Secretory immunoglobulin-A (SIgA) is known to exhibit broad-spectrum antimicrobial activity against a range of viral pathogens through inhibition of pathogen adherence and penetration of the mucosal epithelia, providing a “first line of defense” against common infections (8). On this premise, and sparked by Tomasi et al.’s landmark article on saliva SIgA in cross-country skiers (40), there has been widespread attention (approximately 200 articles) and optimism that saliva SIgA may serve as a noninvasive biomarker of mucosal immunity and upper respiratory tract infection (URTI) risk in athletes and military personnel (9,15,20,30,40). It is somewhat disappointing then that research has not convincingly demonstrated the utility of saliva SIgA as a predictive biomarker for URTI by identifying atypically low values for both an on-the-spot application (against the population reference range) and a monitoring application (against the individual reference range).
Numerous studies have assessed the saliva SIgA response to prolonged strenuous exercise (>1.5 h) to explore the “open-window” hypothesis that immunity may be temporarily compromised after strenuous exercise (44). These studies often show a postexercise decrease in saliva SIgA, lasting for an hour or more (40,43), but discrepancies exist because studies have also shown no change (5) or even an increase in saliva SIgA (7). Numerous factors are thought to contribute to these conflicting findings, including methodological differences in saliva collection and analysis (33), the chosen method to report saliva SIgA (concentration, secretion rate, or relativity to protein/osmolality) (7,42), diurnal variation (43), psychological stress (10), hydration status (18,32), nutritional status (32), smoking (3), and sex (22). Amid the noise of potential confounders, it is perhaps not surprising that studies have fallen someway short of convincingly demonstrating the utility of saliva SIgA to predict URTI.
Tear fluid collection and analysis have been widely used by ophthalmologists to examine specific aspects of ocular immunity (45). However, assessment of mucosal biomarkers through noninvasive tear sampling is a novel concept in exercise and stress immunology, and the relationship between tear SIgA and URTI has not been explored. Transmission of common viral pathogens from the ocular surface to the upper respiratory tract has been demonstrated in both animals (2,6) and humans (4), and thus it is reasonable to postulate that a reduction in locally produced SIgA may compromise host defense beyond the eye and increase susceptibility to URTI. The aqueous component of the tear film is secreted by the lacrimal gland, a single gland, as opposed to the multiple glands that contribute to saliva composition, and thus we suggest that the composition of tear fluid might be more uniform than saliva, providing a clearer signal amid the noise of potential confounders. Recent advances in nanotechnology and microfluidic fabrication have enabled a new generation of ocular measurement devices for both on-the-spot measurements (e.g., the TearLab™ osmolarity device) and continuous monitoring by contact lens (CL) sensors. Indeed, we recently showed the utility of tear osmolarity to track changes in whole-body hydration status (17). It remains unknown whether the increase in tear osmolarity with dehydration was the result of a dehydration-driven decrease in tear flow rate (concentrating effect), which might also alter the availability of SIgA in tear fluid in the same way as dehydration influences SIgA in saliva (18,32). A wearable device that continuously monitors one or more biomarkers offers advantages for the identification of atypical values over on-the-spot measurement. For example, CL sensors have been developed for continuous assessment of glucose and intraocular pressure and have the potential to measure a range of other biomarkers in tear fluid including antibodies (16). With this information in mind, an exploratory investigation of the potential for tear SIgA to assess mucosal immunity and common cold risk is timely and warranted.
To this end, here we present results from three studies. First, in a prospective study, we investigated the utility of tear SIgA and saliva SIgA to predict upper respiratory symptoms (URS) and pathogen-identified URTI in a mixed-sex cohort of CL and non-CL wearers (study 1). Then, using a repeated-measures crossover design, we investigated the effects of prolonged, moderate-intensity exercise stress (study 2) and progressive, modest dehydration (study 3) on tear SIgA and saliva SIgA. We hypothesized that tear SIgA and saliva SIgA would be lower during and in the days before the common cold and that tear SIgA would have greater predictive utility than saliva SIgA.
All studies received local ethics committee approval, and all protocols were conducted in accordance with the Declaration of Helsinki.
Forty recreationally active volunteers gave written informed consent to participate in the study (Fig. 1). Subjects were 26 males and 14 females (males: age, 22 ± 4 yr; height, 179 ± 5 cm; body mass, 73 ± 10 kg; females: age, 22 ± 6 yr; height, 169 ± 4 cm; body mass, 67 ± 20 kg). Subjects were recruited in Bangor, UK, in September at the beginning of the common cold season. Eight of these subjects were CL wearers. Subjects did not self-report URS lasting ≥48 h in the previous month, did not have any underlying health conditions, had no recent diagnosis or test for mononucleosis (within 1 yr), and were not regularly taking medication known to influence immune indices. Subjects were asked to go about their normal daily routines but avoid structured exercise 2 h before sample collection and food and drink 1 h before sample collection.
Subjects provided weekly tear and saliva samples and completed the Jackson common cold questionnaire each day (25). The questionnaire included one dichotomous global question and eight symptom items (headache, sneezing, chills, sore throat, nasal discharge, nasal obstruction, malaise, and cough) scored on a four-point Likert scale (0, not at all; 1, mild; 2, moderate; 3, severe). If subjects answered “yes” to the dichotomous question “Do you think you are suffering from a common cold today?” or reported a symptom score ≥6 for two consecutive days (equivalent to three moderate or two severe symptoms), they were asked to report to the laboratory as soon as possible at a similar time of day to their initial sample (±1 h) for tear and saliva samples and nasopharyngeal and throat swabs. These subjects continued to complete the Jackson common cold questionnaire each day and returned to the laboratory for healthy samples after four consecutive weeks symptom-free (RECOVERY). For subjects who remained healthy, nasopharyngeal and throat swabs and final tear and saliva samples were collected at the end of the 3-wk monitoring period (CON).
Nasopharyngeal and throat swabs
Swabs were collected from subjects using standard procedures (38) both during URS and at the final visit for all subjects. Swabs were immediately placed in viral transport medium and stored at −80°C before real-time PCR analysis, as described (26). real-time PCR was used to screen for a battery of common upper respiratory pathogens: influenza types A and B; respiratory syncytial virus types A and B; metapneumovirus; adenovirus; coronavirus; parainfluenza virus types 1, 2, 3, and 4; human rhinovirus (HRV), bocavirus, Pneumocystis jirovecii, Chlamydophila pneumoniae, Mycoplasma pneumoniae, and Bordetella pertussis.
Thirteen healthy, recreationally active adult males gave written informed consent to participate in the study (age, 23 ± 5 yr; height, 179 ± 8 cm; body mass, 79 ± 9 kg; V˙O2peak, 52.8 ± 5.6 mL·kg−1·min−1). Subjects were nonsmokers who did not take prescription medication or use dietary supplements for the duration of the study or within the preceding month. Subjects were asked to refrain from alcohol, caffeine, over-the-counter medication, and heavy exercise for 24 h preceding all experimental trials and did not self-report URS in the 7 d preceding experimental trials.
To determine V˙O2peak, subjects performed a ramped treadmill exercise test to volitional exhaustion and speed verification, in line with a protocol detailed in an earlier study (14).
Subjects completed two experimental trials in a randomized, crossover design separated by at least 1 wk. The exercise trial (EX) involved 2 h of continuous treadmill running at 65% V˙O2peak in temperate conditions (19°C, 43% RH), whereas the control trial (REST) constituted 2 h of seated rest in the same environment. Subjects reported to the laboratory at 0730 h on day 1 of both trials, where they were provided with a standardized breakfast and a fluid allowance of 35 mL·kg−1·d−1 pro rata for the preexercise period. Subjects remained in the laboratory and engaged in sedentary activities, such as reading, watching television, or browsing the internet after breakfast until preexercise samples. The EX or REST took place from 1100 to 1300 h. Tear and saliva samples were collected before EX (1040 h), after EX (1300 h), 30 min after EX (1330 h), 1 h after EX (1400 h), 24 h later (1040 h, day 2), and at equivalent time points on REST. Nude body mass was recorded before and after EX to estimate fluid losses. During EX, subjects were provided with 3 mL·kg−1·h−1 plain water to partially offset fluid losses through sweating; during REST, fluid intake remained at 35 mL·kg−1·d−1 pro rata. The HR was monitored continuously throughout the 2-h EX or REST period. During EX only, Borg’s RPE was recorded, and 60-s expired gas samples were collected at 10-min intervals, except at 30, 60, and 90 min where fluids were provided instead. After exercise, subjects rested for 1 h and were provided with a standardized meal at 1415 h. Subjects recorded and replicated caloric intake from 1430 h until sleep and were provided with the same breakfast for the following morning (day 2) before returning to the laboratory at 1040 h on day 2 for the 24-h follow-up samples.
Thirteen healthy, recreationally active adult males gave written informed consent to participate in the study (age, 24 ± 4 yr; height, 181 ± 5 cm; body mass, 80 ± 10 kg; V˙O2peak, 56.4 ± 7.8 mL·kg−1·min−1). Subjects took part according to the same criteria outlined in study 2.
Before the main trials, an uphill walking protocol in line with the subsequent exercise to be undertaken during the main trials was used to estimate V˙O2peak (4 km·h−1 at 4% gradient, increasing by 2 to 14 km·h−1, then by 2.5% gradient; 3-min intervals). V˙O2peak data were interpolated to determine a treadmill speed to elicit exercise at 50% V˙O2peak. Whole-body sweat rate was determined by calculating nude body mass loss (BML) during 30-min treadmill exercise at 50% V˙O2peak in an environmental chamber set to 40°C dry-bulb temperature and 40% RH. Sweat rate was used to prescribe an exercise duration expected to elicit 1%, 2%, and 3% BML in the main experimental trials.
The effect of dehydration on tear and saliva SIgA was investigated using a randomized crossover design. Euhydration (EUH) and dehydration (DEH) trials were undertaken by subjects with each trial lasting approximately 28 h and separated by at least 7 d. Subjects reported to the laboratory at 0800 h on day 1 of the trial, where 40 mL·kg−1·d−1 fluids pro rata were provided until 1400 h to ensure euhydration at the start of each trial. Standardized meals were provided at 0830, 1200, 1800, and 2100 h on day 1 and 0900 h on day 2 of each trial. Saliva, tear, and blood samples were collected at four time points: day 1 at 0% BML (1400 h) and 2% BML (1630 h) and on day 2 after overnight fluid restriction (0800 h) and after rehydration (1100 h). Starting at 1400 h, an exercise-heat protocol (40°C, 40% RH) was used to evoke dehydration through sweating; subjects walked uphill (4% gradient, 50% V˙O2peak) for three bouts of equal duration, each bout estimated to elicit 1% BML (36 ± 7 min). During EUH, subjects were provided with fluids to offset BML. The exercise-to-seated rest ratio was 1/1; subjects rested at 20°C in between each exercise bout. After the final exercise bout, subjects remained in the laboratory overnight and engaged in sedentary activities before going to bed at 2300 h. During dehydration, subjects were provided with fluids at a rate of 4 mL·kg−1·d−1 from 1800 to 2300 h; during EUH, they received 40 mL·kg−1·d−1 fluids pro rata. The following morning, subjects woke at 0730 h and, after sample collection, received fluids throughout the course of the morning. During dehydration, subjects were provided with their net fluid losses, and during EUH, subjects received 40 mL·kg−1·d−1 fluids pro rata.
Assessment of plasma osmolality
To assess hydration status, whole-blood samples were collected from an antecubital vein into a 6-mL heparinized Vacutainer (Becton Dickinson, Oxford, UK). Samples were centrifuged (1500g, 4°C, 10 min) to obtain plasma for the immediate determination of osmolality (Posm) in triplicate using a freezing point depression osmometer (model 3300 MO; Advanced Instruments, Norwood, MA).
Tear sample collection, handling, and analysis
In all studies, timed, unstimulated tear samples (approximately 1 μL; collection time, 36 ± 30 s; minimum, 15 s) were collected using 10-μL fire-polished glass microcapillary pipettes (Sigma-Aldrich, St. Louis, MO) from the inferior marginal tear strip of the left eye (19). After the collection, tear samples were expelled into a preweighed microcentrifuge tube, and sample volume was assessed by calculating mass change. Assuming the density of tears to be 1.00 g·mL−1, tear flow rate was calculated by dividing the volume collected by the collection time. Tear samples were diluted 100× in phosphate-buffered saline and stored at −80°C until analysis. After thawing, tear samples were centrifuged, and the concentration of SIgA was determined using enzyme-linked immunosorbent assay (cat. no. 1-1602; Salimetrics, State College, PA). Mean intra-assay coefficient of variation was 1.6% from duplicate standards and samples across all plates and studies. For each study, samples from a single subject were analyzed on the same plate. Tear SIgA secretion rate was determined as the product of tear flow rate and SIgA concentration.
Saliva collection, handling, and analysis
Unstimulated whole-saliva samples were obtained using the passive drool method for 5 min (32). After collection, tubes were weighed to the nearest 1.0 mg, and saliva flow rate was estimated by dividing the sample volume by collection time, assuming 1.00 g·mL−1 saliva density. Immediately after collection, saliva samples were centrifuged, aliquoted into Eppendorf tubes, and frozen at −80°C for later analysis. Concentration of SIgA was determined using the same ELISA kits as used for the tear analysis. Saliva SIgA secretion rate was determined as the product of saliva flow rate and SIgA concentration.
Data are presented as mean ± SD unless otherwise stated. For sample size estimation, we used repeated baseline data from a previous study (18), where a deviation in salivary SIgA concentration outside the normal variability (±1 CV) occurred with an effect size (ES) of 0.98. With α set to 0.05 and power to 0.8, a minimum sample of 11 was calculated to detect a change of this magnitude (G*Power 3.17). Statistical analyses were performed using common statistical software packages (SPSS 22; IBM, Chicago, IL, and GraphPad Prism 5.0, San Diego, CA). Data were checked for normal distribution, and in cases where the assumption of normality was violated, data were natural log transformed before analysis. One-tailed paired and independent t-tests were performed to compare within- and between-group effects in study 1, with Welch correction applied for unequal variance where applicable. Repeated-measures ANOVA was used to assess the main effects and trial–time interactions in studies 2 and 3. F values refer to a time–trial interaction unless otherwise stated. Post hoc Tukey HSD was used to explore interaction effects. ES was calculated (Cohen’s d) for the difference between means for the main outcome variables. Cohen’s d ES values greater than 0.2, 0.5, and 0.8 represent small, medium, and large effects, respectively. For one-tailed tests, 90% confidence intervals (CI) of the difference were calculated, and 95% CI for two-tailed analyses. Area under the curve (AUC) receiver operating characteristic (ROC) analysis was used to assess global diagnostic and predictive utility of secretory immune parameters, with sensitivity, specificity, accuracy, and the diagnostic odds ratio (OR) also calculated. For t-test, ANOVA, and ROC analyses, significance was accepted as P < 0.05.
Laboratory identification of respiratory pathogens
Of the 33 subjects who completed the study, 11 reported with URS (33%, Fig. 1) during the 3-wk period (six females: five non-CL and one CL; five males: two non-CL and three CL). Of these 11 subjects, 9 returned positive swab results after real-time PCR screening (82%). All nine were positive for HRV, and one of these subjects was also positive for coronavirus (URTI). Of the 22 subjects who did not report URS during the monitoring period, 5 returned positive swab results at their final visit (three HRV, one influenza, and one P. jirovecii), leaving 17 subjects who were nonviral shedders and did not report symptoms; this group served as healthy controls (CON: females, 4 non-CL and 2 CL; males, 10 non-CL and 1 CL).
Effect of URTI on tear and saliva SIgA
Tear SIgA concentration was 48% lower in subjects with current URTI than CON (P < 0.05, CI of the difference: −0.10 to −3.98 μg·mL−1; ES: 0.58; Fig. 2A). Tear SIgA secretion rate was 51% lower in subjects with URTI, but this trend did not reach statistical significance (P < 0.10; ES: 0.62; Fig. 2B). There was no difference in saliva SIgA concentration (Fig. 2C) or saliva SIgA secretion rate (Fig. 2D) between URTI and CON.
Change in tear and saliva SIgA before URS and URTI
In the 11 subjects who reported URS during the 3-wk monitoring period, 1-wk pre-URS samples and recovery samples were compared to explore whether tear or saliva SIgA was altered in an individual before URS. Of these 11 subjects, recovery tear SIgA secretion rate was comparable in the four subjects who wore CL (9.3 ± 8.6 μg·min−1) and seven who did not (10.8 ± 6.6 μg·min−1). Tear SIgA concentration was 34% lower during the week preceding URS (P < 0.05, CI of the difference, −0.10 to −2.16 μg·mL−1; ES, 0.82; Fig. 3A). Tear SIgA secretion rate was 46% lower in the week before URS (P < 0.05, CI of the difference, −1.32 to −8.37 μg·min−1; ES, 0.79; Fig. 3B). No change in saliva SIgA concentration or secretion rate was observed before URS (Fig. 3C–D). A subanalysis of the nine subjects with pathogen-identified URTI revealed 32% lower tear SIgA concentration and 40% lower tear SIgA secretion rate before URTI compared with recovery; although not quite statistically significant, the ES calculations represent medium toward large effects (concentration: P = 0.08; ES, 0.72; secretion rate: P = 0.07; ES, 0.59). There was no change in salivary SIgA concentration or secretion rate before URTI.
Utility of tear SIgA to predict subsequent URS
For tear SIgA concentration and secretion rate, ROC analysis was performed to explore the utility of population cutoff values (on-the-spot application) and percent change values from healthy samples (monitoring application) to predict subsequent URS. Tear SIgA secretion rate performed well as AUC was 0.81 (CI, 0.64–0.96; P < 0.01) for absolute tear SIgA secretion rate and 0.74 (CI, 0.57–0.92; P < 0.05) for change in tear SIgA secretion rate. For an on-the-spot application, subjects with a tear SIgA secretion rate <5.5 μg·min−1 were nine times more likely to develop URS in the following week compared with subjects with a tear SIgA secretion rate above this cutoff value (sensitivity, 0.73; specificity, 0.77; accuracy, 76%; OR, 9.1; CI, 1.7–47.7). A tear SIgA secretion rate >5.5 μg·min−1 (negative test) successfully predicted the absence of URS in 85% of cases. For a monitoring application, individuals who experienced a >30% reduction in tear SIgA secretion rate were six times more likely to develop URS in the following week compared with those whose tear SIgA secretion rate did not decrease >30% (sensitivity, 0.64; specificity, 0.77; accuracy, 73%; OR, 6.0; CI, 1.2–29.0). A negative test successfully predicted the absence of URS in 81% of the cases. The AUC was only 0.66 (P = 0.12; CI, 0.48–0.85) for absolute tear SIgA concentration and 0.65 (P = 0.17; CI, 0.46–0.84) for change in tear SIgA concentration and thus not significantly greater than chance.
Effect of prolonged moderate-intensity exercise on tear and saliva SIgA
All 13 subjects completed both experimental trials. Mean HR during EX rose from 136 ± 15 bpm at 10 min to 160 ± 4 bpm at 2 h (mean of all time points, 147 ± 7 bpm), whereas mean HR throughout REST was 59 ± 6 bpm. During EX, RPE rose from 8 ± 2 (“very light”) at 10 min to 15 ± 2 (“hard”) at 2 h. Mean BML was 1.8% ± 0.4% during EX and 0.3% ± 0.5% during REST. Exercise caused an immediate 57% reduction in tear SIgA concentration compared with pre-EX (F = 5.2; P < 0.01; ES, 1.31; Fig. 4B). Saliva SIgA concentration was elevated on EX (main effect of trial: F = 15.1; P < 0.01; ES, 0.55; Fig. 4E). Both tear and saliva flow rates were influenced by exercise; tear flow rate showed a delayed reduction at 1 h after EX versus before EX (F = 3.5; P < 0.05; ES, 1.12; Fig. 4A). Saliva flow rate was lower than REST immediately and 30 min after EX (F = 5.2; P < 0.05; ES, 1.37 after EX, 0.97 at 30 min after EX; Fig. 4D). There was a reduction in tear SIgA secretion rate immediately after EX (44% decrease; ES, 0.47) and at 30 min after EX (55% decrease; ES, 0.67) versus preexercise values (Fig. 4C), although ANOVA did not indicate an interaction. Despite a 23% decrease from pre- to postexercise (ES, 0.45), there was no significant effect of EX on saliva SIgA secretion rate (Fig. 4F).
Effect of dehydration on tear and saliva SIgA
All 13 subjects completed the prescribed exercise duration on both trials. An elevation of Posm occurred at 2% BML and at 0800 h on day 2 (3.0% ± 0.5% BML), compared with 0% BML and EUH (F = 22.7; P < 0.001; Table 1). Neither tear SIgA concentration nor secretion rate were significantly influenced by DEH; however, tear SIgA concentration was higher at 1630 h than at 0800 h, indicating diurnal variation (main effect of time: F = 6.34; P < 0.01; Table 1). DEH did not significantly influence tear flow rate, but tear flow rate was lower at 1630 h than at 0800 h (main effect of time: F = 4.7; P < 0.001; Table 1). Saliva SIgA concentration increased during DEH at 2% BML and at 0800 h on day 2 (3% BML) and returned to baseline upon rehydration (F = 11.0; P < 0.01; Table 1). Saliva flow rate was reduced during dehydration at 2% BML and at 0800 h on day 2 (F = 8.6; P < 0.001; Table 1). There was no effect of DEH on saliva SIgA secretion rate.
The three studies presented here are the first to explore and demonstrate the utility of tear SIgA as a noninvasive biomarker of mucosal immunity and common cold risk. In study 1, we observed that, unlike saliva SIgA, tear SIgA concentration was lower during pathogen-identified URTI (−48%) and in the week before URS (−34%). Availability of SIgA at the ocular surface, reported as tear SIgA secretion rate, tended to be lower during pathogen-identified URTI (−51%) and was significantly reduced the week before URS (−46%). Our data suggest that tear SIgA may have potential for both on-the-spot assessment of an individual’s risk of URS and for a monitoring application whereby a change in tear SIgA secretion rate could be indicative of an individual’s URS risk. Regarding on-the-spot application, absolute tear SIgA secretion rate <5.5 μg·min−1 increased the risk of URS the following week by ninefold. Given the multifactorial underpinning of upper respiratory illness, assessing the likelihood of protection against URS is of great practical importance. Accordingly, tear SIgA secretion rate above 5.5 μg·min−1 predicted subjects free of URS in 85% of cases. For a monitoring application, a decrease in tear SIgA secretion rate >30% resulted in a sixfold increased risk of URS the following week. Absence of a 30% or more decrease in tear SIgA secretion rate predicted subjects free of URS in 81% of cases in our otherwise healthy male and female cohort during the common cold season. In study 2, prolonged exercise caused a transient decrease in tear SIgA (concentration, −57%, and secretion rate, −44%) in line with the “open-window theory” (31). In study 3, dehydration did not significantly influence tear SIgA. Both prolonged exercise (study 2) and dehydration (study 3) brought about a decrease in saliva flow rate and increase in saliva SIgA concentration as shown previously (18,27).
In study 1, we demonstrate that tear SIgA, but not saliva SIgA, was lower during URTI compared with CON. The strength of study 1 was the use of real-time PCR analysis to confirm URTI by identification of common viral pathogens in 9 of 11 subjects (82%) presenting with self-reported URS. HRV was detected in all cases of confirmed URTI, in line with previous evidence that HRV caused 80% of common colds in adults during the seasonal autumn peak (1). We recognize the limitation that we did not screen for the presence of viral pathogens at enrolment, so we cannot discount viral reactivation and conclude that the URTI was due to a primary infection (21). However, given that we chose the common cold season, that none of our subjects self-reported URS in the month before enrolment, and none had been diagnosed with mononucleosis in the previous year, it is plausible that primary infection was prominent in the reported cases of URTI. Our findings exceed the rates of confirmed pathogen detection in 70% of the URS episodes in 200 Finnish students (28); moreover, we detected a substantially higher proportion of pathogen-confirmed URTI from self-reported URS than the approximately 30% reported in elite athletes (12,38). This discrepancy likely occurred because those studies were conducted year-round (12) or during the southern hemisphere summer (38) when the average ambient temperature was approximately 28°C and the common cold incidence is low (likely higher allergy in summer). Athletes engaging in heavy training may be more susceptible to developing URS of noninfectious origin than our cohort, given the high prevalence of airway inflammation and reactivity to airborne allergens in athletes (36). Self-reported URS, irrespective of origin, is a significant economic and social burden, causing absence from work, education and athletic training, poorer performance in work-related tasks (37), and performance decrements in athletic competition (35). Thus, we were principally interested in the utility of tear and saliva SIgA to predict self-reported URS. In the week before URS, both tear SIgA concentration and secretion rate were lower by 34% and 46% (vs healthy), respectively: lower tear SIgA was also evident in the week before URTI in the nine participants with HRV. Because the incubation period for HRV is less than 12 h (24), it is unlikely that tear SIgA decreased as a consequence of the presence of virus, but instead it represented compromised host defense and an increased susceptibility to the common cold.
In light of the potential for developing wearable technology to monitor ocular biomarkers, the influence of CL wear on tear SIgA secretion and indeed URTI risk are important considerations. Numerous studies have investigated the influence of CL wear on tear antimicrobial proteins, but the findings are conflicting, with studies reporting a decrease (34,41) or an increase in tear SIgA with CL wear (29). Moreover, different CL types may have different effects on the tear protein profile; Temel et al. (39) reported an increase in tear SIgA in rigid lens wearers but no effect of soft lenses on tear SIgA compared with nonwearers. Typically, only tear SIgA concentration has been reported, whereas our findings suggest that SIgA secretion rate, taking into account tear flow rate, may be an important factor in preserving the integrity of mucosal defenses at the ocular surface. Moreover, the influence of CL on URTI risk remains unexplored; although CL wearers typically experience increased risk of ocular infections, a very recent report suggests that poor hygiene is a likely contributing factor (11). Our preliminary findings suggest that tear SIgA secretion is comparable in CL and non-CL wearers, but we acknowledge the small N for this comparison and the need for future work to verify this finding.
In contrast to the encouraging results for tear SIgA, we observed no change in saliva SIgA before URS; saliva SIgA concentration and secretion rate before URS were comparable with saliva SIgA in healthy samples. This finding agrees with a study showing no meaningful relationship between saliva SIgA concentration and URS incidence during the winter months in athletes (23) but disagrees with another study showing a progressive decline in saliva SIgA concentration in elite sailors in the 3 wk before URS (30). However, the predictive utility of saliva SIgA concentration in the sailing study (30) should be tempered because the increased frequency of URTI in those experiencing a 30% or greater decline in saliva SIgA concentration was only 2.5-fold compared with sixfold for tear SIgA secretion rate in the present study.
Prolonged, moderate-intensity exercise in study 2 caused a transient decrease in tear SIgA concentration (−57%), which exceeded in magnitude the reduction in tear SIgA concentration before URS. This “open window,” although short lasting, is thus likely to compromise mucosal defense at the ocular surface. In study 3, we demonstrated that unlike saliva, tear SIgA was not affected by dehydration immediately after exercise or after overnight fluid restriction. Although a diurnal change in tear SIgA concentration was highlighted, tear SIgA secretion rate was unaffected by hydration status or time of day, providing a potentially stable signal from which to identify atypical deviations in tear SIgA secretion rate to indicate compromised immunity. Saliva responses to prolonged exercise in study 2 and dehydration in study 3 consisted of a well-characterized decrease in flow rate, concurrent increase in saliva SIgA concentration (concentrating effect), and no overall change in SIgA secretion rate (5,18,27). The discrepancy between the response of tear and saliva SIgA in study 2 may be explained in part by the differences in the neural regulation of tear and saliva secretions. The lacrimal gland receives primarily parasympathetic innervation, with only a minor contribution from the sympathetic nervous system (13). Parasympathetic withdrawal attenuates the rate of tear protein secretion in tears, with likely little effect on the electrolyte or water output (13). Thus, the transient postexercise decrease in tear SIgA concentration may be accounted for by parasympathetic withdrawal. The rate-limiting step for SIgA secretion in mucosal glands is the availability of the polymeric IgA receptor, which transports SIgA across the acinar cell membrane into mucosal secretions. Because alterations in transcriptional regulation of either SIgA or the polymeric IgA receptor would be expected to occur over a longer time scale (minutes to hours), the transient postexercise decrease in tear SIgA concentration was unlikely immunologically driven. Nevertheless, the decrease in tear SIgA after prolonged exercise likely represents a meaningful, albeit temporary, reduction in host defense at the ocular surface.
We conclude that tear SIgA has potential as a noninvasive biomarker of mucosal immunity and common cold risk for those working in the fields of exercise, stress and nutritional immunology, and others. Further research is required to confirm these findings, to determine normative population values, to extend tear fluid analysis beyond SIgA to include other antimicrobial proteins, and to explore the utility of other candidate biomarkers in tears to monitor psychological and physiological status (e.g., stress hormones). Studies are also required to fully understand the influence of CL wear, age, sex, diet, psychological status, and training status, among others, on tear fluid biomarkers. In tandem, further advances in nanotechnology and microfluidics will likely afford the possibility for on-the-spot tear fluid measurement devices and continuous biomonitoring by CL.
Helen Hanstock’s Ph.D. was supported by a 125th anniversary research scholarship from Bangor University. Studies 1 and 2 received no external funding. Study 3 was supported by a graduate student research grant from the European Hydration Institute.
None of the authors had a conflict of interest. The results of the present study do not constitute endorsement by the American College of Sports Medicine.
1. Arruda E, Pitkäranta A, Witek TJ Jr, Doyle CA, Hayden FG. Frequency and natural history of rhinovirus infections in adults during autumn. J Clin Microbiol
. 1997; 35(11): 2864–8.
2. Belser JA, Wadford DA, Xu J, Katz JM, Tumpey TM. Ocular infection of mice with Influenza A (H7) viruses: a site of primary replication and spread to the respiratory tract. J Virol
. 2009; 83(14): 7075–84.
3. Bennet KR, Reade PC. Salivary immunoglobulin A levels in normal subjects, tobacco smokers, and patients with minor aphthous ulceration. Oral Surg Oral Med Oral Pathol
. 1982; 53(5): 461–5.
4. Bischoff WE, Reid T, Russell GB, Peters TR. Transocular entry of seasonal influenza-attenuated virus aerosols and the efficacy of N95 respirators, surgical masks, and eye protection in humans. J Infect Dis
. 2011; 204(2): 193–9.
5. Bishop NC, Blannin AK, Armstrong E, Rickman M, Gleeson M. Carbohydrate and fluid intake affect the saliva flow rate and IgA response to cycling. Med Sci Sports Exerc
. 2000; 32(12): 2046–51.
6. Bitko V, Musiyenko A, Barik S. Viral infection of the lungs through the eye. J Virol
. 2007; 81(2): 783–90.
7. Blannin AK, Robson PJ, Walsh NP, Clark AM, Glennon L, Gleeson M. The effect of exercising to exhaustion at different intensities on saliva immunoglobulin A, protein and electrolyte secretion. Int J Sports Med
. 1998; 19(8): 547–52.
8. Brandtzaeg P. Secretory immunity with special reference to the oral cavity. J Oral Microbiol
. 2013; 5.
9. Carins J, Booth C. Salivary immunoglobulin-A as a marker of stress during strenuous physical training. Aviat Space Environ Med
. 2002; 73(12): 1203–7.
10. Clow A, Hucklebridge F. The impact of psychological stress on immune function in the athletic population. Exerc Immunol Rev
. 2001; 7: 5–17.
11. Cope JR, Collier SA, Rao MM, et al. Contact lens wearer demographics and risk behaviors for contact lens-related eye infections—United States, 2014. MMWR Morb Mortal Wkly Rep
. 2015; 64(32): 865–70.
12. Cox AJ, Gleeson M, Pyne DB, Callister R, Hopkins WG, Fricker PA. Clinical and laboratory evaluation of upper respiratory symptoms in elite athletes. Clin J Sport Med
. 2008; 18(5): 438–45.
13. Dartt DA. Neural regulation of lacrimal gland secretory processes: relevance in dry eye diseases. Prog Retin Eye Res
. 2009; 28: 155–77.
14. Diment BC, Fortes MB, Edwards JP, et al. Exercise intensity and duration effects on in vivo
immunity. Med Sci Sports Exerc
. 2015; 47(7): 1390–8.
15. Fahlman MM, Engels HJ. Mucosal IgA and URTI in American college football players: a year longitudinal study. Med Sci Sports Exerc
. 2005; 37(3): 374–80.
16. Farandos NM, Yetisen AK, Monteiro MJ, Lowe CR, Yun SH. Contact lens sensors in ocular diagnostics. Adv Healthc Mater
. 2015; 4(6): 792–810.
17. Fortes MB, Diment BC, Di Felice U, et al. Tear fluid osmolarity as a potential marker of hydration status. Med Sci Sports Exerc
. 2011; 43(8): 1590–7.
18. Fortes MB, Diment BC, Di Felice U, Walsh NP. Dehydration decreases saliva antimicrobial proteins important for mucosal immunity. Appl Physiol Nutr Metab
. 2012; 37: 850–9.
19. Fullard RJ, Snyder C. Protein levels in nonstimulated and stimulated tears of normal human subjects. Invest Ophthalmol Vis Sci
. 1990; 31: 1119–26.
20. Gleeson M, Bishop N, Oliveira M, McCauley T, Tauler P. Sex differences in immune variables and respiratory infection incidence in an athletic population. Exerc Immunol Rev
. 2011; 17: 122–35.
21. Gleeson M, Bishop N, Oliveira M, McCauley T, Tauler P, Muhamad AS. Respiratory infection risk in athletes: association with antigen-stimulated IL-10 production and salivary IgA secretion. Scand J Med Sci Sports
. 2012; 22(3): 410–7.
22. Gleeson M, McDonald WA, Pyne DB, et al. Salivary IgA levels and infection risk in elite swimmers. Med Sci Sports Exerc
. 1999; 31(1): 67–73.
23. Gleeson M, Pyne DB, Austin JP, et al. Epstein–Barr virus reactivation and upper-respiratory illness in elite swimmers. Med Sci Sports Exerc
. 2002; 34(3): 411–7.
24. Harris JM 2nd, Gwaltney JM Jr. Incubation periods of experimental rhinovirus infection and illness. Clin Infect Dis
. 1996; 6: 1287–90.
25. Jackson GG, Dowling HF, Spiesman IG, Boand AV. Transmission of the common cold to volunteers under controlled conditions. I. The common cold as a clinical entity. AMA Arch Intern Med
. 1958; 101(2): 267–78.
26. Johnston NW, Lambert K, Hussack P, et al. Detection of COPD Exacerbations and compliance with patient-reported daily symptom diaries using a smart phone-based information system [corrected]. Chest
. 2013; 144(2): 507–14.
27. Killer SC, Svendsen IS, Gleeson M. The influence of hydration status during prolonged endurance exercise on salivary antimicrobial proteins. Eur J Appl Physiol
. 2015; 115(9): 1887–95.
28. Mäkelä MJ, Puhakka T, Ruuskanen O, et al. Viruses and bacteria in the etiology of the common cold. J Clin Microbiol
. 1998; 36(2): 539–42.
29. Maurya RP, Bhushan P, Singh VP, et al. Immunoglobulin concentration in tears of contact lens wearers. J Ophthalmic Vis Res
. 2014; 9: 320–3.
30. Neville V, Gleeson M, Folland JP. Salivary IgA as a risk factor for upper respiratory infections in elite professional athletes. Med Sci Sports Exerc
. 2008; 40: 1228–36.
31. Nieman DC. Exercise, infection, and immunity. Int J Sports Med
. 1994; 15: S131–41.
32. Oliver SJ, Laing SJ, Wilson S, Bilzon JL, Walters R, Walsh NP. Salivary immunoglobulin A response at rest and after exercise following a 48 h period of fluid and/or energy restriction. Br J Nutr
. 2007; 97(6): 1109–16.
33. Papacosta E, Nassis GP. Saliva as a tool for monitoring steroid, peptide and immune markers in sport and exercise science. J Sci Med Sport
. 2011; 14(5): 424–34.
34. Pearce DJ, Demirci G, Willcox MD. Secretory IgA epitopes in basal tears of extended-wear soft contact lens wearers and in non-lens wearers. Aust N Z J Ophthalmol
. 1999; 27(3–4): 221–3.
35. Pyne DB, Hopkins WG, Batterham AM, Gleeson M, Fricker PA. Characterising the individual performance responses to mild illness in international swimmers. Br J Sports Med
. 2005; 39: 752–6.
36. Robson-Ansley P, Howatson G, Tallent J, et al. Prevalence of allergy and upper respiratory tract symptoms in runners of the London marathon. Med Sci Sports Exerc
. 2012; 44(6): 999–1004.
37. Smith AP. Effects of the common cold on mood, psychomotor performance, the encoding of new information, speed of working memory and semantic processing. Brain Behav Immun
. 2012; 26(7): 1072–6.
38. Spence L, Brown WJ, Pyne DB, et al. Incidence, etiology, and symptomatology of upper respiratory illness in elite athletes. Med Sci Sports Exerc
. 2007; 39(4): 577–86.
39. Temel A, Kazokoglu H, Taga Y, Orkan AL. The effect of contact lens wear on tear immunoglobulins. CLAO J
. 1991; 17(1): 69–71.
40. Tomasi TB, Trudeau FB, Czerwinski D, Erredge S. Immune parameters in athletes before and after strenuous exercise. J Clin Immunol
. 1982; 2(3): 173–8.
41. Vinding T, Eriksen JS, Nielsen NV. The concentration of lysozyme and secretory IgA in tears from healthy persons with and without contact lens use. Acta Ophthalmol (Copenh)
. 1987; 65(1): 23–6.
42. Walsh NP, Blannin AK, Clark AM, Cook L, Robson PJ, Gleeson M. The effects of high-intensity intermittent exercise on saliva IgA, total protein and alpha-amylase. J Sports Sci
. 1999; 17: 129–34.
43. Walsh NP, Bishop NC, Blackwell J, Wierzbicki SG, Montague JC. Salivary IgA response to prolonged exercise in a cold environment in trained cyclists. Med Sci Sports Exerc
. 2002; 34(10): 1632–7.
44. Walsh NP, Gleeson M, Shephard RJ, et al. Position statement. Part one: immune function and exercise. Exerc Immunol Rev
. 2011; 17: 6–63.
45. Zhou L, Beuerman RW. Tear analysis in ocular surface diseases. Prog Retin Eye Res
. 2012; 31: 527–50.