Evidence map›Paper›PMID 35083456›Full record

ArticleAging pathobiology and therapeutics2021

PathoClock and PhysioClock in mice recapitulate human multimorbidity and heterogeneous aging.

Shabnam Salimi, Christina Pettan-Brewer, Warren Ladiges

Open access · hybridAbstract read
In one paragraph

Article in Aging pathobiology and therapeutics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact, top 72% 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

0 citing papers in PubMed, 0 citations in OpenAlex.

No citing paper in PubMed yet.

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

3 authors at 2 institutions in 1 country.

Shabnam SalimiDepartment of Epidemiology and Public Health, University of Maryland Baltimore, School of Medicine, Baltimore, MD, USA.
Christina Pettan-BrewerDepartment of Comparative Medicine, School of Medicine, University of Washington, Seattle, WA 98195, USA.
Warren LadigesDepartment of Comparative Medicine, School of Medicine, University of Washington, Seattle, WA 98195, USA.
University of Washington · USUniversity of Maryland, Baltimore · US

Funding

Physical resilience is a predictor of healthy aging in miceR01AG057381 · NIA · UNIVERSITY OF WASHINGTON · PI Warren C LADIGES · 2017 to 2026
$4.6M
A geroscience approach for treating Alzheimer's diseaseR01AG067193 · NIA · UNIVERSITY OF WASHINGTON · PI DARVAS, MARTIN C, LADIGES, WARREN C · 2020 to 2024
$4.4M
Pathology of Aging Research NetworkR24AG047115 · NIA · UNIVERSITY OF WASHINGTON · PI LADIGES, WARREN C · 2014 to 2016
$1.1M
Slowing aging by multiplexing (SLAM)R56AG058543 · NIA · UNIVERSITY OF WASHINGTON · PI LADIGES, WARREN C · 2018 to 2018
$867k
DNA methylation markers of Multimorbidity in Aging Humans and MiceK01AG059898 · NIA · UNIVERSITY OF WASHINGTON · PI SALIMI, SHABNAM · 2019 to 2023
$613k
NIA NIH HHS K01 AG059898NIA NIH HHS R01 AG057381NIA NIH HHS R01 AG067193NIA NIH HHS R24 AG047115NIA NIH HHS R56 AG058543
6 · The paper itself

Abstract

backgroundMultimorbidity is a public health concern and an essential component of aging and healthspan but understudied because investigative tools are lacking that can be translatable to capture similarities and differences of the aging process across species and variability between individuals and individual organs.

methodsTo help address this need, body organ disease number (BODN) borrowed from human studies was applied to C57BL/6 (B6) and CB6F1 mouse strains at 8, 16, 24, and 32 months of age, as a measure of systems morbidity based on pathology lesions to develop a mouse PathoClock resembling clinically-based Body Clock in humans, using Bayesian inference. A mouse PhysioClock was also developed based on measures of physiological domains including cardiovascular, neuromuscular, and cognitive function in the same two mouse strains so that alignment with BODN was predictable.

resultsBetween- and within-age variabilities in PathoClock and PhysioClock, as well as between-strain variabilities. Both PathoClock and PhysioClock correlated with chronological age more strongly in CB6F1 than C57BL/6. Prediction models were then developed, designated as PathoAge and PhysioAge, using regression models of pathology and physiology measures on chronological age. PathoAge better predicted chronological age than PhysioAge as the predicted chronological and observed chronological age for PhysioAge were complex rather than linear.

conclusionPathoClock and PhathoAge can be used to capture biological changes that predict BODN, a metric developed in humans, and compare multimorbidity across species. These mouse clocks are potential translational tools that could be used in aging intervention studies.

Indexed as

agingMultimorbiditypathoAgepathoClockpathologyphysioAgephysioClockphysiology

Identifiers

PMID35083456
PMCPMC8789194
OpenAlexW4226160010

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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.