The American Heart Association, the American College of Cardiology, and the European Society of Cardiology now recognize obesity as a chronic disease—yet their cardiovascular prevention playbooks largely still rely on thresholds built in leaner study populations. In everyday practice, risk estimates are often anchored to LDL-cholesterol, blood pressure, and HbA1c, even though obesity biology frequently operates through pathways that those markers fail to fully capture.
That mismatch has consequences. BMI, visceral fat distribution, and hepatic steatosis—signals closely tied to inflammation, insulin resistance, and adverse metabolic remodeling—remain underused or excluded in most cardiovascular risk calculators. As a result, many people living with obesity are categorized as “moderate risk,” despite evidence of cardiometabolic vulnerability that can begin decades before overt diabetes or frank dyslipidemia.
A new discussion in International Journal of Obesity argues that prevention guidelines may be lagging behind therapeutic science. The authors note that contemporary diabetes recommendations already apply cardio-protective medications such as SGLT2 inhibitors and GLP-1 receptor agonists based on cardiovascular outcomes, not solely on whether a patient’s glucose levels meet a specific target.
“Carving therapy decisions strictly from glycaemic thresholds” may therefore be an overly narrow approach when the goal is cardiovascular prevention. Extending the same outcome-based logic to obesity could align treatment algorithms with the underlying mechanisms that generate cardiometabolic risk, including ectopic fat accumulation and metabolic inflammation.
The paper also highlights a shift toward more precise risk stratification. AI-derived imaging biomarkers could quantify phenotypes—such as visceral fat burden and liver fat—that correlate with atherosclerotic processes. Meanwhile, metabolomics may reveal composite metabolic signatures reflecting early vascular stress.
Additionally, polygenic risk scores offer another layer, combining inherited susceptibility with modifiable drivers. Together, these tools could identify high-risk individuals within the “moderate” category and enable earlier, mechanism-informed intervention.
The authors’ overarching point is straightforward: when risk tools ignore obesity-specific physiology, clinicians may miss windows for prevention. Updating thresholds and incorporating modern biomarker technologies could help turn recognition of obesity as a chronic disease into earlier cardiovascular action.
Subject of Research: Cardiometabolic therapy thresholds and cardiovascular risk estimation in obesity
Article Title: Rethinking cardiometabolic therapy thresholds in individuals with obesity
Article References: Khanna, S., Nerlekar, N. & Bhat, A. Rethinking cardiometabolic therapy thresholds in individuals with obesity. Int J Obes (2026). https://doi.org/10.1038/s41366-026-02160-w
Image Credits: AI Generated
DOI: 10.1038/s41366-026-02160-w
Keywords: Obesity; cardiovascular risk; LDL-C; blood pressure; HbA1c; visceral fat; hepatic steatosis; SGLT2 inhibitors; GLP-1 receptor agonists; AI imaging biomarkers; metabolomics; polygenic risk scores
