Association of Ultra-Short-Term Intraocular Pressure Fluctuation With Disease Progression in Primary Angle Closure Glaucoma: The CUPAL Study : Journal of Glaucoma

Secondary Logo

Journal Logo

New Understandings of Glaucoma: Original Studies

Association of Ultra-Short-Term Intraocular Pressure Fluctuation With Disease Progression in Primary Angle Closure Glaucoma: The CUPAL Study

Tan, Shaoying PhD*,†,‡; Yu, Marco PhD§,∥; Baig, Nafees MRCS, FCOphth(HK)‡,¶; Chan, Poemen P. FRCS‡,#,**; Tang, Fang Yao PhD‡,**; Cheung, Carol Y. PhD‡,**; Tham, Clement C.Y. FRCS, FCOphthHK‡,#,**

Author Information
doi: 10.1097/IJG.0000000000002103
  • Free

Abstract

Intraocular pressure (IOP) elevation is a major risk factor for the onset and progression of glaucoma.1,2 IOP is not a constant value, and it varies with metabolism, posture, activities, and environmental factors.3 It fluctuates by 1 mm Hg with every heartbeat.4 The IOP level changes within seconds or minutes have been defined as “ultra-short-term IOP fluctuation”.5 Recent evidence suggested that large IOP fluctuation, including “long-term IOP fluctuation”, which is defined as the IOP variations during months or years and “short-term IOP fluctuation” defined as IOP changes within days or hours, may be an independent risk factor for glaucoma progression, in addition to elevated mean-IOP.6,7 Nevertheless, the roles of IOP related factors in PACG patients have not been clearly identified. We previously identified that “long-term IOP fluctuation” was a significant and independent predictor for subsequent visual field (VF) deterioration in eyes with primary angle closure disease (PACD),8 and “circadian IOP fluctuation” was associated with disease progression in primary angle closure glaucoma (PACG) eyes.9 Since glaucoma is a chronic disease, the IOP fluctuation within a very short time may or may not affect the long-term course of the disease. Still, the continuous high frequency of “ultra-short IOP fluctuation” under daily activities is able to reflect the fluctuation in the systolic cardiac cycle.10 Glaucoma progression has been shown to be associated with hypertension.11 The systolic pressure influences ocular perfusion, and the “ultra-short-term IOP fluctuation” is related to aqueous flow rate, episcleral venous pressure, and external ocular pressure. Therefore, glaucoma progression could potentially be related to the continuous high frequency of spikes in the “ultra-short-term IOP fluctuation”. However, there was no practical way to continuously monitor the “ultra-short-term IOP fluctuations” over a day cycle in previous research, and there is no clinical data to evaluate the relationship of “ultra-short-term IOP fluctuation” during patients’ normal living activities with glaucomatous progression in the published literature.

The Sensimed Triggerfish is a soft silicone contact lens device that allows robust IOP fluctuation measurement over 24 hours, without disrupting the subjects’ normal activities of daily living. The contact lens contains a strain gage surrounding the corneoscleral regions and monitors the circumferential changes. It has been validated that the output shows a well-fitted relationship with the manometrically-measured IOP fluctuation in an enucleated pig eye model.12 The sensor records for 30 seconds at 5-minute intervals during a 24-hour period, which represents 288 readings over 24 hours,13–15 providing the possibility to investigate the “ultra-short-term IOP fluctuation”, which occurs within minutes and seconds. Even though the signal produced by the Triggerfish device may not be the actual IOP in mm Hg, it is still a useful surrogate parameter that correlates well with IOP fluctuations.

We previously documented 24-hour IOP fluctuation using the Sensimed Triggerfish contact lens sensor and showed the relationship between the IOP fluctuations within the sleep-related period, that is, ‘bedtime’ and ‘wake-up’ hours, and the glaucomatous nerve damage in PACG patients. However, the amplitude-frequency components of signal fluctuation within seconds and minutes were filtered out. The effect of daily activities, for example, falling asleep or waking up, on fluctuations were not analyzed in the previous study.9 On the basis of the same cohort of PACG patients and the same set of clinical data, the present study documented and analyzed the “ultra-short-term IOP fluctuation” occurring within seconds and minutes in PACG eyes during the activities, especially in the sleep-related period, undergone by patients in their normal daily living environments. We hypothesize that the “ultra-short-term IOP fluctuation” of the ‘progressive’ and the ‘stable’ PACG eyes would be different under certain daily activities, such as falling asleep or waking up. Monitoring significant IOP fluctuations under specific daily activity or during the most sensitive time period, such as sleep-related periods, could be an additional approach for detecting the risk of disease progression in PACG patients.

MATERIALS AND METHODS

The study protocol was approved by the Ethics Committee for Human Research at the Chinese University of Hong Kong, and in accordance with the tenets of the Declaration of Helsinki and the ICH-GCP guidelines. Informed consent was obtained from all study subjects.

Study Subjects

The current study was a sub-study of the CUHK PACG Longitudinal (CUPAL) Study, an ongoing prospective cohort study.8,11,16 The details of the CUPAL study were described before.5,8,16 To keep comparable disease durations, we only selected, in the current analysis, patients with diagnosed PACG and at least 2 years of follow-up. The diagnosis recruited criteria for PACG were described before.9

Ophthalmic Examinations

The complete ophthalmic examinations results upon recruitment in all recruited patients were collected, including the best-corrected visual acuity (LogMAR), refractive errors, central corneal thickness, and axial length (AL) by ultrasonography. Noncontact specular microscopy (Konan NonconRobo-CA SP-8000 specular microscope, Konan Inc., Hyogo, Japan) was used to measure corneal endothelial cell density. Angle structures were also examined by gonioscopic examination and graded by the Shaffer system. Clinical IOP was measured by Goldmann applanation tonometer on a slit-lamp biomicroscope every 3 months during the follow-up period. The reading in mm Hg was rounded to the next higher integer. Two IOP measurements were taken; yet, a third measurement would be taken if the readings differed by greater than 2 mm Hg. Clinical IOP was defined as the mean of 2 IOP measurements or the median of 3 measurements. Mean of clinical IOP (Mean-IOP) and SD of IOP were calculated for each individual at each 3-month follow-up visit from the first visit till the last visit during the study period. Visual field (VF) was documented by static automated white-on-white threshold perimetry using the Humphrey automated perimetry (Humphrey Field Analyzer II, Carl Zeiss Meditec AG, Jena, Germany). Peripapillary retinal nerve fiber layer (RNFL) thickness was measured at the optic nerve head by using the spectral-domain optical coherence tomography (SD-OCT) imaging system (Spectralis HRA+OCT; software version 3.1, Heidelberg Engineering, Heidelberg, Germany). Serial VF examinations and RNFL thickness scan were conducted every 6 months. All assessments were scheduled within 3 hours of the recruitment day and each follow-up visit.

Definition of “Progressive” Group and “Stable” Group

Functional PACG progression was analyzed retrospectively by applying linear regression to the visual field index (VFI) data obtained from serial Humphrey automated perimetry over the follow-up period. If a significant decrease with P<0.05 in VFI was found, the eye would be classified as ‘progressive’ in VFI. Otherwise, the PACG eyes would be classified as ‘stable’.

Similarly, RNFL thickness progression on OCT was also derived by retrospective analysis by applying linear regression to RNFL thickness data obtained from serial visits in the follow-up period, including global RNFL thickness, superior temporal thickness, superior nasal thickness, inferior nasal thickness, and inferior temporal thickness. If any significant decrease with P<0.05 in RNFL thickness was detected in any specific areas, the eye would be classified as ‘progressive’ in terms of RNFL thickness. The eye would be considered as ‘stable’ in RNFL thickness when changes over all areas of RNFL thickness were not significant during the follow-up period.

Documentation of Ultra-short-term IOP Fluctuation by Contact Lens Sensor

“Ultra-short-term IOP fluctuation” was monitored by Sensimed Triggerfish contact lens sensor (Sensimed AG, Lausanne, Switzerland) in the PACG patients over 24 hours. The contact lens sensor was placed on the selected eyes of the recruited PACG patients on the study day at 13:00 and recording of the sensor output signals was initiated as soon as satisfactory adaptation of the device on the subject’s eye was achieved. Patients were allowed to go home with the contact lens sensor and encouraged to engage in their normal activities of daily living and keep a log of their activities, including the times at which they went to sleep and woke up, in their usual environments. After 24 hours, at 13:00 on the next day, patients would return to the clinic for the removal of the contact lens sensor. The Sensimed Triggerfish contact lens sensor recorded the electric signals for a duration of 30 seconds every 5 minutes. Within each of the 30-second duration, the signal variations were recorded as 288 readings. The robust IOP fluctuation data documented by the sensor was downloaded from the signal recording device and reported as the individual IOP fluctuation profile with a unit of millivolt equivalents (mV eq).

Statistical Analyses

Since each study subject has one’s own daily activities and sleep/wake behavior, nonlinear time warping for each subject should be considered in analyzing 24-hour data. Five sleep-related 1 hour time periods were selected for analysis of the “ultra-short-term IOP fluctuation” associated with sleep, including 1 hour before sleep, 1 hour after falling asleep, 1 hour in the middle of sleep (mid 1 hour sleep-wake), 1 hour before waking, and 1 hour immediately after waking. In addition, 2 day-time 1-hour periods, before lunch (11:00–12:00) and after dinner (20:00–21:00), were also included for analysis, when all of the study subjects were maintaining stable conditions according to their records. The signal spikes caused by blinking were removed manually. The amplitude-frequency profiles of signal fluctuations were quantified in the specific 7 1-hour periods by semivariogram/semivariance.17 The semivariogram of a signal curve is defined mathematically as γΔt=12varst+Δtst, where st represented the signal value at time t, which described the amplitude of fluctuation with frequency measured in terms of Δt. Because of the discontinuous measurement in each 1-hour period, the averaged 15-second amplitude-frequency profiles of signal fluctuation were calculated from 12 periods of 30 seconds within the selected 1-hour durations. The differences in amplitude-frequency profiles of signal fluctuation were compared by permutation tests on the functional t-statistics and the supremum t-statistics18 between the ‘progressive’ and ‘stable’ groups. The functional t-statistics is defined mathematically by TΔt=γ̅1Δtγ̅2Δtvarγ1Δt/n1+varγ2Δt/n2, where γ̅1Δt and γ̅2Δt represent the mean amplitude-frequency profiles of the ‘progressive’ and ‘stable’ groups at frequency Δt, varγ1Δt and varγ2Δt represent the variation of amplitude-frequency profiles of the ‘progressive’ and ‘stable’ groups, and n1 and n2 represent the sample size of the ‘progressive’ and ‘stable’ groups, respectively. The supremum t-statistic is defined as the least upper bound of the functional t-statistics across different frequencies.

Continuous variables were reported as mean (±standard deviation, SD). Statistical significance was considered as P<0.05, and moderate significance was defined as P<0.10. All statistical analyses were conducted using R (version 2.15.2; R Foundation, Vienna, Austria).

RESULTS

Patient Demographics

Twenty-five PACG patients (6 males and 19 females) were recruited. The mean age (±1 SD) was 69.1 (±12.9) years (range, 36.3–89.2 y). The mean follow-up duration (±1 SD) was 41.0 (±8.2) months (range, 24–54 mo). There were 15 right eyes and 10 left eyes. Five patients were classified into VFI ‘progressive’ group with the mean rate (±1 SD) of -0.415 ±0.398%/month on VFI changes, whereas 20 patients were grouped into VFI ‘stable’ group with the mean VFI changes rate (±1 SD) of –0.098±0.165%/month (P=0.010). According to the progression analyses of RNFL thickness from SD-OCT, 16 patients were classified into ‘progressive’ group and 9 patients were classified into ‘stable’ group in terms of RNFL thickness. The results of ophthalmic examinations in ‘stable’ and ‘progressive’ groups are shown in Table 1.

TABLE 1 - Demographic and Ophthalmic Information in ‘Stable’ and ‘Progressive’ Groups Based on the Progression Analysis of Visual Field Index (VFI) in Humphrey Automated perimetry, and Retinal Nerve Fiber Layer (RNFL) Thickness from SD-OCT
VFI Grouping RNFL Grouping
Stable (N=20) Progressive (N=5) P * Stable (N=9) Progressive (N=16) P *
Demographic information
Age, year 68.5±14.2 71.6±6.2 0.668 63.9±15.0 72.0±11.0 0.169
Follow-up duration, month 40.2±8.5 44.4±6.8 0.408 37.3±8.4 43.1±7.7 0.108
Mean-IOP, mm Hg 16.2±1.9 16.6±1.3 0.818 16.1±1.9 16.3±1.8 0.934
S.D.-IOP, mm Hg 2.3±1.1 2.9±1.1 0.243 2.3±1.0 2.5±1.2 0.846
Ophthalmic examinations
 Spherical equivalent, Diopter −1.78±7.33 −2.00±6.92 0.945 −4.93±8.94 1.25±1.57 0.128
 Axial length, mm 22.76±2.43 22.02±2.44 0.053 24.03±3.71 22.42±0.91 0.558
 Central corneal thickness, µm 530.4±45.8 550.8±57.1 0.721 552.1±55.7 523.9±40.4 0.211
 Corneal endothelial cell count, n 2436±739 2501±587 0.921 2339±1001 2511±490 0.803
 Synechial angle closure, degree 276±112 229±90 0.286 317±81 241±113 0.101
 Vertical cup-to-disk ratio 0.69±0.18 0.79±0.20 0.262 0.79±0.14 0.66±0.19 0.106
LogMAR visual acuity 0.69±0.24 0.52±0.26 0.189 0.62±0.23 0.67±0.26 0.525
 Number of medications, n 1.5±1.7 1.5±1.3 0.845 2.0±2.1 1.3±1.3 0.506
Retinal Nerve Fiber Layer (RNFL) Thickness, μm
 Global thickness 77.4±16.5 62.2±9.9 0.067 61.3±10.2 78.6±15.6 0.011
 Supero-temporal thickness 99.5±35.3 96.8±22.2 0.801 77.1±30.1 110.5±28.1 0.023
 Supero-nasal thickness 79.9±33.4 74.4±26.7 0.801 67.5±34.9 84.7±29.2 0.392
 Nasal thickness 57.0±21.9 48.2±8.2 0.199 41.3±18.1 62.5±17.1 0.013
 Infero-nasal thickness 74.7±31.7 57.8±15.3 0.257 60.4±17.0 76.7±33.5 0.294
 Infero-temporal thickness 83.7±27.1 67.0±17.8 0.257 67.5±18.0 86.8±27.5 0.115
 Temporal thickness 75.8±21.8 53.0±13.3 0.019 67.5±28.5 72.6±18.8 0.392
Humphrey automated perimetry
 MD±S.D. (dB) −10.54±6.04 −12.07±6.65 0.621 −12.31±5.11 −10.02±6.54 0.152
 PSD±S.D. (dB) 6.42±3.59 7.49±4.23 0.621 7.57±3.56 6.01±3.67 0.187
 VFI±S.D. (%) 77.6±20.2 71.6±25.1 0.717 74.8±16.1 77.3±23.5 0.388
Rate of Changes on Retinal Nerve Fiber Layer (RNFL) m/mo)
 Global RNFL −0.036±0.458 −0.065±0.273 0.869 0.237±0.606 −0.199±0.128 <0.001
 Superior temporal −0.093±0.473 0.158±0.667 0.447 0.317±0.691 −0.245±0.206 0.008
 Superior nasal 0.050±1.027 −0.272±0.181 0.169 0.394±1.472 −0.244±0.244 0.095
 Inferior nasal −0.027±0.353 0.004±0.314 0.767 0.223±0.403 −0.158±0.205 0.002
 Inferior temporal −0.219±0.392 −0.170±0.378 0.668 −0.052±0.437 −0.297±0.330 0.014
Rate of Changes on VF Index (%/mo) −0.098±0.165 −0.415±0.398 0.010 −0.095±0.152 −0.199±0.295 0.718
Data was represented from Tan S, et al. 2015.9
*Mann-Whitney U test.
LogMAR indicates Logarithm of the Minimum Angle of Resolution; MD, Mean deviation; Mean-IOP, Mean of 3-month clinical documented intraocular pressure during the study period; N, number of subjects; n, number; PSD, pattern standard deviation; RNFL, Retinal nerve fiber layer; SD, standard deviation; SD.-IOP, standard deviation of 3-month clinical documented intraocular pressure during the study period; VFI, Visual field.

Ultra-short-term IOP Fluctuation in PACG Eyes

Figure 1A, B shows an example of signal fluctuations (mV eq) in about 30 seconds during wake-up and sleep period of 1 PACG patients documented by the contact lens sensor, respectively.

F1
FIGURE 1:
Ultra-short-term intraocular pressure (IOP) fluctuation profile (mV eq) recorded over 30 seconds in PACG eyes using the contact lens sensor. A, An example of signal fluctuation in daytime; B, An example of signal fluctuation at nighttime.

Comparison of Ultra-short-term IOP Fluctuation between ‘Progressive’ and ‘Stable’ Groups

The amplitude-frequency profiles of signal fluctuation were compared between ‘progressive’ and ‘stable’ groups defined by VFI (Fig. 2) and RNFL thickness (Fig. 3) in 5 sleep-related 1 hour time periods and 2 day-time 1-hour periods, including 1 hour before sleep, 1 hour after falling asleep, 1 hour in the middle of sleep (mid 1 hour sleep-wake), 1 hour before waking, and 1 hour immediately after waking, before lunch (11:00–12:00) and after dinner (20:00–21:00). The statistical significance of amplitude-frequency profiles of signal fluctuation between ‘progressive’ and ‘stable’ groups was summarized in Table 2. Higher amplitude-frequency profiles of signal fluctuation were found during 1 hour after falling asleep in RNFL ‘progressive’ group compared with RNFL ‘stable’ groups. There were no statistically significant differences observed between the VFI ‘progressive’ and the ‘stable’ group.

F2
FIGURE 2:
The comparison of amplitude-frequency profiles of signal fluctuation between visual field index ‘progressive’ and ‘stable’ groups during 7 one-hour periods, including clock 11:00–12:00 (A,B), clock 20:00–21:00 (C,D), 1 hour before sleep (E,F), 1 hour after falling asleep (G,H), 1 hour in the middle of sleep (I,J), 1 hour before waking (K,L), and 1 hour after waking (M,N).
F3
FIGURE 3:
The comparison of amplitude-frequency profiles of signal fluctuation between RNFL thickness ‘progressive’ and ‘stable’ groups during 7 one-hour periods, including clock 11:00–12:00 (A,B), clock 20:00–21:00 (C,D), 1 hour before sleep (E,F), 1 hour after falling asleep (G,H), 1 hour in the middle of sleep (I,J), 1 hour before waking ( K,L), and 1 hour after waking (M,N).
TABLE 2 - Statistical Significance of Ultra-short-term Amplitude-frequency Profiles of Signal Fluctuation between Progressive and Stable Groups as Defined by VFI and RNFL Thickness
P * Before Lunch (11:00–12:00) After Dinner (20:00–21:00) Sleep 1 Hour Before Sleep 1 Hour After Mid 1 Hour Sleep-Wake Wake 1 Hour Before Wake 1 Hour After
VFI 0.360 0.196 0.404 0.252 0.548 0.664 0.498
RNFL 0.502 0.082 0.908 0.026# 0.480 0.780 0.162
Keys:
*PP-value is by permutation tests on the supremum t-statistics.
#statistically significant with P value of <0.05.
RNFL indicates groups divided by retinal nerve fiber layer thickness in Spectral-Domain Optical Coherence Tomography; VFI, groups divided by visual field index in visual filed test.

DISCUSSION

In this study, output signals from the contact lens sensors were selected in the specific seven 1-hour periods, including 5 sleep-related 1 hour time periods, that is 1 hour before sleep, 1 hour after falling asleep, 1 hour in the middle of sleep (mid 1 hour sleep-wake), 1 hour before waking, and 1 hour immediately after waking, and 2 day-time 1 hour periods, that is before lunch (11:00–12:00) and after dinner (20:00–21:00), by semivariogram/semivariance, which represented detailed recognition of the curvature characteristics in the curves, such as the frequency of fluctuations during a phase. In the current study, significant differences in amplitude-frequency profiles of signal fluctuation presenting “ultra-short-term IOP fluctuation” were found after ‘bedtime’ between RNFL ‘progressive’ and ‘stable’ PACG eyes. The variations in IOP fluctuation within minutes (extent of increase or decrease) were found to be larger in the ‘progressive’ group than in the ‘stable’ group during the first 1 hour of sleeping when there was a major change in body posture and metabolism in PACG patients.

IOP elevation has long been found to be associated with glaucoma progression2 but its role in PACG patients has not been clearly identified. Previous studies have suggested that increased IOP fluctuation is a risk factor for glaucoma progression,2,19 in particular VF deterioration in POAG20 despite normal range of clinic-measured IOP.21–23 We previously identified that “long-term IOP fluctuation” was a significant and independent predictor for subsequent VF deterioration in PACD eyes.8 In the current study, there was no difference in either mean-IOP or SD-IOP, calculated based on 3-month clinical IOP measurements between the ‘progressive’ groups and the ‘stable’ groups (Table 2), indicating that there should be other factors associated with disease progression rather than the IOP level and the “long-term IOP fluctuation” in our PACG cohort. A possible explanation would be that, in the subjects of medically controlled IOP within the normal range, larger “short-term” or “ultra-short-term IOP” fluctuation may play a more important role in disease progression. We previously found “circadian IOP fluctuation”, especially the IOP fluctuation within the ‘bedtime’ and ‘wake-up’ hours, was associated with glaucomatous nerve damage in PACG eyes.9 Unfortunately, the amplitude-frequency components of the signal fluctuations within seconds and minutes, and the effect of daily activities on the fluctuations were filtered out by the analysis.9 Based on the same cohort of PACG patients and the same set of clinical data, the present study documented and analyzed the “ultra-short-term IOP fluctuation” in PACG eyes during the normal living activities of the patients in their usual environments. The most sensitive activity-related time period for detecting significant “ultra-short-term IOP fluctuation”, which is associated with glaucomatous progression was also demonstrated in PACG. Our findings in this study may suggest that ocular microcirculation, mainly affected by blood pressure, ocular perfusion pressure, and IOP, could be a risk factor for disease progression in PACG. These findings also suggested that the higher amplitude-frequency profiles of “ultra-short-term IOP fluctuation” during the first hour of ‘bedtime’ period may be associated with the risk of glaucoma progression, and the ‘bedtime’ period especially in the first hour of sleeping is possible to be the most sensitive time period for detecting significant “ultra-short-term IOP fluctuation” in PACG.

A thorough understanding of IOP fluctuation is essential to facilitating glaucoma diagnosis, improving management, and evaluating therapeutic effects in glaucoma patients. We have evidence that large “long-term” and “short-term” IOP fluctuation may be associated with PACG progression.8,9 The current study added new knowledge on the effect of “ultra-short-term” IOP fluctuation in this disease. “Ultra-short-term IOP fluctuation” is mostly associated with the systolic cardiac cycle and external ocular pressure. In addition, the scleral rigidity plays an important role in the height of IOP spikes in the “ultra-short-term IOP fluctuation”.10 This relationship could be expressed by Friedenwald equation: the change in IOP (dP, mm Hg)=K (rigidity coefficient, normally is 0.02 mm Hg/μL) × the change in volume (dV, μL). “Ultra-short-term IOP fluctuation” is increased by (1) the increase in choroidal blood volume during the systolic cardiac cycle, (2) the addition of external ocular pressure, or (3) during the Valsalva maneuver. (1) and (2) may increase episcleral venous pressure. The acute IOP spikes could be exaggerated in circumstances when the scleral rigidity is increased. Scleral rigidity may also be positively associated with age.24 In addition to cardiac cycle and external ocular pressure, a recent study suggested “ultra-short-term IOP fluctuation” may be affected by blinking or saccadic movement of the eyeball.25 The IOP fluctuation is also related to the effects of ambient environments and activities. On the basis of the findings in this study, we could further suggest that the continuous high frequency of spikes in the “ultra-short-term IOP fluctuation” could also potentially cause damage on the optic nerve and lead to glaucomatous progression, and that the first hour of sleeping could be the most sensitive time period for detecting the significant “ultra-short-term IOP fluctuation” in PACG. In addition to measuring the IOP occasionally or regularly to monitor the 24-hour IOP fluctuation, documenting the amplitude-frequency profiles of “ultra-short-term IOP fluctuation” during the first hour of sleeping would also provide useful clinical information on PACG management. Those PACG patients with higher amplitude-frequency profiles should be followed up more frequently and carefully to detect the glaucomatous progression and may need more effective therapies to control the IOP level and fluctuation.

Since there was a lack of practical means to document “ultra-short-term IOP fluctuation” in the past, the profile of “ultra-short-term IOP fluctuation” and its correlation with glaucomatous progression have still not been well characterized and investigated in the published literature. The key strengths of our study include the fact that this is the first study documenting “ultra-short-term IOP fluctuation”, as measured by the contact lens sensor, in PACG eyes when patients engaged in their normal activities of daily living in their usual environments. Also, this is the first study to evaluate the association of “ultra-short-term IOP fluctuation” with glaucomatous progression in PACG. In addition, this study identified the most sensitive activity-related time period, which was the first hour of sleeping, for detecting the significant amplitude-frequency profiles of “ultra-short-term IOP fluctuation” in PACG. This finding was in line with the conclusion from our previous study: that the “short-term IOP fluctuation”, within the ‘bedtime’ and ‘wake-up’ hours, was associated with glaucomatous nerve damage in PACG eyes.9

The inherent limitations in this study are as follows. (1) The signal output from the contact lens sensor was not the actual IOP reading in mm Hg. (2) The signals from the contact lens sensor could potentially be affected by other corneal properties,26,27 and the effects of these on IOP profiles should be better investigated in future studies. (3) The IOP fluctuation obtained represented only a medically-treated IOP profile. (4) The association of “ultra-short-term IOP fluctuation” with the onset of glaucoma could not be addressed with this study design. (5) Only sleep-related time periods and 2 hours of day-time periods under stable activity could be analyzed in detail in the current study. As each study subject has one’s own daily activities and sleep/wake behavior, non-linear time warping for each subject should be considered in analyzing 24 hours data. However, an appropriate non-linear time warping function would be difficult to define, as the other daily activities were not following the same schedule in the subjects, and hence it would be difficult to perform a 24-hour analysis by registering the sleep and wake behavior. (6) This study has a relatively small sample size, which may not allow the identification of certain characteristics of the IOP fluctuation that may be less strongly associated with disease progression. (7) The observation that the VFI ‘progressive’ group did not show statistically different signal profiles during the activity-related time periods is most likely attributed to the fact that there were only 5 patients in this group. Again, this would have been the result of the relatively small sample size. Nevertheless, there was no significant difference found between RNFL ‘progressive’ and ‘stable’ groups in the MD (P=0.152), PSD (P=0.187), and VFI (P=0.388) in the automated perimetry at recruitment. There was also no significant difference in follow-up durations (Table 1) or disease severity among the 2 groups of subjects.

Further studies should be conducted on a larger cohort and longer follow-up period to evaluate the effects. Meanwhile, the pathway of the continuous high frequency of IOP fluctuation influencing the glaucomatous damage needs to be clarified in the next step. Some evidence showed that hypertension was associated with glaucoma onset and progression,11 and the frequency of IOP fluctuation is able to reflect the fluctuation in the systolic cardiac cycle.10 As such, the relationship among the “ultra-short-term IOP fluctuation”, the systolic pressure and ocular perfusion, as well as the aqueous flow rate, episcleral venous pressure, and external ocular pressure need to be further investigated. The series studies will help us to have more understanding on the mechanism of glaucoma onset and progression, and will also provide the new target for glaucoma management.

In summary, this study provided preliminary data on the “ultra-short-term IOP fluctuation” using a contact lens sensor in PACG eyes during the subjects’ normal activities of daily living. Significant differences in “ultra-short-term IOP fluctuation” were found at specific 1 hour time periods of after ‘bedtime’ between the ‘progressive’ and ‘stable’ eyes in terms of RNFL thickness in PACG. The higher amplitude-frequency profiles of “ultra-short-term IOP fluctuation” around ‘bedtime’ period may be associated with the risk of glaucoma progression, and the first hour of sleeping may be the most sensitive time period for detecting significant “ultra-short-term IOP fluctuation” in PACG.

REFERENCES

1. Heijl A. Perimetry, tonometry and epidemiology: the fate of glaucoma management. Acta Ophthalmol. 2011;89:309–315.
2. Caprioli J, Coleman AL. Intraocular pressure fluctuation a risk factor for visual field progression at low intraocular pressures in the advanced glaucoma intervention study. Ophthalmology. 2008;115:1123–29 e3.
3. Newell FW, Krill AE. Diurnal tonography in normal and glaucomatous eyes. Am J Ophthalmol. 1965;59:840–853.
4. Davson H. Dynamic aspects of cerebrospinal fluid. Dev Med Child Neurol Suppl. 1972;27:1–16.
5. Shaarawy TM, Sherwood MB, Hitchings RA, et al. Glaucoma E-Book. Elsevier Health Sciences; 2014. Available at: https://www.sciencedirect.com/book/9780702051937/glaucoma.
6. Wilensky JT. The role of diurnal pressure measurements in the management of open angle glaucoma. Curr Opin Ophthalmol. 2004;15:90–92.
7. Asrani S, Zeimer R, Wilensky J, et al. Large diurnal fluctuations in intraocular pressure are an independent risk factor in patients with glaucoma. J Glaucoma. 2000;9:134–142.
8. Cheung CY, Li SL, Chan PP, et al. Intraocular pressure control and visual field changes in primary angle closure disease: the CUHK PACG Longitudinal (CUPAL) study. Br J Ophthalmol. 2020;104:629–635.
9. Tan S, Yu M, Baig N, et al. Circadian Intraocular pressure fluctuation and disease progression in primary angle closure glaucoma. Invest Ophthalmol Vis Sci. 2015;56:4994–5005.
10. Friedenwald JS, Stiehler RD. The Mechanism of formation of the aqueous. Trans Am Ophthalmol Soc. 1937;35:184–200.
11. Leske MC, Heijl A, Hyman L, et al. Predictors of long-term progression in the early manifest glaucoma trial. Ophthalmology. 2007;114:1965–1972.
12. Leonardi M, Pitchon EM, Bertsch A, et al. Wireless contact lens sensor for intraocular pressure monitoring: assessment on enucleated pig eyes. Acta Ophthalmol. 2009;87:433–437.
13. Faschinger C, Mossbock G. Continuous 24 h monitoring of changes in intraocular pressure with the wireless contact lens sensor Triggerfish. First results in patients. Ophthalmologe. 2010;107:918–922.
14. Hervas Ontiveros A, Hernandez Martinez P, Garcia-Delpech S, et al. Sensimed Triggerfish((R)) as a new system for continuous recording of 24-hour intraocular pressure in glaucoma patients. Arch Soc Esp Oftalmol. 2013;88:410–411.
15. Sunaric-Megevand G, Leuenberger P, Preussner PR. Assessment of the Triggerfish contact lens sensor for measurement of intraocular pressure variations. Acta Ophthalmol (Copenh). 2014;92:e414–e415.
16. Cheung CY, Li SL, Chan N, et al. Factors associated with long-term intraocular pressure fluctuation in primary angle closure disease: The CUHK PACG Longitudinal (CUPAL) Study. J Glaucoma. 2018;27:703–710.
17. Cressie N, Hawkins Douglas M. Robust estimation of the variogram: I. J Int Ass Math Geol. 1980;12(no.2):10.
18. Ramsay, JO, Hooker Giles, Graves S Springer, NY. Use R! Functional Data Analysis with R and MATLAB Dordrecht: New York: Springer; 2009:166–168.
19. Bengtsson B, Leske MC, Hyman L, et al. Fluctuation of intraocular pressure and glaucoma progression in the early manifest glaucoma trial. Ophthalmology. 2007;114:205–209.
20. The Advanced Glaucoma Intervention Study (AGIS): 7. The relationship between control of intraocular pressure and visual field deterioration.The AGIS Investigators. Am J Ophthalmol. 2000;130:429–440. doi:10.1016/s0002-9394(00)00538-9.
21. Caprioli J. Intraocular pressure fluctuation: an independent risk factor for glaucoma. Arch Ophthalmol. 2007;125:1124–1125.
22. Bergea B, Bodin L, Svedbergh B. Impact of intraocular pressure regulation on visual fields in open-angle glaucoma. Ophthalmology. 1999;106:997–1004; discussion 04-5.
23. Hong S, Seong GJ, Hong YJ. Long-term intraocular pressure fluctuation and progressive visual field deterioration in patients with glaucoma and low intraocular pressures after a triple procedure. Arch Ophthalmol. 2007;125:1010–1013.
24. Pallikaris IG, Kymionis GD, Ginis HS, et al. Ocular rigidity in living human eyes. Invest Ophthalmol Vis Sci. 2005;46:409–414.
25. Downs JC, Burgoyne CF, Seigfreid WP, et al. 24-hour IOP telemetry in the nonhuman primate: implant system performance and initial characterization of IOP at multiple timescales. Invest Ophthalmol Vis Sci. 2011;52:7365–7375.
26. Freiberg FJ, Lindell J, Thederan LA, et al. Corneal thickness after overnight wear of an intraocular pressure fluctuation contact lens sensor. Acta Ophthalmol (Copenh). 2012;90:e534–e539.
27. Hubanova R, Aptel F, Chiquet C, et al. Effect of overnight wear of the Triggerfish((R)) sensor on corneal thickness measured by Visante((R)) anterior segment optical coherence tomography. Acta Ophthalmol (Copenh). 2014;92:e119–e123.
Keywords:

ultra-short-term; intraocular pressure fluctuation; primary angle closure glaucoma; disease progression; contact lens sensor

Copyright © 2022 Wolters Kluwer Health, Inc. All rights reserved.