# Correlation between Imaging Morphological Findings and Laboratory Biomarkers in Patients with Diabetic Macular Edema

Authors: Eleni Dimitriou; Theodoros N. Sergentanis; Vaia Lambadiari; George Theodossiadis; Panagiotis Theodossiadis; Irini Chatziralli
Journal: Journal of Diabetes Research
 (2021)

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## Abstract

Purpose. To investigate the potential association between peripheral blood biomarkers and morphological characteristics of retinal imaging in patients with diabetic macular edema (DME). Methods. Participants in this cross-sectional study were 36 consecutive patients (36 eyes) with treatment-naïve DME, who underwent spectral domain-optical coherence tomography (SD-OCT), fundus photography, and fundus fluorescein angiography (FFA). In addition, peripheral blood samples were taken to evaluate full blood count and biochemical parameters. Correlation between imaging characteristics and laboratory parameters was examined. Results. Eyes with central subfield thickness greater than 405 μm presented significantly higher neutrophils/lymphocytes (p=0.043) and higher lipoprotein (a) compared to eyes with CST<405μm (p=0.003). Presence of hyperreflective foci on SD-OCT was associated with significantly higher white blood cell count (p=0.028). Ellipsoid zone disruption was associated with significantly lower hematocrit (p=0.012), hemoglobin (p=0.009), and red blood cell count (p=0.026), as well as with higher lipoprotein (a) (p=0.015). Macular ischemia on FFA was associated with significantly higher monocytes (p=0.027) and monocytes/HDL (p=0.019). No significant associations were found between laboratory parameters and subretinal fluid, intraretinal fluid, exudates, cysts, disorganization of inner retinal layers, epiretinal membrane, and external limiting membrane condition. Conclusion. Specific imaging morphological characteristics were found to be associated with laboratory parameters in patients with DME. These findings may shed light on the pathophysiology of DME and its correlation with the development of specific clinical signs.


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## Body

## 1. Introduction

Diabetic macular edema (DME) is the most common cause of visual impairment in patients with diabetes mellitus (DM), characterized by exudation and accumulation of extracellular fluid in the macula [1, 2]. The overall prevalence of DME in patients with DM has been estimated to be about 7-14%, while it varies from 0% to 3% in patients with recent DM diagnosis and increases to 28% in patients with DM for more than 20 years [1–5].In the pathogenesis of DME, chronic hyperglycemia promotes a cascade of biochemical pathways and consequent structural alterations in the retinal blood vessels’ wall, including the loss of pericytes and the breakdown of the blood-retinal-barrier, leading to retinal vascular permeability [6, 7]. This breakdown is mainly driven by the production of inflammatory cytokines, with vascular endothelial growth factor (VEGF) to be the most prominent [6, 7]. Moreover, it has been shown that patients with DME have increased levels of proinflammatory mediators in aqueous humor compared to non-DME cases [8], while there is a controversy whether the pathophysiology of DME is mainly attributed to such systemic affection or to a local intraocular response. Of note, several studies have shown that elevated serum lipids, including cholesterol and low-density lipoprotein (LDL), demonstrated a significant association with retinal hard exudates and formation of DME [9–12], while elevated IL-6 has also been correlated with diffuse retinal thickness or severity of DME [13, 14].Nowadays, there is a great development in retinal imaging, especially with the advent of spectral domain-optical coherence tomography (SD-OCT) and swept source-OCT (SS-OCT) [15, 16], as well as OCT angiography [17, 18]. Both OCT and OCTA are noninvasive techniques, enabling the identification of specific morphological characteristics of DME, while OCTA allows the quantification of the foveal avascular zone (FAZ) area besides vessel density [18].Given the current understanding of DME pathogenesis, although much reported information exists on intraocular biomarkers in patients with DME, literature is scarce regarding systemic biomarkers and their correlation with morphological characteristics in retinal imaging. Ghosh et al. examined the relationship between different OCT patterns of DME and systemic risk factors in patients with DME and did not identify any modifiable systemic factor for any of the OCT patterns in DME [19].Based on the above, the purpose of the present study was to investigate the potential association between peripheral blood biomarkers and morphological characteristics of retinal imaging in patients with DME.


## 2. Methods

Participants in this observational, cross-sectional study were 36 consecutive patients with DM type 2 and treatment naïve DME, who were diagnosed and treated at the 2nd Department of Ophthalmology, National and Kapodistrian University of Athens, Athens, Greece, between 1st September 2019 and 31st March 2020. The study protocol adhered to the tenets of the Declaration of Helsinki and was approved by the Institutional Review Board. Written informed consent was obtained from all participants.All patients had nonproliferative diabetic retinopathy (NPDR) and treatment naïve DME, confirmed on SD-OCT, revealingcentralsubfieldthicknessCST≥320μm. One eye of each patient was included. In cases of bilateral DME, the right eye was chosen, so as to avoid selection bias. Patients with other vitreoretinal diseases, uveitis, media opacities, previous vitreoretinal surgery, previous laser photocoagulation, or ocular surgery in the previous 6 months were excluded.Demographic data of patients (age, gender) were recorded, along with the duration of DM. All participants underwent a complete ophthalmologic examination, including best-corrected visual acuity (BCVA) measurement by means of Snellen’s charts (converted to logMAR scale), slit-lamp examination, dilated fundoscopy, color fundus photography using Topcon TRC-50DX (Topcon Corporation), SD-OCT, and fundus fluorescein angiography (FFA) using Spectralis (Spectralis HRA+OCT, Heidelberg Engineering, Heidelberg, Germany). SD-OCT was obtained using a standard acquisition protocol; six radial scans 3 mm long were performed at equally spaced angular orientations centered on the foveola. The OCT volume scan was performed on a20×20 degree cube, consisted of 49 horizontal B-scans with 20 averaged frames per B-scan centered over the fovea. The following SD-OCT variables were recorded at baseline: CST (μm), presence of intraretinal fluid (IRF), subretinal fluid (SRF), cysts, hyperreflective foci (HF), and disorganization of the inner retinal layers (DRIL) and epiretinal membrane (ERM). Ellipsoid zone (EZ) and external limiting membrane (ELM) condition were also assessed. In addition, the presence of exudates on color fundus photography was recorded. The severity of DR was based on color fundus photography and on FFA and was graded according to the international diabetic retinopathy disease severity scale [20]. Moreover, macular ischemia was evaluated on FFA and defined as disruption and enlargement of foveal avascular zone (FAZ). Two investigators (IC, ED) independently evaluated qualitatively the SD-OCT images, the fundus photographs, and the FFA images. The interobserver agreement ranged from very good to perfect for all SD-OCT parameters (k=0.999 for IRF; k=0.999 for SRF; k=0.872 for HF; k=0.851 for DRIL; k=0.999 for ERM; k=0.902 for EZ condition; and k=0.883 for ELM condition), as well as for DR severity assessment on fundus photographs (k=0.901) and ischemia evaluation on FFA (k=0.935).At the same day and following an eight hour overnight fast, all patients underwent a forearm venous puncture for peripheral blood extraction and serum was separated. Full blood count was measured on a Sysmex XE-2100 analyzer (Sysmex Corp. Kobe, Japan), while all biochemical analyses were performed on a Roche Cobas 8000 (Roche, Chicago, IL, USA) in the laboratory of Attikon University Hospital. Specifically, we analyzed the following parameters: glucose, glycated Hb (HbA1c), urea, creatinine, cholesterol, HDL, LDL, triglycerides, apolipoprotein A, apolipoprotein B, lipoprotein (a), homocysteine, vitamin D, and IL-6.


### 2.1. Statistical Analysis

For the description of patients’ characteristics, descriptive statistics were calculated;mean±standarddeviation (SD) was used for continuous variables, while relative frequencies and percentages for categorical variables were reported. All variables were tested for normal distribution with the Shapiro–Wilk test. The associations between laboratory and imaging variables were evaluated with the Mann–Whitney–Wilcoxon test (MWW), as appropriate. In addition, Pearson’s chi-squared test (P) or Fisher’s exact test (F) were also appropriately implemented. According to the a priori power calculation, a sample size of 36 eyes was adequate to achieve 80% power for the detection of an effect size larger or equal to 1.05 (in simple terms, a difference of 1.05∗SD between the compared subgroups), assuming an application of the two-tailed Mann–Whitney–Wilcoxon test for equally sized subgroups at the 5% level of significance. The sample size calculation was performed with G∗Power 3.1.9.2 software (University of Dusseldorf, Germany). Statistical analysis was performed using STATA/SE 13 statistical software (Stata Corporation, College Station, TX, USA). A p value < 0.05 was considered statistically significant.


## 2.1. Statistical Analysis

For the description of patients’ characteristics, descriptive statistics were calculated;mean±standarddeviation (SD) was used for continuous variables, while relative frequencies and percentages for categorical variables were reported. All variables were tested for normal distribution with the Shapiro–Wilk test. The associations between laboratory and imaging variables were evaluated with the Mann–Whitney–Wilcoxon test (MWW), as appropriate. In addition, Pearson’s chi-squared test (P) or Fisher’s exact test (F) were also appropriately implemented. According to the a priori power calculation, a sample size of 36 eyes was adequate to achieve 80% power for the detection of an effect size larger or equal to 1.05 (in simple terms, a difference of 1.05∗SD between the compared subgroups), assuming an application of the two-tailed Mann–Whitney–Wilcoxon test for equally sized subgroups at the 5% level of significance. The sample size calculation was performed with G∗Power 3.1.9.2 software (University of Dusseldorf, Germany). Statistical analysis was performed using STATA/SE 13 statistical software (Stata Corporation, College Station, TX, USA). A p value < 0.05 was considered statistically significant.


## 3. Results

The demographic and clinical characteristics of the study sample are shown in Table1. The mean age of patients was 64.2±8.5years. 58.3% of patients were male and 41.7% female. The mean duration of DM was 11.7±4.7years. The mean BCVA was 0.51±0.29logMAR, while the mean CST was 439.2±79.1μm.Table 1
Demographic and clinical characteristics of the study sample (n=36 patients with diabetic macular edema).
Age (mean±SD, years)64.2±8.5Gender (n, %)Male21 (58.3%)Female15 (41.7%)Duration of diabetes mellitus (mean±SD, years)11.7±4.7HbA1c (mean±SD, %)8.4±1.9Stage of diabetic retinopathy (n, %)Mild10 (27.8%)Moderate17 (47.2%)Severe9 (25.0%)Best-corrected visual acuity (mean±SD, logMAR)0.51±0.29Imaging characteristicsCentral subfield thickness (mean±SD, μm)439.2±79.1Intraretinal fluid (n, %)36 (100%)Subretinal fluid (n, %)9 (25%)Cysts (n, %)2 (5.6%)Hyperreflective foci (n, %)15 (41.7%)Exudates (n, %)13 (36.1%)Disorganization of inner retinal layer (n, %)6 (16.7%)Epiretinal membrane (n, %)2 (5.6%)Ellipsoid zone condition (n, %)Intact23 (63.9%)Disrupted13 (36.1%)External limiting membrane condition (n, %)Intact26 (72.2%)Disrupted10 (27.8%)Macular ischemia (n, %)9 (25%)Regarding the potential association between imaging characteristics and laboratory variables, no significant correlations were found for IRF, SRF, exudates, cysts, DRIL, ERM, and ELM condition.Table2 shows the comparison of laboratory parameters between eyes with HF (n=15) and without HF (n=21). Eyes with HF presented significantly higher white blood cell (WBC) count compared to those without HF (p=0.028, MWW).Table 2
Association between laboratory variables and hyperreflective foci on optical coherence tomography. Laboratory variables are summarized as median (IQR: interquartile range).
HF present (n=15)HF absent (n=21)p value (MWW)Red blood cells (106/μl)4.65 (0.61)4.73 (0.91)0.824White blood cells (103/μl)8.54 (2.10)7.29 (2.26)0.028Neutrophils (103/μl)5.24 (2.08)4.37 (1.41)0.124Lymphocytes (103/μl)2.30 (1.01)1.91 (0.61)0.096Monocytes (103/μl)0.63 (0.20)0.51 (0.27)0.260Platelets (103/μl)248 (37)219 (78)0.073Hematocrit (%)40.6 (4.1)40.7 (3.5)0.547Hemoglobin (g/dl)13.9 (1.5)13.4 (1.5)0.419Monocytes/lymphocytes0.27 (0.14)0.29 (0.19)0.962Neutrophils/lymphocytes2.15 (0.92)2.25 (1.36)0.937Monocytes/HDL0.013 (0.006)0.011 (0.007)0.516Glucose (mg/dl)198 (87)161 (81)0.084HbA1c (%)8.8 (2.5)7.8 (1.8)0.281Urea (mg/dl)37.8 (10.8)37.5 (23.6)0.635Creatinine (mg/dl)0.8 (0.2)0.9 (0.7)0.168Cholesterol (mg/dl)172 (61)147 (56)0.384LDL (mg/dl)94 (56)80 (44)0.310HDL (mg/dl)45 (14)47 (17)0.975Triglycerides (mg/dl)170 (168)122 (89)0.059IL-6 (pg/ml)3.5 (3.7)3.8 (2.2)0.680Apolipoprotein A (mg/dl)145 (35)141 (37)0.334Apolipoprotein B (mg/dl)89 (44)77 (34)0.228Lipoprotein (a) (nmol/l)28.7 (38.6)15.4 (107.3)0.975Homocysteine (μmol/l)14.7 (7.4)17.8 (11.2)0.141Vitamin D (ng/ml)19.8 (12.7)21.6 (18.1)0.506HF: hyperreflective foci; MWW: Mann–Whitney–Wilcoxon’s test.Table3 shows the comparison of laboratory parameters between eyes with intact EZ (n=23) and those with disrupted EZ (n=13). Eyes with disrupted EZ presented significantly lower hematocrit (p=0.012, MWW) and hemoglobin (Hb) (p=0.009, MWW), as well as lower red blood cell (RBC) count (p=0.026, MWW), while they had significantly higher lipoprotein (a) compared to eyes with intact EZ (p=0.015, MWW).Table 3
Association between laboratory variables and ellipsoid zone condition on optical coherence tomography. Laboratory variables are summarized as median (IQR: interquartile range).
EZ intact (n=23)EZ disrupted (n=13)p value (MWW)Red blood cells (106/μl)4.75 (0.71)4.38 (0.53)0.026White blood cells (103/μl)7.81 (3.01)8.09 (2.28)0.417Neutrophils (103/μl)4.64 (2.03)5.29 (3.02)0.174Lymphocytes (103/μl)2.04 (0.62)2.18 (1.00)0.846Monocytes (103/μl)0.54 (0.22)0.57 (0.33)0.818Platelets (103/μl)246 (71)241 (31)0.737Hematocrit (%)41.2 (4.4)38.7 (3.7)0.012Hemoglobin (g/dl)14.0 (1.2)12.9 (1.3)0.009Monocytes/lymphocytes0.27 (0.14)0.28 (0.22)0.659Neutrophils/lymphocytes2.25 (1.25)2.44 (1.53)0.548Monocytes/HDL0.013 (0.006)0.013 (0.007)>0.999Glucose (mg/dl)177 (87)113 (105)0.133HbA1c (%)7.6 (2.3)9.0 (1.5)0.331Urea (mg/dl)36.2 (17.4)42.3 (18.3)0.274Creatinine (mg/dl)0.8 (0.2)1.0 (0.7)0.143Cholesterol (mg/dl)153 (64)166 (39)0.584LDL (mg/dl)86 (55)87 (43)0.902HDL (mg/dl)45 (19)50 (11)0.584Triglycerides (mg/dl)131 (149)138 (120)0.596IL-6 (pg/ml)3.4 (2.1)4.5 (8.0)0.377Apolipoprotein A (mg/dl)142 (36)143 (13)0.750Apolipoprotein B (mg/dl)82 (42)84 (25)0.724Lipoprotein (a) (nmol/l)14.7 (34.8)89.5 (188.4)0.015Homocysteine (μmol/l)15.2 (8.0)18.9 (8.7)0.197Vitamin D (ng/ml)23 (18.1)25.4 (9.8)0.090EZ: ellipsoid zone; MWW: Mann–Whitney–Wilcoxon’s test.Table4 shows the comparison of laboratory parameters between eyes with macular ischemia (n=9) and those without macular ischemia (n=27). Eyes with macular ischemia had significantly higher monocytes (p=0.027, MWW) and monocytes/HDL (p=0.019, MWW) compared to eyes without macular ischemia.Table 4
Association between laboratory variables and macular ischemia on fluorescein angiography. Laboratory variables are summarized as median (IQR: interquartile range).
Macular ischemia presence (n=9)Macular ischemia absence (n=27)p (MWW)Red blood cells (106/μl)4.65 (0.73)4.70 (0.82)0.932White blood cells (103/μl)9.0 (2.49)7.57 (1.96)0.096Neutrophils (103/μl)5.24 (1.96)4.57 (2.03)0.074Lymphocytes (103/μl)2.33 (1.87)2.03 (0.46)0.718Monocytes (103/μl)0.64 (0.38)0.51 (0.19)0.027Platelets (103/μl)243 (86)244 (72)0.430Hematocrit (%)40.5 (6.4)40.7 (5.3)0.503Hemoglobin (g/dl)13.2 (1.4)13.9 (1.4)0.345Monocytes/lymphocytes0.29 (0.21)0.27 (0.14)0.159Neutrophils/lymphocytes2.15 (2.95)2.25 (0.99)0.718Monocytes/HDL0.016 (0.010)0.011 (0.004)0.019Glucose (mg/dl)198 (219)167 (62)0.606HbA1c (%)9.4 (3.0)7.4 (1.8)0.074Urea (mg/dl)40 (16.8)35.8 (20)0.154Creatinine (mg/dl)0.8 (0.5)0.8 (0.2)0.889Cholesterol (mg/dl)172 (80)147 (55)0.216LDL (mg/dl)98 (68)80 (43)0.287HDL (mg/dl)43 (16)48 (19)0.390Triglycerides (mg/dl)211 (190)128 (70)0.198IL-6 (pg/ml)4.2 (6.2)3.0 (2.5)0.149Apolipoprotein A (mg/dl)141 (33)143 (34)0.606Apolipoprotein B (mg/dl)93 (63)81 (33)0.363Lipoprotein (a) (nmol/l)34.7 (92.6)15.4 (59.6)0.363Homocysteine (μmol/l)17.8 (6.8)15.7 (8)0.731Vitamin D (ng/ml)17.9 (13)21.6 (12.3)0.668MWW: Mann–Whitney–Wilcoxon’s test.Table5 shows the comparison of laboratory parameters between eyes with CST above or equal to median (405 μm) and those with CST below median. Eyes with CST≥405μm presented higher neutrophils/lymphocytes (p=0.043, MWW) and higher lipoprotein (a) compared to eyes with CST<405μm (p=0.003, MWW).Table 5
Association between laboratory variables and central subfield thickness on optical coherence tomography. Laboratory variables are summarized as median (IQR: interquartile range).
Centralsubfieldthickness≥405μm (n=18)Centralsubfieldthickness<405μm(n=18)p (MWW)Red blood cells (106/μl)4.6 (0.48)4.79 (0.76)0.097White blood cells (103/μl)7.7 (3.31)7.87 (1.95)0.728Neutrophils (103/μl)5.0 (3.0)4.64 (1.74)0.174Lymphocytes (103/μl)1.97 (0.76)2.08 (1.02)0.282Monocytes (103/μl)0.57 (0.29)0.54 (0.20)0.824Platelets (103/μl)241 (82)251 (68)0.359Hematocrit (%)40.5 (4.7)40.9 (4.1)0.728Hemoglobin (g/dl)13.3 (1.5)13.9 (1.0)0.516Monocytes/lymphocytes0.28 (0.20)0.27 (0.17)0.268Neutrophils/lymphocytes2.74 (1.34)1.95 (0.72)0.043Monocytes/HDL0.012 (0.007)0.013 (0.006)0.950Glucose (mg/dl)159 (92)180 (97)0.179HbA1c (%)8.2 (2.3)7.6 (3.4)0.924Urea (mg/dl)37 (19)38.9 (17.1)0.812Creatinine (mg/dl)0.9 (0.5)0.8 (0.2)0.608Cholesterol (mg/dl)163 (47)153 (64)0.658LDL (mg/dl)95 (43)84 (55)0.716HDL (mg/dl)48 (12)45 (20)0.924Triglycerides (mg/dl)137 (90)122 (207)0.693IL-6 (pg/ml)3.4 (3.6)3.8 (1.9)0.764Apolipoprotein A (mg/dl)142 (32)144 (43)0.937Apolipoprotein B (mg/dl)88 (25)75 (56)0.359Lipoprotein (a) (nmol/l)60.0 (162.1)10.1 (17.4)0.003Homocysteine (μmol/l)18.3 (10.9)15.2 (6.9)0.624Vitamin D (ng/ml)21.7 (13.0)21.3 (18.7)0.776MWW: Mann–Whitney–Wilcoxon’s test.Figures1 and 2 show correlation between laboratory and morphological findings in patients with DME.Figure 1
(A) Optical coherence tomography of a female patient with diabetic macular edema and elevated lipoprotein (a), as well as decreased hematocrit and red blood count, which was associated with the disruption of the ellipsoid zone and an increase in central subfield thickness. (B) Optical coherence tomography of a male patient with diabetic macular edema and elevated white blood cells, which were associated with the presence of hyperreflective foci.
(a)(b)Figure 2
Fluorescein angiography of a male patient with diabetic macular edema, showing macular ischemia, which was associated with higher monocytes and higher monocytes/HDL.


## 4. Discussion

The principal message of the study is that most of the imaging morphological characteristics studied herein were not correlated with laboratory parameters in patients with DME, suggesting that DME may be mainly attributed to a local response more than a systemic effect. However, CST and specific OCT biomarkers, i.e., HF and EZ conditions, as well as macular ischemia on FFA, were found to be associated with laboratory findings in patients with DME, shedding light on the pathophysiology of DME and its correlation with the development of these specific clinical characteristics.Firstly, lipoprotein (a) was found to be associated with high CST and with EZ disruption. Previous studies have shown that high lipoprotein (a) concentration was independently associated with the presence and severity of DR in patients with type 2 DM, regardless of glycemic control [21–25]. Specifically, lipoprotein (a) can affect vascular tone and perfusion, oxidize lipids, and enhance oxidative stress via the generation of reactive oxygen species and inflammatory actions on the vascular wall [26, 27]. Moreover, lipoprotein (a) has been associated with endothelial dysfunction, suggesting that it could be an independent risk factor for diabetic microvascular complications [28, 29]. Although the exact mechanism behind the potential causal relationship between lipoprotein (a) and DME, and especially EZ disruption, remains unclear, it has been hypothesized that elevated lipoprotein (a) concentrations may play a causative role in DME by damaging the microcirculation [30]. In addition, lipoprotein (a) has been involved in the activation of acute inflammation and may be related to more severe DR, including DME severity with higher CST and EZ disruption [31]. Ellipsoid zone disruption has also been associated with decreased RBC count, decreased Hb, and consequently, decreased hematocrit. In patients with DM, RBC have been shown to present variations in size and diameter [32]. Chronic hyperglycemia can lead to nonenzymatic glycation of RBC membrane proteins that would accelerate RBC aging due to negative surface electrical charge [32, 33]. The RBC count has been found to be reduced in patients with microvascular complications, especially in those with longer DM duration [32–34]. Since Hb levels are directly correlated with RBC count, reduced RBC would reflect on Hb concentration and on hematocrit. In our case with DME, hyperglycemia induces the rearrangement of proteins in the plasma membrane, while cytoskeleton proteins also appear to be heavily glycosylated, affecting membrane stability, as it has been mentioned in RBC membrane proteins [32]. Therefore, reduced RBC could represent more severe DR and DME, which may be reflected on EZ disruption, as a result of cytoskeleton weakening.An interesting finding of our study was the association between HF and WBC. Many theories have attempted to explain the pathophysiology of HF, but their precise nature remains elusive. Framme et al. suggested that HF can be leucocytes or RPE cells, indicating retinal inflammation [35]. Coscas et al. supported this concept and postulated that HF are microglia cells activated by inflammation [36]. White blood cells and their subtypes are the biomarkers of inflammatory response because their activation leads to the synthesis of inflammatory cytokines, as it has been previously identified in patients with DR [37, 38]. Therefore, our finding that HF presence was associated with increased WBC is consistent with the hypothesis that an inflammatory component is implemented in HF pathogenesis [39–41].In recent studies, neutrophil (an indicator of inflammation) to lymphocyte (an indicator of physiologic stress) ratio was evaluated in inflammatory diseases, such as coronary artery disease, noncardiac diseases, retinal vein occlusion, age-related macular degeneration and DR [42–47]. Of note, neutrophil-to-lymphocyte ratio is more powerful for prediction of inflammatory diseases than subtypes of WBC alone because it combines the predictive values of two parameters of WBC [44]. In DME, the chronic low-grade inflammation may lead to inflammatory cytokine release, which is commonly responsible for increased vascular permeability [7]. As a result, neutrophil-to-lymphocyte ratio may be defined as an indication of subclinical inflammation, especially in more severe DME, as it was found in our study, showing a significant association between increased neutrophil-to-lymphocyte ratio and higher CST.Moreover, monocyte-to-HDL ratio has been investigated as a new inflammation biomarker and is considered superior to subtypes of WBC, especially in patients with cardiovascular and cerebrovascular diseases, as well as in patients with branch retinal vein occlusion [48–50]. Monocytes are indicators of inflammation, since they are responsible for inflammatory cytokine secretion, while HDL has antioxidant and anti-inflammatory effects [51, 52]. In our study, patients with macular ischemia were found to have increased monocytes, as well as monocyte-to-HDL ratio, which is consistent with other studies in patients with myocardial infarction and limb ischemia, suggesting monocyte-to-HDL ratio as a biomarker of ischemic conditions [53, 54].A potential limitation of this study pertains to the relatively small sample size, although oura priori statistical power calculation showed that it was adequate to achieve 80% power for the detection of an effect size larger or equal to 1.05; further larger studies seem necessary to validate our results.In conclusion, this study investigated the potential correlation between imaging morphological findings and laboratory biomarkers in patients with DME. Our results showed that specific inflammatory biomarkers, such as WBC, monocytes, monocyte-to-HDL, and neutrophil-to-lymphocyte ratios, were associated with more severe disease activity with higher CST and presence of HF and macular ischemia, while EZ disruption was found to be associated with increased lipoprotein (a) and decreased RBC, both of which were involved in microcirculation alterations in patients with DME. These findings may scrutinize the pathophysiology of DME and the pathogenesis of specific clinical signs. However, it should be noted that most of imaging biomarkers studied herein were not correlated with laboratory parameters in patients with DME, suggesting that DME may be mainly attributed to a local response more than a systemic effect. Further studies with a large sample size are needed to justify our results.



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