Accurate disease grading is critical for early diagnosis and effective treatment planning. However, class imbalance and subtle inter-class variations in real-world disease grading datasets make it challenging for traditional classification models to differentiate between neighboring disease stages and preserve ordinal label relationships. Existing approaches emphasize inter-class ordinal relationships but fail to distinguish closely related categories effectively. To address these limitations, we consider disease grading as an ordinal regression problem and adopt a supervised contrastive learning approach to design a hybrid supervised contrastive ordinal learning framework. Our framework consists of three basic modules: 1) prototype-based contrastive ordinal learning, 2) weighted sample-based contrastive learning and 3) disease stage grading using regression. To deal with class imbalance while enhancing intra-class consistency and inter-class separation, we design a distance-based prototype contrastive ordinal loss, which pushes the samples closer to their class centers while maintaining their ordinality. This approach captures subtle differences within closely related disease stages and results in a separable ordinal latent space. Additionally, a per-sample class weighting strategy is integrated into weighted supervised contrastive ordinal learning to prevent class collapse, ensuring balanced gradient contributions and robust inter-class separation. Our approach effectively captures both large-scale and fine-grained variations, enabling precise ordinal classification for disease grading. We validate the framework on diabetic retinopathy and breast cancer datasets, demonstrating its adaptability across medical conditions and potential to enhance diagnostic accuracy in medical imaging applications.