Evidence map›Paper›PMID 39143318›Full record

ArticleNature aging2024

Nonlinear dynamics of multi-omics profiles during human aging.

Xiaotao Shen, Chuchu Wang, Xin Zhou, Wenyu Zhou, Daniel Hornburg, Si Wu, Michael P Snyder

Abstract read
In one paragraph

Article in Nature aging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 295 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
295citing papers in PubMed, 4 pooled it
–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

295 citing papers in PubMed, 4 syntheses or guidelines pooled it.

  1. Pooled it
  2. Guideline
  3. Pooled it
  4. Pooled it
  5. Trial
  6. Trial
  7. Does Age Predict Outcomes of Triceps to Axillary Nerve Transfer?Journal of hand surgery global online · 2027
    Article
  8. Article
  9. Article
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  12. Special Issue "Bioinformatics of Gene Regulations and Structure-2025".International journal of molecular sciences · 2026
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235 more citing papers are in PubMed but not listed here.

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

7 authors.

Xiaotao Shen *Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.ORCID http://orcid.org/0000-0002-9608-9964
Chuchu Wang *Howard Hughes Medical Institute, Stanford University, Stanford, CA, USA.ORCID http://orcid.org/0000-0003-2015-7331
Xin ZhouDepartment of Genetics, Stanford University School of Medicine, Stanford, CA, USA.ORCID http://orcid.org/0000-0001-8089-4507
Wenyu ZhouDepartment of Genetics, Stanford University School of Medicine, Stanford, CA, USA.
Daniel HornburgDepartment of Genetics, Stanford University School of Medicine, Stanford, CA, USA.ORCID http://orcid.org/0000-0002-6618-7774
Si WuDepartment of Genetics, Stanford University School of Medicine, Stanford, CA, USA.
Michael P SnyderDepartment of Genetics, Stanford University School of Medicine, Stanford, CA, USA. mpsnyder@stanford.edu.ORCID http://orcid.org/0000-0003-0784-7987

Funding

Spectrum Stanford Center for clinical and Translational Research and EducationUL1TR001085 · NCATS · STANFORD UNIVERSITY · PI CULLEN, MARK RICHARD, GREENBERG, HARRY BERNARD · 2013 to 2017
$37.1M
Stanford Islet Research CoreP30DK116074 · NIDDK · STANFORD UNIVERSITY · PI Seung K Kim · 2017 to 2026
$19.5M
Longitudinal Multiomics Microbial Profiling in Healthy and Disease IndividualsU54DK102556 · NIDDK · STANFORD UNIVERSITY · PI SNYDER, MICHAEL P., WEINSTOCK, GEORGE M · 2014 to 2015
$7.7M
Longitudinal Multi-Omic Profiles to Reveal Mechanisms of Obesity-Mediated Insulin ResistanceR01DK110186 · NIDDK · STANFORD UNIVERSITY · PI MCLAUGHLIN, TRACEY, SNYDER, MICHAEL P. · 2017 to 2021
$3.2M
Algorithms and Software for Provably Accurate De Novo RNA-Seq AssemblyR01HG008164 · NHGRI · UNIVERSITY OF CALIFORNIA BERKELEY · PI KANNAN, SREERAM, PACHTER, LIOR S · 2015 to 2017
$1.4M
High-throughput sequencer for multi-scale genomic studiesS10OD020141 · OD · STANFORD UNIVERSITY · PI SNYDER, MICHAEL P. · 2015 to 2015
$600k
NCATS NIH HHS UL1 TR001085NHGRI NIH HHS R01 HG008164NIDDK NIH HHS P30 DK116074NIDDK NIH HHS R01 DK110186NIDDK NIH HHS U54 DK102556NIH HHS S10 OD020141
6 · The paper itself

Abstract

Aging is a complex process associated with nearly all diseases. Understanding the molecular changes underlying aging and identifying therapeutic targets for aging-related diseases are crucial for increasing healthspan. Although many studies have explored linear changes during aging, the prevalence of aging-related diseases and mortality risk accelerates after specific time points, indicating the importance of studying nonlinear molecular changes. In this study, we performed comprehensive multi-omics profiling on a longitudinal human cohort of 108 participants, aged between 25 years and 75 years. The participants resided in California, United States, and were tracked for a median period of 1.7 years, with a maximum follow-up duration of 6.8 years. The analysis revealed consistent nonlinear patterns in molecular markers of aging, with substantial dysregulation occurring at two major periods occurring at approximately 44 years and 60 years of chronological age. Distinct molecules and functional pathways associated with these periods were also identified, such as immune regulation and carbohydrate metabolism that shifted during the 60-year transition and cardiovascular disease, lipid and alcohol metabolism changes at the 40-year transition. Overall, this research demonstrates that functions and risks of aging-related diseases change nonlinearly across the human lifespan and provides insights into the molecular and biological pathways involved in these changes.

Indexed as

AgingNonlinear DynamicsAdultAgedCaliforniaFemaleHumansLongitudinal StudiesMaleMetabolomicsMiddle AgedMultiomicsProteomics

Identifiers

PMID39143318
PMCPMC11564093

What Socratic holds

Textmetadata
LicenceCC BY
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 Socratic graph.