Cardiovascular and Metabolic Heterogeneity of Obesity
Bibliographic record
Abstract
The prevalence of obesity has increased globally over the last 2 decades. Although the body mass index has been a convenient and simple index of obesity at the population level, studies have shown that obesity defined by body mass index alone is a remarkably heterogeneous condition with varying cardiovascular and metabolic manifestations across individuals. Adipose tissue is an exquisitely active metabolic organ engaged in cross-talk between various systems; perturbation of adipose tissue results in a pathological response to positive caloric balance in susceptible individuals that directly and indirectly contributes to cardiovascular and metabolic disease. Inadequate subcutaneous adipose tissue expansion in the face of dietary triglycerides leads to visceral and ectopic fat deposition, inflammatory/adipokine dysregulation, and insulin resistance. Conversely, preferential fat storage in the lower body depot may act as a metabolic buffer and protect other tissues from lipotoxicity caused by lipid overflow and ectopic fat. Translational, epidemiological, and clinical studies over the past 30 years have clearly demonstrated a strong link between visceral and ectopic fat and the development of a clinical syndrome characterized by atherogenic dyslipidemia, hyperinsulinemia/glucose intolerance, hypertension, atherosclerosis, and adverse cardiac remodeling/heart failure. This relationship is even more nuanced when clinical entities such as metabolically healthy obesity phenotype and the obesity paradox are considered. Although it is clear that the accumulation of visceral/ectopic fat is a major contributor to cardiovascular and metabolic risk above and beyond the body mass index, implementation of fat distribution assessment into clinical practice remains a challenge. Anthropometric indexes of obesity are easily implemented, but newer imaging-based methods offer improved sensitivity and specificity for measuring specific depots. Lifestyle, pharmacological, and surgical interventions allow a multidisciplinary approach to overweight/obesity that may improve outcomes and align with a public health message to combat the growing epidemic of obesity worldwide and to build healthier lives free of cardiovascular diseases.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".