Tear <sup>1</sup>H Nuclear Magnetic Resonance-Based Metabolomics Application to the Molecular Diagnosis of Aqueous Tear Deficiency and Meibomian Gland Dysfunction Article Swipe
José Manuel Benítez del Castillo
,
M.D. Pinazo-Durán
,
Silvia M. Sanz‐González
,
A.M. Muñoz-Hernández
,
José Javier García‐Medina
,
Vicente Zanón‐Moreno
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1159/000510211
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1159/000510211
Purpose: Meibomian gland dysfunction (MGD) is a major cause of signs and symptoms related to dry eyes (DE) and eyelid inflammation. We investigated the composition of human tears by metabolomic approaches in patients with aqueous tear deficiency and MGD. Methods: Participants in this prospective, case-control pilot study were split into patients with aqueous tear deficiency and MGD (DE-MGD [n = 15]) and healthy controls (CG; n = 20). Personal interviews, ocular surface disease index (OSDI), and ophthalmic examinations were performed. Reflex tears collected by capillarity were processed to 1H nuclear magnetic resonance (NMR) spectroscopy and quantitative data analysis to identify molecules by spectra comparison to library entries of purified standards and/or unknown entities. Statistical analyses were made by the SPSS 22.0 program. Results: Chemometric analysis and 1H NMR spectra comparison revealed the presence of 60 metabolites in tears. Differentiating features were evident in the NMR spectra of the 2 clinical groups, characterized by significant upregulation of phenylalanine, glycerol, and isoleucine, and downregulation of glycoproteins, leucine, and –CH3 lipids, as compared to the CG. The 1H NMR metabolomic analyses of human tears confirmed the applicability of this platform with high predictive accuracy/reliability. Conclusions: Our key distinctive findings support that DE-MGD induces tear metabolomics profile changes. Metabolites contributing to a higher separation from the CG can presumably be used, in the foreseeable future, as DE-MGD biomarkers for better managing the diagnosis and therapy of this disease.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1159/000510211
- https://www.karger.com/Article/Pdf/510211
- OA Status
- bronze
- Cited By
- 13
- References
- 56
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3043422656
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W3043422656Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1159/000510211Digital Object Identifier
- Title
-
Tear <sup>1</sup>H Nuclear Magnetic Resonance-Based Metabolomics Application to the Molecular Diagnosis of Aqueous Tear Deficiency and Meibomian Gland DysfunctionWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-07-16Full publication date if available
- Authors
-
José Manuel Benítez del Castillo, M.D. Pinazo-Durán, Silvia M. Sanz‐González, A.M. Muñoz-Hernández, José Javier García‐Medina, Vicente Zanón‐MorenoList of authors in order
- Landing page
-
https://doi.org/10.1159/000510211Publisher landing page
- PDF URL
-
https://www.karger.com/Article/Pdf/510211Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
bronzeOpen access status per OpenAlex
- OA URL
-
https://www.karger.com/Article/Pdf/510211Direct OA link when available
- Concepts
-
Chemistry, Nuclear magnetic resonance spectroscopy, Internal medicine, Endocrinology, Medicine, StereochemistryTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
13Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2024: 3, 2023: 3, 2022: 3, 2021: 3Per-year citation counts (last 5 years)
- References (count)
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56Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.compared | 169 |
| abstract_inverted_index.controls | 63 |
| abstract_inverted_index.disease. | 233 |
| abstract_inverted_index.features | 139 |
| abstract_inverted_index.findings | 195 |
| abstract_inverted_index.identify | 99 |
| abstract_inverted_index.leucine, | 164 |
| abstract_inverted_index.magnetic | 90 |
| abstract_inverted_index.managing | 226 |
| abstract_inverted_index.patients | 32, 50 |
| abstract_inverted_index.platform | 186 |
| abstract_inverted_index.presence | 132 |
| abstract_inverted_index.program. | 121 |
| abstract_inverted_index.purified | 108 |
| abstract_inverted_index.revealed | 130 |
| abstract_inverted_index.symptoms | 12 |
| abstract_inverted_index.Meibomian | 1 |
| abstract_inverted_index.collected | 82 |
| abstract_inverted_index.confirmed | 181 |
| abstract_inverted_index.diagnosis | 228 |
| abstract_inverted_index.entities. | 112 |
| abstract_inverted_index.glycerol, | 157 |
| abstract_inverted_index.molecules | 100 |
| abstract_inverted_index.processed | 86 |
| abstract_inverted_index.resonance | 91 |
| abstract_inverted_index.standards | 109 |
| abstract_inverted_index.approaches | 30 |
| abstract_inverted_index.biomarkers | 223 |
| abstract_inverted_index.comparison | 103, 129 |
| abstract_inverted_index.deficiency | 36, 54 |
| abstract_inverted_index.ophthalmic | 76 |
| abstract_inverted_index.performed. | 79 |
| abstract_inverted_index.predictive | 189 |
| abstract_inverted_index.presumably | 214 |
| abstract_inverted_index.separation | 209 |
| abstract_inverted_index.Chemometric | 123 |
| abstract_inverted_index.Metabolites | 204 |
| abstract_inverted_index.Statistical | 113 |
| abstract_inverted_index.capillarity | 84 |
| abstract_inverted_index.composition | 24 |
| abstract_inverted_index.distinctive | 194 |
| abstract_inverted_index.dysfunction | 3 |
| abstract_inverted_index.foreseeable | 219 |
| abstract_inverted_index.interviews, | 69 |
| abstract_inverted_index.isoleucine, | 159 |
| abstract_inverted_index.metabolites | 135 |
| abstract_inverted_index.metabolomic | 29, 176 |
| abstract_inverted_index.significant | 153 |
| abstract_inverted_index.Participants | 40 |
| abstract_inverted_index.case-control | 44 |
| abstract_inverted_index.contributing | 205 |
| abstract_inverted_index.examinations | 77 |
| abstract_inverted_index.investigated | 22 |
| abstract_inverted_index.metabolomics | 201 |
| abstract_inverted_index.prospective, | 43 |
| abstract_inverted_index.quantitative | 95 |
| abstract_inverted_index.spectroscopy | 93 |
| abstract_inverted_index.upregulation | 154 |
| abstract_inverted_index.applicability | 183 |
| abstract_inverted_index.characterized | 151 |
| abstract_inverted_index.inflammation. | 20 |
| abstract_inverted_index.downregulation | 161 |
| abstract_inverted_index.glycoproteins, | 163 |
| abstract_inverted_index.phenylalanine, | 156 |
| abstract_inverted_index.Differentiating | 138 |
| abstract_inverted_index.<i>n</i> | 65 |
| abstract_inverted_index.[<i>n</i> | 58 |
| abstract_inverted_index.accuracy/reliability. | 190 |
| abstract_inverted_index.<sup>1</sup>H | 88, 126, 174 |
| abstract_inverted_index.–CH<sub>3</sub> | 166 |
| abstract_inverted_index.<b><i>Methods:</i></b> | 39 |
| abstract_inverted_index.<b><i>Purpose:</i></b> | 0 |
| abstract_inverted_index.<b><i>Results:</i></b> | 122 |
| abstract_inverted_index.<b><i>Conclusions:</i></b> | 191 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 91 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 6 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/3 |
| sustainable_development_goals[0].score | 0.5 |
| sustainable_development_goals[0].display_name | Good health and well-being |
| citation_normalized_percentile.value | 0.85102209 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |