Hepatic steatosis and hepatic iron overload modify the association of iron markers with glucose metabolism disorders and metabolic syndrome

Iron status has been linked with impaired glucose metabolism (IGM), type 2 diabetes mellitus (T2DM) and the metabolic syndrome (MetS), but the role of hepatic steatosis or iron overload on these associations remains uncertain.


| INTRODUC TI ON
Iron is an essential micronutrient required for diverse metabolic processes such as DNA synthesis and oxygen transport. However, excessive iron stores in the liver, pancreas and muscle can be harmful as it has been associated with overproduction of reactive oxygen species, which in turn may be implicated in oxidative stress and cellular damage. 1 Hereditary haemochromatosis -a genetic disorder characterized by massive iron overload -has been reported to be involved in the development of type 2 diabetes mellitus (T2DM), 2 which suggests that iron overload can also cause diabetes. Ferritin is an indicator of body iron. In accordance, several observational studies have shown associations between ferritin concentrations and increased risk of impaired glucose metabolism (IGM), impaired pancreatic beta cell function, decreased insulin sensitivity, metabolic syndrome (MetS) and T2DM in both Eastern and Western countries. [3][4][5][6][7] Despite increasing evidence, the association between ferritin and T2DM and MetS remains inconclusive since ferritin is also an acute phase protein and its synthesis can be stimulated by inflammation, hepatic dysfunction and insulin resistance regardless of iron status. Certain studies have reported that the association of ferritin with T2DM and MetS could be partially explained by hepatic dysfunction on the basis of hepatic enzymes as a marker of hepatic dysfunction. 8,9 However, hepatic steatosis and iron content may contribute further to understand the role of iron and liver dysfunction in the pathogenesis of T2DM and MetS.
The liver serves as a major site for iron storage and in parallel plays a crucial role in iron and glucose homeostasis. Some studies demonstrated that excess iron in the liver may be involved in the pathogenesis of fatty liver, 10,11 while another study observed that fatty liver may disturb iron homeostasis and may lead to iron overload. 12 In addition, fatty liver is suggested to be a forerunner in the development of IGM, T2DM and MetS via hepatic insulin resistance. 13,14 While iron, fatty liver and the metabolic disorders like T2DM and MetS all seem to be interlinked to each other, their interplay between them remains unclear. Based on these premises, the association between ferritin, hepatic steatosis and iron overload with IGM, T2DM and Mets was investigated.
Transferrin, an iron transport protein, is another marker of iron metabolism. Its levels increase with the rise in iron requirements.
Additional investigations of transferrin may help to further understand the role of iron in the pathogenesis of metabolic disorders.
However, studies investigating the relation between transferrin and IGM, T2DM or MetS, in addition to ferritin are sparse. 5,8,15,16 Hence, there is only weak and inconsistent evidence to support this hypothesis.
Thus, the aims of the study were to 1) evaluate the association of ferritin and transferrin concentrations with IGM, T2DM and MetS and 2) analyse interactions between ferritin and hepatic steatosis and iron overload on the association with IGM, T2DM and MetS.

| Study population
The present cross-sectional study is based on the second independ- participated (response 50.1%). Details on the study design, protocols steatosis showed stronger associations with IGM, T2DM and MetS. Transferrin was associated with isolated impaired glucose tolerance but not with T2DM and MetS.

Conclusions:
Our study suggests that ferritin may be associated with glucose metabolism disorders and MetS even in people without hepatic steatosis or iron overload.

Lay summary
The iron marker serum ferritin is associated with prediabetes, diabetes and metabolic syndrome even in people without fatty liver disease or iron overload. But the presence of higher ferritin levels along with either fatty liver disease or iron overload makes the individuals more prone to the risk of these metabolic disorders. and sampling methods have been reported elsewhere. 17,18 All participants provided written informed consent and the study was approved by the medical ethics committee of the University medicine of Greifswald and followed the declaration of Helsinki.

| Interview and physical examination
All participants underwent a standardized computer-assisted personal interview, during which they provided information on sociodemographic and lifestyle factors as well as medical histories and medication use. School education was categorized into 3 groups: <10 years, 10 years and > 10 years, smoking status into current, former and never smokers and alcohol consumption into no (0 g/day), moderate (men 0.1-39.9 g/day and women 0.1-19.9 g/day), and high alcohol (men ≥ 40 g/ day and women ≥ 20 g/day) consumption. Participants who exercised for less than an hour/week in their leisure time during summer or winter were classified as physically inactive. Participants were asked to bring all medications taken 7 days before the time of examination.
Medication data were obtained online using the IDOM program (online drug database led medication assessment) and categorized according to the Anatomical Therapeutical Chemical (ATC) classification index.
Glucose lowering medication was defined by the ATC code A10, and lipid lowering medication by the ATC code C10AB and C10AD.
During the physical examination, standardized measurements of height, weight, waist circumference, hip circumference and blood pressure were performed while the subjects were in light clothing and not wearing shoes. BMI and waist/hip ratio were calculated.
Blood pressure was measured 3 times on the right arm in a sitting position after at least 5-min at rest, using an oscillometric device (OMRON HEM 705-CP). Systolic and diastolic blood pressures were calculated as the average reading of the second and third measurements. Participants were classified as hypertensive based on blood pressure readings ≥ 140/90 mmHg or use of self-reported antihypertensive medication.

| Main outcome measurements: impaired glucose metabolism, unknown type 2 diabetes mellitus and metabolic syndrome
Participants without diagnosed T2DM or taking glucose-lowering agents underwent a standard 75 g oral glucose tolerance test.
Venous blood was sampled after an overnight fast for at least 8h and 2h post glucose solution intake. Measurements of plasma fasting glucose and 2-h glucose levels were measured using a hexokinase method (Dimension Vista 1500, Siemens Healthcare Diagnostics, Eschborn, Germany).

| Hepatic magnetic resonance imaging technique and analysis
Hepatic MR imaging was performed on a 1.5-T MRI system (Magnetom Avanto; Siemens Healthcare AG) with a repetition time of 11.0 msec, 3 echo times of 2.4, 4.8 and 9.6 msec, 10° flip angle, one signal average, bandwidth of 1065Hz/pixel, matrix of 224 × 126 × 32, section thickness of 6.0 mm and a monopolar readout. 21 Following image acquisition, MR datasets were processed by using an offline reconstruction algorithm written in Matlab (Mathworks, Natick, Mass) to estimate proton density fat fraction (PDFF) and create R2* maps. 22 Post-processing was performed on a MacBook Pro Mid 2012 (2.6GHz Core i7; 16GB RAM, 1600 Mhz DDR3; Apple, Cupertino, Calif). Images were then analysed after an operator defined selection of the liver using the region-of-interest tool in Osirix (version 4.6; Pixmeo, Bernex, Switzerland).

| Definition of hepatic steatosis and hepatic iron overload
After determination of PDFF (as percent) and R2* (as sec −1 ), patients were classified by using defined cutoffs of liver fat and liver iron content. Cutoffs of PDFF and R2* were based on histopathological calibrations. These calibrations were defined in an external study described elsewhere. 22 Hepatic steatosis is defined by a PDFF cutoff of 5.1% or higher and hepatic iron overload is defined by a R2* cutoff of 41.0 sec -1 or greater. Calibration was exclusively based on histopathological grading of liver fat and iron content and did not include biochemical findings such as measurement of triglycerides or iron content.

| Statistical analyses
Baseline characteristics of study participants were expressed as median and interquartile range for continuous data and as absolute numbers and percentages for categorical data. Differences between the subjects with NGM, IGM and T2DM were tested by Mann-Whitney U test for continuous data and χ 2 test for categorical data. Partial correlations were calculated between iron markers (ferritin and transferrin) and other continuous covariates after adjusting for age and sex. Multinomial logistic regression analyses were performed to analyse associations of iron markers (ferritin and transferrin) with IGM and T2DM compared to NGM. Further logistic and linear regression models were performed to test associations between iron markers and MetS, fasting glucose, 2-h glucose, fasting insulin and 2-h insulin, HOMA-IR and HbA1c. Associations were analysed based on stepwise adjustment for covariates for all outcomes. The first model was adjusted for age and sex, the second for known diabetes risk factors such as education, smoking, alcohol intake, physical inactivity, BMI, waist/hip ratio, hypertension, triglycerides and total/ HDL cholesterol ratio, renal function markers such as serum creatinine and urinary albumin/creatinine along with the inflammatory markers hs-CRP and leucocytes, and the third ad-

| RE SULTS
Our study sample size varied according to specific outcome and analysis which included 2310 participants for the analysis of IGM and T2DM and 2568 participants for the analysis of MetS. A detailed breakdown of participants used in the analyses after mainly excluding subjects who were nonfasting or with known diabetes is shown in Figure 1. Of note, subjects who had reported known history of diabetes mellitus were excluded from all analyses.
Clinical characteristics of the study participants stratified by groups of glucose metabolism are shown in Table 1 and Table S1. In our sample, 877 individuals (37.9%) had IGM, 141 individuals (6.1%) had T2DM and 812 individuals (31.6%) had MetS. Overall, subjects with IGM and T2DM tended to be older, were less physically active, had a higher prevalence of hypertension, MetS, hepatic steatosis and hepatic iron overload, and exhibited significantly higher concentrations of ferritin, hepatic enzymes, inflammatory markers and metabolic parameters than subjects with NGM (Table 1).
In the correlation analyses, ferritin concentrations were strongly correlated with hepatic iron content (r = 0.71), ALT  (Table 2). However, the association of ferritin with fasting insulin became borderline significant after adjustment for hepatic enzymes.
In the sex-stratified analyses, associations of serum ferritin con- whereas the associations with MetS, fasting glucose, 2-h glucose, HOMA-IR and HbA1c were stronger in men than in women (Table S3).
We observed similar effect modifications by sex for the associations of serum ferritin levels with IGM and MetS (Figure 2; P <.1).
In the effect modification analysis of ferritin by hs-CRP, we ob-    MetS, but not the isolated effect of iron overload or the combined effect of iron overload and ferritin (Table 4).
In the analyses of transferrin, a higher value of transferrin con-  .08

| D ISCUSS I ON
Note: Ferritin was dichotomized into 2 groups as above and below the sex-specific 4th quartile cutpoint (male: 245 µg/L, female: 102.5 µg/L). Main model adjusted for age, sex, education, smoking, alcohol consumption, physical activity, BMI, waist/hip ratio, hypertension, triglycerides, total/HDL cholesterol ratio, serum creatinine, urinary albumin/creatinine, high-sensitive C-reactive protein, leucocytes, alanine aminotransferase, and gammaglutamyl transferase. For MetS, adjusted for all previously mentioned covariates except BMI, waist/hip ratio, triglycerides and total/HDL cholesterol. Significant associations are shown in bold font.  MetS separately in men and women, reported that serum ferritin was strongly related to higher triglycerides in men and glucose concentrations in women. 26 Likewise, our study showed stronger associations of ferritin with glucose-related outcomes in women. In addition, differences in ferritin distributions, genetics and lifestyle characteristics between men and women might partially explain the results as women were less likely to be frequent smokers, hypertensive and diabetic, had lower lipid, ferritin and ALT levels but higher hs-CRP levels in our study (Table S4).
Several possible mechanisms were suggested to explain a link between ferritin and metabolic disorders. One potential cause could be that excess iron may induce oxidative stress and subsequently damage beta cells leading to impaired insulin secretion. 27 Another explanation may be that the high iron stores in the liver impair the liver's capability to extract insulin, thereby leading to hepatic insulin resistance and increased hepatic glucose production. 27 In our study, we found a stronger correlation between ferritin and hepatic enzymes (ALT and GGT) and the association between ferritin and metabolic outcomes (IGM, T2DM and MetS) was moderately attenuated after adjustment for ALT and GGT in our study. These findings were in line with previously published results. 4,8,9,23,24 These results indicated a role of hepatic dysfunction in the relation between ferritin and metabolic outcomes. On the one hand, several studies suggested a link between hyperferritinaemia and nonalcoholic fatty liver disease, 11,12 thus speculating that excess iron may be involved in the pathogenesis of fatty liver, which in turn contributes to the development of T2DM and MetS via hepatic insulin resistance. 13,14 On the other hand, hepatic iron overload was reported to be strongly associated with insulin resistance irrespective of steatosis or liver damage, 30 thus causing insulin resistance directly, which in turn leads to metabolic disorders. 31 Hence, we further explored the role of hepatic steatosis and iron overload in the association between ferritin and metabolic outcomes.
Hepatic steatosis is considered to be a common manifestation of T2DM and MetS. 10 Another iron marker investigated was transferrin, a marker of low iron stores. Its production increases when the body iron stores are low; therefore, transferrin and ferritin were inversely correlated.
However, we observed positive associations of serum transferrin with isolated IGT, 2-h glucose, fasting insulin, 2-h insulin and HbA1c, which were independent of all risk factors including hepatic enzymes.
In accordance, 2 studies have shown transferrin to be associated with higher fasting insulin and 2-h glucose concentrations but not with fasting glucose concentrations, similar to our study. Further, in our study, transferrin showed borderline positive associations with IGM and MetS, but not with T2DM, although positive associations have been reported elsewhere. 5,16,23,37 Despite its inverse correlation with ferritin, positive associations of transferrin with metabolic outcomes probably suggest iron-independent pathways. 5 However, the possible mechanisms behind these associations remain unclear. Some studies suggest that insulin can lead to upregulation of transferrin expression in human hepatocytes, 38,39 while another study reported that serum transferrin may have an antagonistic effect on insulin action, which can lead to insulin resistance. 40 Besides, one could also speculate that the continued state of high glucose concentrations even 2h post meal may have led to upregulation of serum transferrin levels as serum transferrin is said to be highly susceptible to glycation. 41 The main limitation of the study is the cross-sectional study design; thus, no causal inferences can be made. However, we have excluded the participants with known diabetes from the analysis to maintain the temporal sequence of association. Despite our large sample size, it should be mentioned, that among previously undiagnosed T2DM subjects (n = 141), the number of subjects with hepatic iron overload (n = 22) was limited, and therefore, our results will need to be validated by further studies. One cannot exclude the possibility of residual confounding, although we adjusted for some important covariates in our analysis. As multiple hypotheses were tested in the same study sample, one cannot rule out the possibility that this approach yielded some false-positive results. However, we would not expect this to be a major limitation because the main findings were relatively consistent between different models. Major strengths of our study include the large population-based study sample, the availability of ferritin as well as transferrin as measures of iron markers, availability of hepatic MRI data and a lot of covariates for potential confounding.

| CON CLUS ION
Overall, we found that serum ferritin was associated with higher prevalence of IGM, T2DM and MetS in the total population independent of inflammatory markers and hepatic enzymes. Serum transferrin was specifically associated with isolated IGT while serum ferritin was associated with combined IFG + IGT. Moreover, our study suggests that ferritin may be associated with these metabolic outcomes even in people without hepatic steatosis or iron overload. However, in individuals with higher ferritin concentrations, the presence of hepatic steatosis may synergistically increase the risk for IGM, T2DM and MetS, while the presence of hepatic iron overload may synergistically increase the risk for IGM and T2DM but not MetS. Thus, our study shows that hepatic dysfunction may play a significant role in the association between ferritin and metabolic disorders.

ACK N OWLED G EM ENTS
We thank all field workers, study physicians, interviewers and laboratory workers for their contribution to data collection. We also thank all participants of the SHIP study for their contribution to the study.

CO N FLI C T O F I NTE R E S T
The authors do not have any disclosures to report.

DATA AVA I L A B I L I T Y S TAT E M E N T
The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.