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Understanding the Risk of Type 2 Dia...

Ball, Brendan K.,

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Understanding the Risk of Type 2 Diabetes on Alzheimer's Disease /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Understanding the Risk of Type 2 Diabetes on Alzheimer's Disease // Brendan K Ball.
Author:	Ball, Brendan K.,
Description:	1 electronic resource (138 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 86-12, Section: B.
Contained By:	Dissertations Abstracts International86-12B.
Subject:	Cardiovascular disease. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31968613
ISBN:	9798311943444

Understanding the Risk of Type 2 Diabetes on Alzheimer's Disease /
Ball, Brendan K.,

Understanding the Risk of Type 2 Diabetes on Alzheimer's Disease /Brendan K Ball. - 1 electronic resource (138 pages)

Source: Dissertations Abstracts International, Volume: 86-12, Section: B.

Individuals with type 2 diabetes (T2D) are at major risk of developing Alzheimer's disease (AD) during their lifetime. Despite this connection, there is lack of understanding on the biological mechanisms involved in disease pathology. To improve our understanding, I conducted a series of studies with increasing complexity to interrogate the T2D-AD axis. These investigations included an in vitroneuronal culture model stimulated by AD- and T2D-associated metabolites, a computational mouse-to-human translational framework using brain tissue of different mouse disease models, and a cross-disease model that synthesized peripheral blood data of T2D and AD patients. There were overarching themes shared across these studies. I first identified immune signaling mechanisms shared across T2D and AD as demonstrated by differential cytokine responses of neuronal cells and significant inflammatory pathways from gene set enrichment analysis. I next demonstrated that T2D mouse models could predict human AD outcomes better than AD or ADxT2D mouse models, which identified a metabolic component to AD pathology. From cross-disease modeling, I identified blood-based biomarkers of people with T2D that could distinguish between AD and control groups. A panel of these biomarkers were also found to reflect alterations in multiple brain regions, opening the possibility of blood-based testing for AD. As a result of this work, I established a translational modeling platform that integrates human demographic features for data synthesis through cross-species or cross-disease modeling. Overall, these findings show the overlying features shared across T2D and AD, as well as a computational approach to overcome translational barriers in research.

English

ISBN: 9798311943444Subjects--Topical Terms:

3564561
Cardiovascular disease.

Understanding the Risk of Type 2 Diabetes on Alzheimer's Disease /
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520 $a Individuals with type 2 diabetes (T2D) are at major risk of developing Alzheimer's disease (AD) during their lifetime. Despite this connection, there is lack of understanding on the biological mechanisms involved in disease pathology. To improve our understanding, I conducted a series of studies with increasing complexity to interrogate the T2D-AD axis. These investigations included an in vitroneuronal culture model stimulated by AD- and T2D-associated metabolites, a computational mouse-to-human translational framework using brain tissue of different mouse disease models, and a cross-disease model that synthesized peripheral blood data of T2D and AD patients. There were overarching themes shared across these studies. I first identified immune signaling mechanisms shared across T2D and AD as demonstrated by differential cytokine responses of neuronal cells and significant inflammatory pathways from gene set enrichment analysis. I next demonstrated that T2D mouse models could predict human AD outcomes better than AD or ADxT2D mouse models, which identified a metabolic component to AD pathology. From cross-disease modeling, I identified blood-based biomarkers of people with T2D that could distinguish between AD and control groups. A panel of these biomarkers were also found to reflect alterations in multiple brain regions, opening the possibility of blood-based testing for AD. As a result of this work, I established a translational modeling platform that integrates human demographic features for data synthesis through cross-species or cross-disease modeling. Overall, these findings show the overlying features shared across T2D and AD, as well as a computational approach to overcome translational barriers in research.
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