A pro-inflammatory diet is associated with higher body adiposity in kidney transplant recipients
Bibliographic record
Abstract
The dietary inflammatory index (DII) has been associated with obesity and cardiovascular risk factors (CVRF) in the general population. We hypothesized that in kidney transplant recipients (KTR), a positive relationship between DII, body adiposity and CVRF would also be observed. To test this hypothesis, we conducted a cross-sectional study with adult KTR. Body mass index (BMI), body adiposity index (BAI) and waist circumference (WC) were assessed. Total fat mass (FM), trunk FM, and load-capacity index (LCI) were evaluated using dual-energy X-ray absorptiometry. Energy-adjusted DII (E-DII) was estimated based on three 24-h recalls and stratified as anti-inflammatory (E-DII<0) and pro-inflammatory (E-DII>0). CVRF included hypertension, diabetes, dyslipidemia, and metabolic syndrome. A total of 170 KTR, 59% male, with 49.5 (42-57) years and E-DII from -2.89 to 4.78 were evaluated. KTR with E-DII>0, compared to those with E-DII<0, exhibited significantly higher values of BAI, total FM (kg), and LCI. In multiple adjusted linear regression, E-DII was significantly associated with WC, total FM (kg), and trunk FM (kg). Logistic regression analysis indicated that E-DII>0 was significantly associated with obesity, as assessed by BAI. E-DII was not associated with CVRF. The present study suggests that a pro-inflammatory diet is associated with higher total and central body adiposity in KTR. Interventions targeting an anti-inflammatory diet may contribute to reducing excessive body adiposity in this population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.001 |
| 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".