Machine-learning to stratify diabetic patients using novel cardiac biomarkers and integrative genomics

In Utero Nano-Titanium Dioxide Exposure Results in Sexually Dimorphic Weight Gain and Cardiovascular Function in Offspring

Engineered nanomaterials (ENM) are capable of crossing the placental barrier and accumulating in fetal tissue. Specifically, the ENM nano-titanium dioxide (nano-TiO2), has been shown to accumulate in placental and fetal tissue, resulting in decreased birthweight in pups. Additionally, nano-TiO2 is an established cardiac toxicant and regulator of glucose homeostasis, and exposure in utero may lead to serious maladaptive responses in cardiac development and overall metabolism. The current study examines weight gain and cardiac function in male and female Sprague-Dawley rats exposed to 12 mg/m3 nano-TiO2 or filtered air for 6 non-consecutive days in utero between gestational days 12-19. These animals were randomly assigned to receive a grain-based or high-fat diet (60%) between postnatal weeks 12-24 to examine the propensity for weight gain and cardiac response as adults. Our results show a sexually dimorphic response to weight gain with male rats gaining more weight after high-fat diet following in utero nano-TiO2 exposure, and female rats gaining less weight on the high-fat diet respective of exposure. Male rats exposed to nano-TiO2 in utero had reduced ejection fraction prior to diet when compared to air controls. Female rats subjected to in utero nano-TiO2 exposure showed a significant decrease in cardiac output following 12 weeks of high-fat diet. Development of cardiovascular impairments and ultimately cardiac dysfunction and disease following in utero exposures highlights the need for occupational and environmental monitoring of nanoparticulate exposure.

Uncovering the epigenetic regulatory clues of PRRT1 in Alzheimer's disease: a strategy integrating multi-omics analysis with explainable machine learning

BACKGROUND

Alzheimer's disease (AD) is a complex neurodegenerative disorder with a largely unexplored epigenetic landscape.

OBJECTIVE

This study employs an innovative approach that integrates multi-omics analysis and explainable machine learning to explore the epigenetic regulatory mechanisms underlying the epigenetic signature of PRRT1 implicated in AD.

METHODS

Through comprehensive DNA methylation and transcriptomic profiling, we identified distinct epigenetic signatures associated with gene PRRT1 expression in AD patient samples compared to healthy controls. Utilizing interpretable machine learning models and ELMAR analysis, we dissected the complex relationships between these epigenetic signatures and gene expression patterns, revealing novel regulatory elements and pathways. Finally, the epigenetic mechanisms of these genes were investigated experimentally.

RESULTS

This study identified ten epigenetic signatures, constructed an interpretable AD diagnostic model, and utilized various bioinformatics methods to create an epigenomic map. Subsequently, the ELMAR R package was used to integrate multi-omics data and identify the upstream transcription factor MAZ for PRRT1. Finally, experiments confirmed the interaction between MAZ and PRRT1, which mediated apoptosis and autophagy in AD.

CONCLUSION

This study adopts a strategy that integrates bioinformatics analysis with molecular experiments, providing new insights into the epigenetic regulatory mechanisms of PRRT1 in AD and demonstrating the importance of explainable machine learning in elucidating complex disease mechanisms.

Multi-omics insights into the pathogenesis of diabetic cardiomyopathy: epigenetic and metabolic profiles

AIM

Diabetic cardiomyopathy (DbCM), a complex metabolic disease, greatly threatens human health due to therapeutic limitations. Multi-omics approaches facilitate the elucidation of its intrinsic pathological changes.

METHODS

Metabolomics, RNA-seq, proteomics, and assay of transposase-accessible chromatin (ATAC-seq) were utilized to elucidate multidimensional molecular alterations in DbCM.

RESULTS

In the heart and plasma of mice with DbCM, metabolomic analysis demonstrated significant differences in branched-chain amino acids (BCAAs) and lipids. Subsequent RNA-seq and proteomics showed that the key genes, including BCKDHB, PPM1K, Cpt1b, Fabp4, Acadm, Acadl, Acadvl, HADH, HADHA, HADHB, Eci1, Eci2, PDK4, and HMGCS2, were aberrantly regulated, contributing to the disorder of BCAAs and fatty acids. ATAC-seq analysis underscored the pivotal role of epigenetic regulation by revealing dynamic shifts in chromatin accessibility and a robust positive correlation with gene expression patterns in diabetic cardiomyopathy mice. Furthermore, motif analysis identified that KLF15 as a critical transcription factor in DbCM, regulating the core genes implicated with BCAAs metabolism.

CONCLUSION

Our research delved into the metabolic alterations and epigenetic landscape and revealed that KLF15 may be a promising candidate for therapeutic intervention in DbCM.

Interpretable machine learning-driven biomarker identification and validation for Alzheimer's disease

Alzheimer's disease (AD) is a neurodegenerative disorder characterized by limited effective treatments, underscoring the critical need for early detection and diagnosis to improve intervention outcomes. This study integrates various bioinformatics methodologies with interpretable machine learning to identify reliable biomarkers for AD diagnosis and treatment. By leveraging differentially expressed genes (DEGs) analysis, weighted gene co-expression network analysis (WGCNA), and construction of Protein-Protein Interaction (PPI) Networks, we meticulously analyzed the AD dataset from the GEO database to pinpoint Hub genes. Subsequently, various machine learning algorithms were employed to construct diagnostic models, which were then elucidated using SHapley Additive exPlanations (SHAP). To visualize our findings, we generated an insightful bioinformatics map of 10 Hub genes. We then conducted experimental validation on less-studied Hub genes, revealing significant differential mRNA expression of MYH9 and RHOQ in an AD cell model. Finally, we explored the biological significance of these two genes at the single-cell transcriptome level. This study not only introduces interactive SHAP panels for precise decision-making in AD but also offers novel insights into the identification of AD biomarkers through interpretable machine learning diagnostic models. Particularly, MYH9 has emerged as a promising new potential biomarker, pointing the way towards enhanced diagnostic accuracy and personalized therapeutic strategies for AD. Although the mRNA expression patterns of RHOQ are opposite in AD cell models and human brain tissue samples, the role of RHOQ in AD remains worthy of further exploration due to the diversity and complexity of biological molecular regulation.

Screening the Best Risk Model and Susceptibility SNPs for Chronic Obstructive Pulmonary Disease (COPD) Based on Machine Learning Algorithms

Background and Purpose

Chronic obstructive pulmonary disease (COPD) is a common and progressive disease that is influenced by both genetic and environmental factors, and genetic factors are important determinants of COPD. This study focuses on screening the best predictive models for assessing COPD-associated SNPs and then using the best models to predict potential risk factors for COPD.

Methods

Healthy subjects (n=290) and COPD patients (n=233) were included in this study, the Agena MassARRAY platform was applied to genotype the subjects for SNPs. The selected sample loci were first screened by logistic regression analysis, based on which the key SNPs were further screened by LASSO regression, RFE algorithm and Random Forest algorithm, and the ROC curves were plotted to assess the discriminative performance of the models to screen the best prediction model. Finally, the best prediction model was used for the prediction of risk factors for COPD.

Results

One-way logistic regression analysis screened 44 candidate SNPs from 146 SNPs, on the basis of which 44 SNPs were screened or feature ranked using LASSO model, RFE-Caret, RFE-Lda, RFE-lr, RFE-nb, RFE-rf, RFE-treebag algorithms and random forest model, respectively, and obtained ROC curve values of 0.809, 0.769, 0.798, 0.743, 0.686, 0.766, 0.743, 0.719, respectively, so we selected the lasso model as the best model, and then constructed a column-line graph model for the 25 SNPs screened in it, and found that rs12479210 might be the potential risk factors for COPD.

Conclusion

The LASSO model is the best predictive model for COPD and rs12479210 may be a potential risk locus for COPD.

Sorcin can trigger pancreatic cancer-associated new-onset diabetes through the secretion of inflammatory cytokines such as serpin E1 and CCL5

A rise in blood glucose is an early warning sign of underlying pancreatic cancer (PC) and may be an indicator of genetic events in PC progression. However, there is still a lack of mechanistic research on pancreatic cancer-associated new-onset diabetes (PCAND). In the present study, we identified a gene SRI, which possesses a SNP with the potential to distinguish PCAND and Type 2 diabetes mellitus (T2DM), by machine learning on the basis of the UK Biobank database. In vitro and in vivo, sorcin overexpression induced pancreatic β-cell dysfunction. Sorcin can form a positive feedback loop with STAT3 to increase the transcription of serpin E1 and CCL5, which may directly induce β-cell dysfunction. In 88 biopsies, the expression of sorcin was elevated in PC tissues, especially in PCAND samples. Furthermore, plasma serpin E1 levels are higher in peripheral blood samples from PCAND patients than in those from T2DM patients. In conclusion, sorcin may be the key driver in PCAND, and further study on the sorcin-STAT3-serpin E1/CCL5 signaling axis may help us better understand the pathogenesis of PCAND and identify potential biomarkers.

Identification of immune feature genes and intercellular profiles in diabetic cardiomyopathy

BACKGROUND

Diabetic cardiomyopathy (DCM) is a multifaceted cardiovascular disorder in which immune dysregulation plays a pivotal role. The immunological molecular mechanisms underlying DCM are poorly understood.

AIM

To examine the immunological molecular mechanisms of DCM and construct diagnostic and prognostic models of DCM based on immune feature genes (IFGs).

METHODS

Weighted gene co-expression network analysis along with machine learning methods were employed to pinpoint IFGs within bulk RNA sequencing (RNA-seq) datasets. Single-sample gene set enrichment analysis (ssGSEA) facilitated the analysis of immune cell infiltration. Diagnostic and prognostic models for these IFGs were developed and assessed in a validation cohort. Gene expression in the DCM cell model was confirmed through real time-quantitative polymerase chain reaction and western blotting techniques. Additionally, single-cell RNA-seq data provided deeper insights into cellular profiles and interactions.

RESULTS

The overlap between 69 differentially expressed genes in the DCM-associated module and 2483 immune genes yielded 7 differentially expressed immune-related genes. Four IFGs showed good diagnostic and prognostic values in the validation cohort: Proenkephalin (Penk) and retinol binding protein 7 (Rbp7), which were highly expressed, and glucagon receptor and inhibin subunit alpha, which were expressed at low levels in DCM patients (all area under the curves > 0.9). SsGSEA revealed that IFG-related immune cell infiltration primarily involved type 2 T helper cells. High expression of Penk (P < 0.0001) and Rbp7 (P = 0.001) was detected in cardiomyocytes and interstitial cells and further confirmed in a DCM cell model in vitro. Intercellular events and communication analysis revealed abnormal cellular phenotype transformation and signaling communication in DCM, especially between mesenchymal cells and macrophages.

CONCLUSION

The present study identified Penk and Rbp7 as potential DCM biomarkers, and aberrant mesenchymal-immune cell phenotype communication may be an important aspect of DCM pathogenesis.

Artificial intelligence-driven transformations in diabetes care: a comprehensive literature review

Artificial intelligence (AI) has been applied in healthcare for diagnosis, treatments, disease management, and for studying underlying mechanisms and disease complications in diseases like diabetes and metabolic disorders. This review is a comprehensive overview of various applications of AI in the healthcare system for managing diabetes. A literature search was conducted on PubMed to locate studies integrating AI in the diagnosis, treatment, management and prevention of diabetes. As diabetes is now considered a pandemic now so employing AI and machine learning approaches can be applied to limit diabetes in areas with higher prevalence. Machine learning algorithms can visualize big datasets, and make predictions. AI-powered mobile apps and the closed-loop system automated glucose monitoring and insulin delivery can lower the burden on insulin. AI can help identify disease markers and potential risk factors as well. While promising, AI's integration in the medical field is still challenging due to privacy, data security, bias, and transparency. Overall, AI's potential can be harnessed for better patient outcomes through personalized treatment.

Machine learning risk stratification for high-risk infant follow-up of term and late preterm infants

BACKGROUND

Term and late preterm infants are not routinely referred to high-risk infant follow-up programs at neonatal intensive care unit (NICU) discharge. We aimed to identify NICU factors associated with abnormal developmental screening and develop a risk-stratification model using machine learning for high-risk infant follow-up enrollment.

METHODS

We performed a retrospective cohort study identifying abnormal developmental screening prior to 6 years of age in infants born ≥34 weeks gestation admitted to a level IV NICU. Five machine learning models using NICU predictors were developed by classification and regression tree (CART), random forest, gradient boosting TreeNet, multivariate adaptive regression splines (MARS), and regularized logistic regression analysis. Performance metrics included sensitivity, specificity, accuracy, precision, and area under the receiver operating curve (AUC).

RESULTS

Within this cohort, 87% (1183/1355) received developmental screening, and 47% had abnormal results. Common NICU predictors across all models were oral (PO) feeding, follow-up appointments, and medications prescribed at NICU discharge. Each model resulted in an AUC > 0.7, specificity >70%, and sensitivity >60%.

CONCLUSION

Stratification of developmental risk in term and late preterm infants is possible utilizing machine learning. Applying machine learning algorithms allows for targeted expansion of high-risk infant follow-up criteria.

IMPACT

This study addresses the gap in knowledge of developmental outcomes of infants ≥34 weeks gestation requiring neonatal intensive care. Machine learning methodology can be used to stratify early childhood developmental risk for these term and late preterm infants. Applying the classification and regression tree (CART) algorithm described in the study allows for targeted expansion of high-risk infant follow-up enrollment to include those term and late preterm infants who may benefit most.

Autophagy and mitophagy as potential therapeutic targets in diabetic heart condition: Harnessing the power of nanotheranostics

Autophagy and mitophagy pose unresolved challenges in understanding the pathology of diabetic heart condition (DHC), which encompasses a complex range of cardiovascular issues linked to diabetes and associated cardiomyopathies. Despite significant progress in reducing mortality rates from cardiovascular diseases (CVDs), heart failure remains a major cause of increased morbidity among diabetic patients. These cellular processes are essential for maintaining cellular balance and removing damaged or dysfunctional components, and their involvement in the development of diabetic heart disease makes them attractive targets for diagnosis and treatment. While a variety of conventional diagnostic and therapeutic strategies are available, DHC continues to present a significant challenge. Point-of-care diagnostics, supported by nanobiosensing techniques, offer a promising alternative for these complex scenarios. Although conventional medications have been widely used in DHC patients, they raise several concerns regarding various physiological aspects. Modern medicine places great emphasis on the application of nanotechnology to target autophagy and mitophagy in DHC, offering a promising approach to deliver drugs beyond the limitations of traditional therapies. This article aims to explore the potential connections between autophagy, mitophagy and DHC, while also discussing the promise of nanotechnology-based theranostic interventions that specifically target these molecular pathways.

Automated Data Transformation and Feature Extraction for Oxygenation-Sensitive Cardiovascular Magnetic Resonance Images

Oxygenation-sensitive cardiovascular magnetic resonance (OS-CMR) is a novel, powerful tool for assessing coronary function in vivo. The data extraction and analysis however are labor-intensive. The objective of this study was to provide an automated approach for the extraction, visualization, and biomarker selection of OS-CMR images. We created a Python-based tool to automate extraction and export of raw patient data, featuring 3336 attributes per participant, into a template compatible with common data analytics frameworks, including the functionality to select predictive features for the given disease state. Each analysis was completed in about 2 min. The features selected by both ANOVA and MIC significantly outperformed (p < 0.001) the null set and complete set of features in two datasets, with mean AUROC scores of 0.89eatures f 0.94lete set of features in two datasets, with mean AUROC scores that our tool is suitable for automated data extraction and analysis of OS-CMR images.

Physical frailty identification using machine learning to explore the 5-item FRAIL scale, Cardiovascular Health Study index, and Study of Osteoporotic Fractures index

Background

Physical frailty is an important issue in aging societies. Three models of physical frailty assessment, the 5-Item fatigue, resistance, ambulation, illness and loss of weight (FRAIL); Cardiovascular Health Study (CHS); and Study of Osteoporotic Fractures (SOF) indices, have been regularly used in clinical and research studies. However, no previous studies have investigated the predictive ability of machine learning (ML) for physical frailty assessment. The aim was to use two ML algorithms, random forest (RF) and extreme gradient boosting (XGBoost), to predict these three physical frailty assessment models.

Materials and methods

Questionnaires regarding demographic characteristics, lifestyle habits, living environment, and physical frailty assessment were answered by 445 participants aged 60 years and above. The RF and XGBoost algorithms were used to assess their scores for the three physical frailty indices. Furthermore, feature importance and Shapley additive explanations (SHAP) were used to determine the important physical frailty factors.

Results

The XGBoost algorithm obtained higher accuracy for predicting the three physical frailty indices; the areas under the curve obtained by the XGBoost algorithm for the 5-Item FRAIL, CHS, and SOF indices were 0.84. 0.79, and 0.69, respectively. The feature importance and SHAP of the XGBoost algorithm revealed that systolic blood pressure, diastolic blood pressure, age, and body mass index play important roles in all three physical frailty models.

Conclusion

The XGBoost algorithm has a more accurate predictive rate than RF across all three physical frailty assessments. Thus, ML can be a useful tool for the early detection of physical frailty.

Comparative Analysis of Biomarkers in Type 2 Diabetes Patients With and Without Comorbidities: Insights Into the Role of Hypertension and Cardiovascular Disease

Background

Type 2 diabetes mellitus (T2DM) are 90% of diabetes cases, and its prevalence and incidence, including comorbidities, are rising worldwide. Clinically, diabetes and associated comorbidities are identified by biochemical and physical characteristics including glycemia, glycated hemoglobin (HbA1c), and tests for cardiovascular, eye and kidney disease.

Objectives

Diabetes may have a common etiology based on inflammation and oxidative stress that may provide additional information about disease progression and treatment options. Thus, identifying high-risk individuals can delay or prevent diabetes and its complications.

Design

In patients with or without hypertension and cardiovascular disease, as part of progression from no diabetes to T2DM, this research studied the changes in biomarkers between control and prediabetes, prediabetes to T2DM, and control to T2DM, and classified patients based on first-attendance data. Control patients and patients with hypertension, cardiovascular, and with both hypertension and cardiovascular diseases are 156, 148, 61, and 216, respectively.

Methods

Linear discriminant analysis is used for classification method and feature importance, This study examined the relationship between Humanin and mitochondrial protein (MOTSc), mitochondrial peptides associated with oxidative stress, diabetes progression, and associated complications.

Results

MOTSc, reduced glutathione and glutathione disulfide ratio (GSH/GSSG), interleukin-1β (IL-1β), and 8-isoprostane were significant (P < .05) for the transition from prediabetes to t2dm, highlighting importance of mitochondrial involvement. complement component 5a (c5a) is a biomarker associated with disease progression and comorbidities, gsh gssg, monocyte chemoattractant protein-1 (mcp-1), 8-isoprostane being most important biomarkers.

Conclusions

Comorbidities affect the hypothesized biomarkers as diabetes progresses. Mitochondrial oxidative stress indicators, coagulation, and inflammatory markers help assess diabetes disease development and provide appropriate medications. Future studies will examine longitudinal biomarker evolution.

Integrating molecular and clinical variables to predict myocardial recovery

Mechanical unloading and circulatory support with left ventricular assist devices (LVADs) mediate significant myocardial improvement in a subset of advanced heart failure (HF) patients. The clinical and biological phenomena associated with cardiac recovery are under intensive investigation. Left ventricular (LV) apical tissue, alongside clinical data, were collected from HF patients at the time of LVAD implantation (n=208). RNA was isolated and mRNA transcripts were identified through RNA sequencing and confirmed with RT-qPCR. To our knowledge this is the first study to combine transcriptomic and clinical data to derive predictors of myocardial recovery. We used a bioinformatic approach to integrate 59 clinical variables and 22,373 mRNA transcripts at the time of LVAD implantation for the prediction of post-LVAD myocardial recovery defined as LV ejection fraction (LVEF) ≥40% and LV end-diastolic diameter (LVEDD) ≤5.9cm, as well as functional and structural LV improvement independently by using LVEF and LVEDD as continuous variables, respectively. To substantiate the predicted variables, we used a multi-model approach with logistic and linear regressions. Combining RNA and clinical data resulted in a gradient boosted model with 80 features achieving an AUC of 0.731±0.15 for predicting myocardial recovery. Variables associated with myocardial recovery from a clinical standpoint included HF duration, pre-LVAD LVEF, LVEDD, and HF pharmacologic therapy, and LRRN4CL (ligand binding and programmed cell death) from a biological standpoint. Our findings could have diagnostic, prognostic, and therapeutic implications for advanced HF patients, and inform the care of the broader HF population.

Epigenetics in diabetic cardiomyopathy

Diabetic cardiomyopathy (DCM) is a critical complication that poses a significant threat to the health of patients with diabetes. The intricate pathological mechanisms of DCM cause diastolic dysfunction, followed by impaired systolic function in the late stages. Accumulating researches have revealed the association between DCM and various epigenetic regulatory mechanisms, including DNA methylation, histone modifications, non-coding RNAs, and other epigenetic molecules. Recently, a profound understanding of epigenetics in the pathophysiology of DCM has been broadened owing to advanced high-throughput technologies, which assist in developing potential therapeutic strategies. In this review, we briefly introduce the epigenetics regulation and update the relevant progress in DCM. We propose the role of epigenetic factors and non-coding RNAs (ncRNAs) as potential biomarkers and drugs in DCM diagnosis and treatment, providing a new perspective and understanding of epigenomics in DCM.

Literary Identification of Differentially Hydroxymethylated DNA Regions for Type 2 Diabetes Mellitus: A Scoping Minireview

Type 2 diabetes mellitus (T2DM) is a public health condition where environmental and genetic factors can intersect through hydroxymethylation. It was unclear which blood DNA regions were hydroxymethylated in human T2DM development. We aimed to identify the regions from the literature as designed in the ongoing Twins Discordant for Incident T2DM Study. A scoping review was performed using Medical Subject Headings (MeSH) and keyword methods to search PubMed for studies published in English and before 1 August 2022, following our registered protocol. The keyword and MeSH methods identified 12 and 3 records separately, and the keyword-identified records included all from the MeSH. Only three case-control studies met the criteria for the full-text review, including one MeSH-identified record. Increased global levels of 5-hydroxymethylated cytosine (5hmC) in T2DM patients versus healthy controls in blood or peripheral blood mononuclear cells were consistently reported (p < 0.05 for all). Among candidate DNA regions related to the human SOCS3, SREBF1, and TXNIP genes, only the SOCS3 gene yielded higher 5hmC levels in T2DM patients with high poly-ADP-ribosylation than participants combined from those with low PARylation and healthy controls (p < 0.05). Hydroxymethylation in the SOCS3-related region of blood DNA is promising to investigate for its mediation in the influences of environment on incident T2DM.

Assessing the feasibility of applying machine learning to diagnosing non-effusive feline infectious peritonitis

Feline infectious peritonitis (FIP) is a severe feline coronavirus-associated syndrome in cats, which is invariably fatal without anti-viral treatment. In the majority of non-effusive FIP cases encountered in practice, confirmatory diagnostic testing is not undertaken and reliance is given to the interpretation of valuable, but essentially non-specific, clinical signs and laboratory markers. We hypothesised that it may be feasible to develop a machine learning (ML) approach which may be applied to the analysis of clinical data to aid in the diagnosis of disease. A dataset encompassing 1939 suspected FIP cases was scored for clinical suspicion of FIP on the basis of history, signalment, clinical signs and laboratory results, using published guidelines, comprising 683 FIP (35.2%), and 1256 non-FIP (64.8%) cases. This dataset was used to train, validate and evaluate two diagnostic machine learning ensemble models. These models, which analysed signalment and laboratory data alone, allowed the accurate discrimination of FIP and non-FIP cases in line with expert opinion. To evaluate whether these models may have value as a diagnostic tool, they were applied to a collection of 80 cases for which the FIP status had been confirmed (FIP: n = 58 (72.5%), non-FIP: n = 22 (27.5%)). Both ensemble models detected FIP with an accuracy of 97.5%, an area under the curve (AUC) of 0.969, sensitivity of 95.45% and specificity of 98.28%. This work demonstrates that, in principle, ML can be usefully applied to the diagnosis of non-effusive FIP. Further work is required before ML may be deployed in the laboratory as a diagnostic tool, such as training models on datasets of confirmed cases and accounting for inter-laboratory variation. Nevertheless, these results illustrate the potential benefit of applying ML to standardising and accelerating the interpretation of clinical pathology data, thereby improving the diagnostic utility of existing laboratory tests.

Identifying Explainable Machine Learning Models and a Novel SFRP2+ Fibroblast Signature as Predictors for Precision Medicine in Ovarian Cancer

Ovarian cancer (OC) is a type of malignant tumor with a consistently high mortality rate. The diagnosis of early-stage OC and identification of functional subsets in the tumor microenvironment are essential to the development of patient management strategies. However, the development of robust models remains unsatisfactory. We aimed to utilize artificial intelligence and single-cell analysis to address this issue. Two independent datasets were screened from the Gene Expression Omnibus (GEO) database and processed to obtain overlapping differentially expressed genes (DEGs) in stage II-IV vs. stage I diseases. Three explainable machine learning algorithms were integrated to construct models that could determine the tumor stage and extract important characteristic genes as diagnostic biomarkers. Correlations between cancer-associated fibroblast (CAF) infiltration and characteristic gene expression were analyzed using TIMER2.0 and their relationship with survival rates was comprehensively explored via the Kaplan-Meier plotter (KM-plotter) online database. The specific expression of characteristic genes in fibroblast subsets was investigated through single-cell analysis. A novel fibroblast subset signature was explored to predict immune checkpoint inhibitor (ICI) response and oncogene mutation through Tumor Immune Dysfunction and Exclusion (TIDE) and artificial neural network algorithms, respectively. We found that Support Vector Machine-Shapley Additive Explanations (SVM-SHAP), Extreme Gradient Boosting (XGBoost), and Random Forest (RF) successfully diagnosed early-stage OC (stage I). The area under the receiver operating characteristic curves (AUCs) of these models exceeded 0.990. Their overlapping characteristic gene, secreted frizzled-related protein 2 (SFRP2), was a risk factor that affected the overall survival of OC patients with stage II-IV disease (log-rank test: p < 0.01) and was specifically expressed in a fibroblast subset. Finally, the SFRP2+ fibroblast signature served as a novel predictor in evaluating ICI response and exploring pan-cancer tumor protein P53 (TP53) mutation (AUC = 0.853, 95% confidence interval [CI]: 0.829-0.877). In conclusion, the models based on SVM-SHAP, XGBoost, and RF enabled the early detection of OC for clinical decision making, and SFRP2+ fibroblast signature used in diagnostic models can inform OC treatment selection and offer pan-cancer TP53 mutation detection.

An Artificial Intelligence Prediction Model of Insulin Sensitivity, Insulin Resistance, and Diabetes Using Genes Obtained through Differential Expression

Insulin is a powerful pleiotropic hormone that affects processes such as cell growth, energy expenditure, and carbohydrate, lipid, and protein metabolism. The molecular mechanisms by which insulin regulates muscle metabolism and the underlying defects that cause insulin resistance have not been fully elucidated. This study aimed to perform a microarray data analysis to find differentially expressed genes. The analysis has been based on the data of a study deposited in Gene Expression Omnibus (GEO) with the identifier "GSE22309". The selected data contain samples from three types of patients after taking insulin treatment: patients with diabetes (DB), patients with insulin sensitivity (IS), and patients with insulin resistance (IR). Through an analysis of omics data, 20 genes were found to be differentially expressed (DEG) between the three possible comparisons obtained (DB vs. IS, DB vs. IR, and IS vs. IR); these data sets have been used to develop predictive models through machine learning (ML) techniques to classify patients with respect to the three categories mentioned previously. All the ML techniques present an accuracy superior to 80%, reaching almost 90% when unifying IR and DB categories.

The ReadFree tool for the identification of poor readers: a validation study based on a machine learning approach in monolingual and minority-language children

In this study, we validated the "ReadFree tool", a computerised battery of 12 visual and auditory tasks developed to identify poor readers also in minority-language children (MLC). We tested the task-specific discriminant power on 142 Italian-monolingual participants (8-13 years old) divided into monolingual poor readers (N = 37) and good readers (N = 105) according to standardised Italian reading tests. The performances at the discriminant tasks of the "ReadFree tool" were entered into a classification and regression tree (CART) model to identify monolingual poor and good readers. The set of classification rules extracted from the CART model were applied to the MLC's performance and the ensuing classification was compared to the one based on standardised Italian reading tests. According to the CART model, auditory go-no/go (regular), RAN and Entrainment100bpm were the most discriminant tasks. When compared with the clinical classification, the CART model accuracy was 86% for the monolinguals and 76% for the MLC. Executive functions and timing skills turned out to have a relevant role in reading. Results of the CART model on MLC support the idea that ad hoc standardised tasks that go beyond reading are needed.

Detection of diabetic patients in people with normal fasting glucose using machine learning

BACKGROUND

Diabetes mellitus (DM) is a chronic metabolic disease that could produce severe complications threatening life. Its early detection is thus quite important for the timely prevention and treatment. Normally, fasting blood glucose (FBG) by physical examination is used for large-scale screening of DM; however, some people with normal fasting glucose (NFG) actually have suffered from diabetes but are missed by the examination. This study aimed to investigate whether common physical examination indexes for diabetes can be used to identify the diabetes individuals from the populations with NFG.

METHODS

The physical examination data from over 60,000 individuals with NFG in three Chinese cohorts were used. The diabetes patients were defined by HbA1c ≥ 48 mmol/mol (6.5%). We constructed the models using multiple machine learning methods, including logistic regression, random forest, deep neural network, and support vector machine, and selected the optimal one on the validation set. A framework using permutation feature importance algorithm was devised to discover the personalized risk factors.

RESULTS

The prediction model constructed by logistic regression achieved the best performance with an AUC, sensitivity, and specificity of 0.899, 85.0%, and 81.1% on the validation set and 0.872, 77.9%, and 81.0% on the test set, respectively. Following feature selection, the final classifier only requiring 13 features, named as DRING (diabetes risk of individuals with normal fasting glucose), exhibited reliable performance on two newly recruited independent datasets, with the AUC of 0.964 and 0.899, the balanced accuracy of 84.2% and 81.1%, the sensitivity of 100% and 76.2%, and the specificity of 68.3% and 86.0%, respectively. The feature importance ranking analysis revealed that BMI, age, sex, absolute lymphocyte count, and mean corpuscular volume are important factors for the risk stratification of diabetes. With a case, the framework for identifying personalized risk factors revealed FBG, age, and BMI as significant hazard factors that contribute to an increased incidence of diabetes. DRING webserver is available for ease of application ( http://www.cuilab.cn/dring ).

CONCLUSIONS

DRING was demonstrated to perform well on identifying the diabetes individuals among populations with NFG, which could aid in early diagnosis and interventions for those individuals who are most likely missed.

Mitochondrial epigenetics in aging and cardiovascular diseases

Mitochondria are cellular organelles which generate adenosine triphosphate (ATP) molecules for the maintenance of cellular energy through the oxidative phosphorylation. They also regulate a variety of cellular processes including apoptosis and metabolism. Of interest, the inner part of mitochondria-the mitochondrial matrix-contains a circular molecule of DNA (mtDNA) characterised by its own transcriptional machinery. As with genomic DNA, mtDNA may also undergo nucleotide mutations that have been shown to be responsible for mitochondrial dysfunction. During physiological aging, the mitochondrial membrane potential declines and associates with enhanced mitophagy to avoid the accumulation of damaged organelles. Moreover, if the dysfunctional mitochondria are not properly cleared, this could lead to cellular dysfunction and subsequent development of several comorbidities such as cardiovascular diseases (CVDs), diabetes, respiratory and cardiovascular diseases as well as inflammatory disorders and psychiatric diseases. As reported for genomic DNA, mtDNA is also amenable to chemical modifications, namely DNA methylation. Changes in mtDNA methylation have shown to be associated with altered transcriptional programs and mitochondrial dysfunction during aging. In addition, other epigenetic signals have been observed in mitochondria, in particular the interaction between mtDNA methylation and non-coding RNAs. Mitoepigenetic modifications are also involved in the pathogenesis of CVDs where oxygen chain disruption, mitochondrial fission, and ROS formation alter cardiac energy metabolism leading to hypertrophy, hypertension, heart failure and ischemia/reperfusion injury. In the present review, we summarize current evidence on the growing importance of epigenetic changes as modulator of mitochondrial function in aging. A better understanding of the mitochondrial epigenetic landscape may pave the way for personalized therapies to prevent age-related diseases.

Use of Multiple Fluid Biomarkers for Predicting the Co-occurrence of Diabetes and Hypertension Using Machine Learning Approaches

The co-existence of diabetes and hypertension can complicate and affect the management of these diseases. The early detection of these comorbidities can help in developing personalized preventive treatments and thereby, reduce the healthcare burden. The inclusion of readily available fluid biomarkers from different body fluids can be used as diagnostic tools and can facilitate in the designing of treatment strategies. In this work, an attempt has been made using multiple fluid biomarkers to differentiate diabetic from diabetic and hypertensive comorbid (DHC) condition. The fluid biomarkers are obtained from a publicly available dataset for diabetic (N=105) and DHC (N=57) conditions. The features, such as systolic blood pressure, fasting blood glucose, diastolic blood pressure, and total cholesterol are extracted and statistically analyzed. Data balancing technique namely synthetic minority oversampling technique is applied on the minority class to balance the dataset. Machine learning techniques namely, linear discriminant analysis, random forest, K-nearest neighbor, and linear support vector machine are used to perform the classification between the two groups. The results show that systolic blood pressure, diastolic blood pressure, and total cholesterol are elevated in the comorbid condition. These features also exhibit a statistical significance (p<0.001) between the two groups. This study also addresses the data imbalance issue, which is resolved by using an oversampling technique to mitigate the bias resulting from imbalanced data. The LDA classifier achieves a maximum accuracy of 61.2% in distinguishing between the two conditions. Machine learning based approaches may help in the prediction of comorbid conditions. This can act as a guideline for future studies on the progression of diseases and the identification of fluid biomarkers.

Machine learning classifier approaches for predicting response to RTK-type-III inhibitors demonstrate high accuracy using transcriptomic signatures and ex vivo data

Motivation

The application of machine learning (ML) techniques in the medical field has demonstrated both successes and challenges in the precision medicine era. The ability to accurately classify a subject as a potential responder versus a nonresponder to a given therapy is still an active area of research pushing the field to create new approaches for applying machine-learning techniques. In this study, we leveraged publicly available data through the BeatAML initiative. Specifically, we used gene count data, generated via RNA-seq, from 451 individuals matched with ex vivo data generated from treatment with RTK-type-III inhibitors. Three feature selection techniques were tested, principal component analysis, Shapley Additive Explanation (SHAP) technique and differential gene expression analysis, with three different classifiers, XGBoost, LightGBM and random forest (RF). Sensitivity versus specificity was analyzed using the area under the curve (AUC)-receiver operating curves (ROCs) for every model developed.

Results

Our work demonstrated that feature selection technique, rather than the classifier, had the greatest impact on model performance. The SHAP technique outperformed the other feature selection techniques and was able to with high accuracy predict outcome response, with the highest performing model: Foretinib with 89% AUC using the SHAP technique and RF classifier. Our ML pipelines demonstrate that at the time of diagnosis, a transcriptomics signature exists that can potentially predict response to treatment, demonstrating the potential of using ML applications in precision medicine efforts.

Availability and implementation

https://github.com/UD-CRPL/RCDML.

Supplementary information

Supplementary data are available at Bioinformatics Advances online.

Machine learning in the prediction of cardiac surgery associated acute kidney injury with early postoperative biomarkers

Purpose

To establish novel prediction models for predicting acute kidney injury (AKI) after cardiac surgery based on early postoperative biomarkers.

Patients and methods

This study enrolled patients who underwent cardiac surgery in a Chinese tertiary cardiac center and consisted of a discovery cohort (n = 452, from November 2018 to June 2019) and a validation cohort (n = 326, from December 2019 to May 2020). 43 biomarkers were screened using the least absolute shrinkage and selection operator and logistic regression to construct a nomogram model. Three tree-based machine learning models were also established: eXtreme Gradient Boosting (XGBoost), random forest (RF) and deep forest (DF). Model performance was accessed using area under the receiver operating characteristic curve (AUC). AKI was defined according to the Kidney Disease Improving Global Outcomes criteria.

Results

Five biomarkers were identified as independent predictors of AKI and were included in the nomogram: soluble ST2 (sST2), N terminal pro-brain natriuretic peptide (NT-proBNP), heart-type fatty acid binding protein (H-FABP), lactic dehydrogenase (LDH), and uric acid (UA). In the validation cohort, the nomogram achieved good discrimination, with AUC of 0.834. The machine learning models also exhibited adequate discrimination, with AUC of 0.856, 0.850, and 0.836 for DF, RF, and XGBoost, respectively. Both nomogram and machine learning models had well calibrated. The AUC of sST2, NT-proBNP, H-FABP, LDH, and UA to discriminate AKI were 0.670, 0.713, 0.725, 0.704, and 0.749, respectively. In addition, all of these biomarkers were significantly correlated with AKI after adjusting clinical confounders (odds ratio and 95% confidence interval of the third vs. the first tertile: sST2, 3.55 [2.34-5.49], NT-proBNP, 5.50 [3.54-8.71], H-FABP, 6.64 [4.11-11.06], LDH, 7.47 [4.54-12.64], and UA, 8.93 [5.46-15.06]).

Conclusion

Our study provides a series of novel predictive models and five biomarkers for enhancing the risk stratification of AKI after cardiac surgery.

Delirium Prediction Using Machine Learning Interpretation Method and Its Incorporation into a Clinical Workflow

Delirium in hospitalized patients is a worldwide problem, causing a burden on healthcare professionals and impacting patient prognosis. A machine learning interpretation method (ML interpretation method) presents the results of machine learning predictions and promotes guided decisions. This study focuses on visualizing the predictors of delirium using a ML interpretation method and implementing the analysis results in clinical practice. Retrospective data of 55,389 patients hospitalized in a single acute care center in Japan between December 2017 and February 2022 were collected. Patients were categorized into three analysis populations, according to inclusion and exclusion criteria, to develop delirium prediction models. The predictors were then visualized using Shapley additive explanation (SHAP) and fed back to clinical practice. The machine learning-based prediction of delirium in each population exhibited excellent predictive performance. SHAP was used to visualize the body mass index and albumin levels as critical contributors to delirium prediction. In addition, the cutoff value for age, which was previously unknown, was visualized, and the risk threshold for age was raised. By using the SHAP method, we demonstrated that data-driven decision support is possible using electronic medical record data.

Machine learning for spatial stratification of progressive cardiovascular dysfunction in a murine model of type 2 diabetes mellitus

Speckle tracking echocardiography (STE) has been utilized to evaluate independent spatial alterations in the diabetic heart, but the progressive manifestation of regional and segmental cardiac dysfunction in the type 2 diabetic (T2DM) heart remains understudied. Therefore, the objective of this study was to elucidate if machine learning could be utilized to reliably describe patterns of the progressive regional and segmental dysfunction that are associated with the development of cardiac contractile dysfunction in the T2DM heart. Non-invasive conventional echocardiography and STE datasets were utilized to segregate mice into two pre-determined groups, wild-type and Db/Db, at 5, 12, 20, and 25 weeks. A support vector machine model, which classifies data using a single line, or hyperplane, that best separates each class, and a ReliefF algorithm, which ranks features by how well each feature lends to the classification of data, were used to identify and rank cardiac regions, segments, and features by their ability to identify cardiac dysfunction. STE features more accurately segregated animals as diabetic or non-diabetic when compared with conventional echocardiography, and the ReliefF algorithm efficiently ranked STE features by their ability to identify cardiac dysfunction. The Septal region, and the AntSeptum segment, best identified cardiac dysfunction at 5, 20, and 25 weeks, with the AntSeptum also containing the greatest number of features which differed between diabetic and non-diabetic mice. Cardiac dysfunction manifests in a spatial and temporal fashion, and is defined by patterns of regional and segmental dysfunction in the T2DM heart which are identifiable using machine learning methodologies. Further, machine learning identified the Septal region and AntSeptum segment as locales of interest for therapeutic interventions aimed at ameliorating cardiac dysfunction in T2DM, suggesting that machine learning may provide a more thorough approach to managing contractile data with the intention of identifying experimental and therapeutic targets.

Review: Machine learning in precision pharmacotherapy of type 2 diabetes-A promising future or a glimpse of hope?

Precision pharmacotherapy of diabetes requires judicious selection of the optimal therapeutic agent for individual patients. Artificial intelligence (AI), a swiftly expanding discipline, holds substantial potential to transform current practices in diabetes diagnosis and management. This manuscript provides a comprehensive review of contemporary research investigating drug responses in patient subgroups, stratified via either supervised or unsupervised machine learning approaches. The prevalent algorithmic workflow for investigating drug responses using machine learning involves cohort selection, data processing, predictor selection, development and validation of machine learning methods, subgroup allocation, and subsequent analysis of drug response. Despite the promising feature, current research does not yet provide sufficient evidence to implement machine learning algorithms into routine clinical practice, due to a lack of simplicity, validation, or demonstrated efficacy. Nevertheless, we anticipate that the evolving evidence base will increasingly substantiate the role of machine learning in molding precision pharmacotherapy for diabetes.

Interpretable Machine Learning for Inpatient COVID-19 Mortality Risk Assessments: Diabetes Mellitus Exclusive Interplay

People with diabetes mellitus (DM) are at elevated risk of in-hospital mortality from coronavirus disease-2019 (COVID-19). This vulnerability has spurred efforts to pinpoint distinctive characteristics of COVID-19 patients with DM. In this context, the present article develops ML models equipped with interpretation modules for inpatient mortality risk assessments of COVID-19 patients with DM. To this end, a cohort of 156 hospitalised COVID-19 patients with pre-existing DM is studied. For creating risk assessment platforms, this work explores a pool of historical, on-admission, and during-admission data that are DM-related or, according to preliminary investigations, are exclusively attributed to the COVID-19 susceptibility of DM patients. First, a set of careful pre-modelling steps are executed on the clinical data, including cleaning, pre-processing, subdivision, and feature elimination. Subsequently, standard machine learning (ML) modelling analysis is performed on the cured data. Initially, a classifier is tasked with forecasting COVID-19 fatality from selected features. The model undergoes thorough evaluation analysis. The results achieved substantiate the efficacy of the undertaken data curation and modelling steps. Afterwards, SHapley Additive exPlanations (SHAP) technique is assigned to interpret the generated mortality risk prediction model by rating the predictors' global and local influence on the model's outputs. These interpretations advance the comprehensibility of the analysis by explaining the formation of outcomes and, in this way, foster the adoption of the proposed methodologies. Next, a clustering algorithm demarcates patients into four separate groups based on their SHAP values, providing a practical risk stratification method. Finally, a re-evaluation analysis is performed to verify the robustness of the proposed framework.

Machine learning prediction model for post- hepatectomy liver failure in hepatocellular carcinoma: A multicenter study

Introduction

Post-hepatectomy liver failure (PHLF) is one of the most serious complications and causes of death in patients with hepatocellular carcinoma (HCC) after hepatectomy. This study aimed to develop a novel machine learning (ML) model based on the light gradient boosting machines (LightGBM) algorithm for predicting PHLF.

Methods

A total of 875 patients with HCC who underwent hepatectomy were randomized into a training cohort (n=612), a validation cohort (n=88), and a testing cohort (n=175). Shapley additive explanation (SHAP) was performed to determine the importance of individual variables. By combining these independent risk factors, an ML model for predicting PHLF was established. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, positive predictive value, negative predictive value, and decision curve analyses (DCA) were used to evaluate the accuracy of the ML model and compare it to that of other noninvasive models.

Results

The AUCs of the ML model for predicting PHLF in the training cohort, validation cohort, and testing cohort were 0.944, 0.870, and 0.822, respectively. The ML model had a higher AUC for predicting PHLF than did other non-invasive models. The ML model for predicting PHLF was found to be more valuable than other noninvasive models.

Conclusion

A novel ML model for the prediction of PHLF using common clinical parameters was constructed and validated. The novel ML model performed better than did existing noninvasive models for the prediction of PHLF.

Prediction of in-hospital hypokalemia using machine learning and first hospitalization day records in patients with traumatic brain injury

AIMS

Hypokalemia is a common complication following traumatic brain injury, which may complicate treatment and lead to unfavorable outcomes. Identifying patients at risk of hypokalemia on the first day of admission helps to implement prophylactic treatment, reduce complications, and improve prognosis.

METHODS

This multicenter retrospective study was performed between January 2017 and December 2020 using the electronic medical records of patients admitted due to traumatic brain injury. A propensity score matching approach was adopted with a ratio of 1:1 to overcome overfitting and data imbalance during subgroup analyses. Five machine learning algorithms were applied to generate a best-performed prediction model for in-hospital hypokalemia. The internal fivefold cross-validation and external validation were performed to demonstrate the interpretability and generalizability.

RESULTS

A total of 4445 TBI patients were recruited for analysis and model generation. Hypokalemia occurred in 46.55% of recruited patients and the incidences of mild, moderate, and severe hypokalemia were 32.06%, 12.69%, and 1.80%, respectively. Hypokalemia was associated with increased mortality, while severe hypokalemia cast greater impacts. The logistic regression algorithm had the best performance in predicting decreased serum potassium and moderate-to-severe hypokalemia, with an AUC of 0.73 ± 0.011 and 0.74 ± 0.019, respectively. The prediction model was further verified using two external datasets, including our previous published data and the open-assessed Medical Information Mart for Intensive Care database. Linearized calibration curves showed no statistical difference (p > 0.05) with perfect predictions.

CONCLUSIONS

The occurrence of hypokalemia following traumatic brain injury can be predicted by first hospitalization day records and machine learning algorithms. The logistic regression algorithm showed an optimal predicting performance verified by both internal and external validation.

Machine Learning Models for Inpatient Glucose Prediction

PURPOSE OF REVIEW

Glucose management in the hospital is difficult due to non-static factors such as antihyperglycemic and steroid doses, renal function, infection, surgical status, and diet. Given these complex and dynamic factors, machine learning approaches can be leveraged for prediction of glucose trends in the hospital to mitigate and prevent suboptimal hypoglycemic and hyperglycemic outcomes. Our aim was to review the clinical evidence for the role of machine learning-based models in predicting hospitalized patients' glucose trajectory.

RECENT FINDINGS

The published literature on machine learning algorithms has varied in terms of population studied, outcomes of interest, and validation methods. There have been tools developed that utilize data from both continuous glucose monitors and large electronic health records (EHRs). With increasing sample sizes, inclusion of a greater number of predictor variables, and use of more advanced machine learning algorithms, there has been a trend in recent years towards increasing predictive accuracy for glycemic outcomes in the hospital setting. While current models predicting glucose trajectory offer promising results, they have not been tested prospectively in the clinical setting. Accurate machine learning algorithms have been developed and validated for prediction of hypoglycemia and hyperglycemia in the hospital. Further work is needed in implementation/integration of machine learning models into EHR systems, with prospective studies to evaluate effectiveness and safety of such clinical decision support on glycemic and other clinical outcomes.

A Promising Preoperative Prediction Model for Microvascular Invasion in Hepatocellular Carcinoma Based on an Extreme Gradient Boosting Algorithm

Background

The non-invasive preoperative diagnosis of microvascular invasion (MVI) in hepatocellular carcinoma (HCC) is vital for precise surgical decision-making and patient prognosis. Herein, we aimed to develop an MVI prediction model with valid performance and clinical interpretability.

Methods

A total of 2160 patients with HCC without macroscopic invasion who underwent hepatectomy for the first time in West China Hospital from January 2015 to June 2019 were retrospectively included, and randomly divided into training and a validation cohort at a ratio of 8:2. Preoperative demographic features, imaging characteristics, and laboratory indexes of the patients were collected. Five machine learning algorithms were used: logistic regression, random forest, support vector machine, extreme gradient boosting (XGBoost), and multilayer perception. Performance was evaluated using the area under the receiver operating characteristic curve (AUC). We also determined the Shapley Additive exPlanation value to explain the influence of each feature on the MVI prediction model.

Results

The top six important preoperative factors associated with MVI were the maximum image diameter, protein induced by vitamin K absence or antagonist-II, α-fetoprotein level, satellite nodules, alanine aminotransferase (AST)/aspartate aminotransferase (ALT) ratio, and AST level, according to the XGBoost model. The XGBoost model for preoperative prediction of MVI exhibited a better AUC (0.8, 95% confidence interval: 0.74–0.83) than the other prediction models. Furthermore, to facilitate use of the model in clinical settings, we developed a user-friendly online calculator for MVI risk prediction based on the XGBoost model.

Conclusions

The XGBoost model achieved outstanding performance for non-invasive preoperative prediction of MVI based on big data. Moreover, the MVI risk calculator would assist clinicians in conveniently determining the optimal therapeutic remedy and ameliorating the prognosis of patients with HCC.

Manipulation of the miR-378a/mt-ATP6 regulatory axis rescues ATP synthase in the diabetic heart and offers a novel role for lncRNA Kcnq1ot1

Diabetes mellitus has been linked to an increase in mitochondrial microRNA-378a (miR-378a) content. Enhanced miR-378a content has been associated with a reduction in mitochondrial genome-encoded mt-ATP6 abundance, supporting the hypothesis that miR-378a inhibition may be a therapeutic option for maintaining ATP synthase functionality during diabetes mellitus. Evidence also suggests that long noncoding RNAs (lncRNAs), including lncRNA potassium voltage-gated channel subfamily Q member 1 overlapping transcript 1 (Kcnq1ot1), participate in regulatory axes with microRNAs (miRs). Prediction analyses indicate that Kcnq1ot1 has the potential to bind miR-378a. This study aimed to determine if loss of miR-378a in a genetic mouse model could ameliorate cardiac dysfunction in type 2 diabetes mellitus (T2DM) and to ascertain whether Kcnq1ot1 interacts with miR-378a to impact ATP synthase functionality by preserving mt-ATP6 levels. MiR-378a was significantly higher in patients with T2DM and 25-wk-old Db/Db mouse mitochondria, whereas mt-ATP6 and Kcnq1ot1 levels were significantly reduced when compared with controls. Twenty-five-week-old miR-378a knockout Db/Db mice displayed preserved mt-ATP6 and ATP synthase protein content, ATP synthase activity, and preserved cardiac function, implicating miR-378a as a potential therapeutic target in T2DM. Assessments following overexpression of the 500-bp Kcnq1ot1 fragment in established mouse cardiomyocyte cell line (HL-1) cardiomyocytes overexpressing miR-378a revealed that Kcnq1ot1 may bind and significantly reduce miR-378a levels, and rescue mt-ATP6 and ATP synthase protein content. Together, these data suggest that Kcnq1ot1 and miR-378a may act as constituents in an axis that regulates mt-ATP6 content, and that manipulation of this axis may provide benefit to ATP synthase functionality in type 2 diabetic heart.

Environmental chemical exposure dynamics and machine learning-based prediction of diabetes mellitus

BACKGROUND

With dramatically increasing prevalence, diabetes mellitus has imposed a tremendous toll on individual well-being. Humans are exposed to various environmental chemicals, which have been postulated as underappreciated but potentially modifiable diabetes risk factors.

OBJECTIVES

To determine the utility of environmental chemical exposure in predicting diabetes mellitus.

METHODS

A total of 8501 eligible participants from NHANES 2005-2016 were randomly assigned to a discovery (N = 5953) set and a validation (N = 2548) set. We applied random forest (RF) and least absolute shrinkage and selection operator (LASSO) regression with 10-fold cross-validation in the discovery set to select features, and built an optimal model to predict diabetes mellitus, blood insulin, fasting plasma glucose (FPG) and 2-h plasma glucose after oral glucose tolerance test (2-h PG after OGTT).

RESULTS

The machine learning model using LASSO regression predicted diabetes with an area under the receiver operating characteristics (AUROC) of 0.80 and 0.78 in the discovery set and validation set, respectively. The linear model predicted blood insulin level with an R² of 0.42 and 0.40 in the discovery set and validation set, respectively. For FPG, the discovery set and validation set yielded an R² of 0.16 and 0.15, respectively. For 2-h PG after OGTT, the discovery set and validation set yielded an R² of 0.18 and 0.17, respectively.

CONCLUSION

We used environmental chemical exposure, constructed machine learning models and achieved relatively accurate prediction for diabetes, emphasizing the predictive value of widespread environmental chemicals for complicated diseases.

The promise of graphene-based transistors for democratizing multiomics studies

As biological research has synthesized genomics, proteomics, metabolomics, and transcriptomics into systems biology, a new multiomics approach to biological research has emerged. Today, multiomics studies are challenging and expensive. An experimental platform that could unify the multiple omics approaches to measurement could increase access to multiomics data by enabling more individual labs to successfully attempt multiomics studies. Field effect biosensing based on graphene transistors have gained significant attention as a potential unifying technology for such multiomics studies. This review article highlights the outstanding performance characteristics that makes graphene field effect transistor an attractive sensing platform for a wide variety of analytes important to system biology. In addition to many studies demonstrating the biosensing capabilities of graphene field effect transistors, they are uniquely suited to address the challenges of multiomics studies by providing an integrative multiplex platform for large scale manufacturing using the well-established processes of semiconductor industry. Furthermore, the resulting digital data is readily analyzable by machine learning to derive actionable biological insight to address the challenge of data compatibility for multiomics studies. A critical stage of systems biology will be democratizing multiomics study, and the graphene field effect transistor is uniquely positioned to serve as an accessible multiomics platform.

Transcriptomics of single dose and repeated carbon black and ozone inhalation co-exposure highlight progressive pulmonary mitochondrial dysfunction

BACKGROUND

Air pollution is a complex mixture of particles and gases, yet current regulations are based on single toxicant levels failing to consider potential interactive outcomes of co-exposures. We examined transcriptomic changes after inhalation co-exposure to a particulate and a gaseous component of air pollution and hypothesized that co-exposure would induce significantly greater impairments to mitochondrial bioenergetics. A whole-body inhalation exposure to ultrafine carbon black (CB), and ozone (O3) was performed, and the impact of single and multiple exposures was studied at relevant deposition levels. C57BL/6 mice were exposed to CB (10 mg/m3) and/or O3 (2 ppm) for 3 h (either a single exposure or four independent exposures). RNA was isolated from lungs and mRNA sequencing performed using the Illumina HiSeq. Lung pathology was evaluated by histology and immunohistochemistry. Electron transport chain (ETC) activities, electron flow, hydrogen peroxide production, and ATP content were assessed.

RESULTS

Compared to individual exposure groups, co-exposure induced significantly greater neutrophils and protein levels in broncho-alveolar lavage fluid as well as a significant increase in mRNA expression of oxidative stress and inflammation related genes. Similarly, a significant increase in hydrogen peroxide production was observed after co-exposure. After single and four exposures, co-exposure revealed a greater number of differentially expressed genes (2251 and 4072, respectively). Of these genes, 1188 (single exposure) and 2061 (four exposures) were uniquely differentially expressed, with 35 mitochondrial ETC mRNA transcripts significantly impacted after four exposures. Both O3 and co-exposure treatment significantly reduced ETC maximal activity for complexes I (- 39.3% and - 36.2%, respectively) and IV (- 55.1% and - 57.1%, respectively). Only co-exposure reduced ATP Synthase activity (- 35.7%) and total ATP content (30%). Further, the ability for ATP Synthase to function is limited by reduced electron flow (- 25%) and translation of subunits, such as ATP5F1, following co-exposure.

CONCLUSIONS

CB and O3 co-exposure cause unique transcriptomic changes in the lungs that are characterized by functional deficits to mitochondrial bioenergetics. Alterations to ATP Synthase function and mitochondrial electron flow underly a pathological adaptation to lung injury induced by co-exposure.

Enhanced antioxidant capacity prevents epitranscriptomic and cardiac alterations in adult offspring gestationally-exposed to ENM

Maternal engineered nanomaterial (ENM) exposure during gestation has been associated with negative long-term effects on cardiovascular health in progeny. Here, we evaluate an epitranscriptomic mechanism that contributes to these chronic ramifications and whether overexpression of mitochondrial phospholipid hydroperoxide glutathione peroxidase (mPHGPx) can preserve cardiovascular function and bioenergetics in offspring following gestational nano-titanium dioxide (TiO2) inhalation exposure. Wild-type (WT) and mPHGPx (Tg) dams were exposed to nano-TiO2 aerosols with a mass concentration of 12.01 ± 0.50 mg/m3 starting from gestational day (GD) 5 for 360 mins/day for 6 nonconsecutive days over 8 days. Echocardiography was performed in pregnant dams, adult (11-week old) and fetal (GD 14) progeny. Mitochondrial function and global N6-methyladenosine (m6A) content were assessed in adult progeny. MPHGPx enzymatic function was further evaluated in adult progeny and m6A-RNA immunoprecipitation (RIP) was combined with RT-qPCR to evaluate m6A content in the 3'-UTR. Following gestational ENM exposure, global longitudinal strain (GLS) was 32% lower in WT adult offspring of WT dams, with preservation in WT offspring of Tg dams. MPHGPx activity was significantly reduced in WT offspring (29%) of WT ENM-exposed dams, but preserved in the progeny of Tg dams. M6A-RIP-qPCR for the SEC insertion sequence region of mPHGPx revealed hypermethylation in WT offspring from ENM-exposed WT dams, which was thwarted in the presence of the maternal transgene. Our findings implicate that m6A hypermethylation of mPHGPx may be culpable for diminished antioxidant capacity and resultant mitochondrial and cardiac deficits that persist into adulthood following gestational ENM inhalation exposure.

Identification of Blood-Based Glycolysis Gene Associated with Alzheimer's Disease by Integrated Bioinformatics Analysis

BACKGROUND

Alzheimer's disease (AD) is one of many common neurodegenerative diseases without ideal treatment, but early detection and intervention can prevent the disease progression.

OBJECTIVE

This study aimed to identify AD-related glycolysis gene for AD diagnosis and further investigation by integrated bioinformatics analysis.

METHODS

122 subjects were recruited from the affiliated hospitals of Ningbo University between 1 October 2015 and 31 December 2016. Their clinical information and methylation levels of 8 glycolysis genes were assessed. Machine learning algorithms were used to establish an AD prediction model. Receiver operating characteristic curve (AUC) and decision curve analysis (DCA) were used to assess the model. An AD risk factor model was developed by SHapley Additive exPlanations (SHAP) to extract features that had important impacts on AD. Finally, gene expression of AD-related glycolysis genes were validated by AlzData.

RESULTS

An AD prediction model was developed using random forest algorithm with the best average ROC_AUC (0.969544). The threshold probability of the model was positive in the range of 0∼0.9875 by DCA. Eight glycolysis genes (GAPDHS, PKLR, PFKFB3, LDHC, DLD, ALDOC, LDHB, HK3) were identified by SHAP. Five of these genes (PFKFB3, DLD, ALDOC, LDHB, LDHC) have significant differences in gene expression between AD and control groups by Alzdata, while three of the genes (HK3, ALDOC, PKLR) are related to the pathogenesis of AD. GAPDHS is involved in the regulatory network of AD risk genes.

CONCLUSION

We identified 8 AD-related glycolysis genes (GAPDHS, PFKFB3, LDHC, HK3, ALDOC, LDHB, PKLR, DLD) as promising candidate biomarkers for early diagnosis of AD by integrated bioinformatics analysis. Machine learning has the advantage in identifying genes.

Exposure to fine particulate matter promotes platelet activation and thrombosis via obesity-related inflammation

Short-term exposure to fine particulate matter (PM_2.5) increases thrombotic risk particularly in obese individuals, but the underlying mechanisms remain unclear. This study aims to compare the effects of PM_2.5 on inflammation and platelet activation in obese versus normal-weight adults, and investigate potential causal pathways. We conducted a panel study measuring blood markers in 44 obese and 53 normal-weight adults on 3 separate occasions in 2017-2018. Associations between PM_2.5/black carbon (BC) and biomarkers were estimated using mixed-effect models. An interaction analysis compared PM_2.5/BC-related effects between subgroups. Biomarker combinations and mediation analysis were performed to elucidate the biological pathways. There was a significant "low-high-low" trend of PM_2.5 levels across the 3 study periods. Increases in pro-inflammatory cytokines and changes of platelet activation and aggregation markers were associated with PM_2.5/BC in obese subgroup only. Among obese subjects, the combination of pro-inflammatory cytokines and that of platelet markers increased 26.8% (95% CI: 16.0%, 37.9%) and 14.7% (95% CI: 1.9%, 27.0%) per IQR increase in PM_2.5 over 5-day and 7-day averages. Inflammation mediated 24.5% of the pathways through which PM_2.5 promoted platelet activation. This study suggested obese people are susceptible to pro-thrombotic impacts of PM_2.5 exposures. PM_2.5 may aggravate thrombosis through obesity-related inflammation.

Machine learning for prediction of diabetes risk in middle-aged Swedish people

Aims

To study if machine learning methodology can be used to detect persons with increased type 2 diabetes or prediabetes risk among people without known abnormal glucose regulation.

Methods

Machine learning and interpretable machine learning models were applied on research data from Stockholm Diabetes Preventive Program, including more than 8000 people initially with normal glucose tolerance or prediabetes to determine high and low risk features for further impairment in glucose tolerance at follow-up 10 and 20 years later.

Results

The features with the highest importance on the outcome were body mass index, waist-hip ratio, age, systolic and diastolic blood pressure, and diabetes heredity. High values of these features as well as diabetes heredity conferred increased risk of type 2 diabetes. . The machine learning model was used to generate individual, comprehensible risk profiles, where the diabetes risk was obtained for each person in the data set. Features with the largest increasing or decreasing effects on the risk were determined.

Conclusions

The primary application of this machine learning model is to predict individual type 2 diabetes risk in people without diagnosed diabetes, and to which features the risk relates. However, since most features affecting diabetes risk also play a role for metabolic control in diabetes, e.g. body mass index, diet composition, tobacco use, and stress, the tool can possibly also be used in diabetes care to develop more individualized, easily accessible health care plans to be utilized when encountering the patients.

Machine learning analysis of pregnancy data enables early identification of a subpopulation of newborns with ASD

To identify newborns at risk of developing ASD and to detect ASD biomarkers early after birth, we compared retrospectively ultrasound and biological measurements of babies diagnosed later with ASD or neurotypical (NT) that are collected routinely during pregnancy and birth. We used a supervised machine learning algorithm with a cross-validation technique to classify NT and ASD babies and performed various statistical tests. With a minimization of the false positive rate, 96% of NT and 41% of ASD babies were identified with a positive predictive value of 77%. We identified the following biomarkers related to ASD: sex, maternal familial history of auto-immune diseases, maternal immunization to CMV, IgG CMV level, timing of fetal rotation on head, femur length in the 3rd trimester, white blood cell count in the 3rd trimester, fetal heart rate during labor, newborn feeding and temperature difference between birth and one day after. Furthermore, statistical models revealed that a subpopulation of 38% of babies at risk of ASD had significantly larger fetal head circumference than age-matched NT ones, suggesting an in utero origin of the reported bigger brains of toddlers with ASD. Our results suggest that pregnancy follow-up measurements might provide an early prognosis of ASD enabling pre-symptomatic behavioral interventions to attenuate efficiently ASD developmental sequels.

Classification algorithm for congenital Zika Syndrome: characterizations, diagnosis and validation

Zika virus was responsible for the microcephaly epidemic in Brazil which began in October 2015 and brought great challenges to the scientific community and health professionals in terms of diagnosis and classification. Due to the difficulties in correctly identifying Zika cases, it is necessary to develop an automatic procedure to classify the probability of a CZS case from the clinical data. This work presents a machine learning algorithm capable of achieving this from structured and unstructured available data. The proposed algorithm reached 83% accuracy with textual information in medical records and image reports and 76% accuracy in classifying data without textual information. Therefore, the proposed algorithm has the potential to classify CZS cases in order to clarify the real effects of this epidemic, as well as to contribute to health surveillance in monitoring possible future epidemics.

A prediction and interpretation framework of acute kidney injury in critical care

Acute kidney injury (AKI) is a common clinical condition with high mortality and resource consumption. Early identification of high-risk patients to achieve an appropriate allocation of limited clinical resources and timely interventions is of significant importance, which has attracted substantial research to develop prediction models for AKI risk stratification. However, most available AKI prediction models have moderate performance and lack of interpretability, which limits their applicability in supporting care intervention. In this paper, a machine learning-based framework for AKI prediction and interpretation in critical care is presented. First, an ensemble model is developed to predict a patient's risk of AKI within 72 h of admission to the intensive care units. Next, the model is interpreted both globally and locally. For the global interpretation, the important predictors are pinpointed and the detailed relationships between AKI risk and these predictors are illustrated. For the local interpretation, patient-specific analysis is presented to provide a visualized explanation for each individual prediction. Experimental results show that such a prediction and interpretation framework can lead to good prediction and interpretation performance, which has the potential to provide effective clinical decision support.

Use of Machine Learning Approaches in Clinical Epidemiological Research of Diabetes

PURPOSE OF REVIEW

Machine learning approaches-which seek to predict outcomes or classify patient features by recognizing patterns in large datasets-are increasingly applied to clinical epidemiology research on diabetes. Given its novelty and emergence in fields outside of biomedical research, machine learning terminology, techniques, and research findings may be unfamiliar to diabetes researchers. Our aim was to present the use of machine learning approaches in an approachable way, drawing from clinical epidemiological research in diabetes published from 1 Jan 2017 to 1 June 2020.

RECENT FINDINGS

Machine learning approaches using tree-based learners-which produce decision trees to help guide clinical interventions-frequently have higher sensitivity and specificity than traditional regression models for risk prediction. Machine learning approaches using neural networking and "deep learning" can be applied to medical image data, particularly for the identification and staging of diabetic retinopathy and skin ulcers. Among the machine learning approaches reviewed, researchers identified new strategies to develop standard datasets for rigorous comparisons across older and newer approaches, methods to illustrate how a machine learner was treating underlying data, and approaches to improve the transparency of the machine learning process. Machine learning approaches have the potential to improve risk stratification and outcome prediction for clinical epidemiology applications. Achieving this potential would be facilitated by use of universal open-source datasets for fair comparisons. More work remains in the application of strategies to communicate how the machine learners are generating their predictions.

The Need for Precision Medicine to be Applied to Diabetes

Precision medicine refers to the tailoring of medical treatment for an individual based on large amounts of biologic and extrinsic data. The fast advancing fields of molecular biology, gene sequencing, machine learning, and other technologies enable precision medicine to utilize this detailed information to enhance clinical management decision-making for an individual in the real time of the disease course. Traditional clinical decision making is based on reacting to a relatively limited number of phenotypes that are determined by history, physical examination, and conventional lab tests. Precision medicine depends on highly detailed profiling of the patient's genetic, morphologic, and metabolic makeup. The precision medicine approach can be applied to individuals with diabetes to select treatments most likely to offer benefit and least likely to cause side effects, offering prospects of improved clinical outcomes and economic costs savings over current empiric practices. As genetic, metabolomic, immunologic, and other sophisticated testing becomes less expensive and more widespread in the medical record, it is expected that precision medicine will become increasingly applied to diabetes care.