A structural equation approach for modeling metabolic syndrome status in an adult and older North-Eastern Portuguese population
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abstract
The metabolic syndrome (MetS) is characterized by an interrelated cardiometabolic risk factors, specifically central obesity, dysglycemia, dyslipidemia and arterial hypertension. The aim of this study was to analyze the weighting factors for modeling metabolic syndrome status (3-, 4-, and 5-MetS components) in an adult and older North-Eastern Portuguese population. A cross-sectional, observational and retrospective analysis was conducted between January 2019 and December 2020 from patients’ clinical records of 3,581 individuals with MetS condition (18–102 years). A structural equation modelling (SEM) analysis was applied using a standardized root mean square residuals (SRMR) with a path-flow method and a two-step maximum likelihood approach. MetS was diagnosed using Joint Interim Statement (JIS) criteria. Confirmatory model had a good adjustement (SRMR = 0.0334), reporting the following links for weighting factors in MetS status for overall population: waist circumference (WC) (β = 0.24, 95% CI: 0.19–0.29, p <0.001), fasting glucose (FG) (β = 0.17, 95% CI: 0.12– 0.22; p <0.001), systolic blood pressure (SBP) (β = 0.14, 95% CI: 0.09–0.19; p <0.001), dyastolic blood pressure (DBP) (β = 0.06, 95% CI: 0.01–0.11; p <0.001), high-density lipoprotein cholesterol (HDL-c) (β = 0.18, 95% CI: 0.12–0.23; p ≥0.05), and triglycerides (TG) (β = 0.05, 95% CI: 0–0.10; p ≥0.05). Weighting factors with the greatest effect were WC, FG, SBP and DBP, whereas there were no significant effects for HDL and TG. The action of low-density lipoproteins and triglyceride-rich lipoproteins cannot be discarded in the accumulation of atheroma plaques, as well as in the relationship amongst atherosclerosis and major adverse cardiovascular events (MACE). Therefore, the JIS definition has been widely debated to adding a better screening criterion for modelling the MetS diagnosis and progression using other criteria such as waist-to-height ratio (WhtR), waist-to-hip ratio (WHR), mean arterial pressure (MAP) and low-density lipoproteins (LDL) levels. Also, futures multivariate models should include exercise-related variables, i.e., frequency, intensity, time and type (FITT) principles, to measure the impact of the physical exercise on the MetS status change.
This study is a result of the project “GreenHealth-Digital strategies in biological as sets to improve well-being and promote green health” (Norte-01-0145-FEDER-000042), supported by North Portugal Regional Operational Programme (NORTE2020), under the PORTUGAL2020 Partnership Agreement, through the European Regional Development Fund (ERDF)”
