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Risk models and scores for metabolic syndrome: systematic review protocol. | LitMetric

Risk models and scores for metabolic syndrome: systematic review protocol.

BMJ Open

Institute for Health Research, Faculty of Health and Social Sciences, University of Bedfordshire, Luton, UK.

Published: September 2019

AI Article Synopsis

  • - Metabolic syndrome is a serious health issue affecting 20%-25% of adults worldwide, increasing risks for conditions like cardiovascular disease and diabetes, yet risk prediction tools are underutilized in clinical settings.
  • - The review will analyze existing literature from credible databases to evaluate the effectiveness of various risk models for predicting metabolic syndrome, employing quality assessment criteria for included studies.
  • - No ethical approval is needed since primary data won't be collected; findings will be shared through peer-reviewed journals and at relevant conferences.

Article Abstract

Introduction: Metabolic 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 Analysis: Articles 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 Dissemination: This 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 Number: CRD42019139326.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6773348PMC
http://dx.doi.org/10.1136/bmjopen-2018-027326DOI Listing

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