Evidence map›Paper›PMID 35289908›Full record

ArticleThe Journal of nutrition2022

Biomarker-Calibrated Red and Combined Red and Processed Meat Intakes with Chronic Disease Risk in a Cohort of Postmenopausal Women.

Cheng Zheng, Mary Pettinger, G A Nagana Gowda, Johanna W Lampe, Daniel Raftery, Lesley F Tinker, Ying Huang, Sandi L Navarro, Diane M O'Brien, Linda Snetselaar and 4 more

Open access · greenAbstract read
In one paragraph

Article in The Journal of nutrition, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed, 1 pooled it
4.4field-weighted citation impact, top 5% 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

14 citing papers in PubMed, 1 synthesis or guideline pooled it, 22 citations in OpenAlex.

  1. Pooled it
  2. Trial
  3. Review
  4. Article
  5. Observational
  6. Review
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Review
  14. Article
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

14 authors at 6 institutions in 1 country.

Cheng ZhengDepartment of Biostatistics, University of Nebraska Medical Center, Omaha, NE, USA.ORCID 0000-0002-6562-870X
Mary PettingerDivision of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
G A Nagana GowdaDepartment of Anesthesiology and Pain Medicine, University of Washington, Seattle, WA, USA.
Johanna W LampeDivision of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
Daniel RafteryDepartment of Anesthesiology and Pain Medicine, University of Washington, Seattle, WA, USA.ORCID 0000-0003-2467-8118
Lesley F TinkerDivision of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
Ying HuangDivision of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.ORCID 0000-0002-9655-7502
Sandi L NavarroDivision of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.ORCID 0000-0002-4260-2486
Diane M O'BrienInstitute for Arctic Biology, University of Alaska, Fairbanks, AK, USA.
Linda SnetselaarCollege of Public Health, University of Iowa, Iowa City, IA, USA.
Simin LiuDepartment of Epidemiology, School of Public Health, Brown University, Providence, RI, USA.
Robert B WallaceCollege of Public Health, University of Iowa, Iowa City, IA, USA.
Marian L NeuhouserDivision of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
Ross L PrenticeDivision of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
University of Washington · USFred Hutch Cancer Center · USUniversity of Iowa · USBrown University · USUniversity of Alaska Fairbanks · USUniversity of Nebraska Medical Center · US

Funding

Translational Bioimaging Core Shared ResourceP30CA015704 · NCI · FRED HUTCHINSON CANCER RESEARCH CENTER · PI Eric Collisson · 1985 to 2026
$296.4M
PILOT STUDY--CLINICAL NUTRITION RESEARCHP30DK035816 · NIDDK · UNIVERSITY OF WASHINGTON · PI GREGORY J MORTON · 1986 to 2026
$30.4M
Nutrition and Physical Activity Assessment Study (NPAAS)R01CA119171 · NCI · FRED HUTCHINSON CANCER RESEARCH CENTER · PI Marian L Neuhouser · 2006 to 2026
$14.3M
Multiplexed UPLC-MS/MS System for Advanced Target MetabolomicsS10OD021562 · OD · UNIVERSITY OF WASHINGTON · PI RAFTERY, DANIEL · 2016 to 2016
$596k
NCI NIH HHS P30 CA015704NCI NIH HHS R01 CA119171NHLBI NIH HHS HHSN268201600001CNHLBI NIH HHS HHSN268201600002CNHLBI NIH HHS HHSN268201600003CNHLBI NIH HHS HHSN268201600004CNIDA NIH HHS HHSN271201600004CNIDA NIH HHS HHSN271201600004INIDDK NIH HHS P30 DK035816NIH HHS S10 OD021562
6 · The paper itself

Abstract

backgroundThe associations of red and processed meat with chronic disease risk remain to be clarified, in part because of measurement error in self-reported diet.

objectivesWe sought to develop metabolomics-based biomarkers for red and processed meat, and to evaluate associations of biomarker-calibrated meat intake with chronic disease risk among postmenopausal women.

methodsStudy participants were women who were members of the Women's Health Initiative (WHI) study cohorts. These participants were postmenopausal women aged 50-79 y when enrolled during 1993-1998 at 40 US clinical centers with embedded human feeding and nutrition biomarker studies. Literature reports of metabolomics correlates of meat consumption were used to develop meat intake biomarkers from serum and 24-h urine metabolites in a 153-participant feeding study (2010-2014). Resulting biomarkers were used in a 450-participant biomarker study (2007-2009) to develop linear regression calibration equations that adjust FFQ intakes for random and systematic measurement error. Biomarker-calibrated meat intakes were associated with cardiovascular disease, cancer, and diabetes incidence among 81,954 WHI participants (1993-2020).

resultsBiomarkers and calibration equations meeting prespecified criteria were developed for consumption of red meat and red plus processed meat combined, but not for processed meat consumption. Following control for nondietary confounding factors, hazard ratios were calculated for a 40% increment above the red meat median intake for coronary artery disease (HR: 1.10; 95% CI: 1.07, 1.14), heart failure (HR: 1.26; 95% CI: 1.20, 1.33), breast cancer (HR: 1.10; 95% CI: 1.07, 1.13) for, total invasive cancer (HR: 1.07; 95% CI: 1.05, 1.09), and diabetes (HR: 1.37; 95% CI: 1.34, 1.39). HRs for red plus processed meat intake were similar. HRs were close to the null, and mostly nonsignificant following additional control for dietary potential confounding factors, including calibrated total energy consumption.

conclusionsA relatively high-meat dietary pattern is associated with somewhat higher chronic disease risks. These elevations appear to be largely attributable to the dietary pattern, rather than to consumption of red or processed meat per se.

Indexed as

Chronic DiseaseDietMeatAgedBiomarkersCohort StudiesFemaleHumansMiddle AgedPostmenopauseRed MeatRisk FactorsBiomarkerscancercardiovascular diseasediabetesmetabolomicsred and processed meat

Identifiers

PMID35289908
PMCPMC9258528
OpenAlexW4220920864

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.