Type 1 Diabetes Participants Research Articles

Comparison of three insulin bolus calculators to increase time in range of glycemia in a group of poorly controlled adults Type 1 diabetes in a Brazilian public health service

BackgroundA main factor contributing to insufficient glycemic control, during basal/bolus insulin therapy, is poor self-management bolus. Insulin bolus administration frequency is strongly associated with glycated hemoglobin (A1c) in Type 1 Diabetes (T1D). In the present study, we analyzed the performance of two-bolus calculator’s software that could be accessible to T1D patients from a Public Health Service to improve glycemic time in range (TIR) and A1c.MethodsThis prospective, controlled, randomized, parallel intervention clinical trial was carried out with 111 T1D participants on basal/bolus therapy [multiple daily insulin injections (MDI) or subcutaneous infusion pump (CSII)] with basal A1c ≥ 8.5% for 24 weeks. Patients were divided into 3 groups: 2 interventions: COMBO® (bolus calculator) and GLIC (mobile application) and 1 control (CSII group). Anthropometrics and metabolic variables were assessed on basal, 3 and 6 months of follow-up.ResultsTIR was increased in 9.42% in COMBO group (29 ± 12% to 38.9 ± 12.7%; p < 0.001) in 8.39% in the GLIC® group (28 ± 15% to 36.6 ± 15.1%; p < 0.001) while remained stable in CSII group (40 ± 11% to 39.3 ± 10.3%). A1c decrease in 1.08% (p < 0.001), 0.64% (p < 0.001) and 0.38% (p = 0.01) at 6 months in relation to basal in the COMBO, GLIC and CSII respectively. Daily basal insulin dose was reduced by 8.8% (p = 0.01) in the COMBO group.ConclusionThe COMBO and a mobile applicative (GLIC) bolus calculator had a similar and a good performance to optimize the intensive insulin treatment of T1D in the public health system with increase in the TIR and reduction in A1C without increase hypoglycemia prevalence.

The metabolic syndrome is frequent in children and adolescents with type 1 diabetes compared to healthy controls

There is a rise in overweight and obesity among children and adolescents with type 1 diabetes (T1D) in parallel with the rise in the metabolic syndrome (MetS) among children and adolescents. The aim of the study was to describe the prevalence and characteristics of MetS in children and adolescents with T1D compared to their healthy counterparts. The study includes two Danish cohorts; (i) the Copenhagen cross sectional cohort 2016 of 277 children and adolescents with T1D that attend the pediatric outpatient clinic at a large hospital in greater Copenhagen and (ii) the CHAMPS-study DK which is a population-based cohort study of Danish children and adolescents (control cohort). Participants were categorized to have MetS if at least two of the following criteria were met: (i) systolic and/or diastolic blood pressure ≥ 90th percentile, (ii) waist circumference ≥90th percentile, and (iii) triglyceride ≥90th percentile and/or HDL ≤10th percentile. The prevalence of children with Mets in the T1D cohort was higher than in the control cohort (p=0.002). Moreover, participants with T1D had MetS at a lower level of BMI (p < 0.001) and waist circumference (p < 0.001) than participants with MetS from the control cohort (z-scores=0.90 and 1.51). Participants with MetS were younger than the other T1D participants (median 12.8 [9.9,14.8] vs. median 14.6 [11.2,16.9] years, p=0.006). Children and adolescents with T1D have an increased risk of MetS compared to healthy controls and clinicians and caretakers should consider early prevention and health promotion strategies.

545-P: The Impact of Gender on Physical Activity Preferences and Barriers in Adults with Type 1 Diabetes: A Qualitative Study

Exercise studies involving people with type 1 diabetes (T1D) have tested young, fit, men. As such, current insulin adjustment and carbohydrate intake advice may not work for women. A recent secondary analysis found different blood glucose (BG) responses to a weight lifting protocol between male and female T1D participants. It is unclear whether these differences were physiological (i.e., hormones, muscle mass, etc.) or behavioural (i.e., insulin adjustments, carbohydrate intake, etc.) . To explore gender-related differences in physical activity (PA) behaviours and preferences, we recruited men and women with T1D to take part in semi-structured interviews. Questions included demographics, perceived differences between genders (preferred type of PA, motivation, and barriers) , diabetes management strategies (i.e., insulin adjustments, carbohydrate intake, high intensity exercise, etc.) , and PA preferences (frequency, type, motivations, etc.) . Two analysts coded interview transcripts using a framework approach, through open coding of emergent themes. The most common themes were:1) Impact of support on PA success. Both men and women made statements about support, or lack of support, from family members, medical providers, and group exercise. Women reported receiving support from their healthcare team, while men did not.2) Strategies for BG management. This included reducing insulin pre-exercise and consuming extra carbohydrates. Few participants reported using intense exercise.3) Barriers to PA. Men and women reported similar barriers, including hypoglycemia, time limitations, and lack of motivation. Women reported more concerns about dysglycemia.4) Reasons to exercise. The main differences between genders were the importance of enjoyment for men and weight management for women. Overall, men and women with T1D possess similar barriers, sources of support, and BG management strategies around PA, despite differences in motivation. Disclosure J.E.Logan: None. M.Prevost: None. A.Brazeau: Research Support; Eli Lilly and Company, Novo Nordisk, Sanofi. S.Hart: None. M.Maldaner: None. J.E.Yardley: Research Support; Abbott, Dexcom, Inc., LifeScan, Speaker's Bureau; Abbott Diabetes. Funding Heart and Stroke Foundation of Canada, University of Alberta Undergraduate Research Initiative

458-P: Inflammatory Markers and Cardiovascular Autonomic Neuropathy in Type 1 Diabetes

Objective: We aimed to assess the role of chronic inflammation in the pathogenesis of cardiovascular autonomic neuropathy (CAN) , a serious complication of type 1 diabetes (T1D) . Methods: We leveraged an ongoing randomized, placebo-controlled clinical trial targeting inflammation with salsalate in individuals with T1D, to measure a panel of 40 inflammatory markers (Figure) in baseline samples. Measures of CAN included standardized cardiovascular autonomic reflex tests (CARTs) (E/I, Valsalva, 30/15 ratios) and indices of heart rate variability (HRV) . We used Spearman-rank to assess the correlations between inflammatory markers and CAN measures, and linear regression to adjust for age and hemoglobin A1c. Results: The Figure displays the correlation between biomarkers and CAN measures in 58 T1D participants (mean age 51 ± 13 years, and HbA1c 8.4 ± 1.8%) . Amongst all biomarkers, we noted a singular negative correlation between soluble urokinase plasminogen activator receptor (suPAR) and measures of CAN, which remained significant in multivariable analysis (E/I ratio p=0.005, Valsalva p=0.037, RFA p=0.019, LFA p=0.023) . Conclusion: Amongst a large panel of inflammatory markers, we found suPAR to have a notable association with CAN. SuPAR is an immune-mediated signaling glycoprotein which levels are strong predictors of risk in patients with diabetes, thus its role in CAN warrants further exploration. Disclosure L.Ang: None. L.Zhao: None. E.L.Feldman: None. S.Hayek: n/a. R.Pop-busui: Advisory Panel; Averitas Pharma, Inc., Boehringer Ingelheim International GmbH, Nevro Corp., Novo Nordisk, Reata Pharmaceuticals, Inc., Regenacy Pharmaceuticals, Inc. S.V.G.Gunaratnam: None. Y.Huang: None. K.R.Mizokami-stout: None. C.Martin: Advisory Panel; Nevro Corp. J.Reiss: n/a. A.Burant: None. A.Vasbinder: None. C.Launius: None. Funding NIH/NIDDK-1-R01-DK-107956-01

Effect of Ethanol Consumption on the Accuracy of a Glucose Oxidase-Based Subcutaneous Glucose Sensor in Subjects with Type 1 Diabetes.

Continuous glucose monitors (CGM) have improved the management of patients with type 1 diabetes (T1D), with glucose oxidase (GOx)-based sensors being the most used. However, they are potentially subject to both electrochemical and enzymatic interferences, including those related to changes of pH. The objective of this study is to investigate the effect of ethanol, given as beer along with a mixed meal, on the accuracy of a commercial GOx-CGM. Data from 12 T1D participants in a randomized crossover trial to evaluate the effect of meal composition and alcohol consumption on postprandial glucose concentration were used. Absolute error (AE) and mean absolute relative difference (MARD) were calculated. The differences between the alcohol and nonalcohol scenarios were assessed using the Mann–Whitney U and Wilcoxon signed-rank tests. The AE in the alcohol study was low, but significantly greater as compared to the study without alcohol (p-value = 0.0418). The MARD was numerically but not significantly greater. However, both variables were greater at pH < 7.36 and significantly affected by time only in the alcohol arm. In T1D, alcohol consumption affects the accuracy of a GOx-CGM. This effect could be at least partially related to the ethanol-induced changes in pH.

The Potential of Current Noninvasive Wearable Technology for the Monitoring of Physiological Signals in the Management of Type 1 Diabetes: Literature Survey.

BackgroundMonitoring glucose and other parameters in persons with type 1 diabetes (T1D) can enhance acute glycemic management and the diagnosis of long-term complications of the disease. For most persons living with T1D, the determination of insulin delivery is based on a single measured parameter—glucose. To date, wearable sensors exist that enable the seamless, noninvasive, and low-cost monitoring of multiple physiological parameters.ObjectiveThe objective of this literature survey is to explore whether some of the physiological parameters that can be monitored with noninvasive, wearable sensors may be used to enhance T1D management.MethodsA list of physiological parameters, which can be monitored by using wearable sensors available in 2020, was compiled by a thorough review of the devices available in the market. A literature survey was performed using search terms related to T1D combined with the identified physiological parameters. The selected publications were restricted to human studies, which had at least their abstracts available. The PubMed and Scopus databases were interrogated. In total, 77 articles were retained and analyzed based on the following two axes: the reported relations between these parameters and T1D, which were found by comparing persons with T1D and healthy control participants, and the potential areas for T1D enhancement via the further analysis of the found relationships in studies working within T1D cohorts.ResultsOn the basis of our search methodology, 626 articles were returned, and after applying our exclusion criteria, 77 (12.3%) articles were retained. Physiological parameters with potential for monitoring by using noninvasive wearable devices in persons with T1D included those related to cardiac autonomic function, cardiorespiratory control balance and fitness, sudomotor function, and skin temperature. Cardiac autonomic function measures, particularly the indices of heart rate and heart rate variability, have been shown to be valuable in diagnosing and monitoring cardiac autonomic neuropathy and, potentially, predicting and detecting hypoglycemia. All identified physiological parameters were shown to be associated with some aspects of diabetes complications, such as retinopathy, neuropathy, and nephropathy, as well as macrovascular disease, with capacity for early risk prediction. However, although they can be monitored by available wearable sensors, most studies have yet to adopt them, as opposed to using more conventional devices.ConclusionsWearable sensors have the potential to augment T1D sensing with additional, informative biomarkers, which can be monitored noninvasively, seamlessly, and continuously. However, significant challenges associated with measurement accuracy, removal of noise and motion artifacts, and smart decision-making exist. Consequently, research should focus on harvesting the information hidden in the complex data generated by wearable sensors and on developing models and smart decision strategies to optimize the incorporation of these novel inputs into T1D interventions.

IDF21-0636 Effects of COVID-19 lockdown on lifestyle changes and medical challenges in patients with type 1 diabetes in south India

Background: India has become a major epicentre of the Coronavirus (COVID-19) pandemic. The lockdown imposed by the Indian government during the pandemic of COVID-19 has inflicted changes in diet and lifestyle patterns in patients with diabetes. There are unique challenges in addressing management of type 1 diabetes (T1D) during the lockdown period. Aim: To evaluate the effects of nationwide lockdown due to COVID-19 on lifestyle changes and medical challenges in patients with T1D in south India. Method: Data was collected through telephonic interview (between April 30 and May 15, 2020), from a random list of 154 individuals with T1D, who had visited a large tertiary diabetes care centre during the past year. A structured questionnaire was used to collect information on changes in lifestyle, access and challenges to diabetes care, use of technologies such as telemedicine facilities, self-monitoring of blood glucose, and hypoglycemia. Results: The mean age of the participants was 27.3 ± 10.5 years, and 59.1% of the participants were males. Overall, 27.3% of the participants utilized online information like Youtube, Facebook, WhatsApp for control of diabetes of whom, 76.2% reported watching videos about diabetes. Table 1 presents data on access to diabetes care during the lockdown in T1D individuals. Majority (80%) of the participants monitored their blood glucose at home and 38.3% of them had documented episodes (weekly or monthly once) of hypoglycemia and but were able to treat themselves at home. Of the total participants, 49.3% of them reported that their physical activity pattern has changed (50.6% reported no change in their activity) of whom in 52.5% the activity increased, and in 47.5% it decreased. Over 60% of the participants consumed more vegetables, and 38.7% had consumed more fruits, during the lockdown.Table 1Access to diabetes care during the lockdown in T1D individuals.Access to diabetes careTotal (n=154) n(%)Tried to see a Doctor since lockdown (Yes)20(13.0)The main reason for wanting to see a Doctor Treatment or evaluation of diabetes, blood pressure, or heart disease Treatment or evaluation of other chronic diseases Other reason14(70.0) 1(5.0) 5(25.0)The outcome in trying to see the doctor Yes, in-person Yes, by Telemedicine No, tried but could not be seen16(80.0) 2(10.0) 2(10.0)Had any blood tests done since lockdown? Yes – tried and successful Yes – tried but could not be done No11(7.1) 2(1.3) 141(91.6)Have you run out of any of your medications during lockdown? (Yes)9(5.8)Ran out of insulin9 (100.0)What was the main reason for running out of insulin? Lockdown restrictions in going out to buy medications Unable to afford Pharmacy out of stock Others-Pharmacy closed2(22.2) 1(11.1) 5(55.6) 1(11.1)Have you had access to home delivery of medicines? (Yes)48(31.6)Interested in getting medicines home-delivered even after the current situation eases? (Yes)38(25.0) Open table in a new tab Discussion: Among T1D participants, both positive and negative behavioral changes occurred during lockdown due to COVID-19. More research is needed on the long-term effects of COVID-19 on the health and well-being of persons with T1D.

Biochemical Markers of Bone Turnover in Older Adults With Type 1 Diabetes.

Type 1 diabetes (T1D) is characterized by high fracture risk, yet little is known regarding diabetes-related mechanisms or risk factors. Determine whether glycemic control, advanced glycation end products (AGEs), and microvascular complications are associated with bone turnover markers among older T1D adults. Cross-sectional. Epidemiology of Diabetes Interventions and Complications study (6 of 27 clinical centers). 232 T1D participants followed for >30 years. Glycemic control ascertained as concurrent and cumulative hemoglobin A1c (HbA1c); kidney function, by estimated glomerular filtration rates (eGFR); and AGEs, by skin intrinsic fluorescence. Serum procollagen 1 intact N-terminal propeptide (PINP), bone-specific alkaline phosphatase (bone ALP), serum C-telopeptide (sCTX), tartrate-resistant acid phosphatase 5b (TRACP5b), and sclerostin. Mean age was 59.6 ± 6.8 years, and 48% were female. In models with HbA1c, eGFR, and AGEs, adjusted for age and sex, higher concurrent HbA1c was associated with lower PINP [β -3.4 pg/mL (95% CI -6.1, -0.7), P = 0.015 for each 1% higher HbA1c]. Lower eGFR was associated with higher PINP [6.9 pg/mL (95% CI 3.8, 10.0), P < 0.0001 for each -20 mL/min/1.73 m2 eGFR], bone ALP [1.0 U/L (95% CI 0.2, 1.9), P = 0.011], sCTX [53.6 pg/mL (95% CI 32.6, 74.6), P < 0.0001], and TRACP5b [0.3 U/L (95% CI 0.1, 0.4), P = 0.002]. However, AGEs were not associated with any bone turnover markers in adjusted models. HbA1c, eGFR, and AGEs were not associated with sclerostin levels. Among older adults with T1D, poor glycemic control is a risk factor for reduced bone formation, while reduced kidney function is a risk factor for increased bone resorption and formation.

Symptomatic diabetic autonomic neuropathy in type 1 diabetes (T1D): Findings from the T1D exchange

AimsWe aimed to evaluate the contemporary prevalence of and risk factors for symptomatic diabetic autonomic neuropathy (DAN) in participants with type 1 diabetes (T1D) enrolled in the T1D Exchange Clinic Registry. MethodsDAN symptoms and severity were assessed with the Survey of Autonomic Symptoms (SAS) in adults with ≥5 years of T1D participating in the T1D Exchange from years 2010–2017. Associations of demographic, clinical, and laboratory factors with symptomatic DAN were assessed. ResultsOf the 4919 eligible T1D participants, 965 (20%) individuals completed the SAS questionnaire [mean age 40 ± 17 years, median diabetes duration 20 years (IQR: 13,34), 64% female, 90% non-Hispanic White, and 82% with private insurance]. DAN symptoms were present in 166 (17%) of responders with 72% experiencing moderate severity symptoms or worse. Symptomatic DAN participants had higher hemoglobin A1c (p = 0.03), longer duration (p = 0.004), were more likely to be female (p = 0.03), and more likely to have lower income (p = 0.03) versus no DAN symptoms. Symptomatic DAN was associated with diabetic peripheral neuropathy (p < 0.0001), smoking (p = 0.002), cardiovascular disease (p = 0.02), depression (p < 0.001), and opioid use (p = 0.004). ConclusionsDAN symptoms are common in T1D. Socioeconomic factors and psychological comorbidities may contribute to DAN symptoms and should be explored further.

A co-design study to develop supportive interventions to improve psychological and social adaptation among adults with new-onset type 1 diabetes in Denmark and the UK

ObjectiveTo develop supportive interventions for adults with new-onset type 1 diabetes (T1D) to facilitate positive adaptive strategies during their transition into a life with diabetes.DesignThe study used a co-design approach...

Early Follicular Phase of the Menstrual Cycle May Be Associated With More Post-Exercise Hyperglycemia in Female Participants With Type 1 Diabetes

Despite substantial health benefits of exercise in people with type 1 diabetes (T1D), significant knowledge gaps exist around blood glucose (BG) responses to exercise in females with T1D, especially with regards to the menstrual cycle. We compared the impact of aerobic exercise on continuous glucose monitoring (CGM) glucose between the early follicular and late luteal phases of the menstrual cycle in 7 female participants with T1D (mean±SD: age 31±10 years, A1C 7.1±0.5%, VO2peak 33.2±4.3 mL/kg/min). Participants had regular menstrual cycles and were not using hormonal contraceptives. In each phase, participants performed moderate aerobic exercise (cycling at 50% of pre-determined VO2peak) for 45 minutes on an ergometer starting at 5 pm. Meal timing/composition and insulin dosage were kept as consistent as possible between sessions. Blood glucose was measured using CGM for 24 h before exercise and 24 h post exercise. There were no significant differences in mean CGM glucose between the two phases during the 24 h pre, 0-6 h post, overnight (midnight to 6 am) post, or 24 h post exercise. Mean CGM glucose was significantly higher 24 h post as compared to 24 h pre exercise (9.0±1.7 vs 7.3±1.6 mmol/L) in the follicular phase (p=0.0468). There were more post-exercise hyperglycemic episodes in the follicular phase compared with the luteal (2.9±1.7 vs 1.1±0.7, p=0.0453). Female T1D participants may be more prone to post-exercise hyperglycemia requiring insulin correction during the follicular phase of the menstrual cycle.

Physical Activity in Adolescents with and without Type 1 Diabetes during the New Zealand COVID-19 Pandemic Lockdown of 2020.

Physical activity (PA) is an important part of lifestyle management for adolescents with Type 1 diabetes (T1D). Opportunities for PA were reduced by COVID-19 restrictions. Therefore, the purpose of this cross-sectional study was to compare PA among adolescents with and without T1D during the first New Zealand (NZ) COVID-19 lockdown. PA levels of adolescents aged 11–18 years with T1D (n = 33) and healthy controls (n = 34) were assessed through self-reported and parent proxy-reported questionnaires. Overall, PA levels during lockdown were below recommended levels. PA levels did not differ between T1D and control participants (p = 0.212) nor between genders (p = 0.149). Younger adolescents tended to be more active than older adolescents (p = 0.079). PA level was negatively associated with BMI z-score (r = −0.29, p = 0.026) but was not associated with socioeconomic status (SES) or T1D-related parameters. In the T1D group, higher HbA1c was associated with lower school decile (r = −0.58, p < 0.001) and higher BMI z-score (r = 0.68, p < 0.001). Overall, young people were insufficiently active during lockdown, and some sub-groups were more affected than others by the restrictions. Pandemics are likely to be part of our future, and further studies are needed to understand their impact on the health and wellbeing of adolescents.

Impaired exocrine pancreatic function in different stages of type 1 diabetes

IntroductionAim of this study was to investigate the pancreatic exocrine function in patients with type 1 diabetes (T1D) by multiple non-invasive tests.Research design and methodsThe study is a single-center, cross-sectional...

5 - The Association of Exercise Blood Glucose on Postexercise Hypoglycemia in Type 1 Diabetes

Guidelines suggest that people with type 1 diabetes (T1D) start exercise with moderately high blood glucose (8-10 mmol/L) (BG) to reduce the risk of hypoglycemia; however, those fearing hypoglycemia often start higher. This study aimed to determine the impact of afternoon aerobic exercise with a higher BG on postexercise glycemia, as measured by continuous glucose monitoring (CGM). Eleven active T1D participants (8F/3M, 30[8] years, A1C 7.7[0.8]%, VO2peak 38.4[7.7] ml/kg/min) completed 2 exercise sessions in random order: 1) with BG between 8.0-10.0 mmol/L (MOD) and 2) with BG between 12.0-14.0 mmol/L (HI). Participants were asked to keep meal timing/composition and insulin doses consistent between sessions. Participants cycled 45 minutes at 60%VO2peak. Exercise intensity (respiratory exchange ratio, heart rate) was identical between sessions (p>0.05). CGM data (mean[SD]) were assessed between 6:00 pm - 12:00 am (6 hr) and 12:00 am - 6:00 am (nocturnal) after exercise. ANOVA revealed a main effect of condition for mean CGM glucose (MOD vs HI, 6 hr: 8.8[1.0] vs 10.5[2.6] mmol/L; nocturnal: 8.6[1.4] vs 9.8[2.8] mmol/L; p=0.03), percent time in range (MOD vs HI, 6 hr: 76[16] vs 48[28]%; nocturnal: 72[33] vs 58[33]%, p<0.01) and percent time in hyperglycemia (MOD vs HI, 6hr: 24[16]% vs 49[29]%; nocturnal: 27[31] vs 42[33]%, p<0.01). There were no differences with respect to hypoglycemia. Overall, higher exercise BG was not associated with any protection against hypoglycemia, but instead increased hyperglycemia and decreased time in range.

Effects of acute aerobic, resistance and combined exercises on 24-h glucose variability and skeletal muscle signalling responses in type 1 diabetics.

To compare the effect of high-intensity aerobic (AER), resistance (RES), and combined (COMB: RES + AER) exercise, on interstitial glucose (IG) variability and skeletal muscle signalling pathways in type 1 diabetes (T1D). T1D participants (6M/6F) wore a flash glucose monitoring system in four randomized sessions: one control (CONT), and one AER, RES and COMB (40min each). Mean amplitude of glycemic excursions (MAGE), standard deviation (SD) and coefficient variation (CV) of IG were used to compare the 24h post-exercise IG variability. Blood and muscle samples were collected to compare exercise-induced systemic and muscle signalling responses related to metabolic, growth and inflammatory adaptations. Both RES and COMB decreased the 24h MAGE compared to CONT; additionally, COMB decreased the 24h SD and CV. In the 6-12h post-exercise, all exercise modalities reduced the IG CV while SD decreased only after COMB. Both AER and COMB stimulated the PGC-1α mRNA expression and promoted the splicing of IGF-1Ea variant, while Akt and p38MAPK phosphorylation increased only after RES and COMB. Additionally, COMB enhanced eEF2 activation and RES increased myogenin and MRF4 mRNA expression. Blood lactate and glycerol levels and muscle IL-6, TNF-α, and MCP-1 mRNAs increased after all exercise sessions, while serum CK and LDH level did not change. COMB is more effective in reducing IG fluctuations compared to single-mode AER or RES exercise. Moreover, COMB simultaneously activates muscle signalling pathways involved in substrate metabolism and anabolic adaptations, which can help to improve glycaemic control and maintain muscle health in T1D.

Insulin gene promoter methylation status in Greek children and adolescents with Type 1 Diabetes.

The insulin (INS) gene is the one of the most important genes involved in the pathogenesis of Type 1 Diabetes (T1D) after the Major Histocompatibility Complex genes. Studies addressing the issue of hyper- or hypo-methylation status of the INS gene promoter have reported inconsistent results. The majority of studies showed hypomethylation; however a few studies have shown hypermethylation at specific cytosine-guanosine (CpG) sites in the promoter region of the INS gene. The aim of the present study was to analyze the methylation status of the promoter region of the INS gene in Greek children and adolescents with T1D. A total of 20 T1D participants (mean diabetes duration of 6.15±4.12 years) and 20 age- and sex-matched controls were enrolled in the present study. DNA was isolated from whole blood samples, modified using sodium bisulfite and analyzed using PCR and electrophoresis. DNA was then pooled with highly reactive supermagnetic beads at similar molar quantities, submitted for library construction and finally sequenced using next-generation sequencing. The methylation profile at 10 CpG sites around the transcription start site (TSS) of the INS promoter was analysed and expressed as the mean ± standard deviation. The overall mean methylation in patients with T1D did not differ compared with the healthy controls. There was a statistically significant difference between the two groups in hypermethylation at position -345 (P=0.02), while a trend (P=0.06) at position -102 was observed. According to the results of the present study, increased methylation in the INS gene promoter at specific CpG sites around the TSS were already present in childhood T1D. These data may possibly serve as a guide towards the identification of a methylation pattern for detection of development of T1D in genetically predisposed children.

Lipid management for cardiovascular risk reduction in type 1 diabetes.

To review the recent evidence for lipid management in type 1 diabetes (T1D) for cardiovascular risk reduction. Individuals with T1D are at increased risk for cardiovascular morbidity and mortality, with atherosclerosis beginning as early as adolescence. Elevated low-density lipoprotein cholesterol (LDL-C), triglycerides, and lipoprotein (a) are associated with increased cardiovascular risk in T1D. Although high-density lipoprotein cholesterol (HDL-C) in T1D is often normal or higher than in nondiabetic controls, HDL in T1D has structural alterations, which make it proatherogenic rather than cardioprotective. Similarly, although LDL-C is not particularly elevated in T1D, LDL still contributes to cardiovascular risk. Studies in individuals with diabetes have primarily included T2D participants, with a much smaller number of T1D participants; such studies have shown that lipid-lowering therapies, such as statins, ezetimibe, and proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibitors reduce LDL-C levels and cardiovascular events in both those with and without diabetes. Individuals with T1D have increased cholesterol absorption, suggesting that ezetimibe may be particularly effective in T1D. Results of the REDUCE-IT trial show cardiovascular risk reduction from high-dose omega-3 fatty acid (Icosapent Ethyl) therapy in patients with diabetes (primarily type 2 diabetes), independent of triglyceride lowering, but similar data in T1D are currently lacking. Individuals with T1D are at high risk of cardiovascular disease, necessitating close lipid monitoring and management from adolescence through adulthood.

679-P: Endothelial and Hematopoietic Progenitor Cells Are Reduced at Rest and Have an Attenuated Mobilization Response to Exercise in People with Type 1 Diabetes

Aims: Endothelial and Hematopoietic Progenitor Cells (EPCs and HPCs) promote vascular health through secreting proangiogenic factors and replacing damaged cells. Exercise has been shown to be a mobiliser of these rare cells in healthy individuals, however, the response in chronic diseases such as type 1 diabetes (T1D) is unclear. We compared EPC and HPC counts at rest and after acute exercise in people with and without T1D. Methods: Thirty T1D participants (38±12 years, HbA1c 58.5±9.3 mmol/mol-1, diabetes duration 20±13 years) and 30 age, sex, BMI and fitness matched non diabetes controls were recruited. After a maximal exercise test, participants attended a second visit to complete 45mins of walking at 60%VO2peak. Blood samples were taken at rest, immediately and 1 hour post exercise. Circulating levels of two HPCs and five EPC phenotypes were enumerated by flow cytometry. Data (mean±SD) were analysed by mixed model ANOVA and t-test with significance accepted at p≤0.05. Results: The T1D group had significantly lower rest count of HPCs and EPCs, ranging from 21 to 41% lower across all samples compared to the control. Exercise mobilized all cell phenotypes in both groups, with significant increases from rest to post exercise, while HPCs and CD34+CD31+ EPCs remained elevated 1hr post exercise. However, the change (Δ) in HPCs and EPCs from rest to post exercise was greater in the control versus T1D group, with higher CD34+CD45dim HPCs (p=0.03, Δ 48±65 vs. 24±33 cells/ml), as well as CD34+CD31+ (p=0.04, Δ 23±19 vs. 15±25 cells/ml) and CD34+VEGFR2+ EPCs (p=0.02, Δ 24 ±24 vs. 15±31 cells/ml). Conclusion: People with T1D have reduced numbers of HPCs and EPCs at rest and also display a blunted exercise induced increase in these rare blood vessel protecting cells. Further research is needed to understand the clinical significance of an attenuated exercise mobilization of these EPCs and HPCs phenotypes in predicting future cardiovascular complications in T1D. Disclosure G.S. Taylor: None. K. Smith: None. J. Scragg: None. A. Bashir: None. A. Flatt: None. E.J. Stevenson: None. J.A. Shaw: Advisory Panel; Self; Medtronic. Speaker’s Bureau; Self; Novo Nordisk A/S. M.D. Ross: None. D.J. West: None. Funding Diabetes Research &amp; Wellness Foundation (to D.J.W.)

43-LB: Glycemic Control Affects Heart-Rate Variability in Adults with Type 1 Diabetes

People with type 1 diabetes (T1D) have an increased risk for autonomic dysfunction and those with poorly controlled blood glucose levels have an increased risk for cardiovascular disease. The purpose of this study was to determine heart rate variability (HRV) and glucose variability (GV) parameters in less than optimally controlled patients (Group 2: A1C ≥ 7.0%, n = 6]) compared to well-controlled patients (Group 1: A1C ≤ 6.9% [n = 7]) during steady state treadmill exercise took and rest. Methods: Thirteen T1D participants, (32.42 ± 10.46 years), performed steady state treadmill exercise (40% VO2Peak) for 30 minutes. Prior to exercise, participants rested for 30-minutes for determination of resting HRV and GV. Participants wore continuous glucose monitors and a Zephyr Bioharness™ to determine GV and HRV, respectively. Nonparametric, paired samples T-tests were conducted using SPSS 24. Results: In group 2, there were significant differences between the resting period and treadmill exercise in the (1) root mean score of successive differences (RMSSD) (27.71 ± 13.43 vs. 8.01 ± 3.90; z = -2.201, respectively; p &amp;lt;.05); (2) standard deviation of RR intervals (SDNN) (36.54 ± 16.04 vs. 11.68 ± 6.19; z = -2.201, respectively; p &amp;lt;.05); and (3) the stress index (11.17 ± 5.48 vs. 23.07 ± 6.31; z = -2.201, respectively; p &amp;lt;.05). However, during treadmill exercise, there were no significant differences in GV, compared to a resting period. In group 1, there were no significant differences in HRV, during treadmill exercise compared to the resting period. However, there were significant differences in GV (.26 ± .18 vs. .54 ± .38; z = -2.197, respectively; p &amp;lt;.05)., between resting and exercise period. Conclusion: This study suggests that during steady state aerobic exercise, people with poorer glucose control exhibit a pattern of metabolic inflexibility; however, HRV decreased with steady state exercise in both groups suggesting that exercise training may help to normalize HRV in patients with poorly controlled glucose levels. Disclosure M. Park: None. M. Chu: None. Y. Song: None. M. Sevil: None. A. Cinar: Research Support; Self; Dexcom, Inc., JDRF. Stock/Shareholder; Self; Dexcom, Inc., Eli Lilly and Company, Insulet Corporation, Medtronic, Novo Nordisk A/S, Tandem Diabetes Care. L.T. Quinn: None. U.G. Bronas: None. Funding JDRF

742-P: Socioeconomic Position (SEP) Attributes Intersect with Race to Influence Glycemic Control Patterns in Youth with Type 1 Diabetes (T1D) over Time

SEARCH for Diabetes in Youth has identified racial/ethnic disparities in patterns of glycemic control among T1D youth. How SEP and race/ethnicity intersect to influence these disparities is poorly understood. SEARCH T1D participants (n=1,313) age ≤20 years at diagnosis (dx) in 2002-5 were followed 6-13 years with 3+ hemoglobin A1c measures. Group-based trajectory modeling of A1c identified Group 1 (G1, 77%): lower A1c with mild increases (from mean 7.4 to 8.4%) and Group 2 (G2, 23%): high and increasing A1c (from 8.5 to 11.2%). Classification and regression tree analysis modeled patterns in 8 demographic and SEP attributes that distinguished between G2 and G1 (prediction accuracy 0.68). Race/ethnicity was most influential in predicting G2, with 39.6% G2 in minorities vs. 18.2% in non-Hispanic whites (NHWs) (Figure). Among minorities, 52.5% of those with public insurance were in G2 vs. 32.4% of those with private insurance. Among NHWs without college-educated parents, older age at dx (≥10 years) was associated with G2 (34.0% vs. 20.5% in dx &amp;lt;10 years). Among NHWs with college-educated parents, living in a 1-parent household was associated with G2 (23.6% vs. 8.9% in a 2-parent household). Race/ethnicity intersects differentially with select SEP attributes for NHW vs. minorities suggesting more nuanced studies of health disparities are needed. Disclosure A.D. Liese: None. B.A. Reboussin: None. A.R. Kahkoska: Other Relationship; Self; Novo Nordisk A/S. E.A. Frongillo: None. F. Malik: None. G. Imperatore: None. S. Saydah: None. A. Bellatorre: None. J.M. Lawrence: None. D. Dabelea: None. J.A. Mendoza: None. Funding National Institutes of Health (1R01DK117461, 1UC4DK108173)