Evidence map›Paper›PMID 25037050›Full record

ArticleCarcinogenesis2014

Fecal metabolomics: assay performance and association with colorectal cancer.

James J Goedert, Joshua N Sampson, Steven C Moore, Qian Xiao, Xiaoqin Xiong, Richard B Hayes, Jiyoung Ahn, Jianxin Shi, Rashmi Sinha

Open access · bronzeAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Carcinogenesis, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 76 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
76citing papers in PubMed, 1 pooled it
3.7field-weighted citation impact, top 6% 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

76 citing papers in PubMed, 1 synthesis or guideline pooled it, 148 citations in OpenAlex.

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16 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

9 authors at 2 institutions in 1 country.

James J GoedertEpidemiology and Biostatistics Program, Division of Cancer Epidemiology and Genetics, National Cancer Institute, 9609 Medical Center Drive, Bethesda, MD 20892-9704, USA, Information Management Services, 6110 Executive Boulevard, Rockville, MD 20852, USA and Division of Epidemiology, Department of Population Health, New York University School of Medicine, 650 First Avenue, #518, New York, NY 10016, USA goedertj@mail.nih.gov.
Joshua N SampsonEpidemiology and Biostatistics Program, Division of Cancer Epidemiology and Genetics, National Cancer Institute, 9609 Medical Center Drive, Bethesda, MD 20892-9704, USA, Information Management Services, 6110 Executive Boulevard, Rockville, MD 20852, USA and Division of Epidemiology, Department of Population Health, New York University School of Medicine, 650 First Avenue, #518, New York, NY 10016, USA.
Steven C MooreEpidemiology and Biostatistics Program, Division of Cancer Epidemiology and Genetics, National Cancer Institute, 9609 Medical Center Drive, Bethesda, MD 20892-9704, USA, Information Management Services, 6110 Executive Boulevard, Rockville, MD 20852, USA and Division of Epidemiology, Department of Population Health, New York University School of Medicine, 650 First Avenue, #518, New York, NY 10016, USA.
Qian XiaoEpidemiology and Biostatistics Program, Division of Cancer Epidemiology and Genetics, National Cancer Institute, 9609 Medical Center Drive, Bethesda, MD 20892-9704, USA, Information Management Services, 6110 Executive Boulevard, Rockville, MD 20852, USA and Division of Epidemiology, Department of Population Health, New York University School of Medicine, 650 First Avenue, #518, New York, NY 10016, USA.
Xiaoqin XiongInformation Management Services, 6110 Executive Boulevard, Rockville, MD 20852, USA and.
Richard B HayesDivision of Epidemiology, Department of Population Health, New York University School of Medicine, 650 First Avenue, #518, New York, NY 10016, USA.
Jiyoung AhnDivision of Epidemiology, Department of Population Health, New York University School of Medicine, 650 First Avenue, #518, New York, NY 10016, USA.
Jianxin ShiEpidemiology and Biostatistics Program, Division of Cancer Epidemiology and Genetics, National Cancer Institute, 9609 Medical Center Drive, Bethesda, MD 20892-9704, USA, Information Management Services, 6110 Executive Boulevard, Rockville, MD 20852, USA and Division of Epidemiology, Department of Population Health, New York University School of Medicine, 650 First Avenue, #518, New York, NY 10016, USA.
Rashmi SinhaEpidemiology and Biostatistics Program, Division of Cancer Epidemiology and Genetics, National Cancer Institute, 9609 Medical Center Drive, Bethesda, MD 20892-9704, USA, Information Management Services, 6110 Executive Boulevard, Rockville, MD 20852, USA and Division of Epidemiology, Department of Population Health, New York University School of Medicine, 650 First Avenue, #518, New York, NY 10016, USA.
New York University · USInformation Management Services · US

Funding

Microbes and Human MalignanciesZIACP010214 · NCI · DIVISION OF CANCER EPIDEMIOLOGY AND GENETICS · PI VOGTMANN, EMILY · 2010 to 2021
$5.5M
The Oral Microbiome and Upper Aerodigestive Squamous Cell CancerR01CA159036 · NCI · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · PI AHN, JIYOUNG, HAYES, RICHARD BERNARD · 2011 to 2024
$3.5M
Prospective Study of Human Oral Microbiome and Pancreatic Cancer RiskR01CA164964 · NCI · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · PI AHN, JIYOUNG · 2014 to 2017
$2.2M
Intramural NIH HHSNCI NIH HHS R01 CA159036NCI NIH HHS R01 CA164964NCI NIH HHS Z01-CP-010214
6 · The paper itself

Abstract

Metabolomic analysis of feces may provide insights on colorectal cancer (CRC) if assay performance is satisfactory. In lyophilized feces from 48 CRC cases, 102 matched controls, and 48 masked quality control specimens, 1043 small molecules were detected with a commercial platform. Assay reproducibility was good for 527 metabolites [technical intraclass correlation coefficient (ICC) >0.7 in quality control specimens], but reproducibility in 6-month paired specimens was lower for the majority of metabolites (within-subject ICC ≤0.5). In the CRC cases and controls, significant differences (false discovery rate ≤0.10) were found for 41 of 1043 fecal metabolites. Direct cancer association was found with three fecal heme-related molecules [covariate-adjusted 90th versus 10th percentile odds ratio (OR) = 17-345], 18 peptides/amino acids (OR = 3-14), palmitoyl-sphingomyelin (OR = 14), mandelate (OR = 3) and p-hydroxy-benzaldehyde (OR = 4). Conversely, cancer association was inverse with acetaminophen metabolites (OR <0.1), tocopherols (OR = 0.3), sitostanol (OR = 0.2), 3-dehydrocarnitine (OR = 0.4), pterin (OR = 0.3), conjugated-linoleate-18-2N7 (OR = 0.2), N-2-furoyl-glycine (OR = 0.3) and p-aminobenzoate (PABA, OR = 0.2). Correlations suggested an independent role for palmitoyl-sphingomyelin and a central role for PABA (which was stable over 6 months, within-subject ICC 0.67) modulated by p-hydroxy-benzaldehyde. Power calculations based on ICCs indicate that only 45% of metabolites with a true relative risk 5.0 would be found in prospectively collected, prediagnostic specimens from 500 cases and 500 controls. Thus, because fecal metabolites vary over time, very large studies will be needed to reliably detect associations of many metabolites that potentially contribute to CRC.

Indexed as

MetabolomeBiomarkers, TumorCase-Control StudiesColorectal NeoplasmsFecesFemaleHemeHippuratesHumansMaleMetabolomicsMiddle AgedPeptidesReference StandardsBiomarkers, TumorHemeHippuratesPeptidessalicylurate

Identifiers

PMID25037050
PMCPMC4146421
OpenAlexW2106909476

What OpenQuestion holds

Textmetadata
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.