Publications
385 results found
Gimenez M, Tannen AJ, Reddy M, et al., 2017, Revisiting the Relationships Between Measures of Glycemic Control and Hypoglycemia in Continuous Glucose Monitoring Datasets., Diabetes Care, Vol: 41, Pages: 326-332, ISSN: 0149-5992
OBJECTIVE: The Diabetes Control and Complications Trial identified an inverse relationship between HbA1c and severe hypoglycemia. We investigated the relationship between hypoglycemia and HbA1c in a large type 1 diabetes cohort on multiple daily injection or insulin pump therapy using blinded continuous glucose monitoring (CGM) data. The impact of real-time CGM on these relationships and how these relationships differ with biochemical definitions of hypoglycemia have also been assessed. RESEARCH DESIGN AND METHODS: CGM data were obtained from the JDRF CGM randomized control trial. Baseline blinded-CGM data were used to assess time in hypoglycemia in all individuals. End-point data from the CGM intervention group were used to assess the impact of CGM. Percentage times <3.9, 3.3, 3.0, and 2.8 mmol/L were calculated and quadratic regression plots drawn. Relationships were analyzed visually, and ANOVA was used to assess relationships between glycemia and time below threshold. RESULTS: J-shaped relationships were observed for all biochemical hypoglycemia thresholds, with the lowest hypoglycemia risk occurring at HbA1c values between 8.1 and 8.6% (65-70 mmol/mol). The use of an average of 5 days/week of CGM flattened the relationships for 3.3, 3.0, and 2.8 mmol/L, and ANOVA confirmed the loss of relationship for the 3.3 mmol/L threshold using CGM. CONCLUSIONS: The relationship between hypoglycemia and HbA1c in a population with type 1 diabetes is J-shaped. Lower HbA1c values are still associated with increased hypoglycemia risk, although the magnitude of risk depends on biochemical threshold. Real-time CGM may reduce the percentage time spent in hypoglycemia, changing the relationship between HbA1c and hypoglycemia.
Oliver NS, 2017, REPOSE: repositioning insulin pump therapy in Type 1 diabetes, Diabetic Medicine, Vol: 34, Pages: 1656-1657, ISSN: 0742-3071
Srivanichakorn W, Godsland IF, Thomson H, et al., 2017, Fasting plasma glucose and variation in cardiometabolic risk factors in people with high-risk HbA1c-defined prediabetes: a cross-sectional multiethnic study., Diabetes Research and Clinical Practice, Vol: 134, Pages: 183-190, ISSN: 0168-8227
AIMS: Variation in cardiometabolic risk in prediabetes and any impacts of ethnicity on such variation have been little studied. In an ethnically diverse dataset, selected according to a high-risk HbA1c-based definition of prediabetes, we have investigated relationships between glycaemia and cardiometabolic risk factors and the influence of ethnicity on these relationships. METHODS: We undertook a cross-sectional analysis of baseline data from a diabetes prevention study in the UK and a chronic care clinic in Thailand, selected for people without diabetes (fasting plasma glucose <7.0 mmol/l) with HbA1c 6.0% - 6.4% (42-47 mmol/mol). Thai (n=158) and UK White (n=600), South Asian (n=112), Black (n=70) and other/mixed (n=103) groups were distinguished and measurements included fasting plasma glucose (FPG), blood pressure (BP), lipids and insulin resistance-related risk factors (IRFs). RESULTS: Independently of individual characteristics including ethnicity, only systolic BP was weakly associated with FPG (beta coefficient 1.76 (95%CI 0.10 to 3.42), p=0.03), and only LDL-c with IFG (FPG 5.6-<7) (adjusted -0.14 (-0.27, -0.003) p 0.04). There were no significant independent associations with cardiometabolic risk factors when categories of impaired fasting glucose (FPG ≥ 6.1 to <7.0 mmol/L) were considered. Relative to White, South Asian ethnicity was independently associated with lower systolic and diastolic BP, Black with lower triglycerides, cholesterol/HDL-c ratio and having 2 or more IRFs, and Thai with lower cholesterol/HDL-c ratio and all three non-white ethnicities with lower total and LDL cholesterol. CONCLUSION: In high-risk HbA1c-defined prediabetes additional measurement of FPG will add little to evaluation of cardiometabolic risk. Additionally, UK Whites tend to have the most adverse cardiometabolic profile of any ethnic group.
Uduku C, Oliver N, 2017, Pharmacological aspects of closed loop insulin delivery for type 1 diabetes, Current Opinion in Pharmacology, Vol: 36, Pages: 29-33, ISSN: 1471-4892
Insulin deficiency and impaired glucose homeostasis are hallmarks of type 1 diabetes. Since the discovery of insulin, pharmacological and clinical developments have endeavoured to replicate its endogenous pharmacokinetics (PK) and pharmacodynamics (PD). Closed loop insulin delivery systems operate as an artificial pancreas by making automated insulin dose adjustments based on real time continuous glucose monitoring. The increasing adoption of continuous insulin pump therapy and evolving technological advances have seen significant progress in the development of closed loop insulin delivery systems. This article reviews the current landscape of closed loop insulin delivery systems and pharmacological advances that could overcome current barriers.
Herrero P, Bondia J, Oliver N, et al., 2017, A coordinated control strategy for insulin and glucagon delivery in type 1 diabetes, Computer Methods in Biomechanics and Biomedical Engineering, Vol: 20, Pages: 1474-1482, ISSN: 1025-5842
Type 1 diabetes is an autoimmune condition characterised by a pancreatic insulin secretion deficit, resulting in high blood glucose concentrations, which can lead to micro- and macrovascular complications. Type 1 diabetes also leads to impaired glucagon production by the pancreatic α-cells, which acts as a counter-regulatory hormone to insulin. A closed-loop system for automatic insulin and glucagon delivery, also referred to as an artificial pancreas, has the potential to reduce the self-management burden of type 1 diabetes and reduce the risk of hypo- and hyperglycemia. To date, bihormonal closed-loop systems for glucagon and insulin delivery have been based on two independent controllers. However, in physiology, the secretion of insulin and glucagon in the body is closely interconnected by paracrine and endocrine associations. In this work, we present a novel biologically-inspired glucose control strategy that accounts for such coordination. An in silico study using an FDA-accepted type 1 simulator was performed to evaluate the proposed coordinated control strategy compared to its non-coordinated counterpart, as well as an insulin-only version of the controller. The proposed coordinated strategy achieves a reduction of hyperglycemia without increasing hypoglycemia, when compared to its non-coordinated counterpart.
Kennedy ED, Oliver N, 2017, Emerging technologies for diabetes, PRACTICAL DIABETES, Vol: 34, Pages: 240-244, ISSN: 2047-2897
Misra S, Sebastian A, Liu X, et al., 2017, Single antibody positivity associates with two distinct phenotypes in people with young-onset diabetes: insights from the MY DIABETES study, 53rd Annual Meeting of the European-Association-for-the-Study-of-Diabetes (EASD), Publisher: SPRINGER, Pages: S117-S118, ISSN: 0012-186X
Oliver N, 2017, Continuous glucose monitoring adoption in the United Kingdom - an economic and policy perspective, European Endocrinology, Vol: 13, Pages: 73-75, ISSN: 1758-3772
Continuous glucose monitoring (CGM) technology provides real-time glucose concentration data to people with diabetes. The data enable timely treatment decisions that can lead to avoidance or mitigation of hypoglycaemia, with potential cost savings. This commentary discusses CGM implementation and funding policies in the UK, and regional disparities that confront many people with diabetes who could benefit from the technology.
Chaugule S, Oliver N, Klinkenbijl B, et al., 2017, An economic evaluation of continuous glucose monitoring for people with type 1 diabetes and impaired awareness of hypoglycaemia within north west London clinical commissioning groups in England, European Endocrinology, Vol: 13, Pages: 81-85, ISSN: 1758-3772
Objective: To assess the economic impact of providing real time continuous glucose monitoring (CGM) for people with type 1 diabetes (T1D) and impaired awareness of hypoglycaemia (IAH) within North West (NW) London clinical commissioning groups (CCGs). Methods: The eligible population for CGM and inputs for the economic budget impact model developed were derived from published data. The model includes cost of CGM; cost savings associated with lower hypoglycaemia related hospital admissions, accidents and emergency visits; self-monitoring of blood glucose (SMBG) strip usage; and glycated haemoglobin (HbA1c) reduction-related avoided complications and insulin pump use. Results: The cost of CGM for T1D-IAH (n=3,036) in the first year is £10,770,671 and in the fourth year is £11,329,095. The combined cost off-sets related to reduced hypoglycaemia admissions, SMBG strip usage and complications are £8,116,912 and £8,741,026 in years one and four, respectively. The net budget impact within the NW London CCGs is £2,653,760; £2,588,068 in years one and four respectively. Conclusions: Introduction of CGM for T1D-IAH patients will have a minimal budget impact on NW London CCGs, driven by cost of CGM and offsets from lower hypoglycaemia-related costs, reduced SMBG strip usage, avoided HbA1c-related complications and lower insulin pump use.
Malcolm G, Rilstone S, Sivasubramaniyam S, et al., 2017, Managing diabetes at high altitude:personal experience with support from aMultidisciplinary Physical Activity andDiabetes Clinic, BMJ Open Sport and Exercise Medicine, Vol: 3, ISSN: 2055-7647
Objective: Physical activity is important for wellbeing but can be challenging for people with diabetes. Data informing support of specialist activities such as climbing and high altitude trekking is limited. A 42-year-old man with type 1 diabetes (duration 30 years) attended a multidisciplinary Physical Activity and Diabetes clinicplanning to climb Mont Blanc during the summer and trek to Everest Base Camp in the autumn. His aims were to complete these adventures without his diabetes impacting on their success. Methods: We report the information provided that enabledhim to help safely facilitate his objectives, in particular the requirement for frequent checking of blood glucose levels, the effects of altitude on insulin dose requirements, and recognition that Acute Mountain Sicknes may mimic the symptoms of hypoglycemia and vice versa. Real-time continuous glucose monitoring was made available for his treks. Results: The effects of high altitude on blood glucose results and glycaemic variability whilst treated on multiple daily injections of insulinare reported. In addition, we present a first-person account of his experience and lessons learned from managing diabetes at high altitude. Conclusions: A dedicated multidisciplinary Physical Activity & Diabetes clinic delivering individualized, evidence based, patient-focused advice on the effects of altitude on blood glucose levels, and provision of real-time continuous glucose monitoring enabled uneventful completion of a trek to Everest Base Camp in a person with type 1 diabetes.
Petrie JR, Chaturvedi N, Ford I, et al., 2017, Cardiovascular and metabolic effects of metformin in patients with type 1 diabetes (REMOVAL): a double-blind, randomised, placebo-controlled trial, LANCET DIABETES & ENDOCRINOLOGY, Vol: 5, Pages: 597-609, ISSN: 2213-8587
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- Citations: 208
Walkey HC, Kaur A, Bravis V, et al., 2017, Rationale and protocol for the After Diabetes Diagnosis REsearch Support System (ADDRESS): an incident and high risk type 1 diabetes UK cohort study., BMJ Open, Vol: 7, ISSN: 2044-6055
INTRODUCTION: Type 1 diabetes is heterogeneous in its presentation and progression. Variations in clinical presentation between children and adults, and with ethnic group warrant further study in the UK to improve understanding of this heterogeneity. Early interventions to limit beta cell damage in type 1 diabetes are undergoing evaluation, but recruitment is challenging. The protocol presented describes recruitment of people with clinician-assigned, new-onset type 1 diabetes to understand the variation in their manner of clinical presentation, to facilitate recruitment into intervention studies and to create an open-access resource of data and biological samples for future type 1 diabetes research. METHODS AND ANALYSIS: Using the National Institute for Health Research Clinical Research Network, patients >5 years of age diagnosed clinically with type 1 diabetes (and their siblings) are recruited within 6 months of diagnosis. Participants agree to have their clinical, laboratory and demographic data stored on a secure database, for their clinical progress to be monitored using information held by NHS Digital, and to be contacted about additional research, in particular immunotherapy and other interventions. An optional blood sample is taken for islet autoantibody measurement and storage of blood and DNA for future analyses. Data will be analysed statistically to describe the presentation of incident type 1 diabetes in a contemporary UK population. ETHICS AND DISSEMINATION: Ethical approval was obtained from the independent NHS Research Ethics Service. Results will be presented at national and international meetings and submitted for publication to peer-reviewed journals.
Herrero P, Bondia J, Adewuyi O, et al., 2017, Enhancing automatic closed-loop glucose control in type 1 diabetes with an adaptive meal bolus calculator - in silico evaluation under intra- day variability, Computer Methods and Programs in Biomedicine, Vol: 146, Pages: 125-131, ISSN: 0169-2607
Background and ObjectiveCurrent prototypes of closed-loop systems for glucose control in type 1 diabetes mellitus, also referred to as artificial pancreas systems, require a pre-meal insulin bolus to compensate for delays in subcutaneous insulin absorption in order to avoid initial post-prandial hyperglycemia. Computing such a meal bolus is a challenging task due to the high intra-subject variability of insulin requirements. Most closed-loop systems compute this pre-meal insulin dose by a standard bolus calculation, as is commonly found in insulin pumps. However, the performance of these calculators is limited due to a lack of adaptiveness in front of dynamic changes in insulin requirements. Despite some initial attempts to include adaptation within these calculators, challenges remain.MethodsIn this paper we present a new technique to automatically adapt the meal-priming bolus within an artificial pancreas. The technique consists of using a novel adaptive bolus calculator based on Case-Based Reasoning and Run-To-Run control, within a closed-loop controller. Coordination between the adaptive bolus calculator and the controller was required to achieve the desired performance. For testing purposes, the clinically validated Imperial College Artificial Pancreas controller was employed. The proposed system was evaluated against itself but without bolus adaptation. The UVa-Padova T1DM v3.2 system was used to carry out a three-month in silico study on 11 adult and 11 adolescent virtual subjects taking into account inter-and intra-subject variability of insulin requirements and uncertainty on carbohydrate intake.ResultsOverall, the closed-loop controller enhanced by an adaptive bolus calculator improves glycemic control when compared to its non-adaptive counterpart. In particular, the following statistically significant improvements were found (non-adaptive vs. adaptive). Adults: mean glucose 142.2 ± 9.4 vs. 131.8 ± 4.2 mg/dl; perce
Herrero Vinas P, Pesl P, Reddy M, et al., 2017, Atomatic adjustment of Basal insulin infusion rates in type 1 diabetes using run-to-run control and case-based reasoning, Artificial Intelligence in Medicine
People with type 1 diabetes mellitus rely on a basal-bolus insulinregimen to roughly emulate how a non-diabetic person’s body delivers insulin.Adjusting such regime is a challenging process usually conducted by an expertclinical. Despite several guidelines exist for such purpose, they are usuallyimpractical and fall short in achieving optimal glycemic outcomes. Therefore,there is a need for more automated and efficient strategies to adjust such regime.This paper presents, and in silico validates, a novel technique to automaticallyadapt the basal insulin profile of a person with person with type 1 diabetes. Thepresented technique, which is based on Run-to-Run control and Case-BasedReasoning, overcomes some of the limitations of previously proposedapproaches and has been proved to be robust in front of realistic intra-dayvariability. Over a period of 5 weeks on 10 virtual adult subjects, a significantreduction on the percentage of time in hyperglycemia (<70mg/dl) (from 14.3±5.6to 1.6±1.7, p< 0.01), without a significant increase on the percentage of time inhypoglycemia (>180mg/dl) (from 10.2±5.9 to 1.6±1.7, p=0.1), was achieved.
Misra S, Kaur A, Walkey H, et al., 2017, The extent of diabetes misclassification one year after a diagnosis of Type 1 diabetes: data from the after diabetes diagnosis research support system (ADDRESS-2) cohort, 77th Scientific Sessions of the American-Diabetes-Association, Publisher: AMER DIABETES ASSOC, Pages: A411-A412, ISSN: 0012-1797
Walkey HC, Kaur A, Godsland IF, et al., 2017, Clinical presentation and islet autoantibody status in a UK multi-ethnic cohort of children and adults with new-onset Type 1 diabetes-the after diabetes diagnosis research support system-2 (ADDRESS-2), 77th Scientific Sessions of the American-Diabetes-Association, Publisher: American Diabetes Association, Pages: A467-A468, ISSN: 0012-1797
Misra S, Godsland I, Lupak L, et al., 2017, Predictors of C-Peptide in a Young-Onset Diabetes UK Multiethnic Cohort: Results from the MY DIABETES Study, 77th Scientific Sessions of the American-Diabetes-Association, Publisher: AMER DIABETES ASSOC, Pages: A421-A421, ISSN: 0012-1797
Kaur A, Walkey H, Godsland IF, et al., 2017, Characteristics Associated with Diabetic Ketoacidosis in Children and Adults Newly Diagnosed with Type 1 Diabetes: Data from the After Diabetes Diagnosis Research Support System (ADDRESS-2) Cohort, 77th Scientific Sessions of the American-Diabetes-Association, Publisher: AMER DIABETES ASSOC, Pages: A467-A467, ISSN: 0012-1797
Kaur A, Walkey H, Bravis V, et al., 2017, Predictors of glycaemia at the time of diagnosis in people with newly diagnosed Type 1 diabetes: data from the After Diabetes Diagnosis Research Support System (ADDRESS-2) cohort, Diabetes UK Professional Conference 2017, Publisher: Wiley, Pages: 73-73, ISSN: 0742-3071
Misra S, Colclough K, Lupak L, et al., 2017, Clinical and biochemical definitions of Type 1 diabetes do not agree in a multi-ethnic young-onset diabetes cohort: results from the MY DIABETES study, Publisher: WILEY, Pages: 7-7, ISSN: 0742-3071
Chaugule S, Oliver N, Klinkenbijl B, et al., 2017, An economic evaluation of the introduction of continuous glucose monitoring (CGM) devices for people with Type 1 diabetes and impaired awareness of hypoglycaemia within North West London clinical commissioning groups in England, Publisher: WILEY, Pages: 187-187, ISSN: 0742-3071
Misra S, Colclough K, Halleron K, et al., 2017, Type 1 diabetes phenotype in white and South Asian people with young onset diabetes in the UK: results from the MY DIABETES study, Spring Meeting on Clinician Scientists in Training, Publisher: ELSEVIER SCIENCE INC, Pages: 69-69, ISSN: 0140-6736
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Pesl P, Herrero P, Reddy M, et al., 2017, Case-Based Reasoning for Insulin Bolus Advice., J Diabetes Sci Technol, Vol: 11, Pages: 37-42
BACKGROUND: Insulin bolus calculators assist people with Type 1 diabetes (T1D) to calculate the amount of insulin required for meals to achieve optimal glucose levels but lack adaptability and personalization. We have proposed enhancing bolus calculators by the means of case-based reasoning (CBR), an established problem-solving methodology, by individualizing and optimizing insulin therapy for various meal situations. CBR learns from experiences of past similar meals, which are described in cases through a set of parameters (eg, time of meal, alcohol, exercise). This work discusses the selection, representation and effect of case parameters used for a CBR-based Advanced Bolus Calculator for Diabetes (ABC4D). METHODS: We analyzed the usage and effect of selected parameters during a pilot study (n = 10), where participants used ABC4D for 6 weeks. Retrospectively, we evaluated the effect of glucose rate of change before the meal on the glycemic excursion. Feedback from study participants about the choice of parameters was obtained through a nonvalidated questionnaire. RESULTS: Exercise and alcohol were the most frequently used parameters, which was congruent with the feedback from study participants, who found these parameters most useful. Furthermore, cases including either exercise or alcohol as parameter showed a trend in reduction of insulin at the end of the study. A significant difference ( P < .01) was found in glycemic outcomes for meals where glucose rate of change was rising compared to stable rate of change. CONCLUSIONS: Results from the 6-week study indicate the potential benefit of including parameters exercise, alcohol and glucose-rate of change for insulin dosing decision support.
Misra S, Huddy J, Hanna G, et al., 2017, Validation and regulation of point of care devices for medical applications, MEDICAL BIOSENSORS FOR POINT OF CARE (POC) APPLICATIONS, Editors: Narayan, Publisher: WOODHEAD PUBL LTD, Pages: 27-44, ISBN: 978-0-08-100072-4
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- Citations: 7
El-Laboudi AH, Godsland I, Johnston D, et al., 2016, Measures of Glycemic Variability In Type 1 Diabetes and the Effect of Real-Time Continuous Glucose Monitoring, Diabetes Technology & Therapeutics, Vol: 18, Pages: 806-812, ISSN: 1557-8593
Objective: To report the impact of continuous glucose monitoring (CGM) onglycemic variability (GV) indices, factors predictive of change and to correlatevariability with conventional markers of glycaemia.Methods: Data from the JDRF study of CGM in participants with type 1 diabeteswere used. Participants were randomised to CGM or self-monitored blood glucose(SMBG). GV indices at baseline, at 26 weeks in both groups, and at 52 weeks in thecontrol group were analysed. The associations of demographic and clinical factorswith change in GV indices from baseline to 26 weeks were evaluated.Results: Baseline data were available for 448 subjects. GV indices were all outsidenormative ranges (P<0.001). Inter-correlation between GV indices was common and,apart from coefficient of variation (CV), low blood glucose index (LBGI) andpercentage of glycemic risk assessment diabetes equation score attributable tohypoglycaemia (%GRADEhypoglycaemia), all indices correlate positively with HbA1c.There was strong correlation between time spent in hypoglycaemia, and CV, LBGIand %GRADEhypoglycaemia, but not with HbA1c. A significant reduction in all GVindices, except lability index and mean absolute glucose change per unit time (MAG),was demonstrated in the intervention group at 26 weeks compared with the controlgroup. Baseline factors predicting a change in GV with CGM include baselineHbA1c, baseline GV, frequency of daily SMBG and insulin pump use.Conclusions: CGM reduces most GV indices compared with SMBG in people withtype 1 diabetes. The strong correlation between time spent in hypoglycaemia and CV,LBGI and %GRADEhypoglycaemia highlights the value of these metrics in assessinghypoglycaemia as an adjunct to HbA1c in overall assessment of glycaemia.
Hill NE, Campbell C, Buchanan P, et al., 2016, Biochemical, physiological and psychological changes during endurance exercise in people with type 1 diabetes., Journal of Diabetes Science and Technology, Vol: 11, Pages: 529-536, ISSN: 1932-2968
BACKGROUND: Increasing numbers of people with diabetes are adopting exercise programs. Fear of hypoglycemia, hypoglycemia itself, and injuries are major issues for many people with diabetes undertaking physical activity. The purpose of this study was to investigate the effects of type 1 diabetes mellitus on the risk of hypoglycemia, glycemic variability, exercise performance, changes in body composition, changes in insulin dosage, and psychosocial well-being during a multiday endurance exercise event. METHODS: Eleven participants (7 with type 1 diabetes, 4 with normal glucose tolerance) undertook a 15-day, 2300 km cycling tour from Barcelona to Vienna. Data were prospectively collected using bike computers, continuous glucose monitors, body composition analyzers, and mood questionnaires. RESULTS: Mean blood glucose in riders with and without diabetes significantly reduced as the event progressed. Glycemic variability and time spent in hypoglycemia did not change throughout the ride for either set of riders. Riders with diabetes in the lowest quartile of sensor glucose values had significantly reduced power output. Percentage body fat also significantly fell. Hypo- and hyperglycemia provoked feelings of anxiety and worry. CONCLUSIONS: This is the first study to describe a real-time endurance event in type 1 diabetes, and provides important new data that cannot be studied in laboratory conditions. Hypoglycemia continues to occurs in spite of peer support and large reductions in insulin dose. Glycemic variability is shown as a potential barrier to participation in physical activity through effects on mood and psychological well-being.
Agha-Jaffar R, Misra S, Oliver N, et al., 2016, Ethnic variations in glucose and the interaction with fetal growth in a multi-ethnic inner city antenatal cohort, 52nd Annual Meeting of the European-Association-for-the-Study-of-Diabetes (EASD), Publisher: Springer Verlag, Pages: S447-S448, ISSN: 0012-186X
Misra S, Colclough K, Halleron K, et al., 2016, Type 1 diabetes phenotype is similar in UK white and south Asian people with young-onset diabetes: results from the MY DIABETES study, 52nd Annual Meeting of the European-Association-for-the-Study-of-Diabetes (EASD), Publisher: SPRINGER, Pages: S181-S181, ISSN: 0012-186X
Misra S, Shields B, Colclough K, et al., 2016, South Asian individuals with diabetes who are referred for MODY testing in the UK have a lower mutation pick-up rate than white European people, Diabetologia, Vol: 59, Pages: 2262-2265, ISSN: 0012-186X
Agha-Jaffar R, Oliver N, Johnston D, et al., 2016, Gestational diabetes mellitus: does an effective prevention strategy exist?, Nature Reviews Endocrinology, ISSN: 1759-5037
The overall incidence of gestational diabetes mellitus (GDM) is increasing worldwide. Preventing pathological hyperglycaemia during pregnancy could have several benefits: a reduction in the immediate adverse outcomes during pregnancy, a reduced risk of long-term sequelae and a decrease in the economic burden to healthcare systems. In this Review we examine the evidence supporting lifestyle modification strategies in women with and without risk factors for GDM, and the efficacy of dietary supplementation and pharmacological approaches to prevent this disease. A high degree of heterogeneity exists between trials so a generalised recommendation is problematic. In population studies of dietary or combined lifestyle measures, risk of developing GDM is not improved and those involving a physical activity intervention have yielded conflicting results. In pregnant women with obesity, dietary modification might reduce fetal macrosomia but in these patients, low compliance and no significant reduction in the incidence of GDM has been observed in trials investigating physical activity. Supplementation with probiotics or myoinositol have reduced the incidence of GDM but confirmatory studies are still needed. In randomized controlled trials, metformin does not prevent GDM in certain at-risk groups. Given the considerable potential for reducing disease burden, further research is needed to identify strategies that can be easily and effectively implemented on a population level.
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