Challenges and Opportunities in Biomarkers
Hall, J., Petersen, M., Johnson, L., O’Bryant, S., & Team, S. (2023)
Abstract
Background: Depression is both a risk factor for and early symptom of Alzheimer’s disease. Numerous studies have investigated the relationship between AT(N) biomarkers of AD and depression; however, the majority of these have utilized CSF in Non-Hispanic White populations.
Objective: To investigate the relationship between plasma total Tau, Aβ40, Aβ42, NFL and depression in Mexican-Americans and Non-Hispanic Whites.
Methods: The study was a cross-sectional comparison of 645 Mexican American and 644 Non-Hispanic White older adults in a community-based study of cognitive aging who had been categorized as having unimpaired cognition using a consensus based algorithmic approach. Plasma biomarkers were assayed using Simoa technology. The Geriatric Depression Scale assessed depression.
Results: Mexican Americans had significantly higher scores on the GDS. Non-Hispanic Whites had higher Aβ40, and Aβ42 and MAs had higher NFL and Tau. For Mexican Americans, linear regression analyses found NfL and Aβ42 significant predictors of GDS scores whereas for the Non-Hispanic White group none of the biomarkers was significantly related to GDS total score or any of the subscale scores. Those scoring in the depressed range had significantly higher levels of Aβ40, Aβ42, and NFL. When analyzed by ethnicity the depressed Mexican Americans had significantly higher levels of Aβ40, Aβ42, and NFL than the non-depressed. No difference between the depression levels on any of the biomarkers was found for Non-Hispanic Whites.
Conclusions: Findings support the importance of evaluating the effect of ethnicity and level of depressive symptoms when assessing the relationship of AD biomarkers to depression. In cognitively unimpaired MAs depression is related to the Alzheimer’s Disease biomarkers but this is not the case for Non-Hispanic Whites. Higher levels of these biomarkers among depressed cognitively unimpaired Mexican Americans may be an indicator of increased risk for cognitive impairment but not for Non-Hispanic Whites. Longitudinal research is needed to clarify the effect of ethnicity on the biomarker-depression relationship.
Sheetal, A., Reddy, A., Arora, P., Puri, V., & Bharatam, P. V. (2023)
Abstract
Chronic kidney disease is a specific type of Kidney Disease in which, a gradual loss of Kidney Function over a period of 3-30 months is noticeable. Early detection is imperative to prevent this catastrophic event and initiate treatment that may mitigate renal injury. Metabolomics data in Chronic Kidney Disease carries a lot of information about biomarkers. However, it is not clear which of these biomarkers are significant, biostatistical analysis of metabolomics data might provide the clues. In this work, an attempt has been made to find novel biomarkers that may be responsible for causing Chronic Kidney Disease by employing bioinformatics and advanced computational tools. The Chronic Kidney Disease data of the patients (in stages 3 and 4) was selected and data was segregated based on renal and cardiovascular parameters. The study consisted 441 patients and 293 metabolites. Subsequently the identification of top metabolites (as biomarkers) was carried out using statistical methods like t-test, Principal component analysis and partial least square analysis. Nine biomarkers were identified from these statistical analyses i.e Galacturonic acid, p-cresol, L-serine, L-glutamine, Lactose, 2-O-Glycerol-.alpha.-d-galactopyranoside, hexa-TMS, Butanoic acid, 2,4-bis[(trimethylsilyl)oxy]-, trimethylsilyl ester, Pseudo uridine penta-tms and Myo-inositol. The Reactivity of identified metabolites was confirmed by using quantum chemistry calculations in Gaussian software. Heat Map was constructed to find out the variations in concentrations of biomarkers in healthy and CKD patients and the showed the higher concentrations of L-serine, Galacturonic acid , L-glutamine and lower concentrations of Pseudo uridine penta-tms, Butanoic acid, 2,4-bis[(trimethylsilyl)oxy]-, trimethylsilyl ester , 2-O-Glycerol-.alpha.-d-galactopyranoside, hexa-TMS , Myo-inositol , p-cresol , Lactose in Death Patients. The biological significance of identified top metabolites has been evaluated by identifying the metabolic pathways in which the metabolites are involved. The metabolites which were found to be toxic are pseudouridine, L-glutamine, and galactouronic acid as per the previous reported literature. The Variations in concentration of these metabolites are responsible for the Death of patient with Chronic Kidney Disease.
Waddell, O., Frizelle, F., & Keenan, J. I. (2023)
Abstract
Colorectal cancer is the third most diagnosed cancer worldwide with an estimated 1.93 million cases diagnosed in 2020. Over the past few decades there has been a dramatic rise in the incidence of early onset colorectal cancer, defined as colorectal cancer diagnosed in those aged under 50 years. The largest predictor of survival is early stage at diagnosis, therefore ways to improve prompt diagnosis of early onset colorectal cancer at an early stage is an effective way of managing the impact of this rising disease. Diagnosing colorectal cancer in younger patients has unique challenges with patients falling outside the age of most screening programs and early symptoms of colorectal cancer being common, non-specific and initially intermittent.
While colonoscopy remains the gold standard investigation, it is a limited and expensive resource, and current patterns of practice result in large numbers of patients being scoped unnecessarily. The development and use of new and novel non-invasive biomarkers may help (either alone or in combination) identify either symptomatic patients in primary care, or aid with screening asymptomatic patients to focus resources where they are needed most. This review discusses challenges around diagnosing early onset colorectal cancer, with an overview of both current and future methods that might help overcome these challenges. These include increased assessment of familial risk, and the measurement of different biomarkers including faecal haemoglobin, markers of inflammation, gut microbiota, and selected metabolites.
Hogg, M. C. (2023)
Abstract
Transfer RNAs play a crucial role in protein translation where they bring amino acids to the ribosome to be incorporated into nascent polypeptide chains. During stress conditions tRNAs can be cleaved to generate tRNA-derived fragments. Several ribonucleases have been identified that cleave tRNA, however mutations in the stress-induced ribonuclease Angiogenin have been identified in a range of neurological disorders including Amyotrophic Lateral Sclerosis, Parkinson’s Disease, and Alzheimer’s Disease, suggesting that tRNA cleavage may be dysregulated in neurological disease. tRNA fragments have been detected in biofluids indicating they may be of use as biomarkers for neurological diseases. There is considerable variability in the methods used to quantify tRFs from size selection, adapter ligation, removal of RNA modifications, and sequence analysis approaches which can make it difficult to reconcile multiple studies. Here we review the biology of transfer RNAs and the biogenesis of tRNA-derived fragments, with a focus on the methods used to identify and quantify tRNA fragments and how different methodological approaches can influence tRNA fragment detection. We provide an overview of current literature on the identification of tRNA fragments in neurological disease models and patient samples, with a focus on circulating tRNA fragments as potential biomarkers of neurological diseases.
Tsolakis, A. V., Timplalexis, C., Tsolaki, M., & Aifantis, E. C. (2022)
Abstract
Alzheimer’s disease is one of the main challenges of modern medicine since no cure has been found yet, the scientific community still does not fully understand the reasoning behind it, and any interventions found can delay the progress for only a limited amount of time. Over the years, research has shifted from attempts for curing the disease to efforts towards understanding the mechanisms behind it as well as finding tools that will speed up diagnosis many years before its clinical manifestations, when the brain deterioration begins. One of the many promising tools towards this direction is electroencephalography. Electroencephalography employs a variety of different measures that can be used as biomarkers for early diagnosis and differentiation of Alzheimer’s disease from other neurodegenerative disorders. Literature has produced a number of methods that have established reliable correlation between electroencephalography signals and structural abnormalities in Alzheimer’s disease. To that end, the present work proposes the combination of Tsallis Entropy and Higuchi Fractal Dimension within a common classification framework using machine learning techniques for classification among healthy, Mild Cognitive Impairment, and probable Alzheimer’s disease. The proposed methodology is applied on 75 subjects with different feature utilisation scenarios, reaching to an accuracy of 98.03% when classifying a signal epoch, following a 10-fold cross validation, as compared with other similar studies. Nevertheless, in a leave-one-out scenario with the same approach, the average accuracy drops significantly, suggesting that this method could complement other diagnosis approaches but cannot be used on each own.
Vakulenko, M. (2023)
Abstract
This article presents the results of the clinical significance study of changes in the activity of adenosine deaminase and the content of polyamines in the blood plasma of cats with mammary gland tumors. In our work, we investigated the activity of the adenosine deaminase gene in the tissues of mammary gland tumors in cats and compared the activity of this gene with normal mammary gland tissue. In the course of this work, we also found out the frequency of occurrence of mammary gland tumors in the population of domestic cats in the Rostov region and evaluated the prognosis of survival after surgical treatment of breast cancer.
The incidence of mammary gland tumors was detected, which amounted to 0.4% of the domestic cats who have applied to veterinary clinics in Rostov region, where the cats with mammary gland neoplasms amounted to approximately 400 individuals for every 100,000 animals. These data reflect the importance of the problem of mammary gland tumors and emphasize the need to study the pathophysiological foundations of carcinogenesis in order to create molecular genetic approaches for early diagnosis of mammary gland tumors.
The results of the studies showed that the level of expression of the adenosine deaminase gene in the tissues of invasive nonspecific carcinoma and in the tissues of fibroepithelial hyperplasia sharply increases compared to the tissue of a healthy mammary gland, 36 times in the tissues of invasive carcinomas and 131 times in the tissues of fibroepithelial hyperplasia. At the same time, there were no significant differences between the activity of adenosine deaminase in the blood plasma of healthy animals and in the blood plasma of animals with malignant neoplasms of the mammary gland. Measurement of the level of polyamines in the blood of animals showed that the content of putrescine in the erythrocytes of the blood of cats with benign and tumor-like neoplasms of the mammary fibroepithelial hyperplasia significantly increased by 5 times compared with the indicators of the control group. In malignant neoplasms of the mammary gland invasive nonspecific carcinoma, the content of putrescine and spermin in the blood significantly exceeded the control values by 6 and 10 times, respectively.
