Application of machine learning to assess interindividual variability in rapid-acting insulin responses following subcutaneous injection in people with type 1 diabetes.

Coales, Eleanor M., Ajjan, Ramzi A., Pearson, Sam M., O’Mahoney, Lauren L., Kietsiriroje, Noppadol, Brož, Jan, Holmes, Mel and Campbell, Matthew (2021) Application of machine learning to assess interindividual variability in rapid-acting insulin responses following subcutaneous injection in people with type 1 diabetes. Canadian Journal of Diabetes. ISSN 1499-2671

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Abstract

Introduction
Circulating insulin concentrations mediate vascular-inflammatory and prothrombotic factors. However, whether interindividual differences in circulating insulin levels are associated with different inflammatory and prothrombotic profiles in type 1 diabetes (T1D) is unknown. We applied an unsupervised, machine-learning approach to assess whether interindividual differences in rapid-acting insulin levels associate with parameters of vascular health in T1D patients.
Methods
We reanalysed baseline pre-treatment meal-tolerance test data from two randomised control trials in which 32 patients consumed a mixed-macronutrient meal and self-administered a single dose of rapid-acting insulin individualised by carbohydrate-counting. Postprandial serum insulin, tumour necrosis factor alpha (TNFα), plasma fibrinogen, human tissue factor (HTF activity) and plasminogen-activator inhibitor-1 (PAI-1) were measured. Two-step clustering categorised individuals based on shared clinical characteristics. For analyses, insulin pharmacokinetic summary statistics were normalised, allowing standardised intra-individual comparisons.
Results
Despite standardisation of insulin dose, individuals exhibited marked interpersonal variability in peak insulin concentrations (48.63%), time to peak (64.95%), and insulin incremental area under the curve (60.34%). Two clusters were computed: cluster 1 (n=14) representing increased serum insulin concentrations; cluster 2 (n=18) representing reduced serum insulin concentrations (cluster 1: 389.50±177.10 vs. cluster 2: 164.29±;41.91 pmol/L.IU.hr-1; P<0.001). Cluster 2 was characterised by increased fibrinogen, PAI-1, TNFα levels, higher HTF activity, higher HbA1c, BMI, and lower eGDR (increased insulin resistance), older age, and longer diabetes duration (P<0.05 for all analyses).
Conclusions
Reduced serum insulin concentrations are associated with insulin resistance and a prothrombotic milieu in individuals with T1D, and may, therefore, be a marker of adverse vascular outcome.

Item Type: Article
Divisions: Faculty of Health Sciences and Wellbeing > School of Nursing and Health Sciences
Depositing User: Leah Maughan
Date Deposited: 13 Sep 2021 19:36
Last Modified: 14 Sep 2021 13:32
URI: http://sure.sunderland.ac.uk/id/eprint/13957
ORCID for Matthew Campbell: ORCID iD orcid.org/0000-0001-5883-5041

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