Leptin and estrogen signaling crosstalk in the brain modulates energy metabolism
Bibliographic record
Abstract
Leptin and estrogen are key hormones in regulating reproductive function, metabolic health, and body weight. In this review, we explore how the interaction between leptin and estrogen may modulate body weight through changes in metabolism and feeding behaviour. A significant proportion of arcuate neurons coexpress receptors for leptin and estrogen, providing ample opportunity for signal crosstalk to occur. We conducted a narrative literature review and identified the major mechanisms through which leptin and estrogen interact with a focus on signal transduction pathways. G-protein coupled receptor 30 (GPR30) is a good candidate for an inter-pathway connection because it interacts with estrogen receptors and affects the activation of signal transducer and activator of transcription (STAT3), an important downstream factor in both estrogen and leptin signaling pathways. Evidence suggests that estrogen and leptin receptors both utilize hypothalamic STAT3-activating pathways to modulate appetite and lipid storage, and that these pathways may depend on one another for adequate activation. While there are some physiological results to support this point of connection, the cellular and biochemical details remain unclear. Better understanding how leptin and estrogen interact will better inform the treatment of metabolic disorders, including T2D, obesity, and post-menopausal weight gain.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".