Correlation between iron accumulation and sarcopenia in middle-aged and elderly populations

Abstract

Purpose

The goal of this study was to investigate the relationship between iron accumulation and sarcopenia risk.

Method

We conducted a cross-sectional study based on data acquired during the year 2022–2023 from the affiliated hospital of Jiangsu University. Data on age, sex, body mass index (BMI), limb muscle mass, white blood cell (WBC) count, C-reactive protein (CRP) level, serum iron concentration, ferritin level, total iron- binding capacity (TIBC), and transferrin saturation (TSAT) were collected and analyzed. We used t-tests, chi-square tests, binary logistic regression models, and Generalized Additive Models (GAMs) for nonlinear analyses as statistical analyses.

Results

There were 110 participants, including 44 males and 66 females. Binary logistic regression analysis indicated that serum ferritin level was a risk factor for sarcopenia (OR = 1.005, 95% CI], 1.001, 1.009; P = 0.042. Furthermore, nonlinear analysis suggested a potential U-shaped relationship between ferritin levels and the risk of sarcopenia. Specifically, the risk appeared to decrease at ferritin levels below approximately 226.4 µg/L and increase at levels above approximately 241.2 µg/L. Both the overall nonlinear association (P = 0.033) and the nonlinear effect (P = 0.012) were statistically significant.

Conclusion

Serum ferritin levels may be an independent risk factor for sarcopenia in the healthy elderly population. Additionally, a non-linear relationship between ferritin levels and the risk of sarcopenia was revealed. Nevertheless, further research is needed to elucidate the complex pathways connecting iron metabolism to muscle health, and to guide the development of targeted interventions for preventing and managing sarcopenia in this population.

Introduction

Sarcopenia, initially defined by Rosenberg in 1989, describes the progressive muscle loss associated with aging [1]. However, modern definitions now encompass loss of muscle mass, strength, and physical capacity with age. For instance, the European Working Group for Sarcopenia in Older People (EWGOP) defines sarcopenia as muscle atrophy, muscle weakness (measured by hand grip strength), and/or reduced physical capacity, such as gait speed [2, 3]. Sarcopenia contributes to frailty and increases the risk of adverse outcomes, such as physical disability, reduced quality of life, and mortality, thus posing a substantial burden on public health. Epidemiological surveys worldwide have reported sarcopenia prevalence rates ranging from 5 to 13% in those aged 60 and older, with estimates increasing to 11–50% among those aged 80 and older [4]. Furthermore, the global sarcopenia incidence is projected to exceed 200 million by 2060, emphasizing the need for understanding its biology and developing effective treatments [5].

The severity of sarcopenia varies and is influenced by various risk factors, including hormonal and cytokine imbalances, protein synthesis and regulation, motor unit remodeling, sex, genetic factors, and physical inactivity [6, 7]. One emerging factor in sarcopenia research is the role of iron metabolism, which plays a critical role in muscle health and function. Recent studies have provided evidence supporting this connection [8–10]. Iron plays an essential role as a trace element in maintaining various biological functions, including oxygen transport, cellular metabolism, and DNA synthesis [11, 12]. Iron homeostasis ensures continuous absorption, utilization, storage, and recycling of iron within the body [13]. Disruptions in iron regulation can lead to iron-related metabolic disorders, including iron deficiency and iron accumulation, both of which have been linked to sarcopenia [14, 15]. Abnormalities in iron metabolism may affect the balance between muscle protein synthesis and degradation, further exacerbating sarcopenia progression [16].

Despite the recognized link between iron metabolism and sarcopenia [8, 17], gaps remain in understanding the precise role of varying levels of iron. While previous studies have investigated the effects of both iron accumulation and deficiency on sarcopenia [18–20]. A significant limitation of prior research is the reliance on ferritin as the primary biomarker for iron metabolism [21, 22], which may not fully capture the complexity of iron homeostasis. Our study aims to address this gap by employing a more comprehensive approach, incorporating multiple iron metabolism indicators to assess the effects of varying iron levels on sarcopenia. This methodology distinguishes our work from previous studies and offers novel insights into the nuanced relationship between iron regulation and muscle health.

Materials and methods

A cross-sectional study was conducted using data collected between the years 2022–2023 at affiliated hospitals of Jiangsu University. Patient data included age, sex, body mass index (BMI), limb muscle mass, white blood cell (WBC) count, C-reactive protein (CRP) level, serum iron concentration, serum ferritin, serum iron, transferrin, total iron-binding capacity, and transferrin saturation. Several methodological constraints should be noted: [1] The sample size (n = 110) provides limited power to detect modest effects; [2] Key confounders including physical activity levels and dietary iron intake were not measured; [3] Ferritin was used as the sole iron status biomarker despite its known acute-phase reactivity, though we excluded participants with CRP > 10 mg/L to minimize inflammation confounding.

Study population

The study included middle-aged (45–59) and elderly subjects over 60 years old with complete data on specified variables and who provided informed consent, while excluding individuals with missing data, chronic inflammatory disorders, recent blood transfusions, chronic liver disease, kidney disease, rheumatoid disease, endocrine system disease, malignant or hematological disease, muscle disease under drug effect, and individuals with serum C-reactive protein level > 10 mg/L as shown in Fig. 1. Furthermore, the study only collected the participants’ clinical data and did not interfere with any treatment plans. Therefore, it did not cause physiological risks to the subjects, and researchers have attempted to protect the information provided by the subjects. Additionally, the study adhered to ethical standards and protocols for conducting research involving human participants. This study was reviewed and approved by the Biomedical Research Ethics Committee of the Affiliated Hospital of Jiangsu University (SWYXLL20210401-16).

Fig. 1.

Flow diagram of participant selection from hospital medical records. Restricted cubic spline plots for logistic regression analysis of ferritin levels and sarcopenia risk, the solid red line represents the odds ratio (OR) of ferritin for sarcopenia risk, while the dotted red line indicates the 95% confidence interval of the OR. The histogram shows the distribution of ferritin levels across the study population

Laboratory method

Blood indicators

The measurement of hematologic indicators in general and hemoglobin, in particular, is widely available in clinical and research laboratories using flow cytometry on fully automated cell counters. These instruments can be used to count different blood cells with good accuracy. In this study, all subjects had their blood samples collected early in the morning in a fasting state. Hematological tests were performed at the Department of Clinical Laboratory, Affiliated Hospital of Jiangsu University, China. Iron metabolism indicators, including serum iron, total iron binding capacity (TIBC), ferritin, and transferrin saturation (TSAT) were determined as follows: TIBC was determined by enzyme-linked immunosorbent assay (ELISA), serum iron content was determined by iron-zinc colorimetric method, and TSAT was calculated as TSAT = serum iron/ TIBC. In addition, inflammatory markers, such as C-reactive protein (CRP) and white blood cells (WBC), were detected using Mindray’s BC6800 automatic hematology analyzer and corresponding reagents.

Anthropometric measurements

Based on body composition, we used a human body composition analyzer (model TANITA(ME180)) at the outpatient clinic of the Nutrition Department.

Muscle mass

Before the analysis, all subjects went to a changing room and removed their clothes, including jewelry and removable aids. They stood barefoot on the plates of the body composition analyzer with both hands holding the handles while maintaining balance. Under the guidance provided by the dietitian and manufacturer, the subject’s weight, height, and limb muscle mass (appendicular lean mass, ALM) were measured by the same staff. Body mass index (BMI) and appendicular skeletal muscle index (ASMI) were also calculated. BMI = Weight/Height2. ASMI = ALM/BMI. Height and weight were measured using a fixed stadiometer and digital scale according to the standard protocol.

Grip strength

For subject examined, the maximum grip strength of the dominant hand was measured while standing with a spring-loaded dynamometer. At least two tests were performed, and the highest reading was recorded.

Walking Pace

Measurements were taken at least twice following a six-meter (6 m) walk at a normal and uniform pace, and the average value was computed.

Diagnostic criteria for iron accumulation and sarcopenia

Iron deficiency is defined by serum ferritin levels below 15 µg/L in men and women. Generally, serum ferritin levels exceeding 150 µg/L in women or in men are considered high according to the WHO guidelines and laboratory equipment standards. Herein, ferritin levels of 400 µg /L were considered indicative of iron accumulation [23]. According to this criterion, the subjects were divided into two groups: the iron-normal group (N = 79) and the iron accumulation group (N = 31).

Based on the diagnostic criteria for sarcopenia released by the Asian Sarcopenia Group [17]: (1) Decreased muscle strength (grip strength: males < 28 kg, females < 18 kg); (2) Decreased physical function (5-time sit-to-stand test > 12 s or 60-meter walking speed < 1 m/s); (3) Decreased skeletal muscle content (DXA: < 7 kg/m² for males and < 5.4 kg/m² for females). Sarcopenia can be diagnosed if (1), or (2) + (3) are present. According to this criterion, the subjects were divided into a sarcopenia group (N = 31) and a non-sarcopenia group (N = 79).

Statistical methods

Statistical analysis was conducted using SPSS 25.0, and differences were considered statistically significant at P < 0.05. Data are expressed as the mean ± standard deviation . When comparing baseline data, the independent samples t-test was used for normally distributed data. Nonparametric tests were conducted for nonnormally distributed data. The chi-square test was used to compare the counts. Binary logistic regression analysis was used to analyze the relationship between serum ferritin level and sarcopenia. Restricted cubic splines were used to fit the relationship between serum ferritin levels and the different sarcopenia groups, and curve fitting was performed using RStudio (4.2.2).

Result

The cross-sectional study was conducted at the affiliated hospitals of Jiangsu University between June 2022 and July 2023, among 110 subjects, 44 (40%) were males and 66 (60%) were females, with a mean age of 72.46 ± 10.43 years. According to the ferritin levels measured, the subjects were divided into two groups: normal iron group and iron accumulation group, of which 79 were in the normal iron group and 31 in the iron accumulation group. Collectively, 42 (53.16%) were females in the normal iron group and 24 (77.42%) were females in the iron accumulation groups. There was a significant difference in the sex ratio between the two groups (P = 0.019). The proportion of low gait in the iron accumulation group and normal iron groups was 83.9% and 44.3%, respectively. Comparatively, the number of people with a low gait in the iron accumulation group was significantly higher than that in the normal iron group (P < 0.001). However, there were no significant differences between the two groups in the remaining data (Table 1).

Table 1.

Baseline characteristics of subjects stratified by iron status and other relevant health parameters

Variable	Total (N = 110)	Normal iron group (N = 79)	Iron accumulation group (N = 31)	P value
Female (%)	60.00	53.16	77.42	0.019
Height (m)	1.63 ± 0.08	1.63 ± 0.09	1.62 ± 0.07	0.447
Weight (kg)	62.60 ± 9.69	62.89 ± 10.44	61.86 ± 7.54	0.621
BMI (kg/m2)	23.60 ± 3.42	23.59 ± 3.54	23.64 ± 3.13	0.943
Age(years)	72.46 ± 10.43	72.89 ± 9.81	71.39 ± 11.97	0.500
WBC (109/L)	5.89 ± 2.02	6.03 ± 2.18	5.54 ± 1.52	0.257
CRP (mg/L)	3.87 ± 10.88	2.31 ± 1.88	7.86 ± 19.96	0.245
Serum iron(µg/L)	19.51 ± 6.21	19.31 ± 5.41	20.03 ± 7.96	0.588
Ferritin(µg/L)	179.07 ± 169.24	129.36 ± 73.85	305.75 ± 258.45	< 0.001
TIB(µg/L)	53.79 ± 7.77	54.07 ± 7.36	53.09 ± 8.81	0.556
Transferrin saturation (%)	36.14 ± 9.99	35.79 ± 9.02	37.03 ± 12.24	0.561
Limb muscle mass(kg)	18.30 ± 4.47	18.56 ± 4.78	17.63 ± 3.56	0.329
ASMI (kg/m2)	0.79 ± 0.21	0.80 ± 0.21	0.76 ± 0.21	0.436
Low gait (%)	55.50	44.30	83.90	< 0.001
Low grip strength (%)	93.60	91.10	100.00	0.088

Note: BMI: Body Mass Index; ASMI: Appendicular Skeletal Muscle Mass Index

Comparison of clinical indicators between sarcopenia and non-sarcopenia groups

Following the diagnostic criteria for sarcopenia released by the Asian Sarcopenia Group, participants were divided into two groups: the non-sarcopenia group and the sarcopenia group. Among these, there were 78 participants in the non-sarcopenia group, with 49 (62.8%) being females, and 32 participants in the sarcopenia group, with 17 (53.1%) being females. There was no significant difference in the proportion of females between the two groups. A significant difference in BMI was observed between the sarcopenia group (21.11 ± 2.19) and the non-sarcopenia group (24.63 ± 3.31), with a p-value of < 0.001. However, there were no significant differences in iron metabolism-related indicators or ASMI between the two groups. See Table 2.

Table 2.

Comparison of clinical indicators between sarcopenic group and non-sarcopenic group subjects

Variable	Non-sarcopenic group (N = 78)	Sarcopenic group (N = 32)	P value
Age(years)	71.8 ± 10.22	74.09 ± 10.93	0.296
Female (%)	62.80	53.10	0.346
BMI (kg/m2)	24.63 ± 3.31	21.11 ± 2.19	< 0.001
WBC (109/L)	5.78 ± 1.72	6.17 ± 2.61	0.366
CRP (mg/L)	4.66 ± 12.85	1.96 ± 1.14	0.122
Serum iron(µg/L)	19.55 ± 6.57	19.43 ± 5.32	0.927
Ferritin(µg/L)	170.14 ± 94.13	200.84 ± 279.30	0.095
TIB(µg/L)	53.80 ± 7.91	53.76 ± 7.53	0.978
Transferrin saturation (%)	36.19 ± 10.73	36.03 ± 8.03	0.940
Limb muscle mass(kg)	19.4 ± 4.39	15.62 ± 3.48	< 0.001
ASMI (kg/m2)	0.80 ± 0.22	0.75 ± 0.17	0.191

Note: BMI: Body Mass Index; ASMI: Appendicular Skeletal Muscle Mass Index

Binary logistic regression analysis of factors associated with sarcopenia risk

The relationship between ferritin levels and sarcopenia was analyzed using binary logistic regression to correct for confounding factors. Sex, age, BMI, leukocytes, hypersensitive C-reactive protein, ferritin, transferrin saturation, serum iron, and iron accumulation status were included as independent variables. The analysis showed that after adjusting for related confounders, ferritin was a risk factor for sarcopenia (OR = 1.005, 95%CI: 1.001, 1.009, P = 0.042), For each unit increase in ferritin, the odds of sarcopenia increase by 0.5%. Since the confidence interval does not include 1, this result suggests a significant relationship between ferritin levels and sarcopenia risk. while BMI was a protective factor for sarcopenia (OR = 0.545, 95%CI: 0.415, 0.718, P < 0.001). Transferrin Saturation: (OR = 0.884 (95% CI: 0.768–1.018, P = 0.090): this shows a non-significant trend towards a negative association with sarcopenia (P = 0.090). Although the odds ratio is less than 1, the confidence interval includes 1, indicating that this result is not statistically significant. This suggests no clear relationship with sarcopenia in this study. Serum Iron: (OR = 1.125 (95% CI: 0.900-1.407, P = 0.299): it does not show a statistically significant association with sarcopenia (P = 0.299). The confidence interval includes 1, indicating that serum iron levels are not significantly related to sarcopenia in this study. The results of the analysis are presented in Table 3.

Table 3.

Results binary logistic regression analysis of factors associated with sarcopenia

Variable	OR	Wald value	95%CI	P value
Gender	0.953	0.005	0.265,3.434	0.941
Age	1.014	0.16	0.945,1.088	0.690
BMI	0.545	18.695	0.415,0.718	< 0.001
WBC	0.963	0.079	0.742,1.249	0.778
Hypersensitive CRP	0.809	1.292	0.561,1.167	0.256
Ferritin	1.005	4.119	1.001,1.009	0.042
Transferrin saturation	0.884	2.878	0.768,1.018	0.090
Serum iron	1.125	1.077	0.900,1.407	0.299
Iron overload state	5.617	3.528	0.928,34.03	0.060

BMI: Body Mass Index; OR odds ratio; CI: Confidence interval

Nonlinear regression analysis of ferritin levels and their association with sarcopenia risk, adjusting for confounders

The relationship between serum ferritin levels and different sarcopenia groups was modeled and visualized using restricted cubic splines while correcting for confounders such as BMI, age, sex, and leukocytes. The lowest number of nodes under Akaike information criterion (AIC)3 was selected. Figure 2 shows the U-shaped association between ferritin levels and sarcopenia risk, with cutoff values indicated. When ferritin levels were less than 226,428 µg/L, the risk of sarcopenia decreased but gradually increased with ferritin levels greater than 241.2 µg/L. The overall effect was statistically significant (P = 0.033), and the nonlinear effect of ferritin level was significant (P = 0.012). However, the modest sample size and multiple comparisons warrant caution in interpreting these results. The absence of data on physical activity and dietary factors precludes adjustment for these potential confounders.

Fig. 2.

U-shaped relationship between ferritin levels and sarcopenia risk based on restricted cubic spline analysis. Restricted cubic spline plots for logistic regression analysis of ferritin levels and sarcopenia risk, the solid red line represents the odds ratio (OR) of ferritin for sarcopenia risk, while the dotted red line indicates the 95% confidence interval of the OR. The histogram shows the distribution of ferritin levels across the study population. Note the wider confidence intervals at extreme low and high ferritin values, reflecting greater uncertainty in risk estimates at these levels

Discussion

Sarcopenia is a syndrome of generalized loss of muscle mass, strength, or physiology with age [24].Muscle loss, a hallmark of sarcopenia, is associated with an increased fracture risk, impaired physical function, and diminished quality of life [25]. Iron, as a vital micronutrient, plays crucial roles in various biological processes such as enzymatic activity, mitochondrial function, and energy metabolism [12, 26, 27], all of which are essential for muscle health.

Serum ferritin is the primary iron storage protein in the body and plays a key role in regulating iron bioavailability and preventing iron deficiency or iron accumulation diseases [28]. However, the aging process often leads to iron accumulation owing to the absence of an effective excretory route, which contributes to the development of various diseases [29, 30]. In recent years, increasing evidence has shown that iron accumulation in skeletal muscle plays a significant role in the development of sarcopenia, as it can damage muscle cell structure and function, impair the body’s exercise capacity and adaptation, and accelerate the progression of sarcopenia [18]. Ferritin expression is regulated by various factors including inflammation, oxidative stress, and iron levels [31, 32]. Studies have demonstrated that elevated ferritin levels are associated with an increased risk of various adverse body composition changes, including sarcopenia [33, 34]. Notably, one of these studies found that in women over 50 years of age, increased ferritin levels were linked to a higher incidence of sarcopenia, with a 1.52-fold greater risk of sarcopenia observed in the elevated serum ferritin group compared to the normal group, which aligns with our study’s findings. In addition, studies have shown that increased ferritin may promote metabolic processes and protein synthesis in muscle cells by improving iron availability [35], which could contribute to muscle dysfunction. Furthermore, ferritin may also affect the repair and regeneration processes of muscles by regulating iron levels within muscle cells, processes that are crucial in maintaining muscle health and sarcopenia. In a study of mice with iron overload, skeletal muscles exhibited increased oxidative stress and decreased expression of satellite cell markers. After cardiotoxin (CTX)-induced muscle injury, these mice showed delayed muscle regeneration and a decrease in the size of regenerating myofibers [36], highlighting how dysregulated iron levels can impair muscle repair and regeneration. However, other studies have reported contradictory results. For instance, studies have shown that ferritin levels are associated with decreased muscle function and increased risk of sarcopenia [37]. Nakagawa et al. [38] reported a significant negative correlation between serum ferritin levels and muscle strength in a study of 300 patients undergoing hemodialysis. On the other hand, several experimental studies have identified a connection between iron accumulation and muscle disorders within the musculoskeletal system. Altun et al. [39] and Jung et al. [40] observed notably higher non-heme iron levels in the skeletal muscles of elderly rats. Additionally, Xu et al. [41] suggested that iron accumulation could be a characteristic feature of aging skeletal muscle. Recent clinical investigations have highlighted a notable association between iron accumulation and sarcopenia. For instance, a 2014 study involving 1380 middle-aged and elderly Korean women found that individuals with sarcopenia exhibited significantly higher serum ferritin levels, with those with elevated serum ferritin levels facing a 2.02-fold increased risk compared to those with normal levels [22]. In 2017, a study of 639 Italians aged 65 years and above found increased serum ferritin levels in sarcopenic patients along with elevated serum inflammatory markers [42]. Moreover, a cohort study involving 639 hospitalized elderly individuals showed significantly higher serum ferritin levels in the sarcopenia group than in healthy controls, with levels exceeding the normal range (> 145 ug/dl) in patients with sarcopenia [42].

Notably, this study found a potential U-shaped relationship between ferritin levels and sarcopenia risk. While mechanistically plausible, these findings require cautious interpretation due to cross-sectional design, unmeasured lifestyle confounders, and reliance on ferritin alone despite its acute-phase reactivity. The exploratory U-shaped relationship suggests that both low and high ferritin levels may adversely affect muscle health, though the mechanisms likely differ. Iron deficiency may impair mitochondrial function, while iron accumulation could promote oxidative stress. Future studies should validate these associations in larger cohorts using comprehensive iron panels while controlling nutritional and lifestyle factors. Our results should be viewed as hypothesis-generating rather than clinically directive.

In addition, while the overall and nonlinear effects were statistically significant, the effect size was small, and the confidence intervals, particularly at lower ferritin levels, were relatively wide. Threshold values identified (e.g., 226.4 µg/L and 241.2 µg/L) should be interpreted cautiously, as these cutoffs are data-driven and may not generalize across populations. The use of Bonferroni correction for multiple comparisons could further affect the statistical significance of some findings, highlighting the need for replication in larger samples.

Our findings support the fact that both low and high ferritin levels may adversely affect muscle mass and function. Therefore, we propose two distinct biological mechanisms through which ferritin levels may affect sarcopenia risk. First, high Ferritin levels indicate iron accumulation which can lead to oxidative stress and inflammation. Excess iron accumulation in muscle tissue disrupts redox homeostasis, catalyzing the generation of reactive oxygen species (ROS), which in turn contribute to cellular damage. ROS are critical for physiological signaling pathways, but excessive oxidative stress is linked to tissue injury and disease [43]. In the context of muscle, oxidative stress impairs protein synthesis, disrupts mitochondrial function, and promotes muscle atrophy [44]. Elevated ferritin and iron levels have been associated with reduced muscle strength and mass in aging populations [33], and increased iron stores are correlated with markers of chronic inflammation, further exacerbating muscle wasting through inflammatory cytokine production [45]. These mechanisms suggest that iron accumulation, through oxidative stress and inflammation, can contribute to the pathogenesis of sarcopenia. On the other hand, low ferritin levels, which reflect iron deficiency, may negatively affect muscle function due to insufficient oxygen delivery and mitochondrial dysfunction. Iron is a key component of the mitochondrial respiratory chain, necessary for ATP production. Iron deficiency results in reduced mitochondrial quantity and function, impairing oxidative metabolism in skeletal muscle and leading to muscle weakness and fatigue [46, 47]. This disruption in muscle energetics is closely linked to sarcopenia, as decreased ATP production diminishes muscle strength and endurance. These opposing mechanisms illustrate the potential for a U-shaped relationship, where both extremes of ferritin levels—iron accumulation and iron deficiency—adversely affect muscle health through distinct biological pathways. Similar U-shaped associations between iron levels and muscle function have been observed in other study, linking both iron deficiency and iron accumulation to adverse health outcomes such as muscle weakness and sarcopenia [48].

Contrary to expectations, our study found no significant association between CRP levels and sarcopenia risk. This finding is supported by other studies that have also reported no significant link between inflammatory markers and sarcopenia [49, 50]. Although some research suggests that inflammation is associated with an increased risk of sarcopenia [46, 51, 52], our results contribute to a more nuanced understanding of this relationship. Despite significant advancements in understanding the complex etiology of sarcopenia, which involves multiple factors including physical inactivity, hormonal changes, and muscle protein turnover [7], the relationship between serum iron status and sarcopenia remains unclear. While serum iron status and sarcopenia are closely linked, comprehensive evidence to establish a causal relationship between them is currently lacking [53]. The current findings provide a new perspective on the pathogenesis of sarcopenia, highlighting the need for further research to elucidate the underlying mechanisms.

In summary, our study’s findings, combined with existing evidence, suggest that both low and high ferritin levels are associated with an increased risk of sarcopenia, highlighting the importance of maintaining optimal iron status in the prevention and management of sarcopenia. While these findings suggest a potential U-shaped relationship between ferritin levels and sarcopenia risk, it is important to emphasize that this relationship is exploratory and warrants further investigations.

Limitation

Several important limitations should be acknowledged. First, the cross-sectional design precludes causal inferences between iron status and sarcopenia, necessitating future longitudinal studies to establish temporal relationships. Second, our modest sample size may limit the generalizability of findings and statistical power to detect smaller effects. Third, we lacked data on potential confounders including dietary iron intake and physical activity levels that might influence both ferritin levels and muscle health. Fourth, while we observed a statistically significant U-shaped relationship, this finding should be interpreted cautiously due to multiple comparisons in our exploratory analysis. Finally, ferritin alone may not fully capture iron status as it is influenced by both iron stores and acute inflammation. Future research would benefit from larger, multicenter cohorts with comprehensive iron profiling (including hepcidin), detailed covariate assessment, and ideally, interventional designs to clarify these relationships.

Conclusion

This study suggests a possible U-shaped relationship between ferritin levels and the risk of sarcopenia, indicating that both low and high ferritin concentrations may be associated with muscle deterioration. While further validation is needed, these findings provide initial insights into the potential role of iron dysregulation in the development of sarcopenia and may have implications for future preventive and therapeutic strategies.

Ferritin appears to influence sarcopenia risk beyond a certain threshold, though sarcopenia pathogenesis is multifactorial and extends beyond iron metabolism alone. Future research should prioritize longitudinal designs to establish causality and define optimal iron thresholds, while also investigating the mechanistic pathways connecting iron homeostasis, inflammation, and muscle health. Additionally, exploration of iron-modulating interventions could reveal new approaches to mitigate sarcopenia risk in aging populations.

These exploratory insights lay the groundwork for further investigation into iron’s role in muscle aging and may ultimately inform personalized prevention and management strategies for sarcopenia.

Author contributions

M.R.T and H.C conceptualized the study, analyzed the data, and wrote the manuscript. Z.S evaluated the data and edited the manuscript accordingly. Z.GY supervised this study. All authors contributed to the writing and reviewing of the manuscript.

Funding

This work was supported by the Scientific Research Project of Jiangsu Provincial Health Committee in China (to Guo-yang Zhao, M2022119) and the Young and Middle Doctors Training Project of Excellent Talent for Osteoporosis and Bone Mineral Disease (to Guo-yang Zhao, G-X-2019-1107).

Data availability

No datasets were generated or analysed during the current study.

Declarations

Ethical approval and consent to participate

This study was reviewed and approved by the Biomedical Research Ethics Committee of the Affiliated Hospital of Jiangsu University (SWYXLL20210401-16).

Consent for publication

All authors provided consent for publication.

Competing interests

The authors declare no competing interests.