Cell Sex and Sex Hormones Regulate Kidney Metabolism of Glucose and Glutamine: Implications for Diabetic Kidney Disease
Bibliographic record
Abstract
Background: Diabetic kidney disease (DKD) is the major cause of end-stage kidney disease. Male sex is a risk factor for DKD, but the reasons for this predilection are unclear. We demonstrated that androgens accentuate DKD in vivo, and increase enzymes involved in glucose and glutamine metabolism, in male proximal tubular epithelial cells (PTECs). We aimed to determine the effect of cell sex and sex hormones on kidney metabolism. Methods: Male and female PTECs were stimulated with control, dihydrotestosterone (DHT), or estradiol. Sex differences in key metabolites were validated in diabetic mice, and in type 2 diabetic patients and their age- and weight-matched healthy controls (n=180, iCARE cohort). Results: Male PTECs showed significantly higher glycolysis, oxygen consumption (OCR), glucose consumption, oxidative stress, and apoptosis, compared to female PTECs, especially in the presence of DHT. Higher OCR in male PTECs was further enhanced in the presence of glucose and glutamine, but not observed in the presence of pyruvate. Under high glucose, male PTECs showed a decline in OCR and ATP levels over time, and increased lactate production. Male PTECs had significantly higher intracellular levels of TCA cycle metabolites (glutamate, citrate, malate, aspartate) and glutathione metabolites. In turn, female cells had higher levels of pyruvate. In vivo, male sex was linked to increased circulating levels of glucose, lactate, and glutamate in healthy and diabetic mice. Male sex was also independently associated with increased serum levels of glutamate, succinate, fumarate, and 9 metabolites of the glutathione cycle, in healthy and diabetic individuals. Conclusions: This is the first study to demonstrate that the kidney metabolism of glucose and glutamine is modulated by cell sex and sex hormones. Male sex was linked to increased oxidative stress, cell injury, glucose- and glutamine-related enzymes, lactate secretion, and levels of TCA cycle and glutathione metabolites. Our key findings were validated in the blood metabolome of healthy and diabetic humans. Our work has uncovered physiological sex differences that are important for DKD and may lead to new therapeutic paradigms based on patient sex.
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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 | 0.001 | 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".