Evidence map›Paper›PMID 30759867›Full record

ArticleNutrients2019

Personalized Nutrient Profiling of Food Patterns: Nestlé's Nutrition Algorithm Applied to Dietary Intakes from NHANES.

Fabio Mainardi, Adam Drewnowski, Hilary Green

Abstract read
In one paragraph

Article in Nutrients, 2019. 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
–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

8 citing papers in PubMed.

  1. Article
  2. Review
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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

3 authors.

Fabio MainardiNestlé Research, Vers-chez-les-Blanc, 1000 Lausanne 26, Switzerland. Fabio.Mainardi@rd.nestle.com.
Adam DrewnowskiCenter for Public Health Nutrition, University of Washington, Seattle, WA 98195-3410, USA. adrewnow@fredhutch.org.
Hilary GreenNestlé Research, Vers-chez-les-Blanc, 1000 Lausanne 26, Switzerland. hilary.green@rdls.nestle.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Nutrient profiling (NP) models have been used to assess the nutritional quality of single foods. NP methodologies can also serve to assess the quality of total food patterns. The objective of this study was to construct a personalized nutrient-based scoring system for diet quality and optimal calories. The new Nestlé Nutrition Algorithm (NNA) is based on age and gender-specific healthy ranges for energy and nutrient intakes over a 24 h period. To promote nutrient balance, energy and nutrient intakes either below or above pre-defined healthy ranges are assigned lower diet quality scores. NNA-generated diet quality scores for female 2007⁻2014 National Health and Nutrition Examination Survey (NHANES) participants were compared to their Healthy Eating Index (HEI) 2010 scores. Comparisons involved correlations, joint contingency tables, and Bland Altman plots. The NNA approach showed good correlations with the HEI 2010 scores. NNA mean scores for 7 days of two exemplary menu plans (MyPlate and DASH) were 0.88 ± 0.05 (SD) and 0.91 ± 0.02 (SD), respectively. By contrast, diets of NHANES participants scored 0.45 ± 0.14 (SD) and 0.48 ± 0.14 on first and second days, respectively. The NNA successfully captured the high quality of MyPlate and Dietary Approaches to Stop Hypertension (DASH) menu plans and the lower quality of diets actually consumed in the US.

Indexed as

Nutritional StatusNutrition SurveysAdultAgingDietFemaleFoodHumansMaleMiddle AgedNutritional RequirementsNutritive ValueReproducibility of Resultsdietary patternenergy densitynutrient densitynutrient profilingnutritional quality

Identifiers

PMID30759867
PMCPMC6412928

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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.