Lactobacillus rhamnosus Sex-Specifically Attenuates Depressive-like Behavior and Mitigates Metabolic Consequences in Obesity
Bibliographic record
Abstract
Patients with diabetes exhibit an increased prevalence for emotional disorders compared with healthy humans, partially due to a shared pathogenesis including hormone resistance and inflammation, which is also linked to intestinal dysbiosis. The preventive intake of probiotic lactobacilli has been shown to improve dysbiosis along with mood and metabolism. Yet, a potential role of Lactobacillus rhamnosus (Lacticaseibacillus rhamnosus 0030) (LR) in improving emotional behavior in established obesity and the underlying mechanisms are unknown. Female and male C57BL/6N mice were fed a low-fat diet (10% kcal from fat) or high-fat diet (HFD) (45% kcal from fat) for 6 weeks, followed by daily oral gavage of vehicle or 1 × 108 colony-forming units of LR, and assessment of anxiety- and depressive-like behavior. Cecal microbiota composition was analyzed using 16S ribosomal RNA sequencing, plasma and cerebrospinal fluid were collected for metabolomic analysis, and gene expression of different brain areas was assessed using reverse transcriptase quantitative polymerase chain reaction. We observed that 12 weeks of HFD feeding induced hyperinsulinemia, which was attenuated by LR application only in female mice. On the contrary, HFD-fed male mice exhibited increased anxiety- and depressive-like behavior, where the latter was specifically attenuated by LR application, which was independent of metabolic changes. Furthermore, LR application restored the HFD-induced decrease of tyrosine hydroxylase, along with normalizing cholecystokinin gene expression in dopaminergic brain regions; both tyrosine hydroxylase and cholecystokinin are involved in signaling pathways impacting emotional disorders. Our data show that LR attenuates depressive-like behavior after established obesity, with changes in the dopaminergic system in male mice, and mitigates hyperinsulinemia in obese female mice.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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".