Proteomic Analyses Identify Novel Predictors of Diabetic Kidney Disease in Youth-Onset Type 2 Diabetes
Bibliographic record
Abstract
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
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".