Understanding barriers to accurate early laboratory diagnosis and patient centric control of Gestational Diabetes Mellitus
Lead Research Organisation:
University of Warwick
Department Name: Sch of Engineering
Abstract
Gestational diabetes mellitus (GDM) is a condition characterised by high blood glucose levels, with first onset during pregnancy. GDM increases the risk of complications for both mother and child. Evidently, early detection and treatment improve outcomes, but many women are at serious risk of going undiagnosed due to a lack of universally accepted diagnostic criteria, and disagreement over the glucose range deemed healthy. The most commonly used range is reported by the World Health Organisation to be 'somehow arbitrary'. Furthermore, poorly controlled GDM leads to adverse maternal and infant outcomes and increased likelihood of developing type 2 diabetes later in life. On the other hand, tight glucose control increases the risk of severe hypoglycaemia (low glucose levels), which may also compromise the wellbeing of mother and child.
The overall clinical goal is to improve the criteria, enable diagnosis as early as possible in pregnancy, discover better GDM markers and improve management of the condition to ensure that blood glucose levels remain under control throughout pregnancy. In addition to the impact on wellbeing and quality of life for both mother and child, any improvement in the management of this condition will reduce the burden on the national economy.
By facilitating hospital-based training for an engineering scientist, this discipline hop has the following aims:
- gaining for the principal investigator of a full grasp of the clinical challenges preventing the transformation of diagnosis and treatment of GDM;
- the establishment of a permanent network linking engineering science specialists with endocrinologists, obstetricians, pathologists, other allied healthcare colleagues and patients to tackle together the unsolved challenges of effective GDM care;
- the co-creation by the principal investigator and the other stakeholders above of a research strategy to improve GDM care.
The project will look at utilisation of routinely collected NHS data for research, give consideration to glucose variability to go beyond diagnosis based on glucose levels at single time points, and to personalisation for better management of glucose variability throughout pregnancy.
The training will include:
(a) engagement with patients and clinical professionals to understand how GDM is currently managed in the clinic, the practical realities constraining current practice, and how patients and different clinical professionals envisage improving care beyond today's approaches founded on population averages towards personalised alternatives
(b) training in available databases and laboratory sample testing to learn the structure of routinely collected NHS data, identify their limitations, and the implications for data analyses and modelling
(c) training in Good Clinical Practice covering ethical and regulatory requirements for research, the code of conduct for clinic-based research, a researcher's responsibilities towards study participants, limitations of measurements, reliability of data and other areas that are typically left unexplored by engineering and physical sciences researchers, and
(d) experience in recruitment for longitudinal measurements, together with assessment of our ability to recruit and the feasibility of personalisation based on longitudinal data obtained from continuous glucose monitoring, via preliminary data analysis and modelling.
As a long-term goal, the emergent collaboration aims to support the EPSRC Healthy Nation programme via better understanding of individual glucose variability, optimised care through effective diagnosis, patient-specific prediction and evidence-based treatment planning, minimisation of costs of care and reduction of risks to GDM patients and their children. The knowledge acquired from the project will form a platform to translate our findings into a large scale trial, which in turn can form the basis of changing current clinical practice in this field.
The overall clinical goal is to improve the criteria, enable diagnosis as early as possible in pregnancy, discover better GDM markers and improve management of the condition to ensure that blood glucose levels remain under control throughout pregnancy. In addition to the impact on wellbeing and quality of life for both mother and child, any improvement in the management of this condition will reduce the burden on the national economy.
By facilitating hospital-based training for an engineering scientist, this discipline hop has the following aims:
- gaining for the principal investigator of a full grasp of the clinical challenges preventing the transformation of diagnosis and treatment of GDM;
- the establishment of a permanent network linking engineering science specialists with endocrinologists, obstetricians, pathologists, other allied healthcare colleagues and patients to tackle together the unsolved challenges of effective GDM care;
- the co-creation by the principal investigator and the other stakeholders above of a research strategy to improve GDM care.
The project will look at utilisation of routinely collected NHS data for research, give consideration to glucose variability to go beyond diagnosis based on glucose levels at single time points, and to personalisation for better management of glucose variability throughout pregnancy.
The training will include:
(a) engagement with patients and clinical professionals to understand how GDM is currently managed in the clinic, the practical realities constraining current practice, and how patients and different clinical professionals envisage improving care beyond today's approaches founded on population averages towards personalised alternatives
(b) training in available databases and laboratory sample testing to learn the structure of routinely collected NHS data, identify their limitations, and the implications for data analyses and modelling
(c) training in Good Clinical Practice covering ethical and regulatory requirements for research, the code of conduct for clinic-based research, a researcher's responsibilities towards study participants, limitations of measurements, reliability of data and other areas that are typically left unexplored by engineering and physical sciences researchers, and
(d) experience in recruitment for longitudinal measurements, together with assessment of our ability to recruit and the feasibility of personalisation based on longitudinal data obtained from continuous glucose monitoring, via preliminary data analysis and modelling.
As a long-term goal, the emergent collaboration aims to support the EPSRC Healthy Nation programme via better understanding of individual glucose variability, optimised care through effective diagnosis, patient-specific prediction and evidence-based treatment planning, minimisation of costs of care and reduction of risks to GDM patients and their children. The knowledge acquired from the project will form a platform to translate our findings into a large scale trial, which in turn can form the basis of changing current clinical practice in this field.
Planned Impact
This proposed discipline hopping training and the subsequent research will bring benefits to the following areas and individuals.
People with gestational diabetes mellitus (GDM), healthcare professionals and wider society: GDM increases the risk of complications for both mother and child during pregnancy, at childbirth and beyond with increasing prevalence. It is estimated to affect 7% of all pregnancies. Evidence suggests that early detection and treatment improve pregnancy outcomes, whereas mis-diagnosis and a poorly controlled condition can lead to adverse maternal and infant outcomes, including pre-eclampsia, fetal macrosomia and respiratory distress syndrome, to name a few. They also increase the likelihood of developing type 2 diabetes later in life. Indeed, the incidence of type 2 diabetes among women with GDM is 20 to 50% within 5 years of giving birth. Currently, there are two main goals in the management of GDM: early diagnosis and prevention of complications through effective treatment. The existing diagnostic threshold is reported by the World Health Organisation to be 'somehow arbitrary', requiring further research to find ways of improving diagnosis and reduce the costs of treatment. People with GDM and the wider society will benefit from this line of research via improved accuracy and timing of diagnosis, effective personalised management and knowledge about self-management of GDM, ultimately easing the condition, preventing its complications, reducing negative impact on new born child, and reducing the numbers of type 2 diabetes cases due to GDM complications. This approach may also facilitate diagnostic interpretations in other areas of metabolic medicine, for example type 2 diabetes. Overall, this will improve quality of life, mitigate distress, reduce morbidity of patients with GDM, improve wellbeing of their friends and family and the wider community.
Economy: GDM is a growing public health concern and is associated with overall increased healthcare costs. The prevalence of GDM is increasing alongside the prevalence of diabetes mellitus, with the total costs for NHS diabetes care being over £1.5m an hour or 10% of the NHS budget for England and Wales. This equates to over £25,000 spent on diabetes every minute. In total, an estimated £14 billion is spent a year on treating diabetes and its complications. Figures for GDM are influenced by criteria used for diagnosis due to the fact that lower cut-off thresholds identify more women as having GDM, and by general obesity numbers and screening strategies. As is evident from research publications, modest improvements in glycaemic control and diagnostic strategy may generate a significant reduction in the cost of complications, at the same time as increasing the ability to participate productively within society, with significant savings to the UK economy. Improved GDM care also has great economic benefits through a reduced need for hospital admission due to hyper- and hypoglycaemia and costly procedures resulting from admission for GDM-related complications. There is also the potential for improved work productivity and contribution towards national GDP through avoidance of hypoglycaemia, which is essential for the quality of life and fitness to work of many individuals.
Knowledge: This cross-disciplinary initiative will form a critical pathway to economic and social impact through advances in the exploitation of biomedical engineering approaches in healthcare. Two fundamentally novel aspects relevant to healthcare are: (1) quantitative knowledge, developed for the first time, to understand the variety of glucose variations in women developing GDM; (2) a novel personalised approach to early diagnosis and management of GDM, aiming to establish a long-term programme for clinical use that is tailored to individual patients' phenotypes and needs.
People with gestational diabetes mellitus (GDM), healthcare professionals and wider society: GDM increases the risk of complications for both mother and child during pregnancy, at childbirth and beyond with increasing prevalence. It is estimated to affect 7% of all pregnancies. Evidence suggests that early detection and treatment improve pregnancy outcomes, whereas mis-diagnosis and a poorly controlled condition can lead to adverse maternal and infant outcomes, including pre-eclampsia, fetal macrosomia and respiratory distress syndrome, to name a few. They also increase the likelihood of developing type 2 diabetes later in life. Indeed, the incidence of type 2 diabetes among women with GDM is 20 to 50% within 5 years of giving birth. Currently, there are two main goals in the management of GDM: early diagnosis and prevention of complications through effective treatment. The existing diagnostic threshold is reported by the World Health Organisation to be 'somehow arbitrary', requiring further research to find ways of improving diagnosis and reduce the costs of treatment. People with GDM and the wider society will benefit from this line of research via improved accuracy and timing of diagnosis, effective personalised management and knowledge about self-management of GDM, ultimately easing the condition, preventing its complications, reducing negative impact on new born child, and reducing the numbers of type 2 diabetes cases due to GDM complications. This approach may also facilitate diagnostic interpretations in other areas of metabolic medicine, for example type 2 diabetes. Overall, this will improve quality of life, mitigate distress, reduce morbidity of patients with GDM, improve wellbeing of their friends and family and the wider community.
Economy: GDM is a growing public health concern and is associated with overall increased healthcare costs. The prevalence of GDM is increasing alongside the prevalence of diabetes mellitus, with the total costs for NHS diabetes care being over £1.5m an hour or 10% of the NHS budget for England and Wales. This equates to over £25,000 spent on diabetes every minute. In total, an estimated £14 billion is spent a year on treating diabetes and its complications. Figures for GDM are influenced by criteria used for diagnosis due to the fact that lower cut-off thresholds identify more women as having GDM, and by general obesity numbers and screening strategies. As is evident from research publications, modest improvements in glycaemic control and diagnostic strategy may generate a significant reduction in the cost of complications, at the same time as increasing the ability to participate productively within society, with significant savings to the UK economy. Improved GDM care also has great economic benefits through a reduced need for hospital admission due to hyper- and hypoglycaemia and costly procedures resulting from admission for GDM-related complications. There is also the potential for improved work productivity and contribution towards national GDP through avoidance of hypoglycaemia, which is essential for the quality of life and fitness to work of many individuals.
Knowledge: This cross-disciplinary initiative will form a critical pathway to economic and social impact through advances in the exploitation of biomedical engineering approaches in healthcare. Two fundamentally novel aspects relevant to healthcare are: (1) quantitative knowledge, developed for the first time, to understand the variety of glucose variations in women developing GDM; (2) a novel personalised approach to early diagnosis and management of GDM, aiming to establish a long-term programme for clinical use that is tailored to individual patients' phenotypes and needs.
Publications
Eichenlaub M
(2022)
Comment on "Minimal and Maximal Models to Quantitate Glucose Metabolism: Tools to Measure, to Simulate and to Run in Silico Clinical Trials".
in Journal of diabetes science and technology
Eichenlaub MM
(2021)
Bayesian parameter estimation in the oral minimal model of glucose dynamics from non-fasting conditions using a new function of glucose appearance.
in Computer methods and programs in biomedicine
Eichenlaub MM
(2022)
A Glucose-Only Model to Extract Physiological Information from Postprandial Glucose Profiles in Subjects with Normal Glucose Tolerance.
in Journal of diabetes science and technology
Description | The key research findings include: 1. Using Bayesian parameter estimation and a new function of glucose appearance, we developed a model allowing the estimation of insulin sensitivity and the meal-related appearance of glucose from insulin and glucose data from non-fasting and fasting conditions. The oral minimal model (OMM) of glucose dynamics is a prominent method for assessing postprandial glucose metabolism. The model estimates insulin sensitivity and the meal-related appearance of glucose from insulin and glucose data after an oral glucose test. This method attracted thousands of citations, mainly within the group which developed the model. The problem with it was that the method was only commercially available. We developed an alternative tool that we made freely available to the scientific community. Our model yields a similar precision of insulin sensitivity estimates. Furthermore, our procedure shows no deterioration of model fit when data from non-fasting conditions are used. We also showed that the glucose effectiveness parameter of our model is, contrary to previous results, structurally globally identifiable. The identification procedure is implemented in freely accessible MATLAB and Python software packages and can be downloaded from here: https://github.com/manueich/VBA-OMM 2. Continuous glucose monitors are becoming a powerful tool in monitoring blood glucose levels in people without diabetes, particularly for athletes and for maintaining a healthy lifestyle. We have developed a model to extract physiological information on glucose metabolism from glucose profiles obtained from continuous glucose monitors without requiring expensive tests to measure insulin levels. This work is part of my ongoing funded research projects in collaboration with University Hospitals Coventry and Warwickshire. 3. We have developed an automatic algorithm for peak detection from continuous glucose monitoring (CGM). Previously published works have focused on detecting the onset of meal intakes and calculating their carbohydrate load to assist glucose control in type 1 diabetes management; however, no algorithms for automatic peak detection have been developed for type 2 diabetes or control (no diabetes) cases. CGMs are becoming popular in people without diabetes and with type 2 diabetes due to their rising commercial availability and effectiveness. They help understand individuals' dynamic blood responses to food. However, before such information can be extracted for further analysis, the peaks must be selected automatically. This work developed a threshold-based algorithm for entire postprandial peak identification, including starting and endpoints, from data obtained from people with different glucose tolerance levels. This approach does not require significant computational resources and could be integrated into existing mobile phone applications synced with the CGMs. |
Exploitation Route | Implementation in the clinical setting for early diagnosis of the disease and its control. |
Sectors | Healthcare |
Description | This fellowship generated impact through 1. Follow-up funded projects: following the EPSRC Discipline-hopping fellowship, I developed a large-scale 5-year programme on early diagnosis and control of gestational diabetes to continue research initiated by this fellowship, extending it from 'exploration' to 'delivering a clinical trial' with significantly increased research capability. The 5-year programme is based at the University Hospitals Coventry and Warwickshire (UHCW) NHS Trust and is funded by the NHS and industry. I lead it as a Chief Investigator (CI); the team includes clinical and engineering researchers, consultants, research and clinical nurses and dieticians. The follow-up funding covers the costs of numerous blood tests and laboratory analyses, staff time costs, including Saturday overtime, participants' travel, dieticians consultations, software and patients' management, and study visits unrelated to routine care. This large follow-up project has been adopted by the National Institute for Health Research (NIHR) into its portfolio to support this research, including recruiting participants. I now hold an Honorary Contract with UHCW within their Diabetes, Endocrinology and Metabolism Department. 2. Research integrity: The above developments have become possible due to the training component provided to the PI by the EPSRC Fellowship. A significant part of this discipline hopping fellowship was devoted to training for the Chief Investigator (CI), 'hopping' from engineering to healthcare sciences. Training in obtaining Ethical approvals from HRA and Capacity and Capability approvals from the local NHS Trust hospital, as well as integrity training covering research governance, ethical and regulatory considerations, computerised trial management and other aspects of the design, management and interpretation of clinical trials has provided a pathway for the PI to understand research development and governance principles in healthcare, including practical barriers to multidisciplinary research involving healthcare and led the PI to become the Chief Investigator on a large clinically-based project. These unique skills benefit other university academics who require advice/mentoring in cross-disciplinary research integrity. 3. Engagement with industry: lately, the PI has developed a collaboration with Dexcom, a company producing portable continuous glucose monitors (CGMs) and investing in research. The PI secured 200 free portable CGMs to monitor the blood glucose of 50 women throughout their pregnancies. This investment enables patients to make behavioural adjustments in response to changes in their blood glucose levels, opening a window into individual dynamics. Knowledge from CGM measurements relating to the intra- and inter-personal variability of glucose levels in women with gestational diabetes is absent from current practice. It is particularly important in light of the ongoing review of diabetes in pregnancy guidelines conducted by the National Institute for Health and Care Excellence in England. By generating customised knowledge from CGMs and with full support from Dexcom, our research accumulates evidence helping patients benefit from CGM visualisation. 4. Stakeholder engagement: as part of the fellowship, the PI met with patient groups to ensure a beneficial experience for the recruits. |
First Year Of Impact | 2022 |
Sector | Healthcare |
Impact Types | Societal |
Description | Development of a device for in-ear health monitoring and diagnostics |
Amount | £43,293 (GBP) |
Organisation | University of Warwick |
Sector | Academic/University |
Country | United Kingdom |
Start | 11/2023 |
End | 12/2024 |
Description | Early diagnosis and prevention of complications of gestational diabetes |
Amount | £30,000 (GBP) |
Organisation | Dexcom |
Sector | Private |
Country | United States |
Start | 01/2021 |
End | 12/2025 |
Description | Gestational diabetes: early diagnosis and prevention of complications through modelling the complex dynamics of blood glucose variations |
Amount | £103,165 (GBP) |
Funding ID | NK510620 |
Organisation | University Hospitals Coventry and Warwickshire NHS Trust |
Sector | Academic/University |
Country | United Kingdom |
Start | 01/2021 |
End | 12/2025 |
Description | Prediction of Long Covid disease progression to severe disease endpoints - 1 |
Amount | £41,544 (GBP) |
Organisation | Siemens Healthcare |
Sector | Private |
Country | Germany |
Start | 01/2024 |
End | 07/2027 |
Description | Prediction of Long Covid disease progression to severe disease endpoints - 2 |
Amount | £60,000 (GBP) |
Organisation | University Hospitals Coventry and Warwickshire NHS Trust |
Sector | Academic/University |
Country | United Kingdom |
Start | 01/2024 |
End | 04/2027 |
Description | Prediction of Long Covid disease progression to severe disease endpoints - 3 |
Amount | £41,545 (GBP) |
Organisation | University of Warwick |
Sector | Academic/University |
Country | United Kingdom |
Start | 01/2024 |
End | 04/2027 |
Title | Postprandial Peak Identification from Continuous Glucose Monitoring Time Series |
Description | This work developed a local maxima threshold-based algorithm to extract the entire postprandial glucose peaks of real-life datasets and examined whether a threshold-based algorithm could be utilized for this data type from multiple glucose tolerance groups. Achieving a perfect detection of food intake could be challenging as postprandial responses vary between individuals, especially between groups of different glucose tolerance. To produce better detection rates, the algorithm could be adjusted according to the purpose of peak detection. For example, when larger postprandial peaks are of interest, selecting a higher threshold would result in a better precision rate with fewer false positive peaks but more false negatives since more peaks would be excluded. |
Type Of Material | Computer model/algorithm |
Year Produced | 2023 |
Provided To Others? | Yes |
Impact | This algorithm requires insignificant computational resources and could be integrated into existing mobile phone applications synced with continuous glucose monitoring devices. |
Description | Dexcom for gestational diabetes project |
Organisation | Dexcom |
Country | United States |
Sector | Private |
PI Contribution | Submitted proposal regarding early diagnostic of gestational diabetes to secure the additional continues glucose monitors for our extended stud with UHCW |
Collaborator Contribution | Supplied 200 continuous glucose monitors for rfee |
Impact | Multidisciplinary between engineering and medical sciences |
Start Year | 2021 |
Description | Prediction of Long Covid disease progression to severe disease endpoints |
Organisation | Siemens Healthcare |
Country | Germany |
Sector | Private |
PI Contribution | Data analysis, modelling |
Collaborator Contribution | Data |
Impact | Too early to report |
Start Year | 2023 |
Description | Prediction of Long Covid disease progression to severe disease endpoints -2 |
Organisation | University Hospitals Coventry and Warwickshire NHS Trust |
Department | Pathology Department |
Country | United Kingdom |
Sector | Hospitals |
PI Contribution | Modelling and data analysis |
Collaborator Contribution | Clinical data, data interpretations, joint supervisions of PhD students |
Impact | no outcomes yet |
Start Year | 2023 |
Description | Uncovering novel biomarker signatures of pre-eclampsia through deep -omic approaches |
Organisation | University Hospitals Coventry and Warwickshire NHS Trust |
Department | Pathology Department |
Country | United Kingdom |
Sector | Hospitals |
PI Contribution | I provide expertise in complex data analysis and modelling; joint research student supervision |
Collaborator Contribution | Access to data, their expertise in clinical sciences in interpretation results |
Impact | It is too early to report outcome |
Start Year | 2023 |
Description | University Hospitals Coventry and Warwickshire (UHCW) |
Organisation | University Hospitals Coventry and Warwickshire NHS Trust |
Country | United Kingdom |
Sector | Academic/University |
PI Contribution | Following the initial award from EPSRC, the project has been extended to 5 years, to allow recruitments of 3 times more study participants compared with the initial grant aims. The UHCW NHS Trust have committed time and resources to cover the costs of the extended project, i.e. for 4 extra years. This in-kind contribution includes all clinical tests, biomarkers, medical personnel, dieticians' time, travel for participants, research nurses' time. A contract is in place. |
Collaborator Contribution | Contribution includes all clinical tests, biomarkers, medical personnel, dieticians' time, travel for participants, research nurses' time. A contract is in place. |
Impact | Multi-disciplinary: clinical sciences, medicine |
Start Year | 2021 |
Description | A talk or presentation - Stochastic Resonance 40 years - Perugia 12-15 September 2021. |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Professional Practitioners |
Results and Impact | Conference and collaborative activities. Stochastic Resonance 40 years - Perugia 12-15 September 2021 |
Year(s) Of Engagement Activity | 2021 |
URL | https://www.sr40.org/#:~:text=In%202021%20it%20is%20forty,and%20a%20widespread%20research%20subject. |
Description | International onference - 'Postprandial Peak Identification from Continuous Glucose Monitoring Time Series' oral presentation |
Form Of Engagement Activity | Participation in an activity, workshop or similar |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Professional Practitioners |
Results and Impact | Oral presentation of the research, which spackle some further collaborations Archavli A, Randeva H, Khovanova N. Postprandial Peak Identification from Continuous Glucose Monitoring Time Series. 16th Mediterranean Conference on Medical and Biological Engineering and Computing (MEDICON) and the 5th International Conference on Medical and Biological Engineering (CMBEBIH), Sarajevo, Bosnia and Herzegovina, 14 - 16 September 2023. |
Year(s) Of Engagement Activity | 2023 |