Evidence mapPaperPMID 42483609Full record

ReviewHuman mutation2026

From Variant Interpretation to Biomarker Translation: Multi-omics Integration in Inherited Neuromuscular Diseases.

Suming Zhang, Xiaoling Lang, Lunxin Liu

Abstract readReview
In one paragraph

Review in Human mutation, 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

3 authors.

Suming ZhangDepartment of Radiology, Key Laboratory of Obstetric & Gynecologic and Pediatric Disease and Birth Defects of Ministry of Education, West China Second University Hospital, Sichuan University, Chengdu, Sichuan, China, scu.edu.cn.ORCID https://orcid.org/0009-0001-0337-2526
Xiaoling LangOffice of Operations Management, West China Second University Hospital, Sichuan University, Chengdu, Sichuan, China, scu.edu.cn.ORCID https://orcid.org/0009-0001-5245-7219
Lunxin LiuDepartment of Neurosurgery, West China Hospital, Sichuan University, Chengdu, Sichuan, China, scu.edu.cn.ORCID https://orcid.org/0000-0002-8973-8612

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Genetic neuromuscular diseases are highly heterogeneous disorders characterized by diagnostic challenges and limited therapeutic options, underscoring an urgent need for precise biomarkers. The rapid advancement of multi-omics technologies has broadened biomarker discovery from single genomics to multidimensional integrative analyses encompassing transcriptomics, proteomics, and metabolomics. This progression offers opportunities to improve disease diagnosis, subtyping, prognosis assessment, and treatment monitoring. However, translational gaps persist between multi-omics discoveries and clinically applicable biomarkers. This review systematically examines the current application of multi-omics biomarkers in genetic neuromuscular diseases. It provides an in-depth analysis of the multifaceted barriers encountered during the translation process, including technical hurdles, clinical validation complexities, data interpretation challenges, and health system-level obstacles. Furthermore, the review explores emerging solutions including artificial intelligence-assisted decision-making, ethical governance, and policy preparedness. The review aims to offer a framework for constructing a potentially responsible and efficient multi-omics translation in genetic neuromuscular diseases.

Indexed as

BiomarkersNeuromuscular DiseasesGenomicsHumansMetabolomicsMultiomicsProteomicsTranslational Research, BiomedicalBiomarkersartificial intelligencebiomarkersclinical translationgenetic neuromuscular diseasesmulti-omics integrationvariant of uncertain significance

Identifiers

PMID42483609
PMCPMC13386474

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.