Association between novel indices of malnutrition–inflammation complex syndrome and cardiovascular disease in hemodialysis patients
Bibliographic record
Abstract
BACKGROUND: Inflammation and malnutrition are recognized as important risk factors for cardiovascular disease (CVD) in hemodialysis (HD) patients. Owing to substantial short-term variability of serum C-reactive protein (CRP), more reliable markers of malnutrition-inflammation complex syndrome should be sought with stronger associations with the risk of CVD in HD patients. We therefore explored the clinical relevance of a composite inflammatory index (prognostic inflammatory and nutritional index [PINI]) and of muscle protein mass indicators, derived from creatinine kinetics. METHODS: This cross-sectional study included 177 HD patients (89 women and 88 men; median age, 67.73 years). CVD and risk factors were assessed using medical charts, clinical examination, and biochemical measurements performed at inclusion. Lean body mass (LBM) was derived from creatinine kinetic modeling, whereas PINI was calculated as the ratio (CRP xalpha1-acid-glycoprotein)/(albumin x transthyretin). Patients were divided according to the presence or absence of established CVD. RESULTS: The traditional risk factors diabetes (odds ratio [OR], 5.83; p = 0.0045) and smoking (OR, 3.50; p < 0.02) were associated with an increase in prevalent CVD. Low transthyretin (OR, 3.79; p < 0.02) and high levels of CRP (OR, 2.70; p < 0.05), PINI (OR, 3.44; p < 0.02), observed LBM (OR, 3.01; p < 0.05), and the ratio of observed/expected LBM (OR, 4.24; p < 0.01) were associated with CVD after adjustment for age, sex, dialysis center, and dialysis vintage. After additional adjustment for diabetes and smoking, only PINI (OR, 2.85; p = 0.0446) and observed/expected LBM (OR, 2.96; p = 0.0361) were still significant. CONCLUSION: PINI and LBM are associated with increased relative risk for having CVD and could be used routinely to examine the degree of severity of malnutrition inflammation complex syndrome.
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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 | 0.002 |
| 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".