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Tear fluid biomarkers in ocular and systemic disease: potential use for predictive, preventive and personalised medicine.

ABSTRACT: In the field of predictive, preventive and personalised medicine, researchers are keen to identify novel and reliable ways to predict and diagnose disease, as well as to monitor patient response to therapeutic agents. In the last decade alone, the sensitivity of profiling technologies has undergone huge improvements in detection sensitivity, thus allowing quantification of minute samples, for example body fluids that were previously difficult to assay. As a consequence, there has been a huge increase in tear fluid investigation, predominantly in the field of ocular surface disease. As tears are a more accessible and less complex body fluid (than serum or plasma) and sampling is much less invasive, research is starting to focus on how disease processes affect the proteomic, lipidomic and metabolomic composition of the tear film. By determining compositional changes to tear profiles, crucial pathways in disease progression may be identified, allowing for more predictive and personalised therapy of the individual. This article will provide an overview of the various putative tear fluid biomarkers that have been identified to date, ranging from ocular surface disease and retinopathies to cancer and multiple sclerosis. Putative tear fluid biomarkers of ocular disorders, as well as the more recent field of systemic disease biomarkers, will be shown.

SUBMITTER: Hagan S

PROVIDER: S-EPMC4942926 | biostudies-other | 2016

REPOSITORIES: biostudies-other

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Project description: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.

Project description:The early identification of Suboptimal Health Status (SHS) creates a window opportunity for the predictive, preventive, and personalized medicine (PPPM) in chronic diseases. Previous studies have observed the alterations in several mRNA levels in SHS individuals. As a promising "omics" technology offering comprehension of genome structure and function at RNA level, transcriptome profiling can provide innovative molecular biomarkers for the predictive identification and targeted prevention of SHS. To explore the potential biomarkers, biological functions, and signalling pathways involved in SHS, an RNA sequencing (RNA-Seq)-based transcriptome analysis was firstly conducted on buffy coat samples collected from 30 participants with SHS and 30 age- and sex-matched healthy controls. Transcriptome analysis identified a total of 46 differentially expressed genes (DEGs), in which 22 transcripts were significantly increased and 24 transcripts were decreased in the SHS group. A total of 23 transcripts were selected as candidate predictive biomarkers for SHS. Gene Ontology (GO) annotations and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis revealed that several biological processes were related to SHS, such as ATP-binding cassette (ABC) transporter and neurodegeneration. Protein-protein interaction (PPI) network analysis identified 10 hub genes related to SHS, including GJA1, TWIST2, KRT1, TUBB3, AMHR2, BMP10, MT3, BMPER, NTM, and TMEM98. A transcriptome predictive model can distinguish SHS individuals from the healthy controls with a sensitivity of 83.3% (95% confidence interval (CI): 73.9-92.7%), a specificity of 90.0% (95% CI: 82.4-97.6%), and an area under the receiver operating characteristic curve of 0.938 (95% CI: 0.882-0.994). In the present study, we demonstrated that blood (buffy coat) samples appear to be a very promising and easily accessible biological material for the transcriptomic analyses focused on the objective identification of SHS by using our transcriptome predictive model. The pattern of particularly determined DEGs can be used as predictive transcriptomic biomarkers for the identification of SHS in an individual who may, subjectively, feel healthy, but at the level of subcellular mechanisms, the changes can provide early information about potential health problems in this person. Our findings also indicate the potential therapeutic targets in dealing with chronic diseases related to SHS, such as T2DM and CVD, and an early onset of neurodegenerative diseases, such as Alzheimer's and Parkinson's diseases, as well as the findings suggest the targets for personalized interventions as promoted in PPPM.Supplementary informationThe online version contains supplementary material available at 10.1007/s13167-021-00238-1.

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Project description:BackgroundRecognising the early signs of ischemic stroke (IS) in emergency settings has been challenging. Machine learning (ML), a robust tool for predictive, preventive and personalised medicine (PPPM/3PM), presents a possible solution for this issue and produces accurate predictions for real-time data processing.MethodsThis investigation evaluated 4999 IS patients among a total of 10,476 adults included in the initial dataset, and 1076 IS subjects among 3935 participants in the external validation dataset. Six ML-based models for the prediction of IS were trained on the initial dataset of 10,476 participants (split participants into a training set [80%] and an internal validation set [20%]). Selected clinical laboratory features routinely assessed at admission were used to inform the models. Model performance was mainly evaluated by the area under the receiver operating characteristic (AUC) curve. Additional techniques-permutation feature importance (PFI), local interpretable model-agnostic explanations (LIME), and SHapley Additive exPlanations (SHAP)-were applied for explaining the black-box ML models.ResultsFifteen routine haematological and biochemical features were selected to establish ML-based models for the prediction of IS. The XGBoost-based model achieved the highest predictive performance, reaching AUCs of 0.91 (0.90-0.92) and 0.92 (0.91-0.93) in the internal and external datasets respectively. PFI globally revealed that demographic feature age, routine haematological parameters, haemoglobin and neutrophil count, and biochemical analytes total protein and high-density lipoprotein cholesterol were more influential on the model's prediction. LIME and SHAP showed similar local feature attribution explanations.ConclusionIn the context of PPPM/3PM, we used the selected predictors obtained from the results of common blood tests to develop and validate ML-based models for the diagnosis of IS. The XGBoost-based model offers the most accurate prediction. By incorporating the individualised patient profile, this prediction tool is simple and quick to administer. This is promising to support subjective decision making in resource-limited settings or primary care, thereby shortening the time window for the treatment, and improving outcomes after IS.Supplementary informationThe online version contains supplementary material available at 10.1007/s13167-022-00283-4.

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Project description:BackgroundSeveral ocular factors have been identified for primary angle-closure glaucoma (PACG), such as a small cornea, elevated intraocular pressure (IOP), shallow anterior chamber, and short axial length (AL). However, the relationship between the severity of PACG and various ocular parameters [IOP, anterior chamber depth, AL, central corneal thickness] is not fully understood.MethodsA 7-year cross-sectional study. A total of 2254 eyes of 1312 PACG patients (females = 856 [1479 eyes] and males = 456 [775 eyes]) were included. A detailed eye examination was performed. The participants were categorized into gender subgroups followed by subdivision into three different severity groups according to their mean deviation (MD) of the visual fields results as follows: mild (MD ≤ 6 dB), moderate (MD 6-12 dB), and severe (MD > 12 dB) PACG. The associations of ocular biometry with severity of PACG were analyzed using paired Student's t test, multivariate linear regression, and logistic regression analysis.ResultsThere was a significant positive correlation between the MD and AL in the female subgroup (B = 0.663, p = 0.001, 95%CI = 1.070 to 1.255) but not in the male subgroup. Increased AL levels (mild [OR = 1], moderate [OR = 1.047, p = 0.062, 95%CI = 0.947 to 2.462], and severe [OR = 1.274, p < 0.001, 95%CI = 1.114 to 1.457]) were only associated with the severity of PACG in females. Paired Student's t tests showed that the long AL female eyes have a higher MD value than in the short AL female eyes (mean difference = 3.09, t = 6.846, p < 0.001) in the same subjects, but not in the male subgroup (p = 0.648).ConclusionsThe AL was positively and significantly related to the severity of PACG in female but not male subjects. This finding refers to the PACG pathogenesis and suggests the use of AL assessment in glaucoma monitoring, diagnosis, and progression. This may contribute to further development of personalized strategies in preventive medicine.

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Tear fluid biomarkers in ocular and systemic disease: potential use for predictive, preventive and personalised medicine.

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