Abstract
Aims
The evidence relating the pupil light reflex (PLR) and cognition have been inconsistent. In this cross-sectional study, we evaluated the association between the PLR and cognition in community-dwelling middle-aged and older individuals.
Methods
Pupil reactivity was recorded in a subgroup of 403 participants (mean age 60.7 years, 57.3% females) in an epidemiologic study of aging. Ten pupil parameters were calculated to describe pupil constriction to light stimuli. A principal component analysis (PCA) score was used to calculate an overall performance over four cognitive testings. Linear regression was used to assess the association between pupil parameters and PCA scores, adjusting for age, sex, education, medications, health-related quality of life questionnaire, and systemic and ocular comorbidities.
Results
The PCA scores decreased by 0.039 [95% CI (− 0.050, − 0.028)] per year increase in age and were lower in males than females by 0.76 [95% CI (− 0.96, − 0.55)] (p < 0.001). Pupil constriction amplitude in millimeters and the duration from stimulus onset to maximal constriction velocity were significantly associated with cognition after adjusting for (1) age and sex and (2) age, sex, and multiple covariates (p < 0.05).
Conclusions
In this study, we provided moderate evidence suggesting the association between PLR and neuropsychological cognitive measures. The findings suggest the potential of pupil reactivity to serve as a biomarker of brain aging and warrant further longitudinal study to assess if changes in the PLR can predict cognitive decline over time.
Keywords: Pupil light reflex (PLR), Pupil constriction, Cognition, Middle-aged and elderly, Brain aging
Introduction
Most studies of the pupil light reflex (PLR) in alzheimer’s and cognitively impaired individuals showed differences in the PLR compared to normal individuals. These studies identified a wide variety of PLR parameters, including baseline pupil size, constriction latency, amplitude, and velocity/acceleration, etc. [1–6]; however, consistency is lacking. Also, to our knowledge, none of the studies correlated the PLR directly to neuropsychological cognitive measures.
The PLR is mediated by the ocular parasympathetic network via the optic nerve/chiasm/tract, the Edinger-Westphal nucleus, the oculomotor nerves, and the iris sphincter muscles [7–9]. The PLR is also influenced by neuronal activities arising from widespread brain areas that directly or indirectly modulate central nervous system (CNS) sympathetic and parasympathetic outflow [10]. These neuronal networks are subject to changes during the process of aging and CNS neurodegeneration [4, 11–14]. For example, acetylcholine, the neurotransmitter serving the PLR and also memory and learning process [15], showed a decrease in both concentration and function in Alzheimer’s disease [16]. The brainstem noradrenergic locus ceruleus nucleus, activating to disinhibit the Edinger-Westphal nucleus thus mitigate the PLR, showed the earliest Alzheimer-related intraneuronal abnormal Tau protein in the young brains of 10–20 years in a pathology study [17]. This evidence provides a reasonable biological basis for the relation between the PLR and brain aging/Alzheimer’s disease.
The currently available toolkit for assessing Alzheimer’s disease includes neuropsychologic testing, cerebrospinal fluid biomarkers, brain imaging (magnetic resonance imaging and positron emission tomography), and electroencephalography [18–20]. These structural and functional measures can be invasive, tedious, or require sophisticated technology to perform, calling for newer biomarkers to supplement the existing measures. Pupillography offers a non-invasive, easy-to-perform, and relatively cost-effective physiologic measure that can be readily and repeatedly collected, making it appealing for both research and clinical applications.
In our earlier study of the pupil recordings on a subgroup of 403 community-dwelling middle-aged and older adults, we presented the first evidence that the post-illumination pupil response, a measure of intrinsically photosensitive retinal ganglion cell (ipRGC) function, was correlated with neuropsychological cognitive measures [21]. The present study uses the same pupil recordings collected in our previous study to evaluate pupil constriction and its correlation with neuropsychological cognitive measures.
Material and methods
Subjects
The Beaver Dam Offspring Study (BOSS, 2004-present) is a population-based, longitudinal study of aging among community-dwelling adult offspring (age > 18 years) of the epidemiology of hearing loss study (1993–2020) [22] in Beaver Dam, Wisconsin. The majority (98%) of the BOSS participants self-identified as non-Hispanic Whites. The BOSS included detailed sensory assessments (vision, olfaction, hearing), cognitive measures, and covariates (vascular health, alzheimer’s biomarkers, and medical and behavioral history) [23]. The pupil recording was collected on a convenient sample of 403 BOSS participants as an ancillary sub-study as part of the 10-year follow-up examination of the BOSS (2015–2017). We invited participants who had not yet taken part in the 10-year follow-up to also undergo pupillometry and then supplemented that by inviting back participants who had already completed an examination until the target sample size was reached.
The data collection was approved by the University of Wisconsin Madison Health Sciences Institutional Review Board. Written informed consent was obtained from participants before the examination.
Evaluation of cognition
Cognitive testing included trail making test (Parts A and B), a modified Rey auditory verbal learning test, digit symbol substitution test, and verbal fluency test (F, A, and S). A principal component analysis (PCA) score was created for each participant to summarize cognitive tests to yield a more sensitive measure of aging changes [24]. The cognitive testing was conducted either during the same visit as the pupil recording or on a separate visit, with the interval between the two procedures ranging from 0 (on the same day) to 2.25 years (mean ± SD: 0.75 ± 0.75 years).
Pupil recording
The details of the pupil recording algorithm were described elsewhere [21]. Briefly, the binocular infrared pupillometer (DP2000 Human Laboratory Pupillometer, Neur-Optics, Inc., Irvine, CA) recorded the horizontal diameter of the pupil 30 times/sec. The light stimulus subtended a visual angle of 50 by 35 degrees at a viewing distance of 39 mm. The pupil recording of both eyes was collected when the two eyes were stimulated simultaneously. After participants’ sitting in a dark exam room for 5–10 min, the pupillometer began to record pupil reactivity to a pair of 1-s bright light stimuli with spectral bands of 640 ± 10 nm (red light) and 467 ± 17 nm (blue light) at a stimulus intensity of 2.0 log lux. The stimulus presentation was preceded by a 5-s dark period. Each stimulus presentation was followed by a period of darkness ranging from 30–60 s to allow the pupil to settle to baseline. One trial of pupil recording consisted of two consecutive repeats of the red/blue stimulus pair. The total duration of a trial lasted approximately 3 min. Two trials of pupil recording were collected for each individual with an interval of 5–10 min between trials during a single study visit. The pupil parameters were summarized (Fig. 1 and Table 1).
Fig. 1.
An example recording of pupil constriction to red (red) and blue (blue) light stimulation, plotted as the relative pupil diameter to baseline pupil size (%) against time (second). The baseline pupil diameter (BPD) represents the pre-stimulus resting pupil size in the dark. The pupil constriction takes off immediately following the onset of a 1-s light stimulus (gray bar) to reach a minimum pupil diameter (peak constriction). The pupil then resumes a slow dilation to resume its resting state (redilation) over the next 12–14 s (red). When exposed to high-intensity short-wavelength blue light (blue), the pupil demonstrates a prolonged constriction, the post-illumination pupil response (PIPR)
Table 1.
Summary of pupil parameters
| Pupil parameters | Description |
|---|---|
| Baseline pupil diameter (BPD) (mm) | Baseline pupil diameter: average pupil diameter over the 5-s darkness before each stimulus onset |
| Peak constriction (mm) | The minimum pupil diameter achieved following the stimulus onset |
| Constriction amplitude (mm) | The difference between the BPD and peak constriction |
| Constriction amplitude (ratio) | The ratio of constriction amplitude (mm) over BPD |
| Average constriction velocity (mm/s) | Constriction velocity: the first derivative of the pupil diameter (mm) vs. time (sec) curve between the |
| Max constriction velocity (mm/s) | onset of the pupil constriction to peak pupil constriction |
| Max constriction acceleration (mm/s2) | The maximum of the second derivative of the pupil diameter (mm) vs. time (sec) curve from 100 to 400 ms following the stimulus onset |
| Constriction latency (ms) | The duration between stimulus onset and the onset of pupil constriction (the point of the maximum constriction acceleration) |
| T2 | The duration between stimulus onset and the point of maximum constriction velocity |
| T3 | The duration between stimulus onset to peak pupil constriction |
Other variables
The study included detailed ocular history, including refractive error (non-cycloplegic autorefraction), fundus photos graded age-related macular degeneration and diabetic retinopathy, doctor-diagnosed glaucoma, digital image graded cataract, and self-reported cataract surgery [24]. Relevant history, in particular, blinding eye disease (age-related macular degeneration, glaucoma, and cataract/cataract surgery) and concurrent use of medications (benzodiazepine, antihistamine, or beta-blockers) that may affect pupil reactivity were used as covariates in the analysis.
The study also included comorbidity data, including hypertension, diabetes mellitus, obesity, cerebrovascular disease (CVD), depressive symptoms, and information from health-related quality of life questionnaire, the Short Form (SF)-12 physical and mental component summary (PCS and MCS) scores [25]. Relevant comorbidity data were included as potential covariates for cognition, along with educational attainment.
Statistical analysis
Analyses were conducted using R Statistics, version 4.0.3 [26]. The PCA cognitive score was constructed using SAS software, version 9.4 (SAS Institute Inc.), and was described in detail elsewhere[21]. Briefly, the PCA cognitive score, constructed using the cognitive test data and retaining the first component, has a mean of 0 and SD of 1. A single PCA value was calculated for each subject. To reduce the pupil parameter data to a single value for each subject, the pupil parameters were first averaged between red and blue stimulus, then across all available observations (minimum of 4 required out of the maximum of 8) for each participant. Linear regression models were used to assess the association between individual pupil parameters as determinants and PCA cognitive score as outcome, both unadjusted and adjusted for multiple covariates. Assumptions of the linear regression models were examined, and no violations were detected. Statistical significance was set at a p value of 0.05.
The interval between cognitive testing and pupil recording spanned a large range between 0 and 2.25 years. A post hoc sensitivity analysis was performed using the subset of participants who completed cognitive testing and pupil recording on a same day (interval = 0).
Results
A total of 403 participants (172 males and 231 females) were included in the study, with ages ranging from 33–81 years (mean ± SD: 60.7 ± 9.3). The demographic characteristics of the study cohort were described in Table 2. The individual pupil parameters were correlated between the two repeats within a trial, across two trials, the left and right eye, and the red and blue light stimulation (Supplementary Table 1). A total of 168 participants were excluded due to incomplete pupil recording, excessive artifacts, or insufficient available observations. The participants included in the analysis showed a higher rate of depressive symptoms and lower proportion that had cataract extraction than the excluded group. The rest of the baseline characteristics were similar between the two groups (Supplementary Table 2).
Table 2.
Participant characteristics (n = 403). PCS physical component summary, MCS mental component summary, SD standard deviation
| Characteristic (n = 403) | Mean | SD |
|---|---|---|
| Age | 60.7 | 9.3 |
| BMI (kg/m2) | 31.0 | 7.0 |
| SF-12 PCS | 47.9 | 9.5 |
| SF-12 MCS | 53.2 | 7.5 |
| Characteristic | N | Percent (%) |
| Sex | ||
| Female | 231 | 57.3 |
| Male | 172 | 42.7 |
| Education (years) | ||
| 0–12 | 127 | 31.7 |
| 13–15 | 162 | 40.4 |
| 16 + | 112 | 27.9 |
| Systemic comorbidity | ||
| CVD | 42 | 10.6 |
| Diabetes | 46 | 11.4 |
| Hypertension | 205 | 51.0 |
| Depressive symptoms | 66 | 17.5 |
| Systemic medication | ||
| Antihistamine | 67 | 17.8 |
| Benzodiazepine | 26 | 6.9 |
| Beta-blockers | 69 | 18.3 |
| Ocular comorbidity | ||
| Refractive error (right eye) | ||
| Myopia | 123 | 30.8 |
| Emmetropia | 166 | 41.6 |
| Hyperopia | 110 | 27.6 |
| Glaucoma | 11 | 2.7 |
| Cataract | 39 | 12.4 |
| Cataract surgery | 32 | 7.9 |
| ARMD | 16 | 4.4 |
| Diabetic retinopathy | 15 | 4.1 |
The PCA cognitive scores ranged from − 3.72 to 2.07 (mean ± SD: 0.02 ± 0.95). The PCA scores showed an age-related decrease by 0.039 (95% CI [− 0.050, − 0.028]) per year (p < 0.001). On average, the PCA scores were lower in male than female participants by 0.76 (95% CI [− 0.96, − 0.55]) (p < 0.001). The PCA scores were plotted against individual pupil parameters, along with univariable regression outcomes (Fig. 2). In the unadjusted model, the PCA cognitive scores showed a significant correlation (p < 0.05) with all pupil parameters except T3. When adjusting for age and sex, constriction amplitude (in both millimeters and ratio) and T2 remained significantly associated with the PCA scores (Table 3). In subsequent linear regression analysis adding multiple covariates, constriction amplitude in millimeters and T2 remained significant and marginally significant, respectively (Table 4).
Fig. 2.
Scatter plot of the PCA cognitive scores against ten pupil parameters with regression line and standard error (gray), and Pearson correlation coefficient (R)
Table 3.
Summary of the ten individual pupil parameters as (1) mean and SD and (2) linear regression model of the PCA cognitive score on pupil parameters, adjusting for age and sex (n = 235). BPD baseline pupil diameter, T2 the duration from stimulus onset to maximum constriction velocity, T3 the duration from stimulus onset to peak pupil constriction
| Pupil parameters | Mean (SD) | Regression adjusting for age + sex | |
|---|---|---|---|
| B coefficient (95% CI) | p value | ||
| BPD (mm) | 5.39 (0.85) | 0.09 (− 0.04, 0.23) | 0.17 |
| Peak constriction (mm) | 2.76 (0.45) | − 0.001 (− 0.24, 0.23) | 0.99 |
| Constriction amplitude (mm) | 2.63 (0.52) | 0.26 (0.04, 0.49) | 0.02 |
| Constriction amplitude (ratio) | 0.49 (0.04) | 2.70 (0.31, 5.08) | 0.03 |
| Average constriction velocity (mm/s) | 0.06 (0.01) | 10.02 (− 0.19, 20.2) | 0.05 |
| Max constriction velocity (mm/s) | 0.15 (0.03) | 2.79 (− 1.68, 7.25) | 0.22 |
| Max constriction acceleration (mm/s2) | 0.03 (0.01) | 12.41 (− 2.02, 26.8) | 0.09 |
| Constriction latency (ms) | 209 (27.6) | − 0.003 (− 0.007, 0.0006) | 0.10 |
| T2 (ms) | 360 (53.6) | − 0.002 (− 0.004, − 7.46) | 0.04 |
| T3 (ms) | 1512 (186) | 0.0002 (− 0.0003, 0.0008) | 0.39 |
The bold values show statistically significant correlation
Table 4.
Linear regression model of the PCA cognitive scores on pupil constriction amplitude (mm) and T2, adjusting for multiple covariates (n = 235). T2 the duration between stimulus onset and maximum constriction velocity (ms), Time interval the interval between pupil recording and cognitive testing (years)
| Covariates | Constriction amplitude (mm) | T2 | ||
|---|---|---|---|---|
| B coefficient (95% CI) | p value | B coefficient (95% CI) | p value | |
| Pupil parameter | 0.25 (0.02, 0.48) | 0.03 | − 0.002 (− 0.004, −0.00002) | 0.047 |
| Age at pupil recording (years) | − 0.02 (− 0.04, − 0.01) | 0.001 | − 0.03 (− 0.04, −0.02) | < 0.001 |
| Sex (male gender) | − 0.64 (− 0.86, − 0.42) | < 0.001 | − 0.66 (− 0.88, −0.44) | < 0.001 |
| Time interval (years) | − 0.02 (− 0.16, 0.12) | 0.74 | 0.001 (− 0.14, 1.43) | 0.98 |
| BMI (kg/m2) | − 0.01 (− 0.03, 0.006) | 0.23 | − 0.008 (− 0.02, 0.008) | 0.35 |
| Education (16 + years) | 0.21 (0.07, 0.35) | 0.005 | 0.22 (0.08, 0.36) | 0.003 |
| SF-12 PCS | − 0.003 (− 0.02, 0.009) | 0.61 | − 0.0009 (− 0.01, 0.01) | 0.88 |
| SF-12 MCS | − 0.003 (− 0.02, 0.01) | 0.69 | − 0.002 (− 0.02, 0.01) | 0.81 |
| Medication use: | ||||
| Antihistamine | 0.10 (− 0.16, 0.36) | 0.43 | 0.13 (− 0.13, 0.39) | 0.32 |
| Benzodiazepine | 0.20 (− 0.21, 0.61) | 0.34 | 0.18 (− 0.23, 0.59) | 0.38 |
| Betablockers | − 0.04 (− 0.34, 0.26) | 0.80 | 0.006 (− 0.29, 0.31) | 0.97 |
| Cardiovascular disease | 0.18 (− −0.20, 0.56) | 0.36 | 0.17 (− 0.21, 0.55) | 0.38 |
| Depressive symptoms | − 0.04 (− 0.32, 0.23) | 0.74 | − 0.06 (− 0.33, 0.21) | 0.65 |
| Glaucoma | − 0.30 (− 0.95, 0.35) | 0.38 | − 0.13 (− 0.77, 0.51) | 0.69 |
| Any cataract, worse eye | − 0.25 (− 0.58, 0.09) | 0.16 | − 0.26 (− 0.60, 0.07) | 0.12 |
| Cataract extraction | − 0.08 (− 0.60, 0.43) | 0.74 | − -0.16 (− 0.68, 0.35) | 0.53 |
| ARMD, worse eye | − 0.39 (− 0.89, 0.12) | 0.14 | − 0.46 (− 0.96, 0.05) | 0.08 |
The bolded values show a statistically significant correlation
A total of 180 participants had cognitive testing and pupil recording on the same day (interval = 0); 82 (45.5%) were included in the subset for sensitivity analysis. The PCA scores were plotted against individual pupil parameters (Supplementary Fig. 1). The individual pupil parameters and estimated effect in the linear regression model of the PCA cognitive score on pupil parameters, adjusting for age and sex, are similar between the subset (n = 82, Supplementary Table 3) and whole cohort (n = 235, Table 3).
Discussion
In this cross-sectional study of pupil recording in a subgroup of 403 BOSS participants, we examined ten PLR parameters. Nine of the ten parameters (except T3) were correlated with neuropsychological cognitive measures (Fig. 2). However, after adjusting for age and sex, the association was present in only three of the ten parameters (Table 3), suggesting that age and sex had contributed to the association in the other pupil parameters. It is encouraging that after adjusting for age and sex (Table 3) as well as multiple covariates (Table 4), two distinct PLR parameters, one reflecting the magnitude (constriction amplitude in millimeters) and the other the timestamp (T2) of the PLR, remained significantly correlated with PCA scores. The finding suggests that pupil contriction amplitude and T2 might reflect more inherent CNS neurodegenerative processes that underlie participants’ cognitive decline. One such process could be deterioration of the CNS cholinergic system, by either reduced production of acetylcholine or impaired cholinergic function, as acetylcholine serves as the neurotransmitter for ocular parasympathetic Edinger-Westphal nucleus whereas cholinergic neurons densely innervate the hippocampus [27, 28], the CNS hub for memory.
Our study provides a few unique perspectives. First, the study collected pupil recordings among participants of an epidemiological study in the field office. Such an approach did not allow a strict control for confounding factors by including/excluding appropriate participants as in more traditional, laboratory-based pupil research. Instead, these internal and external confounding factors were adjusted by covariate analyses. Careful evaluation of potential confounders is important to ensure the appropriate interpretation of pupil recordings before pupillometry can be widely applied in field-based reasearch. Second, our study correlated the PLR directly to participants’ cognitive scores irrespective of diagnoses of dementia/mild cognitive impairment, whereas the majority of the pupillometry studies on Alzheimer’s/cognitively impaired compared PLR to normal controls. Although the results of our study are not directly comparable to that of others, both supported the potential role of pupillometry in assessing brain aging and CNS neurodegeneration.
Our study has several limitations. First and foremost, the testing interval between pupil recording and cognitive testing spans an extensive range from the same day to up to 2.25 years (mean ± SD: 0.75 ± 0.75 years). At the time of the pupil recording, at least some participants could have shown further cognitive decline. However, such a lag in pupil recording will underestimate rather than overestimate the correlation between the PLR and cognitive measures. In this study, we added the testing interval as one of the covariates in the analysis, and the testing interval did not show significance (Table 4). Also, the findings from the primary and sensitivity analysis showed that the regression coefficients did not vary, suggesting that the difference in testing interval did not play a role in affecting the regression outcomes (Table 3 and Supplementary Table 3). Future prospective studies should ensure simultaneous pupil recording and cognitive testing to reduce the potential variability contributed by testing interval. Second, PLR can be elicited using single wide-spectrum light stimuli instead of averaging between monochromatic red and blue light stimuli. The pupil testing protocol that consisted of a paired red/blue stimulus was initially designed to study the post-illumination pupil response. The linear regression analyses showed that the PLR parameters were correlated between the red and blue stimulus, consistent with Park et al. showing that bright photopically-matched red and blue stimuli (intensity > 1 log cd/m2) produced identical pupil constrictions [29]. Therefore, averaging between the red and blue stimulus should not change the variability profile in individual pupil responses. Using a separate red and blue light stimulus to elicit pupil response would offer the advantage of assessing both pupil constriction and the post-illumination pupil response within a single run of PLR recording. Third, the study shares similar limitations detailed in our earlier work of pupil reactivity and cognition [21], including selection bias from using a convenience sample of the BOSS and lack of ability to generalize due to the predominance of non-Hispanic whites in the cohort. We should also keep in mind that our study was exploratory; therefore, the findings require replication and elucidation by future studies.
Conclusions
In this exploratory cross-sectional study of the PLR in 403 community-dwelling middle-aged and older adults, we found a moderate correlation between neuropsychological cognitive measures and pupil constriction amplitude and the duration between stimulus onset and maximal constriction velocity. The observed correlation between pupil reactivity and cognition is encouraging and warrants future longitudinal study to assess if changes in PLR can predict cognitive decline over time.
Supplementary Material
Funding
This project was supported by the National Institute on Aging [R01AG021917] (KJC, AAP, AJP, CRS, and NM), an unrestricted research grant from Research to Prevent Blindness, Inc. to the Department of Ophthalmology and Visual Sciences (YC, AAP, AJP, CRS, BEK, and KJC) at the University of Wisconsin, and the Clinical and Translational Science Award (CTSA) program through the NIH National Center for Advancing Translational Sciences (NCATS) [1UL1TR002373] (SH).
Footnotes
Ethical approval The study was approved by the University of Wisconsin Madison Health Sciences Institutional Review Board.
Informed consent Written informed consent was obtained from participants of the study.
Supplementary Information The online version contains supplementary material available at https://doi.org/10.1007/s40520-022-02097-w.
Conflict of interest The authors have no relevant financial or non-financial interests to disclose.
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