Influence of sex on hyperfiltration in patients with uncomplicated type 1 diabetes
Bibliographic record
Abstract
The aim of this analysis was to examine sex-based differences in renal segmental resistances in healthy controls (HCs) and patients with type 1 diabetes (T1D). We hypothesized that hyperfiltration—an early hemodynamic abnormality associated with diabetic nephropathy—would disproportionately affect women with T1D, thereby attenuating protection against the development of renal complications. Glomerular hemodynamic parameters were evaluated in HC ( n = 30) and in normotensive, normoalbuminuric patients with T1D and either baseline normofiltration [ n = 36, T1D-N, glomerular filtration rate (GFR) 90–134 ml·min−1·1.73 m2] or hyperfiltration ( n = 32, T1D-H, GFR ≥ 135 ml·min−1·1.73 m2) during euglycemic conditions (4–6 mmol/l). Gomez’s equations were used to derive efferent (RE) and afferent (RA) arteriolar resistances, glomerular hydrostatic pressure (PGLO) from inulin (GFR) and paraaminohippurate [effective renal plasma flow (ERPF)] clearances, plasma protein and estimated ultrafiltration coefficients (KFG). Female patients with T1D with hyperfiltration (T1D-H) had higher RE(1,985 ± 487 vs. 1,381 ± 296 dyne·sec−1·cm−5, P < 0.001) and filtration fraction (FF, 0.20 ± 0.047 vs. 0.16 ± 0.03 P < 0.05) and lower ERPF (876 ± 245 vs. 1,111 ± 298 134 ml·min−1·1.73 m2P < 0.05) compared with male T1D-H patients. Overall, T1D-H patients had higher PGLOand lower RAvs. HC subjects, although there were no sex-based differences. In conclusion, female T1D-H patients had higher REand FF and lower ERPF than their male counterparts with no associated sex differences in RA. Prospective intervention studies should consider sex as a modifier of renal hemodynamic responses to renal protective therapies.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".