Introduction

Besides endophthalmitis, cytomegalovirus (CMV) retinitis, chorioretinitis due to toxoplasmosis or tuberculosis, and other types of intraocular infectious diseases, inflammation also significantly contributes to the pathophysiology of various other vitreoretinal diseases, such as age-related macular degeneration, diabetic retinopathy, retinopathy of prematurity, and even some inherited retinopathy, etc. Precise control of intraocular inflammation, which includes early identification of its causes and quantitative monitoring of its degree, is expected to improve the prognosis of many intraocular diseases. In terms of laboratory diagnosis of pathogen, the main challenge in determining the pathogen of intraocular inflammation is the limited amount of intraocular fluid and nucleic acid or antigen components available for examination. Usually, only approximately 0.1-0.3 mL can be taken by paracentesis or vitrectomy for laboratory examination.

Traditional methods include smear staining under a microscope, and microbial culture to obtain accurate pathogen information, with positive rate varied from 28% to 66%, and the cultivation time can be as long as 3 weeks or even longer, often missing the opportunity for prompt clinical treatment [1- 3]. Metagenomic next-generation sequencing (mNGS) is a genre of new technologies that allows thousands to billions of DNA fragments to be simultaneously and independently sequenced, which can unbiasedly detect trace pathogens from limited volume samples [4-7]. In the same eye center, the positive rates of mNGS and culture were 88.89% (32/36) and 27.78% (10/36), independently [1]. Our recent multicenter prospective study which enrolled 488 patients with intraocular inflammation, from whom vitreous (152) and aqueous humor (336) samples were respectively collected and analyzed using mNGS of cellfree DNA (cfDNA), showed a higher sensitivity (92.2%) and total coincidence rate (81.3%) of mNGS using vitreous samples were slightly higher than those of mNGS using aqueous humor samples (85.4% and 75.4%, respectively) [6].

In addition to nucleic acid testing, microbial antigen testing facilitates the early detection of intraocular inflammation and offers advantages in cost and time efficiency. β-D-glucan (BDG) is a major polysaccharide cell wall component found in many fungal species. Our study showed that the BDG testing value was 1022.78±1362.40 pg/mL in the fungal endophthalmitis group, significantly higher than that of the control group (105.0 ± 180.80 pg/mL, P<0.001). With the prespecified BDG cutoff 107.83 pg/ml as the test-positive, sensitivity was 81.8%, specificity was 87.5% [8]. Another study from our team showed that the positivity rates of BDG testing and mNGS (90.63%) were both significantly higher (P<0.001) than that of microbial cultures (53.13%). The combination of the two tests enhanced sensitivity (93.75%), specificity (100.00%), and overall clinical concordance rate (96.15%) in comparison to the individual tests [9]. Galactomannan (GM), a heat-stable antigen, is a principal constituent of the cell wall, mostly specific for Aspergillus spp., composed of polysaccharides. The mean optical density index (ODI) of GM was 5.77±1.73 in the Aspergillus endophthalmitis group, significantly higher than that in non-Aspergillus intraocular infection (0.19±0.11, P<0.001) and cataract group (0.29±0.27, P< 0.001). At a cut-off ODI of 1.88, the sensitivity and specificity were 100.0% and 100.0%, respectively [10].

Examination of antibody in the intraocular fluid’s aids in the diagnosis of parasitic intraocular infection. In a study of 72 cases with ocular toxocariasis (OT), the positive rate of Toxocara antibody in the intraocular fluid was 84.72% which was much higher than the positive rate of Toxocara antibody in the serum (54.17%). Comparing the titer of IgE in intraocular fluid and serum can further confirm the intraocular parasitic infection, and the positive rate is as high as 92.75% [11]. Moreover, the intraocular IgE level was significantly higher in child OT patients than in adult OT patients (1671.79 ± 1425.97 versus 784.44 ± 544.73 ng/ml, P=0.015) [12]. Quantitative assessment of intraocular inflammation may be crucial for determining the timing of treatment cessation or modification of therapeutic agents or strategies. Our prior research indicated that IL-8 levels, rather than IL-1b, IL-12p70, and TNF-a, in the aqueous humor were significantly correlated with the aqueous concentration of CMV copies. Furthermore, IL-8 levels consistently decreased throughout a regimen of effective treatment involving multiple intravitreal injections of antiviral agents. This finding suggests that intraocular IL-8 may serve as a reliable quantitative laboratory marker for the recovery from cytomegalovirus retinitis [13]. The levels of CMV DNA and interleukin-8 in the aqueous can help the clinician to judge the timing of withdrawal of intravitreal injections of anti-virus drugs or change of anti-virus drugs due to drug resistance [14].

Nowadays, both anti-VEGF drug and anti-inflammation drug such as dexamethasone implant have been reported widely to be used for treatment of diabetic macular edema (DME). Besides elevation of intraocular level of VEGF in the development of diabetic retinopathy, elevated intraocular levels of various inflammatory and pro-inflammatory cytokines have also been observed [15-17]. In relapsed DME cases, significantly higher levels of IL-6, IL-8, IP-10 and MCP-1 were found. Furthermore, MCP-1 levels in the vitreous of DR patients are elevated and higher than in serum, increasing with the progression of DR [18]. The efficacy of intravitreal dexamethasone implants in diminishing DME is associated with baseline concentrations of MCP-1 in the aqueous humor (P=0.028). Elevated baseline levels of MCP- 1 correlate with improved anatomical responses to treatment [19]. Analysis of intraocular fluid in vitreoretinal diseases, such as diabetic macular edema (DME), offers critical insights into the current angiogenic and inflammatory conditions of the patient. This information may aid in selecting optimal treatment options and predicting outcomes following various therapeutic interventions.

Acknowledgement

This work was supported by the National Natural Science Foundation of China (No: 82471081), Beijing Hospitals Authority’s Ascent Programme (DFL20220301), the Excellent Young Talent Innovation Project of Chinese Institutes for Medical Research (CX23YQA02), and the Chinese Institutes for Medical Research (CX23YQ03).

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