An observational study in Diabetes (DM), Diabete Mellitus and Diabete Type 1, sponsored by Forschungsinstitut der Diabetes Akademie Mergentheim. Recruiting at 3 sites in Germany. Open to participants aged 18 Years to 80 Years. Per ClinicalTrials.gov, last updated 2025-12-04.
Sponsored by Forschungsinstitut der Diabetes Akademie Mergentheim · Observational
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.
People with diabetes (PWD) have a higher risk of mental disorders: a systematic review showed the odds of 15 different mental disorders is significantly increased (1). A review indicated that mental health disorders can drive the incidence of diabetes and lead to a negative prognosis (2). Common mental health problems in diabetes include diabetes distress, depression, anxiety, and disordered eating (1,3).
The ADA/EASD's precision medicine consensus statement highlights mental health as "highly relevant" (4). Moreover, mental health data should be "combined with other data … to improve the precision of clinical decision making." A review by the investigators supports that precision mental health in diabetes can be achieved by combining glucose data with behavioral and mental data (5).
A precision medicine approach to mental health requires an in-depth exploration of mental health subgroups. This involves identifying distinct patterns and subgroups who show different courses of mental outcomes as well as understanding the contribution of glucose in relation to physio- and psychological factors. The interplay between mental health, CGM metrics, and behavioral factors is crucial for elucidating mental health trajectories and advancing personalised treatment strategies (5-7).
With the previous DIA-LINK and PRO-MENTAL studies, the investigators set the basis for a precision mental health approach. By combining CGM with ecological momentary assessment (EMA), a methodology allowing repeated daily assessments of mental variables, an innovative approach for precision mental health was established (7). In a recent study, this approach was used to identify individual drivers of diabetes distress using n-of-1 analyses (8). The study supports specific subgroups in which distress is differentially influenced either by the mental perceptions of glucose or the actual glucose values. It was also demonstrated that glycemic control and psychosocial well-being at follow-up were differentially influenced depending on the individual drivers of distress.
Digital twins are a valuable tool for identifying relevant subgroups of PWD who share similar courses of glucose and mental health. A digital twin represents a virtual model of an individual, integrating data from various sources, including CGM, electronic health records, EMA, behaviors, and sensors. A digital twin is ideal to study highly dynamic networks between metabolic, behavioral, and psychological factors (9-11). However, a recent systematic review highlights that although psychosocial factors play a role in glucose control, none of the reviewed digital twins incorporated such factors. The authors conclude that "addressing this deficiency opens new perspectives and opportunities for improving the holistic management of T1D through more comprehensive and inclusive modeling approaches" (9).
With a digital twin, various treatment scenarios can be simulated. This can help diabetologists to develop precise treatment plans addressing both metabolic and psychological needs, ensuring more personalised interventions. Thus, the present project aims to develop a digital twin for precision mental health in diabetes.
Over the past years, a theoretical framework (5,6) and groundwork for a precision mental health approach in diabetes (7,8,12) have been developed. In the PRO-MENTAL study, a longitudinal panel of over 1,300 PWD who regularly complete a 14-day EMA period and questionnaires every 6 months, in addition to using CGM das been established (12).
The objective of the new project is to capitalize on the achievements of the PRO-MENTAL study and to continue the data collection, while also expanding the database by including the following assessments and data:
The digital twin can be used to increase scientific understanding of the complex interplay between glucose and psychosocial factors. Previous research on this interplay has mainly focused on specific parts (13-15), but are lacking a holistic approach. A digital twin enables a more comprehensive analyses of subtypes in which psychosocial factors (e.g., stress, mood, diabetes distress, sleep, mental disorders) and different parameters of metabolic control (e.g., hypoglycemia, time in range) influence each other over time. The use of machine learning/artificial intelligence has the potential to reveal complex networks by considering a multitude of influencing and confounding factors simultaneously.
With the digital twin, different courses of mental health (e.g., depression) and glucose control (e.g., time in range) can be simulated. Simulation will be based on different EMA and glucose profiles, PROs, inflammation status, medication, dietary behavior and physical activity. The investigators also aim at simulating how mental health/glucose control will develop depending on changes in the aforementioned variables. By categorizing a person according to their EMA and CGM profiles (8), the investigators aim to predict whether a person is more or less likely to develop a mental disorder or dysglycemia. The digital twin will also allow the simulation of how different interventional measures may affect the overall course of mental health and glucose. This enables the identification and selection of optimal treatment strategies to positively affect both mental health and glycemia. The ultimate goal is to inform clinical decision-making, thereby translating research into precise clinical diabetes care.
Current work on precision mental health in diabetes is entirely preliminary scientific work with no translation to clinical practice. The development of a digital twin would be the first step of a precision mental health approach in diabetes. It could be used by diabetologists in clinical practice as a clinical decision support system to:
Current guidelines, such as the ADA Standards of Care and the German Psychosocial Guideline, recommend monitoring psychosocial factors (16,17). However, guidelines differ significantly in frequency, dimensions assessed, screening tools, and management of positive results. These discrepancies stem from limited understanding of the natural progression of mental health issues in diabetes, the impact of glucose control and complications on mental health, and the implications of these issues for diabetes care. By enhancing our understanding of mental health trajectories in diabetes and the impact of glucose regulation, this study will inform clinical care practices and guideline development.
The TwinPeaks project will be a prospective, longitudinal, observational, non-interventional study including 1,809 participants overall, with ultimately approx. 6,000 person-years being surveyed.
Work packages:
The project will build directly on the PRO-MENTAL study and expand the previous work with five additional work packages (WP).
WP 1 - Data collection for the development of the digital twin: 1,300 participants with type 1 and type 2 diabetes have already been recruited. The majority have completed a baseline diagnostic interview and are attending online surveys as well as a 14-day EMA periods every 6 months. With the TwinPeaks project, the data collection will be continued but more participants and new variables will be included.
WP 2 - Data management and data preparation: The first step is to clean and prepare the plethora of data from different sources and in different formats for further statistical analysis. A key aspect will be the formatting of the data to account for the different time frames: minutes for CGM data, hours and days for EMA data, months for questionnaire data, and years (12 month and lifetime) for clinical diagnostic interviews. The data from several different sources will be made available through a parameterized interface for data collection and storage. Data-centric solutions will be used as the backend, such as employing a NoSQL database. The NoSQL database allows for the storage of unstructured data from various sources and can be horizontally scaled to handle a growing volume of data. A possible implementation is based on Apache Cassandra, known for its high availability and scalability. Suitable metadata and data standards are defined to ensure high comparability. The infrastructure for data collection and storage follows the recommendations of the German Ethics Council for the use of Big Data in medicine.
WP 3 - Development of the digital twin: Digital twins for subgroups are developed for identifying critical events and starting points for intervention from multimodal data using machine learning and data science techniques. Ultimately, optimized approaches will be provided for the automatic identification of patterns, similarities in the data, and outliers. To develop the digital twin, a hybrid modelling approach is used as the mathematical model. This hybrid model leverages two approaches: 1.) Mechanistic models that are based on mechanistic physiological models of glucose regulation. These models are using ordinary differential equations (ODEs) to simulate the regulation of glucose in PWD. 2.) Long-Short Term Memory (LSTM) models that are exclusively data-driven and are more useful when mechanistic physiological models are not available: e.g., there is no physiological model for the impact of mood, diabetes distress or depression on glucose. Both approaches allow the modelling of dynamic systems, but LSTMs are powerful in data-driven, complex and non-linear scenarios, while ODEs perform well in accurately modelling physiological states. The LSTM can be used to model psychosocial factors that are difficult to formalize, whereas ODEs are used to model glucose control, which is well understood and described by physiological laws. Therefore, the Digital Twin uses ODEs to model glucose trajectories and LSTM to capture psychosocial influences. By combining these two methodologies, a digital twin can be constructed that accurately captures complex relationships from a large data set, while adhering to physiological constraints. For the twinning procedure, an appropriate technique will be chosen based on the data and data fit (e.g., Markov Chain Monte Carlo (MCMC)). Taken together, the combination of ODE and LSTM can be used to learn complex, non-linear relationships in the data and make predictions. MCMC can then be used to quantify the uncertainties of these predictions and calibrate the parameters of the ODE/LSTM model. By combining ODE/LSTM networks for modelling temporal dynamics and MCMC methods for uncertainty quantification and parameter calibration, the accuracy and robustness of digital twins can be improved.
With this approach, anomaly detection will be possible, for example, to identify specific behavioral patterns or characteristics associated with an increased risk for mental disorders or critical metabolic events.
WP 4 - Validation process: Validation of the digital twin is a crucial element that will determine the future clinical usefulness. Thus, new data that serves as ground truth for the predictions and simulations made by the digital twin will be collected.
First, it is important to note the kind of predictions we are aiming for with the digital twin. For glucose, we aim at predicting individual glucose control on an aggregate level such as time in range, time below range, coefficient of variation (CV) or HbA1c within a 6-month timeframe. For mental health, we also aim at predicting individual mental health within a 6-month timeframe on a more general level such the course of (sub)clinical mental disorders, questionnaire scores of e.g. well-being, time with distress, or overall stress level.
For validation of the prediction of glycemic control, CGM and HbA1c data will be continuously assessed every 6 months for the whole sample. Similarly, for the validation of the prediction of mental health, EMA will be continuously assessed every 6 months. For feasibility reasons, clinical diagnostic interviews will only be collected in a subset of the sample. Sample size: Usually, for machine learning models, the ratio between training and test data sets is 80:20 (19). Therefore, for validation purposes, a random subsample of 20% of the full sample (-> 0.2 x 1,809 = 362) will be selected for follow-up interviews.
Based on the validation data, the accuracy (e.g., sensitivity, specificity, positive/negative predictive value) and precision (e.g., difference between simulated time in range and actual time in range) of the predictions from the digital twin are assessed.
8,290 studies on the registry are indexed under Diabetes Mellitus; 1,077 are open to participants now.
This study's planned enrollment of 1,809 is above the median of 246 across 1,936 observational studies indexed under Diabetes Mellitus.
Browse Diabetes Mellitus studies →Forschungsinstitut der Diabetes Akademie Mergentheim is the lead sponsor of 14 studies on the registry; 1 is open to participants now.
Counted across the registry records on this site, refreshed daily.
The study participants are enrolled from the population of people with diabetes treated at several specialised diabetes centres in South Germany. Including the Diabetes Clinic Mergentheim (DCM), Diabetes Practice Mergentheim, DiaMedicum Center Würzburg.
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Exclusion Criteria:
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Informed consent
Before you join, the study team explains what taking part involves — its purpose, what happens, the risks and possible benefits — and answers your questions, so you can decide freely. Consent is usually recorded on a form (the informed consent form, ICF) signed by you or, where the law allows, by someone legally authorised to decide for you.
You can keep asking questions during the study, you are told of new information that matters, and you can choose to leave at any time.
Explained by clinicaltrials.gg, not part of the registry record. The study team decides who can take part.
NYHA class (heart failure)
The New York Heart Association’s four classes of how much heart failure limits physical activity.
Studies set their own limit — many outside heart failure leave out class III–IV, while heart failure studies may ask for a particular class. Check the class this study names.
Explained by clinicaltrials.gg, not part of the registry record. The study team decides who can take part.
The subgroups of patients with type 1 diabetes versus type 2 diabetes may be analysed in individual analyses
Incidence of affective disorders from baseline to follow-up (per structured diagnostic interview)
Diagnoses of affective disorders were assessed at baseline (as part of the PRO-MENTAL study) using the corresponding section of the Brief Diagnostic Interview for Mental Disorders (Mini-DIPS OA, improved version). The PRO-MENTAL cohort is followed up in the TwinPeaks study including a re-assessment of affective disorders using the Mini-DIPS OA at 2-year follow-up. Primary outcome is the 2-year incidence of affective disorders as per structured diagnostic interview.
Time frame: Baseline, 2-year Follow-up
Incidence of anxiety disorders from baseline to follow-up (per structured diagnostic interview)
Diagnoses of anxiety disorders were assessed at baseline (as part of the PRO-MENTAL study) using the corresponding section of the Brief Diagnostic Interview for Mental Disorders (Mini-DIPS OA, improved version). The PRO-MENTAL cohort is followed up in the TwinPeaks study including a re-assessment of anxiety disorders using the Mini-DIPS OA at 2-year follow-up. Primary outcome is the 2-year incidence of anxiety disorders as per structured diagnostic interview.
Time frame: Baseline, 2-year Follow-up
Incidence of eating disorders at from baseline to follow-up (per structured diagnostic interview)
Diagnoses of eating disorders were assessed at baseline (as part of the PRO-MENTAL study) using the corresponding section of the Brief Diagnostic Interview for Mental Disorders (Mini-DIPS OA, improved version). The PRO-MENTAL cohort is followed up in the TwinPeaks study including a re-assessment of eating disorders using the Mini-DIPS OA at 2-year follow-up. Primary outcome is the 2-year incidence of eating disorders as per structured diagnostic interview.
Time frame: Baseline, 2-year Follow-up
Depressive symptoms: Incidence at 2-year Follow-up
Depressive symptoms are assessed with the Patient Health Questionnaire-9 (PHQ-9) at baseline, 12 months, and 24 months. PHQ-9 sum score range: 0 - 27; higher scores indicate higher depressive symptoms; scores of 10 and above are considered as elevated depressive symptoms. The incidence of depressive symptoms at 2-year follow-up compared to baseline (for persons without depressive symptoms at baseline) is a secondary outcome.
Time frame: Baseline, 2-year Follow-up
Depressive symptoms: Remission at 2-year Follow-up
Depressive symptoms are assessed with the Patient Health Questionnaire-9 (PHQ-9) at baseline, 12 months, and 24 months. PHQ-9 sum score range: 0 - 27; higher scores indicate higher depressive symptoms; scores of 10 and above are considered as elevated depressive symptoms. The remission of depressive symptoms at 2-year follow-up compared to baseline (for persons with elevated depressive symptoms at baseline) is a secondary outcome.
Time frame: Baseline, 2-year Follow-up
Anxiety symptoms: Incidence at 2-year Follow-up
Anxiety symptoms are assessed with the Generalized Anxiety Disorders-7 (GAD-7) Questionnaire at baseline, 12 months, and 24 months. GAD-7 sum score range: 0 - 21; higher scores indicate higher anxiety symptoms; scores of 10 and above are considered as elevated anxiety symptoms. The incidence of anxiety symptoms at 2-year follow-up compared to baseline (for persons without anxiety symptoms at baseline) is a secondary outcome.
Time frame: Baseline, 2-year Follow-up
Anxiety symptoms: Remission at 2-year Follow-up
Anxiety symptoms are assessed with the Generalized Anxiety Disorders-7 (GAD-7) Questionnaire at baseline, 12 months, and 24 months. GAD-7 sum score range: 0 - 21; higher scores indicate higher anxiety symptoms; scores of 10 and above are considered as elevated anxiety symptoms. The remission of anxiety symptoms at 2-year follow-up compared to baseline (for persons with elevated anxiety symptoms at baseline) is a secondary outcome.
Time frame: Baseline, 2-year Follow-up
Disordered eating behaviour: Incidence at 2-year Follow-up
Disordered eating behaviours are assessed with the Diabetes Eating Problems Survey-Revised (DEPS-R/DEPS-10) at baseline, 12 months, and 24 months. DEPS-R sum score range: 0 - 80; higher scores indicate more diabetes-related eating problems; scores of 20 and above are considered to indicate disordered eating. The incidence of disordered eating behaviours at 2-year follow-up compared to baseline (for persons without disordered eating at baseline) is a secondary outcome.
Time frame: Baseline, 2-year Follow-up
Disordered eating behaviour: Remission at 2-year Follow-up
Disordered eating behaviours are assessed with the Diabetes Eating Problems Survey-Revised (DEPS-R/DEPS-10) at baseline, 12 months, and 24 months. DEPS-R sum score range: 0 - 80; higher scores indicate more diabetes-related eating problems; scores of 20 and above are considered to indicate disordered eating. The remission of disordered eating behaviours at 2-year follow-up compared to baseline (for persons with disordered eating at baseline) is a secondary outcome.
Time frame: Baseline, 2-year Follow-up
General diabetes distress over time
General diabetes distress is assessed with the Problem Areas in Diabetes Scale (PAID) at baseline, 6-month FU, 12-month FU, 18-month FU, and 24-month FU to detect changes in diabetes distress from baseline to 2-year FU. PAID total score range: 0 - 100; higher scores indicate higher diabetes distress; scores of 40 and above are considered to indicate elevated diabetes distress.
Time frame: Baseline, 2-year Follow-Up
Daily diabetes distress over time
Daily diabetes distress is assessed at baseline, 6-month FU, 12-month FU, 18-month FU, and 24-month FU over each 14 consecutive days using smartphone-based ecological momentary assessment with selected items from the Problem Areas in Diabetes Scale (PAID) adapted for daily assessment (rated on an 11-point scale from 0 - 10; higher values indicate higher mental burden) to detect changes in daily burdens from baseline to 24-month FU. A 10-item sum score ranges from 0 - 100 with higher values indicating higher daily distress.
Time frame: Baseline, 2-year Follow-Up
Glycated hemoglobin (HbA1c) over time
HbA1c (glycated hemoglobin), a laboratory measure of average blood glucose over the past 8 to 12 weeks, is estimated/collected at baseline, 6-month FU, 12-month FU, 18-month FU, and 24-month FU from the participants to detect changes for glycated hemoglobin from baseline to 2-year FU.
Time frame: Baseline, 2-year Follow-up
Glycemic levels (CGM glucose) over time
Automatically recorded daily glucose values are obtained from participants where continuous glucose monitoring (CGM) devices are used. Available glucose data are extracted at baseline, 6-month FU, 12-month FU, 18-month FU, and 24-month FU for each over 14 consecutive days of CGM measurement - parallel to the daily EMA - to detect changes in glucose levels from baseline to 2-year FU.
Time frame: Baseline, 2-year Follow-Up
Subjective health state over time
The subjective health state is assessed at baseline, 12-month FU, and 24-month FU using the visual analogue scale (VAS) of the EuroQol Five Dimensions Questionnaire (EQ-5D) to detect changes in subjective health from baseline to 2-year Follow-up. EQ-5D VAS score range: 0 - 100; higher scores indicate better health states.
Time frame: Baseline, 2-year Follow-up
Wellbeing over time
Wellbeing is assessed at baseline, 12-month FU, and 24-month FU using the WHO-Five Well-being Index (WHO-5) to detect changes in wellbeing from baseline to 24-month FU. WHO-5 sum score range: 0 - 25; higher scores indicate better well-being.
Time frame: Baseline, 2-year Follow-up
General sleep quality over time
General sleep quality assessed at baseline, 6-month FU, 12-month FU, 18-month FU, and 24-month FU using selected items of the Pittsburgh Sleep Quality Index (PSQI) to detect changes in sleep quality from baseline to 2-year FU. A total score range is 0 - 4 each for sleep quality as well as for frequency of sleep problems, whereby higher scores indicate better sleep.
Time frame: Baseline, 2-year Follow-up
Daily sleep quality over time
Daily sleep quality assessed at baseline, 6-month FU, 12-month FU, 18-month FU, and 24-month FU over each 14 days using smartphone-based ecological momentary assessment (EMA) with selected PSQI items adapted for daily assessment (rated on an 11-point scale from 0 - 10) to detect changes in daily sleep quality from baseline to 2-year Follow-up. EMA sleep items are scored on an 11-point scale from 0 - 10 with higher values indicating better sleep quality.
Time frame: Baseline, 2-year Follow-up
Fear of diabetes complications over time
Fear of diabetes complications is assessed at baseline, 12-month FU, and 24-month FU with the short form of the Fear of Diabetes Complications Questionnaire 6-item Short Form (FDCQ-6; requesting frequencies of worries and fears regarding long-term complications of diabetes) to detect changes in fear levels from baseline to 2-year FU. FDCQ-6 sum score range: 0 - 18; higher scores indicate higher fear of complications; scores of 12 and above suggest elevated fear.
Time frame: Baseline, 2-year Follow-up
Diabetes self-management over time
Diabetes self-management behaviors are assessed at baseline, 12-month FU, and 24-month FU with the revised Diabetes Self-Management Questionnaire-Revised (DSMQ-R) to detect changes in diabetes self-management from baseline to 2-year FU. DSMQ-R scale scores range from 0 - 10; higher scores indicate more optimal diabetes adjusted behaviors.
Time frame: Baseline, 2-year Follow-up
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Started Jan 2025 (actual). Primary completion is estimated for Aug 2027, 31 months after the start.
Of 533 non-industry observational studies under Diabetes Mellitus, half reached primary completion within 42 months of starting (the median), counting those still running; a quarter within 16 and three quarters within 175. This study's planned 31 months is below that.
Counting every sponsor, half of 642 studies reached it within 36 months; a quarter within 14 and three quarters within 122.
How these figures are worked out, and which studies are left out of them.
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Forschungsinstitut der Diabetes Akademie Mergentheim