Values of integration between lipidomics and clinical phenomes in patients with acute lung infection, pulmonary embolism, or acute exacerbation of chronic pulmonary diseases: a preliminary study

Abstract

Background The morbidity and mortality of patients with critical illnesses remain high in pulmonary critical care units and a poorly understood correlation between alterations of lipid elements and clinical phenomes remain unelucidated. Methods In the present study, we investigated plasma lipidomic profiles of 30 patients with severe acute pneumonia (SAP), acute pulmonary embolism (APE), and acute exacerbation of chronic pulmonary diseases (AECOPD) or 15 healthy with the aim to compare disease specificity of lipidomic patterns. We defined the specificity of lipidomic profiles in SAP by comparing it to both APE and AECOPD. Analysis of the correlation between altered lipid elements and clinical phenotypes using the lipid-QTL model was then carried out. Results We integrated lipidomic profiles with clinical phenomes measured by score values from the digital evaluation score system and found phenome-associated lipid elements to identify disease-specific lipidomic profiling. The present study demonstrates that lipidomic profiles of patients with acute lung diseases are different from healthy lungs, and there are also disease-specific portions of lipidomics among SAP, APE, or AECOPD. The comprehensive profiles of clinical phenomes or lipidomics are valuable in describing the disease specificity of patient phenomes and lipid elements. The combination of clinical phenomes with lipidomic profiles provides more detailed disease-specific information on panels of lipid elements When compared to the use of each separately. Conclusions Integrating biological functions with disease specificity, we believe that clinical lipidomics may create a new alternative way to understand lipid-associated mechanisms of critical illnesses and develop a new category of disease-specific biomarkers and therapeutic targets. Electronic supplementary material The online version of this article (10.1186/s12967-019-1898-z) contains supplementary material, which is available to authorized users.

Trans‐omic profiling between clinical phenoms and lipidomes among patients with different subtypes of lung cancer

“…We introduced clinical phenomics into trans-omics to evaluate network nodules cross lipidomic and phenomic layers and define phenome-specific lipid panels and lipid-specific phenome groups in acute lung injury patients with or without infections and with or without chronic diseases, as well as in patients with lung cancer subtypes. 8,15,17 To gather our present and previous studies, we found that the variation of trans-omic profiles in patients was TA B L E 8 Lipid elements significantly elevated or declined alone in lung cancer patients survived or nonsurvived (more than twofold), respectively, as compared with healthy control (P-values) more obvious, uncontrolled, and irregular due to the complex and comprehensive influencing factors in clinical practice and measurements. We also found that the integrated panel appeared lung cancer subtype-specific network and the construction to show statistical correlations and mechanistic molecular interactions between lipid metabolisms and phenome appearances in patients with lung cancer.…”

“…[19][20][21][22] Clinical phenomes were furthermore integrated with lipidomic profiles in patients with pulmonary embolism, acute pneumonia, and acute exacerbation of chronic obstructive pulmonary diseases, based on clinical trans-omics principle. 15 Lipidomic profiles difference between health and lung cancer has been defined and it depends upon methodologies of measurement and analysis, sample preparations and sources, and patient populations and status. 8 For example, serum levels of lysoPC (C26:0 and C26:1) and PC (C42:4 and C34:4) were different between stage I NSCLC and healthy patients.…”

“…About 200 µL plasma was collected into a glass tube, into which 10 µL internal standard was added and then 5 mL of methanol:chloroform:formic acid (10:10:1), as reported previously. 3,15 This mixture was incubated at -20 • C overnight after vigorous shaking. Two milliliters of Hajra's reagent (0.2 M H 3 PO 4 , 1 M KCl) were dropped, blended, and centrifuged at 3000 rpm for 5 min.…”

Advancing Semen Evaluation Using Lipidomics

“…Lipidomics has also been utilized to identify lipid profile differences between healthy and diseased human patients. For example, blood plasma from patients with diseases, such as acute lung infections, pulmonary embolism, or acute exacerbation of the chronic pulmonary disease, had a more than 2-fold increase in various lipids compared to healthy patients (109). Lipidomics and liquid chromatography-mass spectrometry may be used to diagnose subclinical coronary artery disease (110) determined that patients with severe coronary calcification tended to have greater levels of monounsaturated triacylglycerols and saturated triacylglycerols.…”

Lipid Mediators in Critically Ill Patients: A Step Towards Precision Medicine

“…Despite improvements in clinical management, mortality remains high and there is no specific treatment, nor are there universally agreed-upon biomarkers for survival and outcome in ARDS. Different types of acute lung diseases have distinct lipid profiles (73) and lipid mediators may represent useful prognostic markers in critically ill patients. LTB4 correlates with lung-injury severity and outcome in patients with ARDS (64,65) and higher pro-inflammatory mediators like PGF2a and selected pro-resolving mediators like 10S,17S-diHDHA were predictive of ARDS development in patients with sepsis (61).…”