Evidence map›Paper›PMID 41282206›Full record

ArticleResearch square2025

How Tea Consumption Interacts with Genetics to Affect Cardiovascular Biomarkers in Older Adults? Findings from the CLHLS.

Jiali Wang, Kaisy Xinhong Ye, Changjiang Li, Min Zou, Luwen Cao, Xiu Wang, Lirong Yu, Lina Sun, Andrea B Maier, Yanyu Wang and 4 more

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Article in Research square, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

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0 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

14 authors.

Jiali WangShandong Second Medical University.
Kaisy Xinhong YeNational University of Singapore.
Changjiang LiShandong Second Medical University.
Min ZouShandong Second Medical University.
Luwen CaoUniversity of Singapore.
Xiu WangBeijing Chuiyangliu Hospital.
Lirong YuShandong Second Medical University.
Lina SunShandong Second Medical University.
Andrea B MaierNational University of Singapore.
Yanyu WangShandong Second Medical University.
Juntang GuoShandong Second Medical University.
Yi ZengNational School of Development, Peking University.
Huashuai ChenDuke University.
Qiushi FengNational University of Singapore.

Funding

THE RANGE OF MALE-FEMALE MORTALITY DIFFERENCESP01AG031719 · NIA · DUKE UNIVERSITY · PI CHRISTENSEN, KAARE · 2009 to 2020
$10.2M
NIA NIH HHS P01 AG031719
6 · The paper itself

Abstract

Background/Objectives: Cardiovascular disease (CVD) has become the leading cause of mortality and disability worldwide. Tea is one of the most common consumed beverages worldwide. Studies have demonstrated that tea consumption has an impact on cardiovascular diseases. This study aims to examine the association between tea consumption and CVD biomarkers and investigate the potential effect of genetic variants on the relationship between tea consumption and CVD biomarkers in older adults. Methods: This prospective cohort study using four waves (2008, 2011, 2014, and 2018) of data from the Chinese Longitudinal Healthy Longevity Survey (CLHLS) to analyze the interactive effect of tea consumption and genetics on CVD biomarkers. The study sample consisted of older adults aged 65-105 from 8 longevity counties across China. The frequency and type of tea consumption and various covariates were investigated using questionnaires, seven blood biomarkers including Plasma glucose (PG),Total cholesterol(TC),Triglyceride(TG),High-sensitive C-reactive protein, (hs-CRP),High-density lipoprotein cholesterol(HDLC),Low-density lipoprotein cholesterol(LDLC) and Hemoglobin were selected from blood biochemical test, genetics were measured using the polygenic risk score(PRS) calculated by PLINK1. Generalized estimation equations (GEE) with a identity link function were adopted to estimate the effect of tea consumption and PRS on CVD biomarkers from both cross-sectional and longitudinal perspective. Results: The results showed that the consumption of tea was associated with higher levels of TC, TG, and LDLC levels, lower levels of HDLC, and age-specific effects on hs-CRP levels, and drinking tea increased TG and LDLC levels of higher PRS older adults, while lowering their HDLC and hs-CRP levels. Conclusions: The current study found that tea consumption had both beneficial and unfavorable effects on cardiovascular biomarkers in the older population. Specifically, it is protective against inflammatory related biomarkers and harmful for lipid metabolism related biomarkers, and these effects were all modified by genetic variants affecting lipid and inflammation metabolism.

Indexed as

ChinaCVDgene-tea interactionolder adultspolygenetic riskteatea consumption

Identifiers

PMID41282206
PMCPMC12633522

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.