491-P: Renal Hemodynamic Dysfunction and Neuropathy in Long-Standing Type 1 Diabetes (T1D): Results from the Canadian Study of Longevity in T1D
Bibliographic record
Abstract
The relationship between renal function and neuropathy is not well established in T1D. Our objective was to determine the relationship between renal hemodynamic function and neuropathy in adults with a ≥50-year history of T1D compared to nondiabetic controls. GFR (inulin), effective renal plasma flow (ERPF, p-aminohippurate) and modified Toronto Clinical Neuropathy Score (mTCNS) were measured in 66 patients with T1D for ≥50-years and in 73 patients without diabetes matched for age and sex. Afferent (RA), efferent (RE) arteriolar resistances and glomerular hydrostatic pressure (PGLO) were estimated using Gomez’s equations. Presence of DKD was defined as eGFRMDRD<60ml/min/1.73m2 or 24-hour urine albumin excretion >30mg/day at the screening visit. Linear regressions were applied to examine the relationships between renal function (dependent variable) and measures of neuropathy (independent variable), adjusted for age, sex and HbA1c in participants with T1D and for age and sex in nondiabetic controls. Greater mTCNS, a measure of neuropathy signs and symptoms, was associated with lower renal blood flow (β=-9.57±4.15, p=0.024) and greater RE (β=32.79±15.13, p=0.034) in adults with T1D after adjusting for age, sex and HbA1c. Participants with T1D and DKD had a greater mTCNS compared to those without DKD (6.3±5.2 vs. 3.8±3.6, p=0.016). In contrast, renal hemodynamic parameters did not associate with measures of neuropathy in nondiabetic controls in multivariable models. Neuropathy score was associated with lower renal perfusion in adults with longstanding T1D. Neurological dysfunction in the presence of diabetes may contribute to impaired renal perfusion resulting in ischemic injury in patients with T1D. Further work is required to understand factors - including the autonomic nervous system - that contribute to maintaining normal renal perfusion in longstanding T1D. Disclosure Y. Lytvyn: None. P. Bjornstad: Advisory Panel; Self; XORTX Therapeutics Inc. Consultant; Self; Bayer AG, Boehringer Ingelheim International GmbH, Bristol-Myers Squibb, Novo Nordisk Foundation. Research Support; Self; Horizon Pharma. L. Lovblom: None. H. Liu: None. J.A. Lovshin: Consultant; Self; AstraZeneca. Consultant; Spouse/Partner; Bayer Inc. Consultant; Self; Boehringer Ingelheim Pharmaceuticals, Inc., Eli Lilly and Company, Novo Nordisk Inc. Research Support; Self; Novo Nordisk Inc. G. Boulet: Advisory Panel; Self; Medtronic. M. Farooqi: None. A. Weisman: None. H.A. Keenan: None. M.H. Brent: Advisory Panel; Self; Bayer AG, Novartis Pharmaceuticals Canada Inc., Roche Canada. N. Paul: None. V. Bril: Advisory Panel; Self; Alexion Pharmaceuticals, Inc., Alnylam Pharmaceuticals, Takeda Canada. Consultant; Self; Akcea Therapeutics, CSL Behring, Sanofi. Research Support; Self; Biogen, Grifols, Momenta Pharmaceuticals. B.A. Perkins: Consultant; Self; Abbott, Boehringer Ingelheim International GmbH, Insulet Corporation. Research Support; Self; Boehringer Ingelheim International GmbH, Novo Nordisk A/S. Other Relationship; Self; Medtronic. D. Cherney: Research Support; Self; Boehringer Ingelheim-Lilly, Merck, Janssen, Sanofi, AstraZeneca and Novo-Nordisk. Other Relationship; Self; from Boehringer Ingelheim-Lilly, Merck, AstraZeneca, Sanofi, Mitsubishi-Tanabe, Abbvie, Janssen, Bayer, Prometic, BMS and Novo-Nordisk.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".