A machine learning-based classification of adult-onset diabetes identifies patients at risk of liver-related complications

The insight to history and trends of transient elastography for assessing liver fibrosis-a bibliometric analysis

Background

Transient elastography (TE) has become a prominent technique for the detection of fibrosis, owing to its non-invasive nature, rapid execution, safety, and ease of repetition. This study aims to conduct a bibliometric analysis of the historical development and trends in the application of TE for the assessment of liver fibrosis.

Methods

In the Web of Science (Core Collection database), we selected the Science Citation Index Expanded database to search for relevant literature from 1 January 1983 to 20 November 2023. We performed a search using the following topic words: transient elastography, liver fibrosis. After screening according to the title, abstract and keyword and removing the repetition, the literature included in the study was finally determined, and full records were downloaded. Bibliometric analysis was performed using VOSviewer and CiteSpace.

Results

Through the bibliometric visualization analysis of 577 articles, it was found that since TE was first reported for the measurement of liver fibrosis in 2003, the number of publications in this field has generally shown an upward trend, and the distribution of publications has shown a bimodal distribution, with peaks in 2010 and 2019. France and China have shown a high contribution in this field with a high number of publications. In terms of contributions from individual research centers, Yonsei University stands out prominently. Throughout the history of research in this field, early studies focused on chronic viral hepatitis, by comparing TE and Fibrosis-4, aspartate aminotransferase to platelet ratio index, FibroTest, liver biopsy and other liver fibrosis detection indicators to verify its diagnostic efficacy. Subsequently, the focus of research gradually shifted to non-alcoholic fatty liver disease and other liver diseases, and the scope of research extended to the establishment of prediction models and efficacy evaluation through TE.

Conclusions

The application scope of TE is gradually expanding, and its safety, simplicity, rapidity, high accuracy, quantitative results, repeatability and good tolerance make it popular in clinical practice. Nowadays, the application of TE is not limited to the diagnosis of liver fibrosis, but has been extended to the establishment of prognostic models and efficacy evaluation of various liver diseases. To explore the deeper value of TE through new research methods such as machine learning models, radiate the advantages of TE to more liver diseases, and combine TE with a variety of non-invasive detection indicators to improve its application value, may be the future development and application prospect of TE in the field of liver fibrosis.

Metabolic dysfunction-associated steatotic liver disease in adults

Metabolic dysfunction-associated steatotic liver disease (MASLD) is the umbrella term that comprises metabolic dysfunction-associated steatotic liver, or isolated hepatic steatosis, through to metabolic dysfunction-associated steatohepatitis, the progressive necroinflammatory disease form that can progress to fibrosis, cirrhosis and hepatocellular carcinoma. MASLD is estimated to affect more than one-third of adults worldwide. MASLD is closely associated with insulin resistance, obesity, gut microbial dysbiosis and genetic risk factors. The obesity epidemic and the growing prevalence of type 2 diabetes mellitus greatly contribute to the increasing burden of MASLD. The treatment and prevention of major metabolic comorbidities such as type 2 diabetes mellitus and obesity will probably slow the growth of MASLD. In 2023, the field decided on a new nomenclature and agreed on a set of research and action priorities, and in 2024, the US FDA approved the first drug, resmetirom, for the treatment of non-cirrhotic metabolic dysfunction-associated steatohepatitis with moderate to advanced fibrosis. Reliable, validated biomarkers that can replace histology for patient selection and primary end points in MASH trials will greatly accelerate the drug development process. Additionally, noninvasive tests that can reliably determine treatment response or predict response to therapy are warranted. Sustained efforts are required to combat the burden of MASLD by tackling metabolic risk factors, improving risk stratification and linkage to care, and increasing access to therapeutic agents and non-pharmaceutical interventions.

Fast Analgesic Effect in Response Test with Topical Phenytoin Cream Correlates with Prolonged Pain Relief After Extended Use in Painful Diabetic Neuropathy

Background: Treatment of painful diabetic neuropathy (PDN) poses several challenges due to the limited effectiveness, high incidence of side effects, and potential drug interactions of oral neuropathic pain medication. Lacking systemic side effects, topical phenytoin cream offers a promising innovative approach to addressing unmet needs in neuropathic pain treatment. In this retrospective study in patients with PDN, we evaluated the analgesic effect of topical phenytoin cream in response tests and after extended use. Methods: We collected data from PDN patients who, prior to prolonged use of phenytoin 10% or 20% cream, either had an open response test (ORET), a single-blind (SIBRET), or a double-blind (DOBRET) placebo-controlled response test with phenytoin cream between November 2016 and February 2023. A positive ORET was defined as pain reduction of at least two points on the 11-point numerical scale (NRS) within 30 min after phenytoin cream application. A positive SIBRET or DOBRET required an additional pain reduction of 1 NRS point in the phenytoin treated area compared to the placebo. In patients with a positive response test, we evaluated the sustained pain reduction and the proportion of patients experiencing minimum pain relief of at least 30% (MPR30: moderate pain relief) and 50% (MPR50: considerable pain relief) after the extended use of phenytoin cream. We also assessed the correlation between the response test analgesic effect and the sustained pain relief. Results: We identified 65 patients with PDN of whom 31 (47.7%) had a positive response test. The median pain reduction in response tests was 3.0 NRS points (IQR 2.0-4.0). Extended use (median 3.3 months, IQR 1.5-12.1]) resulted in a median pain reduction of 4.0 NRS points (IQR 3.0-5.0); 26/31 (83.9%) of patients achieved MPR30, and 21/31 (67.7%) MPR50 achieved pain relief. The response test analgesic effect correlated significantly with sustained pain relief after extended use (τ = 0.72, p < 0.0001). Conclusions: In PDN patients who had a positive phenytoin cream response test, extended use of phenytoin cream provided a significant sustained pain relief.

Machine learning based prediction models for the prognosis of COVID-19 patients with DKA

Patients with Diabetic ketoacidosis (DKA) have increased critical illness and mortality during coronavirus diseases 2019 (COVID-19). The aim of our study was to develop a predictive model for the occurrence of critical illness and mortality in COVID-19 patients with DKA utilizing machine learning. Blood samples and clinical data from 242 COVID-19 patients with DKA collected from December 2022 to January 2023 at Second Xiangya Hospital. Patients were categorized into non-death (n = 202) and death (n = 38) groups, and non-severe (n = 146) and severe (n = 96) groups. We developed five machine learning-based prediction models-Extreme Gradient Boosting (XGB), Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), and Multilayer Perceptron (MLP)-to evaluate the prognosis of COVID-19 patients with DKA. We employed 5-fold cross-validation for model evaluation and used the Shapley Additive Explanations (SHAP) algorithm for result interpretation to ensure reliability. The LR model demonstrated the highest accuracy (AUC = 0.933) in predicting mortality. Additionally, the LR model excelled (AUC = 0.898) in predicting progression to severe disease. This study developed a machine learning-based predictive model for the progression to severe disease or death in COVID-19 patients with DKA, which can serve as a valuable tool to guide clinical treatment decisions.

Exploring the links between types 2 diabetes and liver-related complications: A comprehensive review

In recent decades, the prevalence of type 2 diabetes has been steadily increasing, presenting a significant global public health challenge. These epidemiological trends can be attributed to significant lifestyle changes in modern societies, characterized by sedentary behavior and the consumption of hypercaloric, highly processed foods, along with the aging of the human population. As a result, it has become crucial for both public healthcare systems and healthcare providers to prioritize the management of diabetes and identify its systemic consequences. Emerging research has shed light on the links and risks between diabetes and liver events. This comprehensive review aims to explore the complex interplay between type 2 diabetes mellitus and liver-related outcomes, especially hepatocellular carcinoma and cirrhosis, offering insights into effective methods for detecting liver risk in individuals with diabetes. Additionally, the review will assess the various treatments that could hold the potential for positive outcomes in managing both diabetes and metabolic dysfunction-associated steatotic liver disease and liver fibrosis.

Clinical data mining: challenges, opportunities, and recommendations for translational applications

Clinical data mining of predictive models offers significant advantages for re-evaluating and leveraging large amounts of complex clinical real-world data and experimental comparison data for tasks such as risk stratification, diagnosis, classification, and survival prediction. However, its translational application is still limited. One challenge is that the proposed clinical requirements and data mining are not synchronized. Additionally, the exotic predictions of data mining are difficult to apply directly in local medical institutions. Hence, it is necessary to incisively review the translational application of clinical data mining, providing an analytical workflow for developing and validating prediction models to ensure the scientific validity of analytic workflows in response to clinical questions. This review systematically revisits the purpose, process, and principles of clinical data mining and discusses the key causes contributing to the detachment from practice and the misuse of model verification in developing predictive models for research. Based on this, we propose a niche-targeting framework of four principles: Clinical Contextual, Subgroup-Oriented, Confounder- and False Positive-Controlled (CSCF), to provide guidance for clinical data mining prior to the model's development in clinical settings. Eventually, it is hoped that this review can help guide future research and develop personalized predictive models to achieve the goal of discovering subgroups with varied remedial benefits or risks and ensuring that precision medicine can deliver its full potential.

Algorithms for Early Detection of Silent Liver Fibrosis in the Primary Care Setting

More than one-third of the adult world population has steatotic liver disease (SLD), with a few percent of individuals developing cirrhosis after decades of silent liver fibrosis accumulation. Lack of systematic early detection causes most patients to be diagnosed late, after decompensation, when treatment has limited effect and survival is poor. Unfortunately, no isolated screening test in primary care can sufficiently predict advanced fibrosis from SLD. Recent efforts, therefore, combine several parameters into screening algorithms, to increase diagnostic accuracy. Besides patient selection, for example, by specific characteristics, algorithms include nonpatented or patented blood tests and liver stiffness measurements using elastography-based techniques. Algorithms can be composed as a set of sequential tests, as recommended by most guidelines on primary care pathways. Future use of algorithms that are easy to interpret, cheap, and semiautomatic will improve the management of patients with SLD, to the benefit of global health care systems.