Association of Systemic Medication Use with Glaucoma and Intraocular Pressure: The European Eye Epidemiology Consortium.

PURPOSE: To investigate the association of commonly used systemic medications with glaucoma and intraocular pressure (IOP) in the European population. DESIGN: Meta-analysis of 11 population-based cohort studies of the European Eye Epidemiology Consortium. PARTICIPANTS: The glaucoma analyses included 143 240 participants and the IOP analyses included 47 177 participants. METHODS: We examined associations of 4 categories of systemic medications-antihypertensive medications (ß-blockers, diuretics, calcium channel blockers [CCBs], α-agonists, angiotensin-converting enzyme inhibitors, and angiotensin II receptor blockers), lipid-lowering medications, antidepressants, and antidiabetic medications-with glaucoma prevalence and IOP. Glaucoma ascertainment and IOP measurement method were according to individual study protocols. Results of multivariable regression analyses of each study were pooled using random effects meta-analyses. Associations with antidiabetic medications were examined in participants with diabetes only. MAIN OUTCOME MEASURES: Glaucoma prevalence and IOP. RESULTS: In the meta-analyses of our maximally adjusted multivariable models, use of CCBs was associated with a higher prevalence of glaucoma (odds ratio [OR], 1.23; 95% confidence interval [CI], 1.08 to 1.39). This association was stronger for monotherapy of CCBs with direct cardiac effects (OR, 1.96; 95% CI, 1.23 to 3.12). No other antihypertensive medications, lipid-lowering medications, antidepressants, or antidiabetic medications were associated with glaucoma. Use of systemic ß-blockers was associated with a lower IOP (ß coefficient, -0.33 mmHg; 95% CI, -0.57 to -0.08 mmHg). Monotherapy of both selective systemic ß-blockers (ß coefficient, -0.45 mmHg; 95% CI -0.74 to -0.16 mmHg) and nonselective systemic ß-blockers (ß coefficient, -0.54 mmHg; 95% CI, -0.94 to -0.15 mmHg) was associated with lower IOP. A suggestive association was found between use of high-ceiling diuretics and lower IOP (ß coefficient, -0.30 mmHg; 95% CI, -0.47 to -0.14 mmHg) but not when used as monotherapy. No other antihypertensive medications, lipid-lowering medications, antidepressants, or antidiabetic medications were associated with IOP. CONCLUSIONS: We identified a potentially harmful association between use of CCBs and glaucoma prevalence. Additionally, we observed and quantified the association of lower IOP with systemic ß-blocker use. Both findings potentially are important, given that patients with glaucoma frequently use systemic antihypertensive medications. Determining causality of the CCB association should be a research priority. FINANCIAL DISCLOSURE(S): Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Development and validation of a questionnaire-based myopia proxy in adults: the LifeLines Cohort Study.

AIMS: To build a questionnaire-based myopia proxy and to validate the proxy by confirming its association with educational attainment and a Polygenic Risk Score (PRS) for myopia. METHODS: Data were collected between 2014 and 2017 from 88 646 Dutch adults from the LifeLines Cohort. First, we performed principal component analysis (PCA) to responses of five refraction-status questions. Second, we measured the refractive state in a subset of LifeLines participants (n=326) and performed logistic regression using myopia (mean spherical equivalent <-0.5 D) as a dependent variable and the principal components (PCs) as independent variables. We identified specificity, sensitivity and the classification threshold. Third, the classification equation was applied to the remaining LifeLines participants. The value of the proxy was then explored by calculating its association with educational attainment and a PRS of myopia. RESULTS: A total of 77 096 participants (58.1% women) were eligible for the PCA. The first two PCs had a specificity of 91.9% (95% CI 87.8% to 95.4%) and a sensitivity of 90.4% (95% CI 84.3% to 96.4%) for myopia. The area under the receiver operating characteristic curve was 95.0% (95% CI 92.2% to 97.8%). The age-standardised prevalence of proxy-inferred myopia was 33.8% (95% CI 33.4% to 34.3%). Compared with low education level, the ORs of proxy-inferred myopia were 1.66 (95% CI 1.58 to 1.74, p=5.94×10-90) and 2.54 (95% CI 2.41 to 2.68, p=4.04×10-271) for medium and high education levels, respectively. Similarly, individuals at the top 10% of PRS (vs lower 90%) had an OR of 2.18 (95% CI 1.98 to 2.41, p=6.57×10-56) for proxy-inferred myopia, whereas those at the highest decile had an OR of 4.51 (95% CI 3.9 to 5.21, p=1.74×10-89) when compared with the lowest decile. CONCLUSION: Self-administered refractive error-related questions could be used as an effective tool to capture proxy-inferred myopic cases in a population-based setting.

Genetic and biomarker modulation of arterial stiffness change in the SardiNIA population cohort.

Background and aims: Arterial stiffness (AS), quantified by pulse wave velocity (PWV), arises due to impaired arterial elastic tissue and smooth muscle dysfunction. We aimed to examine the longitudinal association of genetic, lipid and inflammation biomarkers with PWV and how these associations may change with aging. Materials and methods: We utilized genotype and four time-point biomarker data from the SardiNIA cohort [n = 6,301; mean baseline age 43.3 (SD 17.3); 58% females]. To investigate the association of PWV with genetic variants, lipid, and inflammation biomarkers, we employed linear mixed modeling, using age as the time scale. Biomarkers exhibiting significant longitudinal associations were categorized into tertiles and individuals within the second tertile or those with heterozygous alleles were excluded, leaving a cohort of 2,000 individuals. This cohort was further divided into four risk groups: low genetic and low biomarker (L-L), low genetic and high biomarker (L-H), high genetic and low biomarker (H-L), and high genetic and high biomarker risk (H-H). Subsequent analyses focused on these risk groups to assess their association to PWV with time. Results: Using the complete dataset, we found a significant longitudinal association of total cholesterol (TC), triglycerides (TG), fibrinogen (FGN), and total white blood cell count (TWBC) with PWV, all with p < 3.33 × 10-3. After grouping, individuals with homogeneous risk alleles of SNP rs3742207 and high baseline TG levels (H-H group) exhibited a 1.39-fold higher PWV (m/s) (95% CI, 1.17-1.64, p = 1.21 × 10-4) compared to the L-L group. Similarly, individuals in the H-H group of rs3742207-TWBC combination showed 1.75 times higher PWV (95% CI, 1.48-0.2.07, p = 1.01 × 10-10) compared to the L-L group. Similar patterns were observed for groups based on SNP rs7152623-TWBC risk. Furthermore, these associations became more pronounced with increasing age (p < 3.33 × 10-3). Conclusion: The longitudinal association of TG and TWBC biomarkers with PWV varied by SNPs rs3742207 and rs7152623 genotype. Further studies are warranted to investigate the function of genetics, lipids, and inflammation biomarkers on PWV change.

Bioinformatic Prioritization and Functional Annotation of GWAS-Based Candidate Genes for Primary Open-Angle Glaucoma.

Background: Primary open-angle glaucoma (POAG) is the most prevalent glaucoma subtype, but its exact etiology is still unknown. In this study, we aimed to prioritize the most likely 'causal' genes and identify functional characteristics and underlying biological pathways of POAG candidate genes. Methods: We used the results of a large POAG genome-wide association analysis study from GERA and UK Biobank cohorts. First, we performed systematic gene-prioritization analyses based on: (i) nearest genes; (ii) nonsynonymous single-nucleotide polymorphisms; (iii) co-regulation analysis; (iv) transcriptome-wide association studies; and (v) epigenomic data. Next, we performed functional enrichment analyses to find overrepresented functional pathways and tissues. Results: We identified 142 prioritized genes, of which 64 were novel for POAG. BICC1, AFAP1, and ABCA1 were the most highly prioritized genes based on four or more lines of evidence. The most significant pathways were related to extracellular matrix turnover, transforming growth factor-ß, blood vessel development, and retinoic acid receptor signaling. Ocular tissues such as sclera and trabecular meshwork showed enrichment in prioritized gene expression (>1.5 fold). We found pleiotropy of POAG with intraocular pressure and optic-disc parameters, as well as genetic correlation with hypertension and diabetes-related eye disease. Conclusions: Our findings contribute to a better understanding of the molecular mechanisms underlying glaucoma pathogenesis and have prioritized many novel candidate genes for functional follow-up studies.

Autonomic Dysfunction and Blood Pressure in Glaucoma Patients: The Lifelines Cohort Study.

Purpose: We investigated relationship of glaucoma with measurements related to autonomic dysfunction, including heart rate variability (HRV) and blood pressure (BP). Methods: Glaucoma was defined using a questionnaire-based algorithm for 86,841 LifeLines Cohort Study participants. Baseline HRV (root mean square of successive differences [RMSSD]) was calculated from resting electrocardiograms; systolic BP (SBP), diastolic BP (DBP), mean arterial pressure (MAP), and pulse pressure (PP) were oscillometric-based measurements. We used a generalized linear mixed model, adjusted for age, age square, sex, body mass index, and familial relationships to assess the relationship of baseline HRV and BP (continuous and quartiles), hypertension, and antihypertensive medication with glaucoma at follow up (median, 3.8 years). Results: The odds ratio (OR) of glaucoma was 0.95 (95% confidence interval [CI], 0.92-0.99) per unit increase in log-transformed RMSSD (in ms), indicating that autonomous dysfunction (low HRV) is associated with a higher risk of glaucoma. Per 10-mm Hg increase in BP, we found ORs of 1.03 (95% CI, 1.01-1.05; P = 0.015) for SBP, 1.01 (95% CI, 0.97-1.05; P = 0.55) for DBP, 1.03 (95% CI, 1.00-1.06; P = 0.083) for MAP, and 1.04 (95% CI, 1.01-1.07; P = 0.006) for PP. The OR for the lowest versus highest RMSSD quartile was 1.15 (95% CI, 1.05-1.27; P = 0.003). The ORs for the highest versus second quartile were 1.09 (95% CI, 0.99-1.19; P = 0.091) for SBP and 1.13 (95% CI, 1.02-1.24; P = 0.015) for PP. Glaucoma was more common among hypertensives (OR, 1.25; 95% CI, 1.16-1.35; P < 0.001); among those using angiotensin-converting enzyme (ACE) inhibitors (OR, 1.35; 95% CI, 1.18-1.55; P < 0.001); and among those using calcium-channel blockers (OR, 1.19; 95% CI, 1.01-1.40; P = 0.039). Conclusions: Low HRV, high SBP, high PP, and hypertension were associated with glaucoma. Longitudinal studies may elucidate if autonomic dysregulation and high BP also predict glaucoma incidence.

Heritability and the Genetic Correlation of Heart Rate Variability and Blood Pressure in >29 000 Families: The Lifelines Cohort Study.

Dysregulation of the cardiac autonomic nervous system, as indexed by reduced heart rate variability (HRV), has been associated with the development of high blood pressure (BP). However, the underlying pathological mechanisms are not yet fully understood. This study aimed to estimate heritability of HRV and BP and to determine their genetic overlap. We used baseline data of the 3-generation Lifelines population-based cohort study (n=149 067; mean age, 44.5). In-house software was used to calculate root mean square of successive differences and SD of normal-to-normal intervals as indices of HRV based on 10-second resting ECGs. BP was recorded with an automatic BP monitor. We estimated heritabilities and genetic correlations with variance components methods in ASReml software. We additionally estimated genetic correlations with bivariate linkage disequilibrium score regression using publicly available genome-wide association study data. The heritability (SE) estimates were 15.6% (0.90%) for SD of normal-to-normal intervals and 17.9% (0.90%) for root mean square of successive differences. For BP measures, they ranged from 24.4% (0.90%) for pulse pressure to 30.3% (0.90%) for diastolic BP. Significant negative genetic correlations (all P<0.0001) of root mean square of successive differences/SD of normal-to-normal intervals with systolic BP (-0.20/-0.16) and with diastolic BP (-0.15/-0.13) were observed. LD score regression showed largely consistent genetic correlation estimates of root mean square of successive differences/SD of normal-to-normal intervals with systolic BP (range, -0.08 to -0.23) and diastolic BP (range, -0.20 to -0.27). Our study shows a substantial contribution of genetic factors in explaining the variance of HRV and BP measures in the general population. The significant negative genetic correlations between HRV and BP indicate that genetic pathways for HRV and BP partially overlap.