Identification of potential diagnostic biomarkers of acute pancreatitis by serum metabolomic profiles

Lipase-to-Amylase Ratio for the Prediction of Clinically Relevant Postoperative Pancreatic Fistula Following Pancreaticoduodenectomy

OBJECTIVE

Postoperative pancreatic fistula (POPF) represents a leading cause of morbidity and mortality following major pancreatic resections. This study aimed to evaluate the use of postoperative drain fluid lipase-to-amylase ratio (LAR) for the prediction of clinically relevant fistulae (CR-POPF).

METHODS

Consecutive patients undergoing pancreaticoduodenectomy between 2017 and 2021 at a tertiary centre were retrospectively reviewed. Univariable and multivariable analyses were performed to identify predictors for CR-POPF (ISGPS grade B/C). Receiver operating characteristic (ROC) curve analyses were conducted to evaluate the performance of LAR and determine optimum prediction thresholds.

RESULTS

Among 130 patients, 28 (21.5%) developed CR-POPF. Variables positively associated with CR-POPF included soft gland texture, acinar cell density, diagnosis other than PDAC or chronic pancreatitis, resection without neoadjuvant therapy, and postoperative drain fluid lipase, amylase, and LAR (all P <0.05). Multivariable regression analysis identified LAR as an independent predictor of CR-POPF ( P <0.05). ROC curve analysis showed that LAR had moderate ability to predict CR-POPF on POD1 (AUC,0.64; 95%CI,0.54-0.74) and excellent ability on POD3 (AUC,0.85; 95%CI,0.78-0.92) and POD 5 (AUC,0.86; 95%CI,0.79-0.92). Optimum thresholds were consistent over PODs 1 to 5 (ratio>2.6) and associated with 92% sensitivity and 46% to 71% specificity.

CONCLUSIONS

Postoperative drain fluid LAR represents a reliable predictor for the development of CR-POPF. With early prognostication, the postoperative care of patients at risk of developing high-grade fistulas may be optimized.

Identification of a hyperinflammatory sepsis phenotype using protein biomarker and clinical data in the ProCESS randomized trial

Sepsis is a heterogeneous syndrome and phenotypes have been proposed using clinical data. Less is known about the contribution of protein biomarkers to clinical sepsis phenotypes and their importance for treatment effects in randomized trials of resuscitation. The objective is to use both clinical and biomarker data in the Protocol-Based Care for Early Septic Shock (ProCESS) randomized trial to determine sepsis phenotypes and to test for heterogeneity of treatment effect by phenotype comparing usual care to protocolized early, goal-directed therapy(EGDT). In this secondary analysis of a subset of patients with biomarker sampling in the ProCESS trial (n = 543), we identified sepsis phenotypes prior to randomization using latent class analysis of 20 clinical and biomarker variables. Logistic regression was used to test for interaction between phenotype and treatment arm for 60-day inpatient mortality. Among 543 patients with severe sepsis or septic shock in the ProCESS trial, a 2-class model best fit the data (p = 0.01). Phenotype 1 (n = 66, 12%) had increased IL-6, ICAM, and total bilirubin and decreased platelets compared to phenotype 2 (n = 477, 88%, p < 0.01 for all). Phenotype 1 had greater 60-day inpatient mortality compared to Phenotype 2 (41% vs 16%; p < 0.01). Treatment with EGDT was associated with worse 60-day inpatient mortality compared to usual care (58% vs. 23%) in Phenotype 1 only (p-value for interaction = 0.05). The 60-day inpatient mortality was similar comparing EGDT to usual care in Phenotype 2 (16% vs. 17%). We identified 2 sepsis phenotypes using latent class analysis of clinical and protein biomarker data at randomization in the ProCESS trial. Phenotype 1 had increased inflammation, organ dysfunction and worse clinical outcomes compared to phenotype 2. Response to EGDT versus usual care differed by phenotype.

Multi-dimensional metabolomic profiling reveals dysregulated ornithine metabolism hallmarks associated with a severe acute pancreatitis phenotype

To reveal dysregulated metabolism hallmark that was associated with a severe acute pancreatitis (SAP) phenotype. In this study, LC-MS/MS-based targeted metabolomics was used to analyze plasma samples from 106 acute pancreatitis (AP) patients (34 mild, 38 moderate, and 34 severe) admitted within 48 hours from abdominal pain onset and 41 healthy controls. Temporal metabolic profiling was performed on days 1, 3, and 7 after admission. A random forest (RF) was performed to significantly determine metabolite differences between SAP and non-SAP (NSAP) groups. Mass spectrometry imaging (MSI) and immunohistochemistry were conducted for the examination of pancreatic metabolite and metabolic enzyme alterations, respectively, on necrosis and paracancerous tissues. Simultaneously determination of serum and pancreatic tissue metabolic alterations using an L-ornithine-induced AP model to discover metabolic commonalities. Twenty-two significant differential metabolites screened by RF were selected to build an accurate model for the prediction of SAP from NSAP (AUC = 0.955). Six of 22 markers were found by MSI with significant alterations in pancreatic lesions, reduced ornithine-related metabolites were also identified. The abnormally expressed arginase2 and ornithine transcarboxylase were further discovered in combination with time-course metabolic profiling in the SAP animal models, the decreased ornithine catabolites were found at a late stage of inflammation, but ornithine-associated metabolic enzymes were activated during the inflammatory process. The plasma metabolome of AP patients is distinctive, which shows promise for early SAP diagnosis. AP aggravation is linked to the activated ornithine metabolic pathway and its inadequate levels of catabolites in in-situ lesion.

Unraveling the Metabolic Changes in Acute Pancreatitis: A Metabolomics-Based Approach for Etiological Differentiation and Acute Biomarker Discovery

Acute pancreatitis (AP) remains a challenging medical condition, where a deeper metabolic insight could pave the way for innovative treatments. This research harnessed serum metabolomics to discern potential diagnostic markers for AP and distinguish between its biliary (BAP) and alcohol-induced (AAP) forms. Leveraging high-performance liquid chromatography coupled with mass spectrometry, the metabolic signatures of 34 AP patients were contrasted against 26 healthy participants, and then between different etiologies of AP. The results identified metabolites primarily from glycerophospholipids, glycerolipids, fatty acyls, sterol lipids, and pteridines and derivative classes, with the Human Metabolome Database aiding in classification. Notably, these metabolites differentiated AP from healthy states with high AUROC values above 0.8. Another set of metabolites revealed differences between BAP and AAP, but these results were not as marked as the former. This lipidomic analysis provides an introduction to the metabolic landscape of acute pancreatitis, revealing changes in multiple lipid classes and metabolites and identifying these metabolites. Future research could add and discover new diagnostic biomarkers and therapeutic strategies enhancing the management of acute pancreatitis.

Serum metabolomics study for acute attack of chronic pancreatitis

BACKGROUND & AIMS

Chronic pancreatitis (CP) is an inflammatory disease characterized by irreversible changes. However, acute CP attacks can lead to various complications and affect patient prognosis. Therefore, this study aimed to identify reliable candidate metabolic biomarkers for diagnosing acute CP attacks and complement candidate diagnostic markers for CP.

METHODS

A total of 139 serum specimens were prospectively included in three consecutive exploratory, identification, and validation studies. All samples were analyzed for candidate diagnostic biomarkers and metabolic pathways using a liquid chromatography-mass spectrometer.

RESULTS

Serum metabolic profiles differed between patients with CP and non-pancreatic disease controls, and 239 potential metabolic biomarkers for diagnosing CP were identified. Based on identification and validation studies, Diacylglycerol(16:0/18:4), 16-F1-PhytoP, N-(hexacosanoyl)-tetradecasphing-4-enine, carnosic acid, and Auxin b were identified as biomarkers for distinguishing acute attacks from non-acute attacks in patients with CP. The area under the curve of the Diacylglycerol(16:0/18:4) was 0.969 (95% confidence interval, 0.869-1) in the validation study.

CONCLUSIONS

To the best of our knowledge, this is the first prospective cohort study to identify and validate a metabolomic signature in serum for diagnosing acute attacks of CP. In addition, our study identified 239 potential biomarkers for CP diagnosis.

Quantitative metabolic analysis of plasma extracellular vesicles for the diagnosis of severe acute pancreatitis

BACKGROUND

Severe acute pancreatitis (SAP) is the most common gastrointestinal disease and is associated with unpredictable seizures and high mortality rates. Despite improvements in the treatment of acute pancreatitis, the timely and accurate diagnosis of SAP remains highly challenging. Previous research has shown that extracellular vesicles (EVs) in the plasma have significant potential for the diagnosis of SAP since the pancreas can release EVs that carry pathological information into the peripheral blood in the very early stages of the disease. However, we know very little about the metabolites of EVs that might play a role in the diagnosis of SAP.

METHODS

Here, we performed quantitative metabolomic analyses to investigate the metabolite profiles of EVs isolated from SAP plasma. We also determined the metabolic differences of EVs when compared between healthy controls, patients with SAP, and those with mild acute pancreatitis (MAP).

RESULTS

A total of 313 metabolites were detected, mainly including organic acids, amino acids, fatty acids, and bile acids. The results showed that the metabolic composition of EVs derived from SAP and MAP was significantly different from those derived from healthy controls and identified specific differences between EVs derived from patients with SAP and MAP. On this basis, we identified four biomarkers from plasma EVs for SAP detection, including eicosatrienoic acid (C20:3), thiamine triphosphate, 2-Acetylfuran, and cis-Citral. The area under the curve (AUC) was greater than 0.95 for both discovery (n = 30) and validation (n = 70) sets.

CONCLUSIONS

Our data indicate that metabolic profiling analysis of plasma EVs and the screening of potential biomarkers are of significant potential for improving the early diagnosis and severity differentiation of acute pancreatitis.

Correlation between metabolomic profile constituents and feline pancreatic lipase immunoreactivity

Background

Feline pancreatic lipase immunoreactivity (fPLI) is commonly used to diagnose pancreatitis in cats (FP). Untargeted metabolomics has been extensively applied in human and veterinary medicine, but no metabolomic studies regarding FP have been conducted.

Objectives

To identify metabolites significantly associated with increased fPLI.

Animals

Forty‐nine client‐owned cats: 11 clinically healthy and 38 with various clinical conditions.

Methods

Analytical cross‐sectional study with convenience sampling. A panel of 630 metabolites belonging to 26 biochemical classes was quantified in plasma using a commercial metabolomic assay. The correlation between plasma metabolite concentrations and serum fPLI was evaluated using Spearman's rank correlation coefficient (R_s) with Bonferroni correction. Multivariable analysis then was performed to control for glomerular filtration rate, liver damage, and blood glucose concentration. The accuracy of selected metabolites in discriminating between cats with normal (≤3.5 μg/L) and increased (>5.3 μg/L) fPLI was estimated using the area under the receiver operating characteristic curve (AUROC).

Results

Four hundred and seven of 630 metabolites (64.6%) were quantified in all cats. When controlled for potential confounders only 3 sphingolipids were significantly positively correlated with fPLI: 2 cerebrosides: HexCer(d18:1/24:0); (R_s = .56), and HexCer(d18:1/24:1); (R_s = .58) and 1 sphingomyelin: SM C18:0 (R_s = .55). Their AUROCs in identifying cats with increased fPLI were 82% (95% confidence interval [CI 95%], 70%‐94%), 84% (CI 95%, 72%‐96%), and 78% (CI 95%, 65%‐92%), respectively.

Conclusions and Clinical Importance

Selected sphingolipids are moderately positively correlated with fPLI and appear to have fair to moderate diagnostic accuracy in discriminating between cats with normal and increased fPLI.

Identification and validation of a multivariable prediction model based on blood plasma and serum metabolomics for the distinction of chronic pancreatitis subjects from non-pancreas disease control subjects

OBJECTIVE

Chronic pancreatitis (CP) is a fibroinflammatory syndrome leading to organ dysfunction, chronic pain, an increased risk for pancreatic cancer and considerable morbidity. Due to a lack of specific biomarkers, diagnosis is based on symptoms and specific but insensitive imaging features, preventing an early diagnosis and appropriate management.

DESIGN

We conducted a type 3 study for multivariable prediction for individual prognosis according to the TRIPOD guidelines. A signature to distinguish CP from controls (n=160) was identified using gas chromatography-mass spectrometry and liquid chromatography-tandem mass spectrometry on ethylenediaminetetraacetic acid (EDTA)-plasma and validated in independent cohorts.

RESULTS

A Naive Bayes algorithm identified eight metabolites of six ontology classes. After algorithm training and computation of optimal cut-offs, classification according to the metabolic signature detected CP with an area under the curve (AUC) of 0.85 ((95% CI 0.79 to 0.91). External validation in two independent cohorts (total n=502) resulted in similar accuracy for detection of CP compared with non-pancreatic controls in EDTA-plasma (AUC 0.85 (95% CI 0.81 to 0.89)) and serum (AUC 0.87 (95% CI 0.81 to 0.95)).

CONCLUSIONS

This is the first study that identifies and independently validates a metabolomic signature in plasma and serum for the diagnosis of CP in large, prospective cohorts. The results could provide the basis for the development of the first routine laboratory test for CP.

Plasmatic Oxidative and Metabonomic Profile of Patients with Different Degrees of Biliary Acute Pancreatitis Severity

Acute pancreatitis (AP) is an inflammatory process of the pancreas with variable involvement of the pancreatic and peripancreatic tissues and remote organ systems. The main goal of this study was to evaluate the inflammatory biomarkers, oxidative stress (OS), and plasma metabolome of patients with different degrees of biliary AP severity to improve its prognosis. Twenty-nine patients with biliary AP and 11 healthy controls were enrolled in this study. We analyzed several inflammatory biomarkers, multifactorial scores, reactive oxygen species (ROS), antioxidants defenses, and the plasma metabolome of biliary AP and healthy controls. Hepcidin (1.00), CRP (0.94), and SIRI (0.87) were the most accurate serological biomarkers of AP severity. OS played a pivotal role in the initial phase of AP, with significant changes in ROS and antioxidant defenses relating to AP severity. Phenylalanine (p < 0.05), threonine (p < 0.05), and lipids (p < 0.01) showed significant changes in AP severity. The role of hepcidin and SIRI were confirmed as new prognostic biomarkers of biliary AP. OS appears to have a role in the onset and progression of the AP process. Overall, this study identified several metabolites that may predict the onset and progression of biliary AP severity, constituting the first metabonomic study in the field of biliary AP.

Pancreatic Disorders in Children with Inflammatory Bowel Disease

Background and Objectives: Inflammatory bowel disease (IBD) is a chronic condition and mainly affects the intestines, however, the involvement of the other organs of the gastrointestinal tract (upper part, pancreas, and liver) have been observed. The coexistence of IBD with pancreatic pathology is rare, however, it has been diagnosed more frequently during recent years in the pediatric population. This article reviews the current literature on the most common pancreatic diseases associated with IBD in the pediatric population and their relationship with IBD activity and treatment. Materials and Methods: We performed a systematic review of data from published studies on pancreatic disorders, also reported as extraintestinal manifestations (EIMs), among children with IBD. We searched PubMed and Web of Science to identify eligible studies published prior to 25 April 2020. Results: Forty-four papers were chosen for analysis after a detailed inspection, which aimed to keep only the research studies (case control studies and cohort studies) or case reports on children and only those which were written in English. The manifestations of IBD-associated pancreatic disorders range from asymptomatic increase in pancreatic enzymes activity to severe disease such as acute pancreatitis. Acute pancreatitis (AP) induced by drugs, mainly thiopurine, seems to be the most- often-reported pancreatic disease associated with IBD in children. AP associated with other than drug etiologies, and chronic pancreatitis (CP), are rarely observed in the course of pediatric IBD. The pancreatic involvement can be strictly related to the activity of IBD and can also precede the diagnosis of IBD in some pediatric patients. The course of AP is mild in most cases and may occasionally lead to the development of CP, mainly in cases with a genetic predisposition. Conclusions: The involvement of the pancreas in the course of IBD may be considered as an EIM or a separate co-morbid disease, but it can also be a side effect of IBD therapy, therefore a differential diagnosis should always be performed. As the number of IBD incidences with concomitant pancreatic diseases is constantly increasing in the pediatric population, it is important to include pancreatic enzymes level measurement in the workup of IBD.

Metabolomic-based clinical studies and murine models for acute pancreatitis disease: A review

Acute pancreatitis (AP) is one of the most common gastroenterological disorders requiring hospitalization and is associated with substantial morbidity and mortality. Metabolomics nowadays not only help us to understand cellular metabolism to a degree that was not previously obtainable, but also to reveal the importance of the metabolites in physiological control, disease onset and development. An in-depth understanding of metabolic phenotyping would be therefore crucial for accurate diagnosis, prognosis and precise treatment of AP. In this review, we summarized and addressed the metabolomics design and workflow in AP studies, as well as the results and analysis of the in-depth of research. Based on the metabolic profiling work in both clinical populations and experimental AP models, we described the metabolites with potential utility as biomarkers and the correlation between the altered metabolites and AP status. Moreover, the disturbed metabolic pathways correlated with biological function were discussed in the end. A practical understanding of current and emerging metabolomic approaches applicable to AP and use of the metabolite information presented will aid in designing robust metabolomics and biological experiments that result in identification of unique biomarkers and mechanisms, and ultimately enhanced clinical decision-making.

Metabolomics: An emerging potential approach to decipher critical illnesses

Critical illnesses contribute to the maximum morbidity and mortality of hospitalized patients. Acute respiratory distress syndrome (ARDS) and sepsis/septic shock are the two most common acute illnesses associated with intensive care unit (ICU) admission. Once triggered, both have an identical underlying mechanism, portrayed by inflammation and endothelial dysfunction. The diagnosis of ARDS is based on clinical findings, laboratory tests, and radiological imaging. Blood cultures remain the gold standard for the diagnosis of sepsis, with the limitation of time delay and low positive yield. A combination of biomarkers has been proposed to diagnose and prognosticate these acute disorders with strengths and limitations, but still, the gold standard has been elusive to clinicians. In this review article, we illustrate the potential of metabolomics to unravel biomarkers that can be clinically utilized as a rapid prognostic and diagnostic tool associated with specific patient populations (ARDS and sepsis/septic shock) based on the available scientific data.

Clinical Application of Metabolomics in Pancreatic Diseases: A Mini-Review

Metabolomics is a powerful new analytical method to describe the set of metabolites within cellular tissue and bodily fluids. Metabolomics can uncover detailed information about metabolic changes in organisms. The morphology of these metabolites represents the metabolic processes that occur in cells, such as anabolism, catabolism, inhomogeneous natural absorption and metabolism, detoxification, and metabolism of biomass energy. Because the metabolites of different diseases are different, the specificity of the changes can be found by metabolomics testing, which provides a new source of biomarkers for the early identification of diseases and the difference between benign and malignant states. Metabolomics has a wide application potential in pancreatic diseases, including early detection, diagnosis, and identification of pancreatic diseases. However, there are few studies on metabolomics in pancreatic diseases in the literature. This article reviews the application of metabolomics in the diagnosis, prognosis, treatment, and evaluation of pancreatic diseases.

An NMR based panorama of the heterogeneous biology of acute respiratory distress syndrome (ARDS) from the standpoint of metabolic biomarkers

Acute respiratory distress syndrome (ARDS), manifested by intricate etiology and pathophysiology, demands careful clinical surveillance due to its high mortality and imminent life support measures. NMR based metabolomics provides an approach for ARDS which culminates from a wide spectrum of illness thereby confounding early manifestation and prognosis predictors. ¹ H NMR with its manifold applications in critical disease settings can unravel the biomarker of ARDS thus holding potent implications by providing surrogate endpoints of clinical utility. NMR metabolomics which is the current apogee platform of omics trilogy is contributing towards the possible panacea of ARDS by subsequent validation of biomarker credential on larger datasets. In the present review, the physiological derangements that jeopardize the whole metabolic functioning in ARDS are exploited and the biomarkers involved in progression are addressed and substantiated. The following sections of the review also outline the clinical spectrum of ARDS from the standpoint of NMR based metabolomics which is an emerging element of systems biology. ARDS is the main premise of intensivists textbook, which has been thoroughly reviewed along with its incidence, progressive stages of severity, new proposed diagnostic definition, and the preventive measures and the current pitfalls of clinical management. The advent of new therapies, the need for biomarkers, the methodology and the contemporary promising approaches needed to improve survival and address heterogeneity have also been evaluated. The review has been stepwise illustrated with potent biometrics employed to selectively pool out differential metabolites as diagnostic markers and outcome predictors. The following sections have been drafted with an objective to better understand ARDS mechanisms with predictive and precise biomarkers detected so far on the basis of underlying physiological parameters having close proximity to diseased phenotype. The aim of this review is to stimulate interest in conducting more studies to help resolve the complex heterogeneity of ARDS with biomarkers of clinical utility and relevance.

Multifactorial Scores and Biomarkers of Prognosis of Acute Pancreatitis: Applications to Research and Practice

Acute pancreatitis (AP) is a severe inflammation of the pancreas presented with sudden onset and severe abdominal pain with a high morbidity and mortality rate, if accompanied by severe local and systemic complications. Numerous studies have been published about the pathogenesis of AP; however, the precise mechanism behind this pathology remains unclear. Extensive research conducted over the last decades has demonstrated that the first 24 h after symptom onset are critical for the identification of patients who are at risk of developing complications or death. The identification of these subgroups of patients is crucial in order to start an aggressive approach to prevent mortality. In this sense and to avoid unnecessary overtreatment, thereby reducing the financial implications, the proper identification of mild disease is also important and necessary. A large number of multifactorial scoring systems and biochemical markers are described to predict the severity. Despite recent progress in understanding the pathophysiology of AP, more research is needed to enable a faster and more accurate prediction of severe AP. This review provides an overview of the available multifactorial scoring systems and biochemical markers for predicting severe AP with a special focus on their advantages and limitations.

GC-MS based metabolomics strategy to distinguish three types of acute pancreatitis

Acute pancreatitis (AP) is a progressive systemic inflammatory response with high morbidity and high mortality, which is mainly caused by alcohol, bulimia, gallstones and hyperlipidemia. The early diagnosis of different types of AP and further explore potential pathophysiological mechanism of each type of AP is beneficial for optimized treatment strategies and better patient's care. In this study, a metabolomics approach based on gas chromatography-mass spectrometry (GC-MS), and random forests algorithm was established to distinguish biliary acute pancreatitis (BAP), Hyperlipidemia acute pancreatitis (HLAP), and alcoholic acute pancreatitis (AAP), from healthy controls. The classification accuracies for BAP, HLAP, and AAP patients compared with healthy control, were 0.886, 0.906 and 0.857, respectively, by using 5-fold cross-validation method. And some special metabolites for each type of AP were discovered, such as l-Lactic acid, (R)-3-Hydroxybutyric acid, Phosphoric acid, Glycine, Erythronic acid, l-Phenylalanine, d-Galactose, l-Tyrosine, Arachidonic acid, Glycerol 1-hexadecanoate. Furthermore, associations between these metabolites with the metabolism of amino acids, fatty acids were identified. Our studies have illuminated the biomarkers and physiological mechanism of disease in a clinical setting, which suggested that metabolomics is a valuable tool for identifying the molecular mechanisms that are involved in the etiology of BAP, AAP, HLAP and thus novel therapeutic targets.

Metabolite Markers for Characterizing Sasang Constitution Type through GC-MS and 1H NMR-Based Metabolomics Study

Sasang constitutional medicine classifies human beings into four types based on their physical and psychological characteristics. Despite its potential value in achieving personalized medicine, the diagnosis of sasang constitution (SC) type is complex and subjective. In this study, gas chromatography–mass spectrometry and ¹H nuclear magnetic resonance–based metabolic analyses were conducted to find maker metabolites in serum and urine according to different SC types. Although some samples were overlapped on orthogonal projection to latent structure discriminant analysis score plots, serum samples showed separation between different SC types. Levels of lactate, glutamate, triglyceride, and fatty acids in serum and glycolic acid in urine of Tae-Eum type were higher than those of So-Eum and So-Yang type. Fatty acids, triglyceride, and lactate levels were found to be metabolites related to body mass index, indicating that marker metabolites for the diagnosis of SC type could be associated with obese. However, Tae-Eum type showed higher lactate levels in serum than So-Yang type for both normal weight and overweight groups, suggesting that the contents of serum lactate might be dependent on the SC type regardless of body weight. These results suggest that metabolomics analysis could be used to determine SC type.

A review of validated biomarkers obtained through metabolomics

INTRODUCTION

Studying changes in the whole set of small molecules, final products of biochemical reactions in living systems or metabolites, is extremely appealing because they represent the best approach to identifying what occurs in an organism when samples are collected. However, their usefulness as potential biomarkers is limited by discoveries obtained in small groups without proper validation or even confirmation of the chemical structure. Areas covered: During the past 5 years, more than 900 papers have been published on metabolomics for biomarker discovery, but the numbers are much lower when some criteria of validation are applied. In total, 102 papers have been included in this review. The most frequent disease areas in which these markers have been discovered include the following: cancer, diabetes, and related diseases and neurodegenerative, cardiovascular, autoimmune, liver, and kidney diseases. Expert commentary: Metabolomics has been demonstrated as rapidly growing due to the improvements in instrumentation, mainly mass spectrometry, and data mining software. For application in the clinic, the results should be validated in different stages, from analytical validation to validation in independent sets of samples, using thousands of samples from different sources.

Identification of potential diagnostic biomarkers of cerebral infarction using gas chromatography-mass spectrometry and chemometrics

Cerebral infarction (CI) is one of the most common cerebrovascular diseases and remains a major health problem worldwide. In this study, we evaluated the potential diagnostic biomarkers and important relevant metabolic pathways associated with CI. Metabolomics based on gas chromatography-mass spectrometry coupled with the multivariate pattern recognition technique were used to characterize the potential serum metabolic profiles of CI. Forty healthy controls and thirty-three cerebral infarction patients were recruited for the nontargeted global metabolites' study and subsequent targeted fatty acid analysis. Overall, thirty-four endogenous metabolites were found in serum from the untargeted global study, four of which were detected to be significantly different between the CI group and healthy controls, including l-lysine, octadecanoic acid (fatty acid), l-tyrosine and lactic acid. Additionally, fourteen free fatty acids were identified by the subsequent targeted fatty acid analysis, and seven of them were detected to be significantly different between the CI group and healthy controls, which were mainly associated with arachidonic acid metabolism and fatty acid metabolism. Our results suggest several potential diagnostic biomarkers, and serum metabolism research is demonstrated as a powerful tool to explore the pathogenesis of CI.

Cerebral infarction (CI) is one of the most common cerebrovascular diseases and remains a major health problem worldwide.