Evidence mapPaperPMID 39210266Full record

ArticleGenes & nutrition2024

Dry blood spots as a sampling strategy to identify insulin resistance markers during a dietary challenge.

Stephany Gonçalves Duarte, Carlos M Donado-Pestana, Tushar H More, Larissa Rodrigues, Karsten Hiller, Jarlei Fiamoncini

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In one paragraph

Article in Genes & nutrition, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

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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

1 citing paper in PubMed.

  1. Article
4 · The record

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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

6 authors.

Stephany Gonçalves DuarteDepartment of Food Science and Experimental Nutrition, School of Pharmaceutical Sciences, University of São Paulo, Av. Prof. Lineu Prestes, 580, Bloco 14, São Paulo, SP, CEP 05508-900, Brazil.
Carlos M Donado-PestanaDepartment of Food Science and Experimental Nutrition, School of Pharmaceutical Sciences, University of São Paulo, Av. Prof. Lineu Prestes, 580, Bloco 14, São Paulo, SP, CEP 05508-900, Brazil.
Tushar H MoreBraunschweig Integrated Centre of Systems Biology, Technische Universität Braunschweig, Braunschweig, Germany.
Larissa RodriguesDepartment of Food Science and Experimental Nutrition, School of Pharmaceutical Sciences, University of São Paulo, Av. Prof. Lineu Prestes, 580, Bloco 14, São Paulo, SP, CEP 05508-900, Brazil.
Karsten HillerBraunschweig Integrated Centre of Systems Biology, Technische Universität Braunschweig, Braunschweig, Germany.
Jarlei FiamonciniDepartment of Food Science and Experimental Nutrition, School of Pharmaceutical Sciences, University of São Paulo, Av. Prof. Lineu Prestes, 580, Bloco 14, São Paulo, SP, CEP 05508-900, Brazil. jarlei@usp.br.

Funding

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, Brasil 88887.514208/2020-00Fundação de Amparo à Pesquisa do Estado de São Paulo 22/02941-6Fundação de Amparo à Pesquisa do Estado de São Paulo, Brasil 20/16542-0Fundação de Amparo à Pesquisa do Estado de São Paulo, Brasil 21/08657-5Fundação de Amparo à Pesquisa do Estado de São Paulo, Brasil 21/09237-0
6 · The paper itself

Abstract

This study aimed to identify markers of postprandial dysglycemia in the blood of self-described healthy individuals using dry blood spots (DBS) as a sampling strategy. A total of 54 volunteers, including 31 women, participated in a dietary challenge. They consumed a high-fat, high-sugar mixed meal and underwent multiple blood sampling over the course of 150 min to track their postprandial responses. Blood glucose levels were monitored with a portable glucometer and individuals were classified into two groups based on the glucose area under the curve (AUC): High-AUC (H-AUC) and Low-AUC (L-AUC). DBS sampling was performed at the same time points as the assessment of glycemia using Whatman 903 Protein Saver filter paper. A gas chromatography-mass spectrometry-based metabolite profiling was conducted in the DBS samples to assess postprandial changes in blood metabolome. Higher concentrations of metabolites associated with insulin resistance were observed in individuals from the H-AUC group, including sugars and sugar-derived products such as fructose and threonic acid, as well as organic acids and fatty acids such as succinate and stearic acid. Several metabolites detected in the GC-MS analysis remained unidentified, indicating that other markers of hyperglycemia remain to be discovered in DBS. Based on these observations, we demonstrated that the use of DBS as a non-invasive and inexpensive sampling tool allows the identification of metabolites markers of dysglycemia in the postprandial period.

Indexed as

Dietary challengeMetabolomicsMicrosamplingNutritional physiologyPostprandial

Identifiers

PMID39210266
PMCPMC11363552

What Socratic holds

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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 Socratic graph.