Biomarkers in medicine最新文献

Introduction: The use of biomarkers as surrogate endpoints, supported by strong mechanistic, epidemiologic, and/or clinical data, provides drug development programs with endpoints that predict clinical benefit and may be more sensitive to drug effects than clinical endpoints. Neurofilament light chain (NfL) is a marker of neuroaxonal injury that has emerged as a promising biomarker for neurodegenerative diseases.

Methods: We identified Investigational New Drug programs submitted to the U.S. Food and Drug Administration between 2005-2024 that proposed to use NfL as a pharmacodynamic biomarker, biomarker for patient selection or stratification, and/or surrogate endpoint for accelerated approval.

Results: A total of 50 programs were identified with most from the last five years. Of the 50 programs, 94% (n = 47) proposed NfL as a pharmacodynamic biomarker, 8% (n = 4) for patient selection, 52% (n = 26) for patient stratification, and 20% (n = 10) as a surrogate endpoint. Of the programs evaluating NfL as a pharmacodynamic biomarker with available data on NfL levels (n = 21), approximately 50% reported NfL changes that correlated with drug exposure.

Conclusion: This analysis highlights the important role that NfL plays in clinical trials and identifies future areas of research and study design considerations to strengthen the support of NfL as a biomarker.

Background: The Naples Prognostic Score (NPS) is a composite index of inflammation and nutritional status. This study aimed to evaluate the prognostic value of NPS for long-term all-cause mortality in non-ST-elevation myocardial infarction (NSTEMI).

Methods: In this study, 396 consecutive NSTEMI patients who underwent coronary angiography/percutaneous coronary intervention between 1 August 2023, and 31 July 2024, were included. Patients were stratified into low (NPS: 0-1), intermediate (NPS: 2), and high (NPS: 3-4) risk groups. Median follow-up was 433 days. Univariate logistic regression identified predictors of longterm mortality. These were then entered into LASSOpenalized logistic regression for variable selection. Multivariate Cox proportional hazards models assessed the independent predictors of long-term mortality, adjusting for SYNTAX score, hemoglobin, sodium, age, and left ventricular ejection fraction (LVEF).

Results: High-risk NPS patients (n = 91) had higher long-term mortality (17.6%) compared with intermediate-risk (3.7%) and low-risk (3.5%) groups (p < 0.001). In the adjusted Cox model, high-risk NPS independently predicted long-term mortality (HR:3.79; 95% CI 1.55-9.27; p = 0.003), whereas neutrophil-to-lymphocyte ratio (NLR) and C-reactive protein (CRP) were not significant when substituted for NPS.

Conclusion: In NSTEMI patients, NPS independently predicts long-term all-cause mortality beyond traditional risk markers and outperforms isolated inflammatory indices such as NLR and CRP.

Objective: To investigate the relationship between Group B Streptococcus (GBS) infection in pregnant women and adverse pregnancy events (APEs), as well as serum inflammatory factors (IFs) and coagulation parameters.

Methods: A retrospective analysis was conducted on 2,200 late-pregnancy women who delivered at Maternal and Child Health Hospital from March 2020 to January 2023. Data on GBS infection, serotyping, immunofluorescence, coagulation parameters, and APEs were collected. APE incidence was compared between groups, and logistic regression was performed to analyze inflammatory factors and coagulation parameters in GBS-positive women with and without APEs.

Results: Among the participants, 593 (26.95%) were GBS-positive, and 1,607 (73.05%) were GBS-negative. The GBS-positive group had significantly higher rates of preterm birth, intrauterine infection, premature rupture of membranes, placental abruption, postpartum hemorrhage, and meconium-stained amniotic fluid compared to the GBS-negative group (p < 0.05). Logistic regression identified white blood cell count, high-sensitivity C-reactive protein, tumor necrosis factor-α, interleukin-6 (IL-6), interleukin-1β, procalcitonin, and fibrinogen as independent risk factors for APEs (p < 0.05).

Conclusion: GBS infection increases the risk of adverse pregnancy events and is closely associated with alterations in inflammatory and coagulation markers.

Aims: This study aimed to investigate the association between the systemic immune-inflammation index (SII) and prognosis trajectories in ischemic stroke(IS).

Methods: Patients from two tertiary hospitals in Suzhou were included in this study. SII was calculated as neutrophils×platelets/lymphocytes, and patients were categorized into quartiles based on SII values. Latent class growth modeling (LCGM) was employed to describe the trajectories of modified Rankin Scale (mRS) at different time points.Logistic regression models were used to evaluate the association between SII quartiles and prognosis trajectories at multiple time points (14 days, 1 month, 3 months, 6 months)and prognostic trajectories.

Results: Patients in the highest quartile (Q4) of SII had a significantly higher risk of adverse outcomes compared to those in the lowest quartile (Q1). A three-group model was identified as the optimal trajectory model for stroke prognosis. SII was associated with 4.06-fold increased odds (95% CI: 1.64-10.08) of unfavorable prognosis trajectories. Per standard deviation increase in the logarithmic SII, the odds of unfavorable prognosis trajectories were 1.64 (95% CI: 1.18-2.29).

Conclusions: Baseline SII is significantly associated with unfavorable outcome trajectories across multiple time points in IS. These findings highlight the potential value of SII as a predictive biomarker for sequential prognosis in stroke patients.

Aims and background: Gastric cancer is a significant health challenge globally, necessitating the identification of novel biomarkers for improved diagnosis. Our study aimed to address this gap by investigating the diagnostic potential a set of long non-coding RNAs (lncRNAs) in gastric cancer patients compared to healthy controls.

Materials and methods: We conducted a retrospective case-control analysis involving 256 participants, including 128 gastric cancer patients and 128 healthy individuals. Alongside the measurement of serum levels of carcinoembryonic antigen (CEA) and carbohydrate antigen 19-9 (CA19.9), the plasma expression profiling of eight lnc-RNAs was performed in the selected samples.

Results: Our findings revealed significant alterations in CEA and CA19.9 levels in gastric cancer patients compared to controls (p < 0.05). Moreover, upregulated expression of lncRNAs, including HOTIP, BANCR, ZFAS1, TINCR, GC1, and AFAP1-AS1, were all observed in gastric cancer patients (p < 0.05). Excluding lncUGC1 and FAM49B-AS, ROC curve analysis demonstrated the diagnostic potential of lncRNAs in gastric cancer detection, with notable specificity and sensitivity.

Conclusion: Our study disclosed the diagnostic utility of plasma HOTIP, BANCR, ZFAS1, TINCR, GC1, and AFAP1-AS1 lncRNAs as appropriate diagnostic biomarkers for gastric cancer. The identification of dysregulated lncRNAs offers promising avenues for noninvasive diagnostic assessment in gastric cancer.

Aims: This study aimed to investigate the association between the Red cell distribution width-to-albumin ratio (RAR) and in-hospital mortality in patients experiencing Acute Exacerbation of Chronic Obstructive Pulmonary Disease (AECOPD) with concurrent Respiratory Failure (RF).

Methods: This retrospective study included 594 patients diagnosed with AECOPD and RF at the first affiliated hospital of Jinzhou Medical University, China, between January 2021 and September 2023. The primary outcome measure was in-hospital mortality rate. The participants were categorized into three groups according to RAR tertiles: T1 (2.535-3.871), N = 198; T2 (3.88-4.547), N = 196; and T3 (4.56-11.031), N = 200. Logistic regression and subgroup analyses were performed to investigate the relationship between RAR and AECOPD and RF prognosis in patients.

Results: The average age of the participants was 72.1 ± 9.7 years, with 52.2% men (n = 310). The mean RAR was 4.3 ± 1.0%/g/dL. After adjusting for covariates, the odds ratio for in-hospital mortality per unit increase in RAR was 1.74 [95% Confidence Interval: 1.19-2.55], p = 0.004. A linear relationship was observed between RAR and in-hospital mortality among patients with AECOPD and RF.

Conclusion: RAR is an independent risk factor for in-hospital mortality in patients with AECOPD complicated by RF. Elevated RAR levels were associated with increased in-hospital mortality in our patient cohort.

Background: Hepatocellular carcinoma (HCC), a primary contributor to cancer-associated mortality, necessitates enhanced early detection. This study evaluated machine learning models that merge methylated SEPTIN9 (SEPT9) and secreted frizzled-related protein 2 (SFRP2) within circulating cell-free DNA (cfDNA) to detect HCC.

Methods: A cohort of 165 healthy volunteers, 24 precancerous patients of HCC and 112 HCC patients were divided into training and validation sets. Methylated SEPT9 and SFRP2 (mSEPT9/mSFRP2) were detected using real-time PCR. Based on those methylation biomarkers and/or conventional biomarkers (CEA, AFP, CA125, and CA19-9), six machine learning algorithms, including Random Forest (RF), were employed to establish models for the training set. Models were evaluated for area under the ROC curve (AUC), sensitivity, and specificity, and subsequently validated in the validation set.

Results: The RF model outperformed other models. In training, it achieved an AUC of 0.834 (95% CI: 0.745-0.923), exhibiting 69.3% sensitivity and 80.6% specificity for the methylation-specific signature group (mSS group: mSEPT9/mSFRP2). In validation, the RF model for the mSS group showed an AUC of 0.865 (95% CI: 0.811-0.946), with 85.4% sensitivity and 71.4% specificity.

Conclusions: The RF-based model integrating mSEPT9/mSFRP2 in cfDNA can be a promising approach for HCC diagnosis.

Aims: To evaluate the effectiveness of Shock Index (SI), Modified Shock Index (MSI), and Age Shock Index (Age SI) in predicting blood transfusion need, surgical intervention, hospital outcomes, and injury severity in blunt trauma patients.

Methods: This retrospective study included 267 adult patients admitted with blunt trauma to the emergency department of a tertiary university hospital between 1 December 2018, and 1 December 2019. Patients with isolated hand/foot trauma, isolated traumatic brain injury, isolated spinal injuries, or minor trauma (AIS ≤2) were excluded. SI, MSI, and Age SI were calculated and analyzed.

Results: SI (p < 0.001), Age SI (p = 0.001), and MSI (p < 0.001) were significantly associated with blood transfusion and mortality. SI > 0.933 and MSI > 1.159 showed good predictive accuracy for blood transfusion; Age SI > 30.945 showed moderate accuracy. For mortality, SI > 1.015, MSI > 1.333, and Age SI > 67.065 demonstrated good predictive power (all p < 0.001). SI and MSI correlated moderately with injury severity (p < 0.001), with SI > 0.905 and MSI > 1.181 indicating significant predictive value.

Conclusion: SI, MSI, and Age SI can predict early blood transfusion and mortality in blunt trauma patients. SI and MSI appear more reliable than Age SI, particularly in predicting transfusion need and injury severity.