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Record W4381378192 · doi:10.2337/db23-316-or

316-OR: ADA Presidents' Select Abstract: Structural Lesions on Kidney Biopsy in Youth-Onset Type 1 Diabetes (T1D) and Type 2 Diabetes (T2D)

2023· article· en· W4381378192 on OpenAlexaboutno aff
Viji Nair, Abhijit S. Naik, FADHL ALAKWAA, JENNIFER A. SCHAUB, Tim Vigers, Markus Bitzer, Laura Pyle, Frank C. Brosius, Patricia Ladd, Viral N. Shah, Kalie L. Tommerdahl, Kumar Sharma, IAN DE BOER, Pierre‐Jean Saulnier, Helen C. Looker, Robert G. Nelson, J. Hodgin, Petter Bjornstad

Bibliographic record

VenueDiabetes · 2023
Typearticle
Languageen
FieldMedicine
TopicChronic Kidney Disease and Diabetes
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineType 2 diabetesInternal medicineType 1 diabetesCreatinineDiabetes mellitusEndocrinologyUrologyKidneyRenal functionBiopsyKidney diseaseDiabetic nephropathy

Abstract

fetched live from OpenAlex

Recent epidemiological studies suggest a more aggressive clinical course of diabetic kidney disease in youth-onset T2D compared with youth-onset T1D. We compared kidney structural lesions in participants with youth-onset T2D and T1D to determine if youth-onset T2D was associated with greater early tissue injury. Quantitative microscopy was performed on kidney tissue obtained from research kidney biopsies in 27 youth with diabetes (13 T2D, 14 T1D). Group differences in clinical and morphometric parameters were tested using t-tests, Wilcoxon signed-rank test and linear models. At biopsy, the 13 participants with T2D were younger than the 14 with T1D (17±2 vs. 23±2 years; p<0.0001), had shorter diabetes duration (2.4±1.9 vs. 12.3±5.2 years; p<0.0001), but similar HbA1c (6.6±1.0 vs. 7.3±1.0 %; p=0.10) and median urine albumin-to-creatinine ratio (6 [min-max: 1-163] vs. 7 [2-58] mg/g; p=0.96). Youth with T2D exhibited greater glomerular tuft area (17588±4806 vs. 13821±2748 um2, p=0.018), glomerular volume (3.4±1.4 vs. 2.31±0.68 106um3, p=0.018), glomerular nuclear count (101±23 vs. 63±11, p<0.0001) mesangial volume (0.44±0.15 vs. 0.28±0.09 106um3, p=0.002) and mesangial matrix (2291±531 vs. 2001±566 um2, p=0.008). Glomerular sclerosis was only present in one individual with T2D. Despite similar clinical characteristics and considerably shorter diabetes duration, youth with T2D exhibited more severe kidney structural lesions than young persons with youth-onset T1D. Studies are underway to elucidate the metabolic and molecular pathways underlying these structural differences, as well as to delineate potential ultrastructural differences in T2D vs. T1D by electron microscopy. Disclosure V.Nair: None. V.Shah: Advisory Panel; LifeScan Diabetes Institute, Medscape, Consultant; DKSH, Research Support; Novo Nordisk, Tandem Diabetes Care, Inc., Dexcom, Inc., Insulet Corporation, JDRF, National Institutes of Health, Speaker's Bureau; Dexcom, Inc., Insulet Corporation. K.L.Tommerdahl: None. K.Sharma: Advisory Panel; Reata Pharmaceuticals, Inc., Otsuka America Pharmaceutical, Inc. I.De boer: Advisory Panel; AstraZeneca, Boehringer Ingelheim and Eli Lilly Alliance, Boehringer Ingelheim International GmbH, Otsuka America Pharmaceutical, Inc., Bayer Inc., Consultant; George Clinical, Gilead Sciences, Inc., Medscape, Research Support; Dexcom, Inc. P.Saulnier: Board Member; Novo Nordisk, Consultant; Grünenthal Group. H.C.Looker: None. R.G.Nelson: None. J.B.Hodgin: None. P.Bjornstad: Advisory Panel; AstraZeneca, Novo Nordisk, Lilly, Horizon Therapeutics plc, Boehringer Ingelheim (Canada) Ltd., LG Chem, Consultant; Bayer Inc., Bristol-Myers Squibb Company. A.Naik: Advisory Panel; CareDx. F.Alakwaa: None. J.A.Schaub: None. T.B.Vigers: None. M.Bitzer: None. L.Pyle: None. F.C.Brosius: Advisory Panel; Gilead Sciences, Inc. P.E.Ladd: None. Funding National Institute of Diabetes and Digestive and Kidney Diseases; JDRF

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.334
Threshold uncertainty score0.950

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0030.001
Insufficient payload (model declined to judge)0.3340.160

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.

Opus teacher head0.023
GPT teacher head0.284
Teacher spread0.261 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2023
Admission routes1
Has abstractyes

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