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. Author manuscript; available in PMC: 2020 Oct 1.
Published in final edited form as: Am J Ophthalmol. 2019 May 20;206:245–255. doi: 10.1016/j.ajo.2019.05.015

Genetic correlations between diabetes and glaucoma: an analysis of continuous and dichotomous phenotypes

Vincent Laville 1, Jae H Kang 2, Clara C Cousins 3, Adriana I Iglesias 4, Réka Nagy 5, Jessica N Cooke Bailey 6,7, Robert P Igo Jr 6, Yeunjoo E Song 6,7, Daniel I Chasman 8, William G Christen 8, Peter Kraft 9,10, Bernard A Rosner 2,10, Frank Hu 9,11, James F Wilson 5,12, Puya Gharahkhani 13, Alex W Hewitt 14,15, David A Mackey 16, Pirro G Hysi 17, Christopher J Hammond 17, Cornelia M vanDuijn 4,18, Jonathan L Haines 6,7, Veronique Vitart 5, John H Fingert 19, Michael A Hauser 20, Hugues Aschard 1,9, Janey L Wiggs 3, Anthony P Khawaja 21,§, Stuart MacGregor 13,§, Louis R Pasquale 2,22,§; UK Biobank, International Glaucoma Genetics Consortium*; NEIGHBORHOOD Consortium*
PMCID: PMC6864262  NIHMSID: NIHMS1529847  PMID: 31121135

Abstract

Purpose:

A genetic correlation is the proportion of phenotypic variance between traits that is shared on a genetic basis. Here we explore genetic correlations between diabetes- and glaucoma-related traits.

Design:

Cross-sectional study.

Methods:

We assembled genome-wide association study summary statistics from European-derived participants regarding diabetes-related traits like fasting blood sugar (FBS) and type 2 diabetes (T2D) and glaucoma-related traits (intraocular pressure (IOP), central corneal thickness (CCT), corneal hysteresis (CH), corneal resistance factor (CRF), cup-disc ratio (CDR), and primary open-angle glaucoma (POAG)). We included data from the National Eye Institute Glaucoma Human Genetics Collaboration Heritable Overall Operational Database, the UK Biobank and the International Glaucoma Genetics Consortium. We calculated genetic correlation (rg) between traits using linkage disequilibrium score regression. We also calculated genetic correlations between IOP, CCT and selected diabetes-related traits based on individual level phenotype data in two Northern European population-based samples using pedigree information and Sequential Oligogenic Linkage Analysis Routines (SOLAR).

Results:

Overall, there was little rg between diabetes- and glaucoma-related traits. Specifically, we found a non-significant negative correlation between T2D and POAG (rg=−0.14; p=0.16). Using SOLAR, the genetic correlations between measured IOP, CCT, FBS, fasting insulin and hemoglobin A1c, were null. In contrast, genetic correlations between IOP and POAG (rg≥0.45; p≤3.0E-04) and between CDR and POAG were high (rg =0.57; p=2.8E-10). However, genetic correlations between corneal properties (CCT, CRF and CH) and POAG were low (rg range: −0.18 – 0.11) and non-significant (p≥0.07).

Conclusion:

These analyses suggest there is limited genetic correlation between diabetes- and glaucoma-related traits.

Introduction:

Clarifying the relationship between diabetes mellitus and primary open-angle glaucoma (POAG) could help prioritize glaucoma detection efforts and focus glaucoma drug discovery. Studies show that patients with diabetes have higher intraocular pressure (IOP) than patients without diabetes and that increased fasting blood sugar (FBS) is associated with higher IOP.1–6 However, the link between diabetes and IOP is complex as diabetes alters corneal hysteresis (CH) and corneal resistance factor (CRF), possibly confounding the true correlation between diabetes and IOP.7, 8 The Ocular Response Analyzer (ORA) noncontact tonometer (NCT) generates both a Goldmann-correlated IOP (IOPg) and a cornea-compensated IOP (IOPcc), with the latter adjusting for corneal biomechanical properties. Among 110,573 participants in the UK Biobank where IOP was measured with the ORA NCT, self-reported diabetes was associated with higher IOPg but there was no significant difference in IOPcc between subjects with and without diabetes in multivariate analysis.9 A meta-analysis of seven prospective cohort studies also shows that type 2 diabetes (T2D) is associated with increased risk of POAG;10 however, this meta-analysis is not consistent with a study finding that POAG patients with T2D and no diabetic retinopathy had significantly slower rates of retinal nerve fiber layer thinning compared to POAG patients without T2D.11

Several other correlations between diabetes-related traits and IOP are notable. For example, there was a positive association between postprandial glucose level and IOP in patients with and without diabetes.12, 13 Among non-obese individuals,14 there was a positive relationship between insulin resistance and IOP.15 Serum diabetes-related biomarkers positively associated with IOP include hemoglobin A1c (HbA1c),16 high-density lipoprotein (HDL) and triglyceride (TG).2 Several studies also showed a positive correlation between body mass index (BMI), a continuous trait positively linked to T2D,17–19 and IOP.4, 20 Currently, it is unclear if any of these diabetes-related traits translate into increased vulnerability to POAG.

Genetic analyses offer powerful tools to analyze relationships between various traits without confounding by reverse causality, measurement artifact or detection bias. One such tool is linkage disequilibrium (LD) score regression, which estimates the genetic correlation (rg) between traits using genome-wide association study (GWAS) summary statistics.21, 22 For example, Pickrell et al. reported strong genetic correlations between each of the following continuous diabetes-related traits and T2D using LD score regression: fasting blood sugar (FBS), TG, low-density lipoprotein (LDL), HDL and BMI.23 For glaucoma-related traits, a strong genetic correlation between IOP measured with Goldmann applanation tonometry and POAG was reported using GWAS summary data from two large European-derived consortia.24 Using LD score regression in a Japanese population, Shiga et al25 found a positive genetic correlation between T2D and POAG (rg=0.27; p=2.00E-04) but Kinai et al. found no significant correlations between various quantitative diabetes traits and POAG in the same population.26 Another approach is to form panels of genome-wide significant markers for a trait and test them in relation to another trait of interest. In a multiethnic US population (n=69,685), 39 genome-wide significant diabetes alleles were not collectively associated with POAG (n=3,554 cases) after adjustment for T2D.27

A repository of existing GWAS summary statistics and an atlas of genetic cross-correlations can be found at LD Hub.28 Given the preponderance of epidemiological evidence linking diabetes and glaucoma, we tested the hypothesis that there would be genetic correlations between diabetes- and glaucoma related traits. First, we used LD score regression to explore the relations between quantitative glaucoma-related traits (IOP measured using various techniques in the International Glaucoma Genetics Consortium, as well as corneal-compensated IOP (IOPcc) and Goldmann-correlated IOP (IOPg) – both measured with the ORA in the UK BioBank study, central corneal thickness (CCT), CH, CRF, cup-disc ratio (CDR) and POAG) using existing GWAS summary statistics. Next, we performed LD score regression to assess the genetic correlation between diabetes-related traits (2-hour glucose, FBS, HbA1c, fasting insulin (FI), BMI, TG, LDL, HDL and T2D) and glaucoma-related traits. Finally, we compared our estimates of genetic correlations between selected diabetes quantitative traits and glaucoma quantitative traits to values derived from directly measured traits leveraging pedigree information in two Northern European island cohorts.

Methods:

The Institutional Review Board (IRB) of Partners Healthcare prospectively approved the genetic correlation analyses described in this work. The Icahn School of Medicine IRB has a reliance agreement with Partners to conduct this research. These analyses represent a retrospective study of publicly available summary genotype data. The island cohort studies described below were approved by the Scotland National Health Study.

Assembly of Genome-Wide Association Study Summary Statistics

We assembled publicly available GWAS summary statistics and outlined the traits, sample sizes, population characteristics, and trait heritability based on GWAS data for relevant studies in Table 1.29–38 The GWAS summary data were accessed at http://jass.pasteur.fr/selectPhenotypes.html and at http://ldsc.broadinstitute.org. We used the European-derived subgroups of these studies. Details such as study demographics, detailed phenotype collection methods, adjustments for covariates, the genotyping platforms used and number of single nucleotide polymorphisms (SNPs) that passed quality control can be found in references listed in Table 1. The trait heritability based on classic twin studies and family studies as well as the methodology for determining these traits can also be found by referring to the appropriate references in Table 1. Heritability based on classic twin and family studies was high overall and upward of 0.95 for CCT39 (Table 1). As expected, calculations of heritability for all these traits based on summary GWAS data were lower than values estimated from classic twin studies. Several hypotheses for the source of this ‘missing heritability’ have been proposed in the genetics literature.40 In the studies of quantitative diabetes traits, efforts were taken to exclude patients with known diabetes. The studies of blood lipids and BMI contains patients with and without dyslipidemia – there was no concerted effort to exclude patients with diabetes. In the studies of IOP measured in various ways, studies of CDR and in the studies of corneal biophysical properties, less than 1.5% of subjects were on treatment for glaucoma.

Table 1:

Summary genome wide association studies used in this analysis

Trait Description of trait Sample size (Study PMID) Population characteristics Heritability or heritability rangea (Study PMID(s)) Heritability explained by GWAS data (SE)b
FBS Fasting blood sugar 133,010 (22885924) Individuals with physician diagnosis of diabetes were excluded 0.38-0.52 (10064092, 10207722, 11723071) 0.03 (0.01)
2HRG Glucose level 2 hours after oral glucose challenge adjusted for BMI 15,234 (20081857) Individuals with a diagnosis of diabetes, using diabetic medication and/or fasting glucose ≥7 mM) were excluded 0.4 (12898014) 0.10 (0.03)
FI Fasting insulin 108,557 (22885924) See 2HRG 0.36 (17956454) 0.03 (0.01)c
HbA1c Serum hemoglobin A 1c levels 46,368 (20858683) See 2HRG 0.47-0.59 (11872688, 16934002) 0.06 (0.01)
LDL Serum LDL 95,454d (20686565) Patients with and without dyslipidemia. There was no systematic attempt to exclude subjects with diabetes 0.21-0.44 (18165655) 0.12 (0.02)
HDL Serum HDL 99,900d (20686565) See LDL 0.27-0.48 (18165655) 0.14 (0.02)
TG Serum triglyceride 96,598d (20686565) See LDL 0.37 (11309690) 0.14 (0.02)
BMI Calculated based on measured or self-reported weight and height 339,224d (25673413) Includes patients with and without diabetes 0.47-0.89 (22645519) 0.13 (0.01)
T2D Type 2 diabetese 34,840 cases; 114,981 controls (22885922) Type 2 diabetes 0.72 (26678054) 0.05 (0.01)
IOP IOP measured in various ways in the IGGCf 29,578 (28073927) Mostly patients without glaucoma. 0.55 (20851442) 0.13 (0.02)
IOPcc Corneal compensated IOP for the right eye measured with the Reichert tonometer in the UKBBg 76,630 (29785010) Mostly patients without glaucoma NA 0.15 (0.01)h
IOPg IOP Goldmann-correlated for the right eye measured with the Reichert tonometer in the UKBBg 76,630 (29785010) Mostly patients without glaucoma measured with the Reichert tonometer NA 0.19 (0.02)h
CCT Central corneal thickness measured with a pachymeter as the mean of both eyes 17,803 (29760442) Patients without eye disease 0.68-0.95 (19556215, 19420341, 16186354) 0.34 (0.04)
CH Corneal hysteresis of the right eye measured with Reichert tonometer in the UKBBg 76,630 (29785010) Mostly patients without glaucoma NA 0.20 (0.01)h
CRF Corneal resistance factor of the right eye measured with Reichert tonometer in the UKBBg 76,630 (29785010) Mostly patients without glaucoma measured with Reichert tonometer NA 0.25 (0.02)h
CDR Vertical cup-disc ratio measured various ways in the IGGCi 23,899 (28073927) Mostly patients without glaucoma 0.48-0.62 (15939473, 20237253, 14691154, 19458335) 0.31 (0.04)
POAG Primary optic nerve degeneration across IOP values in Neighborhood 3,853 cases; 33,480 controls (26752265) Primary open angle glaucoma 0.70 (28783162) 0.13 (0.03)
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Abbreviations: PMID = PubMed unique identifier; SE = standard error; NA = not available; IOP= Intraocular pressure measured in various ways in the International Glaucoma Genetics Consortium (IGGC); IOPcc = corneal-compensated IOP; UKBB = United Kingdom BioBank; IOPg = Goldmann-correlated IOP; CCT = central corneal thickness; CH = corneal hysteresis; CRF = corneal resistance factor; CDR = cup disc ratio; T2D = Type 2 diabetes; POAG = primary open angle glaucoma.

a

The heritability of a trait is the proportion of trait variance attributable to genetic factors. Estimates for heritability values provided here are based on classic twin or family studies with the exception of POAG. The latter is based on pedigree analysis of insurance claim data using the generic term ‘glaucoma’.

b

Heritability calculated from genome-wide association studies (GWAS) data is the proportion of the heritability that is explained by variants that were genotyped. The formula for estimating heritability can be found in the appendix. All heritability estimates are on the observed scale with the exception of POAG and T2D. The heritability’s of the latter traits were based on the liability scale assuming a population prevalence of 2% and 5% for POAG and T2D, respectively.

c

SE was rounded up to 0.01

d

Sample size for each of these traits in this study.

e

Details regarding how T2D was ascertained are provide in the Appendix.

f

For the 1.4% of participants who were on medical therapy for glaucoma, the measured IOP value was multiplied by 1.3. Patients with a history of laser trabeculoplasty and incisional glaucoma surgery were excluded from analysis.

g

For the 1.5% of participants who were on medical therapy for glaucoma, the measured IOP value was multiplied by 1.3. Patients with a history of any laser trabeculoplasty, any incisional glaucoma surgery, any eye surgery within the previous 4 weeks, active ocular infection, eye injury, corneal graft surgery or refractive laser surgery were excluded.

h

We used inverse rank normalized transformed GWAS summary data for the calculation of heritability for these traits. Raw GWAS data yielded materially similar results (data not shown).

i

1.4% of participants were on medical therapy for glaucoma. Patients with a history of any laser trabeculoplasty and any incisional glaucoma surgery were excluded.

Genetic correlation between traits analyses

The methodology for estimating genetic correlation between traits using high throughput allelic markers has been previously described21 and appears in the Appendix. We provide an overview of the method here. The genetic correlation rg, measures of the covariance between the genetic components of two traits scaled by their respective heritability. It ranges between −1 and +1, although occasional out-of-bounds-estimates arise due to estimation error.41, 42 Negative rg between trait pairs mean that alleles that are positively associated with phenotype 1 are negatively associated phenotype 2. Positive rg between trait pairs mean that there are common alleles positively associated between both traits. An absolute value of rg≥0.5 can be considered as strong while an absolute rg≤0.12 can be regarded as weak. P-values < 6.9E-04 associated with rg were considered as significant to correct for the multiple comparisons made (9 diabetes traits × 8 glaucoma traits). Power calculations41 for all possible bivariate analyses are provided in Supplemental Table 1.

The Orkney and Shetlandic Cohorts: Pedigrees with measured intraocular pressure, central corneal thickness and serum diabetes-related biomarkers.

The Orkney Complex Disease Study (ORCADES) is a family-based, cross-sectional study that seeks to identify genetic factors influencing cardiovascular and other disease risk in the isolated archipelago of the Orkney Isles in northern Scotland.43 In total, 2078 participants aged 16-100 years were recruited between 2005 and 2011, most having three or four grandparents from Orkney, the remainder with two Orcadian grandparents.

The Viking Health Study (VIKING) is a family-based, cross-sectional study that aims to identify genetic factors influencing cardiovascular and other disease risk in the population isolate of the Shetland Islands in northern Scotland. In total, 2105 participants were recruited between 2013 and 2015, each having at least three grandparents from Shetland.

Genetic diversity in both the ORCADES and VIKING populations is less than mainland Scotland, consistent with high levels of endogamy historically.44 In both cohorts, fasting blood samples were collected and many health-related phenotypes, including IOP and CCT as well as environmental exposures were measured. Specifically, serum glucose, fasting insulin and HbA1c were measured. CCT was measured using an ultrasound pachymeter (Heidelberg Engineering; Heidelberg, Germany). IOP was measured with a tonopen (Reichert Technologies; Buffalo, NY).

Genetic correlations in the Orkney and Shetlandic Cohorts

We used SOLAR (Sequential Oligogenic Linkage Analysis Routines) to decompose phenotypic covariances for IOP, CCT and diabetes-related serum biomarkers from our island cohorts into environmental, phenotypic and genetic components using pedigree data. We used measures averaged between both eyes of a participant. We excluded measures from eyes with a history of surgery that might affect CCT or IOP measurements and from participants with keratoconus. HbA1c values from individuals with diabetes or FBS >7mmol/l were also excluded. IOPs were not adjusted for CCT or transformed but were adjusted for age and sex. CCT, adjusted for age and sex, underwent z-score transformation while FBS, HbA1c and FI underwent rank transformation, with FI undergoing natural log transformation first. All serum diabetes biomarkers were further adjusted for sex, age, age2 and BMI. P-values < 0.0042 were considered significant to correct for the multiple comparisons made (2 glaucoma traits × 3 diabetes traits × 2 cohorts).

Results:

Genetic correlation between the various glaucoma-related quantitative traits and POAG revealed significant trends (Table 2). There was a positive genetic association between IOP measured in the IGGC and POAG as previously reported (rg = 0.45; Standard Error (SE) = 0.12; p = 3.0E-04).24 Similarly there were strong positive genetic correlations between IOPcc and POAG (rg = 0.50; SE = 0.09; p = 5.5E-08) and between IOPg and POAG (rg = 0.60; SE = 0.15; p = 4.3E-05). None of the corneal features (CCT, CH or CRF) showed significant genetic correlation with CDR (p≥0.13) or POAG (p≥0.07). Interestingly, while CCT showed strong positive genetic correlations with IOPg (rg=0.58; SE=0.07; p=1.8E-15) and IOPg (rg = 0.48; SE = 0.07; p=3.7E-12), it did not show significant genetic correlation with IOPcc (rg = 0.07; SE = 0.05; p=0.21). Furthermore, there was also a strong positive genetic correlation between CDR and POAG (rg = 0.57; SE = 0.09; p = 2.8E-10). IOPcc showed a positive genetic correlation with CDR (rg = 0.16; SE = 0.05; p = 9.3E-04) that was not significant after correcting for multiple comparisons. We found strong genetic correlations between IOP measured in various ways in the IGGC as well as between IOPg with the following corneal biophysical traits: CCT, CH and CRF (range of rg = 0.31 - 0.81; p≤3.2E-07).

Table 2:

Genetic correlations (standard error) among glaucoma-related traits

IOPcc IOPg CCT CH CRF CDR POAG
IOP 0.81 (0.07)
p=3.8E-34 		1.10 (0.13)
p=2.0E-17 		0.58 (0.07)
p=1.8E-15 		0.39 (0.06)
p=5.0E-10 		0.81 (0.06)
p=2.9E-38 		0.08 (0.07)
p=0.29		 0.45 (0.12)
p=3.0E-04
IOPcc -- 0.77 (0.02)
p~0 		0.07 (0.05)
p=0.21		 −0.32 (0.04)
p=1.1E-19 		0.16 (0.04)
p=7.8E-06 		0.16 (0.05)
p=9.3E-04		 0.50 (0.09)
p=5.5E-08
IOPg -- -- 0.48 (0.07)
p=3.7E-12 		0.30 (0.06)
p=3.2E-07 		0.71 (0.03)
p~0 		0.07 (0.08)
p=0.39		 0.60 (0.15)
p=4.3E-05
CCT -- -- -- 0.64 (0.06)
p=3.0E-24 		0.71 (0.06)
p=8.7E-36 		0.08 (0.06)
p=0.13		 −0.18 (0.10)
p=0.07
CH -- -- -- -- 0.88 (0.01)
p~0 		−0.07 (0.05)
p=0.19		 −0.14 (0.08)
p=0.07
CRF -- -- -- -- -- 0.02 (0.05)
p=0.66		 0.11 (0.07)
0.13
CDR -- -- -- -- -- -- 0.57 (0.09)
p=2.8E-10
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Abbreviations: IOP = intraocular pressure measured with various tonometers in the International Glaucoma Genetics Consortium; IOPcc = corneal-compensated intraocular pressure, as determined in the UK BioBank study; IOPg = Goldmann-correlated intraocular pressure as determined in the UK BioBank study; CCT = central corneal thickness; CH = corneal hysteresis; CRF = corneal resistance factor; CDR = cup disc ratio; POAG = primary open angle glaucoma; NB: In Tables 2, 3, and 4, we use inverse rank normalized transformed GWAS summary data for the right eye for IOPcc, IOPg, CH and CRF. For CCT we use raw GWAS summary data based on the mean from right and left eyes. P-values corrected for multiple comparisons (<6.9E-04) are in bold; p-values < 1E-100 were regarded as ~0.

Next, we examined the genetic correlations between BMI, blood lipid traits and glaucoma-related traits (Table 3) as well as the genetic correlations between diabetes- and glaucoma-related traits (Table 4). Overall, these results were null after correction for multiple comparisons. Notably, there were non-significant inverse genetic correlations between HbA1c and POAG (rg = −0.31; SE = 0.14; p = 0.02) and between T2D and POAG (rg = −0.14; SE = 0.10; p = 0.16).

Table 3:

Genetic correlations (standard error) between body mass index (BMI), blood lipid traits, and glaucoma-related traits

BMI LDL HDL TG
IOP 0.07 (0.04)
p=0.099		 0.14 (0.08)
p=0.059		 0.03 (0.08)
p=0.72		 0.01 (0.05)
p=0.83
IOPcc −0.02 (0.03)
p=0.60		 0.06 (0.05)
p=0.16		 0.06 (0.05)
p=0.22		 −0.01 (0.04)
p=0.81
IOPg 0.02 (0.04)
p=0.67		 0.07 (0.05)
p=0.16		 0.01 (0.06)
p=0.84		 0.00 (0.05)
p=0.97
CCT 0.03 (0.04)
p=0.37		 0.00 (0.07)
p=0.99		 0.07 (0.06)
p=0.24		 −0.07 (0.05)
p=0.21
CH 0.03 (0.03)
p=0.18		 0.01 (0.05)
p=0.84		 −0.02 (0.04)
p=0.65		 0.07 (0.03)
p=0.05
CRF 0.03 (0.02)
p=0.21		 0.04 (0.04)
p=0.38		 0.00 (0.04)
p=0.94		 0.06 (0.03)
p=0.05
CDR 0.00 (0.03)
p=0.92		 0.02 (0.05)
p=0.75		 0.04 (0.05)
p=0.42		 −0.02 (0.04)
p=0.64
POAG −0.04 (0.05)
p=0.41		 0.04 (0.09)
p=0.64		 0.16 (0.08)
p=0.06		 −0.06 (0.07)
p=0.44
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Abbreviations: IOP = intraocular pressure as measured with various tonometera in the International Glaucoma Genetics Consortium; IOPcc = corneal-compensated intraocular pressure measured in the UK BioBank study; IOPg = Goldmann correlated intraocular pressure measured in the UK BioBank; CCT = central corneal thickness; CH = corneal hysteresis; CRF = corneal resistance factor; CDR = cup disc ratio; POAG = primary open angle glaucoma; BMI = body mass index; LDL = low density lipoprotein; HDL = high density lipoprotein; TG = triglyceride.

Table 4:

Genetic correlations (standard error) between diabetes-related traits and glaucoma-related traits

FBS 2HG FI HbA1c T2D
IOP 0.23 (0.09)
p=7.5E-03		 0.17 (0.16)
p=0.29		 0.17 (0.10)
p=9.1E-02		 0.10 (0.10)
p=0.31		 0.08 (0.07)
p=0.30
IOPcc 0.02 (0.06)
p=0.71		 −0.01 (0.09)
p=0.90		 0.02 (0.08)
p=0.83		 −0.01 (0.07)
p=0.84		 0.00 (0.05)
p=0.98
IOPg 0.06 (0.08)
p=0.47		 0.11 (0.12)
p=0.36		 0.02 (0.09)
p=0.84		 −0.03 (0.08)
p=0.71		 −0.03 (0.07)
p=0.62
CCT 0.04 (0.07)
p=0.58		 0.11 (0.13)
p=0.38		 −0.04 (0.08)
p=0.63		 0.11 (0.08)
p=0.13		 0.05 (0.06)
p=0.41
CH 0.03 (0.06)
p=0.58		 0.14 (0.09)
p=0.12		 0.10 (0.06)
p=0.13		 0.03 (0.06)
p=0.60		 0.05 (0.04)
p=0.24
CRF 0.05 (0.06)
p=0.45		 0.13 (0.09)
p=0.14		 0.11 (0.06)
p=0.07		 0.04 (0.05)
p=0.50		 0.06 (0.04)
p=0.16
CDR 0.06 (0.06)
p=0.39		 0.11 (0.10)
p=0.28		 −0.02 (0.09)
p=0.79		 −0.07 (0.08)
p=0.37		 0.07 (0.07)
p=0.26
POAG −0.02 (0.12)
p=0.87		 0.04 (0.16)
p=0.81		 0.00 (0.13)
p=0.99		 −0.31 (0.14)
p=0.02		 −0.14 (0.10)
p=0.16
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Abbreviations: IOP = intraocular pressure measured with various tonometers in the International Glaucoma Genetics Consortium; IOPcc = corneal-compensated intraocular pressure measured in the UK BioBank study; IOPg = Goldmann-correlated intraocular pressure measured in the UK BioBank; CCT = central corneal thickness; CH = corneal hysteresis; CRF = corneal resistance factor; CDR = cup disc ratio; POAG = primary open angle glaucoma; FBS = fasting blood sugar; 2HG = 2 hour glucose; FI = fasting insulin; HbA1c = hemoglobin A1c; T2D = type 2 diabetes.

The ORCADES and VIKING cohorts offered an opportunity to assess the phenotypic correlations between measured glaucoma-related traits and measured serum biomarkers related to diabetes as well as genotypic correlations based on pedigree information, as opposed to genetic biomarkers (Supplemental Tables 2 and 3). Consistent with classic twin studies,39 the heritability of CCT was high (range: 0.78-0.85). Heritability for IOP was 0.13-0.14 in ORCADES and 0.25 in the VIKING study. Phenotypic correlations were very low (<6%) between CCT or IOP and measured diabetes-related serum biomarkers. We found no statistically significant genetic or environmental correlations between diabetes- and glaucoma-related traits after correction for multiple testing in both cohorts (Supplemental Tables 2 and 3). In the VIKING cohort, there was a strong genetic correlation between IOP and CCT (rg = 0.45; p = 9.7E-06). In both cohorts, a modest phenotypic correlation (rp) between IOP and CCT was observed (rp = 0.16; p = 7.8E-08 in ORCADES; rp = 0.26; p = 3.3E-25 in the VIKING study).

Discussion

Using a genome-wide genetic correlation approach, we found no significant relationship between diabetes- and glaucoma-related traits after adjustment for multiple comparisons. These null results must be assessed in context of the power of this study to find significant associations. A consensus estimate of “good” power is based on the square root of the product of the heritability and sample size for the traits having a value >4500.41 The power was considered to be “good” or better for 47 out of 56 bivariate analyses between quantitative diabetes- and quantitative glaucoma-related traits (see Supplemental Table 1). There was one nominal positive association between IOP measured in the IGGC and FBS with subpar power (rg =0.23; p=0.0075; power product=3917) but more adequately powered associations between IOPg and FBS and IOPcc and FBS were definitely null (p≥0.47; power product ≥ 6772; see Table 4 and Supplemental Table 1). T2D did not show any significant genetic correlations with any of the seven quantitative glaucoma-related traits (p≥0.16) and for all of these bivariate analyses there was at least “good” power to observe such an association (power product ≥ 4800; Supplemental Table 3). POAG and T2D are categorical traits and the analysis for genetic correlation between them was slightly underpowered (power product=3957); yet, the result was in the inverse direction (rg=−0.14) and not significant (p=0.16). Our findings using GWAS statistics were consistent with individual level data from two population pedigrees and do not support a genetic relationship between diabetes and glaucoma.

Our result showing a non-significant inverse genetic correlation between T2D and POAG runs contrary to the significant positive correlation between these quantitative traits in a Japanese population.25 The numbers of cases in the genome-wide datasets were comparable between the Asian and our European sample so power differences were unlikely but there could be differences in genetic structure between these groups that account for these differences. For example, LOXL1 was found to be a genome-wide marker for POAG in Japanese subjects,25 but to date LOXL1 markers are not associated with POAG in European-derived Caucasians.45 Using the same Japanese population, Kinai et al. did not find significant genetic correlations between diabetes quantitative traits (HDL, LDL, TG, blood sugar, and HbA1c) and glaucoma, a finding consistent with our results.26 Furthermore, in a US-based multiethnic population, a panel of genome-wide genetic biomarkers for T2D were not associated with POAG.27

Several diabetes quantitative traits are positively related to IOP in epidemiological studies;1–6 yet, we find no genetic correlations between these quantitative diabetes traits and IOP. Overall, while CCT is increased in patients with diabetes based on several studies,46–48 this corneal feature only partially mediated IOP variation in a study from Singapore.6 While CCT is a static biophysical parameter, CH and CRF are dynamic biomechanical properties that are also affected by diabetes control.49, 50 Overall, accounting for CCT, CH and CRF may not completely explain how the diabetic process leads to increased IOP as measured by Goldman applanation tonometry. Nonetheless, the large UK BioBank study suggests there is no relationship between self-reported diabetes and cornea-compensated IOP.9 Of course, both epidemiological51 and genetic correlation analysis24 strongly link IOP to POAG risk, and our study affirms the latter regardless of how IOP is measured. Yet the genetic correlations between any corneal phenotype (CCT, CRF and CH) and POAG are not significant. Furthermore, while genetic correlations between IOP measured in the IGGC and corneal phenotypes and between IOPg and corneal phenotypes are all high, there was no correlation between IOPcc and CCT. Overall these data suggest that from a genetic perspective CCT, CH and CRF quantify features unrelated to POAG, although they may be related to POAG phenotypically.

The epidemiological association between diabetes and glaucoma is somewhat more controversial but most studies indicate a positive association between the two conditions.52 Our genetic correlation study, which is relatively free of bias related to reverse causation or disease detection, indicates a non-significant inverse genetic correlation between T2D and POAG. Furthermore, genetic correlations between IOP and T2D and between CDR and T2D are also null despite adequate power (power product ≥ 4800; Supplemental Table 1). Notably, we found strong genetic correlations between CDR and POAG despite only modest power (power product = 4400; Supplemental Table 1) and modest but non-significant correlations between CDR and IOP, suggesting that, from a genetic perspective, T2D genetic markers are largely not shared with POAG in European populations. These genetic findings may not be applicable to people of other ancestry but do seem adequately powered to address our study question and call for more prospective study of the relationship between diabetes and POAG using a population that is free of disease at baseline and is systematically monitored for both conditions.

Several longitudinal studies found a modest positive association between measured BMI and IOP,53–55 while epidemiological studies of the relation between BMI and incident POAG had mixed results.56, 57 Furthermore some studies suggest that components of the metabolic syndrome are associated with open-angle glaucoma58 but this association may vary by BMI status.59 BMI is a readily obtainable phenotype with the largest summary GWAS data set available among the traits we studied.33 There is strong genetic correlation between BMI and T2D (rg=0.35; SE=0.04; p=4.0E-15; Supplemental Table 4) but no significant correlation between BMI and any of the glaucoma-related traits (p≥0.099; Table 3). These findings suggest that if BMI or metabolic syndrome plays a role in POAG pathogenesis, they may do so through intermediary effects on the glaucomatous process that are not measured in this study.

While these results do not support genetic correlations between diabetes and glaucoma, there are several non-genetic explanations that can be advanced in support of a positive relation between diabetes and glaucoma. For example, it is possible that hyperglycemia leads to the accumulation of advanced-glycation end products60 and fibronectin production61 in the trabecular meshwork leading to increased IOP in patients with T2D. Several reports indicate that experimental diabetes exacerbates IOP-induced optic damage;62–64 however, there is contrary evidence that hyperglycemia was neuroprotective in a rodent model of glaucoma.65 Finally there is an anecdotal report of a rhesus monkey with spontaneous diabetes, elevated IOP, diabetic retinopathy and glaucoma.66

This study has strengths and weaknesses. Strengths include the use of LD score regression, a novel unbiased approach, to assess correlations between many traits where strong positive associations are suspected such as IOP and POAG24 and others where there is controversy such as T2D and POAG.25,26 Furthermore, our genetic correlation analysis between diabetes and glaucoma was extensive as we considered nine diabetes- and eight glaucoma-related traits. We included some studies where the genetic architecture for continuous traits were ascertained in populations where the prevalence of the respective related diseases (T2D and POAG) was minimized. Such approaches allow for the unbiased detection of novel physiologic loci that might be disease-related as well as cross-correlated with another disease. The absence of major genetic correlations between diabetes- and glaucoma-related traits is corroborated by pedigree data obtained in two cohorts. In addition, we leveraged the largest available samples of genetic data on diabetes- and glaucoma-related traits which were largely adequately powered. The cross-correlations within diabetes traits and within glaucoma traits produced expected results. For example, we estimated a strong inverse relation between HDL and T2D (rg = −0.40; SE = 0.06; p = 4.2E-11; Supplemental Table 4), and a strong positive genetic correlation between IOP measured in various ways and POAG, as previously reported.24 Weaknesses include the fact that the study was limited to European populations although some, but not all, data from Japan are consistent with our findings.26 Second, the absence of a statistically significant genetic correlation does not rule out that a minority of genes are truly shared between diabetes- and glaucoma-related traits.

In summary, we found no genetic correlations between comprehensive sets of diabetes- and glaucoma-related traits. These findings were supported in analyses from two island-based cohorts designed to estimate genetic, environmental and phenotypic correlations in directly measured traits that is informed by pedigree data. T2D and related quantitative traits also do not share significant genome-wide SNP heritability with POAG or its related traits. It is therefore reasonable to consider non-genetic factors, including ones that affect the biomechanical properties of the cornea and perhaps even the optic nerve, as mediating the epidemiological associations between diabetes and elevated IOP or POAG. These findings have important implications for our understanding of POAG.

Supplementary Material

1
2
3
4
5

This study explores genetic correlations between diabetes- and glaucoma-related traits. While quantitative glaucoma traits were genetically correlated with primary open-angle glaucoma, none of the diabetes-related traits exhibited genetic correlation with any glaucoma-related trait. Research should focus on non-genetic factors, such as direct effects of diabetes on the trabecular meshwork and the optic nerve, as potential sources of a link between diabetes and glaucoma.

Acknowledgements/Disclosures:

a: Funding/Support: NIH EY015473 (LRP), P30 EY014104 (JLW) and EY022305 (JLW) supported this work. AK is supported by a Moorfields Eye Charity Career Development Fellowship. SM was supported by an Australian Research Council Fellowship. SM, DAM and AWH acknowledge grants 1150144 and 1116360 from the Australian National Health and Medical Research Council.

b: Financial disclosures: The authors have no conflicts of interest to declare with respect to the work described. Unrelated to this work, Dr. Pasquale is a consultant for Bausch & Lomb, Verily and Eyenovia. Anthony Khawaja is a consultant to Allergan and Novartis, has received travel expenses from Thea and has received lecturing fees from Grafton Optical.

c: Other acknowledgements:

ORCADES Acknowledgements:

The Orkney Complex Disease Study (ORCADES) was supported by the Chief Scientist Office of the Scottish Government (CZB/4/276, CZB/4/710), a Royal Society URF to J.F.W., the MRC Human Genetics Unit quinquennial programme “QTL in Health and Disease”, Arthritis Research UK and the European Union framework program 6 EUROSPAN project (contract no. LSHG-CT-2006-018947). DNA extractions were performed at the Wellcome Trust Clinical Research Facility in Edinburgh. We would like to acknowledge the invaluable contributions of the research nurses in Orkney, the administrative team in Edinburgh and the people of Orkney.

VIKING Acknowledgements:

The Viking Health Study – Shetland (VIKING) was supported by the MRC Human Genetics Unit quinquennial programme grant “QTL in Health and Disease”. DNA extractions and genotyping were performed at the Edinburgh Clinical Research Facility, University of Edinburgh. We would like to acknowledge the invaluable contributions of the research nurses in Shetland, the administrative team in Edinburgh and the people of Shetland.

Footnotes

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Supplementary Materials

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