Cell-free plasma telomere length (cf-TL) shows potential as a biomarker for detecting heart failure with AI

Circulating cell-free plasma telomere length (cf-TL) is a cell-free DNA (cfDNA) that has shown its potential as a biomarker for heart diseases. A recent study has explored this blood marker’s potential role in heart diseases, specifically coronary artery disease (CAD) and heart failure (HF) (1^✔ ✔Trusted Source
Cell-free plasma telomere length correlated with the risk of cardiovascular events using machine learning classifiers

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What is cf-TL?

Cell-free DNA (cf-DNA) is the fragmented double-strand DNA released from dying cells into circulating blood which is used as biomarkers for diagnosis and treatments.

Telomeres are the repetitive DNA sequences at the end of the chromosomes to protect them from damage. cf-TLs are telomere fragments that circulate in the bloodstream. By analyzing cf-TL along with other biomarkers, researchers aimed to determine if this marker could help predict these heart conditions.

c f-TL’s Role in Heart Failure

The study found that cf-TL was significantly longer in patients with HF compared to healthy individuals, suggesting that cf-TL might play an important role in HF.

However, no such difference was observed in CAD patients. This limits cf-TL as a biomarker for CAD. When cf-TL was linked to markers of blood vessel health like nitric oxide levels and flow-mediated dilation (FMD), longer cf-TL was associated with blood vessel problems.

AI Advances in cf-TL Detection

Researchers used machine learning (ML) models along with cf-TL to improve disease detection. The models gave positive results in detecting HF with cf-TL as a key factor in prediction. However, cf-TL had minimal impact in detecting CAD. These results highlighted that cf-TL is a strong biomarker in detecting heart failure alone.

The study also concentrated on the importance of blood vessel health, as longer cf-TL was associated with impaired vascular function, a key factor in cardiovascular diseases.

The study was conducted based on retrospective data which limits the ability to predict future outcomes accurately. Another limitation is that the findings were validated within the same group of patients. So, testing in new independent populations is needed to confirm the results.

Reference:

1. Cell-free plasma telomere length correlated with the risk of cardiovascular events using machine learning classifiers – (https://www.nature.com/articles/s41598-024-76686-2)

Source-Medindia