Precision diagnostic approach to predict 5-year risk for microvascular complications in type 1 diabetes.
EBioMedicine
; 80: 104032, 2022 Jun.
Article
em En
| MEDLINE
| ID: mdl-35533498
ABSTRACT
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.Palavras-chave
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Diabetes Mellitus Tipo 1
/
Nefropatias Diabéticas
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Retinopatia Diabética
Tipo de estudo:
Diagnostic_studies
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Etiology_studies
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Prognostic_studies
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Risk_factors_studies
Limite:
Adult
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Humans
Idioma:
En
Ano de publicação:
2022
Tipo de documento:
Article