Impact of High‐Flavanol and High‐Theobromine Chocolate Consumption on Lipid Profile in Women at Risk of Preeclampsia: a Double Blind Randomized Clinical Trial
Bibliographic record
Abstract
Despite discrepancy among results in the literature, flavanol‐rich chocolate consumption during pregnancy could have beneficial effects on the risk of preeclampsia (PE) through improvement of blood pressure and endothelial function. Theobromine, a major constituent of dark chocolate, also possesses vasodilatation properties. However, to our knowledge, no study has investigated the effect of high‐flavanol and high‐theobromine chocolate consumption on lipid profile in pregnant women at risk of PE. Objective To assess the impact of high‐flavanol and high‐theobromine chocolate consumption on lipid profile in pregnant women at risk of PE. Methods A single‐center randomized controlled trial was conducted in women with singleton pregnancy between 11 and 14 weeks gestation. Uterine artery Doppler was performed and women with bilateral diastolic notches and either a uterine artery pulsatility index (PI) >95 th percentile on one side and/or bilateral PI>50 th percentile were considered as potentially eligible for randomization. A total of 131 pregnant women were randomly assigned to either high‐flavanol, high theobromine (HFHT) or low‐flavanol, low‐theobromine (LFLT) chocolate groups. Women had to consume a total of 30 g of chocolate on a daily basis for 12 weeks. Fasting blood samples and anthropometric variables were measured at baseline and at 12 weeks. Results A significant group by time interaction was observed for triglyceride concentrations (p=0.006), with a more pronounced increase in the LFLT group (increases of 0.6 mmol/L in HFHT versus 0.8 mmol/L in LFLT). Significant time effects were observed in total‐C, LDL‐C, HDL‐C and total‐C on HDL‐C ratio (p< 0.0001), with similar increases in both HFHT and LFLT chocolate groups. A significant group by time interaction was noted for body weight (p=0.03), with a greater weight gain in the HFHT group. Conclusion Our results suggest that consumption of HFHT chocolate could have beneficial effects on triglyceride concentrations in pregnant women at risk of PE. Support or Funding Information This research project was supported by Canadian Institute of Health Research by the Jeanne and Jean‐Louis Lévesque Perinatal Research Chair at Laval University.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".