T1D Exchange Clinic Network Research Articles

Design and psychometrics for new measures of health-related quality of life in adults with type 1 diabetes: Type 1 Diabetes and Life (T1DAL)

AimsTo use a three-phase process to develop and validate new self-report measures of diabetes-specific health-related quality of life (HRQOL) for adults with type 1 diabetes. We report on four versions of the Type 1 Diabetes and Life (T1DAL) measure for people age 18–25, 26–45, 46–60, and over 60 years. MethodsWe first conducted qualitative interviews to guide measure creation, then piloted the draft measures. We evaluated psychometric properties at six T1D Exchange Clinic Network sites via completion of T1DAL and validated measures of related constructs. Participants completed the T1DAL again in 4–6 weeks. We used psychometric data to reduce each measure to 23–27 items in length. Finally, we obtained participant feedback on the final measures. ResultsThe T1DAL-Adult measures demonstrated good internal consistency (α = 0.85–0.88) and test–retest reliability (r = 0.77–0.87). Significant correlations with measures of general quality of life, generic and diabetes-specific HRQOL, diabetes burden, self-management, and glycemic control demonstrated validity. Factor analyses yielded 4–5 subscales per measure. Participants were satisfied with the final measures and reported they took 5–10 min to complete. ConclusionsThe strong psychometric properties of the newly developed self-report T1DAL measures for adults with type 1 diabetes make them appropriate for use in clinical research and care.

Effect of Exercise and Meals on Continuous Glucose Monitor Data in Healthy Individuals Without Diabetes.

The aim of these analyses was to characterize the effect of exercise and meals on glucose concentrations in healthy individuals without diabetes. Healthy individuals without diabetes (age ≥6 years) with nonobese body mass index were enrolled at 12 centers within the T1D Exchange Clinic Network. Participants wore a blinded Dexcom G6 for up to ten days. Throughout this sensor wear, participants completed a daily log indicating times and type of any exercise and start times of meals and snacks. A total of 153 participants (age 7-80 years) were included in the analyses. Exercise induced a mean change of -15 ± 18 mg/dL from baseline to nadir sensor glucose level. Mean nadir glucose concentration during nights following exercise days was 82 ± 11 mg/dL compared with 85 ± 11 mg/dL during nights following nonexercise days (P = .05). Mean change from baseline to nadir sensor glucose level during aerobic exercise was -15 ± 18 and -9 ± 12 mg/dL for resistance exercise (P = .25). Overnight nadir glucose during nights following aerobic and resistance exercise was 83 ± 12 and 76 ± 14 mg/dL, respectively (P = .25). Overall mean peak postprandial glucose per participant increased from 93 ± 10 mg/dL premeal to 130 ± 13 mg/dL with an average time to peak glucose per participant of 97 ± 31 minutes. Consumption of alcohol on the day prior did not impact overnight mean or nadir glucose. The present analysis provides important data characterizing the effect of exercise and meals on glucose in healthy individuals without diabetes. These data provide a repository to which future therapies, whether pharmacologic or technologic, can be compared.

Parental Perspectives: Identifying Profiles of Parental Attitudes and Barriers Related to Diabetes Device Use.

Background: Despite the demonstrated benefits of diabetes device use, uptake of insulin pumps and continuous glucose monitors (CGMs) remains quite low. The current study aimed to identify profiles of parents of youth with type 1 diabetes based on their attitudes toward diabetes-specific technology and barriers to diabetes technology uptake. Methods: Online survey data were collected from 471 parents in the T1D Exchange Clinic Network (child's age = 12.0 ± 3.2 years; diabetes duration = 7.0 ± 2.9 years; A1c = 8.4% ± 1.3; 75% using insulin pump; 27% using CGM). Results: K-means cluster analyses revealed five parent profiles: Embracers (50.7%), Burdened (15.7%), Hopeful but Hassled (14.2%), Distrusting (12.7%), and Data Minimalists (6.6%). ANOVAs and chi-square tests identified differences between groups based on diabetes distress, worry over hypoglycemia, device use, and demographic characteristics. Conclusions: Providers encouraging device uptake may benefit from tailoring their approaches based on these distinct groups and their corresponding concerns and needs.

Risk Factors for Cardiovascular Disease (CVD) in Adults with Type 1 Diabetes: Findings from Prospective Real-life T1D Exchange Registry.

Cardiovascular disease (CVD) is a major cause of mortality in adults with type 1 diabetes. We prospectively evaluated CVD risk factors in a large, contemporary cohort of adults with type 1 diabetes living in the United States. Observational study of CVD and CVD risk factors over a median of 5.3 years. The T1D Exchange clinic network. Adults (age ≥ 18 years) with type 1 diabetes and without known CVD diagnosed before or at enrollment. Associations between CVD risk factors and incident CVD were assessed by multivariable logistic regression. The study included 8,727 participants (53% female, 88% non-Hispanic white, median age 33 years [interquartile ratio {IQR} = 21, 48], type 1 diabetes duration 16 years [IQR = 9, 26]). At enrollment, median HbA1c was 7.6% (66 mmol/mol) (IQR = 6.9 [52], 8.6 [70]), 33% used a statin, and 37% used blood pressure medication. Over a mean follow-up of 4.6 years, 325 (3.7%) participants developed incident CVD. Ischemic heart disease was the most common CVD event. Increasing age, body mass index, HbA1c, presence of hypertension and dyslipidemia, increasing duration of diabetes, and diabetic nephropathy were associated with increased risk for CVD. There were no significant gender differences in CVD risk. HbA1c, hypertension, dyslipidemia and diabetic nephropathy are important risk factors for CVD in adults with type 1 diabetes. A longer follow-up is likely required to assess the impact of other traditional CVD risk factors on incident CVD in the current era.

The T1D Exchange Clinic Network and Registry: 10 Years of Enlightenment on the State of Type 1 Diabetes in the United States.

The T1D Exchange Clinic Network and Registry: 10 Years of Enlightenment on the State of Type 1 Diabetes in the United States.

563-P: Autonomic Neuropathy in Type 1 Diabetes (T1D): Findings from the T1D Exchange

Diabetic autonomic neuropathy (DAN) may contribute to significant morbidity, including orthostatic hypotension, arrhythmia, hypoglycemia unawareness, gastroparesis, enteropathy, urinary incontinence and sexual dysfunction in T1D. Contemporary data on DAN impact are scarce. We evaluated the frequency of DAN in adult T1D patients enrolled in the T1D Exchange Clinic Network using the validated Survey of Autonomic Symptoms (SAS). Surveys were emailed to registry participants who consented to be emailed about future studies. DAN was defined as a score >3 on the Symptom Score (SS). Severity of symptoms was graded with the Total Impact Score (TIS) defined as mild (≤25th percentile), moderate (≤50th percentile) and severe (≤75th percentile). Multivariable linear regression and stepwise logistic regression were used to analyze SAS-defined DAN and the correlations between DAN and glycemic control, medical comorbidities and socioeconomic factors. Among 965 T1D participants, DAN frequency was 17%, of which 76% presented with moderate and 24% with severe symptoms. Participants with DAN were older (44±16 vs. 39±17 years), were more often female (73% vs. 62%), had longer T1D duration (28±14 years vs. 23±14 years), and had higher hemoglobin A1c (8.1±1.7% vs. 7.7±1.5%) than those without DAN (all p-values <0.05). Compared with those without DAN, those with DAN were more likely to have peripheral neuropathy (OR 8.10, 95% CI 4.57-14.18), gastroparesis (OR 5.35, 95% CI 2.55-11.23), cardiovascular disease (OR 2.60, 95% CI 1.34-4.91), depression (OR 2.20, 95% CI 1.46-3.32) and use opioids (OR 4.23, 95% CI 1.76-10.13), both p-values <0.01. As ascertained by the short SAS questionnaire, we found that DAN symptoms were common and should be screened for regularly. Traditional glycemic risk factors and the presence of peripheral neuropathy and CVD were associated with DAN. Opioid use also was greater in those with DAN, which might serve as a marker of more painful peripheral neuropathy. Disclosure K.R. Mizokami-Stout: None. R. Bailey: None. N.C. Foster: None. L. Ang: None. G. Aleppo: Advisory Panel; Self; Novo Nordisk Inc. Consultant; Self; Dexcom, Inc., Medtronic MiniMed, Inc. Research Support; Self; AstraZeneca, Dexcom, Inc., Lilly Diabetes, Novo Nordisk Inc. C.J. Levy: Advisory Panel; Self; Novo Nordisk A/S, Sanofi. Employee; Spouse/Partner; Allergan. M.R. Rickels: None. V.N. Shah: Advisory Panel; Self; Sanofi US. Consultant; Self; Dexcom, Inc. S. Polsky: Consultant; Self; Jaeb Center for Health Research. Research Support; Self; Barbara Davis Center for Diabetes, Children's Diabetes Foundation, Dexcom, Inc., Eli Lilly and Company, JDRF, Leona & Harry Helmsley Charitable Trust, National Institute of Diabetes and Digestive and Kidney Diseases, Sanofi US. M. Katz: None. B.A. Nelson: None. A. Carlson: Consultant; Self; Sanofi US. Research Support; Self; Abbott, Dexcom, Inc., JDRF, Medtronic, National Institutes of Health, Novo Nordisk A/S. F. Vendrame: None. R. Pop-Busui: Consultant; Self; Novo Nordisk A/S. Research Support; Self; AstraZeneca. Other Relationship; Self; American Diabetes Association. Funding The Leona M. and Harry B. Helmsley Charitable Trust

Continuous Glucose Monitoring Profiles in Healthy Nondiabetic Participants: A Multicenter Prospective Study.

Use of continuous glucose monitoring (CGM) is increasing for insulin-requiring patients with diabetes. Although data on glycemic profiles of healthy, nondiabetic individuals exist for older sensors, assessment of glycemic metrics with new-generation CGM devices is lacking. To establish reference sensor glucose ranges in healthy, nondiabetic individuals across different age groups using a current generation CGM sensor. Multicenter, prospective study. Twelve centers within the T1D Exchange Clinic Network. Nonpregnant, healthy, nondiabetic children and adults (age ≥6 years) with nonobese body mass index. Each participant wore a blinded Dexcom G6 CGM, with once-daily calibration, for up to 10 days. CGM metrics of mean glucose, hyperglycemia, hypoglycemia, and glycemic variability. A total of 153 participants (age 7 to 80 years) were included in the analyses. Mean average glucose was 98 to 99 mg/dL (5.4 to 5.5 mmol/L) for all age groups except those over 60 years, in whom mean average glucose was 104 mg/dL (5.8 mmol/L). The median time between 70 to 140 mg/dL (3.9 to 7.8 mmol/L) was 96% (interquartile range, 93 to 98). Mean within-individual coefficient of variation was 17 ± 3%. Median time spent with glucose levels >140 mg/dL was 2.1% (30 min/d), and median time spent with glucose levels <70 mg/dL (3.9 mmol/L) was 1.1% (15 min/d). By assessing across age groups in a healthy, nondiabetic population, normative sensor glucose data have been derived and will be useful as a benchmark for future research studies.

Metformin Improves Peripheral Insulin Sensitivity in Youth With Type 1 Diabetes.

Type 1 diabetes in adolescence is characterized by insulin deficiency and insulin resistance (IR), both thought to increase cardiovascular disease risk. We previously demonstrated that adolescents with type 1 diabetes have adipose, hepatic, and muscle IR, and that metformin lowers daily insulin dose, suggesting improved IR. However, whether metformin improves IR in muscle, hepatic, or adipose tissues in type 1 diabetes was unknown. Measure peripheral, hepatic, and adipose insulin sensitivity before and after metformin or placebo therapy in youth with obesity with type 1 diabetes. Double-blind, placebo-controlled clinical trial. Multi-center at eight sites of the T1D Exchange Clinic Network. A subset of 12- to 19-year-olds with type 1 diabetes (inclusion criteria: body mass index ≥85th percentile, HbA1c 7.5% to 9.9%, insulin dosing ≥0.8 U/kg/d) from a larger trial (NCT02045290) were enrolled. Participants were randomized to 3 months of metformin (N = 19) or placebo (N = 18) and underwent a three-phase hyperinsulinemic euglycemic clamp with glucose and glycerol isotope tracers to assess tissue-specific IR before and after treatment. Peripheral insulin sensitivity, endogenous glucose release, rate of lipolysis. Between-group differences in change in insulin sensitivity favored metformin regarding whole-body IR [change in glucose infusion rate 1.3 (0.1, 2.4) mg/kg/min, P = 0.03] and peripheral IR [change in metabolic clearance rate 0.923 (-0.002, 1.867) dL/kg/min, P = 0.05]. Metformin did not impact insulin suppression of endogenous glucose release (P = 0.12). Adipose IR was not assessable with traditional methods in this highly IR population. Metformin appears to improve whole-body and peripheral IR in youth who are overweight/obese with type 1 diabetes.

Insulin Pump Therapy—Patient and Provider Perceptions

This study aimed to identify the benefits and challenges of insulin pump therapy from both the patient and provider perspective. One hundred current multiple daily injection (MDI) users, including 31 former pump users, and 250 current pump users were recruited from myGlu.org, and 61 healthcare providers were recruited from T1D Exchange Clinic Network and American Association of Diabetes Educators to complete an online survey. Reports of decreased HbA1Cs (A1C) after pump initiation differed significantly between patients and providers (p = .001); more former users reported their A1C increased compared to current users (p = .03). Significantly more current and MDI had recent A1Cs &amp;lt; 7.0% than former users (p = .005). The most common reason patients reported stopping pump therapy was an increase in blood glucose; however, providers reported that the most common reason for patients to stop pump therapy was cost. Over 90% of current users were satisfied with their pump and providers report that 92% of their patients were satisfied with their pump; however, only 29% of former pump users reported that they were satisfied with their pump. HCP’s perceptions of the patients’ experience with pumps differs from patients’ experience, which highlights the importance of patient-centered communication to understand an individual patient’s preferences, and challenges in managing diabetes to select an insulin treatment plan that meets both patient and HCP goals. Disclosure M. Perez-Nieves: Employee; Self; Eli Lilly and Company. Stock/Shareholder; Self; Eli Lilly and Company. C. Garey: None. L. Fan: Employee; Self; Eli Lilly and Company. A.H. McAuliffe-Fogarty: Advisory Panel; Self; Novo Nordisk Inc..

Marked Increases in CGM Use Has Not Prevented Increases in HbA1c Levels in Participants in the T1D Exchange (T1DX) Clinic Network

In this report, we examined and compared changes in diabetes technology use and HbA1c levels from data collected in 22,470 participants in the T1DX clinic registry (mean age 26±18 years, duration 14±13 years) between 2016 and 2017 with registry data collected from 25,529 participants (mean age 22±17 years, duration 10±12 years) between 2010 and 2012. There was a moderate increase in insulin pump use across all age groups from 57% in the 2010-12 cohort to 63% in the 2016-17 cohort. In contrast, continuous glucose monitoring (CGM) use rose sharply from 7% to 28%, with the most dramatic increase in the preadolescent and young child groups (4% to 35% for participants &amp;lt;13 years old) (Table). Despite adjustments for differences in age and diabetes duration, mean HbA1c levels increased over time (from 8.2% in 2010-12 to 8.6% in 2016-17), with the greatest rise in participants 13-25 years old (Table). In both cohorts CGM users had lower HbA1c levels than non-users (7.6% in CGM users vs. 8.3% in non-users in 2010-12 and 7.9% in CGM users vs. 8.8% in non-users in 2016-17). Conclusion: In the T1D Exchange registry, HbA1c levels have increased although HbA1c remains lower in CGM users than non-users. Disclosure N.C. Foster: None. K. Miller: None. L. DiMeglio: Advisory Panel; Self; Eli Lilly and Company. Research Support; Self; Dexcom, Inc., Medtronic, Sanofi, Caladrius Biosciences, Inc., Janssen Research &amp; Development, Xeris Pharmaceuticals, Inc., Sanofi. D.M. Maahs: Advisory Panel; Self; Insulet Corporation. Consultant; Self; Abbott. Research Support; Self; Medtronic, Bigfoot Biomedical, Dexcom, Inc., Insulet Corporation, Roche Diabetes Care Health and Digital Solutions. W.V. Tamborlane: Consultant; Self; AstraZeneca, Boehringer Ingelheim GmbH, Eli Lilly and Company, Medtronic MiniMed, Inc., Novo Nordisk Inc., Sanofi, Takeda Pharmaceuticals U.S.A., Inc. R.M. Bergenstal: Research Support; Self; Johnson &amp; Johnson Services, Inc.. Consultant; Self; Johnson &amp; Johnson Services, Inc.. Research Support; Self; Abbott. Advisory Panel; Self; Abbott. Research Support; Self; Becton, Dickinson and Company. Consultant; Self; Becton, Dickinson and Company. Research Support; Self; Boehringer Ingelheim Pharmaceuticals, Inc., AstraZeneca, Takeda Pharmaceuticals U.S.A., Inc., Dexcom, Inc.. Stock/Shareholder; Self; Merck &amp; Co., Inc.. Research Support; Self; Eli Lilly and Company, Sanofi. Advisory Panel; Self; Sanofi, Roche Pharma. Research Support; Self; Novo Nordisk Inc.. Advisory Panel; Self; Novo Nordisk Inc.. Research Support; Self; Medtronic. Consultant; Self; Medtronic. Research Support; Self; Hygieia. Advisory Panel; Self; Hygieia, Glooko, Inc.. Research Support; Self; JAEB Center For Health Research, JDRF, National Institute of Diabetes and Digestive and Kidney Diseases. M.A. Clements: Speaker's Bureau; Self; Medtronic. Advisory Panel; Self; Glooko, Inc. M.R. Rickels: Consultant; Self; Hua Medicine, Xeris Pharmaceuticals, Inc.. E. Smith: None. B.A. Olson: None. R. Beck: Consultant; Self; Eli Lilly and Company. Research Support; Self; Abbott. Consultant; Self; Bigfoot Biomedical. Research Support; Self; Dexcom, Inc.. Consultant; Self; Insulet Corporation. Research Support; Self; Roche Diabetes Care Health and Digital Solutions. Consultant; Self; Merck &amp; Co., Inc., Xeris Pharmaceuticals, Inc..

Prevalence of Celiac Disease in 52,721 Youth With Type 1 Diabetes: International Comparison Across Three Continents.

Celiac disease (CD) has a recognized association with type 1 diabetes. We examined international differences in CD prevalence and clinical characteristics of youth with coexisting type 1 diabetes and CD versus type 1 diabetes only. Data sources were as follows: the Prospective Diabetes Follow-up Registry (DPV) (Germany/Austria); the T1D Exchange Clinic Network (T1DX) (U.S.); the National Paediatric Diabetes Audit (NPDA) (U.K. [England/Wales]); and the Australasian Diabetes Data Network (ADDN) (Australia). The analysis included 52,721 youths <18 years of age with a clinic visit between April 2013 and March 2014. Multivariable linear and logistic regression models were constructed to analyze the relationship between outcomes (HbA1c, height SD score [SDS], overweight/obesity) and type 1 diabetes/CD versus type 1 diabetes, adjusting for sex, age, and diabetes duration. Biopsy-confirmed CD was present in 1,835 youths (3.5%) and was diagnosed at a median age of 8.1 years (interquartile range 5.3-11.2 years). Diabetes duration at CD diagnosis was <1 year in 37% of youths, >1-2 years in 18% of youths, >3-5 years in 23% of youths, and >5 years in 17% of youths. CD prevalence ranged from 1.9% in the T1DX to 7.7% in the ADDN and was higher in girls than boys (4.3% vs. 2.7%, P < 0.001). Children with coexisting CD were younger at diabetes diagnosis compared with those with type 1 diabetes only (5.4 vs. 7.0 years of age, P < 0.001) and fewer were nonwhite (15 vs. 18%, P < 0.001). Height SDS was lower in those with CD (0.36 vs. 0.48, adjusted P < 0.001) and fewer were overweight/obese (34 vs. 37%, adjusted P < 0.001), whereas mean HbA1c values were comparable: 8.3 ± 1.5% (67 ± 17 mmol/mol) versus 8.4 ± 1.6% (68 ± 17 mmol/mol). CD is a common comorbidity in youth with type 1 diabetes. Differences in CD prevalence may reflect international variation in screening and diagnostic practices, and/or CD risk. Although glycemic control was not different, the lower height SDS supports close monitoring of growth and nutrition in this population.

Risk Factors Associated With Severe Hypoglycemia in Older Adults With Type 1 Diabetes.

Severe hypoglycemia is common in older adults with long-standing type 1 diabetes, but little is known about factors associated with its occurrence. A case-control study was conducted at 18 diabetes centers in the T1D Exchange Clinic Network. Participants were ≥60 years old with type 1 diabetes for ≥20 years. Case subjects (n = 101) had at least one severe hypoglycemic event in the prior 12 months. Control subjects (n = 100), frequency-matched to case subjects by age, had no severe hypoglycemia in the prior 3 years. Data were analyzed for cognitive and functional abilities, social support, depression, hypoglycemia unawareness, various aspects of diabetes management, C-peptide level, glycated hemoglobin level, and blinded continuous glucose monitoring (CGM) metrics. Glycated hemoglobin (mean 7.8% vs. 7.7%) and CGM-measured mean glucose (175 vs. 175 mg/dL) were similar between case and control subjects. More case than control subjects had hypoglycemia unawareness: only 11% of case subjects compared with 43% of control subjects reported always having symptoms associated with low blood glucose levels (P < 0.001). Case subjects had greater glucose variability than control subjects (P = 0.008) and experienced CGM glucose levels <60 mg/dL for ≥20 min on 46% of days compared with 33% of days in control subjects (P = 0.10). On certain cognitive tests, case subjects scored worse than control subjects. In older adults with long-standing type 1 diabetes, greater hypoglycemia unawareness and glucose variability are associated with an increased risk of severe hypoglycemia. A study to assess interventions to prevent severe hypoglycemia in high-risk individuals is needed.

Challenges and future directions of the T1D Exchange Clinic Network and registry.

The T1D Exchange Clinic Network consists of 67 clinics throughout the United States. Among the more than 100,000 patients with type 1 diabetes mellitus (T1DM) who receive care at these centers, more than 26,000 have been enrolled in a registry. The registry includes participants over a wide age range, from age &lt;1 to 93 years, and consists of both those newly diagnosed (more than 3000 diagnosed &lt;1 year from the time of enrollment) and those with long-standing diabetes (more than 1000 with T1DM for at least 40 years). Data on diabetes history, insulin administration, diabetes management, monitoring, complications, medical conditions, medications, and laboratory results are collected at enrollment and annually through participant completion of Web-based questionnaires and data extraction from medical records. The clinic registry has provided a rich data set to address important clinical and public health issues, including important observations regarding the current state of treatment of T1DM in diabetes centers in the United States. Challenges encountered during the establishment of the clinic registry include establishment of criteria for a diagnosis of presumed autoimmune T1DM, standardization of data collected across clinics, data quality, and understanding of potential bias. Collecting the data and maximizing data quality has required considerable effort. Even with these efforts, certain data elements are difficult to capture in a meaningful way. A standard T1DM module used by all electronic health records could be developed based on the data collection instruments developed for the T1D Exchange clinic registry.