Total and high molecular weight adiponectin and ethnic-specific differences in adiposity and insulin resistance: a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Ethnic-specific differences in insulin resistance (IR) are well described but the underlying mechanisms are unknown. Adiponectin is an insulin sensitizing adipocytokine that circulates as multiple isoforms, with high molecular weight (HMW) adiponectin associated with greatest insulin sensitivity. The objective of this study is to determine if plasma total and HMW adiponectin concentrations underlie ethnic-specific differences in IR. METHODS: Healthy Canadian Aboriginal, Chinese, European, and South Asian adults (N = 634) were assessed for sociodemographics; lifestyle; fasting plasma insulin, glucose, and total and HMW adiponectin; and adiposity measures [BMI, waist circumference, waist-to-hip ratio, percent body fat, and subcutaneous and visceral adipose tissue (quantified by computed tomography)]. The homeostasis model assessment-insulin resistance (HOMA-IR) assessed IR. RESULTS: South Asians had the greatest HOMA-IR, followed by Aboriginals, Chinese, and Europeans (P < 0.001). Plasma total and HMW adiponectin concentrations were lower in Chinese and South Asians than Aboriginal and Europeans (P < 0.05). Total and HMW adiponectin were inversely associated with HOMA-IR (P < 0.001). Ethnicity modified the relationship between HMW adiponectin and HOMA-IR with stronger effects observed in Aboriginals (P = 0.001), Chinese (P = 0.002), and South Asians (P = 0.040) compared to Europeans. This was not observed for total adiponectin (P = 0.431). At mean total adiponectin concentrations South Asians had higher HOMA-IR than Europeans (P < 0.001). CONCLUSIONS: For each given decrease in HMW adiponectin concentrations a greater increase in HOMA-IR is observed in Aboriginals, Chinese, and South Asians than Europeans. Ethnic-specific differences in HMW adiponectin may account for differences in IR.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".