Tear metabolomics for the diagnosis of primary open-angle glaucoma.

Primary Open-Angle Glaucoma (POAG) is the most prevalent glaucoma type, and the leading cause of irreversible visual impairment and blindness worldwide. Identification of early POAG biomarkers is of enormous value, as there is not an effective treatment for the glaucomatous optic nerve degeneration (OND). In this pilot study, a metabolomic analysis, by using proton (1H) nuclear magnetic resonance (NMR) spectroscopy was conducted in tears, in order to determine the changes of specific metabolites in the initial glaucoma eyes and to discover potential diagnostic biomarkers. A classification model, based on the metabolomic fingerprint in tears was generated as a non-invasive tool to support the preclinical and clinical POAG diagnosis. 1H NMR spectra were acquired from 30 tear samples corresponding to the POAG group (n = 11) and the control group (n = 19). Data were analysed by multivariate statistics (partial least squares-discriminant analysis: PLS-DA) to determine a model capable of differentiating between groups. The whole data set was split into calibration (65%)/validation (35%), to test the performance and the ability for glaucoma discrimination. The calculated PLS-DA model showed an area under the curve (AUC) of 1, as well as a sensitivity of 100% and a specificity of 83.3% to distinguish POAG group versus control group tear data. This model included 11 metabolites, potential biomarkers of the disease. When comparing the study groups, a decrease in the tear concentration of phenylalanine, phenylacetate, leucine, n-acetylated compounds, formic acid, and uridine, was found in the POAG group. Moreover, an increase in the tear concentration of taurine, glycine, urea, glucose, and unsaturated fatty acids was observed in the POAG group. These results highlight the potential of tear metabolomics by 1H NMR spectroscopy as a non-invasive approach to support early POAG diagnosis and in order to prevent visual loss.

Non-invasive biomarkers for mild cognitive impairment and Alzheimer's disease.

Alzheimer's disease is the most common type of dementia in the elderly. It is a progressive degenerative disorder that may begin to develop up to 15 years before clinical symptoms appear. The identification of early biomarkers is crucial to enable a prompt diagnosis and to start effective interventions. In this work, we conducted a metabolomic study using proton Nuclear Magnetic Resonance (1H NMR) spectroscopy in serum samples from patients with neuropathologically confirmed Alzheimer's disease (AD, n = 51), mild cognitive impairment (MCI, n = 27), and cognitively healthy controls (HC, n = 50) to search for metabolites that could be used as biomarkers. Patients and controls underwent yearly clinical follow-ups for up to six years. MCI group included samples from three subgroups of subjects with different disease progression rates. The first subgroup included subjects that remained clinically stable at the MCI stage during the period of study (stable MCI, S-MCI, n = 9). The second subgroup accounted for subjects which were diagnosed with MCI at the moment of blood extraction, but progressed to clinical dementia in subsequent years (MCI-to-dementia, MCI-D, n = 14). The last subgroup was composed of subjects that had been diagnosed as dementia for the first time at the moment of sample collection (incipient dementia, Incp-D, n = 4). Partial Least Square Discriminant Analysis (PLS-DA) models were developed. Three models were obtained, one to discriminate between AD and HC samples with high sensitivity (93.75%) and specificity (94.75%), another model to discriminate between AD and MCI samples (100% sensitivity and 82.35% specificity), and a last model to discriminate HC and MCI with lower sensitivity and specificity (67% and 50%). Differences within the MCI group were further studied in an attempt to determine those MCI subjects that could develop AD-type dementia in the future. The relative concentration of metabolites, and metabolic pathways were studied. Alterations in the pathways of alanine, aspartate and glutamate metabolism, pantothenate and CoA biosynthesis, and beta-alanine metabolism, were found when HC and MCI- D patients were compared. In contrast, no pathway was found disturbed in the comparison of S-MCI with HC groups. These results highlight the potential of 1H NMR metabolomics to support the diagnosis of dementia in a less invasive way, and set a starting point for the study of potential biomarkers to identify MCI or HC subjects at risk of developing AD in the future.

A new 8-oxo-7,8-2'deoxyguanosine nanoporous anodic alumina aptasensor for colorectal cancer diagnosis in blood and urine.

Many important human diseases, and especially cancer, have been related to the overproduction of 8-oxo-7,8-dihydro-2'-deoxyguanosine (8-oxo-dG). This molecule is a product of oxidative stress processes over nucleophilic bases in DNA. In this work, an aptasensor for the rapid, selective and accurate detection of this oncomarker is presented. The aptasensor consists of a nanoporous anodic alumina material loaded with a dye and is functionalized with an aptamer-based "molecular gate". In the presence of target 8-oxo-dG, the capping aptamer displaces from the surface due to the high affinity of the analyte with the capping aptamer, thus inducing delivery of the preloaded fluorescent dye. In contrast, in the absence of 8-oxo-dG, a poor payload delivery is accomplished. This aptamer-based nanodevice has great sensitivity for 8-oxo-dG, resulting in a LOD of 1 nM and a detection time of ca. 60 min. Moreover, the aptasensor is able to accurately detect 8-oxo-dG in unmodified urine and serum without pre-concentration treatments. This diagnostic tool is validated in a set of 38 urine and serum samples from patients diagnosed of colorectal cancer and control patients. These samples are also analyzed using a standardized and specific ELISA kit. The aptasensor displays excellent sensitivity (95.83/100%) and specificity (80/100%) for 8-oxo-dG detection in serum and urine samples, respectively. Our results may serve as a basis for the development of generalized fluorogenic diagnostic platforms for the easy diagnosis of cancer in biofluids as well as for monitoring therapeutic treatments and detection of relapses without the use of expensive equipment or trained personnel.