Impact of Metabolic States on SARS-CoV-2 Vaccine Responses in Mouse Models of Obesity and Diabetes
Bibliographic record
Abstract
The emergence of SARS-CoV-2 has resulted in a significant impact on public health, particularly for individuals with underlying health conditions such as obesity and diabetes. While vaccination efforts have played a crucial role in reducing hospitalizations, it remains unclear whether the effectiveness of these vaccines varies among different population groups. In this study, we investigated the immune responses generated by various SARS-CoV-2 vaccine platforms in mouse models with obesity and diabetes, focusing on both cell-mediated and humoral immune responses. Our findings revealed diminished immune responses in diabetic and obese mice compared to healthy counterparts. After vaccination with adjuvanted subunit or mRNA lipid nanoparticle (LNP) vaccines, both humoral and cell-mediated responses were significantly reduced in diabetic mice. Obese mice also exhibited decreased immunogenicity, albeit to a lesser extent. However, it should be noted that mRNA vaccines demonstrated strong neutralizing responses across all metabolic states, while adjuvanted subunit vaccines elicited higher antibody avidity in mice with type 2 diabetes (T2D) and obesity compared to healthy mice. These results suggest that the impaired humoral and cell-mediated responses observed in altered metabolic states may be linked to chronic inflammation associated with obesity and suboptimal glycemic control in diabetes. Understanding the impact of these metabolic disturbances on vaccine immunogenicity is crucial for developing optimized vaccines that can effectively enhance immune responses and provide long-lasting protection against SARS-CoV-2, even in individuals with obesity and diabetes. By contributing these findings, we support efforts to improve vaccine efficacy in populations affected by metabolic disorders, advancing effective immunization against SARS-CoV-2.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| 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".