Home › Research › Prevention and risk
Precision prevention in exercise: Integrating OMICS biomarkers and artificial intelligence for injury risk assessment
Mohammad Nasb, Lamis Dayoub, Ning Chen. Advanced Exercise and Health Science, 2025-10-04
Topic: Brain proteins, microglia and lab models
Exercise injuries present a significant challenge in medicine and public health, impacting exercise performance, career longevity, and overall quality of life. While OMICS technologies such as genomics, transcriptomics, proteomics, and metabolomics offer unprecedented insights into individual susceptibility, recovery, and performance optimization, their full potential is realized through the integration of artificial intelligence (AI). This article explores the transformative role of OMICS biomarkers in understanding the molecular underpinnings of exercise injuries. Crucially, it emphasizes how AI and advanced data analytics are essential for processing and interpreting the vast, complex datasets generated by multi-OMICS strategies. By leveraging machine learning algorithms, AI can identify subtle patterns and predictive biomarkers, thereby significantly enhancing the accuracy of injury risk assessment, enabling personalized intervention strategies, and facilitating real-time monitoring of exercise, health and recovery. This integration moves beyond traditional biological insights, paving the way for truly predictive and preventive precision exercise medicine. Furthermore, this article addresses the ethical, legal, and logistical challenges associated with the application of OMICS and AI technologies, emphasizing the need for robust regulatory frameworks and equitable access to these advancements. The synergistic combination of OMICS and AI stands as a cornerstone of future precision exercise medicine, promising a safer, more effective, and highly personalized exercise experience.
Key findings
- Exercise injuries present a significant challenge in medicine and public health, impacting exercise performance, career longevity, and overall quality of life.
- By leveraging machine learning algorithms, AI can identify subtle patterns and predictive biomarkers, thereby significantly enhancing the accuracy of injury risk assessment, enabling personalized intervention strategies, and facilitating real-time monitoring of exercise, health and recovery.
- Furthermore, this article addresses the ethical, legal, and logistical challenges associated with the application of OMICS and AI technologies, emphasizing the need for robust regulatory frameworks and equitable access to these advancements.
Why it matters
This article is about using OMICS and artificial intelligence to assess exercise injury risk and contains no information on Alzheimer's disease. It reports no findings relevant to treating, curing or preventing dementia.
New to this subject? Start with our plain-English guide: Prevention →
Study details
- Evidence strengthLow
How this study scored 38/100
Published in a peer-reviewed journal. Scored zero for study design, sample size, effect reported, studied in people and randomised / blinded because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Advanced Exercise and Health Science) |
| Journal's citation record | 0 / 4 | Advanced Exercise and Health Science: journal h-index 8 (OpenAlex, 2026-10-29) |
| Study design | 0 / 12 | No study design recorded — scores zero, not guessed (not stated in the record — scored 0) |
| Strength of evidence | 1 / 5 | Rated “low” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 4 / 5 | 2025 — within 3 year(s) of the newest study here |
| Sample size | 0 / 4 | Participant count not stated — scores zero (not stated in the record — scored 0) |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 0 / 3 | Not stated whether this was studied in people — scores zero (not stated in the record — scored 0) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 2 / 7 | Cited 9 times (OpenAlex, 2026-10-10) |
| Citations per year | 2 / 5 | 3.0 citations a year over 3 year(s) since 2025 |
| Impact for its field and year | 4 / 6 | Cited 8.5× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 0 / 6 | No other study in this library cites it |
| Still being cited | 1 / 4 | Cited 9 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 3 / 4 | Last-listed author Ning Chen: h-index 62 (OpenAlex, whole career) |
| Institution's track record | 0 / 3 | Huazhong Agricultural University: institution h-index 528 (OpenAlex) |
| Multi-centre collaboration | 1 / 3 | OpenAlex lists 3 institution(s) in 1 country |
| Indexed in PubMed | 1 / 2 | Not in PubMed, but has a DOI |
| Free to read | 2 / 2 | Free to read |
| Total | 38 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
- DOI: 10.1016/j.aehs.2025.09.001
- Original source →
- Free full text: Advanced Exercise and Health Science →
Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. Check the citation against the original before you use it.
APA 7th
Nasb, M., Dayoub, L., & Chen, N. (2025). Precision prevention in exercise: Integrating OMICS biomarkers and artificial intelligence for injury risk assessment. Advanced Exercise and Health Science. https://doi.org/10.1016/j.aehs.2025.09.001
Vancouver
Nasb M, Dayoub L, Chen N. Precision prevention in exercise: Integrating OMICS biomarkers and artificial intelligence for injury risk assessment. Advanced Exercise and Health Science. 2025. doi:10.1016/j.aehs.2025.09.001.
BibTeX
@article{nasb2025-81f12896,
author = {Nasb, Mohammad and Dayoub, Lamis and Chen, Ning},
title = {{Precision prevention in exercise: Integrating OMICS biomarkers and artificial intelligence for injury risk assessment}},
journal = {Advanced Exercise and Health Science},
year = {2025},
doi = {10.1016/j.aehs.2025.09.001},
url = {https://doi.org/10.1016/j.aehs.2025.09.001}
}