ACE and Type 2 Diabetes Risk: A Mendelian Randomization Study

Diabetes is a leading cause of death worldwide (1) and an important risk factor for cardiovascular and kidney diseases (2). The global prevalence of diabetes has doubled over the past 25 years (3), and, thus, policies to slow its incidence are urgently required. Exercise, dieting, and some glucose-lowering drugs can prevent or delay the onset of type 2 diabetes (4). Moreover, three (5–7) of five (5–9) large randomized controlled trials (RCTs) have suggested that ACE inhibitors may reduce the risk of type 2 diabetes compared with placebo in people at high risk for cardiovascular outcomes. However, one large RCT in people at low risk for cardiovascular diseases, who also had impaired glucose tolerance (IGT) or impaired fasting glucose (IFG), reported a nonsignificantly lower effect of ACE inhibitors on incident type 2 diabetes (10). Thus, the causal relationship between ACE inhibition and protection from type 2 diabetes remains questionable.

Mendelian randomization (MR) is a statistical method that uses genetic associations with both the putative causal factor (i.e., exposure) and the outcome to infer causality while reducing bias resulting from confounding (because genetic variants are randomly allocated at conception) or reverse causation (because allele assignment precedes disease onset) (11). Conceptually similar to an RCT, the MR approach also enables inferences of the pharmacological effect of a drug using randomly allocated variants in the drug target gene (12).

In this study, we applied MR analyses to test the hypothesis that ACE inhibitors protect against the risk of type 2 diabetes. We identified genetic variants within or near the ACE locus influencing circulating ACE concentrations. Using those variants as instruments for ACE inhibition, we estimated the effect of genetically determined lower ACE concentration on the risk of type 2 diabetes. Providing evidence for the protective effect of ACE inhibitors on type 2 diabetes is of interest because it could guide therapeutic decision making in patients requiring antihypertensive drugs.

The study consisted of three sequential steps (Fig. 1). First, we identified genetic variants within or near the ACE locus associated with ACE concentration in the Outcome Reduction with Initial Glargine INtervention (ORIGIN) (reg. no. NCT00069784, ClinicalTrials.gov) trial to be used as instrumental variables in subsequent MR analyses. Second, we conducted a two-sample MR analysis to assess the relationship between genetically lowered ACE concentration and risk of type 2 diabetes, using summary association statistics from the DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) consortium. Genetic associations were replicated by testing an ACE concentration–lowering genetic risk score (ACE GRS) for association with type 2 diabetes prevalence in the UK Biobank cohort (i.e., one-sample MR). Third, estimates of type 2 diabetes obtained from MR were compared with the estimates obtained from an RCT meta-analysis of ACE inhibitors versus placebo, after standardization for blood pressure change.

MR analyses included individual participant data from the ORIGIN trial and the UK Biobank and summary-level data from up to 26,676 participants with type 2 diabetes and 132,532 control participants from 18 studies as part of the DIAGRAM consortium (13) (Supplementary Table 1). In the ORIGIN trial, a total of 12,537 participants with type 2 diabetes, IGT, or IFG and additional cardiovascular risk factors were randomly allocated to two therapies using a factorial design (insulin glargine vs. standard care, and omega-3 fatty acid supplement vs. placebo) and followed for a median of 6.2 years to monitor cardiovascular events and other health outcomes (14). At baseline, 8,494 participants provided blood samples for proteomic analyses, among whom 5,078 consented to genomic analyses. Following genotyping quality control procedures (Supplementary Material 1), a total of 1,931 participants of European Caucasian ethnicity and 2,216 participants of native Latin ethnicity were included in the genetic analyses.

The UK Biobank is a prospective cohort of >500,000 individuals (aged 40–69 years) recruited across the U.K. and for whom extensive phenotypic and genotypic data were collected (15). From the full data set release issued in July 2017, a total of 343,735 unrelated individuals of British origin had genotyping data suitable for analysis (Supplementary Material 1). Among those individuals, 1,863 had missing phenotypic data, leaving 341,872 participants for our analyses, including 16,320 prevalent cases of type 2 diabetes. Data analyses were conducted under UK Biobank application number 1525.

The primary outcome for the individual participant data analyses was type 2 diabetes prevalence at baseline, as defined by the UK Biobank (15). The primary outcome for analyses using summary-level data was type 2 diabetes, as defined by the DIAGRAM consortium (13).

ACE serum concentration was assayed within a multiplex biomarker panel (Supplementary Table 2). After completion of the ORIGIN trial, 1 mL of serum from each participant was transported to Myriad RBM, Inc. (Austin, TX) to quantify 284 biomarkers (Luminex) related to metabolic and cardiovascular diseases. A total of 238 biomarkers, including ACE, from 8,401 participants were deemed suitable for analysis (16). ACE concentrations were normally distributed. Values were standardized to a mean of 0 and an SD of 1.

We selected genetic variants within 300 kilobases of the ACE locus (namely, cis-single nucleotide polymorphisms [SNPs]) and next removed SNPs not found in the DIAGRAM database or with minor allele frequencies < 0.05, resulting in 870 cis-SNPs that were then tested for their association with ACE concentration in the ORIGIN trial (17). Linear regression analyses for each SNP with ACE concentration were performed separately in each ethnic group, adjusting for ACE inhibitor usage at baseline and for age, sex, and the first five principal components, and then meta-analyzed across the two ethnicities using fixed effects models. A total of 448 cis-SNPs were associated with ACE concentration (P < 0.01), which were then pruned for linkage disequilibrium at a stringent threshold of r² < 0.1.

Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines (18), we performed a meta-analysis of the risk of new-onset type 2 diabetes reported in RCTs examining the effects of ACE inhibitors versus placebo. A structured search was conducted in the PubMed database on 9 May 2019, using the following Medical Subject Heading terms: antihypertensive agents, angiotensin-converting enzyme inhibitors, diabetes mellitus, randomized controlled trial, and meta-analysis. Only studies with a double-blinded, placebo-controlled trial design in adults, assessing the effect of ACE inhibitors in comparison with a placebo and reporting the incidence of new-onset diabetes, were included (Supplementary Material 1). The intervention effect on the incidence of type 2 diabetes was expressed as an odds ratio (OR) with the 95% CI and P value. We used an inverse variance–weighted random effects model because of the wide clinical and methodological variability across trials (R metafor package).

Two-sample MR analysis of effect of ACE concentration change on type 2 diabetes risk used 1) the effect of the ACE concentration–lowering SNPs (calculated in the ORIGIN trial) and 2) the effect of those SNPs on type 2 diabetes risk from the DIAGRAM consortium (13). MR associations were performed using the inverse variance–weighted method by regressing the genetic effect estimates for type 2 diabetes on the genetic effect estimates for ACE concentration adjusted for ACE inhibitor usage at baseline. To determine statistical significance, a bootstrap method was used under the null hypothesis that the ratio of the genetic effect estimate for type 2 diabetes on the genetic effect estimate for the biomarker was equal to zero for all SNPs. The predicted effects on type 2 diabetes were sampled from a normal distribution, with the mean and SD determined from DIAGRAM. A two-tailed P value was calculated using a Z test from 100,000 random simulations.

A weighted GRS underpinning lower ACE serum concentration (ACE GRS) was calculated for each participant of the UK Biobank by summing the number of ACE concentration–lowering alleles inherited at each independent variant, weighted by each SNP–ACE concentration association coefficient measured in SD per effect allele. The score was then normalized such that a 1-unit increase in the GRS corresponded to a 1-SD reduction in genetically determined ACE concentration. Logistic regression was performed to estimate the association between type 2 diabetes prevalence and ACE GRS in the UK Biobank. This model was adjusted for age, sex, ACE inhibitor usage, and the first 10 principal components. To verify whether the effect of ACE inhibition on type 2 diabetes could be mediated through an effect on BMI, three additional logistic regression models of type 2 diabetes risk on ACE GRS were performed. These consisted of two models also adjusted for 1) measured BMI or 2) BMI polygenic risk score (PRS) (calculated on the basis of a method we previously described [19] and using the summary statistics from the Genetic Investigation of ANthropometric Traits [GIANT] consortium [20]); a third model using an ACE GRS excluded the SNP rs72845888, which has previously been reported to be significantly associated with BMI (at P = 4.8 × 10⁻¹¹) (20).

We performed MR-Egger regression to test whether genetic variants included in MR analyses did not exert directional pleiotropy (i.e., where the genetic association with the outcome is through a different causal pathway and not through the exposure of interest) (21). This analysis tested the null hypothesis (i.e., absence of pleiotropy) that the y-intercept of the regression line was equal to zero after fitting the relationships between genetic associations with ACE concentration (independent variable) and type 2 diabetes (dependent variable).

Finally, comparison between estimates of type 2 diabetes risk obtained from MR analysis and that obtained from RCT meta-analysis was performed using a Z test after standardization on blood pressure change (Supplementary Material 3).

Assuming that biological mechanisms underlying the protective effect of ACE inhibition on type 2 diabetes may involve body adiposity, insulin sensitivity, and insulin secretion, we investigated ACE concentration for causal associations with BMI and 15 glucose- and insulin-related traits, including fasting glucose, 2-h postprandial glucose, HbA_1c, and several indices of insulin resistance or β-cell function (22). We performed two-sample MR analyses of ACE concentration change (using the genetic associations estimated in the ORIGIN trial) on BMI (using the GIANT consortium summary-level data) (20) and each of the 15 glucose- and insulin-related traits (using the Meta-Analyses of Glucose and Insulin-related traits Consortium [MAGIC] summary-level data) (23–26). We applied a Bonferroni correction, such that only two-tailed P < 0.05/16 was considered statistically significant.

All statistical analyses were conducted using R version 3.3.2 software. Two-tailed P < 0.05 was considered statistically significant, with adjustment for multiple hypothesis testing applied as appropriate.

We identified 17 independent cis-SNPs significantly associated with ACE concentration in 4,197 participants from the genetic substudy of ORIGIN (Supplementary Table 3 for baseline characteristics). Altogether, these SNPs were able to explain 39% of the variance in ACE serum concentrations (F-statistic value 156), thus supporting its appropriateness to be used as an instrument in subsequent MR analyses (when F > 10) (Supplementary Table 4 and Supplementary Fig. 1).

Through the two-sample MR analysis applied to DIAGRAM, we found that lower ACE concentration was associated with a lower risk of type 2 diabetes (OR per SD 0.92 [95% CI 0.89–0.95]) (Fig. 2A). No pleiotropy was detected to explain the causal relationships between genetically determined ACE concentration change and the risk of type 2 diabetes using MR-Egger regression model (intercept P = 0.221) (Fig. 3).

These genetic findings were replicated in the UK Biobank using one-sample MR. Hereby, we estimated that a 1-SD lower ACE GRS resulted in a 3% lower risk of type 2 diabetes (OR per SD 0.97 [95% CI 0.96–0.99]), with a direction and magnitude of effect consistent with those estimated in the two-sample MR analysis (Fig. 2A and Supplementary Fig. 2). No effect of interaction between ACE inhibitor usage and ACE GRS was found on type 2 diabetes risk (P for interaction = 0.99). Moreover, a 1-SD lower ACE GRS predicted a 0.01 mmol/L lower mean fasting plasma glucose (P = 5.11 × 10⁻³) and a 0.02 mmol/mol lower mean HbA_1c (P = 2.20 × 10⁻²) (Fig. 2B).

As sensitivity analyses, we verified whether adjustment for measured BMI or BMI PRS would affect the association of ACE GRS with type 2 diabetes prevalence. We found that this association was modestly attenuated with adjustment for measured BMI (OR per 1-SD lower ACE GRS 0.97 [95% CI 0.95–0.99]; P = 1.89 × 10⁻⁴) or BMI PRS (0.98 [0.96–0.99]; P = 2.30 × 10⁻³). Similarly, when performing a leave-one-out analysis by excluding the SNP rs72845888 from the ACE GRS calculation, the association of ACE GRS with type 2 diabetes prevalence was attenuated but still significant (0.98 [0.97–0.99]; P = 3.60 × 10⁻²).

To investigate whether the effect of ACE inhibition on type 2 diabetes risk might be cumulative over time, we compared the MR estimates of type 2 diabetes with that obtained from an RCT meta-analysis. Six RCTs of ACE inhibitors versus placebo were included in the meta-analysis, totaling 31,200 participants (Table 1). A total of 1,312 new-onset diabetes cases were reported in the intervention group (n = 15,593) and 1,506 cases in the control group (n = 15,607) with a MAP-lowering effect of ACE inhibitors versus placebo of 2.4 mmHg over a period of 2.9–4.8 years of treatment. This meta-analysis estimated that ACE inhibitor allocation decreased type 2 diabetes incidence by 24% (OR 0.76 [95% CI 0.60–0.97]) (Supplementary Fig. 3). Compared with this, the ACE GRS was associated with a larger decrease in type 2 diabetes risk of 81% per 2.4-mmHg lower MAP (0.19 [0.07–0.51]; P = 0.007 for difference) (Fig. 2C). This result was expected because MR analysis (which appraises the effects of a lifelong exposure on an outcome) may generate a larger effect estimate compared with that obtained from RCTs examining the effects of ACE inhibitors over a limited duration of time.

To further clarify the association of ACE inhibition and type 2 diabetes, we explored potential biological mechanisms linking ACE and type 2 diabetes using a two-sample MR approach. We identified a causal association between lower ACE concentration and lower BMI, whereby a genetically lower ACE concentration predicted a lower BMI (β = −0.02 kg/m² [95% CI −0.03 to −0.007]; P = 7.42 × 10⁻⁴). No significant causal associations between genetically determined ACE concentration and 15 glycemic- and insulin-related traits were detected (P_all ≥ 0.05/16) (Supplementary Table 6).

We demonstrated that genetically lower ACE concentration was associated with lower type 2 diabetes prevalence using two independent analyses, even after adjusting for BMI, and that genetic estimate was larger compared with the effect estimate measured in RCTs of ACE inhibitors versus placebo, when standardizing for blood pressure change. These genetic findings thus reinforce the causal protective effect of ACE inhibitors on type 2 diabetes and suggest a cumulative effect over time.

Here, we have provided the first MR study demonstrating that a genetically lower ACE concentration predicts a lower risk of type 2 diabetes. These findings are consistent with previous results from genetic association studies of ACE variants with type 2 diabetes risk (27,28). The most studied ACE variant (namely, ACE I/D) corresponds to either the presence (insertion [I]) or absence (deletion [D]) of a 287–base pair Alu repeat in intron 16 of the ACE gene. This variant has been reported to account for up to one-half of the variability in serum enzyme levels, with individuals homozygous for allele I having significantly lower levels than carriers of allele D (29). There are multiple SNPs tagging this single I/D SNP, including rs4343. Meta-analyses of the ACE I/D variant showed an association with type 2 diabetes, such that allele I was associated with a 14% lower risk of type 2 diabetes compared with allele D (30). Consistent with this analysis, rs4343-A, which corresponds to allele I, explained 20% of the variance in ACE concentration estimated in the ORIGIN trial, with the strongest negative association with ACE concentration. The allele I was also reported to be nominally associated (P = 8.60 × 10⁻⁷) with a reduced risk of type 2 diabetes, with an OR of 0.97 in the Diabetes Meta-Analysis of Trans-Ethnic consortium (31).

Evidence of causality between ACE inhibitors and protection against type 2 diabetes raises the clinical question of whether ACE inhibitors should be recommended as a first-choice therapy in patients with hypertension at high risk of developing type 2 diabetes. In the Diabetes REduction Assessment with ramipril and rosiglitazone Medication (DREAM) trial, the only dedicated trial studying the effect of ACE inhibitor allocation versus placebo on incident type 2 diabetes to date, ramipril did not significantly reduce the incidence of diabetes in participants having prior IFG or IGT (hazard ratio 0.91 [95% CI 0.79–1.05]) (10). However, plasma glucose levels measured 2 h after an oral glucose load were significantly lower in the ramipril group (10). This suggests that the benefits of ACE inhibitors on type 2 diabetes prevention might be reduced in patients with prior dysglycemia, and the DREAM trial might have been underpowered (with a sample size of 5,269) or of insufficient duration (with a median of 3 years) to detect a reduction in diabetes incidence <20%.

While MR is increasingly used to infer drug effects and reposition drug indications (12), our study suggests a protective effect of ACE inhibitors on type 2 diabetes through a reduction of BMI, which is consistent with experimental models. For example, treatment of mice on a high-fat diet with ACE inhibitors reduced body weight and, consequently, improved insulin sensitivity. This study demonstrated that angiotensin II—the product of ACE—inhibits adipogenic differentiation of human adipocytes, suggesting that ACE inhibitors may prevent type 2 diabetes by promoting the recruitment and differentiation of adipocytes (32). Other mechanisms have also been reported to explain the link between ACE inhibition and glucose homeostasis. Experimental models showed that vasodilation induced by ACE inhibitors could improve blood circulation not only in skeletal muscles, favoring glucose disposal in peripheral tissues (e.g., skeletal muscles, adipose tissue) and promoting insulin action (33,34), but also in the pancreas, promoting insulin secretion (35). Preserving cellular potassium and magnesium pools by blocking ACE could also improve cellular insulin action and secretion (36).

The strengths of our study include the use of MR to estimate a causal relationship between ACE inhibition and type 2 diabetes risk through two independent analyses. Our analyses also revealed a narrower interval estimate through MR compared with RCT meta-analysis. While RCTs provide the highest level of evidence for or against a health intervention, heterogeneity in the diagnosis criteria for type 2 diabetes or in the intervention duration across the studies represents an important limitation to meta-analyses. In contrast, the use of MR has the ability to reflect constant lifelong exposures to a causal factor and can also overcome some RCT pitfalls, such as insufficient duration of follow-up to demonstrate an effect. However, the generalizability and interpretation of our findings are limited by several factors. First, our MR analysis was restricted to individuals of European and Latin American ancestries, and the association of ACE concentration with type 2 diabetes risk may differ in other ethnicities. Second, no significant causal relationships were found between ACE inhibition and either insulin secretion or insulin resistance indexes, although the power of MR was moderate for these traits and not all biological pathways could be explored using MR because of the lack of genetic data or weak genetic instruments. Finally, we could not investigate through our RCT meta-analysis whether receiving ACE inhibitors versus placebo decreases BMI over the follow-up period because BMI change has not been reported as a secondary end point in RCT-related publications (5–9).

Our study represents the first MR analysis of ACE concentration on type 2 diabetes risk and identified ACE as a novel causal factor for type 2 diabetes. Those genetic findings were consistent with meta-analyses of ACE inhibitor RCTs. Although future studies are warranted to better delineate the metabolic actions of ACE inhibitors, current evidence supports the use of ACE inhibitors to protect from type 2 diabetes. These findings also suggest that a patient’s risk of developing type 2 diabetes should be considered when prescribing blood pressure–lowering drugs.

Acknowledgments. The authors thank all the participants for contributing to this project, the GIANT and DIAGRAM consortia, and the UK Biobank for making data available.

Funding and Duality of Interest. Support was provided by the Canadian Institutes of Health Research (CIHR) and Sanofi. The ORIGIN trial and biomarker project were supported by Sanofi and CIHR. The biomarker project was led by ORIGIN investigators at the Population Health Research Institute with active collaboration of Sanofi scientists. Sanofi directly compensated Myriad RBM, Inc., for measurement of the biomarker panel and the Population Health Research Institute for scientific, methodological, and statistical work. Genetic analyses of ORIGIN participants were supported by CIHR (award 125794 to G.P.). M.P. is supported by the E.J. Moran Campbell Internal Career Research Award from McMaster University. G.P. is the Canada Research Chair in Genetic and Molecular Epidemiology and holds the CISCO Professorship in Integrated Health Biosystems. S.H. is an employee of Sanofi and also owns stocks of Sanofi. S.Y. has received research support for ORIGIN from Sanofi through his institution. S.Y. has received research grants, honoraria for lecturing, and travel support from Boehringer Ingelheim, AstraZeneca, Bristol-Myers Squibb, Bayer, and Cadila for work unrelated to the present topic. H.G. holds the McMaster-Sanofi Population Health Institute Chair in Diabetes Research and Care and has received consulting fees from Sanofi, Novo Nordisk, Merck, Eli Lilly, AstraZeneca, Boehringer Ingelheim, Kowa, and Abbott and support for research or continuing education through his institution from Sanofi, Eli Lilly, Merck, Novo Nordisk, Boehringer Ingelheim, and AstraZeneca. Both S.Y. and H.G. hold a patent that ramipril reduces diabetes, but this has been handed over to Sanofi and AstraZeneca. G.P. has received consulting fees from Sanofi, Bristol-Myers Squibb, Lexicomp, and Amgen and support for research through his institution from Sanofi. No other potential conflicts of interest relevant to this article were reported.

Author Contributions. M.P., H.G., and G.P. designed the study, planned the analyses, interpreted the results, and wrote the manuscript. J.S. and M.C. contributed to the statistical and bioinformatics analyses. S.H. suggested including ACE in the biomarker panel. J.B. and S.Y. edited the manuscript. All authors contributed to the critical reading and revision of the manuscript and approved the submitted version of this manuscript. G.P. is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Prior Presentation. Parts of this study were presented as a late-breaking poster at the 79th Scientific Sessions of the American Diabetes Association, San Francisco, CA, 7–11 June 2019, and orally at the 55th Annual Meeting of the European Association for the Study of Diabetes, Barcelona, Spain, 16–20 September 2019.