Dataset Information

NSeP: immune and metabolic biomarkers for early detection of neonatal sepsis-protocol for a prospective multicohort study.

ABSTRACT:

Introduction

Diagnosing neonatal sepsis is heavily dependent on clinical phenotyping as culture-positive body fluid has poor sensitivity, and existing blood biomarkers have poor specificity.A combination of machine learning, statistical and deep pathway biology analyses led to the identification of a tripartite panel of biologically connected immune and metabolic markers that showed greater than 99% accuracy for detecting bacterial infection with 100% sensitivity. The cohort study described here is designed as a large-scale clinical validation of this previous work.

Methods and analysis

This multicentre observational study will prospectively recruit a total of 1445 newborn infants (all gestations)-1084 with suspected early-or late-onset sepsis, and 361 controls-over 4 years. A small volume of whole blood will be collected from infants with suspected sepsis at the time of presentation. This sample will be used for integrated transcriptomic, lipidomic and targeted proteomics profiling. In addition, a subset of samples will be subjected to cellular phenotype and proteomic analyses. A second sample from the same patient will be collected at 24 hours, with an opportunistic sampling for stool culture. For control infants, only one set of blood and stool sample will be collected to coincide with clinical blood sampling. Along with detailed clinical information, blood and stool samples will be analysed and the information will be used to identify and validate the efficacy of immune-metabolic networks in the diagnosis of bacterial neonatal sepsis and to identify new host biomarkers for viral sepsis.

Ethics and dissemination

The study has received research ethics committee approval from the Wales Research Ethics Committee 2 (reference 19/WA/0008) and operational approval from Health and Care Research Wales. Submission of study results for publication will involve making available all anonymised primary and processed data on public repository sites.

Trial registration number

NCT03777670.

SUBMITTER: Chakraborty M

PROVIDER: S-EPMC8718461 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

nSeP: immune and metabolic biomarkers for early detection of neonatal sepsis-protocol for a prospective multicohort study.

Chakraborty Mallinath M Rodrigues Patrícia R S PRS Watkins W John WJ Hayward Angela A Sharma Alok A Hayward Rachel R Smit Elisa E Jones Rebekka R Goel Nitin N Asokkumar Amar A Calvert Jennifer J Odd David D Morris Ian I Doherty Cora C Elliott Sian S Strang Angela A Andrews Robert R Zaher Summia S Sharma Simran S Bell Sarah S Oruganti Siva S Smith Claire C Orme Judith J Edkins Sarah S Craigon Marie M White Daniel D Dantoft Widad W Davies Luke C LC Moet Linda L McLaren James E JE Clarkstone Samantha S Watson Gareth L GL Hood Kerenza K Kotecha Sailesh S Morgan B Paul BP O'Donnell Valerie B VB Ghazal Peter P

BMJ open 20211230 12

<h4>Introduction</h4>Diagnosing neonatal sepsis is heavily dependent on clinical phenotyping as culture-positive body fluid has poor sensitivity, and existing blood biomarkers have poor specificity.A combination of machine learning, statistical and deep pathway biology analyses led to the identification of a tripartite panel of biologically connected immune and metabolic markers that showed greater than 99% accuracy for detecting bacterial infection with 100% sensitivity. The cohort study descri ...[more]

PMID: 37010923

Similar Datasets

Project description:BackgroundNeonatal sepsis (NS), a life-threatening condition, is characterized by organ dysfunction and is the most common cause of neonatal death. However, the pathogenesis of NS is unclear and the clinical inflammatory markers currently used are not ideal for diagnosis of NS. Thus, exploring the link between immune responses in NS pathogenesis, elucidating the molecular mechanisms involved, and identifying potential therapeutic targets is of great significance in clinical practice. Herein, our study aimed to explore immune-related genes in NS and identify potential diagnostic biomarkers. Datasets for patients with NS and healthy controls were downloaded from the GEO database; GSE69686 and GSE25504 were used as the analysis and validation datasets, respectively. Differentially expressed genes (DEGs) were identified and Gene Set Enrichment Analysis (GSEA) was performed to determine their biological functions. Composition of immune cells was determined and immune-related genes (IRGs) between the two clusters were identified and their metabolic pathways were determined. Key genes with correlation coefficient > 0.5 and p < 0.05 were selected as screening biomarkers. Logistic regression models were constructed based on the selected biomarkers, and the diagnostic models were validated.ResultsFifty-two DEGs were identified, and GSEA indicated involvement in acute inflammatory response, bacterial detection, and regulation of macrophage activation. Most infiltrating immune cells, including activated CD8 + T cells, were significantly different in patients with NS compared to the healthy controls. Fifty-four IRGs were identified, and GSEA indicated involvement in immune response and macrophage activation and regulation of T cell activation. Diagnostic models of DEGs containing five genes (PROS1, TDRD9, RETN, LOC728401, and METTL7B) and IRG with one gene (NSUN7) constructed using LASSO algorithm were validated using the GPL6947 and GPL13667 subset datasets, respectively. The IRG model outperformed the DEG model. Additionally, statistical analysis suggested that risk scores may be related to gestational age and birth weight, regardless of sex.ConclusionsWe identified six IRGs as potential diagnostic biomarkers for NS and developed diagnostic models for NS. Our findings provide a new perspective for future research on NS pathogenesis.

Project description:Background: Cancer cachexia is a severe metabolic disorder characterized by progressive weight loss along with a dramatic loss in skeletal muscle and adipose tissue. Like cancer, cachexia progresses in stages starting with pre-cachexia to cachexia and finally to refractory cachexia. In the refractory stage, patients are no longer responsive to therapy and management of weight loss is no longer possible. It is therefore critical to detect cachexia as early as possible. In this study we applied a metabolomics approach to search for early biomarkers of cachexia. Methods: Multi-platform metabolomics analyses were applied to the murine Colon-26 (C26) model of cachexia. Tumor bearing mice (n = 5) were sacrificed every other day over the 14-day time course and control mice (n = 5) were sacrificed every fourth day starting at day 2. Linear regression modeling of the data yielded metabolic trajectories that were compared with the trajectories of body weight and skeletal muscle loss to look for early biomarkers of cachexia. Results: Weight loss in the tumor-bearing mice became significant at day 9 as did the loss of tibialis muscle. The loss of muscle in the gastrocnemius and quadriceps was significant at day 7. Reductions in amino acids were among the earliest metabolic biomarkers of cachexia. The earliest change was in methionine at day 4. Significant alterations in acylcarnitines and lipoproteins were also detected several days prior to weight loss. Conclusion: The results of this study demonstrate that metabolic alterations appear well in advance of observable weight loss. The earliest and most significant alterations were found in amino acids and lipoproteins. Validation of these results in other models of cachexia and in clinical studies will pave the way for a clinical diagnostic panel for the early detection of cachexia. Such a panel would provide a tremendous advance in cachectic patient management and in the design of clinical trials for new therapeutic interventions.

Project description:BackgroundSepsis is a life-threatening organ dysfunction caused by a dysregulated host response to infection. To decrease the high case fatality rates and morbidity for sepsis and septic shock, there is a need to increase the accuracy of early detection of suspected sepsis in prehospital and emergency department settings. This may be achieved by developing risk prediction decision support systems based on artificial intelligence.MethodsThe overall aim of this scoping review is to summarize the literature on existing methods for early detection of sepsis using artificial intelligence. The review will be performed using the framework formulated by Arksey and O'Malley and further developed by Levac and colleagues. To identify primary studies and reviews that are suitable to answer our research questions, a comprehensive literature collection will be compiled by searching several sources. Constrictions regarding time and language will have to be implemented. Therefore, only studies published between 1 January 1990 and 31 December 2020 will be taken into consideration, and foreign language publications will not be considered, i.e., only papers with full text in English will be included. Databases/web search engines that will be used are PubMed, Web of Science Platform, Scopus, IEEE Xplore, Google Scholar, Cochrane Library, and ACM Digital Library. Furthermore, clinical studies that have completed patient recruitment and reported results found in the database ClinicalTrials.gov will be considered. The term artificial intelligence is viewed broadly, and a wide range of machine learning and mathematical models suitable as base for decision support will be evaluated. Two members of the team will test the framework on a sample of included studies to ensure that the coding framework is suitable and can be consistently applied. Analysis of collected data will provide a descriptive summary and thematic analysis. The reported results will convey knowledge about the state of current research and innovation for using artificial intelligence to detect sepsis in early phases of the medical care chain.Ethics and disseminationThe methodology used here is based on the use of publicly available information and does not need ethical approval. It aims at aiding further research towards digital solutions for disease detection and health innovation. Results will be extracted into a review report for submission to a peer-reviewed scientific journal. Results will be shared with relevant local and national authorities and disseminated in additional appropriate formats such as conferences, lectures, and press releases.

Project description:IntroductionMaternal sepsis remains a leading cause of death in pregnancy. Physiological adaptations to pregnancy obscure early signs of sepsis and can result in delays in recognition and treatment. Identifying biomarkers that can reliably diagnose sepsis will reduce morbidity and mortality and antibiotic overuse. We have previously identified an immune-metabolic biomarker network comprising three pathways with a >99% accuracy for detecting bacterial neonatal sepsis. In this prospective study, we will describe physiological parameters and novel biomarkers in two cohorts-healthy pregnant women and pregnant women with suspected sepsis-with the aim of mapping pathophysiological drivers and evaluating predictive biomarkers for diagnosing maternal sepsis.Methods and analysisWomen aged over 18 with an ultrasound-confirmed pregnancy will be recruited to a pilot and two main study cohorts. The pilot will involve blood sample collection from 30 pregnant women undergoing an elective caesarean section. Cohort A will follow 100 healthy pregnant women throughout their pregnancy journey, with collection of blood samples from participants at routine time points in their pregnancy: week 12 'booking', week 28 and during labour. Cohort B will follow 100 pregnant women who present with suspected sepsis in pregnancy or labour and will have at least two blood samples taken during their care pathway. Study blood samples will be collected during routine clinical blood sampling. Detailed medical history and physiological parameters at the time of blood sampling will be recorded, along with the results of routine biochemical tests, including C reactive protein, lactate and white blood cell count. In addition, study blood samples will be processed and analysed for transcriptomic, lipidomic and metabolomic analyses and both qualitative and functional immunophenotyping.Ethics and disseminationEthical approval has been obtained from the Wales Research Ethics Committee 2 (SPON1752-19, 30 October 2019).Trial registration numberNCT05023954.