Evidence map›Paper›PMID 41613876›Full record

ReviewBiophysical reviews2025

NMR metabolomics in genetically engineered mouse models.

Kamil N Aysin, Alexander Yu Rudenko, Sofia S Mariasina, Vladimir I Polshakov

Abstract readReview
In one paragraph

Review in Biophysical reviews, 2025. 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

4 authors.

Kamil N Aysin *Chemistry Department, Lomonosov Moscow State University, Moscow, 119991 Russia.
Alexander Yu Rudenko *Belozersky Institute of Physico-Chemical Biology, Lomonosov Moscow State University, Moscow, Russia.
Sofia S Mariasina *Chemistry Department, Lomonosov Moscow State University, Moscow, 119991 Russia.
Vladimir I PolshakovChemistry Department, Lomonosov Moscow State University, Moscow, 119991 Russia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Metabolomics has become a central approach for elucidating metabolic alterations associated with disease pathogenesis, therapeutic responses, and genetic perturbations. Among the dominant analytical platforms, nuclear magnetic resonance (NMR) spectroscopy is particularly valued for its reproducibility, quantitative accuracy, and minimal sample preparation. These strengths make NMR especially powerful for studies employing genetically engineered mouse models (GEMMs), which remain indispensable for investigating the molecular basis of human disease. This review examines key methodological aspects of NMR metabolomics, including data analysis platforms, the choice of pulse sequences, and strategies to enhance sensitivity and resolution. We summarize applications across major disease areas such as cancer, diabetes, and neurological disorders, with particular emphasis on stable isotope-resolved metabolomics, a powerful approach for dynamic pathway analysis and metabolic flux modeling in intact systems. We also highlight how NMR studies of knockout models have uncovered subtle metabolic perturbations and clarified gene-metabolism relationships. A recurring theme is the evaluation of reproducibility across GEMMs and the challenge of translating metabolic findings from mouse models to human pathophysiology. Finally, we outline current limitations and future directions for advancing the role of NMR metabolomics in preclinical and biomedical research.

Indexed as

Genetically engineered mouse models (GEMM)Isotope labelingModel organismsNMR metabolomics

Identifiers

PMID41613876
PMCPMC12847615

What OpenQuestion holds

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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.