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Proteomics analysis unveils proteins underpinning diabetes-driven white matter hyperintensity growth

Poster No:

141 

Submission Type:

Abstract Submission 

Authors:

Noah Schweitzer1, Sang Joon Son2, Nicholas Fitz3, Chang-Le Chen1, Chang Hyung Hong2, Hyun Woong Roh2, Yong Hyuk Cho2, Bumhee Park4, Na-Rae Kim4, Jin Wook Choi5, Jaeyoun Cheong6, Sangwon Seo7, Young-Sil An8, So Young Moon9, Seung Jin Han10, Bistra Iordanova1, Shaolin Yang11, Howard Aizenstein11, Minjie Wu11

Institutions:

1Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, 2Department of Psychiatry, Ajou University School of Medicine, Suwon, Korea, Republic of, 3Department of Environmental and Occupational Health, University of Pittsburgh, Pittsburgh, PA, 4Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, Korea, Republic of, 5Department of Radiology, Ajou University School of Medicine, Suwon, Korea, Republic of, 6Department of Gastroenterology, Ajou University School of Medicine, Suwon, Korea, Republic of, 7Samsung medical center, Seoul, Korea, Republic of, 8Department of Nuclear Medicine and Molecular Imaging, Ajou University School of Medicine, Suwon, Korea, Republic of, 9Department of Neurology, Ajou University School of Medicine, Suwon, Korea, Republic of, 10Department of Endocrinology and Metabolism, Ajou University School of Medicine, Suwon, Korea, Republic of, 11Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA

First Author:

Noah Schweitzer  
Department of Bioengineering, University of Pittsburgh
Pittsburgh, PA

Co-Author(s):

Sang Joon Son, MD, PhD  
Department of Psychiatry, Ajou University School of Medicine
Suwon, Korea, Republic of
Nicholas Fitz  
Department of Environmental and Occupational Health, University of Pittsburgh
Pittsburgh, PA
Chang-Le Chen  
Department of Bioengineering, University of Pittsburgh
Pittsburgh, PA
Chang Hyung Hong, MD, PhD  
Department of Psychiatry, Ajou University School of Medicine
Suwon, Korea, Republic of
Hyun Woong Roh, MD, PhD  
Department of Psychiatry, Ajou University School of Medicine
Suwon, Korea, Republic of
Yong Hyuk Cho  
Department of Psychiatry, Ajou University School of Medicine
Suwon, Korea, Republic of
Bumhee Park  
Department of Biomedical Informatics, Ajou University School of Medicine
Suwon, Korea, Republic of
Na-Rae Kim  
Department of Biomedical Informatics, Ajou University School of Medicine
Suwon, Korea, Republic of
Jin Wook Choi  
Department of Radiology, Ajou University School of Medicine
Suwon, Korea, Republic of
Jaeyoun Cheong  
Department of Gastroenterology, Ajou University School of Medicine
Suwon, Korea, Republic of
Sangwon Seo  
Samsung medical center
Seoul, Korea, Republic of
Young-Sil An  
Department of Nuclear Medicine and Molecular Imaging, Ajou University School of Medicine
Suwon, Korea, Republic of
So Young Moon  
Department of Neurology, Ajou University School of Medicine
Suwon, Korea, Republic of
Seung Jin Han  
Department of Endocrinology and Metabolism, Ajou University School of Medicine
Suwon, Korea, Republic of
Bistra Iordanova  
Department of Bioengineering, University of Pittsburgh
Pittsburgh, PA
Shaolin Yang, PhD  
Department of Psychiatry, University of Pittsburgh
Pittsburgh, PA
Howard Aizenstein, M.D., PhD.  
Department of Psychiatry, University of Pittsburgh
Pittsburgh, PA
Minjie Wu, PhD  
Department of Psychiatry, University of Pittsburgh
Pittsburgh, PA

Introduction:

White matter hyperintensities (WMH) are surrogate markers of cerebral small vessel disease. There is a need to understand its pathophysiology to prevent cognitive decline and a potential contributing factor is diabetes as it is a chronic macrovascular risk factor[1]. Blood biomarkers might be a useful tool to elucidate the role of diabetes in WMH. There is limited research on blood biomarkers' association with WMH. We aimed to investigate differentially expressed proteins (DEP) in diabetes as detected in blood plasma that have a significant interaction effect with diabetes on WMH.

Methods:

This study was a part of the Biobank Innovations for Chronic Cerebrovascular Disease With ALZheimer's Disease Study (BICWALZS). Blood samples were collected to test for HbA1c. Protein levels were measured with Olink's Cytokine and Neurology panel (https://olink.com/) and transformed into log base-2 values. Participants completed baseline 3T MRI scans which included T1w and T2w-FLAIR sequences. 348 subjects had a baseline MRI (N=245 female, 79 diabetic, mean age 72.0+-7.2). 64 subjects (N=39 female, 11 diabetic, 72.1+-7.5 years) completed a follow-up scan two-years after baseline and held out for a separate longitudinal analysis. WMH on T2w FLAIR images were automatically segmented based on previous method[2]. WMH volume (WMHV) was normalized by intracranial volume and log-transformed. WMHV change was calculated as WMHV at time point 2 minus time point 1. Proteins were tested for differential expression based on diabetes status for each Olink panel. DEP analysis was conducted using the linear model implemented in "limma"[3]. Functional annotation clustering was performed using the DAVID database. A multivariate linear regression model was tested on DEP for interaction between the protein and diabetes on WMHV controlling for age, sex, and scanner site. Proteins with significant interaction effect were tested on the held-out longitudinal sample. Two linear regression models were tested on WMHV change controlling for age at baseline, sex and scanner site: the interaction between diabetes, protein expression and HbA1c levels, protein expression, respectively. We applied the Johnson–Neyman technique to probe and visualize the conditional effect of HbA1c on WMHV change based on protein expression[4]. Throughout the study, multiple comparison was adjusted using Benjamini-Hochberg method.

Results:

We observed 42 and 11 DEP based on diabetes status for Neurology and Cytokine panel, respectively (Fig1A,B). KEGG pathways such as cytokine-cytokine receptor interaction and gene ontology terms such as axon guidance were significantly enriched (Fig1C). The only DEP that had significant interaction effect with diabetes on WMHV were Nerve Growth Factor (NGF)-β (p=5.8E-4, Fig2A) and Carboxypeptidase A2 (CPA2) (p=1.1E-3, Fig2B). WMHV increased with higher NGF-β expression for diabetic subjects and decreased expression in non-diabetic subjects. In the held-out sample, significant interaction effects on WMHV change were observed between NGF-β, diabetes and NGF-, HbA1c, respectively (Fig 2C, p=0.03, 0.019), but not CPA2 (p=0.26, 0.69). Johnson–Neyman analysis indicated the association between WMHV change and NGF-β had a significant negative correlation at HbA1c levels less than 5.59%, and a significant positive correlation at HbA1 levels higher than 7.8% (Fig2D).
Supporting Image: Fig-1_final.png
Supporting Image: Fig-2_final.png
 

Conclusions:

Our proteomic analysis reveals a potential independent pathway through which diabetes contributes to WMH progression. To the best of our knowledge, we are the first to report about an association between NGF, CPA2 with WMH. NGF plays a significant role in neuronal integrity and angiogenesis. Elevated NGF and CPA2, observed in diabetes[5,6], may indicate a worsened diabetic state leading to cerebrovascular complications. Our study emphasizes the importance of managing diabetic health to improve brain health outcomes. Finally, targeting NGF may have potential diagnostic and therapeutic benefits in preventing WMH progression.

Disorders of the Nervous System:

Neurodegenerative/ Late Life (eg. Parkinson’s, Alzheimer’s) 1

Genetics:

Genetics Other

Lifespan Development:

Aging 2

Modeling and Analysis Methods:

Other Methods

Physiology, Metabolism and Neurotransmission :

Physiology, Metabolism and Neurotransmission Other

Keywords:

Aging
Cerebrovascular Disease
Degenerative Disease
White Matter

1|2Indicates the priority used for review

Provide references using author date format

1. Sanahuja, J. et al. Increased Burden of Cerebral Small Vessel Disease in Patients With Type 2 Diabetes and Retinopathy. Diabetes Care 39, 1614-1620 (2016).
2. Wu, M. et al. A fully automated method for quantifying and localizing white matter hyperintensities on MR images. Psychiatry Res 148, 133-142 (2006).
3. Ritchie, M.E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res 43, e47 (2015).
4. D’Alonzo, K.T. The Johnson-Neyman Procedure as an Alternative to ANCOVA. Western Journal of Nursing Research 26, 804-812 (2004).
5. Ding, X.-W., Li, R., Geetha, T., Tao, Y.-X. & Babu, J.R. Nerve growth factor in metabolic complications and Alzheimer's disease: Physiology and therapeutic potential. Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease 1866, 165858 (2020).
6. Lu, Y., Li, Y., Li, G. & Lu, H. Identification of potential markers for type 2 diabetes mellitus via bioinformatics analysis. Mol Med Rep 22, 1868-1882 (2020).