Evidence map›Paper›PMID 25762808›Full record

ArticleThe American journal of clinical nutrition2015

Serum biomarkers of habitual coffee consumption may provide insight into the mechanism underlying the association between coffee consumption and colorectal cancer.

Kristin A Guertin, Erikka Loftfield, Simina M Boca, Joshua N Sampson, Steven C Moore, Qian Xiao, Wen-Yi Huang, Xiaoqin Xiong, Neal D Freedman, Amanda J Cross and 1 more

Registry-linked trialAbstract readMulticenter Study
PubMed Full text at PMC Publisher
In one paragraph

Article in The American journal of clinical nutrition, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT00002540 (Prostate, Lung, Colorectal, and Ovarian), which is not on this map. Cited by 62 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
62citing papers in PubMed, 2 pooled it
13.5field-weighted citation impact, top 1% 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.

NCT00002540 naactive not recruitingnot on this map

Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial

TypeinterventionalSponsorNational Cancer Institute (NCI)Ran1993 to 2027Enrolled76,685ConditionsProstate CarcinomaArmsLaboratory Biomarker Analysis, Medical Examination Assessment, Screening Questionnaire Administration
3 · Its place in the literature

Who cites it

62 citing papers in PubMed, 2 syntheses or guidelines pooled it, 136 citations in OpenAlex.

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  14. Characteristics of Cancer Epidemiology Studies That Employ Metabolomics: A Scoping Review.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2023
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2 more citing papers are in PubMed but not listed here.

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

11 authors at 1 institution in 2 countries.

Kristin A GuertinFrom the Nutritional Epidemiology Branch (KAG, EL, SCM, QX, NDF, and RS), the Biostatistics Branch (JNS), and the Occupational and Environmental Epidemiology Branch (W-YH), Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Department of Health and Human Services, Bethesda, MD; the Innovation Center for Biomedical Informatics and Department of Oncology, Georgetown University Medical Center, Washington, DC (SMB); Information Management Services Inc., Silver Spring, MD (XX); and the Department of Epidemiology and Biostatistics, School of Public Health, Faculty of Medicine, Imperial College London, St. Mary's Campus, Norfolk Place, London, United Kingdom (AJC).
Erikka LoftfieldFrom the Nutritional Epidemiology Branch (KAG, EL, SCM, QX, NDF, and RS), the Biostatistics Branch (JNS), and the Occupational and Environmental Epidemiology Branch (W-YH), Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Department of Health and Human Services, Bethesda, MD; the Innovation Center for Biomedical Informatics and Department of Oncology, Georgetown University Medical Center, Washington, DC (SMB); Information Management Services Inc., Silver Spring, MD (XX); and the Department of Epidemiology and Biostatistics, School of Public Health, Faculty of Medicine, Imperial College London, St. Mary's Campus, Norfolk Place, London, United Kingdom (AJC).
Simina M BocaFrom the Nutritional Epidemiology Branch (KAG, EL, SCM, QX, NDF, and RS), the Biostatistics Branch (JNS), and the Occupational and Environmental Epidemiology Branch (W-YH), Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Department of Health and Human Services, Bethesda, MD; the Innovation Center for Biomedical Informatics and Department of Oncology, Georgetown University Medical Center, Washington, DC (SMB); Information Management Services Inc., Silver Spring, MD (XX); and the Department of Epidemiology and Biostatistics, School of Public Health, Faculty of Medicine, Imperial College London, St. Mary's Campus, Norfolk Place, London, United Kingdom (AJC).
Joshua N SampsonFrom the Nutritional Epidemiology Branch (KAG, EL, SCM, QX, NDF, and RS), the Biostatistics Branch (JNS), and the Occupational and Environmental Epidemiology Branch (W-YH), Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Department of Health and Human Services, Bethesda, MD; the Innovation Center for Biomedical Informatics and Department of Oncology, Georgetown University Medical Center, Washington, DC (SMB); Information Management Services Inc., Silver Spring, MD (XX); and the Department of Epidemiology and Biostatistics, School of Public Health, Faculty of Medicine, Imperial College London, St. Mary's Campus, Norfolk Place, London, United Kingdom (AJC).
Steven C MooreFrom the Nutritional Epidemiology Branch (KAG, EL, SCM, QX, NDF, and RS), the Biostatistics Branch (JNS), and the Occupational and Environmental Epidemiology Branch (W-YH), Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Department of Health and Human Services, Bethesda, MD; the Innovation Center for Biomedical Informatics and Department of Oncology, Georgetown University Medical Center, Washington, DC (SMB); Information Management Services Inc., Silver Spring, MD (XX); and the Department of Epidemiology and Biostatistics, School of Public Health, Faculty of Medicine, Imperial College London, St. Mary's Campus, Norfolk Place, London, United Kingdom (AJC).
Qian XiaoFrom the Nutritional Epidemiology Branch (KAG, EL, SCM, QX, NDF, and RS), the Biostatistics Branch (JNS), and the Occupational and Environmental Epidemiology Branch (W-YH), Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Department of Health and Human Services, Bethesda, MD; the Innovation Center for Biomedical Informatics and Department of Oncology, Georgetown University Medical Center, Washington, DC (SMB); Information Management Services Inc., Silver Spring, MD (XX); and the Department of Epidemiology and Biostatistics, School of Public Health, Faculty of Medicine, Imperial College London, St. Mary's Campus, Norfolk Place, London, United Kingdom (AJC).
Wen-Yi HuangFrom the Nutritional Epidemiology Branch (KAG, EL, SCM, QX, NDF, and RS), the Biostatistics Branch (JNS), and the Occupational and Environmental Epidemiology Branch (W-YH), Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Department of Health and Human Services, Bethesda, MD; the Innovation Center for Biomedical Informatics and Department of Oncology, Georgetown University Medical Center, Washington, DC (SMB); Information Management Services Inc., Silver Spring, MD (XX); and the Department of Epidemiology and Biostatistics, School of Public Health, Faculty of Medicine, Imperial College London, St. Mary's Campus, Norfolk Place, London, United Kingdom (AJC).
Xiaoqin XiongFrom the Nutritional Epidemiology Branch (KAG, EL, SCM, QX, NDF, and RS), the Biostatistics Branch (JNS), and the Occupational and Environmental Epidemiology Branch (W-YH), Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Department of Health and Human Services, Bethesda, MD; the Innovation Center for Biomedical Informatics and Department of Oncology, Georgetown University Medical Center, Washington, DC (SMB); Information Management Services Inc., Silver Spring, MD (XX); and the Department of Epidemiology and Biostatistics, School of Public Health, Faculty of Medicine, Imperial College London, St. Mary's Campus, Norfolk Place, London, United Kingdom (AJC).
Neal D FreedmanFrom the Nutritional Epidemiology Branch (KAG, EL, SCM, QX, NDF, and RS), the Biostatistics Branch (JNS), and the Occupational and Environmental Epidemiology Branch (W-YH), Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Department of Health and Human Services, Bethesda, MD; the Innovation Center for Biomedical Informatics and Department of Oncology, Georgetown University Medical Center, Washington, DC (SMB); Information Management Services Inc., Silver Spring, MD (XX); and the Department of Epidemiology and Biostatistics, School of Public Health, Faculty of Medicine, Imperial College London, St. Mary's Campus, Norfolk Place, London, United Kingdom (AJC).
Amanda J CrossFrom the Nutritional Epidemiology Branch (KAG, EL, SCM, QX, NDF, and RS), the Biostatistics Branch (JNS), and the Occupational and Environmental Epidemiology Branch (W-YH), Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Department of Health and Human Services, Bethesda, MD; the Innovation Center for Biomedical Informatics and Department of Oncology, Georgetown University Medical Center, Washington, DC (SMB); Information Management Services Inc., Silver Spring, MD (XX); and the Department of Epidemiology and Biostatistics, School of Public Health, Faculty of Medicine, Imperial College London, St. Mary's Campus, Norfolk Place, London, United Kingdom (AJC).
Rashmi SinhaFrom the Nutritional Epidemiology Branch (KAG, EL, SCM, QX, NDF, and RS), the Biostatistics Branch (JNS), and the Occupational and Environmental Epidemiology Branch (W-YH), Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Department of Health and Human Services, Bethesda, MD; the Innovation Center for Biomedical Informatics and Department of Oncology, Georgetown University Medical Center, Washington, DC (SMB); Information Management Services Inc., Silver Spring, MD (XX); and the Department of Epidemiology and Biostatistics, School of Public Health, Faculty of Medicine, Imperial College London, St. Mary's Campus, Norfolk Place, London, United Kingdom (AJC).
Georgetown University · US

Funding

Physical Activity, Energy Balance, and CancerZIACP010197 · NCI · DIVISION OF CANCER EPIDEMIOLOGY AND GENETICS · PI ALBANES, DEMETRIUS · 2009 to 2025
$17.5M
Intramural NIH HHS
6 · The paper itself

Abstract

backgroundCoffee intake may be inversely associated with colorectal cancer; however, previous studies have been inconsistent. Serum coffee metabolites are integrated exposure measures that may clarify associations with cancer and elucidate underlying mechanisms.

objectivesOur aims were 2-fold as follows: 1) to identify serum metabolites associated with coffee intake and 2) to examine these metabolites in relation to colorectal cancer.

designIn a nested case-control study of 251 colorectal cancer cases and 247 matched control subjects from the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial, we conducted untargeted metabolomics analyses of baseline serum by using ultrahigh-performance liquid-phase chromatography-tandem mass spectrometry and gas chromatography-mass spectrometry. Usual coffee intake was self-reported in a food-frequency questionnaire. We used partial Pearson correlations and linear regression to identify serum metabolites associated with coffee intake and conditional logistic regression to evaluate associations between coffee metabolites and colorectal cancer.

resultsAfter Bonferroni correction for multiple comparisons (P = 0.05 ÷ 657 metabolites), 29 serum metabolites were positively correlated with coffee intake (partial correlation coefficients: 0.18-0.61; P < 7.61 × 10(-5)); serum metabolites most highly correlated with coffee intake (partial correlation coefficients >0.40) included trigonelline (N'-methylnicotinate), quinate, and 7 unknown metabolites. Of 29 serum metabolites, 8 metabolites were directly related to caffeine metabolism, and 3 of these metabolites, theophylline (OR for 90th compared with 10th percentiles: 0.44; 95% CI: 0.25, 0.79; P-linear trend = 0.006), caffeine (OR for 90th compared with 10th percentiles: 0.56; 95% CI: 0.35, 0.89; P-linear trend = 0.015), and paraxanthine (OR for 90th compared with 10th percentiles: 0.58; 95% CI: 0.36, 0.94; P-linear trend = 0.027), were inversely associated with colorectal cancer.

conclusionsSerum metabolites can distinguish coffee drinkers from nondrinkers; some caffeine-related metabolites were inversely associated with colorectal cancer and should be studied further to clarify the role of coffee in the cause of colorectal cancer. The Prostate, Lung, Colorectal, and Ovarian trial was registered at clinicaltrials.gov as NCT00002540.

Indexed as

CoffeeFeeding BehaviorAgedBiomarkersCase-Control StudiesColorectal NeoplasmsGas Chromatography-Mass SpectrometryHumansLogistic ModelsMetabolomicsMiddle AgedRandomized Controlled Trials as TopicRisk FactorsBiomarkersCoffeecoffeecolorectal cancerdietary intakemetabolitesmetabolomics

Identifiers

PMID25762808
PMCPMC4409687
OpenAlexW2083964309

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

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.