Contributions of HFE polymorphisms to brain and blood iron load, and their links to cognitive and motor function in healthy adults

Abstract Background Brain iron overload is linked to brain deterioration, and cognitive and motor impairment in neurodegenerative disorders and normal aging. Mutations in the HFE gene are associated with iron dyshomeostasis and are risk factors for peripheral iron overload. However, links to brain iron load and cognition are less consistent and data are scarce. Aims and methods Using quantitative susceptibility mapping with magnetic resonance imaging, we investigated whether C282Y and H63D contributed to aging‐related increases in brain iron load and lower cognitive and motor performance in 208 healthy individuals aged 20‐79 years. We also assessed the modulatory effects of HFE mutations on associations between performance and brain iron load, as well as peripheral iron metabolism. Results Independent of age, carriers of either C282Y and/or H63D (HFE‐pos group, n = 66) showed a higher load of iron in putamen than non‐carriers (HFE‐neg group, n = 142), as well as higher transferrin saturation and lower transferrin and transferrin receptors in blood. In the HFE‐neg group, higher putaminal iron was associated with lower working memory. In the HFE‐pos group, higher putaminal iron was instead linked to higher executive function, and lower plasma transferrin was related to higher episodic memory. Iron‐performance associations were modest albeit reliable. Conclusion Our findings suggest that HFE status is characterized by higher regional brain iron load across adulthood, and support the presence of a modulatory effect of HFE status on the relationships between iron load and cognition. Future studies in healthy individuals are needed to confirm the reported patterns.


| INTRODUC TI ON
Iron is an essential metal for numerous biological mechanisms in the brain, where it contributes, for example, to myelin and neurotransmitter syntheses. However, disruption of iron homeostasis can lead to an overload of free iron, which has been linked to cellular dysfunction and destruction via oxidative stress and inflammatory mechanisms. 1 Neurodegenerative disorders and normal aging are characterized by brain iron overload. Adverse effects of higher brain iron load have been observed in normal aging, such as atrophy, brain dysfunction, and poorer motor and cognitive performance. [2][3][4][5][6][7][8][9] Iron-regulating genes may contribute to interindividual variations in markers of iron metabolism. Among the identified iron genes, HFE is one of the most studied. HFE encodes the HFE protein (high Fe2+, or human homeostatic iron regulator protein), which is involved in hepcidin regulation and iron storage mechanisms. 10 Two single nucleotide polymorphisms (SNPs) located in the HFE gene, the C282Y (rs1800562, risk allele: A) and H63D (rs1799945; risk allele: G) mutations, account for most cases of hereditary hemochromatosis. Individuals with the C282Y mutation, and notably homozygotes, are at higher risk for hereditary hemochromatosis, and both mutations were related to neurodegenerative disorders, including Alzheimer's disease. [10][11][12][13] Whereas both mutations have been associated with variations of blood markers of iron metabolism (ie, higher iron, ferritin and transferrin saturation, lower transferrin, and transferrin receptors), C282Y has typically shown stronger associations than H63D. [14][15][16][17][18] A recent study in a Swedish sample of blood donors showed that H63D heterozygotes did not display higher transferrin saturation or ferritin than non-carriers, but both C282Y homo-and heterozygotes were more likely to display elevated values compared to non-carriers. 19 The influence of iron-related genetic polymorphisms has been little studied in relation to brain iron load in gray matter. The few existing studies where C282Y and/or H63D HFE variants were tested yielded mixed results. [20][21][22][23][24] This could be due to different methods of iron quantification and considerably varying sample sizes and sample composition across studies. To our knowledge, only one study examined potential associations between brain iron and cognitive performance as a function of iron-gene profile: Only non-carriers of either H63D or TfC2 (transferrin gene, rs1049296) showed a negative association between iron load in basal ganglia and working-memory performance. 25 This seems counterintuitive, as carriers of alleles associated with already high iron load would be expected to show lower cognitive performance with further increasing iron load.
Motivated by the scarcity of data and mixed findings, we aimed to determine the potential influence of carrying the HFE H63D and/or C282Y mutations (HFE-pos thereafter as opposed to HFEneg, that is, non-carriers) on brain iron load, blood markers of iron metabolism, and relationships between brain and blood iron and cognitive/motor performance in healthy volunteers aged 20-79 years who have not received a clinical diagnosis of iron-related metabolic disorders. We first hypothesized that the HFE-pos group would display elevated brain and blood iron. Second, we tested whether genetic effects on brain and cognition are magnified in older age due to more constraint neural resources, as predicted by the resource-modulation hypothesis. 26 More specifically, we expected stronger effects of the HFE-pos group on brain and blood iron in older age. Finally, we explored relationships between brain iron, blood markers of iron, and cognitive/motor performance encompassing five domains (working memory, executive function, episodic memory, perceptual speed, motor speed). Given the detrimental effects of brain iron overload on cognition, 1,6,27 we expected that high iron load would be more strongly associated with worse performance in the HFE-pos than in the HFE-neg group.
However, given the counterintuitive results reported above, 25 we could not rule out an alternative hypothesis, with a negative association between iron and cognition in the HFE-neg group, but not in HFE-pos group.

| ME THODS
The IronAge study was approved by the Regional Ethical Review Board in Stockholm (number 2016/457-31/2) and conformed with provisions of the Declaration of Helsinki. The protocol consisted of three visits, with blood sampling, cognitive testing, and magnetic resonance imaging (MRI) assessment.

| Participants
Two hundred and thirty-two individuals were recruited through advertisements in newspapers and student websites. Twenty-four individuals were excluded due to incomplete data (N = 15 dropped out before MRI) and incidental brain abnormalities (N = 9). The final sample was composed of 208 individuals ( Table 1).
None of the participants reported any current or past neurological or psychiatric conditions (individuals who were diagnosed with depression and/or were taking antidepressants were excluded if it encompassed a period of 2 years prior to inclusion in the study), and none was taking any psychoactive medication or had substance abuse. Other exclusion criteria concerned ineligibility for MRI (presence of metal in the body, claustrophobia), diagnosed conditions with iron deficiency (eg, anemia) or overload (eg, hemochromatosis), cancer, diabetes, dementia, presence of cognitive/memory complaints, surgery (head, heart, eyes, ears), ulcer, Crohn's disease, HIV, hepatitis, and restless leg syndrome.
The sample was divided into three age groups for the purpose of the analyses, based on our previous study where we identified optimal cutoffs according to the non-linear distribution of iron load in striatum over the adulthood in the same sample. 9 The younger group was aged 20-39 (N = 66), the middle-aged group was aged 40-59 (N = 69), and the older group was aged 60-79 (N = 73).

| Blood sampling
Venous blood was collected before 10 AM while fasting since 8 PM the day before. Serum, plasma, and Li-Heparin samples were brought to the Centre for Clinical Laboratory Studies for immediate analyses (Karolinska Hospital, Stockholm), and DNA extraction was performed at the Biobank at Karolinska Institutet.
Using standard procedures, the following blood markers for iron metabolism were measured: plasma iron (Iron), plasma transferrin (Transf), serum transferrin receptors (Transf-rec), serum ferritin (Ferr), and plasma transferrin saturation (Transf-sat). In addition, C-reactive protein (CRP) was assessed as a general marker of inflammation.

| Genotyping
DNA samples were transferred on PCR plates and sent to the SNP&SEQ Technology Platform, Uppsala University (National Genomics Infrastructure [NGI], SciLifeLab Sweden). The genotyping was performed using a multiplexed primer extension (SBE) chemistry of the iPLEX assay with detection of the incorporated allele by mass spectrometry with a MassARRAY analyzer from Agena Bioscience. [28][29][30] Raw data from the mass reader were converted to genotype data using the Typer software (Agena Bioscience). Both HFE C282Y (rs1800562) and H63D (rs1799945) genotype distributions were in Hardy-Weinberg equilibrium (Ps > .1).
Given the limited number of individual carriers of H63D or C282Y, we pooled the participants in 2 groups: non-carriers of any of the mutations (HFE-neg) and carriers of either the H63D and/or C282Y (HFE-pos).

| Acquisition
Participants were scanned on a GE Discovery

| Quantitative susceptibility mapping
Initially, the total field map was estimated from the complex meGRE images by performing a non-linear least square fitting on a voxelby-voxel basis. 31 The resulting frequency map was then spatially unwrapped using a magnitude image-guided region growth unwrapping algorithm. 32 The background fields (the superimposed field contributions that are not caused by the sources inside the brain and mainly generated by air-tissue interferences) were eliminated using a nonparametric technique based on projection onto dipole fields. 33 Finally, the corrected frequency map was used as input for the fieldto-source inverse problem to calculate the map of susceptibility. We used the recommended non-linear variant of morphology-enabled dipole inversion (MEDI) method to calculate susceptibility maps. 31,34 The MEDI Toolbox (http://weill.corne ll.edu/mri/pages/ qsm.html) was used to generate QSM images ( Figure 1A).
Due to the singularity of the dipole kernel at the center of kspace, the generated QSM images contain relative susceptibility values. Therefore, the QSM images may not necessarily be comparable across subjects in a cohort. A typical approach to address this issue For the analyses, we focused on the basal ganglia (caudate, putamen, pallidum) because iron accumulates most in these regions, and they are relevant for cognitive performance. 2

| Motor and cognitive performance
A battery of cognitive and psychomotor tests was administered by trained staff, following a standardized procedure. Unit-weighted composite scores (mean of z-scores) were computed based on the accuracy in these tasks, and these scores were transformed into T-

| Working memory
The composite score for working memory was based on the number correct for 2-and 3-back, as well as number of correct items in the binding task.

Numerical n-back task
A sequence of single numbers appeared on the screen. During every item presentation, subjects indicated whether the digit on the screen was the same as the one shown 1, 2, or 3 digits back. Each digit was shown for 1.5 seconds, with an interstimulus interval of 0.5s. Three blocks for each condition (1-, 2-, 3-back) were performed in sequential order (1-2-3; 1-2-3). The 1-back block had 13 items, the 2-back block had 14 items, and the 3-back block had 15 items.

Binding task
The binding task assessed the ability to associate visuospatial features in working memory. 41 Five colored uppercase letters were presented in the center of a 5 × 4 grid, accompanied with 5 colored crosses displayed randomly in the other squares of the grid.
Participants were asked to remember the associations between the 5 colored letters with the location of the cross of the same color.
Each trial started with a fixation cross for 2 seconds, and then, consonants were shown.
Five seconds were allocated to this encoding phase, followed by a retention interval of 8 seconds (fixation cross). Participants had to determine whether a black lowercase letter was presented in the correct location by pressing yes or no. In total, 20 trials were administered.

| Episodic memory
The composite score for episodic memory was based on the word list test, as well as cued recall and recognition from the Face-Name Paired-Associates Task.

Word list test
Word list test comprised a list of 16 unrelated concrete nouns, which were presented both orally and visually with a new word appearing every 5s. Immediately after presentation, participants were given two minutes for oral free recall. 42

Face-name paired-associates task
Subjects were shown faces with a fictional first name printed on the right side of the face, forming a face-name pair. Subjects were instructed to remember the name associated with each face and answer orally when asked. During retrieval, each face was presented together with three letters, of which one corresponded to the first letter of the name that was presented together with the face (32 trials). 43 Encoding and retrieval stimuli were presented for 4 seconds. There were eight blocks (4 trials) of encoding and retrieval each, and 7 blocks of an interference task where participants were asked to count backward for 15 seconds to prevent rehearsal and minimize the influence of short-term memory. This was followed by a recognition task (32 trials), where previously presented faces were presented for 7 seconds together with the correct name and two names, of which one was presented in the same block but with a different face and one was new. Subjects had to indicate by button press the correct name. They could also indicate that they did not remember the correct answer. To account for response bias, hits minus false alarms were computed for the recognition task. For TMT-B, circles included both digits and letters, and the task was to connect these in alternating order (1-A, 2-B, 3-C, etc). 44 The test was interrupted by the test leader in case of a mistake and repeated.

Letter and category fluency
For the letter fluency tasks, participants were asked to orally generate as many words as possible within 60 seconds, beginning with the letters F and A, respectively. They were instructed that proper names, numbers, or words with a different suffix were not credited.
For category fluency, participants were asked to orally generate as many words as possible within 60 seconds, belonging to the categories animals and professions, respectively. The four fluency measures were combined into one average score.

Random generation
Participants had to produce a consonant every second (condition 1) or every two seconds (condition 2), whenever a square was presented on the computer screen. 45 A series of 50 random consonants had to be generated in each condition (ie, no vowels, no alphabetic order or reverse order, no repetitions of random sequences, no spelling of words). Scores were corrected for errors, and the two conditions were averaged into a final score.

| Processing speed
The composite score was based on the time in seconds needed to accomplish the TMT-A and number of correctly copied symbols during 90 seconds.

TMT-A
Participants were instructed to draw lines to connect circled digits  in ascending order as rapidly as possible and without lifting the pen.

Digit symbol substitution test
Digit Symbol Substitution Test (DSST) is a general index of perceptual speed. 46 The DSST consists of nine digit-symbol pairs followed by a list of digits. Under each digit, the participant is required to fill in the corresponding symbol as rapidly as possible during 90s. Errors were subtracted from the total score.

| Motor speed
A composite score was created based on the below described two scores from the Purdue Pegboard Test and the main score from the Grooved Pegboard Test.

Purdue Pegboard test
Purdue Pegboard Test (Model 32020, Lafayette Instrument) was used to measure fine motor control (finger and hand dexterity). 47 Participants were instructed to place as many pegs into the peg-holes as possible within 30 seconds. Three conditions were tested: right hand only, left hand only, and both hands simultaneously. In addition, a condition where pegs and washers had to be assembled was conducted, but not included in the total score. Subsequently, two separate scores were generated: (1) average number of pegs inserted by right and left hands, and (2) number of pegs inserted by both hands.

Grooved Pegboard test
The standard apparatus (Lafayette Instruments) was used to assess visual-motor coordination, motor speed, and fine motor control. 44 Participants were instructed to place 25 pegs, one at a time, into key-shaped holes as quickly as possible. The test had two conditions: positioning pegs from left to right on the board using the right hand, and positioning pegs from right to left using their left hand.
The score for the right and the left hand (measured in seconds) was averaged and inversed, such that higher values indicate faster speed.

| Statistical analyses
To identify genetic association and their potential interactions with age on brain iron load, a multivariate analysis of covariance (MANCOVA) was conducted with regional QSM (caudate, putamen, pallidum, cortex) as dependent variables and age group (younger vs. middle-aged vs. older) and HFE status (HFE-neg vs. HFE-pos) as between-subjects factors. Sex, education, and regional volumes were included as covariates. Follow-up analyses of covariance (ANCOVAs) were conducted to identify the significant dependent variables.
To test the genetic association of HFE status on blood markers of iron metabolism, a MANCOVA was conducted with five dependent variables (Iron, Transf, Transf-rec, Transf-sat, Ferr). As above, age group and HFE status were included as between-subjects factors and sex and education as covariates. Follow-up ANCOVAs were conducted to identify the significant dependent variables.
To test the genetic association of HFE status on motor and cognitive functions, a MANCOVA was conducted with five dependent variables (working memory, episodic memory, executive function, perceptual speed, and motor speed). As above, age group and HFE status were included as between-subjects factors and sex and education as covariates. Follow-up ANCOVAs were conducted to identify the significant dependent variables.
To determine whether peripheral and brain iron were related, partial correlations were conducted in the entire sample, adjusting for age, sex, education, and regional volume.
Finally, partial correlation analyses (with same covariates as above) were performed to assess whether brain iron load and blood markers of iron metabolism were related to cognitive performance as a function of HFE status.
Regarding the correlational analyses, in addition to Bonferroni correction for multiple tests, we conducted bootstrapping analyses to confirm the stability of the effects. The bootstrapping analyses were based on 5000 samples. Thus, we also report the bias-corrected (95%) confidence intervals (CIs) of parameter estimates for the correlation coefficients. If 95% confidence intervals for the regression coefficients did not include zero, the effects were considered reliable.
Regarding the distribution of the data across the sample, all QSM and cognitive variables, Iron, Transf, and Transf-sat, were normally distributed. Transf-rec and Ferr were skewed and were therefore log-transformed (log10). Skewness and kurtosis were within the acceptable range [−2; 2] except for Transf-rec(log) due to 3 outliers.
Considering the modest sample size, despite the acceptable skew-

| Prevalence of C282Y and H63D
Allele frequency for the HFE C282Y mutation was 7.7% (A allele), and there were no homozygotes for this SNP. Allele frequency for the HFE H63D mutation was 24.5%, two were homozygous (G allele). One individual was heterozygous for both C282Y and H63D (Table 2).

| Age, HFE status, and brain iron
The MANCOVA conducted on brain QSM iron showed a significant  Table 1 and Figure 1B), where HFE-pos was associated with higher iron load. No interactions between age group and HFE status were significant on the variables of interest (Ps > .05).

| Age, HFE status, and blood markers of iron metabolism
The MANCOVA conducted on blood iron showed a significant main  Figure 1C). Compared with HFE-neg, HFE-pos displayed lower levels of Transf and Transf-rec and higher levels of Transf-sat.
No age group X HFE interactions were significant (Ps > 0.05).

| Age, HFE status, and motor and cognitive functions
The MANCOVA conducted on motor and cognitive functions showed a significant main effect of age group where older age was associated

| Associations between putaminal iron and blood markers of iron metabolism
QSM putamen was negatively related to Transf in the total sample

| Associations between putaminal iron and performance as a function of HFE genotype
In the HFE-neg group, the correlations between QSM in putamen and performance yielded only one significant association with working memory, with higher QSM being associated with lower performance (r = −.21, P = .013; bootstrapping 95% CI [−0.360 to −0.060]).
When comparing HFE groups, the difference between correlations between putaminal QSM and working memory was at trend (z = 1.47, P = .07), whereas the correlations between QSM putamen and executive functioning were significantly different between groups (z = 2.44, P = .007).

| Associations between Transf and performance as a function of HFE genotype
To limit the number of statistical tests, Transf was retained as the blood marker of iron because (1) HFE genotype had a significant effect on levels of Transf, and (2) it was significantly associated with putaminal iron. In the HFE-neg group, Transf was not related to performance (Ps > .21). In the HFE-pos group, adjusting for the same covariates, a negative correlation was found between Transf and episodic memory (r = −.30, P = .015; bootstrapping 95% CI [−0.491 to −0.098]), but not with the other domains (Ps > 0.11; Table 3, Figure 2).
The difference for the correlations between Transf and episodic memory between the two HFE groups was significant (z = 2.10, P = .018). Applying Bonferroni correction on the 20 performed partial correlations (corrected P = .05/20 = .0025), the three correlations with P-values ranging from .013 to .016 within the HFE groups could only be considered trends, although the bootstrapping analyses supported the reliability of these associations.

| Control analyses
Excluding the three participants who were either H63D homozy-

| D ISCUSS I ON
HFE H63D and/or C282Y mutations yielded higher levels of iron in putamen. Moreover, these polymorphisms significantly modulated blood markers of iron metabolism. However, older age did not magnify the combined genetic effect of C282Y and H63D on brain iron and blood markers of iron metabolism. Finally, relationships among putaminal iron, Transf, and cognitive performance differed as a function of HFE status.
Although participants were highly selected volunteers based on strict criteria, the frequencies of C282Y and H63D were comparable with previous reports. In our study, the percentage of 7.7% for C282Y is within the reported frequencies for North European populations, with reported figures between 5% and 10%. 48,49 In our sample, 24.5% of individuals were at least (and for the large majority) heterozygous for H63D, which is also in line with previously reported frequencies. 48

| HFE status effects on brain iron and blood markers of iron
In line with the literature, several blood markers of iron metabolism were altered according to HFE status. C282Y and/or H63D carriers displayed a typical pattern suggesting higher levels of iron such as increased Transf-sat, reduced Transf, and Transf-rec. [14][15][16][17][18] Iron in putamen was increased in those carrying any or both mutations.
This result supports previous studies where C282Y or H63D mutations were related to higher iron in the brain. For both C282Y and H63D HFE variants, Hagemeier et al 23 found a moderate increase in putaminal iron in a sample of 150 individuals, an effect considered non-significant after correction for false discovery rate.
In the large UK Biobank neuroimaging sample, C282Y was most strongly related to higher iron in putamen, and H63D was related to higher striatal susceptibility. 20,22 By contrast, Bartzokis et al 21 reported higher iron in caudate in a sample of 20 male carriers of either H63D and/or TfC2 compared with non-carriers; finally, another study did not find any significant association of either HFE variants with brain iron in a sample of 314 individuals. 24 All MRI methods for brain iron quantification are limited in terms of what is exactly measured at the biological level. This is also true for QSM, whose signal relies on tissue susceptibility. Nevertheless, it is assumed that QSM largely reflects brain iron concentration, as suggested by a post-mortem validation study. 50 The correlations we found between putaminal QSM and blood-based markers of iron (Transf and Ferr) support the assumption that QSM indeed reflects iron concentration status.

| Relation to age
Although increasing age was a significant factor of higher iron load in all investigated regions, 2,6 our results did not support the agingrelated magnification of genetic effects on the brain. 26 Instead, our data suggest that these particular HFE mutations are invariantly associated with iron load across adulthood and do not trigger further iron overload in blood with increasing age, nor in the putamen, a region that typically shows one of the highest age-related increase in iron load. Given the small sample size of the current study, more studies with bigger samples should be performed to confirm this finding.

| Iron-cognition relationships as a function of HFE status
A further aim of the present study was to investigate the relationships between brain iron, cognition and motor function, with the hypothesis that such relationships may differ according to HFE genotype. Based on growing evidence that higher iron load is generally deleterious to cognitive performance in older adults 6,27 and that C282Y/H63D is associated with disturbed iron metabolism leading to iron overload, the expectation that higher iron in the HFEpos group would be related to lower performance was reasonable.  25 In addition, our findings also supported a trend toward a positive association between putaminal iron and executive functioning in the HFE-pos group, as well as a trend where lower Transf (which was negatively correlated with putaminal iron) was related to better episodic memory. These additional data tend to be in favor of an advantage of being carrier (most likely heterozygous) of C282Y or H63D.

TA B L E 3
Partial correlations between iron parameters in brain and blood and cognitive and motor function according to HFE status Note: All partial correlations were controlled for age, sex, and education. Analyses involving QSM (quantitative susceptibility mapping) were additionally controlled for volume of putamen.
Based on our and previous results, C282Y and H63D may exert beneficial effects in healthy individuals with low iron load, which may only become deleterious once a certain threshold of iron load is reached. 53,56 This theory has been discussed in relation to findings that placed the first occurrence and spread of the C282Y mutation centuries ago in some populations of Northern Europe, including Vikings, conferring advantages of increased body iron load in a challenging environment and living conditions. 11,56,57 Our findings support the presence of a modulatory effect of HFE C282Y/H63D status on the relationships between brain iron load and cognitive performance and between blood markers of iron metabolism and cognition in healthy individuals. This seemingly beneficial effect of being carrier of C282Y or H63D requires more studies with larger samples to further evaluate the effects of HFE mutations on diverse outcomes in healthy individuals. It should be acknowledged that other mutations of HFE or other genes associated with HFE function and iron load (HJV, HAMP, and TFR2) likely contribute to brain iron load and may modulate the penetrance of HFE C282Y. However, we focused on common genetic variants of the HFE gene, well described in relation to disorders associated with iron overload. 58,59

| CON CLUS ION
Taken together, our findings suggest that HFE C282Y and H63D mutations contribute to increased brain iron content at the regional level, in addition to blood markers of iron metabolism, across adulthood. In terms of cognition, our results favor a possible advantage of higher blood and brain iron on cognition in healthy carriers of the C282Y and/or H63D HFE mutations. Independent replication studies in healthy populations are needed to confirm the observed associations.

ACK N OWLED G M ENTS
The authors thank Marianne Leissner, Simon Peyda Moore, Nadia F I G U R E 2 Scatterplots of correlations (from top to bottom) between iron (QSM-quantitative susceptibility mapping) in putamen and Transferrin in the whole sample, iron in putamen and working-memory performance as a function of HFE status, iron in putamen and executive-function performance as a function of HFE status, Transferrin and episodic-memory performance as a function of HFE status. Values were adjusted for age, sex, and education, as well as volume of putamen in the analyses including QSM

CO N FLI C T O F I NTE R E S T
The authors of the present study declare that they have no conflict of interest.

AUTH O R CO NTR I B UTI O N S
GK, FM, and GP conceived and designed the experiment. GK and FM collected the data. GK, FM, and GP performed the experiment. GK, FM, FF, EJL, and GP analyzed and interpreted the data. GK, FM, FF, EJL, and GP wrote the paper.

A PPROVA L O F TH E R E S E A RCH PROTO CO L BY A N I N S TITUTI O N A L R E V I E WER B OA R D
The study was approved by the Regional Ethical Review Board in Stockholm (number 2016/457-31/2) and conformed with provisions of the Declaration of Helsinki.

I N FO R M E D CO N S E NT
All participants signed informed consent prior to data collection.

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 from the corresponding author upon reasonable request. The raw data belonged to the present study cannot be made publicly available, because the disclosure of personal data was not included in the research protocol and informed consent document.