Addressing the problem that the existing retinal vessel segmentation models lose topological information in 3D feature extraction， leading to branch breakage， poor continuity in 2D segmentation results， and missing cross-modal associations in vascular analysis and disease prediction， a collaborative framework， MA_DNet（Multi-scale topology-Aware Disease Network）， was proposed. The framework consists of an enhanced segmentation model， MA_Net+， and a disease prediction module. Based on MA_Net（Multi-scale topology-Aware Network）， an intermediate feature retraining module was introduced by MA_Net+ to refine vessel structures and reconnect broken branches. Firstly， the GMSF （Gated Multi-Scale Fusion） module was employed to extract multi-scale spatial convolutions and fuse complex branch features， and the ResMamba module was combined to model long-range topological dependencies within vessels， so as to enhance 3D feature representations， thereby suppressing topological breakage in segmentation results effectively. Then， the convolutional layers of 2D module MA_Net+ were used to further optimize the continuity of local vascular structure. Finally， a cascade prediction module was designed， combining morphological parameters with clinical indicators， so as to establish cross-modal associations between image features and Coronary Artery Disease （CAD） risk. Experimental results show that the MA_Net+ framework achieves a Dice score of 93.02% and a Jaccard index of 87.04% on one subset of the OCTA-500 public dataset， with improvements of 0.28 and 0.37 percentage points， respectively， compared to the IPN-V2+（Image Projection Network V2+） model； on another OCTA-500 subset， the MA_Net+ framework achieves the two indicators of 89.76% and 81.52%， respectively， with gains of 0.35 and 0.57 percentage points， respectively； the disease prediction module of the MA_Net+ framework achieves an AUC（Area Under Curve） of 86.23% on a private dataset. It can be seen that MA_DNet framework enhances the continuity of vascular segmentation effectively through 3D topological modeling and multi-scale fusion mechanism； meanwhile， the framework explores cross-modal correlation prediction between retinal images and CAD risks， offering a new solution for non-invasive cardiovascular diagnosis.