Open Access
Special Issue  |   May 2017
The Growing Need for Validated Biomarkers and Endpoints for Dry Eye Clinical Research
Author Affiliations & Notes
  • Neeta S. Roy
    Department of Ophthalmology, Icahn School of Medicine at Mount Sinai, New York, New York, United States
  • Yi Wei
    Department of Ophthalmology, Icahn School of Medicine at Mount Sinai, New York, New York, United States
  • Eric Kuklinski
    Department of Ophthalmology, Icahn School of Medicine at Mount Sinai, New York, New York, United States
  • Penny A. Asbell
    Department of Ophthalmology, Icahn School of Medicine at Mount Sinai, New York, New York, United States
  • Correspondence: Penny A. Asbell, Department of Ophthalmology, Icahn School of Medicine at Mount Sinai, New York, New York 10029, USA; penny.asbell@mssm.edu
Investigative Ophthalmology & Visual Science May 2017, Vol.58, BIO1-BIO19. doi:10.1167/iovs.17-21709
  • Views
  • PDF
  • Share
  • Tools
    • Alerts
      ×
      This feature is available to Subscribers Only
      Sign In or Create an Account ×
    • Get Citation

      Neeta S. Roy, Yi Wei, Eric Kuklinski, Penny A. Asbell; The Growing Need for Validated Biomarkers and Endpoints for Dry Eye Clinical Research. Invest. Ophthalmol. Vis. Sci. 2017;58(6):BIO1-BIO19. doi: 10.1167/iovs.17-21709.

      Download citation file:


      © ARVO (1962-2015); The Authors (2016-present)

      ×
  • Supplements
Abstract

Purpose: Biomarkers with minimally invasive and reproducible objective metrics provide the key to future paradigm shifts in understanding of the underlying causes of dry eye disease (DED) and approaches to treatment of DED. We review biomarkers and their validity in providing objective metrics for DED clinical research and patient care.

Methods: The English-language literature in PubMed primarily over the last decade was surveyed for studies related to identification of biomarkers of DED: (1) inflammation, (2) point-of-care, (3) ocular imaging, and (4) genetics. Relevant studies in each group were individually evaluated for (1) methodological and analytical details, (2) data and concordance with other similar studies, and (3) potential to serve as validated biomarkers with objective metrics.

Results: Significant work has been done to identify biomarkers for DED clinical trials and for patient care. Interstudy variation among studies dealing with the same biomarker type was high. This could be attributed to biologic variations and/or differences in processing, and data analysis. Correlation with other signs and symptoms of DED was not always clear or present.

Conclusions: Many of the biomarkers reviewed show the potential to serve as validated and objective metrics for clinical research and patient care in DED. Interstudy variation for a given biomarker emphasizes the need for detailed reporting of study methodology, including information on subject characteristics, quality control, processing, and analysis methods to optimize development of nonsubjective metrics. Biomarker development offers a rich opportunity to significantly move forward clinical research and patient care in DED.

Overview: DED is an unmet medical need — a chronic pain syndrome associated with variable vision that affects quality of life, is common with advancing age, interferes with the comfortable use of contact lenses, and can diminish results of eye surgeries, such as cataract extraction, LASIK, and glaucoma procedures. It is a worldwide medical challenge with a prevalence rate ranging from 8% to 50%. Many clinicians and researchers across the globe are searching for better answers to understand the mechanisms related to the development and chronicity of DED. Though there have been many clinical trials for DED, few new treatments have emerged over the last decade. Biomarkers may provide the needed breakthrough to propel our understanding of DED to the next level and the potential to realize our goal of truly personalized medicine based on scientific evidence. Clinical trials and research on DED have suffered from the lack of validated biomarkers and less than objective and reproducible endpoints. Current work on biomarkers has provided the groundwork to move forward. This review highlights primarily ocular biomarkers that have been investigated for use in DED, discusses the methodologic outcomes in providing objective metrics for clinical research, and suggests recommendations for further work.

Dry eye disease (DED) is a multifactorial condition difficult to categorize given the less than precise definitions currently used. One of the most often quoted definitions was developed by over 60 worldwide experts and published as part of the dry eye workshop report (DEWS): Dry eye is a multifactorial disease of the tears and ocular surface that results in symptoms of discomfort, visual disturbance, and tear film instability, with potential damage to the ocular surface. It is accompanied by increased osmolarity of the tear film and inflammation of the ocular surface.1 As more research and information becomes available, the definition will no doubt be modified,2 but it is unlikely to be significantly simplified in the near future given that there is no universally accepted “gold standard” to diagnose DED. Despite the common occurrence of DED, routine diagnosis and clinical evaluation often are subjective and typically based on patient symptom reporting with poor correlation between signs and symptoms.37 While multiple clinical assessments do exist to examine qualitative and quantitative facets of the ocular surface and tear functional unit,8,9 no universal consensus exists as to which of the specific assessments should be included in the diagnostic workup.10 Moreover, established threshold values for defining the distinction between normal and pathologic states on each assessment often are chosen semiarbitrarily, especially as the disease manifests in a spectrum of severity. Additionally, in many of the assessments the measure may be affected by use of drops, touching the eye, and so forth. For example, Schirmer's test, which has been used routinely to determine the amount of tear secretion, is performed by applying a standardized filter paper to the eye for 5 minutes and then measuring the length of wetness on the paper to correlate with tear production; however, its physical presence on the eyelid often stimulates reflex tear secretion, which is distinct from the basal tear production intended to be measured and, thus, can affect the measured levels.10 Other tests, though called objective, require the clinician to score the change on the ocular surface, such as vital dye staining of the cornea, and, therefore, are open to significant observer bias. As a result, poor correlations often are demonstrated between typically used assessment findings (signs) and subjective symptoms where the patient's general pain sensitivity threshold also may be a crucial factor.3,5,1113 
Nonetheless, most clinicians would say, “we know it when we see it and even with current methods we can make the diagnosis of DED during a standard ocular examination.” However, more definitive diagnostic tests, improved ability to determine severity of disease, and methods to determine efficacy of treatment, in the clinical setting and in clinical trials are much needed. 
Experience over the last decade with clinical trials in DED has demonstrated the low success rate with few new treatments reaching regulatory approval.14,15 There are likely several contributing factors to the poor success rates, including ineffective treatments, trial length, environmental influences, heterogeneity of the population and disease, and that signs do not always correlate with symptoms. In addition, the chosen primary endpoints may be less than objective or repeatable; of 57 DED clinical trials listed on clinicaltrials.gov in 2010, 33% used symptoms (dryness, grittiness, redness, and so forth) as primary endpoints, and 40% used signs (corneal staining, Schirmer's test, tear breakup time, and so forth).14 
To address the needs of clinical trials and to expand our understanding of DED, there is an acute need for the identification and validation of biomarkers using minimally invasive methods that will lead to objective metrics to help us create a roadmap for improved understanding of the mechanisms at work in DED, provide better endpoints for clinical trials, and superior patient care. 
Biomarkers as Objective Tools to Support Diagnosis, Treatment Options, and Therapeutic Drug Development
A biomarker is defined as a characteristic that is measured objectively and evaluated as an indicator of normal biological processes, pathogenic processes, or biological responses to a therapeutic intervention.16,17 Further, biomarkers do not come in “one size fits all.” They can be classified as diagnostic biomarkers, monitoring biomarkers, predictive biomarkers, and so forth.1720 As stated by BEST Resource FDA-NIH Biomarker Working Group, “biomarkers should be objective — free of biases by either the patient or observer, reproducible, and provide a metric.” Finally, key characteristics of a usable biomarker include specificity, sensitivity, simplicity, reliability, reproducibility, multiplexing capability, and cost and time needed for the methodology used.21 
Overall, not all biomarkers, as in other fields, will be validated as surrogate endpoints for clinical research involved in testing efficacy and safety of new treatments for DED.22 A surrogate endpoint, in brief, is “expected to predict clinical benefit or harm,”20 and so needs clear evidence of its rationale and its ability to predict clinical benefit. Some biomarkers may best serve clinical trials by enhancing patient selection to provide more uniform subject groups and provide easier comparability between clinical trial results.23 
As we search for biomarkers to better define DED, we are struck again with the definition and the oft-repeated line that DED has a “multifactorial” pathogenesis. Our current knowledge may be more comparable to calling all joint pain “arthritis” with no separation of osteoarthritis and rheumatoid arthritis; obviously, we do not treat all joint pain the same way and instead direct treatment to the specific mechanisms at work. Though patient-reported outcomes are key to understanding and treating symptomatic diseases, such as DED, they have not provided objective repeatable metrics that are needed for clinical trials. Biomarker data will likely lead to better categorization and more effective treatment of DED and maybe even development of companion diagnostics that will associate biomarker status with specific treatments. As such, the scientific, economic and regulatory impact of validated biomarkers and surrogate endpoints have the potential to revolutionize the approach to DED. 
The following sections review studies on biomarkers from human subjects with DED that have the potential to provide minimally invasive objective metrics that could be useful for clinical trials and patient care. 
Biomarkers of Inflammation
Even though the pathogenesis of DED is not fully understood, it is recognized that immune-mediated inflammation has prominent roles in its development and progression.2428 Ocular inflammation, of course, can be part of many diseases and, therefore, is not diagnostic of DED, but it may be useful to determine severity, and has been used in clinical trials and other studies to evaluate efficacy of treatment (listed in Tables 16). For inflammatory biomarker studies on DED patients, two approaches have primarily been used: impression cytology (IC) and tear sampling. 
Table 1
 
Markers Studied in Cells Obtained From Conjunctival Impression Cytology Samples of DED Patients
Table 1
 
Markers Studied in Cells Obtained From Conjunctival Impression Cytology Samples of DED Patients
Table 2
 
Reported Levels of HLA-DR Expression in Cells Obtained From Conjunctival Impression Cytology Samples From DED Patients
Table 2
 
Reported Levels of HLA-DR Expression in Cells Obtained From Conjunctival Impression Cytology Samples From DED Patients
Table 3
 
Observed Differences of IL-6 Levels in Tears From DED Patients and Normals
Table 3
 
Observed Differences of IL-6 Levels in Tears From DED Patients and Normals
Table 4
 
Observed Differences of TNF-α Levels in Tears From DED Patients and Normals
Table 4
 
Observed Differences of TNF-α Levels in Tears From DED Patients and Normals
Table 5
 
Observed Differences of IL-8 Levels in Tears From DED Patients and Normals
Table 5
 
Observed Differences of IL-8 Levels in Tears From DED Patients and Normals
Impression Cytology (IC)
This technique, which involves briefly touching the conjunctival surface to remove cells, has been a key minimally invasive means of sampling cells from the ocular surface. The technique, which initially was used by investigators to examine the cytologic and morphologic characteristics of the ocular surface,29 is now coupled to an array of analytical processing techniques to probe the cellular and molecular expression patterns of inflammatory biomarkers on the ocular surface in DED30,31 (summarized in Table 1). Though the recovered cells have been analyzed by light microscopy, immunocytochemistry, and mRNA polymerase chain reaction, flow cytometry is the most commonly used method as it lends itself to objective measures of multiple inflammatory biomarkers in each sample.30,32 HLA-DR is one of the most common biomarkers of inflammation in DED that has been studied using IC, while little has been done to look at other markers of inflammation (summarized in Table 1). Currently, the Dry Eye Assessment and Management (DREAM) randomized clinical trial of Omega-3 supplements is investigating a series of markers, using IC sampling, to determine common effector cells and their level of activation (Hom MM. IOVS. 2016;57:ARVO E-Abstract 2844).33 
Sampling
The general protocol for IC specimen collection for flow cytometry is based upon a method introduced by Baudouin et al.34 In brief, a porous membrane, following a single anesthetic drop, is applied to the corneal surface. It is important to point out that while most studies have reported the use of either polyether sulfone filters or Eyeprim (Tomlins P, et al. IOVS. 2013;54:ARVO E-Abstract 5430),35 no consensus exists on the size of membrane to be used, which may impact the number of cells recovered for analysis. Once removed from the eye, the samples are placed immediately in physiologic solution, which typically contains 0.05% paraformaldehyde and allows for gentle fixation to maintain the morphologic and antigenic integrity of the cells. This is of particular importance in multicenter trials where samples are stored before being shipped to a central reading center that is masked to subject treatment and to clinical signs or symptoms.32 
Processing
The processing of IC samples for flow cytometry involves mechanical separation of the cells from the membrane by vortex or gentle agitation, followed by recovery using centrifugation. Two important components of this step are the time lapse between collection and processing and the number of cells recovered. While some studies report the time lapse between collection and processing of cells, most studies fail to mention this important detail. As indicated in a study by Epstein et al,32 antigenic integrity of the cells and, thus, the flow cytometer data output of HLA-DR expression, appeared stable up to 4 weeks of storage at 4°C. Another study has reported an even shorter time period of 10 days for obtaining reproducible expression of HLA-DR.36 There also is a lack of information in most studies on the number of cells recovered. In the few studies that have included this information,34,3638 the cell numbers have ranged from 2000 to 200,000 per eye. Unlike blood, where analysis is not limited by cell number, information on cell numbers obtainable from IC samples will be critical in deciding the number of antigens that can be targeted for a study as lower cell numbers may result in statistically insignificant data output for cell subgroup analysis. The cell samples then are processed for immunostaining with conjugated monoclonal antibodies to the intended targets. Antibody dilutions depend on the specific antibody used, the manufacturers' recommendation or, for more stringency, titration for best signal-to-noise ratio. In case of multiple antigen analysis, careful selection of fluorochromes is needed to enhance the ability to differentiate markers. 
Analysis
Analysis of samples involves data acquisition by a flow cytometer and postacquisition analysis. For this, a variety of machines and analysis software are used by different investigators. While this variation cannot be overcome, it is important to recognize that this can be the biggest contributor to variability that may be observed in data reported by various groups. Added to this is the subjective nature of analysis, specifically gating; when multiple parameters are involved this adds to the less than consistent nature of data generated by multiple labs studying the same parameter(s) by flow cytometry. Flow cytometry is a common way to identify biomarkers in many human diseases, such as oncology.39 The processing and analyzing issues that contribute to variability of the results are issues for all studies involving flow cytometry and efforts are being made to address this very issue by “harmonization of flow cytometers” and protocols.39,40 
HLA-DR Expression Variability in DED
Despite the use and adoption of IC for flow cytometry, variability in sampling, processing and analysis, as detailed above, has hampered intergroup reproducibility of results and their comparison. Table 2 lists studies that have looked at HLA-DR expression in IC samples from DED patients and normal individuals. A wide variability in percentage of HLA-DR expression in DED patients is seen among studies, with values ranging from 1.2 to 64.2. For example, a study by Fernandez et al.41 reports the percentage of HLA-DR–positive cells in IC samples from DED patients to be 7.17 ± 6.10, while another study42 shows it to be 56.9 ± 24.6. While the wide range of percentages in DED samples could be attributed to differences in the patient groups (age, proportion of male to female, severity, and so forth), more striking is the variation in percentage of HLA-DR expressed in IC samples from normal subjects (with no history of ocular disease or clinical ophthalmic abnormality), a group where interstudy variation should be minimal. In this group, percentage of HLA-DR–positive cells has ranged from 1.95 ± 1.4643 to 22.1 ± 19.1.44 All of the above once again emphasizes the need for stricter quality control with collective standardization of procedures as well as demonstration of reliability and repeatability of each step, and a need for optimization of nonsubjective data analysis tools. 
Tears
Tears are an accessible source of biological material that can be obtained minimally invasively, and they have been analyzed extensively for a number of biomarkers in DED.4547 Inflammatory mediators released in tears have been recognized as one of the key components in ocular surface inflammation that have prominent roles in the pathophysiology of DED.48 Of the multiple inflammatory biomarkers under investigation, cytokines and chemokines are the most frequently reported and studied in DED.4547 Components of lipid metabolism also have been reported to be correlated with clinical measures of DED, and include secretory phospholipase A2 (sPLA2),49 prostaglandin E2 (PGE2),50 arachidonic acid (AA), docosahexaenoic acid (DHA), eicosapentaenoic acid (EPA), and leukotriene B4 (LTB4).51,52 Using a relatively new technology, isobaric tag for relative and absolute quantitation technology (iTRAQ) proteomics, tear proteins like α-enolase, α-1 acid glycoprotein 1, S100 A8/calgranulin A, S100 A9/calgranulin B, S100 A9/calgranulin B; S100 A4, and S100 A11 have been shown to be upregulated in DED tears.53,54 
Sampling
A number of techniques, including, microcapillary tubes, minisponges, Schirmer's test strips, and tear wash, have been used to collect tears.47 Microcapillary tubes and Schirmer strips are the most frequently used and show comparable outcomes by Western blot analysis,55 whereas different ophthalmic sponges with various extraction buffers have yielded diverse results.56 Tear volumes obtained with the tear wash method vary from patient to patient,57 and there is no evidence supporting its comparability to other methods. 
Processing/Analysis
Luminex, a cytometric bead–based multiplex technology developed by Luminex Corporation (Austin, TX, USA), allows for the simultaneous analysis of multiple cytokines in each sample and processing multiple samples at one time.58 A recent advancement of miniaturized, wall-less multiplex cytokine assay, named DropArray, allows for the relative and absolute quantification of tear cytokines with 1/5 of volume and reagents normally needed for routine Luminex assay,59 possibly allowing for analysis of small tear volumes. In brief, the Luminex method involves loading a fixed volume of diluted tears onto assay plates according to manufacturer's instruction. To ensure consistent results between plates and batches, a serially diluted mixture of cytokine standards with known concentrations, to obtain a standard curve, suitable internal control (pooled tear samples with known concentrations), and external controls (provided in kit) also are loaded onto the assay plate. Following incubation to allow binding of analyte to capture antibodies coated to the beads, a biotinylated detection antibody and its reporter molecule, Streptavidin-PE conjugate, are introduced to complete the reaction on the surface of each microsphere. The plates are read on a laser flow-based detection instrument. The fluorescent intensity and bead counts are measured, and data output is reported as median fluorescent intensity (MFI) and can be translated to concentration based on standard curves for known cytokines/chemokines. Using this technology, Huang et al.60 have shown good measuring repeatability of many immune mediators in tears of DED patients. While intrastudy repeatability appears to be possible with this technology, observed interstudy variation is a serious concern and discussed in the subsequent section. 
TNF-α, IL-6, IL-17a, and IL-8 in DED
Though a number of different cytokines/chemokines have been analyzed in tears of DED patients,4547 this review will focus on 4 cytokines that have shown to be consistently elevated in DED tears compared to non-DED controls, and thought to have a mechanistic role in DED: (1) TNF-α for general inflammatory status of the ocular surface61 (Table 3), (2) IL-6, which has pro- and anti-inflammatory roles, may provide important information on ocular immune status and on treatment effect62 (Table 4), (3) IL-17a, which is secreted by specialized T helper 17 (Th17) subpopulation63 (Table 5), and (4) IL-8, which is important in chemotaxis to mediate macrophage and epithelial innate immunity64 (Table 6). As can be observed in the Tables, a wide range of concentrations has been observed for these cytokines. For example, while tears from DED patients tested for IL-8 showed higher mean values for IL-8 compared to normals (Table 6), the reported concentrations ranged from 74 + 55 pg/mL65 to 6518.3 + 4509.7 pg/mL66 for DED and from 176 + 72 pg/mL67 to 1150 + 50 pg/mL in normals.68 While some of the variability, as with IC studies (Table 2), may be due to biologic variations, the technique used is likely an issue as well. However, the variation in concentrations observed with “normal” subjects is remarkable. It must be noted that under the umbrella of Luminex technology, studies have used assay kits from different manufacturers, different instruments, or even the same instrument with different panels or settings, which may contribute significantly to the wide range of concentrations observed for the same cytokine. Even after the sample processing is completed, data analyzed and reported with different stringent curve fitting models vary significantly, for example, best curve fitting, five-parameter curve fitting, four-parameter curve fitting, cubic spline fitting, or linear polation fitting (Milliplex Analyste User Guide). Few reports describe the analysis algorithm used. Therefore absolute concentrations are likely not comparable between studies and reporting percent change or ratios may be a more useful metric for reporting analyte levels in tears at baseline and with treatment in clinical trials. 
Point of Care Biomarkers
In contrast to the above discussed biomarkers, tear osmolarity and matrix metalloproteinase-9 (MMP-9) are the only biomarkers that are approved by the United States Food and Drug Administration (FDA), have commercially available point-of-care measurement devices, and have the potential to provide objective metrics in DED patient care as well as for clinical research.8,6971 
Tear Osmolarity
High tear osmolarity is considered as one of the “core mechanisms” of DED, and can lead to an increase in inflammation and further damage to the ocular surface.1,72,73 Measurement of tear osmolarity recently has become attractive because of the availability of commercial machines that are FDA approved for point-of-care use.74 These machines can measure osmolarity with very small tear volumes (nanoliters) and are easy to use.72,75 Studies have shown that increased osmolarity is potentially diagnostic of DED (Table 7),72,7678 but there is wide variation from study to study and, in some cases, the DED readings are similar to normal.7981 Furthermore, the cutoff value for DED is not clear82,83 (Table 7). For instance, one study suggested the cutoff value was 308 mOsm/L,80 while another reported 316 mOsm/L.84 Other studies have not shown correlation with clinical signs and symptoms.81,85 Though studies have shown the reliability of some of the available devices using standardized solutions,74,86,87 this does not necessarily indicate reliability when tears are sampled from the ocular surface. Rather than determining one value, it may be best to repeat measurements for each eye to demonstrate stability or lack of stability, indicative of DED.88,89 However, variation in measurements taken within a single session can be high, even among normal subjects.79 
Table 6
 
Observed Differences of IL-17A Levels in Tears From DED Patients and Normals
Table 6
 
Observed Differences of IL-17A Levels in Tears From DED Patients and Normals
Table 7
 
Tear Osmolarity in Clinical Trials involving DED Patients
Table 7
 
Tear Osmolarity in Clinical Trials involving DED Patients
In summary, it remains to be seen if tear osmolarity has the potential to be an objective biomarker for use in clinical trials; further studies will need to specifically elucidate how it can be used, including number of tests per subject, cutoff point, and correlation with clinically relevant findings. 
Matrix Metalloproteinases-9 (MMP-9)
MMP-9 is an enzyme important for tissue remodeling and has important roles in the inflammatory process of DED.90,91 A number of studies show high levels of MMP-9 in tears in DED (Table 8). The FDA-approved office test, called InflammaDry, provides results as positive or negative using a cut-off level of 40 ng/mL in tears. One study has suggested this test provides 85% sensitivity and 94% specificity in diagnosing DED.92 However, the value of MMP-9 as a biomarker for DED is challenged by the observations that there is lack of correlation among MMP-9, other standard tests, and disease symptoms.93 In addition, elevated MMP-9 can be associated with other ocular surface diseases involving inflammation, such as ocular allergy.69 
Table 8
 
MMP-9 in Tears of DED Patients
Table 8
 
MMP-9 in Tears of DED Patients
MMP-9 testing may not be diagnostic for DED but it has the potential to enhance patient selection in clinical trials, especially those evaluating anti-inflammatory treatments. Further research evaluating repeatability in DED and normals without change in intervention, as well as studies correlating clinical tests with DED signs and symptoms, will contribute to understanding its usefulness. 
Ocular Imaging
Imaging may provide minimally invasive metrics about the ocular surface and meibomian glands (MG), and possibly a better differentiation between aqueous deficiency and evaporative dry eye.1,94 
Tear Film Stability and Tear Meniscus
Two features of tears, tear film stability and tear meniscometry, have been shown to be affected in DED.1 The traditional method to assess tear breakup time is through slit-lamp examination using fluorescein dye (FBUT).1 FBUT is measured by observing for dark spots on the ocular surface through a slit-lamp with incident cobalt blue filtered light. The fluorescein illuminates the tear film and the dark spots indicate that the tear film has begun to break up.95 However, this traditional test, though considered objective, likely is impacted by placement of the drops, concentration, volume of fluorescein used, and low test repeatability and reproducibility.94 Newer imaging technologies offer minimally invasive testing methods since, typically, no eye drops or contact with the eye are involved, and analysis provides automated numeric output.8 Noninvasive tear breakup time (NITBUT) is measured through the use of computer software that analyzes reflections of placido rings on the ocular surface. Rapid distortion of the rings is indicative of tear film instability and high NITBUT.96 
NITBUT, such as that obtained using the Oculus Keratograph, has been shown to correlate with DED and provide statistically significant differences between DED and normals.97100 Recent studies have shown good intraexaminer repeatability and interexaminer reproducibility of NITBUT in normals and DED patients.99,101 However, there are varying results comparing NITBUT with the traditional FBUT.99,102106 There also may be variability depending on the machine used.107 More research is needed to determine which method is the better diagnostic tool for DED. 
Tear meniscus height (TMH) is a common aspect of tear meniscometry that can be measured noninvasively using infrared light to capture an image and then measuring the height of the tear meniscus with an electronic ruler,108 or with optical coherence tomography (OCT), which uses high wavelength light waves to take images of the anterior segment that then are analyzed,109 and also by briefly touching the edge of the lower tear meniscus using a specially designed meniscometry strip.110 TMH has been shown to correlate with DED and the traditional measurements for DED.97,110120 Some studies suggest there is good repeatability and reproducibility of TMH measurements in DED and normals.101,114,115,118,120122 However, TMH taken with different machines may not be comparable.111,115,121 
In addition to TMH, tear meniscus cross-sectional area (TMA) and tear meniscus depth (TMD) also are common parameters used to observe the menisci of DED patients.120 TMA and TMD are measured using OCT, similarly to TMH. Since some user input is needed to designate the borders of the tear film to measure, there is some subjectivity to the measurements. However, some studies have shown repeatability and reproducibility in using OCT to measure TMA and TMD.120,122 They also have been shown to correlate with DED and traditional measurements for DED.116,119,120 However, TMA and TMD measurements using OCT have not yet been incorporated in any multicenter clinical trial. 
Overall, there is no consensus on parameters to be evaluated for tear meniscometry, and it is not clear which would be the most clinically significant. 
Meibomian Gland
MGs have an important role in providing lipids to the tear film, which helps to retard the evaporation of tears from the ocular surface.123 MG dysfunction (MGD), defined by the International Workshop on MGD as a “chronic, diffuse abnormality of the MGs, commonly characterized by terminal duct obstruction and/or qualitative/quantitative changes in the glandular secretion,” is a leading cause of evaporative DED.1,123125 Recently, imaging of the MGs under infrared illumination has become easier to perform and can be included in clinical trial testing,126129 but may not be sufficient for diagnosis of MGD or DED. Some studies suggest MG dropout, using direct- or transillumination of the lid, shows a strong relationship between MGD and DED.130134 However, interpretation of meibography is just beginning to be developed and further work will be needed to validate mechanisms of analysis, including development of automated systems and/or reading centers, and correlation of MG changes with clinical findings.135,136 
Lipid Layer
The lipid layer has a critical role in tear film stability and the maintenance of ocular surface health.123,137,138 Alterations in the lipid layer thickness (LLT) have been shown to be a possible good indicator of DED,97,98,139,140 and specifically of MGD.141,142 The lipid layer of the tear film can be assessed using simple, noninvasive imaging technology, such as an interferometer.97 Different studies have found variation in LLT measurements between DED patients and normals; we might expect DED patients to have thinner LLT than normals,142,143 but this is not always observed.144 Further research is needed to demonstrate reliability, repeatability, reproducibility, and validity of LLT in DED. 
Bulbar Redness
Bulbar redness is a nonspecific ocular response due to vasodilation of the conjunctival and/or anterior scleral blood vessels145 and is observed in DED and often is a complaint of patients with DED.146 New imaging technology has been developed that automatically provides a bulbar redness score, and some research suggests that it correlates with patient and observer grading (Gadaria-Rathod N, et al. IOVS. 2013;54:ARVO E-Abstract 527).147 Whether this can serve as a validated biomarker for DED remains to be seen. 
Corneal Surface Anatomy
Morphologic changes of the corneal epithelium and sub-basal nerves have been shown to occur in DED.148155 This has been studied using in vivo confocal microscopy, which allows visualization of the layers of the cornea.156 Some effort has been made to identify potential predictive anatomic biomarkers on the corneal surface and the MGs, primarily using confocal microscopy.149,151 Several groups also are investigating the potential applications of MultiPhoton microscopy for studying ocular surface anatomy.157 
The feasibility of using confocal microscopy for assessing acinar density and diameter, secretion reflectivity, and periglandular inflammation for diagnosis of MGD has been explored by some groups.158160 A comparison between normal and MGD patients regarding the aforementioned morphologic structures and cells showed the potential to diagnose MGD with high sensitivity and specificity.158 Confocal microscopy showed morphologic abnormalities and inflammatory changes in MGs of patients with Sjögren's syndrome and MGD that was not easily distinguishable by the usual clinical exams.160 
Visualization of corneal nerve density may be potentially important where signs do not match symptoms (i.e., patients with dry eye symptoms but with a normal standard clinical examination),149,161 and may be a sign of neuropathic pain.148,155,161163 However, the relationship between DED and corneal nerve changes is not clear. One study has reported that improvement in corneal fluorescein staining score and symptomatology following treatment was evident only in the patient group showing near normal corneal sub-basal nerve fiber length.149 In contrast another study showed that corneal sub-basal nerve fiber length was similar in the patient group that showed clinical improvement and the patient group with no clinical improvement following treatment.151 Notably, though nerve length was similar, sub-basal dendritic cell density was decreased in the patient group that responded positively to treatment.151 Nonetheless, including corneal nerve changes in DED clinical trials, would require extensive instruction for each site and support of automated analysis. Corneal sensitivity testing, an alternate means of measuring corneal innervation, also would be helpful.152,164,165 However, there are only limited data on the repeatability and reliability of available esthesiometers, such as the Cochet-Bennet166 and Belmonte167 esthesiometers. Measuring peripheral cutaneous sensitivity also may be helpful in understanding the pain of DED and provide a more uniform rating of ocular symptoms, but has not been evaluated extensively in humans as yet.168 
Overall, data suggest that many of the ocular imaging measurements are repeatable; however, further investigation on the correlation with other signs and symptoms of DED will likely better elucidate the role of these methods for clinical trial use as objective metrics. 
Genetics
Genetics of human disease is a rapidly growing area for developing biomarkers to identify risk of disease, response to treatment, and so forth. However, little has been done to date on DED,169171 primarily because DED still is considered a multifactorial disease. Genetic studies likely will be more useful when we can better characterize the DED patient population and identify subgroups. 
Considerations for Developing Validated Biomarkers With Objective Endpoints for DED
Considerable efforts already have been initiated, but additional work is needed before we have validated biomarkers with minimally invasive objective metrics that will be generally acceptable in clinical research and for patient management in DED. Results must be reproducible and comparable with multiple studies on the same biomarker. For instance, how useful would testing for cholesterol be if the same subject got significantly different results depending on what laboratory was used? More studies to validate standard operating procedures would add to the acceptance of biomarker usage. Research publications must provide greater detail to allow easier review and comparison with other research on the same biomarkers. Some recommendations include: 
  •  
    Subjects: Studies often described subjects as “healthy controls” and “dry eye patients.” The listing of inclusion and exclusion criteria will help with interstudy comparison of data among subcategories of patients along the DED spectrum.
  •  
    Methodology: Details of techniques used, including quality control, will allow other researchers to repeat the experiment/study which would add validity to using the particular biomarker.
  •  
    Result reporting: Focusing on statistical differences, rate of change rather than absolute values of the reported data, may be more useful. For example for flow cytometry, since gating and analysis still are not automated, it probably is best to look at trends (percent change or ratios) rather than absolute percentage or AUF or any other metric. Similarly for tears, where concentrations appear to vary significantly, between studies, reporting of trends along with concentration will be more useful.
  •  
    Clinical relevance: Does this biomarker provide important information to the clinical setting?
  •  
    Reproducibility: Including repeatability of test, interobserver agreement.
  •  
    Purpose of biomarker: Need to state the use of the biomarker, for example, for diagnosis, establishing severity, assessing change, clinical trial surrogate endpoint, and so forth.
  •  
    Clearly stating sources of potential bias: This would include whether biomarker processing and analysis were masked to clinical attributes and/or treatment of the subjects, funding sources, and all potential sources of conflict of interest.
  •  
    Ease of use: For research setting, multisite clinical trials, and/or in-office patient care.
  •  
    Reading centers: Provide information on standard operating procedures.
  •  
    Reporting: Consider making available information on biomarkers that were not correlated with the disease as well as those that were; development of standardized reporting methodologies.
  •  
    Systemic biomarkers: Not discussed in this review, but a potential source of biomarkers to identify underlying pathogenesis and patients at risk for DED, such as markers for Sjögren's Syndrome.172
  •  
    Statistical Analysis: Methodology should be clearly stated.
Summary/Conclusion
There is a growing body of research and interest in developing biomarkers for DED, for understanding underlying pathophysiology of DED, diagnosis, classification, and treatment efficacy, and for endpoints in clinical trials. Basic research in DED with tissue/organ cultures and animal models will continue to provide direction for potential biomarkers that then will need to be evaluated in patients to validate their role in human disease. All studies, including those from single centers, using small sample size, and so forth, in humans with DED have taken our understanding further and point to areas that would benefit from larger masked studies to validate a biomarker. Multisite clinical trials to date have incorporated biomarkers to monitor inflammatory changes and support drug mechanisms of action. In-office testing now is available for some biomarkers with growing information on their potential usefulness for improved clinical care. Going forward, composite “scores” incorporating several biomarker measurements may be most useful. Our collective efforts have been successful in providing a roadmap for future work in biomarkers in DED. Biomarkers with minimally invasive and reproducible objective metrics will provide the key to future paradigm shifts in understanding of the underlying causes of DED and approaches to treatment of DED. 
Acknowledgments
Supported by the National Eye Institute and the Office of Dietary Supplements, Grant U10EY022881, for the DREAM. DREAM was the recipient of clinical diagnostic instruments from manufacturing companies: TearScience, TearLab, Rapid Pathogen Screening, and Oculus, and Immco provides testing and support free of charge. 
Disclosure: N.S. Roy, MC2 Therapeutics (F); Y. Wei, MC2 Therapeutics (F); E. Kuklinski, Alcon (F), MC2 Therapeutics (F); P.A. Asbell, Alcon (C, F), Allergan (C), MC2 Therapeutics (F), Shire (C), WebMD (C) 
References
The definition and classification of dry eye disease: report of the Definition and Classification Subcommittee of the International Dry Eye WorkShop (2007). Ocul Surf. 2007; 5: 75–92.
Tsubota K, Yokoi N, Shimazaki J, et al. New perspectives on dry eye definition and diagnosis: a consensus report by the Asia Dry Eye Society. Ocul Surf. 2017; 15: 65–76.
Vehof J, Sillevis Smitt-Kamminga N, Nibourg SA, Hammond CJ. Predictors of discordance between symptoms and signs in dry eye disease. Ophthalmology. 2016; 124: 280–286.
Onwubiko SN, Eze BI, Udeh NN, Onwasigwe EN, Umeh RE. Dry eye disease: concordance between the diagnostic tests in African eyes. Eye Contact Lens. 2016; 42: 395–400.
Bartlett JD, Keith MS, Sudharshan L, Snedecor SJ. Associations between signs and symptoms of dry eye disease: a systematic review. Clin Ophthalmol. 2015; 9: 1719–1730.
Schein OD, Tielsch JM, Munoz B, Bandeen-Roche K, West S. Relation between signs and symptoms of dry eye in the elderly. A population-based perspective. Ophthalmology. 1997; 104: 1395–1401.
Bjerrum KB. Test and symptoms in keratoconjunctivitis sicca and their correlation. Acta Ophthalmol Scand. 1996; 74: 436–441.
Zeev MS, Miller DD, Latkany R. Diagnosis of dry eye disease and emerging technologies. Clin Ophthalmol. 2014; 8: 581–590.
Design and conduct of clinical trials: report of the Clinical Trials Subcommittee of the International Dry Eye WorkShop (2007). Ocul Surf. 2007; 5: 153–162.
Savini G, Prabhawasat P, Kojima T, Grueterich M, Espana E, Goto E. The challenge of dry eye diagnosis. Clin Ophthalmol. 2008; 2: 31–55.
Baudouin C, Aragona P, Van Setten G, et al. Diagnosing the severity of dry eye: a clear and practical algorithm. Br J Ophthalmol. 2014; 98: 1168–1176.
Barabino S, Labetoulle M, Rolando M, Messmer EM. Understanding symptoms and quality of life in patients with dry eye syndrome. Ocul Surf. 2016; 14: 365–376.
Toker E, Asfuroglu E. Corneal and conjunctival sensitivity in patients with dry eye: the effect of topical cyclosporine therapy. Cornea. 2010; 29: 133–140.
Chao W, Belmonte C, Benitez Del Castillo JM, et al. Report of the inaugural meeting of the TFOS i(2) = initiating innovation series: targeting the unmet need for dry eye treatment. Ocul Surf. 2016; 14: 264–316.
Novack GD. Why aren't there more pharmacotherapies for dry eye? Ocul Surf. 2014; 12: 227–230.
Group F-NBW. BEST (Biomarkers, EndpointS, and other Tools) Resource. Silver Spring (MD): Food and Drug Administration (US); National Institutes of Health (US); 2016.
Fiore LD, D'Avolio LW . Detours on the road to personalized medicine: barriers to biomarker validation and implementation. JAMA. 2011; 306: 1914–1915.
Biomarkers Definitions Working Group. Biomarkers and surrogate endpoints: preferred definitions and conceptual framework. Clin Pharmacol Ther. 2001; 69: 89–95.
Subramanyam M, Goyal J. Translational biomarkers: from discovery and development to clinical practice. Drug Discov Today Technol. 2016; 21–22: 3–10.
Robb MA, McInnes PM, Califf RM. Biomarkers and surrogate endpoints: developing common terminology and definitions. JAMA. 2016; 315: 1107–1108.
Vignali DA. Multiplexed particle-based flow cytometric assays. J Immunol Methods. 2000; 243: 243–255.
Katz R. Biomarkers and surrogate markers: an FDA perspective. NeuroRx. 2004; 1: 189–195.
Rothmann M. Statistical considerations for surrogate endpoints. Minimal Residue Disease in AML Workshop. Silver Spring, MD; 2013.
Pflugfelder SC, Stern M, Zhang S, Shojaei A. LFA-1/ICAM-1 Interaction as a therapeutic target in dry eye disease. J Ocul Pharmacol Ther. 2017; 33: 5–12.
Ambroziak AM, Szaflik J, Szaflik JP, Ambroziak M, Witkiewicz J, Skopinski P. Immunomodulation on the ocular surface: a review. Cent Eur J Immunol. 2016; 41: 195–208.
Bose T, Diedrichs-Mohring M, Wildner G. Dry eye disease and uveitis: a closer look at immune mechanisms in animal models of two ocular autoimmune diseases. Autoimmun Rev. 2016; 15: 1181–1192.
Wei Y, Asbell PA. The core mechanism of dry eye disease is inflammation. Eye Contact Lens. 2014; 40: 248–256.
Stevenson W, Chauhan SK, Dana R. Dry eye disease: an immune-mediated ocular surface disorder. Arch Ophthalmol. 2012; 130: 90–100.
Egbert PR, Lauber S, Maurice DM. A simple conjunctival biopsy. Am J Ophthalmol. 1977; 84: 798–801.
Lopin E, Deveney T, Asbell PA. Impression cytology: recent advances and applications in dry eye disease. Ocul Surf. 2009; 7: 93–110.
Calonge M, Diebold Y, Saez V, et al. Impression cytology of the ocular surface: a review. Exp Eye Res. 2004; 78: 457–472.
Epstein SP, Gadaria-Rathod N, Wei Y, Maguire MG, Asbell PA. HLA-DR expression as a biomarker of inflammation for multicenter clinical trials of ocular surface disease. Exp Eye Res. 2013; 111: 95–104.
University of Pennsylvania . Dry Eye Assessment and Management Study (DREAM). ClinicalTrails.gov: NCT02128763. 2014–2017.
Baudouin C, Brignole F, Becquet F, Pisella PJ, Goguel A. Flow cytometry in impression cytology specimens. A new method for evaluation of conjunctival inflammation. Invest Ophthalmol Vis Sci.. 1997; 38: 1458–1464.
Lopez-Miguel A, Gutierrez-Gutierrez S, Garcia-Vazquez C, Enriquez-de-Salamanca A. RNA collection from human conjunctival epithelial cells obtained with a new device for impression cytology. Cornea. 2017; 36: 59–63.
Yafawi R, Ko M, Sace FP, John-Baptiste A. Limitations of an ocular surface inflammatory biomarker in impression cytology specimens. Cutan Ocul Toxicol. 2013; 32: 46–53.
Gulati A, Sacchetti M, Bonini S, Dana R. Chemokine receptor CCR5 expression in conjunctival epithelium of patients with dry eye syndrome. Arch Ophthalmol. 2006; 124: 710–716.
Barabino S, Montaldo E, Solignani F, Valente C, Mingari MC, Rolando M. Immune response in the conjunctival epithelium of patients with dry eye. Exp Eye Res. 2010; 91: 524–529.
Kalina T, Flores-Montero J, van der Velden VH, et al. EuroFlow standardization of flow cytometer instrument settings and immunophenotyping protocols. Leukemia. 2012; 26: 1986–2010.
Jamin C, Le Lann L, Alvarez-Errico D, et al. Multi-center harmonization of flow cytometers in the context of the European “PRECISESADS” project. Autoimmun Rev. 2016; 15: 1038–1045.
Fernandez KB, Epstein SP, Raynor GS, et al. Modulation of HLA-DR in dry eye patients following 30 days of treatment with a lubricant eyedrop solution. Clin Ophthalmol. 2015; 9: 1137–1145.
Pisella PJ, Brignole F, Debbasch C, et al. Flow cytometric analysis of conjunctival epithelium in ocular rosacea and keratoconjunctivitis sicca. Ophthalmology. 2000; 107: 1841–1849.
Mastropasqua R, Agnifili L, Fasanella V, et al. Corneoscleral limbus in glaucoma patients: in vivo confocal microscopy and immunocytological study. Invest Ophthalmol Vis Sci.. 2015; 56: 2050–2058.
Trinh L, Brignole-Baudouin F, Raphael M, et al. Th1 and Th2 responses on the ocular surface in uveitis identified by CCR4 and CCR5 conjunctival expression. Am J Ophthalmol. 2007; 144: 580–585.
Willcox MD. Is there a role for inflammation in contact lens discomfort? Eye Contact Lens. 2017; 43: 5–16.
Hagan S, Martin E, Enriquez-de-Salamanca A. Tear fluid biomarkers in ocular and systemic disease: potential use for predictive, preventive and personalised medicine. EPMA J. 2016; 7: 15.
Hagan S, Tomlinson A. Tear fluid biomarker profiling: a review of multiplex bead analysis. Ocul Surf. 2013; 11: 219–235.
Spahiu V, Barry B, Asbell P. Cytokines: key biomarkers in elucidating the pathogenesis of inflammation. J Clin Cell Immunol. 2016; 7: 421.
Wei Y, Du Z, Chen D, Afreen J, Chen V, Asbell P. The role of the secretory group IIa phospholipase A2 (sPLA2-IIa) in ocular surface inflammation. JSM Ophthalmology. 2013; 1: 1.
Chen D, Wei Y, Li X, Epstein S, Wolosin JM, Asbell P. sPLA2-IIa is an inflammatory mediator when the ocular surface is compromised. Exp Eye Res. 2009; 88: 880–888.
Walter SD, Gronert K, McClellan AL, Levitt RC, Sarantopoulos KD, Galor A. Omega-3 Tear film lipids correlate with clinical measures of dry eye. Invest Ophthalmol Vis Sci. 2016; 57: 2472–2478.
Kenchegowda S, He J, Bazan HE. Involvement of pigment epithelium-derived factor, docosahexaenoic acid and neuroprotectin D1 in corneal inflammation and nerve integrity after refractive surgery. Prostaglandins Leukot Essent Fatty Acids. 2013; 88: 27–31.
Zhou L, Beuerman RW. Quantitative proteomic analysis of N-linked glycoproteins in human tear fluid. Methods Mol Biol. 2013; 951: 297–306.
Tong L, Zhou L, Beuerman RW, Zhao SZ, Li XR. Association of tear proteins with Meibomian gland disease and dry eye symptoms. Br J Ophthalmol. 2011; 95: 848–852.
Posa A, Brauer L, Schicht M, Garreis F, Beileke S, Paulsen F. Schirmer strip vs. capillary tube method: noninvasive methods of obtaining proteins from tear fluid. Ann Anat. 2013; 195: 137–142.
Inic-Kanada A, Nussbaumer A, Montanaro J, et al. Comparison of ophthalmic sponges and extraction buffers for quantifying cytokine profiles in tears using Luminex technology. Mol Vis. 2012; 18: 2717–2725.
Lee H, Min K, Kim EK, Kim TI. Minocycline controls clinical outcomes and inflammatory cytokines in moderate and severe meibomian gland dysfunction. Am J Ophthalmol. 2012; 154: 949–957. e1.
Wei Y, Gadaria-Rathod N, Epstein S, Asbell P. Tear cytokine profile as a noninvasive biomarker of inflammation for ocular surface diseases: standard operating procedures. Invest OphthalMol Vis. Sci. 2013; 54: 8327–8336.
Le Guezennec X, Quah J, Tong L, Kim N. Human tear analysis with miniaturized multiplex cytokine assay on “wall-less” 96-well plate. Mol Vis. 2015; 21: 1151–1161.
Huang JF, Zhang Y, Rittenhouse KD, Pickering EH, McDowell MT. Evaluations of tear protein markers in dry eye disease: repeatability of measurement and correlation with disease. Invest Ophthalmol Vis Sci. 2012; 53: 4556–4564.
Feldmann M, Maini RN. Anti-TNF alpha therapy of rheumatoid arthritis: what have we learned? Annu Rev Immunol. 2001; 19: 163–196.
Ghasemi H. Roles of IL-6 in ocular inflammation: a review [published online ahead of print February 1, 2017]. Ocul Immunol Inflamm doi:10.1080/09273948.2016.1277247.
Zenobia C, Hajishengallis G. Basic biology and role of interleukin-17 in immunity and inflammation. Periodontol. 2000. 2015; 69: 142–159.
Ghasemi H, Ghazanfari T, Yaraee R, Faghihzadeh S, Hassan ZM. Roles of IL-8 in ocular inflammations: a review. Ocul Immunol Inflamm. 2011; 19: 401–412.
Tong L, Beuerman R, Simonyi S, Hollander DA, Stern ME. Effects of punctal occlusion on clinical signs and symptoms and on tear cytokine levels in patients with dry eye. Ocul Surf. 2016; 14: 233–241.
Balne PK, Agrawal R, Au VB, et al. Dataset of tear film cytokine levels in dry eye disease (DED) patients with and without HIV infection. Data Brief. 2017; 10: 14–16.
Lam H, Bleiden L, de Paiva CS, Farley W, Stern ME, Pflugfelder SC. Tear cytokine profiles in dysfunctional tear syndrome. Am J Ophthalmol. 2009; 147: 198–205 .e1.
VanDerMeid KR, Su SP, Krenzer KL, Ward KW, Zhang JZ. A method to extract cytokines and matrix metalloproteinases from Schirmer strips and analyze using Luminex. Mol Vis. 2011; 17: 1056–1063.
Lanza NL, Valenzuela F, Perez VL, Galor A. The matrix metalloproteinase 9 point-of-care test in dry eye. Ocul Surf. 2016; 14: 189–195.
Dohlman TH, Ciralsky JB, Lai EC. Tear film assessments for the diagnosis of dry eye. Curr Opin Allergy Clin Immunol. 2016; 16: 487–491.
Bron AJ, Tomlinson A, Foulks GN, et al. Rethinking dry eye disease: a perspective on clinical implications. Ocul Surf. 2014; 12: S1–S31.
Potvin R, Makari S, Rapuano CJ. Tear film osmolarity and dry eye disease: a review of the literature. Clin Ophthalmol. 2015; 9: 2039–2047.
Lemp MA, Bron AJ, Baudouin C, et al. Tear osmolarity in the diagnosis and management of dry eye disease. Am J Ophthalmol. 2011; 151: 792–798. e1.
Rocha G, Gulliver E, Borovik A, Chan CC. Randomized, masked, in vitro comparison of three commercially available tear film osmometers. Clin Ophthalmol. 2017; 11: 243–248.
Versura P, Campos EC. TearLab(R) Osmolarity System for diagnosing dry eye. Expert Rev Mol Diagn. 2013; 13: 119–129.
Suzuki M, Massingale ML, Ye F, et al. Tear osmolarity as a biomarker for dry eye disease severity. Invest Ophthalmol Vis Sci. 2010; 51: 4557–4561.
Utine CA, Bicakcigil M, Yavuz S, Ciftci F. Tear osmolarity measurements in dry eye related to primary Sjögren's syndrome. Curr Eye Res. 2011; 36: 683–690.
Versura P, Profazio V, Campos EC. Performance of tear osmolarity compared to previous diagnostic tests for dry eye diseases. Curr Eye Res. 2010; 35: 553–564.
Bunya VY, Fuerst NM, Pistilli M, et al. Variability of tear osmolarity in patients with dry eye. JAMA Ophthalmol. 2015; 133: 662–667.
Szalai E, Berta A, Szekanecz Z, Szucs G, Modis LJr. Evaluation of tear osmolarity in nonSjögren and Sjögren syndrome dry eye patients with the TearLab system. Cornea. 2012; 31: 867–871.
Messmer EM, Bulgen M, Kampik A. Hyperosmolarity of the tear film in dry eye syndrome. Dev Ophthalmol. 2010; 45: 129–138.
Lin H, Yiu SC. Dry eye disease: a review of diagnostic approaches and treatments. Saudi J Ophthalmol. 2014; 28: 173–181.
Adams M, Cookson VJ, Higgins J, et al. A high-throughput assay to identify modifiers of premature chromosome condensation. J Biomol Screen. 2014; 19: 176–183.
Tomlinson A, Khanal S, Ramaesh K, Diaper C, McFadyen A. Tear film osmolarity: determination of a referent for dry eye diagnosis. Invest Ophthalmol Vis Sci. 2006; 47: 4309–4315.
Massof RW, McDonnell PJ. Latent dry eye disease state variable. Invest Ophthalmol Vis Sci. 2012; 53: 1905–1916.
Yildiz EH, Fan VC, Banday H, et al. Evaluation of a new tear osmometer for repeatability and accuracy, using 0.5-microL (500-Nanoliter) samples. Cornea. 2009; 28: 677–680.
Yoon D, Gadaria-Rathod N, Oh C, Asbell PA. Precision and accuracy of TearLab osmometer in measuring osmolarity of salt solutions. Curr Eye Res. 2014; 39: 1247–1250.
Szczesna-Iskander DH. Measurement variability of the TearLab Osmolarity System. Cont Lens Anterior Eye. 2016; 39: 353–358.
Keech A, Senchyna M, Jones L. Impact of time between collection and collection method on human tear fluid osmolarity. Curr Eye Res. 2013; 38: 428–436.
Chotikavanich S, de Paiva CS, Li de Q, et al. Production and activity of matrix metalloproteinase-9 on the ocular surface increase in dysfunctional tear syndrome. Invest Ophthalmol Vis Sci. 2009; 50: 3203–3209.
Sivak JM, Fini ME. MMPs in the eye: emerging roles for matrix metalloproteinases in ocular physiology. Prog Retin Eye Res. 2002; 21: 1–14.
Sambursky R, Davitt WFIII, Latkany R, et al. Sensitivity and specificity of a point-of-care matrix metalloproteinase 9 immunoassay for diagnosing inflammation related to dry eye. JAMA Ophthalmol. 2013; 131: 24–28.
Lanza NL, McClellan AL, Batawi H, et al. Dry eye profiles in patients with a positive elevated surface matrix metalloproteinase 9 point-of-care test versus negative patients. Ocul Surf. 2016; 14: 216–223.
Nichols KK, Mitchell GL, Zadnik K. The repeatability of clinical measurements of dry eye. Cornea. 2004; 23: 272–285.
Methodologies to diagnose and monitor dry eye disease: report of the Diagnostic Methodology Subcommittee of the International Dry Eye WorkShop (2007). Ocul Surf. 2007; 5: 108–152.
Foulks GN, Forstot SL, Donshik PC, et al. Clinical guidelines for management of dry eye associated with Sjogren disease. Ocul Surf. 2015; 13: 118–132.
Ji YW, Lee J, Lee H, Seo KY, Kim EK, Kim TI. Automated measurement of tear film dynamics and lipid layer thickness for assessment of nonSjögren dry eye syndrome with meibomian gland dysfunction. Cornea. 2017; 36: 176–182.
Menzies KL, Srinivasan S, Prokopich CL, Jones L. Infrared imaging of meibomian glands and evaluation of the lipid layer in Sjögren's syndrome patients and nondry eye controls. Invest Ophthalmol Vis Sci. 2015; 56: 836–841.
Hong J, Sun X, Wei A, et al. Assessment of tear film stability in dry eye with a newly developed keratograph. Cornea. 2013; 32: 716–721.
Wang X, Lu X, Yang J, et al. Evaluation of dry eye and meibomian gland dysfunction in teenagers with myopia through noninvasive keratograph. J Ophthalmol. 2016; 2016: 6761206.
Tian L, Qu JH, Zhang XY, Sun XG. Repeatability and reproducibility of noninvasive keratograph 5M measurements in patients with dry eye disease. J Ophthalmol. 2016; 2016: 8013621.
Cox SM, Nichols KK, Nichols JJ. Agreement between automated and traditional measures of tear film breakup. Optom Vis Sci. 2015; 92: e257–e263.
Hong J, Liu Z, Hua J, et al. Evaluation of age-related changes in noninvasive tear breakup time. Optom Vis Sci. 2014; 91: 150–155.
Jiang Y, Ye H, Xu J, Lu Y. Noninvasive keratograph assessment of tear film break-up time and location in patients with age-related cataracts and dry eye syndrome. J Int Med Res. 2014; 42: 494–502.
Lan W, Lin L, Yang X, Yu M. Automatic noninvasive tear breakup time (TBUT) and conventional fluorescent TBUT. Optom Vis Sci. 2014; 91: 1412–1418.
Abdelfattah NS, Dastiridou A, Sadda SR, Lee OL. Noninvasive imaging of tear film dynamics in eyes with ocular surface disease. Cornea. 2015; 34(suppl)10: S48–S52.
Best N, Drury L, Wolffsohn JS. Clinical evaluation of the oculus keratograph. Cont Lens Anterior Eye. 2012; 35: 171–174.
TF-Scan: meniscus tear height assessment of the tear film quantity. OCULUS, Inc. Arlington, WA. Available at: http://www.oculus.de/us/products/topography/keratograph-5m/software/tf-scan/#produkte_navi. Accessed Feb 15, 2017.
Spirn MJ, Kozak A, Feldman BH, Shah VA. Optical coherence tomography. American Academy of Ophthalmology: protecting sight, empowering lives. EyeWiKi, 2015. Available at: http://eyewiki.aao.org/Optical_Coherence_Tomography, Accessed February 15, 2017.
Dogru M, Ishida K, Matsumoto Y, et al. Strip meniscometry: a new and simple method of tear meniscus evaluation. Invest Ophthalmol Vis Sci. 2006; 47: 1895–1901.
Lee KW, Kim JY, Chin HS, Seo KY, Kim TI, Jung JW. Assessment of the tear meniscus by strip meniscometry and keratograph in patients with dry eye disease according to the presence of meibomian gland dysfunction. Cornea. 2017; 36: 189–195.
Raj A, Dhasmana R, Nagpal RC. Anterior segment optical coherence tomography for tear meniscus evaluation and its correlation with other tear variables in healthy individuals. J Clin Diagn Res. 2016; 10: NC01–NC04.
Akiyama-Fukuda R, Usui T, Yoshida T, Yamagami S. Evaluation of tear meniscus dynamics using anterior segment swept-source optical coherence tomography after topical solution instillation for dry eye. Cornea. 2016; 35: 654–658.
Wei A, Le Q, Hong J, Wang W, Wang F, Xu J. Assessment of lower tear meniscus. Optom Vis Sci. 2016; 93: 1420–1425.
Baek J, Doh SH, Chung SK. Comparison of tear meniscus height measurements obtained with the keratograph and fourier domain optical coherence tomography in dry eye. Cornea. 2015; 34: 1209–1213.
Altan-Yaycioglu R, Sizmaz S, Canan H, Coban-Karatas M. Optical coherence tomography for measuring the tear film meniscus: correlation with Schirmer test and tear-film breakup time. Curr Eye Res. 2013; 38: 736–742.
Ibrahim OM, Dogru M, Ward SK, et al. The efficacy, sensitivity, and specificity of strip meniscometry in conjunction with tear function tests in the assessment of tear meniscus. Invest Ophthalmol Vis Sci. 2011; 52: 2194–2198.
Ibrahim OM, Dogru M, Takano Y, et al. Application of visante optical coherence tomography tear meniscus height measurement in the diagnosis of dry eye disease. Ophthalmology. 2010; 117: 1923–1929.
Czajkowski G, Kaluzny BJ, Laudencka A, Malukiewicz G, Kaluzny JJ. Tear meniscus measurement by spectral optical coherence tomography. Optom Vis Sci. 2012; 89: 336–342.
Qiu X, Gong L, Lu Y, Jin H, Robitaille M. The diagnostic significance of Fourier-domain optical coherence tomography in Sjögren syndrome, aqueous tear deficiency and lipid tear deficiency patients. Acta Ophthalmol. 2012; 90: e359–e366.
Arriola-Villalobos P, Fernandez-Vigo JI, Diaz-Valle D, Almendral-Gomez J, Fernandez-Perez C, Benitez-Del-Castillo JM. Lower tear meniscus measurements using a new anterior segment swept-source optical coherence tomography and agreement with fourier-domain optical coherence tomography. Cornea. 2017; 36: 183–188.
Canan H, Altan-Yaycioglu R, Ulas B, Sizmaz S, Coban-Karatas M. Interexaminer reproducibility of optical coherence tomography for measuring the tear film meniscus. Curr Eye Res. 2014; 39: 1145–1150.
Knop E, Knop N, Millar T, Obata H, Sullivan DA. The international workshop on meibomian gland dysfunction: report of the subcommittee on anatomy, physiology, and pathophysiology of the meibomian gland. Invest Ophthalmol Vis Sci. 2011; 52: 1938–1978.
Nelson JD, Shimazaki J, Benitez-del-Castillo JM, et al. The international workshop on meibomian gland dysfunction: report of the definition and classification subcommittee. Invest Ophthalmol Vis Sci. 2011; 52: 1930–1937.
Nichols KK, Foulks GN, Bron AJ, et al. The international workshop on meibomian gland dysfunction: executive summary. Invest Ophthalmol Vis Sci. 2011; 52: 1922–1929.
Palamar M, Degirmenci C, Ertam I, Yagci A. Evaluation of dry eye and meibomian gland dysfunction with meibography in patients with rosacea. Cornea. 2015; 34: 497–499.
Finis D, Ackermann P, Pischel N, et al. Evaluation of meibomian gland dysfunction and local distribution of meibomian gland atrophy by noncontact infrared meibography. Curr Eye Res. 2015; 40: 982–989.
Srinivasan S, Menzies K, Sorbara L, Jones L. Infrared imaging of meibomian gland structure using a novel keratograph. Optom Vis Sci. 2012; 89: 788–794.
Pult H, Riede-Pult B. Comparison of subjective grading and objective assessment in meibography. Cont Lens Anterior Eye. 2013; 36: 22–27.
Pult H, Purslow C, Murphy PJ. The relationship between clinical signs and dry eye symptoms. Eye (Lond). 2011; 25: 502–510.
Arita R, Itoh K, Maeda S, Maeda K, Tomidokoro A, Amano S. Efficacy of diagnostic criteria for the differential diagnosis between obstructive meibomian gland dysfunction and aqueous deficiency dry eye. Jpn J Ophthalmol. 2010; 54: 387–391.
Arita R, Itoh K, Maeda S, et al. Proposed diagnostic criteria for obstructive meibomian gland dysfunction. Ophthalmology. 2009; 116: 2058–2063. e1.
Yokoi N, Komuro A, Yamada H, Maruyama K, Kinoshita S. A newly developed video-meibography system featuring a newly designed probe. Jpn J Ophthalmol. 2007; 51: 53–56.
Nichols JJ, Berntsen DA, Mitchell GL, Nichols KK. An assessment of grading scales for meibography images. Cornea. 2005; 24: 382–388.
Geerling G, Baudouin C, Aragona P, et al. Emerging strategies for the diagnosis and treatment of meibomian gland dysfunction: proceedings of the OCEAN group meeting. Ocul Surf. 2017; 15: 179–192.
Pult H, Riede-Pult BH, Nichols JJ. Relation between upper and lower lids' meibomian gland morphology, tear film, and dry eye. Optom Vis Sci. 2012; 89: E310–315.
Bron AJ, Tiffany JM, Gouveia SM, Yokoi N, Voon LW. Functional aspects of the tear film lipid layer. Exp Eye Res. 2004; 78: 347–E360.
Craig JP, Tomlinson A. Importance of the lipid layer in human tear film stability and evaporation. Optom Vis Sci. 1997; 74: 8–13.
Pult H, Nichols JJ. A review of meibography. Optom Vis Sci. 2012; 89: E760–E769.
Foulks GN. The correlation between the tear film lipid layer and dry eye disease. Surv Ophthalmol. 2007; 52: 369–374.
Finis D, Pischel N, Schrader S, Geerling G. Evaluation of lipid layer thickness measurement of the tear film as a diagnostic tool for meibomian gland dysfunction. Cornea. 2013; 32: 1549–1553.
Eom Y, Lee JS, Kang SY, Kim HM, Song JS. Correlation between quantitative measurements of tear film lipid layer thickness and meibomian gland loss in patients with obstructive meibomian gland dysfunction and normal controls. Am J Ophthalmol. 2013; 155: 1104–1110. e2.
Blackie CA, Solomon JD, Scaffidi RC, Greiner JV, Lemp MA, Korb DR. The relationship between dry eye symptoms and lipid layer thickness. Cornea. 2009; 28: 789–794.
Jung JW, Park SY, Kim JS, Kim EK, Seo KY, Kim TI. Analysis of factors associated with the tear film lipid layer thickness in normal eyes and patients with dry eye syndrome. Invest Ophthalmol Vis Sci. 2016; 57: 4076–4083.
Papas EB. Key factors in the subjective and objective assessment of conjunctival erythema. Invest Ophthalmol Vis Sci. 2000; 41: 687–691.
Rodriguez JD, Johnston PR, Ousler GWIII, Smith LM, Abelson MB. Automated grading system for evaluation of ocular redness associated with dry eye. Clin Ophthalmol. 2013; 7: 1197–1204.
Wu S, Hong J, Tian L, Cui X, Sun X, Xu J. Assessment of bulbar redness with a newly developed keratograph. Optom Vis Sci. 2015; 92: 892–899.
Belmonte C, Acosta MC, Merayo-Lloves J, Gallar J. What causes eye pain? Curr Ophthalmol Rep. 2015; 3: 111–121.
Kheirkhah A, Dohlman TH, Amparo F, et al. Effects of corneal nerve density on the response to treatment in dry eye disease. Ophthalmology. 2015; 122: 662–668.
Kheirkhah A, Rahimi Darabad R, Cruzat A, et al. Corneal epithelial immune dendritic cell alterations in subtypes of dry eye disease: a pilot in vivo confocal microscopic study. Invest Ophthalmol Vis Sci. 2015; 56: 7179–7185.
Villani E, Garoli E, Termine V, Pichi F, Ratiglia R, Nucci P. Corneal confocal microscopy in dry eye treated with corticosteroids. Optom Vis Sci. 2015; 92: e290–e295.
Labbe A, Liang Q, Wang Z, et al. Corneal nerve structure and function in patients with nonSjögren dry eye: clinical correlations. Invest Ophthalmol Vis Sci. 2013; 54: 5144–5150.
Dastjerdi MH, Dana R. Corneal nerve alterations in dry eye-associated ocular surface disease. Int Ophthalmol Clin. 2009; 49: 11–20.
Villani E, Galimberti D, Viola F, Mapelli C, Ratiglia R. The cornea in Sjogren's syndrome: an in vivo confocal study. Invest Ophthalmol Vis Sci. 2007; 48: 2017–2022.
Belmonte C. Eye dryness sensations after refractive surgery: impaired tear secretion or “phantom” cornea? J Refract Surg. 2007; 23: 598–602.
Villani E, Magnani F, Viola F, et al. In vivo confocal evaluation of the ocular surface morpho-functional unit in dry eye. Optom Vis Sci. 2013; 90: 576–586.
Villani E, Baudouin C, Efron N, et al. In vivo confocal microscopy of the ocular surface: from bench to bedside. Curr Eye Res. 2014; 39: 213–231.
Ibrahim OM, Matsumoto Y, Dogru M, et al. The efficacy, sensitivity, and specificity of in vivo laser confocal microscopy in the diagnosis of meibomian gland dysfunction. Ophthalmology. 2010; 117: 665–672.
Matsumoto Y, Sato EA, Ibrahim OM, Dogru M, Tsubota K. The application of in vivo laser confocal microscopy to the diagnosis and evaluation of meibomian gland dysfunction. Mol Vis. 2008; 14: 1263–1271.
Villani E, Beretta S, De Capitani M, Galimberti D, Viola F, Ratiglia R. In vivo confocal microscopy of meibomian glands in Sjogren's syndrome. Invest Ophthalmol Vis Sci. 2011; 52: 933–939.
Shtein RM, Harper DE, Pallazola V, et al. Discordant dry eye disease (an American Ophthalmological Society thesis). Trans Am Ophthalmol Soc. 2016; 114: T4.
Rosenthal P, Borsook D. Ocular neuropathic pain. Br J Ophthalmol. 2016; 100: 128–134.
Galor A, Levitt RC, McManus KT, et al. Assessment of somatosensory function in patients with idiopathic dry eye symptoms. JAMA Ophthalmol. 2016; 134: 1290–1298.
Levy O, Labbe A, Borderie V, et al. Increased corneal sub-basal nerve density in patients with Sjögren syndrome treated with topical cyclosporine A [published online ahead of print December 13, 2016]. Clin Exp Ophthalmol. doi:10.1111/ceo.12898.
Bourcier T, Acosta MC, Borderie V, et al. Decreased corneal sensitivity in patients with dry eye. Invest Ophthalmol Vis Sci. 2005; 46: 2341–2345.
Chao C, Stapleton F, Badarudin E, Golebiowski B. Ocular surface sensitivity repeatability with Cochet-Bonnet esthesiometer. Optom Vis Sci. 2015; 92: 183–189.
Teson M, Calonge M, Fernandez I, Stern ME, Gonzalez-Garcia MJ. Characterization by Belmonte's gas esthesiometer of mechanical, chemical, and thermal corneal sensitivity thresholds in a normal population. Invest Ophthalmol Vis Sci. 2012; 53: 3154–3160.
Kovacs I, Luna C, Quirce S, et al. Abnormal activity of corneal cold thermoreceptors underlies the unpleasant sensations in dry eye disease. Pain. 2016; 157: 399–417.
Hallak JA, Tibrewal S, Jain S. Depressive symptoms in patients with dry eye disease: a case-control study using the beck depression inventory. Cornea. 2015; 34: 1545–1550.
Vehof J, Wang B, Kozareva D, Hysi PG, Snieder H, Hammond CJ. The heritability of dry eye disease in a female twin cohort. Invest Ophthalmol Vis Sci. 2014; 55: 7278–7283.
Na KS, Mok JW, Kim JY, Joo CK. Proinflammatory gene polymorphisms are potentially associated with Korean nonSjögren dry eye patients. Mol Vis. 2011; 17: 2818–2823.
Suresh L, Malyavantham K, Shen L, Ambrus JLJr. Investigation of novel autoantibodies in Sjogren's syndrome using Sera from the Sjögren's international collaborative clinical alliance cohort. BMC Ophthalmol. 2015; 15: 38.
Leonardi A, Van Setten G, Amrane M, et al. Efficacy and safety of 0.1% cyclosporine A cationic emulsion in the treatment of severe dry eye disease: a multicenter randomized trial. Eur J Ophthalmol. 2016; 26: 287–296.
Cocho L, Fernandez I, Calonge M, et al. Biomarkers in ocular chronic graft versus host disease: tear cytokine- and chemokine-based predictive model. Invest Ophthalmol Vis Sci. 2016; 57: 746–758.
Abud TB, Amparo F, Saboo US, et al. A clinical trial comparing the safety and efficacy of topical tacrolimus versus methylprednisolone in ocular graft-versus-host disease. Ophthalmology 2016; 123: 1449–1457.
Ferrari G, Rabiolo A, Bignami F, et al. Quantifying ocular surface inflammation and correlating it with inflammatory cell infiltration in vivo: a novel method. Invest Ophthalmol Vis Sci. 2015; 56: 7067–7075.
Niu L, Zhang S, Wu J, Chen L, Wang Y. Upregulation of NLRP3 Inflammasome in the tears and ocular surface of dry eye patients. PLoS One. 2015; 10: e0126277.
Moore QL, De Paiva CS, Pflugfelder SC. Effects of dry eye therapies on environmentally induced ocular surface disease. Am J Ophthalmol. 2015; 160: 135–142. e1.
Chen J, Dong F, Chen W, et al. Clinical efficacy of 0.1% pranoprofen in treatment of dry eye patients: a multicenter, randomized, controlled clinical trial. Chin Med J (Engl). 2014; 127: 2407–2412.
Inada N, Ishimori A, Shoji J. CCL20/MIP-3 alpha mRNA expression in the conjunctival epithelium of normal individuals and patients with vernal keratoconjunctivitis. Graefes Arch Clin Exp Ophthalmol. 2014; 252: 1977–1984.
McNamara NA, Gallup M, Porco TC. Establishing PAX6 as a biomarker to detect early loss of ocular phenotype in human patients with Sjogren's syndrome. Invest Ophthalmol Vis Sci. 2014; 55: 7079–7084.
Caffery BE, Joyce E, Heynen ML, Ritter RIII, Jones LA, Senchyna M. Quantification of conjunctival TNF-alpha in aqueous-deficient dry eye. Optom Vis Sci. 2014; 91: 156–162.
Bradley JL, Edwards CS, Fullard RJ. Adaptation of impression cytology to enable conjunctival surface cell transcriptome analysis. Curr Eye Res. 2014; 39: 31–41.
Sheppard JDJr, Singh R, McClellan AJ, et al. Long-term supplementation with n-6 and n-3 PUFAs improves moderate-to-severe keratoconjunctivitis sicca: a randomized double-blind clinical trial. Cornea. 2013; 32: 1297–1304.
Eberwein P, Issleib S, Bohringer D, et al. Conjunctival HLA-DR and CD8 expression detected by impression cytology in ocular graft versus host disease. Mol Vis. 2013; 19: 1492–1501.
Liu X, Wang S, Kao AA, Long Q. The effect of topical pranoprofen 0.1% on the clinical evaluation and conjunctival HLA-DR expression in dry eyes. Cornea. 2012; 31: 1235–1239.
Huang JF, Yafawi R, Zhang M, et al. Immunomodulatory effect of the topical ophthalmic Janus kinase inhibitor tofacitinib (CP-690,550) in patients with dry eye disease. Ophthalmology. 2012; 119: e43–e50.
Corrales RM, Narayanan S, Fernandez I, et al. Ocular mucin gene expression levels as biomarkers for the diagnosis of dry eye syndrome. Invest Ophthalmol Vis Sci. 2011; 52: 8363–8369.
Versura P, Profazio V, Schiavi C, Campos EC. Hyperosmolar stress upregulates HLA-DR expression in human conjunctival epithelium in dry eye patients and in vitro models. Invest Ophthalmol Vis Sci. 2011; 52: 5488–5496.
Caffery B, Heynen ML, Joyce E, Jones L, Ritter RIII, Senchyna M. MUC1 expression in Sjogren's syndrome, KCS, and control subjects. Mol Vis. 2010; 16: 1720–1727.
Sanchez MA, Arriola-Villalobos P, Torralbo-Jimenez P, et al. The effect of preservative-free HP-Guar on dry eye after phacoemulsification: a flow cytometric study. Eye (Lond). 2010; 24: 1331–1337.
Sanchez MA, Torralbo-Jimenez P, Giron N, et al. Comparative analysis of carmellose 0.5% versus hyaluronate 0.15% in dry eye: a flow cytometric study. Cornea. 2010; 29: 167–171.
Massingale ML, Li X, Vallabhajosyula M, Chen D, Wei Y, Asbell PA. Analysis of inflammatory cytokines in the tears of dry eye patients. Cornea. 2009; 28: 1023–1027.
Caffery B, Joyce E, Heynen ML, et al. MUC16 expression in Sjögren's syndrome, KCS, and control subjects. Mol Vis. 2008; 14: 2547–2555.
Mrugacz M, Zak J, Bakunowicz-Lazarczyk A, Wysocka J, Minarowska A. Flow cytometric analysis of HLA-DR antigen in conjunctival epithelial cells of patients with cystic fibrosis. Eye (Lond). 2007; 21: 1062–1066.
Mrugacz M, Zak J, Bakunowicz-Lazarczyk A, Wysocka J, Kaczmarski M. ICAM-1 expression on conjunctival epithelial cells in patients with cystic fibrosis. Cytometry B Clin Cytom. 2007; 72: 204–208.
Rolando M, Barabino S, Mingari C, Moretti S, Giuffrida S, Calabria G. Distribution of conjunctival HLA-DR expression and the pathogenesis of damage in early dry eyes. Cornea. 2005; 24: 951–954.
Brignole F, Pisella PJ, De Saint Jean M, Goldschild M, Goguel A, Baudouin C. Flow cytometric analysis of inflammatory markers in KCS: 6-month treatment with topical cyclosporin A. Invest Ophthalmol Vis Sci. 2001; 42: 90–95.
Brignole F, Pisella PJ, Goldschild M, De Saint Jean M, Goguel A, Baudouin C. Flow cytometric analysis of inflammatory markers in conjunctival epithelial cells of patients with dry eyes. Invest Ophthalmol Vis Sci. 2000; 41: 1356–1363.
Liu Z, Carvajal M, Carothers Carraway CA, Carraway KL, Pflugfelder SC. Increased expression of the type 1 growth factor receptor family in the conjuctival epithelium of patients with keratoconjuctivitis sicca. Am J Ophthalmol. 2000; 129: 472–480.
Baudouin C, Haouat N, Brignole F, Bayle J, Gastaud P. Immunopathological findings in conjunctival cells using immunofluorescence staining of impression cytology specimens. Br J Ophthalmol. 1992; 76: 545–549.
Buggage R, Amrane M, Ismail D, Lemp M, Baudouin C, Baudouin F. The correlation between ocular surface inflammation and corneal fluorescein staining (CFS) in patients with moderate to severe dry eye disease (DED) participating in a randomized clinical trial. Acta Ophthalmol. 2011; 89: doi:10.1111/j.1755-3768.2011.4172.x.
Brignole-Baudouin F, Baudouin C, Aragona P, et al. A multicentre, double-masked, randomized, controlled trial assessing the effect of oral supplementation of omega-3 and omega-6 fatty acids on a conjunctival inflammatory marker in dry eye patients. Acta Ophthalmol. 2011; 89: e591–e597.
Lopez-Miguel A, Teson M, Martin-Montanez V, et al. Dry eye exacerbation in patients exposed to desiccating stress under controlled environmental conditions. Am J Ophthalmol. 2014; 157: 788–798. e2.
Topcu-Yilmaz P, Atakan N, Bozkurt B, et al. Determination of tear and serum inflammatory cytokines in patients with rosacea using multiplex bead technology. Ocul Immunol Inflamm. 2013; 21: 351–359.
Huang D, Luo Q, Yang H, Mao Y. Changes of lacrimal gland and tear inflammatory cytokines in thyroid-associated ophthalmopathy. Invest Ophthalmol Vis Sci. 2014; 55: 4935–4943.
Na KS, Mok JW, Kim JY, Rho CR, Joo CK. Correlations between tear cytokines, chemokines, and soluble receptors and clinical severity of dry eye disease. Invest Ophthalmol Vis Sci. 2012; 53: 5443–5450.
Enriquez-de-Salamanca A, Castellanos E, Stern ME, et al. Tear cytokine and chemokine analysis and clinical correlations in evaporative-type dry eye disease. Mol Vis. 2010; 16: 862–873.
Carreno E, Enriquez-de-Salamanca A, Teson M, et al. Cytokine and chemokine levels in tears from healthy subjects. Acta Ophthalmol. 2010; 88: e250–e258.
Acera A, Rocha G, Vecino E, Lema I, Duran JA. Inflammatory markers in the tears of patients with ocular surface disease. Ophthalmic Res. 2008; 40: 315–321.
Yoon KC, Jeong IY, Park YG, Yang SY. Interleukin-6 and tumor necrosis factor-alpha levels in tears of patients with dry eye syndrome. Cornea. 2007; 26: 431–437.
Meadows JF, Dionne K, Nichols KK. Differential profiling of T-Cell cytokines as measured by protein microarray across dry eye subgroups. Cornea. 2016; 35: 329–335.
Epitropoulos AT, Donnenfeld ED, Shah ZA, et al. Effect of Oral re-esterified omega-3 nutritional supplementation on dry eyes. Cornea. 2016; 35: 1185–1191.
Vehof J, Sillevis Smitt-Kamminga N, Kozareva D, Nibourg SA, Hammond CJ. Clinical characteristics of dry eye patients with chronic pain syndromes. Am J Ophthalmol. 2016; 162: 59–65. e2.
Gokhale M, Stahl U, Jalbert I. In situ osmometry: validation and effect of sample collection technique. Optom Vis Sci. 2013; 90: 359–365.
Tomlinson A, McCann LC, Pearce EI. Comparison of human tear film osmolarity measured by electrical impedance and freezing point depression techniques. Cornea. 2010; 29: 1036–1041.
Sambursky R. Presence or absence of ocular surface inflammation directs clinical and therapeutic management of dry eye. Clin Ophthalmol. 2016; 10: 2337–2343.
Messmer EM, von Lindenfels V, Garbe A, Kampik A. Matrix metalloproteinase 9 testing in dry eye disease using a commercially available point-of-care immunoassay. Ophthalmology. 2016; 123: 2300–2308.
Chan TC, Ye C, Chan KP, Chu KO, Jhanji V. Evaluation of point-of-care test for elevated tear matrix metalloproteinase 9 in post-LASIK dry eyes. Br J Ophthalmol. 2016; 100: 1188–1191.
Sambursky R, Davitt WFIII, Friedberg M, Tauber S. Prospective, multicenter, clinical evaluation of point-of-care matrix metalloproteinase-9 test for confirming dry eye disease. Cornea. 2014; 33: 812–818.
Acera A, Vecino E, Duran JA. Tear MMP-9 levels as a marker of ocular surface inflammation in conjunctivochalasis. Invest Ophthalmol Vis Sci. 2013; 54: 8285–8291.
Table 1
 
Markers Studied in Cells Obtained From Conjunctival Impression Cytology Samples of DED Patients
Table 1
 
Markers Studied in Cells Obtained From Conjunctival Impression Cytology Samples of DED Patients
Table 2
 
Reported Levels of HLA-DR Expression in Cells Obtained From Conjunctival Impression Cytology Samples From DED Patients
Table 2
 
Reported Levels of HLA-DR Expression in Cells Obtained From Conjunctival Impression Cytology Samples From DED Patients
Table 3
 
Observed Differences of IL-6 Levels in Tears From DED Patients and Normals
Table 3
 
Observed Differences of IL-6 Levels in Tears From DED Patients and Normals
Table 4
 
Observed Differences of TNF-α Levels in Tears From DED Patients and Normals
Table 4
 
Observed Differences of TNF-α Levels in Tears From DED Patients and Normals
Table 5
 
Observed Differences of IL-8 Levels in Tears From DED Patients and Normals
Table 5
 
Observed Differences of IL-8 Levels in Tears From DED Patients and Normals
Table 6
 
Observed Differences of IL-17A Levels in Tears From DED Patients and Normals
Table 6
 
Observed Differences of IL-17A Levels in Tears From DED Patients and Normals
Table 7
 
Tear Osmolarity in Clinical Trials involving DED Patients
Table 7
 
Tear Osmolarity in Clinical Trials involving DED Patients
Table 8
 
MMP-9 in Tears of DED Patients
Table 8
 
MMP-9 in Tears of DED Patients
×
×

This PDF is available to Subscribers Only

Sign in or purchase a subscription to access this content. ×

You must be signed into an individual account to use this feature.

×