Effect of dolutegravir-based antiretroviral therapy on glycemic control in female mice
Bibliographic record
Abstract
Dolutegravir (DTG), is recommended for all people with HIV, including pregnant women. Weight gain and hyperglycemia have been reported with DTG use, as well as a signal for neural tube defects (NTDs) that has waned over time. Obesity and hyperglycemia are risk factors for NTDs. We explored the impact of DTG-based antiretroviral therapy (ART) on weight gain and glucose homeostasis in female mice. C57BL/6 mice were treated daily for 9 weeks with water (control), 1xDTG (2.5 mg/kg DTG + 33.3/50 mg/kg emtricitabine/tenofovir-yielding therapeutic levels), or 5xDTG (12.5 mg/kg + 33.3/50 mg/kg emtricitabine/tenofovir). Overnight fasted glucose, weight, and oral glucose tolerance test (OGTT) were measured at 2-8 weeks. Tissue was collected for expression analyses of glucose homeostasis pathways. Weight gain was similar between groups. We observed a transient fasted hyperglycemia with DTG treatment, that peaked at week 6 and resolved by week 9. No significant differences were observed in insulin or OGTT response between groups. DTG was associated with a gradual and persistent decrease in plasma leptin and increase in plasma corticosterone levels compared to controls. Downregulation of genes involved in gluconeogenesis and lipogenesis in liver were observed in DTG-treated mice that remained euglycemic. Muscle and liver leptin receptor expression was elevated with DTG treatment. DTG was associated with transient hyperglycemia, lower leptin and higher corticosterone. Induction of compensatory mechanisms may have aided to restore/maintain euglycemia. This transient nature of the glycemic dysregulation may in part explain the loss of the NTD signal that was observed at the initial roll out of DTG but waned over time.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".