Level of IL-6, TNF, and IL-1β and age-related diseases: a systematic review and meta-analysis

Abstract

Introduction

Chronic low-grade inflammation is an important aspect of morbidity and mortality in older adults. The level of circulating pro-inflammatory cytokines (interleukin (IL)-6, tumor necrosis factor (TNF) or IL-1β) is a risk factor in cardiovascular and neurodegenerative diseases and is also associated with sarcopenia and frailties. The objective of this study was to assess each cytokine: IL-6, TNF, and IL-1β separately in the elderly with comorbidities against controls without diseases according to the data published in the available literature.

Methods

The electronic bibliographic PubMed database was systematically searched to select all the relevant studies published up to July 2023. The total number of the subjects involved in the meta-analysis included patients with diseases (n=8154) and controls (n=33967).

Results

The overall concentration of IL-6 was found to be higher in patients with diseases compared to controls and the difference was statistically significant, with a p-value of <0.001 (SMD, 0.16; 95% CI, 0.12–0.19). The heterogeneity was considerable with Q = 109.97 (P <0.0001) and I2 = 79.2%. The potential diagnostic usefulness of IL-6 was confirmed by odds ratio (OR) analysis (OR: 1.03, 95% CI (1.01; 1.05), p=0.0029). The concentration of both TNF and IL-1β was elevated in the control group compared to patients and amounted to SMD -0.03; 95% CI, -0.09–0.02, p-value 0.533 and SMD-0.29; 95% CI, -0.47– -0.12; p = 0.001, respectively. For TNF, however, the difference was statistically insignificant.

Discussion

IL-6, unlike TNF and IL-1β, could be a useful and convenient marker of peripheral inflammation in older adults with various comorbidities.

Introduction

In developed countries, a trend towards a shift in the age structure towards the elderly population can be observed, which can have health care as well as economic consequences. According to the latest estimates, the number of older people over 60 will double from 756 to 1,400 millions by 2030 (1). The aging of the immune system is a paradox of immunosenescence (deficiencies) and excessive inflammation (inflammaging), causing immune disorders. Inflammaging described as a chronic systemic inflammation associated with chronic age. The changes are mainly attributed to the somatic senescence-associated secretory phenotype (SASP) (2). It has been proposed that the basal inflammatory state and aging is a major factor in the increasing incidence of chronic diseases such as cardiovascular, neurodegenerative, and metabolic diseases (3). The coexisting multimorbidity in the elderly increases the risk of multiple organ failure and death. As the aging of the immune system progresses, the elderly become more susceptible to infectious diseases and cancer, and the risk of dying of influenza and coronavirus disease (COVID-19) also increases (4). It is therefore clear that there is an interplay between the aging of the immune system and age-related diseases. Solutions to prevent and treat age-related diseases can be sought through analyses of both the inflammation severity and immunosenescence (5).

Despite many features of acute inflammation, chronic inflammation is usually low-grade and results in tissue degeneration. There are several mechanisms which activate the development of chronic inflammation, which has been described as the following theories: 1) stress theory, which emphasizes the role of the correlation between excessive stress reaction and stronger pro-inflammatory response in the elderly (6); 2) the theory of oxidation-inflammation, according to which oxidative stress leads to inflammatory aging and affects homeostasis and prevention of health (7); 3) DNA damage theory, where damage to DNA telomeres and mitochondrial DNA leads to point mutations and chromosome rearrangements that contribute to cell aging (8); 4) theory of stem cells aging, where chronic inflammation is one of the main factors responsible for stem cells aging (9); and most widely discussed 5) theory of cytokines, where pro-inflammatory cytokines play an important role in inflammatory aging caused by chronic inflammation.

Aging facilitates a pro-inflammatory state by disrupting the peripheral immune system, which leads to excessive innate immune activity with the release of pro-inflammatory cytokines and a decrease in anti-inflammatory cytokines (10, 11). Different pro-inflammatory cytokines, such as interleukins (IL): IL-1β, IL-6, IL-12, IL-18, interferon (IFN-γ), and tumor necrosis factor (TNF), as well as anti-inflammatory ones, such as IL-4, IL-10, IL-13, and IL-19 which are secreted from immune cells, interact with body cells to mediate the immune responses and thus elicit its most optimum outcome (12–15). Elevated levels of interleukin-6 and TNF (16), as well as IL-1β (17), are associated with diseases, disability, and mortality in older adults. Interleukin-6, also known as ‘the cytokine for gerontologists’, plays a key role in the acute phase response in metabolic control and in the pathogenesis of many chronic diseases (18). IL-6 is produced mainly by the monocytes and macrophages (19). It produces a pleiotropic effect, and although in healthy and younger people its level is usually relatively low, in the elderly its elevated levels may correlate with increased mortality (20–24). Both Sánchez-Castellano et al. (25) and Mehta et al. (26) showed an association between inflammation, including IL-6 levels, and sarcopenia severity. A meta-analysis by Ng et al. (11), in which 34 studies were analyzed, demonstrated that the elderly with depression had significantly higher peripheral levels of IL-1β (p=0.026) and IL-6 (p<0.001) which was not the case in TNF (p=0.351) and C-reactive protein (CRP) levels (p=0.05). IL-1β is a key mediator of inflammatory response and participates in cell proliferation, differentiation, and apoptosis (27). This cytokine is expressed by a wide variety of cells, but macrophages and monocyes are particularly important, where IL-1β is produced in large quantities during infections and other stressful events (28, 29). Overproduction of IL-1β has also been confirmed in major depressive disorder, which may be related to the inflammasome hypothesis (27). High glucose concentration was reported to stimulate the production of IL-1β by pancreatic β cells, which implies the role of this cytokine also in type 2 diabetes (30–32). Since type 2 diabetes in the elderly increases the risk of cardiovascular disease, blocking IL-1β in these patients may reduce the incidence of myocardial infarction and stroke (31). Similar observations were recorded by Alzamil (33) in patients with obesity and with type 2 diabetes, where serum TNF levels in patients with diabetes and obesity were significantly higher than in patients without obesity (p<0.018). Significantly higher serum TNF levels were detected in patients with diabetes and obesity than in non-diabetic group with obesity (p<0.001). TNF is a pro-inflammatory mediator that can produce beneficial effects when activated locally in the tissues but it can be highly harmful when released systemically. It is one of the most important cytokines, produced by several types of cells: monocytes, T-cells, macrophages, fibroblast, adipocytes and smooth muscle cells (34). Generally, TNF is released together with interleukins usually IL-1 (35). In elderly people and centenarians, it has been shown that the level of TNF rises, which significantly increases mortality. Moreover, TNF has been shown to mediate metabolic changes and increased TNF levels have been found in patients with type 2 diabetes (24).

The assessment of chronic inflammation, including the level of pro-inflammatory cytokines in elderly people with comorbidities, may be the key to more effective treatment. Our hypothesis was that independent measurements of cytokines such as IL-6, TNF and IL-1β were significantly associated with the development of age-related diseases. Therefore, the aim of this study was to independently evaluate three cytokines: IL-6, TNF and IL-1β in elderly people with comorbidities compared to disease-free controls.

Materials and methods

Search strategy and study selection

This systematic literature review and meta-analysis followed the requirements of the PRISMA statement (36). We comprehensively searched the Internet literature in the PubMed/Medline database. We focused on scientific papers that contained the word “cytokines” and at least one of the cytokines: IL6 or/and TNF alpha or/and IL-1 beta, and one of the following terms: “cut off” or/and “odds ratio”. Next, we excluded all the papers that involved training or therapy, as such interventions could affect the cytokine levels. Finally, the following filters were also used to obtain our working database: papers in English, free full text available, and study patients’ age above 65. The queries were last updated on 14 August 2023, and Table 1 presents the search strategy using MeSH terms. Detailed search code attached in the Supplementary Material File .

Inclusion and exclusion criteria

The following eligibility requirements were applied: (1) the participants of the study were elderly, additionally filtered in PubMed by the search terms: ‘aged: 65+ years’ (2) the older adults in the study design were clearly divided into two groups: the group with a disease entity and without diseases as the control group; (3) cytokine levels were measured quantitatively; (4) sufficient information and data were provided to estimate the mean value and standard deviation (SD). Reviewers independently evaluated the eligibility of the research papers found, and any disagreement was resolved through discussion. The exclusion criteria were as follows: (1) duplicate publications; (2) reviews, meta-analyses, protocols, editorials, letters, preprints, and unavailable full texts; (3) studies in which a healthy control group was missing or another disease entity was selected as the control group; (4) studies in which interventions were described, i.e. the impact of treatment (therapy), diet, physical activity; (5) studies on animals, (6) non-English research papers.

Data extraction and quality assessment

Each paper was annotated using the following data: the name of first author and the year of publication, the number of included individuals with respect to sex in both study and control groups, the age of study and control groups, IL-6, TNF and IL-1β level, and ratio (OR) values (if applicable in the single model).

Statistical analysis

Statistical analyses were performed using R 4.2.1 software (37), and a “meta” package (38) using a random effects model. For statistical homogeneity, medians and IQRs were converted to means with SDs to maximize the number of studies eligible for meta-analysis (39). Chi-squared and Higgin’s I² tests were used to measure heterogeneity between studies. Cut-off values of 25%, 50%, and 75% were applied to label heterogeneity as low, moderate, or high respectively (40). Heterogeneity between studies was tested using Standardized Mean Difference (SMD) and “metacont” function, whereas studies with OR were analyzed using “metagen” function. Funnel plots were used to assess publication bias. Asymmetry was present in the funnel plots, Egger’s and Begg’s tests were applied to quantitatively assess whether there was any publication bias. The significance threshold for all statistical tests was set at p < 0.05.

Results

Study search and characteristics

A detailed selection of studies which were included in our analysis is shown in Figure 1 . The initial search yielded 371 records. After removing duplicates (n=3), 368 studies were screened. After reviewing the abstracts and titles of the manuscripts, we excluded a total of 330 records from further analysis. Thirty eight publications (records) were qualified for the next stage, of which n = 21 records were removed. The reasons for deleting the publication was: n=11 lack of control group and n=10 irrelevant data - data presented in the form of unadjusted and adjusted models. Finally, n=17 publications were assessed. Due to the fact that some of the publications included more than one tested group, the total number of studies was n=38. For OR analyses, n=6 publications were included, and the total number of studies was n=10.

Figure 1 Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flow diagram showing the paper selection process for inclusion. (Figure not reproduced here; see the original article.)

Publication dates of the included studies ranged from 2008 to 2023. All the included studies reported the measurement of cytokine levels quantified in patients/controls blood and serum or plasma. The total number of the included subjects in the meta-analysis equaled n=8154 patients with diseases and n=33967 controls. With regard to IL-6 concentration investigation,15 publications were included, five of which were used twice in the analysis (41–45) because they involved two groups of patients with a disease and one control group. In turn, the research conducted by Sánchez-Castellano et al. (25) was used three times in IL-6 level assessment (as there were three groups of patients with sarcopenia described by different criteria). Nine publications were included in the evaluation of TNF level, one of which i.e., the research by Sánchez-Castellano et al. (25), was used three times and the research conducted by Jefferis et al. (41) was used twice. Two publications were investigated for IL-1β evaluation and the research carried out by Sánchez-Castellano et al. (25) was used three times ( Table 2 ). For the analysis of the OR for IL-6, five manuscripts were selected where the OR values ​​were publicly available and were reported as 95% Cl. The publications by Tattersall et al. (44) and Wennberg et al. (52) were used twice because two independent models were applied in the study design. The OR analysis for TNF-α included two manuscripts: Wennberg et al. (52) and Anaszewicz et al. (54). OR analysis was not performed for IL-1β due to a lack of data ( Table 3 ).

Analysis of IL-6 based on SMD

Twenty-two studies with the total of 5213 patients with diseases and 28326 controls reported the data on IL-6 concentration. The overall concentration of IL-6 was found to be higher in patients with diseases compared to controls and the difference was significant, with a p-value of <0.001 (SMD, 0.16; 95% CI, 0.12–0.19). The heterogeneity was considerable with Q = 109.97 (P <0.0001) and I² = 79.2% ( Figure 2A ). Egger’s test for the publication bias was also significant (p=0.02) ( Figure 2B ) and the asymmetry of the funnel plot was observed ( Figure 2C ).

Figure 2 Forrest plot (A) Egger test (B) and funnel plot (C) for IL-6. (Figure not reproduced here; see the original article.)

Analysis of TNF based on SMD

The meta-analysis demonstrated that TNF concentration was higher in the controls when compared to patients with diseases (n patients = 2591; n controls = 5262; SMD, -0.03; 95% CI, -0.09–0.02), however, the value was not significant with p – value of 0.533. The observed heterogeneity of the reported studies was high (Q =209.53; P < 0.0001; I² = 94.8%) ( Figure 3A ). Egger’s test for the publication bias was insignificant (p = 0.947) ( Figure 3B ), and the asymmetry of the funnel plot was observed ( Figure 3C ). The outliers analysis revealed that two studies had an exceptional impact on the overall effect.

Figure 3 Forrest plot (A) and Egger test (B) and funnel plot (C) for TNF. (Figure not reproduced here; see the original article.)

Analysis of IL-1β based on SMD

Three hundred and fifty patients with diseases and 379 controls were included in IL-1β serum concentration meta-analysis which showed a tendency toward increased IL-1β concentration in the control group (SMD, -0.29; 95% CI, -0.47– -0.12; p = 0.001). Both overall heterogeneity and Egger’s test for the publication bias were found to be insignificant (Q = 4.48; p=0.214; I² = 33.0% and p =0.213, respectively) ( Figures 4A, B ), but the asymmetry of the funnel plot was observed ( Figure 4C ).

Figure 4 Forrest plot (A) and Egger test (B) and funnel plot (C) for IL-1β. (Figure not reproduced here; see the original article.)

Analysis of IL-6 based on OR

The outcomes of seven studies comprising patients and a control group showed OR higher than 1 (OR: 1.03, 95% CI (1.01; 1.05), p=0.0029) ( Figure 5A ), which indicates a diagnostic usefulness of the analyzed cytokine. The heterogeneity was Q=25.18, p=0.142, I² = 76.2%. The Egger’s test for the publication bias was found significant (p=0.0013, Figure 5B ), and the asymmetry of the funnel plot was observed ( Figure 5C ).

Figure 5 Forrest plot (A) Egger test (B) and funnel plot (C) for IL-6. (Figure not reproduced here; see the original article.)

Analysis of TNF based on OR

The outcomes of three studies including patients showed OR ratio lower than 1 (OR: 0.99, 95% CI (0.98; 1.00), p = 0.07) ( Figure 6A ). The heterogeneity was Q=3.90, p=0.0003, I² = 48.7%. The Egger’s test for the publication bias was not significant (p=0.08, Figure 6B ) and the asymmetry of the funnel plot was not observed ( Figure 6C ).

Figure 6 Forrest plot (A) and Egger test (B) and funnel plot (C) for TNF. (Figure not reproduced here; see the original article.)

Discussion

The adaptive immune response weakens with age whereas the innate immune system becomes chronically activated, which is reflected by an increase in the level of pro-inflammatory cytokines. Persistent, low-grade inflammation contributes significantly to the pathophysiology of a variety of age-related conditions and diseases (56). Sex differences in immune response include the production of cytokines that mediate the immune response between cells. Women show greater production of type 2 cytokines, while men show greater production of type 1 cytokines, which include, among others, IL-1β and TNF (57). This analysis and literature review was conducted to analyze the level/concentration of selected cytokines in the elderly. PubMed database papers were selected to assess the level of IL-6, TNF, and IL-1β in a group of elderly people with diverse comorbidities in comparison to a control group of elderly people without coexisting diseases. Significantly increased levels of IL-6 were recorded in patients compared to disease-free group and the outcomes of seven studies showed OR at a slightly higher level than 1. In turn, no statistically significant difference in TNF level was observed between the controls and the patients, whereas a higher concentration of IL-1β was observed in the control group

Most researchers investigating inflammation and aging focus on IL-6 level assessment mainly in the context of the acute phase reaction while there is a growing body of evidence emphasizing the role of this cytokine in the pathogenesis of chronic diseases (58, 59). Research conducted by Xing et al. (60) also suggested that one of the main functions of IL-6 was to self-limit the inflammatory response by inhibiting the production of TNF and IL-1β. IL-6 confines neutrophil recruitment, favoring their replacement by mononuclear cells (61). Consequently, IL-6 may simultaneously regulate both pro-inflammatory and anti-inflammatory activity contributing to both the development/intensification and the suppression of the acute phase reaction. The switch from the inflammatory burst that follows an inflammatory stimulus to the chronic elevation of IL-6 typical of immune-mediated diseases and encountered in many older adults is still much less understood (59). In our meta-analysis, 15 manuscripts met the inclusion criteria. Five publications were evaluated twice, taking into account two groups of patients with diseases vs. one control group, and one manuscript was analyzed three times. A total of 22 records were assessed. Since IL-6 is produced in skeletal muscles and physical activity can cause up to a 10-fold increase in IL-6 levels (62), our meta-analysis excluded the studies which involved any type of intervention (physical activity). Similarly, studies which assessed the effect of diet were also excluded because a high-fat meal increases plasma IL-6 levels (63), which could bias our analyses. In 17 out of 22 papers analyzed, IL-6 levels were higher in the disease group than in the control group with the highest IL-6 levels reported by Tylutka et al. (53), where the patients with the disease (metabolic syndrome) showed an IL-6 value of 45.7 ± 41.4 pg/ml, which was the highest mean value found in all the manuscripts included in the study. This can be explained by the fact that IL-6 produced by adipose tissue accounts for on average 10 to 35% of its basal circulating level in the organism (64). Taking into account the relationship between IL-6 and obesity, it was hypothesized that the main cause of insulin resistance is the increasing level of IL-6 and other pro-inflammatory cytokines (65). Higher mean IL-6 levels in the control group were reported in the studies by Sánchez-Castellano et al. (25), where sarcopenia was analyzed according to the criterion (Masanés), as well as in studies on liver diseases by Mehta et al. (26). In the recorded assessment of Parkinson’s disease and IL-6 levels, neither incident nor prevalent Parkinson’s disease differed significantly compared to the control group (45). No difference in IL-6 levels between diabetic retinopathy (DR) group and control group was shown in the studies by Ngyuen et al. (50). However, the meta-analysis conducted by Yao et al. (66) with 31 case-control studies (groups with and without diabetic retinopathy) demonstrated a relationship between DR and IL-6 level (SMD: 2.12, 95% CI: 1.53-2.70, P<0.00001). This shows that an increased or decreased level of IL-6 depends on the analyzed disease entity, the group of people included in the study and the age or gender of the study subjects. Our meta-analysis showed that IL-6 level in the group of elderly people with various comorbidities was statistically significantly higher compared to the control group with a p-value of <0.001 (SMD, 0.16; 95% CI, 0.12–0.19), and obvious heterogeneity existed between the studies p <0.0001 and I²  = 79.2%. The OR analysis also demonstrated higher values than 1 ​​(OR: 1.03, 95% CI (1.01; 1.05), p=0.0029), which indicates the potential diagnostic usefulness of IL-6 ( Figure 5A ).

TNF is a pro-inflammatory cytokine that affects macrophage functioning and its numerous functions include a key role in the activation of the pro-inflammatory cytokine cascade, which is why it is called the ‘master regulator’ of pro-inflammatory cytokines (67). TNF is an important contributor to tumorigenesis, progression, invasion, and metastasis and it also plays a critical role in autoimmune diseases (68). In our meta-analysis, nine manuscripts were selected for further analyses. One of them was included twice (41) and one underwent our investigation three times (25), which gave us a total of 12 records. Higher values of TNF compared to the control group were noted in the studies conducted on patients with type 2 diabetes and coronary artery disease by Pu et al. (55), as well as in the studies by Tylutka et al. (53), where patients with metabolic syndrome were analyzed. So far, research focused on the relationship between TNF and insulin resistance has provided ambiguous outcomes. Studies on rats conducted by Hotamisligil et al. (69) proved catabolic effect of TNF on adipose tissue and increased peripheral glucose uptake after neutralizing TNF in obese rats. Research on elderly people conducted by Alzamir (33) demonstrated that serum TNF levels in patients with diabetes and obesity were significantly higher than in non-obese counterparts, however, reports by Miyazaki et al. (70) did not confirm such an association. Interestingly, Jefferis et al. (41) showed that TNF was statistically significantly different in the patients with myocardial infarction than in the control group (p<0.001), but such a difference was not recorded in the group of patients with stroke vs control group (p=0.079), which implied associations between circulating acute phase inflammatory markers and coronary heart disease. Nevertheless, most research papers in our meta-analyses indicated lower TNF values in the group of people with diseases compared to the control, where SMD reached: -0.03; 95% CI, -0.09–0.02, and the value was not significant with p -value of 0.533. Additionally, low OR results: 0.99, 95% CI (0.98; 1.00), p = 0.07 confirmed our assumption that TNF was a poor predictor in diagnostics.

We also evaluated IL-1β, but only two studies that met the selection criteria were reviewed; therefore, the results should be applied with caution. IL-1β is the best characterized and studied pro-inflammatory cytokine of the 11 members of the IL-1 family. Although most research focuses on its production in monocytes or macrophages it is actually produced by various types of immune cells (71). IL-1β is known to contribute to disease pathogenesis in rheumatoid arthritis, gout, inflammatory bowel disease, and type 2 diabetes (72). More recent evidence highlighted a paradoxical role of IL-1β in diabetic retinopathy, multiple sclerosis, and Alzheimer’s disease (73). Research conducted by Sánchez-Castellano et al. (25) in the group of patients with sarcopenia did not reveal any differences between the groups (regardless of the adopted classification scheme for the study). In turn, the research conducted by Morawin et al. (74) showed statistically significantly higher IL-1β values ​​(p=0.02) in the group of patients with sarcopenia n=39, 1406.74 ± 1120.23 pg/mL when compared to patients without diagnosed sarcopenia (n=134, 755.11 ± 353.90 pg/mL). Our meta-analysis showed higher cytokine values in the control group (SMD, -0.29; 95% CI, -0.47– -0.12; p = 0.001), and the overall heterogeneity was not significant (Q = 4.48; p = 0.214; I² = 33.0%). Since the data on cut-offs and ORs was missing, the diagnostic utility of this cytokine could not be assessed and further research needs to be conducted.

Several shortcomings of this meta-analysis should be acknowledged. Firstly, various disease entities were included in the analyses, which may affect the overall results. Moreover, the individuals included in this meta-analysis have a different geographical nationality. The gender of the examined persons is another point that may affect the analyzed cytokines. Another limitation is related to a disproportion between the size of the control group vs. the group with diseases. Some of the included cytokines lacked enough original studies for the analyses of OR (IL-1β) to be performed. Finally, the analyzed cytokines were determined only in peripheral blood: plasma or serum.

Conclusion

Our meta-analysis assessed the levels of IL-6, TNF and IL-1β in older adults in relation to disease entities. An increase in IL-6 levels was found in the group with diseases compared to the control group, regardless of the analyzed disease entity. What may suggest a potential role of IL-6 in the progression/development of age-related diseases is the OR result (OR: 1.03, 95% CI (1.01; 1.05), p=0.0029). The evaluation of IL-6 levels may therefore be helpful in the assessment of a disease progression or treatment optimization. Higher values of TNF and IL-1β were found in the control group. Unfortunately, a limited volume of the available scientific literature on IL-1β means that further research is necessary.

Data availability statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Author contributions

AT: Conceptualization, Writing – original draft, Writing – review and editing, Methodology. ŁW: Data curation, Formal analysis, Writing – review and editing. AZ-L: Conceptualization, Supervision, Writing – review and editing, Methodology.

References

1. De Luca d'AlessandroE BonacciS GiraldiG . Aging populations: the health and quality of life of the elderly. La Clinica Terapeutica. (2011) 162:e13–8.21448536 PubMed
2. LiberaleL BadimonL MontecuccoF LüscherTF LibbyP CamiciGG . Inflammation, aging, and cardiovascular disease: JACC review topic of the week. J Am Coll Cardiol. (2022) 79:837–47. doi: 10.1016/j.jacc.2021.12.017 PMC888167635210039 doi:10.1016/j.jacc.2021.12.017 PubMed
3. FurmanD CampisiJ VerdinE Carrera-BastosP TargS FranceschiC . Chronic inflammation in the etiology of disease across the life span. Nat Med. (2019) 25:1822–32. doi: 10.1038/s41591-019-0675-0 PMC714797231806905 doi:10.1038/s41591-019-0675-0 PubMed
4. HuangC WangY LiX RenL ZhaoJ HuY . Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet. (2020) 395:497–506. doi: 10.1016/S0140-6736(20)30183-5 31986264 PMC7159299 doi:10.1016/S0140-6736(20)30183-5 PubMed
5. Barbé-TuanaF FunchalG SchmitzCRR MaurmannRM BauerME . The interplay between immunosenescence and age-related diseases. Semin immunopathol. (2020) 42:545–557). doi: 10.1007/s00281-020-00806-z 32747977 PMC7398288 doi:10.1007/s00281-020-00806-z PubMed
6. OttavianiE FranceschiC . The neuroimmunology of stress from invertebrates to man. Prog Neurobiol. (1996) 48:421–40. doi: 10.1016/0301-0082(95)00049-6 8804115 doi:10.1016/0301-0082(95)00049-6 PubMed
7. De la FuenteM MiquelJ . An update of the oxidation-inflammation theory of aging: the involvement of the immune system in oxi-inflamm-aging. Curr Pharm design. (2009) 15:3003–26. doi: 10.2174/138161209789058110 19754376 doi:10.2174/138161209789058110 PubMed
8. MeierA FieglerH MuñozP EllisP RiglerD LangfordC . Spreading of mammalian DNA-damage response factors studied by ChIP-chip at damaged telomeres. EMBO J. (2007) 26:2707–18. doi: 10.1038/sj.emboj.7601719 PMC188867817491589 doi:10.1038/sj.emboj.7601719 PubMed
9. JonesDL RandoTA . Emerging models and paradigms for stem cell ageing. Nat Cell Biol. (2011) 13:506–12. doi: 10.1038/ncb0511-506 PMC325797821540846 doi:10.1038/ncb0511-506 PubMed
10. GruverAL HudsonLL SempowskiG . Immunosenescence of ageing. J Pathol: A J Pathological Soc Great Britain Ireland. (2007) 211:144–56. doi: 10.1002/path.2104 PMC193183317200946 doi:10.1002/path.2104 PubMed
11. NgA TamWW ZhangMW HoCS HusainSF McIntyreRS . IL-1β, IL-6, TNF-α and CRP in elderly patients with depression or Alzheimer’s disease: systematic review and meta-analysis. Sci Rep. (2018) 8:12050. doi: 10.1038/s41598-018-30487-6 30104698 PMC6089986 doi:10.1038/s41598-018-30487-6 PubMed
12. DinarelloCA . IL-18: A TH1-inducing, proinflammatory cytokine and new member of the IL-1 family. J Allergy Clin Immunol. (1999) 103:11–24. doi: 10.1016/S0091-6749(99)70518-X 9893178 doi:10.1016/S0091-6749(99)70518-X PubMed
13. LuttmannW MatthiesenT MatthysH VirchowJCJr . Synergistic effects of interleukin-4 or interleukin-13 and tumor necrosis factor-α on eosinophil activation in vitro . Am J Respir Cell Mol Biol. (1999) 20:474–80. doi: 10.1165/ajrcmb.20.3.3326 10030846 doi:10.1165/ajrcmb.20.3.3326 PubMed
14. OpalSM DePaloVA . Anti-inflammatory cytokines. Chest. (2000) 117:1162–72. doi: 10.1378/chest.117.4.1162 10767254 doi:10.1378/chest.117.4.1162 PubMed
15. WongCK HoCY KoFWS ChanCHS HoASS HuiDSC . Proinflammatory cytokines (IL-17, IL-6, IL-18 and IL-12) and Th cytokines (IFN-γ, IL-4, IL-10 and IL-13) in patients with allergic asthma. Clin Exp Immunol. (2001) 125:pp.177–183. doi: 10.1046/j.1365-2249.2001.01602.x PMC190613511529906 doi:10.1046/j.1365-2249.2001.01602.x PubMed
16. De MartinisM FranceschiC MontiD GinaldiL . Inflamm-ageing and lifelong antigenic load as major determinants of ageing rate and longevity. FEBS Lett. (2005) 579:2035–9. doi: 10.1016/j.febslet.2005.02.055 15811314 doi:10.1016/j.febslet.2005.02.055 PubMed
17. AdamsAA BreathnachCC KatepalliMP KohlerK HorohovDW . Advanced age in horses affects divisional history of T cells and inflammatory cytokine production. Mech Ageing Dev. (2008) 129:656–64. doi: 10.1016/j.mad.2008.09.004 18926847 doi:10.1016/j.mad.2008.09.004 PubMed
18. ErshlerWB KellerET . Age-associated increased interleukin-6 gene expression, late-life diseases, and frailty. Annu Rev Med. (2000) 51:245–70. doi: 10.1146/annurev.med.51.1.245 10774463 doi:10.1146/annurev.med.51.1.245 PubMed
19. GabayC . Interleukin-6 and chronic inflammation. Arthritis Res Ther. (2006) 8:1–6. doi: 10.1186/ar1917 PMC322607616899107 doi:10.1186/ar1917 PubMed
20. HubbardRE O’MahonyMS SavvaGM CalverBL WoodhouseKW . Inflammation and frailty measures in older people. J Cell Mol Med. (2009) 13:3103–9. doi: 10.1111/j.1582-4934.2009.00733.x PMC451646919438806 doi:10.1111/j.1582-4934.2009.00733.x PubMed
21. VaradhanR YaoW MatteiniA BeamerBA XueQL YangH . Simple biologically informed inflammatory index of two serum cytokines predicts 10 year all-cause mortality in older adults. Journals Gerontol Ser A: Biomed Sci Med Sci. (2014) 69:165–73. doi: 10.1093/gerona/glt023 PMC403824423689826 doi:10.1093/gerona/glt023 PubMed
22. Puzianowska-KuźnickaM OwczarzM Wieczorowska-TobisK NadrowskiP ChudekJ SlusarczykP . Interleukin-6 and C-reactive protein, successful aging, and mortality: the PolSenior study. Immun Ageing. (2016) 13:1–12. doi: 10.1186/s12979-016-0076-x 27274758 PMC4891873 doi:10.1186/s12979-016-0076-x PubMed
23. Van EppsP OswaldD HigginsPA HornickTR AungH BanksRE . Frailty has a stronger association with inflammation than age in older veterans. Immun Ageing. (2016) 13:1–9. doi: 10.1186/s12979-016-0082-z 27777599 PMC5069820 doi:10.1186/s12979-016-0082-z PubMed
24. ReaIM GibsonDS McGilliganV McNerlanSE AlexanderHD RossOA . Age and age-related diseases: role of inflammation triggers and cytokines. Front Immunol. (2018) 586. doi: 10.3389/fimmu.2018.00586 PMC590045029686666 doi:10.3389/fimmu.2018.00586 PubMed
25. Sánchez-CastellanoC Martín-AragónS Bermejo-BescósP Vaquero-PintoN Miret-CorChadoC Merello de MiguelA . Biomarkers of sarcopenia in very old patients with hip fracture. J Cachexia Sarcopenia Muscle. (2020) 11:478–86. doi: 10.1002/jcsm.12508 PMC711349431912666 doi:10.1002/jcsm.12508 PubMed
26. MehtaM LouissaintJ ParikhNS LongMT TapperEB . Cognitive function, sarcopenia, and inflammation are strongly associated with frailty: a Framingham cohort study. Am J Med. (2021) 134:1530–8. doi: 10.1016/j.amjmed.2021.07.012 PMC900466534464599 doi:10.1016/j.amjmed.2021.07.012 PubMed
27. FerentinosP MaratouE AntoniouA SerrettiA SmyrnisN MoutsatsouP . Interleukin-1 Beta in peripheral blood mononuclear cell lysates as a longitudinal biomarker of response to antidepressants: a pilot study. . Front Psychiatry. (2021) 12:801738. doi: 10.3389/fpsyt.2021.801738 35002816 PMC8738167 doi:10.3389/fpsyt.2021.801738 PubMed
28. KanekoN KurataM YamamotoT MorikawaS MasumotoJ . The role of interleukin-1 in general pathology. Inflammation regeneration. (2019) 39:1–16. doi: 10.1186/s41232-019-0101-5 31182982 PMC6551897 doi:10.1186/s41232-019-0101-5 PubMed
29. BoraschiD . What is IL-1 for? The functions of interleukin-1 across evolution. Front Immunol. (2022) 13:872155. doi: 10.3389/fimmu.2022.872155 35464444 PMC9020223 doi:10.3389/fimmu.2022.872155 PubMed
30. MaedlerK SergeevP RisF OberholzerJ Joller-JemelkaHI SpinasGA . Glucose-induced β cell production of IL-1β contributes to glucotoxicity in human pancreatic islets. J Clin Invest. (2002) 110:851–60. doi: 10.1172/JCI200215318 PMC15112512235117 doi:10.1172/JCI200215318 PubMed
31. DinarelloCA DonathMY Mandrup-PoulsenT . Role of IL-1β in type 2 diabetes. Curr Opin Endocrinol Diabetes Obes. (2010) 17:314–21. doi: 10.1097/MED.0b013e32833bf6dc 20588114 doi:10.1097/MED.0b013e32833bf6dc PubMed
32. DinarelloCA . Interleukin-1 in the pathogenesis and treatment of inflammatory diseases. Blood J Am Soc Hematol. (2011) 117:3720–32. doi: 10.1182/blood-2010-07-273417 PMC308329421304099 doi:10.1182/blood-2010-07-273417 PubMed
33. AlzamilH . Elevated serum TNF-α is related to obesity in type 2 diabetes mellitus and is associated with glycemic control and insulin resistance. J Obes. (2020) 2020 5076858. doi: 10.1155/2020/5076858 32089876 PMC7013317 doi:10.1155/2020/5076858 PubMed
34. PopaC NeteaMG Van RielPL van der MeerJW StalenhoefAF . The role of TNF-α in chronic inflammatory conditions, intermediary metabolism, and cardiovascular risk. J Lipid Res. (2007) 48:751–62. doi: 10.1194/jlr.R600021-JLR200 17202130 doi:10.1194/jlr.R600021-JLR200 PubMed
35. HarvanováG DurankováS BernasovskáJ . The role of cytokines and chemokines in the inflammatory response. Alergologia Polska-Polish J Allergol. (2023) 10:210–9. doi: 10.5114/pja.2023.131708 doi:10.5114/pja.2023.131708
36. PageMJ McKenzieJE BossuytPM BoutronI HoffmannTC MulrowCD . The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. (2021) 372:n71. doi: 10.1136/bmj.n71 33782057 PMC8005924 doi:10.1136/bmj.n71 PubMed
37. R Core Team . R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing (2023). Available at: https://www.R-project.org/.
38. BalduzziS RückerG SchwarzerG . How to perform a meta-analysis with R: a practical tutorial. Evidence-Based Ment Health. (2019) 22:153–60. doi: 10.1136/ebmental-2019-300117 PMC1023149531563865 doi:10.1136/ebmental-2019-300117 PubMed
39. WanX WangW LiuJ TongT . Estimating the sample mean and standard deviation from the sample size, median, range and/or interquartile range. BMC Med Res Method. (2014) 14:1–13. doi: 10.1186/1471-2288-14-135 PMC438320225524443 doi:10.1186/1471-2288-14-135 PubMed
40. HigginsJP ThompsonSG DeeksJJ AltmanDG . Measuring inconsistency in meta-analyses. BMJ. (2003) 327:557–60. doi: 10.1136/bmj.327.7414.557 PMC19285912958120 doi:10.1136/bmj.327.7414.557 PubMed
41. JefferisBJ WhincupPH WelshP WannametheeSG RumleyA LennonLT . Circulating TNFα levels in older men and women do not show independent prospective relations with MI or stroke. Atherosclerosis. (2009) 205:302–8. doi: 10.1016/j.atherosclerosis.2008.12.001 PMC270631519135670 doi:10.1016/j.atherosclerosis.2008.12.001 PubMed
42. PineSR MechanicLE EnewoldL ChaturvediAK KatkiHA ZhengYL . Increased levels of circulating interleukin 6, interleukin 8, C-reactive protein, and risk of lung cancer. J Natl Cancer Institute. (2011) 103:1112–22. doi: 10.1093/jnci/djr216 PMC313958721685357 doi:10.1093/jnci/djr216 PubMed
43. PortaC De AmiciM QuagliniS PaglinoC TaglianiF BonciminoA . Circulating interleukin-6 as a tumor marker for hepatocellular carcinoma. Ann Oncol. (2008) 19:353–8. doi: 10.1093/annonc/mdm448 17962206 doi:10.1093/annonc/mdm448 PubMed
44. TattersallMC DasiewiczAS McClellandRL JarjourNN KorcarzCE MitchellCC . Persistent asthma is associated with carotid plaque in MESA. J Am Heart Assoc. (2022) 11:e026644. doi: 10.1161/JAHA.122.026644 36416156 PMC9851438 doi:10.1161/JAHA.122.026644 PubMed
45. TonTG JainS BiggsML ThackerEL StrotmeyerES BoudreauR . Markers of inflammation in prevalent and incident Parkinson’s disease in the Cardiovascular Health Study. Parkinsonism related Disord. (2012) 18:274–8. doi: 10.1016/j.parkreldis.2011.11.003 PMC328875922119505 doi:10.1016/j.parkreldis.2011.11.003 PubMed
46. GarciaJM SplenserAE KramerJ AlsarrajA FitzgeraldS RamseyD . Circulating inflammatory cytokines and adipokines are associated with increased risk of Barrett's esophagus: a case–control study. Clin Gastroenterol Hepatol. (2014) 12:229–38. doi: 10.1016/j.cgh.2013.07.038 PMC389909223954649 doi:10.1016/j.cgh.2013.07.038 PubMed
47. MarcusGM WhooleyMA GliddenDV PawlikowskaL ZaroffJG OlginJE . Interleukin-6 and atrial fibrillation in patients with coronary artery disease: data from the Heart and Soul Study. Am Heart J. (2008) 155:303–9. doi: 10.1016/j.ahj.2007.09.006 PMC224736618215601 doi:10.1016/j.ahj.2007.09.006 PubMed
48. KaplanRC McGinnAP BairdAE HendrixSL KooperbergC LynchJ . Inflammation and hemostasis biomarkers for predicting stroke in postmenopausal women: the Women's Health Initiative Observational Study. J Stroke Cerebrovascular Dis. (2008) 17:344–55. doi: 10.1016/j.jstrokecerebrovasdis.2008.04.006 PMC307742218984425 doi:10.1016/j.jstrokecerebrovasdis.2008.04.006 PubMed
49. LeeJH JangJH ParkJH JangHJ ParkCS LeeS . The role of interleukin-6 as a prognostic biomarker for predicting acute exacerbation in interstitial lung diseases. PLoS One. (2021) 16:e0255365. doi: 10.1371/journal.pone.0255365 34314462 PMC8315549 doi:10.1371/journal.pone.0255365 PubMed
50. NguyenTT AlibrahimE IslamFA KleinR KleinBE CotchMF . Inflammatory, hemostatic, and other novel biomarkers for diabetic retinopathy: the multi-ethnic study of atherosclerosis. Diabetes Care. (2009) 32:1704–9. doi: 10.2337/dc09-0102 PMC273214419549733 doi:10.2337/dc09-0102 PubMed
51. JennyNS FrenchB ArnoldAM StrotmeyerES CushmanM ChavesPH . Long-term assessment of inflammation and healthy aging in late life: the Cardiovascular Health Study All Stars. Journals Gerontol Ser A: Biomed Sci Med Sci. (2012) 67:970–6. doi: 10.1093/gerona/glr261 PMC343609122367431 doi:10.1093/gerona/glr261 PubMed
52. WennbergAM HagenCE MachuldaMM KnopmanDS PetersenRC MielkeMM . The cross-sectional and longitudinal associations between IL-6, IL-10, and TNFα and cognitive outcomes in the Mayo Clinic Study of Aging. Journals Gerontol: Ser A. (2019) 74:1289–95. doi: 10.1093/gerona/gly217 PMC662558930256904 doi:10.1093/gerona/gly217 PubMed
53. TylutkaA MorawinB WalasŁ. MichałekM GwaraA Zembron-LacnyA . Assessment of metabolic syndrome predictors in relation to inflammation and visceral fat tissue in older adults. Sci Rep. (2023) 13:89. doi: 10.1038/s41598-022-27269-6 36596839 PMC9810713 doi:10.1038/s41598-022-27269-6 PubMed
54. AnaszewiczM WawrzeńczykA CzerniakB BanaśW SochaE LisK . Leptin, adiponectin, tumor necrosis factor α, and irisin concentrations as factors linking obesity with the risk of atrial fibrillation among inpatients with cardiovascular diseases. Kardiologia Polska (Polish Heart Journal). (2019) 77:1055–61. doi: 10.33963/KP.14989 31553329 doi:10.33963/KP.14989 PubMed
55. PuLJ LuL ShenWF ZhangQ ZhangRY ZhangJS . Increased serum glycated albumin level is associated with the presence and severity of coronary artery disease in type 2 diabetic patients. Circ J. (2007) 71:1067–73. doi: 10.1253/circj.71.1067 17587712 doi:10.1253/circj.71.1067 PubMed
56. LiberaleL BonettiNR PuspitasariYM VukolicA AkhmedovA Diaz-CañestroC . TNF-α antagonism rescues the effect of ageing on stroke: Perspectives for targeting inflamm-ageing. Eur J Clin Invest. (2021) 51:e13600. doi: 10.1111/eci.13600 34076259 PMC8596431 doi:10.1111/eci.13600 PubMed
57. BernardiS ToffoliB TononF FrancicaM CampagnoloE FerrettiT . Sex differences in proatherogenic cytokine levels. Int J Mol Sci. (2020) 21:3861. doi: 10.3390/ijms21113861 32485823 PMC7311959 doi:10.3390/ijms21113861 PubMed
58. BauerJ BauerTM KalbT TagaT LengyelG HiranoT . Regulation of interleukin 6 receptor expression in human monocytes and monocyte-derived macrophages. Comparison with the expression in human hepatocytes. J Exp Med. (1989) 170:1537–49. doi: 10.1084/jem.170.5.1537 PMC21894922809509 doi:10.1084/jem.170.5.1537 PubMed
59. MaggioM GuralnikJM LongoDL FerrucciL . Interleukin-6 in aging and chronic disease: a magnificent pathway. Journals Gerontol Ser A: Biol Sci Med Sci. (2006) 61:575–84. doi: 10.1093/gerona/61.6.575 PMC264562716799139 doi:10.1093/gerona/61.6.575 PubMed
60. XingZ GauldieJ CoxG BaumannH JordanaM LeiXF . IL-6 is an antiinflammatory cytokine required for controlling local or systemic acute inflammatory responses. J Clin Invest. (1998) 101:311–20. doi: 10.1172/JCI1368 PMC5085699435302 doi:10.1172/JCI1368 PubMed
61. JonesSA . Directing transition from innate to acquired immunity: defining a role for IL-6. J Immunol. (2005) 175:3463–8. doi: 10.4049/jimmunol.175.6.3463 16148087 doi:10.4049/jimmunol.175.6.3463 PubMed
62. WeigertC HennigeAM BrodbeckK HaringHU SchleicherED . Interleukin-6 acts as insulin sensitizer on glycogen synthesis in human skeletal muscle cells by phosphorylation of Ser473 of Akt. Am J physiology-Endocrinol Metab. (2005) 289:E251–7. doi: 10.1152/ajpendo.00448.2004 15755769 doi:10.1152/ajpendo.00448.2004 PubMed
63. NappoF EspositoK CioffiM GiuglianoG MolinariAM PaolissoG . Postprandial endothelial activation in healthy subjects and in type 2 diabetic patients: role of fat and carbohydrate meals. J Am Coll Cardiol. (2002) 39:1145–50. doi: 10.1016/S0735-1097(02)01741-2 11923038 doi:10.1016/S0735-1097(02)01741-2 PubMed
64. Mohamed-AliV GoodrickS RaweshA KatzDR MilesJM YudkinJS . Subcutaneous adipose tissue releases interleukin-6, but not tumor necrosis factor-α, in vivo . J Clin Endocrinol Metab. (1997) 82:4196–200. doi: 10.1210/jc.82.12.4196 9398739 doi:10.1210/jc.82.12.4196 PubMed
65. AbbatecolaAM FerrucciL GrellaR BandinelliS BonafèM BarbieriM . Diverse effect of inflammatory markers on insulin resistance and insulin-resistance syndrome in the elderly. J Am Geriatrics Soc. (2004) 52:399–404. doi: 10.1111/j.1532-5415.2004.52112.x 14962155 doi:10.1111/j.1532-5415.2004.52112.x PubMed
66. YaoY LiR DuJ LongL LiX LuoN . Interleukin-6 and diabetic retinopathy: a systematic review and meta-analysis. Curr eye Res. (2019) 44:564–74. doi: 10.1080/02713683.2019.1570274 30644770 doi:10.1080/02713683.2019.1570274 PubMed
67. ParameswaranN PatialS . Tumor necrosis factor-α signaling in macrophages. Crit Reviews™ Eukaryotic Gene Expression. (2010) 20:87–103. doi: 10.1615/CritRevEukarGeneExpr.v20.i2 PMC306646021133840 doi:10.1615/CritRevEukarGeneExpr.v20.i2 PubMed
68. ChuWM . Tumor necrosis factor. Cancer Lett. (2013) 328:222–5. doi: 10.1016/j.canlet.2012.10.014 PMC373274823085193 doi:10.1016/j.canlet.2012.10.014 PubMed
69. HotamisligilGS ShargillNS SpiegelmanBM . Adipose expression of tumor necrosis factor-alpha: direct role in obesity-linked insulin resistance. Science. (1993) 259:87–91. doi: 10.1126/science.7678183 7678183 doi:10.1126/science.7678183 PubMed
70. MiyazakiY PipekR MandarinoLJ DeFronzoRA . Tumor necrosis factor α and insulin resistance in obese type 2 diabetic patients. Int J Obes. (2003) 27:88–94. doi: 10.1038/sj.ijo.0802187 12532159 doi:10.1038/sj.ijo.0802187 PubMed
71. Lopez-CastejonG BroughD . Understanding the mechanism of IL-1β secretion. Cytokine Growth factor Rev. (2011) 22:189–95. doi: 10.1016/j.cytogfr.2011.10.001 PMC371459322019906 doi:10.1016/j.cytogfr.2011.10.001 PubMed
72. DinarelloCA SimonA Van Der MeerJW . Treating inflammation by blocking interleukin-1 in a broad spectrum of diseases. Nat Rev Drug Discovery. (2012) 11:633–52. doi: 10.1038/nrd3800 PMC364450922850787 doi:10.1038/nrd3800 PubMed
73. MendiolaAS CardonaAE . The IL-1β phenomena in neuroinflammatory diseases. J Neural Transm. (2018) 125:781–95. doi: 10.1007/s00702-017-1732-9 PMC569997828534174 doi:10.1007/s00702-017-1732-9 PubMed
74. MorawinB TylutkaA BielewiczF Zembron-LacnyA . Diagnostics of inflammaging in relation to sarcopenia. . Front Public Health. (2023) 11:1162385. doi: 10.3389/fpubh.2023.1162385 37465171 PMC10351926 doi:10.3389/fpubh.2023.1162385 PubMed