Editorial: Sex differences in immunometabolism, prophylaxis and therapy
Bibliographic record
Abstract
Editorial on the Research Topic Sex differences in immunometabolism, prophylaxis and therapyThe immune system and metabolism are profoundly intertwined on a whole-body and cellular level, thus affecting homeostatic and pathological processes.Sex differences in immunometabolism have long been appreciated, yet not fully explored.The aim of this Research Topic was to reduce the knowledge gap in our understanding of sex differences in immunometabolism.Premenopausal females typically mount stronger innate and adaptive immune responses than males, which could lead to better protection against infections but also increase the risk for autoimmune disorders.As reviewed by Nytrova and Dolezal, sex bias in multiple sclerosis and neuromyelitis optica spectrum disorders affects the incidence, disease progression and risk of relapse.Biological sex also greatly affects whole-body metabolism.Premenopausal females expand subcutaneous fat whereas males, and females with polycystic ovary syndrome (PCOS), display visceral adiposity which is linked with a higher risk for metabolic disorders.Visceral adiposity is associated with chronic inflammation that attenuates immune responses to infection.It is therefore not surprising that sex differences in immunometabolism can shape responses to vaccines and therapeutic drugs.Several studies have reported that males have a higher risk for severe COVID-19 than females.As summarised in a review article by Rehman et al., this could be due to sex differences in estrogen receptor expression which can affect the HDL to LDL ratio, the nitric oxide synthesis, and others differently in males and females.Females with PCOS are also at higher risk for COVID-19 complications than non-PCOS females, further emphasising the intimate interactions between the immune system and metabolism on the outcome of infection.In a related study, Parker et al. found that induction of SARS-CoV2 antibody responses are more rapid in females.Earlier induction of an antibody response could confer faster clearance of the virus from the respiratory tract, thus explaining the sexbias in disease severity.Sex-bias could also affect the treatment outcome.For instance, a study by Harnett et al. explored the impact of ES-62, a parasitic worm product, in a mouse model of obesity-accelerated aging.The authors found that ES-62 acts protectively in the male mice, but not in the female mice fed a high-fat diet.Specifically, ES-62 improved Frontiers in Immunology frontiersin.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.005 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.011 | 0.014 |
| Insufficient payload (model declined to judge) | 0.025 | 0.017 |
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".