Abstract
Evidence from observational studies for the effect of tea consumption on obesity is inconclusive. This study aimed to verify the causal association between tea consumption and obesity through a two-sample Mendelian randomization (MR) analysis in general population-based datasets. The genetic instruments, single nucleotide polymorphisms (SNPs) associated with tea consumption habits, were obtained from genome-wide association studies (GWAS): UK Biobank, Nurses’ Health Study, Health Professionals Follow-up Study, and Women’s Genome Health Study. The effect of the genetic instruments on obesity was analyzed using the UK Biobank dataset (among ∼500,000 participants). The causal relationship between tea consumption and obesity was analyzed by five methods of MR analyses: inverse variance weighted (IVW) method, MR-Egger regression method, weighted median estimator (WME), weighted mode, and simple mode. Ninety-one SNPs were identified as genetic instruments in our study. A mild causation was found by IVW (odds ratio [OR] = 0.998, 95% confidence interval [CI] = 0.996 to 1.000, p = 0.049]), which is commonly used in two-sample MR analysis, indicating that tea consumption has a statistically significant but medically weak effect on obesity control. However, the other four approaches did not show significance. Since there was no heterogeneity and pleiotropy in this study, the IVW approach has the priority of recommendation. Further studies are needed to clarify the effects of tea consumption on obesity-related health problems in detail.
Highlights
Obesity is a nutrition-related metabolic disorder caused by genetic and environmental determinants (González-Muniesa et al, 2017; Blüher, 2019)
16 single nucleotide polymorphisms (SNPs) were removed for being palindromic with intermediate allele frequencies, and one SNP was removed because of no corresponding outcome data
The causation between tea consumption and obesity was analyzed using the methods of inverse variance weighted (IVW), Mendelian randomization (MR) Egger, weighted median estimator (WME), weighted mode, and simple mode, independently
Summary
Obesity is a nutrition-related metabolic disorder caused by genetic and environmental determinants (González-Muniesa et al, 2017; Blüher, 2019). In the United States, the healthcare expense was about $1,901 per year for each obese person, which extrapolated to about $149.4 billion at the national level (Kim and Basu, 2016). Due to the continuous rise of incidence in the past 50 years, Tea Consumption and Obesity obesity has reached pandemic proportion (Blüher, 2019; Chooi et al, 2019), and is predicted to be 20% by 2025 ((NCD Risk Factor Collaboration (NCD-RisC), 2016). Obesity increases the risk of various diseases, such as type 2 diabetes, cardiovascular disease, dementia and cancers (Blüher, 2019). In spite of the crucial role of diet and exercise in the treatment of obesity, supportive herbal remedies are of increasing concern (Liang et al, 2019)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
