Renal SGLT mRNA expression in human health and disease: a study in two cohorts
Bibliographic record
Abstract
Pharmacological Na+-glucose linked cotransporter (SGLT)2 inhibition is being examined as a renal protection strategy in nondiabetic chronic kidney disease. We quantified renal SGLT mRNA expression in healthy controls (HC), glomerulonephritis (GN), and diabetic kidney disease (DKD) to identify differences in expression across a spectrum of renal diseases. mRNA expression of SGLT1 and SGLT2 in renal tubules and glomeruli, obtained using microdissection and microarray techniques, was evaluated in two large cohorts. The European Renal cDNA bank included HC, GN, and DKD (98 glomeruli and 93 tubulointerstitium). The Nephrotic Syndrome Study Network cohort included 124 adults with membranous nephropathy, minimal change disease, focal segmental glomerulosclerosis, and IgA nephropathy. Within the European Renal cDNA bank, SGLT2 tubular and glomerular log2 mRNA expression significantly differed across HC, GN, and DKD ( P = 0.0009 and P = 0.0004), with the highest expression in HC. Within the Nephrotic Syndrome Study Network, there were no differences in SGLT log2 mRNA expression across GN subtypes. Tubular SGLT2 log2 mRNA expression positively correlated with estimated glomerular filtration rate (by the Modification of Diet in Renal Disease Study equation) and glycated hemoglobin ( r = 0.33 and 0.34, P < 0.05) and inversely correlated with interstitial fibrosis ( r = −0.21, P < 0.05). In conclusion, SGLT2 mRNA expression was lower in DKD compared with HC or GN and inversely related to interstitial fibrosis. The relationships between SGLT mRNA, protein expression, and transporter activity require further elucidation.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 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".