Thymic stromal lymphopoietin: an immune cytokine gene associated with the metabolic syndrome and blood pressure in severe obesity
Bibliographic record
Abstract
A previous expression profiling of VAT (visceral adipose tissue) revealed that the TSLP (thymic stromal lymphopoietin) gene was less expressed in severely obese men with (n=7) compared with without (n=7) the MetS (metabolic syndrome). We hypothesized that TSLP SNPs (single nucleotide polymorphisms) are associated with TSLP gene expression in VAT and with MetS phenotypes. Following validation of lower TSLP expression (P=0.003) in VAT of severely obese men and women with (n=70) compared with without (n=60) the MetS, a detailed genetic investigation was performed at the TSLP locus by sequencing its promoter, exons and intron-exon splicing boundaries using DNA of 25 severely obese subjects. Five tagging SNPs were genotyped in the 130 subjects from the expression analysis to test whether these SNPs contributed to TSLP expression variability (ANOVAs) and then genotyped in two independent samples of severely obese men (total, n=389) and women (total, n=894). In a sex-stratified multistage experimental design, ANOVAs were performed to test whether tagging SNPs were associated with MetS components treated as continuous variables. We observed that the non-coding SNP rs2289277 was associated with TSLP mRNA abundance (P=0.04), as well as with SBP [systolic BP (blood pressure)] (P=0.004) and DBP (diastolic BP) (P=0.0003) in men when adjusting for age, waist circumference, smoking and medication treating hypertension. These novel observations suggest that TSLP expression in VAT may partly explain the inter-individual variability for metabolic impairments in the presence of obesity and that specific SNPs (rs2289277 and/or correlating SNPs) may influence TSLP gene expression as well as BP in obese men.
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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 | 0.000 | 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".