Genetic and Environmental Biomarkers Associated with Triglyceride Levels in Two Groups of Slovak Women

Abstract

Objective: This study analyzed the association between the MLXIPL gene polymorphism (rs3812316) and triglyceride (TG) levels and selected environmental biomarkers in Slovak women at risk for cardiovascular disease compared to a reference sample. Materials and Methods: The studied sample consisted of 200 women at cardiovascular risk (mean age 52.96 ± 6.01 years) and 244 healthy women (mean age 47.52 ± 5.34 years). Participants gave details of their health and lifestyle during their medical examination, and peripheral blood samples were used for biochemical analyses and DNA genotyping. A nested polymerase chain reaction-restriction fragment length polymorphism assay was used to detect the rs 3812316 SNP. Results: We determined that there were significantly different genotype distributions in two TG categories: (1) subjects with normal TG values had a significantly higher G allele frequency than those with elevated TG levels (χ² = 6.1556, df = 2, p = 0.046); and (2) the rare G allele frequency was 0.11 in the cardiovascular risk group and 0.15 in the reference group. Binary regression analysis showed that women with at least one G allele had a significantly lower relative risk of hypertriglyceridemia than women with the CC genotype (OR = 0.399, p = 0.022, 95% CI = 0.182-0.876). Conclusion: This cross-sectional study suggests that MLXIPL rs3812316 genotypes may be associated with TG levels. However, further analysis is advisable because of study limitations.

Introduction

It is widely accepted that elevated triglyceride (TG) and other plasma lipid levels are significant risk factors in the development of cardiovascular disease (CVD), and the World Health Organization (WHO, 2015) rates CVD as the premier global cause of illness and death.

In addition to dyslipidemias, the following classical and modifiable environmental factors have severe impacts on the prevalence of CVD: unhealthy diet, physical inactivity, smoking, alcohol consumption, and psychosocial stress (Grundy et al., 2002; Sun et al., 2014; WHO, 2015). Many of these risk factors are genetically determined, and genome-wide association studies of plasma lipid have dramatically increased the number of common genetic variants with confirmed association with lipoprotein levels and cardiovascular and metabolic traits to more than 150 loci (Mohlke et al., 2008; Teslovich et al., 2010; Willer and Mohlke, 2012; Willer et al., 2013). Although further loci with strong statistical significance have been identified, the size of their effect is modest and accounts for only a small fraction of variability (Mohlke et al., 2008).

Moreover, racial and ethnic variability exists in genetic determinants of lipid profiles (Kurian and Cardarelli, 2007; Deo et al., 2009; Rui et al., 2013; Aung et al., 2014). Therefore, multifactorial analyses of genetic loci and modifiable risk factors are paramount in establishing specific risk factors in various populations and subsequent remedial strategies.

Willer et al. (2013) reported that MLXIPL is one of the 16 genes with a primary association with TGs and high-density lipoprotein (HDL) cholesterol levels, and Cairo et al. (2001) added that this gene is expressed in multiple tissues, including regions of the brain and the intestinal tract. Mlxipl protein (also known as ChREBP—carbohydrate responsive-element binding protein) mediates liver activation of glycolytic and lipogenic regulatory enzymes, including L-pyruvate kinase, acetyl CoA carboxylase, and fatty acid synthase (Kawaguchi et al., 2001); and Herman et al. (2012) record that adipose ChREBP is a major determinant of adipose tissue fatty acid synthesis and systemic insulin sensitivity.

The MLXIPL gene was mapped by Meng et al. (1998) to the 7q11.23 region with 18 exons, and various SNPs were analyzed (Mohlke et al., 2008; Polgár et al., 2010; Radovica et al., 2014). One variant that associated with TG levels is rs3812316 (C771G, His241Gln). Carriers of the rare G-allele present lower TG concentrations than those homozygous for the common allele (Kooner et al., 2008; Ortega-Azorín et al., 2014). Pan et al. (2009) revealed a significant relationship between this SNP and at-risk coronary artery disease patients in China, and Ortega-Azorín et al. (2014) provided novel evidence of gene-diet interaction, supporting AdMedDiet modulation of this polymorphism's effect in determining plasma TG concentration. Only one study on the MLXIPL gene from our country analyzed the association between MLXIPL and other genes, FERHDL and a plasma atherogenic index (Rašlová et al., 2011). However, the particular study examined only a general population of quadragenerians, it was not focused primarily on the TG levels, and it did not analyze modifiable environmental factors.

Our study evaluated the MLXIPL gene rs3812316 variant's association with TG levels in two Slovak women groups, women at high risk of CVD with elevated lipid profiles and a reference sample without these complications. We also assessed gene-environment associations between the studied gene variants and modifiable factors elevating lipid levels.

Materials and Methods

Participants

The study was based on cross-sectional survey data collected between 2009 and 2015 by the Department of Anthropology at Comenius University in Bratislava. This information has previously been utilized to analyze various candidate gene effects on health biomarkers in Slovak women.

The studied sample consisted of 200 women at high risk of CVD (mean age 52.96 ± 6.01 years) who had at least one of the following diagnoses: hypertension, diabetes mellitus, thrombosis, or ischemic heart disease. Of these, 63 women with diagnosed hypercholesterolemia were included in crosstab analyses, but were excluded from lipid profile analyses: binary regression and the general linear model due to possible medication complications which could interfere with TG levels. The reference sample comprised 244 women (mean age 47.52 ± 5.34 years) who presented with no severe disease. The study was performed in cooperation with general practitioners in western and central Slovak areas. Participants were interviewed during their regular medical checkup by medical doctor; and all anthropometric measurements and blood collection were conducted in the morning after at least 12 h of fasting. All participants gave written informed consent for this study, which adhered to the Declaration of Helsinki principles.

Anthropometric measurements

All anthropometric parameters were measured by professional anthropologists with the same instruments, using the methods detailed in Luptáková et al. (2013). Cutoff points for kg/m² BMI levels were as follows: underweight <18.50, normal range 18.50-24.99, overweight ≥25.0, and obese ≥30.0 (WHO, 2000); the WHR 0.80 cutoff was considered for European women (WHO, 2008).

Bioimpedance analyses

Body composition variables were obtained by a bioelectric impedance analyzer with an 800 mA constant excitation current at 50 kHz signal frequency and four-electrode arrangement (BIA 101, Akern, S.r.l.). The reference values for phase angle (an indicator of general body condition) and body cell mass index (BCMI) as the metabolically active body tissue index were used from the BIA Manual: phase angle 4.8-6.0 and BCMI >8. The following fat mass (FM) percentage categories were also considered: low <22.00%, normal 22.0-29.99%, and obese ≥30.00%.

Biochemical analyses

Lipid profiles were analyzed from fasting plasma samples by routine laboratory methods in the Department of Clinical Laboratories of Alpha Medical. The following parameters were examined: total cholesterol (TC), HDL-cholesterol (HDL-C), triglycerides (TG), and LDL-C levels calculated by the Friedewald formula. The TG cutoff level was 2.0 mmol/L, as in Rašlová et al's (2005) dyslipidemia treatment sheet.

DNA analyses

DNA was extracted from peripheral blood samples by the SiMax™ Genomic DNA Extraction Kit; and the MLXIPL gene rs3812316 SNP variant was detected by nested PCR-RFL. The following oligonucleotides amplified the outer region: 5′-atcctcaggcggcagctgcaggggc-3′ and 5′aatggtgcaaacagctcttctcca-3′. The PCR conditions followed initial denaturation (95°C/3 min) with 35 cycles of denaturation (95°C/15 s), annealing (62°C/30 s), and polymerization (72°C/30 s); final extension was then run at 72°C for 5 min. The PCR products (173 bp) were diluted 10 times as a template for subsequent inner PCR with nested primers: 5′-aagggccggactgagtcatggtgaag-3′and 5′-gtgtggtccccgtgctgc-3′. PCR conditions comprised initial denaturation (95°C/3 min), 35 cycles of denaturation (95°C/15 s), annealing (64°C/1 min), and polymerization (72°C/30 s), with the final extension run at 72°C for 3 min. Amplicons (123 bp) were then digested overnight by AluI restriction enzyme, and the fragments (123, 70, and 53 bp) were separated by electrophoresis on 3% agarose gel.

Confirmation of detected genotypes and the polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) test-method were verified by amplification and direct sequencing of eight DNA samples. The amplification was followed by purification with ExoSAP-IT (U.S. Biochemical, Cleveland, Ohio), and purified PCR products became templates for Sanger sequencing reactions (BigDye 3.1, Life Technologies standard manufacturer protocol). Sequencing products were finally analyzed by ABI PRISM 3130 and 3100-Avant Genetic Analyzers (Life Technologies).

Environmental biomarkers

Data on current health, lifestyle, and reproductive history were collected with a questionnaire designed and validated by Kaczmarek (2007). Only the requisite variables were selected and analyzed in this study as follows: physical activity (none-no physical activity, occasionally-from time to time, and regularly-at least two times per week) and smoking (no, occasionally, and regularly-daily). From the Quality of Life questionnaire, the question on self-contentment (yes/no) was chosen. We also evaluated menopausal status, as lower estrogen levels result in worse lipid profiles and frequent occurrence of cardiovascular implications (Akahoshi et al., 1996; Mishra et al., 2013). Women were assumed to be: premenopausal if they had regular menstrual cycle, perimenopausal with irregular cycle in menopausal transition, and postmenopausal if they had amenorrhea for at least 12 months before the examination.

Statistical analyses

Variable data distribution was performed by the “one-sample” Kolmogorov-Smirnov test, and differences in the somatic parameters between women at risk for CVD and the reference sample were tested by analysis of covariance, with control for age. Resultant genotype frequencies were tested for deviation from the Hardy-Weinberg equilibrium by chi-square goodness of fit test. Contingency tables with the chi-square independence test were used to analyze the differences in the genotype distribution between the compared groups. Correlation analysis was then conducted to determine the strongest correlation between TG levels and obesity parameters. Finally, regression tree analysis indicated the most important independent factors influencing TG levels in the studied samples.

The General Linear Model assessed and quantified associations between the genetic and environmental predictors and TG levels. As the TG distribution was skewed to the right, it was log-transformed (logTG). The model passed the standard diagnostic check for normality, homoscedasticity, outliers, and leverage points. Wherever the approximation log(1+x)≈x was valid, the effect was interpreted in the additive manner.

Data processing used R software (R Core Team, 2015) and IBM SPSS version 19.0, with data expressed as the mean and standard deviation. p Values less than 0.05 were considered statistically significant.

Results

Table 1 depicts the mean values of selected somatic and clinical variables and the general nutritional status parameters for both study samples. Women at high risk for CVD had statistically significant higher mean values for all compared variables, except for body height and phase angle. The differences remained unchanged and significant following control for age, thus highlighting that age alone was not responsible for significant differences in the somatic and health parameters of these women.

Table 1.

Mean Values for Selected Somatic, Clinical, and General Nutrition Parameters in Both Study Groups

	Women at CVD risk n = 200		Reference sample n = 244
	Mean	SD	Mean	SD	p	p^a
Age (years)	52.96	6.016	47.52	5.34	<0.001	—
Body height (cm)	162.72	6.16	163.76	5.54	0.067	0.982
Body weight (kg)	80.99	16.912	69.02	12.44	<0.001	<0.001
Waist circum. (cm)	94.08	14.92	81.47	11.48	<0.001	<0.001
Hip circum. (cm)	109.13	12.30	101.41	8.73	<0.001	<0.001
FM (%)	41.76	8.16	35.15	7.79	<0.001	<0.001
BMI (kg/m²)	30.58	6.24	25.74	4.49	<0.001	<0.001
WHR	0.86	0.08	0.80	0.07	<0.001	<0.001
PA	6.37	0.85	6.47	0.89	0.886	0.886
BCMI (kg/m²)	7.90	1.18	7.28	0.71	<0.001	<0.001
SBP (mmHg)	134.97	15.58	118.74	14.03	<0.001	<0.001
DBP (mmHg)	82.30	10.42	76.01	9.02	<0.001	<0.001

p-value of statistical significance (comparison of means), p^a-value of statistical significance adjusted for age (ANCOVA).

CVD, cardiovascular disease; FM, fat mass; n, number of women; BMI, body mass index; WHR, waist to hip ratio; PA, phase angle; BCMI, body cell mass index; SD, standard deviation; SBP, systolic blood pressure; DBP, diastolic blood pressure.

Table 2 shows the prevalence of selected confounders elevating lipid levels in these study groups, where women at risk for CVD had a lower mean level of education and less physical activity and were more obese than women in the reference group. An important difference noted in this study is that the majority of the women at risk for CVD were postmenopausal and most reference subjects were premenopausal.

Table 2.

Prevalence of Selected Confounders Elevating Lipid Levels in Both Study Groups

	Women at CVD risk n (%)	Reference sample n (%)	p
Physical activity
None	52 (26.0)	13 (5.3)	χ² = 37.59
Occasionally	128 (64.0)	199 (81.6)	df = 2
Regularly	20 (10.0)	38 (13.1)	p < 0.001
Smoking
Never	141 (70.5)	179 (73.3)	χ² = 1.18
Occasionally	29 (14.5)	27 (11.1)	df = 2
Regularly	30 (15.0)	37 (15.6)	p = 0.555
Menopausal status
Premenopausal	51 (25.5)	142 (58.2)	χ² = 49.21
Perimenopausal	17 (8.5)	17 (7.0)	df = 2
Postmenopausal	132 (66.0)	85 (34.8)	p < 0.001
FM category
Low	2 (1.0)	16 (6.6)	χ² = 22.04
Normal	13 (6.5)	42 (17.2)	df = 2
Obese	181 (92.5)	186 (76.2)	p < 0.001
Self-content
No	33 (22.9)	34 (21.7)	χ² = 0.07
Yes	111 (77.1)	123 (78.3)	df = 1, p = 0.793
Education
Basic	46 (23.0)	23 (9.4)	χ² = 15.88
Secondary	125 (62.5)	173 (70.9)	df = 2
University	29 (14.5)	48 (19.7)	p < 0.001

χ², chi square; df, degree of freedom; p, value of statistical significance.

Table 3 illustrates the nonparametric Spearman's correlation coefficient analyzing the obesity markers most often correlated with TG levels in these groups. The strongest correlation in women at high risk for CVD was recorded between waist circumference and TG; and in the reference sample it was between waist/hip ratio and TG. The classic BMI obesity marker and the modern FM% parameter gave almost equal TG correlation coefficients.

Table 3.

Correlation Coefficients Between Obesity Variables and TG Values in Both Study Groups

Triglycerides	BMI	Waist circ.	WHR	FM%	FFM%
Women at CVD risk (n = 135)	0.386	0.454	0.385	0.389	−0.389
Reference sample (n = 242)	0.341	0.382	0.399	0.340	−0.339

Value of statistical significance was in all cases less than 0.001.

TG, triglyceride.

Table 4 details MLXIPL genotype distributions according to the women's health and menopausal status and FM and TG categories. The rs3812316 genotype distribution fitted the Hardy-Weinberg equilibrium for the entire group (χ² = 0.66, df = 1, p = 0.42), individually for women at risk for CVD (χ² = 0.137, df = 1, p = 0.711) and also in the reference group (χ² = 1.765, df = 1, p = 0.184). Only the TG categories exhibited significant difference in genotype distribution (χ² = 6.1556, df = 2, p = 0.046). This may be due to more women having the protective GG and GC genotypes in the normal TG-level group than in the elevated TG group. Simple binary logistic regression assessed whether or not genotype variants were associated with TG levels, where additive, dominant, and recessive genetic models composed the independent variables, and TG was the lone dependent variable. The recessive CC versus CG/GG model revealed that women with at least one G allele had significantly lower risk of hypertriglyceridemia than women with the CC genotype (OR = 0.399, 95% CI 0.182-0.876, p = 0.022).

Table 4.

Distribution of the MLXIPL rs3812316 (C/G) Genotype and G Allele Frequency in Slovak Women by Health Status, FM Category, Menopausal Status, and TG Category

	n	CC	CG	GG	G allele frequency
Whole group	436	324	106	6	0.14
Women at CVD risk	195	154	38	3	0.11
Reference sample	241	170	68	3	0.15
		χ² = 4.17, df = 2, p = 0.125
FM category
Low	18	12	6	0	0.17
Normal	54	37	16	1	0.17
Obese	363	274	84	5	0.13
		χ² = 2.22, df = 4, p = 0.700
Premenopausal women	190	142	47	1	0.13
Perimenopausal women	32	25	7	0	0.11
Postmenopausal women	213	157	51	5	0.14
		χ² = 3.10, df = 4, p = 0.540
TG category
Normal TG levels	319	225	90	4	0.15
Hyper TG (>2.00 mmol/L)	56	48	7	1	0.08
		χ² = 6.1556, df = 2, p = 0.046

CVD, cardiovascular disease risk.

Table 5 highlights the results of the general linear model analysis of genetic and environmental biomarkers associated with plasma TG levels (logTG). The functional form of the model was created by combining domain knowledge and the Akaike Information Criterion model selection technique. In this study, the following independent variables were considered: TC, HDL cholesterol, MLXIPL genotypes-additive model, menopausal status, physical activity, FM (%), and smoking. The continuous predictors FM (%) and HDL-cholesterol were centered to obtain biologically meaningful interpretations; and Table 5 summarizes only those predictors which were statistically significantly associated with logTG values.

Table 5.

General Linear Model with logTG Dependent Variable

Predictor	Estimate	SE	t	p
Intercept	−0.038	0.045	−0.841	0.401
HDL-cholesterol	−0.412	0.058	−7.085	< 0.001
TC 5.00-5.50 mmol/L	0.192	0.059	3.240	0.001
TC ≥5.50 mmol/L	0.398	0.051	7.841	< 0.001
FM (%) c and MLXIPL CC	0.018	0.003	5.668	< 0.001
FM (%) c and MLXIPL CG	−0.014	0.006	−2.432	0.016
FM (%) c and MLXIPL GG	−0.027	0.031	−0.897	0.370

TC, total cholesterol; SE, standard error; c, centered predictor; HDL, high-density lipoprotein.

The general linear model revealed that the mean value of TGs is multiplied by 0.662 when HDL cholesterol increases by 1 mmol/L. This infers that it decreases by 66% of the reference value. The model also suggests that individuals with TC levels between 5.00 and 5.50 mmol/L had 1.211 times the mean TG value of individuals with normal TC and those above 5.50 mmol/L had 1.489 times those with TC under 5.00 mmol/L. While neither menopausal status nor MLXIPL genotype was significantly associated with logTG values, the percentage FM (%) with genotype was associated with TG levels. A unit increase of FM (%) was associated with a mean TG increase of 0.018 mmol/L in individuals with the CC genotype and with a mean TG decrease of 0.014 mmol/L in individuals with the CG genotype. It also detected a possible 0.028 decrease in TG level in the GG genotype following a unit FM increase. However, there were only three observations in this category and the association proved nonsignificant for this sample. The general linear model explained 31.5% logTG variability, based on the model R Sq.

Discussion

Our cross-sectional study focused on interactions between the MLXIPL gene rs3812316 variants and environmental biomarkers associated with TG levels in Slovak women.

The analyses revealed significantly different MLXIPL genotype distributions in individuals with normal and elevated TG levels, which were also confirmed by binary regression. The minor G allele seems to have a protective effect on the TG levels, which correlates with the findings of other authors (Willer et al., 2013; Bauer et al., 2016). As TGs are synthesized from fatty acids and glycerol, as a product of lipogenesis in the liver, it seems that polymorphisms within the MLXIPL gene reduce the expression and function of ChREBP, a glucose-responsive transcription factor that regulates fatty acid synthesis. However, the exact consequences of this variant remain unclear (Bauer et al., 2016).

On the other side, central European studies have failed to substantiate the rs3812316 MLXIPL variant's association with TG or TC levels and also the risk of ischemic stroke susceptibility (Vráblik et al., 2008; Polgár et al., 2010). Discrepancies between our results and those published by these authors are most likely due to differences in study design, lipid categorization, and sample size.

Wide frequency variability for the rare G allele has been reported globally (Aung et al., 2014). It varies from 0.05 in Africa and Mexico (Nakayama et al., 2011; Weissglas-Volkov et al., 2013), 0.06 to 0.12 in different parts of India, 0.10 in Europe (Kooner et al., 2008), and up to 0.21/0.26 in Central Asian populations (Nakayama et al., 2011). The frequency of this allele in our study was 0.11 among women at risk for CVD and 0.15 in the reference sample, which are similar to the findings of Vráblik et al. (2008) who reported the frequency of the G allele to be 0.10 for the high TG group, 0.11 for the population sample, and 0.14 for the low TG group.

Analyses of environmental biomarkers confirmed the importance of education, physical activity, and body composition on risk of CVD. Our study revealed that more women with high CVD risk completed only basic education (23.0%) and also that more healthy women had a University degree (19.7%) than women at CVD risk (14.5%). The association between low education and higher rates of mortality from CVD has been described in various articles (Kilander et al., 2001; Lee et al., 2005). According to Beauchamp et al. (2011), women with lower education also had a greater risk of progressing from normal weight to overweight or obesity than those with higher education. In our study we found a high percentage of obesity in both groups; 76.2% in healthy women and 92.5% in women at high CVD risk. To assess obesity, we analyzed the FM% in addition to BMI (as a classical obesity marker) because the former is reported to be a more appropriate indicator of obesity (Frankenfield et al., 2001; Romero-Corral et al., 2008; Müller et al., 2014). However, results in Table 3 show that correlation coefficients between TG and FM% and between TG and BMI are very similar in both study groups. Obesity is very closely connected to physical inactivity, an established risk factor for CVD. In our study, 5.3% of healthy women and 26.0% of women at high CVD risk reported no physical activity. To promote and maintain health, all healthy adults need moderate-intensity aerobic (endurance) physical activity for a minimum of 30 min, 5 days each week or vigorous-intensity aerobic physical activity for a minimum of 20 min, 3 days each week (Haskell et al., 2007). Despite the mean values of BMI and FM (%), which indicated overweight and obesity, we noted malnutrition in both study groups, according to the mean values of the BCMI. As it assess the nutritional status (Talluri et al., 2003; Rymarz et al., 2012), it seems that elevated body mass does not automatically mean increased nutritional quality; and the nutritional status in obese woman appeared worse than in lean individuals. Smoking has been considered another major CVD risk factor since the last century (Kannel et al., 1987). Even though we expected a higher prevalence of smokers among women at risk for CVD than in the reference group, there was no statistically significant difference between these groups.

To determine the best predictor of TG levels in the entire study group, we utilized tree classification analysis (data not shown). It marked, similar to our GLM results in Table 5, the strongest association between TG and lipids, followed by waist circumference and other obesity parameters, including fat free mass (%), FM (%), and BMI (detailed data not presented). Regression analyses evaluated the association between these risk factors and TG levels also related to genotype profile. The combined genotype and FM category was associated with logTG levels. Interestingly, the genotype provided a greater protective effect, because logTG values decreased when the FM increased.

Our study was limited mostly by the sample size, as only six individuals in our study were homozygous for the G allele. For future studies it would be paramount to enlarge the study sample and include individuals with marked hypertriglyceridemia.

In conclusion, our analytic results showed an association between rs3812316 MLXIPL genotypes and TG level categories. In addition, G-allele protective effects were established in combination with FM parameters.

Footnotes

Acknowledgment

This study was supported by KEGA (Cultural and Educational Grant Agency of the Ministry of Education, Science, Research and Sport of the Slovak Republic) grant No. 015UK-4/2015-Genetic Variability in Human Populations—Innovative Book with Manual.

Author Disclosure Statement

No competing financial interests exist.

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