Interactions between the aging brain and motor task complexity across the lifespan: balancing brain activity resource demand and supply.

The Compensation Related Utilization of Neural Circuits Hypothesis (CRUNCH) proposes a framework for understanding task-related brain activity changes as a function of healthy aging and task complexity. Specifically, it affords the following predictions: (i) all adult age groups display more brain activation with increases in task complexity, (ii) older adults show more brain activation compared with younger adults at low task complexity levels, and (iii) disproportionately increase brain activation with increased task complexity, but (iv) show smaller (or no) increases in brain activation at the highest complexity levels. To test these hypotheses, performance on a bimanual tracking task at 4 complexity levels and associated brain activation were assessed in 3 age groups (20-40, 40-60, and 60-80 years, n = 99). All age groups showed decreased tracking accuracy and increased brain activation with increased task complexity, with larger performance decrements and activation increases in the older age groups. Older adults exhibited increased brain activation at a lower complexity level, but not the predicted failure to further increase brain activity at the highest complexity level. We conclude that older adults show more brain activation than younger adults and preserve the capacity to deploy increased neural resources as a function of task demand.

Network-specific differences in transient brain activity at rest are associated with age-related reductions in motor performance.

Multiple functional changes occur in the brain with increasing age. Among those, older adults typically display more restricted fluctuations of brain activity, both during resting-state and task execution. These altered dynamic patterns have been linked to reduced task performance across multiple behavioral domains. Windowed functional connectivity, which is typically employed in the study of connectivity dynamics, however, might not be able to properly characterize moment-to-moment variations of individual networks. In the present study, we used innovation-driven co-activation patterns (ICAP) to overcome this limitation and investigate the length (duration) and frequency (innovation) in which various brain networks emerged across the adult lifespan (N= 92) during a resting-state period. We identified a link between increasing age and a tendency to engage brain areas with distinct functional associations simultaneously as a single network. The emergence of isolated and spatially well-defined visual, motor, frontoparietal, and posterior networks decreased with increased age. This reduction in dynamics of specialized networks mediated age-related performance decreases (i.e., increases in interlimb interference) in a bimanual motor task. Altogether, our findings demonstrated that older compared to younger adults tend to activate fewer network configurations, which include multiple functionally distinct brain areas. The reduction in independent emergence of functionally well-defined and task-relevant networks may reflect an expression of brain dedifferentiation and is likely associated with functional modulatory deficits, negatively impacting motor behavior.

Increased paediatric presentations of severe diabetic ketoacidosis in an Australian tertiary centre during the COVID-19 pandemic.

AIMS: To determine if the frequency of severe diabetic ketoacidosis at presentation of new-onset type 1 diabetes to an Australian tertiary centre increased during the initial period of restrictions resulting from the COVID-19 pandemic (March to May 2020). METHODS: Data were collected on presentations of newly diagnosed type 1 diabetes as well as on all paediatric presentations to the emergency department of a tertiary centre between 2015 and 2020. Data from the period of initial COVID restrictions in Australia (March to May 2020) were compared to the period March to May of the previous 5 years (pre-pandemic periods). RESULTS: The number of new diagnoses of type 1 diabetes was comparable in the pandemic period and pre-pandemic periods (11 in 2020 vs range 6-10 in 2015-2019). The frequency of severe diabetic ketoacidosis was significantly higher in the pandemic period compared to the pre-pandemic periods (45% vs 5%; P <0.003), odds ratio 16.7 (95% CI 2.0, 194.7). The overall frequency of diabetic ketoacidosis was also significantly higher during the pandemic period (73% vs 26%; P <0.007), odds ratio 7.5 (95% CI 1.7, 33.5). None of the individuals tested positive for COVID-19. Presentations of people aged <18 years to the emergency department decreased by 27% in the pandemic period compared to the average of the pre-pandemic periods (4799 vs 6550; range 6268 to 7131). CONCLUSIONS: A significant increase in the frequency of severe diabetic ketoacidosis at presentation of type 1 diabetes was observed during the initial period of COVID-19 restrictions. We hypothesize that concern about presenting to hospital during a pandemic led to a delay in diagnosis. These data have important implications for advocacy of seeking healthcare for non-pandemic-related conditions during a global pandemic.

Stress Modulates the Balance between Hippocampal and Motor Networks during Motor Memory Processing.

The functional interaction between hippocampo- and striato-cortical regions during motor sequence learning is essential to trigger optimal memory consolidation. Based on previous evidence from other memory domains that stress alters the balance between these systems, we investigated whether exposure to stress prior to motor learning modulates motor memory processes. Seventy-two healthy young individuals were exposed to a stressful or nonstressful control intervention prior to training on a motor sequence learning task in a magnetic resonance imaging (MRI) scanner. Consolidation was assessed with an MRI retest after a sleep episode. Behavioral results indicate that stress prior to learning did not influence motor performance. At the neural level, stress induced both a larger recruitment of sensorimotor regions and a greater disengagement of hippocampo-cortical networks during training. Brain-behavior regression analyses showed that while this stress-induced shift from (hippocampo-)fronto-parietal to motor networks was beneficial for initial performance, it was detrimental for consolidation. Our results provide the first experimental evidence that stress modulates the neural networks recruited during motor memory processing and therefore effectively unify concepts and mechanisms from diverse memory fields. Critically, our findings suggest that intersubject variability in brain responses to stress determines the impact of stress on motor learning and subsequent consolidation.

High-protein meals require 30% additional insulin to prevent delayed postprandial hyperglycaemia.

AIM: To determine the amount of additional insulin required for a high-protein meal to prevent postprandial hyperglycaemia in individuals with type 1 diabetes using insulin pump therapy. METHODS: In this randomized cross-over study, 26 participants aged 8-40 years, HbA1c < 65 mmol/mol (8.1%), received a 50 g protein, 30 g carbohydrate, low-fat (< 1 g) breakfast drink over five consecutive days at home. A standard insulin dose (100%) was compared with additional doses of 115, 130, 145 and 160% for the protein, in randomized order. Doses were commenced 15-min pre-drink and delivered over 3 h using a combination bolus with 65% of the standard dose given up front. Postprandial glycaemia was assessed by 4 h of continuous glucose monitoring. RESULTS: The 100% dosing resulted in postprandial hyperglycaemia. From 120 min, ≥ 130% doses resulted in significantly lower postprandial glycaemic excursions compared with 100% (P < 0.05). A 130% dose produced a mean (sd) glycaemic excursion that was 4.69 (2.42) mmol/l lower than control, returning to baseline by 4 h (P < 0.001). From 120 min, there was a significant increase in the risk of hypoglycaemia compared with control for 145% [odds ratio (OR) 25.4, 95% confidence interval (CI) 5.5-206; P < 0.001) and 160% (OR 103, 95% CI 19.2-993; P < 0.001). Some 81% (n = 21) of participants experienced hypoglycaemia following a 160% dose, whereas 58% (n = 15) experienced hypoglycaemia following a 145% dose. There were no hypoglycaemic events reported with 130%. CONCLUSIONS: The addition of 30% more insulin to a standard dose for a high-protein meal, delivered using a combination bolus, improves postprandial glycaemia without increasing the risk of hypoglycaemia.

Young children, adolescent girls and women with type 1 diabetes are more overweight and obese than reference populations, and this is associated with increased cardiovascular risk factors.

AIM: Overweight and obesity are frequently reported in young persons with type 1 diabetes, however its relative magnitude in comparison to the general population is not well understood. This study compared the prevalence of overweight and obesity in young persons with type 1 diabetes to a reference population and explored possible associated factors, including gender, age, HbA1c , insulin regimen, age at diagnosis, diabetes duration, socio-economic status and cardiovascular disease risk factors. METHODS: A cross-sectional review was undertaken of data collected from youth (3-17 years) in 2016 and young adults (18-30 years) in 2015 with a diagnosis of type 1 diabetes for > 3 months attending diabetes centres in Newcastle, Australia. Rates of overweight and obesity were compared with matched population survey results. RESULTS: Data from 308 youth and 283 young adults were included. In girls, significantly higher prevalence of overweight and obesity were seen in the 5-8 (43% vs. 18%), 13-16 (41% vs. 27%), 18-24 (46% vs. 34%) and 25-30 (60% vs. 43%) years age groups; whereas in boys increased prevalence was observed in the 5-8 years age group only (41% vs. 18%). Rates of overweight and obesity increased with age across sexes. In youth, BMI standard deviation score was correlated with socio-economic status, insulin regimen, blood pressure and blood lipids (P < 0.05). In adults, BMI was positively associated with blood pressure, and longer diabetes duration (P < 0.02). CONCLUSIONS: Overweight and obesity are over-represented in young persons with type 1 diabetes, particularly girls. As overweight is associated with other cardiovascular disease markers early intervention is paramount.

Impact of dietary protein on postprandial glycaemic control and insulin requirements in Type 1 diabetes: a systematic review.

AIM: Postprandial hyperglycaemia is a challenge for people living with Type 1 diabetes. In addition to carbohydrate, dietary protein has been shown to contribute to postprandial glycaemic excursions with recommendations to consider protein when calculating mealtime insulin doses. The aim of this review is to identify and synthesize evidence about the glycaemic impact of dietary protein and insulin requirements for individuals with Type 1 diabetes. METHODS: A systematic literature search of relevant biomedical databases was performed to identify research on the glycaemic impact of dietary protein when consumed alone, and in combination with other macronutrients in individuals with Type 1 diabetes. RESULTS: The review included 14 published studies dated from 1992 to 2018, and included studies that researched the impact of protein alone (n = 2) and protein in a mixed meal (n = 12). When protein was consumed alone a glycaemic effect was not seen until ≥ 75 g. In a carbohydrate-containing meal ≥ 12.5 g of protein impacted the postprandial glucose. Inclusion of fat in a high-protein meal enhanced the glycaemic response and further increased insulin requirements. The timing of the glycaemic effect from dietary protein ranged from 90 to 240 min. Studies indicate that the postprandial glycaemic response and insulin requirements for protein are different when protein is consumed alone or with carbohydrate and/or fat. CONCLUSIONS: This systematic review provides evidence that dietary protein contributes to postprandial glycaemic excursions and insulin requirements. These insights have important implications for the education of people with Type 1 diabetes and highlights the need for more effective insulin dosing strategies for mixed macronutrient meals.

Age-related differences in network flexibility and segregation at rest and during motor performance.

Brain networks undergo widespread changes in older age. A large body of knowledge gathered about those changes evidenced an increase of functional connectivity between brain networks. Previous work focused mainly on cortical networks during the resting state. Subcortical structures, however, are of critical importance during the performance of motor tasks. In this study, we investigated age-related changes in cortical, striatal and cerebellar functional connectivity at rest and its modulation by motor task execution. To that end, functional MRI from twenty-five young (mean age 21.5 years) and eighteen older adults (mean age 68.6 years) were analysed during rest and while performing a bimanual tracking task practiced over a two-week period. We found that inter-network connectivity among cortical structures was more positive in older adults both during rest and task performance. Functional connectivity within striatal structures decreased with age during rest and task execution. Network flexibility, the changes in network composition from rest to task, was also reduced in older adults, but only in networks with an age-related increase in connectivity. Finally, flexibility of areas in the prefrontal cortex were associated with lower error scores during task execution, especially in older adults. In conclusion, our findings indicate an age-related reduction in the ability to suppress irrelevant network communication, leading to less segregated and less flexible cortical networks. At the same time, striatal connectivity is impaired in older adults, while cerebellar connectivity shows heterogeneous age-related effects during rest and task execution. Future research is needed to clarify how cortical and subcortical connectivity changes relate to one another.

The ups and downs of low-carbohydrate diets in the management of Type 1 diabetes: a review of clinical outcomes.

Dietary management has been a mainstay of care in Type 1 diabetes since before the discovery of insulin when severe carbohydrate restriction was advocated. The use of insulin facilitated re-introduction of carbohydrate into the diet. Current management guidelines focus on a healthy and varied diet with consideration of glycaemic load, protein and fat. As a result of frustration with glycaemic outcomes, low-carbohydrate diets have seen a resurgence in popularity. To date, low-carbohydrate diets have not been well studied in the management of Type 1 diabetes. Studies looking at glycaemic outcomes from low-carbohydrate diets have largely been cross-sectional, without validated dietary data and with a lack of control groups. The participants have been highly motivated self-selected individuals who follow intensive insulin management practices, including frequent blood glucose monitoring and additional insulin corrections with tight glycaemic targets. These confounders limit the ability to determine the extent of the impact of dietary carbohydrate restriction on glycaemic outcomes. Carbohydrate-containing foods including grains, fruit and milk are important sources of nutrients. Hence, low-carbohydrate diets require attention to vitamin and energy intake to avoid micronutrient deficiencies and growth issues. Adherence to restricted diets is challenging and can have an impact on social normalcy. In individuals with Type 1 diabetes, adverse health risks such as diabetic ketoacidosis, hypoglycaemia, dyslipidaemia and glycogen depletion remain clinical concerns. In the present paper, we review studies published to date and provide clinical recommendations for ongoing monitoring and support for individuals who choose to adopt a low-carbohydrate diet. Strategies to optimize postprandial glycaemia without carbohydrate restriction are presented.

Dietary protein affects both the dose and pattern of insulin delivery required to achieve postprandial euglycaemia in Type 1 diabetes: a randomized trial.

AIM: To quantify the insulin requirement for a high-protein meal compared with a low-protein meal, controlling for carbohydrate and fat content. METHODS: In this crossover study, young people with Type 1 diabetes were randomized to consume a high- (60 g) or low-protein meal (5 g), each containing 30 g carbohydrate and 8 g fat. A variation of the insulin clamp technique was used to determine the insulin requirements to maintain euglycaemia for the following 5 h. RESULTS: A total of 11 participants (mean ± sd age 16.5 ± 2.7 years, HbA1c 52 ± 8.7 mmol/mol [6.9 ± 0.8%], diabetes duration 6.9±5.1 years) completed the study. The mean insulin requirements for the high-protein meal were higher than for the low-protein meal [10.3 (CI 8.2, 12.57) vs 6.7 units (CI 4.7, 8.8); P=0.001], with inter-individual requirements ranging from 0.9 to six times the low-protein meal requirement. Approximately half the additional insulin [1.1 units/h (CI 0.5, 1.8; P=0.001)] was given in the first 2 h, compared with an additional 0.5 units/h (CI -0.2, 1.2; P=0.148) in the second 2 h and 0.1 units (CI -0.6, 0.8; P=0.769) in the final hour. CONCLUSIONS: A high-protein meal requires ~50% more insulin to maintain euglycaemia than a low-protein meal that contains the same quantity of carbohydrate. The majority is required within the first 2 h. Inter-individual differences exist in insulin requirements for dietary protein.

A randomized comparison of three prandial insulin dosing algorithms for children and adolescents with Type 1 diabetes.

AIM: To compare systematically the impact of two novel insulin-dosing algorithms (the Pankowska Equation and the Food Insulin Index) with carbohydrate counting on postprandial glucose excursions following a high fat and a high protein meal. METHODS: A randomized, crossover trial at two Paediatric Diabetes centres was conducted. On each day, participants consumed a high protein or high fat meal with similar carbohydrate amounts. Insulin was delivered according to carbohydrate counting, the Pankowska Equation or the Food Insulin Index. Subjects fasted for 5 h following the test meal and physical activity was standardized. Postprandial glycaemia was measured for 300 min using continuous glucose monitoring. RESULTS: 33 children participated in the study. When compared to carbohydrate counting, the Pankowska Equation resulted in lower glycaemic excursion for 90-240 min after the high protein meal (p < 0.05) and lower peak glycaemic excursion (p < 0.05). The risk of hypoglycaemia was significantly lower for carbohydrate counting and the Food Insulin Index compared to the Pankowska Equation (OR 0.76 carbohydrate counting vs. the Pankowska Equation and 0.81 the Food Insulin Index vs. the Pankowska Equation). There was no significant difference in glycaemic excursions when carbohydrate counting was compared to the Food Insulin Index. CONCLUSION: The Pankowska Equation resulted in reduced postprandial hyperglycaemia at the expense of an increase in hypoglycaemia. There were no significant differences when carbohydrate counting was compared to the Food Insulin Index. Further research is required to optimize prandial insulin dosing.

Age-Related Declines in Motor Performance are Associated With Decreased Segregation of Large-Scale Resting State Brain Networks.

Aging is typically associated with substantial declines in motor functioning as well as robust changes in the functional organization of brain networks. Previous research has investigated the link between these 2 age-varying factors but examinations were predominantly limited to the functional organization within motor-related brain networks. Little is known about the relationship between age-related behavioral impairments and changes in functional organization at the whole brain (i.e., multiple network) level. This knowledge gap is surprising given that the decreased segregation of brain networks (i.e., increased internetwork connectivity) can be considered a hallmark of the aging process. Accordingly, we investigated the association between declines in motor performance across the adult lifespan (20-75 years) and age-related modulations of functional connectivity within and between resting state networks. Results indicated that stronger internetwork resting state connectivity observed as a function of age was significantly related to worse motor performance. Moreover, performance had a significantly stronger association with the strength of internetwork as compared with intranetwork connectivity, including connectivity within motor networks. These findings suggest that age-related declines in motor performance may be attributed to a breakdown in the functional organization of large-scale brain networks rather than simply age-related connectivity changes within motor-related networks.

Optimizing the combination insulin bolus split for a high-fat, high-protein meal in children and adolescents using insulin pump therapy.

AIMS: To determine the optimum combination bolus split to maintain postprandial glycaemia with a high-fat and high-protein meal in young people with Type 1 diabetes. METHODS: A total of 19 young people (mean age 12.9 ± 6.7 years) participated in a randomized, repeated-measures trial comparing postprandial glycaemic control across six study conditions after a high-fat and high-protein meal. A standard bolus and five different combination boluses were delivered over 2 h in the following splits: 70/30 = 70% standard /30% extended bolus; 60/40=60% standard/40% extended bolus; 50/50=50% standard/50% extended bolus; 40/60=40% standard/60% extended bolus; and 30/70=30% standard/70% extended bolus. Insulin dose was determined using the participant's optimized insulin:carbohydrate ratio. Continuous glucose monitoring was used to assess glucose excursions for 6 h after the test meal. RESULTS: Standard bolus and combination boluses 70/30 and 60/40 controlled the glucose excursion up to 120 min. From 240 to 300 min after the meal, the glucose area under the curve was significantly lower for combination bolus 30/70 compared with standard bolus (P=0.004). CONCLUSIONS: High-fat and high-protein meals require a ≥60% insulin:carbohydrate ratio as a standard bolus to control the initial postprandial rise. Additional insulin at an insulin:carbohydrate ratio of up to 70% is needed in the extended bolus for a high fat and protein meal to prevent delayed hyperglycaemia.

Increasing the protein quantity in a meal results in dose-dependent effects on postprandial glucose levels in individuals with Type 1 diabetes mellitus.

AIM: To determine the glycaemic impact of increasing protein quantities when consumed with consistent amounts of carbohydrate in individuals with Type 1 diabetes on intensive insulin therapy. METHODS: Participants with Type 1 diabetes [aged 10-40 years, HbA1c ≤ 64 mmol/mol (8%), BMI ≤ 91st percentile] received a 30-g carbohydrate (negligible fat) test drink daily over 5 days in randomized order. Protein (whey isolate 0 g/kg carbohydrate, 0 g/kg lipid) was added in amounts of 0 (control), 12.5, 25, 50 and 75 g. A standardized dose of insulin was given for the carbohydrate. Postprandial glycaemia was assessed by 5 h of continuous glucose monitoring. RESULTS: Data were collected from 27 participants (15 male). A dose-response relationship was found with increasing amount of protein. A significant negative relationship between protein dose and mean excursion was seen at the 30- and 60-min time points (P = 0.007 and P = 0.002, respectively). No significant relationship was seen at the 90- and 120-min time points. Thereafter, the dose-response relationship inverted, such that there was a significant positive relationship for each of the 150-300-min time points (P < 0.004). Mean glycaemic excursions were significantly greater for all protein-added test drinks from 150 to 300 min (P < 0.005) with the 75-g protein load, resulting in a mean excursion that was 5 mmol/l higher when compared with the control test drink (P < 0.001). CONCLUSIONS: Increasing protein quantity in a low-fat meal containing consistent amounts of carbohydrate decreases glucose excursions in the early (0-60-min) postprandial period and then increases in the later postprandial period in a dose-dependent manner.

Safety and efficacy of the predictive low glucose management system in the prevention of hypoglycaemia: protocol for randomised controlled home trial to evaluate the Suspend before low function.

INTRODUCTION: Innovations with sensor-augmented pump therapy (SAPT) to reduce hypoglycaemia in patients with type 1 diabetes are an ongoing area of research. The predictive low glucose management (PLGM) system incorporates continuous glucose sensor data into an algorithm and suspends basal insulin before the occurrence of hypoglycaemia. The system was evaluated in in-clinic studies, and has informed the parameters of a larger home trial to study its efficacy and safety in real life. METHODS AND ANALYSIS: The aim of this report is to describe the study design and outcome measures for the trial. This is a 6-month, multicentre, randomised controlled home trial to test the PLGM system in children and adolescents with type 1 diabetes. The system is available in the Medtronic MiniMed 640G pump as the 'Suspend before low' feature. Following a run-in period, participants are randomised to either the control arm with SAPT alone or the intervention arm with SAPT and Suspend before low. The primary aim of this study is to evaluate the time spent hypoglycaemic (sensor glucose <3.5 mmol/L) with and without the system. The secondary aims are to determine the number of hypoglycaemic events, the time spent hyperglycaemic, and to evaluate safety with ketosis and changes in glycated haemoglobin. The study also aims to assess the changes in counter-regulatory hormone responses to hypoglycaemia evaluated by a hyperinsulinaemic hypoglycaemic clamp in a subgroup of patients with impaired awareness. Validated questionnaires are used to measure the fear of hypoglycaemia and the impact on the quality of life to assess burden of the disease. ETHICS AND DISSEMINATION: Ethics committee permissions were gained from respective Institutional Review boards. The findings of the study will provide high quality evidence of the ability of the system in the prevention of hypoglycaemia in real life. TRIAL REGISTRATION NUMBER: ACTRN12614000510640, Pre-results.

Influence of dietary protein on postprandial blood glucose levels in individuals with Type 1 diabetes mellitus using intensive insulin therapy.

AIM: To determine the effects of protein alone (independent of fat and carbohydrate) on postprandial glycaemia in individuals with Type 1 diabetes mellitus using intensive insulin therapy. METHODS: Participants with Type 1 diabetes mellitus aged 7-40 years consumed six 150 ml whey isolate protein drinks [0 g (control), 12.5, 25, 50, 75 and 100] and two 150 ml glucose drinks (10 and 20 g) without insulin, in randomized order over 8 days, 4 h after the evening meal. Continuous glucose monitoring was used to assess postprandial glycaemia. RESULTS: Data were collected from 27 participants. Protein loads of 12.5 and 50 g did not result in significant postprandial glycaemic excursions compared with control (water) throughout the 300 min study period (P > 0.05). Protein loads of 75 and 100 g resulted in lower glycaemic excursions than control in the 60-120 min postprandial interval, but higher excursions in the 180-300 min interval. In comparison with 20 g glucose, the large protein loads resulted in significantly delayed and sustained glucose excursions, commencing at 180 min and continuing to 5 h. CONCLUSIONS: Seventy-five grams or more of protein alone significantly increases postprandial glycaemia from 3 to 5 h in people with Type 1 diabetes mellitus using intensive insulin therapy. The glycaemic profiles resulting from high protein loads differ significantly from the excursion from glucose in terms of time to peak glucose and duration of the glycaemic excursion. This research supports recommendations for insulin dosing for large amounts of protein.

In children using intensive insulin therapy, a 20-g variation in carbohydrate amount significantly impacts on postprandial glycaemia.

AIM: To determine if an insulin dose calculated for a meal containing 60 g carbohydrate maintains postprandial glycaemic control for meals containing 40, 50, 70 or 80 g carbohydrate. METHODS: Thirty-four young people (age range 8.5-17.7 years) using intensive insulin therapy consumed five test breakfasts with equivalent fat, protein and fibre contents but differing carbohydrate quantities (40, 50, 60, 70 and 80 g of carbohydrate). The preprandial insulin dose was the same for each meal, based on the subject's usual insulin:carbohydrate ratio for 60 g carbohydrate. Continuous glucose monitoring was used to monitor postprandial glucose over 180 min. RESULTS: The 40-g carbohydrate meal resulted in significantly more hypoglycaemia than the other meals (P = 0.003). There was a one in three chance of hypoglycaemia between 120 and 180 min if an insulin dose for 60 g carbohydrate was given for 40 g carbohydrate. The glucose levels of subjects on the 80-g meal were significantly higher than the 60- and 70-g carbohydrate meals at all time points between 150 and 180 min (P < 0.01). Subjects consuming the 80-g meal were more likely to have significant hyperglycaemia (blood glucose levels ≥ 12 mmol/l) compared with the other meals (P < 0.001). CONCLUSIONS: In patients using intensive insulin therapy, an individually calculated insulin dose for 60 g carbohydrate results in postprandial hypoglycaemia or hyperglycaemia for meals containing 40 and 80 g carbohydrate. To calculate mealtime insulin in order to maintain postprandial control, carbohydrate estimations should be within 10 g of the actual meal carbohydrate.

Can children with Type 1 diabetes and their caregivers estimate the carbohydrate content of meals and snacks?

AIMS: Carbohydrate (CHO) counting allows children with Type 1 diabetes to adjust mealtime insulin dose to carbohydrate intake. Little is known about the ability of children to count CHO and whether a particular method for assessing CHO quantity is better than others. We investigated how accurately children and their caregivers estimate carbohydrate, and whether counting in gram increments improves accuracy compared with CHO portions or exchanges. METHODS: One hundred and two children and adolescents (age range 8.3-18.1 years) on intensive insulin therapy and 110 caregivers independently estimated the CHO content of 17 standardized meals (containing 8-90 g CHO), using whichever method of carbohydrate quantification they had been taught (gram increments, 10-g portions or 15-g exchanges). RESULTS: Seventy-three per cent (n = 2530) of all estimates were within 10-15 g of actual CHO content. There was no relationship between the mean percentage error and method of carbohydrate counting or glycated haemoglobin (HbA(1c)) (P > 0.05). Mean gram error and meal size were negatively correlated (r = -0.70, P < 0.0001). The longer children had been CHO counting the greater the mean percentage error (r = 0.173, P = 0.014). Core foods in non-standard quantities were most frequently inaccurately estimated, while individually labelled foods were most often accurately estimated. CONCLUSIONS: Children with Type 1 diabetes and their caregivers can estimate the carbohydrate content of meals with reasonable accuracy. Teaching CHO counting in gram increments did not improve accuracy compared with CHO portions or exchanges. Large meals tended to be underestimated and snacks overestimated. Repeated age-appropriate education appears necessary to maintain accuracy in carbohydrate estimations.

Children and adolescents on intensive insulin therapy maintain postprandial glycaemic control without precise carbohydrate counting.

AIMS: Carbohydrate (CHO) quantification is used to adjust pre-meal insulin in intensive insulin regimens. However, the precision in CHO quantification required to maintain postprandial glycaemic control is unknown. We determined the effect of a +/-10-g variation in CHO amount, with an individually calculated insulin dose for 60 g CHO, on postprandial glycaemic control. METHODS: Thirty-one children and adolescents (age range 9.5-16.8 years), 17 using continuous subcutaneous insulin infusion (CSII) and 14 using multiple daily injections (MDI), participated. Each subject consumed test lunches of equal macronutrient content, differing only in carbohydrate quantity (50, 60, 70 g CHO), in random order on three consecutive days. For each participant, the insulin dose was the same for each meal, based on their usual insulin : CHO ratio for 60 g CHO. Activity was standardized. Continuous glucose monitoring was used. RESULTS: The CSII and MDI subjects demonstrated no difference in postprandial blood glucose levels (BGLs) for comparable carbohydrate loads (P > 0.05). The 10-g variations in CHO quantity resulted in no differences in BGLs or area under the glucose curves for 2.5 h (P > 0.05). Hypoglycaemic episodes were not significantly different (P = 0.32). The 70-g meal produced higher glucose excursions after 2.5 h, with a maximum difference of 1.9 mmol/l at 3 h (P = 0.01), but the BGLs remained within international postprandial targets. CONCLUSIONS: In patients using intensive insulin therapy, an individually calculated insulin dose for 60 g of carbohydrate maintains postprandial BGLs for meals containing between 50 and 70 g of carbohydrate. A single mealtime insulin dose will cover a range in carbohydrate amounts without deterioration in postprandial control.