Literature DB >> 29864478

Review of animal studies on the cardiovascular effects of caffeine.

Leslie A Beyer1, Mary L Hixon2.   

Abstract

To address the safety of caffeine levels in energy drinks, we previously conducted a detailed evaluation of epidemiology studies in humans consuming coffee/caffeine, in which we assessed multiple health effects (unpublished). To further evaluate the effects of caffeine on the cardiovascular system, we turned to animal studies, which often use pure caffeine (not coffee), frequently at higher doses than those typical of human exposure. We identified key scientific studies and reviews in which effects of coffee or caffeine were evaluated in animals by conducting a comprehensive PubMed literature search and analyzing the results. We found that the human equivalent dose (HED) for the no observed adverse effect level (NOAEL) for cardiovascular effects was 260 mg caffeine (2-3 cups of coffee) for a single dose of caffeine for a 70-kg adult, while the lowest observed adverse effect level (LOAEL) was 770 mg (7-8 cups of coffee) for a 70-kg adult. Overall, the doses associated with possible adverse cardiovascular effects were more than either the amount of caffeine consumed over a 24-hour period in two regular energy shots (400 mg/day) or the amount in two extra strength energy shots (460 mg/day).
Copyright © 2018. Published by Elsevier Ltd.

Entities:  

Keywords:  Caffeine; Coffee; Dietary supplements; Energy drinks; Exposure assessment; Risk assessment

Mesh:

Substances:

Year:  2018        PMID: 29864478     DOI: 10.1016/j.fct.2018.06.002

Source DB:  PubMed          Journal:  Food Chem Toxicol        ISSN: 0278-6915            Impact factor:   6.023


  2 in total

Review 1.  European Cardiac Arrhythmia Society Statement on the cardiovascular events associated with the use or abuse of energy drinks.

Authors:  Samuel Lévy; Luca Santini; Alessandro Capucci; Ali Oto; Maurizio Santomauro; Carla Riganti; Antonio Raviele; Riccardo Cappato
Journal:  J Interv Card Electrophysiol       Date:  2019-09-03       Impact factor: 1.900

2.  Near-infrared spectroscopy and machine learning-based technique to predict quality-related parameters in instant tea.

Authors:  Xiaoli Bai; Lei Zhang; Chaoyan Kang; Bingyan Quan; Yu Zheng; Xianglong Zhang; Jia Song; Ting Xia; Min Wang
Journal:  Sci Rep       Date:  2022-03-09       Impact factor: 4.379

  2 in total

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