Biomarker-based Risk Research Articles

External Validation of Straticyte™, a Quantitative Biomarker-Based Risk Assay in Predicting Oral Cancer

External Validation of Straticyte™, a Quantitative Biomarker-Based Risk Assay in Predicting Oral Cancer

Prediagnostic Amino Acid Metabolites and Risk of Gout, Accounting for Serum Urate: Prospective Cohort Study and Mendelian Randomization.

Our objective was to prospectively investigate prediagnostic population-based metabolome for risk of hospitalized gout (ie, most accurate, severe, and costly cases), accounting for serum urate. We conducted prediagnostic metabolome-wide analyses among 249,677 UK Biobank participants with nuclear magnetic resonance metabolomic profiling (N = 168 metabolites, including eight amino acids) from baseline blood samples (2006-2010) without a history of gout. We calculated multivariable hazard ratios (HRs) for hospitalized incident gout, before and after adjusting for serum urate levels; we included patients with nonhospitalized incident gout in a sensitivity analysis. Potential causal effects were evaluated with two-sample Mendelian randomization. Correcting for multiple testing, 107 metabolites were associated with incidence of hospitalized gout (n = 2,735) before urate adjustment, including glycine and glutamine (glutamine HR 0.64, 95% confidence interval [CI] 0.54-0.75, P = 8.3 × 10-8; glycine HR 0.69, 95% CI 0.61-0.78, P = 3.3 × 10-9 between extreme quintiles), and glycoprotein acetyls (HR 2.48, 95% CI 2.15-2.87, P = 1.96 × 10-34). Associations remained significant and directionally consistent following urate adjustment (HR 0.83, 95% CI 0.70-0.98; HR 0.86, 95% CI 0.76-0.98; HR 1.41, 95% CI 1.21-1.63 between extreme quintiles), respectively; corresponding HRs per SD were 0.91 (95% CI 0.86-0.97), 0.94 (95% CI 0.91-0.98), and 1.10 (95% CI 1.06-1.14). Findings persisted when including patients with nonhospitalized incident gout. Mendelian randomization corroborated their potential causal role on hyperuricemia or gout risk; with change in urate levels of -0.05 mg/dL (95% CI -0.08 to -0.01) and -0.12 mg/dL (95% CI -0.22 to -0.03) per SD of glycine and glutamine, respectively, and odds ratios of 0.94 (95% CI 0.88-1.00) and 0.81 (95% CI 0.67-0.97) for gout. These prospective findings with causal implications could lead to biomarker-based risk prediction and potential supplementation-based interventions with glycine or glutamine.

Psychological outcomes of dementia risk estimation in MCI patients: Results from the PreDADQoL project.

Understanding the impact of biomarker-based dementia risk estimation in people with mild cognitive impairment (MCI) and their care partners is critical for patient care. MCI patients and study partners were counseled on Alzheimer's disease (AD) biomarker and dementia risk was disclosed. Data on mood, quality of life (QoL), and satisfaction with life (SwL) were obtained 1 week and 3 months after disclosure. Seventy-five dyads were enrolled, and two-thirds of the patients opted for biomarker testing. None of the participants experienced clinically relevant depression or anxiety after disclosure. All dyads reported moderate to high QoL and SwL throughout the study. Patients reported more subthreshold depressive symptoms 1 week and lower QoL and SwL 3 months after disclosure. In patients, depression (odds ratio [OR]: 0.76) and anxiety (OR: 0.81) were significant predictors for the decision against biomarker testing. No major psychological harm is to be expected in MCI patients and care partners after dementia risk disclosure. This study is registered in the German clinical trials register (Deutsches Register Klinischer Studien, DRKS): http://www.drks.de/DRKS00011155, DRKS registration number: DRKS00011155, date of registration: 18.08.2017. Patients with mild cognitive impairment (MCI) and study partners were counseled on Alzheimer's disease (AD) biomarker-based dementia risk estimation. About two-thirds of patients opted for biomarker testing and received their dementia risk based on their AD biomarker status. Patients who decided in favor or against CSF biomarker testing differed in psychological features. We did not observe major psychological harm after the dementia risk disclosure. Coping strategies were associated with better subsequent mood and well-being in all participants.

A Neuronal Network-Based Score Predicting Survival in Patients Undergoing Aortic Valve Intervention: The ABC-AS Score.

Background: Despite being the most commonly performed valvular intervention, risk prediction for aortic valve replacement in patients with severe aortic stenosis by currently used risk scores remains challenging. The study aim was to develop a biomarker-based risk score by means of a neuronal network. Methods: In this multicenter study, 3595 patients were divided into test and validation cohorts (70% to 30%) by random allocation. Input variables to develop the ABC-AS score were age, the cardiac biomarker high-sensitivity troponin T, and a patient history of cardiac decompensation. The validation cohort was used to verify the scores' value and for comparison with the Society of Thoracic Surgery Predictive Risk of Operative Mortality score. Results: Receiver operating curves demonstrated an improvement in prediction by using the ABC-AS score compared to the Society of Thoracic Surgery Predictive Risk of Operative Mortality (STS prom) score. Although the difference in predicting cardiovascular mortality was most notable at 30-day follow-up (area under the curve of 0.922 versus 0.678), ABC-AS also performed better in overall follow-up (0.839 versus 0.699). Furthermore, univariate analysis of ABC-AS tertiles yielded highly significant differences for all-cause (p < 0.0001) and cardiovascular mortality (p < 0.0001). Head-to-head comparison between both risk scores in a multivariable cox regression model underlined the potential of the ABC-AS score (HR per z-unit 2.633 (95% CI 2.156-3.216), p < 0.0001), while the STS prom score failed to reach statistical significance (p = 0.226). Conclusions: The newly developed ABC-AS score is an improved risk stratification tool to predict cardiovascular outcomes for patients undergoing aortic valve intervention.

Clinical biomarker-based biological aging and risk of benign prostatic hyperplasia: A large prospective cohort study.

Chronological age (CAge), biological age (BAge), and accelerated age (AAge) are all important for aging-related diseases. CAge is a known risk factor for benign prostatic hyperplasia (BPH); However, the evidence of association of BAge and AAge with BPH is limited. This study aimed to evaluate the association of CAge, Bage, and AAge with BPH in a large prospective cohort. A total of 135,933 males without BPH at enrolment were extracted from the UK biobank. We calculated three BAge measures (Klemera-Doubal method, KDM; PhenoAge; homeostatic dysregulation, HD) based on 16 biomarkers. Additionally, we calculated KDM-BAge and PhenoAge-BAge measures based on the Levine method. The KDM-AAge and PhenoAge-AAge were assessed by the difference between CAge and BAge and were standardized (mean = 0 and standard deviation [SD] = 1). Cox proportional hazard models were applied to assess the associations of CAge, Bage, and AAge with incident BPH risk. During a median follow-up of 13.150 years, 11,811 (8.690%) incident BPH were identified. Advanced CAge and BAge measures were associated with an increased risk of BPH, showing threshold effects at a later age (all P for nonlinearity <0.001). Nonlinear relationships between AAge measures and risk of BPH were also found for KDM-AAge (P = 0.041) and PhenoAge-AAge (P = 0.020). Compared to the balance comparison group (-1 SD < AAge < 1 SD), the accelerated aging group (AAge > 2 SD) had a significantly elevated BPH risk with hazard ratio (HR) of 1.115 (95% CI, 1.000-1.223) for KDM-AAge and 1.180 (95% CI, 1.068-1.303) for PhenoAge-AAge, respectively. For PhenoAge-AAge, subgroup analysis of the accelerated aging group showed an increased HR of 1.904 (95% CI, 1.374-2.639) in males with CAge <50 years and 1.233 (95% CI, 1.088-1.397) in those having testosterone levels <12 nmol/L. Moreover, AAge-associated risk of BPH was independent of and additive to genetic risk. Biological aging is an independent and modifiable risk factor for BPH. We suggest performing active health interventions to slow biological aging, which will help mitigate the progression of prostate aging and further reduce the burden of BPH.

P3 Usefulness of urinary biomarker-based risk score and multiparametric MRI for clinically significant prostate cancer detection in biopsy-naïve patients

P3 Usefulness of urinary biomarker-based risk score and multiparametric MRI for clinically significant prostate cancer detection in biopsy-naïve patients

A State-of-the-art Review on Artificial Intelligence and Machine Learning Applications

Background: The integration of artificial intelligence and machine learning holds great promise for enhancing healthcare institutions and providing fresh perspectives on the origins and advancement of long-term illnesses. In the healthcare sector, artificial intelligence and machine learning are used to address supply and demand concerns, genomic applications, and new advancements in drug development, cancer, and heart disease. Objective: The article explores the ways that machine learning, AI, precision medicine, and genomics are changing healthcare. The essay also discusses how AI's examination of various patient data could enhance healthcare institutions, provide fresh insights into chronic conditions, and advance precision medicine. The potential uses of machine learning for genome analysis are also examined in the paper, particularly about genetic biomarker-based disease risk and symptom prediction. Discussion: The challenges posed by the phenotype-genotype relationship are examined, as well as the significance of comprehending disease pathways in order to create tailored treatments. Moreover, it offers a streamlined and modularized method that predicts how genotypes affect cell properties using machine-learning models, enabling the development of personalized drugs. The collective feedback highlights the rapid interdisciplinary growth of medical genomics following the completion of the Human Genome Project. It also emphasizes how important genomic data is for improving healthcare outcomes and facilitating personalized medicine. Conclusion: The study's conclusions point to a revolutionary shift in healthcare: the application of AI/ML to illness control. Even though these innovations have a lot of potential benefits, problems like algorithm interpretability and ethical issues need to be worked out before they can be successfully incorporated into routine medical practice. Using machine learning in medicine has enormous potential benefits for the biotech industry. Further research, ongoing regulatory frameworks, and collaboration between medical professionals and data analysts are necessary to fully utilize machine learning as well as artificial intelligence in disease management.

Biomarker vs MRI-Enhanced Strategies for Prostate Cancer Screening

Prostate cancer guidelines often recommend obtaining magnetic resonance imaging (MRI) before a biopsy, yet MRI access is limited. To date, no randomized clinical trial has compared the use of novel biomarkers for risk estimation vs MRI-based diagnostic approaches for prostate cancer screening. To evaluate biomarker-based risk estimation (Stockholm3 risk scores or prostate-specific antigen [PSA] levels) with systematic biopsies vs an MRI-enhanced strategy (PSA levels and MRI with systematic and targeted biopsy) for the detection of clinically significant prostate cancer in a screening setting. This open-label randomized clinical trial conducted in Stockholm, Sweden, between April 4, 2018, and December 10, 2020, recruited men aged 50 to 74 years with no history of prostate cancer. Participants underwent blood sampling for PSA and Stockholm3 tests to estimate their risk of clinically significant prostate cancer (Gleason score ≥3 + 4). After the blood tests were performed, participants were randomly assigned in a 2:3 ratio to receive a Stockholm3 test with systematic biopsy (biomarker group) or a PSA test followed by MRI with systematic and targeted biopsy (MRI-enhanced group). Data were analyzed from September 1 to November 5, 2023. In the biomarker group, men with a Stockholm3 risk score of 0.15 or higher underwent systematic biopsies. In the MRI-enhanced group, men with a PSA level of 3 ng/mL or higher had an MRI and those with a Prostate Imaging-Reporting and Data System (PI-RADS) score of 3 or higher (range: 1-5, with higher scores indicating a higher likelihood of clinically significant prostate cancer) underwent targeted and systematic biopsies. Primary outcome was detection of clinically significant prostate cancer (Gleason score ≥3 + 4). Secondary outcomes included detection of clinically insignificant cancer (Gleason score ≤6) and the number of biopsy procedures performed. Of 12 743 male participants (median [IQR] age, 61 [55-67] years), 5134 were assigned to the biomarker group and 7609 to the MRI-enhanced group. In the biomarker group, 8.0% of men (413) had Stockholm3 risk scores of 0.15 or higher and were referred for systematic biopsies. In the MRI-enhanced group, 12.2% of men (929) had a PSA level of 3 ng/mL or higher and were referred for MRI with biopsies if they had a PI-RADS score of 3 or higher. Detection rates of clinically significant prostate cancer were comparable between the 2 groups: 2.3% in the biomarker group and 2.5% in the MRI-enhanced group (relative proportion, 0.92; 95% CI, 0.73-1.15). More biopsies were performed in the biomarker group than in the MRI-enhanced group (326 of 5134 [6.3%] vs 338 of 7609 [4.4%]; relative proportion, 1.43 [95% CI, 1.23-1.66]), and more indolent prostate cancers were detected (61 [1.2%] vs 41 [0.5%]; relative proportion, 2.21 [95% CI, 1.49-3.27]). Findings of this randomized clinical trial indicate that combining a Stockholm3 test with systematic biopsies is comparable with MRI-based screening with PSA levels and systematic and targeted biopsies for detection of clinically significant prostate cancer, but this approach resulted in more biopsies as well as detection of a greater number of indolent cancers. In regions where access to MRI is lacking, the Stockholm3 test can aid in selecting patients for systematic prostate biopsy. ClinicalTrials.gov Identifier: NCT03377881.

A Validated Risk Stratification That Incorporates MAGIC Biomarkers Predicts Long-Term Outcomes in Pediatric Patients with Acute GVHD

Acute graft versus host disease (GVHD) is a common and serious complication of allogeneic hematopoietic cell transplantation (HCT) in children but overall clinical grade at onset only modestly predicts response to treatment and survival outcomes. Two tools to assess risk at initiation of treatment were recently developed. The Minnesota risk system stratifies children for risk of nonrelapse mortality (NRM) according to the pattern of GVHD target organ severity. The Mount Sinai Acute GVHD International Consortium (MAGIC) algorithm of 2 serum biomarkers (ST2 and REG3α) predicts NRM in adult patients but has not been validated in a pediatric population. We aimed to develop and validate a system that stratifies children at the onset of GVHD for risk of 6-month NRM. We determined the MAGIC algorithm probabilities (MAPs) and Minnesota risk for a multicenter cohort of 315 pediatric patients who developed GVHD requiring treatment with systemic corticosteroids. MAPs created 3 risk groups with distinct outcomes at the start of treatment and were more accurate than Minnesota risk stratification for prediction of NRM (area under the receiver operating curve (AUC), .79 versus .62, P = .001). A novel model that combined Minnesota risk and biomarker scores created from a training cohort was more accurate than either biomarkers or clinical systems in a validation cohort (AUC .87) and stratified patients into 2 groups with highly different 6-month NRM (5% versus 38%, P < .001). In summary, we validated the MAP as a prognostic biomarker in pediatric patients with GVHD, and a novel risk stratification that combines Minnesota risk and biomarker risk performed best. Biomarker-based risk stratification can be used in clinical trials to develop more tailored approaches for children who require treatment for GVHD.

Optimization of benzene exposure risk assessment: An integrated approach utilizing internal and external concentrations with a focus on biomarkers S-PMA & t, t-MA

In the majority of occupational settings within China, the concentrations of benzene are observed to fall markedly below the demarcated detection thresholds. Employing traditional risk assessment models, the presence of exceptionally low airborne benzene exposure concentrations may infuse heightened degrees of uncertainty. Consequently, the necessity arises to investigate risk assessment methodologies more apt for the prevalent exposure environment among employees. In the present study, a pharmacokinetic model premised on urinary benzene metabolites (S-PMA and t, t-MA) was employed to ascertain a more precise daily airborne benzene exposure concentration per individual. This value was integrated into the linear multistage model as the ‘internal exposure concentration’. In conjunction with the U.S National Environmental Protection Agency's (EPA) inhalation risk assessment model predicated on the external exposure concentration, the Singapore Ministry of Manpower's (MOM) model, and the linear multistage (LMS) model, the carcinogenic and non-carcinogenic effects of benzene were evaluated for 1781 benzene-exposed employees across 76 enterprises in Jiangsu Province. Findings suggest that in the linear multilevel model assessment, the cancer risk levels based on t, t-MA and S-PMA were higher in the printing and recording media reproduction industry, automobile manufacturing industry, general equipment manufacturing industry and the furniture manufacturing industry (median 2.842 × 10−4, 2.819 × 10−4, 2.809 × 10−4, and 2.678 × 10−4), which align more consistently with the actual benzene exposure circumstances of each industry's study participants, with overall risk levels calculated by the linear multistage model exceeding those of the EPA inhalation risk assessment model and the MOM model. This implies that the linear multistage model of internal exposure, based on the reciprocal of benzene biomarkers S-PMA and t, t-MA for airborne benzene exposure, presents enhanced sensitivity and suitability for the current occupational health risk assessment of workers. Without doubt, biomarker-based benzene exposure risk assessment emerges as the optimal choice.

Biomarker Assessment of a High-Risk, Data-Driven Pediatric Sepsis Phenotype Characterized by Persistent Hypoxemia, Encephalopathy, and Shock.

Identification of children with sepsis-associated multiple organ dysfunction syndrome (MODS) at risk for poor outcomes remains a challenge. We sought to the determine reproducibility of the data-driven "persistent hypoxemia, encephalopathy, and shock" (PHES) phenotype and determine its association with inflammatory and endothelial biomarkers, as well as biomarker-based pediatric risk strata. We retrained and validated a random forest classifier using organ dysfunction subscores in the 2012-2018 electronic health record (EHR) dataset used to derive the PHES phenotype. We used this classifier to assign phenotype membership in a test set consisting of prospectively (2003-2023) enrolled pediatric septic shock patients. We compared profiles of the PERSEVERE family of biomarkers among those with and without the PHES phenotype and determined the association with established biomarker-based mortality and MODS risk strata. Twenty-five PICUs across the United States. EHR data from 15,246 critically ill patients with sepsis-associated MODS split into derivation and validation sets and 1,270 pediatric septic shock patients in the test set of whom 615 had complete biomarker data. None. The area under the receiver operator characteristic curve of the modified classifier to predict PHES phenotype membership was 0.91 (95% CI, 0.90-0.92) in the EHR validation set. In the test set, PHES phenotype membership was associated with both increased adjusted odds of complicated course (adjusted odds ratio [aOR] 4.1; 95% CI, 3.2-5.4) and 28-day mortality (aOR of 4.8; 95% CI, 3.11-7.25) after controlling for age, severity of illness, and immunocompromised status. Patients belonging to the PHES phenotype were characterized by greater degree of systemic inflammation and endothelial activation, and were more likely to be stratified as high risk based on PERSEVERE biomarkers predictive of death and persistent MODS. The PHES trajectory-based phenotype is reproducible, independently associated with poor clinical outcomes, and overlapped with higher risk strata based on prospectively validated biomarker approaches.

Development and validation of the ASGARD risk score for safe monitoring in asymptomatic nonsevere aortic stenosis.

Current guidelines recommend serial echocardiography at minimum 1-2 year intervals for monitoring patients with nonsevere aortic valve stenosis (AS), which is costly and often clinically inconsequential.We aimed to develop and test whether the biomarker-based ASGARD risk score (Aortic Valve Stenosis Guarded by Amplified Risk Determination) can guide the timing of echocardiograms in asymptomatic patients with nonsevere AS. The development cohort comprised 1,093 of 1,589 (69%) asymptomatic patients with mild-to-moderate AS who remained event-free one year after inclusion into the SEAS trial. Cox regression landmark analyses with a 2-year follow-up identified the model (ASGARD) with the lowest Akaike information criterion for association to AS-related composite outcome (heart failure hospitalization, aortic valve replacement, or cardiovascular death). Fine-Gray analyses provided cumulative event rates by ASGARD score quartiles. The ASGARD score was internally validated in the remaining 496 patients (31%) from the SEAS-cohort and externally in 71 asymptomatic outpatients with nonsevere AS from six Copenhagen hospitals. The ASGARD score comprises updated measurements of heart rate and age- and sex-adjusted N-terminal pro-brain natriuretic peptide upon transaortic maximal velocity (Vmax) from the previous year. The ASGARD score had high predictive accuracy across all cohorts (external validation: area under the curve: 0.74 [95% CI, 0.62-0.86]), and similar to an updated Vmax measurement. An ASGARD score ≤50% was associated with AS-related event rates ≤5% for a minimum of 15 months. The ASGARD score could provide a personalized and safe surveillance alternative to routinely planned echocardiograms, so physicians can prioritize echocardiograms for high-risk patients.

Revisiting Post-ICU Admission Fluid Balance Across Pediatric Sepsis Mortality Risk Strata: A Secondary Analysis of a Prospective Observational Cohort Study.

Post-ICU admission cumulative positive fluid balance (PFB) is associated with increased mortality among critically ill patients. We sought to test whether this risk varied across biomarker-based risk strata upon adjusting for illness severity, presence of severe acute kidney injury (acute kidney injury), and use of continuous renal replacement therapy (CRRT) in pediatric septic shock. Ongoing multicenter prospective observational cohort. Thirteen PICUs in the United States (2003-2023). Six hundred and eighty-one children with septic shock. None. Cumulative percent PFB between days 1 and 7 (days 1-7 %PFB) was determined. Primary outcome of interest was complicated course defined as death or persistence of greater than or equal to two organ dysfunctions by day 7. Pediatric Sepsis Biomarker Risk Model (PERSEVERE)-II biomarkers were used to assign mortality probability and categorize patients into high mortality (n = 91), intermediate mortality (n = 134), and low mortality (n = 456) risk strata. Cox proportional hazard regression models with adjustment for PERSEVERE-II mortality probability, presence of sepsis-associated acute kidney injury on day 3, and use of CRRT, demonstrated that time-dependent variable days 1-7%PFB was independently associated with an increased hazard of complicated course. Risk-stratified analyses revealed that each 10% increase in days 1-7 %PFB was associated with increased hazard of complicated course only among patients with high mortality risk strata (adjusted hazard ratio 1.24 (95% CI, 1.08-1.43), p = 0.003). However, this association was not causally mediated by PERSEVERE-II biomarkers. Our data demonstrate the influence of cumulative %PFB on the risk of complicated course in pediatric septic shock. Contrary to our previous report, this risk was largely driven by patients categorized as having a high mortality risk based on PERSEVERE-II biomarkers. Incorporation of such prognostic enrichment tools in randomized trials of restrictive fluid management or early initiation of de-escalation strategies may inform targeted application of such interventions among at-risk patients.

Two machine learning-derived nomogram for predicting the occurrence and severity of acute graft-versus-host disease: a retrospective study based on serum biomarkers.

Acute graft-versus-host disease (aGVHD) is a common complication after allogeneic hematopoietic cell transplantation (allo-HSCT), with high morbidity and mortality. Although glucocorticoids are the standard treatment, only half of patients achieve complete remission. Thus, there is an urgent need to screen biomarkers for the diagnosis of aGVHD to assist in the identification of individuals at risk of aGVHD. This study was to construct prediction models for the occurrence and severity of aGVHD using two machine learning algorithms based on serum biochemical data. Clinical data of 120 patients with hematological diseases who received allo-HSCT were retrospectively analyzed. Seventy-six patients developed aGVHD, including 56 grade I/II and 20 grade III/IV. First, 15 serum biochemical indicators were considered as potential risk factors, and the differences in the levels of indicators between non-aGVHD and aGVHD were observed, followed by evaluation of the diagnostic property. Subsequently, to develop the prediction models for the occurrence and severity of aGVHD, LASSO and random forest (RF) analyses were performed with experimental indicators. Finally, Venn diagram analysis was utilized to obtain shared biomarkers in the two algorithms to construct the nomogram. The model performance was measured by calibration curves. Internal and external validations were performed based on risk score models and ROC curve analyses. Total 12 of 15 indicators exhibited significant differences between the aGVHD and non-aGVHD groups, with AUC values > 0.75. In machine learning analysis, eight features (LAG-3, TLR-2, PD-L1, IP-10, elafin, REG-3α, ST2, TIM3) and seven variables (LAG-3, TLR-2, PD-1, Flt_3, IL-9, elafin, TIM3) were selected to distinguish aGVHD vs. non-aGVHD as well as grade I/II vs. III/IV, respectively. Further, the corresponding nomogram models were established and calibration curves showed that prediction was in good agreement with the actual probability. Biomarker-based risk score model was constructed, which obtained AUC value >0.89 in internal and external datasets. Clinical variables screened through learning algorithm can predict the risk and severity of aGVHD. Our findings may help clinicians develop more personalized and reasonable management strategies.

Biomarker-based risk prediction for the onset of neuroinflammation in X-linked adrenoleukodystrophy

X-linked adrenoleukodystrophy (X-ALD) is highly variable, ranging from slowly progressive adrenomyeloneuropathy to severe brain demyelination and inflammation (cerebral ALD, CALD) affecting males with childhood peak onset. Risk models integrating blood-based biomarkers to indicate CALD onset, enabling timely interventions, are lacking. Therefore, we evaluated the prognostic value of blood biomarkers in addition to current neuroimaging predictors for early detection of CALD. We measured blood biomarkers in a retrospective, male CALD risk-assessment cohort consisting of 134 X-ALD patients and 66 controls and in a phenotype-blinded validation set (25 X-ALD boys, 4-13 years) using Simoa®and Luminex® technologies. Among 25 biomarkers indicating axonal damage, astrocye/microglia activation, or immune-cell recruitment, neurofilament light chain (NfL) had the highest prognostic value for early indication of childhood/adolescent CALD. A plasma NfL cut-off level of 8.33pg/mL, determined in the assessment cohort, correctly discriminated CALD with an accuracy of 96% [95% CI: 80-100] in the validation group. Multivariable logistic regression models revealed that combining NfL with GFAP or cytokines/chemokines (IL-15, IL-12p40, CXCL8, CCL11, CCL22, and IL-4) that were significantly elevated in CALD vs healthy controls had no additional benefit for detecting neuroinflammation. Some cytokines/chemokines were elevated only in childhood/adolescent CALD and already upregulated in asymptomatic X-ALD children (IL-15, IL-12p40, and CCL7). In adults, NfL levels distinguished CALD but were lower than in childhood/adolescent CALD patients with similar (MRI) lesion severity. Blood GFAP did not differentiate CALD from non-inflammatory X-ALD. Biomarker-based risk prediction with a plasma NfL cut-off value of 8.33pg/mL, determined by ROC analysis, indicates CALD onset with high sensitivity and specificity in childhood X-ALD patients. A specific pro-inflammatory cytokine/chemokine profile in asymptomatic X-ALD boys may indicate a primed, immanent inflammatory state aligning with peak onset of CALD. Age-related differences in biomarker levels in adult vs childhood CALD patients warrants caution in predicting onset and progression of CALD in adults. Further evaluations are needed to assess clinical utility of the NfL cut-off for risk prognosis of CALD onset. Austrian Science Fund, European Leukodystrophy Association.

Medical care and biomarker-based assessment of mortality in two cohorts of patients with chronic coronary syndrome 10 years apart

BackgroundThis study aimed to describe the characteristics and mortality of two cohorts of patients with chronic coronary syndrome (CCS) recruited with identical study designs in the same rehabilitation clinics but approximately 10 years apart.MethodsThe KAROLA cohorts included patients with CCS participating in an inpatient cardiac rehabilitation programme in Germany (KAROLA-I: years 1999/2000, KAROLA-II: 2009–2011). Blood samples and information on sociodemographic factors, lifestyle, and medical treatment were collected at baseline, at the end of rehabilitation, and after one year of follow-up. A biomarker-based risk model (ABC-CHD model) and Cox regression analysis were used to evaluate cardiovascular (CV) and non-CV mortality risk.ResultsWe included 1130 patients from KAROLA-I (mean age 58.7 years, 84.4% men) and 860 from KAROLA-II (mean age 60.4 years, 83.4% men). Patients in the KAROLA-I cohort had significantly higher concentrations of CV biomarkers and fewer patients were taking CV medications, except for statins. The biomarker-based ABC-CHD model provided a higher estimate of CV death risk for patients in the KAROLA-I cohort (median 3-year risk, 3.8%) than for patients in the KAROLA-II cohort (median 3-year risk, 2.7%, p-value for difference < 0.001). After 10 years of follow-up, 91 (8.1%) patients in KAROLA-I and 45 (5.2%) in KAROLA-II had died from a CV event.ConclusionsAdvances in disease management over the past 20 years may have led to modest improvements in pharmacological treatment during cardiac rehabilitation and long-term outpatient care for patients with CCS. However, modifiable risk factors such as obesity have increased in the more recent cohort and should be targeted to further improve the prognosis of these patients.

Retracted: CDC20 May Serve as a Potential Biomarker-Based Risk Score System in Predicting the Prognosis of Patients with Hepatocellular Carcinoma.

[This retracts the article DOI: 10.1155/2022/8421813.].

Temporal relationships between BMI and obesity-related predictors of cardiometabolic and breast cancer risk in a longitudinal cohort

Prospective inter-relationships among biomarkers were unexplored, which may provide mechanistic insights into diseases. We investigated the longitudinal associations of BMI change with trajectories of biomarkers related to cardiometabolic or breast cancer risk. A longitudinal study was conducted among 444 healthy women between 2019 to 2021. Cross‑lagged path analysis was used to examine the temporal relationships among BMI, cardiometabolic risk score (CRS), and obesity‑related proteins score (OPS) of breast cancer. Linear mixed-effect models were applied to investigate associations of time-varying BMI with biomarker-based risk score trajectories. Baseline BMI was associated with subsequent change of breast cancer predictors (P = 0.03), and baseline CRS were positively associated with OPS change (P < 0.001) but not vice versa. After fully adjustment of confounders, we found a 0.058 (95%CI = 0.009–0.107, P = 0.020) units increase of CRS and a 1.021 (95%CI = 0.041–1.995, P = 0.040) units increase of OPS as BMI increased 1 kg/m2 per year in postmenopausal women. OPS increased 0.784 (95%CI = 0.053–1.512, P = 0.035) units as CRS increased 1 unit per year. However, among premenopausal women, BMI only significantly affected CRS (β = 0.057, 95%CI = 0.007 to 0.107, P = 0.025). No significant change of OPS with time-varying CRS was found. Higher increase rates of BMI were associated with worse trajectories of biomarker-based risk of cardiometabolic and breast cancer. The longitudinal impact of CRS on OPS is unidirectional. Recommendations such as weight control for the reduction of cardiometabolic risk factors may benefit breast cancer prevention, especially in postmenopausal women.

Prognostic and predictive value of endothelial dysfunction biomarkers in sepsis-associated acute kidney injury: risk-stratified analysis from a prospective observational cohort of pediatric septic shock

BackgroundSepsis-associated acute kidney injury (SA-AKI) is associated with high morbidity, with no current therapies available beyond continuous renal replacement therapy (CRRT). Systemic inflammation and endothelial dysfunction are key drivers of SA-AKI. We sought to measure differences between endothelial dysfunction markers among children with and without SA-AKI, test whether this association varied across inflammatory biomarker-based risk strata, and develop prediction models to identify those at highest risk of SA-AKI.MethodsSecondary analyses of prospective observational cohort of pediatric septic shock. Primary outcome of interest was the presence of ≥ Stage II KDIGO SA-AKI on day 3 based on serum creatinine (D3 SA-AKI SCr). Biomarkers including those prospectively validated to predict pediatric sepsis mortality (PERSEVERE-II) were measured in Day 1 (D1) serum. Multivariable regression was used to test the independent association between endothelial markers and D3 SA-AKI SCr. We conducted risk-stratified analyses and developed prediction models using Classification and Regression Tree (CART), to estimate risk of D3 SA-AKI among prespecified subgroups based on PERSEVERE-II risk.ResultsA total of 414 patients were included in the derivation cohort. Patients with D3 SA-AKI SCr had worse clinical outcomes including 28-day mortality and need for CRRT. Serum soluble thrombomodulin (sTM), Angiopoietin-2 (Angpt-2), and Tie-2 were independently associated with D3 SA-AKI SCr. Further, Tie-2 and Angpt-2/Tie-2 ratios were influenced by the interaction between D3 SA-AKI SCr and risk strata. Logistic regression demonstrated models predictive of D3 SA-AKI risk performed optimally among patients with high- or intermediate-PERSEVERE-II risk strata. A 6 terminal node CART model restricted to this subgroup of patients had an area under the receiver operating characteristic curve (AUROC) 0.90 and 0.77 upon tenfold cross-validation in the derivation cohort to distinguish those with and without D3 SA-AKI SCr and high specificity. The newly derived model performed modestly in a unique set of patients (n = 224), 84 of whom were deemed high- or intermediate-PERSEVERE-II risk, to distinguish those patients with high versus low risk of D3 SA-AKI SCr.ConclusionsEndothelial dysfunction biomarkers are independently associated with risk of severe SA-AKI. Pending validation, incorporation of endothelial biomarkers may facilitate prognostic and predictive enrichment for selection of therapeutics in future clinical trials among critically ill children.Graphical abstract

Comparison of medical care and long-term mortality in two cohorts of patients 10 years apart with chronic coronary syndrome

Abstract Funding Acknowledgements Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Bundesministerium für Bildung und Forschung. Background and introduction Cardiovascular diseases (CVD) remain the most common cause of death, although mortality due to CVD in Germany has decreased continuously over the past 20 years. The reasons for this decline are manifold. Besides the identification of risk factors, the application of algorithms for risk stratification, and the promotion of preventive means, the improvement in treatment strategies was of crucial importance [1, 2]. The ABC-CHD risk model combines novel biomarkers such as N-terminal pro-B-natriuretic peptide (NT-proBNP) and cardiac troponin T (hs-cTnT) with conventional factors in a unifying risk score which has been shown to reliably assess risk for CV mortality in patients with chronic coronary syndrome (CCS) [3]. However, data from the tertiary prevention setting, comparing its performance during different time periods, are scarce. Aims This study aimed to describe the characteristics and mortality of two cohorts of patients with CCS with identical study designs recruited in the same rehabilitation clinics approximately 10 years apart. Methods The KAROLA cohorts included patients with CCS participating in an inpatient cardiac rehabilitation programme in Germany (KAROLA-I: 1999/2000, KAROLA-II: 2009-2011) [details in 4]. Blood samples and information on sociodemographic factors, lifestyle, and medical treatment were collected at baseline, at the end of rehabilitation, and after 1-year follow-up. A biomarker-based risk model (ABC-CHD model) [3] and Cox regression analysis were used to predict cardiovascular (CV) and non-CV mortality risk during 10 years of follow-up. Results We included 1130 patients from KAROLA-I (mean age 58.7 years, 84.4% men) and 860 from KAROLA-II (mean age 60.4 years, 83.4% men). Patients in the KAROLA-I cohort had significantly higher concentrations of CV biomarkers and fewer patients were taking CV medications, except for statins. The ABC-CHD model which also included age, current smoking, history of diabetes, peripheral arterial disease, as well as hs-cTNT, NT-proBNP, and LDL-cholesterol provided a higher estimate of CV death risk for patients in the KAROLA-I cohort (mean 3-year risk, 3.8%) than for patients in the KAROLA-II cohort (mean 3-year risk, 2.7%, p for difference &amp;lt;0.001). After 10 years of follow-up, 91 (8.1%) patients in KAROLA-I and 45 (5.2%) in KAROLA-II had died from a CV event. Non-CV mortality was quite similar among the two cohorts. Notably, when statin medication upon discharge was included as an exposure variable in the Cox model that included age, sex, biomarkers, and clinical variables for the combined analysis with both cohorts, the HR for CV mortality was 0.54 (95% CI 0.31-0.93) in patients taking high-potency statins compared with patients not on statin medication. Conclusions Advances in disease management including pharmacological treatment over the past 20 years may have led to modest improvement of long-term prognosis of patients with CCS.