The Prevalence and Implication of Zinc Deficiency in Patients With Chronic Liver Disease
Bibliographic record
Abstract
BACKGROUND: Patients with liver cirrhosis often exhibit zinc deficiency. Although zinc is involved in many bioactivities, many aspects of clinical implications of zinc deficiency in liver cirrhosis remain unclear. We aimed to reveal the prevalence and implications of zinc deficiency in liver cirrhosis by assessing associations with parameters such as clinical symptoms and laboratory data. METHODS: In 235 cirrhosis patients enrolled at multiple medical institutions in 2009, we assessed how blood zinc levels were associated with their clinical symptoms, patients characteristics, and liver function test results. RESULTS: Blood zinc levels were most strongly correlated with blood albumin levels among the study parameters (r = 0.587, P < 0.0001). When blood albumin levels were ≤ 3.5 g/dL, blood zinc levels were < 70 μg/dL in 88% of patients. Additionally, significant correlations were observed with age (r = -0.253, P = 0.0014), aspartate aminotransferase levels (r = -0.254, P = 0.0020), total bilirubin levels (r = -0.222, P = 0.0053), prothrombin time (r = -0.255, P = 0.0029), branched-chain amino acid to tyrosine ratio (r = 0.357, P < 0.0001), Child-Pugh score (r = 0.469, P < 0.0001), ammonia levels (r = -0.246, P = 0.0028), and total cholesterol levels (r = 0.314, P < 0.0001). Blood zinc levels were significantly lower in patients with edema/ascites (P < 0.0001), those with hepatic encephalopathy (P = 0.0215), those receiving oral diuretics (P = 0.0045), and those receiving oral branched-chain amino acids (P < 0.0001) than in those without these conditions. CONCLUSIONS: Zinc deficiency is prevalent in cirrhosis patients, whereas nitrogen metabolic disorders, particularly hypoalbuminemia, can be an indicator of zinc deficiency. Thus, cirrhosis patients exhibiting a nitrogen metabolic disorder should be examined for the presence of zinc deficiency.
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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 | 0.003 |
| 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".