When's dinner? Does timing of dinner affect the cardiometabolic risk profiles of South‐Asian Canadians at risk for diabetes
Bibliographic record
Abstract
Abstract Aim To explore the relationship between the time dinner is consumed (dinnertime or timing of dinner) and cardiometabolic risk factors among South‐Asian Canadians at risk for diabetes. Methods We recruited 432 South‐Asian adults affiliated with Sikh and Hindu Temples in Metro Vancouver. Participants deemed to be at risk of diabetes underwent a clinical and behavioural assessment. Dinnertime was measured via self‐report. Clinical endpoints included HbA 1c , apolipoprotein, blood pressure, weight, BMI and waist circumference. Results The mean age of participants was 65 years and 59% were male. Dinnertime was categorized into three groups: early (before 18:00 h); average (18:00 to 20:00 h); and late (later than 20:00 h). Among the participants, 19% ( n = 79), 44% ( n = 187) and 37% ( n = 157) reported early, average and late dinnertimes, respectively. Significant differences were found for dinnertime groups and years of residence in Canada, gender and employment. Compared with the early dinnertime group, the late dinnertime group lived in Canada for a shorter duration, comprised a higher proportion of males (66 vs 48%; P = 0.01) and were currently employed (37 vs 22%; P = 0.02). With regard to clinical endpoints, compared with the early dinnertime group, the late dinnertime group had lower systolic blood pressure (135.9 vs 131.7 mmHg; P = 0.03). After controlling for demographic characteristics, this difference was diminished. No significant differences were found between dinnertime and HbA 1c , apolipoprotein, diastolic blood pressure, weight, BMI and waist circumference. Conclusion Findings suggest that, among this sample of South‐Asian Canadians at risk of Type 2 diabetes, there was no association between timing of the evening meal and cardiometabolic profiles.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".