Association of Serum Amyloid A with Kidney Outcomes and All-Cause Mortality in American Indians with Type 2 Diabetes
Bibliographic record
Abstract
BACKGROUND: Serum amyloid A (SAA) induces inflammation and apoptosis in kidney cells and is found to be causing the pathologic changes that are associated with diabetic kidney disease (DKD). Higher serum SAA concentrations were previously associated with increased risk of end-stage renal disease (ESRD) and death in persons with type 2 diabetes and advanced DKD. We explored the prognostic value of SAA in American Indians with type 2 diabetes without DKD or with early DKD. METHODS: SAA concentration was measured in serum samples obtained at the start of follow-up. Multivariate proportional hazards models were employed to examine the magnitude of the risk of ESRD or death across tertiles of SAA concentration after adjustment for traditional risk factors. The C statistic was used to assess the additional predictive value of SAA relative to traditional risk factors. RESULTS: Of 256 participants (mean ± SD glomerular filtration rate [iothalamate] = 148 ± 45 mL/min, and median [interquartile range] urine albumin/creatinine = 39 [14-221] mg/g), 76 developed ESRD and 125 died during a median follow-up period of 15.2 and 15.7 years, respectively. After multivariable proportional hazards regression, participants in the 2 highest SAA tertiles together exhibited a 53% lower risk of ESRD (hazard ratio [HR] 0.47, 95% CI 0.29-0.78), and a 30% lower risk of death (HR 0.70, 95% CI 0.48-1.02), compared with participants in the lowest SAA tertile, although the lower risk of death was not statistically significant. Addition of SAA to the ESRD model increased the C statistic from 0.814 to 0.815 (p = 0.005). CONCLUSIONS: Higher circulating SAA concentration is associated with a reduced risk of ESRD in American Indians with type 2 diabetes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".