Carotid Artery Stiffening With Aging: Structural Versus Load-Dependent Mechanisms in MESA (the Multi-Ethnic Study of Atherosclerosis)

Pewowaruk, Ryan; Tedla, Yacob; Korcarz, Claudia E.; Tattersall, Matthew C.; Stein, James H.; Chesler, Naomi C.; Gepner, Adam D.

doi:10.1161/hypertensionaha.121.18444

Cited by 18 publications

(21 citation statements)

References 30 publications

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“…To differentiate the structural and load‐dependent components of carotid artery stiffness, a participant‐specific exponential model was used to describe arterial mechanics 5 , 7 , 14 , 15 , 16 using a nonlinear stiffness parameter at the common reference pressure of 120/80 mm Hg. Arterial stiffness metrics were calculated at this reference pressure to represent the structural arterial stiffness.…”

Section: Methodsmentioning

confidence: 99%

“…Arterial stiffness is also both a marker of and contributor to abnormal hemodynamics, which is particularly important for end organ damage in high‐flow, low‐resistance organs like the brain and kidneys. Central, elastic arteries stiffen via 2 main mechanisms 5 , 6 : (1) load‐dependent stiffening due to elevated greater blood pressure increasing collagen fiber loading without an intrinsic change to the artery wall and (2) structural stiffening due to growth (intima‐media thickening) and remodeling (collagen accumulation). Structural stiffness is representative of remodeling intrinsic to the artery wall.…”

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confidence: 99%

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Carotid Artery Stiffness Mechanisms Are Associated With End Organ Damage and All‐Cause Mortality: MESA (Multi‐Ethnic Study of Atherosclerosis)

Pewowaruk

Korcarz

Boer

et al. 2023

JAHA

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Background Arterial stiffness can be separated into 2 main mechanisms: (1) load‐dependent stiffening from higher blood pressure and (2) structural stiffening due to remodeling of the vessel wall. The relationship between stiffness mechanisms and end organ damage is unknown. Methods and Results MESA (Multi‐Ethnic Study of Atherosclerosis) participants with carotid ultrasound were included in this study (n=6147). Carotid pulse wave velocity (cPWV) was calculated to represent total stiffness. Structural stiffness was calculated by adjusting cPWV to a 120/80 mm Hg blood pressure with participant‐specific models. Load‐dependent stiffness was the difference of total and structural stiffness. Associations with incident chronic kidney disease (CKD), dementia, and mortality were assessed with adjusted Cox models. During 14.3±4.8 years of follow‐up, 773 CKD events, 535 dementia events, and 1529 deaths occurred. Total cPWV was associated with mortality (hazard ratio [HR], per 1 m/s, 1.04 [95% CI, 1.01–1.08], P =0.02) and dementia (HR, 1.06 [95% CI, 1.01–1.12], P =0.03) but not CKD (HR, 1.03 [95% CI, 0.98–1.08], P =0.33). Structural cPWV was significantly associated with mortality (HR, 1.04 [95% CI, 1.00–1.08], P =0.04) but not CKD (HR, 1.00 [95% CI, 0.94–1.05], P =0.86) or dementia (HR, 1.06 [95% CI, 0.99–1.13], P =0.06). Load‐dependent cPWV was significantly associated with CKD (HR, 1.38 [95% CI, 1.17–1.63], P <0.001) but not mortality (HR, 1.11 [95% CI, 0.99–1.25], P =0.07) or dementia (HR, 1.14 [95% CI, 0.94–1.38], P =0.19). Conclusions The mechanisms of arterial stiffness were associated with all‐cause mortality and CKD. Structural stiffness was associated with all‐cause mortality, and load‐dependent stiffness was associated with CKD. Total stiffness was associated with dementia but load‐dependent and structural stiffness were not.

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Section: Methodsmentioning

confidence: 99%

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confidence: 99%

Carotid Artery Stiffness Mechanisms Are Associated With End Organ Damage and All‐Cause Mortality: MESA (Multi‐Ethnic Study of Atherosclerosis)

Pewowaruk

Korcarz

Boer

et al. 2023

JAHA

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“…The choice of carotid artery non-invasive imaging [ 48 ] with noise-reduction capability is preferred for economic reasons [ 44 , 223 ]. The carotid wall segmentation aids in the detection of plaque build-up [ 224 , 225 ].…”

Section: Deep Learning For Cvd/stroke Risk Assessment In Pd Patients ...mentioning

confidence: 99%

Deep Learning Paradigm for Cardiovascular Disease/Stroke Risk Stratification in Parkinson’s Disease Affected by COVID-19: A Narrative Review

Suri¹,

Maindarkar

Sauseng

et al. 2022

Diagnostics

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Background and Motivation: Parkinson’s disease (PD) is one of the most serious, non-curable, and expensive to treat. Recently, machine learning (ML) has shown to be able to predict cardiovascular/stroke risk in PD patients. The presence of COVID-19 causes the ML systems to become severely non-linear and poses challenges in cardiovascular/stroke risk stratification. Further, due to comorbidity, sample size constraints, and poor scientific and clinical validation techniques, there have been no well-explained ML paradigms. Deep neural networks are powerful learning machines that generalize non-linear conditions. This study presents a novel investigation of deep learning (DL) solutions for CVD/stroke risk prediction in PD patients affected by the COVID-19 framework. Method: The PRISMA search strategy was used for the selection of 292 studies closely associated with the effect of PD on CVD risk in the COVID-19 framework. We study the hypothesis that PD in the presence of COVID-19 can cause more harm to the heart and brain than in non-COVID-19 conditions. COVID-19 lung damage severity can be used as a covariate during DL training model designs. We, therefore, propose a DL model for the estimation of, (i) COVID-19 lesions in computed tomography (CT) scans and (ii) combining the covariates of PD, COVID-19 lesions, office and laboratory arterial atherosclerotic image-based biomarkers, and medicine usage for the PD patients for the design of DL point-based models for CVD/stroke risk stratification. Results: We validated the feasibility of CVD/stroke risk stratification in PD patients in the presence of a COVID-19 environment and this was also verified. DL architectures like long short-term memory (LSTM), and recurrent neural network (RNN) were studied for CVD/stroke risk stratification showing powerful designs. Lastly, we examined the artificial intelligence bias and provided recommendations for early detection of CVD/stroke in PD patients in the presence of COVID-19.Conclusion: The DL is a very powerful tool for predicting CVD/stroke risk in PD patients affected by COVID-19.

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“…For cost reasons, non-invasive protocols for carotid arteries [162] under minimal noise conditions such as harmonic and compound imaging [47,163] are favored for atherosclerosis imaging. The identification of plaque build-up is aided by automated carotid far-wall segmentation [164,165]. Figure 8a shows the step-by-step approach for risk stratification for CVD and stroke risk stratification in ED patients using the AI framework [166].…”

Section: Machine and Deep Learning Framework For Cvd Risk Assessment ...mentioning

confidence: 99%

Cardiovascular/Stroke Risk Assessment in Patients with Erectile Dysfunction—A Role of Carotid Wall Arterial Imaging and Plaque Tissue Characterization Using Artificial Intelligence Paradigm: A Narrative Review

Khanna¹,

Maindarkar

Saxena³

et al. 2022

Diagnostics

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Purpose: The role of erectile dysfunction (ED) has recently shown an association with the risk of stroke and coronary heart disease (CHD) via the atherosclerotic pathway. Cardiovascular disease (CVD)/stroke risk has been widely understood with the help of carotid artery disease (CTAD), a surrogate biomarker for CHD. The proposed study emphasizes artificial intelligence-based frameworks such as machine learning (ML) and deep learning (DL) that can accurately predict the severity of CVD/stroke risk using carotid wall arterial imaging in ED patients. Methods: Using the PRISMA model, 231 of the best studies were selected. The proposed study mainly consists of two components: (i) the pathophysiology of ED and its link with coronary artery disease (COAD) and CHD in the ED framework and (ii) the ultrasonic-image morphological changes in the carotid arterial walls by quantifying the wall parameters and the characterization of the wall tissue by adapting the ML/DL-based methods, both for the prediction of the severity of CVD risk. The proposed study analyzes the hypothesis that ML/DL can lead to an accurate and early diagnosis of the CVD/stroke risk in ED patients. Our finding suggests that the routine ED patient practice can be amended for ML/DL-based CVD/stroke risk assessment using carotid wall arterial imaging leading to fast, reliable, and accurate CVD/stroke risk stratification. Summary: We conclude that ML and DL methods are very powerful tools for the characterization of CVD/stroke in patients with varying ED conditions. We anticipate a rapid growth of these tools for early and better CVD/stroke risk management in ED patients.

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Carotid Artery Stiffening With Aging: Structural Versus Load-Dependent Mechanisms in MESA (the Multi-Ethnic Study of Atherosclerosis)

Cited by 18 publications

References 30 publications

Carotid Artery Stiffness Mechanisms Are Associated With End Organ Damage and All‐Cause Mortality: MESA (Multi‐Ethnic Study of Atherosclerosis)

Carotid Artery Stiffness Mechanisms Are Associated With End Organ Damage and All‐Cause Mortality: MESA (Multi‐Ethnic Study of Atherosclerosis)

Deep Learning Paradigm for Cardiovascular Disease/Stroke Risk Stratification in Parkinson’s Disease Affected by COVID-19: A Narrative Review

Cardiovascular/Stroke Risk Assessment in Patients with Erectile Dysfunction—A Role of Carotid Wall Arterial Imaging and Plaque Tissue Characterization Using Artificial Intelligence Paradigm: A Narrative Review

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