Differences in Abdominal and Liver Fat Accumulation Between Obesity-Matched Diabetic and Non-Diabetic Adults
Bibliographic record
Abstract
PURPOSE: To determine whether type 2 diabetic (T2D) have greater levels of visceral adipose tissue (VAT) and liver fat by comparison to age and obesity-matched non-diabetics (NT2Ds) in White and Asian adults. METHODS: Four thousand, five hundred and four participants were initially recruited from 29 countries in a multicenter, international observational, cross-sectional analysis. From this sample, 2383 White and Asian adults were selected for the focus of this analysis. T2D and NT2D men and women were matched for age, body mass index (BMI) and waist circumference (WC). T2D and NT2D men and women were also compared to participants with either impaired fasting glucose (IFG) and/or impaired glucose tolerance (IGT; (IFG/IGT group)). A secondary analysis determined whether differences exist between NT2Ds and T2Ds in VAT and liver fat accumulation within selected BMI categories for Whites and Asians. Abdominal adipose tissue was measured by computed tomography; liver fat was estimated using the computed tomography-derived mean attenuation. RESULTS: T2Ds and IFG/IGT had elevated levels of VAT and liver fat compared to NT2Ds across gender and ethnicity (p0.05). However, liver fat accumulation was greater in T2Ds compared to IFG/IGT in both White and Asian participants (p<0.05). Within each BMI category, T2Ds had elevated VAT and liver fat compared to age and anthropometrically matched NT2Ds in both Whites and Asians (p<0.05). With few exceptions, abdominal subcutaneous adipose tissue levels were not different in T2Ds or IFG/IGT compared to NT2Ds independent of gender or ethnicity. CONCLUSION: Compared to age and obesity-matched men and women, White and Asian T2Ds, and those with IFG/IGT, present with greater levels of both VAT and liver fat.
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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 | 0.001 | 0.000 |
| 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) | 0.002 | 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".