Devastating Metabolic Consequences of a Life of Plenty: Focus on the Dyslipidemia of Overnutrition
Bibliographic record
Abstract
Although undernutrition and starvation continue to affect a substantial portion of the world's population, billions of people in both developed and developing countries are affected by the opposite problem: consumption of calories that exceed their daily energy expenditure, a condition of overnutrition. The body's response to a positive net energy balance is to store energy, predominantly as triglyceride molecules, in the subcutaneous and visceral fat compartments that expand and ultimately manifest in obesity. The body's fat depot, however, does not have an infinite capacity to store and expand, and at set points, which differ from individual to individual and are also influenced by ethnicity, energy substrates 'spill over', resulting in 'ectopic' fat storage in tissues and organs that are not typically major fat storage depots in lean individuals. A complex web of nutrient overload, chronic inflammation, hormonal action, mitochondrial dysfunction and insulin resistance, to mention some of the factors involved, results in devastating metabolic abnormalities that have far reaching implications for health and disease, leading ultimately to some of the most common chronic diseases of our time; i.e., diabetes mellitus, cancer, chronic liver disease and atherosclerosis. Given the complexity and wide-ranging manifestations of overnutrition (also referred to here as insulin resistant states), we will highlight a specific aspect of the condition, that of dyslipidemia. This review will draw mainly on knowledge acquired from whole body, integrative physiology research in animals and humans affected by overnutrition, and will demonstrate how these types of studies can shed light on our understanding of the pathophysiology of the typical dyslipidemia of obesity, insulin resistance and type 2 diabetes.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".