Metabolic and Body Composition Factors in Subgroups of Obesity: What Do We Know?
Bibliographic record
Abstract
Obesity is thought to be a heterogeneous disorder with several possible etiologies; therefore, by examining subtypes of obesity we attempt to understand obesity's heterogeneous nature. The purpose of this review was to investigate the roles of metabolic, body composition, and cardiovascular disease risk in subtypes of obesity. We briefly consider two subtypes of obesity that have been identified in the literature. One subset of individuals, termed the metabolically healthy, but obese (MHO), despite having large amounts of fat mass compared with at risk obese individuals shows a normal metabolic profile, but remarkably normal to high levels of insulin sensitivity. Preliminary evidence suggests that this could be due at least in part to lower visceral fat levels and earlier onset of obesity. A second subset, termed the metabolically obese, but normal weight (MONW), present with normal body mass index, but have significant risk factors for diabetes, metabolic syndrome, and cardiovascular disease, which could be due to higher fat mass and plasma triglycerides as well as higher visceral fat and liver content. We also briefly consider the potential role of adipose and gastrointestinal hormonal profiles in MHO and MONW individuals, which could lead to a better understanding of potential factors that may regulate their body composition. This information will eventually be invaluable in helping us understand factors that predispose to or protect obese individuals from metabolic and cardiovascular disease. Collectively, a greater understanding of the MHO and MONW individual has important implications for therapeutic decision making, the characterization of subjects in research protocols, and medical education.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".