New diagnostic possibilities in systemic neonatal infections: metabolomics

Development and internal validation of a metabolism-related model for predicting 30-day mortality in neonatal sepsis

OBJECTIVE

Neonatal sepsis, a severe infectious disease associated with high mortality rates, is characterized by metabolic disturbances that play a crucial role in its progression. The aim of this study is to develop a metabolism-related model for assessing 30-day mortality in neonatal sepsis.

METHODS

The clinical data of neonatal sepsis at Ganzhou Women and Children's Health Care Hospital from January 2019 to December 2022 were retrospectively analyzed. Neonatal sepsis cases were divided into survival and non-survival groups. Multivariate logistic regression analysis was used to identify the independent risk factors for 30-day mortality. A nomogram model was developed based on these risk factors. Internal validation of the model was performed using 10-fold cross-validation. The predictive performance was evaluated through receiver operating characteristic (ROC) curves and calibration curve analyses. Decision curve analysis (DCA) was conducted to evaluate the clinical applicability of the developed model.

RESULTS

The study included a total of 156 cases of neonatal sepsis. Multivariate logistic regression analysis revealed that alanine(ALA), citrulline(CIT)), octadecanoylcarnitine(C18) and methionine(MET) were identified as independent risk factors for 30-day mortality of neonatal sepsis. The ROC curve showed an area under the curve of AUC = 0.866 (95% CI 0.796-0.936, P < 0.05). The calibration curve and DCA indicated excellent performance of the model.

CONCLUSION

This study establishes a predictive model for neonatal sepsis-associated 30-day mortality, effectively capturing the perturbations in amino acid metabolism and fatty acid oxidation, thereby demonstrating robust predictive capabilities.

Metabolomics in pediatric lower respiratory tract infections and sepsis: a literature review

Lower respiratory tract infections (LRTIs) are a leading cause of morbidity and mortality in children. The ability of healthcare providers to diagnose and prognose LRTIs in the pediatric population remains a challenge, as children can present with similar clinical features regardless of the underlying pathogen or ultimate severity. Metabolomics, the large-scale analysis of metabolites and metabolic pathways offers new tools and insights that may aid in diagnosing and predicting the outcomes of LRTIs in children. This review highlights the latest literature on the clinical utility of metabolomics in providing care for children with bronchiolitis, pneumonia, COVID-19, and sepsis. IMPACT: This article summarizes current metabolomics approaches to diagnosing and predicting the course of pediatric lower respiratory infections. This article highlights the limitations to current metabolomics research and highlights future directions for the field.

Using Classification Tree Analysis to Predict the Type of Infection in Preterm Neonates: Proof of Concept Study

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BACKGROUND:

Late-onset neonatal sepsis is a major complication in preterm neonates. Early identification of the type of infection could help to improve therapy and outcome depending on the suspected microorganism by tailoring antibiotic treatment to the individual patient based on the predicted organism. Results of blood cultures may take up to 2 days or may remain negative in case of clinical sepsis. Chemical biomarkers may show different patterns in response to different type of microorganisms.

OBJECTIVE:

The aim of this study was to develop, as a proof of concept, a simple classification tree algorithm using readily available information from biomarkers to show that biomarkers can potentially be used in discriminating in the type of infection in preterm neonates suspected of late-onset neonatal sepsis.

DERIVATION COHORT:

A total of 509 suspected late-onset neonatal sepsis episodes in neonates born before less than 32 weeks of gestation were analyzed. To examine model performance, 70% of the original dataset was randomly selected as a derivation cohort (n = 356; training dataset).

VALIDATION COHORT:

The remaining 30% of the original dataset was used as a validation cohort (n = 153; test dataset).

PREDICTION MODEL:

A classification tree prediction algorithm was applied to predict type of infection (defined as no/Gram-positive/Gram-negative sepsis).

RESULTS:

Suspected late-onset neonatal sepsis episodes were classified as no sepsis (80.8% [n = 411]), Gram-positive sepsis (13.9% [n = 71]), and Gram-negative sepsis (5.3% [n = 27]). When the derived classification tree was applied to the test cohort, the overall accuracy was 87.6% (95% CI, 81.3–92.4; p = 0.008). The classification tree demonstrates that interleukin-6 is the most important differentiating biomarker and C-reactive protein and procalcitonin help to further differentiate.

CONCLUSION:

We have developed and internally validated a simple, clinically relevant model to discriminate patients with different types of infection at moment of onset. Further research is needed to prospectively validate this in a larger population and assess whether adaptive antibiotic regimens are feasible.

Knowledge gaps in late-onset neonatal sepsis in preterm neonates: a roadmap for future research

Late-onset neonatal sepsis (LONS) remains an important threat to the health of preterm neonates in the neonatal intensive care unit. Strategies to optimize care for preterm neonates with LONS are likely to improve survival and long-term neurocognitive outcomes. However, many important questions on how to improve the prevention, early detection, and therapy for LONS in preterm neonates remain unanswered. This review identifies important knowledge gaps in the management of LONS and describe possible methods and technologies that can be used to resolve these knowledge gaps. The availability of computational medicine and hypothesis-free-omics approaches give way to building bedside feedback tools to guide clinicians in personalized management of LONS. Despite advances in technology, implementation in clinical practice is largely lacking although such tools would help clinicians to optimize many aspects of the management of LONS. We outline which steps are needed to get possible research findings implemented on the neonatal intensive care unit and provide a roadmap for future research initiatives. IMPACT: This review identifies knowledge gaps in prevention, early detection, antibiotic, and additional therapy of late-onset neonatal sepsis in preterm neonates and provides a roadmap for future research efforts. Research opportunities are addressed, which could provide the means to fill knowledge gaps and the steps that need to be made before possible clinical use. Methods to personalize medicine and technologies feasible for bedside clinical use are described.

Influence of innate immune activation on endocrine and metabolic pathways in infancy

Prematurity is the leading cause of neonatal morbidity and mortality worldwide. Premature infants often require extended hospital stays, with increased risk of developing infection compared with term infants. A picture is emerging of wide-ranging deleterious consequences resulting from innate immune system activation in the newborn infant. Those who survive infection have been exposed to a stimulus that can impose long-lasting alterations into later life. In this review, we discuss sepsis-driven alterations in integrated neuroendocrine and metabolic pathways and highlight current knowledge gaps in respect of neonatal sepsis. We review established biomarkers for sepsis and extend the discussion to examine emerging findings from human and animal models of neonatal sepsis that propose novel biomarkers for early identification of sepsis. Future research in this area is required to establish a greater understanding of the distinct neonatal signature of early and late-stage infection, to improve diagnosis, curtail inappropriate antibiotic use, and promote precision medicine through a biomarker-guided empirical and adjunctive treatment approach for neonatal sepsis. There is an unmet clinical need to decrease sepsis-induced morbidity in neonates, to limit and prevent adverse consequences in later life and decrease mortality.

Metabolomics technology and bioinformatics for precision medicine

Precision medicine is rapidly emerging as a strategy to tailor medical treatment to a small group or even individual patients based on their genetics, environment and lifestyle. Precision medicine relies heavily on developments in systems biology and omics disciplines, including metabolomics. Combination of metabolomics with sophisticated bioinformatics analysis and mathematical modeling has an extreme power to provide a metabolic snapshot of the patient over the course of disease and treatment or classifying patients into subpopulations and subgroups requiring individual medical treatment. Although a powerful approach, metabolomics have certain limitations in technology and bioinformatics. We will review various aspects of metabolomics technology and bioinformatics, from data generation, bioinformatics analysis, data fusion and mathematical modeling to data management, in the context of precision medicine.

Diagnosis of invasive fungal disease in children: a narrative review

Introduction: Invasive fungal diseases (IFD) represent important causes of morbidity and mortality in pediatrics. Early diagnosis and treatment of IFD is associated with better outcome and this entails the need to use fast and highly sensitive and specific methods that can support clinicians in the management of IFD.Areas covered: A narrative review was performed on conventional diagnostic methods such as culture, microscopy and histopathology are still gold standard but are burdened by a lack of sensitivity and specificity; on the other hand, imaging and noninvasive antigen-based such as beta-D-glucan, galactomannan and molecular biomarkers are the most convenient nonculture methods for diagnosis and monitoring effects of therapy. Aim of the present review is to summarize what is available in these fields at end of the second decade of the third millennium and look for future perspectives.Expert opinion: Promising and useful diagnostic methods have been applied in infectious disease diagnosis in clinical practice or in designing platforms. Unfortunately, most of them are not standardized or validated in pediatric population. However, clinicians should be aware of all innovative diagnostic tools to use in combination with conventional diagnostic methods for a better management of pathology and patient.

Diagnostic Challenges and Laboratory Considerations for Pediatric Sepsis

Background

Sepsis is a leading cause of death for children in the US and worldwide. There is a lack of consensus how sepsis is clinically defined, and sepsis definitions and diagnostic guidelines for the pediatric population have remained unchanged for more than a decade now. Current pediatric definitions are largely based on adult guidelines and expert opinion rather than evidence based on outcomes in the pediatric populations. Without a clear definition of sepsis, it is challenging to evaluate the performance of new laboratory tests on the diagnosis and management of sepsis.

Content

This review provides an overview of common etiologies of sepsis in pediatric populations, challenges in defining and diagnosing pediatric sepsis, and current laboratory tests used to identify and monitor sepsis. Strengths and limitations of emerging diagnostic strategies will also be discussed.

Summary

Currently there is no single biomarker that can accurately diagnose or predict sepsis. Current biomarkers such as C-reactive protein and lactate are neither sensitive nor specific for diagnosing sepsis. New biomarkers and rapid pathogen identification assays are much needed. Procalcitonin, although having some limitations, has emerged as a biomarker with demonstrated utility in management of sepsis in adults. Parallel studies analyzing the utility of procalcitonin in pediatric populations are lagging but have shown potential to affect sepsis care in pediatric populations. Multibiomarker approaches and stepwise algorithms show promise in the management of pediatric sepsis. However, a major hurdle is the lack of validated clinical criteria for classification of pediatric sepsis, which is necessary for the development of well-designed studies that can assess the clinical impact of these emerging biomarkers.

Effects of pidotimod and bifidobacteria mixture on clinical symptoms and urinary metabolomic profile of children with recurrent respiratory infections: a randomized placebo-controlled trial

BACKGROUND

Many preschool children develop recurrent respiratory tract infections (RRI). Strategies to prevent RRI include the use of immunomodulators as pidotimod or probiotics, but there is limited evidence of their efficacy on clinical features or on urine metabolic profile.

OBJECTIVE

To evaluate whether pidotimod and/or bifidobacteria can reduce RRI morbidity and influence the urine metabolic profile in preschool children.

MATERIALS AND METHODS

Children aged 3-6 years with RRI were enrolled in a four-arm, exploratory, prospective, randomized, double-blinded, placebo-controlled trial. Patients were randomly assigned to receive pidotimod plus bifidobacteria, pidotimod plus placebo, bifidobacteria plus placebo or double placebo for the first 10 days of each month over 4 consecutive months. Respiratory symptoms and infections were recorded with a daily diary by parents during the study. Metabolomic analyses on urine samples collected before and after treatment were performed.

RESULTS

Compared to placebo, children receiving pidotimod, alone or with bifidobacteria, had more symptom-free days (69 versus 44, p = 0.003; and 65 versus 44, p = 0.02, respectively) and a lower percentage of days with common cold (17% versus 37%, p = 0.005; and 15% versus 37%, p = 0.004, respectively). The metabolomic analysis showed that children treated with Pidotimod (alone or in combination with bifidobacteria) present, respect to children treated with placebo, a biochemical profile characterized by compounds related to the pathway of steroids hormones, hippuric acid and tryptophan. No significant difference in the metabolic profile was found between children receiving bifidobacteria alone and controls.

CONCLUSIONS

Preschool children with RRI treated with pidotimod have better clinical outcomes and a different urine metabolomic profile than subjects receiving placebo. Further investigations are needed to clarify the connection between pidotimod and gut microbiome.

Progress of Research in Neonatal Sepsis

Neonatal sepsis remains a significant global problem with little progress made despite major efforts. At present, there is a lack of an accepted international consensus on the definition, diagnosis, and treatment of neonatal sepsis; the unclear understanding of the pathogenesis of neonatal sepsis leads to blindness in treatment, which will result in an unsatisfactory therapeutic outcome. In addition, some serious diseases caused by noninfectious factors, such as trauma, stress, asphyxia, and so on, have very similar pathophysiological results with neonatal sepsis. In this review we synthesize the recent advances in definition, incidence, causative agents, risk factors, pathophysiology, clinical manifestations, and diagnosis and treatment of neonatal sepsis. Of course, there are still many challenges to neonatal sepsis in many ways.

NMR-Based Plasma Metabolomics at Set Intervals in Newborn Dairy Calves with Severe Sepsis

The aim of this first study was to reveal the new potential biomarkers by a metabolomics approach in severe septic calves. Sepsis is a common cause of morbidity and mortality in newborn dairy calves. The main challenges with the use of biomarkers of sepsis in domestic animals are their availability, cost, and time required to obtain a result. Metabolomics may offer the potential to identify biomarkers that define calf sepsis in terms of combined clinical, physiological, and pathobiological abnormalities. To our knowledge, this is the first study presenting an NMR- (nuclear magnetic resonance-) based plasma metabolomics at set intervals in neonatal septic calves. Twenty neonatal dairy calves with severe sepsis and ten healthy calves were used. Hematological and biochemical health profiles were gathered in plasma samples at set intervals. Similarly, NMR spectra were acquired. All diseased animals (except one) died after 72 hours. Clinical and laboratory results were in accordance with those of severe septic animals. Multivariate analysis on NMR plasma spectra proved to be an excellent tool for faster identification of calves with severe sepsis from healthy animals. The NMR-based metabolomic profile may contribute to the better understanding of severe sepsis in newborn calves.

High-throughput metabolomics enables metabolite biomarkers and metabolic mechanism discovery of fish in response to alkalinity stress

High throughput mass spectrometry (MS)-based metabolomics is a popular platform for small molecule metabolites analyses that are widely used for detecting biomarkers in the research field of environmental assessment. Crucian carp (Carassius carassius, CC) is an economically and ecologically important fish in Asia. It can adapt to extremely high alkalinity, providing us with valuable material to understand the adaptation mechanism for extreme environmental stress. However, the information on the metabolite biomarkers and metabolic mechanisms of CC exposed to alkaline stress is not entirely clear. We applied high-throughput UPLC-Q-TOF/MS combined with chemometrics to identify changes in the metabolome of CC exposed to different concentrations of alkalinity for long term effects. Metabolic differences among alkalinity-treated groups were identified by multivariate statistical analysis. Further, 7 differential metabolites were found after exposure to alkaline conditions. In total, 23 metabolic pathways of these differential metabolites were significantly affected. Alkalinity exposure resulted in widespread change in metabolic profiles in the plasma with disruptions in the phenylalanine metabolism, glycine, serine and threonine metabolism, pyruvate metabolism, tyrosine metabolism, etc. The integrated pathway analysis of the associated metabolites showed that tRNA charging, l-cysteine degradation II, superpathway of methionine degradation, l-serine degradation, tyrosine biosynthesis IV, etc. appear to be the most significantly represented functional categories. Overall, this study demonstrated that metabolic changes in CC played a role in adaptation to the highly alkaline environmental stress.

High throughput mass spectrometry (MS)-based metabolomics is a popular platform for small molecule metabolites analyses that are widely used for detecting biomarkers in the research field of environmental assessment.

Diagnostics for neonatal sepsis: current approaches and future directions

Progress has been made in the reduction of morbidity and mortality from neonatal sepsis. However, diagnosis continues to rely primarily on conventional microbiologic techniques, which can be inaccurate. The objective of this review is to provide the clinician with an overview of the current information available on diagnosing this condition. We review currently available diagnostic approaches for documenting neonatal sepsis and also describe novel approaches for diagnosing infection in neonates who are under development and investigation. Substantial progress has been made with molecular approaches and further development of non-culture-based methods offer promise. The potential ability to incorporate antimicrobial resistance gene testing in addition to pathogen identification may provide a venue to incorporate a predominantly molecular platform into a larger program of neonatal care.

Biomarkers for diagnosis of neonatal sepsis: a literature review

Sepsis is an important cause of mortality and morbidity in neonatal populations. There has been constant search of an ideal sepsis biomarker that have high sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV), so that both the diagnosis and exclusion of neonatal sepsis can be made at the earliest possible and appropriate antibiotics can be started to neonate. Ideal sepsis biomarker will help in guiding us when not to start antibiotics in case of suspect sepsis and total duration of antibiotics course in case of proven sepsis. There are numerous sepsis biomarkers that have been evaluated for early detection of neonatal sepsis but till date there is no single ideal biomarker that fulfills all essential criteria's for being an ideal biomarker. The most commonly used biomarkers are C-reactive protein (CRP) and procalcitonin (PCT), but both have shown varied sensitivity, specificity, PPV and NPV in different studies. We conducted literature search for various neonatal sepsis biomarkers and this review article will cover briefly all the markers with current available evidence.

UPLC-QTOF/MS based metabolomics reveals metabolic alterations associated with severe sepsis

Severe sepsis (SS) remains among the leading causes of death in both developed and developing countries.

Metabolomics in Newborns

Metabolomics is the quantitative analysis of a large number of low molecular weight metabolites that are intermediate or final products of all the metabolic pathways in a living organism. Any metabolic profiles detectable in a human biological fluid are caused by the interaction between gene expression and the environment. The metabolomics approach offers the possibility to identify variations in metabolite profile that can be used to discriminate disease. This is particularly important for neonatal and pediatric studies especially for severe ill patient diagnosis and early identification. This property is of a great clinical importance in view of the newer definitions of health and disease. This review emphasizes the workflow of a typical metabolomics study and summarizes the latest results obtained in neonatal studies with particular interest in prematurity, intrauterine growth retardation, inborn errors of metabolism, perinatal asphyxia, sepsis, necrotizing enterocolitis, kidney disease, bronchopulmonary dysplasia, and cardiac malformation and dysfunction.

Monitoring neonatal fungal infection with metabolomics

The objective of our study was to evaluate the capability of the metabolomics approach to identify the variations of urine metabolites over time related to the neonatal fungal septic condition. The study population included a clinical case of a preterm neonate with invasive fungal infection and 13 healthy preterm controls. This study showed a unique urine metabolic profile of the patient affected by fungal sepsis compared to urine of controls and it was also possible to evaluate the efficacy of therapy in improving patient health.

Neonatal infections due to multi-resistant strains: Epidemiology, current treatment, emerging therapeutic approaches and prevention

Severe infections represent the main cause of neonatal mortality accounting for more than one million neonatal deaths worldwide every year. Antibiotics are the most commonly prescribed medications in neonatal intensive care units. The benefits of antibiotic therapy when indicated are clearly enormous, but the continued and widespread use of antibiotics has generated over the years a strong selective pressure on microorganisms, favoring the emergence of resistant strains. Health agencies worldwide are galvanizing attention toward antibiotic resistance in gram-positive and gram-negative bacteria. Infections in neonatal units due to multidrug and extensively multidrug resistant bacteria are rising and are already seriously challenging antibiotic treatment options. While there is a growing choice of agents against multi-resistant gram-positive bacteria, new options for multi-resistant gram-negative bacteria in the clinical practice have decreased significantly in the last 20 years making the treatment of infections caused by multidrug-resistant pathogens challenging mostly in neonates. Treatment options are currently limited and will be some years before any new treatment for neonates become available for clinical use, if ever. The aim of the review is to highlight the current knowledge on antibiotic resistance in the neonatal population, the possible therapeutic choices, and the prevention strategies to adopt in order to reduce the emergency and spread of resistant strains.

Serial plasma metabolites following hypoxic-ischemic encephalopathy in a nonhuman primate model

Biomarkers that indicate the severity of hypoxic-ischemic brain injury and response to treatment and that predict neurodevelopmental outcomes are urgently needed to improve the care of affected neonates. We hypothesize that sequentially obtained plasma metabolomes will provide indicators of brain injury and repair, allowing for the prediction of neurodevelopmental outcomes. A total of 33 Macaca nemestrina underwent 0, 15 or 18 min of in utero umbilical cord occlusion (UCO) to induce hypoxic-ischemic encephalopathy and were then delivered by hysterotomy, resuscitated and stabilized. Serial blood samples were obtained at baseline (cord blood) and at 0.1, 24, 48, and 72 h of age. Treatment groups included nonasphyxiated controls (n = 7), untreated UCO (n = 11), UCO + hypothermia (HT; n = 6), and UCO + HT + erythropoietin (n = 9). Metabolites were extracted and analyzed using comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry and quantified by PARAFAC (parallel factor analysis). Using nontargeted discovery-based methods, we identified 63 metabolites as potential biomarkers. The changes in metabolite concentrations were characterized and compared between treatment groups. Further comparison determined that 8 metabolites (arachidonic acid, butanoic acid, citric acid, fumaric acid, lactate, malate, propanoic acid, and succinic acid) correlated with early and/or long-term neurodevelopmental outcomes. The combined outcomes of death or cerebral palsy correlated with citric acid, fumaric acid, lactate, and propanoic acid. This change in circulating metabolome after UCO may reflect cellular metabolism and biochemical changes in response to the severity of brain injury and have potential to predict neurodevelopmental outcomes.

Pathfast presepsin assay for early diagnosis of systemic inflammatory response syndrome in patients with nephrolithiasis

It is relatively difficult to diagnose bacterial sepsis in nephrolithiasis patients. The aim of the study is to evaluate the diagnostic ability of presepsin in the differential diagnosis including SIRS, infection, or sepsis and to compare its diagnostic value with other markers, mainly as CRP, procalcitonin (PCT), and white blood cell (WBC) in patients of nephrolithiasis presenting with SIRS. 39 patients of nephrolithiasis who were diagnosed as SIRS were prospectively investigated. Plasma presepsin was detected by Pathfast presepsin assay system; CRP and PCT were measured as well. Additionally, 25 nephrolithiasis patients without SIRS were included. At all timing samples, patients were classified as SIRS or non-SIRS group. Median plasma presepsin levels were significantly increased in the SIRS group compared with non-SIRS group (452 pg/mL versus 178 ng/mL, P < 0.001), and presepsin was markedly elevated even in the early stage of SIRS (584 pg/mL 6 h, 660 pg/mL 24 h versus 452 pg/mL, P < 0.001). According to the receiver-operating characteristic (ROC) analysis, presepsin demonstrated a high diagnostic value compared with either PCT or CRP. In the early stage of SIRS, presepsin remained a highly sensitive (74.7%) and specific (88.4%) diagnostic marker compared with either PCT, CRP, or WBC. Moreover, the areas under the curve (AUCs) of presepsin (84.6%) were also superior to those seen in either PCT (79.6%) or CRP (71.8%). Thus plasma presepsin levels have comparable performance in SIRS for patients with nephrolithiasis.

Potential urine biomarkers from a high throughput metabolomics study of severe sepsis in a large Asian cohort

Non-targeted mass spectrometry was used to characterize peripheral biomarkers associated with the urine metabolome in severe sepsis (SS) patients. This is an efficient and convenient tool for diagnosing and screening of SS in a high-risk population.