Evidence map›Paper›PMID 30753685›Full record

ReviewThe Journal of nutrition2019

Best Practices for Dietary Supplement Assessment and Estimation of Total Usual Nutrient Intakes in Population-Level Research and Monitoring.

Regan L Bailey, Kevin W Dodd, Jaime J Gahche, Johanna T Dwyer, Alexandra E Cowan, Shinyoung Jun, Heather A Eicher-Miller, Patricia M Guenther, Anindya Bhadra, Paul R Thomas and 3 more

Registry-linked trialAbstract readReview
In one paragraph

Review in The Journal of nutrition, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT03400436 (Development of a Total Nutrient Index), which is not on this map. Cited by 56 papers.

0numbers the graph read from it
0cells of the map it votes in
56citing 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.

NCT03400436 completednot on this map

Development of a Total Nutrient Index

TypeobservationalSponsorNational Institute of Health and NutritionRan2011 to 2014Enrolled10,000ConditionsDiet HabitArmsNo intervention in observational studies
3 · Its place in the literature

Who cites it

56 citing papers in PubMed.

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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

13 authors.

Regan L BaileyDepartment of Nutrition Science, Purdue University, West Lafayette, IN.
Kevin W DoddNational Institutes of Health, National Cancer Institute, Rockville, MD.
Jaime J GahcheNational Institutes of Health, Office of Dietary Supplements, Bethesda, MD.
Johanna T DwyerNational Institutes of Health, Office of Dietary Supplements, Bethesda, MD.
Alexandra E CowanDepartment of Nutrition Science, Purdue University, West Lafayette, IN.
Shinyoung JunDepartment of Nutrition Science, Purdue University, West Lafayette, IN.
Heather A Eicher-MillerDepartment of Nutrition Science, Purdue University, West Lafayette, IN.
Patricia M GuentherDepartment of Nutrition and Integrative Physiology, University of Utah, Salt Lake City, UT.
Anindya BhadraDepartment of Statistics, Purdue University, West Lafayette, IN.
Paul R ThomasNational Institutes of Health, Office of Dietary Supplements, Bethesda, MD.
Nancy PotischmanNational Institutes of Health, Office of Dietary Supplements, Bethesda, MD.
Raymond J CarrollDepartment of Statistics, Texas A&M University, College Station, TX.
Janet A ToozeSchool of Medicine, Wake Forest, Winston-Salem, NC.

Funding

Measurement Error, Nutrition, Physical Activity and CancerU01CA057030 · NCI · TEXAS A&M UNIVERSITY · PI CARROLL, RAYMOND J. · 2015 to 2019
$2.8M
Development of a Total Nutrient IndexU01CA215834 · NCI · TEXAS A&M AGRILIFE RESEARCH · PI REGAN BAILEY, JANET A. TOOZE · 2017 to 2026
$2.2M
NCI NIH HHS U01 CA057030NCI NIH HHS U01 CA215834
6 · The paper itself

Abstract

The use of dietary supplements (DS) is pervasive and can provide substantial amounts of micronutrients to those who use them. Therefore when characterizing dietary intakes, describing the prevalence of inadequacy or excess, or assessing relations between nutrients and health outcomes, it is critical to incorporate DS intakes to improve exposure estimates. Unfortunately, little is known about the best methods to assess DS, and the structure of measurement error in DS reporting. Several characteristics of nutrients from DS are salient to understand when comparing to those in foods. First, DS can be consumed daily or episodically, in bolus form and can deliver discrete and often very high doses of nutrients that are not limited by energy intakes. These characteristics contribute to bimodal distributions and distributions severely skewed to the right. Labels on DS often provide nutrient forms that differ from those found in conventional foods, and underestimate analytically derived values. Finally, the bioavailability of many nutrient-containing DS is not known and it may not be the same as the nutrients in a food matrix. Current methods to estimate usual intakes are not designed specifically to handle DS. Two temporal procedures are described to refer to the order that nutrient intakes are combined relative to usual intake procedures, referred to as a "shrinking" the distribution to remove random error. The "shrink then add" approach is preferable to the "add then shrink" approach when users and nonusers are combined for most research questions. Stratifying by DS before usual intake methods is another defensible option. This review describes how to incorporate nutrient intakes from DS to usual intakes from foods, and describes the available methods and fit-for-purpose of different analytical strategies to address research questions where total usual intakes are of interest at the group level for use in nutrition research and to inform policy decisions. Clinical Trial Registry: NCT03400436.

Indexed as

Dietary SupplementsPopulation SurveillancePractice Guidelines as TopicResearch DesignHumansNutritional Requirementsdietary supplementmeasurement errormethodologytotal nutrient intakesusual intakes

Identifiers

PMID30753685
PMCPMC6374152

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

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.