Precision Phenotyping in Cardiometabolic Medicine: Genetic Susceptibility of Diabetes Subtypes and Coronary Artery Disease Risk

Highlights

• Recent large-scale cohort studies demonstrate that while all five adult-onset diabetes subtypes share genetic risk for incident diabetes, only the Moderate Obesity-related Diabetes (MOD) subtype is genetically and causally associated with coronary artery disease (CAD).
• Polygenic Risk Scores (PRS) for the MOD subtype provide a significant early marker for CAD risk in the general population, independent of traditional clinical diagnosis.
• Advances in multi-omics, including proteomics and retinal imaging, offer incremental prognostic value when combined with genome-wide risk scores for predicting major adverse cardiovascular events (MACE) in diabetic patients.
• Mendelian randomization studies clarify the causal roles of metabolic markers like Lipoprotein(a) and GIP, while highlighting that healthy lifestyle interventions can mitigate up to 56% of heart disease risk even in those with high genetic susceptibility.

Background: The Evolution of Diabetes Classification

For decades, adult-onset diabetes was managed as a monolithic entity—Type 2 Diabetes (T2D). However, clinical heterogeneity has long suggested that a binary classification is insufficient for risk stratification. In 2018, the introduction of five distinct clusters—Severe Insulin-Deficient Diabetes (SIDD), Severe Insulin-Resistant Diabetes (SIRD), Moderate Obesity-related Diabetes (MOD), Moderate Age-related Diabetes (MARD), and Severe Autoimmune Diabetes (SAID)—revolutionized our understanding of disease progression. Despite this clinical refinement, the extent to which these subtypes share a genetic basis with atherosclerotic complications like coronary artery disease (CAD) remained elusive until recently. Understanding these genetic intersections is critical for shifting from reactive glucose management to proactive, subtype-specific cardiovascular prevention.

Key Content: Mapping the Genetic Bridge Between Diabetes and CAD

Subtype-Specific Genetic Risk and Incident CAD

A pivotal study utilizing the Malmö Diet and Cancer cohort (N = 24,025) recently addressed whether genetic susceptibility to specific diabetes subtypes predicts future CAD (Pan et al., 2026). During a median follow-up of over 24 years, researchers utilized Polygenic Risk Scores (PRS) for all five subtypes. Notably, while all PRSs were associated with the development of diabetes, only the PRS for Moderate Obesity-related Diabetes (PRSMOD) was a significant predictor of incident CAD. Participants in the highest tertile of PRSMOD faced a 1.10-fold increased risk of developing CAD compared to the lowest tertile. This suggests that the genetic pathways leading to MOD—likely involving adiposity and lipid metabolism—are intrinsically linked to the pathophysiology of atherosclerosis, whereas pathways leading to insulin deficiency (SIDD) or age-related decline (MARD) may not exert the same direct genetic pressure on the coronary arteries.

Causal Evidence through Mendelian Randomization (MR)

To move beyond observational associations, Mendelian randomization has been employed to investigate causality. In the Pan et al. study, MR analysis using large-scale GWAS data (N = 296,525) confirmed a causal effect of the MOD subtype on CAD. This is complemented by other MR studies investigating individual metabolic factors. For instance, Lipoprotein(a) [Lp(a)] has been established as a causal risk factor for cardiovascular disease, with its effect being particularly potent in patients with diabetes (J Am Coll Cardiol, 2024). Interestingly, there is a complex feedback loop: while high Lp(a) increases CAD risk, genetically determined low Lp(a) levels (bottom 10%) are actually associated with an increased risk of T2D, illustrating a paradoxical genetic trade-off between lipid profile and glycemic stability.

The Role of Novel Biomarkers and Multi-Omics

Recent research has expanded the predictive toolkit for the diabetic heart beyond standard biomarkers like HbA1c:

• Proteomics: Large-scale proteomic risk scores derived from nearly 5,000 plasma proteins have shown the ability to improve ASCVD prediction significantly when added to clinical models and polygenic risk scores (JAMA, 2023).
• Retinal Imaging: A population cohort study (GoDARTS) demonstrated that AI-derived retinal vascular parameters (e.g., arterial fractal dimension and venous tortuosity), combined with a CHD PRS, provide incremental prognostic value over the traditional Pooled Cohort Equations (PCE) in patients with T2D (Diabetes Care, 2022).
• Choline Metabolites: MR studies have identified that the cardioprotective effects of SGLT2 inhibitors may be partially mediated by changes in choline and glycine metabolism, providing a metabolic rationale for the reduction in CAD risk observed with these therapies (Diabetes Care, 2022).

Environmental and Lifestyle Modulation

Despite the high impact of genetic susceptibility, environmental factors remain powerful modulators. A prospective nested case-control study in the Swedish Twin Registry found that while T2D increases heart disease risk fourfold, a favorable lifestyle (non-smoking, mild alcohol, physical activity, and healthy weight) can reduce this risk by 56% among diabetic patients (Diabetologia, 2021). This interaction suggests that genetic risk is not destiny; rather, it defines a threshold that can be significantly altered by behavioral interventions.

Expert Commentary: Toward Subtype-Driven Preventive Cardiology

The discovery that only the MOD subtype is genetically tied to CAD risk has profound clinical implications. It suggests that the mechanisms driving the ‘obesity-related’ cluster—such as chronic low-grade inflammation, adipokine dysregulation, and specific lipid partitioning—are the primary drivers of macrovascular risk in this population. In contrast, patients with SIDD or SIRD may require different management focuses, such as aggressive microvascular screening or focused insulin-sensitization, respectively.

However, several controversies persist. For example, the use of GIP (glucose-dependent insulinotropic peptide) as a therapeutic target is under scrutiny. While GLP-1 is established as cardioprotective, prospective studies suggest that elevated GIP levels are associated with increased cardiovascular mortality (Diabetologia, 2020), complicating the long-term outlook for dual-agonist therapies until further causal evidence is established. Furthermore, the role of low-calorie sweeteners like erythritol remains debated; while observational data suggests risk, MR analyses have failed to find a causal link between erythritol and CAD, suggesting that high circulating levels in humans might be a marker of metabolic dysfunction rather than a cause (Diabetes, 2024).

Conclusion

The landscape of diabetes management is shifting from a “one-size-fits-all” approach to a nuanced model of precision medicine. Genetic evidence now confirms that susceptibility to Moderate Obesity-related Diabetes (MOD) is a unique and early causal marker for coronary artery disease. By integrating subtype-specific polygenic risk scores with multi-omic data (proteomics, retinal imaging, and lipidomics), clinicians can better identify high-risk individuals long before the onset of symptomatic heart disease. Future research must now focus on whether subtype-specific therapeutic interventions—such as prioritizing SGLT2 inhibitors or GLP-1 receptor agonists earlier in MOD-susceptible individuals—can effectively alter the natural history of CAD in this population.

References

• Pan M, et al. Genetic Susceptibility to Diabetes Subtypes and Risk of Developing Coronary Artery Disease. Diabetes Care. 2026-03-30. PMID: 41912451.
• Ahlqvist E, et al. Novel subgroups of adult-onset diabetes and their association with outcomes: a data-driven cluster analysis of six variables. Lancet Diabetes Endocrinol. 2018;6(5):361-369.
• Bhakta S, et al. Lipoprotein(a) and Long-Term Cardiovascular Risk in a Multi-Ethnic Pooled Prospective Cohort. J Am Coll Cardiol. 2024;83(16):1511-1525. PMID: 38631771.
• Al-Sharify D, et al. SGLT2 Inhibition, Choline Metabolites, and Cardiometabolic Diseases: A Mediation Mendelian Randomization Study. Diabetes Care. 2022;45(11):2718-2728. PMID: 36161993.
• Gudmundsdottir V, et al. Evaluation of Large-Scale Proteomics for Prediction of Cardiovascular Events. JAMA. 2023;330(8):725-735. PMID: 37606673.
• Yuan S, et al. A healthy lifestyle mitigates the risk of heart disease related to type 2 diabetes: a prospective nested case-control study in a nationwide Swedish twin cohort. Diabetologia. 2021;64(3):530-539. PMID: 33169206.