Construction of Risk Prediction Model of Type 2 Diabetic Kidney Disease Based on Deep Learning (Diabetes Metab J 2024;48:771-9)

Bo Mi Seo; Jong Wook Choi

doi:10.4093/dmj.2024.0464

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Letter Construction of Risk Prediction Model of Type 2 Diabetic Kidney Disease Based on Deep Learning (Diabetes Metab J 2024;48:771-9): Bo Mi Seo¹, Jong Wook Choi²; Diabetes & Metabolism Journal 2024;48(5):1003-1004.
DOI: https://doi.org/10.4093/dmj.2024.0464
Published online: September 12, 2024

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¹Graduate School of Medicine, Hanyang University, Seoul, Korea

²Department of Internal Medicine, Hanyang University College of Medicine, Seoul, Korea

Corresponding author: Jong Wook Choi

Department of Internal Medicine, Hanyang University College of Medicine, 222-1 Wangsimni-ro, Seongdong-gu, Seoul 04763, Korea E-mail: sodasoo@empas.com

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

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See the reply "Construction of Risk Prediction Model of Type 2 Diabetic Kidney Disease Based on Deep Learning (Diabetes Metab J 2024;48:771-9)" in Volume 48 on page 1008.

See the article "Construction of Risk Prediction Model of Type 2 Diabetic Kidney Disease Based on Deep Learning" on page 771.

Diabetic kidney disease (DKD) develops in approximately 40% of patients with diabetes and is the leading cause of end-stage renal disease worldwide [1]. DKD is clinically diagnosed based on reduced renal function or elevated urinary albumin excretion. Once diagnosed, approximately 30% of patients with microalbuminuria progress to macroalbuminuria, even after active intervention [2]. Therefore, it is important to screen individuals for high risk of DKD, who may benefit from early intervention. Although several meta-analyses and regression models for risk prediction using clinical demographics and laboratory data have been suggested, their accuracy is low [3]. Incorporating machine learning methods, such as random forest, decision tree, and support vector machine (SVM), have shown superior results; however, those analyses are prone to over-fitting [4]. Deep learning technology provides solutions to these limitations, as numerous studies employing deep learning technology in diagnosis and prediction have achieved higher accuracy and precision [5].

Yun et al. [6] have built a prediction model using a long short-term memory (LSTM) neural network to assess the risk of DKD. A total of 6,040 patients with type 2 diabetes mellitus (T2DM) was included based on 7-year data from Lee’s United Clinic, Taiwan. The selected risk factors were older age, longer duration of diabetes, family history of DKD, smoking, diabetic retinopathy, mean glycosylated hemoglobin (HbA1c), hypertension, lack of regular exercise, high blood uric acid level, insulin resistance, high body mass index, systolic blood pressure (SBP), high-density lipoprotein cholesterol level, triglycerides, urinary albumin-to-creatinine ratio, cystatin C, meat intake, number of oral hypoglycemic medications, and variability in HbA1c, SBP, and pulse pressure (PP). The LSTM model was trained to use the first 2 years of data to predict the risk of DKD in the next 5 years. The prediction performance of the LSTM model was compared with that of the SVM model in terms of precision, accuracy, recall, and area under the curve (AUC). The results demonstrated that LSTM was superior in all the above parameters. In addition, to verify the effects of the variability of HbA1c, SBP, and PP on the performance of the LSTM model, models were constructed to exclude each of HbA1c, SBP, or PP variability and compared with the LSTM model that included all variability parameters. The LSTM model with all parameters demonstrated higher levels of accuracy and AUC, resulting in enhanced performance.

However, several aspects of this study require further investigation. Although this study was conducted at Lee’s United Clinic, which comprises six clinics, it was a single-center study, and data collection was limited to the Taiwanese population. The presentation of DKD varies considerably among countries and ethnicities. For example, African-American and Asian patients have a higher prevalence of albuminuria than European patients [7]. In addition, several studies have shown that male sex is associated with a higher risk of DKD progression in patients with T2DM [8]. Therefore, ethnicity and sex should be included as risk factors. Moreover, the types of drugs that patients were administered need to be categorized as either renoprotective medications, including sodium-glucose cotransporter-2 inhibitors and renin-angiotensin system blocking drugs, or nephrotoxic medications, such as non-steroidal antiinflammatory drugs. Because the occurrence of acute kidney injury also influences the development of DKD, it would have been more advantageous to consider both baseline creatinine levels and creatinine variability data as risk factors [9]. Finally, although LSTM demonstrated better performance than the SVM model, it remains unclear whether it would be superior to other machine learning models, such as the CatBoost classifier, random forest, or gradient boosted tree, as previous studies have shown higher accuracy and AUC for these models than for SVM models [10].

This study shows the possibility of incorporating deep learning technology to predict DKD development in patients with T2DM, with a relatively longer follow-up duration compared to other previous studies. In addition, although an in-depth analysis of the mechanism by which HbA1c, SBP, and PP affect the development of DKD remains unclear, these variability parameters enhance the performance of the predictive model, which may offer insights into the pathogenesis of DKD. Therefore, this new screening tool uses easily accessible data and, when utilized, is anticipated to be an economical and effective means of predicting DKD.

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CONFLICTS OF INTEREST

No potential conflict of interest relevant to this article was reported.

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Construction of Risk Prediction Model of Type 2 Diabetic Kidney Disease Based on Deep Learning (Diabetes Metab J 2024;48:771-9)

Diabetes Metab J. 2024;48(5):1003-1004. Published online September 12, 2024

DOI: https://doi.org/10.4093/dmj.2024.0464

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Construction of Risk Prediction Model of Type 2 Diabetic Kidney Disease Based on Deep Learning (Diabetes Metab J 2024;48:771-9)

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Seo BM, Choi JW. Construction of Risk Prediction Model of Type 2 Diabetic Kidney Disease Based on Deep Learning (Diabetes Metab J 2024;48:771-9). Diabetes Metab J. 2024;48(5):1003-1004.

DOI: https://doi.org/10.4093/dmj.2024.0464.

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