Bibliographic record
Abstract
Around menopause, women experience several physiological changes including fat redistribution, and cognitive decline. These symptoms have been attributed to changes in the hormonal milieu that accompany menopause, specifically the loss of ovarian estradiol (E2). Ovariectomy (OVX) a standard rodent model of menopause, results in chronically low levels of E2. E2 supplementation reinstates cognition, and body composition to pre-OVX states in mice and rats. Post-menopausal E2 replacement in women has yielded mixed results for cognition and body weight. E2 replacement has also been linked to an increased risk of breast cancer. Thus, effective and safe treatments for post-menopause are currently lacking. Two years preceding menopause, the reproductive hormone, follicle-stimulating hormone (FSH), begins to rise. FSH and E2 levels are inversely related; as E2 levels fall, FSH rises. Chronically elevated FSH was recently implicated in post-menopausal bone loss, weight gain, and cognitive decline. It was suggested that blocking FSH action may represent a novel means to prevent or treat these conditions. Compelling experiments in favour of this hypothesis are, however, lacking. In Chapter 1, I review the literature linking E2 loss or elevated FSH to post-menopausal weight gain, and cognitive decline. In Chapter 2, I investigated FSH actions in fat. Specifically, I examined FSH effects on lipid accumulation and adipocyte gene expression in vitro, as well as the effects of ablating the FSH receptor (FSHR) in adipocytes in OVX mice. FSH treatment did not alter expression levels of adipogenesis or thermogenesis markers, nor did it affect lipid accumulation in white adipocytes differentiated from 3T3-L1 cells or from primary mouse pre-adipocytes. Though I successfully disrupted the FSHR gene (Fshr) in adipocytes using the Cre/lox recombination system, I did not observe changes in body weight or composition, or glucose metabolism in OVX mice on normal chow or high fat diet. Finally, using a newly developed FSHR-3xHA knockin mouse model, I detected FSHR protein expression in mouse ovaries, but not in adipose depots of female mice. Collectively, my results do not show FSH effects on adipogenesis in vitro and challenge the hypothesis that FSH acts through FSHR in adipocytes in vivo. In Chapter 3, I investigated the effects of FSH in the brain. First, I examined Fshr expression in mouse brains using RNAscope in situ hybridization, a highly sensitive assay for detecting mRNA with single transcript resolution. I detected low levels of Fshr in several brain regions. To determine whether these rare transcripts were translated into protein, I used our FSHR-3xHA knockin mouse model. I was unable to detect FSHR protein in these mice. In contrast, I detected another HA-tagged transmembrane protein, NPR2, in brains of HA-Npr2 mice, providing a critical methodological control in these experiments. In preliminary experiments, I assessed the ability of fluorescently labeled FSH to enter the brain following i.v. injection in mice. Thus far, I have been unable to detect FSH in the brain. Collectively, the data suggest that FSH is unlikely to act via its receptor in the brain to mediate its effects, if any.Taken together, my data do not support the hypotheses that FSH acts in adipocytes or the brain. These observations suggest that blocking FSH action will not represent an effective means to prevent or treat post-menopausal bone loss, weight redistribution, or cognitive decline.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".