Catecholamine-dependent β-adrenergic signaling in a pluripotent stem cell model of takotsubo cardiomyopathy

BACKGROUND: Takotsubo syndrome (TTS) is characterized by an acute left ventricular dysfunction and is associated with life-threating complications in the acute phase. The underlying disease mechanism in TTS is still unknown. A genetic basis has been suggested to be involved in the pathogenesis. OBJECTIVES: The aims of the study were to establish an in vitro induced pluripotent stem cell (iPSC) model of TTS, to test the hypothesis of altered β-adrenergic signaling in TTS iPSC-cardiomyocytes (CMs), and to explore whether genetic susceptibility underlies the pathophysiology of TTS. METHODS: Somatic cells of patients with TTS and control subjects were reprogrammed to iPSCs and differentiated into CMs. Three-month-old CMs were subjected to catecholamine stimulation to simulate neurohumoral overstimulation. We investigated β-adrenergic signaling and TTS cardiomyocyte function. RESULTS. Enhanced β-adrenergic signaling in TTS-iPSC-CMs under catecholamine-induced stress increased expression of the cardiac stress marker NR4A1; cyclic adenosine monophosphate levels; and cyclic adenosine monophosphate-dependent protein kinase A-mediated hyperphosphorylation of RYR2-S2808, PLN-S16, TNI-S23/24, and Cav1.2-S1928, and leads to a reduced calcium time to transient 50% decay. These cellular catecholamine-dependent responses were mainly mediated by β-adrenoceptor signaling in TTS. Engineered heart muscles from TTS-iPSC-CMs showed an impaired force of contraction and a higher sensitivity to isoprenaline-stimulated inotropy compared with control subjects. In addition, altered electrical activity and increased lipid accumulation were detected in catecholamine-treated TTS-iPSC-CMs, and were confirmed by differentially expressed lipid transporters CD36 and CPT1C. Furthermore, we uncovered genetic variants in different key regulators of cardiac function. CONCLUSIONS. Enhanced β-adrenergic signaling and higher sensitivity to catecholamine-induced toxicity were identified as mechanisms associated with the TTS phenotype. (International Takotsubo Registry [InterTAK Registry] [InterTAK]; NCT01947621)

Learning meaningful latent space representations for patient risk stratification: model development and validation for dengue and other acute febrile illness

Background: Increased data availability has prompted the creation of clinical decision support systems. These systems utilise clinical information to enhance health care provision, both to predict the likelihood of specific clinical outcomes or evaluate the risk of further complications. However, their adoption remains low due to concerns regarding the quality of recommendations, and a lack of clarity on how results are best obtained and presented. Methods: We used autoencoders capable of reducing the dimensionality of complex datasets in order to produce a 2D representation denoted as latent space to support understanding of complex clinical data. In this output, meaningful representations of individual patient profiles are spatially mapped in an unsupervised manner according to their input clinical parameters. This technique was then applied to a large real-world clinical dataset of over 12,000 patients with an illness compatible with dengue infection in Ho Chi Minh City, Vietnam between 1999 and 2021. Dengue is a systemic viral disease which exerts significant health and economic burden worldwide, and up to 5% of hospitalised patients develop life-threatening complications. Results: The latent space produced by the selected autoencoder aligns with established clinical characteristics exhibited by patients with dengue infection, as well as features of disease progression. Similar clinical phenotypes are represented close to each other in the latent space and clustered according to outcomes broadly described by the World Health Organisation dengue guidelines. Balancing distance metrics and density metrics produced results covering most of the latent space, and improved visualisation whilst preserving utility, with similar patients grouped closer together. In this case, this balance is achieved by using the sigmoid activation function and one hidden layer with three neurons, in addition to the latent dimension layer, which produces the output (Pearson, 0.840; Spearman, 0.830; Procrustes, 0.301; GMM 0.321). Conclusion: This study demonstrates that when adequately configured, autoencoders can produce two-dimensional representations of a complex dataset that conserve the distance relationship between points. The output visualisation groups patients with clinically relevant features closely together and inherently supports user interpretability. Work is underway to incorporate these findings into an electronic clinical decision support system to guide individual patient management