Development and validation of a continuous metabolic syndrome severity score in the Tehran Lipid and Glucose Study.
Mohammadjavad HonarvarSafdar MasoumiLadan MehranDavood KhaliliAtieh AmouzegarFereidoun AziziPublished in: Scientific reports (2023)
Metabolic syndrome (MetS), defined as the coexistence of interrelated cardiometabolic risk factors, is limited by ignoring the severity of the disease and individuals with a pre-metabolic state. We aimed to develop the first age- and sex-specific continuous MetS severity score in the adult population using confirmatory factor analysis (CFA) based on the MetS components in the Middle East. Using data from the population-based Tehran Lipid and Glucose Study (TLGS) I and II datasets, we conducted CFA of the single factor MetS on 8933 adults (20-60 years old) totally, and in age and sex subgroups. We allowed for different factor loadings across the subgroups to formulate age- and sex-specific continuous MetS severity score equations. Thereafter, we validated these equations in the dataset of TLGS III participants. Triglyceride had the highest factor loading across age and sex subgroups, indicating the most correlation with MetS. Except for women aged 40-60 years, waist circumference was the second most significant factor contributing to MetS. Systolic blood pressure was more closely related to MetS in women than in men. Systolic blood pressure and fasting plasma glucose had the weakest correlation with MetS among the 40-60 age group. Moreover, as women age, the contribution of fasting plasma glucose to MetS tended to decline, while it remained relatively constant in men. The resulting MetS severity score was correlated with age and homeostasis model assessment of insulin resistance. Furthermore, the continuous MetS severity score well predicted the traditional MetS according to receiver operating characteristic analysis in the validation dataset. The age- and sex-specific continuous MetS severity score for the West Asian adult population provides a tangible quantitative measure of MetS enabling clinicians to screen and monitor the individuals at risk and assess their metabolic trends.
Keyphrases
- blood pressure
- metabolic syndrome
- insulin resistance
- blood glucose
- polycystic ovary syndrome
- risk factors
- heart failure
- body mass index
- physical activity
- type diabetes
- heart rate
- mass spectrometry
- cardiovascular disease
- fatty acid
- weight loss
- cardiovascular risk factors
- artificial intelligence
- uric acid
- pregnancy outcomes
- machine learning
- body weight