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Interpretable Machine Learning-Derived Nomogram Model for Early Detection of Diabetic Retinopathy in Type 2 Diabetes Mellitus: A Widely Targeted Metabolomics Study
30 Pages Posted: 12 May 2021
More...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 data.
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 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 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 accurate and favorable prediction for DR detection. Further research with larger study populations is needed to confirm our findings.
Funding: This work was supported by Natural Science Foundation of Zhejiang Province (LZ19H020001), Zhejiang Basic Public Welfare Research Project (LGF19H260011), Zhejiang College Students' Science and Technology Innovation activity and Xinmiao Talent Plan (2019R413073), the Initial Scientific Research Funding (KYQD170301), the Major Project of the Eye Hospital of Wenzhou Medical University (YNZD201602). Part of this work was also funded by National Nature Science Foundation of China (81670777).
Declaration of Interest: The authors declare that they have no conflict of interest.
Ethical Approval: This study was conducted according to the Declaration of Helsinki principles. The study protocol was approved by the Ethics Committee of the Eye hospital of WMU (Number: KYK [2017] 46). All participants in the current study were voluntary and provided written informed consent.
Keywords: Diabetic retinopathy; Widely targeted metabolomics; Decision tree; Thiamine triphosphate; Early recognition; Propensity score matching
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