Investigative Ophthalmology & Visual Science Cover Image for Volume 64, Issue 12
September 2023
Volume 64, Issue 12
Open Access
Clinical and Epidemiologic Research  |   September 2023
The Association of Antibiotic Use and the Odds of a New-Onset ICD Code Diagnosis of Age-Related Macular Degeneration: A Large National Case-Control Study
Author Affiliations & Notes
  • John Moir
    Pritzker School of Medicine, University of Chicago, Chicago, Illinois, United States
  • Max Hyman
    The Center for Health and the Social Sciences, University of Chicago, Chicago, Illinois, United States
  • Jessie Wang
    Department of Ophthalmology and Visual Science, University of Chicago Medicine, Chicago, Illinois, United States
  • Andrea Flores
    The Center for Health and the Social Sciences, University of Chicago, Chicago, Illinois, United States
  • Dimitra Skondra
    Department of Ophthalmology and Visual Science, University of Chicago Medicine, Chicago, Illinois, United States
  • Correspondence: Dimitra Skondra, Department of Ophthalmology and Visual Science, University of Chicago Medicine, 924 E 57th St, no. 104, Chicago, IL 60637, USA; [email protected]
Investigative Ophthalmology & Visual Science September 2023, Vol.64, 14. doi:https://doi.org/10.1167/iovs.64.12.14
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      John Moir, Max Hyman, Jessie Wang, Andrea Flores, Dimitra Skondra; The Association of Antibiotic Use and the Odds of a New-Onset ICD Code Diagnosis of Age-Related Macular Degeneration: A Large National Case-Control Study. Invest. Ophthalmol. Vis. Sci. 2023;64(12):14. https://doi.org/10.1167/iovs.64.12.14.

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Abstract

Purpose: The widespread use of antibiotics has many well-documented impacts on the human microbiome, which may be associated with the development of various inflammatory diseases. Despite age-related macular degeneration (AMD) featuring an inflammatory pathogenesis, the relationship between antibiotics and AMD has remained unexplored. We conducted the first study to determine the association between antibiotic exposure and a new-onset International Classification of Diseases (ICD) diagnosis of AMD.

Methods: We performed a case-control analysis of patients aged 55 and older with new-onset AMD between 2008 and 2017 from a nationwide commercial health insurance claims database. Exposure to antibiotics in the two years before the index date was determined for cases and controls matched one-to-one by age, year, region, anemia, hypertension, and a comorbidity index. Conditional multivariable logistic regression, adjusted for AMD risk factors, was performed to calculate odd ratios (OR) and 95% confidence intervals (CI).

Results: Among the antibiotic classes, exposure to aminoglycosides (OR = 1.24; 95% CI, 1.22–1.26) and fluoroquinolones (OR = 1.13; 95% CI, 1.12–1.14) was associated with the greatest odds of a new-onset ICD code diagnosis of AMD. Broad-spectrum antibiotics were associated with nearly three times greater odds of a new-onset ICD code diagnosis of AMD (OR = 1.15; 95% CI, 1.13–1.16) compared to narrow-spectrum antibiotics (OR = 1.05; 95% CI, 1.03–1.07). We also identified a frequency- and duration-dependent association, with a greater cumulative number of antibiotic prescriptions or day supply of antibiotics conferring increased odds of a new-onset ICD code diagnosis of AMD.

Conclusions: Greater cumulative exposure to antibiotics, particularly fluoroquinolones, aminoglycosides, and those with broader-spectrum coverage, may be associated with the development of AMD, a finding that requires further investigation using prospective studies.

Antibiotics have emerged as a cornerstone of modern medicine since their widespread introduction in the 1940s. Within the past two decades, global antibiotic consumption has continued to grow substantially, and rates of antibiotic use in some developing nations now closely mirror the rates observed in the developed world.1,2 These trends are of primary concern because they portend increasing antibiotic resistance.1 A lesser recognized concern is that antibiotics also pose a threat to the composition and diversity of the human microbiome, particularly in industrialized societies with heightened sanitation conventions and prevalent antibiotic use.3 Scholars have postulated that microbial alterations caused by these practices may be responsible for the emergence and progression of modern diseases that are most prevalent in the developed world, including obesity, type 2 diabetes, metabolic syndrome, and asthma.4 Epidemiological studies support this theory, revealing that antibiotics are associated with an increased risk of developing inflammatory bowel disease (IBD), which overwhelmingly impacts Europe, North America, and newly industrialized nations.5,6 
Age-related macular degeneration (AMD) shares several features with these modern inflammatory diseases. A meta-analysis identified a higher prevalence of AMD in those of European ancestry compared to those of Asian or African ancestry, in addition to a higher prevalence of AMD in North American and European regions compared to Asian regions.7 Furthermore, total cases of AMD are projected to continue rising globally throughout the coming decades, including most rapidly in Asia.7 These findings suggest that AMD is currently most ubiquitous in developed regions but could have an increasing burden in regions expected to undergo or complete industrialization in the near future. Finally, although inflammatory, complement, oxidative stress, lipid, and angiogenic pathways are likely implicated in AMD development, the pathogenesis remains incompletely understood.812 Hence, to further understand the factors that contribute to AMD, scholarly attention has turned toward the gut microbiome, inspiring the concept of a “gut-retina axis.”1315 To our knowledge, however, the association between antibiotic exposure and the development of AMD has been unexplored in epidemiological data. 
Thus we conducted a large case-control study of a nationwide commercial health insurance claims database that compared antibiotic usage in patients with new-onset AMD to matched controls from the general population. We investigated the association between new-onset AMD and several antibiotic classes, broad- versus narrow-spectrum antibiotics, or commonly prescribed oral antibiotics. We also examined the association between antibiotic timing and new-onset AMD in addition to frequency-dependent associations, as measured by the cumulative number of antibiotic prescriptions and cumulative day supply of antibiotics. 
Methods
Study Design and Participants
The data originated from a peer-reviewed case-control analysis of cases aged 55 and older with new-onset AMD and their matched controls between 2008 and 2017 in the Merative MarketScan Commercial and Medicare Databases.16 The earliest diagnosis of AMD of any subtype (dry, wet, or unspecified) based on International Classification of Diseases (ICD) coding was designated as the index visit for each case, and its control was identified by matching for year, age, U.S. Census Bureau region, hypertension, anemia, and Charlson Comorbidity Index group (zero, one, two, three or more). The sample included 312,404 cases and 312,376 controls, and it achieved statistical balance.16,17 Complete methodological information including inclusion criteria and diagnostic or procedural coding is available in the Methods section and Supplemental Online Content of our previous study.16 
Ascertainment of Antibiotic Exposures
The exposures in this study were antibiotic classes loosely taken from a previous study of antibiotic exposure and the development of IBD.6 Classes, which were not mutually exclusive, included (1) penicillins, beta-lactamase inhibitors, and combinations, (2) other beta-lactam antibacterial drugs such as cephalosporins, carbapenems, and monobactams, (3) tetracyclines, (4) macrolides, lincosamides, and streptogramins, (5) aminoglycosides, (6) quinolones, (7) trimethoprim and sulfonamides, and (8) other antibacterial drugs. The generic drug names contained within each antibiotic class are listed in Supplementary Table S1. An exposure required at least one prescription drug claim with a relevant National Drug Code in the twenty-four months prior to the index visit. 
We classified each generic drug as broad- or narrow-spectrum (Supplementary Table S2) and summarized the cumulative number of prescriptions (0, 1, 2, 3, 4, 5–9, ≥10) and day supply (0, 1–7, 8–14, 15–28, 29–56, ≥57) of antibiotics in the 24 months before the index visit. Furthermore, we identified the most commonly prescribed oral antibiotics between 2011 and 2020 in the annual “Outpatient Antibiotic Prescription Reports” from the Centers for Disease Control and Prevention, which included amoxicillin, amoxicillin/clavulanic acid, azithromycin, cephalexin, ciprofloxacin, doxycycline, and sulfamethoxazole/trimethoprim (Supplementary Table S3).18 We also summarized the number of prescriptions (zero, one, two, three or more) of each of these antibiotics in the 24 months before the index visit. 
Statistical Analysis
We performed descriptive statistics of the antibiotic exposures for cases and matched controls. For categorical variables, this included frequencies and percentages, and significant differences were determined by χ2 tests. For continuous variables, this included means and standard deviations, and significant differences were determined by two-sample t-tests. 
To test the relationship between AMD and antibiotic class, antibiotic spectrum, number of prescriptions, day supply, the most commonly prescribed oral antibiotics, or the number of prescriptions of the most commonly prescribed oral antibiotics, we performed conditional univariate and multivariable logistic regressions. We implemented conditional logistic regression to account for the matched pairs (i.e., case-control) study design. This technique ensured that cases and controls of the same year, age group, Charlson Comorbidity Score, hypertension status, anemia status, and region were compared.19 The multivariable analyses adjusted for AMD risk factors, including female sex, diabetes, hyperlipidemia, smoking, nonproliferative diabetic retinopathy (DR), and proliferative DR. The multivariable analysis of antibiotic class was repeated in which (1) interaction terms between each antibiotic class and female sex were included, (2) the timing of antibiotic exposure was varied to 0–6 months, 6–12 months, and 1–2 years before the index visit, (3) the subgroup of cases with dry AMD and matched controls was considered, and (4) the subgroup of cases with wet AMD and matched controls was considered. 
Statistical significance was set at α = 0.05. Data cleaning and analysis was performed in SAS software, version 9.4.5 (Cary, NC, USA). The figure was produced with the ggplot2 package, version 3.3.5, in R and RStudio software, versions 3.6.3 and 1.4.1103 (Boston, MA, USA), respectively. The University of Chicago Institutional Review Board exempted this study from full review because personal identifiable information was not available in the data. 
Results
The sample included 312,404 cases and 312,376 matched controls, of which 194,136 cases had dry AMD (193,990 matched controls), 22,206 cases had wet AMD (22,126 matched controls), and 96,063 had unspecified AMD (96,260 matched controls). Descriptive statistics showed a greater percentage of cases were female, diagnosed as smokers, and diagnosed with diabetes, hyperlipidemia, nonproliferative DR, and proliferative DR.16 The average age and Charlson Comorbidity Index score were 74.9 years (standard deviation = 10.3) and 1.1 (standard deviation = 1.5), respectively, for both cases and controls.16 Complete demographic and medical data for cases and controls can be accessed in Table 2 of our previous case-control study.16 The rate of exposure to each antibiotic class, broad- and narrow-spectrum antibiotics, and each of the most commonly prescribed oral antibiotics was greater for cases compared to controls (P < 0.001 for all; Table 1). The mean cumulative number of prescriptions and day supply of antibiotics was greater for cases compared to controls as well (P < 0.001 for both). 
Table 1.
 
Antibiotic Exposures for Cases and Matched Controls
Table 1.
 
Antibiotic Exposures for Cases and Matched Controls
Univariate analyses revealed statistically significant associations between AMD and each antibiotic class (Table 2). Notably, exposure to aminoglycosides increased the odds of a new-onset ICD code diagnosis of AMD by 29% (odds ratio (OR) = 1.29; 95% confidence interval (CI), 1.27–1.31) and exposure to quinolones increased the odds by 17% (OR = 1.17; 95% CI, 1.16–1.18). In multivariable analyses that adjusted for AMD risk factors, increased odds of a new-onset ICD code diagnosis of AMD were observed for exposure to aminoglycosides (OR = 1.24; 95% CI, 1.22–1.26), quinolones (OR = 1.13; 95% CI, 1.12–1.14), macrolides, lincosamides, and streptogramins (OR = 1.05; 95% CI, 1.04–1.06), tetracyclines (OR = 1.03; 95% CI, 1.01–1.05), penicillins, beta-lactamase inhibitors, and combinations (OR = 1.02; 95% CI, 1.01–1.03), and other beta-lactam antibacterial drugs (OR = 1.02; 95% CI, 1.01–1.03). Exposure to trimethoprim and sulfonamides, as well as other antibacterial drugs did not affect the odds of a new-onset ICD code diagnosis of AMD. 
Table 2.
 
Univariate and Multivariable Conditional Logistic Regression Models of Antibiotic Classes for Cases and Matched Controls (N = 624,780)
Table 2.
 
Univariate and Multivariable Conditional Logistic Regression Models of Antibiotic Classes for Cases and Matched Controls (N = 624,780)
Multivariable analyses revealed statistically significant associations between both dry and wet AMD and several antibiotic classes (Table 3). In multivariable analyses, increased odds of a new-onset ICD code diagnosis of dry AMD were observed for exposure to aminoglycosides (OR = 1.22; 95% CI, 1.20–1.25), quinolones (OR = 1.12; 95% CI, 1.10–1.13), macrolides, lincosamides, and streptogramins (OR = 1.04; 95% CI, 1.03–1.06), tetracyclines (OR = 1.03; 95% CI, 1.01–1.05), penicillins, beta-lactamase inhibitors, and combinations (OR = 1.03; 95% CI, 1.01–1.05), and other beta-lactam anti-bacterial drugs (OR = 1.02; 95% CI, 1.01–1.04). Exposure to trimethoprim and sulfonamides, as well as other antibacterial drugs, did not affect the odds of a new-onset ICD code diagnosis of dry AMD. Increased odds of a new-onset ICD code diagnosis of wet AMD were observed for exposure to aminoglycosides (OR = 1.50; 95% CI, 1.40–1.60) and quinolones (OR = 1.32; 95% CI, 1.27–1.38) but not for any of the other antibiotic classes. 
Table 3.
 
Multivariable Conditional Logistic Regression Models of Antibiotic Classes for Cases With Dry AMD and Their Matched Controls (N = 388,126) and Wet AMD and Their Match Controls (N = 44,332)
Table 3.
 
Multivariable Conditional Logistic Regression Models of Antibiotic Classes for Cases With Dry AMD and Their Matched Controls (N = 388,126) and Wet AMD and Their Match Controls (N = 44,332)
Univariate analyses revealed statistically significant associations between AMD and broad- or narrow-spectrum antibiotics (Table 4). Specifically, the increased odds of a new-onset ICD code diagnosis of AMD were twice as great for exposure to broad-spectrum antibiotics (OR = 1.16; 95% CI, 1.15–1.18) compared to narrow-spectrum antibiotics (OR = 1.08; 95% CI, 1.06–1.10). In a multivariable analysis, the increased odds of a new-onset ICD code diagnosis of AMD were three times as great for exposure to broad-spectrum antibiotics (OR = 1.15; 95% CI, 1.13–1.16) compared to narrow-spectrum antibiotics (OR = 1.05; 95% CI, 1.03–1.07). 
Table 4.
 
Univariate and Multivariable Conditional Logistic Regression Models of Antibiotic Spectrum for Cases and Matched Controls (N = 624,780)
Table 4.
 
Univariate and Multivariable Conditional Logistic Regression Models of Antibiotic Spectrum for Cases and Matched Controls (N = 624,780)
Univariate analyses revealed statistically significant associations between AMD and the cumulative number of prescriptions or cumulative day supply of antibiotics (Table 5). In a multivariable analysis, a single prescription of antibiotics increased the odds of a new-onset ICD code diagnosis of AMD by 7% (OR = 1.07; 95% CI, 1.06–1.09) and each additional prescription increased the odds by approximately 4% (two prescriptions: OR = 1.11; 95% CI, 1.10–1.13; three prescriptions: OR = 1.15; 95% CI, 1.13–1.17; four prescriptions: OR = 1.19; 95% CI, 1.16–1.21; five to nine prescriptions: OR = 1.23; 95% CI, 1.21–1.25; ≥10 prescriptions: OR = 1.30; 95% CI, 1.27–1.32). Similarly, one- to seven- and eight- to 14-day supplies of antibiotics increased the odds of a new-onset ICD code diagnosis of AMD by 5% (one- to seven-day supply: OR = 1.05; 95% CI, 1.03–1.07; eight- to 14-day supply: OR = 1.05; 95% CI, 1.04–1.07) and additional day supply further increased the odds (15- to 28-day supply: OR = 1.12; 95% CI, 1.10–1.14; 29- to 56-day supply: OR = 1.22; 95% CI, 1.20–1.24; ≥57-day supply: OR = 1.30; 95% CI, 1.28–1.32). 
Table 5.
 
Univariate and Multivariable Conditional Logistic Regression Models of Number of Prescriptions and Day Supply for Cases and Matched Controls (N = 624,780)
Table 5.
 
Univariate and Multivariable Conditional Logistic Regression Models of Number of Prescriptions and Day Supply for Cases and Matched Controls (N = 624,780)
Univariate analyses revealed statistically significant associations between AMD and each of the most commonly prescribed oral antibiotics (Supplementary Table S4). In multivariable analyses, increased odds of a new-onset ICD code diagnosis of AMD were observed for exposure to azithromycin (OR = 1.05; 95% CI, 1.04–1.07), amoxicillin (OR = 1.05; 95% CI, 1.04–1.06), doxycycline (OR = 1.05; 95% CI, 1.03–1.06), ciprofloxacin (OR = 1.04; 95% CI, 1.03–1.05), and cephalexin (OR = 1.04; 95% CI, 1.02–1.05). Exposure to amoxicillin/clavulanic acid and sulfamethoxazole/trimethoprim did not affect the odds of a new-onset ICD code diagnosis of AMD. Furthermore, the odds of a new-onset ICD code diagnosis of AMD were greater for three or more prescriptions of amoxicillin, azithromycin, cephalexin, ciprofloxacin, or doxycycline compared to two prescriptions, and two prescriptions of each compared to one prescription (Figure; Supplementary Table S5). For amoxicillin/clavulanic acid and sulfamethoxazole/trimethoprim, two prescriptions of each increased the odds of a new-onset ICD code diagnosis of AMD compared to one prescription, but three or more prescriptions of each decreased the odds compared to two prescriptions. 
Figure.
 
Multivariable logistic regression model odds ratios of the number of prescriptions of the most commonly prescribed oral antibiotics for cases and matched controls (n = 624,780). Red represents single prescription, green represents two prescriptions, and blue represents three prescriptions. Bars represent 95% confidence interval.
Figure.
 
Multivariable logistic regression model odds ratios of the number of prescriptions of the most commonly prescribed oral antibiotics for cases and matched controls (n = 624,780). Red represents single prescription, green represents two prescriptions, and blue represents three prescriptions. Bars represent 95% confidence interval.
Finally, in a multivariable analysis of antibiotic class in which interaction terms between each antibiotic class and female sex were included (Supplementary Table S6), aminoglycosides increased the odds of a new-onset ICD code diagnosis of AMD (OR = 1.28; 95% CI, 1.25–1.32); however, being female decreased these odds (OR = 0.95; 95% CI, 0.92–0.98). Penicillins, beta-lactamase inhibitors, and combinations did not affect the odds of a new-onset ICD code diagnosis of AMD; however, being female increased these odds (OR = 1.03; 95% CI, 1.004–1.05). In multivariable analyses in which the timing of antibiotic exposure was varied (Supplementary Table S7), aminoglycosides increased the odds of a new-onset ICD code diagnosis of AMD by 38% if taken zero to six months before the index visit (OR = 1.38; 95% CI, 1.34–1.42) compared to 20% at six to 12 months (OR = 1.20; 95% CI, 1.16–1.24) and 17% at one to two years (OR = 1.17; 95% CI, 1.14–1.20). Exposure to quinolones increased the odds of a new-onset ICD code diagnosis of AMD by 7% at zero to six months (OR = 1.07; 95% CI, 1.06–1.09) compared to 14% at six to 12 months (OR = 1.14; 95% CI, 1.12–1.16) and 10% at one to two years (OR = 1.10; 95% CI 1.09–1.12). Exposure to the other antibiotic classes at zero to six months did not affect the odds of a new-onset ICD code diagnosis of AMD, but exposure at six to 12 months and one to two years did. 
Discussion
In this nationwide, retrospective case-control study of 312,404 cases of new-onset AMD and 312,376 matched controls from the general population, we demonstrated an association between antibiotic exposure and increased odds of a new-onset ICD code diagnosis of AMD. This association persisted after adjustment for well-established AMD risk factors. Fluoroquinolones and aminoglycosides increased the odds of a new-onset ICD code diagnosis of AMD the most, as compared to other antibiotic classes. Notably, there appeared to be a frequency- and duration-dependence to the association, with greater cumulative number of prescriptions and day supply of antibiotics linked with increasing odds. Additionally, exposure to broad-spectrum antibiotics was associated with greater odds of new-onset AMD compared to narrow-spectrum antibiotics. 
These findings support the hypothesis that larger perturbations to the gut microbiome, triggered by either broader antibiotic coverage or greater cumulative exposure, may increase the odds of developing AMD. Although outside the scope of this study, there are several biologically plausible explanations for this finding. Antibiotic exposure has been shown to cause persistent disruptions to the composition of the gut microbiome, even when administered in short courses.20,21 Perturbations to the diversity of the gut microbiome are also more severe for broad-spectrum as compared to narrow-spectrum antibiotics.22 Disruption to the gut microbiome is termed dysbiosis, a state linked with greater intestinal wall permeability that enables translocation of microbial endotoxins, which may be possible sources and triggers of systemic inflammation.23 In a mouse model of neovascular AMD, Andriessen et al.24 found that feeding mice a high-fat diet triggered gut dysbiosis, thereby augmenting intestinal permeability to microbial products that induced systemic inflammation and exacerbated choroidal neovascularization. In AMD, inflammation causes breaks in the retinal pigment epithelium and Bruch's membrane, leading to drusen, progressive degeneration of the photoreceptors, and neovascular development.25 Hence, we suggest that the observed frequency- and duration-dependent association may reflect a trend of sustained guy dysbiosis with inflammatory spikes that eventually disseminate along the gut-retina axis. Additionally, the three-times increased odds of a new-onset ICD code diagnosis of AMD with broad- as compared to narrow-spectrum antibiotics may be indicative that greater deviations from the gut microbiome's homeostatic baseline further exacerbate inflammatory signals along the gut-retina axis. 
The relationship between antibiotics and chronic disease development does not appear to be confined to AMD. Antibiotic exposure in early childhood has been linked with the development of celiac disease, type 1 diabetes, and juvenile idiopathic arthritis.2628 Antibiotic use in adults also appears to increase the risk of IBD and rheumatoid arthritis.6,29 Hence, alterations to the gut microbiome due to widespread antibiotic use may underlie or influence a variety of immune-mediated or inflammatory diseases. 
It is also possible that the association between antibiotic exposure and a new-onset ICD code diagnosis of AMD is secondary to, or at least partially due to, a direct effect of antibiotics on retinal tissue. Studies have identified that fluoroquinolones are linked with connective tissue complications, including Achilles tendon rupture and retinal detachment, the latter of which is speculated to be through damage to the structurally integral collagen in the vitreous body.30,31 Interestingly, cultured human retinal pigment epithelium (RPE) cells treated with ciprofloxacin, a fluoroquinolone, displayed decreased viability with corresponding upregulation of inflammatory and pro-apoptotic genes.32 This raises the possibility that fluoroquinolones exert a cytotoxic effect on RPE cells, thereby raising AMD risk. In a small prospective study of 76 patients who received oral fluoroquinolones and 50 sex-matched controls, Ozcelik-Kose et al.33 found that fluoroquinolones had no effect on retinal degeneration at one week and one month after treatment. The patient sample used in that study was small, limited to a one-month follow-up, and younger in age (mean age, 49.8 years). Comparably, we used a large sample of patients aged 55 years or older with a 24-month lookback period. Our older population better captures those at risk of AMD, whereas a 24-month lookback period extends the time that may be required for antibiotics to exert a degenerative effect on the retina. 
Aminoglycosides and fluoroquinolones were associated with the greatest odds of a new-onset ICD code diagnosis of AMD among the antibiotic classes. For fluoroquinolones, this finding may be partially due to the excellent oral bioavailability of newer agents that allows for greater systemic absorption.34 In addition, fluoroquinolones persistently deplete the abundance of anaerobic gut bacteria such as Bacteroides spp. and Bifidobacterium spp.35 These genera produce short-chain fatty acids, anti-inflammatory mediators that can also reduce gut permeability.36,37 Hence, depletion of these beneficial bacteria by fluoroquinolones may be associated with systemic circulation of inflammatory products that eventually localize to the retina. For aminoglycosides, this finding is less clear, because they generally do not cover gram-positive or anaerobic bacteria. Furthermore, literature detailing the impact of aminoglycosides on the gut microbiome are lacking, compared to other antibiotic classes.35 Additional studies are required to precisely define antibiotic-mediated alterations to the gut microbiome and how these may propagate to the retina. 
Interestingly, fluoroquinolones and aminoglycosides were associated with a greater odds of new-onset coding of wet AMD compared to dry AMD. Although these disease classifications share several similarities, they are also unique in their risk factors and pathogenesis.38 These results suggest that fluoroquinolones and aminoglycosides may interact differently with the pathways that drive development and progression of dry and wet AMD. In support of this, we also found an increasing odds ratio of a new-onset ICD code diagnosis of wet AMD, but not dry AMD, as the number of prescriptions for these two classes of antibiotics increased (Aminoglycosides: one prescription: OR = 1.52; 95% CI, 1.41–1.63; two prescriptions: OR = 1.56; 95% CI, 1.33–1.81; three or more prescriptions: OR = 1.63; 95% CI, 1.34–1.99; Quinolones: one prescription: OR = 1.25; 95% CI, 1.19–1.32; two prescriptions: OR = 1.32; 95% CI, 1.23–1.41; three or more prescriptions: OR = 1.51; 95% CI, 1.41–1.61). Further research is required to understand how these medications may modulate AMD development and progression 
Given that outpatient oral antibiotics comprise more than 200 million prescriptions per year in the United States, we identified that these antibiotic prescriptions may pose a significant burden on AMD development.18 We identified three commonly prescribed antibiotics, including amoxicillin, azithromycin, and trimethoprim/sulfamethoxazole, that failed to increase or decrease the odds of AMD when administered as a single prescription. This finding may help to guide judicious prescription patterns when multiple antibacterial agents are available and appropriate for use. For other commonly prescribed antibiotics that included amoxicillin, azithromycin, cephalexin, ciprofloxacin, or doxycycline, however, we saw evidence of a frequency-dependent association. Interestingly, doxycycline is currently being studied in a clinical trial to identify if it can attenuate the progression of geographic atrophy through its anti-inflammatory effects at low doses.39 Future observational studies should examine the impact of antibiotic dosing on AMD development. 
We found that exposure to fluoroquinolones in the zero to six months preceding AMD diagnosis was associated with the greatest odds of a new-onset ICD code diagnosis of AMD compared to exposure at six to 12 months or one to two years. These data may support the notion that fluoroquinolones mediate an acute inflammatory or cytotoxic effect which accelerates AMD development in patients with a heightened baseline risk. For other antibiotic classes, however, exposure at one to two years generally increased the odds of a new-onset ICD code diagnosis of AMD more than exposure at zero to one year preceding diagnosis. This finding may be indicative of sustained gut dysbiosis and chronic inflammation from long-term antibiotic use. We also investigated the interaction terms between female sex and antibiotic classification, which revealed that the odds of a new-onset ICD code diagnosis of AMD associated with fluoroquinolones was mildly attenuated in females. This finding is counterintuitive, because female sex has been proposed as a risk factor for AMD development. Hence, there is likely an unaccounted variable, such as lifestyle factors, that may help to explain this modest attenuation. For the other antibiotic classes, except for penicillins, female sex did not significantly alter odds of a new-onset ICD code diagnosis of AMD when interaction terms were included, suggesting that sex does not further modulate AMD risk in patients exposed to antibiotics. 
Strengths of this study include the use of MarketScan databases, which offer access to a large, nationwide sample of Americans with employer-sponsored health insurance or Medicare supplemental coverage. This allowed for a precise estimation of the odds of a new-onset ICD code diagnosis of onset AMD following exposure to antibiotics. Second, we showed evidence of a strong frequency-response relationship. The presence of this relationship strongly supports an association between antibiotic exposure and AMD diagnosis. Additionally, our multivariable analyses corroborated and adjusted for well-supported AMD risk factors. Thus our study design minimized possible confounding that would otherwise have compromised internal validity. Finally, this study is likely not susceptible to reverse causation. This is to say that antibiotics are unlikely to be prescribed for symptoms related to AMD in its undiagnosed stages. For this reason, we did not include a lead-in period where antibiotic prescriptions were excluded from cumulative tabulations. 
We acknowledge several limitations to the study design. First, records of pharmacological dispensations may not capture actual antibiotic usage. Second, cases of AMD were identified using diagnosis codes and were not verified with retinal imaging. Such codes may be incorrect or underrepresent AMD, but with a large study population, the effect of aberrant coding is likely small, and it should not differ between cases and controls. With that said, the use of use of a claims database may still give way to misclassification bias in this study. Diagnosis of AMD with ICD coding does not necessarily correspond to the development of new-onset AMD. There may be a delay between development of disease and clinic diagnosis as determined by ICD coding. We did not account for the body mass index of subjects, which is a limitation as obesity has been linked to a greater risk of AMD, though this association is inconsistently characterized.40 Furthermore, we were unable to delineate between oral, topical, intravenous, intravitreal, or other routes of administration—an opportunity for future research to determine whether the effects of antibiotics on AMD development are reserved to certain routes of administration. Additionally, lifetime or early-life exposure to antibiotics were not assessed, which may better capture the sustained gut dysbiosis and chronic inflammation that we hypothesize may contribute to AMD pathogenesis. We were unable to capture the indication for antibiotic prescriptions, which could introduce confounding by indication. For example, it may be that repeated infections are associated with AMD, as opposed to the antibiotics prescribed to treat such infections. Alternatively, individuals with recurrent infections who require more frequent and longer courses of antibiotic therapy could have weakened or dysfunctional immune responses, leading to systemic inflammation that raises AMD risk. However, the mean Charlson Comorbidity Index score for both cases and matched controls was 1.1. This indicates that our study population was afflicted with relatively few comorbidities that might introduce this source of confounding. In addition, with the large sample size used in this study, it is unclear whether statistical significance would also carry clinical significance. However, given the abundance with which antibiotics are prescribed in clinical practice, we believe that there is potential clinical relevance to this finding that merits further exploration. Finally, the retrospective nature of the study only allows for us to draw conclusions regarding the association of antibiotics and AMD. Long-term, prospective epidemiological studies are required to determine whether antibiotics increase risk of AMD development. 
Although it is too early to draw definitive conclusions and to substantially alter clinical practice due to these findings, several results merit consideration and can guide future studies. Our results suggest that use of fewer prescriptions, limiting total days of exposure, and choosing agents with appropriately narrow coverage may be important components of antibiotic stewardship to prevent AMD development. For example, avoidance of aminoglycosides and quinolones when an alternative option is available and clinically efficacious should be considered. Further population-level studies of multiple independent databases are needed to confirm the results herein, given that we carried out the first analysis of antibiotic exposure and a new-onset ICD code diagnosis of AMD. 
Acknowledgments
Supported by the UChicago Institute of Translational Medicine and a Bucksbaum Grant. 
Disclosure: J. Moir, None; M. Hyman, None; J. Wang, None; A. Flores, None; D. Skondra, None 
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Figure.
 
Multivariable logistic regression model odds ratios of the number of prescriptions of the most commonly prescribed oral antibiotics for cases and matched controls (n = 624,780). Red represents single prescription, green represents two prescriptions, and blue represents three prescriptions. Bars represent 95% confidence interval.
Figure.
 
Multivariable logistic regression model odds ratios of the number of prescriptions of the most commonly prescribed oral antibiotics for cases and matched controls (n = 624,780). Red represents single prescription, green represents two prescriptions, and blue represents three prescriptions. Bars represent 95% confidence interval.
Table 1.
 
Antibiotic Exposures for Cases and Matched Controls
Table 1.
 
Antibiotic Exposures for Cases and Matched Controls
Table 2.
 
Univariate and Multivariable Conditional Logistic Regression Models of Antibiotic Classes for Cases and Matched Controls (N = 624,780)
Table 2.
 
Univariate and Multivariable Conditional Logistic Regression Models of Antibiotic Classes for Cases and Matched Controls (N = 624,780)
Table 3.
 
Multivariable Conditional Logistic Regression Models of Antibiotic Classes for Cases With Dry AMD and Their Matched Controls (N = 388,126) and Wet AMD and Their Match Controls (N = 44,332)
Table 3.
 
Multivariable Conditional Logistic Regression Models of Antibiotic Classes for Cases With Dry AMD and Their Matched Controls (N = 388,126) and Wet AMD and Their Match Controls (N = 44,332)
Table 4.
 
Univariate and Multivariable Conditional Logistic Regression Models of Antibiotic Spectrum for Cases and Matched Controls (N = 624,780)
Table 4.
 
Univariate and Multivariable Conditional Logistic Regression Models of Antibiotic Spectrum for Cases and Matched Controls (N = 624,780)
Table 5.
 
Univariate and Multivariable Conditional Logistic Regression Models of Number of Prescriptions and Day Supply for Cases and Matched Controls (N = 624,780)
Table 5.
 
Univariate and Multivariable Conditional Logistic Regression Models of Number of Prescriptions and Day Supply for Cases and Matched Controls (N = 624,780)
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