Characteristics of Abdominal Visceral Adipose Tissue, Metabolic Health and the Gut Microbiome in Adults
Bibliographic record
Abstract
CONTEXT: Compared with the relatively benign effects of increased subcutaneous adipose tissue (SAT), increased visceral adipose tissue (VAT) volume is a causal risk factor for hypertension, hyperlipidemia, type 2 diabetes, and cardiovascular disease. In rodents, increased VAT volume and triglyceride density and ectopic lipid accumulation in kidneys and liver have been induced by alterations in the gut microbiome. However, few studies have characterized these relationships in humans. OBJECTIVE: To evaluate the tissue triglyceride content of VAT and SAT, liver, kidneys, and pancreas in male and female adults and assess associations with markers of glucose tolerance, serum insulin, and lipids and characteristics of the gut microbiome. METHODS: Cross-sectional observational study of healthy human adults (n = 60) at a clinical research center. Body mass index (BMI), body composition, and oral glucose tolerance were assessed. Microbiome analysis was conducted on stool samples using 16S rRNA v3 amplicon sequencing. The triglyceride content of VAT, SAT, liver, kidney and pancreas were determined by assessing proton density fat fraction (PDFF) with magnetic resonance imaging (MRI). RESULTS: Higher VAT PDFF and the ratio of VAT to SAT PDFF were related to higher BMI, HbA1c, HOMA-IR, non-high-density lipoprotein cholesterol, plasma triglycerides, low-density lipoprotein (LDL) cholesterol, and lower high-density lipoprotein (HDL) cholesterol. A higher VAT PDFF and VAT to SAT PDFF ratio were associated with lower alpha diversity and altered beta diversity of the gut microbiome. Differences in VAT were associated with higher relative abundance of the phylum Firmicutes, lower relative abundance of the phylum Bacteroidetes, and enrichment of the bacterial genera Dorea, Streptococcus, and Solobacterium. CONCLUSION: VAT PDFF measured with MRI is related to impaired glucose homeostasis, dyslipidemia, and differences in the gut microbiome, independently of the total body fat percentage.
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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 | 0.001 |
| 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".