Characterization of the Meal-Stimulated Incretin Response and Relationship With Structural Brain Outcomes in Aging and Alzheimer’s Disease

Morris, Jill K.; John, Casey S.; Green, Zachary D.; Wilkins, Heather; Wang, Xiaowan; Kamat, Ashwini; Swerdlow, Russell; Vidoni, Eric D.; Petersen, Melissa; O’Bryant, Sid; Honea, Robyn A.; Burns, Jeffrey M.

doi:10.3389/fnins.2020.608862

Cited by 2 publications

(1 citation statement)

References 76 publications

(82 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The elevated metabolic response may be elicited to compensate for reduced receptor density in the brain. 143 …”

Section: Potential Mechanisms For Links Between Obesity and Brain Healthmentioning

confidence: 99%

Current Perspectives: Obesity and Neurodegeneration - Links and Risks

Kueck,

Morris,

Stanford

2024

DNND

View full text Add to dashboard Cite

Obesity is increasing in prevalence across all age groups. Long-term obesity can lead to the development of metabolic and cardiovascular diseases through its effects on adipose, skeletal muscle, and liver tissue. Pathological mechanisms associated with obesity include immune response and inflammation as well as oxidative stress and consequent endothelial and mitochondrial dysfunction. Recent evidence links obesity to diminished brain health and neurodegenerative diseases such as Alzheimer’s disease (AD) and Parkinson’s disease (PD). Both AD and PD are associated with insulin resistance, an underlying syndrome of obesity. Despite these links, causative mechanism(s) resulting in neurodegenerative disease remain unclear. This review discusses relationships between obesity, AD, and PD, including clinical and preclinical findings. The review then briefly explores nonpharmacological directions for intervention.

show abstract

“…The elevated metabolic response may be elicited to compensate for reduced receptor density in the brain. 143 …”

Section: Potential Mechanisms For Links Between Obesity and Brain Healthmentioning

confidence: 99%

Current Perspectives: Obesity and Neurodegeneration - Links and Risks

Kueck,

Morris,

Stanford

2024

DNND

View full text Add to dashboard Cite

show abstract

Novel Plasma Protein Biomarkers: A Time-Dependent predictive model for Alzheimer's Disease

Zhuang,

Yang,

Ren

et al. 2023

Preprint

View full text Add to dashboard Cite

Background The accurate prediction of Alzheimer's disease (AD) is crucial for the efficient management of its progression. The objective of this research is to construct a new risk predictive model utilizing novel plasma protein biomarkers for predicting AD incidence in the future and analyze their potential biological correlation with AD incidence. Methods A cohort of 440 participants aged 60 years and older from the Alzheimer's Disease Neuroimaging Initiative (ADNI) longitudinal cohort was utilized. The baseline plasma proteomics data was employed to conduct Cox regression, LASSO regression, and cross-validation to identify plasma protein signatures predictive of AD risk. Subsequently, a multivariable Cox proportional hazards model based on these signatures was constructed. The performance of the risk prediction model was evaluated using time-dependent receiver operating characteristic (t-ROC) curves and Kaplan-Meier curves. Additionally, we analyzed the correlations between protein signature expression in plasma and predicted AD risk, the time of AD onset, the expression of protein signatures in cerebrospinal fluid (CSF), the expression of CSF biomarkers, and APOE ε4 genotypes. Results We identified seven protein signatures (APOE, CGA, CRP, CCL26, CCL20, NRCAM, and PYY) that independently predicted AD incidence in the future. The risk prediction model demonstrated area under the ROC curve (AUC) values of 0.77, 0.76, and 0.77 for predicting AD incidence at 4, 6, and 8 years, respectively. Furthermore, the model remained stable in the range of the 3rd to the 12th year (ROC ≥ 0.75). The low-risk group, as defined by the model, exhibited a significantly later AD onset compared to the high-risk group (P < 0.0001). Moreover, all protein signatures exhibited significant correlations with AD risk (P < 0.001) and the time of AD onset (P < 0.01). There was no strong correlation between the protein expression levels in plasma and CSF, as well as AD CSF biomarkers. APOE, CGA, and CRP exhibited significantly lower expression levels in APOE ε4 positive individuals (P < 0.05). Conclusion Our research has successfully identified protein signatures in plasma as potential risk biomarkers that can independently predict AD incidence in the future. Notably, this risk prediction model has demonstrated commendable predictive performance and stability over time. These findings underscore the promising utility of plasma protein signatures in dynamically predicting the risk of Alzheimer's disease, thereby facilitating early screening and intervention strategies.

show abstract

Characterization of the Meal-Stimulated Incretin Response and Relationship With Structural Brain Outcomes in Aging and Alzheimer’s Disease

Cited by 2 publications

References 76 publications

Current Perspectives: Obesity and Neurodegeneration - Links and Risks

Current Perspectives: Obesity and Neurodegeneration - Links and Risks

Novel Plasma Protein Biomarkers: A Time-Dependent predictive model for Alzheimer's Disease

Contact Info

Product

Resources

About