[Improved Mental Health Clinical Practice Informed by Digital Phenotyping]. / Le phénotypage digital pour une pratique clinique en santé mentale mieux informée.

Objectives This review is motivated by the observation that clinical decision-making in mental health is limited by the nature of the measures obtained in conventional clinical interviews and the difficulty for clinicians to make accurate predictions about their patients' future mental states. Our objective is to offer a representative overview of the potential of digital phenotyping coupled with machine learning to address this limitation, while highlighting its own current weaknesses. Methods Through a non-systematic narrative review of the literature, we identify the technological developments that make it possible to quantify, moment by moment and in ecologically valid settings, the human phenotype in various psychiatric populations using the smartphone. Relevant work is also selected in order to determine the usefulness and limitations of machine learning to guide predictions and clinical decision-making. Finally, the literature is explored to assess current barriers to the adoption of such tools. Results Although emerging from a recent field of research, a large body of work already highlights the value of measurements extracted from smartphone sensors in characterizing the human phenotype in behavioral, cognitive, emotional and social spheres that are all impacted by mental disorders. Machine learning permits useful and accurate clinical predictions based on such measures, but suffers from a lack of interpretability that will hamper its use in clinical practice in the near future. Moreover, several barriers identified both on the patient and clinician sides currently hamper the adoption of this type of monitoring and clinical decision support tools. Conclusion Digital phenotyping coupled with machine learning shows great promise for improving clinical practice in mental health. However, the youth of these new technological tools requires a necessary maturation process to be guided by the various concerned actors so that these promises can be fully realized.

MK2 Deletion in Mice Prevents Diabetes-Induced Perturbations in Lipid Metabolism and Cardiac Dysfunction.

Heart disease remains a major complication of diabetes, and the identification of new therapeutic targets is essential. This study investigates the role of the protein kinase MK2, a p38 mitogen-activated protein kinase downstream target, in the development of diabetes-induced cardiomyopathy. Diabetes was induced in control (MK2(+/+)) and MK2-null (MK2(-/-)) mice using repeated injections of a low dose of streptozotocin (STZ). This protocol generated in MK2(+/+) mice a model of diabetes characterized by a 50% decrease in plasma insulin, hyperglycemia, and insulin resistance (IR), as well as major contractile dysfunction, which was associated with alterations in proteins involved in calcium handling. While MK2(-/-)-STZ mice remained hyperglycemic, they showed improved IR and none of the cardiac functional or molecular alterations. Further analyses highlighted marked lipid perturbations in MK2(+/+)-STZ mice, which encompass increased 1) circulating levels of free fatty acid, ketone bodies, and long-chain acylcarnitines and 2) cardiac triglyceride accumulation and ex vivo palmitate ß-oxidation. MK2(-/-)-STZ mice were also protected against all these diabetes-induced lipid alterations. Our results demonstrate the benefits of MK2 deletion on diabetes-induced cardiac molecular and lipid metabolic changes, as well as contractile dysfunction. As a result, MK2 represents a new potential therapeutic target to prevent diabetes-induced cardiac dysfunction.