PS-P08-5: METABOLIC SYNDROME RISK PREDICTION EQUATION AND A SIMPLE SCORE MODEL DERIVATION AND VALIDATION IN JAPANESE GENERAL POPULATION. (January 2023) - Salim, Anwar Ahmed; Kawasoe, Shin; Kubozono, Takuro; Ojima, Satoko; Kawabata, Takeko; Ikeda, Yoshiyuki; Miyahara, Hironori; Tokushige, Koichi; Ohishi, Mitsuru
Abstract : Objective: Predicting metabolic syndrome (MetS) is important for identifying high risk cardiovascular disease individuals and providing preventive interventions. We aimed to develop and validate an equation and a simple MetS score according to the Japanese MetS criteria using Japanese health checkup data. Design and Methods: In total, 54, 198 participants (age, 54.5 ± 10.1 years; men, 46.0%), with baseline and five year follow up data were randomly assigned to Derivation and Validation cohorts (ratio: 2:1). Multivariable logistic regression analysis was performed in derivation cohort and scores were assigned to factors corresponding to beta coefficients. We evaluated predictive ability of the scores using area under the curve (AUC), then applied them to validation cohort to assess reproducibility. For equation model, all variables were treated as continuous except for sex, current smoking, and alcohol consumption, which were treated as binary variables. Results: The primary model (including blood tests) ranged 0 to 27 points had an AUC of 0.81 (sensitivity: 0.81, specificity: 0.81, cut-off score: 14), and consisted of age, sex, blood pressure (BP), body mass index (BMI), serum lipids, glucose measurements, current smoking, and alcohol consumption. The simplified model (excluding blood tests) ranged 0 to 17 points with an AUC of 0.78 (sensitivity: 0.83, specificity: 0.77, cut off score: 15) and included seven variables: age, men, systolic BP, diastolic BP, BMI, alcohol consumption, and current smoking. We classified individuals with a score < 15 as low-risk MetS and ≧ 15 points as high risk MetS, respectively. The incidence of MetS increased constantly as the score increased. Furthermore, the equation model to predict MetS incidence from derivation cohort generated an AUC of 0.85 (sensitivity: 0.86, specificity: 0.55). Analysis of the validation and derivation cohorts yielded similar results. Conclusion: We developed a primary score, an equation model, and a simple score. The simple score is convenient, well validated with acceptable discrimination, and could be used for early detection of MetS in high risk individuals.