IntroductionMetabolic syndrome ‘a clustering of risk factors which includes hypertension central obesity, impaired glucose metabolism with insulin resistance and dyslipidaemia’ affects approximately 20%–25% of the global adult population. Individuals with metabolic syndrome have two to threefold risk of developing cardiovascular disease and a fivefold risk of developing developing diabetes and death from all causes. Although there is rapid proliferation of risk scores for predicting the risk of developing metabolic syndrome later in life, yet, these are seldom used in the practice. Therefore, the purpose of this review is to determine the performance of risk models and scores for predicting the metabolic syndrome.Methods and analysisArticles will be sought for from electronic databases (MEDLINE, CINAHL, PubMed and Web of Science) as well as the Cochrane Library. Further manual search of reference lists and grey literatures will be conducted. The search will cover from the start of indexing to 3 October 2018. Identified studies will be included if they fulfil the study selection criteria. Quality of studies will be appraised using suitable criteria for the risk models. The risk scores in the final sample of the review will be ranked/prioritised based on previous quality criteria for prognostic risk models. Lastly, the impact of the models will be ascertained by tracking citations on Google Scholar.Ethics and disseminationThis study does not require formal ethical approval as primary data will not be collected. The results will be disseminated through a peer-reviewed publication and relevant conference presentations.PROSPERO registration numberCRD42019139326
Purpose 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.
Background:Metabolic syndrome is linked with increased risk of cardiovascular disease, diabetes and all-cause mortality. Despite the high number of models and scores for assessing the risk of developing MetS, there is hardly any used in practical setting. Hence, we conducted a systematic review to determine the performance of risk models and scores for predicting metabolic syndrome.Methods:We systematically searched MEDLINE, CINAHL, PUBMED and Web of Science to identify studies that either derive or validate risk prediction models or scores for predicting the risk of metabolic syndrome. Data concerning models’ statistical properties as well as details of internal or external validations were extracted. Tables were used to compare various components of models and statistical properties. Finally, PROBAST was used to assess the methodological quality (risk of bias) of included studies.Results:A total of 15102 titles were scanned, 29 full papers were analysed in detail and 24 papers were included. The studies reported about the development, validation or both of 40 MetS risk models; out of these, 24 models were studied in details. There is significant heterogeneity between studies in terms of geography/demographics, data type and methodological approach. Majority of the models or risk scores were developed or validated using data from cross-sectional studies, or routine data that were often assembled for other reasons. Various combinations of risk factors (predictors) were considered significant in the respective final model. Similarly, different criteria were used in the diagnosis of MetS, but, NCEP criteria including its modified versions were by far the most widely used (32.5%). There is generally poor reporting quality across the studies, especially concerning statistical data. Any form of internal validation is either not conducted, or not reported in nearly a fifth of the studies. Only two (2) risk models or scores were externally validatedConclusions:There is an abundance of MetS models in the literature. But, their usefulness is doubtful, due to limitations in methodology, poor reporting and lack of external validation and impact studies. Therefore, researchers in the future should focus more on externally validating/ applying such models in a different setting.Protocol: The protocol of this study can be found at https://bmjopen.bmj.com/content/9/9/e027326PROSPERO registration number CRD42019139326
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