Journal of Refractive Surgery

Original Article Supplemental Data

Metabolomic Analysis in Corneal Lenticules From Contact Lens Wearers

Min Li, MD; Lin Liu, MM; Chen Qu, BM; Yuehui Shi, BM; Lina Sun, BM; Xingtao Zhou, MD, PhD; Jun Zou, MD, PhD

Abstract

PURPOSE:

To investigate the mechanisms of pathological changes in corneal stroma and the wearing time of soft contact lenses using the metabolomic method.

METHODS:

Laser scanning confocal microscopy was used to evaluate the pathological changes of corneal stroma between wearing time groups before small incision lenticule extraction. After small incision lenticule extraction, 190 corneal stroma samples were obtained, and a metabolomic method using high performance liquid chromatography coupled with time of flight mass spectrometry was established to analyze the changes in metabolites between wearing time groups.

RESULTS:

Laser scanning confocal microscope results demonstrated that the corneal nerve fiber length, the number of corneal anterior stromal cells, and the number of corneal posterior stromal cells were reduced gradually with increasing wearing time. The metabolomic study demonstrated that 11 biomarkers were identified between patients who did and did not wear soft contact lenses and 6 biomarkers were identified between less than 5 years and more than 5 years of wearing time. These biomarkers participate in energy metabolism, lipid metabolism, inflammatory reactions, and neuroprotecton processes, and partially lead to the pathology of dry eyes, eye inflammation, and corneal nerve fiber length decrease. Five biomarkers in the citrate cycle metabolism pathway were found demonstrating that energy metabolism was seriously disturbed.

CONCLUSIONS:

This study systematically revealed the metabolite mechanism for eye discomfort and related disease after wearing soft contact lenses. The identified biomarkers and related physiology pathways supply a new direction for avoiding the side effects of wearing soft contact lenses.

[J Refract Surg. 2020;36(5):317–325.]

Abstract

PURPOSE:

To investigate the mechanisms of pathological changes in corneal stroma and the wearing time of soft contact lenses using the metabolomic method.

METHODS:

Laser scanning confocal microscopy was used to evaluate the pathological changes of corneal stroma between wearing time groups before small incision lenticule extraction. After small incision lenticule extraction, 190 corneal stroma samples were obtained, and a metabolomic method using high performance liquid chromatography coupled with time of flight mass spectrometry was established to analyze the changes in metabolites between wearing time groups.

RESULTS:

Laser scanning confocal microscope results demonstrated that the corneal nerve fiber length, the number of corneal anterior stromal cells, and the number of corneal posterior stromal cells were reduced gradually with increasing wearing time. The metabolomic study demonstrated that 11 biomarkers were identified between patients who did and did not wear soft contact lenses and 6 biomarkers were identified between less than 5 years and more than 5 years of wearing time. These biomarkers participate in energy metabolism, lipid metabolism, inflammatory reactions, and neuroprotecton processes, and partially lead to the pathology of dry eyes, eye inflammation, and corneal nerve fiber length decrease. Five biomarkers in the citrate cycle metabolism pathway were found demonstrating that energy metabolism was seriously disturbed.

CONCLUSIONS:

This study systematically revealed the metabolite mechanism for eye discomfort and related disease after wearing soft contact lenses. The identified biomarkers and related physiology pathways supply a new direction for avoiding the side effects of wearing soft contact lenses.

[J Refract Surg. 2020;36(5):317–325.]

Vision correction methods include refractive surgery and wearing spectacles or soft contact lenses. Considering motivating factors such as aesthetics and cosmetics, soft contact lenses are becoming increasingly popular. Soft contact lenses currently are among the most commonly used medical devices, with an estimated 150 million soft contact lens users worldwide.1 The value of the soft contact lens market is predicted to reach $13.5 billion by the end of 2020.2 Of course, soft contact lenses are foreign bodies for eyes. They swim within the tear film, thereby having a direct impact on tear film and tissues with potential side effects.3 According to the reports, microbial keratitis is still the most severe complication associated with wearing soft contact lenses, and dry eye symptoms remain despite the advanced technology improvements in soft contact lens materials and care systems.4,5 In addition, wearing soft contact lenses reduces tear film thickness, decreases the number of functional meibomian glands, and alters meibomian gland morphology and function.6–8 It also can decrease the entire corneal thickness, increase the corneal curvature and surface irregularity, and promote squamous metaplasia of superficial conjunctival surface cells.9,10

Some traditional studies partly explained the reasons for side effects such as the changes in the ocular microbiome, the differential expression of inflammatory cytokines and the lipid oxidation, and deposition caused by wearing soft contact lenses.11–13 Although some pathological changes in corneal stroma were found after wearing soft contact lenses, a systematic study to evaluate the effects and mechanism of wearing soft contact lenses is still needed. Metabolomics supplies a holistic approach to biomarker discovery and mechanistic insights into disease onset and progression.14 Some metabolomic studies have been used in the donor corneal, keratoconic corneal, and diabetic corneal stroma.15–17 To our knowledge, there has been no study using a metabolomic method to evaluate the effects on corneal stroma after wearing soft contact lenses.

In this study, we used the laser scanning confocal microscope to evaluate the pathology changes between soft contact lens wearing time groups before small incision lenticule extraction (SMILE). After surgery, 190 corneal stroma samples were analyzed using the metabolomic method to identify the seriously changed metabolites (biomarkers) and related pathways. This can reveal the mechanisms of pathological changes in corneal stroma after wearing soft contact lenses.

Patients and Methods

Materials and Reagents

Acetonitrile and methanol (HPLC grade) was purchased from Honeywell. Formic acid (MS grade) was purchased from Sigma-Aldrich. Ultrapure water was obtained by a Milli-Q water purification system. HPLC n-butanol, acetoacetate (EA), and methanol were purchased from Sinopharm Chemical Reagent Co., Ltd. Commercial standards used for biomarker identification were purchased from Sigma-Aldrich. The laser confocal microscope was purchased from Heidelberg (HRTIII).

Participants

A total of 102 participants (190 corneal stroma samples) were recruited for this study. All samples were divided into four groups according to the wearing time: the no wearing of contact lenses group (NW group, 40 eyes); the less than 5 years of wearing contact lenses group (5W group, 51 eyes); the 5 to 10 years of wearing contact lenses group (5–10W group, 45 eyes); and the more than 10 years of wearing contact lenses group (O10W group, 54 eyes). The inclusion criteria were age 18 to 40 years, manifest refraction spherical equivalent refraction (MRSE) of more than −3.00 diopters (D) and less than −6.00 D, stable myopia for 2 or more years, myopic spherical equivalent increment of less than −0.50 D in 1 year, and corrected distance visual acuity of 20/25 or better. The material of the contact lenses was hydrogel. The average time of wearing soft contact lenses was 8 to 10 hours per day. The average wearing time of each group and the ratios of wearing time of silicone hydrogel soft contact lenses in every group are listed in Table 1. The breakdown of eyes with dry eye, conjunctivitis, and superficial punctate keratitis is also shown in Table 1 and these patients were treated and cured before refractive examination. Patients with any ocular or systemic disease that would present a contraindication to laser refractive surgery were excluded. The characteristics of participants are shown in Table 1. The study was approved by the ethics committee of Shanghai Tenth People's Hospital and conformed to the tenets of the Declaration of Helsinki.

Characteristics of Participants Before SMILE

Table 1:

Characteristics of Participants Before SMILE

Laser Confocal Microscope Detection

The participants were all detected first using laser scanning confocal microscopy, with ×400 magnification and 400 × 400 µm (384 × 384 pixels) to evaluate the number of basal cells in the corneal epithelium, the number of corneal endothelial cells, central corneal sub-cutaneous nerve fiber density (CNFL), and the number of corneal anterior and posterior stromal cells (NCASC and NCPSC). Twenty-five percent of corneal stroma above was the depth of acquiring the NCASC images, whereas 75% depth of corneal stroma was the depth of acquiring the NCPSC images. Before examination, a drop of proparacaine hydrochloride 0.5% (Alcaine; Alcon Laboratories) was delivered to the conjunctival sac. All examinations were performed along the sagittal axis in the central cornea. All patients were examined by the same operator (ML). Three people calculated keratocyte densities and acquired the average results.

Sample Treatment and Analysis

All samples were collected from the participants undergoing small incision lenticule extraction (SMILE). All surgeries were conducted by one surgeon (JZ), were uneventful, and had no severe complications. The corneal stroma samples were then transferred to Eppendorf tubes. The samples were stored at −80 °C until analysis. Corneal stroma samples were defrosted on ice and weighed separately, then the samples were added in liquid nitrogen and ground. Methanol was used for the extraction and the volumes were different from one another according to the sample's weight (1 mL of methanol added in 50 mg of corneal stroma sample). Extraction was performed by vortex-mixing for 5 minutes, and centrifugation at 10,000 rpm for 10 minutes at 4 °C to remove protein precipitation. The supernatants were filtered through 0.22 µm nylon filters and 100 µL filtrates were used for subsequent high performance liquid chromatography coupled with time of flight mass spectrometry (HPLC-TOF-MS) analysis.

A quality control sample was made by pooling the same volume (10 µL) of each corneal stroma sample's filtrate. The quality control sample was injected to monitor experiment stability. A blank sample of methanol, prepared in the same way as corneal stroma samples, was injected after every corneal stroma sample to minimize the carry-over.

HPLC-TOF-MS Analysis

All samples were analyzed on an Agilent-1200 HPLC system coupled with an Agilent-6520 TOF-MS (Agilent Technologies). Separation was performed on a ZORBAX eclipse XDB-C18 column (1.8 µm, 2.1 × 100 mm) with the column temperature at 35 °C. The mobile phases consisting of ultrapure water with 0.1% formic acid (A) and acetonitrile with 0.1% formic acid (B) with gradient change are shown in Table 2. The sample injection volume was 15 µL.

HPLC Gradient Elution Program

Table 2:

HPLC Gradient Elution Program

Both positive and negative ion modes were used for the TOF-MS detection. The parameters of mass detection were set as follows: the flow rate of drying gas (N2) was 9 L min−1 with 350 °C gas temperature; the nebulizer gas pressure was 35 psig; Vcap was 3,800 V in positive and 3,700 V in negative mode; the fragmentor was 160 V; the skimmer was 65 V; and the scan range of mass was 50 to 1,000 m/z. The MS/MS data were acquired in targeted MS/MS mode with three collision energies of 15, 20, and 30 eV.

Data Processing

All raw data from HPLC-TOF-MS were analyzed using Agilent Mass Hunter Qualitative Analysis Software (Agilent Technologies), then the data were output to Agilent Mass Profiler Software (Agilent Technologies), which cleaned the background noises and unrelated ions. In data filtering, the parameters were set as follows: retention time ranging from 0 to 10 minutes with retention time tolerance of 0.1 minute; mass ranging from 50 to 1,000 m/z with mass tolerance of 0.05 Da; and peak relative height of 1.5% or greater. The ion intensities were normalized (linear function transformation) to control the MS response shift through the whole analysis. The output data included retention time, molecular mass, and the corresponding abundance. Principal components analysis (PCA) and partial least squares discriminate analysis (PLSDA) in the SIMCA-P software (version 11; Umetrics) were used for metabolite profile analysis.

A one-way analyses of variance with a Bonferroni correction using SPSS 13.0 for Windows software (SPSS, Inc.) was used for significance analysis. Differences were considered significant at a P value of .05 or less. The metabolites were preliminary identified at the Scripps Center for Metabolomics and Mass Spectrometry, then were confirmed by MS/MS data and standard compounds. The biochemical pathways and reactions of identified metabolites were obtained through the Kyoto Encyclopedia of Genes and Genomes and the Human Metabolome Database.

Results

Laser Confocal Microscope Results

The number of wing cells in corneal epithelium and corneal endothelial cells showed no significant differences in the NW, 5W, 5–10W, and O10W groups before surgery (P = .30 and .80, respectively). The CNFL is shown in Figures 1A–1D and Figure 1M. The CNFL in the NW group was significantly higher than those of the other three groups (P < .01). The CNFL in the 5W group was not significantly different from that of the 5–10W group, but was higher than that of the O10W group (P < .01). The CNFL of the O10W group was also substantially less than that of the 5–10W group (P < .01). The value of CNFL showed a significantly downward trend with extension of wearing time.

(A–D, M) Central corneal subcutaneous nerve fiber density (CNFL), (E–H, N) the number of corneal anterior stromal cells (NCASC), and (I–L, O) the number of corneal posterior stromal cells (NCPSC) of the central cornea in the four groups (NW = no wearing of soft contact lenses; 5W = less than 5 years of wearing soft contact lenses; 5–10W = 5 to 10 years of wearing soft contact lenses; O10W = more than 10 years of wearing soft contact lenses). * Represents the significant difference comparing to the NW group with P ⩽ .05. ** Represents the significant difference comparing to the NW group with P ⩽ .01. ## Represents the significant difference between the two groups with P ⩽ .01.

Figure 1.

(A–D, M) Central corneal subcutaneous nerve fiber density (CNFL), (E–H, N) the number of corneal anterior stromal cells (NCASC), and (I–L, O) the number of corneal posterior stromal cells (NCPSC) of the central cornea in the four groups (NW = no wearing of soft contact lenses; 5W = less than 5 years of wearing soft contact lenses; 5–10W = 5 to 10 years of wearing soft contact lenses; O10W = more than 10 years of wearing soft contact lenses). * Represents the significant difference comparing to the NW group with P ⩽ .05. ** Represents the significant difference comparing to the NW group with P ⩽ .01. ## Represents the significant difference between the two groups with P ⩽ .01.

NCASC in these four groups (Figures 1E–1H, Figure 1N) exhibited significant changes. The NCASC in the NW group was significantly higher than that of the other three groups (P < .01). The NCASC of the 5W group was significantly higher than that of the 5–10W (P = .01) and O10W (P < .01) groups. The NCASC in the O10W group was significantly less than that of the 5–10W group (P < .01). The NCASC also showed a significantly downward relation with the extension of wearing time. Figures 1I–1L and Figure 1O show the NCPSC in these four groups. NCPSC in the O10W group was significantly less than those of the other three groups (P = .01). However, there were no significant differences in the NW, 5W, and 5–10W groups.

Laser scanning confocal microscope data suggested that there were pathological changes that occurred in corneal stroma after wearing soft contact lenses. The decreased CNFL suggested that growth of nerve fibers was altered in the central corneal subcutaneous region. Longer wearing time correlated with more significant impact. Furthermore, the number of corneal anterior stromal cells and corneal posterior stromal cells were substantially altered in groups of soft contact lens wearers along with wearing time.

Metabolic Profiling and Method Validation of HPLC-TOF-MS

To obtain more information regarding metabolites from corneal stroma samples, various polar solvents were compared to optimize extraction results. The separation and detection conditions were also optimized in terms of peak shape and abundance. Typical HPLC-TOF-MS total ion current in both positive and negative mode profiles of the corneal stroma samples are shown in Figure 2.

Typical high performance liquid chromatography coupled with time of flight mass spectrometry total ion current (TIC) profiles of corneal stroma samples in both positive and negative modes.

Figure 2.

Typical high performance liquid chromatography coupled with time of flight mass spectrometry total ion current (TIC) profiles of corneal stroma samples in both positive and negative modes.

To confirm the repeatability of the proposed method, six parallel samples were extracted from a random corneal stroma sample using the preparation method mentioned above. Six parallel samples were injected continuously. The stability of the instrument was demonstrated by the data obtained from quality control samples. Because there were 190 corneal stroma samples, 19 stability data sets were acquired from quality control samples. The relative standard deviations of repeatability and stability of this metabolomic study are shown in Table 3. The data showed that the proposed analysis method could be used in large-scale sample analysis with high repeatability and stability.

Repeatability and Stability Data for the Proposed Method

Table 3:

Repeatability and Stability Data for the Proposed Method

Multivariate Statistical Analysis of HPLC-TOFMS Data

Because 15,340 ions in both positive and negative modes were detected in the HPLC-TOF-MS analysis, it was difficult to identify similarities and differences among the NW, 5W, 5–10W, and O10W groups using traditional statistical methods. Multivariate statistical analysis such as PCA or PLS-DA are important tools for exhibiting patterns of metabolites in various corneal stroma samples. In this study, PCA was first performed using SIMCA-P software. The PCA plot of Figure A (available in the online version of this article) showed that the samples from the NW, 5W, 5–10W, and O10W group samples could be substantially distinguished. The samples of the 5–10W and O10W groups overlapped. The PCA result demonstrated that metabolites among the NW, 5W, 5–10W, and O10W groups were substantially different.

Principal components analysis plot of NW (black triangle), 5W (blue diamond), 5–10W (purple diamond), and O10W (red square) groups. NW = no wearing of soft contact lenses; 5W = less than 5 years of wearing soft contact lenses; 5–10W = 5 to 10 years of wearing soft contact lenses; O10W = more than 10 years of wearing soft contact lenses

Figure A.

Principal components analysis plot of NW (black triangle), 5W (blue diamond), 5–10W (purple diamond), and O10W (red square) groups. NW = no wearing of soft contact lenses; 5W = less than 5 years of wearing soft contact lenses; 5–10W = 5 to 10 years of wearing soft contact lenses; O10W = more than 10 years of wearing soft contact lenses

To identify those metabolites (biomarkers) that contributed to group differences, the supervised multivariate statistical analysis method PLS-DA was used for further analysis. First, to identify the various metabolites between the NW and soft contact lens wearing groups (5W, 5–10W, and O10W), a PLS-DA method was established (R2X = 0.848, R2Y = 0.986, and Q2Y = 0.831). As Figure BA (available in the online version of this article) shows, there was a distinguished classification between the clustering of the NW group samples and other groups' samples. In Figure BB, the corresponding loading plot shows several triangles, and each triangle represents an ion (variable). An ion away from the center indicates that the ion abundance was substantially altered between these two groups. The ability of contribution for the group classification was evaluated using variable importance projection (VIP) in Simca P software. When the VIP is 1.0 or greater, the ion could be considered a potential metabolite bio-marker between the NW group and all other groups.

Partial least squares discriminate analysis plot obtained from the four groups (NW = no wearing of soft contact lenses; 5W = less than 5 years of wearing soft contact lenses; 5–10W = 5 to 10 years of wearing soft contact lenses; O10W = more than 10 years of wearing soft contact lenses). A represents the score plot (red square represents the soft contact lens wearing group sample and green square represents the NW group sample) and B represents the loading plot.

Figure B.

Partial least squares discriminate analysis plot obtained from the four groups (NW = no wearing of soft contact lenses; 5W = less than 5 years of wearing soft contact lenses; 5–10W = 5 to 10 years of wearing soft contact lenses; O10W = more than 10 years of wearing soft contact lenses). A represents the score plot (red square represents the soft contact lens wearing group sample and green square represents the NW group sample) and B represents the loading plot.

A total of 20 ions (VIP of 1.0 or greater) of 15,340 were shown to contribute to the classification of the groups. The 20 substantially altered variables11 were presumed according to accurate MS and MS/MS fragments by searching in metabolite databases ( http://metlin.scripps.edu, http://www.hmdb.ca/) and then confirming using commercial standards (Table 4).

Eleven Identified Biomarkers in Corneal Stroma Between Patients Not Wearing and Wearing Contact Lenses Using LC–Q-TOF-MS

Table 4:

Eleven Identified Biomarkers in Corneal Stroma Between Patients Not Wearing and Wearing Contact Lenses Using LC–Q-TOF-MS

Using the same processes mentioned above, the classification of the 5W, 5–10W, and O10W groups are shown in Figure C (available in the online version of this article). In the end, six potential biomarkers were identified as contributors for group classification. Bio-marker information is shown in Table 5.

Partial least squares discriminate analysis plot obtained from the four groups (NW = no wearing of soft contact lenses; 5W = less than 5 years of wearing soft contact lenses; 5–10W = 5 to 10 years of wearing soft contact lenses; O10W = more than 10 years of wearing soft contact lenses) (up). A represents the score plot (black square represents over 5 years wearing soft contact lenses group sample and red dot represents 5W group sample) and B represents the loading plot.

Figure C.

Partial least squares discriminate analysis plot obtained from the four groups (NW = no wearing of soft contact lenses; 5W = less than 5 years of wearing soft contact lenses; 5–10W = 5 to 10 years of wearing soft contact lenses; O10W = more than 10 years of wearing soft contact lenses) (up). A represents the score plot (black square represents over 5 years wearing soft contact lenses group sample and red dot represents 5W group sample) and B represents the loading plot.

Six Identified Biomarkers in Corneal Stroma Between Patients Wearing Contact Lenses for > 5 Years and < 5 Years Using LC–Q-TOF-MS

Table 5:

Six Identified Biomarkers in Corneal Stroma Between Patients Wearing Contact Lenses for > 5 Years and < 5 Years Using LC–Q-TOF-MS

Related Pathological Process of the Identified Biomarkers and Their Functions

Table 4 displays the metabolites that were significantly altered between the NW group and the other groups. These metabolites are divided into three classes: short chain organic acids, long chain unsaturated fatty acids, and lipids, suggesting that short chain organic acids metabolism, fatty acid metabolism, and lipid metabolism in the corneal stroma were dysfunctional in soft contact lens wearers.

Citrate, oxaloacetate, succinate, pyruvate, and glutamate are all short chain organic acids that were significantly upregulated in our study. The related biological pathways of these five metabolites all participate in the citrate cycle process, suggesting that citrate cycle metabolism was severely disturbed by wearing soft contact lenses. Corneal health relies on a well-balanced avascular oxygen supply. Wearing soft contact lenses reduces the aerobic respiration of glucose. Therefore, the cornea resorts to anaerobic respiration for its energy needs.18 Dysfunction of aerobic respiration of the citrate cycle then occurs. The high levels of citrate, oxaloacetate, succinate, and pyruvate in this study further demonstrate that aerobic respiration of the cornea was inhibited, leading to citrate cycle metabolite accumulation.

Behenyl palmitate, cholesteryl oleate, oleyl palmitate, and oleyl oleate are all lipids. These lipids contribute to form the outermost layer of tear film and help to slow evaporation of the aqueous layer in tear film. After wearing soft contact lenses, lipid metabolism is affected. The low levels of behenyl palmitate, cholesteryl oleate, oleyl palmitate, and oleyl oleate suggest that the synthesis of lipids after wearing soft contact lenses was inhibited. The resulting reduction in the protective function of the lipid layer leads to eye discomfort.

Taurine is the most abundant short chain organic acid found in ocular tissue.19 It inhibits the proliferation and migration of corneal stromal cells and protects against retinal and optic nerve damage.20,21 In this study, the level of taurine was significantly down-regulated, thereby enhancing the proliferation and migration ability of corneal stromal cells. Conversely, low levels of taurine in soft contact lens wearers may reduce an important nutrient substrate for the corneal nerve. This will be verified in a future study.

Arachidonic acid is an inflammatory factor. It is converted to downstream mediators such as prostaglandins and leukotrienes that further fuel the inflammatory cycle.22 The high levels of arachidonic acid in this study suggest that inflammatory activity existed in the corneal stroma of soft contact lens wearers.

Table 5 shows that metabolites were significantly altered between the 5W, 5–10W, and O10W groups. L-carnitine plays an important role in maintaining the ocular surface microenvironment. Khandekar et al23 reported that L-carnitine regulated human corneal epithelial cell volume and ameliorated apoptosis under hyperosmotic stress. Hua et al24 demonstrated that L-carnitine protected human corneal epithelial cells from oxidative stress by reducing declines in antioxidant enzymes and suppressing reactive oxygen species production. The inhibitory effects further reduce membrane lipid oxidative damage and protect the integrity of the tear film lipid layer. The substantially lower levels of L-carnitine in this study suggest that the corneal stroma or ocular surface lacked protective substances, possibly resulting in ocular surface diseases.

In the ocular surface, the glucose content is approximately 40%. High glucose levels in the ocular surface may facilitate the growth of pathogenic micro-organisms and alter the stability of the precorneal tear film.25 In this study, high levels of glucose in the 5W, 5–10W, and O10W groups suggested that pathogen invasion risk was greater.

Prolonged use of soft contact lenses can alter corneal innervations.26 Neuroprotectin D1, biosynthesized from docosahexaenoic acid, which was downregulated in this study, has anti-inflammatory and neuroprotective actions.27,28 In this study, with decreased docosahexaenoic acid levels, the synthesis of neuroprotectin D1 will be affected. Therefore, the anti-inflammatory and neuroprotective effects would be weakened in those wearing contact lenses for more than 5 years.

Sphinganine participates in sphingolipid metabolism. Sphingosine 1-phosphate is a metabolite of sphinganine that participates in neuroactive ligand-receptor interactions. The low levels of sphinganine in this study further suggest that the neuroprotective effect was weakened after wearing soft contact lenses.

Palmitoleic acid and linoleic acid are both long chain unsaturated fatty acids. Linoleic acid is the precursor of arachidonic acid. Because high levels of arachidonic acid are markers of inflammation,22 low levels of linoleic acid will help reduce the development of inflammation.

Discussion

In this study, the laser scanning confocal microscope analysis results showed decreased CNFL, NCASC, and NPCSC in the corneal stroma of patients wearing soft contact lenses that changed with wearing time. The metabolomic study identified 11 biomarkers between the NW and other groups, and 6 biomarkers between the 5W, 5–10W, and O10W groups. The biological function revealed some metabolism mechanism of the eye's discomfort symptoms. For example, the low level expression of lipid metabolites such as behenyl palmitate, cholesteryl oleate, oleyl palmitate, and oleyl oleate may help the development of dry eye symptoms after wearing soft contact lenses. The neuroprotection metabolites such as taurine, docosahexaenoic acid, and sphinganine may play an important role in maintaining normal CNFL. With the downregulation of these metabolites, the CNFL of patients wearing soft contact lenses for long periods is decreased. On the other hand, the high expression level of arachidonic acid, glucose, and linoleic acid may facilitate the inflammation reaction of eyes after wearing soft contact lenses. In addition, the energy metabolism (citrate, oxaloacetate, succinate, pyruvate, and glutamate all participate in the citrate cycle process) dysfunction is another problem worthy of attention. Some intervention methods for regulating the dysfunction of energy metabolism would be a benefit for the population of soft contact lens wearers.

We systematically revealed the significant metabolite changes in energy metabolism, lipid metabolism, inflammation reaction, and neuroprotection process dysfunction in the corneal stroma after wearing soft contact lenses. This metabolism dysfunction partially explained the eyes' discomfort after wearing soft contact lenses and provides a new direction for prevention and treatment of related corneal disease.

References

  1. Patel NB, Hinojosa JA, Zhu M, Robertson DM. Acceleration of the formation of biofilms on contact lens surfaces in the presence of neutrophil-derived cellular debris is conserved across multiple genera. Mol Vis. 2018;24:94–104.
  2. Hexa Research. Contact lenses market analysis, market size, application analysis, regional outlook, competitive strategies and forecasts, 2016 to 2024. http://www.hexaresearch.com/research-report/contact-lenses-industry/
  3. Knop E. Contact lens and ocular surface—After all, it's still a foreign body. Acta Ophthalmologica. 2017;95:1. doi:10.1111/j.1755-3768.2017.03571 [CrossRef]
  4. Verhelst D, Koppen C, Van Looveren J, Meheus A, Tassignon MJBelgian Keratitis Study Group. Clinical, epidemiological and cost aspects of contact lens related infectious keratitis in Belgium: results of a seven-year retrospective study. Bull Soc Belge Ophtalmol. 2005;12(297):7–15.
  5. Itokawa T, Okajima Y, Suzuki T, et al. Association between ocular surface temperature and tear film stability in soft contact lens wearers. Invest Ophthalmol Vis Sci. 2018;59(2):771–775. doi:10.1167/iovs.17-23173 [CrossRef]
  6. Arita R, Itoh K, Inoue K, Kuchiba A, Yamaguchi T, Amano S. Contact lens wear is associated with decrease of meibomian glands. Ophthalmology. 2009;116(3):379–384. doi:10.1016/j.ophtha.2008.10.012 [CrossRef]
  7. Nichols JJ, King-Smith PE. Contact lens and tear film thickness measures associated with different multipurpose care solutions. Ocul Surf. 2005;3:S97–S97. doi:10.1016/S1542-0124(12)70508-6 [CrossRef]
  8. Alghamdi WM, Markoulli M, Holden BA, Papas EB. Impact of duration of contact lens wear on the structure and function of the meibomian glands. Ophthalmic Physiol Opt. 2016;36(2):120–131. doi:10.1111/opo.12278 [CrossRef]
  9. Liu Z, Pflugfelder SC. The effects of long-term contact lens wear on corneal thickness, curvature, and surface regularity. Ophthalmology. 2000;107(1):105–111. doi:10.1016/S0161-6420(99)00027-5 [CrossRef]
  10. Doughty MJ. Objective assessment of squamous metaplasia of conjunctival epithelial cells as associated with soft contact lens wear versus non-lens wearers. Cornea. 2014;33(10):1095–1102. doi:10.1097/ICO.0000000000000203 [CrossRef]
  11. Zhang H, Zhao F, Hutchinson DS, et al. Conjunctival microbiome changes associated with soft contact lens and orthokeratology lens wearing. Invest Ophthalmol Vis Sci. 2017;58(1):128–136. doi:10.1167/iovs.16-20231 [CrossRef]
  12. Pisella PJ, Malet F, Lejeune S, et al. Ocular surface changes induced by contact lens wear. Cornea. 2001;20(8):820–825. doi:10.1097/00003226-200111000-00009 [CrossRef]
  13. Schuett BS, Millar TJ. An experimental model to study the impact of lipid oxidation on contact lens deposition in vitro. Curr Eye Res. 2017;42(9):1220–1227. doi:10.1080/02713683.2017.1307416 [CrossRef]
  14. Ivanisevic J, Want EJ. From samples to insights into metabolism: uncovering biologically relevant information in lc-hrms metabolomics data. Metabolites. 2019;9(12):E308. doi:10.3390/metabo9120308 [CrossRef]
  15. Priyadarsini S, McKay TB, Sarker-Nag A, et al. Complete metabolome and lipidome analysis reveals novel biomarkers in the human diabetic corneal stroma. Exp Eye Res. 2016;153:90–100. doi:10.1016/j.exer.2016.10.010 [CrossRef]
  16. McKay TB, Hjortdal J, Sejersen H, Asara JM, Wu J, Karamichos D. Endocrine and metabolic pathways linked to keratoconus: implications for the role of hormones in the stromal microenvironment. Sci Rep. 2016;6(1):25534. doi:10.1038/srep25534 [CrossRef]
  17. Kryczka T, Szaflik JP, Szaflik J, Midelfart A. Influence of donor age, post-mortem time and cold storage on metabolic profile of human cornea. Acta Ophthalmol. 2013;91(1):83–87. doi:10.1111/j.1755-3768.2011.02271.x [CrossRef]
  18. Leung BK, Bonanno JA, Radke CJ. Oxygen-deficient metabolism and corneal edema. Prog Retin Eye Res. 2011;30(6):471–492. doi:10.1016/j.preteyeres.2011.07.001 [CrossRef]
  19. Heinämäki AA, Muhonen AS, Piha RS. Taurine and other free amino acids in the retina, vitreous, lens, iris-ciliary body, and cornea of the rat eye. Neurochem Res. 1986;11(4):535–542. doi:10.1007/BF00965323 [CrossRef]
  20. Zhou WY, Qi HU, Zhang YG. Effect of taurine on the proliferation and migration of corneal keratocytes. Chinese Journal of Trauma and Disability Medicine. 2007;15:20–21.
  21. Nor Arfuzir NN, Agarwal R, Iezhitsa I, Agarwal P, Sidek S, Ismail NM. Taurine protects against retinal and optic nerve damage induced by endothelin-1 in rats via antioxidant effects. Neural Regen Res. 2018;13(11):2014–2021. doi:10.4103/1673-5374.239450 [CrossRef]
  22. Thakur A, Willcox MD, Stapleton F. The proinflammatory cytokines and arachidonic acid metabolites in human overnight tears: homeostatic mechanisms. J Clin Immunol. 1998;18(1):61–70. doi:10.1023/A:1023291921695 [CrossRef]
  23. Khandekar N, Willcox MD, Shih S, Simmons P, Vehige J, Garrett Q. Decrease in hyperosmotic stress-induced corneal epithelial cell apoptosis by L-carnitine. Mol Vis. 2013;19:1945–1956.
  24. Hua X, Deng R, Li J, et al. Protective effects of L-carnitine against oxidative injury by hyperosmolarity in human corneal epithelial cells. Invest Ophthalmol Vis Sci. 2015;56(9):5503–5511. doi:10.1167/iovs.14-16247 [CrossRef]
  25. David MK, Jennifer L, Robert S, Carol M, Jeanie HS. Correlation of glucose levels in the tear film and blood in normals and diabetics. The Ocular Surface. 2005;3:S84.
  26. 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(1):27–31. doi:10.1016/j.plefa.2012.03.010 [CrossRef]
  27. Bazan NG, Calandria JM, Serhan CN. Rescue and repair during photoreceptor cell renewal mediated by docosahexaenoic acid-derived neuroprotectin D1. J Lipid Res. 2010;51(8):2018–2031. doi:10.1194/jlr.R001131 [CrossRef]
  28. Calandria JM, Bazan NG. Neuroprotectin D1 modulates the induction of pro-inflammatory signaling and promotes retinal pigment epithelial cell survival during oxidative stress. Adv Exp Med Biol. 2010;664:663–670. doi:10.1007/978-1-4419-1399-9_76 [CrossRef]

Characteristics of Participants Before SMILE

VariableNW Group5W Group5–10W GroupO10W GroupP
Gender (% female)55.657.459.758.3.978
Age (years, mean ± SD)25.98 ± 3.6326.72 ± 3.5327.31 ± 3.4827.04 ± 3.65.275
MRSE (D, mean ± SD)−4.23 ± 1.04−4.22 ± 0.99−4.50 ± 0.91−4.30 ± 0.99.372
Optical zone (mm, mean ± SD)6.63 ± 0.096.64 ± 0.096.64 ± 0.096.63 ± 0.10.971
Time of wearing SCL per day (hours, mean ± SD)7.93 ± 1.127.87 ± 0.837.76 ± 0.437.75 ± 0.44.617
Time of wearing SCL (silicone hydrogel ratio)5.88%5.28%3.82%.001
CCT (µm, mean ± SD)565.78 ± 21.22568.00 ± 16.21569.72 ± 20.53572.42 ± 18.02.592
Eyes with SPK ratio (number of eyes)5% (2)7.84% (4)8.89% (4)9.26% (5).085
Eyes with severe dry eye ratio (number of eyes)5% (2)5.88% (3)8.89% (4)7.41% (4).202
Eyes with conjunctivitis ratio (number of eyes)0% (0)1.96% (1)2.22% (1)1.85% (1).599

HPLC Gradient Elution Program

Time (min)A%B%
0982
47822
104060
126040
15982

Repeatability and Stability Data for the Proposed Method

ParameterPositiveNegative
Selected ions (m/z)131.1346.1
Repeatability (n = 6)
  Retention time (min)
    Mean0.848.02
    RSD (%)0.380.55
  Peak area
    Mean1,8345,573
    RSD (%)9.216.98
Stability (n = 19)
  Retention time (min)
    Mean0.858.05
    RSD (%)0.520.56
  Peak area
    Mean1,5275,127
    RSD (%)8.844.79

Eleven Identified Biomarkers in Corneal Stroma Between Patients Not Wearing and Wearing Contact Lenses Using LC–Q-TOF-MS

ModeRetention Time (min)Precise MolecularMolecular FormularCompound NameTrendRelated Pathway
Positive
  10.80214.0192C5H11O7PCitrateUpCitrate cycle metabolism
  20.91131.0762C4H9N3O2OxaloacetateUpCitrate cycle metabolism
  31.18244.0777C9H12N2O6SuccinateUpCitrate cycle metabolism
  43.52204.0965C11H12N2O2TaurineDownTaurine and hypotaurine metabolism
  56.07254.0354C7H14N2O4S2PyruvateDownCitrate cycle metabolism and pyruvate metabolism
  61.03147.0597C5H9NO4Arachidonic acidUpArachidonic acid metabolism
  71.17219.1180C9H17NO5GlutamateDownGlutamatergic synapse
Negative
  81.18190.0179C6H6O7Behenyl palmitateDownLipid metabolism
  91.21193.0799C10H11NO3Cholesteryl oleateDownLipid metabolism
  101.93179.0648C9H9NO3Oleyl palmitateDownLipid metabolism
  113.48344.2074C21H28O4Oleyl oleateDownLipid metabolism

Six Identified Biomarkers in Corneal Stroma Between Patients Wearing Contact Lenses for > 5 Years and < 5 Years Using LC–Q-TOF-MS

ModeRetention Time (min)Precise MolecularMolecular FormularCompound NameTrendRelated Pathway
Positive
  10.80214.0192C5H11O7PSphinganineDownSphingolipid metabolism
  20.91131.0762C4H9N3O2L-carnitineDownLysine degradation
  31.18244.0777C9H12N2O6Linoleic acidDownLinoleic acid metabolism
Negative
  41.17219.1180C9H17NO5Palmitoleic acidDownFatty acid metabolism
  51.18190.0179C6H6O7Docosahexaenoic acidDownBiosynthesis of unsaturated fatty acids
  61.21193.0799C10H11NO3GlucoseUpGlycolysis, pyruvate metabolism, and energy metabolism
Authors

From the Department of Ophthalmology, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, People's Republic of China (ML, LL, CQ, YS, LS, JZ); and the Department of Ophthalmology, Myopia Key Laboratory of the Health Ministry, Eye and ENT Hospital of Fudan University, Shanghai, People's Republic of China (XZ).

Supported by National Natural Science Foundation of China (Grant No. 81500727), the Fundamental Research Funds for the Central Universities (Grant Nos. 22120180053 and 22120170254), Shanghai Municipal Natural Science Foundation (Grant No. 16ZR1426700), and Project of Shanghai Science and Technology (Grant No. 17411950207).

The authors have no financial or proprietary interest in the materials presented herein.

AUTHOR CONTRIBUTIONS

Study concept and design (ML, XZ, JZ); data collection (ML, LL, CQ, YS, LS); analysis and interpretation of data (ML, LL); writing the manuscript (ML); critical revision of the manuscript (LL, CQ, YS, LS, XZ, JZ)

Correspondence to Xingtao Zhou, MD, PhD ( doctzhouxingtao@163.com), and Jun Zou, MD, PhD ( zoujun70@126.com), Department of Ophthalmology, Shanghai Tenth People's Hospital, Tongji University, Shanghai, People's Republic of China.

Received: July 15, 2019
Accepted: March 10, 2020

10.3928/1081597X-20200312-01

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