Profile of Gut Hormones, Pancreatic Hormones and Pro-inflammatory Cytokines in New Zealand Maori
Bibliographic record
Abstract
BACKGROUND: Maori, indigenous people of New Zealand, have at least two times higher prevalence of obesity and diabetes in comparison with the general population in the country. Gut and pancreatic hormone profile differences as well as pro-inflammatory milieu may contribute to this disparity. The aim was to investigate the differences in gut hormones, pancreatic hormones and pro-inflammatory cytokines between Maori and non-Maori individuals. METHODS: This was a cross-sectional study. Fasting blood samples were collected to measure cholecystokinin, ghrelin, gastric inhibitory peptide, glicentin, glucagon-like peptide-1 and -2, oxyntomodulin, secretin, amylin, C-peptide, glucagon, insulin, pancreatic polypeptide, somatostatin, interleukin-6, monocyte chemoattractant protein-1 and tumour necrosis factor-α. Binary logistic regression analysis was conducted in one unadjusted and four adjusted statistical models adjusting for patient-, metabolic- and pancreatitis-related factors. RESULTS: A total of 8 Maori and 85 non-Maori individuals were included. Circulating levels of ghrelin, pancreatic polypeptide and interleukin-6 levels were significantly higher in Maori (P = 0.005, P = 0.003 and P = 0.011, respectively) in both unadjusted and all the four adjusted analyses. Other signaling molecules did not show consistently significant associations with ethnicity. CONCLUSION: Profile of gut hormones, pancreatic hormones and pro-inflammatory cytokines appears to differ between Maori and non-Maori individuals, independent of obesity, diabetes and other covariates. This may go some way to explain the increased propensity to obesity and diabetes in the Maori population.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".