Cardiovascular diseases (CVDs) are the leading cause of death worldwide, emphasizing the critical need for early detection to improve treatment outcomes and prevent severe complications. Changes of the retina can be used to predict CVDs. Traditional methods often use color fundus photography (CFP) to classify CVDs risk levels into discrete categories. However, these methods typically treat the problem as a classification task, potentially overlooking the ordinal relationships among different risk levels. We propose Coral-CVDs to address this limitation by integrating these ordinal relationships into the classification models. This enhancement allows the model to better distinguish between the boundaries of adjacent risk levels. Additionally, we resolve the inconsistency issues present in traditional ordinal regression models and provide mathematical proofs to support our approach. We conducted a series of experiments using the UK Biobank data to validate our hypotheses and the results have shown its effectiveness.