A routine scan that millions of Americans already receive for heart disease screening may now yield far more information than previously thought. Researchers have found that artificial intelligence can analyze coronary artery calcium (CAC) scans to precisely measure the fat surrounding the heart — a biomarker that may predict cardiovascular risk with greater accuracy than conventional methods alone.
What Is Pericardial Fat — and Why Should You Care?
Pericardial fat (also called epicardial adipose tissue, or EAT) is the layer of fat that accumulates directly around the heart and within the pericardial sac. Unlike subcutaneous fat — the fat you can pinch beneath your skin — pericardial fat sits in intimate contact with the myocardium (heart muscle) and coronary arteries.
This proximity matters enormously. Research suggests that pericardial fat is not metabolically inert; it actively secretes inflammatory cytokines and adipokines that can promote atherosclerosis, coronary artery disease, and arrhythmias. A 2026 review published in the cardiology literature noted that epicardial adipose tissue volume serves as “a reliable marker of cardiovascular risk and an attractive tool for early prediction” of adverse cardiac events.
What makes pericardial fat particularly insidious is that you cannot detect it through standard metrics. A person can have a normal body mass index (BMI) and appear outwardly lean while carrying significant amounts of fat around their heart — a phenomenon sometimes called TOFI (Thin Outside, Fat Inside). Studies have consistently shown that pericardial fat correlates more strongly with cardiovascular events than total body weight or BMI alone.
How AI Unlocks Hidden Data in Existing Scans
Here is where the technology becomes compelling. Coronary artery calcium scans are already widely used to assess heart disease risk — they measure calcified plaque in coronary arteries and produce a “CAC score” that guides preventive treatment decisions. Millions of these scans are performed each year.
But until recently, the fat tissue visible on those same scan images was largely ignored. Manually measuring pericardial fat volume from CT imaging is time-consuming and technically demanding, making it impractical for routine clinical use.
Artificial intelligence changes this equation entirely. According to research reported in April 2026, AI algorithms can automatically process standard CAC scan images and extract precise pericardial fat volume measurements — in seconds, with no additional scanning required. The patient does not need to undergo any new procedures; the information has always been there, waiting in the existing imaging data.
The AI-derived pericardial fat measurements, when combined with the traditional CAC score, significantly improved cardiovascular risk stratification. Individuals who might have received a moderate or even low CAC score could be reclassified as higher-risk once their pericardial fat burden was factored in — enabling earlier, more targeted preventive interventions.
The Inflammation Connection
Understanding why pericardial fat is so dangerous requires a brief look at inflammation. Epicardial adipose tissue has a distinct metabolic profile compared to fat elsewhere in the body. It sits in direct anatomical continuity with coronary artery adventitia (outer vessel walls) and can release pro-inflammatory compounds — including tumor necrosis factor-alpha (TNF-α), interleukin-6 (IL-6), and resistin — directly into the coronary vasculature.
This local inflammatory signaling is thought to drive plaque formation and destabilization in the coronary arteries. Chronic inflammation in and around the heart may also impair electrical conduction, potentially contributing to atrial fibrillation and other arrhythmias. Multiple studies have demonstrated that elevated epicardial adipose tissue volume is an independent predictor of major adverse cardiovascular events, even after controlling for traditional risk factors like hypertension, diabetes, and smoking status.
Beyond the CAC Score: A More Complete Picture of Heart Risk
Traditional cardiovascular risk assessment relies on the Framingham Risk Score and its successors, which incorporate age, sex, blood pressure, cholesterol levels, smoking history, and diabetes status. The CAC scan added a powerful imaging dimension to this framework.
AI-assisted pericardial fat measurement represents the next evolution. Research indicates that adding pericardial fat assessment to existing risk models improves predictive accuracy — helping clinicians identify patients who might benefit from more aggressive lipid-lowering therapy, lifestyle modification programs, or closer monitoring, even when their conventional risk scores appear reassuring.
A 2026 study in the cardiology literature found that triglyceride-glucose index combined with epicardial adipose tissue volume provided additive predictive value for adverse cardiac events following bypass surgery — suggesting that fat-based biomarkers hold utility across multiple clinical contexts, from primary prevention to post-surgical care.
What Raises Pericardial Fat Levels?
Several well-established factors contribute to pericardial fat accumulation:
- Visceral obesity — Abdominal fat and pericardial fat tend to track together; reducing central adiposity generally reduces pericardial fat as well.
- Insulin resistance and metabolic syndrome — Impaired glucose metabolism strongly promotes ectopic fat deposition, including around the heart.
- Physical inactivity — Sedentary behavior is independently associated with higher epicardial fat volume.
- Poor diet quality — Diets high in refined carbohydrates, saturated fat, and ultra-processed foods promote systemic inflammation and ectopic fat storage.
- Sleep disruption — Chronic poor sleep has been linked to increased visceral and pericardial fat accumulation.
Lifestyle Strategies Backed by Research
Studies suggest several approaches may help reduce epicardial fat burden, though individuals should consult their healthcare provider before making significant changes to their health regimen:
- Aerobic exercise — Research indicates that regular cardiovascular exercise reduces epicardial adipose tissue volume independently of total weight loss. Even modest reductions in pericardial fat may translate to lower inflammatory burden.
- Mediterranean diet patterns — Anti-inflammatory dietary patterns rich in olive oil, fish, legumes, and vegetables have been associated with lower visceral fat and improved cardiac biomarkers.
- Caloric restriction and weight management — Intentional weight loss reliably reduces pericardial fat; even a 5-10% reduction in body weight can meaningfully improve cardiac metabolic markers.
- Omega-3 fatty acids — Evidence suggests that omega-3s from fatty fish or supplementation may help modulate inflammation in epicardial adipose tissue, though more research is needed.
What This Means for the Future of Heart Health
The integration of AI into cardiac imaging represents a shift from reactive to proactive cardiovascular medicine. By extracting clinically actionable data from scans that patients are already receiving, AI-assisted pericardial fat measurement could help close the gap between risk and detection — identifying vulnerable individuals years before a heart attack or stroke occurs.
For healthcare providers, this technology offers a path toward more personalized risk stratification without adding cost or radiation exposure. For patients, it means that the routine CAC scan they may already be scheduled for could soon reveal a far richer picture of their cardiac health than ever before.
Research in this area is advancing rapidly, and larger clinical trials are expected to further validate pericardial fat as a standard component of cardiovascular risk assessment. Experts suggest that as AI tools become more widely validated and integrated into radiology workflows, measuring heart fat could become as routine as measuring the CAC score itself.
Disclosure: This content is for informational purposes only and is not medical advice. Always consult a qualified healthcare provider before making changes to your health regimen.