Metabolic Fingerprint of Chronic Obstructive Lung Diseases: A New Diagnostic Perspective

Role of inflammatory lipid and fatty acid metabolic abnormalities induced by plastic additives exposure in childhood asthma

Lipid metabolism play an essential role in occurrence and development of asthma, and it can be disturbed by phthalate esters (PAEs) and organophosphate flame retardants (OPFRs). As a chronic inflammatory respiratory disease, the occurrence risk of childhood asthma is increased by PAEs and OPFRs exposure, but it remains not entirely clear how PAEs and OPFRs contribute the onset and progress of the disease. We have profiled the serum levels of PAEs and OPFRs congeners by liquid chromatography coupled with mass spectrometry, and its relationships with the dysregulation of lipid metabolism in asthmatic, bronchitic (acute inflammation) and healthy (non-inflammation) children. Eight PAEs and nine OPFRs congeners were found in the serum of children (1 - 5 years old) from Shenzhen, and their total median levels were 615.16 ng/mL and 17.06 ng/mL, respectively. Moreover, the serum levels of mono-methyl phthalate (MMP), tri-propyl phosphate (TPP) and tri-n-butyl phosphate (TNBP) were significant higher in asthmatic children than in healthy and bronchitic children as control. Thirty-one characteristic lipids and fatty acids of asthma were screened by machine-learning random forest model based on serum lipidome data, and the alterations of inflammatory characteristic lipids and fatty acids including palmitic acids, 12,13-DiHODE, 14,21-DiHDHA, prostaglandin D2 and LysoPA(18:2) showed significant correlated with high serum levels of MMP, TPP and TNBP. These results imply PAEs and OPFRs promote the occurrence of childhood asthma via disrupting inflammatory lipid and fatty acid metabolism, and provide a novel sight for better understanding the effects of plastic additives on childhood asthma.

Metabolite Alterations in Autoimmune Diseases: A Systematic Review of Metabolomics Studies

Autoimmune diseases, characterized by the immune system's loss of self-tolerance, lack definitive diagnostic tests, necessitating the search for reliable biomarkers. This systematic review aims to identify common metabolite changes across multiple autoimmune diseases. Following PRISMA guidelines, we conducted a systematic literature review by searching MEDLINE, ScienceDirect, Google Scholar, PubMed, and Scopus (Elsevier) using keywords "Metabolomics", "Autoimmune diseases", and "Metabolic changes". Articles published in English up to March 2023 were included without a specific start date filter. Among 257 studies searched, 88 full-text articles met the inclusion criteria. The included articles were categorized based on analyzed biological fluids: 33 on serum, 21 on plasma, 15 on feces, 7 on urine, and 12 on other biological fluids. Each study presented different metabolites with indications of up-regulation or down-regulation when available. The current study's findings suggest that amino acid metabolism may serve as a diagnostic biomarker for autoimmune diseases, particularly in systemic lupus erythematosus (SLE), multiple sclerosis (MS), and Crohn's disease (CD). While other metabolic alterations were reported, it implies that autoimmune disorders trigger multi-metabolite changes rather than singular alterations. These shifts could be consequential outcomes of autoimmune disorders, representing a more complex interplay. Further studies are needed to validate the metabolomics findings associated with autoimmune diseases.

Biosensing chips for cancer diagnosis and treatment: a new wave towards clinical innovation

Recent technological advances in nanoscience and material designing have led to the development of point-of-care devices for biomolecule sensing and cancer diagnosis. In situ and portable sensing devices for bedside, diagnosis can effectively improve the patient’s clinical outcomes and reduce the mortality rate. Detection of exosomal RNAs by immuno-biochip with increased sensitivity and specificity to diagnose cancer has raised the understanding of the tumor microenvironment and many other technology-based biosensing devices hold great promise for clinical innovations to conquer the unbeatable fort of cancer metastasis. Electrochemical biosensors are the most sensitive category of biomolecule detection sensors with significantly low concentrations down to the atomic level. In this sense, this review addresses the recent advances in cancer detection and diagnosis by developing significant biological sensing devices that are believed to have better sensing potential than existing facilities.

Therapeutic applications of curcumin nanomedicine formulations in cystic fibrosis

Medicinal applications of turmeric-derived curcumin have been known to mankind for long ages. Its potential in managing "cystic fibrosis" has also been evaluated. This autosomal recessive genetic disease is caused by mutations in the cystic fibrosis transmembrane conductance regulator (CFTR) which involves an impaired secretion of chloride ions and leads to hypersecretion of thick and sticky mucus and serious complications including airway obstruction, chronic lung infection, and inflammatory reactions. This narrative review aims to highlight the available evidence for the efficacy of curcumin nanoformulations in its potential treatment of cystic fibrosis. Recent research has shown that curcumin acts on the localized mutant CFTR ion channel at the plasma membrane. Preclinical studies have also shown that curcumin nanoformulations have promising effects in the treatment of cystic fibrosis. In this context, the purpose of this narrative review is to highlight the general bioactivity of curcumin, the types of formulations and related studies, thus opening new therapeutic perspectives for CF.

Metabolomic Analysis of Plasma from Breast Cancer Patients Using Ultra-High-Performance Liquid Chromatography Coupled with Mass Spectrometry: An Untargeted Study

Breast cancer (BC) is one of the leading causes of cancer mortality in women worldwide, and therefore, novel biomarkers for early disease detection are critically needed. We performed herein an untargeted plasma metabolomic profiling of 55 BC patients and 55 healthy controls (HC) using ultra-high performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UHPLC/Q-TOF-MS). Pre-processed data revealed 2494 ions in total. Data matrices’ paired t-tests revealed 792 ions (both positive and negative) which presented statistically significant changes (FDR < 0.05) in intensity levels between cases versus controls. Metabolites identified with putative names via MetaboQuest using MS/MS and mass-based approaches included amino acid esters (i.e., N-stearoyl tryptophan, L-arginine ethyl ester), dipeptides (ile-ser, met-his), nitrogenous bases (i.e., uracil derivatives), lipid metabolism-derived molecules (caproleic acid), and exogenous compounds from plants, drugs, or dietary supplements. LASSO regression selected 16 metabolites after several variables (TNM Stage, Grade, smoking status, menopausal status, and race) were adjusted. A predictive conditional logistic regression model on the 16 LASSO selected ions provided a high diagnostic performance with an area-under-the-curve (AUC) value of 0.9729 (95% CI 0.96−0.98) on all 55 samples. This study proves that BC possesses a specific metabolic signature that could be exploited as a novel metabolomics-based approach for BC detection and characterization. Future studies of large-scale cohorts are needed to validate these findings.

Reversal of brain aging by targeting telomerase: A nutraceutical approach

Telomeres, the protective caps of chromosomes, shorten with age, as telomerase, the enzyme responsible for the compensation of telomere erosion, is inactive in the majority of cells. Telomere shortening and subsequent cell senescence lead to tissue aging and age‑related diseases. Neurodegenerative disorders, characterized by the progressive loss of neurons among other hallmarks of aged tissue, and poor cognitive function, have been associated with a short telomere length. Thus, telomerase activity has emerged as a therapeutic target, with novel agents being under investigation. The present study aimed to examine the effects of a novel natural telomerase activator, 'Reverse™', containing Centella asiatica extract, vitamin C, zinc and vitamin D3 on the brains of 18‑month‑old rats. The administration of the 'Reverse™' supplement for 3 months restored telomerase reverse transcriptase (TERT) expression in the brains of rats, as revealed by ELISA and immunohistochemistry. In addition, the findings from PCR‑ELISA demonstrated an enhanced telomerase activity in the cerebellum and cortex cells in the brains of rats treated with the 'Reverse™' supplement. The histopathological findings confirmed a structural reversibility effect close to the differentiation observed in the young control group of rats treated with two capsules/kg body weight of the 'Reverse™' supplement. On the whole, the findings of the present study provide a strong indication that an increased telomerase activity and TERT expression may be achieved not only in the postnatal or embryonic period, but also in the brains of middle‑aged rats through nutraceutical supplementation. The use of the 'Reverse™' supplement may thus contribute to the potential alleviation of a number of central nervous system diseases.

The snapshot of metabolic health in evaluating micronutrient status, the risk of infection and clinical outcome of COVID-19

COVID-19 has re-established the significance of analyzing the organism through a metabolic perspective to uncover the dynamic interconnections within the biological systems. The role of micronutrient status and metabolic health emerge as pivotal in COVID-19 pathogenesis and the immune system's response. Metabolic disruption, proceeding from modifiable factors, has been proposed as a significant risk factor accounting for infection susceptibility, disease severity and risk for post-COVID complications. Metabolomics, the comprehensive study and quantification of intermediates and products of metabolism, is a rapidly evolving field and a novel tool in biomarker discovery. In this article, we propose that leveraging insulin resistance biomarkers along with biomarkers of micronutrient deficiencies, will allow for a diagnostic window and provide functional therapeutic targets. Specifically, metabolomics can be applied as: a. At-home test to assess the risk of infection and propose nutritional support, b. A screening tool for high-risk COVID-19 patients to develop serious illness during hospital admission and prioritize medical support, c(i). A tool to match nutritional support with specific nutrient requirements for mildly ill patients to reduce the risk for hospitalization, and c(ii). for critically ill patients to reduce recovery time and risk of post-COVID complications, d. At-home test to monitor metabolic health and reduce post-COVID symptomatology. Metabolic rewiring offers potential virtues towards disease prevention, dissection of high-risk patients, taking actionable therapeutic measures, as well as shielding against post-COVID syndrome.

Genistein: An Integrative Overview of Its Mode of Action, Pharmacological Properties, and Health Benefits

Genistein is an isoflavone first isolated from the brooming plant Dyer's Genista tinctoria L. and is widely distributed in the Fabaceae family. As an isoflavone, mammalian genistein exerts estrogen-like functions. Several biological effects of genistein have been reported in preclinical studies, such as the antioxidant, anti-inflammatory, antibacterial, and antiviral activities, the effects of angiogenesis and estrogen, and the pharmacological activities on diabetes and lipid metabolism. The purpose of this review is to provide up-to-date evidence of preclinical pharmacological activities with mechanisms of action, bioavailability, and clinical evidence of genistein. The literature was researched using the most important keyword "genistein" from the PubMed, Science, and Google Scholar databases, and the taxonomy was validated using The Plant List. Data were also collected from specialized books and other online resources. The main positive effects of genistein refer to the protection against cardiovascular diseases and to the decrease of the incidence of some types of cancer, especially breast cancer. Although the mechanism of protection against cancer involves several aspects of genistein metabolism, the researchers attribute this effect to the similarity between the structure of soy genistein and that of estrogen. This structural similarity allows genistein to displace estrogen from cellular receptors, thus blocking their hormonal activity. The pharmacological activities resulting from the experimental studies of this review support the traditional uses of genistein, but in the future, further investigations are needed on the efficacy, safety, and use of nanotechnologies to increase bioavailability and therapeutic efficacy.

A Clinical Trial for the Identification of Metabolic Biomarkers in Hashimoto’s Thyroiditis and in Psoriasis: Study Protocol

Hashimoto’s thyroiditis and psoriasis are inflammatory disorders that significantly impact patients’ quality of life, stressing the need for novel biomarkers of early diagnosis. This randomized clinical trial (NCT04693936) aims to identify Hashimoto’s thyroiditis’ and psoriasis’ metabolic biomarkers and to investigate the effect of environmental factors on the disease-related metabolic imprint and quality of life. Patients with Hashimoto’s thyroiditis, patients with psoriasis, and healthy individuals aged 18–60 will be recruited, enrolled according to eligibility criteria (medical history, clinical thyroid markers and the PASI score) and randomized to two groups. The intervention group will receive a combination of nutraceuticals for 6 months as part of a Mediterranean diet, and the control group will follow their usual diet. Data will be collected at baseline and the end of the study, including metabolite levels, lifestyle and anthropometric measurements, adherence to the Mediterranean diet (through the Mediterranean Diet Score) and disease-specific quality of life (through the Thyroid Patient Report Outcome for Hashimoto’s group, and the Dermatology Life Quality Index for the psoriasis group). This study will investigate metabolic biomarkers and related changes in Hashimoto’s thyroiditis and psoriasis and evaluate the association of metabolic changes with dietary factors and quality of life.

Application of Metabolomics in Pediatric Asthma: Prediction, Diagnosis and Personalized Treatment

Asthma in children remains a significant public health challenge affecting 5–20% of children in Europe and is associated with increased morbidity and societal healthcare costs. The high variation in asthma incidence among countries may be attributed to differences in genetic susceptibility and environmental factors. This respiratory disorder is described as a heterogeneous syndrome of multiple clinical manifestations (phenotypes) with varying degrees of severity and airway hyper-responsiveness, which is based on patient symptoms, lung function and response to pharmacotherapy. However, an accurate diagnosis is often difficult due to diversities in clinical presentation. Therefore, identifying early diagnostic biomarkers and improving the monitoring of airway dysfunction and inflammatory through non-invasive methods are key goals in successful pediatric asthma management. Given that asthma is caused by the interaction between genes and environmental factors, an emerging approach, metabolomics—the systematic analysis of small molecules—can provide more insight into asthma pathophysiological mechanisms, enable the identification of early biomarkers and targeted personalized therapies, thus reducing disease burden and societal cost. The purpose of this review is to present evidence on the utility of metabolomics in pediatric asthma through the analysis of intermediate metabolites of biochemical pathways that involve carbohydrates, amino acids, lipids, organic acids and nucleotides and discuss their potential application in clinical practice. Also, current challenges on the integration of metabolomics in pediatric asthma management and needed next steps are critically discussed.

An original biomarker for the risk of developing cardiovascular diseases and their complications: Telomere length

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We studied a sample of patients with coronary heart disease.

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We analyzed the telomere length in this sample.

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We compared telomere length in patients and conventionally healthy study participants.

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Patients with coronary heart disease had shorter telomerestelomeres.

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These patients had an increased risk of cardiovascular complication.

Aim

The aim of this work was to study the effect of telomere length in the chromosomes of nuclear blood cells in individuals with coronary heart disease (CHD) on the development of cardiovascular complications (CVC).

Materials and methods

DNA was isolated from nuclear blood cells of 498 study participants. The telomere length was determined by real-time polymerase chain reaction. The investigation of each sample was repeated three times. Five years after the end of this study, a telephone survey of 119 patients with CHD was conducted in order to obtain data on the presence of CVC.

Results

According to the results obtained, a decrease in telomere length in patients with coronary heart disease increases the risk of subsequent development of cardiovascular complications.

Conclusion

Patients with coronary heart disease with shorter telomeres compared with conventionally healthy study participants had an increased risk of cardiovascular complications within 5 years after telomere analysis.

Prediction of Autoimmune Diseases by Targeted Metabolomic Assay of Urinary Organic Acids

Autoimmune diseases (ADs) are chronic disorders characterized by the loss of self-tolerance, and although being heterogeneous, they share common pathogenic mechanisms. Self-antigens and inflammation markers are established diagnostic tools; however, the metabolic imbalances that underlie ADs are poorly described. The study aimed to employ metabolomics for the detection of disease-related changes in autoimmune diseases that could have predictive value. Quantitative analysis of 28 urine organic acids was performed using Gas Chromatography-Mass Spectrometry in a group of 392 participants. Autoimmune thyroiditis, inflammatory bowel disease, psoriasis and rheumatoid arthritis were the most prevalent autoimmune diseases of the study. Statistically significant differences were observed in the tricarboxylate cycle metabolites, succinate, methylcitrate and malate, the pyroglutamate and 2-hydroxybutyrate from the glutathione cycle and the metabolites methylmalonate, 4-hydroxyphenylpyruvate, 2-hydroxyglutarate and 2-hydroxyisobutyrate between the AD group and the control. Artificial neural networks and Binary logistic regression resulted in the highest predictive accuracy scores (66.7% and 74.9%, respectively), while Methylmalonate, 2-Hydroxyglutarate and 2-hydroxybutyrate were proposed as potential biomarkers for autoimmune diseases. Urine organic acid levels related to the mechanisms of energy production and detoxification were associated with the presence of autoimmune diseases and could be an adjunct tool for early diagnosis and prediction.

Internet of Medical Things: An Effective and Fully Automatic IoT Approach Using Deep Learning and Fine-Tuning to Lung CT Segmentation

Several pathologies have a direct impact on society, causing public health problems. Pulmonary diseases such as Chronic obstructive pulmonary disease (COPD) are already the third leading cause of death in the world, leaving tuberculosis at ninth with 1.7 million deaths and over 10.4 million new occurrences. The detection of lung regions in images is a classic medical challenge. Studies show that computational methods contribute significantly to the medical diagnosis of lung pathologies by Computerized Tomography (CT), as well as through Internet of Things (IoT) methods based in the context on the health of things. The present work proposes a new model based on IoT for classification and segmentation of pulmonary CT images, applying the transfer learning technique in deep learning methods combined with Parzen’s probability density. The proposed model uses an Application Programming Interface (API) based on the Internet of Medical Things to classify lung images. The approach was very effective, with results above 98% accuracy for classification in pulmonary images. Then the model proceeds to the lung segmentation stage using the Mask R-CNN network to create a pulmonary map and use fine-tuning to find the pulmonary borders on the CT image. The experiment was a success, the proposed method performed better than other works in the literature, reaching high segmentation metrics values such as accuracy of 98.34%. Besides reaching 5.43 s in segmentation time and overcoming other transfer learning models, our methodology stands out among the others because it is fully automatic. The proposed approach has simplified the segmentation process using transfer learning. It has introduced a faster and more effective method for better-performing lung segmentation, making our model fully automatic and robust.

Alterations in telomere length and mitochondrial DNA copy number in human lymphocytes on short-term exposure to moderate hypoxia

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Exposure to moderate hypoxia for 24 h significantly increased telomere length.

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Telomere elongation is related to the duration of hypoxia exposure.

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Mitochondrial DNA copy number was unaffected by hypoxia exposure.

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Mitochondrial DNA copy number is a more stable marker than telomere length alteration under hypoxia.

Hypoxia is related to a variety of diseases, such as cardiovascular and inflammatory diseases and various cancers. Telomere length (TL) may vary according to the hypoxia level and cell types. To the best of our knowledge, no study has investigated the effect of moderate hypoxia on TL and mitochondrial DNA copy number (mtDNAcn) in human lymphocytes. Therefore, in this study, we analyzed the effect of moderate hypoxia on TL in correlation with mtDNAcn. This study included 32 healthy male nonsmoker’s subjects; in this cohort, we had previously studied sister chromatid exchange and microsatellite instability. Blood samples from each subject were divided into three groups: a control group and two experimental groups exposed to moderate hypoxia for 12 or 24 h. Relative TL and mtDNAcn were measured by a quantitative real-time polymerase chain reaction. The TL in the control group did not significantly differ from that in the experimental group subjected to hypoxia for 12 h; however, the TL in the 24 h hypoxia–treated experimental group was significantly higher than that in the control group. The correlation between TL and mtDNAcn was not statistically significant in the two hypoxic states. The increase in TL was observed on exposure to hypoxia for 24 h and not for 12 h; thus, the findings suggest that telomere elongation is related to hypoxia exposure duration. The mtDNAcn in the two experimental groups did not significantly differ from that in the control group. These observations suggest that mtDNAcn alterations show more genetic stability than TL alterations. To the best of our knowledge, this is the first in vitro study on human lymphocytes reporting an increase in TL and no alteration in mtDNAcn after short-time exposure to moderate hypoxia.

Integrative clinical, genomics and metabolomics data analysis for mainstream precision medicine to investigate COVID-19

Despite significant scientific and medical discoveries, the genetics of novel infectious diseases like COVID-19 remains far from understanding. SARS-CoV-2 is a single-stranded RNA respiratory virus that causes COVID-19 by binding to the ACE2 receptor in the lung and other organs. Understanding its clinical presentation and metabolomic and genetic profile will lead to the discovery of diagnostic, prognostic and predictive biomarkers, which may lead to more effective medical therapy. It is important to investigate correlations and overlap between reported diagnoses of a patient with COVID-19 in clinical data with identified germline and somatic mutations, and highly expressed genes from genomics data analysis. Timely model clinical, genomics and metabolomics data to find statistical patterns across millions of features to identify underlying biological pathways, modifiable risk factors and actionable information that supports early detection and prevention of COVID-19, and development of new therapies for better patient care. Next, ensuring security reconcile noise, need to build and train machine learning prognostic models to find actionable information that supports early detection and prevention of COVID-19. Based on the myriad data, applying appropriate machine learning algorithms to stratify patients, understand scenarios, optimise decision-making, identify high-risk rare variants (including ACE2, TMPRSS2) and making medically relevant predictions. Innovative and intelligent solutions are required to improve the traditional symptom-driven practice, and allow earlier interventions using predictive diagnostics and tailor better personalised treatments, when confronted with the challenges of pandemic situations.

Metabolic profiling of organic and fatty acids in chronic and autoimmune diseases

Metabolomics is a powerful tool of omics that permits the simultaneous identification of metabolic perturbations in several autoimmune and chronic diseases. Several parameters can affect a metabolic profile, from the population characteristics to the selection of the analytical method. In the current chapter, we summarize the main analytical methods and results of the metabolic profiling of fatty and organic acids performed in human metabolomic studies for asthma, COPD, psoriasis and Hashimoto's thyroiditis. We discuss the most significant metabolic alterations associated with these diseases, after comparison of either a single patient's group with healthy controls or several patient's subgroups of different disease severity and phenotype with healthy controls or of a patient's group before and after treatment. Finally, we present critical metabolic patterns that are associated with each disease and their potency for the unraveling of disease pathogenesis, prediction, diagnosis, patient stratification and treatment selection.