gms | German Medical Science

Introduction: Obesity is a complex disorder and an important risk factor for several chronic diseases. Obesity is usually defined by anthropometric measures such as Body Mass Index, which are easy to apply but cannot adequately quantify volume and location of adipose tissue depots. However, different adipose tissue depots have different local and systemic metabolic effects, influencing overall cardiometabolic risk [1]. Therefore, distribution patterns of individual adipose tissue depots, representing subphenotypes of body composition, might represent a better measure to assess cardiometabolic risk. Individual adipose tissue depots can be quantified reliably and non-invasively by magnetic resonance imaging (MRI) [2].

This study aimed to 1) identify subphenotypes of body composition by patterns of adipose tissue distribution derived by MRI, 2) evaluate the association between these subphenotypes and overall risk, as well as individual cardiometabolic risk factors.

Methods: We used data from the KORA-MRI study, a sample from a population-based cohort where N=400 individuals without history of cardiovascular disease underwent whole-body MRI [3]. Visceral (VAT), subcutaneous (SAT), bone marrow (BMAT), cardiac, renal sinus, hepatic, pancreatic and skeletal muscle fat were measured. Overall cardiovascular risk was calculated by the SCORE2 and Framingham risk scores [4].

Subphenotypes of body composition were identified by k-means clustering on data of the 8 adipose tissue depots, standardized by sex. Cluster stability was assessed using bootstrapping and the Jaccard Index. Association between the subphenotypes and risk scores/factors was calculated by linear regression.

Results: A total of 299 individuals with complete adipose tissue data were included in the analysis (41.1 % female, 56.5 ± 9.1 years, 30.1 % with BMI ≥30 kg/m²).

We identified k=5 stable body composition subphenotypes, denoted by Roman numerals I-V. Participants with phenotype I had lower than average values of all adipose tissue depots and the lowest overall cardiovascular risk (SCORE2 median 1.8 %). Phenotype II had average values of adipose tissue depots, except for remarkably high BMAT values, and higher cardiovascular risk than phenotype I (5.3 %). Phenotype III had highest levels of BMAT, renal sinus and muscle fat of all phenotypes and highest cardiovascular risk (7.2 %).

Phenotype IV had elevated VAT, SAT, hepatic and cardiac fat, but lower BMAT, renal hilus and muscle fat than phenotype III. Cardiovascular risk was comparable between phenotypes II and IV (5.3 % vs 5.6 %). For phenotype V, levels of all adipose tissues were elevated, however most strikingly those of pancreatic fat. Cardiovascular risk was second highest (6.4 %).

Linear regression showed that compared to phenotype I (reference), both phenotype III and V were associated with a 3.8 times higher SCORE2.

Conclusion: Different subphenotypes of body composition are associated with different degrees of cardiometabolic risk. We identified a subphenotype that was characterized by higher BMAT and muscle fat and higher cardiovascular risk, although visceral and hepatic fat were not remarkably elevated. This phenotype might be difficult to detect in clinical practice, however represents a subgroup vulnerable to cardiometabolic disease.

The authors declare that they have no competing interests.

The authors declare that an ethics committee vote is not required.