Lipoprotein(a) and Small Dense LDL Profiles and Statin Response in Type 2 Diabetes Mellitus: A Case-Control Study

Lipoprotein(a) and Small Dense LDL Profiles and Statin Response in Type 2 Diabetes Mellitus: 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 Article Lipoprotein(a) and Small Dense LDL Profiles and Statin Response in Type 2 Diabetes Mellitus: A Case-Control Study Sophida Suta, Bonggochpass Pinsawas, Apinya Surawit, Suphawan Ophakas, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7577991/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 Lipoprotein subfraction research has emerged as a promising approach for risk stratification, warranting investigation in type 2 diabetes mellitus (T2DM), cardiovascular disease (CVD), and lipid-lowering therapy. This cross-sectional, case-control study aimed to characterize lipoprotein particle profiles, including small dense LDL (sdLDL) and lipoprotein(a) [Lp(a)], in individuals with T2DM and CVD compared with healthy controls, and to evaluate their associations with clinical risk factors. Fasting plasma samples from 118 participants were analyzed: T2DM ( n = 52), CVD ( n = 14), and healthy controls ( n = 52). Lipoprotein levels and subclasses were quantified using proton nuclear magnetic resonance ( 1 H NMR) spectroscopy with Bruker’s In-Vitro Diagnostic research (IVDr) Lipoprotein Subclass Analysis (B.I.LISA). Participants had a mean age of 46.9 ± 9.0 years, and 79% were female. Compared with controls, individuals with T2DM had significantly higher Lp(a) ( p = 0.012), triglycerides, total cholesterol, LDL, and apolipoprotein B100 (apoB100) ( p < 0.05), while HDL remained unchanged; sdLDL levels were higher but not statistically significant. Statin therapy reduced large buoyant and intermediate-density LDL particles but had limited effects on sdLDL and Lp(a). Physical activity was associated with higher HDL and lower LDL ( p < 0.001). In conclusions, Lp(a) and sdLDL contribute to CVD risk in T2DM, with partial response to statin therapy. Health sciences/Biomarkers Health sciences/Cardiology Health sciences/Diseases Health sciences/Endocrinology Health sciences/Medical research Type 2 diabetes mellitus cardiovascular disease lipoprotein subclass lipoprotein(a) nuclear magnetic resonance spectroscopy physical activity Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Cardiovascular disease (CVD) is a leading cause of global mortality 1 , and individuals with type 2 diabetes mellitus (T2DM) face a significantly elevated risk 2 to 4 times higher than the general population 2 , 3 . The global prevalence of diabetes among adults is projected to rise to 7.7%, affecting 439 million adults by 2030, with a particularly significant increase in low- and middle-income countries 4 . One crucial but underappreciated link between T2DM and CVD is diabetic dyslipidemia, as metabolic abnormalities associated with these conditions can exacerbate CVD risk. T2DM is a chronic metabolic disorder characterized by insufficient insulin production and/or insulin resistance. These changes lead to increased production of very low-density lipoprotein (VLDL), intermediate-density lipoprotein (IDL), and low-density lipoprotein (LDL), particularly small dense low-density lipoprotein (sdLDL), while simultaneously reducing high-density lipoprotein (HDL) levels 5 . Additionally, this dyslipidemia promotes chronic inflammation, contributing to atherosclerosis and an increased risk of CVD 6 , 7 . Several studies suggest that smaller, dense LDL particles are more atherogenic than larger ones. These smaller particles are believed to penetrate the endothelial lining of blood vessels more easily, contributing to atherosclerosis. However, traditional lipid panels do not differentiate between LDL particle sizes, potentially underestimating cardiovascular risk in certain patients 8 , 9 . Beyond the well-established role of LDL and HDL in CVD, another evolving lipoprotein marker, lipoprotein(a) [Lp(a)], has gained attention for its potential impact on atherosclerosis. Lp(a) is a genetically regulated, independent, and causal risk factor for atherosclerotic cardiovascular diseases. It shares structural similarities with LDL particles and contains apoB100, which is covalently linked to plasminogen-like apolipoprotein(a) [apo(a)] 10 , 11 . Lp(a) is more atherogenic due to its pro-inflammatory and anti-fibrinolytic properties 12 , 13 . Unlike other lipoproteins, Lp(a) is primarily genetically regulated and remains largely unaffected by lifestyle modifications or lipid-lowering therapies, making it a particularly challenging yet critical marker for CVD risk assessment. Elevated Lp(a) is associated with an elevated risk of CVD 14 . Non-genetic factors, such as diet and physical activity/exercise, have minimal influence on Lp(a) plasma levels 15 . Moreover, while lipid-lowering drugs generally do not significantly affect Lp(a) levels, Tsimikas et al. reported that statins may modestly increase Lp(a) levels by approximately 10–15% 16 . Currently, medical assessments for CVD related to high LDL or low HDL levels primarily focus on measuring cholesterol quantity rather than evaluating the particle number and particle size characteristics of LDL and HDL 17 . However, the concentration and particle number of VLDL, LDL, and HDL are not constant; they vary among individuals and can change in response to lipid-modifying drugs or lifestyle modifications 18 , 19 . With the rise of high-resolution lipid profiling technologies, proton nuclear magnetic resonance ( 1 H NMR) spectroscopy now enables detailed analysis of lipoprotein particle size and concentration 20 – 22 . However, the clinical application of these parameters remains inconsistent. This study aims to characterize lipoprotein particle profiles including sdLDL and Lp(a) in individuals with T2DM and CVD compared to healthy controls, and to evaluate their associations with clinical risk factors. Our goal is to clarify the contribution of these particles to residual cardiovascular risk and assess their utility in guiding risk stratification and therapeutic interventions. Methods Subjects and study design This cross-sectional, case-control study utilized archived plasma samples from two previously conducted studies: (1) the Siriraj One Health Study (SIOH), a prospective cohort study of working adults in urban Bangkok 23 , and (2) the Diabetes Registry and Care Quality under the Universal Coverage Policy at Primary Care Units (DIRECT), a multicenter study conducted in many regions, Thailand 24 . Both studies involved adult participants with relatively homogeneous characteristics within the urbanized area as previously described. Eligible adults (adults (≥ 18 years) were categorized into three groups: (1) T2DM group (based on the International Classification of Diseases 10th Revision ICD-10: E11–E14; n = 52), (2) CVD group (ICD-10: I20–I25, I60–I64; n = 14), and (3) healthy controls ( n = 52) with no history of diabetes, CVD, or lipid-lowering therapy. Controls were age- and sex-matched to T2DM participants. Demographic data, clinical history, medication use including the type and dosage of lipid-lowering drugs (e.g., statins, gemfibrozil, nicotinic acid, cholestyramine, fibrates, and ezetimibe) duration of diagnosis, food frequency, and physical activity were obtained from the two previous studies 23 , 24 . The study protocol was approved by the Institutional Review Board of the Faculty of Medicine Siriraj Hospital, Mahidol University (COA No. Si 542/2024, Si 381/2023, and Si 330/2017). All methods were carried out in accordance with relevant guidelines and regulations (Declaration of Helsinki). Plasma samples used in this study had been previously collected with written informed consent under prior approved research protocols, and their subsequent use for the present study was approved by the ethics committee. Biochemistries measurements Fasting plasma samples collected in EDTA tubes were used to measure hemoglobin A1C (HbA1c) via a turbidimetric inhibition immunoassay, while insulin levels were measured using a sandwich immunoassay with the electrochemiluminescence (ECLIA) technique. The homeostatic model assessment for insulin resistance (HOMA-IR) was calculated using the formula 25 : $$\:\frac{\left[\right[fasting\:insulin\:(\mu\:U/mL)]\:\times\:\:[fasting\:plasma\:glucose\:(mg/dL)\left]\right]}{405}$$ Similarly, the homeostatic model assessment of beta cell function (HOMA-β) was calculated using the formula: $$\:\frac{\:fasting\:insulin\:(\mu\:U/mL)\:}{fasting\:glucose\:(mg/dL)\:\--\:63}$$ Additionally, Lp(a) levels were measured using an immunoturbidimetric assay. Urine samples were analyzed for microalbuminuria, assessed by the albumin/creatinine ratio (MAU) using an immunoturbidimetric assay. All measurements were conducted at an accredited clinical laboratory (Siriraj Hospital, Bangkok, Thailand). Physical activity assessment Physical activity was categories into: occupational, household, transportation, and exercise. The frequency (days per week) and duration (hours and/or minutes per day) of each activity were analyzed to determine the metabolic equivalent tasks (METs). Intensity levels were classified according to METs, following the criteria of Chirdkiatisak et al. 26 . Physical activity was categorized as follows: light physical activity: 6 METs. Physical activity was calculated using the formula: $$\:MET\:value\:\times\:\:\left(\left(hours\:\times\:60\right)+minute\right)\times\:day\left(s\right)\:per\:week$$ The total MET-minutes per week were then summed across all activities. Food frequency consumption Food consumption data were assessed using the Thai semi-quantitative food frequency questionnaire (Thai semi-FFQ), a validated tool developed by our group to evaluate habitual dietary intake in individuals at risk for metabolic syndrome in urban Thai 27 . This semi-FFQ included 91 food items classified into five major groups: fruits ( n = 18), beverages ( n = 10), snacks and desserts ( n = 29), à la carte dishes ( n = 7), and rice with toppings ( n = 8), each with five standardized portion sizes. Participants reported consumption frequency and portion size, which were converted into average daily intake. Finally, all food items were calculated proportionally per serving using the formula 28 : $$\:\left[frequency\:per\:month\:or\:week\:or\:day\right]\times\:\:\frac{serving\:size}{propotion}$$ 1 H NMR measurement and processing Fasting EDTA-frozen plasma sample (− 80 ◦C) were quantitatively analyzed for lipoprotein particle size at the Singapore Phenome Centre (Nanyang Technological University, Singapore) using a 600 MHz Avance III HD spectrometer based on In-Vitro Diagnostic research (IVDr) system Bruker BioSpin, Germany). Briefly, plasma samples (350 µL) were mixed with 75 mM sodium phosphate buffer (pH 7.4) in a 1:1 ratio and vortexed for approximately 1 minute. Following vortexing, 600 µL of the mixture was transferred into a 5-mm Bruker SampleJet NMR tube. NMR measurements were conducted on a Bruker 600 MHz Avance III HD spectrometer (IVDr), equipped with a 5-mm broadband inverse (BBI) Z-gradient high-resolution probe and fitted with a Bruker SampleJet robot, with the cooling system set to 5°C. The acquisition pulse sequences used were 1D NOESY and Carr-Purcell-Meiboom-Gill (CPMG), with the probe temperature set at 36.85°C (310 K). Lipoprotein subclasses (112 in total) were analyzed using the Bruker IVDr Lipoprotein Subclass Analysis method (B.I.-LISA) (Supplementary Table S1 ). The lipoprotein data included information on the chemical components of apolipoprotein-A1 (apoA1), apolipoprotein-A2 (apoA2), apolipoprotein B100 (apoB100), the ratio of apo-A1 to apoB100, cholesterol (CH), free cholesterol (FC), phospholipids (PL), particle number (PN), triglycerides (TG), VLDL, IDL, LDL, HDL, and the LDL-to-HDL cholesterol ratio. VLDL, LDL, and HDL were further subdivided into specific density subclasses, with VLDL having 5 subclasses, LDL having 6 subclasses, and HDL having 4 subclasses 29 . The major lipoprotein subclasses are distinguished by their density ranges as follows: VLDL (0.950–1.006 kg/L), IDL (1.006–1.019 kg/L), LDL (1.019–1.063 kg/L), and HDL (1.063–1.210 kg/L). LDL subclasses are further defined by density: LDL-1 (1.019–1.031 kg/L) and LDL-2 (1.031–1.034 kg/L) are classified as large buoyant LDL; LDL-3 through LDL-5 (1.034–1.044 kg/L) are classified as intermediate-density LDL; and LDL-6 (1.044–1.063 kg/L) is classified as small dense LDL (sdLDL) 30 . A full description of the lipoprotein annotations is provided in Supplementary Table S2 . Statistical analyses Lp(a) values below the limit of detection (LOD < 7 mg/dL) were imputed as 3.5 mg/dL (LOD/2) for statistical analysis 31 , 32 . This approach was considered appropriate in the context of moderate levels of censored data. Additionally, a sensitivity analysis was performed using LOD/√2 to evaluate the impact on statistical estimates (Supplementary Table S3). All data were reported as mean ± standard deviation (SD) or as percentages. Group comparisons were performed using independent t-tests or one-way ANOVA. If the assumptions of normality or homogeneity of variance were violated, non-parametric alternatives such as the Mann Whitney U test or Kruskal–Wallis test were used. Categorical variables were compared using chi-squared test. p < 0.05 was considered statistically significant. Multiple regression analyses were performed to assess independent associations between risk factors, and Pearson’s correlation analyses were used to evaluate correlations between various parameters and lipoprotein particle number. Results Baseline characteristics Table 1 presents the baseline characteristics of the participants, with a mean age of 46.9 ± 9.00 years (female 79%). Participants in the T2DM and CVD had significantly higher body weight, body mass index (BMI), waist circumference (WC), fat mass, and body fat percentage than the healthy control group ( p < 0.001), indicating overall adiposity in those groups. Systolic and diastolic blood pressure (SBP and DBP) were also significantly higher in the T2DM and CVD groups ( p < 0.001), consistent with an increased cardiovascular risk burden among individuals with these conditions. Table 1 The main characteristics of the study population. Continuous data are presented as the mean ± standard deviation (SD), while ordinal data are expressed as frequency (n) and percentage (%). The T2DM group was diagnosed based on ICD-10 codes E11–E14. The CVD group was diagnosed based on ICD-10 codes I20–I25 and I60–I64. T2DM, type 2 diabetes mellitus; CVD, cardiovascular diseases; BMI, body mass index; WC, waist circumference; SBP, systolic blood pressure; DBP, diastolic blood pressure; HbA1c, hemoglobin A1C; MAU, urine microalbumin; HOMA-IR, homeostatic model assessment for insulin resistance; HOMA-β, homeostatic model assessment of beta cell function; BUN, blood urea nitrogen; eGFR, estimated glomerular filtration rate; AST, aspartate transaminase; ALT, alanine transaminase. p value in bold indicate significant differences ( p < 0.05), analyzed by one-way ANOVA. variable Control T2DM CVD p value n = 52 n = 52 n = 14 Age, years 42.5 ± 5.93 44.7 ± 10.66 53.4 ± 10.40 0.004 Gender, n (%) 0.477 Male 11.0 (21.15) 11.0 (21.15) 5.0 (35.71) Female 41.0 (78.85) 41.0 (78.85) 9.0 (64.29) Height (cm) 160.4 ± 7.42 160.6 ± 7.65 159.0 ± 10.18 0.835 Weight (kg) 65.1 ± 13.89 80.5 ± 17.84 78.0 ± 17.57 < 0.001 BMI (kg/m 2 ) 25.2 ± 4.00 31.0 ± 5.47 30.7 ± 5.91 < 0.001 WC (cm) 83.4 ± 10.92 97.3 ± 14.90 96.0 ± 17.22 < 0.001 Skeletal muscle mass (kg) 23.2 ± 5.52 26.1 ± 5.60 24.9 ± 6.79 0.045 Fat (kg) 22.6 ± 7.26 33.0 ± 11.28 30.9 ± 10.72 < 0.001 %Fat (%) 34.3 ± 6.82 40.3 ± 7.84 40.1 ± 6.30 < 0.001 SBP (mmHg) 123.8 ± 11.69 137.5 ± 15.59 137.6 ± 12.15 < 0.001 DBP (mmHg) 72.6 ± 12.27 81.6 ± 10.38 79.5 ± 15.38 < 0.001 Fasting blood glucose (mg/dl) 98.9 ± 9.18 134.0 ± 52.80 127.6 ± 29.39 < 0.001 Insulin (µU/ml) 10.6 ± 5.86 20.7 ± 20.33 28.6 ± 42.94 0.004 HbA1c (%) 5.4 ± 0.29 7.1 ± 1.85 6.8 ± 1.26 < 0.001 MAU (mg/g.creatinine) 17.6 ± 86.96 43.6 ± 82.62 16.9 ± 25.46 0.252 HOMA-IR 2.6 ± 1.61 6.7 ± 5.97 9.2 ± 14.33 < 0.001 HOMA-β 5.9 ± 2.89 11.2 ± 32.85 9.7 ± 12.56 0.491 Creatinine (µmg/dL) 0.7 ± 0.10 0.7 ± 0.23 0.7 ± 0.14 0.675 eGFR (mL/min/1.73m 2 ) 106.7 ± 8.38 102.2 ± 19.66 97.4 ± 14.02 0.424 AST (U/L) 20.4 ± 5.45 22.9 ± 8.65 26.2 ± 5.74 0.266 ALT (U/L) 22.6 ± 14.13 27.5 ± 18.39 30.9 ± 11.22 0.484 Smoking history, n (%) 0.736 Never 48.0 (94.12) 46.0 (93.88) 10.0 (90.91) Ever, but have stopped. 1.0 (1.96) 2.0 (4.08) 1.0 (9.09) Currently 2.0 (3.92) 1.0 (2.04) 0.0 (0.00) Alcohol consumption, n (%) 0.604 Never 18.0 (36.73) 28.0 (57.14) 7.0 (63.64) Quitted 6.0 (12.24) 5.0 (10.20) 1.0 (9.09) 1 time per week 1.0 (2.04) 0.0 (0.00) 0.0 (0.00) Physical activity (MET-min/week) 8788.3 ± 6026.71 6833.9 ± 3781.34 9554.5 ± 6817.69 0.141 Frequency of food group daily intake Fruits 0.3 ± 0.54 0.2 ± 0.21 0.4 ± 0.28 0.523 Beverages 0.2 ± 0.15 0.2 ± 0.15 0.2 ± 0.13 0.862 Snacks and desserts 0.1 ± 0.07 0.1 ± 0.05 0.1 ± 0.07 0.725 A la carte 0.1 ± 0.10 0.1 ± 0.21 0.1 ± 0.11 0.464 Rice with toppings 0.1 ± 0.15 0.3 ± 0.55 0.2 ± 0.19 0.101 Indicators of glucose metabolism differedmarkedly between groups. Fasting blood glucose, fasting insulin, and HbA1c levels were highest in the T2DM group ( p < 0.001). Accordingly, insulin resistance was evident in T2DM patients; both HOMA-IR and HOMA-β were significantly elevated in the T2DM group compared to the CVD and the healthy control groups. Furthermore, HOMA-IR was positive correlation with HbA1c, indicating that individuals with higher insulin resistance tend to have higher HbA1c levels (r = 0.45, 95% CI: 0.308–0.580, p < 0.0001) (see Supplementary Fig. S1 ). Additionally, renal function markers (e.g., creatinine and estimated glomerular filtration rate [eGFR]) and liver enzymes (aspartate transaminase [AST] and alanine transaminase [ALT]) showed no significant differences among the group. Lifestyle factors including smoking history, alcohol consumption, physical activity, or the frequency of daily food group intake were also no significant differences among the groups. Medication histories for participants in the T2DM and CVD groups are detailed in Supplementary Table S4. Notably, the duration of disease and the type and dose of lipid-lowering drugs did not significantly differ between the T2DM and CVD groups. Lp(a) measurement Almost all participants had Lp(a) concentration in the low-risk range ( 125 nmol/L) (see Supplementary Fig. S2 ). We observed that the T2DM group not receiving lipid-lowering therapy had significantly higher plasma Lp(a) levels compared to the healthy control group ( p = 0.012) (Table 2 ). Within the T2DM group, there was no significant difference in Lp(a) concentrations between those receiving lipid-lowering therapy and those not receiving treatment. For the CVD group, a comparison with the control group was not feasible, as only one participant in the CVD group was not on lipid-lowering medication. We also found that Lp(a) was significantly correlated with LDL cholesterol (r = 0.35 95%CI [0.13 to 0.54]; p = 0.002) and total LDL particle number (r = 0.30 95% CI [0.06 to 0.50]; p = 0.011) but not with LDL-5 and LDL-6 particle numbers in healthy controls, unlike in individuals with T2DM. (see Supplementary Fig. S3 and Supplementary Fig. S4). These results suggest that although Lp(a) is elevated in T2DM, their variation appears largely independent of the traditional lipid profile and LDL subclassed distribution in this cohort. Table 2 Comparison of concentration and particle size of lipoprotein between the case and control groups. Data are expressed as mean ± standard deviation (SD). The T2DM group was diagnosed based on ICD-10 codes E11–E14, and the CVD group was diagnosed based on ICD-10 codes I20–I25 and I60–I64; "not received" refers to participants not receiving lipid-lowering drugs (statins); "received" refers to participants receiving lipid-lowering drugs (statins). Based on the 1 H NMR (Bruker IVDr/B.I.LISA) classification, LDL subclasses are defined by density as follows: LDL-1 (1.019–1.031 kg/L) and LDL-2 (1.031–1.034 kg/L) are classified as large buoyant LDL; LDL-3 through LDL-5 (1.034–1.044 kg/L) are classified as intermediate-density LDL; and LDL-6 (1.044–1.063 kg/L) is classified as small dense LDL (sdLDL). T2DM, type 2 diabetes mellitus; CVD, cardiovascular diseases; VLDL, very-low-density lipoprotein; IDL, intermediate-density lipoprotein; LDL, low-density lipoprotein; HDL, high-density lipoprotein; ApoA1, apolipoprotein A1; ApoA2, apolipoprotein A2; ApoB100, apolipoprotein B100. variable Control T2DM p value a p value b CVD p value c not received received All not received received mean ± SD mean ± SD mean ± SD mean ± SD mean ± SD mean ± SD Lp(a), nmol/L 30.0 ± 40.91 73.9 ± 105.46 53.3 ± 83.34 0.436 0.012 42.8 ± 40.09 3.5 - 45.8 ± 40.03 0.301 Lipids and apolipoprotein, mg/dL Triglyceride 93.6 ± 35.84 121.1 ± 37.27 137.7 ± 65.26 0.295 0.004 119.6 ± 47.56 126.5 - 119.1 ± 49.46 0.028 Cholesterol 204.3 ± 29.60 216.2 ± 30.14 180.8 ± 47.96 0.004 0.125 169.2 ± 43.09 126.5 - 172.5 ± 42.99 0.001 LDL 122.4 ± 24.91 131.1 ± 28.06 100.7 ± 36.29 0.002 0.196 92.7 ± 28.74 66.7 - 94.7 ± 28.88 < 0.001 HDL 59.7 ± 13.29 57.5 ± 11.05 51.7 ± 8.15 0.034 0.503 48.0 ± 11.40 32.3 - 49.2 ± 10.89 0.004 ApoA1 154.9 ± 24.99 153.2 ± 18.93 143.2 ± 16.68 0.050 0.781 137.0 ± 20.64 110.4 - 139.0 ± 19.95 0.017 ApoA2 32.1 ± 4.29 32.6 ± 3.78 30.9 ± 3.70 0.108 0.629 28.8 ± 4.58 22.7 - 29.2 ± 4.41 0.014 ApoB100 89.4 ± 15.52 98.7 ± 19.73 84.3 ± 26.83 0.040 0. 035 78.8 ± 21.39 64.0 - 79.9 ± 21.81 0.042 LDL/HDL 2.1 ± 0.57 2.4 ± 0.73 2.0 ± 0.71 0.048 0.115 2.0 ± 0.62 2.1 - 2.0 ± 0.65 0.422 ApoB100/ApoA1 0.6 ± 0.14 0.7 ± 0.17 0.6 ± 0.19 0.206 0.077 0.6 ± 0.15 0.6 - 0.6 ± 0.15 0.814 Lipoprotein particle number, nmol/L Total particle number 1624.7 ± 282.24 1795.0 ± 358.68 1532.0 ± 487.91 0. 040 0.035 1432.7 ± 388.84 1162.7 - 1453.4 ± 396.56 0.042 VLDL particle number 109.5 ± 46.42 144.1 ± 48.51 159.8 ± 74.53 0.399 0.006 145.5 ± 55.75 150.7 - 145.1 ± 58.00 0.016 IDL particle number 62.5 ± 25.01 84.1 ± 30.33 74.6 ± 32.29 0.288 0.002 77.4 ± 35.48 57.9 - 78.9 ± 36.46 0.076 LDL particle number 1388.4 ± 267.69 1492.4 ± 331.28 1209.9 ± 420.85 0.013 0.166 1126.4 ± 314.17 840.4 - 1148.4 ± 315.57 0.003 LDL-1 particle number 217.1 ± 54.64 220.7 ± 58.49 161.7 ± 55.21 0.001 0.807 173.0 ± 54.19 115.4 - 177.4 ± 53.70 0.009 LDL-2 particle number 193.5 ± 65.83 191.0 ± 62.37 115.7 ± 51.59 < 0.001 0.879 117.1 ± 36.12 103.7 - 118.1 ± 37.38 < 0.001 LDL-3 particle number 221.5 ± 61.99 226.4 ± 59.85 147.6 ± 68.15 < 0.001 0.760 147.8 ± 44.77 112.1 - 150.5 ± 45.36 < 0.001 LDL-4 particle number 242.2 ± 62.27 262.3 ± 79.70 204.3 ± 79.35 0.013 0.255 199.0 ± 59.78 146.6 - 203.1 ± 60.20 0.023 LDL-5 particle number 223.1 ± 75.07 265.2 ± 106.35 249.8 ± 100.52 0.599 0.059 215.1 ± 90.59 174.3 - 218.2 ± 93.50 0.736 LDL-6 particle number 300.1 ± 98.06 347.2 ± 119.95 356.8 ± 149.92 0.808 0.086 302.1 ± 115.43 247.5 - 306.3 ± 119.02 0.948 a Statistically significant difference between "not received" and "received" groups in the T2DM b Statistically significant difference between "not received" in the T2DM compared to the control group c Statistically significant difference between all of the CVD group compared to the control group. p value in bold indicate significant differences ( p < 0.05), analyzed by Mann-Whitney U test. 1 H NMR measurement Lipoprotein concentrations and particle numbers are presented in Table 2 , the T2DM group (not receiving lipid-lowering drugs) demonstrated a clear dyslipidemia profile relative to control. Triglyceride and apoB100 levels were significantly higher in T2DM patients without lipid-lowering treatment than in the healthy controls group ( p 0.05), while HDL levels were lower in the T2DM than in the healthy control group, indicating a high-risk condition for atherosclerosis. Furthermore, the T2DM group had significantly elevated total atherogenic particle count defined as the sum of VLDL, IDL, and LDL particle numbers compared to the healthy control group ( p = 0.035), with VLDL and IDL particles number contributing most to this difference. Interestingly, the total LDL particle number did not significantly differ between T2DM and healthy control groups. However, subclass analysis revealed a trend toward higher sdLDL particles (LDL-6 subclass) in the T2DM group, though this was not statistically significant. Furthermore, we observed that elevated triglyceride levels were strongly associated with a greater number of sdLDL particles (Supplementary Fig. S5). Within T2DM participants, those receiving lipid-lowering therapy had significantly lower lipid levels and fewer large buoyant and intermediate-density LDL particles than those not on treatment. However, sdLDL particle numbers did not differ significantly between the two subgroups. In all CVD groups, both those receiving and not receiving lipid-lowering therapy had significantly lower lipid levels and large buoyant LDL particles compared to healthy, but not sdLDL particle numbers. Moreover, we found a strong link between lipid profiles and lipoprotein particles. These findings confirm that participants in the T2DM patients had an atherogenic shift in their lipoprotein profile, characterized by elevated triglyceride, apoB100, a surplus of remnant (VLDL/IDL) particles, an excess remnant (VLDL/IDL) particle, and a propensity toward more sdLDL particle number even when overall LDL particle counts was similar to that of healthy individuals. Lipid distribution patterns in lipoproteins and lipoprotein subclasses Radar plots analysis was employed to visualize the distributions of lipid and apolipoprotein across lipoprotein subclasses in each group (Fig. 1 and Supplementary Table S5). These plots revealed distinct patterns between groups. The T2DM group (not receiving lipid-lowering drugs) had higher levels of triglycerides, cholesterol, and apolipoproteins within VLDL and LDL subclasses compared to the healthy control group. In particular, the sdLDL in T2DM carried greater concentration of triglycerides and cholesterol than those in the healthy control group, consistent with an accumulation of cholesterol-rich, small dense LDL particles in diabetes. T2DM patients who received lipid-lowering therapy showed partial improvement in this subclass lipid profile. The cholesterol and apoB100 content within the large and intermediate-density LDL particles was reduced relative to the untreated T2DM patients, but the more atherogenic subclasses (sdLDL) remained largely elevated, and triglyceride-rich particles (especially VLDL and subclasses LDL-5 and LDL-6 particle number) persisted at high levels. Similarly, the CVD group receiving lipid-lowering therapy exhibited elevated triglyceride and free cholesterol levels across multiple VLDL, IDL, and LDL subclasses compared to the healthy control group. This indicates that even with statin treatment, many T2DM patients experienced incomplete normalization of their lipoprotein profiles, notably retaining excess triglyceride-rich lipoproteins and sdLDL particles, which may contribute to their residual cardiovascular risk. Effect of clinical variables and lipid-lowering therapy We examined the associations between various clinical factors (BMI and glycemic indices) and lipoprotein measure, as well as the impact of lipid-lowering therapy, using multivariable regression analysis (Fig. 2 and Supplementary Table S6). An increased BMI was significantly associated with a more atherogenic lipid profile. Specifically, higher BMI correlated with higher triglyceride levels (+ 2.42 [95% CI: +0.89 to + 3.95]; p = 0.002), while showering a modest but significant decrease in total cholesterol, LDL, and HDL levels. Additionally, individuals with higher BMI were significantly associated with greater numbers of VLDL particle number (+ 3.24 [95% CI: +1.48 to + 5.00]; p < 0.001) and IDL particle number (+ 1.20 [95% CI: +0.31 to + 2.08]; p = 0.009). In contrast, the large buoyant and intermediate-density LDL particles significantly decreased as BMI increased, whereas the sdLDL subclasses showed the opposite trend. Poor glycemic control was associated with adverse changes in the lipoprotein profile. Neither FBG nor HbA1c levels were correlated with Lp(a). Both, however, were positively correlated with triglyceride concentrations, with HbA1c showing a much stronger association [+ 14.53 (95% CI: +9.06 to + 20.00); p < 0.0001]. In addition, HbA1c was inversely correlated with HDL levels ( p < 0.01). In contrast, FBG showed only weak associations. These findings indicate that higher glycemic levels, particularly as reflected by HbA1c, are linked to elevated triglycerides and reduced HDL. In addition, elevated HbA1c levels were significantly associated with increased numbers of VLDL, IDL, and sdLDL particles (all p < 0.0001). Notably, the strongest association was observed for sdLDL [+ 33.08 (95% CI: +19.66 to + 46.50)], highlighting its particular relevance in the context of diabetes-related dyslipidemia. In contrast, HOMA-IR levels showed no significant associations with most lipoprotein parameters. The use of lipid-lowering medication (e.g., statins) had a marked impact on the lipoprotein particle profile. Patients receiving lipid-lowering therapy showed no significant difference in Lp(a) levels but had significantly lower total cholesterol, LDL, and apoB100 levels ( p < 0.05). Analysis of LDL subclasses revealed significantly lower total LDL particle numbers (− 270.99 [95% CI: −468.16 to − 73.81]; p = 0.008). Treatment was associated with significant reductions in large buoyant and intermediate-density LDL particles, whereas VLDL and IDL particles were not significantly affected. Importantly, sdLDL particles remained unchanged with lipid-lowering therapy ( p > 0.05). Additionally, we also observed that using a high intensity of lipid-lowering drugs (atorvastatin 40–80 mg and/or simvastatin > 40 mg daily) did not significantly further improve Lp(a) level or the overall lipoprotein profile compared to a low- and moderate- intensity therapy (Supplementary Table S7). As shown in Fig. 3 , lipid-lowering therapy increased the proportion of LDL composed of large buoyant and intermediate-density LDL particles, while the relative abundance of sdLDL particles was higher in treated patients compared to untreated ones. In other words, although therapy substantially reduces the total number of LDL particles, it disproportionately depletes the larger subclasses, resulting in a greater proportion of the remaining LDL consisting of small dense particles. Consistent with prior evidence, these residual sdLDL particles possess greater atherogenic potential and contribute more to arterial plaque formation 33 . These findings suggest that while standard lipid-lowering therapy is effective in lowering total LDL and reducing large buoyant and intermediate-density LDL particles, it fails to adequately reduce Lp(a) and sdLDL. Elevated BMI and poor glycemic control appear to aggravate this pattern by favoring the formation of triglyceride-rich lipoproteins and sdLDL, which may help explain the residual CVD risk in treated T2DM and CVD patients. Association of exercise activity with lipoproteins and lipoprotein subclasses The relationship between exercise intensities and lipoproteins, including their subclasses, is presented in Fig. 4 , with a focus solely on exercise activity. Among healthy controls (Fig. 4 a), higher levels of exercise were associated with a trend toward lower total cholesterol and LDL levels, although this reduction did not reach statistical significance. In contrast, HDL levels showed a statistically significant increase with greater exercise (estimated break-point: 504.0 ± 62.17 MET-min/week, p < 0.001). Exercise was also associated with a slight reduction in the smallest LDL particles (LDL-6 subclass), whereas large buoyant and intermediate-density LDL particles showed a non-significant increasing trend. Higher levels of exercise were not associated with significant reductions in Lp(a) levels. In individuals with T2DM, with or without lipid-lowering therapy, exercise activity showed minimal influence on lipoprotein subclasses, with overlapping confidence intervals across the full range of physical activity (Fig. 4 b). Similarly, among patients with established CVD receiving lipid-lowering therapy, no consistent association was observed between exercise levels and lipoprotein measures, likely reflecting advanced disease status and pharmacological modification (Fig. 4 c). Overall, these observations reinforce the importance of regular physical activity in modulating lipid metabolism. Maintaining an adequate exercise regimen was associated with beneficial changes, particularly increasing HDL and potentially lowering the proportion of atherogenic sdLDL, which may be essential for long-term management of dyslipidemia. However, exercise alone appears to be beneficial primarily in healthy individuals, with limited effects in those with T2DM or established CVD. Our findings suggest that a combination of consistent exercise and appropriate medical therapy should be pursued for more effective control of LDL and comprehensive cardiovascular risk reduction in these high-risk groups. Discussion This cross-sectional, case-control study employed high-resolution 1 H NMR spectroscopy to profile lipoprotein subclasses and assess cardiometabolic risk in individuals with T2DM and CVD. The use of the Bruker IVDr Lipoprotein Subclass Analysis (B.I.LISA) platform enables detailed quantification of lipoprotein characteristics beyond conventional lipid panels. Our findings demonstrated significantly elevated levels of Lp(a) in individuals with T2DM. This is consistent with the report by Abdullah et al., which showed that patients with T2DM and prediabetes had higher mean levels of Lp(a) compared with healthy controls and a greater prevalence of abnormally elevated Lp(a) 34 . Several studies further suggest that, in patients with type 2 diabetes, elevated Lp(a) is associated with an increased risk of cardiovascular disease. The underlying mechanisms remain unclear, but both genetic and non-genetic factors are likely contributors, and additional interactions between glucose, insulin, and Lp(a) may also play a role 35 – 37 . Additionally, Lp(a) levels showed significantly correlation with LDL and LDL particle number concentrations only in healthy individuals, suggesting an independent pathophysiological pathway. This dissociation may be attributed to metabolic disturbances such as insulin resistance and chronic low-grade inflammation, which are known to upregulate Lp(a) synthesis independently of LDL pathway 37 . Conversely, among healthy individuals, Lp(a) positively correlated with LDL, consistent with prior evidence suggesting shared metabolic or structural features 11 . Elevated Lp(a) is a recognized causal and independent risk factor for atherosclerotic CVD 11 , 38 , with greater risk observed in the presence of elevated LDL 39 . In T2DM, elevated triglyceride levels and poor glycemic control were strongly associated with an increase in sdLDL particles. Hirano et al. demonstrated a positive correlation between sdLDL levels and triglyceride concentrations in healthy individuals. More recently, a study in a Chinese cohort revealed that each 0.1 mmol/L increase in sdLDL was significantly associated with higher odds of pre-diabetes and newly diagnosed T2DM, independent of LDL 40 . This observation is consistent with the Japan Diabetes Complications Study (JDCS), which identified higher serum triglycerides as a key determinant of sdLDL burden 41 . Poor glycemic control may also promote the formation of sdLDL through non-enzymatic glycation of apolipoproteins, further enhancing their atherogenic potential 42 . SdLDL particles are considered highly atherogenic due to their longer circulation time, increased oxidative susceptibility, and enhanced arterial wall penetration factors that collectively contribute to a heightened risk of cardiovascular events 43 , 44 . These findings underscore the importance of intensive management of both hypertriglyceridemia and hyperglycemia to mitigate sdLDL burden and reduce cardiovascular risk in patients with T2DM. Lipid-lowering therapy, particularly with statins, had a pronounced effect on reducing large buoyant LDL particles and overall LDL levels. However, our findings confirm previous reports that statins do not effectively lower Lp(a) concentrations. In fact, statin therapy may lead to a modest increase in Lp(a) levels, typically by approximately 10–15% 16,45 . The underlying mechanism remains unclear. Current lipid-lowering medications have not achieved meaningful reductions in Lp(a) levels, with the exception of proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibitors, which have been shown to reduce Lp(a) concentrations by approximately 27% 46 . Numerous recent studies suggest that statins can effectively reduce the number of LDL particle 47 , 48 . Our study confirms that statin therapy is more effective at reducing LDL levels than decreasing the number of LDL particles, particularly sdLDL particles. Mechanistically, statins lower serum cholesterol levels primarily by inhibiting HMG-CoA reductase activity 49 and upregulating low-density lipoprotein receptors (LDLR) expression 50 . Because sdLDL particles exhibit lower affinity for LDLR due to altered apoB structure 51 , they are less effectively cleared from circulation compared to large buoyant LDL particles 51 While some studies have reported that high-dose statin therapy reduces sdLDL levels 52 , 53 , our study did not observe significant reductions in sdLDL with either low- nor high-intensity statin therapy. This discrepancy may be due to persistent insulin resistance and metabolic dysregulation in T2DM, which promotes sdLDL formation and blunts statin efficacy. These findings highlight a key limitation of statin monotherapy in addressing the full spectrum of atherogenic lipoproteins in high-risk populations. Adjunctive strategies, such as emerging agents targeting triglyceride metabolism (e.g., apoC-III inhibitors or ANGPTL3 inhibitors) 54 , 55 , may offer additional benefits by reducing sdLDL burden. Moreover, more personalized approaches to lipid management that consider residual risk markers such as sdLDL and Lp(a) may be warranted to optimize cardiovascular prevention in patients with T2DM and CVD. We also found that physical activity significantly improved HDL levels and trended toward lower LDL levels, consistent with finding from the ELSA-Brasil study reported that moderate and vigorous physical activity were significantly associated with higher HDL and lower LDL and TG levels 56 . However, physical activity did not significantly alter Lp(a) levels, which are primarily genetically determined and resistant to lifestyle intervention 57 . Overall, this study highlights the limitations of standard lipid-lowering therapies in fully mitigating residual cardiovascular risk in T2DM and CVD populations. It underscores the importance of sdLDL and Lp(a) as independent, treatment-resistant risk factors. Our findings support a need for adjunctive strategies such as novel lipid-lowering agents or lifestyle interventions tailored to sdLDL reduction in managing high-risk individuals. Although sdLDL and Lp(a) are increasingly recognized as important residual risk factors, current guidelines do not recommend their routine measurement due to limited evidence. Our findings provide novel insights from an Asian cohort, highlighting potential ethnic differences in lipoprotein patterns compared with Western populations. Future longitudinal and interventional studies are needed to clarify causal pathways, evaluate novel lipid-lowering agents, and determine how sdLDL and Lp(a) can be integrated into risk prediction and management strategies for high-risk patients with T2DM and CVD This study has several notable strengths. First, inclusion of both statin-treated and untreated T2DM and CVD patients provided real-world insights into the residual cardiovascular risk under current lipid-lowering therapies. Second, the study integrated data on lifestyle factors, including physical activity and dietary intake, allowing for the exploration of their association with lipoprotein subclasses. There are several potential limitations to this study. First, the small sample size, especially in the CVD group, may reduce generalizability. Second, the cross-sectional design of the study precludes causal inference. While associations between lipoprotein parameters and clinical or lifestyle variables were identified, temporal and causal relationships cannot be established. Third, liver function data were obtained retrospectively and may not reflect current metabolic status. Fourth, dietary intake and physical activity were assessed through self-report, which may have led to under- or overestimation due to inherent reporting biases. Fifth, although statin treatment was recorded, data on patient adherence were not available, which may influence lipid profile outcomes. Conclusions Lp(a) and sdLDL are important residual cardiovascular risk markers in T2DM. While statins reduce LDL and large buoyant and intermediate-density LDL particles, they have limited effect on sdLDL and Lp(a). Physical activity improves HDL and LDL profiles but does not lower Lp(a). These findings highlight the need for advanced lipid profiling and additional therapeutic strategies to address persistent lipid abnormalities and improve risk management in high-risk patients. Declarations Acknowledgments We thank the principal investigators and all members of the SIOH and DIRECT projects for providing data and samples for this research. The authors are also grateful to Prof. Wang Yulan and the team from Nanyang Technological University, Singapore, for their support and suggestions regarding NMR analysis. We also thank the management of the Siriraj Medical Research Center for providing office space and laboratory facilities for this study. Author contribution statement S.S. and K.M. contributed to the conceptualization, methodology, visualization, project administration, and original draft preparation. P.M., B.P., S.P., S.S., S.O., and A.S. contributed to the investigation and data curation. A.S. was responsible for software development and formal analysis. Y.W., Y.Y.D., X.S., and T.S. contributed to resources and to writing—review and editing. K.M. also contributed to writing—review and editing. The corresponding author (K.M.) attests that all listed authors meet the authorship criteria and that no individuals meeting the criteria have been omitted. All authors have read and approved the final version of the manuscript. Funding This work was supported by Siriraj research development fund, Gant number (IO) R016733021, Faculty of medicine Siriraj Hospital, Mahidol University. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interest The authors declare no competing interests. Statement on consent Written informed consent was not required for this study as determined by the Institutional Review Board of Siriraj Hospital, Mahidol University, because the plasma samples were de-identified and obtained from the institutional biobank (COA No. Si 542/2024). 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07:23:06","extension":"png","order_by":19,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":535741,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFigure4.png","url":"https://assets-eu.researchsquare.com/files/rs-7577991/v1/e70532839c654cec34d002f5.png"},{"id":92476719,"identity":"82bc2fea-4433-4841-bc2b-d3865983a32f","added_by":"auto","created_at":"2025-09-30 07:23:05","extension":"png","order_by":20,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":94389,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineGraphicalAbstract.png","url":"https://assets-eu.researchsquare.com/files/rs-7577991/v1/b210dd13b7b3177f8eea15a3.png"},{"id":92478452,"identity":"33690a0d-dfbb-4108-bbd4-9e907f6de81b","added_by":"auto","created_at":"2025-09-30 07:31:05","extension":"xml","order_by":21,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":267785,"visible":true,"origin":"","legend":"","description":"","filename":"41049690d37945a595f4a03cbecf5b641structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7577991/v1/bef366a9d97b4c1372f5cd93.xml"},{"id":92476723,"identity":"2bd51dd4-df15-4ad4-abef-ba127cd99997","added_by":"auto","created_at":"2025-09-30 07:23:05","extension":"html","order_by":22,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":284778,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7577991/v1/9af76b6ed849ab33884f6d1f.html"},{"id":92476700,"identity":"bec36100-6a3a-4dd6-98f2-3c8963043d8a","added_by":"auto","created_at":"2025-09-30 07:23:05","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1951725,"visible":true,"origin":"","legend":"\u003cp\u003eRadar plots of lipid and apolipoprotein concentrations across lipoproteins and their subclasses. Plots show relative levels of plasma triglycerides, cholesterol, free cholesterol, phospholipids and apolipoproteins across different lipoprotein fractions. Radial distance from the center indicates the relative concentration of each component, with greater distance representing higher abundance in the corresponding lipoprotein subclass. T2DM, type 2 diabetes mellitus; CVD, cardiovascular disease\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7577991/v1/1675eee8da39e7994abdbb2e.jpg"},{"id":92478446,"identity":"050251b6-f01e-4adc-ba06-6d389cd15e94","added_by":"auto","created_at":"2025-09-30 07:31:05","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":624816,"visible":true,"origin":"","legend":"\u003cp\u003eAssociations between lipoprotein subclasses, clinical variables, and lipid-lowering drugs use. Dot plots show the b-coefficients and their 95% confidence intervals for associations between differential lipoprotein subclasses and selected clinical outcomes or treatment status. Values below zero indicate an inverse relationship, while values above zero indicate a direct positive relationship. VLDL, very-low-density lipoprotein; IDL, intermediate-density lipoprotein LDL, low-density lipoprotein; HDL, high-density lipoprotein; ApoA1, apolipoprotein A1; ApoA2, apolipoprotein A2; ApoB100, apolipoprotein B100\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-7577991/v1/5fecba2d4a0be7deb758af21.png"},{"id":92479690,"identity":"ae75aa83-1a5d-4b0c-a4d9-33e5709e77d3","added_by":"auto","created_at":"2025-09-30 07:39:05","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":431223,"visible":true,"origin":"","legend":"\u003cp\u003eRatio of LDL subclasses to total LDL particle concentration across study groups. Box plots represent the mean; error bars indicate the minimum and maximum values. *, \u003cem\u003ep\u003c/em\u003e\u0026lt;0.05; **, \u003cem\u003ep\u003c/em\u003e\u0026lt;0.01 indicate significant differences by Mann-Whitney U test.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-7577991/v1/36658d3374c4e7adf3a56d97.png"},{"id":92476714,"identity":"6e762619-87a5-4b0c-9dd1-e3467209c109","added_by":"auto","created_at":"2025-09-30 07:23:05","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":8156734,"visible":true,"origin":"","legend":"\u003cp\u003eCubic spline models showing associations between exercise activity and lipoproteins and their subclasses. Models are adjusted for age, sex, and BMI. Smoothed lines represent the estimated regression curves; shaded areas indicate 95% confidence intervals. T2DM, type 2 diabetes mellitus; CVD, cardiovascular disease; HDL, high-density lipoprotein; LDL, low-density lipoprotein; VLDL, very-low-density lipoprotein; IDL, intermediate-density lipoprotein; Lp(a), lipoprotein (a)\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7577991/v1/86a7b675ef24cd5298372b9e.jpg"},{"id":96248825,"identity":"24db4e0b-a9eb-40d0-a3ad-62dbb44becc9","added_by":"auto","created_at":"2025-11-19 07:29:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7621128,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7577991/v1/0c0b38d1-bd2b-425d-a3d0-42e9d7422d7c.pdf"},{"id":92476710,"identity":"59f50f69-5ce0-43c8-a0ae-ad2243fc8d70","added_by":"auto","created_at":"2025-09-30 07:23:05","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":728362,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile1SupplementaryFigure.docx","url":"https://assets-eu.researchsquare.com/files/rs-7577991/v1/11522d155f3bc70fb52125b0.docx"},{"id":92479693,"identity":"e3916bcf-c690-448c-8356-82581025c109","added_by":"auto","created_at":"2025-09-30 07:39:05","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":88299,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile2SupplementaryTable.docx","url":"https://assets-eu.researchsquare.com/files/rs-7577991/v1/9776fcd308ad78870cab4431.docx"},{"id":92479703,"identity":"d9265e32-7c25-4035-991c-4de37e11159c","added_by":"auto","created_at":"2025-09-30 07:39:06","extension":"png","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":292028,"visible":true,"origin":"","legend":"","description":"","filename":"GraphicalAbstract.png","url":"https://assets-eu.researchsquare.com/files/rs-7577991/v1/4d0c939c7309fa8fdaaa2553.png"}],"financialInterests":"No competing interests reported.","formattedTitle":"Lipoprotein(a) and Small Dense LDL Profiles and Statin Response in Type 2 Diabetes Mellitus: A Case-Control Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCardiovascular disease (CVD) is a leading cause of global mortality\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e, and individuals with type 2 diabetes mellitus (T2DM) face a significantly elevated risk 2 to 4 times higher than the general population\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. The global prevalence of diabetes among adults is projected to rise to 7.7%, affecting 439\u0026nbsp;million adults by 2030, with a particularly significant increase in low- and middle-income countries\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. One crucial but underappreciated link between T2DM and CVD is diabetic dyslipidemia, as metabolic abnormalities associated with these conditions can exacerbate CVD risk. T2DM is a chronic metabolic disorder characterized by insufficient insulin production and/or insulin resistance. These changes lead to increased production of very low-density lipoprotein (VLDL), intermediate-density lipoprotein (IDL), and low-density lipoprotein (LDL), particularly small dense low-density lipoprotein (sdLDL), while simultaneously reducing high-density lipoprotein (HDL) levels\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Additionally, this dyslipidemia promotes chronic inflammation, contributing to atherosclerosis and an increased risk of CVD\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Several studies suggest that smaller, dense LDL particles are more atherogenic than larger ones. These smaller particles are believed to penetrate the endothelial lining of blood vessels more easily, contributing to atherosclerosis. However, traditional lipid panels do not differentiate between LDL particle sizes, potentially underestimating cardiovascular risk in certain patients\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eBeyond the well-established role of LDL and HDL in CVD, another evolving lipoprotein marker, lipoprotein(a) [Lp(a)], has gained attention for its potential impact on atherosclerosis. Lp(a) is a genetically regulated, independent, and causal risk factor for atherosclerotic cardiovascular diseases. It shares structural similarities with LDL particles and contains apoB100, which is covalently linked to plasminogen-like apolipoprotein(a) [apo(a)]\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Lp(a) is more atherogenic due to its pro-inflammatory and anti-fibrinolytic properties\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Unlike other lipoproteins, Lp(a) is primarily genetically regulated and remains largely unaffected by lifestyle modifications or lipid-lowering therapies, making it a particularly challenging yet critical marker for CVD risk assessment. Elevated Lp(a) is associated with an elevated risk of CVD\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Non-genetic factors, such as diet and physical activity/exercise, have minimal influence on Lp(a) plasma levels\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Moreover, while lipid-lowering drugs generally do not significantly affect Lp(a) levels, Tsimikas et al. reported that statins may modestly increase Lp(a) levels by approximately 10\u0026ndash;15%\u003csup\u003e16\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eCurrently, medical assessments for CVD related to high LDL or low HDL levels primarily focus on measuring cholesterol quantity rather than evaluating the particle number and particle size characteristics of LDL and HDL\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. However, the concentration and particle number of VLDL, LDL, and HDL are not constant; they vary among individuals and can change in response to lipid-modifying drugs or lifestyle modifications\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. With the rise of high-resolution lipid profiling technologies, proton nuclear magnetic resonance (\u003csup\u003e1\u003c/sup\u003eH NMR) spectroscopy now enables detailed analysis of lipoprotein particle size and concentration\u003csup\u003e\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. However, the clinical application of these parameters remains inconsistent. This study aims to characterize lipoprotein particle profiles including sdLDL and Lp(a) in individuals with T2DM and CVD compared to healthy controls, and to evaluate their associations with clinical risk factors. Our goal is to clarify the contribution of these particles to residual cardiovascular risk and assess their utility in guiding risk stratification and therapeutic interventions.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eSubjects and study design\u003c/h2\u003e\u003cp\u003eThis cross-sectional, case-control study utilized archived plasma samples from two previously conducted studies: (1) the Siriraj One Health Study (SIOH), a prospective cohort study of working adults in urban Bangkok\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e, and (2) the Diabetes Registry and Care Quality under the Universal Coverage Policy at Primary Care Units (DIRECT), a multicenter study conducted in many regions, Thailand\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Both studies involved adult participants with relatively homogeneous characteristics within the urbanized area as previously described. Eligible adults (adults (\u0026ge;\u0026thinsp;18 years) were categorized into three groups: (1) T2DM group (based on the International Classification of Diseases 10th Revision ICD-10: E11\u0026ndash;E14; \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;52), (2) CVD group (ICD-10: I20\u0026ndash;I25, I60\u0026ndash;I64; \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;14), and (3) healthy controls (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;52) with no history of diabetes, CVD, or lipid-lowering therapy. Controls were age- and sex-matched to T2DM participants. Demographic data, clinical history, medication use including the type and dosage of lipid-lowering drugs (e.g., statins, gemfibrozil, nicotinic acid, cholestyramine, fibrates, and ezetimibe) duration of diagnosis, food frequency, and physical activity were obtained from the two previous studies\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. The study protocol was approved by the Institutional Review Board of the Faculty of Medicine Siriraj Hospital, Mahidol University (COA No. Si 542/2024, Si 381/2023, and Si 330/2017). All methods were carried out in accordance with relevant guidelines and regulations (Declaration of Helsinki). Plasma samples used in this study had been previously collected with written informed consent under prior approved research protocols, and their subsequent use for the present study was approved by the ethics committee.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eBiochemistries measurements\u003c/h3\u003e\n\u003cp\u003eFasting plasma samples collected in EDTA tubes were used to measure hemoglobin A1C (HbA1c) via a turbidimetric inhibition immunoassay, while insulin levels were measured using a sandwich immunoassay with the electrochemiluminescence (ECLIA) technique. The homeostatic model assessment for insulin resistance (HOMA-IR) was calculated using the formula\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:\\frac{\\left[\\right[fasting\\:insulin\\:(\\mu\\:U/mL)]\\:\\times\\:\\:[fasting\\:plasma\\:glucose\\:(mg/dL)\\left]\\right]}{405}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eSimilarly, the homeostatic model assessment of beta cell function (HOMA-β) was calculated using the formula:\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:\\frac{\\:fasting\\:insulin\\:(\\mu\\:U/mL)\\:}{fasting\\:glucose\\:(mg/dL)\\:\\--\\:63}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAdditionally, Lp(a) levels were measured using an immunoturbidimetric assay. Urine samples were analyzed for microalbuminuria, assessed by the albumin/creatinine ratio (MAU) using an immunoturbidimetric assay. All measurements were conducted at an accredited clinical laboratory (Siriraj Hospital, Bangkok, Thailand).\u003c/p\u003e\n\u003ch3\u003ePhysical activity assessment\u003c/h3\u003e\n\u003cp\u003ePhysical activity was categories into: occupational, household, transportation, and exercise. The frequency (days per week) and duration (hours and/or minutes per day) of each activity were analyzed to determine the metabolic equivalent tasks (METs). Intensity levels were classified according to METs, following the criteria of Chirdkiatisak et al.\u003csup\u003e26\u003c/sup\u003e. Physical activity was categorized as follows: light physical activity: \u0026lt;3 METs, moderate physical activity: 3\u0026ndash;6 METs, and vigorous physical activity: \u0026gt;6 METs. Physical activity was calculated using the formula:\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:MET\\:value\\:\\times\\:\\:\\left(\\left(hours\\:\\times\\:60\\right)+minute\\right)\\times\\:day\\left(s\\right)\\:per\\:week$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe total MET-minutes per week were then summed across all activities.\u003c/p\u003e\n\u003ch3\u003eFood frequency consumption\u003c/h3\u003e\n\u003cp\u003eFood consumption data were assessed using the Thai semi-quantitative food frequency questionnaire (Thai semi-FFQ), a validated tool developed by our group to evaluate habitual dietary intake in individuals at risk for metabolic syndrome in urban Thai\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. This semi-FFQ included 91 food items classified into five major groups: fruits (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;18), beverages (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;10), snacks and desserts (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;29), \u0026agrave; la carte dishes (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;7), and rice with toppings (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;8), each with five standardized portion sizes. Participants reported consumption frequency and portion size, which were converted into average daily intake. Finally, all food items were calculated proportionally per serving using the formula\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e:\u003cdiv id=\"Equd\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equd\" name=\"EquationSource\"\u003e\n$$\\:\\left[frequency\\:per\\:month\\:or\\:week\\:or\\:day\\right]\\times\\:\\:\\frac{serving\\:size}{propotion}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003csup\u003e\u003cb\u003e1\u003c/b\u003e\u003c/sup\u003e\u003cb\u003eH NMR measurement and processing\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFasting EDTA-frozen plasma sample (\u0026minus;\u0026thinsp;80 ◦C) were quantitatively analyzed for lipoprotein particle size at the Singapore Phenome Centre (Nanyang Technological University, Singapore) using a 600 MHz Avance III HD spectrometer based on In-Vitro Diagnostic research (IVDr) system Bruker BioSpin, Germany). Briefly, plasma samples (350 \u0026micro;L) were mixed with 75 mM sodium phosphate buffer (pH 7.4) in a 1:1 ratio and vortexed for approximately 1 minute. Following vortexing, 600 \u0026micro;L of the mixture was transferred into a 5-mm Bruker SampleJet NMR tube. NMR measurements were conducted on a Bruker 600 MHz Avance III HD spectrometer (IVDr), equipped with a 5-mm broadband inverse (BBI) Z-gradient high-resolution probe and fitted with a Bruker SampleJet robot, with the cooling system set to 5\u0026deg;C. The acquisition pulse sequences used were 1D NOESY and Carr-Purcell-Meiboom-Gill (CPMG), with the probe temperature set at 36.85\u0026deg;C (310 K). Lipoprotein subclasses (112 in total) were analyzed using the Bruker IVDr Lipoprotein Subclass Analysis method (B.I.-LISA) (Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). The lipoprotein data included information on the chemical components of apolipoprotein-A1 (apoA1), apolipoprotein-A2 (apoA2), apolipoprotein B100 (apoB100), the ratio of apo-A1 to apoB100, cholesterol (CH), free cholesterol (FC), phospholipids (PL), particle number (PN), triglycerides (TG), VLDL, IDL, LDL, HDL, and the LDL-to-HDL cholesterol ratio. VLDL, LDL, and HDL were further subdivided into specific density subclasses, with VLDL having 5 subclasses, LDL having 6 subclasses, and HDL having 4 subclasses\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. The major lipoprotein subclasses are distinguished by their density ranges as follows: VLDL (0.950\u0026ndash;1.006 kg/L), IDL (1.006\u0026ndash;1.019 kg/L), LDL (1.019\u0026ndash;1.063 kg/L), and HDL (1.063\u0026ndash;1.210 kg/L). LDL subclasses are further defined by density: LDL-1 (1.019\u0026ndash;1.031 kg/L) and LDL-2 (1.031\u0026ndash;1.034 kg/L) are classified as large buoyant LDL; LDL-3 through LDL-5 (1.034\u0026ndash;1.044 kg/L) are classified as intermediate-density LDL; and LDL-6 (1.044\u0026ndash;1.063 kg/L) is classified as small dense LDL (sdLDL)\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. A full description of the lipoprotein annotations is provided in Supplementary Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e.\u003c/p\u003e\n\u003ch3\u003eStatistical analyses\u003c/h3\u003e\n\u003cp\u003eLp(a) values below the limit of detection (LOD\u0026thinsp;\u0026lt;\u0026thinsp;7 mg/dL) were imputed as 3.5 mg/dL (LOD/2) for statistical analysis\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. This approach was considered appropriate in the context of moderate levels of censored data. Additionally, a sensitivity analysis was performed using LOD/\u0026radic;2 to evaluate the impact on statistical estimates (Supplementary Table S3). All data were reported as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) or as percentages. Group comparisons were performed using independent t-tests or one-way ANOVA. If the assumptions of normality or homogeneity of variance were violated, non-parametric alternatives such as the Mann Whitney U test or Kruskal\u0026ndash;Wallis test were used. Categorical variables were compared using chi-squared test. \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant. Multiple regression analyses were performed to assess independent associations between risk factors, and Pearson\u0026rsquo;s correlation analyses were used to evaluate correlations between various parameters and lipoprotein particle number.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003eBaseline characteristics\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the baseline characteristics of the participants, with a mean age of 46.9\u0026thinsp;\u0026plusmn;\u0026thinsp;9.00 years (female 79%). Participants in the T2DM and CVD had significantly higher body weight, body mass index (BMI), waist circumference (WC), fat mass, and body fat percentage than the healthy control group (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating overall adiposity in those groups. Systolic and diastolic blood pressure (SBP and DBP) were also significantly higher in the T2DM and CVD groups (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), consistent with an increased cardiovascular risk burden among individuals with these conditions.\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\u003eThe main characteristics of the study population. Continuous data are presented as the mean \u0026plusmn; standard deviation (SD), while ordinal data are expressed as frequency (n) and percentage (%). The T2DM group was diagnosed based on ICD-10 codes E11\u0026ndash;E14. The CVD group was diagnosed based on ICD-10 codes I20\u0026ndash;I25 and I60\u0026ndash;I64. T2DM, type 2 diabetes mellitus; CVD, cardiovascular diseases; BMI, body mass index; WC, waist circumference; SBP, systolic blood pressure; DBP, diastolic blood pressure; HbA1c, hemoglobin A1C; MAU, urine microalbumin; HOMA-IR, homeostatic model assessment for insulin resistance; HOMA-β, homeostatic model assessment of beta cell function; BUN, blood urea nitrogen; eGFR, estimated glomerular filtration rate; AST, aspartate transaminase; ALT, alanine transaminase. \u003cem\u003ep\u003c/em\u003e value in bold indicate significant differences (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), analyzed by one-way ANOVA.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\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\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e\u003cp\u003evariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eControl\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003eT2DM\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003eCVD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cem\u003ep value\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;52\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;52\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;14\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eAge, years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e42.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026plusmn; 5.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e44.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026plusmn; 10.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e53.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026plusmn; 10.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eGender, \u003cem\u003en\u003c/em\u003e(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.477\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(21.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e11.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(21.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e5.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e(35.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e41.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(78.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e41.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(78.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e9.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e(64.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eHeight (cm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e160.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026plusmn; 7.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e160.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026plusmn; 7.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e159.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026plusmn; 10.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.835\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eWeight (kg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e65.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026plusmn; 13.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e80.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026plusmn; 17.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e78.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026plusmn; 17.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\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\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e25.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026plusmn; 4.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e31.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026plusmn; 5.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e30.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026plusmn; 5.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\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\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eWC (cm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e83.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026plusmn; 10.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e97.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026plusmn; 14.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e96.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026plusmn; 17.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\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\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eSkeletal muscle mass (kg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026plusmn; 5.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e26.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026plusmn; 5.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e24.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026plusmn; 6.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e0.045\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eFat (kg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026plusmn; 7.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e33.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026plusmn; 11.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e30.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026plusmn; 10.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\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\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e%Fat (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e34.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026plusmn; 6.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e40.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026plusmn; 7.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e40.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026plusmn; 6.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\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\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eSBP (mmHg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e123.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026plusmn; 11.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e137.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026plusmn; 15.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e137.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026plusmn; 12.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\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\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eDBP (mmHg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e72.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026plusmn; 12.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e81.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026plusmn; 10.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e79.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026plusmn; 15.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\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\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eFasting blood glucose (mg/dl)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e98.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026plusmn; 9.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e134.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026plusmn; 52.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e127.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026plusmn; 29.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\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\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eInsulin (\u0026micro;U/ml)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026plusmn; 5.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e20.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026plusmn; 20.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e28.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026plusmn; 42.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eHbA1c (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026plusmn; 0.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026plusmn; 1.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e6.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026plusmn; 1.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\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\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eMAU (mg/g.creatinine)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026plusmn; 86.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e43.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026plusmn; 82.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e16.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026plusmn; 25.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.252\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eHOMA-IR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026plusmn; 1.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026plusmn; 5.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e9.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026plusmn; 14.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\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\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eHOMA-β\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026plusmn; 2.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e11.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026plusmn; 32.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e9.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026plusmn; 12.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.491\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eCreatinine (\u0026micro;mg/dL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026plusmn; 0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026plusmn; 0.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026plusmn; 0.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.675\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eeGFR (mL/min/1.73m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e106.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026plusmn; 8.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e102.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026plusmn; 19.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e97.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026plusmn; 14.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.424\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eAST (U/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026plusmn; 5.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e22.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026plusmn; 8.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e26.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026plusmn; 5.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.266\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eALT (U/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026plusmn; 14.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e27.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026plusmn; 18.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e30.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026plusmn; 11.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.484\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eSmoking history, \u003cem\u003en\u003c/em\u003e(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.736\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNever\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e48.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(94.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e46.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(93.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e10.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e(90.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEver, but have stopped.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(1.96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(4.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e(9.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCurrently\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(3.92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(2.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e(0.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eAlcohol consumption, \u003cem\u003en\u003c/em\u003e(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.604\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNever\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(36.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e28.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(57.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e7.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e(63.64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQuitted\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(12.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(10.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e(9.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;1 time per month\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(32.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e13.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(26.53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e(18.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 time per month\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(12.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(6.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e(9.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003enearly 1 time per week\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(4.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e(0.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;1 time per week\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(2.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e(0.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003ePhysical activity (MET-min/week)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8788.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026plusmn; 6026.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6833.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026plusmn; 3781.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e9554.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026plusmn; 6817.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.141\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e\u003cp\u003eFrequency of food group daily intake\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFruits\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026plusmn; 0.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026plusmn; 0.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026plusmn; 0.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.523\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBeverages\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026plusmn; 0.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026plusmn; 0.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026plusmn; 0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.862\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSnacks and desserts\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026plusmn; 0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026plusmn; 0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026plusmn; 0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.725\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eA la carte\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026plusmn; 0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026plusmn; 0.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026plusmn; 0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.464\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRice with toppings\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026plusmn; 0.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026plusmn; 0.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026plusmn; 0.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.101\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eIndicators of glucose metabolism differedmarkedly between groups. Fasting blood glucose, fasting insulin, and HbA1c levels were highest in the T2DM group (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Accordingly, insulin resistance was evident in T2DM patients; both HOMA-IR and HOMA-β were significantly elevated in the T2DM group compared to the CVD and the healthy control groups. Furthermore, HOMA-IR was positive correlation with HbA1c, indicating that individuals with higher insulin resistance tend to have higher HbA1c levels (r\u0026thinsp;=\u0026thinsp;0.45, 95% CI: 0.308\u0026ndash;0.580, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) (see Supplementary Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Additionally, renal function markers (e.g., creatinine and estimated glomerular filtration rate [eGFR]) and liver enzymes (aspartate transaminase [AST] and alanine transaminase [ALT]) showed no significant differences among the group. Lifestyle factors including smoking history, alcohol consumption, physical activity, or the frequency of daily food group intake were also no significant differences among the groups. Medication histories for participants in the T2DM and CVD groups are detailed in Supplementary Table S4. Notably, the duration of disease and the type and dose of lipid-lowering drugs did not significantly differ between the T2DM and CVD groups.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eLp(a) measurement\u003c/h3\u003e\n\u003cp\u003eAlmost all participants had Lp(a) concentration in the low-risk range (\u0026lt;\u0026thinsp;75 nmol/L), while approximately 12% of individuals exceeded the high-risk (\u0026gt;\u0026thinsp;125 nmol/L) (see Supplementary Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). We observed that the T2DM group not receiving lipid-lowering therapy had significantly higher plasma Lp(a) levels compared to the healthy control group (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.012) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Within the T2DM group, there was no significant difference in Lp(a) concentrations between those receiving lipid-lowering therapy and those not receiving treatment. For the CVD group, a comparison with the control group was not feasible, as only one participant in the CVD group was not on lipid-lowering medication. We also found that Lp(a) was significantly correlated with LDL cholesterol (r\u0026thinsp;=\u0026thinsp;0.35 95%CI [0.13 to 0.54]; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002) and total LDL particle number (r\u0026thinsp;=\u0026thinsp;0.30 95% CI [0.06 to 0.50]; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.011) but not with LDL-5 and LDL-6 particle numbers in healthy controls, unlike in individuals with T2DM. (see Supplementary Fig. S3 and Supplementary Fig. S4). These results suggest that although Lp(a) is elevated in T2DM, their variation appears largely independent of the traditional lipid profile and LDL subclassed distribution in this cohort.\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\u003eComparison of concentration and particle size of lipoprotein between the case and control groups. Data are expressed as mean \u0026plusmn; standard deviation (SD). The T2DM group was diagnosed based on ICD-10 codes E11\u0026ndash;E14, and the CVD group was diagnosed based on ICD-10 codes I20\u0026ndash;I25 and I60\u0026ndash;I64; \"not received\" refers to participants not receiving lipid-lowering drugs (statins); \"received\" refers to participants receiving lipid-lowering drugs (statins). Based on the \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003eH NMR (Bruker IVDr/B.I.LISA) classification, LDL subclasses are defined by density as follows: LDL-1 (1.019\u0026ndash;1.031 kg/L) and LDL-2 (1.031\u0026ndash;1.034 kg/L) are classified as large buoyant LDL; LDL-3 through LDL-5 (1.034\u0026ndash;1.044 kg/L) are classified as intermediate-density LDL; and LDL-6 (1.044\u0026ndash;1.063 kg/L) is classified as small dense LDL (sdLDL). T2DM, type 2 diabetes mellitus; CVD, cardiovascular diseases; VLDL, very-low-density lipoprotein; IDL, intermediate-density lipoprotein; LDL, low-density lipoprotein; HDL, high-density lipoprotein; ApoA1, apolipoprotein A1; ApoA2, apolipoprotein A2; ApoB100, apolipoprotein B100.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"17\"\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\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c16\" colnum=\"16\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c17\" colnum=\"17\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" morerows=\"2\" nameend=\"c2\" namest=\"c1\" rowspan=\"3\"\u003e\u003cp\u003evariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c4\" namest=\"c3\" rowspan=\"2\"\u003e\u003cp\u003eControl\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c8\" namest=\"c5\"\u003e\u003cp\u003eT2DM\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cem\u003ep value\u003c/em\u003e\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cem\u003ep value\u003c/em\u003e\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"6\" nameend=\"c16\" namest=\"c11\"\u003e\u003cp\u003eCVD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c17\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cem\u003ep value\u003c/em\u003e\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003enot received\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003ereceived\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u003cp\u003eAll\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u003cp\u003enot received\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c16\" namest=\"c15\"\u003e\u003cp\u003ereceived\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003emean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026plusmn; SD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003emean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026plusmn; SD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003emean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026plusmn; SD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003emean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c12\"\u003e\u003cp\u003e\u0026plusmn; SD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c13\"\u003e\u003cp\u003emean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c14\"\u003e\u003cp\u003e\u0026plusmn; SD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c15\"\u003e\u003cp\u003emean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c16\"\u003e\u003cp\u003e\u0026plusmn; SD\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eLp(a), nmol/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026plusmn; 40.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e73.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026plusmn; 105.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e53.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026plusmn; 83.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.436\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e0.012\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e42.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e\u0026plusmn; 40.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e3.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e45.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e\u0026plusmn; 40.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e0.301\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003e\u003cem\u003eLipids and apolipoprotein, mg/dL\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTriglyceride\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e93.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026plusmn; 35.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e121.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026plusmn; 37.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e137.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026plusmn; 65.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.295\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e119.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e\u0026plusmn; 47.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e126.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e119.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e\u0026plusmn; 49.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e\u003cb\u003e0.028\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCholesterol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e204.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026plusmn; 29.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e216.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026plusmn; 30.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e180.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026plusmn; 47.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.125\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e169.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e\u0026plusmn; 43.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e126.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e172.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e\u0026plusmn; 42.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLDL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e122.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026plusmn; 24.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e131.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026plusmn; 28.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e100.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026plusmn; 36.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.196\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e92.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e\u0026plusmn; 28.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e66.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e94.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e\u0026plusmn; 28.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHDL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e59.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026plusmn; 13.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e57.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026plusmn; 11.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e51.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026plusmn; 8.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e0.034\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.503\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e48.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e\u0026plusmn; 11.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e32.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e49.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e\u0026plusmn; 10.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eApoA1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e154.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026plusmn; 24.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e153.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026plusmn; 18.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e143.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026plusmn; 16.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.050\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.781\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e137.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e\u0026plusmn; 20.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e110.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e139.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e\u0026plusmn; 19.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e\u003cb\u003e0.017\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eApoA2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e32.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026plusmn; 4.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e32.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026plusmn; 3.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e30.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026plusmn; 3.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.108\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.629\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e28.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e\u0026plusmn; 4.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e22.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e29.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e\u0026plusmn; 4.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e\u003cb\u003e0.014\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eApoB100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e89.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026plusmn; 15.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e98.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026plusmn; 19.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e84.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026plusmn; 26.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e0.040\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.\u003cb\u003e035\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e78.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e\u0026plusmn; 21.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e64.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e79.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e\u0026plusmn; 21.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e\u003cb\u003e0.042\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLDL/HDL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026plusmn; 0.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026plusmn; 0.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026plusmn; 0.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e0.048\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.115\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e2.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e\u0026plusmn; 0.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e2.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e2.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e\u0026plusmn; 0.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e0.422\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eApoB100/ApoA1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026plusmn; 0.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026plusmn; 0.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026plusmn; 0.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.206\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.077\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e\u0026plusmn; 0.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e0.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e\u0026plusmn; 0.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e0.814\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"17\" nameend=\"c17\" namest=\"c1\"\u003e\u003cp\u003e\u003cem\u003eLipoprotein particle number, nmol/L\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal particle number\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1624.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026plusmn; 282.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1795.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026plusmn; 358.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1532.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026plusmn; 487.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.\u003cb\u003e040\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e0.035\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1432.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e\u0026plusmn; 388.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e1162.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e1453.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e\u0026plusmn; 396.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e\u003cb\u003e0.042\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVLDL particle number\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e109.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026plusmn; 46.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e144.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026plusmn; 48.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e159.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026plusmn; 74.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.399\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e0.006\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e145.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e\u0026plusmn; 55.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e150.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e145.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e\u0026plusmn; 58.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e\u003cb\u003e0.016\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIDL particle number\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e62.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026plusmn; 25.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e84.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026plusmn; 30.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e74.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026plusmn; 32.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.288\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e77.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e\u0026plusmn; 35.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e57.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e78.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e\u0026plusmn; 36.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e0.076\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLDL particle number\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1388.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026plusmn; 267.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1492.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026plusmn; 331.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1209.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026plusmn; 420.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e0.013\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.166\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1126.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e\u0026plusmn; 314.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e840.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e1148.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e\u0026plusmn; 315.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLDL-1 particle number\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e217.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026plusmn; 54.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e220.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026plusmn; 58.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e161.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026plusmn; 55.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.807\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e173.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e\u0026plusmn; 54.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e115.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e177.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e\u0026plusmn; 53.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e\u003cb\u003e0.009\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLDL-2 particle number\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e193.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026plusmn; 65.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e191.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026plusmn; 62.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e115.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026plusmn; 51.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.879\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e117.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e\u0026plusmn; 36.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e103.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e118.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e\u0026plusmn; 37.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLDL-3 particle number\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e221.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026plusmn; 61.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e226.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026plusmn; 59.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e147.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026plusmn; 68.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.760\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e147.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e\u0026plusmn; 44.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e112.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e150.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e\u0026plusmn; 45.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLDL-4 particle number\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e242.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026plusmn; 62.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e262.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026plusmn; 79.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e204.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026plusmn; 79.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e0.013\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.255\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e199.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e\u0026plusmn; 59.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e146.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e203.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e\u0026plusmn; 60.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e\u003cb\u003e0.023\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLDL-5 particle number\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e223.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026plusmn; 75.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e265.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026plusmn; 106.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e249.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026plusmn; 100.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.599\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.059\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e215.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e\u0026plusmn; 90.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e174.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e218.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e\u0026plusmn; 93.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e0.736\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLDL-6 particle number\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e300.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026plusmn; 98.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e347.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026plusmn; 119.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e356.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026plusmn; 149.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.808\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.086\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e302.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e\u0026plusmn; 115.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e247.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e306.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e\u0026plusmn; 119.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e0.948\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"17\"\u003e\u003csup\u003ea\u003c/sup\u003e Statistically significant difference between \"not received\" and \"received\" groups in the T2DM\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"17\"\u003e\u003csup\u003eb\u003c/sup\u003e Statistically significant difference between \"not received\" in the T2DM compared to the control group\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"17\"\u003e\u003csup\u003ec\u003c/sup\u003e Statistically significant difference between all of the CVD group compared to the control group.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"17\"\u003e\u003cem\u003ep\u003c/em\u003e value in bold indicate significant differences (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), analyzed by Mann-Whitney U test.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003csup\u003e\u003cb\u003e1\u003c/b\u003e\u003c/sup\u003e\u003cb\u003eH NMR measurement\u003c/b\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eLipoprotein concentrations and particle numbers are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the T2DM group (not receiving lipid-lowering drugs) demonstrated a clear dyslipidemia profile relative to control. Triglyceride and apoB100 levels were significantly higher in T2DM patients without lipid-lowering treatment than in the healthy controls group (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Mean concentration of total cholesterol and LDL levels were higher in the T2DM group than the healthy control (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05), while HDL levels were lower in the T2DM than in the healthy control group, indicating a high-risk condition for atherosclerosis. Furthermore, the T2DM group had significantly elevated total atherogenic particle count defined as the sum of VLDL, IDL, and LDL particle numbers compared to the healthy control group (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.035), with VLDL and IDL particles number contributing most to this difference. Interestingly, the total LDL particle number did not significantly differ between T2DM and healthy control groups. However, subclass analysis revealed a trend toward higher sdLDL particles (LDL-6 subclass) in the T2DM group, though this was not statistically significant. Furthermore, we observed that elevated triglyceride levels were strongly associated with a greater number of sdLDL particles (Supplementary Fig. S5). Within T2DM participants, those receiving lipid-lowering therapy had significantly lower lipid levels and fewer large buoyant and intermediate-density LDL particles than those not on treatment. However, sdLDL particle numbers did not differ significantly between the two subgroups.\u003c/p\u003e\u003cp\u003eIn all CVD groups, both those receiving and not receiving lipid-lowering therapy had significantly lower lipid levels and large buoyant LDL particles compared to healthy, but not sdLDL particle numbers. Moreover, we found a strong link between lipid profiles and lipoprotein particles. These findings confirm that participants in the T2DM patients had an atherogenic shift in their lipoprotein profile, characterized by elevated triglyceride, apoB100, a surplus of remnant (VLDL/IDL) particles, an excess remnant (VLDL/IDL) particle, and a propensity toward more sdLDL particle number even when overall LDL particle counts was similar to that of healthy individuals.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eLipid distribution patterns in lipoproteins and lipoprotein subclasses\u003c/h2\u003e\u003cp\u003eRadar plots analysis was employed to visualize the distributions of lipid and apolipoprotein across lipoprotein subclasses in each group (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Supplementary Table S5). These plots revealed distinct patterns between groups. The T2DM group (not receiving lipid-lowering drugs) had higher levels of triglycerides, cholesterol, and apolipoproteins within VLDL and LDL subclasses compared to the healthy control group. In particular, the sdLDL in T2DM carried greater concentration of triglycerides and cholesterol than those in the healthy control group, consistent with an accumulation of cholesterol-rich, small dense LDL particles in diabetes. T2DM patients who received lipid-lowering therapy showed partial improvement in this subclass lipid profile. The cholesterol and apoB100 content within the large and intermediate-density LDL particles was reduced relative to the untreated T2DM patients, but the more atherogenic subclasses (sdLDL) remained largely elevated, and triglyceride-rich particles (especially VLDL and subclasses LDL-5 and LDL-6 particle number) persisted at high levels. Similarly, the CVD group receiving lipid-lowering therapy exhibited elevated triglyceride and free cholesterol levels across multiple VLDL, IDL, and LDL subclasses compared to the healthy control group. This indicates that even with statin treatment, many T2DM patients experienced incomplete normalization of their lipoprotein profiles, notably retaining excess triglyceride-rich lipoproteins and sdLDL particles, which may contribute to their residual cardiovascular risk.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eEffect of clinical variables and lipid-lowering therapy\u003c/h2\u003e\u003cp\u003eWe examined the associations between various clinical factors (BMI and glycemic indices) and lipoprotein measure, as well as the impact of lipid-lowering therapy, using multivariable regression analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Supplementary Table S6). An increased BMI was significantly associated with a more atherogenic lipid profile. Specifically, higher BMI correlated with higher triglyceride levels (+\u0026thinsp;2.42 [95% CI: +0.89 to +\u0026thinsp;3.95]; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002), while showering a modest but significant decrease in total cholesterol, LDL, and HDL levels. Additionally, individuals with higher BMI were significantly associated with greater numbers of VLDL particle number (+\u0026thinsp;3.24 [95% CI: +1.48 to +\u0026thinsp;5.00]; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and IDL particle number (+\u0026thinsp;1.20 [95% CI: +0.31 to +\u0026thinsp;2.08]; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.009). In contrast, the large buoyant and intermediate-density LDL particles significantly decreased as BMI increased, whereas the sdLDL subclasses showed the opposite trend.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003ePoor glycemic control was associated with adverse changes in the lipoprotein profile. Neither FBG nor HbA1c levels were correlated with Lp(a). Both, however, were positively correlated with triglyceride concentrations, with HbA1c showing a much stronger association [+\u0026thinsp;14.53 (95% CI: +9.06 to +\u0026thinsp;20.00); \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001]. In addition, HbA1c was inversely correlated with HDL levels (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). In contrast, FBG showed only weak associations. These findings indicate that higher glycemic levels, particularly as reflected by HbA1c, are linked to elevated triglycerides and reduced HDL. In addition, elevated HbA1c levels were significantly associated with increased numbers of VLDL, IDL, and sdLDL particles (all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Notably, the strongest association was observed for sdLDL [+\u0026thinsp;33.08 (95% CI: +19.66 to +\u0026thinsp;46.50)], highlighting its particular relevance in the context of diabetes-related dyslipidemia. In contrast, HOMA-IR levels showed no significant associations with most lipoprotein parameters.\u003c/p\u003e\u003cp\u003eThe use of lipid-lowering medication (e.g., statins) had a marked impact on the lipoprotein particle profile. Patients receiving lipid-lowering therapy showed no significant difference in Lp(a) levels but had significantly lower total cholesterol, LDL, and apoB100 levels (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Analysis of LDL subclasses revealed significantly lower total LDL particle numbers (\u0026minus;\u0026thinsp;270.99 [95% CI: \u0026minus;468.16 to \u0026minus;\u0026thinsp;73.81]; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.008). Treatment was associated with significant reductions in large buoyant and intermediate-density LDL particles, whereas VLDL and IDL particles were not significantly affected. Importantly, sdLDL particles remained unchanged with lipid-lowering therapy (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Additionally, we also observed that using a high intensity of lipid-lowering drugs (atorvastatin 40\u0026ndash;80 mg and/or simvastatin\u0026thinsp;\u0026gt;\u0026thinsp;40 mg daily) did not significantly further improve Lp(a) level or the overall lipoprotein profile compared to a low- and moderate- intensity therapy (Supplementary Table S7). As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, lipid-lowering therapy increased the proportion of LDL composed of large buoyant and intermediate-density LDL particles, while the relative abundance of sdLDL particles was higher in treated patients compared to untreated ones. In other words, although therapy substantially reduces the total number of LDL particles, it disproportionately depletes the larger subclasses, resulting in a greater proportion of the remaining LDL consisting of small dense particles. Consistent with prior evidence, these residual sdLDL particles possess greater atherogenic potential and contribute more to arterial plaque formation\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. These findings suggest that while standard lipid-lowering therapy is effective in lowering total LDL and reducing large buoyant and intermediate-density LDL particles, it fails to adequately reduce Lp(a) and sdLDL. Elevated BMI and poor glycemic control appear to aggravate this pattern by favoring the formation of triglyceride-rich lipoproteins and sdLDL, which may help explain the residual CVD risk in treated T2DM and CVD patients.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eAssociation of exercise activity with lipoproteins and lipoprotein subclasses\u003c/h2\u003e\u003cp\u003eThe relationship between exercise intensities and lipoproteins, including their subclasses, is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, with a focus solely on exercise activity. Among healthy controls (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea), higher levels of exercise were associated with a trend toward lower total cholesterol and LDL levels, although this reduction did not reach statistical significance. In contrast, HDL levels showed a statistically significant increase with greater exercise (estimated break-point: 504.0\u0026thinsp;\u0026plusmn;\u0026thinsp;62.17 MET-min/week, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Exercise was also associated with a slight reduction in the smallest LDL particles (LDL-6 subclass), whereas large buoyant and intermediate-density LDL particles showed a non-significant increasing trend. Higher levels of exercise were not associated with significant reductions in Lp(a) levels. In individuals with T2DM, with or without lipid-lowering therapy, exercise activity showed minimal influence on lipoprotein subclasses, with overlapping confidence intervals across the full range of physical activity (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb). Similarly, among patients with established CVD receiving lipid-lowering therapy, no consistent association was observed between exercise levels and lipoprotein measures, likely reflecting advanced disease status and pharmacological modification (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec). Overall, these observations reinforce the importance of regular physical activity in modulating lipid metabolism. Maintaining an adequate exercise regimen was associated with beneficial changes, particularly increasing HDL and potentially lowering the proportion of atherogenic sdLDL, which may be essential for long-term management of dyslipidemia. However, exercise alone appears to be beneficial primarily in healthy individuals, with limited effects in those with T2DM or established CVD. Our findings suggest that a combination of consistent exercise and appropriate medical therapy should be pursued for more effective control of LDL and comprehensive cardiovascular risk reduction in these high-risk groups.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis cross-sectional, case-control study employed high-resolution \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003eH NMR spectroscopy to profile lipoprotein subclasses and assess cardiometabolic risk in individuals with T2DM and CVD. The use of the Bruker IVDr Lipoprotein Subclass Analysis (B.I.LISA) platform enables detailed quantification of lipoprotein characteristics beyond conventional lipid panels.\u003c/p\u003e\u003cp\u003eOur findings demonstrated significantly elevated levels of Lp(a) in individuals with T2DM. This is consistent with the report by Abdullah et al., which showed that patients with T2DM and prediabetes had higher mean levels of Lp(a) compared with healthy controls and a greater prevalence of abnormally elevated Lp(a)\u003csup\u003e34\u003c/sup\u003e. Several studies further suggest that, in patients with type 2 diabetes, elevated Lp(a) is associated with an increased risk of cardiovascular disease. The underlying mechanisms remain unclear, but both genetic and non-genetic factors are likely contributors, and additional interactions between glucose, insulin, and Lp(a) may also play a role\u003csup\u003e\u003cspan additionalcitationids=\"CR36\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. Additionally, Lp(a) levels showed significantly correlation with LDL and LDL particle number concentrations only in healthy individuals, suggesting an independent pathophysiological pathway. This dissociation may be attributed to metabolic disturbances such as insulin resistance and chronic low-grade inflammation, which are known to upregulate Lp(a) synthesis independently of LDL pathway\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. Conversely, among healthy individuals, Lp(a) positively correlated with LDL, consistent with prior evidence suggesting shared metabolic or structural features\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Elevated Lp(a) is a recognized causal and independent risk factor for atherosclerotic CVD\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e, with greater risk observed in the presence of elevated LDL\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eIn T2DM, elevated triglyceride levels and poor glycemic control were strongly associated with an increase in sdLDL particles. Hirano et al. demonstrated a positive correlation between sdLDL levels and triglyceride concentrations in healthy individuals. More recently, a study in a Chinese cohort revealed that each 0.1 mmol/L increase in sdLDL was significantly associated with higher odds of pre-diabetes and newly diagnosed T2DM, independent of LDL\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. This observation is consistent with the Japan Diabetes Complications Study (JDCS), which identified higher serum triglycerides as a key determinant of sdLDL burden\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. Poor glycemic control may also promote the formation of sdLDL through non-enzymatic glycation of apolipoproteins, further enhancing their atherogenic potential\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. SdLDL particles are considered highly atherogenic due to their longer circulation time, increased oxidative susceptibility, and enhanced arterial wall penetration factors that collectively contribute to a heightened risk of cardiovascular events\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e,\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. These findings underscore the importance of intensive management of both hypertriglyceridemia and hyperglycemia to mitigate sdLDL burden and reduce cardiovascular risk in patients with T2DM.\u003c/p\u003e\u003cp\u003eLipid-lowering therapy, particularly with statins, had a pronounced effect on reducing large buoyant LDL particles and overall LDL levels. However, our findings confirm previous reports that statins do not effectively lower Lp(a) concentrations. In fact, statin therapy may lead to a modest increase in Lp(a) levels, typically by approximately 10\u0026ndash;15%\u003csup\u003e16,45\u003c/sup\u003e. The underlying mechanism remains unclear. Current lipid-lowering medications have not achieved meaningful reductions in Lp(a) levels, with the exception of proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibitors, which have been shown to reduce Lp(a) concentrations by approximately 27%\u003csup\u003e46\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eNumerous recent studies suggest that statins can effectively reduce the number of LDL particle\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e,\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. Our study confirms that statin therapy is more effective at reducing LDL levels than decreasing the number of LDL particles, particularly sdLDL particles. Mechanistically, statins lower serum cholesterol levels primarily by inhibiting HMG-CoA reductase activity\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e and upregulating low-density lipoprotein receptors (LDLR) expression\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e. Because sdLDL particles exhibit lower affinity for LDLR due to altered apoB structure\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e, they are less effectively cleared from circulation compared to large buoyant LDL particles\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e While some studies have reported that high-dose statin therapy reduces sdLDL levels\u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e,\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e, our study did not observe significant reductions in sdLDL with either low- nor high-intensity statin therapy. This discrepancy may be due to persistent insulin resistance and metabolic dysregulation in T2DM, which promotes sdLDL formation and blunts statin efficacy. These findings highlight a key limitation of statin monotherapy in addressing the full spectrum of atherogenic lipoproteins in high-risk populations. Adjunctive strategies, such as emerging agents targeting triglyceride metabolism (e.g., apoC-III inhibitors or ANGPTL3 inhibitors)\u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e,\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e, may offer additional benefits by reducing sdLDL burden. Moreover, more personalized approaches to lipid management that consider residual risk markers such as sdLDL and Lp(a) may be warranted to optimize cardiovascular prevention in patients with T2DM and CVD.\u003c/p\u003e\u003cp\u003eWe also found that physical activity significantly improved HDL levels and trended toward lower LDL levels, consistent with finding from the ELSA-Brasil study reported that moderate and vigorous physical activity were significantly associated with higher HDL and lower LDL and TG levels\u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e. However, physical activity did not significantly alter Lp(a) levels, which are primarily genetically determined and resistant to lifestyle intervention\u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e. Overall, this study highlights the limitations of standard lipid-lowering therapies in fully mitigating residual cardiovascular risk in T2DM and CVD populations. It underscores the importance of sdLDL and Lp(a) as independent, treatment-resistant risk factors. Our findings support a need for adjunctive strategies such as novel lipid-lowering agents or lifestyle interventions tailored to sdLDL reduction in managing high-risk individuals.\u003c/p\u003e\u003cp\u003e Although sdLDL and Lp(a) are increasingly recognized as important residual risk factors, current guidelines do not recommend their routine measurement due to limited evidence. Our findings provide novel insights from an Asian cohort, highlighting potential ethnic differences in lipoprotein patterns compared with Western populations. Future longitudinal and interventional studies are needed to clarify causal pathways, evaluate novel lipid-lowering agents, and determine how sdLDL and Lp(a) can be integrated into risk prediction and management strategies for high-risk patients with T2DM and CVD\u003c/p\u003e\u003cp\u003eThis study has several notable strengths. First, inclusion of both statin-treated and untreated T2DM and CVD patients provided real-world insights into the residual cardiovascular risk under current lipid-lowering therapies. Second, the study integrated data on lifestyle factors, including physical activity and dietary intake, allowing for the exploration of their association with lipoprotein subclasses. There are several potential limitations to this study. First, the small sample size, especially in the CVD group, may reduce generalizability. Second, the cross-sectional design of the study precludes causal inference. While associations between lipoprotein parameters and clinical or lifestyle variables were identified, temporal and causal relationships cannot be established. Third, liver function data were obtained retrospectively and may not reflect current metabolic status. Fourth, dietary intake and physical activity were assessed through self-report, which may have led to under- or overestimation due to inherent reporting biases. Fifth, although statin treatment was recorded, data on patient adherence were not available, which may influence lipid profile outcomes.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eLp(a) and sdLDL are important residual cardiovascular risk markers in T2DM. While statins reduce LDL and large buoyant and intermediate-density LDL particles, they have limited effect on sdLDL and Lp(a). Physical activity improves HDL and LDL profiles but does not lower Lp(a). These findings highlight the need for advanced lipid profiling and additional therapeutic strategies to address persistent lipid abnormalities and improve risk management in high-risk patients.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the principal investigators and all members of the SIOH and DIRECT projects for providing data and samples for this research. The authors are also grateful to Prof. Wang Yulan and the team from Nanyang Technological University, Singapore, for their support and suggestions regarding NMR analysis. We also thank the management of the Siriraj Medical Research Center for providing office space and laboratory facilities for this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contribution statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eS.S. and K.M. contributed to the conceptualization, methodology, visualization, project administration, and original draft preparation. P.M., B.P., S.P., S.S., S.O., and A.S. contributed to the investigation and data curation. A.S. was responsible for software development and formal analysis. Y.W., Y.Y.D., X.S., and T.S. contributed to resources and to writing\u0026mdash;review and editing. K.M. also contributed to writing\u0026mdash;review and editing. The corresponding author (K.M.) attests that all listed authors meet the authorship criteria and that no individuals meeting the criteria have been omitted. All authors have read and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by Siriraj research development fund, Gant number (IO) R016733021, Faculty of medicine Siriraj Hospital, Mahidol University. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interest\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatement on consent\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWritten informed consent was not required for this study as determined by the Institutional Review Board of Siriraj Hospital, Mahidol University, because the plasma samples were de-identified and obtained from the institutional biobank (COA No. Si 542/2024).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthic statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study protocol was approved by the Institutional Review Board of Siriraj Hospital, Mahidol University (COA No. Si 542/2024).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of AI and AI-assisted technologies\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors used ChatGPT (OpenAI) for grammar and spelling checking and to improve the readability of the manuscript. All content was reviewed and edited by the authors to ensure scientific accuracy and integrity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSupplementary information: The online version contains supplementary data available at\u0026nbsp;\u003c/p\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eData availability\u003c/h2\u003e\u003cp\u003eThe original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.\u003c/p\u003e\u003c/div\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eJoseph, P. et al. Reducing the global burden of cardiovascular disease, part 1: the epidemiology and risk factors. \u003cem\u003eCirc. 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Use of Lipoprotein (a) in clinical practice: A biomarker whose time has come. A scientific statement from the National Lipid Association. \u003cem\u003eJ. Clin. Lipidol.\u003c/em\u003e \u003cb\u003e13\u003c/b\u003e, 374\u0026ndash;392. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:10.1016/j.jacl.2019.04.010\u003c/span\u003e\u003cspan address=\"https://doi.org:10.1016/j.jacl.2019.04.010\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2019).\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":"Type 2 diabetes mellitus, cardiovascular disease, lipoprotein subclass, lipoprotein(a), nuclear magnetic resonance spectroscopy, physical activity","lastPublishedDoi":"10.21203/rs.3.rs-7577991/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7577991/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eLipoprotein subfraction research has emerged as a promising approach for risk stratification, warranting investigation in type 2 diabetes mellitus (T2DM), cardiovascular disease (CVD), and lipid-lowering therapy. This cross-sectional, case-control study aimed to characterize lipoprotein particle profiles, including small dense LDL (sdLDL) and lipoprotein(a) [Lp(a)], in individuals with T2DM and CVD compared with healthy controls, and to evaluate their associations with clinical risk factors. Fasting plasma samples from 118 participants were analyzed: T2DM (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;52), CVD (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;14), and healthy controls (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;52). Lipoprotein levels and subclasses were quantified using proton nuclear magnetic resonance (\u003csup\u003e1\u003c/sup\u003eH NMR) spectroscopy with Bruker\u0026rsquo;s In-Vitro Diagnostic research (IVDr) Lipoprotein Subclass Analysis (B.I.LISA). Participants had a mean age of 46.9\u0026thinsp;\u0026plusmn;\u0026thinsp;9.0 years, and 79% were female. Compared with controls, individuals with T2DM had significantly higher Lp(a) (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.012), triglycerides, total cholesterol, LDL, and apolipoprotein B100 (apoB100) (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), while HDL remained unchanged; sdLDL levels were higher but not statistically significant. Statin therapy reduced large buoyant and intermediate-density LDL particles but had limited effects on sdLDL and Lp(a). Physical activity was associated with higher HDL and lower LDL (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In conclusions, Lp(a) and sdLDL contribute to CVD risk in T2DM, with partial response to statin therapy.\u003c/p\u003e","manuscriptTitle":"Lipoprotein(a) and Small Dense LDL Profiles and Statin Response in Type 2 Diabetes Mellitus: A Case-Control Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-30 07:23:00","doi":"10.21203/rs.3.rs-7577991/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":"1b609154-fe52-4fa3-b098-96644a81d6a7","owner":[],"postedDate":"September 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":55454973,"name":"Health sciences/Biomarkers"},{"id":55454974,"name":"Health sciences/Cardiology"},{"id":55454976,"name":"Health sciences/Diseases"},{"id":55454978,"name":"Health sciences/Endocrinology"},{"id":55454979,"name":"Health sciences/Medical research"}],"tags":[],"updatedAt":"2025-11-18T03:23:44+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-30 07:23:00","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7577991","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7577991","identity":"rs-7577991","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}