Evidence map›Paper›PMID 41959427›Full record

ArticlebioRxiv : the preprint server for biology2026

Monocytes are biological sensors of aging and frailty in humans.

Chanhong Min, Charles Ezenwanne, Yoseph W Dance, Nico Macaluso, Ladaisha Thompson, Lolita Nidadavolu, Akhil Katuri, Crystal Szczesny, Jacqueline Langdon, Edward J Pearce and 3 more

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in bioRxiv : the preprint server for biology, 2026. 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
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.

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

13 authors.

Chanhong MinDepartment of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, USA.
Charles EzenwanneInstitute for Nanobiotechnology (INBT), Johns Hopkins University, Baltimore, Maryland, USA.
Yoseph W DanceDepartment of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, USA.
Nico MacalusoInstitute for Nanobiotechnology (INBT), Johns Hopkins University, Baltimore, Maryland, USA.
Ladaisha ThompsonDepartment of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, USA.
Lolita NidadavoluDepartment of Geriatric Medicine and Gerontology, Johns Hopkins School of Medicine, Baltimore, Maryland, USA.
Akhil KaturiDepartment of Geriatric Medicine and Gerontology, Johns Hopkins School of Medicine, Baltimore, Maryland, USA.
Crystal SzczesnyDepartment of Geriatric Medicine and Gerontology, Johns Hopkins School of Medicine, Baltimore, Maryland, USA.
Jacqueline LangdonDepartment of Geriatric Medicine and Gerontology, Johns Hopkins School of Medicine, Baltimore, Maryland, USA.
Edward J PearceDepartment of Molecular Microbiology and Immunology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, USA.
Peter AbadirDepartment of Geriatric Medicine and Gerontology, Johns Hopkins School of Medicine, Baltimore, Maryland, USA.
Jeremy D WalstonInstitute for Nanobiotechnology (INBT), Johns Hopkins University, Baltimore, Maryland, USA.
Jude M PhillipDepartment of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, USA.ORCID 0000-0002-2999-5254

Funding

Technological Assessment and Solutions Core - RC4P30AG021334 · NIA · JOHNS HOPKINS UNIVERSITY · PI Jeremy D Walston · 2003 to 2026
$33.2M
Defining a biophysical basis for cell types, cell states and cellular heterogeneity at single-cell resolutionR35GM157099 · NIGMS · JOHNS HOPKINS UNIVERSITY · PI Jude Marvin Phillip · 2025 to 2026
$762k
NIA NIH HHS P30 AG021334NIGMS NIH HHS R35 GM157099
6 · The paper itself

Abstract

Among older adults, frailty is clinically identifiable and characterized by increased vulnerability to adverse health outcomes. Although various tools exist to identify frailty, diagnosis often depends on late-stage clinical symptoms, which typically limit proactive intervention options. We considered whether aging and frailty information may be encoded in the single-cell behaviors and dynamic responses of primary human monocytes. Combining high-content imaging, single-cell behavior profiling, and machine learning, we demonstrated unique age- and frailty-dependent monocyte behaviors at baseline and following exposure to inflammatory stressors. Using these single-cell behaviors, we developed a deep learning neural network model called scTRAIT. scTRAIT accurately predicts the frailty status of older donors, including the capability to track and forecast longitudinal changes in frailty status. Collectively, these findings demonstrate that aging and frailty information are robustly encoded within single-cell behaviors, establishing monocytes as significant biological sensors of aging and frailty in humans.

Indexed as

agingdeep learningfrailtyMonocytessingle-cell behaviors

Identifiers

PMID41959427
PMCPMC13060867

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.