Exhaustive exercise and vitamins C and E modulate thyroid hormone levels at low and high altitudes

Thyroid hormones play an important role in cell growth and differentiation and regulation of oxygen consumption and thermogenesis. The effect of altitude and vitamin supplementation on thyroid hormone levels in animals or humans performing acute exhaustive exercise have not been investigated before. Therefore, we thought to test whether exhaustive exerciseinduced stress with antioxidant supplementation was capable of modulating the level of thyroid hormones at different altitudes. Serum levels of T4 (Thyroxin), T3 (Triiodothyronine), and TSH (Thyroid Stimulating Hormone) were measured in rats (N=36) born and bred in low altitude (600 m above sea level) and high altitude (2200 m above sea level) following forced swimming with or without vitamins C and E (25 mg/kg) pre-treatments. Thyroid levels were significantly decreased in resting rats at high altitude compared to low altitude, and swimming exercise moderately increased T3 and TSH at both high and low altitudes, whereas T4 was markedly increased (62 %) at low altitude compared to a moderate high altitude increase (28 %). Co-administration of vitamins C and E augmented the observed forced swimminginduced thyroid release. However, the conversion of T4 to T3 was reduced in both altitude areas following swimming exercise and vitamin pre-treatment had no effect. We conclude that acute stress induced thyroidal hormones in rats, which was augmented by antioxidant drugs in both high and low altitude areas. These findings may play an important role in the human pathophysiology of thyroid gland at different altitudes

Untargeted Lipidomic Biomarkers for Liver Cancer Diagnosis: A Tree-Based Machine Learning Model Enhanced by Explainable Artificial Intelligence

Background and Objectives: Liver cancer ranks among the leading causes of cancer-related mortality, necessitating the development of novel diagnostic methods. Deregulated lipid metabolism, a hallmark of hepatocarcinogenesis, offers compelling prospects for biomarker identification. This study aims to employ explainable artificial intelligence (XAI) to identify lipidomic biomarkers for liver cancer and to develop a robust predictive model for early diagnosis. Materials and Methods: This study included 219 patients diagnosed with liver cancer and 219 healthy controls. Serum samples underwent untargeted lipidomic analysis with LC-QTOF-MS. Lipidomic data underwent univariate and multivariate analyses, including fold change (FC), t-tests, PLS-DA, and Elastic Network feature selection, to identify significant biomarker candidate lipids. Machine learning models (AdaBoost, Random Forest, Gradient Boosting) were developed and evaluated utilizing these biomarkers to differentiate liver cancer. The AUC metric was employed to identify the optimal predictive model, whereas SHAP was utilized to achieve interpretability of the model's predictive decisions. Results: Notable alterations in lipid profiles were observed: decreased sphingomyelins (SM d39:2, SM d41:2) and increased fatty acids (FA 14:1, FA 22:2) and phosphatidylcholines (PC 34:1, PC 32:1). AdaBoost exhibited a superior classification performance, achieving an AUC of 0.875. SHAP identified PC 40:4 as the most efficacious lipid for model predictions. The SM d41:2 and SM d36:3 lipids were specifically associated with an increased risk of low-onset cancer and elevated levels of the PC 40:4 lipid. Conclusions: This study demonstrates that untargeted lipidomics, in conjunction with explainable artificial intelligence (XAI) and machine learning, may effectively identify biomarkers for the early detection of liver cancer. The results suggest that alterations in lipid metabolism are crucial to the progression of liver cancer and provide valuable insights for incorporating lipidomics into precision oncology

Combining the Strengths of the Explainable Boosting Machine and Metabolomics Approaches for Biomarker Discovery in Acute Myocardial Infarction

Acute Myocardial Infarction (AMI), a common disease that can have serious consequences, occurs when myocardial blood flow stops due to occlusion of the coronary artery. Early and accurate prediction of AMI is critical for rapid prognosis and improved patient outcomes. Metabolomics, the study of small molecules within biological systems, is an effective tool used to discover biomarkers associated with many diseases. This study intended to construct a predictive model for AMI utilizing metabolomics data and an explainable machine learning approach called Explainable Boosting Machines (EBM). The EBM model was trained on a dataset of 102 prognostic metabolites gathered from 99 individuals, including 34 healthy controls and 65 AMI patients. After a comprehensive data preprocessing, 21 metabolites were determined as the candidate predictors to predict AMI. The EBM model displayed satisfactory performance in predicting AMI, with various classification performance metrics. The model's predictions were based on the combined effects of individual metabolites and their interactions. In this context, the results obtained in two different EBM modeling, including both only individual metabolite features and their interaction effects, were discussed. The most important predictors included creatinine, nicotinamide, and isocitrate. These metabolites are involved in different biological activities, such as energy metabolism, DNA repair, and cellular signaling. The results demonstrate the potential of the combination of metabolomics and the EBM model in constructing reliable and interpretable prediction outputs for AMI. The discussed metabolite biomarkers may assist in early diagnosis, risk assessment, and personalized treatment methods for AMI patients. This study successfully developed a pipeline incorporating extensive data preprocessing and the EBM model to identify potential metabolite biomarkers for predicting AMI. The EBM model, with its ability to incorporate interaction terms, demonstrated satisfactory classification performance and revealed significant metabolite interactions that could be valuable in assessing AMI risk. However, the results obtained from this study should be validated with studies to be carried out in larger and well-defined samples

Platelet Metabolites as Candidate Biomarkers in Sepsis Diagnosis and Management Using the Proposed Explainable Artificial Intelligence Approach

Background: Sepsis is characterized by an atypical immune response to infection and is a dangerous health problem leading to significant mortality. Current diagnostic methods exhibit insufficient sensitivity and specificity and require the discovery of precise biomarkers for the early diagnosis and treatment of sepsis. Platelets, known for their hemostatic abilities, also play an important role in immunological responses. This study aims to develop a model integrating machine learning and explainable artificial intelligence (XAI) to identify novel platelet metabolomics markers of sepsis. Methods: A total of 39 participants, 25 diagnosed with sepsis and 14 control subjects, were included in the study. The profiles of platelet metabolites were analyzed using quantitative 1H-nuclear magnetic resonance (NMR) technology. Data were processed using the synthetic minority oversampling method (SMOTE)-Tomek to address the issue of class imbalance. In addition, missing data were filled using a technique based on random forests. Three machine learning models, namely extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and kernel tree boosting (KTBoost), were used for sepsis prediction. The models were validated using cross-validation. Clinical annotations of the optimal sepsis prediction model were analyzed using SHapley Additive exPlanations (SHAP), an XAI technique. Results: The results showed that the KTBoost model (0.900 accuracy and 0.943 AUC) achieved better performance than the other models in sepsis diagnosis. SHAP results revealed that metabolites such as carnitine, glutamate, and myo-inositol are important biomarkers in sepsis prediction and intuitively explained the prediction decisions of the model. Conclusion: Platelet metabolites identified by the KTBoost model and XAI have significant potential for the early diagnosis and monitoring of sepsis and improving patient outcomes

Explainable Boosting Machines Identify Key Metabolomic Biomarkers in Rheumatoid Arthritis

Background and Objectives: Rheumatoid arthritis (RA) is a chronic autoimmune disease characterised by joint inflammation and pain. Metabolomics approaches, which are high-throughput profiling of small molecule metabolites in plasma or serum in RA patients, have so far provided biomarker discovery in the literature for clinical subgroups, risk factors, and predictors of treatment response using classical statistical approaches or machine learning models. Despite these recent developments, an explainable artificial intelligence (XAI)-based methodology has not been used to identify RA metabolomic biomarkers and distinguish patients with RA. This study constructed a XAI-based EBM model using global plasma metabolomics profiling to identify metabolites predictive of RA patients and to develop a classification model that can distinguish RA patients from healthy controls. Materials and Methods: Global plasma metabolomics data were analysed from RA patients (49 samples) and healthy individuals (10 samples). SMOTE technique was used for class imbalance in data preprocessing. EBM, LightGBM, and AdaBoost algorithms were applied to generate a discriminatory model between RA and controls. Comprehensive performance metrics were calculated, and the interpretability of the optimal model was assessed using global and local feature descriptions. Results: A total of 59 samples were analysed, 49 from RA patients, and 10 from healthy subjects. The EBM generated better results than LightGBM and AdaBoost by attaining an AUC of 0.901 (95% CI: 0.847-0.955) with 87.8% sensitivity which helps prevent false negative early RA diagnosis. The primary biomarkers EBM-based XAI identified were N-acetyleucine, pyruvic acid, and glycerol-3-phosphate. EBM global explanation analysis indicated that elevated pyruvic acid levels were significantly correlated with RA, whereas N-acetyleucine exhibited a nonlinear relationship, implying possible protective effects at specific concentrations. Conclusions: This study underscores the promise of XAI and evidence-based medicine methodology in developing biomarkers for RA through metabolomics. The discovered metabolites offer significant insights into RA pathophysiology and may function as diagnostic biomarkers or therapeutic targets. Incorporating EBM methodologies integrated with XAI improves model transparency and increases the therapeutic applicability of predictive models for RA diagnosis/management. Furthermore, the transparent structure of the EBM model empowers clinicians to understand and verify the reasoning behind each prediction, thereby fostering trust in AI-assisted decision-making and facilitating the integration of metabolomic insights into routine clinical practice

Co-administration of Vitamins E and C protects against stress-induced hepatorenal oxidative damage and effectively improves lipid profile at both low and high altitude

The aim of this study was to evaluate the effect of co- administration of vitamins E and C on exhaustive exercise induced-stress in regards to hepatorenal function in rats native to low altitude (LA) and high altitude (HA). In both LA and HA areas, native wistar rats of each area were divided into three groups of 6 rats each, which include stress-free control, forced swimming-induced experimental stress and experimental stress plus vitamins E and C treatment. Lipid profile and Liver and kidney functions were assessed in both groups. HA and LA rats exhibit similar baseline levels of liver and kidney function as well as lipid metabolism profiles. However, HA rats showed decreased levels of antioxidant markers with an increased level of lipid peroxidation. Exhaustive swimming exercise induced a significant increase in the liver and kidney function of rats at both altitudes accompanied with a decrease in antioxidants levels. However, the magnitude of change observed in HA rats was more profound. Also at LA, forced swimming exercise resulted in a significant increase in serum total cholesterol (TChol), triacylglycerides (TAG) and high-density lipoprotein cholesterol (HDL). However, in HA rats, forced swimming exercise caused a significant decrease in serum TChol and low-density lipoprotein (LDL), except for HDL levels which were significantly elevated. Pre- and co-administration of vitamins E and C counteracted the induction of liver and/or kidney function by exhaustive exercise, and lowered TChol and LDL levels in rats at either altitude. In conclusion, at native high altitude: kidney and liver function essentially remained stable; response to stress included more profound oxidative damage to liver and kidney tissues as well as augmented deterioration in lipid metabolism compared to low altitude; and combined administration of vitamins E and C protected against observed oxidative stress damage to liver and kidney tissues and preserved lipid metabolism. At low altitude, combined administration of vitamin E and C protected against stress-induced oxidative damage to the liver and kidney and did preserve normal lipid metabolism, except for HDL. These novel findings reveal the pathophysiological changes in the liver function, kidney function and lipid metabolism occurring at high altitude specifically under stress, and demonstrate the efficacy of combined supplementation of vitamins E and C to normalize these changes.Key words: Exercise, oxidative stress, vitamin E, vitamin C, altitude, rats

Enhancing type 2 diabetes mellitus prediction by integrating metabolomics and tree-based boosting approaches

Background Type 2 diabetes mellitus (T2DM) is a global health problem characterized by insulin resistance and hyperglycemia. Early detection and accurate prediction of T2DM is crucial for effective management and prevention. This study explores the integration of machine learning (ML) and explainable artificial intelligence (XAI) approaches based on metabolomics panel data to identify biomarkers and develop predictive models for T2DM.Methods Metabolomics data from T2DM (n = 31) and healthy controls (n = 34) were analyzed for biomarker discovery (mostly amino acids, fatty acids, and purines) and T2DM prediction. Feature selection was performed using the least absolute shrinkage and selection operator (LASSO) regression to enhance the model's accuracy and interpretability. Advanced three tree-based ML algorithms (KTBoost: Kernel-Tree Boosting; XGBoost: eXtreme Gradient Boosting; NGBoost: Natural Gradient Boosting) were employed to predict T2DM using these biomarkers. The SHapley Additive exPlanations (SHAP) method was used to explain the effects of metabolomics biomarkers on the prediction of the model.Results The study identified multiple metabolites associated with T2DM, where LASSO feature selection highlighted important biomarkers. KTBoost [Accuracy: 0.938; CI: (0.880-0.997), Sensitivity: 0.971; CI: (0.847-0.999), Area under the Curve (AUC): 0.965; CI: (0.937-0.994)] demonstrated its effectiveness in using complex metabolomics data for T2DM prediction and achieved better performance than other models. According to KTBoost's SHAP, high levels of phenylactate (pla) and taurine metabolites, as well as low concentrations of cysteine, laspartate, and lcysteate, are strongly associated with the presence of T2DM.Conclusion The integration of metabolomics profiling and XAI offers a promising approach to predicting T2DM. The use of tree-based algorithms, in particular KTBoost, provides a robust framework for analyzing complex datasets and improves the prediction accuracy of T2DM onset. Future research should focus on validating these biomarkers and models in larger, more diverse populations to solidify their clinical utility

Derangement of hemopoiesis and hematological indices in Khat (Catha edulis) - treated rats

The purpose of this study was to identify the sub-acute toxic effects of Khat (Catha edulis) on hemopoiesis and hematological indices of white albino rats. Two groups, each of 10 rats, were used. In the experimental group, a hydro-ethanol extract of C. edulis was administered orally to rats, daily, in single doses of 500 mg/kg body weight, for for weeks. The control group received equivalent amounts of normal saline. Our results show, for the first time, that oral administration of C. edulis hydro-ethanol extract caused significant derangement in hemopoiesis and in gross hematological indices in rats, characterized by macrocytic anemia and leucopenia. Our data show statistically significant decreases in total leukocytes count (TLC) in which, hemoglobin concentration (Hb. conc.), packed cell volume (PCV), and red cell count (RCC), accompanied by significant increases in mean cell volume (MCV), red blood cell distribution width (RDW) and platelets count with no change in mean hemoglobin concentration (MHC). In peripheral blood smears (PBS) of treated rats, there were evidences of dyserythropoiesis- impaired hemoglobinization, macrocytosis, poikilocytosis and anisocytosis, and dysgranulopoiesis- giant forms, hypersegmented neutrophils and bizarre nuclear shapes. In conclusion, our results indicate that oral administration of a hydro-ethanol extract of C. edulis adversely affected blood cell formation and induced macrocytic anemia and leukopenia in rats. However, the exact mechanisms of these hematological changes produced by Khat are still in need for further studies.Keywords:Catha edulis, hemopoiesis, anemia, leukopenia, ratsAfrican Journal of Biotechnology, Vol. 13(2), pp. 349-355, 8 January, 201

Metabolomics Biomarker Discovery to Optimize Hepatocellular Carcinoma Diagnosis: Methodology Integrating AutoML and Explainable Artificial Intelligence

Background: This study aims to assess the efficacy of combining automated machine learning (AutoML) and explainable artificial intelligence (XAI) in identifying metabolomic biomarkers that can differentiate between hepatocellular carcinoma (HCC) and liver cirrhosis in patients with hepatitis C virus (HCV) infection. Methods: We investigated publicly accessible data encompassing HCC patients and cirrhotic controls. The TPOT tool, which is an AutoML tool, was used to optimize the preparation of features and data, as well as to select the most suitable machine learning model. The TreeSHAP approach, which is a type of XAI, was used to interpret the model by assessing each metabolite's individual contribution to the categorization process. Results: TPOT had superior performance in distinguishing between HCC and cirrhosis compared to other AutoML approaches AutoSKlearn and H2O AutoML, in addition to traditional machine learning models such as random forest, support vector machine, and k-nearest neighbor. The TPOT technique attained an AUC value of 0.81, showcasing superior accuracy, sensitivity, and specificity in comparison to the other models. Key metabolites, including L-valine, glycine, and DL-isoleucine, were identified as essential by TPOT and subsequently verified by TreeSHAP analysis. TreeSHAP provided a comprehensive explanation of the contribution of these metabolites to the model's predictions, thereby increasing the interpretability and dependability of the results. This thorough assessment highlights the strength and reliability of the AutoML framework in the development of clinical biomarkers. Conclusions: This study shows that AutoML and XAI can be used together to create metabolomic biomarkers that are specific to HCC. The exceptional performance of TPOT in comparison to traditional models highlights its capacity to identify biomarkers. Furthermore, TreeSHAP boosted model transparency by highlighting the relevance of certain metabolites. This comprehensive method has the potential to enhance the identification of biomarkers and generate precise, easily understandable, AI-driven solutions for diagnosing HCC