Relationship of Anthropometric Indices to Abdominal Body Composition: A Multi-Ethnic New Zealand Magnetic Resonance Imaging Study
Bibliographic record
Abstract
BACKGROUND: Conventional anthropometric indices (body mass index (BMI) and waist circumference (WC)) have limitations, in part, due to ethnic differences in fat distribution. Assessment of abdominal body composition using magnetic resonance imaging (MRI) is increasingly used to gain deeper insights into the pathophysiology of diabetes mellitus, cardiovascular diseases and metabolic syndrome, but the knowledge of abdominal volumes in indigenous populations is scarce. This study aimed to assess abdominal fat distribution and total abdominal volume using MRI in a multi-ethnic cohort that includes Maori (the indigenous people of New Zealand) and Pacific Islanders (PI). METHODS: MRI was used to quantify subcutaneous adipose tissue (SAT) volume, visceral adipose tissue (VAT) volume and total abdominal (TAb) volume by two independent raters in a blinded fashion. WC and BMI were also measured. Multinomial regression was used to compare the volumes between ethnic groups. Linear regression was used to investigate the ethnicity-specific associations between anthropometric indices and abdominal volumes. Three statistical models were built to adjust for age, sex, prediabetes/diabetes status and other covariates. RESULTS: A total of 87 individuals (37 Caucasians, 24 Maori/PI and 26 others) were studied. Maori/PI had a significantly higher VAT volume compared with Caucasians across all statistical models, with the highest odds ratio of 2.1 (95% confidence interval: 1.1 - 4.2; P = 0.026). SAT and TAb volumes did not differ significantly between the groups. WC explained up to 72.9% of variance in VAT volume among Maori/PI and up to 50.7% among Caucasians. BMI explained up to 67.6% of variance in VAT volume among Maori/PI and up to 52.1% among Caucasians. CONCLUSIONS: Greater visceral fat deposition among Maori/PI might go some way towards explaining the increased rates of metabolic disorders observed in this ethnic group. Conventional anthropometric indices do not correspond to the same abdominal volumes across different ethnic groups.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".