ORIGINAL RESEARCH published: 18 January 2022 doi: 10.3389/fmed.2021.800973 Associations Between Diabetic Retinopathy and Parkinson’s Disease: Results From the Catalonian Primary Care Cohort Study Didac Mauricio 1,2,3,4*† , Bogdan Vlacho 1† , Joan Barrot de la Puente 1,5 , Xavier Mundet-Tudurí 1,6 , Jordi Real 1,4 , Jaime Kulisevsky 7 , Emilio Ortega 1,8,9 , Esmeralda Castelblanco 1,10 , Josep Julve 11* and Josep Franch-Nadal 1,4,12 1 DAP-Cat Group, Unitat de Suport a la Recerca Barcelona, Fundació Institut Universitari per a la recerca a l’Atenció Primària de Salut Jordi Gol i Gurina, Barcelona, Spain, 2 Departament of Medicine, University of Vic-Central University of Catalonia, Edited by: Catalonia, Spain, 3 Department of Endocrinology and Nutrition, Hospital de la Santa Creu i Sant Pau, Barcelona, Spain, 4 Anna Maria Roszkowska, CIBER of Diabetes and Associated Metabolic Diseases, Instituto de Salud Carlos III, Madrid, Spain, 5 Primary Health Care University of Messina, Italy Center Dr. Jordi Nadal i Fàbregas (Salt), Gerència d’Atenció Primaria, Institut Català de la Salut, Girona, Spain, 6 Faculty of Medicine, Department of Medicine, Autonomous University of Barcelona, Barcelona, Spain, 7 Movement Disorders Unit, Reviewed by: Neurology Department, Hospital de la Santa Creu i Sant Pau, Barcelona, Spain, 8 Department of Endocrinology and Nutrition, Weiping Jia, Institut d’Investigacions Biomèdiques August Pi i Suñer, Hospital Clinic, Barcelona, Spain, 9 CIBER of Physiopathology of Shanghai Sixth People’s Obesity and Nutrition, Instituto de Salud Carlos III, Madrid, Spain, 10 Division of Endocrinology, Metabolism and Lipid Hospital, China Research, Washington University School of Medicine in St. Louis, St. Louis, MO, United States, 11 Institut de Recerca de Antonio Ferrante, l’Hospital de la Santa Creu i Sant Pau, Barcelona, Spain, 12 Primary Health Care Center Raval Sud, Gerència d’Atenció South Australia Pathology, Australia Primaria, Institut Català de la Salut, Barcelona, Spain *Correspondence: Didac Mauricio didacmauricio@gmail.com The purpose of this study was to assess the risk of occurrence of Parkinson’s disease Josep Julve (PD) among subjects with type 2 diabetes and diabetic retinopathy (DR) in our large JJulve@santpau.cat primary health care database from Catalonia (Spain). A retrospective cohort study with † These authors have contributed pseudo-anonymized routinely collected health data from SIDIAP was conducted from equally to this work and share first authorship 2008 to 2016. We calculated the number of events, time to event, cumulative incidence, and incidence rates of PD for subjects with and without DR and for different stages of Specialty section: DR. The proportional hazards regression analysis was done to assess the probability This article was submitted to Ophthalmology, of occurrence between DR and PD. In total, 26,453 type 2 diabetic subjects with DR a section of the journal were identified in the database, and 216,250 subjects without DR at inclusion. During Frontiers in Medicine the follow-up period, 1,748 PD events occurred. PD incidence rate and cumulative Received: 24 October 2021 incidence were higher among subjects with DR (16.95 per 10,000 person-years and Accepted: 22 December 2021 Published: 18 January 2022 0.83%, respectively). In the unadjusted analysis, subjects with DR were at 1.25 times Citation: higher risk (hazard ratio: 1.22, 95% confidence interval: 1.06; 1.41) of developing PD Mauricio D, Vlacho B, Barrot de la during the study period. However, we did not find any statistically significant HR for DR Puente J, Mundet-Tudurí X, Real J, Kulisevsky J, Ortega E, in any models after adjusting for different risk factors (age, sex, duration of diabetes, Castelblanco E, Julve J and smoking, body mass index, glycosylated hemoglobin, comorbidities). In conclusion, in Franch-Nadal J (2022) Associations our primary health care population database, DR was not associated with an increased Between Diabetic Retinopathy and Parkinson’s Disease: Results From the risk of PD after adjusting for different risk factors. In our retrospective cohort study, age, Catalonian Primary Care Cohort male sex, and diabetes duration were independent risk factors for developing PD. Study. Front. Med. 8:800973. doi: 10.3389/fmed.2021.800973 Keywords: age, diabetic retinopathy, diabetes type 2, Parkinson’s disease, primary care, real world data (RWD) Frontiers in Medicine | www.frontiersin.org 1 January 2022 | Volume 8 | Article 800973 Mauricio et al. Diabetic Retinopathy and Parkinson’s Disease INTRODUCTION possible effect of survivor bias among the patients with diabetes may interfere with the results (7). Diabetes mellitus (DM) is a metabolic disorder that may result Evidence suggests that DR and PD share similar in an unfavorable impact in different organs and leading to pathophysiological characteristics and mechanisms (dopamine numerous complications (1). The complications of diabetes reduction, increased α-Synuclein expression, and abnormal are generally classified as macrovascular and microvascular. neurotrophic factors expression) related to disrupted dopamine Large blood vessels are affected by macrovascular complications, activity since both brain and retina express D1-like and D2-like and depending on the location, three types of complications dopamine receptors (17). The phosphorylation of α-Synuclein exist: coronary artery disease, peripheral artery disease, and as a result of the dopamine in abnormal regulation in the cerebrovascular disease. On the other hand, microvascular retinal layers may be a reason for neurodegeneration in the complications affect blood microvessels, leading to complications retina and brain (18). Therefore, a close association between such as diabetic peripheral neuropathy, diabetic nephropathy, pathophysiological mechanisms of DR and PD may be expected. and diabetic retinopathy (DR). However, unlike classical vascular To our knowledge, there is only one large population database complications, DM may affect almost every organ system and study from South Korea aimed to investigate this association. damage other tissues or cell types (1). In addition to vascular The authors found that the incidence of PD was higher among tissue, damage to non-vascular tissue also happens in DR, DM subjects and even higher among subjects with DM and DR; one of the most common complications among subjects with however, they acknowledged that important variables related to DM. Due to the retina’s particular neurovascular structure, the DM were not available for the analysis, such as duration of neuro-dysfunction and neurodegeneration caused by DM is diabetes, and glycated hemoglobin (19). On this background, we an important component of this complication. For example, undertook the current study to assess the risk of occurrence of PD it was previously reported that different neuronal damage and among subjects with DR in our large primary health care database functional changes could occur in the retina due to poor DM from Catalonia (Spain). control. These changes include loss of dendrites and synaptic activity, neural apoptosis, thinning of the inner retina, ganglion MATERIALS AND METHODS cell loss, reactive microglial activation, and deficits in the retina’s electrophysiological activity, dark adaptation, contrast Study Design and Data Source sensitivity, or color vision (2, 3). Overall, the complications We used a retrospective cohort of subjects with T2DM attended of diabetes are far from vascular only, and there are a lot of in primary health care centers from the Catalonian Health other non-classical chronic complications of diabetes, including Institute–ICS, using the pseudo-anonymized routinely collected neurodegenerative complications (1). health data from the SIDIAP (Sistema d’Informació per al Parkinson’s disease (PD) is the second most common desenvolupament de la Investigació en Atenció Primària) neurodegenerative disease. Generally, this chronic disease is database. This database is a well-validated data source for the more prevalent among older adults and Hispanic people (4). study of diabetes in Spain (20, 21), collecting different data related So far, many risk factors have been reported to be related to to health problems, clinical and diagnostic procedures, laboratory PD, such as pesticides, dietary factors, melanoma, traumatic parameters, and information on medication prescribed and brain injury, and diabetes (4–7). The role of DM as a dispensed. The data were collected for the period between risk factor for neurodegenerative diseases involves different January 1, 2008, and December 31, 2016. pathways of cellular metabolic injury such as impaired insulin signaling and inflammatory and oxidative stress, which can Definition of Eligibility Criteria lead to mitochondrial dysfunction, neuroinflammation, synaptic We included all subjects aged 30 years or above, with a register of plasticity and other neuronal dysfunction and degeneration (8). T2DM defined as the presence of relevant ICD-10 (International So far, different studies have been conducted to evaluate the Classification of Diseases, 10th Revision) diagnostic codes and risk of DM on PD. In cohort studies from Finland, Denmark, sub-codes (E11 and E14). Subjects with diagnostic codes for other UK, Taiwan, and South Korea (9–14), type 2 diabetes mellitus types of diabetes (type 1, gestational or other) or without T2DM (T2DM) was a risk factor for PD. However, no such associations codes were excluded from the analysis. Those subjects with were found in cohort studies in the US (15, 16). preexisting primary and secondary Parkinson’s disease (ICD-10: The results related to the association between DM and PD G20 and G21) were also excluded from the study population. reported in meta-analyses have also been heterogeneous. In one meta-analysis with 11 observational studies (four cohort and Definition of Variables seven case-control studies) on the association of diabetes as a risk At inclusion, variables related to DR and clinical characteristics factor for PD, the authors concluded that diabetes appears to be a of the subjects were collected. DR was defined as the presence risk factor for PD (6). Another meta-analysis with observational of diagnostic codes and sub-codes (ICD-10: E11.3, E14.3, studies indicated that T2DM increases the risk of future PD; and H36) and/or abnormal (pathologic) results for fundus however, no associations were found when the authors changed photography. In those with available fundus photography data, the exposure definition to any type of diabetes (7). Moreover, DR was stratified in different stages using the Early Treatment the authors pointed out that the association between DM and Diabetic Retinopathy Study (ETDRS) classification: no apparent PD tended to change depending on the study design since a retinopathy (NDR), mild non-proliferative retinopathy (NPDR), Frontiers in Medicine | www.frontiersin.org 2 January 2022 | Volume 8 | Article 800973 Mauricio et al. Diabetic Retinopathy and Parkinson’s Disease TABLE 1 | Code list. Statistical Analysis For the variables collected at inclusion, we used descriptive Variable Definition statistics. The number and frequencies for the qualitative Codes used for definition of the study variables variables were calculated, while we estimated the means and Type 2 diabetes ICD-10-CM Codes: E11.xx; E14.xx standard deviation for the quantitative variables. mellitus At follow-up, for the primary study event (PD), we Cardiovascular ICD-10-CM Codes:I21.xx; I22.xx; I23.xx; calculated the number of subjects and events for each group diseases I25.xx; G45.xx; G46.xx; I60.xx; I61.xx (presence/absence of DR and each stage of DR), time to I62.xx; I63.xx; I64.xx event (time to a diagnosis of PD after a diagnosis of DR), Diabetic retinopathy ICD-10-CM Codes: E11.3; E14.3; cumulative incidence and incidence rates (person/year). To H36;H36.0;H36.8 and/or fundus assess the probability of occurrence between DR and PD, we photography: mild non-proliferative retinopathy (NPDR), moderate NPDR, used proportional hazards regression analysis. The hazard ratios severe NPDR, proliferative diabetic (HR) for the primary outcome event were calculated with retinopathy (PRD), and diabetic macular corresponding 95% confidence intervals (CI), and statistical edema (DME) significance was established as a p < 0.05. Additionally, adjusted Dyslipidemia ICD-10-CM Codes: E78;E78.9 and/or HRs were calculated using different clinically important variables Lipid-lowering drugs as risk factors. Gradually we added different risk factors to the Hypertension ICD-10-CM Codes: 10 and/or model, starting with age and sex in the first model, adding T2DM Antihypertensive agents duration, smoking status, hypertension, dyslipidemia, and BMI Parkinson’s disease as ICD-10-CM Codes: G20 event in the second model, and adding CKD, CVD, and HbA1c in the Parkinson’s disease as ICD-10-CM Codes: G20;G21.xx third model. Moreover, to assess the effect of diabetes duration exclusion criteria and HbA1c on the association of DR with risk of incident PD, Chronic kidney disease CKD-EPI glomerular filtration rate <60 we performed additional models with different combinations ml/min/1.73 m2 and or albumin/creatinine of these two variables with the other relevant variables from ratio>30 mg/g the first model. We also performed a sensitivity analysis with Antithrombotic agents ATC/DDD codes: B01A the estimates from different models and, also, stratification for Antihypertensive ATC/DDD codes: C02; C03; C07; C08; diabetes duration and HbA1c. Data management and all analyses agents C09 were performed using R statistical software, version 3.6.1. Antidiabetics agents ATC/DDD codes: A10 Lipid-Lowering agents ATC/DDD codes: C10 Institutional Review Board Statement xx, sub codes. The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Institutional Review Board (or Ethics Committee) of IDIAP Jordi Gol i Gurina Foundation (protocol code P13/028 and date of moderate NPDR, severe NPDR, proliferative diabetic retinopathy approval 03/04/2013). (PRD), and diabetic macular edema (DME) (22). We also collected variables related to sociodemographic characteristics RESULTS (age, sex) and toxic habits (tobacco use). Duration of T2DM was calculated. Due to the under-reporting in our database, Subjects Characteristics dyslipidemia and hypertension were identified as a combination From 2008 until 2016, 250,363 subjects with T2DM were of diagnostic code and/or treatment for these diseases. In identified in the SIDIAP database. We excluded 1,850 individuals addition, chronic kidney disease (CKD) was defined as a who had a previous diagnostic category of PD and 5,838 combination of CKD-EPI glomerular filtration rate <60 individuals without T2DM diagnostic codes. In total, 216,250 ml/min/1.73 m2 and/or an albumin/creatinine ratio >30 mg/g. subjects did not have DR at baseline, while 26,453 had DR by To identify cardiovascular disease, we used diagnostic codes diagnostic code and/or diagnosis by fundus photography. The alone. Moreover, concomitant medication (antihypertensive, study flowchart is presented in Figure 1. antiplatelet, lipid-lowering, antidiabetic drugs), laboratory The clinical characteristics of the cohort and different groups parameters [lipid profile, renal profile, glycated hemoglobin at inclusion are presented in Table 2. The mean age of the (HbA1c)], and clinical variables [systolic and diastolic blood study subjects was 65.3 years. There were more males (57.5%), pressure, body mass index (BMI)] were collected. The detailed and the average duration of T2DM was 5.35 years. There information on the different codes used in defining the study were differences between the study groups regarding age, variables is included as Table 1 code list. comorbidities, laboratory parameters, and concomitant drug use. During follow-up, we collected data related to PD as a primary Subjects with DR were, on average, 2.7 years older and with study event. PD was defined as the presence of a diagnostic code more comorbidity, especially cardiovascular diseases and CKD, for Parkinson’s disease (ICD-10: G20). The follow-up period was compared with those without DR. We also observed higher mean defined as the time between the inclusion in the study and the values for HbA1c, lower glomerular filtration ratios, and slightly primary study event. lower BMI among subjects with DR. Regarding concomitant Frontiers in Medicine | www.frontiersin.org 3 January 2022 | Volume 8 | Article 800973 Mauricio et al. Diabetic Retinopathy and Parkinson’s Disease FIGURE 1 | Study flowchart. drug use, higher percentages were also observed for all drug person-years and 0.99%, respectively). High incidence rate and classes of interest in the DR group. cumulative incidence were observed among individuals with DR identified by diagnostic code but without grading of DR according to fundus photography (27.77 per 10,000 person-years Parkinson’s Disease Incidence Among the and 1.33%, respectively). Groups Information on the time of follow-up, events, and cumulative Factors Predicting Parkinson’s Disease incidence are presented in Table 3. The average time to the We observed statistically significant un-adjusted HR (Unadj-HR) event among study subjects was 4.8 years. In total, 1,748 PD for the primary study event between the groups (Table 3). The events occurred, with an incidence rate of 14.25 per 10,000 subjects with DR had a 1.22 times higher risk of developing person-years and a cumulative incidence of 0.72%. PD incidence PD during the study period. Additionally, we also calculated and cumulative incidence were higher among subjects with DR the Unadj-HR considering the stage of DR. The highest Unadj- (16.95 per 10,000 person-years and 0.82%, respectively). When HR was observed among the subjects with moderate NPDR. the different stages of DR were compared, the highest incidence These subjects were at a 1.50 times higher risk of developing PD rate and cumulative incidence of PD was observed among those compared with those without DR. Further, a higher risk (Unadj- with moderate non-proliferative retinopathy (20.73 per 10,000 HR: 2.02) of developing PD was observed for the group with Frontiers in Medicine | www.frontiersin.org 4 January 2022 | Volume 8 | Article 800973 Mauricio et al. Diabetic Retinopathy and Parkinson’s Disease TABLE 2 | Clinical characteristics of the subjects at study inclusion. All subjects Group without DR Group with DR p-value N = 242,703 N = 216,250 N = 26,453 Age, mean (SD), years 65.3 (11.5) 65.0 (11.5) 67.7 (11.5) <0.001 Sex (male), n (%) 142,749 (57.4) 127,534 (57.5) 15,215 (56.7) Smoking habit, n (%) <0.001 No smoker 140,585 (57.9) 124,530 (57.6) 16,055 (60.7) Ex-smoker 32,843 (13.5) 29,782 (13.8) 3,061 (11.6) Current smoker 69,275 (28.5) 61,938 (28.6) 7,337 (27.7) Comorbidities, n (%) Dyslipidemia 125,617 (51.8) 111,117 (51.4) 14,500 (54.8) <0.001 Hypertension 155,115 (63.9) 136,395 (63.1) 18,720 (70.8) <0.001 Cardiovascular diseases 30,014 (12.4) 25,424 (11.8) 4,590 (17.4) <0.001 Chronic kidney disease 37,804 (15.6) 31,529 (14.6) 6,275 (23.7) 0.000 Clinical variables, mean, (SD) Diabetes duration, (years) 5.35 (5.39) 4.98 (5.04) 8.41 (6.92) 0.000 BMI (kg/m2 ) 30.6 (5.16) 30.6 (5.15) 30.2 (5.24) <0.001 SBP (mmHg) 134 (14.9) 134 (14.7) 137 (16.3) <0.001 DBP (mmHg) 76.7 (9.72) 76.9 (9.62) 75.4 (10.4) <0.001 Laboratory parameters, mean, (SD) HbA1c (%) 7.18 (1.52) 7.12 (1.48) 7.74 (1.72) 0.000 HbA1c (mmol/mol) 55.0 (16.6) 54.3 (16.2) 61.1 (18.8) Total cholesterol (mg/dl) 195 (41.1) 195 (40.9) 188 (42.5) <0.001 HDL cholesterol (mg/dl) 48.6 (12.8) 48.6 (12.8) 48.8 (13.2) 0.005 LDL cholesterol (mg/dl) 114 (34.3) 115 (34.2) 109 (34.9) <0.001 Triglycerides (mg/dl) 167 (121) 168 (123) 159 (109) <0.001 Creatinine (mg/dl) 0.91 (0.30) 0.90 (0.28) 0.96 (0.43) <0.001 Albumin / Creatinine ratio 35.9 (142) 31.2 (123) 74.0 (239) <0.001 (mg/g) Glomerular filtration 76.7 (20.5) 77.5 (20.1) 69.4 (22.8) <0.001 (ml/min/1.73 m2 ) Concomitant medications, n (%) Antithrombotic 76,507 (31.5) 64,823 (30.0) 11,684 (44.2) 0.000 Antihypertensive 154,166 (63.5) 135,576 (62.7) 18,590 (70.3) <0.001 Antidiabetics 181,237 (74.7) 158,665 (73.4) 22,572 (85.3) 0.000 Lipid-lowering 123,969 (51.1) 109,697 (50.7) 14,272 (54.0) <0.001 BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; HbA1c, glycosylated hemoglobin; SD, standard deviation. DR identified by diagnostic code, but without grading of DR factors for PD in these models. Table 5 shows the results of these compared with the group without DR. additional models. Figure 2 and Table 4 show the results of the different multivariable proportional hazards analysis models. In the proportional hazards analysis adjusting the models for different Sensitivity Analysis risk factors, we did not find any statistically significant HRs for In the sensitivity analysis, stratifying by diabetes duration or DR in any models. Age and male sex were independent risk HbA1c, similar tendencies were observed for the HRs for DR factors in all of the models. T2DM duration was a risk factor in observed in the previously described models. However, having an the second and third models, especially for subjects with T2DM HbA1c between 9.1 and 10% and DR was negatively associated duration of more than 20 years. In contrast, having a BMI over with PD when including additional relevant variables in this 39 kg/m2 and being an ex-smoker decreased the risk of PD. In additional model (age, sex, T2DM duration) or variables in model the additional models, adjusting only for age, sex, and diabetes 3 (age, sex, smoking, T2DM duration, dyslipidemia, CVD, HTA, duration and/or HbA1c, we did not find a significant association CKD, IMC). The results of this sensitivity analysis are shown in of DR with PD. The age, sex and diabetes duration remained risk Table 6 and Figure 3. Frontiers in Medicine | www.frontiersin.org 5 January 2022 | Volume 8 | Article 800973 Mauricio et al. Diabetic Retinopathy and Parkinson’s Disease TABLE 3 | Parkinson’s disease events among the study groups, stage of diabetic retinopathy and un-adjusted hazards ratios. Variable N subjects Person-years Time free from Parkinson’s Incidence rate Cumulative Un-adjusted HR the event (years) disease events per 10000-Year incidence 95% CI [LI; Ul] All subjects 242,703 1226699.67 4.82 1,748 14.25 0.72 – Group without DR 216,250 1097519.23 4.86 1,529 13.93 0.71 Ref Group with DR 26,453 129180.43 4.56 219 16.95 0.83 1.22 [1.06; 1.41] Stage of DR No apparent diabetic 216,250 1097519.23 4.86 1,529 13.93 0.71 Ref retinopathy (NDR) Mild non-proliferative 14,632 72673.13 4.63 109 14.99 0.75 1.08 [0.89; 1.31] diabetic retinopathy (NPDR) Moderate (NPDR) 6,211 29900.12 4.48 62 20.74 0.99 1.50 [1.16; 1.93] Severe (NPDR) 1,517 7669.25 4.91 3 3.91 0.20 0.28 [0.09; 0.87] Proliferative diabetic 837 3736.21 4.11 5 13.38 0.60 0.98 [0.41; 2.35] retinopathy (PRD) Diabetic macular edema 556 2238.91 3.56 4 17.87 0.72 1.33 [0.50; 3.54] (DME) Unknown stage* 2,700 12962.82 4.65 36 27.77 1.33 2.02 [1.45; 2.81] DR, diabetic retinopathy; NDR, no apparent diabetic retinopathy; NPDR, non-proliferative diabetic retinopathy; PRD, proliferative diabetic retinopathy; DME, diabetic macular edema; ref, reference group; HR, hazard ratio; 95% CI, 95% confidence interval; LI, lower limit; Ul, upper limit. *Subjects having diabetic retinopathy by diagnostic code but without fundus photography/stage of DR. DISCUSSION the two studies, the incidence rate was slightly higher among subjects with DR in our study (16.95 vs. 15.51 per 10,000 person- The results from our retrospective cohort study from a primary years, respectively). Comparing groups without DR between care database from 2008 until 2016 showed no increased the two studies, we observed a higher incidence rate than in risk for developing PD among subjects with previous diabetic the South Korea study (13.93 vs. 8.39 per 10,000 person-years, retinopathy in fully adjusted models. Instead, age, male gender, respectively). An explanation for this difference could be that our and longer T2DM duration conferred an increased risk of PD non-DR population was older than the South Korean population, over time independently among T2DM subjects. and it is well known that PD increases rapidly with age (4). In To date only one similar observational study with routinely general, our T2DM population was relatively older, and we had collected health data from South Korea investigated the smaller differences between the groups in age (with or without relationship between T2DM retinopathy and PD (16). DR). In particular, this could also explain the differences in the Comparing the clinical characteristics of the DR subjects in unadjusted HR observed in our study (Unadj-HR: 1.22 95% both studies, subjects from our study were on average 6.7 CI: 1.06; 1.41) compared with the South Korean study, where years older, with a higher proportion of males (8.5%), 14.3% more pronounced differences in un-adjusted HR were observed more smokers, and were also more obese (average difference (Unadj-HR: 5.72, 95% CI: 5.41; 6.05) (19). For our definition 5.7 kg/m2 ). The differences found between subjects with DR of DR, we intentionally included fundus photography, which from the two studies are not surprising. It is well reported is a gold standard screening method with a high sensitivity to that the characteristics of eastern Asian people with T2DM are detect DR, which could prevent possible misclassification of different from European T2DM subjects (23). In Asian countries, this condition (25). Possible overestimation of DR due to the T2DM subjects are younger and have a lower BMI than those utilization of only one diagnostic code related to DR (ICD-10: from the US or European countries. Compared to Caucasians, H36) in the South Korean study could be one of the reasons Asians have more lipotoxicity and insulin resistance due to the for significant associations found between DR and PD in the greater visceral adiposity, which is more metabolically adverse multivariable proportional hazards regression analysis models. (23). People with T2DM from Asia also tend to have a higher In our multivariable model, besides basic clinical factors such incidence of renal complications and ischemic strokes but lower as age and sex, we evaluated the effect of duration of T2DM, coronary heart disease or peripheral arterial disease (24). Our BMI, smoking status and HbA1c. Longer diabetes duration study observed a higher percentage of subjects with CKD in the has been previously established as a strong risk factor for DR group (23.7%). However, these data cannot be compared to microvascular complications, especially DR (26, 27). Indeed, we the South Korea study due differences in the definition of these also observed that T2DM duration was an independent risk variables. For instance, in the South Korean study, end-stage factor for the development of PD in our multivariable model renal disease was defined by diagnostic code, while in our study, analysis. Other similar studies have also reported that diabetes we defined CKD by values of CKD-EPI glomerular filtration rate is an independent risk factor for PD. For example, a recently <60 ml/min/1.73 m2 and/or an albumin/creatinine ratio >30 published observational study with the same database as ours mg/g. Regarding the differences in the events of PD between reported an increased risk of PD among T2DM subjects (adj-HR Frontiers in Medicine | www.frontiersin.org 6 January 2022 | Volume 8 | Article 800973 Mauricio et al. Diabetic Retinopathy and Parkinson’s Disease TABLE 4 | Adjusted hazard ratios for different variables. Model 1 Model 2 Model 3 Predictor HR 95% HR HR 95% CI [LI; UI] 95% CI [LI; Ul] CI [LI; Ul] Group with DR, 1.06 1.01 1.01 ref: Group without 0.92; 1.22 0.87; 1.17 0.88; 1.17 DR Sex (male) 1.35 1.07 1.38 1.22; 1.48 1.07; 1.08 1.24; 1.54 Age (years) 1.08 1.39 1.07 1.07; 1.08 1.25; 1.55 1.07; 1.08 T2DM duration 1.23 1.22 6–10 years 1.10; 1.37 1.10; 1.36 T2DM duration 1.24 1.23 11–15 years 1.07; 1.43 1.06; 1.43 T2DM duration 1.34 1.33 16–20 years 1.07; 1.68 1.06; 1.68 T2DM duration 1.57 1.56 more than 20 1.20; 2.05 1.19; 2.04 years Ex-smoker 0.69 0.69 0.56; 0.84 0.56; 0.84 Current smoker 0.93 0.92 0.82; 1.05 0.82; 1.04 Dyslipidemia 1.07 1.06 0.96; 1.17 0.97; 1.18 Hypertension 0.94 0.93 0.85; 1.05 0.84; 1.04 BMI 24.9–29.9 1.02 1.02 kg/m2 0.86; 1.22 0.86; 1.22 BMI 30.0–34.9 0.94 0.93 kg/m2 0.78; 1.13 0.77; 1.12 BMI 35.0–39.9 0.90 0.89 kg/m2 0.70; 1.14 0.70; 1.14 BMI more than 0.63 0.63 39.9 kg/m2 0.41; 0.95 0.41; 0.94 BMI (missing) 0.93 0.93 0.77; 1.11 0.78;1.12 HbA1c 6.5–7% 0.98 0.85; 1.14 HbA1c 7.1–8% 1.00 0.87; 1.14 HbA1c 8.1–9% 1.08 0.90; 1.30 HbA1c 9.1–10% 0.96 0.73; 1.26 HbA1c more than 0.73 10% 0.53; 1.00 HbA1c (missing) 0.93 FIGURE 2 | Unadjusted and adjusted hazard ratios for Parkinson’s disease 0.81; 1.08 according to diabetic retinopathy status and stage of diabetic retinopathy. (A) CKD 1.03 Unadjusted hazard ratios for Parkinson’s disease for different stages of 0.91; 1.17 diabetic retinopathy. DR, diabetic retinopathy; NDR, no apparent diabetic CVD 1.08 retinopathy; NPDR, non-proliferative diabetic retinopathy; PRD, proliferative 0.95; 1.24 diabetic retinopathy; DME, diabetic macular edema. (B) Unadjusted and Observations 242,703 242,703 242,703 adjusted hazards ratios for Parkinson’s disease for subjects with and without R2 Nagelkerke 0.026 0.027 0.027 diabetic retinopathy; Model 1: adjusted for age and sex; Model 2: adjusted for, age, sex, diabetes duration, smoking status, hypertension, dyslipidemia and BMI, body mass index; CKD, chronic kidney disease; CVD, Cardiovascular disease; DR, body mass index; Model 3: adjusted for age, sex, diabetes duration, smoking diabetic retinopathy; HbA1c, glycosylate hemoglobin; HR, hazard ratio; 95% CI, 95% status, hypertension, dyslipidemia, CKD, CVD and BMI and HbA1c. confidence interval; LI, lower limit; Ul, upper limit; T2DM, type 2 diabetes mellitus. Bold values means statistically significant Hazard ratios. Frontiers in Medicine | www.frontiersin.org 7 January 2022 | Volume 8 | Article 800973 Mauricio et al. Diabetic Retinopathy and Parkinson’s Disease TABLE 5 | Additional models for adjusted hazard ratios for different variables. for diabetes duration, the HRs remained in the same direction; however, there was no association of DR with the development Model 1.1 Model 1.2 Model 1.3 of PD. Stratification for HbA1c revealed that a value between 9.1 Predictor HR HR HR and 10% in the presence of DR was negatively associated with 95% CI [LI; UI] 95% CI 95% CI [LI; Ul] PD. This finding has to be taken cautiously as it was only found [LI; Ul] for this HbA1c interval; additionally, there is no clear clinical Group with DR, 1.01 1.01 1.06 explanation for this based on current knowledge. ref: Group without 0.88; 1.17 0.87; 1.16 0.92; 1.23 The results from studies examining an association between DR BMI/obesity and PD have so far been inconsistent. For most Sex (male) 1.35 1.35 1.35 of the longitudinal studies, according to a recent review on 1.23; 1.49 1.23; 1.49 1.23; 1.49 the epidemiology of PD, no associations between BMI and Age (years) 1.07 1.07 1.08 1.07; 1.08 1.07; 1.08 1.07; 1.08 PD were observed (4). However, in a Spanish retrospective T2DM duration 1.23 1.23 study, overweight and obese individuals were associated with 6–10 years 1.10; 1.38 1.11; 1.38 higher PD risk (adj-HR: 1.21; 95% CI 1.15–1.27), compared with T2DM duration 1.25 1.36 normal weight (28). This could be explained due to the different 11–15 years 1.08; 1.44 1.08; 1.70 population characteristics or different variables considered in the T2DM duration 1.36 1.25 HR analysis. Another prospective study from Finland reported 16–20 years 1.08; 1.70 1.08; 1.44 that overweight or obese subjects were more at risk of developing T2DM duration 1.59 1.59 PD than those with a BMI <23 kg/m2 (33). We observed more than 20 1.22; 2.08 1.22; 2.08 surprising results in our multivariable models, where those years subjects with a BMI >39.9 kg/m2 had a 41% decreased risk of HbA1c 6.5–7% 0.98 1.00 0.85; 1.13 0.87; 1.15 developing PD compared with subjects with a BMI <24.9 kg/m2 . HbA1c 7.1–8% 0.99 1.04 We do not have an explanation for this association which was 0.87; 1.14 0.91; 1.19 otherwise only restricted to the highest stratum of body weight. HbA1c 8.1–9% 1.07 1.14 Therefore, further studies are warranted to address this question. 0.89; 1.29 0.95; 1.37 So far one recent systemic review and meta-analysis, including HbA1c 9.1–10% 0.95 1.00 27 observational studies, addressed the relationship between DR 0.72; 1.25 0.76; 1.32 and systemic neurodegeneration (34). The authors reported that HbA1c more than 0.72 0.75 systemic neurodegeneration was statistically associated with DR. 10% 0.52; 0.99 0.54; 1.03 However, in a smaller subgroup meta-analysis related to the HbA1c (missing) 0.92 0.94 severity of DR, the authors did not observe associations with 0.80; 1.06 0.82; 1.08 systemic neurodegeneration. Moreover, they only included one Observations 242,703 242,703 242,703 study that analyzed the relationship between DR and PD that R2 Nagelkerke 0.026 0.026 0.026 was summarized but not included in the quantitative review (34). HbA1c, glycosylate hemoglobin; HR, hazard ratio; 95% CI, 95% confidence interval; LI, Another systematic review on the potential association between lower limit; Ul, upper limit; T2DM, type 2 diabetes mellitus. diabetic retinopathy and hyperkinetic disorders found that DR Bold values means statistically significant Hazard ratios. could be indirectly related with striatopallidal microangiopathy; further, the authors suggested that the severity of the DR and glycemic fluctuation could be associated with increased risk or of 1.19, 95% CI: 1.13; 1.25) and subjects with prediabetes (adj-HR worse prognosis of hyperkinetic disorders (35). of 1.07, 95% CI: 1.00; 1.14) compared to those subjects without The results of our study should be interpreted with these conditions (28). Despite the different periods of observation caution, keeping in mind potential limitations. Firstly, this was and definition of the study outcome, as well as the variable of a retrospective observational study with pseudo-anonymized exposure (prediabetes state and T2DM), a similar number of routinely collected health data where the presence of missing events for PD were observed in this study (PD among T2DM, data is inherent. There is a possibility that not all subjects 1,789 events) compared with our study (PD with and without with DR were captured. For this reason, we used the fundus DR, 1,748 events) (28). Knowing the fact that subjects with photography results and combined them with diagnostic codes. prediabetes are at an increased risk of developing T2DM (29), Secondly, a numerical imbalance between the different groups that the diagnosis of T2DM is usually delayed by between 4 and occurred due to the study design and lack of randomization. 7 years (30), and that both prediabetes and T2DM increase with Thirdly, we did not have information on the treatment of PD; age (31), could explain findings in our model that subjects with therefore, we only used the diagnostic code to define the main longer T2DM duration had a higher risk for PD. Similar results study event. Besides these limitations, our study includes a were also reported in a recently published case-control study large population of subjects attended in primary health care from Denmark, in which the authors did not find an association centers which increases the statistical power. Moreover, in between DR and PD; further, it should be pointed out that in our multivariable hazard ratio models, we considered more this study subjects with Parkinson’s disease had a longer duration potentially confounding variables (T2DM duration) related to of diabetes (32). In our sensitivity analysis, when we stratified PD than previously published studies. Frontiers in Medicine | www.frontiersin.org 8 January 2022 | Volume 8 | Article 800973 TABLE 6 | Sensitivity analysis. Frontiers in Medicine | www.frontiersin.org Mauricio et al. Stratum Subgroup Model HR 95% CI LI 95% CI Ul p Adjusted by N Overall 1 1.06 0.92 1.23 0.40 Sex (male) + Age (years) 242,703 Overall 1.1 1.01 0.88 1.17 0.85 Sex (male) + Age (years) + T2DM duration + HbA1c 242,703 Overall 1.2 1.01 0.87 1.16 0.92 Sex (male) + Age (years) + T2DM duration 242,703 Overall 1.3 1.06 0.92 1.23 0.41 Sex (male) + Age (years) + HbA1c 242,703 Overall 2 1.01 0.86 1.17 0.89 Sex (male) + Age (years) + Smoking + T2DM duration + 242,703 Dyslipidemia + HTA + BMI Overall 3 1.01 0.88 1.17 0.88 Sex (male) + Age (years) + Smoking + T2DM duration + 242,703 Dyslipidemia + CVD + HTA + CKD + HbA1c + BMI T2DM duration T2DM duration less 6 years 1.1 1.06 0.82 1.37 0.65 Sex (male) + Age (years) + HbA1c 138,553 T2DM duration T2DM duration 6–10 years 1.1 1.03 0.81 1.31 0.79 Sex (male) + Age (years) + HbA1c 64,161 T2DM duration T2DM duration 11–15 years 1.1 0.81 0.57 1.15 0.23 Sex (male) + Age (years) + HbA1c 27,449 T2DM duration T2DM duration 16–20 years 1.1 1.04 0.63 1.71 0.89 Sex (male) + Age (years) + HbA1c 8,140 T2DM duration T2DM duration more than 20 years 1.1 1.23 0.71 2.12 0.46 Sex (male) + Age (years) + HbA1c 4,400 HbA1c HbA1c less 6.5% 1.1 1.10 0.84 1.45 0.49 Sex (male) + Age (years) + T2DM duration 79,068 HbA1c HbA1c 6.5–7% 1.1 1.09 0.75 1.60 0.63 Sex (male) + Age (years) + T2DM duration 37,778 HbA1c HbA1c 7.1–8% 1.1 1.22 0.91 1.64 0.18 Sex (male) + Age (years) + T2DM duration 41,252 HbA1c HbA1c 8.1–9% 1.1 1.18 0.79 1.75 0.42 Sex (male) + Age (years) + T2DM duration 18,471 HbA1c HbA1c 9.1–10% 1.1 0.21 0.06 0.66 0.01 Sex (male) + Age (years) + T2DM duration 9,607 HbA1c HbA1c more than 10% 1.1 0.73 0.32 1.67 0.45 Sex (male) + Age (years) + T2DM duration 11,957 HbA1c HbA1c (missing) 1.1 0.82 0.57 1.19 0.31 Sex (male) + Age (years) + T2DM duration 44,570 T2DM duration T2DM duration less 6 years 3 1.06 0.82 1.38 0.64 Sex (male) + Age (years) + Smoking + Dyslipidemia + 138,553 9 CVD + HTA + CKD + HbA1c + BMI T2DM duration T2DM duration 6–10 years 3 1.02 0.80 1.29 0.87 Sex (male) + Age (years) + Smoking + Dyslipidemia + 64,161 CVD + HTA + CKD + HbA1c + BMI T2DM duration T2DM duration 11–15 years 3 0.80 0.57 1.16 0.25 Sex (male) + Age (years) + Smoking + Dyslipidemia + 27,449 CVD + HTA + CKD + HbA1c + BMI T2DM duration T2DM duration 16–20 years 3 1.03 0.62 1.69 0.91 Sex (male) + Age (years) + Smoking + Dyslipidemia + 8,140 CVD + HTA + CKD + HbA1c + BMI T2DM duration T2DM duration more than 20 years 3 1.22 0.70 2.11 0.48 Sex (male) + Age (years) + Smoking + Dyslipidemia + 4,400 CVD + HTA + CKD + HbA1c + BMI HbA1c HbA1c less 6.5% 3 1.10 0.84 1.45 0.48 Sex (male) + Age (years) + Smoking + T2DM duration + 79,068 Dyslipidemia + CVD + HTA + CKD + BMI HbA1c HbA1c 6.5–7% 3 1.10 0.75 1.61 0.61 Sex (male) + Age (years) + Smoking + T2DM duration + 37,778 Diabetic Retinopathy and Parkinson’s Disease Dyslipidemia + CVD + HTA + CKD + BMI January 2022 | Volume 8 | Article 800973 HbA1c HbA1c 7.1–8% 3 1.23 0.92 1.65 0.17 Sex (male) + Age (years) + Smoking + T2DM duration + 41,252 Dyslipidemia + CVD + HTA + CKD + BMI HbA1c HbA1c 8.1–9% 3 1.17 0.78 1.73 0.45 Sex (male) + Age (years) + Smoking + T2DM duration + 18,471 Dyslipidemia + CVD + HTA + CKD + BMI HbA1c HbA1c 9.1–10% 3 0.20 0.06 0.66 0.01 Sex (male) + Age (years) + Smoking + T2DM duration + 9,607 Dyslipidemia + CVD + HTA + CKD + BMI HbA1c HbA1c more than 10% 3 0.71 0.31 1.62 0.41 Sex (male) + Age (years) + Smoking + T2DM duration + 11,957 Dyslipidemia + CVD + HTA + CKD + BMI HbA1c HbA1c (missing) 3 0.82 0.57 1.19 0.31 Sex (male) + Age (years) + Smoking + T2DM duration + 44,570 Dyslipidemia + CVD + HTA + CKD + BMI BMI, body mass index; CKD, chronic kidney disease; CVD, Cardiovascular disease; DR, diabetic retinopathy; HbA1c, glycosylate hemoglobin; HR, hazard ratio; 95% CI, 95% confidence interval; LI, lower limit; Ul, upper limit; T2DM, type 2 diabetes mellitus. Mauricio et al. Diabetic Retinopathy and Parkinson’s Disease FIGURE 3 | Hazard ratios for Parkinson’s disease according retinopathy status. Sensitivity analysis. M1: Sex (male) + Age (years). M 1.1 Sex (male) + Age (years) + Diabetes duration + HbA1c. M1.2 Sex (male) + Age (years) + Diabetes duration. M1.3 Sex (male) + Age (years) + HbA1c. M2: Sex (male) + Age (years) + Smoking + Diabetes duration + Dyslipidemia + HTA + BMI. M3: Sex (male) + Age (years) + Smoking + Diabetes duration + Dyslipidemia + HTA +CKD + HbA1c + BMI. In conclusion, in our primary health care population database, Requests to access these datasets should be directed to Dídac DR was not associated with an increased risk of PD after Mauricio, didacmauricio@gmail.com. adjusting for different risk factors. The detection of DR is mainly based on retinal vascular changes that do not always ETHICS STATEMENT coincide with retinal neurodegeneration (33). Even though a strong biological background exists between DR and PD, The studies involving human participants were further mechanistic studies and well-designed clinical studies are reviewed and approved by IDIAP Jordi Gol i Gurina needed to investigate the possible relationship between these two Foundation (protocol code P13/028 and date of neurodegenerative conditions. approval 03/04/2013). Written informed consent for participation was not required for this study DATA AVAILABILITY STATEMENT in accordance with the national legislation and the institutional requirements. The data analyzed in this study is subject to the following licenses/restrictions: restrictions apply to the availability of AUTHOR CONTRIBUTIONS some or all data generated or analyzed during this study because they were used under license. The corresponding DM: conceptualization and methodology. JR: software, author will on request detail the restrictions and any formal analysis, and data curation. JR, BV, and DM: conditions under which access to some data may be provided. validation. XM-T: resources. BV: writing—original draft Frontiers in Medicine | www.frontiersin.org 10 January 2022 | Volume 8 | Article 800973 Mauricio et al. Diabetic Retinopathy and Parkinson’s Disease preparation. BV and DM: writing—review and editing. ACKNOWLEDGMENTS DM, JJ, JR, EO, EC, JB, JK, and JF-N: supervision. All authors have read and agreed to the published version of We thank Amanda Prowse for her editorial support of the manuscript. this manuscript. REFERENCES 19. Lee SE, Han K, Baek JY, Ko KS, Lee KU, Koh EH. Association between diabetic retinopathy and parkinson disease: the Korean national health 1. Mauricio D, Alonso N, Gratacòs M. Chronic diabetes complications: the insurance service database. J Clin Endocrinol Metab. (2018) 103:3231– need to move beyond classical concepts. Trends Endocrinol Metab. (2020) 8. doi: 10.1210/jc.2017-02774 31:287–95. doi: 10.1016/j.tem.2020.01.007 20. 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Diabetic Retinopathy and Parkinson’s Disease of the potential link between retinopathy and hyperkinetic disorders in Publisher’s Note: All claims expressed in this article are solely those of the authors diabetes. Park Relat Disord. (2020) 80:194–8. doi: 10.1016/j.parkreldis.2020. and do not necessarily represent those of their affiliated organizations, or those of 10.025 the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or Conflict of Interest: DM and JF-N received advisory and/or speaking fees from endorsed by the publisher. Astra-Zeneca. Ascensia. Boehringer Ingelheim. GSK. Lilly. MSD. Novartis. Novo Nordisk. and Sanofi and received research grants to the institution from Astra- Zeneca. GSK. Lilly. MSD. Novartis. Novo Nordisk. Sanofi. and Boehringer. EO has Copyright © 2022 Mauricio, Vlacho, Barrot de la Puente, Mundet-Tudurí, Real, received advisory and or speaking fees from Astra-Zeneca, Boehringer Ingelheim, Kulisevsky, Ortega, Castelblanco, Julve and Franch-Nadal. This is an open-access Lilly, MSD, Novo Nordisk, Sanofi, and Amgen; he has received research grants to article distributed under the terms of the Creative Commons Attribution License (CC the institution from MSD and Amgen. BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original The remaining authors declare that the research was conducted in the absence of publication in this journal is cited, in accordance with accepted academic practice. any commercial or financial relationships that could be construed as a potential No use, distribution or reproduction is permitted which does not comply with these conflict of interest. terms. 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