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Record W4396990430 · doi:10.1681/asn.20223311s1117c

Proteomic Analyses Identify Novel Predictors of Diabetic Kidney Disease in Youth-Onset Type 2 Diabetes

2022· article· en· W4396990430 on OpenAlexaff
Laura Pyle, Tim Vigers, Laure K. El ghormli, Ian H. de Boer, Robert G. Nelson, Anita T. Layton, Kumar Sharma, Sushrut S. Waikar, Hiddo J.L. Heerspink, Neil H. White, Kalie L. Tommerdahl, Amy S. Shah, Rose Gubitosi‐Klug, Petter Bjornstad

Bibliographic record

VenueJournal of the American Society of Nephrology · 2022
Typearticle
Languageen
FieldMedicine
TopicChronic Kidney Disease and Diabetes
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsDiabetes mellitusType 2 diabetesKidney diseaseMedicineDiseaseInternal medicineEndocrinology

Abstract

fetched live from OpenAlex

Background: Diabetic kidney disease (DKD) develops by young adulthood in up to 50% of people with youth-onset type 2 diabetes (Y-T2D), increasing risk of dialysis and premature death. Understanding mechanisms responsible for early DKD is key to management and prevention; accordingly, we sought to identify multiprotein signatures of DKD in Y-T2D. Methods: We measured 7604 Aptamers in 374 baseline plasma samples from the Treatment Options for type 2 Diabetes in Adolescents and Youth (TODAY) study, using the SomaScan 7K Proteomic (SomaLogic) platform. Urine albumin-to-creatinine ratio (UACR) was assessed annually for up to 15 years. Incident micro- and macroalbuminuria were defined as UACR ≥30 and ≥300 mg/g on ≥2 of 3 measures. We evaluated prediction of micro- and macroalbuminuria in separate Cox regression models adjusted for HbA1c, triglycerides, blood pressure, and estimated insulin sensitivity. Gene set enrichment analysis (GSEA) identified pathways of interest. The false discovery rate was controlled at 5% and we report q-values. Results: Participants were 14±2 years of age, 37% male; 43% developed either micro-or macroalbuminuria. Seven proteins predicted time to microalbuminuria, while 8 proteins predicted time to macroalbuminuria, with 2 proteins in common: nerve epidermal growth factor-like 1 (NELL1) (micro: HR 1.56 per 1 SD [95% CI 1.33, 1.83], q=0.0003; macro: 1.95 [1.44-2.63], q=0.017) and FAM189A2 (micro: 1.58 [1.33, 1.87], q=0.0008; macro: 1.79 [1.36, 2.35], q=0.038). GSEA identified gene sets, including one related to semaphorin interactions, associated with microalbuminuria and macroalbuminuria (Figure).Figure.: Top pathways from Gene Set Enrichment Analysis for microalbuminuria (top) and macroalbuminuria (bottom).Conclusions: Novel proteins, including those interacting with semaphorins, which play an important role in inflammation and cellular repair, predict incident albuminuria in Y-T2D. Funding: NIDDK Support

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)0.001
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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.024
GPT teacher head0.306
Teacher spread0.282 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

Explore more

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