In atherosclerosis and obesity, the recruitment of monocytes and the infiltration of macrophages into the endothelium and adipose tissue [
Increased adiposity, particularly visceral obesity, is associated with subclinical inflammation [
Monocytes act on the expression of inflammatory markers and are strongly involved in all stages of atherosclerosis [
It has been shown that the concentrations of each monocyte subtype may vary according to the clinical manifestation of CAD [
However, the relationship between nontraditional indicators of adiposity and the monocyte subtypes has not yet been established. Given the relationship between adipose tissue and subclinical inflammation and between monocyte concentration and inflammatory marker expression and considering the importance of this triad for the development and progression of CAD, the aim of this study was to evaluate the correlation between the indicators of adiposity, inflammatory markers, and monocyte subtypes in patients with stable CAD.
This was a cross-sectional study conducted using baseline data from a randomized clinical trial including patients >40 years who were diagnosed with CAD by cineangiocoronariography and were clinically stable (more than 60 days without new symptoms, hospitalization, or emergency visit due to cardiovascular disease or new cardiovascular events) [
Patients who underwent surgery <90 days previously; had cancer, active infection, or inflammatory diseases; were currently using immunosuppressants; were chronically using anti-inflammatories and immunosuppressive drugs; had grade III obesity (BMI ≥ 40 mg/kg2); and were undergoing splenectomy and dialysis were excluded.
This study was approved by the Research Ethics Committee of IC/FUC (No. 534.850), and all individuals agreed to participate by signing the informed consent form.
Data were obtained from medical records and in medical and nutritional consultations; collection was conducted by trained investigators. Sociodemographic, current and cardiac history, and lifestyle variables were evaluated. Patients were categorized as nonsmokers, current smokers, or past smokers (interruption for more than six months [
Body weight (kg) and height (cm) were measured using an anthropometric electronic scale (Welmy LCD W110H®) and a coupled stadiometer. To measure weight, the patients were asked to be barefoot, empty their pockets, remove adornments, and retain the minimum amount of clothing. By positioning the patient in the centre of the platform, the height was obtained with the patient standing barefoot, with their arms along the sides of their body and their hands facing their thighs.
The waist circumference was measured at the midpoint between the last costal arch and the iliac crest with the patient in the expired supine position. The hip circumference was obtained by identifying the largest diameter of the hip through the major trochanters. The neck circumference was measured with the patient standing with the head positioned in the Frankfurt horizontal plane, measuring the neck perpendicularly to its long axis, at its largest diameter. These indicators were obtained with the aid of an inelastic and flexible tape measure (0 to 143 cm).
The mathematical formulas used for the calculation of traditional and nontraditional indicators of adiposity [
Fifteen millilitres of peripheral blood was collected from patients after 12 h of fasting. Serum triglycerides were evaluated by the enzymatic colorimetric method and HDL-cholesterol by immunoprecipitation (Roche modular P Chemistry Analyzer®). The concentration of high-sensitivity C-reactive protein (hs-CRP) was evaluated by the turbidimetry technique (Roche Cobas Integra 400 Plus Chemistry Analyzer®) [
The cytokines interleukin- (IL-) 2, IL-4, IL-6, IL-10, and interferon-gamma (INF-
The percentage of the different monocyte subtypes was evaluated from peripheral blood that was collected in EDTA tubes using a protocol adapted from Lee et al. [
Immediately after labelling, cells were washed with PBS containing 5% foetal bovine serum, resuspended, and analysed by flow cytometry (FACScanto II, BD Biosciences®). Prior to data acquisition, the sensitivity and overall equipment performance were assessed with CST (Cytometer Setup and Tracking Beads (BD Biosciences®)).
For each sample, at least 8,000 monocyte gate events were acquired and defined according to size (forward side scatter (FSC)) and granularity (side scatter (SSC)) characteristics. Data were analysed with BD software (FACSDiva version 6.1.3, BD Biosciences®) and presented as percentages, as previously described by Ulrich et al. [
Sample size calculation was performed using the WinPepi® program for Windows. Considering a Pearson’s correlation coefficient (
Analyses were performed using the Statistical Package for Social Sciences, SPSS version 24.0 (IBM Corp., Armonk, NY, USA). Quantitative variables were described as mean ± standard deviation (normal distribution) or median and interquartile range (non-normal distribution). Categorical variables were described as absolute values and percentages. Analysis of variance (ANOVA), Kruskal–Wallis, and Pearson’s chi-squared tests were used for comparisons between groups according to the nutritional status classified by BMI. Pearson’s correlation coefficient was used for the evaluation of correlations, and adjusted partial correlation was used to evaluate the independent correlations of sex and age. For the correlations, non-normal variables (hs-CRP, LAP, and VAI) were transformed logarithmically. A significance level of 5% was used.
In total, 97 patients were evaluated (81.4% men and 18.6% women) with a mean age of 58.0 ± 11.7 years; 94.8% of patients were Caucasian and had a mean educational level of 9.5 ± 3.1 years. Regarding BMI, 13.4% of the patients were classified as having an adequate BMI (BMI, 18.5–24.9 kg/m2), 39.2% were overweight (BMI, 25–29.9 kg/m2), and 47.4% were classified as obesity (BMI ≥ 30 kg/m2).
Table
Baseline clinical characteristics according to body mass index (
BMI 18.5 to 24.9 ( |
BMI 25.0 to 29.9 ( |
BMI ≥ 30.0 ( |
| |
---|---|---|---|---|
Age (years) | 54.6 ± 18.0 | 59.5 ± 8.3 | 57.8 ± 12.1 | 0.44 |
Sex | 0.90 | |||
Male | 10 (12.7) | 31 (39.2) | 38 (48.1) | |
Female | 3 (16.7) | 7 (38.9) | 8 (44.4) | |
Smoking1 | 9 (14.8) | 20 (32.8) | 32 (52.5) | 0.25 |
Alcohol abuse | 0 | 1 (25) | 3 (75) | 0.49 |
IPAQ | 0.54 | |||
Active | 4 (12.9) | 12 (38.7) | 15 (48.4) | |
Minimally active | 4 (10.8) | 18 (48.6) | 15 (40.5) | |
Inactive | 5 (17.2) | 8 (27.6) | 16 (55.2) | |
Type 2 diabetes mellitus | 3 (15) | 8 (40) | 9 (45) | 0.96 |
Dyslipidemia | 4 (7.3) | 24 (43.6) | 27 (49.1) | 0.12 |
Hypertension | 4 (6.8) | 25 (42.4) | 30 (50.8) | 0.06 |
BMI (kg/m2) | 23.8 ± 1.2 | 27.4 ± 1.4 | 33.4 ± 3.6 | <0.001 |
WC (cm) | ||||
Male | 88.4 ± 6.4 | 96.6 ± 5.2 | 110.0 ± 9.2 | <0.001 |
Female | 84.3 ± 3.1 | 92.5 ± 7.5 | 101.4 ± 5.4 | 0.002 |
HC (cm) | ||||
Male | 93.9 ± 3.9 | 99.6 ± 3.7 | 109.1 ± 8.7 | <0.001 |
Female | 96.3 ± 4.2 | 100.1 ± 6.1 | 110.1 ± 5.8 | 0.003 |
WHR | ||||
Male | 0.94 ± 0.06 | 0.97 ± 0.06 | 1.00 ± 0.06 | 0.002 |
Female | 0.88 ± 0.04 | 0.93 ± 0.06 | 0.92 ± 0.06 | 0.41 |
NC (cm) | ||||
Male | 37.4 ± 1.5 | 39.3 ± 2.2 | 42.7 ± 3.1 | <0.001 |
Female | 33.6 ± 1.7 | 35.3 ± 2.5 | 37.2 ± 1.6 | 0.002 |
LAP (cm/mmol/l) | ||||
Male | 27.67 (21–53) | 42.09 (28–71) | 80.18 (58–21) | <0.001 |
Female | 30.14 (13–39) | 38.55 (35–51) | 85.09 (49–113) | 0.019 |
VAI (log) | ||||
Male | 3.35 (2.4–4.7) | 3.64 (2.7–7.5) | 5.18 (3.4–8.3) | 0.09 |
Female | 3.44 (1.6–4.6) | 4.76 (3.1–6.1) | 7.05 (3.3–9.3) | 0.16 |
DAAT (cm2) | ||||
Male | 170.0 ± 43.8 | 224.2 ± 34.2 | 309.9 ± 61.8 | <0.001 |
Female | 104.3 ± 14.1 | 143.4 ± 20.3 | 179.4 ± 25.0 | 0.002 |
IL-2 (pg/mL) | 18.8 ± 1.7 | 19.6 ± 3.1 | 18.6 ± 1.9 | 0.19 |
IL-4 (pg/mL) | 27.5 ± 5.5 | 27.4 ± 7.4 | 27.2 ± 6.1 | 0.99 |
IL-6 (pg/mL) | 17.4 ± 3.0 | 17.7 ± 4.6 | 18.5 ± 5.5 | 0.68 |
IL-10 (pg/mL) | 16.8 ± 4.4 | 16.5 ± 4.6 | 17.2 ± 8.3 | 0.90 |
INF- |
16.1 ± 3.9 | 18.3 ± 5.6 | 17.5 ± 4.0 | 0.33 |
hs-CPR (mg/L) | 0.10 (0.1–0.4) | 0.14 (0.02–0.3) | 0.21 (0.1–0.4) | 0.81 |
Fibrinogen (mg/dL) | 298.9 ± 69.5 | 283.8 ± 63.9 | 284.0 ± 64.1 | 0.74 |
Mon1 (%) | 62.1 ± 15.0 | 59.5 ± 14.2 | 57.3 ± 17.9 | 0.61 |
Mon2 (%) | 4.9 ± 1.9 | 5.8 ± 2.2 | 5.2 ± 2.5 | 0.39 |
Mon3 (%) | 15.3 ± 8.7 | 16.5 ± 8.2 | 18.4 ± 9.6 | 0.45 |
Values are presented as mean ± standard deviation, median (interquartile range), or
Baseline characteristics of cardiovascular history and drugs (
Anterior wall myocardial infarction (%) | 22.5 |
Inferior wall myocardial infarction (%) | 21.3 |
Percutaneous coronary intervention (%) | 86.9 |
Disease in 1 to 2 vessels (%) | 65.5 |
Coronary artery bypass graft (%) | 4.8 |
Ejection fraction (mean ± SD) | 56.9 ± 12.4 |
Killip I (%) | 87.8 |
Family history of CAD (%) | 44 |
Drugs (%) | |
Aspirin | 86.6 |
Clopidogrel | 51.5 |
Betablockers | 85.5 |
ACE inhibitor/ARB | 79.4 |
Diuretics | 19.6 |
Statins | 81.5 |
AMI: acute myocardial infarct; CAD: coronary artery disease; ACE: angiotensin-converting enzyme; ARB: angiotensin receptor blocker.
Figures
Correlation between percentages of monocyte subgroups (%) and inflammatory markers (pg/mL) in patients with adequate BMI. Mon1 was positively correlated with INF-
Correlations between percentages of monocyte subgroups (%) and inflammatory markers (pg/mL) in overweight individuals. Mon1 correlated positively with IL-4 (a) and IL-10 (b). Mon3 correlated positively with IL-2 (c) and negatively with IL-14 (d).
Correlation between the percentage of monocyte subgroups (%) and inflammatory markers (pg/mL) in individuals with obesity. Mon1 correlated positively with IL-4 (a), IL-6 (b), and IL-10 (c). Mon3 correlated negatively with IL-4 (d), IL-6 (e), and IL-10 (f).
The neck circumference correlated negatively with IL-4 concentration in patients with adequate BMI (
In overweight individuals, after adjusting for sex and age, the LAP and VAI correlated with IL-4 and fibrinogen and, in obese subjects, the BMI, neck, waist, and hip circumferences, and DAAT correlated positively with IL-6, but only the BMI correlated positively with hs-CRP. There was no correlation between the indicators of adiposity and inflammation among patients with adequate BMI. Table
Partial correlation (
BMI 25 to 29.9 ( |
BMI ≥ 30.0 ( | |||
---|---|---|---|---|
IL-4 | FBR | IL-6 | hs-CRP | |
BMI (kg/m2) | −0.09 | 0.32 | 0.46 |
0.33 |
Neck circumference (cm) | −0.18 | 0.12 | 0.34 |
0.20 |
Waist circumference (cm) | −0.20 | 0.24 | 0.34 |
0.16 |
Hip circumference (cm) | −0.008 | 0.07 | 0.42 |
0.10 |
WHC | −0.21 | 0.20 | −0.04 | 0.11 |
LAP (cm/mmol/l) | −0.45 |
0.35 |
−0.13 | −0.26 |
VAI (log) | −0.37 |
0.26 | −0.08 | −0.26 |
DAAT (cm2) | −0.19 | 0.26 | 0.36 |
0.12 |
1Adjusted for sex and age. BMI: body mass index; WHC: waist-hip circumference; LAP: lipid accumulation product; VAI: visceral adiposity index; DAAT: deep-abdominal-adipose tissue; IL: interleukin; FBR: fibrinogen; hs-CRP: high-sensitivity C-reactive protein.
In patients with adequate BMI, no correlation was observed between the percentage of monocytes and the indicators of adiposity. Among overweight individuals, a positive correlation was observed between the percentage of Mon2 and the hip circumference (
In overweight patients, after adjustment for sex and age, there was no correlation between the monocyte subtypes and the indicators of adiposity. In obese patients, the hip circumference remained positively correlated with Mon1 (
Obesity is a worldwide epidemic and a cardiovascular risk factor that contributes to a 20% increased risk of acute myocardial infarction [
Similarly, we observed correlations between traditional and nontraditional indicators of adiposity and inflammation in overweight patients, regardless of factors such as sex and age. Finally, we did not observe correlations between monocyte subtypes and nontraditional indicators of adiposity. This suggests that in patients with CAD, especially those with obesity, the assessment of adiposity through simple and easy-to-obtain measures to identify additional cardiovascular risk is still useful.
Similar to Rothe et al. [
In obese individuals, Mon1 cells were positively related to inflammatory markers, in agreement with previous findings from experimental studies, in which Gr1 +/Ly6C monocytes (Mon1 in murine) presented a more inflammatory profile in atherosclerosis models [
A positive correlation of BMI with IL-6 and hs-CRP was observed in obese patients, even after adjusting for sex and age. On the other hand, new indicators of adiposity were positively related to IL-6 in obese patients and negatively with IL-4 in overweight patients, suggesting that patients with greater visceral fat accumulation have suppressed IL-4. In addition, it can be hypothesized that the use of drugs with metabolic effects, such as statins, can influence the relationships between inflammatory markers and the new indicators that represent the VAT since many of these indexes use markers of lipid profile in their mathematical formulas.
In relation to inflammatory markers, fibrinogen concentrations are known to correlate with TAV (identified using an imaging method) in obese individuals [
In the I LIKE HOMe study [
These conflicting results may be explained because although there are data from both
Our study has several limitations. Most of the patients were Caucasian men; the number of patients with adequate BMI was small; we did not perform imaging tests as a gold standard method to evaluate the body fat distribution and identify TAV and TAS compartments; our findings do not define causality, considering the cross-sectional design of the study; we did not specifically evaluate the atopic state of the participants; there was no control group including individuals without CAD; and we did not evaluate the important cytokines, monocyte chemoattractant protein-1 (MCP-1), and tumour necrosis factor-alpha (TNF-
However, the results aim to enrich the knowledge on the behaviour of monocyte subtypes in patients with CAD who are undergoing treatment. Furthermore, we sought to understand the correlation between monocyte subtypes, low-grade inflammation, and different AIs according to the presence or absence of obesity in these individuals. In addition, some previous studies did not follow a standardized methodology for the identification of monocyte subtypes, which was defined more recently in a consensus published in 2016 [
In our study, conducted in patients with stable CAD, we identified correlations between traditional and nontraditional indicators of adiposity, inflammatory markers, and monocyte subtypes, regardless of nutritional status according to BMI. However, this relationship needs to be evaluated further because inherent variables, such as metabolic and hemodynamic changes, as well as drug use, may have an influence on these correlations.
The values behind the means, standard deviations and other measures reported used to support the findings of this study are available from the corresponding author upon request.
The authors declare that there are no conflicts of interest.
SBG and AM designed the study protocol; SBG, VLP, MMK, LDD, ASQ, and AM were involved in drafting the manuscript; SBG, AM, VLP, and ASQ critically reviewed the manuscript for the intellectual quality of the study. The authors had full access to all data in the study. SBG, AM, and VLP were responsible for the contents of the article. All authors have given their final approval to the final version of the manuscript.
This study was funded by the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) and the Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul (FAPERGS).
Table S1: mathematical formulas used for calculation of traditional and nontraditional indicators of adiposity. Figure S1: example of a representative gating strategy of the FACS monocyte analysis of our study. Mon1: classic monocytes; Mon2: intermediate monocytes; Mon3: nonclassic monocytes.