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. 2021 Mar 9;10(5):1148. doi: 10.3390/jcm10051148

Association of Genetic Polymorphisms in Oxidative Stress and Inflammation Pathways with Glaucoma Risk and Phenotype

Makedonka Atanasovska Velkovska 1, Katja Goričar 2, Tanja Blagus 2, Vita Dolžan 2, Barbara Cvenkel 1,3,*
Editors: Jose Javier Garcia-Medina, Maria Dolores Pinazo-Duran
PMCID: PMC7967191  PMID: 33803434

Abstract

Oxidative stress and neuroinflammation are involved in the pathogenesis and progression of glaucoma. Our aim was to evaluate the impact of selected single-nucleotide polymorphisms in inflammation and oxidative stress genes on the risk of glaucoma, the patients’ clinical characteristics and the glaucoma phenotype. In total, 307 patients with primary open-angle glaucoma or ocular hypertension were enrolled. The control group included 339 healthy Slovenian blood donors. DNA was isolated from peripheral blood. Genotyping was performed for SOD2 rs4880, CAT rs1001179, GPX1 rs1050450, GSTP1 rs1695, GSTM1 gene deletion, GSTT1 gene deletion, IL1B rs1143623, IL1B rs16944, IL6 rs1800795 and TNF rs1800629. We found a nominally significant association of GSTM1 gene deletion with decreased risk of ocular hypertension and a protective role of IL1B rs16944 and IL6 rs1800629 in the risk of glaucoma. The CT and TT genotypes of GPX1 rs1050450 were significantly associated with advanced disease, lower intraocular pressure and a larger vertical cup–disc ratio. In conclusion, genetic variability in IL1B and IL6 may be associated with glaucoma risk, while GPX and TNF may be associated with the glaucoma phenotype. In the future, improved knowledge of these pathways has the potential for new strategies and personalised treatment of glaucoma.

Keywords: glaucoma, inflammation, oxidative stress, phenotype, polymorphism, susceptibility

1. Introduction

There is growing interest in the correlation between oxidative stress, inflammation, apoptosis and primary open-angle glaucoma (POAG) initiation and progression [1,2,3,4,5]. Reactive oxygen species (ROS) are formed in the eyes, following a wide variety of stressors and are largely implicated in glaucoma pathogenesis. Similarly, immune-inflammatory response mediators have recently become a target of interest in glaucoma [6,7,8,9,10].

POAG progression has been linked to an increase in oxidative stress (OS) markers [11,12], and although it has been hypothesised that OS plays an early role in the development of glaucomatous optic neuropathy [13], the link between OS and clinical glaucoma parameters remains to be elucidated [1]. In glaucoma, increased intraocular pressure (IOP), vascular dysregulation and immune system activation can trigger several changes in the retina and optic nerve, including disrupted axonal transport and neurofilament accumulation, microvascular abnormalities, extracellular matrix remodelling and glial cell activation. These alterations can lead to secondary damage such as excitotoxicity, neurotrophin deprivation, oxidative damage, mitochondrial dysfunction and, eventually, retinal ganglion cell death [5,14]. In addition, neurodegeneration extends beyond the retina and optic nerve into the central nervous system [3,15].

Oxidative stress arises due to disturbed equilibrium between the pro-oxidant/antioxidant status that further takes part in the generation of ROS and free radicals, both potentially toxic for neuronal cells. The human body produces oxygen free radicals and other ROS as by-products through numerous physiological and biochemical processes [16]. At the same time, antioxidants, further supported with antioxidant enzymes superoxide dismutase (SOD), catalase (CAT), glutathione reductase and glutathione peroxidase (GPX), help to regulate the level of ROS [17,18]. Decreased antioxidant defence, together with increasing pro-oxidants in the aqueous humour [12,19], ocular tissues [20,21] and blood [22], has been reported in glaucoma. Several antioxidant enzymes such as SOD, CAT and GPX have been found in the aqueous humour [23,24]. The enzyme CAT is the primary scavenger of ROS, and its deficiency and/or genetic variants are associated with a higher risk of diabetes complications and vitiligo [25,26]. GPX has antioxidant effects and catalyses the reduction of hydrogen peroxide by two molecules of glutathione as part of a ROS defence system.

Glaucoma is a neurodegenerative optic neuropathy and, similarly to other neurodegenerative diseases, is also associated with neuroinflammation where astrocytes and microglia play a major role [27]. The presence of reactive astrocytes, microglial activation and the release of inflammatory mediators such as cytokines, ROS, nitric oxide (NO) and tumour necrosis factor-α (TNF-α) cause a state of chronic inflammation that may exert neurotoxic effects [28]. Immune inflammatory response mediators like proteolytic enzymes and pro-inflammatory cytokines (TNF-α, IL-1β, IL-6, IL-12, and NO) have recently become a target of glaucoma research [15,29,30,31,32,33,34,35,36,37,38,39,40]. TNF-α, a major immunomodulator and inflammatory cytokine, has been suggested to mediate the apoptotic death of retinal ganglion cells in glaucoma patients [34]. Interleukin-1, an inflammatory cytokine, is implicated in ischaemic and excitotoxic damage in the retina [41]. Interleukin-6, a proinflammatory cytokine, modulates neuronal survival and protects retinal ganglion cells from ischaemic and excitotoxic damage [42].

Moreover, further research concerning the functions, effectors and signalling pathways of the above molecules and their interactions may lead to new strategies for the treatment of glaucoma [10,43].

Genetic factors, such as single-nucleotide polymorphisms (SNPs), have been shown to modify these pathways, but their impact on the risk of glaucoma and disease course has not been confirmed yet. Studies evaluating the effect of genetic polymorphisms or alterations in proteins’ functions in the oxidative-stress- and inflammation-related genes on the risk of glaucoma have found conflicting results [44,45]. Although the association of genetic variability of inflammation and oxidative stress genes with the risk of glaucoma has already been explored to some extent, the association with patients’ clinical characteristics and glaucoma phenotype has not been studied yet.

Therefore, the aim of our study was to investigate the associations of selected SNPs in inflammation and oxidative stress pathways with the risk of glaucoma, as well as the associations between selected SNPs, patients’ clinical characteristics and the glaucoma phenotype.

2. Materials and Methods

2.1. Study Participants

The study included patients older than 40 years with POAG or ocular hypertension (OHT) attending the Glaucoma Clinic at the Department of Ophthalmology of the University Medical Centre Ljubljana, Slovenia, from October 2018 until May 2020. The study was approved by the Slovenian Medical Ethics Committee (KME 30/05/11). All subjects signed informed consent in accordance with the Declaration of Helsinki.

Ocular hypertension was defined according to the Guidelines and Terminology of the European Glaucoma Society as IOP higher than 21 mmHg without changes at the optic nerve head and visual field defects [46]. POAG was defined as untreated IOP higher than 21 mmHg, characteristic glaucoma changes at the optic nerve head and/or corresponding visual field defects. Patients with primary juvenile open-angle glaucoma, secondary causes of OHT/glaucoma and non-glaucomatous optic neuropathy and patients after prior intraocular surgery (except non-complicated cataract surgery with more than 6 months after surgery) were excluded. Patients were treated according to the recommendations of the European Glaucoma Society guidelines [47]. Lowering of the IOP was achieved by topical medications, laser trabeculoplasty or surgery, depending on the severity of glaucoma, life expectancy, status of the fellow eye and the rate of progression.

Data on the course of disease and treatment were obtained from medical records. In all patients, the following data were recorded: sex, age, smoking status, a family history of glaucoma, IOP, central corneal thickness (CCT), visual field parameters (with best-corrected visual acuity), vertical cup–disc (C/D) ratio, current ocular diagnosis and systemic diseases. The presence of diabetes, arterial hypertension (AH), hyperlipidaemia and heart disease was also recorded.

The glaucoma phenotype was assessed with the severity of disease, C/D ratio, IOP and CCT. The severity of glaucoma was based on the visual field index mean defect (MD) of the Octopus standard automated perimetry (G program, dynamic strategy) and classified into early (MD < 6 dB), moderate (MD 6–12 dB) and advanced (MD > 12 dB) disease. The IOP was measured by Goldman applanation tonometry. The mean IOP based on all measurements during follow-up was calculated for each eye. CCT values were measured with a manual ultrasound pachymeter (Pachmate-DGH, Tehnology Inc., Exton, PA, USA).

The control group included 339 healthy, unrelated Slovenian blood donors without any systemic disease. For the control group, data about age and sex were available.

2.2. DNA Isolation and Genotyping

Nine candidate genes were selected based on their direct involvement in oxidative stress pathways and signalling cascades of inflammation. Only functional polymorphisms with a minor allele frequency above 5% were included in the study: non-synonymous SNPs in the coding region previously associated with enzyme activity or SNPs in the 5’ untranslated region previously associated with altered gene or protein expression levels were SOD2 rs4880, CAT rs1001179, GPX1 rs1050450, GSTP1 rs1695, IL1B rs1143623, IL1B rs16944, IL6 rs1800795 and TNF rs1800629 (Supplementary Materials Table S1) [48]. We also determined the presence of homozygous GSTM1 and GSTT1 gene deletion.

Genomic DNA was isolated from peripheral blood samples using the E.Z.N.A.® SQ II Blood DNA Kit (Omega Bio-tek, Inc., Norcross, GA, USA) following the manufacturer’s instructions. GSTM1 and GSTT1 were genotyped using multiplex PCR that enabled the detection of homozygous gene deletion. In brief, both genes were simultaneously amplified in a single-step PCR together with the β-globin gene (HBB) as the internal positive control, as previously described [49] (Table S2, Figure S1). Genotyping of 8 SNPs was performed with competitive allele-specific PCR (KASP), using the KASP Master mix and custom KASP genotyping assays (all from LGC, Middlesex, UK) according to the manufacturer’s instructions (KBiosciences, Herts, UK and LGC Genomics, UK) (Table S2, Figure S2). Ten percent of the samples were genotyped in duplicate as quality control, and all the results were concordant.

2.3. Statistical Analysis

Continuous and categorical variables were described with the median and interquartile range (25–75%) or frequencies, respectively. Fisher’s exact test was used to compare the distribution of categorical variables, while nonparametric Mann–Whitney or Kruskal-Wallis tests were used to compare the distribution of continuous variables. Deviation from the Hardy–Weinberg equilibrium (HWE) was evaluated using the standard chi-square test. Both dominant and additive genetic models were used in the analysis. The association of polymorphisms with glaucoma risk was evaluated using logistic regression to calculate non-adjusted and adjusted odds ratios (ORs) and 95% confidence intervals (CIs). For the IOP, CCT and C/D ratio, data from the most affected eye were used in the analysis. If a measurement was available for only one eye, the measurement of that eye was used in the analysis.

All statistical tests were two sided. As 11 SNPs were investigated, the Bonferroni correction was used to account for multiple comparisons: p-values below 0.005 were considered statistically significant, while p-values between 0.005 and 0.050 were considered nominally significant. All statistical tests were two sided. In risk analysis, for a polymorphism with a minor allele frequency of 0.10, 0.30 and 0.50, this study had 80% power to detect ORs of 1.9, 1.6 and 1.54 or more, respectively. Power calculation was conducted using PS Power and Sample Size Calculation version 3.0 [50]. The statistical analyses were carried out using IBM SPSS Statistics version 21.0 (IBM Corporation, Armonk, NY, USA).

3. Results

A total of 307 patients, 235 with POAG and 72 with OHT, participated in this study. The median age of patients was 70 (interquartile range 64–78) years. Among the patients, 139 (45.3%) were male and 168 (54.7%) were female. The control group consisted of 339 healthy blood donors with the median age of 49 (interquartile range 45–55) years. Among the controls, 251 (74.0%) were male and 88 (26.0%) were female. Patients with POAG or OHT were significantly older than controls (p < 0.001). There were more females among patients with POAG or OHT compared to controls (p < 0.001). In total, 31.9% of patients reported a family history of glaucoma. Clinical characteristics of all patients, their smoking history and accompanying systemic diseases are presented separately for the OHT and POAG groups in Table 1.

Table 1.

Clinical characteristics of patients.

Characteristic Cases
(n = 307)		 OHT
(n = 72)		 POAG
(n = 235)		 p-Value
Sex Male, n (%) 139 (45.3) 34 (47.2) 105 (44.7) 0.787
Female, n (%) 168 (54.7) 38 (52.8) 130 (55.3)
Age (years) Median (25–75%) 70 (64–78) 64 (54–69) 72 (66–79) <0.001
IOP (mmHg) Right eye, (median (25–75%) 19.71 (16.8–23) 22.6 (20.1–24.2) 18.59 (16.3–22.0) <0.001
Left eye, (median (25–75%) 19.78 (16.7–23) 22.5 (19.7–24.2) 18.48 (16.3–22.0) <0.001
CCT (µm) Right eye, (median (25–75%) 547 (521–574) 565.5 (533–595) 541 (518–570) <0.001
Left eye, (median (25–75%) 548.5 (522.5–577.0) {1} 570.5 (532.3–602.5) 543 (519.5–571.0) <0.001
C/D ratio Right eye, (median (25–75%) 0.8 (0.5–0.9) 0.35 (0.3–0.5) 0.9 (0.7–1.0) <0.001
Left eye, (median (25–75%) 0.8 (0.5–1) 0.3 (0.3–0.5) 0.9 (0.7–1.0) <0.001
Family history of glaucoma No, n (%) 209 (68.1) 51 (70.8) 158 (67.2) 0.665
Yes, n (%) 98 (31.9) 21 (29.2) 77 (32.8)
Arterial hypertension {1} No, n (%) 140 (45.8) 38 (52.8%) 102 (43.6) 0.179
Yes, n (%) 166 (54.2) 34 (47.2%) 132 (56.4)
Diabetes No, n (%) 280 (91.2) 64 (88.9%) 216 (91.9) 0.476
Yes, n (%) 27 (8.8) 8 (11.1%) 19 (8.1)
Hyperlipidaemia No, n (%) 209 (68.1) 53 (73.6%) 156 (66.4) 0.312
Yes, n (%) 98 (31.9) 19 (26.4%) 79 (3.6)
Heart disease {1} No, n (%) 240 (78.4) 63 (87.5%) 177 (75.6) 0.034
Yes, n (%) 66 (21.6) 9 (12.5%) 57 (24.4)
Smoking No, n (%) 204 (66.4) 40 (55.6%) 164 (69.8) 0.045
Currently, n (%) 26 (8.5) 6 (8.3) 20 (8.5)
Former, n (%) 77 (25.1) 26 (36.1) 51 (21.7)
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{ }—missing data; OHT—ocular hypertension; POAG—primary open-angle glaucoma; IOP—intraocular pressure; CCT—central corneal thickness; C/D ratio—vertical cup–disc ratio.

Genotypes’ distributions for the investigated SNPs in both patient and control groups are presented in Table 2. When susceptibility analysis was performed, GSTM1 gene deletion was nominally significantly associated with the risk for POAG or OHT. The carriers of GSTM1 gene deletion had lower odds for developing POAG or OHT (OR = 0.50; 95% CI = 0.30–0.83; p = 0.007).

Table 2.

Genotype frequencies of the control (n = 339) and patient (n = 307) groups with the risk of glaucoma or OHT.

SNP Genotype Controls
n (%)		 Cases
n (%)		 OR (95% CI) adj. padj-Value
SOD2 rs4880 CC 92 (27.1) 78 (25.4) Reference
CT 160 (47.2) 136 (44.3) 1.11 (0.60–2.06) 0.745
TT 87 (25.7) 93 (30.3) 1.15 (0.58–2.26) 0.690
CT + TT 247 (72.9) 229 (74.6) 1.12 (0.63–2.00) 0.691
CAT rs1001179 CC 193 (57.1) {1} 184 (59.9) Reference
CT 122 (36.1) 105 (34.2) 0.95 (0.56–1.60) 0.840
TT 23 (6.8) 18 (5.9) 1.31 (0.42–4.08) 0.642
CT + TT 145 (42.9) 123 (40.1) 0.99 (0.59–1.63) 0.954
GPX1 rs1050450 CC 170 (50.1) 157 (51.1) Reference
CT 130 (38.3) 125 (40.7) 0.86 (0.51–1.47) 0.592
TT 39 (11.5) 25 (8.1) 0.49 (0.19–1.28) 0.146
CT + TT 169 (49.9) 150 (48.9) 0.78 (0.47–1.30) 0.345
GSTP1 rs1695 AA 150 (44.2) 141 (45.9) Reference
AG 152 (44.8) 128 (41.7) 0.98 (0.58–1.67) 0.940
GG 37 (10.9) 38 (12.4) 1.84 (0.79–4.28) 0.157
AG + GG 189 (55.8) 166 (54.1) 1.11 (0.67–1.83) 0.686
GSTP1 rs1138272 CC 274 (80.8) 254 (82.7) Reference
CT + TT 65 (19.2) 53 (17.3) 1.25 (0.65–2.41) 0.513
GSTM1 gene deletion No deletion 136 (40.1) 150 (48.9) Reference
Deletion 203 (59.9) 157 (51.1) 0.50 (0.30–0.83) 0.007
GSTT1 gene deletion No deletion 288 (85) 254 (82.7) Reference
Deletion 51 (15) 53 (17.3) 0.58 (0.30–1.12) 0.103
IL1B rs1143623 GG 174 (51.3) 145 (47.2) Reference
GC 136 (40.1) 135 (44) 1.25 (0.74–2.11) 0.408
CC 29 (8.6) 27 (8.8) 1.50 (0.61–3.69) 0.378
GC + CC 165 (48.7) 162 (52.8) 1.29 (0.78–2.12) 0.323
IL1B rs16944 TT 44 (13) 44 (14.3) Reference
TC 145 (42.8) 143 (46.6) 0.69 (0.32–1.47) 0.332
CC 150 (44.2) 120 (39.1) 0.62 (0.28–1.35) 0.227
TC + CC 295 (87.0) 263 (85.7) 0.65 (0.32–1.34) 0.248
IL6 rs1800795 GG 120 (35.4) 111 (36.2) Reference
GC 151 (44.5) 154 (50.2) 0.71 (0.41–1.24) 0.226
CC 68 (20.1) 42 (13.7) 0.67 (0.32–1.39) 0.277
GC + CC 219 (64.6) 196 (63.8) 0.70 (0.41–1.17) 0.174
TNF rs1800629 GG 228 (67.3) 234 (76.2) Reference
GA + AA 111 (32.7) 73 (23.8) 0.66 (0.38–1.17) 0.156
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{ }—missing data; adj–adjusted for age and sex; SNP–single nucleotide polymorphism. Nominally significant values have been marked in bold.

Next, susceptibility analysis was performed separately for patients with POAG (n = 235) and OHT (n = 72), as shown in Table 3. The carriers of GSTM1 gene deletion had lower odds for developing OHT (OR = 0.43; 95% CI = 0.22–0.81; p = 0.009) but not for developing POAG (OR = 0.61; 95% CI = 0.31–1.18; p = 0.141).

Table 3.

Genotype frequencies of selected polymorphisms and their association with the risk of OHT (n = 72) and POAG (n = 235).

OHT POAG
SNP Genotype n (%) OR (95% CI) adj. padj-Value n (%) OR (95% CI) adj. padj-Value
SOD2 rs4880 CC 15 (20.8) Reference 63 (26.8) Reference
CT 39 (54.2) 1.37 (0.63–3.00) 0.426 97 (41.3) 0.90 (0.41–2.02) 0.806
TT 18 (25) 1.12 (0.46–2.71) 0.798 75 (31.9) 1.23 (0.51–2.93) 0.643
CT + TT 57 (79.2) 1.28 (0.61–2.66) 0.515 172 (73.2) 1.02 (0.49–2.14) 0.953
CAT rs1001179 CC 49 (68.1) Reference 135 (57.4) Reference
CT 20 (27.8) 0.66 (0.33–1.30) 0.230 85 (36.2) 1.36 (0.68–2.69) 0.381
TT 3 (4.2) 0.40 (0.07–2.19) 0.290 15 (6.4) 3.79 (0.92–15.55) 0.064
CT + TT 23 (31.9) 0.62 (0.32–1.20) 0.157 100 (42.6) 1.54 (0.80–2.96) 0.201
GPX1 rs1050450 CC 45 (62.5) Reference 112 (47.7) Reference
CT 22 (30.6) 0.66 (0.33–1.30) 0.228 103 (43.8) 0.93 (0.47–1.85) 0.834
TT 5 (6.9) 0.36 (0.10–1.26) 0.111 20 (8.5) 0.70 (0.20–2.44) 0.581
CT + TT 27 (37.5) 0.59 (0.31–1.12) 0.104 123 (52.3) 0.89 (0.46–1.72) 0.725
GSTP1 rs1695 AA 38 (52.8) Reference 103 (43.8) Reference
AG 26 (36.1) 0.92 (0.47–1.79) 0.800 102 (43.4) 0.92 (0.46–1.84) 0.819
GG 8 (11.1) 1.37 (0.49–3.83) 0.554 30 (12.8) 2.70 (0.85–8.60) 0.092
AG + GG 34 (47.2) 0.99 (0.53–1.86) 0.987 132 (56.2) 1.11 (0.58–2.13) 0.747
GSTP1 rs1138272 CC 62 (86.1) Reference 192 (81.7) Reference
CT + TT 10 (13.9) 1.09 (0.47–2.55) 0.837 43 (18.3) 1.44 (0.60–3.47) 0.410
GSTM1 gene deletion No deletion 36 (50) Reference 114 (48.5) Reference
Deletion 36 (50) 0.43 (0.22–0.81) 0.009 121 (51.5) 0.61 (0.31–1.18) 0.141
GSTT1 gene deletion No deletion 60 (83.3) Reference 194 (82.6) Reference
Deletion 12 (16.7) 0.61 (0.26–1.44) 0.258 41 (17.4) 0.50 (0.22–1.16) 0.107
IL1B rs1143623 GG 38 (52.8) Reference 107 (45.5) Reference
GC 32 (44.4) 1.16 (0.61–2.19) 0.657 103 (43.8) 1.12 (0.56–2.24) 0.745
CC 2 (2.8) 0.47 (0.10–2.24) 0.341 25 (10.6) 2.87 (0.95–8.65) 0.061
GC + CC 34 (47.2) 1.04 (0.56–1.95) 0.893 128 (54.5) 1.34 (0.70–2.57) 0.384
IL1B rs16944 TT 5 (6.9) Reference 39 (16.6) Reference
TC 34 (47.2) 1.38 (0.45–4.28) 0.574 109 (46.4) 0.34 (0.13–0.90) 0.030
CC 33 (45.8) 1.44 (0.46–4.47) 0.533 87 (37) 0.32 (0.12–0.86) 0.024
TC + CC 67 (93.1) 1.41 (0.48–4.16) 0.536 196 (83.4) 0.33 (0.13–0.82) 0.017
IL6 rs1800795 GG 25 (34.7) Reference 86 (36.6) Reference
GC 36 (50) 0.89 (0.45–1.80) 0.755 118 (50.2) 0.44 (0.21–0.93) 0.031
CC 11 (15.3) 0.77 (0.30–1.96) 0.577 31 (13.2) 0.49 (0.19–1.29) 0.149
GC + CC 47 (65.3) 0.86 (0.44–1.66) 0.654 149 (63.4) 0.46 (0.23–0.91) 0.025
TNF rs1800629 GG 56 (77.8) Reference 178 (75.7) Reference
GA + AA 16 (22.2) 0.62 (0.30–1.28) 0.196 57 (24.3) 0.67 (0.31–1.41) 0.289
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adj—adjusted for age and sex; OHT—ocular hypertension; POAG—primary open-angle glaucoma; SNP—single nucleotide polymorphism. Nominally significant values have been marked in bold.

IL6 and IL1B polymorphisms showed nominally significant association with POAG but not with OHT. The carriers of the IL1B rs16944 polymorphism had lower odds for developing POAG in the dominant and additive genetic model (OR = 0.33; 95% CI = 0.13–0.82; p = 0.017). IL6 rs1800795 was associated with lower odds for developing POAG in the dominant genetic model (OR = 0.46; 95% CI = 0.23–0.91; p = 0.025) (Table 3).

The distribution of genotype frequencies for the investigated polymorphisms was compared between groups of patients with OHT without visual field defects and patients with glaucoma of different severity (Table 4). The results showed statistically significant differences among groups only for the distribution of GPX1 rs1050450 genotypes in the dominant model. The carriers of at least one polymorphic GPX1 allele had less OHT and more POAG (p = 0.025; Table 4).

Table 4.

Association of selected polymorphisms with OHT and POAG severity.

SNP Genotype OHT
(n = 72)		 Early POAG
(n = 62)		 Moderate POAG
(n = 54)		 Severe
POAG (n = 119)		 p-Value *
n (%) n (%) n (%) n (%)
SOD2 rs4880 CC 15 (19.2) 15 (19.2) 15 (19.2) 33 (42.3) Padd = 0.681
CT 39 (28.7) 27 (19.9) 22 (16.2) 48 (35.3)
TT 18 (19.4) 20 (21.5) 17 (18.3) 38 (40.9)
CT + TT 57 (24.9) 47 (20.5) 39 (17) 86 (37.6) Pdom = 0.722
CAT rs1001179 CC 49 (26.6) 31 (16.8) 35 (19) 69 (37.5) Padd = 0.149
CT 20 (19) 23 (21.9) 18 (17.1) 44 (41.9)
TT 3 (16.7) 8 (44.4) 1 (5.6) 6 (33.3)
CT + TT 23 (18.7) 31 (25.2) 19 (15.4) 50 (40.7) Pdom = 0.158
GPX1 rs1050450 CC 45 (28.7) 33 (21) 19 (12.1) 60 (38.2) Padd = 0.134
CT 22 (17.6) 24 (19.2) 29 (23.2) 50 (40)
TT 5 (20) 5 (20) 6 (24) 9 (36)
CT + TT 27 (18) 29 (19.3) 35 (23.3) 59 (39.3) Pdom = 0.025
GSTP1 rs1695 AA 38 (27) 26 (18.4) 23 (16.3) 54 (38.3) Padd = 0.628
AG 26 (20.3) 30 (23.4) 21 (16.4) 51 (39.8)
GG 8 (21.1) 6 (15.8) 10 (26.3) 14 (36.8)
AG + GG 34 (20.5) 36 (21.7) 31 (18.7) 65 (39.2) Pdom = 0.570
GSTP1 rs1138272 CC 62 (24.4) 49 (19.3) 41 (16.1) 102 (40.2) 0.299
CT + TT 10 (18.9) 13 (24.5) 13 (24.5) 17 (32.1)
GSTM1 gene deletion No deletion 36 (24) 23 (15.3) 30 (20) 61 (40.7) 0.195
Deletion 36 (22.9) 39 (24.8) 24 (15.3) 58 (36.9)
GSTT1 gene deletion No deletion 60 (23.6) 50 (19.7) 46 (18.1) 98 (38.6) 0.940
Deletion 12 (22.6) 12 (22.6) 8 (15.1) 21 (39.6)
IL1B rs1143623 GG 38 (26.2) 27 (18.6) 22 (15.2) 58 (40) Padd = 0.358
GC 32 (23.7) 27 (20) 26 (19.3) 50 (37)
CC 2 (7.4) 8 (29.6) 6 (22.2) 11 (40.7)
GC + CC 34 (21) 35 (21.6) 32 (19.8) 61 (37.7) Pdom = 0.526
IL1B rs16944 TT 5 (11.4) 14 (31.8) 7 (15.9) 18 (40.9) Padd = 0.074
TC 34 (23.8) 30 (21) 30 (21) 49 (34.3)
CC 33 (27.5) 18 (15) 17 (14.2) 52 (43.3)
TC + CC 67 (25.5) 48 (18.3) 47 (17.9) 101 (38.4) Pdom = 0.079
IL6 rs1800795 GG 25 (22.5) 24 (21.6) 23 (20.7) 39 (35.1) Padd = 0.862
GC 36 (23.4) 31 (20.1) 23 (14.9) 64 (41.6)
CC 11 (26.2) 7 (16.7) 8 (19) 16 (38.1)
GC + CC 47 (24) 38 (19.4) 31 (15.8) 80 (40.8) Pdom = 0.617
TNF rs1800629 GG 56 (23.9) 46 (19.7) 43 (18.4) 89 (38) 0.873
GA + AA 16 (21.9) 16 (21.9) 11 (15.1) 30 (41.1)
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* Comparison of all 4 groups using Fisher’s exact test; add—additive model; dom—dominant model; OHT—ocular hypertension; POAG—primary open-angle glaucoma; SNP—single nucleotide polymorphism. Statistically significant values have been marked in bold.

We also analysed the association of the investigated polymorphisms with the glaucoma phenotype, such as the IOP, CCT and C/D ratio (Table 5). The results showed a statistically significant association of GPX1 rs1050450 with the IOP and C/D ratio in the dominant model. The carriers of at least one polymorphic GPX1 allele had a lower IOP (p = 0.019) and a slightly increased C/D ratio (p = 0.035). In addition, a statistically significant association of TNF rs1800629 with the CCT was found in the dominant model. The carriers of at least one polymorphic TNF rs1800629 allele had a larger CCT (p = 0.001).

Table 5.

Association of investigated polymorphisms with the IOP, CCT and C/D ratio.

SNP Genotype IOP Max
Median (25–75%)		 p-Value CCT Min.
Median (25–75%)		 p-Value C/D Max.
Median (25–75%)		 p-Value
SOD2 rs4880 CC 21 (17.5–26) Padd = 0.301 539 (520.75–571) Padd = 0.422 0.85 (0.6–1) Padd = 0.246
CT 20.8 (17.5–23.3) 548 (520–577) 0.8 (0.5–1)
TT 20.2 (17.3–23) 541 (514.5–573) 0.9 (0.7–1)
CT + TT 20.6 (17.5–23) Pdom = 0.139 544 (518.5–573) Pdom = 0.590 0.9 (0.6–1) Pdom = 0.985
CAT rs1001179 CC 20.8 (17.5–23.7) Padd = 0.574 543 (518.25–573.75) Padd = 0.331 0.85 (0.6–1) Padd = 0.517
CT 20.6 (17.4–23.7) 541 (519.5–571.5) 0.9 (0.6–1)
TT 22.5 (17.5–26) 557 (532.5–584.5) 0.8 (0.6–0.9)
CT + TT 20.8 (17.5–24) Pdom = 0.759 544 (520–573) Pdom = 0.994 0.9 (0.6–1) Pdom = 0.690
GPX1 rs1050450 CC 21.2 (17.9–24) Padd = 0.061 551 (520.5–575) Padd = 0.245 0.8 (0.5–1) Padd = 0.105
CT 19.4 (17.2–23) 538 (519–571) 0.9 (0.7–1)
TT 19 (17.2–24) 540 (501.5–572) 0.9 (0.6–1)
CT + TT 19.3 (17.2–23.2) Pdom = 0.019 538.5 (518–571) Pdom = 0.094 0.9 (0.675–1) Pdom = 0.035
GSTP1 rs1695 AA 20.5 (17.6–23.5) Padd = 0.991 546 (519.5–575) Padd = 0.154 0.8 (0.55–1) Padd = 0.572
AG 21 (17.2–24) 537.5 (515.5–570) 0.9 (0.6–1)
GG 20.1 (17.7–24.1) 547.5 (531–574.75) 0.9 (0.7–1)
AG + GG 20.9 (17.4–24) Pdom = 0.899 540.5 (520–572.25) Pdom = 0.598 0.9 (0.6–1) Pdom = 0.357
GSTP1 rs1138272 CC 20.6 (17.5–24) 0.924 544 (520–573) 0.965 0.9 (0.6–1) 0.846
CT + TT 21 (17.4–23.7) 540 (519.5–575.5) 0.9 (0.6–1)
GSTM1 gene deletion No deletion 21 (17.2–24) 0.967 544 (515–576) 0.894 0.9 (0.6–1) 0.823
Deletion 20.6 (17.6–23.5) 541 (520–572.5) 0.8 (0.6–1)
GSTT1 gene deletion No deletion 20.7 (17.4–23.9) 0.354 541.5 (518–573) 0.199 0.9 (0.6–1) 0.994
Deletion 21 (18–24) 544 (529.5–577) 0.9 (0.6–1)
IL1B rs1143623 GG 21 (17.6–24) Padd = 0.268 539 (518–575) Padd = 0.382 0.9 (0.6–1) Padd = 0.843
GC 20.2 (17.2–23) 544 (520–571) 0.8 (0.6–1)
CC 21 (17.6–24) 557 (530–573) 0.9 (0.6–1)
GC + CC 20.3 (17.3–23) Pdom = 0.128 547 (521–571.5) Pdom = 0.383 0.85 (0.6–1) Pdom = 0.970
IL1B rs16944 TT 20.4 (17.7–23.1) Padd = 0.411 551 (529.25–570) Padd = 0.617 0.9 (0.7–1) Padd = 0.541
TC 20.4 (17.5–23) 544 (520–574) 0.8 (0.6–1)
CC 21 (17.2–25) 538.5 (518–573.75) 0.9 (0.5–1)
TC + CC 20.8 (17.4–24) Pdom = 0.989 541 (519–574) Pdom = 0.508 0.9 (0.6–1) Pdom = 0.319
IL6 rs1800795 GG 20 (17.6–23.3) Padd = 0.184 546 (520–578) Padd = 0.354 0.8 (0.5–1) Padd = 0.342
GC 20.6 (17.2–23.4) 541.5 (520.75–573) 0.9 (0.6–1)
CC 21.8 (18.4–26) 534 (502.75–569.25) 0.8 (0.4–1)
GC + CC 21 (17.3–24) Pdom = 0.491 540.5 (518–571) Pdom = 0.390 0.9 (0.6–1) Pdom = 0.451
TNF rs1800629 GG 20.5 (17.2–23.2) 0.166 540 (517.75–570) 0.001 0.9 (0.6–1) 0.539
GA + AA 21.7 (17.7–24.1) 564 (529.5–589.5) 0.9 (0.6–1)
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add—additive model; dom—dominant model; IOP—intraocular pressure; CCT—central corneal thickness; C/D ratio—vertical cup–disc ratio; SNP—single nucleotide polymorphism. Statistically significant values have been marked in bold.

We also analysed the association between patients’ clinical characteristics and the glaucoma phenotype. Cases with AH had a significantly lower maximal median IOP compared to those without AH (p < 0.001) and a greater C/D ratio (p = 0.008) (Table 6). Similarly, ischaemic heart disease was significantly associated with lower IOP (p = 0.002). However, patients with glaucoma were older compared to OHT subjects (p < 0.001) with a higher prevalence of heart disease (p = 0.034). Diabetes was significantly associated with a larger CCT (p = 0.030).

Table 6.

Association of patients’ clinical characteristics with the glaucoma phenotype.

Characteristic IOP Max.
Median (25–75%)		 p-Value CCT Min.
Median (25–75%)		 p-Value C/D Ratio Max.
Median (25–75%)		 p-Value
Smoking No, n (%) 20.1 (17.5–23.7) 0.356 541 (520–573) 0.589 0.9 (0.6–1) 0.019
Yes, n (%) 21 (17.4–24) 544 (520–573) 0.8 (0.5–1)
Family history No, n (%) 21 (17.7–24) 0.073 544 (520–574) 0.632 0.9 (0.6–1) 0.695
Yes, n (%) 20 (17.1–23.2) 540.5 (518.8–570.5) 0.9 (0.6–1)
AH No, n (%) 22 (18.6–24.2) <0.001 548.5 (526.8–575.5) 0.050 0.8 (0.5–1) 0.008
Yes, n (%) 19.4 (16.9–22.8) 538 (512–570.3) 0.9 (0.6–1)
Diabetes No, n (%) 20.6 (17.2–23.7) 0.071 541 (518.3–573) 0.030 0.9 (0.6–1) 0.543
Yes, n (%) 22 (19.3–24) 558 (540–580) 0.9 (0.4–1)
Hyperlipidaemia No, n (%) 21 (17.6–24) 0.078 543 (518.5–574) 0.959 0.8 (0.6–1) 0.541
Yes, n (%) 19.7 (17.2–23.1) 543.5 (520–570) 0.9 (0.6–1)
Heart disease No, n (%) 21 (17.7–24) 0.002 544.5 (520–575.5) 0.173 0.8 (0.6–1) 0.485
Yes, n (%) 19 (16.7–21.5) 536.5 (515.8–562.3) 0.9 (0.6–1)
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AH—arterial hypertension; IOP—intraocular pressure; CCT—central corneal thickness; C/D ratio—vertical cup–disc ratio. Statistically significant values have been marked in bold.

In the multivariable analysis including GPX1 rs1050450, AH and diabetes, both GPX1 rs1050450 (p = 0.014) and patients’ clinical characteristics remained significantly associated with the IOP (p < 0.001 and p = 0.019, respectively). In the multivariable analysis including GPX1 rs1050450, AH and smoking, only GPX1 rs1050450 remained significantly associated with the C/D ratio (p = 0.027). In the multivariable model including TNF rs1800629 and diabetes, both variables were significantly associated with the CCT (p < 0.001 and p = 0.015).

4. Discussion

In this study, we evaluated the associations of selected SNPs in antioxidative and inflammation pathways with the risk of glaucoma and the glaucoma phenotype. Our main finding was that in the antioxidative pathways, GSTM1 gene deletion may play a protective role in the development of OHT, while inflammatory pathway polymorphisms such as IL1B rs16944 and IL6 rs1800795 may play a protective role in the development of POAG. Interestingly, GPX1 rs1050450 polymorphism was mainly associated with the severity of POAG and with the phenotype such as the IOP and C/D ratio.

In our study, we observed that the carriers of GSTM1 gene deletion had lower odds for developing OHT, but we found no association between GSTT1 gene deletion and the risk of OHT or POAG. GSTs play an important role in cellular protection against oxidative stress. Homozygous GSTM1 and GSTT1 deletion results in the absence of the encoded enzymes and may thus impair detoxification and inactivation of reactive metabolites generated during oxidative stress. GSTM1 and GSTT1 gene deletion was extensively investigated with regard to glaucoma risk. Whereas some studies have found a significantly higher frequency of the GSTM1 null genotype in patients with POAG, especially in smokers compared to controls [51,52,53], others have reported increased risk with a GSTM1 positive phenotype, or in combination with a GSTT1 null genotype, [54,55] or did not find any association with the risk of glaucoma [56,57,58]. Many factors might account for the differences in results between similar studies, such as differences in sample size, types of glaucoma and ethnic and geographical GSTM1 null and GSTT1 null distribution in populations. A recent meta-analysis suggested that there might be a significant association between GSTM1 polymorphisms and increased susceptibility to glaucoma [57].

In our study, GPX1 rs1050450 was not associated with the risk of glaucoma. We also found no association between the CAT rs1001179 promoter variant and the risk of glaucoma. CAT and GPX1 directly participate in the inactivation of hydrogen peroxide, while GPX1 also participates in the detoxification of reactive secondary metabolites of oxidative stress, such as various lipid hydroperoxides. The most common GPX1 rs1050450 polymorphism codes for Pro198Leu substitution and leads to decreased enzyme activity; therefore, the capacity for antioxidant defence may also be decreased [59]. Defence capacity against ROS may also be decreased in carriers of a common functional CAT rs1001179 polymorphism that influences transcription factor binding in the promoter region and is associated with decreased catalase levels [60]. Associations of GPX1 polymorphisms have only been studied in other neurodegenerative diseases such as Alzheimer’s disease [61] and Parkinson’s disease [48] but not in glaucoma. Likewise, CAT rs1001179 genotype frequencies did not differ significantly between cases and controls in previous studies [24,62]. Only the synonymous SNP CAT rs769217 was significantly associated with POAG in the Chinese population [62]. We also found no association between SOD rs4880 and glaucoma. Similarly, no difference in the allele and genotype frequency in SNPs rs4880 between POAG cases and controls was reported by Zhou et al. [63].

With regard to inflammatory pathway polymorphisms, we found a protective effect of IL1B rs16944 and IL6 rs1800795 on the development of POAG. Both polymorphisms are located in the promoter region and may lead to altered gene expression. In patients with POAG, significantly increased mRNA expression of the IL1B gene has been found in blood and significantly increased IL-1B protein expression in the aqueous humour compared to controls [64]. In an animal model of acute glaucoma, upregulation of IL-1B caused an increase in retinal ganglion cell death [65]. Polymorphisms in the IL1B promoter region (rs16944 and rs1143634—not analysed in our study) have already been investigated for an association with POAG. Whereas positive associations were reported in Caucasian populations [66,67], associations with POAG were not observed in Asian populations [68,69,70,71]. Meta-analysis evaluating the role of these two SNPs of IL1B in the susceptibility to glaucoma did not find any association, but the conclusions should be interpreted with caution as only a small number of studies was included [72].

With regard to IL6, Zimmermann et al. suggested that the promoter IL6 rs1800795 polymorphism is unlikely to be a major risk factor for POAG [73]. In addition, a recent systematic review and meta-analysis found a statistically significant glaucoma risk associated only with rs1524107, but not with rs1800795, which was investigated in our study [42]. However, when patients with early to moderate glaucoma were compared to patients with advanced glaucoma, the IL6 rs1800795 C allele as well as the GC genotype were protective against less severe forms of normal-tension glaucoma [69].

We are not aware of any studies investigating the association of SNPs involved in the inflammatory and oxidative stress pathways with the glaucoma phenotype, including the severity of glaucoma, IOP, C/D ratio and CCT. We found a statistically significant association of GPX1 rs1050450 with the severity of glaucoma. In the dominant model, the frequency of at least one polymorphic GPX allele increased with the severity of glaucoma (19.3% in early, 23.3% in moderate and 39.3% in advanced glaucoma). The GPX1 rs1050450 CT and TT genotypes were reported to be associated with increased risk of damage caused by oxidative stress, such as in coronary heart disease and cancer [74,75]. The GPX1 rs1050450 CT and TT genotypes were associated with increased risk of POAG in the Polish population, but the link with glaucoma phenotypes has not been investigated [76]. Antioxidant enzymes, such as GPX, are an important defence system against oxidative stress, which may play a major pathophysiological role in glaucoma. An increase in oxidative stress markers in serum and aqueous humour with a decrease in serum antioxidant stress markers was present in glaucoma patients compared to controls. However, despite a decrease in serum GPX, there was an important increase in GPX in the aqueous humour [11]. This may indicate a protective response of the eye against oxidative stress and may wear off in the long term [19].

Glaucoma is a heritable disease, and siblings of POAG cases have a tenfold-increased risk of developing the disease [77]. The C/D ratio, IOP and CCT used clinically to predict POAG risk are heritable traits related to the disease and may be associated with genetic variability in inflammation and oxidative stress pathways. In our study, carriers of at least one polymorphic GPX1 rs1050450 allele had statistically lower IOP and a lower C/D ratio. Patients with advanced glaucoma require lower target IOP to prevent progression of disease than those with early glaucoma or ocular hypertension [78]. In our study, approximately 30% of all cases had advanced disease. Therefore, those with lower IOP had presumably advanced glaucoma, which might partly explain the link between GPX1 polymorphisms and lower IOP and severity of glaucoma. However, the association between this SNP and the C/D ratio is not clear. We found no previous studies analysing the association of the GPX1 SNP with the glaucoma phenotype.

In the last decade, genome-wide association studies (GWAS) have identified over 50 C/D-ratio-associated loci, but only 9 of these have been associated with POAG [79,80]. Up till now, multiple IOP-associated loci were identified using large and multi-ethnic biobank-based cohorts. Among the significant results were also loci at genes previously associated with POAG but not previously known to influence IOP. This indicates that genetic variation at these genes mediates the increased POAG risk via raised IOP rather than via direct effect on retinal ganglion cells [79,80]. The identified loci explained 17% of the variance of IOP in the EPIC–Norfolk Eye Study [81].

The central corneal thickness has been associated with increased POAG development and progression [82,83], but it is uncertain whether this relationship is caused by IOP measurement artefacts or whether the relationship is biologically causal [84]. In our study, the GG genotype of TNF rs1800629 was associated with a low CCT. The link between TNF polymorphisms and the CCT has not been investigated. However, recently, the results of GWAS for the CCT have suggested that the CCT may not be a heritable trait for POAG and that the CCT–glaucoma association observed in studies is due to IOP measurement artefacts rather than biological causality [80].

Multiple epidemiological studies have reported the role of hypertension as a risk factor for POAG [85,86]. Treatment of hypertensive patients with beta-blockers results in nocturnal hypotension, which is a potential risk factor for glaucomatous optic neuropathy [87,88]. The mechanisms by which hypertension induces optic nerve damage are still unclear. Whether or not an association exists between diabetes mellitus and glaucoma has been an issue of debate, but findings from several studies in recent years suggest that the risk of glaucoma among diabetic patients may be greater than once believed [89,90,91,92,93]. Patients with POAG may suffer from ischaemic heart disease more often than those without glaucoma [93]. We also analysed the association between clinical characteristics (AH, diabetes, ischaemic heart disease, family history of glaucoma) with the glaucoma phenotype (IOP, C/D ratio, CCT). In our study, patients with AH had significantly lower maximal median IOP and a greater C/D ratio compared to those without AH. A possible explanation is that among cases with AH, there were older patients with advanced glaucoma, requiring lower target IOP compared to cases with OHT. Similarly, in our study, patients with ischaemic heart disease had significantly lower IOP. As patients with glaucoma were older compared to OHT subjects and with a higher prevalence of heart disease, they required lower target IOP to prevent progression, which could explain our results. Diabetes was significantly associated with a larger CCT in our study. Our observation is in line with the findings of a meta-analysis that suggested that diabetes and hyperglycaemia are associated with a thicker cornea [94]. We found no other associations between the glaucoma phenotype or clinical characteristics with the investigated SNPs.

One of the limitations of our study was that the number of control subjects and cases was small compared to larger studies investigating genetic factors. Furthermore, we investigated only a limited number of polymorphisms in the oxidative stress and inflammation pathways. The strength of our study was that we used a pathway-based approach and selected SNPs with a known functional effect that are common in the Caucasian population. Perhaps other polymorphisms of the genes involved in these pathways, not investigated in our study, may have had a potential impact. Another limitation was that only data on age and sex were available for the control group. However, all controls were healthy blood donors without any self-reported systemic disease. Furthermore, the chance of controls having undiagnosed glaucoma was small due to the low prevalence of the disease in this age group, estimated to be between 0.2% and 1.1% in a Caucasian population of the same age [95,96,97]. Another strength of our study was that all patients attended the same glaucoma unit with the same treatment approach and follow-up, unambiguous diagnostic criteria and classification of phenotype. Furthermore, all patients and controls originated from a genetically homogenous population, thus limiting possible bias due to the population structure [98,99].

Although our findings should be interpreted with caution, there is further perspective to this research. Ganglion cell death in glaucoma is a complex process triggered by different molecular mechanisms, among which oxidative stress and activation of inflammation by retinal glial cells play an important role. Improving antioxidant defence and addressing inflammation pathways might stimulate cell survival and boost the cells’ ability to withstand pathological insult. Several studies have shown the potential protective effect of antioxidants on retinal ganglion cells [100,101,102,103], while there is experimental evidence that modulation of inflammation reduces retinal ganglion cell death [102]. Therefore, improved knowledge of these pathways might help to establish predictive biomarkers to improve treatment strategies for glaucoma. Patients could be stratified into groups with detectable deficits in oxidative stress and/or inflammation pathways, so supplementary therapy could be more specific and treatment personalised. Our study investigated genes and SNPs with broad implications in glaucoma and other neurodegenerative diseases that share similar biomarkers [104,105]. This type of study on glaucoma and similar diseases may help to design inflammation and oxidative stress pathway gene panels that could be used in testing patients with different but related diseases in order to personalise treatment and potentially improve treatment outcomes.

In conclusion, we used a pathway-based approach to address the relationship between oxidative stress and inflammation polymorphisms, and POAG risk. We found some indications for a possible association of genetic variability in GSTM1 with OHT. While IL1B and IL6 may be associated with the risk of glaucoma, GPX and TNF may affect the glaucoma phenotype. However, the evidence presented here is limited and further association and functional studies are required.

Acknowledgments

We thank all the patients for their participation in the study. We would like to thank Saša Mohar, head nurse of the glaucoma unit, for contribution to patient enrolment. We would also like to thank Savica Soldat from the pharmacogenetics laboratory for help with the laboratory work.

Supplementary Materials

The following are available online at https://www.mdpi.com/2077-0383/10/5/1148/s1, Table S1: Characteristics of investigated polymorphisms, variant allele frequency and agreement with Hardy-Weinberg equilibrium in controls, Table S2: Primers used for multiplex PCR (a) and thermal cycling conditions used for genotyping for multiplex PCR (b) and KASP chemistry (c), Figure S1: Representative gel image of GSTT1 and GSTM1 genotyping analysis, Figure S2: Representative cluster image for IL6 rs1800795 analysis obtained after KASP competitive allele specific PCR. References [106,107,108,109,110,111] are cited in the supplementary materials.

Click here for additional data file. (387.1KB, pdf)

Author Contributions

Conceptualisation, V.D. and B.C.; methodology, V.D. and B.C.; software, K.G.; validation, M.A.V., V.D., K.G., T.B. and B.C.; formal analysis, M.A.V., K.G. and T.B.; investigation, M.A.V., K.G., V.D. and B.C.; resources, V.D. and B.C.; data curation, M.A.V., K.G. and T.B.; writing—original draft preparation, M.A.V. and B.C.; writing—M.A.V., K.G., V.D. and B.C.; review and editing, V.D. and B.C.; visualisation, M.A.V. and K.G.; supervision, V.D. and B.C.; project administration, M.A.V., K.G., V.D. and T.B.; funding acquisition, none. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Slovenian Research Agency (ARRS) (grant nos. P1-0170 and P3-0333).

Institutional Review Board Statement

The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Slovenian Medical Ethics Committee (KME 30/05/11).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The datasets used and/or analysed for the current study are available from the corresponding author on reasonable request.

Conflicts of Interest

The authors declare no conflict of interest.

Footnotes

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Click here for additional data file. (387.1KB, pdf)

Data Availability Statement

The datasets used and/or analysed for the current study are available from the corresponding author on reasonable request.

Articles from Journal of Clinical Medicine are provided here courtesy of Multidisciplinary Digital Publishing Institute (MDPI)

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