Medical nutrition therapy for gestational diabetes mellitus in Australia : what has changed in 10 years and how does current practice compare with best practice?

Background: The present study aimed to report Australian dietetic practice regarding management of gestational diabetes mellitus (GDM) and to make comparisons with the findings from a 2009 survey of dietitians and with the Academy of Nutrition and Dietetics Evidence-Based Nutrition Practice Guidelines (NPG). Methods: Cross-sectional surveys were conducted in 2019 and 2009 of dietitians providing medical nutrition therapy (MNT) to women with GDM in Australia. The present study compares responses on demographics, dietetic assessment and interventions, and guideline use in 2019 vs. 2009. Results: In total, 149 dietitians (2019) and 220 (2009) met survey inclusion criteria. In both surveys >60% of respondents reported dietary interventions aiming for >45% energy from carbohydrate, 15%–25% energy from protein and 15%–30% energy from fat. Many variations in MNT found in 2009 continued to be evident in 2019, including the percentage of energy from carbohydrate aimed for (30%–65% in 2019 vs. 20%–75% in 2009) and the wide range in the recommended minimum daily carbohydrate intake (40–220 and 60–300 g). Few dietitians reported aiming for the NPG minimum of 175 g of carbohydrate daily in both surveys (32% in 2019 vs. 26% in 2009). There were, however, some significant increases in MNT consistent with NPG recommendations in 2019 vs. 2009, including the minimum frequency of visits provided (49%, n = 61 vs. 33%, n = 69; p < 0.001) and provision of gestational weight gain advice (59%, n = 95 vs. 40%, n = 195; p < 0.05). Conclusions: Although many dietitians continue to provide MNT consistent with existing NPG, there is a need to support greater uptake, especially for recommendations regarding carbohydrate intake

Managing diabetes in preschool children

This article is a new chapter in the ISPAD Clinical Practice Consensus Guidelines Compendium. The complete set of guidelines can be found for free download at www.ispad.org. The evidence grading system used in the ISPAD Guidelines is the same as that used by the American Diabetes Association

Glucose management for exercise using continuous glucose monitoring (CGM) and intermittently scanned CGM (isCGM) systems in type 1 diabetes: position statement of the European Association for the Study of Diabetes (EASD) and of the International Society for Pediatric and Adolescent Diabetes (ISPAD) endorsed by JDRF and supported by the American Diabetes Association (ADA)

Physical exercise is an important component in the management of type 1 diabetes across the lifespan. Yet, acute exercise increases the risk of dysglycaemia, and the direction of glycaemic excursions depends, to some extent, on the intensity and duration of the type of exercise. Understandably, fear of hypoglycaemia is one of the strongest barriers to incorporating exercise into daily life. Risk of hypoglycaemia during and after exercise can be lowered when insulin‐dose adjustments are made and/or additional carbohydrates are consumed. Glycaemic management during exercise has been made easier with continuous glucose monitoring (CGM) and intermittently scanned continuous glucose monitoring (isCGM) systems; however, because of the complexity of CGM and isCGM systems, both individuals with type 1 diabetes and their healthcare professionals may struggle with the interpretation of given information to maximise the technological potential for effective use around exercise (ie, before, during and after). This position statement highlights the recent advancements in CGM and isCGM technology, with a focus on the evidence base for their efficacy to sense glucose around exercise and adaptations in the use of these emerging tools, and updates the guidance for exercise in adults, children and adolescents with type 1 diabetes

Interaction Testing and Polygenic Risk Scoring to Estimate the Association of Common Genetic Variants with Treatment Resistance in Schizophrenia

Importance: About 20% to 30% of people with schizophrenia have psychotic symptoms that do not respond adequately to first-line antipsychotic treatment. This clinical presentation, chronic and highly disabling, is known as treatment-resistant schizophrenia (TRS). The causes of treatment resistance and their relationships with causes underlying schizophrenia are largely unknown. Adequately powered genetic studies of TRS are scarce because of the difficulty in collecting data from well-characterized TRS cohorts. Objective: To examine the genetic architecture of TRS through the reassessment of genetic data from schizophrenia studies and its validation in carefully ascertained clinical samples. Design, Setting, and Participants: Two case-control genome-wide association studies (GWASs) of schizophrenia were performed in which the case samples were defined as individuals with TRS (n = 10501) and individuals with non-TRS (n = 20325). The differences in effect sizes for allelic associations were then determined between both studies, the reasoning being such differences reflect treatment resistance instead of schizophrenia. Genotype data were retrieved from the CLOZUK and Psychiatric Genomics Consortium (PGC) schizophrenia studies. The output was validated using polygenic risk score (PRS) profiling of 2 independent schizophrenia cohorts with TRS and non-TRS: a prevalence sample with 817 individuals (Cardiff Cognition in Schizophrenia [CardiffCOGS]) and an incidence sample with 563 individuals (Genetics Workstream of the Schizophrenia Treatment Resistance and Therapeutic Advances [STRATA-G]). Main Outcomes and Measures: GWAS of treatment resistance in schizophrenia. The results of the GWAS were compared with complex polygenic traits through a genetic correlation approach and were used for PRS analysis on the independent validation cohorts using the same TRS definition. Results: The study included a total of 85490 participants (48635 [56.9%] male) in its GWAS stage and 1380 participants (859 [62.2%] male) in its PRS validation stage. Treatment resistance in schizophrenia emerged as a polygenic trait with detectable heritability (1% to 4%), and several traits related to intelligence and cognition were found to be genetically correlated with it (genetic correlation, 0.41-0.69). PRS analysis in the CardiffCOGS prevalence sample showed a positive association between TRS and a history of taking clozapine (r2 = 2.03%; P =.001), which was replicated in the STRATA-G incidence sample (r2 = 1.09%; P =.04). Conclusions and Relevance: In this GWAS, common genetic variants were differentially associated with TRS, and these associations may have been obscured through the amalgamation of large GWAS samples in previous studies of broadly defined schizophrenia. Findings of this study suggest the validity of meta-analytic approaches for studies on patient outcomes, including treatment resistance

Nutritional management of children and adolescents on insulin pump therapy: a survey of Australian practice

Objective: The aims of the survey were to review nutritional care provided to children on insulin pump therapy (IPT) and to identify areas of consensus in medical nutrition therapy. Interventions were compared with existing evidence for best practice. Method: A questionnaire was sent to Dieticians in tertiary pediatric diabetes centers in Australia. Data were gathered on clinic demographics, reasons for commencement of pump therapy, and time spent in medical nutrition therapy. Details of nutrition education strategies were identified. Outcomes from nutrition interventions were reported. Results: A 100% response rate was achieved (n = 12). A number of nutrition therapy interventions were provided to children on IPT. These included carbohydrate counting, glycemic index (GI), and carbohydrate exchanges. At most centers, nutrition education involved teaching dose adjustment for meals based on the carbohydrate content of the meal with estimations to within 5 g. All centers taught GI. The format of nutrition education, including number and length of consults, varied greatly between centers. Only one center had developed nutrition guidelines for managing insulin pump patients. Conclusions: Most pediatric diabetes centers in Australia did not follow nutrition guidelines for the management of children on IPT. There were inconsistencies in the number and length of nutrition consultations provided. Some strategies employed in nutrition education were not supported by existing guidelines for best practice. Differences between centers highlighted gaps in the evidence for nutrition therapy interventions in children on pumps

Postprandial glucose metabolism in children and adolescents with type 1 diabetes mellitus: potential targets for improvement

: The main goal of therapeutic management of type 1 Diabetes Mellitus (T1DM) is to maintain optimal glycemic control to prevent acute and long-term diabetes complications and to enable a good quality of life. Postprandial glycemia makes a substantial contribution to overall glycemic control and variability in diabetes and, despite technological advancements in insulin treatments, optimal postprandial glycemia is difficult to achieve. Several factors influence postprandial blood glucose levels in children and adolescents with T1DM, including nutritional habits and adjustment of insulin doses according to meal composition. Additionally, hormone secretion, enteroendocrine axis dysfunction, altered gastrointestinal digestion and absorption, and physical activity play important roles. Meal-time routines, intake of appropriate ratios of macronutrients, and correct adjustment of the insulin dose for the meal composition have positive impacts on postprandial glycemic variability and long-term cardiometabolic health of the individual with T1DM. Further knowledge in the field is necessary for management of all these factors to be part of routine pediatric diabetes education and clinical practice. Thus, the aim of this report is to review the main factors that influence postprandial blood glucose levels and metabolism, focusing on macronutrients and other nutritional and lifestyle factors, to suggest potential targets for improving postprandial glycemia in the management of children and adolescents with T1DM

Biting off more than you can chew: is it possible to precisely count carbohydrate?

Aim: Carbohydrate counting is used to adjust premeal insulin to carbohydrate intake in intensive insulin regimens. The aim of the present study was to determine the potential variability in the carbohydrate content of a slice of bread (one ‘exchange’) from that reported on the label and hence, the potential variability in carbohydrate intake when consuming a serve. Methods: A cross-sectional survey of 11 different loaves of bread commonly consumed by children with type 1 diabetes was undertaken. All slices in each loaf were weighed to an accuracy of ±1 g; and the reported carbohydrate content per 100 g of each loaf was used to determine the carbohydrate content of the mean, minimum and maximum slice in each loaf of bread. Results: There was no difference between the reported and the mean estimated carbohydrate content of a slice. The minimum slice of bread across all loaves was estimated to contain only 10.0 g of carbohydrate, whereas the maximum slice contained an estimated 20.7 g of carbohydrate. The greatest variation in carbohydrate amount within a loaf was 12.3 g. Conclusions: In commercially available loaves of bread in Australia, the carbohydrate content of a slice can vary by up to 45% of that reported on the label, in accordance with Food Standards Australia New Zealand. This highlights a practical limitation inherent with the commonly held view that food labels can facilitate accuracy in carbohydrate counting in 1-g increments

The relationship between carbohydrate and the mealtime insulin dose in type 1 diabetes

A primary focus of the nutritional management of type 1 diabetes has been on matching prandial insulin therapy with carbohydrate amount consumed. Different methods exist to quantify carbohydrate including counting in one gram increments, 10 g portions or 15 g exchanges. Clinicians have assumed that counting in one gram increments is necessary to precisely dose insulin and optimize postprandial control. Carbohydrate estimations in portions or exchanges have been thought of as inadequate because they may result in less precise matching of insulin dose to carbohydrate amount. However, studies examining the impact of errors in carbohydrate quantification on postprandial glycemia challenge this commonly held view. In addition it has been found that a single mealtime bolus of insulin can cover a range of carbohydrate intake without deterioration in postprandial control. Furthermore, limitations exist in the accuracy of the nutrition information panel on a food label. This article reviews the relationship between carbohydrate quantity and insulin dose, highlighting limitations in the evidence for a linear association. These insights have significant implications for patient education and mealtime insulin dose calculations

The role of dietary protein and fat in glycaemic control in type 1 diabetes: implications for intensive diabetes management

A primary focus of the management of type 1 diabetes has been on matching prandial insulin therapy with carbohydrate amount consumed. However, even with the introduction of more flexible intensive insulin regimes, people with type 1 diabetes still struggle to achieve optimal glycaemic control. More recently, dietary fat and protein have been recognised as having a significant impact on postprandial blood glucose levels. Fat and protein independently increase the postprandial glucose excursions and together their effect is additive. This article reviews how the fat and protein in a meal impact the postprandial glycaemic response and discusses practical approaches to managing this in clinical practice. These insights have significant implications for patient education, mealtime insulin dose calculations and dosing strategies

Screening Practices for Disordered Eating in Paediatric Type 1 Diabetes Clinics

Background: Type 1 Diabetes (T1D) is associated with increased risk of eating disorders. This study aimed to (1) assess adherence of Australasian paediatric T1D clinics to international guidelines on screening for disordered eating and (2) identify barriers and enablers to the use of screening tools for the identification of disordered eating. Methods: A 24-item survey covering five content domains: clinic characteristics, identification of disordered eating, screening tool use, training and competence, and pathways for referral, was sent to Australasian clinics caring for &ge;150 children and adolescents with T1D. Results: Of 13 eligible clinics, 10 participated. Two reported rates of disordered eating of &gt;20%, while eight reported rates &lt; 5%. All clinics used the routine clinical interview as the primary method of screening for disordered eating. Only one used screening tools; these were not diabetes-specific or routinely used. Barriers to use of screening tools included shortage of time and lack of staff confidence around use (n = 7, 70%). Enablers included staff training in disordered eating. Conclusions: Screening tools for disordered eating are not utilised by most Australasian paediatric T1D clinics. Overall, low reported rates of disordered eating suggest that it may be undetected, potentially missing an opportunity for early intervention