Adipocyte Size, Adipose Tissue Fibrosis, Macrophage Infiltration and Disease Risk Are Different in Younger and Older Individuals with Childhood and Adulthood Onset Obesity
Bibliographic record
Abstract
The timing of obesity onset and age have been shown to affect the risk of obesity-related comorbidities, although the impact of each of these factors on markers of adipose tissue function remains unclear. The aim of this study is to determine whether differences in regional adipose tissue characteristics vary with age and age of obesity onset, and whether these differences are associated with the markers of cardiometabolic health. Adipose tissue samples were from 80 female bariatric surgery candidates who were classified by age of obesity onset and age into 4 groups: 1) younger adults (<40 y) with childhood-onset obesity (< 18 y) (Childhood-Young); 2) younger adults with adulthood-onset obesity (>18 y) (Adulthood-Young); 3) older adults (>55 y) with childhood-onset obesity (Childhood-Old); and 4) older adults with adulthood-onset obesity (Adulthood-Old). Adipocyte diameter, adipose tissue fibrosis and macrophage infiltration were determined in subcutaneous (SAT) and visceral adipose tissue (VAT). Clinical parameters were obtained from participants’ medical records. Visceral adipocyte size in the Childhood-Young group was the smallest of all the groups. Age affected visceral infiltration of M1-like cells in both older groups, whereas onset, specifically childhood-onset, was related to visceral infiltration of M2-like cells in the Childhood-Old group. Fibrosis accumulation in SAT and VAT varied with age and onset, particularly in the Childhood-Old group having the lowest fibrosis levels. Markers of cardiometabolic health (fasting glucose, glycated hemoglobin, total, HDL- and LDL-cholesterol and triglyceride concentrations) were positively and well associated with adipose tissue characteristics of the Childhood-Old group but not of the Adulthood-Young group. Older adults with childhood onset obesity, who had the greatest duration of obesity exposure, were particularly vulnerable to the cardiometabolic effects associated with perturbations in adipose tissue characteristics. These results, suggest that age and age of obesity onset may have independent and cumulative effects on obesity pathology. Sylvia Santosa holds a Canada Research Chair – Tier 2 in Clinical Nutrition. This research is also supported by a Discovery Grant from The Natural Science and Engineering Research Council of Canada.
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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) | 0.001 | 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".