Clinical serum lipidomic profiling revealed potential lipid biomarkers for early diabetic retinopathy
Open accessAbstract
Diabetic retinopathy (DR) is a serious complication of diabetes featuring abnormal lipid metabolism. However, the specific lipid molecules associated with onset and progression remain unclear. We used a broad-targeted lipidomics approach to assess the lipid changes that occur before the proliferative retinopathy stage and to identify novel lipid biomarkers to distinguish between patients without DR (NDR) and with non-proliferative DR (NPDR). Targeted lipomics analysis was carried out on serum samples from patients with type I diabetes, including 20 NDRs and 20 NPDRs. The results showed that compared with the NDR group, 102 lipids in the NPDR group showed specific expressions. Four lipid metabolites including TAG58:2-FA18:1 were obtained using the Least Absolute Shrink And Selection Operator (LASSO) and Support Vector Machine Recursive Feature Elimination (SVM-RFE) methods. The four-lipid combination diagnostic models showed good predictive ability in both the discovery and validation sets, and were able to distinguish between NDR patients and NPDR patients. The identified lipid markers significantly improved diagnostic accuracy within the NPDR group. Our findings help to better understand the complexity and individual differences of DR lipid metabolism.
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Most omics-based DR studies reported relatively balanced case-control ratios (~1:1).
“Notably, most omics studies reported relatively balanced case–control ratios (~1:1) [56], contrasting with the frequent class imbalance seen in clinical datasets.”
From Artificial intelligence-powered prediction of diabetic complications: from clinical data to molecular omics (2026) · cites this paper as [56]
The Results (Data processing and analysis; page unavailable) report balanced NDR and NPDR groups in this lipidomics study: 20 per group in the discovery cohort and 11 per group in validation. This supports the 1:1 ratio as an example, but the paper does not establish that most omics-based DR studies have balanced groups or that clinical datasets frequently have class imbalance. (LLM, conf=0.88)
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- Authors
- Sun, W., Su, M., Zhuang, L., Ding, Y., Zhang, Q., & Lyu, D.
- Affiliations
- Hainan Provincial Hospital of Traditional Chinese Medicine (CN)
- Chinese Academy of Tropical Agricultural Sciences (CN)
- Corresponding author
- Lyu, Daizhu
- ORCID
- Zhang, Qianhui
- Venue
- Scientific Reports
- Published
- 2024-07-02
- Volume
- 14
- Issue
- 1
- Pages
- 15148-15148
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC11219800
- OpenAlex
- W4400255625
- Cited by
- 8
- References
- 31
- Citations by year
- 2026: 6 · 2025: 2
- FWCI
- 1.09
- Type
- journal article
- Language
- en
- Open access
- gold
- dx.doi.org
- Locations
- Scientific Reports (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Wen Sun (820MS148)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Metabolomics and Mass Spectrometry Studies
- Health Sciences › Medicine › Ophthalmology › Retinal Diseases and Treatments
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Research
- Keywords
- diabetic retinopathy, non-proliferative diabetic retinopathy, serum lipidomics, lipid biomarkers, type 1 diabetes, lipid metabolism, diagnostic biomarkers, early diagnosis
- MeSH terms
- Lipidomics, Adult, Diabetes Mellitus, Type 1, Diabetic Retinopathy, Female, Humans, Lipids, Male, Middle Aged, Biomarkers, Lipid Metabolism
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