The Role of Whole Food Plant-Based Food Intake on Postprandial Glycemia in Type 1 Diabetes
Bibliographic record
Abstract
CONTEXT: A whole food plant-based diet (WFPBD), minimally processed foods with limited consumption of animal products, is associated with improved health outcomes. The benefits of WFPBD are underexplored in individuals with type 1 diabetes (T1D). OBJECTIVE: The primary objective of this analysis is to evaluate the association between WFPBD on glycemia in individuals with T1D. METHODS: Utilizing prospectively collected meal events from the Type 1 Diabetes Exercise Initiative, we examined the effect of WFPBD intake on glycemia, determined by the plant-based diet index (PDI). The PDI calculates overall, healthful (hPDI), and unhealthy PDI (uPDI) to evaluate for degree of processed foods and animal products (ie, WFPBD). Mixed effects linear regression model assessed time in range (TIR), time above range, and time below range. RESULTS: We analyzed 7938 meals from 367 participants. TIR improved with increasing hPDI scores, conferring a 4% improvement in TIR between highest and lowest hPDI scores (high hPDI: 75%, low hPDI: 71%; P < .001). Compared with meals with low hPDI, meals with high hPDI had lower glucose excursion (high hPDI: 53 mg/dL, low hPDI: 62 mg/dL; P < .001) and less time >250 mg/dL (high hPDI: 8%, low hPDI: 14%; P < .001). These effects were present but less pronounced by PDI (high PDI: 74%, low PDI: 71%; P = .01). No differences in time below 70 mg/dL and 54 mg/dL were observed by PDI or hPDI. CONCLUSION: Meal events with higher hPDI were associated with 4% postprandial TIR improvement. These benefits were seen primarily in WFPBD meals (captured by hPDI) and less pronounced plant-based meals (captured by PDI), emphasizing the benefit of increasing unprocessed food intake over limiting animal products alone.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".