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Background <p>Intravitreal anti-vascular endothelial growth factor (VEGF) agents improve visual acuity in diabetic macular edema (DME). However, resistance, non-response, or recurrence occurs in many patients. Predictive biomarkers for anti-VEGF response are lacking. We aim to identify cytokine markers predictive of anti-VEGF response and elucidate cytokines involved in poor-response DME pathogenesis.</p> Methods <p>A luminex assay was carried out to measure the concentration of cytokines in the aqueous humor. A predictive model based on baseline cytokines was constructed in a discovery set that comprised 46 responders and 20 non-responders. In addition, an analysis of baseline cytokines of 15 responders and 12 non-responders was conducted as a validation set. The performance of the nomogram was determined using the area under the receiver operating characteristic curve (AUC) and Hosmer–Lemeshow goodness of fit test.</p> Results <p>Baseline concentrations of angiogenesis-related cytokines VEGF (<i>P</i> &lt; 0.0001), placenta growth factor (PIGF) (<i>P</i> &lt; 0.0001), angiopoietin-2 (Ang-2) (<i>P</i> &lt; 0.0001), inflammatory factor interleukin-6 (IL-6) (<i>P</i> &lt; 0.0001), IL-8 (<i>P</i> &lt; 0.0001), chemokine monocyte chemoattractant protein-1 (MCP-1) (<i>P</i> &lt; 0.0001), and adhesion factor intercellular adhesion molecule-1 (ICAM-1) (<i>P</i> &lt; 0.001) were significantly increased compared to controls. The prediction nomogram model included five baseline cytokines: VEGF, IL-6, Ang-2, MCP-1, and ICAM-1 were constructed. The AUC for the discovery set was 0.85 (95% CI: 0.74–0.96) and for the internal validation was 0.84, indicating that the prediction model has good predictive accuracy. The Hosmer–Lemeshow goodness of fit test showed good model calibration (<i>P</i> = 0.295). The levels of Ang-2 (<i>P</i> = 0.0042), IL-6 (<i>P</i> &lt; 0.0001), IL-8 (<i>P</i> &lt; 0.0001), MCP-1 (<i>P</i> &lt; 0.0001), and PIGF (<i>P</i> &lt; 0.0001) were still significantly increased at the 6-month timepoint after multiple injections of anti-VEGF drugs for non-response group patients.</p> Conclusion <p>The baseline cytokine-based model helped to assess the individual probability of response to anti-VEGF therapy. There are cytokines beyond VEGF that are involved in the pathogenesis of DME, therapeutic regimens targeting these cytokines may improve the visual acuity and reduce macular edema.</p>

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Development of a predictive model based on aqueous cytokines data for response to anti-VEGF therapy in diabetic macular edema

  • 期刊论文

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  • 发表日期：

    2026年3月13日

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  • Fuhua Yang,
  • Yi Gong,
  • Xiaoying Pan,
  • Jinzhi Zhao,
  • Rongguo Yu,
  • Liangzhang Tan,
  • Emmanuel Eric Pazo,
  • Xiaomin Zhang,
  • Xiaorong Li

摘要

Background

Intravitreal anti-vascular endothelial growth factor (VEGF) agents improve visual acuity in diabetic macular edema (DME). However, resistance, non-response, or recurrence occurs in many patients. Predictive biomarkers for anti-VEGF response are lacking. We aim to identify cytokine markers predictive of anti-VEGF response and elucidate cytokines involved in poor-response DME pathogenesis.

Methods

A luminex assay was carried out to measure the concentration of cytokines in the aqueous humor. A predictive model based on baseline cytokines was constructed in a discovery set that comprised 46 responders and 20 non-responders. In addition, an analysis of baseline cytokines of 15 responders and 12 non-responders was conducted as a validation set. The performance of the nomogram was determined using the area under the receiver operating characteristic curve (AUC) and Hosmer–Lemeshow goodness of fit test.

Results

Baseline concentrations of angiogenesis-related cytokines VEGF (P < 0.0001), placenta growth factor (PIGF) (P < 0.0001), angiopoietin-2 (Ang-2) (P < 0.0001), inflammatory factor interleukin-6 (IL-6) (P < 0.0001), IL-8 (P < 0.0001), chemokine monocyte chemoattractant protein-1 (MCP-1) (P < 0.0001), and adhesion factor intercellular adhesion molecule-1 (ICAM-1) (P < 0.001) were significantly increased compared to controls. The prediction nomogram model included five baseline cytokines: VEGF, IL-6, Ang-2, MCP-1, and ICAM-1 were constructed. The AUC for the discovery set was 0.85 (95% CI: 0.74–0.96) and for the internal validation was 0.84, indicating that the prediction model has good predictive accuracy. The Hosmer–Lemeshow goodness of fit test showed good model calibration (P = 0.295). The levels of Ang-2 (P = 0.0042), IL-6 (P < 0.0001), IL-8 (P < 0.0001), MCP-1 (P < 0.0001), and PIGF (P < 0.0001) were still significantly increased at the 6-month timepoint after multiple injections of anti-VEGF drugs for non-response group patients.

Conclusion

The baseline cytokine-based model helped to assess the individual probability of response to anti-VEGF therapy. There are cytokines beyond VEGF that are involved in the pathogenesis of DME, therapeutic regimens targeting these cytokines may improve the visual acuity and reduce macular edema.