Cross-over comparative study of cold-induced brown adipose tissue activity in Greenlandic Inuit and Danes: rationale, design, and methodology
Bibliographic record
Abstract
Brown adipose tissue (BAT) is essential for non-shivering thermogenesis, a key survival mechanism for Arctic populations exposed to chronic cold. As BAT dissipates energy as heat, it presents a potential target for improving cardiometabolic health and treating obesity. The Arctic Inuit represents a unique metabolic model due to distinct genetic and environmental adaptations. This study advances methods in cardiometabolic research by investigating BAT activation in Arctic Inuit and ethnic Danes under cold exposure. A comparative crossover study of 20 Inuit and Danes includes two sessions: (A) thermal comfort and (B) 2 hours of individualised cooling. Each session concludes with fat biopsies and [18F]FDG-PET/CT scans to quantify BAT activity and volume. Additional measures include blood sampling and infrared thermography (IRT). The cooling protocol and biological sampling are designed to capture key metabolic signatures of BAT activation, enabling detailed insight into thermogenic function and its cardiometabolic implications. PET/CT scans contribute valuable insights into metabolic processes and the ethical considerations balance the benefit of unique insight against radiation risk. Given limitations in accessibility and radiation exposure, this study also evaluates IRT as a low-risk, accessible alternative to PET/CT scans. This methodological advancement supported approval by the North Denmark Region ethics committee (N-20220042). As [18 F]FDG-PET/CT is not available in Arctic Greenland, data collection was conducted in Denmark with an accessible Inuit population. The study forms part of a broader study on climate and health, approved by the ethics committee for Greenland.
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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".