DOI: https://doi.org/10.21203/rs.3.rs-1860127/v1
Abstract: The incidence of diabetes is increasing globally and prediabetes is clinically important. However, the relationship between diabetic duration and ocular motor cranial nerve palsy (CNP) was not evaluated in large scale study and studies on ocular motor CNP in pre-diabetes are sparse.
Backgroud/Objectives: This study aimed to assess the association between glycemic status and the incidence of ocular motor CNP.
Subjects/Methods: This is a retrospective nationwide population-based cohort study using National Health Insurance Service (NHIS) of South Korea data from 2009. Health checkup data of 4,067,842 individuals aged between 20 and 90 years between January 1, 2009, and December 31, 2018, were analyzed. The subjects were classified based on glycemic status as nondiabetes, impaired fasting glucose (IFG), newly detected diabetes, diabetes duration <5 years, and diabetes duration ≥5 years. The primary end point of this study was incidence of CNP. Hazard ratio (HR) and 95% confidence interval (CI) of CNP were estimated using Cox proportional hazards regression analysis. Model 3 adjusted age, sex, smoking status, alcohol consumption, physical activity of individuals and body mass index in the analysis.
Results: We identified 5,835 incident CNP cases and 4,062,007 control cases during the follow-up period (average, 6.3 years). In the adjusted model 3, the adjusted HR for the IFG group was 1.104 (95% CI 1.035 - 1.177), for the newly detected diabetes group was 1.793 (95% CI 1.6 - 2.009), for the diabetes duration <5 years group was 1.959 (95% CI 1.768 - 2.169) and for the diabetes duration ≥5 years group was 2.606 (95% CI 2.38 - 2.854). Using the Kaplan-Meier curve, the log-rank test showed increase in the incidence of CNP according to the duration of diabetes (p < 0.001).
Conclusions: Our population-based large-scale cohort study suggests that both IFG and diabetes significantly increased the risk of the development of ocular motor CNP compared to the normal glycemic status.
The acquired 3rd, 4th, and 6th cranial nerve palsies which cause diplopia due to the extraocular muscle paralysis are commonly observed in neuro-ophthalmology practice [1–6]. The main pathophysiology of the disease is considered as atherosclerotic changes involving small vessels, so-called presumed microvascular ischemia especially in older adults of 50 years or older [6–10]. The incidence of these palsies increases with age, and the majority of patients had one or more vasculopathic risk factors such as diabetes mellitus, hypertension, and hyperlipidemia [1–6, 11, 12]. Diabetes mellitus is one of the major risk factors for ocular motor nerve palsy, and the association between ocular motor cranial nerve palsy (CNP) and diabetes mellitus is well established through the previous studies [6, 13–17].
In a large Caucasian diabetic population follow-up study on mainly type 2 diabetes, 0.4% were hospitalized with ophthalmoplegia [18, 19], while a Japanese study reported the prevalence of ophthalmoplegia among diabetic patients to be 0.97% which is ten times more than non-diabetic patients at 0.13% [14, 19]. Also, ophthalmoplegia among diabetic patients was more frequently seen in individuals with a long duration of diabetes [14, 19, 20].
Prediabetes is the at-risk status of diabetes, while diagnosis criteria are on the debate [21–23]. The World Health Organization (WHO) defined impaired fasting glucose (IFG) as fasting plasma glucose (FPG) of 6.1-6.9mmol/L (110 to 125 mg/dL) and impaired glucose tolerance (IGT) as 2 hours (hr) plasma glucose of 7.8–11.0 mmol/L (140–200 mg/dL) after ingestion of 75 g of oral glucose load or a combination of the two based on a 2 hr oral glucose tolerance test (OGTT) [22]. However, studies on CNP in pre-diabetes are sparse. In the light of the available literature, this study aimed to assess the association between ocular motor nerve palsy and glycemic status in diabetic patients as well as people with impaired fasting glucose (IFG) or normal glucose tolerance, and recognize risk factors of ophthalmoplegia according to the glycemic status in a large cohort representative of the Korean general population. In this report, we conducted a incidence study for the ocular motor (third, fourth, and sixth cranial nerves) CNP and its related risk factors for Korean adults according to the glycemic categories, who are 20 years or older of age using the National Health Insurance Service (NHIS)-National Sample Cohort (NSC) database.
This study was approved by the Samsung Medical Center Institutional Review Board (IRB; IRB no. SMC 2020-09-050), and the requirement for informed consent from the individual patients was waived because the data used were public and anonymized under confidentiality guidelines.
The Korean National Health Insurance (NHI) is a single health care insurance system that covers; 97% of the total Korean population; the remaining 3% are “medical protection” beneficiaries. Information on individuals’ use of medical facilities, prescription records, and diagnostic codes configured in the form of Korean Standard Classification of Diseases (KCD)-7 codes (7th Revision), with a few changes to Korean situations based on the International Classification of Diseases-10 (10th Revision) codes, is recorded in the National Health Insurance Service (NHIS) database. In addition, the NHIS provides a biennial health examination program for all beneficiaries aged > 20 years, which consists of anthropometry, a self-administered questionnaire on past medical history or health-related behavior, and laboratory tests [24]. This database has been considered to be representative of the Korean population and is used in research through anonymization and deidentification.
From 2009 to 2018, 4,233,273 NHIS health examinations were conducted (Fig. 1). Among them, participants with regular checkups were included (n = 4,233,273) and incomplete data (n = 151,372) were excluded. Also, patients, who had a previous history of CNP (n = 2,872) and who were newly diagnosed with CNP or death within 1 year after the date of examination (n = 11,187) were excluded (1-year time lag). As a result, 4,067,842 eligible subjects were followed up for CNP after examination date until December 31, 2018.
Diabetes and glucose tolerance status of subjects were categorized into the groups according to ICD10 codes (E10.x–E14.x), and fasting glucose measurements during the health screening. This definition was based on the consensus of relevant findings widely used in previous studies. The subjects were classified based on glycemic status as nondiabetes (fasting glucose, 100 mg/dL), IFG (fasting glucose 100–125 mg/dL), newly detected diabetes, diabetes duration < 5 years, and diabetes duration ≥ 5 years. IFG and diabetes were diagnosed according to the criteria of the American Diabetes Association. If diabetes was diagnosed during the cohort period, it was defined as "newly detected diabetes". Patients’ clinical courses were assessed until December 2018.
From the Korean NHIS data, height, weight, waist circumference, and blood pressure (systolic and diastolic) of the eligible individuals were collected. Detailed information on the lifestyle of subjects was obtained through standardized self-reported questionnaires. Subjects were classified according to smoking status as a nonsmoker, former smoker, or current smoker. Individuals who consumed 30 g of alcohol per day were classified as being heavy alcohol consumers. Regular physical activity was defined as the performance of strenuous exercise for at least 20 minutes (once per week). Baseline comorbidities (hypertension, dyslipidemia, cardiovascular disease, and cerebrovascular disease) of subjects were identified based on a combination of the past medical history questionnaires, KCD-7 codes, and data from the prescription database. Blood sampling was conducted after overnight fasting, and serum glucose, total cholesterol, triglyceride, high-density lipoprotein (HDL), low-density lipoprotein (LDL), and creatinine concentrations were measured. The estimated glomerular filtration rate (eGFR) of subjects was calculated using the MDRD Study equation. We defined chronic kidney disease (CKD) as an eGFR, 60 mL/min/1.73 m2.
The outcome variable was CNP. Incident ocular motor CN palsy among the defined diabetes mellitus (DM) cohort with age 20 years or more was identified using the ICD-10-CM codes for third CN palsy (H49.0), fourth CN palsy (H49.1), and sixth CN palsy (H49.2). Anyone with comorbid dysthyroid exophthalmos (H06.2), thyrotoxicosis (E05), or myasthenia gravis (G70.0) was excluded.
Variables including sex, smoking status, drinking amount, and concomitant comorbidity were considered as possible confounders of the association between third, fourth, and sixth CNP and DM. The comorbidities of patients were based on patient’s previous diagnoses, and defined comorbidities were hypertension, dyslipidemia, and chronic kidney diseases.
The baseline characteristics between these groups were compared using the Student’s t-test or analysis of variance for continuous variables and the chi-square test for categorical variables. The data are expressed as numbers and percentages (categorical) or the mean ± standard deviation (continuous).
Hazard ratios (HRs) and 95% confidence intervals (CIs) were estimated using Cox proportional- hazard models to analyze the association between the glycemic status and the incident ocular motor CNP. Incidence rate of CNP was calculated by dividing the number of events by 1,000 person-years. Four different models were created by adjusting confounding variables in a stepwise manner and compared independently. Model 1 was unadjusted, and in model 2, we adjusted the variant ‘age’ and ‘sex’, and in model 3, we adjusted variant “smoking status (never smoked, former smoker, current smoker)”, “drinking amount (never drinker, moderate drinker, heavy drinker)”, “physical activity (no exercise, 1–2 times a week, > 3 times a week)” and “body mass index” in addition to those adjusted in model 2. We also conducted clinically relevant subgroup analyses and calculated P-values for the interactions between glycemic status and subgroups in the development of ocular motor CNP.
Furthermore, HRs and 95% confidence intervals (CIs) of CNP development according to each group, were estimated using Cox proportional-hazard models to analyze the association between variables including categorical variables such as age (< 65, ≥ 65 year old), body mass index (BMI) (< 18.5, 18.5 ~ 23, 23 ~ 25, 25 ~ 30, and > 30 kg/m2), smoking status (never smoked, former smoker, and current smoker), alcohol consumption (never drinker, mild drinker, and heavy drinker), socioeconomic status (SES) (Medical aid and Q1: lower 25%, Q2: lower 25 ~ 50%, Q3: 50 ~ upper 75%, and Q4: upper 75 ~ 100%), menarche age (< 12, < 14, <16, and > 16 years old), menopause age (< 40, < 45, <50, < 55, and > 55 years old), oral contraceptive use history (no, < 1 year, and > 1year), parity (0, 1, and > 2 times), feeding duration (never, <6months, 6months ~ 1year, and > 1year) and continuous variables such as age, fasting plasma glucose (FPG), systolic blood pressure (SBP), diastolic blood pressure (DBP), BMI (average), waist circumference. The cumulative incidence of CNP in each group was also calculated using the Kaplan-Meier curve.
All statistical analyses were carried out using SAS software (version 9.4; SAS Institute Inc., Cary, NC, USA). P-values < .05 were considered statistically significant
We followed up 4,067,842 eligible subjects. Of eligible subjects, 5,835 (0.14) developed ocular motor CNP. They were assigned to the CNP group, while the remaining 4,062,007 subjects were assigned to the control group (Fig. 1). The mean observation period was 6.3 years. Among all of the subjects, there were 3,713,183 in the nondiabetes group, 921,362 in the IFG group, 121,057 in the newly detected diabetes group, 119,982 in the diabetes duration < 5 years group, and 113,620 in the diabetes duration ≥ 5 years group. Table 1 shows the baseline characteristics of the subjects categorized by the glycemic status. Of 4,067,842 patients, the proportion of males was 51.8% in the nondiabetes group, 62.2% in the IFG group, 71.3% the newly detected diabetes group, 58.4% in the diabetes duration < 5 years group, and 55% in the diabetes duration ≥ 5 years group. Other comorbidities like hypertension, dyslipidemia, chronic kidney disease were significantly more common in diabetic patients. Among them, those diagnosed with hypertension accounted for 20.32% in the nondiabetes group, 34.37% in the IFG group, 44.96% in the newly detected diabetes group, 64.96% in the diabetes duration < 5 years group, and 69.51% in the diabetes duration ≥ 5 years group. The longer the onset period was, the higher the percentage of hypertension diagnosed (p < 0.0001). The characteristics of subjects not included in this study are also summarized Supplementary Table 1. Age, sex, smoking status, the presence of chronic kidney disease or dyslipidemia were significantly associated with the incident CNP.
| No diabetes (n = 2,791,821) | IFG (n = 921,362) | New onset (n = 121,057) | Diabetes duration < 5yrs (n = 119,982) | Diabetes duration ≥ 5yrs (n = 113,620) | p value | |
|---|---|---|---|---|---|---|
| Age (years) | 44.9 ± 13.86 | 49.68 ± 13.29 | 51.85 ± 12.65 | 58.32 ± 11.09 | 61.93 ± 9.94 | < 0.0001 |
| Sex (male) | 1,446,061 (51.8) | 573,133 (62.2) | 86,318 (71.3) | 70,069 (58.4) | 62,492 (55) | < 0.0001 |
| aHypertension | 567,227 (20.32) | 316,691 (34.37) | 54,432 (44.96) | 77,944 (64.96) | 78,976 (69.51) | < 0.0001 |
| SBP (mmHg) | 121.19 ± 129.02 | 123.95 ± 193.49 | 119.9 ± 135.63 | 111.84 ± 105.25 | 107.69 ± 82.11 | < 0.0001 |
| DBP (mmHg) | 120.48 ± 14.46 | 125.8 ± 15.08 | 129.8 ± 16.02 | 128.7 ± 15.6 | 129 ± 15.93 | < 0.0001 |
| CKD | 301,280 (10.79) | 121,843 (13.22) | 17,113 (14.14) | 25,489 (21.24) | 36,804 (32.39) | < 0.0001 |
| bDyslipidemia | 388,725 (13.92) | 205,098 (22.26) | 34,413 (28.43) | 57,889 (48.25) | 53,397 (47) | < 0.0001 |
| Cholesterol | 192.7 ± 39.02 | 202.12 ± 43.83 | 207.91 ± 47.49 | 195.75 ± 47.03 | 188.62 ± 46.34 | < 0.0001 |
| HDL | 75.29 ± 9.82 | 78.34 ± 10.12 | 80.58 ± 10.52 | 79.08 ± 10.07 | 77.64 ± 10 | < 0.0001 |
| LDL | 57.24 ± 33.22 | 55.71 ± 32.3 | 53.86 ± 30.1 | 52.41 ± 31.67 | 52.03 ± 32.23 | < 0.0001 |
| TG | 104.58 (104.52, 104.65) | 127.74 (127.59, 127.89) | 160.77 (160.21, 161.34) | 149.9 (149.42, 150.39) | 138.38 (137.93, 138.83) | < 0.0001 |
| Income, low | 492,182 (17.63) | 152,779 (16.58) | 22,328 (18.44) | 22,082 (18.4) | 20,168 (17.75) | < 0.0001 |
| cBMI | 23.33 ± 3.15 | 24.35 ± 3.19 | 25.01 ± 3.45 | 25.36 ± 3.33 | 24.67 ± 3.14 | < 0.0001 |
| Waist Circum. | 78.88 ± 9.32 | 82.4 ± 8.97 | 85.01 ± 9.23 | 86.17 ± 9 | 85.29 ± 8.49 | < 0.0001 |
| Smoking | < 0.0001 | |||||
| Non | 1,716,940 (61.5) | 501,864 (54.47) | 56,595 (46.75) | 69,048 (57.55) | 71,298 (62.75) | |
| Ex | 357,845 (12.82) | 162,725 (17.66) | 22,204 (18.34) | 22,516 (18.77) | 20,446 (18) | |
| Current | 717,036 (25.68) | 256,773 (27.87) | 42,258 (34.91) | 28,418 (23.69) | 21,876 (19.25) | |
| Drinker | < 0.0001 | |||||
| Non | 1,455,661 (52.14) | 436,775 (47.41) | 52,296 (43.2) | 72,915 (60.77) | 76,246 (67.11) | |
| Mild | 1,144,658 (41) | 388,021 (42.11) | 51,496 (42.54) | 36,228 (30.19) | 29,613 (26.06) | |
| dHeavy | 191,502 (6.86) | 96,566 (10.48) | 17,265 (14.26) | 10,839 (9.03) | 7,761 (6.83) | |
| eRegular Exercise | 484,653 (17.36) | 176,060 (19.11) | 23,005 (19) | 26,596 (22.17) | 27,785 (24.45) | < 0.0001 |
| n = number, IFG = impaired fasting glucose, Ref. = reference, yrs = years, SBP = systolic blood pressure, DBP = diastolic blood pressure, CKD = chronic kidney disease, HDL = high density lipoprotein, LDL = low density lipoprotein, TG = triglycerides, WC = waist circumference. | ||||||
| aHypertension was defined as being prescribed at least once per year for antihypertensive drugs under ICD-10-CM codes I10-I13, I15, or BP ≥ 140/90 mmHg. | ||||||
| bDyslipidemia: Dyslipidemia was defined as a total cholesterol level of 240 mg/dL or higher, or at least once per year for prescription of a lipid-lowering drug under ICD-10-CM code E78. | ||||||
| cBMI: BMI was calculated as weight (kg) divided by height (m) squared. | ||||||
| dDrinking amount: Individuals who consumed more than 30 g of alcohol per day were defined as heavy drinkers. | ||||||
| eRegular exercise: Regular physical activity was defined as an individual doing high-intensity exercise for at least 20 minutes three times per week or at least 30 minutes of moderate-intensity exercise five times per week. | ||||||
When the subjects were compared according to the glycemic status, the incidence rate of CNP was 1.42 times higher in the IFG group, 2.63 times higher in the newly detected diabetes group, 3.51 times higher in the diabetes duration < 5 years group, and 5.19 times higher in the diabetes duration ≥ 5 years group compared to the nondiabetes group (all p < 0.001) (Table 2).
| Glycemic status | Patients (n) | CNP events | Rate | HR (95% CI) | ||
|---|---|---|---|---|---|---|
| Model 1* | Model 2 ** | Model 3 *** | ||||
| No | 2,791,821 | 3038 | 0.1318 | 1 (Ref.) | 1 (Ref.) | 1 (Ref.) |
| IFG | 921,362 | 1415 | 0.18744 | 1.423 (1.336,1.516) | 1.12 (1.051,1.193) | 1.104 (1.035,1.177) |
| New onset | 121,057 | 337 | 0.34585 | 2.631 (2.351,2.944) | 1.838 (1.641,2.058) | 1.793 (1.6,2.009) |
| < 5yrs | 119,982 | 443 | 0.46159 | 3.505 (3.172,3.873) | 2.052 (1.854,2.271) | 1.959 (1.768,2.169) |
| ≥ 5yrs | 113620 | 602 | 0.6803 | 5.185 (4.751,5.659) | 2.692 (2.459,2.946) | 2.606 (2.38,2.854) |
| CNP = cranial nerve palsy, n = number, IFG = impaired fasting glucose, Ref. = reference, yrs = years. | ||||||
| Incidence rate per 1000. | ||||||
| * Model 1: Non-adjusted; ** Model 2: adjusted for age and sex; *** Model 3; adjusted for age, sex, smoking, drinking, physical activity and BMI. | ||||||
The adjusted HRs (aHRs) for major clinical variables showed consistent results. In the analysis of the five subgroups, the aHR for the IFG group was 1.12 (95% CI 1.051–1.193)) in model 2 and 1.104 (95% CI 1.035–1.177) in model 3. The aHR for the newly detected diabetes group was 1.838 (95% CI 1.641–2.058) in model 2 and 1.793 (95% CI 1.6–2.009) in model 3, whereas the aHR for the diabetes duration < 5 years group was 2.052 (95% CI 1.854–2.271) in model 2 and 1.959 (95% CI 1.768–2.169) in model 3. At last, aHR for the diabetes duration ≥ 5 years group was 2.692 (95% CI 2.459–2.946) in model 2 and 2.606 (95% CI 2.38–2.854) in model 3.
The risk of ocular motor CNP in subgroup analysis according to age, sex, smoking status, and the presence of hypertension
Supplementary Table 1 shows the incidence rates and multivariable-adjusted HRs for the development of ocular motor CNP according to age, sex, smoking status, and the presence of hypertension. The diabetes duration ≥ 5 years group with relatively younger age less than 65 years (HR 3.227, 95% CI 2.881, 3.615) (Fig. 2), male gender (HR 2.860, 95% CI 2.564, 3.190) (Fig. 3), current smoking (HR 3.134, 95% 2.592, 3.790) (Supplementary Fig. 1), and without hypertension (HR 2.976, 95% CI 2.554, 3.466) (Fig. 4) showed higher risk for ocular motor CNP development compared to groups without each of them.
The cumulative incidence of CNP in each group was calculated using the Kaplan-Meier curve (Fig. 5). The log-rank test also showed a significant difference in the incidence of CNP between the groups (p < 0.001).
In this retrospective nationwide population-based cohort study, the incidence of ocular motor CNP among Koreans significantly increased according to the glycemic status (nondiabetes, IFG, newly detected, duration < 5 years, and duration ≥ 5 years groups). In particular, the results showed that the risk of ocular motor CNP was significantly increased even in subjects with IFG compared to non-diabetes subjects. The term “prediabetes” was adopted to identify the condition in which blood glucose levels are above normal but below that of DM [25, 26]. It is reported that up to 70% of individuals with prediabetes develop type 2 DM [27]. In addition, prediabetes itself is related to other conditions including obesity and hypertension [28]. The possibility of involvement of small unmyelinated nerve fibers in prediabetes was also previously reported, which was expressed as a dysfunction of cardiac autonomic activity and impairment of nerve fibers associated with pain and temperature [29]. Also, in the Diabetes Prevention Program (DPP) study, which was a cross-sectional study on diabetic patients, 8% of patients with prediabetes had vasculopathy such as retinopathy and coronary heart disease [29, 30]. Our study suggested that prediabetic status is also related to increased risk of ocular motor CNP. Intensive lifestyle interventions are reported to have a beneficial effect for slowing the rate of progression to diabetes in prediabetic patients in previous studies [30, 31]. Currently, it is debatable whether or not drug treatment should be initiated in prediabetes [21, 29, 30, 32]. Despite controversies over the guidelines relating to the criteria for prediabetes [21] and pharmacotherapy [32], studies suggested the importance of preventive action in prediabetic status to lessen the possibility of progression of diabetes (5–10%) and its complications [21, 30, 32]. Our study is the first to report on the association between prediabetes and ocular motor CNP, which suggests that prediabetes can cause peripheral neuropathies relating to ocular movement and early treatment in prediabetic patients can potentially reduce the risk of this type of complication. Further prospective studies are needed to clarify the effect of the treatment in this group of patients.
In this study, the risk of ocular motor CNP was higher with longer diabetes duration. These results were consistent after adjusting for various potential confounders. We found that individuals with prediabetes group (IFG) had a 1.10 times higher risk of developing CNP compared with nondiabetic subjects, while the newly detected group had a 1.79 times higher, within 5 years of diabetes group had a 1.96 times higher, and 5 years or more group had a 2.60 times higher risk of developing ocular motor CNP. It is well known that longer diabetes duration is a major risk factor for diabetic complications such as macrovascular and microvascular events [15, 19, 20]. Since the majority of acquired ocular motor CNP is also a microvascular ischemic disorder [6, 12, 20], it can be assumed that the duration of diabetes also increases the risk of this type of neuropathy. Stephenson et al. analyzed the risk factor of diabetic peripheral neuropathy and revealed that in the case of long duration of DM, the risk of peripheral neuropathy was significantly increased [33]. Specifically, people diagnosed with diabetes at a younger age are expected to have a longer lifetime risk of these cranial neuropathies, suggesting the need for intensive glycemic management and optimization of other risk factors.
Although the exact pathophysiology of diabetic neuropathy is not fully understood, numerous hypotheses have been proposed [34–36]. In hyperglycemic status, nerve perfusion is reduced [37, 38]. Hyalinization of the basal lamina and increased wall thickness of the blood vessels supplying the peripheral nerve induce nerve ischemia [36–38]. In addition, various mechanisms such as polyol pathway hyperactivity, microglial activation, sodium and calcium channel expression change, and oxidative stress have been found to induce metabolic disorders in the nerve itself [34, 36]. Based on this, we hypothesized that those mechanisms may also cause cranial nerve disorder.
In our study, we found that diabetic patients with young age, male gender, smoking, and hypertension showed a higher risk of ocular motor CNP compared to those without them. According to Eman et al., who analyzed clinical data from The Saudi National Diabetes Registry (SNDR), the most significant risk factors for CNP in diabetic patients were age ≥ 45 years, diabetes duration ≥ 10 years, and male gender, while smoking and hypertension were not significant [19]. In the same contexts with Saudi, in the Caucasian diabetic patients, male gender was an independent risk factor for CNP [37]. Another study also reported that males developed neuropathic complications at 63 years, approximately 4 years earlier than did females (at 67 years) [38]. While DM and HTN are known risk factors of CNP, several studies revealed that HTN did not increase the risk of CNP significantly. Patel et al. reported that patients with DM were six times more likely to have 6th CNP, while there was no significant association between HTN and 6th CNP [39]. Jacobson et al. suggested that left ventricular hypertrophy, one of the peripheral organ injuries, was considered to be an independent risk factor for CNP, rather than HTN itself [11]. Further research on the exact pathomechanisms underlying the association between various risk factors and the development of CNP is required.
This study has several limitations. First, as this study has a retrospective design, the causal relationship between the diseases cannot be confirmed. Second, due to the nature of the data, patients who did not use health care services, and patients whose physicians registered incorrect diagnostic codes could not be counted. However, the acute nature of the symptom and its significant impact on the visual function caused by ocular motor CNP might lead the majority of patients to use healthcare services.
In conclusion, this population-based study showed that the incidence of ocular motor CNP increased according to glycemic status (non-diabetes, IFG, new-onset, within 5 years and over 5 years groups) and the longer duration of diabetes increases the likelihood of this complication. Also, prediabetic people, who are below the diagnostic threshold for diabetes were at more risk of cranial neuropathy, compared with normal glycemic status. Relatively younger age, male gender, and smoking patients could have a higher risk of developing ocular motor CNP related to diabetes or prediabetes.
Financial Support
This research was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (No. NRF-2021R1A2C1007718).
Proprietary Interest or Competing Interest
None of the authors have financial disclosure.
Supplementary Table 1 is not available with this version