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1.
Nguyen Phuoc Long Sang Jun Yoon Nguyen Hoang Anh Tran Diem Nghi Dong Kyu Lim Yu Jin Hong Soon-Sun Hong Sung Won Kwon 《Metabolomics : Official journal of the Metabolomic Society》2018,14(8):109
Introduction
Metabolomics is an emerging approach for early detection of cancer. Along with the development of metabolomics, high-throughput technologies and statistical learning, the integration of multiple biomarkers has significantly improved clinical diagnosis and management for patients.Objectives
In this study, we conducted a systematic review to examine recent advancements in the oncometabolomics-based diagnostic biomarker discovery and validation in pancreatic cancer.Methods
PubMed, Scopus, and Web of Science were searched for relevant studies published before September 2017. We examined the study designs, the metabolomics approaches, and the reporting methodological quality following PRISMA statement.Results and Conclusion
The included 25 studies primarily focused on the identification rather than the validation of predictive capacity of potential biomarkers. The sample size ranged from 10 to 8760. External validation of the biomarker panels was observed in nine studies. The diagnostic area under the curve ranged from 0.68 to 1.00 (sensitivity: 0.43–1.00, specificity: 0.73–1.00). The effects of patients’ bio-parameters on metabolome alterations in a context-dependent manner have not been thoroughly elucidated. The most reported candidates were glutamic acid and histidine in seven studies, and glutamine and isoleucine in five studies, leading to the predominant enrichment of amino acid-related pathways. Notably, 46 metabolites were estimated in at least two studies. Specific challenges and potential pitfalls to provide better insights into future research directions were thoroughly discussed. Our investigation suggests that metabolomics is a robust approach that will improve the diagnostic assessment of pancreatic cancer. Further studies are warranted to validate their validity in multi-clinical settings.2.
Background
Early detection screening of asymptomatic populations for low prevalence cancers requires a highly specific test in order to limit the cost and anxiety produced by falsely positive identifications. Most solid cancers are a heterogeneous collection of diseases as they develop from various combinations of genetic lesions and epigenetic modifications. Therefore, it is unlikely that a single test will discriminate all cases of any particular cancer type. We propose a novel, intuitive biomarker panel design that accommodates disease heterogeneity by allowing for diverse biomarker selection that increases diagnostic accuracy.Methods
Using characteristics of nine pancreatic ductal adenocarcinoma (PDAC) biomarkers measured in human sera, we modeled the behavior of biomarker panels consisting of a sum of indicator variables representing a subset of biomarkers within a larger biomarker data set. We then chose a cutoff for the sum to force specificity to be high and delineated the number of biomarkers required for adequate sensitivity of PDAC in our panel design.Results
The model shows that a panel consisting of 40 non-correlated biomarkers characterized individually by 32% sensitivity at 95% specificity would require any 7 biomarkers to be above their respective thresholds and would result in a panel specificity and sensitivity of 99% each.Conclusions
A highly accurate blood-based diagnostic panel can be developed from a reasonable number of individual serum biomarkers that are relatively weak classifiers when used singly. A panel constructed as described is advantageous in that a high level of specificity can be forced, accomplishing a prerequisite for screening asymptomatic populations for low-prevalence cancers.3.
Background
Many studies have investigated the prognostic role of biomarkers in colorectal liver metastases (CRLM). However, no biomarker has been established in routine clinical practice. The aim of this study was to scrutinize the current literature for biomarkers evaluated by immunohistochemistry as prognostic markers in patients with resected CRLM.Methods
A systematic review was performed according to the PRISMA guidelines. Articles were identified in the PubMed database with selected search terms and by cross-references search. The REMARK quality criteria were applied. Markers were included if they reported the prognostic impact of immunohistochemical markers in a multivariable setting in relation to overall survival (OS). A meta-analysis was conducted when more than one original article provided survival data of a marker.Results
In total, 26 biomarkers were identified as independent significant markers for OS in resected CRLM. These biomarkers were found to be involved in multiple oncogenic signalling pathways that control cell growth, apoptosis, angiogenesis and evasion of immune detection. Among these biomarker candidates were Ki-67, EGFR, p53, hTERT, CD34, TSP-1, KISS1, Aurora kinase A and CDX2. CD34 and TSP-1 were reported as significantly associated with survival by more than one study and where therefore pooled in a meta-analysis.Conclusion
A number of independent prognostic biomarkers for resected CRLM were identified. However, most markers were evaluated in a retrospective setting with small patient cohorts, without external validation. Large, prospective, multicentre studies with standardised methods are needed before biomarkers can translated into the clinic.4.
Kai Yang Fan Zhang Peng Han Zhuo-zhong Wang Kui Deng Yuan-yuan Zhang Wei-wei Zhao Wei Song Yu-qing Cai Kang Li Bin-bin Cui Zheng-Jiang Zhu 《Metabolomics : Official journal of the Metabolomic Society》2018,14(9):110
Introduction
Colorectal cancer (CRC) is a clinically heterogeneous disease, which necessitates a variety of treatments and leads to different outcomes. Only some CRC patients will benefit from neoadjuvant chemotherapy (NACT).Objectives
An accurate prediction of response to NACT in CRC patients would greatly facilitate optimal personalized management, which could improve their long-term survival and clinical outcomes.Methods
In this study, plasma metabolite profiling was performed to identify potential biomarker candidates that can predict response to NACT for CRC. Metabolic profiles of plasma from non-response (n?=?30) and response (n?=?27) patients to NACT were studied using UHPLC–quadruple time-of-flight)/mass spectrometry analyses and statistical analysis methods.Results
The concentrations of nine metabolites were significantly different when comparing response to NACT. The area under the receiver operating characteristic curve value of the potential biomarkers was up to 0.83 discriminating the non-response and response group to NACT, superior to the clinical parameters (carcinoembryonic antigen and carbohydrate antigen 199).Conclusion
These results show promise for larger studies that could result in more personalized treatment protocols for CRC patients.5.
Hong Zheng Minjiang Chen Siming Lu Liangcai Zhao Jiansong Ji Hongchang Gao 《Metabolomics : Official journal of the Metabolomic Society》2017,13(10):121
Introduction
Liver cirrhosis (LC) is an advanced liver disease that can develop into hepatocellular carcinoma. Hepatitis B virus (HBV) infection is one of the main causes of LC. Therefore, there is an urgent need for developing a new method to monitor the progression of HBV-related LC (HBV-LC).Objectives
In this study, we attempted to examine serum metabolic changes in healthy individuals as well as patients with HBV and HBV-LC. Furthermore, potential metabolite biomarkers were identified to evaluate patients progressed from health to HBV-LC.Methods
Metabolic profiles in the serum of healthy individuals as well as patients with HBV and HBV-LC were detected using an NMR-based metabolomic approach. Univariate and multivariate analyses were conducted to analyze serum metabolic changes during HBV-LC progression. Moreover, potential metabolite biomarkers were explored by receiver operating characteristic curve analysis.Results
Serum metabolic changes were closely associated with the progression of HBV-LC, mainly involving energy metabolism, protein metabolism, lipid metabolism and microbial metabolism. Serum histidine was identified as a potential biomarker for HBV patients. Acetate, formate, pyruvate and glutamine in the serum were identified as a potential biomarker panel for patients progressed from HBV to HBV-LC. In addition, phenylalanine, unsaturated lipid, n-acetylglycoprotein and acetone in the serum could be considered as a potential common biomarkers panel for these patients.Conclusion
NMR-based serum metabolomic approach could be a promising tool to monitor the progression of liver disease. Different metabolites may reflect different stages of liver disease.6.
Applications of metabolomics in the study and management of preeclampsia: a review of the literature
Rachel S. Kelly Rachel T. Giorgio Bo L. Chawes Natalia I. Palacios Kathryn J. Gray Hooman Mirzakhani Ann Wu Kevin Blighe Scott T. Weiss Jessica Lasky-Su 《Metabolomics : Official journal of the Metabolomic Society》2017,13(7):86
Introduction
Preeclampsia represents a major public health burden worldwide, but predictive and diagnostic biomarkers are lacking. Metabolomics is emerging as a valuable approach to generating novel biomarkers whilst increasing the mechanistic understanding of this complex condition.Objectives
To summarize the published literature on the use of metabolomics as a tool to study preeclampsia.Methods
PubMed and Web of Science were searched for articles that performed metabolomic profiling of human biosamples using either Mass-spectrometry or Nuclear Magnetic Resonance based approaches and which included preeclampsia as a primary endpoint.Results
Twenty-eight studies investigating the metabolome of preeclampsia in a variety of biospecimens were identified. Individual metabolite and metabolite profiles were reported to have discriminatory ability to distinguish preeclamptic from normal pregnancies, both prior to and post diagnosis. Lipids and carnitines were among the most commonly reported metabolites. Further work and validation studies are required to demonstrate the utility of such metabolites as preeclampsia biomarkers.Conclusion
Metabolomic-based biomarkers of preeclampsia have yet to be integrated into routine clinical practice. However, metabolomic profiling is becoming increasingly popular in the study of preeclampsia and is likely to be a valuable tool to better understand the pathophysiology of this disorder and to better classify its subtypes, particularly when integrated with other omic data.7.
Hye-Jeong Song Eun-Suk Yang Jong-Dae Kim Chan-Young Park Min-Sun Kyung Yu-Seop Kim 《Biomedical engineering online》2018,17(2):152
Background
Screening test using CA-125 is the most common test for detecting ovarian cancer. However, the level of CA-125 is diverse by variable condition other than ovarian cancer. It has led to misdiagnosis of ovarian cancer.Methods
In this paper, we explore the 16 serum biomarker for finding alternative biomarker combination to reduce misdiagnosis. For experiment, we use the serum samples that contain 101 cancer and 92 healthy samples. We perform two major tasks: Marker selection and Classification. For optimal marker selection, we use genetic algorithm, random forest, T-test and logistic regression. For classification, we compare linear discriminative analysis, K-nearest neighbor and logistic regression.Results
The final results show that the logistic regression gives high performance for both tasks, and HE4-ELISA, PDGF-AA, Prolactin, TTR is the best biomarker combination for detecting ovarian cancer.Conclusions
We find the combination which contains TTR and Prolactin gives high performance for cancer detection. Early detection of ovarian cancer can reduce high mortality rates. Finding a combination of multiple biomarkers for diagnostic tests with high sensitivity and specificity is very important.8.
Yoshio Araki Kazuhiro Yoshikawa Sho Okamoto Masaki Sumitomo Mikio Maruwaka Toshihiko Wakabayashi 《BMC neurology》2010,10(1):112
Background
Moyamoya disease (MMD) is an uncommon cerebrovascular condition with unknown etiology characterized by slowly progressive stenosis or occlusion of the bilateral internal carotid arteries associated with an abnormal vascular network. MMD is a major cause of stroke, specifically in the younger population. Diagnosis is based on only radiological features as no other clinical data are available. The purpose of this study was to identify novel biomarker candidate proteins differentially expressed in the cerebrospinal fluid (CSF) of patients with MMD using proteomic analysis.Methods
For detection of biomarkers, CSF samples were obtained from 20 patients with MMD and 12 control patients. Mass spectral data were generated by surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS) with an anion exchange chip in three different buffer conditions. After expression difference mapping was undertaken using the obtained protein profiles, a comparative analysis was performed.Results
A statistically significant number of proteins (34) were recognized as single biomarker candidate proteins which were differentially detected in the CSF of patients with MMD, compared to the control patients (p < 0.05). All peak intensity profiles of the biomarker candidates underwent classification and regression tree (CART) analysis to produce prediction models. Two important biomarkers could successfully classify the patients with MMD and control patients.Conclusions
In this study, several novel biomarker candidate proteins differentially expressed in the CSF of patients with MMD were identified by a recently developed proteomic approach. This is a pilot study of CSF proteomics for MMD using SELDI technology. These biomarker candidates have the potential to shed light on the underlying pathogenesis of MMD.9.
Zhongwei Xu Kaimin Xu Shijia Ding Jiao Luo Tingmei Chen Aiguo Zhou Zhenxing Wen Jian Zhang 《Metabolomics : Official journal of the Metabolomic Society》2017,13(6):73
Introduction
Non-traumatic osteonecrosis of the femoral head (NTONFH) is a progressive disease, always leading to hip dysfunction if no early intervention was applied. The difficulty for early diagnosis of NTONFH is due to the slight symptoms at early stages as well as the high cost for screening patients by using magnetic resonance imaging.Objective
The aim was to detect biomarkers of early-stage NTONFH, which was beneficial to the exploration of a cost-effective approach for the early diagnose of the disease.Methods
Metabolomic approaches were employed in this study to detect biomarkers of early-stage NTONFH (22 patients, 23 controls), based on the platform of ultra-performance liquid chromatography tandem quadrupole time-of-flight mass spectrometry (UPLC-QTOF-MS) and the uses of multivariate statistic analysis, putative metabolite identification, metabolic pathway analysis and biomarker analysis.Results
In total, 33 serum metabolites were found altered between NTONFH group and control group. In addition, glycerophospholipid metabolism and pyruvate metabolism were highly associated with the disease.Conclusion
The combination of LysoPC (18:3), l-tyrosine and l-leucine proved to have a high diagnostic value for early-stage NTONFH. Our findings may contribute to the protocol for early diagnosis of NTONFH and further elucidate the underlying mechanisms of the disease.10.
Yiwen Sun Zexuan Zhu Zhu-Hong You Zijie Zeng Zhi-An Huang Yu-An Huang 《BMC systems biology》2018,12(9):121
Background
MicroRNA (miRNA) plays a key role in regulation mechanism of human biological processes, including the development of disease and disorder. It is necessary to identify potential miRNA biomarkers for various human diseases. Computational prediction model is expected to accelerate the process of identification.Results
Considering the limitations of previously proposed models, we present a novel computational model called FMSM. It infers latent miRNA biomarkers involved in the mechanism of various diseases based on the known miRNA-disease association network, miRNA expression similarity, disease semantic similarity and Gaussian interaction profile kernel similarity. FMSM achieves reliable prediction performance in 5-fold and leave-one-out cross validations with area under ROC curve (AUC) values of 0.9629+/??0.0127 and 0.9433, respectively, which outperforms the state-of-the-art competitors and classical algorithms. In addition, 19 of top 25 predicted miRNAs have been validated to have associations with Colonic Neoplasms in case study.Conclusions
A factored miRNA similarity based model and miRNA expression similarity substantially contribute to the well-performing prediction. The list of the predicted most latent miRNA biomarkers of various human diseases is publicized. It is anticipated that FMSM could serve as a useful tool guiding the future experimental validation for those promising miRNA biomarker candidates.11.
Takeo Moriya Yoshinori Satomi Hiroyuki Kobayashi 《Metabolomics : Official journal of the Metabolomic Society》2016,12(12):179
Introduction
Human plasma metabolomics offer powerful tools for understanding disease mechanisms and identifying clinical biomarkers for diagnosis, efficacy prediction and patient stratification. Although storage conditions can affect the reliability of data from metabolites, strict control of these conditions remains challenging, particularly when clinical samples are included from multiple centers. Therefore, it is necessary to consider stability profiles of each analyte.Objectives
The purpose of this study was to extract unstable metabolites from vast metabolome data and identify factors that cause instability.Method
Plasma samples were obtained from five healthy volunteers, were stored under ten different conditions of time and temperature and were quantified using leading-edge metabolomics. Instability was evaluated by comparing quantitation values under each storage condition with those obtained after ?80 °C storage.Result
Stability profiling of the 992 metabolites showed time- and temperature-dependent increases in numbers of significantly changed metabolites. This large volume of data enabled comparisons of unstable metabolites with their related molecules and allowed identification of causative factors, including compound-specific enzymatic activity in plasma and chemical reactivity. Furthermore, these analyses indicated extreme instability of 1-docosahexaenoylglycerol, 1-arachidonoylglycerophosphate, cystine, cysteine and N6-methyladenosine.Conclusion
A large volume of data regarding storage stability was obtained. These data are a contribution to the discovery of biomarker candidates without misselection based on unreliable values and to the establishment of suitable handling procedures for targeted biomarker quantification.12.
Background
Many mathematical and statistical models and algorithms have been proposed to do biomarker identification in recent years. However, the biomarkers inferred from different datasets suffer a lack of reproducibilities due to the heterogeneity of the data generated from different platforms or laboratories. This motivates us to develop robust biomarker identification methods by integrating multiple datasets.Methods
In this paper, we developed an integrative method for classification based on logistic regression. Different constant terms are set in the logistic regression model to measure the heterogeneity of the samples. By minimizing the differences of the constant terms within the same dataset, both the homogeneity within the same dataset and the heterogeneity in multiple datasets can be kept. The model is formulated as an optimization problem with a network penalty measuring the differences of the constant terms. The L1 penalty, elastic penalty and network related penalties are added to the objective function for the biomarker discovery purpose. Algorithms based on proximal Newton method are proposed to solve the optimization problem.Results
We first applied the proposed method to the simulated datasets. Both the AUC of the prediction and the biomarker identification accuracy are improved. We then applied the method to two breast cancer gene expression datasets. By integrating both datasets, the prediction AUC is improved over directly merging the datasets and MetaLasso. And it’s comparable to the best AUC when doing biomarker identification in an individual dataset. The identified biomarkers using network related penalty for variables were further analyzed. Meaningful subnetworks enriched by breast cancer were identified.Conclusion
A network-based integrative logistic regression model is proposed in the paper. It improves both the prediction and biomarker identification accuracy.13.
Guanshi Zhang Elda Dervishi Suzanna M. Dunn Rupasri Mandal Philip Liu Beomsoo Han David S. Wishart Burim N. Ametaj 《Metabolomics : Official journal of the Metabolomic Society》2017,13(4):43
Introduction
Ketosis is a prevalent metabolic disease of transition dairy cows that affects milk yield and the development of other periparturient diseases.Objectives
The objective of this study was to retrospectively metabotype the serum of dairy cows affected by ketosis before clinical signs of disease, during the diagnosis of ketosis, and after the diagnosis of disease and identify potential predictive and diagnostic serum metabolite biomarkers for the risk of ketosis.Methods
Targeted metabolomics was used to identify and quantify 128 serum metabolites in healthy (CON, n?=?20) and ketotic (n?=?6) cows by DI/LC-MS/MS at ?8 and ?4 weeks prepartum, during the disease week, and at +4 and +8 weeks after parturition.Results
Significant changes were detected in the levels of several metabolite groups including amino acids, glycerophospholipids, sphingolipids, acylcarnitines, and biogenic amines in the serum of ketotic cows during all time points studied.Conclusions
Results of this study support the idea that ketosis is preceded and associated and followed by alterations in multiple metabolite groups. Moreover, two sets of predictive biomarker models and one set of diagnostic biomarker model with very high sensitivity and specificity were identified. Overall, these findings throw light on the pathobiology of ketosis and some of the metabolites identified might serve as predictive biomarkers for the risk of ketosis. The data must be considered as preliminary given the lower number of ketotic cows in this study and more research with a larger cohort of cows is warranted to validate the results.14.
Ray O. Bahado-Singh Amit Lugade Jayson Field Zaid Al-Wahab BeomSoo Han Rupasri Mandal Trent C. Bjorndahl Onur Turkoglu Stewart F. Graham David Wishart Kunle Odunsi 《Metabolomics : Official journal of the Metabolomic Society》2018,14(1):6
Introduction
Endometrial cancer (EC) is associated with metabolic disturbances including obesity, diabetes and metabolic syndrome. Identifying metabolite biomarkers for EC detection has a crucial role in reducing morbidity and mortality.Objective
To determine whether metabolomic based biomarkers can detect EC overall and early-stage EC.Methods
We performed NMR and mass spectrometry based metabolomic analyses of serum in EC cases versus controls. A total of 46 early-stage (FIGO stages I–II) and 10 late-stage (FIGO stages III–IV) EC cases constituted the study group. A total of 60 unaffected control samples were used. Patients and controls were divided randomly into a discovery group (n?=?69) and an independent validation group (n?=?47). Predictive algorithms based on biomarkers and demographic characteristics were generated using logistic regression analysis.Results
A total of 181 metabolites were evaluated. Extensive changes in metabolite levels were noted in the EC versus the control group. The combination of C14:2, phosphatidylcholine with acyl-alkyl residue sum C38:1 (PCae C38:1) and 3-hydroxybutyric acid had an area under the receiver operating characteristics curve (AUC) (95% CI)?=?0.826 (0.706–0.946) and a sensitivity?=?82.6%, and specificity?=?70.8% for EC overall. For early EC prediction: BMI, C14:2 and PC ae C40:1 had an AUC (95% CI)?=?0.819 (0.689–0.95) and a sensitivity?=?72.2% and specificity?=?79.2% in the validation group.Conclusions
EC is characterized by significant perturbations in important cellular metabolites. Metabolites accurately detected early-stage EC cases and EC overall which could lead to the development of non-invasive biomarkers for earlier detection of EC and for monitoring disease recurrence.15.
Jamie V. de Seymour Stephanie Tu Xiaoling He Hua Zhang Ting-Li Han Philip N. Baker Karolina Sulek 《Metabolomics : Official journal of the Metabolomic Society》2018,14(6):79
Introduction
Intrahepatic cholestasis of pregnancy (ICP) is a common maternal liver disease; development can result in devastating consequences, including sudden fetal death and stillbirth. Currently, recognition of ICP only occurs following onset of clinical symptoms.Objective
Investigate the maternal hair metabolome for predictive biomarkers of ICP.Methods
The maternal hair metabolome (gestational age of sampling between 17 and 41 weeks) of 38 Chinese women with ICP and 46 pregnant controls was analysed using gas chromatography–mass spectrometry.Results
Of 105 metabolites detected in hair, none were significantly associated with ICP.Conclusion
Hair samples represent accumulative environmental exposure over time. Samples collected at the onset of ICP did not reveal any metabolic shifts, suggesting rapid development of the disease.16.
Background
In this study, we optimized the process for enhancing amylase production from Pseudomonas balearica VITPS19 isolated from agricultural lands in Kolathur, India.Methods
Process optimization for enhancing amylase production from the isolate was carried out by Response Surface Methodology (RSM) with optimized chemical and physical sources using Design expert v.7.0. A central composite design was used to evaluate the interaction between parameters. Interaction between four factors–maltose (C-source), malt extract (Nsource), pH, and CaCl2 was studied.Results
The factors pH and CaCl2 concentration were found to affect amylase production. Validation of the experiment showed a nearly twofold increase in alpha amylase production.Conclusion
Amylase production was thus optimized and increased yield was achieved.17.
Wenzel Schoening Volker Schmitz Jelena Klawitter Uwe Christians Jost Klawitter 《Metabolomics : Official journal of the Metabolomic Society》2017,13(9):102
Introduction
Although current immunosuppressive protocols have dramatically improved 1-year survival of kidney transplants, there has been less progress in terms of long-term graft survival over the last two decades. The key to avoiding late graft loss is early diagnosis and differentiation between anti-allograft immune processes and immunosuppressant toxicity (IS-Tox). Modern bioanalytical technologies have opened new opportunities for the development of sensitive and specific diagnostic tools. There is an immediate need for biomarkers that are able to differentiate between renal allograft rejection and immunosuppressant toxicity.Objective
To test our hypothesis that changes of metabolite patterns in urine have the potential to serve as a non-invasive combinatorial biomarker that can differentiate between allograft immune reactions and IS-Tox.Methods
We used 1H-NMR spectroscopy and Luminex multiplexing for metabolic profiling of rat urine and the analysis of protein biomarkers in urine and plasma, respectively, to compare the effects of chronic allograft rejection in a Fisher-to-Lewis rat transplant model with IS-Tox induced by cyclosporine, tacrolimus and/or sirolimus in Lewis rats.Results
Our results showed that, while IS-Tox caused changes in metabolite patterns that are typically associated with proximal tubule damage, rejection caused more profuse changes not specifically focused on a particular kidney region. Moreover, metabolite pattern changes were more sensitive than changes in protein markers that were evident only during the later stages of rejection.Conclusion
The present study provides first proof-of-concept that longitudinal monitoring of urine metabolite markers has the potential to differentiate between early renal allograft rejection and immunosuppressant nephrotoxicity.18.
Thong Huy Cao Donald J. L. Jones Paulene A. Quinn Daniel Chu Siong Chan Narayan Hafid Helen M. Parry Mohapradeep Mohan Jatinderpal K. Sandhu Stefan D. Anker John G. Cleland Kenneth Dickstein Gerasimos Filippatos Hans L. Hillege Marco Metra Piotr Ponikowski Nilesh J. Samani Dirk J. Van Veldhuisen Faiez Zannad Aeilko H. Zwinderman Adriaan A. Voors Chim C. Lang Leong L. Ng 《Clinical proteomics》2018,15(1):35
Background
Current risk prediction models in heart failure (HF) including clinical characteristics and biomarkers only have moderate predictive value. The aim of this study was to use matrix assisted laser desorption ionisation mass spectrometry (MALDI-MS) profiling to determine if a combination of peptides identified with MALDI-MS will better predict clinical outcomes of patients with HF.Methods
A cohort of 100 patients with HF were recruited in the biomarker discovery phase (50 patients who died or had a HF hospital admission vs. 50 patients who did not have an event). The peptide extraction from plasma samples was performed using reversed phase C18. Then samples were analysed using MALDI-MS. A multiple peptide biomarker model was discovered that was able to predict clinical outcomes for patients with HF. Finally, this model was validated in an independent cohort with 100 patients with HF.Results
After normalisation and alignment of all the processed spectra, a total of 11,389 peptides (m/z) were detected using MALDI-MS. A multiple biomarker model was developed from 14 plasma peptides that was able to predict clinical outcomes in HF patients with an area under the receiver operating characteristic curve (AUC) of 1.000 (p?=?0.0005). This model was validated in an independent cohort with 100 HF patients that yielded an AUC of 0.817 (p?=?0.0005) in the biomarker validation phase. Addition of this model to the BIOSTAT risk prediction model increased the predictive probability for clinical outcomes of HF from an AUC value of 0.643 to an AUC of 0.823 (p?=?0.0021). Moreover, using the prediction model of fourteen peptides and the composite model of the multiple biomarker of fourteen peptides with the BIOSTAT risk prediction model achieved a better predictive probability of time-to-event in prediction of clinical events in patients with HF (p?=?0.0005).Conclusions
The results obtained in this study suggest that a cluster of plasma peptides using MALDI-MS can reliably predict clinical outcomes in HF that may help enable precision medicine in HF.19.
Youzhong Liu Sara Forcisi Mourad Harir Magali Deleris-Bou Sibylle Krieger-Weber Marianna Lucio Cédric Longin Claudine Degueurce Régis D. Gougeon Philippe Schmitt-Kopplin Hervé Alexandre 《Metabolomics : Official journal of the Metabolomic Society》2016,12(4):69
Introduction
Bacterial malolactic fermentation (MLF) has a considerable impact on wine quality. The yeast strain used for primary fermentation can systematically stimulate (MLF+ phenotype) or inhibit (MLF?) bacteria and the MLF process as a function of numerous winemaking practices, but the underlying molecular evidence still remains a mystery.Objectives
The goal of the study was to elucidate such evidence by the direct comparison of extracellular metabolic profiles of MLF+ and MLF? phenotypes.Methods
We have applied a non-targeted metabolomic approach combining ultrahigh-resolution FT-ICR-MS analysis, powerful statistical tools and a comprehensive wine metabolite database.Results
We discovered around 2500 unknown masses and 800 putative biomarkers involved in phenotypic distinction. For the putative biomarkers, we also developed a biomarker identification workflow and elucidated the exact structure (by UPLC-Q-ToF–MS2) and/or exact physiological impact (by in vitro tests) of several novel biomarkers, such as D-gluconic acid, citric acid, trehalose and tripeptide Pro-Phe-Val. In addition to valid biomarkers, molecular evidence was reflected by unprecedented chemical diversity (around 3000 discriminant masses) that characterized both the yeast phenotypes. While distinct chemical families such as phenolic compounds, carbohydrates, amino acids and peptides characterize the extracellular metabolic profiles of the MLF+ phenotype, the MLF? phenotype is associated with sulphur-containing peptides.Conclusion
The non-targeted approach used in this study played an important role in finding new and unexpected molecular evidence.20.
Chaofu Ke Xiaohong Zhu Yuxia Zhang Yueping Shen 《Metabolomics : Official journal of the Metabolomic Society》2018,14(9):117