TY - JOUR A1 - Balonov, Ilja A1 - Kurlbaum, Max A1 - Koschker, Ann-Cathrin A1 - Stier, Christine A1 - Fassnacht, Martin A1 - Dischinger, Ulrich T1 - Changes in plasma metabolomic profile following bariatric surgery, lifestyle intervention or diet restriction — insights from human and rat studies JF - International Journal of Molecular Sciences N2 - Although bariatric surgery is known to change the metabolome, it is unclear if this is specific for the intervention or a consequence of the induced bodyweight loss. As the weight loss after Roux-en-Y Gastric Bypass (RYGB) can hardly be mimicked with an evenly effective diet in humans, translational research efforts might be helpful. A group of 188 plasma metabolites of 46 patients from the randomized controlled Würzburg Adipositas Study (WAS) and from RYGB-treated rats (n = 6) as well as body-weight-matched controls (n = 7) were measured using liquid chromatography tandem mass spectrometry. WAS participants were randomized into intensive lifestyle modification (LS, n = 24) or RYGB (OP, n = 22). In patients in the WAS cohort, only bariatric surgery achieved a sustained weight loss (BMI −34.3% (OP) vs. −1.2% (LS), p ≤ 0.01). An explicit shift in the metabolomic profile was found in 57 metabolites in the human cohort and in 62 metabolites in the rodent model. Significantly higher levels of sphingolipids and lecithins were detected in both surgical groups but not in the conservatively treated human and animal groups. RYGB leads to a characteristic metabolomic profile, which differs distinctly from that following non-surgical intervention. Analysis of the human and rat data revealed that RYGB induces specific changes in the metabolome independent of weight loss. KW - metabolomics KW - phosphatidylcholines KW - sphingolipids KW - branched-chain amino acids KW - obesity KW - Roux-en-Y Gastric Bypass KW - rodent model KW - insulin resistance Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-304462 SN - 1422-0067 VL - 24 IS - 3 ER - TY - JOUR A1 - Bliziotis, Nikolaos G. A1 - Kluijtmans, Leo A. J. A1 - Tinnevelt, Gerjen H. A1 - Reel, Parminder A1 - Reel, Smarti A1 - Langton, Katharina A1 - Robledo, Mercedes A1 - Pamporaki, Christina A1 - Pecori, Alessio A1 - Van Kralingen, Josie A1 - Tetti, Martina A1 - Engelke, Udo F. H. A1 - Erlic, Zoran A1 - Engel, Jasper A1 - Deutschbein, Timo A1 - Nölting, Svenja A1 - Prejbisz, Aleksander A1 - Richter, Susan A1 - Adamski, Jerzy A1 - Januszewicz, Andrzej A1 - Ceccato, Filippo A1 - Scaroni, Carla A1 - Dennedy, Michael C. A1 - Williams, Tracy A. A1 - Lenzini, Livia A1 - Gimenez-Roqueplo, Anne-Paule A1 - Davies, Eleanor A1 - Fassnacht, Martin A1 - Remde, Hanna A1 - Eisenhofer, Graeme A1 - Beuschlein, Felix A1 - Kroiss, Matthias A1 - Jefferson, Emily A1 - Zennaro, Maria-Christina A1 - Wevers, Ron A. A1 - Jansen, Jeroen J. A1 - Deinum, Jaap A1 - Timmers, Henri J. L. M. T1 - Preanalytical pitfalls in untargeted plasma nuclear magnetic resonance metabolomics of endocrine hypertension JF - Metabolites N2 - Despite considerable morbidity and mortality, numerous cases of endocrine hypertension (EHT) forms, including primary aldosteronism (PA), pheochromocytoma and functional paraganglioma (PPGL), and Cushing’s syndrome (CS), remain undetected. We aimed to establish signatures for the different forms of EHT, investigate potentially confounding effects and establish unbiased disease biomarkers. Plasma samples were obtained from 13 biobanks across seven countries and analyzed using untargeted NMR metabolomics. We compared unstratified samples of 106 PHT patients to 231 EHT patients, including 104 PA, 94 PPGL and 33 CS patients. Spectra were subjected to a multivariate statistical comparison of PHT to EHT forms and the associated signatures were obtained. Three approaches were applied to investigate and correct confounding effects. Though we found signatures that could separate PHT from EHT forms, there were also key similarities with the signatures of sample center of origin and sample age. The study design restricted the applicability of the corrections employed. With the samples that were available, no biomarkers for PHT vs. EHT could be identified. The complexity of the confounding effects, evidenced by their robustness to correction approaches, highlighted the need for a consensus on how to deal with variabilities probably attributed to preanalytical factors in retrospective, multicenter metabolomics studies. KW - confounders KW - metabolomics KW - multicenter KW - plasma NMR KW - preanalytical conditions Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-282930 SN - 2218-1989 VL - 12 IS - 8 ER - TY - JOUR A1 - Reel, Smarti A1 - Reel, Parminder S. A1 - Erlic, Zoran A1 - Amar, Laurence A1 - Pecori, Alessio A1 - Larsen, Casper K. A1 - Tetti, Martina A1 - Pamporaki, Christina A1 - Prehn, Cornelia A1 - Adamski, Jerzy A1 - Prejbisz, Aleksander A1 - Ceccato, Filippo A1 - Scaroni, Carla A1 - Kroiss, Matthias A1 - Dennedy, Michael C. A1 - Deinum, Jaap A1 - Eisenhofer, Graeme A1 - Langton, Katharina A1 - Mulatero, Paolo A1 - Reincke, Martin A1 - Rossi, Gian Paolo A1 - Lenzini, Livia A1 - Davies, Eleanor A1 - Gimenez-Roqueplo, Anne-Paule A1 - Assié, Guillaume A1 - Blanchard, Anne A1 - Zennaro, Maria-Christina A1 - Beuschlein, Felix A1 - Jefferson, Emily R. T1 - Predicting hypertension subtypes with machine learning using targeted metabolites and their ratios JF - Metabolites N2 - Hypertension is a major global health problem with high prevalence and complex associated health risks. Primary hypertension (PHT) is most common and the reasons behind primary hypertension are largely unknown. Endocrine hypertension (EHT) is another complex form of hypertension with an estimated prevalence varying from 3 to 20% depending on the population studied. It occurs due to underlying conditions associated with hormonal excess mainly related to adrenal tumours and sub-categorised: primary aldosteronism (PA), Cushing’s syndrome (CS), pheochromocytoma or functional paraganglioma (PPGL). Endocrine hypertension is often misdiagnosed as primary hypertension, causing delays in treatment for the underlying condition, reduced quality of life, and costly antihypertensive treatment that is often ineffective. This study systematically used targeted metabolomics and high-throughput machine learning methods to predict the key biomarkers in classifying and distinguishing the various subtypes of endocrine and primary hypertension. The trained models successfully classified CS from PHT and EHT from PHT with 92% specificity on the test set. The most prominent targeted metabolites and metabolite ratios for hypertension identification for different disease comparisons were C18:1, C18:2, and Orn/Arg. Sex was identified as an important feature in CS vs. PHT classification. KW - metabolomics KW - machine learning KW - hypertension KW - primary aldosteronism KW - pheochromocytoma/paraganglioma KW - Cushing syndrome KW - biomarkers Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-286161 SN - 2218-1989 VL - 12 IS - 8 ER - TY - JOUR A1 - Arlt, Wiebke A1 - Biehl, Michael A1 - Taylor, Angela E. A1 - Hahner, Stefanie A1 - Libé, Rossella A1 - Hughes, Beverly A. A1 - Schneider, Petra A1 - Smith, David J. A1 - Stiekema, Han A1 - Krone, Nils A1 - Porfiri, Emilio A1 - Opocher, Giuseppe A1 - Bertherat, Jerôme A1 - Mantero, Franco A1 - Allolio, Bruno A1 - Terzolo, Massimo A1 - Nightingale, Peter A1 - Shackleton, Cedric H. L. A1 - Bertagna, Xavier A1 - Fassnacht, Martin A1 - Stewart, Paul M. T1 - Urine Steroid Metabolomics as a Biomarker Tool for Detecting Malignancy in Adrenal Tumors JF - The Journal of Clinical Endocrinology & Metabolism N2 - Context: Adrenal tumors have a prevalence of around 2% in the general population. Adrenocortical carcinoma (ACC) is rare but accounts for 2–11% of incidentally discovered adrenal masses. Differentiating ACC from adrenocortical adenoma (ACA) represents a diagnostic challenge in patients with adrenal incidentalomas, with tumor size, imaging, and even histology all providing unsatisfactory predictive values. Objective: Here we developed a novel steroid metabolomic approach, mass spectrometry-based steroid profiling followed by machine learning analysis, and examined its diagnostic value for the detection of adrenal malignancy. Design: Quantification of 32 distinct adrenal derived steroids was carried out by gas chromatography/mass spectrometry in 24-h urine samples from 102 ACA patients (age range 19–84 yr) and 45 ACC patients (20–80 yr). Underlying diagnosis was ascertained by histology and metastasis in ACC and by clinical follow-up [median duration 52 (range 26–201) months] without evidence of metastasis in ACA. Steroid excretion data were subjected to generalized matrix learning vector quantization (GMLVQ) to identify the most discriminative steroids. Results: Steroid profiling revealed a pattern of predominantly immature, early-stage steroidogenesis in ACC. GMLVQ analysis identified a subset of nine steroids that performed best in differentiating ACA from ACC. Receiver-operating characteristics analysis of GMLVQ results demonstrated sensitivity = specificity = 90% (area under the curve = 0.97) employing all 32 steroids and sensitivity = specificity = 88% (area under the curve = 0.96) when using only the nine most differentiating markers. Conclusions: Urine steroid metabolomics is a novel, highly sensitive, and specific biomarker tool for discriminating benign from malignant adrenal tumors, with obvious promise for the diagnostic work-up of patients with adrenal incidentalomas. KW - adrenal cortex hormones KW - urine KW - adrenal cortex neoplasms KW - mass spectrometry KW - metabolomics Y1 - 2011 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-154682 VL - 96 IS - 12 SP - 3775 EP - 3784 ER -