Does dietary protein intake correlate with markers suggestive of early diabetic nephropathy in children and adolescents with Type 1 diabetes mellitus?
Bibliographic record
Abstract
AIMS: To examine the relationship between dietary protein intake and possible early markers of diabetic nephropathy (creatinine clearance (CrCI), kidney volume and albumin excretion rate (AER)). METHODS: One hundred and forty-five subjects with diabetes for 5-10 years, divided into three pubertal groups, participated. Kidney volume was measured by ultrasound, and serum creatinine and HbA1c were assayed. Two or three 24-h urine collections were obtained for albumin, creatinine and urea excretion rates. Dietary protein intake was estimated from urinary urea nitrogen excretion rate. Glomerular filtration rate was estimated by creatinine clearance. RESULTS: Mean protein intake was 1.22 +/- 0.48 g x kg(-1) x day(-1) Protein intake was significantly higher in males than females (P < 0.0001) and highest in prepubertal compared to mid-pubertal and post-pubertal subjects (P < 0.001). In multiple regression analysis, protein intake was positively associated with CrCl (P < 0.0001), and male sex (P < 0.0001) and negatively associated with body surface area (P = 0.0013) and age (P = 0.01). Kidney volume and AER were not related to dietary protein intake. CONCLUSIONS: This cross-sectional study failed to show a significant relationship between dietary protein intake and markers of early nephropathy, other than CrCl. However, a longitudinal, prospective study is required to definitively assess the role of protein intake in the evolution of diabetic nephropathy.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".