Association between macro- and microvascular damage and sarcopenia index in individuals with type 2 diabetes mellitus
Bibliographic record
Abstract
Sarcopenia was recently reported to be relevant to an increased macro-and microvascular disease risk. Sarcopenia index (SI) has been identified as a surrogate marker for sarcopenia. The aim of the present study was to investigate the association between macro- and microvascular disease and SI in patients with type 2 diabetes mellitus (T2DM). A total of 783 patients with T2DM were enrolled in this cross-sectional study. The SI was calculated by (serum creatinine [mg/dL]/cystatin C [mg/L]) × 100. The subjects were divided into three groups according to SI tertiles: T1 (41.27–81.37), T2 (81.38– 99.55), and T3 (99.56–192.31). Parameters of macro- and microvascular complications, including diabetic retinopathy (DR), micro- and macroalbuminuria (MAU), diabetic peripheral neuropathy (DPN), and lower extremity peripheral artery disease (LEAD) were evaluated. Multivariate logistic regression analysis revealed that when taking the top tertile of SI as a reference, an increasing trend of the prevalence of DR, MAU, DPN, and LEAD were presented (all P for trend < 0.05), where the OR (95% CI) for DR prevalence was 1.967 (1.252–3.090) in T2, 2.195 (1.278–3.769) in T1, for MAU was 1.805 (1.149–2.837) in T2, 2.537 (1.490–4.320) in T1, for DPN was 2.244 (1.485–3.391) in T2, 3.172 (1.884–5.341) in T1, and for LEAD was 2.017 (1.002–4.057) in T2, 2.405 (1.107–5.225) in T1 (all P < 0.05). Patients with lower SI were more inclined to have an increased risk of macro- and microvascular damage in T2DM population, which may be related to sarcopenia.
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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 | 0.001 | 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 | 0.001 |
| 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".