Skip to main content
Download PDF

Exploring the associations of plasma proteins with frailty based on Mendelian randomization

BMC Immunology volume 25, Article number: 86 (2024) Cite this article

Abstract

Background

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

Methods

Associations of plasma proteins with 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 based on MR-identified target proteins.

Results

After false discovery rate (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 analysis suggested BIRC2, PSME1, APOE, and CTNNB1 as key intervention targets.

Conclusion

This study employed MR design to investigate the association of circulating plasma proteins with frailty, identified five proteins negatively associated with frailty risk and 49 proteins positively associated with frailty.

Peer Review reports

Introduction

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 of plasma protein levels with the onset of frailty. Darvin et al. found that an increase in the concentration of inflammatory glycoproteins within the body was associated with a heightened risk of developing frailty [8]. Another multi-cohort study identified several proteins related to frailty, 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 variations, which utilizes single nucleotide polymorphisms (SNPs) as instrumental variables (IVs) 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.

Circulating 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 (GWASs) 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 aimed to analyze the potential causal relationships of plasma proteins with frailty using a MR study design. We also explored the core targets and mechanisms of frailty, providing a basis for its prevention and treatment.

Materials and methods

Design principles

A flowchart of the study design is shown in Fig. 1. 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 IVs must be independent of any potential confounding factors; and (3) the exclusion restriction assumption, which asserts that the IVs should not be directly associated with the outcome except through the exposure factor [10]. In this study, plasma proteins were considered as the exposure factors and frailty as the outcome. SNPs that were strongly associated with plasma proteins were selected as the IVs.

Fig. 1
figure 1

The overall study design. Abbreviations: GO, gene ontology; IV, instrumental variables; MR-PRESSO, MR pleiotropy residual sum and outlier; pQTL, protein quantitative trait loci

Full size image

Data sources

Characteristics of the study populations are shown in Table S1. In brief, 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 TwinGene cohort. In both cohorts, the frailty index (FI) was calculated based on entries related to various health deficits, including symptoms, signs, abnormal lab values, diseases, and disabilities. The FI for each individual was determined as the proportion of identified deficits to the total number of assessed deficits: FI = [Number of identified deficits] / [Total number of assessed deficits] [14]. For the UK Biobank cohort, 49 binary deficits were used, while 44 deficits were considered in the TwinGene cohort. A higher FI indicates greater frailty, correlating directly with the severity of frailty.

Selection of IVs

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 associations of plasma proteins with frailty were assessed using multiple methods, including inverse variance weighted (IVW), MR-Egger regression, weighted median, maximum-likelihood, and MR-PRESSO tests, to verify the reliability of the results. IVW was used as the primary MR analysis method [16]. The heterogeneity of the IVs was evaluated using Cochran’s Q test [17]. If P > 0.05, indicating no heterogeneity, a fixed-effects IVW model was used; otherwise, a random-effects model was applied. Additionally, several sensitivity analyses were conducted to examine the consistency of the associations. MR-Egger regression was employed to assess potential pleiotropy, with P > 0.05 suggesting the absence of horizontal pleiotropy [18]. The weighted median method provided valid estimates even when up to 50% of the IVs were invalid [19]. The maximum-likelihood method, assuming no heterogeneity or pleiotropy, yielded results comparable to IVW but with smaller standard errors and more unbiased estimates [20]. The MR-PRESSO test was used to detect outliers and re-estimate the corrected association effect [21]. Together, these methods helped ensure the stability and reliability of the findings.

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

Results

IV selection results

A total of 29,461 SNPs reported to be associated with levels of 4,709 plasma proteins were screened. Among these, 5,707 SNPs were not available in the frailty outcome database. Ultimately, 23,754 SNPs associated with 4,563 plasma proteins were included as IVs for subsequent analysis.

MR analysis results

IVW was employed as the primary analysis method, which indicated nominal associations of 776 plasma proteins with the occurrence of frailty. After FDR correction, associations for 54 proteins remained significantly significant (PFDR<0.05) (Fig. 2).

Fig. 2
figure 2

Volcano plot of Mendelian randomization results for 776 plasma proteins and the risk of frailty. The x-axis represents the beta values, y-axis represents the negative log10-transformed P-value. Proteins highlighted with color indicate PFDR < 0.05

Full size image

Cochran’s Q test identified heterogeneity in two proteins, thus a random-effects model was used; and 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)] was negatively associated with 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)] were positively associated with the risk of frailty (Table 2). MR-Egger regression indicated that seven proteins showing associations with frailty were potentially influenced by horizontal pleiotropy (P < 0.05), necessitating further investigation. MR-PRESSO test identified three proteins with outliers, and after outlier correction, the causal effect estimates remained similar. Results from weighted median and maximum-likelihood methods showed associations consistent with the IVW method.

Table 1 Associations of plasma protein concentrations and the reduced risk of frailty (top 5)
Full size table
Table 2 Associations of plasma protein concentrations and the increased risk of frailty (top 5)
Full size table

PPI network construction and GO functional enrichment analysis results

The PPI network analysis of the 54 proteins achieving statistically significant after FDR correction identified 28 nodes and 35 edges (Fig. 3). Among these, the top five targets based on Degree centrality were APOE, CTNNB1, B2M, WNT5A, APOB, and MMP3. These findings suggested that these circulating proteins might serve as core therapeutic targets for the prevention and treatment of frailty.

Fig. 3
figure 3

Protein-protein interaction (PPI) network. (A). PPI network exported from STRING database. (B). Annotations for the nodes and edges in the PPI network

Full size image

The GO functional enrichment analysis yielded a total of 284 entries, comprising 203 entries in biological process (BP), 66 entries in cellular component (CC), and 15 entries in molecular function (MF) (Fig. 4). 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.

Fig. 4
figure 4

Gene ontology (GO) enrichment analysis of the putative causal proteins of frailty. The x-axis indicates negative log10-transformed P-values, and the y-axis indicates GO items. Abbreviations: BP, biological process; CC, cellular component; MF, molecular function

Full size image

Discussion

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

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 [22]. It has been reported that BIRC2 expression levels decrease significantly with aging, suggesting a crucial regulatory role in human aging processes [23]. Multiple studies consistently indicate that BIRC2 has neuroprotective effects and can inhibit apoptosis [24, 25], 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 [26], 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 [27]. In two cohort studies of long-lived individuals, BIRC2 and PSME1 exhibited higher serum levels in carriers of the APOE2 allele, suggesting their potential as neuroprotective agents [28, 29]. 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 [30].

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 [31]. Research indicates that APOE2 exerts neuroprotective effects [28, 32] and contributes to extending healthy lifespan during aging processes [33]. Conversely, APOE4 is considered as a risk factor for Alzheimer’s disease and other neurodegenerative disorders [34, 35], potentially increasing frailty risk among carriers through effects on lipid metabolism, inflammatory responses, or neurological functions [36,37,38]. Furthermore, GWASs have reported a statistically significant association between APOE and cognitive aging, a phenotype linked to frailty [39], which is consistent with our findings. However, other observational studies have not consistently found associations between different APOE genotypes and frailty [40, 41], necessitating further research to explore the relationship and underlying biological mechanisms. APOB is the principal protein of low-density lipoproteins [42]. Previous studies have proposed APOB as a biomarker for predicting cardiovascular risk [43, 44], and Stewart et al. found that individuals with cardiovascular disease (CVD) experience accelerated frailty with increasing risk factors [45]. 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.

β-catenin, encoded by the CTNNB1 gene, plays a crucial role in cell adhesion and regulation of the Wnt signaling pathway [46]. 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 [47]. Sarcopenia, a key feature of physical frailty, is characterized by muscle loss [48]. Yin et al. identified a potential causal relationship between CTNNB1 and sarcopenia, suggesting its critical role in the pathogenesis of muscle loss and proposing CTNNB1 as a promising therapeutic target for sarcopenia [49], 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 [50].

This study had several strengths and limitations. Firstly, it explored the potential causal relationships of circulating plasma proteins with 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 IVW, weighted median, MR-Egger regression, and MR-PRESSO test during the analysis process to enhance result robustness through mutual validation. Thirdly, by integrating construction of PPI networks and GO enrichment analysis, we further explored the core targets and potential pathogenic mechanisms of frailty. However, the study also had limitations. Firstly, it included only European populations, which reduced population stratification biases, but limited the generalizability of results to populations of other ethnicities. Secondly, there might be issues related to horizontal pleiotropy that could affect the results, necessitating further validation. Thirdly, the study was unable to assess the dose-response relationships of circulating proteins with frailty occurrence, only indicating the presence of a potential causal association.

In summary, this study employed MR design to investigate the associations of circulating plasma proteins with frailty. The results suggested statistically significant associations of proteins including BIRC2, PSME1, APOE, and CTNNB1, with 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.

Data availability

Data will be made available on request.

Abbreviations

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:

MR pleiotropy residual sum and outlier

PPI:

Protein-protein interaction

pQTL:

Protein quantitative trait loci

SNP:

Single nucleotide polymorphisms

References

  1. Hoogendijk EO, et al. Frailty: implications for clinical practice and public health. Lancet. 2019;394(10206):1365–75.

    Article  PubMed  Google Scholar 

  2. Fried LP, et al. Frailty in older adults: evidence for a phenotype. J Gerontol Biol Sci Med Sci. 2001;56(3):M146–56.

    Article  CAS  Google Scholar 

  3. To TL et al. Prevalence of Frailty among Community-Dwelling older adults in Asian countries: a systematic review and Meta-analysis. Healthc (Basel), 2022. 10(5).

  4. Jiao J, et al. Prevalence and associated factors for frailty among elder patients in China: a multicentre cross-sectional study. BMC Geriatr. 2020;20(1):100.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Taylor JA, et al. Multisystem physiological perspective of human frailty and its modulation by physical activity. Physiol Rev. 2023;103(2):1137–91.

    Article  CAS  PubMed  Google Scholar 

  6. Belenguer-Varea A et al. Effect of familial longevity on Frailty and Sarcopenia: a case-control study. Int J Environ Res Public Health. 2023;20(2).

  7. Dato S, et al. Frailty phenotypes in the elderly based on cluster analysis: a longitudinal study of two Danish cohorts. Evidence for a genetic influence on frailty. Age (Dordr). 2012;34(3):571–82.

    Article  PubMed  Google Scholar 

  8. Darvin K, et al. Plasma protein biomarkers of the geriatric syndrome of frailty. J Gerontol Biol Sci Med Sci. 2014;69(2):182–6.

    Article  CAS  Google Scholar 

  9. Liu F, et al. Late-life plasma proteins associated with prevalent and incident frailty: a proteomic analysis. Aging Cell. 2023;22(11):e13975.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Sanderson E et al. Mendelian randomization. Nat Rev Methods Primers. 2022;2.

  11. Ferkingstad E, et al. Large-scale integration of the plasma proteome with genetics and disease. Nat Genet. 2021;53(12):1712–21.

    Article  CAS  PubMed  Google Scholar 

  12. Evans DM, Davey Smith G. Mendelian randomization: New Applications in the coming age of hypothesis-free causality. Annu Rev Genomics Hum Genet. 2015;16:327–50.

    Article  CAS  PubMed  Google Scholar 

  13. Atkins JL, et al. A genome-wide association study of the frailty index highlights brain pathways in ageing. Aging Cell. 2021;20(9):e13459.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Mitnitski AB, Mogilner AJ, Rockwood K. Accumulation of deficits as a proxy measure of aging. ScientificWorldJournal. 2001;1:323–36.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Pierce BL, Ahsan H, Vanderweele TJ. Power and instrument strength requirements for mendelian randomization studies using multiple genetic variants. Int J Epidemiol. 2011;40(3):740–52.

    Article  PubMed  Google Scholar 

  16. Lee CH, et al. Comparison of two Meta-analysis methods: inverse-variance-weighted average and weighted Sum of Z-Scores. Genomics Inf. 2016;14(4):173–80.

    Article  Google Scholar 

  17. Greco MF, et al. Detecting pleiotropy in mendelian randomisation studies with summary data and a continuous outcome. Stat Med. 2015;34(21):2926–40.

    Article  Google Scholar 

  18. Burgess S, Thompson SG. Interpreting findings from mendelian randomization using the MR-Egger method. Eur J Epidemiol. 2017;32(5):377–89.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Bowden J, et al. Consistent estimation in mendelian randomization with some Invalid instruments using a weighted median estimator. Genet Epidemiol. 2016;40(4):304–14.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Milligan BG. Maximum-likelihood estimation of relatedness. Genetics. 2003;163(3):1153–67.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Verbanck M, et al. Detection of widespread horizontal pleiotropy in causal relationships inferred from mendelian randomization between complex traits and diseases. Nat Genet. 2018;50(5):693–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Zadoroznyj A, Dubrez L. Cytoplasmic and nuclear functions of cIAP1. Biomolecules. 2022;12(2).

  23. Balliu B, et al. Genetic regulation of gene expression and splicing during a 10-year period of human aging. Genome Biol. 2019;20(1):230.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Varfolomeev E, et al. IAP antagonists induce autoubiquitination of c-IAPs, NF-kappaB activation, and TNFalpha-dependent apoptosis. Cell. 2007;131(4):669–81.

    Article  CAS  PubMed  Google Scholar 

  25. Marivin A, et al. The inhibitor of apoptosis (IAPs) in adaptive response to Cellular stress. Cells. 2012;1(4):711–37.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Sebastiani P, et al. Protein signatures of centenarians and their offspring suggest centenarians age slower than other humans. Aging Cell. 2021;20(2):e13290.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Zhang F, et al. Carfilzomib alleviated osteoporosis by targeting PSME1/2 to activate Wnt/beta-catenin signaling. Mol Cell Endocrinol. 2022;540:111520.

    Article  CAS  PubMed  Google Scholar 

  28. Sebastiani P, et al. A serum protein signature of APOE genotypes in centenarians. Aging Cell. 2019;18(6):e13023.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Gurinovich A, et al. Effect of longevity genetic variants on the molecular aging rate. Geroscience. 2021;43(3):1237–51.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Adelof J, et al. PA28alpha overexpressing female mice maintain exploratory behavior and capacity to prevent protein aggregation in hippocampus as they age. Aging Cell. 2021;20(4):e13336.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Serrano-Pozo A, Das S, Hyman BT. APOE and Alzheimer’s disease: advances in genetics, pathophysiology, and therapeutic approaches. Lancet Neurol. 2021;20(1):68–80.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Goldberg TE, Huey ED, Devanand DP. Association of APOE e2 genotype with Alzheimer’s and Non-alzheimer’s neurodegenerative pathologies. Nat Commun. 2020;11(1):4727.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Kulminski AM, et al. Protective role of the apolipoprotein E2 allele in age-related disease traits and survival: evidence from the Long Life Family Study. Biogerontology. 2016;17(5–6):893–905.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Smith JD. Apolipoprotein E4: an allele associated with many diseases. Ann Med. 2000;32(2):118–27.

    Article  CAS  PubMed  Google Scholar 

  35. Koutsodendris N, et al. Apolipoprotein E and Alzheimer’s Disease: findings, hypotheses, and potential mechanisms. Annu Rev Pathol. 2022;17:73–99.

    Article  CAS  PubMed  Google Scholar 

  36. Mourtzi N, et al. Apolipoprotein epsilon4 allele is associated with frailty syndrome: results from the hellenic longitudinal investigation of ageing and diet study. Age Ageing. 2019;48(6):917–21.

    Article  PubMed  Google Scholar 

  37. Jin X, et al. Association of APOE epsilon4 genotype and lifestyle with cognitive function among Chinese adults aged 80 years and older: a cross-sectional study. PLoS Med. 2021;18(6):e1003597.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Snejdrlova M, et al. APOE polymorphism as a potential determinant of functional fitness in the elderly regardless of nutritional status. Neuro Endocrinol Lett. 2011;32(Suppl 2):51–4.

    CAS  PubMed  Google Scholar 

  39. Davies G, et al. A genome-wide association study implicates the APOE locus in nonpathological cognitive ageing. Mol Psychiatry. 2014;19(1):76–87.

    Article  CAS  PubMed  Google Scholar 

  40. Chhetri JK, et al. Apolipoprotein E polymorphism and Frailty: apolipoprotein epsilon4 allele is Associated with fatigue but not Frailty Syndrome in a Community-Dwelling older Population Cohort. J Nutr Health Aging. 2021;25(4):410–5.

    Article  CAS  PubMed  Google Scholar 

  41. Rockwood K, Nassar B, Mitnitski A. Apolipoprotein E-polymorphism, frailty and mortality in older adults. J Cell Mol Med. 2008;12(6B):2754–61.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Mahley RW, et al. Plasma lipoproteins: apolipoprotein structure and function. J Lipid Res. 1984;25(12):1277–94.

    Article  CAS  PubMed  Google Scholar 

  43. Behbodikhah J et al. Apolipoprotein B and cardiovascular disease: biomarker and potential therapeutic target. Metabolites. 2021;11(10).

  44. Gigante B, et al. Elevated ApoB serum levels strongly predict early cardiovascular events. Heart. 2012;98(16):1242–5.

    Article  CAS  PubMed  Google Scholar 

  45. Stewart R. Cardiovascular Disease and Frailty: what are the mechanistic links? Clin Chem. 2019;65(1):80–6.

    Article  CAS  PubMed  Google Scholar 

  46. Valenta T, Hausmann G, Basler K. The many faces and functions of beta-catenin. EMBO J. 2012;31(12):2714–36.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Kuipers A, et al. Gene expression profiling suggests downregulation of wnt Pathway Signaling with Aging. Innov Aging. 2020;4(Supplement1):142–142.

    Article  PubMed Central  Google Scholar 

  48. Nascimento CM, et al. Sarcopenia, frailty and their prevention by exercise. Free Radic Biol Med. 2019;132:42–9.

    Article  CAS  PubMed  Google Scholar 

  49. Yin KF, et al. Systematic druggable genome-wide mendelian randomization identifies therapeutic targets for Sarcopenia. J Cachexia Sarcopenia Muscle. 2024.

  50. Dodds R, Sayer AA. Sarcopenia and frailty: new challenges for clinical practice. Clin Med (Lond). 2016;16(5):455–8.

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

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

Funding

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

Author information

Authors and Affiliations

  1. School of Public Health, Zhejiang Chinese Medical University, Hangzhou, 310053, China

    Shuhui Chen, Hao Lin, Bin Liu & Yingying Mao

  2. College of Basic Medical Science, Zhejiang Chinese Medical University, Hangzhou, 310053, China

    Hejing Pan, Yaling Xu & Lin Huang

Authors
  1. Shuhui Chen
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Hao Lin
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Bin Liu
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Hejing Pan
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Yaling Xu
    View author publications

    You can also search for this author inPubMed Google Scholar

  6. Yingying Mao
    View author publications

    You can also search for this author inPubMed Google Scholar

  7. Lin Huang
    View author publications

    You can also search for this author inPubMed Google Scholar

Contributions

S.H.Chen wrote the original draft and contributed to writing—review and editing, methodology, and data curation. H.Lin, B.Liu and H.J.Pan contributed to methodology and data curation. Y.Y.Mao and L.Huang supervised the project, and contributed to the validation of the results. All authors reviewed the manuscript.

Corresponding authors

Correspondence to Yingying Mao or Lin Huang.

Ethics declarations

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.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, S., Lin, H., Liu, B. et al. Exploring the associations of plasma proteins with frailty based on Mendelian randomization. BMC Immunol 25, 86 (2024). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12865-024-00677-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12865-024-00677-1

Keywords

BMC Immunology

ISSN: 1471-2172

Contact us