Obesity paradox in cardiovascular disease: where do we stand?
Bibliographic record
Abstract
Salvatore Carbone,1 Justin M Canada,1 Hayley E Billingsley,1,2 Mohammad S Siddiqui,3 Andrew Elagizi,4 Carl J Lavie41VCU Pauley Heart Center, Department of Internal Medicine, Richmond, VA, USA; 2Kinesiology and Health Sciences, College of Humanities & Science, Richmond, VA, USA; 3Division of Gastroenterology, Hepatology and Nutrition, Virginia Commonwealth University, Richmond, VA, USA; 4John Ochsner Heart and Vascular Institute, Ochsner Clinical School-the University of Queensland School of Medicine, New Orleans, LA, USAAbstract: Obesity is associated with an increased risk of developing cardiovascular disease (CVD), particularly heart failure (HF) and coronary heart disease (CHD). The mechanisms through which obesity increases CVD risk involve changes in body composition that can affect hemodynamics and alters heart structure. Pro-inflammatory cytokines produced by the adipose tissue itself which can induce cardiac dysfunction and can promote the formation of atherosclerotic plaques. When obesity and HF or CHD coexist, individuals with class I obesity present a more favorable prognosis compared to individuals who are normal or underweight. This phenomenon has been termed the “obesity paradox.” Obesity is defined as an excess fat mass (FM), but individuals with obesity typically also present with an increased amount of lean mass (LM). The increase in LM may explain part of the obesity paradox as it is associated with improved cardiorespiratory fitness (CRF), a major determinant of clinical outcomes in the general population, but particularly in those with CVD, including HF. While increased LM is a stronger prognosticator in HF compared to FM, in patients with CHD excess FM can exert protective effects particularly when not associated with increased systemic inflammation. In the present review, we discuss the mechanisms through which obesity may increase the risk for CVD, and how it may exert protective effects in the setting of established CVD, with a focus on body composition. We also highlight the importance of measuring or estimating CRF, including body composition-adjusted measures of CRF (ie, lean peak oxygen consumption) for an improved risk status stratification in patients with CVD and finally, we discuss the potential non-pharmacologic therapeutics, such as exercise training and dietary interventions, aimed at improving CRF and perhaps clinical outcomes.Keywords: obesity, cardiovascular disease, obesity paradox, body composition, cardiorespiratory fitness
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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 | 0.015 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.008 | 0.016 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.012 | 0.021 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".