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Serum Metabolomic Analysis Suggests Impairment of Myocardial Energy Production in Takotsubo Syndrome.

ABSTRACT: Takotsubo syndrome is a complex entity that, although it usually has a good prognosis, can be life threatening. While recent advances have improved the knowledge of takotsubo syndrome, many aspects of its etiology still remain uncertain. Metabolomics, a hypothesis generating approach, could provide novel pathophysiology information about this disease. Serum samples were obtained from takotsubo (n = 19) and acute myocardial infarction patients (n = 8) at the cath lab and, in the case of takotsubo, again once the patient had recovered, 3 months after the main event. 1H NMR spectra of the serum were acquired at 9.4T using a CPMG pulse sequence (32 ms effective delay). Supervised and unsupervised pattern recognition approaches where applied to the data. Pattern recognition was able to differentiate between takotsubo and acute myocardial infarction during the acute phase with 95% accuracy. Myocardial infarction patients showed an increase in lipid signals, a known risk factor for the disease while takotsubo patients showed a relative increase in acetate that could suggest a reduced turnover of the Krebs cycle. When comparing acute and recovered phases, we could detect an increase in alanine and creatine once patients recovered. Our results demonstrate that takotsubo syndrome is metabolically different than AMI, showing limited myocardial energy production capacity during the acute phase. We achieved high classification success against AMI; however, this study should be considered as a proof of concept regarding clinical application of metabolic profiling in takotsubo cardiomyopathy.

SUBMITTER: Nunez-Gil IJ

PROVIDER: S-EPMC8303832 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature

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Serum Metabolomic Analysis Suggests Impairment of Myocardial Energy Production in Takotsubo Syndrome.

Nuñez-Gil Iván J IJ Andrés Mireia M Benito Begoña B Bernardo Esther E Vedia Oscar O Ferreira-Gonzalez Ignacio I Barba Ignasi I

Metabolites 20210703 7

<h4>Introduction</h4>Takotsubo syndrome is a complex entity that, although it usually has a good prognosis, can be life threatening. While recent advances have improved the knowledge of takotsubo syndrome, many aspects of its etiology still remain uncertain. Metabolomics, a hypothesis generating approach, could provide novel pathophysiology information about this disease.<h4>Methods and results</h4>Serum samples were obtained from takotsubo (<i>n</i> = 19) and acute myocardial infarction patient ...[more]

PMID: 34357333

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Project description:ImportanceMachine learning algorithms enable the automatic classification of cardiovascular diseases based on raw cardiac ultrasound imaging data. However, the utility of machine learning in distinguishing between takotsubo syndrome (TTS) and acute myocardial infarction (AMI) has not been studied.ObjectivesTo assess the utility of machine learning systems for automatic discrimination of TTS and AMI.Design, settings, and participantsThis cohort study included clinical data and transthoracic echocardiogram results of patients with AMI from the Zurich Acute Coronary Syndrome Registry and patients with TTS obtained from 7 cardiovascular centers in the International Takotsubo Registry. Data from the validation cohort were obtained from April 2011 to February 2017. Data from the training cohort were obtained from March 2017 to May 2019. Data were analyzed from September 2019 to June 2021.ExposureTransthoracic echocardiograms of 224 patients with TTS and 224 patients with AMI were analyzed.Main outcomes and measuresArea under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity of the machine learning system evaluated on an independent data set and 4 practicing cardiologists for comparison. Echocardiography videos of 228 patients were used in the development and training of a deep learning model. The performance of the automated echocardiogram video analysis method was evaluated on an independent data set consisting of 220 patients. Data were matched according to age, sex, and ST-segment elevation/non-ST-segment elevation (1 patient with AMI for each patient with TTS). Predictions were compared with echocardiographic-based interpretations from 4 practicing cardiologists in terms of sensitivity, specificity, and AUC calculated from confidence scores concerning their binary diagnosis.ResultsIn this cohort study, apical 2-chamber and 4-chamber echocardiographic views of 110 patients with TTS (mean [SD] age, 68.4 [12.1] years; 103 [90.4%] were female) and 110 patients with AMI (mean [SD] age, 69.1 [12.2] years; 103 [90.4%] were female) from an independent data set were evaluated. This approach achieved a mean (SD) AUC of 0.79 (0.01) with an overall accuracy of 74.8 (0.7%). In comparison, cardiologists achieved a mean (SD) AUC of 0.71 (0.03) and accuracy of 64.4 (3.5%) on the same data set. In a subanalysis based on 61 patients with apical TTS and 56 patients with AMI due to occlusion of the left anterior descending coronary artery, the model achieved a mean (SD) AUC score of 0.84 (0.01) and an accuracy of 78.6 (1.6%), outperforming the 4 practicing cardiologists (mean [SD] AUC, 0.72 [0.02]) and accuracy of 66.9 (2.8%).Conclusions and relevanceIn this cohort study, a real-time system for fully automated interpretation of echocardiogram videos was established and trained to differentiate TTS from AMI. While this system was more accurate than cardiologists in echocardiography-based disease classification, further studies are warranted for clinical application.

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Serum Metabolomic Analysis Suggests Impairment of Myocardial Energy Production in Takotsubo Syndrome.

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Serum Metabolomic Analysis Suggests Impairment of Myocardial Energy Production in Takotsubo Syndrome.

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