Factor analysis of ethnic variation in the multiple metabolic (insulin resistance) syndrome in three Canadian populations
Bibliographic record
Abstract
This study describes and compares the pattern of risk factor clustering in multiple metabolic (insulin resistance) syndrome (MMS) in three Canadian ethnic groups (Indians, Inuit, non-Aboriginal Canadians). Three cross-sectional, population-based sample surveys in three contiguous regions of Canada were conducted during the late 1980s and early 1990s (Ontario, Manitoba, Northwest Territories). The combined dataset consists of 873 Cree-Ojibwa Indians from northern Ontario and Manitoba, 387 Inuit from the Northwest Territories, and 2,670 non-Aboriginal Canadians (predominantly of European origin) in the province of Manitoba. The samples are representative of the noninstitutionalized, adult population of their respective catchment areas. Factor analysis transformed 10 anthropometric and metabolic variables into three uncorrelated factors. Three factors, which together account for 64.3% of the variance, can be identified: an "obesity factor" (factor loadings for weight, height, waist and hip girth, and HDL-cholesterol); a "blood pressure factor" (factor loadings for mean systolic and diastolic BP and total cholesterol); and a "lipid/glucose factor" (factor loadings for triglycerides, total cholesterol, HDL, and fasting plasma glucose). Fasting insulin is available for only a subset of the data and separate analysis shows that it groups with glucose. Factor scores generated by the factor analysis differ according to ethnic group, diabetes status, and sex on multivariate analysis of variance. Indians have the highest scores for all three factors. Inuit have the lowest obesity scores and are not significantly different from non-Aboriginal people with regard to the other two factors. MMS is prevalent in diverse ethnic groups but varies in the pattern of phenotypic expression, with some components more prominent in some groups.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.000 | 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 teacher head, 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".