Analysis for warning factors of type 2 diabetes mellitus complications with Markov blanket based on a Bayesian network model

Comparison of conventional mathematical model and machine learning model based on recent advances in mathematical models for predicting diabetic kidney disease

Previous research suggests that mathematical models could serve as valuable tools for diagnosing or predicting diseases like diabetic kidney disease, which often necessitate invasive examinations for conclusive diagnosis. In the big-data era, there are several mathematical modeling methods, but generally, two types are recognized: conventional mathematical model and machine learning model. Each modeling method has its advantages and disadvantages, but a thorough comparison of the two models is lacking. In this article, we describe and briefly compare the conventional mathematical model and machine learning model, and provide research prospects in this field.

Healthcare Big Data in Hong Kong: Development and Implementation of Artificial Intelligence-Enhanced Predictive Models for Risk Stratification

Routinely collected electronic health records (EHRs) data contain a vast amount of valuable information for conducting epidemiological studies. With the right tools, we can gain insights into disease processes and development, identify the best treatment and develop accurate models for predicting outcomes. Our recent systematic review has found that the number of big data studies from Hong Kong has rapidly increased since 2015, with an increasingly common application of artificial intelligence (AI). The advantages of big data are that i) the models developed are highly generalisable to the population, ii) multiple outcomes can be determined simultaneously, iii) ease of cross-validation by for model training, development and calibration, iv) huge numbers of useful variables can be analyzed, v) static and dynamic variables can be analyzed, vi) non-linear and latent interactions between variables can be captured, vii) artificial intelligence approaches can enhance the performance of prediction models. In this paper, we will provide several examples (cardiovascular disease, diabetes mellitus, Brugada syndrome, long QT syndrome) to illustrate efforts from a multi-disciplinary team to identify data from different modalities to develop models using territory-wide datasets, with the possibility of real-time risk updates by using new data captured from patients. The benefit is that only routinely collected data are required for developing highly accurate and high-performance models. AI-driven models outperform traditional models in terms of sensitivity, specificity, accuracy, area under the receiver operating characteristic and precision-recall curve, and F1 score. Web and/or mobile versions of the risk models allow clinicians to risk stratify patients quickly in clinical settings, thereby enabling clinical decision-making. Efforts are required to identify the best ways of implementing AI algorithms on the web and mobile apps.

A Bayesian network model for predicting cardiovascular risk

BACKGROUND AND OBJECTIVE

Cardiovascular diseases are the leading death cause in Europe and entail large treatment costs. Cardiovascular risk prediction is crucial for the management and control of cardiovascular diseases. Based on a Bayesian network built from a large population database and expert judgment, this work studies interrelations between cardiovascular risk factors, emphasizing the predictive assessment of medical conditions, and providing a computational tool to explore and hypothesize such interrelations.

METHODS

We implement a Bayesian network model that considers modifiable and non-modifiable cardiovascular risk factors as well as related medical conditions. Both the structure and the probability tables in the underlying model are built using a large dataset collected from annual work health assessments as well as expert information, with uncertainty characterized through posterior distributions.

RESULTS

The implemented model allows for making inferences and predictions about cardiovascular risk factors. The model can be utilized as a decision- support tool to suggest diagnosis, treatment, policy, and research hypothesis. The work is complemented with a free software implementing the model for practitioners' use.

CONCLUSIONS

Our implementation of the Bayesian network model facilitates answering public health, policy, diagnosis, and research questions concerning cardiovascular risk factors.

Construction and evaluation of networks among multiple postoperative complications

BACKGROUND AND OBJECTIVE

Postoperative complications confer an increased risk of reoperation, prolonged length of hospital stay, and increased mortality. Many studies have attempted to identify the complex associations among complications to preemptively interrupt their progression, but few studies have looked at complications as a whole to reveal and quantify their possible trajectories of progression. The main objective of this study was to construct and quantify the association network among multiple postoperative complications from a comprehensive perspective to elucidate the possible evolution trajectories.

METHODS

In this study, a Bayesian network model was proposed to analyze the associations among 15 complications. Prior evidence and score-based hill-climbing algorithms were used to build the structure. The severity of complications was graded according to their connection to death, with the association between them quantified using conditional probabilities. The data of surgical inpatients used in this study were collected from four regionally representative academic/teaching hospitals in a prospective cohort study in China.

RESULTS

In the network obtained, 15 nodes represented complications or death, and 35 arcs with arrows represented the directly dependent relationship between them. With three grades classified on that basis, the correlation coefficients of complications within grades increased with increased grade, ranging from -0.11 to -0.06, 0.16, and 0.21 to 0.4 in grade 1 to grade 3, respectively. Moreover, the probability of each complication in the network increased with the occurrence of any other complication, even mild complications. Most seriously, once cardiac arrest requiring cardiopulmonary resuscitation occurs, the probability of death will be up to 88.1%.

CONCLUSIONS

The present evolving network can facilitate the identification of strong associations among specific complications and provides a basis for the development of targeted measures to prevent further deterioration in high-risk patients.

Identifying risk factors of developing type 2 diabetes from an adult population with initial prediabetes using a Bayesian network

Background

It is known that people with prediabetes increase their risk of developing type 2 diabetes (T2D), which constitutes a global public health concern, and it is associated with other diseases such as cardiovascular disease.

Methods

This study aimed to determine those factors with high influence in the development of T2D once prediabetes has been diagnosed, through a Bayesian network (BN), which can help to prevent T2D. Furthermore, the set of features with the strongest influences on T2D can be determined through the Markov blanket. A BN model for T2D was built from a dataset composed of 12 relevant features of the T2D domain, determining the dependencies and conditional independencies from empirical data in a multivariate context. The structure and parameters were learned with the bnlearn package in R language introducing prior knowledge. The Markov blanket was considered to find those features (variables) which increase the risk of T2D.

Results

The BN model established the different relationships among features (variables). Through inference, a high estimated probability value of T2D was obtained when the body mass index (BMI) was instantiated to obesity value, the glycosylated hemoglobin (HbA1c) to more than 6 value, the fatty liver index (FLI) to more than 60 value, physical activity (PA) to no state, and age to 48-62 state. The features increasing T2D in specific states (warning factors) were ranked.

Conclusion

The feasibility of BNs in epidemiological studies is shown, in particular, when data from T2D risk factors are considered. BNs allow us to order the features which influence the most the development of T2D. The proposed BN model might be used as a general tool for prevention, that is, to improve the prognosis.

Artificial intelligence based prediction models for individuals at risk of multiple diabetic complications: A systematic review of the literature

AIM

The aim of this review is to examine the effectiveness of artificial intelligence in predicting multimorbid diabetes-related complications.

BACKGROUND

In diabetic patients, several complications are often present, which have a significant impact on the quality of life; therefore, it is crucial to predict the level of risk for diabetes and its complications.

EVALUATION

International databases PubMed, CINAHL, MEDLINE and Scopus were searched using the terms artificial intelligence, diabetes mellitus and prediction of complications to identify studies on the effectiveness of artificial intelligence for predicting multimorbid diabetes-related complications. The results were organized by outcomes to allow more efficient comparison.

KEY ISSUES

Based on the inclusion/exclusion criteria, 11 articles were included in the final analysis. The most frequently predicted complications were diabetic neuropathy (n = 7). Authors included from two to a maximum of 14 complications. The most commonly used prediction models were penalized regression, random forest and Naïve Bayes model neural network.

CONCLUSION

The use of artificial intelligence can predict the risks of diabetes complications with greater precision based on available multidimensional datasets and provides an important tool for nurses working in preventive health care.

IMPLICATIONS FOR NURSING MANAGEMENT

Using artificial intelligence contributes to a better quality of care, better autonomy of patients in diabetes management and reduction of complications, costs of medical care and mortality.

Development of a Nomogram-Based Tool to Predict Neurocognitive Impairment Among HIV-positive Charter Participants

Background:

Despite the widespread use of combination antiretroviral therapy (cART), HIV-associated neurocognitive impairment (NCI) persists in people living with HIV (PLWH). Studies have generated inconsistent results regarding etiological factors for NCI in PLWH. Furthermore, a user-friendly and readily available predictive tool is desirable in clinical practice to screen PLWH for NCI.

Objective:

This study aimed to identify factors associated with NCI using a large and diverse sample of PLWH and build a nomogram based on demographic, clinical, and behavioral variables.

Methods:

We performed Bayesian network analysis using a supervised learning technique with the Markov Blanket (MB) algorithm. Logistic regression was also conducted to obtain the adjusted regression coefficients to construct the nomogram.

Results:

Among 1,307 participants, 21.6% were neurocognitively impaired. During the MB analysis, age provided the highest amount of mutual information (0.0333). Logistic regression also showed that old age (>50 vs. ≤50 years) had the strongest association (OR=2.77, 95% CI=1.99-3.85) with NCI. The highest possible points on the nomogram were 626, translated to a nomogram-predicted probability of NCI to be approximately 0.95. The receiver operating characteristic (ROC) curve's concordance index was 0.75, and the nomogram's calibration plot exhibited an excellent agreement between observed and predicted probabilities.

Conclusion:

The nomogram used variables that can be easily measured in clinical settings and, thus, easy to implement within a clinic or web-interface platform. The nomogram may help clinicians screen for patients with a high probability of having NCI and thus needing a comprehensive neurocognitive assessment for early diagnosis and appropriate management.

Diabetic Wound-Healing Science

Diabetes mellitus is an increasingly prevalent chronic metabolic disease characterized by prolonged hyperglycemia that leads to long-term health consequences. It is estimated that impaired healing of diabetic wounds affects approximately 25% of all patients with diabetes mellitus, often resulting in lower limb amputation, with subsequent high economic and psychosocial costs. The hyperglycemic environment promotes the formation of biofilms and makes diabetic wounds difficult to treat. In this review, we present updates regarding recent advances in our understanding of the pathophysiology of diabetic wounds focusing on impaired angiogenesis, neuropathy, sub-optimal chronic inflammatory response, barrier disruption, and subsequent polymicrobial infection, followed by current and future treatment strategies designed to tackle the various pathologies associated with diabetic wounds. Given the alarming increase in the prevalence of diabetes, and subsequently diabetic wounds, it is imperative that future treatment strategies target multiple causes of impaired healing in diabetic wounds.

Simultaneous Determination of Saponins and Lignans in Rat Plasma by UPLC- MS/MS and its Application to a Pharmacokinetic Study of Shenqi Jiangtang Granule

BACKGROUND

Shenqi Jiangtang Granule (SJG), a classical prescription of traditional Chinese medicine, is widely used to treat diabetes and its complications. Although, the clinical efficacy of SJG, is sufficient, the pharmacokinetic behavior of various substances in the plasma of SJG is unknown.

OBJECTIVE

The aim of this study was to investigate the plasma pharmacokinetics during absorption of SJG after oral administration in rats.

METHODS

A rapid and accurate ultra-high performance liquid chromatography/tandem mass spectrometry (UPLC- MS/MS) method was developed for the simultaneous determination of eight analytes in SJG, including gomisin D, schisandrin A, schisandrin B, schizandrol A, schizandrol B, ginsenoside Rd, ginsenoside Re and notoginsenoside Ft1. The analysis was carried out on a BEH C18 column (2.1 mm × 50 mm, 1.7 μm) with gradient elution at a flow rate of 0.2 mL/min in a mobile phase consisting of 0.1% formic acid water and acetonitrile. In addition, lignans and saponins were detected in positive ion mode and negative ion mode, respectively.

RESULTS

Eight analytes in SJG, including gomisin D, schisandrin A, schisandrin B, schizandrol A, schizandrol B, ginsenoside Rd, ginsenoside Re and notoginsenoside Ft1, showed good linearity (R2 in the range of 0.9955 ~ 0.9999). The lower limit of quantification (LLOQ) was 5, 0.8, 0.8, 8, 0.8, 5, 0.6 and 10 ng/mL. The accuracy and precision of all analytes were at ±15%. Matrix effect and average extraction recovery were > 85%. All analytes performed well under four storage conditions.

CONCLUSION

The results showed that in vivo absorption and exposure of gomisin D and ginsenoside Rd were better than other analytes, while schizandrol B and notoginsenoside Ft1 were poorly absorbed. This approach could be applied to study the pharmacokinetic characteristics of various analytes in plasma after oral administration of SJG in rats.

Natural Triterpenoids Isolated from Akebia trifoliata Stem Explants Exert a Hypoglycemic Effect via α-Glucosidase Inhibition and Glucose Uptake Stimulation in Insulin-Resistant HepG2 Cells

The inhibition of α-glucosidase activity is a prospective approach to attenuate postprandial hyperglycemia in the treatment of type 2 diabetes mellitus (T2DM). Herein, the inhibition of α-glucosidase by three compounds T₁ -T₃ of Akebia trifoliata stem, namely hederagenin (T₁ ), 3-epiakebonoic acid (T₂ ), and arjunolic acid (T₃ ) were investigated using enzyme kinetics and molecular docking analysis. The three triterpenoids exhibited excellent inhibitory activities against α-glucosidase. T₁ -T₃ showed the strongest inhibition with IC₅₀ values of 42.1±5.4, 19.6±3.2, and 11.2±2.3 μM, respectively, compared to the acarbose positive control (IC₅₀ =106.3±8.2). Enzyme inhibition kinetics showed that triterpenoids T₁ -T₃ demonstrated competitive, mixed, and noncompetitive-type inhibition against α-glucosidase, respectively. The inhibition constant (K_i ) values were 21.21, 7.70, and 3.18 μM, respectively. Docking analysis determined that the interaction of ligands T₁ -T₃ and α-glucosidase was mainly forced by hydrogen bonds and hydrophobic interactions, which could result in improved binding to the active site of the target enzyme. The insulin resistant (IR)-HepG2 cell model used in this study (HepG2 cells exposed to 10^-7  M insulin for 24 h) and glucose uptake assays showed that compounds T₁ -T₃ had no cytotoxicity with concentrations ranging from 6.25 to 25 μM and displayed significant stimulation of glucose uptake in IR-HepG2 cells. Thus, triterpenoids T₁ -T₃ showed dual therapeutic effects of α-glucosidase inhibition and glucose uptake stimulation and could be used as potential medicinal resources to investigate new antidiabetic agents for the prevention or treatment of diabetes.