For this study, we analyzed a dataset collected for an ongoing investigation, which has been described previously.
20 Data collection for the current analysis extended from October 2011 through November 2018, and the overall goal of the larger investigation has been to assess ocular and general health associations with the LAZ trait. Potential subjects for this investigation included consecutive patients who presented for regularly scheduled examination by one of nine attending doctors in the primary eye care service of an urban teaching facility in Chicago, Illinois, USA. Length and time period of each attending investigator's participation varied, with some contributing subject data over the entire study period and others contributing for shorter timeframes. Patients were included if they had their pupils adequately dilated (≥ 6 mm), were ≥ 18 years of age, provided informed consent, and completed a written questionnaire to supplement demographic and lifestyle information.
The examination included ocular/systemic medical history, Snellen acuity testing, pupil testing, motility/binocular testing, color vision screening, confrontation visual fields, pre-dilation subjective refraction, slit lamp evaluation, Goldmann applanation tonometry, and funduscopic examination that included binocular indirect ophthalmoscopy of the retinal periphery.
After initial work-up, student clinicians offered study participation prior to pupillary dilation and provided the questionnaires for completion without assistance. Faculty investigators learned of subject participation at the time of their final patient assessment, and they searched for the LAZ trait using direct and indirect, bright slit lamp illumination with 16 to 25× magnification. The criterion for existence of the LAZ trait was presence of radially oriented zonular fibers, pigmented or nonpigmented, with anterior tips that extended substantially (> 1.0 mm) beyond (central to) the normal anterior capsular zonular insertion zone located about 1.5 mm anterior to the lens equator.
34 Further detail on LAZ morphology and detection is presented elsewhere.
11,20,26,35 To ensure definitive LAZ cases, we excluded subjects with < 5 LAZ fibers.
20,26,35 To provide estimation of the number/density of LAZ, investigators used the following arbitrary scale based on countable LAZ fibers: grade trace = 0 to 4, grade 1+ = 5 to 9, grade 2+ = 10 to 19, grade 3+ = 20 to 49, and grade 4+ = ≥ 50.
In addition to other ocular features, investigators examined subjects for KS formation.
2,4,6,8 We considered a KS present when there was “fine pigment dusting” on the central aspect of the posterior cornea, and we considered “fine pigment dusting” present when individual pigment granules could not be “counted” because they were too fine, numerous, and coalesced (
Fig. 2). Larger, coarse pigment flecks and pigment associated with guttata were not considered evidence of a KS, even when central.
Data for other variables (
Tables 1,
2) relied on current history, the medical record, and the questionnaire. Race/ethnicity was classified as: (1) “Black/African American, (2) Asian, (3) Hispanic, black or white, (4) non-Hispanic white, and (5) other.” Based on category frequencies, final analyses used groupings of “Black/African American” versus “not Black/African American.”
Education level was determined by questionnaire: “What is your highest level of education? (1) Less than high school degree, (2) High school degree, (3) Vocational school or some college but no degree, (4) College Associate's or bachelor's degree, and (5) College Master's, Professional, or Doctoral degree.” For final analyses, we grouped subjects according to any formal education beyond a high school degree versus less than or equal to a high school degree.
For smoking status, we asked: “Have you ever been a smoker? (1) Yes, currently, (2) Previously: quit < 12 months ago, (3) Previously: quit > 12 months ago, and (4) Never or rarely: smoked less than a total of 50 cigarettes (2½ packs) over my lifetime.” We then grouped subjects as “current,” “former,” or “never” smokers, and then “ever” versus “never” smokers for final models. We considered subjects as former smokers if they had quit smoking > 1 year prior to the examination, and we asked ever smokers to indicate what they had smoked (i.e. “(1) Cigarettes only or nearly always, (2) Cigars / pipe only or nearly always, and (3) Other, please describe.” We also asked subjects how much they had smoked (half pack increments if cigarettes), the age they began smoking, and when they had stopped if applicable.
To determine alcohol use status, we asked: “Do you drink alcohol? (1) Yes, I do currently, (2) Previously: quit < 12 months ago, (3) Previously: quit > 12 months ago, and (4) Never or rarely because I have not drunk alcohol more than 10 times during my life.” We considered subjects as former drinkers if they had quit drinking > 1 year prior to our examination. Otherwise, they were considered a current drinker. To improve categorization, we asked subjects how many days per week they drank (less than weekly, 1–2 days, 3–4 days, or 5–7 days), how much they usually drank (1–2 drinks, 3–4, 5–7, or ≥8). We also asked how many years they had drunk alcohol and, if applicable, when they had stopped. Based on information obtained, we categorized subjects as “current,” “former,” or “never” drinkers,” and regression model explorations included various combinations of these categories (e.g. “current” versus “ever/never” drinker and “ever” versus “never” drinker).
We considered subjects to have diabetes if they were taking prescribed medication or stopped taking it against medical advice. Likewise, we considered subjects to have a formal hypertension diagnosis only if they were taking medication at the time of examination or stopped taking it against medical advice. Blood pressure was obtained prior to eye drop instillation for pupillary dilation. Student clinicians obtained these measurements using either automated wrist cuffs or manual arm sphygmomanometers.
Body mass index (BMI, kg/m
2) was derived using height and weight information collected via the questionnaire. During analyses, BMI was explored as a continuous variable and using combinations of standard BMI categories for adults (i.e. “underweight” [< 18.5], “normal or healthy weight” [18.5 to 24.9], “overweight” [25 to 29.9], and “obese” [≥30]).
36
To explore cholesterol-lowering medication use, we used two approaches. For one, we determined if there was a medical record notation indicating current use of cholesterol-lowering medications, and for the other approach, we also asked via questionnaire about a history of high cholesterol and medication use (i.e. “Have you ever been diagnosed with high cholesterol and taken medication for it?” Because subjects often did not know the names of medication(s), we did not assess specific cholesterol medications or classes.
We used spherical-equivalent values for refractive error, which were based on the pre-dilation subjective refraction. We excluded eyes with a history of trauma, uveitis, and intraocular surgery.
Statistical analyses were performed using the SAS System, Release 9.3 for Microsoft Windows (SAS Institute, Cary, NC, USA). We used the Student's
t-test for univariate comparisons involving normal distributions and the Wilcoxon rank-sum test for non-normal variables. Chi-squared tests were used for categorical variables. An exploratory analysis was first performed using all available variables (see
Tables 1,
2) to search for potential relationships, and we used multiple logistic regression to model explanatory variables against the dependent variable (i.e. presence of a KS). Model building included stepwise, forward, and backward regression techniques, and variables were explored using varied continuous and categorical formats. Assumptions were checked for analyses, and variables were assessed for correlation and interaction. Our research followed the tenets of the Declaration of Helsinki, subjects provided written informed consent prior to participation, and institutional review board approval was received.