Machine learning with validation to detect diabetic microvascular complications using clinical and metabolomics data

Abstract

AIMS: Using machine learning integrated with clinical and metabolomic data to identify biomarkers associated with diabetic kidney disease (DKD) and diabetic retinopathy (DR), and to improve the performance of DKD/DR detection models beyond traditional risk factors. METHODS: We examined a population-based cross-sectional sample of 2,772 adults with type 1 or type 2 diabetes from Singapore Epidemiology of Eye Diseases study (SEED, 2004-2011). LASSO logistic regression (LASSO) and gradient boosting decision tree (GBDT) were used to select markers of prevalent DKD (defined as an eGFR < 60ml/min/1.73m^2) and prevalent DR (defined as an ETDRS severity level >= 20) from an expanded set of 19 established risk factors and 220 NMR-quantified circulating metabolites. Risk assessment models were developed based on the variable selection results and externally validated in UK Biobank (n=5,843, 2007-2010). Model performance (AUC with 95% CI, sensitivity, and specificity) of machine learning was compared to that of traditional logistic regression adjusted for age, gender, diabetes duration, HbA1c%, systolic BP, and BMI. RESULTS: SEED participants had a median age of 61.7 years, with 49.1% female, 20.2% having DKD, and 25.4% having DR. UK Biobank participants had a median age of 61.0 years, with 39.2% female, 6.4% having DKD, and 5.7% having DR. Both algorithms identified diabetes duration, insulin usage, age, and tyrosine as the most important factors of both DKD and DR. DKD was additionally associated with CVD, hypertension medication, and three metabolites (lactate, citrate, and cholesterol esters to total lipids ratio in intermediate-density-lipoprotein); While DR was additionally associated with HbA1c, blood glucose, pulse pressure, and alanine. Machine-learned models for DKD and DR detection outperformed traditional logistic regression in both internal (AUC: 0.832-0.838 vs. 0.743 for DKD, and 0.779-0.790 vs. 0.764 for DR) and external validation (AUC: 0.737-0.790 vs. 0.692 for DKD, and 0.778 vs. 0.760 for DR). CONCLUSIONS: Machine-learned biomarkers suggested insulin resistance to be a primary factor associated with diabetic microvascular complications. Integrating machine learning with biomedical big data enabled biomarker discovery from a wide range of correlated variables, which may facilitate our understanding of the disease mechanisms and improve disease screening.

Competing Interest Statement

The authors have declared no competing interest.

Funding Statement

This study was supported by the National Medical Research Council, NMRC/StaR/016/2013, NMRC/CIRG/1371/2013, NMRC/CIRG/1417/2015, and OFLCG/001/2017.

Author Declarations

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

Yes

The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

Institutional Review Board of SingHealth gave ethical approval for this work.

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Yes

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I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable.

Yes

Data Availability

SEED data are available from the Singapore Eye Research Institutional Ethics Committee for researchers who meet the criteria for access to confidential data. Interested researchers can send data access requests to the Singapore Eye Research Institute using the following email address: seri@seri.com.sg. The UK Biobank datasets can be requested by bona fide researchers for approved projects, including replication, through HTTPS://WWW.UKBIOBANK.AC.UK/.

HTTPS://WWW.UKBIOBANK.AC.UK/

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