Low serum leptin levels are associated with malnutrition status according to malnutrition‐inflammation score in patients undergoing chronic hemodialysis
Bibliographic record
Abstract
INTRODUCTION: Leptin is an adipokine secreted from adipocytes that mediate lipid metabolism and inflammation. This cross-sectional study investigated the relationship between serum leptin level and nutrition status evaluated by malnutrition-inflammation score (MIS) among patients undergoing hemodialysis (HD). METHODS: This study included 100 patients on HD. Nutritional status was based on MIS (malnutrition ≥7 points). Body composition, biochemistry data, and serum leptin level were evaluated. FINDINGS: Of 100 subjects, 33 (33.0%) were categorized as having malnutrition. Compared with subjects in the well-nourished group, those in the malnutrition group had on average an older age, longer HD duration, and lower height, weight, body mass index, waist circumference, body fat mass, serum triglyceride level, and creatinine level. Serum leptin levels were also significantly lower in the malnutrition group (P < 0.001), whereas C-reactive protein (CRP) levels were higher (P = 0.002). Multivariable linear regression analysis revealed that HD duration (β = 2.06, P = 0.009), serum leptin level (β = -5.16, P < 0.001), CRP level (β = 3.33, P < 0.001), and albumin level (β = -1.95, P = 0.008) were factors independently associated with MIS. The discriminative power of serum leptin level to predict malnutrition was 0.834 (95% confidence interval: 0.747-0.901, P < 0.001). DISCUSSION: Low serum leptin level was associated with malnutrition, and serum leptin level may be a valuable marker for nutrition assessment in patients undergoing HD.
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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 | 0.002 |
| 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 | 0.000 |
| 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".