Evidence map›Paper›PMID 41622288›Full record

ReviewHereditas2026

Unraveling the complexities of caffeine: metabolism, genetics, evolution, and health.

Xinyi Liu, Shuhua Xu

Abstract readReview
In one paragraph

Review in Hereditas, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Xinyi LiuCenter for Evolutionary Biology, School of Life Sciences, Fudan University, Shanghai, 200438, China.
Shuhua XuCenter for Evolutionary Biology, School of Life Sciences, Fudan University, Shanghai, 200438, China. xushua@fudan.edu.cn.

Funding

National Natural Science Foundation of China 32030020
6 · The paper itself

Abstract

Caffeine (1,3,7-trimethylxanthine) is the most widely consumed psychoactive substance globally, yet individuals differ strikingly in their physiological and health responses. These differences arise from the combined effects of genetic variation, evolutionary history, lifestyle exposures, and cultural practices. In this review, we synthesize current knowledge on the pharmacokinetics of caffeine, emphasizing the central role of the hepatic enzyme CYP1A2 and its transcriptional regulator AHR in determining metabolic rate. We summarize functional polymorphisms, haplotypes, and genome-wide association signals that underlie the spectrum from slow to rapid metabolizers, and integrate population-genetic and ancient DNA evidence to reveal how evolutionary pressures have shaped global patterns of caffeine tolerance. We further examine non-genetic modulators—including smoking, pregnancy, diet, and commonly prescribed medications—that dynamically influence CYP1A2 activity. Finally, we discuss how genetic variation interacts with coffee and tea’s diverse bioactive components to influence cardiovascular, neurological, and metabolic outcomes. By uniting pharmacogenomics, evolutionary genetics, and epidemiology, this review highlights the need for personalized caffeine intake recommendations that account for genetic background, beverage preparation methods, and individual health status.

Indexed as

CaffeineEvolution, MolecularAnimalsBiological EvolutionCoffeeCytochrome P-450 CYP1A2Genetic VariationGenome-Wide Association StudyHumansPharmacogeneticsPolymorphism, GeneticReceptors, Aryl HydrocarbonCaffeineCoffeeCYP1A2 protein, humanCytochrome P-450 CYP1A2Receptors, Aryl HydrocarbonCaffeineCardiovascular healthCYP1A2Evolutionary geneticsPharmacogenomicsTea versus coffee

Identifiers

PMID41622288
PMCPMC12954933

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.