Objective and subjective sleep measures are associated with HbA1c and insulin sensitivity in the general population: Findings from the ORISCAV-LUX-2 study
Bibliographic record
Abstract
AIM: To analyze the association of objective and subjective sleep measures with HbA1c and insulin sensitivity in the general population. METHODS: Using a cross-sectional design, data from 1028 participants in the ORISCAV-LUX-2 study from the general population in Luxembourg were analyzed. Objective sleep measures were assessed using accelerometers whereas subjective measures were assessed using the Pittsburgh Sleep Quality Index (PSQI) questionnaire. Sleep measures were defined as predictors, while HbA1c and quantitative insulin sensitivity check index (QUICKI) scores were considered outcomes. Linear and spline regression models were fitted by progressively adjusting for demographic and lifestyle variables in the total sample population as well as by stratified analyses using gender, obesity status, depressive symptoms and diabetes status. RESULTS: In fully adjusted models, total and deep sleep durations were associated with lower HbA1c (mmol/mol) levels, whereas sleep coefficients of variation (%) and poor sleep efficiency, as measured by PSQI scores (units), were associated with higher HbA1c levels. In stratified models, such associations were observed mainly in men, and in subjects who had depressive symptoms, were overweight and no diabetes. In addition, total sleep, deep sleep, coefficients of variation and poor sleep efficiency as measured by PSQI revealed non-linear associations. Similarly, greater insulin sensitivity was associated with longer total sleep time and with PSQI-6 (use of sleep medication). CONCLUSION: Associations were more frequently observed between sleep characteristics and glycaemic control with the use of objective sleep measures. Also, such associations varied within subgroups of the population. Our results highlight the relevance of measuring sleep patterns as key factors in the prevention of diabetes.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".