Weighted gene co-expression network analysis to identify key modules and hub genes related to hyperlipidaemia

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Methods: The microarray dataset of GSE66676 obtained from patients with hyperlipidaemia was downloaded. Weighted gene co-expression network (WGCNA) analysis was used to analyse the gene expression profile, and the royal blue module was considered to have the highest correlation. Gene Ontology (GO) functional and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were implemented for the identification of genes in the royal blue module using the Database for Annotation, Visualization and Integrated Discovery (DAVID) online tool (version 6.8; http://david.abcc.ncifcrf.gov). A protein-protein interaction (PPI) network was established by using the online STRING tool. Then, several hub genes were identified by the MCODE and cytoHubba plug-ins in Cytoscape software. Results: The significant module (royal blue) identified was associated with TC, TG and non-HDL-C. GO and KEGG enrichment analyses revealed that the genes in the royal blue module were associated with carbon metabolism, steroid biosynthesis, fatty acid metabolism and biosynthesis pathways of unsaturated fatty acids. SQLE (degree = 17) was revealed as a key molecule associated with hypercholesterolaemia (HCH), and SCD was revealed as a key molecule associated with hypertriglyceridaemia (HTG). RT-qPCR analysis also confirmed the above results based on our HCH/HTG samples. Conclusions: SQLE and SCD are related to hyperlipidaemia, and SQLE/SCD may be new targets for cholesterol-lowering or triglyceride-lowering therapy, respectively. Endocrinology & Metabolism Nutrition & Dietetics Weighted gene co-expression network analysis Hyperlipidaemia Significant modules Hub genes Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Background Coronary artery disease (CAD) has become a prominent cause of morbidity, mortality, disability, high healthcare costs and functional deterioration and accounts for approximately 30% of all deaths worldwide [1-3]. Hyperlipidaemia (HLP) is a major risk factor for CAD and its complications. Comprehensive lipid-lowering therapy is recommended for patients with CAD by the 2013 American College of Cardiology (ACC)/American Heart Association (AHA) guidelines for the treatment of blood cholesterol to reduce the risk of cardiovascular events [4]. The guidelines emphasize that lipid-lowering therapy should not focus solely on decreasing low-density lipoprotein cholesterol (LDL-C) levels. Several compelling studies proved that lowering total cholesterol (TC) [5], triglyceride (TG) [5] and LDL-C [6] levels is more effective in reducing cardiovascular risk than lowering LDL-C levels alone [7]. The “6 percent effect” of statins refers to the fact that doubling the dose of statins only decreases LDL-C levels by 6.4%, and PCSK9 inhibitors combined with statins are recommended for patients with acute coronary syndrome (ACS) with a high risk of cardiovascular events [8]. Thus, the identification of novel therapeutic targets for HLP is expected to further reduce the risk of cardiovascular disease. Microarray analysis might serve as a novel and practical approach to identify susceptibility genes associated with HLP [9]. However, the reproducibility and sensitivity of microarray analysis based on differentially expressed genes may be limited [10, 11]. Gene co-expression network-based methods have been widely used in processing microarray data and have especially been used to identify meaningful functional modules [12, 13]. Weighted gene co-expression network analysis (WGCNA) is one of the most effective methods of gene co-expression network analysis. Instead of simply identifying the differentially expressed genes, a scale-free network of gene-gene interactions is generated by WGCNA, and several significant modules composed of genes with similar functions could be identified by WGCNA; in addition, it can be used to further analyse the correlation between modules and phenotypes or clinical characteristics [14]. Therefore, WGCNA could be utilized to construct a co-expression network and identify significant modules in the network, which may help us to illuminate the intrinsic characteristics of HLP and provide new insights into potential genetic biomarkers, signalling pathways and molecular mechanisms involved in HLP. Materials And Methods Hyperlipidaemia microarray datasets The microarray dataset obtained from patients with HLP (GSE66676) was downloaded from the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih.gov/geo/) database, which is based on the platform of the GPL6244 Affymetrix Human Gene 1.0 ST Array. Gene expression value matrices were obtained from the original files in CEL format after normalizing the expression values by using RMA methods in R software (version 4.0.0). [15]. Then, the Bioconductor package was used to transform the probe identification numbers (IDs) into gene symbols [16]. When multiple probe IDs corresponded to the same gene, the average expression value was used as the expression value. Construction of the weighted gene co-expression network WGCNA is a widely used systems biology method that is usually used to establish a scale-free network based on gene expression data profiles [12]. The co-expression network was constructed by selecting the genes whose variance was greater than all the quartiles of variance. After the sample cluster tree was constructed, cut height = 35 was used to screen the samples for subsequent studies. To ensure the reliability of the results of the network construction, the outlier samples were eliminated, and the samples in cluster 1 were selected to build the sample dendrogram and trait heatmap. The appropriate soft threshold power (soft power = 9) was selected according to the standard scale-free networks, and the adjacency values between all differentially expressed genes were calculated using a power function. Then, the adjacency values were transformed into a topological overlap matrix (TOM), and the corresponding dissimilarity (1-TOM) values were calculated. Module identification was accomplished with the dynamic tree cut method by hierarchically clustering genes using 1-TOM as the distance measure with a minimum size cut-off of 30 and a deep split value of 2 for the resulting dendrogram. To verify the stability of the identified modules, a module preservation function was used to calculate module preservation and quality statistics in the WGCNA package [17]. Identification of the module of interest and functional annotation Pearson correlation analysis was used to assess the correlations between modules and clinical characteristics to identify biologically meaningful modules. All genes associated with the significant module were subjected to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses by using the Database for Annotation, Visualization and Integrated Discovery (DAVID) online tool (version 6.8; http://david.abcc.ncifcrf.gov). P < 0.05 was set as the cut-off criterion. Hub gene analysis The degree of module membership (MM) was defined as the correlation between the gene expression profile and the module eigengenes (Mes). The degree of gene significance (GS) was defined as the absolute value of the correlation between the gene and external traits. In general, modules with increased MS and GS values among all the identified modules were selected for further analysis of their biological function [18]. The protein-protein interaction (PPI) network of genes in the selected module was constructed by the Search Tool for the Retrieval of Interacting Genes database (version 11.0; http://www.string-db.org) [19] and then visualized using Cytoscape software [20]. Molecular complex detection (MCODE) [21] was used to identify the most valuable clustering module. An MCODE score > 4 was the threshold for inclusion in further analysis. CytoHubba, a Cytoscape plugin, was used to identify hub genes in the PPI network; it provides 11 methods to explore important nodes in biological networks, of which degree has a better performance [22]. Sample verification and diagnostic criteria A total of 462 (229 males, 49.57%; 233 females, 50.43%) unrelated participants with normal lipid levels and 485 (236 males, 48.66%; 249 females, 51.34%) unrelated subjects with hypercholesterolaemia (HCH, TC > 5.17 mmol/l) and 474 (232 males, 49.16%; 241 females, 50.84%) unrelated participants with hypertriglyceridaemia (HTG, TG > 1.70 mmol/l) were randomly recruited from the Physical Examination Center of the Affiliated Hospital of Guizhou Medical University. The age ranged from 24 to 82 years. There was no difference in age distribution or sex ratio between the control and HCH or HTG groups. Patients suffering from HCH did not have a history of HTG, and patients suffering from HTG did not have a history of HCH. All participants were basically healthy and had no history of myocardial infarction, CAD, type 2 diabetes mellitus (T2DM) or ischaemic stroke. They were not taking any medicines that could alter serum lipid levels. All subjects had signed written informed consent. The research protocol was approved by the Ethics Committee of the Affiliated Hospital of Guizhou Medical University. Epidemiological analysis Universally standardized methods and protocols were used to conduct the epidemiological survey [23]. Detailed lifestyle and demographic characteristics were collected with a standard set of questionnaires. Alcohol consumption (0 (non-drinker), < 25 g/day and ≥ 25 g/day) and smoking status (0 (non-smoker), < 20 cigarettes/day and ≥ 20 cigarettes/day) were divided into three different subgroups. Waist circumference, BMI, height, blood pressure and weight were measured as previously described [24]. Biochemical assays Fasting venous blood samples of 5 ml were collected from each subject. A portion of the sample (2 ml) was placed in a tube and used to measure serum lipid levels. The remaining sample (3 ml) was collected in a glass tube containing anticoagulants (14.70 g/L glucose, 13.20 g/L trisodium citrate, 4.80 g/L citric acid) and utilized to extract deoxyribonucleic acid (DNA). The methods for performing serum ApoA1, HDL-C, ApoB, TG, LDL-C and TC measurements were described in a previous study [25]. All determinations were conducted using an autoanalyser (Type 7170A; Hitachi Ltd., Tokyo, Japan) in the Clinical Science Experiment Center of the Affiliated Hospital of Guizhou Medical University. Quantitative real-time PCR Peripheral blood monocytes (PBMCs) were isolated from blood samples with TRIzol reagent, which was used to extract the total RNA that was then reverse-transcribed into cDNA by using the PrimeScript RT reagent kit (Takara Bio, Japan). The obtained cDNA was used as a template for RT-qPCR. Table 1 shows that specific primer sequences, which were designed by Sangon Biotech (Shanghai, China), were used to detect the 2 hub genes. Quantitative RT-PCR was performed using a Taq PCR Master Mix Kit (Takara) on an ABI Prism 7500 sequence-detection system (Applied Biosystems, USA) using RT Reaction Mix in a total volume of 20 μL with the following reaction conditions: pre-denaturation at 95°C for 30 seconds, then 40 cycles of 95°C for 30 seconds and 60°C for 30 seconds. Table 1 PCR primers for quantitative real-time PCR Gene Forward primer Reverse primer SQLE TCTGGGGGTTAAGAGCAGTG GTGTCTACACTTACCATCTGTGGC SCD CTTGCGATATGCTGTGGTGC GGCTCCTAGCCTAATCCCCT GAPDH GCAACTAGGATGGTGTGGCT TCCCATTCCCCAGCTCTCATA Diagnostic criteria The values of serum ApoB (0.80-1.05 g/L), HDL-C (1.16-1.42 mmol/L), ApoA1 (1.20-1.60 g/L), TC (3.10-5.17 mmol/L), TG (0.56-1.70 mmol/L), the ApoA1/ApoB ratio (1.00-2.50) and LDL-C (2.70-3.10 mmol/L) were defined as normal at our Clinical Science Experiment Center. Subjects with TG > 1.70 mmol/L were defined as having hypertriglyceridaemia, and TC > 5.17 mmol/L was defined as having hypercholesterolaemia [26]. Participants with a fasting plasma (blood) glucose value ≥ 7.0 mmol/L were defined as having diabetes [27]. The diagnostic criteria of hypertension [28], obesity, normal weight and overweight were described in our previous study [29]. Statistical analyses SPSS (Version 22.0) was used to process the research data. The results are presented as the mean ± SD except for TG levels, which are presented as medians and interquartile ranges. The differences in the general characteristics except for TG between HCH/HTG patients and controls were analysed by independent-samples t tests. The Kruskal-Wallis and Mann-Whitney nonparametric tests were used to detect the difference in TG levels between patients with HCH/HTG and controls. The chi-square test was utilized to assess the differences in the proportion of smokers, age distribution and alcohol consumption between patients with HCH/HTG and controls. Heat mapping of the correlation models and bioinformatic analysis were performed in R software (version 4.0.0). A P value < 0.05 was considered to be statistically significant. Results Data pre-processing Gene expression profiles were obtained after normalization of the data and removing the outliers, and a total of 20,284 gene symbols were identified from 67 samples. Additional details about the gene expression profile and the sample phenotypes are presented in Additional file 1: Tables S1 and S2. Weighted gene co ‑ expression networks The sample cluster tree and sample dendrogram and trait heatmap are shown in Additional file 2: Figures S1 and S2. The gene expression profiles of 42 samples in cluster 1 were selected to build the weighted gene co-expression network. After the soft threshold (β = 9) was determined ( Figure 1 ), the weighted gene co-expression network was constructed by selecting the genes whose variance was greater than all the quartiles of variance. The adjacency matrix and correlation matrix of the gene expression profile were calculated and then transformed into a topological overlap matrix (TOM), and a clustering tree of genes based on the gene–gene non-ω similarity was obtained ( Figure 2 ). Combined with the TOM, the gene modules of each gene network were identified by the hierarchical average linkage clustering method, and twenty gene modules were identified by the dynamic tree cut algorithm (cut height = 0.25) ( Figure 3 ). The grey module contains all the genes that do not belong to the other modules and were excluded from subsequent analysis. Identification of the modules of interest and functional annotation The identification of modules that were significantly related to clinical phenotype was of high biological significance. In this study, we noticed that the royal blue module was associated with TC ( r 2 = 0.38, P = 0.01), TG ( r 2 = 0.41, P = 0.007) and non-HDL-C ( r 2 = 0.32, P = 0.04), and the genes in the royal blue module were studied in the subsequent analyses ( Figure 4 ). GO and KEGG pathway enrichment analyses were used to further explore the biological functions of the genes in the royal blue module. Furthermore, we noticed that a total of 101 genes ( Additional file: Tables S3 ) in the royal blue module were significantly correlated with the following pathways: hsa01100: metabolic pathways, hsa01130: biosynthesis of antibiotics, hsa00100: steroid biosynthesis, hsa01212: fatty acid metabolism, and hsa01040: biosynthesis of unsaturated fatty acids. The cell components, biological processes, molecular functions and KEGG pathway analysis of the royal blue module are also shown in Figure 5 , and more detailed information is presented in Additional file: Tables S4 and S5 . PPI network construction and module analysis of DEGs A PPI network including 93 notes and 333 edges was constructed by the STRING online tool. As shown in Figure 6 , the hub genes SQLE (degree = 17) and SCD (degree = 5) were identified by cytoHubba plug-ins in MCODE1 and MCODE2, respectively. Thus, we speculate that the genes mentioned above may be significantly correlated with blood lipid metabolism. Validation analysis by RT-qPCR As shown in Figure 7A , the RT-qPCR results revealed that the expression of SQLE in the HCH group and SCD in the HTG group was higher than that in healthy subjects. At the same time, we also noticed that SQLE was positively correlated with TC ( Figure 7C ) levels in the HCH group and that SCD was positively correlated with TG levels in the HTG group ( Figure 7D ). Common and biochemical characteristics As mentioned in Table 2 , the sex ratio, age and height were similar between the controls and patients with HCH/HTG. Serum HDL-C and ApoA1 levels and the ApoA1/ApoB ratio were significantly higher, and the proportion of smokers, proportion of drinkers, systolic blood pressure, waist circumference, weight, diastolic blood pressure, glucose level, pulse pressure, body mass index (BMI), and serum LDL-C, ApoB, TG and TC levels were significantly lower in controls than in patients with hyperlipidaemia. Table 2 Comparison of demographic, lifestyle characteristics and serum lipid levels of the participants Characteristic Control （ n=462 ） HCH (n=485) HTG (n=474) P HCH vs. controls P HTG vs. controls Male/female 3 229/233 236/249 232/241 0.780 0.870 Age (years) 1 57.60± 8.81 58.13±9.69 57.10±7.61 0.379 0.359 Height (cm) 1 159.83±8.20 160.66±7.93 159.92±8.11 0.114 0.771 Weight (kg) 1 58.98±9.90 62.66±10.09 60.52±11.08 1.97E-8 0.021 Body mass index (kg/m²) 1 23.05±3.32 24.21±3.12 23.61±3.67 3.59E-8 0.014 Waist circumference 1 74.50±8.47 78.37±8.76 81.10 ±9.21 1.00E-11 4.10E-28 Smoking, n % 3 Non-smoker 355 331 349 ≤ 20 cigarettes/day 98 114 73 > 20 cigarettes/day 9 40 52 1.73E-5 4.42E-8 Alcohol, n % 3 Non-drinker 377 354 363 ≤ 25 g/day 45 55 44 > 25 g/day 40 76 67 0.002 0.031 SBP (mmHg) 1 135.49±22.58 139.52±22.56 141.15±20.42 0.006 1.08E-4 D BP (mmHg) 1 82.45±12.42 84.01±11.71 85.15±11.72 0.047 0.001 PP (mmHg) 1 53.04±17.77 55.51±18.19 56.00±14.43 0.035 0.007 Glu (mmol/L) 1 6.14±1.42 6.44±1.58 6.35±1.32 0.002 0.015 TC (mmol/L) 1 4.37±0.64 5.80±0.50 4.46±0.37 9.42E-47 0.007 TG (mmol/L) 2 0.99(0.53) 1.15(0.45) 2.30(1.03) 4.46E-9 1.69E-84 HDL-C (mmol/L) 1 1.64±0.48 1.48±0.43 1.44±0.46 2.56E-8 7.22E-11 LDL-C (mmol/L) 1 2.50±0.55 3.52±0.80 2.79±0.86 4.89E-32 1.86E-9 ApoA1 (g/L) 1 1.33±0.24 1.23±0.25 1.25±0.24 3.49E-11 4.66E-8 ApoB (g/L) 1 0.98±0.17 1.02±0.18 1.07±0.21 0.002 1.06E-11 ApoA1/ApoB 1 1.39±0.33 1.25±0.40 1.22±0.39 1.91E-8 7.62E-12 SBP Systolic blood pressure; DBP Diastolic blood pressure; PP Pulse pressure; Glu Glucose; HDL-C high-density lipoprotein cholesterol; LDL-C low-density lipoprotein cholesterol; Apo Apolipoprotein; TC Total cholesterol ; TG Triglyceride . 1 Mean ± SD determined by t-test. 2 Median (interquartile range) tested by the Wilcoxon-Mann-Whitney test. 3 The rate or constituent ratio between the different groups was analyzed by the chi-square test. Discussion Several recent studies have shown that hypertension, smoking, obesity, age, dyslipidaemia, lack of exercise, sex and diabetes mellitus are common risk factors for cardiovascular disease [30, 31]. A comprehensive understanding of the potential molecular mechanisms involved in the pathogenesis of HLP is helpful for its prevention and treatment. As a novel and practical approach to the identification of HLP susceptibility genes, a microarray analysis using WGCNA may be helpful for the diagnosis of hyperlipidaemia [14]. WGCNA could be used to build a scale-free co-expression network of lipid-associated genes by detecting gene-to-gene interactions rather than simply focusing on the differentially expressed genes (DEGs). Co-expressed genes were enriched in different modules by hierarchical average linkage cluster analysis. In the present research, we analysed a dataset from HLP patients (GSE66676) by using WGCNA and identified that the royal blue module was significantly associated with TC, TG and non-HDL. Furthermore, KEGG enrichment analyses of the genes in the royal blue module indicated that the enriched genes in this module might have significant potential biological functions that are closely related to metabolic pathways, steroid biosynthesis, fatty acid metabolism and biosynthesis of unsaturated fatty acids. Two hub genes ( SQLE and SCD ) were identified in the royal blue module that were detected by MCODE analysis. Moreover, the verification results were highly consistent with the above findings, and we found that the expression of the SQLE gene in patients with HCH and the SCD gene in patients with HTG was higher than that in healthy controls. Therefore, the identified SQLE gene was associated with the onset of HCH, the SCD gene was associated with the onset of HTG, and the underlying molecular mechanisms of these genes might be slightly different. In addition, SQLE and SCD were previously reported to be statin responsive, and they are known to be involved in sterol metabolism and transport; at the same time, there were significant changes in expression levels in the B-cells in response to statin treatment [32], and therefore, SQLE and SCD may be new targets for lipid-lowering therapy. Fatty acids and cholesterol are essential lipids involved in many crucial biological processes; however, excessive free fatty acids and free cholesterol are major risk factors for type 2 diabetes and atherosclerosis [33]. Previous studies on intermediate metabolites in cholesterol biosynthesis have shown that the first oxidative step in cholesterol biosynthesis is catalysed by squalene monooxygenase ( SQLE ), a crucial regulator downstream of HMG-CoA reductase ( HMGCR ) in cholesterol synthesis [34]. Meanwhile, SQLE is suggested as the second rate-limiting enzyme in cholesterol synthesis [35, 36]. Inhibition of SQLE expression could effectively reduce cholesterol synthesis [37, 38], and the cholesterol-lowering effect is caused by the combination of multiple levels. First, SQLE and HMGCR act as direct targets of the sterol regulatory element binding protein 2 ( SREBP2) transcription factor and play a crucial regulatory role in most cholesterol biosensor genes [39, 40]. Second, the N-terminus of the SQLE protein may contain a cholesterol-sensitive region that mediates the protease degradation of SQLE in a cholesterol-dependent manner by relying on an E3 ubiquitin ligase such as MARCH [41]. Interestingly, oleate acts as an unsaturated fatty acid and can stabilize SQLE by blocking MARCH6 -mediated degradation [42]. In addition, Masanori Honsho et al . also noticed that inhibition of SQLE expression through elevating plasmalogen levels may be a novel and alternative potential method to reduce cholesterol synthesis [43]. Similarly, the KEGG analyses in the current study indicated that SQLE was mainly involved in metabolic pathways and steroid biosynthesis. Metabolic risk factors such as insulin resistance, obesity, hypertension and dyslipidaemia are correlated with each other, so their combination is generally referred to as “metabolic syndrome” (MetS). Abnormal stearoyl-coenzyme A desaturase ( SCD ) expression/activity has been noticed in subjects with metabolic syndrome, indicating that SCD may be related to the pathogenesis of metabolic syndrome. By querying the GENE database in NCBI, we noticed that SCD (also known as SCD1 ; FADS5 ; SCDOS ; hSCD1 ; MSTP008 ; gene ID: 6319, HGNC: 10571, OMIM: 604031) is positioned on chromosome 10q24.31 (exon count: 6) and encodes a biological synthase, which is mainly involved in the metabolism of fatty acids, especially oleic acid. This protein is an intact membrane protein located in the endoplasmic reticulum and is a member of the fatty acid desaturase family. Herman-Edelstein M et al . proved that SREBPs are transcription factors that activate the synthesis of fatty acids (FAs), triglycerides (TGs), and cholesterol, and SREBP2 activates cholesterol production, whereas SREBP1 primarily activates FA and TG synthesis [44]. ATP-citrate lyase ( ACLY ), a cytosolic enzyme that generates acetyl-CoA for cholesterol and de novo fatty acid synthesis, is a potential target for hypolipidaemic intervention [45]. ACLY acts as a critical enzyme involved in de novo fatty acid synthesis and catalyses the conversion of citrate to cytosolic acetyl-CoA. Acetyl-CoA is converted to malonyl CoA via acetyl-CoA carboxylase ( ACC ), which plays a key role in the first committed step in the synthesis of fatty acids [46]. SCD is another key rate-limiting enzyme in fatty acid metabolism downstream of ACLY ; it can convert different saturated fatty acids into monounsaturated fatty acids, and its expression is directly regulated by SREBP1 [47-50]. Both animal and human studies have shown that SCD is associated with obesity and insulin resistance [51, 52]. Mice with the SCD gene exhibited reduced diet-induced weight gain and improved insulin resistance compared to wild-type controls [53]. Deletion of the SCD1 gene product in mice could effectively improve insulin sensitivity, reduce plasma non-HDL cholesterol and triglyceride levels and liver lipid accumulation and increase beneficial HDL cholesterol levels [54]. Daniel Castellano-Castillo et al . also found a negative relationship between SCD DNA methylation and BMI and the MetS index [55]. In the current study, we also noticed that SCD was mainly involved in fatty acid metabolism and the biosynthesis pathways of unsaturated fatty acids. Unhealthy lifestyle factors such as excessive drinking and cigarette smoking have been linked to HLP [56, 57]. In the present study, we found that the percentage of participants who smoked was greater in the hyperlipidaemic group than in the normal group. In recent years, the influence of smoking on HLP has attracted increasing attention. Several recent studies have indicated the existence of lower HDL-C and higher TC, LDL-C and TG levels in smokers than in non-smokers [57]. Moderate drinking reduced the incidence of cardiovascular events, and the potential mechanism may be related to increased HDL-C and ApoA1 levels [58]. However, frequent binge drinking was correlated with an increased risk of CAD mortality because it will lead to a number of serious health problems, including dyslipidaemia, abnormal liver function and myocardial infarction [59]. Therefore, the preventive effect of a healthy lifestyle on hyperlipidaemia should not be ignored when exploring new therapeutic targets for hyperlipidaemia. This research had several limitations. First, this is a single-centre study comprising a small patient number, and large multicentre studies are necessary to validate our findings. Second, the molecular mechanisms of SQLE and SCD involved in HLP are still not fully defined and require further cytology and animal experiments to further outline their respective roles in vivo and in vitro . Conclusions WGCNA identified that the royal blue module was significantly associated with TC, TG and non-HDL. GO and KEGG enrichment analyses revealed that the hub genes of SQLE were associated with TC and that SCD was associated with TG metabolism. The verification results of RT-qPCR revealed that the expression of SQLE in hypercholesterolaemia and SCD in hypertriglyceridaemia was higher than that in normal controls, which further increased the credibility of the conclusion. Thus, we speculated that SQLE may be a novel target for cholesterol-lowering therapy and that SCD may be a novel target for triglyceride-lowering therapy. Abbreviations WGCNA: weighted gene co-expression network analysis; HCH: hypercholesterolemia; HTG: hypertriglyceridemia; SQLE: squalene epoxidase; SCD: stearoyl-CoA desaturase; DAVID: Database for Annotation, Visualization and Integrated Discovery; T2DM: Type 2 diabetes mellitus; GO: Gene Ontology; HDL-C: High-density lipoprotein cholesterol; IS: Ischemic stroke; KEGG: Kyoto Encyclopedia of Genes and genomes; LDL-C: Low-density lipoprotein cholesterol; MCODE: Molecular Complex Detection; Apo: Apolipoprotein; PPI: Protein-protein interaction; GEO: Gene Expression Omnibus; BMI: Body mass index; TG: Triglyceride; RT-qPCR: Quantitative real time polymerase chain reaction; TC: Total cholesterol; ACC: acetyl CoA carboxylase; ACLY: ATP - Citrate Lyase; FAs: fatty acids; MetS: metabolic syndrome; HMGCR: HMG-CoA reductase; SREBP2: sterol regulatory element binding protein 2; CAD: Coronary artery disease; HLP: Hyperlipidaemia; ACC: American College of Cardiology; AHA: American Heart Association; ACS: acute coronary syndrome; TOM: topological overlap matrix; MM: module membership; Mes: module eigengenes; Mes: module eigengenes; DNA: deoxyribonucleic acid; PBMCs: Peripheral blood monocytes; DEGs: differentially expressed genes. Declarations Acknowledgements We thank all the participants of this study. Authors’ contributions F.-J.L. and P.-F.Z. conceived the study, carried out the epidemiological survey and collected the samples, participated in the design, and drafted the manuscript. Y.Z.G. performed the statistical analyses. H.W.P. helped to modify the manuscript. W.L. conceived the study, participated in the design, carried out the epidemiological survey, and helped to draft the manuscript. All authors read and approved the final manuscript. Funding This study was supported by the National Natural Science Foundation of China (No. 81960047). Availability of data and materials The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Ethics approval and consent to participate The study design was approved by the Ethics Committee of the Affiliated Hospital of Guizhou Medical University. Informed consent was obtained from all participants. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. Author details 1 Department of Cardiology, Affiliated Hospital of Guizhou Medical University, 28 Guyi Street, Guiyang 550002, Guizhou, People’s Republic of China 2 Department of Cardiology, The Central Hospital of ShaoYang, 36 QianYuan lane, Shaoyang 422000, Hunan, People’s Republic of China 3 Graduate School of Guangxi Medical University, 22 Shuangyong Road, Nanning 530021, Guangxi, People’s Republic of China 4 Department of Cardiology, Hunan Provincial People’s Hospital & First Affiliated Hospital of Hunan Normal University, Changsha, Hunan, China References Houston M: The role of noninvasive cardiovascular testing, applied clinical nutrition and nutritional supplements in the prevention and treatment of coronary heart disease. Ther Adv Cardiovasc Dis 2018, 12(3):85-108. 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Khounphinith E, Yin R-X, Cao X-L, Huang F, Wu J-Z, Li H: TIMD4 rs6882076 SNP Is Associated with Decreased Levels of Triglycerides and the Risk of Coronary Heart Disease and Ischemic Stroke. Int J Med Sci 2019, 16(6):864-871. Greenland P, Knoll MD, Stamler J, Neaton JD, Dyer AR, Garside DB et al : Major risk factors as antecedents of fatal and nonfatal coronary heart disease events. JAMA 2003, 290(7):891-897. Yusuf S, Hawken S, Ounpuu S, Dans T, Avezum A, Lanas F et al : Effect of potentially modifiable risk factors associated with myocardial infarction in 52 countries (the INTERHEART study): case-control study. Lancet 2004, 364(9438):937-952. Bolotin E, Armendariz A, Kim K, Heo SJ, Boffelli D, Tantisira K et al : Statin-induced changes in gene expression in EBV-transformed and native B-cells. Hum Mol Genet 2014, 23(5):1202-1210. Wang Y-J, Bian Y, Luo J, Lu M, Xiong Y, Guo S-Y et al : Cholesterol and fatty acids regulate cysteine ubiquitylation of ACAT2 through competitive oxidation. Nat Cell Biol 2017, 19(7):808-819. Honsho M, Abe Y, Fujiki Y: Dysregulation of Plasmalogen Homeostasis Impairs Cholesterol Biosynthesis. The Journal of biological chemistry 2015, 290(48):28822-28833. Hidaka Y, Satoh T, Kamei T: Regulation of squalene epoxidase in HepG2 cells. J Lipid Res 1990, 31(11):2087-2094. Gonzalez R, Carlson JP, Dempsey ME: Two major regulatory steps in cholesterol synthesis by human renal cancer cells. Arch Biochem Biophys 1979, 196(2):574-580. Hidaka Y, Hotta H, Nagata Y, Iwasawa Y, Horie M, Kamei T: Effect of a novel squalene epoxidase inhibitor, NB-598, on the regulation of cholesterol metabolism in Hep G2 cells. J Biol Chem 1991, 266(20):13171-13177. Sawada M, Matsuo M, Hagihara H, Tenda N, Nagayoshi A, Okumura H et al : Effect of FR194738, a potent inhibitor of squalene epoxidase, on cholesterol metabolism in HepG2 cells. Eur J Pharmacol 2001, 431(1):11-16. Sharpe LJ, Brown AJ: Controlling cholesterol synthesis beyond 3-hydroxy-3-methylglutaryl-CoA reductase (HMGCR). J Biol Chem 2013, 288(26):18707-18715. Cai D, Wang J, Gao B, Li J, Wu F, Zou JX et al : RORγ is a targetable master regulator of cholesterol biosynthesis in a cancer subtype. Nature communications 2019, 10(1):4621. Zelcer N, Sharpe LJ, Loregger A, Kristiana I, Cook EC, Phan L et al : The E3 ubiquitin ligase MARCH6 degrades squalene monooxygenase and affects 3-hydroxy-3-methyl-glutaryl coenzyme A reductase and the cholesterol synthesis pathway. Mol Cell Biol 2014, 34(7):1262-1270. Stevenson J, Luu W, Kristiana I, Brown AJ: Squalene mono-oxygenase, a key enzyme in cholesterol synthesis, is stabilized by unsaturated fatty acids. Biochem J 2014, 461(3):435-442. Honsho M, Abe Y, Fujiki Y: Dysregulation of Plasmalogen Homeostasis Impairs Cholesterol Biosynthesis. J Biol Chem 2015, 290(48):28822-28833. Herman-Edelstein M, Thomas MC, Thallas-Bonke V, Saleem M, Cooper ME, Kantharidis P: Dedifferentiation of immortalized human podocytes in response to transforming growth factor-β: a model for diabetic podocytopathy. Diabetes 2011, 60(6):1779-1788. Pearce NJ, Yates JW, Berkhout TA, Jackson B, Tew D, Boyd H et al : The role of ATP citrate-lyase in the metabolic regulation of plasma lipids. Hypolipidaemic effects of SB-204990, a lactone prodrug of the potent ATP citrate-lyase inhibitor SB-201076. Biochem J 1998, 334 ( Pt 1)(Pt 1):113-119. Scaglia N, Chisholm JW, Igal RA: Inhibition of stearoylCoA desaturase-1 inactivates acetyl-CoA carboxylase and impairs proliferation in cancer cells: role of AMPK. PLoS One 2009, 4(8):e6812. Jones DT, Valli A, Haider S, Zhang Q, Smethurst EA, Schug ZT et al : 3D Growth of Cancer Cells Elicits Sensitivity to Kinase Inhibitors but Not Lipid Metabolism Modifiers. Mol Cancer Ther 2019, 18(2):376-388. Ntambi JM, Miyazaki M: Regulation of stearoyl-CoA desaturases and role in metabolism. Prog Lipid Res 2004, 43(2):91-104. Forrest LM, Lough CM, Chung S, Boudyguina EY, Gebre AK, Smith TL et al : Echium oil reduces plasma triglycerides by increasing intravascular lipolysis in apoB100-only low density lipoprotein (LDL) receptor knockout mice. Nutrients 2013, 5(7):2629-2645. Herman-Edelstein M, Scherzer P, Tobar A, Levi M, Gafter U: Altered renal lipid metabolism and renal lipid accumulation in human diabetic nephropathy. J Lipid Res 2014, 55(3):561-572. Rahman SM, Dobrzyn A, Lee SH, Dobrzyn P, Miyazaki M, Ntambi JM: Stearoyl-CoA desaturase 1 deficiency increases insulin signaling and glycogen accumulation in brown adipose tissue. Am J Physiol Endocrinol Metab 2005, 288(2):E381-387. García-Serrano S, Moreno-Santos I, Garrido-Sánchez L, Gutierrez-Repiso C, García-Almeida JM, García-Arnés J et al : Stearoyl-CoA desaturase-1 is associated with insulin resistance in morbidly obese subjects. Mol Med 2011, 17(3-4):273-280. Ntambi JM, Miyazaki M, Stoehr JP, Lan H, Kendziorski CM, Yandell BS et al : Loss of stearoyl-CoA desaturase-1 function protects mice against adiposity. Proc Natl Acad Sci U S A 2002, 99(17):11482-11486. MacDonald MLE, Singaraja RR, Bissada N, Ruddle P, Watts R, Karasinska JM et al : Absence of stearoyl-CoA desaturase-1 ameliorates features of the metabolic syndrome in LDLR-deficient mice. J Lipid Res 2008, 49(1):217-229. Castellano-Castillo D, Moreno-Indias I, Sanchez-Alcoholado L, Ramos-Molina B, Alcaide-Torres J, Morcillo S et al : Altered Adipose Tissue DNA Methylation Status in Metabolic Syndrome: Relationships Between Global DNA Methylation and Specific Methylation at Adipogenic, Lipid Metabolism and Inflammatory Candidate Genes and Metabolic Variables. J Clin Med 2019, 8(1):87. Waskiewicz A, Sygnowska E: Alcohol intake and cardiovascular risk factor profile in men participating in the WOBASZ study. Kardiol Pol 2013, 71(4):359-365. Rao Ch S, Subash YE: The effect of chronic tobacco smoking and chewing on the lipid profile. J Clin Diagn Res 2013, 7(1):31-34. Ruixing Y, Shangling P, Hong C, Hanjun Y, Hai W, Yuming C et al : Diet, alcohol consumption, and serum lipid levels of the middle-aged and elderly in the Guangxi Bai Ku Yao and Han populations. Alcohol 2008, 42(3):219-229. Pai JK, Mukamal KJ, Rimm EB: Long-term alcohol consumption in relation to all-cause and cardiovascular mortality among survivors of myocardial infarction: the Health Professionals Follow-up Study. Eur Heart J 2012, 33(13):1598-1605. Supplementary Files Additionalfile1.xlsx Additionalfile2.docx Cite Share Download PDF Status: Published Journal Publication published 04 Mar, 2021 Read the published version in Nutrition & Metabolism → Version 2 posted Editorial decision: Minor revision 18 Feb, 2021 Editor assigned by journal 20 Jan, 2021 Reviewers invited by journal 20 Jan, 2021 Submission checks completed at journal 20 Jan, 2021 Editor invited by journal 20 Jan, 2021 You are reading this latest preprint version Show more versions Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-54056","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research","associatedPublications":[],"authors":[{"id":8892404,"identity":"e1a009aa-226e-49fb-bc6b-45c8fbbd8d22","order_by":0,"name":"Fu Jun Liao","email":"","orcid":"","institution":"Affiliated Hospital of Guizhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Fu","middleName":"Jun","lastName":"Liao","suffix":""},{"id":8892405,"identity":"32d56dab-b380-46b0-855b-52518f46b44a","order_by":1,"name":"Peng-Fei Zheng","email":"","orcid":"","institution":"Central Hospital of Shaoyang","correspondingAuthor":false,"prefix":"","firstName":"Peng-Fei","middleName":"","lastName":"Zheng","suffix":""},{"id":8892406,"identity":"2990b83d-fadf-4e51-b14b-bbf22f23961c","order_by":2,"name":"Yao-Zong Guan","email":"","orcid":"","institution":"Guangxi Medical University First Affiliated Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yao-Zong","middleName":"","lastName":"Guan","suffix":""},{"id":8892407,"identity":"6183224d-776d-48f3-88d0-fbaca221aed6","order_by":3,"name":"Hong Wei Pan","email":"","orcid":"","institution":"Hunan Provincial People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Hong","middleName":"Wei","lastName":"Pan","suffix":""},{"id":8892408,"identity":"a4fc41b9-1ff8-4626-8b2d-036c078e8097","order_by":4,"name":"Wei Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3klEQVRIiWNgGAWjYBACPmYgkVDBJsfG3nzwAYMBEVrYwFrO8Bnz8RxLNiBOC4hgbJNLnCeRYyZBlMPY2HkMHzxgM0ts4zmWVvmj4I48A/vhoxvwO4zH2CCBJ824jb352G0eg2eGDTxpaTfwa+HdJpEgcUwWZMttBoPDjA0SPGaEtGz/kWDwn7EN6JfCHwaH7YnRso0hIYFNEaSFgcfgcCIRWvg/SyQcYDNmAwayNFBLchshv/DzH0v8+PMfm5x8e/PBjz/+HLbtZz98DK8WLPaSpnwUjIJRMApGATYAAKbURSFG1STEAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0003-1792-8182","institution":"Guizhou Medical University","correspondingAuthor":true,"prefix":"","firstName":"Wei","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2020-08-05 10:48:20","currentVersionCode":2,"declarations":"","doi":"10.21203/rs.3.rs-54056/v2","doiUrl":"https://doi.org/10.21203/rs.3.rs-54056/v2","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12986-021-00555-2","type":"published","date":"2021-03-04T15:05:39+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":5339484,"identity":"e4debd51-59b0-4bca-af10-b3eecf1cb2ec","added_by":"auto","created_at":"2021-01-28 15:17:49","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":166021,"visible":true,"origin":"","legend":"Analysis of network topology for various soft-thresholding powers. The left panel shows the scale-free fit index (y-axis) as a function of the soft-thresholding power (x-axis). The right panel displays the mean connectivity (degree, y-axis) as a function of the soft-thresholding power (x-axis).","description":"","filename":"OnlineFigure1.png","url":"https://assets-eu.researchsquare.com/files/rs-54056/v2/e8160efe68633c8138124c8b.png"},{"id":5339346,"identity":"41a25b3b-9a45-439f-9ba0-b874b0692b72","added_by":"auto","created_at":"2021-01-28 15:14:49","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":208006,"visible":true,"origin":"","legend":"Heatmap plot of topological overlap in the gene network. In the heatmap, each row and column correspond to a gene, light color denotes low topological overlap, and progressively darker red denotes higher topological overlap. Darker squares along the diagonal correspond to modules. The gene dendrogram and module assignment are shown along the left and top.","description":"","filename":"OnlineFigure2.png","url":"https://assets-eu.researchsquare.com/files/rs-54056/v2/31ef109de60db38b9bbb1eba.png"},{"id":5339351,"identity":"ceec1d60-31d1-4e5c-b27b-b94ffba2e5d1","added_by":"auto","created_at":"2021-01-28 15:14:50","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":286325,"visible":true,"origin":"","legend":"Clustering dendrogram of genes. Gene clustering tree (dendrogram) obtained by hierarchical clustering of adjacency-based dissimilarity. The colored row below the dendrogram indicates module membership identified by the dynamic tree cut method, together with assigned merged module colors and the original module colors.","description":"","filename":"OnlineFigure3.png","url":"https://assets-eu.researchsquare.com/files/rs-54056/v2/11cbc5d7c39babd3335d3af4.png"},{"id":5339353,"identity":"a0cdffa4-edc7-480f-8d89-14eeff072faf","added_by":"auto","created_at":"2021-01-28 15:14:50","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":872410,"visible":true,"origin":"","legend":"Module-feature associations. Each row corresponds to a modulEigengene and the column to the clinical phenotype. Each cell contains the corresponding correlation in the first line and the P-value in the second line. The table is color-coded by correlation according to the color legend.","description":"","filename":"OnlineFigure4.png","url":"https://assets-eu.researchsquare.com/files/rs-54056/v2/4c25620af952b85996d4970f.png"},{"id":5339545,"identity":"a93dd189-6e03-45c8-a1ff-4ab80e2018ad","added_by":"auto","created_at":"2021-01-28 15:20:49","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":110930,"visible":true,"origin":"","legend":"GO functional and KEGG pathway enrichment analyses for genes in the object module. The x-axis shows the number of genes and the y-axis shows the GO and KEGG pathway terms. The -log10 (P-value) of each term is colored according to the legend. (A): GO functional enrichment analysis. (B): KEGG pathway enrichment analysis.","description":"","filename":"OnlineFigure5.png","url":"https://assets-eu.researchsquare.com/files/rs-54056/v2/2587a18336af5bf572b98385.png"},{"id":5339487,"identity":"f2486e8a-7a55-4ed9-b531-f376107f93a0","added_by":"auto","created_at":"2021-01-28 15:17:50","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1046046,"visible":true,"origin":"","legend":"PPI network construction and identification of hub genes. (A) PPI network of genes in magenta module. The edge shows the interaction between two genes. Significant modules identified from the PPI network using the MCODE with a score \u003e 4.0. (A-1) Molecular-1 with MCODE score = 17.29. (A-2) Molecular-2 with MCODE score = 4.4.","description":"","filename":"OnlineFigure6.png","url":"https://assets-eu.researchsquare.com/files/rs-54056/v2/5563601ffcfa2ae430b3ceaf.png"},{"id":5339652,"identity":"aa688ef1-43dd-4c52-98b3-c1b8a836d921","added_by":"auto","created_at":"2021-01-28 15:23:49","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":210986,"visible":true,"origin":"","legend":"Validation with RT-qPCR (A) and the relationship between genes and lipid parameter in Control (B), HCH (C) and HTG (D). *P \u003c 0.001. ","description":"","filename":"OnlineFigure7.png","url":"https://assets-eu.researchsquare.com/files/rs-54056/v2/03f3980ba9cf5a9c26434e45.png"},{"id":13652657,"identity":"3b4cf859-487f-452a-8bfe-814e7b1d163d","added_by":"auto","created_at":"2021-09-17 09:50:22","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2712393,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-54056/v2/340c6380-c169-4920-9e1a-6c7f9fcb424e.pdf"},{"id":5339488,"identity":"2de1f048-4926-46e8-a042-1b153eb955e9","added_by":"auto","created_at":"2021-01-28 15:17:50","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":15456154,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-54056/v2/15992a158fa6f53f0c267af0.xlsx"},{"id":5339350,"identity":"56761778-f1bc-4cb3-ab68-164c73ec2ac1","added_by":"auto","created_at":"2021-01-28 15:14:50","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":734322,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile2.docx","url":"https://assets-eu.researchsquare.com/files/rs-54056/v2/e64205673e75ef5ca7e8cac7.docx"}],"financialInterests":"","formattedTitle":"Weighted gene co-expression network analysis to identify key modules and hub genes related to hyperlipidaemia","fulltext":[{"header":"Background","content":"\u003cp\u003eCoronary artery disease (CAD) has become a prominent cause of morbidity, mortality, disability, high healthcare costs and functional deterioration and accounts for approximately 30% of all deaths worldwide [1-3]. Hyperlipidaemia (HLP) is a major risk factor for CAD and its complications. Comprehensive lipid-lowering therapy is recommended for patients with CAD by the 2013 American College of Cardiology (ACC)/American Heart Association (AHA) guidelines for the treatment of blood cholesterol to reduce the risk of cardiovascular events [4]. The guidelines emphasize that lipid-lowering therapy should not focus solely on decreasing low-density lipoprotein cholesterol (LDL-C) levels. Several compelling studies proved that lowering total cholesterol (TC) [5], triglyceride (TG) [5] and LDL-C [6] levels is more effective in reducing cardiovascular risk than lowering LDL-C levels alone [7]. The \u0026ldquo;6 percent effect\u0026rdquo; of statins refers to the fact that doubling the dose of statins only decreases LDL-C levels by 6.4%, and PCSK9 inhibitors combined with statins are recommended for patients with acute coronary syndrome (ACS) with a high risk of cardiovascular events [8]. Thus, the identification of novel therapeutic targets for HLP is expected to further reduce the risk of cardiovascular disease.\u003c/p\u003e\n\u003cp\u003eMicroarray analysis might serve as a novel and practical approach to identify susceptibility genes associated with HLP [9]. However, the reproducibility and sensitivity of microarray analysis based on differentially expressed genes may be limited [10, 11]. Gene co-expression network-based methods have been widely used in processing microarray data and have especially been used to identify meaningful functional modules [12, 13]. Weighted gene co-expression network analysis (WGCNA) is one of the most effective methods of gene co-expression network analysis. Instead of simply identifying the differentially expressed genes, a scale-free network of gene-gene interactions is generated by WGCNA, and several significant modules composed of genes with similar functions could be identified by WGCNA; in addition, it can be used to further analyse the correlation between modules and phenotypes or clinical characteristics [14]. Therefore, WGCNA could be utilized to construct a co-expression network and identify significant modules in the network, which may help us to illuminate the intrinsic characteristics of HLP and provide new insights into potential genetic biomarkers, signalling pathways and molecular mechanisms involved in HLP.\u003c/p\u003e"},{"header":"Materials And Methods","content":"\u003cp\u003e\u003cstrong\u003eHyperlipidaemia\u003c/strong\u003e\u003cstrong\u003e microarray datasets\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe microarray dataset obtained from patients with HLP (GSE66676) was downloaded from the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih.gov/geo/) database, which is based on the platform of the GPL6244 Affymetrix Human Gene 1.0 ST Array. Gene expression value matrices were obtained from the original files in CEL format after normalizing the expression values by using RMA methods in R software (version 4.0.0). [15]. Then, the Bioconductor package was used to transform the probe identification numbers (IDs) into gene symbols [16]. When multiple probe IDs corresponded to the same gene, the average expression value was used as the expression value.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConstruction of the weighted gene co-expression network\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWGCNA is a widely used systems biology method that is usually used to establish a scale-free network based on gene expression data profiles [12]. The co-expression network was constructed by selecting the genes whose variance was greater than all the quartiles of variance. After the sample cluster tree was constructed, cut height = 35 was used to screen the samples for subsequent studies. To ensure the reliability of the results of the network construction, the outlier samples were eliminated, and the samples in cluster 1 were selected to build the sample dendrogram and trait heatmap. The appropriate soft threshold power (soft power = 9) was selected according to the standard scale-free networks, and the adjacency values between all differentially expressed genes were calculated using a power function. Then, the adjacency values were transformed into a topological overlap matrix (TOM), and the corresponding dissimilarity (1-TOM) values were calculated. Module identification was accomplished with the dynamic tree cut method by hierarchically clustering genes using 1-TOM as the distance measure with a minimum size cut-off of 30 and a deep split value of 2 for the resulting dendrogram. To verify the stability of the identified modules, a module preservation function was used to calculate module preservation and quality statistics in the WGCNA package [17].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIdentification of the module of\u0026nbsp;interest and\u0026nbsp;functional annotation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePearson correlation analysis was used to assess the correlations between modules and clinical characteristics to identify biologically meaningful modules. All genes associated with the significant module were subjected to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses by using the Database for Annotation, Visualization and Integrated Discovery (DAVID) online tool (version 6.8; http://david.abcc.ncifcrf.gov). \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05 was set as the cut-off criterion.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHub gene analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe degree of module membership (MM) was defined as the correlation between the gene expression profile and the module eigengenes (Mes). The degree of gene significance (GS) was defined as the absolute value of the correlation between the gene and external traits. In general, modules with increased MS and GS values among all the identified modules were selected for further analysis of their biological function [18]. The protein-protein interaction (PPI) network of genes in the selected module was constructed by the Search Tool for the Retrieval of Interacting Genes database (version 11.0; http://www.string-db.org) [19] and then visualized using Cytoscape software [20]. Molecular complex detection (MCODE) [21] was used to identify the most valuable clustering module. An MCODE score \u0026gt; 4 was the threshold for inclusion in further analysis. CytoHubba, a Cytoscape plugin, was used to identify hub genes in the PPI network; it provides 11 methods to explore important nodes in biological networks, of which degree has a better performance [22].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSample verification and diagnostic criteria\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 462 (229 males, 49.57%; 233 females, 50.43%) unrelated participants with normal lipid levels and 485 (236 males, 48.66%; 249 females, 51.34%) unrelated subjects with hypercholesterolaemia (HCH, TC \u0026gt; 5.17 mmol/l) and 474 (232 males, 49.16%; 241 females, 50.84%) unrelated participants with hypertriglyceridaemia (HTG, TG \u0026gt; 1.70 mmol/l) were randomly recruited from the Physical Examination Center of the Affiliated Hospital of Guizhou Medical University. The age ranged from 24 to 82 years. There was no difference in age distribution or sex ratio between the control and HCH or HTG groups. Patients suffering from HCH did not have a history of HTG, and patients suffering from HTG did not have a history of HCH. All participants were basically healthy and had no history of myocardial infarction, CAD, type 2 diabetes mellitus (T2DM) or ischaemic stroke. They were not taking any medicines that could alter serum lipid levels. All subjects had signed written informed consent. The research protocol was approved by the Ethics Committee of the Affiliated Hospital of Guizhou Medical University.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEpidemiological analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUniversally standardized methods and protocols were used to conduct the epidemiological survey [23]. Detailed lifestyle and demographic characteristics were collected with a standard set of questionnaires. Alcohol consumption (0 (non-drinker), \u0026lt; 25 g/day and \u0026ge; 25 g/day) and smoking status (0 (non-smoker), \u0026lt; 20 cigarettes/day and \u0026ge; 20 cigarettes/day) were divided into three different subgroups. Waist circumference, BMI, height, blood pressure and weight were measured as previously described [24].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBiochemical assays\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFasting venous blood samples of 5 ml were collected from each subject. A portion of the sample (2 ml) was placed in a tube and used to measure serum lipid levels. The remaining sample (3 ml) was collected in a glass tube containing anticoagulants (14.70 g/L glucose, 13.20 g/L trisodium citrate, 4.80 g/L citric acid) and utilized to extract deoxyribonucleic acid (DNA). The methods for performing serum ApoA1, HDL-C, ApoB, TG, LDL-C and TC measurements were described in a previous study [25]. All determinations were conducted using an autoanalyser (Type 7170A; Hitachi Ltd., Tokyo, Japan) in the Clinical Science Experiment Center of the Affiliated Hospital of Guizhou Medical University.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eQuantitative real-time PCR\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePeripheral blood monocytes (PBMCs) were isolated from blood samples with TRIzol reagent, which was used to extract the total RNA that was then reverse-transcribed into cDNA by using the PrimeScript RT reagent kit (Takara Bio, Japan). The obtained cDNA was used as a template for RT-qPCR. \u003cstrong\u003eTable 1\u003c/strong\u003e shows that specific primer sequences, which were designed by Sangon Biotech (Shanghai, China), were used to detect the 2 hub genes. Quantitative RT-PCR was performed using a Taq PCR Master Mix Kit (Takara) on an ABI Prism 7500 sequence-detection system (Applied Biosystems, USA) using RT Reaction Mix in a total volume of 20 \u0026mu;L with the following reaction conditions: pre-denaturation at 95\u0026deg;C for 30 seconds, then 40 cycles of 95\u0026deg;C for 30 seconds and 60\u0026deg;C for 30 seconds.\u003c/p\u003e\u003cp style='margin:0in;text-align:justify;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cstrong\u003e\u003cspan style=\"font-size:16px;color:black;\"\u003eTable 1\u003c/span\u003e\u003c/strong\u003e\u003cspan style=\"color:black;\"\u003e\u0026nbsp;\u003c/span\u003e\u003cspan style=\"font-size:16px;color:black;\"\u003ePCR primers for quantitative real-time PCR\u003c/span\u003e\u003c/p\u003e\n\u003ctable style=\"width:463.25pt;border-collapse:collapse;border:none;\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 74.65pt;border-top: 1pt solid windowtext;border-left: none;border-bottom: 1pt solid windowtext;border-right: none;padding: 0in 5.4pt;height: 18.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:justify;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cstrong\u003e\u003cspan style=\"font-size:15px;color:black;\"\u003eGene\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 196.55pt;border-top: 1pt solid windowtext;border-left: none;border-bottom: 1pt solid windowtext;border-right: none;padding: 0in 5.4pt;height: 18.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:justify;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cstrong\u003e\u003cspan style=\"font-size:15px;color:black;\"\u003eForward primer\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 192.05pt;border-top: 1pt solid windowtext;border-left: none;border-bottom: 1pt solid windowtext;border-right: none;padding: 0in 5.4pt;height: 18.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:justify;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cstrong\u003e\u003cspan style=\"font-size:15px;color:black;\"\u003eReverse primer\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 74.65pt;padding: 0in 5.4pt;height: 18.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:justify;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style=\"font-size:15px;color:black;\"\u003eSQLE\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 196.55pt;padding: 0in 5.4pt;height: 18.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:justify;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style=\"font-size:15px;color:black;\"\u003eTCTGGGGGTTAAGAGCAGTG\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 192.05pt;padding: 0in 5.4pt;height: 18.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:justify;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style=\"font-size:15px;color:black;\"\u003eGTGTCTACACTTACCATCTGTGGC\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 74.65pt;padding: 0in 5.4pt;height: 18.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:justify;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style=\"font-size:15px;color:black;\"\u003eSCD\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 196.55pt;padding: 0in 5.4pt;height: 18.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:justify;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style=\"font-size:15px;color:black;\"\u003eCTTGCGATATGCTGTGGTGC\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 192.05pt;padding: 0in 5.4pt;height: 18.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:justify;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style=\"font-size:15px;color:black;\"\u003eGGCTCCTAGCCTAATCCCCT\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 74.65pt;border-top: none;border-right: none;border-left: none;border-image: initial;border-bottom: 1pt solid windowtext;padding: 0in 5.4pt;height: 18.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:justify;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style=\"font-size:15px;color:black;\"\u003eGAPDH\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 196.55pt;border-top: none;border-right: none;border-left: none;border-image: initial;border-bottom: 1pt solid windowtext;padding: 0in 5.4pt;height: 18.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:justify;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style=\"font-size:15px;color:black;\"\u003eGCAACTAGGATGGTGTGGCT\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 192.05pt;border-top: none;border-right: none;border-left: none;border-image: initial;border-bottom: 1pt solid windowtext;padding: 0in 5.4pt;height: 18.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:justify;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style=\"font-size:15px;color:black;\"\u003eTCCCATTCCCCAGCTCTCATA\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\u003cbr\u003e\u003cp\u003e\u003cstrong\u003eDiagnostic criteria\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe values of serum ApoB (0.80-1.05 g/L), HDL-C (1.16-1.42 mmol/L), ApoA1 (1.20-1.60 g/L), TC (3.10-5.17 mmol/L), TG (0.56-1.70 mmol/L), the ApoA1/ApoB ratio (1.00-2.50) and LDL-C (2.70-3.10 mmol/L) were defined as normal at our Clinical Science Experiment Center. Subjects with TG \u0026gt; 1.70 mmol/L were defined as having hypertriglyceridaemia, and TC \u0026gt; 5.17 mmol/L was defined as having hypercholesterolaemia [26]. Participants with a fasting plasma (blood) glucose value \u0026ge; 7.0 mmol/L were defined as having diabetes [27]. The diagnostic criteria of hypertension [28], obesity, normal weight and overweight were described in our previous study [29].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSPSS (Version 22.0) was used to process the research data. The results are presented as the mean \u0026plusmn; SD except for TG levels, which are presented as medians and interquartile ranges. The differences in the general characteristics except for TG between HCH/HTG patients and controls were analysed by independent-samples t tests. The Kruskal-Wallis and Mann-Whitney nonparametric tests were used to detect the difference in TG levels between patients with HCH/HTG and controls. The chi-square test was utilized to assess the differences in the proportion of smokers, age distribution and alcohol consumption between patients with HCH/HTG and controls. Heat mapping of the correlation models and bioinformatic analysis were performed in R software (version 4.0.0). A \u003cem\u003eP\u003c/em\u003e value \u0026lt; 0.05 was considered to be statistically significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eData pre-processing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGene expression profiles were obtained after normalization of the data and removing the outliers, and a total of 20,284 gene symbols were identified from 67 samples. Additional details about the gene expression profile and the sample phenotypes are presented in \u003cstrong\u003eAdditional file 1: Tables S1 and S2.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWeighted gene co\u003c/strong\u003e\u003cstrong\u003e‑\u003c/strong\u003e\u003cstrong\u003eexpression networks\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe sample cluster tree and sample dendrogram and trait heatmap are shown in \u003cstrong\u003eAdditional file 2: \u003c/strong\u003e\u003cstrong\u003eFigures\u003c/strong\u003e\u003cstrong\u003e S1 and S2. \u003c/strong\u003eThe gene expression profiles of 42 samples in cluster 1 were selected to build the weighted gene co-expression network. After the soft threshold (\u0026beta; = 9) was determined (\u003cstrong\u003eFigure 1\u003c/strong\u003e), the weighted gene co-expression network was constructed by selecting the genes whose variance was greater than all the quartiles of variance. The adjacency matrix and correlation matrix of the gene expression profile were calculated and then transformed into a topological overlap matrix (TOM), and a clustering tree of genes based on the gene\u0026ndash;gene non-\u0026omega; similarity was obtained (\u003cstrong\u003eFigure 2\u003c/strong\u003e). Combined with the TOM, the gene modules of each gene network were identified by the hierarchical average linkage clustering method, and twenty gene modules were identified by the dynamic tree cut algorithm (cut height = 0.25) (\u003cstrong\u003eFigure 3\u003c/strong\u003e). The grey module contains all the genes that do not belong to the other modules and were excluded from subsequent analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIdentification of the modules of\u0026nbsp;interest and\u0026nbsp;functional annotation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe identification of modules that were significantly related to clinical phenotype was of high biological significance. In this study, we noticed that the royal blue module was associated with \u003cem\u003eTC\u003c/em\u003e (\u003cem\u003er \u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e = 0.38, \u003cem\u003eP\u003c/em\u003e = 0.01), TG (\u003cem\u003er \u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e = 0.41, \u003cem\u003eP\u003c/em\u003e = 0.007) and non-HDL-C (\u003cem\u003er \u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e = 0.32, \u003cem\u003eP\u003c/em\u003e = 0.04), and the genes in the royal blue module were studied in the subsequent analyses (\u003cstrong\u003eFigure 4\u003c/strong\u003e). GO and KEGG pathway enrichment analyses were used to further explore the biological functions of the genes in the royal blue module. Furthermore, we noticed that a total of 101 genes (\u003cstrong\u003eAdditional file: Tables S3\u003c/strong\u003e) in the royal blue module were significantly correlated with the following pathways: hsa01100: metabolic pathways, hsa01130: biosynthesis of antibiotics, hsa00100: steroid biosynthesis, hsa01212: fatty acid metabolism, and hsa01040: biosynthesis of unsaturated fatty acids. The cell components, biological processes, molecular functions and KEGG pathway analysis of the royal blue module are also shown in \u003cstrong\u003eFigure 5\u003c/strong\u003e\u003cstrong\u003e,\u003c/strong\u003e and more detailed information is presented in \u003cstrong\u003eAdditional file: Tables S4 and S5\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePPI network construction and module analysis of DEGs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA PPI network including 93 notes and 333 edges was constructed by the STRING online tool. As shown in \u003cstrong\u003eFigure 6\u003c/strong\u003e, the hub genes \u003cem\u003eSQLE\u003c/em\u003e (degree = 17) and \u003cem\u003eSCD \u003c/em\u003e(degree = 5) were identified by cytoHubba plug-ins in MCODE1 and MCODE2, respectively. Thus, we speculate that the genes mentioned above may be significantly correlated with blood lipid metabolism.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eValidation analysis by RT-qPCR\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs shown in \u003cstrong\u003eFigure 7A\u003c/strong\u003e, the RT-qPCR results revealed that the expression of \u003cem\u003eSQLE \u003c/em\u003ein the HCH group and \u003cem\u003eSCD \u003c/em\u003ein the HTG group was higher than that in healthy subjects. At the same time, we also noticed that \u003cem\u003eSQLE\u003c/em\u003e was positively correlated with TC (\u003cstrong\u003eFigure 7C\u003c/strong\u003e) levels in the HCH group and that \u003cem\u003eSCD\u003c/em\u003e was positively correlated with TG levels in the HTG group (\u003cstrong\u003eFigure 7D\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCommon and biochemical characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs mentioned in \u003cstrong\u003eTable 2\u003c/strong\u003e, the sex ratio, age and height were similar between the controls and patients with HCH/HTG. Serum HDL-C and ApoA1 levels and the ApoA1/ApoB ratio were significantly higher, and the proportion of smokers, proportion of drinkers, systolic blood pressure, waist circumference, weight, diastolic blood pressure, glucose level, pulse pressure, body mass index (BMI), and serum LDL-C, ApoB, TG and TC levels were significantly lower in controls than in patients with hyperlipidaemia.\u003c/p\u003e\u003cp style='margin:0in;text-align:justify;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cstrong\u003e\u003cspan style=\"font-size:15px;color:black;\"\u003eTable 2\u0026nbsp;\u003c/span\u003e\u003c/strong\u003e\u003cspan style=\"font-size:16px;color:black;\"\u003eComparison of demographic, lifestyle characteristics and serum lipid levels of the participants\u003c/span\u003e\u003c/p\u003e\n\u003ctable style=\"width:531.6pt;margin-left:-42.55pt;border-collapse:collapse;border: none;\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 126.4pt;border-top: 1pt solid windowtext;border-left: none;border-bottom: 1pt solid windowtext;border-right: none;padding: 0in 5.4pt;height: 24.45pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:left;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003eCharacteristic\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72.4pt;border-top: 1pt solid windowtext;border-left: none;border-bottom: 1pt solid windowtext;border-right: none;padding: 0in 5.4pt;height: 24.45pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003eControl\u003c/span\u003e\u003cspan style=\"font-family:DengXian;color:black;\"\u003e（\u003c/span\u003e\u003cspan style=\"color:black;\"\u003en=462\u003c/span\u003e\u003cspan style=\"font-family:DengXian;color:black;\"\u003e）\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69.1pt;border-top: 1pt solid windowtext;border-left: none;border-bottom: 1pt solid windowtext;border-right: none;padding: 0in 5.4pt;height: 24.45pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003eHCH\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003e(n=485)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69.1pt;border-top: 1pt solid windowtext;border-left: none;border-bottom: 1pt solid windowtext;border-right: none;padding: 0in 5.4pt;height: 24.45pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003eHTG\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003e(n=474)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94pt;border-top: 1pt solid windowtext;border-left: none;border-bottom: 1pt solid windowtext;border-right: none;padding: 0in 5.4pt;height: 24.45pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cem\u003e\u003cspan style=\"color:black;\"\u003eP\u003c/span\u003e\u003c/em\u003e\u003cstrong\u003e\u003cem\u003e\u003cspan style=\"color:black;\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/em\u003e\u003c/strong\u003e\u003cem\u003e\u003csub\u003e\u003cspan style=\"color:black;\"\u003eHCH vs. controls\u003c/span\u003e\u003c/sub\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100.6pt;border-top: 1pt solid windowtext;border-left: none;border-bottom: 1pt solid windowtext;border-right: none;padding: 0in 5.4pt;height: 24.45pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cem\u003e\u003cspan style=\"color:black;\"\u003eP\u003c/span\u003e\u003c/em\u003e\u003cstrong\u003e\u003cem\u003e\u003cspan style=\"color:black;\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/em\u003e\u003c/strong\u003e\u003cem\u003e\u003csub\u003e\u003cspan style=\"color:black;\"\u003eHTG vs. controls\u003c/span\u003e\u003c/sub\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 126.4pt;padding: 0in 5.4pt;height: 20.8pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:justify;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003eMale/female\u003csup\u003e3\u003c/sup\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72.4pt;padding: 0in 5.4pt;height: 20.8pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003e229/233\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69.1pt;padding: 0in 5.4pt;height: 20.8pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003e236/249\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69.1pt;padding: 0in 5.4pt;height: 20.8pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003e232/241\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94pt;padding: 0in 5.4pt;height: 20.8pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003e0.780\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100.6pt;padding: 0in 5.4pt;height: 20.8pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003e0.870\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 126.4pt;padding: 0in 5.4pt;height: 20.2pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:justify;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003eAge (years)\u003csup\u003e1\u003c/sup\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72.4pt;padding: 0in 5.4pt;height: 20.2pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003e57.60\u0026plusmn;\u003c/span\u003e\u003cspan style=\"color:black;\"\u003e8.81\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69.1pt;padding: 0in 5.4pt;height: 20.2pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003e58.13\u0026plusmn;9.69\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69.1pt;padding: 0in 5.4pt;height: 20.2pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003e57.10\u0026plusmn;7.61\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94pt;padding: 0in 5.4pt;height: 20.2pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003e0.379\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100.6pt;padding: 0in 5.4pt;height: 20.2pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003e0.359\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 126.4pt;padding: 0in 5.4pt;height: 20.8pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:justify;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003eHeight (cm)\u003csup\u003e1\u003c/sup\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72.4pt;padding: 0in 5.4pt;height: 20.8pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003e159.83\u0026plusmn;8.20\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69.1pt;padding: 0in 5.4pt;height: 20.8pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003e160.66\u0026plusmn;7.93\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69.1pt;padding: 0in 5.4pt;height: 20.8pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003e159.92\u0026plusmn;8.11\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94pt;padding: 0in 5.4pt;height: 20.8pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003e0.114\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100.6pt;padding: 0in 5.4pt;height: 20.8pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003e0.771\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 126.4pt;padding: 0in 5.4pt;height: 20.2pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:justify;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003eWeight (kg)\u003csup\u003e1\u003c/sup\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72.4pt;padding: 0in 5.4pt;height: 20.2pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003e58.98\u0026plusmn;9.90\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69.1pt;padding: 0in 5.4pt;height: 20.2pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003e62.66\u0026plusmn;10.09\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69.1pt;padding: 0in 5.4pt;height: 20.2pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003e60.52\u0026plusmn;11.08\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94pt;padding: 0in 5.4pt;height: 20.2pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003e1.97E-8\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100.6pt;padding: 0in 5.4pt;height: 20.2pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003e0.021\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 126.4pt;padding: 0in 5.4pt;height: 20.8pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:justify;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003eBody mass index (kg/m\u0026sup2;)\u003csup\u003e1\u003c/sup\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72.4pt;padding: 0in 5.4pt;height: 20.8pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003e23.05\u0026plusmn;3.32\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69.1pt;padding: 0in 5.4pt;height: 20.8pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003e24.21\u0026plusmn;3.12\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69.1pt;padding: 0in 5.4pt;height: 20.8pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003e23.61\u0026plusmn;3.67\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94pt;padding: 0in 5.4pt;height: 20.8pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003e3.59E-8\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100.6pt;padding: 0in 5.4pt;height: 20.8pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003e0.014\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 126.4pt;padding: 0in 5.4pt;height: 20.2pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:justify;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003eWaist circumference\u003c/span\u003e\u003csup\u003e\u003cspan style=\"color:black;\"\u003e1\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72.4pt;padding: 0in 5.4pt;height: 20.2pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003e74.50\u0026plusmn;8.47\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69.1pt;padding: 0in 5.4pt;height: 20.2pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003e78.37\u0026plusmn;8.76\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69.1pt;padding: 0in 5.4pt;height: 20.2pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003e81.10\u003c/span\u003e\u003cspan style=\"color:black;\"\u003e\u0026plusmn;9.21\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94pt;padding: 0in 5.4pt;height: 20.2pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003e1.00E-11\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100.6pt;padding: 0in 5.4pt;height: 20.2pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003e4.10E-28\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 126.4pt;padding: 0in 5.4pt;height: 20.2pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:justify;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003eSmoking, \u003cem\u003en\u003c/em\u003e %\u003csup\u003e3\u003c/sup\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72.4pt;padding: 0in 5.4pt;height: 20.2pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69.1pt;padding: 0in 5.4pt;height: 20.2pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69.1pt;padding: 0in 5.4pt;height: 20.2pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94pt;padding: 0in 5.4pt;height: 20.2pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100.6pt;padding: 0in 5.4pt;height: 20.2pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 126.4pt;padding: 0in 5.4pt;height: 20.8pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:justify;font-size:14px;font-family:\"Calibri\",sans-serif;text-indent:10.5pt;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003eNon-smoker\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72.4pt;padding: 0in 5.4pt;height: 20.8pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003e355\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69.1pt;padding: 0in 5.4pt;height: 20.8pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003e331\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69.1pt;padding: 0in 5.4pt;height: 20.8pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003e349\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94pt;padding: 0in 5.4pt;height: 20.8pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100.6pt;padding: 0in 5.4pt;height: 20.8pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 126.4pt;padding: 0in 5.4pt;height: 20.8pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:justify;font-size:14px;font-family:\"Calibri\",sans-serif;text-indent:10.5pt;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003e\u0026le; 20 cigarettes/day\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72.4pt;padding: 0in 5.4pt;height: 20.8pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003e98\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69.1pt;padding: 0in 5.4pt;height: 20.8pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003e114\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69.1pt;padding: 0in 5.4pt;height: 20.8pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003e73\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94pt;padding: 0in 5.4pt;height: 20.8pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100.6pt;padding: 0in 5.4pt;height: 20.8pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 126.4pt;padding: 0in 5.4pt;height: 20.8pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:justify;font-size:14px;font-family:\"Calibri\",sans-serif;text-indent:10.5pt;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003e\u0026gt; 20 cigarettes/day\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72.4pt;padding: 0in 5.4pt;height: 20.8pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003e9\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69.1pt;padding: 0in 5.4pt;height: 20.8pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003e40\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69.1pt;padding: 0in 5.4pt;height: 20.8pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003e52\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94pt;padding: 0in 5.4pt;height: 20.8pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003e1.73E-5\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100.6pt;padding: 0in 5.4pt;height: 20.8pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003e4.42E-8\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 126.4pt;padding: 0in 5.4pt;height: 20.8pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:justify;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003eAlcohol, \u003cem\u003en\u003c/em\u003e %\u003csup\u003e3\u003c/sup\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72.4pt;padding: 0in 5.4pt;height: 20.8pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69.1pt;padding: 0in 5.4pt;height: 20.8pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69.1pt;padding: 0in 5.4pt;height: 20.8pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94pt;padding: 0in 5.4pt;height: 20.8pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100.6pt;padding: 0in 5.4pt;height: 20.8pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 126.4pt;padding: 0in 5.4pt;height: 20.8pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:justify;font-size:14px;font-family:\"Calibri\",sans-serif;text-indent:10.5pt;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003eNon-drinker\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72.4pt;padding: 0in 5.4pt;height: 20.8pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003e377\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69.1pt;padding: 0in 5.4pt;height: 20.8pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003e354\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69.1pt;padding: 0in 5.4pt;height: 20.8pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003e363\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94pt;padding: 0in 5.4pt;height: 20.8pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100.6pt;padding: 0in 5.4pt;height: 20.8pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 126.4pt;padding: 0in 5.4pt;height: 20.8pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:justify;font-size:14px;font-family:\"Calibri\",sans-serif;text-indent:10.5pt;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003e\u0026le; 25 g/day\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72.4pt;padding: 0in 5.4pt;height: 20.8pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003e45\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69.1pt;padding: 0in 5.4pt;height: 20.8pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003e55\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69.1pt;padding: 0in 5.4pt;height: 20.8pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003e44\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94pt;padding: 0in 5.4pt;height: 20.8pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100.6pt;padding: 0in 5.4pt;height: 20.8pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 126.4pt;padding: 0in 5.4pt;height: 20.8pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:justify;font-size:14px;font-family:\"Calibri\",sans-serif;text-indent:10.5pt;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003e\u0026gt; 25 g/day\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72.4pt;padding: 0in 5.4pt;height: 20.8pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003e40\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69.1pt;padding: 0in 5.4pt;height: 20.8pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003e76\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69.1pt;padding: 0in 5.4pt;height: 20.8pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003e67\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94pt;padding: 0in 5.4pt;height: 20.8pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003e0.002\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100.6pt;padding: 0in 5.4pt;height: 20.8pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003e0.031\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 126.4pt;padding: 0in 5.4pt;height: 20.2pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:justify;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003eSBP (mmHg)\u003csup\u003e1\u003c/sup\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72.4pt;padding: 0in 5.4pt;height: 20.2pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003e135.49\u0026plusmn;22.58\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69.1pt;padding: 0in 5.4pt;height: 20.2pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003e139.52\u0026plusmn;22.56\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69.1pt;padding: 0in 5.4pt;height: 20.2pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003e141.15\u0026plusmn;20.42\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94pt;padding: 0in 5.4pt;height: 20.2pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003e0.006\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100.6pt;padding: 0in 5.4pt;height: 20.2pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003e1.08E-4\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 126.4pt;padding: 0in 5.4pt;height: 20.8pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:justify;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003eD\u003c/span\u003e\u003cspan style=\"color:black;\"\u003eBP (mmHg)\u003csup\u003e1\u003c/sup\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72.4pt;padding: 0in 5.4pt;height: 20.8pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003e82.45\u0026plusmn;12.42\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69.1pt;padding: 0in 5.4pt;height: 20.8pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003e84.01\u0026plusmn;11.71\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69.1pt;padding: 0in 5.4pt;height: 20.8pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003e85.15\u0026plusmn;11.72\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94pt;padding: 0in 5.4pt;height: 20.8pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003e0.047\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100.6pt;padding: 0in 5.4pt;height: 20.8pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003e0.001\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 126.4pt;padding: 0in 5.4pt;height: 20.2pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:justify;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003ePP (mmHg) \u003csup\u003e1\u003c/sup\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72.4pt;padding: 0in 5.4pt;height: 20.2pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003e53.04\u0026plusmn;17.77\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69.1pt;padding: 0in 5.4pt;height: 20.2pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003e55.51\u0026plusmn;18.19\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69.1pt;padding: 0in 5.4pt;height: 20.2pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003e56.00\u0026plusmn;14.43\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94pt;padding: 0in 5.4pt;height: 20.2pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003e0.035\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100.6pt;padding: 0in 5.4pt;height: 20.2pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003e0.007\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 126.4pt;padding: 0in 5.4pt;height: 20.8pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:justify;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003eGlu (mmol/L) \u003csup\u003e1\u003c/sup\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72.4pt;padding: 0in 5.4pt;height: 20.8pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003e6.14\u0026plusmn;1.42\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69.1pt;padding: 0in 5.4pt;height: 20.8pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003e6.44\u0026plusmn;1.58\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69.1pt;padding: 0in 5.4pt;height: 20.8pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003e6.35\u0026plusmn;1.32\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94pt;padding: 0in 5.4pt;height: 20.8pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003e0.002\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100.6pt;padding: 0in 5.4pt;height: 20.8pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003e0.015\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 126.4pt;padding: 0in 5.4pt;height: 20.2pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:justify;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003eTC (mmol/L) \u003csup\u003e1\u003c/sup\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72.4pt;padding: 0in 5.4pt;height: 20.2pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003e4.37\u0026plusmn;0.64\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69.1pt;padding: 0in 5.4pt;height: 20.2pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003e5.80\u0026plusmn;0.50\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69.1pt;padding: 0in 5.4pt;height: 20.2pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003e4.46\u0026plusmn;0.37\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94pt;padding: 0in 5.4pt;height: 20.2pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003e9.42E-47\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100.6pt;padding: 0in 5.4pt;height: 20.2pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003e0.007\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 126.4pt;padding: 0in 5.4pt;height: 23.35pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:justify;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003eTG (mmol/L)\u003csup\u003e2\u003c/sup\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72.4pt;padding: 0in 5.4pt;height: 23.35pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003e0.99(0.53)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69.1pt;padding: 0in 5.4pt;height: 23.35pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003e1.15(0.45)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69.1pt;padding: 0in 5.4pt;height: 23.35pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003e2.30(1.03)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94pt;padding: 0in 5.4pt;height: 23.35pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003e4.46E-9\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100.6pt;padding: 0in 5.4pt;height: 23.35pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003e1.69E-84\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 126.4pt;padding: 0in 5.4pt;height: 20.2pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:justify;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003eHDL-C (mmol/L)\u003csup\u003e1\u003c/sup\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72.4pt;padding: 0in 5.4pt;height: 20.2pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003e1.64\u0026plusmn;0.48\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69.1pt;padding: 0in 5.4pt;height: 20.2pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003e1.48\u0026plusmn;0.43\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69.1pt;padding: 0in 5.4pt;height: 20.2pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style=\"color:black;\"\u003e1.44\u0026plusmn;0.46\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94pt;padding: 0in 5.4pt;height: 20.2pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003e2.56E-8\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100.6pt;padding: 0in 5.4pt;height: 20.2pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003e7.22E-11\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 126.4pt;padding: 0in 5.4pt;height: 20.8pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:justify;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003eLDL-C (mmol/L)\u003csup\u003e1\u003c/sup\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72.4pt;padding: 0in 5.4pt;height: 20.8pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003e2.50\u0026plusmn;0.55\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69.1pt;padding: 0in 5.4pt;height: 20.8pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003e3.52\u0026plusmn;0.80\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69.1pt;padding: 0in 5.4pt;height: 20.8pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style=\"color:black;\"\u003e2.79\u0026plusmn;0.86\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94pt;padding: 0in 5.4pt;height: 20.8pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003e4.89E-32\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100.6pt;padding: 0in 5.4pt;height: 20.8pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003e1.86E-9\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 126.4pt;padding: 0in 5.4pt;height: 20.2pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:justify;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003eApoA1 (g/L)\u003csup\u003e1\u003c/sup\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72.4pt;padding: 0in 5.4pt;height: 20.2pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003e1.33\u0026plusmn;0.24\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69.1pt;padding: 0in 5.4pt;height: 20.2pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003e1.23\u0026plusmn;0.25\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69.1pt;padding: 0in 5.4pt;height: 20.2pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style=\"color:black;\"\u003e1.25\u0026plusmn;0.24\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94pt;padding: 0in 5.4pt;height: 20.2pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003e3.49E-11\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100.6pt;padding: 0in 5.4pt;height: 20.2pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003e4.66E-8\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 126.4pt;padding: 0in 5.4pt;height: 20.8pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:justify;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003eApoB (g/L)\u003csup\u003e1\u003c/sup\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72.4pt;padding: 0in 5.4pt;height: 20.8pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003e0.98\u0026plusmn;0.17\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69.1pt;padding: 0in 5.4pt;height: 20.8pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003e1.02\u0026plusmn;0.18\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69.1pt;padding: 0in 5.4pt;height: 20.8pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style=\"color:black;\"\u003e1.07\u0026plusmn;0.21\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94pt;padding: 0in 5.4pt;height: 20.8pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003e0.002\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100.6pt;padding: 0in 5.4pt;height: 20.8pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003e1.06E-11\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 126.4pt;border-top: none;border-right: none;border-left: none;border-image: initial;border-bottom: 1pt solid windowtext;padding: 0in 5.4pt;height: 20.8pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:justify;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003eApoA1/ApoB\u003csup\u003e1\u003c/sup\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72.4pt;border-top: none;border-right: none;border-left: none;border-image: initial;border-bottom: 1pt solid windowtext;padding: 0in 5.4pt;height: 20.8pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003e1.39\u0026plusmn;0.33\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69.1pt;border-top: none;border-right: none;border-left: none;border-image: initial;border-bottom: 1pt solid windowtext;padding: 0in 5.4pt;height: 20.8pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003e1.25\u0026plusmn;0.40\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69.1pt;border-top: none;border-right: none;border-left: none;border-image: initial;border-bottom: 1pt solid windowtext;padding: 0in 5.4pt;height: 20.8pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style=\"color:black;\"\u003e1.22\u0026plusmn;0.39\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94pt;border-top: none;border-right: none;border-left: none;border-image: initial;border-bottom: 1pt solid windowtext;padding: 0in 5.4pt;height: 20.8pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style=\"color:black;\"\u003e1.91E-8\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100.6pt;border-top: none;border-right: none;border-left: none;border-image: initial;border-bottom: 1pt solid windowtext;padding: 0in 5.4pt;height: 20.8pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;text-align:center;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:18.0pt;'\u003e\u003cspan style=\"color:black;\"\u003e7.62E-12\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp style='margin:0in;text-align:justify;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:150%;'\u003e\u003cem\u003e\u003cspan style=\"font-size:16px;line-height:150%;color:black;\"\u003eSBP\u003c/span\u003e\u003c/em\u003e\u003cspan style=\"font-size:16px;line-height:150%;color:black;\"\u003e\u0026nbsp;Systolic blood pressure;\u003cem\u003e\u0026nbsp;DBP\u0026nbsp;\u003c/em\u003eDiastolic blood pressure;\u003cem\u003e\u0026nbsp;PP\u003c/em\u003e Pulse pressure;\u003cem\u003e\u0026nbsp;Glu\u0026nbsp;\u003c/em\u003eGlucose;\u003cem\u003e\u0026nbsp;HDL-C\u0026nbsp;\u003c/em\u003ehigh-density lipoprotein cholesterol; \u003cem\u003eLDL-C\u003c/em\u003e low-density lipoprotein cholesterol; \u003cem\u003eApo\u0026nbsp;\u003c/em\u003eApolipoprotein;\u003cem\u003e\u0026nbsp;TC\u003c/em\u003e Total cholesterol\u003cem\u003e; TG\u0026nbsp;\u003c/em\u003eTriglyceride\u003cem\u003e.\u003c/em\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp style='margin:0in;text-align:justify;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:150%;'\u003e\u003cspan style=\"font-size:15px;line-height:150%;color:black;\"\u003e1 Mean \u0026plusmn; SD determined by t-test.\u003c/span\u003e\u003c/p\u003e\n\u003cp style='margin:0in;text-align:justify;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:150%;'\u003e\u003cspan style=\"font-size:15px;line-height:150%;color:black;\"\u003e2 Median (interquartile range) tested by the Wilcoxon-Mann-Whitney test.\u003c/span\u003e\u003c/p\u003e\n\u003cp style='margin:0in;text-align:justify;font-size:14px;font-family:\"Calibri\",sans-serif;line-height:150%;'\u003e\u003cspan style=\"font-size:16px;line-height:150%;color:black;\"\u003e3 The rate or constituent ratio between the different groups was analyzed by the chi-square test.\u003c/span\u003e\u003c/p\u003e\n\u003cp style='margin:0in;text-align:justify;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style=\"font-size:16px;color:black;\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n\u003cp style='margin:0in;text-align:justify;font-size:14px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style=\"font-size:16px;color:black;\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eSeveral recent studies have shown that hypertension, smoking, obesity, age, dyslipidaemia, lack of exercise, sex and diabetes mellitus are common risk factors for cardiovascular disease [30, 31]. A comprehensive understanding of the potential molecular mechanisms involved in the pathogenesis of HLP is helpful for its prevention and treatment. As a novel and practical approach to the identification of HLP susceptibility genes, a microarray analysis using WGCNA may be helpful for the diagnosis of hyperlipidaemia [14]. WGCNA could be used to build a scale-free co-expression network of lipid-associated genes by detecting gene-to-gene interactions rather than simply focusing on the differentially expressed genes (DEGs). Co-expressed genes were enriched in different modules by hierarchical average linkage cluster analysis. In the present research, we analysed a dataset from HLP patients (GSE66676) by using WGCNA and identified that the royal blue module was significantly associated with TC, TG and non-HDL. Furthermore, KEGG enrichment analyses of the genes in the royal blue module indicated that the enriched genes in this module might have significant potential biological functions that are closely related to metabolic pathways, steroid biosynthesis, fatty acid metabolism and biosynthesis of unsaturated fatty acids. Two hub genes (\u003cem\u003eSQLE\u003c/em\u003e and \u003cem\u003eSCD\u003c/em\u003e) were identified in the royal blue module that were detected by MCODE analysis. Moreover, the verification results were highly consistent with the above findings, and we found that the expression of the \u003cem\u003eSQLE\u003c/em\u003e gene in patients with HCH and the \u003cem\u003eSCD\u003c/em\u003e gene in patients with HTG was higher than that in healthy controls. Therefore, the identified \u003cem\u003eSQLE\u003c/em\u003e gene was associated with the onset of HCH, the \u003cem\u003eSCD\u003c/em\u003e gene was associated with the onset of HTG, and the underlying molecular mechanisms of these genes might be slightly different. In addition, \u003cem\u003eSQLE\u003c/em\u003e and \u003cem\u003eSCD\u003c/em\u003e were previously reported to be statin responsive, and they are known to be involved in sterol metabolism and transport; at the same time, there were significant changes in expression levels in the B-cells in response to statin treatment [32], and therefore, \u003cem\u003eSQLE\u003c/em\u003e and \u003cem\u003eSCD\u003c/em\u003e may be new targets for lipid-lowering therapy.\u003c/p\u003e\n\u003cp\u003eFatty acids and cholesterol are essential lipids involved in many crucial biological processes; however, excessive free fatty acids and free cholesterol are major risk factors for type 2 diabetes and atherosclerosis [33]. Previous studies on intermediate metabolites in cholesterol biosynthesis have shown that the first oxidative step in cholesterol biosynthesis is catalysed by squalene monooxygenase (\u003cem\u003eSQLE\u003c/em\u003e), a crucial regulator downstream of HMG-CoA reductase (\u003cem\u003eHMGCR\u003c/em\u003e) in cholesterol synthesis [34]. Meanwhile, \u003cem\u003eSQLE\u003c/em\u003e is suggested as the second rate-limiting enzyme in cholesterol synthesis [35, 36]. Inhibition of \u003cem\u003eSQLE\u003c/em\u003e expression could effectively reduce cholesterol synthesis [37, 38], and the cholesterol-lowering effect is caused by the combination of multiple levels. First, \u003cem\u003eSQLE \u003c/em\u003eand\u003cem\u003e HMGCR\u003c/em\u003e act as direct targets of the sterol regulatory element binding protein 2 (\u003cem\u003eSREBP2)\u003c/em\u003e transcription factor and play a crucial regulatory role in most cholesterol biosensor genes [39, 40]. Second, the N-terminus of the \u003cem\u003eSQLE\u003c/em\u003e protein may contain a cholesterol-sensitive region that mediates the protease degradation of \u003cem\u003eSQLE\u003c/em\u003e in a cholesterol-dependent manner by relying on an E3 ubiquitin ligase such as \u003cem\u003eMARCH\u003c/em\u003e [41]. Interestingly, oleate acts as an unsaturated fatty acid and can stabilize \u003cem\u003eSQLE\u003c/em\u003e by blocking \u003cem\u003eMARCH6\u003c/em\u003e-mediated degradation [42]. In addition, Masanori Honsho \u003cem\u003eet al\u003c/em\u003e. also noticed that inhibition of \u003cem\u003eSQLE\u003c/em\u003e expression through elevating plasmalogen levels may be a novel and alternative potential method to reduce cholesterol synthesis [43]. Similarly, the KEGG analyses in the current study indicated that \u003cem\u003eSQLE\u003c/em\u003e was mainly involved in metabolic pathways and steroid biosynthesis.\u003c/p\u003e\n\u003cp\u003eMetabolic risk factors such as insulin resistance, obesity, hypertension and dyslipidaemia are correlated with each other, so their combination is generally referred to as \u0026ldquo;metabolic syndrome\u0026rdquo; (MetS). Abnormal\u0026nbsp;stearoyl-coenzyme A desaturase (\u003cem\u003eSCD\u003c/em\u003e)\u0026nbsp;expression/activity has been noticed in subjects with metabolic syndrome, indicating that \u003cem\u003eSCD\u003c/em\u003e may be related to the pathogenesis of metabolic syndrome. By querying the GENE database in NCBI, we noticed that \u003cem\u003eSCD\u003c/em\u003e (also known as \u003cem\u003eSCD1\u003c/em\u003e;\u003cem\u003e FADS5\u003c/em\u003e; \u003cem\u003eSCDOS\u003c/em\u003e; \u003cem\u003ehSCD1\u003c/em\u003e; \u003cem\u003eMSTP008\u003c/em\u003e; gene ID: 6319, HGNC: 10571, OMIM: 604031) is positioned on chromosome 10q24.31 (exon count: 6) and encodes a biological synthase, which is mainly involved in the metabolism of fatty acids, especially oleic acid. This protein is an intact membrane protein located in the endoplasmic reticulum and is a member of the fatty acid desaturase family. Herman-Edelstein M \u003cem\u003eet al\u003c/em\u003e. proved that \u003cem\u003eSREBPs\u003c/em\u003e are transcription factors that activate the synthesis of fatty acids (FAs), triglycerides (TGs), and cholesterol, and \u003cem\u003eSREBP2\u003c/em\u003e activates cholesterol production, whereas \u003cem\u003eSREBP1\u003c/em\u003e primarily activates FA and TG synthesis [44]. ATP-citrate lyase (\u003cem\u003eACLY\u003c/em\u003e), a cytosolic enzyme that generates acetyl-CoA for cholesterol and \u003cem\u003ede novo\u003c/em\u003e fatty acid synthesis, is a potential target for hypolipidaemic intervention [45]. \u003cem\u003eACLY\u003c/em\u003e acts as a critical enzyme involved in \u003cem\u003ede novo\u003c/em\u003e fatty acid synthesis and catalyses the conversion of citrate to cytosolic acetyl-CoA. Acetyl-CoA is converted to malonyl CoA via acetyl-CoA carboxylase (\u003cem\u003eACC\u003c/em\u003e), which plays a key role in the first committed step in the synthesis of fatty acids [46]. \u003cem\u003eSCD\u003c/em\u003e is another key rate-limiting enzyme in fatty acid metabolism downstream of \u003cem\u003eACLY\u003c/em\u003e; it can convert different saturated fatty acids into monounsaturated fatty acids, and its expression is directly regulated by \u003cem\u003eSREBP1\u003c/em\u003e [47-50]. Both animal and human studies have shown that \u003cem\u003eSCD \u003c/em\u003eis associated with obesity and insulin resistance [51, 52]. Mice with the \u003cem\u003eSCD\u003c/em\u003e gene exhibited reduced diet-induced weight gain and improved insulin resistance compared to wild-type controls [53]. Deletion of the \u003cem\u003eSCD1\u003c/em\u003e gene product in mice could effectively improve insulin sensitivity, reduce plasma non-HDL cholesterol and triglyceride levels and liver lipid accumulation and increase beneficial HDL cholesterol levels [54]. Daniel Castellano-Castillo \u003cem\u003eet al\u003c/em\u003e. also found a negative relationship between\u003cem\u003e SCD\u003c/em\u003e DNA methylation and BMI and the MetS index [55]. In the current study, we also noticed that\u003cem\u003e SCD\u003c/em\u003e was mainly involved in fatty acid metabolism and the biosynthesis pathways of unsaturated fatty acids.\u003c/p\u003e\n\u003cp\u003eUnhealthy lifestyle factors such as excessive drinking and cigarette smoking have been linked to HLP [56, 57]. In the present study, we found that the percentage of participants who smoked was greater in the hyperlipidaemic group than in the normal group. In recent years, the influence of smoking on HLP has attracted increasing attention. Several recent studies have indicated the existence of lower HDL-C and higher TC, LDL-C and TG levels in smokers than in non-smokers [57]. Moderate drinking reduced the incidence of cardiovascular events, and the potential mechanism may be related to increased HDL-C and ApoA1 levels [58]. However, frequent binge drinking was correlated with an increased risk of CAD mortality because it will lead to a number of serious health problems, including dyslipidaemia, abnormal liver function and myocardial infarction [59]. Therefore, the preventive effect of a healthy lifestyle on hyperlipidaemia should not be ignored when exploring new therapeutic targets for hyperlipidaemia.\u003c/p\u003e\n\u003cp\u003eThis research had several limitations. First, this is a single-centre study comprising a small patient number, and large multicentre studies are necessary to validate our findings. Second, the molecular mechanisms of\u003cem\u003e SQLE\u003c/em\u003e and \u003cem\u003eSCD\u003c/em\u003e involved in HLP are still not fully defined and require further cytology and animal experiments to further outline their respective roles \u003cem\u003ein vivo\u003c/em\u003e and \u003cem\u003ein vitro\u003c/em\u003e.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eWGCNA identified that the royal blue module was significantly associated with TC, TG and non-HDL. GO and KEGG enrichment analyses revealed that the hub genes of \u003cem\u003eSQLE\u003c/em\u003e were associated with TC and that \u003cem\u003eSCD\u003c/em\u003e was associated with TG metabolism. The verification results of RT-qPCR revealed that the expression of \u003cem\u003eSQLE\u003c/em\u003e in hypercholesterolaemia and SCD in hypertriglyceridaemia was higher than that in normal controls, which further increased the credibility of the conclusion. Thus, we speculated that \u003cem\u003eSQLE\u003c/em\u003e may be a novel target for cholesterol-lowering therapy and that \u003cem\u003eSCD \u003c/em\u003emay be a novel target for triglyceride-lowering therapy.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eWGCNA: weighted gene co-expression network analysis; HCH: hypercholesterolemia; HTG: hypertriglyceridemia; SQLE: squalene epoxidase; SCD: stearoyl-CoA desaturase; DAVID: Database for Annotation, Visualization and Integrated Discovery; T2DM: Type 2 diabetes mellitus; GO: Gene Ontology; HDL-C: High-density lipoprotein cholesterol; IS: Ischemic stroke; KEGG: Kyoto Encyclopedia of Genes and genomes; LDL-C: Low-density lipoprotein cholesterol; MCODE: Molecular Complex Detection; Apo: Apolipoprotein; PPI: Protein-protein interaction; GEO: Gene Expression Omnibus; BMI: Body mass index; TG: Triglyceride; RT-qPCR: Quantitative real time polymerase chain reaction; TC: Total cholesterol; ACC: acetyl CoA carboxylase; ACLY: ATP - Citrate Lyase; FAs: fatty acids; MetS: metabolic syndrome; HMGCR: HMG-CoA reductase; SREBP2: sterol regulatory element binding protein 2; CAD: Coronary artery disease; HLP: Hyperlipidaemia; ACC: American College of Cardiology; AHA: American Heart Association; ACS: acute coronary syndrome; TOM: topological overlap matrix; MM: module membership; Mes: module eigengenes; Mes: module eigengenes; DNA: deoxyribonucleic acid; PBMCs: Peripheral blood monocytes; DEGs: differentially expressed genes.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank all the participants of this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eF.-J.L. and P.-F.Z. conceived the study, carried out the epidemiological survey and collected the samples, participated in the design, and drafted the manuscript. Y.Z.G. performed the statistical analyses. H.W.P. helped to modify the manuscript. W.L. conceived the study, participated in the design, carried out the epidemiological survey, and helped to draft the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the National Natural Science Foundation of China (No. 81960047).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study design was approved by the Ethics Committee of the Affiliated Hospital of Guizhou Medical University. Informed consent was obtained from all participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor details\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1 \u003c/sup\u003eDepartment of Cardiology, Affiliated Hospital of Guizhou Medical University, 28 Guyi Street, Guiyang 550002, Guizhou, People\u0026rsquo;s Republic of China\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e2 \u003c/sup\u003eDepartment of Cardiology, The Central Hospital of ShaoYang, 36 QianYuan lane, Shaoyang 422000, Hunan, People\u0026rsquo;s Republic of China\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e3 \u003c/sup\u003eGraduate School of Guangxi Medical University, 22 Shuangyong Road, Nanning 530021, Guangxi, People\u0026rsquo;s Republic of China\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e4\u003c/sup\u003eDepartment of Cardiology, Hunan Provincial People\u0026rsquo;s Hospital \u0026amp; First Affiliated Hospital of Hunan Normal University, Changsha, Hunan, China\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eHouston M: The role of noninvasive cardiovascular testing, applied clinical nutrition and nutritional supplements in the prevention and treatment of coronary heart disease. 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Eur Heart J 2012, 33(13):1598-1605.\u003c/li\u003e\n\u003c/ol\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":"nutrition-and-metabolism","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nuam","sideBox":"Learn more about [Nutrition \u0026 Metabolism](http://nutritionandmetabolism.biomedcentral.com/)","snPcode":"12986","submissionUrl":"https://submission.nature.com/new-submission/12986/3","title":"Nutrition \u0026 Metabolism","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Weighted gene co-expression network analysis, Hyperlipidaemia, Significant modules, Hub genes","lastPublishedDoi":"10.21203/rs.3.rs-54056/v2","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-54056/v2","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eThe purpose of this study was to explore the potential molecular targets of hyperlipidaemia and the related molecular mechanisms.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eThe microarray dataset of GSE66676 obtained from patients with hyperlipidaemia was downloaded. Weighted gene co-expression network (WGCNA) analysis was used to analyse the gene expression profile, and the royal blue module was considered to have the highest correlation. Gene Ontology (GO) functional and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were implemented for the identification of genes in the royal blue module using the Database for Annotation, Visualization and Integrated Discovery (DAVID) online tool (version 6.8; http://david.abcc.ncifcrf.gov). A protein-protein interaction (PPI) network was established by using the online STRING tool. Then, several hub genes were identified by the MCODE and cytoHubba plug-ins in Cytoscape software.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eThe significant module (royal blue) identified was associated with TC, TG and non-HDL-C. GO and KEGG enrichment analyses revealed that the genes in the royal blue module were associated with carbon metabolism, steroid biosynthesis, fatty acid metabolism and biosynthesis pathways of unsaturated fatty acids. \u003cem\u003eSQLE\u003c/em\u003e (degree = 17) was revealed as a key molecule associated with hypercholesterolaemia (HCH), and \u003cem\u003eSCD \u003c/em\u003ewas revealed as a key molecule associated with hypertriglyceridaemia (HTG). RT-qPCR analysis also confirmed the above results based on our HCH/HTG samples.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003e\u003cem\u003eSQLE\u003c/em\u003e and \u003cem\u003eSCD\u003c/em\u003e are related to hyperlipidaemia, and \u003cem\u003eSQLE/SCD\u003c/em\u003e may be new targets for cholesterol-lowering or triglyceride-lowering therapy, respectively.\u003c/p\u003e","manuscriptTitle":"Weighted gene co-expression network analysis to identify key modules and hub genes related to hyperlipidaemia","msid":"","msnumber":"","nonDraftVersions":[{"code":2,"date":"2021-01-28 15:14:48","doi":"10.21203/rs.3.rs-54056/v2","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Minor revision","date":"2021-02-19T00:00:00+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2021-01-21T00:00:00+00:00","index":"","fulltext":""},{"type":"reviewersInvited","content":"","date":"2021-01-21T00:00:00+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2021-01-20T23:00:00+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2021-01-20T23:00:00+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nutrition-and-metabolism","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nuam","sideBox":"Learn more about [Nutrition \u0026 Metabolism](http://nutritionandmetabolism.biomedcentral.com/)","snPcode":"12986","submissionUrl":"https://submission.nature.com/new-submission/12986/3","title":"Nutrition \u0026 Metabolism","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}},{"code":1,"date":"2020-08-07 16:16:38","doi":"10.21203/rs.3.rs-54056/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revision","date":"2020-12-02T00:00:00+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2020-09-16T12:00:00+00:00","index":1,"fulltext":"Recommendation: Reviewer's comments unavailable due to the journal's policy.\n"},{"type":"reviewerAgreed","content":"","date":"2020-09-09T12:00:00+00:00","index":1,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2020-08-25T12:00:00+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2020-08-06T12:00:00+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2020-08-05T12:00:00+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2020-08-05T12:00:00+00:00","index":"","fulltext":""},{"type":"submitted","content":"","date":"2020-08-04T12:00:00+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nutrition-and-metabolism","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nuam","sideBox":"Learn more about [Nutrition \u0026 Metabolism](http://nutritionandmetabolism.biomedcentral.com/)","snPcode":"12986","submissionUrl":"https://submission.nature.com/new-submission/12986/3","title":"Nutrition \u0026 Metabolism","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1079b8c5-815b-428a-8c91-0217381586f7","owner":[],"postedDate":"January 28th, 2021","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":260998,"name":"Endocrinology \u0026 Metabolism"},{"id":260999,"name":"Nutrition \u0026 Dietetics"}],"tags":[],"updatedAt":"2021-03-07T15:07:14+00:00","versionOfRecord":{"articleIdentity":"rs-54056","link":"https://doi.org/10.1186/s12986-021-00555-2","journal":{"identity":"nutrition-and-metabolism","isVorOnly":false,"title":"Nutrition \u0026 Metabolism"},"publishedOn":"2021-03-04 15:05:39","publishedOnDateReadable":"March 4th, 2021"},"versionCreatedAt":"2021-01-28 15:14:48","video":"","vorDoi":"10.1186/s12986-021-00555-2","vorDoiUrl":"https://doi.org/10.1186/s12986-021-00555-2","workflowStages":[]},"version":"v2","identity":"rs-54056","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-54056","identity":"rs-54056","version":["v2"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}