomarkers involved in distinct pathophysiological pathways of inflammation, fibrosis and calcification are elevated in patients with hemodynamically significant volume overload. Since they are also related to mortality, their clinical role needs to be explored further, also in context with the routine biomarker NT-proBNP.

Evaluation of Circulating Tumor DNA and Carcinoembryonic Antigen Levels and Relationship with Clinicopathological Risk Factors and Prognostic Indices in Colorectal Cancer Patients

Objective: Colorectal cancer (CRC) is a leading cause of cancer-related mortality worldwide. ctDNA has emerged as a promising biomarker for CRC management, offering real-time insights into tumor burden and genetic mutations. This study investigates the correlation between CEA levels, ctDNA, clinicopathological factors, and treatment outcomes in early and advanced CRC patients. Methods: The study retrospectively analyzed data from CRC patients, including early-stage disease and metastatic disease. ctDNA levels, demographic data, and clinical parameters such as CEA, inflammatory indexes, and tumor characteristics were evaluated to determine correlations with treatment outcomes. Results: The study included 20 patients, with 60% diagnosed at the metastatic stage. The study found no statistically significant correlation between ctDNA levels and disease stage, recurrence, or overall survival. While CEA and ctDNA levels were measured, they did not demonstrate a significant relationship with treatment outcomes. Notably, ctDNA levels were higher in metastatic patients, though this was not statistically significant. Conclusion: Despite its potential, the integration of ctDNA as a routine biomarker in CRC care faces challenges, including variability in measurement techniques and cost-effectiveness. However, ctDNA's ability to guide personalized treatment strategies and monitor disease recurrence holds promise. The study's findings align with previous research, suggesting ctDNA as a poor prognostic indicator, though further research is needed. ctDNA represents a significant advance in CRC management, offering non-invasive, real-time insights into tumor dynamics. Ongoing research is expected to solidify its role in personalized treatment planning, potentially leading to more effective and tailored therapies for CRC patients.

Impact of second-generation antipsychotics monotherapy or combined therapy in cytokine, lymphocyte subtype, and thyroid antibodies for schizophrenia: a retrospective study

BackgroundSchizophrenia (SCZ) shares high clinical relevance with the immune system, and the potential interactions of psychopharmacological drugs with the immune system are still an overlooked area. Here, we aimed to identify whether the second-generation antipsychotics (SGA) monotherapy or combined therapy of SGA with other psychiatric medications influence the routine blood immunity biomarkers of patients with SCZ.MethodsMedical records of inpatients with SCZ from January 2019 to June 2023 were retrospectively screened from June 2023 to August 2023. The demographic data and peripheral levels of cytokines (IL-2, IL-4, IL-6, TNF-α, INF-γ, and IL-17 A), lymphocyte subtype proportions (CD3+, CD4+, CD8 + T-cell, and natural killer (NK) cells), and thyroid autoimmune antibodies (thyroid peroxidase antibody (TPOAb), and antithyroglobulin antibody (TGAb)) were collected and analyzed.Results30 drug-naïve patients, 64 SGA monotherapy (20 for first-episode SCZ, 44 for recurrent SCZ) for at least one week, 39 combined therapies for recurrent SCZ (18 with antidepressant, 10 with benzodiazepine, and 11 with mood stabilizer) for at least two weeks, and 23 used to receive SGA monotherapy (had withdrawn for at least two weeks) were included despite specific medication. No difference in cytokines was found between the SGA monotherapy sub-groups (p > 0.05). Of note, SGA monotherapy appeared to induce a down-regulation of IFN-γ in both first (mean [95% confidence interval]: 1.08 [0.14–2.01] vs. 4.60 [2.11–7.08], p = 0.020) and recurrent (1.88 [0.71–3.05] vs. 4.60 [2.11–7.08], p = 0.027) episodes compared to drug-naïve patients. However, the lymphocyte proportions and thyroid autoimmune antibodies remained unchanged after at least two weeks of SGA monotherapy (p > 0.05). In combined therapy groups, results mainly resembled the SGA monotherapy for recurrent SCZ (p > 0.05).ConclusionThe study demonstrated that SGA monotherapy possibly achieved its comfort role via modulating IFN-γ, and SGA combined therapy showed an overall resemblance to monotherapy.

Application of physiological network mapping in the prediction of survival in critically ill patients with acute liver failure

Reduced functional connectivity of physiological systems is associated with poor prognosis in critically ill patients. However, physiological network analysis is not commonly used in clinical practice and awaits quantitative evidence. Acute liver failure (ALF) is associated with multiorgan failure and mortality. Prognostication in ALF is highly important for clinical management but is currently dependent on models that do not consider the interaction between organ systems. This study aims to examine whether physiological network analysis can predict survival in patients with ALF. Data from 640 adult patients admitted to the ICU for paracetamol-induced ALF were extracted from the MIMIC-III database. Parenclitic network analysis was performed on the routine biomarkers using 28-day survivors as reference population and network clusters were identified for survivors and non-survivors using k-clique percolation method. Network analysis showed that liver function biomarkers were more clustered in survivors than in non-survivors. Arterial pH was also found to cluster with serum creatinine and bicarbonate in survivors compared with non-survivors, where it clustered with respiratory nodes indicating physiologically distinctive compensatory mechanism. Deviation along the pH-bicarbonate and pH-creatinine axes significantly predicts mortality independent of current prognostic indicators. These results demonstrate that network analysis can provide pathophysiologic insight and predict survival in critically ill patients with ALF.

Serum CD5L as potential biomarker of thyroid hormone status during pregnancy.

The thyroid hormone (TH) status is routinely assessed by thyrotropin (TSH) and thyroxine (T4). Both biomarkers are mainly regulated by TH receptor beta, whereas many peripheral organs employ the alpha receptor. Serum cluster of differentiation 5-like molecule (CD5L) is a liver-derived protein under control of both TH receptor isoforms. However, clinical data on its relation to TH status are sparse. An additional biomarker of TH status is needed in particular during pregnancy, where the routine biomarkers become dynamically disturbed. This study aimed to determine possible covariates regulating serum CD5L and to test its potential suitability as additional TH biomarker during pregnancy. A sandwich ELISA for serum CD5L was established using newly raised antibodies. Circadian effects and the impact of liver disease on serum CD5L concentrations were assessed. Serum samples from pregnant women with well-characterized TH and trace element status were analyzed, and CD5L concentrations were correlated with other indicators of TH status including TSH, fT4, fT3, copper, and selenium concentrations. The new quantitative assay for CD5L showed high accuracy. Serum CD5L was stable in dilution and refreezing experiments and did not show strong circadian variance or dependency on liver disease. In serum of pregnant women, CD5L correlated positively to fT3, but not to fT4 or TSH. Significant positive correlations of CD5L were observed with serum levels of the TH-responsive trace elements selenium and copper. The data support the potential suitability of serum CD5L as an additional marker of TH status, with potential value for pregnancy and thyroid disease.

Application of machine learning algorithms in predicting carotid artery plaques using routine health assessments.

Cardiovascular diseases (CVD) constitute a grave global health challenge, engendering significant socio-economic repercussions. Carotid artery plaques (CAP) are critical determinants of CVD risk, and proactive screening can substantially mitigate the frequency of cardiovascular incidents. However, the unequal distribution of medical resources precludes many patients from accessing carotid ultrasound diagnostics. Machine learning (ML) offers an effective screening alternative, delivering accurate predictions without the need for advanced diagnostic equipment. This study aimed to construct ML models that utilize routine health assessments and blood biomarkers to forecast the onset of CAP. In this study, seven ML models, including LightGBM, LR, multi-layer perceptron (MLP), NBM, RF, SVM, and XGBoost, were used to construct the prediction model, and their performance in predicting the risk of CAP was compared. Data on health checkups and biochemical indicators were collected from 19,751 participants at the Beijing MJ Health Screening Center for model training and validation. Of these, 6,381 were diagnosed with CAP using carotid ultrasonography. In this study, 21 indicators were selected. The performance of the models was evaluated using the accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), F1 score, and area under the curve (AUC) value. Among the seven ML models, the light gradient boosting machine (LightGBM) had the highest AUC value (85.4%). Moreover, age, systolic blood pressure (SBP), gender, low-density lipoprotein cholesterol (LDL-C), and total cholesterol (CHOL) were the top five predictors of carotid plaque formation. This study demonstrated the feasibility of predicting carotid plaque risk using ML algorithms. ML offers effective tools for improving public health monitoring and risk assessment, with the potential to improve primary care and community health by identifying high-risk individuals and enabling proactive healthcare measures and resource optimization.

Early differential diagnosis between acute inflammatory demyelinating polyneuropathy and acute-onset chronic inflammatory demyelinating polyneuropathy in children: Clinical factors and routine biomarkers

BackgroundTo identify clinical factors and biomarkers that could contribute to early differential diagnosis of acute inflammatory demyelinating polyneuropathy (AIDP) and acute-onset chronic inflammatory demyelinating polyneuropathy (A-CIDP) in the pediatric population, with limited evidence. MethodsWe conducted an observational retrospective study of children diagnosed with AIDP and A-CIDP between January 2014 and December 2022. Demographic data, clinical features, and routine biomarkers were also analyzed. Statistical analysis was used to identify significant features with high sensitivity and specificity. ResultsWe included 91 AIDP and 17 A-CIDP patients. The A-CIDP group had an older median age (6.33 vs. 4.33 years, p = 0.017), required more complex immunotherapies (p < 0.001), and showed a longer time to nadir over 2 weeks (76.5 % vs. 7.7 %, p < 0.001). Gastrointestinal dysfunction (29.4 % vs. 6.59 %, p = 0.014) and numbness (35.3 % vs. 12.1 %, p = 0.027) were more prevalent in A-CIDP. The AIDP patients had a longer median hospitalization stays (13 vs. 11 days, p < 0.05), more prodromal events (90.1 % vs. 64.7 %, p = 0.013), and more frequent cranial nerve palsy (61.5 % vs. 5.88 %, p < 0.001). The disability scores on admission, discharge, and peak were worse in the AIDP group (p < 0.001). AIDP patients showed higher cerebrospinal fluid protein (p = 0.039), albumin quotient (p = 0.048), leukocytes (p = 0.03), neutrophils (p = 0.010), platelet count (p = 0.005), systemic inflammatory index (SII) (p = 0.009), and gamma-glutamyl transferase (p = 0.039). Multivariable regression identified two independent predictors of early A-CIDP detection: time from onset to peak beyond 2 weeks (OR = 37.927, 95%CI = 7.081–203.15) and lower modified Rankin Scale score on admission (OR = 0.308, 95%CI = 0.121–0.788). ConclusionOur study found that when the condition continued to deteriorate beyond two weeks with a lower mRS on admission and possibly less cranial nerve involvement, we may favor the diagnosis of pediatric A-CIDP rather than AIDP.

Prediction of gestational diabetes mellitus by multiple biomarkers at early gestation

BackgroundIt remains unclear which early gestational biomarkers can be used in predicting later development of gestational diabetes mellitus (GDM). We sought to identify the optimal combination of early gestational biomarkers in predicting GDM in machine learning (ML) models.MethodsThis was a nested case-control study including 100 pairs of GDM and euglycemic (control) pregnancies in the Early Life Plan cohort in Shanghai, China. High sensitivity C reactive protein, sex hormone binding globulin, insulin-like growth factor I, IGF binding protein 2 (IGFBP-2), total and high molecular weight adiponectin and glycosylated fibronectin concentrations were measured in serum samples at 11–14 weeks of gestation. Routine first-trimester blood test biomarkers included fasting plasma glucose (FPG), serum lipids and thyroid hormones. Five ML models [stepwise logistic regression, least absolute shrinkage and selection operator (LASSO), random forest, support vector machine and k-nearest neighbor] were employed to predict GDM. The study subjects were randomly split into two sets for model development (training set, n = 70 GDM/control pairs) and validation (testing set: n = 30 GDM/control pairs). Model performance was evaluated by the area under the curve (AUC) in receiver operating characteristics.ResultsFPG and IGFBP-2 were consistently selected as predictors of GDM in all ML models. The random forest model including FPG and IGFBP-2 performed the best (AUC 0.80, accuracy 0.72, sensitivity 0.87, specificity 0.57). Adding more predictors did not improve the discriminant power.ConclusionThe combination of FPG and IGFBP-2 at early gestation (11–14 weeks) could predict later development of GDM with moderate discriminant power. Further validation studies are warranted to assess the utility of this simple combination model in other independent cohorts.

Predicting early-stage coronary artery disease using machine learning and routine clinical biomarkers improved by augmented virtual data.

Coronary artery disease (CAD) is a highly prevalent disease with modifiable risk factors. In patients with suspected obstructive CAD, evaluating the pre-test probability model is crucial for diagnosis, although its accuracy remains controversial. Machine learning (ML) predictive models can help clinicians detect CAD early and improve outcomes. This study aimed to identify early-stage CAD using ML in conjunction with a panel of clinical and laboratory tests. The study sample included 3316 patients enrolled in the Ludwigshafen Risk and Cardiovascular Health (LURIC) study. A comprehensive array of attributes was considered, and an ML pipeline was developed. Subsequently, we utilized five approaches to generating high-quality virtual patient data to improve the performance of the artificial intelligence models. An extension study was carried out using data from the Young Finns Study (YFS) to assess the results' generalizability. Upon applying virtual augmented data, accuracy increased by approximately 5%, from 0.75 to -0.79 for random forests (RFs), and from 0.76 to -0.80 for Gradient Boosting (GB). Sensitivity showed a significant boost for RFs, rising by about 9.4% (0.81-0.89), while GB exhibited a 4.8% increase (0.83-0.87). Specificity showed a significant boost for RFs, rising by ∼24% (from 0.55 to 0.70), while GB exhibited a 37% increase (from 0.51 to 0.74). The extension analysis aligned with the initial study. Accurate predictions of angiographic CAD can be obtained using a set of routine laboratory markers, age, sex, and smoking status, holding the potential to limit the need for invasive diagnostic techniques. The extension analysis in the YFS demonstrated the potential of these findings in a younger population, and it confirmed applicability to atherosclerotic vascular disease.

Routine cardiac biomarkers for the prediction of incident major adverse cardiac events in patients with glomerulonephritis: a real-world analysis using a global federated database

Rationale & objectiveGlomerulonephritis (GN) is a leading cause of chronic kidney disease (CKD). Major adverse cardiovascular events (MACE) are prolific in CKD. The risk of MACE in GN cohorts is multifactorial. We investigated the prognostic significance of routine cardiac biomarkers, Troponin I and N-terminal pro-BNP (NT-proBNP) in predicting MACE within 5 years of GN diagnosis.Study DesignRetrospective cohort study.Setting & participantsData were obtained from TriNetX, a global federated health research network of electronic health records (EHR).Exposure or predictorBiomarker thresholds: Troponin I: 18 ng/L, NT-proBNP: 400 pg/mL.OutcomesPrimary outcome: Incidence of major adverse cardiovascular events (MACE). Secondary outcome: was the risk for each individual component of the composite outcome.Analytical Approach1:1 propensity score matching using logistic regression. Cox proportional hazard models were used to assess the association of cardiac biomarkers with the primary and secondary outcomes, reported as Hazard Ratio (HR) and 95% confidence intervals (CI). Survival analysis was performed which estimates the probability of an outcome over a 5-year follow-up from the index event.ResultsFollowing PSM, 34,974 and 18,218 patients were analysed in the Troponin I and NTproBNP cohorts, respectively. In the Troponin I all cause GN cohort, 3,222 (9%) developed composite MACE outcome HR 1.79; (95% CI, 1.70, 1.88, p < 0.0001). In the NTproBNP GN cohort, 1,686 (9%) developed composite MACE outcome HR 1.99; (95% CI, 1.86, 2.14, p < 0.0001).LimitationsThe data are derived from EHR for administrative purposes; therefore, there is the potential for data errors or missing data.ConclusionsIn GN, routinely available cardiac biomarkers can predict incident MACE. The results suggest the clinical need for cardiovascular and mortality risk profiling in glomerular disease using a combination of clinical and laboratory variables.

Circulating microRNAs as potential biomarkers of physical activity in geriatric patients with HCV

BackgroundCirculating microRNAs have been implicated in a diverse array of biological and pathological phenomena. Their potential utility as noninvasive biomarkers for screening and diagnosing various diseases has been proposed.ObjectiveThis study aimed to explore the potential role of the miRNAs miR-122 and miR-486 as molecular biomarkers in the pathogenesis of hepatitis C virus (HCV) infection. Thus, miR-122 and miR-486 were detected in the serum of HCV patients and healthy controls. Moreover, the potential correlations of miR-122 and miR-486 with viral complications, such as physical activity, pain, muscle fatigue, and HCV infection, were identified.MethodsA total of 150 subjects aged 30 to 66 years were included in this study. The patients were classified as patients with chronic hepatitis C virus (CHC) (n = 110) or healthy controls (n = 40). Real-time polymerase chain reaction (PCR) analyses were performed to determine miR-122 and miR-486 expression. Physical activity (PA), pain score, HCV genotyping, viral overload, aspartate transaminase (AST), alanine transaminase (ALT), lactic acid dehydrogenase (LDH), creatine kinase (CK), and antioxidant status were also estimated by using prevalidated questionnaires, PCR, and spectrophotometric analyses.ResultsCompared with those in normal controls, significant increases in the serum levels of miR-122 and miR-486 were reported in patients with CHC. In physically active CHC patients, there was a significant correlation between the expression of miRNAs and increased alanine transaminase (ALT), aspartate transaminase (AST), fibrosis scores, and inflammation activity, but no association was reported for hepatitis C virus (HCV) RNA or viral load. Additionally, significant decreases in LDH, CK, GSSG, and pain scores and increases in TAC, GSH, and the GSH/GSSG ratio were reported. Moreover, the expression of miR-122 and miR-486 was positively correlated with changes in body mass index (BMI) and liver fibrosis stage, as well as negatively correlated with sex, PA, TAC, GSH, GSSG, and the GSH/GSSG ratio.ConclusionMiR-122 and miR-486 expression levels were strongly correlated with physical activity, pain perception, and muscle fatigue biomarkers in HCV-infected patients. These miRNA levels were associated with elevated AST, ALT, fibrosis scores, LDH, CK, and antioxidant status, thus suggesting their potential as biomarkers for disease severity and oxidative stress. However, no correlation was observed with viral load or HCV-RNA expression, thus implying that these miRNAs may impact disease progression and symptoms through host factors, rather than directly affecting viral replication. In summary, the results demonstrated that molecular studies of miR-22 and miR-468 and their associations with PA, pain, adiposity, sex differences, and muscle fatigue, as well as routine biomarkers, could be useful as prognostic nanoninvasive biomarkers, thus providing novel therapeutic targets for CHC infection.

Assessment of cell cycle arrest biomarkers and neutrophil gelatinase-associated lipocalin to distinguish acute kidney injury from other diseases in dogs.

Cell cycle arrest biomarkers (tissue inhibitor of metalloproteinase-2 [uTIMP-2] and insulin-like growth factor binding protein 7 [uIGFBP7]), and neutrophil gelatinase-associated lipocalin (NGAL) variables are valuable biomarkers for early diagnosis of acute kidney injury (AKI) in people. To evaluate uTIMP-2, uIGFBP7, fractional excretion of NGAL (FeNGAL), and urinary to serum NGAL ratio (u/sNGAL) in healthy dogs, dogs with AKI, dogs with chronic kidney disease (CKD), and critically ill (CI) dogs. Forty-two client-owned dogs (healthy, n = 10; AKI, n = 11; CKD, n = 11; CI, n = 10). Prospective, observational study. After assessment of routine renal biomarkers, stress (uTIMP-2, uIGFBP7) and damage (NGAL) biomarkers were measured, using ELISA kits, and normalized to urinary creatinine (uCr). Normalized uTIMP-2 and [uTIMP-2] × [uIGFBP7]/uCr were significantly higher in the AKI group (median 151.9 [range, 2.2-534.2] and 62.9 [1.1-266.8] pg/mL respectively), compared to healthy dogs (0.3 [0.2-74.7]; P < .001 and 0.16 [0.1-58.1] pg/mL; P < .001), dogs with CKD (0.7 [0.3-742.5]; P = .04 and 0.37 [0.2-180.1] pg/mL; P = .03) and CI dogs (1.9 [0.2-37.0]; P = .03 and 0.8 [0.1-16.1] pg/mL; P = .02). Fractional excretion of NGAL was significantly higher in dogs with AKI (54.17 [7.93-155.32] %), than in healthy (0.03 [0.01-0.21] %; P < .001) and CI dogs (3.05 [0.05-28.86] %; P = .02). Normalized uTIMP-2, [uTIMP-2] × [uIGFBP7]/uCr, and FeNGAL can be valuable renal biomarkers for early diagnosis of AKI in dogs.

Brain-body mechanisms contribute to sexual dimorphism in amyotrophic lateral sclerosis.

Amyotrophic lateral sclerosis (ALS) is the most common form of human motor neuron disease. It is characterized by the progressive degeneration of upper and lower motor neurons, leading to generalized motor weakness and, ultimately, respiratory paralysis and death within 3-5 years. The disease is shaped by genetics, age, sex and environmental stressors, but no cure or routine biomarkers exist for the disease. Maleindividuals have a higher propensity to develop ALS, and a different manifestation of the disease phenotype, than femaleindividuals. However, the mechanisms underlying these sex differences remain a mystery. In this Review, we summarize the epidemiology of ALS, examine the sexually dimorphic presentation of the disease and highlight the genetic variants and molecular pathways that might contribute to sex differences in humans and animal models of ALS. We advance the idea that sexual dimorphism in ALS arises from the interactions between the CNS and peripheral organs, involving vascular, metabolic, endocrine, musculoskeletal and immune systems, which are strikingly different between male and femaleindividuals. Finally, we review the response to treatments in ALS and discuss the potential to implement future personalized therapeutic strategies for the disease.

Biomarker-guided detection of acute kidney injury in abdominal aortic surgery: the new and the old.

Acute kidney injury (AKI) is a common complication in patients undergoing major vascular surgery. Despite significant research efforts in this area, the incidence of AKI remains high, posing a significant challenge to healthcare systems, especially in situations where resources are limited. Early prediction of AKI severity and individualized postoperative care is therefore essential. The primary objective of this exploratory study was to assess the diagnostic value of urine cell-cycle arrest biomarkers [(TIMP-2) × (IGFBP7)] and soluble urokinase plasminogen activator receptor (suPAR) for predicting moderate or severe AKI within 24 h after open aortic surgery, and compared to routine kidney biomarkers. Seventy-five patients undergoing elective aortic surgery were included. Clinical parameters, urine and blood samples were collected preoperatively, immediately postoperatively, and 24 h later. AKI was defined using KDIGO criteria. Individual and combined diagnostic performance of biomarkers were evaluated. Of the 75 patients, 61% developed AKI, of which 28% developed moderate or severe AKI within 24 h of surgery. Baseline demographics, comorbidities and kidney parameters did not differ between patients with moderate or severe AKI (AKI II/III) and none or mild AKI (AKI 0/I), except for higher preoperative suPAR levels in later AKI II/III patients. Urine osmolality, Cystatin C and serum creatinine had the highest predictive power for AKI II/III with AUCs of 0.75-0.72. (TIMP-2) × (IGFBP7), and neither (TIMP-2) × (IGFBP7) nor suPAR individually showed superior diagnostic value. Combining CysC or SCr with urine osmolality and 6 h urine output gave the best performance with AUCs of 0.86 (95% CI, 0.74-0.96) and 0.85 (95% CI, 0.75-0.95) respectively. Our study suggests that routine parameters like urine osmolality, CysC, SCr and 6 h urine output perform best in predicting postoperative AKI after aortic surgery compared to the new biomarkers (TIMP-2) × (IGFBP7) and suPAR. Combining biomarkers, particularly CysC or SCr with urine output, urine osmolality, may enhance diagnostic accuracy. Further validation in larger cohorts and clinical settings is warranted to establish their clinical utility.

The role of myeloperoxidase as a biomarker in atherosclerotic cardiovascular disease

Myeloperoxidase (MPO), a heme-containing protein integral to the innate immune system, plays a pivotal role in both bactericidal activity and inflammation regulation. In this comprehensive review, we aim to delve into the extensive literature on MPO’s involvement in cardiovascular disease. Through exhaustive searches of reputable databases such as PubMed, Scopus, and Web of Science, we conducted a thorough analysis to elucidate MPO’s potential as a predictor for cardiovascular disease risk. Evidence suggests that MPO serves as a valuable biomarker, particularly in identifying vulnerable lesions predisposed to rupture,leading to myocardial infarction, especially in the context of acute coronary syndrome. Its early release in response to symptom onset positions MPO as a promising tool for triaging chest pain patients in emergency departments, offering advantages over conventional markers like cardiac troponins. Moreover, studies have demonstrated a correlation between elevated MPO levels and higher mortality rates in patients with acute coronary syndrome, both in short- and long-term follow-up. However, the widespread adoption of MPO as a routine clinical biomarker faces significant challenges. Standardizing measurement techniques and determining the optimal timing of assessments are crucial for ensuring reliability and comparability across studies. Furthermore, while MPO shows promise as a complement to existing risk stratification protocols, questions remain regarding its superiority over troponins and its utility in stable coronary artery disease. Addressing these issues necessitates large-scale prospective cohort studies to clarify MPO’s comparative effectiveness alongside conventional biomarkers. Despite these challenges, MPO emerges as a potentially valuable addition to risk assessment strategies, particularly for patients with persistently negative troponin levels, helping guide therapeutic interventions and enhancing clinical decision-making in cardiovascular medicine.

Integrating routine blood biomarkers and artificial intelligence for supporting diagnosis of silicosis in engineered stone workers

AbstractEngineered stone silicosis (ESS), primarily caused by inhaling respirable crystalline silica, poses a significant occupational health risk globally. ESS has no effective treatment and presents a rapid progression from simple silicosis (SS) to progressive massive fibrosis (PMF), with respiratory failure and death. Despite the use of diagnostic methods like chest x‐rays and high‐resolution computed tomography, early detection of silicosis remains challenging. Since routine blood tests have shown promise in detecting inflammatory markers associated with the disease, this study aims to assess whether routine blood biomarkers, coupled with machine learning techniques, can effectively differentiate between healthy individuals, subjects with SS, and PMF. To this end, 107 men diagnosed with silicosis, ex‐workers in the engineered stone (ES) sector, and 22 healthy male volunteers as controls not exposed to ES dust were recruited. Twenty‐one primary biochemical markers derived from peripheral blood extraction were obtained retrospectively from clinical hospital records. Relief‐F features selection technique was applied, and the resulting subset of 11 biomarkers was used to build five machine learning models, demonstrating high performance with sensitivities and specificities in the best case greater than 82% and 89%, respectively. The percentage of lymphocytes, the angiotensin‐converting enzyme, and lactate dehydrogenase indexes were revealed, among others, as blood biomarkers with significant cumulative importance for the machine learning models. Our study reveals that these biomarkers could detect a chronic inflammatory status and potentially serve as a supportive tool for the diagnosis, monitoring, and early detection of the progression of silicosis.

Deciphering AKI in Burn Patients: Correlations between Clinical Clusters and Biomarkers.

Acute kidney injury (AKI) is a significant complication in burn patients, impacting outcomes substantially. This study explores the heterogeneity of AKI in burn patients by analyzing creatinine time-series data to identify distinct AKI clusters and evaluating routine biomarkers' predictive values. A retrospective cohort analysis was performed on 2608 adult burn patients admitted to Hangang Sacred Heart Hospital's Burn Intensive Care Unit (BICU) from July 2010 to December 2022. Patients were divided into four clusters based on creatinine trajectories, ranging from high-risk, severe cases to lower-risk, short-term care cases. Cluster A, characterized by high-risk, severe cases, showed the highest mortality and severity, with significant predictors being PT and TB. Cluster B, representing intermediate recovery cases, highlighted PT and albumin as useful predictors. Cluster C, a low-risk, high-resilience group, demonstrated predictive values for cystatin C and eGFR cys. Cluster D, comprising lower-risk, short-term care patients, indicated the importance of PT and lactate. Key biomarkers, including albumin, prothrombin time (PT), cystatin C, eGFR cys, and total bilirubin (TB), were identified as significant predictors of AKI development, varying across clusters. Diagnostic accuracy was assessed using area under the curve (AUC) metrics, reclassification metrics (NRI and IDI), and decision curve analysis. Cystatin C and eGFR cys consistently provided significant predictive value over creatinine, with AUC values significantly higher (p < 0.05) in each cluster. This study highlights the need for a tailored, biomarker-driven approach to AKI management in burn patients, advocating for the integration of diverse biomarkers in clinical practice to facilitate personalized treatment strategies. Future research should validate these biomarkers prospectively to confirm their clinical utility.