Evidence map›Paper›PMID 37659696›Full record

SynthesisAdvances in nutrition (Bethesda, Md.)2023

Systematic Review of Nutrient Profile Models Developed for Nutrition-Related Policies and Regulations Aimed at Noncommunicable Disease Prevention -An Update.

Caroline Martin, Mylène Turcotte, Juliette Cauchon, Amélie Lachance, Sonia Pomerleau, Véronique Provencher, Marie-Ève Labonté

Abstract readSystematic Review
In one paragraph

Synthesis in Advances in nutrition (Bethesda, Md.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed, 2 pooled it
–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

19 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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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

7 authors.

Caroline MartinCentre Nutrition, santé et société (NUTRISS), Institute of Nutrition and Functional Foods (INAF), Université Laval, Québec, QC, Canada; School of Nutrition, Université Laval, Québec, QC, Canada.
Mylène TurcotteCentre Nutrition, santé et société (NUTRISS), Institute of Nutrition and Functional Foods (INAF), Université Laval, Québec, QC, Canada.
Juliette CauchonCentre Nutrition, santé et société (NUTRISS), Institute of Nutrition and Functional Foods (INAF), Université Laval, Québec, QC, Canada; School of Nutrition, Université Laval, Québec, QC, Canada.
Amélie LachanceCentre Nutrition, santé et société (NUTRISS), Institute of Nutrition and Functional Foods (INAF), Université Laval, Québec, QC, Canada; School of Nutrition, Université Laval, Québec, QC, Canada.
Sonia PomerleauCentre Nutrition, santé et société (NUTRISS), Institute of Nutrition and Functional Foods (INAF), Université Laval, Québec, QC, Canada.
Véronique ProvencherCentre Nutrition, santé et société (NUTRISS), Institute of Nutrition and Functional Foods (INAF), Université Laval, Québec, QC, Canada; School of Nutrition, Université Laval, Québec, QC, Canada.
Marie-Ève LabontéCentre Nutrition, santé et société (NUTRISS), Institute of Nutrition and Functional Foods (INAF), Université Laval, Québec, QC, Canada; School of Nutrition, Université Laval, Québec, QC, Canada. Electronic address: marie-eve.labonte@fsaa.ulaval.ca.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Nutrient profiling (NP) models are useful for characterizing the healthfulness of foods and for underpinning various nutrition-related public health strategies. Recently, there has been a rapid increase in the number of NP models developed by different organizations worldwide. A systematic review (SR) summarizing the key characteristics of NP models with applications in government-led nutrition policies was carried out in 2016 and published by Labonté et al. [4]. Given the continuous proliferation of NP models, the current study aimed to update this SR. Systematic searches were performed in databases of both the peer-reviewed (n = 7) and grey (n = 1) literature to identify publications related to NP published between May 2016 and September 2020. The full text of relevant publications was assessed independently by 2 reviewers to build a list of potential models. Each model was classified as "already identified in the original SR" or as "newly identified." The eligibility of the "newly identified" models, and of some models excluded from the previous SR because their details were not known at that time, were then assessed independently by 2 reviewers based on pre-established criteria. A total of 151 potential NP models were assessed for eligibility, of which 93 were "newly identified," 28 were originally excluded from the previous SR, and 30 were identified from additional online searches during the eligibility assessment stage. Twenty-six models met the inclusion criteria. Their most frequent applications were food labeling (n = 17) and regulation of food marketing to children (n = 7). They all included nutrients to limit, with sodium, saturated fat, and total sugars being the most frequently considered. Content or face validity testing was conducted for 11 (42%) of the included models. As NP models are increasingly used worldwide to support public health strategies, having an up-to-date resource listing them and detailing their characteristics is crucial. PROSPERO #CRD42021259041.

Indexed as

Noncommunicable DiseasesChildFoodHumansNutrientsNutrition PolicyNutritive ValueNutrientsfood qualityfood regulationhealthfulnesshealthy foodnutrient profilingnutritional qualitynutrition policypublic healthsystematic reviewvalidation

Identifiers

PMID37659696
PMCPMC10721541

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.