Robust metabolic syndrome risk score based on triangular areal similarity

Area of Science:

• Cardiology
• Metabolic Health
• Biostatistics

Background:

• Current metabolic syndrome (MetS) risk calculations often rely on sample-specific characteristics, limiting their universal applicability.
• There is a need for robust, sample-independent methods to accurately quantify MetS risk across diverse populations.

Purpose of the Study:

• To introduce and evaluate a novel sample-independent risk quantification method for metabolic syndrome (MetS).
• To assess the diagnostic accuracy and robustness of the proposed method compared to existing risk scores.

Main Methods:

• Development of the 'triangular areal similarity' (TAS) method using three-axis radar charts based on five MetS factors.
• Evaluation of TAS using large-scale Korean (n=72,332) and American (n=11,286) datasets, stratified by sex, age, and race.

Main Results:

• The TAS risk score showed a strong positive correlation with the number of abnormal MetS factors.
• The proposed score demonstrated high diagnostic accuracy and robustness, outperforming previous risk scores.
• The method exhibited superior performance and stability on cross-national datasets.

Conclusions:

• The novel sample-independent TAS method provides a precise and stable approach for metabolic syndrome risk prediction.
• This method has the potential to significantly enhance the accuracy of MetS risk assessment in clinical practice.