American Journal of Epidemiology Advance Access published November 25, 2015 American Journal of Epidemiology © The Author 2015. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: [email protected].
DOI: 10.1093/aje/kwv204
Original Contribution Diabetes Pathology and Risk of Primary Open-Angle Glaucoma: Evaluating Causal Mechanisms by Using Genetic Information
Ling Shen, Stefan Walter, Ronald B. Melles, M. Maria Glymour, and Eric Jorgenson* * Correspondence to Dr. Eric Jorgenson, Kaiser Permanente Division of Research, 2000 Broadway, Oakland, CA 94612 (e-mail: [email protected]).
Although type 2 diabetes (T2D) predicts glaucoma, the potential for unmeasured confounding has hampered causal conclusions. We performed separate sample genetic instrumental variable analyses using the Genetic Epidemiology Research Study on Adult Health and Aging cohort (n = 69,685; 1995–2013) to estimate effects of T2D on primary open-angle glaucoma (POAG; 3,554 cases). Genetic instrumental variables for overall and mechanism-specific (i.e., linked to T2D via influences on adiposity, β-cell function, insulin regulation, or other metabolic processes) T2D risk were constructed by using 39 genetic polymorphisms established to predict T2D in other samples. Instrumental variable estimates indicated that T2D increased POAG risk (odds ratio = 2.53, 95% confidence interval: 1.04, 6.11). The instrumental variable for β-cell dysregulation also significantly predicted POAG (odds ratioβ-cell = 5.26, 95% confidence interval: 1.75, 15.85), even among individuals without diagnosed T2D, suggesting that metabolic dysregulation may increase POAG risk prior to T2D diagnosis. The T2D risk variant in the melatonin receptor 1B gene (MTNR1B) predicted risk of POAG independently of T2D status, indicating possible pleiotropic physiological functions of melatonin, but instrumental variable effect estimates were significant even excluding MTNR1B variants. To our knowledge, this is the first genetic instrumental variable study of T2D and glaucoma, providing a novel approach to evaluating this hypothesized relationship. Our findings substantially bolster observational evidence that T2D increases POAG risk. genetic instrumental variables; Mendelian randomization; normal tension glaucoma; primary open angle glaucoma; type 2 diabetes
Abbreviations: CI, confidence interval; GERA, Genetic Epidemiology Research Study on Adult Health and Aging; GIPR, gastric inhibitory polypeptide receptor gene; GRI, genetic risk index; GWAS, genome-wide association studies; HbA1c, hemoglobin A1c; ICD-9-CM, International Classification of Diseases, Ninth Revision, Clinical Modification; KPNC, Kaiser Permanente Northern California; MTNR1B, melatonin receptor 1B gene; NTG, normal-tension glaucoma; OR, odds ratio; POAG, primary open-angle glaucoma; SNP, single-nucleotide polymorphism; T2D, type 2 diabetes.
optic nerve damage in these patients (4, 8). Although both incident (6, 7) and prevalent (4, 5, 8, 9) glaucoma have been associated with T2D, some studies found no association (10–13). Many lifestyle and environmental factors affecting T2D risk may also influence POAG. For example, aerobic exercise and obesity may influence diabetes and intraocular pressure and, therefore, POAG risk (14, 15). Furthermore, T2D is a complex disorder, characterized by dysregulation in multiple, interactive processes, which may have distinct biological effects on POAG. T2D diagnoses are typically preceded by a long period of subtler metabolic changes. These changes, even if not meeting
Glaucoma is the second leading cause of blindness worldwide. Primary open-angle glaucoma (POAG) accounts for 90% of US glaucoma cases (1). The overall prevalence of POAG was 1.9% in 2010, reaching 7.9% among individuals over the age of 80 years. Although elevated intraocular pressure is a major risk factor, 10%–20% of POAG cases have normal or low intraocular pressure, referred to as “normaltension glaucoma” (NTG) (2). Type 2 diabetes (T2D) predicts POAG (3–7), but a causal link has not been established. T2D patients often have elevated intraocular pressure, which may give rise to glaucomatous 1
Downloaded from http://aje.oxfordjournals.org/ at Laurentian University on January 4, 2016
Initially submitted April 20, 2015; accepted for publication July 29, 2015.
2 Shen et al.
physiological dimensions of T2D. If T2D causally affects the risk of POAG, the genetic instrumental variables influencing T2D will also predict POAG. In this study, we aim to gain further understanding of the influence of T2D on POAG and NTG through separate-sample genetic instrumental variable analyses in the large Genetic Epidemiology Research Study on Adult Health and Aging (GERA) cohort nested in the Kaiser Permanente Northern California (KPNC) health plan system.
diagnostic thresholds, may also increase intraocular pressure and POAG risk. Traditional observational studies cannot dissect these biological mechanisms underlying T2D and the risk of POAG. Given the ambiguous temporal order, the possibility of shared common causes, and the complex T2D pathogenesis, the causal relation between T2D and POAG has been difficult to establish. The association between T2D and NTG is even less well understood, with no observational evidence reported to date. Genetic instrumental variable studies, sometimes called Mendelian randomization designs, can be conceptualized as naturally occurring or pseudo-randomized trials. The genetic instrumental variable is valuable when it is not possible to comprehensively adjust for confounding, establish temporal order, or undertake a randomized trial (Figure 1A). Large genomewide association studies (GWAS) have identified numerous single-nucleotide polymorphisms (SNPs) associated with T2D and implicated key molecular processes in T2D pathogenesis (16, 17). These SNPs can be combined to create genetic instrumental variables with maximal statistical power and subdivided on the basis of the biological mechanisms underlying the risk of T2D, such as impaired β-cell function, adiposity, or insulin responsiveness. Overall and mechanism-specific genetic instrumental variables can then be used to evaluate whether the influence of T2D on POAG is specific to certain
METHODS Sample
A) Instrumental Variables
Hypothesized Mechanisms
Exposure/Outcome of Interest Unmeasured Confounders
Adiposity β-Cell Function Genes Confirmed to Predict T2D
Insulin Regulation
T2D
Primary Open-Angle Glaucoma
Undefined Metabolic Processes
B)
Unmeasured Confounders
Genes Related to β-Cell Function Confirmed to Predict T2D
β-Cell Function
Metabolic Dysregulation
T2D
Primary Open-Angle Glaucoma
MTNR1B Melatonin Figure 1. Conceptual models for genetic instrumental variable analyses of type 2 diabetes (T2D) and primary open-angle glaucoma (POAG), Genetic Epidemiology Research Study on Adult Health and Aging cohort, 1995–2013. The original model motivating the analyses, assuming that the 39 polymorphisms influenced POAG only via T2D and that the 39 polymorphisms could be grouped into mechanisms of influence on T2D related to adiposity, β-cell function, insulin regulation, or other mechanisms (A); a revised model representing the structure most consistent with results, including a direct pathway from the melatonin receptor 1B gene (MTNR1B) polymorphisms to POAG via melatonin and direct effects of metabolic dysregulation even for patients who do not meet diagnostic criteria for T2D (B). The revised model postulates that the major mechanisms of metabolic dysregulation relevant to POAG act via β-cell function, but we consider the elimination of other mechanisms speculative because confidence intervals for these subscores included the β-cell function coefficient and were not statistically distinguishable.
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The GERA cohort comprises 110,266 adult members of KPNC, a nonprofit integrated health-care-delivery organization with 3.5 million active members. KPNC membership reflects the general population of northern California, with the exception being underrepresentation at socioeconomic extremes (18). The GERA cohort has previously been described in detail (19, 20), and cohort descriptions are provided in Web Table 1 available at http://aje.oxfordjournals.org/. Among the non-Hispanic white participants (n = 83,285), our analyses were restricted to those at least 35 years of age upon sample collection (n = 80,953). We excluded 1,343 individuals with
Genetic Estimates of Effects of T2D on Glaucoma 3
diagnoses of other glaucoma subtypes, including narrow or closure angle glaucoma, pseudo-exfoliation glaucoma, and pigmentary glaucoma (International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM), codes 365.02, 365.2, 365.52, and 365.13, respectively), and 9,592 individuals with indication of any secondary cause of glaucoma or whose glaucoma status was ambiguous, diagnosed as borderline glaucoma, preglaucoma, or unspecified glaucoma (ICD-9-CM codes 365.0, 365.00, 365.7, and 365.9) or ocular hypertension (ICD-9-CM code 365.04), or documented with intraocular pressure ≥22 mm Hg or cupdisc ratio differences between eyes >0.2 but without a more specific diagnosis). Outcome definitions
Diabetes status
GERA participants were linked to the KPNC diabetes registry, identifying KPNC members with diabetes based on the electronic health records (21–23) through December 2013. Registry eligibility is determined by pharmacy prescription for diabetes medications, abnormal HbA1c or glucose values, and outpatient, emergency room, and hospitalization diagnoses of diabetes. A validation study found that the registry was 99.5% sensitive for diagnosed diabetes (22). T2D was distinguished from type 1 diabetes on the basis of age at diagnosis (≤20 years) or self-report. Genotyping, quality control, imputation, and genetic ancestry
Genotyping used Affymetrix Axiom (Affymetrix, Santa Clara, California) arrays as previously described (24, 25), and quality-control procedures have been described in detail (20). Following quality control, genotypes were prephased with Shape-IT v2.r727 (26) and then imputed to the 1000 Genomes Project (March 2012 release) (27) using IMPUTE2 v2.3.0 (28). The IMPUTE2 “info” metric, estimating correlation of the imputed and true genotype, was used as a criterion in defining the genetic instrumental variable (29). Eigenvectors were computed with EIGENSTRAT (30) using 41,228 SNPs (19). These principal components were used to adjust for genetic ancestry. Genetic instrumental variables Overall T2D genetic risk index. The T2D genetic risk indexes (GRIs) were polygenic risk indices constructed to
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Glaucoma was diagnosed by KPNC ophthalmologists through comprehensive eye examinations, typically on the basis of optic nerve tissue defects and corresponding visual field loss. Individuals were identified by electronic health records from 1995 to 2013 as POAG cases if they had ≥1 diagnoses for primary open angle glaucoma (ICD-9-CM codes 365.01, 365.05, 365.1, 365.10, 365.11, 365.12, and 365.15). NTG cases were a subset of POAG cases with ICD-9-CM diagnosis code 365.12. Outcomes of POAG and NTG cases were compared with those of individuals with no diagnosis for any glaucoma (ICD-9-CM codes 365.xx).
approximate the genetically induced probability of T2D based on known SNPs identified in external T2D GWAS. To ensure that the instrumental variable was valid and unequivocally predisposed to T2D, we chose SNPs and weights from the largest T2D meta-analyses—41 SNPs in Morris et al.’s genome-wide association study of T2D among individuals of overwhelmingly European descent (16). A more recent report by Mahajan et al. (17) identified 74 SNPs associated with T2D in transancestry populations, and we evaluated an alternative T2D GRI based on these 74 SNPs as sensitivity analysis. SNPs included in the Morris score were all included or in high linkage disequilibrium with an SNP in the larger Mahajan score. Not only have these large scale meta-analyses confirmed previously implicated T2D susceptible loci, the genetic discoveries also extend insight into T2D pathogenesis. Three SNPs, rs163184, rs12571751, and rs2334499, were not imputed well (info ≤ 0.8) and were not included in our T2D GRIs, resulting in 39 well-typed or imputed SNPs reported by Morris et al. (16) in our primary instrumental variable and 71 SNPs reported by Mahajan et al. (17) in the alternative instrumental variable. The β weights for each SNP (estimated in the external GWAS) were applied to each GERA participant’s allele count for that SNP and summed across all SNPs into a single polygenic score approximating the probability of developing T2D given the alleles included in the score (Web Appendix 1 provides formulas applied). Because the T2D GRI approximates the genetically induced probability of T2D, estimated coefficients for this T2D GRI can be interpreted as testing the association of T2D on the outcome. The original GWAS which provided effect estimates for each SNP were based on logistic models, so the correspondence between the T2D GRI and the probability of T2D is imperfect; simulations showing the performance of this approach under the null are shown in Web Appendix 2 and Web Figure 1. This weighted construction of instrumental variables, using β coefficients from previous large scale GWAS (16, 17), avoids weak-instruments bias (31–33) and improves statistical power. Mechanism-specific T2D risk index. T2D represents several interacting processes of metabolic dysregulation, which may have distinct consequences on POAG. To elucidate aspects of diabetic physiology that may influence risk of POAG/NTG, we grouped the T2D GRI (based on 39 SNPs from Morris et al. (16) for their relatively large effect sizes on T2D risk among individuals of European descent) into 4 subscores related to adiposity, β-cell function, insulin regulation, and other metabolic processes. The SNP assignment was based on a literature review (references and justifications are in Web Table 2). These subscores were used for model validation and mechanism-specific analyses. Glycemic trait index. T2D is characterized by elevated plasma glucose arising from impaired β-cell function and insulin resistance and is associated with several quantitative measures of glucose metabolism, including fasting glucose levels, fasting insulin, and glycated hemoglobin A1c (HbA1c) which reflects the average blood glucose concentration in the previous 120 days. We constructed genetic instrumental variables related to these traits (fasting glucose GRI, fasting insulin GRI, and HbA1c GRI) from SNPs reported in recent largescale GWAS and tested their associations with POAG.
11.4 38 26.05 (4.8) 14.5
53.7
9.8
27.21 (5.3)
516
51.9 1,845
1,779
T2D
Body mass indexc,d
27.45 (5.4)
6,491
59.6 37,419
57.6 38,067
≥ College educationc
Abbreviations: NTG, normal-tension glaucoma; POAG, primary open-angle glaucoma; SD, standard deviation; T2D, type 2 diabetes. a P value for test of independence between the 39 single-nucleotide polymorphism type 2 diabetes genetic risk index and covariates. The overall T2D genetic risk index was constructed on the basis of 39 single-nucleotide polymorphisms and weights reported in a meta-analysis of genome-wide association studies (16). b Two-sided P values were from linear regression using each covariate to predict the overall T2D genetic risk index constructed from 39 single-nucleotide polymorphisms, adjusting for the first 10 ancestry principal components (n = 66,131). c A total of 66,063 eligible samples had valid education responses from the health survey, and 69,625 had at least 1 documented body mass index measure. d Body mass index expressed as weight (kg)/height (m)2.
5.4 × 10−183
0.09
31.49 (6.5) 26.97 (5.1) 55.9
59.7 190
186
333 72.38 (9.6) 3,554 73.62 (9.6) 63.69 (12.0)
66,131
0.90
0.69
3,166 60.6
47.9
3,155 58.7
44.8 62,973 63.82 (12.2)
36,943
7,045 67.92 (9.7)
% No. No.
%
Mean (SD)
No.
%
Mean (SD)
No.
%
Mean (SD)
No.
%
Mean (SD)
T2D T2D Status No T2D NTG POAG
Glaucoma Status
36,032
0.12
P Valuea,b Mean (SD)
Female
From 70,018 participants, we identified 3,554 POAG, 333 NTG (Table 1), and 7,045 (10.1%) T2D cases. T2D was not
Age at DNA collection, years
RESULTS
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We confirmed the strength of the genetic instrumental variables (Web Tables 3 and 4) in predicting T2D using logistic and linear models and examined potential associations with covariates (age, sex, education, and body mass index) as part of genetic instrumental variable assumption verification. Next, we used logistic regression to estimate the association between diagnosed T2D and POAG. This conventional estimate could be biased due to unmeasured confounding, motivating the instrumental variable analysis. We also examined the relative timing and age of T2D and POAG diagnoses to demonstrate the ambiguous temporality of these 2 conditions. Our primary analysis assessed the association of T2D GRIs and POAG in a logistic regression model; because of the weights used to construct the T2D GRI, the coefficients from these models are the separate-sample instrumental variable effect estimates for the association of T2D and POAG (33–35). We repeated the instrumental variable analysis using the mechanism-specific T2D GRIs. Heterogeneity tests, measured as I 2 (percentage of the total variation across different subscores due to heterogeneity) and Cochran’s Q statistics were used to assess whether mechanism-specific instrumental variable estimates differ from each other. Large differences in subscore instrumental variable estimates will result in significant heterogeneity or high I 2. Finally, we used overadjusted models (adjusting for T2D status) and instrumental variable models stratified by T2D status to assess whether the genetic instrumental variable predicted the outcome independently of measured T2D status (32). Such an independent association would indicate either a direct effect of the genetic instrumental variable on POAG (pleiotropic pathways) or that diagnosed T2D does not fully capture the diabetic phenotype, for example, if prediabetes metabolic changes influence POAG risk. These two possibilities are not mutually exclusive, but it is important to evaluate whether the pleiotropic pathway fully accounts for the instrumental variable effect estimate. To distinguish whether significant associations in overadjusted models reflected pleiotropic effects of the genetic instrumental variable on POAG or biological effects mediated through subclinical diabetes-related pathophysiology, we assessed whether the instrumental variable estimates persisted when we modified the instrumental variable to exclude the most strongly associated individual SNPs (32). P values were based on 2-sided statistical tests unless otherwise noted. Relevant Uniform Resource Locators (URLs) for statistical software and the Database of Genotypes and Phenotypes (dbGAP) accession number for the GERA cohort are provided in Web Appendix 3.
No Glaucoma
Statistical analysis
Covariates
Covariates. Models were adjusted for age at DNA collection, sex, and the first 10 ancestry principal components. We evaluated education (self-reported college degree versus no college degree) and body mass index (median of the available body mass index measures from electronic health records) in the association with T2D GRI.
Table 1. Characteristics of the Sample (n = 70,018), by Glaucoma and Type 2 Diabetes Status, and Association of Covariates With the Type 2 Diabetes Genetic Risk Index, Genetic Epidemiology Research Study on Adult Health and Aging Cohort, 1995–2013
4 Shen et al.
Genetic Estimates of Effects of T2D on Glaucoma 5
Table 2. Associations of Overall and Mechanism-Specific Genetic Risk Indexes With T2D Status, Adjusted for Age at DNA Collection, Sex, and the First 10 Ancestry Principal Components, Genetic Epidemiology Research Study on Adult Health and Aging Cohort, 1995–2013 ORa
95% CI
1.38
1.35, 1.41
P Value
Nagalkerke’s Partial R 2, %
RDb
95% CI
P Value
2.18
0.64
0.60, 0.68
8.1 × 10−212
2.02
0.73
0.68, 0.78
2.5 × 10−193
1.93
0.72
0.67, 0.77
1.4 × 10−184
0.06
0.54
0.31, 0.76
2.2 × 10−6
1.50
0.78
0.72, 0.84
6.2 × 10−141
Overall Genetic Risk Index T2D GRI, 71 SNPsc T2D GRI, 39 SNPs
d
T2D GRI, 37 SNPs
e
1.37 1.36
1.34, 1.40 1.33, 1.39
1.6 × 10−204 3.1 × 10 5.6 × 10
−188 −180
Mechanism-Specific Subscores Adiposity β-cell function
1.06 1.31
1.03, 1.08 1.28, 1.34
2.3 × 10−6 9.4 × 10
−139
f
−5
rs10830963 G allele
1.08
1.04, 1.12
4.7 × 10
rs8108269 G allele
1.08
1.04, 1.11
6.4 × 10−5
β-cell function, excluding 2 SNPsg
1.30
1.27, 1.33
2.0 × 10−130
1.41
0.77
0.71, 0.83
2.2 × 10−132
1.13
1.10, 1.15
2.2 × 10−24
0.26
0.72
0.58, 0.86
1.3 × 10−24
0.24
0.67
0.53, 0.80
2.6 × 10−22
Insulin Others
1.12
1.09, 1.14
2.0 × 10
−22
Abbreviations: CI, confidence interval; GRI, genetic risk index; NTG, normal-tension glaucoma; OR, odds ratio; RD, risk difference; SNP, single-nucleotide polymorphism; T2D, type 2 diabetes. a The association was tested in a logistic regression model predicting T2D and represented as the odds ratios for each increment of standard deviation in overall and mechanism-specific genetic risk index. For rs10830963_G and rs8108269_G, the estimated odds ratios were the risk of T2D associated with the G allele assuming a log-additive genetic model. b The association was tested in a linear regression model predicting T2D and presented as the risk difference for each 1-point increase in overall and mechanism-specific genetic risk index. c TheT2D GRI was constructed on the basis of 71 SNPs and weights reported in the trans-ancestry meta-analysis of genome-wide association studies (17). d TheT2D GRI was constructed on the basis of 39 SNPs and weights reported in a meta-analysis of genome-wide association studies (16). e The T2D GRI was constructed on the basis of the SNPs and weights reported in a meta-analysis of genome-wide association studies (16), but it excluded rs10830963 and rs8108269 that showed significant effects in the overadjusted instrumental variable model. f Mechanism-specific subscores were constructed on the basis of the SNPs that were thought to influence adiposity, β-cell function, insulin, and other unknown metabolic processes. The SNPs and weights were reported in a meta-analysis of genome-wide association studies. g Excluding SNPs rs10830963 and rs8108269.
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of association varied among mechanism-specific subscores, because of the different numbers and effect sizes of SNPs included in each subscore. The pancreatic β-cell function subscore included the most SNPs and showed the strongest association, explaining about 1.5% of T2D risk variance. As expected, T2D status was significantly associated with POAG (OR = 1.32, 95% CI: 1.19, 1.45). Genetic instrumental variable analyses (Table 3) indicated that T2D increased the odds of POAG (OR = 2.53, 95% CI: 1.04, 6.11) but not NTG (OR = 0.63, 95% CI: 0.04, 10.59). Among the mechanismspecific subscores, only the β-cell function subscore predicted increased odds of POAG (OR = 5.26, 95% CI: 1.75, 15.85). Adjustment for diagnosed T2D status partially but not fully attenuated the association between the T2D GRIs and POAG. Stratified by T2D status, the β-cell function subscore predicted higher odds of POAG among non-T2D subjects (OR = 5.34, 95% CI: 1.59, 18.01) but not among T2D patients (OR = 0.69, 95% CI: 0.04, 10.69). Effect estimates for other subscores were not statistically distinguishable, and heterogeneity tests were not significant (I 2 = 0 and Q = 0.15) (P = 0.93). Sixteen out of the 19 SNPs related to β-cell function had odds ratio point estimates above 1 for
associated with NTG, but we observed a substantially higher prevalence of T2D among POAG cases than among individuals without glaucoma (14.5% vs. 9.8%) (P < 0.0001). Among POAG cases with comorbid T2D (n = 526), 53.7% were diagnosed with T2D before the first diagnosis of POAG, and the average age at their first POAG diagnosis was 70.7 (standard deviation, 9.5) years, almost identical to the average age at first POAG diagnosis among those without T2D, 69.6 (standard deviation, 10.3) years. The T2D GRI was not associated with age, sex, education, or body mass index. The T2D GRI strongly predicted T2D status in both logistic and linear regression models (Table 2). The odds of measured T2D status increased with each standard deviation increment in overall T2D GRI (odds ratio (OR) = 1.37, 95% confidence interval (CI): 1.34, 1.40) (P = 3.1 × 10−188) and all 4 mechanismspecific subscores. The overall T2D GRIs derived from 71 SNPs, and 39 SNPs had similar predictive power explaining about 2% of the variance (the Nagalkerke partial R 2). The T2D GRI score derived from 39 SNPs ranged from 0.0202 to 0.4550 (mean = 0.1120, standard deviation, 0.0437) and was, on average, about 0.0145 units (or about 13%) higher in subjects with T2D than in those without T2D. The strength
6 Shen et al.
Table 3. Instrumental Variable Estimates for the Effect of T2D on Primary Open-Angle Glaucoma and Normal-Tension Glaucoma in the Instrumental Variable Modelsa, Overadjusted Instrumental Variable Modelsb, and Instrumental Variable Models Stratifying T2D Status, for Each Genetic Risk Index, Genetic Epidemiology Research Study on Adult Health and Aging Cohort, 1995–2013 Overadjusted Model, Controlling for T2D
Instrumental Variable Estimates
OR
95% CI
P Value
OR
P Value
Instrumental Variable Estimates Among Individuals With T2D OR
P Value
Instrumental Variable Estimates Among Individuals Without T2D OR
P Value
POAG Outcome T2D GRI, 39 SNPsc
2.53 1.04, 6.11
0.04
1.90
0.16
0.75
0.79
2.27
0.10
T2D GRI, 37 SNPsd
2.11 0.86, 5.12
0.10
1.58
0.32
0.47
0.50
1.7
0.30
Adiposity
1.32 0.02, 78.65
0.89
1.05
0.98
0.24
0.79
1.45
0.87
β-cell function
DOI: 10.1093/aje/kwv204
Original Contribution Diabetes Pathology and Risk of Primary Open-Angle Glaucoma: Evaluating Causal Mechanisms by Using Genetic Information
Ling Shen, Stefan Walter, Ronald B. Melles, M. Maria Glymour, and Eric Jorgenson* * Correspondence to Dr. Eric Jorgenson, Kaiser Permanente Division of Research, 2000 Broadway, Oakland, CA 94612 (e-mail: [email protected]).
Although type 2 diabetes (T2D) predicts glaucoma, the potential for unmeasured confounding has hampered causal conclusions. We performed separate sample genetic instrumental variable analyses using the Genetic Epidemiology Research Study on Adult Health and Aging cohort (n = 69,685; 1995–2013) to estimate effects of T2D on primary open-angle glaucoma (POAG; 3,554 cases). Genetic instrumental variables for overall and mechanism-specific (i.e., linked to T2D via influences on adiposity, β-cell function, insulin regulation, or other metabolic processes) T2D risk were constructed by using 39 genetic polymorphisms established to predict T2D in other samples. Instrumental variable estimates indicated that T2D increased POAG risk (odds ratio = 2.53, 95% confidence interval: 1.04, 6.11). The instrumental variable for β-cell dysregulation also significantly predicted POAG (odds ratioβ-cell = 5.26, 95% confidence interval: 1.75, 15.85), even among individuals without diagnosed T2D, suggesting that metabolic dysregulation may increase POAG risk prior to T2D diagnosis. The T2D risk variant in the melatonin receptor 1B gene (MTNR1B) predicted risk of POAG independently of T2D status, indicating possible pleiotropic physiological functions of melatonin, but instrumental variable effect estimates were significant even excluding MTNR1B variants. To our knowledge, this is the first genetic instrumental variable study of T2D and glaucoma, providing a novel approach to evaluating this hypothesized relationship. Our findings substantially bolster observational evidence that T2D increases POAG risk. genetic instrumental variables; Mendelian randomization; normal tension glaucoma; primary open angle glaucoma; type 2 diabetes
Abbreviations: CI, confidence interval; GERA, Genetic Epidemiology Research Study on Adult Health and Aging; GIPR, gastric inhibitory polypeptide receptor gene; GRI, genetic risk index; GWAS, genome-wide association studies; HbA1c, hemoglobin A1c; ICD-9-CM, International Classification of Diseases, Ninth Revision, Clinical Modification; KPNC, Kaiser Permanente Northern California; MTNR1B, melatonin receptor 1B gene; NTG, normal-tension glaucoma; OR, odds ratio; POAG, primary open-angle glaucoma; SNP, single-nucleotide polymorphism; T2D, type 2 diabetes.
optic nerve damage in these patients (4, 8). Although both incident (6, 7) and prevalent (4, 5, 8, 9) glaucoma have been associated with T2D, some studies found no association (10–13). Many lifestyle and environmental factors affecting T2D risk may also influence POAG. For example, aerobic exercise and obesity may influence diabetes and intraocular pressure and, therefore, POAG risk (14, 15). Furthermore, T2D is a complex disorder, characterized by dysregulation in multiple, interactive processes, which may have distinct biological effects on POAG. T2D diagnoses are typically preceded by a long period of subtler metabolic changes. These changes, even if not meeting
Glaucoma is the second leading cause of blindness worldwide. Primary open-angle glaucoma (POAG) accounts for 90% of US glaucoma cases (1). The overall prevalence of POAG was 1.9% in 2010, reaching 7.9% among individuals over the age of 80 years. Although elevated intraocular pressure is a major risk factor, 10%–20% of POAG cases have normal or low intraocular pressure, referred to as “normaltension glaucoma” (NTG) (2). Type 2 diabetes (T2D) predicts POAG (3–7), but a causal link has not been established. T2D patients often have elevated intraocular pressure, which may give rise to glaucomatous 1
Downloaded from http://aje.oxfordjournals.org/ at Laurentian University on January 4, 2016
Initially submitted April 20, 2015; accepted for publication July 29, 2015.
2 Shen et al.
physiological dimensions of T2D. If T2D causally affects the risk of POAG, the genetic instrumental variables influencing T2D will also predict POAG. In this study, we aim to gain further understanding of the influence of T2D on POAG and NTG through separate-sample genetic instrumental variable analyses in the large Genetic Epidemiology Research Study on Adult Health and Aging (GERA) cohort nested in the Kaiser Permanente Northern California (KPNC) health plan system.
diagnostic thresholds, may also increase intraocular pressure and POAG risk. Traditional observational studies cannot dissect these biological mechanisms underlying T2D and the risk of POAG. Given the ambiguous temporal order, the possibility of shared common causes, and the complex T2D pathogenesis, the causal relation between T2D and POAG has been difficult to establish. The association between T2D and NTG is even less well understood, with no observational evidence reported to date. Genetic instrumental variable studies, sometimes called Mendelian randomization designs, can be conceptualized as naturally occurring or pseudo-randomized trials. The genetic instrumental variable is valuable when it is not possible to comprehensively adjust for confounding, establish temporal order, or undertake a randomized trial (Figure 1A). Large genomewide association studies (GWAS) have identified numerous single-nucleotide polymorphisms (SNPs) associated with T2D and implicated key molecular processes in T2D pathogenesis (16, 17). These SNPs can be combined to create genetic instrumental variables with maximal statistical power and subdivided on the basis of the biological mechanisms underlying the risk of T2D, such as impaired β-cell function, adiposity, or insulin responsiveness. Overall and mechanism-specific genetic instrumental variables can then be used to evaluate whether the influence of T2D on POAG is specific to certain
METHODS Sample
A) Instrumental Variables
Hypothesized Mechanisms
Exposure/Outcome of Interest Unmeasured Confounders
Adiposity β-Cell Function Genes Confirmed to Predict T2D
Insulin Regulation
T2D
Primary Open-Angle Glaucoma
Undefined Metabolic Processes
B)
Unmeasured Confounders
Genes Related to β-Cell Function Confirmed to Predict T2D
β-Cell Function
Metabolic Dysregulation
T2D
Primary Open-Angle Glaucoma
MTNR1B Melatonin Figure 1. Conceptual models for genetic instrumental variable analyses of type 2 diabetes (T2D) and primary open-angle glaucoma (POAG), Genetic Epidemiology Research Study on Adult Health and Aging cohort, 1995–2013. The original model motivating the analyses, assuming that the 39 polymorphisms influenced POAG only via T2D and that the 39 polymorphisms could be grouped into mechanisms of influence on T2D related to adiposity, β-cell function, insulin regulation, or other mechanisms (A); a revised model representing the structure most consistent with results, including a direct pathway from the melatonin receptor 1B gene (MTNR1B) polymorphisms to POAG via melatonin and direct effects of metabolic dysregulation even for patients who do not meet diagnostic criteria for T2D (B). The revised model postulates that the major mechanisms of metabolic dysregulation relevant to POAG act via β-cell function, but we consider the elimination of other mechanisms speculative because confidence intervals for these subscores included the β-cell function coefficient and were not statistically distinguishable.
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The GERA cohort comprises 110,266 adult members of KPNC, a nonprofit integrated health-care-delivery organization with 3.5 million active members. KPNC membership reflects the general population of northern California, with the exception being underrepresentation at socioeconomic extremes (18). The GERA cohort has previously been described in detail (19, 20), and cohort descriptions are provided in Web Table 1 available at http://aje.oxfordjournals.org/. Among the non-Hispanic white participants (n = 83,285), our analyses were restricted to those at least 35 years of age upon sample collection (n = 80,953). We excluded 1,343 individuals with
Genetic Estimates of Effects of T2D on Glaucoma 3
diagnoses of other glaucoma subtypes, including narrow or closure angle glaucoma, pseudo-exfoliation glaucoma, and pigmentary glaucoma (International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM), codes 365.02, 365.2, 365.52, and 365.13, respectively), and 9,592 individuals with indication of any secondary cause of glaucoma or whose glaucoma status was ambiguous, diagnosed as borderline glaucoma, preglaucoma, or unspecified glaucoma (ICD-9-CM codes 365.0, 365.00, 365.7, and 365.9) or ocular hypertension (ICD-9-CM code 365.04), or documented with intraocular pressure ≥22 mm Hg or cupdisc ratio differences between eyes >0.2 but without a more specific diagnosis). Outcome definitions
Diabetes status
GERA participants were linked to the KPNC diabetes registry, identifying KPNC members with diabetes based on the electronic health records (21–23) through December 2013. Registry eligibility is determined by pharmacy prescription for diabetes medications, abnormal HbA1c or glucose values, and outpatient, emergency room, and hospitalization diagnoses of diabetes. A validation study found that the registry was 99.5% sensitive for diagnosed diabetes (22). T2D was distinguished from type 1 diabetes on the basis of age at diagnosis (≤20 years) or self-report. Genotyping, quality control, imputation, and genetic ancestry
Genotyping used Affymetrix Axiom (Affymetrix, Santa Clara, California) arrays as previously described (24, 25), and quality-control procedures have been described in detail (20). Following quality control, genotypes were prephased with Shape-IT v2.r727 (26) and then imputed to the 1000 Genomes Project (March 2012 release) (27) using IMPUTE2 v2.3.0 (28). The IMPUTE2 “info” metric, estimating correlation of the imputed and true genotype, was used as a criterion in defining the genetic instrumental variable (29). Eigenvectors were computed with EIGENSTRAT (30) using 41,228 SNPs (19). These principal components were used to adjust for genetic ancestry. Genetic instrumental variables Overall T2D genetic risk index. The T2D genetic risk indexes (GRIs) were polygenic risk indices constructed to
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Glaucoma was diagnosed by KPNC ophthalmologists through comprehensive eye examinations, typically on the basis of optic nerve tissue defects and corresponding visual field loss. Individuals were identified by electronic health records from 1995 to 2013 as POAG cases if they had ≥1 diagnoses for primary open angle glaucoma (ICD-9-CM codes 365.01, 365.05, 365.1, 365.10, 365.11, 365.12, and 365.15). NTG cases were a subset of POAG cases with ICD-9-CM diagnosis code 365.12. Outcomes of POAG and NTG cases were compared with those of individuals with no diagnosis for any glaucoma (ICD-9-CM codes 365.xx).
approximate the genetically induced probability of T2D based on known SNPs identified in external T2D GWAS. To ensure that the instrumental variable was valid and unequivocally predisposed to T2D, we chose SNPs and weights from the largest T2D meta-analyses—41 SNPs in Morris et al.’s genome-wide association study of T2D among individuals of overwhelmingly European descent (16). A more recent report by Mahajan et al. (17) identified 74 SNPs associated with T2D in transancestry populations, and we evaluated an alternative T2D GRI based on these 74 SNPs as sensitivity analysis. SNPs included in the Morris score were all included or in high linkage disequilibrium with an SNP in the larger Mahajan score. Not only have these large scale meta-analyses confirmed previously implicated T2D susceptible loci, the genetic discoveries also extend insight into T2D pathogenesis. Three SNPs, rs163184, rs12571751, and rs2334499, were not imputed well (info ≤ 0.8) and were not included in our T2D GRIs, resulting in 39 well-typed or imputed SNPs reported by Morris et al. (16) in our primary instrumental variable and 71 SNPs reported by Mahajan et al. (17) in the alternative instrumental variable. The β weights for each SNP (estimated in the external GWAS) were applied to each GERA participant’s allele count for that SNP and summed across all SNPs into a single polygenic score approximating the probability of developing T2D given the alleles included in the score (Web Appendix 1 provides formulas applied). Because the T2D GRI approximates the genetically induced probability of T2D, estimated coefficients for this T2D GRI can be interpreted as testing the association of T2D on the outcome. The original GWAS which provided effect estimates for each SNP were based on logistic models, so the correspondence between the T2D GRI and the probability of T2D is imperfect; simulations showing the performance of this approach under the null are shown in Web Appendix 2 and Web Figure 1. This weighted construction of instrumental variables, using β coefficients from previous large scale GWAS (16, 17), avoids weak-instruments bias (31–33) and improves statistical power. Mechanism-specific T2D risk index. T2D represents several interacting processes of metabolic dysregulation, which may have distinct consequences on POAG. To elucidate aspects of diabetic physiology that may influence risk of POAG/NTG, we grouped the T2D GRI (based on 39 SNPs from Morris et al. (16) for their relatively large effect sizes on T2D risk among individuals of European descent) into 4 subscores related to adiposity, β-cell function, insulin regulation, and other metabolic processes. The SNP assignment was based on a literature review (references and justifications are in Web Table 2). These subscores were used for model validation and mechanism-specific analyses. Glycemic trait index. T2D is characterized by elevated plasma glucose arising from impaired β-cell function and insulin resistance and is associated with several quantitative measures of glucose metabolism, including fasting glucose levels, fasting insulin, and glycated hemoglobin A1c (HbA1c) which reflects the average blood glucose concentration in the previous 120 days. We constructed genetic instrumental variables related to these traits (fasting glucose GRI, fasting insulin GRI, and HbA1c GRI) from SNPs reported in recent largescale GWAS and tested their associations with POAG.
11.4 38 26.05 (4.8) 14.5
53.7
9.8
27.21 (5.3)
516
51.9 1,845
1,779
T2D
Body mass indexc,d
27.45 (5.4)
6,491
59.6 37,419
57.6 38,067
≥ College educationc
Abbreviations: NTG, normal-tension glaucoma; POAG, primary open-angle glaucoma; SD, standard deviation; T2D, type 2 diabetes. a P value for test of independence between the 39 single-nucleotide polymorphism type 2 diabetes genetic risk index and covariates. The overall T2D genetic risk index was constructed on the basis of 39 single-nucleotide polymorphisms and weights reported in a meta-analysis of genome-wide association studies (16). b Two-sided P values were from linear regression using each covariate to predict the overall T2D genetic risk index constructed from 39 single-nucleotide polymorphisms, adjusting for the first 10 ancestry principal components (n = 66,131). c A total of 66,063 eligible samples had valid education responses from the health survey, and 69,625 had at least 1 documented body mass index measure. d Body mass index expressed as weight (kg)/height (m)2.
5.4 × 10−183
0.09
31.49 (6.5) 26.97 (5.1) 55.9
59.7 190
186
333 72.38 (9.6) 3,554 73.62 (9.6) 63.69 (12.0)
66,131
0.90
0.69
3,166 60.6
47.9
3,155 58.7
44.8 62,973 63.82 (12.2)
36,943
7,045 67.92 (9.7)
% No. No.
%
Mean (SD)
No.
%
Mean (SD)
No.
%
Mean (SD)
No.
%
Mean (SD)
T2D T2D Status No T2D NTG POAG
Glaucoma Status
36,032
0.12
P Valuea,b Mean (SD)
Female
From 70,018 participants, we identified 3,554 POAG, 333 NTG (Table 1), and 7,045 (10.1%) T2D cases. T2D was not
Age at DNA collection, years
RESULTS
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We confirmed the strength of the genetic instrumental variables (Web Tables 3 and 4) in predicting T2D using logistic and linear models and examined potential associations with covariates (age, sex, education, and body mass index) as part of genetic instrumental variable assumption verification. Next, we used logistic regression to estimate the association between diagnosed T2D and POAG. This conventional estimate could be biased due to unmeasured confounding, motivating the instrumental variable analysis. We also examined the relative timing and age of T2D and POAG diagnoses to demonstrate the ambiguous temporality of these 2 conditions. Our primary analysis assessed the association of T2D GRIs and POAG in a logistic regression model; because of the weights used to construct the T2D GRI, the coefficients from these models are the separate-sample instrumental variable effect estimates for the association of T2D and POAG (33–35). We repeated the instrumental variable analysis using the mechanism-specific T2D GRIs. Heterogeneity tests, measured as I 2 (percentage of the total variation across different subscores due to heterogeneity) and Cochran’s Q statistics were used to assess whether mechanism-specific instrumental variable estimates differ from each other. Large differences in subscore instrumental variable estimates will result in significant heterogeneity or high I 2. Finally, we used overadjusted models (adjusting for T2D status) and instrumental variable models stratified by T2D status to assess whether the genetic instrumental variable predicted the outcome independently of measured T2D status (32). Such an independent association would indicate either a direct effect of the genetic instrumental variable on POAG (pleiotropic pathways) or that diagnosed T2D does not fully capture the diabetic phenotype, for example, if prediabetes metabolic changes influence POAG risk. These two possibilities are not mutually exclusive, but it is important to evaluate whether the pleiotropic pathway fully accounts for the instrumental variable effect estimate. To distinguish whether significant associations in overadjusted models reflected pleiotropic effects of the genetic instrumental variable on POAG or biological effects mediated through subclinical diabetes-related pathophysiology, we assessed whether the instrumental variable estimates persisted when we modified the instrumental variable to exclude the most strongly associated individual SNPs (32). P values were based on 2-sided statistical tests unless otherwise noted. Relevant Uniform Resource Locators (URLs) for statistical software and the Database of Genotypes and Phenotypes (dbGAP) accession number for the GERA cohort are provided in Web Appendix 3.
No Glaucoma
Statistical analysis
Covariates
Covariates. Models were adjusted for age at DNA collection, sex, and the first 10 ancestry principal components. We evaluated education (self-reported college degree versus no college degree) and body mass index (median of the available body mass index measures from electronic health records) in the association with T2D GRI.
Table 1. Characteristics of the Sample (n = 70,018), by Glaucoma and Type 2 Diabetes Status, and Association of Covariates With the Type 2 Diabetes Genetic Risk Index, Genetic Epidemiology Research Study on Adult Health and Aging Cohort, 1995–2013
4 Shen et al.
Genetic Estimates of Effects of T2D on Glaucoma 5
Table 2. Associations of Overall and Mechanism-Specific Genetic Risk Indexes With T2D Status, Adjusted for Age at DNA Collection, Sex, and the First 10 Ancestry Principal Components, Genetic Epidemiology Research Study on Adult Health and Aging Cohort, 1995–2013 ORa
95% CI
1.38
1.35, 1.41
P Value
Nagalkerke’s Partial R 2, %
RDb
95% CI
P Value
2.18
0.64
0.60, 0.68
8.1 × 10−212
2.02
0.73
0.68, 0.78
2.5 × 10−193
1.93
0.72
0.67, 0.77
1.4 × 10−184
0.06
0.54
0.31, 0.76
2.2 × 10−6
1.50
0.78
0.72, 0.84
6.2 × 10−141
Overall Genetic Risk Index T2D GRI, 71 SNPsc T2D GRI, 39 SNPs
d
T2D GRI, 37 SNPs
e
1.37 1.36
1.34, 1.40 1.33, 1.39
1.6 × 10−204 3.1 × 10 5.6 × 10
−188 −180
Mechanism-Specific Subscores Adiposity β-cell function
1.06 1.31
1.03, 1.08 1.28, 1.34
2.3 × 10−6 9.4 × 10
−139
f
−5
rs10830963 G allele
1.08
1.04, 1.12
4.7 × 10
rs8108269 G allele
1.08
1.04, 1.11
6.4 × 10−5
β-cell function, excluding 2 SNPsg
1.30
1.27, 1.33
2.0 × 10−130
1.41
0.77
0.71, 0.83
2.2 × 10−132
1.13
1.10, 1.15
2.2 × 10−24
0.26
0.72
0.58, 0.86
1.3 × 10−24
0.24
0.67
0.53, 0.80
2.6 × 10−22
Insulin Others
1.12
1.09, 1.14
2.0 × 10
−22
Abbreviations: CI, confidence interval; GRI, genetic risk index; NTG, normal-tension glaucoma; OR, odds ratio; RD, risk difference; SNP, single-nucleotide polymorphism; T2D, type 2 diabetes. a The association was tested in a logistic regression model predicting T2D and represented as the odds ratios for each increment of standard deviation in overall and mechanism-specific genetic risk index. For rs10830963_G and rs8108269_G, the estimated odds ratios were the risk of T2D associated with the G allele assuming a log-additive genetic model. b The association was tested in a linear regression model predicting T2D and presented as the risk difference for each 1-point increase in overall and mechanism-specific genetic risk index. c TheT2D GRI was constructed on the basis of 71 SNPs and weights reported in the trans-ancestry meta-analysis of genome-wide association studies (17). d TheT2D GRI was constructed on the basis of 39 SNPs and weights reported in a meta-analysis of genome-wide association studies (16). e The T2D GRI was constructed on the basis of the SNPs and weights reported in a meta-analysis of genome-wide association studies (16), but it excluded rs10830963 and rs8108269 that showed significant effects in the overadjusted instrumental variable model. f Mechanism-specific subscores were constructed on the basis of the SNPs that were thought to influence adiposity, β-cell function, insulin, and other unknown metabolic processes. The SNPs and weights were reported in a meta-analysis of genome-wide association studies. g Excluding SNPs rs10830963 and rs8108269.
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of association varied among mechanism-specific subscores, because of the different numbers and effect sizes of SNPs included in each subscore. The pancreatic β-cell function subscore included the most SNPs and showed the strongest association, explaining about 1.5% of T2D risk variance. As expected, T2D status was significantly associated with POAG (OR = 1.32, 95% CI: 1.19, 1.45). Genetic instrumental variable analyses (Table 3) indicated that T2D increased the odds of POAG (OR = 2.53, 95% CI: 1.04, 6.11) but not NTG (OR = 0.63, 95% CI: 0.04, 10.59). Among the mechanismspecific subscores, only the β-cell function subscore predicted increased odds of POAG (OR = 5.26, 95% CI: 1.75, 15.85). Adjustment for diagnosed T2D status partially but not fully attenuated the association between the T2D GRIs and POAG. Stratified by T2D status, the β-cell function subscore predicted higher odds of POAG among non-T2D subjects (OR = 5.34, 95% CI: 1.59, 18.01) but not among T2D patients (OR = 0.69, 95% CI: 0.04, 10.69). Effect estimates for other subscores were not statistically distinguishable, and heterogeneity tests were not significant (I 2 = 0 and Q = 0.15) (P = 0.93). Sixteen out of the 19 SNPs related to β-cell function had odds ratio point estimates above 1 for
associated with NTG, but we observed a substantially higher prevalence of T2D among POAG cases than among individuals without glaucoma (14.5% vs. 9.8%) (P < 0.0001). Among POAG cases with comorbid T2D (n = 526), 53.7% were diagnosed with T2D before the first diagnosis of POAG, and the average age at their first POAG diagnosis was 70.7 (standard deviation, 9.5) years, almost identical to the average age at first POAG diagnosis among those without T2D, 69.6 (standard deviation, 10.3) years. The T2D GRI was not associated with age, sex, education, or body mass index. The T2D GRI strongly predicted T2D status in both logistic and linear regression models (Table 2). The odds of measured T2D status increased with each standard deviation increment in overall T2D GRI (odds ratio (OR) = 1.37, 95% confidence interval (CI): 1.34, 1.40) (P = 3.1 × 10−188) and all 4 mechanismspecific subscores. The overall T2D GRIs derived from 71 SNPs, and 39 SNPs had similar predictive power explaining about 2% of the variance (the Nagalkerke partial R 2). The T2D GRI score derived from 39 SNPs ranged from 0.0202 to 0.4550 (mean = 0.1120, standard deviation, 0.0437) and was, on average, about 0.0145 units (or about 13%) higher in subjects with T2D than in those without T2D. The strength
6 Shen et al.
Table 3. Instrumental Variable Estimates for the Effect of T2D on Primary Open-Angle Glaucoma and Normal-Tension Glaucoma in the Instrumental Variable Modelsa, Overadjusted Instrumental Variable Modelsb, and Instrumental Variable Models Stratifying T2D Status, for Each Genetic Risk Index, Genetic Epidemiology Research Study on Adult Health and Aging Cohort, 1995–2013 Overadjusted Model, Controlling for T2D
Instrumental Variable Estimates
OR
95% CI
P Value
OR
P Value
Instrumental Variable Estimates Among Individuals With T2D OR
P Value
Instrumental Variable Estimates Among Individuals Without T2D OR
P Value
POAG Outcome T2D GRI, 39 SNPsc
2.53 1.04, 6.11
0.04
1.90
0.16
0.75
0.79
2.27
0.10
T2D GRI, 37 SNPsd
2.11 0.86, 5.12
0.10
1.58
0.32
0.47
0.50
1.7
0.30
Adiposity
1.32 0.02, 78.65
0.89
1.05
0.98
0.24
0.79
1.45
0.87
β-cell function
