Abstract
Background
This cohort study aims to examine the relationship between the occurrence of cranial nerve palsy (CNP) affecting the third, fourth, or sixth cranial nerve and the subsequent risk of stroke, with a particular focus on the modulating effect of age on this association.
Methods and Results
We established a cohort of individuals diagnosed with third, fourth, or sixth CNP who underwent national health screening within 2 years of diagnosis from 2010 to 2017. A control group was matched by sex and age at a ratio of 1:5. Participants were followed until December 31, 2019. We use multivariable Cox proportional hazards regression analyses to assess the association between ocular motor CNP and subsequent stroke stratified by age. Covariates including lifestyle, health behavior, underlying comorbidities, and Charlson comorbidity index score were also adjusted. Compared with the control group, the ocular motor CNP group had a higher risk of stroke after adjusting for potential confounders (hazard ratio [HR], 1.23 [95% CI,, 1.08–1.39]). The risk of stroke increased by 8.91 times in individuals with ocular motor CNP who were in their 30s (HR, 8.91 [95% CI, 1.63–48.66]). The risk increased by 2.49 times in those who were in their 40s, 1.78 times in those who were in their 50s, and 1.32 times in those who were in their 60s (HRs, 2.49, 1.78, and 1.32 [95% CI, 1.39–4.45, 1.31–2.42, and 1.08–1.62], respectively). However, for those who were in their 20s, 70s, or 80s, the incidence of stroke did not significantly increase.
Conclusions
Our study establishes an association between ocular motor CNP and an increased risk of stroke, particularly in young adults.
Keywords: age factor, cranial nerve palsy, diplopia, ischemic stroke, population‐based study, stroke, young adults
Subject Categories: Epidemiology, Ischemic Stroke
Nonstandard Abbreviations and Acronyms
- CCI
Charlson comorbidity index
- CNP
cranial nerve palsy
- NHIS
Korean National Health Insurance Service
Clinical Perspective.
What Is New?
This study reveals a strong link between ocular motor cranial nerve palsy and increased stroke risk, particularly in younger adults.
Individuals in their 30s with ocular motor cranial nerve palsy have up to an 8‐fold higher risk of subsequent stroke compared with controls.
What Are the Clinical Implications?
These findings emphasize the importance of recognizing ocular motor cranial nerve palsy as a potential indicator of elevated stroke risk, especially in younger patients, urging clinicians to be vigilant during evaluations, particularly for patients in their 30s and 40s.
Cranial nerve palsies of the third, fourth, and sixth nerves can result in limited eye movement and diplopia, prompting patients to seek urgent medical care. One of the most common causes of ocular motor cranial nerve palsy (CNP) is microvascular ischemia, which is commonly observed in older patients with diabetes, hypertension, and hyperlipidemia. 1 , 2 , 3 As these patients often share similar atherosclerotic risk factors, numerous studies have explored the relationship between ocular motor cranial nerve palsy and stroke. 4 , 5 , 6 , 7 , 8 , 9 Previous research using the Sample Cohort database of the Korean National Health Insurance Service (NHIS) comprising 1 million individuals has demonstrated an increased risk of stroke following ocular motor CNP. 4 , 5 , 7
Given the rarity of ocular motor CNP, this study aimed to investigate its association with stroke risk in a larger population to ensure an adequate sample size for robust analysis. We sought to establish a cohort of patients diagnosed with ocular motor CNP and a 1:5 matched control group by sex and age using health insurance and examination data from NHIS covering the Korean general population of 50 million. Furthermore, this study aimed to explore whether stroke risk associated with ocular motor cranial nerve palsy might differ by age.
Methods
Data Availability Statement
The anonymized data sets and materials used in this study are accessible to the public through the NHIS of South Korea's website (https://nhiss.nhis.or.kr/). Access to these data requires authorization, adhering to the NHIS's data usage policies. Raw data are not allowed to be openly shared according to the policy of NHIS.
Data Source and Study Population
This study used data from South Korea's NHIS, covering ≈96% of the country's 51 million citizens. The data set contained medical records, including outpatient and inpatient histories and medication prescriptions. In addition, biennial national health screening program data for individuals aged ≥20 years were used, encompassing self‐reported health behaviors, anthropometric measurements (eg, blood pressure, height, weight), and laboratory results. These data are also linked to demographic factors such as income and socioeconomic status.
We employed a personalized database from NHIS of South Korea to create a cohort of patients diagnosed with cranial nerve palsy of the third, fourth, or sixth cranial nerve for the first time between 2010 and 2017 who underwent the national health examination within 2 years before CNP diagnosis. We selected a control group, comprising individuals who underwent health examinations in the same period and had no CNP diagnosis, matched at a ratio of 1:5 on the basis of sex and age through propensity scoring. Our objective was to investigate any difference in the risk of subsequent stroke between the patient group with CNP and the control group. Eligible individuals were followed for stroke from 1 year after the date of their CNP diagnosis (the 1‐year time lag) until December 31, 2017. We employed this 1‐year time lag in sensitivity analysis to circumvent the issue of reverse causation (immortal time bias).
This study adhered to the tenets of the Declaration of Helsinki. The study protocol was approved by the Institutional Review Board (IRB) of Samsung Medical Center (No. SMC 2020‐09‐050). The requirement for informed consent was waived by the Institutional Review Board, as our study solely used anonymous and deidentified information.
Definition of Ocular Motor CNP
The diagnosis of ocular motor CNP was established on the basis of the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD‐10‐CM) codes of H49.0 for third CNP, H49.1 for fourth CNP, and H49.2 for sixth CNP. Individuals with a prior history of dysthyroid exophthalmos (code: H06.2), thyrotoxicosis (code: E05), or myasthenia gravis (code: G70.0) were excluded from the cohort.
Definition of Stroke
The diagnosis of stroke was established on the basis of the ICD‐10‐CM code of I63 (cerebral infarction) made during hospitalization and according to brain imaging such as computed tomography and magnetic resonance imaging. 10 Individuals with a prior history of stroke, including those with ICD‐10‐CM codes I63 and I64 (stroke, not specified as hemorrhage or infarction), were excluded from the CNP cohort.
Covariates
Baseline comorbidities were assessed on the basis of the patient's medical history, clinical records, and pharmacy codes in the ICD‐10‐CM. Hypertension was defined as a blood pressure of ≥140/90 mm Hg or the presence of at least 1 antihypertensive medication prescription in a year with ICD‐10‐CM codes of I10, I11, I12, I13, and I15. Diabetes was determined by a fasting glucose level of ≥126 mg/dL or at least 1 prescription claim in a year. Dyslipidemia was diagnosed as a total cholesterol level of ≥240 mg/dL or the presence of at least 1 antihyperlipidemic medication prescription in a year under ICD‐10 code E78. Chronic kidney disease was determined by an estimated glomerular filtration rate of <60 mL/min per 1.73 m2. Sleep apnea was defined under ICD‐10 code G47.33. Charlson comorbidity index (CCI) score was also used to calculate the adjusted odds ratio.
Standardized self‐reported questionnaires were used to collect general health behavior and lifestyle information during national health examination. Information about smoking status, alcohol consumption, and regular physical activity were collected through these questionnaires. Obesity was defined as a body mass index of ≥25 kg/m2. A low income level was defined as the lower one fifth of the whole population.
Blood samples were drawn after overnight fasting. Serum levels of glucose, total cholesterol, triglycerides, high‐density lipoprotein cholesterol, low‐density lipoprotein cholesterol, hemoglobin, serum creatinine, aspartate aminotransferase, alanine aminotransferase, and γ‐GTP were measured.
Statistical Analysis
Baseline characteristics of the study participants according to ocular motor CNP were compared using ANOVA for continuous variables or χ2 test for categorical variables. The incidence rate of stroke was calculated by dividing the number of events by 1000 person‐years. We performed multivariable Cox proportional hazards regression analyses to evaluate the association of ocular motor CNP with subsequent stroke and calculated hazard ratios (HRs) and 95% CIs. Five models were constructed, taking various confounding factors into account. Model 1 was an unadjusted model. Model 2 was adjusted for age and sex. Model 3 was adjusted for age, sex, smoking status, alcohol consumption, physical activity, income level, and obesity. Model 4 was adjusted for age, sex, smoking status, alcohol consumption, physical activity, income level, obesity, diabetes, hypertension, dyslipidemia, chronic kidney disease, and sleep apnea. Finally, model 5 was adjusted for CCI score in addition to factors adjusted in model 4.
Additionally, the study conducted subgroup analysis to determine if the risk of subsequent stroke varied depending on the presence of ocular motor CNP and age. The analysis was performed in increments of 10 years. All statistical analyses were carried out using SAS software version 9.4 (SAS Institute Inc., Cary, NC).
RESULTS
This study used a customized NHIS database cohort comprising the Korean population aged ≥20 years who participated in the national health screening program. Of a total of 60,781 individuals diagnosed with ocular motor CNP for the first time from 2010 to 2017, 52,076 were included in this study after excluding those with a previous history of thyroid eye disease, hyperthyroidism, or myasthenia gravis. Of these, 23 642 participated in the national health screening within 2 years before their ocular motor CNP diagnosis. After excluding those with missing health screening variables, 22,588 remained. After excluding those with a previous claim for stroke (codes: I63 and I64), 17 388 remained. After applying a 1‐year lag period, 16,648 remained. After 1:5 matching by sex and age, 16,374 were enrolled in the ocular motor CNP group and 81,870 were enrolled in the control group (Figure 1). Study participants were followed until December 31, 2019.
Figure 1. Flowchart of this study.
CNP indicates cranial nerve palsy.
Table 1 presents baseline characteristics of the study population. Individuals with an ocular motor CNP history had a higher likelihood of being obese, having comorbidities such as diabetes, hypertension, dyslipidemia, and chronic kidney disease, and having a CCI score of ≥2 compared with the controls.
Table 1.
Baseline Characteristics of the Study Population
| Total | Ocular motor cranial nerve palsy | P value | ||
|---|---|---|---|---|
| No | Yes | |||
| No. | 98,244 | 81,870 | 16,374 | |
| Male, n (%) | 61,848 (62.95) | 51,540 (62.95) | 10,308 (62.95) | 1 |
| Age, y, mean±SD | 58.45±13.06 | 58.45±13.06 | 58.45±13.06 | 1 |
| 20–39 | 2,370 (2.41) | 1,975 (2.41) | 395 (2.41) | |
| 40–64 | 6,582 (6.7) | 5,485 (6.7) | 1,097 (6.7) | |
| ≥65 | 14,400 (14.66) | 12,000 (14.66) | 2,400 (14.66) | |
| Current smoker, n (%) | 22,095 (22.49) | 18,487 (22.58) | 3,608 (22.03) | 0.1266 |
| Drinking, n (%)* | 44,059 (44.85) | 37,291 (45.55) | 6,768 (41.33) | <0.0001 |
| Regular physical activity | 21,433 (21.82) | 17,831 (21.78) | 3,602 (22) | 0.5363 |
| Income, low† | 19,766 (20.12) | 16,594 (20.27) | 3,172 (19.37) | 0.009 |
| Obesity‡ | 35,850 (36.49) | 29,528 (36.07) | 6,322 (38.61) | <0.0001 |
| Diabetes | 16,495 (16.79) | 12,053 (14.72) | 4,442 (27.13) | <0.0001 |
| Hypertension | 40,322 (41.04) | 32,772 (40.03) | 7,550 (46.11) | <0.0001 |
| Dyslipidemia | 30,044 (30.58) | 24,125 (29.47) | 5,919 (36.15) | <0.0001 |
| Chronic kidney disease§ | 6,259 (6.37) | 5,105 (6.24) | 1,154 (7.05) | 0.0001 |
| Sleep apnea‖ | 97 (0.1) | 78 (0.1) | 19 (0.12) | 0.4399 |
| CCI score | 1.32±1.44 | 1.14±1.31 | 2.21±1.71 | <0.0001 |
| ≥3 | 18,318 (18.65) | 11,943 (14.59) | 6,375 (38.93) | <0.0001 |
| BMI, mean±SD | 24.07±3.16 | 24.03±3.14 | 24.26±3.24 | <0.0001 |
| WC, mean±SD | 82.47±8.88 | 82.35±8.83 | 83.09±9.11 | <0.0001 |
| Fasting glucose | 103.52±29.12 | 101.98±25.71 | 111.2±41.35 | <0.0001 |
| SBP, mean±SD | 125.05±15.14 | 124.94±15.08 | 125.55±15.42 | <0.0001 |
| DBP, mean±SD | 77.11±9.93 | 77.07±9.88 | 77.31±10.18 | 0.0047 |
| Total cholesterol, mean±SD | 195.7±37.85 | 195.99±37.6 | 194.24±39.06 | <0.0001 |
| Glomerular filtration rate | 88.94±45.33 | 88.86±44.13 | 89.33±50.9 | 0.2264 |
| Follow up duration | ||||
| Mean±SD | 4.36±2.24 | 4.37±2.23 | 4.3±2.25 | 0.0003 |
BMI indicates body mass index; CCI, Charlson comorbidity index; DBP, diastolic blood pressure; SBP, systolic blood pressure; SD, standard deviation; and WC, waist circumference.
Drinking was defined if average daily alcohol consumption was >0 g.
Low income meant medical aid beneficiary and income <25%.
Obesity was defined as BMI ≥25 kg/m2.
Chronic kidney disease was defined as estimated glomerular filtration rate < 60 mL/min.
Sleep apnea was defined when the diagnostic code was G47.3.
During a mean follow‐up of 4.36 years (SD, 2.24), the incidence rate of stroke was 5.07 and 3.51 per 1000 person‐year in CNP and control groups, respectively. After adjusting for age and sex, the CNP group had a 1.44 times higher risk of stroke than controls (model 2: HR, 1.44 [95% CI, 1.28–1.62], Table 2). Even after further adjusting for underlying comorbidities, health behaviors, and CCI score, the CNP group had a significantly increased risk of stroke by 1.23 times compared with controls (model 5: HR, 1.23 [95% CI, 1.08–1.39] Table 2).
Table 2.
Risk of Stroke According to CNP Event
| CNP | N | Stroke | Person‐year | Incidence rate* | HR (95% CI) | ||||
|---|---|---|---|---|---|---|---|---|---|
| Model 1† | Model 2‡ | Model 3§ | Model 4‖ | Model 5¶ | |||||
| No | 81 870 | 1257 | 357718.01 | 3.514 | 1 (Ref.) | 1 (Ref.) | 1 (Ref.) | 1 (Ref.) | 1 (Ref.) |
| Yes | 16 374 | 357 | 70421.14 | 5.070 | 1.445 (1.285–1.625) | 1.44 (1.28–1.619) | 1.439 (1.279–1.619) | 1.339 (1.189–1.508) | 1.226 (1.084–1.387) |
CNP indicates ocular motor cranial nerve palsy; HR, and hazard ratio.
Incidence rate was calculated as per 1000 person‐year.
Model 1 was unadjusted.
Model 2 was adjusted for sex and age.
Model 3 was adjusted for sex, age, smoking, drinking, physical activity, income, and obesity.
Model 4 was adjusted for sex, age, smoking, drinking, physical activity, income, obesity, diabetes, hypertension, and dyslipidemia.
Model 5 was adjusted for sex, age, smoking, drinking, physical activity, income, obesity, diabetes, hypertension, dyslipidemia, chronic kidney disease, sleep apnea, and Charlson comorbidity index score.
The Kaplan–Meier survival curve showed a significant increase in the incidence probability of stroke over a maximum of 8 years of follow‐up observation in individuals with ocular motor CNP history compared with controls (Figure 2). This study also examined the impact of ocular motor CNP history on stroke occurrence by age. The risk of stroke increased by 8.91 times in individuals with ocular motor CNP who were in their 30s (HR, 8.91 [95% CI, 1.63–48.66]; Table 3). The risk increased by 2.49 times in those who were in their 40s, 1.78 times in those who were in their 50s, and 1.32 times in those who were in their 60s (HRs, 2.49, 1.78, and 1.32 [95% CI, 1.39–4.45, 1.31–2.42, and 1.08–1.62, respectively). However, in those who were in their 20s, 70s, or 80s, the incidence of stroke did not significantly increase. The P value for interaction regarding age differences was statistically significant in all models.
Figure 2. Kaplan–Meier survival curve showing a significant increase in the incidence probability of stroke over a maximum of 8 years of follow‐up observation in individuals with ocular motor CNP history compared with controls.
CNP indicates cranial nerve palsy.
Table 3.
Age‐Related Stroke Risk According to CNP Event
| Age, y | CNP | N | Stroke event | HR (95% CI) | ||||
|---|---|---|---|---|---|---|---|---|
| Model 1* | Model 2† | Model 3‡ | Model 4§ | Model 5‖ | ||||
| 20s | No | 1975 | 1 | 1 (Ref.) | 1 (Ref.) | 1 (Ref.) | 1 (Ref.) | 1 (Ref.) |
| Yes | 395 | 2 | 10.137 (0.919–111.788) | 10.171 (0.922–112.162) | 9.975 (0.905–110.01) | 9.876 (0.896–108.922) | 9.13 (0.828–100.71) | |
| 30s | No | 5485 | 2 | 1 (Ref.) | 1 (Ref.) | 1 (Ref.) | 1 (Ref.) | 1 (Ref.) |
| Yes | 1,097 | 4 | 10.072 (1.845–54.986) | 10.076 (1.846–55.009) | 10.08 (1.846–55.03) | 9.745 (1.785–53.204) | 8.909 (1.631–48.658) | |
| 40s | No | 12000 | 31 | 1 (Ref.) | 1 (Ref.) | 1 (Ref.) | 1 (Ref.) | 1 (Ref.) |
| Yes | 2400 | 18 | 2.95 (1.65–5.273) | 2.951 (1.651–5.274) | 2.945 (1.647–5.264) | 2.724 (1.524–4.871) | 2.488 (1.39–4.453) | |
| 50s | No | 21240 | 139 | 1 (Ref.) | 1 (Ref.) | 1 (Ref.) | 1 (Ref.) | 1 (Ref.) |
| Yes | 4248 | 59 | 2.163 (1.595–2.933) | 2.163 (1.595–2.933) | 2.152 (1.587–2.918) | 1.964 (1.447–2.665) | 1.778 (1.307–2.418) | |
| 60s | No | 23490 | 417 | 1 (Ref.) | 1 (Ref.) | 1 (Ref.) | 1 (Ref.) | 1 (Ref.) |
| Yes | 4698 | 128 | 1.569 (1.287–1.912) | 1.567 (1.286–1.91) | 1.565 (1.283–1.908) | 1.445 (1.184–1.763) | 1.324 (1.082–1.62) | |
| 70s | No | 15255 | 571 | 1 (Ref.) | 1 (Ref.) | 1 (Ref.) | 1 (Ref.) | 1 (Ref.) |
| Yes | 3051 | 123 | 1.094 (0.9–1.329) | 1.092 (0.898–1.327) | 1.095 (0.901–1.33) | 1.027 (0.845–1.25) | 0.949 (0.778–1.156) | |
| ≥80 | No | 2425 | 96 | 1 (Ref.) | 1 (Ref.) | 1 (Ref.) | 1 (Ref.) | 1 (Ref.) |
| Yes | 485 | 23 | 1.19 (0.755–1.876) | 1.179 (0.748–1.858) | 1.178 (0.747–1.857) | 1.137 (0.721–1.793) | 1.061 (0.672–1.674) | |
| P for interaction | <0.0001 | <0.0001 | <0.0001 | 0.0001 | 0.0002 | |||
CNP indicates ocular motor cranial nerve palsy; HR, hazard ratio.
Model 1 was unadjusted.
Model 2 was adjusted for sex and age.
Model 3 was adjusted for sex, age. smoking, drinking, physical activity, income, and obesity.
Model 4 was adjusted for sex, age. smoking, drinking, physical activity, income, obesity. diabetes, hypertension, and dyslipidemia.
Model 5 was adjusted for sex, age. smoking, drinking, physical activity, income, obesity. diabetes, hypertension, dyslipidemia, chronic kidney disease, sleep apnea, and Charlson comorbidity index score.
Discussion
This nationwide, population‐based cohort study investigated the association between preceding ocular motor CNP diagnosis and subsequent stroke risk. After adjusting for age, sex, and medical comorbidities related to cerebrovascular disease, this study found that an ocular motor CNP diagnosis increased the risk of stroke. Specifically, young adults in their 30s with a history of ocular motor CNP had a risk of subsequent stroke that was up to 8 times higher than those in the control group. This finding is consistent with previous studies using sample cohort of 1 million provided by the NHIS of South Korea. 4 , 5 , 7 A recent meta‐analysis has demonstrated an HR of 5.96 of subsequent stroke after isolated ocular motor CNP within the first year. The HR reduced to 3.27 after 5 years. It remained high at 2.9 up to 12 years. 9 Microvascular ischemia has been reported to be the most common cause, especially in those with isolated ocular motor CNPs who were aged >50 years. 11 , 12 , 13 Underlying mechanisms of increased risk after ocular motor CNP are believed to be related to common comorbidities such as diabetes, hypertension, and hyperlipidemia. 14 , 15 , 16 , 17 Studies support the hypothesis that microvascular ischemia, a common cause of ocular motor CNP, might lead to subsequent macrovascular ischemia such as cerebral artery occlusion. 18 , 19
Interestingly, our subgroup analysis revealed that the risk of stroke associated with ocular motor CNP varied according to age. Previously, Rim et al reported that the HR of 5‐year stroke‐free survival after ocular motor CNP is 4.20 for younger subjects (aged <65 years) and 3.25 for older subjects (aged ≥65 years). 7 However, since they divided age into only 2 categories on the basis of the age of 65 years, the boundary for younger adults was not clear. In addition, the risk difference was not significant. Furthermore, the number of subjects in their entire ocular motor CNP group was only 466, which was a considerably smaller sample size compared with the sample size of the present study as they analyzed a sample cohort instead of nationwide data. In our study, we used a nationwide population‐based cohort and finally analyzed a total of 16,374 patients with ocular motor CNP. In our age‐stratified analysis, subjects in their 30s had a significantly higher subsequent stroke risk of 8.9 times after a diagnosis of ocular motor CNP compared with the control group. Similarly, the HR for those in their 20s was high, at 9.13, although statistical significance was not obtained due to a small number of stroke cases in this age group. HRs for subjects in their 40s, 50s, and 60s were 2.49, 1.78, and 1.32, respectively, showing a gradual decrease. In contrast, there was no significant increase in stroke risk in individuals aged ≥70 years. These results suggest that the occurrence of ocular motor CNP itself in younger individuals might serve as an important alarm sign for increased stroke risk. Similar findings have been reported regarding an increased risk of cardiovascular disease complications in younger individuals with conditions such as hyperlipidemia and diabetes compared with those who develop these conditions later in life. 20 , 21 Further research is needed to elucidate the underlying mechanisms driving this age‐related disparity and identify potential preventive strategies tailored to younger patients. The study highlights the importance of considering ocular motor CNP as a potential marker for increased stroke risk, especially in younger individuals.
Our study has several strengths that can support the reliability of our results. The large sample size of our study was derived from the NHIS database, which included data from 96% of the Korean population, allowing for robust comparisons. We conducted a 1:5 matching solely on the basis of age and sex, which could subject us to criticism for not considering other confounders. However, as seen in Table S1, our analysis for confounders extended beyond just age and sex. It included socioeconomic status factors such as smoking, drinking, physical activity, and income; atherosclerotic risks like obesity, diabetes, hypertension, and dyslipidemia; as well as other potential confounders such as chronic kidney disease, sleep apnea, and the CCI score, which indicates the severity of conditions. The results showed that these variables did not significantly impact the overall outcomes of the study, suggesting that, despite not achieving perfect matching, these factors did not substantially influence the results. These findings ensure more accurate estimates of the association between ocular motor CNP and stroke risk.
However, several limitations of our study should be acknowledged. First, diagnoses of ocular motor CNP and stroke were based on ICD‐10‐CM codes, which might be subjected to misclassification. Although the diagnosis of stroke is well validated, 10 ocular motor CNP is a rare condition and difficult to validate. Indeed, by defining CNP in the same manner as previous studies, 4 , 5 , 7 , 8 our research aligns with established methodologies, which enhances the credibility of our findings. This approach ensures consistency in comparison with prior research, allowing for a more reliable interpretation of the results within the context of existing literature. Second, the study population was limited to an East Asian population, making it challenging to generalize our findings to other ethnic groups. Third, the observational design of our study precludes establishing a causal relationship between ocular motor CNP and stroke risk. Finally, we did not account for potential confounders such as medication status, which might influence stroke risk in patients with ocular motor CNP. However, it is not explicitly detailed in our report whether tests were conducted specifically for differential diagnosis during the hospitalization period; it is reasonable to assume that conditions like brain lesions, increased intracranial pressure, multiple sclerosis, and infections would have been differentiated during this time.
In conclusion, our study provides evidence of an association between ocular motor CNP and an increased risk of stroke, particularly in younger individuals. These findings underscore the importance of thorough evaluation and management in patients with ocular motor CNP to prevent and reduce the risk of subsequent stroke. Future studies are needed to investigate the underlying mechanisms of this association and potential preventive strategies tailored to younger patients.
Sources of Funding
This research was supported by the National Research Foundation of Korea grant funded by the Korean government (Ministry of Science and Information and Communications Technology; No. NRF‐2021R1A2C1007718), the Republic of Korea, to Dr Park. The sponsor or funding organization had no role in the design or conduct of this research.
Disclosures
None.
Supporting information
Table S1
This manuscript was sent to Monik C. Jiménez, SM, ScD, Associate Editor, for review by expert referees, editorial decision, and final disposition.
Supplemental Material is available at https://www.ahajournals.org/doi/suppl/10.1161/JAHA.123.033437
For Sources of Funding and Disclosures, see page 7.
Contributor Information
Kyung‐Ah Park, Email: kparkoph@skku.edu.
Sei Yeul Oh, Email: syoh@skku.edu.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Table S1
Data Availability Statement
The anonymized data sets and materials used in this study are accessible to the public through the NHIS of South Korea's website (https://nhiss.nhis.or.kr/). Access to these data requires authorization, adhering to the NHIS's data usage policies. Raw data are not allowed to be openly shared according to the policy of NHIS.