| TYPE Original Research PUBLISHED 22 November 2022 DOI 10.3389/fped.2022.887771 Different profiles of lipoprotein particles associate various EDITED BY Dimpna Calila Albert-Brotons, King Faisal Specialist Hospital & Research degrees of cardiac involvement Centre, Saudi Arabia REVIEWED BY in adolescents with Hayato Tada, Kanazawa University, Japan morbid obesity Stefano Cacciatore, International Centre for Genetic Engineering and Biotechnology (ICGEB), South Africa José M. Siurana1,2*†, Anna Sabaté-Rotés2,3†, Núria Amigó4,5,6, *CORRESPONDENCE Neus Martínez-Micaelo4,5, Larry Arciniegas7, Lucia Riaza8, José M. Siurana Eduard Mogas7, Ferran Rosés-Noguer2,3, Paula S. Ventura9,10‡ jmsiurana@hotmail.com † and Diego Yeste2,7,11‡ These authors have contributed equally to this 1 work and share first authorship Department of Pediatric Cardiology, Hospital HM Nens, HM Hospitales, Barcelona, Spain, 2 Autonomous University of Barcelona, Barcelona, Spain, 3Department of Pediatric Cardiology, Vall ‡ These authors have contributed equally to this d’Hebron University Hospital, Barcelona, Spain, 4Biosfer Teslab, Reus, Spain, 5Department of Basic work and share senior authorship Medical Sciences, Universitat Rovira I Virgili, Institut D’Investigació Sanitària Pere Virgili (IISPV), Tarragona, Spain, 6Centro de Investigación Biomédica en Red de Diabetes y Enfermedades SPECIALTY SECTION Metabólicas Asociadas (CIBERDEM), Instituto de Salud Carlos III (ISCIII), Madrid, Spain, 7Department This article was submitted to Pediatric of Pediatric Endocrinology, Vall d’Hebron University Hospital, Barcelona, Spain, 8Department of Cardiology, a section of the journal Frontiers in Pediatric Radiology, Vall d’Hebron University Hospital, Barcelona, Spain, 9Department of Pediatric Pediatrics Endocrinology, Hospital HM Nens, HM Hospitales, Barcelona, Spain, 10Fundació Institut d’Investigació RECEIVED 02 March 2022 en Ciències de la Salut Germans Trias i Pujol (IGTP), Badalona, Spain, 11Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Instituto de Salud Carlos III (ISCIII), Madrid, ACCEPTED 02 November 2022 Spain PUBLISHED 22 November 2022 CITATION Siurana JM, Sabaté-Rotés A, Amigó N, Introduction: Dyslipidemia secondary to obesity is a risk factor related to Martínez-Micaelo N, Arciniegas L, Riaza L, cardiovascular disease events, however a pathological conventional lipid Mogas E, Rosés-Noguer F, Ventura PS and profile (CLP) is infrequently found in obese children. The objective is to Yeste D (2022) Different profiles of lipoprotein evaluate the advanced lipoprotein testing (ALT) and its relationship with particles associate various degrees of cardiac involvement in adolescents with morbid cardiac changes, metabolic syndrome (MS) and inflammatory markers in a obesity. population of morbidly obese adolescents with normal CLP and without type Front. Pediatr. 10:887771. 2 diabetes mellitus, the most common scenario in obese adolescents. doi: 10.3389/fped.2022.887771 Methods: Prospective case-control research of 42 morbidly obese adolescents COPYRIGHT and 25 normal-weight adolescents, whose left ventricle (LV) morphology and © 2022 Siurana, Sabaté-Rotés, Amigó, function had been assessed. The ALT was obtained by proton nuclear Martínez-Micaelo, Arciniegas, Riaza, Mogas, Rosés-Noguer, Ventura and Yeste. This is an magnetic resonance spectroscopy, and the results were compared according open-access article distributed under the terms to the degree of cardiac involvement – normal heart, mild LV changes, and of the Creative Commons Attribution License severe LV changes (specifically LV remodeling and systolic dysfunction) – (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original and related to inflammation markers [highly-sensitive C-reactive protein and author(s) and the copyright owner(s) are glycoprotein A (GlycA)] and insulin-resistance [homeostatic model credited and that the original publication in this assessment for insulin-resistance (HOMA-IR)]. A second analysis was journal is cited, in accordance with accepted academic practice. No use, distribution or performed to compare our results with the predominant ALT when only reproduction is permitted which does not body mass index and metabolic syndrome criteria were considered. comply with these terms. Results: The three cardiac involvement groups showed significant increases in HOMA-IR, inflammatory markers and ALT ratio LDL-P/HDL-P (40.0 vs. 43.9 vs. 47.1, p 0.012). When only cardiac change groups were considered, differences in small LDL-P (565.0 vs. 625.1 nmol/L, p 0.070), VLDL size and GlycA demonstrated better utility than just traditional risk factors to predict which subjects could present severe LV changes [AUC: 0.79 (95% CI: 0.54–1)]. In Frontiers in Pediatrics 01 frontiersin.org Siurana et al. 10.3389/fped.2022.887771 the second analysis, an atherosclerotic ALT was detected in morbidly obese subjects, characterized by a significant increase in large VLDL-P, small LDL-P, ratio LDL-P/ HDL-P and ratio HDL-TG/HDL-C. Subjects with criteria for MS presented overall worse ALT (specially in triglyceride-enriched particles) and remnant cholesterol values. Conclusions: ALT parameters and GlycA appear to be more reliable indicators of cardiac change severity than traditional CV risk factors. Particularly, the overage of LDL-P compared to HDL-P and the increase in small LDL-P with cholesterol-depleted LDL particles appear to be the key ALT’s parameters involved in LV changes. Morbidly obese adolescents show an atherosclerotic ALT and those with MS present worse ALT values. KEYWORDS morbid obesity, adolescents, lipoprotein subclasses, atherosclerotic phenotype, metabolic syndrome, cardiac changes, systolic dysfunction, small LDL particles Introduction changes, coronary heart disease or CVD events (13–15), however to our knowledge, none have analyzed which ALT Cardiovascular diseases (CVD) are the main cause of death components are closely associated with cardiac changes in in the world (1) and, if the trend initiated in 2,000 continues, obese adolescents. Our group showed in a previous CVD will also become the main cause of death among publication that morbidly obese adolescents presented left contemporary children. Obesity is a multifactorial disease that ventricular remodeling and systo-diastolic dysfunctions, includes several preceding disorders of CVD, such as high closely related to body mass index (BMI) (16). The objective blood pressure (BP), insulin resistance, diabetes and of the current study is to evaluate the ALT and its dyslipidemia. However, the majority of obese children present relationship with cardiac changes, metabolic syndrome (MS) a pre-pathological condition, therefore identifying which of and inflammatory markers in the same cohort of morbidly them are at increased risk of developing CVD is a challenge obese adolescents with normal CLP and without type 2 for pediatricians. diabetes mellitus (T2-DM), the most common scenario in In the conventional lipid profile (CLP), the increase in obese adolescents. cholesterol in low-density lipoproteins (LDL-C) has been demonstrated as one of the most important factors associated with CVD risk (2), but the role of high-density lipoprotein cholesterol (HDL-C) or triglycerides (TG) remains unclear Methods (3). Additionally, the remnant cholesterol – a derivative from Study population the CLP that accounts for the cholesterol enclosed in the very low-density lipoproteins (VLDL), intermediate-density Prospective observational case-control research of 67 lipoproteins (IDL) and chylomicrons – has been also adolescents of both genders between 10 and 17 years old. associated with increased prevalence of ischemic heart disease Subjects with morbid obesity were recruited from (4) and myocardial infarction (5). On the other hand, the endocrinology clinics and control participants were recruited advanced lipoprotein testing (ALT), assessed by proton from healthy volunteers in the cardiology and sports medicine nuclear magnetic resonance spectroscopy (1H-NMR) of clinics. plasma, provides much information about lipoprotein Subjects with current infectious or recent acute characteristics as it quantifies the concentration, particle size inflammatory processes, history of prematurity or birth weight and composition (cholesterol or TG) of each lipoprotein <2,000 g, and smokers or those whose pathologies could affect subclass. Previous studies have shown how in situations of the cardiovascular system – such as congenital heart disease, apparent normality, specific lipoprotein alterations are highly chronic kidney disease, transplant, rheumatic diseases, and associated with future CVD events (6–9). Thus, the concept HIV infection – were excluded. Similarly, obese subjects with of atherogenic dyslipidemia – applied to subjects with hypo T2-DM and control subjects who practiced >7 weekly sport HDL-C, hypertriglyceridemia and normal LDL-C, but hours, a threshold explained in our previous study (16), were increases in some ALT components – has been associated also excluded in this analysis. with an elevated risk of CVD events (10–12). Subjects were classified depending on the degree of cardiac Various studies have evaluated the ALT in obese adults and involvement and three groups were established: no cardiac adolescents and established its utility in predicting arterial wall changes (n 25), formed by control and obese adolescents Frontiers in Pediatrics 02 frontiersin.org Siurana et al. 10.3389/fped.2022.887771 without LV changes; mild cardiac changes (n 17), consisting of acetyl groups of N-acetylglucosamine and N-acetylgalactosamine 15 obese adolescents with LV remodeling and 2 control bond to plasmatic proteins. Glyc A is able to detect low-grade adolescents with border systolic dysfunction; and severe chronic inflammation in obesity and insulin resistance’s disorders cardiac changes (n 25), constituted by morbidly obese (21–23), and in atherosclerosis progression (24). adolescents with LV remodeling and systolic dysfunction, based on the threshold below defined. For the second analysis, subjects were reclassified according to their BMI, calculated from tables of the Barcelona Longitudinal Growth Study Echocardiographic image acquisition 1995–2017 (17), in two groups: subjects with morbid obesity and analysis (SDS-BMI ≥4; n 42), and with normal weight (BMI 5th–85th percentile, n 25). Finally, the last classification of subjects was Patients were examined using a Vivid S60 commercial performed on the base of MS criteria from Cook et al. (18), ultrasound scanner (GE Vingmed Ultrasound AS, Horten, identifying two groups: subjects without MS (n 53), and with Norway) with a phased-array transducer (GE 3-MHz; GE MS (n 14). Vingmed Ultrasound AS). Images were obtained at rest in the Written informed consent for participation was obtained supine or left lateral decubitus position in the standard and the Institutional Review Board at Vall d’Hebron Hospital tomographic views of the LV (parasternal long and short axis approved the protocol (PR-AMI-273/2018). All subjects and apical 4-chamber, 2-chamber, and long-axis views). All provided assent and an informed consent was signed by their echocardiographic images were obtained prospectively by an parents/legal guardians. experienced pediatric cardiologist, according to the criteria of the American Society of Echocardiography (ASE) (25). To evaluate the LV geometry, relative wall thickness (RWT) Clinical and laboratory assessment and LV mass were calculated using LV linear dimensions and following the recommendation of ASE (25). The LV mass was Demographic data of age, gender and clinical status were determined by the adjusted Devereux’s equation and the obtained from patient anamnesis. Blood pressure was resultant value was indexed to height to the power of 2.7 obtained using a Welch Allyn Spot Vital Signs Monitor (LVMI, g/m2.7). LV geometry was categorized as normal or (4200B, Hillrom, Batesville, Indiana) after subjects had pathological (concentric remodeling, eccentric hypertrophy and rested for 5 min, in a supine position and with an concentric hypertrophy) considering the following cutoff values appropriately sized cuff giving measured mid-arm (95th percentile in the pediatric population): LVMI >45 g/m2.7 circumference, according to the criteria of the European in males and >40 g/m2.7 in females (26), and RWT >0.41 (27). Society of Hypertension (19), which also defines the high The LV function was determined by two-dimensional speckle BP values by age and gender. tracking echocardiography, a well-validated and precise method to EDTA blood samples were obtained at the time of quantify ventricular function with lower variability than LV enrollment at the participating hospitals in the morning ejection fraction in pediatric patients (28). Strain and strain rate after at least 8 h of fasting. Samples for lipoprotein particle (SR) were calculated according to the criteria of the ASE (25) analysis were aliquoted, stored in liquid nitrogen (−80°C) and the European Association of Cardiovascular Imaging (28). and shipped on dry ice to Biosfer Teslab, where the ALT Two-dimensional video loops were obtained for each patient in was measured by 1H-NMR spectroscopy, based on the apical four, three, and two-chamber views, acquiring images at a LipoScale test® (20). For each main lipoprotein class frame rate of >65 frames/s. Offline image processing was (VLDL, LDL and HDL) we obtained large, medium and performed using EchoPAC (version 11.2, GE Vingmed small subclass particle concentrations (VLDL-P, LDL-P, Ultrasound, Horten, Norway). After manually tracing along the HDL-P), mean particle size and composition (ratio VLDL- endomyocardial border, the software automatically generated the TG/VLDL-C, ratio LDL-TG/LDL-C, ratio HDL-TG/HDL- epicardial border and the six segments, which were accepted C). Additionally, tests performed on blood samples from all after a visual inspection. To determine SR and midline strain, at subjects were: fasting glycemia and insulin, glycated least 17 out of 18 segments had to be included. Measured hemoglobin and the CLP (total cholesterol, HDL-C, LDL-C parameters were LV end-systolic global longitudinal strain (GLS, and TG). To evaluate insulin resistance, the homeostasis %) and LV early diastolic global longitudinal SR (early GLSR, 1/ model assessment of insulin resistance (HOMA-IR) was sec). Resultant values were calculated by adding the strain of all calculated using the equation: fasting insulin (µU/ml) × accepted segments and dividing the value by the total number of fasting glucose (mmol/L)/22.5. Inflammation markers segments. The GLS cutoff value which defined systolic evaluated were highly-sensitive C-reactive protein (hs-CPR) dysfunction in this study was −16.7%, which corresponds to the and an NMR derived glycoprotein biomarker, termed lowest GLS value reported in a meta-analysis of LV strain glycoprotein A (GlycA), arising from the concentration of the measures by echocardiography in children (29). Frontiers in Pediatrics 03 frontiersin.org Siurana et al. 10.3389/fped.2022.887771 TABLE 1 Clinical and laboratory characteristics of subjects depending on cardiac involvement degree groups. Variable No cardiac Mild cardiac Severe cardiac P value P value No vs disorder changes changes Mild vs Severe (N = 25) (N = 17) (N = 25) No vs No vs Mild vs Mild Severe Severe Age (years) 14 [13-15] 14 [13-16] 14 [13-15] 0.179 0.743 0.239 0.352 BMI (kg/m2) 19.5 [17.9-22.7] 35.2 [32.7-38.9] 37.8 [34.9-40.2] <0.001 <0.001 0.238 <0.001 BMI SD -0.1 [-0.9-1.1] 7.0 [5.9-8.4] 7.8 [6.4-9.0] <0.001 <0.001 0.303 <0.001 SBP (mmHg) 113 [101-119] 120 [114-128] 126 [119-141] 0.011 <0.001 0.178 <0.001 DBP (mmHg) 64 [57-69] 66 [59-76] 76 [67-81] 0.307 0.001 0.040 0.002 BP ≥p90 (%) 12 35 68 0.124 <0.001 0.059 <0.001 LABORATORY PARAMETERS Fasting glucose (mg/dL) 79 [71-84] 82 [72-90] 83 [78-88] 0.279 0.022 0.662 0.094 HbA1c (%) (N=57) 5.2 [5.1-5.5] 5.3 [5.2-5.5] 5.4 [5.2-5.6] 0.291 0.172 0.650 0.315 HOMA-IR 1.4 [1.0-2.3] 3.6 [1.8-5.0] 6.6 [3.8-8.2] 0.019 <0.001 0.013 <0.001 Classical lipid profile Total Cholesterol (mg/dL) 173 [164-188] 181 [143-201] 179 [169-198] 0.749 0.282 0.729 0.610 LDL-C (mg/dL) 103 [91-113] 98 [77-120] 106 [96-121] 0.635 0.327 0.254 0.433 HDL-C (mg/dL) 52 [45-60] 44 [41-55] 44 [41-48] 0.048 0.001 0.283 0.003 Triglycerides (mg/dL) 71 [60-92] 95 [75-118] 87 [74-140] 0.026 0.010 0.933 0.018 Remnant cholesterol (mg/dL) 18.9 [13.5-22.5] 24.2 [17.5-30.3] 22.0 [18.1-36.9] 0.037 0.019 0.977 0.032 HDL-C ≤40 mg/dL (%) 0 18 14 0.059 0.235 0.672 0.115 Triglycerides ≥150 mg/dL (%) 0 12 16 0.158 0.110 1.000 0.126 Inflammatory markers Highly sensitive C-reactive 0.02 [0.01-0.09] 0.12 [0.06-0.24] 0.24 [0.14-0.52] 0.012 <0.001 0.041 <0.001 protein (mg/dL) (N=63) Glycoprotein A (µmol/L) 618 [583-747] 707 [659-830] 845 [731-982] 0.030 <0.001 0.034 <0.001 Lipoprotein Particles VLDL-P (nmol/L) Total 30.1 [25.5-40.7] 45.0 [29.3-55.1] 37.4 [31.6-69.5] 0.041 0.007 0.691 0.016 Large 0.8 [0.7-1.2] 1.3 [0.9-1.6] 1.1 [0.9-1.6] 0.024 0.007 0.866 0.013 Medium 3.3 [2.6-5.0] 4.7 [3.6-8.0] 5.0 [2.7-6.2] 0.022 0.114 0.635 0.070 Small 26.1 [22.7-33.6] 39.3 [25.0-47.3] 30.1 [27.6-59.9] 0.046 0.005 0.596 0.014 LDL-P (nmol/L) Total 1023.6 1017.8 1123.0 0.868 0.034 0.115 0.086 [913.5-1131.3] [818.1-1182.7] [1015.8-1245.2] Large 165.0 [151.6-174.9] 163.8 [129.8-188.7] 172.3 [147.3-183.0] 0.582 0.720 0.497 0.752 Medium 300.4 [248.3-356.3] 299.0 [146.3-378.2] 335.7 [257.6-381.7] 0.635 0.290 0.244 0.402 Small 560.0 [517.1-599.2] 565.0 [522.8-639.0] 625.1 [551.7-685.3] 0.390 0.003 0.070 0.011 HDL-P (µmol/L) Total 26.5 [23.7-29.3] 24.3 [20.6-29.0] 24.2 [22.1-25.6] 0.244 0.015 0.547 0.064 Large 0.22 [0.2-0.3] 0.26 [0.2-0.3] 0.25 [0.2-0.3] 0.001 0.035 0.155 0.003 Medium 9.3 [8.6-10.4] 9.0 [8.1-10.3] 8.9 [7.9-9.3] 0.363 0.034 0.391 0.112 Small 16.8 [14.5-18.9] 15.2 [12.0-19.2] 14.8 [13.2-16.4] 0.405 0.029 0.599 0.129 Size (nm) VLDL size 42.3 [42.0-42.4] 42.4 [42.3-42.4] 42.1 [42.0-42.3] 0.337 0.218 0.037 0.106 LDL size 21.1 [20.9-21.2] 21.0 [20.7-21.2] 21.0 [20.9-21.1] 0.060 0.049 0.635 0.072 HDL size 8.3 [8.2-8.3] 8.3 [8.2-8.4] 8.3 [8.2-8.3] 0.434 0.233 0.990 0.488 Composition Ratio VLDL-TG/VLDL-C 3.70 [3.4-4.0] 3.62 [3.5-4.1] 3.9 [3.5-4.2] 0.929 0.367 0.513 0.643 Ratio IDL-TG/IDL-C 1.20 [1.1-1.4] 1.20 [1.1-1.4] 1.10 [1.1-1.2] 0.868 0.148 0.163 0.245 (continued) Frontiers in Pediatrics 04 frontiersin.org Siurana et al. 10.3389/fped.2022.887771 TABLE 1 Continued Variable No cardiac Mild cardiac Severe cardiac P value P value No vs disorder changes changes Mild vs Severe (N = 25) (N = 17) (N = 25) No vs No vs Mild vs Mild Severe Severe Ratio LDL-TG/LDL-C 0.11 [0.1-0.1] 0.12 [0.1-0.1] 0.14 [0.1-0.2] 0.660 0.006 0.044 0.017 Ratio HDL-TG/HDL-C 0.23 [0.2-0.3] 0.29 [0.2-0.4] 0.30 [0.2-0.4] 0.011 0.005 0.992 0.007 Ratio LDL-P/HDL-P 40.0 [31.7-47.6] 43.9 [34.4-45.9] 47.1 [40.7-53.1] 0.646 0.005 0.037 0.012 Non-HDL-P (nmol/L) 1034.7 1122.0 1153.1 0.929 0.013 0.141 0.053 [934.9-1148.1] [837.8-1228.5] [1042.3-1282.1] Values expressed in median and 25-75% IQR; P value determined using Kruskal-Wallis test followed by Dunn’s post hoc test for multiple comparisons; Dichotomous variables (Fisher exact test); BMI, Body Mass Index; SD, Standard deviation; SBP, Systolic blood pressure; DBP, Diastolic blood pressure; HOMA-IR, Homeostatic model assessment insulin resistance. Statistical analysis represent 90% confidence intervals around the centroid of each data cluster. Finally, we built a linear fitting model, Data from the study were analyzed using descriptive statistics. and by computing the area under the curve (AUC) and the Quantitative results were expressed as median and 25–75 95% confidence interval of a receiver operating interquartile range, while qualitative or dichotomous variables characteristics (ROC) curve we evaluated and quantified were expressed as percentages. Chi-square test (χ 2) and Fisher’s how accurately the selected variables were able to exact test were used, according to the size and characteristic of discriminate between groups. Patients were randomly qualitative variables, to compare proportions and to study assigned to training (60%) and test (40%) sets. We relationships between them. The comparison between performed 10-fold cross-validation with 100 replicates on quantitative variables was made by nonparametric Mann– the training data during the model construction process and Whitney U test or nonparametric Kruskal–Wallis test followed tested the model on the hold-out data. Analysis was by Dunn’s post hoc test for multiple comparisons. To estimate performed using the R statistical software version 4.1.1 correlations between parameters, the Pearson and Spearman’s (Chapman & Hall/CRC Computational Biology Series). Rho correlation coefficients were calculated as appropriate for Additionally, a logistic regression was performed to model the type of the data. Data was analyzed by using IBM SPSS the probability of dichotomous events (mild or severe Statistics for Windows, version 23.0 (IBM Corp., Armonk, cardiac changes, and no changes or severe cardiac changes) N.Y.) and values of p < 0.05 were considered significant. with the selected variables in the previous statistical analysis. A three-step multivariate analysis was applied to identify important variables and patterns that allowed distinguishing between the three cardiac involvement groups. In the first Results step, we applied three statistical approaches to identify the variables that make the largest contributions to the A total of 42 adolescents with obesity and 25 adolescents discrimination between groups. These approaches include without obesity, age-matched, were included in our study. No the Wilcoxon rank-sum test, the Random Forest, and the subject was excluded, although HbA1c was not registered in Partial Least Squares discriminant analysis (PLS-DA). Those ten subjects of the obese group and hs-CPR values were variables that resulted significant with a p-value <0.05 were missed in one control and three obese subjects. Main clinical, selected as the candidate for the Wilcoxon rank-sum test, laboratory and echocardiographic data from the three cardiac and the 5–10 most important variables were determined by groups are summarized in Table 1. No significant differences the variable importance score or the variable importance in were noted in age, but most obese subjects were classified in projection (VIP) score using the Random Forest or the the mild or severe cardiac change groups. However, the PLSA-DA, respectively. To avoid overfitting, 10-fold cross- significant increase observed in BP, HOMA-IR and validation was performed. In the second step, by using a inflammation markers among the three groups suggests a Venn diagram we selected the most prominent variables relationship between the severity of cardiac changes and the and those that will be included in the model by determining worsening in CV risk factors, except in the conventional lipid those that overlap by the statistical approaches. In the third profile, where no differences were found among both groups step, we used the Principal Component Analysis (PCA) as with cardiac changes. In contrast, significant differences were an unsupervised method to visualize the capacity of the detected in the ALT (Table 1), and a pathological phenotype selected variables to drive group separation. Ellipses in PCA related to cardiac changes was defined (Figure 1): increases in Frontiers in Pediatrics 05 frontiersin.org Siurana et al. 10.3389/fped.2022.887771 concentrations of total VLDL-P, large VLDL-P, total LDL-P, 23.9 mg/dl, p 0.010) (Figure 1). Finally, data of subjects small LDL-P, ratio LDL-P/HDL-P, and ratio HDL-TG/ categorized by MS criteria are summarized in Table 3. HDL-C, and decreases in concentrations of total HDL-P and Obese adolescents who accomplished MS criteria showed medium HDL-P, and in the LDL size. overall worse BP, insulin resistance and hs-CRP values than Data of LV geometry and speckle tracking determinations any of the previous groups. Their CLP was characterized from the echocardiographic evaluation are summarized in by marked differences in triglycerides (78 vs 124 mg/dL, Supplementary Table S1. p < 0.001) and remnant cholesterol (19.6 vs 30.3 mg/dL, Table 2 shows the clinical characteristics, laboratory p < 0.001), while the ALT was characterized by significant parameters and ALT of subjects classified by obesity index. increases in: total VLDL-P, large VLDL-P, ratio LDL-P/ The obese subjects exhibited, in addition to the pathological HDL-P and ratio HDL-TG/HDL-C (Figure 1). The ALT phenotype observed in subjects with cardiac changes, a resultant ALT did not differ when only obese subjects were mild, though significant, difference in the concentration of categorized by MS criteria, remarking the central role of HDL-C (52 vs. 44 mg/dl, p < 0.001), triglycerides (77 vs. hypertriglyceridemia in subjects with MS (Supplemental 91 mg/dl, p < 0.001) and remnant cholesterol (19.1 vs. Table S2). FIGURE 1 The lipid contour is a graphical model to facilitate the lipoprotein profile interpretation and its association with cardiovascular risk. The colored silhouettes represent the patient groups’ values with respect to the values of an apparently healthy population (black circle) (30). The study group’s contour delimits a smaller central area when the variables have values associated with an increased risk of developing CVD (i.e., values higher than the reference population’s mean for L-VLDL-P, VLDL-P, Rem-C, S-LDL-P, LDL-P, HDL-TG/HDL-C variables; or lower than reference population’s mean for LDL-Ø, HDL-P and M-HDL-P variables). Colored silhouettes are represented in percentage of increase or reduction regarding data in Tables 1–3. Bottom of the figure: VLDL-P, total VLDL particles; L-VLDL-P, large VLDL particles; LDL-P, total LDL particles; S- LDL-P, small LDL particles; HDL-P, total HDL particles; M-HDL-P, medium HDL particles; LDL-Ø, LDL particles diameter; HDL-TG/HDL-C, triglyceride enriched HDL; Rem C, remnant cholesterol. Frontiers in Pediatrics 06 frontiersin.org Siurana et al. 10.3389/fped.2022.887771 TABLE 2 Clinical and laboratory characteristics of subjects depending on body mass index groups. Variable Non-obese (N = 25) Obese (N = 42) p value Age (years) 14 [13-15] 14 [13-15] 0.726 Male 52% (N=13) 33% (N=14) 0.198 BMI (kg/m2) 19.4 [17.9-22.1] 36.8 [34.3-39.8] <0.001 BMI SD -0.2 [-0.9-0.7] 7.5 [6.4-8.5] <0.001 Waist circumference (cm) 69 [65-74] 111 [102-120] <0.001 SBP (mmHg) 108 [101-118] 124 [118-134] <0.001 DBP (mmHg) 60 [60-68] 74 [65-78] <0.001 LABORATORY PARAMETERS Fasting glucose (mg/dL) 76 [71-84] 83 [75-89] 0.012 HbA1c (%) (N = 57) 5.2 [5.1-5.5] 5.3 [5.2-5.5] 0.218 HOMA-IR 1.2 [1.0-1.7] 5 [3.6-7.9] <0.001 Classical lipid profile Total Cholesterol (mg/dL) 180 [164-191] 178 [156-197] 0.613 LDL-C (mg/dL) 102 [90-115] 105 [91-118] 0.604 HDL-C (mg/dL) 52 [48-61] 44 [41-49] <0.001 Triglycerides (mg/dL) 77 [60-90] 91 [72-130] 0.006 Remnant cholesterol (mg/dL) 19.1 [14.1-22.5] 23.9 [18.0-36.3] 0.010 Inflammatory markers Highly-sensitive C-reactive protein (mg/dL) 0.02 [0.01-0.05] 0.22 [0.13-0.45] <0.001 Glycoprotein A (µmol/L) 620 [583-687] 802 [705-938] <0.001 LIPOPROTEIN PARTICLES VLDL-P (nmol/L) Total 30.0 [25.5-40.7] 42.8 [31.5-63.7] 0.002 Large 0.8 [0.7-1.2] 1.3 [0.9-1.6] 0.001 Medium 3.7 [2.8-5.4] 4.6 [3.0-6.5] 0.072 Small 26.1 [22.4-33.6] 37.3 [27.3-55.6] 0.002 LDL-P (nmol/L) Total 1023.6 [904.7-1136.9] 1096.5 [977.4-1196.7] 0.133 Large 165.1 [150.6-183.3] 164.4 [145.2-182.5] 0.726 Medium 300.4 [225.2-356.3] 332.1 [249.9-384.6] 0.371 Small 560.0 [517.1-607.0] 616.2 [527.3-676.3] 0.019 HDL-P (µmol/L) Total 27.0 [24.2-30.1] 24.2 [21.7-26.4] 0.001 Large 0.2 [0.2-0.3] 0.2 [0.2-0.3] 0.010 Medium 9.6 [8.7-10.9] 8.9 [7.9-9.4] 0.018 Small 17.3 [14.8-19.2] 17.8 [12.8-17.0] 0.005 Size (nm) VLDL size 42.3 [42.2-42.4] 42.2 [42.0-42.4] 0.371 LDL size 21.1 [21.0-21.2] 21.0 [20.9-21.1] 0.058 HDL size 8.3 [8.2-8.3] 8.3 [8.2-8.3] 0.123 Composition Ratio VLDL-TG/VLDL-C 3.7 [3.4-3.9] 3.9 [3.5-4.2] 0.249 Ratio IDL-TG/IDL-C 1.2 [1.1-1.4] 1.1 [1.1-1.3] 0.042 Ratio LDL-TG/LDL-C 0.1 [0.1-0.1] 0.1 [0.1-0.2] 0.023 Ratio HDL-TG/HDL-C 0.2 [0.2-0.3] 0.3 [0.2-0.4] 0.002 Ratio LDL-P/HDL-P 37.1 [31.7-45.2] 46.2 [38.9-51.8] 0.001 Non-HDL-P (nmol/L) 1034.7 [915.5-1163.1] 1127.8 [1002.7-1245.4] 0.068 Values expressed in median and 25-75% IQR; P value calculated by non-parametric Mann-Whitney U test; Dichotomous variables (Fisher exact test); BMI, Body Mass Index; SD, Standard deviation; SBP, Systolic blood pressure; DBP, Diastolic blood pressure; HOMA-IR, Homeostatic model assessment insulin resistance. Frontiers in Pediatrics 07 frontiersin.org Siurana et al. 10.3389/fped.2022.887771 TABLE 3 Clinical and laboratory characteristics of subjects depending on metabolic syndrome (MS) diagnosis. Variable No MS (<3 factors) (N = 53) MS (≥3 factors) (N = 14) p value Age (years) 14 [13-15] 14 [13-15] 0.825 2 BMI (kg/m ) 32.1 [19.4-37.0] 38.1 [35.9-43.0] <0.001 BMI SD 5.7 [-0.1-7.5] 7.9 [6.5-10.3] 0.001 SBP (mmHg) 118 [106-122] 131 [126-144] <0.001 DBP (mmHg) 67 [59-74] 77 [69-80] 0.002 LABORATORY PARAMETERS Fasting glucose (mg/dL) 79 [72-86] 84 [82-89] 0.024 HbA1c (%) (N = 57) 5.3 [5.1-5.5] 5.4 [5.1-5.6] 0.515 HOMA-IR 2.3 [1.3-4.7] 7.4 [4.2-8.9] <0.001 Classical lipid profile Total Cholesterol (mg/dL) 176 [164-193] 188 [164-207] 0.185 LDL-C (mg/dL) 103 [90-117] 105 [95-121] 0.633 HDL-C (mg/dL) 49 [43-56] 42 [36-47] 0.003 Triglycerides (mg/dL) 78 [64-99] 124 [103-163] <0.001 Remnant cholesterol (mg/dL) 19.6 [14.6-24.1] 30.3 [25.2-44.4] <0.001 Inflammatory markers Highly sensitive C-reactive protein (mg/dL) 0.09 [0.02-0.21] 0.37 [0.15-0.50] 0.001 Glycoprotein A (µmol/L) 701 [616-787] 868 [777-999] 0.001 LIPOPROTEIN PARTICLES VLDL-P (nmol/L) Total 31.7 [26.9-45.0] 59.6 [47.6-82.5] <0.001 Large 0.9 [0.7-1.3] 1.6 [1.3-1.8] <0.001 Medium 3.7 [2.6-5.4] 6.1 [4.5-9.4] <0.001 Small 28.3 [23.3-38.6] 52.2 [40.1-71.4] <0.001 LDL-P (nmol/L) Total 1033.2 [913.5-1175.2] 1092.5 [998.0-1256.9] 0.235 Large 165.1 [146.0-183.3] 161.1 [147.8-180.5] 0.841 Medium 311.5 [232.6-368.0] 347.7 [256.5-377.1] 0.600 Small 575.8 [529.4-627.0] 644.2 [518.6-714.6] 0.112 HDL-P (µmol/L) Total 25.5 [23.0-28.7] 23.5 [20.3-26.0] 0.099 Large 0.2 [0.2-0.3] 0.3 [0.2-0.3] 0.158 Medium 9.1 [8.4-9.9] 8.3 [7.5-9.5] 0.079 Small 16.2 [13.9-18.7] 14.6 [12.3-16.5] 0.096 Size (nm) VLDL size 42.3 [42.0-42.4] 42.2 [42.1-42.4] 0.877 LDL size 21.0 [20.9-21.2] 20.9 [20.8-21.0] 0.112 HDL size 8.3 [8.2-8.3] 8.3 [8.3-8.3] 0.316 Composition Ratio VLDL-TG/VLDL-C 3.7 [3.5-4.1] 4.0 [3.5-4.2] 0.267 Ratio IDL-TG/IDL-C 1.2 [1.1-1.4] 1.1 [1.1-1.2] 0.064 Ratio LDL-TG/LDL-C 0.1 [0.1-0.1] 0.1 [0.1-0.2] 0.007 Ratio HDL-TG/HDL-C 0.2 [0.2-0.3] 0.4 [0.3-0.5] <0.001 Ratio LDL-P/HDL-P 42.1 [33.6-47.3] 49.5 [43.3-52.8] 0.006 Non-HDL-P (nmol/L) 1066.3 [934.9-1194.3] 1127.8 [1027.1-1317.5] 0.087 Values expressed in median and 25-75% IQR; P value calculated by non-parametric Mann-Whitney U test; Dichotomous variables (Fisher exact test); BMI, Body Mass Index; SD, Standard deviation; SBP, Systolic blood pressure; DBP, Diastolic blood pressure; HOMA-IR, Homeostatic model assessment insulin resistance. Frontiers in Pediatrics 08 frontiersin.org Siurana et al. 10.3389/fped.2022.887771 TABLE 4 Bivariate correlations between lipoprotein particles and left ventricle structural and functional parameters, insulin resistance index, inflammatory marker and triglycerides. Remodeling Systolic GLS Early GLSR HOMA hs-CRP TG VLDL-P (nmol/L) Total 0.37* 0.30† −0.28† 0.52* 0.23 0.96* Large 0.39* 0.32* −0.31* 0.53* 0.25† 0.91* Medium 0.30† 0.12 −0.12 0.35† 0.08 0.91* † Small 0.38* 0.33* −0.30 0.53* 0.25† 0.95* LDL-P (nmol/L) Total 0.18 0.16 −0.11 0.28† 0.21 0.33* Large −0.05 −0.08 0.13 0.06 −0.04 0.28† Medium 0.07 0.05 −0.03 0.22 0.14 0.35* † † † Small 0.30 0.32* −0.25 0.30 0.34* 0.21 HDL-P (µmol/L) Total −0.24† −0.42* 0.44* −0.33* −0.51* 0.14 Large 0.40* 0.11 −0.23 0.25† 0.17 0.49* Medium −0.19 −0.36* 0.39* −0.23 −0.39* 0.06 Small −0.20 −0.36* 0.37* −0.30† −0.47* 0.13 Size (nm) VLDL size 0.06 −0.19 0.16 −0.09 −0.21 0.29† LDL size −0.25† −0.25† 0.23 −0.07 −0.25† 0.17 HDL size 0.13 0.15 −0.17 0.15 0.27† −0.09 Composition Ratio VLDL-TG/VLDL-C 0.05 0.19 −0.04 0.14 0.25† −0.04 Ratio IDL-TG/IDL-C −0.16 −0.20 0.22 −0.36* −0.32* 0.45* Ratio LDL-TG/LDL-C 0.29† 0.29† −0.25† 0.47* 0.30† 0.65* Ratio HDL-TG/HDL-C 0.40* 0.30† −0.33* 0.52* 0.33* 0.88* † Ratio LDL-P/HDL-P 0.27 0.42* −0.37* 0.45* 0.51* 0.18 † Non-HDL-P (nmol/L) 0.23 0.20 −0.15 0.33 0.24 0.42* Remnant cholesterol (mg/dl) 0.35* 0.23 −0.25† 0.46† 0.20 0.97* GLS, global longitudinal strain; GLSR, global longitudinal strain rate; HOMA-IR, homeostatic model assessment insulin resistance; hs-CRP, highly-sensitive C-reactive protein; TG, triglycerides. Spearman coefficient. Values expressed in r. *p < 0.01. † p < 0.05. Correlation analysis between ALT components and component of the ALT, so this inflammatory marker was variables of LV geometry and function, HOMA-IR and chosen for the multivariable analysis. Correlation analysis inflammatory markers are summarized in Table 4. The between ALT and CLP components are summarized in ratios LDL-P/HDL-P and HDL-TG/HDL-C were the Supplementary Table S3. Triglycerides appeared particularly variables better correlated to cardiac changes as well as to associated with VLDL-P and large HDL-P. insulin resistance and inflammatory parameters. Moreover, The final multivariable model with the largest AUC [0.79 considering the specific subclasses of lipoprotein particles, (95% CI: 0.54–1)] for distinguishing mild cardiac change the results indicated that large VLDL-P, small LDL-P and subjects from those with severe cardiac changes identified total HDL-P (inversely) subclasses have the best correlation the following pattern: HOMA-IR, GlycA, VLDL-diameter to LV remodeling and systo-diastolic dysfunction, while and large HDL-P (Figure 2). In contrast, for differentiating lipoprotein particles rich in triglycerides (VLDL-P, TG- normal heart subjects from those with severe cardiac enriched HDL and cholesterol-depleted LDL) had the changes the largest AUC [0.91 (95% CI: 0.74–1)] resulted highest association with HOMA-IR. Lastly, total HDL-P and from the following variables: BMI standard deviation, small LDL-P were particles related with hs-CRP values, HOMA-IR, systolic BP, diastolic BP, GlycA, small VLDL-P, whereas GlycA proved to be better correlated with every small LDL-P and ratio HDL-TG/HDL-C (Figure 3). To Frontiers in Pediatrics 09 frontiersin.org Siurana et al. 10.3389/fped.2022.887771 FIGURE 2 Multivariable model to differentiate mild cardiac change subjects (1) than those with severe cardiac changes (2). The variables included in the model were those that overlap by at least two of the three statistical approaches. An area under the curve (AUC) of 0.79 (95% CI: 0.54–1) was obtained with the next pattern: HOMA-IR, glycoprotein A (Glyc-A), VLDL diameter and large HDL-P. Predictive accuracy = 0.8; p-value = 0.051; out-of-bag error = 0.27. assess the usefulness of lipoprotein subclasses and Discussion glycoprotein A in the multivariable diagnostic model, ROC curves with only traditional CV risk factors (BMI standard This study showed that morbidly obese adolescents with LV deviation, HOMA-IR, systolic and diastolic BP) were changes presented a pathological phenotype in the ALT despite developed and compared with the previous analysis. exhibiting normal values in the CLP (Figure 1). Large VLDL-P, Figure 4 shows the comparative ROC curve analysis. A small LDL-P and total HDL-P were the subclasses more closely binomial logistic regression was performed on mild vs. related to cardiac changes, while the ratio which highlighted the severe cardiac changes groups, where remaining excess of LDL-P in relation to HDL-P appeared to be the independent parameters of the final model (adjusted R 2 = variable more closely associated with severity in the LV 0.53, p < 0.0001) were HOMA-IR (OR 1.4; 95% CI: 1.0–1.9; changes. Furthermore, when obese adolescents were classified p < 0.050), VLDL size (OR 0.0; 95% CI: 0.0–0.9; p < 0.050) by MS criteria or obesity index, the resulting ALT was and large HDL-P (OR 0.0; 95% CI: 0.0–0.3; p < 0.050); and determined by the high triglyceridemia of these subjects, and on normal vs. severe cardiac changes groups, where the hence, predominated TG-enriched lipoproteins, like VLDL remaining independent parameter of the final model and TG-enriched HDL (increase in the ratio HDL-TG/HDL- (adjusted R 2 = 0.84, p < 0.0001) was the BMI standard C) (Figure 1). Additionally, the inflammatory markers deviation (OR 2.8; 95% CI: 1.2–6.6; p 0.010). exhibited a correlation with the aforementioned pathological Reproducibility of echocardiographic parameters has been ALT and especially with lower levels of total HDL-P and an demonstrated previously (16), showing good intraclass altered LDL subclass distribution, moved to the smallest LDL- correlation coefficients. P. Finally, multivariable models showed the relevance of Frontiers in Pediatrics 10 frontiersin.org Siurana et al. 10.3389/fped.2022.887771 FIGURE 3 Multivariable model to differentiate normal heart subjects (0) from those with severe cardiac changes (2). The variables included in the model were those that overlap by the three statistical approaches. An area under the curve (AUC) of 0.91 (95% CI: 0.74–1) was obtained with the next pattern: body mass index standard deviation (BMIsd), HOMA-IR, systolic blood pressure (SBP), diastolic blood pressure (DBP), glycoprotein A (Glyc-A), small VLDL-P, small LDL-P and ratio HDL-TG/HDL-C. Predictive accuracy = 0.84; p-value = 0.004; out-of-bag error = 0.1. VLDL size and large HDL-P—as well as BP, insulin resistance and compositionally cholesterol-depleted (34). The small and inflammation—in the changes observed in obese LDL-P possesses an increased atherogenicity – on account of adolescent’s hearts. Our results have highlighted the mechanisms like endothelial barrier crossing facility or importance of ALT and GlycA as differentiating indicators of oxidation susceptibility (34) – and it has been postulated as cardiac severity in adolescents with a similar degree of a predictor risk factor for coronary heart disease (35). obesity, where minimum differences can be found in BMI, BP Similarly, the relevance of LDL-P concentration to predict or insulin resistance levels. future CVD events rather than LDL-C has been also Previous studies performed in obese or diabetic children documented in the Framingham Offspring Study (6), where have shown similar results. Thus, the most frequently the highest risk was attributed to subjects with high LDL-P reported ALT was defined by increases of small LDL-P and and low LDL-C. Our study, performed in a population with VLDL-P and decreases of LDL size and large-medium HDL-P low LDL-C, has shown a significant increase in the small (13, 31–33). LDL-P subclass in the obese group. Additionally, in the VLDL are the main carriers of plasmatic triglycerides, and descriptive statistics between subjects with severe cardiac hence appear particularly increased in subjects with changes and those with mild cardiac changes, the small hypertriglyceridemia. In contrast, LDL are cholesterol- LDL-P and cholesterol-depleted LDL (increase in the ratio enriched lipoproteins and their concentration is not so LDL-TG/LDL-C) seemed to be the best differentiating ALT triglyceride-dependent, however in hypertriglyceridemia parameters, having also been found correlated with LV conditions the LDL particles have been found to be smaller remodeling, systo-diastolic dysfunction, insulin resistance and Frontiers in Pediatrics 11 frontiersin.org Siurana et al. 10.3389/fped.2022.887771 FIGURE 4 Comparative analysis between ROC curves considering only traditional cardiovascular risk factors (body mass index standard deviation, blood pressure and HOMA-IR) and ROC curves shown in Figures 2, 3. (A) When Normal heart subjects were compared to those with severe cardiac changes, the addition of lipoprotein subclasses and glycoprotein A to the model with traditional risk factors did not change the area under the curve (AUC) (0.92 vs. 0.91) [Only traditional risk factors: AUC 0.92 (95% CI: 0.77–1); predictive accuracy = 0.88; p-value = 0.0001; out-of-bag error = 0.1]. (B) When mild cardiac change subjects were compared to those with severe cardiac changes, the addition of lipoprotein subclasses and glycoprotein A caused an increase in the AUC (0.58 vs. 0.79) [only traditional risk factors: AUC 0.58 (95% CI: 0.3–0.86); predictive accuracy = 0.6; p-value = 0.596; out-of-bag error = 0.32]. Frontiers in Pediatrics 12 frontiersin.org Siurana et al. 10.3389/fped.2022.887771 inflammation. Concerning triglyceride levels, a strong positive also were the subjects with the highest triglyceride levels. correlation was found with all subclasses of VLDL-P and large However, a close relationship with cardiac changes was not HDL-P, but apparently, their levels were independent of the demonstrated. small LDL-P, as reported in the Framingham Offspring This study has some limitations. The cross-sectional design Study. Regarding VLDL-P, greater significant differences were and small sample size limit the extraction of causal conclusions. noted in adolescents with obesity and metabolic syndrome, The criteria for the degrees of cardiac involvement have been especially in large and small subclasses, and VLDL size defined by the authors and have not been previously tested in seemed to be useful to differentiate levels of cardiac other publications. They are based on the concept that severity involvement. The hypersecretion of large VLDL-P, as a result increases with the addition of cardiac changes, from only LV of overnutrition and insulin resistance, has been previously remodeling to systolic dysfunction, defined by GLS, a widely proposed as a key pathological mechanism in atherogenic studied parameter linked to mortality (50). The diastolic function dyslipidemia (36, 37). was not included in the group definition criteria because of the Cardioprotective functions of HDL include, in addition to lack of a single parameter for its identification, but the early the reverse cholesterol transport to the liver, the inhibition of GLSR was incorporated to the correlation analysis instead. LDL oxidation and anti-inflammatory actions in the In conclusion, our results have shown that morbidly obese endothelium (38, 39). However, in conditions of adolescents present an atherosclerotic ALT despite showing hypertriglyceridemia and insulin resistance, the overstimulated no pathological concentrations in the conventional lipid cholesteryl-ester-transfer protein enriches HDL composition profile. Subjects that meet the criteria for metabolic syndrome in TG by means of exchanging TG by cholesteryl esters with present overall worse ALT and remnant cholesterol values other lipoprotein subclasses (40–42). Consequently, TG- because of the highest triglyceride levels. enriched HDL have their beneficial features reduced – in T2- Furthermore, when the obese adolescents are classified by DM patients by up to 52% of the HDL antioxidative capacity the degree of cardiac change, ALT and GlycA appear to be (43) – and have been associated with atheroma plaque more reliable indicators of severity than traditional CV risk formation (44). Our results have highlighted the prevalence of factors as BMI, BP or insulin resistance. Particularly, the TG-enriched HDL among participants with cardiac changes overage of LDL-P compared to HDL-P and the increase in and especially in those with MS, as well as a relationship small LDL-P with cholesterol-depleted LDL particles appear between TG-enriched HDL and LDL and the insulin to be the key ALT’s parameters involved in LV changes. resistance. Hence, the HDL’s cardioprotective function might be decreased in these subjects. Furthermore, a reduced number of total and medium HDL- Data availability statement P concentration and size has been associated with higher CVD risk in adults (45, 46), and previous studies conducted in obese The raw data supporting the conclusions of this article will adolescents with insulin resistance or T2-DM noted a be made available by the authors, without undue reservation. significant decrease in large HDL-P (47). Nevertheless, these findings were not reproduced in the descriptive analysis of our cohort, where the significant reduction was shown in the Ethics statement total HDL-P concentration, without significant distinctions between the subclasses, however the large HDL-P reduction The studies involving human participants were reviewed was identified as a differentiating factor between mild and and approved by Institutional Review Board at Vall d’Hebron severe cardiac changes in the multivariable analysis, which Hospital approved the protocol (PR-AMI-273/2018). Written gives prominence to the widely recognized role of smaller informed consent to participate in this study was provided by HDL size in CVD (48). the participants’ legal guardian/next of kin. Remnant cholesterol marks the overall load of TG-enriched lipoproteins (VLDL and IDL), which in situations of hypertriglyceridemia can carry as much or more cholesterol Author contributions than LDL (49). How it interacts in the atherosclerosis physiopathology is still unclear but its association with CVD, JMS, AS, DY, PSV and NA designed the study, conducted the as a factor related to coronary artery disease, has been bibliographic search and wrote different parts of the manuscript. established and an increased risk – up to 2.7 times in LA and EM contributed to gathering patient data and to the concentrations ≥39 mg/dl – has been reported as independent discussion of the study results. LR, NM and FR participated in of obesity (5). The highest remnant cholesterol values in the the results evaluation, analysis and discussion. JMS and DY present study were near to this hypothetical threshold and coordinated all authors’ participation. All authors contributed to belonged to obese adolescents with metabolic syndrome, who the article and approved the submitted version. Frontiers in Pediatrics 13 frontiersin.org Siurana et al. 10.3389/fped.2022.887771 Funding Conflict of interest J.M.S. received grant funding for this project from the NA has a patent and is stock owner of Biosfer Teslab, the Spanish Paediatric Cardiology and Congenital company that commercializes the lipoprotein profiling Cardiopathies Society (J.V. Comas 2019). D.Y. used funds described in the present manuscript. This study received to finance the analysis of blood samples from “The funding from Novo Nordisk Pharma S.A. through “The Growth and Development group” of the Vall d’Hebron Growth and Development Group” of the Vall d’Hebron Research Institute, partially financed by Novo Nordisk Research Institute. The funder was not involved in the study Pharma S.A. design, collection, analysis, interpretation of data, the writing of this article or the decision to submit it for publication. All authors declare no other competing interests. Acknowledgments The authors wish to acknowledge the work of cardiologists Publisher’s note and endocrinologist Dr. Ariadna Campos and Dr. Maria Clemente from Vall d’Hebron Hospital, and Dr. Marisa All claims expressed in this article are solely those of the Torres, Dr. Georges Akel and Dr. Zelmira Bosch from HM authors and do not necessarily represent those of their Nens Hospital for their contributions to patient recruitment affiliated organizations, or those of the publisher, the editors and image analysis related to this study. We also wish to and the reviewers. Any product that may be evaluated in this thank Dr. Roser Ferrer from the biochemistry department of article, or claim that may be made by its manufacturer, is not Vall d’Hebron Hospital. The project was funded by the guaranteed or endorsed by the publisher. Spanish Paediatric Cardiology and Congenital Cardiopathies Society and by “The Growth and Development group” (Vall d’Hebron Research Institute). 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