Evidence map›Paper›PMID 19838915›Full record

ReviewNutrition and cancer2009

Well-done meat intake, heterocyclic amine exposure, and cancer risk.

Wei Zheng, Sang-Ah Lee

Open access · greenAbstract readReview
In one paragraph

Review in Nutrition and cancer, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 93 papers, 7 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
93citing papers in PubMed, 7 pooled it
6.4field-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

93 citing papers in PubMed, 7 syntheses or guidelines pooled it, 262 citations in OpenAlex.

  1. Guideline
  2. Pooled it
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  8. Testing front-of-package warnings to discourage red meat consumption: a randomized experiment with US meat consumers.The international journal of behavioral nutrition and physical activity · 2021
    Trial
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  11. Frequent Detection of Abasic Sites andChemical research in toxicology · 2026
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  13. Review
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33 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

2 authors at 1 institution in 1 country.

Wei ZhengVanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN 37203-1738, USA. wei.zheng@vanderbilt.edu
Sang-Ah Lee
Vanderbilt University · US

Funding

The Nashville Breast Health StudyR01CA100374 · NCI · VANDERBILT UNIVERSITY · PI ZHENG, WEI · 2004 to 2009
$4.2M
NCI NIH HHS R01 CA100374NCI NIH HHS R01CA100374
6 · The paper itself

Abstract

High intake of meat, particularly red and processed meat, has been associated with an increased risk of a number of common cancers such as breast, colorectum, and prostate in many epidemiological studies. Heterocyclic amines (HCAs) are a group of mutagenic compounds found in cooked meats, particularly well-done meats. HCAs are some of most potent mutagens detected using the Ames/salmonella tests and have been clearly shown to induce tumors in experimental animal models. Over the past 10 years, an increasing number of epidemiological studies have evaluated the association of well-done meat intake and meat carcinogen exposure with cancer risk. The results from these epidemiologic studies were evaluated and summarized in this review. The majority of these studies have shown that high intake of well-done meat and high exposure to meat carcinogens, particularly HCAs, may increase the risk of human cancer.

Indexed as

AminesAnimalsBreast NeoplasmsColorectal NeoplasmsCookingDietFemaleHeterocyclic CompoundsHot TemperatureHumansMaleMeatMeat ProductsMutagensNeoplasmsPancreatic NeoplasmsAminesHeterocyclic CompoundsMutagens

Identifiers

PMID19838915
PMCPMC2769029
OpenAlexW2008368893

What OpenQuestion holds

Textmetadata
LicenceTDM
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.