Machine learning approach to predicting albuminuria in persons with type 2 diabetes: An analysis of the LOOK AHEAD Cohort.
Albuminuria and estimated glomerular filtration rate (e-GFR) are early markers of renal disease and cardiovascular outcomes in persons with diabetes. Although body composition has been shown to predict systolic blood pressure, its application in predicting albuminuria is unknown. In this study, we h...
| Publicado en: | Journal of Clinical Hypertension Vol. 23; no. 12; pp. 2137 - 2146 |
|---|---|
| Autores principales: | , , |
| Formato: | research Journal Article |
| Publicado: |
Wiley-Blackwell
Dec2021
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=154293993&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 154293993 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 15246175 VT0 jtl: Journal of Clinical Hypertension issn: 15246175 maglogo: Y pubinfo: dt: Dec2021 vid: 23 iid: 12 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 154293993 153861858 154293993 NLM34847294 154293993 10.1111/jch.14397 NLM34847294 154293993 ppf: 2137 ppct: 9 formats: tig: atl: Machine learning approach to predicting albuminuria in persons with type 2 diabetes: An analysis of the LOOK AHEAD Cohort. aug: au: Khitan, Zeid Nath, Tanmay Santhanam, Prasanna affil: Division of Nephrology, Department of Medicine, Joan C Edwards School of Medicine, Marshall University, Huntington West Virginia,, USA sug: subj: Diabetes Mellitus, Type 2 Complications Hypertension Diabetes Mellitus, Type 2 Epidemiology Diabetes Mellitus, Type 2 Diagnosis Albuminuria Epidemiology Human Albuminuria Diagnosis Glomerular Filtration Rate Risk Factors Comparative Studies Multicenter Studies Evaluation Research Validation Studies Scales ab: Albuminuria and estimated glomerular filtration rate (e-GFR) are early markers of renal disease and cardiovascular outcomes in persons with diabetes. Although body composition has been shown to predict systolic blood pressure, its application in predicting albuminuria is unknown. In this study, we have used machine learning methods to assess the risk of albuminuria in persons with diabetes using body composition and other determinants of metabolic health. This study is a comparative analysis of the different methods to predict albuminuria in persons with diabetes mellitus who are older than 40 years of age, using the LOOK AHEAD study cohort-baseline characteristics. Age, different metrics of body composition, duration of diabetes, hemoglobin A1c, serum creatinine, serum triglycerides, serum cholesterol, serum HDL, serum LDL, maximum exercise capacity, systolic blood pressure, diastolic blood pressure, and the ankle-brachial index are used as predictors of albuminuria. We used Area under the curve (AUC) as a metric to compare the classification results of different algorithms, and we show that AUC for the different models are as follows: Random forest classifier-0.65, gradient boost classifier-0.61, logistic regression-0.66, support vector classifier -0.61, multilayer perceptron -0.67, and stacking classifier-0.62. We used the Random forest model to show that the duration of diabetes, A1C, serum triglycerides, SBP, Maximum exercise Capacity, serum creatinine, subtotal lean mass, DBP, and subtotal fat mass are important features for the classification of albuminuria. In summary, when applied to metabolic imaging (using DXA), machine learning techniques offer unique insights into the risk factors that determine the development of albuminuria in diabetes. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|