Circulatory miRNAs as Correlates of Elevated Intra-Pancreatic Fat Deposition in a Mixed Ethnic Female Cohort: The TOFI_Asia Study
Bibliographic record
Abstract
Ectopic lipid accumulation, including intra-pancreatic fat deposition (IPFD), exacerbates type 2 diabetes risk in susceptible individuals. Dysregulated circulating microRNAs (miRNAs) have been identified as correlating with clinical measures of pancreatitis, pancreatic cancer and type 1 diabetes. The aim of the current study was therefore to examine the association between circulating abundances of candidate miRNAs, IPFD and liver fat deposition as quantified using magnetic resonance imaging (MRI) and spectroscopy (MRS). Asian Chinese (n = 34; BMI = 26.7 ± 4.2 kg/m2) and European Caucasian (n = 34; BMI = 28.0 ± 4.5 kg/m2) females from the TOFI_Asia cohort underwent MRI and MRS analysis of pancreas (MR-%IPFD) and liver fat (MR-%liver fat), respectively, to quantify ectopic lipid deposition. Plasma miRNA abundances of a subset of circulatory miRNAs associated with IPFD and liver fat deposition were quantified by qRT-PCR. miR-21-3p and miR-320a-5p correlated with MR-%IPFD, plasma insulin and HOMA2-IR, but not MR-%liver fat. MR-%IPFD remained associated with decreasing miR-21-3p abundance following multivariate regression analysis. miR-21-3p and miR-320a were demonstrated to be negatively correlated with MR-%IPFD, independent of ethnicity. For miR-21-3p, this relationship persists with the inclusion of MR-%liver fat in the model, suggesting the potential for a wider application as a specific circulatory correlate of IPFD.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".