Adherence to the Mediterranean Diet and Cardiovascular Risk Factors among the Lebanese Population: A Nationwide Cross-Sectional Post Hoc Study
Bibliographic record
Abstract
Objective: This study aims to identify the association between adherence to healthy eating, using the Lebanese Mediterranean Diet Scale (LMDS), and cardiovascular risk factors in the Lebanese population. Materials and Methods: A cross-sectional study using a multistage cluster sample was conducted in Lebanon. Sociodemographic characteristics were collected through structured interviews and self-administered questionnaires. The LMDS assessed dietary habits. The associations between diabetes, dyslipidemia, and cardiovascular disease were investigated using stratification analysis. Results: The study included 2048 people (mean age: 41.54 ± 17.09 years). Higher adherence to the Mediterranean diet was associated with older age (Beta = 0.175, p < 0.001), being female (Beta = 0.085, p = 0.001), being married (Beta = 0.054, p = 0.047), participating in regular physical activity (Beta = 0.142, p < 0.001), and having cardiovascular disease (Beta = 0.115, p < 0.001) and diabetes (Beta = 0.055, p = 0.043). Adherence was, however, negatively associated with being a smoker (Beta = −0.083, p = 0.002), a previous smoker (Beta = −0.059, p = 0.026), and having higher distress levels (Beta = −0.079, p = 0.002). Stratification analysis by diabetes, dyslipidemia, and cardiovascular disease (CVD) consistently demonstrated these associations. Conclusions: These findings suggest that demographic and health factors influence the Lebanese population’s adherence to the Mediterranean diet. Older age, female gender, married status, physical activity, CVD, and diabetes were all found to be associated with adherence to the Mediterranean diet in the Lebanese population. In contrast, smoking and distress were inversely associated with it.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".