Evidence map›Paper›PMID 38762186›Full record

Observational studyThe American journal of clinical nutrition2024

Calibration of citrus intake assessed by food frequency questionnaires using urinary proline betaine in an observational study setting.

Yang Hu, Molin Wang, Walter C Willett, Meir Stampfer, Liming Liang, Frank B Hu, Eric Rimm, Lorraine Brennan, Qi Sun

Abstract readObservational Study
In one paragraph

Observational study in The American journal of clinical nutrition, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
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

4 citing papers in PubMed.

  1. Trial
  2. Article
  3. Article
  4. Review
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.

Yang HuDepartment of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, United States.
Molin WangDepartment of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, United States; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, United States.
Walter C WillettDepartment of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, United States; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, United States; Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States.
Meir StampferDepartment of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, United States; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, United States; Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States.
Liming LiangDepartment of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, United States; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, United States.
Frank B HuDepartment of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, United States; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, United States; Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States.
Eric RimmDepartment of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, United States; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, United States; Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States.
Lorraine BrennanInstitute of Food and Health, School of Agriculture and Food Science, University College Dublin, Dublin, Ireland.
Qi SunDepartment of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, United States; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, United States; Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States. Electronic address: qisun@hsph.harvard.edu.

Funding

Statistical MethodsP01CA087969 · NCI · BRIGHAM AND WOMEN'S HOSPITAL · PI ELIASSEN, A. HEATHER, TAMIMI, RULLA M · 2000 to 2019
$77.8M
Validity of Diet and Activity Measures in WomenP01CA055075 · NCI · HARVARD UNIVERSITY (SCH OF PUBLIC HLTH) · PI FUCHS, CHARLES S · 1991 to 2009
$41.8M
Life Course Cancer Epidemiology Cohort in WomenU01CA176726 · NCI · HARVARD UNIVERSITY D/B/A HARVARD SCHOOL OF PUBLIC HEALTH · PI ELIASSEN, A. HEATHER, WILLETT, WALTER C. · 2018 to 2025
$22.4M
Long Term Multidisciplinary Study of Cancer in Women: The Nurses Health StudyUM1CA186107 · NCI · BRIGHAM AND WOMEN'S HOSPITAL · PI ELIASSEN, A. HEATHER, STAMPFER, MEIR · 2014 to 2023
$22.3M
Cancer Epidemiology Cohort in Male Health ProfessionalsU01CA167552 · NCI · HARVARD UNIVERSITY D/B/A HARVARD SCHOOL OF PUBLIC HEALTH · PI Lorelei Mucci, Walter C. Willett · 2017 to 2026
$17.0M
Life Course Cancer Epidemiology Cohort in WomenUM1CA176726 · NCI · HARVARD SCHOOL OF PUBLIC HEALTH · PI WILLETT, WALTER C. · 2013 to 2017
$15.7M
RISK FACTORS FOR CVD IN WOMENR01HL034594 · NHLBI · TULANE UNIVERSITY OF LOUISIANA · PI JoAnn Elisabeth Manson, Lu Qi · 1985 to 2026
$13.8M
Dietary Etiology of Heart DiseaseR01HL035464 · NHLBI · HARVARD UNIVERSITY (SCH OF PUBLIC HLTH) · PI ERIC B RIMM, Qi Sun · 1986 to 2026
$13.6M
Cancer Epidemiology Cohort in Male Health ProfessionalsUM1CA167552 · NCI · HARVARD SCHOOL OF PUBLIC HEALTH · PI WILLETT, WALTER C. · 2012 to 2016
$11.5M
DIETARY PATTERNS AND RISK OF CARDIOVASCULAR DISEASER01HL060712 · NHLBI · HARVARD UNIVERSITY (SCH OF PUBLIC HLTH) · PI HU, FRANK B, QI, QIBIN · 1998 to 2021
$8.0M
Validity of Diet and Activity Measures in MenU01CA152904 · NCI · HARVARD SCHOOL OF PUBLIC HEALTH · PI RIMM, ERIC B, STAMPFER, MEIR · 2010 to 2013
$7.2M
Metabolomics Core for the Dietary Biomarkers Development Center at Harvard UniversityU2CDK129670 · NIDDK · HARVARD UNIVERSITY D/B/A HARVARD SCHOOL OF PUBLIC HEALTH · PI SUN, QI · 2021 to 2025
$7.0M
NCI NIH HHS P01 CA055075NCI NIH HHS P01 CA087969NCI NIH HHS U01 CA152904NCI NIH HHS U01 CA167552NCI NIH HHS U01 CA176726NCI NIH HHS UM1 CA167552NCI NIH HHS UM1 CA176726NCI NIH HHS UM1 CA186107NHLBI NIH HHS R01 HL034594NHLBI NIH HHS R01 HL035464NHLBI NIH HHS R01 HL060712NIA NIH HHS R21 AG070375NIDDK NIH HHS R01 DK119268NIDDK NIH HHS R01 DK120870NIDDK NIH HHS R01 DK126698NIDDK NIH HHS U2C DK129670NIEHS NIH HHS R01 ES022981
6 · The paper itself

Abstract

backgroundWhether observational study can be employed to establish calibration equations for self-reported dietary intake using food biomarkers is unknown.

objectivesThis study aims to demonstrate the feasibility of obtaining calibration equations based on food biomarkers and 7-d diet records (7DDRs) to correct measurement errors of food frequency questionnaires (FFQs) in an observational study setting.

methodsThe study population consisted of 669 males and 749 females from the Women's and Men's Lifestyle Validation Studies. In the training set, the biomarker-predicted intake derived by regressing 7DDR-assessed intake on urinary proline betaine concentration was regressed on the FFQ-assessed intake to obtain the calibration equations. The regression coefficients were applied to the test set to calculate the calibrated FFQ intake. We examined total citrus as well as individual citrus fruits/beverages.

resultsUrinary proline betaine was moderately correlated with orange juice intake (Pearson correlation [r] = 0.53 for 7DDR and 0.48 for FFQ) but only weakly correlated with intakes of orange (r = 0.12 for 7DDR and 0.15 for FFQ) and grapefruit (r = 0.14 for 7DDR and 0.09 for FFQ). The FFQ-assessed citrus intake was systematically higher than the 7DDR-assessed intake, and after calibrations, the mean calibrated FFQ measurements were almost identical to 7DDR assessments. In the test set, the mean intake levels from 7DDRs, FFQs, and calibrated FFQs were 62.5, 75.3, and 63.2 g/d for total citrus; 41.6, 42.5, and 41.9 g/d for orange juice; 11.8, 24.3, and 12.3 g/d for oranges; and 8.3, 9.3, and 8.6 g/d for grapefruit, respectively. We observed larger differences between calibrated FFQ and 7DDR assessments at the extreme ends of intake, although, on average, good agreements were observed for all citrus except grapefruit.

conclusionsOur 2-step calibration approach has the potential to be adapted to correct systematic measurement error for other foods/nutrients with established food biomarkers in a cost effective way.

Indexed as

BiomarkersCitrusAdultAgedBetaineCalibrationDietDiet RecordsDiet SurveysFemaleHumansMaleMiddle AgedProlineSurveys and QuestionnairesBetaineBiomarkersProlinestachydrinecalibration analysisdiet recordsmeasurement errorNMR-spectroscopyurinary food biomarker

Identifiers

PMID38762186
PMCPMC11251408

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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.