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Background and Purpose: Atherogenic index of plasma (AIP) is a newly identified as marker of lipid metabolism and glucose metabolism, showing significant predictive value in individuals with cardiovascular disease. This study aimed to explore the correlation between AIP and early neurological deterioration (END) in ischemic stroke patients after mechanical thrombectomy (MT)., Methods: Patients with anterior circulation large artery occlusive stroke who underwent MT were retrospectively enrolled from 2 stroke center in China. The AIP is a logarithmically transformed ratio of triglycerides to high-density lipoprotein cholesterol. END was defined as an increase of ≥ 4 point in National Institutes of Health Stroke Scale within 24 hours after surgery. Multivariable regression analysis and restricted cubic spline was utilized to determine the association of AIP index with risk of END., Results: Of 601 patients (mean age, 70.2 ± 12.1 years; 62.1 % of male) enrolled, 91 (15.1 %) experienced postoperative END. After adjustment for potential confounders, higher AIP levels were significantly associated with an increased risk of END after MT treatment (Per 1-standard deviation increase; odd ratio, 1.474; 95 % confidence interval, 1.162-1.869, P = 0.001). Similar results were confirmed when the AIP was analyzed as a categorical variable. Restricted cubic spline further demonstrated a linear relationship between AIP and risk of END (P = 0.001 for linearity)., Conclusions: The present study found that a higher AIP index were associated with END in acute ischemic stroke patients following MT treatment for emergent large vessel occlusion., Competing Interests: Declaration of competing interest All the authors declare that there is no conflict of interest., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Asthma is a prevalent chronic disease characterized by airflow obstruction, causing breathing difficulties and wheezing. This study investigates the association between the C-reactive protein to albumin ratio (CAR) and asthma prevalence, as well as all-cause and respiratory mortality among asthma patients, using data from the 2001-2018 National Health and Nutrition Examination Survey. We included participants aged 20 years and older with complete CAR data, excluding those who were pregnant or lost to follow-up. The analysis employed weighted logistic regression and Cox proportional hazards models with stepwise adjustment, restricted cubic spline analysis for nonlinear relationships, and time-dependent ROC curves for predictive accuracy. Results showed that the highest CAR quartile significantly increased the risk of asthma (OR 1.56, 95% CI 1.38-1.78), all-cause mortality (HR 2.20, 95% CI 1.67-2.89), and respiratory mortality (HR 2.56, 95% CI 1.30-5.38). The impact of CAR on all-cause mortality was particularly significant in hypertensive patients. These findings highlight CAR's potential as a valuable biomarker for predicting asthma prevalence and mortality, underscoring its role in asthma management and prognostication., (© 2024. The Author(s).)

The 2017 11th Workshop on Recent Issues in Bioanalysis (11th WRIB) took place in Los Angeles/Universal City, California on 3-7 April 2017 with participation of close to 750 professionals from pharmaceutical/biopharmaceutical companies, biotechnology companies, contract research organizations and regulatory agencies worldwide. WRIB was once again a 5-day, weeklong event - a full immersion week of bioanalysis, biomarkers and immunogenicity. As usual, it was specifically designed to facilitate sharing, reviewing, discussing and agreeing on approaches to address the most current issues of interest including both small and large molecule analysis involving LCMS, hybrid ligand binding assay (LBA)/LCMS and LBA approaches. This 2017 White Paper encompasses recommendations emerging from the extensive discussions held during the workshop, and is aimed to provide the bioanalytical community with key information and practical solutions on topics and issues addressed, in an effort to enable advances in scientific excellence, improved quality and better regulatory compliance. Due to its length, the 2017 edition of this comprehensive White Paper has been divided into three parts for editorial reasons. This publication (Part 2) covers the recommendations for biotherapeutics, biomarkers and immunogenicity assays using hybrid LBA/LCMS and regulatory agencies' inputs. Part 1 (LCMS for small molecules, peptides and small molecule biomarkers) and Part 3 (LBA: immunogenicity, biomarkers and pharmacokinetic assays) are published in Volume 9 of Bioanalysis, issues 22 and 24 (2017), respectively.

Aims: We evaluated the added prognostic value of a multi-time point-based multimarker panel of biomarkers in patients with acute heart failure (AHF)., Methods and Results: Seven circulating biomarkers [NT-proBNP, high sensitivity cardiac troponin T (hs-cTnT), soluble ST2 (sST2), growth differentiation factor 15 (GDF-15), cystatin-C, galectin-3, and high sensitivity C-reactive protein (hs-CRP)] were measured at baseline and on days 2, 5, 14, and 60 in 1161 patients enrolled in the RELAX-AHF trial. Patients with BNP ≥350 ng/L or NT-proBNP ≥1400 ng/L, mild to moderate renal impairment, and systolic blood pressure >125 mmHg were included in the trial. Time-dependent Cox regression analysis was utilized to evaluate the incremental value of serial measurement of biomarkers. Added value of individual biomarkers and their combination, on top of a pre-specified baseline model, was quantified with the gain in the C-index. Serial biomarker evaluation showed incremental predictive value over baseline measurements alone for the prediction of 180-day cardiovascular mortality except for galectin-3. While a repeat measurement as early as day 2 was adequate for NT-proBNP and cystatin-C in terms of maximizing discriminatory accuracy, further measurements on days 14 and 60 provided added value for hs-cTnT, GDF-15, sST2, and hs-CRP. Individual biomarker additions on top of the baseline model showed additional prognostic value. The greatest prognostic gain was, however, attained with the combination of NT-proBNP, hs-cTnT, GDF-15, and sST2, which yielded 0.08 unit absolute increment in the C-index to 0.87 (95% confidence interval 0.83-0.91]., Conclusion: In patients with AHF and mild to moderate renal impairment, a multimarker approach based on a panel of serially evaluated biomarkers provides the greatest prognostic improvement unmatched by a single time point-based single marker strategy., (© 2017 The Authors. European Journal of Heart Failure © 2017 European Society of Cardiology.)

Posttraumatic stress disorder (PTSD) is associated with increased risk for Type 2 diabetes and cardiovascular disease (cardiometabolic disease), warranting research into targeted prevention strategies. In the present case-control study of 160 young (mean age 32.7 years) male military veterans, we aimed to assess whether PTSD status predicted increased markers of cardiometabolic risk in otherwise healthy individuals, and further, to explore biological pathways between PTSD and these increased markers of cardiometabolic risk. Toward these aims, we compared measures of cardiometabolic risk, namely insulin resistance (IR) (HOMA-IR), metabolic syndrome (MetS) and prediabetes, between 80 PTSD cases and 80 controls without PTSD. We then determined whether PTSD-associated increases in HOMA-IR were correlated with select biological variables from pathways previously hypothesized to link PTSD with cardiometabolic risk, including systemic inflammation (increased C-reactive protein, interleukin-6, and tumor necrosis factor α), sympathetic over-activity (increased resting heart rate), and neuroendocrine dysregulation (increased plasma cortisol or serum brain-derived neurotrophic factor (BDNF)). We found PTSD diagnosis was associated with substantially higher HOMA-IR (cases 4.3±4.3 vs controls 2.4±2.0; p<0.001), and a higher frequency of MetS (cases 21.3% vs controls 2.5%; p<0.001), but not prediabetes (cases 20.0% vs controls 18.8%; p>0.05). Cases also had increased pro-inflammatory cytokines (p<0.01), heart rate (p<0.001), and BDNF (p<0.001), which together predicted increased HOMA-IR (adjusted R 2 =0.68, p<0.001). Results show PTSD diagnosis in young male military veterans without cardiometabolic disease is associated with increased IR, predicted by biological alterations previously hypothesized to link PTSD to increased cardiometabolic risk. Findings support further research into early, targeted prevention of cardiometabolic disease in individuals with PTSD., (Copyright © 2017 Elsevier Ltd. All rights reserved.)

Accurate risk prediction models are needed to identify different risk groups for individualized prevention and treatment strategies. In the Nurses' Health Study, to examine the effects of several biomarkers and genetic markers on the risk of rheumatoid arthritis (RA), a three-phase nested case-control (NCC) design was conducted, in which two sequential NCC subcohorts were formed with one nested within the other, and one set of new markers measured on each of the subcohorts. One objective of the study is to evaluate clinical values of novel biomarkers in improving upon existing risk models because of potential cost associated with assaying biomarkers. In this paper, we develop robust statistical procedures for constructing risk prediction models for RA and estimating the incremental value (IncV) of new markers based on three-phase NCC studies. Our method also takes into account possible time-varying effects of biomarkers in risk modeling, which allows us to more robustly assess the biomarker utility and address the question of whether a marker is better suited for short-term or long-term risk prediction. The proposed procedures are shown to perform well in finite samples via simulation studies., (© 2015, The International Biometric Society.)

Background: Accurate risk prediction plays a key role in disease prevention and disease management; emergence of new biomarkers may lead to an important question about how much improvement in prediction accuracy it would achieve by adding the new markers into the existing risk prediction tools., Purpose: In large prospective cohort studies, the standard full-cohort design, requiring marker measurement on the entire cohort, may be infeasible due to cost and low rate of the clinical condition of interest. To overcome such difficulties, nested case-control (NCC) studies provide cost-effective alternatives but bring about challenges in statistical analyses due to complex data sets generated., Methods: To evaluate prognostic accuracy of a risk model, Cai and Zheng proposed a class of nonparametric inverse probability weighting (IPW) estimators for accuracy measures in the time-dependent receiver operating characteristic curve analysis. To accommodate a three-phase NCC design in Nurses' Health Study, we extend the double IPW estimators of Cai and Zheng to develop risk prediction models under time-dependent generalized linear models and evaluate the incremental values of new biomarkers and genetic markers., Results: Our results suggest that aggregating the information from both the genetic markers and biomarkers substantially improves the accuracy for predicting 5-year and 10-year risks of rheumatoid arthritis., Conclusions: Our method provided robust procedures to evaluate the incremental value of new biomarkers allowing for complex sampling designs.

Systemic inflammation is a major factor influencing the outcome and quality of patient with chronic obstructive pulmonary disease (COPD) and acute exacerbations (AECOPD). Because of the inflammatory complexity, a great challenge is still confronted to optimize the identification and validation of disease-specific biomarkers. This study aimed at developing a new protocol of specific biomarker evaluation by integrating proteomic profiles of inflammatory mediators with clinical informatics in AECOPD patients, understand better their function and signal networks. Plasma samples were collected from healthy non-smokers or patients with stable COPD (sCOPD) or AECOPD on days 1 and 3 of the admission and discharging day (day 7-10). Forty chemokines were measured using a chemokine multiplex antibody array. Clinical informatics was achieved by a Digital Evaluation Score System (DESS) for assessing severity of patients. Chemokine data was compared among different groups and its correlation with DESS scores was performed by SPSS software. Of 40 chemokines, 30 showed significant difference between sCOPD patients and healthy controls, 16 between AECOPD patients and controls and 13 between AECOPD patients and both sCOPD and controls, including BTC, IL-9, IL-18Bpa, CCL22,CCL23, CCL25, CCL28, CTACK, LIGHT, MSPa, MCP-3, MCP-4 and OPN. Of them, some had significant correlation with DESS scores. There is a disease-specific profile of inflammatory mediators in COPD and AECOPD patients which may have a potential diagnostics together with clinical informatics of patients. Our preliminary study suggested that integration of proteomics with clinical informatics can be a new way to validate and optimize disease-special biomarkers., (© 2011 The Authors Journal compilation © 2011 Foundation for Cellular and Molecular Medicine/Blackwell Publishing Ltd.)

Accumulating evidence has suggested that thyroid hormone levels affect the prognosis of acute ischemic stroke (AIS), but the results have been inconsistent.

ObjectiveTo explore the values of neutrophil to lymphocyte ratio (NLR), platelet to lymphocyte ratio (PLR), neutrophil to albumin ratio (NAR), prognostic nutritional index (PNI), systemic immune inflammatory index (SII) and red cell distribution width to albumin ratio (RA) for evaluating the risk of 30-day mortality of ischemic stroke or hemorrhagic stroke patients.MethodsIn this cohort study, the data of 1,601 patients diagnosed with stroke were extracted from the Medical Information Mart for Intensive Care III (MIMIC-III) database. Among them, 908 were hemorrhagic stroke patients and 693 were ischemic stroke patients. Demographic and clinical variables of patients were collected. Univariate and multivariable Cox regression were performed to evaluate the predictive values of NLR, PLR, SII, NAR, RA, and PNI for 30-day mortality in hemorrhagic stroke or ischemic stroke patients. The receiver operator characteristic (ROC) curves were plotted to assess the predictive values of NLR, NAR, and RA for 30-day mortality of hemorrhagic stroke patients.ResultsAt the end of follow-up, 226 hemorrhagic stroke patients and 216 ischemic stroke patients died. The elevated NLR level was associated with increased risk of 30-day mortality in hemorrhagic stroke [hazard ratio (HR) = 1.17, 95% confidence interval (CI): 1.06–1.29]. The increased NAR level was associated with elevated risk of 30-day mortality in hemorrhagic stroke (HR = 1.16, 95% CI: 1.02–1.30). The high RA level was linked with increased risk of 30-day mortality (HR = 1.44, 95% CI: 1.23–1.69). No significant correlation was observed in these inflammation biomarkers with the risk of 30-day mortality in ischemic stroke patients. The area under the curves (AUCs) of NLR, RA, and NAR for evaluating the risk of 30-day mortality of hemorrhagic stroke patients were 0.552 (95% CI: 0.503–0.601), 0.644 (95% CI: 0.590–0.699) and 0.541 (95% CI: 0.490–0.592).ConclusionNLR, NAR, and RA were potential prognostic biomarkers for predicting 30-day mortality of hemorrhagic stroke patients, which might provide clinicians an easy and cheap way to quickly identify patients with high risk of mortality.