Obesity and Cardiovascular Disease: The Emerging Role of Inflammation
Bibliographic record
Abstract
Obesity is a growing public health challenge across the globe. It is associated with increased morbidity and mortality. Cardiovascular disease (CVD) is the leading cause of mortality for people with obesity. Current strategies to reduce CVD are largely focused on addressing traditional risk factors such as dyslipidemia, type 2 diabetes (T2D) and hypertension. Although this approach is proven to reduce CVD, substantial residual risk remains for people with obesity. This necessitates a better understanding of the etiology of CVD in people with obesity and alternate therapeutic approaches. Reducing inflammation may be one such strategy. A wealth of animal and human data indicates that obesity is associated with adipose tissue and systemic inflammation. Inflammation is a known contributor to CVD in humans and can be successfully targeted to reduce CVD. Here we will review the etiology and pathogenesis of inflammation in obesity associated metabolic disease as well as CVD. We will review to what extent these associations are causal based on human genetic studies and pharmacological studies. The available data suggests that anti-inflammatory treatments can be used to reduce CVD, but off-target effects such as increased infection have precluded its broad therapeutic application to date. The role of anti-inflammatory therapies in improving glycaemia and metabolic parameters is less established. A number of clinical trials are currently ongoing which are evaluating anti-inflammatory agents to lower CVD. These studies will further clarify whether anti-inflammatory agents can safely reduce CVD.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".