Evidence map›Paper›PMID 38932492›Full record

ArticleAging cell2024

Integrated multi-omic analyses uncover the effects of aging on cell-type regulation in glucose-responsive tissues.

Peng Xu, Yimeng Kong, Nicholette D Palmer, Maggie C Y Ng, Bin Zhang, Swapan K Das

Abstract read
In one paragraph

Article in Aging cell, 2024. 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. Biomarkers of ageing of humans and non-human primates.Nature reviews. Molecular cell biology · 2025
    Review
  3. Review
  4. Article
  5. Review
  6. Review
  7. Article
  8. 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

6 authors.

Peng XuCenter of Clinical Laboratory Medicine, Zhongda Hospital, School of Medicine, Advanced Institute for Life and Health, Southeast University, Nanjing, China.ORCID 0000-0001-8007-9879
Yimeng KongCenter of Clinical Laboratory Medicine, Zhongda Hospital, School of Medicine, Advanced Institute for Life and Health, Southeast University, Nanjing, China.
Nicholette D PalmerDepartment of Biochemistry, Wake Forest University School of Medicine, Winston-Salem, North Carolina, USA.
Maggie C Y NgDivision of Genetic Medicine, Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, Tennessee, USA.
Bin ZhangDepartment of Genetics & Genomic Sciences, Mount Sinai Center for Transformative Disease Modeling, Icahn Genomics Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
Swapan K DasDepartment of Internal Medicine, Section on Endocrinology and Metabolism, Wake Forest University School of Medicine, Winston-Salem, North Carolina, USA.

Funding

Integrative Network Biology Approaches to Identify, Characterize and Validate Molecular Subtypes in Alzheimer's DiseaseU01AG046170 · NIA · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI WANG, MINGHUI, ZHANG, BIN · 2013 to 2022
$26.0M
Genetics of African American Type 2 DiabetesR01DK066358 · NIDDK · WAKE FOREST UNIVERSITY HEALTH SCIENCES · PI NG, MAGGIE · 2003 to 2019
$6.2M
Mapping Diabetes Susceptibility by Expression Traits in African AmericansR01DK090111 · NIDDK · WAKE FOREST UNIVERSITY HEALTH SCIENCES · PI DAS, SWAPAN KUMAR · 2011 to 2015
$2.5M
Integrative Study of Genomics, Transcriptomics and Metabolomics in Type 2 DiabetesU01DK105556 · NIDDK · WAKE FOREST UNIVERSITY HEALTH SCIENCES · PI NG, MAGGIE · 2015 to 2019
$2.1M
Identifying Genetic Drivers of Expression Networks Causing Insulin ResistanceR01DK118243 · NIDDK · WAKE FOREST UNIVERSITY HEALTH SCIENCES · PI DAS, SWAPAN KUMAR · 2018 to 2020
$1.1M
NIA NIH HHS U01 AG046170NIDDK NIH HHS R01 DK066358NIDDK NIH HHS R01 DK090111NIDDK NIH HHS R01 DK118243NIDDK NIH HHS U01 DK105556NIH HHS R01 DK066358NIH HHS R01 DK090111NIH HHS R01 DK118243NIH HHS U01AG046170NIH HHS U01 DK105556
6 · The paper itself

Abstract

Aging significantly influences cellular activity and metabolism in glucose-responsive tissues, yet a comprehensive evaluation of the impacts of aging and associated cell-type responses has been lacking. This study integrates transcriptomic, methylomic, single-cell RNA sequencing, and metabolomic data to investigate aging-related regulations in adipose and muscle tissues. Through coexpression network analysis of the adipose tissue, we identified aging-associated network modules specific to certain cell types, including adipocytes and immune cells. Aging upregulates the metabolic functions of lysosomes and downregulates the branched-chain amino acids (BCAAs) degradation pathway. Additionally, aging-associated changes in cell proportions, methylation profiles, and single-cell expressions were observed in the adipose. In the muscle tissue, aging was found to repress the metabolic processes of glycolysis and oxidative phosphorylation, along with reduced gene activity of fast-twitch type II muscle fibers. Metabolomic profiling linked aging-related alterations in plasma metabolites to gene expression in glucose-responsive tissues, particularly in tRNA modifications, BCAA metabolism, and sex hormone signaling. Together, our multi-omic analyses provide a comprehensive understanding of the impacts of aging on glucose-responsive tissues and identify potential plasma biomarkers for these effects.

Indexed as

AgingGlucoseAdipose TissueAnimalsHumansMetabolomicsMiceMultiomicsGlucoseadiposeagingcell‐typemetabolitemulti‐omicmusclenetwork

Identifiers

PMID38932492
PMCPMC11320340

What OpenQuestion holds

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LicenceCC BY
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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.