Journal of Clinical Medicine Article The Circulating Fatty Acid Transporter Soluble CD36 Is Not Associated with Carotid Atherosclerosis in Subjects with Type 1 and Type 2 Diabetes Mellitus Esmeralda Castelblanco 1,2 , Lucía Sanjurjo 3 , Maria Barranco-Altirriba 1 , Mireia Falguera 4,5 , Marta Hernández 4,6 , Berta Soldevila 2,7 , Maria-Rosa Sarrias 3,8 , Josep Franch-Nadal 2,9 , Juan Antonio Arroyo 10,11 , José-Manuel Fernandez-Real 12,13 , Nuria Alonso 2,6,11, * and Didac Mauricio 1,2,11, * 1 Department of Endocrinology & Nutrition, Hospital de la Santa Creu i Sant Pau, IIB Sant Pau, 08041 Barcelona, Spain; esmeraldacas@gmail.com (E.C.); barmaria95@gmail.com (M.B.-A.) 2 Centre for Biomedical Research on Diabetes and Associated Metabolic Diseases (CIBERDEM), 08907 Barcelona, Spain; bsolde@hotmail.com (B.S.); josep.franch@gmail.com (J.F.-N.) 3 Innate Immunity Group, Health Sciences Research Institute Germans Trias i Pujol (IGTP), 08916 Badalona, Spain; lsanxurxo@gmail.com (L.S.); mrsarrias@igtp.cat (M.-R.S.) 4 Biomedical Research Institute of Lleida, University of Lleida, 25198 Lleida, Spain; mireiafalguera@hotmail.com (M.F.); martahernandezg@gmail.com (M.H.) 5 Primary Health Care Centre Cervera, Gerència d’Atenció Primaria, Institut Català de la Salut, 25200 Cervera, Spain 6 Department of Endocrinology and Nutrition, University Hospital Arnau de Vilanova, IRBLleida, 25198 Lleida, Spain 7 Department of Endocrinology & Nutrition, University Hospital Germans Trias i Pujol, IGTP, 08916 Badalona, Spain 8 Centre for Biomedical Research on Liver and Digestive Diseases (CIBEREHD), 28029 Madrid, Spain 9 DAP-Cat group, Unitat de Suport a la Recerca Barcelona, Fundació Institut Universitari per a la Recerca a l’Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), 08007 Barcelona, Spain 10 Department of Internal Medicine, Hypertension and Vascular Risk Unit, Hospital de la Santa Creu i Sant Pau, 08041 Barcelona, Spain; jarroyo@santpau.cat 11 Department of Medicine, Autonomous University of Barcelona, 08193 Bellaterra, Spain 12 Department of Diabetes, Endocrinology & Nutrition, Hospital Dr Josep Trueta, IDIBGI, 17007 Girona, Spain; jmfreal@idibgi.org 13 Centre for Biomedical Research on Physiopathology of Obesity and Nutrition (CIBEROBN), 17007 Girona, Spain * Correspondence: nalonso32416@yahoo.es (N.A.); didacmauricio@gmail.com (D.M.); Tel.: +34-934-978-860 (N.A.); +34-935-565-661 (D.M.)  Received: 16 April 2020; Accepted: 29 May 2020; Published: 2 June 2020  Abstract: This study aimed to determine the association of fatty acid transporter plasma soluble cluster of differentiation 36 (sCD36) with subclinical carotid atherosclerosis (SCA). A cross-sectional study was conducted in 1023 subjects, 225 with type 1 diabetes (T1D), 276 with type 2 diabetes (T2D) and 522 who were nondiabetic. Carotid atherosclerotic plaque (CAP) presence was determined using B-mode carotid ultrasound imaging. sCD36 were analysed by ELISA, and CD36 surface receptor and mRNA expression were measured by flow cytometry and real-time PCR. Logistic regression models were used to evaluate sCD36 as a biomarker of SCA. Up to 376 (36.75%) participants had at least one CAP, 76 T1D, 164 T2D and 136 without diabetes, while the remaining 647 (63.25%) did not have any CAP. There were no differences in sCD36 between patients with and without CAP in T1D (p = 0.287) or T2D (p = 0.513). Although nondiabetic subjects with plaques had lower sCD36 levels than those without (p = 0.023), the multivariate models revealed no association of sCD36 with CAP in any of the three study groups. No differences were found in surface CD36 or CD36 mRNA expression between J. Clin. Med. 2020, 9, 1700; doi:10.3390/jcm9061700 www.mdpi.com/journal/jcm J. Clin. Med. 2020, 9, 1700 2 of 12 the patients with and without CAP. sCD36 is not associated with SCA in type 1 or type 2 diabetic or in nondiabetic subjects. Keywords: atherosclerotic plaque; carotid atherosclerosis; sCD36; type 1 diabetes mellitus; type 2 diabetes mellitus 1. Introduction Accelerated atherosclerosis is the primary cause of the increased cardiovascular morbidity and mortality associated with type 1 (T1D) and type 2 diabetes (T2D). Apart from other important cardiovascular risk factors, and although it has not been clearly established, there is some evidence that points to hyperglycemia as a contributing factor to the diabetes-associated macrovascular burden [1]. In addition, there is an urgent need to define new markers that contribute to the detection and prediction of atherosclerosis in patients with diabetes. There are several factors that have been described to be involved in the pathogenesis of atherosclerosis in patients with diabetes. Among these, we find hyperglycemia, dyslipidemia and chronic inflammation [2]. In this sense, results obtained in studies performed in animal models of diabetes have suggested that atherosclerosis might be accelerated, in part, due to the synergistic effects of altered glucose and lipid metabolism [3]. On the other hand, there is solid evidence from experimental, clinical and epidemiological studies showing that hyperglycemia and its associated metabolic disturbances are linked to an increased inflammatory response that contributes through different pathways to the inflammatory response involved in the pathogenesis of the atherosclerotic process [2,3]. The cluster of differentiation 36 (CD36) is an 88-kDa transmembrane glycoprotein expressed in many cell types, and plays an essential role in lipid metabolism, angiogenesis and inflammation. This fatty acid transporter is related to several disease conditions such as cancer biology, insulin resistance, stroke, diabetes nephropathy and atherosclerosis [4,5]. Atherosclerosis develops as a result of lipid uptake by macrophages of the vascular wall, leading to the development of foam cells and smooth muscle cell proliferation [6]. In macrophages, the fatty acid transporter CD36 promotes atherosclerosis through the uptake of oxidized low-density lipoprotein (ox-LDL) and subsequent foam cell development [4], and the release of inflammatory mediators [5]. Previous studies demonstrated the involvement of CD36 in the progression of atherosclerosis, and also that deletion or blockage of the CD36-induced signalling pathway decreased the development of atherosclerotic lesions in mice [7,8]. For these reasons, CD36 has been proposed as a biomarker of future cardiovascular disease in diabetes with potential clinical applicability [9]. The circulating form of CD36 (sCD36) was reported to be related with metabolic syndrome for the first time in a small group of subjects: sCD36 was increased in T2D individuals compared with both lean (5-fold) and obese nondiabetic subjects (2- to 3-fold) [10]. These results were also reported by other studies from the one group [11–13]. However, other researchers have found an inverse relationship between sCD36 and several components of the metabolic syndrome [14,15]. In subjects with altered glucose tolerance, sCD36 was also reported to be related to markers of liver injury such as alanine aminotransferase (ALT) and aspartate transaminase (AST) [16]. Moreover, the fatty acid transporter sCD36 has been considered a potential biomarker of atherosclerosis given that, as mentioned before in animal models of atherosclerosis, absence of CD36 has been reported to result in a reduction of atherosclerosis [8]. Further, some authors have reported an association between higher sCD36 concentration and carotid intima-media thickness (cIMT), a surrogate marker of atherosclerosis [11,17]. Additionally, Handberg et al. reported that, in patients from the general population with internal carotid stenotic plaques, those who are symptomatic show higher sCD36 concentrations compared with those who are asymptomatic [18]. Finally, in another study in patients with stage 5 chronic kidney disease, higher sCD36 concentrations were associated with an increased risk of CV mortality [19]. J. Clin. Med. 2020, 9, 1700 3 of 12 In this study, we aimed to assess whether the circulating concentrations of sCD36 are associated with presence of carotid plaques in subjects with T1D, T2D, and without diabetes, all of them with normal kidney function and free of previous cardiovascular disease. 2. Experimental Section 2.1. Design and Study Population This cross-sectional study included 1023 participants recruited from the University Hospitals Germans Trias i Pujol (Badalona, Spain) and Arnau de Vilanova (Lleida, Spain). Among them, 225 with T1D and 276 with T2D were selected from two previous studies [20,21]; 522 individuals were selected from a population-based study in our region (participants without diabetes on the basis of HbA1c < 6.5% and glucose < 126 mg/dL) [22]. Further, 50 participants, 22 with and 28 without carotid atherosclerotic plaque, were enrolled at the University Hospital Germans Trias i Pujol to perform the sub-studies of flow-cytometric analysis and mRNA expression of CD36. Inclusion criteria for all the study participants included the following: age 20–85 years; free from established chronic kidney disease (glomerular filtration rate < 60 mL/min and/or urine albumin/creatinine ratio > 299 mg/g); absence of clinical cardiovascular disease or associated revascularisation procedures, such as cerebrovascular disease, coronary heart disease, or peripheral vascular disease (including the diagnosis of diabetic foot disease). A subject was classified as having hypertension or dyslipidaemia if he/she was taking medication for the given condition. Information about medication was obtained after a careful review of the clinical records. Anthropometric measures, including waist circumference, height, body weight, and sitting blood pressure, were obtained by standardized methods, and information about smoking habits and alcohol consumption was collected as previously described [20,21]. Blood samples were collected from subjects for all analyses in a fasting state. Standard laboratory methods were used to determine blood tests of glucose, HbA1c, creatinine, lipid profile, blood profile and ALT and urine tests (the latter only in diabetic subjects) [21]. Blood samples for sCD36 measurements were processed immediately after extraction and stored at −80 ◦ C at institutional biobanks until analysed. Protocols for flow cytometry assays and real-time PCR started immediately after collection. Local ethics committees of University Hospitals Arnau de Vilanova (PI-13-095 and 12/2009), Germans Trias i Pujol (PI-15-147) and Primary Health Care University Research Institute (IDIAP) Jordi Gol (P12/043) approved the study, following the Declaration of Helsinki. Before inclusion, all participants provided written informed consent. 2.2. Carotid Ultrasound Imaging Carotid B-mode ultrasound imaging was performed using a Sequoia 512 (Siemens, North Rhine, Westphalia, Germany) or LOGIQ E9 (General Electric, Wauwatosa, WI, USA) with 15-MHz linear-array probes. The presence of atherosclerotic plaques was identified using B-mode and colour Doppler examinations. The presence of atherosclerotic plaques at different carotid artery sites was defined as a cIMT ≥ 1.5 mm protruding into the lumen, according to the criterion given in the ASE Consensus Statement [23] and the Mannheim cIMT Consensus [24]. This criterion was used in two relevant studies showing the predictive value of atherosclerotic plaques for cardiovascular risk, the Atherosclerosis Risk in Communities study (ARIC) [25] and the Framingham Offspring Study [26]. Plaques were also classified as echolucent (lipid- and haemorrhage-rich plaques) or echogenic/mixed (fibrotic or fibro-fatty plaque and mixed when patients had both echolucent and echo-rich plaques), depending on plaque echogenicity on ultrasound examination, as previously described [27]. All ultrasound procedures were performed by experienced researchers following a standardized protocol. 2.3. Determination of sCD36 by ELISA To assess the concentration of plasma sCD36, we used a commercial ELISA kit (Nordic BioSite, Täby, Sweden) following the instructions of the manufacturer. In brief, standards and samples, in appropriate dilutions, were incubated in duplicate for 2 h on the plate shaker at room temperature J. Clin. Med. 2020, 9, 1700 4 of 12 (RT). After washing four times, a detection antibody was added and incubated for 2 h on the plate shaker at RT. After four washes, a solution of streptavidin conjugated with horseradish peroxidase was incubated for 50 min again on the plate shaker protected from light at RT. After washing four times, a 50 µL substrate solution (TMB (tetramethylbenzidine)) was added, and this solution was incubated and protected from light for 20 min at RT. Then, 50 µL of stop solution was used to stop the colour development. Absorbance was read at 450 nm using SpectraMax 340PC384 (Molecular Devices, LLC, Sunnyvale, CA, USA). A log-log curve fit was used to analyse the results. The calibration was performed with recombinant human CD36 in a concentration range of 1.95–250 ng/mL. A value of 0.05 ng/mL was assigned to values of sCD36 lower than the detection limit (nondiabetic group, n = 64/522; T1D, n = 41/225 and T2D, n = 24/276). The intra-assay and inter-assay precision coefficients provided by the ELISA manufacturer were 4–6% and 8–12%, respectively. 2.4. Flow-Cytometric Analysis The flow-cytometric analysis was designed to determine if there were differences in the expression of the surface receptor CD36 in circulating mononuclear cells between subjects with and without atherosclerotic plaques. This analysis included 50 subjects, 22 patients with and 28 without carotid atherosclerotic plaques. Blood was incubated with ammonium chloride (BD Pharm Lyse™, San Jose, CA, USA) for 10 min to lyse erythrocytes. The cells were washed with PBS and then incubated with monoclonal antibodies against CD36 (Miltenyi Biotec, Bergisch Gladbach, Germany), CD3 and CD14 (BD Biosciences, San Jose, CA, USA). Flow-cytometric analysis was performed on a Fortessa SORP flow cytometer (BD Biosciences, San Jose, CA, USA) using the sample acquisition and analysis software FACSDiva v6.2 (BD Biosciences, San Jose, CA, USA). 2.5. Real-Time PCR In the 50 patients mentioned above, an analysis of CD36 mRNA expression was carried out to find differences between subjects with and without atherosclerotic plaque. After lysing, the erythrocytes with ammonium chloride (BD Pharm Lyse™, San Jose, CA, USA), the cells were washed with PBS and disrupted with QUIzol Lysis Reagent (Qiagen, Hilden, Germany). The mRNA Mini Kit (Qiagen, Hilden, Germany) was used to extract total mRNA. Total RNA (1 µg) was reverse-transcribed using the Transcriptor First Strand cDNA Synthesis Kit (Roche, Basel, Switzerland). Each reaction was then amplified in a LightCycler® 480 PCR system using SYBR Green I Master (Roche, Basel, Switzerland). The CD36 primer pairs used in the reaction were forward primer 50 –›30 (GAGAACTGTTATGGGGCTAT) and reverse primer 50 –›30 (TTCAACTGGAGAGGCAAAGG). The expression level of glyceraldehyde 3-phosphate dehydrogenase (GAPDH) was used to normalise gene expression values before analysing the results. 2.6. Statistical Analyses The R statistical software, version 3.3.1, and SPSS software (version 22, IBM, SPSS, Chicago, IL, USA) was used for all handling of data, statistical analysis and figure construction. The results of the quantitative measurements are expressed as mean (standard deviation) or median (interquartile range), while for qualitative variables, absolute and relative frequencies are used. Analysis of variance (ANOVA), the Mann–Whitney test, or the Kruskal–Wallis test was used to determine the differences between patients with and without carotid atherosclerotic plaque in the T1D, T2D and control groups. The chi-squared test or Fisher’s exact test was used to evaluate the differences in qualitative variables. Tukey’s correction and Spearman’s rank correlation coefficient were used to account for multiple tests and correlations, respectively. A logistic regression model was used to determine the associations of variables with the presence of atherosclerotic plaque in every study group. In all these models, the variables of the bivariate analysis with a p-value < 0.1 and clinical relevance were used. The Hosmer–Lemeshow test evaluated in all models tested the goodness-of-fit assumption. Receiver operating characteristic (ROC) curves and DeLong’s test for correlated ROC J. Clin. Med. 2020, 9, 1700 5 of 12 curves were used to check the logistic regression models. Furthermore, multinomial logistic regression models were performed to determine the variables associated with the burden of carotid atherosclerotic plaque, one plaque or multiple plaques; patients without plaque were used as the reference. A p value < 0.05 was established as statistically significant. 3. Results A total of 1023 individuals, 376 (36.75%) with and 647 (63.25%) without a carotid atherosclerotic plaque, were included in the study. In the T1D group (n = 225), 33.8% had atherosclerotic plaques; of 276 subjects with type 2 diabetes, 59.4% had plaques, and 26.1% of nondiabetic subjects (n = 522) had plaques. In the overall study population, the mean age was 51 ± 12.5 years, and up to 45.4% were men. In T1D, patients with at least one atherosclerotic plaque were older and had a higher proportion of tobacco exposure, hypertension, dyslipidaemia and antiplatelet treatment. Moreover, they had higher BMI (body mass index), SBP (systolic blood pressure) and ALT (alanine aminotransferase) than those without plaque. On the other hand, in the T2D group, patients with at least one atherosclerotic plaque were older and had a higher proportion of alcohol consumption, tobacco exposure and hypertension. Further, they had increased SBP and mean platelet volume (MPV) (Table 1). Finally, nondiabetic subjects had different values in all variables except tobacco exposure, platelets, lymphocytes and MPV (Table S1). Table 1. Clinical and anthropometrical characteristics of the type 1 and type 2 diabetes groups by the presence or absence of atherosclerotic plaques. Type 1 Diabetes Type 2 Diabetes No Plaque Plaque p No Plaque Plaque p n 149 76 112 164 Sex, men 69 (46.3%) 40 (52.6%) 0.449 53 (47.3%) 90 (54.9%) 0.267 Age, years 41.0 [36.0;47.0] 51.0 [45.8;59.2] <0.001 56.5 [48.0;64.0] 61.0 [54.0;68.0] <0.001 BMI, Kg/m2 24.7 [22.1;27.9] 26.1 [23.9;28.1] 0.014 30.4 [28.3;34.7] 30.0 [27.3;34.3] 0.143 Obesity 21 (14.3%) 15 (19.7%) 0.392 63 (56.8%) 83 (50.6%) 0.379 Alcohol, g/day 2.25 [0.00;7.25] 2.65 [0.02;6.98] 0.749 0.56 [0.00;8.27] 3.03 [0.00;11.2] 0.029 Tobacco 67 (45.0%) 49 (64.5%) 0.009 48 (42.9%) 101 (61.6%) 0.003 sBP, mmHg 124 (15.6) 135 (17.0) <0.001 136 (17.7) 142 (19.4) 0.006 dBP, mmHg 74.3 (9.32) 75.0 (10.6) 0.611 77.8 (10.3) 75.9 (10.7) 0.153 Hypertension 17 (11.4%) 38 (50.0%) <0.001 48 (42.9%) 105 (64.0%) 0.001 Dyslipidaemia 41 (27.5%) 55 (72.4%) <0.001 48 (42.9%) 81 (49.4%) 0.344 Statins 64 (43.0%) 32 (42.1%) 1.000 40 (35.7%) 76 (46.3%) 0.103 Antiplatelet 28 (18.8%) 35 (46.1%) <0.001 35 (31.2%) 64 (39.0%) 0.232 Glucose, mg/dL 162 [118;206] 146 [98.0;209] 0.308 147 [116;198] 148 [119;175] 0.576 Creatinine, mg/dL 0.76 (0.15) 0.79 (0.16) 0.262 0.77 [0.68;0.90] 0.80 [0.69;0.94] 0.353 ALT, mg/dL 16.0 [12.0;20.0] 18.0 [14.0;25.5] 0.005 19.0 [16.0;30.0] 20.0 [16.0;27.0] 0.703 Triglycerides, mg/dL 64.0 [49.0;85.0] 69.5 [55.0;92.0] 0.086 120 [84.5;175] 114 [84.8;157] 0.254 T Cholesterol, mg/dL 184 (28.2) 178 (28.6) 0.191 186 (38.2) 186 (35.6) 0.979 HDL, mg/dL 64.0 [54.2;75.0] 58.5 [52.8;71.2] 0.120 46.0 [39.0;57.0] 49.0 [42.5;60.0] 0.051 LDL, mg/dL 103 [87.0;119] 98.0 [83.0;111] 0.205 109 [88.5;131] 108 [88.4;126] 0.613 HbA1c, % 7.30 [6.90;7.80] 7.60 [7.00;8.20] 0.122 7.70 [6.85;8.70] 7.50 [6.80;8.30] 0.321 Haemoglobin, g/dL 14.0 (1.21) 14.0 (1.45) 0.900 13.8 (1.45) 13.7 (1.30) 0.695 Haematocrit, % 41.5 (3.31) 41.5 (3.89) 0.910 41.2 (3.65) 41.1 (3.47) 0.857 Platelets, ×109 /L 225 [200;255] 221 [189;259] 0.513 220 [190;267] 227 [186;278] 0.611 Lymphocytes, ×109 /L 1.90 [1.50;2.31] 1.83 [1.44;2.21] 0.375 1.99 [1.67;2.46] 2.06 [1.62;2.52] 0.562 MPV, fL 9.70 [9.00;10.5] 9.75 [9.17;10.3] 0.841 9.20 [8.75;9.70] 9.40 [9.05;9.90] 0.020 IMT 0.63 [0.57;0.70] 0.63 [0.57;0.69] 0.670 0.73 [0.67;0.81] 0.84 [0.73;0.92] <0.001 Mean (SD) or median [Interquartile range are used for continuous variables]; frequency (percentage) is used for categorical variables. Soluble CD36 in quartiles was Ln transformed. 3.1. Circulating Soluble CD36 in the Study Groups The median plasma concentration of sCD36 was different between patients with and without atherosclerotic plaques in the whole population (2.58 ng/mL vs. 3.17 ng/mL; p = 0.009). In head-to-head J. Clin. Med. 2020, 9, 1700 6 of 12 J. Clin. Med. 2020, 9, x FOR PEER REVIEW 6 of 13 comparisons, the concentration of sCD36 was different between nondiabetic subjects with and without atherosclerotic plaques (2.02 ng/mL vs. 3.04 ng/mL; p = 0.023). Conversely, there was no difference difference between T1D patients with and without plaques (3.65 ng/mL vs. 3.66 ng/mL; p = 0.287) or between T1D patients with and without plaques (3.65 ng/mL vs. 3.66 ng/mL; p = 0.287) or between T2D between T2D patients with and without atherosclerotic plaques (2.66 ng/mL vs. 2.57 ng/mL; p = 0.513) patients with and without atherosclerotic plaques (2.66 ng/mL vs. 2.57 ng/mL; p = 0.513) (Figure 1). (Figure 1). Furthermore, according to the atherosclerotic plaque burden, sCD36 concentration was Furthermore, according to the atherosclerotic plaque burden, sCD36 concentration was different different between T2D patients with one plaque vs. multiple plaques (3.44 ng/mL vs. 1.70 ng/mL; p = between T2D patients with one plaque vs. multiple plaques (3.44 ng/mL vs. 1.70 ng/mL; p = 0.043). 0.043). Although in the T2D and nondiabetic groups, sCD36 concentrations were higher in echolucent Although in the T2D and nondiabetic groups, sCD36 concentrations were higher in echolucent compared to echogenic/mixed plaques, no significant differences were found. In T1D, compared to echogenic/mixed plaques, no significant differences were found. In T1D, echogenic/mixed echogenic/mixed plaques showed non-significantly higher levels of sCD36 (Table S2). plaques showed non-significantly higher levels of sCD36 (Table S2). Figure 1. Concentrations of sCD36 according to the presence of atherosclerotic plaque by group. Figure 1. Concentrations of sCD36 according to the presence of atherosclerotic plaque by group. Soluble CD36 in quartiles was Ln transformed. Soluble CD36 in quartiles was Ln transformed. 3.2. Soluble CD36 as a Marker of Subclinical Carotid Atherosclerosisf 3.2. Soluble CD36 as a Marker of Subclinical Carotid Atherosclerosisf In the logistic regression model of the whole group of study participants, the variables In the logistic independently regression associated with the model presenceof the whole group plaque of atherosclerotic of study participants, were older age (OR the variables = 1.084, p independently < 0.001), higher associated systolic with bloodthe presence pressure (OR of atherosclerotic = 1.023, p < 0.001), plaque were older tobacco age (OR exposure (OR= 1.084, = 2.979, p< 0.001), higher systolic blood pressure (OR = 1.023, p < 0.001), tobacco p < 0.001), higher BMI (OR = 1.043, p = 0.015), T1D (OR = 2.778, p < 0.001) and T2D (OR = 2.034, exposure (OR = 2.979, p < 0.001), p higher = 0.011) BMI (ORS3). (Table = 1.043, In thep =nondiabetic 0.015), T1Dcontrol (OR = 2.778, group, p