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BMC Geriatrics
Published in: BMC Geriatrics 1/2024

Open Access 01-12-2024 | Research

Unraveling the relationship between high-sensitivity C-reactive protein and frailty: evidence from longitudinal cohort study and genetic analysis

Authors: Yu-Feng Luo, Zi-Jian Cheng, Yan-Fei Wang, Xi-Yuan Jiang, Shu-Feng Lei, Fei-Yan Deng, Wen-Yan Ren, Long-Fei Wu

Published in: BMC Geriatrics | Issue 1/2024

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Abstract

Background

This study aimed to investigate the association of high-sensitivity C-reactive protein (hs-CRP) with incident frailty as well as its effects on pre-frailty progression and regression among middle-aged and older adults.

Methods

Based on the frailty index (FI) calculated with 41 items, 6890 eligible participants without frailty at baseline from China Health and Retirement Longitudinal Study (CHARLS) were categorized into health, pre-frailty, and frailty groups. Logistic regression models were used to estimate the longitudinal association between baseline hs-CRP and incident frailty. Furthermore, a series of genetic approaches were conducted to confirm the causal relationship between CRP and frailty, including Linkage disequilibrium score regression (LDSC), pleiotropic analysis, and Mendelian randomization (MR). Finally, we evaluated the association of hs-CRP with pre-frailty progression and regression.

Results

The risk of developing frailty was 1.18 times (95% CI: 1.03–1.34) higher in participants with high levels of hs-CRP at baseline than low levels of hs-CRP participants during the 3-year follow-up. MR analysis suggested that genetically determined hs-CRP was potentially positively associated with the risk of frailty (OR: 1.06, 95% CI: 1.03–1.08). Among 5241 participants with pre-frailty at baseline, we found pre-frailty participants with high levels of hs-CRP exhibit increased odds of progression to frailty (OR: 1.39, 95% CI: 1.09–1.79) and decreased odds of regression to health (OR: 0.84, 95% CI: 0.72–0.98) when compared with participants with low levels of hs-CRP.

Conclusions

Our results suggest that reducing systemic inflammation is significant for developing strategies for frailty prevention and pre-frailty reversion in the middle-aged and elderly population.
Appendix
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Literature
1.
Fried LP, et al. Frailty in older adults: evidence for a phenotype. J Gerontol A Biol Sci Med Sci. 2001;56(3):M146–56.CrossRefPubMed
2.
Yamada Y, et al. Prevalence of frailty and Prefrailty in people with human immunodeficiency virus aged 50 or older: a systematic review and Meta-analysis. Open forum Infect Diseases. 2022;9(5)
3.
Vermeiren S, et al. Frailty and the prediction of negative health outcomes: a Meta-analysis. J Am Med Dir Assoc. 2016;17(12):1163 e1–1163 e17.CrossRefPubMed
4.
Yang X, et al. Impact of frailty on mortality and hospitalization in chronic heart failure: a systematic review and Meta-analysis. J Am Heart Assoc. 2018;7(23):e008251.CrossRefPubMedCentralPubMed
5.
Kojima G. Frailty as a predictor of disabilities among community-dwelling older people: a systematic review and meta-analysis. Disabil Rehabil. 2017;39(19):1897–908.CrossRefPubMed
6.
Gale CR, et al. Inflammatory markers and incident frailty in men and women: the English longitudinal study of ageing. Age (Dordr). 2013;35(6):2493–501.CrossRefPubMedCentralPubMed
7.
Soysal P, et al. Inflammation and frailty in the elderly: a systematic review and meta-analysis. Ageing Res Rev. 2016;31:1–8.CrossRefPubMed
8.
Zhu Y, et al. C-reactive protein, frailty and overnight hospital admission in elderly individuals: a population-based study. Arch Gerontol Geriatr. 2016;64:1–5.CrossRefPubMed
9.
Livshits G, et al. Shared genetic influence on frailty and chronic widespread pain: a study from TwinsUK. Age Ageing. 2018;47(1):119–25.CrossRefPubMed
10.
Young AC, et al. The identification of hereditary and environmental determinants of frailty in a cohort of UK twins. Twin Res Hum Genet. 2016;19(6):600–9.CrossRefPubMed
11.
Kim S, et al. Association of healthy aging with parental longevity. Age (Dordr). 2013;35(5):1975–82.CrossRefPubMed
12.
Mekli K, et al. Genetic variant of Interleukin-18 gene is associated with the frailty index in the English longitudinal study of ageing. Age Ageing. 2015;44(6):938–42.CrossRefPubMedCentralPubMed
13.
Barzilay JI, et al. Insulin resistance and inflammation as precursors of frailty - the cardiovascular health study. Arch Intern Med. 2007;167(7):635–41.CrossRefPubMed
14.
Monti D, et al. Inflammaging and human longevity in the omics era. Mech Ageing Dev. 2017;165(Pt B):129–38.CrossRefPubMed
15.
Kamil RJ, et al. Association of Hearing Impairment with Incident Frailty and Falls in older adults. J Aging Health. 2016;28(4):644–60.CrossRefPubMed
16.
Zhao Y, et al. Cohort profile: the China health and retirement longitudinal study (CHARLS). Int J Epidemiol. 2014;43(1):61–8.CrossRefPubMed
17.
Han S, et al. Systemic inflammation accelerates the adverse effects of air pollution on metabolic syndrome: findings from the China health and retirement longitudinal study (CHARLS). Environ Res. 2022;215(Pt 1):114340.CrossRefPubMed
18.
19.
Kobsar D, et al. Classification accuracy of a single tri-axial accelerometer for training background and experience level in runners. J Biomech. 2014;47(10):2508–11.CrossRefPubMed
20.
Qin TT, et al. Body mass index moderates the relationship between C-reactive protein and depressive symptoms: evidence from the China health and retirement longitudinal study. Sci Rep. 2017:7.
21.
22.
23.
Genomes Project C. et al., A global reference for human genetic variation. Nature., 2015. 526(7571):68–74.
24.
Ray D, Chatterjee N. A powerful method for pleiotropic analysis under composite null hypothesis identifies novel shared loci between type 2 diabetes and prostate Cancer. PLoS Genet. 2020;16(12):e1009218.CrossRefPubMedCentralPubMed
25.
26.
Verbanck M, et al. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Eur J Hum Genet. 2019;27:854–5.
27.
Ong JS, MacGregor S. Implementing MR-PRESSO and GCTA-GSMR for pleiotropy assessment in Mendelian randomization studies from a practitioner's perspective. Genet Epidemiol. 2019;43(6):609–16.CrossRefPubMedCentralPubMed
28.
Hemani G, et al. The MR-base platform supports systematic causal inference across the human phenome. Elife. 2018:7.
29.
Xu S, et al. A novel penalized inverse-variance weighted estimator for Mendelian randomization with applications to COVID-19 outcomes. Biometrics. 2023;79(3):2184–95.
30.
Zhao QY, et al. Statistical inference in two-sample summary-data Mendelian randomization using robust adjusted profile score. Ann Stat. 2020;48(3):1742–69.MathSciNetCrossRef
31.
Metadata
Title
Unraveling the relationship between high-sensitivity C-reactive protein and frailty: evidence from longitudinal cohort study and genetic analysis
Authors
Yu-Feng Luo
Zi-Jian Cheng
Yan-Fei Wang
Xi-Yuan Jiang
Shu-Feng Lei
Fei-Yan Deng
Wen-Yan Ren
Long-Fei Wu
Publication date
01-12-2024
Publisher
BioMed Central
Published in
BMC Geriatrics / Issue 1/2024
Electronic ISSN: 1471-2318
DOI
https://doi.org/10.1186/s12877-024-04836-2

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