Evidence map›Paper›PMID 35849013›Full record

ArticleThe American journal of clinical nutrition2022

Medical conditions associated with coffee consumption: Disease-trajectory and comorbidity network analyses of a prospective cohort study in UK Biobank.

Can Hou, Yu Zeng, Wenwen Chen, Xin Han, Huazhen Yang, Zhiye Ying, Yao Hu, Yajing Sun, Yuanyuan Qu, Fang Fang and 1 more

Open access · greenAbstract read
In one paragraph

Article in The American journal of clinical nutrition, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
5.6field-weighted citation impact, top 3% of its field
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

13 citing papers in PubMed, 32 citations in OpenAlex.

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  10. Genome-wide association studies of coffee intake in UK/US participants of European ancestry uncover cohort-specific genetic associations.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2024
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

11 authors at 3 institutions in 3 countries.

Can HouWest China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China.
Yu ZengWest China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China.
Wenwen ChenDivision of Nephrology, Kidney Research Institute, State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, China.
Xin HanWest China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China.
Huazhen YangWest China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China.
Zhiye YingWest China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China.
Yao HuWest China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China.
Yajing SunWest China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China.
Yuanyuan QuWest China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China.
Fang FangInstitute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden.ORCID 0000-0002-3310-6456
Huan SongWest China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China.
Sichuan University · CNKarolinska Institutet · SEUniversity of Iceland · IS

Funding

Medical Research Council MC_PC_17228Medical Research Council MC_QA137853
6 · The paper itself

Abstract

backgroundHabitual coffee consumption has been associated with multiple health benefits. A comprehensive analysis of disease trajectory and comorbidity networks in relation to coffee consumption is, however, currently lacking.

objectivesWe aimed to comprehensively examine the health outcomes associated with habitual coffee consumption, through clarifying its disease trajectory and comorbidity networks.

methodsBased on the UK Biobank cohort, we included 395,539 individuals with available information on coffee intake collected at recruitment between 2006 and 2010. These individuals were categorized as having low (<1 cup per day), moderate (1-3 cups), and high (≥4 cups) levels of coffee intake, and were followed through 2020 to ascertain 496 medical conditions. Cox regression was used to assess the associations between high-level coffee intake and the risk of medical conditions with a prevalence ≥0.5% in the study population, after adjusting for multiple confounders, using low-level coffee intake as the reference. Disease-trajectory and comorbidity network analyses were then applied to visualize the temporal and nontemporal relationships between the medical conditions that had an inverse association with high-level coffee intake.

resultsDuring a median follow-up of 11.8 years, 31 medical conditions were found to be associated with high-level coffee intake, among which 30 showed an inverse association (HRs ranged from 0.61 to 0.94). The inverse associations were more pronounced for women, compared with men. Disease-trajectory and comorbidity network analyses of these 30 conditions identified 4 major clusters of medical conditions, mainly in the cardiometabolic and gastrointestinal systems, among both men and women; 1 cluster of medical conditions following alcohol-related disorders, primarily among men; as well as a cluster of estrogen-related conditions among women.

conclusionsHabitual coffee consumption was associated with lower risks of many medical conditions, especially those in the cardiometabolic and gastrointestinal systems and those related to alcohol use and estrogen regulation.

Indexed as

Cardiovascular DiseasesCoffeeBiological Specimen BanksCaffeineCohort StudiesComorbidityEstrogensFemaleHumansMaleProspective StudiesRisk FactorsUnited KingdomCaffeineCoffeeEstrogensalcohol-related disorderscaffeinecardiometabolic diseasescoffeeestrogen-related conditionsgastrointestinal diseases

Identifiers

PMID35849013
PMCPMC9437992
OpenAlexW4285733636

What OpenQuestion holds

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LicenceCC BY-NC
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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.