Abstract
Background
Diagnosis and prognostication of severe traumatic brain injury (sTBI) continue to be problematic despite years of research efforts. There are currently no clinically reliable ...biomarkers, though advances in protein biomarkers are being made. Utilizing Omics technology, particularly metabolomics, may provide new diagnostic biomarkers for sTBI. Several published studies have attempted to determine the specific metabolites and metabolic pathways involved; these studies will be reviewed.
Aims
This scoping review aims to summarize the current literature concerning metabolomics in sTBI, review the comprehensive data, and identify commonalities, if any, to define metabolites with potential clinical use. In addition, we will examine related metabolic pathways through pathway analysis.
Methods
Scoping review methodology was used to examine the current literature published in Embase, Scopus, PubMed, and Medline. An initial 1090 publications were identified and vetted with specific inclusion criteria. Of these, 20 publications were selected for further examination and summary. Metabolic data was classified using the Human Metabolome Database (HMDB) and arranged to determine the ‘recurrent’ metabolites and classes found in sTBI. To help understand potential mechanisms of injury, pathway analysis was performed using these metabolites and the Kyoto Encyclopedia of Genes and Genomes (KEGG) Pathway Database.
Results
Several metabolites related to sTBI and their effects on biological pathways were identified in this review. Across the literature, proline, citrulline, lactate, alanine, valine, leucine, and serine all decreased in adults post sTBI, whereas both octanoic and decanoic acid increased. Hydroxy acids and organooxygen compounds generally increased following sTBI, while most carboxylic acids decreased. Pathway analysis showed significantly affected glycine and serine metabolism, glycolysis, branched-chain amino acid (BCAA) metabolism, and other amino acid metabolisms. Interestingly, no tricarboxylic acid cycle metabolites were affected.
Conclusion
Aside from a select few metabolites, classification of a metabolic profile proved difficult due to significant ambiguity between study design, sample size, type of sample, metabolomic detection techniques, and other confounding variables found in sTBI literature. Given the trends found in some studies, further metabolomics investigation of sTBI may be useful to identify clinically relevant metabolites.
Follicular lymphoma (FL) is a cancer of B-cells, representing the second most common type of non-Hodgkin lymphoma and typically diagnosed at advanced stage in older adults. In contrast to the wide ...range of available molecular genetic data, limited data relating the metabolomic features of follicular lymphoma are known. Metabolomics is a promising analytical approach employing metabolites (molecules < 1 kDa in size) as potential biomarkers in cancer research. In this pilot study, we performed proton nuclear magnetic resonance spectroscopy (
H-NMR) on 29 cases of FL and 11 control patient specimens. The resulting spectra were assessed by both unsupervised and supervised statistical methods. We report significantly discriminant metabolomic models of common metabolites distinguishing FL from control tissues. Within our FL case series, we also report discriminant metabolomic signatures predictive of progression-free survival.
At the end of 2019, the coronavirus disease 2019 (COVID-19) pandemic increased the hospital burden of COVID-19 caused by the SARS-Cov-2 and became the most significant health challenge for nations ...worldwide. The severity and high mortality of COVID-19 have been correlated with various demographic characteristics and clinical manifestations. Prediction of mortality rate, identification of risk factors, and classification of patients played a crucial role in managing COVID-19 patients. Our purpose was to develop machine learning (ML)-based models for the prediction of mortality and severity among patients with COVID-19. Identifying the most important predictors and unraveling their relationships by classification of patients to the low-, moderate- and high-risk groups might guide prioritizing treatment decisions and a better understanding of interactions between factors. A detailed evaluation of patient data is believed to be important since COVID-19 resurgence is underway in many countries.
The findings of this study revealed that the ML-based statistically inspired modification of the partial least square (SIMPLS) method could predict the in-hospital mortality among COVID-19 patients. The prediction model was developed using 19 predictors including clinical variables, comorbidities, and blood markers with moderate predictability (
= 0.24) to separate survivors and non-survivors. Oxygen saturation level, loss of consciousness, and chronic kidney disease (CKD) were the top mortality predictors. Correlation analysis showed different correlation patterns among predictors for each non-survivor and survivor cohort separately. The main prediction model was verified using other ML-based analyses with a high area under the curve (AUC) (0.81-0.93) and specificity (0.94-0.99). The obtained data revealed that the mortality prediction model can be different for males and females with diverse predictors. Patients were classified into four clusters of mortality risk and identified the patients at the highest risk of mortality, which accentuated the most significant predictors correlating with mortality.
An ML model for predicting mortality among hospitalized COVID-19 patients was developed considering the interactions between factors that may reduce the complexity of clinical decision-making processes. The most predictive factors related to patient mortality were identified by assessing and classifying patients into different groups based on their sex and mortality risk (low-, moderate-, and high-risk groups).
Iran with an area of 1.648 million km2 is located between the Caspian Sea and the Persian Gulf. The Iranian population consists of multiethnic groups that have been influenced by various invasions ...and migration throughout history. Studies have revealed the presence of more than 47 different β-globin gene mutations responsible for β-Thalassemia in Iran. This paper is an attempt to study the origin of β-Thalassemia mutations in different parts of Iran. Distribution of β-Thalassemia mutations in Iran shows different patterns in different areas. β-Thalassemia mutations have been a reflection of people and area in correlation with migration and origin of ancestors. We compared the frequencies of β-globin mutations in different regions of Iran with those derived from neighboring countries. The analysis provided evidence of complementary information about the genetic admixture and migration of some mutations, as well as the remarkable genetic classification of the Iranian people and ethnic groups.
Breast cancer is the most common malignancy and the second leading cause of cancer deaths among women worldwide after lung cancer. Mitochondria play a central role in the regulation of cellular ...function, metabolism, and cell death in cancer cells. We aim to examine the mitochondrial polymorphisms of complex I in association with breast cancer in an Iranian cohort.
This experimental study includes 53 patients with breast cancer and 35 healthy control patients. In addition, tumor-adjacent normal breast tissue was obtained from each patient. The DNA of the tissue cells was extracted and analyzed for complex I mutations using a PCR sequencing method. Our results show 94 mtDNA complex I variants in tumor tissues. A10398G was the most prevalent polymorphism and strongly correlated with Her2 receptor in tumor tissue samples. Mitochondrial DNA (mtDNA) mutations have been widely linked to the etiology of numerous disorders. The mtDNA mutations screening on A10398G along with other mutations might provide insight on the role of mitochondrial mutations in breast cancer.
Sarcoidosis is a disorder characterized by granulomatous inflammation of unclear etiology. In this study we evaluated whether veterans with sarcoidosis exhibited different plasma metabolomic and ...metallomic profiles compared with civilians with sarcoidosis. A case control study was performed on veteran and civilian patients with confirmed sarcoidosis. Proton nuclear magnetic resonance spectroscopy (
H NMR), hydrophilic interaction liquid chromatography mass spectrometry (HILIC-MS) and inductively coupled plasma mass spectrometry (ICP-MS) were applied to quantify metabolites and metal elements in plasma samples. Our results revealed that the veterans with sarcoidosis significantly differed from civilians, according to metabolic and metallomics profiles. Moreover, the results showed that veterans with sarcoidosis and veterans with COPD were similar to each other in metabolomics and metallomics profiles. This study suggests the important role of environmental risk factors in the development of different molecular phenotypic responses of sarcoidosis. In addition, this study suggests that sarcoidosis in veterans may be an occupational disease.
Until recently, the study of mycobacterial diseases was trapped in culture-based technology that is more than a century old. The use of nucleic acid amplification is changing this, and powerful new ...technologies are on the horizon. Metabolomics, which is the study of sets of metabolites of both the bacteria and host, is being used to clarify mechanisms of disease, and can identify changes leading to better diagnosis, treatment, and prognostication of mycobacterial diseases. Metabolomic profiles are arrays of biochemical products of genes in their environment. These complex patterns are biomarkers that can allow a more complete understanding of cell function, dysfunction, and perturbation than genomics or proteomics. Metabolomics could herald sweeping advances in personalized medicine and clinical trial design, but the challenges in metabolomics are also great. Measured metabolite concentrations vary with the timing within a condition, the intrinsic biology, the instruments, and the sample preparation. Metabolism profoundly changes with age, sex, variations in gut microbial flora, and lifestyle. Validation of biomarkers is complicated by measurement accuracy, selectivity, linearity, reproducibility, robustness, and limits of detection. The statistical challenges include analysis, interpretation, and description of the vast amount of data generated. Despite these drawbacks, metabolomics provides great opportunity and the potential to understand and manage mycobacterial diseases.
There is no known marker to screen patients with sarcoidosis to determine the risk of progression to pulmonary fibrosis. We aimed to identify potential noninvasive biomarkers for early detection of ...pulmonary fibrosing sarcoidosis.
A case-control study was performed on African Americans with confirmed sarcoidosis included 31 subjects with pulmonary fibrosis vs. 36 without pulmonary fibrosis. Plasma samples were analyzed by liquid chromatography-mass spectrum. Multivariate statistical analysis models were developed in a training set based on 50 age- and sex-matched samples to identify metabolites involved in the discrimination. Principal component analysis and orthogonal partial least squares-discriminant (OPLS) analysis coupled to the most influential variables were used to derive significant metabolic discriminations.
Of the datasets from 171 feature peaks, 14 features including p-coumaroylagmatine and palmitoylcarnitine showed significant differences between fibrosing and non-fibrosing pulmonary sarcoidosis (p = 0.001). OPLS analysis presented clear separation between two groups with an acceptable goodness of fit (R(2) = 0.522) and predictive power (Q(2)=0.322). Discriminating metabolites involved collagen pathway metabolites especially those in the arginine-proline pathway.
Metabolomics can provide a useful tool to detect pulmonary fibrosis in patients with sarcoidosis. Two discriminating metabolites, p-coumaroylagmatine and palmitoylcarnitine may be potential markers for fibrosing pulmonary sarcoidosis.
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