Evidence map›Paper›PMID 35404230›Full record

ArticleeLife2022

Automated, high-dimensional evaluation of physiological aging and resilience in outbred mice.

Zhenghao Chen, Anil Raj, G V Prateek, Andrea Di Francesco, Justin Liu, Brice E Keyes, Ganesh Kolumam, Vladimir Jojic, Adam Freund

Open access · goldAbstract read
In one paragraph

Article in eLife, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed
2.1field-weighted citation impact, top 12% 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

21 citing papers in PubMed, 28 citations in OpenAlex.

  1. Article
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  5. Fifteen years of the Diversity Outbred mouse model: a review.Mammalian genome : official journal of the International Mammalian Genome Society · 2026
    Review
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  7. Life-long behavioral screen reveals an architecture of vertebrate aging.bioRxiv : the preprint server for biology · 2025
    Article
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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

9 authors.

Zhenghao ChenCalico Life Sciences LLC, South San Francisco, South San Francisco, United States.
Anil RajCalico Life Sciences LLC, South San Francisco, South San Francisco, United States.ORCID 0000-0003-4412-0883
G V PrateekCalico Life Sciences LLC, South San Francisco, South San Francisco, United States.
Andrea Di FrancescoCalico Life Sciences LLC, South San Francisco, South San Francisco, United States.ORCID 0000-0001-6867-8203
Justin LiuCalico Life Sciences LLC, South San Francisco, South San Francisco, United States.ORCID 0000-0002-5338-6491
Brice E KeyesCalico Life Sciences LLC, South San Francisco, South San Francisco, United States.
Ganesh KolumamCalico Life Sciences LLC, South San Francisco, South San Francisco, United States.
Vladimir JojicCalico Life Sciences LLC, South San Francisco, South San Francisco, United States.
Adam FreundCalico Life Sciences LLC, South San Francisco, South San Francisco, United States.ORCID 0000-0002-7956-5332

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Behavior and physiology are essential readouts in many studies but have not benefited from the high-dimensional data revolution that has transformed molecular and cellular phenotyping. To address this, we developed an approach that combines commercially available automated phenotyping hardware with a systems biology analysis pipeline to generate a high-dimensional readout of mouse behavior/physiology, as well as intuitive and health-relevant summary statistics (resilience and biological age). We used this platform to longitudinally evaluate aging in hundreds of outbred mice across an age range from 3 months to 3.4 years. In contrast to the assumption that aging can only be measured at the limits of animal ability via challenge-based tasks, we observed widespread physiological and behavioral aging starting in early life. Using network connectivity analysis, we found that organism-level resilience exhibited an accelerating decline with age that was distinct from the trajectory of individual phenotypes. We developed a method, Combined Aging and Survival Prediction of Aging Rate (CASPAR), for jointly predicting chronological age and survival time and showed that the resulting model is able to predict both variables simultaneously, a behavior that is not captured by separate age and mortality prediction models. This study provides a uniquely high-resolution view of physiological aging in mice and demonstrates that systems-level analysis of physiology provides insights not captured by individual phenotypes. The approach described here allows aging, and other processes that affect behavior and physiology, to be studied with improved throughput, resolution, and phenotypic scope.

Indexed as

AgingSystems BiologyAnimalsMicePhenotypeagingcomputational biologyhealthspanmousephysiologyresiliencesystems biology

Identifiers

PMID35404230
PMCPMC9000950
OpenAlexW4226143830

What OpenQuestion 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 OpenQuestion graph.