Dataset Information

Novel approach to analysis of the immune system using an ungated model of immune surface marker abundance to predict health outcomes.

ABSTRACT: Traditionally, the immune system is understood to be divided into discrete cell types that are identified via surface markers. While some cell type distinctions are no doubt discrete, others may in fact vary on a continum, and even within discrete types, differences in surface marker abundance could have functional implications. Here we propose a new way of looking at immune data, which is by looking directly at the values of the surface markers without dividing the cells into different subtypes. To assess the merit of this approach, we compared it with manual gating using cytometry data from the Singapore Longitudinal Aging Study (SLAS) database. We used two different neural networks (one for each method) to predict the presence of several health conditions. We found that the model built using raw surface marker abundance outperformed the manual gating one and we were able to identify some markers that contributed more to the predictions. This study is intended as a brief proof-of-concept and was not designed to predict health outcomes in an applied setting; nonetheless, it demonstrates that alternative methods to understand the structure of immune variation hold substantial progress.

SUBMITTER: Provost G

PROVIDER: S-EPMC9351261 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Novel approach to analysis of the immune system using an ungated model of immune surface marker abundance to predict health outcomes.

Provost G G Lavoie F B FB Larbi A A Ng T P TP Ying C Tan Tze CTT Chua M M Fulop T T Cohen A A AA

Immunity & ageing : I & A 20220804 1

Traditionally, the immune system is understood to be divided into discrete cell types that are identified via surface markers. While some cell type distinctions are no doubt discrete, others may in fact vary on a continum, and even within discrete types, differences in surface marker abundance could have functional implications. Here we propose a new way of looking at immune data, which is by looking directly at the values of the surface markers without dividing the cells into different subtypes ...[more]

PMID: 35927749

Similar Datasets

Project description:AimsThere are limited data examining the role of immune checkpoint (IC) ligands in the pathophysiology of heart failure (HF). Therefore, we explore this in three HF animal models and in three different human cohorts (healthy, stable, and worsening HF).Methods and resultsTranscriptomic analyses of cardiac tissue of three different HF mouse models revealed differentially expressed IC receptors and their ligands compared with control mice. Based on this observation, serum levels of three well-known IC ligands (i.e. sPD-L1, sPD-L2 and galectin-9) were measured in stable HF patients from the Vitamin D Chronic Heart Failure (VitD-CHF) study (n = 101), as well as healthy individuals from the Prevention of Renal and Vascular End-stage Disease (PREVEND) study (n = 58). sPD-L1, sPD-L2, and galectin-9 were all associated with New York Heart Association classification. In multivariate linear regression analyses, all three IC ligands were associated with galectin-3 (β = 0.230, β = 0.283, and β = 0.304, respectively). sPD-L1 and galectin-9 were also associated with hs-troponin-T (β = 0.386 and β = 0.314). Regarding prognosis, higher serum levels of sPD-L1 and galectin-9 were significantly associated with increased risk for HF hospitalization and all-cause mortality [hazard ratio 1.69 (1.09-2.59) and hazard ratio 1.50 (1.06-2.12)]. Furthermore, the importance of IC ligands was tested in another stage of HF, namely worsening HF patients. In the worsening HF cohort (The BIOlogy Study to Tailored Treatment in Chronic Heart Failure) (n = 2032), sPD-L2 and galectin-9 were associated with New York Heart Association classification and significantly predicted outcome with an increased relative risk of 15% and 20%, after multivariable adjustment, respectively.ConclusionsIC ligands are expressed in cardiac disease models, and serum levels of IC ligands are elevated in HF patients, are associated with disease severity, and significantly predict prognosis. These data indicate a potential role for IC ligands in HF pathogenesis.

Project description:BackgroundCoronary artery calcium (CAC) is a strong predictor of cardiovascular events across all racial and ethnic groups. CAC can be quantified on nonelectrocardiography (ECG)-gated computed tomography (CT) performed for other reasons, allowing for opportunistic screening for subclinical atherosclerosis.ObjectivesThe authors investigated whether incidental CAC quantified on routine non-ECG-gated CTs using a deep-learning (DL) algorithm provided cardiovascular risk stratification beyond traditional risk prediction methods.MethodsIncidental CAC was quantified using a DL algorithm (DL-CAC) on non-ECG-gated chest CTs performed for routine care in all settings at a large academic medical center from 2014 to 2019. We measured the association between DL-CAC (0, 1-99, or ≥100) with all-cause death (primary outcome), and the secondary composite outcomes of death/myocardial infarction (MI)/stroke and death/MI/stroke/revascularization using Cox regression. We adjusted for age, sex, race, ethnicity, comorbidities, systolic blood pressure, lipid levels, smoking status, and antihypertensive use. Ten-year atherosclerotic cardiovascular disease risk was calculated using the pooled cohort equations.ResultsOf 5,678 adults without ASCVD (51% women, 18% Asian, 13% Hispanic/Latinx), 52% had DL-CAC >0. Those with DL-CAC ≥100 had an average 10-year ASCVD risk of 24%; yet, only 26% were on statins. After adjustment, patients with DL-CAC ≥100 had increased risk of death (HR: 1.51; 95% CI: 1.28-1.79), death/MI/stroke (HR: 1.57; 95% CI: 1.33-1.84), and death/MI/stroke/revascularization (HR: 1.69; 95% CI: 1.45-1.98) compared with DL-CAC = 0.ConclusionsIncidental CAC ≥100 was associated with an increased risk of all-cause death and adverse cardiovascular outcomes, beyond traditional risk factors. DL-CAC from routine non-ECG-gated CTs identifies patients at increased cardiovascular risk and holds promise as a tool for opportunistic screening to facilitate earlier intervention.

Dataset Information

Novel approach to analysis of the immune system using an ungated model of immune surface marker abundance to predict health outcomes.

Publications

Novel approach to analysis of the immune system using an ungated model of immune surface marker abundance to predict health outcomes.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets