Evidence map›Paper›PMID 38961613›Full record

ArticleAging cell2024

Transcriptomic and metabolomic changes might predict frailty in SAMP8 mice.

Letizia Dacomo, Pietro La Vitola, Laura Brunelli, Letizia Messa, Edoardo Micotti, Luisa Artioli, Elena Sinopoli, Greta Cecutti, Susanna Leva, Stella Gagliardi and 7 more

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 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

12 citing papers in PubMed.

  1. Article
  2. Review
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  6. Frontiers in pharmacology · 2026
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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

17 authors.

Letizia DacomoDepartment of Neuroscience, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy.
Pietro La VitolaDepartment of Neuroscience, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy.
Laura BrunelliDepartment of Environmental Health Sciences, Laboratory of Metabolites and Proteins in Translational Research, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy.
Letizia MessaDepartment of Electronics, Information and Bioengineering (DEIB) Politecnico di Milano, Milan, Italy.
Edoardo MicottiDepartment of Neuroscience, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy.
Luisa ArtioliDepartment of Neuroscience, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy.
Elena SinopoliDepartment of Neuroscience, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy.
Greta CecuttiDepartment of Neuroscience, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy.
Susanna LevaDepartment of Neuroscience, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy.
Stella GagliardiMolecular Biology and Transcriptomics, IRCCS Mondino Foundation, Pavia, Italy.
Orietta PansarasaCellular Model and Neuroepigenetics, IRCCS Mondino Foundation, Pavia, Italy.
Stephana CarelliDepartment of Pediatrics, Center of Functional Genomics and Rare Diseases, Buzzi Children's Hospital, Milan, Italy.ORCID 0000-0003-4603-396X
Antonio GuaitaGolgi Cenci Foundation, Milan, Italy.
Roberta PastorelliDepartment of Environmental Health Sciences, Laboratory of Metabolites and Proteins in Translational Research, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy.
Gianluigi ForloniDepartment of Neuroscience, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy.ORCID 0000-0001-5374-3914
Cristina CeredaDepartment of Pediatrics, Center of Functional Genomics and Rare Diseases, Buzzi Children's Hospital, Milan, Italy.
Claudia BalducciDepartment of Neuroscience, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy.ORCID 0000-0002-3870-835X

Funding

Fondazione Cariplo 2017-0557
6 · The paper itself

Abstract

Frailty is a geriatric, multi-dimensional syndrome that reflects multisystem physiological change and is a transversal measure of reduced resilience to negative events. It is characterized by weakness, frequent falls, cognitive decline, increased hospitalization and dead and represents a risk factor for the development of Alzheimer's disease (AD). The fact that frailty is recognized as a reversible condition encourages the identification of earlier biomarkers to timely predict and prevent its occurrence. SAMP8 (Senescence-Accelerated Mouse Prone-8) mice represent the most appropriate preclinical model to this aim and were used in this study to carry transcriptional and metabolic analyses in the brain and plasma, respectively, upon a characterization at cognitive, motor, structural, and neuropathological level at 2.5, 6, and 9 months of age. At 2.5 months, SAMP8 mice started displaying memory deficits, muscle weakness, and motor impairment. Functional alterations were associated with a neurodevelopmental deficiency associated with reduced neuronal density and glial cell loss. Through transcriptomics, we identified specific genetic signatures well distinguishing SAMP8 mice at 6 months, whereas plasma metabolomics allowed to segregate SAMP8 mice from SAMR1 already at 2.5 months of age by detecting constitutively lower levels of acylcarnitines and lipids in SAMP8 at all ages investigated correlating with functional deficits and neuropathological signs. Our findings suggest that specific genetic alterations at central level, as well as metabolomic changes in plasma, might allow to early assess a frail condition leading to dementia development, which paves the foundation for future investigation in a clinical setting.

Indexed as

FrailtyTranscriptomeAgingAnimalsDisease Models, AnimalMaleMetabolomicsMiceagingdementiafrailtymotor impairmentneuronal losspredictive biomarkerssenescent SAMP8 mice

Identifiers

PMID38961613
PMCPMC11464142

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Registered trials

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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.