Association of polymorphisms within the transforming growth factor‐β1 gene with diabetic nephropathy and serum cholesterol and triglyceride concentrations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
AIM: The TGF-β gene participates in the development of chronic kidney disease. We investigated whether the 869 T > C, 915 G > C and -800 G > A polymorphisms of TGF-β1 are associated with diabetic nephropathy (DN). METHODS: Polymorphisms were genotyped in 439 type 2 diabetes mellitus patients, 233 with diabetic nephropathy (DN+) and 206 without (DN-). The sample was characterized for relevant clinical and biochemical parameters. RESULTS: The 869 T > C (P = 0.016; odds ratio (OR) = 1.818, 95% confidence interval (CI) = 1.128-2.930) and the 915 G > C polymorphisms (P = 0.008, OR = 4.073, 95% CI = 1.355-12.249) were associated with diabetic nephropathy. The 869 T > C variant was associated with total cholesterol levels: CC + CT genotypes had a mean cholesterol concentration of 5.62 ± 1.40 mmol/L vs a mean concentration of 5.15 ± 1.40 mmol/L for the TT genotype (P = 0.011). Triglycerides were also higher in CC + CT genotypes (2.49 ± 1.56 mmol/L) in comparison with TT homozygotes (2.1 ± 1.22 mmol/L, P = 0.042). Multivariate logistic regression showed that the polymorphisms 869 T > C and 915 G > C were independent predictors for DN (P = 0.049 and 0.046, respectively). CONCLUSION: The 869 T > C and 915 G > C polymorphisms within the TGF-β1 gene were associated with DN+. Lower cholesterol and triglycerides levels were observed in TT homozygotes for the 869 T > C polymorphism. The TGF-β1 869 T allele seems to confer protection against DN+.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 it