Frontiers in endocrinology · 2025
Morphological and functional alterations in type 2 diabetes pancreata assessed with MRI-based metrics and [18F]FP-(+)-DTBZ PET.
СтатьяНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Nejati Seyed Faraz, Sadabad Faranak Ebrahimian, Ren Rui, Huang Yuan, Bini Jason
Аннотация
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.
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.