Coronary heart disease (CHD) is a leading cause of death, highlighting the importance of risk stratification and prognostic biomarkers in CHD. There is a growing body of evidence supporting the potential value of heat shock proteins (HSPs) in the pathogenesis of atherosclerosis. Here, we explored the relationship between serum anti-HSP27 levels and a genetic variant, rs2868371, in the HSB1 gene in the Stroke and Heart Atherosclerotic Disorders (MASHAD) cohort study carried out in Mashhad. A total of 8776 subjects were recruited. Anti-HSP27 levels were measured using an in-house enzyme-linked immunosorbent assay (ELISA), followed by genotyping using a TaqMan® probe-based assay. Demographic, biochemical, and hematological characteristics of the population were evaluated in all subjects. Kaplan-Meier curves were utilized, while logistic regression models were used to evaluate the relationship between genotypic frequencies and clinical characteristics of the population. No significant difference in anti-HSP27 levels was demonstrated between subjects with and without CHD. The frequencies of the CC, CG, and GG genotypes were 68%, 26.8%, and 5.2%, respectively. CAD patients with GG and GC genotypes had a lower risk of myocardial infarction (MI) compared with the reference group after adjusting for confounding factors [OR=0.27 (95% CI=0.08-0.94), P=0.040]. Our data revealed a relationship between the genetic variant in the HSB1 gene and the risk of developing CHD, supporting further research on the potential value of this emerging marker in predicting cardiovascular disease.
F. Sadabadi, M. Saberi-Karimian, H. Ghazizadeh et al.· Acta Medica Iranica· 0 citations
Objective This study aimed to develop a machine learning (ML) framework to predict incident type 2 diabetes mellitus (T2DM) using routinely available hematological and renal biomarkers, and to assess their added predictive value over conventional clinical risk factors. Methods We analyzed data from 6093 diabetes-free participants from the prospective Mashhad Stroke and Heart Atherosclerotic Disorder (MASHAD) cohort. Predictors included white blood cell count (WBC), red blood cell count (RBC), red cell distribution width (RDW), and other hematological/renal factors. We employed logistic regression and multiple ML models (Random Forest, XGBoost, LightGBM), optimized via grid search and cross-validation. Results Multivariate logistic regression identified RBC, WBC, and RDW as independent predictors of T2DM. The Random Forest model achieved the highest performance with a ROC-AUC of 0.73, an accuracy of 0.67, and correctly identified 223 of 347 incident T2DM cases Using a probability threshold of 0.20, the Random Forest model achieved a sensitivity of 0.618, a specificity of 0.707, a positive predictive value (PPV) of 0.327, and a negative predictive value (NPV) of 0.889 on the independent test set Feature importance analysis identified metabolic syndrome, BMI, uric acid, and age as the strongest contributors, while WBC, RBC, and NLR were the most influential hematological predictors. Conclusion Routine hematological indices, including RBC, WBC, and RDW, were independently associated with incident T2DM, while metabolic syndrome, BMI, uric acid, and age contributed most strongly to overall model prediction. ML provides a complementary approach for early risk stratification, although further validation is required before clinical implementation.
Niloufar Kamkar, Saleh Behzadi, Vahid Mahdavizadeh et al.· American Heart Journal Plus:...· 0 citations
