Author
Aoife M. Egan
Other affiliations: Connolly Hospital Blanchardstown, University College Hospital, University Hospital Galway ...read more
Bio: Aoife M. Egan is an academic researcher from Mayo Clinic. The author has contributed to research in topics: Pregnancy & Gestational diabetes. The author has an hindex of 18, co-authored 73 publications receiving 1173 citations. Previous affiliations of Aoife M. Egan include Connolly Hospital Blanchardstown & University College Hospital.
Papers
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University of Toronto1, Lunenfeld-Tanenbaum Research Institute2, University of Calgary3, King's College London4, University of Cambridge5, Sunnybrook Research Institute6, Rabin Medical Center7, University of California, Santa Barbara8, University of Southern California9, University of Ottawa10, University of Western Ontario11, McMaster University12, Cambridge University Hospitals NHS Foundation Trust13, Nemours Foundation14, University Health Network15, University of East Anglia16, Mount Sinai Hospital, Toronto17, Hospital de Sant Pau18, Ottawa Hospital19, Norfolk and Norwich University Hospital20, Sunnybrook Health Sciences Centre21, Glasgow Royal Infirmary22, Université du Québec23, Queen's University24, Royal Victoria Infirmary25, Leeds Teaching Hospitals NHS Trust26, Edinburgh Royal Infirmary27, University of Southampton28, University College Hospital29, University of Manchester30, South Tees Hospitals NHS Trust31, Russells Hall Hospital32, Kingston General Hospital33, Royal University Hospital34, JDRF35
TL;DR: In this paper, the effectiveness of continuous glucose monitoring (CGM) on maternal glucose control and obstetric and neonatal health outcomes was examined in women with Type 1 diabetes and planning pregnancy.
398 citations
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National University of Ireland1, Medical University of Vienna2, University of Sydney3, Carlos III Health Institute4, Katholieke Universiteit Leuven5, Erasmus University Rotterdam6, University of Copenhagen7, University of Southern Denmark8, University of Padua9, Poznan University of Medical Sciences10, University of St. Gallen11, VU University Amsterdam12, University of Graz13, VU University Medical Center14
TL;DR: The prevalence of GDM diagnosed by the IADPSG/WHO 2013 GDM criteria in European pregnant women with a BMI ≥29.0 kg/m2 is substantial, and poses a significant health burden to these pregnancies and to the future health of the mother and her offspring.
Abstract: Accurate prevalence estimates for gestational diabetes mellitus (GDM) among pregnant women in Europe are lacking owing to the use of a multitude of diagnostic criteria and screening strategies in both high-risk women and the general pregnant population. Our aims were to report important risk factors for GDM development and calculate the prevalence of GDM in a cohort of women with BMI ≥29 kg/m2 across 11 centres in Europe using the International Association of the Diabetes and Pregnancy Study Groups (IADPSG)/WHO 2013 diagnostic criteria. Pregnant women (n = 1023, 86.3% European ethnicity) with a BMI ≥29.0 kg/m2 enrolled into the Vitamin D and Lifestyle Intervention for GDM Prevention (DALI) pilot, lifestyle and vitamin D studies of this pan-European multicentre trial, attended for an OGTT during pregnancy. Demographic, anthropometric and metabolic data were collected at enrolment and throughout pregnancy. GDM was diagnosed using IADPSG/WHO 2013 criteria. GDM treatment followed local policies. The number of women recruited per country ranged from 80 to 217, and the dropout rate was 7.1%. Overall, 39% of women developed GDM during pregnancy, with no significant differences in prevalence across countries. The prevalence of GDM was high (24%; 242/1023) in early pregnancy. Despite interventions used in the DALI study, a further 14% (94/672) had developed GDM when tested at mid gestation (24–28 weeks) and 13% (59/476) of the remaining cohort at late gestation (35–37 weeks). Demographics and lifestyle factors were similar at baseline between women with GDM and those who maintained normal glucose tolerance. Previous GDM (16.5% vs 7.9%, p = 0.002), congenital malformations (6.4% vs 3.3%, p = 0.045) and a baby with macrosomia (31.4% vs 17.9%, p = 0.001) were reported more frequently in those who developed GDM. Significant anthropometric and metabolic differences were already present in early pregnancy between women who developed GDM and those who did not. The prevalence of GDM diagnosed by the IADPSG/WHO 2013 GDM criteria in European pregnant women with a BMI ≥29.0 kg/m2 is substantial, and poses a significant health burden to these pregnancies and to the future health of the mother and her offspring. Uniform criteria for GDM diagnosis, supported by robust evidence for the benefits of treatment, are urgently needed to guide modern GDM screening and treatment strategies.
111 citations
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TL;DR: It is shown that in the already high-risk settings of both GDM and PGDM, excessive GWG confers an additive risk for LGA birth weight, macrosomia, and gestational hypertension.
Abstract: Context: Women who have diabetes mellitus during pregnancy are at higher risk of adverse outcomes. Excessive gestational weight gain (GWG) is also emerging as a risk factor for maternofetal complications, and in 2009, the Institute of Medicine published recommendations for appropriate GWG. It is unclear whether excessive GWG confers additional risk to women with diabetes in pregnancy and whether Institute of Medicine recommendations are applicable to this population. Objective: The objective of this study was to examine whether excessive GWG in pregnancies complicated by diabetes mellitus is associated with higher adverse obstetric outcomes. Design: This was an observational study. Setting: The study was conducted at five antenatal centers along the Irish Atlantic seaboard. Participants: 802 women with diabetes in pregnancy participated in the study. Main Outcome Measure: Maternal outcomes examined included preeclampsia, gestational hypertension, and cesarean delivery. Fetal outcomes included large for ge...
110 citations
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TL;DR: The recommended way of measuring plasma glucose and the threshold used to define what is normal or abnormal have gone through several iterations over the past two decades are reviewed in this article.
104 citations
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TL;DR: This study highlights the challenges facing clinicians in improving exercise levels in patients, and the need to identify the specific barriers to exercise in the individual to improve health outcomes.
Abstract: Background: Although regular exercise is a critical component of the management of type 2 diabetes, many patients do not meet their exercise targets. Lack of exercise is associated with obesity and adverse cardiovascular outcomes.
Aim: We aimed to assess exercise habits in obese Irish patients with type 2 diabetes to determine if patients are adhering to exercise guidelines and to identify perceived barriers to exercise in this group.
Design: A cross-sectional study of obese patients with type 2 diabetes attending routine outpatient diabetes clinics at our institution, a public teaching hospital located on the outskirts of Dublin City.
Methods: A total of 145 obese patients with type 2 diabetes were administered a questionnaire to evaluate exercise habits and perceived barriers to exercise. Anthropometric details were measured.
Results: About 47.6% ( n = 69) of patients exercised for <150 minutes per week (40% of males, 62% of females; P = 0.019) and these patients had a higher body mass index than those meeting targets (35 vs. 33.5 kg/m2; P = 0.02). Perceived barriers to exercise were varied, with lack of time and physical discomfort being the most common. Reported barriers to exercise varied with age, gender and marital status.
Conclusions: This study highlights the challenges facing clinicians in improving exercise levels in patients, and the need to identify the specific barriers to exercise in the individual to improve health outcomes.
71 citations
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01 Jan 2014
TL;DR: These standards of care are intended to provide clinicians, patients, researchers, payors, and other interested individuals with the components of diabetes care, treatment goals, and tools to evaluate the quality of care.
Abstract: XI. STRATEGIES FOR IMPROVING DIABETES CARE D iabetes is a chronic illness that requires continuing medical care and patient self-management education to prevent acute complications and to reduce the risk of long-term complications. Diabetes care is complex and requires that many issues, beyond glycemic control, be addressed. A large body of evidence exists that supports a range of interventions to improve diabetes outcomes. These standards of care are intended to provide clinicians, patients, researchers, payors, and other interested individuals with the components of diabetes care, treatment goals, and tools to evaluate the quality of care. While individual preferences, comorbidities, and other patient factors may require modification of goals, targets that are desirable for most patients with diabetes are provided. These standards are not intended to preclude more extensive evaluation and management of the patient by other specialists as needed. For more detailed information, refer to Bode (Ed.): Medical Management of Type 1 Diabetes (1), Burant (Ed): Medical Management of Type 2 Diabetes (2), and Klingensmith (Ed): Intensive Diabetes Management (3). The recommendations included are diagnostic and therapeutic actions that are known or believed to favorably affect health outcomes of patients with diabetes. A grading system (Table 1), developed by the American Diabetes Association (ADA) and modeled after existing methods, was utilized to clarify and codify the evidence that forms the basis for the recommendations. The level of evidence that supports each recommendation is listed after each recommendation using the letters A, B, C, or E.
9,618 citations
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TL;DR: A diagnosis of gestational diabetes mellitus (GDM) (diabetes diagnosed in the second or third trimester of pregnancy that is not clearly overt diabetes) or chemical-induced diabetes (such as in the treatment of HIV/AIDS or after organ transplantation)
Abstract: 1. Type 1 diabetes (due to b-cell destruction, usually leading to absolute insulin deficiency) 2. Type 2 diabetes (due to a progressive insulin secretory defect on the background of insulin resistance) 3. Gestational diabetes mellitus (GDM) (diabetes diagnosed in the second or third trimester of pregnancy that is not clearly overt diabetes) 4. Specific types of diabetes due to other causes, e.g., monogenic diabetes syndromes (such as neonatal diabetes and maturity-onset diabetes of the young [MODY]), diseases of the exocrine pancreas (such as cystic fibrosis), and drugor chemical-induced diabetes (such as in the treatment of HIV/AIDS or after organ transplantation)
2,339 citations
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1,896 citations
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Ljubljana University Medical Centre1, King's College London2, Vita-Salute San Raffaele University3, Stanford University4, American Diabetes Association5, University of Padua6, Harvard University7, University of Amsterdam8, University of Sydney9, University of Colorado Denver10, University of Sheffield11, University of Washington12, University of Cambridge13, Shanghai Jiao Tong University14, University of Virginia15, JDRF16, Katholieke Universiteit Leuven17, University of East Anglia18, San Antonio River Authority19, Steno Diabetes Center20, University of Montpellier21, University of Florida22, Nihon University23, Yale University24, Tel Aviv University25
TL;DR: This article summarizes the ATTD consensus recommendations for relevant aspects of CGM data utilization and reporting among the various diabetes populations.
Abstract: Improvements in sensor accuracy, greater convenience and ease of use, and expanding reimbursement have led to growing adoption of continuous glucose monitoring (CGM). However, successful utilization of CGM technology in routine clinical practice remains relatively low. This may be due in part to the lack of clear and agreed-upon glycemic targets that both diabetes teams and people with diabetes can work toward. Although unified recommendations for use of key CGM metrics have been established in three separate peer-reviewed articles, formal adoption by diabetes professional organizations and guidance in the practical application of these metrics in clinical practice have been lacking. In February 2019, the Advanced Technologies & Treatments for Diabetes (ATTD) Congress convened an international panel of physicians, researchers, and individuals with diabetes who are expert in CGM technologies to address this issue. This article summarizes the ATTD consensus recommendations for relevant aspects of CGM data utilization and reporting among the various diabetes populations.
1,776 citations
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San Antonio River Authority1, Ljubljana University Medical Centre2, University of Amsterdam3, University of Colorado Denver4, University of Washington5, King's College London6, Vita-Salute San Raffaele University7, Stanford University8, University of Padua9, Harvard University10, University of Sheffield11, University of Cambridge12, Shanghai Jiao Tong University13, Princess Margaret Hospital for Children14, University of Virginia15, JDRF16, University of East Anglia17, Copenhagen University Hospital18, University of Montpellier19, Yale University20
TL;DR: This article summarizes the ATTD consensus recommendations and represents the current understanding of how CGM results can affect outcomes.
Abstract: Measurement of glycated hemoglobin (HbA1c) has been the traditional method for assessing glycemic control. However, it does not reflect intra- and interday glycemic excursions that may lead to acute events (such as hypoglycemia) or postprandial hyperglycemia, which have been linked to both microvascular and macrovascular complications. Continuous glucose monitoring (CGM), either from real-time use (rtCGM) or intermittently viewed (iCGM), addresses many of the limitations inherent in HbA1c testing and self-monitoring of blood glucose. Although both provide the means to move beyond the HbA1c measurement as the sole marker of glycemic control, standardized metrics for analyzing CGM data are lacking. Moreover, clear criteria for matching people with diabetes to the most appropriate glucose monitoring methodologies, as well as standardized advice about how best to use the new information they provide, have yet to be established. In February 2017, the Advanced Technologies & Treatments for Diabetes (ATTD) Congress convened an international panel of physicians, researchers, and individuals with diabetes who are expert in CGM technologies to address these issues. This article summarizes the ATTD consensus recommendations and represents the current understanding of how CGM results can affect outcomes.
1,173 citations