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Plasma Metabolic Profile with Machine Learning Reveals Distinct Diagnostic and Biological Signatures for Pathologic
Ziheng Qi1, Jiao Qi2,3,4,5, Ye Zhang2,3,4,5
1School of Chemistry and Molecular Engineering, East China Normal University, Shanghai, 200241, P. R. China.
Abstract:
Pathologic myopia (PM), characterized by serious myopic macular degeneration (MMD), is a detrimental subtype of high myopia (HM) and has become one of the leading causes of blindness worldwide. In this concern, precise and high-throughput molecular diagnosis and further pathologic insights are urgently needed. Here, through the combined strategy of nanoparticle-enhanced laser desorption/ionization mass spectrometry-based rapid metabolic analysis (<30 s) and machine learning, a precise molecular diagnostic approach of PM (HM with MMD grade ≥ 2) is proposed, which achieves areas under the curve of 0.874 and 0.889 for diagnosing PM and early-stage PM, respectively. Further, the biomarkers indicate the PM-associated systemic metabolic reprogramming of amino acid and lipid metabolism, which may mediate dysfunctional oxidative stress, inflammation, hormone/neurotransmitter systems, and energy metabolism. Notably, MMD grade 4, featuring characteristic macula atrophy, exhibits specificity in this metabolic reprogramming. Of these biomarkers, azelaic acid shows a significant protective effect in the ARPE-19 cells under abnormal oxidative stress, which may be involved in PM development as a key antioxidative active metabolite. This work will contribute to PM molecular diagnosis and pathology exploration.
Insights
Pathologic myopia (PM) can now be precisely diagnosed using rapid metabolic analysis and machine learning. This approach identifies metabolic changes linked to PM and its vision-threatening complications.
Area of Science:
- Ophthalmology
- Metabolomics
- Biomarker Discovery
Background:
- Pathologic myopia (PM) with myopic macular degeneration (MMD) is a leading cause of global blindness.
- Accurate molecular diagnosis and understanding of PM pathology are critically needed.
Purpose of the Study:
- To develop a rapid, high-throughput molecular diagnostic approach for PM using metabolomics and machine learning.
- To identify metabolic biomarkers associated with PM and MMD progression.
Main Methods:
- Utilized nanoparticle-enhanced laser desorption/ionization mass spectrometry for rapid metabolic profiling (<30 seconds).
- Applied machine learning algorithms to diagnose PM (MMD grade ≥ 2) and early-stage PM.
- Analyzed systemic metabolic reprogramming, focusing on amino acid and lipid metabolism.
Main Results:
- Achieved high diagnostic accuracy for PM (AUC=0.874) and early-stage PM (AUC=0.889).
- Identified PM-associated metabolic reprogramming involving oxidative stress, inflammation, and energy metabolism.
- Found specific metabolic alterations in advanced MMD (grade 4) and identified azelaic acid as a potential protective metabolite.
Conclusions:
- This novel approach enables precise molecular diagnosis of PM.
- Metabolic reprogramming is central to PM pathology, offering new insights into disease mechanisms.
- Azelaic acid shows potential as a key antioxidative metabolite in PM development.
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