ArticleDiabetology & metabolic syndrome2026
Clustering adipokines, metabolic, oxidative stress, and inflammatory markers associated with cardiovascular disease risk in patients with metabolic syndrome.
Article in Diabetology & metabolic syndrome, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundMetabolic syndrome (MetS) is a cluster of metabolic disturbances that significantly increases the risk of cardiovascular disease (CVD). Beyond classical risk factors, adipokines, oxidative stress, and inflammatory pathways play crucial roles in the pathogenesis of CVD. This study aimed to identify adipokine- and oxidative stress-related biomarker clusters associated with cardiovascular risk score (CVRS) in individuals with MetS.
methodsA cross-sectional study was conducted on 190 adults (aged 20–50 years) diagnosed with MetS according to international criteria. Serum levels of leptin, adiponectin, visfatin, and omentin-1 were measured alongside oxidative stress markers (TOS, TAC, ox-LDL, SOD, GPx) and inflammatory biomarkers (TNF-α, IL-6, hs-CRP). CVRS was calculated using the modified McMahan risk model. Associations between biomarker clusters and CVRS were evaluated using principal component analysis (PCA).
resultsAcross CVRS quartiles, FBG, HOMA-IR, HbA1c, TG, TOS, ox-LDL, leptin, and visfatin increased significantly (p < 0.01), while HDL-C, TAC, and adiponectin decreased (p < 0.01). PCA identified five components explaining 59.6% of the total variance: oxidative stress-related, lipid profile-related, inflammation-related, adipokine-related, and glucose-related factors. All five components were independent predictors of CVRS in linear regression analysis (p < 0.05).
conclusionAdipokine imbalance—characterized by elevated leptin and visfatin and reduced adiponectin—together with oxidative and inflammatory stress, contributes significantly to cardiovascular risk in MetS. PCA-derived biomarker components offer an integrative and mechanistic insight into cardiometabolic risk stratification and may guide future biomarker-based risk assessment strategies.
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