Diabetes, Metabolic Syndrome and Obesity (Apr 2022)
Development and Validation of a Simple Risk Model for Predicting Metabolic Syndrome (MetS) in Midlife: A Cohort Study
Abstract
Musa S Ibrahim, Dong Pang, Gurch Randhawa, Yannis Pappas Institute for Health Research, University of Bedfordshire, Putteridge Bury Luton, Bedfordshire, LU2 8LE, EnglandCorrespondence: Musa S Ibrahim, Tel +447918698231, Email [email protected]: To develop and validate a simple risk model for predicting metabolic syndrome in midlife using a prospective cohort data.Design: Prospective cohort study.Participants: A total of 7626 members of the 1958 British birth cohort (individuals born in the first week of March 1958) participated in the biomedical survey at age 45 and have completed information on metabolic syndrome.Methods: Variables utilised were obtained prospectively at birth, 7, 16, 23 and 45 years. Multivariable logistic regression was used to develop a total of ten (10) MetS risk prediction models taking the life course approach. Measures of discrimination and calibration were used to evaluate the performance of the models. A pragmatic criteria developed was used to select one model with the most potential to be useful. The internal validity (overfitting) of the selected model was assessed using bootstrap technique of Stata.Main Outcome Measure: Metabolic syndrome was defined based on the NCEP-ATP III clinical criteria.Results: There is high prevalence of MetS among the cohort members (19.6%), with males having higher risk as compared to females (22.8% vs 16.4%, P < 0.001). Individuals with MetS are more likely to have higher levels of HbA1c and low HDL-cholesterol. Similarly, regarding the individual components of MetS, male cohort members are more likely to have higher levels of glycaemia (HbA1c), BP and serum triglycerides. In contrast, female cohort members have lower levels of HDL-cholesterol and higher levels of waist circumference. Furthermore, a total of ten (10) MetS risk prediction models were developed taking the life course approach. Of these, one model with the most potential to be applied in practical setting was selected. The model has good accuracy (AUROC 0.91 (0.90, 0.92)), is well calibrated (Hosmer-Lemeshow 6.47 (0.595)) and has good internal validity.Conclusion: Early life factors could be included in a risk model to predict MetS in midlife. The developed model has been shown to be accurate and has good internal validity. Therefore, interventions targeting socioeconomic inequality could help in the wider prevention of MetS. However, the validity of the developed model needs to be further established in an external population.Keywords: metabolic syndrome, prediction model, risk score, development and validation of risk model, 1958 British birth cohort, national child development study
