ArticleBMC cardiovascular disorders2026
Role of combined serum homocysteine and small dense LDL-C in predicting cardiovascular events in obesity: a retrospective clinical study.
Article in BMC cardiovascular disorders, 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
backgroundTo evaluate serum homocysteine (Hcy) and small dense low-density lipoprotein cholesterol (sdLDL-C) as predictors of cardiovascular risk in obese patients and their incremental value for prediction models.
methodsA retrospective cohort study was conducted on obese patients. Baseline data and laboratory parameters, including Hcy, sdLDL-C, and traditional cardiovascular risk factors, were collected. Cardiovascular events were recorded during follow-up. Multivariable logistic regression was used to assess the independent associations of Hcy and sdLDL-C with cardiovascular risk. Model performance was assessed using receiver operating characteristic (ROC) curves, calibration plots, and decision curve analysis (DCA).
resultsCardiovascular events were strongly associated with elevated Hcy and sdLDL-C. Hcy levels showed a dose-response relationship with event rates. After adjustment for age, sex, body mass index, smoking status, hypertension, diabetes, conventional lipid profiles, high-sensitivity C-reactive protein, and estimated glomerular filtration rate, serum homocysteine (Hcy) remained independently associated with cardiovascular risk (OR = 1.90, 95% CI: 1.54–2.35, P < 0.001). sdLDL-C also showed significant predictive value (OR = 1.21, 95% CI: 1.13–1.30, P < 0.001). The traditional model’s area under the ROC curve (AUC) was 0.849. Adding Hcy or sdLDL-C individually improved discrimination, while the combined model achieved a significantly higher AUC of 0.972 (95% CI: 0.953–0.991), with superior calibration and clinical net benefit.
conclusionIn obese patients, Hcy and sdLDL-C are independent risk markers for cardiovascular events. Their combined use significantly enhances risk prediction model performance, aiding refined risk stratification.
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