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Morphological and functional alterations in type 2 diabetes pancreata assessed with MRI-based metrics and
Seyed Faraz Nejati1, Faranak Ebrahimian Sadabad1, Rui Ren2
1Positron Emission Tomography (PET)Center, Yale Biomedical Imaging Institute, Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, United States.
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 individuals, 16 healthy obese volunteers (HOVs), and five prediabetes individuals-who underwent [18F]FP-(+)-DTBZ PET to quantify vesicular monoamine transporter type 2 (VMAT2) density [standardized uptake value ratio (SUVR-1)], T1-weighted MRI for 3D morphology metric analysis, and an arginine stimulation test to measure acute (AIRarg) and maximum (AIRargMAX) insulin responses. Least Absolute Shrinkage and Selection Operator (LASSO) regression models identified the optimal combination of positron emission tomography (PET), MRI, and clinical variables to predict beta-cell function for the whole pancreas and its subregions.
Results:
Compared to HOVs, individuals with T2D exhibited significantly reduced AIRarg and AIRargMAX. Only the 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 acute and maximum insulin responses. This synergistic approach offers a novel combination of biomarkers for staging disease and evaluating therapeutic interventions.
Insights
Combining PET imaging of beta-cell mass and MRI morphology metrics improves prediction of beta-cell function in type 2 diabetes. This synergistic approach offers novel biomarkers 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 and function is crucial for T2D management.
- Current methods for assessing beta-cell mass and function have limitations.
Purpose of the Study:
- To evaluate if combining positron emission tomography (PET)-derived beta-cell mass (BCM) estimates with magnetic resonance imaging (MRI)-based morphology metrics enhances 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, 5 prediabetes).
- Utilized [18F]FP-(+)-DTBZ PET for vesicular monoamine transporter type 2 (VMAT2) density (SUVR-1) and T1-weighted MRI for 3D morphology.
- Arginine stimulation test measured acute (AIRarg) and maximum (AIRargMAX) insulin responses.
- Least Absolute Shrinkage and Selection Operator (LASSO) regression identified predictive variables.
Main Results:
- T2D individuals showed significantly reduced AIRarg and AIRargMAX compared to healthy obese volunteers.
- Pancreas body volume was significantly smaller in the T2D cohort.
- PET-derived SUVR-1 and clinical covariates best predicted AIRarg for the whole pancreas.
- Predicting AIRargMAX required integrating MRI morphology metrics with SUVR-1 and clinical covariates.
Conclusions:
- Combining PET-derived BCM estimates and MRI morphology metrics with machine learning improves prediction of beta-cell function in T2D.
- This synergistic approach provides novel biomarkers for disease staging and evaluating therapeutic interventions.
- The study highlights the potential of multimodal imaging in understanding and managing T2D.
