Diet quality and associations with lactate and metabolic syndrome in bipolar disorder
Bibliographic record
Abstract
BACKGROUND: Nutrition is largely affected in bipolar disorder (BD), however, there is a lack of understanding on the relationship between dietary categories, BD, and the prevalence of metabolic syndrome. The objective of this study is to examine dietary trends in BD and it is hypothesized that diets with increased consumption of seafood and high-fiber carbohydrates will be correlated to improved patient outcomes, and a lower frequency of metabolic syndrome. METHODS: This retrospective cohort study includes two French cohorts. The primary cohort, FACE-BD, includes 268 stable BD patients. The second cohort, I-GIVE, includes healthy controls, both stable and acute BD and schizophrenia patients. Four dietary categories were assessed: meat, seafood, low-fiber and high-fiber carbohydrates. Dietary data from two food frequency questionnaires were normalized using min-max scaling and assessed using various statistical analyses. RESULTS: In our primary cohort, the increased high-fiber carbohydrate consumption was correlated to lower prevalence of metabolic syndrome and improved mood. Low-fiber carbohydrate consumption is associated with higher BMI, while higher seafood consumption was correlated to improved mood and delayed age of onset. Results were not replicated in our secondary cohort. LIMITATIONS: Our populations were small and two different dietary questionnaires were used; thus, results were used to examine similarities in trends. CONCLUSIONS: Overall, various dietary trends were associated with metabolic syndrome, BMI, lactate, mood and age of onset. Improving our understanding of nutrition in BD can provide mechanistic insight, clinically relevant nutritional guidelines for precision medicine and ultimately improve the quality of lives for those with BD.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".