The Dietary Phase 2 Protein Inducer Sulforaphane Can Normalize the Kidney Epigenome and Improve Blood Pressure in Hypertensive Rats
Bibliographic record
Abstract
BACKGROUND: Our previous studies have shown that broccoli sprouts high in the glucosinolate glucoraphanin decreases renal and vascular oxidative stress and inflammation as well as blood pressure in spontaneously hypertensive stroke-prone (SHRSP) rats. The objective of this study was to determine whether the metabolite of glucoraphanin, sulforaphane, was responsible for this improved blood pressure and whether this is associated with normalization of renal methylated DNA. METHODS: Sulforaphane was given by gavage to SHRSP and Sprague Dawley (SD) rats over 4 months and blood pressure measured under anesthesia just before euthanasia. Renovascular morphology was determined by histology and methylated deoxycytosine levels analyzed using high-performance liquid chromatography. RESULTS: Mean arterial pressure was 20% higher in vehicle-treated SHRSP when compared to SD. Sulforaphane administration to SHRSP improved blood pressure and lowered this difference to 11%. Vehicle-treated SHRSP had significantly increased wall:lumen ratios in renal arteries, increased numbers of vascular smooth muscle cells (VSMCs), increased renal protein nitration, and decreased (11%) renal DNA methylation compared to SD. Sulforaphane administration to SHRSP significantly lowered arterial wall:lumen ratio by 35%, reduced the number of VSMCs, reduced the level of protein nitration, and increased methylated deoxycytosine levels by 14%. CONCLUSIONS: Sulforaphane administration rectified pathological abnormalities in SHRSP kidneys and significantly improved blood pressure. This was associated with normalization of global kidney DNA methylation suggesting that DNA methylation could be associated with hypertension.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".