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Can Biomarker-Based Monitoring Detect the Early Development of Diastolic Dysfunction During Chemotherapy?

Aug 2026 · Medicina · Vol 62, pp. 1634 · 0 citations · 16 references

TL;DR

The regression analyses suggest that this phenotype is multifactorial, with echocardiographic indices capturing the functional component and circulating biomarkers reflecting complementary aspects of the underlying biological response.

Abstract

Background and Objectives: Although modern oncologic therapies have substantially improved cancer survival and overall prognosis, they have also led to an increasing burden of cardiovascular toxicity. Preventing severe complications such as heart failure and death remains a major priority, while maintaining the need for effective and potentially curative cancer therapy. Careful patient monitoring and early detection of cardiovascular complications may allow the timely implementation of cardioprotective strategies and treatments that could delay or prevent myocardial toxicity. Materials and Methods: A total of 92 women with breast cancer were enrolled in a prospective observational cohort study. All patients received an anthracycline-based chemotherapy regimen, cyclophosphamide, docetaxel and trastuzumab. Biomarker assessment, including NT-proBNP, high-sensitivity cardiac troponin I (hs-cTnI), Gal-3, and GDF-15, was performed at baseline and at the initiation of trastuzumab-based therapy. A comprehensive diastolic function assessment was performed, including transmitral Doppler flow parameters and tissue velocities. Patients were followed for 12 months, and all cardiovascular events occurring during the follow-up period were recorded. Results: Biological and ecocardiographic parameters were analyzed and multiple models of prediction were made. To identify predictors of estimated left ventricular filling pressure (eLVFP), stepwise multiple linear regression analyses were performed, and four significant predictors of left ventricular end-diastolic filling pressure were identified: one echocardiographic parameter (baseline LV filling pressure) and three biological variables (changes in Gal-3, GDF-15, and hs-cTnI levels after treatment). The overall regression model was highly significant (p < 0.001) and explained 77.07% of the variance in the dependent variable. Gal-3 was the strongest predictor of left ventricular filling pressure (p < 0.001). Conclusions: The regression analyses suggest that this phenotype is multifactorial, with echocardiographic indices capturing the functional component and circulating biomarkers reflecting complementary aspects of the underlying biological response. This multimodal approach may therefore help identify patients with early treatment-related cardiac changes and potentially those at increased risk of subsequent CTRCD, particularly during a period when conventional LVEF-based surveillance may still appear reassuring.

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