Mass spectrometry analysis of human tear fluid biomarkers specific for ocular and systemic diseases in the context of 3P medicine

Over the last two decades, a large number of non-communicable/chronic disorders reached an epidemic level on a global scale such as diabetes mellitus type 2, cardio-vascular disease, several types of malignancies, neurological and eye pathologies—all exerted system’s enormous socio-economic burden to primary, secondary, and tertiary healthcare. The paradigm change from reactive to predictive, preventive, and personalized medicine (3PM/PPPM) has been declared as an essential transformation of the overall healthcare approach to benefit the patient and society at large. To this end, specific biomarker panels are instrumental for a cost-effective predictive approach of individualized prevention and treatments tailored to the person. The source of biomarkers is crucial for specificity and reliability of diagnostic tests and treatment targets. Furthermore, any diagnostic approach preferentially should be noninvasive to increase availability of the biomaterial, and to decrease risks of potential complications as well as concomitant costs. These requirements are clearly fulfilled by tear fluid, which represents a precious source of biomarker panels. The well-justified principle of a “sick eye in a sick body” makes comprehensive tear fluid biomarker profiling highly relevant not only for diagnostics of eye pathologies but also for prediction, prognosis, and treatment monitoring of systemic diseases. One prominent example is the Sicca syndrome linked to a cascade of severe complications that include dry eye, neurologic, and oncologic diseases. In this review, protein profiles in tear fluid are highlighted and corresponding biomarkers are exemplified for several relevant pathologies, including dry eye disease, diabetic retinopathy, cancers, and neurological disorders. Corresponding analytical approaches such as sample pre-processing, differential proteomics, electrophoretic techniques, high-performance liquid chromatography (HPLC), enzyme-linked immuno-sorbent assay (ELISA), microarrays, and mass spectrometry (MS) methodology are detailed. Consequently, we proposed the overall strategies based on the tear fluid biomarkers application for 3P medicine practice. In the context of 3P medicine, tear fluid analytical pathways are considered to predict disease development, to target preventive measures, and to create treatment algorithms tailored to individual patient profiles.

Homocysteine metabolism as the target for predictive medical approach, disease prevention, prognosis, and treatments tailored to the person

Homocysteine (Hcy) metabolism is crucial for regulating methionine availability, protein homeostasis, and DNA-methylation presenting, therefore, key pathways in post-genomic and epigenetic regulation mechanisms. Consequently, impaired Hcy metabolism leading to elevated concentrations of Hcy in the blood plasma (hyperhomocysteinemia) is linked to the overproduction of free radicals, induced oxidative stress, mitochondrial impairments, systemic inflammation and increased risks of eye disorders, coronary artery diseases, atherosclerosis, myocardial infarction, ischemic stroke, thrombotic events, cancer development and progression, osteoporosis, neurodegenerative disorders, pregnancy complications, delayed healing processes, and poor COVID-19 outcomes, among others. This review focuses on the homocysteine metabolism impairments relevant for various pathological conditions. Innovative strategies in the framework of 3P medicine consider Hcy metabolic pathways as the specific target for in vitro diagnostics, predictive medical approaches, cost-effective preventive measures, and optimized treatments tailored to the individualized patient profiles in primary, secondary, and tertiary care.

Superficial temperature distribution patterns before and after physical activity in school children are indicative for personalized exercise coaching and disease prevention

Background Thermoregulation is highly individual and predictive for potentially cascading pathologies. Altered and deficient thermoregulation is considered an important diagnostic indicator which can be of great clinical utility for specialized screening programs and individualized prediction and prevention of severe pathologies triggered early in life. Working hypothesis Individual thermoregulation can be objectively assessed by thermovision camera before and after exercises in school children stratified by age and gender that may be of great clinical utility for personalized training early in life in the framework of 3P medicine. Study design In this study, 60 female and male primary school children were exposed to physical exercises in the form of 45-min general fitness training. The subjects under examination were stratified by age: group 1 (7-year-olds), group 2 (9-year-olds), and group 3 (12-year-olds). Superficial body temperature patterns were measured by means of thermovision camera before and immediately after exercises, as well as after the 15-min recovery time. Temperature patterns were analyzed in 12 areas of the body front and back, covering trunk and upper and lower limbs. Results The obtained results revealed an individual and age-depended difference in response of the body to exercises. The first measurement prior to exercise (measurement 1) revealed no statistically significant differences in the mean surface temperature of all analyzed areas between 7- and 9-year-old children. Further, 7- and 9-year-old children did not differ significantly in the mean temperature recorded in the trunk compared to the 12-year-old children. However, in 12-year-old children, statistically significant higher values of the mean temperature of the upper and lower limbs, were observed compared to the group of 7-year-olds and significantly higher values of the mean temperature of the lower limbs compared to the group of 9-year-olds. Immediately after exercises (measurement 2), a statistically significant decrease in the temperature was noted in all groups and in all areas of the body. The greatest temperature change was observed in 12-year-olds, while the least one was measured in the youngest subjects. The statistically significant relation between the average trunk temperature of 7-year-old and 12-year-old children was observed: lower values of the mean temperature of the front and back of the trunk were noted in the group of 12-year-old children compared to the group of 7-year-olds. A significantly lower average temperature of the back of the trunk compared to the youngest group was also recorded in 9-year-old children. The study performed after the 15-min recovery time (measurement 3) showed an increase in the average temperature of all analyzed areas. In all subjects, the mean temperature recorded in measurement 3 did not differ significantly from the initial ones (measurement 1, prior to exercises). Only the mean temperature of the trunk back of 12-year-old children was significantly lower after the rest period compared to the initial examination. In all groups, the temperatures after exercises followed by a 15-min recovery returned to the initial ones, except of the trunk backs of 12-year-old children, where the temperature was lower than before exercises. Conclusions and expert recommendations in the framework of 3PM Thermovision analysis is an effective tool to assess individual thermoregulation and to stratify school children for personalized exercise coaching. Body exercise-based disease prevention early in life is effective when tailored to the person: multi-parametric guidance for prescribing exercises individually is needed. Contextually, proposed individualized training approach should be adapted to the age-dependent particularities and individual thermoregulation.

N6-methyladenosine-related non-coding RNAs are potential prognostic and immunotherapeutic responsiveness biomarkers for bladder cancer

Background Bladder cancer (BC) is a commonly occurring malignant tumor of the urinary system, demonstrating high global morbidity and mortality rates. BC currently lacks widely accepted biomarkers and its predictive, preventive, and personalized medicine (PPPM) is still unsatisfactory. N6-methyladenosine (m6A) modification and non-coding RNAs (ncRNAs) have been shown to be effective prognostic and immunotherapeutic responsiveness biomarkers and contribute to PPPM for various tumors. However, their role in BC remains unclear. Methods m6A-related ncRNAs (lncRNAs and miRNAs) were identified through a comprehensive analysis of TCGA, starBase, and m6A2Target databases. Using TCGA dataset (training set), univariate and least absolute shrinkage and selection operator (LASSO) regression analyses were performed to develop an m6A-related ncRNA–based prognostic risk model. Kaplan-Meier analysis of overall survival (OS) and receiver operating characteristic (ROC) curves were used to verify the prognostic evaluation power of the risk model in the GSE154261 dataset (testing set) from Gene Expression Omnibus (GEO). A nomogram containing independent prognostic factors was developed. Differences in BC clinical characteristics, m6A regulators, m6A-related ncRNAs, gene expression patterns, and differentially expressed genes (DEGs)–associated molecular networks between the high- and low-risk groups in TCGA dataset were also analyzed. Additionally, the potential applicability of the risk model in the prediction of immunotherapeutic responsiveness was evaluated based on the “IMvigor210CoreBiologies” data set. Results We identified 183 m6A-related ncRNAs, of which 14 were related to OS. LASSO regression analysis was further used to develop a prognostic risk model that included 10 m6A-related ncRNAs (BAALC-AS1, MIR324, MIR191, MIR25, AC023509.1, AL021707.1, AC026362.1, GATA2-AS1, AC012065.2, and HCP5). The risk model showed an excellent prognostic evaluation performance in both TCGA and GSE154261 datasets, with ROC curve areas under the curve (AUC) of 0.62 and 0.83, respectively. A nomogram containing 3 independent prognostic factors (risk score, age, and clinical stage) was developed and was found to demonstrate high prognostic prediction accuracy (AUC = 0.83). Moreover, the risk model could also predict BC progression. A higher risk score indicated a higher pathological grade and clinical stage. We identified 1058 DEGs between the high- and low-risk groups in TCGA dataset; these DEGs were involved in 3 molecular network systems, i.e., cellular immune response, cell adhesion, and cellular biological metabolism. Furthermore, the expression levels of 8 m6A regulators and 12 m6A-related ncRNAs were significantly different between the two groups. Finally, this risk model could be used to predict immunotherapeutic responses. Conclusion Our study is the first to explore the potential application value of m6A-related ncRNAs in BC. The m6A-related ncRNA–based risk model demonstrated excellent performance in predicting prognosis and immunotherapeutic responsiveness. Based on this model, in addition to identifying high-risk patients early to provide them with focused attention and targeted prevention, we can also select beneficiaries of immunotherapy to deliver personalized medical services. Furthermore, the m6A-related ncRNAs could elucidate the molecular mechanisms of BC and lead to a new direction for the improvement of PPPM for BC.