Evidence map›Paper›PMID 33381804›Full record

ArticleThe American journal of clinical nutrition2021

Risk of metabolic syndrome and metabolic phenotypes in relation to biomarker-calibrated estimates of energy and protein intakes: an investigation from the Women's Health Initiative.

Alexi Vasbinder, Lesley F Tinker, Marian L Neuhouser, Mary Pettinger, Lauren Hale, Chongzhi Di, Oleg Zaslavsky, Laura L Hayman, Xioachen Lin, Charles Eaton and 5 more

Open access · greenAbstract read
In one paragraph

Article in The American journal of clinical nutrition, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
1.4field-weighted citation impact, top 16% 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

8 citing papers in PubMed, 12 citations in OpenAlex.

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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

15 authors at 6 institutions in 1 country.

Alexi VasbinderBiobehavioral Nursing and Health Informatics, University of Washington School of Nursing, Seattle, WA, USA.
Lesley F TinkerPublic Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
Marian L NeuhouserPublic Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
Mary PettingerPublic Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
Lauren HaleProgram in Public Health, Department of Family, Population, and Preventive Medicine, Renaissance School of Medicine, Stony Brook University, Stony Brook, NY, USA.
Chongzhi DiPublic Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
Oleg ZaslavskyBiobehavioral Nursing and Health Informatics, University of Washington School of Nursing, Seattle, WA, USA.
Laura L HaymanDepartment of Nursing, University of Massachusetts Boston, Boston, MA, USA.
Xioachen LinDepartment of Epidemiology, Center for Global Cardiometabolic Health, Brown University, Providence, RI, USA.
Charles EatonDepartment of Family Medicine and Epidemiology, Alpert Medical School, Brown University, Providence, RI, USA.
Di WangBiobehavioral Nursing and Health Informatics, University of Washington School of Nursing, Seattle, WA, USA.
Ashley SchermanBiobehavioral Nursing and Health Informatics, University of Washington School of Nursing, Seattle, WA, USA.
Marcia L StefanickDepartment of Medicine, Stanford Prevention Research Center, Stanford, CA, USA.
Wendy E BarringtonChild, Family, Population Health Nursing, University of Washington School of Nursing, Seattle, WA, USA.
Kerryn W RedingBiobehavioral Nursing and Health Informatics, University of Washington School of Nursing, Seattle, WA, USA.
University of Washington · USFred Hutch Cancer Center · USBrown University · USNeurobehavioral Systems · USStony Brook University · USUniversity of Massachusetts Chan Medical School · US

Funding

Omics and Symptom Science Training ProgramT32NR016913 · NINR · UNIVERSITY OF WASHINGTON · PI CYNTHIA M DOUGHERTY, Margaret McLean Heitkemper · 2017 to 2026
$3.2M
Biomarkers of Long-Term Fatigue in Breast Cancer Survivors Treated with RadiationF31NR018588 · NINR · UNIVERSITY OF WASHINGTON · PI VASBINDER, ALEXI L · 2019 to 2020
$89k
NHLBI NIH HHS HHSN268201600001CNHLBI NIH HHS HHSN268201600002CNHLBI NIH HHS HHSN268201600003CNHLBI NIH HHS HHSN268201600004CNHLBI NIH HHS HHSN268201600018CNINR NIH HHS F31 NR018588NINR NIH HHS T32 NR016913
6 · The paper itself

Abstract

backgroundMetabolic syndrome (MetS) is associated with increased mortality independent of BMI, resulting in discordant metabolic phenotypes, such as metabolically healthy obese and metabolically unhealthy normal-weight individuals. Studies investigating dietary intake in MetS have reported mixed results, due in part to the limitations of self-reported measures.

objectivesTo investigate the role of biomarker-calibrated estimates of energy and protein in MetS and metabolic phenotypes.

methodsPostmenopausal participants from the Women's Health Initiative (WHI) study who were free of MetS at baseline, had available data from FFQs at baseline, and had components of MetS at Year 3 (n = 3963) were included. Dietary energy and protein intakes were estimated using biomarker calibration methods. MetS was defined as 3 or more of the following: elevated serum triglycerides (≥150 mg/dL), low HDL cholesterol (<50 mg/dL), hypertension [systolic blood pressure (BP) ≥130 or diastolic BP ≥85 mmHg], elevated serum glucose (>100 mg/dL), and abdominal adiposity (waist circumference > 89 cm). Models were adjusted for age, WHI study component, race/ethnicity, education, income, smoking, recreational physical activity, disease history, and parity.

resultsFor every 10% increment in total calibrated energy intake, women were at a 1.37-fold elevated risk of MetS (95% CI, 1.15-1.63); a 10% increment in calibrated total protein intake was associated with a 1.21-fold elevated risk of MetS (95% CI, 1.00-1.47). Specifically, animal protein intake was associated with MetS (OR, 1.08; 95% CI, 1.02-1.14), whereas vegetable protein intake was not (OR, 0.99; 95% CI, 0.95-1.03). No differences were seen when examining metabolic phenotypes.

conclusionsWe found that higher calibrated total energy, total protein, and total animal protein intakes were strongly associated with MetS. If replicated in clinical trials, these results will have implications for the promotion of energy and animal protein restrictions for the reduction of MetS risks.

Indexed as

AgedBiomarkersDietary ProteinsEnergy IntakeFemaleHumansMetabolic SyndromeMiddle AgedBiomarkersDietary Proteinsbiomarkerbody compositiondietenergy intakemetabolic syndromeprotein intake

Identifiers

PMID33381804
PMCPMC7948844
OpenAlexW3113472800

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

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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.