Obesity phenotypes and atherogenic dyslipidemias
Bibliographic record
Abstract
BACKGROUND: Obesity and atherogenic dyslipidemias are interrelated disorders that substantially contribute to the development of atherosclerotic cardiovascular disease (ASCVD) and the broader cardiovascular-kidney-metabolic (CKM) syndrome. The heterogeneity of obesity, particularly the role of visceral, ectopic and dysfunctional adipose tissue, drives the dyslipidemia phenotype characterized by hypertriglyceridemia, reduced high-density lipoprotein cholesterol and elevated apolipoprotein B-containing lipoproteins. METHODS: We review the epidemiological studies, imaging analyses, genetic data and clinical trial evidence linking obesity phenotypes with dyslipidemias and cardiovascular outcomes. We further evaluated lifestyle, pharmacological and surgical strategies targeting obesity-related lipid abnormalities. RESULTS: Visceral and ectopic fat accumulation, rather than body mass index alone, strongly predicts adverse lipid patterns, insulin resistance and cardiovascular events. Atherogenic dyslipidemias contribute to substantial residual ASCVD risk despite low-density lipoprotein cholesterol lowering. Lifestyle interventions-including diet modification, physical activity and improved cardiorespiratory fitness-demonstrate favourable effects on lipid metabolism independent of weight loss. Traditional pharmacotherapies such as statins, fibrates and omega-3 fatty acids offer partial benefits, while novel incretin-based agents and dual or triple receptor agonists provide robust weight loss and metabolic improvements. Metabolic and bariatric surgery remains an effective approach for sustained weight reduction and remission of dyslipidemias in the appropriate patient and is increasingly integrated with pharmacotherapy. CONCLUSIONS: Atherogenic dyslipidemias are a hallmark of high-risk obesity phenotypes and a major contributor to ASCVD within the CKM framework. Precision medicine approaches that incorporate obesity phenotyping, targeted treatment strategies and population-level interventions are needed to reduce the burden of dyslipidemias and their cardiovascular sequelae.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".