Serum ferritin levels are positively associated with ... - Springer Link

J Bone Miner Metab (2014) 32:683–690 DOI 10.1007/s00774-013-0540-z

ORIGINAL ARTICLE

Serum ferritin levels are positively associated with bone mineral density in elderly Korean men: the 2008–2010 Korea National Health and Nutrition Examination Surveys Kyung Shik Lee • Ji Su Jang • Dong Ryul Lee • Yang Hyun Kim • Ga Eun Nam • Byoung-duck Han • Kyung Do Han • Kyung Hwan Cho Seon Mee Kim • Youn Seon Choi • Do Hoon Kim

•

Received: 30 May 2013 / Accepted: 31 October 2013 / Published online: 14 December 2013 Ó The Japanese Society for Bone and Mineral Research and Springer Japan 2013

Abstract A possible negative effect of iron overload on bone metabolism has been suggested by the fact that patients with hemochromatosis, thalassemia, and sickle cell anemia have lower bone mineral density than the general population. However, the influence of iron overload on bone health in the general population is uncertain. The aim of this study was to investigate the relationship between serum ferritin levels and bone mineral density (BMD) in elderly Koreans. A total of 2,943 subjects aged 65 years and over who participated in the 2008–2010 Korea National Health and Nutrition Examination Surveys were included in this study. Age, physical activity, current smoking status, alcohol consumption, education level, household income, and dietary assessment were surveyed by a face-to-face interview. BMD was measured at the lumbar spine and femur by dual-energy X-ray Electronic supplementary material The online version of this article (doi:10.1007/s00774-013-0540-z) contains supplementary material, which is available to authorized users. K. S. Lee D. R. Lee Department of Family Medicine, Wonkwang University College of Medicine, Gunpo-si, Gyeonggi-do, South Korea J. S. Jang Seocho Public Health Center, Seocho City Office, Seoul, South Korea Y. H. Kim G. E. Nam B. Han K. H. Cho S. M. Kim Y. S. Choi D. H. Kim (&) Department of Family Medicine, Korea University College of Medicine, Anam-dong 5-Ga, Seongbuk-gu, Seoul 136-701, South Korea e-mail: [email protected] K. Do Han Department of Biostatistics, Catholic University College of Medicine, Seoul, South Korea

absorptiometry, and other biochemical markers, including serum ferritin, 25-hydroxyvitamin D3, serum alkaline phosphatase, and parathyroid hormone, were assayed. After adjusting for age and body mass index, we found an association between BMD of the total lumbar spine, total femur, and femur neck and levels of alkaline phosphatase, parathyroid hormone, vitamin D3, and daily intake of calcium and protein. Serum ferritin levels were positively associated with BMD of the total lumbar spine, total femur, and femur neck after adjusting for all covariates in men, but not in women. This study suggests a positive association between serum ferritin levels and BMD in elderly South Korean men without hematologic disorders. Further study is warranted to verify the effects of iron on bone metabolism. Keywords Ferritin Osteoporosis Bone mineral density Iron Elderly

Introduction Osteoporosis is a disease characterized by low bone mass and micro-architectural deterioration of bone tissue, which leads to enhanced bone fragility and a consequent increase in fracture risk [1]. The diagnosis of osteoporosis thus centers on the assessment of bone mass and quality. Because there are no widely available clinical tools that can satisfactorily assess bone quality, the diagnosis of osteoporosis depends at present upon the measurement of skeletal bone mass [2]. The clinical significance of osteoporosis lies in the enhanced risk of fractures, with their attendant morbidity and mortality. Low bone mass is the most important risk factor of fracture, although other skeletal abnormalities also contribute to bone fragility [3].

123

684

The relationship between total body iron and bone mineral density (BMD) has attracted clinicians’ attention. The possible negative impact of iron overload on bone metabolism has been suggested by the fact that patients with hemochromatosis, thalassemia, and sickle cell anemia have a lower BMD than the general population [4–8]. However, the influence of increased serum ferritin levels, and, more generally, the effect of iron on BMD in elderly people without hematologic disorders is uncertain. Therefore, we analyzed the relationship between serum ferritin levels and BMD in the elderly South Korean population using data from the 2008–2010 Korea National Health and Nutrition Examination Surveys (KNHANES).

Materials and methods Overview and subjects This study was based on data gathered from the KNHANES in 2008, 2009, and 2010 [9]. The KNHANES have been conducted by the Division of Chronic Disease Surveillance under the Korea Centers for Disease Control and Prevention (KCDC) since 1998, and the surveys are designed to assess national health and nutrition status. The survey consists of an interview about health status, a nutritional assessment, and a health check-up. A complex, stratified, multistage cluster sampling design with proportional allocation was used for the selected household units that participate in the survey. Trained interviewers conducted face-to-face interviews with a structured questionnaire. From a total of 3,749 participants aged C65 years who were measured for BMD, subjects who presented with current thyroid disease, chronic hepatitis B or C, liver cirrhosis, chronic kidney disease, malignancy, or pulmonary and extrapulmonary tuberculosis were excluded. Subjects who had medical histories of current osteoporosis treatment, hormone replacement therapy, or hysterectomy were also excluded. Those who did not respond to questions about past medical histories and those who had not fasted for more than 8 h before measurement were excluded. Finally, we enrolled 2,943 subjects in our study, and divided them into two groups by gender for all analyses: 1,374 men and 1,569 women. The demographic variables that were considered as confounding factors in this study were age, education level, household income level, alcohol consumption, smoking status, and physical activity. We surveyed physical activity using the International Physical Activity Questionnaire (IPAQ) [10], and subjects were divided into three groups based on whether they were inactive (sedentary), minimally active, or showed health-enhancing physical activity.

123

J Bone Miner Metab (2014) 32:683–690

Subjects classified into the inactive group reported no activity or some activity, but not enough to fall under the categories for minimally active or health-enhancing physical activity. The minimally active group comprised subjects who reported activity meeting one of the following three criteria: (1) 3 or more days of vigorous activity of at least 20 min per day, (2) 5 or more days of moderateintensity activity and/or walking of at least 30 min per day, or (3) 5 or more days of any combination of walking, moderate-intensity, or vigorous-intensity activities achieving a minimum of at least 600 metabolic equivalent task minutes per week (MET-min/wk). The health-enhancing physical activity group was defined as those who showed any one of the following two criteria: (1) vigorous-intensity activity for at least 3 days per week and accumulating at least 1,500 MET-min/week, or (2) 7 or more days of any combination of walking or moderate- or vigorous-intensity activities accumulating at least 3,000 MET-min/week. Participants were divided into three groups based on average alcohol intake per day during the month before the interview. The categories were non-drinker, light to moderate drinker (\30 g/day), and heavy drinker ([30 g/day) [11]. Participants were categorized as non-smoker, exsmoker, or current smoker in accordance with their answers on the self-reported questionnaire. The study protocol was approved by the Korea Ministry of Health and Welfare, and the study was conducted according to the Ethical Principles for Medical Research Involving Human Subjects as defined by the Helsinki Declaration. All subjects provided written informed consent. Anthropometric measurements Trained staff members took measurements of the subjects. The heights (cm) and weights (kg) of the subjects were measured to the nearest 0.1 cm and 0.1 kg, respectively, with light clothes on and without shoes. Body mass index (BMI) was calculated by dividing weight (kg) by the square of height (m2). Biochemical measurements Blood samples were taken after a minimum of 8 h of fasting. All laboratory examinations were conducted in the Neodin Medical Institute, Seoul, Korea. Serum alkaline phosphatase levels were measured enzymatically using a Hitachi automatic analyzer 7600 (Hitachi, Tokyo, Japan). Parathyroid hormone levels were measured by the chemiluminescence immunoassay method with a LIAISON (DiaSorin, USA). Serum 25-hydroxyvitamin D3 [25(OH)D3] levels were measured by the radioimmunoassay method with a 1470 WIZARD gamma counter


(PerkinElmer, Finland). Serum ferritin levels were measured by an immunoradiometric assay method with a 1470 WIZARD gamma counter (PerkinElmer).

685 Table 1 Principal clinical characteristics and bone mineral density of Korean adults in KNHANES 2008–2010 Variables

Male (unweighted, n = 1,374)

Female (unweighted, n = 1,569)

p value*

Age

71.7 ± 0.2

72.7 ± 0.2

\0.001

Bone mineral density measurement In the KNHANES, whole-body dual-energy X-ray absorptiometry (DEXA) was performed with a QDR Discovery (formerly known as the QDR 4500A) fan beam densitometer (Hologic, Inc., Bedford, MA, USA) following the procedures recommended by the manufacturer. All subjects were dressed in light clothes and removed all jewelry and other items that could interfere with the examination. Bone mineral density (g/cm2) was measured with lumbar and hip scans at the lumbar spine (L1–4) and the total femur. The results of DEXA were analyzed using the standard techniques of the Korean Society of Osteoporosis and Hologic Discovery software (version 13.1). Nutrition assessment Daily food intake was assessed using the 24-h recall method and a food frequency questionnaire to determine food consumed the previous day. Daily intake of protein and calcium were calculated using a food database developed for the KNHANES and the food composition table published by the National Rural Living Science Institute under the Rural Development Administration. Statistical analyses Data are presented as mean ± standard error (SEs) or as percentages. In order to compare baseline characteristics, the v2 test was used for categorical variables and Student’s t test was used for continuous variables. Analysis of variance (ANOVA) was used to find associations between the BMD levels of subjects at the lumbar spine, total femur, and femur neck and lifestyle, education, and income. Multivariate linear regressions were used to examine the relationships between BMD and socioeconomic status, health behavior, alkaline phosphatase, parathyroid hormone, 25(OH)D3, serum ferritin, and daily intake of protein and calcium. All statistical tests were two-tailed, and statistical significance was defined as p \ 0.05. The SAS software package version 9.2 for Windows (SAS Institute, Cary, NC, USA) was used for all analyses.

Height (cm)

164.8 ± 0.2

150.6 ± 0.2

\0.001

Weight (kg)

62.8 ± 0.3

55.0 ± 0.3

\0.001

BMI (kg/m2)

23.1 ± 0.1

24.2 ± 0.1

\0.001

Alkaline phosphatase (IU/L)

245.6 ± 2.6

265.8 ± 2.2

\0.001

Parathyroid hormone (pg/mL) 25-OH vitamin D3 (ng/mL)

66.9 ± 1.0

73.0 ± 1.1

\0.001

22.3 ± 0.3

18.4 ± 0.3

\0.001

Serum ferritin (ng/mL)

127.7 ± 6.2

77.1 ± 3.1

\0.001

Lumbar spine BMD (g/cm2) Total femur BMD (g/cm2)

0.930 ± 0.005

0.746 ± 0.004

\0.001

0.880 ± 0.005

0.704 ± 0.003

\0.001

Femur neck BMD (g/cm2)

0.701 ± 0.004

0.557 ± 0.003

\0.001

Femur trochanter BMD (g/cm2) Femur intertrochanter BMD (g/cm2)

0.637 ± 0.004

0.507 ± 0.002

\0.001

1.058 ± 0.006

0.855 ± 0.004

\0.001

Daily intake of protein (g)

63.0 ± 1.1

44.7 ± 0.8

\0.001

Daily intake of calcium (mg) Alcohol intake

478.7 ± 12.3

362.2 ± 15.7

\0.001

67.9 (1.5)

34.4 (1.5)

26.7 (1.4)

6.1 (0.8)

Heavy drinker

\0.001

Current smoker

Inactive (sedentary)

28.2 (1.6)

Minimally active

42.1 (1.7)

40.4 (1.6)

Health-enhancing activity

29.6 (1.8)

16.7 (1.1)

42.9 (1.5)

29.4 (1.6)

6.2 (0.8)

1.6 (1.1)

10.9 (1.2)

\0.001

Education level

\0.001

Household income Highest quartile

\0.001 \0.001

Metabolic equivalent of task

College graduate

\0.001 \0.001

Cigarette smoking

Data are presented as mean ± SE or percentage (SE) KNHANES Korea National Health and Nutrition Examination Survey, BMI body mass index, BMD bone mineral density * p values were obtained by v2 test and Student’s t test and were all \0.001

Results The basic characteristics of subjects are presented in Table 1. The mean age was 71.7 ± 0.2 years for males and

72.7 ± 0.2 years for females. The averages and ratios of the other variables were significantly different between males and females.

123

686


Table 2 Comparison of lumbar spine, total femur, and femur neck BMDs according to health behavior and socioeconomic status Variables

Male (unweighted, n = 1,374) Lumbar spine BMD (g/cm2)

Total femur BMD (g/cm2)

Female (unweighted, n = 1,569) Femur neck BMD (g/cm2)

Lumbar spine BMD (g/cm2)

Total femur BMD (g/cm2)

Femur neck BMD (g/cm2)

Alcohol intake Yes

0.929 ± 0.007

0.886 ± 0.006

0.706 ± 0.005

0.748 ± 0.007

0.716 ± 0.006

0.571 ± 0.006

No p value*

0.932 ± 0.009 0.77

0.867 ± 0.008 0.056

0.689 ± 0.008 0.062

0.745 ± 0.005 0.697

0.698 ± 0.004 0.009

0.549 ± 0.003 \0.001

Current smoker Yes

0.912 ± 0.01

0.868 ± 0.009

0.689 ± 0.008

0.698 ± 0.014

0.662 ± 0.013

0.53 ± 0.01

No

0.937 ± 0.006

0.884 ± 0.006

0.704 ± 0.005

0.749 ± 0.004

0.707 ± 0.004

0.559 ± 0.003

p value*

0.024

0.099

0.087

0.001

0.001

0.005

Physical activity Inactive

0.919 ± 0.01

0.851 ± 0.009

0.671 ± 0.007

0.744 ± 0.007

0.687 ± 0.005

0.543 ± 0.005

Minimally active

0.933 ± 0.007

0.884 ± 0.006

0.706 ± 0.006

0.751 ± 0.006

0.712 ± 0.005

0.563 ± 0.005

Health enhancing activity

0.936 ± 0.01

0.902 ± 0.008

0.721 ± 0.007

0.74 ± 0.008

0.728 ± 0.007

0.575 ± 0.006

p value

0.382

0.001

\0.001

0.55

\0.001

\0.001

Education level Less than middle school

0.912 ± 0.006

0.87 ± 0.006

0.693 ± 0.005

0.742 ± 0.004

0.701 ± 0.004

0.554 ± 0.003

More than high school

0.974 ± 0.01

0.904 ± 0.008

0.722 ± 0.006

0.801 ± 0.016

0.746 ± 0.015

0.594 ± 0.013

p value*

\0.001

\0.001

\0.001

0.001

0.004

0.002

Household income Less than highest quartile

0.923 ± 0.006

0.874 ± 0.005

0.696 ± 0.005

0.74 ± 0.004

0.703 ± 0.004

0.556 ± 0.003

Highest quartile

0.984 ± 0.002

0.931 ± 0.015

0.739 ± 0.012

0.782 ± 0.014

0.716 ± 0.012

0.563 ± 0.01

p value*

0.001

\0.001

0.001

0.005

0.289

0.529

Data are presented as mean ± SE BMD bone mineral density * p values were obtained by Student’s t test

p values were obtained by analysis of variance (ANOVA)

Table 2 shows a comparison of BMD for the lumbar spine, total femur, and femur neck according to lifestyle variables such as alcohol intake, smoking, physical activity, educational level, and household income. The average BMD of the total femur and femur neck in female alcohol drinkers was significantly higher than in non-drinkers. The average BMDs of the total lumbar spine in male smokers and the total lumbar spine, total femur, and femur neck in female smokers were significantly lower than for non-smokers of both genders. The average BMDs of the total femur and femur neck in both genders significantly increased proportionally with physical activity. Subjects with more than a high school education showed higher BMDs at all locations compared with subjects with less than a middle school education for both genders. Males in the highest income quartile had a higher average BMD at all locations compared with males in any other income quartile, and the same was true for females (Table 2). Table 3 shows the age- and BMI-adjusted linear regression between BMD and the clinical variables, including levels of alkaline phosphatase, parathyroid

123

hormone, 25(OH)D3, serum ferritin, and daily intake of protein and calcium. Alkaline phosphatase was negatively associated with BMD of the lumbar spine, total femur, and femur neck in both genders. 25(OH)D3 levels were positively associated with BMD of the total femur and femur neck in both genders. Daily intake of calcium was positively associated with BMD for the lumbar spine, total femur, and femur neck in males. Serum ferritin levels were positively associated with BMD of the total femur and femur neck in males. Table 4 shows various multivariable adjusted linear regression analyses between BMD and serum ferritin levels. Model 1 was adjusted for age, body mass index, education level, monthly income, alcohol drinking, cigarette smoking, and physical activity, while Model 2 was adjusted for the variables in Model 1 plus alkaline phosphatase, parathyroid hormone, and 25(OH)D3. Model 3 considered daily intake of calcium and protein along with the variables in Model 2. Finally, serum ferritin levels were positively associated with BMD for the lumbar spine, total femur and femur neck in males.


687

Table 3 Age- and BMI-adjusted linear regression between BMD and clinical variables Variables

Male (unweighted, n = 1,374) b


SE

p value*

b

SE

p-value*

Lumbar spine BMD Alkaline phosphatase (IU/L)

-0.000259

0.000062

\0.001

-0.000302

0.000044

\0.001

Parathyroid hormone (pg/mL)

-0.007944

0.011957

0.507

-0.013615

0.008528

0.111

25-OH vitamin D3 (ng/mL)

0.000370

0.000627

0.556

0.000180

0.000581

0.756


0.014605

0.005188

0.005

0.001290

0.004901

0.793


0.000029

0.000148

0.845

0.000484

0.000186

0.01

Daily intake of calcium (mg) Total femur BMD

0.000030

0.000013

0.029

0.000013

0.000008

0.103

Alkaline phosphatase (IU/L)

-0.000286

0.000054

\0.001

-0.000263

0.000030

\0.001


-0.020328

0.009689

0.037

-0.027656

0.006492

\0.001


0.002075

0.000527

\0.001

0.001524

0.000395

\0.001


0.014675

0.004315

\0.001

0.005013

0.003303

0.13


0.000329

0.000117

0.005

0.000374

0.000132

0.005

Daily intake of calcium (mg)

0.000030

0.000012

0.011

0.000011

0.000006

0.09

Femur neck BMD Alkaline phosphatase (IU/L)

-0.000251

0.000048

\0.001

-0.000163

0.000029

\0.001


-0.016731

0.008873

0.06

-0.022070

0.005714

\0.001


0.001679

0.000474

\0.001

0.001355

0.000380

\0.001


0.011004

0.004173

0.009

0.003768

0.003052

0.218


0.000316

0.000121

0.009

0.000258

0.000103

0.013

Daily intake of calcium (mg)

0.000039

0.000011

0.001

0.000005

0.000006

0.466

*

p values were obtained by multivariable linear regression analysis after adjusting for age and body mass index. Serum ferritin and parathyroid hormone levels were logarithmically transformed because of the skewed nature of their distribution

Table 4 Multivariable adjusted linear regression between BMD and serum ferritin levels Variables

Male (unweighted, n = 1,374)


b

SE

p value

b

SE

p value

Lumbar spine BMD

0.014954

0.005043

0.003

0.001667

0.004624

0.719

Total femur BMD

0.012175

0.004182

0.004

0.002802

0.002802

0.372

Femur neck BMD

0.009222

0.004064

0.024

0.001562

0.002885

0.589

Lumbar spine BMD

0.015308

0.004996

0.002

0.000669

0.004619

0.885

Total femur BMD Femur neck BMD

0.012120 0.009251

0.003929 0.003890

0.002 0.018

0.001556 0.000602

0.003027 0.002860

0.607 0.833

0.018029

0.005297

0.001

0.001333

0.004725

0.778

Model 1*

Model 2

Model 3à Lumbar spine BMD Total femur BMD

0.010766

0.004201

0.011

0.001928

0.003089

0.533

Femur neck BMD

0.008004

0.004067

0.049

0.000481

0.002856

0.866

* Model 1 is adjusted for age, body mass index, year of survey, education level, monthly household income, alcohol consumption, smoking status, and physical activity levels

Model 2 is adjusted for alkaline phosphatase, parathyroid hormone, and 25-OH vitamin D3 along with covariates in model 1 Model 3 is adjusted for daily intake of protein and calcium along with covariates in model 2. Serum ferritin and parathyroid hormone levels were logarithmically transformed because of the skewed nature of their distribution

à

123

688


Fig. 1 The prevalence of osteoporosis or osteopenia and bone mineral density (BMD) at lumbar spine, total femur, and femur neck in three tertile groups according to serum ferritin levels. The bar graph indicates the prevalence (percentage) of osteoporosis or osteopenia in each tertile group and the line graph shows the change in BMD at lumbar spine, total femur, and femur neck in three groups by ferritin level. Ranges for tertile groups: tertile 1 (\63.61 ng/mL),

tertile 2 (63.61–127.63 ng/mL), tertile 3 ([127.63 ng/mL) for men; tertile 1 (\45.68 ng/mL), tertile 2 (45.68–80.81 ng/mL), tertile 3 ([80.81 ng/mL) for women. In the male group, p values for linear trend are 0.017, 0.001, \0.001, 0.022 for lumbar spine, femur total, femur neck BMD, and prevalence of osteoporosis, respectively, while p values for linear trend test are all [0.05 in female group

Figure 1 shows the prevalence of osteoporosis or osteopenia and BMD at the lumbar spine, total femur, and femur neck in three tertile groups according to serum ferritin levels. In males, the prevalence of osteoporosis or osteopenia decreased and BMD increased as serum ferritin levels increased by tertile categories. Although there was no statistically significant pattern in females, the more ferritin levels increased in tertiles, the more prevalence rates of osteoporosis decreased.

intracellular iron balance. Under steady-state conditions, serum ferritin levels correlate with total body iron stores. Thus, the serum ferritin level is the most convenient laboratory test for estimating iron stores [20]. As mentioned above, initial studies emphasized that an iron overload in patients with hematologic diseases might have deleterious effects on bone health. However, an association between an increase in iron store and BMD is still ambiguous, especially in the general population without hematologic diseases. Previous studies on an association of iron with bone metabolism were based largely on animal models or unique populations of humans. There are also a few large population studies which reported significant positive association of iron intake with bone health [21–23]. Some in vivo studies have been conducted in order to determine the effects of iron overload on the development of osteoporosis. Vernejoul et al. [24] treated pigs with a total dose of 10.8 g of parenteral iron spaced over 36 days. Bone histomorphometry showed that osteoblast recruitment and the collagen synthesis rate were decreased in the iron-overloaded pigs. Mean wall thickness, an indication of the amount of bone synthesized, was also lowered. Isomura et al. [25] showed that iron overload for 4 weeks in postmenopausal rats led to significantly increased excretion of urinary deoxypyridinoline as a specific marker of bone resorption. In addition, an iron-overloaded diet induced significant increases in serum osteopontin, which may reflect enhancement of bone resorption and osteoclast activity. Tsay et al. [26] reported that enhanced bone

Discussion In this study, we analyzed the association between serum ferritin levels and bone mineral density in the elderly South Korean population. Our results indicate a positive association between serum ferritin levels and BMD independently of many possible confounders. In the 1960s, Lynch and Seftel et al. [12, 13] noted that osteoporosis in middleaged male Bantu subjects in Johannesburg might be related to severe iron overloading. Eyres et al. [14] reported that osteoporosis in patients with hemochromatosis is related to excess iron. Similarly, various studies on beta-thalassemia and sickle cell anemia have been conducted based on the hypothesis that iron overload in several pathologic conditions could induce osteoporosis [8, 15–18]. At 12 nm in diameter, ferritin is a large protein, and is formed from a spherical protein coat of apoferritin that surrounds a core of hydrous ferric oxide [19]. Ferritin plays a central role in the maintenance of the delicate

123


resorption is most probably related to an increase in oxidative stress in iron overload status. However, Buyukbese et al. [27] found no statistically significant difference in ferritin levels between osteoporotic postmenopausal women and a control group. Guggenbuhl et al. [28] found no direct link between bone mineral density and either transferrin saturation or ferritin levels. In contrast, some studies have suggested that iron deficiency, rather than excess, may lead to osteoporosis. Katsumata et al. [29] reported that serum osteocalcin concentration, bone mineral density, and the mechanical strength of the femur are significantly lower in iron-deficient rats. A DEXA analysis done by Medeiros et al. [30] revealed a pattern of significantly reduced bone density in iron-deficient rats. In addition, Harris et al. [22] showed that increasing levels of iron intake were associated with greater bone mineral density of the lumbar spine and femur in healthy postmenopausal women. Maurer et al. [31] found that dietary iron was associated with a greater positive change in bone mineral density at the trochanter and Ward’s triangle in postmenopausal women using hormone replacement therapy. In the same manner, we identified a positive correlation between serum ferritin levels and bone mineral density in elderly Korean men. A positive trend in the association between serum ferritin levels and BMD was also shown in elderly Korean women, although it did not reach statistical significance (Supplementary Fig. 1). In addition, the ferritin tertiles had a negative correlation with the prevalence rates of osteoporosis without statistical significance in the female group (Fig. 1). The significant difference in serum mean ferritin levels between the male and female groups may contribute to this gender difference in the association between ferritin levels and BMD. Therefore, we performed additional correlation analysis between serum ferritin and BMD in the lowest tertile subgroup (unweighted, n = 981) of both genders (ferritin\53.75 ng/mL) to clarify the association of serum iron stores in subjects with low ferritin levels with BMD. In this subgroup analysis, elderly men (unweighted, n = 330) had still a significant positive correlation between ferritin levels and BMD, but elderly women (unweighted, n = 651) showed an insignificant positive correlation (supplementary Table 1). This result suggests that the gender difference in the association between serum ferritin levels and BMD may result from factors other than the difference in serum mean ferritin levels, although the precise mechanism is unclear. In previous studies, the possibility that iron overload in hematologic disorders might induce osteoporosis has been raised. In contrast, in healthy elderly men without hematologic diseases, iron may paradoxically increase bone mineralization. This possibility is partially based on the metabolic process of vitamin D. The hydroxylation of

689

vitamin D requires a three-component system involving a flavoprotein, an iron–sulfur protein (renal ferredoxin), and a cytochrome P-450, in order that 25-hydroxyvitamin D3 can be converted into 1,25-dehydroxyvitamin D3 [32]. In addition, iron is an element which is required for the process of collagen maturation in bone [33, 34]. Therefore, in the general population without hematologic disorders, iron stores may have a positive correlation with BMD. Further studies are needed to determine the principles governing bone formation and resorption in states of iron overload without underlying disorders. There were several limitations to our study. First, its cross-sectional design means that it would be impossible to infer any causal relationship between elevated ferritin levels and an increase in BMD. Second, our study did not include a biochemical assessment of bone resorption and formation status in addition to bone mineral density. Additionally, the role of novel biomarkers in the interaction between serum ferritin and BMD, such as serum osteocalcin which is considered to play a role in crosstalk between bone and energy metabolism, also needs to be investigated in a future study [35]. Furthermore, the accuracy of self-reported dietary intake is limited. However, it is worth noting that despite these limitations, our study was able to identify a possible positive effect of elevated iron stores on bone metabolism, while previous studies were unable to do so and instead have emphasized the negative impact of iron overload on bone health. In conclusion, serum ferritin concentration is positively associated with bone mineral density in elderly South Korean men, even after adjusting for covariates such as age, BMI, current smoking status, alcohol consumption, physical activity level, 25(OH)D3, parathyroid hormone, serum alkaline phosphatase, and daily intake of calcium and protein. Conflict of interest

All authors have no conflicts of interest.

References 1. Kanis JA, McCloskey EV, Johansson H, Cooper C, Rizzoli R, Reginster JY (2013) European guidance for the diagnosis and management of osteoporosis in postmenopausal women. Osteoporos Int 24:23–57 2. Kanis JA (2002) Diagnosis of osteoporosis and assessment of fracture risk. Lancet 359:1929–1936 3. Kanis JA, Black D, Cooper C, Dargent P, Dawson-Hughes B, De Laet C, Delmas P, Eisman J, Johnell O, Jonsson B, Melton L, Oden A, Papapoulos S, Pols H, Rizzoli R, Silman A, Tenenhouse A (2002) A new approach to the development of assessment guidelines for osteoporosis. Osteoporos Int 13:527–536 4. Valenti L, Varenna M, Fracanzani AL, Rossi V, Fargion S, Sinigaglia L (2009) Association between iron overload and osteoporosis in patients with hereditary hemochromatosis. Osteoporos Int 20:549–555 Epub 2008 Jul 2026

123

690 5. Mahachoklertwattana P, Sirikulchayanonta V, Chuansumrit A, Karnsombat P, Choubtum L, Sriphrapradang A, Domrongkitchaiporn S, Sirisriro R, Rajatanavin R (2003) Bone histomorphometry in children and adolescents with beta-thalassemia disease: iron-associated focal osteomalacia. J Clin Endocrinol Metab 88:3966–3972 6. Voskaridou E, Terpos E (2004) New insights into the pathophysiology and management of osteoporosis in patients with beta thalassaemia. Br J Haematol 127:127–139 7. Salama OS, Al-Tonbary YA, Shahin RA, Eldeen OA (2006) Unbalanced bone turnover in children with beta-thalassemia. Hematology 11:197–202 8. Sarrai M, Duroseau H, D’Augustine J, Moktan S, Bellevue R (2007) Bone mass density in adults with sickle cell disease. Br J Haematol 136:666–672 9. (2008–2010) The Forth and Fifth Korean National Health and Nutrition Examination Survey (KNHANES IV and V). Korea Centers for Disease Control and Prevention 10. Hagstromer M, Oja P, Sjostrom M (2006) The international physical activity questionnaire (IPAQ): a study of concurrent and construct validity. Public Health Nutr 9:755–762 11. Agarwal DP (2002) Cardioprotective effects of light-moderate consumption of alcohol: a review of putative mechanisms. Alcohol Alcohol 37:409–415 12. Seftel HC, Malkin C, Schmaman A, Abrahams C, Lynch SR, Charlton RW, Bothwell TH (1966) Osteoporosis, scurvy, and siderosis in Johannesburg bantu. Br Med J 1:642–646 13. Lynch SR, Berelowitz I, Seftel HC, Miller GB, Krawitz P, Charlton RW, Bothwell TH (1967) Osteoporosis in Johannesburg Bantu males. Its relationship to siderosis and ascorbic acid deficiency. Am J Clin Nutr 20:799–807 14. Eyres KS, McCloskey EV, Fern ED, Rogers S, Beneton M, Aaron JE, Kanis JA (1992) Osteoporotic fractures: an unusual presentation of haemochromatosis. Bone 13:431–433 15. Eren E, Yilmaz N (2005) Biochemical markers of bone turnover and bone mineral density in patients with beta-thalassaemia major. Int J Clin Pract 59:46–51 16. Mahachoklertwattana P, Chuansumrit A, Sirisriro R, Choubtum L, Sriphrapradang A, Rajatanavin R (2003) Bone mineral density, biochemical and hormonal profiles in sub-optimally treated children and adolescents with beta-thalassaemia disease. Clin Endocrinol (Oxf) 58:273–279 17. Bielinski BK, Darbyshire PJ, Mathers L, Crabtree NJ, Kirk JM, Stirling HF, Shaw NJ (2003) Impact of disordered puberty on bone density in beta-thalassaemia major. Br J Haematol 120:353–358 18. Sadat-Ali M, Sultan O, Al-Turki H, Alelq A (2011) Does high serum iron level induce low bone mass in sickle cell anemia? Biometals 24:19–22 Epub 2010 Nov 2026 19. Theil EC (1987) Ferritin: structure, gene regulation, and cellular function in animals, plants, and microorganisms. Annu Rev Biochem 56:289–315 20. Orino K, Lehman L, Tsuji Y, Ayaki H, Torti SV, Torti FM (2001) Ferritin and the response to oxidative stress. Biochem J 357:241–247

123

J Bone Miner Metab (2014) 32:683–690 21. Angus RM, Sambrook PN, Pocock NA, Eisman JA (1988) Dietary intake and bone mineral density. Bone Miner 4:265–277 22. Harris MM, Houtkooper LB, Stanford VA, Parkhill C, Weber JL, Flint-Wagner H, Weiss L, Going SB, Lohman TG (2003) Dietary iron is associated with bone mineral density in healthy postmenopausal women. J Nutr 133:3598–3602 23. Michaelsson K, Holmberg L, Mallmin H, Wolk A, Bergstrom R, Ljunghall S (1995) Diet, bone mass, and osteocalcin: a crosssectional study. Calcif Tissue Int 57:86–93 24. de Vernejoul MC, Pointillart A, Golenzer CC, Morieux C, Bielakoff J, Modrowski D, Miravet L (1984) Effects of iron overload on bone remodeling in pigs. Am J Pathol 116:377–384 25. Isomura H, Fujie K, Shibata K, Inoue N, Iizuka T, Takebe G, Takahashi K, Nishihira J, Izumi H, Sakamoto W (2004) Bone metabolism and oxidative stress in postmenopausal rats with iron overload. Toxicology 197:93–100 26. Tsay J, Yang Z, Ross FP, Cunningham-Rundles S, Lin H, Coleman R, Mayer-Kuckuk P, Doty SB, Grady RW, Giardina PJ, Boskey AL, Vogiatzi MG (2010) Bone loss caused by iron overload in a murine model: importance of oxidative stress. Blood 116:2582–2589 Epub 2010 Jun 2516 27. Buyukbese MA, Cetinus E, Cetinkaya A, Aras S (2005) Ferritin levels in postmenopausal women do not seem to play a significant role in osteoporosis. South Med J 98:845 28. Guggenbuhl P, Deugnier Y, Boisdet JF, Rolland Y, Perdriger A, Pawlotsky Y, Chales G (2005) Bone mineral density in men with genetic hemochromatosis and HFE gene mutation. Osteoporos Int 16:1809–1814 Epub 2005 Jun 1801 29. Katsumata S, Tsuboi R, Uehara M, Suzuki K (2006) Dietary iron deficiency decreases serum osteocalcin concentration and bone mineral density in rats. Biosci Biotechnol Biochem 70:2547–2550 Epub 2006 Oct 2547 30. Medeiros DM, Plattner A, Jennings D, Stoecker B (2002) Bone morphology, strength and density are compromised in iron-deficient rats and exacerbated by calcium restriction. J Nutr 132:3135–3141 31. Maurer J, Harris MM, Stanford VA, Lohman TG, Cussler E, Going SB, Houtkooper LB (2005) Dietary iron positively influences bone mineral density in postmenopausal women on hormone replacement therapy. J Nutr 135:863–869 32. DeLuca HF (1976) Metabolism of vitamin D: current status. Am J Clin Nutr 29:1258–1270 33. Tuderman L, Myllyla R, Kivirikko KI (1977) Mechanism of the prolyl hydroxylase reaction. 1. Role of co-substrates. Eur J Biochem 80:341–348 34. Prockop DJ (1971) Role of iron in the synthesis of collagen in connective tissue. Fed Proc 30:984–990 35. Movahed A, Larijani B, Nabipour I, Kalantarhormozi M, Asadipooya K, Vahdat K, Akbarzadeh S, Farrokhnia M, Assadi M, Amirinejad R, Bargahi A, Sanjdideh Z (2012) Reduced serum osteocalcin concentrations are associated with type 2 diabetes mellitus and the metabolic syndrome components in postmenopausal women: the crosstalk between bone and energy metabolism. J Bone Miner Metab 30:683–691