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Lifestyle factors (LSFs) are increasingly recognized as instrumental in both the development and control of diseases. Despite their importance, there is a lack of methods to extract relations between LSFs and diseases from the literature, a step necessary to consolidate the currently available knowledge into a structured form. As simple co-occurrence-based relation extraction (RE) approaches are unable to distinguish between the different types of LSF-disease relations, context-aware transformer-based models are required to extract and classify these relations into specific relation types. No comprehensive LSF–disease RE system existed, primarily due to the lack of a suitable corpus for developing it. We present LSD600, the first corpus specifically designed for LSF-disease RE, comprising 600 abstracts with 1900 relations of eight distinct types between 5,027 diseases and 6,930 LSF entities. We evaluated LSD600’s quality by training a RoBERTa model on the corpus, achieving an F-score of 68.5% for the multi-label RE task on the held-out test set. We further validated LSD600 by using the trained model on the two Nutrition-Disease and FoodDisease datasets, where it achieved F-scores of 70.7% and 80.7%, respectively. Building on these performance results, LSD600 and the RE system trained on it can be valuable resources to fill the existing gap in this area and pave the way for downstream applications.