Growth differentiation factor-15 as a predictor of acute myocardial infarction: a multivariable modeling approach

Globally, acute myocardial infarction (AMI) is a predominant cause of morbidity and mortality. Identifying reliable biomarkers to enhance risk prediction models remains a priority. This study assesses the role of growth differentiation factor-15 (GDF-15) as a predictor of AMI and its incremental value in refining current risk assessment models. A case-control study was established involving 45 AMI cases and 45 controls. Demographic, clinical, and biochemical parameters were evaluated. Logistic regression models were developed to assess the relationship between GDF-15 and AMI, adjusting for conventional risk factors and biomarkers. The prediction ability of models with and without GDF-15 was compared using the area under the curve (AUC). GDF-15 values were markedly elevated in AMI patients relative to controls. Incorporating GDF-15 into predictive models substantially improved their discriminative ability, demonstrating that GDF-15 was a robust independent predictor of AMI, enhancing diagnostic sensitivity and specificity across multiple models. Adjusting for demographic, lifestyle, and clinical risk factors, inclusion of GDF-15 led to notable AUC enhancements in Model 2 (32.88%) and Model 3 (19.66%). Models 4 and 5, which included additional biomarkers, demonstrated modest AUC improvements (2.57% and 0.61%, respectively), highlighting GDF-15's incremental value, even in models already incorporating a wide range of established biomarkers. In conclusion, GDF-15 is a robust and independent predictor of AMI, consistently improving the diagnostic performance of multivariable models. Its incorporation enhanced sensitivity, specificity, predictive values, and AUC (up to 0.999), underlining its effectiveness in risk stratification and early diagnosis of AMI.