Abstract
Vision loss is a risk factor for dementia, but it is unknown whether stroke-related vision impairment is linked to dementia risk in stroke survivors. This secondary analysis aimed to quantify the association between stroke-related vision impairment and time to incident dementia diagnosis, from time of stroke, using the Arthrosclerosis Risk in Communities study dataset. We included participants who sustained a non-fatal probable or definite ischemic, incident stroke captured from hospital surveillance during the study period and excluded those who were diagnosed with incident dementia prior to or less than half a year after the incident stroke. The association between stroke-related vision impairment (binary) and time from incident stroke to dementia diagnosis was analyzed using a Fine-Gray survival model to account for the competing risk of death, adjusting for age at incident stroke, stroke severity, biological sex, education and race-center. Among 787 stroke survivors, 31% were diagnosed with dementia during the follow-up period and 19.5% had stroke-related vision impairment. The presence of stroke-related vision impairment was not significantly associated with dementia diagnosis (HR=1.18; 95% CI 0.85, 1.63; p = 0.32). While results suggest that stroke-related vision impairment corresponds to a higher cumulative incidence of dementia, the association was not statistically significant.
Keywords: stroke-related vision impairment, dementia risk, survival analysis, secondary data
Introduction
Strokes are extremely common events in the United States (Tsao et al., 2023). Almost 800,000 people survive strokes and live with disabling consequences (Tsao et al., 2023). For example, stroke-related vision impairment—such as visual field defects, where part of the visual field cannot be seen due to the damage from the stroke—is a common post-stroke disability (F. J. Rowe et al., 2019). Stroke-related vision impairment has been shown to cause challenges such as decreased independence, reduced mobility, increased risk of falls, and greater difficulty with activities of daily living, all of which can negatively impact overall quality of life (Hepworth et al., 2016; F. J. Rowe, 2017).
We are interested in exploring the relationship between stroke-related vision impairment and cognitive outcomes after stroke, given that age-related vision loss is a known risk factor for dementia in general populations (Burton et al., 2021). Our most recent work that studied stroke survivors quantified associations between stroke-related vision impairment and prevalent dementia (Hreha, et al., in press). We found that stroke-related vision impairment was associated with a statistically significant doubling in odds of dementia compared to normal cognition and mild cognitive impairment after adjustment of potential confounders. When we further adjusted for stroke severity however, this odds ratio was slightly attenuated and there was no evidence of statistical significance. This study led us to conclude that among stroke survivors, while there is some evidence that stroke-related vision impairment is associated with odds of dementia, stroke severity may be a confounding factor.
Prior research has not addressed whether stroke-related vision impairment is linked to time of dementia development in stroke survivors. Investigating the link may be critical, as some vision impairment such as hemianopia is not correctable and therefore may contribute to more impairment and less overall activity. One possible mechanism linking uncorrectable visual impairment to cognitive decline and dementia is reduced sensory input, which may lead to cognitive under-stimulation and diminished engagement (Nagarajan et al., 2022; Whitson et al., 2018; Zheng et al., 2018). Therefore, in the current study, we aimed to quantify the association between stroke-related vision impairment and time to incident dementia diagnosis, from time of stroke. Our hypothesis was that the hazard of developing incident dementia would be higher for stroke survivors with stroke-related vision impairment, compared to stroke survivors without stroke-related vision impairment symptoms.
Methods
The Atherosclerosis Risk in Communities (ARIC) study is an ongoing, long-term, prospective cohort study initiated in 1987, designed to investigate atherosclerotic disease and its risk factors in a community-based population (“The Atherosclerosis Risk in Communities (ARIC) Study,” 1989). The ARIC cohort—almost 16,000 participants—is from four communities: Forsyth County, North Carolina; Jackson, Mississippi; Minneapolis, Minnesota; and Washington County, Maryland. The study involved a home visit, several clinic examinations, annual or semiannual telephone follow-ups, and diagnosis of clinical events through surveillance of hospitalization records (Wright et al., 2021).
Cohort selection
Using ARIC study cohort data, we conducted a secondary analysis with participants who had an incident probable or definite ischemic stroke captured by the surveillance data. Stroke events were determined from stroke hospitalizations captured from cohort surveillance between 1987 and 2022 (for all sites except Jackson, which was only available through 2020). Physician documentation of new symptoms of stroke-related vision impairment (hemianopia, diplopia, and/or cranial nerve palsy) present on or leading to the hospitalization were abstracted. We excluded anyone who did not have a stroke severity score, measured by the National Institutes of Health Stroke Scale (NIHSS), since this was an important potential confounder for our analysis (Koton et al., 2022). We also excluded participants who were classified as having dementia (described below in Definition of Outcomes) prior to or less than half a year after the incident stroke, or if they had a stroke prior to their baseline visit. Figure 1 is a CONSORT flow diagram of cohort selection.
Figure 1:
CONSORT diagram describing cohort selection
Definition of primary exposure
The primary exposure was defined as stroke-related vision impairment. In the cohort surveillance, along with stroke diagnosis, the presence of hemianopia, diplopia, and cranial nerve palsy either present at, leading to hospital admission, or occurring during hospitalization was captured. Participants with at least one of these conditions at their incident ischemic stroke event were given a status of stroke-related vision impairment.
Definition of potential confounders and descriptive covariates
We captured descriptive data for each participant regarding age, sex, race-center, level of education, and stroke severity score. Age was taken from time of incident stroke, while sex, race-center, and years of education were self-reported at visit 1 (ARIC baseline). Sex was coded as male or female. Self-reported race data were collected at baseline, but ethnicity was not reported or evaluated. Race-center is a categorical variable that jointly encodes participant race and study center to account for their non-independence. Because the racial composition varied by center (e.g., only Black participants were enrolled at Jackson, MS, and nearly all participants at Minneapolis, MN, and Washington County, MD, were White), using this combined variable helps control for potential confounding due to center-specific racial distributions. The categories are: (1) Black, Jackson Mississippi, (2) Black, Forsyth County, North Carolina, (3) White, Forsyth County, North Carolina, (4) White, Minneapolis, Minnesota, and (5) White, Washington County, Maryland. Education was coded as a categorical variable with levels representing the highest level of education completed. These categories are: (1) did not complete high school, (2) high school, General Education Development, or vocational school, or (3) at least some college. Stroke severity score, which was determined using an algorithm for retrospective scoring with the NIHSS, was taken from the incident ischemic stroke event (Koton et al., 2022). While the continuous score can be categorized into four levels: minor (NIHSS score of ≤5), mild (NIHSS score of 6–10), moderate (NIHSS score of 11–15), and severe (NIHSS score of ≥16), the present study used the continuous score for improved precision in regression analyses.
Definition of outcomes
The survival outcome was the time to incident dementia development, from time of incident stroke. The process for classifying dementia in the ARIC study has been described in detail ( Knopman et al., 2016; Gottesman et al., 2017). Briefly, dementia development was categorized into three levels. Level 1 included adjudicated dementia from complete evaluation at the ARIC Neurocognitive Study (NCS) visits. Level 2 additionally included participants who did not attend ARIC-NCS visits but were classified as having dementia based on criteria from (1) the Telephone Interview for Cognitive Status-Modified (TICS), (2) informant telephone interviews using a modified version of the Clinical Dementia Rating (CDR) and the Functional Activities Questionnaire (FAQ) or (3) the eight item Dementia Screening Interview (AD8) or Six Item Screener (SIS) (Knopman et al., 2016). Level 3, which was used for this analysis, additionally included dementia cases identified by surveillance based on discharge hospitalization of ICD-9 and ICD-10 (International Classification of Diseases, Ninth and Tenth Revision) codes, or death certificate codes. Participants were censored at date of last participant contact prior to December 4, 2020, if they had not yet developed dementia according to the level 3 definition. The time origin (or time-zero) for this outcome is the time of incident stroke. Death before level 3 dementia development was treated as a competing event. For this study, participants were considered at risk for dementia development until they (1) were diagnosed with dementia, (2) died, (3) are lost to follow up for other reasons, or (4) administratively censored at last contact prior to December 4, 2020.
Statistical analysis
Demographic characteristics are presented for the overall cohort and stratified by stroke-related vision impairment (yes/no). Continuous variables are displayed with their mean and standard deviation, as well as their median, minimum, and maximum values; categorical variables are displayed with their frequency count and percent. Time from incident stroke to dementia development was visualized using cumulative incidence curves and was analyzed using a Fine-Gray survival model to account for the competing risk of death, or control for the fact that people may die before they develop or are diagnosed with dementia (Fine & Gray, 1999). The primary exposure is stroke-related vision impairment (binary), and potential confounders include age at incident stroke, biological sex, stroke severity using the continuous NIHSS score, race-center, and level of education. In other words, to quantify this relationship while accounting for potential confounders, we ran a Fine-Gray survival model to estimate the subdistribution hazard ratio (HR) of dementia development, which compares the instantaneous risk of dementia development among those with stroke-related vision impairment to those without, assuming the individuals have not already experienced a dementia diagnosis and accounting for death as a competing risk.
We ran a sensitivity analysis using a Cox proportional-hazards (Cox PH) approach to model the cause-specific hazard of dementia development (Latouche et al., 2013). We report hazard ratios, 95% confidence intervals (CIs), and p-values. For statistical tests, the nominal type I error level was set to 0.05. All analyses were performed using R.4.1.3.(R Core Team, 2024).
Results
Cohort characteristics
The final analytic cohort comprised 787 people who sustained a probable or definite incident ischemic stroke. Of those included, 31.4% developed dementia during follow up, and the average time to dementia development was 9.4 years (SD, 6.42). On average, the cohort was 69 (SD, 8.41) years old at the incident stroke and 51% female. 19.5% or 154 people had symptoms of stroke-related vision impairment at the time of incident stroke. Specifically, there were 115 people who had a diagnosis of homonymous hemianopia, 31 with diplopia, and 23 with a cranial nerve palsy. Several participants had symptoms of multiple types of stroke-related vision impairment. See Table 1 for additional descriptive statistics of the cohort.
Table 1.
Descriptive statistics of the cohort
| Stroke-Related Vision Impairment | |||
|---|---|---|---|
|
| |||
| Overall (N=787) |
Yes (N=154) |
No (N=633) |
|
|
| |||
| Event | |||
| Dementia Diagnosis | 247 (31.4%) | 53 (34.4%) | 194 (30.6%) |
| Competing Event (Death) | 406 (51.6%) | 83 (53.9%) | 323 (51.0%) |
| Censored (Non-death) | 134 (17.0%) | 18 (11.7%) | 116 (18.3%) |
| Time to Dementia Diagnosis (Years) a | |||
| Mean (SD) | 9.40 (6.42) | 8.46 (5.75) | 9.66 (6.58) |
| Median [Min, Max] | 7.92 [0.616, 26.4] | 7.80 [0.805, 22.4] | 8.08 [0.616, 26.4] |
| Age at Incident Stroke (Years) | |||
| Mean (SD) | 69.3 (8.41) | 68.8 (8.55) | 69.4 (8.38) |
| Median [Min, Max] | 69.2 [47.1, 91.6] | 68.0 [48.3, 91.6] | 69.4 [47.1, 90.4] |
| Stroke Severity Score (National Institutes of Health Stroke Severity) | |||
| NIHSS ≤5 | 544 (69.1%) | 81 (52.6%) | 463 (73.1%) |
| NIHSS 6–10 | 154 (19.6%) | 33 (21.4%) | 121 (19.1%) |
| NIHSS 11–15 | 56 (7.1%) | 25 (16.2%) | 31 (4.9%) |
| NIHSS ≥16 | 33 (4.2%) | 15 (4.2%) | 18 (2.8%) |
| Stroke Type | |||
| Probable/Definite Thrombotic Brain Infarction (TIB) | 611 (77.6%) | 104 (67.5%) | 507 (80.1%) |
| Probable/Definite Non-carotid Embolic Brain Infarction (EIB) | 176 (22.4%) | 50 (32.5% | 126 (19.9%) |
| Sex | |||
| Female | 399 (50.7%) | 72 (46.8%) | 327 (51.7%) |
| Male | 388 (49.3%) | 82 (53.2%) | 306 (48.3%) |
| # Years Education | |||
| Did Not Complete High School | 266 (33.8%) | 45 (29.2%) | 221 (34.9%) |
| High School, General Educational Development, or Vocational School | 295 (37.5%) | 58 (37.7%) | 237 (37.4%) |
| At Least Some College | 226 (28.7%) | 51 (33.1%) | 175 (27.6%) |
| Race- Center | |||
| Black, Forsyth County, North Carolina | 32 (4.1%) | 5 (3.2%) | 27 (4.3%) |
| Black, Jackson, Mississippi | 266 (33.8%) | 54 (35.1%) | 212 (33.5%) |
| White, Forsyth County, North Carolina | 137 (17.4%) | 22 (14.3%) | 115 (18.2%) |
| White, Minneapolis, Minnesota | 170 (21.6%) | 48 (31.2%) | 122 (19.3%) |
| White, Washington County, Maryland | 182 (23.1%) | 25 (16.2%) | 157 (24.8%) |
This calculation only includes the participants with a dementia diagnosis and is calculated from time of incident stroke. NIHSS= National Institutes of Health Stroke Scale
Figure 1, which is a cumulative incidence curve that is plotted over 30 years, displays the cumulative incidence of dementia development over time, where time 0 is the time of incident stroke. The plot, stratified by stroke-related vision impairment, demonstrates higher cumulative incidence of dementia across time for those with stroke-related vision impairment, but as shown by the wide and overlapping confidence bands across time, the data did not support significant differences by vision status. The associated risk table displays the number of participants in each group at risk at each time point, and the number of cumulative events (or dementia developments) at each time point in parentheses. For example, there are 11 people at risk at year 20, and 51 people already have developed dementia, died or are lost to follow up.
The adjusted HR for stroke-related vision impairment from the Fine-Gray model was 1.18 (95% CI 0.85, 1.63; P = 0.32), which yields results similar to what is shown with the unadjusted curves.
Sensitivity analysis
The Cox PH regression model, which quantifies the cause-specific hazard ratio and wherein death is treated as a censoring event, resulted in the cause-specific HR for stroke-related vision impairment on dementia diagnosis being 1.18 (95% CI 0.85, 1.63; P = 0.30). This aligns almost identically with the Fine-Gray results.
We also report on another cumulative incidence plot (Figure 3), for descriptive purposes, further stratifying by age at incident stroke (categorized into < 70 years and 70 + years). We see that, in the 70+ group, there is a moderately higher cumulative incidence of dementia across time for those with stroke-related vision impairment than without. In the < 70 group, there appears to be no difference. Even in the 70+ group, we again do not have strong evidence to confirm this relationship, due to the large confidence intervals at each time point.
Figure 3.
Cumulative incidence curve for dementia, stratified by stroke-related vision impairment and age, and risk table
Discussion
This study is the first to examine the association of stroke-related vision impairment and time to incident dementia in a population of stroke survivors. Our work builds on previous findings, which revealed an association between stroke-related vision impairment and prevalent dementia among stroke survivors (Hreha et al., in press). In this analysis, we did not find statistically significant evidence that stroke-related vision impairment is associated with the time to dementia post-stroke.
Previous research has established that stroke alone is an independent risk factor for dementia (Ivan et al., 2004; Koton et al., 2022), meaning that the baseline risk for dementia in our sample of all stroke survivors was already elevated. Therefore, we are likely looking for a smaller effect size than we would see in a broader population. Alternatively, it may be the case that there is no true association. Despite the inconclusive results, our findings highlight the potential for future research specifically aimed at collecting detailed, prospective information in stroke survivors, about vision status and cognitive outcomes, to more definitively address this issue.
In our analysis we combined several vision deficits due to the small number of individuals with these specific conditions in the cohort. However, we know there is heterogeneity in the stroke-related vision impairment variable, as our composite variable includes symptoms of homonymous hemianopia, diplopia and/or cranial nerve palsy. Diplopia and cranial nerve palsy are typically binary in nature—present or absent—without gradations of severity. In addition, diplopia and cranial nerve palsies have been reported to improve or are correctable with interventions such as prisms or spot patching (Politzer, 1996; F. Rowe & VIS group UK, 2011). In contrast, homonymous hemianopia results in permanent vision loss and can vary significantly in severity. Research found that differences in severity of the vision loss lead to differences in symptom burden, functional challenges, and recovery trajectories (F. J. Rowe et al., 2013). Therefore, we speculate for our cohort, any cases of severe visual impairment may be influencing cognitive trajectories differently from mild visual impairment. We do not, however, have information on the severity of the conditions from this dataset. In addition, the limited sample size precluded us from examining how the specific visual impairments may contribute to or drive dementia risk, differentially.
There is evidence that individuals with post-stroke vision impairment, particularly those with a homonymous hemianopia, use compensatory strategies to perform everyday tasks (Pollock et al., 2011). A recent meta-analysis concluded that visual skills training shows positive effects in improving cognitive function for people living post-stroke, especially in combination with high cognitive load and in an early phase of rehabilitation (Niering & Seifert, 2024). Because this study is a secondary data analysis, we do not have information about whether interventions such as compensatory strategies or vision rehabilitation in general were used by participants. A possibility is that individuals in our cohort were receiving vision rehabilitation or compensating effectively—thereby supporting task performance—and this may have buffered against accelerated cognitive decline, potentially explaining why vision impairment did not appear to increase dementia risk significantly.
Although the subdistribution and cause-specific HRs account for competing risks in different manners, the former estimating the cumulative incidence of dementia diagnosis among all individuals still “at risk”, including those who experienced a competing event, and the latter censoring at competing events, the similarity in their estimates suggests a consistent association between stroke-related vision impairment and dementia, which supports the robustness across different modeling approaches, despite the lack of statistical significance. This may also indicate that the presence of death as a competing risk does not substantially alter the magnitude or direction of the association.
Study strengths
To our knowledge, this is the first data analysis using a long-standing dataset to determine if stroke-related vision impairment adds to the risk of dementia beyond the risk posed by stroke alone. Despite some limitations, the dataset is the largest available to us that provides the combination of stroke, dementia, and vision impairment data that we required to initiate exploration of our research questions. Additionally, the dataset includes a diverse population and long follow-up period after stroke.
Limitations
Several limitations impact the interpretation of our results. First, the sample is geographically restricted to the four ARIC field centers. This may limit the generalizability of the results to other populations. Second, we included the race-center variable but are unable to disentangle race from region as White participants were included from only three centers and Black participants from only two centers. We acknowledge that race is a social construct and that the race-center variable does not fully capture the complex sociocultural and structural factors it is often used to represent, however, we included it for consistency with other ARIC studies. Third, as mentioned above, our sample size was small and therefore we might not have enough power to detect clinically meaningful group differences between vision-impaired versus non-vision impaired stroke survivors. Fourth, since this is a secondary analysis, we do not know important individual factors such as cognitive reserve, social support, driving status, specifics details about the vision impairment such as the specific side of visual field loss, or utilization of rehabilitation services at any point for cognitive impairments. These potential confounders or mediators could be contributing to the relationships explored in this work.
Future Directions
One important area for future work is to pursue this question prospectively, and with large clinical trials, in order to address the limitations noted above. Stroke-related vision impairments may be modifiable, offering opportunities to improve quality of life and possibly lower risk of downstream stroke-related sequalae. It would also be interesting to determine if homonymous hemianopia alone is associated with time to incident dementia post-stroke. Additionally, if a prospective trial is developed, collecting data on driving status and access to public transportation would be essential to determine whether these factors mediate the relationship between stroke-related vision impairment and time to dementia.
Conclusions
This study found that 31.4% of stroke survivors in this cohort developed dementia within the study follow-up period—a large proportion, which aligns with the well-established role of stroke as a risk factor for dementia. However, our results do not indicate that stroke-related vision impairment is a significant independent risk factor for developing incident dementia. It is still important that clinicians continue to interpret post-stroke vision impairment as a potential marker of overall neurological vulnerability.
Figure 2.
Cumulative incidence curve for dementia, stratified by stroke-related vision impairment and risk table
Acknowledgements
This work was supported by National Institutes of Health under Grant K01HD106010 (PI: Kimberly Hreha), the Duke Pepper Older Americans Independence Center (NIA P30AG028716) and the Biostatistics, Epidemiology and Research Design (BERD) Methods Core funded through Grant Award Number UL1TR002553 from the National Center for Advancing Translational Sciences (NCATS), a component of the National Institutes of Health (NIH). We also want to acknowledge. The Atherosclerosis Risk in Communities study, which has been funded in whole or in part with Federal funds from the National Heart, Lung, and Blood Institute, National Institutes of Health, Department of Health and Human Services, under Contract nos. (75N92022D00001, 75N92022D00002, 75N92022D00003, 75N92022D00004, 75N92022D00005). The ARIC Neurocognitive Study is supported by U01HL096812, U01HL096814, U01HL096899, U01HL096902, and U01HL096917 from the NIH (NHLBI, NINDS, NIA and NIDCD). The authors thank the staff and participants of the ARIC study for their important contributions.
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