Evidence map›Paper›PMID 33299266›Full record

ArticleTrends in food science & technology2019

Molecular annotation of food - towards personalized diet and precision health.

Junai Gan, Justin B Siegel, J Bruce German

Open access · greenAbstract read
In one paragraph

Article in Trends in food science & technology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
2.9field-weighted citation impact, top 9% 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

7 citing papers in PubMed, 35 citations in OpenAlex.

  1. Article
  2. Article
  3. Review
  4. Mutexa: A Computational Ecosystem for Intelligent Protein Engineering.Journal of chemical theory and computation · 2023
    Review
  5. Review
  6. Article
  7. 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

3 authors at 1 institution in 1 country.

Junai GanDepartment of Food Science and Technology, University of California, Davis, CA, United States.
Justin B SiegelDepartment of Chemistry, University of California, Davis, CA, United States.
J Bruce GermanDepartment of Food Science and Technology, University of California, Davis, CA, United States.
University of California, Davis · US

Funding

Workshop on Environmental Technology Transfer and EntrepreneurshipP42ES004699 · NIEHS · UNIVERSITY OF CALIFORNIA DAVIS · PI HAMMOCK, BRUCE D · 1987 to 2021
$50.1M
Activation of probiotic bifidobacteria by milk glyansR01AT008759 · NCCIH · UNIVERSITY OF CALIFORNIA AT DAVIS · PI GERMAN, BRUCE, LEBRILLA, CARLITO B · 2014 to 2018
$4.2M
Enzymatic formation of the hydrocarbon skeletons underlying diterpenoid diversityR01GM076324 · NIGMS · IOWA STATE UNIVERSITY · PI PETERS, REUBEN JOHN · 2006 to 2019
$3.4M
Mechanism of Activation of Probiotic Bifidobacteria by Prebiotic Milk GlycansR01AT007079 · NCCIH · UNIVERSITY OF CALIFORNIA AT DAVIS · PI GERMAN, BRUCE, LEBRILLA, CARLITO B · 2012 to 2016
$2.2M
NCCIH NIH HHS R01 AT007079NCCIH NIH HHS R01 AT008759NIEHS NIH HHS P42 ES004699NIGMS NIH HHS R01 GM076324
6 · The paper itself

Abstract

backgroundPersonalized diet requires matching human genotypic and phenotypic features to foods that increase the chance of achieving a desired physiological health outcome. New insights and technologies will help to decipher the intricacies of diet-health relationships and create opportunities for breakthroughs in dietary interventions for personal health management. SCOPE AND APPROACH: This article describes the scientific progress towards personalized diet and points out the need for integrating high-quality data on food. A framework for molecular annotation of food is presented, focusing on what aspects should be measured and how these measures relate to health. Strategies of applying trending technologies to improve personalized diet and health are discussed, highlighting challenges and opportunities for transforming data into insights and actions. KEY FINDINGS AND

conclusionsThe goal of personalized diet is to enable individuals and caregivers to make informed dietary decisions for targeted health management. Achieving this goal requires a better understanding of how molecular properties of food influence individual eating behavior and health outcomes. Annotating food at a molecular level encompasses characterizing its chemical composition and modifications, physicochemical structure, and biological properties. Features of molecular properties in the food annotation framework are applicable to varied conditions and processes from raw materials to meals. Applications of trending technologies, such as omics techniques, wearable biosensors, and artificial intelligence, will support data collection, data analytics, and personalized dietary actions for targeted health management.

Indexed as

DataDietFoodHealthMolecular propertyPersonalized nutrition

Identifiers

PMID33299266
PMCPMC7723349
OpenAlexW2964116700

What OpenQuestion holds

Textmetadata
LicenceTDM
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.