Cardiovascular disease is the primary cause of death also in diabetic women [
Low plasma levels of high-density lipoprotein cholesterol (HDL-C) have been largely recognized as a risk factor for coronary heart disease (CHD) [
HDL class comprises very heterogeneous particles that can be separated by different methods, including two-dimensional gel electrophoresis that separates Apo-AI containing HDL particles according to their size and lipid content [
We have recently shown that type 2 diabetes determines a shift in the distribution of HDL particles; in particular, when assessing HDL subpopulation distribution by two-dimensional gel electrophoresis, diabetic women had HDL that are selectively depleted in the large lipid-rich
Besides their role in RCT, HDL particles exert their antiatherosclerotic role through several other mechanisms, such as a reduction of inflammation, endothelial dysfunction, and LDL oxidation [
Several inflammatory markers and adipokines have been subjected to intensive studies for their role in insulin resistance and atherosclerosis. In particular, high-sensitivity C-reactive protein (hsCRP) and interleukin-6 (IL-6) have been clearly involved in both insulin resistance and atherosclerosis prediction [
In that same population of CHD-free women with and without type 2 diabetes [
Study population has been previously described elsewhere [
Exclusion criteria for all participants were as follows: pregnancy, hormonal replacement therapy, oral contraceptive use or multivitamin supplementation, current treatment with
Lifestyle and clinical data were collected through a standardized questionnaire.
BMI and blood pressure (BP) were measured according to standard procedures. Type 2 diabetes was diagnosed according to ADA criteria [
All the participants gave their informed consent and the study was approvedby the local ethical committee.
After a 12- to 14-hour fasting, blood samples were collected from all participants for the determination of the study parameters. Blood was drown in a 10 mL tube containing EDTA (0.15% final concentration) and in a regular 10 mL tube. After collection, plasma and serum were immediately separated at 2,500 rpm for 30 minutes at 4°C, and aliquots were stored at −80°C until analysis. Fasting plasma glucose and serum creatinine levels were measured with standard automated laboratory methods (Roche Diagnostics, Milan, Italy). Glycated haemoglobin
All lipid and lipoprotein measurements were performed at the Lipid Metabolism Laboratory, Tufts University. Plasma total cholesterol (TC) and triglycerides levels were measured by automated enzymatic assays [
Apo-A-I containing HDL subpopulations in plasma were measured by nondenaturing two-dimensional gel electrophoresis, as previously described [
All inflammatory markers measurements were performed at the Lipid Metabolism Laboratory, Tufts University. Measurements of hsCRP were performed on a Hitachi 911 (Roche Diagnostics, Indianapolis, Indiana) using the hsCRP kit from Wako Chemicals. Within- and between-run coefficients of variation were <5%. Plasma concentrations of interleukin- (IL-) 6 were determined by an ELISA assay (R&D Systems, Minneapolis, Minnesota).
The numerical data are expressed as mean and standard deviation (SD). Examined variables were normally distributed as verified by Kolmogorov-Smirnov test; consequently, the parametric approach has been used. For each parameter, we performed statistical comparisons between women with and without diabetes applying Student’s
Finally, linear regression models were estimated in order to assess the possible dependence of hsCRP on all examined variables; firstly, we estimated all univariate models; subsequently, a multivariate regression analysis was performed including in the model only the variables significantly associated with inflammatory markers levels in the univariate approach. The same analysis was performed in order to assess the dependence of IL-6.
Statistical analysis was performed using the SPSS program, version 11.0, for Windows (SPSS Inc., Chicago, IL).
Clinical characteristics of the 160 CHD-free women, 80 with and 80 without type 2 diabetes, participating in the study have been previously described [
Lipid profile, Apo-AI containing HDL subpopulations distribution, and markers of systemic inflammation in women with and without type 2 diabetes.
Total population | Women with type 2 diabetes | Women without type 2 diabetes |
| |
---|---|---|---|---|
|
160 | 80 | 80 | |
Postmenopausal ( |
86 | 43 | 43 | |
Age (yrs) |
|
|
|
— |
Menopausal duration (yrs) |
|
|
|
— |
BMI (Kg/m2) |
|
|
|
<0.001 |
Waist circumference (cm) |
|
|
|
<0.001 |
Systolic BP (mmHg)* |
|
|
|
<0.001 |
Diastolic BP (mmHg) |
|
|
|
<0.001 |
Fasting BG (mg/dL)* |
|
|
|
<0.001 |
|
||||
Lipid and lipoprotein profile | ||||
Total-C (mg/dL)# |
|
|
|
— |
LDL-C (mg/dL)# |
|
|
|
— |
Triglycerides (mg/dL) |
|
|
|
0.001 |
HDL-C (m/dL)* |
|
|
|
<0.0001 |
Apo-AI (mg/dL)# |
|
|
|
0.04 |
Apo-AII (mg/dL) |
|
|
|
0.01 |
|
||||
Apo-AI containing HDL subpopulations profile | ||||
α-1 HDL (mg/dL) |
|
|
|
0.006 |
α-2 HDL (mg/dL) |
|
|
|
0.005 |
α-3 HDL (mg/dL) |
|
|
|
0.02 |
Pre-α-1 (mg/dL) |
|
|
|
0.025 |
|
||||
Markers of systemic inflammation | ||||
hsCRP (mg/L)* |
|
|
|
0.001 |
IL-6 (pg/mL)* |
|
|
|
— |
Data are
As shown in Table
When comparing circulating levels of principal Apo-AI containing HDL subpopulations (Table
Diabetic women also had 2-fold higher hsCRP serum levels than nondiabetic ones (age- and BMI-adjusted
Overall, markers of systemic inflammation significantly correlated with metabolic and lipid parameters and HDL subpopulations. In particular, hsCRP levels positively correlated with IL-6 in both the diabetic group (
As shown in Table
Correlation coefficients (
Total population | Women with type 2 diabetes | Women without type 2 diabetes | ||||
---|---|---|---|---|---|---|
hsCRP | IL-6 | hsCRP | IL-6 | hsCRP | IL-6 | |
Age | — | 0.17* | — | — | — | 0.24* |
Menopause duration | — | — | — | — | — | — |
BMI | 0.62 |
0.48 |
0.55 |
0.35* | 0.58 |
0.49 |
Waist C | 0.57 |
0.37 |
0.45 |
— | 0.53 |
0.35* |
Systolic BP | — | 0.30 |
— | 0.24* | — | 0.24* |
Diastolic BP | — | 0.29 |
— | 0.27* | — | — |
Fasting BG | 0.35 |
0.35 |
— | — | 0.25* | — |
Fasting insulin | 0.51 |
0.43 |
0.45 |
0.36 |
0.52 |
0.44 |
Creatinine | — | — | −0.25* | — | — | — |
|
||||||
Lipid and Apo-AI containing HDL subpopulations profile | ||||||
Total-C | — | — | — | — | — | — |
LDL-C | — | — | — | — | — | — |
Triglycerides | 0.28 |
0.19* | 0.28* | — | — | — |
HDL-C | −0.23* | −0.29 |
−0.26* | — | — | — |
Apo-AI | — | −0.19* | — | — | — | — |
Apo-AII | −0.19* | −0.32 |
−0.23* | −027* | — | −0.25* |
α-1 HDL | −0.19* | −0.24* | −0.22* | — | — | −0.24* |
α-2 HDL | — | −0.33* | — | −0.27* | — | — |
α-3 HDL | — | 0.18* | — | — | — | — |
Pre-α-1 HDL | −0.023* | −0.24* | −0.21* | — | — | — |
Only significant correlation coefficients (Spearman test) are shown.
In the whole study population, both hsCRP and IL-6 positively correlated with triglycerides and inversely correlated with HDL-C and Apo-AII concentrations; IL-6 also showed inverse correlation with Apo-AI levels (
Significant correlations of inflammatory markers with specific Apo-AI containing HDL subclasses were also noted. Notably, hsCRP and IL-6 showed significant inverse correlations with the larger lipid-rich
Similar correlations were also noted when separately considering diabetic women and controls, although these correlations were less numerous, especially in controls (Table
In particular, in women with diabetes, hsCRP significantly correlated with BMI (
In women without diabetes, hsCRP levels showed a significant correlation with BMI (
At univariate regression analysis (Table
Univariate and multivariate regression analysis between hsCRP and IL-6 and metabolic, lipid, and Apo-AI containing HDL subpopulations profile in total population.
hsPCR | IL-6 | |||||||
---|---|---|---|---|---|---|---|---|
Univariate regression | Multivariate regression | Univariate regression | Multivariate regression | |||||
|
|
|
|
|
|
|
| |
Anthropometric and metabolic parameters | ||||||||
Diabetes | 3.251 | 0.001 | — | — | 0.800 | 0.061 | — | — |
BMI | 0.41 | <0.001 | 0.24 | 0.03 | 0.14 | <0.001 | 0.141 | 0.003 |
Waist C | 0.11 | 0.001 | — | — | 0.03 | 0.02 | — | — |
Systolic BP | — | — | — | — | 0.03 | 0.01 | — | — |
Fasting BG | 0.03 | 0.005 | — | — | 0.01 | 0.009 | 0.011 | 0.02 |
Fasting insulin | 0.09 | 0.004 | — | — | 0.04 | 0.009 | — | — |
|
||||||||
Lipid and Apo-AI containing HDL subpopulations profile | ||||||||
HDL-C |
|
0.005 | — |
|
0.002 | — | ||
Apo-AI | — | — | — | — |
|
0.003 | — | — |
Apo-AII |
|
0.04 | — | — |
|
0.004 | — | — |
α-1 HDL |
|
0.04 | — | — | — | — | — | — |
α-2 HDL | — | — | — | — |
|
0.009 | — | — |
α-3 HDL | — | — | — | — | 0.11 | 0.04 | — | — |
Pre-α-1 HDL |
|
0.007 |
|
0.083 |
|
0.03 | — | — |
Only significant
IL-6 was significantly associated with BMI, waist circumference, systolic BP, fasting blood glucose, and insulin levels and negatively with HDL-C, Apo-AI, and Apo-AII levels. A trend was also noted for an association with diabetes. IL-6 also showed significant associations with almost all the HDL subclasses explored, specifically a negative association with
Multivariate regression analysis was performed including in the model only HDL subpopulations and not HDL-C levels, to avoid colinearity. As a result (Table
Low levels of HDL-C are a mainstay of diabetic dyslipidemia and a largely recognized CHD risk factor [
HDL particles may be particularly atheroprotective in women, where each 1 mg/dL increase in HDL-C is associated with a 3% decrease in CHD risk versus 2% in men [
The antiatherosclerotic role of these particles may be also mediated by the modulation of inflammation, since atherosclerosis today is considered an inflammatory disease.
We have recently shown that diabetic women have a less atheroprotective HDL subpopulation pattern [
Both hsCRP and IL-6 are well-characterized inflammatory markers in type 2 diabetes [
Our results also confirm the association of inflammatory markers with adiposity and insulin resistance, since BMI, waist circumference, fasting blood glucose and insulin, and systolic and diastolic BP were all significantly associated with inflammatory markers, especially in women with diabetes. However, these associations are probably driven by the deleterious effects of obesity, since at multivariate analysis BMI was the strongest correlate of inflammatory markers in our dataset, even more than diabetes itself, which was no longer significant at multivariate analysis, although fasting blood glucose was still significantly associated with IL-6 levels.
When the potential relationships between inflammation and lipid profile, with particular regard to HDL particles, were assessed, we found significant correlations between inflammatory markers and HDL-C levels and Apo-AI and Apo-AII concentrations, especially in diabetic women, whereas no associations were noted with other lipid fractions. Although many of these correlations disappeared when separating women with and without diabetes, probably because of the smaller sample size, these results are in accordance with those of the ATTICA study, where a significant correlation between HDL-C concentrations and markers of systemic inflammation was shown [
The potential anti-inflammatory role of HDL particles is sustained by several lines of evidence. Thus, besides their role in RCT, HDL particles may have several other antiatherosclerotic mechanisms, including the modulation of oxidation, inflammation, and endothelial dysfunction [
Indeed, as reported in numerous studies [
The anti-inflammatory properties of HDL particles have been also sustained by proteomics analysis, revealing more than 50 proteins associated with HDL, most of which are with specific anti-inflammatory or antioxidant functions.
However, the link between inflammation and HDL particles is complex. Thus, the protective role of HDL-C levels appears to be attenuated by acute or chronic inflammation [
Anti-inflammatory effects of HDL particles may be also particularly relevant in acute coronary syndrome (ACS), where vascular inflammation strongly affects plaque vulnerability [
These and other experimental lines of evidence indicate that, under inflammatory conditions, HDL particles lose their protective capacity shifting toward a proatherogenic pattern [
It is becoming apparent that different HDL particles may show peculiar “qualities” that may influence RCT process, as well as their antioxidant or anti-inflammatory potential, rendering them atheroprotective or proatherogenic [
In CHD patients, Asztalos et al. showed distinct alterations in HDL subpopulation distribution, as assessed by nondenaturing two-dimensional electrophoresis [
Since these modifications could negatively influence anti-inflammatory properties of HDL particles, we also tested the hypothesis that different HDL LpA-I and LpA-I:A-II subclasses may be differently associated with inflammation. Our data confirm this hypothesis, since markers of inflammation negatively correlated with large lipid-rich
Although the small sample size is a limitation, in our study population, the use of lipid-lowering medications, anti-inflammatory drugs, and glitazones was accurately excluded to avoid their confounding effect on the relationship between inflammatory markers and lipid variables.
Another limitation is the cross-sectional design of our study that does not allow us to determine whether a specific HDL profile is less “anti-inflammatory” or, on the contrary, it is the higher inflammatory state which modifies HDL particles distribution toward a proatherogenic pattern.
In conclusion, our data show that HDL-C and the more atheroprotective HDL subpopulations are inversely associated with inflammatory markers, suggesting that different HDL particles may exert a different role in inflammation. However, caution must be taken when interpreting these associations that need to be confirmed in larger populations.
The functionality of HDL particles is a matter of growing investigation and, while waiting for validated markers in the clinical practice, the measurement of specific HDL subfractions might be useful to better evaluate the CVD risk in diabetic subjects.
The authors declare that there is no conflict of interests regarding the publication of this paper.