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Article

Dietary Nitrate Intake Is Associated with Decreased Incidence of Open-Angle Glaucoma: The Rotterdam Study

by
Joëlle E. Vergroesen
1,2,
Tosca O. E. de Crom
2,
Lauren C. Blekkenhorst
3,
Caroline C. W. Klaver
1,2,4,5,
Trudy Voortman
2,6 and
Wishal D. Ramdas
1,*
1
Department of Ophthalmology, Erasmus MC University Medical Center, P.O. Box 2040, 3000 CA Rotterdam, The Netherlands
2
Department of Epidemiology, Erasmus MC University Medical Center, P.O. Box 2040, 3000 CA Rotterdam, The Netherlands
3
School of Medical and Health Sciences, Edith Cowan University, Joondalup, WA 6027, Australia
4
Department of Ophthalmology, Radboud University Medical Center, P.O. Box 9101, 6500 HB Nijmegen, The Netherlands
5
Institute of Molecular and Clinical Ophthalmology, University of Basel, CH-4031 Basel, Switzerland
6
Division of Human Nutrition and Health, Wageningen University & Research, P.O. Box 17, 6700 AA Wageningen, The Netherlands
*
Author to whom correspondence should be addressed.
Nutrients 2022, 14(12), 2490; https://doi.org/10.3390/nu14122490
Submission received: 22 May 2022 / Revised: 10 June 2022 / Accepted: 13 June 2022 / Published: 15 June 2022
(This article belongs to the Special Issue Effects of Nutrients on Eye Health)
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Abstract

:
Previous studies suggest that nitric oxide is involved in the regulation of the intraocular pressure (IOP) and in the pathophysiology of open-angle glaucoma (OAG). However, prospective studies investigating the association between dietary nitrate intake, a source of nitric oxide, and incident (i)OAG risk are limited. We aimed to determine the association between dietary nitrate intake and iOAG, and to evaluate the association between dietary nitrate intake and IOP. From 1991 onwards, participants were followed each five years for iOAG in the Rotterdam Study. A total of 173 participants developed iOAG during follow-up. Cases and controls were matched on age (mean ± standard deviation: 65.7 ± 6.9) and sex (%female: 53.2) in a case:control ratio of 1:5. After adjustment for potential confounders, total dietary nitrate intake was associated with a lower iOAG risk (odds ratio (OR) with corresponding 95% confidence interval (95% CI): 0.95 (0.91–0.98) for each 10 mg/day higher intake). Both nitrate intake from vegetables (OR (95% CI): 0.95 (0.91–0.98) for each 10 mg/day higher intake) and nitrate intake from non-vegetable food sources (OR (95% CI): 0.63 (0.41–0.96) for each 10 mg/day higher intake) were associated with a lower iOAG risk. Dietary nitrate intake was not associated with IOP. In conclusion, dietary nitrate intake was associated with a reduced risk of iOAG. IOP-independent mechanisms may underlie the association with OAG.

1. Introduction

Glaucoma is an eye disease that causes the most cases of irreversible blindness worldwide. Currently, more than 80 million people worldwide have glaucoma, of which approximately 11 million are estimated to be bilaterally blind [1]. A high intraocular pressure (IOP) is a well-known modifiable risk factor but, since glaucoma can progress despite an “adequate” IOP, it is very likely that IOP-independent mechanisms play a role as well. Therefore, more knowledge about other potential risk factors is urgently needed for optimal prevention and treatment strategies.
Several studies have investigated the association between nutrition and open-angle glaucoma (OAG) [2]. Studies on the intake of dark green leafy vegetables showed an inverse association with OAG [3,4,5]. This may in part be explained by the substantial amount of dietary nitrate that green leafy vegetables contain (2000–5000 mg/kg) [6,7,8], along with phylloquinone, lutein, folate, α-tocopherol, and kaempferol [9]. Due to the nitrate–nitrite–nitric oxide pathway, nitrate is an important source of nitric oxide (NO). Different studies have suggested that NO, known as endothelium-derived relaxing factor [10], plays a role in the regulation of IOP, by increasing the conventional outflow facility [3,11,12,13,14,15]. Abnormal function and degradation of endothelial cells are associated with reduced NO bioavailability and, subsequently, progression of glaucoma [16]. Additionally, the endothelium of Schlemm’s canal (SC) reacts to physiological levels of shear stress, by aligning with the direction of flow and by increasing the production of NO. NO production by SC cells has a homeostatic signaling function during times of elevated IOP, when SC narrows and shear stress on SC cells increases. Shear-stimulated production of NO by SC cells would then increase outflow facility, normalizing IOP [17,18,19,20]. This process may be compromised in glaucoma, as SC cells isolated from glaucomatous eyes have shown to be either shear-unresponsive or lifted from their substrate in the presence of shear stress [21]. IOP-independent effects of dietary nitrate have also been suggested. Dietary nitrate has shown to have beneficial effects on blood pressure, endothelial function, reperfusion injury, and platelet aggregation [22]. All of these may be involved in the pathophysiology of OAG, but studies investigating whether dietary nitrate intake relates to the risk of incident (i) OAG are limited.
The aim of this study was to determine the association between dietary nitrate intake and iOAG. We also examined the association between dietary nitrate intake and IOP, as an OAG risk factor, and we studied whether potential associations with iOAG were explained by IOP or, indirectly, by blood pressure.

2. Materials and Methods

2.1. Study Design and Population

Participants were derived from three independent cohorts from the prospective population-based Rotterdam Study (RS-I, RS-II, RS-III), designed to assess determinants of age-related diseases in the middle-aged and elderly population (45+ years). Enrollment for the ophthalmic part started in 1991; after the baseline visit, participants were invited for follow-up visits with intervals of approximately five years [23]. Of 8679 participants with ophthalmic examinations, 7008 had baseline measurements of dietary nitrate intake. Of those, 173 participants developed iOAG during follow-up. Since age is strongly associated with iOAG risk [24] and dietary intake [25,26], and dietary intake is different for females compared to males [27], we chose to use a case–control design. We matched cases and controls on age (maximum difference of three years) and sex, in a 1:5 ratio, and sampled without replacement. The final dataset consisted of 173 cases and 865 controls.

2.2. Ophthalmic Assessment

The eye examinations included Goldmann applanation tonometry (Haag-Streit AG, Bern, Switzerland), and visual field testing (Humphrey Field Analyzer; HFA II 740; Carl Zeiss, Oberkochen, Germany). All participants underwent visual field testing using the Humphrey Field Analyzer (HFA; Carl Zeiss Meditec, Jena, Germany). A second supra-threshold test was performed when a visual field defect appeared to be present. Details have been described elsewhere [28]. If the second supra-threshold test showed at least one overlapping abnormality in the same hemifield, Goldmann kinetic perimetry (RS-I-1 and RS-I-3; Haag-Streit) or full-threshold HFA (all other cohort visits) was performed on both eyes. If abnormalities were consecutive and reproducible, thus present on the Goldmann or full-threshold test and on both supra-threshold tests, visual field loss was considered to be present. Defects had to be in a consistent hemifield and at least one depressed test point had to have exactly the same location on all fields. Glaucoma specialists examined fundus photographs, ophthalmic examination reports, medical histories, and MRI scans of the brain to exclude all other possible causes of visual field loss. Discrepancies were resolved by consensus. iOAG cases had an open anterior chamber angle and no history or signs of secondary glaucoma [28]. For IOP, three measurements were taken from each eye, the median value of which was recorded [29]. For iOAG cases, we used IOP measurements of the affected eye. If both eyes were affected or unaffected, a random eye was selected. IOP was not included in the definition of iOAG.

2.3. Dietary Nitrate Data

Dietary intake was assessed at baseline using food frequency questionnaires (FFQs) as described in detail elsewhere [30]. Both FFQs were previously validated and showed reasonable to good estimates of nutrient intake [31,32,33]. All food items were assessed based on the frequency of consumption, the number of servings per day as well as on the preparation methods. We calculated dietary nitrate intake separately from vegetables and non-vegetable food sources, because of their possible contradicting health effects [34,35,36,37,38,39]. Nitrate intake for each vegetable was calculated using a comprehensive database, including nitrate data for 178 vegetables from over 250 publications [40]. Nitrate intake from vegetables (mg/day) was calculated by multiplying the amount of each vegetable (g/day) by the median nitrate content (mg/g) for that individual vegetable. Nitrate intake from non-vegetable food sources was obtained from an earlier developed dietary nitrate and nitrate database [41]. Nitrate intake from non-vegetable food sources was estimated by multiplying the amount of the food item (g/day) by the mean nitrate value (mg/g) of that food item. If no nitrate value was available for a specific food item, we considered a value of 0 mg/g. Total dietary nitrate intake (mg/day) was calculated by summing the nitrate intake from vegetables and nitrate intake from non-vegetable food sources. Participants with unreliable dietary intake (total energy intake <500 kcal/day or >5000 kcal/day) were excluded.

2.4. Covariates

Education level was assessed with questionnaires and categorized into: primary education, lower education, intermediate education, or higher education. Smoking status was obtained using questionnaires and participants were classified as non-smoker, former smoker or current smoker. At the research center, blood pressure was measured at the right brachial artery with the participant in sitting position. The mean of two consecutive measurements was used. Hypertension was defined as a resting blood pressure exceeding 140/90 mmHg or the use of blood pressure-lowering medication. Medication data on blood pressure-lowering medications (antihypertensives, diuretics, beta blockers, calcium channel blockers, and renin-angiotensin-aldosterone system agents) were collected with questionnaires [42]. Weight and height were measured at the research center. Body mass index (BMI) was calculated as weight in kilograms divided by height in meters squared. Total energy intake was obtained from the previously described FFQs. Diet quality was defined as adherence to the Dutch dietary guidelines, with a scoring range from 0 (no adherence) to 14 (full adherence). Details have been described elsewhere [43]. For physical activity, two different questionnaires were used: a validated adapted version of the Zutphen Physical Activity Questionnaire [44] and the LASA Physical Activity Questionnaire [45]. Data were recalculated into metabolic equivalent of task (MET)-hours per week, and a z-standardized score was included in the analyses.

2.5. Statistical Analyses

Differences in baseline characteristics between cases and controls were evaluated using chi-square tests and independent-samples t-tests. We adjusted dietary nitrate intake for total energy intake by applying the nutrient residual method and analyzing the dietary nitrate intake adjusted for total energy intake. One-way ANOVA was used to compare the baseline characteristics of participants in the different quintiles of total dietary nitrate intake. The dose–response relationship between dietary nitrate intake and predicted iOAG probability or IOP, was examined using generalized additive modelling. We performed multivariable conditional logistic regression analyses to calculate odds ratios (ORs) with corresponding 95% confidence intervals (CI) for iOAG and hypertension. ORs can be interpreted as the difference in odds per increase of 10 mg/day intake of dietary nitrate keeping energy intake constant (iso-energetic). Additionally, we modelled dietary nitrate intake in quintiles with the first quintile (Q1) as reference category to test for evidence of linear trends. The median value for each category as continuous variables was used in separate conditional logistic regression models. The final models included BMI, total energy intake, diet quality, physical activity, and follow-up time. Follow-up duration was calculated from the baseline until the last visit with reliable ophthalmic examination or the first visit with iOAG diagnosis. To assess potential reverse causality, we analyzed the association between dietary nitrate intake and iOAG in cumulative follow-up intervals. Additionally, we observed the effect of including IOP (potential mediator in the association with iOAG) or education level and smoking status (lifestyle factors affecting nutrition quality) in the models. The association of dietary nitrate with IOP at follow-up, and diastolic and systolic blood pressure at baseline, was assessed by performing multivariable linear regression analysis, adjusting for the same covariates as mentioned above. The blood pressure analyses were additionally adjusted for use of blood pressure-lowering medications. Statistical analyses were performed using SPSS v25.0 (SPSS Inc., Chicago, IL, USA) and R v3.6.1 (R Inc., Boston, MA, USA), with packages DescTools, mgcv, ggplot2, dplyr and ggforestplot. A p-value < 0.05 was considered statistically significant.

3. Results

The baseline characteristics of cases and controls are displayed in Table 1. Participants with iOAG had a significantly lower BMI and their diet quality score was higher. As expected, they had a significantly higher IOP. Dietary nitrate intake was significantly different between cases and controls. Baseline characteristics according to quintiles of total dietary nitrate intake are presented in Table 2. Higher consumers of dietary nitrate more often had a higher education. Additionally, their BMI, total energy intake and diet quality score were higher.
Figure 1 presents a graphic representation of the dose–response relationship between dietary nitrate intake, iOAG and IOP analyzed in separate generalized additive multivariable-adjusted models. For iOAG, similar dose–response relationships were found for total dietary nitrate intake (Figure 1A), nitrate intake from vegetables (Figure 1B) and nitrate intake from non-vegetable food sources (Figure 1C), i.e., they were linear across the reported range of intake. For IOP, a different dose–response relationship was found for nitrate intake from non-vegetable food sources and IOP (Figure 1F) compared to the relationship with total dietary nitrate intake and nitrate intake from vegetables (Figure 1D,E). The association of nitrate intake from non-vegetable food sources with IOP was linear across the reported range of intake, whereas the associations of total dietary nitrate intake and nitrate intake from vegetables with IOP were not.
In the multivariable-adjusted model (Figure 2A; model 1), each 10 mg/day higher total dietary nitrate intake was associated with a 5% reduction in the risk of iOAG (OR (95% CI): 0.95 (0.91–0.98)). Participants in the highest quintile (Q5: mean 213.0 mg/day) had the largest risk reduction (OR (95% CI): 0.38 (0.20–0.72)) compared to participants in the lowest quintile (Q1: mean 48.8 mg/day) (p-trend = 0.002). For nitrate intake from vegetables, we observed a 5% reduction in the risk of iOAG (OR (95% CI): 0.95 (0.91–0.98)) for each 10 mg/day higher intake (Figure 2B; model 1). The difference in iOAG risk was 61% when comparing the highest (Q5: mean 196.8 mg/day) and lowest (Q1: mean 34.6 mg/day) nitrate intake from vegetables (OR (95% CI): 0.39 (0.20–0.73)) (p-trend = 0.003). For nitrate intake from non-vegetable food sources, we observed a 37% reduction in the risk of iOAG (OR (95% CI): 0.63 (0.41–0.96)) for each 10 mg/day higher intake (Figure 2C; model 1), but we did not observe a significant trend (p-trend = 0.08). Additional adjustment of the aforementioned analyses with IOP (Figure 2; model 2) or with education level and smoking status (Figure 2; model 3) did not change the results. When analyzing the cumulative follow-up intervals, a higher intake of dietary nitrate intake was associated with a lower iOAG risk during every cumulative follow-up interval after 10 years of follow-up (Figure S1).
For IOP as outcome, we observed no significant associations with total dietary nitrate intake (beta (95% CI): 0.02 (−0.02–0.06) for each 10 mg/day higher intake) and nitrate intake from vegetables (beta (95% CI): 0.02 (−0.02–0.06) for each 10 mg/day higher intake) (Table 3). We did observe a borderline significant association between nitrate intake from non-vegetable food sources and IOP (beta (95% CI): −0.45 (−0.96–0.06) for each 10 mg/day higher intake) (p-trend = 0.09). We found no significant associations between dietary nitrate intake and diastolic blood pressure (Table S1) and systolic blood pressure (Table S2). Only nitrate intake from non-vegetable food sources was associated with a lower risk of hypertension (OR (95% CI): 0.65 (0.45–0.94) for each 10 mg/day higher intake) (p-trend = 0.06) (Table S3).

4. Discussion

In this case–control study embedded within a prospective population-based cohort, we found that dietary nitrate intake showed a strong association with a decreased incidence of OAG. No significant associations were observed between dietary nitrate intake and IOP. Additionally, no clear associations were observed between dietary nitrate intake and blood pressure.
To our knowledge, we are the first to assess the association between dietary nitrate and iOAG, stratified by source (vegetables vs. non-vegetable food sources). The Nurses’ Health Study and the Health Professionals Follow-up Study reported a pooled multivariable rate ratio (MVRR) of 0.79 (95% CI 0.66–0.93; p-trend = 0.02) for the highest quintile of dietary nitrate intake (~240 mg/day) as compared with the lowest quintile (~80 mg/day) [3]. When additionally adjusted for other dietary factors, this pooled MVRR decreased to 0.67 (95% CI 0.52–0.85; p-trend = 0.01) [3]. We found a similar result and trend. A nitrate intake of ~200 mg can be achieved by consuming 100 g spinach (nitrate: 1926 mg/kg), 130 g beets (nitrate: 1581 mg/kg), 190 g endive (nitrate: 1054 mg/kg) or 115 g kale (nitrate: 1748 mg/kg) [40]. These are very feasible portion sizes, as the Dutch dietary guidelines recommend consuming at least 200 g of vegetables daily [43]. As we did not observe an association between dietary nitrate intake and IOP, the association between dietary nitrate and iOAG may be explained by other, IOP-independent, mechanisms.
Dietary nitrate intake may affect the risk of iOAG due to its beneficial effects on blood pressure, endothelial function, reperfusion injury, and platelet aggregation (Figure S2). These effects are likely to occur as a result of enhanced NO production through the nitrate–nitrite–NO pathway [22]. Previous research has shown that a higher dietary nitrate intake was associated with significantly wider retinal arterioles [46]. Widening of retinal arteriolar caliber is not only associated with a lower risk of cardiovascular and cerebrovascular diseases [47,48], but also with a lower risk of glaucoma [49]. Eyes with primary OAG were 2.7 times more likely to have generalized arteriolar narrowing, and narrower retinal arterioles were significantly associated with higher OAG prevalence and incidence [50,51,52,53,54]. Thus, the association between nitrate and OAG may be explained by increased retinal arteriolar caliber caused by nitrate, which affects blood pressure. Previous population-based studies have suggested that IOP is associated with systemic blood pressure levels [55,56,57,58,59,60,61,62,63,64]. Nevertheless, in our study, we found no clear association between dietary nitrate intake and IOP or blood pressure.
Although research into the IOP-independent pathways by which dietary nitrate intake could influence glaucoma incidence are limited, we would like to highlight their potential in explaining in part or in combination the inverse association found in this study. Endothelial dysfunction in glaucoma has been associated with an imbalance between endothelin-1 and NO [65]. Dietary nitrate could thus potentially lower the incidence of OAG by upregulating the NO levels, hereby improving endothelial function. Glaucomatous retinal ganglion cell loss has previously been associated with increased oxidant levels [66,67,68,69], a theory that is supported by the fact that administration of antioxidants protects retinal cells from injury following retinal ischemia and reperfusion [70,71,72,73]. Retinal ischemia can thus potentially impact optic nerve degeneration [74]. Increased NO bioavailability acts on the balance between antioxidants and prooxidant agents [75]. NO can eliminate oxidants, reduce equivalents provided by superoxide, and prevent the reaction of peroxide [76]. Dietary nitrate has shown to suppress radical formation and to be a scavenger of potentially damaging reactive oxygen and nitrogen species, suggesting that it may also exhibit antioxidant effects [76,77]. This is one mechanism that may play a role in the observed association between dietary nitrate intake and iOAG. Moreover, adhesion and aggregation of platelets is inhibited by NO. Modulation of platelet function is an important therapeutic strategy in preventing and treating atherosclerosis, a disease considered to increase glaucoma risk [78,79]. Thus, mediation of platelet aggregation is one other mechanism that could underlie the association between dietary nitrate intake and iOAG.
This study has several strengths. We used a prospective population-based design, allowing repeated eye examinations, and thus prospectively ascertaining iOAG cases, according to a well-established OAG definition [28] and IOP measurements. Additionally, dietary data were collected using validated FFQs, which included a wide variety of food items commonly consumed in the Dutch population. By using dietary information from baseline assessments, we limited selection bias and the risk of reverse causality, since all included participants were free of iOAG at this visit. Moreover, the questionnaire was administered to cases and controls under similar conditions. Furthermore, we assessed the association between dietary nitrate intake and iOAG over cumulative follow-up periods to provide insight into possible reversed causality. The persistence of the association over time implies that reverse causality is unlikely. The availability of robust data on possible confounders allowed us to reach an independent association between dietary nitrate intake and iOAG. Given that our cases and controls were matched on age and sex, it is very unlikely that our findings were affected by the association of age and sex with dietary (nitrate) intake. We performed additional matching on BMI (with a range of 2.0 kg/m2), since the controls in this study had a significantly higher BMI than the iOAG cases, and a higher BMI appears to be associated with lower iOAG risk [80,81,82,83,84,85]. However, additional matching on BMI did not change the association between dietary nitrate intake and iOAG or IOP (Tables S4 and S5, respectively). Limitations should also be considered when interpreting our results. By assessing the association in time, thus only looking at incident disease, we limited the number of iOAG cases, and therefore also IOP measurements. As the iOAG cases did not have exorbitant IOP measurements typically associated with OAG (mean 16.2 mmHg; interquartile range 13–18 mmHg), this may have limited our possibilities to detect statistically significant IOP-lowering effects of dietary nitrate intake. By using the FFQ, we relied on the participants’ memory for collecting information for as far back as one month. Additionally, the FFQ is known to under- or over-report certain foods, leading to non-differential misclassification. Additionally, based on the FFQs, nitrate intake over the past year or month was determined, which does not per definition reflect long term intake as participants may change dietary habits over time. However, since dietary information was collected at baseline, with all participants free of iOAG, it is unlikely that such misclassification would result in false-positive findings. If glaucoma presence would have an effect on dietary nitrate intake, this would not be applicable to our study. Despite the limitations, the low respondent burden makes the FFQ an easy and effective data collection tool. It additionally allows for calculation of the total energy intake, which is a large benefit [86]. Although the analyses were adjusted for multiple confounders, we were unable to adjust for other possible confounders such as family history of glaucoma, since this was only available for a small subset of participants. We did consider the risk factor myopia, for which we adjusted by including education level into model 3. We also included spherical equivalent into the model (data not shown), but this did not change the results. Lastly, residual confounding cannot completely be excluded. In summary, a higher dietary nitrate intake reduces the risk of iOAG. The effect was independent of the IOP. Our findings confirm earlier reported associations between dietary nitrate intake and OAG. However, intervention studies are necessary before the association between dietary nitrate intake and iOAG can be considered as an important public health implication.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/nu14122490/s1, Figure S1: Multivariable-adjusted odds ratios with corresponding 95% confidence intervals (CIs) for incident open-angle glaucoma per 10 g/day increase in total dietary nitrate intake (A), nitrate intake from vegetables (B), and nitrate intake from non-vegetable food sources (C), shown per cumulative follow-up interval; Table S1: Multivariable adjusted beta (95% confidence interval) of diastolic blood pressure, by quintiles of nitrate intake; Table S2: Multivariable adjusted beta (95% confidence interval) of systolic blood pressure, by quintiles of nitrate intake; Table S3: Multivariable adjusted odds ratio (95% confidence interval) of hypertension, by quintiles of nitrate intake; Figure S2: Beneficial health effects of dietary nitrate; Table S4: Multivariable adjusted odds ratio (95% confidence interval) of incident open-angle glaucoma by nitrate intake; Table S5: Multivariable adjusted beta (95% confidence interval) of intraocular pressure by nitrate intake.

Author Contributions

Conceptualization, C.C.W.K., T.V. and W.D.R.; Methodology, J.E.V., T.O.E.d.C., T.V. and W.D.R.; Formal Analysis, J.E.V.; Data Curation, L.C.B., C.C.W.K. and T.V.; Writing—Original Draft Preparation, J.E.V.; Writing—Review and Editing, J.E.V., T.O.E.d.C., C.C.W.K., T.V. and W.D.R.; Visualization, J.E.V.; Supervision, C.C.W.K. and W.D.R.; Funding Acquisition, C.C.W.K. and W.D.R. All authors have read and agreed to the published version of the manuscript.

Funding

This study was supported by the following foundations that contributed through Uitzicht: Stichting Glaucoomfonds, Landelijke Stichting voor Blinden en Slechtzienden (LSBS), Oogfonds. Additional support was given by Rotterdamse Stichting Blindenbelangen (RSB), Stichting Lijf en Leven, Henkes stichting, Stichting voor Ooglijders, Stichting Blindenhulp, Erasmus Medical Center, Erasmus University, Netherlands Organization for the Health Research and Development (ZonMw), the Research Institute for Diseases in the Elderly (RIDE), the Ministry of Education, Culture and Science, the Ministry for Health, Welfare and Sports, the European Commission (DG XII), and the Municipality of Rotterdam. The sponsor or funding organization had no role in the design or conduct of this research.

Institutional Review Board Statement

The Rotterdam Study has been approved by the Medical Ethics Committee of Erasmus MC (registration number MEC 02.1015) and by the Dutch Ministry of Health, Welfare, and Sport (Population Screening Act WBO, license number 1071272-159521-PG). The Rotterdam Study has been entered into the Netherlands National Trial Register (NTR; www.trialregister.nl) and into the WHO International Clinical Trials Registry Platform (ICTRP; www.who.int/ictrp/network/primary/en/) under shared catalog number NTR6831.

Informed Consent Statement

All participants provided written informed consent to participate in the study and to have their information obtained from treating physicians.

Data Availability Statement

Data can be obtained upon request. Requests should be directed towards the management team of the Rotterdam Study ([email protected]), which has a protocol for approving data requests. Due to restrictions based on privacy regulations and informed consent of the participants, data cannot be made freely available in a public repository.

Acknowledgments

The authors are grateful to the study participants, the staff from the Rotterdam Study and the participating general practitioners and pharmacists.

Conflicts of Interest

The author declares no conflict of interest.

References

  1. Quigley, H.A.; Broman, A.T. The number of people with glaucoma worldwide in 2010 and 2020. Br. J. Ophthalmol. 2006, 90, 262–267. [Google Scholar] [CrossRef] [Green Version]
  2. Ramdas, W.D. The relation between dietary intake and glaucoma: A systematic review. Acta Ophthalmol. 2018, 96, 550–556. [Google Scholar] [CrossRef] [PubMed]
  3. Kang, J.H.; Willett, W.C.; Rosner, B.A.; Buys, E.; Wiggs, J.L.; Pasquale, L.R. Association of Dietary Nitrate Intake with Primary Open-Angle Glaucoma: A Prospective Analysis From the Nurses’ Health Study and Health Professionals Follow-up Study. JAMA Ophthalmol. 2016, 134, 294–303. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  4. Coleman, A.L.; Stone, K.L.; Kodjebacheva, G.; Yu, F.; Pedula, K.L.; Ensrud, K.E.; Cauley, J.A.; Hochberg, M.C.; Topouzis, F.; Badala, F.; et al. Glaucoma Risk and the Consumption of Fruits and Vegetables Among Older Women in the Study of Osteoporotic Fractures. Am. J. Ophthalmol. 2008, 145, 1081–1089. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  5. Giaconi, J.A.; Yu, F.; Stone, K.L.; Pedula, K.L.; Ensrud, K.E.; Cauley, J.A.; Hochberg, M.C.; Coleman, A.L.; Study of Osteoporotic Fractures Research Group. The association of consumption of fruits/vegetables with decreased risk of glaucoma among older African-American women in the study of osteoporotic fractures. Am. J. Ophthalmol. 2012, 154, 635–644. [Google Scholar] [CrossRef] [Green Version]
  6. Brkić, D.; Bošnir, J.; Bevardi, M.; Bošković, A.G.; Miloš, S.; Lasić, D.; Krivohlavek, A.; Racz, A.; Ćuić, A.M.; Trstenjak, N.U. Nitrate in Leafy Green Vegetables and Estimated Intake. Afr. J. Tradit. Complement. Altern. Med. 2017, 14, 31–41. [Google Scholar]
  7. Sweazea, K.L.; Johnston, C.S.; Miller, B.; Gumpricht, E. Nitrate-Rich Fruit and Vegetable Supplement Reduces Blood Pressure in Normotensive Healthy Young Males without Significantly Altering Flow-Mediated Vasodilation: A Randomized, Double-Blinded, Controlled Trial. J. Nutr. Metab. 2018, 2018, 1729653. [Google Scholar] [CrossRef]
  8. Iammarino, M.; Di Taranto, A.; Cristino, M. Monitoring of nitrites and nitrates levels in leafy vegetables (spinach and lettuce): A contribution to risk assessment. J. Sci. Food Agric. 2014, 94, 773–778. [Google Scholar] [CrossRef]
  9. Morris, M.C.; Wang, Y.; Barnes, L.L.; Bennett, D.A.; Dawson-Hughes, B.; Booth, S.L. Nutrients and bioactives in green leafy vegetables and cognitive decline: Prospective study. Neurology 2018, 90, e214–e222. [Google Scholar] [CrossRef]
  10. Bauer, V.; Sotníková, R. Nitric oxide—The endothelium-derived relaxing factor and its role in endothelial functions. Gen. Physiol. Biophys. 2010, 29, 319–340. [Google Scholar] [CrossRef]
  11. Saccà, S.C.; Gandolfi, S.; Bagnis, A.; Manni, G.; Damonte, G.; Traverso, C.E.; Izzotti, A. The Outflow Pathway: A Tissue with Morphological and Functional Unity. J. Cell. Physiol. 2016, 231, 1876–1893. [Google Scholar] [CrossRef] [PubMed]
  12. Dismuke, W.M.; Mbadugha, C.C.; Ellis, D.Z. NO-induced regulation of human trabecular meshwork cell volume and aqueous humor outflow facility involve the BKCa ion channel. Am. J. Physiol. Cell Physiol. 2008, 294, C1378–C1386. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  13. Stamer, W.D.; Lei, Y.; Boussommier-Calleja, A.; Overby, D.R.; Ethier, C.R. eNOS, a pressure-dependent regulator of intraocular pressure. Investig. Ophthalmol. Vis. Sci. 2011, 52, 9438–9444. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  14. Galassi, F.; Renieri, G.; Sodi, A.; Ucci, F.; Vannozzi, L.; Masini, E. Nitric oxide proxies and ocular perfusion pressure in primary open angle glaucoma. Br. J. Ophthalmol. 2004, 88, 757–760. [Google Scholar] [CrossRef] [Green Version]
  15. Lidder, S.; Webb, A.J. Vascular effects of dietary nitrate (as found in green leafy vegetables and beetroot) via the nitrate-nitrite-nitric oxide pathway. Br. J. Clin. Pharmacol. 2013, 75, 677–696. [Google Scholar] [CrossRef] [Green Version]
  16. Doganay, S.; Evereklioglu, C.; Turkoz, Y.; Er, H. Decreased nitric oxide production in primary open-angle glaucoma. Eur. J. Ophthalmol. 2002, 12, 44–48. [Google Scholar] [CrossRef]
  17. Kotikoski, H.; Vapaatalo, H.; Oksala, O. Nitric oxide and cyclic GMP enhance aqueous humor outflow facility in rabbits. Curr. Eye Res. 2003, 26, 119–123. [Google Scholar] [CrossRef]
  18. Borghi, V.; Bastia, E.; Guzzetta, M.; Chiroli, V.; Toris, C.B.; Batugo, M.R.; Carreiro, S.T.; Chong, W.K.; Gale, D.C.; Kucera, D.J.; et al. A novel nitric oxide releasing prostaglandin analog, NCX 125, reduces intraocular pressure in rabbit, dog, and primate models of glaucoma. J. Ocul. Pharmacol. Ther. 2010, 26, 125–132. [Google Scholar] [CrossRef]
  19. Mäepea, O.; Bill, A. The pressures in the episcleral veins, Schlemm’s canal and the trabecular meshwork in monkeys: Effects of changes in intraocular pressure. Exp. Eye Res. 1989, 49, 645–663. [Google Scholar] [CrossRef]
  20. Mäepea, O.; Bill, A. Pressures in the juxtacanalicular tissue and Schlemm’s canal in monkeys. Exp. Eye Res. 1992, 54, 879–883. [Google Scholar] [CrossRef]
  21. Ashpole, N.E.; Overby, D.R.; Ethier, C.R.; Stamer, W.D. Shear stress-triggered nitric oxide release from Schlemm’s canal cells. Investig. Ophthalmol. Vis. Sci. 2014, 55, 8067–8076. [Google Scholar] [CrossRef] [PubMed]
  22. Bondonno, C.P.; Croft, K.D.; Hodgson, J.M. Dietary nitrate, nitric oxide, and cardiovascular health. Crit. Rev. Food Sci. Nutr. 2016, 56, 2036–2052. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  23. Ikram, M.A.; Brusselle, G.G.O.; Murad, S.D.; van Duijn, C.M.; Franco, O.H.; Goedegebure, A.; Klaver, C.C.W.; Nijsten, T.E.C.; Peeters, R.P.; Stricker, B.H.; et al. The Rotterdam Study: 2018 update on objectives, design and main results. Eur. J. Epidemiol. 2017, 32, 807–850. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  24. Coleman, A.L.; Miglior, S. Risk factors for glaucoma onset and progression. Surv. Ophthalmol. 2008, 53 (Suppl. 1), S3–S10. [Google Scholar] [CrossRef] [PubMed]
  25. Drewnowski, A.; Shultz, J.M. Impact of aging on eating behaviors, food choices, nutrition, and health status. J. Nutr. Health Aging 2001, 5, 75–79. [Google Scholar] [PubMed]
  26. Hiza, H.A.; Casavale, K.O.; Guenther, P.M.; Davis, C.A. Diet quality of Americans differs by age, sex, race/ethnicity, income, and education level. J. Acad. Nutr. Diet. 2013, 113, 297–306. [Google Scholar] [CrossRef]
  27. Grzymisławska, M.; Puch, E.A.; Zawada, A.; Grzymisławski, M. Do nutritional behaviors depend on biological sex and cultural gender? Adv. Clin. Exp. Med. 2020, 29, 165–172. [Google Scholar] [CrossRef] [Green Version]
  28. Springelkamp, H.; Wolfs, R.C.; Ramdas, W.D.; Hofman, A.; Vingerling, J.R.; Klaver, C.C.; Jansonius, N.M. Incidence of glaucomatous visual field loss after two decades of follow-up: The Rotterdam Study. Eur. J. Epidemiol. 2017, 32, 691–699. [Google Scholar] [CrossRef] [Green Version]
  29. Dielemans, I.; Vingerling, J.R.; Hofman, A.; Grobbee, D.E.; de Jong, P.T. Reliability of intraocular pressure measurement with the Goldmann applanation tonometer in epidemiological studies. Graefes Arch. Clin. Exp. Ophthalmol. 1994, 232, 141–144. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  30. Ikram, M.A.; Brusselle, G.; Ghanbari, M.; Goedegebure, A.; Ikram, M.K.; Kavousi, M.; Kieboom, B.C.T.; Klaver, C.C.W.; de Knegt, R.J.; Luik, A.I.; et al. Objectives, design and main findings until 2020 from the Rotterdam Study. Eur. J. Epidemiol. 2020, 35, 483–517. [Google Scholar] [CrossRef]
  31. Klipstein-Grobusch, K.; den Breeijen, J.H.; Goldbohm, R.A.; Geleijnse, J.M.; Hofman, A.; Grobbee, D.E.; Witteman, J.C. Dietary assessment in the elderly: Validation of a semiquantitative food frequency questionnaire. Eur. J. Clin. Nutr. 1998, 52, 588–596. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  32. Goldbohm, R.A.; van den Brandt, P.A.; Brants, H.A.; van’t Veer, P.; Al, M.; Sturmans, F.; Hermus, R.J. Validation of a dietary questionnaire used in a large-scale prospective cohort study on diet and cancer. Eur. J. Clin. Nutr. 1994, 48, 253–265. [Google Scholar] [PubMed]
  33. Feunekes, G.I.; Van Staveren, W.A.; De Vries, J.H.; Burema, J.; Hautvast, J.G. Relative and biomarker-based validity of a food-frequency questionnaire estimating intake of fats and cholesterol. Am. J. Clin. Nutr. 1993, 58, 489–496. [Google Scholar] [CrossRef] [PubMed]
  34. Bogovski, P.; Bogovski, S. Special report animal species in which n-nitroso compounds induce cancer. Int. J. Cancer 1981, 27, 471–474. [Google Scholar] [CrossRef]
  35. Keszei, A.P.; Goldbohm, R.A.; Schouten, L.J.; Jakszyn, P.; van den Brandt, P.A. Dietary N-nitroso compounds, endogenous nitrosation, and the risk of esophageal and gastric cancer subtypes in the Netherlands Cohort Study. Am. J. Clin. Nutr. 2013, 97, 135–146. [Google Scholar] [CrossRef] [Green Version]
  36. Knekt, P.; Järvinen, R.; Dich, J.; Hakulinen, T. Risk of colorectal and other gastro-intestinal cancers after exposure to nitrate, nitrite and N-nitroso compounds: A follow-up study. Int. J. Cancer 1999, 80, 852–856. [Google Scholar] [CrossRef]
  37. Aschebrook-Kilfoy, B.; Ward, M.H.; Gierach, G.L.; Schatzkin, A.; Hollenbeck, A.R.; Sinha, R.; Cross, A.J. Epithelial ovarian cancer and exposure to dietary nitrate and nitrite in the NIH-AARP Diet and Health Study. Eur. J. Cancer Prev. 2012, 21, 65–72. [Google Scholar] [CrossRef] [Green Version]
  38. Cross, A.J.; Freedman, N.D.; Ren, J.; Ward, M.H.; Hollenbeck, A.R.; Schatzkin, A.; Sinha, R.; Abnet, C.C. Meat consumption and risk of esophageal and gastric cancer in a large prospective study. Am. J. Gastroenterol. 2011, 106, 432–442. [Google Scholar] [CrossRef] [Green Version]
  39. Dellavalle, C.T.; Daniel, C.R.; Aschebrook-Kilfoy, B.; Hollenbeck, A.R.; Cross, A.J.; Sinha, R.; Ward, M.H. Dietary intake of nitrate and nitrite and risk of renal cell carcinoma in the NIH-AARP Diet and Health Study. Br. J. Cancer 2013, 108, 205–212. [Google Scholar] [CrossRef] [Green Version]
  40. Blekkenhorst, L.C.; Prince, R.L.; Ward, N.C.; Croft, K.D.; Lewis, J.R.; Devine, A.; Shinde, S.; Woodman, R.J.; Hodgson, J.M.; Bondonno, C.P. Development of a reference database for assessing dietary nitrate in vegetables. Mol. Nutr. Food Res. 2017, 61, 1600982. [Google Scholar] [CrossRef] [Green Version]
  41. Inoue-Choi, M.; Virk-Baker, M.K.; Aschebrook-Kilfoy, B.; Cross, A.J.; Subar, A.F.; Thompson, F.E.; Sinha, R.; Ward, M.H. Development and calibration of a dietary nitrate and nitrite database in the NIH-AARP Diet and Health Study. Public Health Nutr. 2016, 19, 1934–1943. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  42. ATC/DDD Index 2022. Available online: https://www.whocc.no/atc_ddd_index/ (accessed on 14 June 2022).
  43. Voortman, T.; Kiefte-de Jong, J.C.; Ikram, M.A.; Stricker, B.H.; van Rooij, F.J.A.; Lahousse, L.; Tiemeier, H.; Brusselle, G.G.; Franco, O.H.; Schoufour, J.D. Adherence to the 2015 Dutch dietary guidelines and risk of non-communicable diseases and mortality in the Rotterdam Study. Eur. J. Epidemiol. 2017, 32, 993–1005. [Google Scholar] [CrossRef] [PubMed]
  44. Caspersen, C.J.; Bloemberg, B.P.; Saris, W.H.; Merritt, R.K.; Kromhout, D. The prevalence of selected physical activities and their relation with coronary heart disease risk factors in elderly men: The Zutphen Study, 1985. Am. J. Epidemiol. 1991, 133, 1078–1092. [Google Scholar] [CrossRef]
  45. Stel, V.S.; Smit, J.H.; Pluijm, S.M.; Visser, M.; Deeg, D.J.; Lips, P. Comparison of the LASA Physical Activity Questionnaire with a 7-day diary and pedometer. J. Clin. Epidemiol. 2004, 57, 252–258. [Google Scholar] [CrossRef] [PubMed]
  46. Gopinath, B.; Liew, G.; Lewis, J.R.; Blekkenhorst, L.C.; Bondonno, C.; Burlutsky, G.; Hodgson, J.M.; Mitchell, P. Association of dietary nitrate intake with retinal microvascular structure in older adults. Eur. J. Nutr. 2020, 59, 2057–2063. [Google Scholar] [CrossRef]
  47. McGeechan, K.; Liew, G.; Macaskill, P.; Irwig, L.; Klein, R.; Klein, B.E.K.; Wang, J.J.; Mitchell, P.; Vingerling, J.R.; DeJong, P.T.V.M. Meta-analysis: Retinal vessel caliber and risk for coronary heart disease. Ann. Intern. Med. 2009, 151, 404–413. [Google Scholar] [CrossRef]
  48. Ikram, M.K.; De Jong, F.J.; Bos, M.J.; Vingerling, J.R.; Hofman, A.; Koudstaal, P.J.; De Jong, P.; Breteler, M.M.B. Retinal vessel diameters and risk of stroke: The Rotterdam Study. Neurology 2006, 66, 1339–1343. [Google Scholar] [CrossRef]
  49. Chan, K.K.W.; Tang, F.; Tham, C.C.Y.; Young, A.L.; Cheung, C.Y. Retinal vasculature in glaucoma: A review. BMJ Open Ophthalmol. 2017, 1, e000032. [Google Scholar] [CrossRef]
  50. Mitchell, P.; Leung, H.; Wang, J.J.; Rochtchina, E.; Lee, A.J.; Wong, T.Y.; Klein, R. Retinal vessel diameter and open-angle glaucoma: The Blue Mountains Eye Study. Ophthalmology 2005, 112, 245–250. [Google Scholar] [CrossRef]
  51. Kawasaki, R.; Wang, J.J.; Rochtchina, E.; Lee, A.J.; Wong, T.Y.; Mitchell, P. Retinal vessel caliber is associated with the 10-year incidence of glaucoma: The Blue Mountains Eye Study. Ophthalmology 2013, 120, 84–90. [Google Scholar] [CrossRef]
  52. Amerasinghe, N.; Aung, T.; Cheung, N.; Fong, C.W.; Wang, J.J.; Mitchell, P.; Saw, S.M.; Wong, T.Y. Evidence of retinal vascular narrowing in glaucomatous eyes in an Asian population. Investig. Ophthalmol. Vis. Sci. 2008, 49, 5397–5402. [Google Scholar] [CrossRef] [PubMed]
  53. Wang, S.; Xu, L.; Wang, Y.; Wang, Y.; Jonas, J.B. Retinal vessel diameter in normal and glaucomatous eyes: The Beijing eye study. Clin. Exp. Ophthalmol. 2007, 35, 800–807. [Google Scholar] [CrossRef] [PubMed]
  54. Yoo, E.; Yoo, C.; Lee, B.R.; Lee, T.E.; Kim, Y.Y. Diagnostic Ability of Retinal Vessel Diameter Measurements in Open-Angle Glaucoma. Investig. Ophthalmol. Vis. Sci. 2015, 56, 7915–7922. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  55. Bengtsson, B. Some factors affecting the distribution of intraocular pressures in a population. Acta Ophthalmol. 1972, 50, 33–46. [Google Scholar] [CrossRef]
  56. Bulpitt, C.J.; Hodes, C.; Everitt, M.G. Intraocular pressure and systemic blood pressure in the elderly. Br. J. Ophthalmol. 1975, 59, 717–720. [Google Scholar] [CrossRef] [Green Version]
  57. Kahn, H.A.; Leibowitz, H.M.; Ganley, J.P.; Kini, M.M.; Colton, T.; Nickerson, R.S.; Dawber, T.R. The Framingham Eye Study. II. Association of ophthalmic pathology with single variables previously measured in the Framingham Heart Study. Am. J. Epidemiol. 1977, 106, 33–41. [Google Scholar] [CrossRef]
  58. Klein, B.E.; Klein, R. Intraocular pressure and cardiovascular risk variables. Arch. Ophthalmol. 1981, 99, 837–839. [Google Scholar] [CrossRef]
  59. Klein, B.E.; Klein, R.; Linton, K.L. Intraocular pressure in an American community. The Beaver Dam Eye Study. Investig. Ophthalmol. Vis. Sci. 1992, 33, 2224–2228. [Google Scholar]
  60. Wu, S.Y.; Leske, M.C. Associations with intraocular pressure in the Barbados Eye Study. Arch. Ophthalmol. 1997, 115, 1572–1576. [Google Scholar] [CrossRef]
  61. Tielsch, J.M.; Katz, J.; Sommer, A.; Quigley, H.A.; Javitt, J.C. Hypertension, perfusion pressure, and primary open-angle glaucoma. A population-based assessment. Arch. Ophthalmol. 1995, 113, 216–221. [Google Scholar] [CrossRef]
  62. Dielemans, I.; Vingerling, J.R.; Algra, D.; Hofman, A.; Grobbee, D.E.; de Jong, P.T. Primary open-angle glaucoma, intraocular pressure, and systemic blood pressure in the general elderly population. The Rotterdam Study. Ophthalmology 1995, 102, 54–60. [Google Scholar] [CrossRef]
  63. Healey, P.R.; Mitchell, P.; Smith, W.; Wang, J.J. The influence of age and intraocular pressure on the optic cup in a normal population. J. Glaucoma 1997, 6, 274–278. [Google Scholar] [CrossRef] [PubMed]
  64. Foster, P.J.; Machin, D.; Wong, T.Y.; Ng, T.P.; Kirwan, J.F.; Johnson, G.J.; Khaw, P.T.; Seah, S.K. Determinants of intraocular pressure and its association with glaucomatous optic neuropathy in Chinese Singaporeans: The Tanjong Pagar Study. Investig. Ophthalmol. Vis. Sci. 2003, 44, 3885–3891. [Google Scholar] [CrossRef]
  65. Resch, H.; Garhofer, G.; Fuchsjäger-Mayrl, G.; Hommer, A.; Schmetterer, L. Endothelial dysfunction in glaucoma. Acta Ophthalmologica 2009, 87, 4–12. [Google Scholar] [CrossRef]
  66. McMonnies, C. Reactive oxygen species, oxidative stress, glaucoma and hyperbaric oxygen therapy. J. Optom. 2018, 11, 3–9. [Google Scholar] [CrossRef]
  67. Kumar, D.M.; Agarwal, N. Oxidative stress in glaucoma: A burden of evidence. J. Glaucoma 2007, 16, 334–343. [Google Scholar] [CrossRef]
  68. Ferreira, S.M.; Lerner, S.F.; Brunzini, R.; Evelson, P.A.; Llesuy, S.F. Oxidative stress markers in aqueous humor of glaucoma patients. Am. J. Ophthalmol. 2004, 137, 62–69. [Google Scholar] [CrossRef]
  69. Izzotti, A.; Saccà, S.C.; Cartiglia, C.; De Flora, S. Oxidative deoxyribonucleic acid damage in the eyes of glaucoma patients. Am. J. Med. 2003, 114, 638–646. [Google Scholar] [CrossRef]
  70. Chidlow, G.; Schmidt, K.G.; Wood, J.P.; Melena, J.; Osborne, N.N. Alpha-lipoic acid protects the retina against ischemia-reperfusion. Neuropharmacology 2002, 43, 1015–1025. [Google Scholar] [CrossRef]
  71. Pan, H.; He, M.; Liu, R.; Brecha, N.C.; Yu, A.C.; Pu, M. Sulforaphane protects rodent retinas against ischemia-reperfusion injury through the activation of the Nrf2/HO-1 antioxidant pathway. PLoS ONE 2014, 9, e114186. [Google Scholar] [CrossRef]
  72. Xu, Y.P.; Han, F.; Tan, J. Edaravone protects the retina against ischemia/reperfusion-induced oxidative injury through the PI3K/Akt/Nrf2 pathway. Mol. Med. Rep. 2017, 16, 9210–9216. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  73. Seong, H.; Ryu, J.; Yoo, W.S.; Kim, S.J.; Han, Y.S.; Park, J.M.; Kang, S.S.; Seo, S.W. Resveratrol Ameliorates Retinal Ischemia/Reperfusion Injury in C57BL/6J Mice via Downregulation of Caspase-3. Curr. Eye Res. 2017, 42, 1650–1658. [Google Scholar] [CrossRef] [PubMed]
  74. Renner, M.; Stute, G.; Alzureiqi, M.; Reinhard, J.; Wiemann, S.; Schmid, H.; Faissner, A.; Dick, H.B.; Joachim, S.C. Optic Nerve Degeneration after Retinal Ischemia/Reperfusion in a Rodent Model. Front. Cell. Neurosci. 2017, 11, 254. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  75. Félétou, M.; Vanhoutte, P.M. Endothelium-derived hyperpolarizing factor: Where are we now? Arterioscler. Thromb. Vasc. Biol. 2006, 26, 1215–1225. [Google Scholar] [CrossRef]
  76. Wink, D.A.; Miranda, K.M.; Espey, M.G.; Pluta, R.M.; Hewett, S.J.; Colton, C.; Vitek, M.; Feelisch, M.; Grisham, M.B. Mechanisms of the antioxidant effects of nitric oxide. Antioxid. Redox Signal. 2001, 3, 203–213. [Google Scholar] [CrossRef]
  77. Clifford, T.; Howatson, G.; West, D.J.; Stevenson, E.J. The potential benefits of red beetroot supplementation in health and disease. Nutrients 2015, 7, 2801–2822. [Google Scholar] [CrossRef]
  78. Song, X.; Li, P.; Li, Y.; Yan, X.; Yuan, L.; Zhao, C.; An, Y.; Chang, X. Strong association of glaucoma with atherosclerosis. Sci. Rep. 2021, 11, 8792. [Google Scholar] [CrossRef]
  79. Song, X.; Li, P.; Yuan, L.; Li, Y.; Yan, X.; Zhao, C.; An, Y.; Chang, X. Strong Association of Glaucoma with Atherosclerosis and Potential Therapeutic Effect of Methazolamide on Atherosclerosis. Res. Sq. 2021, 1–13. [Google Scholar] [CrossRef]
  80. Lin, S.C.; Pasquale, L.R.; Singh, K.; Lin, S.C. The Association between Body Mass Index and Open-angle Glaucoma in a South Korean Population-based Sample. J. Glaucoma 2018, 27, 239–245. [Google Scholar] [CrossRef]
  81. Ramdas, W.D.; Wolfs, R.C.; Hofman, A.; de Jong, P.T.; Vingerling, J.R.; Jansonius, N.M. Lifestyle and risk of developing open-angle glaucoma: The Rotterdam study. Arch. Ophthalmol. 2011, 129, 767–772. [Google Scholar] [CrossRef] [Green Version]
  82. Na, K.-S.; Kim, J.-H.; Paik, J.-S.; Cho, W.-K.; Ha, M.; Park, Y.-G.; Yang, S.-W. Underweight increases the risk of primary open-angle glaucoma in diabetes patients: A Korean nationwide cohort study. Medicine 2020, 99, e19285. [Google Scholar] [CrossRef] [PubMed]
  83. Kim, A.Y.; Han, K.E.; Jun, R.M.; Choi, K.R. Progression of Visual Field Loss and Body Mass Index in Normal Tension Glaucoma. J. Korean Ophthalmol. Soc. 2017, 58, 1404–1409. [Google Scholar] [CrossRef] [Green Version]
  84. Berdahl, J.P.; Fleischman, D.; Zaydlarova, J.; Stinnett, S.; Allingham, R.R.; Fautsch, M.P. Body Mass Index Has a Linear Relationship with Cerebrospinal Fluid Pressure. Investig. Ophthalmol. Vis. Sci. 2012, 53, 1422–1427. [Google Scholar] [CrossRef] [PubMed]
  85. Fleischman, D.; Berdahl, J.P.; Zaydlarova, J.; Stinnett, S.; Fautsch, M.P.; Allingham, R.R. Cerebrospinal fluid pressure decreases with older age. PLoS ONE 2012, 7, e52664. [Google Scholar] [CrossRef] [PubMed]
  86. Willett, W.; Stampfer, M.J. Total energy intake: Implications for epidemiologic analyses. Am. J. Epidemiol. 1986, 124, 17–27. [Google Scholar] [CrossRef]
Nutrients 14 02490 g001 550
Figure 1. Graphic presentation of the multivariable-adjusted dose–response relationship between incident open-angle glaucoma (iOAG), intraocular pressure (IOP), and energy adjusted dietary nitrate intake obtained by generalized additive regression models; total dietary nitrate intake (A,D), nitrate intake from vegetables (B,E), and nitrate intake from non-vegetable food sources (C,F). Dotted lines represent 95% confidence intervals. The reference value is the value associated with the mean nitrate intake for all participants. The rug plot along the x- and y-axis of each graph depicts each observation.
Figure 1. Graphic presentation of the multivariable-adjusted dose–response relationship between incident open-angle glaucoma (iOAG), intraocular pressure (IOP), and energy adjusted dietary nitrate intake obtained by generalized additive regression models; total dietary nitrate intake (A,D), nitrate intake from vegetables (B,E), and nitrate intake from non-vegetable food sources (C,F). Dotted lines represent 95% confidence intervals. The reference value is the value associated with the mean nitrate intake for all participants. The rug plot along the x- and y-axis of each graph depicts each observation.
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Figure 2. Odds ratios (95% confidence interval (CI)) for open-angle glaucoma by total dietary nitrate intake (A), nitrate intake from vegetables (B), and nitrate intake from non-vegetable food sources (C) (as continuous variables and quintiles) analyzed using conditional logistic regression. Model 1: adjusted for body mass index, total energy intake, diet quality, physical activity, and follow-up time. Model 2: model 1 additionally adjusted for intraocular pressure. Model 3: model 1 additionally adjusted for education level and smoking status.
Figure 2. Odds ratios (95% confidence interval (CI)) for open-angle glaucoma by total dietary nitrate intake (A), nitrate intake from vegetables (B), and nitrate intake from non-vegetable food sources (C) (as continuous variables and quintiles) analyzed using conditional logistic regression. Model 1: adjusted for body mass index, total energy intake, diet quality, physical activity, and follow-up time. Model 2: model 1 additionally adjusted for intraocular pressure. Model 3: model 1 additionally adjusted for education level and smoking status.
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Table
Table 1. Baseline characteristics of participants that did and did not develop incident open-angle glaucoma (iOAG) during follow-up.
Table 1. Baseline characteristics of participants that did and did not develop incident open-angle glaucoma (iOAG) during follow-up.
No iOAG (N = 865)iOAG (N = 173)p-Value
Age, years, mean (SD)64.8 (7.0)65.7 (6.9)0.12
Sex, female, N (%)460 (53.2)92 (53.2)>0.99
Education, N (%) 0.77
Primary education101 (11.7)21 (12.1)
Lower education376 (43.5)78 (45.1)
Intermediate education250 (28.9)53 (30.6)
Higher education131 (15.1)21 (12.1)
Smoking status, N (%) 0.79
Non-smoker281 (32.5)54 (31.2)
Former smoker410 (47.4)81 (46.8)
Current smoker170 (19.7)38 (21.9)
Hypertension, N (%)491 (56.8)92 (53.2)0.46
SBP, mmHg, mean (SD)137.6 (20.6)136.6 (20.9)0.58
DBP, mmHg, mean (SD)77.1 (11.6)75.0 (12.5)0.03
BMI, kg/m2, mean (SD)27.1 (4.1)25.9 (3.3)<0.001
Total energy intake, kcal/day, mean (SD)2119.1 (594.5)2054.3 (515.0)0.19
Diet quality, mean (SD)6.6 (1.9)7.0 (1.9)0.04
Physical activity, MET hours/week, mean (SD)0.0 (0.9)0.1 (0.9)0.07
IOP, mmHg, mean (SD)14.1 (2.9)16.4 (3.9)<0.001
Follow-up time, years, mean (SD)9.5 (4.7)10.9 (5.3)<0.001
Total dietary nitrate intake, mg/day, mean (SD)109.8 (78.4)92.8 (47.1)<0.001
Nitrate intake from vegetables, mg/day, mean (SD)94.2 (76.3)77.4 (45.2)<0.001
Nitrate intake from non-vegetable food sources, mg/day, mean (SD) 15.6 (7.9)15.4 (10.9)0.78
Abbreviations: iOAG, incident open-angle glaucoma; N, number; SBP, systolic blood pressure; DBP, diastolic blood pressure; BMI, body mass index; MET, metabolic equivalent of task; IOP, intraocular pressure; SD, standard deviation.
Table
Table 2. Baseline characteristics of participants by energy adjusted total dietary nitrate intake (1st, 3rd, and 5th quintiles).
Table 2. Baseline characteristics of participants by energy adjusted total dietary nitrate intake (1st, 3rd, and 5th quintiles).
KERRYPNXQ1 (N = 205)Q3 (N = 206)Q5 (N = 205)p ANOVA
iOAG, N (%)38 (18.5)38 (18.4)20 (9.8)0.07
Age, years, mean (SD)66.4 (7.1)65.7 (6.9)62.5 (6.0)<0.001
Sex, female, N (%)93 (45.4)124 (60.2)119 (58.0)0.03
Education, N (%) 0.005
Primary education31 (15.1) 29 (14.1) 7 (3.4)
Lower education86 (42.0) 89 (43.2) 94 (45.8)
Intermediate education61 (30.0) 58 (28.2) 60 (29.3)
Higher education24 (11.7)28 (13.6)43 (21.0)
Smoking status, N (%) 0.73
Non-smoker69 (33.7) 75 (36.4) 60 (29.3)
Former smoker88 (42.9) 91 (44.2) 106 (51.7)
Current smoker46 (22.4)40 (19.4)39 (19.0)
Hypertension, N (%)118 (57.5)123 (59.7)118 (57.6)0.39
SBP, mmHg, mean (SD)140.8 (20.4)138.9 (22.9)137.0 (19.7)0.04
DBP, mmHg, mean (SD)77.7 (12.3)76.5 (12.5)78.4 (10.6)0.02
BMI, kg/m2, mean (SD)26.3 (3.6)26.8 (3.9)28.2 (4.6)<0.001
Total energy intake, kcal/days, mean (SD)2233.2 (673.9)2024.8 (484.4)2140.5 (569.8)0.002
Diet quality, mean (SD)6.0 (1.8)7.2 (1.8)7.0 (2.0)<0.001
Physical activity, MET hours/week, mean (SD)−0.1 (0.9)0.1 (0.9)0.1 (0.9)0.06
IOP, mmHg, mean (SD)14.6 (3.1)14.6 (3.1)14.1 (3.4)0.33
Follow-up time, years, mean (SD)9.6 (4.5)10.4 (5.2)9.2 (4.8)0.07
Total dietary nitrate intake, mg/day, mean (SD)48.8 (15.7)86.4 (11.4)213.0 (91.7)<0.001
Nitrate intake from vegetables, mg/day, mean (SD)35.1 (14.5)71.2 (10.1)196.8 (91.4)<0.001
Nitrate intake from non-vegetable food sources, mg/day, mean (SD) 13.7 (4.6)15.2 (5.7)16.3 (6.7)<0.001
Abbreviations: N, number; Q, quintile; ANOVA, analysis of variance; iOAG, incident open-angle glaucoma; SBP, systolic blood pressure; DBP, diastolic blood pressure; BMI, body mass index; MET, metabolic equivalent of task; IOP, intraocular pressure; SD, standard deviation.
Table
Table 3. Multivariable adjusted beta (95% confidence interval) of intraocular pressure (IOP), by quintiles of nitrate intake.
Table 3. Multivariable adjusted beta (95% confidence interval) of intraocular pressure (IOP), by quintiles of nitrate intake.
Beta a per 1 Unit Increasep-ValueQ1Q2Q3Q4Q5p-Trend b
Total dietary nitrate intake
(10 mg/day)		Model 10.02
(−0.02–0.06)		0.350.00−0.04
(−0.89–0.80)		−0.25
(−1.05–0.55)		−0.22
(−0.98–0.53)		−0.15
(−0.99–0.69)		0.78
Model 20.02
(−0.02–0.06)		0.390.00−0.02
(−0.87–0.83)		−0.30
(−1.11–0.50)		−0.23
(−0.99–0.54)		−0.20
(−1.06–0.66)		0.69
Nitrate intake from vegetables
(10 mg/day)		Model 10.02
(−0.02–0.06)		0.290.000.33
(−0.48–1.13)		0.17
(−0.61–0.95)		−0.13
(−0.85–0.60)		0.11
(−0.69–0.91)		0.91
Model 20.02
(−0.02–0.06)		0.320.000.33
(−0.48–1.14)		0.18
(−0.61–0.97)		−0.12
(−0.85–0.62)		0.05
(−0.76–0.87)		0.82
Nitrate intake from non-vegetable food sources (10 mg/day)Model 1−0.45
(−0.96–0.06)		0.090.000.37
(−0.52–1.25)		0.05
(−0.73–0.84)		−0.15
(−0.92–0.62)		−0.29
(−1.05–0.47)		0.09
Model 2−0.46
(−0.98–0.05)		0.080.000.37
(−0.53–1.26)		0.05
(−0.74–0.84)		−0.18
(−0.96–0.60)		−0.31
(−1.08–0.45)		0.08
Model 1: adjusted for body mass index, total energy intake, diet quality, physical activity, and follow-up time. Model 2: model 1 additionally adjusted for education level and smoking status. a Betas (95%CI) for intraocular pressure (IOP) by total dietary nitrate intake, nitrate intake from vegetables, and nitrate intake from non-vegetable food sources (as continuous variables) analyzed using linear regression. b Test for trend conducted using median value for each quintile (total dietary nitrate intake: quintile 1 = 48.8 mg/day; quintile 2 = 69.0 mg/day; quintile 3 = 86.4 mg/day; quintile 4 = 114.0 mg/day; quintile 5 = 213.0 mg/day; nitrate intake from vegetables: quintile 1 = 34.6 mg/day; quintile 2 = 54.2 mg/day; quintile 3 = 72.0 mg/day; quintile 4 = 98.1 mg/day; quintile 5 = 196.8 mg/day; nitrate intake from non-vegetable food sources: quintile 1 = 10.1 mg/day; quintile 2 = 11.9 mg/day; quintile 3 = 14.1 mg/day; quintile 4 = 15.8 mg/day; quintile 5 = 21.1 mg/day).
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Vergroesen, J.E.; de Crom, T.O.E.; Blekkenhorst, L.C.; Klaver, C.C.W.; Voortman, T.; Ramdas, W.D. Dietary Nitrate Intake Is Associated with Decreased Incidence of Open-Angle Glaucoma: The Rotterdam Study. Nutrients 2022, 14, 2490. https://doi.org/10.3390/nu14122490

AMA Style

Vergroesen JE, de Crom TOE, Blekkenhorst LC, Klaver CCW, Voortman T, Ramdas WD. Dietary Nitrate Intake Is Associated with Decreased Incidence of Open-Angle Glaucoma: The Rotterdam Study. Nutrients. 2022; 14(12):2490. https://doi.org/10.3390/nu14122490

Chicago/Turabian Style

Vergroesen, Joëlle E., Tosca O. E. de Crom, Lauren C. Blekkenhorst, Caroline C. W. Klaver, Trudy Voortman, and Wishal D. Ramdas. 2022. "Dietary Nitrate Intake Is Associated with Decreased Incidence of Open-Angle Glaucoma: The Rotterdam Study" Nutrients 14, no. 12: 2490. https://doi.org/10.3390/nu14122490

APA Style

Vergroesen, J. E., de Crom, T. O. E., Blekkenhorst, L. C., Klaver, C. C. W., Voortman, T., & Ramdas, W. D. (2022). Dietary Nitrate Intake Is Associated with Decreased Incidence of Open-Angle Glaucoma: The Rotterdam Study. Nutrients, 14(12), 2490. https://doi.org/10.3390/nu14122490

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