Exploring the Association between Plasma Proteins and Frailty Based on Mendelian Randomization and Network Pharmacology

doi:10.21203/rs.3.rs-5287035/v1

Background

Frailty is an emerging global burden of disease, characterized as an age-related clinical syndrome. Recent studies have suggested a potential link between plasma protein levels and the onset of frailty. This study aims to analyze the potential causal relationship between plasma proteins and frailty using a Mendelian Randomization (MR) study design.

Methods

Associations between plasma proteins and frailty were assessed using inverse variance weighted (IVW), MR-Egger regression, weighted median, maximum-likelihood method, and MR-PRESSO test. Protein-protein interaction network construction and gene ontology functional enrichment analysis were conducted on MR-identified target proteins.

Results

After FDR correction, MR analysis identified five plasma proteins, including BIRC2 [OR = 0.978, 95%CI(0.967–0.990)] and PSME1 [OR = 0.936, 95%CI(0.909–0.965)], as protective factors against frailty, and 49 proteins, including APOB [OR = 1.053, 95%CI(1.037–1.069)] and CYP3A4 [OR = 1.098, 95%CI(1.068,1.128)], as risk factors. Network pharmacology suggested BIRC2, PSME1, APOE, and CTNNB1 as key intervention targets.

Conclusion

This study employed MR design integrated with network pharmacology analysis to investigate the association between circulating plasma proteins and frailty, identified 5 plasma proteins negatively associated with frailty risk and 49 plasma proteins positively associated with frailty.

Mendelian randomization

Network pharmacology

Frailty

Protein

Frailty is an emerging global burden of disease, characterized as an age-related clinical syndrome primarily defined by the decline of multiple physiological systems and increased vulnerability to stressors [1]. Frailty is commonly categorized into three subtypes: physical frailty, social frailty, and cognitive frailty. It is frequently associated with a range of adverse outcomes, such as frequent falls, cognitive decline, increased risk of disability, and higher mortality rates, imposing a substantial burden on individuals and society [2]. The prevalence of frailty in the population of Asian countries is reported to be approximately 20.5%, while in China, it is about 18.0% [3, 4]. Notably, the prevalence of frailty exhibits a significant upward trend with advancing age. Although we cannot halt the natural progression of aging, effective interventions can ameliorate the condition of frailty and may even potentially reverse it [5].

The etiology and pathogenesis of frailty remain unclear; however, existing research indicates that the development of frailty is influenced by both environmental and genetic factors [6, 7]. Recent studies have suggested a potential link between plasma protein levels and the onset of frailty. Darvin et al. found that an increase in the concentration of inflammatory glycoproteins within the body is associated with a heightened risk of developing frailty [8]. Another multi-cohort study identified several proteins related to frailty, which are enriched in pathways and upstream regulators involved in lipid metabolism, angiogenesis, inflammation, and cellular senescence [9]. However, traditional observational studies are susceptible to confounding factors and reverse causation, rendering causal inference results less reliable. Mendelian randomization (MR) is a method of causal inference based on genetic variation, which utilizes single nucleotide polymorphisms (SNPs) as instrumental variables (IV) to detect and quantify potential causal relationships [10]. Since alleles are randomly assigned to offspring and determined before birth, MR studies can effectively control for confounding biases and the effects of reverse causation.

Plasma proteins play a crucial role in a range of biological processes in the human body. As downstream products of the central dogma of molecular biology, they also serve as intermediate phenotypes in disease pathways. Investigating protein quantitative trait loci (pQTL) can elucidate disease mechanisms and identify novel drug targets [11, 12]. With the advancement of genome-wide association studies (GWAS) on plasma proteins and the availability of comprehensive genomic datasets, their application in disease etiology research has become increasingly prevalent in recent years. Therefore, this study aims to analyze the potential causal relationship between plasma proteins and frailty using a MR study design. Additionally, by incorporating network pharmacology methods, we will explore the core targets and mechanisms of frailty, providing a basis for its prevention and treatment.

Design Principles

MR studies must satisfy the following three core assumptions: (1) the association assumption, which requires that the IV is strongly associated with the exposure factor; the use of weak IVs may lead to biased results; (2) the independence assumption, which stipulates that the IV must be independent of any potential confounding factors; and (3) the exclusion restriction assumption, which asserts that the IV should not be directly associated with the outcome except through the exposure factor [10]. In this study, plasma proteins are considered as the exposure factors and frailty as the outcome. SNPs that are strongly associated with plasma proteins are selected as the IVs.

Data sources

Plasma protein genetic association data were sourced from the deCODE database (https://www.decode.com/summarydata/), where 4,907 plasma proteins were assessed in a cohort of 35,559 individuals from Iceland using the SomaScan v4 platform [11]. Genetic association data for frailty originated from a GWAS involving 175,226 individuals [13], including 164,610 participants from the UK Biobank cohort and 10,616 participants from the Swedish Twin Registry. Based on entries related to symptoms, disabilities, and diagnosed diseases in both cohorts, the frailty index (FI) was calculated as the proportion of existing health deficits to the total deficits, correlating directly with the severity of frailty [14].

Selection of IV

The IVs used in this study were selected based on the following criteria: (1) each SNP must reach genome-wide significance (P<5×10^-8); (2) there should be no linkage disequilibrium between SNPs (parameters set to r²<0.001, kb=10,000); (3) SNPs located within the human major histocompatibility complex (MHC) region were excluded; (4) the strength of the IVs was assessed by calculating the F-statistic, with F>10 as the threshold to exclude weak IV bias. The calculation formula is as follows: R²=β²×2×MAF×(1-MAF), F=[R²×(N-1-K)]/[K×(1-R²)], where R² is the proportion of variance explained by the SNP, β is the effect size of the SNP on the exposure factor, MAF is the minor allele frequency, N is the sample size, and K is the number of included SNPs [15].

Statistical Analysis

The association between plasma proteins and frailty will be assessed using several methods, including inverse variance weighted (IVW), MR-Egger regression, weighted median, maximum-likelihood method, and MR-PRESSO tests. IVW will be used as the primary MR analysis method [16]. The heterogeneity of the IVs will be evaluated using Cochran's Q test [17]. If P>0.05, indicating no heterogeneity, a fixed-effects IVW model will be used; otherwise, a random-effects model will be applied. MR-Egger regression will be employed to assess potential pleiotropy, with P>0.05 suggesting the absence of horizontal pleiotropy [18]. The MR-PRESSO test will be used to detect outliers and re-estimate the corrected association effect [19].

MR analysis will be conducted using R software version 4.3.2, with the “MendelianRandomization” and “MRPRESSO” packages. The false discovery rate (FDR) method will be used to adjust P-values for multiple comparisons, with statistical significance set at P_FDR<0.05.

Protein-Protein Interaction (PPI) Network Construction and Gene Ontology (GO) Functional Enrichment Analysis

PPI networks are complex webs of interactions between proteins that play critical roles in signal transduction, gene expression regulation, and energy and material metabolism within organisms. Constructing a PPI network for key proteins can help elucidate the biological mechanisms underlying disease progression. The target proteins identified through MR will be imported into the STRING database (https://string-db.org/) to construct a PPI network. Subsequently, GO functional enrichment analysis will be performed to define and describe the functions of genes and proteins. GO functional enrichment analysis is a commonly used bioinformatics method to reveal the functional enrichment of a set of genes within the gene ontology, which is categorized into three main classes: biological process (BP), cellular component (CC), and molecular function (MF). By comparing the functional enrichment levels across different gene sets, this analysis aims to uncover the primary functional characteristics and biological processes involved, thereby providing a comprehensive understanding of the biological significance of the gene set. This analysis will be conducted using the “clusterProfiler” package in R.

IV Selection Results

A total of 29,461 SNPs were screened and found to be associated with levels of 4,709 plasma proteins. Among these, 5,707 SNPs did not match the frailty outcome database. Ultimately, 23,754 SNPs associated with 4,563 plasma proteins were included as IV for analysis.

MR Analysis Results

IVW was employed as the primary analysis method, indicating an association between 776 plasma proteins and the occurrence of frailty. After FDR correction, 54 proteins remained significantly associated (P_FDR<0.05) (Figure 1).

Cochran's Q test identified heterogeneity in 2 proteins, thus a random-effects model was used; the remaining 52 proteins were analyzed using a fixed-effects model. Results revealed that proteins such as BIRC2 [OR=0.978, 95% CI (0.967-0.990)] and PSME1 [OR=0.936, 95% CI (0.909-0.965)] may serve as protective factors for frailty (Table 1), while proteins like APOB [OR=1.053, 95% CI (1.037-1.069)] and CYP3A4 [OR=1.098 , 95%CI(1.068,1.128)] may act as risk factors for frailty (Table 2). MR-Egger regression indicated that 7 proteins showed associations with frailty potentially influenced by horizontal pleiotropy (P<0.05), necessitating further investigation. MR-PRESSO identified 3 proteins with outliers, and after outlier correction, the causal associations remained similar. Results from weighted median and maximum-likelihood methods showed associations consistent with IVW.

Table 1 Association between predicted plasma proteins from genes and the reduced risk of frailty (Top 5)

	SNPs	β	se	P value	OR（95%CI）	P_{Cochran's Q}	PMR-Egger	PFDR.adjust
BIRC2
Inverse-variance weighted	6	-0.022	0.006	2.92E-04	0.978 (0.967,0.990)	0.099		2.91E-02
MR-Egger	6	/	/	/	/		0.008
Weighted median	6	-0.025	0.006	7.04E-05	0.975 (0.963,0.987)
Maximum-likelihood method	6	-0.022	0.008	7.74E-03	0.978 (0.963,0.994)
MR-PRESSO	6	-0.022	0.008	4.49E-02	0.978 (0.963,0.994)
PSME1
Inverse-variance weighted	3	-0.066	0.015	1.92E-05	0.936 (0.909,0.965)	0.268		4.87E-03
MR-Egger	3	/	/	/	/		0.667
Weighted median	3	-0.068	0.016	2.00E-05	0.934 (0.905,0.964)
Maximum-likelihood method	3	-0.066	0.016	2.50E-05	0.936 (0.908,0.965)
TMEM106B
Inverse-variance weighted	3	-0.089	0.023	1.30E-04	0.915 (0.874,0.957)	0.108		1.70E-02
MR-Egger	3	/	/	/	/		0.063
Weighted median	3	-0.106	0.026	4.60E-05	0.899 (0.854,0.946)
Maximum-likelihood method	3	-0.090	0.024	1.66E-04	0.914 (0.872,0.958)
LYPLAL1
Inverse-variance weighted	2	-0.095	0.026	3.03E-04	0.909 (0.864,0.957)	0.835		2.94E-02
Maximum-likelihood method	2	-0.095	0.027	4.20E-04	0.909 (0.862,0.959)
CDH1
Inverse-variance weighted	3	-0.074	0.018	3.82E-05	0.928 (0.896,0.962)	0.236		7.26E-03
MR-Egger	3	/	/	/	/		0.091
Weighted median	3	-0.067	0.019	4.02E-04	0.935 (0.901,0.971)
Maximum-likelihood method	3	-0.075	0.018	4.93E-05	0.928 (0.895,0.962)

Table 2 Association between predicted plasma proteins from genes and the increased risk of frailty (Top 5)

	SNPs	β	se	P value	OR（95%CI）	P_{Cochran's Q}	PMR-Egger	PFDR.adjust
APOB
Inverse-variance weighted	16	0.051	0.008	5.62E-11	1.053 (1.037,1.069)	0.058		1.28E-07
MR-Egger	16	/	/	/	/		0.946
Weighted median	16	0.047	0.010	4.01E-06	1.049 (1.028,1.070)
Maximum-likelihood method	16	0.052	0.010	2.23E-07	1.053 (1.033,1.074)
MR-PRESSO	16	0.051	0.010	1.22E-04	1.053 (1.032,1.073)
CYP3A4
Inverse-variance weighted	11	0.093	0.014	1.46E-11	1.098 (1.068,1.128)	0.760		6.66E-08
MR-Egger	11	/	/	/	/		0.631
Weighted median	11	0.106	0.019	3.97E-08	1.112 (1.071,1.155)
Maximum-likelihood method	11	0.094	0.014	3.48E-11	1.099 (1.069,1.130)
MR-PRESSO	11	0.093	0.011	8.60E-06	1.098 (1.074,1.122)
STOM
Inverse-variance weighted	4	0.154	0.025	1.35E-09	1.167 (1.110,1.226)	0.150		1.54E-06
MR-Egger	4	/	/	/	/		0.796
Weighted median	4	0.124	0.035	4.07E-04	1.132 (1.057,1.212)
Maximum-likelihood method	4	0.157	0.034	4.73E-06	1.170 (1.094,1.252)
MR-PRESSO	4	0.154	0.034	1.99E-02	1.167 (1.092,1.247)
KCNMB3
Inverse-variance weighted	15	0.058	0.009	1.66E-10	1.060 (1.041,1.079)	0.107		2.52E-07
MR-Egger	15	/	/	/	/		0.424
Weighted median	15	0.046	0.012	2.13E-04	1.047 (1.022,1.072)
Maximum-likelihood method	15	0.059	0.011	1.17E-07	1.060 (1.038,1.084)
MR-PRESSO	15	0.058	0.011	1.24E-04	1.060 (1.037,1.083)
LRRK2
Inverse-variance weighted	6	0.114	0.019	1.76E-09	1.120 (1.080,1.163)	0.386		1.61E-06
MR-Egger	6	/	/	/	/		0.039
Weighted median	6	0.099	0.024	4.61E-05	1.104 (1.053,1.158)
Maximum-likelihood method	6	0.115	0.020	5.85E-09	1.122 (1.079,1.166)
MR-PRESSO	6	0.114	0.019	2.03E-03	1.120 (1.079,1.164)

PPI Network Construction and GO Functional Enrichment Analysis Results

The PPI network analysis of the 54 proteins identified 28 nodes and 35 edges (Figure 2). Among these, the top five targets based on Degree centrality were APOE, CTNNB1, B2M, WNT5A, APOB, and MMP3. These findings suggest that these circulating proteins may serve as core therapeutic targets for frailty treatment.

The GO functional enrichment analysis yielded a total of 284 entries, comprising 203 entries in BP, 66 entries in CC, and 15 entries in MF (Figure 3). The BP entries primarily involved negative regulation of neuron projection development, protein refolding, and negative regulation of cell projection organization. The CC entries mainly included entries related to clathrin-coated endocytic vesicle, coated vesicle membrane, and endocytic vesicle membrane. The MF entries predominantly covered functions such as lipoprotein particle receptor binding, MHC class II protein complex binding, and low-density lipoprotein particle receptor binding.

This study assessed the potential causal association between plasma proteins and frailty using two-sample MR methods, and explored the core targets and pathogenic mechanisms of frailty through network pharmacology analysis. MR analysis results indicated a negative correlation between frailty risk and circulating proteins such as cIAP1 and PSME1, and a positive correlation with 49 circulating proteins including APOE and β-catenin. Additionally, in the PPI network, proteins like BIRC2, PSME1, APOE, and CTNNB1 were interconnected, suggesting their potential as critical intervention targets for frailty prevention.

BIRC2, also known as cIAP1, is a member of the Inhibitor of Apoptosis Proteins (IAP) family, primarily involved in regulating cellular apoptosis and the NF-κB signaling pathway [20]. It has been reported that BIRC2 expression levels significantly decrease with aging, suggesting a crucial regulatory role in human aging processes [21]. Multiple studies consistently indicate that BIRC2 has neuroprotective effects and can inhibit apoptosis [22, 23], reinforcing evidence that BIRC2 may act as a protective factor against frailty. Additionally, gene knockout studies in mice have shown that this protein protects neurons from apoptosis by stimulating antioxidant pathways [24], further suggesting its potential role in preventing or alleviating frailty symptoms. By inhibiting apoptosis and providing neuroprotection, BIRC2 may contribute to maintaining bodily functions and mitigating the onset of frailty.

PSME1 is a member of the Proteasome activator subunit (PSME) gene family, playing a crucial role in intracellular protein degradation and metabolism, which are essential for maintaining cellular homeostasis and normal physiological functions [25]. In two studies involving cohorts of long-lived individuals, BIRC2 and PSME1 exhibited higher serum levels in carriers of the APOE2 allele, suggesting their potential as neuroprotective agents [26, 27]. This finding was further corroborated in animal experiments, where mice overexpressing PSME1 did not display typical signs of aging but showed enhanced cognitive abilities and improved memory function [28].

Apolipoprotein E (APOE) is positioned centrally in the PPI network diagram, connected with apolipoprotein B (APOB), both being crucial members of the apolipoprotein family. APOE is primarily involved in the metabolism and conversion of lipoproteins, characterized by three common alleles known as E2, E3, and E4 [29]. Research indicates that APOE2 exerts neuroprotective effects [26, 30] and contributes to extending healthy lifespan during aging processes [31]. Conversely, APOE4 is considered a risk factor for Alzheimer's disease and other neurodegenerative disorders [32, 33], potentially increasing frailty risk among carriers through effects on lipid metabolism, inflammatory responses, or neurological functions [34–36]. Furthermore, genome-wide association studies have shown significant associations between APOE and cognitive aging, a phenotype linked to frailty [37], consistent with our study findings. However, other observational studies have not consistently found significant associations between different APOE genotypes and frailty [38, 39], necessitating further research to explore the relationship and mechanisms linking APOE to frailty. APOB is the principal protein of low-density lipoproteins [40]. Previous studies have proposed APOB as a biomarker for predicting cardiovascular risk [41, 42], and Stewart et al. found that individuals with cardiovascular disease (CVD) experience accelerated frailty with increasing risk factors [43]. Nevertheless, current research on the association between APOB and frailty remains limited, necessitating larger-scale cohort studies, experimental investigations, and other methodologies to strengthen evidence.

Beta-catenin (β-catenin), encoded by the CTNNB1 gene, plays a crucial role in cell adhesion and regulation of the Wnt signaling pathway [44]. Studies have found that CTNNB1 is associated with aging; its expression increases by 4% for every 10 years of age, with this association accelerating after the age of 60 years [45]. Sarcopenia, a key feature of physical frailty, is characterized by muscle loss [46]. Yin et al. identified a significant causal relationship between CTNNB1 and sarcopenia, suggesting its critical role in the pathogenesis of muscle loss and proposing CTNNB1 as a potential therapeutic target for sarcopenia [47], which aligns with our study findings. Strategies such as enhancing physical exercise, nutritional supplementation, or other interventions may potentially prevent or delay the onset of physical frailty [48].

This study has several strengths and limitations. Firstly, it explores the causal relationship between circulating plasma proteins and frailty from a genetic perspective using large-scale GWAS data, thereby avoiding confounding biases and reverse causation commonly found in traditional observational epidemiological studies. Secondly, the study employed multiple statistical analysis methods such as inverse variance weighting, weighted median, MR-Egger regression, and MR-PRESSO tests during the analysis process to enhance result robustness through mutual validation. Thirdly, by integrating construction of PPI networks and GO enrichment analysis, it strengthens the evidence of associations while further exploring the core targets and pathogenic mechanisms of frailty. However, the study also has limitations. Firstly, it included only European population cohorts, which, while reducing some population stratification biases, limits the generalizability of results to other ethnicities. Secondly, there may be issues related to horizontal pleiotropy that could affect the results, necessitating further validation. Thirdly, the study was unable to confirm a dose-response relationship between circulating proteins and frailty occurrence, only indicating the presence of causal association.

In summary, this study employed MR design integrated with network pharmacology analysis to investigate the association between circulating plasma proteins and frailty. The results suggest significant associations between cIAP1, PSME1, APOE, β-catenin, and frailty, indicating their potential roles as core targets in frailty pathogenesis. Further in vivo and in vitro experiments are needed to validate these findings rigorously, explore the biological mechanisms of frailty, and identify novel drug targets aimed at reducing disease incidence and alleviating societal burden.

BP	biological process
CC	cellular component
FDR	false discovery rate
FI	frailty index
GO	Gene Ontology
GWAS	genome-wide association studies
IV	instrumental variables
IVW	inverse-variance weighted
MF	molecular function
MHC	major histocompatibility complex
MR	Mendelian randomization
MR-PRESSO test	MR pleiotropy residual sum and outlier test
PPI	Protein-Protein Interaction
pQTL	protein quantitative trait loci
SNP	single nucleotide polymorphisms

Acknowledgements

We would like to thank all the participants in the study.

Author contributions

H.S.Chen wrote the original draft and contributed to writing—review and editing, methodology, and data curation. J.H.Pan contributed to methodology and data curation. L.Y.Xu contributed to software development. Y.Y.Mao contributed to validation and investigation. L.Huang supervised the project. All authors reviewed the manuscript.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Data availability

Data will be made available on request.

Ethics approval and consent to participate

Our analysis used publicly available GWAS summary statistics. No new data were collected, and no new ethical approval was required.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

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No competing interests reported.

Supplementarytable.xlsx

Exploring the Association between Plasma Proteins and Frailty Based on Mendelian Randomization and Network Pharmacology

Status:

Version 1

Abstract

Background

Methods

Results

Conclusion

Figures

Introduction

Materials and methods

Results

Discussion

Abbreviations

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1