Next Article in Journal
Genome-Wide Profiling of the ACTIN Gene Family and Its Implications for Agronomic Traits in Brassica Napus: A Bioinformatics Study
Previous Article in Journal
Current Overview of Spinocerebellar Ataxia Type 7 in Mexican Population: Challenges in Specialized Care for a Rare Disease
Previous Article in Special Issue
Cognitive Effects of Simulated Galactic Cosmic Radiation Are Mediated by ApoE Status, Sex, and Environment in APP Knock-In Mice
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
IJMS
Volume 25
Issue 19
10.3390/ijms251910751
ijms-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Plasma Proteomic Biomarkers in Alzheimer’s Disease and Cardiovascular Disease: A Longitudinal Study

by
Laurie A. Theeke
1,
Ying Liu
2,
Silas Wang
3,
Xingguang Luo
4,
R. Osvaldo Navia
5,
Danqing Xiao
6,
Chun Xu
7,
Kesheng Wang
8,* and
The Alzheimer and Disease Neuroimaging Initiative
†
1
Department of Community of Acute and Chronic Care, School of Nursing, The George Washington University, Ashburn, VA 20147, USA
2
Department of Biostatistics and Epidemiology, College of Public Health, East Tennessee State University, Johnson City, TN 37614, USA
3
Department of Statistics & Data Science, Dietrich College of Humanities and Social Sciences, Carnegie Mellon University, Pittsburgh, PA 15213, USA
4
Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06516, USA
5
Department of Medicine and Rockefeller Neuroscience Institute, West Virginia University, Morgantown, WV 26506, USA
6
Department of STEM, School of Arts and Sciences, Regis College, Weston, MA 02493, USA
7
Department of Health and Biomedical Sciences, College of Health Affairs, University of Texas Rio Grande Valley, Brownsville, TX 78520, USA
8
Department of Biobehavioral Health & Nursing Science, College of Nursing, University of South Carolina, Columbia, 1601 Greene Street, Columbia, SC 29208, USA
*
Author to whom correspondence should be addressed.
†
Note: Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu) (accessed on 26 October 2022). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf.
Int. J. Mol. Sci. 2024, 25(19), 10751; https://doi.org/10.3390/ijms251910751 (registering DOI)
Submission received: 27 August 2024 / Revised: 23 September 2024 / Accepted: 30 September 2024 / Published: 6 October 2024
(This article belongs to the Special Issue Advanced Science in Alzheimer’s Disease)
Browse Figure
Versions Notes

Abstract

:
The co-occurrence of Alzheimer’s disease (AD) and cardiovascular diseases (CVDs) in older adults highlights the necessity for the exploration of potential shared risk factors. A total of 566 adults were selected from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, including 111 individuals with AD, 383 with mild cognitive impairment (MCI), and 410 with CVD. The multivariable linear mixed model (LMM) was used to investigate the associations of AD and CVD with longitudinal changes in 146 plasma proteomic biomarkers (measured at baseline and the 12-month follow-up). The LMM showed that 48 biomarkers were linked to AD and 46 to CVD (p < 0.05). Both AD and CVD were associated with longitudinal changes in 14 biomarkers (α1Micro, ApoH, β2M, BNP, complement C3, cystatin C, KIM1, NGAL, PPP, TIM1, THP, TFF3, TM, and VEGF), and both MCI and CVD were associated with 12 biomarkers (ApoD, AXL, BNP, Calcitonin, CD40, C-peptide, pM, PPP, THP, TNFR2, TTR, and VEGF), suggesting intricate connections between cognitive decline and cardiovascular health. Among these, the Tamm Horsfall Protein (THP) was associated with AD, MCI, CVD, and APOE-ε4. This study provides valuable insights into shared and distinct biological markers and mechanisms underlying AD and CVD.

1. Introduction

Alzheimer’s disease (AD), the leading cause of major neurocognitive disorders, affected 6.7 million Americans aged 65 and older in 2023 with projected increases to 13.8 million by 2060 [1]. Concurrently, cardiovascular diseases (CVDs) such as atherosclerosis, stroke, hypertension, myocardial infarction, coronary heart disease, and congestive heart failure are the foremost causes of disability and mortality globally for people aged 45 years and older [2,3]. CVDs impact 126.9 million adults in the United States (U.S.), a number that increases with age [4]. These conditions are particularly prevalent in the aging population, especially among adults aged 65 years and older, constituting an increasing public health problem [3,5,6,7].
Previous studies suggested that there is considerable overlap between AD and CVDs in terms of lifestyle risk factors, pathophysiology, and clinical presentations, but there might also be a direct causal association of CVDs and cardiovascular risk factors with AD and MCI [3,7,8,9]. For example, CVDs and risk factors are associated with an increased risk of AD and mild cognitive impairment (MCI), while AD and CVDs share similar genetic biomarkers and biochemical profiles, suggesting a complex interplay of genetic predispositions and environmental triggers [7,8]. Several genes have been linked to both AD and CVDs including apolipoprotein E (APOE), MTHFR, HFE, FTO, and ARHGAP26 [8,10,11,12,13,14]. Most importantly, one study reported a shared APOE-ε4 allele among several chronic health conditions, including AD, CVDs, and metabolic phenotypes [15]. A more recent study reported that ACE1 and several APO proteins were associated with AD, MCI, CVDs, and the APOE-ε4 allele [16]. However, the mechanisms remain unclear. It has been proposed that certain genetic variants influence AD through multiple cardiovascular risk factors, [17] while some genes could interact with cardiovascular risk factors in the development of AD [18]. Additionally, an epigenetic/epigenomic influence in cardiovascular complications is also involved in the development of neurodegenerative disorders such as AD [19]. Therefore, there is still a gap in understanding the precise mechanistic pathways for the cause–effect and/or shared pathology between AD and CVDs [3].
Despite the established connections, there has been limited focus on the longitudinal changes in proteomic biomarkers in people suffering from both AD and CVD. Given the high prevalence of comorbid CVD in people with AD, exploring these biomarker changes is crucial for understanding the relationships between these conditions. This study aims to (1) examine the longitudinal changes in plasma proteomic biomarkers in adults who have been diagnosed with cognitive impairment (AD and MCI) and CVD while adjusting for covariates and (2) detect the overlapping biomarkers among AD, MCI, and CVD, thereby offering insights into potential shared pathophysiological mechanisms.

2. Results

2.1. Baseline Descriptive Statistics

At baseline, no significant differences were observed in age and education across groups defined by AD diagnostic status or CVD presence, indicating that these factors did not systematically vary among the groups studied (Table 1). Gender was not associated with AD but was associated with CVD. APOE-ε4 was associated with AD diagnostic status but not with CVD.

2.2. Multivariable LMM Analysis

The LMM showed that AD and CVD were associated with longitudinal changes in 48 and 46 plasma proteomic biomarkers, respectively (p < 0.05, Table S1). Both AD and CVD were associated with longitudinal changes in fourteen proteomic biomarkers (α1Micro, ApoH, β2M, BNP, complement C3, cystatin C, KIM1, NGAL, PPP, TIM1, THP, TFF3, TM, and VEGF) (Table 2), of which four were also associated with MCI. Furthermore, MCI and CVD were associated with twelve proteomic biomarkers (ApoD, AXL, BNP, Calcitonin, CD40, C-peptide, pM, PPP, THP, TNFR2, TTR, and VEGF), with eight listed in Table 3. Moreover, the single biggest risk gene for sporadic AD, APOE-ε4, was associated with 22 proteomic biomarkers (Table S1).
The THP was associated with AD, MCI, CVD, and APOE-ε4 (Table 2) and with age (p = 0.001). The multiple comparisons for the THP between AD diagnosis, CVD status, and APOE genotypes at baseline and the 12-month follow-up are illustrated in Table 4. There were significant differences between AD and CN, between CVD and non-CVD, and between APOE-ε4-1+ and APOE-ε4-0 at baseline and at the 12-month follow-up, as well as between MCI and CN at the 12-month follow-up.

2.3. Correlation Analysis and Variable Cluster Analysis

Pearson and Spearman correlation analyses were conducted for the baseline data of 22 proteins which were associated with AD or MCI and CVD in LMM analysis (Table 2 and Table 3), and the coefficients are presented in Table S2. Based on variable cluster analysis, the twenty-two proteins were clustered into eight clusters (eight, three, two, two, one, one, three, and two variables for each cluster, Figure 1). Cluster 1 included eight proteins (α1Micro, β2M, BNP, cystatin C, NGAL, TFF3, THP, and TNFR2), while cluster 2 included three proteins (CD40, TIM1, and VEGF). AXL and TM were in cluster 3, and ApoH and TTR were in cluster 4. One protein (Calcitonin) was in cluster 5 and one protein (KIM1) was in cluster 6. Three proteins (C-peptide, pM, and PPP) were in cluster 7, and two proteins (ApoD and C3) were in cluster 8. About 65.1% of the total variation in the data could be accounted for by the eight clusters. Table S3 describes the variables in each cluster and the corresponding 1-R2 ratio values. Small values of the 1-R2 ratio indicate that the variable has a strong correlation with its own cluster and a weak correlation with the other clusters.

3. Discussion

This study underscores the complex relationships among AD, CVDs, and proteomic biomarkers in these conditions. There is established evidence that the pathway to the development of CVDs and AD includes regulating the transcription and translation of specific biomarkers, which in turn affects the structure and physiological function of resident cells in both the cardiovascular and central nervous system. After controlling for known potential confounding factors (age, gender, education, and APOE-ε4 allele), the main findings of this study include that (1) AD and CVDs were associated with longitudinal changes in 48 and 46 proteomic biomarkers, respectively, and both AD and CVDs were associated with longitudinal changes in 14 proteomic biomarkers; (2) both MCI and CVDs were associated with longitudinal changes in 12 proteomic biomarkers; and (3) APOE-ε4 was associated with 22 proteomic biomarkers. Interestingly, AD, CVDs, and APOE-ε4 were associated with α1Micro, β2M, complement C3, cystatin C, and THP. Furthermore, MCI, CVDs, and APOE-ε4 were associated with THP, Calcitonin, and TNFR2. Additionally, THP was associated with AD, MCI, CVD, and APOE-ε4.

3.1. Proteomic Biomarkers Associated with AD, CVDs, and APOE-ε4

Interestingly, both AD and CVDs were associated with longitudinal changes in 14 biomarkers (α1Micro, ApoH, β2M, BNP, complement C3, cystatin C, KIM1, NGAL, PPP, TIM1, THP, TFF3, TM, and VEGF); among them, five proteins (α1Micro, β2M, complement C3, cystatin C, and THP) were associated with APOE-ε4 (Table 2).
Alpha-1-microglobulin (α1M) is a small plasma protein that plays a role in various physiological processes, including the regulation of the immune response and protection against oxidative stress. One previous study found that α1M may play a role in AD [20], and one study of α1M in acute kidney injury and cardiovascular events reported higher α1M levels in those with CKD, diabetes, hypertension, and heart failure [21]. Beta-2 microglobulin (β2M) is a low-molecular-weight protein that is a component of the major histocompatibility complex (MHC) class 1 molecules, present on the surface of nucleated cells. It has been primarily recognized for its role as a marker of immune system activity and renal function. It has been suggested as a potential biomarker for preclinical AD and might have varied functions throughout various stages of preclinical AD progression, possibly related to energy metabolism in the pathological mechanism of AD [22,23]. β2M is one of the genes that appear to mediate the association of diet with incident cardiovascular disease and all-cause mortality [24], and there are moderate positive associations between β2M levels and CVD events and mortality [25]. Recently, it was reported that β2M plays an active role in both brain injury and cognitive disorders in animal models [26].
Complement C3 is a central component of the complement system and plays a crucial role in inflammation and immunity and the pathophysiology of both CVDs and AD. It is well established that complement C3 is associated with atherosclerosis and CVDs [27,28]. The activation of C3 leads to the recruitment of inflammatory cells to the vessel wall, contributing to the formation and progression of atherosclerotic plaques. One recent study found that the complement C3 system is involved in the progression of atherosclerosis by vascular remodeling [29], while another study suggested that complement C3 is associated with a higher future risk of coronary heart disease [30]. In AD, complement C3 may reflect stage-associated biomarker changes in AD [31], and the presence of APOE-ε4 has been associated with elevated C3 levels, resulting in elevated Alzheimer’s neurodegeneration, noting that amyloid has a regulating effect on the complement system and is linked to subsequent tau protein pathology [32]. C3 is one of the blood biomarkers for use in point-of-care diagnosis tools for AD [20], with one recent study confirming that C3 could be a biomarker in the early diagnosis of AD [33].
Regarding the cystatin C protein, cystatin C is known to have neuroprotective properties. It is represented in neurodegenerative diseases [34] and is a therapeutic candidate that can potentially prevent brain damage and neurodegeneration [35]. One recent study provided evidence for the use of saliva samples to source biomarkers (including cystatin C) for the early detection of cognitive impairment in individuals on the AD continuum and potentially other neurodegenerative diseases [36]. However, the role of cystatin C in CVD showed inconsistent results. Previous studies revealed that cystatin C is historically linked to its utility as a marker of renal function which is closely linked to cardiovascular health. It can be viewed as an alternative predictor of complications for CVDs [37], and cystatin C could be promising as a biomarker in the diagnosis of coronary artery disease, aneurysm, adiposity, peripheral arterial disease, and coronary artery calcification [38]. A meta-analysis found that there is a significant association between high levels of cystatin C and the development of cardiovascular events or mortality in individuals with normal renal function [39]. One recent study also found that cystatin C independently predicted major cardiovascular events, the development of chronic kidney disease, and cardiovascular and all-cause mortality. The prediction of long-term mortality was independent of the improved estimation of GFR [40]. However, Mendelian randomization analyses did not support a causal role of cystatin C in the etiology of CVDs, and as such, therapeutics targeting lowering circulating cystatin C are unlikely to be effective in preventing CVDs [41].

3.2. Proteomic Biomarkers Associated with MCI, CVDs, and APOE-ε4

The present study further found that MCI and CVDs were associated with 12 biomarkers (ApoD, AXL, BNP, Calcitonin, CD40, C-peptide, pM, PPP, THP, TNFR2, TTR, and VEGF), while MCI, CVDs, and the APOE-ε4 allele were associated with THP, Calcitonin, and TNFR2 (Table 2 and Table 3).
The current findings of cytokines (TNF-α, TNFR2, and VEGF) associated with CVDs are congruent with the results of previous studies for TNFR2 in myocardial infarction in the Swedish population [42] and TNF-α [43]. The findings of this study are important in understanding how VEGF contributes to cardiovascular illness. An alteration in VEGF has been associated with several diseases (e.g., kidney, hypertension, cancer, or diabetes), but this study was the first report showing that VEGF is increased in CVDs. VEGF represents inflammatory processes that were present in several tissues including the heart and brain. These CVD-associated biomarkers could be used to inform clinical applications, such as pharmacotherapies targeting neutralizing inflammatory biomarkers which have displayed potential for effectiveness toward treating CVDs, such as atherosclerosis, although contradictory findings indicate that there is still a need for designing more precise therapy [44]. VEGF was positively associated with AD, MCI, and CVDs in our study. Thus, the biomarkers shared by AD, MCI, and CVDs in the current study can be used to further identify shared pathophysiology for AD, MCI, and CVDs. VEGF, as an intracellular player together with other cytokines, may determine vascular risk factors and their contribution to cognitive impairment and the development of CVDs and AD. Understanding the complex interactions between VEGF and other signaling pathways could tell us how molecular discovery can inform future drug development and clinical trial design [45].
TNFR2 is one of two receptors of the cytokines and has proinflammatory effects. A recent study suggested that stimulating TNFR2 has the potential to strongly modulate the balance between effector T cells and Treg cells that may impact disease in both positive and negative manners [46]. Previous studies have shown that TNFR2 has a neuroprotective function. For example, a study in human post-mortem AD brain tissues demonstrated that TNFR2 levels were decreased [47], while individuals with low levels of TNFR2 were more likely to develop AD [48]. The neuroprotective role of TNFR2 signaling has been reported in a review article [49]. Furthermore, both animal model and human studies have suggested TNFR2 as a therapeutic target [50,51,52]. The present study revealed that TNFR2 was positively associated with CVD but negatively associated with MCI, whereas no significant association was found with AD. These findings suggest a differential role of TNFR2 in CVDs versus neurocognitive function and the complex pathogenesis mechanism of AD, MCI, and CVDs.

3.3. Proteomic Biomarkers Associated with AD, MCI, CVDs, and APOE-ε4

We found that AD, MCI, and CVDs were associated with BNP, PPP, THP, and VEGF, while THP was also associated with the APOE-ε4 allele (Table 2).
The THP, also known as uromodulin, is associated with kidney function along with graft survival, CVDs, glucose metabolism, end-stage renal disease, systemic inter-organ signaling, and overall mortality [53,54]. For example, one previous study found that higher levels of urinary uromodulin were associated with a lower risk of eGFR decline, a lower risk of incident chronic kidney disease (CKD), and a lower risk of mortality in the aged population [55]. One recent study demonstrated that higher serum uromodulin levels were associated with lower mortality and major adverse cardiovascular events in a white CKD population from Germany [56]. A recent study reported that they were able to predict, with accuracy, the risk of a person with MCI progressing to dementia due to AD in a time period of up to four years by using a machine learning-based panel composed of 12 plasma proteins (ApoB, Calcitonin, C-peptide, CRP, IGFBP-2, Interleukin-3, Interleukin-8, PARC, Serotransferrin, THP, TLSP 1-309, and TN-C) that included THP [57].

3.4. Relationship among Shared Proteomic Biomarkers

The present study found that both AD and CVDs were associated with fourteen proteomic biomarkers, and both MCI and CVDs were associated with twelve proteomic biomarkers; among them, four biomarkers were shared by AD, MCI, and CVDs (Table 2 and Table 3). Based on the 22 shared proteins, correlation analyses and variable cluster analysis were conducted (Tables S2 and S3 and Figure 1). The twenty-two proteins were clustered into eight clusters. Proteins within a cluster revealed strong correlations with each other, which implies similar pathways. However, further pathway analysis is required to validate the results.
AD is an age-related neurodegenerative disorder which is characterized by the progressive accumulation in the brain parenchyma of β-amyloid A(β) plaques (Aβ peptides) and neurofibrillary tangles (tau protein), and the most widely used biomarkers for AD include the APOE genotype, CSF Aβ42, pTau and tTau, and findings from imaging setups such as MRI and PET together with a battery of cognition tests [34,58,59,60]. Previous studies have shown that the levels of Aβ42 are fully decreased at least 5 to 10 years before the conversion of MCI to AD dementia [61], the abnormal accumulation of Aβ can start decades before the dementia stage [62], and the first Aβ plaques occur at least 10 years, and probably 20–30 years, before the first symptoms [63]. Previous studies have also shown that amyloid-Aβ is increased in atherosclerotic arteries, indicating that amyloid-Aβ is involved in cardiovascular disease [64,65,66]. More recently, a study reported that ACE1 and several APO proteins are associated with AD, MCI, CVDs, and the APOE-ε4 allele [16]. The shared proteins among AD, MCI, and CVDs support previous studies that suggested that AD, MCI, and CVDs shared similar genetic biomarkers and biochemical profiles [7,8,10,11,12,13,14,16]. Furthermore, among the twenty-two shared proteins, seven biomarkers were associated with APOE-ε4 (Table 2 and Table 3), providing additional evidence that the APOE-ε4 allele plays an important role in AD, MCI, CVDs, and metabolic phenotypes [15,16].

3.5. Strengths and Limitations

We are aware of the strengths of the current study. First, we have taken into account potential confounding factors in our analysis since the presence of certain confounding factors, such as variations in age, sex, and the APOE-ε4 allele, may contribute to variations in findings [43] and false results. Second, we used longitudinal analysis, which is particularly useful for evaluating the relationship between risk factors and the development of diseases, such as AD and CVDs.
We acknowledge some limitations of the current study. First, we were unable to model the interactions between genetic, environmental, and direct effects nor document that genetic and environmental factors are considered risk factors for CVDs and AD since both diseases are considered complex and multifactorial. Second, the modest sample size in the current study may result in false positive or negative findings; thus, a future study with larger sample sizes is needed for accurate type-1 error rate control. To confirm the utility of these biomarkers, it is crucial to validate them in studies with larger samples. Third, in the future, further studies are needed to explore the inflammatory responses in the central nervous system of patients with AD and CVD to gain a better understanding of the disease’s etiology and risk factors.

4. Materials and Methods

4.1. Dataset

To achieve the aims of this study, data were analyzed from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu) (accessed on 26 October 2022). Initiated in 2003, the ADNI is an ongoing, longitudinal, multicenter study representing a collaborative effort as a public–private partnership, led by Principal Investigator Michael W. Weiner, MD. The primary goal of the ADNI is to test whether serial magnetic resonance imaging (MRI), positron emission tomography (PET), other biological markers, and clinical and neuropsychological assessment can be combined to measure the progression of MCI and early AD. The ADNI provides services in the U.S. and Canada. The use of ADNI data to perform a secondary data analysis is exempt from the Institutional Review Board.

4.2. Measures

The demographic variables included in this study were age, gender, and educational levels (https://adni.bitbucket.io/reference/ptdemog.html, accessed on 26 October 2022). Gender was a dichotomous variable that was self-reported as either male or female. Age and education were continuous variables expressed in total years. CVDs were identified using the MedHist data within the ADNI (https://adni.bitbucket.io/reference/medhist.html, accessed on 26 October 2022) with participants classified based on their history of CVDs, defined as either yes (with history) or no (without history). Data on APOE-ε4 genotypes were extracted from the ADNI database (https://adni.loni.usc.edu/updated-apoe-results-2, accessed on 26 October 2022). APOE genotyping was performed on DNA samples obtained from blood samples. APOE-ε4 carriers were defined as individuals with at least one ε4 allele (ε4/ε4 designated as APOE-ε4-2, ε4/ε3 or ε4/ε2 as APOE-ε4-1+), while non-carriers were defined as individuals with no ε4 allele (APOE-ε4-0) (Table 1).
Plasma samples were collected from a subset of these participants 12 months after the baseline assessment. Plasma samples were assayed for 190 analytes using the “Human DiscoveryMAP”, developed on the Luminex xMAP platform by Rules-Based Medicine. A total of 146 plasma proteomic biomarkers passed the strict ADNI quality control from a subset of the ADNI-1 dataset known as the “Biomarkers Consortium Plasma Proteomics Project RBM multiplex data” (https://adni.bitbucket.io/reference/rbmqc.html, accessed on 26 October 2022) [67]. More details about the Biomarkers Consortium Project “Use of Targeted Multiplex Proteomic Strategies to Identify Plasma-Based Biomarkers in Alzheimer’s Disease” are listed at https://adni.loni.usc.edu/wp-content/uploads/2010/12/BC-Plasma-Proteomics-Analysis-Plan.pdf, accessed on 26 October 2022. There were 566 adults measured for plasma proteomic biomarkers at baseline in the ADNI-1 dataset. This study included the demographics, AD diagnosis, medical history information, APOE genotypes, and protein data, and there were 111 individuals with AD, 383 with mild cognitive impairment (MCI), and 410 with CVD.

4.3. Statistical Methods

The categorical variables were depicted using raw counts along with the corresponding proportions, while continuous variables were presented as means ± standard deviations (SDs). The Chi-square test was used to examine the differences in categorical variables for both AD diagnoses and CVDs. For continuous variables, differences among groups based on AD and CVD diagnoses were evaluated using the one-way Analysis of Variance (ANOVA). Given the potential for a skewed distribution among the protein data, Z scores were calculated for each protein to standardize the data, using the mean and standard deviation.
A multivariable linear mixed model (LMM) was used to explore the impact of AD, CVDs, and the APOE-ε4 allele on the longitudinal changes in 146 proteins (from baseline to 12 months). The model was adjusted for age, gender, and education.
Y it = μ t + β i x i t + γ z i + α i + ε i t     i = 1 , … , n ;   t = 1 , … , T
where Yit is the value of the outcome for individual i at follow-up time t, μt is an intercept varying with time, xit is a vector of time-varying variables such as the follow-up visit, zi is a vector of time-invariant variables such as gender and education, αi denotes the random effects where each has a normal distribution with a mean of 0 and constant variance, and β are fixed effects. εij is a random distribution term. i = 1,…, Ij is the level-1 individual i indicator, and t = 1,…,T is the level-2 indicator, such as the follow-up visit.
Both Pearson and Spearman correlation analyses were performed to test the associations among AD- or MCI- and CVD-associated proteins using the Z scores. To further test the relationship among AD -or MCI- and CVD-associated proteins, the variable cluster procedure (VARCLUS) in SAS 9.4 (SAS Institute, Cary, NC, USA) was used to identify clusters of variables that exhibited strong correlations within each cluster and weak correlations with variables in other clusters, offering insight into the potential underlying patterns and relationships among the proteins. A good fit for each item was indicated by higher squared correlation (R2) values within its own cluster, low R2 values with the next closest cluster, and low 1 − R2 ratios (the ratio of 1 − R2 for a variable’s own cluster to 1 − R2 for its nearest cluster) [68,69].
1 − R 2   r a t i o = 1 − R 2   o w n   c l u s t e r 1 − R 2   n e x t   c l o s e s t   c l u s t e r
All statistical analyses were performed using SAS (version 9.4). A p < 0.05 was considered statistically significant.

5. Conclusions

The findings from our study align with prior experimental and observational studies on the connection between biomarkers and two diseases (AD and/or CVD) after controlling for several potential confounding factors. Moreover, we observed a correlation between certain proteins and the APOE-ε4 allele. Together, these biomarkers hold significant potential for early detection, prognostication, and perhaps even the development of targeted treatment therapies for AD and CVD. This potential can only be realized through the confirmation and validation of these findings in studies with larger sample sizes and well-defined traits/diseases. The further testing of additional biomarkers could contribute to the creation of a predictive scoring system, offering guidance for timely treatment interventions and future meta-analysis.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/ijms251910751/s1.

Author Contributions

Conceptualization, L.A.T., D.X., C.X., and K.W.; methodology, D.X., C.X., and K.W.; software, Y.L., S.W., X.L., and K.W.; validation, L.A.T., D.X., C.X., and R.O.N.; formal analysis, Y.L., S.W., X.L., and K.W.; resources, K.W.; data curation, K.W.; writing—original draft preparation, L.A.T., D.X., C.X., and K.W.; writing—review and editing, L.A.T., Y.L., S.W., X.L., R.O.N., D.X., C.X., and K.W.; visualization, D.X., C.X., R.O.N., and K.W.; supervision, K.W. The Alzheimer’s Disease Neuroimaging Initiative data used in the preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). All authors have read and agreed to the published version of the manuscript.

Funding

This research received no funding.

Institutional Review Board Statement

There was an Institutional Review Board exemption for the current study due to secondary data analysis. Data used in the preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the original study. The data used in the preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu).

Data Availability Statement

The data used in the preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu).

Acknowledgments

The present study is a secondary data analysis. The original study was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). The ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd. and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research provides funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. 2023 Alzheimer’s Disease Facts and Figures. Alzheimer’s Dement 2023, 19, 1598–1695. [CrossRef] [PubMed]
  2. Roth, G.A.; Mensah, G.A.; Johnson, C.O.; Addolorato, G.; Ammirati, E.; Baddour, L.M.; Barengo, N.C.; Beaton, A.Z.; Benjamin, E.J.; Benziger, C.P.; et al. Global Burden of Cardiovascular Diseases and Risk Factors, 1990–2019: Update From the GBD 2019 Study. J Am Coll Cardiol 2020, 76, 2982–3021. [Google Scholar] [CrossRef]
  3. Waigi, E.W.; Webb, R.C.; Moss, M.A.; Uline, M.J.; McCarthy, C.G.; Wenceslau, C.F. Soluble and Insoluble Protein Aggregates, Endoplasmic Reticulum Stress, and Vascular Dysfunction in Alzheimer’s Disease and Cardiovascular Diseases. GeroScience 2023, 45, 1411–1438. [Google Scholar] [CrossRef] [PubMed]
  4. Virani, S.S.; Alonso, A.; Aparicio, H.J.; Benjamin, E.J.; Bittencourt, M.S.; Callaway, C.W.; Carson, A.P.; Chamberlain, A.M.; Cheng, S.; Delling, F.N.; et al. Heart Disease and Stroke Statistics—2021 Update: A Report From the American Heart Association. Circulation 2021, 143(8), e254–e743. [Google Scholar] [CrossRef]
  5. Carter, C.J.; France, J.; Crean, S.; Singhrao, S.K. The Porphyromonas Gingivalis/Host Interactome Shows Enrichment in GWASdb Genes Related to Alzheimer’s Disease, Diabetes and Cardiovascular Diseases. Front. Aging Neurosci. 2017, 9, 408. [Google Scholar] [CrossRef] [PubMed]
  6. Gong, S.; Su, B.B.; Tovar, H.; Mao, C.; Gonzalez, V.; Liu, Y.; Lu, Y.; Wang, K.-S.; Xu, C. Polymorphisms Within RYR3 Gene Are Associated With Risk and Age at Onset of Hypertension, Diabetes, and Alzheimer’s Disease. Am. J. Hypertens. 2018, 31, 818–826. [Google Scholar] [CrossRef] [PubMed]
  7. Tini, G.; Scagliola, R.; Monacelli, F.; La Malfa, G.; Porto, I.; Brunelli, C.; Rosa, G.M. Alzheimer’s Disease and Cardiovascular Disease: A Particular Association. Cardiol. Res. Pract. 2020, 2020, 1–10. [Google Scholar] [CrossRef] [PubMed]
  8. Mur, J.; McCartney, D.L.; Walker, R.M.; Campbell, A.; Bermingham, M.L.; Morris, S.W.; Porteous, D.J.; McIntosh, A.M.; Deary, I.J.; Evans, K.L.; et al. DNA Methylation in APOE: The Relationship with Alzheimer’s and with Cardiovascular Health. AD Transl. Res. Clin. Interv. 2020, 6, e12026. [Google Scholar] [CrossRef] [PubMed]
  9. De Bruijn, R.F.; Ikram, M.A. Cardiovascular Risk Factors and Future Risk of Alzheimer’s Disease. BMC Med. 2014, 12, 130. [Google Scholar] [CrossRef] [PubMed]
  10. Eriksson, U.K.; Bennet, A.M.; Gatz, M.; Dickman, P.W.; Pedersen, N.L. Nonstroke Cardiovascular Disease and Risk of Alzheimer Disease and Dementia. Alzheimer Dis. Assoc. Disord. 2010, 24, 213–219. [Google Scholar] [CrossRef]
  11. Kotze, M.J.; Van Rensburg, S.J. Pathology Supported Genetic Testing and Treatment of Cardiovascular Disease in Middle Age for Prevention of Alzheimer’s Disease. Metab. Brain Dis. 2012, 27, 255–266. [Google Scholar] [CrossRef] [PubMed]
  12. Mahley, R.W. Apolipoprotein E: From Cardiovascular Disease to Neurodegenerative Disorders. J. Mol. Med. 2016, 94, 739–746. [Google Scholar] [CrossRef]
  13. Ray, M.; Ruan, J.; Zhang, W. Variations in the Transcriptome of Alzheimer’s Disease Reveal Molecular Networks Involved in Cardiovascular Diseases. Genome Biol. 2008, 9, R148. [Google Scholar] [CrossRef]
  14. Wang, K.; Lu, Y.; Morrow, D.F.; Xiao, D.; Xu, C.; The Alzheimer’s Disease Neuroimaging Initiative. Associations of ARHGAP26 Polymorphisms with Alzheimer’s Disease and Cardiovascular Disease. J. Mol. Neurosci. 2022, 72, 1085–1097. [Google Scholar] [CrossRef]
  15. Belloy, M.E.; Napolioni, V.; Greicius, M.D. A Quarter Century of APOE and Alzheimer’s Disease: Progress to Date and the Path Forward. Neuron 2019, 101, 820–838. [Google Scholar] [CrossRef]
  16. Xu, C.; Garcia, D.; Lu, Y.; Ozuna, K.; Adjeroh, D.A.; Wang, K.; on behalf of the Alzheimer’s Disease Neuroimaging Initiative. Levels of Angiotensin-Converting Enzyme and Apolipoproteins Are Associated with Alzheimer’s Disease and Cardiovascular Diseases. Cells 2021, 11, 29. [Google Scholar] [CrossRef]
  17. Broce, I.J.; Tan, C.H.; Fan, C.C.; Jansen, I.; Savage, J.E.; Witoelar, A.; Wen, N.; Hess, C.P.; Dillon, W.P.; Glastonbury, C.M.; et al. Dissecting the Genetic Relationship between Cardiovascular Risk Factors and Alzheimer’s Disease. Acta. Neuropathol. 2019, 137, 209–226. [Google Scholar] [CrossRef]
  18. Bessi, V.; Balestrini, J.; Bagnoli, S.; Mazzeo, S.; Giacomucci, G.; Padiglioni, S.; Piaceri, I.; Carraro, M.; Ferrari, C.; Bracco, L.; et al. Influence of ApoE Genotype and Clock T3111C Interaction with Cardiovascular Risk Factors on the Progression to Alzheimer’s Disease in Subjective Cognitive Decline and Mild Cognitive Impairment Patients. J. Pers. Med. 2020, 10, 45. [Google Scholar] [CrossRef] [PubMed]
  19. Sharma, V.K.; Mehta, V.; Singh, T.G. Alzheimer’s Disorder: Epigenetic Connection and Associated Risk Factors. Curr. Neuropharmacol. 2020, 18, 740–753. [Google Scholar] [CrossRef] [PubMed]
  20. Jammeh, E.; Zhao, P.; Carroll, C.; Pearson, S.; Ifeachor, E. Identification of Blood Biomarkers for Use in Point of Care Diagnosis Tool for Alzheimer’s Disease. In Proceedings of the 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Orlando, FL, USA, 16–20 August 2016; IEEE: Orlando, FL, USA; pp. 2415–2418. [Google Scholar]
  21. Amatruda, J.G.; Estrella, M.M.; Garg, A.X.; Thiessen-Philbrook, H.; McArthur, E.; Coca, S.G.; Parikh, C.R.; Shlipak, M.G.; TRIBE-AKI Consortium. Urine Alpha-1-Microglobulin Levels and Acute Kidney Injury, Mortality, and Cardiovascular Events Following Cardiac Surgery. Am. J. Nephrol. 2021, 52, 673–683. [Google Scholar] [CrossRef] [PubMed]
  22. Huang, Y.-M.; Ma, Y.-H.; Gao, P.-Y.; Wang, Z.-B.; Huang, L.-Y.; Hou, J.-H.; Tan, L.; Yu, J.-T. Plasma Β2-Microglobulin and Cerebrospinal Fluid Biomarkers of Alzheimer’s Disease Pathology in Cognitively Intact Older Adults: The CABLE Study. Alzheimer's Res. Ther. 2023, 15, 69. [Google Scholar] [CrossRef]
  23. Zhang, Y.; Yang, Y.; Wang, C.; Chen, W.; Chen, X.; Wu, F.; He, H. Copper Metabolism-Related Genes in Entorhinal Cortex for Alzheimer’s Disease. Sci. Rep. 2023, 13, 17458. [Google Scholar] [CrossRef]
  24. Kim, Y.; Lu, S.; Ho, J.E.; Hwang, S.; Yao, C.; Huan, T.; Levy, D.; Ma, J. Proteins as Mediators of the Association Between Diet Quality and Incident Cardiovascular Disease and All-Cause Mortality: The Framingham Heart Study. JAHA 2021, 10, e021245. [Google Scholar] [CrossRef] [PubMed]
  25. Shi, F.; Sun, L.; Kaptoge, S. Association of Beta-2-Microglobulin and Cardiovascular Events and Mortality: A Systematic Review and Meta-Analysis. Atherosclerosis 2021, 320, 70–78. [Google Scholar] [CrossRef]
  26. Chen, F.; Liu, J.; Li, F.-Q.; Wang, S.-S.; Zhang, Y.-Y.; Lu, Y.-Y.; Hu, F.-F.; Yao, R.-Q. Β2-Microglobulin Exacerbates Neuroinflammation, Brain Damage, and Cognitive Impairment after Stroke in Rats. Neural. Regen. Res. 2023, 18, 603–608. [Google Scholar] [CrossRef] [PubMed]
  27. Bao, X.; Borné, Y.; Johnson, L.; Muhammad, I.F.; Persson, M.; Niu, K.; Engström, G. Comparing the Inflammatory Profiles for Incidence of Diabetes Mellitus and Cardiovascular Diseases: A Prospective Study Exploring the ‘Common Soil’ Hypothesis. Cardiovasc. Diabetol. 2018, 17, 87. [Google Scholar] [CrossRef] [PubMed]
  28. Hertle, E.; Van Greevenbroek, M.M.; Arts, I.C.; Van Der Kallen, C.J.; Geijselaers, S.L.; Feskens, E.J.; Jansen, E.H.; Schalkwijk, C.G.; Stehouwer, C.D. Distinct Associations of Complement C3a and Its Precursor C3 with Atherosclerosis and Cardiovascular Disease: The CODAM Study. Thromb. Haemost. 2014, 111, 1102–1111. [Google Scholar] [CrossRef] [PubMed]
  29. Garcia-Arguinzonis, M.; Diaz-Riera, E.; Peña, E.; Escate, R.; Juan-Babot, O.; Mata, P.; Badimon, L.; Padro, T. Alternative C3 Complement System: Lipids and Atherosclerosis. IJMS 2021, 22, 5122. [Google Scholar] [CrossRef]
  30. Sacks, F.M.; Furtado, J.D.; Jensen, M.K. Protein-Based HDL Subspecies: Rationale and Association with Cardiovascular Disease, Diabetes, Stroke, and Dementia. Biochim. Biophys. Acta Mol. Cell Biol. Lipids 2022, 1867, 159182. [Google Scholar] [CrossRef]
  31. Hu, W.T.; Watts, K.D.; Tailor, P.; Nguyen, T.P.; Howell, J.C.; Lee, R.C.; Seyfried, N.T.; Gearing, M.; Hales, C.M.; Levey, A.I.; et al. CSF Complement 3 and Factor H Are Staging Biomarkers in Alzheimer’s Disease. Acta Neuropathol. Commun. 2016, 4, 14. [Google Scholar] [CrossRef] [PubMed]
  32. Bonham, L.W.; Desikan, R.S.; Yokoyama, J.S. The Relationship between Complement Factor C3, APOE Ε4, Amyloid and Tau in Alzheimer’s Disease. Acta Neuropathol. Commun. 2016, 4, 65. [Google Scholar] [CrossRef]
  33. Wang, T.; Wang, X.; Yao, Y.; Zhao, C.; Yang, C.; Han, Y.; Cai, Y. Association of Plasma Apolipoproteins and Levels of Inflammation-Related Factors with Different Stages of Alzheimer’s Disease: A Cross-Sectional Study. BMJ Open 2022, 12, e054347. [Google Scholar] [CrossRef]
  34. Simonsen, A.H.; Hagnelius, N.-O.; Waldemar, G.; Nilsson, T.K.; McGuire, J. Protein Markers for the Differential Diagnosis of Vascular Dementia and Alzheimer’s Disease. Int. J. Proteom. 2012, 2012, 1–8. [Google Scholar] [CrossRef] [PubMed]
  35. Mathews, P.M.; Levy, E. Cystatin C in Aging and in Alzheimer’s Disease. Ageing Res. Rev. 2016, 32, 38–50. [Google Scholar] [CrossRef] [PubMed]
  36. McNicholas, K.; François, M.; Liu, J.-W.; Doecke, J.D.; Hecker, J.; Faunt, J.; Maddison, J.; Johns, S.; Pukala, T.L.; Rush, R.A.; et al. Salivary Inflammatory Biomarkers Are Predictive of Mild Cognitive Impairment and Alzheimer’s Disease in a Feasibility Study. Front. Aging Neurosci. 2022, 14, 1019296. [Google Scholar] [CrossRef] [PubMed]
  37. Gevorgyan, M.M.; Voronina, N.P.; Goncharova, N.V.; Kozaruk, T.V.; Russkikh, G.S.; Bogdanova, L.A.; Korolenko, T.A. Cystatin C as a Marker of Progressing Cardiovascular Events during Coronary Heart Disease. Bull Exp. Biol. Med. 2017, 162, 421–424. [Google Scholar] [CrossRef] [PubMed]
  38. Wu, H.; Du, Q.; Dai, Q.; Ge, J.; Cheng, X. Cysteine Protease Cathepsins in Atherosclerotic Cardiovascular Diseases. J. Atheroscler. Thromb. 2018, 25, 111–123. [Google Scholar] [CrossRef] [PubMed]
  39. Einwoegerer, C.F.; Domingueti, C.P. Association Between Increased Levels of Cystatin C and the Development of Cardiovascular Events or Mortality: A Systematic Review and Meta-Analysis. Arq. Bras. Cardiol. 2018, 111, 796–807. [Google Scholar] [CrossRef]
  40. West, M.; Kirby, A.; Stewart, R.A.; Blankenberg, S.; Sullivan, D.; White, H.D.; Hunt, D.; Marschner, I.; Janus, E.; Kritharides, L.; et al. Circulating Cystatin C Is an Independent Risk Marker for Cardiovascular Outcomes, Development of Renal Impairment, and Long-Term Mortality in Patients With Stable Coronary Heart Disease: The LIPID Study. JAHA 2022, 11, e020745. [Google Scholar] [CrossRef] [PubMed]
  41. Van Der Laan, S.W.; Fall, T.; Soumaré, A.; Teumer, A.; Sedaghat, S.; Baumert, J.; Zabaneh, D.; Van Setten, J.; Isgum, I.; Galesloot, T.E.; et al. Cystatin C and Cardiovascular Disease. J. Am. Coll. Cardiol. 2016, 68, 934–945. [Google Scholar] [CrossRef] [PubMed]
  42. Carlsson, A.C.; Jansson, J.-H.; Söderberg, S.; Ruge, T.; Larsson, A.; Ärnlöv, J. Levels of Soluble Tumor Necrosis Factor Receptor 1 and 2, Gender, and Risk of Myocardial Infarction in Northern Sweden. Atherosclerosis 2018, 272, 41–46. [Google Scholar] [CrossRef] [PubMed]
  43. Ain, Q.U.; Sarfraz, M.; Prasesti, G.K.; Dewi, T.I.; Kurniati, N.F. Confounders in Identification and Analysis of Inflammatory Biomarkers in Cardiovascular Diseases. Biomolecules 2021, 11, 1464. [Google Scholar] [CrossRef] [PubMed]
  44. Ajoolabady, A.; Bi, Y.; McClements, D.J.; Lip, G.Y.H.; Richardson, D.R.; Reiter, R.J.; Klionsky, D.J.; Ren, J. Melatonin-Based Therapeutics for Atherosclerotic Lesions and beyond: Focusing on Macrophage Mitophagy. Pharmacol. Res. 2022, 176, 106072. [Google Scholar] [CrossRef] [PubMed]
  45. Apte, R.S.; Chen, D.S.; Ferrara, N. VEGF in Signaling and Disease: Beyond Discovery and Development. Cell 2019, 176, 1248–1264. [Google Scholar] [CrossRef] [PubMed]
  46. Mehta, A.K.; Gracias, D.T.; Croft, M. TNF Activity and T Cells. Cytokine 2018, 101, 14–18. [Google Scholar] [CrossRef] [PubMed]
  47. Zhao, M.; Cribbs, D.H.; Anderson, A.J.; Cummings, B.J.; Su, J.H.; Wasserman, A.J.; Cotman, C.W. The Induction of the TNF Death Domain Signaling Pathway in Alzheimer’s Disease Brain. Neurochem. Res. 2003, 28, 307–318. [Google Scholar] [CrossRef]
  48. Zhao, A.; Li, Y.; Deng, Y. TNF Receptors Are Associated with Tau Pathology and Conversion to Alzheimer’s Dementia in Subjects with Mild Cognitive Impairment. Neurosci. Lett. 2020, 738, 135392. [Google Scholar] [CrossRef]
  49. Ortí-Casañ, N.; Wu, Y.; Naudé, P.J.W.; De Deyn, P.P.; Zuhorn, I.S.; Eisel, U.L.M. Targeting TNFR2 as a Novel Therapeutic Strategy for Alzheimer’s Disease. Front. Neurosci. 2019, 13, 49. [Google Scholar] [CrossRef] [PubMed]
  50. Pillai, J.A.; Bebek, G.; Khrestian, M.; Bena, J.; Bergmann, C.C.; Bush, W.S.; Leverenz, J.B.; Bekris, L.M. TNFRSF1B Gene Variants and Related Soluble TNFR2 Levels Impact Resilience in Alzheimer’s Disease. Front. Aging Neurosci. 2021, 13, 638922. [Google Scholar] [CrossRef] [PubMed]
  51. Ortí-Casañ, N.; Zuhorn, I.S.; Naudé, P.J.W.; De Deyn, P.P.; Van Schaik, P.E.M.; Wajant, H.; Eisel, U.L.M. A TNF Receptor 2 Agonist Ameliorates Neuropathology and Improves Cognition in an Alzheimer’s Disease Mouse Model. Proc. Natl. Acad. Sci. USA 2022, 119, e2201137119. [Google Scholar] [CrossRef] [PubMed]
  52. Ortí-Casañ, N.; Wajant, H.; Kuiperij, H.B.; Hooijsma, A.; Tromp, L.; Poortman, I.L.; Tadema, N.; De Lange, J.H.E.; Verbeek, M.M.; De Deyn, P.P.; et al. Activation of TNF Receptor 2 Improves Synaptic Plasticity and Enhances Amyloid-β Clearance in an Alzheimer’s Disease Mouse Model with Humanized TNF Receptor 2. JAD 2023, 94, 977–991. [Google Scholar] [CrossRef] [PubMed]
  53. Steubl, D.; Buzkova, P.; Ix, J.H.; Devarajan, P.; Bennett, M.R.; Chaves, P.H.M.; Shlipak, M.G.; Bansal, N.; Sarnak, M.J.; Garimella, P.S. Association of Serum and Urinary Uromodulin and Their Correlates in Older Adults—The Cardiovascular Health Study. Nephrology 2020, 25, 522–526. [Google Scholar] [CrossRef] [PubMed]
  54. Thielemans, R.; Speeckaert, R.; Delrue, C.; De Bruyne, S.; Oyaert, M.; Speeckaert, M.M. Unveiling the Hidden Power of Uromodulin: A Promising Potential Biomarker for Kidney Diseases. Diagnostics 2023, 13, 3077. [Google Scholar] [CrossRef]
  55. Devuyst, O.; Olinger, E.; Rampoldi, L. Uromodulin: From Physiology to Rare and Complex Kidney Disorders. Nat. Rev. Nephrol. 2017, 13, 525–544. [Google Scholar] [CrossRef]
  56. Steubl, D.; Schneider, M.P.; Meiselbach, H.; Nadal, J.; Schmid, M.C.; Saritas, T.; Krane, V.; Sommerer, C.; Baid-Agrawal, S.; Voelkl, J.; et al. Association of Serum Uromodulin with Death, Cardiovascular Events, and Kidney Failure in CKD. CJASN 2020, 15, 616–624. [Google Scholar] [CrossRef]
  57. Araújo, D.C.; Veloso, A.A.; Gomes, K.B.; De Souza, L.C.; Ziviani, N.; Caramelli, P. A Novel Panel of Plasma Proteins Predicts Progression in Prodromal Alzheimer’s Disease. JAD 2022, 88, 549–561. [Google Scholar] [CrossRef]
  58. Jack, C.R.; Bennett, D.A.; Blennow, K.; Carrillo, M.C.; Dunn, B.; Haeberlein, S.B.; Holtzman, D.M.; Jagust, W.; Jessen, F.; Karlawish, J.; et al. NIA-AA Research Framework: Toward a Biological Definition of Alzheimer’s Disease. Alzheimer’s Dement. 2018, 14, 535–562. [Google Scholar] [CrossRef] [PubMed]
  59. Ahmed, R.M.; Paterson, R.W.; Warren, J.D.; Zetterberg, H.; O’Brien, J.T.; Fox, N.C.; Halliday, G.M.; Schott, J.M. Biomarkers in Dementia: Clinical Utility and New Directions. J. Neurol. Neurosurg. Psychiatry 2014, 85, 1426–1434. [Google Scholar] [CrossRef] [PubMed]
  60. Khoury, R.; Patel, K.; Gold, J.; Hinds, S.; Grossberg, G.T. Recent Progress in the Pharmacotherapy of Alzheimer’s Disease. Drugs Aging 2017, 34, 811–820. [Google Scholar] [CrossRef] [PubMed]
  61. Buchhave, P. Cerebrospinal Fluid Levels Ofβ-Amyloid 1-42, but Not of Tau, Are Fully Changed Already 5 to 10 Years Before the Onset of Alzheimer Dementia. Arch. Gen. Psychiatry 2012, 69, 98. [Google Scholar] [CrossRef]
  62. Villemagne, V.L.; Burnham, S.; Bourgeat, P.; Brown, B.; Ellis, K.A.; Salvado, O.; Szoeke, C.; Macaulay, S.L.; Martins, R.; Maruff, P.; et al. Amyloid β Deposition, Neurodegeneration, and Cognitive Decline in Sporadic Alzheimer’s Disease: A Prospective Cohort Study. Lancet Neurol. 2013, 12, 357–367. [Google Scholar] [CrossRef] [PubMed]
  63. Jansen, W.J.; Ossenkoppele, R.; Knol, D.L.; Tijms, B.M.; Scheltens, P.; Verhey, F.R.J.; Visser, P.J.; Aalten, P.; Aarsland, D.; Alcolea, D.; et al. Prevalence of Cerebral Amyloid Pathology in Persons Without Dementia: A Meta-Analysis. JAMA 2015, 313, 1924. [Google Scholar] [CrossRef]
  64. Tibolla, G.; Norata, G.D.; Meda, C.; Arnaboldi, L.; Uboldi, P.; Piazza, F.; Ferrarese, C.; Corsini, A.; Maggi, A.; Vegeto, E.; et al. Increased Atherosclerosis and Vascular Inflammation in APP Transgenic Mice with Apolipoprotein E Deficiency. Atherosclerosis 2010, 210, 78–87. [Google Scholar] [CrossRef]
  65. Austin, S.A.; Sens, M.A.; Combs, C.K. Amyloid Precursor Protein Mediates a Tyrosine Kinase-Dependent Activation Response in Endothelial Cells. J. Neurosci. 2009, 29, 14451–14462. [Google Scholar] [CrossRef]
  66. De Meyer, G.R.Y.; De Cleen, D.M.M.; Cooper, S.; Knaapen, M.W.M.; Jans, D.M.; Martinet, W.; Herman, A.G.; Bult, H.; Kockx, M.M. Platelet Phagocytosis and Processing of β-Amyloid Precursor Protein as a Mechanism of Macrophage Activation in Atherosclerosis. Circ. Res. 2002, 90, 1197–1204. [Google Scholar] [CrossRef] [PubMed]
  67. Guo, L.-H.; Alexopoulos, P.; Wagenpfeil, S.; Kurz, A.; Perneczky, R. Plasma Proteomics for the Identification of Alzheimer Disease. Alzheimer Dis. Assoc. Disord. 2013, 27, 337–342. [Google Scholar] [CrossRef] [PubMed]
  68. Aggarwal, V.; Kosian, S. Feature Selection and Dimension Reduction Techniques in SAS; EXL Service: New York, NY, USA, 2011. [Google Scholar]
  69. Muthén, B.; Kaplan, D. A Comparison of Some Methodologies for the Factor Analysis of Non-normal Likert Variables. Br. J. Math. Stat. 1985, 38, 171–189. [Google Scholar] [CrossRef]
Ijms 25 10751 g001
Figure 1. Oblique variable clustering analysis of 22 proteins.
Figure 1. Oblique variable clustering analysis of 22 proteins.
Ijms 25 10751 g001
Table 1. Descriptive statistics at baseline.
Table 1. Descriptive statistics at baseline.
VariableADMCICNF/χ2, pCVDNon-CVDt/χ2, p
Age (mean ± SD)74.8 ± 8.174.8 ± 7.775.1 ± 5.80.05, 0.952775.2 ± 7.273.9 ± 7.83.60, 0.0584
Gender
    Male65248304.39, 0.1113258854.38, 0.0364 *
    Female4613528 14069
Education (mean ± SD)15.1 ± 3.215.6 ± 3.015.7 ± 2.81.31, 0.269915.4 ± 3.115.8 ± 2.81.70, 0.1930
APOE- ε4
   03617853 54.80, <0.0001 ***187801.10, 0.2953
   1+752055 21174
Abbreviations: CN: cognitive normal; MCI: mild cognitive impairment; AD: Alzheimer’s disease; CVD: cardiovascular disease; SD: standard deviation. p value is based on Chi-square test or F test/t test in ANOVA. * p < 0.05; *** p < 0.0001.
Table 2. Multivariable linear mixed model for AD and CVD shared proteins.
Table 2. Multivariable linear mixed model for AD and CVD shared proteins.
VariableAD vs. CN (t, p)MCI vs. CN (t, p)CVD vs. Non-CVD (t, p)APOE-ε4-1+ vs. 0 (t, p)12 Months (t, p)
A1Micro2.40, 0.0168 *1.80, 0.07204.68, <0.0001 ***−2.01, 0.0454 *−5.20, <0.0001 ***
ApoH2.20, 0.0279 *−1.55, 0.12252.90, 0.0039 **−0.54, 0.5874−15.40, <0.0001 ***
β2M3.10, 0.0020 **0.28, 0.78134.52, <0.0001 ***−2.61, 0.0092 **−0.97, 0.3314
BNP5.53, <0.0001 ***4.94, <0.0001 ***3.82, 0.0001 **−0.40, 0.68663.728, 0.0011 **
Complement C32.66, 0.0080 **−1.44, 0.15042.07, 0.0391 *−2.30, 0.0221 *2.94, 0.0034 **
Cystatin C2.31, 0.0213 *−1.24, 0.21575.78, <0.0001 ***−2.49, 0.0130 *−1.65, 0.1001
KIM1−3.06, 0.0023 **0.74, 0.46173.12, 0.0019 **0.01, 0.9900−5.47, <0.0001 ***
NGAL2.83, 0.0048 **0.60, 0.54652.53, 0.0117 *−1.61 0.1080−3.94, <0.0001 ***
PPP3.53, 0.0005 **3.08, 0.0022 **2.15, 0.0322 *1.31, 0.19042.77, 0.0059 **
TFF32.84, 0.0046 **0.30, 0.76534.30, <0.0001 ***−1.37, 0.17064.09, <0.0001 ***
THP−3.51, 0.0005 **−2.91, 0.0038 **−4.10, <0.0001 ***2.90, 0.0038 **−9.98, <0.0001 ***
TIM13.29, 0.0011 **0.68, 0.49992.14, 0.0328 *−1.51, 0.13210.72, 0.4744
TM2.14, 0.0324 *0.90, 0.36654.19, <0.0001 ***−0.74, 0.4626−1.46, 0.1455
VEGF3.63, 0.0003 **2.40, 0.0169 *4.13, <0.0001 ***−1.70, 0.08951.92, 0.0553
Abbreviations: CN: cognitive normal; MCI: mild cognitive impairment; AD: Alzheimer’s disease; CVD: cardiovascular disease; LMM: linear mixed model; t-value is based on multivariable LMM adjusted for age, gender, education, and APOE-ε4. p value is based on multivariable LMM. * p < 0.05; ** p < 0.01; *** p < 0.0001.
Table 3. Multivariable linear mixed model for MCI and CVD shared proteins.
Table 3. Multivariable linear mixed model for MCI and CVD shared proteins.
VariableAD vs. CN (t, p)MCI vs. CN (t, p)CVD vs. non-CVD (t, p)APOE-ε4-1+ vs. 0 (t, p)12 Months (t, p)
ApoD−1.84, 0.0670−3.12, 0.0019 **−2.00, 0.0456 *−0.92, 0.3576−0.78, 0.4374
AXL1.62, 0.10672.64, 0.0084 **2.28, 0.0233 *−0.25, 0.8027−5.62, <0.0001 ***
Calcitonin1.86, 0.06332.75, 0.0061 **2.17, 0.0302 *2.51, 0.0124 *−2.34, 0.0197 *
CD401.15, 0.2498−2.61, 0.0093 **−0.93, 0.3543−0.42, 0.62782.97, 0.0032 **
C-peptide1.14, 0.25582.03, 0.0429 *4.77, <0.0001 ***0.22, 0.82811.05, 0.2947
pM0.56, 0.57842.10, 0.0365 *3.63, 0.0003 **−1.15, 0.25251.95, 0.0517
TNFR20.64, 0.5208−2.07, 0.0385 *3.96, <0.0001 ***−2.38, 0.0175 *−3.43, 0.0007 **
TTR−1.42, 0.1562−3.69, 0.0002 **2.68, 0.0075 **1.10, 0.2701−10.65, <0.0001 ***
Abbreviations: CN: cognitive normal; MCI: mild cognitive impairment; AD: Alzheimer’s disease; CVD: cardiovascular disease; LMM: linear mixed model; t-value is based on multivariable LMM adjusted for age, gender, education, and APOE-ε4. p value is based on multivariable LMM. * p < 0.05; ** p < 0.01; *** p < 0.0001.
Table 4. Multiple comparison for the THP in linear mixed model analysis.
Table 4. Multiple comparison for the THP in linear mixed model analysis.
ComparisonVisitDifference ± SEt, p
AD vs. CNBaseline−0.52 ± 0.16−3.16, 0.0048 **
12 months−0.60 ± 0.16−3.66, 0.0008 **
MCI vs. CNBaseline−0.31 ± 0.14−2.22, 0.0695
12 months−0.50 ± 0.14−3.53, 0.0013 **
AD vs. MCIBaseline−0.20 ± 0.10−1.97, 0.1219
12 months−0.10 ± 0.11−0.99, 0.5863
CVD vs. non-CVDBaseline−0.39 ± 0.09−4.32, <0.0001 ***
12 months−0.33 ± 0.09−3.55, 0.0004 **
APOE-ε4-1+ vs. 0Baseline 0.24 ± 0.092.73, 0.0066 **
12 months 0.26 ± 0.092.94, 0.0034 **
Abbreviations: CN: cognitive normal; MCI: mild cognitive impairment; AD: Alzheimer’s disease; CVD: cardiovascular disease; LMM: linear mixed model; t-value is based on multivariable LMM adjusted for age, gender, education, and APOE-ε4. p value is based on multivariable LMM. SE = standard error; p value is Tukey–Kramer Test ** p < 0.01; *** p < 0.0001.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Theeke, L.A.; Liu, Y.; Wang, S.; Luo, X.; Navia, R.O.; Xiao, D.; Xu, C.; Wang, K.; The Alzheimer and Disease Neuroimaging Initiative. Plasma Proteomic Biomarkers in Alzheimer’s Disease and Cardiovascular Disease: A Longitudinal Study. Int. J. Mol. Sci. 2024, 25, 10751. https://doi.org/10.3390/ijms251910751

AMA Style

Theeke LA, Liu Y, Wang S, Luo X, Navia RO, Xiao D, Xu C, Wang K, The Alzheimer and Disease Neuroimaging Initiative. Plasma Proteomic Biomarkers in Alzheimer’s Disease and Cardiovascular Disease: A Longitudinal Study. International Journal of Molecular Sciences. 2024; 25(19):10751. https://doi.org/10.3390/ijms251910751

Chicago/Turabian Style

Theeke, Laurie A., Ying Liu, Silas Wang, Xingguang Luo, R. Osvaldo Navia, Danqing Xiao, Chun Xu, Kesheng Wang, and The Alzheimer and Disease Neuroimaging Initiative. 2024. "Plasma Proteomic Biomarkers in Alzheimer’s Disease and Cardiovascular Disease: A Longitudinal Study" International Journal of Molecular Sciences 25, no. 19: 10751. https://doi.org/10.3390/ijms251910751

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Article metric data becomes available approximately 24 hours after publication online.
Back to TopTop