Coronary Flow Reserve and Glycemic Variability in Patients with Coronary Artery Disease

Diabetic microvascular disease in non-classical beds: the hidden impact beyond the retina, the kidney, and the peripheral nerves

Diabetes microangiopathy, a hallmark complication of diabetes, is characterised by structural and functional abnormalities within the intricate network of microvessels beyond well-known and documented target organs, i.e., the retina, kidney, and peripheral nerves. Indeed, an intact microvascular bed is crucial for preserving each organ's specific functions and achieving physiological balance to meet their respective metabolic demands. Therefore, diabetes-related microvascular dysfunction leads to widespread multiorgan consequences in still-overlooked non-traditional target organs such as the brain, the lung, the bone tissue, the skin, the arterial wall, the heart, or the musculoskeletal system. All these organs are vulnerable to the physiopathological mechanisms that cause microvascular damage in diabetes (i.e., hyperglycaemia-induced oxidative stress, inflammation, and endothelial dysfunction) and collectively contribute to abnormalities in the microvessels' structure and function, compromising blood flow and tissue perfusion. However, the microcirculatory networks differ between organs due to variations in haemodynamic, vascular architecture, and affected cells, resulting in a spectrum of clinical presentations. The aim of this review is to focus on the multifaceted nature of microvascular impairment in diabetes through available evidence of specific consequences in often overlooked organs. A better understanding of diabetes microangiopathy in non-target organs provides a broader perspective on the systemic nature of the disease, underscoring the importance of recognising the comprehensive range of complications beyond the classic target sites.

Deep learning-based coronary computed tomography analysis to predict functionally significant coronary artery stenosis

Fractional flow reserve derived from coronary CT (FFR-CT) is a noninvasive physiological technique that has shown a good correlation with invasive FFR. However, the use of FFR-CT is restricted by strict application standards, and the diagnostic accuracy of FFR-CT analysis may potentially be decreased by severely calcified coronary arteries because of blooming and beam hardening artifacts. The aim of this study was to evaluate the utility of deep learning (DL)-based coronary computed tomography (CT) data analysis in predicting invasive fractional flow reserve (FFR), especially in cases with severely calcified coronary arteries. We analyzed 184 consecutive cases (241 coronary arteries) which underwent coronary CT and invasive coronary angiography, including invasive FFR, within a three-month period. Mean coronary artery calcium scores were 963 ± 1226. We evaluated and compared the vessel-based diagnostic accuracy of our proposed DL model and a visual assessment to evaluate functionally significant coronary artery stenosis (invasive FFR < 0.80). A deep neural network was trained with consecutive short axial images of coronary arteries on coronary CT. Ninety-one coronary arteries of 89 cases (48%) had FFR-positive functionally significant stenosis. On receiver operating characteristics (ROC) analysis to predict FFR-positive stenosis using the trained DL model, average area under the curve (AUC) of the ROC curve was 0.756, which was superior to the AUC of visual assessment of significant (≥ 70%) coronary artery stenosis on CT (0.574, P = 0.011). The sensitivity, specificity, positive and negative predictive value (PPV and NPV), and accuracy of the DL model and visual assessment for detecting FFR-positive stenosis were 82 and 36%, 68 and 78%, 59 and 48%, 87 and 69%, and 73 and 63%, respectively. Sensitivity and NPV for the prediction of FFR-positive stenosis were significantly higher with our DL model than visual assessment (P = 0.0004, and P = 0.024). DL-based coronary CT data analysis has a higher diagnostic accuracy for functionally significant coronary artery stenosis than visual assessment.

Impaired coronary flow reserve in patients with poor type 2 diabetes control: Preliminary results from prospective microvascular dysfunction registry

BACKGROUND

Type 2 diabetes (DM) is a common comorbidity associated with cardiovascular disease, especially when poor glucose control is present. Extracardiac microcirculatory complications prevalence is well documented, however coronary microcirculatory dysfunction (CMD) seem to be underreported in this group.

METHODS

The present study analyzed coronary physiology measurements (coronary flow reserve [CFR], index of microcirculatory resistance [IMR], resistance reserve ratio [RRR]) in 47 diabetic patients (21 subjects with poor glycemia control defined as fasting glucose levels > 7.2 mmol/L and 26 with normal fasting glucose), and compared to 54 non-diabetic controls, who had undergone coronary angiography due to symptoms of chronic coronary syndrome. The median age of patients was 65.5 [59.0; 73.0] years old, 74% male, similar in terms of cardiovascular risk factors and prior myocardial infarction. Insulin was used by 19% of diabetic patients with poor glucose control and by 15% of those with DM and low fasting glucose.

RESULTS

Prevalence of CMD was 38% in poor glycemia control patients, 27% in DM-patients with proper glucose control and 31% of non-diabetics. Median CFR values were the lowest in poor DM control patients compared to both, normal fasting glucose (1.75 [1.37; 2.32] vs. 2.30 [1.75; 2.85], p = 0.026) and to non-diabetics (1.75 [1.37; 2.32] vs. 2.15 [1.50; 2.95], p = 0.045). Levels of IMR, RRR and MRR did not differ significantly between compared groups (p > 0.05 for all comparisons).

CONCLUSIONS

Poor glycemia control in type 2 DM might be associated with a higher prevalence of CMD driven by decreased coronary flow reserve, however, further research in larger groups of patients should be performed to confirm this observation.

Defining the Phenotypes for Heart Failure With Preserved Ejection Fraction

PURPOSE OF REVIEW

Heart failure with preserved ejection fraction (HFpEF) imposes a significant burden on society and healthcare. The lack in efficacious therapies is likely due to the significant heterogeneity of HFpEF. In this review, we define various phenotypes based on underlying comorbidities or etiologies, discuss phenotypes arrived at by novel methods, and explore therapeutic targets.

RECENT FINDINGS

A few studies have used machine learning methods to uncover sub-phenotypes within HFpEF in an unbiased manner based on clinical features, echocardiographic findings, and biomarker levels. We synthesized the literature and propose three broad phenotypes: (1) young, with few comorbidities, usually obese and with low natriuretic peptide levels, (2) obese with substantive cardiometabolic burden and comorbidities and impaired ventricular relaxation, (3) old, multimorbid, with high rates of atrial fibrillation, renal and coronary artery disease, chronic obstructive pulmonary disease, and left ventricular hypertrophy. We also propose potential therapeutic strategies for these phenotypes.

Discrepancy between plaque vulnerability and functional severity of angiographically intermediate coronary artery lesions

This study sought to investigate the relationship between physiological severity and plaque vulnerability of intermediate coronary artery stenoses as assessed by fractional flow reserve (FFR) and near-infrared spectroscopy-intravascular ultrasound (NIRS-IVUS). We included vessels where both FFR and NIRS-IVUS were performed. A positive FFR was defined as FFR ≤ 0.80. Lipid core burden index of the entire target vessel (TV-LCBI), maximum LCBI in 4 mm (maxLCBI_4mm), and maximum plaque burden (PB) were evaluated using NIRS-IVUS. A vulnerable plaque was defined as a lipid-rich plaque (maxLCBI4_mm ≥ 400) with large PB (≥ 70%). A total of 59 vessels of 45 patients were included. Median FFR value was 0.75 [interquartile 0.72, 0.82]. An FFR value of ≤ 0.80 was observed in 42 vessels (71%). TV-LCBI (correlation coefficient [CC] = - 0.331, p = 0.011), lesion length (CC = - 0.350, p = 0.007), and PB (CC = - 0.230, p = 0.080) negatively correlated with FFR value, while maxLCBI_4mm did not (CC = - 0.156, p = 0.24). The prevalence of vulnerable plaques (26.2% vs. 29.4%, p > 0.99) and mean TV-LCBI, maxLCBI_4mm, and PB values were not significantly different between the vessels with FFR ≤ 0.80 and those with FFR > 0.80. In multivariable logistic models, diabetes mellitus (p = 0.003) and hemoglobin A1c (p = 0.012) were associated with the presence of a vulnerable plaque. In conclusion, the results of the present study suggested that FFR may reflect total lipid burden but not necessarily plaque vulnerability. In patients with coronary artery disease and a high likelihood of rapid atherosclerosis progression, such as diabetes mellitus patients, assessing plaque vulnerability in addition to the functional severity of coronary artery lesions may help stratify better the risk of future events.