Acylation Stimulating Protein, Complement C3 and Lipid Metabolism in Ketosis-Prone Diabetic Subjects
Bibliographic record
Abstract
BACKGROUND: Ketosis-prone diabetes (KPDM) is new-onset diabetic ketoacidosis without precipitating factors in non-type 1 diabetic patients; after management, some are withdrawn from exogenous insulin, although determining factors remain unclear. METHODS: Twenty KPDM patients and twelve type 1 diabetic patients (T1DM), evaluated at baseline, 12 and 24 months with/without insulin maintenance underwent a standardized mixed-meal tolerance test (MMTT) for 2 h. RESULTS: At baseline, triglyceride and C3 were higher during MMTT in KPDM vs. T1DM (p<0.0001) with no differences in non-esterified fatty acids (NEFA) while Acylation Stimulating Protein (ASP) tended to be higher. Within 12 months, 11 KPDM were withdrawn from insulin treatment (KPDM-ins), while 9 were maintained (KPDM+ins). NEFA was lower in KPDM-ins vs. KPDM+ins at baseline (p = 0.0006), 12 months (p<0.0001) and 24 months (p<0.0001) during MMTT. NEFA in KPDM-ins decreased over 30-120 minutes (p<0.05), but not in KPDM+ins. Overall, C3 was higher in KPDM-ins vs KPDM+ins at 12 months (p = 0.0081) and 24 months (p = 0.0019), while ASP was lower at baseline (p = 0.0024) and 12 months (p = 0.0281), with a decrease in ASP/C3 ratio. CONCLUSIONS: Notwithstanding greater adiposity in KPDM-ins, greater NEFA decreases and lower ASP levels during MMTT suggest better insulin and ASP sensitivity in these patients.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".