Distinct compositional alterations in plasma lipoproteins in type 2 diabetes: a cross-sectional study of healthy individuals and diabetics with and without cardiovascular comorbidities

Distinct compositional alterations in plasma lipoproteins in type 2 diabetes: a cross-sectional study of healthy individuals and diabetics with and without cardiovascular comorbidities | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Distinct compositional alterations in plasma lipoproteins in type 2 diabetes: a cross-sectional study of healthy individuals and diabetics with and without cardiovascular comorbidities Elena Tsay, Diyora Kurmaeva, Sharofiddin Nuriddinov, Vladimir Tsoy, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7318923/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 25 Dec, 2025 Read the published version in Cardiovascular Diabetology → Version 1 posted 12 You are reading this latest preprint version Abstract Background. Type 2 diabetes mellitus (T2DM) increases the risk of cardiovascular disease (CVD), largely by alterations in the blood lipids and the metabolism of circulating lipoproteins (LPs). We studied whether the presence of additional risk factors, such as hypertension, or CVD itself, is associated with further alterations in the LP profiles in individuals with T2DM. Methods. We performed LP profiling using ¹H NMR spectroscopy and quantified 65 parameters in 393 healthy controls (HC) and in 390 T2DM patients with and without cardiovascular comorbidities. Univariate and multivariate analyses were used to assess alterations in LPs in diabetic patients. Results. Triglycerides in all major LP classes, as well as particle numbers of very low-density lipoproteins (VLDL) and intermediate-density lipoproteins (IDL) were increased in T2DM compared to HC. In contrast, particle numbers of low-density lipoproteins (LDL) and high-density lipoproteins (HDL) were reduced, suggesting slower lipolytic conversion of IDL to LDL and impaired clearance of triglyceride-enriched HDL. Univariate and multivariate analyses converged in identifying distinct LP profiles associated with T2DM, while differences between patients with and without hypertension or CVD were minor, indicating that T2DM is the primary factor driving LP dysregulation. T2DM, with or without cardiovascular comorbidities, also causes differential disruption of the correlation structure among LPs. Conclusions. T2DM is associated with major alterations in LP metabolism independent of hypertension or CVD. Thus, early lipid management in T2DM is important to mitigate CVD risk. Further research is needed to elucidate how T2DM progresses to CVD in relation to atherogenic LPs. Type 2 Diabetes Mellitus Lipoprotein Metabolism Dyslipidemia Cardiovascular Disease Risk ¹H NMR Spectroscopy Figures Figure 1 Figure 2 Research insights What is currently known about this topic? Type 2 diabetes mellitus (T2DM) raises plasma VLDL and lowers HDL, increasing cardiovascular disease (CVD) risk. Early lipid management in T2DM is crucial to mitigate CVD risk. What is the key research question? Does hypertension or CVD comorbidity further alter lipoprotein metabolism in T2DM? What is new? We found dysregulation in both major lipoprotein fractions and smaller, atherogenic subfractions. CVD comorbidities are linked to minor changes in cholesterol and ApoB in LDL and HDL subfractions. CVD comorbidities in T2DM were also linked to disrupted correlations among lipoproteins. How might this study influence clinical practice? Lipoproteins dysregulations we identified prove new mechanistic insights into T2DM. Changes in lipoprotein levels may be promising targets for early lipid therapy to reduce CVD risk. Introduction Type 2 Diabetes Mellitus (T2DM) is a chronic metabolic disorder characterized by hyperglycemia due to peripheral insulin resistance and inadequate pancreatic insulin secretion [1]. T2DM has many microvascular and macrovascular complications which significantly affect quality of life [2] and increase risk of death, particularly from cardiovascular disease (CVD) [3, 4]. Alterations in lipid and lipoprotein (LP) metabolism in T2DM are linked to insulin resistance and are believed to be involved in the etiology of CVD [5, 6]. T2DM is associated with elevated particle numbers of very low-density lipoproteins (VLDL) and reduced numbers of high-density lipoproteins (HDL) [7, 8]. The increase in VLDL is primarily due to enhanced hepatic secretion and impaired clearance driven by insulin resistance. Elevated VLDL leads to increased conversion to intermediate-density lipoproteins (IDL) through LP lipase-mediated lipolysis. Studies also indicate that low-density lipoproteins ( LDL) particles in T2DM exhibit a smaller and denser phenotype [9]. These small dense LDL particles have a higher propensity to penetrate the arterial wall and are more prone to oxidative modification, thereby promoting atherogenesis[10]. Furthermore, both the triglyceride ( TG ) and the cholesterol ( Chol )) contents of LPs differ between patients with T2DM and healthy individuals, contributing to hypertriglyceridemia, hypercholesterolemia, and decreased HDL-mediated reverse Chol transport [11–14]. All these alterations significantly contribute to increased CVD risk in patients with T2DM. Hypertension is also associated with insulin resistance and is approximately twice as frequent in patients with T2DM compared with healthy individuals, and further increases risk of CVD [15]. It is not clear whether this is because of more extensive alterations in the blood lipid profile [16–19]. Besides traditional lipid analysis, assessing compositional alterations in LP subfractions is crucial for understanding the mechanisms linking T2DM with CVD and informing risk assessment and management strategies [20]. Here, we analyzed 65 plasma LP parameters in a large group of T2DM patients with and without hypertension and coronary heart disease (CHD), and in healthy controls (HC), by using a recently-developed model based on proton nuclear magnetic resonance ( 1 H NMR) spectroscopy [21]. We also investigated alterations in co-variation among different LPs to identify changes in LP metabolism likely to be mechanistically linked to T2DM. Materials and methods Study population This study recruited patients diagnosed with T2DM from the Republican Specialized Scientific and Practical Medical Center of Endocrinology in Tashkent (Uzbekistan) between July 2021 and August 2022. Exclusion criteria included current cancer diagnosis, pregnancy or lactation, and confirmed type 1 or gestational diabetes mellitus. A total of 390 T2DM patients (later referred to as DA group) aged 20 to 88 years were enrolled, of whom 88 had no hypertension or CHD (DO subgroup), 105 T2DM patients had hypertension (DH subgroup), and 197 had both hypertension and CHD (DHC subgroup). Recruitment of healthy control (HC) group was conducted between July 2021 and July 2022 at the Center for Advanced Technologies in Tashkent (Uzbekistan) through social media and local newspapers. For the HC group, 393 healthy individuals (normal blood glucose concentrations, normal blood pressure, no chronic medical conditions, and no regular medication use) were recruited. Clinical, anthropometric, and medical history data were collected at the time of hospital admission for T2DM patients or during visits to the recruitment center for HC. Each participant completed a detailed questionnaire probing for demographic information and lifestyle factors, including smoking and alcohol consumption. Fasting blood samples were collected between 6 and 10 AM, centrifuged in EDTA-containing vacutainers, and plasma was stored at -80°C until analysis. The study was conducted in accordance with the Declaration of Helsinki and approved by the Ethical Committee of the Ministry of Public Health of the Republic of Uzbekistan (No. 6/11-1694). All participants provided written informed consent. Chemicals and reagents All chemicals and reagents were procured from Sigma-Aldrich (Søborg, Denmark). These included deuterium oxide (D 2 O, 99.9% atom D), monobasic sodium phosphate (NaH 2 PO 4 , ≥ 99%), dibasic sodium phosphate (Na 2 HPO 4 , ≥ 98%), sodium salt of 3-(trimethylsilyl)propionic-2,2,3,3-d4 acid (TSP, 98% atom D, ≥ 98%), and sodium azide (NaN 3 , ≥ 99.5%). Purified water was obtained using a Millipore lab water system (Merck KGaA, Darmstadt, Germany) equipped with a 0.22 µm filter membrane. Sample collection and preparation for ¹H NMR analysis Blood plasma samples were thawed at room temperature and aliquots of 350 µL were carefully mixed with an equal volume of phosphate buffer in 2.0 mL Eppendorf tubes. The phosphate buffer was prepared as previously described [22], using NaH 2 PO 4 and Na 2 HPO 4 solutions. A 600 µL portion of the plasma-buffer mixture was then transferred into 5 mm OD (103.5 mm length) SampleJet tubes (Bruker BioSpin, Germany). Sample preparation and measurements were randomized, and pooled control human blood plasma samples were analyzed at regular intervals throughout the entire measurement sequence. ¹H NMR spectral data acquisition and processing The ¹H NMR spectra of plasma samples were acquired as previously described [21]. Briefly, the Bruker Avance III 600 MHz NMR spectrometer was used to acquire 1D NOESY spectra with water suppression ( noesygppr1d ) with 32 scans, collected into 131,072 data points, with a spectral width of 30 ppm, a 90° pulse, a recycle delay (d1) of 4 s, and a mixing time of 0.01 s. All NMR spectra were imported into the SigMa software [23], and concentrations of LPs were predicted using 0.6–1.4 ppm region of the 1 H NMR spectra, representing methyl protons of Chol and fatty acids and methylene protons of fatty acids, using partial least squares (PLS) regression, as described previously [21]. Statistical data analysis To assess potential confounding factors, continuous variables (e.g., age, body mass index (BMI)) were analyzed using Student’s t-test, while categorical variables (e.g., gender, smoking status) were evaluated with the chi-square test. Differences in plasma LPs between the T2DM patients and HC groups were evaluated using one-way ANOVA with Benjamini–Hochberg’s false discovery rate (FDR-p) correction (5%). An FDR-p value of < 0.05 was considered statistically significant. Multiple linear regression (MLR) and logistic regression analysis (LRA) were used to assess the impact of T2DM on LPs, considering disease status (T2DM versus HC) as the independent variable and adjusting for significant confounders (sex, age, BMI, blood pressure, and alcohol consumption). Confounding effects of medication use were accounted when evaluating differences among T2DM subgroups only (DO, DH, and DHC). Partial least squares-discriminant analysis (PLS-DA) [24] was used to identify key LP classifiers distinguishing T2DM patients from the HC group, following previously described optimization and validation protocol [25]. All statistical analyses were conducted using MATLAB (R2024a, The MathWorks, Inc., Natick, USA) with customized scripts. Results Characteristics of the study population A total of 783 participants were included in the study (Table 1). DA patients were older and had higher BMI, blood glucose (BG), and both systolic (SB) and diastolic blood pressure (DB) compared to HC. Gender distribution and smoking status did not differ significantly, though alcohol consumption was higher in HC. The DO subgroup included more males than DH and DHC, while no differences were found in smoking, alcohol use, BG, or T2DM duration. Age, BMI, SB, DB, and medication use increased progressively across the T2DM subgroups. Lipoprotein profile alterations in type 2 diabetes Multivariable linear regression (MLR), adjusted for key confounders (age, gender, BMI, systolic/diastolic blood pressure, and alcohol consumption), identified 62 LPs significantly different between HC and DA. Nearly all LPs also showed significant differences between HC and at least one T2DM subgroup (Table 2). After further adjusting for medication use, MLR comparing the three T2DM subgroups (DO, DH, DHC) revealed only six LPs with significant differences. Figure 1A presents results from both LRA and MLR, highlighting two distinct LP clusters on the odds ratio vs. fold change plot, with marker size reflecting effect size. The first cluster includes TG , cholesterol ester ( CE) , and apolipoprotein B (ApoB) in VLDL and IDL particle numbers, positively associated with T2DM. The second cluster, negatively associated with T2DM, includes Chol , apolipoprotein A1 ( ApoA1) , ApoB , and phospholipids (Phoslip) in HDL and LDL subfractions (Table 2 and Supplementary Table 1). Similar clustering was observed when LRA and MLR results were jointly analyzed across HC and all T2DM subgroups, with no significant differences among the subgroups (Supplementary Fig. 1). Triglyceride enrichment was one of the most consistent alterations across diabetic groups. Compared to HC, DA showed markedly elevated Plasma TG (154 vs. 113 mg/dL), with this increase persisting in all diabetic subgroups. VLDL TG and IDL TG were also elevated, peaking in DH, though differences between DO, DH, and DHC were not significant, suggesting that VLDL and IDL triglyceride enrichment is a core feature of T2DM rather than its comorbidities. HDL2b TG was modestly elevated in DA and increased progressively across subgroups, reaching significance between DO and DHC, while HDL2a TG remained unchanged. LDL and LDL1 triglyceride levels were stable in DA overall, yet significantly elevated in DH and DHC compared to HC. These elevations likely result from increased hepatic VLDL production driven by insulin resistance and free fatty acid flux, coupled with impaired lipolysis [26]. This leads to VLDL and IDL accumulation and sustained triglyceride enrichment in HDL2b and LDL1, indicating disrupted LP remodeling and increased atherogenic risk. Cholesterol concentrations were reduced across all LPs in DA and all diabetic subgroups compared to HC. VLDL Chol and IDL Chol showed no increase from HC to DA or DO. Among diabetic subgroups, only minor declines were observed in LDL2 Chol (DO to DHC) and HDL2a Chol (DH to DHC), possibly reflecting greater lipid-lowering therapy use in DHC. The most striking reduction was a 50% decrease in HDL2b Chol in DA, underscoring the loss of cardioprotective HDL. These findings suggest that T2DM is linked to both overall cholesterol depletion and HDL particle remodeling. Notably, reductions were already evident in DO and not further worsened by hypertension or CHD, indicating early onset. The uniform decline across LPs may result from impaired hepatic synthesis or reduced intestinal absorption [27]. Specifically, reduced HDL Chol likely reflects impaired reverse cholesterol transport [28]. Free cholesterol levels in plasma and HDL2b decreased in DA, while VLDL FreeChol increased compared to HC, a pattern consistent across all diabetic subgroups (DO, DH, DHC), with no significant differences among them. This redistribution from HDL to TG -rich VLDL suggests impaired reverse cholesterol transport and enhanced hepatic VLDL secretion, likely driven by insulin resistance and reduced LP clearance [29]. Elevated VLDL FreeChol reflects cholesterol retention in remnant particles, while reduced HDL2b FreeChol points to diminished HDL-mediated cholesterol efflux in T2DM. Cholesteryl ester ( CE ) levels mirrored trends in total and free cholesterol, showing broad reductions across all LPs in DA and diabetic subgroups compared to HC. However, VLDL CE and IDL CE were significantly elevated in DA, unlike VLDL Chol and IDL Chol , with no differences among diabetic subgroups. This suggests that CE depletion is independent of comorbidities. The rise in VLDL CE and IDL CE may reflect increased cholesterol ester transfer protein (CETP)-mediated transfer of CE from HDL to TG -rich LPs, contributing to a more atherogenic LP profile in diabetes [30]. Phospholipid levels in LPs followed the patterns of Chol and CE , showing reductions in DA and all diabetic subgroups compared to HC. The steepest declines occurred in LDL1–LDL3 and HDL2b (up to 30%). LDL5 Phoslip and HDL2a Phoslip remained unchanged. In contrast, VLDL Phoslip consistently increased from HC to DA and progressively across DO, DH, and DHC, reflecting enhanced hepatic VLDL output and impaired lipolysis, which shift phospholipids toward triglyceride-rich remnant particles in insulin-resistant T2DM [31]. Like most other LP molecules, ApoA1 levels were decreased both in plasma and in HDL main and in HDL subfractions consistently across all T2DM subgroups compared to HC. The largest reduction was observed in HDL2b ApoA1 levels from being around 9 mg/dL while HC group contained mean concentration of 16 mg/dL. Given the central role of ApoA1 in maintaining HDL function, ApoA1 reduction, especially in large HDL2b particles, suggests impaired reverse cholesterol transport via ABCA1 and diminished HDL functionality in T2DM. This loss affects both the quantity and quality of HDL, weakening its atheroprotective role. The consistent decline across diabetic subgroups highlights ApoA1 as a key marker of HDL dysfunction, contributing to lipid accumulation and increased vascular risk. The same trend of decrease was observed for ApoB as in ApoA1 , where its concentrations were reduced in plasma, and in LDL and in all LDL subfractions (LDL1-LDL5), except LDL6 ApoB , in all T2DM groups compared to HC. In contrast, VLDL and IDL levels of ApoB were consistently increased in all T2DM groups consistently from being around 6 mg/dL in HC for both particles to nearly 8 mg/dL in diabetic patients. The pattern indicates a shift of ApoB from smaller, cholesterol-rich LDL to larger, triglyceride-rich VLDL and IDL particles, reflecting impaired remnant catabolism. Increased VLDL TG , VLDL CE , and VLDL ApoB suggest hepatic overproduction or reduced clearance. This redistribution lowers LDL-associated ApoB , potentially underestimating atherogenic risk in T2DM. The remodeling toward triglyceride-rich remnants highlights a more atherogenic phenotype and emphasizes the need for subclass-specific ApoB profiling in diabetic dyslipidemia. Discriminating type 2 diabetes from healthy individuals To identify the cluster LPs with the strongest classification power for distinguishing T2DM patients from HC, a PLS-DA-based iterative variable selection was performed. Using a training set comprising 70% of T2DM and 70% of HC participants, we conducted variable selection by retaining only LPs with significant contributions based on their VIP scores and selectivity ratios. After 1,000 iterations, only LPs consistently selected in all cases were retained. The final classification model for DA versus HC included 14 LPs and demonstrated high performance, with an AUC of 0.92 on the independent test set (30% from each group; Fig. 1B). The model’s validity was confirmed by a permutation test (2,000). The selected LPs aligned with MLR and LRA results, with IDL ApoB being the strongest discriminator, followed by HDL-associated Chol, FreeChol , and ApoA1 , as well as total plasma or main-fraction levels of ApoA1 , CE , and phospholipids. Similar classification accuracy was achieved for HC versus DO, DH, and DHC comparisons, with the same 14 LPs consistently among the top classifiers (Supplementary Fig. 1). However, no valid PLS-DA models could be developed to distinguish the diabetic subgroups from each other (Supplementary Fig. 1). Altered correlations between lipoproteins in patients with type 2 diabetes Apart from studying differences in LPs concentrations between T2DM and HC groups using MLR or identifying T2DM-associated LPs through LRA, we further investigated alterations in the correlation structure among LPs. This complementary approach offers orthogonal insights into the reorganization of lipid metabolism in diabetic dyslipidemia. Our analysis revealed that approximately 10% of positive correlations between LP pairs observed in HC were attenuated in the DA group, while the proportion of significant negative correlations nearly doubled (2.7% to 5.2%) (Supplementary Fig. 2). Comparable patterns were observed across all diabetic subgroups (DO, DH, and DHC), with the most pronounced disruption in the DO group, where more than 30% of previously significant correlations present in HC were lost. Concurrently, the number of significant negative correlations nearly tripled. We further examined LP pairs exhibiting the most pronounced disruptions in correlation structure between the HC and T2DM groups or among diabetic subgroups. Strong positive correlations observed in HC between HDL2bL CE and HDL Chol (R = 0.75) or HDL2a Chol (R = 0.71) were attenuated in all diabetic subgroups, particularly in DH (R = 0.04–0.22) (Fig. 2). Similar patterns emerged among other HDL subfractions; for instance, the robust correlation between HDL2b Phoslip and HDL2a Chol in HC (R = 0.80) declined across all diabetic groups, with the sharpest drop in DH (R = 0.20). Likewise, the correlation between HDL2b Phoslip and HDL2a ApoA1 in HC (R = 0.71) was markedly reduced in DH and DHC (R = 0.29 and 0.27, respectively). Notably, HDL2a Chol and HDL Phoslip both showed moderate correlations with plasma CE in HC (R = 0.46–0.50), which were lost in DO (R = –0.03), but remained significant though slightly weakened in DH and DHC (R = 0.36–0.42) (Supplementary Fig. 3). Intriguingly, weak correlations in HC between HDL2a Phoslip and HDL3 Chol (R = 0.24), and between HDL2a TG and IDL TG (R = 0.27), progressively increased across diabetic subgroups, doubling in DHC. A similar trend was observed between HDL3 Chol and four VLDL variables (VLDL ApoB , VLDL CE , VLDL Phoslip , VLDL FreeChol ), with correlations increasing from HC (R = –0.04 to 0.17) to DA (R = 0.30–0.43), reaching highest values in DH (R = 0.45–0.52). Likewise, the lack of correlation between HDL2b CE and IDL CE in HC (R = 0.07) evolved into a strong correlation in DHC (R = 0.51). LDL subfractions also showed disrupted patterns in T2DM. Strong correlations in HC between LDL1 Phoslip and HDL2a TG , LDL3 CE and LDL5 Phoslip , and LDL2 ApoB and MainFrac Phoslip (R = 0.47–0.63) were consistently reduced across diabetic subgroups (R = –0.13 to 0.16). Surprisingly, notable negative correlations emerged: LDL3 CE , weakly or non-correlated with VLDL Chol (R = 0.18) and LDL TG (R = 0.29) in HC, exhibited strong negative correlations in DO (R = –0.40), indicating a significant shift in covariation. A similar but more pronounced trend was observed between IDL CE and HDL2a Chol . Discussion Nearly all LPs (58 out of 65) were significantly different between diabetic patients (DA group) and healthy controls (HC group), consistent with the characteristic dyslipidemia of T2DM, marked by elevated VLDL particles and triglycerides, and reduced HDL particles and HDL Chol levels. HDL2b particles were depleted in cholesterol and enriched in triglycerides, indicating altered composition and impaired function of HDL in T2DM. This aligns with evidence that triglyceride enrichment accelerates HDL catabolism, thereby lowering HDL Chol levels [32]. Increased VLDL-related ApoB , triglycerides, and cholesterol reflect heightened systemic fatty acid availability, enhanced hepatic de novo lipogenesis, and impaired LP catabolism [33]. The latter is evident in elevated IDL ApoB and reduced particle numbers of most LDL subclasses, suggesting that the conversion of IDL to LDL is a rate-limiting step in the VLDL → IDL → LDL cascade. This likely promotes formation of small, dense, highly atherogenic LDL particles [34]. One of the novelties of our study is the minimal differences observed in LP profiles among the three diabetic subgroups (DO, DH, and DHC). After FDR correction of MLR multiple comparison results, only five LP variables remained significantly different between at least two subgroups. Among triglycerides, key LPs differentiating HC from DA, only the main fraction and HDL2b subfraction showed significantly higher triglyceride levels in DH compared to DO, while the remaining TG variables did not differ across subgroups. Additionally, LDL2 Chol and LDL2 ApoB , large and less dense atherogenic particles, were moderately lower in DH than in DO, likely due to greater medication use. The smallest observed difference was reduced HDL2a Chol in DH compared to DHC. These minimal subgroup differences likely reflect a shared metabolic dysregulation in T2DM, with subtle variations such as lower LDL2 Chol and LDL2 ApoB in DH, and reduced HDL2a Chol in DH versus DHC, possibly attributable to increased use of lipid-lowering or antihypertensive medications and/or subgroup-specific modulation of LP metabolism [31]. Previous studies examining traditional blood lipid profiles found more pronounced abnormalities in T2DM patients with hypertension than those without, though without adjusting for confounders [16, 17]. Other studies reported better lipid profiles in T2DM patients with CVD compared to those without, potentially reflecting more intensive lipid-lowering therapy [18], while some reported mixed outcomes (e.g., higher TG and HDL Chol , but lower LDL Chol in T2DM with CVD) [19]. However, none evaluated the comprehensive LP profile as done here. Our findings suggest that T2DM itself is the primary driver of LP dysregulation, and that hypertension and CHD do not further exacerbate these alterations but instead contribute to CVD risk through mechanisms independent of LP metabolism. Another intriguing finding from our study is the altered correlation patterns between LPs in T2DM. The disrupted correlation structure driven by insulin resistance–mediated dysregulation of hepatic lipid metabolism, which enhances hepatic VLDL overproduction and impairs LP remodeling by LP lipase (LPL) and hepatic lipase (HL), leading to triglyceride enrichment and accelerated clearance of HDL particles [35]. This remodeling process compromises the structural integrity and function of HDL, thereby weakening its physiological correlation with other HDL subfractions and cholesterol content. Subsequently, impaired lipolytic conversion of VLDL to LDL results in the accumulation of IDL and small dense LDL particles, disrupting the typical LDL–IDL–HDL metabolic continuum and giving rise to inverse correlations not observed in normoglycemic individuals [27]. The emergence of negative correlations, especially between HDL and IDL components, and within LDL fractions, may reflect competitive or compensatory shifts in lipid flux through divergent metabolic pathways, a hallmark of advanced diabetic dyslipidemia. Our results are internally consistent and indicate that T2DM is the primary driver of dysregulated LP profiles and the associated increase in cardiovascular risk. These findings carry important clinical implications, suggesting that dyslipidemia management in T2DM should target the lipid and LP abnormalities inherent to diabetes itself, independent of other risk factors such as hypertension or existing CVD. This highlights the importance of initiating appropriate lipid-lowering therapy early in the course of T2DM, regardless of comorbid hypertension or CHD. The consistent reduction in HDL-related variables and increase in VLDL-related parameters indicate that standard lipid panels may inadequately reflect the full extent of dyslipidemia in T2DM. Expanded LP profiling, including subfractions and molecular components such as apolipoproteins and phospholipids, may offer more precise biomarkers for disease monitoring. The altered correlation patterns among LPs suggest a complex, diabetes-driven reorganization of lipid metabolism. This supports the need for integrated metabolic treatment strategies that not only lower LDL cholesterol but also enhance HDL functionality and reduce VLDL and triglyceride levels. Further mechanistic studies on the role of insulin resistance, inflammation, and oxidative stress in modulating LP metabolism could deepen our understanding of diabetic dyslipidemia. Longitudinal studies are also warranted to investigate how these alterations on LP profiles evolve over time and contribute to cardiovascular outcomes in T2DM. This study has limitations, including a relatively small sample size within T2DM subgroups, which may limit statistical power and generalizability. The cross-sectional design also precludes causal inference. Future studies with larger, multi-center cohorts and longitudinal frameworks are necessary to validate and expand upon these findings. Conclusion This study demonstrates significant alterations in LP profiles in T2DM, characterized by elevated triglycerides and VLDL, and reduced cholesterol and HDL, independent of hypertension or CVD. Notably, the disruption extended to smaller, atherogenic subfractions, underscoring the depth of metabolic imbalance in T2DM. The presence of cardiovascular comorbidities introduced only modest additional changes, mainly affecting cholesterol and ApoB content in LDL and HDL subfractions. Furthermore, a correlation analysis revealed a significant breakdown in the physiological relationships among lipoproteins in T2DM, which was further altered in those with cardiovascular complications. Our findings suggest that T2DM alone is the principal driver of lipoprotein abnormalities, while cardiovascular comorbidities contribute minimally to additional compositional changes. These results underscore the need for early lipid intervention to mitigate cardiovascular risk. Lipoprotein profiling in diabetic patients may improve risk stratification and guide early lipid-lowering interventions. Future longitudinal studies are warranted to confirm these findings and explore causal mechanisms. Abbreviations ANOVA – Analysis of Variance ApoA1 – Apolipoprotein A1 ApoB – Apolipoprotein B BMI – Body Mass Index CE – Cholesteryl Esters CHD – Coronary Heart Disease Chol – Cholesterol CI – Confidence Interval CVD – Cardiovascular Disease DA – All patients with type 2 diabetes mellitus (T2DM), with or without comorbidities DH – Diabetic patients with hypertension DHC – Diabetic patients with hypertension and coronary heart disease DO – Diabetic patients without hypertension or coronary heart disease FDR – False Discovery Rate FreeChol – Free (unesterified) Cholesterol HC – Healthy Controls HDL – High-Density Lipoprotein IDL – Intermediate-Density Lipoprotein LDL – Low-Density Lipoprotein LRA – Logistic Regression Analysis LP – Lipoprotein NMR – Nuclear Magnetic Resonance OR – Odds Ratio Phoslip – Phospholipids PLS-DA – Partial Least Squares Discriminant Analysis SD – Standard Deviation T2DM – Type 2 Diabetes Mellitus TG – Triglycerides VLDL – Very Low-Density Lipoprotein Declarations Ethics approval and consent to participate. The study was conducted in accordance with the principles outlined in the Declaration of Helsinki and approved by the Ethical Committee of the Ministry of Public Health of the Republic of Uzbekistan (Approval No. 6/11-1694). Written informed consent was obtained from all participants prior to their inclusion in the study. Consent for publication. This manuscript is not under consideration elsewhere and will not be submitted to another journal until a final decision regarding its acceptability has been made. Competing interests The authors declare no competing interests. Author Contributions. B.K, and D.D. acquired funding and were involved in the conception and design of the study. Z.K., contributed to sample collection and recruitment of patients. E.T., D.K., S.N., and V.T., S.A., B.S.S., and B.K. were involved in sample preparation, data acquisition, data cleaning, and data analysis. B.K. wrote the manuscript. F.M., and S.B.E. contributed to the interpretation of the results. All authors contributed to the discussion, edited the manuscript, and approved the final manuscript. B.K. and D.D. are the guarantors of this work and, as such, have full access to all the data included in the study and take responsibility for the integrity of the data and the accuracy of the results. Acknowledgements. Data was generated though accessing research infrastructure at UCPH, including FOODHAY (Food and Health Open Innovation Laboratory, Danish Roadmap for Research Infrastructure). Funding. This work was supported by the University of Copenhagen (Data+ project, Strategy 2013 funds), modernizing Uzbekistan national innovation system (MUNIS) project (project number: REP-1/12 ), basic funding from the Agency of Innovative Development under the Ministry of Higher Education, Science and Innovations of the Republic of Uzbekistan (project number: REP 25112021-127), and International credit mobility (KA171) in the Erasmus + program 2021-2027, funded by the European Union. 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Tables Table 1 – Characteristics of the Study Population HC ( N = 393) DA ( N = 390) P DO ( N = 88) DH ( N = 105) DHC ( N = 197) P Females 212 (54%) 186 (48%) 0.08 25 (28%) 56 (53%) 105 (53%) 2E-4 Age (years) 43.8 (15.9) 57.8 (11.6) 8E-37 50.5 (13.1) 56.3 (11.2) 61.9 (9) 8E-15 BMI (kg/m 2 ) 26.2 (5) 29.8 (5.1) 5E-19 28.6 (4.3) 29.8 (4.8) 30.3 (5.4) 0.02 Smoking (yes) 120 (31%) 122 (31%) 0.82 29 (33%) 29 (28%) 64 (32%) 0.64 Alcohol (yes) 164 (42%) 123 (32%) 0.003 30 (34%) 28 (27%) 65 (33%) 0.45 BG (mmol/L) 4.4 (0.8) 10.2 (3.7) 2E-62 10.3 (3.8) 10.3 (3.9) 10.1 (3.4) 0.87 SB (mmHg) 111.8 (15.9) 126.7 (18.5) 9E-15 118.9 (14.5) 129.9 (17.9) 128.6 (19.5) 4E-05 DB (mmHg) 74.5 (11.9) 80.7 (10.1) 3E-08 76.8 (8.9) 81.4 (10.7) 82 (9.9) 2E-4 T2DM (years) na 11.2 (8.1) na 9.8 (8.4) 11.2 (6.9) 11.9 (8.5) 0.16 Med. (yes) na 377 (97%) na 78 (89%) 102 (97%) 197 (100%) 5E-06 Hyp. (yes) na 300 (77%) na 54 (61%) 85 (81%) 161 (82%) 4E-4 Llow. (yes) na 138 (35%) na 11 (13%) 26 (25%) 101 (51%) 6E-11 Anhyp. (yes) na 255 (65%) na 29 (33%) 67 (64%) 159 (81%) 4E-14 HC = healthy control individuals ( N = 393); DA = all patients with Type 2 Diabetes Mellitus (T2DM) ( N = 390); DO = diabetic patients without hypertension or coronary heart disease ( N = 88); DH = diabetic patients with hypertension ( N = 105); DHC = diabetic patients with both hypertension and coronary heart disease ( N = 197). Data are presented as mean (standard deviation (SD)) for continuous variables, or as n (%) for categorical variables. P-values were determined using one-way ANOVA or t-test for continuous variables, and the chi-square test for categorical variables. *BMI = body mass index; BG = blood glucose concentration; SB = systolic blood pressure; DB = diastolic blood pressure; Med = regular medication use; Hyp = regular use of hypoglycemic drugs; Llow = regular use of lipid-lowering drugs; Anhyp = regular use of antihypertensive drugs. Table 2 – Concentrations of lipoproteins in healthy individuals and in patients with Type 2 Diabetes Mellitus Lipoproteins HC, M(SD) DA, M(SD) DO, M(SD) DH, M(SD) DHC, M(SD) P 1 Plasma TG 113.2 (52.9) 153.9 (72.7) 140 (68) 167.5 (82.2) 152.8 (68.3) a,b,c,d 4 LDL TG 15.7 (5.8) 17.2 (7.5) 16.2 (8) 18.6 (8) 17 (6.9) c 5 VLDL TG 72.5 (45) 99.6 (59.8) 88 (53.9) 110.3 (68.8) 99.1 (56.4) a,c,d 6 IDL TG 9.1 (4.6) 13.3 (6.9) 12.5 (7.3) 14.5 (7.9) 13.1 (6) a,b,c,d 7 LDL1 TG 4 (2.1) 5.2 (2.9) 4.8 (3.1) 5.6 (3.2) 5.1 (2.6) c,d 8 HDL2b TG 1.8 (1.1) 2.5 (1.7) 2.2 (1.7) 2.8 (1.8) 2.5 (1.6) a,c,d,e 9 HDL2a TG 2.6 (1.7) 2.3 (1.4) 2.2 (1.4) 2.3 (1.4) 2.4 (1.5) 10 Plasma Chol 183.9 (46.2) 152.4 (40.7) 156 (38) 156.9 (43.8) 148.3 (39.9) a,b,c,d 13 LDL Chol 103.1 (31.3) 78.2 (29.7) 83.3 (29.3) 79.7 (33.1) 75.2 (27.7) a,b,c,d 14 HDL Chol 38.3 (12) 26.2 (9.2) 26.5 (9.7) 25.4 (8.8) 26.5 (9.1) a,b,c,d 15 VLDL Chol 11.9 (8.6) 16.9 (11.2) 14.1 (10.3) 19.1 (12.3) 17 (10.7) c,d 16 IDL Chol 6.7 (3.6) 8.1 (4) 7.1 (3.9) 8.9 (4) 8.2 (4) c,d 17 LDL1 Chol 27.2 (10.8) 20.9 (10.9) 22.1 (11) 21.9 (11.9) 19.8 (10.3) a,b,c,d 18 LDL2 Chol 14.8 (6.3) 9.7 (5.7) 11 (5.4) 10.1 (6.5) 8.9 (5.4) a,b,c,d,f 19 LDL3 Chol 14.8 (5.6) 10.4 (4.8) 10.8 (4.8) 10.8 (5.2) 10 (4.6) a,b,c,d 20 LDL4 Chol 15.7 (4.9) 12.1 (4.3) 12.8 (4.4) 12.3 (4.6) 11.6 (4) a,b,c,d 21 LDL5 Chol 11.8 (4.6) 9.5 (3.9) 9.2 (3.5) 10.1 (4.2) 9.4 (3.9) a,b,c,d 22 HDL2b Chol 14.2 (7.2) 7.5 (5.1) 8.1 (5.2) 7.5 (5.5) 7.3 (4.9) a,b,c,d 23 HDL2a Chol 17.6 (3.8) 13.3 (3.3) 13.6 (3.3) 12.6 (3.5) 13.5 (3.2) a,b,c,d,g 24 HDL3 Chol 19.1 (3.4) 17.3 (4.1) 17.6 (3.8) 17 (4.3) 17.4 (4.1) a,c,d 25 Plasma FreeChol 52.4 (11.5) 46.7 (11.1) 46.3 (10.4) 48.6 (11.8) 45.9 (10.9) a,b,c,d 26 VLDL FreeChol 7.1 (4) 10.7 (5.2) 9.5 (5) 11.7 (5.8) 10.7 (5) a,b,c,d 27 HDL2b FreeChol 5 (2.1) 2.9 (1.6) 3.1 (1.9) 2.9 (1.7) 2.8 (1.5) a,b,c,d 28 Plasma Phoslip 203.6 (45.2) 183.4 (45.2) 181.9 (38.9) 187.7 (50.1) 181.7 (45.1) a,b,c,d 31 LDL Phoslip 69.1 (21.3) 52.8 (20.7) 56 (19.9) 53.7 (23.3) 50.9 (19.4) a,b,c,d 32 HDL Phoslip 67.9 (18.1) 53.8 (17.2) 52.4 (17.3) 53 (17.7) 54.8 (16.9) a,b,c,d 33 VLDL Phoslip 22.9 (13.4) 32.6 (17.2) 29.1 (16.2) 35.9 (19.2) 32.4 (16.3) a,b,c,d 34 LDL1 Phoslip 13.3 (8.8) 9.1 (6.5) 9.4 (6.3) 9.4 (7.9) 8.9 (5.8) a,b,c,d 35 LDL2 Phoslip 7.4 (3.9) 5.2 (3.3) 5.6 (3.2) 5.6 (3.9) 4.8 (3) a,c,d 36 LDL3 Phoslip 10 (3.3) 7.5 (3) 7.7 (2.9) 7.8 (3.3) 7.3 (2.9) a,b,c,d 37 LDL5 Phoslip 7.5 (2.7) 7.5 (2.7) 7 (2.4) 8 (2.7) 7.5 (2.7) a 38 HDL2b Phoslip 15.7 (11.2) 10.8 (9.1) 11.5 (9.9) 10.9 (10.1) 10.4 (8.1) a,b,c,d 39 HDL2a Phoslip 36.2 (9) 35.6 (10.7) 36 (9.4) 35 (11) 35.7 (11.1) 40 Plasma ApoA1 121 (21.7) 100.3 (20.6) 101.5 (19.1) 98.2 (22.3) 100.8 (20.3) a,b,c,d 43 HDL ApoA1 101.2 (21.4) 81.8 (17.3) 82.8 (16.8) 80.8 (18) 81.9 (17.3) a,b,c,d 44 HDL2b ApoA1 16 (9.9) 9.3 (7.9) 9.5 (8.6) 9.8 (8.9) 8.9 (7) a,b,c,d 45 HDL2a ApoA1 36.5 (8.2) 31.7 (7.9) 32.2 (7.1) 30.5 (8) 32 (8.1) a,b,c,d 46 HDL3 ApoA1 54.4 (11.1) 46.2 (9.6) 46.2 (9.3) 45.5 (9.3) 46.7 (10) a,b,c,d 47 Plasma ApoB 93.7 (21.8) 86.3 (21) 87.4 (21.2) 89.4 (22.1) 84.2 (20.2) a,d 50 LDL ApoB 75.3 (18.4) 63 (17.3) 65.6 (17.4) 64.5 (18.9) 60.9 (16.1) a,b,c,d 51 VLDL ApoB 5.8 (3) 8.1 (3.5) 7.2 (3.4) 8.7 (3.7) 8.2 (3.3) a,b,c,d 52 IDL ApoB 6 (1.7) 7.8 (2.1) 7.6 (2.3) 8.2 (2.2) 7.7 (2) a,b,c,d 53 LDL1 ApoB 17.2 (5.1) 15.5 (5.7) 16.5 (5.6) 16 (6.2) 14.9 (5.4) a,d 54 LDL2 ApoB 9.4 (3.3) 6.9 (3.5) 7.6 (3.2) 7 (3.9) 6.4 (3.3) a,b,c,d,f 55 LDL3 ApoB 9.6 (3.4) 7.7 (3) 7.7 (3) 8 (3.1) 7.5 (2.9) a,b,c,d 56 LDL4 ApoB 9.5 (3.2) 7.5 (2.8) 7.5 (2.7) 7.8 (3) 7.3 (2.6) a,b,c,d 57 LDL5 ApoB 7.7 (3) 6.4 (2.7) 6 (2.4) 6.8 (3.2) 6.4 (2.6) a,b,c,d 58 LDL6 ApoB 9.9 (8.5) 10.7 (9) 9.6 (7.1) 11.9 (10.2) 10.5 (9.1) 59 Plasma CE 219.2 (56.7) 176.7 (51.6) 183.4 (48.6) 181.1 (55.3) 171.3 (50.5) a,b,c,d 60 VLDL CE 12.1 (6.9) 17 (8.8) 15.3 (8.5) 18.7 (9.5) 17 (8.3) a,b,c,d 61 IDL CE 7.6 (2.9) 9.5 (3.6) 9.1 (3.8) 10 (3.4) 9.4 (3.6) a,b,c,d 62 LDL CE 107.5 (39.1) 83 (36.8) 87.5 (37.1) 86.2 (39.1) 79.4 (35.2) a,b,c,d 63 LDL1 CE 25.4 (13.1) 19.6 (12.5) 21.1 (12) 21.3 (14.4) 18.1 (11.6) a,b,c,d 64 LDL3 CE 16.3 (5.8) 11.7 (5.9) 12.3 (6.1) 12.1 (6.4) 11.2 (5.4) a,b,c,d 65 HDL2b CE 17.3 (8.4) 11.9 (7.5) 12.5 (7.1) 12.5 (7.9) 11.3 (7.6) a,b,c,d Concentrations of LPs (mg/dL) in healthy control individuals (HC, N = 393), all patients with Type 2 Diabetes Mellitus (DA, N = 393), diabetic patients without hypertension or coronary heart disease (DO, N = 88), diabetic patients with hypertension (DH, N = 105), and diabetic patients with both hypertension and coronary heart disease (DHC, N = 197). Data are presented as mean (M) (standard deviation (SD)). P-values were determined by multiple linear regression (MLR) analysis after adjusting for confounders. The significance threshold was set at P < 0.05 after false discovery rate (5%) correction. * a: significant difference between HC and DA; b: HC vs. DO; c: HC vs. DH; d: HC vs. DHC; e: DO vs. DH; f: DO vs. DHC; g: DH vs. DHC. TG = triglycerides; Chol = cholesterol; FreeChol = free cholesterol; CE = cholesterol ester; Phoslip = phospholipids; ApoA1 = apo apolipoprotein A1; ApoB = apolipoprotein B; VLDL = very low-density LP; IDL = intermediate-density LP; LDL = low-density LP; HDL = high-density LP. **Concentrations of LP molecules in main and subfraction are omitted from the table, for a complete list see Supplementary Table 1. Additional Declarations No competing interests reported. Supplementary Files SupplementaryTable1.xlsx SupplementaryFigure1.pdf Supplementary Figure 1 - Blood plasma LPs differentiating healthy individuals (HC, N = 393) from patients with Type 2 Diabetes Mellitus (T2DM) subgroups including diabetic patients without hypertension or coronary heart disease (DO, N = 88), diabetic patients with hypertension (DH, N = 105), and diabetic patients with both hypertension and coronary heart disease (DHC, N = 197) as well as between the subgroups themselves. A: Scatter plot of adjusted odds ratios versus mean fold changes (FC) between the two groups. Each dot represents a LP variable, with dot size proportional to its effect size (%) as determined by multiple linear regression (MLR) analysis after adjusting for confounders. Odds ratios were obtained from logistic regression analysis (LRA). LP names are shown only for variables that met the significance threshold ( P < 0.05 after false discovery rate (5%) correction) in both LRA and MLR analyses. The color code insert indicates whether LP molecules are free in blood plasma or belong to one of the four major (sub)fractions: VLDL, IDL, LDL, or HDL. B: Results from partial least squares-discriminant analysis (PLS-DA) used to identify key LP classifiers distinguishing the two groups. Normalized variable importance in projection (VIP) scores and the selectivity ratio plot highlight LP variables consistently selected during iterative PLS-DA model optimization. 1 - Specificity versus Sensitivity plot shows the area under the curve (AUC) of the receiver operating characteristic curve from the optimized PLS-DA model, developed using a training set (TR – 70% of individuals from both groups) and tested on a test set (TS – 30%). LV = number of latent variables used to build the model; Error% = misclassification rate for TR and TS. The lower-right panel shows results of a permutation test (2,000). SupplementaryFigure2.pdf Supplementary Figure 2 – Heatmap of pairwise correlation coefficients among 65 LP variables in healthy control individuals (HC, lower left triangle, n = 393) and patients with Type 2 Diabetes Mellitus (T2DM, upper right triangle). The T2DM group includes all patients with T2DM (DA, n = 393), diabetic patients without hypertension or coronary heart disease (DO, n = 88), those with hypertension (DH, n = 105), and those with both hypertension and coronary heart disease (DHC, n = 197). Bi-clustering based on Euclidean distance and average linkage was applied to the correlation matrix derived from the HC group, and the same LP ordering was used across all heatmaps. The color bar (upper right) indicates Pearson correlation coefficient (R) values ranging from +1 to –1. The distribution plot (lower right) shows the frequency of R values across the correlation matrices from the HC and T2DM datasets. Note: Percentages in parentheses indicate the proportion of LP variable pairs with significant positive (+) or negative (–) correlations. SupplementaryFigure3.pdf Supplementary Figure 3 - Disrupted correlation structure of blood plasma LPs in patients with Type 2 Diabetes Mellitus. Scatter plots show the correlation between the concentrations (mg/dL) of specific LP pairs in healthy controls (HC, N = 393), all patients with Type 2 Diabetes Mellitus (DA, N = 393), diabetic patients without hypertension or coronary heart disease (DO, N = 88), diabetic patients with hypertension (DH, N = 105), and diabetic patients with both hypertension and coronary heart disease (DHC, N = 197). Each panel displays the Pearson correlation coefficient (R), with asterisks (*) indicating statistically significant correlations (P < 0.05) within the respective group. 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12:53:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7318923/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7318923/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12933-025-03039-2","type":"published","date":"2025-12-25T15:58:25+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":89287426,"identity":"c6f3d0a6-7d3c-410b-8c9b-5bdb8d8c3ac2","added_by":"auto","created_at":"2025-08-18 11:39:49","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":570150,"visible":true,"origin":"","legend":"\u003cp\u003eBlood plasma LPs differentiating healthy individuals (HC, \u003cem\u003eN\u003c/em\u003e = 393) from patients with Type 2 Diabetes Mellitus (DA, \u003cem\u003eN\u003c/em\u003e = 390). \u003cstrong\u003eA:\u003c/strong\u003e Scatter plot of adjusted odds ratios versus mean fold changes (FC) between DA and HC groups. Each dot represents a LP variable, with dot size proportional to its effect size (%) as determined by multiple linear regression (MLR) analysis after adjusting for confounders. Odds ratios were obtained from logistic regression analysis (LRA). LP names are shown only for variables that met the significance threshold (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05 after false discovery rate (5%) correction) in both LRA and MLR analyses. The color code insert indicates whether LP molecules are free in blood plasma or belong to one of the four major (sub)fractions: VLDL, IDL, LDL, or HDL. \u003cstrong\u003eB:\u003c/strong\u003eResults from partial least squares-discriminant analysis (PLS-DA) used to identify key LP classifiers distinguishing DA from HC individuals. Normalized variable importance in projection (VIP) scores and the selectivity ratio plot highlight 14 LP variables consistently selected during iterative PLS-DA model optimization. 1 - Specificity versus Sensitivity plot shows the area under the curve (AUC) of the receiver operating characteristic curve from the optimized PLS-DA model, developed using a training set (TR – 70% of individuals from both groups) and tested on a test set (TS – 30%). LV = number of latent variables used to build the model; Error% = misclassification rate for TR and TS. The lower-right panel shows results of a permutation test (2,000), indicating model validity. AUC values for the true datasets (TR: 0.91; TS: 0.92) were significantly higher than those for the permuted datasets (TR: 0.57; TS: 0.61).\u003c/p\u003e","description":"","filename":"fig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7318923/v1/c01ae5a6c85b340f0e6fff9e.jpg"},{"id":89287784,"identity":"5d753df3-fea1-4c84-9560-d9b42c755dc1","added_by":"auto","created_at":"2025-08-18 11:47:49","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":805324,"visible":true,"origin":"","legend":"\u003cp\u003eDisrupted correlation structure of blood plasma LPs in patients with Type 2 Diabetes Mellitus. Scatter plots show the correlation between the concentrations (mg/dL) of specific LP pairs in healthy controls (HC, \u003cem\u003eN\u003c/em\u003e = 393), all patients with Type 2 Diabetes Mellitus (DA, \u003cem\u003eN\u003c/em\u003e= 393), diabetic patients without hypertension or coronary heart disease (DO, \u003cem\u003eN\u003c/em\u003e= 88), diabetic patients with hypertension (DH, \u003cem\u003eN\u003c/em\u003e = 105), and diabetic patients with both hypertension and coronary heart disease (DHC, \u003cem\u003eN\u003c/em\u003e = 197). Each panel displays the Pearson correlation coefficient (R), with asterisks (*) indicating statistically significant correlations (P \u0026lt; 0.05) within the respective group.\u003c/p\u003e","description":"","filename":"fig2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7318923/v1/1023b74f4f5c62f08eac9e1d.jpg"},{"id":99172512,"identity":"019b35f4-49be-4588-b26c-293efc2c5772","added_by":"auto","created_at":"2025-12-29 16:10:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2603737,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7318923/v1/7b6e4ffd-d66f-478e-bda8-1c0212ae14a7.pdf"},{"id":89287785,"identity":"00a4569f-1603-444b-8221-3c4cd7c1810d","added_by":"auto","created_at":"2025-08-18 11:47:50","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":33273,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7318923/v1/2e6a35b9e359ea18f8f22356.xlsx"},{"id":89287433,"identity":"7be5bedc-9bb2-4365-a993-c4bfa3dd7a95","added_by":"auto","created_at":"2025-08-18 11:39:50","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":1963508,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary\u003c/strong\u003e \u003cstrong\u003eFigure 1\u003c/strong\u003e - Blood plasma LPs differentiating healthy individuals (HC, \u003cem\u003eN\u003c/em\u003e = 393) from patients with Type 2 Diabetes Mellitus (T2DM) subgroups including diabetic patients without hypertension or coronary heart disease (DO, N = 88), diabetic patients with hypertension (DH, N = 105), and diabetic patients with both hypertension and coronary heart disease (DHC, N = 197) as well as between the subgroups themselves. \u003cstrong\u003eA:\u003c/strong\u003e Scatter plot of adjusted odds ratios versus mean fold changes (FC) between the two groups. Each dot represents a LP variable, with dot size proportional to its effect size (%) as determined by multiple linear regression (MLR) analysis after adjusting for confounders. Odds ratios were obtained from logistic regression analysis (LRA). LP names are shown only for variables that met the significance threshold (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05 after false discovery rate (5%) correction) in both LRA and MLR analyses. The color code insert indicates whether LP molecules are free in blood plasma or belong to one of the four major (sub)fractions: VLDL, IDL, LDL, or HDL. \u003cstrong\u003eB:\u003c/strong\u003e Results from partial least squares-discriminant analysis (PLS-DA) used to identify key LP classifiers distinguishing the two groups. Normalized variable importance in projection (VIP) scores and the selectivity ratio plot highlight LP variables consistently selected during iterative PLS-DA model optimization. 1 - Specificity versus Sensitivity plot shows the area under the curve (AUC) of the receiver operating characteristic curve from the optimized PLS-DA model, developed using a training set (TR – 70% of individuals from both groups) and tested on a test set (TS – 30%). LV = number of latent variables used to build the model; Error% = misclassification rate for TR and TS. The lower-right panel shows results of a permutation test (2,000).\u003c/p\u003e","description":"","filename":"SupplementaryFigure1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7318923/v1/bab21db2d3bb49bea6717a60.pdf"},{"id":89287434,"identity":"164dbbc4-9da4-49c3-94c5-074af14488d4","added_by":"auto","created_at":"2025-08-18 11:39:50","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":4344313,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure 2 –\u003c/strong\u003e Heatmap of pairwise correlation coefficients among 65 LP variables in healthy control individuals (HC, lower left triangle, n = 393) and patients with Type 2 Diabetes Mellitus (T2DM, upper right triangle). The T2DM group includes all patients with T2DM (DA, n = 393), diabetic patients without hypertension or coronary heart disease (DO, n = 88), those with hypertension (DH, n = 105), and those with both hypertension and coronary heart disease (DHC, n = 197). Bi-clustering based on Euclidean distance and average linkage was applied to the correlation matrix derived from the HC group, and the same LP ordering was used across all heatmaps. The color bar (upper right) indicates Pearson correlation coefficient (R) values ranging from +1 to –1. The distribution plot (lower right) shows the frequency of R values across the correlation matrices from the HC and T2DM datasets. Note: Percentages in parentheses indicate the proportion of LP variable pairs with significant positive (+) or negative (–) correlations.\u003c/p\u003e","description":"","filename":"SupplementaryFigure2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7318923/v1/a2e34691da0d32308c860214.pdf"},{"id":89288691,"identity":"cbb5cda8-b351-420a-92fc-04d6232a9807","added_by":"auto","created_at":"2025-08-18 11:55:50","extension":"pdf","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":4324676,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure 3 - \u003c/strong\u003eDisrupted correlation structure of blood plasma LPs in patients with Type 2 Diabetes Mellitus. Scatter plots show the correlation between the concentrations (mg/dL) of specific LP pairs in healthy controls (HC, \u003cem\u003eN\u003c/em\u003e = 393), all patients with Type 2 Diabetes Mellitus (DA, \u003cem\u003eN\u003c/em\u003e= 393), diabetic patients without hypertension or coronary heart disease (DO, \u003cem\u003eN\u003c/em\u003e= 88), diabetic patients with hypertension (DH, \u003cem\u003eN\u003c/em\u003e = 105), and diabetic patients with both hypertension and coronary heart disease (DHC, \u003cem\u003eN\u003c/em\u003e = 197). Each panel displays the Pearson correlation coefficient (R), with asterisks (*) indicating statistically significant correlations (P \u0026lt; 0.05) within the respective group.\u003c/p\u003e","description":"","filename":"SupplementaryFigure3.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7318923/v1/6d6dc293229280ef8a1770a9.pdf"},{"id":89287436,"identity":"c3c96afa-0117-41ba-a24b-eb3660d03fb3","added_by":"auto","created_at":"2025-08-18 11:39:50","extension":"pptx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":2508547,"visible":true,"origin":"","legend":"","description":"","filename":"GA.pptx","url":"https://assets-eu.researchsquare.com/files/rs-7318923/v1/69702d8ce7bab2e3c6c228a0.pptx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Distinct compositional alterations in plasma lipoproteins in type 2 diabetes: a cross-sectional study of healthy individuals and diabetics with and without cardiovascular comorbidities","fulltext":[{"header":"Research insights","content":"\u003cp\u003e\u003cstrong\u003eWhat is currently known about this topic?\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eType 2 diabetes mellitus (T2DM) raises plasma VLDL and lowers HDL, increasing cardiovascular disease (CVD) risk. Early lipid management in T2DM is crucial to mitigate CVD risk.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWhat is the key research question?\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDoes hypertension or CVD comorbidity further alter lipoprotein metabolism in T2DM?\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWhat is new?\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe found dysregulation in both major lipoprotein fractions and smaller, atherogenic subfractions.\u003c/p\u003e\n\u003cp\u003eCVD comorbidities are linked to minor changes in cholesterol and ApoB in LDL and HDL subfractions.\u003c/p\u003e\n\u003cp\u003eCVD comorbidities in T2DM were also linked to disrupted correlations among lipoproteins.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHow might this study influence clinical practice?\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLipoproteins dysregulations we identified prove new mechanistic insights into T2DM.\u003c/p\u003e\n\u003cp\u003eChanges in lipoprotein levels may be promising targets for early lipid therapy to reduce CVD risk.\u003c/p\u003e"},{"header":"Introduction","content":"\u003cp\u003eType 2 Diabetes Mellitus (T2DM) is a chronic metabolic disorder characterized by hyperglycemia due to peripheral insulin resistance and inadequate pancreatic insulin secretion [1]. T2DM has many microvascular and macrovascular complications which significantly affect quality of life [2] and increase risk of death, particularly from cardiovascular disease (CVD) [3, 4]. Alterations in lipid and lipoprotein (LP) metabolism in T2DM are linked to insulin resistance and are believed to be involved in the etiology of CVD [5, 6].\u003c/p\u003e\u003cp\u003eT2DM is associated with elevated particle numbers of very low-density lipoproteins (VLDL) and reduced numbers of high-density lipoproteins (HDL) [7, 8]. The increase in VLDL is primarily due to enhanced hepatic secretion and impaired clearance driven by insulin resistance. Elevated VLDL leads to increased conversion to intermediate-density lipoproteins (IDL) through LP lipase-mediated lipolysis. Studies also indicate that low-density lipoproteins \u003cb\u003e(\u003c/b\u003eLDL) particles in T2DM exhibit a smaller and denser phenotype [9]. These small dense LDL particles have a higher propensity to penetrate the arterial wall and are more prone to oxidative modification, thereby promoting atherogenesis[10]. Furthermore, both the triglyceride (\u003cem\u003eTG\u003c/em\u003e) and the cholesterol (\u003cem\u003eChol\u003c/em\u003e)) contents of LPs differ between patients with T2DM and healthy individuals, contributing to hypertriglyceridemia, hypercholesterolemia, and decreased HDL-mediated reverse \u003cem\u003eChol\u003c/em\u003e transport [11\u0026ndash;14]. All these alterations significantly contribute to increased CVD risk in patients with T2DM.\u003c/p\u003e\u003cp\u003eHypertension is also associated with insulin resistance and is approximately twice as frequent in patients with T2DM compared with healthy individuals, and further increases risk of CVD [15]. It is not clear whether this is because of more extensive alterations in the blood lipid profile [16\u0026ndash;19]. Besides traditional lipid analysis, assessing compositional alterations in LP subfractions is crucial for understanding the mechanisms linking T2DM with CVD and informing risk assessment and management strategies [20]. Here, we analyzed 65 plasma LP parameters in a large group of T2DM patients with and without hypertension and coronary heart disease (CHD), and in healthy controls (HC), by using a recently-developed model based on proton nuclear magnetic resonance (\u003csup\u003e1\u003c/sup\u003eH NMR) spectroscopy [21]. We also investigated alterations in co-variation among different LPs to identify changes in LP metabolism likely to be mechanistically linked to T2DM.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy population\u003c/h2\u003e\u003cp\u003eThis study recruited patients diagnosed with T2DM from the Republican Specialized Scientific and Practical Medical Center of Endocrinology in Tashkent (Uzbekistan) between July 2021 and August 2022. Exclusion criteria included current cancer diagnosis, pregnancy or lactation, and confirmed type 1 or gestational diabetes mellitus. A total of 390 T2DM patients (later referred to as DA group) aged 20 to 88 years were enrolled, of whom 88 had no hypertension or CHD (DO subgroup), 105 T2DM patients had hypertension (DH subgroup), and 197 had both hypertension and CHD (DHC subgroup). Recruitment of healthy control (HC) group was conducted between July 2021 and July 2022 at the Center for Advanced Technologies in Tashkent (Uzbekistan) through social media and local newspapers. For the HC group, 393 healthy individuals (normal blood glucose concentrations, normal blood pressure, no chronic medical conditions, and no regular medication use) were recruited. Clinical, anthropometric, and medical history data were collected at the time of hospital admission for T2DM patients or during visits to the recruitment center for HC. Each participant completed a detailed questionnaire probing for demographic information and lifestyle factors, including smoking and alcohol consumption. Fasting blood samples were collected between 6 and 10 AM, centrifuged in EDTA-containing vacutainers, and plasma was stored at -80\u0026deg;C until analysis. The study was conducted in accordance with the Declaration of Helsinki and approved by the Ethical Committee of the Ministry of Public Health of the Republic of Uzbekistan (No. 6/11-1694). All participants provided written informed consent.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eChemicals and reagents\u003c/h3\u003e\n\u003cp\u003eAll chemicals and reagents were procured from Sigma-Aldrich (S\u0026oslash;borg, Denmark). These included deuterium oxide (D\u003csub\u003e2\u003c/sub\u003eO, 99.9% atom D), monobasic sodium phosphate (NaH\u003csub\u003e2\u003c/sub\u003ePO\u003csub\u003e4\u003c/sub\u003e, \u0026ge;\u0026thinsp;99%), dibasic sodium phosphate (Na\u003csub\u003e2\u003c/sub\u003eHPO\u003csub\u003e4\u003c/sub\u003e, \u0026ge;\u0026thinsp;98%), sodium salt of 3-(trimethylsilyl)propionic-2,2,3,3-d4 acid (TSP, 98% atom D, \u0026ge;\u0026thinsp;98%), and sodium azide (NaN\u003csub\u003e3\u003c/sub\u003e, \u0026ge;\u0026thinsp;99.5%). Purified water was obtained using a Millipore lab water system (Merck KGaA, Darmstadt, Germany) equipped with a 0.22 \u0026micro;m filter membrane.\u003c/p\u003e\n\u003ch3\u003eSample collection and preparation for ¹H NMR analysis\u003c/h3\u003e\n\u003cp\u003eBlood plasma samples were thawed at room temperature and aliquots of 350 \u0026micro;L were carefully mixed with an equal volume of phosphate buffer in 2.0 mL Eppendorf tubes. The phosphate buffer was prepared as previously described [22], using NaH\u003csub\u003e2\u003c/sub\u003ePO\u003csub\u003e4\u003c/sub\u003e and Na\u003csub\u003e2\u003c/sub\u003eHPO\u003csub\u003e4\u003c/sub\u003e solutions. A 600 \u0026micro;L portion of the plasma-buffer mixture was then transferred into 5 mm OD (103.5 mm length) SampleJet tubes (Bruker BioSpin, Germany). Sample preparation and measurements were randomized, and pooled control human blood plasma samples were analyzed at regular intervals throughout the entire measurement sequence.\u003c/p\u003e\n\u003ch3\u003e¹H NMR spectral data acquisition and processing\u003c/h3\u003e\n\u003cp\u003eThe \u0026sup1;H NMR spectra of plasma samples were acquired as previously described [21]. Briefly, the Bruker Avance III 600 MHz NMR spectrometer was used to acquire 1D NOESY spectra with water suppression (\u003cem\u003enoesygppr1d\u003c/em\u003e) with 32 scans, collected into 131,072 data points, with a spectral width of 30 ppm, a 90\u0026deg; pulse, a recycle delay (d1) of 4 s, and a mixing time of 0.01 s. All NMR spectra were imported into the SigMa software [23], and concentrations of LPs were predicted using 0.6\u0026ndash;1.4 ppm region of the \u003csup\u003e1\u003c/sup\u003eH NMR spectra, representing methyl protons of \u003cem\u003eChol\u003c/em\u003e and fatty acids and methylene protons of fatty acids, using partial least squares (PLS) regression, as described previously [21].\u003c/p\u003e\n\u003ch3\u003eStatistical data analysis\u003c/h3\u003e\n\u003cp\u003eTo assess potential confounding factors, continuous variables (e.g., age, body mass index (BMI)) were analyzed using Student\u0026rsquo;s t-test, while categorical variables (e.g., gender, smoking status) were evaluated with the chi-square test. Differences in plasma LPs between the T2DM patients and HC groups were evaluated using one-way ANOVA with Benjamini\u0026ndash;Hochberg\u0026rsquo;s false discovery rate (FDR-p) correction (5%). An FDR-p value of \u0026lt;\u0026thinsp;0.05 was considered statistically significant. Multiple linear regression (MLR) and logistic regression analysis (LRA) were used to assess the impact of T2DM on LPs, considering disease status (T2DM versus HC) as the independent variable and adjusting for significant confounders (sex, age, BMI, blood pressure, and alcohol consumption). Confounding effects of medication use were accounted when evaluating differences among T2DM subgroups only (DO, DH, and DHC). Partial least squares-discriminant analysis (PLS-DA) [24] was used to identify key LP classifiers distinguishing T2DM patients from the HC group, following previously described optimization and validation protocol [25]. All statistical analyses were conducted using MATLAB (R2024a, The MathWorks, Inc., Natick, USA) with customized scripts.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eCharacteristics of the study population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 783 participants were included in the study (Table 1). DA patients were older and had higher BMI, blood glucose (BG), and both systolic (SB) and diastolic blood pressure (DB) compared to HC. Gender distribution and smoking status did not differ significantly, though alcohol consumption was higher in HC. The DO subgroup included more males than DH and DHC, while no differences were found in smoking, alcohol use, BG, or T2DM duration. Age, BMI, SB, DB, and medication use increased progressively across the T2DM subgroups.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLipoprotein profile alterations in type 2 diabetes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMultivariable linear regression (MLR), adjusted for key confounders (age, gender, BMI, systolic/diastolic blood pressure, and alcohol consumption), identified 62 LPs significantly different between HC and DA. Nearly all LPs also showed significant differences between HC and at least one T2DM subgroup (Table 2). After further adjusting for medication use, MLR comparing the three T2DM subgroups (DO, DH, DHC) revealed only six LPs with significant differences. Figure 1A presents results from both LRA and MLR, highlighting two distinct LP clusters on the odds ratio vs. fold change plot, with marker size reflecting effect size. The first cluster includes \u003cem\u003eTG\u003c/em\u003e, cholesterol ester (\u003cem\u003eCE)\u003c/em\u003e, and apolipoprotein B \u003cem\u003e(ApoB)\u003c/em\u003e in VLDL and IDL particle numbers, positively associated with T2DM. The second cluster, negatively associated with T2DM, includes \u003cem\u003eChol\u003c/em\u003e, apolipoprotein A1 (\u003cem\u003eApoA1)\u003c/em\u003e, \u003cem\u003eApoB\u003c/em\u003e, and \u003cem\u003ephospholipids (Phoslip)\u003c/em\u003e in HDL and LDL subfractions (Table 2 and Supplementary Table 1). Similar clustering was observed when LRA and MLR results were jointly analyzed across HC and all T2DM subgroups, with no significant differences among the subgroups (Supplementary Fig. 1).\u003c/p\u003e\n\u003cp\u003eTriglyceride enrichment was one of the most consistent alterations across diabetic groups. Compared to HC, DA showed markedly elevated Plasma\u003cem\u003eTG\u003c/em\u003e (154 vs. 113 mg/dL), with this increase persisting in all diabetic subgroups. VLDL\u003cem\u003eTG\u003c/em\u003e and IDL\u003cem\u003eTG\u003c/em\u003e were also elevated, peaking in DH, though differences between DO, DH, and DHC were not significant, suggesting that VLDL and IDL triglyceride enrichment is a core feature of T2DM rather than its comorbidities. HDL2b\u003cem\u003eTG\u003c/em\u003e was modestly elevated in DA and increased progressively across subgroups, reaching significance between DO and DHC, while HDL2a\u003cem\u003eTG\u003c/em\u003e remained unchanged. LDL and LDL1 triglyceride levels were stable in DA overall, yet significantly elevated in DH and DHC compared to HC. These elevations likely result from increased hepatic VLDL production driven by insulin resistance and free fatty acid flux, coupled with impaired lipolysis [26]. This leads to VLDL and IDL accumulation and sustained triglyceride enrichment in HDL2b and LDL1, indicating disrupted LP remodeling and increased atherogenic risk.\u003c/p\u003e\n\u003cp\u003eCholesterol concentrations were reduced across all LPs in DA and all diabetic subgroups compared to HC. VLDL\u003cem\u003eChol\u003c/em\u003e and IDL\u003cem\u003eChol\u003c/em\u003e showed no increase from HC to DA or DO. Among diabetic subgroups, only minor declines were observed in LDL2\u003cem\u003eChol\u003c/em\u003e (DO to DHC) and HDL2a\u003cem\u003eChol\u003c/em\u003e (DH to DHC), possibly reflecting greater lipid-lowering therapy use in DHC. The most striking reduction was a 50% decrease in HDL2b\u003cem\u003eChol\u003c/em\u003e in DA, underscoring the loss of cardioprotective HDL. These findings suggest that T2DM is linked to both overall cholesterol depletion and HDL particle remodeling. Notably, reductions were already evident in DO and not further worsened by hypertension or CHD, indicating early onset. The uniform decline across LPs may result from impaired hepatic synthesis or reduced intestinal absorption [27]. Specifically, reduced HDL\u003cem\u003eChol\u003c/em\u003e likely reflects impaired reverse cholesterol transport [28].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFree cholesterol levels in plasma and HDL2b decreased in DA, while VLDL\u003cem\u003eFreeChol\u003c/em\u003e increased compared to HC, a pattern consistent across all diabetic subgroups (DO, DH, DHC), with no significant differences among them. This redistribution from HDL to \u003cem\u003eTG\u003c/em\u003e-rich VLDL suggests impaired reverse cholesterol transport and enhanced hepatic VLDL secretion, likely driven by insulin resistance and reduced LP clearance [29]. Elevated VLDL\u003cem\u003eFreeChol\u003c/em\u003e reflects cholesterol retention in remnant particles, while reduced HDL2b\u003cem\u003eFreeChol\u003c/em\u003e points to diminished HDL-mediated cholesterol efflux in T2DM. Cholesteryl ester (\u003cem\u003eCE\u003c/em\u003e) levels mirrored trends in total and free cholesterol, showing broad reductions across all LPs in DA and diabetic subgroups compared to HC. However, VLDL\u003cem\u003eCE\u003c/em\u003e and IDL\u003cem\u003eCE\u003c/em\u003e were significantly elevated in DA, unlike VLDL\u003cem\u003eChol\u003c/em\u003e and IDL\u003cem\u003eChol\u003c/em\u003e, with no differences among diabetic subgroups. This suggests that \u003cem\u003eCE\u003c/em\u003e depletion is independent of comorbidities. The rise in VLDL\u003cem\u003eCE\u003c/em\u003e and IDL\u003cem\u003eCE\u003c/em\u003e may reflect increased cholesterol ester transfer protein\u0026nbsp;(CETP)-mediated transfer of \u003cem\u003eCE\u003c/em\u003e from HDL to \u003cem\u003eTG\u003c/em\u003e-rich LPs, contributing to a more atherogenic LP profile in diabetes [30].\u003c/p\u003e\n\u003cp\u003ePhospholipid levels in LPs followed the patterns of \u003cem\u003eChol\u003c/em\u003e and \u003cem\u003eCE\u003c/em\u003e, showing reductions in DA and all diabetic subgroups compared to HC. The steepest declines occurred in LDL1–LDL3 and HDL2b (up to 30%). LDL5\u003cem\u003ePhoslip\u003c/em\u003e and HDL2a\u003cem\u003ePhoslip\u003c/em\u003e remained unchanged. In contrast, VLDL\u003cem\u003ePhoslip\u003c/em\u003e consistently increased from HC to DA and progressively across DO, DH, and DHC, reflecting enhanced hepatic VLDL output and impaired lipolysis, which shift phospholipids toward triglyceride-rich remnant particles in insulin-resistant T2DM [31].\u003c/p\u003e\n\u003cp\u003eLike most other LP molecules, \u003cem\u003eApoA1\u003c/em\u003e levels were decreased both in plasma and in HDL main and in HDL subfractions consistently across all T2DM subgroups compared to HC. The largest reduction was observed in HDL2b\u003cem\u003eApoA1\u003c/em\u003e levels from being around 9 mg/dL while HC group contained mean concentration of 16 mg/dL. Given the central role of \u003cem\u003eApoA1\u003c/em\u003e in maintaining HDL function, \u003cem\u003eApoA1\u003c/em\u003e reduction, especially in large HDL2b particles, suggests impaired reverse cholesterol transport via ABCA1 and diminished HDL functionality in T2DM. This loss affects both the quantity and quality of HDL, weakening its atheroprotective role. The consistent decline across diabetic subgroups highlights \u003cem\u003eApoA1\u003c/em\u003e as a key marker of HDL dysfunction, contributing to lipid accumulation and increased vascular risk. The same trend of decrease was observed for \u003cem\u003eApoB\u003c/em\u003e as in \u003cem\u003eApoA1\u003c/em\u003e, where its concentrations were reduced in plasma, and in LDL and in all LDL subfractions (LDL1-LDL5), except LDL6\u003cem\u003eApoB\u003c/em\u003e, in all T2DM groups compared to HC. In contrast, VLDL and IDL levels of \u003cem\u003eApoB\u003c/em\u003e were consistently increased in all T2DM groups consistently from being around 6 mg/dL in HC for both particles to nearly 8 mg/dL in diabetic patients. The pattern indicates a shift of \u003cem\u003eApoB\u003c/em\u003e from smaller, cholesterol-rich LDL to larger, triglyceride-rich VLDL and IDL particles, reflecting impaired remnant catabolism. Increased VLDL\u003cem\u003eTG\u003c/em\u003e, VLDL\u003cem\u003eCE\u003c/em\u003e, and VLDL\u003cem\u003eApoB\u003c/em\u003e suggest hepatic overproduction or reduced clearance. This redistribution lowers LDL-associated \u003cem\u003eApoB\u003c/em\u003e, potentially underestimating atherogenic risk in T2DM. The remodeling toward triglyceride-rich remnants highlights a more atherogenic phenotype and emphasizes the need for subclass-specific \u003cem\u003eApoB\u003c/em\u003e profiling in diabetic dyslipidemia.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDiscriminating type 2 diabetes from healthy individuals\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo identify the cluster LPs with the strongest classification power for distinguishing T2DM patients from HC, a PLS-DA-based iterative variable selection was performed. Using a training set comprising 70% of T2DM and 70% of HC participants, we conducted variable selection by retaining only LPs with significant contributions based on their VIP scores and selectivity ratios. After 1,000 iterations, only LPs consistently selected in all cases were retained. The final classification model for DA versus HC included 14 LPs and demonstrated high performance, with an AUC of 0.92 on the independent test set (30% from each group; Fig. 1B). The model’s validity was confirmed by a permutation test (2,000). The selected LPs aligned with MLR and LRA results, with IDL\u003cem\u003eApoB\u003c/em\u003e being the strongest discriminator, followed by HDL-associated \u003cem\u003eChol, FreeChol\u003c/em\u003e, and \u003cem\u003eApoA1\u003c/em\u003e, as well as total plasma or main-fraction levels of \u003cem\u003eApoA1\u003c/em\u003e, \u003cem\u003eCE\u003c/em\u003e, and phospholipids. Similar classification accuracy was achieved for HC versus DO, DH, and DHC comparisons, with the same 14 LPs consistently among the top classifiers (Supplementary Fig. 1). However, no valid PLS-DA models could be developed to distinguish the diabetic subgroups from each other (Supplementary Fig. 1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAltered correlations between lipoproteins in patients with type 2 diabetes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eApart from studying differences in LPs concentrations between T2DM and HC groups using MLR or identifying T2DM-associated LPs through LRA, we further investigated alterations in the correlation structure among LPs. This complementary approach offers orthogonal insights into the reorganization of lipid metabolism in diabetic dyslipidemia. Our analysis revealed that approximately 10% of positive correlations between LP pairs observed in HC were attenuated in the DA group, while the proportion of significant negative correlations nearly doubled (2.7% to 5.2%) (Supplementary Fig. 2). Comparable patterns were observed across all diabetic subgroups (DO, DH, and DHC), with the most pronounced disruption in the DO group, where more than 30% of previously significant correlations present in HC were lost. Concurrently, the number of significant negative correlations nearly tripled.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe further examined LP pairs exhibiting the most pronounced disruptions in correlation structure between the HC and T2DM groups or among diabetic subgroups. Strong positive correlations observed in HC between HDL2bL\u003cem\u003eCE\u003c/em\u003e and HDL\u003cem\u003eChol\u003c/em\u003e (R = 0.75) or HDL2a\u003cem\u003eChol\u003c/em\u003e (R = 0.71) were attenuated in all diabetic subgroups, particularly in DH (R = 0.04–0.22) (Fig. 2). Similar patterns emerged among other HDL subfractions; for instance, the robust correlation between HDL2b\u003cem\u003ePhoslip\u003c/em\u003e and HDL2a\u003cem\u003eChol\u003c/em\u003e in HC (R = 0.80) declined across all diabetic groups, with the sharpest drop in DH (R = 0.20). Likewise, the correlation between HDL2b\u003cem\u003ePhoslip\u003c/em\u003e and HDL2a\u003cem\u003eApoA1\u003c/em\u003e in HC (R = 0.71) was markedly reduced in DH and DHC (R = 0.29 and 0.27, respectively). Notably, HDL2a\u003cem\u003eChol\u003c/em\u003e and HDL\u003cem\u003ePhoslip\u003c/em\u003e both showed moderate correlations with plasma \u003cem\u003eCE\u003c/em\u003e in HC (R = 0.46–0.50), which were lost in DO (R = –0.03), but remained significant though slightly weakened in DH and DHC (R = 0.36–0.42) (Supplementary Fig. 3). Intriguingly, weak correlations in HC between HDL2a\u003cem\u003ePhoslip\u003c/em\u003e and HDL3\u003cem\u003eChol\u003c/em\u003e (R = 0.24), and between HDL2a\u003cem\u003eTG\u003c/em\u003e and IDL\u003cem\u003eTG\u003c/em\u003e (R = 0.27), progressively increased across diabetic subgroups, doubling in DHC. A similar trend was observed between HDL3\u003cem\u003eChol\u003c/em\u003e and four VLDL variables (VLDL\u003cem\u003eApoB\u003c/em\u003e, VLDL\u003cem\u003eCE\u003c/em\u003e, VLDL\u003cem\u003ePhoslip\u003c/em\u003e, VLDL\u003cem\u003eFreeChol\u003c/em\u003e), with correlations increasing from HC (R = –0.04 to 0.17) to DA (R = 0.30–0.43), reaching highest values in DH (R = 0.45–0.52). Likewise, the lack of correlation between HDL2b\u003cem\u003eCE\u003c/em\u003e and IDL\u003cem\u003eCE\u003c/em\u003e in HC (R = 0.07) evolved into a strong correlation in DHC (R = 0.51). LDL subfractions also showed disrupted patterns in T2DM. Strong correlations in HC between LDL1\u003cem\u003ePhoslip\u003c/em\u003e and HDL2a\u003cem\u003eTG\u003c/em\u003e, LDL3\u003cem\u003eCE\u003c/em\u003e and LDL5\u003cem\u003ePhoslip\u003c/em\u003e, and LDL2\u003cem\u003eApoB\u003c/em\u003e and MainFrac\u003cem\u003ePhoslip\u003c/em\u003e (R = 0.47–0.63) were consistently reduced across diabetic subgroups (R = –0.13 to 0.16). Surprisingly, notable negative correlations emerged: LDL3\u003cem\u003eCE\u003c/em\u003e, weakly or non-correlated with VLDL\u003cem\u003eChol\u003c/em\u003e (R = 0.18) and LDL\u003cem\u003eTG\u003c/em\u003e (R = 0.29) in HC, exhibited strong negative correlations in DO (R = –0.40), indicating a significant shift in covariation. A similar but more pronounced trend was observed between IDL\u003cem\u003eCE\u003c/em\u003e and HDL2a\u003cem\u003eChol\u003c/em\u003e.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eNearly all LPs (58 out of 65) were significantly different between diabetic patients (DA group) and healthy controls (HC group), consistent with the characteristic dyslipidemia of T2DM, marked by elevated VLDL particles and triglycerides, and reduced HDL particles and HDL\u003cem\u003eChol\u003c/em\u003e levels. HDL2b particles were depleted in cholesterol and enriched in triglycerides, indicating altered composition and impaired function of HDL in T2DM. This aligns with evidence that triglyceride enrichment accelerates HDL catabolism, thereby lowering HDL\u003cem\u003eChol\u003c/em\u003e levels [32]. Increased VLDL-related \u003cem\u003eApoB\u003c/em\u003e, triglycerides, and cholesterol reflect heightened systemic fatty acid availability, enhanced hepatic de novo lipogenesis, and impaired LP catabolism [33]. The latter is evident in elevated IDL\u003cem\u003eApoB\u003c/em\u003e and reduced particle numbers of most LDL subclasses, suggesting that the conversion of IDL to LDL is a rate-limiting step in the VLDL \u0026rarr; IDL \u0026rarr; LDL cascade. This likely promotes formation of small, dense, highly atherogenic LDL particles [34].\u003c/p\u003e\n\u003cp\u003eOne of the novelties of our study is the minimal differences observed in LP profiles among the three diabetic subgroups (DO, DH, and DHC). After FDR correction of MLR multiple comparison results, only five LP variables remained significantly different between at least two subgroups. Among triglycerides, key LPs differentiating HC from DA, only the main fraction and HDL2b subfraction showed significantly higher triglyceride levels in DH compared to DO, while the remaining \u003cem\u003eTG\u003c/em\u003e variables did not differ across subgroups. Additionally, LDL2\u003cem\u003eChol\u003c/em\u003e and LDL2\u003cem\u003eApoB\u003c/em\u003e, large and less dense atherogenic particles, were moderately lower in DH than in DO, likely due to greater medication use. The smallest observed difference was reduced HDL2a\u003cem\u003eChol\u003c/em\u003e in DH compared to DHC. These minimal subgroup differences likely reflect a shared metabolic dysregulation in T2DM, with subtle variations such as lower LDL2\u003cem\u003eChol\u003c/em\u003e and LDL2\u003cem\u003eApoB\u003c/em\u003e in DH, and reduced HDL2a\u003cem\u003eChol\u003c/em\u003e in DH versus DHC, possibly attributable to increased use of lipid-lowering or antihypertensive medications and/or subgroup-specific modulation of LP metabolism [31]. Previous studies examining traditional blood lipid profiles found more pronounced abnormalities in T2DM patients with hypertension than those without, though without adjusting for confounders [16, 17]. Other studies reported better lipid profiles in T2DM patients with CVD compared to those without, potentially reflecting more intensive lipid-lowering therapy [18], while some reported mixed outcomes (e.g., higher \u003cem\u003eTG\u003c/em\u003e and HDL\u003cem\u003eChol\u003c/em\u003e, but lower LDL\u003cem\u003eChol\u003c/em\u003e in T2DM with CVD) [19]. However, none evaluated the comprehensive LP profile as done here. Our findings suggest that T2DM itself is the primary driver of LP dysregulation, and that hypertension and CHD do not further exacerbate these alterations but instead contribute to CVD risk through mechanisms independent of LP metabolism.\u003c/p\u003e\n\u003cp\u003eAnother intriguing finding from our study is the altered correlation patterns between LPs in T2DM. The disrupted correlation structure driven by insulin resistance\u0026ndash;mediated dysregulation of hepatic lipid metabolism, which enhances hepatic VLDL overproduction and impairs LP remodeling by LP lipase (LPL) and hepatic lipase (HL), leading to triglyceride enrichment and accelerated clearance of HDL particles [35]. This remodeling process compromises the structural integrity and function of HDL, thereby weakening its physiological correlation with other HDL subfractions and cholesterol content. Subsequently, impaired lipolytic conversion of VLDL to LDL results in the accumulation of IDL and small dense LDL particles, disrupting the typical LDL\u0026ndash;IDL\u0026ndash;HDL metabolic continuum and giving rise to inverse correlations not observed in normoglycemic individuals [27]. The emergence of negative correlations, especially between HDL and IDL components, and within LDL fractions, may reflect competitive or compensatory shifts in lipid flux through divergent metabolic pathways, a hallmark of advanced diabetic dyslipidemia.\u003c/p\u003e\n\u003cp\u003eOur results are internally consistent and indicate that T2DM is the primary driver of dysregulated LP profiles and the associated increase in cardiovascular risk. These findings carry important clinical implications, suggesting that dyslipidemia management in T2DM should target the lipid and LP abnormalities inherent to diabetes itself, independent of other risk factors such as hypertension or existing CVD. This highlights the importance of initiating appropriate lipid-lowering therapy early in the course of T2DM, regardless of comorbid hypertension or CHD.\u003c/p\u003e\n\u003cp\u003eThe consistent reduction in HDL-related variables and increase in VLDL-related parameters indicate that standard lipid panels may inadequately reflect the full extent of dyslipidemia in T2DM. Expanded LP profiling, including subfractions and molecular components such as apolipoproteins and phospholipids, may offer more precise biomarkers for disease monitoring. The altered correlation patterns among LPs suggest a complex, diabetes-driven reorganization of lipid metabolism. This supports the need for integrated metabolic treatment strategies that not only lower LDL cholesterol but also enhance HDL functionality and reduce VLDL and triglyceride levels. Further mechanistic studies on the role of insulin resistance, inflammation, and oxidative stress in modulating LP metabolism could deepen our understanding of diabetic dyslipidemia. Longitudinal studies are also warranted to investigate how these alterations on LP profiles evolve over time and contribute to cardiovascular outcomes in T2DM.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study has limitations, including a relatively small sample size within T2DM subgroups, which may limit statistical power and generalizability. The cross-sectional design also precludes causal inference. Future studies with larger, multi-center cohorts and longitudinal frameworks are necessary to validate and expand upon these findings.\u0026nbsp;\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study demonstrates significant alterations in LP profiles in T2DM, characterized by elevated triglycerides and VLDL, and reduced cholesterol and HDL, independent of hypertension or CVD. Notably, the disruption extended to smaller, atherogenic subfractions, underscoring the depth of metabolic imbalance in T2DM. The presence of cardiovascular comorbidities introduced only modest additional changes, mainly affecting cholesterol and ApoB content in LDL and HDL subfractions. Furthermore, a correlation analysis revealed a significant breakdown in the physiological relationships among lipoproteins in T2DM, which was further altered in those with cardiovascular complications. Our findings suggest that T2DM alone is the principal driver of lipoprotein abnormalities, while cardiovascular comorbidities contribute minimally to additional compositional changes. These results underscore the need for early lipid intervention to mitigate cardiovascular risk. Lipoprotein profiling in diabetic patients may improve risk stratification and guide early lipid-lowering interventions. Future longitudinal studies are warranted to confirm these findings and explore causal mechanisms.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eANOVA \u0026ndash; Analysis of Variance\u003c/p\u003e\n\u003cp\u003eApoA1 \u0026ndash; Apolipoprotein A1\u003c/p\u003e\n\u003cp\u003eApoB \u0026ndash; Apolipoprotein B\u003c/p\u003e\n\u003cp\u003eBMI \u0026ndash; Body Mass Index\u003c/p\u003e\n\u003cp\u003eCE \u0026ndash; Cholesteryl Esters\u003c/p\u003e\n\u003cp\u003eCHD \u0026ndash; Coronary Heart Disease\u003c/p\u003e\n\u003cp\u003eChol \u0026ndash; Cholesterol\u003c/p\u003e\n\u003cp\u003eCI \u0026ndash; Confidence Interval\u003c/p\u003e\n\u003cp\u003eCVD \u0026ndash; Cardiovascular Disease\u003c/p\u003e\n\u003cp\u003eDA \u0026ndash; All patients with type 2 diabetes mellitus (T2DM), with or without comorbidities\u003c/p\u003e\n\u003cp\u003eDH \u0026ndash; Diabetic patients with hypertension\u003c/p\u003e\n\u003cp\u003eDHC \u0026ndash; Diabetic patients with hypertension and coronary heart disease\u003c/p\u003e\n\u003cp\u003eDO \u0026ndash; Diabetic patients without hypertension or coronary heart disease\u003c/p\u003e\n\u003cp\u003eFDR \u0026ndash; False Discovery Rate\u003c/p\u003e\n\u003cp\u003eFreeChol \u0026ndash; Free (unesterified) Cholesterol\u003c/p\u003e\n\u003cp\u003eHC \u0026ndash; Healthy Controls\u003c/p\u003e\n\u003cp\u003eHDL \u0026ndash; High-Density Lipoprotein\u003c/p\u003e\n\u003cp\u003eIDL \u0026ndash; Intermediate-Density Lipoprotein\u003c/p\u003e\n\u003cp\u003eLDL \u0026ndash; Low-Density Lipoprotein\u003c/p\u003e\n\u003cp\u003eLRA \u0026ndash; Logistic Regression Analysis\u003c/p\u003e\n\u003cp\u003eLP \u0026ndash; Lipoprotein\u003c/p\u003e\n\u003cp\u003eNMR \u0026ndash; Nuclear Magnetic Resonance\u003c/p\u003e\n\u003cp\u003eOR \u0026ndash; Odds Ratio\u003c/p\u003e\n\u003cp\u003ePhoslip \u0026ndash; Phospholipids\u003c/p\u003e\n\u003cp\u003ePLS-DA \u0026ndash; Partial Least Squares Discriminant Analysis\u003c/p\u003e\n\u003cp\u003eSD \u0026ndash; Standard Deviation\u003c/p\u003e\n\u003cp\u003eT2DM \u0026ndash; Type 2 Diabetes Mellitus\u003c/p\u003e\n\u003cp\u003eTG \u0026ndash; Triglycerides\u003c/p\u003e\n\u003cp\u003eVLDL \u0026ndash; Very Low-Density Lipoprotein\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate.\u003c/strong\u003e The study was conducted in accordance with the principles outlined in the Declaration of Helsinki and approved by the Ethical Committee of the Ministry of Public Health of the Republic of Uzbekistan (Approval No. 6/11-1694). Written informed consent was obtained from all participants prior to their inclusion in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication.\u0026nbsp;\u003c/strong\u003eThis manuscript is not under consideration elsewhere and will not be submitted to another journal until a final decision regarding its acceptability has been made.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions.\u003c/strong\u003e B.K, and D.D. acquired funding and were involved in the conception and design of the study. Z.K., contributed to sample collection and recruitment of patients. E.T., D.K., S.N., and V.T., S.A., B.S.S., and B.K. were involved in sample preparation, data acquisition, data cleaning, and data analysis. B.K. wrote the manuscript. F.M., and S.B.E. contributed to the interpretation of the results. All authors contributed to the discussion, edited the manuscript, and approved the final manuscript. B.K. and D.D. are the guarantors of this work and, as such, have full access to all the data included in the study and take responsibility for the integrity of the data and the accuracy of the results.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements.\u0026nbsp;\u003c/strong\u003eData was generated though accessing research infrastructure at UCPH, including FOODHAY (Food and Health Open Innovation Laboratory, Danish Roadmap for Research Infrastructure).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding.\u0026nbsp;\u003c/strong\u003eThis work was supported by the University of Copenhagen (Data+ project, Strategy 2013 funds), modernizing Uzbekistan national innovation system (MUNIS) project (project number: REP-1/12 ), basic funding from the Agency of Innovative Development under the Ministry of Higher Education, Science and Innovations of the Republic of Uzbekistan (project number: REP 25112021-127), and International credit mobility (KA171) in the Erasmus + program 2021-2027, funded by the European Union.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data generated during and/or analyzed in the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBatista TM, Haider N, Kahn CR: Defining the underlying defect in insulin action in type 2 diabetes. \u003cem\u003eDiabetologia \u003c/em\u003e2021, 64(5):994-1006.\u003c/li\u003e\n\u003cli\u003eMa C-X, Ma X-N, Guan C-H, Li Y-D, Mauricio D, Fu S-B: Cardiovascular disease in type 2 diabetes mellitus: progress toward personalized management. \u003cem\u003eCardiovascular Diabetology \u003c/em\u003e2022, 21(1):74.\u003c/li\u003e\n\u003cli\u003ePearson-Stuttard J, Bennett J, Cheng YJ, Vamos EP, Cross AJ, Ezzati M, Gregg EW: Trends in predominant causes of death in individuals with and without diabetes in England from 2001 to 2018: an epidemiological analysis of linked primary care records. \u003cem\u003eThe Lancet Diabetes \u0026amp; Endocrinology \u003c/em\u003e2021, 9(3):165-173.\u003c/li\u003e\n\u003cli\u003eHarding JL, Shaw JE, Peeters A, Davidson S, Magliano DJ: Age-Specific Trends From 2000\u0026ndash;2011 in All-Cause and Cause-Specific Mortality in Type 1 and Type 2 Diabetes: A Cohort Study of More Than One Million People. \u003cem\u003eDiabetes Care \u003c/em\u003e2016, 39(6):1018-1026.\u003c/li\u003e\n\u003cli\u003eBrunzell JD, Davidson M, Furberg CD, Goldberg RB, Howard BV, Stein JH, Witztum JL: Lipoprotein Management in Patients With Cardiometabolic Risk: Consensus statement from the American Diabetes Association and the American College of Cardiology Foundation. \u003cem\u003eDiabetes Care \u003c/em\u003e2008, 31(4):811-822.\u003c/li\u003e\n\u003cli\u003eLuciani L, Pedrelli M, Parini P: Modification of lipoprotein metabolism and function driving atherogenesis in diabetes. \u003cem\u003eAtherosclerosis \u003c/em\u003e2024, 394:117545.\u003c/li\u003e\n\u003cli\u003eChehade JM, Gladysz M, Mooradian AD: Dyslipidemia in type 2 diabetes: prevalence, pathophysiology, and management. \u003cem\u003eDrugs \u003c/em\u003e2013, 73(4):327-339.\u003c/li\u003e\n\u003cli\u003eZhang P, Gao J, Pu C, Zhang Y: Apolipoprotein status in type 2 diabetes mellitus and its complications (Review). \u003cem\u003eMol Med Rep \u003c/em\u003e2017, 16(6):9279-9286.\u003c/li\u003e\n\u003cli\u003eGupta A, Gupta R: Current Understanding of Diabetic Dyslipidemia: A Review. \u003cem\u003eJournal of the Indian Institute of Science \u003c/em\u003e2023, 103(1):287-307.\u003c/li\u003e\n\u003cli\u003eRizzo M, Berneis K: Low-density lipoprotein size and cardiovascular risk assessment. \u003cem\u003eQJM: An International Journal of Medicine \u003c/em\u003e2006, 99(1):1-14.\u003c/li\u003e\n\u003cli\u003eSokooti S, Flores-Guerrero JL, Heerspink HJL, Connelly MA, Bakker SJL, Dullaart RPF: Triglyceride-rich lipoprotein and LDL particle subfractions and their association with incident type 2 diabetes: the PREVEND study. \u003cem\u003eCardiovascular Diabetology \u003c/em\u003e2021, 20(1):156.\u003c/li\u003e\n\u003cli\u003eJomard A, Osto E: High Density Lipoproteins: Metabolism, Function, and Therapeutic Potential. \u003cem\u003eFrontiers in Cardiovascular Medicine \u003c/em\u003e2020, 7.\u003c/li\u003e\n\u003cli\u003eHern\u0026aacute;ez \u0026Aacute;, Soria-Florido MT, Schr\u0026ouml;der H, Ros E, Pint\u0026oacute; X, Estruch R, Salas-Salvad\u0026oacute; J, Corella D, Ar\u0026oacute;s F, Serra-Majem L\u003cem\u003e et al\u003c/em\u003e: Role of HDL function and LDL atherogenicity on cardiovascular risk: A comprehensive examination. \u003cem\u003ePLOS ONE \u003c/em\u003e2019, 14(6):e0218533.\u003c/li\u003e\n\u003cli\u003eCho K-H: The Current Status of Research on High-Density Lipoproteins (HDL): A Paradigm Shift from HDL Quantity to HDL Quality and HDL Functionality. \u003cem\u003eInternational Journal of Molecular Sciences \u003c/em\u003e2022, 23(7):3967.\u003c/li\u003e\n\u003cli\u003eSowers JR, Epstein M, Frohlich ED: Diabetes, Hypertension, and Cardiovascular Disease. \u003cem\u003eHypertension \u003c/em\u003e2001, 37(4):1053-1059.\u003c/li\u003e\n\u003cli\u003eOyelola OO, Ajayi AA, Babalola RO, Stein EA: Plasma lipids, lipoproteins, and apolipoproteins in Nigerian diabetes mellitus, essential hypertension, and hypertensive-diabetic patients. \u003cem\u003eJ Natl Med Assoc \u003c/em\u003e1995, 87(2):113-118.\u003c/li\u003e\n\u003cli\u003eJha J, Malhotra V, Jha O, Gupta S: Study of Lipid Profile in Diabetes Mellitus with and without Hypertension. \u003cem\u003eJournal of Basic and Applied Research in Biomedicine \u003c/em\u003e2022, 5(1):31-36.\u003c/li\u003e\n\u003cli\u003eMonteiro AM, Palma I: Lipid profile and persistent lipid abnormalities in diabetic patients \u0026ndash; a retrospective study. \u003cem\u003eRevista Portuguesa de Endocrinologia, Diabetes e Metabolismo \u003c/em\u003e2016, 11(2):197-201.\u003c/li\u003e\n\u003cli\u003eLeiter LA, Lundman P, da Silva PM, Drexel H, J\u0026uuml;nger C, Gitt AK, investigators obotD: Persistent lipid abnormalities in statin-treated patients with diabetes mellitus in Europe and Canada: results of the Dyslipidaemia International Study. \u003cem\u003eDiabetic Medicine \u003c/em\u003e2011, 28(11):1343-1351.\u003c/li\u003e\n\u003cli\u003eSyv\u0026auml;nne M, Taskinen M-R: Lipids and lipoproteins as coronary risk factors in non-insulin-dependent diabetes mellitus. \u003cem\u003eThe Lancet \u003c/em\u003e1997, 350:S20-S23.\u003c/li\u003e\n\u003cli\u003eKhakimov B, Hoefsloot HCJ, Mobaraki N, Aru V, Kristensen M, Lind MV, Holm L, Castro-Mejia JL, Nielsen DS, Jacobs DM\u003cem\u003e et al\u003c/em\u003e: Human Blood Lipoprotein Predictions from H-1 NMR Spectra: Protocol, Model Performances, and Cage of Covariance. \u003cem\u003eAnalytical Chemistry \u003c/em\u003e2022, 94(2):628-636.\u003c/li\u003e\n\u003cli\u003eCentelles SM, Hoefsloot HCJ, Khakimov B, Ebrahimi P, Lind MV, Kristensen M, de Roo N, Jacobs DM, van Duynhoven J, Gannet C\u003cem\u003e et al\u003c/em\u003e: Toward Reliable Lipoprotein Particle Predictions from NMR Spectra of Human Blood: An Interlaboratory Ring Test. \u003cem\u003eAnalytical Chemistry \u003c/em\u003e2017, 89(15):8004-8012.\u003c/li\u003e\n\u003cli\u003eKhakimov B, Mobaraki N, Trimigno A, Aru V, Engelsen SB: Signature Mapping (SigMa): An efficient approach for processing complex human urine H-1 NMR metabolomics data. \u003cem\u003eAnalytica Chimica Acta \u003c/em\u003e2020, 1108:142-151.\u003c/li\u003e\n\u003cli\u003eSt\u0026aring;hle L, Wold S: Partial least squares analysis with cross‐validation for the two‐class problem: A Monte Carlo study. \u003cem\u003eJournal of Chemometrics \u003c/em\u003e1987, 1.\u003c/li\u003e\n\u003cli\u003eKhakimov B, Poulsen SK, Savorani F, Acar E, Gurdeniz G, Larsen TM, Astrup A, Dragsted LO, Engelsen SB: New Nordic Diet versus Average Danish Diet: A Randomized Controlled Trial Revealed Healthy Long-Term Effects of the New Nordic Diet by GC-MS Blood Plasma Metabolomics. \u003cem\u003eJournal of Proteome Research \u003c/em\u003e2016, 15(6):1939-1954.\u003c/li\u003e\n\u003cli\u003eHirano T: Pathophysiology of Diabetic Dyslipidemia. \u003cem\u003eJ Atheroscler Thromb \u003c/em\u003e2018, 25(9):771-782.\u003c/li\u003e\n\u003cli\u003eKrauss RM: Lipids and Lipoproteins in Patients With Type 2 Diabetes. \u003cem\u003eDiabetes Care \u003c/em\u003e2004, 27(6):1496-1504.\u003c/li\u003e\n\u003cli\u003eShiu SW, Wong Y, Tan KC: Pre-\u0026beta;1 HDL in type 2 diabetes mellitus. \u003cem\u003eAtherosclerosis \u003c/em\u003e2017, 263:24-28.\u003c/li\u003e\n\u003cli\u003eChait A, Ginsberg HN, Vaisar T, Heinecke JW, Goldberg IJ, Bornfeldt KE: Remnants of the Triglyceride-Rich Lipoproteins, Diabetes, and Cardiovascular Disease. \u003cem\u003eDiabetes \u003c/em\u003e2020, 69(4):508-516.\u003c/li\u003e\n\u003cli\u003eDullaart RP, de Vries R, Kwakernaak AJ, Perton F, Dallinga-Thie GM: Increased large VLDL particles confer elevated cholesteryl ester transfer in diabetes. \u003cem\u003eEur J Clin Invest \u003c/em\u003e2015, 45(1):36-44.\u003c/li\u003e\n\u003cli\u003eVerg\u0026egrave;s B: Pathophysiology of diabetic dyslipidaemia: where are we? \u003cem\u003eDiabetologia \u003c/em\u003e2015, 58(5):886-899.\u003c/li\u003e\n\u003cli\u003eLamarche B, Uffelman KD, Carpentier A, Cohn JS, Steiner G, Barrett PH, Lewis GF: Triglyceride enrichment of HDL enhances in vivo metabolic clearance of HDL apo A-I in healthy men. \u003cem\u003eJ Clin Invest \u003c/em\u003e1999, 103(8):1191-1199.\u003c/li\u003e\n\u003cli\u003eAdiels M, Taskinen MR, Packard C, Caslake MJ, Soro-Paavonen A, Westerbacka J, Vehkavaara S, H\u0026auml;kkinen A, Olofsson SO, Yki-J\u0026auml;rvinen H\u003cem\u003e et al\u003c/em\u003e: Overproduction of large VLDL particles is driven by increased liver fat content in man. \u003cem\u003eDiabetologia \u003c/em\u003e2006, 49(4):755-765.\u003c/li\u003e\n\u003cli\u003eGinsberg HN, MacCallum PR: The obesity, metabolic syndrome, and type 2 diabetes mellitus pandemic: Part I. Increased cardiovascular disease risk and the importance of atherogenic dyslipidemia in persons with the metabolic syndrome and type 2 diabetes mellitus. \u003cem\u003eJ Cardiometab Syndr \u003c/em\u003e2009, 4(2):113-119.\u003c/li\u003e\n\u003cli\u003eAdiels M, Olofsson S-O, Taskinen M-R, Bor\u0026eacute;n J: Overproduction of Very Low\u0026ndash;Density Lipoproteins Is the Hallmark of the Dyslipidemia in the Metabolic Syndrome. \u003cem\u003eArteriosclerosis, Thrombosis, and Vascular Biology \u003c/em\u003e2008, 28(7):1225-1236.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1 \u0026ndash; Characteristics of the Study Population\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"681\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eHC (\u003cem\u003eN\u0026nbsp;\u003c/em\u003e= 393)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eDA (\u003cem\u003eN\u0026nbsp;\u003c/em\u003e= 390)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eDO (\u003cem\u003eN\u0026nbsp;\u003c/em\u003e= 88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eDH (\u003cem\u003eN\u003c/em\u003e = 105)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eDHC (\u003cem\u003eN\u003c/em\u003e = 197)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eFemales\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e212 (54%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e186 (48%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e25 (28%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e56 (53%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e105 (53%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e2E-4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e43.8 (15.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e57.8 (11.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e8E-37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e50.5 (13.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e56.3 (11.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e61.9 (9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e8E-15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e26.2 (5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e29.8 (5.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e5E-19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e28.6 (4.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e29.8 (4.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e30.3 (5.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eSmoking (yes)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e120 (31%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e122 (31%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e29 (33%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e29 (28%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e64 (32%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eAlcohol (yes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e164 (42%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e123 (32%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e30 (34%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e28 (27%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e65 (33%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eBG (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e4.4 (0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e10.2 (3.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e2E-62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e10.3 (3.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e10.3 (3.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e10.1 (3.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eSB (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e111.8 (15.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e126.7 (18.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e9E-15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e118.9 (14.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e129.9 (17.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e128.6 (19.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e4E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eDB (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e74.5 (11.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e80.7 (10.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e3E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e76.8 (8.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e81.4 (10.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e82 (9.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e2E-4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eT2DM (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003ena\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e11.2 (8.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003ena\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e9.8 (8.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e11.2 (6.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e11.9 (8.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eMed. (yes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003ena\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e377 (97%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003ena\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e78 (89%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e102 (97%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e197 (100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e5E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eHyp. (yes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003ena\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e300 (77%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003ena\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e54 (61%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e85 (81%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e161 (82%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e4E-4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eLlow. (yes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003ena\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e138 (35%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003ena\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e11 (13%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e26 (25%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e101 (51%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e6E-11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eAnhyp. (yes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003ena\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e255 (65%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003ena\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e29 (33%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e67 (64%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e159 (81%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e4E-14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eHC = healthy control individuals (\u003cem\u003eN\u003c/em\u003e = 393); DA = all patients with Type 2 Diabetes Mellitus (T2DM) (\u003cem\u003eN\u003c/em\u003e = 390); DO = diabetic patients without hypertension or coronary heart disease (\u003cem\u003eN\u003c/em\u003e = 88); DH = diabetic patients with hypertension (\u003cem\u003eN\u003c/em\u003e = 105); DHC = diabetic patients with both hypertension and coronary heart disease (\u003cem\u003eN\u003c/em\u003e = 197). Data are presented as mean (standard deviation (SD)) for continuous variables, or as n (%) for categorical variables. P-values were determined using one-way ANOVA or t-test for continuous variables, and the chi-square test for categorical variables. *BMI = body mass index; BG = blood glucose concentration; SB = systolic blood pressure; DB = diastolic blood pressure; Med = regular medication use; Hyp = regular use of hypoglycemic drugs; Llow = regular use of lipid-lowering drugs; Anhyp = regular use of antihypertensive drugs.\u003c/p\u003e\n\u003cp\u003eTable 2 \u0026ndash; Concentrations of lipoproteins in healthy individuals and in patients with Type 2 Diabetes Mellitus\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"639\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eLipoproteins\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eHC, M(SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003eDA, M(SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eDO, M(SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eDH, M(SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eDHC, M(SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003ePlasma\u003cem\u003eTG\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e113.2 (52.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e153.9 (72.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e140 (68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e167.5 (82.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e152.8 (68.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003ea,b,c,d\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eLDL\u003cem\u003eTG\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e15.7 (5.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e17.2 (7.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e16.2 (8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e18.6 (8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e17 (6.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003ec\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eVLDL\u003cem\u003eTG\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e72.5 (45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e99.6 (59.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e88 (53.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e110.3 (68.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e99.1 (56.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003ea,c,d\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eIDL\u003cem\u003eTG\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e9.1 (4.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e13.3 (6.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e12.5 (7.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e14.5 (7.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e13.1 (6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003ea,b,c,d\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eLDL1\u003cem\u003eTG\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e4 (2.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e5.2 (2.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e4.8 (3.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.6 (3.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e5.1 (2.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003ec,d\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eHDL2b\u003cem\u003eTG\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e1.8 (1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e2.5 (1.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e2.2 (1.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e2.8 (1.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e2.5 (1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003ea,c,d,e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eHDL2a\u003cem\u003eTG\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e2.6 (1.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e2.3 (1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e2.2 (1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e2.3 (1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e2.4 (1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003ePlasma\u003cem\u003eChol\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e183.9 (46.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e152.4 (40.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e156 (38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e156.9 (43.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e148.3 (39.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003ea,b,c,d\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eLDL\u003cem\u003eChol\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e103.1 (31.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e78.2 (29.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e83.3 (29.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e79.7 (33.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e75.2 (27.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003ea,b,c,d\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eHDL\u003cem\u003eChol\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e38.3 (12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e26.2 (9.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e26.5 (9.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e25.4 (8.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e26.5 (9.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003ea,b,c,d\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eVLDL\u003cem\u003eChol\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e11.9 (8.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e16.9 (11.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e14.1 (10.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e19.1 (12.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e17 (10.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003ec,d\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eIDL\u003cem\u003eChol\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e6.7 (3.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e8.1 (4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e7.1 (3.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e8.9 (4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e8.2 (4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003ec,d\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eLDL1\u003cem\u003eChol\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e27.2 (10.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e20.9 (10.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e22.1 (11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e21.9 (11.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e19.8 (10.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003ea,b,c,d\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eLDL2\u003cem\u003eChol\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e14.8 (6.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e9.7 (5.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e11 (5.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e10.1 (6.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e8.9 (5.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003ea,b,c,d,f\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eLDL3\u003cem\u003eChol\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e14.8 (5.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e10.4 (4.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e10.8 (4.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e10.8 (5.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e10 (4.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003ea,b,c,d\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eLDL4\u003cem\u003eChol\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e15.7 (4.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e12.1 (4.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e12.8 (4.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e12.3 (4.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e11.6 (4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003ea,b,c,d\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eLDL5\u003cem\u003eChol\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e11.8 (4.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e9.5 (3.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e9.2 (3.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e10.1 (4.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e9.4 (3.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003ea,b,c,d\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eHDL2b\u003cem\u003eChol\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e14.2 (7.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e7.5 (5.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e8.1 (5.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e7.5 (5.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e7.3 (4.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003ea,b,c,d\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eHDL2a\u003cem\u003eChol\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e17.6 (3.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e13.3 (3.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e13.6 (3.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e12.6 (3.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e13.5 (3.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003ea,b,c,d,g\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eHDL3\u003cem\u003eChol\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e19.1 (3.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e17.3 (4.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e17.6 (3.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e17 (4.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e17.4 (4.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003ea,c,d\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003ePlasma\u003cem\u003eFreeChol\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e52.4 (11.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e46.7 (11.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e46.3 (10.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e48.6 (11.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e45.9 (10.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003ea,b,c,d\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eVLDL\u003cem\u003eFreeChol\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e7.1 (4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e10.7 (5.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e9.5 (5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e11.7 (5.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e10.7 (5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003ea,b,c,d\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eHDL2b\u003cem\u003eFreeChol\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e5 (2.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e2.9 (1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e3.1 (1.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e2.9 (1.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e2.8 (1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003ea,b,c,d\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003ePlasma\u003cem\u003ePhoslip\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e203.6 (45.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e183.4 (45.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e181.9 (38.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e187.7 (50.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e181.7 (45.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003ea,b,c,d\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eLDL\u003cem\u003ePhoslip\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e69.1 (21.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e52.8 (20.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e56 (19.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e53.7 (23.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e50.9 (19.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003ea,b,c,d\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eHDL\u003cem\u003ePhoslip\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e67.9 (18.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e53.8 (17.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e52.4 (17.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e53 (17.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e54.8 (16.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003ea,b,c,d\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eVLDL\u003cem\u003ePhoslip\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e22.9 (13.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e32.6 (17.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e29.1 (16.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e35.9 (19.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e32.4 (16.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003ea,b,c,d\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eLDL1\u003cem\u003ePhoslip\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e13.3 (8.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e9.1 (6.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e9.4 (6.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e9.4 (7.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e8.9 (5.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003ea,b,c,d\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eLDL2\u003cem\u003ePhoslip\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e7.4 (3.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e5.2 (3.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e5.6 (3.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.6 (3.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e4.8 (3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003ea,c,d\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eLDL3\u003cem\u003ePhoslip\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e10 (3.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e7.5 (3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e7.7 (2.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e7.8 (3.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e7.3 (2.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003ea,b,c,d\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eLDL5\u003cem\u003ePhoslip\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e7.5 (2.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e7.5 (2.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e7 (2.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e8 (2.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e7.5 (2.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003ea\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eHDL2b\u003cem\u003ePhoslip\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e15.7 (11.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e10.8 (9.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e11.5 (9.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e10.9 (10.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e10.4 (8.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003ea,b,c,d\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eHDL2a\u003cem\u003ePhoslip\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e36.2 (9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e35.6 (10.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e36 (9.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e35 (11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e35.7 (11.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003ePlasma\u003cem\u003eApoA1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e121 (21.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e100.3 (20.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e101.5 (19.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e98.2 (22.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e100.8 (20.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003ea,b,c,d\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eHDL\u003cem\u003eApoA1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e101.2 (21.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e81.8 (17.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e82.8 (16.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e80.8 (18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e81.9 (17.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003ea,b,c,d\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eHDL2b\u003cem\u003eApoA1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e16 (9.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e9.3 (7.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e9.5 (8.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e9.8 (8.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e8.9 (7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003ea,b,c,d\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eHDL2a\u003cem\u003eApoA1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e36.5 (8.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e31.7 (7.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e32.2 (7.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e30.5 (8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e32 (8.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003ea,b,c,d\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eHDL3\u003cem\u003eApoA1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e54.4 (11.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e46.2 (9.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e46.2 (9.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e45.5 (9.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e46.7 (10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003ea,b,c,d\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003ePlasma\u003cem\u003eApoB\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e93.7 (21.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e86.3 (21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e87.4 (21.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e89.4 (22.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e84.2 (20.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003ea,d\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eLDL\u003cem\u003eApoB\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e75.3 (18.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e63 (17.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e65.6 (17.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e64.5 (18.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e60.9 (16.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003ea,b,c,d\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eVLDL\u003cem\u003eApoB\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e5.8 (3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e8.1 (3.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e7.2 (3.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e8.7 (3.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e8.2 (3.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003ea,b,c,d\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eIDL\u003cem\u003eApoB\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e6 (1.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e7.8 (2.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e7.6 (2.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e8.2 (2.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e7.7 (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003ea,b,c,d\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eLDL1\u003cem\u003eApoB\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e17.2 (5.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e15.5 (5.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e16.5 (5.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e16 (6.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e14.9 (5.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003ea,d\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eLDL2\u003cem\u003eApoB\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e9.4 (3.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e6.9 (3.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e7.6 (3.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e7 (3.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e6.4 (3.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003ea,b,c,d,f\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eLDL3\u003cem\u003eApoB\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e9.6 (3.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e7.7 (3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e7.7 (3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e8 (3.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e7.5 (2.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003ea,b,c,d\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eLDL4\u003cem\u003eApoB\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e9.5 (3.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e7.5 (2.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e7.5 (2.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e7.8 (3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e7.3 (2.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003ea,b,c,d\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eLDL5\u003cem\u003eApoB\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e7.7 (3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e6.4 (2.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e6 (2.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e6.8 (3.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e6.4 (2.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003ea,b,c,d\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eLDL6\u003cem\u003eApoB\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e9.9 (8.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e10.7 (9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e9.6 (7.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e11.9 (10.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 10.5 (9.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003ePlasma\u003cem\u003eCE\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e219.2 (56.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e176.7 (51.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e183.4 (48.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e181.1 (55.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e171.3 (50.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003ea,b,c,d\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eVLDL\u003cem\u003eCE\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e12.1 (6.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e17 (8.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e15.3 (8.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e18.7 (9.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e17 (8.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003ea,b,c,d\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eIDL\u003cem\u003eCE\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e7.6 (2.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e9.5 (3.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e9.1 (3.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e10 (3.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e9.4 (3.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003ea,b,c,d\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eLDL\u003cem\u003eCE\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e107.5 (39.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e83 (36.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e87.5 (37.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e86.2 (39.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e79.4 (35.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003ea,b,c,d\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eLDL1\u003cem\u003eCE\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e25.4 (13.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e19.6 (12.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e21.1 (12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e21.3 (14.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e18.1 (11.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003ea,b,c,d\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eLDL3\u003cem\u003eCE\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e16.3 (5.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e11.7 (5.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e12.3 (6.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e12.1 (6.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e11.2 (5.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003ea,b,c,d\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eHDL2b\u003cem\u003eCE\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e17.3 (8.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e11.9 (7.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e12.5 (7.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e12.5 (7.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e11.3 (7.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003ea,b,c,d\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eConcentrations of LPs (mg/dL) in healthy control individuals (HC, N = 393), all patients with Type 2 Diabetes Mellitus (DA, N = 393), diabetic patients without hypertension or coronary heart disease (DO, N = 88), diabetic patients with hypertension (DH, N = 105), and diabetic patients with both hypertension and coronary heart disease (DHC, N = 197). Data are presented as mean (M) (standard deviation (SD)). P-values were determined by multiple linear regression (MLR) analysis after adjusting for confounders. The significance threshold was set at P \u0026lt; 0.05 after false discovery rate (5%) correction. * a: significant difference between HC and DA; b: HC vs. DO; c: HC vs. DH; d: HC vs. DHC; e: DO vs. DH; f: DO vs. DHC; g: DH vs. DHC. \u003cem\u003eTG\u003c/em\u003e = triglycerides; \u003cem\u003eChol\u003c/em\u003e = cholesterol; \u003cem\u003eFreeChol\u003c/em\u003e = free cholesterol; \u003cem\u003eCE\u003c/em\u003e = cholesterol ester; \u003cem\u003ePhoslip\u003c/em\u003e = phospholipids; \u003cem\u003eApoA1\u003c/em\u003e = apo apolipoprotein A1; \u003cem\u003eApoB\u003c/em\u003e = apolipoprotein B; VLDL = very low-density LP; IDL = intermediate-density LP; LDL = low-density LP; HDL = high-density LP. **Concentrations of LP molecules in main and subfraction are omitted from the table, for a complete list see Supplementary Table 1.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"cardiovascular-diabetology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"cvdb","sideBox":"Learn more about [Cardiovascular Diabetology](http://cardiab.biomedcentral.com/)","snPcode":"12933","submissionUrl":"https://submission.nature.com/new-submission/12933/3","title":"Cardiovascular Diabetology","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Type 2 Diabetes Mellitus, Lipoprotein Metabolism, Dyslipidemia, Cardiovascular Disease Risk, ¹H NMR Spectroscopy","lastPublishedDoi":"10.21203/rs.3.rs-7318923/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7318923/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground.\u003c/h2\u003e\u003cp\u003eType 2 diabetes mellitus (T2DM) increases the risk of cardiovascular disease (CVD), largely by alterations in the blood lipids and the metabolism of circulating lipoproteins (LPs). We studied whether the presence of additional risk factors, such as hypertension, or CVD itself, is associated with further alterations in the LP profiles in individuals with T2DM.\u003c/p\u003e\u003ch2\u003eMethods.\u003c/h2\u003e\u003cp\u003eWe performed LP profiling using \u0026sup1;H NMR spectroscopy and quantified 65 parameters in 393 healthy controls (HC) and in 390 T2DM patients with and without cardiovascular comorbidities. Univariate and multivariate analyses were used to assess alterations in LPs in diabetic patients.\u003c/p\u003e\u003ch2\u003eResults.\u003c/h2\u003e\u003cp\u003eTriglycerides in all major LP classes, as well as particle numbers of very low-density lipoproteins (VLDL) and intermediate-density lipoproteins (IDL) were increased in T2DM compared to HC. In contrast, particle numbers of low-density lipoproteins (LDL) and high-density lipoproteins (HDL) were reduced, suggesting slower lipolytic conversion of IDL to LDL and impaired clearance of triglyceride-enriched HDL. Univariate and multivariate analyses converged in identifying distinct LP profiles associated with T2DM, while differences between patients with and without hypertension or CVD were minor, indicating that T2DM is the primary factor driving LP dysregulation. T2DM, with or without cardiovascular comorbidities, also causes differential disruption of the correlation structure among LPs.\u003c/p\u003e\u003ch2\u003eConclusions.\u003c/h2\u003e\u003cp\u003eT2DM is associated with major alterations in LP metabolism independent of hypertension or CVD. Thus, early lipid management in T2DM is important to mitigate CVD risk. Further research is needed to elucidate how T2DM progresses to CVD in relation to atherogenic LPs.\u003c/p\u003e","manuscriptTitle":"Distinct compositional alterations in plasma lipoproteins in type 2 diabetes: a cross-sectional study of healthy individuals and diabetics with and without cardiovascular comorbidities","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-18 11:39:45","doi":"10.21203/rs.3.rs-7318923/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-09T03:18:23+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-02T16:01:29+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-24T19:42:36+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-17T13:08:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"49172236940934196086410301092742420123","date":"2025-09-10T00:14:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"157275956511753878185663673067027108509","date":"2025-09-09T18:55:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"75369628159714130020317743302951001986","date":"2025-09-09T13:56:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"231522016699240825672301734842623444546","date":"2025-09-08T08:18:04+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-11T09:05:38+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-08T07:57:09+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-08T01:47:02+00:00","index":"","fulltext":""},{"type":"submitted","content":"Cardiovascular Diabetology","date":"2025-08-07T12:37:47+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"cardiovascular-diabetology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"cvdb","sideBox":"Learn more about [Cardiovascular Diabetology](http://cardiab.biomedcentral.com/)","snPcode":"12933","submissionUrl":"https://submission.nature.com/new-submission/12933/3","title":"Cardiovascular Diabetology","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d0212a77-6d31-4b93-9cdf-8899e6295d10","owner":[],"postedDate":"August 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-12-29T16:06:28+00:00","versionOfRecord":{"articleIdentity":"rs-7318923","link":"https://doi.org/10.1186/s12933-025-03039-2","journal":{"identity":"cardiovascular-diabetology","isVorOnly":false,"title":"Cardiovascular Diabetology"},"publishedOn":"2025-12-25 15:58:25","publishedOnDateReadable":"December 25th, 2025"},"versionCreatedAt":"2025-08-18 11:39:45","video":"","vorDoi":"10.1186/s12933-025-03039-2","vorDoiUrl":"https://doi.org/10.1186/s12933-025-03039-2","workflowStages":[]},"version":"v1","identity":"rs-7318923","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7318923","identity":"rs-7318923","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}