Heterogeneous associations of insoluble dietary fibre intake with subsequent glycosylated Hb levels among Chinese adults with type 2 diabetes: a quantile regression approach
Bibliographic record
Abstract
Dietary fibre intake has been suggested to reduce blood glucose levels in diabetic patients, particularly when glycosylated Hb (HbA1c) levels are high. In the present study, we used a quantile regression (QR) approach to characterise the possible heterogeneous associations of dietary fibre intake with HbA1c levels in Chinese diabetic patients. A total of 497 diabetic patients participated in the baseline survey in 2006 and in the follow-up survey in 2011, both of which were conducted in Pudong New Area of Shanghai, China. Structured in-person interviews were conducted to collect information on demographic characteristics and lifestyle factors. Dietary intake was assessed using a validated FFQ. Blood samples were collected during the interviews for biochemical assays. QR models were used to examine the heterogeneous associations of dietary factors with HbA1c levels. A significant marginal association of insoluble dietary fibre intake with subsequent HbA1c levels was observed only when the HbA1c level was over 6·8%. The associations appeared to be greater when the quantile levels of HbA1c were higher. The coefficient estimates were -0·174 (95% CI -0·433, -0·025) at the quantile of 0·60, -0·200 (95% CI -0·306, -0·008) at 0·70, -0·221 (95% CI -0·426, -0·117) at 0·80, and -0·389 (95% CI -0·516, -0·018) at 0·90. A similar pattern was observed for the associations of dietary glycaemic index (GI) value with HbA1c levels. In conclusion, the present results indicate that the associations of insoluble dietary fibre intake and GI value with subsequent HbA1c levels depend on glycaemic control status in Chinese diabetic patients. More studies are required to confirm our findings.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".