Low‐density lipoprotein cholesterol in adolescents and young adults with type 1 diabetes: Data from the Australasian Diabetes Data Network registry
Bibliographic record
Abstract
AIM: To determine low-density lipoprotein cholesterol (LDL-C) screening frequency and levels, and factors associated with elevated LDL-C, in Australasian youth with type 1 diabetes (T1D). METHODS: Data were extracted from the Australasian Diabetes Data Network (ADDN), a prospective clinical quality registry, on all T1D healthcare visits attended by young people aged 16-25 years (with T1D duration of >1 year) between January 2011 and December 2020. The primary outcomes were elevated LDL-C > 2.6 mmol/L (100 mg/dL) and threshold for treatment: >3.4 mmol/L (130 mg/dL), according to consensus guidelines. Multivariable Generalised Estimated Equations (GEE) were used to examine factors associated with elevated LDL-C across all visits. RESULTS: A cohort of 6338 young people (52.6% men) were identified, of whom 1603 (25.3%) had ≥1 LDL-C measurement documented. At last measurement, mean age, age at T1D diagnosis and T1D duration were 18.3 ± 2.4, 8.8 ± 4.5 and 8.9 ± 4.8 years, respectively. LDL-C was elevated in 737 (46.0%) and at the treatment threshold in 250 (15.6%). In multivariable GEE elevated LDL-C continuously was associated with older age (OR = 0.07; 0.01-0.13, p = 0.02), female sex (OR = 0.31; 0.18-0.43; p < 0.001), higher HbA1c (OR = 0.04; 0.01-0.08; p = 0.01) and having an elevated BMI (OR = 0.17, 0.06-0.39, p < 0.001). CONCLUSIONS: LDL-C screening and levels are suboptimal in this cohort, increasing future cardiovascular complication risk. There is an urgent need to understand how healthcare services can support improved screening and management of dyslipidaemia in this population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".