Yuhui Pang, Chaokun Luo, Qingruo Zhang, Xiongze Zhang, Nanying Liao, Yuying Ji, Lan Mi, Yuhong Gan, Yongyue Su, Feng Wen, Hui Chen
{"title":"多组学集成与机器学习识别早期糖尿病视网膜病变，糖尿病黄斑水肿和抗vegf治疗反应。","authors":"Yuhui Pang, Chaokun Luo, Qingruo Zhang, Xiongze Zhang, Nanying Liao, Yuying Ji, Lan Mi, Yuhong Gan, Yongyue Su, Feng Wen, Hui Chen","doi":"10.1167/tvst.13.12.23","DOIUrl":null,"url":null,"abstract":"<p><strong>Purpose: </strong>Identify optimal metabolic features and pathways across diabetic retinopathy (DR) stages, develop risk models to differentiate diabetic macular edema (DME), and predict anti-vascular endothelial growth factor (anti-VEGF) therapy response.</p><p><strong>Methods: </strong>We analyzed 108 aqueous humor samples from 78 type 2 diabetes mellitus patients and 30 healthy controls. Ultra-high-performance liquid chromatography-high-resolution-mass-spectrometry detected lipidomics and metabolomics profiles. DME patients received ≥3 anti-VEGF treatments, categorized into strong and weak response groups. Machine learning (ML) screened prospective metabolic features, developing prediction models.</p><p><strong>Results: </strong>Key metabolic features identified in the metabolomics and lipidomics datasets included n-acetyl isoleucine (odds ratio [OR] = 1.635), cis-aconitic acid (OR = 3.296), and ophthalmic acid (OR = 0.836) for DR. For early-DR, n-acetyl isoleucine (OR = 1.791) and decaethylene glycol (PEG-10) (OR = 0.170) were identified as key markers. L-kynurenine (OR = 0.875), niacinamide (OR = 0.843), and linoleoyl ethanolamine (OR = 0.941) were identified as significant indicators for DME. Trigonelline (OR = 1.441) and 4-methylcatechol-2-sulfate (OR = 1.121) emerged as predictors for strong response to anti-VEGF. Predictive models achieved R² values of 99.9%, 97.7%, 93.9%, and 98.4% for DR, early-DR, DME, and strong response groups in the calibration set, respectively, and validated well with R² values of 96.3%, 96.8%, 79.9%, and 96.3%.</p><p><strong>Conclusions: </strong>This research used ML to identify differential metabolic features from metabolomics and lipidomics datasets in DR patients. It implies that metabolic indicators can effectively predict early disease progression and potential weak responders to anti-VEGF therapy in DME eyes.</p><p><strong>Translational relevance: </strong>The identified metabolic indicators may aid in predicting the early progression of DR and optimizing therapeutic strategies for DME.</p>","PeriodicalId":23322,"journal":{"name":"Translational Vision Science & Technology","volume":"13 12","pages":"23"},"PeriodicalIF":2.6000,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11645727/pdf/","citationCount":"0","resultStr":"{\"title\":\"Multi-Omics Integration With Machine Learning Identified Early Diabetic Retinopathy, Diabetic Macula Edema and Anti-VEGF Treatment Response.\",\"authors\":\"Yuhui Pang, Chaokun Luo, Qingruo Zhang, Xiongze Zhang, Nanying Liao, Yuying Ji, Lan Mi, Yuhong Gan, Yongyue Su, Feng Wen, Hui Chen\",\"doi\":\"10.1167/tvst.13.12.23\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Purpose: </strong>Identify optimal metabolic features and pathways across diabetic retinopathy (DR) stages, develop risk models to differentiate diabetic macular edema (DME), and predict anti-vascular endothelial growth factor (anti-VEGF) therapy response.</p><p><strong>Methods: </strong>We analyzed 108 aqueous humor samples from 78 type 2 diabetes mellitus patients and 30 healthy controls. Ultra-high-performance liquid chromatography-high-resolution-mass-spectrometry detected lipidomics and metabolomics profiles. DME patients received ≥3 anti-VEGF treatments, categorized into strong and weak response groups. Machine learning (ML) screened prospective metabolic features, developing prediction models.</p><p><strong>Results: </strong>Key metabolic features identified in the metabolomics and lipidomics datasets included n-acetyl isoleucine (odds ratio [OR] = 1.635), cis-aconitic acid (OR = 3.296), and ophthalmic acid (OR = 0.836) for DR. For early-DR, n-acetyl isoleucine (OR = 1.791) and decaethylene glycol (PEG-10) (OR = 0.170) were identified as key markers. L-kynurenine (OR = 0.875), niacinamide (OR = 0.843), and linoleoyl ethanolamine (OR = 0.941) were identified as significant indicators for DME. Trigonelline (OR = 1.441) and 4-methylcatechol-2-sulfate (OR = 1.121) emerged as predictors for strong response to anti-VEGF. Predictive models achieved R² values of 99.9%, 97.7%, 93.9%, and 98.4% for DR, early-DR, DME, and strong response groups in the calibration set, respectively, and validated well with R² values of 96.3%, 96.8%, 79.9%, and 96.3%.</p><p><strong>Conclusions: </strong>This research used ML to identify differential metabolic features from metabolomics and lipidomics datasets in DR patients. 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Multi-Omics Integration With Machine Learning Identified Early Diabetic Retinopathy, Diabetic Macula Edema and Anti-VEGF Treatment Response.
Purpose: Identify optimal metabolic features and pathways across diabetic retinopathy (DR) stages, develop risk models to differentiate diabetic macular edema (DME), and predict anti-vascular endothelial growth factor (anti-VEGF) therapy response.
Methods: We analyzed 108 aqueous humor samples from 78 type 2 diabetes mellitus patients and 30 healthy controls. Ultra-high-performance liquid chromatography-high-resolution-mass-spectrometry detected lipidomics and metabolomics profiles. DME patients received ≥3 anti-VEGF treatments, categorized into strong and weak response groups. Machine learning (ML) screened prospective metabolic features, developing prediction models.
Results: Key metabolic features identified in the metabolomics and lipidomics datasets included n-acetyl isoleucine (odds ratio [OR] = 1.635), cis-aconitic acid (OR = 3.296), and ophthalmic acid (OR = 0.836) for DR. For early-DR, n-acetyl isoleucine (OR = 1.791) and decaethylene glycol (PEG-10) (OR = 0.170) were identified as key markers. L-kynurenine (OR = 0.875), niacinamide (OR = 0.843), and linoleoyl ethanolamine (OR = 0.941) were identified as significant indicators for DME. Trigonelline (OR = 1.441) and 4-methylcatechol-2-sulfate (OR = 1.121) emerged as predictors for strong response to anti-VEGF. Predictive models achieved R² values of 99.9%, 97.7%, 93.9%, and 98.4% for DR, early-DR, DME, and strong response groups in the calibration set, respectively, and validated well with R² values of 96.3%, 96.8%, 79.9%, and 96.3%.
Conclusions: This research used ML to identify differential metabolic features from metabolomics and lipidomics datasets in DR patients. It implies that metabolic indicators can effectively predict early disease progression and potential weak responders to anti-VEGF therapy in DME eyes.
Translational relevance: The identified metabolic indicators may aid in predicting the early progression of DR and optimizing therapeutic strategies for DME.
期刊介绍：
Translational Vision Science & Technology (TVST), an official journal of the Association for Research in Vision and Ophthalmology (ARVO), an international organization whose purpose is to advance research worldwide into understanding the visual system and preventing, treating and curing its disorders, is an online, open access, peer-reviewed journal emphasizing multidisciplinary research that bridges the gap between basic research and clinical care. A highly qualified and diverse group of Associate Editors and Editorial Board Members is led by Editor-in-Chief Marco Zarbin, MD, PhD, FARVO.
The journal covers a broad spectrum of work, including but not limited to:
Applications of stem cell technology for regenerative medicine,
Development of new animal models of human diseases,
Tissue bioengineering,
Chemical engineering to improve virus-based gene delivery,
Nanotechnology for drug delivery,
Design and synthesis of artificial extracellular matrices,
Development of a true microsurgical operating environment,
Refining data analysis algorithms to improve in vivo imaging technology,
Results of Phase 1 clinical trials,
Reverse translational ("bedside to bench") research.
TVST seeks manuscripts from scientists and clinicians with diverse backgrounds ranging from basic chemistry to ophthalmic surgery that will advance or change the way we understand and/or treat vision-threatening diseases. TVST encourages the use of color, multimedia, hyperlinks, program code and other digital enhancements.
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