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ORIGINAL PAPERS: Blood vessels

A genetic risk score for fasting plasma glucose is independently associated with arterial stiffness

a Mendelian randomization study

Gottsäter, Mikael; Hindy, George; Orho-Melander, Marju; Nilsson, Peter M.; Melander, Olle

Author Information
doi: 10.1097/HJH.0000000000001646

Abstract

INTRODUCTION

The stiffening of large elastic arteries that occurs with increasing age is an important contributor to hypertension, especially in the elderly [1,2]. Arterial stiffness has also been shown to be an independent predictor of cardiovascular events and mortality [3,4]. In addition to the strong inter-correlation of arterial stiffness with blood pressure, several cross-sectional studies have shown arterial stiffness to be associated with a number of clinical conditions such as diabetes, dyslipidemia and adiposity [5–7]. Markers of these conditions have been able to predict arterial stiffness after a long follow-up time. However, observational studies of this kind cannot prove causality.

Despite associations of arterial stiffness with several cardiovascular risk markers, genome-wide associations studies (GWAS) have identified only one genetic variant to be associated with arterial stiffness and results have not been concurrent [8,9]. This significant locus in the 3’-BCL11B gene desert on chromosome 14 was identified in a multicenter GWAS in 20 364 individuals from nine European community-based cohorts [9].

Mendelian randomization is an analysis technique, which uses genetic information to achieve an unbiased detection of causal effects [10]. Benefitting from the random allocation of alleles at conception and the absence of confounding factors or reverse causality, in Mendelian randomization, the genetic variant is used as an instrumental variable [11].

The aim of this study was to use a Mendelian randomization approach to explore a potential causal relationship between arterial stiffness and cardiometabolic risk factors by the use of genetic risk score (GRS) of blood pressure, adiposity, disturbed glucose metabolism and dyslipidemia.

METHODS

Study population

The study population is part of the Malmö Diet and Cancer (MDC) study, a large population-based cohort consisting of men and women from the city of Malmö, Sweden [12,13]. A subset of the population, 6103 individuals, took part in the cardiovascular arm of this cohort investigated in 1991–1994 [14]. Of these original 6103 participants, 3734 individuals were re-investigated in 2007–2012, of which 3056 individuals underwent measurement of pulse wave velocity (PWV). This has been previously described including analysis of nonparticipation at the re-investigation [15] and PWV measurements [5]. The study was approved by the regional ethics review board, Lund, Sweden (ID 532-2006). A written informed consent was obtained from all participants.

Altogether, there is complete data on 2853 individuals constituting the study population for our analyses.

Measurement of arterial stiffness

Arterial stiffness was measured as carotid–femoral pulse wave velocity (c-f PWV) using an applanation tonometry technique (SphygmoCor, Atcor Medical, Australia) with participants in a quiet room in supine position after 5 min of rest. The carotid–femoral distance was calculated using the so-called subtraction method defining the distance as the suprasternal notch to the umbilicus plus the umbilicus to the measuring point at the femoral artery minus the suprasternal notch to the measuring point at the carotid artery. Transit time was calculated using the foot-to-foot method with simultaneous ECG registration measuring the time from the peak of the R-wave on the ECG to the foot of the pulse wave at the carotid and femoral arteries, respectively. The number of successful c-f PWV measurements in each individual varied from one to five, with a goal of three measurements (86.7% of cases). Results are based on mean values from these measurements.

Genotyping

The single nucleotide polymorphisms (SNPs) were genotyped using a MALDI-TOF mass spectrometer (Sequenom Mass Array, Sequenom, San Diego, California, USA). Proxy SNPs were found using SNAP version 2.2. SNPs that failed this analysis were analyzed individually using the Taqman or KASPar allelic discrimination method on an ABI 790HT (Applied Biosystems, Life Technologies, Carlsbad, California, USA), according to instructions provided by the manufacturer. Individuals with less than 60% successful genotyped SNPs were excluded. SNPs with a genotype success rate of less than 90% or deviation from the Bonferroni-corrected Hardy–Weinberg Equilibrium in each set of SNPs for each trait were excluded from the analysis. At least 25% of individuals were also genotyped with a different method (Human OmniExpress Bead Chip; Illumina, San Diego, California, USA) to check for concordance, which was more than 98% for all included SNPs.

Construction of genetic risk scores

Construction of weighted GRS for SBP, BMI, low-density lipoprotein cholesterol (LDLc), high-density lipoprotein cholesterol (HDLc), triglycerides, fasting plasma glucose (FPG) and type 2 diabetes (T2D) were performed using publications from large multicenter genome-wide association studies (GWASs) [16–24]. The SNPs included in the GRSs were weighted by the reported effect size in GWAS. The SNPs for FPG were all discovered in nondiabetic individuals [16]. Of the 15 SNPs included in the FPG GRS, 7 overlapped with the SNPs in the T2D GRS. A GRS for T2D without the seven SNPs overlapping in the FPG GRS was created. This GRS is referred to as T2D41 GRS (as it includes 41 SNPs). Also for the other GRSs, a few SNPs had shown GWAS significance for several traits and, thus, were included in several scores. The references and number of included SNPs for the respective GRSs are presented in Supplementary Table 1, https://links.lww.com/HJH/A883 and complete tables of all included SNPs are presented in Supplementary Tables 2–8, https://links.lww.com/HJH/A883. The genotype at each locus was coded as 0, 1 or 2 depending on the number of alleles, previously shown to increase the risk factor in question. With information from previous publications, each allele was weighted according to the estimated effect size. We both calculated a GRS for each individual and used a multivariate Mendelian randomization wherever causal estimates calculated from each SNP were combined.

Statistical analysis

Statistical calculations were performed using IBM SPSS Statistics, version 22 (IBM Corp., Armonk, New York, USA), PLINK (version 1.07) and R (version 3.31). C-f PWV, LDLc, HDLc, triglycerides and FPG were natural logarithm transformed to achieve normal distributions.

The associations between each GRS and its respective trait as well as the associations between each trait and c-f PWV were calculated with multiple linear regression.

For the GRSs calculated for each individual, multiple linear regression was used to investigate the association between the respective GRS and c-f PWV with adjustment for age, sex and mean arterial pressure (MAP). Logistic regression was used to calculate the odds ratio of GRS for T2D. Whenever the instrumental variable was SBP GRS, ongoing blood pressure-lowering treatment was added to the analysis and additionally, sensitivity analyses without antihypertensive medication was performed. Whenever the instrumental variable was LDLc GRS, HDLc GRS or triglycerides GRS, lipid-lowering treatment was added to the analysis and, sensitivity analysis without individuals on statin treatment was performed in addition to the main analyses. Whenever the instrumental variable was FPG GRS, individuals with diabetes were excluded. To estimate the causal effect of the trait on c-f PWV, we performed a two-stage least squares regression [10]. Firstly, the traits were regressed on their respective GRS and the fitted values were saved. Secondly, c-f PWV was regressed on the predicted fitted values. Adjustments were made for age, sex and MAP in both stages.

Also, an inverse-variance weighted Mendelian randomization regression was performed according to a previously described approach [25,26]. This was performed as a complementary approach, in order to correct for potential bias of pleiotropic effects of a SNP on any of the other studied traits. Shortly, the β coefficients from the multiple linear regression of each of the 183 SNPs on c-f PWV were regressed on the β coefficients from the multiple linear regression of the same SNPs on each trait. This regression was inverse-variance weighted using standard errors of each SNP–PWV association and the intercept was fixed to zero.

RESULTS

The characteristics of the final study population of 2853 individuals are presented in Table 1. The population with c-f PWV data but without a complete data set (n = 203) had a higher proportion of women (70.0 versus 59.8%, P = 0.004), otherwise there were no significant differences between the study population and individuals excluded because of missing data.

TABLE 1
TABLE 1:
Characteristics of the study population, n = 2853

All traits were associated with c-f PWV as presented in Table 2. Each GRS, except for SBP GRS, was significantly associated with its respective trait. This is presented in Table 3.

TABLE 2
TABLE 2:
Association of traits with carotid–femoral pulse wave velocity, adjusted for age and sex
TABLE 3
TABLE 3:
Association of genetic risk scores with its respective trait, measured at the Malmö Diet and Cancer follow-up examination

There were significant associations between c-f PWV and both FPG GRS (β = 0.03, P = 0.03) and T2D GRS (β = 0.03, P = 0.04). There was no significant association between T2D41 GRS and c-f PWV (β = 0.03, P = 0.07). In sensitivity analyses without individuals with diabetes the significance for FPG GRS remained (β = 0.04, P = 0.02). There were borderline significant associations for SBP GRS (β = −0.03, P = 0.05) and triglycerides GRS (β = 0.03, P = 0.05), but none of these remained in sensitivity analyses excluding individuals with blood pressure-lowering treatment and lipid-lowering treatment, respectively. For the other GRSs there were no significant associations with c-f PWV (see Table 4).

TABLE 4
TABLE 4:
Association between genetic risk scores and carotid–femoral pulse wave velocity adjusted for age, sex, mean arterial pressure and ongoing blood pressure-lowering treatment

In two-stage least squares regression, the instrumental variable, one standard deviation (SD) increment of FPG GRS, was associated with 0.034 SD (95% CI 0.003–0.065) or 0.08 m/s (95% CI 0.007–0.16) higher c-f PWV.

A total of 183 unique SNPs (r2 < 0.2) that were reported in GWAS to associate with SBP, BMI, LDLc, HDLc, triglycerides, FPG and T2D were included in the inverse-variance weighted Mendelian randomization. There was a significant association between c-f PWV β coefficients and FPG β coefficients (β = 0.28, P = 0.006), but not with T2D β coefficients (β = 0.006, P = 0.88). There were no significant associations between c-f PWV β coefficients and BMI (β = −0.03, P = 0.82), SBP (β = 0.002, P = 0.98), LDLc (β = −0.05, P = 0.32), HDLc (β = −0.02, P = 0.73) or triglyceride (β = 0.09, P = 0.18) β coefficients. This is presented in Table 5. When excluding eight SNPs were associated with FPG but not with T2D, FPG β coefficients were still significantly associated with PWV β coefficients (β = 0.25, P = 0.018) and T2D results remained insignificant (β = 0.04, P = 0.33).

TABLE 5
TABLE 5:
Inverse-variance weighted Mendelian randomization of cardiometabolic traits and carotid–femoral pulse wave velocity adjusted for age, sex, mean arterial pressure and blood pressure lowering treatment at follow-up

DISCUSSION

This population-based study using Mendelian randomization methods found an independent association of β coefficients for FPG with arterial stiffness measured as c-f PWV. Whenever using the individual GRS method, both FPG and T2D were associated with c-f PWV. Multivariable inverse-variance weighted Mendelian randomization was used to correct for potential bias because of pleiotropic effects, which violates a basic assumption of Mendelian randomization. Therefore, the significant results for T2D from individual GRS method in our study is interpreted as an effect of pleiotropy. This is further supported by the T2D41 GRS results – the fact that the T2D GRS lost its significance with c-f PWV after removing seven SNPs also included in the FPG GRS.

Previous studies have found associations between arterial stiffness and diabetes, FPG, triglycerides and HDLc [5–7]. However, the exact mechanisms behind this are unclear and it is not known whether it is an effect of hyperglycemia per se or rather caused by more complex metabolic changes associated with insulin resistance. Arterial stiffness is known to be associated with increasing number of traits of the metabolic syndrome, which supports the role of insulin resistance [27,28]. On the other hand, the increased arterial stiffness in type 1 diabetes points towards a more prominent role of hyperglycemia per se [27]. This is supported by the results from our study where only the genetic disposition of fasting hyperglycemia was associated with arterial stiffness. Even though an increased fasting plasma glucose is one of the T2D criteria, the genetic predisposition of T2D is likely to incorporate several other complex metabolic traits including insulin resistance and abdominal fat accumulation. The SNPs associated with fasting glycemia included in our study, on the other hand, were established in a population without diabetes and should reflect genetic variation of plasma glucose within the normal range with no or weaker tendency of progression to overt T2D. Increased levels of plasma glucose could be harmful to arterial wall through its abilities to facilitate the formation of advanced glycation end-products (AGEs) and enhancement of collagen cross-linking in the arterial wall, decreasing its elastic properties [29,30]. Hyperglycemia has also been shown to induce the angiotensin II production in the arterial wall, a factor relevant for arterial remodeling [31].

Several epidemiological studies have shown that impaired fasting glucose (IFG) is associated with arterial stiffness even in levels below the threshold for a diabetes diagnosis [27,32–34]. This has also been demonstrated in data from the MDC cohort [5]. Such data support our results and indicate that hyperglycemia is harmful to the arterial wall even without the presence of overt diabetes. Our study suggests that macrovascular effects of hyperglycemia could be one pathway, linking plasma glucose to target organ damage and cardiovascular events. On the basis of the results from our study, every treatment, pharmacological or nonpharmacological, that lowers fasting glycemia effectively would have the potential to also reduce arterial stiffening and lower the risk for cardiovascular events and death [3].

The relationship between GRS for T2D and arterial stiffness has previously been investigated in a Mendelian randomization study of 11 385 individuals from Shanghai, China [35]. The results showed a positive relationship between arterial stiffness measured by brachial-ankle PWV and a 34 SNPs GRS for T2D (OR = 1.24, P = 0.008), thereby showing concurrent results with the individual GRS results from our study.

Strengths of this study include the community-based population and the thoroughly measured outcome, c-f PWV, which is the gold standard for measuring arterial stiffness [36]. As one SNP explains very little of a trait variance, GRSs were used as instrumental variables instead of individual SNPs in order to increase power. However, a GRS may include several SNPs that have pleiotropic effects on other cardiometabolic traits, thus, violating a basic assumption of an instrumental variable in Mendelian randomization [11]. This was addressed by the use of inverse-variance weighted Mendelian randomization, which should reduce this problem [25,26]. Inverse-variance weighted Mendelian randomization method has been applied in previous publications to address the issue of pleiotropy [37,38]. The widespread use of lipid-lowering and blood pressure-lowering drug treatment in our study diluting the effect of genetic variants on the phenotype is problematic. This was addressed both by adjustment and also by sensitivity analyses. The limitations above should, however, not result in any false rejections of the null hypothesis. Therefore, we have focused on the positive rather than negative findings in this study knowing that negative results might be explained by lack of power. Finally, in Mendelian randomization, the genetic variant used as an instrumental variable should be reliably associated to the marker. The SNPs used to construct the GRSs have in meta-analysis been shown to be associated with their respective phenotypes. However, in our cohort, the absence of significant associations between SBP GRS and SBP violates an assumption of Mendelian randomization making SBP GRS an unsuitable instrumental variable [11].

In conclusion, our study shows that SNPs raising fasting plasma glucose but not SNPs associated with T2D were associated with arterial stiffness. This suggests a causal stiffening effect of glycemia on the arterial wall, independently of diabetes status. This is, to our knowledge, the first study investigating the relationship of cardiometabolic SNPs and arterial stiffness using inverse-variance weighted Mendelian randomization, as a method of accounting for pleiotropy. The absence of significant associations between T2D SNPs and arterial stiffness in our study is, in our opinion, surprising and needs confirmation in future studies.

ACKNOWLEDGEMENTS

This study was supported by the Medical Research Council of Sweden (grant K2011-65X-20752-04-6), the Region Skåne County Council, the Ernhold Lundstrom Foundation, European Research Council StG282255.

Conflicts of interest

There are no conflicts of interest.

Reviewer's Summary Evaluation

Reviewer 1

Strengths

  • To point out that plasma glucose levels, irrespective of the presence of diabetes, may affect arterial stiffness by using the genetic risk scores to support a causal relationship.
  • Robust methodological approach; a large number of unique SNPs (183) included in the Mendelian randomization.
  • The working hypothesis of lowering fasting glucose to reduce arterial stiffness and, therefore, CV risk.

Weaknesses

  • The negative findings on the role of diabetes are likely due to the relatively small cohort and lack of power of the study.

REFERENCES

1. O’Rourke MF, Safar ME, Dzau V. The Cardiovascular Continuum extended: aging effects on the aorta and microvasculature. Vasc Med 2010; 15:461–468.
2. Nilsson PM, Boutouyrie P, Cunha P, Kotsis V, Narkiewicz K, Parati G, et al. Early vascular ageing in translation: from laboratory investigations to clinical applications in cardiovascular prevention. J Hypertens 2013; 31:1517–1526.
3. Ben-Shlomo Y, Spears M, Boustred C, May M, Anderson SG, Benjamin EJ, et al. Aortic pulse wave velocity improves cardiovascular event prediction: an individual participant meta-analysis of prospective observational data from 17,635 subjects. J Am Coll Cardiol 2014; 63:636–646.
4. Vlachopoulos C, Aznaouridis K, Stefanadis C. Prediction of cardiovascular events and all-cause mortality with arterial stiffness: a systematic review and meta-analysis. J Am Coll Cardiol 2010; 55:1318–1327.
5. Gottsater M, Ostling G, Persson M, Engstrom G, Melander O, Nilsson PM. Nonhemodynamic predictors of arterial stiffness after 17 years of follow-up: the Malmö Diet and Cancer study. J Hypertens 2015; 33:957–965.
6. Johansen NB, Vistisen D, Brunner EJ, Tabak AG, Shipley MJ, Wilkinson IB, et al. Determinants of aortic stiffness: 16-year follow-up of the Whitehall II study. PLoS One 2012; 7:e37165.
7. Cecelja M, Chowienczyk P. Dissociation of aortic pulse wave velocity with risk factors for cardiovascular disease other than hypertension: a systematic review. Hypertension 2009; 54:1328–1336.
8. Tarasov KV, Sanna S, Scuteri A, Strait JB, Orru M, Parsa A, et al. COL4A1 is associated with arterial stiffness by genome-wide association scan. Circ Cardiovasc Genet 2009; 2:151–158.
9. Mitchell GF, Verwoert GC, Tarasov KV, Isaacs A, Smith AV, Yasmin, et al. Common genetic variation in the 3’-BCL11B gene desert is associated with carotid-femoral pulse wave velocity and excess cardiovascular disease risk: the AortaGen Consortium. Circ Cardiovasc Genet 2012; 5:81–90.
10. Davey Smith G, Hemani G. Mendelian randomization: genetic anchors for causal inference in epidemiological studies. Hum Mol Genet 2014; 23:R89–98.
11. Sheehan NA, Didelez V, Burton PR, Tobin MD. Mendelian randomisation and causal inference in observational epidemiology. PLoS Med 2008; 5:e177.
12. Berglund G, Elmstähl S, Janzon L, Larsson SA. The Malmö Diet and Cancer study. Design and feasibility. J Intern Med 1993; 233:45–51.
13. Manjer J, Carlsson S, Elmstahl S, Gullberg B, Janzon L, Lindström M, et al. The Malmö Diet and Cancer Study: representativity, cancer incidence and mortality in participants and nonparticipants. Eur J Cancer Prev 2001; 10:489–499.
14. Hedblad B, Nilsson P, Janzon L, Berglund G. Relation between insulin resistance and carotid intima-media thickness and stenosis in nondiabetic subjects. Results from a cross-sectional study in Malmö, Sweden. Diabet Med 2000; 17:299–307.
15. Rosvall M, Persson M, Ostling G, Nilsson PM, Melander O, Hedblad B, et al. Risk factors for the progression of carotid intima-media thickness over a 16-year follow-up period: the Malmö Diet and Cancer Study. Atherosclerosis 2015; 239:615–621.
16. Dupuis J, Langenberg C, Prokopenko I, Saxena R, Soranzo N, Jackson AU, et al. New genetic loci implicated in fasting glucose homeostasis and their impact on type 2 diabetes risk. Nat Genet 2010; 42:105–116.
17. Ehret GB, Munroe PB, Rice KM, Bochud M, Johnson AD, et al. International Consortium for Blood Pressure Genome-Wide Association Studies. Genetic variants in novel pathways influence blood pressure and cardiovascular disease risk. Nature 2011; 478:103–109.
18. Levy D, Ehret GB, Rice K, Verwoert GC, Launer LJ, Dehghan A, et al. Genome-wide association study of blood pressure and hypertension. Nat Genet 2009; 41:677–687.
19. Morris AP, Voight BF, Teslovich TM, Ferreira T, Segre AV, Steinthorsdottir V, et al. Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes. Nat Genet 2012; 44:981–990.
20. Newton-Cheh C, Johnson T, Gateva V, Tobin MD, Bochud M, Coin L, et al. Genome-wide association study identifies eight loci associated with blood pressure. Nat Genet 2009; 41:666–676.
21. Speliotes EK, Willer CJ, Berndt SI, Monda KL, Thorleifsson G, Jackson AU, et al. Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index. Nat Genet 2010; 42:937–948.
22. Teslovich TM, Musunuru K, Smith AV, Edmondson AC, Stylianou IM, Koseki M, et al. Biological, clinical and population relevance of 95 loci for blood lipids. Nature 2010; 466:707–713.
23. Voight BF, Scott LJ, Steinthorsdottir V, Morris AP, Dina C, Welch RP, et al. Twelve type 2 diabetes susceptibility loci identified through large-scale association analysis. Nat Genet 2010; 42:579–589.
24. Zeggini E, Scott LJ, Saxena R, Voight BF, Marchini JL, Hu T, et al. Meta-analysis of genome-wide association data and large-scale replication identifies additional susceptibility loci for type 2 diabetes. Nat Genet 2008; 40:638–645.
25. Burgess S, Dudbridge F, Thompson SG. Re: “Multivariable Mendelian randomization: the use of pleiotropic genetic variants to estimate causal effects”. Am J Epidemiol 2015; 181:290–291.
26. Burgess S, Thompson SG. Multivariable Mendelian randomization: the use of pleiotropic genetic variants to estimate causal effects. Am J Epidemiol 2015; 181:251–260.
27. Stehouwer CD, Henry RM, Ferreira I. Arterial stiffness in diabetes and the metabolic syndrome: a pathway to cardiovascular disease. Diabetologia 2008; 51:527–539.
28. Scuteri A, Cunha PG, Rosei EA, Badariere J, Bekaert S, Cockcroft JR, et al. Arterial stiffness and influences of the metabolic syndrome: a cross-countries study. Atherosclerosis 2014; 233:654–660.
29. Sell DR, Monnier VM. Molecular basis of arterial stiffening: role of glycation - a mini-review. Gerontology 2012; 58:227–237.
30. van Eupen MG, Schram MT, van Sloten TT, Scheijen J, Sep SJ, van der Kallen CJ, et al. Skin autofluorescence and pentosidine are associated with aortic stiffening: the Maastricht Study. Hypertension 2016; 68:956–963.
31. Lavrentyev EN, Estes AM, Malik KU. Mechanism of high glucose induced angiotensin II production in rat vascular smooth muscle cells. Circ Res 2007; 101:455–464.
32. Webb DR, Khunti K, Silverman R, Gray LJ, Srinivasan B, Lacy PS, et al. Impact of metabolic indices on central artery stiffness: independent association of insulin resistance and glucose with aortic pulse wave velocity. Diabetologia 2010; 53:1190–1198.
33. Xu L, Jiang CQ, Lam TH, Cheng KK, Yue XJ, Lin JM, et al. Impact of impaired fasting glucose and impaired glucose tolerance on arterial stiffness in an older Chinese population: the Guangzhou Biobank Cohort Study-CVD. Metabolism 2010; 59:367–372.
34. Shin JY, Lee HR, Lee DC. Increased arterial stiffness in healthy subjects with high-normal glucose levels and in subjects with prediabetes. Cardiovasc Diabetol 2011; 10:30.
35. Xu M, Huang Y, Xie L, Peng K, Ding L, Lin L, et al. Diabetes and risk of arterial stiffness: a Mendelian randomization analysis. Diabetes 2016; 65:1731–1740.
36. Laurent S, Cockcroft J, Van Bortel L, Boutouyrie P, Giannattasio C, Hayoz D, et al. Expert concencus document on arterial stiffness: methodological issues and clinical applications. Eur Heart J 2006; 27:2588–2605.
37. Do R, Willer CJ, Schmidt EM, Sengupta S, Gao C, Peloso GM, et al. Common variants associated with plasma triglycerides and risk for coronary artery disease. Nat Genet 2013; 45:1345–1352.
38. White J, Swerdlow DI, Preiss D, Fairhurst-Hunter Z, Keating BJ, Asselbergs FW, et al. Association of lipid fractions with risks for coronary artery disease and diabetes. JAMA Cardiol 2016; 1:692–699.
Keywords:

aging; arterial stiffness; diabetes mellitus; epidemiology; hyperglycemia; Mendelian randomization

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