LPA kringle IV type 2 is associated with type 2 diabetes in a Chinese population with very high cardiovascular risk

kringle IV heart Abstract The connection between lipoprotein(a) [Lp(a)] levels and the risks of cardiovascular disease and diabetes remains poorly understood. Lp(a) is encoded by the LPA gene, and evidence suggests that the kringle IV type 2 (KIV-2) variant is particularly important to Lp(a) isoform size. A large isoform size, represented as a high number of KIV-2 repeats in LPA , is associated with low serum Lp(a) concentrations and an increased risk of type 2 diabetes. We investigated the associations among Lp(a) concentrations, LPA KIV-2 repeats, and type 2 diabetes in a Chinese population of 1,863 consecutive patients with very high cardiovascular risk, as identified by coronary angiography. Individuals with Lp(a) levels in the top tertile (67.86 (35.34-318.50) mg/dL) had a lower risk of diabetes compared to those in the bottom tertile (7.38 (0.60-12.91) mg/dL) . There was an inverse association between the number of KIV-2 repeats and serum Lp(a) concentrations. This study demonstrated that a high number of LPA KIV-2 repeats are associated with increased risk of type 2 diabetes in a Chinese population with very high cardiovascular risk, which suggests that large Lp(a) isoform size, associated with low Lp(a) concentration, has a causal effect on type 2 diabetes. randomized clinical trials demonstrating reduced risk of diabetes when targeting large Lp(a) isoforms. Our findings have some implications for prevention and treatment of type 2 diabetes and CHD. Identification of large Lp(a) isoform size represented as a high number of LPA KIV-2 repeats may help to prevent type 2 diabetes in a population with very high cardiovascular risk. Use of Lp(a) lowering drugs to reduce cardiovascular risk in such a population is feasible, as these therapies are unlikely to increase the number of KIV-2 repeats which is determined by genetics. Our results do not support a differential management of Lp(a) levels for the prevention of type 2 diabetes and CHD.


Introduction
Type 2 diabetes and cardiovascular disease may have common genetic and environmental antecedents according to the well-known "common soil hypothesis" (1). The description of metabolic syndrome largely bolstered the development of the "common soil hypothesis"(1, 2).
Obesity represents a key driver for the occurrence of metabolic syndrome(2, 3). Increased visceral and ectopic fat deposition releases excess fatty acids and a variety of adipokines that elicit metabolic risk factors, which plays a major role in the development of insulin resistance and predispose to both type 2 diabetes and cardiovascular disease(2, 3). However, lipoprotein (a) [Lp(a)] may be an exception based on its opposite effects on cardiovascular disease and type 2 diabetes. Lp(a) is a low-density lipoprotein (LDL) like particle mainly produced by the liver, consisting of an apolipoprotein B100 molecule covalently bounded to an apolipoprotein(a)(4).
Prospective epidemiological studies have demonstrated that elevated concentrations of Lp(a) are associated with increased risk of ischemic cardiovascular disease(5-7). On the contrary, observational studies found that increased Lp(a) levels are associated with lower risk of type 2 diabetes(8, 9), although inconsistent results were also reported in early small sample studies (10,11). The first prospective study of Lp(a) levels and the risk of type 2 diabetes is the Women's Health Study, which reported an inverse relationship between Lp(a) levels and incident diabetes in US women(8). Then this finding was replicated in a Danish general population(8).
Observational studies may suffer from many potential biases, including confounding and reverse causation, which limits them to identify causal associations robustly (12). Genetic studies of polymorphisms affecting Lp(a) levels can be used to substantiate the causality. The serum Lp(a) concentrations were found to be a large extent genetically determined via by guest, on July 20, 2018 www.jlr.org Downloaded from variations in the LPA gene which encodes apolipoprotein(a) (13). Among the variants, the LPA kringle IV type 2 (KIV-2) repeat polymorphism is particularly important, which is defined by a 5.5 kb repeat that exists in multiple copy numbers from 1 to more than 40 copies (13).
Therefore, the KIV-2 repeat polymorphism determines the number of kringle structures in apolipoprotein(a), which affect the size of the protein (13). As the size of apolipoprotein(a) inversely correlates with its hepatic production rate, small Lp(a) isoforms are associated with higher Lp(a) concentrations, while large Lp(a) isoforms with lower concentrations (13). Single nucleotide polymorphisms (SNP) at the LPA locus like rs10455872 and rs3798220 were also associated with an increased level of Lp(a) (13,14). Several studies using Mendelian randomization approaches have demonstrated that LPA variants were associated with the risk of coronary heart disease (CHD) and myocardial infarction (14)(15)(16)(17), which supports a causal role of Lp(a) in ischemic cardiovascular disease.
Similarly, Mendelian randomization studies were used to assess the causality for an association between Lp(a) and type 2 diabetes. In a study of the Danish general population, high KIV-2 sum of repeats were shown to be associated with increased risk of type 2 diabetes, but no association was observed between rs10455872 and diabetes (18). In another study, rs10455872 which explained 26.8% of the variability in Lp(a) levels was not associated with risk of type 2 diabetes(19). Therefore, the causality between Lp(a) and type 2 diabetes may not be mediated by the concentrations of Lp(a) per se, but a causal role of large Lp(a) isoform size cannot be excluded. Until recently, one study using a novel genetic approach confirmed that it is a high number of KIV-2 repeats that are associated causally with increased risk of type 2 diabetes, and not low Lp(a) concentrations per se(20). 1863 patients were included in the current study. The study was approved by the Ethics Committee of Zhongshan Hospital, and all participants gave written informed consent.

Clinical assessment
Baseline information about medical history and health-related behaviors was collected. The smoking or alcohol drinking state was defined as never smoking or drinking, current smokers or drinkers (smoked or consumed alcohol regularly in the past 6 months), or ever smokers or drinkers (cessation of smoking or alcohol drinking for more than 6 months). BMI was calculated as weight divided by height squared (kg/m 2 ). Waist circumference was measured at the midway between the lower rib margin and the iliac crest with patients standing properly.
Diabetes mellitus was defined as the following criteria: 1) diagnosis of diabetes made previously by a physician and/or 2) use of insulin or oral hypoglycemic agents and/or 3) a fasting plasma glucose (FPG) ≥7mmol/L and/or 4) a 2-hour postprandial glucose (2-h PPG) ≥11.1mmol/L and/or 5) a glycosylated hemoglobin (HbA1c) ≥6.5%. Hypertension was defined as the following criteria: 1) diagnosis of hypertension made previously by a physician and/or 2) a systolic blood pressure ≥140 mmHg and/or 3) a diastolic blood pressure ≥90mmHg and/or 4) treatment with antihypertensive medications.

Laboratory assays
Venous blood samples were obtained in the morning after at least 12 hours overnight fast before coronary angiography. Fasting glucose, 2-hour postprandial glucose, triglyceride, total cholesterol, high-density lipoprotein cholesterol (HDL-C), apolipoprotein A-I (apoAI), and apolipoprotein B (apoB) were measured by enzymatic methods (Roche Diagnostics, Basel, Switzerland) using Hitachi 7600 biochemistry autoanalyzer (Hitachi High-Technologies Crop., Tokyo, Japan). Low-density lipoprotein (LDL-C) was calculated using the Friedewald formula.
Serum Lp(a) levels were measured by using goat monoclonal antibody (DiaSys Diagnostic Systems GmbH, Germany) by particle-enhanced immune transmission turbidimetry in Hitachi 7600 biochemistry autoanalyzer (Hitachi High-Technologies Crop., Tokyo, Japan). For the laboratory test of serum Lp(a) concentrations, the coefficient of variation (CV) within group was 6-8%, and the CV between groups was 8-10%. HbA1c was determined by high performance liquid chromatography using the Bio-Rad Variant II analyzer (Bio-Rad Laboratories, Hercules, CA, USA).

Genotyping
Patients with proper quality DNA samples (n = 1487) were genotyped for KIV-2 repeat polymorphism. Genomic DNA was extracted from peripheral leukocytes according to established protocols. The number of KIV-2 repeats in LPA from genomic DNA were determined by quantitative real-time polymerase chain reaction (qPCR) as reported by Lanktree et al (26). Briefly, multiplexed qPCR reactions were carried out in the Applied Biosystems ViiA7 Real-Time PCR system. Primers and probes for the exons 4 and 5 of LPA KIV-2 were designed using Applied Biosystems FileBuilder 3.1 (sequences are given in supplemental Table   S1). qPCR reactions were also carried out for RNase P (RNAP), an endogenous single-copy control gene. The number of KIV-2 repeats as determined by qPCR was calculated as the difference in cycle thresholds (CT) between target and control probes (ΔCT). TheΔCT was calculated for the exon 4 (ΔCT4) and exon 5 (ΔCT5) probes separately for every patient. Then the average difference betweenΔCT4 andΔCT5 (ΔΔCT) was calculated for all patients, and individuals whose ΔΔCT was greater than two standard deviations from the mean were by guest, on July 20, 2018 www.jlr.org Downloaded from excluded from the analysis. Finally, the average ofΔCT4 andΔCT5 ( ∆C T ) was then used as the relative KIV-2 repeat number for further analysis.

Angiographic analysis
Two experienced cardiologists who were blinded to the study protocol performed the coronary angiography and reviewed the angiographic findings. Then a percentage stenosis was given to the major epicardial arteries and their sub-branches. Finally, patients with a ≥ 50% stenosis in one or more coronary vessels were diagnosed as significant coronary stenosis.

Statistical analysis
Continuous variables were expressed as means ± SD or median (interquartile range) and categorical variables as percentages. The one-way ANOVA and the χ 2 test were used to compare differences of continuous variables and categorical variables between groups, respectively. Pearson correlation analysis was used to examine the relationship between Lp(a) levels and the number of KIV-2 repeats in LPA. We divided the distribution of Lp(a) levels or the number of KIV-2 repeats into tertiles. Logistic regression analysis was used to investigate the independent association of Lp(a) or the number of KIV-2 repeats with type 2 diabetes, and the adjusted odds ratios (ORs) were calculated in relation to each tertile increase of Lp(a) concentrations or each tertile decrease of KIV-2 repeats numbers. Linear regression analysis was used to examine the association of the number of KIV-2 repeats in LPA with serum Lp(a) levels. Non-normally distributed values were natural log-transformed before analysis.
We performed an Mendelian randomization analysis (two-stage regression) to examine the causal association of serum Lp(a) levels with type 2 diabetes. An instrumental variable method was used. The instrumental variable the number of KIV-2 repeats in LPA is expected to act as a by guest, on July 20, 2018 www.jlr.org Downloaded from non-confounded and unbiased marker for Lp(a) levels, and is used to estimate the causal effects of Lp(a) levels on type 2 diabetes. In stage 1, the association of the number of KIV-2 repeats in LPA with Lp(a) levels was analyzed using linear regression analysis and the β-coefficient was documented. The F-statistic from the linear regression analysis of Lp(a) on this polymorphism was obtained, and F-statistic > 10 suggests that potential bias due to weak instruments should not be substantial. In stage 2, the association of genetically predicted Lp(a) levels (calculated according to the equation from stage 1) with type 2 diabetes was analyzed using logistic regression. Three models were performed in stage 2. Model 1 and model 2 were unadjusted and adjusted for age, sex and BMI. Model 3 additionally adjusted for smoking status, drinking status, systolic blood pressure, diastolic blood pressure, antihypertensive medication, significant coronary stenosis, total cholesterol, triglyceride, LDL-C, HDL-C, apoA-I, apoB and lipidlowering drugs.
Mendelian randomization analyses were performed using R software version 3.4.2. Other analyses were performed using SPSS software version 19.0. Statistical tests were two-tailed and p values <0.05 were considered statistical significant.

Results
Among 1863 participants, 75.4% were men and 24.6% were women, with a mean age of 62.7 years. The mean BMI was 24 drugs. Table 1 shows the baseline characteristics of the study participants by tertiles of serum Lp(a). Serum Lp(a) levels were inversely associated with FPG, 2-h PPG, HbA1c, diabetes and triglyceride, and positively associated with total cholesterol, LDL-C, apoB and significant coronary stenosis. The observational OR for type 2 diabetes was decreased across the tertiles of serum Lp(a) in crude model, model adjusted for age, sex and BMI, and multifactorially adjusted model ( Figure   4). In the instrumental variable analysis, the estimated causal OR for type 2 diabetes was decreased for genetically elevated Lp(a) levels (Figure 4). In a comparison of individuals with genetically predicted Lp(a) values in the top tertile versus bottom tertile, the crude OR for type 2 diabetes was 0.59 (95%CI 0.45-0.79; P for trend < 0.001) (Figure 4). The association remained the same after adjustment for age, sex and BMI (OR=0.59, 95%CI 0.44-0.79; P for trend < 0.001). Further adjustment for smoking and drinking status, blood pressure levels, antihypertensive medication, significant coronary stenosis, lipid parameters and lipid-lowering drugs, the association was more pronounced (OR=0.43, 95%CI 0.30-0.61; P for trend < 0.001).

Discussion
We observed a strong inverse association between serum Lp(a) levels and type 2 diabetes in a Chinese population with very high cardiovascular risk. We demonstrated that the number of However, in Mendelian randomization analyses, the LPA rs10455872 which explained 26.8% of the variability in Lp(a) levels, was not associated with risk of type 2 diabetes(19). In another study of Danish general population, the number of KIV-2 repeats was causally associated with type 2 diabetes in Mendelian randomization analyses, but rs10455872 did not affect the risk of diabetes (18). In our study, the Mendelian randomization analyses using KIV-2 repeats polymorphism as an instrument variable also suggests a causal effect of Lp(a) on type 2 diabetes.
These studies including ours indicate that low Lp(a) concentrations alone may not be causally associated with type 2 diabetes, but there may be a causal relationship between large Lp(a) isoform size and type 2 diabetes. Recently, by using a novel genetic approach, one study found that SNPs that were associated selectively with the number of KIV-2 repeats were associated with type 2 diabetes, but SNPs that were associated selectively with Lp(a) concentrations were not associated with type 2 diabetes(20). These results reassure that it is a high number of KIV-insulin resistance, which causes type 2 diabetes finally.
Although the inverse association of Lp(a) levels with type 2 diabetes in Chinese populations have been reported previously, ours is the first study to investigate the causality of this observed association. As disease status cannot alter genotype and genotype-disease associations are not likely to be confounded by lifestyle factors, we are able to demonstrate a causal role of Lp(a) in type 2 diabetes in a Chinese population by using Mendelian randomization analyses with LPA KIV-2 repeat polymorphism as the instrumental variable. In consistent with the observational effect, the genetically predicted Lp(a) levels was inversely association with type 2 diabetes in the current study. Furtherly, the genetic effect was stronger than the observed effect, with a 57% risk reduction comparing individuals with genetically predicted Lp(a) values in the top tertile versus bottom tertile. It should be noticed that the individuals in the 2 nd tertile of Lp(a) levels are at lower risk than those in the 3 rd tertile as an observational effect as shown in Figure 4. Although confounders were adjusted as much as possible in our study, it is possible that our observations still suffered from some residual confounding due to inability to adjust the model for all potential confounders. In Mendelian randomization analysis, we computed the non-confounded genetically predicted Lp(a) levels. The risk of type 2 diabetes decreased gradually with increasing genetically predicted Lp(a) levels as expected as shown in Figure 4.
We consider that the real relationship between Lp(a) levels and type 2 diabetes was presented by using genetic approaches. Although a Mendelian randomization study may be considered nature's own randomized intervention trial profiting from random assortment of alleles from parents to offspring, the possibility of reverse causality still cannot be totally excluded.
Therefore, final proof of causality between Lp(a) and type 2 diabetes still requires future repeats may help to prevent type 2 diabetes in a population with very high cardiovascular risk.
Use of Lp(a) lowering drugs to reduce cardiovascular risk in such a population is feasible, as these therapies are unlikely to increase the number of KIV-2 repeats which is determined by genetics. Our results do not support a differential management of Lp(a) levels for the prevention of type 2 diabetes and CHD.
Our study has some limitations. Our study indicates that it is the isoform size of Lp(a) that is causally associated with risk of type 2 diabetes. However, as Lp(a) isoforms were not measured directly in our study, we are unable to provide specific information about the association of Lp(a) isoform size with type 2 diabetes. Further, we measured the number of KIV-2 repeats with qPCR, which cannot estimate the number of KIV-2 repeats in an allele-specific manner.
The obtained value is the sum of the number of repeats at the 2 different LPA alleles, which represents an average particle size. As it is the large Lp(a) isoform that causes diabetes and not the average particle size, our measurements may introduce some biases to the results. Besides, our assay may be biased because of its inability to account for non-expression of alleles.
However, the current gold standard method like electrophoresis with immunoblotting is technically challenging, laborious, and time consuming which restricts its application in genetic studies. Therefore, the limitations of the qPCR methods are balanced by the fact that it provides a fast and cost-effective method of evaluation of Lp(a) isoform size from genomic DNA in by guest, on July 20, 2018 www.jlr.org Downloaded from genetic epidemiology studies. The new genetic approach(20) using SNPs associated selectively with Lp(a) concentrations or with KIV-2 repeats has its advantages, and is worth being applied in future studies of Chinese populations to confirm that it is a high number of KIV-2 repeats that are associated causally with increased risk of type 2 diabetes.
In conclusion, our study demonstrated that a high number of LPA KIV-2 repeats are associated with increased risk of type 2 diabetes in a Chinese population with very high cardiovascular risk. Our results indicate that large Lp(a) isoform size, which is associated with low Lp(a) concentrations, has a causal effect on type 2 diabetes. More studies in Chinese populations are needed to confirm our findings, and studies to understand the pathophysiological mechanism underlying the causal association between Lp(a) isoform size and type 2 diabetes are also warranted.      Risk estimates for the tertiles of Lp(a) levels were calculated by logistic regression analyses using the first tertile as the reference. T1: the first tertile; T2: the second tertile; T3: the third tertile. *The multifactorial analysis adjusted for age, sex, BMI, smoking status, drinking status, systolic blood pressure, diastolic blood pressure, antihypertensive medication, significant coronary stenosis, total cholesterol, triglyceride, LDL-C, HDL-C, apoA-I, apoB and lipidlowering drugs.