INTRODUCTION
G laucoma represents the main cause of irrevesible blindness worldwide[1-2]. The elevated intraocular pressure (IOP) remains the main risk factor[3], but recent theories refer to other pathogenic pathways[4] contributing to progressive loss of ganglion cells and vision decay[1]. These theories implicate aberrant immunity[5] enhanced inflammatory response[6], vascular dysregulation[7-9] ischemia[10], hypoxia[11]or increased oxidative stress[6,12].
For ocular or systemic pathologies with inflammatory compound, the analysis of cytokines in biological fluids has become extremely attractive for research[13-17]. In glaucoma,previous studies measured inflammatory cytokines in aqueous humor (AH) and reported elevated levels of different interleukins[17-18].
Methods such as the enzyme-linked immunosorbent assay(ELISA) or polymerase chain reaction (PCR) are extremely useful when assessing these types of molecules, despite their great disadvantage of impossible multi-parameter measurements from small volumes. Cytometric bead assay techniques offer this possibility, in a fast, reproducible and highly accurate manner, allowing simultaneous detection of multiple cytokines from small volume samples[4-5].
The aim of our study was to investigate the inflammatory profile in primary open angle glaucoma (POAG) patients and assess the changes induced in the cytokines expression in the AH, when another systemic pro-inflammatory disease, such as diabetes mellitus (DM), was additionally present.
SUBJECTS AND METHODS
Ethical Approval Study protocol respected the declaration of Helsinki, as revised in 2013, and was approved by the local university Ethics Committee. Informed consent was obtained from all participants, prior to their inclusion in the study.
This cross-sectional study included 87 eyes from 87 patients recruited during September 2015-September 2016. Patients were distributed in four groups: 26 eyes from 26 diabetic patients,16 eyes from 16 POAG patients and 21 eyes from 21 diabetic POAG patients; healthy controls (24 eyes) were recruited from 24 patients undergoing conventional cataract surgery.
The AH was obtained under sterile conditions before the cataract surgery. At the beginning of the surgery, through an anterior chamber (AC) paracentesis, a 30G needle attached to a tuberculin syringe (1 mL) was inserted to extract a total volume of 100 μL AH. The samples were immediately transferred to polyethylene sterile tubes (Eppendorf 3810X®),stored at -80℃, within two hours from harvesting, until further analysis. The needle was not in contact with iris, lens or corneal endothelium, nor the AC contained any viscoelastic substance. No anti-inflammatory medication was administered preoperatory to the patients.
A Luminex® cytokine polystyrene color bead based multiplex assay (Human Cytokine Premixed Kit A FCST03 LHSC000e,RnD Systems®) was used to measure the concentrations of the following cytokines: interleukin-1 receptor antagonist (IL-1Ra), interleukin-1α (IL-1α), interleukin-1β (IL-1β), interleukin 5 (IL-5), interleukin 6 (IL-6), interleukin 10 (IL-10), interleukin 17 (IL-17), granulocyte macrophage colony stimulating factor(GM-CSF), interferon-gamma (IFN-γ), monocyte chemotactic protein-1 (CCL2), macrophage inflammatory protein 1-alpha(CCL3), macrophage inflammatory protein 1-beta (CCL4),CXC motif chemokine 5 (CXCL5), interleukin 8 (CXCL8/IL-8),basic fibroblast growth factor (bFGF), vascular endothelial growth factor (VEGF), α tumor necrosis factor (TNFα). The assay was conducted according to manufacturer’s indications.For the glaucoma groups, we have included only POAG cases,where the diagnosis had been established prior to the study initiation, based on the European Glaucoma Society criteria[19].For all patients, demographic data (age, sex), duration of glaucoma/diabetes, glaucoma treatment, best corrected visual acuity in decimals by Snellen charts, and preoperative IOP(mm Hg) by Goldmann tonometer were recorded. Automated standard perimetry (central 24-2, Humphrey® Carl Zeiss Meditech) quantified the mean deviation (MD) and pattern standard deviation (PSD) in decibels (dB). Classification of glaucoma damage was based on Hodapp criteria, described elsewhere[20]. More than 85% of patients used more than one type of IOP lowering substance, all containing preservatives.Diabetic patients had only mild forms of diabetic retinopathy(DR) or none at all; blood sugar level (mg/dL) and glycosylated hemoglobin (HbA1c, %) were measured in all these patients.Cataract surgery was indicated by the ophthalmologist when lens opacification became visually significant and prevented proper fundus monitoring. Only uncomplicated cases of cataract surgery were selected. Patients with history of ocular trauma, uveitis, previous eye surgeries, including laser therapy,age-related macular degeneration, retinal vascular occlusions or systemic autoimmune/inflammatory diseases that could have influenced the ocular local and systemic inflammation status, were excluded.
Statistical Analysis Data were analyzed both qualitatively and quantitatively. Qualitative analysis was based on “heat map” graphs (Excel®, Microsoft, Office) showing minimum to maximum values; this processing rapidly identified dispersion,extreme values and allowed for Gaussian or non-Gaussian data categorizing in the study groups.
Quantitative analysis was performed using the SPSS® 20.0(IBM Corp., New York, NY, USA) software. For categorical variables, the frequency distribution and percentages were calculated and compared by χ2 test. For numerical variables in parametric distribution, two-sample independent t-test was performed. Statistical significance was accepted at P＜0.05. For differences in IOP, duration of POAG/DM and cytokine concentrations, the Mann-Whitney U test was used.Correlations between cytokine concentrations and patients’demographic data, including age and IOP, were calculated by Spearman correlation test. One-way analysis of variance(ANOVAs) were used to test for differences in cytokine levels among multiple groups, then a post-hoc analysis was used (Tukey honest significant difference test) to identify the specific differences between groups. Interaction tests and multiple regression analysis were further employed for prediction calculations.
RESULTS
A total of 87 AH samples were collected from 87 eyes in 87 patients. Groups characteristics, clinical and demographical,could be followed in Table 1.
The eyes with combined pathology (POAG+DM) had the youngest age when compared to healthy controls (P＜0.001),POAG patients (P=0.000) or DM eyes (P=0.000), respectively.Also there has been noted a larger proportion of males among glaucoma patients compared to healthy controls or diabetic patients. Mean IOP was significantly higher in the diabetic POAG group, when compared to POAG patients(P=0.04), DM eyes (P＜0.001) or healthy subjects (P＜0.001),respectively. Similar number of topical anti-glaucoma substances was used to control the IOP in the glaucoma groups. Functional parameters (MD, PSD) were comparable between glaucoma patients (P＞0.05).
Table 1 Clinical and demographic data in the study groups mean±SD, n (%)
Parametera Control group(n=24)DM group(n=26)POAG group(n=16)POAG+DM group(n=21) Pa Age (y) 72.33±11.26 69.04±9.46 75.69±5.54 59.95±3.89a ＜0.05 Sex ratio (M:F) 1:2 1:1.16 2:1 2:1 0.873 IOP (mm Hg) 14.21±2.68 15.50±1.9 18.19±4.3 20.33±2.3a 0.05 Anti-glaucoma substances (number) - - 3±0.87 2.85±1.02 0.48 Glaucoma therapy Treated eyes 16 (100) 21 (100)Active substance BB 11 (68) 16 (76) 0.129 PGA 16 (100) 21 (100) -AA 3 (18.75) 4 (19) 0.891 CAI 5 (31) 9 (42) 0.093 Number of drops 1 2 (12.5) 3 (14.2) 0.681≥2 14 (87.5) 18 (85.8) 0.462 MD (dB) - - -10.59±8.35 -8.13±7.9 0.083 PSD (dB) - - 3.25±4.22 2.05±0.90a 0.260 Duration of glaucoma (mo) - - 45.63±26.48 36.86±15.86 0.581 Duration of diabetes (mo) - 133.56±77.82 - 115.62±81.24 0.119 Blood sugar level (mg/dL) - 177.12±19.54 - 155.05±21.83 0.127 HbA1c (%) - 8.12±1.81 - 7.09±1.35 0.095
IOP: Intraocular pressure; BB: Beta blocker; PGA: Prostaglandin analogue; AA: Alpha agonists; CAI: Carbonic anhydrase inhibitors; MD: Mean deviation; PSD: Pattern standard deviation; HbA1c: Glycosylated hemoglobin; SD: Standard deviation. aSignificance of differences between groups: χ2 test, two samples independent t-test or Mann-Whitney U test.
Cytokine Analysis From the 21 molecules, six could not be accurately dosed (IL-1β, IL-5, IL-6, IL-10, IL-17, IFNγ),since results were obtained by extrapolation; as such they were excluded from further analysis. Table 2 illustrates all means,medians and range variation for the remaining molecules, as quantitatively measured in each study group.
One way ANOVA analysis identified the significantly different concentrations of cytokines between the study groups: CCL4(P=0.003), CCL5 (P＜0.001), CXCL5 (P＜0.001), CXCL8(P=0.004), IL-1α (P＜0.001), IL-1Ra (P＜0.001) and TNFα(P=0.05). A post-hoc analysis identified the specific differences in cytokine concentrations between POAG and diabetic POAG eyes. As such, IL-2 (P=0.009) and CXCL5 (P＜0.001) were significantly different in diabetic POAG eyes vs POAG eyes.The influence of IOP (≤/＞18 mm Hg), age (≤/＞60 years old)and sex (males vs females) was studied upon the levels of the separation markers. Table 3 resumes the changes in cytokine expression when these limits were tested.
Results showed that the mean concentrations of CXCL5,CXCL8, IL-1α, CCL5 and CCL4 increased in a significant manner, if the IOP exceeded 18 mm Hg (P＜0.05). Alltogether,younger patients exhibited a stronger inflammatory response(CXCL5, CXCL8, IL-1α, CCL5), since all analyzed cases with an age below 60y produced higher concentrations of cytokines as compared to older age (P＜0.05). IL-2, normally expressed as anti-inflammatory molecule, exhibited lower levels if patients had higher IOPs or younger age, suggesting a failure of the compensatory mechanisms once the IOP increases and more inflammation is triggered.
Correlation analysis showed that in diabetic patients the IOP correlated significantly with the levels of CCL4 (ρ=0.535,P=0.005) and CXCL5 (ρ=0.537, P=0.005). An inverse negative correlation was found for CXCL8 with the age of the subjects (ρ=-0.424, P=0.03) suggesting a more prominent inflammatory response in younger patients. A similar agerelated finding was detected in the POAG group regarding age of the subjects and CCL5 level (ρ=-0.497, P=0.05). Also the IOP was correlated with the CXCL8 level (ρ=0.600, P=0.014).Related to the functional damage in glaucoma patients, the MD values in the POAG group were inversely correlated with TNFα (ρ=-0.561, P=0.024), yet localized glaucoma damage(PSD) was not correlated with any inflammatory molecule.
For diabetic POAG patients, the age was correlated with CXCL8 (ρ=-0.536, P=0.04) and CCL5 (ρ=0.592, P=0.06)levels. IOP was correlated with a larger number of molecules from the “separation markers” category, such as CXCL5(ρ=0.650, P=0.005), TNFα (ρ=0.539, P=0.001), IL-1α(ρ=0.544, P=0.04) or IL-1Ra (ρ=0.513, P=0.02). Also in these patients, glaucoma duration was correlated both with the TNFα level (ρ=0.561, P=0.024) and the number of IOP lowering substances (ρ=0.438, P=0.04).
Table 2 Aqueous cytokine concentrations in the study groups mean±SD
IL-1Ra: Interleukin-1 receptor antagonist; IL-1α: Interleukin-1α; IL-2: Interleukin 2; IL-4: Interleukin 4; GM-CSF: Granulocyte macrophage colony stimulating factor; CCL2: Monocyte chemotactic protein-1; CCL3: Macrophage inflammatory protein 1-alpha; CCL4: Macrophage inflammatory protein 1-beta; CCL5(RANTES): Chemokine (C-C motif) ligand 5; CXCL5: CXC Motif chemokine 5; CXCL8/IL-8: Interleukin 8; bFGF: Basic fibroblast growth factor; VEGF: Vascular endothelial growth factor; TNFα: α tumor necrosis factor. a Control group; bDiabetes group; cPrimary open angle glaucoma group; dPrimary open angle glaucoma+diabetes group; eOne way ANOVA analysis was performed to compare the four groups; P＜0.05 for statistically significant difference.
Cytokines (pg/mL) CTRa eyes DMb eyes POAGc eyes POAG+DMd eyes Pe IL-1α 9.51±4.83 11.71±8.99 14.73±5.02 18.60±2.69 0.000 Median 8.7 10.93 16.55 18.18 Range 1.05-22.40 3.34-36.01 5.34-21.99 13.03-23.12 IL-1Ra 656.35-518.31 1319.75±832.76 2276.10±1301.65 2960.02+/2675.07 0.000 Median 538 801.245 1374 2397 Range 126.94-2516.07 179.45-5431.78 243.97-12729 1084.14-12729 IL-2 107.29+/26.74 87.55±29.32 98.08±33.20 68.56±20.24 0.000 Median 113.70 86.24 94.40 74.23 Range 13.49-138.49 30.63-142.96 49.39-165.28 30.63-98.82 IL-4 39.37±12.62 37.53±11.62 41.95±13.76 46.44-7.23 0.06 Median 40 37.29 39.58 45.42 Range 8.84-55.10 14.05-57.55 19.13-72.40 32.87-59.99 G-CSF 43.51±70.31 42.55±28.83 74.60+/168.74 62.64+/66.75 0.636 Median 26.81 33.37 2.89 33.38 Range 1.33-277.24 3.30-119.94 1.15-652.44 11.35-290.64 GM-CSF 2.82±0.85 2.84±1.00 3.01±0.73 3.18±0.63 0.442 Median 2.3 2.7 3.04 3.2 Range 1.18-4.66 1.77-5.43 1.72-4.05 1.99-4.05 CXCL8 (IL-8) 12.13±3.68 19.25±8.51 22.02±13.61 29.51±11.92 0.004 Median 5.09 15.83 20.46 25.62 Range 1.04-97.75 12.39-44.51 4.93-43.30 13.76-46.25 bFGF 39.46±31.71 36.98±10.26 52.71-32.62 44.42±26.56 0.259 Median 33.04 32.72 37.50 37.37 Range 11.48-74.56 2.86-69.74 23.96-114.05 21.06-134.09 VEGF 50.02±19.89 96.42±60.17 78.66±55.53 84.11±72.05 0.162 Median 52.39 64.84 50.38 62.29 Range 7.23-91.12 23.83-505.15 16.47-236.49 30.81-356.65 CCL2 (MCP-1) 449±260.81 464.24±276.32 383.59±249.32 505.12±397.33 0.376 Median 395.64 407.16 363.12 439.62 Range 29.58-1054.44 71.27±1224.13 177.18-913.69 249.92-1913.00 CCL3 (MIP-1α) 138.26±54.08 141.50±54.39 158.26±29.01 167.04±16.11 0.1 Median 126.08 133.32 167.48 166.21 Range 43.96-315.92 52.75-338.24 99.46-205.12 135.16-202.17 CCL4 (MIP-1β) 9.78±8.80 11.32±5.22 25.59±29.41 19.73±13.63 0.003 Median 7.85 10.59 15.00 14.27 Range 2.21-44.43 1.54±21.74 4.51-119.22 5.81-61.73 CCL5 (RANTES) 2.49±1.07 3.49±2.32 4.30+/1.61 4.81±1.39 0.000 Median 2.36 3.22 3.93 4.96 Range 1.05-5.65 1.87-11.04 1.48-6.82 2.34-6.83 CXCL5 (ENA-78) 19.34±9.22 27.19±10.06 24.79±10.48 40.61-3.16 0.000 Median 20.86 24.73 20.36 40.11 Range 2.99-34.13 14.09-43.22 10.37-52.50 34.19-46.55 TNFα 2.41±0.95 2.68±0.83 2.84±0.60 3.27±0.68 0.05 Median 2.31 2.52 2.76 3.17 Range 1.21-20.96 1.67-5.31 1.95-3.94 2.55-5.31
Figure 1 Normal probability plot (P-P) expected vs observed cumulated probability for TNFα throughout the study A: POAG group(R2=0.842); B: DM group (R2=0.580); C: POAG+DM group (R2=0.281).
Table 3 Influence of IOP, age and sex upon the cytokine concentrations mean±SD
CXCL5: CXC motif chemokine 5; CXCL8/IL-8: Interleukin 8; IL-1α: Interleukin-1α; TNFα: α tumor necrosis factor; CCL4: Macrophage inflammatory protein 1-beta; CCL5 (RANTES): Chemokine (C-C motif) ligand 5; IL-2: Interleukin 2; IL-1Ra: Interleukin-1 receptor antagonist. aStatistical difference, t-test, P＜0.05.
Age (y) Pa IOP (mm Hg) Pa Sex Pa≤60 ＞60 ≤18 ＞18 M F CXCL5 34.27±10.94 23.59±10.27 0.012 24.17±10.40 35.34±10.85 0.002 30.78±10.45 24.28±12.14 0.04 CXCL8 26.48±18.86 16.48±13.98 0.006 16.21±16.54 29.30±13.08 0.000 23.23±16.18 17.11±16.71 0.08 IL-1α 15.62±6.42 11.92±5.54 0.000 11.64±6.18 17.06±4.06 0.000 14.31±4.53 12.27±7.38 0.112 TNFα 3.04±0.78 3.02±2.67 0.619 2.68±0.98 3.80±3.49 0.024 3.18±3.03 2.88±0.66 0.09 CCL4 16.99+/12.12 14.97+/18.40 0.577 12.04±8.03 23.96±25.03 0.029 18.66±19.54 12.61±11.19 0.07 CCL5 4.23±1.98 3.35±1.77 0.04 3.36±1.97 4.40±1.48 0.008 3.09±1.6 3.45±2.15 0.276 IL-2 82.28±32.05 95.28±28.93 0.05 92.73±30.35 85.04±31.15 0.281 97.73±30.35 85.04±31.15 0.375 IL-1Ra 2232.43±2398 1388.53±1976 0.07 1458±2002.17 2263.37±2548.92 0.111 1574.94±1239.51 185.86±2864.83 0.555 Cytokine(pg/mL)
Further statistical analysis allowed for creation of inflammatory mathematical models for each study group. The “separation”markers elicited either direct pro-inflammatory effect (CXCL5,CXCL8) or they could indirectly modulate the Th1 cells activity (IL-1α, IL-1Ra, IL-2, CCL4, CCL5, TNFα) to increase inflammation. Still, regardless of the activated inflammatory pathway, all molecules increased TNFα’s expression. As such,a step wise multiple regression analysis studied the influence of the “separation” markers upon TNFα level in each category of patients (Figure 1).
In the POAG group, the highest level of statistical relevance was found: R2=0.842, F(1,16)=14.639, P≤0.001; thus in POAG eyes, TNFα level was largely explained by the levels of IL-2,IL-1α, CXCL8 and CXCL5 all together. For diabetic patients,the same inflammatory molecules explained less of the TNFα concentration, R2=0.580, F(1,24)=4.844, P=0.006, while in the diabetic POAG eyes, IL-2, IL-1α, CXCL8 explained the least of TNFα concentration compared to all the other study groups (R2=0.281), with borderline statistical significance F(1,19)=2.358, P=0.05. In the latter model, CXCL5 was eliminated from the predictors, to avoid statistical redundancy due to its high level of correlation with IL-2 (ρ=0.847,P＜0.001). Glaucoma treatment influenced very little the level of TNFα in our study: R2=0.06, F(1,36)=4.663, P=0.017,similar to the IOP influence upon the level of this cytokine:R2=0.052, F(1,36)=5.974, P=0.034. Other parameters did not reach statistical significance as predictors for our TNFα inflammatory model.
DISCUSSION
Altered cytokines composition in AH have been identified in multiple ocular diseases: glaucoma, age related macular degeneration, diabetic retinopathy or uveitis[13]. Our study assessed the changes in cytokine expression in diabetic POAG patients, based on the hypothesis that additional proinflammatory pathology (DM) could induce identifiable changes in AH composition in POAG eyes.
The global analysis of all cytokines that were significantly different between groups (CXCL5, CXCL8, IL-1α, IL-1,IL-2) point out that all molecules belong to the early “acute inflammatory” phase reactants, capable of increasing the expression of one another. Additionally, these cytokines have been reported as direct or indirect regulators of TNFα expression[21-22].
TNFα as pivotal inflammatory marker in POAG or diabetes is not new[21-22]. However, the original element of this study resides in the attempt to create a model for the inflammation in diabetic POAG eyes based on some cytokines that were differently expressed in this category and the end-point inflammatory pathway they trigger (TNFα over-expression).Thus, authors aimed to explain how much of the TNFα’s level could be predicted based on the indentified “separation markers”. As such in simple POAG cases, 80% of TNFα’s level could be explained by an increase in IL-2, IL-1α, CXCL8 and CXCL5 expression, while in diabetic eyes, the model predicted less (＜60%) from the TNFα’s level. In diabetic POAG patients, the same molecules could not explain the TNFα’s increased expression in a relevant manner (＜30%).
This finding could be only explained by the high proportion of cytokines that exhibit a polymorphic role and influence the production or release of other cytokine types, through multiple pathways that cannot be assessed and fully identified in a cross-sectional study. Our results show that the inflammation induced additionally by diabetes in POAG eyes changed the cytokine inflammatory profile in AH, but the mechanism was not similar to non-diabetic POAG eyes, at least for the model we tested. Collateral inflammatory pathways, co-stimulatory signals and cytokines should be further investigated.
The TNFα molecule can normally be found in healthy eyes.Depending on microenvironment changes, it can exhibit both neuroprotective and neurodegenerative properties[23]. In normal conditions, retinal tissue expresses receptors for TNFα; unless hypoxia develops, the increased TNFα levels do not trigger apoptotic signals for the ganglion cells[24]. This could support the results we obtained in the inflammatory model in POAG eyes. Still, Sawada et al[22] reported increased TNFα levels in hypertensive glaucoma patients compared to healthy subjects,through ischemic mechanical mechanism. Moreover, Huang et al[17] reported increased expression of TNFα associated with the increase in IOP in glaucoma patients; in this direction, our study found similar results for all glaucoma patients with an IOP over 18 mm Hg.
We have chosen 18 mm Hg as cut-off value, because according to Hodapp criteria[20], our glaucoma patients were categorized as moderate to advanced glaucoma. In consequence, similar to Advaned Glaucoma Interventional Study (AGIS)[24], we have chosen this limit to investigate the cytokines levels in our study in a dychotomic manner. Since Fingeret et al[25]reported an increased glaucoma risk after the age of 60y, we studied this cut-off values also in our patients. Our results showed that mean concentrations of cytokines (CXCL5,CXCL8, IL-1α, CCL5 and CCL4) increase in a significant manner when IOP increases, alltogether with a stronger inflammatory response ellicited at younger ages. Mean IOP and the intensity of treatment (number of anti-glaucoma medications) influenced the TNFα inflammatory model very little in this study, yet our results are similar to other published data, which found little or no correlation between the intensity of treatment and the cytokine expression[13,26].Nevertheless, the authors acknowledge other studies[27], where multiple therapy generated additive inflammation in the anterior segment in glaucoma patients. Still, in our study, the intensity of treatment upon the TNFα inflammatory profile in POAG patients was not statistically significant; this group homogeneity could be explained by the fact that all POAG patients received prostaglandin analogue (PGA), and in both glaucoma subgroups, over 85% of subjects received more than two topical substances. The very little number of patients with a single type of medication (PGA) in each glaucoma group (2 eyes with POAG eyes vs 3 eyes with diabetic POAG) and the administration of fixed combinations (PGA+β blocker, CAI+β blocker), might have made the statistical calculations very hazardous and, for the authors, impossible to assess correctly which substance or combination of substances influenced the TNFα model in a significant manner, if the case in this study.Regarding topical treatment in our POAG patients, all medication used during the study contained preservatives,
which are known to increase the ocular inflammation[27].Very few patients received only one IOP lowering substance.Therefore, corruption of our data due to these two major confounders (preservatives and multiple therapy) has been diminished and homogeneity among subjects was achieved.Authors are aware that the best profiles might have been obtained from naïve glaucoma patients, before starting any treatment, or leaving the patients without treatment after a proper washout period. Still, for ethical reasons this was neither possible, nor applicable in our patients. Antiglaucoma medication with preservatives reflects the clinical reality of the local national medical system, since public health insurance, at the time of the study, covered only the costs for these types of anti-glaucoma medications. The present research was conducted in a cross-sectional manner.The concept of predictive models was imported from other research branches[28], but usually involves dynamic processes.Authors would like to acknowledge that mathematical validity cannot reproduce the dynamic homeostatic and adaptive changes that occur spontaneously in biological systems[29].Further investigations in longitudinal studies and better standardizations on a wider number of subjects are needed for proper validation of our results and in order to obtain a better understanding of such complex in vivo interactions.
In conclusion, our study revealed that diabetes induced a significant change in the cytokine expression in primary open angle glaucoma patients and modified both the signaling molecules and the inflammatory pathways from the initial TNFα model. Further studies that could identify collateral molecules and co-stimulatory pathways in combined ocular neurodegenerative diseases are needed.
ACKNOWLEDGEMENTS
Authors’ contributions: Concept: Pantalon A; Design:Pantalon A, Feraru C, Chiseliță D; Supervision: Feraru C,Chiseliță D; Resource: Pantalon A; Materials: Pantalon A,Constantinescu D; Data collection and/or processing: Pantalon A, Obadă O; Analysis and/or interpretation: Pantalon A;Literature research: Pantalon A, Obadă O; Critical reviews:Chiseliță D, Feraru C, Constantinescu D.
Conflicts of Interest: Pantalon A, None; Obadă O, None;Constantinescu D, None; Feraru C, None; Chiseliță D, None.
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