Search

Abstract The prediction utility of Framingham Risk Score in populations with low conventional cardiovascular risk burden is limited, particularly among women. Gender-specific markers to predict cardiovascular risk in overtly healthy people are lacking. In this study we hypothesize that postprandial responses triggered by a high-calorie meal test differ by gender in their ability to triage asymptomatic subjects into those with and without subclinical atherosclerosis. A total of 101 healthy Chinese subjects (46 females, 55 males) at low risk of coronary heart disease completed the study. Subjects underwent cardiovascular imaging and postprandial blood phenotyping after consuming a standardized macronutrient meal. Prediction models were developed using logistic regression and subsequently subjected to cross-validation to obtain a de-optimized receiver operating characteristic (ROC) curve. Distinctive gender differences in postprandial trajectories of glucose, lipids and inflammatory markers were observed. We used gender-specific association with different combinations of postprandial predictors to develop 2 models for predicting risk of subclinical atherosclerosis in males (ROC AUC = 0.7867, 95% CI 0.6567, 0.9166) and females (ROC AUC = 0.9161, 95% CI 0.8340, 0.9982) respectively. We report novel postprandial models for predicting subclinical atherosclerosis in apparently healthy Asian subjects using a gender-specific approach, complementing the conventional Framingham Risk Score. Clinical Trial Registration: The trial was registered at clinicaltrials.gov as NCT03531879.

BackgroundSubclinical atherosclerosis can be present in individuals with an optimal cardiovascular risk factor profile. Traditional risk scores such as the Framingham risk score do not adequately capture risk stratification in low-risk individuals. The aim of this study was to determine if markers of metabolic syndrome and insulin resistance can better stratify low-risk individuals.MethodsA cross-sectional study of 101 healthy participants with a low Framingham risk score and no prior morbidities was performed to assess prevalence of subclinical atherosclerosis using computed tomography (CT) and ultrasound. Participants were compared between groups based on Metabolic Syndrome (MetS) and Insulin-Sensitivity Index (ISI-cal) scores.ResultsTwenty three individuals (23%) had subclinical atherosclerosis with elevated CT Agatston score ≥1. Presence of both insulin resistance (ISI-cal

Identifying biomarkers in non-small cell lung cancer (NSCLC) can improve diagnosis and patient stratification. We evaluated plasmas and sera for interleukins (IL)-11, IL-6, IL-8, IL-17A, and IL-33 as biomarkers in primary NSCLC patients undergoing surgical treatment against normal volunteers. Exhaled-breath condensates (EBCs), a potential source without invasive procedures, were explored in normal individuals. Due to separate recruitment criteria and intrinsic cohort differences, the NSCLC and control cohorts were not well matched for age (median age: 65 vs. 40 years; p < 0.0001) and smoking status (p = 0.0058). Interleukins were first assessed through conventional ELISA. IL-11 was elevated in NSCLC plasma compared to controls (49.71 ± 16.90 vs. 27.67 ± 14.06 pg/mL, respectively, p < 0.0001) but undetectable in sera and EBCs by conventional ELISA. Therefore, high-sensitivity PCR-based IL-11 ELISA was repeated, albeit with concentration discrepancies. IL11 gene and protein upregulation by RT-qPCR and immunohistochemistry, respectively, were validated in NSCLC tumors. The lack of detection sensitivity across IL-6, IL-8, IL-17A, and IL-33 suggests the need for further, precise assays. Surprisingly, biomarker concentrations can be dissimilar across paired plasmas and sera. Our results identified a need to optimize detection limits for biomarker detection and caution against over-reliance on just one form of blood sample for biomarker assessment.

Introduction: Cardiovascular disease was the top cause of deaths and disability in Singapore in 2018, contributing extensively to the local healthcare burden. Primary prevention identifies at‑risk individuals for the swift implementation of preventive measures. This has been traditionally done using the Singapore‑adapted Framingham Risk Score (SG FRS). However, its most recent recalibration was more than a decade ago. Recent changes in patient demographics and risk factors have undermined the accuracy of SG FRS, and the rising popularity of wearable health metrics has led to new data types with the potential to improve risk prediction. Methods: In healthy Singaporeans enrolled in SingHEART study (absence of any clinical outcomes), we investigated improvements in SG FRS to predict myocardial infarction risk based on high/low classification of the Agatston score (surrogate outcome). Logistic regression, receiver operating characteristic and net reclassification index (NRI) analyses were conducted. Results: We demonstrated a significant improvement in the area under curve (AUC) of SG FRS (AUC = 0.641) after recalibration and incorporation of additional variables (fasting blood glucose and wearable‑derived activity levels) (AUC = 0.774) (P < 0.001). SG FRS++ significantly increases accuracy in risk prediction (NRI = 0.219, P = 0.00254). Conclusion: Existing Singapore cardiovascular disease risk prediction guidelines should be updated to improve risk prediction accuracy. Recalibrating existing risk functions and utilising wearable metrics that provide a large pool of objective health data can improve existing risk prediction tools. Lastly, activity levels and prediabetic state are important factors for coronary heart disease risk stratification, especially in low‑risk individuals. [ABSTRACT FROM AUTHOR]