Proteomic Analysis Uncovers Multiprotein Signatures Associated with Early Diabetic Kidney Disease in Youth with Type 2 Diabetes Mellitus
Bibliographic record
Abstract
Key Points Proteomics analyses identified seven proteins predictive of time to development of albuminuria among youth with type 2 diabetes in the Treatment Options for Type 2 Diabetes in Adolescents and Youth cohort, 118 proteins predictive of time to development of hyperfiltration, and three proteins predictive of time to rapid eGFR decline. Seven proteins were predictive of all three outcomes (SEM4A, PSB3, dihydroxyphenylalanine decarboxylase, C1RL1, T132A, pyruvate carboxylase, and C1-esterase inhibitor) and have been implicated in immune regulatory mechanisms, metabolic dysregulation, proteostasis, and cellular signaling pathways. Elastic net Cox proportional hazards model identified distinct multiprotein signatures (38–68 proteins) of time to albuminuria, hyperfiltration, and rapid eGFR decline with concordance for models with clinical covariates and selected proteins between 0.81 and 0.96, whereas the concordance for models with clinical covariates only was between 0.56 and 0.63. Background The onset of diabetic kidney disease (DKD) in youth with type 2 diabetes (T2D) mellitus often occurs early, leading to complications in young adulthood. Risk biomarkers associated with the early onset of DKD are urgently needed in youth with T2D. Methods We conducted an in-depth analysis of 6596 proteins (SomaScan 7K) in 374 baseline plasma samples from the Treatment Options for Type 2 Diabetes in Adolescents and Youth study to identify multiprotein signatures associated with the onset of albuminuria (urine albumin-to-creatinine ratio ≥30 mg/g), a rapid decline in eGFR (annual eGFR decline >3 ml/min per 1.73 m 2 and/or ≥3.3% at two consecutive visits), and hyperfiltration (≥135 ml/min per 1.73 m 2 at two consecutive visits). Elastic net Cox regression with ten-fold cross-validation was applied to the top 100 proteins (ranked by P value) to identify multiprotein signatures of time to development of DKD outcomes. Results Participants in the Treatment Options for Type 2 Diabetes in Adolescents and Youth study (14±2 years, 63% female, 7±6 months diabetes duration) experienced high rates of early DKD: 43% developed albuminuria, 48% hyperfiltration, and 16% rapid eGFR decline. Increased levels of seven and three proteins were predictive of shorter time to develop albuminuria and rapid eGFR decline, respectively; 118 proteins predicted time to development of hyperfiltration. Elastic net Cox proportional hazards models identified multiprotein signatures of time to incident early DKD with concordance for models with clinical covariates and selected proteins between 0.81 and 0.96, whereas the concordance for models with clinical covariates only was between 0.56 and 0.63. Conclusions Our research sheds new light on proteomic changes early in the course of youth-onset T2D that associate with DKD. Proteomic analyses identified promising risk factors that predict DKD risk in youth with T2D and could deepen our understanding of DKD mechanisms and potential interventions. Clinical Trial registry name and registration number: NCT00081328.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| 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".