Changes in olive oil consumption and long-term body weight changes in 3 United States prospective cohort studies
Bibliographic record
Abstract
BACKGROUND: Olive oil intake is inversely associated with risk of cardiometabolic diseases. However, its energy density has raised concerns about weight gain. OBJECTIVE: To examine the associations between long-term changes in olive oil consumption and changes in body weight in three prospective cohort studies. METHODS: We examined data from 121,119 females and males from the Nurses' Health Study (NHS, 1990-2010), NHSII (1991-2015), and Health Professionals Follow-up Study (HPFS, 1990-2014), aged 65 y or younger and who were free from chronic disease at baseline. We assessed the associations between changes in olive oil intake within each 4-y interval and concurrent body weight changes using multivariable linear regression models. Results across the 3 cohorts were pooled using inverse-variance weights. RESULTS: ) was between 25.9 and 26.1 across the 3 cohorts. The mean weight change over each of the 4-y follow-up cycles was highest in the NHSII (1.8 kg; 95% CI [confidence interval]: -6.8, 11.3 kg), followed by the NHS (1.2 kg; 95% CI: -6.8, 9.1 kg), and lastly the health professionals follow-up study HPFS (0.9 kg; 95% CI: -5.4, 7.3 kg). After multivariable adjustment, each ½ tablespoon (7 g) serving per day increment in olive oil consumption was inversely associated with body weight (β coefficient: -0.09 kg, 95% CI: -0.11, -0.08 kg; P < 0.0001). In contrast, each 7 g serving per day increase in other types of added fat (vegetable oils, butter, and margarine) was positively associated with changes in body weight. Results were consistent in stratified analyses by age and BMI. In substitution analyses, replacing margarine, butter, and other vegetable oils with equal amounts of olive oil was associated with less weight gain. CONCLUSIONS: A long-term increase in olive oil intake was inversely associated with body weight in middle-aged adults in the United States. Conversely, increased consumption of other added fats, such as butter and margarine, was positively associated with body weight.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".