Altered features of body composition in older adults with type 2 diabetes and prediabetes compared with matched controls
Bibliographic record
Abstract
BACKGROUND: Ageing is accompanied by muscle loss and fat gain, which may elevate the risk of type 2 diabetes (T2D). However, there is a paucity of data on the distribution of regional lean and fat tissue in older adults with T2D or prediabetes compared with healthy controls. The objective of this study was to compare regional body composition [by dual-energy x-ray absorptiometry (DXA)], muscle and subcutaneous adipose tissue (SAT) thicknesses (by ultrasound), and ultrasound-based muscle texture features in older adults with T2D or prediabetes compared with normoglycaemic controls. METHODS: Eighteen adults > 60 years with T2D or prediabetes (T2D group) were individually matched to normoglycaemic participants [healthy matched (HM) group] for age (±5 years), sex, and body fat (±2.5%). In a single study visit, all participants received a whole-body DXA scan and ultrasound assessment of the abdomen and anterior thigh. At these two landmarks, we used ultrasound to measure muscle and SAT thickness, as well as texture features of the rectus femoris and rectus abdominis. We also conducted an exploratory subanalysis on a subset of participants (n = 14/18 in the T2D group and n = 10/18 in the HM group) who underwent additional assessments including strength testing of the knee extensors (using a Biodex dynamometer), and a fasting blood sample for the measurement of circulating markers of glucose metabolism [glucose, insulin, c-peptide, and the homoeostatic model assessment of insulin resistance (HOMA-IR)]. RESULTS: , P = 0.045; rectus femoris local binary pattern (LBP) entropy: 4.65 ± 0.05 vs. 4.59 ± 0.08 A.U., P = 0.007]. When all participants who underwent additional assessments were pooled (n = 24), we observed that certain ultrasound-based muscle texture features correlated significantly with muscle strength (rectus abdominis histogram skew vs. power during an isokinetic contraction at 60°/s: r = 0.601, P = 0.003) and insulin resistance (rectus femoris LBP entropy vs. HOMA-IR: r = 0.419, P = 0.042). CONCLUSIONS: Our findings suggest a novel body composition phenotype specific to older adults with T2D or prediabetes. We are also the first to report that ultrasound-based texture features correspond with functional outcomes. Future larger scale studies are needed to uncover the mechanisms underpinning these regional body composition differences.
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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 | 0.001 |
| 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".