Metabolic health and cardiometabolic risk clusters help remodel cardiometabolic disease prognosis

Lean, metabolically unhealthy people have a risk of cardiovascular disease (CVD) that is higher than the risk seen in obese, metabolically healthy people. Recently, new cluster analyzes (computer-based grouping of people) have also identified large heterogeneity in the risk of type 2 diabetes and CVD and their response to treatment.

These findings reveal that there may be a vast yet undiscovered treasure to be raised in the field of cardiometabolic research. In a review article in The Lancet Diabetes & EndocrinologyNorbert Stephan from Helmholtz Munich, the German Center for Diabetes Research (DZD) and the University of Tübingen, and Matthias B. Schulze from the German Institute of Human Nutrition Potsdam-Rehbruecke and DZD highlight how these new risk stratification concepts can help better implementation of precision medicine in clinical practice.

Among the top 20 global risk factors for years of life lost in 2040, the three metabolic risks—high blood pressure, high BMI, and high fasting plasma glucose—will be the largest risk variables. Based on these and other established risk factors, such as low HDL-cholesterol and high triglycerides, the concept of metabolic health has attracted much attention in the scientific community. It focuses on the aggregation of important risk factors, which allows the identification of impaired metabolic health.

To date, in most of the more than 1,000 studies addressing this topic, people are considered metabolically healthy if they have less than 2 of the following metabolic risk factors—high blood pressure, high plasma glucose, low HDL-cholesterol, and high triglycerides, or pharmacological treatments for these conditions—exist. Thus, subphenotypes, such as metabolically unhealthy normal weight (MUHNW) and metabolically healthy obese (MHO) individuals, were identified that differed greatly in their CVD risk.

In a meta-analysis, Matthias Schulze, Norbert Stefan and colleagues found that compared to metabolically healthy normal weight (MHNW) people, the risk of CVD was increased by 45% in people with MHO and by 100% in people with MUHNW. In their current review article, Norbert Stephan and Matthias Schulze not only summarize the knowledge of these relationships, but also highlight their new definition of metabolic health.

Taking into account the risk factors hypertension, diabetes and high waist-to-hip ratio, they found in two very large studies (US National Health and Nutrition Examination Survey III and UK Biobank) that the risk of CVD mortality increased by 100% in people with MUHNW but not elevated in people with MHO. Matthias Schulze emphasizes that “these data reveal the importance of considering the impact of body fat distribution in the definition of metabolic health.”

Norbert Stephan adds, “Do the new cardiometabolic risk clusters also help to identify subgroups of people at different risk of cardiometabolic diseases?” To answer this question, the authors of this review article discuss findings from the most important dimensionality reduction approaches of the data, which can be summarized under the term “cluster analysis”.

These studies were primarily conducted in patients with diabetes or in people at risk of type 2 diabetes. Cluster approaches are also based on routinely available clinical variables, but may include more complex data such as genetics. Among the subgroups resulting from these cluster analyzes are people who have predominantly low insulin secretion, insulin resistance, fatty liver, visceral obesity, mild age-related diabetes, mild obesity-related diabetes, or other, more complex phenotypes.

Norbert Stephan and Matthias Schulze conclude: “In terms of cardiometabolic risk stratification, both the metabolic health concept and cluster approaches are not considered superior to established risk prediction models.” However, both approaches may be informative to better predict cardiometabolic risk in subgroups, such as individuals in different BMI categories or people with type 2 diabetes.”

They also emphasize that applicability of the concepts by treating physicians and communication of cardiometabolic risk with patients may be easier for the concept of metabolic health. The authors then point out that being metabolically healthy or unhealthy, or being assigned to a specific cardiometabolic risk cluster, will in most cases be a transitional distribution.

Furthermore, they conclude that approaches to identify cardiometabolic risk clusters have provided evidence that they can be used to allocate individuals to specific pathophysiological risk groups. The extent to which this allocation may improve risk assessment and treatment response remains to be carefully investigated.

Provided by the German Diabetes Research Center DZD