Dietary Fiber Intake, Cardiovascular Risk Factors, and Kidney Function: A Mediation Analysis
Bibliographic record
Abstract
Background: Higher fiber intake may be associated with higher eGFR but the mechanisms underlying this association are poorly understood. Considering that higher fiber intake is linked to improved cardiovascular (CV) risk factors, we hypothesize that the effect of fiber intake on eGFR could be mediated by these CV factors. Methods: CARTaGENE is a population survey of healthy adults. We used multiple linear regression to study the association between fiber intake and eGFR while adjusting for confounding factors, including age, sex, diabetes, hypertension, dyslipidemia, body mass index [BMI], smoking, prior CV disease, physical activity and caloric intake. We assessed whether CV risk factors lie in the causal pathway between fiber intake and eGFR through mediation analyses. Results: We included 9,854 of the CARTaGENE participants with a completed food questionnaire (mean age: 53 years, 56% males). The main comorbidities were hypertension (25%), diabetes (8%) and cardiovascular disease (7%). The median daily fiber intake was 17.2g (IQR 10.7-23.7) and the mean eGFR was 87.3 ±14.6 mL/min/1.73 m2. After adjustment for the above factors, fiber intake was associated with higher eGFR and serum HDL levels, and lower BMI, glycated hemoglobin and triglyceride levels (Table). Other risk factors were found to be non-significant. The mediation analysis demonstrated that only 10% of the effect of fiber intake on eGFR was mediated through BMI and triglyceride levels. Conclusions: Higher dietary fiber intake is associated with higher eGFR and better control of certain cardiovascular risk factors. While the association between fiber intake and kidney function may be marginally mediated by healthy weight and triglyceride levels, further studies are needed to understand the mechanisms underlying this association.Association between dietary fiber intake and clinical variables.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".