Conventional and unconventional T cell responses contribute to the prediction of clinical outcome and causative bacterial pathogen in sepsis patients.

Sepsis is characterised by a dysfunctional host response to infection culminating in life-threatening organ failure that requires complex patient management and rapid intervention. Timely diagnosis of the underlying cause of sepsis is crucial, and identifying those at risk of complications and death is imperative for triaging treatment and resource allocation. Here, we explored the potential of explainable machine learning models to predict mortality and causative pathogen in sepsis patients. By using a modelling pipeline employing multiple feature selection algorithms, we demonstrate the feasibility to identify integrative patterns from clinical parameters, plasma biomarkers and extensive phenotyping of blood immune cells. Whilst no single variable had sufficient predictive power, models that combined five and more features showed a macro area under the curve (AUC) of 0.85 to predict 90 day mortality after sepsis diagnosis, and a macro AUC of 0.86 to discriminate between Gram-positive and Gram-negative bacterial infections. Parameters associated with the cellular immune response contributed the most to models predictive of 90 day mortality, most notably, the proportion of T cells among PBMCs, together with expression of CXCR3 by CD4+ T cells and CD25 by mucosal-associated invariant T (MAIT) cells. Frequencies of Vδ2+ γδ T cells had the most profound impact on the prediction of Gram-negative infections, alongside other T cell-related variables and total neutrophil count. Overall, our findings highlight the added value of measuring the proportion and activation patterns of conventional and unconventional T cells in the blood of sepsis patients in combination with other immunological, biochemical and clinical parameters.

Low circulatory levels of total cholesterol, HDL-C and LDL-C are associated with death of patients with sepsis and critical illness: systematic review, meta-analysis, and perspective of observational studies.

BACKGROUND: Mechanistic studies have established a biological role of sterol metabolism in infection and immunity with clinical data linking deranged cholesterol metabolism during sepsis with poorer outcomes. In this systematic review we assess the relationship between biomarkers of cholesterol homeostasis and mortality in critical illness. METHODS: We identified articles by searching a total of seven electronic databases from inception to October 2023. Prospective observational cohort studies included those subjects who had systemic cholesterol (Total Cholesterol (TC), HDL-C or LDL-C) levels assessed on the first day of ICU admission and short-term mortality recorded. Meta-analysis and meta-regression were used to evaluate overall mean differences in serum cholesterol levels between survivors and non-survivors. Study quality was assessed using the Newcastle-Ottawa Scale. FINDINGS: From 6469 studies identified by searches, 24 studies with 2542 participants were included in meta-analysis. Non-survivors had distinctly lower HDL-C at ICU admission -7.06 mg/dL (95% CI -9.21 to -4.91, p < 0.0001) in comparison with survivors. Corresponding differences were also seen less robustly for TC -21.86 mg/dL (95% CI -31.23 to -12.49, p < 0.0001) and LDL-C -8.79 mg/dL (95% CI, -13.74 to -3.83, p = 0.0005). INTERPRETATION: Systemic cholesterol levels (TC, HDL-C and LDL-C) on admission to critical care are inversely related to mortality. This finding is consistent with the notion that inflammatory and metabolic setpoints are coupled, such that the maladaptive-setpoint changes of cholesterol in critical illness are related to underlying inflammatory processes. We highlight the potential of HDL-biomarkers as early predictors of severity of illness and emphasise that future research should consider the metabolic and functional heterogeneity of HDLs. FUNDING: EU-ERDF-Welsh Government Ser Cymru programme, BBSRC, and EU-FP7 ClouDx-i project (PG).

Low levels of small HDL particles predict but do not influence risk of sepsis.

BACKGROUND: Low levels of high-density lipoprotein (HDL) cholesterol have been associated with higher rates and severity of infection. Alterations in inflammatory mediators and infection are associated with alterations in HDL cholesterol. It is unknown whether the association between HDL and infection is present for all particle sizes, and whether the observed associations are confounded by IL-6 signalling. METHODS: In the UK Biobank, ~ 270,000 individuals have data on HDL subclasses derived from nuclear magnetic resonance analysis. We estimated the association of particle count of total HDL and HDL subclasses (small, medium, large, and extra-large HDL) with sepsis, sepsis-related death, and critical care admission in a Cox regression model. We subsequently utilised genetic data from UK Biobank and FinnGen to perform Mendelian randomisation (MR) of each HDL subclass and sepsis to test for a causal relationship. Finally, we explored the role of IL-6 signalling as a potential causal driver of changes in HDL subclasses. RESULTS: In observational analyses, higher particle count of small HDL was associated with protection from sepsis (Hazard ratio, HR 0.80; 95% CI 0.74-0.86, p = 4 × 10-9 comparing Quartile 4, highest quartile of HDL to Quartile 1, lowest quartile of HDL), sepsis-related death (HR 0.80; 95% CI 0.74-0.86, p = 2 × 10-4), and critical care admission with sepsis (HR 0.72 95% CI 0.60-0.85, p = 2 × 10-4). Parallel associations with other HDL subclasses were likely driven by changes in the small HDL compartment. MR analyses did not strongly support causality of small HDL particle count on sepsis incidence (Odds ratio, OR 0.98; 95% CI 0.89-1.07, p = 0.6) or death (OR 0.94, 95% CI 0.75-1.17, p = 0.56), although the estimate on critical care admission with sepsis supported protection (OR 0.73, 95% CI 0.57-0.95, p = 0.02). Bidirectional MR analyses suggested that increased IL-6 signalling was associated with reductions in both small (beta on small HDL particle count - 0.16, 95% CI - 0.10 to - 0.21 per natural log change in SD-scaled CRP, p = 9 × 10-8).and total HDL particle count (beta - 0.13, 95% CI - 0.09 to - 0.17, p = 7 × 10-10), but that the reverse effect of HDL on IL-6 signalling was largely null. CONCLUSIONS: Low number of small HDL particles are associated with increased hazard of sepsis, sepsis-related death, and sepsis-related critical care admission. However, genetic analyses did not strongly support this as causal. Instead, we demonstrate that increased IL-6 signalling, which is known to alter infection risk, could confound associations with reduced HDL particle count, and suggest this may explain part of the observed association between (small) HDL particle count and sepsis.

Phenotypic associations with the HMOX1 GT(n) repeat in European populations.

HO-1 is a key enzyme in the management of heme in humans. A GT(n) repeat length in the gene HMOX1, has previously been widely associated with a variety of phenotypes, including susceptibility and outcomes in diabetes, cancer, infections, and neonatal jaundice. However, studies are generally small and results inconsistent. In this study, we imputed the GT(n) repeat length in two European cohorts (UK Biobank, UK, n = 463,005, recruited 2006-onwards; and Avon Longitudinal Study of Parents and Children, ALSPAC, UK, n = 937, recruited 1990 onwards), with the reliability of imputation tested in other cohorts (1000 Genomes, Human Genome Diversity Project and UK-Personal Genome Project). Subsequently, we measured the relationship between repeat length and previously identified associations (diabetes, COPD, pneumonia and infection related mortality in UK Biobank; neonatal jaundice in ALSPAC) and performed a phenome-wide association study (PheWAS) in UK Biobank. Despite high quality imputation (correlation between true repeat length and imputed repeat length >0.9 in test cohorts), clinical associations were not identified in either the PheWAS or specific association studies. These findings are robust to definitions of repeat length and sensitivity analyses. Despite multiple smaller studies identifying associations across a variety of clinical settings; we could not replicate or identify any relevant phenotypic associations with the HMOX1 GT(n) repeat.

Prevalence of electronic screening for sepsis in National Health Service acute hospitals in England.

Sepsis is a worldwide public health problem. Rapid identification is associated with improved patient outcomes-if followed by timely appropriate treatment. OBJECTIVES: Describe digital sepsis alerts (DSAs) in use in English National Health Service (NHS) acute hospitals. METHODS: A Freedom of Information request surveyed acute NHS Trusts on their adoption of electronic patient records (EPRs) and DSAs. RESULTS: Of the 99 Trusts that responded, 84 had an EPR. Over 20 different EPR system providers were identified as operational in England. The most common providers were Cerner (21%). System C, Dedalus and Allscripts Sunrise were also relatively common (13%, 10% and 7%, respectively). 70% of NHS Trusts with an EPR responded that they had a DSA; most of these use the National Early Warning Score (NEWS2). There was evidence that the EPR provider was related to the DSA algorithm. We found no evidence that Trusts were using EPRs to introduce data driven algorithms or DSAs able to include, for example, pre-existing conditions that may be known to increase risk.Not all Trusts were willing or able to provide details of their EPR or the underlying algorithm. DISCUSSION: The majority of NHS Trusts use an EPR of some kind; many use a NEWS2-based DSA in keeping with national guidelines. CONCLUSION: Many English NHS Trusts use DSAs; even those using similar triggers vary and many recreate paper systems. Despite the proliferation of machine learning algorithms being developed to support early detection of sepsis, there is little evidence that these are being used to improve personalised sepsis detection.

Gene filtering strategies for machine learning guided biomarker discovery using neonatal sepsis RNA-seq data.

Machine learning (ML) algorithms are powerful tools that are increasingly being used for sepsis biomarker discovery in RNA-Seq data. RNA-Seq datasets contain multiple sources and types of noise (operator, technical and non-systematic) that may bias ML classification. Normalisation and independent gene filtering approaches described in RNA-Seq workflows account for some of this variability and are typically only targeted at differential expression analysis rather than ML applications. Pre-processing normalisation steps significantly reduce the number of variables in the data and thereby increase the power of statistical testing, but can potentially discard valuable and insightful classification features. A systematic assessment of applying transcript level filtering on the robustness and stability of ML based RNA-seq classification remains to be fully explored. In this report we examine the impact of filtering out low count transcripts and those with influential outliers read counts on downstream ML analysis for sepsis biomarker discovery using elastic net regularised logistic regression, L1-reguarlised support vector machines and random forests. We demonstrate that applying a systematic objective strategy for removal of uninformative and potentially biasing biomarkers representing up to 60% of transcripts in different sample size datasets, including two illustrative neonatal sepsis cohorts, leads to substantial improvements in classification performance, higher stability of the resulting gene signatures, and better agreement with previously reported sepsis biomarkers. We also demonstrate that the performance uplift from gene filtering depends on the ML classifier chosen, with L1-regularlised support vector machines showing the greatest performance improvements with our experimental data.

Variation in ERAP2 has opposing effects on severe respiratory infection and autoimmune disease.

ERAP2 is an aminopeptidase involved in immunological antigen presentation. Genotype data in human samples from before and after the Black Death, an epidemic due to Yersinia pestis, have marked changes in allele frequency of the single-nucleotide polymorphism (SNP) rs2549794, with the T allele suggested to be deleterious during this period, while ERAP2 is also implicated in autoimmune diseases. This study explored the association between variation at ERAP2 and (1) infection, (2) autoimmune disease, and (3) parental longevity. Genome-wide association studies (GWASs) of these outcomes were identified in contemporary cohorts (UK Biobank, FinnGen, and GenOMICC). Effect estimates were extracted for rs2549794 and rs2248374, a haplotype tagging SNP. Additionally, cis expression and protein quantitative trait loci (QTLs) for ERAP2 were used in Mendelian randomization (MR) analyses. Consistent with decreased survival in the Black Death, the T allele of rs2549794 showed evidence of association with respiratory infection (odds ratio; OR for pneumonia 1.03; 95% CI 1.01-1.05). Effect estimates were larger for more severe phenotypes (OR for critical care admission with pneumonia 1.08; 95% CI 1.02-1.14). In contrast, opposing effects were identified for Crohn disease (OR 0.86; 95% CI 0.82-0.90). This allele was shown to associate with decreased ERAP2 expression and protein levels, independent of haplotype. MR analyses suggest that ERAP2 expression may be mediating disease associations. Decreased ERAP2 expression is associated with severe respiratory infection with an opposing association with autoimmune diseases. These data support the hypothesis of balancing selection at this locus driven by autoimmune and infectious disease.

Immune and metabolic markers for identifying and investigating severe Coronavirus disease and Sepsis in children and young people (pSeP/COVID ChYP study): protocol for a prospective cohort study.

INTRODUCTION: Early recognition and appropriate management of paediatric sepsis are known to improve outcomes. A previous system's biology investigation of the systemic immune response in neonates to sepsis identified immune and metabolic markers that showed high accuracy for detecting bacterial infection. Further gene expression markers have also been reported previously in the paediatric age group for discriminating sepsis from control cases. More recently, specific gene signatures were identified to discriminate between COVID-19 and its associated inflammatory sequelae. Through the current prospective cohort study, we aim to evaluate immune and metabolic blood markers which discriminate between sepses (including COVID-19) from other acute illnesses in critically unwell children and young persons, up to 18 years of age. METHODS AND ANALYSIS: We describe a prospective cohort study for comparing the immune and metabolic whole-blood markers in patients with sepsis, COVID-19 and other illnesses. Clinical phenotyping and blood culture test results will provide a reference standard to evaluate the performance of blood markers from the research sample analysis. Serial sampling of whole blood (50 µL each) will be collected from children admitted to intensive care and with an acute illness to follow time dependent changes in biomarkers. An integrated lipidomics and RNASeq transcriptomics analyses will be conducted to evaluate immune-metabolic networks that discriminate sepsis and COVID-19 from other acute illnesses. This study received approval for deferred consent. ETHICS AND DISSEMINATION: The study has received research ethics committee approval from the Yorkshire and Humber Leeds West Research Ethics Committee 2 (reference 20/YH/0214; IRAS reference 250612). Submission of study results for publication will involve making available all anonymised primary and processed data on public repository sites. TRIAL REGISTRATION NUMBER: NCT04904523.

Altered IL-6 signalling and risk of tuberculosis disease: a meta-analysis and Mendelian randomisation study.

IL-6 responses are ubiquitous in Mycobacterium tuberculosis (Mtb) infections, but their role in determining human tuberculosis (TB) disease risk is unknown. We used single nucleotide polymorphisms (SNPs) in and near the IL-6 receptor (IL6R) gene, focusing on the non-synonymous variant, rs2228145, associated with reduced classical IL-6 signalling, to assess the effect of altered IL-6 activity on TB disease risk. We identified 16 genome wide association studies (GWAS) of TB disease collating 17,982 cases of TB disease and 972,389 controls across 4 continents. Meta-analyses and Mendelian randomisation analyses revealed that reduced classical IL-6 signalling was associated with lower odds of TB disease, a finding replicated using multiple, independent SNP instruments and 2 separate exposure variables. Our findings establish a causal relationship between IL-6 signalling and the outcome of Mtb infection, suggesting IL-6 antagonists do not increase the risk of TB disease and should be investigated as adjuncts in treatment.

The effect of interleukin-6 signaling on severe malaria: A Mendelian randomization analysis.

OBJECTIVES: Severe malaria remains a deadly disease for many young children in low- and middle-income countries. Levels of interleukin (IL)-6 have been shown to identify cases of severe malaria and associate with severity, but it is unknown if this association is causal. METHODS: A single nucleotide polymorphism (SNP; rs2228145) in the IL-6 receptor was chosen as a genetic variant that is known to alter IL-6 signaling. We tested this, then took this forward as an instrument to perform Mendelian randomization (MR) in MalariaGEN, a large cohort study of patients with severe malaria at 11 worldwide sites. RESULTS: In MR analyses using rs2228145, we did not identify an effect of decreased IL-6 signaling on severe malaria (odds ratio 1.14, 95% confidence interval 0.56-2.34, P = 0.713). The estimates of the association with any severe malaria subphenotype were similarly null, although with some imprecision. Further analyses using other MR approaches had similar results. CONCLUSION: These analyses do not support a causal role for IL-6 signaling in the development of severe malaria. This result suggests IL-6 may not be causal for severe outcomes in malaria, and that therapeutic manipulation of IL-6 is unlikely to be a suitable treatment for severe malaria.

An observational and Mendelian randomisation study on iron status and sepsis.

Iron deficiency is associated with a substantial burden of morbidity. However, supplementation of iron has been linked to increased rates of serious infection in randomised trials of children in sub-Saharan Africa. Randomised trials in other settings have been inconclusive and it is unknown if changes in levels of iron biomarkers are linked to sepsis in these other settings. We used genetic variants associated with levels of iron biomarkers as instrumental variables in a Mendelian randomisation (MR) analysis to test the hypothesis that increasing levels of iron biomarkers increase the risk of sepsis. In observational and MR analyses we found that increases in iron biomarkers increase the odds of sepsis. In stratified analyses, we show that this risk may be larger in those with iron deficiency and/or anaemia. Taken together, results here suggest a required caution in supplementation of iron and underline the role of iron homeostasis in severe infection.

Therapeutic potential of IL6R blockade for the treatment of sepsis and sepsis-related death: A Mendelian randomisation study.

BACKGROUND: Sepsis is characterised by dysregulated, life-threatening immune responses, which are thought to be driven by cytokines such as interleukin 6 (IL-6). Genetic variants in IL6R known to down-regulate IL-6 signalling are associated with improved Coronavirus Disease 2019 (COVID-19) outcomes, a finding later confirmed in randomised trials of IL-6 receptor antagonists (IL6RAs). We hypothesised that blockade of IL6R could also improve outcomes in sepsis. METHODS AND FINDINGS: We performed a Mendelian randomisation (MR) analysis using single nucleotide polymorphisms (SNPs) in and near IL6R to evaluate the likely causal effects of IL6R blockade on sepsis (primary outcome), sepsis severity, other infections, and COVID-19 (secondary outcomes). We weighted SNPs by their effect on CRP and combined results across them in inverse variance weighted meta-analysis, proxying the effect of IL6RA. Our outcomes were measured in UK Biobank, FinnGen, the COVID-19 Host Genetics Initiative (HGI), and the GenOSept and GainS consortium. We performed several sensitivity analyses to test assumptions of our methods, including utilising variants around CRP and gp130 in a similar analysis. In the UK Biobank cohort (N = 486,484, including 11,643 with sepsis), IL6R blockade was associated with a decreased risk of our primary outcome, sepsis (odds ratio (OR) = 0.80; 95% confidence interval (CI) 0.66 to 0.96, per unit of natural log-transformed CRP decrease). The size of this effect increased with severity, with larger effects on 28-day sepsis mortality (OR = 0.74; 95% CI 0.47 to 1.15); critical care admission with sepsis (OR = 0.48, 95% CI 0.30 to 0.78) and critical care death with sepsis (OR = 0.37, 95% CI 0.14 to 0.98). Similar associations were seen with severe respiratory infection: OR for pneumonia in critical care 0.69 (95% CI 0.49 to 0.97) and for sepsis survival in critical care (OR = 0.22; 95% CI 0.04 to 1.31) in the GainS and GenOSept consortium, although this result had a large degree of imprecision. We also confirm the previously reported protective effect of IL6R blockade on severe COVID-19 (OR = 0.69, 95% CI 0.57 to 0.84) in the COVID-19 HGI, which was of similar magnitude to that seen in sepsis. Sensitivity analyses did not alter our primary results. These results are subject to the limitations and assumptions of MR, which in this case reflects interpretation of these SNP effects as causally acting through blockade of IL6R, and reflect lifetime exposure to IL6R blockade, rather than the effect of therapeutic IL6R blockade. CONCLUSIONS: IL6R blockade is causally associated with reduced incidence of sepsis. Similar but imprecisely estimated results supported a causal effect also on sepsis related mortality and critical care admission with sepsis. These effects are comparable in size to the effect seen in severe COVID-19, where IL-6 receptor antagonists were shown to improve survival. These data suggest that a randomised trial of IL-6 receptor antagonists in sepsis should be considered.

Immune-metabolic adaptations in pregnancy: A potential stepping-stone to sepsis.

Physiological shifts during pregnancy predispose women to a higher risk of developing sepsis resulting from a maladapted host-response to infection. Insightful studies have delineated subtle point-changes to the immune system during pregnancy. Here, we present an overlay of these point-changes, asking what changes and when, at a physiological, cellular, and molecular systems-level in the context of sepsis. We identify distinct immune phases in pregnancy delineated by placental hormone-driven changes in homeostasis setpoints of the immune and metabolic systems that subtly mirrors changes observed in sepsis. We propose that pregnancy immune-metabolic setpoint changes impact feedback thresholds that increase risk for a maladapted host-response to infection and thus act as a stepping-stone to sepsis. Defining maternal immune-metabolic setpoint changes is not only vital for tailoring the right diagnostic tools for early management of maternal sepsis but will facilitate an unravelling of the pathophysiological pathways that predispose an individual to sepsis.

HMOX1 STR polymorphism and malaria: an analysis of a large clinical dataset.

BACKGROUND: Inducible expression of heme oxygenase-1 (encoded by the gene HMOX1) may determine protection from heme released during malaria infections. A variable length, short tandem GT(n) repeat (STR) in HMOX1 that may influence gene expression has been associated with outcomes of human malaria in some studies. In this study, an analysis of the association between variation at the STR in HMOX1 on severe malaria and severe malaria subtypes is presented in a large, prospectively collected dataset (MalariaGEN). METHODS: The HMOX1 STR was imputed using a recently developed reference haplotype panel designed for STRs. The STR was classified by total length and split into three alleles based on an observed trimodal distribution of repeat lengths. Logistic regression was used to assess the association between this repeat on cases of severe malaria and severe malaria subtypes (cerebral malaria and severe malarial anaemia). Individual analyses were performed for each MalariaGEN collection site and combined for meta-analysis. One site (Kenya), had detailed clinical metadata, allowing the assessment of the effect of the STR on clinical variables (e.g. parasite count, platelet count) and regression analyses were performed to investigate whether the STR interacted with any clinical variables. RESULTS: Data from 17,960 participants across 11 collection sites were analysed. In logistic regression, there was no strong evidence of association between STR length and severe malaria (Odds Ratio, OR: 0.96, 95% confidence intervals 0.91-1.02 per ten GT(n) repeats), although there did appear to be an association at some sites (e.g., Kenya, OR 0.90, 95% CI 0.82-0.99). There was no evidence of an interaction with any clinical variables. CONCLUSIONS: Meta-analysis suggested that increasing HMOX1 STR length is unlikely to be reliably associated with severe malaria. It cannot be ruled out that repeat length may alter risk in specific populations, although whether this is due to chance variation, or true variation due to underlying biology (e.g., gene vs environment interaction) remains unanswered.

CD1d-dependent rewiring of lipid metabolism in macrophages regulates innate immune responses.

Alterations in cellular metabolism underpin macrophage activation, yet little is known regarding how key immunological molecules regulate metabolic programs in macrophages. Here we uncover a function for the antigen presenting molecule CD1d in the control of lipid metabolism. We show that CD1d-deficient macrophages exhibit a metabolic reprogramming, with a downregulation of lipid metabolic pathways and an increase in exogenous lipid import. This metabolic rewiring primes macrophages for enhanced responses to innate signals, as CD1d-KO cells show higher signalling and cytokine secretion upon Toll-like receptor stimulation. Mechanistically, CD1d modulates lipid import by controlling the internalization of the lipid transporter CD36, while blocking lipid uptake through CD36 restores metabolic and immune responses in macrophages. Thus, our data reveal CD1d as a key regulator of an inflammatory-metabolic circuit in macrophages, independent of its function in the control of T cell responses.

mSep: investigating physiological and immune-metabolic biomarkers in septic and healthy pregnant women to predict feto-maternal immune health - a prospective observational cohort study protocol.

INTRODUCTION: Maternal 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 ANALYSIS: Women 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 DISSEMINATION: Ethical approval has been obtained from the Wales Research Ethics Committee 2 (SPON1752-19, 30 October 2019). TRIAL REGISTRATION NUMBER: NCT05023954.

Evaluation of perturbed iron-homeostasis in a prospective cohort of patients with COVID-19.

Background: Marked reductions in serum iron concentrations are commonly induced during the acute phase of infection. This phenomenon, termed hypoferremia of inflammation, leads to inflammatory anemia, but could also have broader pathophysiological implications. In patients with coronavirus disease 2019 (COVID-19), hypoferremia is associated with disease severity and poorer outcomes, although there are few reported cohorts. Methods: In this study, we leverage a well characterised prospective cohort of hospitalised COVID-19 patients and perform a set of analyses focussing on iron and related biomarkers and both acute severity of COVID-19 and longer-term symptomatology. Results: We observed no associations between acute serum iron and long-term outcomes (including fatigue, breathlessness or quality of life); however, lower haemoglobin was associated with poorer quality of life. We also quantified iron homeostasis associated parameters, demonstrating that among 50 circulating mediators of inflammation IL-6 concentrations were strongly associated with serum iron, consistent with its central role in inflammatory control of iron homeostasis. Surprisingly, we observed no association between serum hepcidin and serum iron concentrations. We also observed elevated erythroferrone concentrations in COVID-19 patients with anaemia of inflammation. Conclusions: These results enhance our understanding of the regulation and pathophysiological consequences of disturbed iron homeostasis during SARS-CoV-2 infection.

HMOX1 genetic polymorphisms and outcomes in infectious disease: A systematic review.

INTRODUCTION: Heme-oxygenase 1 (HMOX1) is a critical stress response gene that catalyzes the multistep oxidation of heme. A GT(n) repeat of variable length in the promoter in has been associated with a wide range of human diseases, including infections. This paper aims to summarise and systematically review associations between the length of the HMOX1 GT(n) promoter and infectious disease in humans. METHODS: A search using relevant terms was performed in PubMED and EMBASE through to 15/01/21 identifying all research that studied an association between the HMOX1 GT(n) repeat polymorphism and the incidence and/or outcome of any human infectious disease. Citations were screened for additional studies. Potential studies were screened for inclusion by two authors. Data was extracted on allele frequency, genotype, strength of association, mechanism of genotyping, and potential biases. A narrative review was performed across each type of infection. RESULTS: 1,533 studies were identified in the search, and one via citation screening. Sixteen studies were ultimately included, seven in malaria, three in HIV, three in sepsis, and one each in pneumonia, hepatitis C, and acute respiratory distress syndrome (ARDS). Sample sizes for nearly all studies were small (biggest study, n = 1,646). Allelic definition was different across all included studies. All studies were at some risk of bias. In malaria, three studies suggested that longer alleles were associated with reduced risk of severe malaria, particularly malaria-induced renal dysfunction, with four studies identifying a null association. In sepsis, two studies suggested an association with longer alleles and better outcomes. CONCLUSIONS: Despite the importance of HMOX1 in survival from infection, and the association between repeat length and gene expression, the clinical data supporting an association between repeat length and incidence and/or outcome of infection remain inconclusive.