Smoking as a risk factor for visual field progression in exfoliation glaucoma patients in Sweden

Abstract

Purpose: The present study aimed to identify if smoking was a risk factor for visual field progression in exfoliation glaucoma patients.

Methods: Prospective nonrandomized cohort study. The study included patients with diagnosed exfoliation glaucoma. All included patients were followed for at least three years with reliable visual fields. At least five reliable visual fields were needed to be included in the study. Exfoliation glaucoma was defined using the European Glaucoma Society Guidelines. The visual fields were tested using the 24-2 test point pattern of the Humphrey Field Analyzer. Smoking was assessed through questionnaires. Outcomes: Visual field progression. Three different approaches were used: difference in mean deviation (MD), rate of progression (ROP), and guided progression analysis (GPA). 

Results: Totally, n=113 patients were included; of them, n=57 were smokers. Smoking was a significant predictor for visual field progression in the three models (MD/ROP/GPA) studied (p=<0.001/p=<0.001/p=<0.001).  Other predictors were in the MD model: IOP at diagnosis (p=0.04) and SLT treatment (p=0.001). In the ROP model: MD (p=0.01), VFI (p=0.005), number of medications (p=0.001) and SLT (p=0.001). In the GPA model: the number of medications (p=0.002).  

Conclusions: Smoking strongly predicted visual field deterioration in all the models studied. Therefore, patients should be advised to quit smoking when establishing the glaucoma diagnosis to slow down the progression of the disease. 

Background

Glaucoma is a common eye disease that affects the optic nerve. This progressive disease is characterised by a continuous loss of ganglion cells in the retina. Usually, visual field defects develop in glaucoma, and in advanced cases, glaucoma can even lead to blindness (1). A standard classification of glaucoma divides glaucoma into primary and secondary. The primary is the one without a known aetiology. Primary open-angle glaucoma (POAG) and secondary exfoliation glaucoma (EXFG) are the two most common clinical manifestations of glaucoma in Sweden (2). Exfoliation glaucoma is due to the accumulation of a greyish protein-based material in the eye's anterior chamber. Exfoliation material has been identified in different eye structures, but even extraocular organs revealed the presence of exfoliation material (3-6).  

Risk factors for the onset and progression of glaucoma have been well described in previous studies. Older age, elevated IOP, ethnicity, family history of glaucoma, exfoliation, disc haemorrhage and myopia have been described as risk factors for the development of POAG (7-9). However, the evidence found was scarce regarding risk factors that only can induce the development of EXFG. Therefore, it's possible to speculate that similar risk factors can apply to EXFG. However, genetic causes showed a different role between POAG and EXFG (10). Considering risk factors for glaucoma progression, the ones described in the Early Manifest Glaucoma Trial (EMGT)(11) were: higher IOP, exfoliation, increased damage at baseline, higher age, and disc haemorrhages. Other studies confirmed that age and IOP were significant risk factors for glaucoma progression in POAG patients (12-16). Very little evidence was found about specific risk factors for visual field deterioration in EXFG. 

Smoking has been extensively described as a risk factor for health. Smoking has been identified as a significant risk factor for cardiovascular disease, carotid disease, and peripheral artery disease.(17) Quitting smoking was associated with a 30% reduction in all-cause mortality. (18) Even an increased risk for dementia, Alzheimer's disease, and vascular dementia have been described. (19) In the eye, the association between smoking and glaucoma has been described in previous studies with different results. (20-27) Even four systematic reviews showing different results were found in the literature. (28-31) Edwards, R, showed a weak association between smoking and glaucoma; the review did not consider the smoking status (current or former). Jain, V showed more robust evidence of an association between smoking and glaucoma among current smokers than among former smokers. Bonovas showed an increased risk with current smoking, the pooled estimate OR was 1.37 but no risk in the case of former smokers. However, Zhou, Y showed no association between smoking and glaucoma, both in current and former smokers (pooled RR 0.97 in both). Results were variable due to differences in the studies included, different populations, study designs, how smoking was assessed and classified, etc. All the studies in the above-mentioned systematic reviews ranked glaucoma as a binary variable (Yes/No), included only POAG patients, and did not consider the disease's stage and/or progression. 

The present study aimed to identify if smoking was a risk factor for visual field progression in EXFG. 

Methods

The present was a prospective nonrandomized cohort study. The study followed the STROBE guidelines. (32) All patients diagnosed with exfoliation glaucoma attending the Ophthalmology Department at the Skaraborg’s Hospital were asked if they wanted to participate in the study. In case of an affirmative answer, patients were called to a special recruiting appointment. To diminish “recall bias” patients were advised about the nature of the study before the recruitment appointment. The recruiting period was from January 2014 until December 2018 (five years).

Inclusion criteria: 

1. Patients diagnosed with exfoliation glaucoma. Exfoliation glaucoma was defined according to the European Glaucoma Society. (33)

2. Age ≤ 85 years at recruiting. 

Exclusion criteria:

1. Patients who could not perform at least five reliable visual fields three years after recruiting. Reliable visual fields were: false positives ≤ 15% and/or false negatives ≤ 20%, and/or fixation losses ≤ 30%. 

2. Patients with advanced visual field deterioration, defined as mean deviation (MD) ≥ -18 dB and/or visual field index (VFI) ≤40%, were excluded. Patients with damaged visual fields were excluded to avoid "floor effects.(34, 35)

3. Patients who were operated on glaucoma surgery during the follow-up period. Uneventful cataract surgery and/or selective laser treatment (SLT) were not considered exclusion criteria. 

4. Patients suffering from another eye disease during the follow-up period that could modify visual fields, i.e., central vein occlusion, retinal diseases, etc. 

5. Patients dropping out of the control period for different reasons like dementia, moving to another city, etc. 

6. Patients who administrated nicotine through other ways than cigarette smoking, like chewing tobacco, smoking pipes, pills, etc., both at the recruiting visit and during the three-year follow-up period. 

The risk factors studied at recruiting were smoking, age, sex, unilateral/bilateral glaucoma, visual acuity, refractive errors, IOP at diagnosis and recruitment, number of medications, central corneal thickness (CCT) measurement, gonioscopy evaluation: depth and pigmentation, cup-disc (C/D) ratio, diabetes, hypertension, cardiovascular diseases (including angina, heart attacks, heart failure, stroke, TIA and peripheral arterial diseases), migraine and family history for glaucoma.

Risk factors studied during the three years were: IOP reduction, SLT treatment, cataract operation, and the number of medications used. 

At the recruiting visit, all participants filled out a questionnaire regarding smoking. The questions were as follows: 1) Did you smoke once in your life? (Yes/No). 2) Did you smoke more than 100 cigarettes in your life? (Yes/No). 3) Are you still smoking? (Yes/No). 4) How many cigarettes per day have you smoked? 5) When did you begin smoking? 6) When did you quit smoking? The total number of cigarettes smoked in the whole life was calculated as the number of cigarettes per day x 365 x smoking years. Smoking was studied and classified according to the definitions of the Centers for Disease Control and Prevention (CDC) National Health Interview Services (Division of Health Interview Statistics. National Center for Health Statistics. 3311 Toledo Rd Hyattsville, USA). Current smoker: An adult who has smoked 100 cigarettes in their lifetime and who currently smokes cigarettes. Former smoker: An adult who has smoked at least 100 cigarettes in their lifetime but who had quit smoking at the time of the interview. No smoker: An adult who has never smoked, or who has smoked less than 100 cigarettes in their lifetime. (https://www.cdc.gov/nchs/nhis/tobacco/tobacco_glossary.htm)

Furthermore, the questionnaire included questions regarding hypertension, cardiovascular diseases, diabetes, migraine, and a family history of glaucoma. Hypertension was studied as "using medicines against high blood pressure" (Yes/No). Cardiovascular diseases (angina, heart attack, heart failure, stroke, TIA, and peripheral artery diseases) were registered as present/absent (Yes/No). Diabetes was classified as "using medicines against diabetes" (Yes/No). Migraine was described as "suffered from migraine" (Yes/No). Finally, a family history of glaucoma was defined as having near relative suffering from glaucoma. Near relative was defined: as father and/or mother and/or siblings. The answer was registered as "Yes/No." 

Every patient was ophthalmological examined at the recruiting visit. Age was registered as age at the recruiting visit and age at diagnosis based on the patient's records. Sex was registered as male/female. In addition, the unilateral/bilateral presence of glaucoma was recorded. If both eyes were suffering from glaucoma, both eyes were registered, but only one eye, chosen at random, was included in the study. 

The best-corrected visual acuity (BCVA) was tested using a Snellen chart. Refractive errors were registered based on the information sent by the optician/optometrist. The IOP was measured with an applanation tonometer. The central corneal thickness (CCT) values were obtained using an ultrasound device (Tomey Pachymetry; Tomey Corp, Nagoya 451–0051, Japan). A gonioscopy was performed using a goniolens with undilated pupils to assess the trabecular meshwork. Gonioscopy was classified using the Shaffer's system (0-4). The pigmentation of the trabecular meshwork was ranked 0-3. Afterwards, the patient's pupils were dilated with 2.5 % Phenylephrine and Tropicamide 0.5% (Bausch & Lomb UK Ltd, 106 London Road-Kingston-upon-Thames-Surrey-KT2 6TN-England). The presence of exfoliation was recorded. In the case of pseudophakia, if exfoliation was not present at recruitment, patients were included if exfoliation was registered at least twice in patients’ medical records. The appearance of the optic nerve was studied using a 90-D lens, and the average vertical cup-to-disc ratio (C/D ratio) was measured. 

The risk factors for progression under the three-year follow-up were measured: IOP reduction was calculated as the difference in IOP between the IOP at diagnosis and the IOP three years after recruitment. The SLT treatment was measured as present or absent per patient (Yes/No). Cataract operation was counted as present or absent (Yes/No). Eye drops at the end of the three years were measured as the number of medications (compounds).

The primary endpoint of the study was the visual field's progress. All patients were examined using the Humphrey Field Analyzer device (Carl Zeiss, Carl-Zeiss-Straße 22, 73447 Oberkochen, Germany) and the test point pattern 24-2. A trained ophthalmic nurse did the examinations. Visual field progression was assessed in three different ways. 

The first approach was based on the "mean deviation" (MD) values. The difference in MD values was calculated from the beginning to the end of the study. Higher MD values indicated higher progression. The MD parameter was chosen since several studies still use MD as an indicator of progression. (36-39) However, cataract development can also modify MD values. 

The second method used to analyze progression was based on the "visual field index" (VFI) values. The machine calculated the VFI and performed a regression analysis calculating the "rate of progression" (ROP). The device calculated the ROP as the percentage of VFI deterioration (%)/year. The ROP calculation is a "trend analysis." 

Finally, guided progression analysis (GPA) was the third progression method. The GPA is included in the device and performed automatically (GPA Alert). The GPA differs from the ROP because it is an "event analysis." The machine compares every single point to similar points detected in prior examinations. The GPA alert showed: no, possible or likely progression. In the present study, the results were evaluated as "no progression" or "progression," The term “progression” included both "possible" and "likely" progression.

Statistics

The SPSS statistical software was used for statistical analysis (IBM, 1 New Orchard Road Armonk, NY 10504, USA). All the studied variables were tested in two steps. The first step used univariate linear regression analysis for continuous endpoint variables (MD and ROP). Meanwhile, the dichotomous endpoint (GPA) used a univariate logistic regression. The variables that showed significant values in the univariate analysis model were included as covariates in a multivariate analysis. Multivariate linear regression analysis was used in case the endpoints were continuous (MD and ROP). In the case of a dichotomous variable (GPA), a multivariate logistic regression analysis was performed. In addition, a subgroup analysis among the smokers using a similar strategy was done. The significance level was set at 0.05. 

A post-hoc power calculation was done using the "PS free software" of the Vanderbilt University (https://biostat.app.vumc.org/wiki/Main/PowerSampleSize). 

Results

In total, n = 113 patients were included in this study; seventeen (n = 17) patients were excluded. The reasons for exclusion were nine (n = 9) patients did not attend the check-up visits and/or could not perform reliable visual fields, four patients (n = 4) developed other eye diseases during the three years follow-up, three patients (n = 3) were glaucoma operated and one (n = 1) patient began using “snus” (Moist tobacco packaged in ready-to-use pouches that are placed between teeth and gums) under the three years follow-up.

The general characteristics of the cohort were the following: the average age at diagnosis was 69.05 (± 6.6) years, and the average age at recruitment was 71.65 (± 7.27) years. Regarding sex distribution, n = 59 (52.21%) was female and n = 54 (47.79%) was male (Chi-square; p = 0.46). Smoking was reported by n = 57 (50.44%) meanwhile n = 56 (49.56%) reported no smoking (Chi-square; p = 0.92). Glaucoma was presented unilateral in n = 72 (63.71%) and bilateral in n = 41 (36.29%) (Chi-square; p = < 0.001). At diagnosis, the average IOP value was 32.81 mmHg (± 6.59) (range: 24–55). Visual acuity at diagnosis was 0.83 (± 0.24). Refractive errors showed to be 0.52 D (± 1.64), range: -4.25/+3.25 D. The average CCT values were 542.52 µm (± 33.31). The gonioscopy at recruitment showed an average anterior chamber depth of 3.19 (± 0.62), and the pigmentation of the trabecular meshwork was 2.48 (± 0.54). The C/D ratio at recruitment was 0.71 (± 0.18). The average time between diagnosis and the recruiting appointment was 2.79 (± 1.29) years.

The IOP at diagnosis was 32.81 mmHg (± 6.59). After three years of follow-up, the average IOP was 16.8 mmHg (± 2.88). The number of cataract patients operated on during the three-year follow-up was n = 15. The SLT treatment was performed in n = 22 patients. The number of medications at three years follow-up was 2.68 (± 0.9).

Regarding visual field parameters at baseline (recruitment), the MD was − 6.42dB (± 4.96), and the VFI was 85.6% (± 14.42). After three years of follow-up, the visual field parameters were: MD -9.96dB (± 5.31), and the VFI was 75.22% (± 19.02). The rate of progression (ROP) was − 2.65% (± 2.26)/ year. The GPA showed that n = 63 progressed and n = 50 not progressed (Chi-square; p = 0.06).

The cohort was studied according to smoking status. Smoker patients were younger at glaucoma diagnosis than no-smokers. The average age at diagnosis among smokers was 67.5 (± 6.71) years; meanwhile, among no smokers was 70.24 (± 6.49) years (T-test; p = 0.04). The IOP at diagnosis was higher among smokers than among smokers. The average IOP among smokers was 33.72 (± 6.55) mmHg; meanwhile, IOP among no smokers was 30.96 (± 6.37) mmHg (T-test; p = 0.02). Glaucoma diagnosis was presented more often bilateral among smokers than among no smokers. Among smokers, 37% were diagnosed with bilateral glaucoma; meanwhile, only 20% were bilateral among no smokers (Chi-square; p = 0.03). The visual field parameters at recruitment differed between smokers and no smokers. Both the MD and the VFI values showed a more damaged visual among smokers than no smokers at recruitment (T-test; p = 0.02 for MD and p = 0.03 for VFI). See Table 1.

M: Male. F: Female. SD: Standard deviation. IOP: Intraocular pressure. CCT: Central cornea thickness. MD: Mean deviation. VFI: Visual field index.

During the three years of follow-up, the number of patients treated with SLT was significantly higher among smokers than among no smokers. Among smokers, 30% were treated with SLT; meanwhile, only 11% were SLT treated among the no smokers patients (Chi-square; p = 0.005). At the end of the three-year follow-up period, the MD and the VFI values still differed between the smokers and the no-smoker patients. Smokers showed a more damaged visual field than no smokers (T-test; p = < 0.001 for MD and p = < 0.001 for VFI). In addition, the progression parameters (MD/ROP and GPA) differed between smokers and no smokers after three years of follow-up. The MD difference was 2.20 (± 1.85) dB among no smokers; meanwhile, it was 5.06 (± 2.86) dB among smokers (T-test; p = < 0.001). The ROP also showed a significant difference; among no smokers, it was − 1.63 (± 1.71) %/year; meanwhile, it was − 3.83 (± 2.31) %/year among smokers (T-test; p = < 0.001). The GPA also showed a significant difference between smokers and no smokers. The GPA classified n = 32 (56%) patients as no progressor among no smokers; meanwhile, only n = 10 (18%) patients were classified as no progressors in the smoker group (Chi-square; p = < 0.001). The relative risk (RR) for GPA progression among smokers was 1.95. See Table 2.

IOP: Intraocular pressure. MD: Mean deviation. VFI: Visual field index. SLT: Selective laser trabeculoplasty. ROP: rate of progression.

The post-hoc power calculation performed with the PS software based on a Chi-square using the data from the GPA model showed that for an alfa error of 0.05, sample sizes of n = 56 and n = 57, and relative risk of 1.95, the power was 95%.

All the independent variables were first tested in a univariate regression model for the dependent variables: MD, ROP, and GPA. Then, the three dependent variables were tested separately. The following independent variables were included: at diagnosis: IOP, and age. At recruitment: age, sex, smoking, diabetes, hypertension, migraine, family history, cardiovascular diseases, IOP, visual acuity, unilateral/bilateral, phakia/pseudophakia, refractive errors, CCT, anterior chamber depth, trabecular meshwork pigmentation, C/D ratio, time diagnosis-recruitment, VFI, MD. At three years of follow-up: SLT treatments, cataract operations, IOP, and the number of medications at three years follow-up. Only variables showing a significant value (p = < 0.05) were included in a multivariable analysis. In the multivariable linear regression MD model, the variables that showed a significant association were smoking (p = < 0.001), IOP at diagnosis (p = 0.04), and SLT treatment (p = 0.01). Please see Table 3.

β coeff.: β coefficient. SE: Standard error. IOP: Intraocular pressure. MD: Mean deviation. VFI: Visual field index. SLT: Selective Laser Treatment.

(*) Significant values

In the multivariate linear regression ROP model, the variables associated with progression were smoking (p = < 0.001), MD at recruitment (p = 0.01), VFI at recruitment (p = 0.005), the number of medications at three years’ follow-up (p = 0.001) and, the SLT treatment (p = 0.001). Please see Table 4.

β coeff.: β coefficient. SE: Standard error. IOP: Intraocular pressure. MD: Mean deviation. ROP: Rate of progression. SLT: Selective Laser Treatment.

(*) Significant values.

In the multivariate logistic regression GPA model, the variables associated with progress were smoking (p = < 0.001) and the number of medications at three years’ follow-up (p = 0.002). Please see Table 5.

GPA: Guided progression analysis. OR: Odds ratio. IOP: Intraocular pressure. MD: Mean deviation. VFI: Visual field index.

(*) Significant values.

Discussion

The present study showed an association between former smoking and visual field deterioration in EXFG patients in Sweden. At the baseline, the age of glaucoma diagnosis was lower among smokers than among no smokers. Smokers showed even a higher IOP at diagnosis. All included patients were sent to us by an optometrist who checked the IOP when patients wanted to buy eyeglasses. Probably as smokers developed higher IOP was detected by the optometrists earlier than no smokers. The bilateral presentation of glaucoma was more common among smokers than no smokers. Probably some kind of systemic factors induced by nicotine use could explain that glaucoma showed often bilateral among smokers than no smokers. Another difference between the two groups at inclusion was that the MD and VFI values showed increased visual field damage among smokers than no smokers. This increased damage in the visual field could be mediated through an increased IOP in the smokers but even by the nicotine effects or both. After three year follow-up period, the smokers showed increased progress in their visual field's damage than the no smokers. The three models used (i.e., MD difference/ROP/GPA) showed significant results. In the MD model, the difference between the MD values from the beginning to the end of the study was 2 dB among no smokers and around 5 dB among smokers. Similar results were shown in the ROP model; the progression among smokers (-3.83%/year) was more than double that among no smokers (-1.63%/year). The GPA model also showed that 44% were classified as progression in the no smoker meanwhile 82% were classified as progression in the smoker group. Probably the increased damage visual field at inclusion, the faster progression was detected.

Three different models (MD, VFI, and GPA) for visual field damage were used; still, there is no consensus about the best method. (38) Smoking showed a statistically significant association with visual field deterioration in the three models studied. Other predictors for progression were: IOP at diagnosis, MD and VFI at recruitment, amount of medications at three years' follow-up, and SLT treatment. It's easy to understand that IOP at diagnosis was a risk factor for visual field progression. Exfoliation glaucoma characterized by high IOP and increased IOP will induce damage to the ganglion cells, thus visual field deterioration. The other parameters that showed association were the MD and the VFI values at recruitment. These parameters are directly related to the high IOP values at diagnosis. Patients with a more deteriorated visual field at diagnosis already had a loss of ganglion cells, which can explain an increased progression among these patients.

Visual field deterioration in exfoliation glaucoma patients was evaluated using three different models: MD, VFI, and GPA. Both the MD and the VFI are continuous variables. The MD values can theoretically vary between 0 and − 30 Decibels (dB); meanwhile, the VFI can range between 0-100 percent (%). On the other hand, the GPA evaluated visual field deterioration as a dichotomous variable: progression/ no progression. For this dichotomous variable, logistic regression analysis was the most suitable approach. The advantage of the GPA model is that it's possible to obtain odds ratios (OR) from the function (“Exp B”) and measure relative risks (RR). In the case of the unadjusted model, the OR was 5.67 and the adjusted of 8.77. This OR is not related to the levels of progression as the variable is not continuous. The OR says that smokers have a five to nine-fold increased risk of being placed among "progressors” than non-smokers. The RR for smokers to develop progression was 1.95; this means that smokers are 95% more likely to develop visual field progression than no smokers. Even GPA seems to be a good method to analyse visual field damage as a binary variable, which probably simplifies how the disease develops (continue). Finally, the three models completed each other and added more information about diseases’ progression.

The association between smoking and developing glaucoma has been controversial. Smoking has been described in a previous study as a risk factor for glaucoma development, but this evidence was not shown in another study. (23) (40) No evidence in the literature was found about associations between smoking and risk for progression in glaucoma. Smoking has been pointed out as a risk factor for increased IOP regardless of glaucoma. (21, 22). Similar results were found in the present study; smokers had higher IOP values than non-smokers. At diagnosis, the smoking glaucoma patients had an IOP of 33.72 (± 6.55) mmHg versus an IOP of 30.96 (± 6.37) mmHg (t-test; p = 0.02) among non-smokers. It’s possible to speculate that visual field deterioration was due to an increased IOP. However, when adjusting for IOP in the three models (MD/ROP and GPA), smoking remained a predictor for progression. The reasons for increased IOP due to cigarette smoking are still unclear. It is possible that the anticholinergic effects of nicotine can increase IOP. (22)

A relationship between the number of cigarettes and progression was detected in the subgroup analysis. Patients who smoked a higher amount of cigarettes showed an increased progression. These results were shown both in the unadjusted (MD: p = 0.008; ROP: p = 0.006 and GPA: p = 0.04) and the adjusted (MD: p = 0.005; ROP: p = < 0.001 and GPA: p = 0.03) analysis in the three models studied. However, IOP at diagnosis when adjusting for smoking was found to be significant only in the MD model (p = 0.005) but not in the ROP model (p = 0.88) and the GPA model (p = 0.72). It's possible that visual field deterioration associated with an increased nicotine intake was not mediated through an increased IOP. Nicotine induces vasoconstriction and a decreased blood flow in the optic nerve; Mehra KS described this in 1976. (41) These findings were described by several reports later on. (42, 43) Exfoliation glaucoma has been associated with an increased risk of vascular obliteration. Exfoliation deposits have been found in the vessels in the eye (44, 45). An already reduced blood flow in exfoliation glaucoma patients could be accentuated by nicotine use.

Interestingly, most patients began smoking at 17.08 (± 2.62) and finished smoking at 45.17 (± 13.97) years. The average age at recruitment was 72.27 (± 7.24) years, and only three (n = 3) patients were still smoking at inclusion. If nicotine would have a direct IOP increasing effect, it isn't easy to believe that this could be maintained so many years after quitting smoking. Probably other mechanisms besides increased IOP can explain visual field progression. Exfoliation glaucoma has been shown to be a genetic disease; several genes have been studied. A previously published article (46) showed an association between smoking and Endothelial nitric oxide synthase (NOS3) gene variants in POAG patients. The results applied to both current and former smokers. The authors concluded that smoking could be a risk factor (RR = 1.63) for developing POAG mediated through the rs7830 NOS3. Exfoliation glaucoma has been associated with several genes. The most studied is the LOXL1 gene. (10, 47–49) The most common SNPs associated with exfoliation are the: LOXL1_rs3825942, LOXL1_rs2165241, and LOXL1_rs1048661. (47, 49) In the present study, smokers develop glaucoma earlier than no smokers. This indicates that smoking could induce genetic changes leading to exfoliation earlier in life. Even glaucoma among smokers was presented more often as bilateral than in no smokers, showing probable systemic effects of smoking and/or genetic mechanisms. The hypothesis of genetic alterations in LOXL1 due to smoking renders further studies.

Several predictors for glaucoma progression were identified in the present study. Besides smoking, other predictors were: IOP at diagnosis (MD model), MD and VFI at recruitment (ROP model), SLT treatment (MD/ROP model), and the number of medications at three years of follow-up (ROP/GPA model). The IOP at diagnosis was high: 32.81 mmHg (± 6.59). This is consistent with previous results (50); exfoliation glaucoma is a "high pressure" glaucoma. Elevated IOP is a known predictor of glaucoma progression. (12–16) High values in MD and low VFI resulted in an increased progression. High MD values at recruitment induced increased progress, consistent with previous results. (36) Increased progression must be considered when glaucoma diagnosis is established, so patients with high MD and/or low VFI should be monitored often to detect progression and stop the evolution of this disease. The SLT treatments and the number of medications had an inverse relationship with progression; let's say that increased SLT and the number of drugs resulted in a decreased progression. Both methods reduced IOP and slowed down glaucoma progression.

The study has certain limitations. All included patients and their parents were born in Scandinavia. Three (n = 3) of them were born in Finland, and the rest in Sweden. Therefore, results from the present study probably cannot be applied to other populations. All patients were suffering from exfoliation glaucoma; results cannot be applied to other types of glaucoma. Furthermore, exfoliation glaucoma is the most common glaucoma type in Sweden. (50)

Another limitation is that patients with very damaged visual fields were not included. This was to avoid "floor effects," in which visual field changes cannot say if the disease is progressing or not. The results from the study can be applied to early and moderate glaucoma. (39) Another limitation is that only patients smoking cigarettes were included. Other ways of nicotine intake were excluded. Another limitation of the study is that most included patients were "former" smokers. The patients were consecutively included from our Ophthalmology Department. Even though this is a limitation and results cannot be extrapolated to "current" smokers, the sample resembles our daily practice.

The present study showed an association between smoking and visual field progression in the three models studied. Damage to visual fields could be mediated through increased IOP, nicotine effects, or both. The mechanisms remained still unclear. Regardless of the mechanisms, it's crucial to include smoking in the routine ophthalmology examination of glaucoma patients. To ask about smoking has not been a standard question in our Department and probably is not in several other Ophthalmology Departments worldwide.

Furthermore, it seems essential to consider if the patient had smoked in the past when making clinical decisions. Probably the IOP should be reduced even more among smokers than among non-smokers. SLT treatment or glaucoma surgery should be considered earlier in smokers than in non-smokers to reduce IOP and slow down the progression of this disabling eye disease.

In conclusion, smoking seems to be a risk factor for glaucoma progression in exfoliation glaucoma. Patients should be asked about smoking habits when the glaucoma diagnosis is established. Patients should be informed and helped to quit smoking.

Declarations

Ethics approval and consent to participate

The study adhered to the tenets of the declaration of Helsinki. All patients signed informed consent. Ethical approval was granted by the University of Gothenburg (DN: 119-12). 

 Consent for publication

Not applicable

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Competing interests

The author declares not having competing interests.

Funding

No funding was received for the present study

Author’s contribution 

There is just one author of this article. 

Acknowledgments

Not applicable. 

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