Vitamin D and cholesterol metabolism overlap significantly in the pathways that contribute to their biosynthesis. However, our understanding of their independent and co-regulation is limited. Cardiovascular disease is the leading cause of death globally and atherosclerosis, the pathology associated with elevated cholesterol, is the leading cause of cardiovascular disease. It is therefore important to understand vitamin D metabolism as a contributory factor. From the literature, we compile evidence of how these systems interact, relating the understanding of the molecular mechanisms involved to the results from observational studies. We also present the first systems biology pathway map of the joint cholesterol and vitamin D metabolisms made available using the Systems Biology Graphical Notation (SBGN) Markup Language (SBGNML). It is shown that the relationship between vitamin D supplementation, total cholesterol, and LDL-C status, and between latitude, vitamin D, and cholesterol status are consistent with our knowledge of molecular mechanisms. We also highlight the results that cannot be explained with our current knowledge of molecular mechanisms: (i) vitamin D supplementation mitigates the side-effects of statin therapy; (ii) statin therapy does not impact upon vitamin D status; and critically (iii) vitamin D supplementation does not improve cardiovascular outcomes, despite improving cardiovascular risk factors. For (iii), we present a hypothesis, based on observations in the literature, that describes how vitamin D regulates the balance between cellular and plasma cholesterol. Answering these questions will create significant opportunities for advancement in our understanding of cardiovascular health.

Systems medicine (SM) has emerged as a powerful tool for studying the human body at the systems level with the aim of improving our understanding, prevention and treatment of complex diseases. Being able to automatically extract relevant features needed for a given task from high-dimensional, heterogeneous data, deep learning (DL) holds great promise in this endeavour. This review paper addresses the main developments of DL algorithms and a set of general topics where DL is decisive, namely, within the SM landscape. It discusses how DL can be applied to SM with an emphasis on the applications to predictive, preventive and precision medicine. Several key challenges have been highlighted including delivering clinical impact and improving interpretability. We used some prototypical examples to highlight the relevance and significance of the adoption of DL in SM, one of them is involving the creation of a model for personalized Parkinson’s disease. The review offers valuable insights and informs the research in DL and SM.

Essential thrombocythaemia (ET) is driven by somatic mutations involving the JAK2, CALR and MPL genes. Approximately 10% of patients lack driver mutations and are referred as ‘triple-negative’ ET (TN-ET). The diagnosis of TN-ET, however, relies on bone marrow examination that is not always performed in routine practice, and thus in the real-world setting, there are a group of cases with suspected TN-myeloproliferativeneoplasm.In this real-world cohort, patients with suspected TN-ET were initially rescreened for JAK2, CALR and MPL and then targeted next-generation sequencing (NGS) was applied.The 35 patients with suspected TN-ET had a median age at diagnosis of 43 years (range 16–79) and a follow-up of 10 years (range 2–28). The median platelet count was 758×109/L (range 479–2903). Thrombosis prior to and following diagnosis was noted in 20% and 17% of patients. Six patients were JAK2V617F and two patients were CALR positive on repeat screening. NGS results showed that 24 of 27 patients harboured no mutations. Four mutations were noted in three patients.There was no evidence of clonality for the majority of patients with suspected TN-ET with targeted NGS analysis. Detection of driver mutations in those who were previously screened suggests that regular rescreening is required. This study also questions the diagnosis of TN-ET without the existence of a clonal marker.

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