Background: The role of uric acid in the development of diabetic peripheral neuropathy remains unclear. This study aimed to determine the association between uric acid and peripheral neuropathy among type 2 diabetes mellitus (T2DM) patients. Methods: We conducted a nationwide cross-sectional study based on the diabetes and hypertension study of the Medical Research Network of the Consortium of Thai Medical Schools. Adult T2DM patients from 831 public hospitals in Thailand were evaluated. The serum uric acid level was categorized into five groups based on quintiles (<4.4, 4.4–5.3, 5.3–6.2, 6.2–7.3, and >7.3 mg/dL). A multivariate logistic regression model was used to assess the independent association between serum uric acid level and peripheral neuropathy. Results: In total, 7,511 T2DM patients with available data about serum uric acid levels were included in the analysis. The mean age of the participants was 61.7±10.9 years, and approximately 35.6% were men. The prevalence rate of peripheral neuropathy was 3.0%. Moreover, the prevalence rates of peripheral neuropathy stratified according to uric acid levels <4.4, 4.4–5.3, 5.3–6.2, 6.2–7.3, and >7.3 mg/dL were 2.5%, 2.8%, 2.4%, 2.5%, and 4.7%, respectively. A serum uric acid level ≥7.3 mg/dL was found to be associated with an increase in odds ratio (1.54; 95% confidence interval, 1.02–2.32) for peripheral neuropathy compared with a serum uric acid level <4.4 mg/dL. Conclusion Serum uric acid level is independently associated with peripheral neuropathy in T2DM patients, and elevated serum uric acid levels should be considered a risk factor for diabetic peripheral neuropathy in clinical practice.

Sepsis-associated acute kidney injury (SA-AKI) is a serious complication in critically ill patients, resulting in higher mortality, morbidity, and cost. The intricate pathophysiology of SA-AKI requires vigilant clinical monitoring and appropriate, prompt intervention. While traditional statistical analyses have identified severe risk factors for SA-AKI, the results have been inconsistent across studies. This has led to growing interest in leveraging artificial intelligence (AI) and machine learning (ML) to predict SA-AKI better. ML can uncover complex patterns beyond human discernment by analyzing vast datasets. Supervised learning models like XGBoost and RNN-LSTM have proven remarkably accurate at predicting SA-AKI onset and subsequent mortality, often surpassing traditional risk scores. Meanwhile, unsupervised learning reveals clinically relevant sub-phenotypes among diverse SA-AKI patients, enabling more tailored care. In addition, it potentially optimizes sepsis treatment to prevent SA-AKI through continual refinement based on patient outcomes. However, utilizing AI/ML presents ethical and practical challenges regarding data privacy, algorithmic biases, and regulatory compliance. AI/ML allows early risk detection, personalized management, optimal treatment strategies, and collaborative learning for SA-AKI management. Future directions include real-time patient monitoring, simulated data generation, and predictive algorithms for timely interventions. However, a smooth transition to clinical practice demands continuous model enhancements and rigorous regulatory oversight. In this article, we outlined the conventional methods used to address SA-AKI and explore how AI and ML can be applied to diagnose and manage SA-AKI, highlighting their potential to revolutionize SA-AKI care.