Muscle and Visceral Fat: Metabolic and Inflammatory Burden
Visceral fat—a major driver of systemic inflammation and insulin resistance—is measurable in CAC scans. Increased visceral fat volume is a powerful predictor of metabolic syndrome, type 2 diabetes, and CVD. AI-CVD’s ability to quantify visceral fat improves risk stratification for these conditions. Myosteatosis, characterized by fat infiltration into skeletal muscle, is an emerging biomarker of systemic metabolic dysfunction. AI-driven measurement of thoracic skeletal muscle density from CAC scans has shown strong predictive value for HF, AF, CHD), and all-cause mortality. Recent studies demonstrate that combining myosteatosis with CAC scores amplifies risk prediction, particularly for males, making it a critical addition to AI-CVD.
Enhanced Risk Stratification with AI-CVD™
HeartLung's AI-CVD™ leverages advanced AI algorithms to accurately quantify visceral fat and measure thoracic skeletal muscle density from CAC scans. This data, combined with CAC scores and traditional risk factors such as BMI, cholesterol levels, and blood pressure, enhances the predictive accuracy for metabolic and various cardiovascular conditions, particularly in males. The comprehensive evaluation provided by AI-CVD™ enables early intervention and personalized treatment plans, helping to manage and mitigate the risks associated with high visceral fat volume, myosteatosis, and systemic metabolic dysfunction.
Clinical Implications of Visceral Fat and Myosteatosis
Visceral fat, located deep within the abdominal cavity, surrounds vital organs and contributes significantly to metabolic and inflammatory processes. Its presence is strongly associated with an increased risk of metabolic syndrome, type 2 diabetes, and cardiovascular disease (CVD). By incorporating visceral fat measurements into HeartLung's AI-CVD™ assessments, healthcare professionals can gain valuable insights into a patient's metabolic health and inflammatory burden.
Myosteatosis involves the infiltration of fat into skeletal muscle, which can significantly impact muscle function and overall metabolic health. This condition is associated with systemic metabolic dysfunction, increasing the risk of heart failure, atrial fibrillation, coronary heart disease, and all-cause mortality. By incorporating myosteatosis measurements into HeartLung's AI-CVD™ assessments, healthcare professionals can gain a deeper understanding of a patient's cardiometabolic health.
AI Measurement of Myosteatosis in Cardiac CT
Predicts Atrial Fibrillation and Heart Failure in MESA
Myosteatosis, the pathological accumulation of fat within skeletal muscle, is a critical marker of metabolic dysfunction and systemic health and is linked to the risk of metabolic dysfunction-associated steatotic liver disease (MASLD). Risk factors for myosteatosis include aging, lifestyle factors such as inactivity, type 2 diabetes mellitus (T2DM), and muscle injury. Fat infiltration into skeletal muscle is modulated by many regulators, genes, and signaling pathways. The condition is distinct from myopenia, or general loss of muscle mass, which is another common feature of aging. The relationship between myosteatosis, T2DM, and cardiovascular disease (CVD) is an area of growing interest, with accumulating evidence indicating the three are closely related. Myosteatosis is associated with subclinical coronary atherosclerosis, particularly in patients with T2DM.
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While there is no established reference standard to quantify myosteatosis, computed tomography (CT) has been the most utilized modality for myosteatosis measurement in research settings, with lower muscle radiodensity, in Hounsfield Units (HU), indicating higher fat infiltration. CT offers high spatial resolution and reproducibility, making it one of the standards for evaluating muscle composition in both research and clinical settings.
The AI-CVD initiative aims to extract all useful information from CAC scans, including coronary and non-coronary findings to improve the identification of persons at risk of a wide range of CVD and other conditions for earlier preventive efforts. Additionally, this initiative enables automated opportunistic screening in coronary CT angiography (CCTA) and low-dose lung cancer screening CT scans. We have previously published on other components of AI-CVD from CAC scans in the Multi-Ethnic Study of Atherosclerosis (MESA), including bone mineral density, cardiac chamber volumetry, and left ventricular mass measurement. We used AI-CVD algorithms to detect myosteatosis based on measurements of the mean HU of thoracic skeletal muscle in MESA CAC scans. Historically, most studies have assessed myosteatosis using abdominal CT scans, focusing on a single slice at the L3 level and analyzing a limited region of interest within the paraspinal and psoas muscles. Two recent studies have performed myosteatosis measurements in lung CT scans at the T12 and T4 vertebrae levels for lung-related diseases. However, no studies have evaluated total skeletal muscle within the cardiac CT field of view on all available slices. In this paper, we focus on the prognostic value of myosteatosis in CAC scans obtained at baseline examinations of MESA participants for incident atrial fibrillation (AF), heart failure (HF), and total CVD prediction over 15 years of follow-up.
Figure 3. Cumulative Incidence of CVD by Skeletal Muscle Density Quartiles
Figure 4. Cumulative Incidence of AF by Skeletal Muscle Density Quartiles
Figure 6a-c. Incidence of CVD, AF, and HF by Agatston CAC Score and Myosteatosis
