Association of Fibroblast Growth Factor 23 with Blood Pressure in Primary Proteinuric Glomerulopathies
Bibliographic record
Abstract
INTRODUCTION: Fibroblast growth factor 23 (FGF23) has direct effects on the vasculature and myocardium, and high levels of FGF23 are a risk factor for cardiovascular disease (CVD); however, the impact of FGF23 on CVD in primary proteinuric glomerulopathies has not been addressed. METHODS: The associations of baseline plasma intact FGF23 levels with resting blood pressure (BP) and lipids over time among adults and children with proteinuric glomerulopathies enrolled in the Nephrotic Syndrome Study Network (NEPTUNE) were analyzed using generalized estimating equation regression analyses. Models were adjusted for age, sex, glomerular diagnosis, follow-up time, estimated glomerular filtration rate, urine protein/creatinine ratio, obesity, and serum phosphorous levels. RESULTS: Two hundred and four adults with median FGF23 77.5 (IQR 51.3-119.3) pg/mL and 93 children with median FGF23 62.3 (IQR 44.6-83.6) pg/mL were followed for a median of 42 (IQR 20.5-54) months. In adjusted models, each 1 µg/mL increase in FGF23 was associated with a 0.3 increase in systolic BP index at follow-up (p < 0.001). Greater baseline FGF23 was associated with greater odds of hypertensive BP (OR = 1.0003; 95% CI 1.001-1.006, p = 0.03) over time. Compared to tertile 1, tertile 2 (OR = 2.1; 95% CI 1.12-3.99, p = 0.02), and tertile 3 (OR = 3; 95% CI 1.08-8.08, p = 0.04), FGF23 levels were associated with greater odds of hypertensive BP over time. Tertile 2 was associated with greater triglycerides compared to tertile 1 (OR = 48.1; 95% CI 4.4-91.9, p = 0.03). CONCLUSION: Overall, higher baseline FGF23 was significantly associated with hypertensive BP over time in individuals with proteinuric glomerulopathies. Further study of FGF23 as a therapeutic target for reducing CVD in proteinuric glomerular disease is warranted.
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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) | 0.001 | 0.001 |
| 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) | 0.002 | 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".