Arterial stiffness in patients with type 1 diabetes and its comparison to cardiovascular risk evaluation tools

Cardiovascular disease in type 1 diabetes, an underestimated danger: Epidemiological and pathophysiological data

Cardiovascular disease (CV) is a common complication of type 1 diabetes (T1D) and a leading cause of death. T1D patients are more likely to develop CV disease (CVD) early in life and show a reduction of life expectancy of at least 11 years. Patients with a young age of T1D onset have a substantially higher CV risk. The reasons for increased atherosclerosis in T1D patients are not entirely explained. In addition to the typical CV risk factors, long-term hyperglycemia has a significant impact by inducing oxidative stress, vascular inflammation, monocyte adhesion, arterial wall thickening and endothelial dysfunction. Additionally, CVD in T1D is also associated with nephropathy. However, CVD risk is still significantly increased in T1D patients, in good glycemic control without additional CV risk factors, indicating the involvement of supplementary potential factors. By increasing oxidative stress, vascular inflammation, and endothelial dysfunction, hypoglycemia and glucose variability may exacerbate CVD. Moreover, significant qualitative and functional abnormalities of lipoproteins are present in even well-controlled T1D patients and are likely to play a role in the development of atherosclerosis and the promotion of CVD. According to recent research, immune system dysfunction, which is typical of auto-immune T1D, may also promote CVD, likely via inflammatory pathways. In addition, T1D patients who are overweight or obese exhibit an additional CV risk due to pathophysiological mechanisms that are similar to those seen in T2D.

Impaired Cardiovagal Activity as a Link Between Hyperglycemia and Arterial Stiffness in Adults With Type 2 Diabetes Mellitus Among an Eastern Indian Population: A Cross-sectional Study

OBJECTIVES

Cardiac autonomic neuropathy (CAN) is one of the most common yet overlooked complications of type 2 diabetes mellitus (T2DM). Individuals with T2DM with CAN have a 5-fold higher rate of cardiovascular morbidity and mortality. The presence of CAN in T2DM could potentially lead to arterial stiffness. However, only sparse data are available suggesting any association between autonomic dysfunction and arterial stiffness in T2DM.

METHODS

We recruited 80 people with T2DM and 74 healthy controls for our study. Heart rate variability (HRV) testing was performed to assess autonomic function. Assessment of arterial stiffness was done by measuring the brachial pulse wave velocity (baPWV) and augmentation index (AI).

RESULTS

The time-domain parameters were significantly decreased (p<0.001) and frequency-domain parameters, such as total power and high-frequency band expressed as a normalized unit, were found to be significantly reduced in people with T2DM (p<0.001). Both baPWV and AI were significantly higher in people with T2DM compared with healthy controls (p<0.001). We observed a moderate correlation between standard deviation of normal to normal interval (SDNN) and baPWV (r=-0.437, p=0.002) and AI (r=-0.403, p=0.002). A multiple linear regression model showed an association between SDNN and arterial stiffness parameters, such as baPWV and AI, which were statistically significant (p<0.05) in a fully adjusted model that included the conventional risk factors for atherosclerosis.

CONCLUSIONS

Impaired cardiovagal activity is an independent risk factor for the development of arterial stiffness. Incorporation of HRV testing into the diabetes management protocol would have potential benefits for identifying individuals at high risk of developing cardiovascular events. Hence, preventive measures can be taken as early as possible to improve patient outcomes.

Type 1 Diabetes and Cardiovascular Health

Type 1 diabetes (T1D) is associated with an increased risk of cardiovascular disease (CVD). CVD occurs much earlier in people with T1D than in the general population, and several risk factors have been identified some of which are modifiable. Risk prediction models and imaging tests to detect early signs of CVD have not been extensively validated. Strategies to promote cardiovascular health (CVH) in T1D include identifying risk factors, early treatment to achieve CVH targets, and improving the education of health care providers and people with T1D.

Sex Differences in Cardiovascular Disease and Cardiovascular Risk Estimation in Patients With Type 1 Diabetes

CONTEXT

Patients with type 1 diabetes (T1D) have higher cardiovascular disease (CVD) risk than the general population.

OBJECTIVE

This observational study aims to evaluate sex-related differences in CVD prevalence and CVD risk estimates in a large cohort of T1D adults.

METHODS

We conducted a multicenter, cross-sectional study involving 2041 patients with T1D (mean age 46 years; 44.9% women). In patients without pre-existing CVD (primary prevention), we used the Steno type 1 risk engine to estimate the 10-year risk of developing CVD events.

RESULTS

CVD prevalence (n = 116) was higher in men than in women aged ≥55 years (19.2 vs 12.8%, P = .036), but comparable between the 2 sexes in those aged <55 years (P = .91). In patients without pre-existing CVD (n = 1925), mean 10-year estimated CVD risk was 15.4 ± 0.4% without any significant sex difference. However, stratifying this patient group by age, the 10-year estimated CVD risk was significantly higher in men than in women until age 55 years (P < .001), but this risk equalized after this age. Carotid artery plaque burden was significantly associated with age ≥55 years and with a medium and high 10-year estimated CVD risk, without any significant sex difference. Diabetic retinopathy and sensory-motor neuropathy were also associated with higher 10-year CVD risk and female sex.

CONCLUSION

Both men and women with T1D are at high CVD risk. The 10-year estimated CVD risk was higher in men aged <55 years than in women of similar age, but these sex differences disappeared at age ≥55 years, suggesting that female sex was no longer protective.

Fully automatic carotid arterial stiffness assessment from ultrasound videos based on machine learning

Arterial stiffness (AS) refers to the loss of arterial compliance and alterations in vessel wall properties. The study of local carotid stiffness (CS) is particularly important since carotid artery stiffening raises the risk of stroke, cognitive impairment, and dementia. So, stiffness measurement as a screening tool approach is crucial because it can reduce mortality and facilitate therapy planning. This study aims to evaluate the stiffness of the CCA using machine learning (ML) through the features of diameter change (ΔD) and stiffness parameters. This study was conducted in seven stages: data collection, preprocessing, CCA segmentation, CCA lumen diameter (DCCA) computing during cardiac cycles, denoising signals of DCCA, computational of AS parameters, and stiffness assessment using ML. The 51 videos (with 25 s) of CCA B-mode ultrasound (US) were used and analyzed. Each US video yielded approximately 750 sequential frames spanning about 24 cardiac cycles. Firstly, US preset settings with time gain compensation with a U-pattern were employed to enhance CCA segmentations. The study showed that auto region-growing, employed three times, is appropriate for segmenting walls with a short running time (4.56 s/frame). The diameter computed for frames constructs a signal (diameter signal) with noisy parts in the shape of peak variance and an un-smooth side. Among the 12 employed smoothing methods, spline fitting with a mean peak difference per cycle (MPDCY) of 0.58 pixels was the most effective for the diameter signal. The authors propose the MPDCY as a new selection criterion for smoothing methods with highly preserved peaks. The ΔD (D_sys-D_dia) determined in this study was validated by statistical analysis as a viable replacement for manual ΔD measurement. Statistical analysis was carried out by Mann-Whitney t-test with a p-value of 0.81, regression line R² = 0.907, and there was no difference in means between the two groups for box plots. The stiffness parameters of the carotid arteries were calculated based on auto-ΔD and pulse pressure. Five ML models, including K-nearest neighbor (KNN), support vector machine (SVM), decision tree (DT), logistic regression (LR), and random forest (RF), fed by distension (ΔD) and five stiffness parameters, were used to distinguish between the stiffened and un-stiffened CCA. Except for SVM, all models performed excellently in terms of specificity, sensitivity, precision, and area under the curve (AUC). In addition, the scatterplot and statistical analysis of the fed features confirm these remarkable outcomes. The scatter plot demonstrates that a linear hyperline can easily distinguish between the two classes. The statistical analysis shows that the stiffness parameters computed from the database of this work were statistically (p < 0.05) distributed into the non-stiffness and stiffness groups. The presented models are validated by applying them to additional datasets. Applying models to other datasets reveals a model performance of 100%. The proposed ML models could be applied in clinical practice to detect CS early, which is essential for preventing stroke.

Radial artery pulse wave velocity: a new characterization technique and the instabilities associated with the respiratory phase and breath-holding

Objective. Pulse wave velocity (PWV) is a key diagnostic parameter of the cardiovascular system's state. However, approaches aimed at PWV characterization often suffer from inevitable drawbacks. Statistical results demonstrating how closely PWV in the radial artery (RA) and the respiration phase correlate, as well as RA PWV evolution during breath-holding (BH), have not yet been presented in the literature. The aims of this study are (a) to propose a simple robust technique for measuring RA PWV, (b) to reveal the phase relation between the RA PWV and spontaneous breathing, and (c) to disclose the influence of BH on the RA PWV.Approach.The high-resolution remote breathing monitoring method Sorption-Enhanced Infrared Thermography (SEIRT) and the new technique aimed at measuring RA PWV described in this paper were used synchronously, and their measurement data were processed simultaneously.Main results. Spontaneous breathing leaves a synchronous 'trace' on the RA PWV. The close linear correlation of the respiration phase and the phase of concomitant RA PWV changes is statistically confirmed in five tested people (Pearson's r is of the order of 0.5-0.8, P < 0.05). The BH appreciably affects the RA PWV. A phenomenon showing that the RA PWV is not indifferent to hypoxia is observed for the first time.Significance.The proposed technique for RA PWV characterization has high prospects in biomedical diagnostics. The presented pilot study deserves attention in the context of the mutual interplay between respiratory and cardiovascular systems. It may also be useful in cases where peripheral pulse wave propagation helps assess respiratory function.

Time-varying parameters of glycemic control and glycation in relation to arterial stiffness in patients with type 1 diabetes

BACKGROUND

A substantial proportion of type 1 diabetes (T1D) patients free from known cardiovascular disease (CVD) show premature arterial stiffening, with age, blood pressure, and HbA1c-as gold standard of glycemic control-as main predictors. However, the relationship of arterial stiffness with other time-varying parameters of glycemic control and glycation has been far less explored. This study investigated the relationship of arterial stiffness with several short- and long-term parameters of glycemic control and glycation in patients with T1D, such as advanced glycation end-products (AGEs) and continuous glucose monitoring (CGM)-derived parameters.

METHODS

Cross-sectional study at a tertiary care centre including 54 patients with T1D free from known CVD. Arterial stiffness was assessed with carotid-femoral pulse wave velocity (cf-PWV). Current level and 10-year history of HbA1c were evaluated, and skin AGEs, urinary AGEs, and serum soluble AGE-receptor (sRAGE) concentrations. CGM for 7 days was used to determine time in range, time in hyper- and hypoglycemia, and glycemic variability.

RESULTS

Cf-PWV was associated with current HbA1c (r_s = + 0.28), mean 10-years HbA1c (r_s = + 0.36), skin AGEs (r_s = + 0.40) and the skin AGEs-to-sRAGE ratio (r_s = + 0.40), but not with urinary AGE or serum sRAGE concentrations; and not with any of the CGM-parameters. Multiple linear regression for cf-PWV showed that the model with the best fit included age, T1D duration, 24-h mean arterial pressure and mean 10-years HbA1c (adjusted R² = 0.645, p < 0.001).

CONCLUSIONS

Longer-term glycemic exposure as reflected by current and mean 10-years HbA1c is a key predictor of arterial stiffness in patients with T1D, while no relationship was found with any of the short-term CGM parameters. Our findings stress the importance of early and sustained good glycemic control to prevent premature CVD in patients with T1D and suggest that HbA1c should continue to be used in the risk assessment for diabetic complications. The role of skin glycation, as a biomarker for vascular aging, in the risk assessment for CVD is an interesting avenue for further research.