2356-PUB: The Relationship between Hypoglycaemia, Weight, and Quality of Life among Patients with Type 1 Diabetes: Observations from the DEPICT-2 Trial
Bibliographic record
Abstract
Insulin-treatment in type 1 diabetes mellitus (T1DM) is associated with elevated risk of hypoglycaemia and weight gain, which may act as a barrier to achieving optimal glycaemic control. For patients inadequately controlled by insulin alone, adjunct dapagliflozin can reduce body weight and improve glycaemic control without increased risk of hypoglycaemia. This study aimed to empirically quantify the inter-relationships between hypoglycaemia, body mass index (BMI) and quality of life using DEPICT-2 trial data. A two-stage linear regression framework evaluated (1) the relationship between hypoglycaemic fear score (HFS) and the occurrence of severe hypoglycaemia, the number of documented symptomatic events and patient age, and (2) the relationship between health-related utility (EQ-5D) and prognostic factors for utility, including HFS and BMI. Model selection was based on clinically-relevant factors. A linked evidence approach correlated the relationship between treatment, hypoglycaemia incidence and HbA1c over 52-weeks, to the relationships captured within the regression models. HFS increased as a function of incidence of severe hypoglycaemia (coefficient estimate (CE): 14.62, p=0.004) and frequency of symptomatic events (log transposed, CE: 1.32, p=0.0026). In turn, increased HFS and increased BMI were both independently associated with a significant reduction in utility (CE 0.0024, p<0.001 and 0.0026, p=0.0016 respectively). In DAPA-treated patients, attainment of target glycaemic control was not associated with an increased rate of hypoglycaemic events. This study demonstrates that fear of hypoglycaemia, driven by increased incidence of hypoglycaemia, and BMI are significant determinants of quality of life for people with T1DM. These findings support the role of dapagliflozin in T1DM management, which was associated with achievement of glycaemic control targets and weight loss, without increased hypoglycaemia. Disclosure J. Gordon: Research Support; Self; AstraZeneca, Bristol-Myers Squibb Company, GlaxoSmithKline plc., Novartis AG, Novo Nordisk A/S, Pfizer Inc., Takeda Canada, Takeda Pharmaceutical Company Limited. L.M. Beresford-Hulme: None. H. Bennett: Consultant; Self; AstraZeneca. Employee; Self; Health Economics and Outcomes Research Ltd. A. Tank: Employee; Self; AstraZeneca. C. Edmonds: Employee; Self; AstraZeneca. P. McEwan: Consultant; Self; AstraZeneca. Employee; Self; Health Economics and Outcomes Research Ltd.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".