WeChat Mini Program
Old Version Features

340-OR: ADA Presidents' Select Abstract: Structural Neuroimaging and Cognitive Function in Long-Duration Type 1 Diabetes

Diabetes(2023)

Boston

Cited 0|Views2
Abstract
Cognitive decline is an increasingly important complication in people with type 1 diabetes (T1D) as they age. We previously reported that compared with nondiabetic people, those with long-duration T1D have impaired cognitive function, similar to people with type 2 diabetes. To characterize factors associated with cognitive decline in people with T1D, 52 participants of the Joslin Medalist Study (“Medalists”) with T1D≥50 years, and 20 age-matched nondiabetic controls, underwent clinical evaluation, cognitive assessments, and neuroimaging (Alzheimer Disease Neuroimaging Initiative-3 protocol). Compared to controls, Medalists had 49.4 (±21.9) cm3 (p=0.03) lower total brain volume, and 2.7 (±0.8) cm3, 3.0 (±1.5) cm3 and 3.1 (±1.5) cm3 lower volumes (p<0.05), respectively, in the deep gray matter region (thalamus, putamen, caudate and globus pallidus), the Alzheimer disease signature region (hippocampus, parahippocampus, entorhinal cortex, inferior parietal lobule, precuneus and cuneus), and the occipital lobe. No significant differences were observed in frontal, temporal, or parietal lobe volumes. In assessing small vessel disease, the number of white matter hyperintensities (WMHs) or microvascular hemorrhages did not differ between Medalists and controls, nor did regional cerebral blood flow measures (via arterial spin labeling). Among Medalists, reduced total and all regional brain volumes were associated (p<0.05) with worse motor skills, female sex, lower education status, increased age, and longer diabetes duration. Increased WMHs were associated with worse renal function and higher coronary artery calcification scores. Volumes did not associate with other cognitive domains (recall, working memory, or executive function). These findings suggest that cognitive decline in people with long-duration T1D is related mainly to abnormalities in parenchymal CNS rather than neurovascular changes. Definitive confirmation of this finding by brain histopathology is underway. Disclosure H. Shah: None. M. Desalvo: None. A. Haidar: None. S. Jangolla: None. M. Yu: None. J. Gauthier: None. N.A. Ziemniak: None. I. Wu: None. T. Billah: None. L. Ning: None. A. Adam: None. Y. Rathi: None. G.L. King: Research Support; Janssen Research & Development, LLC. Funding National Institute of Diabetes and Digestive and Kidney Diseases (3P30DK036836-34S1); Thomas J. Beatson, Jr. Foundation
More
Translated text
求助PDF
上传PDF
Bibtex
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
  • Pretraining has recently greatly promoted the development of natural language processing (NLP)
  • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
  • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
  • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
  • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined