Evidence map›Paper›PMID 41644627›Full record

ArticleNPJ digital medicine2026

Neck-to-knee dixon MRI thigh volume as a superior mass biomarker for Sarcopenia: evidence from the UK biobank.

Hyeon Su Kim, Hyunwoo Park, Junseok Kang, Hyunbin Kim, Bonsang Gu, Bhola Shivam, Jun-Il Yoo

Abstract read
In one paragraph

Article in NPJ digital medicine, 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

7 authors.

Hyeon Su KimProgram in Biomedical Science and Engineering, Inha University, Incheon, South Korea.
Hyunwoo ParkDepartment of Internal Medicine, Hallym University Medical Center, Hallym University College of Medicine, Anyang, South Korea.
Junseok KangHarvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA.
Hyunbin KimProgram in Biomedical Science and Engineering, Inha University, Incheon, South Korea.
Bonsang GuDepartment of Biomedical Research Institute, Inha University Hospital, Incheon, South Korea.
Bhola ShivamDepartment of Biomedical Sciences, College of Medicine, and Program in Biomedical Science & Engineering, Inha University, Incheon, South Korea.
Jun-Il YooDepartment of Biomedical Sciences, College of Medicine, and Program in Biomedical Science & Engineering, Inha University, Incheon, South Korea. furim@hanmail.net.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Sarcopenia assessment requires biomarkers capturing muscle-specific strength beyond single-slice measurements. We developed an automated MRI framework segmenting 27 pelvic-thigh musculoskeletal structures to investigate muscle distribution as functional biomarkers. Among 37,004 UK Biobank participants (64.5 ± 7.9 years), transformer-based segmentation achieved Dice similarity coefficient of 0.896. Dixon MRI-derived thigh muscle volume showed exceptional DEXA concordance (r = 0.936). Posterior/anterior (P/A) muscle ratio independently predicted adverse outcomes: weak grip strength (OR 1.60, 95%CI 1.45-1.77), sarcopenia (OR 1.42, 95%CI 1.13-1.78), mortality (OR 1.49, 95%CI 1.23-1.81), and falls (OR 1.12, 95%CI 1.05-1.20), all p < 0.005, while left/right asymmetry showed no associations. Automated MRI phenotyping reveals muscle distribution patterns, particularly reduced anterior compartment volume, predict functional decline independent of total muscle mass, supporting evolution toward composition-aware sarcopenia criteria.

Identifiers

PMID41644627
PMCPMC13013591

What Socratic holds

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LicenceCC BY-NC-ND
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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 Socratic graph.