Puig-Jové et al. Cardiovascular Diabetology (2022) 21:257 Cardiovascular Diabetology https://doi.org/10.1186/s12933-022-01652-z RESEARCH Open Access The novel inflammatory biomarker GlycA and triglyceride-rich lipoproteins are associated with the presence of subclinical myocardial dysfunction in subjects with type 1 diabetes mellitus Carlos Puig-Jové1,2†, Josep Julve3,4†, Esmeralda Castelblanco5, M Teresa Julián3,6, Núria Amigó3,7,8, Henrik U Andersen9, Tarunveer S Ahluwalia9,10, Peter Rossing9,11, Dídac Mauricio3,12,13, Magnus T Jensen14 and Núria Alonso2,3,6* Abstract Background Subjects with Type 1 diabetes mellitus (T1DM) have an increased incidence of heart failure (HF). Several pathophysiological mechanisms have been involved in its development. The aim of this study was to analyze the potential contribution of the advanced lipoprotein profile and plasma glycosylation (GlycA) to the presence of subclinical myocardial dysfunction in subjects with T1DM. Methods We included subjects from a Danish cohort of T1DM subjects (Thousand & 1 study) with either diastolic and/or systolic subclinical myocardial dysfunction, and a control group without myocardial dysfunction, matched by age, sex and HbA1c. All underwent a transthoracic echocardiogram and an advanced lipoprotein profile obtained by using the NMR-based Liposcale® test. GlycA NMR signal was also analyzed. Systolic dysfunction was defined as left ventricular ejection fraction ≤ 45% and diastolic dysfunction was considered as E/e′≥12 or E/e′ 8–12 + volume of the left atrium > 34 ml/m2. To identify a metabolic profile associated with the presence of subclinical myocardial dysfunction, a multivariate supervised model of classification based on least squares regression (PLS-DA regression) was performed. Results One-hundred forty-six subjects had diastolic dysfunction and 18 systolic dysfunction. Compared to the control group, patients with myocardial dysfunction had longer duration of diabetes (p = 0.005), and higher BMI (p = 0.013), serum NTproBNP concentration (p = 0.001), systolic blood pressure (p < 0.001), albuminuria (p < 0.001), and incidence of advanced retinopathy (p < 0.001). The supervised classification model identified a specific pattern † These authors contributed equally to this study Dídac Mauricio, Magnus T Jensen and Núria Alonso share senior authorship *Correspondence: Núria Alonso nalonso32416@yahoo.es Full list of author information is available at the end of the article © The Author(s) 2022. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. Puig-Jové et al. Cardiovascular Diabetology (2022) 21:257 Page 2 of 11 associated with myocardial dysfunction, with a capacity to discriminate patients with myocardial dysfunction from controls. PLS-DA showed that triglyceride-rich lipoproteins (TGRLs), such as VLDL (total VLDL particles, large VLDL subclass and VLDL-TG content) and IDL (IDL cholesterol content), as well as the plasma concentration of GlycA, were associated with the presence of subclinical myocardial dysfunction. Conclusion Proatherogenic TGRLs and the proinflammatory biomarker Glyc A are strongly associated to myocardial dysfunction in T1DM. These findings suggest a pivotal role of TGRLs and systemic inflammation in the development of subclinical myocardial dysfunction in T1DM. Keywords Myocardial dysfunction, Heart failure, Type 1 diabetes, Lipoproteins, GlycA Background including the myocardium. When the capacity for stor- Diabetes mellitus (DM) and heart failure (HF) are two age and oxidation of mobilized FAs is exceeded, they multifaceted entities that involve high morbidity and can be transformed in other reactive species that further mortality when both conditions coexist [1, 2]. The risk of potentiates myocardial lipotoxicity. This cause of non- HF is increased both in subjects with type 2 DM (T2DM) ischemic and non-hypertensive cardiomyopathy is often [3] and type 1 DM (T1DM) [4]. Indeed, DM is highly referred to as diabetic or “lipotoxic” cardiomyopathy. prevalent amongst patients with HF [5, 6], especially Diabetic dyslipidaemia may also contribute to the dia- those with HF and preserved ejection fraction (HFpEF) betic myocardial dysfunction. Particularly, the excess flux [7, 8]. of mobilized FAs to the liver promotes overproduction of In population-based studies, the risk of HF in patients TG-rich lipoproteins (TGRLs) and their remnants. Eleva- with diabetes (particularly T2DM) is significantly tions in circulating TGRLs are frequently associated with increased following adjustment for well-established increased concentrations of remnant cholesterol and HF risk factors [9]. The resulting specific form of car- with reduced high-density lipoproteins (HDL) choles- diomyopathy is known as “diabetic cardiomyopathy” terol, and all contribute to the development of ischemic [10]. Although the concept of diabetic cardiomyopathy heart disease [24]. However, their contribution, if any, on is often considered in individuals affected by T2DM, a non-ischemic cardiomyopathy remains poorly explored. metabolically-induced cardiomyopathy, independent of To the best of our knowledge, no previous studies in hypertension, nephropathy or ischemic heart disease is T1DM patients have studied the relationship between also evident in individuals with T1DM [11, 12]. metabolic advanced profile with subclinical HF, defined Several pathophysiological mechanisms directly affect- as presence of impaired cardiac diastolic and/or systolic ing the structure and function of the myocardium have function without previous clinical manifestation of HF. been proposed to contribute to the development of dia- Thus, the present study aims to analyze the contribution betic cardiomyopathy. Among those described are hyper- of inflammation (GlycA) and an advanced lipoprotein glycemia, hyperinsulinemia, inflammation and increased profile to the presence of subclinical myocardial dysfunc- levels of circulating fatty acids (FAs) and triglycerides tion in a well-defined cohort of T1DM patients. (TGs) [13–17]. Systemic inflammation plays a key role in HF etiopathogenesis [18, 19]. In that sense, GlycA Methods has been described as a “composite biomarker of sys- Study population temic inflammation” since its signal on nuclear magnetic Our study population was selected from the Thousand resonance (NMR) spectra represents both the levels and & 1 study cohort study. This study was carried out at degree of glycosylation of various acute phase proteins. the Steno Diabetes Center in Copenhagen (SDCC) with GlycA NMR signal has been reported to be associated cardiology examinations conducted at the Department with increased risk of CV events, peripheral arterial dis- of Cardiology, Copenhagen University Hospital Herlev- ease, and mortality even after adjusting for other inflam- Gentofte. It was conducted between April 1st, 2010, matory markers [20, 21]. and April 1st, 2012, and was based on a large cohort of Lipotoxicity and cardiac lipid accumulation are other 1.093 patients with T1DM without known heart disease. factors that have been related to the etiopathogenesis of Patients were included if they were 18 years of age or diabetic cardiomyopathy [22]. In this line, myocardial older, attending the outpatient clinic at the SDCC, diag- metabolism studies have shown a reduced myocardial nosed with T1DM, without known heart disease (defined glucose uptake and an increased uptake of FAs in subjects as any known HF, coronary artery disease [including with T1DM [23]. In T1DM, insulin deficiency promotes previous myocardial infarction, stable angina, previ- the mobilization of FAs from fat pads which results in an ous percutaneous coronary intervention, or coronary increased availability of excess FAs in different tissues, artery bypass surgery], atrial fibrillation or atrial flutter, Puig-Jové et al. Cardiovascular Diabetology (2022) 21:257 Page 3 of 11 left bundle branch block, congenital heart disease, pace- for body surface area. LV mass was determined by the maker or implantable cardioverter defibrillator implan- linear method and indexed for body surface area. tation), and if willing to participate in the study. The Subclinical myocardial dysfunction was defined when study population has been described in detail elsewhere patients had systolic and/or diastolic myocardial dysfunc- [25]. Briefly, from the total study population included in tion on the echocardiogram in the absence of HF symp- the study, 15.5% (n = 169) of the participants had grossly toms. Systolic dysfunction was defined as LVEF ≤ 45% abnormal systolic or diastolic function. determined by Simpson´s biplane method and diastolic From the original cohort of Thousand & 1 study, we dysfunction was considered if there was evidence of long- finally included a subgroup of 304 patients with T1DM, standing LV filling pressure defined as E/e′ >12 (where E comprising 154 patients with myocardial dysfunction is diastolic mitral early inflow velocity and e′ is pulsed- (the myocardial dysfunction group) and 150 controls. wave early diastolic tissue doppler velocity) or E/e′ 8–12 For the myocardial dysfunction group, we selected all and LAV > 34 ml/m2. T1DM patients who had either diastolic and/or systolic myocardial dysfunction and enough serum sample for Biochemistry conducting the NMR spectroscopy analysis. Addition- Information about biochemistry such as HbA1c, p-cre- ally, we included 150 patients from the same cohort who atinine and albuminuric status was collected from elec- had no echocardiographic alterations (the control group) tronic patient files at the SDCC from the ambulatory matched by age, glycated hemoglobin (HbA1c) and visit closest to study inclusion, which was maximally ± 4 gender. months from inclusion. This information was collected after analyzing the echocardiography. Ethical consideration The urinary albumin excretion rate (UAER) was mea- The original study was performed in accordance with the sured in 24-h sterile urine collections by enzyme immu- second Helsinki declaration and approved by the regional noassay. Patients were categorized as normoalbuminuric ethics committee (H-3-2009-139) and the Danish Data if the UAER, in two out of three consecutive measure- Protection Agency (00934-Geh-2010-003). All subjects ments, was < 30 mg/24 h, microalbuminuric if the UAER gave written informed consent. was between 30 and 300 mg/24 h, and macroalbumin- uric if the UAER > 300 mg/24 h. HbA1c was measured by Study visit high-performance liquid chromatography (normal range: Prior to the echocardiographic examination, all patients 21–46 mmol/mol [4.1–6.4%]; Variant; Bio-Rad Laborato- received study information, signed the consent form and ries, Munich, Germany), and serum creatinine concen- filled out a questionnaire with information about lifestyle tration was measured by an enzymatic method (Hitachi factors, including smoking, exercise, alcohol consump- 912; Roche Diagnostics, Mannheim, Germany). The esti- tion and cardiorespiratory symptoms. The use of cardio- mated glomerular filtration rate (eGFR) was calculated by vascular treatments, such as lipid lowering medication the MDRD method. (statins) and antihypertensive medication (beta blockers, calcium antagonists, diuretics, angiotensin-converting Lipoprotein and glycoprotein analysis by NMR enzyme inhibitors or angiotensin II receptor antago- spectroscopy (advanced profile) nists), was also recorded. Blood pressure was measured Serum samples were shipped on dry ice from the SDCC in the supine position. to the Biosfer Teslab facilities (Reus, Spain) for Liposcale® lipoprotein and glycoprotein analysis. Samples were kept Echocardiogram at −80°C until the NMR analysis. 200 µl of serum was Echocardiography was performed with a General Elec- diluted with 50 µl deuterated water and 300 µl of 50 mM tric, Vivid 7 Dimension imaging system device (GE Ving- phosphate buffer solution at pH 7.4. 1H-NMR spectra med Ultrasound AS, Horten, Norway) with a 3.5-MHz were recorded at 306 K on a Bruker Avance III 600 spec- transducer in accordance with the recommendations trometer operating at a proton frequency of 600.20 MHz from the European Association of Echocardiography/ (14.1 T). American Society of Echocardiography [26]. Echocar- diographic examinations were read and analysed using Lipoprotein analysis the General Electric EchoPAC software (BT11), record- Lipoprotein profiling was obtained by using the Lipo- ing three consecutive heart cycles. Left ventricle ejection scale® test (IVD-CE), a previously reported method fraction (LVEF) was determined by Simpson’s biplane based on a two-dimensional 1H-NMR diffusion-ordered method. Left atrial volume (LAV) was determined by the spectroscopy (DOSY) approach for lipoprotein profile recommended biplane area-length method and indexed characterization including lipid content (cholesterol and triglyceride concentration), size and particle number of Puig-Jové et al. Cardiovascular Diabetology (2022) 21:257 Page 4 of 11 the main lipoprotein classes [27]. The methyl signal was chemometrics- and metabolomics-based analyses deconvoluted by using 9 lorentzian functions to deter- when a large number of variables are used to classify mine the lipid concentration of the large, medium and two-stage conditions. PLS-DA relates the X matrix small subclasses of the main lipoprotein classes: very (input data including NMR derived parameters, clini- low-density lipoproteins (VLDL), low-density lipopro- cal and anthropometric variables), and the Y matrix teins (LDL) and HDL, and their size associated diffusion (presence or absence of myocardial disfunction) to coefficients. Then, the lipid concentration was combined find the maximum discrimination between classes with their associated particle volume in order to quantify (study groups). The PLS-DA method reduces the the number of particles required to transport the mea- dimensionality of the initial dataset (X matrix), creat- sured lipid concentration of each lipoprotein subclass. ing a new multidimensional dataset for each individ- Weighted average VLDL, LDL and HDL particle sizes ual maximizing the total variance of data using just a were calculated from various subclass concentrations by few components (the latent scores), obtained from the summing the known diameter of each subclass multi- specific contribution of each variable (loadings) to the plied by its relative percentage of subclass particle num- new multidimensional axis [31]. ber. The variation coefficients for the particle numbers On the other hand, to avoid overfitting and correct the were between 2% and 4%, and for the particle sizes they multiple testing effects, we auto scaled and cross-vali- were lower than 0.3%. dated by the permutation Venetian Blinds cross valida- tion method by using 10 splits of the input data. The area Glycoprotein analysis under the curve (AUC) was used to evaluate the capacity The region of the 1H-NMR spectrum where the glyco- of the NMR and clinical variables to distinguish between proteins resonate (2.15–1.90 ppm) was deconvoluted the two groups (with and without subclinical myocardial using several analytical functions associated with spe- dysfunction). cific plasmatic sugar-protein bonds according to a pre- The following subset of 19 clinical and biochemi- viously published procedure [28]. For each function, cal variables was selected after a GA approach: diabetes the total area (proportional to concentration), height, duration, body mass index (BMI), hemoglobin, inter- position and bandwidth were determined. The area of mediate-density lipoproteins (IDL) cholesterol content the specific glycoprotein signal defined as the GlycA (IDL-C), LDL cholesterol content (LDL-C), VLDL TG NMR signal arose from the acetyl groups of N-acetyl- content (VLDL-TG), total VLDL particles (VLDL Par- glucosamine and N-acetylgalactosamine bonded to ticles), Large VLDL, total LDL particles (LDL Particles), plasmatic proteins [29]. Consistent with that, a larger total HDL particles (HDL Particles); Large HDL, Medium GlycA area reflects a higher level of plasmatic gly- HDL, LDL size (LDL-Z), HDL size (HDL-Z), GlycA area, cosylation. The height-to-width (H/W) ratio of the H/W GlycA ratio (H/W GlycA), HDL-TG/HDL-C ratio GlycA signal, associated with its molecular aggrega- (HDL ratio), Small VLDL/total VLDL particles ratio (% tion state, was also reported. The H/W parameter, Small VLDL) and Small LDL/total LDL particles ratio (% increased during inflammatory processes, reflects the Small LDL). sugar-protein bond flexibility, indicating glycosylation Finally, we assessed the discrimination capacity of in accessible regions of the proteins. Height is defined NMR biomarkers to predict the presence of myocar- as the difference from baseline to the maximum of the dial dysfunction when these variables were added to the corresponding NMR peaks and the width value corre- model including only traditional clinical variables (age, sponds to the peak width at half height. sex, eGFR, NTproBNP, BMI, diabetes duration and sys- tolic blood pressure) by using logistic regression. Statistical analysis We used widely described chemometric methods [30] Results in order to identify a specific lipoprotein/glycopro- Clinical characteristics of our study population tein profile associated with myocardial dysfunction. Our study population comprised a total of 304 T1DM Briefly, multivariate statistical analyses were com- subjects (53.6% women, 50.7% subjects with myocar- puted in MATLAB, Ver. 7.10.0 using PLS-Toolbox, dial dysfunction) with a median [min;max] age of 62.1 Ver. 5.2.2 (Eigenvector Research Inc., Manson, WA, [22.5;87.0] years at inclusion, median diabetes duration United States) after application of a genetic algorithm of 33 years and a median HbA1c value of 8.0%. Clinical (GA) for variable selection to optimize the predictive characteristics by group are shown in Table 1. Briefly, ability of the model. Partial least squares discrimi- there were no differences regarding gender, age, HbA1c nant analysis (PLS-DA) models were used as a super- value, smoking habit, total cholesterol, LDL cholesterol vised classification method between the study groups. and TGs values between the groups. From a total of 154 This is a well established and widely used method in subjects with myocardial dysfunction, 146 (94.8%) and Puig-Jové et al. Cardiovascular Diabetology (2022) 21:257 Page 5 of 11 Table 1 Descriptive analysis of clinical variables by group Variable Myocardial Control p Dysfunction (n 150) value (n 154) Sex (men) 72 (46.8%) 69 (46.0%) 0.987 Age (years) 61.0 (11.7) 60.6 (11.1) 0.756 Diabetes duration (years) 35.1 (14.9) 30.1 (15.5) 0.005 EF (%) 55.8 (7.58) 58.6 (5.24) < 0.001 E/e´ Cocient < 0.001 E/e´ <8 8 (5.19%) 92 (61.3%) E/e´ 8–12 67 (43.5%) 58 (38.7%) E/e´ >12 79 (51.3%) 0 (0.00%) LAV (ml/m2) 34.1 (7.67) 28.9 (5.52) < 0.001 Diastolic HF < 0.001 No 8 (5.19%) 150 (100%) Yes 146 (94.8%) 0 (0.00%) Systolic HF < 0.001 No 136 (88.3%) 150 (100%) Yes 18 (11.7%) 0 (0.00%) Height (m) 1.71 (0.09) 1.73 (0.10) 0.037 Weight (Kg) 76.4 (15.1) 75.1 (13.7) 0.422 BMI (Kg/m2) 26.1 (3.93) 25.0 (3.66) 0.013 Diastolic BP (mmHg) 73.5 (11.2) 71.4 (9.53) 0.073 Systolic BP (mmHg) 143 (17.7) 136 (17.9) 0.002 Statin use 0.039 No 52 (33.8%) 69 (46.0%) Yes 102 (66.2%) 81 (54.0%) Antihypertensive use < 0.001 No 29 (19.8%) 59 (39.3%) Yes 125 (81.2%) 91 (60.7%) Smoking habit 0.140 Never smoker 51 (33.1%) 61 (40.7%) Current smoker 25 (16.2%) 30 (20.0%) Ex-smoker 78 (50.6%) 59 (39.3%) NTproBNP (pg/mL) 352 [163;591] 249 [116;449] < 0.001 MRproANP (pmol/L) 100 [69.2;144] 82.3 [56.4;112] < 0.001 HbA1c value (%) 8.19 (1.11) 8.01 (1.20) 0.178 Total cholesterol (mmol/L) 4.77 (0.99) 4.72 (0.93) 0.620 LDL cholesterol (mmol/L) 2.44 (0.70) 2.44 (0.80) 0.997 Triglycerides (mmol/L) 1.12 (0.78) 1.07 (0.60) 0.595 24 h albumin in urine (mg/24 h) 309 (1587) 18.0 (26.0) 0.026 eGFR value (mL/min/1.73m2) 75.4 (26.2) 83.7 (21.0) 0.003 Albuminuria status < 0.001 Normoalbuminuria 73 (47.4%) 110 (73.3%) Microalbuminuria 42 (27.3%) 35 (23.3%) Macroalbuminuria 39 (25.3%) 5 (3.33%) Retinopathy status global (worst eye) < 0.001 Normal 37 (24.2%) 55 (36.7%) Simplex retinopathy 63 (41.2%) 79 (52.7%) Proliferative retinopathy 53 (34.6%) 16 (10.7%) Results are expressed in mean (SD) for continuous variables, frequency and percentage for categorical variables and median [25th;75th] for variables not normally distributed. EF: ejection fraction, E/e´: estimated left ventricular filling pressure, LAV: left atrial volume, HF: heart failure. BMI: body mass index, BP: blood pressure, NTproBNP: N-terminal fragment of pro-B-type natriuretic peptide, MRproANP: Mid-regional pro atrial natriuretic peptide, HbA1c: glycated hemoglobin, LDL: low- density lipoproteins, eGFR: estimated glomerular filtration rate Puig-Jové et al. Cardiovascular Diabetology (2022) 21:257 Page 6 of 11 Table 2 Lipoprotein and glycoprotein NMR advanced profile in and serum cardiac biomarkers of HF were also observed cases and controls between the two groups. In summary, subjects with myo- Variable Myocardial Control p cardial dysfunction had lower mean (SD) ejection frac- Dysfunction (n 150) value (n 154) tion (55.8 ± 7.5% vs. 58.6 ± 5.2%, p < 0.001), higher LAV VLDL-P (nmol/L) 41.3 (26.5) 44.4 (30.3) 0.343 (34.1 ± 7.6 ml/m2 vs. 28.9 ± 5.5 ml/m2, p < 0.001) and Large VLDL-P 1.11 (0.65) 1.17 (0.65) 0.406 higher median [25th;75th] concentrations of N-terminal Medium VLDL-P 3.66 (2.31) 4.08 (3.53) 0.215 fragment of pro-B-type natriuretic peptide (NTproBNP) Small VLDL-P 36.5 (24.5) 39.1 (27.0) 0.377 (352 [163;591] pg/mL vs. 249 [116;449] pg/mL, LDL-P (nmol/L) 1217 (231) 1230 (235) 0.608 p = 0.001), and mid-regional pro atrial natriuretic peptide Large LDL-P 172 (32.7) 173 (30.7) 0.757 (MRproANP) (100 [69.2;144] pmol/L vs. 82.3 [56.4;112] Medium LDL-P 353 (115) 363 (109) 0.457 pmol/L, p < 0.001). Also, the percentage of subjects with Small LDL-P 691 (134) 694 (138) 0.845 E/e′ >8 on echocardiography was higher in subjects HDL-P (nmol/L) 32.6 (6.59) 32.4 (6.38) 0.708 with myocardial dysfunction compared with those with- Large HDL-P 0.29 (0.05) 0.29 (0.04) 0.891 out myocardial dysfunction (94.8% vs. 38.7%, p < 0.001). Medium HDL-P 11.2 (2.55) 11.0 (2.26) 0.451 Additionally, the use of statin and antihypertensive thera- Small HDL-P 21.1 (4.58) 21.1 (4.61) 0.895 pies was more frequent in subjects with myocardial dys- Total-P/HDL-P 40.1 (11.5) 41.3 (14.2) 0.442 function than in those without (p = 0.039 and p < 0.001 LDL-P/HDL-P 38.8 (10.9) 39.8 (13.3) 0.465 respectively). Finally, subjects with myocardial dysfunc- VLDL-C (mg/dL) 15.2 (10.80) 13.8 (9.35) 0.198 tion were more likely to have advanced stages of retinop- IDL-C (mg/dL) 11.6 (4.98) 10.1 (3.96) 0.004 athy and albuminuria (p < 0.001 for both comparisons) LDL-C (mg/dL) 120 (22.80) 119 (23.40) 0.813 than those without. HDL-C (mg/dL) 65.1 (16.40) 66.7 (17.80) 0.401 VLDL-TG (mg/dL) 57.8 (39.30) 53.9 (33.10) 0.353 Lipoprotein and glycoprotein NMR advanced profile in IDL-TG (mg/dL) 11.9 (3.90) 10.7 (3.25) 0.003 subjects with myocardial dysfunction and controls LDL-TG (mg/dL) 16.5 (4.46) 15.5 (4.15) 0.048 Results from the lipoprotein and glycoprotein profile are HDL-TG (mg/dL) 18.2 (4.83) 17.2 (4.98) 0.073 shown in Table 2. Most remarkably, compared with con- VLDL-Z (nm, diameter) 41.9 (0.44) 42.0 (0.45) 0.921 trols, subjects with myocardial dysfunction presented a LDL-Z (nm, diameter) 21.0 (0.31) 21.0 (0.34) 0.687 more proatherogenic and pro-inflammatory profile, asso- HDL-Z (nm, diameter) 8.28 (0.07) 8.28 (0.08) 0.633 ciated with increased IDL cholesterol and TG content Cholesterol total (mg/dL) 212 (31.2) 210 (30.60) 0.582 (p = 0.004 and 0.003 respectively) and a greater GlycA TG total (mg/dL) 104 (47.1) 97.2 (39.40) 0.155 area (p < 0.001). The LDL lipid composition, defined as HDL-C (mg/dL) 65.1 (16.4) 66.7 (17.80) 0.401 LDL-TG / LDL-C, was also increased in the myocardial Ratio VLDL (VLDL-TG/ VLDL-C) 4.19 (1.34) 4.41 (1.41) 0.160 dysfunction group, reflecting increased remnant levels Ratio LDL (LDL-TG/ LDL-C) 0.14 (0.03) 0.13 (0.03) 0.012 and neutral lipid heteroexchange (TGs and cholesteryl Ratio IDL (IDL-TG/IDL-C) 1.08 (0.18) 1.12 (0.23) 0.123 esters) from TG-rich particles to LDL particles, and vice Ratio HDL (HDL-TG/ HDL-C) 0.29 (0.10) 0.27 (0.10) 0.061 versa, by the cholesteryl ester transfer protein. % VLDL (Small/total VLDL-P) 0.88 (0.04) 0.88 (0.04) 0.916 % LDL (Small/total LDL-P) 0.57 (0.06) 0.57 (0.06) 0.491 Lipoprotein and glycoprotein signature associated with % HDL (Small/total HDL-P) 0.65 (0.04) 0.65 (0.05) 0.596 GlycA area (1,39*102µmol/L) 4.93 (0.94) 4.66 (0.85) 0.009 the presence of myocardial dysfunction H/W GlycA 16.9 (3.09) 15.8 (2.80) 0.001 We used a multivariant classification approach based on Results are expressed in mean (SD). VLDL: very-low-density lipoproteins, P: PLS-DA in order to identify a specific lipoprotein and particles, LDL: low-density lipoproteins, HDL: high-density lipoproteins, C: glycoprotein profile associated with myocardial dysfunc- cholesterol, IDL: intermediate-density lipoproteins, TG: triglycerides, Z: size, H/W: height-to-width ratio tion in subjects with T1DM. Figure 1 shows the good performance of the classification method by using a ROC curve analysis, and the contribution of each variable to 18 (11.7%) had diastolic and systolic dysfunction, respec- the model, summarized in the loadings plot of the princi- tively. Subjects with myocardial dysfunction showed, pal two latent multidimensional variables (LV1 and LV2). in comparison with controls, a longer diabetes dura- This supervised classification model succeeded in iden- tion (35.1 ± 14.9 years vs. 30.1 ± 15.5 years; p = 0.005), tifying a specific pattern associated with myocardial dys- a higher BMI (26.1 ± 3.9 kg/m2 vs. 25.0 ± 3.7 kg/m2; function, with a capacity to discriminate diabetic patients p = 0.013), a higher systolic blood pressure (143 mmHg with myocardial dysfunction from the rest of the indi- vs. 136 mmHg; p < 0.001) and a lower eGFR (75.4 ± 26.2 viduals without CVD, in a modest but significant way in mL/min/1.73m2 vs. 83.7 ± 21.0 mL/min/1.73m2; relation to fortuitously obtaining the correct classifica- p = 0.003). Differences in echocardiographic parameters tion (area under the ROC curve 0.63, p < 1.1801e-012). Puig-Jové et al. Cardiovascular Diabetology (2022) 21:257 Page 7 of 11 Fig. 1 A: Estimated and cross-validated ROC curve of the PLS-DA classification model. B: Contribution of each variable to the multidimensional latent variables LV1 and LV2. PLS-DA relates the X matrix (input data including NMR derived parameters, clinical and anthropometric variables) and the Y matrix (presence or absence of myocardial disfunction) to find the maximum discrimination between both groups. AUROC: area under the ROC curve, PLS-DA: partial least squares discriminant analysis, LV: latent variable, NMR: nuclear magnetic resonance In that sense, we used the PLS-DA classification Supplementary Fig. 1 shows the predicted probabil- approach to investigate which variables discriminated ity of myocardial dysfunction in a model (model 1) best between subjects with myocardial dysfunction including only classical risk variables (age, sex, eGFR, and controls (non-myocardial dysfunction subjects). NTproBNP, BMI, diabetes duration and systolic blood LV1 showed that the variables with the most significant pressure > 140mmHg). We built a second model contribution to explain the presence of myocardial (model 2) including both classical variables and the dysfunction in T1DM subjects beyond well-established NMR-assessed biomarkers. The inclusion of the NMR risk factors included: NMR-determined GlycA area variables in this second model significantly increased and H/W GlycA ratio as well as TGRLs, such as VLDL the area under the ROC curve from 0.62 [0.56–0.68] (total VLDL particles, large VLDL subclass and VLDL- to 0.67 [0.61–0.73], with a net reclassification analysis TG content) and IDL (IDL cholesterol content). In the improvement considering NMR-assessed parameters second latent variable (i.e. after the first discriminating of 21%. analysis based on inflammatory parameters and VLDL and IDL related parameters was accomplished), other Discussion clinical variables including diabetes duration and LDL To our knowledge, this is the first study to date evaluat- related characteristics (LDL-Z) became discriminative, ing the association between a metabolic advanced pro- whereas VLDL related variables were no longer dis- file with subclinical myocardial dysfunction assessed criminant. Furthermore, we compared the predicted with echocardiography in subjects with T1DM. The probability of the presence of myocardial dysfunc- analysis has been performed in a well characterized tion when NMR variables were added together with large cohort of T1DM patients without prior CVD. traditional risk factors by using logistic regressions. Our data showed that the inflammation biomarker Puig-Jové et al. Cardiovascular Diabetology (2022) 21:257 Page 8 of 11 GlycA as well as VLDL-related variables (total VLDL also been described in T1DM women with previous pre- particles, large VLDL subclass and VLDL-TG content) eclampsia with carotid atherosclerosis [44]. and IDL-related variables (IDL cholesterol content) Additionally, the role of TGRLs in HF has been previ- were associated with the presence of myocardial dys- ously established. In two prospective studies of 113,554 function in T1DM subjects. individuals from the general population in Denmark, a Although the underlying mechanisms explaining the higher risk of HF for stepwise higher non-fasting TGs myocardial dysfunction remain still obscure, accumulat- (i.e., TGRLs, which also include the chylomicron rem- ing evidence supports the role of chronic inflammation nants) was found [45]. in its manifestation and progress to HF [32–33]. In sup- Although evidence supporting a relationship between port of this, our data suggest that the circulating levels cholesterol remnants and non-ischemic causes of HF of GlycA are related to myocardial dysfunction in T1DM is limited, the induction of VLDL receptor (VLDLR) subjects. Our finding is consistent with the significance abundance and TGRL uptake by cardiomyocytes in an of circulating GlycA, as it integrates in a single metrics experimental postprandial setting [46] suggest a role for the plasma concentrations [34] and glycosylation states VLDLR in atrial cardiomyopathy and ventricular dys- [29, 35] of several abundant acute-phase inflammatory function [47, 48]. Indeed, an association of atrial fibrilla- proteins. Notably, GlycA exhibits lower intraindividual tion with abnormal VLDL-related lipid metabolism has variability and greater analytical precision than other been suggested by some authors [48] and an increased inflammatory markers [34], thereby suggesting a role as risk of developing atrial fibrillation among patients with a potential novel biomarker linked to systemic inflam- T1DM compared with the general population has been mation and CVD assessment [36–38]. In line with this, reported [49]. the serum levels of GlycA are directly associated with According to previous studies, the circulating baseline those of C-reactive protein (CRP) [39], which is a well- levels of GlycA exhibit significant positive associations established diagnostic biomarker of inflammation used with incident CVD event rates and associated mortality in clinical practical clinical. Noteworthy, the advantage [37]. Remarkably, such correlations remain significant offered by GlycA over CRP is that it may integrate mul- even after adjusting for several other established CVD tiple inflammatory pathways by capturing the global sig- risk factors. In our study, the discriminating capability nal of several proteins and, therefore, better captures the of our PLS-DA model was not improved when includ- degree of systemic inflammation [40]. In addition, the ing either the 24 h-urinary albumin excretion rate or measurement of GlycA is associated with higher reli- NTproBNP as input variables, maintaining GlycA as the ability and lower intra-individual variability than CRP best discriminating variable for the presence of myocar- because it is detected similarly in both serum and plasma dial disfunction in subjects with T1DM (data not shown). samples, in fasting and non-fasting states, after short or The multivariant approach used in the present study long-term storage [34], and also after repeated intraindi- by using the PLS-DA methodology helped in identifying vidual determinations over time [41]. concomitant lipoprotein characteristics (TGRLs) that, A main finding of our study was that GlycA values together with the proinflammatory GlycA, could mainly allowed identification of T1DM subjects with and with- account for myocardial dysfunction in T1DM subjects out subclinical myocardial dysfunction. Our data are in on top of the commonly used traditional and well-estab- line with other study that support the concept that GlycA lished risk factors, including BMI, gender, HbA1c or is associated with an increased risk of HF, particularly diabetes duration. Inflammation showed the higher dis- HFpEF, which is independent of traditional CVD risk criminant ability distinguishing myocardial dysfunction factors and other inflammatory markers [42]. To our among T1DM patients, with the GlycA area being the knowledge, data about GlycA concentrations in subjects highest contributor to the first multidimensional latent with T1DM are lacking and for the first time our results variable (LV1). Of note, diabetes duration helped to dis- regarding GlycA further suggest a role for inflammatory criminate myocardial dysfunction from non-myocardial mechanisms in the pathogenesis of myocardial dysfunc- dysfunction T1DM patients only once the inflammatory tion in these patients. signature was considered, as shown in the second latent Our data also showed that some characteristics of variable (LV2) of the multivariable classification analy- VLDL-related particles, together with GlycA, could help sis. Furthermore, this second multidimensional latent to better discriminate between T1DM subjects with and variable showed a loss of association between isolated without subclinical myocardial dysfunction. Regard- increased TG-rich parameters and myocardial dysfunc- ing T1DM, the advanced characteristics of VLDL has tion. Therefore, our data revealed an interaction between been positively and independently associated with arte- GlycA and TGRLs in T1DM subjects with myocardial rial stiffness [43]. Likewise, similar associations have dysfunction. This association was lost in those patients without myocardial dysfunction. Puig-Jové et al. Cardiovascular Diabetology (2022) 21:257 Page 9 of 11 Our study had some limitations that should be taken clarify the potential clinical applications of these findings into account. We matched individuals with and without as well as to investigate their biological basis. subclinical myocardial dysfunction by age, sex and gly- Abbreviations cemic control. This may have resulted in a biased com- DM Diabetes mellitus. parison because, according to original Thousand & 1 HF Heart failure. study results, patients with myocardial dysfunction were T2DM Type 2 diabetes mellitus. T1DM Type 1 diabetes mellitus. likely to be older and have longer diabetes duration than HFpEF Heart failure with preserved ejection fraction. subjects with normal function. Therefore, by selecting CVD Cardiovascular disease. subjects who were similar for these criteria, at least one FAs Fatty acids. TGs Triglycerides. group may be unrepresentative of the population from NMR Nuclear magnetic resonance. which it came, and the abnormalities observed in the TGRLs Triglycerides-rich lipoproteins. NMR profile may be different from a truly representa- HDL High density lipoproteins. SDCC Steno diabetes center in Copenhagen. tive population. Furthermore, considering the condition HbA1c Glycated hemoglobin. of hypertension as a risk factor for myocardial dysfunc- LVEF Left ventricle ejection fraction. tion, is important to emphasize that the use of antihyper- LAV Left atrial volume. UAER Urinary albumin excretion rate. tensive therapies was significantly more frequent in the eGFR Estimated glomerular filtration rate. group of subjects with myocardial dysfunction. Never- DOSY Diffusion-ordered spectroscopy. theless, due to the fact that these drugs can be used both VLDL Very low-density lipoproteins. LDL Low-density lipoproteins. as an antihypertensive treatment as well as a nephro- GA Genetic algorithm. protective intervention, we consider this as a limitation PLS-DA Partial least square discriminant analysis. to assess the condition of hypertension associated with AUC Area under the curve. BMI Body mass index. myocardial dysfunction. Additionally, the small number IDL Intermediate-density lipoprotein. of subjects who presented systolic dysfunction within NTproBNP N-terminal fragment of pro-B-type natriuretic peptide. subjects with myocardial dysfunction (n = 18, 11.7%) MRproANP Mid-regional pro atrial natriuretic peptide. CRP C-reactive protein. implies that the results obtained in the present study VLDLR VLDL receptor. should be primordially referred to diastolic dysfunction. hsCRP High-sensitivity CRP. Finally, since this study is cross-sectional we cannot infer causality between the metabolic profile and presence of Supplementary Information myocardial dysfunction. Possible strengths should also be The online version contains supplementary material available at https://doi. org/10.1186/s12933-022-01652-z. mentioned, since the current population is selected from a large study of ambulatory T1DM patients without pre- Supplementary Material 1: Supplementary Fig. 1: Comparison of the vious known heart disease. This cohort had the advan- distribution of the predicted probabilities showing the classification tage of a relatively low HbA1c and may therefore be at performance of the presence of MCD between two different models, being the x-axis the predicted probabilities for both classes and the y-axis a lower risk of HF than typical T1DM patients and that the count of observations. A: Model 1 includes classical risk variables may potentially have strengthened our findings. (age, sex, eGFR, NTproBNP, BMI, diabetes duration and systolic blood pressure >140mmHg). B: Model 2 includes classical risk variables and the NMR-assessed biomarkers. The inclusion of NMR parameters significantly Conclusion increased the AUROC from 0.62 [0.56–0.68] to 0.67 [0.61–0.73], with a NRI The present study uncovered associations between sub- considering NMR-assessed parameters of 21%. MCD: myocardial dysfunc- clinical myocardial dysfunction and advanced NMR tion, BMI: body mass index, NMR: nuclear magnetic resonance, AUROC: area under the ROC curve, NRI: net reclassification improvement. metabolic characteristics in patients with T1DM that were hidden in conventional analyses. The GlycA area Acknowledgements and the H/W GlycA ratio, as well as TGRLs (VLDL and We want to particularly acknowledge the participants of the Thousand & 1 IDL) related variables, were revealed as strong contribu- study and all the collaboration provided from SDCC. tors of subclinical myocardial dysfunction. According Author contributions to the aforementioned results, we propose a pivotal role CPJ, JJ, PR, DM and NAL contributed to the study design, data analysis of TGRLs characteristics and systemic inflammation and interpretation, and the writing of the manuscript. HUA, PR and MTJe reflected by the GlycA biomarker in subclinical myocar- contributed to the acquisition, analysis and interpretation of data. EC, MTJu, NAM and TSA contributed to data interpretation and discussion. PR, DM and dial dysfunction in T1DM patients. NAL contributed to the study coordination, conduct of the study and writing Estimation of the presence of subclinical myocardial of the manuscript. All authors critically reviewed the manuscript and agreed dysfunction among T1DM patients and its relation- with the final version of the manuscript. ship with lipoprotein and glycoprotein characteristics Funding revealed in this study still deserves more attention in The support for the original Thousand & 1 Study has been provided by the future. Therefore, further studies will be required to the European Foundation for the Study of Diabetes/Pfizer European Programme 2010 for Research into Cardiovascular Risk Reduction in Puig-Jové et al. Cardiovascular Diabetology (2022) 21:257 Page 10 of 11 Patients with Diabetes; and The Danish Heart Foundation (number 12-04- 2. Lindman BR, Dávila-Román VG, Mann DL, McNulty S, Semigran MJ, Lewis GD, R90-A3840-22725). This work was supported by Grants from Fundació La et al. Cardiovascular phenotype in HFpEF patients with or without diabetes: a MARATÓ de TV3 (201602- 30-31 to NAL and JJ), Instituto de Salud Carlos RELAX trial ancillary study. J Am Coll Cardiol. 2014;64(6):541–9. III (PI17- 00232 to JJ, PI17-01362 to NAL) and by CIBER -Consorcio Centro 3. Shah AD, Langenberg C, Rapsomaniki E, Denaxas S, Pujades-Rodriguez M, de Investigación Biomédica en Red- (CB15/00071), Instituto de Salud Gale CP, et al. Type 2 diabetes and incidence of a wide range of cardiovascu- Carlos III, Ministerio de Ciencia e Innovación, Spain, with co-funding from lar diseases: a cohort study in 1?9 million people. Lancet. 2015;26(385 Suppl the European Regional Development Fund (ERDF). JJ is supported by 1):86. funds provided by ISCIII (Grant CPII18/00004, Miguel Servet II program). 4. Avogaro A, Azzolina D, Fadini GP, Baldi I. Incidence of heart failure in patients This research was partially funded by the Agencia de Gestió d’Ajuts with type 1 diabetes: a systematic review of observational studies. J Endocri- Universitaris I de Recerca AGAUR, grant number 2020-PANDE-00083. nol Invest. 2021;44(4):745–53. 5. Nichols GA, Hillier TA, Erbey JR, Brown JB. Congestive heart failure in Data availability type 2 diabetes: prevalence, incidence, and risk factors. Diabetes Care. Primary material is held by the authors. 2001;24:1614–9. 6. From AM, Leibson CL, Bursi F, Redfield MM, Weston SA, Jacobsen SJ, et al. Dia- betes in heart failure: prevalence and impact on outcome in the population. Declarations Am J Med. 2006;119:591–9. 7. McHugh K, DeVore AD, Wu J, Matsouaka RA, Fonarow GC, Heidenreich PA, et Ethics approval and consent to participate al. 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