Correlation between Systemic Inflammation and Morphological Changes of Retinal Neurovascular Unit in Patients with Early Signs of Diabetic Retinopathy: An OCT and OCT-Angiography Study

Diabetic retinopathy (DR) is a major cause of preventable blindness in the global working-age population [1], with a prevalence reaching 34.6% among diabetic adults [2]. Due to the insidious and asymptomatic characteristics of DR at onset, diabetic eyes frequently progress to the sight-threatening severe DR and miss optimal time for treatment when DR is diagnosed, resulting in an irreversible visual impairment and heavy financial burden of treatment to diabetic individuals. Therefore, investigating the mechanism and potential biomarkers considering the occurrence of DR is meaningful for early observation, timely diagnosis, and further understanding pathogenesis of DR.

Currently, the concept of retinal neurovascular unit (RNVU), primarily including retinal microvasculature and retinal neuron, was introduced into DR [3]. Compared with traditional DR evaluation, RNVU is more comprehensive for its evaluation not merely focusing on retinal microvascular alterations but also retinal neuronal changes, and it plays a crucial role on early changes of DR [4]. Previous research showed that retinal ganglion cell was the most susceptible cell type of retinal neuron in a diabetic animal model [5]. Sohn et al. [6] demonstrated that morphological retinal neuronal changes of ganglion cell loss by quantifying thickness of retinal nerve fiber layer (RNFL) and ganglion cell layer (GCL) precede the clinical visible microvascular lesions in DR. Moreover, retinal microvascular density decreased before the clinical diagnosis of DR [7]. Thus, retinal microvascular alterations and neuronal changes are two important pathways in the early pathogenesis of DR.

Optical coherence tomography (OCT) and OCT angiography (OCTA) are effective and sensitive modalities for detecting early morphological changes of RNVU components. OCTA allows the quantification of retinal microvascular alterations by measuring the size of foveal avascular zone (FAZ) and macular vessel density (VD) [7, 8]. Early retinal neuronal changes of ganglion cell loss can be identified by thickness of RNFL and GCL on OCT, representing axons and soma of the ganglion cell, respectively [6]. However, these imaging-derived RNVU parameters cannot be widely applied by comprehensive ophthalmologists, endocrinologists, and primary care physicians in clinical practice, as interpretation of these parameters results depends on retinal specialists, and a more convenient method is necessary for detecting early changes of RNVU.

Meanwhile, inflammation is considered as an essential step in the early pathogenesis of DR [9]. The expression of pro-inflammatory cytokines increases in the vitreous of the early DR patients [10]. However, the extraction of vitreous fluid is invasive and high expense, which limits its application in clinical work. Recently, some easily obtained systemic inflammation biomarkers based on calculation of peripheral white blood cells were proposed, encompassing neutrophil-to-lymphocyte ratio (NLR), monocyte-to-lymphocyte ratio (MLR), and systemic immune-inflammatory index (SII). These biomarkers were reliable markers of systemic inflammation and demonstrated to correlate with various ocular diseases, such as retinal vein occlusion (RVO) [11], age-related macular degeneration (AMD) [12], and retinitis pigmentosa (RP) [13]. Nowadays, epidemiological and clinical studies have reported the correlation between systemic inflammation biomarkers and DR. Rajendrakumar et al. [14] reported that NLR was a promising potential predictor for DR incidence, especially in diabetic patients aged less than 65 years. Recent studies found that MLR and SII were independent risk factors for DR [15, 16]. The aforementioned studies shed light on the potential association between systemic inflammation biomarkers and the occurrence of DR. However, no previous studies have elucidated the correlation between systemic inflammation biomarkers and early morphological RNVU changes of DR.

This study aimed to explore the correlation between systemic inflammation biomarkers and morphological changes of RNVU under OCT and OCTA in patients with early signs of DR. The results of our study may help a better understanding on the role of systemic inflammation on morphological changes of RNVU in early DR and acquire easily obtained biomarkers for monitoring early changes of DR.

This was a retrospective, observational, and cross-sectional study conducted at Fujian Provincial Hospital in Fuzhou, China, from December 2023 to April 2024. The study was performed according to the principles established in the Declaration of Helsinki and was approved by the Ethics Committee of Fujian Provincial Hospital. The ID number of the ethics approval is K2023-10-006.

A total of 93 type 2 diabetic mellitus (T2DM) patients with early signs of DR (170 eyes) were enrolled in this study and hospitalized in the Endocrinology Department of Fujian Provincial Hospital for diabetes management. To serve as a control reference group for comparison with the study group, an additional 37 age-, sex-, and axial length (AL)-matched healthy subjects (64 eyes) were enrolled as control group from Ophthalmic Outpatient Department at the same hospital. With the exception of having DM and hypertension, the inclusion and exclusion criteria of control group were the same as for the T2DM patients with early signs of DR.

Inclusion criteria were as follows: (1) patients with the diagnosis of T2DM; (2) patients diagnosed with early signs of DR defined by ranging from level 10 to level 35 on Early Treatment Diabetic Retinopathy Study (ETDRS) DR severity scale (DRSS) score [17], including preclinical DR, microaneurysms only, and mild NPDR; (3) refractive errors between −3.00 diopter (D) and +3.00 D spherical equivalent; (4) AL between 22 and 24.5 mm. Exclusion criteria were as follows: (1) without examination records of blood sample, OCT, or OCTA; (2) the images of fundus examination with visible eye motion or with poor image quality (defined as signal strength less than 20 dB on OCT and less than 7 on OCTA, respectively); (3) other retinal diseases, including uveitis, RVO, RP, AMD, etc.; (4) with glaucoma or ocular hypertension; (5) history of retinal treatment including intravitreal anti-VEGF injection, retinal laser, or vitreoretinal surgery; (6) history of medicine for anti-inflammation or immunosuppression in the past 6 months; (7) with other diseases, which might affect blood test results, such as kidney or liver diseases, autoimmune disorders, hematological diseases, and malignant tumor; (8) with other diabetic complications, including diabetic nephropathy, diabetic peripheral neuropathy, etc.; (9) the images of OCT with any evidence of cysts, or dissociation of the retinal inner layers, or epiretinal membranes, or atrophy at retinal pigment epithelium.

All subjects underwent a fasting venous blood sample collection on the first day of hospitalization before receiving any therapy. Peripheral blood cell counts and biochemical measurements were conducted on an automated hematology analyzer (Sysmex Corporation, Kobe, Japan).

NLR was computed as neutrophil count/lymphocyte count. SII was calculated as platelet count × neutrophil count/lymphocyte count [18]. MLR was computed as monocyte count/lymphocyte count [19].

All subjects underwent a series of ophthalmologic examinations, including slit-lamp biomicroscopy, visual acuity, intraocular pressure, AL, ultrawide-field fundus photographs, OCT, and OCTA. DR severity level was determined independently by two experienced ophthalmologists, using ultrawide-field fundus photographs according to ETDRS classification [17].

RNVU parameters analyzed in this paper contain two aspects, including retinal neuron and retinal microvasculature. Retinal neuronal parameters include the thickness of RNFL, GCL, and inner nuclear layer (INL). Retinal microvascular parameters include FAZ perimeter (FAZp), FAZ area (FAZa), and VD. Examples of each RNVU parameter are provided in Figure 1.

Retinal neuronal parameters were quantified by SD-OCT (Heidelberg Spectralis version 1.9.10.0, Heidelberg Engineering, Heidelberg, Germany). A 6 × 6 mm macular scanning range mode was selected, and the IR and OCT 30° ART examination procedure was used to obtain B-scan images. The Spectralis segmentation software was used to segment individual retinal layer thickness, including RNFL, GCL, and INL (shown in Fig. 1a) [20]. The mean thickness of each retinal layer within a 6 mm diameter macular ETDRS circle [21] was measured automatically using the inbuilt Spectralis mapping software, Heidelberg Eye Explorer (version 6.0 c). The 6 mm diameter ETDRS circle encompassed nine macular regions: a fovea-centered circle (0–1 mm diameter), four inner annuli (1–3 mm diameter), and four outer annuli (3–6 mm diameter). Retinal neuronal parameters were estimated based on average thicknesses of macular RNFL, GCL, and INL across the entire 6 mm diameter ETDRS circle.

Retinal microvascular parameters were measured and quantified by OCTA. All participants were examined by the SD-OCT system (Carl Zeiss, Inc., Fremont, CA, USA) equipped with Cirrus HD-OCT 5000 device (AngioPlex) using the angiography 3 × 3 mm acquisition protocol. Retinal microvascular parameters including FAZp, FAZa, and VD in the superficial capillary plexus (shown in Fig. 1b, c), were calculated by the Carl Zeiss Meditec Density Exerciser (version 10.0.12787) on OCTA [6].

Statistical analysis was performed using SPSS software (SPSS, Inc., 24.0, Chicago, IL, USA). Continuous variables are described using mean ± standard deviation, and categorical variables are presented as numbers and percentages. All continuous variables were tested for normal distribution using the Kolmogorov-Smirnov test. Systemic inflammation biomarkers stratified by quartiles of each RNVU parameter were analyzed using ANOVA analysis. Spearman correlation and its significance were used to investigate the correlation between systemic inflammation biomarkers and RNVU parameters. To consider possible characteristics of different variables, univariable and multivariable generalized linear models (GLMs) were adopted to further correlation investigation. Considering SII value ranging between hundreds, which were different with variables of NLR and MLR, we used SII per 100 unit instead of SII in GLM analysis. Variables with p value of 0.05 or smaller in univariable analysis were included in multivariable analysis. GLM methods were adopted for RNFL, GCL, FAZp, and VD as dependent variables. Subgroup analysis was performed on the basis of DM duration, considering its potential influence on VD. A value of p < 0.05 was considered statistically significant.

A total of 102 patients (204 eyes) with complete clinical records and ophthalmic examinations were originally reviewed. Eyes with poor image quality (n = 12), with macular epiretinal membrane (n = 9), with history of retinal laser (n = 9), with renal dysfunction (n = 4) were excluded. Finally, 170 eyes from 93 T2DM patients were enrolled in the final analysis.

The demographic and laboratory parameters of the study group and controls are summarized in Table 1. In the study group, the mean age of the study group was 58.28 ± 9.62 years, with 54 (58.1%) male and 39 (41.9%) female patients. The mean duration of DM and glycosylated hemoglobin A1c (HbA1c) were 9.46 ± 6.66 years and 9.63 ± 2.26%, respectively. The mean values of NLR, MLR, and SII were 2.66 ± 1.38, 0.27 ± 0.12, and 616.30 ± 341.69, respectively. Ocular findings and RNVU parameters of the study group and controls are also summarized in Table 1.

We analyzed systemic inflammation biomarkers in diabetic patients stratified by the quartiles of each RNVU parameter, including RNFL, GCL, INL, FAZp, FAZa, and VD, respectively. Patients with thicker quartiles of RNFL tend to have lower NLR and SII (shown in Fig. 2a). Patients with thicker GCL quartiles were found to have lower SII (shown in Fig. 2b). Patients with higher quartiles of VD were found to have lower NLR and SII in general (shown in Fig. 2c). Detailed results were presented in the online supplementary material at https://doi.org/10.1159/000545097 (for screening details, see online suppl. File 1).

Spearman correlation analysis was adopted to investigate the correlation between systemic inflammation biomarkers and parameters of RNVU in patients with early signs of DR. The correlation matrix is shown in Figure 3.

RNFL thickness was significantly correlated with MLR (MLR: r = −0.210, p = 0.008). GCL thickness was significantly correlated with NLR and MLR (NLR: r = −0.183, p = 0.017; MLR: r = −0.235, p = 0.002). FAZp was significantly correlated with NLR and MLR (NLR: r = 0.153, p = 0.046; MLR: r = 0.187, p = 0.014). FAZa was positively correlated with MLR (r = 0.189, p = 0.014). VD was significantly correlated with NLR (NLR: r = −0.188, p = 0.014).

To control the possible confounding effects of studied factors, GLM methods were adopted. Table 2 shows the association of systemic inflammation biomarkers and VD in patients with early signs of DR through GLM analysis. In univariable GLM, DM duration (p = 0.032), prevalence of hypertension (p = 0.019), GCL (p = 0.020), FAZp (p = 0.000), FAZa (p = 0.000), and SII_{per 100 unit} (p = 0.031) were significantly associated with VD. Variables with p value <0.05 in univariable analysis were used to construct the final multivariate GLM. In multivariable GLM, DM duration (p = 0.001), FAZp (p = 0.000), FAZa (p = 0.005), and SII_{per 100 unit} (p = 0.012) were still significantly associated with VD.

Subgroup analysis was conducted to consider potential effects of DM duration on the association between systemic inflammation biomarkers and VD. Detailed results of subgroup analysis are shown in Table 3. Multivariable GLM analysis presented that SII_{per 100 unit} remained significantly related with DM duration over 10 years (p = 0.001).

In this study, we investigated the correlation between peripheral blood white cells-derived systemic inflammation biomarkers and morphological changes of RNVU parameters using OCT and OCTA in diabetic patients with early signs of DR. The major findings are as follows: (1) Systemic inflammation biomarkers are statistically significantly correlated with early RNVU changes, in both retinal microvascular alterations and retinal neuronal changes. (2) SII is independently associated with VD, which supports the hypothesis that SII may be a potential biomarker for detecting early retinal microvascular alterations of DR. To the best of our knowledge, this is the first study to explore the association between systemic inflammation biomarkers and early RNVU changes of DR.

Previously, attempts have been made to investigate the association between systemic inflammation biomarkers and DR, considering the role of systemic inflammation on DR pathogenesis. He et al. [22] found that NLR was positively associated with the risk of DR when its value was less than 4.778. Yue et al. [16] reported that MLR was an independent risk factor for the occurrence of DR. These studies were in agreement with our conclusion that systemic inflammation biomarkers were associated with DR. And we further found NLR and MLR were significantly associated with early retinal microvascular and neuronal changes, which more comprehensively elucidate the impact of systemic inflammation on two primary DR pathogenic pathways. In addition, Huang et al. [23] reported that systemic inflammation biomarkers were significantly correlated with various manifestations of microvascular changes including microvascular leakage and capillary non-perfusion areas under ultrawide-field FA. Unlike Huang et al.’s study [23] was based on mild NPDR to PDR, we further demonstrated that SII was independently associated with decreased VD in patients without apparent clinically retinal microvascular alterations, which supports the possible contribution of systemic inflammation to early insidious retinal microvascular impairment of DR.

FAZ and VD under OCTA, representing retinal microvascular part of RNVU, are known as sensitive indicators for monitoring early retinal microvascular alterations [7]. Enlarged FAZ represents the loss of integrity of blood vessels [24]. The decrease of VD in the superficial capillary plexus indicates early reduced capillary flow and capillary dropout in the diabetic retina [25]. In the present study, we found that FAZp, FAZa, and VD were significantly associated with systemic inflammation biomarkers. This suggests that systemic inflammation mediated by leukocytes may be involved in the loss of integrity of foveal vessels and capillary dropout before apparent clinically detectable DR. Despite these correlation coefficients being modest in this study, our findings were consistent with previous evidence [22], which showed similar small but statistically significant correlation. Importantly, He et al. [22] further substantiated the clinical value of systemic inflammation biomarker through a large-scale population study of 2,772 participants, demonstrating that even a weak correlation retained significant predictive value for DR occurrence. This suggested that our findings with similar modest correlation could still offer valuable insights. Moreover, our study extended the correlation of systemic inflammation with early diabetic retinal microvascular and neuronal alterations (e.g., FAZ, VD, and GCL), further revealing potential mechanistic links between systemic inflammation and two primary DR pathogenic pathways.

Thickness of RNFL and GCL quantified by OCT represent the early retinal neuron changes of ganglion cells loss [26]. In the present study, RNFL and GCL were significantly associated with systemic inflammation biomarkers. Previous research showed hyperreflective foci number, an OCT-based retinal inflammation biomarker, was inversely correlated with macular sensitivity in diabetic macular edema (DME), possibly linking retinal inflammation to functional retinal neuronal impairment [27]. Meanwhile, hyperreflective focus was also significantly associated with systemic inflammation biomarkers in patients with DME, suggesting retinal inflammation in DME may correlate with systemic inflammation [18]. Therefore, we speculate that systemic inflammation biomarkers may reflect the contribution of retinal inflammation to early retinal neuronal changes, or participate in the process of early retinal neuronal changes. Further research is needed to clarify the association among systemic inflammation, retinal inflammation, and retinal neuronal changes.

Systemic inflammation biomarkers aforementioned are calculated from peripheral leukocyte subtypes, and mechanisms behind them are explained by the function of these cells. Neutrophils contribute to endothelial cell apoptosis and diabetic blood-retinal barrier breakdown [28]. Lymphocytes participate in adaptive immunity [29]. Activated platelets release inflammatory mediators, which regulate the inflammatory response between leukocytes and endothelial cells [30]. Since the balance of these leukocytes function instead of using it alone, SII is a more reliable biomarker to reflect systemic inflammatory status. Additionally, SII has been proved to be a positive and independent predictor for DR stages in T2DM patients [31]. Despite no established consensus considering the normal range of SII, in related studies, most cutoff values of SII for specific diseases from receiver operating characteristic curve analysis are around 600 × 10⁹/L [32‒35]. In the current study, we further found that SII was independently and inversely associated with VD in patients with early DR. This suggests SII is a potential biomarker for detecting early retinal microvascular alterations and inspires us to evaluate the degree of microvascular changes from the elevated value of SII. Moreover, based on the elevated SII, ophthalmologists, endocrinologists, and primary care physicians can identify diabetic patients at risk of retinal microvascular impairment and prioritize them for retinal examinations to screen for DR.

The strengths of the present study are as follows. This is the first study to investigate the association between systemic inflammation biomarkers and morphological changes of RNVU in early DR, which has not been reported before. Additionally, compared to expensive and invasive ocular inflammation biomarkers only available during surgical procedures, SII is a more economical, easier accessible and understanding biomarker for monitoring early retinal microvascular alterations. Certain limitations of this study are also needed to be acknowledged. First, the causal relationship between systemic inflammation biomarkers and RNVU in diabetic patients cannot be determined due to the cross-sectional study design and the relatively small sample size. Further longitudinal studies of larger sample sizes are required to validate the findings. Second, the approach of evaluating RNVU in current retinal imaging is relatively limited. The focus of the present study considering RNVU is on the retinal ganglion cell loss and microvascular alterations quantified by RNFL, GCL, FAZ, and VD. However, RNVU also contains glial and inflammatory cells. These cannot be quantified with current OCT or OCTA examinations. Third, this is a single-center study from an Asian population, which means the conclusions may not extend to other races and need further research.

NLR, MLR, and SII are associated with parameters of RNVU, at the aspects of retinal microvasculature and retinal neuron in patients with early signs of DR, which emphasizes the vital contribution of systemic inflammation to pathogenesis pathway of early DR. SII is significantly independently correlated with VD, which serves SII as a potential and promising biomarker for detecting early retinal microvascular alterations.

The study was approved by the Ethics Committee of Fujian Provincial Hospital (the ID-number of the ethics approval is K2023-10-006) and performed according to the principles established in the Declaration of Helsinki. The requirement for informed consent was waived by the Ethics Committee of Fujian Provincial Hospital because of the retrospective nature of the study.

The authors have no conflicts of interest to declare.

This study was supported by Natural Science Foundation of Fujian Province (2022J011006).

Hanli Guo, Wenjie Wu, and Qiong Li designed this study. Ningxuan Jin and Huazhi Ma collected and double checked the data. Hanli Guo, Yue Huang, and Yulong Huang analyzed the data. Hanli Guo wrote the paper. Wenjie Wu and Qiong Li provided critical revision to the article. All authors read and approved the final manuscript.

All data generated or analyzed during this study are included in this article and its online supplementary material. Further inquiries can be directed to the corresponding author.