RESUMO
OBJECTIVE: To estimate the pterygium ocular surface state, and compare with healthy eyes and dry eyes. To investigate the inflammation due to pterygia growth by tear Lymphotoxin-alpha (LT α) test. DESIGN: Prospective, single-center study. PARTICIPANTS: 400 patients, divided into 100 pterygium group, 100 mild dry eye group, 100 moderate dry eye group, and 100 age-and sex-matched normal controls. METHODS: The non-invasive break-up time (NIBUT), tear meniscus height (TMH) test, corneal fluorescein staining (CFS), meibomian gland loss score (MGs), and lipid layer thickness (LLT) were evaluated in all patients. Pterygium status and ocular status in the pterygium group were collected. The tear LT α test was conducted in the pterygium patients group. RESULT: Pterygium can affect the ocular surface, leading to decreased tear film stability. The TMH, NIBUT, CFS, MGs, and lipid layer thickness can provide insights into this phenomenon. The presence of pterygium can change the structure and condition of the ocular surface. Tear LT α testing shows an abnormal decrease in LT α levels in pterygium patients. This indicates an immune-inflammation microenvironment that causes tissue repair deficiency. CONCLUSION: The dry eye triggered by the growth of pterygium may originate from the tear film instability due to pterygia. As an inflammatory index, LT α in the development of pterygium and the aggravation of dry eye patients can indicate that the ocular surface is in different inflammatory states. Future tear testing in LT α may be a potential indicator to assess the inflammatory status of the dry eye.
RESUMO
To explore the correlation between tear LT-a, pterygium status, and dry eye indicators. We established a diagnostic model to evaluate active pterygium. A retrospective study was conducted between June 2021 and June 2023 on 172 patients, comprising 108 men and 64 women. The study analyzed LT-a and various ocular parameters in all participants. The data was collected using Excel software and analyzed using SPSS 25.0 statistical software and Medcalc. We made a nomogram diagnostic model to different diagnosed the state of pterygium. This study found that pterygium has progressive eye surface damage during the active state. There was no significant difference in dry eye indicators between the two groups. However, the concentration of LT-a in the active group was significantly lower than that in the inactive group (P < 0.001). We observed that increased pterygium grade corresponded to a worse ocular surface condition. In addition, LT-a was significantly positively correlated with disease duration, but negatively correlated with age, pterygium size, active pterygium state, and LLT value. The optimal intercept value for evaluating active pterygium in Lt-a was ≤ 0.49 dg/ml. We screened three variables for evaluating active pterygium through Single and Multiple regression analysis: LT-a grading, pterygium size, and congestion score. Finally, we made a reliable diagnostic nomogram model. Pterygium development triggers immune inflammation. Our model based on LT-a identifies active pterygium for personalized treatment options and new research directions.
