Plasma Proteome Association with Coronary Heart Disease and Carotid Intima Media Thickness: results from the KORA F4 study

Plasma Proteome Association with Coronary Heart Disease and Carotid Intima Media Thickness: results from the KORA F4 study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Plasma Proteome Association with Coronary Heart Disease and Carotid Intima Media Thickness: results from the KORA F4 study Mohamed A. Elhadad, Monica del C. Gómez-Alonso, Chien-Wei Chen, and 15 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3234719/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Background and aims: Atherosclerosis is the main cause of stroke and coronary heart disease (CHD), both leading mortality causes worldwide. Proteomics, as a high-throughput method, could provide helpful insights into the pathological mechanisms underlying atherosclerosis. In this study, we characterized the associations of plasma protein levels with CHD and with carotid intima-media thickness (CIMT), as a surrogate measure of atherosclerosis. Methods: The discovery phase included 1000 participants from the KORA F4 study, whose plasma protein levels were quantified using the aptamer-based SOMAscan proteomics platform. We evaluated the associations of plasma protein levels with CHD using logistic regression, and with CIMT using linear regression. For both outcomes we applied two models: an age-sex adjusted model, and a model additionally adjusted for body mass index, smoking status, physical activity, diabetes status, hypertension status, low density lipoprotein, high density lipoprotein, and triglyceride levels (fully-adjusted model). The replication phase included a matched case-control sample from the independent KORA F3 study, using ELISA-based measurements of galectin-4. Pathway analysis was performed with nominally associated proteins (p-value < 0.05) from the fully-adjusted model. Results: In the KORA F4 sample, after Bonferroni correction, we found CHD to be associated with five proteins using the age-sex adjusted model: galectin-4 (LGALS4), renin (REN), cathepsin H (CTSH), and coagulation factors X and Xa (F10). The fully-adjusted model yielded only the positive association of galectin-4 (OR = 1.58, 95% CI = 1.3 - 1.93), which was successfully replicated in the KORA F3 sample (OR = 1.40, 95% CI = 1.09 - 1.88). For CIMT, we found four proteins to be associated using the age-sex adjusted model namely: cytoplasmic protein NCK1 (NCK1), insulin-like growth factor-binding protein 2 (IGFBP2), growth hormone receptor (GHR), and GDNF family receptor alpha-1 (GFRA1). After assessing the fully-adjusted model, only NCK1 remained significant (ꞵ = 0.017, p-value = 1.39e-06). Upstream regulators of galectin-4 and NCK1 identified from pathway analysis were predicted to be involved in inflammation pathways. Conclusions: Our proteome-wide association study identified galectin-4 to be associated with CHD and NCK1 to be associated with CIMT. Inflammatory pathways underlying the identified associations highlight the importance of inflammation in the development and progression of CHD. Galectin-4 NCK1 coronary artery disease stroke carotid intima media thickness atherosclerosis cardiovascular disease proteomics. Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Atherosclerosis, which is a cornerstone pathophysiological process of multiple disease forms including coronary heart disease (CHD) and stroke, is the leading cause of mortality worldwide ( 1 ). Epidemiological studies of CHD and stroke have successfully identified major metabolic risk factors such as obesity, diabetes, dyslipidemia, and hypertension, which in turn helped to develop preventive strategies and to identify new drug targets ( 2 ). Atherosclerosis is characterized by thickening of both the media and intima of the arterial wall, followed by plaque formation. Where this process impedes the proper delivery of oxygen-rich blood to the heart, it results in the development of CHD ( 3 ). Specific mechanisms of atherosclerosis involve endothelial dysfunction, lipid accumulation, inflammation, oxidative stress, angiogenesis, matrix degradation, and thrombosis ( 3 ). A marker of early atherosclerosis is carotid intima media thickness (CIMT), an ultrasound-based measurement of arterial wall thickness ( 4 ). CIMT is clinically used to predict risk of atherosclerotic diseases like CHD and stroke ( 4 – 9 ). An increase in CIMT of 0.1 mm has been found to be associated with 15% higher risk for myocardial infarction and 18% higher risk for stroke ( 10 ). Identifying proteins linked to specific atherogenic-mechanisms sheds light onto the molecular pathophysiology of cardiometabolic disturbances. Although there have been multiple studies that found highly expressed proteins within atherosclerotic tissue, only a small number of these findings was replicated when using blood samples ( 11 – 13 ). Proteomics can provide valuable clues to the underlying pathological mechanisms at the molecular level. The plasma proteome has been extensively characterized in cardiovascular disease, because of its availability and proximity to the cardiovascular system. Several studies applying mass spectrometry to quantify plasma proteins in small sample numbers have identified proteins associated with CHD ( 14 – 20 ). A study using an aptamer-based platform, capable of profiling more than thousand analytes in plasma, detected many protein changes in the context of myocardial injury in a cohort of the Framingham Heart Studies, thus underscoring the potential of proteomics tools when applied to large human cohorts ( 21 ). In the present study, we use quantitative data for 1129 proteins in plasma samples from 1000 individuals of the population-based KORA F4 study from a high-throughput aptamer-based analysis ( 23 ). We explore the association of plasma protein levels with both CHD and CIMT, and additionally validate our findings in an independent sample from the KORA F3 cohort. We further explore possible roles of the associated proteins in the pathogenesis of atherosclerosis and CHD using pathway analysis. Materials and methods Study design of the discovery cohort The KORA (Cooperative health research in the Region of Augsburg) study is an independent population-based cohort study in southern Germany ( 22 ). The study was approved by the ethics committee of the Bavarian Medical Association and was carried out in accordance with the principles of the Declaration of Helsinki. All study participants signed written informed consent prior to their participation in the study. We used data from the KORA F4 study (n = 3080), which is the follow-up of the KORA S4 survey that was conducted between 2006 and 2008. A subsample of 1000 individuals was randomly selected from the deeply phenotyped KORA F4 participants for plasma protein measurement using the SOMAscan aptamer-based proteomics platform ( 23 ). CHD status validation Stratification of subjects for CHD association was performed according to Fig. 1 . We validated self-reported CHD using self-reported history of myocardial infarction (MI) and characteristic electrocardiogram (ECG) MI changes. The PC-based BioSys system (Hörmann Medizintechnik, Zwönitz, Germany) was used to record ECGs, which were manually assessed by two cardiologists. Infarction-characteristic changes in the ECG were identified by consensus. We used the validated CHD status as a binary variable in subsequent statistical analysis. CIMT measurement Ultrasound measurements of the extracranial carotid arteries have been performed in KORA participants as previously described ( 24 , 25 ). Briefly, all measurements were conducted by two sonographers according to a standardized protocol ( 26 ). Optimal images of both common carotid arteries (CCA) were identified and stored. Then, CIMT was ascertained over a length of 10 mm beginning at 0–5 mm of the dilatation of the distal CCA using an automated edge detection reading system (Prowin software, Medical Technologies International, USA). The final CIMT value was calculated as the average of the measurements of three frozen images from both the left and right CCA. Measurements of inter-sonographer (n = 30 CIMT measurements) and inter-reader variations (n = 50 CIMT measurements) showed coefficients of variations of 1.9% and 3.0% and Spearman correlation coefficients of ≥ 0.89 (Supplementary information: Supplementary Fig. 1) ( 25 ). Assessment of model covariates BMI was calculated by dividing each participant’s weight in kilograms by the square of the participant´s height. Participants were categorized as hypertensive if they had a blood pressure measurement (≥ 140 / 90 mmHg) or if they were receiving medical treatment for hypertension. Diabetes was defined by self-report or current use of glucose-lowering agents. Diabetes status was validated by asking the participant’s responsible physician. Lipid levels included low-density lipoprotein (LDL), high-density lipoprotein (HDL), and triglycerides. Self-reported smoking status was categorized as non-, former, or current smoker. Leisure time physical activity was assessed with two separate questions concerning leisure time sport activity in winter and in summer (cycling included). Possible answers were: 1) > 2 hours, 2) 1–2 hours, 3) < 1 hour, and 4) none per week. Participants who had a total score < 5, obtained by summing the numbers 1) – 4) from the winter and summer questions, were classified as physically active. Proteomics measurements Plasma samples were sent to SomaLogic Inc. (Boulder Colorado, USA) for proteomics measurement by means of the SOMAscan platform ( 23 , 27 ). Samples and bead coupled SOMAmers were mixed in solution. Free proteins were washed out and SOMAmer-protein complexes were photocleaved off the beads. Dextran sulfate was added to the solution as an anionic competitor, allowing non-cognate complexes to dissociate. SOMAmer-protein complexes were captured onto new avidin-coated beads by protein biotin tags. Free SOMAmers were washed out. Afterwards, SOMAmers were released from complexes in a denaturing buffer. Thus, individual protein concentrations were transformed into a corresponding SOMAmer concentration. SOMAmers were hybridized to complementary sequences on a microarray and quantified by fluorescence. The resulting raw intensities were processed using SOMALogic’s data analysis workflow, which utilizes the standard samples included on each plate and entails hybridization normalization, median signal normalization, and signal calibration to control for interplate differences. Protein intensities were reported in relative fluorescence units. One sample failed SOMAscan quality control, leaving a total of 999 samples from 483 males and 516 females. Additionally, we removed 29 aptamers that failed SOMAscan quality control, and 5 additional aptamers as recommended by the SOMAscan assay change log issued on December 22, 2016, leaving 1095 aptamers for further analysis. Study design of the replication cohort For replication analysis, a matched CHD case-control sample was drawn from the KORA F3 cohort, which is a follow-up study from the previous KORA S3 survey, which enrolled German nationals between 25- and 74-years old living in the region of Augsburg, South Germany. The KORA F3 cohort consists of 3988 participants, whose data were collected between 2004 and 2005. In total, 330 samples were drawn including 165 CHD cases selected according to criteria identical to the discovery phase, as well as 165 sex and age (5-year interval) matched controls. To validate the results of our discovery phase, the replication was performed using ELISA-based measures of the significant proteins obtained with the fully-adjusted model. However, only galectin-4 was assessed to be associated with CHD. Due to the lack of CIMT measures in KORA F3, NCK1 was not tested. Enzyme-linked immunosorbent assay (ELISA) for galectin-4 An ELISA pre-coated with antibodies against galectin-4 was purchased from MyBiosource (San Diego, CA, U.S.A, MBS9715486) and analyzed according to the manufacturer’s protocol. A standard curve was prepared using recombinant galectin-4 in concentrations ranging from 96 to 3 pg/ml with a measurement error below 12%. Plasma samples were diluted in a 1:5 ratio using the dilution buffer provided by the kit. Absorption at 450 nm was measured with the Fluorostar Omega microplate reader (BMG LabTech, Ortenberg, Germany). Pre-processing of protein data All protein values including either SOMAscan- or ELISA-based measures, were log2 transformed and standardized to have a mean of 0 and a standard deviation of 1, to be used for further statistical analysis. Statistical Analysis Estimated associations between CHD and each plasma protein were calculated by applying iterative multiple logistic regression analysis (dichotomous outcome), while associations between CIMT and each plasma protein were calculated using a linear regression model (continuous outcome). Both CHD and CIMT associations were tested according to the following models: model 1 adjusted by age and sex only; model 2 adjusted for age, sex, body mass index (BMI), smoking status, physical activity, low-density lipoprotein (LDL), high-density lipoprotein (HDL), triglyceride levels, diabetes status, and hypertension status. Subjects with missing values were excluded from the respective analysis. To account for protein multiple testing, we applied Bonferroni correction resulting in a significant threshold of 4.57E-05 (0.05/1095 proteins) for both CHD and CIMT outcomes. Systems biology analysis Systems biology analysis of CHD- and CIMT-nominally associated proteins (p < 0.05) identified with the fully-adjusted model, was performed using the Ingenuity Pathways Knowledge (Qiagen, Redwook City, CA), a database of biological interactions and processes spanning from molecular (proteins, genes) to organism (diseases) levels. Ingenuity Pathway Analysis (IPA) uses enrichment analysis-approaches to calculate the significance of observing a candidate protein/gene set within the context of biological systems. IPA calculates the p-value for enrichment or overlap between the test set and the IPA knowledge base using Fisher´s Exact test. Significant activation was considered at z-score > 2 and significant inhibition at z-score < − 2. Pathway analyses included causal networks and identification of upstream regulators. Results Baseline characteristics of the study population are described in Table 1 . Stratification of participants in our discovery cohort KORA F4 for CHD association is displayed in Fig. 1 . There were 982 individuals included in the fully-adjusted model of which 76 had CHD. Non-CHD participants were younger with a mean age of 58.7 years and comprised more women (53.2%), compared to participants with CHD who had a mean age of 64.8 years and comprised less women (31.6%). Participants with CHD had a higher mean of CIMT (0.91) as compared to those without (0.87, p-value: 0.017). Table 1 Baseline characteristics of the KORA F4 study sample: discovery phase. Fully-adjusted model Coronary Heart Disease (CHD) P-value No Yes n 906 76 Age (mean, SD) 58.74 (7.69) 64.75 (6.82) < 0.001 Sex = female (%) 482 (53.2%) 24 (31.6%) < 0.001 BMI (mean, SD) 27.54 (4.46) 30.23 (5.27) < 0.001 LDL (mean, SD) 140.69 (34.27) 127.95 (33.36) 0.002 HDL (mean, SD) 57.71 (15.12) 52.50 (15.83) 0.004 Triglycerides (mean, SD) 127.20 (88.52) 148.51 (72.89) 0.041 Smoking status 0.013 Non-smoker 391 (43.2%) 26 (34.2%) Former smoker 373 (41.2%) 44 (57.9%) Current smoker 142 (15.7%) 6 (7.9%) Physically active = yes 571 (63.0%) 43 (56.6%) 0.321 Diabetes status = yes 67 (7.4%) 14 (18.4%) 0.002 Hypertension status = yes 334 (36.9%) 53 (69.7%) < 0.001 CIMT (mean, SD) 0.87 (0.13) 0.91 (0.15) 0.019 BMI: body mass index; CHD: coronary heart disease; HDL: high-density lipoprotein; LDL: low-density lipoprotein; CIMT: carotid intima-media thickness; SD: standard deviation In total, five proteins were found to be significantly associated with CHD in the sex-age adjusted model (Table 2 , Fig. 2 , Supplementary Table 1). Two of these proteins showed a protective effect namely, coagulation factor X (OR = 0.66, 95% CI = 0.55–0.80) and its activated form coagulation factor Xa (OR = 0.65, 95% CI = 0.53–0.79) and three showed negative effects namely, cathepsin H (OR = 1.64, 95% CI = 1.3–2.09), galectin-4 (OR = 1.70, 95% CI = 1.41–2.08) and renin (OR = 1.73, 95% CI = 1.38–2.17). Only galectin-4 remained significantly associated with CHD in the fully-adjusted model (OR = 1.58, 95% CI = 1.30–1.93) (Table 2 , Fig. 2 , Supplementary Table 2). Table 2 Associations between CHD and plasma proteins in both the discovery and replication phases of the study. a) Discovery phase: SOMAScan measurements Model 1: Age-sex adjusted model* KORA F4 sample: n = 78 CHD vs. 908 non-CHD Protein UniProt Gene Symbol OR Lower_CI Upper_CI P-value Galectin-4 P56470 LGALS4 1.70 1.41 2.08 7.93E-08 Renin P00797 REN 1.73 1.38 2.17 1.91E-06 Coagulation factor Xa P00742 F10 0.65 0.53 0.79 1.10E-05 Coagulation Factor X P00742 F10 0.66 0.55 0.80 1.23E-05 Cathepsin H P09668 CTSH 1.64 1.30 2.09 4.21E-05 Model 2: Fully-adjusted model** KORA F4 sample: n = 76 CHD vs. 906 non-CHD Protein UniProt Gene Symbol OR Lower_CI Upper_CI P-value Galectin-4 P56470 LGALS4 1.58 1.30 1.93 5.50E-06 b) Replication phase: ELISA-based measures Model 2: Fully-adjusted model*** KORA F3 sample: n = 165 CHD vs. 165 non-CHD Protein UniProt Gene Symbol OR Lower_CI Upper_CI P-value Galectin-4 P56470 LGALS4 1.40 1.09 1.88 1.37E-02 *Model 1: Results from 1095 assessed proteins with the SOMAscan platform. **Model 2: Full model adjusted by age, sex, body mass index (BMI), low density lipoprotein (LDL), high density lipoprotein (HDL), triglyceride levels, diabetes status, hypertension status, smoking status (categorized as never, former or current smoker) and physical activity. Results from 1095 assessed proteins with the SOMAscan platform. ***Results from testing galectin-4 only. For CIMT, four proteins in total were found to be significantly associated in the age-sex adjusted model after Bonferroni adjustment, these were GDNF family receptor alpha-1 (GFRA1) (β = 0.017, p-value = 1.91E-06), cytoplasmic protein NCK1 (NCK1) (β = 0.017, p-value = 3.59E-06), insulin-like growth factor-binding protein 2 (IGFBP2) (β = -0.015, p-value = 4.47E-05), and growth hormone receptor (GHR) (β = 0.016, p-value = 2.46E-05) (Table 3 , Fig. 3 , Supplementary table 3 ). In the fully-adjusted model, only NCK1 (β = 0.017, p-value = 1.39E-06) remained significantly associated with CIMT after Bonferroni adjustment (Table 3 , Fig. 3 , Supplementary table 4 ). Table 3 Associations between CIMT and plasma proteins in the discovery phase of the study. a) Discovery phase: SOMAScan measurements Model 1: Age-sex adjusted model* KORA F4 sample: n = 893 Protein UniProt Gene Symbol Beta SE P-value GDNF family receptor alpha-1 P56159 GFRA1 0.017 3.61E-03 1.91E-06 Cytoplasmic protein NCK1 P16333 NCK1 0.017 3.65E-03 3.59E-06 Growth hormone receptor P10912 GHR 0.016 3.73E-03 2.46E-05 Insulin-like growth factor-binding protein 2 P18065 IGFBP2 -0.015 3.69E-03 4.47E-05 Model 2: Fully-adjusted model** KORA F4 sample: n = 889 Protein UniProt Gene Symbol Beta SE P-value Cytoplasmic protein NCK1 P16333 NCK1 0.017 3.56E-03 1.39E-06 *Model 1: Results from 1095 assessed proteins with the SOMAscan platform. **Model 2: Full model adjusted by age, sex, body mass index (BMI), physical activity, low density lipoprotein (LDL), high density lipoprotein (HDL), triglyceride levels, diabetes status, hypertension status and smoking status (categorized as never, former or current smoker). Results from 1095 assessed proteins with the SOMAscan platform. To validate our results, we tested our fully-adjusted model findings in an independent cohort of KORA F3 using ELISA-measured levels of galectin-4. Characteristics of participants are shown in supplementary table 5 a. The association between galectin-4 and CHD was successfully replicated in the fully-adjusted case-control study (OR = 1.40, 95% CI = 1.09–1.88, p-value = 1.37E-02; Supplementary table 5 b). Finally, to gain insights into enriched signaling pathways and biological mechanisms, Ingenuity Pathway Analysis was performed using nominally associated proteins (p-value < 0.05) identified with the fully-adjusted model including 106 CHD-associated proteins or 66 CIMT-associated proteins. For CHD, the unique significant causal network (z-score = -2.11) including galectin-4 and 43 CHD-associated proteins, predicted the activation of peroxisome proliferator activated receptor alpha (PPARA), which might directly increase the expression of galectin-4. This finding was also predicted by the causal network with the lowest p-value (FDR = 5.37E-02), which included 68 CHD-associated proteins. Figure 4 a shows the summary of PPARA-predicted activation via the two networks. For CIMT, the unique significant causal network (z-score = -2.0) including NCK1 and four CIMT-associated proteins, identified interleukin-9 as an inhibited upstream regulator (Fig. 4 b). Discussion The aim of the present study was to identify protein associations with CHD and CIMT, the latter as a surrogate of atherosclerosis risk. In the age-sex adjusted model, proteomic analysis showed differential abundance of five proteins with CHD and four with CIMT. Following further adjustment of the model for BMI, smoking status, lipid measurements, hypertension, and diabetes status, the quantitative difference of galectin-4 for CHD and NCK1 for CIMT remained significant. Moreover, the association of galectin-4 with CHD was further validated using ELISA-based measurements in an independent study. Galectin-4 is a member of the beta-galactoside-binding proteins, and has important functions in lipid raft stabilization, protein apical trafficking, cell adhesion, as well as wound healing ( 28 ). Galectin-4 may be involved in atherosclerosis by enhancing lipid raft stabilization, which may subsequently affect redox signaling pathways ( 29 ). Schroder et al. reported galectin-4 to be correlated with myocardial blood flow reserve, a gold standard diagnostic to clinically assess coronary microvascular dysfunction, in women with angina pectoris and non-obstructive CHD ( 30 ). The authors conjectured that galectin-4’s promotion of cell adhesion contributed to the association ( 30 ). A Swedish population-based study found galectin-4 to be significantly associated with incident coronary events (hazard ratio (HR) = 1.34, 95% confidence interval (CI) = 1.14–1.57) and incident heart failure (HR = 1.26, 95% CI: 1.03–1.54) ( 31 ). Another study compared heart failure patients to controls both recruited in the outpatient clinic at Karolinska University Hospital, finding galectin-4 to be significantly associated with heart failure (HR = 2.6; FDR adjusted p-value 0.005) ( 32 ). In addition, galectin-4 has been reported to be associated with hospitalization linked to obesity ( 33 ) and ST-segment elevation myocardial infarction ( 34 ). All listed reports are in line with our finding of galectin-4’s association with CHD. The pathway analysis of CHD-associated proteins suggests that the interplay of galectin-4 and the predicted activated status of both p38 MAPk signaling and interleukin-1B, representatives of inflammation pathways ( 35 ), takes place via the peroxisome proliferator activated receptor alpha (PPARA). PPARG-deficient macrophages have been found to display an elevated production of pro-inflammatory cytokines including interleukin-1B ( 36 ). Among the five CHD-associated proteins using the age-sex adjusted model, two were found to be associated with a higher- and two with a lower-CHD risk. Our reported association of renin with higher CHD-risk might have been lost when using the fully-adjusted model due to the adjustment for hypertension ( 37 ). However, renin has been reported to be positively associated with CHD ( 38 , 39 ). Renin is a member of the renin-angiotensin-aldosterone system, which, via its active peptide angiotensin II, contributes to atherosclerosis development, not only by promoting hypertension but also through multiple direct actions on vessels ( 40 ). Cathepsin H was an additional protein associated with a higher CHD-risk in the age-sex adjusted model. Cathepsin H is a lysosomal cysteine protease important in the overall degradation of lysosomal proteins ( 41 ). Its atherogenic role could lead the transformation of LDL to an atherogenic moiety, which in turn induces macrophage foam cell formation ( 42 ). Proteins associated with lower-CHD risk associations included coagulation factor X as well as its active form coagulation factor Xa. A pathogenetic mechanism of CHD includes thrombotic vessel occlusion followed by rupture of an atherosclerotic plaque ( 3 ). It is therefore not surprising that constituents and a regulating protein of the coagulation cascade were found significantly associated with CHD in the present study. Factor Xa exerts also non-hemostatic effects by activation of protease-activated receptors-1 (PAR-1) and PAR-2, which have been associated with atherosclerosis, inflammation, and fibrosis ( 43 ). Such counterintuitive associations were reported before: where the concentrations of coagulation factor X and prothrombin were lower in blood from patients with CHD having more than 50% stenosis compared with those without CHD ( 44 ). Brummel-Ziedins et al. hypothesized that despite the depletion of coagulation factors, the balance between tissue factor and tissue factor pathway inhibitor is the primary driver of a hypercoagulable state in patients with CHD ( 44 ). We also identified proteins to be associated with CIMT. NCK1 was the unique significant protein positively associated with CIMT in the fully-adjusted model. NCK1 is reported to be involved in different pathways leading to the progression of atherosclerosis ( 45 ). For instance, it is associated with vascular permeability, which allows the uptake of low-density lipoproteins (LDL) and thereby stimulates inflammation ( 45 , 46 ). The resulting oxidative stress increase in endothelial cells decreases nitric oxide and thereby supports endothelial cell dysfunction ( 47 ). Alfaidi et al. showed in an in-vivo study that NCK1-knockdown reduced NF-κB signaling and thereby inflammation in endothelial cells ( 45 ). Additionally, pathway analysis revealed interleukin-9 to indirectly inhibit expression of NCK1, SPTAN1, and CFL1, while the opposite effect was predicted for CMA1. Interleukin-9 has been reported to decrease expression of human NCK1 in MDA-MB-231 human breast cells, and to differentially regulate actin cytoskeleton-related proteins such as NCK1 ( 48 ). Interestingly, both SPTAN1 and CFL1 are filamentous cytoskeletal and actin-related proteins, while CMA1 could participate in extracellular matrix degradation ( 49 ). We were unable to validate the NCK1 findings using ELISA-measurements due to the lack of CIMT measurements in the validation sample. We were able to confirm three additional proteins associated with CIMT using our age-sex adjusted model namely GFRA1, IGFBP2 and GHR, which have been all reported to be linked to CVD, mortality, and atherosclerosis ( 50 , 51 ). IGFBP2 was the unique obtained negative association. The same direction effect has been reported as a strong association with type 2 diabetes in a comparison study of incident type 2 diabetes and coronary heart disease in the KORA cohort ( 52 ). In this study, we identified proteins specifically associated with either CHD or CIMT. Previous reports on KORA F4 have already stated a non-linear relation between CIMT and CHD risk ( 53 ). Thus, this complex relationship amongst the two phenotypes could help to explain the specificity of our findings. A major strength of our study is the proteome-wide approach, which covers proteins at low abundance levels in plasma. By conducting a hypothesis-free analysis, we were able to analyze the association of a wide array of plasma proteins with CHD and CIMT. An additional strength is the availability of an independent sample, which we could use to validate initial results with an alternative measurement technique that provides absolute concentrations. Our study also has limitations. The lack of patient differentiation by CHD severity could, for instance, be attenuating some associations. Manifestations of early-stage CHD differ from the late-stage CHD. In the latter, protein levels change due to myocardial injury and physiological compensation. Additionally, the difference in plaque vulnerability and extent of atherosclerosis between stable and unstable CHD ( 54 ), may also impact the plasma proteome. Finally, due to the cross-sectional nature of our study, temporal relations cannot be inferred. In summary, our proteome-wide study identified a new association of galectin-4 with CHD. Galectin-4 may be involved in atherosclerosis by enhancing lipid raft stabilization, which subsequently affects redox signaling pathways. Moreover, we report the association of NCK1 with CIMT. Declarations Ethics approval and consent to participate The KORA (Cooperative health research in the Region of Augsburg) study is an independent population-based cohort study in southern Germany (22). The study was approved by the ethics committee of the Bavarian Medical Association and was carried out in accordance with the principles of the Declaration of Helsinki. All study participants signed written informed consent prior to their participation in the study. Consent for publication Not applicable Availability of data and materials The informed consent given by KORA study participants does not cover data posting in public databases. However, data are available upon request by means of a project agreement from KORA (https://helmholtz-muenchen.managed-otrs.com/external). Competing interests The authors declare that they have no competing interests. Funding The KORA study was initiated and financed by the Helmholtz Zentrum München – German Research Center for Environmental Health, which is funded by the German Federal Ministry of Education and Research (BMBF) and by the State of Bavaria. Data collection in the KORA study is done in cooperation with the University Hospital of Augsburg. Furthermore, KORA research was supported within the Munich Center of Health Sciences (MC Health), Ludwig-Maximilians-Universität, as part of LMUinnovativ. This research received funding from the German Centre for Cardiovascular Research (DZHK) under grant number DZHK B 19-017 SE and 81X2400136. Authors' contributions MAE, MCGA, CWC and SN interpreted the data, wrote, and revised the manuscript. CWC and MAE participated in the design of the study and analyzed the data. MW conceived the research question, participated in its design and revised the manuscript. EH participated in the design and measurement of the protein replication and revised the manuscript. AP, MW, CG, JG, KS, SK, WR, JS, WK, MN, UV, EH, TD, CM were involved in the data collection, data management, and preparation of their respective cohorts. All authors contributed to the writing of the article, critically reviewed it, and approved the final version for submission. Acknowledgements We thank Ulrike Lissner for technical support in ELISA measurements. 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Additional Declarations No competing interests reported. Supplementary Files SuppTable1CHDsexagemodel.xlsx SuppTable2CHDfullyadjustedmodel.xlsx SuppTable3CIMTsexagemodel.xlsx SuppTable4CIMTfullyadjustedmodel.xlsx SuppTable5KORAF3CHDGal4.xlsx Supplementaryinformation.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Major revision 31 Oct, 2023 Reviews received at journal 11 Sep, 2023 Reviewers agreed at journal 19 Aug, 2023 Reviewers invited by journal 16 Aug, 2023 Editor assigned by journal 11 Aug, 2023 Submission checks completed at journal 11 Aug, 2023 First submitted to journal 04 Aug, 2023 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-3234719","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":225656561,"identity":"e8d7cd80-2fec-48d2-895b-be1dc218baec","order_by":0,"name":"Mohamed A. 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1","display":"","copyAsset":false,"role":"figure","size":1224936,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlow diagram of subjects´ stratification for CHD association.\u003c/strong\u003e Coronary heart disease (CHD) was validated using self-reported history of myocardial infraction (MI) and characteristic electrocardiogram MI changes.\u003c/p\u003e","description":"","filename":"Fig1CHDsubjectsstratification.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3234719/v1/6efcd714728f8f0261a4aff2.jpg"},{"id":41709586,"identity":"fc4597da-e272-405c-b299-c9a9165a77c5","added_by":"auto","created_at":"2023-08-17 14:36:28","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":132498,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eVolcano plot showing the association between plasma proteins and CHD. \u003c/strong\u003eA) Age-sex adjusted model. B) Fully-adjusted model: age, sex, body mass index (BMI), physical activity, low-density lipoprotein (LDL), high-density lipoprotein (HDL), triglyceride levels, diabetes status, hypertension status and smoking status included as covariates. Each dot on the figure represents the odds ratio of a single protein with Bonferroni significant proteins labelled. OR: odds ratio.\u003c/p\u003e","description":"","filename":"Fig2VolcanoPlotCHD.png","url":"https://assets-eu.researchsquare.com/files/rs-3234719/v1/f4f7b004d1bd997cc52117a9.png"},{"id":41707450,"identity":"686ba417-aee9-405f-bd33-9c9d6da286a1","added_by":"auto","created_at":"2023-08-17 14:28:28","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":128975,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eVolcano plot showing the association between plasma proteins and CIMT. \u003c/strong\u003eA) Age-sex adjusted model. B) Fully-adjusted model: age, sex, body mass index (BMI), physical activity, low-density lipoprotein (LDL), high-density lipoprotein (HDL), triglyceride levels, diabetes status, hypertension status and smoking status included as covariates. Each dot on the figure represents the β-coefficients of a single protein with Bonferroni significant proteins labelled.\u003c/p\u003e","description":"","filename":"Fig3VolcanoPlotCIMT.png","url":"https://assets-eu.researchsquare.com/files/rs-3234719/v1/59465fb64be34a670b9581fd.png"},{"id":41709587,"identity":"33cfbcdd-6be3-4089-b4eb-2d069c02be5d","added_by":"auto","created_at":"2023-08-17 14:36:28","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1158025,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePredicted upstream regulators of galectin-4 or NCK1 and associated proteins. \u003c/strong\u003eResults obtained when analyzing nominally significant (p \u0026lt; 0.05) CHD-associated proteins using the fully-adjusted model. a) PPARA was predicted to directly increase expression of galectin-4 (LGALS4): selected pathways of the two significant causal networks predicting the activation of peroxisome proliferator activated receptor alpha (PPARA). Activation of P38 mitogen-activated protein kinase (P38MAPK) leads to activation and phosphorylation of PPARA. Interleukin-1 beta (IL1B) usually decreases the activity of PPARA; however, IL1B is predicted to be inhibited. b) Unique significant inhibited causal network including NCK1: predicted inhibition of interleukin-9 (IL9) could indirectly increase expression of NCK1, SPTAN1 and CFL1, and indirectly decrease expression of CMA1. Full line: direct interaction; segmented line: indirect interaction. Black arrows: associated proteins identified by the fully-adjusted model in the discovery phase.\u003c/p\u003e","description":"","filename":"Fig4PredictedUpstreamRegulators.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3234719/v1/a3010094ad0aa6b69ce3c5e2.jpg"},{"id":41710583,"identity":"1313f188-d7e9-4165-97a2-91a30d78577d","added_by":"auto","created_at":"2023-08-17 14:44:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":963331,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3234719/v1/351250dc-0262-48e3-bebd-5851d346b776.pdf"},{"id":41709590,"identity":"262b0f77-0b2a-4adc-a264-c214d89f2ae3","added_by":"auto","created_at":"2023-08-17 14:36:28","extension":"xlsx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":212008,"visible":true,"origin":"","legend":"","description":"","filename":"SuppTable1CHDsexagemodel.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3234719/v1/7b5c512bbc42e1b916cd4e09.xlsx"},{"id":41707455,"identity":"a6503c55-7a4f-43f5-9bec-5f88b76461fe","added_by":"auto","created_at":"2023-08-17 14:28:28","extension":"xlsx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":206983,"visible":true,"origin":"","legend":"","description":"","filename":"SuppTable2CHDfullyadjustedmodel.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3234719/v1/ec96e4db7def3a75cf73b871.xlsx"},{"id":41709588,"identity":"14dcff02-3bb5-4e27-93b2-74d174880f18","added_by":"auto","created_at":"2023-08-17 14:36:28","extension":"xlsx","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":157985,"visible":true,"origin":"","legend":"","description":"","filename":"SuppTable3CIMTsexagemodel.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3234719/v1/0b5f58939c9b114ca36653ad.xlsx"},{"id":41707460,"identity":"d6f576bd-e22b-4f7e-bd2f-d173c3219d10","added_by":"auto","created_at":"2023-08-17 14:28:28","extension":"xlsx","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":152462,"visible":true,"origin":"","legend":"","description":"","filename":"SuppTable4CIMTfullyadjustedmodel.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3234719/v1/1d666b0ce37b5e91824ff5e7.xlsx"},{"id":41707453,"identity":"ef565fec-0379-469f-b95f-3ac74ed9b0a4","added_by":"auto","created_at":"2023-08-17 14:28:28","extension":"xlsx","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":18514,"visible":true,"origin":"","legend":"","description":"","filename":"SuppTable5KORAF3CHDGal4.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3234719/v1/48d4c33a10f4c093347650cd.xlsx"},{"id":41707457,"identity":"3f4508fb-4097-43d0-b14d-54e72a4c33c5","added_by":"auto","created_at":"2023-08-17 14:28:28","extension":"docx","order_by":11,"title":"","display":"","copyAsset":false,"role":"supplement","size":44919,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryinformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-3234719/v1/e07f22a59f52224f278a9c4e.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Plasma Proteome Association with Coronary Heart Disease and Carotid Intima Media Thickness: results from the KORA F4 study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAtherosclerosis, which is a cornerstone pathophysiological process of multiple disease forms including coronary heart disease (CHD) and stroke, is the leading cause of mortality worldwide (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Epidemiological studies of CHD and stroke have successfully identified major metabolic risk factors such as obesity, diabetes, dyslipidemia, and hypertension, which in turn helped to develop preventive strategies and to identify new drug targets (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAtherosclerosis is characterized by thickening of both the media and intima of the arterial wall, followed by plaque formation. Where this process impedes the proper delivery of oxygen-rich blood to the heart, it results in the development of CHD (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Specific mechanisms of atherosclerosis involve endothelial dysfunction, lipid accumulation, inflammation, oxidative stress, angiogenesis, matrix degradation, and thrombosis (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). A marker of early atherosclerosis is carotid intima media thickness (CIMT), an ultrasound-based measurement of arterial wall thickness (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). CIMT is clinically used to predict risk of atherosclerotic diseases like CHD and stroke (\u003cspan additionalcitationids=\"CR5 CR6 CR7 CR8\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). An increase in CIMT of 0.1 mm has been found to be associated with 15% higher risk for myocardial infarction and 18% higher risk for stroke (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIdentifying proteins linked to specific atherogenic-mechanisms sheds light onto the molecular pathophysiology of cardiometabolic disturbances. Although there have been multiple studies that found highly expressed proteins within atherosclerotic tissue, only a small number of these findings was replicated when using blood samples (\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eProteomics can provide valuable clues to the underlying pathological mechanisms at the molecular level. The plasma proteome has been extensively characterized in cardiovascular disease, because of its availability and proximity to the cardiovascular system. Several studies applying mass spectrometry to quantify plasma proteins in small sample numbers have identified proteins associated with CHD (\u003cspan additionalcitationids=\"CR15 CR16 CR17 CR18 CR19\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). A study using an aptamer-based platform, capable of profiling more than thousand analytes in plasma, detected many protein changes in the context of myocardial injury in a cohort of the Framingham Heart Studies, thus underscoring the potential of proteomics tools when applied to large human cohorts (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn the present study, we use quantitative data for 1129 proteins in plasma samples from 1000 individuals of the population-based KORA F4 study from a high-throughput aptamer-based analysis (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). We explore the association of plasma protein levels with both CHD and CIMT, and additionally validate our findings in an independent sample from the KORA F3 cohort. We further explore possible roles of the associated proteins in the pathogenesis of atherosclerosis and CHD using pathway analysis.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design of the discovery cohort\u003c/h2\u003e \u003cp\u003eThe KORA (Cooperative health research in the Region of Augsburg) study is an independent population-based cohort study in southern Germany (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). The study was approved by the ethics committee of the Bavarian Medical Association and was carried out in accordance with the principles of the Declaration of Helsinki. All study participants signed written informed consent prior to their participation in the study.\u003c/p\u003e \u003cp\u003eWe used data from the KORA F4 study (n\u0026thinsp;=\u0026thinsp;3080), which is the follow-up of the KORA S4 survey that was conducted between 2006 and 2008. A subsample of 1000 individuals was randomly selected from the deeply phenotyped KORA F4 participants for plasma protein measurement using the SOMAscan aptamer-based proteomics platform (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eCHD status validation\u003c/h2\u003e \u003cp\u003eStratification of subjects for CHD association was performed according to Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. We validated self-reported CHD using self-reported history of myocardial infarction (MI) and characteristic electrocardiogram (ECG) MI changes. The PC-based BioSys system (H\u0026ouml;rmann Medizintechnik, Zw\u0026ouml;nitz, Germany) was used to record ECGs, which were manually assessed by two cardiologists. Infarction-characteristic changes in the ECG were identified by consensus. We used the validated CHD status as a binary variable in subsequent statistical analysis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eCIMT measurement\u003c/h2\u003e \u003cp\u003eUltrasound measurements of the extracranial carotid arteries have been performed in KORA participants as previously described (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Briefly, all measurements were conducted by two sonographers according to a standardized protocol (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Optimal images of both common carotid arteries (CCA) were identified and stored. Then, CIMT was ascertained over a length of 10 mm beginning at 0\u0026ndash;5 mm of the dilatation of the distal CCA using an automated edge detection reading system (Prowin software, Medical Technologies International, USA). The final CIMT value was calculated as the average of the measurements of three frozen images from both the left and right CCA. Measurements of inter-sonographer (n = 30 CIMT measurements) and inter-reader variations (n = 50 CIMT measurements) showed coefficients of variations of 1.9% and 3.0% and Spearman correlation coefficients of \u0026ge;\u0026thinsp;0.89 (Supplementary information: Supplementary Fig.\u0026nbsp;1) (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eAssessment of model covariates\u003c/h2\u003e \u003cp\u003eBMI was calculated by dividing each participant\u0026rsquo;s weight in kilograms by the square of the participant\u0026acute;s height. Participants were categorized as hypertensive if they had a blood pressure measurement (\u0026ge;\u0026thinsp;140 / 90 mmHg) or if they were receiving medical treatment for hypertension. Diabetes was defined by self-report or current use of glucose-lowering agents. Diabetes status was validated by asking the participant\u0026rsquo;s responsible physician. Lipid levels included low-density lipoprotein (LDL), high-density lipoprotein (HDL), and triglycerides. Self-reported smoking status was categorized as non-, former, or current smoker. Leisure time physical activity was assessed with two separate questions concerning leisure time sport activity in winter and in summer (cycling included). Possible answers were: 1)\u0026thinsp;\u0026gt;\u0026thinsp;2 hours, 2) 1\u0026ndash;2 hours, 3)\u0026thinsp;\u0026lt;\u0026thinsp;1 hour, and 4) none per week. Participants who had a total score\u0026thinsp;\u0026lt;\u0026thinsp;5, obtained by summing the numbers 1) \u0026ndash; 4) from the winter and summer questions, were classified as physically active.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eProteomics measurements\u003c/h2\u003e \u003cp\u003ePlasma samples were sent to SomaLogic Inc. (Boulder Colorado, USA) for proteomics measurement by means of the SOMAscan platform (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Samples and bead coupled SOMAmers were mixed in solution. Free proteins were washed out and SOMAmer-protein complexes were photocleaved off the beads. Dextran sulfate was added to the solution as an anionic competitor, allowing non-cognate complexes to dissociate. SOMAmer-protein complexes were captured onto new avidin-coated beads by protein biotin tags. Free SOMAmers were washed out. Afterwards, SOMAmers were released from complexes in a denaturing buffer. Thus, individual protein concentrations were transformed into a corresponding SOMAmer concentration. SOMAmers were hybridized to complementary sequences on a microarray and quantified by fluorescence. The resulting raw intensities were processed using SOMALogic\u0026rsquo;s data analysis workflow, which utilizes the standard samples included on each plate and entails hybridization normalization, median signal normalization, and signal calibration to control for interplate differences. Protein intensities were reported in relative fluorescence units. One sample failed SOMAscan quality control, leaving a total of 999 samples from 483 males and 516 females. Additionally, we removed 29 aptamers that failed SOMAscan quality control, and 5 additional aptamers as recommended by the SOMAscan assay change log issued on December 22, 2016, leaving 1095 aptamers for further analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStudy design of the replication cohort\u003c/h2\u003e \u003cp\u003eFor replication analysis, a matched CHD case-control sample was drawn from the KORA F3 cohort, which is a follow-up study from the previous KORA S3 survey, which enrolled German nationals between 25- and 74-years old living in the region of Augsburg, South Germany. The KORA F3 cohort consists of 3988 participants, whose data were collected between 2004 and 2005. In total, 330 samples were drawn including 165 CHD cases selected according to criteria identical to the discovery phase, as well as 165 sex and age (5-year interval) matched controls.\u003c/p\u003e \u003cp\u003eTo validate the results of our discovery phase, the replication was performed using ELISA-based measures of the significant proteins obtained with the fully-adjusted model. However, only galectin-4 was assessed to be associated with CHD. Due to the lack of CIMT measures in KORA F3, NCK1 was not tested.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eEnzyme-linked immunosorbent assay (ELISA) for galectin-4\u003c/h2\u003e \u003cp\u003eAn ELISA pre-coated with antibodies against galectin-4 was purchased from MyBiosource (San Diego, CA, U.S.A, MBS9715486) and analyzed according to the manufacturer\u0026rsquo;s protocol. A standard curve was prepared using recombinant galectin-4 in concentrations ranging from 96 to 3 pg/ml with a measurement error below 12%. Plasma samples were diluted in a 1:5 ratio using the dilution buffer provided by the kit. Absorption at 450 nm was measured with the Fluorostar Omega microplate reader (BMG LabTech, Ortenberg, Germany).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003ePre-processing of protein data\u003c/h2\u003e \u003cp\u003eAll protein values including either SOMAscan- or ELISA-based measures, were log2 transformed and standardized to have a mean of 0 and a standard deviation of 1, to be used for further statistical analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eEstimated associations between CHD and each plasma protein were calculated by applying iterative multiple logistic regression analysis (dichotomous outcome), while associations between CIMT and each plasma protein were calculated using a linear regression model (continuous outcome). Both CHD and CIMT associations were tested according to the following models: model 1 adjusted by age and sex only; model 2 adjusted for age, sex, body mass index (BMI), smoking status, physical activity, low-density lipoprotein (LDL), high-density lipoprotein (HDL), triglyceride levels, diabetes status, and hypertension status.\u003c/p\u003e \u003cp\u003eSubjects with missing values were excluded from the respective analysis. To account for protein multiple testing, we applied Bonferroni correction resulting in a significant threshold of 4.57E-05 (0.05/1095 proteins) for both CHD and CIMT outcomes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eSystems biology analysis\u003c/h2\u003e \u003cp\u003eSystems biology analysis of CHD- and CIMT-nominally associated proteins (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) identified with the fully-adjusted model, was performed using the Ingenuity Pathways Knowledge (Qiagen, Redwook City, CA), a database of biological interactions and processes spanning from molecular (proteins, genes) to organism (diseases) levels. Ingenuity Pathway Analysis (IPA) uses enrichment analysis-approaches to calculate the significance of observing a candidate protein/gene set within the context of biological systems. IPA calculates the p-value for enrichment or overlap between the test set and the IPA knowledge base using Fisher\u0026acute;s Exact test. Significant activation was considered at z-score\u0026thinsp;\u0026gt;\u0026thinsp;2 and significant inhibition at z-score\u0026thinsp;\u0026lt;\u0026thinsp;\u0026minus;\u0026thinsp;2. Pathway analyses included causal networks and identification of upstream regulators.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eBaseline characteristics of the study population are described in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Stratification of participants in our discovery cohort KORA F4 for CHD association is displayed in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. There were 982 individuals included in the fully-adjusted model of which 76 had CHD. Non-CHD participants were younger with a mean age of 58.7 years and comprised more women (53.2%), compared to participants with CHD who had a mean age of 64.8 years and comprised less women (31.6%). Participants with CHD had a higher mean of CIMT (0.91) as compared to those without (0.87, p-value: 0.017).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of the KORA F4 study sample: discovery phase.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eFully-adjusted model\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eCoronary Heart Disease (CHD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e906\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (mean, SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e58.74 (7.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64.75 (6.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u0026thinsp;=\u0026thinsp;female (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e482 (53.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24 (31.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (mean, SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27.54 (4.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30.23 (5.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL (mean, SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e140.69 (34.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e127.95 (33.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL (mean, SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57.71 (15.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52.50 (15.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriglycerides (mean, SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e127.20 (88.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e148.51 (72.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e391 (43.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26 (34.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFormer smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e373 (41.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44 (57.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e142 (15.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (7.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhysically active\u0026thinsp;=\u0026thinsp;yes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e571 (63.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43 (56.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.321\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes status\u0026thinsp;=\u0026thinsp;yes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e67 (7.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (18.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension status\u0026thinsp;=\u0026thinsp;yes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e334 (36.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53 (69.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCIMT (mean, SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.87 (0.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.91 (0.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eBMI: body mass index; CHD: coronary heart disease; HDL: high-density lipoprotein; LDL: low-density lipoprotein; CIMT: carotid intima-media thickness; SD: standard deviation\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn total, five proteins were found to be significantly associated with CHD in the sex-age adjusted model (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Supplementary Table\u0026nbsp;1). Two of these proteins showed a protective effect namely, coagulation factor X (OR\u0026thinsp;=\u0026thinsp;0.66, 95% CI\u0026thinsp;=\u0026thinsp;0.55\u0026ndash;0.80) and its activated form coagulation factor Xa (OR\u0026thinsp;=\u0026thinsp;0.65, 95% CI\u0026thinsp;=\u0026thinsp;0.53\u0026ndash;0.79) and three showed negative effects namely, cathepsin H (OR\u0026thinsp;=\u0026thinsp;1.64, 95% CI\u0026thinsp;=\u0026thinsp;1.3\u0026ndash;2.09), galectin-4 (OR\u0026thinsp;=\u0026thinsp;1.70, 95% CI\u0026thinsp;=\u0026thinsp;1.41\u0026ndash;2.08) and renin (OR\u0026thinsp;=\u0026thinsp;1.73, 95% CI\u0026thinsp;=\u0026thinsp;1.38\u0026ndash;2.17). Only galectin-4 remained significantly associated with CHD in the fully-adjusted model (OR\u0026thinsp;=\u0026thinsp;1.58, 95% CI\u0026thinsp;=\u0026thinsp;1.30\u0026ndash;1.93) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Supplementary Table\u0026nbsp;2).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociations between CHD and plasma proteins in both the discovery and replication phases of the study.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003ea) Discovery phase: SOMAScan measurements\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eModel 1: Age-sex adjusted model*\u003c/p\u003e \u003cp\u003eKORA F4 sample: n\u0026thinsp;=\u0026thinsp;78 CHD vs. 908 non-CHD\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProtein\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUniProt\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGene Symbol\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLower_CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUpper_CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGalectin-4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP56470\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLGALS4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.93E-08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRenin\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP00797\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eREN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.91E-06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCoagulation factor Xa\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP00742\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.10E-05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCoagulation Factor X\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP00742\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.23E-05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCathepsin H\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP09668\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCTSH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.21E-05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eModel 2: Fully-adjusted model**\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eKORA F4 sample: n\u0026thinsp;=\u0026thinsp;76 CHD vs. 906 non-CHD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eProtein\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eUniProt\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eGene Symbol\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eOR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eLower_CI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eUpper_CI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eP-value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGalectin-4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP56470\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLGALS4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.50E-06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eb) Replication phase: ELISA-based measures\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eModel 2: Fully-adjusted model***\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eKORA F3 sample: n\u0026thinsp;=\u0026thinsp;165 CHD vs. 165 non-CHD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eProtein\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eUniProt\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eGene Symbol\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eOR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eLower_CI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eUpper_CI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eP-value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGalectin-4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP56470\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLGALS4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.37E-02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e*Model 1: Results from 1095 assessed proteins with the SOMAscan platform.\u003c/p\u003e \u003cp\u003e**Model 2: Full model adjusted by age, sex, body mass index (BMI), low density lipoprotein (LDL), high density lipoprotein (HDL), triglyceride levels, diabetes status, hypertension status, smoking status (categorized as never, former or current smoker) and physical activity. Results from 1095 assessed proteins with the SOMAscan platform.\u003c/p\u003e \u003cp\u003e***Results from testing galectin-4 only.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFor CIMT, four proteins in total were found to be significantly associated in the age-sex adjusted model after Bonferroni adjustment, these were GDNF family receptor alpha-1 (GFRA1) (β\u0026thinsp;=\u0026thinsp;0.017, p-value\u0026thinsp;=\u0026thinsp;1.91E-06), cytoplasmic protein NCK1 (NCK1) (β\u0026thinsp;=\u0026thinsp;0.017, p-value\u0026thinsp;=\u0026thinsp;3.59E-06), insulin-like growth factor-binding protein 2 (IGFBP2) (β = -0.015, p-value\u0026thinsp;=\u0026thinsp;4.47E-05), and growth hormone receptor (GHR) (β\u0026thinsp;=\u0026thinsp;0.016, p-value\u0026thinsp;=\u0026thinsp;2.46E-05) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Supplementary table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In the fully-adjusted model, only NCK1 (β\u0026thinsp;=\u0026thinsp;0.017, p-value\u0026thinsp;=\u0026thinsp;1.39E-06) remained significantly associated with CIMT after Bonferroni adjustment (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Supplementary table \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociations between CIMT and plasma proteins in the discovery phase of the study.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003ea) Discovery phase: SOMAScan measurements\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eModel 1: Age-sex adjusted model*\u003c/p\u003e \u003cp\u003eKORA F4 sample: n\u0026thinsp;=\u0026thinsp;893\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProtein\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUniProt\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGene Symbol\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBeta\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGDNF family receptor alpha-1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP56159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGFRA1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.61E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.91E-06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCytoplasmic protein NCK1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP16333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNCK1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.65E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.59E-06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGrowth hormone receptor\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP10912\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.73E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.46E-05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eInsulin-like growth factor-binding protein 2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP18065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIGFBP2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.69E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.47E-05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eModel 2: Fully-adjusted model**\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eKORA F4 sample: n\u0026thinsp;=\u0026thinsp;889\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eProtein\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eUniProt\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eGene Symbol\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eBeta\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eSE\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eP-value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCytoplasmic protein NCK1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP16333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNCK1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.56E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.39E-06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e*Model 1: Results from 1095 assessed proteins with the SOMAscan platform.\u003c/p\u003e \u003cp\u003e**Model 2: Full model adjusted by age, sex, body mass index (BMI), physical activity, low density lipoprotein (LDL), high density lipoprotein (HDL), triglyceride levels, diabetes status, hypertension status and smoking status (categorized as never, former or current smoker). Results from 1095 assessed proteins with the SOMAscan platform.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo validate our results, we tested our fully-adjusted model findings in an independent cohort of KORA F3 using ELISA-measured levels of galectin-4. Characteristics of participants are shown in supplementary table \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea. The association between galectin-4 and CHD was successfully replicated in the fully-adjusted case-control study (OR\u0026thinsp;=\u0026thinsp;1.40, 95% CI\u0026thinsp;=\u0026thinsp;1.09\u0026ndash;1.88, p-value\u0026thinsp;=\u0026thinsp;1.37E-02; Supplementary table \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003eFinally, to gain insights into enriched signaling pathways and biological mechanisms, Ingenuity Pathway Analysis was performed using nominally associated proteins (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05) identified with the fully-adjusted model including 106 CHD-associated proteins or 66 CIMT-associated proteins. For CHD, the unique significant causal network (z-score = -2.11) including galectin-4 and 43 CHD-associated proteins, predicted the activation of peroxisome proliferator activated receptor alpha (PPARA), which might directly increase the expression of galectin-4. This finding was also predicted by the causal network with the lowest p-value (FDR\u0026thinsp;=\u0026thinsp;5.37E-02), which included 68 CHD-associated proteins. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea shows the summary of PPARA-predicted activation via the two networks. For CIMT, the unique significant causal network (z-score = -2.0) including NCK1 and four CIMT-associated proteins, identified interleukin-9 as an inhibited upstream regulator (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe aim of the present study was to identify protein associations with CHD and CIMT, the latter as a surrogate of atherosclerosis risk. In the age-sex adjusted model, proteomic analysis showed differential abundance of five proteins with CHD and four with CIMT. Following further adjustment of the model for BMI, smoking status, lipid measurements, hypertension, and diabetes status, the quantitative difference of galectin-4 for CHD and NCK1 for CIMT remained significant. Moreover, the association of galectin-4 with CHD was further validated using ELISA-based measurements in an independent study.\u003c/p\u003e \u003cp\u003eGalectin-4 is a member of the beta-galactoside-binding proteins, and has important functions in lipid raft stabilization, protein apical trafficking, cell adhesion, as well as wound healing (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). Galectin-4 may be involved in atherosclerosis by enhancing lipid raft stabilization, which may subsequently affect redox signaling pathways (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Schroder et al. reported galectin-4 to be correlated with myocardial blood flow reserve, a gold standard diagnostic to clinically assess coronary microvascular dysfunction, in women with angina pectoris and non-obstructive CHD (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). The authors conjectured that galectin-4\u0026rsquo;s promotion of cell adhesion contributed to the association (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). A Swedish population-based study found galectin-4 to be significantly associated with incident coronary events (hazard ratio (HR)\u0026thinsp;=\u0026thinsp;1.34, 95% confidence interval (CI)\u0026thinsp;=\u0026thinsp;1.14\u0026ndash;1.57) and incident heart failure (HR\u0026thinsp;=\u0026thinsp;1.26, 95% CI: 1.03\u0026ndash;1.54) (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). Another study compared heart failure patients to controls both recruited in the outpatient clinic at Karolinska University Hospital, finding galectin-4 to be significantly associated with heart failure (HR\u0026thinsp;=\u0026thinsp;2.6; FDR adjusted p-value 0.005) (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). In addition, galectin-4 has been reported to be associated with hospitalization linked to obesity (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e) and ST-segment elevation myocardial infarction (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). All listed reports are in line with our finding of galectin-4\u0026rsquo;s association with CHD. The pathway analysis of CHD-associated proteins suggests that the interplay of galectin-4 and the predicted activated status of both p38 MAPk signaling and interleukin-1B, representatives of inflammation pathways (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e), takes place via the peroxisome proliferator activated receptor alpha (PPARA). PPARG-deficient macrophages have been found to display an elevated production of pro-inflammatory cytokines including interleukin-1B (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAmong the five CHD-associated proteins using the age-sex adjusted model, two were found to be associated with a higher- and two with a lower-CHD risk. Our reported association of renin with higher CHD-risk might have been lost when using the fully-adjusted model due to the adjustment for hypertension (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). However, renin has been reported to be positively associated with CHD (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). Renin is a member of the renin-angiotensin-aldosterone system, which, via its active peptide angiotensin II, contributes to atherosclerosis development, not only by promoting hypertension but also through multiple direct actions on vessels (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCathepsin H was an additional protein associated with a higher CHD-risk in the age-sex adjusted model. Cathepsin H is a lysosomal cysteine protease important in the overall degradation of lysosomal proteins (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). Its atherogenic role could lead the transformation of LDL to an atherogenic moiety, which in turn induces macrophage foam cell formation (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eProteins associated with lower-CHD risk associations included coagulation factor X as well as its active form coagulation factor Xa. A pathogenetic mechanism of CHD includes thrombotic vessel occlusion followed by rupture of an atherosclerotic plaque (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). It is therefore not surprising that constituents and a regulating protein of the coagulation cascade were found significantly associated with CHD in the present study. Factor Xa exerts also non-hemostatic effects by activation of protease-activated receptors-1 (PAR-1) and PAR-2, which have been associated with atherosclerosis, inflammation, and fibrosis (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e). Such counterintuitive associations were reported before: where the concentrations of coagulation factor X and prothrombin were lower in blood from patients with CHD having more than 50% stenosis compared with those without CHD (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e). Brummel-Ziedins et al. hypothesized that despite the depletion of coagulation factors, the balance between tissue factor and tissue factor pathway inhibitor is the primary driver of a hypercoagulable state in patients with CHD (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWe also identified proteins to be associated with CIMT. NCK1 was the unique significant protein positively associated with CIMT in the fully-adjusted model. NCK1 is reported to be involved in different pathways leading to the progression of atherosclerosis (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e). For instance, it is associated with vascular permeability, which allows the uptake of low-density lipoproteins (LDL) and thereby stimulates inflammation (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e). The resulting oxidative stress increase in endothelial cells decreases nitric oxide and thereby supports endothelial cell dysfunction (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e). Alfaidi et al. showed in an in-vivo study that NCK1-knockdown reduced NF-κB signaling and thereby inflammation in endothelial cells (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e). Additionally, pathway analysis revealed interleukin-9 to indirectly inhibit expression of NCK1, SPTAN1, and CFL1, while the opposite effect was predicted for CMA1. Interleukin-9 has been reported to decrease expression of human NCK1 in MDA-MB-231 human breast cells, and to differentially regulate actin cytoskeleton-related proteins such as NCK1 (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e). Interestingly, both SPTAN1 and CFL1 are filamentous cytoskeletal and actin-related proteins, while CMA1 could participate in extracellular matrix degradation (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e). We were unable to validate the NCK1 findings using ELISA-measurements due to the lack of CIMT measurements in the validation sample.\u003c/p\u003e \u003cp\u003eWe were able to confirm three additional proteins associated with CIMT using our age-sex adjusted model namely GFRA1, IGFBP2 and GHR, which have been all reported to be linked to CVD, mortality, and atherosclerosis (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e). IGFBP2 was the unique obtained negative association. The same direction effect has been reported as a strong association with type 2 diabetes in a comparison study of incident type 2 diabetes and coronary heart disease in the KORA cohort (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this study, we identified proteins specifically associated with either CHD or CIMT. Previous reports on KORA F4 have already stated a non-linear relation between CIMT and CHD risk (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e). Thus, this complex relationship amongst the two phenotypes could help to explain the specificity of our findings.\u003c/p\u003e \u003cp\u003eA major strength of our study is the proteome-wide approach, which covers proteins at low abundance levels in plasma. By conducting a hypothesis-free analysis, we were able to analyze the association of a wide array of plasma proteins with CHD and CIMT. An additional strength is the availability of an independent sample, which we could use to validate initial results with an alternative measurement technique that provides absolute concentrations. Our study also has limitations. The lack of patient differentiation by CHD severity could, for instance, be attenuating some associations. Manifestations of early-stage CHD differ from the late-stage CHD. In the latter, protein levels change due to myocardial injury and physiological compensation. Additionally, the difference in plaque vulnerability and extent of atherosclerosis between stable and unstable CHD (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e), may also impact the plasma proteome. Finally, due to the cross-sectional nature of our study, temporal relations cannot be inferred.\u003c/p\u003e \u003cp\u003eIn summary, our proteome-wide study identified a new association of galectin-4 with CHD. Galectin-4 may be involved in atherosclerosis by enhancing lipid raft stabilization, which subsequently affects redox signaling pathways. Moreover, we report the association of NCK1 with CIMT.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cem\u003eEthics approval and consent to participate\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe KORA (Cooperative health research in the Region of Augsburg) study is an independent population-based cohort study in southern Germany (22). The study was approved by the ethics committee of the Bavarian Medical Association and was carried out in accordance with the principles of the Declaration of Helsinki. All study participants signed written informed consent prior to their participation in the study.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConsent for publication\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAvailability of data and materials\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe informed consent given by KORA study participants does not cover data posting in public databases. However, data are available upon request by means of a project agreement from KORA (https://helmholtz-muenchen.managed-otrs.com/external).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCompeting interests\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe KORA study was initiated and financed by the Helmholtz Zentrum M\u0026uuml;nchen \u0026ndash; German Research Center for Environmental Health, which is funded by the German Federal Ministry of Education and Research (BMBF) and by the State of Bavaria. Data collection in the KORA study is done in cooperation with the University Hospital of Augsburg. Furthermore, KORA research was supported within the Munich Center of Health Sciences (MC Health), Ludwig-Maximilians-Universit\u0026auml;t, as part of LMUinnovativ. This research received funding from the German Centre for Cardiovascular Research (DZHK) under grant number DZHK B 19-017 SE and 81X2400136.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAuthors' contributions\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eMAE, MCGA, CWC and SN interpreted the data, wrote, and revised the manuscript. CWC and MAE participated in the design of the study and analyzed the data. MW conceived the research question, participated in its design and revised the manuscript. EH participated in the design and measurement of the protein replication and revised the manuscript. AP, MW, CG, JG, KS, SK, WR, JS, WK, MN, UV, EH, TD, CM were involved in the data collection, data management, and preparation of their respective cohorts. All authors contributed to the writing of the article, critically reviewed it, and approved the final version for submission.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAcknowledgements\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe thank Ulrike Lissner for technical support in ELISA measurements.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAuthors' information (optional)\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBenjamin EJ, Virani SS, Callaway CW, Chamberlain AM, Chang AR, Cheng S, et al. Heart Disease and Stroke Statistics-2018 Update: A Report From the American Heart Association. Circulation. 2018;137(12):e67\u0026ndash;492.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHajar R. Risk Factors for Coronary Artery Disease: Historical Perspectives. 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Atherosclerosis. 2020;296:32\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAgewall S. Acute and stable coronary heart disease: different risk factors. Eur Heart J. 2008;29(16):1927\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\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":"cardiovascular-diabetology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"cvdb","sideBox":"Learn more about [Cardiovascular Diabetology](http://cardiab.biomedcentral.com/)","snPcode":"12933","submissionUrl":"https://submission.nature.com/new-submission/12933/3","title":"Cardiovascular Diabetology","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Galectin-4, NCK1, coronary artery disease, stroke, carotid intima media thickness, atherosclerosis, cardiovascular disease, proteomics.","lastPublishedDoi":"10.21203/rs.3.rs-3234719/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3234719/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground and aims: \u003c/strong\u003eAtherosclerosis is the main cause of stroke and coronary heart disease (CHD), both leading mortality causes worldwide. Proteomics, as a high-throughput method, could provide helpful insights into the pathological mechanisms underlying atherosclerosis. In this study, we characterized the associations of plasma protein levels with CHD and with carotid intima-media thickness (CIMT), as a surrogate measure of atherosclerosis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eThe discovery phase included 1000 participants from the KORA F4 study, whose plasma protein levels were quantified using the aptamer-based SOMAscan proteomics platform. We evaluated the associations of plasma protein levels with CHD using logistic regression, and with CIMT using linear regression. For both outcomes we applied two models: an age-sex adjusted model, and a model additionally adjusted for body mass index, smoking status, physical activity, diabetes status, hypertension status, low density lipoprotein, high density lipoprotein, and triglyceride levels (fully-adjusted model). The replication phase included a matched case-control sample from the independent KORA F3 study, using ELISA-based measurements of galectin-4. Pathway analysis was performed with nominally associated proteins (p-value \u0026lt; 0.05) from the fully-adjusted model.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e In the KORA F4 sample, after Bonferroni correction, we found CHD to be associated with five proteins using the age-sex adjusted model: galectin-4 (LGALS4), renin (REN), cathepsin H (CTSH), and coagulation factors X and Xa (F10). The fully-adjusted model yielded only the positive association of galectin-4 (OR = 1.58, 95% CI = 1.3 - 1.93), which was successfully replicated in the KORA F3 sample (OR = 1.40, 95% CI = 1.09 - 1.88). For CIMT, we found four proteins to be associated using the age-sex adjusted model namely: cytoplasmic protein NCK1 (NCK1), insulin-like growth factor-binding protein 2 (IGFBP2), growth hormone receptor (GHR), and GDNF family receptor alpha-1 (GFRA1). After assessing the fully-adjusted model, only NCK1 remained significant (ꞵ = 0.017, p-value = 1.39e-06). Upstream regulators of galectin-4 and NCK1 identified from pathway analysis were predicted to be involved in inflammation pathways.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e Our proteome-wide association study identified galectin-4 to be associated with CHD and NCK1 to be associated with CIMT. Inflammatory pathways underlying the identified associations highlight the importance of inflammation in the development and progression of CHD.\u003c/p\u003e","manuscriptTitle":"Plasma Proteome Association with Coronary Heart Disease and Carotid Intima Media Thickness: results from the KORA F4 study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-08-17 14:28:23","doi":"10.21203/rs.3.rs-3234719/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revision","date":"2023-10-31T13:02:50+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2023-09-11T13:05:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"c2fb9fc8-6d09-4ff7-bba2-a00ec8c766d1","date":"2023-08-19T18:10:12+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2023-08-16T16:59:06+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2023-08-11T07:57:20+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2023-08-11T04:28:59+00:00","index":"","fulltext":""},{"type":"submitted","content":"Cardiovascular Diabetology","date":"2023-08-04T12:24:04+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"cardiovascular-diabetology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"cvdb","sideBox":"Learn more about [Cardiovascular Diabetology](http://cardiab.biomedcentral.com/)","snPcode":"12933","submissionUrl":"https://submission.nature.com/new-submission/12933/3","title":"Cardiovascular Diabetology","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6c896fa2-7865-4d20-bf30-e3bcb5b5ab56","owner":[],"postedDate":"August 17th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-05-08T14:00:35+00:00","versionOfRecord":[],"versionCreatedAt":"2023-08-17 14:28:23","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3234719","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3234719","identity":"rs-3234719","version":["v1"]},"buildId":"J0_U0BvcaRcwD8yVFaRlm","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}