Clinical Research Directory

Precision Subclassification of Mental Health in Diabetes: Digital Twins for Precision Mental Health to Track Subgroups

Mental conditions and disorders (e.g. distress, depressive, anxiety, and eating disorders) are more prevalent in people with diabetes (PWD) and associated with reduced quality of life and impaired glycaemic outcomes. Evidence supports a complex network between psychosocial factors and glycaemic control that can be highly variable between persons. It is assumed that subgroups exist that show different trajectories of glycaemia and mental health. Belonging to a particular subgroup may be linked with a higher risk of developing mental health problems compared to others. This suggests that it is possible to treat individuals in different subgroups in a manner that optimizes their treatment and can improve health outcomes. Accurate characterisation can inform more individualized care. This calls for a more personalised approach considering the idiosyncrasies of different subgroups. Over 3 years, the investigators have established the basis of a precision mental health approach for diabetes using n-of-1 analyses. By utilizing combined ecological momentary assessment (EMA: repeated daily sampling of psychosocial factors in everyday life) and continuous glucose monitoring (CGM), intensive longitudinal data per person could be collected. This enables the analysis of individual associations between glycaemic parameters and psychosocial variables and identification of individual sources of diabetes distress in each person. The objective of the present study is to use of the n-of-1 approach to identify subgroups of PWD who share common characteristics in the associations between glucose and psychosocial variables. The identified subgroups shall be used to develop a digital twin for precision mental health in diabetes. The digital twin serves as representation of a real person, allowing to make simulations and predictions of the course of mental health and glycaemia. These predictions can inform diabetes care and lead to more precise, personalised treatment decisions. To achieve this, a longitudinal panel including over 1,400 PWD who continuously complete EMA and questionnaire surveys and measure glucose levels using CGM was developed. Over 1000 clinical interviews to diagnose mental disorders have been conducted to identify major mental health conditions and map mental outcomes. To identify subgroups and develop the digital twin, the sampling will be expanded aiming at a total of 1,809 PWD. Incidence and remission of mental disorders will be determined via repeated interviews. The complex networks between clinical, metabolic, and psychosocial data will be analysed using machine learning, leading to new insights with the potential to shape future guidelines. These results will be used by the digital twin to predict courses of glycaemic control and mental health, translating the individual evidence into direct treatment suggestions.

Behavioral Therapy and GLP-1 Analogue Effects on Binge Eating, Weight, and Coping in Obesity

This study is a clinical, longitudinal, non-randomized, prospective observational study that seeks to compare the treatment effects and safety of using GLP-1 analogues versus not using appetite suppressants during a lifestyle treatment program that includes individual consultations every fourth month and 10 weeks of CBT-E group therapy in patients with both obesity and BED. The primary objective of this study is to evaluate the impact on BED symptomatology, while the secondary objectives include examining the potential adoption of alternative harmful coping mechanisms. Additionally, the study will assess psychological well-being and weight changes and their consequent influence on obesity-associated comorbid conditions. Adult patients with coexisting obesity and BED presenting at the Obesity clinic at Haukeland University Hospital, Bergen, Norway, will be included Patients will be divided into two groups: Group-GLP1 (n = 40), who will use GLP-1 analogues, and Group-NoMED (n = 40), who will not use appetite suppressants. Both groups will otherwise follow the routine standardized patient care pathway with follow-up controls every four months and participation in CBT group therapy sessions. Changes in symptoms of BED, alternative harmful coping strategies and mental health will be recorded at baseline and 12 months using patient-reported questionnaires, as well as anthropometric and biochemical data.

Conceptualisation of a Network Approach to Complete the Classification of Eating Disorders With the Aim of Better Personalising Their Management

Eating disorders (EDBs) are mental disorders with a great impact on the lives of individuals as well as a great cost imposed on society. The treatments currently available rely on different forms of psychotherapies with an unsatisfactory response rate as well as a considerable relapse rate . One of the reasons underlying the low success of available therapeutic modalities would be the heterogeneity of the clinical presentation of EDs, making their classification and the personalization of their treatment more difficult.The investigators hypothesize that a secondarily dimensional classification approach (respecting the categorical classification currently in use) of EDs based on the different dimensions disturbed in these disorders as well as in their comorbidities, would better represent their nosographic reality, which could provide patients with more personalized and potentially more effective care.