Evidence map›Paper›PMID 39019160›Full record

ArticleThe Journal of nutrition2024

Aging Modulates the Effect of Dietary Glycemic Index on Gut Microbiota Composition in Mice.

Ying Zhu, Emily N Yeo, Kelsey M Smith, Andrew S Greenberg, Sheldon Rowan

Abstract read
In one paragraph

Article in The Journal of nutrition, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Review
  2. Biomedicines · 2026
    Review
  3. Article
  4. Article
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

5 authors.

Ying ZhuJM-USDA Human Nutrition Research Center on Aging, Tufts University, Boston, MA, United States; Division of Biochemical and Molecular Nutrition, Friedman School of Nutrition Science and Policy, Tufts University, Boston, MA, United States.
Emily N YeoDivision of Biochemical and Molecular Nutrition, Friedman School of Nutrition Science and Policy, Tufts University, Boston, MA, United States; Department of Integrative Physiology, University of Colorado Boulder, Boulder, CO, United States.
Kelsey M SmithJM-USDA Human Nutrition Research Center on Aging, Tufts University, Boston, MA, United States; Division of Biochemical and Molecular Nutrition, Friedman School of Nutrition Science and Policy, Tufts University, Boston, MA, United States.
Andrew S GreenbergJM-USDA Human Nutrition Research Center on Aging, Tufts University, Boston, MA, United States; Division of Biochemical and Molecular Nutrition, Friedman School of Nutrition Science and Policy, Tufts University, Boston, MA, United States; Division of Endocrinology, Diabetes and Metabolism, Tufts University School of Medicine, Boston, MA, United States.
Sheldon RowanJM-USDA Human Nutrition Research Center on Aging, Tufts University, Boston, MA, United States; Division of Biochemical and Molecular Nutrition, Friedman School of Nutrition Science and Policy, Tufts University, Boston, MA, United States; Department of Ophthalmology, Tufts University School of Medicine, Boston, MA, United States. Electronic address: Sheldon.rowan@tufts.edu.

Funding

Transgenic CoreP30DK046200 · NIDDK · TUFTS MEDICAL CENTER · PI HU, FRANK B · 1992 to 2021
$25.0M
Research Training Program in Nutrition, Obesity and Metabolic DisordersT32DK124170 · NIDDK · TUFTS UNIVERSITY BOSTON · PI GREENBERG, ANDREW S · 2020 to 2024
$870k
NIDDK NIH HHS P30 DK046200NIDDK NIH HHS T32 DK124170
6 · The paper itself

Abstract

backgroundGut microbiome composition profoundly impacts host physiology and is modulated by several environmental factors, most prominently diet. The composition of gut microbiota changes over the lifespan, particularly during the earliest and latest stages. However, we know less about diet-aging interactions on the gut microbiome. We previously showed that diets with different glycemic indices, based on the ratio of rapidly digested amylopectin to slowly digested amylose, led to altered composition of gut microbiota in male C57BL/6J mice.

objectivesHere, we examined the role of aging in influencing dietary effects on gut microbiota composition and aimed to identify gut bacterial taxa that respond to diet and aging.

methodsWe studied 3 age groups of male C57BL/6J wild-type mice: young (4 mo), middle-aged (13.5 mo), and old (22 mo), all fed either high glycemic (HG) or low glycemic (LG) diets matched for caloric content and macronutrient composition. Fecal microbiome composition was determined by 16S rDNA metagenomic sequencing and was evaluated for changes in α- and β-diversity and bacterial taxa that change by age, diet, or both.

resultsYoung mice displayed lower α-diversity scores than middle-aged counterparts but exhibited more pronounced differences in β-diversity between diets. In contrast, old mice had slightly lower α-diversity scores than middle-aged mice, with significantly higher β-diversity distances. Within-group variance was lowest in young, LG-fed mice and highest in old, HG-fed mice. Differential abundance analysis revealed taxa associated with both aging and diet. Most differential taxa demonstrated significant interactions between diet and aging. Notably, several members of the Lachnospiraceae family increased with aging and HG diet, whereas taxa from the Bacteroides_H genus increased with the LG diet. Akkermansia muciniphila decreased with aging.

conclusionsThese findings illustrate the complex interplay between diet and aging in shaping the gut microbiota, potentially contributing to age-related disease.

Indexed as

AgingDietFecesGastrointestinal MicrobiomeGlycemic IndexMice, Inbred C57BLAnimalsBacteriaMaleMiceRNA, Ribosomal, 16SRNA, Ribosomal, 16Sagingdietglycemic indexmicrobiomeresistant starch

Identifiers

PMID39019160
PMCPMC11393168

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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.