Type 1 Diabetes Exchange Registry Research Articles

The emotional burden of type 1 diabetes: A cross-sectional study to understand associations between diabetes distress and glucose metrics in adulthood.

Advancements in type 1 diabetes (T1D) management, such as continuous glucose monitoring (CGM), have helped people achieve narrower glucose ranges, but associations between CGM and diabetes distress are unclear. Although higher HbA1c is associated with higher distress, associations with other glucose metrics are unknown. To better understand this relationship, we characterized diabetes distress in a sample of CGM users and compared differences in glucose metrics (measured via CGM) between those with higher versus lower distress. CGM users with T1D from the T1D Exchange Registry completed an online survey including diabetes distress (DDS-2) and shared CGM data (N = 199). CGM metrics were computed from all available data within 3 months prior to survey completion. Participants were grouped by distress level: lower (DDS-2 < 3, n = 120) or higher (DDS-2 ≥ 3, n = 79). Welch's t-tests were used to compare mean differences in CGM metrics between groups and MANCOVA was used to further probe mean differences. Approximately 39.7% participants reported higher diabetes distress. Welch's t-tests revealed participants with higher distress spent significantly more time in higher glucose ranges (above 180 mg/dL and above 250 mg/dL), less time in target glucose ranges (between 70 and 180 mg/dL and between 70 and 140 mg/dL) and had higher glucose management index values compared to those with lower distress (p < 0.01). MANCOVA models showed similar results. CGM users continue to experience diabetes distress. Moreover, higher distress appears to be associated with hyperglycaemia. These findings provide support for broader screening efforts for diabetes distress.

Correlation between triglyceride-glucose index and diabetic kidney disease risk in adults with type 1 diabetes mellitus

BackgroundThe triglyceride-glucose (TyG) index is identified as an alternative indicator of insulin resistance (IR) and is associated with macro- and micro-vascular diseases among patients with type 2 diabetes mellitus. The relationship between the TyG index and IR and its impact on diabetic kidney disease (DKD) remains unclear among adults with type 1 diabetes mellitus(T1DM).MethodsThis study comprised a cross-sectional analysis using data from the Guangdong T1DM Translational Medicine Study (GTT) and a longitudinal analysis using data from the type 1 diabetes (T1D) Exchange registry study. Correlation analysis was used to investigate the association between the TyG index and IR. Logistic regression and Cox proportional hazards regression were performed to explore the impact of the TyG index on DKD risk.ResultsThe GTT Study included 836 adults (216 with DKD and 620 without DKD). A significant correlation existed between the TyG index and the estimated glucose disposal rate (r=-0.64, p < 0.01). The TyG index was a risk factor for DKD after confounder adjustment (OR = 1.34, 95% CI:1.03–1.74). The T1D Exchange registry study included 8,771 adults (2,050 with DKD and 6,721 without DKD). After adjusting confounding factors, the TyG index was identified as an independent risk factor for DKD at enrollment, with the highest risk of DKD incidence observed in the highest TyG tertile group (OR = 1.92, 95%CI:1.67–2.20). During a median follow-up of 44.58(21.84, 67.09) months, the risk of developing DKD was increased by 32% at every 1 SD increase of the TyG index over time among participants without DKD at enrollment.ConclusionsThe TyG index could be used to assess IR and was identified as an independent risk factor of DKD among adults with T1DM.

Insights into Knowledge and Attitudes About Autoantibody Screening from People Affected by Type1 Diabetes: A Brief Report.

Screening for islet-specific autoantibodies can identify individuals at risk for type1 diabetes (T1D). Despite calls for increased nationwide autoantibody screening efforts, it is unclear how many individuals have participated in screening among people who may benefit from it. Moreover, knowledge and perceptions of autoantibody screening in real-world samples are not well understood. We surveyed a sample of individuals (aged 18+years old) from T1D Exchange Registry with a personal or family history of T1D to assess their self-reported T1D autoantibody knowledge, experiences, and attitudes. Participants belonged to one of three groups: adults with T1D who had a biological child without T1D or future plans for a child (PWD); parents without T1D who had a biological child with T1D and one or more biological children without T1D (Caregivers); and first-degree adult children or siblings to a person with T1D (Relatives). Descriptive analyses (means, standard deviations, frequencies) are presented by participant groups. A total of 510 participants enrolled in the study. Across groups, participants reported feeling a little to somewhat knowledgeable about autoantibody screening and positive perceptions of autoantibody screening in general. However, few participants had screened their child without T1D (PWDs, 21.94%; Caregivers, 46.30%) or themselves (Relatives, 19.23%). Among those who had screened, participants reported generally positive experiences. Among those who had not screened, many participants were "undecided" about autoantibody screening (PWD, 38.46%; Caregivers, 40.52%; Relatives, 44.44%). Influences reported for participants' decisions to screen, not screen, or their current indecision differed by group: PWDs (21.70%) and Caregivers (26.87%) most often reported self-initiated research as an influence and Relatives reported they had not previously considered screening (48.28%). Results highlight the need for more accessible information about screening, including real experiences from those who have screened.

Cluster analysis of adult individuals with type 1 diabetes: Treatment pathways and complications over a five-year follow-up period

AimsTo identify subgroups of adults with type 1 diabetes and analyse their treatment pathways and risk of diabetes-related complications over a 5-year follow-up. MethodsWe performed a k-means cluster analysis using the T1DExchange Registry (n = 6,302) to identify subgroups based on demographic and clinical characteristics. Annual reassessments linked treatment trajectories with these clusters, considering drug and technology use. Complication risks were analysed using Cox regression. ResultsFive clusters were identified: 1) A favourable combination of all variables (31.67 %); 2) Longer diabetes duration (22.63 %); 3) Higher HbA1c levels (13.28 %); 4) Higher BMI (15.25 %); 5) Older age at diagnosis (17.17 %). Two-thirds of patients remained in their initial cluster annually. Technology adoption showed improved glycaemic control over time. Cox proportional hazards showed different risk patterns: Cluster 1 had low complication risk; Cluster 2 had the highest risk for retinopathy, coronary artery disease and autonomic neuropathy; Cluster 3 had the highest risk for albuminuria, depression and diabetic ketoacidosis; Cluster 4 had increased risk for multiple complications; Cluster 5 had the highest risk for hypertension and severe hypoglycaemia, with elevated coronary artery disease risk. ConclusionsClinical characteristics can identify subgroups of patients with T1DM showing differences in treatment and complications during follow-up.

Exploring the intersection of income and race in an age diverse sample of people with Type 1 diabetes.

Prior literature suggests that the social gradient in health is racialised such that Black individuals receive fewer health benefits from socioeconomic status than White individuals, yet scarce research studies examine whether this relationship persists in the context of Type 1 diabetes (T1D). Further, most research studies on racial health disparities in T1D outcomes focus on health during early life. We used data from the 2017-2018 wave of the T1D Exchange Registry (N=11,963) and examined the relationship between household income, race and HbA1c in an age diverse sample of people with T1D. Results revealed that the inverse association between income and HbA1c is stronger for Black T1Ds compared to White T1Ds. Despite this, Black T1Ds with an income of $100,000 or more had higher HbA1c values compared to White individuals with an income of less than $25,000. Further, Black adolescents with T1D had particularly high HbA1c values. There is an urgent need for more research on the interpersonal and structural barriers associated with suboptimal glycaemic control among Black individuals.

Weight Gain and Glycemic Control in Adults with Type 1 Diabetes in the T1D Exchange Registry.

Aim: The impact of weight gain on insulin dosage and glycemic control in adults with type 1 diabetes (T1D) aged 25 years and older was investigated in the T1D Exchange Registry participants. Methods: Participants were categorized into four groups based on their change in weight from T1D Exchange registry enrollment to year 5: stable weight (-5 to <5 lb), gained 5 to <10 lb, gained 10 to <20 lb, or gained ≥20 lb. Those who lost >5 lb were excluded. The primary outcomes were glucose control, as measured by glycosylated hemoglobin (HbA1c), and total daily insulin dose (TDD) at year 5. Linear regression models were used to evaluate the association between weight gain, HbA1c, and TDD. Results: There were 1969 participants included in the analyses. The mean ± standard deviation age was 45 ± 13 years, 57% were female, and 92% were White non-Hispanic. For those with an enrollment HbA1c <8.0%, the mean HbA1c at year 5 was higher for those who gained ≥20 lb compared to those with a stable weight of -5 to <5 lb (7.4% ± 1.1% vs. 7.2% ± 0.8%, respectively; P = 0.005). For this cohort, the mean TDD at year 5 increased from 49 ± 25 to 61 ± 29 U for those who gained ≥20 lb, while decreased from 45 ± 27 to 44 ± 25 U for those with stable weight of -5 to <5 lb (P < 0.001). Among participants with an enrollment HbA1c ≥9.0%, the mean HbA1c at year 5 was statistically insignificant at 8.4% ± 1.3% for those who gained ≥20 lb compared to 9.2% ± 1.7% for those with a stable weight of -5 to <5 lb (P = 0.09). Conclusion: Significant weight gain in adults with T1D who had good to adequate glycemic control was associated with modest deterioration in glucose control despite an increase in TDD. Worsening glucose control may indicate insulin resistance related to weight gain despite significantly increased insulin dosage which was insufficient to maintain adequate glycemic control.

669-P: Factor Analysis of the Diabetes-Specific Risk-Taking Inventory

Introduction: Adolescence is marked by increased risk-taking behaviors and challenges engaging in optimal type 1 diabetes (T1D) treatment. We developed the Diabetes-Specific Risk-Taking Inventory (DSRI) to assess risky adolescent T1D self-management behaviors. In the current study, we conducted confirmatory factor analyses (CFAs) for the DSRI and examined model fit. Methods: We surveyed a national sample of 224 adolescents (age 15-18 years; M age 16.9 ± 1.1 years; 49% female, 11% identified as Black, Indigenous, or Persons of Color; 76.8% used insulin pumps; M HbA1c= 8.5% ± 1.3); from the T1D Exchange registry in a cross-sectional design. Participants completed the DSRI, and we collected the most recent HbA1c value from registry data. We performed CFAs in R using the Lavaan package to evaluate three models: 1) a one-factor structure, 2) a two-factor structure reflecting items higher and lower in risk (item level of risk assessed by surveying diabetes health care providers), and 3) another two-factor structure reflecting items that associate with higher HbA1c (&amp;gt;9%) and lower HbA1c (&amp;lt;9%). Results: The DSRI measure contains 31 items; however, one item was removed from CFA analyses due to a nonsignificant factor loading value. The one-factor CFA model demonstrated poor fit χ2(399, n=224) =1714.94, p&amp;lt;0.001, RMSEA=0.12, CFI=0.52, TLI=0.49, as did the two-factor structure differentiating between higher and lower HbA1c χ2(399, n=224) =1121.02, p&amp;lt;0.001, RMSEA=0.090, CFI=0.72, TLI=0.69. The two-factor CFA reflecting higher and lower risk items model demonstrated improved fit χ2(399, n=224) =896.19, p&amp;lt;0.001, RMSEA=0.085, CFI=0.77, TLI=0.75. Conclusions: The DSRI is a valuable self-report measure that providers may use to screen for high-risk behaviors not routinely assessed in T1D clinical care. The results suggest that the high-risk vs. low-risk two-factor model is slightly better fitting than the one-factor model; however, additional factor structures should be assessed to identify a more robust structural model. Disclosure A.Monzon: None. J.Pierce: None. R.M.Wasserman: None. Funding The Leona M. and Harry B. Helmsley Charitable Trust (2016PG-T1D053); National Institutes of Diabetes and Digestive and Kidney Diseases (1K12DK097696)

668-P: Exploring Attitudes toward Autoantibody Screening in People Living with T1D, Caregivers, and First-Degree Relatives

Background: T1D autoantibodies can be detected months to years before disease onset, but few people undergo screening. We explored attitudes toward autoantibody screening in people with T1D and caregivers and relatives of people with T1D. Methods: Participants were recruited from the T1D Exchange Registry or referred by an Online Registry participant. Guided by the Health Belief Model, we conducted 4 focus groups with 26 participants (11 adults with T1D with a child without T1D; 8 caregivers to a child with T1D and a child without T1D; 7 biological siblings to a person with T1D). Transcripts were analyzed using NVivo. Results: Responses are summarized in Table 1. Most participants held positive attitudes toward screening. The most common perceived benefit was to obtain knowledge; the anticipated emotional burden of a positive screen was the most frequently reported barrier. Anxiety and relief were emotions associated with a positive and negative screen, respectively. Participants desired information about antibody screening and its interpretation and accuracy. A healthcare providers’ recommendation might prompt the decision to screen. Conclusions: Participants expressed positive attitudes and perceived benefits of autoantibody screening but also reported barriers to screening, particularly the emotional burden of a positive result. Disclosure M.Peter: None. K.S.M.Chapman: None. J.L.Dunne: Employee; Janssen Research &amp; Development, LLC. C.S.Kelly: None. W.Wolf: None.

58-LB: Associations between Diabetes Distress and CGM Metrics in Adults in the T1D Exchange Registry

Research on diabetes distress in continuous glucose monitor (CGM) users with T1D is divided. Some studies suggest CGM users experience less distress compared to non-users, whereas other studies find no differences in distress. While research shows higher distress is related to higher HbA1c, more work is needed to understand how distress is related to CGM metrics. Thus, we recruited adult CGM users with T1D (n = 199) from the T1D Exchange Registry to complete an online survey about diabetes characteristics and a screener of diabetes distress (DDS-2). CGM metrics (defined in Table 1) were computed from CGM data within 12 months prior to survey completion. Participants were grouped into lower (DDS-2 &amp;lt; 3, n = 120) or higher (DDS-2 ≥ 3, n = 79) diabetes distress. We used Welch’s t-tests to compare mean differences in blood glucose (BG) experiences and CGM metrics between participants with lower and higher diabetes distress. As shown in Table 1, participants with higher diabetes distress reported higher HbA1c, and - via CGM metrics - experienced more time in higher BG ranges and less time within standard target ranges, had higher blood glucose variability, and had higher GMI compared to those with lower distress. Associations between distress and CGM metrics replicated in a MANCOVA after adjusting for duration of T1D. Our results suggest, in CGM users, higher perceived diabetes distress is associated with more time in hyperglycemia. Disclosure C. S. Kelly: None. H. Nguyen: None. K. S. M. Chapman: None. E. M. Cornelius: None. M. Peter: None. W. Wolf: None.

17-LB: Diabetic Nephropathy Prediction with Machine-Learning Models for Patients with Type 1 Diabetes

Cardiovascular disease (CVD) is the leading cause of global mortality in type 1 Diabetes (T1D). This risk remains largely under-managed, leading to a future significant CVD burden. Diabetic nephropathy (DN) is considered a risk factor for CVD and overall mortality in patients with T1D. Identifying risk factors that contribute to the development and progression of DN is therefore important in T1D. Early identification of DN may support earlier intervention for patients with T1D and in turn, reduce future risk for CVD. Using the T1D Exchange Registry database of individuals with T1D in the USA, we sought to develop a machine-learning algorithm to predict patients with DN. With performance metrics of F1-score = 0.67, AUC = 0.78, in our Random Forest model, and F1-score= 0.66, AUC = 0.77 in our Logistics Regression model demonstrates the potential to assist in mitigating DN development and progression. Disclosure S. Sripada: None. S. Sripada: None. S. Belapurkar: Research Support; Dexcom, Inc., Tandem Diabetes Care, Inc. Other Relationship; T1D Exchange. Research Support; Medtronic.

Development and validation of fear of hypoglycemia screener: results from the T1D exchange registry

BackgroundFear of Hypoglycemia (FoH) in people with diabetes has a significant impact on their quality of life, psychological well-being, and self-management of disease. There are a few questionnaires assessing FoH in people living with diabetes, but they are more often used in research than clinical practice. This study aimed to develop and validate a short and actionable FoH screener for adults living with type 1 diabetes (T1D) for use in routine clinical practice.MethodsWe developed an initial screener based on literature review and, interviews with healthcare providers (HCPs) and people with T1D. We developed a cross-sectional web-based survey, which was then conducted to examine the reliability and validity of the screener. Adults (aged ≥ 18 years) with diagnosis of T1D for ≥ 1 year were recruited from the T1D Exchange Registry (August–September 2020). The validation analyses were conducted using exploratory factor analyses, correlation, and multivariable regression models for predicting cut-off scores for the final screener.ResultsThe final FoH screener comprised nine items assessing two domains, “worry” (6-items) and “avoidance behavior” (three items), in 592 participants. The FoH screener showed good internal consistency (Cronbach’s α = 0.88). The screener also demonstrated high correlations (r = 0.71–0.75) with the Hypoglycemia Fear Survey and moderate correlations with depression, anxiety, and diabetes distress scales (r = 0.44–0.66). Multivariable regression analysis showed that higher FoH screener scores were significantly associated with higher glycated hemoglobin (HbA1c) (b = 0.04) and number of comorbidities (b = 0.03).ConclusionsThis short FoH screener demonstrated good reliability and validity. Further research is planned to assess clinical usability to identify patients with FoH and assist effective HCP-patient conversations.

Risky self-management behaviors in adolescents with type 1 diabetes: Measurement validation for the Diabetes-Specific Risk-Taking Inventory.

Among persons with type 1 diabetes (T1D), adolescents often experience the greatest challenge achieving optimal treatment engagement and glycemic targets. Risk-taking behaviors often increase during adolescence and may interfere with engagement in T1D care. We developed the Diabetes-Specific Risk-Taking Inventory (DSRI) to assess risky T1D self-management behaviors in adolescents with T1D. In the current study, we aimed to examine the DSRI's psychometric properties. We surveyed a national sample of 224 adolescents from the T1D Exchange registry (M age=16.9 ± 1.1, 49% female, M A1c=8.5% ± 1.3, 76.8% on insulin pumps) in a cross-sectional design. Participants completed the DSRI and measures of engagement, general risk-taking, and executive functioning and reported on incidence of severe hypoglycemia and diabetic ketoacidosis over the past year. The DSRI demonstrated reliability (internal consistency: α=0.89; test-retest reliability: r=0.86, p < 0.01). Concurrent validity was demonstrated through significant associations between the DSRI and T1D engagement (r=-0.75), general risk-taking (r=0.57), executive dysfunction (r=0.34), and report of severe hypoglycemia over the past year (r=0.22). The DSRI accounted for unique variance in adolescents' most recent glycated hemoglobin, above and beyond other variables, indicating its incremental validity. Overall, initial psychometrics suggest the DSRI is a reliable and valid measure of risks that adolescents may take with their T1D care. This innovative self-report measure has potential to be an actionable clinical tool to screen for high-risk behaviors not routinely assessed in T1D clinical care.

629-P: Unique Associations between Health Insurance and HbA1c in Adults with Type 1 Diabetes

Diabetes outcomes are affected by socioeconomic factors including health insurance. While some research has examined the impact of health insurance in people with type 1 diabetes (T1D) (e.g., disruptions in health insurance on health outcomes) , it is less clear whether health insurance type (e.g., private versus government-sponsored) shares a unique association with diabetes outcomes. Thus, we examined relationships between demographics, socioeconomic status, and health insurance type with HbA1c in adults in the T1D Exchange Registry - an online longitudinal study of people living with T1D. Participants (N = 7725) were 42.2 years old (SD = 14.6) on average and 75.4% reported their gender as female. Most participants (92.2%) reported their race as White and 5.1% identified as Hispanic. Many participants used insulin pumps (71.3%) and continuous glucose monitors (CGM, 83.0%) . Self-reported HbA1c was 7.3% (SD = 1.58) on average. Health insurance type was categorized into private (79.0%) , Medicaid (9.6%) , Medicare (9.5%) , or no health insurance (1.9%) . Health insurance type, demographic factors (i.e., age, gender, race, ethnicity) , socioeconomic factors (i.e., income, education level) , and diabetes technology use (i.e., insulin pump, CGM) were entered simultaneously into a linear regression with HbA1c as the outcome variable. Private insurance was used as the reference group. Statistical significance was set at p &amp;lt; .05. We found lower HbA1c was associated with identifying race as White (Β = -0.21) , having higher income (Β = -0.10) , having higher education (Β = -0.17) , using an insulin pump (Β = -0.46) and using CGM (Β = -0.72) . Higher HbA1c was associated with identifying as female (Β = 0.14) and having either Medicaid (Β = 0.23) or no health insurance (Β = 0.30) . These results suggest, above and beyond other socioeconomic and demographic factors, health insurance type may contribute to small but meaningful differences in HbA1c. Disclosure C.Kelly: None. H.Nguyen: None. J.Liu: None. K.S.M.Chapman: None. M.Peter: None. C.Leon: None. K.Laferriere: None. J.Ravelson: None. W.Wolf: None.

289-OR: Automated Insulin Delivery Use among 12,065 T1D Exchange Registry Participants

Use of automated insulin delivery (AID) in people with type 1 diabetes (T1D) has increased in recent years. We analyzed data from the T1D Exchange Registry - an online longitudinal study following adults and children with T1D - to describe characteristics of AID use and its association with self-reported HbA1c, occurrence of diabetic ketoacidosis (DKA) symptoms, and occurrence of severe hypoglycemia events (SHE) . Of 12,065 participants (69.5% female, 87.5% non-Hispanic White, mean age 37.9 yrs, mean T1D duration 19.9 yrs) , 26.4% reported using AID, 43.6% using insulin pump without AID, and 30.0% using multiple daily injections (MDI) . Private insurance was reported in 77.4% of AID users, 74.9% pump without AID, and 66.3% MDI. Among the 3,185 AID users, 50.6% used predictive low glucose suspend (PLGS) features, including Medtronic 640G and Tandem Basal-IQ; 49.4% used hybrid closed-loop (HCL) , including DIY looping, Medtronic Auto Mode, and Tandem Control-IQ. Average HbA1c was lower in AID users than those using MDI and pump without AID; Incidence of SHE was lower among HCL users compared with PLGS users and AID users compared with MDI users (Table 1) . These cross-sectional real-world data support findings of previous clinical studies showing significant improvements in HbA1c with AID use, with HCL use providing additional protection against SHE. Disclosure J.Liu: None. W.Wolf: None. K.Miller: Research Support; Dexcom, Inc., Tandem Diabetes Care, Inc. C.Kelly: None. K.S.M.Chapman: None. M.Peter: None. D.Finan: None. H.Nguyen: None. K.Laferriere: None. C.Leon: None.

92-OR: Persistence of Impaired Awareness of Hypoglycemia, Severe Hypoglycemic Events, and Suboptimal Glycemic Control Despite Advanced Diabetes Technologies

Trials of continuous glucose monitors (CGMs) and hybrid closed-loop systems (HCLS) demonstrate improvements in glycemia with reductions in hypoglycemia in T1D, but there is limited real-world data on how these technologies impact the prevalence of impaired awareness of hypoglycemia (IAH) , severe hypoglycemic events (SHEs) , and glycemic control. We conducted a one-time online survey of adults with T1D in the T1D Exchange Registry and online communities. Self-reported medical histories (insulin delivery method, HbA1c, IAH, SHEs) and CGM data were collected. 2044 patients (mean age 43 y, mean T1D duration 26 y, 72% female, 95% White) completed the survey. Most reported using CGMs (92%) ; 953 also used HCLS. Mean HbA1c was 6.89%; 41.5% had an HbA1C of ≥7%; 30.7% reported IAH; 19.8% had ≥1 SHE in the prior year; 12.0% had ≥2 SHEs in the prior year; 9.6% had IAH + ≥1 SHE; and 6.6% had IAH + ≥2 SHEs (Table) . Rates of SHEs and IAH + SHEs were lower in CGM users and CGM + HCLS users than in non-CGM users; however, among CGM + HCLS users, 16.6% reported ≥1 SHE, 8.7% ≥2 SHEs, 7.8% IAH + ≥1 SHE, and 4.7% IAH + ≥2 SHEs. Also, 35.6% of CGM + HCLS users had an HbA1C of ≥7%. In a patient cohort with high rates of technology adoption, rates of SHEs and IAH remained high, with a significant proportion of patients not achieving targeted glycemic control, indicating the need for novel T1D treatments. Disclosure J. Pettus: Advisory Panel; Lilly, MannKind Corporation, Novo Nordisk, Sanofi. Consultant; Carmot Therapeutics, Inc., Diasome. L. Titievsky: Employee; Intercept Pharmaceuticals, Inc., Pfizer Inc., Vertex Pharmaceuticals Incorporated. Stock/Shareholder; Intercept Pharmaceuticals, Inc., Pfizer Inc., Vertex Pharmaceuticals Incorporated. K. Hagan: Employee; Vertex Pharmaceuticals Incorporated. T. Liu: Employee; Takeda Pharmaceutical Company Limited, Vertex Pharmaceuticals Incorporated. Stock/Shareholder; Seattle Genetics, Inc. K. Chandarana: Employee; Vertex Pharmaceuticals Incorporated. J.L. Gaglia: Advisory Panel; Dompé, Regeneron Pharmaceuticals Inc. Consultant; Vertex Pharmaceuticals Incorporated. Research Support; Avotres Inc., Dompé, Janssen Research &amp; Development, LLC, Provention Bio, Inc. Stock/Shareholder; Vertex Pharmaceuticals Incorporated. W. Wolf: None. J. Bispham: Employee; PPD Inc., T1D Exchange. K.S.M. Chapman: None. D. Finan: None. J. Sherr: Advisory Panel; Bigfoot Biomedical, Inc., Cecelia Health, Insulet Corporation, Medtronic, Vertex Pharmaceuticals Incorporated. Consultant; Insulet Corporation, Lexicon Pharmaceuticals, Inc. Research Support; Dexcom, Inc., Insulet Corporation, Jaeb Center for Health Research, JDRF, Medtronic, National Institute of Diabetes and Digestive and Kidney Diseases. Speaker's Bureau; Lilly Diabetes.

652-P: Gaps Remain in Achieving Target T1D Glycemic Goals Despite Advanced Technologies

Continuous glucose monitoring (CGM) metrics and self-reported disease characteristics (severe hypoglycemic events [SHEs], HbA1c) warrant further description in people with T1D using CGM and pumps, including hybrid closed-loop systems (HCLS) . We conducted a one-time online survey of adults with T1D in the T1D Exchange Registry or online communities, where ∼50% of participants contributed up to 1 year of CGM data. Patients were asked about their medical history (SHEs, HbA1c) , while glucose management indicator (GMI) , prolonged hypoglycemic events (&amp;lt;54 mg/dL) , time in and below range (TIR/TBR) , and coefficient of variation (CV) were derived from CGM data. Patients who completed the survey and contributed CGM data (N=926) had a mean age of 42 y and T1D duration of 25 y; 73% were female; 96% were white; 94% had ≥1 year of CGM use. Mean HbA1c was 6.6% (69.0% had HbA1c &amp;lt;7%) . While most patients met consensus glycemic targets (HbA1c, GMI, TIR, TBR, and CV) , with higher proportions observed in those using HCLS pumps than in those using pump + CGM (not HCLS) and MDI + CGM (Table) , patients continued to have significant hypoglycemia based on CGM data and an average of 1.1 SHEs in the prior year. Despite improvements in glycemic control (TIR, TBR, and self-reported HbA1c) with advanced technologies, many patients still cannot achieve clinical targets and experience significant hypoglycemia, highlighting the unmet need for novel T1D treatments. Disclosure L.M.Laffel: Advisory Panel; Medtronic, Roche Diabetes Care, Consultant; Boehringer Ingelheim International GmbH, Dexcom, Inc., Dompé, Insulet Corporation, Janssen Pharmaceuticals, Inc., Lilly Diabetes, Novo Nordisk, Provention Bio, Inc. K.S.M.Chapman: None. D.Finan: None. R.M.Bergenstal: Advisory Panel; Hygieia, Medtronic, Roche Diabetes Care, Zealand Pharma A/S, Consultant; Abbott Diabetes, Ascensia Diabetes Care, Bigfoot Biomedical, Inc., CeQur SA, Dexcom, Inc., Eli Lilly and Company, Novo Nordisk, Onduo LLC, Sanofi, United HealthCare Services, Inc., Research Support; Abbott Diabetes, Dexcom, Inc., Eli Lilly and Company, Insulet Corporation, Medtronic, Novo Nordisk, Sanofi. J.Liu: None. L.Titievsky: Employee; Intercept Pharmaceuticals, Inc., Pfizer Inc., Vertex Pharmaceuticals Incorporated, Stock/Shareholder; Intercept Pharmaceuticals, Inc., Pfizer Inc., Vertex Pharmaceuticals Incorporated. K.Hagan: Employee; Vertex Pharmaceuticals Incorporated. T.Liu: Employee; Takeda Pharmaceutical Company Limited, Vertex Pharmaceuticals Incorporated, Stock/Shareholder; Seattle Genetics, Inc. K.Chandarana: Employee; Vertex Pharmaceuticals Incorporated. J.L.Gaglia: Advisory Panel; Dompé, Regeneron Pharmaceuticals Inc., Consultant; Vertex Pharmaceuticals Incorporated, Research Support; Avotres Inc., Dompé, Janssen Research &amp; Development, LLC, Provention Bio, Inc., Stock/Shareholder; Vertex Pharmaceuticals Incorporated. W.Wolf: None. J.Bispham: Employee; PPD Inc., T1D Exchange.

Evaluating Factors Associated With Continuous Glucose Monitoring Utilization With the Type 1 Diabetes Exchange Registry.

The 2022 American Diabetes Association (ADA) Standards of Care recommends considering use of continuous glucose monitoring (CGM) for insulin-managed diabetes mellitus (DM), but equitable access remains challenging. This study evaluates socioeconomic and demographic metrics associated with CGM use. RStudio 2021.09.1+372 was utilized to perform uni- and bivariable analysis, as well as binomial logistic regression modeling for categorical CGM use (yes/no) on the most recent cross-section from the Type 1 Diabetes Exchange (T1DX) Registry 2016-2018 cohort (n = 22 418). Compared with White Non-Hispanic participants, Black Non-Hispanic (OR = 0.45, CI = 0.36-0.57, P < 0.001) and American Indian/Alaskan Native individuals (OR = 0.33, CI = 0.14-0.70, P = 0.008) had lower odds of CGM use. Compared with private insurance, government insurance had reduced odds of CGM use (OR = 0.59, CI = 0.52-0.66, P < 0.001). Individuals earning $100,000 or more were twice as likely to use CGMs (OR = 2.06, CI = 1.75-2.45, P < 0.001) compared with those earning <$25,000 annually. Subgroup analysis based on income bracket demonstrated that government insured individuals earning <$25,000 annually were the least likely to use CGMs (OR = 0.44, CI = 0.32-0.61, P < 0.001), as compared with private insurance. T1DX Registry data demonstrate that CGM use follows the inverse care law, with health technology utilization inversely related to disease burden. Federal policies promoting CGM use in Medicare and Medicaid populations can facilitate the ADA's recommendation for patients with insulin-managed diabetes mellitus.

Tobacco use patterns and clinical outcomes in the T1D exchange

AimsThis study examined associations between tobacco use and diabetes outcomes using the T1D Exchange Registry. MethodsAdult participants (N = 933) completed standardized questionnaires including self-reported outcomes: past year serious hypoglycemic and diabetic ketoacidosis episodes, diabetes self-care, diabetes distress, and self-monitoring of blood glucose. Chart-extracted outcomes included HbA1c, nephrology and neuropathy diagnoses, and BMI. We examined the relation of tobacco use status (never, former, current) and frequency of use (daily versus less than daily) to these outcomes. ResultsThe majority had never used tobacco (55%, n = 515); 27% (n = 252) were former users and 18% (n = 166) were current users (with 31% using daily). Tobacco status was associated with HbA1c, BMI, self-care, distress, and blood glucose monitoring frequency. Across most outcomes, current users evidenced worse values relative to never users, and former users were largely similar to never users. Daily use was associated with significantly worse outcomes on HbA1c, diabetes self-care, and distress scores relative to less than daily use. ConclusionsThese cross-sectional comparisons suggest that current tobacco use is associated with worse status on important clinical diabetes indicators. Former users did not evidence these deleterious associations. Findings point to potential diabetes-specific motivators that could inform tobacco cessation interventions.

Glycemic outcomes among rural patients in the type 1 diabetes T1D Exchange registry, January 2016–March 2018: a cross-sectional cohort study

IntroductionDoes rural status influence glycemic outcomes among participants in the type 1 diabetes T1D Exchange clinic registry?Research design and methodsData from the T1D Exchange clinic registry between January 2016 and...

Relationships Between Socioeconomic Status, Insurance Coverage for Diabetes Technology and Adverse Health in Patients With Type 1 Diabetes.

Insulin pumps and continuous glucose monitors (CGM) have many benefits in the management of type 1 diabetes. Unfortunately disparities in technology access occur in groups with increased risk for adverse effects (eg, low socioeconomic status [SES], public insurance). Using 2015 to 2016 data from 4,895 participants from the T1D Exchange Registry, a structural equation model (SEM) was fit to explore the hypothesized direct and indirect relationships between SES, insurance features, access to diabetes technology, and adverse clinical outcomes (diabetic ketoacidosis, hypoglycemia). SEM was estimated using the maximum likelihood method and standardized path coefficients are presented. Higher SES and more generous insurance coverage were directly associated with CGM use (β = 1.52, SE = 0.12, P < .0001 and β = 1.21, SE = 0.14, P < .0001, respectively). Though SES displayed a small inverse association with pump use (β = -0.11, SE = 0.04, P = .0097), more generous insurance coverage displayed a stronger direct association with pump use (β = 0.88, SE = 0.10, P < .0001). CGM use and pump use were both directly associated with fewer adverse outcomes (β = -0.23, SE = 0.06, P = .0002 and β = -0.15, SE = 0.04, P = .0002, respectively). Both SES and insurance coverage demonstrated significant indirect effects on adverse outcomes that operated through access to diabetes technology (β = -0.33, SE = 0.09, P = .0002 and β = -0.40, SE = 0.09, P < .0001, respectively). The association between SES and insurance coverage and adverse outcomes was primarily mediated through diabetes technology use, suggesting that disparities in diabetes outcomes have the potential to be mitigated by addressing the upstream disparities in technology use.