Evidence mapPaperPMID 41465849Full record

ReviewLife (Basel, Switzerland)2025

Advancing Precision Diagnosis of Sarcopenic Obesity Through Digital Technologies, Wearables and Omics Data.

Grigorios Panagiotou, Soren Brage

Abstract readReview
In one paragraph

Review in Life (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

2 authors.

Grigorios PanagiotouMRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge CB2 0QQ, UK.
Soren BrageMRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge CB2 0QQ, UK.

Funding

Medical Research Council MC_UU_0006/4National Institute for Health Research CL-2022-14-004NIHR Cambridge Biomedical Research Centre IS-BRC-1215-20014
6 · The paper itself

Abstract

Sarcopenic obesity, the coexistence of excess adiposity with loss of muscle mass and function, is becoming increasingly prevalent. The condition is linked to higher morbidity and mortality but its diagnosis remains limited by reliance on body composition methods that are costly, inaccessible, and/or involve radiation exposure. Recent advances in bioinformatics, data analytics, and digital health technologies create opportunities for scalable, precise approaches to detection. This narrative review synthesizes current evidence from the published literature on online medical libraries (Pubmed, Medline, Scopus, Google Scholar) until September 2025 on multi-omics, digital phenotyping and eHealth research, highlighting how these tools can refine risk stratification and extend diagnostic reach beyond traditional methods. We describe the potential utility of wearable sensor technologies, and smartphone-based body composition methods, as well as genomics, proteomics, transcriptomics and metabolomics. Such approaches, alone or in combination, may enable earlier identification of sarcopenic obesity, including in individuals who are not routinely prioritized for screening. We conclude that integrating biological and digital data offers promise for advancing precision diagnostics in sarcopenic obesity, enabling more tailored prevention and intervention strategies while ultimately reducing healthcare burden. Further research is required to determine the feasibility, clinical utility and scalability of such innovations before their widespread implementation.

Indexed as

digital toolseHealthmuscle lossprecision diagnosisremote methodologiessarcopeniasarcopenic obesity

Identifiers

PMID41465849
PMCPMC12735172

What Socratic 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 Socratic graph.