Association Between the Number of Metabolic Syndrome Components and Markers of Subclinical Vascular Damage: A Retrospective Cross-Sectional Study

Vollständiger Abstract

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Background: Metabolic syndrome (MetS) refers to the coexistence of cardiometabolic disorders, such as hyperglycemia, elevated blood pressure, visceral obesity, or features of atherogenic dyslipidemia. A diagnosis of MetS significantly increases the risk of cardiovascular disease (CVD). However, the exact relationship between the number of MetS components, regardless of their constellation, and features of subclinical cardiovascular dysfunction remains unclear. This study aimed to explore the relationship between the number of MetS components and features of subclinical atherosclerosis and arterial stiffness (AS) in patients without clinically overt CVD. Methods: This retrospective study included 100 patients (mean age: 58.8 ± 16.81 years), without signs of acute illness or diagnosed atherosclerotic CVD, who were admitted to a clinic for scheduled internal medicine diagnostics. Participants were assessed for MetS components and underwent cardiovascular tests, including Doppler ultrasonography of the carotid, vertebral, and lower limb arteries; measurement of intima–media thickness (IMT) in the carotid (cIMT), common femoral (cfIMT), and superficial femoral (sfIMT) arteries; and assessment of AS using carotid–femoral pulse wave velocity (cfPWV) and pulse wave analysis (PWA). Results: Univariate analysis revealed significant correlations between the number of MetS components and IMT in all vascular beds: cIMT (R = 0.371; p < 0.001), cfIMT (R = 0.343; p < 0.001), and sfIMT (R = 0.516; p < 0.001). Significant correlations were also found for several AS and PWA parameters: cfPWV (R = 0.398; p < 0.001), central pulse pressure (cPP) (R = 0.223; p < 0.026), and end-systolic pressure (ESP) (R = 0.241; p = 0.016). Meanwhile, in the multivariable analysis adjusted for sex, age, and body mass index (BMI), only the association between the subendocardial viability ratio (SEVR) and the number of MetS components was significant (β = 0.33; 95% CI 0.084–0.577; p = 0.009); however, this association does not remain significant after Benjamini–Hochberg correction (p = 0.108). Conclusions: Although the number of MetS components correlated significantly with several parameters of subclinical atherosclerosis and AS, most associations were explained by confounding variables. These results do not support the use of a simple count of MetS components as an indicator of subclinical cardiovascular dysfunction related to atherosclerosis and AS.