Advanced lipoprotein, glycoprotein and lipidomic profiles in hyperalphalipoproteinemia with and without atherosclerotic cardiovascular disease: a case–control study

Advanced lipoprotein, glycoprotein and lipidomic profiles in hyperalphalipoproteinemia with and without atherosclerotic cardiovascular disease: a case–control study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Advanced lipoprotein, glycoprotein and lipidomic profiles in hyperalphalipoproteinemia with and without atherosclerotic cardiovascular disease: a case–control study David Ceacero-Marín, Bárbara Fernández-Cidón, Xavier Pintó Sala, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8981110/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Despite the well-established protective role of HDL-cholesterol against atherosclerotic cardiovascular disease (ASCVD), several studies have observed a higher incidence of ASCVD in individuals with elevated HDL-cholesterol concentrations. This condition, known as hyperalphalipoproteinemia, appears to modify HDL composition, potentially resulting in a loss of its anti-atherogenic properties and, in some cases, a pro-atherogenic effect. This study aimed to assess advanced lipoprotein, lipidomic and inflammatory biomarker profiles using nuclear magnetic resonance (NMR) spectroscopy in patients diagnosed with hyperalphalipoproteinemia who have suffered a cardiovascular event. A case-control study was conducted on a cohort of patients diagnosed with hyperalphalipoproteinemia and ASCVD (n = 29) and a control group of individuals with hyperalphalipoproteinemia but without ASCVD (n = 41). A conventional lipid profile and an advanced lipoprotein, lipidomic and inflammatory biomarker profiles were performed using NMR spectroscopy. Patients with hyperalphalipoproteinemia and ASCVD showed higher concentrations of VLDL particles (particularly small VLDL), HDL particles (particularly small HDL) and higher triglyceride content in both lipoproteins, although serum concentrations of HDL-cholesterol and triglycerides were comparable within groups. Furthermore, ASCVD group presented an altered lipidomic profile, characterised by decreased concentrations of lysophosphatidylcholine, and increased concentrations of saturated fatty acids. In addition, these patients exhibited elevated concentrations of amyloid A protein in both serum and isolated HDL3 fraction. Patients with hyperalphalipoproteinemia and ASCVD showed a lipoprotein and lipidomic profile with metabolic, structural, and compositional alterations consistent with a more atherogenic pattern. This profile could not be detected by a conventional lipid profile. Nuclear Magnetic Resonance Biomolecular Lipoproteins HDL Cholesterol HDL Lipoproteins Lipidomic Cardiovascular Diseases Figures Figure 1 Introduction High-density lipoproteins (HDL) are heterogeneous particles with a complex composition that can vary significantly depending on individual genetic and environmental characteristics [ 1 ]. Epidemiological studies have shown that low serum HDL cholesterol (HDL-C) concentrations are associated with an increased risk of atherosclerotic cardiovascular disease (ASCVD) [ 2 ], but clinical trials have not demonstrated that the use of drugs to increase HDL-C has a preventive effect against ASCVD [ 3 ]. The relationship between HDL-C, ASCVD risk, and overall mortality is U-shaped [ 4 ]. Individuals with HDL-C concentrations at the lower and upper distribution curves exhibit the highest overall and cardiovascular mortality, with a higher risk of all-cause mortality in men [ 4 ]. Extremely high HDL-C concentrations were significantly associated with increased risk of ASCVD mortality and increased risk for coronary heart disease and ischemic stroke [ 5 ]. Notably, these high concentrations are characteristic of hyperalphalipoproteinemia (HALP), which is normally defined as HDL-C concentrations above the 90th percentile of the population [ 6 ]. HALP predisposes individuals to atherosclerotic disease [ 7 ]. In addition, patients present a wide spectrum of heterogeneous and nonspecific clinical manifestations, making it difficult to identify ASCVD risk in HALP patients. The discordance between elevated HDL-cholesterol and cardiovascular risk may be explained by structural and compositional remodeling of HDL that impairs its function. The aforementioned dysfunction attenuates the anti-inflammatory and antioxidant properties of HDL [ 8 ]. Indeed, it may even render the particles pro-atherogenic, thereby promoting endothelial injury in individuals with HALP [ 4 ]. Currently, clinical laboratories measure HDL-C. However, the antiatherogenic functions of HDL are not directly dependent on its cholesterol content. Therefore, measuring HDL-C does not reflect the functionality of HDL [ 1 ]. Similarly, measuring apolipoprotein A1, instead of HDL-C, has not been shown to be a better predictor of ASCVD [ 9 ]. Currently, reverse cholesterol transport can be assessed directly by measuring the cholesterol efflux capacity of macrophages (CEC) [ 10 ]. However, due to technical complexity and the lack of standardisation, the implementation of this technique in clinical practice is very limited. Analysis of the structure and composition of HDL is considered the most viable alternative for the assessment of HDL functionality [ 11 ]. Therefore, it is essential to assess the structure and composition of HDL particles in order to assess their functionality, given the established correlation between these structural characteristics and their cardioprotective properties [ 12 ]. In this context, nuclear magnetic resonance (NMR) spectroscopy of lipoproteins has emerged as the method of choice to determine the structure and composition of plasma lipoproteins. This technology is high-performance, reproducible, scalable, cost-effective, and meets laboratory metrological requirements. Indeed, HDL particle concentration (HDL-P) has been demonstrated to be a superior predictor of cardiovascular risk [ 13 ] in comparison to its cholesterol content. Similarly, lipidomics studies have shown to improve the prediction of individual ASCVD risk compared to current protocols for ASCVD screening in the general population [ 14 ]. In this sense, lipidomics may help to identify compositional patterns associated with reduced HDL functionality [ 15 ]. Conventional lipid profile (total cholesterol, triglycerides (TG), HDL-C, and LDL-C) has not been shown to be useful to assess cardiovascular risk in patients with HALP [ 16 ]. Consequently, it is essential to develop indirect biomarkers that assess HDL functionality. The objective of this study is to analyse an advanced lipoprotein profile in HALP patients who have suffered an ischaemic event. The profile includes lipidomic and inflammation biomarkers, and lipoprotein structure and composition assessed by NMR spectroscopy. The results will be compared with those from subjects with HALP without a history of ASCVD. Additionally, the objective was to develop a model, based on NMR parameters, to characterise and discriminate both groups. Methods Study design and participants This is a cross-sectional, retrospective case-control study conducted at a tertiary hospital in Barcelona, Spain. Two groups of patients under the age of 75 were studied: ASCVD-group (n = 29): Patients with ASCVD and HALP. Control-group (n = 41): Patients without ASCVD and HALP. In the cohort of patients with ASCVD, a retrospective evaluation was conducted to ascertain whether they had a history of hospitalization for acute coronary syndrome, stroke, coronary atherosclerosis, or lower limb atherosclerosis. Ischemic events occurring within the three months preceding study inclusion were excluded. Control-group was matched with the ASCVD-group according to sex, age, HDL-C, and TG. Subjects were selected from a primary care center in the same area population. Patients with renal failure (estimated glomerular filtration rate (eGFR) according to CKD-EPI 4.52 mmol/L), alcohol abuse (> 2 standard drinks/day), or those undergoing treatment with fibrates, barbiturates, phenytoin or omega-3 fatty acids were excluded from the study. Subjects were defined to have HALP if they presented HDL-C concentrations above the 90th percentile (P90) for age and gender in at least two laboratory tests, without the presence of secondary causes of high HDL-C. The P90 threshold of HDL-C was estimated from a retrospective laboratory database of primary care subjects from the same geographical area as the study. The database included 302,938 individuals aged 18–75 years who underwent testing between 2021 and 2023. Subjects with evidence of liver disease (aspartate aminotransferase, alanine aminotransferase, gamma-glutamyltransferase, or alkaline phosphatase outside the laboratory reference interval), renal impairment (eGFR 5 mg/L were excluded. P90 was calculated in a sex-specific manner for the overall 18–75-year age range. During the initial visit, patients carried out: a 17-item dietary questionnaire assessing adherence to a low-calorie Mediterranean diet [ 17 ], two physical activity questionnaires: RAPA1 and RAPA 2 (Rapid Assessment of Physical Activity) [ 18 ], two measurements of blood pressure and heart rate (obtaining the average of both measurements), height, weight and abdominal circumference. Laboratory analysis Conventional lipoprotein profile: The analysis of total cholesterol, HDL-C, LDL-C, TG, apolipoprotein A and apolipoprotein B was conducted using the Cobas® 8000 analyser (Roche Diagnostics, Rotkreuz, Switzerland). Sample size calculation The primary endpoint for sample size estimation was the between-group difference in the change of HDL-TG. Assuming a difference of 6.73 mg/dL between ASCVD and control groups and a common standard deviation of 4.5 mg/dL [ 22 ], a two-sided α = 0.05, and 95% power, the minimum sample size required per group was 13 participants/group. Statistical analysis Univariable analyses were performed to evaluate the association of each variable with the presence of ASCVD. Variables were adjusted (adjusted p -Value) for waist circumference, arterial hypertension, nutrition and physical activity RAPA1-derived. Given the large number of variables analysed and, consequently, the multiple hypothesis tests performed, the Benjamini-Hochberg (BH) procedure was applied to correct for multiple comparisons (adjusted p-Value (BH)). This approach facilitates the regulation of the false discovery rate (FDR), defined as the expected proportion of falsely significant results among those considered significant, which was set at 5% (q < 0.05). To characterise and discriminate the presence of ASCVD in individuals with HALP, a multivariate logistic regression model was constructed using a backward stepwise selection procedure. Variables that remained significant after adjustment for clinical covariates (adjusted p -Value (BH) < 0.05) were selected. The model was adjusted for age, sex, waist circumference, arterial hypertension, nutrition, and physical activity. This methodological approach permitted the integration of the molecular variables into a single model, thereby deriving a predictive equation and elucidating the manner in which molecular alterations are incorporated into a comprehensive pattern associated with ASCVD when comparing both groups. Diagnostic accuracy of the model was evaluated using a receiver operating characteristic (ROC) curve analysis. In addition, cross-validation and bootstrap validation of the ROC analysis were performed to avoid the risk of overfitting and increase predictive power. The analyses were performed using RStudio statistical software, version 4.4.3. Results To define HALP in subjects aged 18–75 years, the cutoff point obtained for the P90 of HDL-C concentrations in men (n = 131,140) was 1.75 mmol/L (67.67 mg/dL) and in women (n = 171,798) was 2.14 mmol/L (82.75 mg/dL). Clinical characteristics of both groups are described in Table 1 . Table 1 Clinical data of patients and control subjects. Variables Control-group (n = 41) ASCVD-group (n = 29) p-Value Age (years) 57 (50–63) 55 (51–61) 0.830 Male sex 18 (43.9%) 15 (51.7%) 0.687 Family history of ASCVD 6 (14.6%) 9 (31%) 0.177 Type 1 diabetes mellitus 0 0 - Type 2 diabetes mellitus 0 (0%) 1 (3.4%) 0.414 Arterial hypertension 0 (0%) 11 (37.9%) < 0.001 Inflammatory/autoimmune disease 0 (0%) 3 (10.3%) 0.068 Liver disease 0 (0%) 2 (6.9%) 0.168 Thromboembolic disease 0 (0%) 1 (3.4%) 0.414 Early menopause before age 40, women 2 (8.7%) 1 (7.1%) 1.000 Polycystic ovary syndrome, women 0 1 (7.1%) 0.378 Mean systolic blood pressure (mm Hg) 128.14 ± 14.63 134.29 ± 12.14 0.066 Mean diastolic blood pressure (mm Hg) 82.43 ± 7.55 81.64 ± 6.85 0.657 Heart rate (bpm) 76.51 ± 7.28 72.12 ± 10.65 0.063 Body mass index (kg/m 2 ) 23.52 ± 2.30 27.60 ± 4.00 < 0.001 Abdominal circumference (cm) 82.00 (73.00–88.00) 90.00 (80.00–97.00) < 0.001 Active smoker 3 (7.3%) 4 (13.8%) 0.438 Low/moderate alcohol consumption 14 (34.1%) 9 (31%) 0.988 Nutrition (Dietary questionnaire) 10 (3) 8 (3) 0.027 Physical activity (RAPA1-derived): Moderate/vigorous physical activity 28 (68.3%) 13 (44.8%) 0.086 RAPA1 (Rapid Assessment of Physical Activity 1) 0.066 - Sedentary/Light physical activity 13 (31.7%) 16 (55.2%) - Moderate physical activity 17 (41.5%) 5 (17.2%) - Vigorous physical activity 11 (26.8%) 8 (27.6%) RAPA2 (Rapid Assessment of Physical Activity 1) 0.018 - Strength training 19 (46.3%) 6 (20.7%) - Balance training 12 (29.3%) 5 (17.2%) - Strength and balance training 6 (14.6%) 1 (3.4%) Atherosclerotic cardiovascular disease Acute myocardial infarction/Cardiovascular accident 0 13 (44.8%) - Angina pectoris 0 5 (17.2%) - Stroke 0 2 (7.0%) - Coronary atherosclerosis 0 5 (17.2%) Peripheral artery disease 0 4 (13.8%) - Treatment of patients Statins 0 26 (89.7%) - PCSK9 inhibitors 0 5 (17.2%) - Ezetimibe 0 16 (55.2%) - Acetylsalicylic acid 0 22 (75.9%) - Data are expressed as mean (standard deviation) or median (interquartile range) depending on the normal distribution and homoscedasticity of continuous variables and number of cases (percentage) for qualitative variables. Categorical variables were compared using the χ² test or Fisher's exact test. Quantitative variables were compared using Student's t-test, Welch's t-test or Mann–Whitney U test, depending on assumptions of normality and homoscedasticity. ASCVD: Atherosclerotic Cardiovascular Disease; RAPA: Rapid Assessment of Physical Activity; PCSK9: proprotein convertase subtilisin/kexin type 9. The results of the univariable analysis are presented in Table 2 and Table 3 . Table 2 Advanced lipoproteins and glycoproteins profiles by nuclear magnetic resonance spectroscopy in the ASCVD and control groups. Variables (units) Control-group (n = 41) ASCVD-group (n = 29) p -Value Adjusted p -Value Adjusted p -Value (BH) Cholesterol (mmol/L) 5.33 (4.88–5.64) 4.44 (3.90–4.93) < 0.001 0.139 0.267 TG (mmol/L) 0.76 (0.62–0.85) 0.78 (0.58–1.00) 0.668 0.651 0.705 HDL-C (mmol/L) 2.36 (2.04–2.59) 2.28 (2.11–2.47) 0.712 0.244 0.396 LDL-C (mmol/L) 2.64 (2.22–2.93) 1.68 (1.33–2.10) < 0.001 0.036 0.117 Apolipoprotein A1 (g/L) 1.94 (1.83–2.08) 1.92 (1.74–2.11) 0.525 0.870 0.870 Apolipoprotein B (g/L) 0.79 (0.15) 0.70 (0.22) 0.078 0.540 0.638 Glucose (mmol/L) 4.60 (4.30–4.90) 5.10 (4.80–5.40) < 0.001 0.144 0.267 Creatinine (µmol/L) 69.93 (13.58) 74.07 (13.25) 0.207 0.512 0.638 Glomerular filtration rate (mL/min/1.73 m 2 ) 90 (89–90) 90 (84–90) 0.160 0.527 0.638 Hs-CRP (mg/L) 0.49 (0.28–1.00) 1.60 (0.70–3.00) < 0.001 0.093 0.241 SAA in serum (mg/dL) 2.69 (1.95–4.30) 4.50 (3.15–6.17) 0.011 0.006 0.027 SAA in HDL3 fraction (mg/dL) 1.52 (1.05–2.41) 2.87 (1.64–3.50) 0.018 0.005 0.027 VLDL particles VLDL-P (nmol/L) 26.34 (22.60–29.26) 31.70 (29.66–34.67) < 0.001 0.084 0.111 Large VLDL-P (nmol/L) 0.73 (0.59–0.83) 0.92 (0.73–1.07) 0.002 0.072 0.111 Medium VLDL-P (nmol/L) 3.01 (2.05–3.93) 2.56 (1.59–3.73) 0.328 0.874 0.874 Small VLDL-P (nmol/L) 22.44 (19.64–24.61) 28.73 (26.81–29.75) < 0.001 0.009 0.018 VLDL-Z (nm) 42.26 (0.24) 42.07 (0.35) 0.015 < 0.001 0.004 VLDL-C (mg/dL) 8.37 (5.75–10.35) 8.61 (6.24–9.79) 0.668 0.824 0.874 VLDL-TG (mg/dL) 35.41 (29.52–40.58) 47.34 (42.41–52.37) < 0.001 0.004 0.012 VLDL-TG/VLDL-C 4.45 (3.85–5.20) 5.62 (4.92–6.74) < 0.001 0.001 0.004 LDL particles LDL-P (nmol/L) 1196.55 (160.40) 1038. (218.12) 0.002 0.177 0.309 Large LDL-P (nmol/L) 204.67 (189.50–220.43) 166.80 (146.69–186.54) < 0.001 0.008 0.058 Medium LDL-P (nmol/L) 398.86 (359.02–475.73) 283.33 (234.16–345.20) < 0.001 0.079 0.275 Small LDL-P (nmol/L) 596.24 (548.74–618.12) 560.75 (492.15–609.87) 0.252 0.944 0.944 LDL-Z (nm) 21.37 (0.22) 21.11 (0.25) < 0.001 0.017 0.079 LDL-TG (mg/dL) 14.55 (13.61–17.12) 11.41 (9.59–14.45) 0.001 0.131 0.293 LDL-TG/LDL-C 0.12 (0.11–0.13) 0.12 (0.10–0.13) 0.233 0.146 0.293 IDL particles IDL-C (mg/dL) 7.89 (2.77) 8.50 (3.09) 0.406 0.437 0.556 IDL-TG (mg/dL) 8.98 (1.92) 10.87 (2.08) < 0.001 0.004 0.055 IDL-TG/IDL-C 1.13 (1.05–1.36) 1.29 (1.19–1.42) 0.044 0.115 0.293 HDL particles HDL-P (µmol/L) 34.55 (3.18) 37.37 (5.24) 0.014 0.015 0.039 Large HDL-P (nmol/L) 321.90 (43.21) 290.08 (55.72) 0.013 0.350 0.400 Medium HDL-P (µmol/L) 13.77 (1.71) 13.46 (2.67) 0.590 0.612 0.612 Small HDL-P (µmol/L) 20.46 (2.76) 23.61 (3.59) < 0.001 0.005 0.035 HDL-Z (nm) 8.33 (0.06) 8.26 (0.06) < 0.001 0.024 0.048 HDL-TG (mg/dL) 14.65 (12.57–15.57) 17.16 (14.74–18.76) < 0.001 0.009 0.035 HDL-TG/HDL-C 0.16 (0.14–0.18) 0.20 (0.17–0.21) < 0.001 0.036 0.057 Glicoprotein profile Glyc-B (µmol/L) 330.57 (26.64) 328.21 (38.34) 0.776 0.360 0.556 Glyc-A (µmol/L) 595.46 (563.18–630.54) 602.76 (511.31–654.12) 0.990 0.531 0.620 H/W Glyc-B 4.17 (3.92–4.38) 4.20 (3.77–4.57) 0.896 0.415 0.556 H/W Glyc-A 14.99 (14.16–15.53) 15.29 (13.91–16.96) 0.482 0.852 0.918 Results are expressed as mean (standard deviation) or median (interquartile range), depending on normality and homoscedasticity. Categorical variables were compared using the χ² test or Fisher's exact test. Quantitative variables were compared using Student's t-test, Welch's t-test or Mann–Whitney U test, depending on assumptions of normality and homoscedasticity. p-value: unadjusted p-value from the univariable analysis; Adjusted p-value: p-value after adjustment for covariates (abdominal circumference, arterial hypertension, nutrition, and physical activity); Adjusted p-value (BH): p-value adjusted for the covariates and for multiple comparisons using the Benjamini–Hochberg procedure (false discovery rate control). VLDL: very low-density lipoproteins, LDL: low-density lipoproteins, HDL: high-density lipoproteins, C: cholesterol, IDL: intermediate-density lipoproteins, P: particles; hs-CRP: ultra-sensitive C-reactive protein; TG: triglycerides; Z: diameter, Glyc: glycoprotein, SAA: serum amyloid A, H/W: height-to-width ratio. Table 3 Lipidomic profile by nuclear magnetic resonance spectroscopy in the ASCVD and control groups. p -Value and p -Value corrected by Benjamini–Hochberg (BH) are showed. Variables (units) Control-group (n = 41) ASCVD-group (n = 29) p -Value Adjusted p -Value Adjusted p -Value (BH) Lipidomic profile Cholesterol esters (mmol/L) 3.15 (3.07–3.27) 2.94 (2.66–3.08) 0.002 0.358 0.477 Free cholesterol (mmol/L) 2.00 (1.91–2.11) 1.76 (1.66–1.92) 0.002 0.661 0.756 Glycerophospholipids (except lysophosphatidylcholine) (mmol/L) 3.95 (0.41) 3.81 (0.54) 0.237 0.913 0.913 Phosphatidylcholine (mmol/L) 2.71 (0.26) 2.75 (0.35) 0.569 0.240 0.385 Sphingomyelin (mmol/L) 1.02 (0.98–1.05) 0.96 (0.88–1.06) 0.070 0.814 0.868 Phosphatidylcholine/ Sphingomyelin 2.68 (0.20) 2.83 (0.32) 0.031 0.241 0.385 Lysophosphatidylcholine (mmol/L) 0.74 (0.05) 0.68 (0.07) < 0.001 0.009 0.049 Lysophosphatidylcholine/ Sphingomyelin 0.73 (0.03) 0.70 (0.06) 0.011 < 0.001 0.002 Polyunsaturated fatty acids (mmol/L) 14.70 (1.82) 15.27 (2.49) 0.297 0.232 0.385 Linoleic acid (mmol/L) 4.02 (0.61) 3.54 (0.53) 0.001 0.068 0.218 Saturated fatty acids (mmol/L) 7.60 (0.51) 8.25 (1.40) 0.022 < 0.001 0.002 Omega-6 and omega-7 fatty acids (mmol/L) 5.92 (0.67) 5.84 (0.77) 0.780 0.160 0.367 Omega-9 fatty acid (mmol/L) 2.73 (2.57–2.92) 2.84 (2.53–3.10) 0.139 0.322 0.468 Omega-3 fatty acid (mmol/L) 0.40 (0.36–0.44) 0.43 (0.39–0.48) 0.100 0.104 0.277 Docosahexaenoic acid (mmol/L) 0.18 (0.02) 0.17 (0.03) 0.098 0.639 0.756 Arachidonic acid + eicosapentaenoic acid (mmol/L) 1.64 (0.23) 1.82 (0.28) 0.008 0.061 0.218 Results are expressed as mean (standard deviation) or median (interquartile range), depending on normality and homoscedasticity. Categorical variables were compared using the χ² test or Fisher's exact test. Quantitative variables were compared using Student's t-test, Welch's t-test or Mann–Whitney U test, depending on assumptions of normality and homoscedasticity. p-value: unadjusted p-value from the univariable analysis; Adjusted p-value: p-value after adjustment for covariates (abdominal circumference, arterial hypertension, nutrition, and physical activity); Adjusted p-value (BH): p-value adjusted for the covariates and for multiple comparisons using the Benjamini–Hochberg procedure (false discovery rate control). VLDL: very low-density lipoproteins, LDL: low-density lipoproteins, HDL: high-density lipoproteins, C: cholesterol, IDL: intermediate-density lipoproteins, P: particles; hs-CRP: ultra-sensitive C-reactive protein; TG: triglycerides; Z: diameter, Glyc: glycoprotein, SAA: serum amyloid A, H/W: height-to-width ratio. To perform the multiple logistic regression model, non-significant variables were eliminated (adjusted p-value (BH) > 0.05). Therefore, the preselected variables were: VLDL-TG, Small VLDL-P, VLDL-Z, HDL-P, Small HDL-P, HDL-TG, HDL-Z, LPC, SFA, serum SAA, and SAA in HDL3 ( Supplementary Fig. 1 ). Subsequently, variables with high correlation (ρ > 0.6) or multicollinearity (variance inflation factor > 3) were eliminated. Finally, after applying the backward stepwise selection procedure to the logistic model, the variables with the greatest discriminative ability between both groups were: HDL-Z, HDL-TG, VLDL-Z, and SFA (Table 4 ). Table 4 Results of the multiple logistic regression analysis adjusted by sex, age, abdominal circumference, arterial hypertension, nutrition and physical activity. Variables (units) Coefficients OR CI 95% p -Value R² Constant 647.05 – – 0.022 0.874 HDL-Z (per 0.1 nm) -2.723 0.066 0.046–0.968 0.047 HDL-TG (mg/dL) 0.470 1.600 1.015–2.522 0.043 VLDL-Z (per 0.1 nm) -1.106 0.331 0.121–0.902 0.031 SFA (mmol/L) 3.46 31.8 1.2–814.6 0.037 Sex -0.580 0.558 0.046–6.878 0.650 Age -0.030 0.970 0.806–1.167 0.745 Abdominal circumference 0.127 1.135 0.966–1.333 0.124 Arterial hypertension 24.01 – – 0.995 Nutrition 0.113 1.119 0.590–2.124 0.730 Physical activity (RAPA1-derived) -2.062 0.127 0.007–2.473 0.173 OR: odds ratio; 95% CI: 95% confidence interval; RAPA: Rapid Assessment of Physical Activity; R 2 : Nagelkerke's pseudo-R 2 ; HDL-Z: diameter of HDL; HDL-TG: triglyceride content of HDL; VLDL-Z: diameter of VLDL; SFA: saturated fatty acids. In the multiple logistic regression model (adjusted by age, sex, waist circumference, arterial hypertension, nutrition and physical activity), HDL-TG (OR [95%CI]: 1.60 [1.02–2.52]; p = 0.043), and SFA (OR [95%CI]: 31.8 [1.2–814.6); p = 0.037) were independently and positively associated with ASCVD-group. In contrast, HDL-Z (per 0.1 nm) (OR [95%CI]: 0.066 [0.046–0.968]; p = 0.047) and VLDL-Z (per 0.1 nm) (OR [95%CI]: 0.331 [0.121–0.902); p = 0.031) were independently and inversely associated (Table 4 ). The final model showed excellent apparent discrimination (area under the curve (AUC) [95%CI] of 0.981 [0.958–1.000]) (Fig. 1 ). Internal validation confirmed robust performance, with a mean cross-validated AUC of 0.846 [0.766–0.936] and bootstrap-estimated AUC of 0.929 [0.839–0.981]. The model showed an apparent sensitivity of 100%, specificity of 90.2% and 94.3% of accuracy. Internal validation yielded sensitivity and specificity of 84.4% and 85.0%, respectively. Bootstrap internal validation yielded sensitivity and specificity of 89.2% and 92.8%, respectively. The overall model was statistically significant (likelihood ratio test: χ²=73.25, p < 0.001). Model calibration was assessed with the Hosmer–Lemeshow goodness-of-fit test (g = 10; χ²=3.66, p = 0.909), indicating adequate calibration. Akaike Information Criterion of the model was 43.72. The model showed a Nagelkerke pseudo-R² of 0.874, consistent with good overall model fit. Discussion In this study, we present, for the first time, the assessment of a lipoprotein, lipidomic, and inflammatory biomarker profile, based on NMR, in patients with HALP and ASCVD, compared to control subjects with HALP without ASCVD. The observed changes in the ASCVD-group related to LDL, free and esterified cholesterol can be explained by the fact that all these patients are undergoing secondary prevention with lipid-lowering treatment (Table 1 ). In contrast, the conventional lipoprotein profile —defined by HDL-C and TG— did not show significant discrepancies between groups ( p = 0.705 and p = 0.396, respectively). However, relevant and significant differences in the structure and composition of lipoproteins were detected when the advanced lipid profile was analyzed by NMR spectroscopy. In the present study, patients with HALP and ASCVD showed higher concentrations of VLDL-P ( p = 0.111) and small VLDL particles ( p = 0.018). In addition, VLDL-Z was lower compared to the control group ( p = 0.004). Furthermore, the ASCVD-group exhibited higher HDL-P concentrations ( p = 0.039) and an increase in small HDL-P ( p = 0.035), which resulted in a decrease in HDL-Z ( p = 0.048). In addition, an increase in TG content was observed in VLDL, IDL and HDL lipoproteins ( p = 0.012, p = 0.055 and p = 0.035, respectively) in the ASCVD-group. The increase in the number of large VLDL particles may result in their transformation into small and dense VLDL particles due to a possible decrease in the lipoprotein lipase (LPL) activity. This process results in the accumulation of small, dense, and highly atherogenic remnant particles [ 22 ]. Indeed, in patients treated with statins who have achieved therapeutic LDL-C targets, the concentration of small VLDL-P was strongly associated with the risk of ASCVD [ 23 ]. This TG enrichment suggests an imbalance in lipid distribution and an alteration in lipoprotein remodeling, possibly mediated by an increase in cholesterol ester transport protein (CETP) activity and a decrease in LPL activity. This process leads to the generation of TG-rich lipoproteins (TRL). Consequently, this increase in VLDL-TG could generate an excess of atherogenic TRL remnants. In this context, an intensification of CETP-mediated exchange is expected, in which TRLs transfer TG to HDL in exchange for cholesterol esters, which explains the increase in HDL-TG concentrationss. A strong relationship has been observed between increased HDL-TG concentrations and the risk of premature cardiovascular disease [ 22 ]. Consequently, HDL-TG has been proposed as a good marker to assess HDL functionality and to evaluate the risk of ASCVD in patients with atherosclerosis [ 24 ]. Furthermore, smaller HDL particles with higher TG content exhibit lower cholesterol uptake, greater susceptibility to oxidation, faster catabolism by endothelial lipase [ 25 ], and, presumably, lower HDL functionality [ 26 ]. TG-enriched HDL becomes a superior substrate for lipases (particularly hepatic lipase), thus promoting TG hydrolysis and particle contraction. This is consistent with the decrease in HDL-Z and the increase in small HDL-P concentration. Few studies have evaluated the relationship between HDL size and ASCVD risk beyond HDL-C. In addition, evidence is even more limited in individuals with HALP. In patients with established coronary artery disease (CAD), some studies show that higher small HDL concentrations are associated with a better prognosis [ 27 ]. However, other studies have reported a positive correlation between small HDL particle size and carotid intima-media thickness [ 28 ]. Pullinger CR et al [ 29 ] demonstrated in a meta-analysis that elevated concentrations of pre-beta-1 HDL (the smallest, disc-shaped form of HDL) are strongly associated with coronary heart disease (CHD) and myocardial infarction, regardless of traditional factors. This supports the hypothesis that certain small forms of HDL may behave as risk markers. Similarly, other studies have reported that small HDL concentrations are elevated in CHD [ 30 ] and CAD [ 31 ]. In addition, small HDL particles have been associated with a lower CEC compared with medium and large HDL particles [ 32 ]. This finding could reflect alterations in HDL functionality or HDL maturation, leading to the possible accumulation of small HDL particles. Consequently, measurement of a classic lipid profile (TG and HDL-C) was inadequate to discriminate between HALP patients with and without ASCVD. However, advanced analysis of the lipidic profile using NMR allowed the identification of an altered pattern of lipoprotein composition associated with ASCVD, potentially compatible with a remodelling of HDL particles that may condition their functionality. Furthermore, conventional lipid-lowering treatments (Table 1 ) did not improve either structure or composition of HDL. Consequently, there is a need to develop new therapeutic strategies that specifically modify composition and/or structure of HDL, or alternatively, promote non-pharmacological interventions. Such interventions may include nutritional guidelines based on the Mediterranean diet and exercise programmes, with the aim of restoring the antiatherogenic properties of these lipoproteins [ 33 , 34 ]. As shown in Table 1 , the control group had greater adherence to the Mediterranean diet and higher levels of physical activity. Mediterranean dietary intervention and lifestyle improvements are associated with reduced TG content in HDL and VLDL, as well as reduced VLDL-P, related to antiatherogenic changes [ 35 ]. Phenol-rich olive oil enhances HDL function, thus improving CEC, increasing HDL size and fluidity, and reducing oxidative stress [ 36 ]. In this regard, higher diet quality or a more anti-inflammatory diet (DASH) was associated with less small VLDL-P and HDL-P [ 37 ]. Similarly, current moderate and vigorous leisure-time physical activity showed a higher HDL antioxidant capacity [ 38 , 39 ]. Regarding the glycoprotein profile by NMR, no specific variable has been linked to a particular proinflammatory pattern. However, a different glycoprotein pattern has been identified, which differs from that observed in patients with type 2 diabetes mellitus [ 40 ], hypertriglyceridemia [ 41 ] or obesity [ 42 ], who exhibit increased concentrations of these glycoproteins. An increase in SAA concentration has been observed in total serum and isolated HDL3 serum of patients with HALP and ASCVD ( p = 0.027). This finding suggests that these patients exhibit a chronic inflammatory state, which is associated with an increased risk of developing ASCVD and higher mortality rates [ 43 ]. It is hypothesized that the incorporation of SAA into HDL particles converts atheroprotective HDL into dysfunctional HDL. Increased SAA concentration is associated with an alteration in its anti-inflammatory properties, although HDL-C concentration is not affected [ 44 ]. The lipidomic study highlighted a decrease in the concentrations of LPC ( p = 0.049), as well as an increase in SFA concentrations ( p = 0.002) in the ASCVD-group. The reduction in amphipathic/surface lipids (such as LPC) in the study group is consistent with the increased HDL-TG/HDL-C ratio (p = 0.057) observed in the ASCVD-group. TG enrichment increases the susceptibility of HDL to the action of hepatic and endothelial lipase, which accelerates its catabolism and reduces its plasma residence time. Consequently, shorter circulation time could limit HDL maturation and LPC accumulation in the particle, resulting in lower plasma LPC concentration. Similarly, the decrease in the LPC/sphingomyelin ratio ( p = 0.002) in the ASCVD group suggests phospholipid remodeling towards a more rigid lipoprotein monolayer [ 45 ] that is less favourable for Lecithin-cholesterol acyltransferase activity, consistent with a possible structurally less functional HDL profile despite high HDL-C concentrations. These results are consistent with studies that have associated reduced LPC concentrations with an increased risk of cardiovascular events in patients undergoing hemodialysis [ 46 ]. The high SFA concentrations observed in the ASCVD-group ( p = 0.002) are consistent with an adverse cardiometabolic profile in these patients. Observational studies have shown that a higher average intake of SFA is associated with adverse cardiovascular outcomes [ 47 ]. Collectively, the observed lipidomic pattern is consistent with a remodeling of the lipidome towards a relatively more saturated profile with lower availability of membrane/amphipathic lipids, potentially relevant for the composition and functionality of circulating lipoproteins. This phenotype have been described in high-risk cardiometabolic contexts [ 48 ]. In our cohort with HALP, these findings suggest that plasma lipidomics provides complementary information to the conventional lipid profile and may help to discriminate individuals with HALP and ASCVD. Multiple logistic regression analysis identified an NMR-based lipoprotein and lipidomic pattern associated with the presence of ASCVD in patients with HALP, characterised by small VLDL and HDL particles, TG-enriched HDL, and higher SFA content. This model showed an excellent discriminatory power between HALP patients with and without ASCVD and identified a combination of variables potentially compatible with functional alterations in HDL. This equation was adjusted by sex, age, abdominal circumference and arterial hypertension and showed good diagnostic sensitivity (100%), specificity (90.2%) and AUC of 0.981. The similarity of AUC, sensitivity, and specificity in model performance and internal validation procedures (cross-validation and bootstrap) supports the internal robustness and stability of the model, suggesting limited overfitting in the study sample. However, external validation is still required before clinical generalisation. In summary, patients with HALP and ASCVD exhibited a lipoprotein and lipidomic profile of metabolic, structural, and compositional alterations that cannot be detected by a conventional lipid profile. These findings highlight the importance of developing new diagnostic strategies specifically designed to identify changes in the composition and/or structure of HDL in patients with HALPIn addition, novel therapeutic strategies and non-pharmacological interventions may help to improve risk stratification and could be relevant for the clinical management of ASCVD risk in patients with HALP. Conclusions Patients with HALP and ASCVD showed differences in lipoprotein structure and composition consistent with a more atherogenic pattern, which are not detected by the conventional lipid profile. Advanced lipid profile analysis showed increased concentrations of HDL-P and VLDL-P in the ASCVD-group, at the expense of small HDL and VLDL particles. Additionally, HDL, IDL and VLDL particles showed an increased TG content. Lipidomic analysis revealed decreased concentrations of LPC and increased SFA concentrations in patients with HALP and ASCVD. In addition, these patients exhibited elevated concentrations of amyloid A protein in both serum and isolated HDL3 fraction. Combined analysis of advanced lipoprotein, lipidomics and inflammatory biomarkers profiles using NMR can characterise and discriminate subjects with HALP and ASCVD. Declarations Acknowledgements This work has been developed in the context of the PhD program in Biochemistry, Molecular Biology and Biomedicine of the Autonomous University of Barcelona, Spain. Funding This work has been funded by the Col·legi de Farmacèutics de Barcelona [grant number S/0006276-2023]. The funding sponsors did not participate in any way in the design of the study, the collection, analysis or interpretation of data, the writing of the article, or the decision to publish the results. Author contributions Conceptualization: D. Ceacero-Marín, B. Fernández-Cidón, X. Pintó, M. Fanlo-Maresma, V. Esteve-Luque, N. Amigó, E. Corbella, L.F. Dubón, A. Riera-Mestre, and M.J. Castro-Castro; Data curation: D. Ceacero-Marín, B. Fernández-Cidón, N. Amigó, E. Corbella, and M.J. Castro-Castro; Formal analysis: D. Ceacero-Marín, B. Fernández-Cidón, X. Pintó, E. Corbella, A. Riera-Mestre, and M.J. Castro-Castro; Funding acquisition: D. Ceacero-Marín, B. Fernández-Cidón, and M.J. Castro-Castro; Investigation: D. Ceacero-Marín, B. Fernández-Cidón, X. Pintó, M. Fanlo-Maresma, V. Esteve-Luque, N. Amigó, E. Corbella, L.F. Dubón, A. Riera-Mestre, and M.J. Castro-Castro; Methodology: D. Ceacero-Marín, B. Fernández-Cidón, X. Pintó, M. Fanlo-Maresma, V. Esteve-Luque, N. Amigó, E. Corbella, A. Riera-Mestre, and M.J. Castro-Castro; Project administration: D. Ceacero-Marín, B. Fernández-Cidón, X. Pintó, A. Riera-Mestre, and M.J. Castro-Castro; Resources, X. Pintó, L.F. Dubón, and A. Riera-Mestre; Supervision: B. Fernández-Cidón, X. Pintó, M. Fanlo-Maresma, V. Esteve-Luque, L.F. Dubón, A. Riera-Mestre, and M.J. Castro-Castro; Validation: X. Pintó, N. Amigó, and A. Riera-Mestre; Visualization, X. Pintó, L.F. Dubón, and A. Riera-Mestre; writing—original draft: D. Ceacero-Marín; and writing—review & editing: D. Ceacero-Marín, B. Fernández-Cidón, X. Pintó, M. Fanlo-Maresma, V. Esteve-Luque, N. Amigó, E. Corbella, L.F. Dubón, A. Riera-Mestre, and M.J. Castro-Castro. Data availability The data that support the findings of this study are available from the corresponding author on reasonable request. Conflict of interest D. Ceacero-Marín, B. Fernández-Cidón, X. Pintó, M. Fanlo-Maresma, V. Esteve-Luque, E. Corbella, L.F. Dubón, A. Riera-Mestre, and M.J. Castro-Castro declare that they have no conflict of interest. N. Amigó is a stock owner of Biosfer Teslab and is a patent holder of the lipoprotein profiling described in the present manuscript. Ethical approval and Informed consent The study protocol conforms to the ethical guidelines of the 1975 Declaration of Helsinki. The study protocol has been priorly approved by the Institution's ethics committees on research on humans (the hospital and primary care centre (approvals numbers: PR119/23 and Ref. 25/134-P, respectively)). Written informed consent was obtained from each patient included in the study. References von Eckardstein A, Nordestgaard BG, Remaley AT et al (2023) High-density lipoprotein revisited: biological functions and clinical relevance. Eur Heart J 44:1394–1407. https://doi.org/10.1093/eurheartj/ehac605 Gordon T, Castelli WP, Hjortland MC, Kannel WB, Dawber TR (1977) High-density lipoprotein as a protective factor against coronary heart disease: the Framingham Study. Am J Med 62:707–714. https://doi.org/10.1016/0002-9343(77)90874-9 Schandelmaier S, Briel M, Saccilotto R et al (2017) Niacin for primary and secondary prevention of cardiovascular events. Cochrane Database Syst Rev 6:CD009744. https://doi.org/10.1002/14651858 Madsen CM, Varbo A, Nordestgaard BG (2017) Extreme high high-density lipoprotein cholesterol is paradoxically associated with high mortality in men and women: two prospective cohort studies. Eur Heart J 38:2478–2486. https://doi.org/10.1093/eurheartj/ehx163 Hirata A, Sugiyama D, Watanabe M et al (2018) Association of extremely high levels of high-density lipoprotein cholesterol with cardiovascular mortality in a pooled analysis of 9 cohort studies including 43,407 individuals: The EPOCH-JAPAN study. J Clin Lipidol 12:674–684e5. https://doi.org/10.1016/j.jacl.2018.01.014 Giammanco A, Noto D, Barbagallo CM et al (2021) Hyperalphalipoproteinemia and beyond: the role of HDL in cardiovascular diseases. Life (Basel) 11:581. https://doi.org/10.3390/life11060581 Yamashita S, Maruyama T, Hirano KI et al (2000) Molecular mechanisms, lipoprotein abnormalities and atherogenicity of hyperalphalipoproteinemia. Atherosclerosis 152:271–285. https://doi.org/10.1016/S0021-9150(00)00574-8 Navab M, Reddy ST, van Lenten BJ, Fogelman AM (2011) HDL and cardiovascular disease: atherogenic and atheroprotective mechanisms. Nat Rev Cardiol 8:222–232. https://doi.org/10.1038/nrcardio.2010.222 Di Angelantonio E, Gao P, Pennells L, Kaptoge S, Caslake M et al (2012) Lipid-related markers and cardiovascular disease prediction. JAMA 307:2499–2506. https://doi.org/10.1001/jama.2012.6571 Khera AV, Demler OV, Adelman SJ et al (2017) Cholesterol efflux capacity, high-density lipoprotein particle number, and incident cardiovascular events: an analysis from the JUPITER Trial. Circulation 135:2494–2504. https://doi.org/10.1161/CIRCULATIONAHA.116.025678 Thakkar H, Vincent V, Sen A et al (2021) Changing perspectives on HDL: from simple quantity measurements to functional quality assessment. J Lipids 2021:5585521. https://doi.org/10.1155/2021/5585521 Marsche G, Heine GH, Stadler JT, Holzer M (2020) Current understanding of the relationship of HDL composition, structure and function to their cardioprotective properties in chronic kidney disease. Biomolecules 10:1348. https://doi.org/10.3390/biom10091348 Toth PP, Grabner M, Punekar RS et al (2014) Cardiovascular risk in patients achieving low-density lipoprotein cholesterol and particle targets. Atherosclerosis 235:585–591. https://doi.org/10.1016/j.atherosclerosis.2014.05.914 Eichelmann F, Sellem L, Wittenbecher C et al (2022) Deep lipidomics in human plasma: cardiometabolic disease risk and effect of dietary fat modulation. Circulation 146:21–35. https://doi.org/10.1161/CIRCULATIONAHA.121.056805 Gómez Rosso L, Lhomme M, Meroño T et al (2014) Altered lipidome and antioxidative activity of small, dense HDL in normolipidemic rheumatoid arthritis: relevance of inflammation. Atherosclerosis 237:652–660. https://doi.org/10.1016/j.atherosclerosis.2014.09.034 Hancock-Cerutti W, Lhomme M, Dauteuille C et al (2017) Paradoxical coronary artery disease in humans with hyperalphalipoproteinemia is associated with distinct differences in the high-density lipoprotein phosphosphingolipidome. J Clin Lipidol 11(5):1192–1200e3. https://doi.org/10.1016/j.jacl.2017.06.018 Álvarez-Álvarez I, Martínez-González MÁ, Sánchez-Tainta A et al (2019) Adherence to an energy-restricted Mediterranean diet score and prevalence of cardiovascular risk factors in the PREDIMED-Plus: a cross-sectional study. Rev Esp Cardiol (Engl Ed) 72:925–934. https://doi.org/10.1016/j.rec.2018.08.010 Topolski TD, LoGerfo J, Patrick DL, Williams B, Walwick J et al (2006) The Rapid Assessment of Physical Activity (RAPA) among older adults. Prev Chronic Dis 3:A118 Gil M, Samino S, Barrilero R, Correig X (2019) Lipid profiling using 1H NMR spectroscopy. Methods Mol Biol 2037:35–47. https://doi.org/10.1007/978-1-4939-9690-2_3 Fuertes-Martín R, Taverner D, Vallvé JC et al (2018) Characterization of 1H NMR plasma glycoproteins as a new strategy to identify inflammatory patterns in rheumatoid arthritis. J Proteome Res 17:3730–3739. https://doi.org/10.1021/acs.jproteome.8b00411 Hirano T, Nohtomi K, Koba S, Muroi A, Ito Y (2008) A simple and precise method for measuring HDL-cholesterol subfractions by a single precipitation followed by homogeneous HDL-cholesterol assay. J Lipid Res 49:1130–1136. https://doi.org/10.1194/jlr.D700027-JLR200 Fernández-Cidón B, Candás-Estébanez B, Gil-Serret M et al (2021) Physicochemical properties of lipoproteins assessed by nuclear magnetic resonance as a predictor of premature cardiovascular disease: the PRESARV-SEA study. J Clin Med 10:1379. https://doi.org/10.3390/jcm10071379 Lawler PR, Akinkuolie AO, Chu AY et al (2017) Atherogenic lipoprotein determinants of cardiovascular disease and residual risk among individuals with low low-density lipoprotein cholesterol. J Am Heart Assoc 6:e005549. https://doi.org/10.1161/JAHA.117.005549 Girona J, Amigó N, Ibarretxe D et al (2019) HDL triglycerides: a new marker of metabolic and cardiovascular risk. Int J Mol Sci 20:3151. https://doi.org/10.3390/ijms20133151 Rye KA, Bursill CA, Lambert G, Tabet F, Barter PJ (2009) The metabolism and anti-atherogenic properties of HDL. J Lipid Res 50:S195–S200. https://doi.org/10.1194/jlr.R800034-JLR200 Verwer BJ, Scheffer PG, Vermue RP, Pouwels PJ, Diamant M et al (2020) NAFLD is related to post-prandial triglyceride-enrichment of HDL particles in association with endothelial and HDL dysfunction. Liver Int 40:2439–2444. https://doi.org/10.1111/liv.14597 Duparc T, Ruidavets JB, Genoux A et al (2020) Serum level of HDL particles are independently associated with long-term prognosis in patients with coronary artery disease: the GENES study. Sci Rep 10:8138. https://doi.org/10.1038/s41598-020-65100-2 Watanabe H, Söderlund S, Soro-Paavonen A et al (2006) Decreased high-density lipoprotein particle size, prebeta-, and large HDL subspecies concentration in Finnish low-HDL families: relationship with intima-media thickness. Arterioscler Thromb Vasc Biol 26:897–902. https://doi.org/10.1161/01.ATV.0000209577.04246.c0 Pullinger CR, O'Connor PM, Naya-Vigne JM et al (2021) Levels of prebeta-1 high-density lipoprotein are a strong independent positive risk factor for coronary heart disease and myocardial infarction: a meta-analysis. J Am Heart Assoc 10:e018381. https://doi.org/10.1161/JAHA.120.018381 Kontush A (2015) HDL particle number and size as predictors of cardiovascular disease. Front Pharmacol 6:218. https://doi.org/10.3389/fphar.2015.00218 Xu RX, Li S, Li XL et al (2015) High-density lipoprotein subfractions in relation with the severity of coronary artery disease: a Gensini score assessment. J Clin Lipidol 9:26–34. https://doi.org/10.1016/j.jacl.2014.11.003 Mutharasan RK, Thaxton CS, Berry J et al (2017) HDL efflux capacity, HDL particle size, and high-risk carotid atherosclerosis in a cohort of asymptomatic older adults: the Chicago Healthy Aging Study. J Lipid Res 58:600–606. https://doi.org/10.1194/jlr.P069039 Hernáez Á, Castañer O, Elosua R et al (2017) Mediterranean diet improves high-density lipoprotein function in high-cardiovascular-risk individuals: a randomized controlled trial. Circulation 135:633–643. https://doi.org/10.1161/CIRCULATIONAHA.116.023712 Stanton KM, Kienzle V, Dinnes DLM et al (2022) Moderate- and high-intensity exercise improves lipoprotein profile and cholesterol efflux capacity in healthy young men. J Am Heart Assoc 11:e023386. https://doi.org/10.1161/JAHA.121.023386 Candás-Estébanez B, Fernández-Cidón B, Corbella E et al (2024) The impact of the Mediterranean diet and lifestyle intervention on lipoprotein subclass profiles among metabolic syndrome patients: findings of a randomized controlled trial. Int J Mol Sci 25:1338. https://doi.org/10.3390/ijms25021338 Hernáez Á, Fernández-Castillejo S, Farràs M et al (2014) Olive oil polyphenols enhance high-density lipoprotein function in humans. Arterioscler Thromb Vasc Biol 34:2115–2119. https://doi.org/10.1161/ATVBAHA.114.303374 Millar SR, Navarro P, Harrington JM, Shivappa N, Hébert JR, Perry IJ, Phillips CM (2021) Comparing dietary score associations with lipoprotein particle subclass profiles: a cross-sectional analysis of a middle-to older-aged population. Clin Nutr 40:4720–4729. https://doi.org/10.1016/j.clnu.2021.06.005 Viadas R, Toloba A, Fernández I et al (2023) Association of physical activity with high-density lipoprotein functionality in a population-based cohort: the REGICOR study. Rev Esp Cardiol (Engl Ed) 76:86–93. https://doi.org/10.1016/j.rec.2022.04.009 Hernáez Á, Soria-Florido MT, Castañer O et al (2021) Leisure time physical activity is associated with improved HDL functionality in high cardiovascular risk individuals: a cohort study. Eur J Prev Cardiol 28:1392–1401. https://doi.org/10.1177/2047487320925625 Amigó N, Castelblanco E, Julve J et al (2025) Advanced serum lipoprotein and glycoprotein profiling for cardiovascular event prediction in type 2 diabetes mellitus: the LIPOCAT study. Cardiovasc Diabetol 24:88. https://doi.org/10.1186/s12933-025-02636-5 Moreno-Vedia J, Rosales R, Ozcariz E et al (2022) Triglyceride-rich lipoproteins and glycoprotein A and B assessed by 1H-NMR in metabolic-associated fatty liver disease. Front Endocrinol (Lausanne) 12:775677. https://doi.org/10.3389/fendo.2021.775677 Manmadhan A, Lin BX, Zhong J et al (2019) Elevated GlycA in severe obesity is normalized by bariatric surgery. Diabetes Obes Metab 21:178–182. https://doi.org/10.1111/dom.13481 Mayer FJ, Binder CJ, Krychtiuk KA et al (2019) The prognostic value of serum amyloid A for long-term mortality among patients with subclinical carotid atherosclerosis. Eur J Clin Invest 49:e13095. https://doi.org/10.1111/eci.13095 Vuilleumier N, Dayer JM, von Eckardstein A, Roux-Lombard P (2013) Pro- or anti-inflammatory role of apolipoprotein A-1 in high-density lipoproteins? Swiss Med Wkly 143:w13781. https://doi.org/10.4414/smw.2013.13781 Martínez-Beamonte R, Lou-Bonafonte JM, Martínez-Gracia MV, Osada J (2013) Sphingomyelin in high-density lipoproteins: structural role and biological function. Int J Mol Sci 9(4):7716–7741. https://doi.org/10.3390/ijms14047716 Lee YK, Lee DH, Kim JK et al (2013) Lysophosphatidylcholine, oxidized low-density lipoprotein and cardiovascular disease in Korean hemodialysis patients: analysis at 5 years of follow-up. J Korean Med Sci 28:268–273. https://doi.org/10.3346/jkms.2013.28.2.268 Maki KC, Dicklin MR, Kirkpatrick CF (2021) Saturated fats and cardiovascular health: current evidence and controversies. J Clin Lipidol 15:765–772. https://doi.org/10.1016/j.jacl.2021.09.049 Heimerl S, Fischer M, Baessler A et al (2014) Alterations of plasma lysophosphatidylcholine species in obesity and weight loss. PLoS ONE 9:e111348. https://doi.org/10.1371/journal.pone.0111348 Supplementary Files FigSup1.tiff Supplemental Figure 1. Boxplot of variables with statistical significance (adjusted p-value (BH) <0.05) after adjustment for abdominal circumference, high blood pressure, nutrition, and physical activity. Graphicalabstract.tif Graphical abstract. Graphical summary of the study conducted COIallauthorsform.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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19:44:05","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8981110/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8981110/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104322750,"identity":"38a5a187-6615-4ee0-8050-b9bd8d936012","added_by":"auto","created_at":"2026-03-10 13:27:16","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":9780,"visible":true,"origin":"","legend":"\u003cp\u003eROC analysis of the multiple logistic equation based on: HDL-Z, HDL-TG, VLDL-Z, and saturated fatty acids.\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-8981110/v1/36911415ba3336edaf2babc0.png"},{"id":107481130,"identity":"0c5f93d7-8751-4db4-bf7c-ae1c61a131d1","added_by":"auto","created_at":"2026-04-22 02:15:58","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":974340,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8981110/v1/106171fb-9565-4a28-9dff-38df1d383e4d.pdf"},{"id":104322760,"identity":"4999508d-01af-46aa-ba5c-284b0d9329aa","added_by":"auto","created_at":"2026-03-10 13:27:19","extension":"tiff","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":280714,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplemental Figure 1.\u003c/strong\u003e Boxplot of variables with statistical significance (adjusted p-value (BH) \u0026lt;0.05) after adjustment for abdominal circumference, high blood pressure, nutrition, and physical activity.\u003c/p\u003e","description":"","filename":"FigSup1.tiff","url":"https://assets-eu.researchsquare.com/files/rs-8981110/v1/cad7a4802203e613a087aaa7.tiff"},{"id":104322748,"identity":"0429d359-a1d9-475b-9079-f49f46eee8ea","added_by":"auto","created_at":"2026-03-10 13:27:16","extension":"tif","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":306244,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGraphical abstract. \u003c/strong\u003eGraphical summary of the study conducted\u003c/p\u003e","description":"","filename":"Graphicalabstract.tif","url":"https://assets-eu.researchsquare.com/files/rs-8981110/v1/952cae0ecaa1ae98f80abeae.tif"},{"id":104322765,"identity":"bb8c9963-c06b-43ee-8f49-46169c7aec14","added_by":"auto","created_at":"2026-03-10 13:27:20","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":36076,"visible":true,"origin":"","legend":"","description":"","filename":"COIallauthorsform.docx","url":"https://assets-eu.researchsquare.com/files/rs-8981110/v1/e320a24feffe1ffce6dfecc0.docx"}],"financialInterests":"","formattedTitle":"Advanced lipoprotein, glycoprotein and lipidomic profiles in hyperalphalipoproteinemia with and without atherosclerotic cardiovascular disease: a case–control study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHigh-density lipoproteins (HDL) are heterogeneous particles with a complex composition that can vary significantly depending on individual genetic and environmental characteristics [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eEpidemiological studies have shown that low serum HDL cholesterol (HDL-C) concentrations are associated with an increased risk of atherosclerotic cardiovascular disease (ASCVD) [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], but clinical trials have not demonstrated that the use of drugs to increase HDL-C has a preventive effect against ASCVD [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe relationship between HDL-C, ASCVD risk, and overall mortality is U-shaped [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Individuals with HDL-C concentrations at the lower and upper distribution curves exhibit the highest overall and cardiovascular mortality, with a higher risk of all-cause mortality in men [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Extremely high HDL-C concentrations were significantly associated with increased risk of ASCVD mortality and increased risk for coronary heart disease and ischemic stroke [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Notably, these high concentrations are characteristic of hyperalphalipoproteinemia (HALP), which is normally defined as HDL-C concentrations above the 90th percentile of the population [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. HALP predisposes individuals to atherosclerotic disease [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. In addition, patients present a wide spectrum of heterogeneous and nonspecific clinical manifestations, making it difficult to identify ASCVD risk in HALP patients.\u003c/p\u003e \u003cp\u003eThe discordance between elevated HDL-cholesterol and cardiovascular risk may be explained by structural and compositional remodeling of HDL that impairs its function. The aforementioned dysfunction attenuates the anti-inflammatory and antioxidant properties of HDL [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Indeed, it may even render the particles pro-atherogenic, thereby promoting endothelial injury in individuals with HALP [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCurrently, clinical laboratories measure HDL-C. However, the antiatherogenic functions of HDL are not directly dependent on its cholesterol content. Therefore, measuring HDL-C does not reflect the functionality of HDL [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Similarly, measuring apolipoprotein A1, instead of HDL-C, has not been shown to be a better predictor of ASCVD [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCurrently, reverse cholesterol transport can be assessed directly by measuring the cholesterol efflux capacity of macrophages (CEC) [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. However, due to technical complexity and the lack of standardisation, the implementation of this technique in clinical practice is very limited. Analysis of the structure and composition of HDL is considered the most viable alternative for the assessment of HDL functionality [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTherefore, it is essential to assess the structure and composition of HDL particles in order to assess their functionality, given the established correlation between these structural characteristics and their cardioprotective properties [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this context, nuclear magnetic resonance (NMR) spectroscopy of lipoproteins has emerged as the method of choice to determine the structure and composition of plasma lipoproteins. This technology is high-performance, reproducible, scalable, cost-effective, and meets laboratory metrological requirements.\u003c/p\u003e \u003cp\u003eIndeed, HDL particle concentration (HDL-P) has been demonstrated to be a superior predictor of cardiovascular risk [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] in comparison to its cholesterol content.\u003c/p\u003e \u003cp\u003eSimilarly, lipidomics studies have shown to improve the prediction of individual ASCVD risk compared to current protocols for ASCVD screening in the general population [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. In this sense, lipidomics may help to identify compositional patterns associated with reduced HDL functionality [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eConventional lipid profile (total cholesterol, triglycerides (TG), HDL-C, and LDL-C) has not been shown to be useful to assess cardiovascular risk in patients with HALP [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Consequently, it is essential to develop indirect biomarkers that assess HDL functionality.\u003c/p\u003e \u003cp\u003eThe objective of this study is to analyse an advanced lipoprotein profile in HALP patients who have suffered an ischaemic event. The profile includes lipidomic and inflammation biomarkers, and lipoprotein structure and composition assessed by NMR spectroscopy. The results will be compared with those from subjects with HALP without a history of ASCVD. Additionally, the objective was to develop a model, based on NMR parameters, to characterise and discriminate both groups.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and participants\u003c/h2\u003e \u003cp\u003eThis is a cross-sectional, retrospective case-control study conducted at a tertiary hospital in Barcelona, Spain. Two groups of patients under the age of 75 were studied:\u003c/p\u003e \u003cp\u003eASCVD-group (n\u0026thinsp;=\u0026thinsp;29): Patients with ASCVD and HALP.\u003c/p\u003e \u003cp\u003eControl-group (n\u0026thinsp;=\u0026thinsp;41): Patients without ASCVD and HALP.\u003c/p\u003e \u003cp\u003eIn the cohort of patients with ASCVD, a retrospective evaluation was conducted to ascertain whether they had a history of hospitalization for acute coronary syndrome, stroke, coronary atherosclerosis, or lower limb atherosclerosis. Ischemic events occurring within the three months preceding study inclusion were excluded.\u003c/p\u003e \u003cp\u003eControl-group was matched with the ASCVD-group according to sex, age, HDL-C, and TG. Subjects were selected from a primary care center in the same area population.\u003c/p\u003e \u003cp\u003ePatients with renal failure (estimated glomerular filtration rate (eGFR) according to CKD-EPI\u0026thinsp;\u0026lt;\u0026thinsp;60 mL/min), severe disease, cirrhosis, hypertriglyceridemia (\u0026gt;\u0026thinsp;4.52 mmol/L), alcohol abuse (\u0026gt;\u0026thinsp;2 standard drinks/day), or those undergoing treatment with fibrates, barbiturates, phenytoin or omega-3 fatty acids were excluded from the study.\u003c/p\u003e \u003cp\u003eSubjects were defined to have HALP if they presented HDL-C concentrations above the 90th percentile (P90) for age and gender in at least two laboratory tests, without the presence of secondary causes of high HDL-C.\u003c/p\u003e \u003cp\u003eThe P90 threshold of HDL-C was estimated from a retrospective laboratory database of primary care subjects from the same geographical area as the study. The database included 302,938 individuals aged 18\u0026ndash;75 years who underwent testing between 2021 and 2023. Subjects with evidence of liver disease (aspartate aminotransferase, alanine aminotransferase, gamma-glutamyltransferase, or alkaline phosphatase outside the laboratory reference interval), renal impairment (eGFR\u0026thinsp;\u0026lt;\u0026thinsp;60 mL/min), thyroid disease (thyrotropin outside the reference interval), or C-reactive protein\u0026thinsp;\u0026gt;\u0026thinsp;5 mg/L were excluded. P90 was calculated in a sex-specific manner for the overall 18\u0026ndash;75-year age range.\u003c/p\u003e \u003cp\u003eDuring the initial visit, patients carried out: a 17-item dietary questionnaire assessing adherence to a low-calorie Mediterranean diet [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], two physical activity questionnaires: RAPA1 and RAPA 2 (Rapid Assessment of Physical Activity) [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], two measurements of blood pressure and heart rate (obtaining the average of both measurements), height, weight and abdominal circumference.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eLaboratory analysis\u003c/h3\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eConventional lipoprotein profile:\u003c/h2\u003e \u003cp\u003eThe analysis of total cholesterol, HDL-C, LDL-C, TG, apolipoprotein A and apolipoprotein B was conducted using the Cobas\u0026reg; 8000 analyser (Roche Diagnostics, Rotkreuz, Switzerland).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSample size calculation\u003c/h3\u003e\n\u003cp\u003eThe primary endpoint for sample size estimation was the between-group difference in the change of HDL-TG. Assuming a difference of 6.73 mg/dL between ASCVD and control groups and a common standard deviation of 4.5 mg/dL [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], a two-sided α\u0026thinsp;=\u0026thinsp;0.05, and 95% power, the minimum sample size required per group was 13 participants/group.\u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eUnivariable analyses were performed to evaluate the association of each variable with the presence of ASCVD. Variables were adjusted (adjusted \u003cem\u003ep\u003c/em\u003e-Value) for waist circumference, arterial hypertension, nutrition and physical activity RAPA1-derived.\u003c/p\u003e \u003cp\u003eGiven the large number of variables analysed and, consequently, the multiple hypothesis tests performed, the Benjamini-Hochberg (BH) procedure was applied to correct for multiple comparisons (adjusted p-Value (BH)). This approach facilitates the regulation of the false discovery rate (FDR), defined as the expected proportion of falsely significant results among those considered significant, which was set at 5% (q\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eTo characterise and discriminate the presence of ASCVD in individuals with HALP, a multivariate logistic regression model was constructed using a backward stepwise selection procedure. Variables that remained significant after adjustment for clinical covariates (adjusted \u003cem\u003ep\u003c/em\u003e-Value (BH)\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were selected. The model was adjusted for age, sex, waist circumference, arterial hypertension, nutrition, and physical activity.\u003c/p\u003e \u003cp\u003eThis methodological approach permitted the integration of the molecular variables into a single model, thereby deriving a predictive equation and elucidating the manner in which molecular alterations are incorporated into a comprehensive pattern associated with ASCVD when comparing both groups. Diagnostic accuracy of the model was evaluated using a receiver operating characteristic (ROC) curve analysis.\u003c/p\u003e \u003cp\u003eIn addition, cross-validation and bootstrap validation of the ROC analysis were performed to avoid the risk of overfitting and increase predictive power.\u003c/p\u003e \u003cp\u003eThe analyses were performed using RStudio statistical software, version 4.4.3.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eTo define HALP in subjects aged 18\u0026ndash;75 years, the cutoff point obtained for the P90 of HDL-C concentrations in men (n\u0026thinsp;=\u0026thinsp;131,140) was 1.75 mmol/L (67.67 mg/dL) and in women (n\u0026thinsp;=\u0026thinsp;171,798) was 2.14 mmol/L (82.75 mg/dL).\u003c/p\u003e \u003cp\u003eClinical characteristics of both groups are described in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClinical data of patients and control subjects.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl-group (n\u0026thinsp;=\u0026thinsp;41)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eASCVD-group (n\u0026thinsp;=\u0026thinsp;29)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep-Value\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57 (50\u0026ndash;63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55 (51\u0026ndash;61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.830\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale sex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18 (43.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (51.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.687\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily history of ASCVD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (14.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (31%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.177\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eType 1 diabetes mellitus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eType 2 diabetes mellitus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (3.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.414\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArterial hypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (37.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInflammatory/autoimmune disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (10.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.068\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiver disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (6.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.168\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThromboembolic disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (3.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.414\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEarly menopause before age 40, women\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (8.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (7.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePolycystic ovary syndrome, women\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (7.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.378\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean systolic blood pressure (mm Hg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e128.14\u0026thinsp;\u0026plusmn;\u0026thinsp;14.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e134.29\u0026thinsp;\u0026plusmn;\u0026thinsp;12.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.066\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean diastolic blood pressure (mm Hg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e82.43\u0026thinsp;\u0026plusmn;\u0026thinsp;7.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e81.64\u0026thinsp;\u0026plusmn;\u0026thinsp;6.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.657\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeart rate (bpm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e76.51\u0026thinsp;\u0026plusmn;\u0026thinsp;7.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72.12\u0026thinsp;\u0026plusmn;\u0026thinsp;10.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.063\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody mass index (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.52\u0026thinsp;\u0026plusmn;\u0026thinsp;2.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.60\u0026thinsp;\u0026plusmn;\u0026thinsp;4.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbdominal circumference (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e82.00\u0026nbsp;(73.00\u0026ndash;88.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90.00\u0026nbsp;(80.00\u0026ndash;97.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eActive smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (7.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (13.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.438\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow/moderate alcohol consumption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14 (34.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (31%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.988\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNutrition (Dietary questionnaire)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.027\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhysical activity (RAPA1-derived): Moderate/vigorous physical activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28 (68.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 (44.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.086\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eRAPA1 (Rapid Assessment of Physical Activity 1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.066\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e- Sedentary/Light physical activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13 (31.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16 (55.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e- Moderate physical activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17 (41.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (17.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e- Vigorous physical activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (26.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (27.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eRAPA2 (Rapid Assessment of Physical Activity 1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.018\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e- Strength training\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19 (46.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (20.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e- Balance training\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (29.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (17.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e- Strength and balance training\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (14.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (3.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eAtherosclerotic cardiovascular disease\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcute myocardial infarction/Cardiovascular accident\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 (44.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAngina pectoris\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (17.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStroke\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (7.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoronary atherosclerosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (17.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeripheral artery disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (13.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eTreatment of patients\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStatins\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26 (89.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePCSK9 inhibitors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (17.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEzetimibe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16 (55.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcetylsalicylic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22 (75.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cem\u003eData are expressed as mean (standard deviation) or median (interquartile range) depending on the normal distribution and homoscedasticity of continuous variables and number of cases (percentage) for qualitative variables. Categorical variables were compared using the χ\u0026sup2; test or Fisher's exact test. Quantitative variables were compared using Student's t-test, Welch's t-test or Mann\u0026ndash;Whitney U test, depending on assumptions of normality and homoscedasticity. ASCVD: Atherosclerotic Cardiovascular Disease; RAPA: Rapid Assessment of Physical Activity; PCSK9: proprotein convertase subtilisin/kexin type 9.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe results of the univariable analysis are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAdvanced lipoproteins and glycoproteins profiles by nuclear magnetic resonance spectroscopy in the ASCVD and control groups.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables (units)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl-group (n\u0026thinsp;=\u0026thinsp;41)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eASCVD-group\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;29)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAdjusted\u003c/p\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAdjusted\u003c/p\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-Value (BH)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCholesterol (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.33 (4.88\u0026ndash;5.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.44 (3.90\u0026ndash;4.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.267\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.76 (0.62\u0026ndash;0.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.78 (0.58\u0026ndash;1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.668\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.651\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.705\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-C (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.36 (2.04\u0026ndash;2.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.28 (2.11\u0026ndash;2.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.712\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.244\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.396\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL-C (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.64 (2.22\u0026ndash;2.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.68 (1.33\u0026ndash;2.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.036\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.117\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApolipoprotein A1 (g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.94 (1.83\u0026ndash;2.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.92 (1.74\u0026ndash;2.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.525\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.870\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.870\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApolipoprotein B (g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.79 (0.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.70 (0.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.540\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.638\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlucose (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.60 (4.30\u0026ndash;4.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.10 (4.80\u0026ndash;5.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.267\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine (\u0026micro;mol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e69.93 (13.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74.07 (13.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.207\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.512\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.638\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlomerular filtration rate (mL/min/1.73 m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e90 (89\u0026ndash;90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90 (84\u0026ndash;90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.527\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.638\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHs-CRP (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.49 (0.28\u0026ndash;1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.60 (0.70\u0026ndash;3.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.241\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSAA in serum (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.69 (1.95\u0026ndash;4.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.50 (3.15\u0026ndash;6.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.006\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.027\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSAA in HDL3 fraction (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.52 (1.05\u0026ndash;2.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.87 (1.64\u0026ndash;3.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.005\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.027\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVLDL particles\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVLDL-P (nmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26.34 (22.60\u0026ndash;29.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.70 (29.66\u0026ndash;34.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.111\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLarge VLDL-P (nmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.73 (0.59\u0026ndash;0.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.92 (0.73\u0026ndash;1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.111\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedium VLDL-P (nmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.01 (2.05\u0026ndash;3.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.56 (1.59\u0026ndash;3.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.328\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.874\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.874\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmall VLDL-P (nmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.44 (19.64\u0026ndash;24.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.73 (26.81\u0026ndash;29.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.009\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.018\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVLDL-Z (nm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42.26 (0.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42.07 (0.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVLDL-C (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.37 (5.75\u0026ndash;10.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.61 (6.24\u0026ndash;9.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.668\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.824\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.874\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVLDL-TG (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35.41 (29.52\u0026ndash;40.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47.34 (42.41\u0026ndash;52.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.012\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVLDL-TG/VLDL-C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.45 (3.85\u0026ndash;5.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.62 (4.92\u0026ndash;6.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLDL particles\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL-P (nmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1196.55 (160.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1038. (218.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.309\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLarge LDL-P (nmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e204.67 (189.50\u0026ndash;220.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e166.80 (146.69\u0026ndash;186.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.008\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.058\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedium LDL-P (nmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e398.86 (359.02\u0026ndash;475.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e283.33 (234.16\u0026ndash;345.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.275\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmall LDL-P (nmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e596.24 (548.74\u0026ndash;618.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e560.75 (492.15\u0026ndash;609.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.252\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.944\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.944\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL-Z (nm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21.37 (0.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.11 (0.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.017\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.079\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL-TG (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.55 (13.61\u0026ndash;17.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.41 (9.59\u0026ndash;14.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.293\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL-TG/LDL-C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.12 (0.11\u0026ndash;0.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.12 (0.10\u0026ndash;0.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.233\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.293\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIDL particles\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIDL-C (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.89 (2.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.50 (3.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.406\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.556\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIDL-TG (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.98 (1.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.87 (2.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIDL-TG/IDL-C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.13 (1.05\u0026ndash;1.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.29 (1.19\u0026ndash;1.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.293\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHDL particles\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-P (\u0026micro;mol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34.55 (3.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37.37 (5.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.015\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.039\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLarge HDL-P (nmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e321.90 (43.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e290.08 (55.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.400\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedium HDL-P (\u0026micro;mol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.77 (1.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.46 (2.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.590\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.612\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.612\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmall HDL-P (\u0026micro;mol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20.46 (2.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.61 (3.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.005\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.035\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-Z (nm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.33 (0.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.26 (0.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.024\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.048\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-TG (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.65 (12.57\u0026ndash;15.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.16 (14.74\u0026ndash;18.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.009\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.035\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-TG/HDL-C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.16 (0.14\u0026ndash;0.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.20 (0.17\u0026ndash;0.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.036\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.057\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGlicoprotein profile\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlyc-B (\u0026micro;mol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e330.57 (26.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e328.21 (38.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.776\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.360\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.556\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlyc-A (\u0026micro;mol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e595.46 (563.18\u0026ndash;630.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e602.76 (511.31\u0026ndash;654.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.990\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.531\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.620\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH/W Glyc-B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.17 (3.92\u0026ndash;4.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.20 (3.77\u0026ndash;4.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.896\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.415\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.556\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH/W Glyc-A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.99 (14.16\u0026ndash;15.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.29 (13.91\u0026ndash;16.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.482\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.852\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.918\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cem\u003eResults are expressed as mean (standard deviation) or median (interquartile range), depending on normality and homoscedasticity. Categorical variables were compared using the χ\u0026sup2; test or Fisher's exact test. Quantitative variables were compared using Student's t-test, Welch's t-test or Mann\u0026ndash;Whitney U test, depending on assumptions of normality and homoscedasticity. p-value: unadjusted p-value from the univariable analysis; Adjusted p-value: p-value after adjustment for covariates (abdominal circumference, arterial hypertension, nutrition, and physical activity); Adjusted p-value (BH): p-value adjusted for the covariates and for multiple comparisons using the Benjamini\u0026ndash;Hochberg procedure (false discovery rate control).\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cem\u003eVLDL: very low-density lipoproteins, LDL: low-density lipoproteins, HDL: high-density lipoproteins, C: cholesterol, IDL: intermediate-density lipoproteins, P: particles; hs-CRP: ultra-sensitive C-reactive protein; TG: triglycerides; Z: diameter, Glyc: glycoprotein, SAA: serum amyloid A, H/W: height-to-width ratio.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLipidomic profile by nuclear magnetic resonance spectroscopy in the ASCVD and control groups. \u003cem\u003ep\u003c/em\u003e-Value and \u003cem\u003ep\u003c/em\u003e-Value corrected by Benjamini\u0026ndash;Hochberg (BH) are showed.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables (units)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl-group (n\u0026thinsp;=\u0026thinsp;41)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eASCVD-group\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;29)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAdjusted \u003cem\u003ep\u003c/em\u003e-Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAdjusted \u003cem\u003ep\u003c/em\u003e-Value (BH)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eLipidomic profile\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCholesterol esters (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.15 (3.07\u0026ndash;3.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.94 (2.66\u0026ndash;3.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.358\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.477\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFree cholesterol (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.00 (1.91\u0026ndash;2.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.76 (1.66\u0026ndash;1.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.661\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.756\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlycerophospholipids (except lysophosphatidylcholine) (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.95 (0.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.81 (0.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.237\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.913\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.913\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhosphatidylcholine (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.71 (0.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.75 (0.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.569\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.385\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSphingomyelin (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.02 (0.98\u0026ndash;1.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.96 (0.88\u0026ndash;1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.814\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.868\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhosphatidylcholine/\u003c/p\u003e \u003cp\u003eSphingomyelin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.68 (0.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.83 (0.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.241\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.385\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLysophosphatidylcholine (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.74 (0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.68 (0.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.009\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.049\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLysophosphatidylcholine/\u003c/p\u003e \u003cp\u003eSphingomyelin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.73 (0.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.70 (0.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePolyunsaturated fatty acids (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14.70 (1.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15.27 (2.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.297\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.232\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.385\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLinoleic acid (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.02 (0.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.54 (0.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.218\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSaturated fatty acids (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.60 (0.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.25 (1.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOmega-6 and omega-7 fatty acids (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.92 (0.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.84 (0.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.780\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.367\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOmega-9 fatty acid (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.73 (2.57\u0026ndash;2.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.84 (2.53\u0026ndash;3.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.322\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.468\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOmega-3 fatty acid (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.40 (0.36\u0026ndash;0.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.43 (0.39\u0026ndash;0.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.277\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDocosahexaenoic acid (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.18 (0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.17 (0.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.098\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.639\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.756\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArachidonic acid\u0026thinsp;+\u0026thinsp;eicosapentaenoic acid (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.64 (0.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.82 (0.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.218\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cem\u003eResults are expressed as mean (standard deviation) or median (interquartile range), depending on normality and homoscedasticity. Categorical variables were compared using the χ\u0026sup2; test or Fisher's exact test. Quantitative variables were compared using Student's t-test, Welch's t-test or Mann\u0026ndash;Whitney U test, depending on assumptions of normality and homoscedasticity. p-value: unadjusted p-value from the univariable analysis; Adjusted p-value: p-value after adjustment for covariates (abdominal circumference, arterial hypertension, nutrition, and physical activity); Adjusted p-value (BH): p-value adjusted for the covariates and for multiple comparisons using the Benjamini\u0026ndash;Hochberg procedure (false discovery rate control).\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cem\u003eVLDL: very low-density lipoproteins, LDL: low-density lipoproteins, HDL: high-density lipoproteins, C: cholesterol, IDL: intermediate-density lipoproteins, P: particles; hs-CRP: ultra-sensitive C-reactive protein; TG: triglycerides; Z: diameter, Glyc: glycoprotein, SAA: serum amyloid A, H/W: height-to-width ratio.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTo perform the multiple logistic regression model, non-significant variables were eliminated (adjusted p-value (BH)\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Therefore, the preselected variables were: VLDL-TG, Small VLDL-P, VLDL-Z, HDL-P, Small HDL-P, HDL-TG, HDL-Z, LPC, SFA, serum SAA, and SAA in HDL3 (\u003cb\u003eSupplementary Fig.\u0026nbsp;1\u003c/b\u003e). Subsequently, variables with high correlation (ρ\u0026thinsp;\u0026gt;\u0026thinsp;0.6) or multicollinearity (variance inflation factor\u0026thinsp;\u0026gt;\u0026thinsp;3) were eliminated. Finally, after applying the backward stepwise selection procedure to the logistic model, the variables with the greatest discriminative ability between both groups were: HDL-Z, HDL-TG, VLDL-Z, and SFA (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of the multiple logistic regression analysis adjusted by sex, age, abdominal circumference, arterial hypertension, nutrition and physical activity.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables (units)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoefficients\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCI 95%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eR\u0026sup2;\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e647.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"10\" rowspan=\"11\"\u003e \u003cp\u003e0.874\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-Z (per 0.1 nm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-2.723\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.046\u0026ndash;0.968\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.047\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-TG (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.470\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.015\u0026ndash;2.522\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.043\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVLDL-Z (per 0.1 nm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.121\u0026ndash;0.902\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.031\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSFA (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.2\u0026ndash;814.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.037\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.580\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.558\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.046\u0026ndash;6.878\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.650\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.970\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.806\u0026ndash;1.167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.745\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbdominal circumference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.966\u0026ndash;1.333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.124\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArterial hypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.995\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNutrition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.590\u0026ndash;2.124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.730\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhysical activity (RAPA1-derived)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-2.062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.007\u0026ndash;2.473\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.173\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cem\u003eOR: odds ratio; 95% CI: 95% confidence interval; RAPA: Rapid Assessment of Physical Activity; R\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e: \u003cem\u003eNagelkerke's pseudo-R\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e; \u003cem\u003eHDL-Z: diameter of HDL; HDL-TG: triglyceride content of HDL; VLDL-Z: diameter of VLDL; SFA: saturated fatty acids.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn the multiple logistic regression model (adjusted by age, sex, waist circumference, arterial hypertension, nutrition and physical activity), HDL-TG (OR [95%CI]: 1.60 [1.02\u0026ndash;2.52]; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.043), and SFA (OR [95%CI]: 31.8 [1.2\u0026ndash;814.6); \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.037) were independently and positively associated with ASCVD-group. In contrast, HDL-Z (per 0.1 nm) (OR [95%CI]: 0.066 [0.046\u0026ndash;0.968]; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.047) and VLDL-Z (per 0.1 nm) (OR [95%CI]: 0.331 [0.121\u0026ndash;0.902); \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.031) were independently and inversely associated (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe final model showed excellent apparent discrimination (area under the curve (AUC) [95%CI] of 0.981 [0.958\u0026ndash;1.000]) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Internal validation confirmed robust performance, with a mean cross-validated AUC of 0.846 [0.766\u0026ndash;0.936] and bootstrap-estimated AUC of 0.929 [0.839\u0026ndash;0.981].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe model showed an apparent sensitivity of 100%, specificity of 90.2% and 94.3% of accuracy. Internal validation yielded sensitivity and specificity of 84.4% and 85.0%, respectively. Bootstrap internal validation yielded sensitivity and specificity of 89.2% and 92.8%, respectively.\u003c/p\u003e \u003cp\u003eThe overall model was statistically significant (likelihood ratio test: χ\u0026sup2;=73.25, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Model calibration was assessed with the Hosmer\u0026ndash;Lemeshow goodness-of-fit test (g\u0026thinsp;=\u0026thinsp;10; χ\u0026sup2;=3.66, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.909), indicating adequate calibration. Akaike Information Criterion of the model was 43.72. The model showed a Nagelkerke pseudo-R\u0026sup2; of 0.874, consistent with good overall model fit.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we present, for the first time, the assessment of a lipoprotein, lipidomic, and inflammatory biomarker profile, based on NMR, in patients with HALP and ASCVD, compared to control subjects with HALP without ASCVD.\u003c/p\u003e \u003cp\u003eThe observed changes in the ASCVD-group related to LDL, free and esterified cholesterol can be explained by the fact that all these patients are undergoing secondary prevention with lipid-lowering treatment (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn contrast, the conventional lipoprotein profile \u0026mdash;defined by HDL-C and TG\u0026mdash; did not show significant discrepancies between groups (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.705 and \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.396, respectively). However, relevant and significant differences in the structure and composition of lipoproteins were detected when the advanced lipid profile was analyzed by NMR spectroscopy.\u003c/p\u003e \u003cp\u003eIn the present study, patients with HALP and ASCVD showed higher concentrations of VLDL-P (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.111) and small VLDL particles (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.018). In addition, VLDL-Z was lower compared to the control group (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004).\u003c/p\u003e \u003cp\u003eFurthermore, the ASCVD-group exhibited higher HDL-P concentrations (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.039) and an increase in small HDL-P (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.035), which resulted in a decrease in HDL-Z (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.048).\u003c/p\u003e \u003cp\u003eIn addition, an increase in TG content was observed in VLDL, IDL and HDL lipoproteins (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.012, \u003cem\u003ep\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.055 and \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.035, respectively) in the ASCVD-group.\u003c/p\u003e \u003cp\u003eThe increase in the number of large VLDL particles may result in their transformation into small and dense VLDL particles due to a possible decrease in the lipoprotein lipase (LPL) activity. This process results in the accumulation of small, dense, and highly atherogenic remnant particles [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Indeed, in patients treated with statins who have achieved therapeutic LDL-C targets, the concentration of small VLDL-P was strongly associated with the risk of ASCVD [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis TG enrichment suggests an imbalance in lipid distribution and an alteration in lipoprotein remodeling, possibly mediated by an increase in cholesterol ester transport protein (CETP) activity and a decrease in LPL activity. This process leads to the generation of TG-rich lipoproteins (TRL). Consequently, this increase in VLDL-TG could generate an excess of atherogenic TRL remnants.\u003c/p\u003e \u003cp\u003eIn this context, an intensification of CETP-mediated exchange is expected, in which TRLs transfer TG to HDL in exchange for cholesterol esters, which explains the increase in HDL-TG concentrationss.\u003c/p\u003e \u003cp\u003eA strong relationship has been observed between increased HDL-TG concentrations and the risk of premature cardiovascular disease [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Consequently, HDL-TG has been proposed as a good marker to assess HDL functionality and to evaluate the risk of ASCVD in patients with atherosclerosis [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFurthermore, smaller HDL particles with higher TG content exhibit lower cholesterol uptake, greater susceptibility to oxidation, faster catabolism by endothelial lipase [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], and, presumably, lower HDL functionality [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTG-enriched HDL becomes a superior substrate for lipases (particularly hepatic lipase), thus promoting TG hydrolysis and particle contraction. This is consistent with the decrease in HDL-Z and the increase in small HDL-P concentration.\u003c/p\u003e \u003cp\u003eFew studies have evaluated the relationship between HDL size and ASCVD risk beyond HDL-C. In addition, evidence is even more limited in individuals with HALP.\u003c/p\u003e \u003cp\u003eIn patients with established coronary artery disease (CAD), some studies show that higher small HDL concentrations are associated with a better prognosis [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHowever, other studies have reported a positive correlation between small HDL particle size and carotid intima-media thickness [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePullinger CR et al [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] demonstrated in a meta-analysis that elevated concentrations of pre-beta-1 HDL (the smallest, disc-shaped form of HDL) are strongly associated with coronary heart disease (CHD) and myocardial infarction, regardless of traditional factors. This supports the hypothesis that certain small forms of HDL may behave as risk markers.\u003c/p\u003e \u003cp\u003eSimilarly, other studies have reported that small HDL concentrations are elevated in CHD [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] and CAD [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn addition, small HDL particles have been associated with a lower CEC compared with medium and large HDL particles [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. This finding could reflect alterations in HDL functionality or HDL maturation, leading to the possible accumulation of small HDL particles.\u003c/p\u003e \u003cp\u003eConsequently, measurement of a classic lipid profile (TG and HDL-C) was inadequate to discriminate between HALP patients with and without ASCVD. However, advanced analysis of the lipidic profile using NMR allowed the identification of an altered pattern of lipoprotein composition associated with ASCVD, potentially compatible with a remodelling of HDL particles that may condition their functionality.\u003c/p\u003e \u003cp\u003eFurthermore, conventional lipid-lowering treatments (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) did not improve either structure or composition of HDL. Consequently, there is a need to develop new therapeutic strategies that specifically modify composition and/or structure of HDL, or alternatively, promote non-pharmacological interventions.\u003c/p\u003e \u003cp\u003eSuch interventions may include nutritional guidelines based on the Mediterranean diet and exercise programmes, with the aim of restoring the antiatherogenic properties of these lipoproteins [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the control group had greater adherence to the Mediterranean diet and higher levels of physical activity.\u003c/p\u003e \u003cp\u003eMediterranean dietary intervention and lifestyle improvements are associated with reduced TG content in HDL and VLDL, as well as reduced VLDL-P, related to antiatherogenic changes [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Phenol-rich olive oil enhances HDL function, thus improving CEC, increasing HDL size and fluidity, and reducing oxidative stress [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this regard, higher diet quality or a more anti-inflammatory diet (DASH) was associated with less small VLDL-P and HDL-P [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSimilarly, current moderate and vigorous leisure-time physical activity showed a higher HDL antioxidant capacity [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRegarding the glycoprotein profile by NMR, no specific variable has been linked to a particular proinflammatory pattern. However, a different glycoprotein pattern has been identified, which differs from that observed in patients with type 2 diabetes mellitus [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], hypertriglyceridemia [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e] or obesity [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e], who exhibit increased concentrations of these glycoproteins.\u003c/p\u003e \u003cp\u003eAn increase in SAA concentration has been observed in total serum and isolated HDL3 serum of patients with HALP and ASCVD (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.027). This finding suggests that these patients exhibit a chronic inflammatory state, which is associated with an increased risk of developing ASCVD and higher mortality rates [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIt is hypothesized that the incorporation of SAA into HDL particles converts atheroprotective HDL into dysfunctional HDL. Increased SAA concentration is associated with an alteration in its anti-inflammatory properties, although HDL-C concentration is not affected [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe lipidomic study highlighted a decrease in the concentrations of LPC (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.049), as well as an increase in SFA concentrations (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002) in the ASCVD-group.\u003c/p\u003e \u003cp\u003eThe reduction in amphipathic/surface lipids (such as LPC) in the study group is consistent with the increased HDL-TG/HDL-C ratio (p\u0026thinsp;=\u0026thinsp;0.057) observed in the ASCVD-group. TG enrichment increases the susceptibility of HDL to the action of hepatic and endothelial lipase, which accelerates its catabolism and reduces its plasma residence time. Consequently, shorter circulation time could limit HDL maturation and LPC accumulation in the particle, resulting in lower plasma LPC concentration.\u003c/p\u003e \u003cp\u003eSimilarly, the decrease in the LPC/sphingomyelin ratio (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002) in the ASCVD group suggests phospholipid remodeling towards a more rigid lipoprotein monolayer [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e] that is less favourable for Lecithin-cholesterol acyltransferase activity, consistent with a possible structurally less functional HDL profile despite high HDL-C concentrations.\u003c/p\u003e \u003cp\u003eThese results are consistent with studies that have associated reduced LPC concentrations with an increased risk of cardiovascular events in patients undergoing hemodialysis [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe high SFA concentrations observed in the ASCVD-group (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002) are consistent with an adverse cardiometabolic profile in these patients. Observational studies have shown that a higher average intake of SFA is associated with adverse cardiovascular outcomes [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCollectively, the observed lipidomic pattern is consistent with a remodeling of the lipidome towards a relatively more saturated profile with lower availability of membrane/amphipathic lipids, potentially relevant for the composition and functionality of circulating lipoproteins. This phenotype have been described in high-risk cardiometabolic contexts [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn our cohort with HALP, these findings suggest that plasma lipidomics provides complementary information to the conventional lipid profile and may help to discriminate individuals with HALP and ASCVD.\u003c/p\u003e \u003cp\u003eMultiple logistic regression analysis identified an NMR-based lipoprotein and lipidomic pattern associated with the presence of ASCVD in patients with HALP, characterised by small VLDL and HDL particles, TG-enriched HDL, and higher SFA content. This model showed an excellent discriminatory power between HALP patients with and without ASCVD and identified a combination of variables potentially compatible with functional alterations in HDL.\u003c/p\u003e \u003cp\u003eThis equation was adjusted by sex, age, abdominal circumference and arterial hypertension and showed good diagnostic sensitivity (100%), specificity (90.2%) and AUC of 0.981. The similarity of AUC, sensitivity, and specificity in model performance and internal validation procedures (cross-validation and bootstrap) supports the internal robustness and stability of the model, suggesting limited overfitting in the study sample. However, external validation is still required before clinical generalisation.\u003c/p\u003e \u003cp\u003eIn summary, patients with HALP and ASCVD exhibited a lipoprotein and lipidomic profile of metabolic, structural, and compositional alterations that cannot be detected by a conventional lipid profile.\u003c/p\u003e \u003cp\u003eThese findings highlight the importance of developing new diagnostic strategies specifically designed to identify changes in the composition and/or structure of HDL in patients with HALPIn addition, novel therapeutic strategies and non-pharmacological interventions may help to improve risk stratification and could be relevant for the clinical management of ASCVD risk in patients with HALP.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003ePatients with HALP and ASCVD showed differences in lipoprotein structure and composition consistent with a more atherogenic pattern, which are not detected by the conventional lipid profile.\u003c/p\u003e \u003cp\u003eAdvanced lipid profile analysis showed increased concentrations of HDL-P and VLDL-P in the ASCVD-group, at the expense of small HDL and VLDL particles. Additionally, HDL, IDL and VLDL particles showed an increased TG content.\u003c/p\u003e \u003cp\u003eLipidomic analysis revealed decreased concentrations of LPC and increased SFA concentrations in patients with HALP and ASCVD. In addition, these patients exhibited elevated concentrations of amyloid A protein in both serum and isolated HDL3 fraction.\u003c/p\u003e \u003cp\u003eCombined analysis of advanced lipoprotein, lipidomics and inflammatory biomarkers profiles using NMR can characterise and discriminate subjects with HALP and ASCVD.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work has been developed in the context of the PhD program in Biochemistry, Molecular Biology and Biomedicine of the Autonomous University of Barcelona, Spain.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work has been funded by the Col\u0026middot;legi de Farmac\u0026egrave;utics de Barcelona [grant number S/0006276-2023].\u003c/p\u003e\n\u003cp\u003eThe funding sponsors did not participate in any way in the design of the study, the collection, analysis or interpretation of data, the writing of the article, or the decision to publish the results.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: D. Ceacero-Mar\u0026iacute;n, B. Fern\u0026aacute;ndez-Cid\u0026oacute;n, X. Pint\u0026oacute;, M. Fanlo-Maresma, V. Esteve-Luque, N. Amig\u0026oacute;, E. Corbella, L.F. Dub\u0026oacute;n, A. Riera-Mestre, and M.J. Castro-Castro; Data curation: D. Ceacero-Mar\u0026iacute;n, B. Fern\u0026aacute;ndez-Cid\u0026oacute;n, N. Amig\u0026oacute;, E. Corbella, and M.J. Castro-Castro; Formal analysis: D. Ceacero-Mar\u0026iacute;n, B. Fern\u0026aacute;ndez-Cid\u0026oacute;n, X. Pint\u0026oacute;, E. Corbella, A. Riera-Mestre, and M.J. Castro-Castro; Funding acquisition: D. Ceacero-Mar\u0026iacute;n, B. Fern\u0026aacute;ndez-Cid\u0026oacute;n, and M.J. Castro-Castro; Investigation: D. Ceacero-Mar\u0026iacute;n, B. Fern\u0026aacute;ndez-Cid\u0026oacute;n, X. Pint\u0026oacute;, M. Fanlo-Maresma, V. Esteve-Luque, N. Amig\u0026oacute;, E. Corbella, L.F. Dub\u0026oacute;n, A. Riera-Mestre, and M.J. Castro-Castro; Methodology: D. Ceacero-Mar\u0026iacute;n, B. Fern\u0026aacute;ndez-Cid\u0026oacute;n, X. Pint\u0026oacute;, M. Fanlo-Maresma, V. Esteve-Luque, N. Amig\u0026oacute;, E. Corbella, A. Riera-Mestre, and M.J. Castro-Castro; Project administration: D. Ceacero-Mar\u0026iacute;n, B. Fern\u0026aacute;ndez-Cid\u0026oacute;n, X. Pint\u0026oacute;, A. Riera-Mestre, and M.J. Castro-Castro; Resources, X. Pint\u0026oacute;, L.F. Dub\u0026oacute;n, and A. Riera-Mestre; Supervision: B. Fern\u0026aacute;ndez-Cid\u0026oacute;n, X. Pint\u0026oacute;, M. Fanlo-Maresma, V. Esteve-Luque, L.F. Dub\u0026oacute;n, A. Riera-Mestre, and M.J. Castro-Castro; Validation: X. Pint\u0026oacute;, N. Amig\u0026oacute;, and A. Riera-Mestre; Visualization, X. Pint\u0026oacute;, L.F. Dub\u0026oacute;n, and A. Riera-Mestre; writing\u0026mdash;original draft: D. Ceacero-Mar\u0026iacute;n; and writing\u0026mdash;review \u0026amp; editing: D. Ceacero-Mar\u0026iacute;n, B. Fern\u0026aacute;ndez-Cid\u0026oacute;n, X. Pint\u0026oacute;, M. Fanlo-Maresma, V. Esteve-Luque, N. Amig\u0026oacute;, E. Corbella, L.F. Dub\u0026oacute;n, A. Riera-Mestre, and M.J. Castro-Castro.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eD. Ceacero-Mar\u0026iacute;n, B. Fern\u0026aacute;ndez-Cid\u0026oacute;n, X. Pint\u0026oacute;, M. Fanlo-Maresma, V. Esteve-Luque, E. Corbella, L.F. Dub\u0026oacute;n, A. Riera-Mestre, and M.J. Castro-Castro\u0026nbsp;declare that they have no conflict of interest.\u003c/p\u003e\n\u003cp\u003eN. Amig\u0026oacute; is a stock owner of Biosfer Teslab and is a patent holder of the lipoprotein profiling described in the present manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval and Informed consent\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study protocol conforms to the ethical guidelines of the 1975 Declaration of Helsinki. The study protocol has been priorly approved by the Institution\u0026apos;s ethics committees on research on humans (the hospital and primary care centre (approvals numbers: PR119/23 and Ref. 25/134-P, respectively)). Written informed consent was obtained from each patient included in the study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003evon Eckardstein A, Nordestgaard BG, Remaley AT et al (2023) High-density lipoprotein revisited: biological functions and clinical relevance. Eur Heart J 44:1394\u0026ndash;1407. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/eurheartj/ehac605\u003c/span\u003e\u003cspan address=\"10.1093/eurheartj/ehac605\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGordon T, Castelli WP, Hjortland MC, Kannel WB, Dawber TR (1977) High-density lipoprotein as a protective factor against coronary heart disease: the Framingham Study. Am J Med 62:707\u0026ndash;714. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/0002-9343(77)90874-9\u003c/span\u003e\u003cspan address=\"10.1016/0002-9343(77)90874-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchandelmaier S, Briel M, Saccilotto R et al (2017) Niacin for primary and secondary prevention of cardiovascular events. Cochrane Database Syst Rev 6:CD009744. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/14651858\u003c/span\u003e\u003cspan address=\"10.1002/14651858\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMadsen CM, Varbo A, Nordestgaard BG (2017) Extreme high high-density lipoprotein cholesterol is paradoxically associated with high mortality in men and women: two prospective cohort studies. Eur Heart J 38:2478\u0026ndash;2486. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/eurheartj/ehx163\u003c/span\u003e\u003cspan address=\"10.1093/eurheartj/ehx163\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHirata A, Sugiyama D, Watanabe M et al (2018) Association of extremely high levels of high-density lipoprotein cholesterol with cardiovascular mortality in a pooled analysis of 9 cohort studies including 43,407 individuals: The EPOCH-JAPAN study. J Clin Lipidol 12:674\u0026ndash;684e5. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jacl.2018.01.014\u003c/span\u003e\u003cspan address=\"10.1016/j.jacl.2018.01.014\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGiammanco A, Noto D, Barbagallo CM et al (2021) Hyperalphalipoproteinemia and beyond: the role of HDL in cardiovascular diseases. Life (Basel) 11:581. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/life11060581\u003c/span\u003e\u003cspan address=\"10.3390/life11060581\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYamashita S, Maruyama T, Hirano KI et al (2000) Molecular mechanisms, lipoprotein abnormalities and atherogenicity of hyperalphalipoproteinemia. Atherosclerosis 152:271\u0026ndash;285. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S0021-9150(00)00574-8\u003c/span\u003e\u003cspan address=\"10.1016/S0021-9150(00)00574-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNavab M, Reddy ST, van Lenten BJ, Fogelman AM (2011) HDL and cardiovascular disease: atherogenic and atheroprotective mechanisms. Nat Rev Cardiol 8:222\u0026ndash;232. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/nrcardio.2010.222\u003c/span\u003e\u003cspan address=\"10.1038/nrcardio.2010.222\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDi Angelantonio E, Gao P, Pennells L, Kaptoge S, Caslake M et al (2012) Lipid-related markers and cardiovascular disease prediction. JAMA 307:2499\u0026ndash;2506. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1001/jama.2012.6571\u003c/span\u003e\u003cspan address=\"10.1001/jama.2012.6571\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhera AV, Demler OV, Adelman SJ et al (2017) Cholesterol efflux capacity, high-density lipoprotein particle number, and incident cardiovascular events: an analysis from the JUPITER Trial. Circulation 135:2494\u0026ndash;2504. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1161/CIRCULATIONAHA.116.025678\u003c/span\u003e\u003cspan address=\"10.1161/CIRCULATIONAHA.116.025678\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThakkar H, Vincent V, Sen A et al (2021) Changing perspectives on HDL: from simple quantity measurements to functional quality assessment. J Lipids 2021:5585521. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1155/2021/5585521\u003c/span\u003e\u003cspan address=\"10.1155/2021/5585521\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMarsche G, Heine GH, Stadler JT, Holzer M (2020) Current understanding of the relationship of HDL composition, structure and function to their cardioprotective properties in chronic kidney disease. Biomolecules 10:1348. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/biom10091348\u003c/span\u003e\u003cspan address=\"10.3390/biom10091348\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eToth PP, Grabner M, Punekar RS et al (2014) Cardiovascular risk in patients achieving low-density lipoprotein cholesterol and particle targets. Atherosclerosis 235:585\u0026ndash;591. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.atherosclerosis.2014.05.914\u003c/span\u003e\u003cspan address=\"10.1016/j.atherosclerosis.2014.05.914\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEichelmann F, Sellem L, Wittenbecher C et al (2022) Deep lipidomics in human plasma: cardiometabolic disease risk and effect of dietary fat modulation. Circulation 146:21\u0026ndash;35. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1161/CIRCULATIONAHA.121.056805\u003c/span\u003e\u003cspan address=\"10.1161/CIRCULATIONAHA.121.056805\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eG\u0026oacute;mez Rosso L, Lhomme M, Mero\u0026ntilde;o T et al (2014) Altered lipidome and antioxidative activity of small, dense HDL in normolipidemic rheumatoid arthritis: relevance of inflammation. Atherosclerosis 237:652\u0026ndash;660. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.atherosclerosis.2014.09.034\u003c/span\u003e\u003cspan address=\"10.1016/j.atherosclerosis.2014.09.034\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHancock-Cerutti W, Lhomme M, Dauteuille C et al (2017) Paradoxical coronary artery disease in humans with hyperalphalipoproteinemia is associated with distinct differences in the high-density lipoprotein phosphosphingolipidome. J Clin Lipidol 11(5):1192\u0026ndash;1200e3. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jacl.2017.06.018\u003c/span\u003e\u003cspan address=\"10.1016/j.jacl.2017.06.018\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e\u0026Aacute;lvarez-\u0026Aacute;lvarez I, Mart\u0026iacute;nez-Gonz\u0026aacute;lez M\u0026Aacute;, S\u0026aacute;nchez-Tainta A et al (2019) Adherence to an energy-restricted Mediterranean diet score and prevalence of cardiovascular risk factors in the PREDIMED-Plus: a cross-sectional study. Rev Esp Cardiol (Engl Ed) 72:925\u0026ndash;934. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.rec.2018.08.010\u003c/span\u003e\u003cspan address=\"10.1016/j.rec.2018.08.010\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTopolski TD, LoGerfo J, Patrick DL, Williams B, Walwick J et al (2006) The Rapid Assessment of Physical Activity (RAPA) among older adults. Prev Chronic Dis 3:A118\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGil M, Samino S, Barrilero R, Correig X (2019) Lipid profiling using 1H NMR spectroscopy. Methods Mol Biol 2037:35\u0026ndash;47. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/978-1-4939-9690-2_3\u003c/span\u003e\u003cspan address=\"10.1007/978-1-4939-9690-2_3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFuertes-Mart\u0026iacute;n R, Taverner D, Vallv\u0026eacute; JC et al (2018) Characterization of 1H NMR plasma glycoproteins as a new strategy to identify inflammatory patterns in rheumatoid arthritis. J Proteome Res 17:3730\u0026ndash;3739. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1021/acs.jproteome.8b00411\u003c/span\u003e\u003cspan address=\"10.1021/acs.jproteome.8b00411\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHirano T, Nohtomi K, Koba S, Muroi A, Ito Y (2008) A simple and precise method for measuring HDL-cholesterol subfractions by a single precipitation followed by homogeneous HDL-cholesterol assay. J Lipid Res 49:1130\u0026ndash;1136. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1194/jlr.D700027-JLR200\u003c/span\u003e\u003cspan address=\"10.1194/jlr.D700027-JLR200\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFern\u0026aacute;ndez-Cid\u0026oacute;n B, Cand\u0026aacute;s-Est\u0026eacute;banez B, Gil-Serret M et al (2021) Physicochemical properties of lipoproteins assessed by nuclear magnetic resonance as a predictor of premature cardiovascular disease: the PRESARV-SEA study. J Clin Med 10:1379. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/jcm10071379\u003c/span\u003e\u003cspan address=\"10.3390/jcm10071379\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLawler PR, Akinkuolie AO, Chu AY et al (2017) Atherogenic lipoprotein determinants of cardiovascular disease and residual risk among individuals with low low-density lipoprotein cholesterol. J Am Heart Assoc 6:e005549. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1161/JAHA.117.005549\u003c/span\u003e\u003cspan address=\"10.1161/JAHA.117.005549\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGirona J, Amig\u0026oacute; N, Ibarretxe D et al (2019) HDL triglycerides: a new marker of metabolic and cardiovascular risk. Int J Mol Sci 20:3151. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/ijms20133151\u003c/span\u003e\u003cspan address=\"10.3390/ijms20133151\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRye KA, Bursill CA, Lambert G, Tabet F, Barter PJ (2009) The metabolism and anti-atherogenic properties of HDL. J Lipid Res 50:S195\u0026ndash;S200. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1194/jlr.R800034-JLR200\u003c/span\u003e\u003cspan address=\"10.1194/jlr.R800034-JLR200\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVerwer BJ, Scheffer PG, Vermue RP, Pouwels PJ, Diamant M et al (2020) NAFLD is related to post-prandial triglyceride-enrichment of HDL particles in association with endothelial and HDL dysfunction. Liver Int 40:2439\u0026ndash;2444. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/liv.14597\u003c/span\u003e\u003cspan address=\"10.1111/liv.14597\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDuparc T, Ruidavets JB, Genoux A et al (2020) Serum level of HDL particles are independently associated with long-term prognosis in patients with coronary artery disease: the GENES study. Sci Rep 10:8138. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41598-020-65100-2\u003c/span\u003e\u003cspan address=\"10.1038/s41598-020-65100-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWatanabe H, S\u0026ouml;derlund S, Soro-Paavonen A et al (2006) Decreased high-density lipoprotein particle size, prebeta-, and large HDL subspecies concentration in Finnish low-HDL families: relationship with intima-media thickness. Arterioscler Thromb Vasc Biol 26:897\u0026ndash;902. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1161/01.ATV.0000209577.04246.c0\u003c/span\u003e\u003cspan address=\"10.1161/01.ATV.0000209577.04246.c0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePullinger CR, O'Connor PM, Naya-Vigne JM et al (2021) Levels of prebeta-1 high-density lipoprotein are a strong independent positive risk factor for coronary heart disease and myocardial infarction: a meta-analysis. J Am Heart Assoc 10:e018381. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1161/JAHA.120.018381\u003c/span\u003e\u003cspan address=\"10.1161/JAHA.120.018381\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKontush A (2015) HDL particle number and size as predictors of cardiovascular disease. Front Pharmacol 6:218. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fphar.2015.00218\u003c/span\u003e\u003cspan address=\"10.3389/fphar.2015.00218\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu RX, Li S, Li XL et al (2015) High-density lipoprotein subfractions in relation with the severity of coronary artery disease: a Gensini score assessment. J Clin Lipidol 9:26\u0026ndash;34. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jacl.2014.11.003\u003c/span\u003e\u003cspan address=\"10.1016/j.jacl.2014.11.003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMutharasan RK, Thaxton CS, Berry J et al (2017) HDL efflux capacity, HDL particle size, and high-risk carotid atherosclerosis in a cohort of asymptomatic older adults: the Chicago Healthy Aging Study. J Lipid Res 58:600\u0026ndash;606. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1194/jlr.P069039\u003c/span\u003e\u003cspan address=\"10.1194/jlr.P069039\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHern\u0026aacute;ez \u0026Aacute;, Casta\u0026ntilde;er O, Elosua R et al (2017) Mediterranean diet improves high-density lipoprotein function in high-cardiovascular-risk individuals: a randomized controlled trial. Circulation 135:633\u0026ndash;643. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1161/CIRCULATIONAHA.116.023712\u003c/span\u003e\u003cspan address=\"10.1161/CIRCULATIONAHA.116.023712\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStanton KM, Kienzle V, Dinnes DLM et al (2022) Moderate- and high-intensity exercise improves lipoprotein profile and cholesterol efflux capacity in healthy young men. J Am Heart Assoc 11:e023386. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1161/JAHA.121.023386\u003c/span\u003e\u003cspan address=\"10.1161/JAHA.121.023386\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCand\u0026aacute;s-Est\u0026eacute;banez B, Fern\u0026aacute;ndez-Cid\u0026oacute;n B, Corbella E et al (2024) The impact of the Mediterranean diet and lifestyle intervention on lipoprotein subclass profiles among metabolic syndrome patients: findings of a randomized controlled trial. Int J Mol Sci 25:1338. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/ijms25021338\u003c/span\u003e\u003cspan address=\"10.3390/ijms25021338\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHern\u0026aacute;ez \u0026Aacute;, Fern\u0026aacute;ndez-Castillejo S, Farr\u0026agrave;s M et al (2014) Olive oil polyphenols enhance high-density lipoprotein function in humans. Arterioscler Thromb Vasc Biol 34:2115\u0026ndash;2119. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1161/ATVBAHA.114.303374\u003c/span\u003e\u003cspan address=\"10.1161/ATVBAHA.114.303374\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMillar SR, Navarro P, Harrington JM, Shivappa N, H\u0026eacute;bert JR, Perry IJ, Phillips CM (2021) Comparing dietary score associations with lipoprotein particle subclass profiles: a cross-sectional analysis of a middle-to older-aged population. Clin Nutr 40:4720\u0026ndash;4729. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.clnu.2021.06.005\u003c/span\u003e\u003cspan address=\"10.1016/j.clnu.2021.06.005\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eViadas R, Toloba A, Fern\u0026aacute;ndez I et al (2023) Association of physical activity with high-density lipoprotein functionality in a population-based cohort: the REGICOR study. Rev Esp Cardiol (Engl Ed) 76:86\u0026ndash;93. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.rec.2022.04.009\u003c/span\u003e\u003cspan address=\"10.1016/j.rec.2022.04.009\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHern\u0026aacute;ez \u0026Aacute;, Soria-Florido MT, Casta\u0026ntilde;er O et al (2021) Leisure time physical activity is associated with improved HDL functionality in high cardiovascular risk individuals: a cohort study. Eur J Prev Cardiol 28:1392\u0026ndash;1401. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/2047487320925625\u003c/span\u003e\u003cspan address=\"10.1177/2047487320925625\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAmig\u0026oacute; N, Castelblanco E, Julve J et al (2025) Advanced serum lipoprotein and glycoprotein profiling for cardiovascular event prediction in type 2 diabetes mellitus: the LIPOCAT study. Cardiovasc Diabetol 24:88. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12933-025-02636-5\u003c/span\u003e\u003cspan address=\"10.1186/s12933-025-02636-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoreno-Vedia J, Rosales R, Ozcariz E et al (2022) Triglyceride-rich lipoproteins and glycoprotein A and B assessed by 1H-NMR in metabolic-associated fatty liver disease. Front Endocrinol (Lausanne) 12:775677. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fendo.2021.775677\u003c/span\u003e\u003cspan address=\"10.3389/fendo.2021.775677\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eManmadhan A, Lin BX, Zhong J et al (2019) Elevated GlycA in severe obesity is normalized by bariatric surgery. Diabetes Obes Metab 21:178\u0026ndash;182. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/dom.13481\u003c/span\u003e\u003cspan address=\"10.1111/dom.13481\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMayer FJ, Binder CJ, Krychtiuk KA et al (2019) The prognostic value of serum amyloid A for long-term mortality among patients with subclinical carotid atherosclerosis. Eur J Clin Invest 49:e13095. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/eci.13095\u003c/span\u003e\u003cspan address=\"10.1111/eci.13095\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVuilleumier N, Dayer JM, von Eckardstein A, Roux-Lombard P (2013) Pro- or anti-inflammatory role of apolipoprotein A-1 in high-density lipoproteins? Swiss Med Wkly 143:w13781. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4414/smw.2013.13781\u003c/span\u003e\u003cspan address=\"10.4414/smw.2013.13781\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMart\u0026iacute;nez-Beamonte R, Lou-Bonafonte JM, Mart\u0026iacute;nez-Gracia MV, Osada J (2013) Sphingomyelin in high-density lipoproteins: structural role and biological function. Int J Mol Sci 9(4):7716\u0026ndash;7741. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/ijms14047716\u003c/span\u003e\u003cspan address=\"10.3390/ijms14047716\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee YK, Lee DH, Kim JK et al (2013) Lysophosphatidylcholine, oxidized low-density lipoprotein and cardiovascular disease in Korean hemodialysis patients: analysis at 5 years of follow-up. J Korean Med Sci 28:268\u0026ndash;273. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3346/jkms.2013.28.2.268\u003c/span\u003e\u003cspan address=\"10.3346/jkms.2013.28.2.268\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMaki KC, Dicklin MR, Kirkpatrick CF (2021) Saturated fats and cardiovascular health: current evidence and controversies. J Clin Lipidol 15:765\u0026ndash;772. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jacl.2021.09.049\u003c/span\u003e\u003cspan address=\"10.1016/j.jacl.2021.09.049\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHeimerl S, Fischer M, Baessler A et al (2014) Alterations of plasma lysophosphatidylcholine species in obesity and weight loss. PLoS ONE 9:e111348. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0111348\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0111348\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Nuclear Magnetic Resonance, Biomolecular, Lipoproteins, HDL, Cholesterol, HDL, Lipoproteins, Lipidomic, Cardiovascular Diseases","lastPublishedDoi":"10.21203/rs.3.rs-8981110/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8981110/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDespite the well-established protective role of HDL-cholesterol against atherosclerotic cardiovascular disease (ASCVD), several studies have observed a higher incidence of ASCVD in individuals with elevated HDL-cholesterol concentrations. This condition, known as hyperalphalipoproteinemia, appears to modify HDL composition, potentially resulting in a loss of its anti-atherogenic properties and, in some cases, a pro-atherogenic effect.\u003c/p\u003e \u003cp\u003eThis study aimed to assess advanced lipoprotein, lipidomic and inflammatory biomarker profiles using nuclear magnetic resonance (NMR) spectroscopy in patients diagnosed with hyperalphalipoproteinemia who have suffered a cardiovascular event.\u003c/p\u003e \u003cp\u003eA case-control study was conducted on a cohort of patients diagnosed with hyperalphalipoproteinemia and ASCVD (n\u0026thinsp;=\u0026thinsp;29) and a control group of individuals with hyperalphalipoproteinemia but without ASCVD (n\u0026thinsp;=\u0026thinsp;41).\u003c/p\u003e \u003cp\u003eA conventional lipid profile and an advanced lipoprotein, lipidomic and inflammatory biomarker profiles were performed using NMR spectroscopy.\u003c/p\u003e \u003cp\u003ePatients with hyperalphalipoproteinemia and ASCVD showed higher concentrations of VLDL particles (particularly small VLDL), HDL particles (particularly small HDL) and higher triglyceride content in both lipoproteins, although serum concentrations of HDL-cholesterol and triglycerides were comparable within groups. Furthermore, ASCVD group presented an altered lipidomic profile, characterised by decreased concentrations of lysophosphatidylcholine, and increased concentrations of saturated fatty acids. In addition, these patients exhibited elevated concentrations of amyloid A protein in both serum and isolated HDL3 fraction.\u003c/p\u003e \u003cp\u003ePatients with hyperalphalipoproteinemia and ASCVD showed a lipoprotein and lipidomic profile with metabolic, structural, and compositional alterations consistent with a more atherogenic pattern. This profile could not be detected by a conventional lipid profile.\u003c/p\u003e","manuscriptTitle":"Advanced lipoprotein, glycoprotein and lipidomic profiles in hyperalphalipoproteinemia with and without atherosclerotic cardiovascular disease: a case–control study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-10 13:24:22","doi":"10.21203/rs.3.rs-8981110/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e15fd3cc-5464-4301-8e85-cf8a5bc33091","owner":[],"postedDate":"March 10th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-16T22:00:33+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-10 13:24:22","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8981110","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8981110","identity":"rs-8981110","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}