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Record W4403638688 · doi:10.1172/jci183984

Attenuated kidney oxidative metabolism in young adults with type 1 diabetes

2024· article· en· W4403638688 on OpenAlexaff
Ye Ji Choi, Gabriel Richard, Guanshi Zhang, Jeffrey B. Hodgin, Dawit Demeke, Yingbao Yang, Jennifer A. Schaub, Ian M. Tamayo, Abhijit S. Naik, Viji Nair, Carissa Birznieks, Alexis MacDonald, Phoom Narongkiatikhun, Susan J. Gross, Lynette Driscoll, Maureen Flynn, Kalie L. Tommerdahl, Kristen J. Nadeau, Viral N. Shah, Tim Vigers, Janet K. Snell‐Bergeon, Jessica Kendrick, Daniël H. van Raalte, Luping Li, Pottumarthi Prasad, Patricia Ladd, Bennett B. Chin, David Z. Cherney, Phillip J. McCown, Fadhl Alakwaa, Edgar A. Otto, Frank C. Brosius, Pierre‐Jean Saulnier, Victor G. Puelles, Jesse A. Goodrich, Kelly Street, Manjeri A. Venkatachalam, A. López Ruiz, Ian H. de Boer, Robert G. Nelson, Laura Pyle, Denis P. Blondin, Kumar Sharma, Matthias Kretzler, Petter Bjornstad

Bibliographic record

VenueJournal of Clinical Investigation · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicDiabetes and associated disorders
Canadian institutionsInstitute of Population and Public HealthUniversity of TorontoCentre Hospitalier Universitaire de SherbrookeUniversité de Sherbrooke
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesNational Heart, Lung, and Blood InstituteUniversity of MichiganCenters for Disease Control and PreventionBoettcher FoundationAmerican Heart Association
KeywordsOxidative metabolismOxidative phosphorylationDiabetes mellitusMetabolismMedicineType 2 diabetesKidneyEndocrinologyInternal medicineBiologyPhysiologyBiochemistry

Abstract

fetched live from OpenAlex

BACKGROUNDIn type 1 diabetes (T1D), impaired insulin sensitivity may contribute to the development of diabetic kidney disease (DKD) through alterations in kidney oxidative metabolism.METHODSYoung adults with T1D (n = 30) and healthy controls (HCs) (n = 20) underwent hyperinsulinemic-euglycemic clamp studies, MRI, 11C-acetate PET, kidney biopsies, single-cell RNA-Seq, and spatial metabolomics to assess this relationship.RESULTSParticipants with T1D had significantly higher glomerular basement membrane (GBM) thickness compared with HCs. T1D participants exhibited lower insulin sensitivity and cortical oxidative metabolism, correlating with higher insulin sensitivity. Proximal tubular transcripts of TCA cycle and oxidative phosphorylation enzymes were lower in T1D. Spatial metabolomics showed reductions in tubular TCA cycle intermediates, indicating mitochondrial dysfunction. The Slingshot algorithm identified a lineage of proximal tubular cells progressing from stable to adaptive/maladaptive subtypes, using pseudotime trajectory analysis, which computationally orders cells along a continuum of states. This analysis revealed distinct distribution patterns between T1D and HCs, with attenuated oxidative metabolism in T1D attributed to a greater proportion of adaptive/maladaptive subtypes with low expression of TCA cycle and oxidative phosphorylation transcripts. Pseudotime progression associated with higher HbA1c, BMI, and GBM, and lower insulin sensitivity and cortical oxidative metabolism.CONCLUSIONThese early structural and metabolic changes in T1D kidneys may precede clinical DKD.TRIAL REGISTRATIONClinicalTrials.gov NCT04074668.FUNDINGUniversity of Michigan O'Brien Kidney Translational Core Center grant (P30 DK081943); CROCODILE studies by National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) (P30 DK116073), Juvenile Diabetes Research Foundation (JDRF) (2-SRA-2019-845-S-B), Boettcher Foundation, Intramural Research Program at NIDDK and Centers for Disease Control and Prevention (CKD Initiative) under Inter-Agency Agreement #21FED2100157DPG.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.018
GPT teacher head0.304
Teacher spread0.287 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations8
Published2024
Admission routes1
Has abstractyes

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