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Li J, Guo C, Wang T, Xu Y, Peng F, Zhao S, Li H, Jin D, Xia Z, Che M, Zuo J, Zheng C, Hu H, Mao G. Interpretable machine learning-derived nomogram model for early detection of diabetic retinopathy in type 2 diabetes mellitus: a widely targeted metabolomics study. Nutr Diabetes 2022; 12:36. [PMID: 35931671 PMCID: PMC9355962 DOI: 10.1038/s41387-022-00216-0] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Revised: 07/10/2022] [Accepted: 07/18/2022] [Indexed: 01/20/2023] Open
Abstract
Objective Early identification of diabetic retinopathy (DR) is key to prioritizing therapy and preventing permanent blindness. This study aims to propose a machine learning model for DR early diagnosis using metabolomics and clinical indicators. Methods From 2017 to 2018, 950 participants were enrolled from two affiliated hospitals of Wenzhou Medical University and Anhui Medical University. A total of 69 matched blocks including healthy volunteers, type 2 diabetes, and DR patients were obtained from a propensity score matching-based metabolomics study. UPLC-ESI-MS/MS system was utilized for serum metabolic fingerprint data. CART decision trees (DT) were used to identify the potential biomarkers. Finally, the nomogram model was developed using the multivariable conditional logistic regression models. The calibration curve, Hosmer–Lemeshow test, receiver operating characteristic curve, and decision curve analysis were applied to evaluate the performance of this predictive model. Results The mean age of enrolled subjects was 56.7 years with a standard deviation of 9.2, and 61.4% were males. Based on the DT model, 2-pyrrolidone completely separated healthy controls from diabetic patients, and thiamine triphosphate (ThTP) might be a principal metabolite for DR detection. The developed nomogram model (including diabetes duration, systolic blood pressure and ThTP) shows an excellent quality of classification, with AUCs (95% CI) of 0.99 (0.97–1.00) and 0.99 (0.95–1.00) in training and testing sets, respectively. Furthermore, the predictive model also has a reasonable degree of calibration. Conclusions The nomogram presents an accurate and favorable prediction for DR detection. Further research with larger study populations is needed to confirm our findings.
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Affiliation(s)
- Jushuang Li
- Division of Epidemiology and Health Statistics, Department of Preventive Medicine, School of Public Health & Management, Wenzhou Medical University, Wenzhou, Zhejiang, China.,Center on Evidence-Based Medicine & Clinical Epidemiological Research, School of Public Health & Management, Wenzhou Medical University, Wenzhou, Zhejiang, China.,Department of Medical Statistics and Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Chengnan Guo
- Division of Epidemiology and Health Statistics, Department of Preventive Medicine, School of Public Health & Management, Wenzhou Medical University, Wenzhou, Zhejiang, China.,Center on Evidence-Based Medicine & Clinical Epidemiological Research, School of Public Health & Management, Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Tao Wang
- Division of Epidemiology and Health Statistics, Department of Preventive Medicine, School of Public Health & Management, Wenzhou Medical University, Wenzhou, Zhejiang, China.,Center on Evidence-Based Medicine & Clinical Epidemiological Research, School of Public Health & Management, Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Yixi Xu
- Division of Epidemiology and Health Statistics, Department of Preventive Medicine, School of Public Health & Management, Wenzhou Medical University, Wenzhou, Zhejiang, China.,Center on Evidence-Based Medicine & Clinical Epidemiological Research, School of Public Health & Management, Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Fang Peng
- Division of Epidemiology and Health Statistics, Department of Preventive Medicine, School of Public Health & Management, Wenzhou Medical University, Wenzhou, Zhejiang, China.,Center on Evidence-Based Medicine & Clinical Epidemiological Research, School of Public Health & Management, Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Shuzhen Zhao
- Division of Epidemiology and Health Statistics, Department of Preventive Medicine, School of Public Health & Management, Wenzhou Medical University, Wenzhou, Zhejiang, China.,Center on Evidence-Based Medicine & Clinical Epidemiological Research, School of Public Health & Management, Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Huihui Li
- Division of Epidemiology and Health Statistics, Department of Preventive Medicine, School of Public Health & Management, Wenzhou Medical University, Wenzhou, Zhejiang, China.,Center on Evidence-Based Medicine & Clinical Epidemiological Research, School of Public Health & Management, Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Dongzhen Jin
- Division of Epidemiology and Health Statistics, Department of Preventive Medicine, School of Public Health & Management, Wenzhou Medical University, Wenzhou, Zhejiang, China.,Center on Evidence-Based Medicine & Clinical Epidemiological Research, School of Public Health & Management, Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Zhezheng Xia
- Division of Epidemiology and Health Statistics, Department of Preventive Medicine, School of Public Health & Management, Wenzhou Medical University, Wenzhou, Zhejiang, China.,Center on Evidence-Based Medicine & Clinical Epidemiological Research, School of Public Health & Management, Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Mingzhu Che
- Division of Epidemiology and Health Statistics, Department of Preventive Medicine, School of Public Health & Management, Wenzhou Medical University, Wenzhou, Zhejiang, China.,Center on Evidence-Based Medicine & Clinical Epidemiological Research, School of Public Health & Management, Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Jingjing Zuo
- Center on Clinical Research, School of Ophthalmology & Optometry, Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Chao Zheng
- The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Honglin Hu
- Department of Endocrinology, the First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China.
| | - Guangyun Mao
- Division of Epidemiology and Health Statistics, Department of Preventive Medicine, School of Public Health & Management, Wenzhou Medical University, Wenzhou, Zhejiang, China. .,Center on Evidence-Based Medicine & Clinical Epidemiological Research, School of Public Health & Management, Wenzhou Medical University, Wenzhou, Zhejiang, China. .,Center on Clinical Research, School of Ophthalmology & Optometry, Wenzhou Medical University, Wenzhou, Zhejiang, China.
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Richmond JE, Bulloch AG, Bauce L, Lukowiak K. Evidence for the presence, synthesis, immunoreactivity, and uptake of GABA in the nervous system of the snail Helisoma trivolvis. J Comp Neurol 1991; 307:131-43. [PMID: 1856317 DOI: 10.1002/cne.903070112] [Citation(s) in RCA: 25] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
In the present study several techniques were employed to test the hypothesis that gamma-aminobutyric acid (GABA) is a neurotransmitter in the central nervous system (CNS) of the pond snail Helisoma trivolvis (Mollusca, Pulmonata). First, by using chromatographic techniques, the presence of GABA and its differential distribution among the ganglia constituting the CNS was demonstrated. Second, de novo synthesis of 3H-GABA from 3H-glutamate was shown by the CNS. Levels of both endogenous and newly synthesized GABA were greatest in the buccal, cerebral, and pedal ganglia. Third, indirect immunohistochemistry of wholemounts revealed a central network of GABA-like immunoreactive neurons. With the possible exceptions of two pairs of fibers in nerve trunks, all projections from GABA-immunoreactive neurons were confined to the CNS, suggesting a predominantly central role for GABA. Stained neurons were found on the dorsal surface of the buccal ganglia and throughout the cerebral and pedal ganglia. No GABA-immunoreactive cell bodies were observed in the parietal, pleural, or visceral ganglia. Finally, uptake of 3H-GABA was examined autoradiographically in sectioned ganglia. A pattern of radiolabelled cells was observed that closely resembled the distribution of GABA-immunoreactive neurons. The data described above fulfill several criteria necessary to establish GABA as a transmitter in the nervous system of Helisoma. Taken together with previously obtained pharmacological evidence demonstrating that GABA acts on Helisoma central neurons, GABA is considered to be a strong candidate for a neurotransmitter in Helisoma.
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Affiliation(s)
- J E Richmond
- Department of Medical Physiology, University of Calgary, Alberta, Canada
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