In hemodialysis, adiponectin, and pro‐brain natriuretic peptide levels may be subjected to variations in body mass index
Bibliographic record
Abstract
Adiponectin exerts cardiovascular protective actions, although some studies have shown the opposite. In hemodialysis, obese subjects display lower mortality rates despite hypoadiponectinemia, while higher adiponectin concentrations correlate with an elevated cardiovascular risk in nonobese subjects. The aim of the study is to suggest that adiponectin level variations are associated with differences in the body mass index (BMI). The interplay between adiponectin and pro-brain natriuretic peptide (Pro-BNP) levels may vary according to body fat mass. Fifty-two chronic hemodialysis patients were divided into three groups. Group A, BMI<25 (n=20); Group B, BMI 25 to 30 (n=21), and Group C, BMI>30 (n=11). Diabetics: Group A 10%; Group B 6 29%; Group C 55%, P=0.027. Determinations: Adiponectin, Pro-BNP, insulin, insulin resistance (HOMA), troponin T, nutritional status, ultrafiltration rates, C-reactive protein (CRP), vascular accesses, and echocardiography. Group A: adiponectinemia positively and significantly correlated with Pro-BNP, CRP, and troponin T. As BMI increased, adiponectin, Pro-BNP, and malnutrition significantly decreased, while insulin, HOMA, and ultrafiltration rates significantly increased. Cardiac restriction was significantly higher in obese patients. In all groups, Pro-BNP and troponin T displayed a strong positive correlation. In low-BMI subjects, high Pro-BNP and adiponectin, low myocardial restriction, and worse nutritional status were prevalent. In obesity, hypoadiponectinemia stimulates cardiac remodeling, cardiac hypertrophy, and decreased stretching, rendering Pro-BNP levels low despite high ultrafiltration rates. Thus, adiponectin correlates inversely with BMI, probably playing different cardiovascular roles as BMI changes.
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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 | 0.000 |
| 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".