Cell Sex and Sex Hormones Modulate Glucose and Glutamine Kidney Metabolism: Implications for Diabetic Kidney Disease
Bibliographic record
Abstract
Background: Male sex predisposes to diabetic kidney disease (DKD). We uncovered androgen-induced perturbations in kidney metabolic proteins that may drive faster DKD progression in men. Our goal is to characterize cell sex- and sex hormone-specific alterations in the kidney cell metabolism. Methods: Human primary proximal tubule epithelial cells (PTEC) from 3 male and 3 female donors were stimulated with control, dihydrotestosterone (DHT), or estradiol (EST). We assessed glycolysis (extracellular acidification rate, ECAR) and oxygen consumption rate (OCR) in a Seahorse analyzer. We also studied sex differences in 16-week-old diabetic Akita mice. Results: Male PTEC showed significantly higher ECAR, OCR, superoxide levels and apoptosis, compared to female PTEC (p<0.05). Higher OCR in male PTEC was further enhanced in the presence of glutamine as a unique susbstrate. In male PTEC, ECAR was increased by DHT, whereas OCR was increased by DHT and EST. Further, glucose levels in the media were reduced by DHT. DHT-induced metabolic changes were prevented by androgen receptor (AR) inhibitors. ATP, superoxide and apoptosis were increased by DHT, especially in male PTEC. Under hyperglycemia (25mM glucose), male cells showed a more rapid decline in OCR, and DHT increased superoxide and ATP levels. Transcriptional regulator analysis predicted that PTBP1, MCM4, and KDM5D (Y-linked) regulate proteins increased by DHT. Targets of PTBP1 and MCM4 include enzymes involved in glucose and glutamine metabolism (TKT, GLUD1). In vivo, diabetes increased kidney gene expression of Tkt, Glud1, and glutamine transporter Slc38a3 in males, but not females. Conclusions: PTEC metabolism is influenced by cell sex and sex hormones. Male PTEC show higher glycolysis, oxygen consumption, and respiratory capacity than female PTEC, and a higher propensity to oxidize glutamine in the mitochondria. Importantly, glutamine plays a key role as anaplerotic substrate for the TCA cycle in diabetes. Our in vivo data support the link between male sex and regulation of glutamine metabolism, and suggests that kidney utilization of glutamine in DKD is sex-specific. By understanding and monitoring how these metabolic changes occur in male and female patients, our findings may contribute to a more personalized management of DKD.
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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 | 0.000 |
| 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) | 0.002 | 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".