Detection of Lower Albuminuria Levels and Early Development of Diabetic Kidney Disease Using an Artificial Intelligence-Based Rule Extraction Approach

Abstract

The aim of the present study was to determine the lowest cut-off value for albuminuria levels, which can be used to detect diabetic kidney disease (DKD) using the urinary albumin-to-creatinine ratio (UACR). National Health and Nutrition Examination Survey (NHANES) data for US adults were used, and participants were classified as having diabetes or prediabetes based on a self-report and physiological measures. The study dataset comprised 942 diabetes and 524 prediabetes samples. This study clarified the significance of the lower albuminuria (UACR) levels, which can detect DKD, using an artificial intelligence-based rule extraction approach. The diagnostic rules (15 concrete rules) for both samples were extracted using a recursive-rule eXtraction (Re-RX) algorithm with continuous attributes (continuous Re-RX) to discriminate between prediabetes and diabetes datasets. Continuous Re-RX showed high test accuracy (77.56%) and a large area under the receiver operating characteristics curve (75%), which derived the two cut-off values (6.1 mg/g Cr and 71.00 mg/g Cr) for the lower albuminuria level in the UACR to detect early development of DKD. The early cut-off values for normoalbuminuria (NA) and microalbuminuria (MA) will be determined to help detect CKD and DKD, and to detect diabetes before MA develop and to prevent diabetic complications.

Poor Glycemic Control Can Increase the Plasma Kidney Injury Molecule-1 Concentration in Normoalbuminuric Children and Adolescents with Diabetes Mellitus

“…Recent studies have suggested lower UACR cutoffs to detect NA and MA for the earlier detection of CKD and DN [9]. Furthermore, many studies have reported individual MA cutoffs for different diabetic conditions such as 10.00 mg/g Cr for diabetic retinopathy and 19.25 mg/g Cr for hypertension [6]. Likewise, our study demonstrated that lowering the UACR threshold (to <10.00 mg/g Cr) could lead to the early detection of DN development.…”

“…Microalbuminuria (MA) has been utilized as an indicator of incipient DN and the severity of DN can depend upon the degree of MA [5]. Nevertheless, numerous studies have demonstrated that MA could not be a specific indicator of renal injury and its utility in predicting DN progression is limited because various pathological changes and an eGFR decline are observed in the presence of NA [2,6]. Progressive renal decline could be initiated at even 10% NA, 30% MA and 50% proteinuric states in DM patients [7].…”

Artificial Intelligence for the Diagnosis and Treatment of Diabetes Kidney Disease: a systematic review

“…The details of the selected articles are presented in Table -3 The scheme of this study is summarized into four different categories based on the AI framework in use. The categories include 1) predictive AI model for the early detection of DN (40,44,45,46,48,51,54,55,59,61), 2) AI Framework for the diagnosis of DN (42,50,57), 3) predictive model for the progression of DKD (39,41,49,58,62), 4) the management of DN for existing patients OR early management indication to DM patients without DKD with the help of AI (43,47,52,53,56,60). The studies included in this systematic review are from various parts of the world.…”

The evolution and future of diabetic kidney disease research: a bibliometric analysis

“…Moreover, artificial intelligence is widely applied in the analysis of clinical indicators, digital imaging data, and digital pathological data in renal diseases [46,47] and improves the diagnosis and prognostication of DKD [48][49][50]. High-performance models built by artificial intelligence may contribute to more effective and accurate interventions in the clinical practice of DKD.…”