Rabson-Mendenhall syndrome: a rare case of severe insulin resistance

Insulin resistance syndromes are a heterogeneous group of disorders with variable clinical phenotypes, associated with increased blood glucose and insulin levels. A 20-year-old female, diabetic for 12 years, reported with hyperglycemia not responding to high dose of insulin; therefore, insulin dosage was increased but did not lead to appropriate glycemic control. Investigations revealed hyperglycemia (random blood glucose 23 mmol/L) glycosylated hemoglobin (HbA1c) 9.2%. Ultrasonogram of the abdomen showed prominent ovaries with fatty liver. Echocardiography revealed mild mitral, pulmonary and tricuspid regurgitation and pulmonary hypertension. Based on the clinical features, skin changes and the onset of type 2 diabetes mellitus, Rabson-Mendenhall syndrome (RMS) was considered. In last admission, she was admitted for hyperglycemic control and treated with intravenous fluids, insulin infusion, metformin, pioglitazone, linagliptin, hydroxychloroquine, sulphonylurea, antibiotics. There is no complete cure for the condition and the current treatments are difficult and not very promising. BIRDEM Med J 2022; 12(1): 83-87

Self-Monitoring Blood Glucose Method and Outcome: A Review

Self-monitoring of blood glucose (SMBG) is a valuable technique for diabetes mellitus treatment. Patients with diabetes frequently monitor their blood glucose levels in order to identify hypoglycemia and modify their insulin dosage as necessary. In many large-scale outcome studies, self-monitoring of blood glucose (SMBG) in the management of diabetes plays a vital role, contributing significantly to the outcomes. It is recommended that the patient keep track of their SMBG readings in a log book. For interpreting the SMBG findings, information regarding food intake, medication, and activity may be useful. An explanation of the practical components of the process is required to assess a patient's grasp of SMBG knowledge. For SMBG lancing treatments to be effective, the patient must have a thorough understanding of the stages involved. With many studies suggesting the benefits of SMBG other studies say that SMBG has little clinical effectiveness in improving glycemic control in patients with T2DM who are taking oral medications or eating a low-carbohydrate diet alone, and is thus unlikely to be cost-effective. However, if patients have the ability to modify their treatment dosage then it can be much more effective. In this review we will be looking at the SMBG techniques, outcomes and the relationship with glucose management.

Prediction of Personal Glycemic Responses to Food for Individuals With Type 1 Diabetes Through Integration of Clinical and Microbial Data

<b><i>OBJECTIVE</i></b><i> </i>Despite technological advances, results from various clinical trials repeatedly showed that many individuals with type 1 diabetes (T1D) do not achieve their glycemic goals. One of the major challenges in disease management is the administration of an accurate amount of insulin for each meal which will match the expected postprandial glycemic response (PPGR). <p><b><i>RESEARCH DESIGN AND METHODS</i></b><i> </i>We recruited individuals with T1D using continuous glucose monitoring (CGM) and continuous subcutaneous insulin infusion (CSII) devices simultaneously to a prospective cohort and profiled them for 2 weeks. Participants were asked to report real-time dietary intake using a designated mobile app. We measured their PPGRs and devised machine-learning algorithms for PPGR prediction, which integrate glucose measurements, insulin dosages, dietary habits, blood parameters, anthropometrics, exercise, and gut microbiota. Data of the PPGR of 1,057 healthy individuals to 47,863 meals were also integrated into the model. The performance of the models was evaluated using 10-fold cross validation.</p> <p><b><i>RESULTS</i></b><i> </i>121 individuals with T1D, 75 adults and 46 children, were included in the study. PPGR to 6,377 meals was measured. Our PPGR prediction model substantially outperforms a baseline model emulating standard of care (correlation of R=0.59 compared to R=0.40 for predicted and observed PPGR respectively, p <10⁻¹⁰). The model was robust across different subpopulations. Feature attribution analysis revealed that glucose levels at meal initiation, glucose trend 30 minutes prior to meal, meal carbohydrate content and meal’s carbohydrate/fat ratio were the most influential features to the model. </p> <p><b><i>CONCLUSIONS</i></b><i> </i>Our model enables a more accurate prediction of PPGR and therefore may allow a better adjustment of the required insulin dosage for meals. It can be further implemented in closed-loop systems and may lead to rationally designed nutritional interventions personally tailored for individuals with T1D based on meals with expected low glycemic response. </p>

Prediction of Personal Glycemic Responses to Food for Individuals With Type 1 Diabetes Through Integration of Clinical and Microbial Data

<b><i>OBJECTIVE</i></b><i> </i>Despite technological advances, results from various clinical trials repeatedly showed that many individuals with type 1 diabetes (T1D) do not achieve their glycemic goals. One of the major challenges in disease management is the administration of an accurate amount of insulin for each meal which will match the expected postprandial glycemic response (PPGR). <p><b><i>RESEARCH DESIGN AND METHODS</i></b><i> </i>We recruited individuals with T1D using continuous glucose monitoring (CGM) and continuous subcutaneous insulin infusion (CSII) devices simultaneously to a prospective cohort and profiled them for 2 weeks. Participants were asked to report real-time dietary intake using a designated mobile app. We measured their PPGRs and devised machine-learning algorithms for PPGR prediction, which integrate glucose measurements, insulin dosages, dietary habits, blood parameters, anthropometrics, exercise, and gut microbiota. Data of the PPGR of 1,057 healthy individuals to 47,863 meals were also integrated into the model. The performance of the models was evaluated using 10-fold cross validation.</p> <p><b><i>RESULTS</i></b><i> </i>121 individuals with T1D, 75 adults and 46 children, were included in the study. PPGR to 6,377 meals was measured. Our PPGR prediction model substantially outperforms a baseline model emulating standard of care (correlation of R=0.59 compared to R=0.40 for predicted and observed PPGR respectively, p <10⁻¹⁰). The model was robust across different subpopulations. Feature attribution analysis revealed that glucose levels at meal initiation, glucose trend 30 minutes prior to meal, meal carbohydrate content and meal’s carbohydrate/fat ratio were the most influential features to the model. </p> <p><b><i>CONCLUSIONS</i></b><i> </i>Our model enables a more accurate prediction of PPGR and therefore may allow a better adjustment of the required insulin dosage for meals. It can be further implemented in closed-loop systems and may lead to rationally designed nutritional interventions personally tailored for individuals with T1D based on meals with expected low glycemic response. </p>

Application of Nano-Insulin Pump in Children with Diabetic Ketoacidosis

Type 1 diabetes is an insulin-dependent type of diabetes that is most common among children. Due to absolute deficiency of insulin in patients, diabetic ketoacidosis (DKA) can easily ensue. Insulin pump can simulate the physiological secretion of islet, but increases the risk of pain and infection in children due to its traumatic effect. This study aimed to analyze the application effect of nano-insulin pump in children with DKA. Children with DKA admitted to our hospital from May 2018 to May 2020 were included in this study and, according to the random number table method, they were divided into two groups, with each group containing 36 cases. The first group received traditional insulin pump infusion (IP), while the second group received nano-insulin pump infusion (NIP). It was found that the reduction of FBG and PBG in NIP group was greater than that in IP group. The recovery time of urine ketone, blood ketone, glucose, venous pH, and other clinical indicators in the NIP group were all lower than those in the IP group (P < 0.05). The length of hospital stay, insulin dosage, incidence of hypoglycemia, and infusion site infection rate in the NIP group were all lower than those in the IP group (P <0.05). The findings indicate that the application of nano-insulin pump in children with DKA had a significant effect and could quickly and obviously correct the levels of blood glucose and ketone body in children.

PATTERN OF GLYCEMIC STATUS AFTER STEROID ADMINISTRATION FOR FETAL LUNG MATURITY IN NORMOGLYCEMIC PREGNANT WOMEN ; A CLINICAL ANALYSIS

Objectives To study the patterns of glycemic status in response to steroid administered to women with risk of preterm delivery between 24 weeks and 36 weeks 6 days of gestation in normoglycemic subjects and to evaluate if maternal characteristics predicted the development of hyperglycemia and if Insulin was necessary in the glycemic management Design : longitudinal study Participants : 76 antenatal women, normoglycemic status between 24 weeks and 36 week 6 days of gestation Methods : Antenatal women who screened negative for Gestational Diabetes Mellitus by 75 gm GTT who received Injection Betamethasone for risk of preterm delivery . Fasting and Postprandial blood sugar levels were recorded from day 1 to 7 after steroid administration. Results Forty seven out of seventy six patients had hyperglycemia of varying severity. Among the risk factors associated with hyperglycemia, age>25 years, family history ofdiabetes and hypertension and BMI >25 have statistically significant association with hyperglycemia. Insulin was started in a total of 40 patients of 47 hyperglycemic patients (85.1%). Mean Insulin dosage required for day 1 was9.66 units. Among the 40 patients started on Insulin 15 (37.5 %) had to be continued on Insulin on Day 7 after steroid administration. Conclusion Significant hyperglycemia can occur in normoglycemic women also leading to serious maternal- fetal consequences . Testing of all antenatal patients especially in age group more than 25years, BMI over 25, hypertensive patients, family history of diabetes who are at risk for development of hyperglycemia ie recommended and start insulin accordingly thus preventing complications.

Type 1 Diabetes and Addison’s Disease: When the Diagnosis Is Suggested by the Continuous Glucose Monitoring System

Our objective is to emphasize the important role of continuous glucose monitoring (CGM) in suggesting adrenal insufficiency in patients affected by type 1 diabetes. We describe an adolescent girl with type 1 diabetes and subsequent latent Addison’s disease diagnosed based on a recurrent hypoglycemic trend detected by CGM. In patients with type 1 diabetes, persistent unexplained hypoglycemic episodes at dawn together with reduced insulin requirement arouse souspicionof adrenal insufficiency. Adrenal insufficiency secondary to autoimmune Addison’s disease, even if rarely encountered among young patients, may be initially symptomless and characterized by slow progression up to acute adrenal crisis, which represents a potentially life-threatening condition. Besides glycometabolic assessment and adequate insulin dosage adjustment, type 1 diabetes needs prompt recognition of potentially associated autoimmune conditions. Among these, Addison’s disease can be suspected, although latent or paucisymptomatic, through periodic and careful evaluation of CGM data.

Metabolic control and hypoglycaemia in people with type 1 diabetes: insulin pump therapy vs. intensified insulin therapy in an unselected cohort in routine care

Aims The aim of this study was to compare individuals with type 1 diabetes with continuous subcutaneous insulin infusion (CSII) and intensified insulin therapy (ICT) in routine care regarding metabolic control and treatment satisfaction. Methods Individuals with type 1 diabetes (CSII n = 74; ICT n = 163) were analysed regarding metabolic control, frequency of hypoglycaemia and treatment satisfaction (DTSQs range 0–36). Results Individuals with CSII (duration of CSII: 14.1 ± 7.2 years) were younger (51.1 ± 15.8 vs. 56.2 ± 16.2 years, p = 0.023), had longer diabetes duration (28.7 ± 12.4 vs. 24.6 ± 14.3 years, p = 0.033), lower insulin dosage (0.6 ± 0.2 vs. 0.7 ± 0.4 IU/kg, p = 0.004), used more frequently short-acting analogue insulin (90.5% vs. 48.5%, p < 0.001) and flash/continuous glucose monitoring (50.0% vs. 31.9%, p = 0.009) than people with ICT. HbA1c was similar between CSII and ICT (7.1 ± 0.8%/54.4 ± 9.1 mmol/mol vs. 7.2 ± 1.0%/55.7 ± 10.9 mmol/mol, p = 0.353). Individuals with CSII had higher frequency of non-severe hypoglycaemia per week (in people with blood glucose monitoring: 1.9 ± 1.7 vs. 1.2 ± 1.6, p = 0.014; in people with flash/continuous glucose monitoring: 3.3 ± 2.2 vs. 2.1 ± 2.0, p = 0.006). Prevalence of polyneuropathy (18.9% vs. 38.0%, p = 0.004) and systolic blood pressure (138.0 ± 16.4 vs. 143.9 ± 17.1 mmHg, p = 0.014) was lower in CSII. Satisfaction with diabetes treatment (26.7 ± 7.3 vs. 26.0 ± 6.8, p = 0.600) did not differ between CSII and ICT. Conclusions CSII and ICT yielded comparable metabolic control and treatment satisfaction but CSII was associated with higher incidence of non-severe hypoglycaemia and lower insulin dosage.