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| Name | Class |
|---|---|
| University of Warwick | OTHER |
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This observational study aims to recruit up to thirty T1DM patients from a diabetic outpatient clinic at the University Hospital Coventry and Warwickshire for a two-phase study. The first phase involves attending an inpatient protocol for up to thirty-six hours in a calorimetry room at the Human Metabolism Research Unit under controlled conditions, followed by a phase of free-living, for up to three days, in which participants will go about their normal daily activities without restriction. Throughout the study, the participants will wear commercially available wearable sensors to measure and record physiological signals (e.g., electrocardiogram and continuous glucose monitor). Data collected will be used to develop and validate an AI model using state-of-the-art deep-learning methods for the purpose of non-invasive glycaemic event detection.
The study volunteers will be asked to an attend an 'inpatient' facility for up to 36 hrs dedicated to advanced metabolic measurement (HMRU). They will be asked to consume prepared meals of varying macronutrient content as part of a balanced diet, and performed prescribed physical activity. During this time the volunteers will be measured by instrumentation which will investigate the chemical concentration in respired gases (e.g. whole-body calorimeters, metabolic carts); bloods, saliva and urine samples will be taken. If the participant then wishes, we will ask them to continue to wear the wearable devices in a home setting for a maximum one week.
The data derived from this study will allow new tools and mathematical models to be developed that can be used to analyse and simulate patient metabolic response. It is envisaged this study will give further evidence to support future research into glucose utilisation in diseased metabolic populations.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Type1diabetes patients | Males and females diagnosed with T1D, aged over 18 years old who are currently under the care of the Warwickshire Institute for the Study of Diabetes, Endocrinolgy and Metabolism (WISDEM) at the University Hospitals Coventry and Warwickshire. |
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| Measure | Description | Time Frame |
|---|---|---|
| Interstitial Glucose | As measured by a continuous glucose monitor [NOTE] Observational study thus a key measurement not a true outcome measure. | For the duration of the study, up to 5 days |
| Measure | Description | Time Frame |
|---|---|---|
| ECG -Interval across different fiducial points | As measured by an ambulatory ECG device [NOTE] Observational study thus a key measurement not a true outcome measure. The interval across different fiducial points (P.Q.R,S,T) is one of the features that are useful to quantify the difference in ECG signals for different glycaemic events. | For the duration of the study, up to 5 days |
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Inclusion Criteria:
The study will be open to all individuals living independently, over 18 years without acute illness or ongoing clinical investigation, or volunteers with a stable medical condition may be included. Volunteers with an ongoing medical condition will only be included after detailed consultation with our clinical and dietetics members of the team; however, it is imperative that volunteers are able to provide written informed consent.
Exclusion Criteria:
Whilst the study employs a deliberately open inclusion criterion, the following exclusion measures will be employed:
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The population will be recruited from the Warwickshire Institute for the study of diabetes, Endocrinology and Metabolism, at the University Hospitals Coventry and Warwickshire. WISDEM is a flagship partnership between the hospitals and the University of Warwick Medical School created to tackle diabetes and related metabolic conditions.
| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| John G Hattersley, PhD | Contact | +44 (0) 24 7696 6068 | john.hattersley@uhcw.nhs.uk | |
| Leandro Pechhia, PhD | Contact | +44 (0) 24 7657 3383 | L.Pecchia@warwick.ac.uk |
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| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 31932608 | Background | Porumb M, Stranges S, Pescape A, Pecchia L. Precision Medicine and Artificial Intelligence: A Pilot Study on Deep Learning for Hypoglycemic Events Detection based on ECG. Sci Rep. 2020 Jan 13;10(1):170. doi: 10.1038/s41598-019-56927-5. | |
| Background | Porumb M, Griffen C, Hattersley J, Pecchia L. Nocturnal low glucose detection in healthy elderly from one-lead ECG using convolutional denoising autoencoders. Biomedical Signal Processing and Control. 2020;62:102054. | ||
| 37072364 |
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| ID | Term |
|---|---|
| D008659 | Metabolic Diseases |
| ID | Term |
|---|---|
| D009750 | Nutritional and Metabolic Diseases |
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Saliva samples for circadian biomarkers (cortisol, melatonin) Plasma for endocrine markers (insulin and glucose)
| ECG - Slope across different fiducial points | As measured by an ambulatory ECG device [NOTE] Observational study thus a key measurement not a true outcome measure. The Slope across different fiducial points (P.Q.R,S,T) is one of the features that are useful to quantify the difference in ECG signals for different glycaemic events. | For the duration of the study, up to 5 days |
| ECG - Indices of Heart Rate Variability | As measured by an ambulatory ECG device [NOTE] Observational study thus a key measurement not a true outcome measure. Heart rate variability (HRV) is the fluctuation in the time intervals between adjacent heartbeats. There are several indices that are useful to quantify the difference in ECG signals for different glycaemic events such as Ultra Low Frequency (ULF) (≤0.003 Hz), Very Low Frequency (VLF) (0.0033-0.04 Hz), Low Frequency (LF) (0.04-0.15 Hz) and High Frequency (HF) (0.15-0.4 Hz) | For the duration of the study, up to 5 days |
| Blood Pressure (Systolic and Diastolic) | As measured by an ambulatory blood pressure device [NOTE] Observational study thus a key measurement not a true outcome measure. | For the duration of the study, up to 5 days |
| Derived |
| Cisuelo O, Stokes K, Oronti IB, Haleem MS, Barber TM, Weickert MO, Pecchia L, Hattersley J. Development of an artificial intelligence system to identify hypoglycaemia via ECG in adults with type 1 diabetes: protocol for data collection under controlled and free-living conditions. BMJ Open. 2023 Apr 18;13(4):e067899. doi: 10.1136/bmjopen-2022-067899. |