scholarly journals Targeted metabolomics as a tool in discriminating endocrine from primary hypertension

2020 ◽  
Author(s):  
Zoran Erlic ◽  
Parminder Reel ◽  
Smarti Reel ◽  
Laurence Amar ◽  
Alessio Pecori ◽  
...  
Keyword(s):  
Multivariate Analyses ◽  
Cushing Syndrome ◽  
Area Under The Curve ◽  
Primary Hypertension ◽  
Liquid Chromatography Mass Spectrometry ◽  
Targeted Metabolomics ◽  
Metabolic Pattern ◽  
Machine Learning Approach ◽  
Arginine Aspartate ◽  
Stored Blood

Abstract Context Identification of patients with endocrine forms of hypertension (EHT) (primary hyperaldosteronism [PA], pheochromocytoma/paraganglioma [PPGL] and Cushing syndrome [CS]) provides the basis to implement individualized therapeutic strategies. Targeted metabolomics (TM) have revealed promising results in profiling cardiovascular diseases and endocrine conditions associated with hypertension. Objective Use TM to identify distinct metabolic patterns between primary hypertension (PHT) and EHT and test its discriminating ability. Design Retrospective analyses of PHT and EHT patients from a European multicentre study (ENSAT-HT). TM was performed on stored blood samples using liquid chromatography mass spectrometry. To identify discriminating metabolites a “classical approach” (CA) (performing a series of univariate and multivariate analyses) and a “machine learning approach” (MLA) (using Random Forest) were used. Patients The study included 282 adult patients (52% female; mean age 49 years) with proven PHT (n=59) and EHT (n=223 with 40 CS, 107 PA and 76 PPGL), respectively. Results From 155 metabolites eligible for statistical analyses, 31 were identified discriminating between PHT and EHT using the CA and 27 using the MLA, of which 15 metabolites (C9, C16, C16:1, C18:1, C18:2, arginine, aspartate, glutamate, ornithine, spermidine, lysoPCaC16:0, lysoPCaC20:4, lysoPCaC24:0, PCaeC42:0, SM C18:1, SM C20:2) were found by both approaches. The ROC curve built on the top 15 metabolites from the CA provided an area under the curve (AUC) of 0.86, which was similar to the performance of the 15 metabolites from MLA (AUC 0.83). Conclusions TM identifies distinct metabolic pattern between PHT and EHT providing promising discriminating performance.

scholarly journals SUN-LB97 Targeted Metabolomics as a Screening Tool in the Diagnosis of Endocrine Hypertension

2020 ◽  
Vol 4 (Supplement_1) ◽  
Author(s):  
Zoran Erlic ◽  
Laurence Amar ◽  
Casper K Larsen ◽  
Martina Tetti ◽  
Christina Pamporaki ◽  
...  
Keyword(s):  
Screening Tool ◽  
Cushing Syndrome ◽  
Area Under The Curve ◽  
Targeted Metabolomics ◽  
Adequate Treatment ◽  
Metabolic Pattern ◽  
Global Epidemic ◽  
Endocrine Hypertension ◽  
Cardiovascular Morbidity And Mortality ◽  
Common Group

Abstract Arterial hypertension [HT] is a global epidemic that requires adequate treatment to reduce cardiovascular morbidity and mortality. Secondary causes of HT and specifically endocrine hypertension [EHT] (primary hyperaldosteronism [PA], pheochromocytoma/paraganglioma [PPGL] and Cushing syndrome [CS]) can potentially be cured by surgery or treated by targeted medication. However, diagnosis of EHT requires expertise in test selection and interpretation of test results. The availability of experts outnumbers its demand. Thus, preselecting tools are necessary to identify patients who require further referral to an expert. Since targeted metabolomics [TM] is a new method showing promising results in profiling cardiovascular diseases and endocrine conditions associated with HT, we tested the ability of TM in discriminating primary hypertension [PHT] from EHT cases. The study included 282 adult patients (52% female; mean age 49 years) from the European multicentre consortium ENSAT-HT (www.ensat-ht.eu). Of these, 59 were diagnosed with PHT and 223 with EHT (40 CS, 107 PA and 76 PPGL). TM was performed on stored blood samples with a mass spectrometry based approach using the AbsoluteIDQTM p180 Kit (BIOCRATES Life Sciences, Austria). In total, 188 metabolites were determined, of which 155 were eligible for statistical analyses according to established selection criteria. To identify relevant discriminating metabolites, a series of univariate and multivariate analyses were applied. Since the distribution of the patients between the clinical entities was different according to sex (p<0.001) and age (p=0.001), analyses were also performed separately for each sex and age group (cut-off 50 years). Thereby, we identified 4 common metabolites (C18:1, C18:2, spermidine, ornithine) from the comparison of PHT with each endocrine hypertension subgroup (CS, PA, PPGL) separately. The ROC curve for discrimination between PHT and EHT built upon these 4 metabolites had an area under the curve (AUC) of 0.79 (95%CI 0.73-0.85). In the comparison of PHT and EHT as a common group 38 metabolites were identified. Using the top 15 metabolites from the latter comparison (C3-DC, C9, C16, C16:1, C18:1, C18:2, arginine, aspartate, glutamate, ornithine, spermidine, lysoPCaC20:4, PCaaC38:6, PCaaC40:6, PCaaC42:1) the AUC was 0.86 (95%CI 0.81-0.91). We conclude that TM is associated with distinct metabolic pattern in PHT and EHT and is a promising pre-screening tool for identifying EHT patients.

scholarly journals A non-targeted LC–MS metabolic profiling of pregnancy: longitudinal evidence from healthy and pre-eclamptic pregnancies

Metabolomics ◽  
2021 ◽  
Vol 17 (2) ◽  
Author(s):  
Tiina Jääskeläinen ◽  
◽  
Olli Kärkkäinen ◽  
Jenna Jokkala ◽  
Anton Klåvus ◽  
...  
Keyword(s):  
Large Scale ◽  
Scale Effects ◽  
Late Pregnancy ◽  
Metabolite Profile ◽  
Adverse Outcomes ◽  
Liquid Chromatography Mass Spectrometry ◽  
Targeted Metabolomics ◽  
Serum Samples ◽  
Healthy Women ◽  
Maternal Metabolism

Abstract Introduction Maternal metabolism changes substantially during pregnancy. However, few studies have used metabolomics technologies to characterize changes across gestation. Objectives and methods We applied liquid chromatography–mass spectrometry (LC–MS) based non-targeted metabolomics to determine whether the metabolic profile of serum differs throughout the pregnancy between pre-eclamptic and healthy women in the FINNPEC (Finnish Genetics of Preeclampsia Consortium) Study. Serum samples were available from early and late pregnancy. Results Progression of pregnancy had large-scale effects to the serum metabolite profile. Altogether 50 identified metabolites increased and 49 metabolites decreased when samples of early pregnancy were compared to samples of late pregnancy. The metabolic signatures of pregnancy were largely shared in pre-eclamptic and healthy women, only urea, monoacylglyceride 18:1 and glycerophosphocholine were identified to be increased in the pre-eclamptic women when compared to healthy controls. Conclusions Our study highlights the need of large-scale longitudinal metabolomic studies in non-complicated pregnancies before more detailed understanding of metabolism in adverse outcomes could be provided. Our findings are one of the first steps for a broader metabolic understanding of the physiological changes caused by pregnancy per se.

CPVA: a web-based metabolomic tool for chromatographic peak visualization and annotation

2020 ◽  
Vol 36 (12) ◽  
pp. 3913-3915
Author(s):  
Hemi Luan ◽  
Xingen Jiang ◽  
Fenfen Ji ◽  
Zhangzhang Lan ◽  
Zongwei Cai ◽  
...  
Keyword(s):  
False Positive ◽  
Supplementary Information ◽  
Liquid Chromatography Mass Spectrometry ◽  
Targeted Metabolomics ◽  
Metabolomics Data ◽  
Web Based ◽  
Tremendous Amount ◽  
Chromatographic Peaks ◽  
User Friendly

Abstract Motivation Liquid chromatography–mass spectrometry-based non-targeted metabolomics is routinely performed to qualitatively and quantitatively analyze a tremendous amount of metabolite signals in complex biological samples. However, false-positive peaks in the datasets are commonly detected as metabolite signals by using many popular software, resulting in non-reliable measurement. Results To reduce false-positive calling, we developed an interactive web tool, termed CPVA, for visualization and accurate annotation of the detected peaks in non-targeted metabolomics data. We used a chromatogram-centric strategy to unfold the characteristics of chromatographic peaks through visualization of peak morphology metrics, with additional functions to annotate adducts, isotopes and contaminants. CPVA is a free, user-friendly tool to help users to identify peak background noises and contaminants, resulting in decrease of false-positive or redundant peak calling, thereby improving the data quality of non-targeted metabolomics studies. Availability and implementation The CPVA is freely available at http://cpva.eastus.cloudapp.azure.com. Source code and installation instructions are available on GitHub: https://github.com/13479776/cpva. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals A pilot study of discovery and validation of peritoneal endometriosis biomarkers in peritoneal fluid and serum

2020 ◽  
Author(s):  
See Ling Loy ◽  
Jieliang Zhou ◽  
Liang Cui ◽  
Tse Yeun Tan ◽  
Tat Xin Ee ◽  
...  
Keyword(s):  
Peritoneal Fluid ◽  
Liquid Chromatography Mass Spectrometry ◽  
Operating Characteristics ◽  
Targeted Metabolomics ◽  
Serum Samples ◽  
Pilot Studies ◽  
Potential Biomarker ◽  
Receiver Operating Characteristics Curve ◽  
Peritoneal Endometriosis ◽  
Validation Set

ObjectiveTo identify potential serum biomarkers in women with peritoneal endometriosis (PE) by first looking at its source in the peritoneal fluid (PF).DesignCase-control pilot studies, comprising independent discovery and validation sets.SettingKK Women’s and Children’s Hospital, Singapore.Patient(s)Women with laparoscopically confirmed PE and absence of endometriosis (control).Intervention(s)None.Main Outcome Measure(s)In the discovery set, we used untargeted liquid chromatography-mass spectrometry (LC-MS/MS) metabolomics, multivariable and univariable analyses to generate global metabolomic profiles of PF for endometriosis and to identify potential metabolites that could distinguish PE (n=10) from controls (n=31). Using targeted metabolomics, we validated the identified metabolites in PF and sera of cases (n=16 PE) and controls (n=19). We performed the area under the receiver-operating characteristics curve (AUC) analysis to evaluate the diagnostic performance of PE metabolites.Result(s)In the discovery set, PF phosphatidylcholine (34:3) and phenylalanyl-isoleucine were significantly increased in PE than controls groups, with AUC 0.77 (95% confidence interval 0.61-0.92; p=0.018) and AUC 0.98 (0.95-1.02; p<0.001), respectively. In the validation set, phenylalanyl-isoleucine retained discriminatory performance to distinguish PE from controls in both PF (AUC 0.77; 0.61-0.92; p=0.006) and serum samples (AUC 0.81; 0.64-0.99; p=0.004).Conclusion(s)Our preliminary results propose phenylalanyl-isoleucine as a potential biomarker of PE, which may be used as a minimally-invasive diagnostic biomarker of PE.

scholarly journals Identification and Quantification by Targeted Metabolomics of Antibiotic-Responsive Urinary Small Phenolic Molecules Derived from the Intestinal Microbiota in Mice

2019 ◽  
Vol 4 (1) ◽  
pp. 85
Author(s):  
Mark E. Obrenovich ◽  
George Eugene Jaskiw ◽  
Renliang Zhang ◽  
Belinda Willard ◽  
Curtis J. Donskey
Keyword(s):  
High Performance ◽  
Bacterial Species ◽  
Anaerobic Bacteria ◽  
Liquid Chromatography Mass Spectrometry ◽  
Targeted Metabolomics ◽  
Mouse Urine ◽  
Urinary Levels ◽  
Low Levels ◽  
Pathogen Colonization ◽  
The Relationship

Background: Urinary levels of small molecules generated by the gut microbiota (GMB) constitute potential biomarkers for the state of the GMB. Such metabolites include numerous small phenolic molecules linked to anaerobic bacteria, particularly Clostridium species. Due to multiple technical challenges, however, the relationship between these chemicals and the GMB remains poorly characterized. Improved, high-performance liquid chromatography-mass spectrometry (LC-MS)-based metabolomic analysis can now reliably separate and quantify low levels of multiple small phenolic molecules and their structural isomers.Methods: CF-1 (female mice) were treated over 2 consecutive days with either i) vehicle, ii) one of 2 different antibiotic regimens (clindamycin or piperacillin/tazobactam) known to inhibit intestinal anaerobes and promote colonization by Clostridium difficile and other pathogens or iii) an antibiotic (aztreonam) that suppresses facultative Gram-negative bacteria but not enterococci or anaerobes and does not promote pathogen colonization Urine collected 24 hours after the last treatment was analyzed by LC-MS.Results: We identified over 25 compounds, many of which had not been previously reported in mouse urine. Eleven small phenolic molecules showed significant antibiotic-related changes. Urinary levels of the hydroxyphenylpropionic acids were suppressed by clindamycin and piperacillin/tazobactam treatment, but were elevated in aztreonam-treated mice. In addition, aztreonam treatment was associated with elevated levels of the dihydroxyhydrocinnamic acids.Conclusions: Profiles of differential changes in urinary small phenolic molecules may provide an index of anaerobic bacterial species in the GMB and could prove useful in monitoring susceptibility to overgrowth of pathogens such as C. difficile.

scholarly journals Untargeted Metabolomics and Polyamine Profiling in Serum before and after Surgery in Colorectal Cancer Patients

Metabolites ◽  
2020 ◽  
Vol 10 (12) ◽  
pp. 487
Author(s):  
Yu Ra Lee ◽  
Ki-Yong An ◽  
Justin Jeon ◽  
Nam Kyu Kim ◽  
Ji Won Lee ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Metabolic Pathways ◽  
Sphingolipid Metabolism ◽  
Proline Metabolism ◽  
Liquid Chromatography Mass Spectrometry ◽  
Targeted Metabolomics ◽  
Serum Samples ◽  
Physiological Alterations ◽  
Before And After ◽  
Colorectal Cancer Patients

Colorectal cancer is one of the most prevalent cancers in Korea and globally. In this study, we aimed to characterize the differential serum metabolomic profiles between pre-operative and post-operative patients with colorectal cancer. To investigate the significant metabolites and metabolic pathways associated with colorectal cancer, we analyzed serum samples from 68 patients (aged 20–71, mean 57.57 years). Untargeted and targeted metabolomics profiling in patients with colorectal cancer were performed using liquid chromatography-mass spectrometry. Untargeted analysis identified differences in sphingolipid metabolism, steroid biosynthesis, and arginine and proline metabolism in pre- and post-operative patients with colorectal cancer. We then performed quantitative target profiling of polyamines, synthesized from arginine and proline metabolism, to identify potential polyamines that may serve as effective biomarkers for colorectal cancer. Results indicate a significantly reduced serum concentration of putrescine in post-operative patients compared to pre-operative patients. Our metabolomics approach provided insights into the physiological alterations in patients with colorectal cancer after surgery.

scholarly journals MON-188 The Diagnostic Value of DHEAS in Subtyping Patients with Cushing Syndrome

2020 ◽  
Vol 4 (Supplement_1) ◽  
Author(s):  
Mengsi Liu ◽  
keying Zhu ◽  
Huan Chen ◽  
Wenhuan Feng ◽  
Dalong Zhu ◽  
...  
Keyword(s):  
Cushing Syndrome ◽  
Laboratory Data ◽  
Area Under The Curve ◽  
Dehydroepiandrosterone Sulfate ◽  
Diagnostic Value ◽  
Acth Level ◽  
Plasma Acth ◽  
Clinical Value ◽  
Cushing Disease ◽  
Diagnostic Sensitivity And Specificity

Abstract Objective: Serum dehydroepiandrosterone sulfate (DHEAS) can be used to assess the integrity of the hypothalamic-pituitary-adrenal (HPA) axis. The aim of this study was to evaluate the clinical value of DHEAS in differentiating adrenal Cushing syndrome (ACS) from Cushing’ disease (CD). Methods: We recruited 100 patients with Cushing syndrome, 36 with CD and 64 with ACS. 72 sex-, age- and BMI-matched nonfunctional adrenal adenomas (NFAAs) were served as controls. Clinical and laboratory data were collected. DHEAS levels were measured and DHEAS ratio was calculated by dividing the measured DHEAS by the lower limit of the respective reference range (age- and sex-matched). Results: 1) No significant differences in age, sex, or BMI were detected among the NFAAs, ACS and CD groups. Compared to NFAAs group, ACS patients had lower plasma ACTH levels [1.11(1.11,1.74) vs 5.0 ± 2.9 pmol/L, P&lt;0.01], lower DHEAS levels (24.00 ± 20.72 vs 189.05 ± 82.03 ug/dL, P &lt; 0.01) and lower DHEAS ratio [0.58(0.27,0.98) vs 5.34 ± 3.0]; Plasma ACTH (22.12 ± 14.22 pmol/L), DHEAS (309.4 ± 201.1 ug/dL) and DHEAS ratio (10.51 ± 7.65) in CD patients were significantly higher compared to those in NFAAs and ACS patients (all P&lt;0.01). 2) In ACS patients, there were 53 patients with suppressed ACTH level of &lt;2.0 pmol/L, 11 patients without plasma ACTH suppression (≥2.0pmol/L). Compared to NFAAs, lower DHEAS and DHEAS ratio were detected in these two groups, and no significant differences were found in the DHEAS [15(15, 23.5) vs 23.8 ± 14.4 ug/dL, P=0.86] and DHEAS ratio [0.58(0.27, 0.80) vs 1.0(0.25,2.09) ug/dL, P=0.40] between the two groups. 3) ROC analysis showed that the area under the curve (AUC) of plasma ACTH, serum DHEAS and DHEAS ratio in diagnosing 0.954, 0.997 and 0.990 respectively. The optimal cut-off values for DHEAS and its ratio were 79.1ug/dL, and 2.09, respectively. The diagnostic sensitivity and specificity of plasma ACTH (&lt;2.0pmol/L) were 84.1 and 100%, those of DHEAS were 97.5% and 100%, and those of DHEAS ratio were 95% and 100%, respectively. Conclusions: Patients with different subtype of Cushing syndrome showed distinctive DHEAS levels and DHEAS ratio. DHEAS and DHEAS ratio are useful in differential diagnosis of Cushing syndrome. Especially, when the plasma ACTH level is not conclusive. The measurement of DHEAS may offer a supplementary test to diagnosis ACS from CD. Keywords: Adrenal Cushing syndrome; Cushing disease; Adrenocorticotropic hormone; Dehydroepiandrosterone sulfate

scholarly journals Aneurysm rebleeding before therapy: a predictable disaster?

2019 ◽  
Vol 131 (5) ◽  
pp. 1473-1480 ◽  
Author(s):  
Marvin Darkwah Oppong ◽  
Meltem Gümüs ◽  
Daniela Pierscianek ◽  
Annika Herten ◽  
Andreas Kneist ◽  
...  
Keyword(s):  
Odds Ratio ◽  
Multivariate Analyses ◽  
Area Under The Curve ◽  
Final Analysis ◽  
Ruptured Aneurysms ◽  
Aneurysm Occlusion ◽  
Independent Risk Factors ◽  
Good Diagnostic Accuracy ◽  
Current Guidelines ◽  
Score Point

OBJECTIVECurrent guidelines for subarachnoid hemorrhage (SAH) include early aneurysm treatment within 72 hours after ictus. However, aneurysm rebleeding remains a crucial complication of SAH. The aim of this study was to identify independent predictors allowing early stratification of SAH patients for rebleeding risk.METHODSAll patients admitted to the authors’ institution with ruptured aneurysms during a 14-year period were eligible for this retrospective study. Demographic and radiographic parameters, aneurysm characteristics, medical history, and medications as well as baseline parameters at admission (blood pressure and laboratory parameters) were evaluated in univariate and multivariate analyses. A novel risk score was created using independent risk factors.RESULTSData from 984 cases could be included into the final analysis. Aneurysm rebleeding occurred in 58 cases (5.9%), and in 48 of these cases (82.8%) rerupture occurred within 24 hours after SAH. Of over 30 tested associations, preexisting arterial hypertension (p = 0.02; adjusted odds ratio [aOR] 2.56, 1 score point), aneurysm location at the basilar artery (p = 0.001, aOR 4.5, 2 score points), sac size ≥ 9 mm (p = 0.04, aOR 1.9, 1 score point), presence of intracerebral hemorrhage (p = 0.001, aOR 4.29, 2 score points), and acute hydrocephalus (p < 0.001, aOR 6.27, 3 score points) independently predicted aneurysm rebleeding. A score built upon these parameters (0–9 points) showed a good diagnostic accuracy (p < 0.001, area under the curve 0.780) for rebleeding prediction.CONCLUSIONSCertain patient-, aneurysm-, and SAH-specific parameters can reliably predict aneurysm rerupture. A score developed according to these parameters might help to identify individuals that would profit from immediate aneurysm occlusion.

scholarly journals Estimation of the Ambit of Breast Cancer with a Modified Resnet Analysis Using Machine Learning Approach

2021 ◽  
Vol 18 ◽  
pp. 183-190
Author(s):  
C. K. Narayanappa ◽  
G. R., Poornima ◽  
Basavaraj V. Hiremath
Keyword(s):  
Breast Cancer ◽  
Supervised Classification ◽  
Area Under The Curve ◽  
Breast Cancer Diagnosis ◽  
Learning Approach ◽  
Classification Models ◽  
Mortality And Morbidity ◽  
Machine Learning Approach ◽  
Sensitivity Specificity ◽  
Cancerous Cells

Breast Cancer has been one of the most common reasons for mortality and morbidity among the females around the world especially in developing countries. In this regard, Mammography is a popular screening technique for breast cancer diagnosis so as to label the existence of cancerous cells. The present work encompasses the design and development of a M-ResNet (Modified ResNet) approach so as to classify the breast cancer into benign and malignant conditions with the inclusions for supervised classification models with the training of both upper as well as the lower layers of the designed networks. The efficacy of the developed approach was evaluated using various performance evaluators such as those of sensitivity, specificity, accuracy and F1-Score. Bi-Rads score was used as a basis for the classification process wherein a score of 0-3 correlated to benign and it is non-cancerous nature of tissues whereas malignancy was denoted by a score of 4 and above. InBreast dataset, a publicly available online dataset with 112 breast images were used for the evaluation of the developed paradigm. The present paradigm portrayed an accuracy of 96.43% with Area Under the Curve (AUC) of 95.63%.

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