News

Black Tea, Citrus Products Lowers Ovarian Cancer Risk

By

Tea and citrus products are associated with a lower risk of developing ovarian cancer, according to a recent study.

Researchers at the University of East Anglia in the United Kingdom found that women who consumed food and drinks high in containing flavonols (found in tea, red wine, apples and grapes) and flavanones (found in citrus fruit and juices) significantly decrease their risk of developing epithelial ovarian cancer, the fifth-leading cause of cancer death among women.

Ovarian cancer affects more than 6,500 women in the UK each year. In the United States, about 20,000 women are diagnosed with ovarian cancer each year.

"This is the first large-scale study looking into whether habitual intake of different flavonoids can reduce the risk of epithelial ovarian cancer," researcher Aedin Cassidy, who led the study, said in a statement. "The main sources of these compounds include tea and citrus fruits and juices, which are readily incorporated into the diet, suggesting that simple changes in food intake could have an impact on reducing ovarian cancer risk."

For the study, Cassidy and her colleagues examined he dietary habits of nearly 172,000 women aged between 25 and 55 for more than three decades.

"We found that women who consume foods high in two sub-groups of powerful substances called flavonoids -- flavonols and flavanones -- had a significantly lower risk of developing epithelial ovarian cancer," Cassidy said.

In particular, researchers found that drinking just a couple of cups of black tea every day was associated with a 31 percent reduction in risk.

The research was the first to comprehensively examine the six major flavonoid subclasses present in the normal diet with ovarian cancer risk, and the first to investigate the impact of polymers and anthocyanins.

The findings are detailed in the American Journal of Clinical Nutrition.

© 2024 University Herald, All rights reserved. Do not reproduce without permission.
Join the Discussion
Real Time Analytics