Almost every mental health disorder is more prevalent in people with diabetes compared to people without diabetes. Is has been consistently shown, that people with diabetes and mental health issues (e.g. distress, depression, anxiety, eating disorder) have worse glycaemic control and worse quality of life. Thus, mental health issues can have a substantial impact on glycaemic control but are also an outcome of diabetes therapy itself. Achieving optimal quality of life is therefore considered an important goal of diabetes therapy. Research suggests a complex network between psychosocial factors and glycaemic control that can be highly variable between persons. It is therefore assumed that subgroups of people with diabetes exist that show different trajectories of glycaemic control and mental health. Being in one subgroup can be more strongly linked to the likelihood of developing mental illness and complications relative to others. This suggests that it may be possible to treat individuals in different subgroups in a manner that optimises their treatment and health outcomes. Accurate characterisation of the heterogeneity in people with diabetes may help individualise care and improve outcomes. This calls for a more personalised approach that considers the idiosyncrasies of different subgroups, which in in line with the ADA/EASD's call for precision medicine in diabetes. In the last 3 years, we have established the basis of a precision mental health approach in diabetes by employing n-of-1 analyses. By using the combination of ecological momentary assessment (EMA: a methodology that allows the repeated daily sampling of psychosocial factors in people's daily life) and continuous glucose monitoring (CGM), we were able to collect intensive longitudinal data per person. With this approach, we have analysed individual associations between glycaemic parameters and psychosocial variables and identified specific sources of diabetes distress per person. Our objective is to make use of this n-of-1 approach and identify subgroups of people who share certain characteristics in their associations between glucose and psychosocial variables. We aim to feed these identified subgroups into the development of a digital twin for precision mental health in diabetes. With the digital twin, the aim is to have a representation of a real person based on the identified subgroups that allows to make simulations and predictions of the course of glycaemic control and mental health. These predictions can inform therapy and lead to more personalised, precise treatment decisions. Ultimately, the digital twin can serve as a clinical decision support system. In order to achieve these objectives, we have already built a longitudinal panel of 1,400 participants that continuously complete EMA and questionnaire surveys along with measuring glucose levels using CGM. To map mental health, we have already conducted \>700 clinical diagnostic interviews to diagnose mental disorders (e.g. depression, anxiety, eating disorders). For identifying subgroups and developing the digital twin, we will expand the data collection and aim at including a total of 1,809 participants. Further metabolic data (e.g. HbA1c) will be collected and the incidence and remission of mental disorders will be determined by repeated clinical diagnostic interviews. By using machine learning, the complex networks between clinical, metabolic and psychosocial data will be analysed for different subgroups, leading to new insights that have the potential to shape future guidelines. This will then be used by the digital twin approach to make predictions on future glycaemic control and mental health, thereby directly translating the new scientific evidence into actionable treatment suggestions.