Validation of the Use of Peripheral Blood Mononuclear Cells as Surrogate Model for Skeletal Muscle Tissue in Nutrigenomic Studies
Bibliographic record
Abstract
Peripheral blood mononuclear cells (PBMCs) offer a significant promise for gene expression analyses as a substitute for tissues that are not easily accessible. The objective of this study was to validate the use of PBMCs for gene expression analysis as a marker of nutritional intervention as an alternative to skeletal muscle tissue (SMT) biopsies. We performed a transcriptome comparison of PBMCs versus SMT after an 8-week supplementation with n-3 polyunsaturated fatty acid (PUFA) in 16 obese and insulin-resistant subjects. Expression levels of 48,803 transcripts were assessed by the Human-6 v3 Expression BeadChips (Illumina, San Diego, CA). In SMT, 36,738 (75%) transcripts were detected, whereas 34,182 (70%) transcripts were detected in PBMCs. Further, 88% (32,341) of these transcripts were coexpressed in both tissues. Importantly, a strong correlation (r = 0.84, p < 0.0001) was observed between transcript expression levels of PBMCs and SMT after n-3 PUFA supplementation. In conclusion, PBMCs express the majority of transcripts expressed in SMT subsequent to n-3 PUFA supplementation and their expression levels are comparable. In the interest of practicalities and cost, these results support the use of PBMCs as a surrogate model for SMT gene expression in nutrigenomic studies. Further research on PBMC and SMT gene expression in response to other nutritional exposures is warranted.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".