Evidence map›Paper›PMID 32788283›Full record

ArticleDiabetes care2020

Metabolomic Signatures of Long-term Coffee Consumption and Risk of Type 2 Diabetes in Women.

Dong Hang, Oana A Zeleznik, Xiaosheng He, Marta Guasch-Ferre, Xia Jiang, Jun Li, Liming Liang, A Heather Eliassen, Clary B Clish, Andrew T Chan and 9 more

Open access · bronzeAbstract read
In one paragraph

Article in Diabetes care, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
27citing papers in PubMed, 2 pooled it
5.0field-weighted citation impact, top 4% 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

27 citing papers in PubMed, 2 syntheses or guidelines pooled it, 50 citations in OpenAlex.

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  20. Machine Learning in Nutrition Research.Advances in nutrition (Bethesda, Md.) · 2022
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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

19 authors at 4 institutions in 2 countries.

Dong HangDepartment of Epidemiology and Biostatistics, Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, School of Public Health, Nanjing Medical University, Nanjing, China.
Oana A ZeleznikChanning Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA.
Xiaosheng HeClinical and Translational Epidemiology Unit and Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA.
Marta Guasch-FerreDepartment of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA.
Xia JiangProgram in Genetic Epidemiology and Statistical Genetics, Harvard T.H. Chan School of Public Health, Boston, MA.ORCID 0000-0001-8525-1404
Jun LiDepartment of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA.
Liming LiangDepartment of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA.ORCID 0000-0001-8261-3174
A Heather EliassenChanning Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA.ORCID 0000-0003-3519-8638
Clary B ClishBroad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA.
Andrew T ChanChanning Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA.ORCID 0000-0001-8259-9245
Zhibin HuDepartment of Epidemiology and Biostatistics, Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, School of Public Health, Nanjing Medical University, Nanjing, China.
Hongbing ShenDepartment of Epidemiology and Biostatistics, Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, School of Public Health, Nanjing Medical University, Nanjing, China.
Kathryn M WilsonChanning Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA.
Lorelei A MucciDepartment of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA.
Qi SunDepartment of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA.
Frank B HuDepartment of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA.ORCID 0000-0002-8480-1563
Walter C WillettDepartment of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA.
Edward L GiovannucciDepartment of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA.
Mingyang SongDepartment of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA mingyangsong@mail.harvard.edu.ORCID 0000-0002-1324-0316
Brigham and Women's Hospital · USHarvard University · USBroad Institute · USNanjing Medical University · CN

Funding

Statistical MethodsP01CA087969 · NCI · BRIGHAM AND WOMEN'S HOSPITAL · PI ROSNER, BERNARD A · 2000 to 2019
$77.8M
Risk Factors for Breast Cancer in Younger NursesR01CA050385 · NCI · HARVARD UNIVERSITY (SCH OF PUBLIC HLTH) · PI ELIASSEN, A. HEATHER, WILLETT, WALTER C. · 1989 to 2019
$32.3M
Life Course Cancer Epidemiology Cohort in WomenU01CA176726 · NCI · HARVARD UNIVERSITY D/B/A HARVARD SCHOOL OF PUBLIC HEALTH · PI ELIASSEN, A. HEATHER, WILLETT, WALTER C. · 2018 to 2025
$22.4M
Long Term Multidisciplinary Study of Cancer in Women: The Nurses Health StudyUM1CA186107 · NCI · BRIGHAM AND WOMEN'S HOSPITAL · PI ELIASSEN, A. HEATHER, STAMPFER, MEIR · 2014 to 2023
$22.3M
BIOCHEMICAL MARKERS IN THE NURSES'HEALTH STUDY COHORTR01CA049449 · NCI · BRIGHAM AND WOMEN'S HOSPITAL · PI HANKINSON, SUSAN E · 1989 to 2008
$11.3M
Inflammation and Colorectal NeoplasiaR01CA137178 · NCI · MASSACHUSETTS GENERAL HOSPITAL · PI CHAN, ANDREW T · 2009 to 2019
$6.5M
Sociodemographic Disparities in SLE Incidence: Behavioral and Psychosocial FactorsR01AR057327 · NIAMS · BRIGHAM AND WOMEN'S HOSPITAL · PI COSTENBADER, KAREN H · 2009 to 2019
$6.2M
Prospective study of biomarkers and risk factors for ALS incidence and progressioR01NS045893 · NINDS · HARVARD UNIVERSITY (SCH OF PUBLIC HLTH) · PI ASCHERIO, ALBERTO · 2003 to 2016
$4.0M
Metabolomics and risk of Parkinson's DiseaseR01NS089619 · NINDS · HARVARD SCHOOL OF PUBLIC HEALTH · PI ASCHERIO, ALBERTO · 2015 to 2019
$3.3M
Circulating plasma metabolites, diet, and risk of type 2 diabetesR01DK112940 · NIDDK · BRIGHAM AND WOMEN'S HOSPITAL · PI HU, FRANK B, MORA, SAMIA · 2018 to 2021
$2.9M
Dietary sulfur, the gut microbiome, and colorectal cancerR01CA202704 · NCI · MASSACHUSETTS GENERAL HOSPITAL · PI CHAN, ANDREW T, GARRETT, WENDY S. · 2016 to 2020
$2.8M
Psychological stress, associate biologic mediators, and ovarian cancer riskR01CA163451 · NCI · BRIGHAM AND WOMEN'S HOSPITAL · PI TWOROGER, SHELLEY S · 2012 to 2015
$1.7M
NCI NIH HHS K99 CA215314NCI NIH HHS P01 CA087969NCI NIH HHS R00 CA215314NCI NIH HHS R01 CA049449NCI NIH HHS R01 CA050385NCI NIH HHS R01 CA137178NCI NIH HHS R01 CA163451NCI NIH HHS R01 CA202704NCI NIH HHS U01 CA176726NCI NIH HHS UM1 CA186107NIAMS NIH HHS R01 AR057327NIDDK NIH HHS K24 DK098311NIDDK NIH HHS R01 DK112940NINDS NIH HHS R01 NS045893NINDS NIH HHS R01 NS089619
6 · The paper itself

Abstract

objectiveCoffee may protect against multiple chronic diseases, particularly type 2 diabetes, but the mechanisms remain unclear. RESEARCH DESIGN AND

methodsLeveraging dietary and metabolomic data in two large cohorts of women (the Nurses' Health Study [NHS] and NHSII), we identified and validated plasma metabolites associated with coffee intake in 1,595 women. We then evaluated the prospective association of coffee-related metabolites with diabetes risk and the added predictivity of these metabolites for diabetes in two nested case-control studies (

resultsOf 461 metabolites, 34 were identified and validated to be associated with total coffee intake, including 13 positive associations (primarily trigonelline, polyphenol metabolites, and caffeine metabolites) and 21 inverse associations (primarily triacylglycerols [TAGs] and diacylglycerols [DAGs]). These associations were generally consistent for caffeinated and decaffeinated coffee, except for caffeine and its metabolites that were only associated with caffeinated coffee intake. The three cholesteryl esters positively associated with coffee intake showed inverse associations with diabetes risk, whereas the 12 metabolites negatively associated with coffee (5 DAGs and 7 TAGs) showed positive associations with diabetes. Adding the 15 diabetes-associated metabolites to a classical risk factor-based prediction model increased the C-statistic from 0.79 (95% CI 0.76, 0.83) to 0.83 (95% CI 0.80, 0.86) (

conclusionsCoffee consumption is associated with widespread metabolic changes, among which lipid metabolites may be critical for the antidiabetes benefit of coffee. Coffee-related metabolites might help improve prediction of diabetes, but further validation studies are needed.

Indexed as

DietAdultCaffeineCase-Control StudiesCoffeeDiabetes Mellitus, Type 2FemaleHumansHypoglycemic AgentsMetabolomeMetabolomicsMiddle AgedProspective StudiesRisk FactorsCaffeineCoffeeHypoglycemic Agents

Identifiers

PMID32788283
PMCPMC7510042
OpenAlexW3048432031

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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.