Why might South Asians be so susceptible to central obesity and its atherogenic consequences? The adipose tissue overflow hypothesis
Bibliographic record
Abstract
The rates of coronary disease have accelerated dramatically amongst South Asians, driven to an important extent by the atherogenic dyslipidemia and type 2 diabetes that have become so common amongst them. These precursors of vascular disease appear at lower absolute amounts of adipose tissue in South Asians than in whites. In this paper, we set out a new hypothesis--the adipose tissue overflow hypothesis--to account for these findings. The adipose tissue mass within our bodies can be divided into three different compartments: superficial subcutaneous adipose tissue, deep subcutaneous adipose tissue and visceral adipose tissue. The superficial subcutaneous adipose tissue compartment is the primary compartment, is present throughout the body, and constitutes the vast majority of the adipose tissue in the lower limb. With energy excess, the secondary adipose tissue compartments--the deep subcutaneous (mainly upper body) and the visceral adipose tissue compartments--become more prominent. Superficial subcutaneous adipose tissue is relatively inert metabolically, whereas the other two compartments are characterized by higher transmembrane fatty acid flux rates and thus are more closely linked to dyslipidemia and dysglycemia. We hypothesize that the superficial subcutaneous adipose tissue compartment is larger in whites than in South Asians. If so, as obesity develops, South Asians exhaust the storage capacity of their superficial subcutaneous adipose tissue compartment before whites do and that is why they develop the metabolic complications of upper body obesity at lower absolute masses of adipose tissue than white people.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".