ArticleBiomedical engineering online2025
Clinical value of serum uric acid and homocysteine levels in predicting the occurrence of atrial fibrillation in patients with type 2 diabetes mellitus.
Article in Biomedical engineering online, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe objective of this study was to evaluate the predictive value of serum uric acid (UA) and homocysteine (Hcy) levels for atrial fibrillation (AF) development in type 2 diabetes mellitus (T2DM) patients.
methodsClinical data of 400 patients diagnosed with T2DM between January 2020 and August 2023 were retrospectively analyzed. They were categorized into AF group and non-AF group according to whether AF occurred or not. The predictive efficacy of serum UA and Hcy on the occurrence of AF in patients with T2DM was analyzed by using the receiver operating characteristic (ROC). The combined values were derived using regression coefficients, enabling joint prediction. Logistic regression analysis was employed to identify the influential factors. The nomogram prediction model was developed using R software based on the screened influencing factors, with internal validation performed via the Bootstrap method. ROC curves, calibration curves, and decision curves were plotted to evaluate the efficacy of the model.
resultsCompared with the non-AF group, the total bilirubin (TBIL), DBIL/TBIL, total protein (TP), UA, Hcy, cystatin C (Cys C), and large platelet ratio (PLCR) levels were significantly higher in the AF group, whereas triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and platelet count (PLT) levels were significantly lower (p < 0.05). The area under the curve (AUC) predicted by the combination of serum UA and serum Hcy was 0.928, which was higher than that of UA (Z = 2.635, p = 0.008) and Hcy (Z = 4.629, p < 0.001). UA, Hcy, TP, PLCR, TG, and LDL-C were all influential factors for AF in patients with T2DM (p < 0.05). The nomogram model constructed on the basis of the above independent influences predicted an AUC of 0.946 (95% CI: 0.924-0.968) for the occurrence of AF, with p = 0.134 in the Hosmer-Lemeshow test. In addition, calibration curve and decision curve analyses showed good agreement and clinical benefit for this nomogram model.
conclusionSerum UA and Hcy levels exhibited some predictive value for the occurrence of AF in patients with T2DM. The nomogram model incorporating demographic and serological parameters demonstrated good diagnostic performance and may serve as a valuable predictive tool for AF occurrence.
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.