Cardiovascular Risk Stratification in Diabetic Retinopathy via Atherosclerotic Pathway in COVID-19/non-COVID-19 Frameworks using Artificial Intelligence Paradigm: A Narrative Review

Diabetes is one of the main causes of the rising cases of blindness in adults. This microvascular complication of diabetes is termed diabetic retinopathy (DR) and is associated with an expanding risk of cardiovascular events in diabetes patients. DR, in its various forms, is seen to be a powerful indicator of atherosclerosis. Further, the macrovascular complication of diabetes leads to coronary artery disease (CAD). Thus, the timely identification of cardiovascular disease (CVD) complications in DR patients is of utmost importance. Since CAD risk assessment is expensive for lowincome countries, it is important to look for surrogate biomarkers for risk stratification of CVD in DR patients. Due to the common genetic makeup between the coronary and carotid arteries, lowcost, high-resolution imaging such as carotid B-mode ultrasound (US) can be used for arterial tissue characterization and risk stratification in DR patients. The advent of artificial intelligence (AI) techniques has facilitated the handling of large cohorts in a big data framework to identify atherosclerotic plaque features in arterial ultrasound. This enables timely CVD risk assessment and risk stratification of patients with DR. Thus, this review focuses on understanding the pathophysiology of DR, retinal and CAD imaging, the role of surrogate markers for CVD, and finally, the CVD risk stratification of DR patients. The review shows a step-by-step cyclic activity of how diabetes and atherosclerotic disease cause DR, leading to the worsening of CVD. We propose a solution to how AI can help in the identification of CVD risk. Lastly, we analyze the role of DR/CVD in the COVID-19 framework

Explainable Artificial Intelligence Paves the Way in Precision Diagnostics and Biomarker Discovery for the Subclass of Diabetic Retinopathy in Type 2 Diabetics

Diabetic retinopathy (DR), a common ocular microvascular complication of diabetes, contributes significantly to diabetes-related vision loss. This study addresses the imperative need for early diagnosis of DR and precise treatment strategies based on the explainable artificial intelligence (XAI) framework. The study integrated clinical, biochemical, and metabolomic biomarkers associated with the following classes: non-DR (NDR), non-proliferative diabetic retinopathy (NPDR), and proliferative diabetic retinopathy (PDR) in type 2 diabetes (T2D) patients. To create machine learning (ML) models, 10% of the data was divided into validation sets and 90% into discovery sets. The validation dataset was used for hyperparameter optimization and feature selection stages, while the discovery dataset was used to measure the performance of the models. A 10-fold cross-validation technique was used to evaluate the performance of ML models. Biomarker discovery was performed using minimum redundancy maximum relevance (mRMR), Boruta, and explainable boosting machine (EBM). The predictive proposed framework compares the results of eXtreme Gradient Boosting (XGBoost), natural gradient boosting for probabilistic prediction (NGBoost), and EBM models in determining the DR subclass. The hyperparameters of the models were optimized using Bayesian optimization. Combining EBM feature selection with XGBoost, the optimal model achieved (91.25 ± 1.88) % accuracy, (89.33 ± 1.80) % precision, (91.24 ± 1.67) % recall, (89.37 ± 1.52) % F1-Score, and (97.00 ± 0.25) % the area under the ROC curve (AUROC). According to the EBM explanation, the six most important biomarkers in determining the course of DR were tryptophan (Trp), phosphatidylcholine diacyl C42:2 (PC.aa.C42.2), butyrylcarnitine (C4), tyrosine (Tyr), hexadecanoyl carnitine (C16) and total dimethylarginine (DMA). The identified biomarkers may provide a better understanding of the progression of DR, paving the way for more precise and cost-effective diagnostic and treatment strategies

Collaborative denoising autoencoder for high glycated haemoglobin prediction.

A pioneering study is presented demonstrating that the presence of high glycated haemoglobin (HbA1c) levels in a patient’s blood can be reliably predicted from routinely collected clinical data. This paves the way for performing early detection of Type-2 Diabetes Mellitus (T2DM). This will save healthcare providers a major cost associated with the administration and assessment of clinical tests for HbA1c. A novel collaborative denoising autoencoder framework is used to address this challenge. The framework builds an independent denoising autoencoder model for the high and low HbA1c level, which extracts feature representations in the latent space. A baseline model using just three features: patient age together with triglycerides and glucose level achieves 76% F1-score with an SVM classifier. The collaborative denoising autoencoder uses 78 features and can predict HbA1c level with 81% F1-score

Inflammation Is A Common Factor Between Central And Peripheral Neurodegeneration

The studies in this dissertation investigate neurodegenerative conditions of the central and peripheral nervous system utilizing bioinformatics and systems biology approaches. Various neurodegenerative conditions are associated with neuroinflammation or the inflammation of nervous tissue. We utilized Parkinson’s disease as our system for neuroinflammation in the central nervous system and diabetic peripheral neuropathy for the peripheral nervous system. Parkinson’s disease is associated with loss of dopaminergic neurons in the substantia nigra and consequent loss of dopamine signaling in the striatum of the central nervous system. Characteristics of Parkinson’s Disease include symptoms such as shaking, rigidity, slowness of movement, difficulty walking, dementia, depression, anxiety, sleeping disorders, and hallmark formation of misfolded α-synuclein aggregates called Lewy bodies. Diabetic peripheral neuropathy is a microvascular complication associated with diabetes mellitus. Degeneration of the peripheral nervous system in diabetes presents as neuropathic pain in the periphery with eventual loss of sensation in a stocking and glove like pattern. The loss of sensation is an underlying cause of diabetic foot syndrome which is the leading cause of lower limb amputations. This dissertation consists of three studies. The first study compared multiple murine models of diabetic peripheral neuropathy at different stages of the disease against human subjects in effort to identify an underlying cause of disease using publicly available microarray transcriptomic data. Pathway and network analysis were performed in conjunction on differentially expressed genes identified by comparing healthy controls to diabetic mice and progressive to non-progressive human subjects with diabetic peripheral neuropathy. Clusters of pathways in this network were related to inflammation, degradation, apoptosis, as well as kinase and immune signaling, as conserved changes across multiple time points, models, and species of DPN. These observed pathways, commonly disrupted across progression, species, and various murine models of the disease, are likely the key responses associated with diabetic peripheral neuropathy. The second study further investigated a single high dose streptozotocin model of type 1 diabetes mellitus by comparing tissues related to diabetic peripheral neuropathy (sciatic nerve and dorsal root ganglia) and diabetic nephropathy (renal glomerulus and cortex). RNA-sequencing identified differentially expressed genes in each complication-prone tissue between healthy controls and streptozotocin-treated mice. Genes with a conserved directional change were analysed using network and pathway analysis. Clusters related to DNA-damage response, oxidative stress, and immune response were represented in shared genes between diabeticnephropathy and diabetic peripheral neuropathy tissue experiencing a common directional change. These cluster themes are likely key conserved disruptions in microvascular complication-prone tissue. The third study explored neuroinflammation of the central nervous system utilizing mice overexpressing α-synuclein under the mouse thymidine1 promoter as an animal model of Parkinson’s disease. This murine model exhibits parkinsonian motor and non-motor symptoms as well as α-synuclein aggregation pathology. Early activation of microglia, the resident innate immune cells of the brain, and an inflammatory response can be measured in the brains of these animals as early as one month of age. RNA and DNA were extracted from microglia isolated from these animals at 3 and 13 months of age for RNA-sequencing and reduced representation bisulfite sequencing, respectively. The time points for tissue collection involve the beginning of motor symptoms at 3 months and 13 months is immediately prior to a loss of 40% of dopamine signaling which occurs at 14 months of age. The overexpression of α-synuclein-induced both genomic methylation and gene expression changes that are indicative of an immunologically activated M1 state of microglia. Correlation between gene expression and a change in methylation status were investigated but only intronic CG rich sites held a significant correlation with observed gene expression (r=-0.15, p=0.008). Profiling the changes induced by α-synuclein provides valuable insight into the systems contributing to disease progression. Overall, these results warrant further investigation into the role inflammation plays on the progression of neurodegenerative diseases. Our wide range of models and techniques lends strength to the notion of common immune activation pathways induced by a variety of disease insults in both the central and peripheral nervous systems

The Involvement Of Polyol Pathway In Hypgerglycemia And Cadmium Toxicity In The Establishment Of Diabetic Neprhopathy

Diabetic nephropathy (DN) is a major cause of end-stage renal disease (ESRD), where prolonged exposure to hyperglycemia induces damage to proximal tubule cells of the kidney. Since progression to ESRD correlates to pathological changes in the tubular segments of the kidney, the effects of hyperglycemia in the PT portion of the nephron may be particularly relevant to the progression of DN. Development of this disease also is likely to occur in the context of exposure to other renal toxins, and the heavy metal cadmium (Cd2+) may be the most relevant due to the accumulation of this metal in the major cell type involved in glucose reabsorption: proximal tubule cells. Preliminary microarray analysis has shown human proximal tubule (HPT) cells exposed acutely and chronically to Cd2+ have an increased expression of an aldose reductase (AR) isoform, AKR1B10. This isoform along with AKR1B1 and sorbitol dehydrogenase (SORD) are involved in glucose metabolism under hyperglycemic conditions via the polyol pathway. The goal of this study was to verify and extend these observations in culture of HPT cells. For this purpose, HPT cells were exposed to one of the three following treatments; 5.5 (control), 7.5, 11, or 16 mM glucose concentrations for 8 days; 9, 27, 45 μM Cd2+ for 24 hours (acute), or 4.5, 9, 27 μM Cd2+ for 13 days (chronic). Real-time PCR was used to measure the expression level of these enzymes. Exposures to either hyperglycemia or Cd2+ stimulated a significant induction of AKR1B10 in HPT cells; however, exposure to these renal toxins had no effect on AKR1B1 or SORD expression. We also observed glucose-induced loss of epithelial morphology that correlated to an induction of N- Cadherin (CDH2), a mesenchymal marker. These results are suggestive of potential synergistic effects of Cd2+ and hyperglycemia in the toxic responses of the proximal tubules during the development of DN

Cardiac and Mitochondrial Impacts of Acute Pulmonary Xenobiotic Exposure

With every breath, we breathe in foreign materials, xenobiotic particles. These particles can interact with our tissues and influence the systems within. Airborne xenobiotic particles classically refer to ambient particulate matter (PM), but with technological advancement and the flourishing of nanotechnology, engineered nanomaterials (ENMs) have become entwined in the definition. Because the term xenobiotic particles encompasses particles with a broad range of size, shapes and chemical composition, definition of the properties that induce toxicity can be difficult, yet crucial to understand and predicting which of these characteristics is capable of inducing toxicity. This concept is crucial as nanotechnology moves forward and continues to introduce particles with new, unique properties. Beginning to identify the health impacts of xenobiotic particles it is important to consider ambient air pollution and the solid fraction of this mixture, particulate matter (PM). PM itself is a non-uniform, composite particle containing particles ranging in size and chemical composition. Further, PM composition can vary geographically and has been shown to differentially affect cardiovascular susceptibilities and outcomes. Within the Appalachian region, coal is a multi-billion dollar industry and comes in two forms: underground and surface mining. Surface mining is growing throughout the region due to its less labor-intensive methods, which employs large machinery to remove the soil and rock from on top of mineral deposits. One form of surface mining utilizes explosives to remove this overburden, mountaintop removal mining (MTM). Even though the mining companies attempt to abate fugitive dust, the populations surrounding these mining operations have a higher incidence of chronic cardiovascular disease mortality rates. This suggests that the PM created by MTM (PMMTM) may induce cardiac stress leading to cardiovascular disease. Nanotechnology is rapidly growing into a multi-billion dollar industry and is already incorporated into consumer products including everything from sporting equipment and food storage to personal care products and biomedical applications. With the rapid growth of nanotechnology, the toxicological impact of the ENMs driving expansion cannot keep pace with the advancement. Nano-sized particles differ in their physicochemical properties as compared to their micron-sized counterparts and while these properties imbue them with the novel applications driving nanotechnology, they may also be driving toxicological impacts. ENMs are carefully and methodically produced in particles of varying size, shape and chemical composition to accomplish different consumer-based end-products. Multi-walled carbon nanotubes (MWCNT) are a rapidly growing ENM with uniquely strong and electrical properties making it useful in everything from sporting equipment to electronics. Titanium dioxide (nano-TiO2) is a relatively inert ENM widely used as a photocatalyst and pigment in paints and personal care products. Exposure to these materials has shown adverse pulmonary and cardiovascular effects but the cardiac functional impacts following exposure have not been well characterized. Further, the subcellular cardiac mechanisms impacted by xenobiotic exposure have not been well defined. The mitochondrion may be a target of xenobiotic exposure propagating toxicity. Within the cardiomyocyte, mitochondrial analyses are further complicated by the presence of spatially and biochemically distinct subpopulations of mitochondria: the subsarcolemmal (SSM) and interfibrillar (IFM) mitochondria. The SSM sit below the sarcolemma while the IFM reside within the contractile apparatus. The goal of the current studies was to investigate the cardiac and mitochondrial impacts following an acute pulmonary xenobiotic exposure. To complete this goal, we utilized pulmonary exposure techniques, state of the art echocardiographic assessment, and mitochondrial functional analyses following xenobiotic exposure. Following a pulmonary exposure to PMMTM, we identified a significant decrease in cardiac ejection fraction and fractional shortening concomitant with an increase in cardiac apoptosis. Investigation into the source of apoptotic signaling suggested the mitochondria as central into apoptotic initiation and leads to both SSM and IFM respiratory dysfunction. Similarly, when we exposed animals to MWCNT we identified cardiac dysfunction developing after SSM and IFM respiratory dysfunction. Yet, further investigation into the mitochondrial affects identified that the IFM produced more reactive oxygen species (ROS) following exposure. Finally, following exposure to nano-vTiO2 cardiac diastolic dysfunction was observed indicative of restrictive filling during diastole. Following exposure, there was a significant decrease in mitochondrial respiratory function and an increase in ROS production and damage in the IFM. To attenuate the mitochondrial ROS production and damage leading to cardiac dysfunction, we utilized a novel transgenic animal overexpressing the antioxidant mitochondrial phospholipid hydroperoxide glutathione peroxidase (mPHGPx). MPHGPx has been previously shown to be efficient in protecting the inner mitochondrial membrane (IMM) from ROS damage and preserve the mitochondrion\u27s function and proteome. The IMM is essential to protect as the complexes within the mitochondrial electron transport chain (ETC) reside within the locale. Overexpressing mPHGPx attenuated mitochondrial ROS production and damage as well as the cardiac diastolic dysfunction observed following exposure to nano-TiO2. (Abstract shortened by UMI.)