ArticleNeuropsychiatric disease and treatment2025
Hyperuricemia in Chinese Patients with Mood Disorders: Prevalence, Related Factors, and Predictive Model.
Article in Neuropsychiatric disease and treatment, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Correlation Analysis of Blood Lithium Metabolism Kinetics and Concentration-to-Dose Ratio in Patients With Hypertension and Manic Episodes.Human psychopharmacology · 2026Article
- Association Between Triglyceride-Glucose Index and Hyperuricemia in Hospitalized Patients with Depression: A Cross-Sectional Study.Neuropsychiatric disease and treatment · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: Previous studies have shown that serum uric acid (UA) levels are significantly higher in patients with bipolar disorder (BD) than in patients with depressive disorder (DD), schizophrenia, and healthy controls. Currently, studies generally report that there is a complex bidirectional interaction between mood disorders (MD) and hyperuricemia (HUA). We investigated the prevalence and related factors of hyperuricemia in Chinese patients with mood disorders to find out potential mechanisms and build a predictive model. Patients and Methods: A total of 771 patients with mood disorders who met the criteria were enrolled. The demographic and disease characteristics of MD patients were collected by a self-designed questionnaire. Depression severity was assessed by the Hamilton Depression Scale (HAMD-17). The Positive and Negative Symptom Scale (PANSS) was used to assess psychotic symptoms. The Nurse's Guided Assessment of Suicide Risk Scale (NGASR) was used to assess suicide risk. Laboratory parameters of metabolism include blood cell-related parameters, electrolytes, liver function-related parameters, etc. Results: The prevalence of hyperuricemia was 21.68% in patients with depressive disorder and 39.25% in patients with depressive episodes of bipolar disorder (DEBD). Among MD patients with or without hyperuricemia, there were some differences such as gender, mood stabilizers, systolic blood pressure etc. The independent risk factors of MD patients with hyperuricemia followed: lithium carbonate, urea (Ur), triglycerides (TG), lactate dehydrogenase (LDH), and white blood cell (WBC) (all p<0.05). After combining the independent risk factors with platelets (PLT), albumin (ALB), alanine aminotransferase (ALT), and C-peptide by multivariate Logistic regression, we obtained an optimal predictive model. Conclusion: MD patients treated with lithium carbonate should be closely monitored for uric acid levels. We suggested that hepatic TG accumulation and psychiatric drug-induced hepatocellular damage may contribute to hyperuricemia in patients with mood disorders.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.