Obesity: Pathophysiology and Clinical Management
Bibliographic record
Abstract
Obesity is an increasingly serious socioeconomic and clinical problem. Between (1/4)-(1/3) of population in the developed countries can be classified as obese. Four major etiological factors for development of obesity are genetic determinants, environmental factors, food intake and exercise. Obesity increases the risk of the development of various pathologic conditions including: insulin-resistant diabetes mellitus, cardiovascular disease, non-alcoholic fatty liver disease, endocrine problems, and certain forms of cancer. Thus, obesity is a negative determinant for longevity. In this review we provide broad overview of pathophysiology of obesity. We also discuss various available, and experimental therapeutic methods. We highlight functions of adipocytes including fat storing capacity and secretory activity resulting in numerous endocrine effects like leptin, IL-6, adiponectin, and resistin. The anti-obesity drugs are classified according to their primary action on energy balance. Major classes of these drugs are: appetite suppressants, inhibitors of fat absorption (i.e. orlistat), stimulators of thermogenesis and stimulators of fat mobilization. The appetite suppressants are further divided into noradrenergic agents, (i.e. phentermine, phendimetrazine, benzphetamine, diethylpropion), serotoninergic agents (i.e. dexfenfluramine), and mixed noradrenergic-serotoninergic agents (i.e. sibutramine). Thus, we highlight recent advances in the understanding of the central neural control of energy balance, current treatment strategies for obesity and the most promising targets for the development of novel anti-obesity drugs.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".