@article{BalonovKurlbaumKoschkeretal.2023,
  author    = {Balonov, Ilja and Kurlbaum, Max and Koschker, Ann-Cathrin and Stier, Christine and Fassnacht, Martin and Dischinger, Ulrich},
  title     = {Changes in plasma metabolomic profile following bariatric surgery, lifestyle intervention or diet restriction — insights from human and rat studies},
  series = {International Journal of Molecular Sciences},
  volume    = {24},
  journal   = {International Journal of Molecular Sciences},
  number    = {3},
  issn      = {1422-0067},
  doi       = {10.3390/ijms24032354},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-304462},
  year      = {2023},
  abstract  = {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 W{\"u}rzburg 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.},
  language  = {en}
}
@article{BliziotisKluijtmansTinneveltetal.2022,
  author    = {Bliziotis, Nikolaos G. and Kluijtmans, Leo A. J. and Tinnevelt, Gerjen H. and Reel, Parminder and Reel, Smarti and Langton, Katharina and Robledo, Mercedes and Pamporaki, Christina and Pecori, Alessio and Van Kralingen, Josie and Tetti, Martina and Engelke, Udo F. H. and Erlic, Zoran and Engel, Jasper and Deutschbein, Timo and N{\"o}lting, Svenja and Prejbisz, Aleksander and Richter, Susan and Adamski, Jerzy and Januszewicz, Andrzej and Ceccato, Filippo and Scaroni, Carla and Dennedy, Michael C. and Williams, Tracy A. and Lenzini, Livia and Gimenez-Roqueplo, Anne-Paule and Davies, Eleanor and Fassnacht, Martin and Remde, Hanna and Eisenhofer, Graeme and Beuschlein, Felix and Kroiss, Matthias and Jefferson, Emily and Zennaro, Maria-Christina and Wevers, Ron A. and Jansen, Jeroen J. and Deinum, Jaap and Timmers, Henri J. L. M.},
  title     = {Preanalytical pitfalls in untargeted plasma nuclear magnetic resonance metabolomics of endocrine hypertension},
  series = {Metabolites},
  volume    = {12},
  journal   = {Metabolites},
  number    = {8},
  issn      = {2218-1989},
  doi       = {10.3390/metabo12080679},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-282930},
  year      = {2022},
  abstract  = {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.},
  language  = {en}
}
@article{ReelReelErlicetal.2022,
  author    = {Reel, Smarti and Reel, Parminder S. and Erlic, Zoran and Amar, Laurence and Pecori, Alessio and Larsen, Casper K. and Tetti, Martina and Pamporaki, Christina and Prehn, Cornelia and Adamski, Jerzy and Prejbisz, Aleksander and Ceccato, Filippo and Scaroni, Carla and Kroiss, Matthias and Dennedy, Michael C. and Deinum, Jaap and Eisenhofer, Graeme and Langton, Katharina and Mulatero, Paolo and Reincke, Martin and Rossi, Gian Paolo and Lenzini, Livia and Davies, Eleanor and Gimenez-Roqueplo, Anne-Paule and Assi{\´e}, Guillaume and Blanchard, Anne and Zennaro, Maria-Christina and Beuschlein, Felix and Jefferson, Emily R.},
  title     = {Predicting hypertension subtypes with machine learning using targeted metabolites and their ratios},
  series = {Metabolites},
  volume    = {12},
  journal   = {Metabolites},
  number    = {8},
  issn      = {2218-1989},
  doi       = {10.3390/metabo12080755},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-286161},
  year      = {2022},
  abstract  = {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.},
  language  = {en}
}
@article{BliziotisKluijtmansSotoetal.2022,
  author    = {Bliziotis, Nikolaos G. and Kluijtmans, Leo A. J. and Soto, Sebastian and Tinnevelt, Gerjen H. and Langton, Katharina and Robledo, Mercedes and Pamporaki, Christina and Engelke, Udo F. H. and Erlic, Zoran and Engel, Jasper and Deutschbein, Timo and N{\"o}lting, Svenja and Prejbisz, Aleksander and Richter, Susan and Prehn, Cornelia and Adamski, Jerzy and Januszewicz, Andrzej and Reincke, Martin and Fassnacht, Martin and Eisenhofer, Graeme and Beuschlein, Felix and Kroiss, Matthias and Wevers, Ron A. and Jansen, Jeroen J. and Deinum, Jaap and Timmers, Henri J. L. M.},
  title     = {Pre- versus post-operative untargeted plasma nuclear magnetic resonance spectroscopy metabolomics of pheochromocytoma and paraganglioma},
  series = {Endocrine},
  volume    = {75},
  journal   = {Endocrine},
  number    = {1},
  doi       = {10.1007/s12020-021-02858-z},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-326574},
  pages     = {254-265},
  year      = {2022},
  abstract  = {Purpose Pheochromocytomas and Paragangliomas (PPGL) result in chronic catecholamine excess and serious health complications. A recent study obtained a metabolic signature in plasma from PPGL patients; however, its targeted nature may have generated an incomplete picture and a broader approach could provide additional insights. We aimed to characterize the plasma metabolome of PPGL patients before and after surgery, using an untargeted approach, and to broaden the scope of the investigated metabolic impact of these tumors. Design A cohort of 36 PPGL patients was investigated. Blood plasma samples were collected before and after surgical tumor removal, in association with clinical and tumor characteristics. Methods Plasma samples were analyzed using untargeted nuclear magnetic resonance (NMR) spectroscopy metabolomics. The data were evaluated using a combination of uni- and multi-variate statistical methods. Results Before surgery, patients with a nonadrenergic tumor could be distinguished from those with an adrenergic tumor based on their metabolic profiles. Tyrosine levels were significantly higher in patients with high compared to those with low BMI. Comparing subgroups of pre-operative samples with their post-operative counterparts, we found a metabolic signature that included ketone bodies, glucose, organic acids, methanol, dimethyl sulfone and amino acids. Three signals with unclear identities were found to be affected. Conclusions Our study suggests that the pathways of glucose and ketone body homeostasis are affected in PPGL patients. BMI-related metabolite levels were also found to be altered, potentially linking muscle atrophy to PPGL. At baseline, patient metabolomes could be discriminated based on their catecholamine phenotype.},
  language  = {en}
}
@article{ArltBiehlTayloretal.2011,
  author    = {Arlt, Wiebke and Biehl, Michael and Taylor, Angela E. and Hahner, Stefanie and Lib{\´e}, Rossella and Hughes, Beverly A. and Schneider, Petra and Smith, David J. and Stiekema, Han and Krone, Nils and Porfiri, Emilio and Opocher, Giuseppe and Bertherat, Jer{\^o}me and Mantero, Franco and Allolio, Bruno and Terzolo, Massimo and Nightingale, Peter and Shackleton, Cedric H. L. and Bertagna, Xavier and Fassnacht, Martin and Stewart, Paul M.},
  title     = {Urine Steroid Metabolomics as a Biomarker Tool for Detecting Malignancy in Adrenal Tumors},
  series = {The Journal of Clinical Endocrinology \& Metabolism},
  volume    = {96},
  journal   = {The Journal of Clinical Endocrinology \& Metabolism},
  number    = {12},
  doi       = {10.1210/jc.2011-1565},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-154682},
  pages     = {3775 -- 3784},
  year      = {2011},
  abstract  = {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.},
  language  = {en}
}