June 2013
Volume 54, Issue 15
Free
ARVO Annual Meeting Abstract  |   June 2013
Associations between Systemic Factors and Longitudinal Analyses of Intraocular Pressure: Mean, Peak and Variability
Author Affiliations & Notes
  • Thasarat Vajaranant
    Ophthalmology and Visual Sciences, University of Illinois at Chicago, Chicago, IL
  • Charlotte Joslin
    Ophthalmology and Visual Sciences, University of Illinois at Chicago, Chicago, IL
    Division of Epidemiology and Biostatistics, School of Public Health, University of Illinois at Chicago, Chicago, IL
  • Footnotes
    Commercial Relationships Thasarat Vajaranant, None; Charlotte Joslin, None
  • Footnotes
    Support None
Investigative Ophthalmology & Visual Science June 2013, Vol.54, 3519. doi:https://doi.org/
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      Thasarat Vajaranant, Charlotte Joslin; Associations between Systemic Factors and Longitudinal Analyses of Intraocular Pressure: Mean, Peak and Variability. Invest. Ophthalmol. Vis. Sci. 2013;54(15):3519. doi: https://doi.org/.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract
 
Purpose
 

Intraocular pressure (IOP) mean, peak and variability are risk factors for primary open-angle glaucoma development and progression, and systemic factors may affect IOP. Here, we characterized 12-year IOP mean, peak and variability in non-glaucomatous eyes at baseline, and determined systemic factors associated with long-term IOP variables.

 
Methods
 

We used the dbGaP Age-Related Eye Disease Study dataset. Glaucoma was an exclusion criterion to enrollment, and we excluded subjects who reported glaucoma during follow-up to minimize effects of IOP treatment. Repeated IOP measurements (OD only), taken annually, were analyzed to assess the effect of disease status on mean IOP using 2 methods (SAS v9.3, Cary, NC): 1) a t-test to compare effect of disease status; and 2) mixed-effects regression models for longitudinal data analysis with repeated measures. Method 1: descriptive statistics for the IOP were computed; and compared by t-test based on baseline self-reported systemic disease status (diabetes (DM), hypertension (HTN), smoking, cancer), obesity (BMI > 30) and age-related macular degeneration status; antioxidant treatment; and subsequent all-cause and cardiovascular mortality. Method 2: random-effects models were fit using PROC MIXED to test the trend across time, and to assess the fixed effects of systemic disease status. Null hypotheses were tested by comparing model -2LogLikelihood values among nested models.

 
Results
 

Of 3,914 analyzed subjects; 55% were female and 95.7% were White (mean age = 69.4 ± 5 yrs). Analyses totaled 31,431 IOP measurements during a 9.6 mean yrs (max = 12.8). T-test of mortality had no effect on mean IOP (p = 0.30), suggesting no survival effect and validating inclusion of subjects, regardless of death. Table 1 summarizes overall long-term IOP variables. Less visit number and baseline DM, HTN and obesity statistically significantly predicts higher IOP mean, peak and variability in t-tests. Final multivariable mixed-effects regression models identified a mean IOP of 15.8 at baseline, with only visit number (estimate = -0.014, p <.0001), baseline DM (estimate = 0.712, p <.0001) and obesity (estimate = 0.597, p <.0001) significant in predicting IOP.

 
Conclusions
 

In this older cohort with no glaucoma at baseline, overall IOP decreases over time. During the 12-year period, baseline DM and obesity were significantly associated with an increased IOP.

  
Keywords: 568 intraocular pressure  
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