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Keywords:

  • obesity;
  • phospholipid transfer protein;
  • phospholipid transfer protein gene;
  • type 2 diabetes mellitus;
  • waist circumference

Abstract

  1. Top of page
  2. Abstract
  3. Introduction
  4. Subjects and methods
  5. Results
  6. Discussion
  7. Conflict of interest statement
  8. References

Abstract.  Dullaart RPF, Vergeer M, de Vries R, Kappelle PJWH, Dallinga-Thie GM (University Medical Center Groningen, University of Groningen, Groningen; and Academic Medical Center Amsterdam, Amsterdam; The Netherlands). Type 2 diabetes mellitus interacts with obesity and common variations in PLTP to affect plasma phospholipid transfer protein activity. J Intern Med 2012; 271: 490–498.

Background.  Phospholipid transfer protein (PLTP) is an emerging cardiometabolic risk marker that is important in high-density lipoprotein (HDL) and triglyceride metabolism. Plasma PLTP activity is elevated in type 2 diabetes mellitus, whereas glucose may regulate PLTP gene transcription in vitro. Of interest, common PLTP variations that predict cardiovascular disease have been identified recently. We investigated whether the diabetic state is able to amplify relationships between obesity and PLTP gene variations with circulating PLTP levels.

Subjects and methods.  Plasma PLTP activity (using a phospholipid vesicles–HDL system), PLTP gene score [number of PLTP activity-decreasing alleles based on two tagging polymorphisms (rs378114 and rs60- 65904)] and waist circumference were determined in two Dutch cohorts comprising 237 patients with type 2 diabetes and 78 control subjects.

Results.  Patients with diabetes were more obese (< 0.001 for prevalence of increased waist circumference) and had 13% higher plasma PLTP activity (< 0.001). PLTP gene score was not different in diabetic and control subjects (= 0.40). PLTP activity was highest in patients with diabetes with an enlarged waist and lowest in control subjects with a normal waist circumference (< 0.001). Multiple linear regression analysis revealed a positive interaction between diabetes status and waist circumference on PLTP activity (β = 0.200, = 0.005). Furthermore, diabetes status (β = −0.485, = 0.046) or HbA1c (β = −0.240, = 0.035) interacted with PLTP gene score to affect PLTP activity.

Conclusions.  Type 2 diabetes and enlarged waist circumference interact to impact on plasma PLTP activity. Diabetes may also amplify the association between plasma PLTP activity and common PLTP gene variations. Our findings support the hypothesis that diabetes–environment and diabetes–gene interactions govern plasma PLTP activity.


Abbreviations:
anova

analysis of variance

apo

apolipoprotein

AU

arbitrary unit

BMI

body mass index

CETP

cholesteryl ester transfer protein

CRP

high-sensitivity C-reactive protein

DALI study

Diabetes Atorvastatin Lipid Intervention study

EDTA

ethylenediaminetetraacetic acid

HbA1c

glycated haemoglobin

HDL

high-density lipoprotein

MetS

metabolic syndrome

NCEP-ATP-III

national cholesterol education programme adult treatment panel III

PLTP

phospholipid transfer protein

SNP

single-nucleotide polymorphism

VLDL

very low-density lipoprotein

Introduction

  1. Top of page
  2. Abstract
  3. Introduction
  4. Subjects and methods
  5. Results
  6. Discussion
  7. Conflict of interest statement
  8. References

Phospholipid transfer protein (PLTP) belongs to the lipid transfer/lipopolysaccharide binding protein family and is expressed in several tissues and cell systems including liver, macrophages and adipose tissue [1–4]. PLTP is a high-density lipoprotein (HDL)-associated lipid transfer protein that is able to transfer phospholipids towards HDL during lipolysis of triglyceride-rich lipoproteins and to remodel HDL into different sized (both small and large) particles [1–4]. Studies in mice support the notion that PLTP increases hepatic very low-density lipoprotein (VLDL) production [5, 6]. Moreover, PLTP exchanges α-tocopherol between lipoproteins and is associated with pro-inflammatory proteins in plasma [7, 8].

Although the potential role of PLTP in the development of atherosclerosis has not been unequivocally established, available evidence from human studies mostly supports the possibility that elevated plasma PLTP activity is related positively to (subclinical) atherosclerosis [2, 3, 9–12]. Genetic variations in PLTP that are associated with plasma PLTP activity, lipid phenotype and obesity-related variables, as well as cardiovascular disease, have been identified recently [13–16].

During the past few years, evidence has accumulated to support the possibility that plasma PLTP activity is elevated in insulin-resistant states, such as type 2 diabetes mellitus, obesity and metabolic syndrome (MetS) [3, 4, 17–22]. Circulating PLTP activity is closely related to glucose and lipid homoeostasis. Plasma PLTP increases in parallel with increments in circulating free fatty acid levels and decreases in response to acipimox administration, insulin infusion and weight loss [3, 4, 18, 19, 23–27]. Of note, it has been reported that high glucose stimulates PLTP promoter activity in vitro, possibly via nuclear hormone receptor-dependent mechanisms [28]. Taken together, these findings raise the hypothesis that the diabetic state may influence relationships of obesity and common genetic variations in PLTP with plasma PLTP activity levels in humans.

Given an emerging role of PLTP as a possible cardiometabolic risk factor, we sought to determine whether the diabetic state interacts with obesity to impact on plasma PLTP activity. We also investigated whether the presence of diabetes mellitus may amplify the observed association between plasma PLTP activity and common PLTP gene variations. These possibilities were tested in two cohorts, comprising type 2 diabetic and control subjects, in which we previously identified two PLTP-tagging single-nucleotide polymorphisms (SNPs) that are associated with plasma PLTP activity [16].

Subjects and methods

  1. Top of page
  2. Abstract
  3. Introduction
  4. Subjects and methods
  5. Results
  6. Discussion
  7. Conflict of interest statement
  8. References

Subjects included in the present study were participants from two previous studies: the Groningen case–control study, which was originally designed to examine whether intima–media thickness is related to plasma PLTP activity [10]; and the Diabetes Atorvastatin Lipid Intervention (DALI) study, a double-blind, placebo-controlled, randomized, multicentre study, which evaluated the effect of atorvastatin on lipid metabolism, endothelial function, coagulation and inflammatory markers [29, 30]. The protocols were approved by the medical ethics committees of the participating centres. All participants provided written informed consent.

The inclusion and exclusion criteria for the Groningen and DALI studies have been described in detail elsewhere [10, 29, 30]. In brief, the participants of the Groningen study were recruited by advertisement in local newspapers. Subjects with and without previously diagnosed type 2 diabetes mellitus (defined according to World Health Organization criteria) were included. Smokers and subjects using lipid-lowering drugs, insulin and thiazolidones were excluded from the Groningen cohort. Patients with diabetes participating in the DALI study had a diabetes duration of at least 1 year, were free of clinically manifest cardiovascular disease at entry and had a glycated haemoglobin (HbA1c) level <10%. When applicable, lipid-lowering drugs were withdrawn at least 8 weeks before the start of the run-in phase. Premenopausal women were excluded. Plasma lipid inclusion criteria were levels of fasting total cholesterol between 4.0 and 8.0 mmol L−1 and triglycerides between 1.5 and 6.0 mmol L−1. Further exclusion criteria for participation in either cohort were clinically manifest cardiac disease (history of myocardial infarction, coronary intervention or major ischaemia on an electrocardiogram) and pregnancy. For the present study, we included all subjects who had complete data with respect to obesity measures and PLTP gene score. Consequently, a total of 78 control subjects and 67 patients with diabetes from the Groningen cohort and 170 patients with diabetes from the DALI cohort were included.

Blood pressure was measured using routine clinical procedures. Body mass index (BMI) was calculated as the ratio between weight and height squared (in kg m−2). Waist circumference was measured as the smallest girth between the rib cage and iliac crest, and waist/hip ratio was measured as the waist circumference divided by the hip circumference. The revised national cholesterol education programme adult treatment panel III (NCEP-ATP-III) criteria were applied for classification of MetS, and a waist circumference >102 cm for men and >88 cm for women [31] was used to indicate central obesity. Venous blood was obtained after an overnight fast.

Laboratory analyses

Venous blood was collected in ethylenediaminetetraacetic acid (EDTA)-containing tubes (1.5 mg mL−1), which were placed on ice immediately. Plasma was obtained by centrifugation at 1400 g for 15 min at 4 °C. Plasma glucose and HbA1c were measured shortly after blood collection. Plasma samples for measurement of PLTP activity, lipids and apolipoproteins were stored at −80 °C until analysis. In both studies, total levels of cholesterol and triglycerides were measured using routine enzymatic colorimetric assays, and HDL cholesterol was measured with a direct homogeneous method [10, 29]. Non-HDL cholesterol was calculated as the difference between total and HDL cholesterol. High-sensitivity C-reactive protein (CRP) was assayed by nephelometry (Dade Behring, Marburg, Germany) in the Groningen cohort [22] and by enzyme immunoassay (Dako, Copenhagen, Denmark) in the DALI study [32]. Apolipoprotein E (apoE) was measured by nephelometry (Wako, Osaka, Japan) [30].

Plasma PLTP activity was measured with a phospholipid vesicles–HDL system, using [14C]-labelled dipalmitoyl phosphatidylcholine as previously described [10, 32, 33]. Briefly, plasma samples (1 μL) were incubated with [14C]-labelled phosphatidylcholine vesicles and excess pooled normal HDL for 45 min at 37 °C. The method is specific for PLTP activity; the phospholipid transfer-promoting property of cholesteryl ester transfer protein (CETP) does not interfere with the assay. Levels of plasma PLTP activity vary linearly with the amount of plasma added to the incubation system. PLTP activity was related to the activity in human reference pooled plasma and was expressed in arbitrary units (AU; 100 AU corresponds to 13.6 μmol phosphatidylcholine transferred per mL per hour). The inter-assay coefficient of variation of the measurement of PLTP activity is 5%.

Variation in PLTP was determined using a gene score that is based on two common PLTP-tagging SNPs [rs378114 (c.330−117G>A) and rs6065904 (c.705+256C>T)], as described in detail elsewhere [16]. The PLTP score represents the number (from 0 to 4) of plasma PLTP activity-decreasing alleles (for rs378114: presence of 0, 1 or 2 G alleles; for rs6065904: presence of 0, 1 or 2 T alleles) and has been found to inversely predict plasma PLTP activity in both the Groningen and the DALI cohorts [16]. A higher PLTP gene score is also linearly associated with less PLTP mRNA expression in human liver specimens [16].

Statistical analysis

spss 18 was used for data analysis. Results are expressed as mean ± SD or median (interquartile range) unless stated otherwise. Differences in variables between diabetic and nondiabetic subjects were determined by unpaired t-tests or Mann–Whitney U tests where appropriate. Differences between diabetic and nondiabetic subjects with and without an enlarged waist circumference were determined by one-way analysis of variance (anova) with subsequent Bonferroni correction for multiple comparisons. Logarithmically transformed triglyceride values were used in regression analyses. Between-group differences in proportions were assessed by chi-square analysis. Multivariate linear regression analysis was applied to determine differences between diabetic and control subjects after adjustment for age and sex. Multivariate linear regression analyses were also carried out to determine the independent contribution of variables to plasma PLTP activity. Additionally, interaction terms between diabetes status, HbA1c, PLTP gene score and obesity variables were calculated. To this end, a distribution of centred to the mean was made for continuous variables by subtracting the individual value of the variable of interest from its group mean value. Interaction terms were considered to be statistically significant at two-sided P-values <0.1 [34]. Otherwise, the level of significance was set at two-sided < 0.05.

Results

  1. Top of page
  2. Abstract
  3. Introduction
  4. Subjects and methods
  5. Results
  6. Discussion
  7. Conflict of interest statement
  8. References

A total of 237 patients with diabetes and 78 control subjects were included in the study. Patients with diabetic were older, had higher systolic and diastolic blood pressure and were more obese than control subjects, but the male/female ratio was similar between the groups (Table 1). Average diabetes duration was 9 ± 7 years. Fifty-two patients with diabetes (22%) were current smokers (< 0.001 compared to control subjects, because of exclusion of smokers in the Groningen study). Nineteen of the patients with diabetes were treated with diet alone. Oral blood glucose-lowering drugs alone (metformin and/or sulfonylurea) were used by 130 patients, whereas 43 patients used insulin alone and 45 used insulin in combination with oral blood glucose-lowering drugs (mainly metformin). A total of 210 (89%) patients with diabetes fulfilled the criteria for MetS compared to 21 control subjects (27%, < 0.001).

Table 1.   Clinical characteristics, plasma lipids, phospholipid transfer protein (PLTP) activity and variation in PLTP in 237 type 2 diabetic and 78 control subjects
 Type 2 diabetic subjects (= 237)Control subjects (= 78)P-valueP-value*
  1. BMI, body mass index; CRP, high-sensitivity C-reactive protein; HDL, high-density lipoprotein; SNPs, single-nucleotide polymorphisms.

  2. Data are given as mean (SD), median (interquartile range) or number (%).

  3. *P-values for difference between type 2 diabetic and control subjects adjusted for age, sex and smoking. PLTP gene score: derived from two tagging SNPs (rs378114 and rs6065904) associated with plasma PLTP activity.

Age (years)59 ± 856 ± 90.003 
Sex (m/f)129/10844/340.87 
Systolic blood pressure (mmHg)151 ± 21130 ± 18<0.001<0.001
Diastolic blood pressure (mmHg)87 ± 982 ± 10<0.001<0.001
BMI (kg m−2)30.2 ± 5.126.0 ± 4.1<0.001<0.001
Waist circumference (cm)105 ± 1490 ± 13<0.001<0.001
Enlarged waist174 (73%)18 (23%)<0.001 
Waist/hip ratio0.98 ± 0.100.90 ± 0.08<0.001<0.001
Glucose (mmol L−1)10.0 ± 3.15.7 ± 1.0<0.001<0.001
HbA1c (%)7.9 ± 1.35.4 ± 0.4<0.001<0.001
CRP (mg L−1)2.77 (1.29–5.43)1.36 (0.51–2.61)<0.001<0.001
Total cholesterol (mmol L−1)5.80 ± 0.965.70 ± 1.030.430.86
Non-HDL cholesterol (mmol L−1)4.70 ± 0.994.22 ± 1.11<0.0010.004
HDL cholesterol (mmol L−1)1.11 ± 0.311.48 ± 0.42<0.001<0.001
Triglycerides (mmol L−1)2.20 (1.76–2.97)1.26 (0.89–2.02)<0.001<0.001
ApoE (g L−1)0.041 ± 0.0110.040 ± 0.0100.0270.021
PLTP activity (AU)107 ± 1795 ± 11<0.001<0.001
PLTP gene score0: = 160: = 10.40 
 1: = 591: = 20  
 2: = 922: = 30  
 3: = 513: = 21  
 4: = 194: = 6  

As shown in Table 1, systolic and diastolic blood pressure, BMI, waist circumference, waist/hip ratio, fasting plasma glucose, HbA1c and CRP levels were higher in patients with diabetes than in control subjects. Plasma total cholesterol was similar between diabetic and control subjects. Non-HDL cholesterol, triglycerides and apoE levels were higher, whereas HDL cholesterol was lower in patients with diabetes (Table 1). All these differences remained significant after adjustment for age, sex and smoking. Plasma PLTP activity was on average 13% higher in patients with diabetes; this difference remained significant after adjustment for age, sex, smoking and study cohort (< 0.001). The distribution of PLTP gene score was not different in diabetic compared to control subjects (Table 1; median gene score (interquartile range): 2 (1–3) in both diabetic and control subjects).

As shown in Fig. 1, plasma PLTP activity was higher in patients with diabetes with an enlarged waist (= 174), compared to patients with diabetes with a normal waist circumference (= 63), control subjects with an enlarged waist (= 18) and control subjects with a normal waist circumference (= 60) (anova, < 0.001). These differences remained significant after adjustment for age, sex, smoking and study cohort (anova, < 0.001), after additional adjustment for PLTP gene score (anova, < 0.001) and also after excluding the 88 patients with diabetes using insulin (= 227, anova, < 0.001).

image

Figure 1.  Plasma phospholipid transfer protein activity according to diabetes status and enlarged waist circumference. Data are means ± SEM. (a) Nondiabetic subjects with normal waist (n = 60); (b) nondiabetic subjects with enlarged waist (n = 18); (c) type 2 diabetic subjects with normal waist (n = 63); (d) type 2 diabetic subjects with enlarged waist (n = 174). anova, P < 0.001.

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Multiple linear regression analysis was carried out to determine the extent to which plasma PLTP activity was governed by PLTP gene score, diabetes status and obesity. In initial models that included either BMI or waist circumference, it was found that plasma PLTP activity was related to BMI (ß = 0.179, = 0.002) or waist circumference (ß = 0.255, < 0.001), independently of the presence of diabetes (< 0.001, data not shown). When BMI and waist circumference were included together, PLTP activity was found to be determined by waist circumference alone (ß = 0.258, = 0.002) and not by BMI (ß = 0.01, = 0.95). Therefore, waist circumference was selected as the primary obesity variable for evaluating diabetes–obesity interactions to impact on plasma PLTP. As shown in Table 2, plasma PLTP activity was independently and inversely related to PLTP gene score and positively related to diabetes status and enlarged waist circumference after adjustment for age, sex, smoking, CRP and study cohort (model 1). We next examined whether the presence of diabetes modified the relationship between plasma PLTP activity and enlarged waist circumference. As shown in model 2, there was a positive interaction between diabetes status and waist circumference on PLTP activity. When HbA1c, non-HDL cholesterol, triglycerides and apoE levels were added to the models, PLTP activity remained independently related to enlarged waist (model 3), and there was still an interaction between diabetes status and waist circumference on PLTP activity (model 4). When patients with diabetes using insulin were excluded, the interaction between diabetes status and waist circumference on PLTP activity also remained significant (β = 0.213, =0.013 and β = 0.180, = 0.038 without and with additional adjustment for HbA1c, non-HDL cholesterol, triglycerides and apoE, respectively; data not shown). In further analyses, there was also an interaction between diabetes status and waist/hip ratio on PLTP activity (model 2: β = 0.219, < 0.001; model 4: β = 0.215, = 0.001, data not shown).

Table 2.   Multiple linear regression models showing independent relationships between plasma phospholipid transfer protein (PLTP) activity and PLTP gene score, diabetes status and waist circumference, and interaction between diabetes status and enlarged waist in 237 patients with type 2 diabetes and 78 control subjects
 Model 1Model 2Model 3Model 4
ßP-valueßP-valueßP-valueßP-value
  1. CRP, high-sensitivity C-reactive protein; DALI, Diabetes Atorvastatin Lipid Intervention; HDL, high-density lipoprotein; SNPs, single-nucleotide polymorphisms.

  2. PLTP gene score: derived from two tagging SNPs (rs378114 and rs6065904) associated with plasma PLTP activity. ß: standardized regression coefficient. Logarithmically transformed values of triglycerides and C-reactive protein (CRP) levels are used in the analyses.

  3. All models are adjusted for age, sex, smoking, CRP and study cohort (DALI cohort, Groningen cohort).

  4. Other statistical determinants of PLTP activity included in the multivariate linear regression analyses:

  5. Model 1: diabetes status, enlarged waist circumference, PLTP gene score;

  6. Model 2: diabetes status, enlarged waist circumference, PLTP gene score and interaction between waist circumference and diabetes status;

  7. Model 3: diabetes status, enlarged waist circumference, PLTP gene score, glycated haemoglobin (HbA1c), non-high-density lipoprotein (HDL) cholesterol, ln triglycerides and apolipoprotein E (apoE);

  8. Model 4: diabetes status, enlarged waist circumference, PLTP gene score, HbA1c, non-HDL cholesterol, ln triglycerides, apoE and interaction between waist circumference and diabetes status.

Age−0.1200.019−0.0980.057−0.100.053−0.0870.094
Sex (men vs. women)−0.0730.162−0.1370.015−0.0670.21−0.1170.043
Smoking (yes/no)0.1270.0220.1230.0250.1280.0210.1220.028
Ln CRP0.0350.540.010.940.0010.99−0.0220.71
DALI vs. Groningen cohort−0.1520.026−0.1290.058−0.0110.900.0020.98
PLTP gene score−0.280<0.001−0.284<0.001−0.265<0.001−0.271<0.001
Diabetes mellitus (yes/no)0.1550.0210.1730.0100.1010.170.1130.125
Enlarged waist circumference (yes/no)0.2080.0010.0760.320.1760.0060.0700.38
Diabetes–waist circumference interaction  0.2000.005  0.1620.028
HbA1c    0.1740.0400.1830.030
Non-HDL cholesterol    −0.0210.74−0.0260.68
Ln triglycerides    0.0590.440.0390.61
ApoE    0.1840.0040.1890.003

Finally, we determined whether diabetes status and the degree of chronic hyperglycaemia, as reflected by the HbA1c level, interacted with PLTP gene score to affect plasma PLTP activity (Table 3). Remarkably, diabetes status showed an interaction with PLTP gene score on PLTP activity after adjustment for age, sex, smoking, CRP, study cohort and enlarged waist circumference (model 1), as well as after additional adjustment for non-HDL cholesterol, triglycerides and apoE (model < 0.001 compared to control subjects, because of exclusion of smokers in the Groningen study3). Thus, the presence of diabetes amplified the association between plasma PLTP activity and the number of PLTP activity-decreasing alleles. This interaction was essentially unaltered after exclusion of patients with diabetes using insulin (model 1: β = −0.449, = 0.085; model 3: β = −0.418, =0.098, data not shown). Likewise, interactions between the HbA1c level and the PLTP gene score on PLTP activity were observed in analysis without (model 2; graphically depicted in Fig. 2) and with (model 4) additional adjustment for non-HDL cholesterol, triglycerides and apoE.

Table 3.   Multiple linear regression models showing relationships between plasma phospholipid transfer protein (PLTP) activity and PLTP gene score, diabetes status and HbA1c, and interactions between PLTP gene score and diabetes status and HbA1c in 78 nondiabetic patients and 237 type 2 diabetic subjects
 Model 1Model 2Model 3Model 4
ßP-valueßP-valueßP-valueßP-value
  1. DALI, Diabetes Atorvastatin Lipid Intervention; HDL, high-density lipoprotein; SNPs, single-nucleotide polymorphisms.

  2. PLTP gene score: derived from two tagging SNPs (rs378114 and rs6065904) associated with plasma PLTP activity. ß: standardized regression coefficient. Logarithmically transformed values of triglycerides and C-reactive protein (CRP) levels are used in the analyses.

  3. All models are adjusted for age, sex, smoking, CRP and study cohort (DALI cohort, Groningen cohort).

  4. Other statistical determinants of PLTP activity included in the multivariate linear regression analyses:

  5. Model 1: PLTP gene score, diabetes status, enlarged waist circumference and interaction between PLTP gene score and diabetes status.

  6. Model 2: PLTP gene score, diabetes status, HbA1c, enlarged waist circumference and interaction of PLTP gene score with HbA1c.

  7. Model 3: PLTP gene score, diabetes status, enlarged waist circumference, non-HDL cholesterol, ln triglycerides, apolipoprotein E (apoE) and interaction between PLTP gene score and diabetes status.

  8. Model 4: PLTP gene score, diabetes status, glycated haemoglobin (HbA1c), enlarged waist circumference, non-high-density lipoprotein (HDL) cholesterol, ln triglycerides, apoE and interaction between PLTP gene score and HbA1c.

Age−0.1250.014−0.1300.027−0.1060.042−0.1060.041
Sex (men vs. women)−0.0720.167−0.0750.130−0.0660.22−0.0680.21
Smoking (yes/no)0.1260.0220.1270.0200.1220.0270.1250.024
Ln CRP0.0470.410.0250.660.0210.710.0070.90
DALI vs. Groningen cohort−0.1520.026−0.0640.83−0.0800.28−0.0040.96
PLTP gene score0.1530.49−0.272<0.0010.1040.64−0.263<0.001
Diabetes mellitus (yes/no)0.3700.0040.1010.140.3560.0060.1040.16
HbA1c  0.3930.002  0.3550.008
Diabetes–PLTP gene score interaction−0.4850.046  −0.4230.084  
HbA1c–PLTP gene score interaction  −0.2400.035  −0.2030.078
Enlarged waist circumference (yes/no)0.2090.0010.1960.0010.1920.0020.1820.004
Non-HDL cholesterol    −0.0060.92−0.0150.81
Ln triglycerides    0.0640.410.0590.44
ApoE    0.1780.0050.1770.005
image

Figure 2.  Graphical presentation of the interaction between the HbA1c level (presented in categories) and PLTP gene score [number of phospholipid transfer protein (PLTP) activity-decreasing alleles] on plasma PLTP activity (data derived from Table 3, model 2; interaction term: β = −0.240, P = 0.035).

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Discussion

  1. Top of page
  2. Abstract
  3. Introduction
  4. Subjects and methods
  5. Results
  6. Discussion
  7. Conflict of interest statement
  8. References

In the present study, we have demonstrated for the first time that the presence of diabetes strengthens the relationship between plasma PLTP activity and enlarged waist circumference independently of the influence of common PLTP variations. Furthermore, our results suggest that the association between plasma PLTP activity and variations in PLTP is modified by the diabetic state, meaning that plasma PLTP activity is more strongly affected in individuals with diabetes mellitus for a given PLTP gene score. Such an interaction was also observed when using the HbA1c level as a measure of the degree of chronic hyperglycaemia. Taken together, our findings support the hypothesis that diabetes–environment and diabetes–gene interactions are involved in the regulation of circulating PLTP activity.

In this study, plasma PLTP activity was more strongly related to waist circumference than to BMI. Of note, both BMI and waist circumference are correlated with visceral abdominal as well as with subcutaneous abdominal fat tissue [35]. Therefore, these obesity measures do not fully discriminate between relationships between plasma PLTP activity and either obesity per se or a centrally distributed adiposity pattern. Of further note, PLTP is expressed in visceral as well as in subcutaneous adipose tissue, although PLTP mRNA has been found to be related positively to BMI in subcutaneous but not in visceral adipose tissue [3, 36]. Nonetheless, a more centrally distributed obesity pattern may be regarded as a major driving force leading to increasing free fatty acid delivery to the liver and hence to disturbances in hepatic lipid homoeostasis [37], which are likely to include elevations in plasma PLTP activity [22]. We did not specifically assess visceral and subcutaneous fat areas. Yet, we consider the present finding that the diabetic state interacted positively with waist circumference to affect PLTP activity robust, as this interaction was independent of plasma CRP, which is known to be elevated in (central) obesity [38], and was replicated using the waist/hip ratio as a measure of obesity.

The present observation that the diabetic state and, in alternative analyses, the HbA1c level interact with the PLTP gene score to modulate circulating PLTP activity clearly supports the hypothesis that the extent to which genetic variation in PLTP affects circulating PLTP activity levels may be amplified by the degree of chronic hyperglycaemia in vivo. The PLTP gene score used here is strongly associated with hepatic mRNA expression [16]. Earlier studies have demonstrated that high glucose stimulates PLTP promoter activity in vitro [28]. Accordingly, PLTP mRNA expression is modulated by nuclear receptors, such as the peroxisome proliferator-activated receptor, the liver X-receptor and the farnesoid X-receptor [28, 39, 40]. In the present study, we did not aim to delineate the mechanisms whereby exposure to hyperglycaemia may modify PLTP expression.

Several other aspects of our study should be addressed. In view of the possibility that PLTP activity is higher in cigarette smokers [41], and given the exclusion of smokers in the Groningen cohort, we took account of smoking status in the multivariable analyses. Indeed, current smoking was found to be associated with higher plasma PLTP levels in this study. Furthermore, the notion that PLTP has pro-inflammatory properties [8, 42] provides an additional argument to evaluate whether or not interactions between diabetes and obesity on circulating PLTP activity are confounded by enhanced chronic subclinical inflammation. We also included the plasma level of apoE, which has been considered to be involved in activating PLTP [30, 43] and in stimulating the ability of PLTP to remodel HDL particles [44]. A strong correlation between plasma PLTP activity and apoE was observed that was independent of plasma apoB-containing lipoproteins and obesity. Of note, interactions between diabetes status and obesity and between diabetes status and PLTP gene score to affect plasma PLTP activity were not essentially influenced by inclusion of apoE in the multivariable analysis.

The current findings are based on an analysis of two cohorts combined. In the Groningen cohort, insulin-treated patients with diabetes were excluded [10], whereas a considerable number of DALI participants used insulin [29]. Furthermore, mild to moderate hypertriglyceridaemia was a selection criterion for the DALI study [29], which probably explains the high prevalence of enlarged waist circumference among patients with diabetes. For these reasons, we took account of the origin of the study cohort in all multivariable analyses. In this regard, it is relevant that all interactions remained essentially unaltered after exclusion of insulin-treated patients. Moreover, the analyses concerning PLTP gene variation that we used here are based on a recently described PLTP gene score [16]. This approach was chosen in the expectation that statistical power to detect diabetes status–PLTP gene and HbA1c–PLTP interactions was better compared to that using each PLTP gene variation separately. Finally, it should be appreciated that the cross-sectional design is a limitation of the present study.

In conclusion, our findings support the hypothesis that diabetes–environment and diabetes–gene interactions are involved in the regulation of plasma activity of PLTP, an emerging cardiometabolic risk factor. The present observations impact on the supposition that improving metabolic control and weight reduction may coordinately and perhaps even synergistically affect lipoprotein metabolism via PLTP regulation.

References

  1. Top of page
  2. Abstract
  3. Introduction
  4. Subjects and methods
  5. Results
  6. Discussion
  7. Conflict of interest statement
  8. References
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