IL-6 and TNF-α as Predictors of Mortality in Octogenarians: Evidence from a Brazilian Cohort

IL-6 and TNF-α as Predictors of Mortality in Octogenarians: Evidence from a Brazilian Cohort | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article IL-6 and TNF-α as Predictors of Mortality in Octogenarians: Evidence from a Brazilian Cohort Ingridy Fátima Alves Rodrigues, Audrey Cecília Tonet-Furioso, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8818082/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Inflammaging—characterized by chronic low-grade inflammation associated with aging—has been consistently linked to an increased risk of mortality, with cytokines acting as central mediators of this process. Therefore, investigating biomarkers capable of predicting outcomes in the oldest-old becomes particularly relevant, especially among Brazilian older adults, whose sociodemographic and health contexts differ from those observed in developed countries. Accordingly, this study aimed to evaluate whether inflammatory cytokines predict mortality in individuals aged 80 years and older, based on a retrospective Brazilian cohort. Between 2016 and 2018, sociodemographic and anthropometric variables were collected, and peripheral blood samples were obtained to measure inflammatory and anti-inflammatory cytokines. In 2024 and 2025, mortality occurrence was ascertained through information from family members and caregivers, copies of death certificates, interviews, and consultation of the National Registry of Deceased Individuals. In univariate Cox regression models, each unit increase in TNF-α concentration was associated with a 30% increase in mortality risk, while each unit increase in IL-6 levels was associated with a 1% increase in the risk of death, suggesting the potential utility of these cytokines as prognostic biomarkers in the oldest-old. Aging. Immunosenescence. Cytokines. Mortality. Octogenarians Figures Figure 1 Figure 2 Introduction Population aging poses challenges and increasing demands for the care of older adults and the promotion of healthy longevity, particularly among individuals aged 80 years and older (PAHO, 2023). In this context, predictors of chronic diseases and mortality based on circulating biomolecules are highly desirable due to their utility in refining risk scores and advancing the understanding of disease pathophysiology. Inflammatory biomarkers deserve particular attention because of their involvement in the development of numerous chronic conditions and in mortality outcomes. Inflammatory processes, including inflammaging, are associated with a higher prevalence of aging-related diseases, which rank among the leading causes of death (Baune et al., 2010). Chronic low-grade inflammation, referred to as inflammaging, is recognized as a hallmark of biological aging (Tartiere, Freije, López-Otín, 2024) and, per se, confers increased susceptibility to a range of disorders, including cancer, osteoporosis, sarcopenia, depression, and dementia, as well as physical and cognitive disabilities. Although understood as an adaptive, multifactorial, and dynamic mechanism in response to cumulative antigenic stress throughout life (Fulop et al., 2018 ), this persistent inflammatory process disrupts systemic homeostasis and promotes the accumulation of cellular damage that contributes to frailty and premature death (Ferrucci & Fabbri, 2018 ). While inflammaging is characterized by persistent subclinical alterations in circulating levels of different classes of immunological mediators—including chemokines, regulatory and growth factors, and pro- or anti-inflammatory cytokines—pro-inflammatory cytokines appear to contribute more substantially to mortality risk, particularly among the oldest-old (PAHO, 2023; Panela & Ma, 2024; Tylutka, Walas, & Zembron-Lacny, 2024 ). During the COVID-19 pandemic, for example, altered levels of IL-6 and TNF-α were identified as predictors of mortality among affected adults (Mandel et al., 2020 ), highlighting the need to improve or enhance immune health to increase resistance to infectious diseases in older adults (Teissier, Boulanger, & Cox, 2022 ). Although chronic inflammation associated with aging underlies the development of age-related disorders, the possibility that specific levels of immunomarkers are associated with adverse outcomes such as frailty and mortality remains insufficiently explored. Moreover, both elevated and reduced levels may exacerbate these risks, particularly among the oldest-old (Fulop et al., 2018 ). Therefore, in light of the considerations above, this study aimed to investigate the association between levels of inflammatory cytokines as predictors of mortality or longevity among individuals aged 80 years and older. Methods This analytical, comparative, retrospective cohort study was conducted based on two data collection phases. The first phase took place between 2016 and 2018 and included participants aged 80 years and older at baseline. During this phase, sociodemographic variables, anthropometric measurements, and peripheral blood samples were collected from 222 individuals for cytokine quantification at a laboratory facility. Details regarding recruitment procedures, blood sample collection, and clinical assessments have been described in previous studies (Neri et al., 2023 ; Filho et al., 2025; Amorim et al., 2021; Rodrigues et al., 2021 ).The second phase of the study was conducted between 2024 and 2025, during which mortality among participants enrolled in the first phase was ascertained based on interviews with family members and/or caregivers, as well as through access to death certificates or consultation of the National Registry of Deceased Individuals. Serum samples obtained during the baseline phase were stored at − 80°C until thawed for the analysis of immunological mediators. Concentrations of interferon-gamma (IFN-γ), interleukin-2 (IL-2), IL-4, IL-6, IL-10, and tumor necrosis factor-alpha (TNF-α) were measured using a multiplex flow cytometry–based assay, employing a bead-based immunoassay kit (Human TH1/TH2 CBA Cytokine Kit II) manufactured by BD Biosciences® (San Diego, CA, USA). Serum samples were processed according to the manufacturer’s protocol, and data acquisition was performed by recording a minimum of 300 events per cytokine bead on a BD FACSVerse flow cytometer using the FL4 channel. Data were analyzed using FCAP software, version 3.0 (BD Biosciences®, San Diego, CA, USA). Standard curves for each cytokine were generated using the mediator standard mixture provided by the manufacturer, and cytokine concentrations in serum samples were determined by interpolation from the corresponding standard curves. Data analysis was conducted in three sequential steps to evaluate baseline levels of the cytokines IFN-γ, TNF-α, IL-10, IL-6, IL-4, and IL-2 as potential predictors of mortality during the period between the two study phases. Initially, descriptive analyses of the study variables were performed. For continuous variables, measures of central tendency and dispersion were calculated, including mean, standard deviation, percentiles, median, minimum, and maximum values. Categorical variables were summarized using absolute frequencies and proportions. Adherence to normality assumptions was assessed using the Shapiro–Wilk test, in accordance with recommendations from the nonparametric statistics literature (Conover, 1999 ). Survival analysis was performed using the Kaplan–Meier estimator (Kleinbaum & Klein, 2005 ). Follow-up time was calculated from the date of assessment at the first wave until death or the last contact with surviving participants. To identify predictors of mortality, the classical Cox proportional hazards model was applied, which estimates the effect of each predictor on time to event through hazard ratios, under the assumption of proportional hazards over the follow-up period. Estimation was based on partial likelihood, without parametric specification of the baseline hazard function. Analyses were conducted using the coxph() function from the survival package (Therneau, 2024) in R software, version 4.5.1 (R Core Team, 2025). The study was conducted in accordance with ethical and scientific principles as established by Brazilian Ministry of Health Resolution No. 466, of December 12, 2012, and in compliance with the principles of the General Data Protection Law (LGPD) (Brazil, 2018). All responses were recorded in the REDCap (Research Electronic Data Capture) digital platform, with restricted access. The first phase of the project was approved by a Research Ethics Committee (REC) under approval number 1.185.879, and the second phase of data collection received approval from a Research Ethics Committee under approval number 6.603.725. In both phases of the study, participation was voluntary. All participants were fully informed about the study objectives, as well as the potential risks and benefits, and provided written informed consent prior to enrollment. Clinical trial number: not applicable. Results The final analytical sample comprised 129 participants, most of whom were female (62%) and self-identified as White (60%). After the sample losses described in Figure 1, 37 participants survived and 92 died during the follow-up period. Initially, the sociodemographic characteristics of the sample were analyzed according to mortality status (survivors and deceased). Among surviving individuals (N = 37), 24 were female (64.9%) and 13 were male (35.1%), with a median age of 91 years (range: 85–98 years). Among participants who died during follow-up (N = 92¹), 56 were female (60.9%) and 36 were male (39.1%), with a median age of 85 years (range: 78–101 years) (Table 1). Table 1 Sociodemographic characteristics according to mortality status. Brasília, 2025 Variables Survivors (N = 37ᵃ) Deceased (N = 92ᵃ) Sex Female 24 (64.86) 56 (60.87) Male 13 (35.14) 36 (39.13) Age (years) 91.00 (85.00; 98.00) 85.00 (78.00; 101.00) Marital status Married 10 (27.03) 8 (8.79) Single 27 (72.97) 83 (91.21) (Missing data) 0 1 Race/ethnicity (IBGE classification) Asian 0 (0.00) 2 (2.20) White 16 (43.24) 61 (67.03) Indigenous 5 (13.51) 1 (1.10) Brown (Pardo) 16 (43.24) 25 (27.47) Black 0 (0.00) 2 (2.20) (Missing data) 0 1 Weight (kg) 62.70 (30.00; 86.10) 60.20 (37.00; 88.60) (Missing data) 0 19 Height (cm) 155.00 (137.00; 175.00) 156.00 (133.00; 178.00) (Missing data) 0 19 Body mass index (kg/m²) 25.50 (13.70; 38.22) 25.56 (15.16; 35.47) (Missing data) 0 19 Legend: ᵃn (%); Median (Min; Max) Next, baseline cytokine concentrations were compared between survivors and deceased participants (Table 2). From a descriptive perspective, median levels of IFN-γ, TNF-α, IL-6, and IL-2 were numerically higher in the deceased group, whereas IL-10 and IL-4 showed very similar median values between groups. It is important to note that missing data were present for IFN-γ, IL-10, IL-6, IL-4, and IL-2 measurements, corresponding to 9 surviving participants and 19 deceased participants, as well as for TNF-α measurements, which were missing for 10 survivors and 21 deceased participants. These losses occurred due to inability to attend laboratory collection or insufficient sample volume for analysis. Table 2 Baseline cytokine concentrations according to mortality status. Brasília, 2025 Variables Survivors (N = 37ᵃ) Deceased (N = 92ᵃ) IFN-γ 5.67 (3.88; 11.05) 6.27 (3.70; 30.46) TNF-α 1.45 (0.41; 3.55) 1.80 (0.27; 6.28) IL-10 3.48 (2.30; 4.82) 3.39 (2.09; 394.00) IL-6 4.20 (0.34; 23.86) 5.57 (0.13; 168.45) IL-4 1.92 (0.13; 5.23) 1.66 (0.23; 4.81) IL-2 8.95 (8.00; 17.83) 9.01 (8.00; 838.00) Legend: ᵃn (%); Median (Min; Max). Note: Data are presented as median (minimum; maximum). N indicates the number of participants per group. Missing values are reported by variable. Survival analysis was performed using the Kaplan–Meier estimator, considering the time elapsed between the date of assessment at the first study phase and the date of death or the last contact with surviving participants. Figure 1 presents the cumulative overall survival of the sample, which was 14.1% at 9.038 years (95% CI: 3.4%–58.5%). Finally, variables were individually tested using the Cox proportional hazards model (univariate Cox models) to assess the association of each variable with time to the event of death, based on the proportional hazards (PH) assumption, which evaluates the constancy of relative risk over time. All variables included in the analyses met the proportional hazards assumption (Table 3). Table 3 Univariate Cox proportional hazards models – variables meeting the proportional hazards assumption Predictor HR (95% CI) p-value N Events Female sex 0.82 (0.53; 1.28) 0.3883 119 82 Married marital status 0.46 (0.22; 0.95) 0.0371 118 81 White race 1.40 (0.89; 2.21) 0.1478 118 81 BMI (kg/m²) 0.97 (0.93; 1.02) 0.2869 103 66 TNF-α 1.30 (1.00; 1.68) 0.0482 90 63 IL-10 1.00 (1.00; 1.01) 0.1278 93 65 IL-6 1.01 (1.00; 1.02) 0.0359 93 65 IL-4 0.85 (0.65; 1.12) 0.2514 93 65 IL-2 1.00 (1.00; 1.00) 0.7033 93 65 IFN-γ 1.04 (0.97; 1.11) 0.2309 93 65 Note : Univariate Cox proportional hazards regression models (hazard ratio, HR) for mortality, including predictors that met the proportional hazards (PH) assumption. 95% CI = 95% confidence interval. N = number of participants included in each model; Events = number of deaths observed. BMI = body mass index. Cytokines refer to baseline concentrations. In univariate Cox proportional hazards analysis, three variables showed a statistically significant association with mortality: being married (HR = 0.46; 95% CI: 0.22–0.95; p = 0.037), TNF-α (HR = 1.30; 95% CI: 1.00–1.68; p = 0.048), and IL-6 (HR = 1.01; 95% CI: 1.00–1.02; p = 0.036). Married marital status was associated with a lower risk of death, whereas higher baseline concentrations of TNF-α and IL-6 were associated with an increased mortality risk. The remaining variables were not significant predictors of mortality. Discussion The variables identified as predictors of mortality (TNF-α and IL-6) are consistent with the inflammaging paradigm, as they demonstrate an association between pro-inflammatory cytokines and poorer prognosis, namely death. Increased serum concentrations of TNF-α and IL-6 were associated with a higher risk of mortality, whereas being married emerged as a protective factor against death. Inflammaging alters immune responses with advancing age, leading to a weakened adaptive immune response and a chronically activated innate immune system, which is reflected in increased levels of pro-inflammatory cytokines (Tylutka, Walas, & Zembron-Lacny, 2024). In the present study, elevated baseline concentrations of TNF-α were identified as predictors of mortality among octogenarians. TNF-α is a key pro-inflammatory cytokine and one of the most important mediators of the immune response. When present at elevated levels, TNF-α has been associated with increased severity of several cardiovascular diseases, which are recognized as the leading causes of death worldwide (Rolski et al., 2023). TNF-α also plays a relevant role in tumor initiation and progression, as well as in the development of autoimmune diseases (Chu, 2013). In addition, it is involved in metabolic disorders, atherosclerosis (Fard et al., 2021), and coronary artery disease (Pu et al., 2007). In bone tissue, TNF-α is closely associated with skeletal disorders, including osteoporosis, fracture healing, intervertebral disc pathologies, cartilage degeneration, and rheumatoid arthritis (Yao et al., 2024). Several therapeutic approaches have been investigated with the aim of modulating the immune response and enhancing immune resilience against age-related alterations (Nguyen & Cho, 2025). However, to date, the results of anti–TNF-α therapy in cardiac conditions have been inconsistent and remain controversial (Rolski et al., 2023). TNF-α was among the first pro-inflammatory cytokines to be associated with cardiac diseases. Both cardiomyocytes and macrophages are capable of producing TNF-α, which acts through autocrine and paracrine mechanisms, contributing to the persistence of myocardial inflammation (Zhang & Dhalla, 2024). Similar to the present study, Bruunsgaard et al. (2003), in a Danish cohort, identified TNF-α as a prognostic marker of mortality and frailty among 126 centenarians, whereas IL-6 did not affect survival. Conversely, Giovannini et al. (2011), in an Italian study involving 362 octogenarians, reported that elevated IL-6 levels were associated with a significantly higher risk of death, while TNF-α levels were not significantly related to the outcome. These findings highlight the heterogeneous impact of inflammaging-related biomarkers on longevity across different populations. IL-6 is a cytokine with both anti-inflammatory and pro-inflammatory functions (Scheller et al., 2011; Amin et al., 2020), although it is classically regarded as a pro-inflammatory cytokine (Zhang & Dhalla, 2024). Significantly elevated levels are commonly observed in older adults with chronic diseases. Notably, IL-6 is also produced by skeletal muscle and its circulating concentrations may vary in response to physical activity (Tylutka, Walas, & Zembron-Lacny, 2024). Its association with atherosclerosis is well established, with increased circulating levels linked to both the development and severity of the disease (Zhang & Dhalla, 2024). In addition, elevated IL-6 levels have been associated with obesity and insulin resistance, as this cytokine is also produced by adipose tissue (Abbatecola et al., 2004). Furthermore, during the recent SARS-CoV-2 pandemic, IL-6 emerged as a predictor of mortality among adults with COVID-19 (Rosyid et al., 2024). In the present study, elevated IL-6 levels were identified as predictors of mortality among octogenarians, in agreement with findings from a large U.S. cohort study involving 1,122 middle-aged adults (mean age 47.8 years), in which this association was observed to be significantly stronger in women than in men (Hooten et al., 2023). Additionally, this cohort study including octogenarians and nonagenarians demonstrated a protective effect of marriage on all-cause mortality. Being married or living with a partner was associated with a lower risk of death. These findings are consistent with a prospective cohort study using data from the U.S. National Health and Nutrition Examination Survey, which included older adults aged 60 years and older and showed that being married, living with a partner, or being separated was associated with a lower risk of mortality compared with individuals who had never married (Wang & Yi, 2023). This evidence highlights marital status as a favorable condition for better health across multiple dimensions, reinforcing the positive relationship between marriage and survival. Conversely, a Danish cohort study found that widowhood was associated with increased mortality in older age for both men and women when compared with married individuals (Blanner et al., 2020). However, widowhood was associated with more favorable outcomes than divorce regarding the onset of cognitive impairment in a cohort of older adults in the United States (Liu et al., 2019). The present study has as a limitation the reduction in sample size from the initial cohort (n = 221) to the final analytical sample (n = 129). However, attrition is inherent to longitudinal studies involving older populations and is primarily driven by death, worsening health conditions, disabilities that preclude continued participation, loss to follow-up, or refusal. Such losses are strongly associated with both follow-up duration and advanced age of participants. In this context, Okpara et al. (2023), in a cohort study following women aged 55 years and older for approximately six years, reported an attrition rate of 30.2%. Similarly, Jacobsen et al. (2021), in a 10-year cohort study including participants aged 65 years and older, observed an attrition rate of 77% by the end of the follow-up period. Therefore, the cumulative sample attrition observed in the present study falls within the range expected for cohorts involving the oldest-old and does not constitute an atypical finding or one incompatible with the longitudinal design adopted. This attrition should be interpreted in light of the baseline characteristics of the sample and the epidemiological context of advanced aging. Among the strengths of this study is the follow-up of a Brazilian cohort of octogenarians, a population that remains underrepresented in the scientific literature, particularly in middle-income countries. The assessment of inflammatory markers (IL-6 and TNF-α) in very old individuals contributes to a better understanding of the biological mechanisms associated with mortality at advanced ages. Given the scarcity of studies including populations aged 80 years and older, the analysis of these biomarkers allows for the exploration of the role of chronic low-grade inflammation in extreme aging and its relationship with clinically relevant outcomes, thereby expanding the scientific evidence in an age group that is still poorly represented in the literature. Conclusion The present study demonstrated a positive association between elevated levels of the cytokines IL-6 and TNF-α and mortality among very old adults, and additionally showed a protective effect of marital status on this outcome. IL-6 and TNF may be associated with multiple health conditions in very old adults that predispose to mortality. Therefore, these cytokines can be considered important mediators of different prognostic trajectories and likely predictive biomarkers of mortality in this population, potentially contributing to future clinical assessments and guiding therapeutic decision-making. The findings provide relevant evidence to support the discussion on the role of systemic inflammation in advanced aging and may inform future investigations and care strategies targeted at this age group. Future research should consider longitudinal follow-up with repeated measurements of the assessed cytokines in order to evaluate their trajectories over time, as well as the influence of comorbidities and health-related events throughout advanced aging. In addition, the inclusion of nutritional, cognitive, and frailty-related variables, along with the monitoring of lifestyle behaviors and clinical interventions, may contribute to the development of more robust predictive models of mortality. Abbreviations APS Primary Health Care BMI Body Mass Index CBA Cytometric Bead Array CI Confidence Interval CNPq National Council for Scientific and Technological Development DCNT Chronic Non-Communicable Diseases HR Hazard Ratio IFN-γ Interferon-gamma IL-2 Interleukin-2 IL-4 Interleukin-4 IL-6 Interleukin-6 IL-10 Interleukin-10 SASP Senescence-Associated Secretory Phenotype TNF-α Tumor Necrosis Factor-alpha Declarations Ethics approval and consent to participate This study was approved by the Research Ethics Committee of the Universidade Católica de Brasília (Comitê de Ética em Pesquisa da Universidade Católica de Brasília), approval number 6.603.725, in accordance with the Declaration of Helsinki and Brazilian ethical regulations. All participants provided written informed consent at baseline assessment. Consent for publication Not applicable. Availability of data and materials The datasets generated and/or analyzed during the current study are not publicly available due to ethical and privacy restrictions involving older adults but are available from the corresponding author upon reasonable request. Competing interests The authors declare that they have no competing interests. Funding This study was supported by the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq). The funding body had no role in the design of the study; collection, analysis, and interpretation of data; or in writing the manuscript. Authors’ contributions IFAR conceived and designed the study, defined the research question, conducted the literature review, performed data collection and analysis, and drafted the final manuscript. ACTF provided technical and logistical support, overseeing the laboratory analyses and contributing to the methodological description. FMCA contributed to the writing and critical discussion of the results. OTN critically reviewed the manuscript and suggested revisions where appropriate. KHCVS supervised all stages of the research, performed the statistical analyses, and conducted the overall critical revision of the manuscript. All authors read and approved the final manuscript. Acknowledgements The authors thank all participants and their families for their valuable contribution to this study. The authors also acknowledge the support of the research and laboratory teams involved in data collection and cytokine analyses. Conflicts of Interest and Funding The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. This work was funded by the Brazilian National Council for Scientific and Technological Development (CNPq), Call for Proposals No. 21/2023, Grant No. 382958/2025-0. References Abbatecola AM, Ferrucci L, Grella R, Bandinelli S, Bonafè M, Barbieri M, et al. 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The pro- and anti-inflammatory properties of the cytokine interleukin-6. Biochimica et Biophysica Acta (BBA) – Mol Cell Res [Internet]. 2011;1813(5):878–88. Available from: https://www.sciencedirect.com/science/article/pii/S0167488911000425 Tartiere AG, Freije P, López-Otín C. The hallmarks of aging as a conceptual framework for health and longevity research. Front Aging [Internet]. 2024 Jan 15 [cited 2024 Mar 12];5. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10824251/ Teissier T, Boulanger E, Cox LS. Interconnections between Inflammageing and Immunosenescence during Ageing. Cells [Internet]. 2022 Jan 21 [cited 2026 Jan 23];11(3):359. Available from: https://pubmed.ncbi.nlm.nih.gov/35159168/ Tylutka A, Walas Ł, Zembron-Lacny A. Level of IL-6, TNF, and IL-1β and age-related diseases: a systematic review and meta-analysis. Front Immunol [Internet]. 2024;15. Available from: https://www.frontiersin.org/journals/immunology/articles/ 10.3389/fimmu.2024.1330386/full Wang L, Yi Z. Marital status and all-cause mortality rate in older adults: a population-based prospective cohort study. BMC Geriatr [Internet]. 2023 Apr 4 [cited 2026 Jan 23];23(1). Available from: https://link.springer.com/article/ 10.1186/s12877-023-03880-8 Yao Q, He L, Bao C, Yan X, Ao J. The role of TNF-α in osteoporosis, bone repair and inflammatory bone diseases: A review. Tissue Cell [Internet]. 2024 Jun 27 [cited 2026 Jan 30];89:102422. Available from: https://www.sciencedirect.com/science/article/pii/S004081662400123X Zhang H, Dhalla NS. The Role of Pro-Inflammatory Cytokines in the Pathogenesis of Cardiovascular Disease. Int J Mol Sci [Internet]. 2024 Jan 16 [cited 2024 Mar 10];25(2):1082–2. Available from: https://www.mdpi.com/1422-0067/25/2/1082#B9-ijms-25-01082 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 26 Mar, 2026 Editor assigned by journal 23 Mar, 2026 Editor invited by journal 02 Mar, 2026 Submission checks completed at journal 01 Mar, 2026 First submitted to journal 01 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8818082","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":612781023,"identity":"f3859c41-ffc3-462a-8220-e8a9b36fc521","order_by":0,"name":"Ingridy Fátima Alves Rodrigues","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABDElEQVRIiWNgGAWjYBACNhCRACYZDBgSQBR7A4g8AOQSrYXnAH4tSBphaiQS8Gvhk26/9uFB2WEG8/bmbR8e/DosJz/z8QNmnpo7DObSB7CbL3OmeEbCucMMMmeOFc9I7DtsbHA7zYCZ59gzBsu+BOxaJHKSGRLbDjNISOQYMyT2HE7cIJ1gwMzbcJjB4AwOX6BrqZ8/8/gHAlrSDyO0JPw4nMBwg4egLcwMCefSeSR4jhUzJDakG244k1NwcM6xZzyWPdi1yM9If8z4o8xaToK9eTPjjz/W8vLtxzc+eFNzR86cB7sWYMyBwr8ZIs3YBhE7ABLHpQGYPh4AiToo5w9udaNgFIyCUTByAQABcF1SEyUWPAAAAABJRU5ErkJggg==","orcid":"","institution":"Universidade Católica de Brasília","correspondingAuthor":true,"prefix":"","firstName":"Ingridy","middleName":"Fátima Alves","lastName":"Rodrigues","suffix":""},{"id":612781024,"identity":"c4427638-3370-4bfc-b952-21e44b65d7c9","order_by":1,"name":"Audrey Cecília Tonet-Furioso","email":"","orcid":"","institution":"Universidade Católica de Brasília","correspondingAuthor":false,"prefix":"","firstName":"Audrey","middleName":"Cecília","lastName":"Tonet-Furioso","suffix":""},{"id":612781025,"identity":"39aaae96-fe34-47b5-958a-b971c73749af","order_by":2,"name":"Flávia Maria Campos de Abreu","email":"","orcid":"","institution":"Universidade Católica de Brasília","correspondingAuthor":false,"prefix":"","firstName":"Flávia","middleName":"Maria Campos","lastName":"de Abreu","suffix":""},{"id":612781026,"identity":"4be00863-cd71-45a9-a899-13c04a88027f","order_by":3,"name":"Otávio Toledo Nóbrega","email":"","orcid":"","institution":"University of Brasília","correspondingAuthor":false,"prefix":"","firstName":"Otávio","middleName":"Toledo","lastName":"Nóbrega","suffix":""},{"id":612781027,"identity":"48b0ef82-0f4f-413b-843c-b920021e5920","order_by":4,"name":"Karla Helena Coelho Vilaça e Silva","email":"","orcid":"","institution":"Universidade Católica de Brasília","correspondingAuthor":false,"prefix":"","firstName":"Karla","middleName":"Helena Coelho Vilaça e","lastName":"Silva","suffix":""}],"badges":[],"createdAt":"2026-02-07 21:38:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8818082/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8818082/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105578158,"identity":"778eeece-2581-4032-94da-6a27268c9e89","added_by":"auto","created_at":"2026-03-27 14:02:36","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":174360,"visible":true,"origin":"","legend":"\u003cp\u003eParticipant flow, follow-up losses, and composition of the final sample\u003c/p\u003e\n\u003cp\u003eIdentification, eligibility, exclusion, and inclusion stages of participants, illustrating the flow toward the final analytical sample of the study.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8818082/v1/44b6655968396f0c05518049.png"},{"id":105578159,"identity":"b79a7c13-e541-429a-b2da-0532eb4aed82","added_by":"auto","created_at":"2026-03-27 14:02:36","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":153143,"visible":true,"origin":"","legend":"\u003cp\u003eOverall Kaplan–Meier survival curve\u003c/p\u003e\n\u003cp\u003eOverall Kaplan–Meier survival curve of the study population, showing cumulative survival probability over time (years). Shaded areas represent 95% confidence intervals, and tick marks indicate censored observations. The number of participants at risk at each time point is displayed below the curve.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8818082/v1/62ed3d8ae5c9494ff6b08c67.png"},{"id":108180673,"identity":"54733e90-d453-4a3f-a2ec-cd69d6454bc1","added_by":"auto","created_at":"2026-04-30 08:51:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":500558,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8818082/v1/d8d3fe58-7e78-42fc-835e-f1ca8b9a680c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"IL-6 and TNF-α as Predictors of Mortality in Octogenarians: Evidence from a Brazilian Cohort","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePopulation aging poses challenges and increasing demands for the care of older adults and the promotion of healthy longevity, particularly among individuals aged 80 years and older (PAHO, 2023). In this context, predictors of chronic diseases and mortality based on circulating biomolecules are highly desirable due to their utility in refining risk scores and advancing the understanding of disease pathophysiology. Inflammatory biomarkers deserve particular attention because of their involvement in the development of numerous chronic conditions and in mortality outcomes. Inflammatory processes, including inflammaging, are associated with a higher prevalence of aging-related diseases, which rank among the leading causes of death (Baune et al., 2010).\u003c/p\u003e \u003cp\u003eChronic low-grade inflammation, referred to as inflammaging, is recognized as a hallmark of biological aging (Tartiere, Freije, L\u0026oacute;pez-Ot\u0026iacute;n, 2024) and, per se, confers increased susceptibility to a range of disorders, including cancer, osteoporosis, sarcopenia, depression, and dementia, as well as physical and cognitive disabilities. Although understood as an adaptive, multifactorial, and dynamic mechanism in response to cumulative antigenic stress throughout life (Fulop et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), this persistent inflammatory process disrupts systemic homeostasis and promotes the accumulation of cellular damage that contributes to frailty and premature death (Ferrucci \u0026amp; Fabbri, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). While inflammaging is characterized by persistent subclinical alterations in circulating levels of different classes of immunological mediators\u0026mdash;including chemokines, regulatory and growth factors, and pro- or anti-inflammatory cytokines\u0026mdash;pro-inflammatory cytokines appear to contribute more substantially to mortality risk, particularly among the oldest-old (PAHO, 2023; Panela \u0026amp; Ma, 2024; Tylutka, Walas, \u0026amp; Zembron-Lacny, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDuring the COVID-19 pandemic, for example, altered levels of IL-6 and TNF-α were identified as predictors of mortality among affected adults (Mandel et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), highlighting the need to improve or enhance immune health to increase resistance to infectious diseases in older adults (Teissier, Boulanger, \u0026amp; Cox, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAlthough chronic inflammation associated with aging underlies the development of age-related disorders, the possibility that specific levels of immunomarkers are associated with adverse outcomes such as frailty and mortality remains insufficiently explored. Moreover, both elevated and reduced levels may exacerbate these risks, particularly among the oldest-old (Fulop et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTherefore, in light of the considerations above, this study aimed to investigate the association between levels of inflammatory cytokines as predictors of mortality or longevity among individuals aged 80 years and older.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThis analytical, comparative, retrospective cohort study was conducted based on two data collection phases. The first phase took place between 2016 and 2018 and included participants aged 80 years and older at baseline. During this phase, sociodemographic variables, anthropometric measurements, and peripheral blood samples were collected from 222 individuals for cytokine quantification at a laboratory facility. Details regarding recruitment procedures, blood sample collection, and clinical assessments have been described in previous studies (Neri et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Filho et al., 2025; Amorim et al., 2021; Rodrigues et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).The second phase of the study was conducted between 2024 and 2025, during which mortality among participants enrolled in the first phase was ascertained based on interviews with family members and/or caregivers, as well as through access to death certificates or consultation of the National Registry of Deceased Individuals.\u003c/p\u003e \u003cp\u003eSerum samples obtained during the baseline phase were stored at \u0026minus;\u0026thinsp;80\u0026deg;C until thawed for the analysis of immunological mediators. Concentrations of interferon-gamma (IFN-γ), interleukin-2 (IL-2), IL-4, IL-6, IL-10, and tumor necrosis factor-alpha (TNF-α) were measured using a multiplex flow cytometry\u0026ndash;based assay, employing a bead-based immunoassay kit (Human TH1/TH2 CBA Cytokine Kit II) manufactured by BD Biosciences\u0026reg; (San Diego, CA, USA). Serum samples were processed according to the manufacturer\u0026rsquo;s protocol, and data acquisition was performed by recording a minimum of 300 events per cytokine bead on a BD FACSVerse flow cytometer using the FL4 channel. Data were analyzed using FCAP software, version 3.0 (BD Biosciences\u0026reg;, San Diego, CA, USA). Standard curves for each cytokine were generated using the mediator standard mixture provided by the manufacturer, and cytokine concentrations in serum samples were determined by interpolation from the corresponding standard curves.\u003c/p\u003e \u003cp\u003eData analysis was conducted in three sequential steps to evaluate baseline levels of the cytokines IFN-γ, TNF-α, IL-10, IL-6, IL-4, and IL-2 as potential predictors of mortality during the period between the two study phases. Initially, descriptive analyses of the study variables were performed. For continuous variables, measures of central tendency and dispersion were calculated, including mean, standard deviation, percentiles, median, minimum, and maximum values. Categorical variables were summarized using absolute frequencies and proportions. Adherence to normality assumptions was assessed using the Shapiro\u0026ndash;Wilk test, in accordance with recommendations from the nonparametric statistics literature (Conover, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1999\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSurvival analysis was performed using the Kaplan\u0026ndash;Meier estimator (Kleinbaum \u0026amp; Klein, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Follow-up time was calculated from the date of assessment at the first wave until death or the last contact with surviving participants. To identify predictors of mortality, the classical Cox proportional hazards model was applied, which estimates the effect of each predictor on time to event through hazard ratios, under the assumption of proportional hazards over the follow-up period. Estimation was based on partial likelihood, without parametric specification of the baseline hazard function. Analyses were conducted using the coxph() function from the \u003cb\u003esurvival\u003c/b\u003e package (Therneau, 2024) in R software, version 4.5.1 (R Core Team, 2025).\u003c/p\u003e \u003cp\u003e The study was conducted in accordance with ethical and scientific principles as established by Brazilian Ministry of Health Resolution No. 466, of December 12, 2012, and in compliance with the principles of the General Data Protection Law (LGPD) (Brazil, 2018). All responses were recorded in the REDCap (Research Electronic Data Capture) digital platform, with restricted access. The first phase of the project was approved by a Research Ethics Committee (REC) under approval number 1.185.879, and the second phase of data collection received approval from a Research Ethics Committee under approval number 6.603.725.\u003c/p\u003e \u003cp\u003eIn both phases of the study, participation was voluntary. All participants were fully informed about the study objectives, as well as the potential risks and benefits, and provided written informed consent prior to enrollment.\u003c/p\u003e \u003cp\u003eClinical trial number: not applicable.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe final analytical sample comprised 129 participants, most of whom were female (62%) and self-identified as White (60%). After the sample losses described in Figure 1, 37 participants survived and 92 died during the follow-up period.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eInitially, the sociodemographic characteristics of the sample were analyzed according to mortality status (survivors and deceased). Among surviving individuals (N = 37), 24 were female (64.9%) and 13 were male (35.1%), with a median age of 91 years (range: 85\u0026ndash;98 years). Among participants who died during follow-up (N = 92\u0026sup1;), 56 were female (60.9%) and 36 were male (39.1%), with a median age of 85 years (range: 78\u0026ndash;101 years) (Table 1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u0026nbsp;\u003c/strong\u003eSociodemographic characteristics according to mortality status. Bras\u0026iacute;lia, 2025\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cstrong\u003eSurvivors (N = 37ᵃ)\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cstrong\u003eDeceased (N = 92ᵃ)\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003eFemale\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e24 (64.86)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e56 (60.87)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003eMale\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e13 (35.14)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e36 (39.13)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cstrong\u003eAge (years)\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e91.00 (85.00; 98.00)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e85.00 (78.00; 101.00)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cstrong\u003eMarital status\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003eMarried\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e10 (27.03)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e8 (8.79)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003eSingle\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e27 (72.97)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e83 (91.21)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e(Missing data)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e0\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e1\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cstrong\u003eRace/ethnicity (IBGE classification)\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\u003ctd\u003e\u003c/td\u003e\u003ctd\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003eAsian\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e0 (0.00)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e2 (2.20)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003eWhite\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e16 (43.24)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e61 (67.03)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003eIndigenous\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e5 (13.51)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e1 (1.10)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003eBrown (Pardo)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e16 (43.24)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e25 (27.47)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003eBlack\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e0 (0.00)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e2 (2.20)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e(Missing data)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e0\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e1\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cstrong\u003eWeight (kg)\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e62.70 (30.00; 86.10)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e60.20 (37.00; 88.60)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e(Missing data)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e0\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e19\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cstrong\u003eHeight (cm)\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e155.00 (137.00; 175.00)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e156.00 (133.00; 178.00)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e(Missing data)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e0\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e19\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cstrong\u003eBody mass index (kg/m\u0026sup2;)\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e25.50 (13.70; 38.22)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e25.56 (15.16; 35.47)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e(Missing data)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e0\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e19\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eLegend:\u003c/strong\u003e ᵃn (%); Median (Min; Max)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNext, baseline cytokine concentrations were compared between survivors and deceased participants (Table 2). From a descriptive perspective, median levels of IFN-\u0026gamma;, TNF-\u0026alpha;, IL-6, and IL-2 were numerically higher in the deceased group, whereas IL-10 and IL-4 showed very similar median values between groups.\u003c/p\u003e\n\u003cp\u003eIt is important to note that missing data were present for IFN-\u0026gamma;, IL-10, IL-6, IL-4, and IL-2 measurements, corresponding to 9 surviving participants and 19 deceased participants, as well as for TNF-\u0026alpha; measurements, which were missing for 10 survivors and 21 deceased participants. These losses occurred due to inability to attend laboratory collection or insufficient sample volume for analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u0026nbsp;\u003c/strong\u003eBaseline cytokine concentrations according to mortality status. Bras\u0026iacute;lia, 2025\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\u003cstrong\u003eSurvivors (N = 37ᵃ)\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\u003cstrong\u003eDeceased (N = 92ᵃ)\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003eIFN-\u0026gamma;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e5.67 (3.88; 11.05)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e6.27 (3.70; 30.46)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003eTNF-\u0026alpha;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e1.45 (0.41; 3.55)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e1.80 (0.27; 6.28)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003eIL-10\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e3.48 (2.30; 4.82)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e3.39 (2.09; 394.00)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003eIL-6\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e4.20 (0.34; 23.86)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e5.57 (0.13; 168.45)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003eIL-4\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e1.92 (0.13; 5.23)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e1.66 (0.23; 4.81)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003eIL-2\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e8.95 (8.00; 17.83)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e9.01 (8.00; 838.00)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eLegend:\u0026nbsp;\u003c/strong\u003eᵃn (%); Median (Min; Max).\u003c/p\u003e\n\u003cp\u003eNote: Data are presented as median (minimum; maximum). N indicates the number of participants per group. Missing values are reported by variable.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSurvival analysis was performed using the Kaplan\u0026ndash;Meier estimator, considering the time elapsed between the date of assessment at the first study phase and the date of death or the last contact with surviving participants. Figure 1 presents the cumulative overall survival of the sample, which was 14.1% at 9.038 years (95% CI: 3.4%\u0026ndash;58.5%).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFinally, variables were individually tested using the Cox proportional hazards model (univariate Cox models) to assess the association of each variable with time to the event of death, based on the proportional hazards (PH) assumption, which evaluates the constancy of relative risk over time.\u003c/p\u003e\n\u003cp\u003eAll variables included in the analyses met the proportional hazards assumption (Table 3).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u0026nbsp;\u003c/strong\u003eUnivariate Cox proportional hazards models \u0026ndash; variables meeting the proportional hazards assumption\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"586\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\u003cstrong\u003ePredictor\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\u003cstrong\u003eHR (95% CI)\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\u003cstrong\u003eN\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\u003cstrong\u003eEvents\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003eFemale sex\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e0.82 (0.53; 1.28)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e0.3883\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e119\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e82\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003eMarried marital status\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e0.46 (0.22; 0.95)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e0.0371\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e118\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e81\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003eWhite race\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e1.40 (0.89; 2.21)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e0.1478\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e118\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e81\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003eBMI (kg/m\u0026sup2;)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e0.97 (0.93; 1.02)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e0.2869\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e103\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e66\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003eTNF-\u0026alpha;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e1.30 (1.00; 1.68)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e0.0482\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e90\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e63\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003eIL-10\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e1.00 (1.00; 1.01)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e0.1278\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e93\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e65\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003eIL-6\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e1.01 (1.00; 1.02)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e0.0359\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e93\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e65\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003eIL-4\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e0.85 (0.65; 1.12)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e0.2514\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e93\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e65\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003eIL-2\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e1.00 (1.00; 1.00)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e0.7033\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e93\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e65\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003eIFN-\u0026gamma;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e1.04 (0.97; 1.11)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e0.2309\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e93\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e65\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eNote\u003c/strong\u003e: Univariate Cox proportional hazards regression models (hazard ratio, HR) for mortality, including predictors that met the proportional hazards (PH) assumption.\u003cbr\u003e\u0026nbsp;95% CI = 95% confidence interval.\u003cbr\u003e\u0026nbsp;N = number of participants included in each model; Events = number of deaths observed.\u003cbr\u003e\u0026nbsp;BMI = body mass index.\u003c/p\u003e\n\u003cp\u003eCytokines refer to baseline concentrations.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn univariate Cox proportional hazards analysis, three variables showed a statistically significant association with mortality: being married (HR = 0.46; 95% CI: 0.22\u0026ndash;0.95; p = 0.037), TNF-\u0026alpha; (HR = 1.30; 95% CI: 1.00\u0026ndash;1.68; p = 0.048), and IL-6 (HR = 1.01; 95% CI: 1.00\u0026ndash;1.02; p = 0.036). Married marital status was associated with a lower risk of death, whereas higher baseline concentrations of TNF-\u0026alpha; and IL-6 were associated with an increased mortality risk. The remaining variables were not significant predictors of mortality.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe variables identified as predictors of mortality (TNF-\u0026alpha; and IL-6) are consistent with the inflammaging paradigm, as they demonstrate an association between pro-inflammatory cytokines and poorer prognosis, namely death. Increased serum concentrations of TNF-\u0026alpha; and IL-6 were associated with a higher risk of mortality, whereas being married emerged as a protective factor against death.\u003c/p\u003e\n\u003cp\u003eInflammaging alters immune responses with advancing age, leading to a weakened adaptive immune response and a chronically activated innate immune system, which is reflected in increased levels of pro-inflammatory cytokines (Tylutka, Walas, \u0026amp; Zembron-Lacny, 2024).\u003c/p\u003e\n\u003cp\u003eIn the present study, elevated baseline concentrations of TNF-\u0026alpha; were identified as predictors of mortality among octogenarians. TNF-\u0026alpha; is a key pro-inflammatory cytokine and one of the most important mediators of the immune response. When present at elevated levels, TNF-\u0026alpha; has been associated with increased severity of several cardiovascular diseases, which are recognized as the leading causes of death worldwide (Rolski et al., 2023).\u003c/p\u003e\n\u003cp\u003eTNF-\u0026alpha; also plays a relevant role in tumor initiation and progression, as well as in the development of autoimmune diseases (Chu, 2013). In addition, it is involved in metabolic disorders, atherosclerosis (Fard et al., 2021), and coronary artery disease (Pu et al., 2007). In bone tissue, TNF-\u0026alpha; is closely associated with skeletal disorders, including osteoporosis, fracture healing, intervertebral disc pathologies, cartilage degeneration, and rheumatoid arthritis (Yao et al., 2024).\u003c/p\u003e\n\u003cp\u003eSeveral therapeutic approaches have been investigated with the aim of modulating the immune response and enhancing immune resilience against age-related alterations (Nguyen \u0026amp; Cho, 2025). However, to date, the results of anti\u0026ndash;TNF-\u0026alpha; therapy in cardiac conditions have been inconsistent and remain controversial (Rolski et al., 2023).\u003c/p\u003e\n\u003cp\u003eTNF-\u0026alpha; was among the first pro-inflammatory cytokines to be associated with cardiac diseases. Both cardiomyocytes and macrophages are capable of producing TNF-\u0026alpha;, which acts through autocrine and paracrine mechanisms, contributing to the persistence of myocardial inflammation (Zhang \u0026amp; Dhalla, 2024).\u003c/p\u003e\n\u003cp\u003eSimilar to the present study, Bruunsgaard et al. (2003), in a Danish cohort, identified TNF-\u0026alpha; as a prognostic marker of mortality and frailty among 126 centenarians, whereas IL-6 did not affect survival. Conversely, Giovannini et al. (2011), in an Italian study involving 362 octogenarians, reported that elevated IL-6 levels were associated with a significantly higher risk of death, while TNF-\u0026alpha; levels were not significantly related to the outcome. These findings highlight the heterogeneous impact of inflammaging-related biomarkers on longevity across different populations.\u003c/p\u003e\n\u003cp\u003eIL-6 is a cytokine with both anti-inflammatory and pro-inflammatory functions (Scheller et al., 2011; Amin et al., 2020), although it is classically regarded as a pro-inflammatory cytokine (Zhang \u0026amp; Dhalla, 2024). Significantly elevated levels are commonly observed in older adults with chronic diseases. Notably, IL-6 is also produced by skeletal muscle and its circulating concentrations may vary in response to physical activity (Tylutka, Walas, \u0026amp; Zembron-Lacny, 2024).\u003c/p\u003e\n\u003cp\u003eIts association with atherosclerosis is well established, with increased circulating levels linked to both the development and severity of the disease (Zhang \u0026amp; Dhalla, 2024). In addition, elevated IL-6 levels have been associated with obesity and insulin resistance, as this cytokine is also produced by adipose tissue (Abbatecola et al., 2004). Furthermore, during the recent SARS-CoV-2 pandemic, IL-6 emerged as a predictor of mortality among adults with COVID-19 (Rosyid et al., 2024).\u003c/p\u003e\n\u003cp\u003eIn the present study, elevated IL-6 levels were identified as predictors of mortality among octogenarians, in agreement with findings from a large U.S. cohort study involving 1,122 middle-aged adults (mean age 47.8 years), in which this association was observed to be significantly stronger in women than in men (Hooten et al., 2023).\u003c/p\u003e\n\u003cp\u003eAdditionally, this cohort study including octogenarians and nonagenarians demonstrated a protective effect of marriage on all-cause mortality. Being married or living with a partner was associated with a lower risk of death. These findings are consistent with a prospective cohort study using data from the U.S. National Health and Nutrition Examination Survey, which included older adults aged 60 years and older and showed that being married, living with a partner, or being separated was associated with a lower risk of mortality compared with individuals who had never married (Wang \u0026amp; Yi, 2023). This evidence highlights marital status as a favorable condition for better health across multiple dimensions, reinforcing the positive relationship between marriage and survival.\u003c/p\u003e\n\u003cp\u003eConversely, a Danish cohort study found that widowhood was associated with increased mortality in older age for both men and women when compared with married individuals (Blanner et al., 2020). However, widowhood was associated with more favorable outcomes than divorce regarding the onset of cognitive impairment in a cohort of older adults in the United States (Liu et al., 2019).\u003c/p\u003e\n\u003cp\u003eThe present study has as a limitation the reduction in sample size from the initial cohort (n = 221) to the final analytical sample (n = 129). However, attrition is inherent to longitudinal studies involving older populations and is primarily driven by death, worsening health conditions, disabilities that preclude continued participation, loss to follow-up, or refusal. Such losses are strongly associated with both follow-up duration and advanced age of participants.\u003c/p\u003e\n\u003cp\u003eIn this context, Okpara et al. (2023), in a cohort study following women aged 55 years and older for approximately six years, reported an attrition rate of 30.2%. Similarly, Jacobsen et al. (2021), in a 10-year cohort study including participants aged 65 years and older, observed an attrition rate of 77% by the end of the follow-up period.\u003c/p\u003e\n\u003cp\u003eTherefore, the cumulative sample attrition observed in the present study falls within the range expected for cohorts involving the oldest-old and does not constitute an atypical finding or one incompatible with the longitudinal design adopted. This attrition should be interpreted in light of the baseline characteristics of the sample and the epidemiological context of advanced aging.\u003c/p\u003e\n\u003cp\u003eAmong the strengths of this study is the follow-up of a Brazilian cohort of octogenarians, a population that remains underrepresented in the scientific literature, particularly in middle-income countries. The assessment of inflammatory markers (IL-6 and TNF-\u0026alpha;) in very old individuals contributes to a better understanding of the biological mechanisms associated with mortality at advanced ages. Given the scarcity of studies including populations aged 80 years and older, the analysis of these biomarkers allows for the exploration of the role of chronic low-grade inflammation in extreme aging and its relationship with clinically relevant outcomes, thereby expanding the scientific evidence in an age group that is still poorly represented in the literature.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe present study demonstrated a positive association between elevated levels of the cytokines IL-6 and TNF-\u0026alpha; and mortality among very old adults, and additionally showed a protective effect of marital status on this outcome.\u003c/p\u003e\n\u003cp\u003eIL-6 and TNF may be associated with multiple health conditions in very old adults that predispose to mortality. Therefore, these cytokines can be considered important mediators of different prognostic trajectories and likely predictive biomarkers of mortality in this population, potentially contributing to future clinical assessments and guiding therapeutic decision-making.\u003c/p\u003e\n\u003cp\u003eThe findings provide relevant evidence to support the discussion on the role of systemic inflammation in advanced aging and may inform future investigations and care strategies targeted at this age group. Future research should consider longitudinal follow-up with repeated measurements of the assessed cytokines in order to evaluate their trajectories over time, as well as the influence of comorbidities and health-related events throughout advanced aging. In addition, the inclusion of nutritional, cognitive, and frailty-related variables, along with the monitoring of lifestyle behaviors and clinical interventions, may contribute to the development of more robust predictive models of mortality.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAPS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePrimary Health Care\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBMI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBody Mass Index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCBA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCytometric Bead Array\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eConfidence Interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCNPq\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNational Council for Scientific and Technological Development\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDCNT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eChronic Non-Communicable Diseases\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHazard Ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIFN-γ\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInterferon-gamma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIL-2\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInterleukin-2\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIL-4\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInterleukin-4\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIL-6\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInterleukin-6\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIL-10\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInterleukin-10\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSASP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSenescence-Associated Secretory Phenotype\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTNF-α\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTumor Necrosis Factor-alpha\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Research Ethics Committee of the Universidade Cat\u0026oacute;lica de Bras\u0026iacute;lia (Comit\u0026ecirc; de \u0026Eacute;tica em Pesquisa da Universidade Cat\u0026oacute;lica de Bras\u0026iacute;lia), approval number 6.603.725, in accordance with the Declaration of Helsinki and Brazilian ethical regulations.\u003c/p\u003e\n\u003cp\u003eAll participants provided written informed consent at baseline assessment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analyzed during the current study are not publicly available due to ethical and privacy restrictions involving older adults but are available from the corresponding author upon reasonable request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the Conselho Nacional de Desenvolvimento Cient\u0026iacute;fico e Tecnol\u0026oacute;gico (CNPq).\u003c/p\u003e\n\u003cp\u003eThe funding body had no role in the design of the study; collection, analysis, and interpretation of data; or in writing the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIFAR conceived and designed the study, defined the research question, conducted the literature review, performed data collection and analysis, and drafted the final manuscript.\u003c/p\u003e\n\u003cp\u003eACTF provided technical and logistical support, overseeing the laboratory analyses and contributing to the methodological description.\u003c/p\u003e\n\u003cp\u003eFMCA contributed to the writing and critical discussion of the results.\u003c/p\u003e\n\u003cp\u003eOTN critically reviewed the manuscript and suggested revisions where appropriate.\u003c/p\u003e\n\u003cp\u003eKHCVS supervised all stages of the research, performed the statistical analyses, and conducted the overall critical revision of the manuscript.\u003c/p\u003e\n\u003cp\u003eAll authors read and approved the final manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank all participants and their families for their valuable contribution to this study. The authors also acknowledge the support of the research and laboratory teams involved in data collection and cytokine analyses.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest and Funding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n\u003cp\u003eThis work was funded by the Brazilian National Council for Scientific and Technological Development (CNPq), Call for Proposals No. 21/2023, Grant No. 382958/2025-0.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbbatecola AM, Ferrucci L, Grella R, Bandinelli S, Bonaf\u0026Atilde;\u0026uml; M, Barbieri M, et al. Diverse Effect of Inflammatory Markers on Insulin Resistance and Insulin-Resistance Syndrome in the Elderly. 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Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.mdpi.com/1422-0067/25/2/1082#B9-ijms-25-01082\u003c/span\u003e\u003cspan address=\"https://www.mdpi.com/1422-0067/25/2/1082#B9-ijms-25-01082\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-geriatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bgtc","sideBox":"Learn more about [BMC Geriatrics](http://bmcgeriatr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bgtc/default.aspx","title":"BMC Geriatrics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Aging. Immunosenescence. Cytokines. Mortality. Octogenarians","lastPublishedDoi":"10.21203/rs.3.rs-8818082/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8818082/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eInflammaging\u0026mdash;characterized by chronic low-grade inflammation associated with aging\u0026mdash;has been consistently linked to an increased risk of mortality, with cytokines acting as central mediators of this process. Therefore, investigating biomarkers capable of predicting outcomes in the oldest-old becomes particularly relevant, especially among Brazilian older adults, whose sociodemographic and health contexts differ from those observed in developed countries. Accordingly, this study aimed to evaluate whether inflammatory cytokines predict mortality in individuals aged 80 years and older, based on a retrospective Brazilian cohort. Between 2016 and 2018, sociodemographic and anthropometric variables were collected, and peripheral blood samples were obtained to measure inflammatory and anti-inflammatory cytokines. In 2024 and 2025, mortality occurrence was ascertained through information from family members and caregivers, copies of death certificates, interviews, and consultation of the National Registry of Deceased Individuals. In univariate Cox regression models, each unit increase in TNF-α concentration was associated with a 30% increase in mortality risk, while each unit increase in IL-6 levels was associated with a 1% increase in the risk of death, suggesting the potential utility of these cytokines as prognostic biomarkers in the oldest-old.\u003c/p\u003e","manuscriptTitle":"IL-6 and TNF-α as Predictors of Mortality in Octogenarians: Evidence from a Brazilian Cohort","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-27 14:02:32","doi":"10.21203/rs.3.rs-8818082/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-03-26T07:47:17+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-23T09:00:21+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-02T07:55:03+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-02T00:56:35+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Geriatrics","date":"2026-03-02T00:53:16+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-geriatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bgtc","sideBox":"Learn more about [BMC Geriatrics](http://bmcgeriatr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bgtc/default.aspx","title":"BMC Geriatrics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8a28be84-4383-4657-8c37-ecc71a46f785","owner":[],"postedDate":"March 27th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-27T14:02:32+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-27 14:02:32","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8818082","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8818082","identity":"rs-8818082","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}