Abstract:
Background: A coronary artery calcium (CAC) score of zero is widely used to defer further cardiac testing, on the assumption that it reliably excludes clinically significant coronary artery disease (CAD). However, CAC detects only calcified plaque and cannot identify non-calcified disease, which may be more prevalent in certain risk-factor subgroups. We evaluated five traditional cardiovascular risk factors to determine whether any is associated with coronary plaque burden despite CAC = 0.
Methods: We analysed 573 patients with CAC = 0 from a coronary CT angiography registry (n = 1293 total). Two segment-level outcomes were derived: a three-tier worst-segment stenosis severity category (Normal [0%] / Non-obstructive [<50%] / Obstructive [≥50%]) and the Segment Involvement Score (SIS), a count of segments with any detectable plaque. Five risk factors (diabetes, hypertension, hypercholesterolaemia, family history of CAD, smoking) were screened univariably against both outcomes using Fisher's test and Mann-Whitney U test respectively. The risk factor showing significant association was carried forward to multivariable modelling (ordinal logistic regression for severity; negative binomial regression for SIS), adjusted for age and sex.
Results: Diabetes was the only risk factor significantly associated with both outcomes (stenosis severity p=0.001; SIS p=0.003). Hypertension, hypercholesterolaemia, family history, and smoking showed no significant association. After adjusting for age and sex, diabetes remained independently associated with worse severity (OR 2.55, 95% CI [1.31–4.76], p = 0.004) and greater disease extent (IRR 3.29, 95% CI [1.86–5.90], p < 0.0001).
Conclusion: Among traditional cardiovascular risk factors, diabetes was independently associated with coronary plaque burden in patients with CAC = 0 after adjustment for age and sex. This suggests a CAC of zero may be less reassuring in diabetic patients, although prospective studies are required before recommending routine CCTA referral on the basis of diabetes alone.
Biography:
Lathiksha Babu is an MBBS student at University College London (UCL) and holds an iBSc in Clinical Neurology. Her research interests include clinical neuroscience, cardiovascular imaging, and the application of artificial intelligence in medicine. She has contributed to projects investigating the cognitive effects of antiseizure medications in nonepileptic populations, AI-assisted coronary artery disease screening, and neuromodulation for postural orthostatic tachycardia syndrome. She is particularly interested in translational research that bridges emerging technologies with clinical practice to improve patient outcomes and advance evidence-based medicine.
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