TY - JOUR

T1 - Precision diagnostic approach to predict 5-year risk for microvascular complications in type 1 diabetes

AU - Al-Sari, Naba

AU - Kutuzova, Svetlana

AU - Suvitaival, Tommi

AU - Henriksen, Peter

AU - Pociot, Flemming

AU - Rossing, Peter

AU - McCloskey, Douglas

AU - Legido-Quigley, Cristina

N1 - Copyright © 2022 The Authors. Published by Elsevier B.V. All rights reserved.

PY - 2022

Y1 - 2022

N2 - BACKGROUND: Individuals with long standing diabetes duration can experience damage to small microvascular blood vessels leading to diabetes complications (DCs) and increased mortality. Precision diagnostic tailors a diagnosis to an individual by using biomedical information. Blood small molecule profiling coupled with machine learning (ML) can facilitate the goals of precision diagnostics, including earlier diagnosis and individualized risk scoring.METHODS: Using data in a cohort of 537 adults with type 1 diabetes (T1D) we predicted five-year progression to DCs. Prediction models were computed first with clinical risk factors at baseline and then with clinical risk factors and blood-derived molecular data at baseline. Progression of diabetic kidney disease and diabetic retinopathy were predicted in two complication-specific models.FINDINGS: The model predicts the progression to diabetic kidney disease with accuracy: 0.96 ± 0.25 and 0.96 ± 0.06 area under curve, AUC, with clinical measurements and with small molecule predictors respectively and highlighted main predictors to be albuminuria, glomerular filtration rate, retinopathy status at baseline, sugar derivatives and ketones. For diabetic retinopathy, AUC 0.75 ± 0.14 and 0.79 ± 0.16 with clinical measurements and with small molecule predictors respectively and highlighted key predictors, albuminuria, glomerular filtration rate and retinopathy status at baseline. Individual risk scores were built to visualize results.INTERPRETATION: With further validation ML tools could facilitate the implementation of precision diagnosis in the clinic. It is envisaged that patients could be screened for complications, before these occur, thus preserving healthy life-years for persons with diabetes.FUNDING: This study has been financially supported by Novo Nordisk Foundation grant NNF14OC0013659.

AB - BACKGROUND: Individuals with long standing diabetes duration can experience damage to small microvascular blood vessels leading to diabetes complications (DCs) and increased mortality. Precision diagnostic tailors a diagnosis to an individual by using biomedical information. Blood small molecule profiling coupled with machine learning (ML) can facilitate the goals of precision diagnostics, including earlier diagnosis and individualized risk scoring.METHODS: Using data in a cohort of 537 adults with type 1 diabetes (T1D) we predicted five-year progression to DCs. Prediction models were computed first with clinical risk factors at baseline and then with clinical risk factors and blood-derived molecular data at baseline. Progression of diabetic kidney disease and diabetic retinopathy were predicted in two complication-specific models.FINDINGS: The model predicts the progression to diabetic kidney disease with accuracy: 0.96 ± 0.25 and 0.96 ± 0.06 area under curve, AUC, with clinical measurements and with small molecule predictors respectively and highlighted main predictors to be albuminuria, glomerular filtration rate, retinopathy status at baseline, sugar derivatives and ketones. For diabetic retinopathy, AUC 0.75 ± 0.14 and 0.79 ± 0.16 with clinical measurements and with small molecule predictors respectively and highlighted key predictors, albuminuria, glomerular filtration rate and retinopathy status at baseline. Individual risk scores were built to visualize results.INTERPRETATION: With further validation ML tools could facilitate the implementation of precision diagnosis in the clinic. It is envisaged that patients could be screened for complications, before these occur, thus preserving healthy life-years for persons with diabetes.FUNDING: This study has been financially supported by Novo Nordisk Foundation grant NNF14OC0013659.

U2 - 10.1016/j.ebiom.2022.104032

DO - 10.1016/j.ebiom.2022.104032

M3 - Journal article

C2 - 35533498

VL - 80

JO - EBioMedicine

JF - EBioMedicine

SN - 2352-3964

M1 - 104032

ER -