Abstract
OBJECTIVE
To assess longitudinal trends in glycemic metrics, prevalence of severe hypoglycemic events (SHEs), impaired awareness of hypoglycemia (IAH), and technology use (continuous glucose monitoring [CGM], automated insulin delivery [AID]) in a real-world U.S. cohort of adults with type 1 diabetes.
RESEARCH DESIGN AND METHODS
This was a cross-sectional study of adults with type 1 diabetes conducted ∼2 years after participants enrolled in the original retrospective observational study. Participants self-reported technology use, insulin delivery method, glycated hemoglobin (HbA1c), IAH, and SHEs. Change was assessed among these variables from the initial and follow-up study.
RESULTS
Approximately 2 years after the original survey, 1,056 adults responded to the follow-up survey and were eligible for analysis (53% response rate; mean [SD] age: 46 [16] years; mean [SD] type 1 diabetes duration: 29 [16] years; 71% female; 97% White). Most reported using CGM in the original study (91.8%) and at follow-up (94.4%), while the use of AID increased 17.7%. In the original study, 61.7% reported HbA1c <7% vs. 67.4% at follow-up. Proportions of individuals with IAH and SHEs remained high at ∼30% and ∼20%, respectively, in both studies.
CONCLUSIONS
Although most participants used CGM and the use of AID increased, approximately one-third of respondents did not achieve HbA1c targets, ∼20% continued to have SHEs in the last year, and ∼30% had IAH. This highlights that while CGM and AID systems are a significant advancement, their use alone has not mitigated the risk of severe hypoglycemia, and glucose management still remains suboptimal.
Graphical Abstract
Introduction
The management of type 1 diabetes has evolved in recent years with iterations of pumps and continuous glucose monitoring (CGM), but the insulin-associated iatrogenic risk of hypoglycemia remains (1,2). Severe hypoglycemia is a common complication of insulin therapy in people with type 1 diabetes (3,4). Severe hypoglycemia is a potentially life-threatening condition that may present as confusion, coma, seizure, or cardiac arrhythmia and is defined by neurocognitive impairment and the requirement of external assistance for recovery (5). Repeated episodes of hypoglycemia can lead to a blunted counterregulatory response and an inability to recognize low blood glucose, a condition referred to as impaired awareness of hypoglycemia (IAH) (6,7). IAH increases the risk of severe hypoglycemia by up to sixfold (8,9).
Despite advancements in type 1 diabetes management options, many individuals still struggle to reach the recommended glycated hemoglobin (HbA1c) target of <7%. Additionally, some people continue to have severe hypoglycemic events (SHEs) and IAH (10–12). This is largely due to limitations of current therapies, which include the narrow therapeutic range of insulin and differences between pharmacologic insulin administration into the subcutaneous tissue and regulated physiologic insulin secretion from pancreatic β-cells. Additionally, glucose monitors measure interstitial fluid glucose, which lags blood glucose and may not accurately reflect blood glucose levels when these change rapidly. Taken together, these can lead to an unintended mismatch between insulin administration and physiologic requirements, resulting in risk of both hyper- and hypoglycemia (11,13).
Longitudinal trends in SHEs among individuals with type 1 diabetes are not well described in the current literature, and information about glycemic metrics and use of technology over time is currently limited. To assess whether trends have changed over time within this study population, we conducted a second survey of participants from the original cohort (1) to evaluate hypoglycemia, glycemic metrics, use of technology, and type 1 diabetes characteristics over time. In addition, information about SHEs, such as where participants were treated and what they believed caused their SHEs, were captured. This study evaluated the changes in SHEs, IAH, and glycemic metrics (HbA1c) over time in a high-technology–using population to assess how changes in technology use improve glucose management and mitigate SHEs.
Research Design and Methods
Study Design
This was a cross-sectional study in which participants from an original cohort (1) (composed of adults ≥18 years of age) with type 1 diabetes, who were recruited from the T1D Exchange Registry and T1D Exchange Online Community, were invited to respond to a second online survey ∼2 years after their initial data collection. The original survey was conducted from February to April 2021, and the follow-up survey was conducted from April to May 2023. This resulted in two points of data collection covering two distinct 12-month periods for each participant. Specifically, participants from the original cohort who completed at least one type 1 diabetes–related question; provided information about their CGM use, insulin delivery method, and SHEs; and agreed to be contacted for additional research opportunities were asked to participate in a one-time follow-up survey conducted via an online platform. Participants were required to be residents of the U.S. Women who were currently pregnant were not eligible to participate in the follow-up study, and those who gave birth in the 12 months before the survey were excluded from the analysis.
In both the original and follow-up surveys, participants were asked to report the number of SHEs they had in the prior 12 months, in addition to providing information about glycemic metrics and their use of technology for glycemic monitoring and insulin delivery.
The study was conducted in accordance with the current guidelines for Good Pharmacoepidemiology Practices and in accordance with local and applicable laws and regulations. An Institutional Review Board (WCG IRB, Puyallup, WA) reviewed and approved the study documents. This was an observational study, and the participants provided authorization for use and disclosure of their personal health information as described in the informed consent form. Before providing web-based consent, participants were informed of the purpose and potential benefits and risks of the study.
Study Procedures and Assessments
Data collected from the participants via the online surveys included demographics and clinical characteristics such as HbA1c levels, diabetic ketoacidosis (DKA) events, IAH (determined using the modified Gold score) (14), insulin delivery methods, and daily insulin dose. SHEs were self-reported and defined as severe low blood glucose events accompanied by altered mental and/or physical functioning that required assistance from another person for recovery. Information about how many SHEs participants had during the 12 months before responding to each survey was collected. In the second survey, for those who reported having one or more SHEs, additional questions were asked about the situation that led to SHEs and how SHEs were treated.
Statistical Analyses
The study population included participants who provided a response to the survey question about the number of SHEs they had in the 12 months before responding to the follow-up survey. All analyses were descriptive, and no statistical hypothesis testing was performed. Participants completed two surveys at distinct time points, with data analyzed at the cohort level.
Continuous variables were summarized using the following descriptive summary statistics: the number of participants (n), mean, and SD. Categorical variables were summarized using counts of participants and percentages. Enrolled participants who did not provide answers to a survey question were summarized using the “missing” category.
Data and Resource Availability
The data that support the findings of this study are available from T1D Exchange, but restrictions apply to the availability of these data, which were used under contract for the current study and, therefore, are not publicly available. Data are, however, available upon reasonable request and with permission from the T1D Exchange.
Results
Participant Disposition and Baseline Characteristics
The original cohort consisted of individuals with type 1 diabetes who responded to the initial survey. The follow-up cohort consisted of individuals with type 1 diabetes who responded to a follow-up survey ∼2 years later and, therefore, reported the numbers of SHEs they had during two distinct intervals; that is, the 12 months before responding to the original and follow-up surveys. Participant demographics and background clinical characteristics comparing the original cohort (n = 2,044) and the respondents who responded to the follow-up survey ∼2 years later (n = 1,056) are summarized in Table 1. Both the mean (SD) ages of participants (43.0 [15.6] vs. 46.3 [15.6] years) and duration of diabetes (26.3 [15.3] vs. 29.0 [15.5] years) increased from the original survey to the follow-up survey. Most respondents in both surveys were female and White. Participants were representative of all geographic regions in the U.S. (Midwest, Northeast, South, and West). In both cohorts, >99% of participants had health insurance, with most holding private health insurance.
Table 1.
Demographics and background clinical characteristics
| Original cohort (n = 2,044) | Follow-up cohort (n = 1,056) | |
|---|---|---|
| Demographics | ||
| Age, mean (SD) (years) | 43.0 (15.6) | 46.3 (15.6) |
| Female sex | 1,474 (72.1) | 749 (70.9) |
| White race | 1,949 (95.4) | 1,022 (96.8) |
| Hispanic or Latino ethnicity | 100 (4.9) | 45 (4.3) |
| Have health insurance | 2,029 (99.3) | 1,049 (99.3) |
| Have private health insurance | 1,591 (77.8) | 803 (76.0) |
| Clinical characteristics | ||
| Duration since diagnosis, mean (SD) (years) | 26.3 (15.3) | 29.0 (15.5) |
| BMI (kg/m²) category | ||
| Underweight (<18.5) | 22 (1.1) | 13 (1.2) |
| Normal weight (18.5–24.9) | 794 (38.8) | 405 (38.4) |
| Overweight (25–29.9) | 646 (31.6) | 337 (31.9) |
| Obesity (≥30) | 567 (27.7) | 288 (27.3) |
| Missing | 15 (0.7) | 13 (1.2) |
| Disease-related clinical characteristics | ||
| No. of DKA episodes in the past 12 months* category | ||
| 0 events | 1,799 (88.0) | 987 (93.5) |
| 1 event | 99 (4.8) | 34 (3.2) |
| >1 events | 104 (5.1) | 27 (2.6) |
| Missing | 42 (2.1) | 8 (0.8) |
Data are presented as n (%), unless indicated otherwise.
*The DKA question was, “In the past 12 months, how many times did you experience diabetes-related ketoacidosis.”
The distribution of BMI was similar between surveys, and the proportions of DKA events were nominally higher in the original study compared with the follow-up study.
Use of Technology for Glycemic Monitoring and Insulin Delivery
Data from participants who responded to both surveys (n = 1,056), including use of technology (i.e., CGM, insulin pumps, and automated insulin delivery [AID]) at the time of each survey, are summarized in Table 2. In both surveys, most participants reported using CGM at the time of the surveys (91.8% of participants in the original survey and 94.4% of participants in the follow-up survey). The percentages of participants who started (6.6%) or stopped (2.1%) CGM between the two studies were small. While insulin pump use was relatively constant across the two surveys (78.4% in the original survey and 80.0% in the follow-up survey), more participants used AID (a hybrid closed-loop system [HCLS]) in the follow-up survey (65.1%) than in the original survey (47.4%).
Table 2.
Technology types and use, IAH, and SHEs in participants who responded in both studies
| Characteristic | Original study (n = 1,056) | Follow-up study (n = 1,056) |
|---|---|---|
| Current CGM user | ||
| Yes | 969 (91.8) | 997 (94.4) |
| No | 87 (8.2) | 59 (5.6) |
| Insulin pump user | ||
| Yes | 828 (78.4) | 845 (80.0) |
| No | 228 (21.6) | 211 (20.0) |
| AID feature use* | ||
| Hybrid closed-loop | 501 (47.4) | 687 (65.1) |
| Low glucose suspend or predictive low glucose suspend | 64 (6.1) | 42 (4.0) |
| Manual mode | 261 (24.7) | 51 (4.8) |
| Missing† | 2 (0.2) | N/A |
| Not applicable‡ | 0 (0.0) | 65 (6.2) |
| IAH§ | ||
| Yes | 323 (30.6) | 307 (29.1) |
| No | 733 (69.4) | 749 (70.9) |
| No. of SHEs | ||
| ≥1 | 177 (16.8) | 201 (19.0) |
| ≥2 | 101 (9.6) | 106 (10.0) |
Data are presented as n (%). N/A, does not apply.
*The AID question was only shown to participants who selected “insulin pump” as a method of insulin delivery. †“Missing” refers to participants who selected insulin pump as a method of insulin delivery but did not respond to this question about AID use. ‡“Not applicable” refers to participants who do not have a closed-loop or AID feature. §IAH was measured using the modified Gold score.
SHEs
Shown in Table 2 are the numbers and percentages of participants who had one or more or two or more SHEs in the 12 months before responding to each survey. The percentages of participants who reported having one or more SHE in the prior 12 months were 16.8% for the original survey and 19.0% for the follow-up survey. Similar percentages of participants reported having two or more SHEs in the 12 months before each survey (9.6% for the original survey and 10.0% for the follow-up survey).
Additional information about the SHEs reported in the follow-up survey is shown in Table 3. Among participants who reported having one or more SHEs in the 12 months before the follow-up survey, the mean (SD) number of SHEs reported was 2.4 (2.7). The most common factors that led to the SHEs reported in the study were overestimated insulin dose or carbohydrate count (57.7%), physical activity (36.3%), and delayed or smaller meals (20.9%). Among participants who had at least one SHE, nearly 90% reported that at least one SHE was treated outside of a health care setting. Approximately 14% were treated by an emergency medical technician (EMT), ambulance, or first responder; yet, only 2.5% were treated with an outpatient visit, and 2.5% were treated with inpatient hospitalization.
Table 3.
SHEs, IAH, and glycemic metrics in the follow-up survey
| SHEs | Participants with ≥1 SHE (n = 201) |
| SHEs reported, mean (SD), n | 2.4 (2.7) |
| SHEs reported | |
| 1 | 95 (47.3) |
| 2 | 56 (27.9) |
| 3 | 19 (9.5) |
| 4 | 11 (5.5) |
| ≥5 | 20 (10.0) |
| Event or situation that led to SHE* (check all that apply) | |
| Overestimated insulin dose/carbohydrate count | 116 (57.7) |
| Physical activity | 73 (36.3) |
| Delayed or smaller meal | 42 (20.9) |
| Other | 38 (18.9) |
| Alcohol consumption | 19 (9.5) |
| Missed a meal after injecting insulin | 16 (8.0) |
| Accidentally dosed additional insulin injection | 3 (1.5) |
| Treatment setting of SHEs* (check all that apply) | |
| ≥1 SHEs treated in non–health care setting (e.g., at home by a spouse or friend) | 178 (88.6) |
| ≥1 SHEs treated by EMT/ambulance/first responder | 29 (14.4) |
| ≥1 SHEs treated at an outpatient visit | 5 (2.5) |
| ≥1 SHE treated with inpatient hospitalization | 5 (2.5) |
| IAH | Overall (n = 1,056) |
| IAH modified Gold score, mean (SD) | 2.9 (1.5) |
| Change in modified Gold score from original to follow-up study, mean (SD) | −0.0 (1.2) |
| Glycemic metrics measured by HbA1c values | Overall (n = 1,056) |
| Most recent HbA1c (%) within the past 12 months | (n = 1,045) |
| n | 1,044 |
| Mean (SD) | 6.6 (1.0) |
| Change in most recent HbA1c (%) from original to follow-up study | |
| n | 1,046 |
| Mean (SD) | −0.2 (0.7) |
| Most recent HbA1c (%) within the past 12 months <6.5%† | (n = 1,045) |
| Yes | 495 (47.4) |
| Most recent HbA1c (%) within the past 12 months <7%† | (n = 1,045) |
| Yes | 704 (67.4) |
Data are presented as n (%), unless indicated otherwise.
*For “check all that apply” questions, percentages sum to more than 100%. Summary was based on the five most recent SHEs in the 12 months prior to the follow-up study. †Denominator was the number of participants with most recent HbA1c within 12 months before the follow-up study. Percentages were based on column totals.
The percentages of participants with self-reported SHEs in the 12 months before each survey are shown in Fig. 1A. Similar percentages of participants in each study reported having one or more SHEs in the 12 months before responding to the survey. In the original cohort (n = 2,044), 7.8% of participants reported one SHE and 12.0% reported two or more SHEs. In the follow-up cohort (n = 1,056), 9.0% of participants reported one SHE and 10.0% reported two or more SHEs. Additionally, we assessed the association between technology use and the number of SHEs that occurred during the 12 months before each survey (Fig. 1B and C). Among participants who did not use CGM, 34.3% in the original cohort and 30.5% in the follow-up cohort reported one or more SHEs. Among participants using CGM with multiple daily injections (MDIs) for insulin delivery, 23.0% in the original cohort and 14.7% in the follow-up cohort reported one or more SHE. For those who used CGM and a pump for insulin delivery (excluding those using HCLS), 19.0% in the original cohort and 17.2% in the follow-up cohort reported one or more SHEs. Lastly, for participants using CGM and a pump with AID (HCLS), excluding low glucose suspend or predictive low glucose suspend, 16.6% in the original cohort and 19.6% in the follow-up cohort reported one or more SHEs (Fig. 1B). Similar trends were also observed among participants who had two or more SHEs in the original and follow-up cohorts even with the use of technology (Fig. 1C). Among those using CGM and a pump (excluding those using HCLS), 11.8% in the original cohort and 9.3% in the follow-up cohort reported two or more SHEs. For those who used AID, 8.7% in the original cohort and 10.1% in the follow-up cohort reported two or more SHEs. It is important to note that the timing of AID adoption and SHE events were not captured.
Figure 1.
Percentages of participants with SHEs. Original cohort: blue; follow-up cohort: green. A: Numbers of SHEs reported for the overall cohorts (original cohort, n = 2,044; follow-up cohort, n = 1,056). B and C: Participants with one or more SHEs (B) and with two or more SHEs (C). Error bars represent 95% CIs. SHEs were defined as severe low blood glucose events accompanied by altered mental and/or physical functioning that required assistance from another person for recovery. Nine CGM users from the original study and three CGM users from the follow-up study with pump type unknown were excluded from analysis. Description of each technology type: CGM + MDI, currently using a CGM, and insulin delivery method does not include insulin pump (MDI includes injections via vial/syringe, insulin pen, or insulin smart pen); CGM + Pump, currently using a CGM, excluding hybrid closed loop; CGM + AID, currently using a CGM, insulin delivery method includes insulin pump, and AID use is “hybrid closed loop.”
IAH
For participants who responded to both surveys, the number and percentage of participants with IAH (defined as a modified Gold score of ≥4) are shown in Table 2. A similar percentage of participants reported having IAH in the original and follow-up surveys (30.6% and 29.1%, respectively).
Changes in IAH status between the surveys are summarized in Table 3. The mean (SD) modified Gold score for the follow-up cohort was 2.9 (1.5) and the mean (SD) change in modified Gold score from the original to follow-up survey was −0.0 (1.2). Across the 1,056 respondents who responded to both surveys, the IAH status of most participants was the same in original and follow-up surveys (61.3% reported no IAH in either study; 20.9% reported IAH in both studies).
Glycemic Metrics (HbA1c)
Summarized in Table 3 are HbA1c values for participants in the follow-up cohort and changes in HbA1c from the original to follow-up study among those who responded to both surveys. The mean (SD) HbA1c value was 6.6% (1.0) and the change in mean (SD) HbA1c value from the original to the follow-up study was small (−0.2% [0.7]). At the time of the follow up survey, 47.4% of participants had an HbA1c <6.5% and 67.4% of participants had an HbA1c <7.0%.
Conclusions
In this retrospective, cross-sectional cohort study of 1,056 U.S.-based adults who participated in two surveys (original and follow-up), we observed an increase in technology use, with similar rates of SHEs and IAH, and similar glycemic outcomes over the two survey periods. Of participants who responded to both surveys, only 61.7% in the original and 67.4% in the follow-up survey met the American Diabetes Association/European Association for the Study of Diabetes (12) HbA1c target, despite the vast majority of participants using CGM (>91% in the original survey and >94% in the follow-up survey) and a substantial increase in AID (HCLS) use (17.7% increase in follow-up survey). This study demonstrates that despite the use of the most advanced diabetes technologies available, such as CGMs, pumps, and AID, a notable proportion of participants still do not attain glycemic targets and continue to be at risk for micro- and macrovascular complications (15,16).
While CGM and insulin pump therapy use was relatively consistent across both time points, there was a notable increase in AID (HCLS) use. Specifically, 17.7% of participants initiated the use of AID (HCLS) between the two surveys, reflecting an increasing adoption of advanced diabetes technology. Despite 65.1% of participants using AID at follow-up, the percentage of participants who reported at least one SHE was higher in the follow-up study (19.0%) than in the original study (16.8%). Notably, the most frequently reported factor leading to SHEs was overestimation of insulin dose or carbohydrate count (57.7%). This could potentially limit the ability of advanced diabetes technologies, such as AID, from mitigating risk of SHEs because most AID systems require users to manually enter carbohydrates consumed, which can be prone to inaccurate estimation (17). This finding underscores the persistent challenge of hypoglycemia in type 1 diabetes clinical care, even with the tools available today.
Studying and capturing SHEs is challenging because most events occur outside the health care setting and thus are not captured in claims or electronic health care records databases; therefore, the burden of SHEs is underrepresented (18). In this follow-up study, participants reported that most SHEs were treated outside the health care setting (i.e., without EMT intervention, a visit to an outpatient care center, or inpatient hospitalization). Notably, the primary factor leading to SHEs was overestimation of insulin dose or carbohydrate count, which could potentially limit the ability of advanced diabetes technologies, such as an AID system, from mitigating risk of SHEs. Current generation AID systems rely on user-input data regarding meal size, either as discrete carbohydrate counts or with a qualitative approach to meal size; therefore, error in estimation cannot be overcome by technology (19,20). In addition, device fatigue resulting in reduced use of automated features or the system as a whole or an individual’s false sense of security due to technology that may lead to more intensive meal-time bolusing strategies as well as strengthening of settings that impact automation (e.g., lowering system targets, lowering insulin-to-carbohydrate ratios); any of these may increase the risk of SHEs. Future iterations of these systems seek to adopt a full closed-loop approach, where the need for meal announcements is obviated. However, in the interim, conversations in the clinic can be rooted in examining episodes of hypoglycemia, potentially reviewing CGM data, and discussing factors that led to the situation with a primary focus on behavioral modifications.
Caution must be used in interpreting CGM data without regard to clinical symptomatology. This concept is supported by previous research that reported a high discordance between sensor-detected and patient-reported hypoglycemia (21). For example, a recent study found that many individuals report symptomatic hypoglycemia when CGM data show glucose levels above the hypoglycemic range (i.e., 70 mg/dL). Conversely, individuals with type 1 diabetes reported no symptoms in more than half of the CGM-recorded instances of level 2 hypoglycemia (sensor glucose <54 mg/dL) (21). The discordance with level 2 hypoglycemic events and the inability to capture level 3 hypoglycemic events (SHEs) using a CGM shows the importance of engaging directly with people with type 1 diabetes in order to best understand their hypoglycemic experience.
IAH was reported by 29.1% of participants in the follow-up survey, which is consistent with the literature (22). For most participants, presence or absence of IAH persisted across the surveys. IAH is associated with a higher risk of SHEs, even in individuals who use CGM (6). Future research will be required to assess the potential to resolve IAH with technology; however, our data suggest persistence of IAH despite CGM and AID use.
This U.S.-based cohort of adults with type 1 diabetes, drawn from the T1D Exchange Registry and Online Community, represents a highly engaged and well-managed population; the majority of participants who responded to both surveys were White, female, and held private health insurance. While not fully representative of the larger type 1 diabetes population, this group may offer insight into the evolving landscape of type 1 diabetes care, particularly as CGM and AID technology continue to become more widely adopted. CGM is now recognized as the standard of care for adults living with type 1 diabetes, and guidelines recommend AID systems should be the preferred insulin delivery method in this population (23).
Study participants were asked to self-report data from the past 12 months, and the data are therefore subject to recall and recency bias. However, to ensure comparability across both surveys, the same questionnaire items and definitions from the original survey were used in this follow-up survey. This consistency minimizes the risk that the observed differences reflect changes in patient-reported outcomes rather than a variation in measurement. The temporality of SHEs and HbA1c values relative to changes in the use of CGM and AID was not captured; therefore, reported changes in either HbA1c or SHEs could not readily be attributed to changes in technology. Furthermore, interruptions in supplies, such as a lapse in CGM or pump supplies (i.e., infusion sets, CGM sensors), could have prevented those who use a particular technology from being on it at the time of an event. This level of detail was beyond what was feasible to capture in the surveys. The surveys were conducted in 2021 and 2023 and collected data from the 12 months prior to survey completion; therefore, it is also important to note that potential confounding may be present given the coronavirus disease 2019 pandemic (i.e., lifestyle changes or increased insulin resistance in the setting of coronavirus disease 2019 infection) (24), which was not examined or controlled for.
Additionally, this was a follow-up study and thus limited by participant attrition across the two surveys. Our sample size was limited to participants who responded to the original survey and opted to participate in the follow-up study; therefore, attrition bias is possible. However, demographics and background characteristics of the participants were assessed within the original study population and the follow-up population and results were generally similar between studies. Analysis was done at the cohort level, and changes in outcomes at the individual level were not assessed. Analyses were purely descriptive, and no hypothesis testing was performed. Given these limitations, differences in outcomes may indicate association but not causation. Data for variables such as HbA1c, number of SHEs, and Gold score did not have any implausible values or outliers that would have impacted the overall conclusions for the key study variables.
This study highlights a critical challenge that remains with current type 1 diabetes management; even in a highly engaged cohort with a high uptake of diabetes technology, a notable proportion of participants continue to experience SHEs and maintain glycemic averages higher than the recommended target range. Despite the increased uptake of advanced technologies, a substantial unmet need remains, underscoring the necessity of innovative strategies and therapies to improve care for individuals with type 1 diabetes.
This article contains supplementary material online at https://doi.org/10.2337/figshare.31079971.
Article Information
Acknowledgments. Eliza-Beth Lerch (E.-B.L.) provided medical writing support under the guidance of the authors, and Alexandra Battaglia (A.B.) provided assistance with graphics. E.-B.L. and A.B. are employees of Vertex Pharmaceuticals Incorporated and may hold stock and/or stock options at the company. The T1D Exchange Registry is funded by The Leona M. and Harry B. Helmsley Charitable Trust grant G-2103-05086.
J.P. is an editor of Diabetes Care but was not involved in any of the decisions regarding review of the manuscript or its acceptance.
Duality of Interest. J.L.S. has received grants or contracts from Abbott Diabetes, Dexcom, Breakthrough T1D, Insulet, Medtronic, the National Institutes of Health, and Provention Bio; has received consulting fees from Abbott Diabetes, Insulet, Medscape, Medtronic Diabetes, Vertex Pharmaceuticals, and Ypsomed; has participated in advisory boards for Abbott Diabetes, Cecelia Health, Insulet, MannKind, Medtronic Diabetes, StartUp Health T1D Moonshot, and Vertex Pharmaceuticals Incorporated; and has received support for attending meetings and/or travel from Vertex Pharmaceuticals Incorporated. R.L.M., T.P., and J.S.S. are employees at Vertex Pharmaceuticals Incorporated and own stocks/stock options of Vertex Pharmaceuticals. M.E.P. and E.M.C. were employees of T1D Exchange during the course of the study. No other potential conflicts of interest relevant to this article were reported.
Author Contributions. R.L.M., T.P., J.S.S., M.E.P., and E.M.C. conducted the analyses of the study data. M.E.P. and E.M.C. acquired study data. All authors were involved in interpreting the data, reviewing and drafting the manuscript, and approving the final version of the manuscript to be published. All authors contributed to the development of the study design. All authors agreed to be accountable for the work in the manuscript. R.L.M. and J.P. are the guarantors of this work and, as such, had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.
Prior Presentation. Parts of this study were presented in abstract form at the 84th Scientific Sessions of the American Diabetes Association, Orlando, FL, 21–24 June 2024.
Handling Editors. The journal editor responsible for overseeing the review of the manuscript was Stephen S. Rich.
Funding Statement
This study was funded by Vertex Pharmaceuticals Incorporated.
Footnotes
This article is featured in a podcast available at diabetescareonair.libsyn.com/site.
Supporting information
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