Sex differences in systemic inflammation and immune function in diet‐induced obesity rodent models: A systematic review
Bibliographic record
Abstract
Understanding sex differences in immunological responses in the context of obesity is important to improve health outcomes. This systematic review aimed to investigate sex differences in systemic inflammation, immune cell phenotype, and function in diet-induced obesity (DIO) animal models. A systematic search in Medline, Embase, and CINAHL from inception to April 2023 was conducted, using a combination of the following concepts: sex, obesity, cytokines, and immune cell phenotypes/function. Forty-one publications reporting on systemic inflammation (61%), cell phenotype (44%), and/or function (7%) were included. Females had lower systemic inflammation compared with males in response to DIO intervention and a higher proportion of macrophage (M)2-like cells compared with males that had a higher proportion of M1-like in adipose tissue. Although there were no clear sex differences in immune function, high-fat DIO intervention remains an important factor in the development of immune dysfunction in both males and females, including disturbances in cytokine production, proliferation, and migration of immune cells. Yet, the mechanistic links between diet and obesity on such immune dysfunction remain unclear. Future studies should investigate the role of diet and obesity in the functionality of immune cells and employ adequate methods for a high-quality investigation of sex differences in this context.
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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".