Association Between Serum Ferritin Levels and Frailty Risk in Community-Dwelling Older Adults: A Nationwide Population-Based Study

Association Between Serum Ferritin Levels and Frailty Risk in Community-Dwelling Older Adults: A Nationwide Population-Based Study | 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 Association Between Serum Ferritin Levels and Frailty Risk in Community-Dwelling Older Adults: A Nationwide Population-Based Study Min-gu Kang, Il-Young Jang, Ji Yeon Baek, Yunju Jo, Dongryeol Ryu, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7644766/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Background Ferritin is a key iron storage protein and a marker of iron metabolism, linked to various health outcomes beyond hematologic conditions. This study examined the association between serum ferritin levels and the frailty index (FI), a comprehensive measure of health status and biological aging, in community-dwelling older adults. Methods We performed a population-based, cross-sectional analysis using data on 6,722 participants aged 65 years and older from the Korea National Health and Nutrition Examination Survey. A 36-item deficit accumulation FI was developed, encompassing physical, cognitive, psychological, and social domains. Participants were classified as non-frail (FI ≤ 0.15), pre-frail (0.15 0.25). Serum ferritin levels were measured using an immunoradiometric assay, and individuals were categorized into sex-specific quartiles. Results In both women and men, the FI exhibited a non-linear pattern across ferritin quartiles, with the lowest FI observed in the third quartile (Q3). Among women, FI was significantly higher in both the lowest (Q1) and highest (Q4) quartiles compared to Q3 ( P < 0.001 and 0.001, respectively). Consistently, women in Q1 and Q4 had significantly higher odds of frailty compared to Q3, at 2.03 and 1.35 times, respectively ( P < 0.001 and 0.049, respectively). Although a similar trend was observed in men, the associations were not statistically significant. Conclusions Both low and high serum ferritin concentrations were associated with increased frailty risk, particularly in women. These findings suggest that serum ferritin may potentially serve as a predictive biomarker for frailty in older adults. ferritin frailty index systemic inflammation oxidative stress biomarker Figures Figure 1 Figure 2 Figure 3 Figure 4 Background Frailty in older adults is a significant geriatric syndrome marked by increased vulnerability to various stressors due to diminished physiological capacity, more accurately reflecting an individual's overall well-being and functional ability than chronological age [ 1 , 2 ]. The clinical importance of frailty is underscored by its link to adverse health outcomes such as disability, falls, and mortality, necessitating robust assessment tools [ 2 , 3 ]. While no definitive criterion exists, the "phenotypic frailty" or Fried criteria, focusing mainly on physical aspects, are widely used for their simplicity [ 4 ]. However, the "frailty index" developed by Rockwood et al. [ 5 , 6 ], which includes a broad range of deficits such as social, psychological, and cognitive factors, is regarded as a more comprehensive and superior predictor of key outcomes like hospitalization and death [ 7 , 8 ]. Recent longitudinal studies have highlighted the frailty index as the most reliable indicator of biological age [ 9 ], advocating for its priority use in frailty clinical research. This approach not only enhances the accuracy of frailty assessment but also supports the development of reliable biomarkers, thereby driving advancements in frailty research and promoting more effective interventions to improve quality of life and extend healthy living among the older adults. Ferritin, a key iron storage protein in the body, serves as a crucial biomarker for iron metabolism and is implicated in a variety of health conditions beyond hematologic disorders. Elevated circulating ferritin level, indicative of iron overload, increase oxidative stress, leading to tissue damage [ 10 ]. Moreover, blood ferritin concentration is recognized as an inflammation marker [ 11 , 12 ], showing positive correlations with various chronic diseases, including cardiovascular diseases, diabetes, non-alcoholic fatty liver disease, and neurodegenerative disorders [ 13 – 16 ]. Conversely, low ferritin levels can signal poor health due to nutritional deficiencies, including iron deficiency, reduced energy levels, and compromised immune function [ 17 – 19 ]. Given its broad impact on these diverse health issues, ferritin levels provide significant insights into an individual's overall health status. Consequently, ferritin may serve as a valuable blood biomarker for frailty, reflecting physical, social, and mental conditions as well as biological age. Despite this potential, clinical research on the relationship between circulating ferritin concentration and frailty risk remains limited. To address this gap, we aimed to determine the association between serum ferritin concentration and the Rockwood frailty index in a nationally representative sample of community-dwelling older adults. Methods Study Population This cross-sectional study utilized data from the Korea National Health and Nutrition Examination Survey (KNHANES) collected between 2008 and 2012. Since 1998, KNHANES has been conducted regularly to evaluate the health and nutritional status of the Korean population, track health risk factors and chronic disease prevalence, and support health policy and program development in Korea [ 20 ]. KNHANES employs a complex, multi-stage probability sampling method to represent the entire non-institutionalized population of Korea. The annual survey employs a three-stage sampling design: initially, primary sample units (PSUs) consisting of 50 to 60 households are selected from census blocks or resident registration addresses. Subsequently, 20–25 households are chosen from each PSU via field surveys. Finally, all individuals aged 1 year and older in the selected households are surveyed. During the study period, 8,036 individuals aged 65 and older participated in KNHANES. Our analysis included 6,722 participants, after excluding 194 individuals with over 20% missing data on frailty assessment variables and 1,120 individuals lacking ferritin data. (Fig. 1 ). All participants provided informed consent before participating in KNHANES, and personal data were anonymized prior to public release. The study was approved by the Institutional Review Board of Chonnam National University Bitgoeul Hospital, which waived the requirement for additional informed consent (IRB No. CNUBH-2024-013). The study adhered to the Declaration of Helsinki principles. Serum Ferritin Concentration Measurement Blood samples for serum ferritin measurement were collected from participants aged 10 and older with their consent. After a minimum 8-hour overnight fast, blood samples were drawn from the antecubital vein in the morning. Samples were refrigerated immediately and transported to the Central Testing Institute (Neodin Medical, Inc., Seoul, Korea). The serum ferritin level was determined using an immunoradiometric assay with a 1470 WIZARD gamma-Counter (PerkinElmer, Turku, Finland). The coefficient of variation value was less than 5%. Frailty-Related Factors Evaluation Trained nurses measured blood pressure on the right arm using a mercury sphygmomanometer (Baumanometer® Wall Unit 33(0850); W.A. Baum, Copiague, NY, USA) after participants had been seated quietly for at least 5 minutes. Blood pressure was measured three times, and the second and third measurements were averaged. Blood samples were also collected during the survey. Body mass index (BMI) was calculated as weight in kilograms divided by height in meters squared (kg/m²). Education level and lifestyle factors were self-reported. Education level was categorized as elementary school or lower, middle school, high school, and college or higher. Smokers were defined as those who had smoked five or more packs of cigarettes in their lifetime and were currently smoking. Medical conditions were classified based on doctor diagnoses. Frailty index The frailty index was constructed using standard procedures [ 21 ] and previous KNHANES-based indices [ 22 , 23 ]. The index, ranging from 0 (best condition) to 1 (worst condition), comprised 36 items surveyed between 2008 and 2012. These items covered comorbidities (e.g., arthritis, asthma, cancer, cardiovascular disease, diabetes, dyslipidemia, hypertension, stroke), functional abilities (e.g., inactivity, limited exercise capacity, daily living limitations, social activity limitations, self-care inability, chewing difficulty), symptoms (e.g., pain, fatigue, dyspnea, weight loss, depression, anxiety, suicidal ideation, stress), and laboratory values (e.g., blood pressure, heart rate, pulmonary function, BUN, creatinine, cholesterol, triglycerides, HDL cholesterol, fasting glucose, vitamin D, urine protein), as well as smoking status and BMI. (Supplementary Table 1). Participants were classified into non-frail (≤ 0.15), pre-frail (0.15–0.25), and frail (> 0.25) categories based on established criteria [ 24 , 25 ]. Statistical Analysis To derive national-level estimates, we employed a complex sample analysis method incorporating assigned weights. The data from annual surveys were pooled, with each year's sample analyzed independently. Results are presented as means with standard errors (SEs) for continuous variables, or as counts with percentages for categorical variables. General linear models were used to compare continuous variables, while categorical variables were analyzed using cross-tabulation methods. Potential confounding factors included age, sex, education, smoking status, diabetes, dyslipidemia, cardiovascular diseases, and BMI. We assessed differences in serum ferritin levels across frailty statuses and variations in the frailty index among serum ferritin quartiles using general linear models. The relationship between serum ferritin levels and the Rockwood frailty index was examined using linear regression analysis. Multiple logistic regression was utilized to evaluate the risk of frailty across serum ferritin quartiles. A threshold of P < 0.05 was set for statistical significance, and all analyses were performed using SPSS software, version 21.0 (IBM Corporation, Armonk, NY, USA). Results Baseline Characteristics of Study Participants Table 1 summarizes the baseline characteristics of 6,722 participants aged 65 years and older. Among the women, 848 (22.0%) were classified as non-frail, 1,441 (37.3%) as pre-frail, and 1,570 (40.7%) as frail, with mean ages of 71.9, 72.1, and 73.0 years, respectively ( P < 0.001). Among the men, 1,127 (39.4%) were categorized as non-frail, 1,079 (37.7%) as pre-frail, and 657 (22.9%) as frail, with mean ages of 71.0, 71.7, and 72.5 years, respectively ( P < 0.001). In both men and women, transitioning from non-frail to pre-frail and frail groups was associated with lower levels of education, higher rates of smoking, and increased prevalence of diabetes, dyslipidemia, and cardiovascular diseases (all P < 0.001). Additionally, BMI was observed to increase across these groups ( P < 0.001 in women and P = 0.006 in men). Table 1 Baseline characteristics of the study participants according to frailty status Variables Women (N = 3,859) Men (N = 2,863) Non-frail (N = 848) Pre-frail (N = 1,441) Frail (N = 1,570) P value Non-frail (N = 1,127) Pre-frail (N = 1,079) Frail (N = 657) P value Age (years), mean (SE) 71.9 (0.2) 72.1 (0.2) 73.0 (0.2) < 0.001 71.0 (0.2) 71.7 (0.2) 72.5 (0.2) < 0.001 Level of education, % < 0.001 < 0.001 1st 2nd 3rd 4th 80.0% 8.3% 9.0% 2.7% 84.3% 7.7% 6.6% 1.4% 90.0% 6.1% 3.2% 0.7% 41.4% 17.3% 22.2% 19.1% 47.9% 20.4% 20.6% 11.2% 62.7% 15.5% 16.1% 5.7% Smoking, n (%) 21 (2.7%) 43 (3.0%) 110 (7.6%) < 0.001 193 (18.4%) 310 (30.0%) 227 (36.8%) < 0.001 Diabetes, n (%) 70 (8.4%) 259 (19.7%) 444 (31.1%) < 0.001 137 (11.3%) 258 (24.7%) 207 (32.9%) < 0.001 Dyslipidemia, n (%) 124 (15.4%) 335 (23.4%) 453 (30.8%) < 0.001 85 (7.6%) 143 (13.4%) 120 (19.3%) < 0.001 Cardiovascular disease (MI, angina), n (%) 14 (1.4%) 66 (4.1%) 151 (9.7%) < 0.001 50 (4.0%) 84 (7.6%) 89 (13.9%) < 0.001 BMI (kg/m 2 ), mean (SE) 23.2 (0.1) 24.2 (0.1) 24.9 (0.1) < 0.001 23.0 (0.1) 23.4 (0.1) 23.3 (0.2) 0.006 Continuous and categorial variables were compared using general linear model and crosstabs analyses in a complex sample analysis method, respectively. Bold numbers indicate statistically significant values. SE, standard error; BMI, body mass index. Serum Ferritin Concentrations and Frailty Status Serum ferritin concentrations by frailty status were analyzed using a general linear model within a complex sample analysis framework (Fig. 2 ). In both women and men, no significant differences in serum ferritin concentrations were observed among the non-frail, pre-frail, and frail groups, irrespective of whether adjustments for confounding variables were made before or after the analysis. Association Between Serum Ferritin Levels and Frailty Index Linear regression analyses were conducted to determine the independent relationship between serum ferritin levels and the Rockwood frailty index (Table 2 ). In women, higher serum ferritin levels were consistently correlated with a higher frailty index, both before and after adjusting for potential confounders, including age, educational level, smoking status, diabetes, dyslipidemia, cardiovascular diseases, and BMI ( P = 0.005 to 0.018). However, in men, no statistically significant association between serum ferritin concentration and the frailty index was observed, regardless of the adjustment model used. Table 2 Serum ferritin levels and frailty index: assessment by multiple linear regression analysis Adjustment Dependent variable: frailty index β SE P value Women Unadjusted 0.000079 0.000030 0.009 Age and BMI 0.000070 0.000025 0.005 Multivariable 0.000059 0.000025 0.018 Men Unadjusted 0.000008 0.000014 0.557 Age and BMI 0.000007 0.000012 0.543 Multivariable -0.000002 0.000008 0.817 General linear model analysis was performed with frailty index as a dependent variable, and with serum ferritin level (ng/mL) as an independent variable. Multivariable adjustment model includes age, level of education, smoking, diabetes, dyslipidemia, cardiovascular diseases, and body mass index as confounding factors. Bold numbers indicate statistically significant values. β, regression coefficient; SE, standard error; BMI, body mass index. Threshold Effect of Serum Ferritin on Frailty Risk To determine whether there was a threshold effect between serum ferritin concentration and frailty, participants were categorized into four groups according to serum ferritin concentration quartiles (Fig. 3 ). The frailty index in both women and men varied non-linearly across quartiles, with the lowest values observed in the third quartile (Q3). Therefore, the differences in frailty index among the groups were analyzed using Q3 as the reference. In women, the frailty index was significantly higher in both the lowest quartile (Q1, serum ferritin ≤ 36 ng/mL) and the highest quartile (Q4, serum ferritin > 90 ng/mL) compared to Q3 (59 < serum ferritin ≤ 90 ng/mL), regardless of adjustments for confounding variables ( P 153 ng/mL) compared to Q3 (89 < serum ferritin ≤ 153 ng/mL) in the unadjusted model ( P = 0.040 and 0.030, respectively). However, after adjusting for potential confounders such as age and BMI, the statistical significance for Q4 disappeared, and a significant increase in frailty index was observed only in the Q1 group ( P = 0.041). The risk of frailty according to serum ferritin quartile groups was assessed using logistic regression analyses (Fig. 4 ). In women, compared to Q3 in the unadjusted model, Q1 and Q4 showed significantly higher odds ratios for frailty, at 1.83 and 1.50 times, respectively ( P < 0.001 and 0.006, respectively). This statistical significance persisted even after multivariable adjustment for various confounding variables ( P < 0.001 and 0.049, respectively). In men, the unadjusted model also showed 1.39-fold and 1.41-fold higher odds ratios for frailty in Q1 and Q4 compared to Q3 (both P = 0.042). However, after adjusting for variables such as age and BMI, no significant differences were observed among the four quartile groups. Discussion Circulating ferritin, a marker traditionally used to assess iron storage, has been increasingly linked to various disease states, especially in geriatric populations. This large-scale study, involving community-dwelling older adults aged 65 and above, reveals a non-linear association between serum ferritin concentrations and the frailty index in women. Specifically, women in the lowest and highest serum ferritin quartiles exhibited a significantly higher risk of frailty. Although a similar trend was observed in men, the association between serum ferritin levels and frailty attenuated after adjusting for confounding variables. This pioneering investigation into the general population underscores the potential of serum ferritin, which is widely used in clinical practice, as a predictive biomarker for frailty in older adults. Both the phenotype model and the cumulative deficit model are utilized to define frailty [ 2 ]; however, the Rockwood frailty index, which offers a thorough evaluation of an individual's health status and is expressed as a continuous variable, demonstrates superior sensitivity in detecting changes in the health status of older adults and predicting health outcomes compared to the dichotomous classification of the Fried criteria based on five physical criteria [ 4 – 6 ]. This index not only captures a wide array of health deficits but also incorporates their cumulative impact, offering a more holistic view of a person’s health condition [ 4 – 8 ]. The KNHANES provides an extensive dataset required to calculate the Rockwood frailty index. This comprehensive data, collected with high reliability and representing the national population, includes detailed information on various health parameters, lifestyle factors, and clinical measures [ 20 ]. Consequently, KNHANES is uniquely positioned as an optimal cohort for conducting frailty research, leveraging big data to enhance the precision and applicability of health assessments in geriatric populations. Frailty, marked by muscle weakness, decreased energy, lower levels of physical activity, and reduced resilience, is gaining recognition as a critical clinical issue in rapidly aging societies [ 1 ]. As a result, there is growing interest in discovering potential biomarkers for the early identification of those at high risk of developing frailty [ 26 ]. Although numerous clinical studies have explored the association between circulating ferritin levels and various chronic diseases that may predispose individuals to frailty, research directly examining its impact on frailty itself remains limited. Our literature review identified only two studies addressing the connection between blood ferritin and frailty; however, these studies utilized the Edmonton Frailty Scale [ 27 ] and the Fried criteria [ 28 ], respectively, which may not comprehensively capture the broad spectrum of frailty, including overall health and functional capacity. To address this gap, our study is the first to leverage extensive national datasets, generating a comprehensive frailty index that encompasses medical, functional, and psychosocial deficits related to aging to explore the link between ferritin and frailty. This approach provides robust clinical evidence supporting the potential role of ferritin as a biomarker for frailty development. High serum ferritin, indicative of iron overload, contributes to the development of frailty through multiple, intertwined pathophysiological mechanisms. Firstly, iron overload can catalyze the formation of reactive oxygen species (ROS), leading to oxidative stress and cellular damage, particularly affecting mitochondrial function [ 10 ]. This oxidative stress accelerates cellular aging and impairs muscle function, contributing to sarcopenia—a key component of frailty [ 29 ]. Additionally, excess iron can cause chronic inflammation by activating pro-inflammatory pathways [ 11 , 12 ], which further exacerbates muscle wasting and reduces physical resilience. Iron-induced inflammation can also impair the immune response [ 12 ], increasing susceptibility to infections and reducing overall physiological robustness. Moreover, iron deposits in organs such as the heart and liver can lead to organ dysfunction [ 30 , 31 ], thereby compromising the body's ability to respond to stressors. Finally, iron overload may disrupt endocrine functions, particularly those regulating metabolism and muscle maintenance, thus exacerbating physical decline [ 30 , 32 ]. Collectively, these processes undermine the physiological reserves and adaptability of older individuals, promoting the onset and progression of frailty. On the other hand, low serum ferritin levels, indicative of iron deficiency, are also associated with an increased risk of frailty through distinct but equally impactful mechanisms. Iron is crucial for various physiological functions, including oxygen transport, DNA synthesis, and mitochondrial energy production [ 33 ]. Iron deficiency leads to anemia, resulting in reduced oxygen delivery to tissues and diminished physical endurance [ 19 ], which can contribute to muscle weakness and fatigue—core components of frailty. Furthermore, insufficient iron impairs mitochondrial function and reduces ATP production [ 34 ], leading to decreased muscle strength and physical performance. Iron is also essential for proper immune function; deficiency can lead to compromised immunity, increasing vulnerability to infections and further exacerbating frailty [ 17 ]. Additionally, iron plays a role in neurotransmitter synthesis and cognitive function, and deficiency may contribute to cognitive decline and reduced coordination [ 35 ], both of which are critical in maintaining physical independence. Therefore, both iron overload and deficiency disrupt key physiological processes, each promoting frailty through different but interrelated pathways. The particularly interesting findings of our study including women and men aged 65 years and older, the non-linear association between blood ferritin concentration and frailty was more pronounced in women than in men, likely due to gender-specific physiological and biological factors. Postmenopausal women, who constituted a significant portion of our female cohort, experience changes in iron metabolism that can lead to increased iron accumulation, heightening the risk of iron overload [ 36 ]. Conversely, women are also more prone to iron deficiency due to a history of lower baseline iron stores and higher lifetime iron demands. Additionally, the loss of estrogen's protective effects post-menopause can exacerbate oxidative stress and inflammation [ 37 ], which are key contributors to frailty. Women also tend to have a higher prevalence of conditions like osteoporosis and sarcopenia [ 38 ], which can be worsened by both iron deficiency and overload, thereby increasing their vulnerability to frailty. Social and behavioral factors, including differences in diet and health-seeking behavior, may further influence iron status [ 39 ] and its impact on frailty more significantly in women. Thus, these combined factors likely explain the more pronounced association between blood ferritin levels and frailty observed in women in our study. Our study's primary strength is the use of complex sample analysis methods with weighted adjustments, which allows for the estimation of national-level statistics, thereby enhancing the generalizability of our findings. Moreover, the large sample size enabled us to adjust for various confounding factors, increasing the statistical rigor of our results. However, there are several limitations to consider when interpreting our data. A major limitation is the cross-sectional design, which does not allow for establishing a causal link between serum ferritin levels and frailty. Additionally, the KNHANES dataset lacks measurements of systemic inflammation and oxidative stress, preventing us from determining if the increased frailty risk associated with high or low ferritin levels is directly due to these factors. Another limitation is the reliance on self-reported data, which may introduce recall and social desirability biases. Finally, as our study focused exclusively on a Korean population, the findings may not be applicable to other demographic groups, particularly Caucasians. Conclusions Our study identified a non-linear association between serum ferritin concentrations and the frailty index in a nationally representative cohort of older adults aged 65 and above. Both low and high ferritin concentrations were linked to an increased risk of frailty, with this relationship being more pronounced in women than in men. Future research is needed to investigate whether interventions aimed at maintaining ferritin concentrations within an optimal range can mitigate frailty, which reflects biological aging. Abbreviations KNHANES Korea National Health and Nutrition Examination Survey PSU Primary sample unit BMI Body mass index SE standard error ROS reactive oxygen species OR Odds ratio CI Confidence interval Declarations Ethics approval and consent to participate The KNHANES data were collected with informed consent from all participants under the approval of the KCDC (now KDCA). The study was approved by the Institutional Review Board of Chonnam National University Bitgoeul Hospital, which waived the requirement for additional informed consent (IRB No. CNUBH-2024-013). Consent for publication Not applicable. This study used publicly available, de-identified data. Clinical trial number not applicable. Competing Interests The authors declare no conflicts of interest related to this work. Funding This research was supported by grants from the Korean ARPA-H Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health and Welfare, Republic of Korea [Grant No. RS-2024-00507256]; the Korea Health Technology R&D Project through KHIDI, funded by the Ministry of Health and Welfare, Republic of Korea [Grant No. RS-2024-00401934]; and the Asan Institute for Life Science, Asan Medical Center, Seoul, Republic of Korea [grant number: 2022IP0072]. Author Contribution M.G. Kang conceptualized and designed the study, conducted the investigation, curated the data, and drafted the original manuscript as well as reviewed and edited it. He also administered the project. I.Y. Jang contributed to the investigation, reviewed and edited the manuscript, and assisted in project administration. J.Y. Baek contributed to the investigation, reviewed and edited the manuscript, and assisted in project administration. Y.J. Jo contributed to the investigation, reviewed and edited the manuscript, and assisted in project administration. D.R. Ryu contributed to the investigation, reviewed and edited the manuscript, assisted in project administration, and supervised the study. H.W. Jung conceptualized and designed the study, contributed to the investigation, reviewed and edited the manuscript, and supervised the study. B.J. Kim conceptualized and designed the study, conducted the investigation, drafted the original manuscript, reviewed and edited it, administered the project, and supervised the study. Data Availability The datasets generated and/or analysed during the current study are available from the KNHANES website: https://knhanes.kdca.go.kr/knhanes/main.do. References Lee H, Lee E, Jang IY. Frailty and Comprehensive Geriatric Assessment. J Korean Med Sci. 2020;35(3):e16. Clegg A, Young J, Iliffe S, Rikkert MO, Rockwood K. Frailty in elderly people. Lancet (London England). 2013;381(9868):752–62. Kong X, Qi W, Xing F, Zhu S, Sun Y, Duan H, Wu Y. Association of Abnormal Sleep Duration and Sleep Disturbance with Physical Activity in Older Adults: Between- and within-Person Effects. J Am Med Dir Assoc. 2024;25(2):368–74. Fried LP, Tangen CM, Walston J, Newman AB, Hirsch C, Gottdiener J, Seeman T, Tracy R, Kop WJ, Burke G, et al. Frailty in older adults: evidence for a phenotype. journals Gerontol Ser Biol Sci Med Sci. 2001;56(3):M146–156. Rockwood K, Mitnitski A. Frailty in relation to the accumulation of deficits. journals Gerontol Ser Biol Sci Med Sci. 2007;62(7):722–7. Rockwood K, Song X, MacKnight C, Bergman H, Hogan DB, McDowell I, Mitnitski A. A global clinical measure of fitness and frailty in elderly people. CMAJ: Can Med Association J = J de l'Association medicale canadienne. 2005;173(5):489–95. Theou O, Brothers TD, Mitnitski A, Rockwood K. Operationalization of frailty using eight commonly used scales and comparison of their ability to predict all-cause mortality. J Am Geriatr Soc. 2013;61(9):1537–51. Blodgett J, Theou O, Kirkland S, Andreou P, Rockwood K. Frailty in NHANES: Comparing the frailty index and phenotype. Arch Gerontol Geriatr. 2015;60(3):464–70. Li X, Ploner A, Wang Y, Magnusson PK, Reynolds C, Finkel D, Pedersen NL, Jylhävä J, Hägg S. Longitudinal trajectories, correlations and mortality associations of nine biological ages across 20-years follow-up. eLife 2020, 9. Puntarulo S. Iron, oxidative stress and human health. Mol Aspects Med. 2005;26(4–5):299–312. Moen IW, Bergholdt HKM, Mandrup-Poulsen T, Nordestgaard BG, Ellervik C. Increased Plasma Ferritin Concentration and Low-Grade Inflammation-A Mendelian Randomization Study. Clin Chem. 2018;64(2):374–85. Kernan KF, Carcillo JA. Hyperferritinemia and inflammation. Int Immunol. 2017;29(9):401–9. Sun L, Franco OH, Hu FB, Cai L, Yu Z, Li H, Ye X, Qi Q, Wang J, Pan A, et al. Ferritin concentrations, metabolic syndrome, and type 2 diabetes in middle-aged and elderly chinese. J Clin Endocrinol Metab. 2008;93(12):4690–6. Zhang N, Yu X, Xie J, Xu H. New Insights into the Role of Ferritin in Iron Homeostasis and Neurodegenerative Diseases. Mol Neurobiol. 2021;58(6):2812–23. Brinza C, Floria M, Popa IV, Burlacu A. The Prognostic Performance of Ferritin in Patients with Acute Myocardial Infarction: A Systematic Review. Diagnostics (Basel Switzerland) 2022, 12(2). Suárez-Ortegón MF, Ensaldo-Carrasco E, Shi T, McLachlan S, Fernández-Real JM, Wild SH. Ferritin, metabolic syndrome and its components: A systematic review and meta-analysis. Atherosclerosis. 2018;275:97–106. Recalcati S, Invernizzi P, Arosio P, Cairo G. New functions for an iron storage protein: the role of ferritin in immunity and autoimmunity. J Autoimmun. 2008;30(1–2):84–9. Blankenhaus B, Braza F, Martins R, Bastos-Amador P, González-García I, Carlos AR, Mahu I, Faisca P, Nunes JM, Ventura P, et al. Ferritin regulates organismal energy balance and thermogenesis. Mol metabolism. 2019;24:64–79. Camaschella C. Iron deficiency. Blood. 2019;133(1):30–9. Kweon S, Kim Y, Jang MJ, Kim Y, Kim K, Choi S, Chun C, Khang YH, Oh K. Data resource profile: the Korea National Health and Nutrition Examination Survey (KNHANES). Int J Epidemiol. 2014;43(1):69–77. Searle SD, Mitnitski A, Gahbauer EA, Gill TM, Rockwood K. A standard procedure for creating a frailty index. BMC Geriatr. 2008;8:24. Kang MG, Kim OS, Hoogendijk EO, Jung HW. Trends in Frailty Prevalence Among Older Adults in Korea: A Nationwide Study From 2008 to 2020. J Korean Med Sci. 2023;38(29):e157. Kang MG, Jung HW. Association Between Oral Health and Frailty in Older Korean Population: A Cross-Sectional Study. Clin Interv Aging. 2022;17:1863–72. Won CW, Lee Y, Lee S, Kim M. Development of Korean Frailty Index for Primary Care (KFI-PC) and Its Criterion Validity. Annals geriatric Med Res. 2020;24(2):125–38. Kim DH, Glynn RJ, Avorn J, Lipsitz LA, Rockwood K, Pawar A, Schneeweiss S. Validation of a Claims-Based Frailty Index Against Physical Performance and Adverse Health Outcomes in the Health and Retirement Study. journals Gerontol Ser Biol Sci Med Sci. 2019;74(8):1271–6. Kim SJ, Jo Y, Park SJ, Ji E, Lee JY, Choi E, Baek JY, Jang IY, Jung HW, Kim K, et al. Metabolomic profiles of ovariectomized mice and their associations with body composition and frailty-related parameters in postmenopausal women. J Endocrinol Investig. 2024;47(10):2551–63. Zawadzki B, Mazur G, Butrym A. Iron Dysregulation and Frailty Syndrome. J Clin Med 2021, 10(23). Kumar A, Dhar M, Agarwal M, Mukherjee A, Saxena V. Predictors of Frailty in the Elderly Population: A Cross-Sectional Study at a Tertiary Care Center. Cureus. 2022;14(10):e30557. Chen M, Wang Y, Deng S, Lian Z, Yu K. Skeletal muscle oxidative stress and inflammation in aging: Focus on antioxidant and anti-inflammatory therapy. Front cell Dev biology. 2022;10:964130. Chen WJ, Kung GP, Gnana-Prakasam JP. Role of Iron in Aging Related Diseases. Antioxid (Basel Switzerland) 2022, 11(5). Huang X, Song Y, Wei L, Guo J, Xu W, Li M. The emerging roles of ferroptosis in organ fibrosis and its potential therapeutic effect. Int Immunopharmacol. 2023;116:109812. Tenuta M, Cangiano B, Rastrelli G, Carlomagno F, Sciarra F, Sansone A, Isidori AM, Gianfrilli D, Krausz C. Iron overload disorders: Growth and gonadal dysfunction in childhood and adolescence. Pediatr Blood Cancer. 2024;71(7):e30995. Gupta D. Role of Iron (Fe) in Body. IOSR J Appl Chem. 2014;7:38–46. Duan G, Li J, Duan Y, Zheng C, Guo Q, Li F, Zheng J, Yu J, Zhang P, Wan M et al. Mitochondrial Iron Metabolism: The Crucial Actors in Diseases. Molecules 2022, 28(1). Yavuz BB, Cankurtaran M, Haznedaroglu IC, Halil M, Ulger Z, Altun B, Ariogul S. Iron deficiency can cause cognitive impairment in geriatric patients. J Nutr Health Aging. 2012;16(3):220–4. Jian J, Pelle E, Huang X. Iron and menopause: does increased iron affect the health of postmenopausal women? Antioxid Redox Signal. 2009;11(12):2939–43. Ishikawa A, Matsushita H, Shimizu S, Morita N, Hanai R, Sugiyama S, Watanabe K, Wakatsuki A. Impact of Menopause and the Menstrual Cycle on Oxidative Stress in Japanese Women. J Clin Med 2023, 12(3). Nielsen BR, Abdulla J, Andersen HE, Schwarz P, Suetta C. Sarcopenia and osteoporosis in older people: a systematic review and meta-analysis. Eur Geriatr Med. 2018;9(4):419–34. Joung-Won Lee e. Iron Status and Its Relations with Nutrient Intake, Coffee Drinking, and Smoking in Korean Urban Adults. J Community Nutr. 2003;5(1):44–50. Additional Declarations No competing interests reported. Supplementary Files SupplementaryTable1.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 10 Dec, 2025 Reviews received at journal 17 Nov, 2025 Reviews received at journal 09 Nov, 2025 Reviewers agreed at journal 08 Nov, 2025 Reviewers agreed at journal 01 Nov, 2025 Reviewers invited by journal 27 Oct, 2025 Editor invited by journal 29 Sep, 2025 Editor assigned by journal 25 Sep, 2025 Submission checks completed at journal 25 Sep, 2025 First submitted to journal 17 Sep, 2025 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-7644766","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":540403521,"identity":"5f7296ca-ee8e-47bc-acfb-52a24ac31e41","order_by":0,"name":"Min-gu Kang","email":"","orcid":"","institution":"Chonnam National University Bitgoeul Hospital","correspondingAuthor":false,"prefix":"","firstName":"Min-gu","middleName":"","lastName":"Kang","suffix":""},{"id":540403522,"identity":"c52e9d35-325a-415f-aa25-9af789eab5e5","order_by":1,"name":"Il-Young Jang","email":"","orcid":"","institution":"University of Ulsan College of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Il-Young","middleName":"","lastName":"Jang","suffix":""},{"id":540403523,"identity":"7eda6ac6-e6e9-4a8a-97d1-8b913022f58a","order_by":2,"name":"Ji Yeon Baek","email":"","orcid":"","institution":"University of Ulsan College of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Ji","middleName":"Yeon","lastName":"Baek","suffix":""},{"id":540403524,"identity":"a55e9d63-4964-4c13-9ee0-9ccc1463de35","order_by":3,"name":"Yunju Jo","email":"","orcid":"","institution":"Gwangju Institute of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Yunju","middleName":"","lastName":"Jo","suffix":""},{"id":540403525,"identity":"af0f7ede-8b4d-485e-b06c-2b470aa9c65d","order_by":4,"name":"Dongryeol Ryu","email":"","orcid":"","institution":"Gwangju Institute of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Dongryeol","middleName":"","lastName":"Ryu","suffix":""},{"id":540403526,"identity":"51e663b5-1e4d-408e-9c84-72aa4b9abb6e","order_by":5,"name":"Hee-Won Jung","email":"","orcid":"","institution":"University of Ulsan College of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Hee-Won","middleName":"","lastName":"Jung","suffix":""},{"id":540403527,"identity":"7fa0f3c5-23bd-4bec-8de5-9fb5b4c3966e","order_by":6,"name":"Beom-Jun Kim","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA90lEQVRIiWNgGAWjYFACNjYGCQMbOfv2BpgIcwNjA271UC0VacYGPAdgIoxEaGE4czhxg0QCkVoMrh1Le2DZlsa4XfLx0w0/GOrk+CUSGxhn7sGj5XbacQPJNhtmy9lpZjd7GA4bS84AatnwDJ+W9DYJybY0NobbCWa3GRgOJG64AdTy4ABBLYd5GG4e/wbUUldPhJa0YxISZw5LGNzgAdnCnGAA0rIBjxbJ22npBsBANpDsySm72WNw2HBmz8OGgzPwaOG7nWb2GBiV9f3sx7fd+FFRJ8/PnnzwYQ8eLSDALIFwJ4QioAEYdR8IqRgFo2AUjIKRDQB3SVoCTbjECQAAAABJRU5ErkJggg==","orcid":"","institution":"University of Ulsan College of Medicine","correspondingAuthor":true,"prefix":"","firstName":"Beom-Jun","middleName":"","lastName":"Kim","suffix":""}],"badges":[],"createdAt":"2025-09-18 03:38:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7644766/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7644766/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":95320396,"identity":"4ced3e10-73be-410e-aa19-1ad5cc58ca68","added_by":"auto","created_at":"2025-11-06 16:38:48","extension":"tif","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":2114386,"visible":true,"origin":"","legend":"","description":"","filename":"Figure1Flowdiagramofthestudyparticipants.tif","url":"https://assets-eu.researchsquare.com/files/rs-7644766/v1/21d879b8b3b20e64af982d71.tif"},{"id":95320392,"identity":"5f55ed87-20f9-4358-bdb2-24add535eb5f","added_by":"auto","created_at":"2025-11-06 16:38:47","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":48006,"visible":true,"origin":"","legend":"","description":"","filename":"Associationbetweenferritinandfrailty.docx","url":"https://assets-eu.researchsquare.com/files/rs-7644766/v1/e956a6701d104881595e243e.docx"},{"id":95320375,"identity":"fd2674bc-7e0c-48ab-92d2-a186a41f8de7","added_by":"auto","created_at":"2025-11-06 16:38:45","extension":"tif","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":5735026,"visible":true,"origin":"","legend":"","description":"","filename":"Figure2Differencesinserumferritinlevelsaccordingtothefrailtystatus.tif","url":"https://assets-eu.researchsquare.com/files/rs-7644766/v1/61884f5ae8a4f7594620e1b6.tif"},{"id":95320343,"identity":"d976e0b9-2cb6-4e78-bbe5-0be0bd29ec10","added_by":"auto","created_at":"2025-11-06 16:38:40","extension":"tif","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":6910854,"visible":true,"origin":"","legend":"","description":"","filename":"Figure3Differencesinfrailtyindexaccordingtoserumferritinquartiles.tif","url":"https://assets-eu.researchsquare.com/files/rs-7644766/v1/85f7edb13f3a3d8e7f6b9da7.tif"},{"id":95320369,"identity":"e3418cf7-9caf-4f6f-8a20-1f44e4963aa6","added_by":"auto","created_at":"2025-11-06 16:38:42","extension":"tif","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":7397366,"visible":true,"origin":"","legend":"","description":"","filename":"Figure4Logisticregressionofprefrailandfrailoddsbyserumferritinquartiles.tif","url":"https://assets-eu.researchsquare.com/files/rs-7644766/v1/bc87c337580b4c65e342de70.tif"},{"id":95320368,"identity":"0483c1ee-00b8-468f-a538-2b66db532911","added_by":"auto","created_at":"2025-11-06 16:38:42","extension":"json","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":9152,"visible":true,"origin":"","legend":"","description":"","filename":"740bd1073f794c5d91227fa6f5b7c5e0.json","url":"https://assets-eu.researchsquare.com/files/rs-7644766/v1/1615f9dc341f42309641b329.json"},{"id":95320318,"identity":"39862cdf-6115-4a11-9fab-4f83accfc8c5","added_by":"auto","created_at":"2025-11-06 16:38:39","extension":"docx","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":22413,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7644766/v1/75287cd11757f1deb4558f55.docx"},{"id":95320342,"identity":"726b10ae-5592-424b-b943-d646a12d904d","added_by":"auto","created_at":"2025-11-06 16:38:40","extension":"xml","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":114674,"visible":true,"origin":"","legend":"","description":"","filename":"740bd1073f794c5d91227fa6f5b7c5e01enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-7644766/v1/f0aca61539b562c1b12528ff.xml"},{"id":95320321,"identity":"c121d4bb-c1f7-4102-a5dd-7be69ee96396","added_by":"auto","created_at":"2025-11-06 16:38:39","extension":"tif","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":2114386,"visible":true,"origin":"","legend":"","description":"","filename":"Figure1Flowdiagramofthestudyparticipants.tif","url":"https://assets-eu.researchsquare.com/files/rs-7644766/v1/aa28ffa72a3a6b9268332e65.tif"},{"id":95320374,"identity":"c0b9ba9d-ab6a-4371-8f8b-60762bfcccb1","added_by":"auto","created_at":"2025-11-06 16:38:45","extension":"tif","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":5735026,"visible":true,"origin":"","legend":"","description":"","filename":"Figure2Differencesinserumferritinlevelsaccordingtothefrailtystatus.tif","url":"https://assets-eu.researchsquare.com/files/rs-7644766/v1/11acb234a32c58fbdb8a7c21.tif"},{"id":95320320,"identity":"150998df-cb3d-4d31-b2b6-3cbfaf4920bd","added_by":"auto","created_at":"2025-11-06 16:38:39","extension":"tif","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":6910854,"visible":true,"origin":"","legend":"","description":"","filename":"Figure3Differencesinfrailtyindexaccordingtoserumferritinquartiles.tif","url":"https://assets-eu.researchsquare.com/files/rs-7644766/v1/51766d14808d6b29646794e4.tif"},{"id":95320346,"identity":"f9dc98ce-ff0a-47c2-b9ff-ccb8ea13e46a","added_by":"auto","created_at":"2025-11-06 16:38:40","extension":"tif","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":7397366,"visible":true,"origin":"","legend":"","description":"","filename":"Figure4Logisticregressionofprefrailandfrailoddsbyserumferritinquartiles.tif","url":"https://assets-eu.researchsquare.com/files/rs-7644766/v1/af7ad240b377fb88152ca31f.tif"},{"id":95320393,"identity":"7ff09d51-14c0-4c0f-86ef-74a3e6396db1","added_by":"auto","created_at":"2025-11-06 16:38:47","extension":"png","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":215378,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFigure1Flowdiagramofthestudyparticipants.png","url":"https://assets-eu.researchsquare.com/files/rs-7644766/v1/62f3bdb6cd678de90530402d.png"},{"id":95320347,"identity":"dc436cd8-b742-4bbd-9d3d-b3dc36d69873","added_by":"auto","created_at":"2025-11-06 16:38:40","extension":"png","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1839643,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFigure2Differencesinserumferritinlevelsaccordingtothefrailtystatus.png","url":"https://assets-eu.researchsquare.com/files/rs-7644766/v1/877e436b7fda911d22fe183d.png"},{"id":95320370,"identity":"7a610111-7a3a-4da1-aaa6-ee4b15012402","added_by":"auto","created_at":"2025-11-06 16:38:42","extension":"png","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":856847,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFigure3Differencesinfrailtyindexaccordingtoserumferritinquartiles.png","url":"https://assets-eu.researchsquare.com/files/rs-7644766/v1/38a9aa87f776c22cc9952ef7.png"},{"id":95320371,"identity":"d417a6bd-69d4-47f1-8b9d-244513108c04","added_by":"auto","created_at":"2025-11-06 16:38:43","extension":"png","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":3597168,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFigure4Logisticregressionofprefrailandfrailoddsbyserumferritinquartiles.png","url":"https://assets-eu.researchsquare.com/files/rs-7644766/v1/cd76becef5e5ff57fbd20f68.png"},{"id":95320348,"identity":"f3306227-f2b9-4392-8445-42569140dc5a","added_by":"auto","created_at":"2025-11-06 16:38:41","extension":"xml","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":115680,"visible":true,"origin":"","legend":"","description":"","filename":"740bd1073f794c5d91227fa6f5b7c5e01structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7644766/v1/4fe4b8de5801195828c748d3.xml"},{"id":95320390,"identity":"29fa1bf9-cc59-40ef-90c7-cfa562bd5251","added_by":"auto","created_at":"2025-11-06 16:38:47","extension":"html","order_by":17,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":124755,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7644766/v1/474836b89b7b5a2b8e6d40f8.html"},{"id":95320345,"identity":"10443c06-16df-4091-a2c3-2682483e40c8","added_by":"auto","created_at":"2025-11-06 16:38:40","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1908112,"visible":true,"origin":"","legend":"\u003cp\u003eFlow diagram of the study participants.\u003c/p\u003e","description":"","filename":"Figure1Flowdiagramofthestudyparticipants.png","url":"https://assets-eu.researchsquare.com/files/rs-7644766/v1/a102ab5de851c25df084a700.png"},{"id":95320344,"identity":"fbe16844-9fcd-4d23-9ec3-a99942b37c8e","added_by":"auto","created_at":"2025-11-06 16:38:40","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":7771454,"visible":true,"origin":"","legend":"\u003cp\u003eDifferences in serum ferritin levels according to the frailty status.\u003c/p\u003e\n\u003cp\u003eThe estimated means with 95% confidence intervals were generated and compared using general linear model analysis in a complex sample analysis method. Multivariable adjusted model: adjusted for age, level of education, smoking, diabetes, dyslipidemia, cardiovascular diseases, and body mass index.\u003c/p\u003e","description":"","filename":"Figure2Differencesinserumferritinlevelsaccordingtothefrailtystatus.png","url":"https://assets-eu.researchsquare.com/files/rs-7644766/v1/8f0222328cee6d7a6f59842e.png"},{"id":95320391,"identity":"0f23943e-c4d9-48f6-9a2a-b24c5a6bee35","added_by":"auto","created_at":"2025-11-06 16:38:47","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2341131,"visible":true,"origin":"","legend":"\u003cp\u003eDifferences in frailty index according to serum ferritin quartiles in women (A) and men (B). The estimated means with 95% confidence intervals were generated and compared using general linear model analysis in a complex sample analysis method. Multivariable adjusted model: adjusted for age, level of education, smoking, diabetes, dyslipidemia, cardiovascular diseases, and body mass index. Asterisk indicates statistically significant difference from the Q3. Q, quartile. Ferritin quartiles in women: Q1 = serum ferritin ≤ 36 (ng/mL), Q2 = 36 \u0026lt; serum ferritin ≤ 59, Q3 = 59 \u0026lt; serum ferritin ≤ 90, Q4 = serum ferritin \u0026gt; 90. Ferritin quartiles in men: Q1 = serum ferritin ≤ 52 (ng/mL), Q2 = 52 \u0026lt; serum ferritin ≤ 89, Q3 = 89 \u0026lt; serum ferritin ≤ 153, Q4 = serum ferritin \u0026gt; 153.\u003c/p\u003e","description":"","filename":"Figure3Differencesinfrailtyindexaccordingtoserumferritinquartiles.png","url":"https://assets-eu.researchsquare.com/files/rs-7644766/v1/074dff5b4591f5bdd363b919.png"},{"id":95524252,"identity":"2292b0b1-4dfc-4ffd-8edc-85ffa2e75216","added_by":"auto","created_at":"2025-11-10 10:02:33","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":14043286,"visible":true,"origin":"","legend":"\u003cp\u003eLogistic regression of pre-frail and frail odds by serum ferritin quartiles.\u003c/p\u003e\n\u003cp\u003eMultivariable adjusted model: adjusted for age, level of education, smoking, diabetes, dyslipidemia, cardiovascular diseases, and body mass index. OR, odds ratio; CI, confidence interval; Q, quartile. Ferritin quartiles in women: Q1 = serum ferritin ≤ 36 (ng/mL), Q2 = 36 \u0026lt; serum ferritin ≤ 59, Q3 = 59 \u0026lt; serum ferritin ≤ 90, Q4 = serum ferritin \u0026gt; 90. Ferritin quartiles in men: Q1 = serum ferritin ≤ 52 (ng/mL), Q2 = 52 \u0026lt; serum ferritin ≤ 89, Q3 = 89 \u0026lt; serum ferritin ≤ 153, Q4 = serum ferritin \u0026gt; 153.\u003c/p\u003e","description":"","filename":"Figure4Logisticregressionofprefrailandfrailoddsbyserumferritinquartiles.png","url":"https://assets-eu.researchsquare.com/files/rs-7644766/v1/46e64c83d2ba0fad7602cce8.png"},{"id":95320319,"identity":"bb37909a-b40b-4947-bd2f-7fc7656b95a2","added_by":"auto","created_at":"2025-11-06 16:38:39","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":22413,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7644766/v1/14963355fd4f993ddee2a81c.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association Between Serum Ferritin Levels and Frailty Risk in Community-Dwelling Older Adults: A Nationwide Population-Based Study","fulltext":[{"header":"Background","content":"\u003cp\u003eFrailty in older adults is a significant geriatric syndrome marked by increased vulnerability to various stressors due to diminished physiological capacity, more accurately reflecting an individual's overall well-being and functional ability than chronological age [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The clinical importance of frailty is underscored by its link to adverse health outcomes such as disability, falls, and mortality, necessitating robust assessment tools [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. While no definitive criterion exists, the \"phenotypic frailty\" or Fried criteria, focusing mainly on physical aspects, are widely used for their simplicity [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. However, the \"frailty index\" developed by Rockwood et al. [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], which includes a broad range of deficits such as social, psychological, and cognitive factors, is regarded as a more comprehensive and superior predictor of key outcomes like hospitalization and death [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Recent longitudinal studies have highlighted the frailty index as the most reliable indicator of biological age [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], advocating for its priority use in frailty clinical research. This approach not only enhances the accuracy of frailty assessment but also supports the development of reliable biomarkers, thereby driving advancements in frailty research and promoting more effective interventions to improve quality of life and extend healthy living among the older adults.\u003c/p\u003e\u003cp\u003eFerritin, a key iron storage protein in the body, serves as a crucial biomarker for iron metabolism and is implicated in a variety of health conditions beyond hematologic disorders. Elevated circulating ferritin level, indicative of iron overload, increase oxidative stress, leading to tissue damage [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Moreover, blood ferritin concentration is recognized as an inflammation marker [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], showing positive correlations with various chronic diseases, including cardiovascular diseases, diabetes, non-alcoholic fatty liver disease, and neurodegenerative disorders [\u003cspan additionalcitationids=\"CR14 CR15\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Conversely, low ferritin levels can signal poor health due to nutritional deficiencies, including iron deficiency, reduced energy levels, and compromised immune function [\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Given its broad impact on these diverse health issues, ferritin levels provide significant insights into an individual's overall health status. Consequently, ferritin may serve as a valuable blood biomarker for frailty, reflecting physical, social, and mental conditions as well as biological age. Despite this potential, clinical research on the relationship between circulating ferritin concentration and frailty risk remains limited. To address this gap, we aimed to determine the association between serum ferritin concentration and the Rockwood frailty index in a nationally representative sample of community-dwelling older adults.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy Population\u003c/h2\u003e\u003cp\u003eThis cross-sectional study utilized data from the Korea National Health and Nutrition Examination Survey (KNHANES) collected between 2008 and 2012. Since 1998, KNHANES has been conducted regularly to evaluate the health and nutritional status of the Korean population, track health risk factors and chronic disease prevalence, and support health policy and program development in Korea [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. KNHANES employs a complex, multi-stage probability sampling method to represent the entire non-institutionalized population of Korea. The annual survey employs a three-stage sampling design: initially, primary sample units (PSUs) consisting of 50 to 60 households are selected from census blocks or resident registration addresses. Subsequently, 20\u0026ndash;25 households are chosen from each PSU via field surveys. Finally, all individuals aged 1 year and older in the selected households are surveyed. During the study period, 8,036 individuals aged 65 and older participated in KNHANES. Our analysis included 6,722 participants, after excluding 194 individuals with over 20% missing data on frailty assessment variables and 1,120 individuals lacking ferritin data. (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). All participants provided informed consent before participating in KNHANES, and personal data were anonymized prior to public release. The study was approved by the Institutional Review Board of Chonnam National University Bitgoeul Hospital, which waived the requirement for additional informed consent (IRB No. CNUBH-2024-013). The study adhered to the Declaration of Helsinki principles.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eSerum Ferritin Concentration Measurement\u003c/h3\u003e\n\u003cp\u003eBlood samples for serum ferritin measurement were collected from participants aged 10 and older with their consent. After a minimum 8-hour overnight fast, blood samples were drawn from the antecubital vein in the morning. Samples were refrigerated immediately and transported to the Central Testing Institute (Neodin Medical, Inc., Seoul, Korea). The serum ferritin level was determined using an immunoradiometric assay with a 1470 WIZARD gamma-Counter (PerkinElmer, Turku, Finland). The coefficient of variation value was less than 5%.\u003c/p\u003e\n\u003ch3\u003eFrailty-Related Factors Evaluation\u003c/h3\u003e\n\u003cp\u003eTrained nurses measured blood pressure on the right arm using a mercury sphygmomanometer (Baumanometer\u0026reg; Wall Unit 33(0850); W.A. Baum, Copiague, NY, USA) after participants had been seated quietly for at least 5 minutes. Blood pressure was measured three times, and the second and third measurements were averaged. Blood samples were also collected during the survey. Body mass index (BMI) was calculated as weight in kilograms divided by height in meters squared (kg/m\u0026sup2;). Education level and lifestyle factors were self-reported. Education level was categorized as elementary school or lower, middle school, high school, and college or higher. Smokers were defined as those who had smoked five or more packs of cigarettes in their lifetime and were currently smoking. Medical conditions were classified based on doctor diagnoses.\u003c/p\u003e\n\u003ch3\u003eFrailty index\u003c/h3\u003e\n\u003cp\u003eThe frailty index was constructed using standard procedures [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] and previous KNHANES-based indices [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The index, ranging from 0 (best condition) to 1 (worst condition), comprised 36 items surveyed between 2008 and 2012. These items covered comorbidities (e.g., arthritis, asthma, cancer, cardiovascular disease, diabetes, dyslipidemia, hypertension, stroke), functional abilities (e.g., inactivity, limited exercise capacity, daily living limitations, social activity limitations, self-care inability, chewing difficulty), symptoms (e.g., pain, fatigue, dyspnea, weight loss, depression, anxiety, suicidal ideation, stress), and laboratory values (e.g., blood pressure, heart rate, pulmonary function, BUN, creatinine, cholesterol, triglycerides, HDL cholesterol, fasting glucose, vitamin D, urine protein), as well as smoking status and BMI. (Supplementary Table\u0026nbsp;1). Participants were classified into non-frail (\u0026le;\u0026thinsp;0.15), pre-frail (0.15\u0026ndash;0.25), and frail (\u0026gt;\u0026thinsp;0.25) categories based on established criteria [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eTo derive national-level estimates, we employed a complex sample analysis method incorporating assigned weights. The data from annual surveys were pooled, with each year's sample analyzed independently. Results are presented as means with standard errors (SEs) for continuous variables, or as counts with percentages for categorical variables. General linear models were used to compare continuous variables, while categorical variables were analyzed using cross-tabulation methods. Potential confounding factors included age, sex, education, smoking status, diabetes, dyslipidemia, cardiovascular diseases, and BMI. We assessed differences in serum ferritin levels across frailty statuses and variations in the frailty index among serum ferritin quartiles using general linear models. The relationship between serum ferritin levels and the Rockwood frailty index was examined using linear regression analysis. Multiple logistic regression was utilized to evaluate the risk of frailty across serum ferritin quartiles. A threshold of \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was set for statistical significance, and all analyses were performed using SPSS software, version 21.0 (IBM Corporation, Armonk, NY, USA).\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003eBaseline Characteristics of Study Participants\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes the baseline characteristics of 6,722 participants aged 65 years and older. Among the women, 848 (22.0%) were classified as non-frail, 1,441 (37.3%) as pre-frail, and 1,570 (40.7%) as frail, with mean ages of 71.9, 72.1, and 73.0 years, respectively (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Among the men, 1,127 (39.4%) were categorized as non-frail, 1,079 (37.7%) as pre-frail, and 657 (22.9%) as frail, with mean ages of 71.0, 71.7, and 72.5 years, respectively (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In both men and women, transitioning from non-frail to pre-frail and frail groups was associated with lower levels of education, higher rates of smoking, and increased prevalence of diabetes, dyslipidemia, and cardiovascular diseases (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Additionally, BMI was observed to increase across these groups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 in women and \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006 in men).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBaseline characteristics of the study participants according to frailty status\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u003cp\u003eWomen (N\u0026thinsp;=\u0026thinsp;3,859)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e\u003cp\u003eMen (N\u0026thinsp;=\u0026thinsp;2,863)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNon-frail (N\u0026thinsp;=\u0026thinsp;848)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePre-frail (N\u0026thinsp;=\u0026thinsp;1,441)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eFrail (N\u0026thinsp;=\u0026thinsp;1,570)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNon-frail (N\u0026thinsp;=\u0026thinsp;1,127)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003ePre-frail (N\u0026thinsp;=\u0026thinsp;1,079)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eFrail (N\u0026thinsp;=\u0026thinsp;657)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (years), mean (SE)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e71.9 (0.2)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e72.1 (0.2)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e73.0 (0.2)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e71.0 (0.2)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e71.7 (0.2)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e72.5 (0.2)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLevel of education, %\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1st\u003c/p\u003e\u003cp\u003e2nd\u003c/p\u003e\u003cp\u003e3rd\u003c/p\u003e\u003cp\u003e4th\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e80.0%\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e8.3%\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e9.0%\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e2.7%\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e84.3%\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e7.7%\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e6.6%\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e1.4%\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e90.0%\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e6.1%\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e3.2%\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e0.7%\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e41.4%\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e17.3%\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e22.2%\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e19.1%\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e47.9%\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e20.4%\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e20.6%\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e11.2%\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e62.7%\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e15.5%\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e16.1%\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e5.7%\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoking, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e21 (2.7%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e43 (3.0%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e110 (7.6%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e193 (18.4%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e310 (30.0%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e227 (36.8%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetes, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e70 (8.4%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e259 (19.7%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e444 (31.1%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e137 (11.3%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e258 (24.7%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e207 (32.9%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDyslipidemia, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e124 (15.4%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e335 (23.4%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e453 (30.8%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e85 (7.6%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e143 (13.4%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e120 (19.3%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCardiovascular disease (MI, angina), n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e14 (1.4%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e66 (4.1%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e151 (9.7%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e50 (4.0%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e84 (7.6%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e89 (13.9%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e), mean (SE)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e23.2 (0.1)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e24.2 (0.1)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e24.9 (0.1)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e23.0 (0.1)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e23.4 (0.1)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e23.3 (0.2)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e0.006\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003eContinuous and categorial variables were compared using general linear model and crosstabs analyses in a complex sample analysis method, respectively. Bold numbers indicate statistically significant values. SE, standard error; BMI, body mass index.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eSerum Ferritin Concentrations and Frailty Status\u003c/h3\u003e\n\u003cp\u003eSerum ferritin concentrations by frailty status were analyzed using a general linear model within a complex sample analysis framework (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In both women and men, no significant differences in serum ferritin concentrations were observed among the non-frail, pre-frail, and frail groups, irrespective of whether adjustments for confounding variables were made before or after the analysis.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eAssociation Between Serum Ferritin Levels and Frailty Index\u003c/h2\u003e\u003cp\u003eLinear regression analyses were conducted to determine the independent relationship between serum ferritin levels and the Rockwood frailty index (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In women, higher serum ferritin levels were consistently correlated with a higher frailty index, both before and after adjusting for potential confounders, including age, educational level, smoking status, diabetes, dyslipidemia, cardiovascular diseases, and BMI (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005 to 0.018). However, in men, no statistically significant association between serum ferritin concentration and the frailty index was observed, regardless of the adjustment model used.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSerum ferritin levels and frailty index: assessment by multiple linear regression analysis\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eAdjustment\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eDependent variable: frailty index\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eβ\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eWomen\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnadjusted\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.000079\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.000030\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.009\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge and BMI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.000070\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.000025\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.005\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMultivariable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.000059\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.000025\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.018\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eMen\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnadjusted\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.000008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.000014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.557\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge and BMI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.000007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.000012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.543\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMultivariable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.000002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.000008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.817\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eGeneral linear model analysis was performed with frailty index as a dependent variable, and with serum ferritin level (ng/mL) as an independent variable. Multivariable adjustment model includes age, level of education, smoking, diabetes, dyslipidemia, cardiovascular diseases, and body mass index as confounding factors. Bold numbers indicate statistically significant values. β, regression coefficient; SE, standard error; BMI, body mass index.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eThreshold Effect of Serum Ferritin on Frailty Risk\u003c/h2\u003e\u003cp\u003eTo determine whether there was a threshold effect between serum ferritin concentration and frailty, participants were categorized into four groups according to serum ferritin concentration quartiles (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The frailty index in both women and men varied non-linearly across quartiles, with the lowest values observed in the third quartile (Q3). Therefore, the differences in frailty index among the groups were analyzed using Q3 as the reference. In women, the frailty index was significantly higher in both the lowest quartile (Q1, serum ferritin\u0026thinsp;\u0026le;\u0026thinsp;36 ng/mL) and the highest quartile (Q4, serum ferritin\u0026thinsp;\u0026gt;\u0026thinsp;90 ng/mL) compared to Q3 (59\u0026thinsp;\u0026lt;\u0026thinsp;serum ferritin\u0026thinsp;\u0026le;\u0026thinsp;90 ng/mL), regardless of adjustments for confounding variables (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 to 0.002). In men, the frailty index was initially higher in Q1 (serum ferritin\u0026thinsp;\u0026le;\u0026thinsp;52 ng/mL) and Q4 (serum ferritin\u0026thinsp;\u0026gt;\u0026thinsp;153 ng/mL) compared to Q3 (89\u0026thinsp;\u0026lt;\u0026thinsp;serum ferritin\u0026thinsp;\u0026le;\u0026thinsp;153 ng/mL) in the unadjusted model (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.040 and 0.030, respectively). However, after adjusting for potential confounders such as age and BMI, the statistical significance for Q4 disappeared, and a significant increase in frailty index was observed only in the Q1 group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.041).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe risk of frailty according to serum ferritin quartile groups was assessed using logistic regression analyses (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). In women, compared to Q3 in the unadjusted model, Q1 and Q4 showed significantly higher odds ratios for frailty, at 1.83 and 1.50 times, respectively (\u003cem\u003eP\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.001 and 0.006, respectively). This statistical significance persisted even after multivariable adjustment for various confounding variables (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 and 0.049, respectively). In men, the unadjusted model also showed 1.39-fold and 1.41-fold higher odds ratios for frailty in Q1 and Q4 compared to Q3 (both \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.042). However, after adjusting for variables such as age and BMI, no significant differences were observed among the four quartile groups.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eCirculating ferritin, a marker traditionally used to assess iron storage, has been increasingly linked to various disease states, especially in geriatric populations. This large-scale study, involving community-dwelling older adults aged 65 and above, reveals a non-linear association between serum ferritin concentrations and the frailty index in women. Specifically, women in the lowest and highest serum ferritin quartiles exhibited a significantly higher risk of frailty. Although a similar trend was observed in men, the association between serum ferritin levels and frailty attenuated after adjusting for confounding variables. This pioneering investigation into the general population underscores the potential of serum ferritin, which is widely used in clinical practice, as a predictive biomarker for frailty in older adults.\u003c/p\u003e\u003cp\u003eBoth the phenotype model and the cumulative deficit model are utilized to define frailty [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]; however, the Rockwood frailty index, which offers a thorough evaluation of an individual's health status and is expressed as a continuous variable, demonstrates superior sensitivity in detecting changes in the health status of older adults and predicting health outcomes compared to the dichotomous classification of the Fried criteria based on five physical criteria [\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. This index not only captures a wide array of health deficits but also incorporates their cumulative impact, offering a more holistic view of a person\u0026rsquo;s health condition [\u003cspan additionalcitationids=\"CR5 CR6 CR7\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The KNHANES provides an extensive dataset required to calculate the Rockwood frailty index. This comprehensive data, collected with high reliability and representing the national population, includes detailed information on various health parameters, lifestyle factors, and clinical measures [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Consequently, KNHANES is uniquely positioned as an optimal cohort for conducting frailty research, leveraging big data to enhance the precision and applicability of health assessments in geriatric populations.\u003c/p\u003e\u003cp\u003eFrailty, marked by muscle weakness, decreased energy, lower levels of physical activity, and reduced resilience, is gaining recognition as a critical clinical issue in rapidly aging societies [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. As a result, there is growing interest in discovering potential biomarkers for the early identification of those at high risk of developing frailty [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Although numerous clinical studies have explored the association between circulating ferritin levels and various chronic diseases that may predispose individuals to frailty, research directly examining its impact on frailty itself remains limited. Our literature review identified only two studies addressing the connection between blood ferritin and frailty; however, these studies utilized the Edmonton Frailty Scale [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] and the Fried criteria [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], respectively, which may not comprehensively capture the broad spectrum of frailty, including overall health and functional capacity. To address this gap, our study is the first to leverage extensive national datasets, generating a comprehensive frailty index that encompasses medical, functional, and psychosocial deficits related to aging to explore the link between ferritin and frailty. This approach provides robust clinical evidence supporting the potential role of ferritin as a biomarker for frailty development.\u003c/p\u003e\u003cp\u003eHigh serum ferritin, indicative of iron overload, contributes to the development of frailty through multiple, intertwined pathophysiological mechanisms. Firstly, iron overload can catalyze the formation of reactive oxygen species (ROS), leading to oxidative stress and cellular damage, particularly affecting mitochondrial function [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. This oxidative stress accelerates cellular aging and impairs muscle function, contributing to sarcopenia\u0026mdash;a key component of frailty [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Additionally, excess iron can cause chronic inflammation by activating pro-inflammatory pathways [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], which further exacerbates muscle wasting and reduces physical resilience. Iron-induced inflammation can also impair the immune response [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], increasing susceptibility to infections and reducing overall physiological robustness. Moreover, iron deposits in organs such as the heart and liver can lead to organ dysfunction [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], thereby compromising the body's ability to respond to stressors. Finally, iron overload may disrupt endocrine functions, particularly those regulating metabolism and muscle maintenance, thus exacerbating physical decline [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Collectively, these processes undermine the physiological reserves and adaptability of older individuals, promoting the onset and progression of frailty.\u003c/p\u003e\u003cp\u003eOn the other hand, low serum ferritin levels, indicative of iron deficiency, are also associated with an increased risk of frailty through distinct but equally impactful mechanisms. Iron is crucial for various physiological functions, including oxygen transport, DNA synthesis, and mitochondrial energy production [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Iron deficiency leads to anemia, resulting in reduced oxygen delivery to tissues and diminished physical endurance [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], which can contribute to muscle weakness and fatigue\u0026mdash;core components of frailty. Furthermore, insufficient iron impairs mitochondrial function and reduces ATP production [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], leading to decreased muscle strength and physical performance. Iron is also essential for proper immune function; deficiency can lead to compromised immunity, increasing vulnerability to infections and further exacerbating frailty [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Additionally, iron plays a role in neurotransmitter synthesis and cognitive function, and deficiency may contribute to cognitive decline and reduced coordination [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], both of which are critical in maintaining physical independence. Therefore, both iron overload and deficiency disrupt key physiological processes, each promoting frailty through different but interrelated pathways.\u003c/p\u003e\u003cp\u003eThe particularly interesting findings of our study including women and men aged 65 years and older, the non-linear association between blood ferritin concentration and frailty was more pronounced in women than in men, likely due to gender-specific physiological and biological factors. Postmenopausal women, who constituted a significant portion of our female cohort, experience changes in iron metabolism that can lead to increased iron accumulation, heightening the risk of iron overload [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Conversely, women are also more prone to iron deficiency due to a history of lower baseline iron stores and higher lifetime iron demands. Additionally, the loss of estrogen's protective effects post-menopause can exacerbate oxidative stress and inflammation [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], which are key contributors to frailty. Women also tend to have a higher prevalence of conditions like osteoporosis and sarcopenia [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], which can be worsened by both iron deficiency and overload, thereby increasing their vulnerability to frailty. Social and behavioral factors, including differences in diet and health-seeking behavior, may further influence iron status [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] and its impact on frailty more significantly in women. Thus, these combined factors likely explain the more pronounced association between blood ferritin levels and frailty observed in women in our study.\u003c/p\u003e\u003cp\u003eOur study's primary strength is the use of complex sample analysis methods with weighted adjustments, which allows for the estimation of national-level statistics, thereby enhancing the generalizability of our findings. Moreover, the large sample size enabled us to adjust for various confounding factors, increasing the statistical rigor of our results. However, there are several limitations to consider when interpreting our data. A major limitation is the cross-sectional design, which does not allow for establishing a causal link between serum ferritin levels and frailty. Additionally, the KNHANES dataset lacks measurements of systemic inflammation and oxidative stress, preventing us from determining if the increased frailty risk associated with high or low ferritin levels is directly due to these factors. Another limitation is the reliance on self-reported data, which may introduce recall and social desirability biases. Finally, as our study focused exclusively on a Korean population, the findings may not be applicable to other demographic groups, particularly Caucasians.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eOur study identified a non-linear association between serum ferritin concentrations and the frailty index in a nationally representative cohort of older adults aged 65 and above. Both low and high ferritin concentrations were linked to an increased risk of frailty, with this relationship being more pronounced in women than in men. Future research is needed to investigate whether interventions aimed at maintaining ferritin concentrations within an optimal range can mitigate frailty, which reflects biological aging.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eKNHANES\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eKorea National Health and Nutrition Examination Survey\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePSU\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ePrimary sample unit\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\"\u003eSE\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003estandard error\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eROS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ereactive oxygen species\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eOR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eOdds ratio\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\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003cp\u003eThe KNHANES data were collected with informed consent from all participants under the approval of the KCDC (now KDCA). The study was approved by the Institutional Review Board of Chonnam National University Bitgoeul Hospital, which waived the requirement for additional informed consent (IRB No. CNUBH-2024-013).\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eConsent for publication\u003c/h2\u003e\u003cp\u003eNot applicable. This study used publicly available, de-identified data.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003cp\u003enot applicable.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003cp\u003eThe authors declare no conflicts of interest related to this work.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis research was supported by grants from the Korean ARPA-H Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health and Welfare, Republic of Korea [Grant No. RS-2024-00507256]; the Korea Health Technology R\u0026amp;D Project through KHIDI, funded by the Ministry of Health and Welfare, Republic of Korea [Grant No. RS-2024-00401934]; and the Asan Institute for Life Science, Asan Medical Center, Seoul, Republic of Korea [grant number: 2022IP0072].\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eM.G. Kang conceptualized and designed the study, conducted the investigation, curated the data, and drafted the original manuscript as well as reviewed and edited it. He also administered the project. I.Y. Jang contributed to the investigation, reviewed and edited the manuscript, and assisted in project administration. J.Y. Baek contributed to the investigation, reviewed and edited the manuscript, and assisted in project administration. Y.J. Jo contributed to the investigation, reviewed and edited the manuscript, and assisted in project administration. D.R. Ryu contributed to the investigation, reviewed and edited the manuscript, assisted in project administration, and supervised the study. H.W. Jung conceptualized and designed the study, contributed to the investigation, reviewed and edited the manuscript, and supervised the study. B.J. Kim conceptualized and designed the study, conducted the investigation, drafted the original manuscript, reviewed and edited it, administered the project, and supervised the study.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated and/or analysed during the current study are available from the KNHANES website: https://knhanes.kdca.go.kr/knhanes/main.do.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLee H, Lee E, Jang IY. Frailty and Comprehensive Geriatric Assessment. J Korean Med Sci. 2020;35(3):e16.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eClegg A, Young J, Iliffe S, Rikkert MO, Rockwood K. Frailty in elderly people. Lancet (London England). 2013;381(9868):752\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKong X, Qi W, Xing F, Zhu S, Sun Y, Duan H, Wu Y. Association of Abnormal Sleep Duration and Sleep Disturbance with Physical Activity in Older Adults: Between- and within-Person Effects. J Am Med Dir Assoc. 2024;25(2):368\u0026ndash;74.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFried LP, Tangen CM, Walston J, Newman AB, Hirsch C, Gottdiener J, Seeman T, Tracy R, Kop WJ, Burke G, et al. Frailty in older adults: evidence for a phenotype. journals Gerontol Ser Biol Sci Med Sci. 2001;56(3):M146\u0026ndash;156.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRockwood K, Mitnitski A. Frailty in relation to the accumulation of deficits. journals Gerontol Ser Biol Sci Med Sci. 2007;62(7):722\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRockwood K, Song X, MacKnight C, Bergman H, Hogan DB, McDowell I, Mitnitski A. A global clinical measure of fitness and frailty in elderly people. CMAJ: Can Med Association J = J de l'Association medicale canadienne. 2005;173(5):489\u0026ndash;95.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTheou O, Brothers TD, Mitnitski A, Rockwood K. Operationalization of frailty using eight commonly used scales and comparison of their ability to predict all-cause mortality. J Am Geriatr Soc. 2013;61(9):1537\u0026ndash;51.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBlodgett J, Theou O, Kirkland S, Andreou P, Rockwood K. Frailty in NHANES: Comparing the frailty index and phenotype. Arch Gerontol Geriatr. 2015;60(3):464\u0026ndash;70.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi X, Ploner A, Wang Y, Magnusson PK, Reynolds C, Finkel D, Pedersen NL, Jylh\u0026auml;v\u0026auml; J, H\u0026auml;gg S. Longitudinal trajectories, correlations and mortality associations of nine biological ages across 20-years follow-up. eLife 2020, 9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePuntarulo S. Iron, oxidative stress and human health. Mol Aspects Med. 2005;26(4\u0026ndash;5):299\u0026ndash;312.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMoen IW, Bergholdt HKM, Mandrup-Poulsen T, Nordestgaard BG, Ellervik C. Increased Plasma Ferritin Concentration and Low-Grade Inflammation-A Mendelian Randomization Study. Clin Chem. 2018;64(2):374\u0026ndash;85.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKernan KF, Carcillo JA. Hyperferritinemia and inflammation. Int Immunol. 2017;29(9):401\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSun L, Franco OH, Hu FB, Cai L, Yu Z, Li H, Ye X, Qi Q, Wang J, Pan A, et al. Ferritin concentrations, metabolic syndrome, and type 2 diabetes in middle-aged and elderly chinese. J Clin Endocrinol Metab. 2008;93(12):4690\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang N, Yu X, Xie J, Xu H. New Insights into the Role of Ferritin in Iron Homeostasis and Neurodegenerative Diseases. Mol Neurobiol. 2021;58(6):2812\u0026ndash;23.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBrinza C, Floria M, Popa IV, Burlacu A. The Prognostic Performance of Ferritin in Patients with Acute Myocardial Infarction: A Systematic Review. Diagnostics (Basel Switzerland) 2022, 12(2).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSu\u0026aacute;rez-Orteg\u0026oacute;n MF, Ensaldo-Carrasco E, Shi T, McLachlan S, Fern\u0026aacute;ndez-Real JM, Wild SH. Ferritin, metabolic syndrome and its components: A systematic review and meta-analysis. Atherosclerosis. 2018;275:97\u0026ndash;106.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRecalcati S, Invernizzi P, Arosio P, Cairo G. New functions for an iron storage protein: the role of ferritin in immunity and autoimmunity. J Autoimmun. 2008;30(1\u0026ndash;2):84\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBlankenhaus B, Braza F, Martins R, Bastos-Amador P, Gonz\u0026aacute;lez-Garc\u0026iacute;a I, Carlos AR, Mahu I, Faisca P, Nunes JM, Ventura P, et al. Ferritin regulates organismal energy balance and thermogenesis. Mol metabolism. 2019;24:64\u0026ndash;79.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCamaschella C. Iron deficiency. Blood. 2019;133(1):30\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKweon S, Kim Y, Jang MJ, Kim Y, Kim K, Choi S, Chun C, Khang YH, Oh K. Data resource profile: the Korea National Health and Nutrition Examination Survey (KNHANES). Int J Epidemiol. 2014;43(1):69\u0026ndash;77.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSearle SD, Mitnitski A, Gahbauer EA, Gill TM, Rockwood K. A standard procedure for creating a frailty index. BMC Geriatr. 2008;8:24.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKang MG, Kim OS, Hoogendijk EO, Jung HW. Trends in Frailty Prevalence Among Older Adults in Korea: A Nationwide Study From 2008 to 2020. J Korean Med Sci. 2023;38(29):e157.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKang MG, Jung HW. Association Between Oral Health and Frailty in Older Korean Population: A Cross-Sectional Study. Clin Interv Aging. 2022;17:1863\u0026ndash;72.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWon CW, Lee Y, Lee S, Kim M. Development of Korean Frailty Index for Primary Care (KFI-PC) and Its Criterion Validity. Annals geriatric Med Res. 2020;24(2):125\u0026ndash;38.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKim DH, Glynn RJ, Avorn J, Lipsitz LA, Rockwood K, Pawar A, Schneeweiss S. Validation of a Claims-Based Frailty Index Against Physical Performance and Adverse Health Outcomes in the Health and Retirement Study. journals Gerontol Ser Biol Sci Med Sci. 2019;74(8):1271\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKim SJ, Jo Y, Park SJ, Ji E, Lee JY, Choi E, Baek JY, Jang IY, Jung HW, Kim K, et al. Metabolomic profiles of ovariectomized mice and their associations with body composition and frailty-related parameters in postmenopausal women. J Endocrinol Investig. 2024;47(10):2551\u0026ndash;63.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZawadzki B, Mazur G, Butrym A. Iron Dysregulation and Frailty Syndrome. J Clin Med 2021, 10(23).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKumar A, Dhar M, Agarwal M, Mukherjee A, Saxena V. Predictors of Frailty in the Elderly Population: A Cross-Sectional Study at a Tertiary Care Center. Cureus. 2022;14(10):e30557.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChen M, Wang Y, Deng S, Lian Z, Yu K. Skeletal muscle oxidative stress and inflammation in aging: Focus on antioxidant and anti-inflammatory therapy. Front cell Dev biology. 2022;10:964130.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChen WJ, Kung GP, Gnana-Prakasam JP. Role of Iron in Aging Related Diseases. Antioxid (Basel Switzerland) 2022, 11(5).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHuang X, Song Y, Wei L, Guo J, Xu W, Li M. The emerging roles of ferroptosis in organ fibrosis and its potential therapeutic effect. Int Immunopharmacol. 2023;116:109812.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTenuta M, Cangiano B, Rastrelli G, Carlomagno F, Sciarra F, Sansone A, Isidori AM, Gianfrilli D, Krausz C. Iron overload disorders: Growth and gonadal dysfunction in childhood and adolescence. Pediatr Blood Cancer. 2024;71(7):e30995.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGupta D. Role of Iron (Fe) in Body. IOSR J Appl Chem. 2014;7:38\u0026ndash;46.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDuan G, Li J, Duan Y, Zheng C, Guo Q, Li F, Zheng J, Yu J, Zhang P, Wan M et al. Mitochondrial Iron Metabolism: The Crucial Actors in Diseases. Molecules 2022, 28(1).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYavuz BB, Cankurtaran M, Haznedaroglu IC, Halil M, Ulger Z, Altun B, Ariogul S. Iron deficiency can cause cognitive impairment in geriatric patients. J Nutr Health Aging. 2012;16(3):220\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJian J, Pelle E, Huang X. Iron and menopause: does increased iron affect the health of postmenopausal women? Antioxid Redox Signal. 2009;11(12):2939\u0026ndash;43.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eIshikawa A, Matsushita H, Shimizu S, Morita N, Hanai R, Sugiyama S, Watanabe K, Wakatsuki A. Impact of Menopause and the Menstrual Cycle on Oxidative Stress in Japanese Women. J Clin Med 2023, 12(3).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNielsen BR, Abdulla J, Andersen HE, Schwarz P, Suetta C. Sarcopenia and osteoporosis in older people: a systematic review and meta-analysis. Eur Geriatr Med. 2018;9(4):419\u0026ndash;34.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJoung-Won Lee e. Iron Status and Its Relations with Nutrient Intake, Coffee Drinking, and Smoking in Korean Urban Adults. J Community Nutr. 2003;5(1):44\u0026ndash;50.\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":"ferritin, frailty index, systemic inflammation, oxidative stress, biomarker","lastPublishedDoi":"10.21203/rs.3.rs-7644766/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7644766/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eFerritin is a key iron storage protein and a marker of iron metabolism, linked to various health outcomes beyond hematologic conditions. This study examined the association between serum ferritin levels and the frailty index (FI), a comprehensive measure of health status and biological aging, in community-dwelling older adults.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eWe performed a population-based, cross-sectional analysis using data on 6,722 participants aged 65 years and older from the Korea National Health and Nutrition Examination Survey. A 36-item deficit accumulation FI was developed, encompassing physical, cognitive, psychological, and social domains. Participants were classified as non-frail (FI\u0026thinsp;\u0026le;\u0026thinsp;0.15), pre-frail (0.15\u0026thinsp;\u0026lt;\u0026thinsp;FI\u0026thinsp;\u0026le;\u0026thinsp;0.25), or frail (FI\u0026thinsp;\u0026gt;\u0026thinsp;0.25). Serum ferritin levels were measured using an immunoradiometric assay, and individuals were categorized into sex-specific quartiles.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eIn both women and men, the FI exhibited a non-linear pattern across ferritin quartiles, with the lowest FI observed in the third quartile (Q3). Among women, FI was significantly higher in both the lowest (Q1) and highest (Q4) quartiles compared to Q3 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 and 0.001, respectively). Consistently, women in Q1 and Q4 had significantly higher odds of frailty compared to Q3, at 2.03 and 1.35 times, respectively (\u003cem\u003eP\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.001 and 0.049, respectively). Although a similar trend was observed in men, the associations were not statistically significant.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eBoth low and high serum ferritin concentrations were associated with increased frailty risk, particularly in women. These findings suggest that serum ferritin may potentially serve as a predictive biomarker for frailty in older adults.\u003c/p\u003e","manuscriptTitle":"Association Between Serum Ferritin Levels and Frailty Risk in Community-Dwelling Older Adults: A Nationwide Population-Based Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-06 16:38:24","doi":"10.21203/rs.3.rs-7644766/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-10T07:22:21+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-17T17:42:02+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-09T07:19:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"331006271141531279476061522841485738141","date":"2025-11-08T22:43:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"261597332978129766560032269749849353569","date":"2025-11-02T02:32:06+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-27T08:34:40+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-29T12:37:40+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-25T13:25:26+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-25T13:23:53+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Geriatrics","date":"2025-09-18T03:25:19+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":"72939a36-9d0e-41f8-8548-242ca00e7624","owner":[],"postedDate":"November 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-12-18T17:53:15+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-06 16:38:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7644766","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7644766","identity":"rs-7644766","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}