Disaggregating Asian cardiovascular phenotypes through cardiac CT adipose tissue imaging

Asian populations are frequently treated as a homogeneous group in cardiovascular research, a practice that obscures substantial heterogeneity in risk, disease burden, and underlying pathophysiology [1]. In fact, growing evidence demonstrates that cardiovascular outcomes vary markedly across Asian subgroups, with consistently higher rates of atherosclerotic cardiovascular disease observed among South Asian and Filipino populations compared with East Asian groups, differences that are incompletely explained by traditional risk factors or socioeconomic variables alone [2]. Aggregation of diverse ethnicities into a single “Asian” category masks subgroup-specific trajectories shaped by genetics, migration patterns, acculturation, social determinants of health, and metabolic profiles, and risks perpetuating misclassification of both high-risk and low-risk individuals [1]. Disaggregating Asian cardiovascular data has already yielded clinically actionable insights, including the recognition of South Asian ancestry as a risk-enhancing factor in contemporary prevention guidelines [3], yet most large datasets still lack sufficient granularity to capture within-group variation. Moving beyond population averages toward subgroup-specific characterization is therefore essential not only to improve risk estimation, but also to uncover distinct biological pathways that may drive cardiovascular disease across Asian populations and inform more precise, equitable preventive strategies.

In this issue of the American Journal of Preventive Cardiology, Wang et al. [4] report a cross-sectional cardiac CT analysis of 2702 Asian patients with stable coronary artery disease from a multi-ethnic cohort in Singapore, comparing epicardial adipose tissue (EAT), coronary plaque characteristics, and perivascular adipose tissue (PVAT) across East, South, and Southeast Asian subgroups. Using non-contrast CT for EAT and coronary CT angiography for PVAT, the authors demonstrate that South Asian patients exhibit a distinct EAT phenotype characterized by lower total EAT volume but higher EAT attenuation and a greater proportion of high-density EAT, findings suggestive of a more pro-inflammatory adipose tissue profile. Notably, these differences were observed despite similar coronary plaque burden, plaque composition, and PVAT attenuation across ethnic groups, and persisted after adjustment for traditional cardiovascular risk factors, including the higher prevalence of diabetes among South Asian participants. Although the absolute differences in EAT attenuation were modest and the study lacked longitudinal outcomes, these findings highlight heterogeneity in cardiac adipose tissue biology within Asian populations and suggests that ethnic-specific cardiovascular risk may operate through mechanisms beyond conventional plaque-based imaging markers.

To interpret these findings, it is important to place them within the broader biological context of epicardial adipose tissue. EAT is increasingly recognized as a metabolically active visceral fat depot with direct paracrine and endocrine effects on the coronary vasculature and myocardium [5]. Beyond total volume, qualitative features of EAT measured by cardiac CT, particularly tissue attenuation and the proportion of higher-density fat, are thought to reflect adipose inflammation, fibrosis, and altered cellular composition [6,7]. Prior studies have linked higher EAT attenuation to adverse cardiometabolic profiles [8], coronary inflammation [7], and incident cardiovascular events [9], suggesting that EAT may capture a dimension of risk that is not fully represented by luminal stenosis or plaque burden alone. In this context, the observation by Wang et al. that South Asian patients exhibit higher EAT attenuation and a greater proportion of high-density EAT despite lower overall EAT volume aligns with emerging evidence that cardiovascular risk in certain populations may be driven less by the quantity of visceral fat and more by its inflammatory phenotype. The dissociation between EAT characteristics and plaque burden further supports the concept that EAT may reflect a broader myocardial inflammatory milieu rather than localized, plaque-driven processes.

This study provides important descriptive clarity regarding phenotypic heterogeneity in cardiac adipose tissue across Asian subgroups, demonstrating that differences in EAT biology persist even within a shared healthcare environment and after adjustment for traditional risk factors. This study builds on preexisting literature examining coronary plaques on cardiac CT in Asian subgroups [10]. At the same time, the analysis does not establish whether these EAT characteristics are causally related to cardiovascular risk, nor does it determine their prognostic significance, as the cross-sectional design and absence of longitudinal outcomes preclude inference regarding incident events, disease progression, or treatment response. The modest absolute differences in attenuation further underscore that these findings should be interpreted as hypothesis-generating rather than clinically actionable. Thus, while the study advances understanding of subgroup-specific cardiac adipose tissue phenotypes, it leaves unanswered whether EAT quality confers incremental risk beyond established imaging and clinical markers, a question that will require prospective validation and outcome-driven analyses.

From a clinical perspective, the findings by Wang et al. do not support immediate changes in risk stratification or management strategies for Asian patients with coronary artery disease. However, the study reinforces the broader principle that cardiovascular risk in Asian populations, particularly among South Asians, may not be adequately captured by traditional risk factors or plaque-centric imaging alone. In this context, qualitative or quantitative assessment of cardiac adipose tissue may eventually complement existing imaging frameworks by offering insight into inflammatory or metabolic risk phenotypes, particularly in patients whose plaque burden appears discordant with their clinical risk profile. Future research should prioritize longitudinal studies linking EAT attenuation and high-density EAT metrics to hard cardiovascular outcomes, as well as investigations integrating cardiac fat phenotyping with metabolic biomarkers, genomics, environmental and social determinants of health. Emerging therapies with anti-inflammatory and metabolic effects, including colchicine, glucagon-like peptide-1 receptor agonists, and sodium–glucose cotransporter-2 inhibitors, also warrant evaluation for their potential to modify EAT phenotypes across distinct Asian subgroups, which may help elucidate mechanisms of benefit and heterogeneity in treatment response. Importantly, while the present study relied on manual or semi-manual segmentation of EAT, automated artificial intelligence–based approaches for EAT quantification are now widely available and highly reproducible [11], enabling scalable assessment in large and longitudinal cohorts. Deeper phenotyping using radiomic features[12,13] may further refine risk characterization by capturing tissue heterogeneity not discernible with conventional measures. Collectively, such efforts will determine whether cardiac adipose tissue phenotyping remains a descriptive marker of ethnic heterogeneity or evolves into a clinically actionable tool for precision prevention.

In summary, this study underscores the importance of disaggregating Asian populations in cardiovascular research and highlights meaningful heterogeneity in cardiac adipose tissue phenotypes across Asian subgroups. By demonstrating distinct epicardial adipose tissue characteristics in South Asian patients despite similar coronary plaque burden, the findings suggest that ethnic-specific cardiovascular risk may be driven by biological mechanisms not captured by conventional plaque-based imaging. Although the results are descriptive and not yet clinically actionable, they provide a compelling rationale for integrating cardiac fat phenotyping into future longitudinal studies, therapeutic investigations, and AI-enabled imaging frameworks. Ultimately, such efforts may advance precision prevention strategies that better reflect the biological diversity within Asian populations.

Declaration of competing interest

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:

Sadeer Al-Kindi has patent #US20260038116A1 pending to Case Western Reserve University. Sadeer Al-Kindi has patent #US20250217970A1 pending to Case Western Reserve University. Sadeer Al-Kindi has patent #US20240321461A1 pending to Case Western Reserve University. Sadeer Al-Kindi has patent #US20240169525A1 pending to Case Western Reserve University. Sadeer Al-Kindi and Ron Blankstein are executive editors for AJPC Khurram Nasir is the editor in chief of AJPC If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.