Association of serum iron metabolism with muscle mass and frailty in older adults: A cross-sectional study of community-dwelling older adults

Abstract

This study aimed to explore the correlation between serum ferritin and additional biomarkers associated with iron metabolism, as well as their connection to muscle atrophy and frailty in the community-dwelling middle-aged and elderly population. The study included 110 middle-aged and elderly participants. Participants were categorized into an iron accumulation group (31 cases) and a normal iron group (79 cases) based on the standard ferritin values for men and women. Based on the criteria of the Asian Working Group on Muscular Dystrophy, participants were classified into a sarcopenia group (31 cases) and a non-sarcopenia group (79 cases). Using the Fried frailty syndrome criteria, participants were categorized into non-frailty (7 cases), pre-frailty (50 cases), and frailty (53 cases) groups. We employed multiple linear regression, binary logistic regression, partial correlation analysis, and ordinal logistic regression to assess the associations between iron metabolism indices and the presence of muscle atrophy and frailty. Compared with the normal iron group, the iron overload group had significantly higher ferritin, weight loss, fatigue, slow gait, and frailty scores (P < .05). Among the 3 models we set, ferritin was not significantly correlated with muscle mass in models 1 and 3 (P > .05), ferritin was positively correlated with muscle mass in model 2 (P_model2 = .048), but Transferrin saturation was positively correlated with muscle mass in all 3 models (P_model1 = .047, P_model2 = .026, P_model3 = .024). Ferritin, body mass index and iron overload were the influencing factors of sarcopenia (P_ferritin = .027, P_BMI < .001, P_{iron overload} = .028). Ferritin was positively correlated with weight loss, fatigue, slow gait, frailty score, and frailty grade (P < .05). Age, gender and ferritin were the influencing factors of frailty classification (P < .05). Disrupted iron metabolism can lead to decreased muscle mass and function among the middle-aged and elderly, increasing frailty risk. It’s crucial to prioritize community-based frailty screening and prevention, focusing on iron utilization as well as storage, since accelerating the body’s iron metabolism cycle might influence muscle health more significantly than iron reserves.

1. Introduction

Aging is a natural biological process that manifests diversely among individuals, with certain individuals exhibiting sustained robust health at their advanced years. The primary impacts on the motor system involve osteoporosis, sarcopenia, and degenerative changes in joints. Sarcopenia is characterized by the loss of muscle mass, decreased muscle strength, and impaired muscle function. Sarcopenia is closely linked to aging, and the risk of osteoporotic fractures increases gradually with age. As a result, fragility fractures are common in ordinary elderly individuals due to the simultaneous presence of sarcopenia and osteoporosis in their daily lives.

Iron is essential for maintaining the normal function of muscle cells. However, the body has a limited capacity to excrete iron, and an excess of iron may result in prolonged tissue damage. Research indicates an age-related buildup of iron in skeletal muscle.^[1,2] Accumulation of iron can impact mitochondrial function, induce ferroptosis, trigger insulin resistance, and activate the ubiquitin-proteasome system, ultimately resulting in reduced skeletal muscle function and atrophy. While we now better comprehend the epidemiology and clinical manifestations of sarcopenia, its pathogenesis remains incompletely understood.^[3] Recently, there has been increased interest in the role of trace elements in muscle metabolism. Numerous studies have established connections between magnesium, selenium, and calcium, and various skeletal muscle diseases.^[4–6] Iron is a crucial trace element for cell metabolism, survival, and proliferation. Numerous studies have indicated that a lack of iron may result in anemia, neurocognitive dysfunction, and diminished functional capacity.^[7,8] Excessive iron accumulation, however, can potentially initiate osteoporosis, neurodegeneration, and cardiovascular disease.^[9–11] Previous studies have also validated the association between iron accumulation, iron overload in experimental animals, and muscle diseases within the realm of skeletal muscle research.^[12,13] However, there is limited reporting on the association between serum iron overload and muscle mass in humans.

Frailty stands as an escalating global health concern, bearing substantial implications for both clinical practice and public health. The prevalence of frailty is expected to surge alongside the rapid expansion of the aging population. It is marked by a decline in functioning across diverse physiological systems, coupled with an increased susceptibility to stressors. Individuals grappling with frailty face an elevated risk of adverse outcomes, encompassing falls, hospitalization, and mortality.^[14] Within the context of the accelerated aging of the global population, there is a notable rise in the number of older individuals grappling with both sarcopenia and frailty. This scenario has elevated it to a pivotal geriatric syndrome in clinical care. Sarcopenia and physical frailty share fundamental characteristics, including compromised physical function, notably in mobility, a high prevalence, and a close correlation with adverse health outcomes, such as disability and mortality in the elderly. Notably, they exhibit potential reversibility and have been concurrently studied from their inception.^[15,16] Thus, we investigated the relationship between sarcopenia and iron metabolism in frail and nonfrail older adults residing in the community. Additionally, we explored the link between iron metabolism and frailty indicators, with the hope that the findings of this study can be generalized to the broader elderly population.

This study aimed to explore the correlation between sarcopenia and serum ferritin levels, as well as other biomarkers associated with iron metabolism, in both frail and non-frail older adults.

2. Materials and methods

Recruitment closed from November 2022 through July 2023. This study enrolled a total of 110 middle-aged and elderly patients, including 44 males and 66 females, with a mean age of 72.46 ± 10.43 years. Patients were categorized into an iron accumulation group (31 cases) and a normal iron group (79 cases) based on the standard ferritin values for males and females. Patients were divided into a sarcopenia group (n = 31) and a non-sarcopenia group (n = 79). According to the Fried frailty syndrome criteria, 5 components – unexpected weight loss, slow movement, reduced grip strength, fatigue, and physical inactivity – were used to identify the recruited subjects as frail. Patients were categorized into non-frail (7 cases), pre-frail (50 cases), and frail (53 cases) based on their scores. All participants provided informed consent before enrollment in the study. All subjects were of Han Chinese ethnicity. We confirm that all methods were performed in accordance with the relevant guidelines and regulations. The datasets used and/or analyzed during the current study available from the corresponding author on reasonable request.

2.1. Blood biomarkers

Hematology tests in Yancheng and Zhenjiang were conducted at the Clinical Laboratory Department of the Affiliated Hospital of Jiangsu University. Laboratory analysis included measurements of the following iron metabolism parameters: serum iron, total iron binding capacity (TIBC), ferritin, and transferrin saturation (TSAT). Inflammatory markers comprised C-reactive protein (CRP), white blood cell count (WBC), and 25-hydroxyvitamin D. The 25-hydroxyvitamin D level was assessed using a chemiluminescence immunoassay analyzer manufactured by Shenzhen New Industry Biomedical Engineering Co., LTD (Shenzhen, China). WBC was detected using the Mindray BC6800 automatic blood cell analyzer and corresponding reagents. The normal reference range for WBC was 3.5 to 9.5 × 10⁹/L. Total iron binding capacity was assessed using enzyme-linked immunosorbent assay, and serum iron was measured via ferrozine colorimetric assay (normal reference range 9.5–5 μmol/L). The automatic biochemical analyzer AU5800 (Beckman Coulter Company [Fuzhou City, China]) was used for both measurements.

2.2. Participant inclusion/exclusion criteria

Individuals aged between 60 and 80 years old are eligible for inclusion in this study. Exclusion criteria encompass those with chronic liver and kidney diseases, rheumatic diseases, endocrine system disorders, metabolic bone or muscle ailments, malignant tumors, and blood disorders. Additionally, participants who have recently used medications impacting muscle function, such as psychotropic drugs, anti-epileptic drugs, muscle relaxants, and sex hormone medications, are excluded. Moreover, individuals requiring mobility assistance or experiencing general life challenges are also excluded from the study.

2.3. Body composition analyzer measures body composition

The body composition analyzer was utilized to measure muscle mass in the trunk and limbs, as well as body moisture and fat in various areas. In the Zhenjiang Area, the test was conducted by the operator at the orthopedic clinic of the Affiliated Hospital of Jiangsu University, following the guidelines provided by the chief physician of the nutrition department and the manufacturer. Participants were instructed to stand on the body composition analyzer’s electrode plate barefoot and on an empty stomach. They were asked to hold the handle with both hands while maintaining balance. Lean soft tissue mass in the body is categorized into arms, legs, and trunk regions. Limb muscle mass is determined by combining the muscle mass of both the upper and lower limbs. In the Yancheng Area, the test was carried out by the operator at Yanfu Orthopaedic Hospital’s orthopedic clinic, following the regulations of the orthopedic chief physician and the manufacturer. The specific testing procedures are identical to those mentioned above. To minimize errors stemming from the testing equipment itself, we opted for the identical model of body composition analyzer (model TANITA [MC-180]).

2.4. Frailty measurements

We used specific criteria to assess frailty, which included: unintentional or involuntary weight loss, defined by a body mass index (BMI) below 18.5 kg/m² and/or a weight loss of more than 10 pounds (4.5 kg) in the last 6 months; slowness, measured by comparing the average of two 6 m fast gait speed test results to the lowest quintile of values stratified by sex and height; weakness, indicated by a grip strength lower than 28.0 kg for men and 18.0 kg for women; exhaustion, evaluated by the vitality domain in the SF-12, with individuals scoring less than 10 out of 15 (based on a previous population-based study of frailty) classified as exhausted; and Physical inactivity, assessed by the Longitudinal Ageing Physical Activity Questionnaire (LAPAQ), which recorded the frequency and duration (in minutes) of 6 different activities (walking outside, bicycling, gardening, light and heavy household activities, and sports activities) in the past 2 weeks. A participant was considered physically inactive if their overall average daily time spent on physical activities was in the lowest quintile for their sex. We categorized participants as frail if they had 3 or more components, pre-frail if they had 1 or 2 components, and robust if they had none of the components.

2.5. Measurement of grip strength

Utilize a spring-type grip device to assess grip strength while standing with extended elbows. If elderly individuals are unable to stand independently, measurements should be taken while they are seated. Conduct the test a minimum of 2 times, applying maximum force with the dominant hand or both hands, and record the highest reading.

2.6. Gait speed test

Measure the time it takes to walk 6 meters at a consistent, normal pace without acceleration or deceleration, and record the average speed after at least 2 measurements.

2.7. Definition of sarcopenia

We used the 2019 criteria from the Asian Working Group for Sarcopenia^[17] to identify sarcopenia, a condition of muscle loss. We calculated the Skeletal Muscle Index (SMI) as the ratio of Appendicular Lean Mass, which was measured by the body composition analyzer, over the square of height. We considered participants to have low SMI if their ratio was <7.0 kg/m² for men or <5.4 kg/m² for women. We defined low gait speed as an average speed of 2 trials <1.0 m/s. We classified participants as having sarcopenia if they had both low SMI and low lower limb strength or gait speed, possible sarcopenia if they had either low lower limb strength or gait speed but not low SMI, and non-sarcopenia if they had neither low SMI nor low lower limb strength or gait speed. Because of the limited number of participants, we divided the subjects into 2 groups: the sarcopenia group and the non-sarcopenia group.

2.8. Serum ferritin

Serum ferritin (µg/L) was quantified through electrochemiluminescence immunoassay. Human iron storage categories were determined in accordance with WHO guidelines^[18] and the laboratory equipment standards employed in our facility. Iron deficiency was designated by serum ferritin levels <15 µg/L in both men and women, while iron overload was identified by serum ferritin exceeding 150 µg/L in women and >400 µg/L in men. Serum ferritin levels outside these ranges were classified as indicative of normal iron storage status.

2.9. Muscle mass

Muscle mass was measured using a body composition analyzer (model TANITA MC-180). Lean skeletal muscle mass (ASM) (kg) was calculated as the lean muscle mass of the limbs. BMI (kg/m²) was defined as weight divided by height squared. To further quantify muscle mass, appendicular skeletal muscle index (ASMI) was calculated as ASM divided by BMI.

2.10. Statistical analysis

We employed the one-sample Shapiro–Wilk test to evaluate normal data distribution. Results showing conformity to a normal distribution were expressed as mean ± standard deviation. We employed the 1-sample Shapiro–Wilk test to evaluate normal data distribution. Results showing conformity to a normal distribution were expressed as mean ± standard deviation. All estimates were calculated while considering sample weights. Linear regression models (for continuous variables) and weighted chi-square tests (for categorical variables) were employed to compare baseline characteristics. Linear regression was employed to analyze the relationship between ferritin, transferrin saturation, and ASMI. Binary logistic regression was employed to analyze the relationship between ferritin, BMI, low activity, iron overload, age, gender, WBC, CRP, 25 (OH) D, and sarcopenia. Partial correlation analysis was employed to study the correlation between ferritin and frailty indicators, frailty score, and frailty classification. Ordinal logistic regression was employed to analyze the relationship between age, gender, ferritin, BMI, CRP, WBC, TIBC, and 25 (OH) D and frailty classification. A significance level of P < .05 was applied for statistical significance.

2.11. Statement of ethics

Written informed consent was obtained from all recruited patients. This study has been approved by the Medical Ethics Committee of the Affiliated Hospital of Jiangsu University. Approval number: SWYXLL20210401-16.

3. Results

3.1. Baseline characteristics of study participants

Based on the inclusion and exclusion criteria illustrated in Figure 1, a total of 110 subjects were included in the final analysis. Participants were classified based on their iron status, with 79 in the iron-normal group and 31 in the iron-overload group, as indicated in Table 1. Individuals in the iron-overload group exhibited higher levels of ferritin, weight loss, fatigue, slow gait, and frailty scores compared to those in the normal iron group. Women constituted 60.00% of the total, with 53.16% in the iron-normal group and 77.42% in the iron-overload group. A significant difference in gender ratio between the 2 groups was observed (P = .019). There were no significant differences in WBC and CRP levels between the iron overload group and the iron normal group, which is consistent with our intention to exclude the effect of inflammation on ferritin when we designed the experiment. The frailty score in the iron-overload group was significantly higher than that in the iron-normal group, indicating a potential association between iron overload and frailty in the elderly (P < .001). Patients with iron overload exhibit varying degrees of abnormalities in inflammatory markers, iron metabolism, and frailty assessment, potentially linked to physiological changes induced by iron overload in the elderly. A thorough investigation of these associations will enhance our understanding of the relationship between iron metabolism and frailty in old age.

Figure 1.

The participant selection flow-chart.

Table 1.

Baseline characteristics of study participants.

Characteristic	lron status			P value
n	Overall (N = 110)	Normal (N = 79)	Overload (N = 31)
Female	60.00%	53.16%	77.42%	.019
Height (m)	1.63 ± 0.08	1.63 ± 0.09	1.62 ± 0.07	.447
Weight (kg)	62.60 ± 9.69	62.89 ± 10.44	61.86 ± 7.54	.621
BMI (kg/m2)	23.60 ± 3.42	23.59 ± 3.54	23.64 ± 3.13	.943
Age (yr)	72.46 ± 10.43	72.89 ± 9.81	71.39 ± 11.97	.500
WBC (1000 cells/µL)	5.89 ± 2.02	6.03 ± 2.18	5.54 ± 1.52	.257
CRP (mg/L)	2.25 ± 1.53	2.19 ± 1.53	2.44 ± 1.56	.613
Serum iron (µmol/L)	19.51 ± 6.21	19.31 ± 5.41	20.03 ± 7.96	.588
Ferritin (µg/L)	179.07 ± 169.24	129.36 ± 73.85	305.75 ± 258.45	<.001
TIBC (µmol/L)	53.79 ± 7.77	54.07 ± 7.36	53.09 ± 8.81	.556
TSAT (%)	36.14 ± 9.99	35.79 ± 9.02	37.03 ± 12.24	.561
25 (OH) D (ng/mL)	16.40 ± 5.83	16.23 ± 6.03	16.86 ± 5.36	.609
Muscle mass (kg)	42.90 ± 10.65	43.35 ± 10.57	41.76 ± 10.94	.485
Trunk muscle mass (kg)	19.92 ± 3.67	20.08 ± 3.87	19.52 ± 3.13	.475
Limb muscle mass (kg)	18.30 ± 4.47	18.56 ± 4.78	17.63 ± 3.56	.329
ASMI	0.79 ± 0.21	0.80 ± 0.21	0.76 ± 0.21	.434
Weight loss	0.16 ± 0.37	0.04 ± 0.19	0.48 ± 0.51	<.001
Exhaustion	0.06 ± 0.25	0.00 ± 0.00	0.23 ± 0.43	<.001
Slow pace	0.55 ± 0.50	0.44 ± 0.50	0.84 ± 0.37	<.001
Low level of physical activity	0.54 ± 0.50	0.49 ± 0.50	0.65 ± 0.49	.155
Low grip strength	0.94 ± 0.25	0.91 ± 0.29	1.00 ± 0.00	.088
Frailty score	2.25 ± 1.18	1.89 ± 1.01	3.19 ± 1.05	<.001

Mean ± SD for continuous variables: P value was calculated by linear regression model. Percent for categorical variables: P value was calculated by weighted chi-square test.

25 (OH) D = 25-hydroxyvitamin D, ASMI = appendicular skeletal muscle index, BMI = body mass index, CRP = C-reactive protein, TSAT = transferrin saturation, WBC = white blood cell count.

3.2. The impact of serum ferritin and transferrin saturation on muscle mass

Table 2 presents the results of the multiple linear regression analysis. In the unadjusted model, transferrin saturation is significantly associated with muscle mass (Model 1: β = 0.004, 95% CI: 0.00006, 0.0079). After adjusting for covariates, this significant association remains evident in the fully adjusted models (Model 2: β = 0.004, 95% CI: 0.0005, 0.008; Model 3: β = 0.005, 95% CI: 0.001, 0.009). Interestingly, in Model 2, ferritin shows a positive correlation with ASMI (Model 2: β = 0.0003, 95% CI: 0.000008, 0.0005, P = .043). It appears that in our recruited participants, the expected inverse relationship between ferritin levels and muscle mass, as suggested by previous clinical database studies, is not observed. We find that TSAT better explains the relationship between iron metabolism and muscle mass in this population compared to ferritin.

Table 2.

Relationship between ferritin, TSAT and ASMI.

Dependent variable	Independent variable	Model 1			Model 2			Model 3
		B	95% CI	P value	B	95% CI	P value	B	95% CI	P value
ASMI	Ferritin	0.0002	−0.00003 to 0.0004	.081	0.0003	0.000008–0.0005	.043	0.0002	−0.00003 to 0.0005	.085
	TSAT	0.0040	0.00006–0.0079	.047	0.0040	0.0005–0.008	.026	0.005	0.001–0.009	.024

Model 1: no covariates were adjusted; Model 2: gender, age were adjusted; Model 3: gender, age, BMI, WBC, CRP were adjusted.

ASMI = appendicular skeletal muscle index, BMI = body mass index, CRP = C-reactive protein, TSAT = transferrin saturation, WBC = white blood cell count.

3.3. Binary logistic analysis of influencing factors of sarcopenia

Following the Asian criteria for sarcopenia diagnosis, participants were categorized into sarcopenic and non-sarcopenic groups, and the variables of this study were collectively included in a binary logistic regression analysis. The findings indicate associations between sarcopenia and ferritin, BMI, and iron overload (OR_BMI = 0.561 [95% CI: 0.429–0.734], P < .001); (OR_Ferritin = 1.006 [95% CI: 1.001–1.011], P = .027); (OR_{Iron_overload} = 0.114 [95% CI: 0.016–0.795], P = .028). It can be observed that for each additional unit of ferritin, the probability of developing sarcopenia increases by 0.6%. Age, gender, WBC, CRP, and 25 (OH) D individually do not impact the occurrence of sarcopenia. Refer to Table 3 for details.

Table 3.

Binary logistic analysis of influencing factors of sarcopenia.

	B	W	P value	OR	95% CI
BMI	−0.577	17.746	.000	0.561	0.429	0.734
Age	−0.082	3.090	.079	0.922	0.841	1.009
Gender	−0.207	0.096	.757	0.813	0.220	3.001
WBC	−0.045	0.123	.726	0.956	0.741	1.232
CRP	−0.212	1.170	.279	0.809	0.551	1.188
Ferritin	0.006	4.895	.027	1.006	1.001	1.011
TSAT	−0.070	2.835	.092	0.933	0.860	1.011
25 (OH) D	0.065	1.440	.230	1.068	0.959	1.188
Iron overload	2.175	4.799	.028	8.801	1.257	61.60

25 (OH) D = 25-hydroxyvitamin D, BMI = body mass index, CRP = C-reactive protein, TSAT = transferrin saturation, WBC = white blood cell count.

3.4. Correlation analysis between ferritin and frailty indicators

To further investigate the correlation between ferritin and various frailty indicators, considering the varying impact of age, gender, and BMI on frailty, we employed partial correlation analysis by controlling for the effects of age, gender, and BMI to examine the association between ferritin and various frailty indicators. The results reveal a significant positive correlation between ferritin and weight loss, exhaustion, slow pace, frailty score, and frailty grading. Refer to Table 4 for details.

Table 4.

Partial correlation analysis between ferritin and frailty indicators.

	Ferritin	Weight loss	exhaustion	Slow pace	Low level of physical activity	Low grip strength	Frailty score	Classification of frailty
Ferritin	–	0.450***	0.506***	0.23*	0.093	0.058	0.45***	0.231*
Weight loss	0.450***	–	0.282**	0.301**	0.132	0.151	0.673***	0.476***
exhaustion	0.506***	0.282**	–	0.091	−0.032	−0.021	0.374***	0.066
Slow pace	0.233*	0.301**	0.091	–	0.254**	0.202*	0.743***	0.730***
Low level of physical activity	0.093	0.132	−0.032	0.254**	–	0.14	0.553***	0.450***
Low grip strength	0.058	0.151	−0.021	0.202*	0.14	–	0.446***	0.581***
Frailty score	0.450***	0.673***	0.374***	0.743***	0.553***	0.446***	–	0.842***
Classification of frailty	0.231*	0.476***	0.066	0.730***	0.449***	0.581***	0.841***	–

Controlling variables: age, gender, and body mass index.

^***P < .001;

^**P < .01;

^*P < .05.

3.5. Analysis results of influencing factors of frailty classification

Incorporating the relevant indicators of this study into an ordered logistic regression analysis, we found associations between age, gender, and ferritin levels with frailty grading. Other factors, including BMI, CRP, WBC, TSAT, and 25 (OH) D, do not individually influence frailty grading. Refer to Table 5 for details.

Table 5.

Multivariate ordered logistic regression analysis of influencing factors of Classification of frailty in 3 groups.

	β	P value	Wald	OR	95% CI
BMI	−0.099	0.155	2.023	0.906	0.791, 1.038
Age	0.193	0.000	31.23	1.212	1.133, 1.297
Gender	−1.775	0.002	9.146	0.170	0.053, 0.536
CRP	0.000	0.996	0.332	0.999	0.949, 1.054
WBC	0.069	0.564	0.000	1.072	0.847, 1.356
Ferritin	0.010	0.001	11.26	1.010	1.004, 1.016
TSAT	0.009	0.737	0.113	1.010	0.956, 1.065
25 (OH) D	−0.048	0.252	1.313	0.952	0.878, 1.034

25 (OH) D = 25-hydroxyvitamin D, BMI = body mass index, CRP = C-reactive protein, TSAT = transferrin saturation, WBC = white blood cell count.

4. Discussion

To explore the association of serum ferritin and other iron metabolism-related biomarkers with muscle atrophy and frailty in middle-aged and elderly, we used multiple statistical methods (e.g., multiple linear regression, binary logistic regression, partial correlation analysis, and ordinal logistic regression) to assess the association of iron metabolism measures with muscle atrophy and frailty in 110 middle-aged and older participants. The results showed that disturbed iron metabolism may contribute to the decline of muscle mass and function and increase the risk of frailty in middle-aged and elderly people. Community-based frailty screening and prevention should prioritize iron utilization and storage, as accelerating the body’s cycling of iron metabolism may have a greater impact on muscle health than iron stores. This provides new perspectives for future research and community health management.

Iron is an essential trace element in the human body, playing a crucial role in various physiological functions. It serves as a key component in fundamental metabolic pathways like oxygen transport, electron transfer, and energy metabolism. Additionally, iron participates in the regulation of gene expression, cell proliferation, differentiation, apoptosis, immune defense, and other processes. Sophisticated and complex regulatory mechanisms control iron metabolism in the body to maintain normal iron homeostasis. Many diseases, including anemia, infectious diseases, cardiovascular diseases, and metabolic diseases, can disrupt iron homeostasis. This disruption, in turn, exacerbates iron homeostasis disorders, forming a vicious circle.

Sarcopenia, a common skeletal muscle disease in the elderly, is characterized by a progressive decline in muscle mass and function. This decline can lead to serious consequences such as falls and disability, imposing a significant burden on the elderly. Sarcopenia’s occurrence and development involve various factors, including aging, mitochondrial dysfunction, oxidative stress, apoptosis, and inflammatory response. In recent years, increasing evidence has demonstrated a close relationship between sarcopenia and iron overload. Iron overload in skeletal muscle can damage the structure and function of muscle cells, reduce the body’s exercise ability and adaptability, and worsen the occurrence of sarcopenia. This study aims to explore the relationship between iron metabolism, muscle mass, and aging in the middle-aged and elderly community.

Serum ferritin is a vital clinical indicator of bodily iron storage,^[19] and the buildup of iron may serve as a marker for the aging of skeletal muscles.^[20] Chung et al^[21] demonstrated that elevated ferritin levels correlate with an augmented risk of diverse adverse body compositions. Moreover, the occurrence of low muscle mass in women over 50 rises in tandem with escalating ferritin levels. The risk of low muscle mass in groups with elevated serum ferritin is 1.52 times that of the normal group. Within the skeletal muscle research domain, certain studies^[22] indicate that iron overload can disrupt the balance between muscle protein synthesis and degradation, leading to oxidative stress-dependent skeletal muscle atrophy. Reardon and Allen^[13] animal experiments reveal that, compared to control mice, those with iron overload experience an increase in iron content in the tibialis anterior muscle groups. Post-intervention, the iron group exhibits lower skeletal muscle weight. The iron group exhibited reduced performance in the endurance test and generated less force in the strength test.

Unlike experimental studies, the results of clinical studies are still controversial. However, in a longitudinal study with 698 participants, Bartali et al^[23] did not find a significant association between serum iron levels and physical function. Waters et al^[24] observed a significant association between iron intake and gait speed in 315 older adults. In a study with 300 hemodialysis patients, Nakagawa et al^[25] reported a significant inverse association, albeit slight, between higher serum ferritin and lower muscle strength. Therefore, we focused on another measure of iron metabolism: iron-binding capacity and transferrin saturation. Iron-binding capacity measures the amount of transferrin in circulating blood. Under normal conditions, 100 mL of serum contains sufficient transferrin to bind 4.4 to 8 μmol (250–450 μg) of iron. Given that the normal serum iron concentration is approximately 1.8 μmol per deciliter (100 μg per deciliter), roughly one-third of transferrin is saturated with iron. Unsaturated or potential iron-binding capacity can be easily determined by radioiron or spectrophotometry. The sum of Unsaturated or potential iron-binding capacity and serum iron represents the TIBC. TIBC can also be measured directly. The commonly used numerical calculation is transferrin saturation, which equals serum iron divided by the total iron-binding capacity value.

In our previous literature research, we discovered that Chen et al^[26] analyzed data from the National Health and Nutrition Examination Survey (NHANES) spanning 2015 to 2018. Serum ferritin, ASM, BMI, and confounding factors were extracted for analysis. Multiple linear regression analysis and smooth curve fitting were employed to examine the relationship between serum ferritin and muscle mass.

Interestingly, in this study, we discovered that nearly all recruited patients could independently manage daily activities and assist their children’s families with specific responsibilities like grocery shopping, school drop-offs, and occasional participation in community exercises like square dancing and walking. This group of elderly individuals essentially represents the broader population of Chinese seniors. In this cohort of recruited patients, as shown in Table 2, we observe that ferritin does not significantly impact muscle mass; conversely, the transferrin saturation index exhibits a notable difference.

Ferritin comprises 2 components: I-chain ferritin, involved in iron storage, and H-chain ferritin, linked to stress responses. Serum ferritin is predominantly composed of I-chain subunits. Transferrin availability mirrors the presence of transferrin, responsible for ferrying iron in and out of cells. Investigating the impact of iron availability on sarcopenia development could be a novel research avenue. It is reasonable to speculate that, in community screenings for specific populations, we should not only concentrate on ferritin values but also evaluate the influence of transferrin saturation on muscle mass in this group.

When screening the potential elderly population for sarcopenia prevention in the community, it’s crucial to account for the influence of daily family and fitness activities on the muscle (mass and strength) of the elderly. This approach enhances the screening and prevention of sarcopenia risk in the elderly population. Simultaneously, to delve deeper into the impact of iron metabolism on the living status and activity abilities of middle-aged and elderly individuals, we have introduced the concept of frailty. In 2001, Fried and colleagues delineated the clinical manifestations of frailty in connection with physiological traits, signifying identifiable biological syndromes. Employing this frailty characteristic, an elderly individual can be diagnosed with frailty if they meet 3 or more of the 5 criteria indicating signs or symptoms.

Based on the test results and questionnaire surveys of the recruited subjects, we observed a significant positive correlation between ferritin and slow gait speed, fatigue, and weight loss, after eliminating the confounding factor of age. Nevertheless, studies revealing no substantial link between ferritin and muscle mass suggest that ferritin induces frailty in middle-aged and elderly individuals not solely based on muscle mass. Consideration should extend to osteoporosis, neurodegeneration, and cardiovascular disease resulting from iron overload. Hence, in the elderly population, particularly in community nursing homes, low activity is often prevalent, and its impact on the muscles of elderly residents is more pronounced compared to those living independently and handling specific family and social duties. If we consider the influence of elevated ferritin on the frailty index of the elderly, targeted and effective treatments can be administered based on individual circumstances, significantly enhancing the quality of life for the elderly.

Frailty is widespread and correlates with adverse outcomes and heightened healthcare costs. Due to the accelerating aging of the global population, the impact on the world is expected to intensify. Therefore, addressing the issue of frailty is an urgent global health concern. Our country is undergoing rapid aging, and managing frailty is challenging due to limited resources and healthcare accessibility. Despite significant progress in frailty research domestically and internationally over the past few decades, awareness of frailty is widespread within medical disciplines, particularly in the fields of nursing and the life management of the elderly. Our discoveries regarding the link between ferritin and frailty are anticipated to enhance the acceptance and feasibility of frailty screening in clinical practice. Additionally, they may contribute to the formulation of strategies for preventing frailty, ultimately enhancing the quality of life for older adults already in a frail stage.

5. Conclusion

Abnormal iron metabolism could contribute to the decline in muscle mass and frailty in middle-aged and elderly individuals. Emphasis should be placed on screening and preventing frailty within the community. Increased focus is needed on patients’ daily activities during history collection, considering not only iron storage but also iron utilization.

6. Limitation

The limitations of this paper are 2-fold: Firstly, given the cross-sectional nature of this study, establishing a causal relationship between serum ferritin or TSAT and muscle loss is not possible. Secondly, the sample size is inadequate, and testing a larger sample of the elderly community population would enhance the robustness of our findings.

Author contributions

Conceptualization: Anpei Ma, Honggu Chen, Hong Yin, Ziyi Zhang, Tingxia Han.

Data curation: Anpei Ma, Honggu Chen, Hong Yin, Ziyi Zhang, Guoyang Zhao, Aihua Chen, Tingxia Han.

Formal analysis: Anpei Ma, Honggu Chen, Hong Yin, Ziyi Zhang, Aihua Chen.

Investigation: Anpei Ma, Honggu Chen, Hong Yin, Ziyi Zhang.

Methodology: Anpei Ma, Hong Yin.

Project administration: Anpei Ma, Guoyang Zhao.

Resources: Guoyang Zhao.

Software: Anpei Ma, Honggu Chen, Hong Yin, Ziyi Zhang, Caifeng Luo.

Supervision: Guoyang Zhao, Caifeng Luo, Aihua Chen, Ruo Zhuang.

Visualization: Ruo Zhuang.

Writing – original draft: Anpei Ma, Honggu Chen, Hong Yin, Ziyi Zhang, Guoyang Zhao.

Writing – review & editing: Anpei Ma, Guoyang Zhao, Caifeng Luo, Ruo Zhuang.