Literature DB >> 34222074

Validity of continuous metabolic syndrome score for predicting metabolic syndrome; a systematic review and meta-analysis.

Maryam Khazdouz1, Motahareh Hasani1, Sanaz Mehranfar2, Hanieh-Sadat Ejtahed3,4, Shirin Djalalinia5,6, Armita Mahdavi Gorabi7, Mohammad Esmaeili-Abdar8, Shahrokh Karbalahi Saleh9, Seyed Masoud Arzaghi10, Hoda Zahedi11, Amir Kasaeian12,13,14, Mostafa Qorbani15,16.   

Abstract

BACKGROUND: Nowadays, use of continuous metabolic syndrome (cMetS) score has been suggested to improve recognition of metabolic syndrome (MetS). The aim of this study was to evaluate the validity of cMetS scores for predicting MetS.
METHODS: We searched the electronic databases included MEDLINE/PubMed, Embase, ISI Web of Science, and Scopus from 1 January 1980 to 30 September 2020. Observational studies on participants with different cMetS scores were included in this meta-analysis. The sensitivity, specificity, positive likelihood ratio (LR+), negative likelihood ratio (LR) and diagnostic odds ratio (DOR) with 95% CI were calculated.
RESULTS: Ten studies involving a total of 25,073 participants were included. All studies had cross-sectional design. The pooled sensitivity and specificity of cMetS scores for predicting MetS were 0.90 (95% CI: 0.83 to 0.95) and 0.86 (95% CI: 0.83 to 0.89), respectively. Moreover, cMetS scores had the pooled LR+ of 6.5 (95% CI: 5.0 to 8.6), and a pooled (LR-) of 0.11 (95% CI: 0.063 to 0.21). The pooled DOR of cMetS scores to predict MetS were 57 (95% CI: 26 to 127).
CONCLUSIONS: The high sensitivity and specificity of cMetS scores indicates that it has a high accuracy to predict the risk of MetS. Furthermore, the cMetS scores has a good ability to rule out healthy people. STUDY REGISTRATION: This study was registered as PROSPERO CRD42020157273. © Springer Nature Switzerland AG 2021.

Entities:  

Keywords:  Continuous metabolic syndrome score (cMetS); Metabolic syndrome; Sensitivity

Year:  2021        PMID: 34222074      PMCID: PMC8212237          DOI: 10.1007/s40200-021-00771-w

Source DB:  PubMed          Journal:  J Diabetes Metab Disord        ISSN: 2251-6581


  33 in total

Review 1.  Principles and practical application of the receiver-operating characteristic analysis for diagnostic tests.

Authors:  M Greiner; D Pfeiffer; R D Smith
Journal:  Prev Vet Med       Date:  2000-05-30       Impact factor: 2.670

2.  Continuous and Dichotomous Metabolic Syndrome Definitions in Youth Predict Adult Type 2 Diabetes and Carotid Artery Intima Media Thickness: The Cardiovascular Risk in Young Finns Study.

Authors:  Costan G Magnussen; Sanith Cheriyan; Matthew A Sabin; Markus Juonala; Juha Koskinen; Russell Thomson; Michael R Skilton; Mika Kähönen; Tomi Laitinen; Leena Taittonen; Nina Hutri-Kähönen; Jorma S A Viikari; Olli T Raitakari
Journal:  J Pediatr       Date:  2015-12-09       Impact factor: 4.406

Review 3.  The prevalence of metabolic syndrome in Latin America: a systematic review.

Authors:  F Márquez-Sandoval; G Macedo-Ojeda; D Viramontes-Hörner; J D Fernández Ballart; J Salas Salvadó; B Vizmanos
Journal:  Public Health Nutr       Date:  2011-04-13       Impact factor: 4.022

4.  Prevalence of obesity and trends in the distribution of body mass index among US adults, 1999-2010.

Authors:  Katherine M Flegal; Margaret D Carroll; Brian K Kit; Cynthia L Ogden
Journal:  JAMA       Date:  2012-01-17       Impact factor: 56.272

5.  The relation of childhood BMI to adult adiposity: the Bogalusa Heart Study.

Authors:  David S Freedman; Laura Kettel Khan; Mary K Serdula; William H Dietz; Sathanur R Srinivasan; Gerald S Berenson
Journal:  Pediatrics       Date:  2005-01       Impact factor: 7.124

6.  Obesity and metabolic syndrome among a representative sample of Iranian adolescents.

Authors:  Mehryar Mehrkash; Roya Kelishadi; Sakineh Mohammadian; Firouzeh Mousavinasab; Mostafa Qorbani; Mohammad Esmaeil Fazl Hashemi; Hamid Asayesh; Parinaz Poursafa; Nina Shafa
Journal:  Southeast Asian J Trop Med Public Health       Date:  2012-05       Impact factor: 0.267

7.  Prevalence of the metabolic syndrome in American adolescents: findings from the Third National Health and Nutrition Examination Survey.

Authors:  Sarah D de Ferranti; Kimberlee Gauvreau; David S Ludwig; Ellis J Neufeld; Jane W Newburger; Nader Rifai
Journal:  Circulation       Date:  2004-10-11       Impact factor: 29.690

8.  QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies.

Authors:  Penny F Whiting; Anne W S Rutjes; Marie E Westwood; Susan Mallett; Jonathan J Deeks; Johannes B Reitsma; Mariska M G Leeflang; Jonathan A C Sterne; Patrick M M Bossuyt
Journal:  Ann Intern Med       Date:  2011-10-18       Impact factor: 25.391

9.  Population-based metabolic syndrome risk score and its determinants: The Isfahan Healthy Heart Program.

Authors:  Mohsen Hosseini; Nizal Sarrafzadegan; Roya Kelishadi; Mehri Monajemi; Sedigheh Asgary; Hossein Molavi Vardanjani
Journal:  J Res Med Sci       Date:  2014-12       Impact factor: 1.852

10.  Continuous Metabolic Syndrome Scores for Children Using Salivary Biomarkers.

Authors:  Ping Shi; J Max Goodson; Mor-Li Hartman; Hatice Hasturk; Tina Yaskell; Jorel Vargas; Maryann Cugini; Roula Barake; Osama Alsmadi; Sabiha Al-Mutawa; Jitendra Ariga; Pramod Soparkar; Jawad Behbehani; Kazem Behbehani; Francine Welty
Journal:  PLoS One       Date:  2015-09-29       Impact factor: 3.240

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