Abstract P518: Prospective Study of Skipping Meals to Lose Weight as a Predictor of Incident Type 2 Diabetes and Coronary Heart Disease: The Canadian 1995 Nova Scotia Health Survey
Bibliographic record
Abstract
Introduction: Skipping meals is an increasingly common practice to lose weight among North American adults of all bodyweights. However, due to a lack of long-term studies, the long-term effect of skipping meals to lose weight on cardiometabolic health outcomes such as a diagnosis of type 2 diabetes mellitus (T2DM) or incident coronary heart disease (CHD) remains unknown, although previous short-term studies of skipping meals and risk factors for T2DM and CHD have suggested plausible biological pathways for a relationship to exist in either direction, protective or harmful. Hypothesis: We assessed the hypotheses that skipping meals to lose weight was associated with long-term risk of incident T2DM and CHD in the Canadian 1995 Nova Scotia Health Survey (NSHS95), and that these associations were influenced by cardiometabolic risk factors. Methods: Skipping meals to lose weight was assessed via questionnaire in a cohort of 2,898 adults in the NSHS95 and was linked to population-based health care administrative databases to determine incidence of T2DM and/or CHD in the following 23 years. Multivariable Cox proportional hazards models were used to estimate hazard ratios (HR) and 95% confidence intervals (CI) for T2DM and CHD. Results: During 23 years of follow-up, 430 incident cases of T2DM and 632 incident cases of CHD were diagnosed. Compared to participants who did not skip meals to lose weight, those who did skip meals to lose weight (2.7%) had an 87% higher risk of T2DM (multivariable-adjusted HR=1.87, 95% CI: 1.11-3.17). This association was no longer present after adjustment for baseline body mass index (BMI) (HR=1.42, 0.83-2.42). After stratification by BMI, skipping meals was associated with T2DM among participants who had BMI <25 kg/m 2 (n=1,030; HR=4.42, 1.01-19.30) but not among participants with BMIs of 25-29.9 kg/m 2 (n=1,123; HR=1.07, 0.39-2.96) or 30+ kg/m 2 (n=586; HR=1.21, 0.61-2.39). The multivariable-adjusted (including BMI) association was also present within participants with elevated cholesterol (n=1,450; HR=1.88, 1.00-3.53) and high blood pressure (n=1,363; HR=2.07, 1.11-3.85), but not among those without. No significant association was observed between skipping meals to lose weight and CHD risk before (HR=1.14, 0.67-1.96) or after adjustment for BMI (HR=1.05, 0.61-1.81), or within subgroups. Conclusion: These findings suggest that skipping meals to lose weight may be a predictive modifiable risk factor for developing T2DM over time, especially among people with a BMI <25 kg/m 2 , potentially working in connection or iteration with other T2DM risk factors. With the growing number of popular diets that include skipping meals, future studies are warranted to understand perturbations of potential metabolic consequences.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".