Evidence map›Paper›PMID 31712321›Full record

Trial reportDiabetes2020

Impaired Metabolic Flexibility to High-Fat Overfeeding Predicts Future Weight Gain in Healthy Adults.

Brittany Begaye, Karyne L Vinales, Tim Hollstein, Takafumi Ando, Mary Walter, Clifton Bogardus, Jonathan Krakoff, Paolo Piaggi

Erratum issuedOpen access · bronzeAbstract readRandomized Controlled Trial
In one paragraph

Trial report in Diabetes, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 45 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
45citing papers in PubMed, 1 pooled it
3.9field-weighted citation impact, top 6% 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

45 citing papers in PubMed, 1 synthesis or guideline pooled it, 73 citations in OpenAlex.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

8 authors at 2 institutions in 2 countries.

Brittany BegayeObesity and Diabetes Clinical Research Section, Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Department of Health and Human Services, Phoenix, AZ.
Karyne L VinalesObesity and Diabetes Clinical Research Section, Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Department of Health and Human Services, Phoenix, AZ.
Tim HollsteinObesity and Diabetes Clinical Research Section, Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Department of Health and Human Services, Phoenix, AZ.ORCID 0000-0002-4048-2978
Takafumi AndoObesity and Diabetes Clinical Research Section, Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Department of Health and Human Services, Phoenix, AZ.
Mary WalterClinical Core Laboratory, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD.
Clifton BogardusObesity and Diabetes Clinical Research Section, Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Department of Health and Human Services, Phoenix, AZ.
Jonathan KrakoffObesity and Diabetes Clinical Research Section, Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Department of Health and Human Services, Phoenix, AZ.
Paolo PiaggiObesity and Diabetes Clinical Research Section, Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Department of Health and Human Services, Phoenix, AZ paolo.piaggi@gmail.com paolo.piaggi@nih.gov.ORCID 0000-0003-2774-9161
National Institutes of Health · USUniversity of Pisa · IT

Funding

Factors which predict variance in weight changeZIADK069029 · NIDDK · NATIONAL INSTITUTE OF DIABETES AND DIGESTIVE AND KIDNEY DISEASES · PI KRAKOFF, JONATHAN · 2009 to 2025
$15.0M
6 · The paper itself

Abstract

The ability to switch fuels for oxidation in response to changes in macronutrient composition of diet (metabolic flexibility) may be informative of individuals' susceptibility to weight gain. Seventy-nine healthy, weight-stable participants underwent 24-h assessments of energy expenditure and respiratory quotient (RQ) in a whole-room calorimeter during energy balance (EBL) (50% carbohydrate, 30% fat) and then during 24-h fasting and three 200% overfeeding diets in a crossover design. Metabolic flexibility was defined as the change in 24-h RQ from EBL during fasting and standard overfeeding (STOF) (50% carbohydrate, 30% fat), high-fat overfeeding (HFOF) (60% fat, 20% carbohydrate), and high-carbohydrate overfeeding (HCOF) (75% carbohydrate, 5% fat) diets. Free-living weight change was assessed after 6 and 12 months. Compared with EBL, RQ decreased on average by 9% during fasting and by 4% during HFOF but increased by 4% during STOF and by 8% during HCOF. A smaller decrease in RQ, reflecting a smaller increase in lipid oxidation rate, during HFOF but not during the other diets predicted greater weight gain at both 6 and 12 months. An impaired metabolic flexibility to acute HFOF can identify individuals prone to weight gain, indicating that an individual's capacity to oxidize dietary fat is a metabolic determinant of weight change.

Indexed as

AdolescentAdultDietary CarbohydratesDietary FatsEnergy MetabolismFastingFemaleHumansMaleMiddle AgedWeight GainYoung AdultDietary CarbohydratesDietary Fats

Identifiers

PMID31712321
PMCPMC6971489
OpenAlexW2988593428

What OpenQuestion holds

Textmetadata
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.