Ignite Creation Date:
2024-05-06 @ 7:57 PM
Last Modification Date:
2024-10-26 @ 3:17 PM
Study NCT ID:
NCT06195566
Status:
NOT_YET_RECRUITING
Last Update Posted:
2024-01-08
First Post:
2023-12-22
Brief Title:
Development of PI-ML Algorithm for Prediction of the Real-time Risk for Developing Pre-diabetes
Sponsor:
Jelizaveta Sokolovska
Organization:
University of Latvia
Study Overview
Official Title:
Physics Informed Machine Learning-based Prediction and Reversion of Impaired Fasting Glucose Management
Status:
NOT_YET_RECRUITING
Status Verified Date:
2023-12
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
PRAESIIDIUM
Brief Summary:
In this prospective non-randomized monocentric study data will be collected from otherwise healthy individuals with overweightobese grade I to increase data availability in the pre-diabetes field impaired glucose intolerance and to validate the outputs of an algorithm for the physics-informed machine learning PIML designed to estimate the real-time risk of prediabetes Each participant will take part in the study for 4 months including 3 onsite visits
During the screening visit participants eligibility will be determined by checking the inclusion and exclusion criteria after detailed information and obtaining informed consent by the investigator Blood will be withdrawn for exclusion of existing prediabetesdiabetes at the fasted state For women in reproductive age a urinary pregnancy test will be performed After getting the results of blood tests glucose and HbA1c participants will be asked to participate in study
On the visit 1 eligible participants will arrive at the study centre in a fasting state Blood samples will be collected and participants will get vials and instructions for collection of stool and urine samples Anthropometric data lifestyle habit cigarette alcohol consumption and family history will be collected A 6-minute walking test to determine VO2 max will then be performed Participants will receive a blinded Abbott Libre Pro glucose sensor which they will wear for the next 14-days Further participants will be provided with a Fitbit Charge 5 health and fitness wristband For validation purposes some part of study participants will be kindly asked to test newly develop wrist-worn device EDIBit With the help of 24-hour food recall study subjects will be trained by medical staff on how to correctly enter their food intake in the Study app for completion of digital 3-day food diaries They will be asked to fill in the diaries for 3 days after study visit1 and 3 days before study visit2 They will also receive a food frequency questionnaire during visit1
The second study visit will run nearly identical to study visit1 except for food frequency questionnaire which will be omitted During this visit participants will receive information sheets on physical activity and dietary recommendations
The third and last visit will run nearly identically to the study visit2 except that no new glucose sensor will be inserted and also stool samples will not be collected
During the screening visit participants eligibility will be determined by checking the inclusion and exclusion criteria after detailed information and obtaining informed consent by the investigator Blood will be withdrawn for exclusion of existing prediabetesdiabetes at the fasted state For women in reproductive age a urinary pregnancy test will be performed After getting the results of blood tests glucose and HbA1c participants will be asked to participate in study
On the visit 1 eligible participants will arrive at the study centre in a fasting state Blood samples will be collected and participants will get vials and instructions for collection of stool and urine samples Anthropometric data lifestyle habit cigarette alcohol consumption and family history will be collected A 6-minute walking test to determine VO2 max will then be performed Participants will receive a blinded Abbott Libre Pro glucose sensor which they will wear for the next 14-days Further participants will be provided with a Fitbit Charge 5 health and fitness wristband For validation purposes some part of study participants will be kindly asked to test newly develop wrist-worn device EDIBit With the help of 24-hour food recall study subjects will be trained by medical staff on how to correctly enter their food intake in the Study app for completion of digital 3-day food diaries They will be asked to fill in the diaries for 3 days after study visit1 and 3 days before study visit2 They will also receive a food frequency questionnaire during visit1
The second study visit will run nearly identical to study visit1 except for food frequency questionnaire which will be omitted During this visit participants will receive information sheets on physical activity and dietary recommendations
The third and last visit will run nearly identically to the study visit2 except that no new glucose sensor will be inserted and also stool samples will not be collected
Detailed Description:
Noncommunicable diseases NCDs such as cancer cardiovascular diseases and diabetes represent 74 of the disease burden globally and are the major causes of preventable premature deaths 12
Diabetes is a chronic NCD characterized by elevated glucose blood levels In 2021 the prevalence of diabetes in Europe was 1 in 11 adults 61 million a figure projected to rise to 69 million by 2045 3 The global management and treatment of diabetes cost 988 billion dollars in 2021 Despite these expenditures diabetes remains the third leading cause of death accounting for 67 million deaths with 11 millions of these in Europe alone Among the different types type-2 diabetes T2D comprises 90 of total diabetes cases primarily presenting in adulthood 34 Risk factors for T2D include genetic predisposition family history metabolic syndrome obesity physical inactivity age and ethnicity There are 541 million adults worldwide with Impaired Glucose Tolerance IGT a significant risk factor for T2D 56 IGT and Impaired Fasting Glucose IFG represent intermediate conditions within the healthy-to-T2D transition and are symptoms of prediabetes 78 Notably prediabetes represents an early-stage condition that can be reversed Studies show that T2D progression can be reduced by approximately 58 within three years through lifestyle modifications Physical activity of 30-54 minutes at least 2-5 days per week is recommended as well as a healthy diet 9 Efforts have been made to develop non-invasive diabetes risk prediction models based on clinically available parameters 10 The onset of T2D involves complex multiscale mechanisms starting from molecular tissue and organ levels leading to dysfunction in physiological processes Chronic inflammatory biomarkers play a significant role in T2D pathogenesis Recognizing this multi-level approach is a step towards personalized disease diagnosis However there remain challenges related to modelling the healthy-to-prediabetes transition from both case study and methodological perspectives 51314 The objective of this project is the development of a prototype tool aimed at real-time prediction of prediabetic risk This tool will incorporate a series of patient-specific mathematical models simulating metabolism pancreas hormone production microbiome metabolites inflammatory processes and immune system response These models were initially developed during the FP7 MISSION-T2D project and further developed into the implementation of an integrated multilevel and patient-specific model incorporating genetic metabolic and nutritional data for the simulation and prediction of metabolic and inflammatory processes in the onset and progression of T2D 14-18 The prediction algorithm will utilize a physics-informed machine learning PIML approach combining a comprehensive dataset from both existing and new clinical trials with continuous data input through wearable sensors 19 The final algorithm will be hosted on a web-based platform where both medical professionals and patients can input data from multiple sources
A dedicated prospective observational study described in this application will be conducted in Latvia recruiting adult participants with metabolic risk factors - overweight and obesity grade I for data collection purposes to validate the developed machine learning PIML algorithm for pre-diabetes real-time risk prediction
Data collections has three main purposes I Input Data for the in-silico MT2D model
The input of the simulations includes the following discrete starting parameters gender MF weight height number of sessions of physical activity 0 1 2 3 duration of the bout of physical activity 30 60 90 min intensity in terms of VO2max 40 60 3 meals per day in each meal are specified the carbohydrates low medium high proteins low medium high and fats low medium high
II Validation of the MT2D outputs Output numerical values of the simulations from the model include
1 Inflammation markers recorded every 8h B-cells B-1 B-2 PBL TH Th1 Th2 Th17 Treg CTL Treg NK MA DC EP ADIP number volume IgM IgG IgG1 IgG2 IC IL-2 IL-12 IFN-g IL-4 TNF-a TGF-b IL-10 IL-6 IL-18 IL-23 IFN-b IL-1b LPS leptin
a Metabolic outcomes recorded every minute arterial concentrations glu pyr lac ala gly FFA tgl O2 CO2 organs 22 metabolites hormones insulin glucagon epinephrinefasting glucose rate of appearance glucose alanine triglyceridestotal daily energy balance VO2max anthropometric measures BW BMI fat mass fat free mass
III Data for trainingvalidation of the physics-informed machine learning PIML algorithm demographic Data health-related Data lifestyle Data eg food consumption data and physical activity data continuous ingestion through wearable sensors Continuous Glucose Monitoring CGM and tracker of physical activity eg Fitbit Charge 5 EDIBit These devices such as smartwatches and fitnessactivity trackers eg Fitbit Fibion Apple Watch are equipped with sensors that can track a variety of health metrics including physical activity heart rate sleep patterns and increasingly also for glucose monitoring non-invasive continuous glucose monitoring are still under development 2021 This data can be used to identify patterns and trends in a persons health which can help with the early detection of diabetesprediabetes 22 Machine learning ML models show potential in enhancing early detection by analysing various risk factors and predicting outcomes However before these models are integrated into healthcare systems and clinical practices they must be rigorously evaluated One of the most robust methods for such evaluation is through external validation using longitudinal cohorts 23-26 During the clinical study participants may be supported by a digital assistant which can be used to make automatic voice calls to participants to collect data and provide user support and follow-up The digital assistant uses pre-defined dialogues designed by trial staff and vocal responses from participants are recorded as textdata in the trial database The function of the digital assistant is regarded as a complement to other methods for data capture such as questionnaires wearables messaging regular phone calls
A subset of the participant will receive a study-dedicated e-SIM to avoid using another mobile phone on which number the participant could be called by the digital assistant The participants will be informed of the subscriber number used by the digital assistant so that they can recognise its incoming calls and freely decide whether to accept or reject them on a personal choice Participants will be able to stop this automated service at any time
During the clinical study participants may be supported by a digital assistant to help people better comply with the clinical study
A subset of the participant will receive a study-dedicated e-SIM to avoid using another mobile phone on which number the participant could be called by the digital assistant The participants will be informed of the subscriber number used by the digital assistant so that they can recognise its incoming calls and freely decide whether to accept or reject them on a personal choice Participants will be able to stop this automated service at any time
Timeline and probands The study will run for 15 months During this period 75 individuals will be followed for 4 months The recruitment will take place from January 2024 until March 2025 parallel to the study period
Subject identification For each participant who has signed the Participant Informed Consent Form the investigator must allocate a unique two letter and three-digit Participant Identification Number
All documents forms and data including bio-materials - urine blood and stool samples files will be tagged with this Participant ID Each participant eligible and not eligible will be documented in the Screening and Enrolment Log
Data management The data of the participants will be entered in an eCRFs An eCRF will be provided by the project consortium partners Spindox Labs and CheckHealth The eCRF will be maintained by staff at the University of Latvia All study data will be captured in the eCRF and monitored by the monitor All processes will be handled in accordance with standard operating procedures SOPs
Collected data from wearable sensors and remote-assisted questionnaires will be managed by the LinkWatch cloud-based storage for the entire duration of the project This includes regular back-up procedures and security provisions in accordance with the GDPR and the CHK security policy The security policy is based on the framework recommended by the Swedish Contingency Agency Reg 2016679UE following ISOIEEE 27000
No paper forms of the questionnaires will be used except informed form Consent form will be stored in archive at University of Latvia
Macronutrient food intake will be obtained through a three-day food diary That will be provided in different ways - initializing the macronutrient database with finish Finelli database using Open food facts for packaged food Meanwhile participants will be asked to double information writing manually food diary for three days
In visit 1 and visit 3 food frequency questionnaire and in each visit 24h recall will be obtained Physical activity will be tracked using a commercially available FitBit Charge 5 tracker
Between visit 1 and visit 2 specific recommendations will not be provided This phase will serve as a data collection period to accurately assess participants current lifestyle factors To provide a higher diversity of collected data recommendations for physical activity and a healthy diet will be provided to the participants at visit 2 These prescriptions will align with the relevant WHO guidelines and summarized in hand-out materials
In patients with T2DM regular exercise increases insulin sensitivity and secretion improving glucose tolerance It is noted that a single bout of exercise can enhance insulin sensitivity while long-term exercise is required to improve pancreatic function in T2DM patients Myokines are secreted factors from skeletal muscle adipose tissue liver gut etc proposed as cross-talk organ mediators involved in metabolic adaptations to exercise IL-6 IL-2 IL-10 leptin will be assessed for PIML validation purposes
Blood samples from enrolled patients at the three cohort visits Visit 1 Visit 2 and Visit 3 will be collected and initially processed to obtain plasma samples and temporarily stored at -80C by the recruiting site see below blood samples processing before shipment
Plasma samples will be shipped to the project partner Italian Liver Foundation FIF in Basovizza Trieste Italy At FIF plasma samples will be stored at the at a temperature -80C and access-controlled freezer for 10 years
At FIF batched samples will be unfrozen and further processed to determine plasma abundances of interleukins IL-2 IL-6 IL-10 and leptin
Blood will be collected for determination of clinical blood tests on the study site and for sample transfer to FIF for further biomarker analysis and preservation at the University of Latvia
Participants will collect their faecal samples at home together two samples - in first and second visit respectively The primary goal of faecal samples collection is to create a long-lasting PRAESIIDIUM biobank In a subsequent moment faecal samples might be processed for microbiome analysis After arrival at local hospital sample should be frozen at -80C until processing Further processing - upon availability of additional funds Fondazione Italiana Fegato ONLUS Italy
Diabetes is a chronic NCD characterized by elevated glucose blood levels In 2021 the prevalence of diabetes in Europe was 1 in 11 adults 61 million a figure projected to rise to 69 million by 2045 3 The global management and treatment of diabetes cost 988 billion dollars in 2021 Despite these expenditures diabetes remains the third leading cause of death accounting for 67 million deaths with 11 millions of these in Europe alone Among the different types type-2 diabetes T2D comprises 90 of total diabetes cases primarily presenting in adulthood 34 Risk factors for T2D include genetic predisposition family history metabolic syndrome obesity physical inactivity age and ethnicity There are 541 million adults worldwide with Impaired Glucose Tolerance IGT a significant risk factor for T2D 56 IGT and Impaired Fasting Glucose IFG represent intermediate conditions within the healthy-to-T2D transition and are symptoms of prediabetes 78 Notably prediabetes represents an early-stage condition that can be reversed Studies show that T2D progression can be reduced by approximately 58 within three years through lifestyle modifications Physical activity of 30-54 minutes at least 2-5 days per week is recommended as well as a healthy diet 9 Efforts have been made to develop non-invasive diabetes risk prediction models based on clinically available parameters 10 The onset of T2D involves complex multiscale mechanisms starting from molecular tissue and organ levels leading to dysfunction in physiological processes Chronic inflammatory biomarkers play a significant role in T2D pathogenesis Recognizing this multi-level approach is a step towards personalized disease diagnosis However there remain challenges related to modelling the healthy-to-prediabetes transition from both case study and methodological perspectives 51314 The objective of this project is the development of a prototype tool aimed at real-time prediction of prediabetic risk This tool will incorporate a series of patient-specific mathematical models simulating metabolism pancreas hormone production microbiome metabolites inflammatory processes and immune system response These models were initially developed during the FP7 MISSION-T2D project and further developed into the implementation of an integrated multilevel and patient-specific model incorporating genetic metabolic and nutritional data for the simulation and prediction of metabolic and inflammatory processes in the onset and progression of T2D 14-18 The prediction algorithm will utilize a physics-informed machine learning PIML approach combining a comprehensive dataset from both existing and new clinical trials with continuous data input through wearable sensors 19 The final algorithm will be hosted on a web-based platform where both medical professionals and patients can input data from multiple sources
A dedicated prospective observational study described in this application will be conducted in Latvia recruiting adult participants with metabolic risk factors - overweight and obesity grade I for data collection purposes to validate the developed machine learning PIML algorithm for pre-diabetes real-time risk prediction
Data collections has three main purposes I Input Data for the in-silico MT2D model
The input of the simulations includes the following discrete starting parameters gender MF weight height number of sessions of physical activity 0 1 2 3 duration of the bout of physical activity 30 60 90 min intensity in terms of VO2max 40 60 3 meals per day in each meal are specified the carbohydrates low medium high proteins low medium high and fats low medium high
II Validation of the MT2D outputs Output numerical values of the simulations from the model include
1 Inflammation markers recorded every 8h B-cells B-1 B-2 PBL TH Th1 Th2 Th17 Treg CTL Treg NK MA DC EP ADIP number volume IgM IgG IgG1 IgG2 IC IL-2 IL-12 IFN-g IL-4 TNF-a TGF-b IL-10 IL-6 IL-18 IL-23 IFN-b IL-1b LPS leptin
a Metabolic outcomes recorded every minute arterial concentrations glu pyr lac ala gly FFA tgl O2 CO2 organs 22 metabolites hormones insulin glucagon epinephrinefasting glucose rate of appearance glucose alanine triglyceridestotal daily energy balance VO2max anthropometric measures BW BMI fat mass fat free mass
III Data for trainingvalidation of the physics-informed machine learning PIML algorithm demographic Data health-related Data lifestyle Data eg food consumption data and physical activity data continuous ingestion through wearable sensors Continuous Glucose Monitoring CGM and tracker of physical activity eg Fitbit Charge 5 EDIBit These devices such as smartwatches and fitnessactivity trackers eg Fitbit Fibion Apple Watch are equipped with sensors that can track a variety of health metrics including physical activity heart rate sleep patterns and increasingly also for glucose monitoring non-invasive continuous glucose monitoring are still under development 2021 This data can be used to identify patterns and trends in a persons health which can help with the early detection of diabetesprediabetes 22 Machine learning ML models show potential in enhancing early detection by analysing various risk factors and predicting outcomes However before these models are integrated into healthcare systems and clinical practices they must be rigorously evaluated One of the most robust methods for such evaluation is through external validation using longitudinal cohorts 23-26 During the clinical study participants may be supported by a digital assistant which can be used to make automatic voice calls to participants to collect data and provide user support and follow-up The digital assistant uses pre-defined dialogues designed by trial staff and vocal responses from participants are recorded as textdata in the trial database The function of the digital assistant is regarded as a complement to other methods for data capture such as questionnaires wearables messaging regular phone calls
A subset of the participant will receive a study-dedicated e-SIM to avoid using another mobile phone on which number the participant could be called by the digital assistant The participants will be informed of the subscriber number used by the digital assistant so that they can recognise its incoming calls and freely decide whether to accept or reject them on a personal choice Participants will be able to stop this automated service at any time
During the clinical study participants may be supported by a digital assistant to help people better comply with the clinical study
A subset of the participant will receive a study-dedicated e-SIM to avoid using another mobile phone on which number the participant could be called by the digital assistant The participants will be informed of the subscriber number used by the digital assistant so that they can recognise its incoming calls and freely decide whether to accept or reject them on a personal choice Participants will be able to stop this automated service at any time
Timeline and probands The study will run for 15 months During this period 75 individuals will be followed for 4 months The recruitment will take place from January 2024 until March 2025 parallel to the study period
Subject identification For each participant who has signed the Participant Informed Consent Form the investigator must allocate a unique two letter and three-digit Participant Identification Number
All documents forms and data including bio-materials - urine blood and stool samples files will be tagged with this Participant ID Each participant eligible and not eligible will be documented in the Screening and Enrolment Log
Data management The data of the participants will be entered in an eCRFs An eCRF will be provided by the project consortium partners Spindox Labs and CheckHealth The eCRF will be maintained by staff at the University of Latvia All study data will be captured in the eCRF and monitored by the monitor All processes will be handled in accordance with standard operating procedures SOPs
Collected data from wearable sensors and remote-assisted questionnaires will be managed by the LinkWatch cloud-based storage for the entire duration of the project This includes regular back-up procedures and security provisions in accordance with the GDPR and the CHK security policy The security policy is based on the framework recommended by the Swedish Contingency Agency Reg 2016679UE following ISOIEEE 27000
No paper forms of the questionnaires will be used except informed form Consent form will be stored in archive at University of Latvia
Macronutrient food intake will be obtained through a three-day food diary That will be provided in different ways - initializing the macronutrient database with finish Finelli database using Open food facts for packaged food Meanwhile participants will be asked to double information writing manually food diary for three days
In visit 1 and visit 3 food frequency questionnaire and in each visit 24h recall will be obtained Physical activity will be tracked using a commercially available FitBit Charge 5 tracker
Between visit 1 and visit 2 specific recommendations will not be provided This phase will serve as a data collection period to accurately assess participants current lifestyle factors To provide a higher diversity of collected data recommendations for physical activity and a healthy diet will be provided to the participants at visit 2 These prescriptions will align with the relevant WHO guidelines and summarized in hand-out materials
In patients with T2DM regular exercise increases insulin sensitivity and secretion improving glucose tolerance It is noted that a single bout of exercise can enhance insulin sensitivity while long-term exercise is required to improve pancreatic function in T2DM patients Myokines are secreted factors from skeletal muscle adipose tissue liver gut etc proposed as cross-talk organ mediators involved in metabolic adaptations to exercise IL-6 IL-2 IL-10 leptin will be assessed for PIML validation purposes
Blood samples from enrolled patients at the three cohort visits Visit 1 Visit 2 and Visit 3 will be collected and initially processed to obtain plasma samples and temporarily stored at -80C by the recruiting site see below blood samples processing before shipment
Plasma samples will be shipped to the project partner Italian Liver Foundation FIF in Basovizza Trieste Italy At FIF plasma samples will be stored at the at a temperature -80C and access-controlled freezer for 10 years
At FIF batched samples will be unfrozen and further processed to determine plasma abundances of interleukins IL-2 IL-6 IL-10 and leptin
Blood will be collected for determination of clinical blood tests on the study site and for sample transfer to FIF for further biomarker analysis and preservation at the University of Latvia
Participants will collect their faecal samples at home together two samples - in first and second visit respectively The primary goal of faecal samples collection is to create a long-lasting PRAESIIDIUM biobank In a subsequent moment faecal samples might be processed for microbiome analysis After arrival at local hospital sample should be frozen at -80C until processing Further processing - upon availability of additional funds Fondazione Italiana Fegato ONLUS Italy
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None