Green tea does not appear to reduce lung cancer risk, more studies needed to understand health benefits

By Audrey Yow

- Last updated on GMT

Green tea does not appear to reduce lung cancer risk, more studies needed to understand health benefits © Getty Images
Green tea does not appear to reduce lung cancer risk, more studies needed to understand health benefits © Getty Images

Related tags Green tea Cancer Tea Nutrition Antioxidant

Green tea intake does not appear to reduce the risk of lung cancer, but researchers say more investigations are needed to understand the potential health benefits of its bioactive compounds.

Researchers used a two-sample Mendelian randomisation approach to investigate any causal link between green tea consumption (exposure) and the risk of lung cancer (outcome). Their analysis revealed no significant associations between green tea intake and any lung cancer subtypes.

“Through both primary inverse-variance weighted analyses and various sensitivity analyses, our results consistently showed no significant associations between green tea intake and lung cancer risk,”​ the researchers wrote in Frontiers in Nutrition​.

“While our study does not confirm the protective effect of green tea consumption on lung cancer at a population level, the biological plausibility for green tea's anticancer potential cannot be entirely dismissed.”

Lung cancer is the most common global cancer in terms of incidence and mortality, which makes it a public health concern. Previous studies have been done to investigate green tea consumption, but the findings were inconsistent. Thus, the researchers aimed to clarify any causal link between green tea consumption and the risk of lung cancer.

For the two-sample Mendelian randomisation approach, green tea consumption data was sourced from the UK Biobank dataset (exposure), which consists of 64,949 participants of European descent, both males and females, who reported their green tea intake. The genetic association data for various types of lung cancer were sourced from multiple databases (outcome).

The researchers then carried out a Genome-Wide Association Study (GWAS) to identify genetic variants that are associated with green tea consumption and their potential effects on lung cancer risk. Primary inverse-variance weighted (IVW) analyses and various sensitivity tests were also applied in the study.

No significant associations were found between green tea intake and any lung cancer subtypes. These findings were consistent when applying multiple Mendelian randomisation methods.

However, the researchers highlighted the substantial body of research that underscores the potential health benefits of green tea.

Green tea is rich in polyphenols, particularly a catechin known as epigallocatechin-3-gallate (EGCG), which is recognised for its antioxidant, anti-inflammatory, and anticarcinogenic properties. This has been substantiated by numerous studies that offer compelling evidence of the potential anti-cancer attributes of green tea and EGCG.

“Our conclusions stand in stark contrast to a considerable volume of prior research, which has observed a potential protective effect of green tea consumption against lung cancer, particularly among non-smokers,”​ said the researchers.

“The observed protective effect was particularly striking in Asian populations, potentially indicating genetic or lifestyle interactions.”

Despite the novel insights of this study, the researchers acknowledged that there were potential limitations.

“Green tea does not appear to offer protective benefits against lung cancer at a population level. However, lung cancer's complex aetiology and green tea's potential health benefits suggest more research is needed. Further studies should include diverse populations, improved exposure measurements and randomised controlled trials, are warranted,”​ the researchers concluded.

Source: Frontiers in Nutrition

DOI: 10.3389/fnut.2024.1265878

“Investigating the potential causal association between consumption of green tea and risk of lung cancer: a study utilizing Mendelian randomization”

Authors: Jieming Lu, Ye Lin et al​.

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