Diabetic cardiomyopathy: Pathophysiology, theories and evidence to date

The prevalence of type 2 diabetes (T2D) has increased worldwide and doubled over the last two decades. It features among the top 10 causes of mortality and morbidity in the world. Cardiovascular disease is the leading cause of complications in diabetes and within this, heart failure has been shown to be the leading cause of emergency admissions in the United Kingdom. There are many hypotheses and well-evidenced mechanisms by which diabetic cardiomyopathy as an entity develops. This review aims to give an overview of these mechanisms, with particular emphasis on metabolic inflexibility. T2D is associated with inefficient substrate utilisation, an inability to increase glucose metabolism and dependence on fatty acid oxidation within the diabetic heart resulting in mitochondrial uncoupling, glucotoxicity, lipotoxicity and initially subclinical cardiac dysfunction and finally in overt heart failure. The review also gives a concise update on developments within clinical imaging, specifically cardiac magnetic resonance studies to characterise and phenotype early cardiac dysfunction in T2D. A better understanding of the pathophysiology involved provides a platform for targeted therapy in diabetes to prevent the development of early heart failure with preserved ejection fraction

Fast-tracking the deep residual network training for arrhythmia classification by leveraging the power of dynamical systems

Arrhythmia, characterised by irregular heartbeats, poses significant health risks. Residual networks, a subset of deep learning architectures, have emerged as a potent tool in detecting electrocardiogram (ECG) signal anomalies. However, the enhanced accuracy and capabilities afforded by increasing network depth in these models come at the cost of heightened computational demands, posing a considerable challenge to their practical applicability. Addressing this critical bottleneck, this paper presents a methodology for the resource-economical development of machine learning-enabled systems for arrhythmia detection. The proposed methodology, grounded in the dynamical systems perspective of residual networks, initiates the training process with a shallow network, and then progressively augments its depth. We validate the method rigorously on the PhysioNet’s MIT-BIH arrhythmia dataset using heartbeat spectrograms as training inputs. The results show that the proposed training necessitates a minimum of 39.47% fewer parameters per epoch, when compared with the conventional vanilla training, a feat achieved without sacrificing, and in fact potentially enhancing, the overall performance. Our findings suggest the methodology not only drastically reduces training time but also promises significant savings in energy consumption and environmental costs, offering a glimpse into a future of more sustainable and resource-efficient machine learning developments in arrhythmia detection

Does stress perfusion imaging improve the diagnostic accuracy of late gadolinium enhanced cardiac magnetic resonance for establishing the etiology of heart failure?

Background Late gadolinium enhanced cardiovascular magnetic resonance (LGE-CMR) has excellent specificity, sensitivity and diagnostic accuracy for differentiating between ischemic cardiomyopathy (ICM) and non-ischemic dilated cardiomyopathy (NICM). CMR first-pass myocardial perfusion imaging (perfusion-CMR) may also play role in distinguishing heart failure of ischemic and non-ischemic origins, although the utility of additional of stress perfusion imaging in such patients is unclear. The aim of this retrospective study was to assess whether the addition of adenosine stress perfusion imaging to LGE-CMR is of incremental value for differentiating ICM and NICM in patients with severe left ventricular systolic dysfunction (LVSD) of uncertain etiology. Methods We retrospectively identified 100 consecutive adult patients (median age 69 years (IQR 59–73)) with severe LVSD (mean LV EF 26.6 ± 7.0%) referred for perfusion-CMR to establish the underlying etiology of heart failure. The cause of heart failure was first determined on examination of CMR cine and LGE images in isolation. Subsequent examination of complete adenosine stress perfusion-CMR studies (cine, LGE and perfusion images) was performed to identify whether this altered the initial diagnosis. Results On LGE-CMR, 38 patients were diagnosed with ICM, 46 with NICM and 16 with dual pathology. With perfusion-CMR, there were 39 ICM, 44 NICM and 17 dual pathology diagnoses. There was excellent agreement in diagnoses between LGE-CMR and perfusion-CMR (κ 0.968, p<0.001). The addition of adenosine stress perfusion images to LGE-CMR altered the diagnosis in only two of the 100 patients. Conclusion The addition of adenosine stress perfusion-CMR to cine and LGE-CMR provides minimal incremental diagnostic yield for determining the etiology of heart failure in patients with severe LVSD

Patient-specific three-dimensional torso models for analysing cardiac activity

Standard electrocardiogram (ECG) is routinely used for recording cardiac electrical activity but lacks 3D details. Body surface potential mapping techniques have been developed more recently. A major challenge related to this technique is the projection of body signals back to their original sources in the heart. For an accurate projection, one needs to take into account patient-specific electrophysiological data of tissues surrounding the heart. Much information regarding physiological variations, as well as the exact position of organs in the torso, can be obtained from magnetic resonance (MR) images. Here, a patient-specific methodology for building 3D torso models from transverse MR images is proposed. Torso contour detection is based on edge detection using a canny filter and indicating contour points with a polar coordinate system. Organ detection is performed using an interpolation technique with Active Contour modelling. Results show that accurate torso models can be constructed with short processing time

Embracing uncertainty flexibility: harnessing a supervised tree kernel to empower ensemble modelling for 2D echocardiography-based prediction of right ventricular volume

The right ventricular (RV) function deterioration strongly predicts clinical outcomes in numerous circumstances. To boost the clinical deployment of ensemble regression methods that quantify RV volumes using tabular data from the widely available two-dimensional echocardiography (2DE), we propose to complement the volume predictions with uncertainty scores. To this end, we employ an instance-based method which uses the learned tree structure to identify the nearest training samples to a target instance and then uses a number of distribution types to more flexibly model the output. The probabilistic and point-prediction performances of the proposed framework are evaluated on a relatively small-scale dataset, comprising 100 end-diastolic and end-systolic RV volumes. The reference values for point performance were obtained from MRI. The results demonstrate that our flexible approach yields improved probabilistic and point performances over other state-of-the art methods. The appropriateness of the proposed framework is showcased by providing exemplar cases. The estimated uncertainty embodies both aleatoric and epistemic types. This work aligns with trustworthy artificial intelligence since it can be used to enhance the decision-making process and reduce risks. The feature importance scores of our framework can be exploited to reduce the number of required 2DE views which could enhance the proposed pipeline’s clinical application

Infarct Size Following Treatment With Second‐ Versus Third‐Generation P2Y₁₂ Antagonists in Patients With Multivessel Coronary Disease at ST‐Segment Elevation Myocardial Infarction in the CvLPRIT Study

Background-Third-generation P2Y12 antagonists (prasugrel and ticagrelor) are recommended in guidelines on ST-segment elevation myocardial infarction. Mechanisms translating their more potent antiplatelet activity into improved clinical outcomes versus the second-generation P2Y12 antagonist clopidogrel are unclear. The aim of this post hoc analysis of the Complete Versus Lesion-Only PRImary PCI Trial-CMR (CvLPRIT-CMR) substudy was to assess whether prasugrel and ticagrelor were associated with reduced infarct size compared with clopidogrel in patients undergoing primary percutaneous coronary intervention. Methods and Results-CvLPRIT-CMR was a multicenter, prospective, randomized, open-label, blinded end point trial in 203 STsegment elevation myocardial infarction patients with multivessel disease undergoing primary percutaneous coronary intervention with either infarct-related artery-only or complete revascularization. P2Y12 inhibitors were administered according to local guidelines. The primary end point of infarct size on cardiovascular magnetic resonance was not significantly different between the randomized groups. P2Y12 antagonist administration was not randomized. Patients receiving clopidogrel (n=70) compared with those treated with either prasugrel or ticagrelor (n=133) were older (67.8±12 versus 61.5±10 years, P < 0.001), more frequently had hypertension (49% versus 29%, P=0.007), and tended to have longer symptom-to-revascularization time (234 versus 177 minutes, P=0.05). Infarct size (median 16.1% [quartiles 1-3, 10.5-27.7%] versus 12.1% [quartiles 1-3, 4.8-20.7%] of left ventricular mass, P=0.013) and microvascular obstruction incidence (65.7% versus 48.9%, P=0.022) were significantly greater in patients receiving clopidogrel. Infarct size remained significantly different after adjustment for important covariates using both generalized linear models (P=0.048) and propensity score matching (P=0.025). Conclusions-In this analysis of CvLPRIT-CMR, third-generation P2Y12 antagonists were associated with smaller infarct size and lower microvascular obstruction incidence versus the second-generation P2Y12 antagonist clopidogrel for ST-segment elevation myocardial infarction

Infarct size following complete revascularization in patients presenting with STEMI: a comparison of immediate and staged in-hospital non-infarct related artery PCI subgroups in the CvLPRIT study

Background: The CvLPRIT study showed a trend for improved clinical outcomes in the complete revascularisation (CR) group in those treated with an immediate, as opposed to staged in-hospital approach in patients with multivessel coronary disease undergoing primary percutaneous intervention (PPCI). We aimed to assess infarct size and left ventricular function in patients undergoing immediate compared with staged CR for multivessel disease at PPCI. Methods: The Cardiovascular Magnetic Resonance (CMR) substudy of CvLPRIT was a multicentre, prospective, randomized, open label, blinded endpoint trial in PPCI patients with multivessel disease. These data refer to a post-hoc analysis in 93 patients randomized to the CR arm (63 immediate, 30 staged) who completed a pre-discharge CMR scan (median 2 and 4 days respectively) after PPCI. The decision to stage non-IRA revascularization was at the discretion of the treating interventional cardiologist. Results: Patients treated with a staged approach had more visible thrombus (26/30 vs. 31/62, p = 0.001), higher SYNTAX score in the IRA (9.5, 8–16 vs. 8.0, 5.5–11, p = 0.04) and a greater incidence of no-reflow (23.3 % vs. 1.6 % p < 0.001) than those treated with immediate CR. After adjustment for confounders, staged patients had larger infarct size (19.7 % [11.7–37.6] vs. 11.6 % [6.8–18.2] of LV Mass, p = 0.012) and lower ejection fraction (42.2 ± 10 % vs. 47.4 ± 9 %, p = 0.019) compared with immediate CR. Conclusions: Of patients randomized to CR in the CMR substudy of CvLPRIT, those in whom the operator chose to stage revascularization had larger infarct size and lower ejection fraction, which persisted after adjusting for important covariates than those who underwent immediate CR. Prospective randomized trials are needed to assess whether immediate CR results in better clinical outcomes than staged CR

A randomised, open label, active comparator trial assessing the effects of 26 weeks of liraglutide or sitagliptin on cardiovascular function in young obese adults with type 2 diabetes

Aim To compare the effects of a glucagon‐like peptide‐1 receptor agonist and a dipeptidyl peptidase‐4 inhibitor on magnetic resonance imaging‐derived measures of cardiovascular function. Materials and methods In a prospective, randomized, open‐label, blinded endpoint trial liraglutide (1.8 mg) and sitagliptin (100 mg) were compared in asymptomatic, non‐insulin treated young (aged 18‐50 years) adults with obesity and type 2 diabetes. The primary outcome was difference in circumferential peak early diastolic strain rate change (PEDSR), a biomarker of cardiac diastolic dysfunction 26 weeks after randomization. Secondary outcomes included other indices of cardiac structure and function, HbA1c and body weight. Results Seventy‐six participants were randomized (54% female, mean ± SD age 44 ± 6 years, diabetes duration 4.4 years, body mass index 35.3 ± 6.1 kg m−2), of whom 65% had ≥1 cardiovascular risk factor. Sixty‐one participants had primary outcome data available. There were no statistically significant between‐group differences (intention‐to‐treat; mean [95% confidence interval]) in PEDSR change (−0.01 [−0.07, +0.06] s−1), left ventricular ejection fraction (−1.98 [−4.90, +0.94]%), left ventricular mass (+1.14 [−5.23, +7.50] g) or aortic distensibility (−0.35 [−0.98, +0.28] mmHg−1 × 10−3) after 26 weeks. Reductions in HbA1c (−4.57 [−9.10, −0.37] mmol mol−1) and body weight (−3.88 [−5.74, −2.01] kg) were greater with liraglutide. Conclusion There were no differences in cardiovascular structure or function after short‐term use of liraglutide and sitagliptin in younger adults with obesity and type 2 diabetes. Longer studies in patients with more severe cardiac dysfunction may be necessary before definitive conclusions can be made about putative pleiotropic properties of incretin‐based therapies