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For most patients, experiencing several weeks of polyuria, polydipsia, fatigue, and significant weight loss may indicate the presence of diabetes. Furthermore, adults displaying the phenotypic appearance of type 2 diabetes (particularly those who are obese and not initially insulin-requiring), may have islet cell autoantibodies, suggesting autoimmune-mediated β cell destruction and a diagnosis of latent autoimmune diabetes of adults (LADA). The categorization of glucose homeostasis is...
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The hemodynamic response function as a type 2 diabetes biomarker: a data-driven approach.

Pedro Guimarães1, Pedro Serranho1,2, João V Duarte1,3

  • 1University of Coimbra, Coimbra Institute for Biomedical Imaging and Translational Research (CIBIT), Institute for Nuclear Sciences Applied to Health (ICNAS), Coimbra, Portugal.

Frontiers in Neuroinformatics
|January 22, 2024
PubMed
Summary

We can accurately identify Type 2 diabetes mellitus (T2DM) patients by analyzing brain hemodynamic response function (HRF) alterations. These unique HRF patterns show promise as early biomarkers for neurophysiological changes in T2DM.

Keywords:
deep learningfunctional magnetic resonance imaginghemodynamic responseneuroimagingtype 2 diabetes

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Area of Science:

  • Neuroscience
  • Medical Imaging
  • Biomarker Discovery

Background:

  • Type 2 diabetes mellitus (T2DM) is associated with early neurophysiological changes preceding structural or vascular damage.
  • Understanding these early brain alterations is crucial for timely intervention.
  • Current methods may not fully capture subtle, early-stage neural dysfunction.

Purpose of the Study:

  • To develop and validate an unbiased, data-driven approach to detect and characterize hemodynamic response function (HRF) alterations in T2DM patients.
  • To explore the potential of these HRF changes as early biomarkers for T2DM.
  • To identify specific brain regions and neural mechanisms affected in early T2DM.

Main Methods:

  • Utilized task-based event-related functional magnetic resonance imaging (fMRI) with a visual speed discrimination task.
  • Employed deep learning (DL) and explainability techniques combined with deconvolution generalized linear models.
  • Performed relevance analysis to identify key brain regions involved in discrimination.

Main Results:

  • Achieved up to 95% accuracy in discriminating T2DM patients based on HRF.
  • Demonstrated higher classification performance with increased stimulus contrast and in the left visual hemifield.
  • Identified extrastriate visual cortex, parietal cortex, and insula as critical regions for discrimination.

Conclusions:

  • A data-driven classification of HRF can accurately differentiate T2DM patients.
  • HRF alterations serve as promising biomarkers for early neurophysiological changes in T2DM.
  • This approach offers a novel method for understanding T2DM-related brain dysfunction.