Assessing The Impact of The Diet on Cardiometabolic Outcomes: Are Two Consecutive Measures Post‐Intervention Really Necessary?
Bibliographic record
Abstract
Background Important day‐to‐day variations in the blood concentrations of risk factors may lessen our ability to detect significant effects of any given nutritional intervention on cardiometabolic risk. In theory, relying on several consecutive measurements of a biomarker outcome should decrease variability and hence increase statistical power. However, there is very little empirical evidence supporting such a theory in nutrition research. The purpose of this study was to examine how using the mean of two consecutive measures vs. only one measure post treatment influences the capacity to detect a change in often‐used cardiometabolic risk factors in response to nutritional interventions. Methods We used data from two randomized double‐blind crossover trials that have assessed the impact of docosahexaenoic acid (DHA) on cardiometabolic risk factors, the first one in the context of a supplementation study and the second one in the context of fully‐controlled feeding conditions. We used equations and simulations to compare the effect of using the mean of two consecutive measures vs. only one measure post treatment on the capacity to detect a change in often‐used cardiometabolic risk factors in response to nutritional interventions. Results We found that the reduction in sample size or the gain in statistical power attributed to using the mean of two consecutive measures vs. a single measure is marginal for serum total cholesterol (C), non‐HDL‐C, HDL‐C, LDL‐C, apolipoprotein B100, triglycerides (TG) and C‐reactive protein, even when attempting to assess small effect sizes. For example, the difference in the required sample size to detect a medium effect size (e.g. 0.5) for the change in TG in response to supplemented DHA between using one vs. the mean of two consecutive TG measurements post treatment is 4 subjects (71 vs. 67 subjects respectively). For LDL‐C, this difference is 2 subjects (67 vs. 65 subjects). Conclusions These data indicate that the reduction in sample size and hence the gain in statistical power attributed to using the mean of two measures taken on consecutive days vs. a single measure is small and unjustified to detect a change in cardiometabolic risk factors in response to DHA in the context of crossover nutritional studies. Support or Funding Information JA is a recipient of PhD Scholarships from the Canadian Institutes of Health Research (CIHR) and Fonds de recherche du Québec – Santé (FRQ‐S).
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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 | 0.174 | 0.381 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".