Abstract
The association between the systemic immune-inflammation index (SII) and the risk of sarcopenia has not yet been revealed. The purpose of this study was to investigate the relationship between the SII and sarcopenia in individuals aged 18–59 years. All data for this study are from the National Health and Nutrition Examination Survey (NHANES) database, including 7258 participants (age range: 18–59 years). We divided SII values by quartiles (quartiles 1–4: 0.3–3.1, 3.2–4.4, 4.4–6.2, and 6.2–58.5). We constructed a multivariate logistic regression model to assess the association between the SII and the risk of sarcopenia, and an interaction test was run to test the stability of the model and identify high-risk individuals with sarcopenia. Compared to nonsarcopenia participants, sarcopenia patients had a significantly higher SII value (weighted average: 6.65 vs. 5.16) (P = 0.002). Multivariate logistic regression results showed a positive linear relationship between the SII and sarcopenia (OR [odds ratio] = 1.12, 95% CI [confidence interval] 1.03–1.21). Compared to the quartile 1 group, the quartile 4 group was associated with a higher risk of sarcopenia (OR = 3.94, 95% CI 1.42–10.94). Compared with the quartile 1 group, the OR value of the quartile 2 to quartile 4 groups showed an upwards trend (Ptrend < 0.001) as the level of SII increased. Subgroup analysis also indicate that the correlation between higher SII values and the risk of sarcopenia was stable. There was a significant positive linear relationship between SII and sarcopenia, indicating that higher SII values can increase the risk of sarcopenia in individuals aged 18–59 in the United States. The findings of this study will be beneficial in promoting the use of SII alone or in combination with other tools for the risk screening of sarcopenia in communities or large populations.
Similar content being viewed by others
Introduction
Sarcopenia is a progressive, systemic skeletal muscle disease1,2 characterized by decreased body function, muscle strength, and mass. Due to differences in the definition, evaluation tools, and diagnostic cut-off values of sarcopenia, the prevalence of sarcopenia varies greatly in different countries or regions3,4. In the past few decades, the prevalence of sarcopenia has been increasing, with over 10–16% of the general population aged 60 and over suffering from sarcopenia3,5. Epidemiological statistical results show that the prevalence rates of sarcopenia in the community, hospitalized, and care-home populations are 10%, 23%, and 38%, respectively6. The prevalence of sarcopenia in men and women is 14% and 12%, respectively6. Studies have shown that sarcopenia is highly correlated with cachexia7,8,9,10 and increases the risk of falls and fractures. In addition, sarcopenia is also believed to be associated with activity disorders, low bone mass, metabolic disorders, depression, and hospital mortality11,12,13,14,15, which undoubtedly reduces the quality of life of patients and increases the medical burden. In view of the enormous harm of sarcopenia to public health, it is of important clinical and public health value to identify and screen high-risk populations with sarcopenia early in the community and provide targeted prevention and treatment recommendations.
Currently, there is still no international consensus on the diagnostic methods and standards for sarcopenia. The commonly used diagnostic criteria were proposed by working groups such as the European Working Group on Sarcopenia in Older People (EWGSOP)16,17, the International Working Group on Sarcopenia (IWGS)18, the Asian Working Group for Sarcopenia (AWGS)1, and the Foundation for the National Institutes (FNIH)19. However, the vast majority of criteria recommend that sarcopenia be defined based on low muscle content and/or low muscle strength and/or low physical fitness15,16,17,18,19. The measurement of muscle mass relies on instruments such as CT, MR, and dual-energy X-ray absorptiometry (DXA), which is not convenient for use in large-scale population screening. In recent years, different scales have been widely used in population screening due to their rapid and simple advantages, including the sarcopenia-five scale (SARC-F)20, SARC-F combined with calf circumference (SARC-CalF) scale21, and SARC-F adding elderly and body mass index (BMI) information scale (SARC-F + EBM)22,23, but those scales have difficulty taking into account the sensitivity and specificity of the diagnosis of sarcopenia. Therefore, the efficacy of those scales in the diagnosis or screening of sarcopenia still needs to be further verified. In this context, simple and efficient screening tools or evaluation indicators for sarcopenia need to be explored and developed.
The systemic immune-inflammation index (SII) is an index for evaluating inflammation based on neutrophil, lymphocyte, and platelet counts24,25, which can more objectively reflect changes in the level of inflammation in the body. SII can be calculated through a routine blood examination, and it has the advantages of being fast, efficient, simple, and low in cost. Previous studies have confirmed that the SII has good clinical application value in the diagnosis of chronic diseases such as tumours, cardiovascular diseases, osteoporosis, and rheumatoid arthritis26,27,28,29. Research suggests that chronic low-grade inflammation associated with ageing may be an important cause of sarcopenia30. With age, the body presents a chronic inflammatory state30. The levels of various inflammatory substances and proinflammatory factors increase, promote protein decomposition and inhibit protein synthesis through various signalling pathways31,32,33, resulting in an imbalance in muscle tissue synthesis and metabolism. Shi et al.34 found that higher levels of SII were associated with an increased risk of low muscle mass in a large population, and they pointed out that anti-inflammatory therapy may be a possible pathway to alleviate low muscle mass status. Another study showed that NLRP3 inflammasomes and cell death can lead to muscle dysfunction by reducing glycolytic potential and muscle fibre size35. In addition, Karanth et al.36 analysed cross-sectional data and concluded that measures need to be taken to prevent sarcopenia in populations with high levels of systemic inflammation. Based on the causal relationship between inflammation levels and sarcopenia, the SII may have clinical application value in predicting the risk of sarcopenia. However, there is currently a lack of research to confirm this conjecture. Our hypothesis is that there is a potential correlation between the SII and sarcopenia. In addition, using the SII for a simple risk assessment of 18–59-year-olds has better value, which may help us identify susceptible populations earlier and take more preventive measures. To fill this gap, we used population data from the National Health and Nutrition Examination Survey (NHANES) database to investigate the relationship between the SII and sarcopenia, which will provide new measurement tools and options for community or large-scale population screening for sarcopenia.
Methods
Data sources and study participants
The data for this study were obtained from the NHANES database. NHANES is a cross-sectional survey conducted by the Centers for Disease Control and Prevention (CDC) of the United States37 and is aimed at collecting health and nutrition information for the general population in the United States. The survey participants of NHANES are determined through stratified cluster multistage sampling, which can be used as a representative sample of American residents. Since the independent variable and end point outcome of this study are SII and sarcopenia, respectively, we only selected two survey periods, 2011–2012 and 2013–2014, with sufficient measurement data to calculate those two variables. All participants signed an informed consent form38; NHANES was approved by the Ethics Review Committee of the National Center for Health Statistics (Protocol # 2011–17)38.
A total of 19,931 participants were included in the 2011–2014 survey cycle. Since SII- and sarcopenia-related data are the core data of this study, all participants lacking data on those two variables were not included for further analysis. Specifically, participants who were only missing covariate data were not excluded, and those missing values were set as dummy variables in the statistical analysis. There were no other exclusion or inclusion criteria. A total of 7258 participants were eventually included. The inclusion and exclusion process is shown in Fig. 1.
Determination of sarcopenia outcomes
According to FNIH sarcopenia project criteria (FNIH)19,39, we will determine whether participants in the 2011–2014 cycle have health issues with sarcopenia based on data from the NHANES database. According to the FNIH standard, the judgement factors for sarcopenia include weakness and muscle loss19. The quantitative judgement indicators for these two factors are grip strength and appendicular skeletal muscle mass (ASM). Both grip strength and ASM require BMI adjustment19, and the adjusted grip strength and ASM are named grip strengthBMI and ASMBMI, respectively. In this study, the cut-off values of grip strengthBMI for males and females were < 1.0 and < 0.5619, respectively. The ASMBMI-cutoff values for males and females were < 0.789 and < 0.512, respectively19. Based on the diagnostic criteria and cutoff values of FNIH, we can diagnose whether each participant has sarcopenia based on their grip strengthBMI and ASMBMI data.
During the 2011–2014 cycle, body composition (including muscle mass) was measured using DXA, and all results were calculated using HologicQDR-4500 software (version Apex 3.2, Hologic, USA)40,41. Participants were asked not to undergo other radiological examinations the day before or on the day of the DXA evaluation. Specific information and data on body composition measurements can be found on the CDC website40,41. The grip strength test was conducted using a Takei dynamometer (TKK 5401, Takei Scientific Instruments, Tokyo, Japan), and measurement details can be found on the CDC website and measurement manual42,43. The measurement work related to clinical and laboratory indicators is carried out by trained medical workers. In addition, the detailed process of sample collection and processing has been described in detail in the NHANES Laboratory/Medical Technician Procedure Manual (LPM)40,41,42,43.
Measurement of SII
The SII is an immune inflammatory index calculated based on the counts of neutrophils, platelets, and lymphocytes in peripheral blood. The calculation formula is SII = platelet count × neutrophil count/lymphocyte count44. Blood sample collection for all participants was completed at the NHANES Mobile Examination Center (MEC). The instruments used for the analysis of whole blood cell counts in the 2011–2012 and 2013–2014 cycles were the Coulter HMX (Coulter Electronics Ltd., Bedfordshire, UK)45 and Beckman Coulter DXH 800 (Beckman Coulter, Brea, CA, USA)46, respectively.
Assessment of covariates
The selection of covariates in this study was mainly based on the literature and variables recognized by academia47,48,49,50,51, and they primarily consisted of sociodemographic factors. The covariates mainly included age (years), sex, race (non-Hispanic white, non-Hispanic black, American Mexican, and others), BMI, and smoking status (never, former, current). Based on BMI values, we divided BMI into three groups: < 25, 25–24.99, and ≥ 30. In addition, we extracted six diseases, including diabetes52, chronic kidney disease (CKD)53, hypertension54, chronic obstructive pulmonary disease (COPD)55, coronary heart disease (CHD)56 and hyperlipidaemia57, as covariates. The diagnosis of these diseases was based on clear diagnostic criteria52,53,54,55,56,57 and matched the measurement indicators in the NHANES database for diagnosis; or, each participant included in this study reported taking disease-related drugs, such as hypoglycaemic drugs, lipid-lowering drugs, etc.; or, participants reported having been diagnosed by a doctor.
Statistical analyses
The data analysis of this study was conducted in accordance with CDC statistical guidelines58. All data extraction and analysis were performed using R software (version 4.2.2, http://www.R-project.org, The R Foundation) and EmpowerStats software (version 4.1, www.empowerstats.com, X&Y solutions, Inc. Boston, MA, USA). All tests were bilateral and had the following test level: α = 0.05. Continuous variables were represented by weighted means and 95% confidence intervals (CIs); classification variables were expressed using frequency and weighted percentages (%). We used a bivariate logistic regression model to evaluate the relationship between the SII and sarcopenia. Due to the large absolute value of SII and to make the data have higher statistical efficiency and facilitate clinical application, we divided all SII values by 100 as the original data. Therefore, in this study, each unit increase in SII means an increase of 100 in the absolute value of SII. To test the stability of the impact of SII on sarcopenia in different regression models, we divided SII values into four groups according to the quartile. The quartile ranges of SII were 0.3–3.1 (quartile 1), 3.2–4.4 (quartile 2), 4.4–6.2 (quartile 3), and 6.2–58.5 (quartile 4). SII was entered into the model as a continuous variable and as a categorical variable (quartile grouping). To further test the stability of the relationship between SII and sarcopenia, we constructed three regression models to gradually adjust for confounding factors: Model 1 did not adjust for covariates; Model 2 adjusted for age, sex, BMI, and ethnicity; and based on Model 2, Model 3 further adjusted for smoking status, diabetes, CKD, hypertension, COPD, CHD, and hyperlipidaemia.
We further conducted interaction tests (subgroup analysis) on these covariates to help us evaluate the reliability of the conclusions of this study from a comprehensive perspective. Considering the multiple comparisons in subgroup analysis, we adjusted the P-value using Bonferroni correction to obtain a conservative threshold for determining significant differences (P = 0.05/8 = 0.006).
Ethics approval and consent to participate
The protocols of NHANES were approved by the institutional review board of the National Center for Health Statistics, CDC (https://www.cdc.gov/nchs/nhanes/irba98.htm). NHANES has obtained written informed consent from all participants.
Results
Baseline characteristics of the study participants
A total of 7258 participants were included in this study, including 88 sarcopenia patients and 7170 nonsarcopenia participants. The age range for all participants was 18–59 years, with a weighted average age of 38.5 years. The weighted average ages of participants with sarcopenia and nonsarcopenia were 45.39 and 38.45, respectively, with a statistically significant difference (P = 0.0003). The weighted average values (and 95% CI) of SII in sarcopenia and nonsarcopenia participants were 6.65 (95% CI 5.79–7.52) and 5.16 (95% CI 5.03–5.30), respectively, with a statistically significant difference (P = 0.002). The weighted average ages of the quartile 1–4 groups were 37.5, 38.12, 39.32, and 39.07, respectively, with statistically significant differences between the groups (P = 0.022). The frequency of sarcopenia in the quartile 1–4 groups was 11, 17, 21, and 39, respectively, with a statistically significant difference between the groups (P = 0.0005). The weighted average (and 95% CI) SII values in the quartile 1–4 groups were 2.42 (95% CI 2.40–2.46), 3.81 (95% CI 3.79–3.83), 5.25 (95% CI 5.22–5.27), and 8.84 (95% CI 8.67–9.01), respectively, with statistically significant differences between the groups (P < 0.001). The specific characteristics of all 7258 participants are shown in Table 1.
Association between SII and sarcopenia
We constructed three logistic regression models to explore the independent impact of SII on sarcopenia. The unadjusted Model 1 results showed that an increase in SII was associated with a higher risk of sarcopenia (OR [odds ratio] = 1.10, 95% CI 1.05–1.15). Both the microadjustment model (Model 2) and the overall covariate adjustment model (Model 3) showed that a higher SII is associated with a higher risk of sarcopenia (Table 2), and their corresponding effect values (and 95% CI) were OR = 1.14 (95% CI 1.07–1.21) and OR = 1.12 (95% CI 1.03–1.21), respectively, indicating a positive linear relationship between SII and sarcopenia. In Model 1 (OR = 4.97, 95% CI 2.37–10.42), Model 2 (OR = 4.53, 95% CI 2.06–9.93) and Model 3 (OR = 3.94, 95% CI 1.42–10.94), the quartile 4 group was associated with a higher risk of sarcopenia than the quartile 1 group. Compared with the quartile 1 group, the OR value of the quartile 2–4 groups showed an upwards trend (Ptrend < 0.001) as the level of SII increased. The above univariate and multivariate logistic regression analysis results indicate that there is a potential positive correlation between SII values and the risk of sarcopenia.
Subgroup analysis
To further test the reliability of this regression model, we conducted a subgroup analysis of covariates (Fig. 2). We found that higher SII values are associated with the risk of sarcopenia, a conclusion that is highly similar across age, sex, race, CKD, hypertension, diabetes, BMI, and hyperlipidaemia subgroups (all P for interaction > 0.006) (Fig. 2).
Discussion
Sarcopenia is believed to be closely and positively correlated with cachexia, decreased physiological function, and increased mortality59,60, which makes it necessary to pay attention to the screening, diagnosis, prevention, and treatment of sarcopenia. Considering the enormous harm of sarcopenia to public health, early identification of high-risk populations with sarcopenia and recommendations for prevention or treatment may be the most effective way to reduce the harm of sarcopenia to public health. Therefore, it is an important task to find an evaluation tool or indicator for sarcopenia suitable for large-scale population screening to achieve this goal. To the best of our knowledge, this study is the first to explore the correlation between SII and the risk of sarcopenia, which will provide reliable data and more options for community screening among people at risk for sarcopenia. This study revealed a positive linear relationship between SII and the occurrence of sarcopenia (OR = 1.12, 95% CI 1.03–1.21, P = 0.009), indicating that an increase in SII will significantly increase the risk of sarcopenia. In addition, compared to the quartile 1 group (SII: 0.3–3.1), the quartile 4 group (SII: > 6.2) had a significantly increased risk of sarcopenia by 294% (OR = 3.94, 95% CI 1.42–10.94, Ptrend < 0.001). The interaction tests indicate that the correlation between higher SII values and the risk of sarcopenia is not affected by age, sex, race, CKD, hypertension, diabetes, BMI, and hyperlipidaemia. The above three stable conclusions suggest that higher SII levels are associated with a higher risk of sarcopenia. In addition, there is believed to be a causal relationship between inflammation and sarcopenia, and studies have pointed out that inflammation-related factors can be used as therapeutic targets for sarcopenia35,61,62. We are very encouraged by these conclusions because they indicate that an objective, easy-to-operate, and scalable SII has great potential to become a risk assessment indicator for sarcopenia that can be used in community or large-scale population screening.
The SII is a comprehensive inflammatory index that reflects the immune and inflammatory status of the host and is calculated by measuring neutrophils, lymphocytes, and platelets in the peripheral blood63. Therefore, the data acquisition and calculation of SII has the advantages of accuracy, objectivity, efficiency, and ease of promotion. The increase in SII values reflects an increase in neutrophil and platelet counts, as well as an increase in multiple cytokine levels (and/or a decrease in lymphocyte counts). The decrease in lymphocytes during inflammation can increase the production of proinflammatory cytokines, induce oxidative stress, and promote cell apoptosis64, thereby aggregating inflammation to promote the development of disease. Ageing is often accompanied by an increase in inflammatory markers and related factors, and sarcopenia is also considered to be an age-related disease30. Significantly elevated levels of inflammation and oxidative stress are believed to be important factors inducing sarcopenia in middle-aged and elderly people30,65. Inflammatory cytokines may antagonize the anabolism of insulin-like growth factor-1 (IGF-1)66, reducing the level of muscle IGF-1, which can lead to increased protein catabolism and muscle anabolism disorders. With age, circulating levels of inflammatory markers such as interleukin-6 (IL-6), C-reactive protein (CRP), tumour necrosis factor α (TNF-α), and IL-1β significantly increase and are directly related to decreased muscle mass and strength67,68,69. IL-6 is involved in the regulation of myoprotein turnover and is considered to be a catabolic cytokine70,71. Inflammation can also indirectly reduce the concentration of growth hormone (GH) and IGF-1 in the body, which can have a negative impact on skeletal muscle72,73, inducing the occurrence of sarcopenia. TNF-α inhibits the synthesis of muscle proteins by regulating the PI3K/Akt/mTOR signalling pathway and promotes muscle atrophy by activating the expression of a series of muscle growth inhibitory factors, such as atrogin-1, nuclear factor kappa (NF-κB), and myostatin74,75. In addition, many inflammatory factors (such as IL-15 and CRP) or inflammatory signalling pathways (e.g., MAPK/NF-κB/Wnt/mTOR signalling pathways) are believed to be associated with the occurrence of sarcopenia76,77,78,79,80. Based on the above information, we believe that the SII has a solid physiological and pathological basis in predicting the occurrence of sarcopenia, and this is also reflected in our research conclusions. Therefore, SII as a biomarker for sarcopenia warrants further research. Our research findings have increased the understanding of the relationship between the SII and sarcopenia. In the US population aged 18–59 years, SII values may be used for screening individuals at risk for sarcopenia. Moreover, anti-inflammatory therapy may be a potential pathway for the treatment of sarcopenia.
Early identification of high-risk individuals with sarcopenia in specific populations can help the public health system or clinical subspecialties provide more accurate healthcare advice. The results of subgroup analysis showed that in the population with IFG (or IGT) (OR = 1.3), BMI ≥ 30 (OR = 1.1), or age 50–59 years (OR = 1.2), each unit increase in SII (absolute value increase of 100) increased their risk of sarcopenia by 30%, 10%, and 20%, respectively. Research on the relationship between IFG (or IGT) and sarcopenia is still lacking, and our research provides the most basic addition to this topic. Previous studies have shown that serum levels of various inflammatory factors, including IL-6, TNF-α, CRP, plasminogen activator inhibitor-1, and fibrinogen, in IFG (or IGT) populations are significantly higher than those in healthy individuals81,82. Moreover, IGT patients generally have more insulin resistance83, which is often a chronic inflammatory reaction, compared to healthy people. The IFG population has also been considered to be in a low-inflammatory microenvironment for a long time84. Chronic persistence and low-inflammatory status are the main characteristics of obesity85 and are also important causes for insulin resistance. Research suggests that obesity is associated with an increase in the expression of inflammatory mediators in adipose tissue which disrupt metabolic homeostasis, and obese individuals are in a chronic inflammatory state of imbalance between proinflammatory and anti-inflammatory immune cells86,87. Previous studies have confirmed the association between inflammation and sarcopenia30,33,67,68. Seung Jae Heo et al. found that the risk of sarcopenia increased significantly with every 5 consecutive years of age increase88; our study showed that the risk of sarcopenia increased more significantly with the increase of SII values in the population aged 50–59 years. Yoo et al. found that chronic inflammatory status in the elderly can cause inflammatory cytokines to antagonize the synthesis and metabolism of IGF-1, leading to muscle synthesis disorders63.
Strengths and limitations
The topic of this study has obvious advantages, which will be beneficial to promoting the screening of individuals at risk for sarcopenia in the community. Prior to this study, there were no studies exploring the relationship between SII and sarcopenia. This study is the first to explore the relationship between SII and the risk of sarcopenia and confirms a linear positive correlation between the two. Second, the participants included in this study were between the ages of 18–59, and conducting risk screening for sarcopenia in this age group (not elderly individuals) can yield better screening and prevention value. Our research conclusions are based on survey data from people aged 18–59, which means that the conclusions of this study have better applicability to the target population. SII does not rely on complex instruments but can be calculated with routine blood tests and has objectivity and repeatability, which will be very beneficial for community doctors or large-scale population screening applications. Considering the enormous public health hazards of sarcopenia, we believe that SII can be used alone or in combination with other tools (questionnaires) to increase the efficiency and accuracy of screening for sarcopenia.
However, we must admit that this study also has the following limitations. First, this was an observational study, which means that the determination of causal relationships still requires further development in the future, requiring high-quality cohort studies, metabolomics studies, or Mendelian randomization studies. Second, due to the lack of diagnostic criteria for sarcopenia in the 18–59 age group, we applied FNIH sarcopenia project criteria to define sarcopenia in this study. And the major limitation is that the criteria were developed mainly for use in older adults. Further development of diagnostic criteria for sarcopenia in the 18–59-year-old population is needed to further test the conclusions of this study. Third, there were differences in the timing of DXA evaluation and blood sample collection for each participant, which may affect the homogeneity of measurement results. Therefore, we need to consider the impact of the accuracy of the measurement results on the reliability of this conclusion. Fourth, some key covariates (e.g., physical activity level and dietary habits) should be included in the analysis as they may have a critical impact on sarcopenia. However, due to the lack of raw data and the difficulty in collecting and quantifying some variables (such as dietary habits), we were unable to include that information. Last, the target population of this study is based on the United States population, which may make the conclusions of this study unsuitable for extrapolation to populations in other countries or continents.
Conclusions
After adjusting for multiple covariates, we found a significant positive linear correlation between SII and sarcopenia, indicating that a higher SII can increase the risk of sarcopenia in people aged 18–59 in the United States. The quartile 4 group had a higher risk of sarcopenia than the quartile 1 group. Given the predictive value of SII for the risk of sarcopenia, the findings of this study will be beneficial in promoting the use of SII alone or in combination with other tools for risk screening in communities or large populations.
Availability of data and materials
The datasets generated and analyzed in the current study are available at NHANES website: https://www.cdc.gov/nchs/nhanes/index.htm.
Abbreviations
- SII:
-
Systemic immune-inflammation index
- NHANES:
-
National Health and Nutrition Examination Survey
- OR:
-
Odds ratio
- CI:
-
Confidence interval
- EWGSOP:
-
European Working Group on Sarcopenia in Older People
- IWGS:
-
International Working Group on Sarcopenia
- AWGS:
-
Asian Working Group for Sarcopenia
- FNIH:
-
Foundation for the National Institutes
- DXA:
-
Dual-energy X-ray absorptiometry
- SARC-F:
-
Sarcopenia-five
- SARC-CalF:
-
SARC-F combined with calf circumference
- BMI:
-
Body mass index
- SARC-F + EBM:
-
SARC-F adding elderly and body mass index information scale
- CDC:
-
Centers for Disease Control and Prevention
- ASM:
-
Appendicular skeletal muscle mass
- CKD:
-
Chronic kidney disease
- COPD:
-
Chronic obstructive pulmonary disease
- CHD:
-
Coronary heart disease
- IGF-1:
-
Insulin-like growth factor-1
- IL-6:
-
Interleukin-6
- CRP:
-
C-reactive protein
- TNF-α:
-
Tumor necrosis factor α
- GH:
-
Growth hormone
- IFG:
-
Impaired fasting glucose
- IGT:
-
Impaired glucose tolerance
References
Chen, L. et al. Asian working group for sarcopenia: 2019 Consensus update on sarcopenia diagnosis and treatment. J. Am. Med. Dir. Assoc. 21(3), 300–307. https://doi.org/10.1016/j.jamda.2019.12.012 (2020).
Cruz-Jentoft, A. J. & Sayer, A. A. Sarcopenia. Lancet 393(10191), 2636–2646. https://doi.org/10.1016/S0140-6736(19)31138-9 (2019).
Yuan, S. & Larsson, S. C. Epidemiology of sarcopenia: Prevalence, risk factors, and consequences. Metabolism https://doi.org/10.1016/j.metabol.2023.155533 (2023).
Sayer, A. A. & Cruz-Jentoft, A. Sarcopenia definition, diagnosis and treatment: consensus is growing. Age Ageing 51(10), afac220. https://doi.org/10.1093/ageing/afac220 (2022).
Shafiee, G. et al. Prevalence of sarcopenia in the world: A systematic review and meta-analysis of general population studies. J. Diabet. Metab. Disord. 16(1), 21. https://doi.org/10.1186/s40200-017-0302-x (2017).
Papadopoulou, S. K., Tsintavis, P., Potsaki, P. & Papandreou, D. Differences in the prevalence of sarcopenia in community-dwelling, nursing home and hospitalized individuals: A systematic review and meta-analysis. J. Nutr. Health Aging 24(1), 83–90. https://doi.org/10.1007/s12603-019-1267-x (2020).
Orsso, C. E. et al. Mapping ongoing nutrition intervention trials in muscle, sarcopenia, and cachexia: A scoping review of future research. J. Cachexia Sarcopenia Muscle 13(3), 1442–1459. https://doi.org/10.1002/jcsm.12954 (2022).
Yedigaryan, L., Gatti, M., Marini, V., Maraldi, T. & Sampaolesi, M. Shared and divergent epigenetic mechanisms in cachexia and sarcopenia. Cells 11(15), 2293. https://doi.org/10.3390/cells11152293 (2022).
Candow, D. G. et al. Creatine supplementation for older adults: focus on sarcopenia, osteoporosis, frailty and cachexia. Bone 162, 116467. https://doi.org/10.1016/j.bone.2022.116467 (2022).
Petermann-Rocha, F., Pell, J. P., Celis-Morales, C. & Ho, F. K. Frailty, sarcopenia, cachexia and malnutrition as comorbid conditions and their associations with mortality: A prospective study from UK biobank. J Public Health 44(2), e172–e180. https://doi.org/10.1093/pubmed/fdaa226 (2022).
Liu, D. et al. Mitochondrial quality control in sarcopenia: Updated overview of mechanisms and interventions. Aging Dis. 12(8), 2016. https://doi.org/10.14336/AD.2021.0427 (2021).
Nishikawa, H., Asai, A., Fukunishi, S., Nishiguchi, S. & Higuchi, K. Metabolic syndrome and sarcopenia. Nutrients 13(10), 3519. https://doi.org/10.3390/nu13103519 (2021).
Coll, P. P. et al. The prevention of osteoporosis and sarcopenia in older adults. J. Am. Geriatr. Soc. 69(5), 1388–1398. https://doi.org/10.1111/jgs.17043 (2021).
Chang, K., Hsu, T., Wu, W., Huang, K. & Han, D. Is sarcopenia associated with depression? A systematic review and meta-analysis of observational studies. Age Ageing 46(5), 738–746. https://doi.org/10.1093/ageing/afx094 (2017).
Shu, X. et al. Diagnosis, prevalence, and mortality of sarcopenia in dialysis patients: A systematic review and meta-analysis. J. Cachexia Sarcopenia Muscle 13(1), 145–158. https://doi.org/10.1002/jcsm.12890 (2022).
Wallengren, O. et al. Comparison of the 2010 and 2019 diagnostic criteria for sarcopenia by the European working group on sarcopenia in older people (EWGSOP) in two cohorts of Swedish older adults. BMC Geriatr. 21(1), 600. https://doi.org/10.1186/s12877-021-02533-y (2021).
Patel, H. P. et al. Prevalence of sarcopenia in community-dwelling older people in the UK using the European working group on sarcopenia in older people (EWGSOP) definition: Findings from the hertfordshire cohort study (HCS). Age Ageing 42(3), 378–384. https://doi.org/10.1093/ageing/afs197 (2013).
Fielding, R. A. et al. Sarcopenia: An undiagnosed condition in older adults. Current consensus definition prevalence etiology, and consequences. International working group on sarcopenia. J. Am. Med. Dir. Assoc. 12(4), 249–256. https://doi.org/10.1016/j.jamda.2011.01.003 (2011).
Studenski, S. A. et al. The FNIH sarcopenia project: Rationale, study description, conference recommendations, and final estimates. J. Gerontol. Ser. A 69(5), 547–558. https://doi.org/10.1093/gerona/glu010 (2014).
Wu, A. H. et al. Prognostic utility of self-reported sarcopenia (SARC-F) in the multiethnic cohort. J. Cachexia Sarcopenia Muscle 13(2), 987–1002. https://doi.org/10.1002/jcsm.12916 (2022).
Yang, M. et al. Screening sarcopenia in community-dwelling older adults: SARC-F vs SARC-F combined with calf circumference (SARC-CalF). J. Am. Med. Dir. Assoc. 19(3), 271–277. https://doi.org/10.1016/j.jamda.2017.12.016 (2018).
Kurita, N., Wakita, T., Kamitani, T., Wada, O. & Mizuno, K. SARC-F validation and SARC-F+EBM derivation in musculoskeletal disease: the SPSS-OK study. J. Nutr. Health Aging 23(8), 732–738. https://doi.org/10.1007/s12603-019-1222-x (2019).
Krzymińska-Siemaszko, R., Deskur-śmielecka, E., Kaluźniak-Szymanowska, A., Lewandowicz, M. & Wieczorowska-Tobis, K. Comparison of diagnostic performance of SARC-F and its two modified versions (SARC-CalF and SARC-F+EBM) in community-dwelling older adults from Poland. Clin. Interv. Aging 15, 583–594. https://doi.org/10.2147/CIA.S250508 (2020).
Geraghty, J. R. et al. Systemic immune-inflammation index predicts delayed cerebral vasospasm after aneurysmal subarachnoid hemorrhage. Neurosurgery 89(6), 1071–1079. https://doi.org/10.1093/neuros/nyab354 (2021).
Yücel, K. B. et al. The relationship between systemic immune inflammation index and survival in patients with metastatic renal cell carcinomatreated withtyrosine kinase inhibitors. Sci. Rep. 12(1), 16559. https://doi.org/10.1038/s41598-022-20056-3 (2022).
Zhang, J. et al. Systemic immune-inflammation index is associated with decreased bone mass density and osteoporosis in postmenopausal women but not in premenopausal women. Endocr. Connect 12(2), e220461. https://doi.org/10.1530/EC-22-0461 (2023).
Xiao, S. et al. Association of systemic immune inflammation index with all-cause, cardiovascular disease, and cancer-related mortality in patients with cardiovascular disease: a cross-sectional study. J. Inflamm. Res. 16, 941–961. https://doi.org/10.2147/JIR.S402227 (2023).
Liu, B., Wang, J., Li, Y., Li, K. & Zhang, Q. The association between systemic immune-inflammation index and rheumatoid arthritis: Evidence from NHANES 1999–2018. Arthritis Res. Ther. 25(1), 34. https://doi.org/10.1186/s13075-023-03018-6 (2023).
Candemir, M., Kiziltunç, E., Nurkoç, S. & Şahinarslan, A. Relationship between systemic immune-inflammation index (sii) and the severity of stable coronary artery disease. Angiology 72(6), 575–581. https://doi.org/10.1177/0003319720987743 (2021).
Jensen, G. L. Inflammation: Roles in aging and sarcopenia. J. Parenter. Enter. Nutr. 32(6), 656–659. https://doi.org/10.1177/0148607108324585 (2008).
Sciorati, C. et al. Pharmacological blockade of TNFalpha prevents sarcopenia and prolongs survival in aging mice. Aging 12(23), 23497–23508. https://doi.org/10.18632/aging.202200 (2020).
Rodrigues, A. C. Z. et al. Heart and neural crest derivative 2-induced preservation of sympathetic neurons attenuates sarcopenia with aging. J. Cachexia Sarcopenia Muscle 12(1), 91–108. https://doi.org/10.1002/jcsm.12644 (2021).
Daily, J. W. & Park, S. Sarcopenia is a cause and consequence of metabolic dysregulation in aging humans: Effects of gut dysbiosis, glucose dysregulation, diet and lifestyle. Cells 11(3), 338. https://doi.org/10.3390/cells11030338 (2022).
Shi, L., Zhang, L., Zhang, D. & Chen, Z. Association between systemic immune-inflammation index and low muscle mass in US adults: A cross-sectional study. BMC Public Health 23(1), 1416. https://doi.org/10.1186/s12889-023-16338-8 (2023).
Jimenez-Gutierrez, G. E. et al. Molecular mechanisms of inflammation in sarcopenia: Diagnosis and therapeutic update. Cells 11(15), 2359. https://doi.org/10.3390/cells11152359 (2022).
Karanth, S. D. et al. Inflammation in relation to sarcopenia and sarcopenic obesity among older adults living with chronic comorbidities: Results from the National Health and Nutrition Examination Survey 1999–2006. Nutrients 13(11), 3957. https://doi.org/10.3390/nu13113957 (2021).
Cetners for disease control and prevention about the National health and nutrition examination survey. NHANES. https://www.cdc.gov/nchs/nhanes/index.htm. Accessed 20 Mar 2023.
National Center for Health Statistics. Centers for Disease Control and Prevention NCHS research ethics review board (ERB) approval. https://www.cdc.gov/nchs/nhanes/irba98.htm. Accessed 20 Mar 2023.
Dam, T. T. et al. An evidence-based comparison of operational criteria for the presence of sarcopenia. J. Gerontol. Ser. A Biol. Sci. Med. Sci. 69(5), 584–590. https://doi.org/10.1093/gerona/glu013 (2014).
Cetners for disease control and preventio about the National health and nutrition examination survey. NHANES. https://wwwn.cdc.gov/Nchs/Nhanes/2011-2012/DXX_G.htm#DXDLALE. Accessed 20 Mar 2023.
Cetners for disease control and prevention about the National health and nutrition examination survey. NHANES. https://wwwn.cdc.gov/Nchs/Nhanes/2013-2014/DXX_H.htm#DXDLALE. Accessed 20 Mar 2023.
Cetners for disease control and prevention about the National health and nutrition examination survey. NHANES. https://wwwn.cdc.gov/Nchs/Nhanes/2011-2012/MGX_G.htm#Component_Description. Accessed 20 Mar 2023.
Cetners for disease control and prevention about the National health and nutrition examination survey. NHANES. https://wwwn.cdc.gov/Nchs/Nhanes/2013-2014/MGX_H.htm#MGXH1T1. Accessed 20 Mar 2023.
Hu, B. et al. Systemic immune-inflammation index predicts prognosis of patients after curative resection for hepatocellular carcinoma. Clin Cancer Res 20(23), 6212–6222. https://doi.org/10.1158/1078-0432.CCR-14-0442 (2014).
Cetners for disease control and prevention about the National health and nutrition examination survey. NHANES. https://wwwn.cdc.gov/Nchs/Nhanes/2011-2012/CBC_G.htm#LBXMPSI. Accessed 20 Mar 2023.
Cetners for disease control and prevention. about the National health and nutrition examination survey. NHANES. https://wwwn.cdc.gov/Nchs/Nhanes/2013-2014/CBC_H.htm#LBXPLTSI. Accessed 20 Mar 2023.
Yang Q, Chan P. Skeletal muscle metabolic alternation develops sarcopenia. Aging Dis 2022;13(3):801. https://doi.org/10.14336/AD.2021.1107.
Darroch P, O Brien WJ, Mazahery H, Wham C. Sarcopenia prevalence and risk factors among residents in aged care. Nutrients 2022;14(9):1837. https://doi.org/10.3390/nu14091837.
Sri-On, J., Fusakul, Y., Kredarunsooksree, T., Paksopis, T. & Ruangsiri, R. The prevalence and risk factors of sarcopenia among Thai community-dwelling older adults as defined by the Asian working group for sarcopenia (AWGS-2019) criteria: A cross-sectional study. BMC Geriatr. 22(1), 786. https://doi.org/10.1186/s12877-022-03471-z (2022).
Wilkinson, T. J. et al. Association of sarcopenia with mortality and end-stage renal disease in those with chronic kidney disease: A UK biobank study. J. Cachexia Sarcopenia Muscle 12(3), 586–598. https://doi.org/10.1002/jcsm.12705 (2021).
Kaga, H. et al. Prediabetes is an independent risk factor for sarcopenia in older men, but not in older women: The Bunkyo health study. J. Cachexia Sarcopenia Muscle 13(6), 2835–2842. https://doi.org/10.1002/jcsm.13074 (2022).
Bonham, P. A. et al. 2021 guideline for management of patients with lower-extremity wounds due to diabetes mellitus and/or neuropathic disease. J. Wound Ostomy Cont. Nurs. 49(3), 267–285. https://doi.org/10.1097/WON.0000000000000860 (2022).
Rovin, B. H. et al. KDIGO 2021 clinical practice guideline for the management of glomerular diseases. Kidney Int. 100(4 Supplement), S1-276. https://doi.org/10.1016/j.kint.2021.05.021 (2021).
Jacobsen, A. P. et al. A cohort study and meta-analysis of isolated diastolic hypertension: Searching for a threshold to guide treatment. Eur. Heart J. 42(21), 2119–2129. https://doi.org/10.1093/eurheartj/ehab111 (2021).
Macleod, M. et al. Chronic obstructive pulmonary disease exacerbation fundamentals: Diagnosis, treatment, prevention and disease impact. Respirology 26(6), 532–551. https://doi.org/10.1111/resp.14041 (2021).
Kianoush, S. et al. An update on the utility of coronary artery calcium scoring for coronary heart disease and cardiovascular disease risk prediction. Curr. Atheroscler. Rep. https://doi.org/10.1007/s11883-016-0565-6 (2016).
Chen, C., Chen, K., Hsu, C., Chiu, W. & Li, Y. J. A guideline-based decision support for pharmacological treatment can improve the quality of hyperlipidemia management. Comput. Methods Programs Biomed. 97(3), 280–285. https://doi.org/10.1016/j.cmpb.2009.12.004 (2010).
NHANES Tutorials. https://wwwn.cdc.gov/nchs/nhanes/tutorials/default.aspx. Accessed 20 Mar 2023.
Davies, B. et al. Differential association of frailty and sarcopenia with mortality and disability: Insight supporting clinical subtypes of frailty. J. Am. Med. Dir. Assoc. 23(10), 1712–1716. https://doi.org/10.1016/j.jamda.2022.03.013 (2022).
Fröhlich, A. K. et al. JCSM: Growing together with cachexia and sarcopenia research. J. Cachexia Sarcopenia Muscle 12(6), 1359–1367. https://doi.org/10.1002/jcsm.12886 (2021).
Livshits, G. & Kalinkovich, A. A cross-talk between sestrins, chronic inflammation and cellular senescence governs the development of age-associated sarcopenia and obesity. Ageing Res. Rev. 86, 101852. https://doi.org/10.1016/j.arr.2023.101852 (2023).
Chen, F. et al. Inflammation-dependent downregulation of mir-532-3p mediates apoptotic signaling in human sarcopenia through targetingbak1. Int. J. Biol. Sci. 16(9), 1481–1494. https://doi.org/10.7150/ijbs.41641 (2020).
Mori, K. et al. Prognostic role of the systemic immune–inflammation index in upper tract urothelial carcinoma treated with radical nephroureterectomy: Results from a large multicenter international collaboration. Cancer Immunol. Immunother. 70(9), 2641–2650. https://doi.org/10.1007/s00262-021-02884-w (2021).
Liu, P. et al. The mechanisms of lysophosphatidylcholine in the development of diseases. Life Sci. 247, 117443. https://doi.org/10.1016/j.lfs.2020.117443 (2020).
Toth, M. J., Ades, P. A., Tischler, M. D., Tracy, R. P. & Lewinter, M. M. Immune activation is associated with reduced skeletal muscle mass and physical function in chronic heart failure. Int. J. Cardiol. 109(2), 179–187. https://doi.org/10.1016/j.ijcard.2005.06.006 (2006).
Yoo, S. et al. Role of exercise in age-related sarcopenia. J. Exerc. Rehabil. 14(4), 551–558. https://doi.org/10.12965/jer.1836268.134 (2018).
Kamper, R. S. et al. Associations between inflammatory markers, body composition, and physical function: The copenhagen sarcopenia study. J. Cachexia Sarcopenia Muscle 12(6), 1641–1652. https://doi.org/10.1002/jcsm.12832 (2021).
Pan, L. et al. Inflammation and sarcopenia: A focus on circulating inflammatory cytokines. Exp. Gerontol. 154, 111544. https://doi.org/10.1016/j.exger.2021.111544 (2021).
Schaap, L. A. et al. Higher inflammatory marker levels in older persons: Associations with 5-year change in muscle mass and muscle strength. J. Gerontol. Ser. A Biol. Sci. Med. Sci. 64A(11), 1183–1189. https://doi.org/10.1093/gerona/glp097 (2009).
Xiao, J. et al. Ire1α arm of unfolded protein response in muscle-specific tgf-β signaling-mediated regulation of muscle cell immunological properties. Cell Mol. Biol. Lett. 28(1), 15. https://doi.org/10.1186/s11658-023-00429-w (2023).
Zoico, E. & Roubenoff, R. The role of cytokines in regulating protein metabolism and muscle function. Nutr. Rev. 60(2), 39–51. https://doi.org/10.1301/00296640260085949 (2002).
Boutin, R. D., Yao, L., Canter, R. J. & Lenchik, L. Sarcopenia: Current concepts and imaging implications. AJR Am. J. Roentgenol. 205(3), W255–W266. https://doi.org/10.2214/AJR.15.14635 (2015).
Roubenoff, R. & Hughes, V. A. Sarcopenia: Current concepts. J. Gerontol. Ser. A Biol. Sci. Med. Sci. 55(12), M716–M724. https://doi.org/10.1093/gerona/55.12.M716 (2000).
Huang, K. C. et al. Capsaicin alleviates cisplatin-induced muscle loss and atrophy in vitro and in vivo. J. Cachexia Sarcopenia Muscle 14(1), 182–197. https://doi.org/10.1002/jcsm.13120 (2023).
Sakuma, K., Aoi, W. & Yamaguchi, A. Molecular mechanism of sarcopenia and cachexia: Recent research advances. Pflugers Arch. 469(5–6), 573–591. https://doi.org/10.1007/s00424-016-1933-3 (2017).
Wu, J. et al. Tnf-α contributes to sarcopenia through caspase-8/caspase-3/gsdme-mediated pyroptosis. Cell Death Discov. 9(1), 76. https://doi.org/10.1038/s41420-023-01365-6 (2023).
Sirago, G., Picca, A., Calvani, R., Coelho-Júnior, H. J. & Marzetti, E. Mammalian target of rapamycin (mTOR) signaling at the crossroad of muscle fiber fate in sarcopenia. Int. J. Mol. Sci. 23(22), 13823. https://doi.org/10.3390/ijms232213823 (2022).
Gao, J. et al. Resistin as a systemic inflammation-related biomarker for sarcopenia in patients with chronic obstructive pulmonary disease. Front. Nutr. 9, 921399. https://doi.org/10.3389/fnut.2022.921399 (2022).
Yalcin, A., Silay, K., Balik, A. R., Avcioğlu, G. & Aydin, A. S. The relationship between plasma interleukin-15 levels and sarcopenia in outpatient older people. Aging Clin. Exp. Res. 30(7), 783–790. https://doi.org/10.1007/s40520-017-0848-y (2018).
Oflazoglu, U. et al. The relationship between sarcopenia detected in newly diagnosed colorectal cancer patients and FGF21, irisin and CRP levels. Eur. Geriatr. Med. 13(4), 795–803. https://doi.org/10.1007/s41999-022-00635-3 (2022).
Dong, J., Yang, Z. & Chen, Y. Older age, higher body mass index and inflammation increase the risk for new-onset diabetes and impaired glucose tolerance in patients on peritoneal dialysis?. Perit. Dial. Int. J. Int. Soc. Perit. Dial. 36(3), 277–283. https://doi.org/10.3747/pdi.2015.00182 (2016).
Müller, S. et al. Impaired glucose tolerance is associated with increased serum concentrations of interleukin 6 and co-regulated acute-phase proteins but not tnf-α or its receptors. Diabetologia 45(6), 805–812. https://doi.org/10.1007/s00125-002-0829-2 (2002).
Carrasco-Zanini, J. et al. Proteomic signatures for identification of impaired glucose tolerance. Nat. Med. 28(11), 2293–2300. https://doi.org/10.1038/s41591-022-02055-z (2022).
Ghachem, A., Brochu, M. & Dionne, I. J. Differential clusters of modifiable risk factors for impaired fasting glucoseversus impaired glucose tolerance in adults 50 years of age and older. Ther. Adv. Chronic Dis. 10, 254182129. https://doi.org/10.1177/2040622319854239 (2019).
Lee, Y. S., Wollam, J. & Olefsky, J. M. An integrated view of immunometabolism. Cell 172(1), 22–40. https://doi.org/10.1016/j.cell.2017.12.025 (2018).
Figueroa-Vega, N. et al. Analysis of the percentages of monocyte subsets and ilc2s, their relationships with metabolic variables and response to hypocaloric restriction in obesity. PLoS One 15(2), e228637. https://doi.org/10.1371/journal.pone.0228637 (2020).
Hotamisligil, G. S., Shargill, N. S. & Spiegelman, B. M. Adipose expression of tumor necrosis factor-α: Direct role in obesity-linked insulin resistance. Science 259(5091), 87–91. https://doi.org/10.1126/science.7678183 (1993).
Heo, S. J. & Jee, Y. S. Characteristics of age classification into five-year intervals to explain sarcopenia and immune cells in older adults. Medicina 59(10), 1700. https://doi.org/10.3390/medicina59101700 (2023).
Acknowledgements
The authors thank the participants of the NHANES databases.
Funding
This work was supported by the National Natural Science Foundation of China (No.82004383, No. 82004386,No. 81974574), Guangdong Basic and Applied Basic Research Foundation (No.2022A1515220131, No.2022A1515010385, No. 2022A1515011700, No.2023A1515012626), the Project of Administration of Traditional Chinese Medicine of Guangdong Province (No.20241125), the Science and Technology Program of Guangzhou (No.2023A03J0238), the National key research and development program (2021YFC1712804), the Research Fund for Bajian Talents of Guangdong Provincial Hospital of Chinese Medicine (No. BJ2022KY01), and Project of Philosophy and Social Science Planning of Guangzhou in 2022 (No. 2022GZQN42).
Author information
Authors and Affiliations
Contributions
J.Z., L.F.Z., J.L. and L.Z. conceived the study design and are responsible for the overall content. G.L., Y.D., G.Z., and J.P. analyzed and interpreted the data. W.Y., K.H., and J.Z. prepared the manuscript. J.Z. and L.F.Z.: contributed equally to this work. All authors approved the submitted and final versions. All the authors agree to publish this article.
Corresponding authors
Ethics declarations
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.
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, 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 changes were made. 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/4.0/.
About this article
Cite this article
Zhao, J., Zeng, L., Liang, G. et al. Higher systemic immune-inflammation index is associated with sarcopenia in individuals aged 18–59 years: a population-based study. Sci Rep 13, 22156 (2023). https://doi.org/10.1038/s41598-023-49658-1
Received:
Accepted:
Published:
DOI: https://doi.org/10.1038/s41598-023-49658-1
- Springer Nature Limited
This article is cited by
-
Association between systemic immune-inflammation index and sarcopenic obesity in middle-aged and elderly Chinese adults: a cross-sectional study and mediation analysis
Lipids in Health and Disease (2024)
-
Comparative evaluation of screening tools for sarcopenia in patients with axial spondyloarthritis
Scientific Reports (2024)
-
Association of systemic inflammatory indices with anthropometric measures, metabolic factors, and liver function in non-alcoholic fatty liver disease
Scientific Reports (2024)