@article{MontellanoKluterRueckeretal.2022,
  author    = {Montellano, Felipe A. and Kluter, Elisabeth J. and R{\"u}cker, Viktoria and Ungeth{\"u}m, Kathrin and Mackenrodt, Daniel and Wiedmann, Silke and Dege, Tassilo and Quilitzsch, Anika and Morbach, Caroline and Frantz, Stefan and St{\"o}rk, Stefan and Haeusler, Karl Georg and Kleinschnitz, Christoph and Heuschmann, Peter U.},
  title     = {Cardiac dysfunction and high-sensitive C-reactive protein are associated with troponin T elevation in ischemic stroke: insights from the SICFAIL study},
  series = {BMC Neurology},
  volume    = {22},
  journal   = {BMC Neurology},
  number    = {1},
  doi       = {10.1186/s12883-022-03017-1},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-300119},
  year      = {2022},
  abstract  = {Background Troponin elevation is common in ischemic stroke (IS) patients. The pathomechanisms involved are incompletely understood and comprise coronary and non-coronary causes, e.g. autonomic dysfunction. We investigated determinants of troponin elevation in acute IS patients including markers of autonomic dysfunction, assessed by heart rate variability (HRV) time domain variables. Methods Data were collected within the Stroke Induced Cardiac FAILure (SICFAIL) cohort study. IS patients admitted to the Department of Neurology, W{\"u}rzburg University Hospital, underwent baseline investigation including cardiac history, physical examination, echocardiography, and blood sampling. Four HRV time domain variables were calculated in patients undergoing electrocardiographic Holter monitoring. Multivariable logistic regression with corresponding odds ratios (OR) and 95\% confidence intervals (CI) was used to investigate the determinants of high-sensitive troponin T (hs-TnT) levels ≥14 ng/L. Results We report results from 543 IS patients recruited between 01/2014-02/2017. Of those, 203 (37\%) had hs-TnT ≥14 ng/L, which was independently associated with older age (OR per year 1.05; 95\% CI 1.02-1.08), male sex (OR 2.65; 95\% CI 1.54-4.58), decreasing estimated glomerular filtration rate (OR per 10 mL/min/1.73 m2 0.71; 95\% CI 0.61-0.84), systolic dysfunction (OR 2.79; 95\% CI 1.22-6.37), diastolic dysfunction (OR 2.29; 95\% CI 1.29-4.02), atrial fibrillation (OR 2.30; 95\% CI 1.25-4.23), and increasing levels of C-reactive protein (OR 1.48 per log unit; 95\% CI 1.22-1.79). We did not identify an independent association of troponin elevation with the investigated HRV variables. Conclusion Cardiac dysfunction and elevated C-reactive protein, but not a reduced HRV as surrogate of autonomic dysfunction, were associated with increased hs-TnT levels in IS patients independent of established cardiovascular risk factors.},
  language  = {en}
}
@article{LippertFassnachtRonchi2022,
  author    = {Lippert, Juliane and Fassnacht, Martin and Ronchi, Cristina L.},
  title     = {The role of molecular profiling in adrenocortical carcinoma},
  series = {Clinical Endocrinology},
  volume    = {97},
  journal   = {Clinical Endocrinology},
  number    = {4},
  doi       = {10.1111/cen.14629},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-258382},
  pages     = {460-472},
  year      = {2022},
  abstract  = {Adrenocortical carcinoma (ACC) is a rare, aggressive cancer with still partially unknown pathogenesis, heterogenous clinical behaviour and no effective treatment for advanced stages. Therefore, there is an urgent clinical unmet need for better prognostication strategies, innovative therapies and significant improvement of the management of the individual patients. In this review, we summarize available studies on molecular prognostic markers and markers predictive of response to standard therapies as well as newly proposed drug targets in sporadic ACC. We include in vitro studies and available clinical trials, focusing on alterations at the DNA, RNA and epigenetic levels. We also discuss the potential of biomarkers to be implemented in a clinical routine workflow for improved ACC patient care.},
  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}
}