Association between low-carbohydrate-diet score, glycemia and cardiovascular risk factors in adults with type 1 diabetes
Bibliographic record
Abstract
BACKGROUND AND AIMS: Low-carbohydrate-diets (LCDs) are gaining popularity in individuals with type 1 diabetes (T1D). However, the impact of such diets on glycemia and cardiovascular risk factors is debated. This study aims to evaluate associations between low-carbohydrate intakes using LCD score with glycemia and cardiovascular risk factors (lipid profile) in adults with T1D or LADA in Québec, Canada. METHODS AND RESULTS: This is a cross-sectional study using data collected in the BETTER registry (02/2019 and 04/2021) including self-reported 24-h dietary recalls to calculate LCD scores, waist circumference, level-2 and level-3 hypoglycemic episodes and measured biochemical data (HbA1c, LDL-cholesterol and non-HDL-cholesterol). Participants were divided into quartiles (Q) based on LCD scores. Two hundred eighty-five adults (aged 48.2 ± 15.0 years; T1D duration 25.9 ± 16.2 years) were included. Categorical variables underwent Chi-squared/Fisher's Exact tests, while continuous variables underwent ANOVA tests. Mean carbohydrate intake ranged from 31.2 ± 6.9% (Q1) to 56.5 ± 6.8% (Q4) of total daily energy. Compared to Q4, more people in Q1 reported HbA1c ≤ 7% [≤53.0 mmol/mol] (Q1: 53.4% vs. Q4: 29.4%; P = 0.011). The same results were found in the models adjusted for age, sex and T1D duration. A greater proportion of participants in Q1 never experienced level-3 hypoglycemia compared to Q3 (Q1: 60.0% vs. Q3: 31.0%; P = 0.004). There were no differences across quartiles for frequency of level-2 hypoglycemia events and lipid profile (LDL-cholesterol and non-HDL-cholesterol). CONCLUSIONS: Low-carbohydrate intakes are associated with higher probabilities of reaching HbA1c target and of never having experienced level-3 hypoglycemia. No associations with level-2 hypoglycemia frequency, nor cardiovascular risk factors were observed.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".