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Morphological and Functional Alterations in Type 2 Diabetes Pancreata assessed with MRI-based metrics and [ 18
Objective:
To determine if combining PET-derived beta-cell mass (BCM) estimates with MRI- based morphology metrics improves the prediction of beta-cell functional mass in type 2 diabetes (T2D).
Methods:
We performed a retrospective analysis of 40 participants; 19 T2D, 16 healthy obese volunteers (HOV), 5 prediabetes, who underwent [ 18 F]FP-(+)-DTBZ PET to quantify vesicular monoamine transporter type 2 (VMAT2) density (SUVR-1), T1-weighted MRI for 3D morphology metric analysis, and an arginine stimulus test to measure acute (AIRarg) and maximum (AIRargMAX) insulin responses. Lasso regression models identified the optimal combination of PET, MRI, and clinical variables to predict beta-cell function for the whole pancreas and its subregions.
Results:
Compared to HOV, individuals with T2D exhibited significantly reduced AIRarg and AIRargMAX. Only pancreas body volume was significantly smaller in the T2D cohort. For the whole pancreas, a model including PET-derived SUVR-1 and a subset of clinical covariates best predicted acute beta-cell function (AIRarg). However, predicting maximum functional reserve (AIRargMAX) required the addition of MRI-based morphology metrics in combination with SUVR-1 and a subset of clinical covariates.
Conclusion:
We combined PET imaging of BCM and MRI morphology metrics with a robust machine learning-based variable selection method to extract useful PET- and MRI-based metrics for predicting functional and not-fully functional BCM. This synergistic approach offers a novel combination of biomarkers for staging disease and evaluating therapeutic interventions.
Insights
Combining PET imaging of beta-cell mass (BCM) and MRI morphology metrics accurately predicts beta-cell function in type 2 diabetes (T2D). This approach enhances the prediction of functional BCM for disease staging and therapeutic evaluation.
Area of Science:
- Endocrinology
- Medical Imaging
- Metabolic Diseases
Background:
- Type 2 diabetes (T2D) is characterized by impaired beta-cell function.
- Accurate assessment of beta-cell mass (BCM) and function is crucial for T2D management.
- Current methods for BCM estimation have limitations in predicting functional capacity.
Purpose of the Study:
- To evaluate if combining Positron Emission Tomography (PET)-derived BCM estimates with Magnetic Resonance Imaging (MRI)-based morphology metrics improves the prediction of beta-cell functional mass in T2D.
- To identify optimal combinations of imaging and clinical variables for predicting beta-cell function.
Main Methods:
- Retrospective analysis of 40 participants (19 T2D, 16 healthy obese volunteers, 5 prediabetes).
- Utilized [18F]FP-(+)-DTBZ PET for vesicular monoamine transporter type 2 (VMAT2) density (SUVR-1) and T1-weighted MRI for 3D morphology.
- Assessed acute (AIRarg) and maximum (AIRargMAX) insulin responses via arginine stimulus test.
- Employed Lasso regression for variable selection to predict beta-cell function.
Main Results:
- T2D participants showed significantly reduced acute and maximum insulin responses compared to healthy obese volunteers.
- Only pancreas body volume was significantly smaller in the T2D cohort.
- PET-derived SUVR-1 and clinical covariates best predicted acute beta-cell function for the whole pancreas.
- Predicting maximum functional reserve required integrating MRI morphology metrics with PET data and clinical covariates.
Conclusions:
- Combining PET imaging of BCM and MRI morphology metrics with machine learning effectively predicts functional and non-fully functional BCM.
- This synergistic approach provides novel biomarkers for T2D disease staging.
- The combined imaging strategy aids in evaluating the efficacy of therapeutic interventions for T2D.
