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Structural Equation Modelling for Predicting the Relative Contribution of Each Component in the Metabolic Syndrome Status Change

Author

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  • José E. Teixeira

    (Research Centre in Sports Sciences, Health and Human Development (CIDESD), 5001-801 Vila Real, Portugal
    Department of Sport Sciences, Instituto Politécnico de Bragança (IPB), 5300-253 Bragança, Portugal)

  • José A. Bragada

    (Research Centre in Sports Sciences, Health and Human Development (CIDESD), 5001-801 Vila Real, Portugal
    Department of Sport Sciences, Instituto Politécnico de Bragança (IPB), 5300-253 Bragança, Portugal)

  • João P. Bragada

    (North East Local Health Unit (ULSNE)—Health Care Unit of Santa Maria, 5301-852 Bragança, Portugal)

  • Joana P. Coelho

    (North East Local Health Unit (ULSNE)—Health Care Unit of Santa Maria, 5301-852 Bragança, Portugal)

  • Isabel G. Pinto

    (North East Local Health Unit (ULSNE)—Health Care Unit of Santa Maria, 5301-852 Bragança, Portugal)

  • Luís P. Reis

    (North East Local Health Unit (ULSNE)—Health Care Unit of Santa Maria, 5301-852 Bragança, Portugal)

  • Paula O. Fernandes

    (Applied Management Research Unit (UNIAG), Instituto Politécnico de Bragança (IPB), 5300-253 Bragança, Portugal)

  • Jorge E. Morais

    (Research Centre in Sports Sciences, Health and Human Development (CIDESD), 5001-801 Vila Real, Portugal
    Department of Sport Sciences, Instituto Politécnico de Bragança (IPB), 5300-253 Bragança, Portugal)

  • Pedro M. Magalhães

    (Department of Sport Sciences, Instituto Politécnico de Bragança (IPB), 5300-253 Bragança, Portugal)

Abstract

Understanding the factor weighting in the development of metabolic syndrome (MetS) may help to predict the progression for cardiovascular and metabolic diseases. Thus, the aim of this study was to develop a confirmatory model to describe and explain the direct and indirect effect of each component in MetS status change. A total of 3581 individuals diagnosed with MetS, aged 18–102 years, were selected between January 2019 and December 2020 from a community-representative sample of Portuguese adults in a north-eastern Portuguese region to test the model’s goodness of fit. A structural equation modelling (SEM) approach and a two-way ANOVA (age × body composition) were performed to compare the relative contribution of each MetS component using joint interim statement (JIS). Waist circumference (β = 0.189–0.373, p < 0.001), fasting glucose (β = 0.168–0.199, p < 0.001) and systolic blood pressure (β = 0.140–0.162, p < 0.001) had the highest direct effect on the change in MetS status in the overall population and concerning both sexes. Moreover, diastolic blood pressure (DBP), triglycerides (TG) and high-density lipoprotein cholesterol (HDL-c) had a low or non-significant effect. Additionally, an indirect effect was reported for age and body composition involving the change in MetS status. The findings may suggest that other components with higher specificity and sensitivity should be considered to empirically validate the harmonised definition of MetS. Current research provides the first multivariate model for predicting the relative contribution of each component in the MetS status change, specifically in Portuguese adults.

Suggested Citation

  • José E. Teixeira & José A. Bragada & João P. Bragada & Joana P. Coelho & Isabel G. Pinto & Luís P. Reis & Paula O. Fernandes & Jorge E. Morais & Pedro M. Magalhães, 2022. "Structural Equation Modelling for Predicting the Relative Contribution of Each Component in the Metabolic Syndrome Status Change," IJERPH, MDPI, vol. 19(6), pages 1-14, March.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:6:p:3384-:d:770248
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    References listed on IDEAS

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    1. Mahdieh Khodarahmi & Mohammad Asghari-Jafarabadi & Mahdieh Abbasalizad Farhangi, 2019. "A structural equation modeling approach for the association of a healthy eating index with metabolic syndrome and cardio-metabolic risk factors among obese individuals," PLOS ONE, Public Library of Science, vol. 14(7), pages 1-20, July.
    2. Jae Seung Chang & Jun Namkung, 2021. "Effects of Exercise Intervention on Mitochondrial Stress Biomarkers in Metabolic Syndrome Patients: A Randomized Controlled Trial," IJERPH, MDPI, vol. 18(5), pages 1-19, February.
    3. Muhammad Daniel Azlan Mahadzir & Kia Fatt Quek & Amutha Ramadas, 2020. "Process Evaluation of a Nutrition and Lifestyle Behavior Peer Support Program for Adults with Metabolic Syndrome," IJERPH, MDPI, vol. 17(8), pages 1-13, April.
    4. Alba Marcos-Delgado & Natalia Hernández-Segura & Tania Fernández-Villa & Antonio J. Molina & Vicente Martín, 2021. "The Effect of Lifestyle Intervention on Health-Related Quality of Life in Adults with Metabolic Syndrome: A Meta-Analysis," IJERPH, MDPI, vol. 18(3), pages 1-14, January.
    5. Amal F. Alshammary & Khalid Khalaf Alharbi & Naif Jameel Alshehri & Vishal Vennu & Imran Ali Khan, 2021. "Metabolic Syndrome and Coronary Artery Disease Risk: A Meta-Analysis of Observational Studies," IJERPH, MDPI, vol. 18(4), pages 1-16, February.
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    Cited by:

    1. Sunghyun Hong & Minsuk Oh & Youngwon Kim & Justin Y. Jeon, 2022. "Association of Absolute and Relative Handgrip Strength with Prevalent Metabolic Syndrome in Adults: Korea National Health and Nutrition Examination Survey 2014–2018," IJERPH, MDPI, vol. 19(19), pages 1-14, October.
    2. Diana Bernardo & Carlos Carvalho & Raquel Leirós-Rodríguez & Jorge Mota & Paula Clara Santos, 2023. "Comparison of the Portuguese Version of the Pregnancy Physical Activity Questionnaire (PPAQ) with Accelerometry for Classifying Physical Activity among Pregnant Women with Obesity," IJERPH, MDPI, vol. 20(2), pages 1-11, January.
    3. Pedro Forte & Luís Branquinho & Ricardo Ferraz, 2022. "The Relationships between Physical Activity, Exercise, and Sport on the Immune System," IJERPH, MDPI, vol. 19(11), pages 1-6, June.

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