A Review of Insulin-Dosing Formulas for Continuous Subcutaneous Insulin Infusion (CSII) for Adults with Type 1 Diabetes

Insulin Management for Gestational and Type 2 Diabetes in Pregnancy

Insulin is preferred as the first-line agent for glucose management of gestational diabetes mellitus and type 2 diabetes in pregnancy when nutritional and lifestyle modifications are unable to achieve pregnancy-specific glucose targets. Individual heterogeneity in defects of insulin secretion or sensitivity in liver and muscle, unique genetic influences on pregnancy glycemic regulation, and variable cultural and lifestyle behaviors that affect meal, activity, sleep, and occupational schedules necessitate a personalized approach to insulin regimens. Newer insulin preparations have been developed to mimic the physiologic release of endogenous insulin, maintaining appropriate basal levels to cover hepatic gluconeogenesis and simulate the rapid, meal-related, bolus rise of insulin. Such physiologic basal-bolus dosing of insulin can be administered safely, achieving tighter glycemic control while reducing episodes of hypoglycemia. Insulin initiation and titration require understanding the pharmacodynamics of different insulin preparations in addition to a patient's glycemic profiles, effect of variable nutritional intake and mealtimes, physical activity, stress, timing of sleep cycles, and cultural habits. Educating and empowering patients to learn how their glucose responds to insulin, portion and content of meals, and physical activity can increase personal engagement in therapy, flexibility in eating patterns, and improved glycemic control. This Clinical Expert Series article is focused on optimizing insulin management (initiation, dosing, and titration) of gestational and type 2 diabetes in pregnancy.

Multiple basal infusion rates in open-loop insulin delivery systems: is there a metabolic benefit?

Objective

To evaluate glycemic control according to the number of daily basal rates (BRs) in type 1 diabetes patients using continuous subcutaneous insulin infusion (CSII).

Subjects and methods

Cross-sectional study of patients treated with an open-loop CSII for at least 6 months and using a flash glucose monitoring system. Patients were divided into 2 groups: group 1 (G1) and group 2 (G2), with ≤4 and >4 BRs/24h, respectively. The groups were compared regarding HbA1c, time in range (TIR), time above range (TAR), time below range (TBR), glucose management indicator (GMI), glucose variability and data related to hypoglycemia. Regression models were performed.

Results

The study included 99 patients (n = 55 in G1; n = 44 in G2). Median (Interquartile range) overall age was 30 (17) years, with 19.5 (48) and 51 (77) months of CSII use, respectively. The median number of different BRs was 3 (2) for G1 and 6 (2) for G2. There were no differences concerning age, sex, educational stage, weight, and insulin analog used. G2 had longer disease duration, longer CSII use, and higher total basal daily dose/kg. No significant differences regarding HbA1c, median glucose, GMI, TIR, TAR, and CV were found. G2 patients had more hypoglycemia, more asymptomatic hypoglycemia, and higher TBR. After adjusting for potential confounders, G1 maintained a lower risk of asymptomatic hypoglycemia.

Conclusion

Programming open-loop CSII devices with more than 4 BRs does not improve metabolic control. Additionally, it seems to be a risk factor for hypoglycemia and was an independent predictor for asymptomatic hypoglycemia.

Fasting plasma glucose level-based formula for estimating starting daily dose in basal-bolus insulin therapy

There is no standard formula for estimating the starting daily dose (SDD) of basal-bolus insulin therapy (BBT). We aimed to develop a formula for estimating SDD and evaluate its efficacy and safety in patients with type 2 diabetes hospitalized for BBT. In the first study (n = 104), we retrospectively analyzed the relationship between peak daily dose (PDD) during hospitalization and clinical parameters. The PDD was significantly associated with fasting plasma glucose (FPG) (R = 0.449, P < 0.0001) and HbA1c levels (R = 0.384, P < 0.0001) but not body weight, body mass index, body surface area, or serum C-peptide levels. Based on the results, we developed a formula for estimating SDD using FPG levels: SDD (U/day) = 0.08 × FPG (mg/dL). In the second study (n = 405), we assessed efficacy and safety of the formula by evaluating the M-value from the daily glucose profile and assessing the frequency of hypoglycemia (blood glucose level < 70 mg/dL). When BBT was initiated using the FPG level-based formula, the M-values decreased from 61.0 ± 52.8 to 12.8 ± 10.8 (P < 0.0001), and hypoglycemia was observed in only 3/405 cases (0.74%) under the SDD. The FPG level-based formula is useful for estimating SDD in BBT and is safe for clinical use.

Evaluation of a Web-Based Simulation Tool for Self-Management Support in Type 1 Diabetes: A Pilot Study

OBJECTIVE

To develop and evaluate a novel Web-based Simulation Tool (WST) that brings simulation technologies to people with Type 1 Diabetes (T1D), enabling unique patient-data interactions seamlessly on a daily basis.

METHODS

A pilot clinical trial was conducted to assess system usability. The study consisted of one week of observation (Phase 1) and four weeks of interaction with WST (Phase 2). Responses to Technology Acceptance (TA) and Diabetes Distress Scale (DDS) questionnaires were collected, and follow-up interviews were conducted after Phase 2.

RESULTS

Fifteen participants with T1D using Control-IQ technology (age: 36 ± 13 years, HbA1c: 6.5% ± 0.7%) completed all study procedures. Generated simulation models achieved a median Mean Absolute Relative Difference (MARD) of 6.8% [interquartile range, IQR: 5.1%, 9.1%]. A decrease in expected benefits (likely explained by issues with the third-party data collection system) and an increase in expected burdens were observed. On a 1-5 scale, ease of use, trust, and usefulness scores were 3 [3,4], 4 [3,4], and 4 [3,4], respectively. Time below 70 mg/dL decreased between Phases 1 and 2 (1.6% [0.7%,3.7%] vs 0.8% [0.5%,3.0%]). A reduction in mean emotional burden was also observed (2.5 ± 1.1 vs 2.1 ± 0.8).

CONCLUSIONS

Results indicate that there was a learning curve to WST, but also that most participants trusted the system and found it useful in their diabetes care.

SIGNIFICANCE

Simulation technologies like WST could be used by educators and patients to facilitate diabetes self-management, leading to better diabetes literacy and reducing associated distress.

Insulin pumps in children - a systematic review

BACKGROUND

Insulin pump therapy is a real breakthrough in managing diabetes Mellitus, particularly in children. It can deliver a tiny amount of insulin and decreases the need for frequent needle injections. It also helps to maintain adequate and optimal glycemic control to reduce the risk of metabolic derangements in different tissues. Children are suitable candidates for pump therapy as they need a more freestyle and proper metabolic control to ensure adequate growth and development. Therefore, children and their caregivers should have proper education and training and understand the proper use of insulin pumps to achieve successful pump therapy. The pump therapy continuously improves to enhance its performance and increase its simulation of the human pancreas. Nonetheless, there is yet a long way to reach the desired goal.

AIM

To review discusses the history of pump development, its indications, types, proper use, special conditions that may enface the children and their families while using the pump, its general care, and its advantages and disadvantages.

METHODS

We conducted comprehensive literature searches of electronic databases until June 30, 2022, related to pump therapy in children and published in the English language.

RESULTS

We included 118 articles concerned with insulin pumps, 61 were reviews, systemic reviews, and meta-analyses, 47 were primary research studies with strong design, and ten were guidelines.

CONCLUSION

The insulin pump provides fewer needles and can provide very tiny insulin doses, a convenient and more flexible way to modify the needed insulin physiologically, like the human pancreas, and can offer adequate and optimal glycemic control to reduce the risk of metabolic derangements in different tissues.

In vivo degradation forms, anti-degradation strategies, and clinical applications of therapeutic peptides in non-infectious chronic diseases

Current medicinal treatments for diseases comprise largely of two categories: small molecular (chemical) (e.g., aspirin) and larger molecular (peptides/proteins, e.g., insulin) drugs. Whilst both types of therapeutics can effectively treat different diseases, ranging from well-understood (in view of pathogenesis and treatment) examples (e.g., flu), to less-understood chronic diseases (e.g., diabetes), classical small molecule drugs often possess significant side-effects (a major cause of drug withdrawal from market) due to their low- or non-specific targeting. By contrast, therapeutic peptides, which comprise short sequences from naturally occurring peptides/proteins, commonly demonstrate high target specificity, well-characterized modes-of-action, and low or non-toxicity in vivo. Unfortunately, due to their small size, linear permutation, and lack of tertiary structure, peptidic drugs are easily subject to rapid degradation or loss in vivo through chemical and physical routines, thus resulting in a short half-life and reduced therapeutic efficacy, a major drawback that can reduce therapeutic efficiency. However, recent studies demonstrate that the short half-life of peptidic drugs can be significantly extended by various means, including use of enantiomeric or non-natural amino acids (AAs) (e.g., L-AAs replacement with D-AAs), chemical conjugation [e.g., with polyethylene glycol], and encapsulation (e.g., in exosomes). In this context, we provide an overview of the major in vivo degradation forms of small therapeutic peptides in the plasma and anti-degradation strategies. We also update on the progress of small peptide therapeutics that are either currently in clinical trials or are being successfully used in clinical therapies for patients with non-infectious diseases, such as diabetes, multiple sclerosis, and cancer.

Basal insulin requirement in patients with type 1 diabetes depends on the age and body mass index

AIMS/INTRODUCTION

To investigate the basal insulin requirement in patients with type 1 diabetes who are on multiple daily injections (MDI) and to assess the patient characteristics that affect the percent of total daily basal insulin dose to the total daily insulin dose (%TBD/TDD).

MATERIALS AND METHODS

The subjects of this study were 67 inpatients with type 1 diabetes who were served diabetic meals of 25-30 kcal/kg standard body weight during several weeks of hospitalization. The basal insulin requirement was adjusted to keep the blood glucose level from bedtime to before breakfast within a 30 mg/dL difference. The bolus insulin dose before the meal was adjusted to keep the blood glucose level below 140 and 200 mg/dL before and 2 h after each meal, respectively. The total daily insulin dose (TDD), the percent of total daily basal insulin dose (TBD) to TDD (%TBD/TDD), and clinical characteristics were collected.

RESULTS

The median (Q1, Q3) of TDD was 33.0 (26.0, 49.0) units, and the %TBD/TDD was 24.1 ± 9.8%. The %TBD/TDD was positively correlated with the body mass index (BMI) and negatively correlated with the age at the onset and at the examination according to a univariate analysis. However, the %TBD/TDD was dependent on the BMI (β = 0.340, P = 0.004) and the age at examination (β = -0.288, P = 0.012) according to the multiple regression analysis.

CONCLUSIONS

The average %TBD/TDD in patients with type 1 diabetes on MDI was approximately 24% under inpatient conditions. The basal insulin requirement was dependent on the BMI and the age at examination.

Insulin dose reduction in dapagliflozin combination therapy for type 1 diabetes mellitus: the RISING-STAR study

To clarify the frequency of hypoglycemia in patients with type 1 diabetes mellitus receiving dapagliflozin combination therapy to reduce their basal insulin dose. Sixty subjects were assigned to two groups according to their basal insulin-to-total daily dose (TDD) ratio: group A (basal insulin/TDD <40%) and group B (≥40%). Reduction of the basal insulin dose was instituted in group B, but not in group A. The number of hypoglycemic events per day and ketosis frequency were the primary and secondary endpoints, respectively. The hypoglycemia frequency before and after the intervention was 0.23 and 0.26 times/day in group A and 0.19 and 0.23 times/day in group B, respectively, with no significant difference between the groups. The total insulin dose reduction was approximately 10% in both groups. Ketosis frequency increased significantly after the intervention (from 0.013 to 0.086 times/day in group A and 0.013 to 0.059 times/day in group B). Time-in-range, mean amplitude of glycemic excursion, and glycated hemoglobin A1c improved in both groups. No significant difference in hypoglycemia frequency was observed between patients with and without reduction of the basal insulin dose. The combination therapy improved glycemic control and patient satisfaction regarding hyperglycemia. Nevertheless, adequate attention to ketosis is crucial.

Diurnal Variation of Real-Life Insulin Sensitivity Factor Among Children and Adolescents With Type 1 Diabetes Using Ultra-Long-Acting Basal Insulin Analogs

BACKGROUND

Estimation of insulin sensitivity factor (ISF) is essential for correction insulin doses calculation. This study aimed to assess real-life ISF among children and adolescents with type 1 diabetes using ultra-long-acting basal insulin analogs and to detect factors associated with ISF among those patients.

METHODS

This prospective observational study was conducted at Sohag University Hospital, Egypt, and included 93 participants aged 6-18 years, diagnosed with T1DM for at least 1 year and using insulin glargine 300 Units/mL or insulin degludec 100 Units/mL as basal insulin. The ISF, insulin-to-carbohydrate ratio (ICR) and insulin doses were initially assessed then adjusted as required. The participants were regularly contacted throughout the follow-up period. Glycemic control parameters were assessed after 3 months.

RESULTS

The ISF showed diurnal variation with higher correction dose requirements for the morning than for the rest of the day (p < 0.001). This pattern of diurnal variation was found in participants with different pubertal stages and in participants using either type of ultra-long acting basal insulin analogs. There was no significant difference between the ISF calculated according to the 1800 rule [1800/Total daily insulin dose (TDD)] and the morning ISF (p = 0.25). The 1800 rule-calculated ISF was significantly lower than the actual ISF for the afternoon (p < 0.001) and the evening (p < 0.001). ISF at different times of the day were significantly correlated with age, body mass index, pubertal stage, diabetes duration, TDD, and ICR. Multiple regression analysis revealed that ICR was the most significant factor associated with ISF. Linear regression analysis revealed that the ISF (in mg/dL) for any time of the day could be estimated as 5.14 × ICR for the same time of the day (coefficient = 5.14, 95% confidence interval: 5.10-5.19, R ² = 0.95, p < 0.001).

CONCLUSION

Diurnal variation of ISF that had to be considered for proper calculation of correction doses. This diurnal variation was found in children and adolescents with different pubertal stages. The 1800 rule was appropriate for the morning correction doses but not in the afternoon or the evening. The TDD and the ICR could be used for ISF estimation.

Dynamic Insulin Basal Needs Estimation and Parameters Adjustment in Type 1 Diabetes

Technology advances have made possible improvements such as Continuous Glucose Monitors, giving the patient a glucose reading every few minutes, or insulin pumps, allowing more personalized therapies. With the increasing number of available closed-loop systems, new challenges appear regarding algorithms and functionalities. Several of the analysed systems in this paper try to adapt to changes in some patients’ conditions and, in several of these systems, other variables such as basal needs are considered fixed from day to day to simplify the control problem. Therefore, these systems require a correct adjustment of the basal needs profile which becomes crucial to obtain good results. In this paper a novel approach tries to dynamically determine the insulin basal needs of the patient and use this information within a closed-loop algorithm, allowing the system to dynamically adjust in situations of illness, exercise, high-fat-content meals or even partially blocked infusion sites and avoiding the need for setting a basal profile that approximately matches the basal needs of the patient. The insulin sensitivity factor and the glycemic target are also dynamically modified according to the situation of the patient. Basal insulin needs are dynamically determined through linear regression via the decomposition of previously dosed insulin and its effect on the patient’s glycemia. Using the obtained value as basal insulin needs and other mechanisms such as basal needs modification through its trend, ISF and glycemic targets modification and low-glucose-suspend threshold, the safety of the algorithm is improved. The dynamic basal insulin needs determination was successfully included in a closed-loop control algorithm and was simulated on 30 virtual patients (10 adults, 10 adolescent and 10 children) using an open-source python implementation of the FDA-approved (Food and Drug Administration) UVa (University of Virginia)/Padova Simulator. Simulations showed that the proposed system dynamically determines the basal needs and can adapt to a partial blockage of the insulin infusion, obtaining similar results in terms of time in range to the case in which no blockage was simulated. The proposed algorithm can be incorporated to other current closed-loop control algorithms to directly estimate the patient’s basal insulin needs or as a monitoring channel to detect situations in which basal needs may differ from the expected ones.

Race/ethnicity and challenges for optimal insulin therapy

AIMS

We aimed to review insulin dosing recommendations, insulin regulation and its determinants, glycaemic response to carbohydrates, and the efficacy and safety of insulin therapy in different races/ethnicities.

METHODS

We searched for articles in PubMed and Google Scholar databases up to 31 March 2021, with the following keywords: "ethnicity", "diabetes", "insulin", "history of insulin", "insulin therapy", "food/rice", "carbohydrate intake", "insulin resistance", "BMI", "insulin dosing", "insulin sensitivity", "insulin response", "glycaemic index", "glycaemic response", "efficacy and safety", with interposition of the Boolean operator "AND".In addition, we reviewed the reference lists of the articles found.

RESULTS

The differential effect of race/ethnicity has not yet been considered in current insulin therapy guidelines. Nevertheless, body size and composition, body mass index, fat distribution, diet, storage, and energy expenditure vary significantly across populations. Further, insulin sensitivity, insulin response, and glycaemicresponse to carbohydrates differ by ethnicity. These disparities may lead to different insulin requirements, adversely impacting the efficacy and safety of insulin therapy among ethnic groups.

CONCLUSIONS

Race/ethnicity affects glucose metabolism and insulin regulation.Until now, international guidelines addressing racial/ethnic-specific clinical recommendations are limited. Comprehensive updated insulin therapy guidelines by ethnicity are urgently needed.

Pharmacokinetic-pharmacodynamic modelling of the hypoglycaemic effect of pulsatile administration of human insulin in rats

The relationship between the plasma insulin (INS) concentration–time course and plasma glucose concentration–time course during and after pulsatile INS administration to rats was characterized using a pharmacokinetic–pharmacodynamic (PK–PD) model. A total INS dose of 0.5 IU/kg was intravenously injected in 2 to 20 pulses over a 2-h period. Compared with the single bolus administration, the area under the effect-time curve (AUE) increased depending on the number of pulses, and the AUEs for more than four pulses plateaued at a significantly larger value, which was similar to that after the infusion of a total of 0.5 IU/kg of INS over 2 h. No increase in plasma INS concentration occurred after pulsatile administration. Two indirect response models primarily reflecting the receptor-binding process (IR model) or glucose transporter 4 (GLUT4) translocation (GT model) were applied to describe the PK–PD relationship after single intravenous bolus administration of INS. These models could not explain the observed data after pulsatile administration. However, the IR-GT model, which was a combination of the IR and GT models, successfully explained the effects of pulsatile administration and intravenous infusion. These results indicate that the receptor-binding process and GLUT4 translocation are responsible for the change in AUE after pulsatile administration.

Prospective evaluation of insulin-to-carbohydrate ratio in children and adolescents with type 1 diabetes using multiple daily injection therapy

AIM

Assessment of insulin-to-carbohydrate ratio (ICR) in children and adolescents with type 1 diabetes mellitus (T1DM) using multiple daily injection (MDI) therapy.

METHODS

This prospective observational study was conducted over a 2-year period at Sohag University Hospital, Egypt. Children and adolescents aged 4 to 17 years, diagnosed with T1DM for at least 1 year, with fasting serum C-peptide levels <0.24 ng/dL and whose parents accepted to shift their management to flexible MDI using carbohydrate counting, were included. Participants were initially hospitalized for estimation of ICR and insulin doses, then followed-up monthly for further adjustments. Insulin doses, ICR, and glycemic control parameters were assessed after 3 months.

RESULTS

The study included 201 participants, 110 (54.7%) of them were males. The median age was 9.5 years (interquartile range: 7-12.5 years). Bolus insulin requirements estimated by the 500 rule were significantly lower than the actual doses used by the study participants for all meals (P < .001). Bolus insulin requirement for morning meal was significantly higher compared to other meals (P < .001). Linear regression analyses between ICR for different meals and the reciprocal of total daily dose (TDD) in 96 participants with optimum glycemic control revealed that ICR could be calculated as 301 to 309/TDD for morning meal (R² = 0.97, P < .001), 317 to 331/TDD for afternoon meal (R² = 0.96, P < .001), and 362 to 376/TDD for evening meal (R² = 0.98, P < .001).

CONCLUSIONS

Bolus insulin requirements showed diurnal variation. Using 301 to 309/TDD, 317 to 331/TDD, and 362 to 376/TDD formulas would be more appropriate than the 500 rule for initial estimation of ICR for morning, afternoon, and evening meals, respectively.

Update on Serum Glucose and Metabolic Management of Clinical Nuclear Medicine Studies: Current Status and Proposed Future Directions

Management of a patient's blood glucose or metabolism in nuclear medicine studies has become an integral aspect of daily work primarily due to the increasing use of F-18 flurodeoxyglucose (FDG) positron emission tomography (PET). Newer tracers such as F-18 Fluciclovine and C-11 Choline, are in theory subject to metabolic shifts and changes based on patients' insulin levels, and also require attention to achieving optimum patient preparation. Metabolic derangements can also affect other studies, such as gastric emptying (GE), the results of which are dependent upon the patient's blood glucose level during the time of imaging. The growing variety of diabetic medications has increased the complexity of the instructions which need to be given to patients. Current guidelines for patient preparation were developed in the past and have only slowly evolved with the introduction of newer oral medications. In addition to older insulin formulations newer formulations with different profiles of onset, duration, and consistency of action are being used. The wide spectrum of newer drugs now in use for treating diabetes has not been accompanied by any updated consensus on how to manage these drugs for imaging studies which require blood glucose level management. In this article we review these newer diabetes medications primarily to raise awareness of the changing landscape. Our focus will be on suggestions to optimize patient preparation and management for these studies. For each scenario, our suggestions will be given as summary proposals for best patient management. Our hope is that this discussion will stimulate multicenter studies to provide data to support new practice guidelines for metabolically dependent nuclear medicine procedures.

Design and Prestudy Assessment of a Dashboard for Presenting Self-Collected Health Data of Patients With Diabetes to Clinicians: Iterative Approach and Qualitative Case Study

Background

Introducing self-collected health data from patients with diabetes into consultation can be beneficial for both patients and clinicians. Such an initiative can allow patients to be more proactive in their disease management and clinicians to provide more tailored medical services. Optimally, electronic health record systems (EHRs) should be able to receive self-collected health data in a standard representation of medical data such as Fast Healthcare Interoperability Resources (FHIR), from patients systems like mobile health apps and display the data directly to their users—the clinicians. However, although Norwegian EHRs are working on implementing FHIR, no solution or graphical interface is available today to display self-collected health data.

Objective

The objective of this study was to design and assess a dashboard for displaying relevant self-collected health data from patients with diabetes to clinicians.

Methods

The design relied on an iterative participatory process involving workshops with patients, clinicians, and researchers to define which information should be available and how it should be displayed. The assessment is based on a case study, presenting an instance of the dashboard populated with data collected from one patient with diabetes type 1 (in-house researcher) face-to-face by 14 clinicians. We performed a qualitative analysis based on usability, functionality, and expectation by using responses to questionnaires that were distributed to the 14 clinicians at the end of the workshops and collected before the participants left. The qualitative assessment was guided by the Standards for Reporting Qualitative Research.

Results

We created a dashboard permitting clinicians to assess the reliability of self-collected health data, list all collected data including medical calculations, and highlight medical situations that need to be investigated to improve the situation of the patients. The dashboard uses a combination of tables, graphs, and other visual representations to display the relevant information. Clinicians think that this type of solution will be useful during consultations every day, especially for patients living in remote areas or those who are technologically interested.

Conclusions

Displaying self-collected health data during consultations is not enough for clinicians; the data reliability has to be assured and the relevant information needs to be extracted and displayed along with the data to ease the introduction during a medical encounter. The prestudy assessment showed that the system provides relevant information to meet clinicians’ need and that clinicians were eager to start using it during consultations. The system has been under testing in a medical trial since November 2018, and the first results of its assessment in a real-life situation are expected in the beginning of next year (2020).

A mobile health monitoring-and-treatment system based on integration of the SSN sensor ontology and the HL7 FHIR standard

BACKGROUND

Mobile health (MH) technologies including clinical decision support systems (CDSS) provide an efficient method for patient monitoring and treatment. A mobile CDSS is based on real-time sensor data and historical electronic health record (EHR) data. Raw sensor data have no semantics of their own; therefore, a computer system cannot interpret these data automatically. In addition, the interoperability of sensor data and EHR medical data is a challenge. EHR data collected from distributed systems have different structures, semantics, and coding mechanisms. As a result, building a transparent CDSS that can work as a portable plug-and-play component in any existing EHR ecosystem requires a careful design process. Ontology and medical standards support the construction of semantically intelligent CDSSs.

METHODS

This paper proposes a comprehensive MH framework with an integrated CDSS capability. This cloud-based system monitors and manages type 1 diabetes mellitus. The efficiency of any CDSS depends mainly on the quality of its knowledge and its semantic interoperability with different data sources. To this end, this paper concentrates on constructing a semantic CDSS based on proposed FASTO ontology.

RESULTS

This realistic ontology is able to collect, formalize, integrate, analyze, and manipulate all types of patient data. It provides patients with complete, personalized, and medically intuitive care plans, including insulin regimens, diets, exercises, and education sub-plans. These plans are based on the complete patient profile. In addition, the proposed CDSS provides real-time patient monitoring based on vital signs collected from patients' wireless body area networks. These monitoring include real-time insulin adjustments, mealtime carbohydrate calculations, and exercise recommendations. FASTO integrates the well-known standards of HL7 fast healthcare interoperability resources (FHIR), semantic sensor network (SSN) ontology, basic formal ontology (BFO) 2.0, and clinical practice guidelines. The current version of FASTO includes 9577 classes, 658 object properties, 164 data properties, 460 individuals, and 140 SWRL rules. FASTO is publicly available through the National Center for Biomedical Ontology BioPortal at https://bioportal.bioontology.org/ontologies/FASTO .

CONCLUSIONS

The resulting CDSS system can help physicians to monitor more patients efficiently and accurately. In addition, patients in rural areas can depend on the system to manage their diabetes and emergencies.

Treatment of mild-to-moderate hypoglycemia in patients with type 1 diabetes treated with insulin pump therapy: are current recommendations effective?

AIMS

Mild-to-moderate hypoglycemia (blood glucose < 4.0 mmol/L) is recommended to be treated with 15 g of carbohydrates and to repeat the treatment if hypoglycemia persists after 15 min. This recommendation was established before intensive insulin therapy and based on studies using insulin with different pharmacokinetic profiles from actual insulin analogs showing that 15 g of glucose increases blood glucose by ~ 1.5 mmol/L in 15 min. We aimed to explore the effect of current recommended treatment of mild-to-moderate hypoglycemia in type 1 diabetes (T1D) participants and factors associated with treatment effectiveness.

METHODS

This is a secondary analysis from three observational inpatient studies with a standardized and supervised treatment (16 g carbohydrates) of hypoglycemia (< 3.3 mmol/L with symptoms or < 3.0 mmol/L without symptom) in participants (47 adults-10 adolescents) with T1D using continuous subcutaneous insulin infusion ("insulin pumps"; CSII)).

RESULTS

Twenty-seven participants presented a total of 48 hypoglycemia episodes treated by a single intake of 16 g of carbohydrates. Time required for normoglycemia recovery was 19.5 ± 12.0 min. The rise in plasma glucose following treatment was 0.85 ± 0.66 mmol/L in 15 min. Eighteen episodes (38%) were resolved (> 4.0 mmol/L) 15-min post-treatment. Glycemia at the time of treatment (< 3.2 mmol/L; p < 0.001) and a higher proportion of total daily insulin from basal doses (p = 0.03) were associated with a slower post-treatment plasma glucose rise.

CONCLUSIONS

These results raise the possibility that sixteen grams of carbohydrates could be insufficient to treat a large proportion of hypoglycemia episodes in T1D patients treated with CSII. Factors affecting treatment effectiveness need to be investigated.

Assessment of factors determining an HbA1c concentration ≤7.5% in patients with type 1 diabetes

BACKGROUND

Establishing an optimal insulin regimen is crucial for maintaining glycemic control in patients with type 1 diabetes (T1D). The aim of the present study was to determine the insulin dose required to achieve an HbA1c concentration ≤7.5% in Japanese patients with T1D.

METHODS

The present multicenter cross-sectional study was performed at three institutes in Japan. Information was collected regarding patient age, sex, body weight, body mass index (BMI), HbA1c, total daily insulin dose (TDD), and total basal insulin dose (TBD), and the effects of these factors on achieving HbA1c ≤7.5% were investigated.

RESULTS

Of 107 patients with T1D, 92 had no detectable endogenous insulin secretion: 39 had HbA1c ≤7.5% (well-controlled group) and 53 had HbA1c >7.5% (poorly controlled group). No significant differences in age, sex, height, body weight, BMI, diabetes duration, stage of diabetic kidney disease, treatment, or TDD were noted between the poorly and well-controlled groups. The TBD as a percentage of TDD (%TBD) was lower in patients with well-controlled diabetes ( P < 0.05) after adjustment for age, gender, and diabetes duration. In the well-controlled group, TDD was correlated with body weight ( R = 0.51), BMI ( R = 0.44), body surface area ( R = 0.41), and TBD ( R = 0.73; P < 0.01 for all), but TBD was not correlated with BMI or body surface area. In our population, a %TBD of approximately 30% was appropriate, without considering BMI.

CONCLUSIONS

To achieve HbA1c ≤7.5 in patients with T1D, TDD should be calculated based on body weight, and the %TBD should be set at 30% in the Japanese population.

Effect of carbohydrate counting using bolus calculators on glycemic control in type 1 diabetes patients during continuous subcutaneous insulin infusion

The present study examined the long‐term efficacy of insulin pump therapy for type 1 diabetes patients when carried out using carbohydrate counting with bolus calculators for 1 year. A total of 22 type 1 diabetes patients who had just started continuous subcutaneous insulin infusion were examined and divided into two groups: one that was educated about carbohydrate counting using bolus calculators (n = 14); and another that did not use bolus calculators (n = 8). After 1 year, the hemoglobin A1c levels of the patient group that used bolus calculators decreased persistently and significantly (P = 0.0297), whereas those of the other group did not. The bodyweight, total daily dose of insulin and bolus percentage of both groups did not change. Carbohydrate counting using bolus calculators is necessary to achieve optimal and persistent glycemic control in patients undergoing continuous subcutaneous insulin infusion.