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

Updates in the Diagnosis of Chronic Pancreatitis: Current Approaches and New Possibilities

This review provides a comprehensive update on the diagnostic approaches to chronic pancreatitis (CP), emphasizing recent advancements in imaging techniques, biomarker research, and multivariable scoring systems. Despite substantial progress in these areas, current diagnostic algorithms have limitations, particularly for early and non-calcific CP. Traditional criteria have focused on classic diagnostic signs, but "minimal change" CP is increasingly recognized through advanced imaging and function tests. This article aims to guide clinicians in applying current methods and available strategies for CP diagnosis and outline research efforts in the field.

Associations of prenatal metal exposure with child neurodevelopment and mediation by perturbation of metabolic pathways

Prenatal exposure to metals has been associated with impaired neurodevelopment in children, but the detailed molecular mechanisms remain largely unknown. Based on the Wuhan Healthy Baby Cohort, China (N = 1088), eleven metals were measured in maternal urine during early pregnancy (13.1 ± 1.1 weeks) and metabolomics profiling was conducted in cord blood. Neurodevelopment was evaluated using the Bayley Scales of Infant Development in 2-year-old children to obtain the mental development index (MDI) and psychomotor development index (PDI). After false discovery rate correction, higher maternal urinary levels of manganese, nickel, aluminum, rubidium, gallium, and the summary score of metals were only significantly associated with lower MDI scores. The weighted quantile sum index of the metal mixture showed a significant inverse association with MDI and PDI scores, with aluminum contributing the most to the associations. Histidine, beta-alanine, purine, and pyrimidine metabolism significantly mediated the above associations, suggesting that disturbances in amino acids, neurotransmitter and neuroendocrine metabolism may be important mediators in contributing to impaired neurodevelopment of children.

Causal roles of immune cells and metabolites in chronic pancreatitis: a mendelian randomization study

BACKGROUND

Previous research has established a correlation between immune cells and an increased likelihood of Chronic pancreatitis (CP). However, studies investigating the causal relationship remain limited.

METHODS

This study utilized publicly available genome-wide association study (GWAS) databases and conducted a two-sample Mendelian randomization (MR) analysis to examine the causal relationships (CRs) among 731 immune cells, 1,400 metabolites, and CP. Mediation MR analysis was also performed to assess whether metabolites serve as mediators in the relationship between immune cells and CP.

RESULTS

Our study identified four immune cell types that act as risk factors for CP, with odds ratios (OR) ranging between 1.076 and 1.177. In contrast, three immune cell types were found to serve as protective factors, exhibiting OR values between 0.846 and 0.913. Additionally, four metabolites were implicated as risk factors for CP, with OR values ranging from 1.243 to 1.334. On the other hand, eight metabolites were discovered to have a protective effect, with OR values between 0.580 and 0.871. Mediation analysis revealed that cholesterol levels mediate the causal relationship between immune cell cells and CP, with a mediation effect of 0.00918, accounting for 9.18% of the total effect.

CONCLUSIONS

Our findings provide valuable insights into the genetic underpinnings of CP, highlighting the role of immune cells and plasma metabolites in its pathogenesis. The mediation analysis further suggests that the presence of CD25 on IgD-CD38-B cells may facilitate CP development through the elevation of cholesterol levels. These results not only deepen our understanding of CP but also suggest potential biological targets for therapeutic intervention. Future clinical research should focus on these mediators to develop more effective treatment strategies for CP.

A Reproducibility Crisis for Clinical Metabolomics Studies

Cancer is a leading cause of world-wide death and a major subject of clinical studies focused on the identification of new diagnostic tools. An in-depth meta-analysis of 244 clinical metabolomics studies of human serum samples highlights a reproducibility crisis. A total of 2,206 unique metabolites were reported as statistically significant across the 244 studies, but 72% (1,582) of these metabolites were identified by only one study. Further analysis shows a random disparate disagreement in reported directions of metabolite concentration changes when detected by multiple studies. Statistical models revealed that 1,867 of the 2,206 metabolites (85%) are simply statistical noise. Only 3 to 12% of these metabolites reach the threshold of statistical significance for a specific cancer type. Our findings demonstrate the absence of a detectable metabolic response to cancer and provide evidence of a serious need by the metabolomics community to establish widely accepted best practices to improve future outcomes.

Genetically predicted gut bacteria, circulating bacteria-associated metabolites and pancreatic ductal adenocarcinoma: a Mendelian randomisation study

Pancreatic ductal adenocarcinoma (PDAC) has high mortality and rising incidence rates. Recent data indicate that the gut microbiome and associated metabolites may play a role in the development of PDAC. To complement and inform observational studies, we investigated associations of genetically predicted abundances of individual gut bacteria and genetically predicted circulating concentrations of microbiome-associated metabolites with PDAC using Mendelian randomisation (MR). Gut microbiome-associated metabolites were identified through a comprehensive search of Pubmed, Exposome Explorer and Human Metabolome Database. Single Nucleotide Polymorphisms (SNPs) associated by Genome-Wide Association Studies (GWAS) with circulating levels of 109 of these metabolites were collated from Pubmed and the GWAS catalogue. SNPs for 119 taxonomically defined gut genera were selected from a meta-analysis performed by the MiBioGen consortium. Two-sample MR was conducted using GWAS summary statistics from the Pancreatic Cancer Cohort Consortium (PanScan) and the Pancreatic Cancer Case-Control Consortium (PanC4), including a total of 8,769 cases and 7,055 controls. Inverse variance-weighted MR analyses were performed along with sensitivity analyses to assess potential violations of MR assumptions. Nominally significant associations were noted for genetically predicted circulating concentrations of mannitol (odds ratio per standard deviation [ORSD] = 0.97; 95% confidence interval [CI]: 0.95-0.99, p = 0.006), methionine (ORSD= 0.97; 95%CI: 0.94-1.00, p = 0.031), stearic acid (ORSD= 0.93; 95%CI: 0.87-0.99, p = 0.027), carnitine = (ORSD=1.01; 95% CI: 1.00-1.03, p = 0.027), hippuric acid (ORSD= 1.02; 95%CI: 1.00-1.04, p = 0.038) and 3-methylhistidine (ORSD= 1.05; 95%CI: 1.01-1.10, p = 0.02). Two gut microbiome genera were associated with reduced PDAC risk; Clostridium sensu stricto 1 (OR: 0.88; 95%CI: 0.78-0.99, p = 0.027) and Romboutsia (OR: 0.87; 95%CI: 0.80-0.96, p = 0.004). These results, though based only on genetically predicted gut microbiome characteristics and circulating bacteria-related metabolite concentrations, provide evidence for causal associations with pancreatic carcinogenesis.

The role of gut microbiome and its metabolites in pancreatitis

Gut microbiome plays a vital role in the intestinal ecosystem and has close association with metabolites. Due to the development of metabolomics and microbiomics, recent studies have observed that alteration of either the gut microbiome or metabolites may have effects on the progression of pancreatitis. Several new treatments based on the gut microbiome or metabolites have been studied extensively in recent years. Gut microbes, such as Bifidobacterium, Akkermansia, and Lactobacillus, and metabolites, such as short-chain fatty acids, bile acids, vitamin, hydrogen sulfide, and alcohol, have different effects on pancreatitis. Some preliminary studies about new intervention measures were based on the gut microbiome and metabolites such as diet, prebiotic, herbal medicine, and fecal microbiota transplantation. This review aims to summarize the recent advances about the gut microbiome, metabolites, and pancreatitis in order to determine the potential beneficial role of the gut microbiome and metabolites in pancreatitis.

A review of disease risk prediction methods and applications in the omics era

Risk prediction and disease prevention are the innovative care challenges of the 21st century. Apart from freeing the individual from the pain of disease, it will lead to low medical costs for society. Until very recently, risk assessments have ushered in a new era with the emergence of omics technologies, including genomics, transcriptomics, epigenomics, proteomics, and so on, which potentially advance the ability of biomarkers to aid prediction models. While risk prediction has achieved great success, there are still some challenges and limitations. We reviewed the general process of omics-based disease risk model construction and the applications in four typical diseases. Meanwhile, we highlighted the problems in current studies and explored the potential opportunities and challenges for future clinical practice.

The role of EUS in the diagnosis of early chronic pancreatitis

The diagnosis of early chronic pancreatitis (ECP) is challenging due to the lack of standardized diagnostic criteria. EUS has been considered a sensitive diagnostic modality for chronic pancreatitis (CP), with advancements in technique such as EUS-guided fine needle aspiration and biopsy (EUS-FNA/FNB) being developed. However, their role in the diagnosis of ECP remains unelucidated. This review thereby aimed to provide an overview of the clinical landscape of EUS in the field of ECP.

Plasma Metabolite Profiling Identifies Nondiabetic Chronic Pancreatitis Patients With Metabolic Alterations Progressing to Prediabetes Before HbA1c

INTRODUCTION

Diabetes (T3cDM) secondary to chronic pancreatitis (CP) arises due to endocrine dysfunction and metabolic dysregulations. Currently, diagnostic tests are not available to identify patients who may progress from normoglycemia to hyperglycemia in CP. We conducted plasma metabolomic profiling to diagnose glycemic alterations early in the course of disease.

METHODS

Liquid chromatography-tandem mass spectrometry was used to generate untargeted, targeted plasma metabolomic profiles in patients with CP, controls (n = 445) following TRIPOD guidelines. Patients were stratified based on glucose tolerance tests following ADA guidelines. Multivariate analysis was performed using partial least squares discriminant analysis to assess discriminatory ability of metabolites among stratified groups. COMBIROC and logistic regression were used to derive biomarker signatures. AI-ML tool (Rapidminer) was used to verify these preliminary results.

RESULTS

Ceramide, lysophosphatidylethanolamine, phosphatidylcholine, lysophosphatidic acid (LPA), phosphatidylethanolamine, carnitine, and lysophosphatidylcholine discriminated T3cDM CP patients from healthy controls with AUC 93% (95% CI 0.81-0.98, P < 0.0001), and integration with pancreatic morphology improved AUC to 100% (95% CI 0.93-1.00, P < 0.0001). LPA, phosphatidylinositol, and ceramide discriminated nondiabetic CP with glycemic alterations (pre-diabetic CP); AUC 66% (95% CI 0.55-0.76, P = 0.1), and integration enhanced AUC to 74% (95% CI 0.55-0.88, P = 0.86). T3cDM was distinguished from prediabetic by LPA, phosphatidylinositol, and sphinganine (AUC 70%; 95% CI 0.54-0.83, P = 0.08), and integration improved AUC to 83% (95% CI 0.68-0.93, P = 0.05). CombiROC cutoff identified 75% and 78% prediabetes in validation 1 and 2 cohorts. Random forest algorithm assessed performance of integrated panel demonstrating AUC of 72% in predicting glycemic alterations.

DISCUSSION

We report for the first time that a panel of metabolites integrated with pancreatic morphology detects glycemia progression before HbA1c in patients with CP.

Early Diagnosis and Prognosis Prediction of Pancreatic Cancer Using Engineered Hybrid Core-Shells in Laser Desorption/Ionization Mass Spectrometry

Effective detection of bio-molecules relies on the precise design and preparation of materials, particularly in laser desorption/ionization mass spectrometry (LDI-MS). Despite significant advancements in substrate materials, the performance of single-structured substrates remains suboptimal for LDI-MS analysis of complex systems. Herein, designer Au@SiO2@ZrO2 core-shell substrates are developed for LDI-MS-based early diagnosis and prognosis of pancreatic cancer (PC). Through controlling Au core size and ZrO2 shell crystallization, signal amplification of metabolites up to 3 orders is not only achieved, but also the synergistic mechanism of the LDI process is revealed. The optimized Au@SiO2@ZrO2 enables a direct record of serum metabolic fingerprints (SMFs) by LDI-MS. Subsequently, SMFs are employed to distinguish early PC (stage I/II) from controls, with an accuracy of 92%. Moreover, a prognostic prediction scoring system is established with enhanced efficacy in predicting PC survival compared to CA19-9 (p < 0.05). This work contributes to material-based cancer diagnosis and prognosis.

Strategy for Identifying a Robust Metabolomic Signature Reveals the Altered Lipid Metabolism in Pituitary Adenoma

Despite the well-established connection between systematic metabolic abnormalities and the pathophysiology of pituitary adenoma (PA), current metabolomic studies have reported an extremely limited number of metabolites associated with PA. Moreover, there was very little consistency in the identified metabolite signatures, resulting in a lack of robust metabolic biomarkers for the diagnosis and treatment of PA. Herein, we performed a global untargeted plasma metabolomic profiling on PA and identified a highly robust metabolomic signature based on a strategy. Specifically, this strategy is unique in (1) integrating repeated random sampling and a consensus evaluation-based feature selection algorithm and (2) evaluating the consistency of metabolomic signatures among different sample groups. This strategy demonstrated superior robustness and stronger discriminative ability compared with that of other feature selection methods including Student's t-test, partial least-squares-discriminant analysis, support vector machine recursive feature elimination, and random forest recursive feature elimination. More importantly, a highly robust metabolomic signature comprising 45 PA-specific differential metabolites was identified. Moreover, metabolite set enrichment analysis of these potential metabolic biomarkers revealed altered lipid metabolism in PA. In conclusion, our findings contribute to a better understanding of the metabolic changes in PA and may have implications for the development of diagnostic and therapeutic approaches targeting lipid metabolism in PA. We believe that the proposed strategy serves as a valuable tool for screening robust, discriminating metabolic features in the field of metabolomics.

Metabolomic machine learning predictor for diagnosis and prognosis of gastric cancer

Gastric cancer (GC) represents a significant burden of cancer-related mortality worldwide, underscoring an urgent need for the development of early detection strategies and precise postoperative interventions. However, the identification of non-invasive biomarkers for early diagnosis and patient risk stratification remains underexplored. Here, we conduct a targeted metabolomics analysis of 702 plasma samples from multi-center participants to elucidate the GC metabolic reprogramming. Our machine learning analysis reveals a 10-metabolite GC diagnostic model, which is validated in an external test set with a sensitivity of 0.905, outperforming conventional methods leveraging cancer protein markers (sensitivity < 0.40). Additionally, our machine learning-derived prognostic model demonstrates superior performance to traditional models utilizing clinical parameters and effectively stratifies patients into different risk groups to guide precision interventions. Collectively, our findings reveal the metabolic landscape of GC and identify two distinct biomarker panels that enable early detection and prognosis prediction respectively, thus facilitating precision medicine in GC.

MultiClassMetabo: A Superior Classification Model Constructed Using Metabolic Markers in Multiclass Metabolomics

Multiclass metabolomics has become a popular technique for revealing the mechanisms underlying certain physiological processes, different tumor types, or different therapeutic responses. In multiclass metabolomics, it is highly important to uncover the underlying biological information on biosamples by identifying the metabolic markers with the most associations and classifying the different sample classes. The classification problem of multiclass metabolomics is more difficult than that of the binary problem. To date, various methods exist for constructing classification models and identifying metabolic markers consisting of well-established techniques and newly emerging machine learning algorithms. However, how to construct a superior classification model using these methods remains unclear for a given multiclass metabolomic data set. Herein, MultiClassMetabo has been developed for constructing a superior classification model using metabolic markers identified in multiclass metabolomics. MultiClassMetabo can enable online services, including (a) identifying metabolic markers by marker identification methods, (b) constructing classification models by classification methods, and (c) performing a comprehensive assessment from multiple perspectives to construct a superior classification model for multiclass metabolomics. In summary, MultiClassMetabo is distinguished for its capability to construct a superior classification model using the most appropriate method through a comprehensive assessment, which makes it an important complement to other available tools in multiclass metabolomics. MultiClassMetabo can be accessed at http://idrblab.cn/multiclassmetabo/.

Plasma metabolomics and proteomics reveal novel molecular insights and biomarker panel for cholelithiasis

BACKGROUND

Cholelithiasis is a gastrointestinal disease that is associated with the highest socioeconomic cost. A diagnosis of cholelithiasis based on clinical features is significantly limited, and direct molecular insights into cholelithiasis and the relationship between cholelithiasis and clinical biochemical parameters are unclear.

OBJECTIVES

Uncovering direct molecular insights into cholelithiasis and the relationship between cholelithiasis and clinical biochemical parameters. Identifying sensitive and specific biomarkers for this disease.

METHODS

Parallel metabolomic and proteomic analyses of plasma from cholelithiasis patients (CPs) and healthy control individuals (HCs) without cholelithiasis were performed using ultrahigh-performance liquid chromatography-tandem mass spectrometry. A multimodule coexpression network analysis and integrated machine learning methods, including least absolute shrinkage and selection operator, random forest, and support vector machine, were used for bioinformatic analyses. An independent cohort and the cross-validation of the combination of two cohorts were used to evaluate the diagnostic performance of the panel.

RESULTS

Arachidonic acid metabolism was significantly different between the CP and HC groups. Glyceraldehyde-3-phosphate dehydrogenase, actin beta, phosphoglycerate mutase 1, Enolase 1, Myeloperoxidase, and actin alpha 1 were identified as potential proteins related to cholelithiasis. The correlation between the merged modules and clinical biochemical tests was calculated. A diagnostic panel composed of four candidate biomarkers, including 3-oxotetradecanoic acid, 12-hydroxydodecanoic acid, hemoglobin subunit delta (HBD), and fibrinogen beta chain (FGB), was proposed based on three modules that were significantly associated with cholelithiasis. The classification according to the diagnostic panel detected CPs with an area under the curve (AUC) of 0.955. External validation in an independent cohort resulted in similar accuracy (AUC=0.995).

CONCLUSIONS

This study provided some direct molecular insights into cholelithiasis by showing the differences in plasma metabolic and protein profiles between CPs and HCs and presented a potential biomarker panel with two metabolites (3-oxotetradecanoic acid, 12-hydroxydodecanoic acid) and two proteins (HBD, FGB) for predicting cholelithiasis. We also explored the potential correlation of clinical biochemical parameters with combined modules. These findings may provide some reference for the diagnosis of cholelithiasis in clinical practice.

Personalized prediction for multiple chronic diseases by developing the multi-task Cox learning model

Personalized prediction of chronic diseases is crucial for reducing the disease burden. However, previous studies on chronic diseases have not adequately considered the relationship between chronic diseases. To explore the patient-wise risk of multiple chronic diseases, we developed a multitask learning Cox (MTL-Cox) model for personalized prediction of nine typical chronic diseases on the UK Biobank dataset. MTL-Cox employs a multitask learning framework to train semiparametric multivariable Cox models. To comprehensively estimate the performance of the MTL-Cox model, we measured it via five commonly used survival analysis metrics: concordance index, area under the curve (AUC), specificity, sensitivity, and Youden index. In addition, we verified the validity of the MTL-Cox model framework in the Weihai physical examination dataset, from Shandong province, China. The MTL-Cox model achieved a statistically significant (p<0.05) improvement in results compared with competing methods in the evaluation metrics of the concordance index, AUC, sensitivity, and Youden index using the paired-sample Wilcoxon signed-rank test. In particular, the MTL-Cox model improved prediction accuracy by up to 12% compared to other models. We also applied the MTL-Cox model to rank the absolute risk of nine chronic diseases in patients on the UK Biobank dataset. This was the first known study to use the multitask learning-based Cox model to predict the personalized risk of the nine chronic diseases. The study can contribute to early screening, personalized risk ranking, and diagnosing of chronic diseases.

Intra-pancreatic fat deposition across the pancreatitis spectrum and the influence of gut hormones

BACKGROUND AND AIMS

Acute pancreatitis (AP) and chronic pancreatitis (CP) often represent parts of the spectrum of disease. While growing evidence indicates that intra-pancreatic fat deposition (IPFD) plays an important role in the pathogenesis of pancreatitis, no study of living individuals has investigated IPFD in both AP and CP. Further, the associations between IPFD and gut hormones remain to be elucidated. The aims were to investigate the associations of IPFD with AP, CP, and health; and to study whether gut hormones affect these associations.

METHODS

Magnetic resonance imaging on the same 3.0 Tesla scanner was used to determine IPFD in 201 study participants. These participants were arranged into the health, AP, and CP groups. Gut hormones (ghrelin, glucagon-like peptide-1, gastric inhibitory peptide, peptide YY, and oxyntomodulin) were measured in blood, both after an 8-hour overnight fasting and after ingestion of a standardised mixed meal. A series of linear regression analyses was run, accounting for age, sex, ethnicity, body mass index, glycated haemoglobin, and triglycerides.

RESULTS

Both the AP group and CP group had significantly higher IPFD in comparison with the health group, consistently across all models (p for trend 0.027 in the most adjusted model). Ghrelin in the fasted state had a significant positive association with IPFD in the AP group (but not the CP or health group), consistently across all models (p = 0.019 in the most adjusted model). None of the studied gut hormones in the postprandial state was significantly associated with IPFD.

CONCLUSION

Fat deposition in the pancreas is similarly high in individuals with AP and those with CP. The gut-brain axis, and more specifically overexpression of ghrelin, may contribute to increased IPFD in individuals with AP.

Tripterygium wilfordii protects against an animal model of autoimmune hepatitis

ETHNOPHARMACOLOGICAL RELEVANCE

Tripterygium wilfordii tablets (TWT) is widely used to treat autoimmune diseases such as rheumatoid arthritis. Celastrol, one main active ingredient in TWT, has been shown to produce a variety of beneficial effects, including anti-inflammatory, anti-obesity, anti-cancer, and immunomodulatory. However, whether TWT could protect against Concanavalin A (Con A)-induced hepatitis remains unclear.

THE AIM OF THE STUDY

This study aims to investigate the protective effect of TWT against Con A-induced hepatitis and elucidate the underlying mechanism.

MATERIALS AND METHODS

Metabolomic analysis, pathological analysis, biochemical analysis, qPCR and Western blot analysis and the Pxr-null mice were used in this study.

RESULTS

The results indicated that TWT and its active ingredient celastrol could protect against Con A-induced acute hepatitis. Plasma metabolomics analysis revealed that metabolic perturbations related to bile acid and fatty acid metabolism induced by Con A were reversed by celastrol. The level of itaconate in the liver was increased by celastrol and speculated as an active endogenous compound mediating the protective effect of celastrol. Administration of 4-octanyl itaconate (4-OI) as a cell-permeable itaconate mimicker was found to attenuate Con A-induced liver injury through activation of the pregnane X receptor (PXR) and enhancement of the transcription factor EB (TFEB)-mediated autophagy.

CONCLUSIONS

Celastrol increased itaconate and 4-OI promoted activation of TFEB-mediated lysosomal autophagy to protect against Con A-induced liver injury in a PXR-dependent manner. Our study reported a protective effect of celastrol against Con A-induced AIH via an increased production of itaconate and upregulation of TFEB. The results highlighted that PXR and TFEB-mediated lysosomal autophagic pathway may offer promising therapeutic target for the treatment of autoimmune hepatitis.

Small molecule metabolites: discovery of biomarkers and therapeutic targets

Metabolic abnormalities lead to the dysfunction of metabolic pathways and metabolite accumulation or deficiency which is well-recognized hallmarks of diseases. Metabolite signatures that have close proximity to subject's phenotypic informative dimension, are useful for predicting diagnosis and prognosis of diseases as well as monitoring treatments. The lack of early biomarkers could lead to poor diagnosis and serious outcomes. Therefore, noninvasive diagnosis and monitoring methods with high specificity and selectivity are desperately needed. Small molecule metabolites-based metabolomics has become a specialized tool for metabolic biomarker and pathway analysis, for revealing possible mechanisms of human various diseases and deciphering therapeutic potentials. It could help identify functional biomarkers related to phenotypic variation and delineate biochemical pathways changes as early indicators of pathological dysfunction and damage prior to disease development. Recently, scientists have established a large number of metabolic profiles to reveal the underlying mechanisms and metabolic networks for therapeutic target exploration in biomedicine. This review summarized the metabolic analysis on the potential value of small-molecule candidate metabolites as biomarkers with clinical events, which may lead to better diagnosis, prognosis, drug screening and treatment. We also discuss challenges that need to be addressed to fuel the next wave of breakthroughs.

A systems approach points to a therapeutic role for retinoids in asparaginase-associated pancreatitis

Among drug-induced adverse events, pancreatitis is life-threatening and results in substantial morbidity. A prototype example is the pancreatitis caused by asparaginase, a crucial drug used to treat acute lymphoblastic leukemia (ALL). Here, we used a systems approach to identify the factors affecting asparaginase-associated pancreatitis (AAP). Connectivity Map analysis of the transcriptomic data showed that asparaginase-induced gene signatures were potentially reversed by retinoids (vitamin A and its analogs). Analysis of a large electronic health record database (TriNetX) and the U.S. Federal Drug Administration Adverse Events Reporting System demonstrated a reduction in AAP risk with concomitant exposure to vitamin A. Furthermore, we performed a global metabolomic screening of plasma samples from 24 individuals with ALL who developed pancreatitis (cases) and 26 individuals with ALL who did not develop pancreatitis (controls), before and after a single exposure to asparaginase. Screening from this discovery cohort revealed that plasma carotenoids were lower in the cases than in controls. This finding was validated in a larger external cohort. A 30-day dietary recall showed that the cases received less dietary vitamin A than the controls did. In mice, asparaginase administration alone was sufficient to reduce circulating and hepatic retinol. Based on these data, we propose that circulating retinoids protect against pancreatic inflammation and that asparaginase reduces circulating retinoids. Moreover, we show that AAP is more likely to develop with reduced dietary vitamin A intake. The systems approach taken for AAP provides an impetus to examine the role of dietary vitamin A supplementation in preventing or treating AAP.

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.

Salivary Polyamines Help Detect High-Risk Patients with Pancreatic Cancer: A Prospective Validation Study

Pancreatic cancer is one of the most malignant cancer types and has a poor prognosis. It is often diagnosed at an advanced stage because of the absence of typical symptoms. Therefore, it is necessary to establish a screening method for the early detection of pancreatic cancer in high-risk individuals. This is a prospective validation study conducted in a cohort of 1033 Japanese individuals (male, n = 467, age = 63.3 ± 11.5 years; female, n = 566, age = 64.2 ± 10.6 years) to evaluate the use of salivary polyamines for screening pancreatic diseases and cancers. Patients with pancreatic cancer were not included; however, other pancreatic diseases were treated as positive cases for accuracy verification. Of the 135 individuals with elevated salivary polyamine markers, 66 had pancreatic diseases, such as chronic pancreatitis and pancreatic cysts, and 1 had gallbladder cancer. These results suggest that the salivary polyamine panel is a useful noninvasive pancreatic disease screening tool.

Fat-soluble vitamin and phytochemical metabolites: Production, gastrointestinal absorption, and health effects

Consumption of diets rich in fruits and vegetables, which provide some fat-soluble vitamins and many phytochemicals, is associated with a lower risk of developing certain degenerative diseases. It is well accepted that not only the parent compounds, but also their derivatives formed upon enzymatic or nonenzymatic transformations, can produce protective biological effects. These derivatives can be formed during food storage, processing, or cooking. They can also be formed in the lumen of the upper digestive tract during digestion, or via metabolism by microbiota in the colon. This review compiles the known metabolites of fat-soluble vitamins and fat-soluble phytochemicals (FSV and FSP) that have been identified in food and in the human digestive tract, or could potentially be present based on the known reactivity of the parent compounds in normal or pathological conditions, or following surgical interventions of the digestive tract or consumption of xenobiotics known to impair lipid absorption. It also covers the very limited data available on the bioavailability (absorption, intestinal mucosa metabolism) and summarizes their effects on health. Notably, despite great interest in identifying bioactive derivatives of FSV and FSP, studying their absorption, and probing their putative health effects, much research remains to be conducted to understand and capitalize on the potential of these molecules to preserve health.

Changes of serum metabolites levels during neoadjuvant chemoradiation and prediction of the pathological response in locally advanced rectal cancer

INTRODUCTION

Previous studies have explored prediction value of serum metabolites in neoadjuvant chemoradiation therapy (NCRT) response for rectal cancer. To date, limited literature is available for serum metabolome changes dynamically through NCRT.

OBJECTIVES

This study aimed to explore temporal change pattern of serum metabolites during NCRT, and potential metabolic biomarkers to predict the pathological response to NCRT in locally advanced rectal cancer (LARC) patients.

METHODS

Based on dynamic UHPLC-QTOF-MS untargeted metabolomics design, this study included 106 LARC patients treated with NCRT. Biological samples of the enrolled patients were collected in five consecutive time-points. Untargeted metabolomics was used to profile serum metabolic signatures from LARC patients. Then, we used fuzzy C-means clustering (FCM) to explore temporal change patterns in metabolites cluster and identify monotonously changing metabolites during NCRT. Repeated measure analysis of variance (RM-ANOVA) and multilevel partial least-squares discriminant analysis (ML-PLS-DA) were performed to select metabolic biomarkers. Finally, a panel of dynamic differential metabolites was used to build logistic regression prediction models.

RESULTS

Metabolite profiles showed a clearly tendency of separation between different follow-up panels. We identified two clusters of 155 serum metabolites with monotonously changing patterns during NCRT (74 decreased metabolites and 81 increased metabolites). Using RM-ANOVA and ML-PLS-DA, 8 metabolites (L-Norleucine, Betaine, Hypoxanthine, Acetylcholine, 1-Hexadecanoyl-sn-glycero-3-phosphocholine, Glycerophosphocholine, Alpha-ketoisovaleric acid, N-Acetyl-L-alanine) were further identified as dynamic differential biomarkers for predicting NCRT sensitivity. The area under the ROC curve (AUC) of prediction model combined with the baseline measurement was 0.54 (95%CI = 0.43 ~ 0.65). By incorporating the variability indexes of 8 dynamic differential metabolites, the prediction model showed better discrimination performance than baseline measurement, with AUC = 0.67 (95%CI 0.57 ~ 0.77), 0.64 (0.53 ~ 0.75), 0.60 (0.50 ~ 0.71), and 0.56 (0.45 ~ 0.67) for the variability index of difference, linear slope, ratio, and standard deviation, respectively.

CONCLUSION

This study identified eight metabolites as dynamic differential biomarkers to discriminate NCRT-sensitive and resistant patients. The changes of metabolite level during NCRT show better performance in predicting NCRT sensitivity. These findings highlight the clinical significance of metabolites variabilities in metabolomics analysis.

Simulation training of laparoscopic pancreaticojejunostomy and stepwise training program on a 3D-printed model

AIM

Laparoscopic pancreaticojejunostomy is among the most difficult and high-risk operations. Surgeons with low or moderate seniority rarely are allowed to perform this surgery in clinics. Therefore, there is an urgent need to develop a reliable simulation training model focused on laparoscopic pancreaticojejunostomy and an effective stepwise training program.

METHODS

Surgeons with different working experiences or exposure to different training programs at Sir Run Run Shaw Hospital were divided into four groups. Each was required to perform laparoscopic pancreaticojejunostomy using a designed three-dimensional dry lab model. All surgeons' baseline characteristics and surgical performance, including operation time and score, were recorded and analysed. The authenticity of the model was evaluated by four senior surgeons.

RESULTS

The surgeon group with higher seniority had an older average age, longer working time, and had completed more laparoscopic cholecystectomy and laparoscopic common bile duct exploration procedures. Meanwhile, the surgeon group with higher seniority presented better operation performance, including shorter operation time and higher operation score in their initial simulation training. Resident surgeons who underwent stepwise training with the laparoscopic biliary-enteric anastomosis training program showed better initial performance than resident surgeons who underwent stepwise training with the laparoscopic basic suture training program. After repeated training, the surgeons showed improved surgical performance.

CONCLUSION

Our pancreaticojejunostomy model showed a good degree of discernibility, as surgeons with more experience performed better with the model for their initial simulation training in laparoscopic pancreaticojejunostomy. Stepwise training of the laparoscopic biliary-enteric anastomosis training program helped surgeons obtain a better initial performance, and repeated simulation training on this model improved the surgeon's operative performance.

Serum metabolome alterations in patients with early nonalcoholic fatty liver disease

BACKGROUND

Although metabolomic analysis for patients with nonalcoholic fatty liver disease (NAFLD) was a promising approach to identify novel biomarkers as targets for the diagnosis of NAFLD, the serum metabolomics profile of early-stage NAFLD patients from central China remain unknown.

OBJECTIVE

The aim of the present study was to explore the metabolic characteristics of patients with early-stage NAFLD based on the ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) technology, to identify differential metabolites and perform functional analysis, and especially, to establish a novel early NAFLD clinical diagnostic tool.

RESULTS

Compared with healthy controls, serum metabolite species increased significantly in early stage NAFLD patients. Expression of 88 metabolites including 1-naphthylmethanol, rosavin, and theophylline were up-regulated in early NAFLD, while 68 metabolites including 2-hydroxyphenylacetic acid and lysophosphatidylcholine (24:1(15Z)) were down-regulated. The early NAFLD classifier achieved a strong diagnostic efficiency in the discovery phases (80.99%) and was successfully verified in the validation phases (75.23%).

CONCLUSIONS

These results advance our understanding about the composition and biological functions of serum metabolites of early NAFLD. In addition, serum metabolic markers can serve as an efficient diagnostic tool for the early-stage NAFLD.

Clinical and translational markers of severity and prognosis in chronic pancreatitis

PURPOSE OF REVIEW

The incidence of chronic pancreatitis as a progressive inflammation and fibrosis syndrome is on the rise due to increasing awareness and improved imaging modalities. Numerous classification systems have been suggested in recent years to describe the disease, but only few of them have been used to classify the severity and prognostic significance of the disease. Biomarkers for severity and (early) chronic pancreatitis diagnosis are not yet ready for clinical application.

RECENT FINDINGS

In using the M-ANNHEIM and Chronic Pancreatitis Prognosis Score (COPPS) classification system, the severity assessment and short- and medium-term disease progression is available. A prospectively validated biomarker for early chronic pancreatitis diagnosis is not yet available, metabolome-based approaches seem to have the greatest potential for clinical translation.

SUMMARY

Currently, due to the lack of universal definition for the early disease stage of chronic pancreatitis, it is difficult to accurately classify these patient cohorts in existing scoring systems. In principle, setting up a suitable scoring system would allow surveillance and establish a therapy approaches flanked by corresponding biomarker panel development. Therapy management of chronic pancreatitis and monitoring by means of scoring systems (such as the COPPS) would make a decisive contribution to improving patient treatment.

Targeting Fibrosis: The Bridge That Connects Pancreatitis and Pancreatic Cancer

Pancreatic fibrosis is caused by the excessive deposits of extracellular matrix (ECM) and collagen fibers during repeated necrosis to repair damaged pancreatic tissue. Pancreatic fibrosis is frequently present in chronic pancreatitis (CP) and pancreatic cancer (PC). Clinically, pancreatic fibrosis is a pathological feature of pancreatitis and pancreatic cancer. However, many new studies have found that pancreatic fibrosis is involved in the transformation from pancreatitis to pancreatic cancer. Thus, the role of fibrosis in the crosstalk between pancreatitis and pancreatic cancer is critical and still elusive; therefore, it deserves more attention. Here, we review the development of pancreatic fibrosis in inflammation and cancer, and we discuss the therapeutic strategies for alleviating pancreatic fibrosis. We further propose that cellular stress response might be a key driver that links fibrosis to cancer initiation and progression. Therefore, targeting stress proteins, such as nuclear protein 1 (NUPR1), could be an interesting strategy for pancreatic fibrosis and PC treatment.