Impact of lipid markers and high-sensitivity C-reactive protein on the value of the 99th percentile upper reference limit for high-sensitivity cardiac troponin I

    Impact of lipid markers and high-sensitivity C-reactive protein on the value of the 99th percentile upper reference limit for high-sensitivity cardiac troponin I Magdalena Krintus, Marek Kozinski, Tomasz Fabiszak, Magdalena Kuligowska-Prusinska, Ewa Laskowska, Lieselotte Lennartz, Lena NowakLos, Jacek Kubica, Grazyna Sypniewska PhD PII: DOI: Reference:

S0009-8981(16)30393-X doi: 10.1016/j.cca.2016.09.020 CCA 14505

To appear in:

Clinica Chimica Acta

Received date: Revised date: Accepted date:

28 July 2016 1 September 2016 25 September 2016

Please cite this article as: Krintus Magdalena, Kozinski Marek, Fabiszak Tomasz, Kuligowska-Prusinska Magdalena, Laskowska Ewa, Lennartz Lieselotte, Nowak-Los Lena, Kubica Jacek, Sypniewska Grazyna, Impact of lipid markers and high-sensitivity C-reactive protein on the value of the 99th percentile upper reference limit for highsensitivity cardiac troponin I, Clinica Chimica Acta (2016), doi: 10.1016/j.cca.2016.09.020

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ACCEPTED MANUSCRIPT Impact of lipid markers and high-sensitivity C-reactive protein on the value of the 99th percentile upper reference limit for high-sensitivity cardiac troponin I

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Running title: Lipids, hs-CRP and 99th URL for hs-cTnI

Magdalena Krintus1*, Marek Kozinski2*, Tomasz Fabiszak3,

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Magdalena Kuligowska-Prusinska1 , Ewa Laskowska2, Lieselotte Lennartz4,

Department of Laboratory Medicine, Nicolaus Copernicus University, Collegium Medicum,

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Lena Nowak-Los1, Jacek Kubica3, Grazyna Sypniewska PhD1

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Bydgoszcz, Poland

Department of Principles of Clinical Medicine, Nicolaus Copernicus University, Collegium

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Medicum, Bydgoszcz, Poland

Department of Cardiology and Internal Medicine, Nicolaus Copernicus University,

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Collegium Medicum, Bydgoszcz, Poland Abbott Laboratories, Wiesbaden, Germany

*Magdalena Krintus and Marek Kozinski have equally contributed to the present article and should be considered first authors.

Corresponding author: Marek Kozinski MD, PhD, Department of Principles of Clinical Medicine, Nicolaus Copernicus University, Collegium Medicum, 9 Sklodowskiej-Curie Street, 85-094 Bydgoszcz, Poland, Phone: +48 52 585 40 23, Fax: +48 52 585 40 24, E-mail: [email protected]

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ACCEPTED MANUSCRIPT Abstract

Objectives: i) to assess the relationship between lipid markers and high-sensitivity C-reactive

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protein (hs-CRP), and high-sensitivity cardiac troponin I (hs-cTnI) in the reference

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population, and ii) to evaluate the impact of lipid markers and hs-CRP on the 99th percentile

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upper reference limit (URL) for hs-cTnI.

Methods: 531 questionnaire-identified presumably healthy individuals were enrolled in a

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single-center, cross-sectional study. Surrogate biomarkers for diabetes, myocardial and renal

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dysfunction were used to refine the healthy cohort (n=408). Lipid profile, total cholesterol:high-density lipoprotein cholesterol (HDL-C) ratio, non-HDL-C, apolipoprotein AI (apoAI), apolipoprotein B (apoB), apoB:apoAI ratio, lipoprotein(a), small dense low-

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density lipoprotein cholesterol (LDL-C) and hs-CRP were determined.

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Results: Individuals with detectable vs. non-detectable hs-cTnI concentrations more often showed elevated LDL-C (60% vs. 46%; p=0.002), apoB (73% vs. 61%; p=0.008), apoB:apoAI ratio (53% vs. 40%; p=0.005) and lipoprotein(a) (15% vs. 7%; p=0.015). The

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apoB:apoAI ratio and to a lesser extent other lipid markers, but not hs-CRP, were positively associated with hs-cTnI concentration in univariate and multivariate analyses. Exclusion of individuals with elevated apoB:apoAI ratio or apoB, but not hs-CRP, lowered the 99th percentile URL in the healthy cohort respectively by 12.9% (6.2 vs 5.4 ng/L) and 14.5% (6.2 vs 5.3 ng/L). The corresponding reduction for both lipid biomarkers in the presumably healthy population was 24.0% (7.5 vs. 5.7 ng/L). Conclusion: Our study demonstrates that atherogenic lipid markers, particularly apoB:apoAI ratio or apoB, influence the 99th percentile URL for hs-cTnI.

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ACCEPTED MANUSCRIPT Keywords: lipids, lipoproteins, apolipoproteins, high-sensitivity C-reactive protein, highsensitivity cardiac troponin, 99th percentile URL.

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1. Introduction

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Detection of the rise and/or fall of cardiac biomarker concentrations, preferably cardiac troponin (cTn), with at least one value above the 99th percentile upper reference limit (URL), continues to be the essential component for the diagnosis of myocardial infarction (MI) [1].

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According to the Third Universal Definition of Myocardial Infarction, the 99th percentile URL should be determined in a normal reference population for each specific assay with

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appropriate quality control in each laboratory [1]. Importantly, recently endorsed highsensitivity cardiac troponin (hs-cTn) assays with their improved precision at the 99th

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percentile URL are able to detect measurable cTn concentrations in a substantial proportion of

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with suspected MI [2-10].

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healthy individuals and have been proven to facilitate clinical decision making in patients

The selection of a normal reference population for the 99th percentile URL determination remains a critical but also controversial issue as the characteristics of the

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reference population have a profound effect on the 99th percentile URL value. Recently we and others have demonstrated that multiple factors may influence the determination of this critically important decision limit (i.e. selection criteria including biomarkers, statistical approach, gender, smoking status and age of reference individuals) [11-16]. Unfortunately, no uniform protocol for the selection of a healthy reference population has been established so far. The CLSI document Reference Intervals in the Clinical Laboratory (EP28-A3c) [17] advocates a questionnaire-based approach for determining laboratory test reference intervals, while a recent expert review by Sandoval and Apple [18] recommends that the selection process for reference populations to determine the 99th percentile URL should address: clinical history and medication use, surrogate biomarkers for diabetes, myocardial and renal 3

ACCEPTED MANUSCRIPT dysfunction, sufficient sample size with diverse distribution of ages, appropriate statistical analysis, inclusion of an imaging modality if financially feasible, and finally description of

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specimen type.

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However, none of these recommendations include testing for lipid markers or high-

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sensitivity C-reactive protein (hs-CRP). We hypothesize that inclusion of these biomarkers, which are important risk factors and/or mediators of atherosclerosis [19-30] in the selection of a reference population may facilitate identification of truly healthy individuals. Therefore, in

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the present study we aimed: i) to assess the relationship between lipid markers (total

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cholesterol [TC], low-density lipoprotein cholesterol [LDL-C], high-density lipoprotein cholesterol [HDL-C], triglycerides [TG], non-HDL-cholesterol [non-HDL-C], the TC:HDL-C ratio, apolipoprotein AI [apoAI], apolipoprotein B [apoB], the apoB:apoAI ratio,

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lipoprotein(a) [Lp(a)], and small dense LDL-C [sdLDL-C]) and hs-CRP, and hs-cTnI in a reference healthy population, and ii) to evaluate the impact of the investigated lipid markers

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2. Methods

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and hs-CRP on the 99th percentile URL for hs-cTnI.

2.1. Study design

The study was designed as a community-based, single-center, cross-sectional study. Fasting plasma and serum samples were obtained between March and August 2013 and also between July and August 2015 from presumably healthy Caucasian individuals aged 18-70 years, recruited in various workplaces in Bydgoszcz, Poland. Of 639 eligible individuals from the general population, 91 subjects were excluded due to hypertension, diabetes mellitus (n=10) or both (n=7) as identified by a prespecified questionnaire (Supplemental Figure 1), completed by the participants prior to blood draw. 531 participants declared good health, were free of known cardiac diseases (had no history of cardiac disease, cardiac treatment, including 4

ACCEPTED MANUSCRIPT lipid-lowering therapy, cardiac intervention), hypertension or diabetes mellitus and therefore were enrolled in the study. Additional exclusion criteria included pregnancy, any current

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infection or previously diagnosed chronic inflammatory disease. No cardiac imaging studies

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were obtained in this reference population. All participants provided informed written consent

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for inclusion in this study. The study protocol was approved by the Local Ethics Committee in accordance with the Declaration of Helsinki. Biomarkers were used to further refine the healthy cohort as follows: B-type natriuretic peptide (BNP) <35 ng/L [24], glycated

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hemoglobin (HbA1c) <48 mmol/mol (<6.5%) [25], and estimated glomerular filtration rate

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(eGFR) >60 mL/min/1.73m [2,12]. Using these parameters, we progressively excluded subjects at risk of myocardial dysfunction, diabetes and moderate to severe kidney

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2.2. Biomarker measurements

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dysfunction.

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All measurements were performed at the Department of Laboratory Medicine, Nicolaus University,

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Bydgoszcz,

Poland.

Serum

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ethylenediaminetetraacetic acid (EDTA) plasma were separated from venous blood samples

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and following centrifugation (except for HbA1c which was measured in whole blood) routine laboratory measurements were performed in fresh serum (creatinine, basic lipid profile, hscTnI) and only BNP was measured in fresh EDTA plasma samples. Then all remaining serum aliquots were stored at -70°C until assayed together for apoAI, apoB, Lp(a), sd-LDL-C and hs-CRP. HbA1c, BNP, creatinine, basic lipid profile (TC, HDL-C and TG) were measured on the Abbott ARCHITECT ci8200 analyzer using commercially available tests (Abbott Laboratories, Wiesbaden, Germany). LDL-C concentration was calculated using the Friedewald equation, except for subjects with TG >2.26 mmol/L, in which case LDL-C was

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ACCEPTED MANUSCRIPT measured directly. Non-HDL-C concentration as well as the TC:HDL-C and apoB:apoAI ratios were calculated. Serum cTnI was measured using the ARCHITECT STAT high

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sensitive Troponin-I immunoassay with a limit of detection (LoD) of 1.9 ng/L. The lowest

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cTnI concentration corresponding to a total CV of 10% was 3.6 ng/L [31]. This assay uses

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calibrators which are matched to a large stock of internal reference standards to ensure calibrator lot consistency. Calibrator B with a value close to the critical decision limit is traceable to NIST SRM 2921 (20 ng/L). In turn, by use of the low control (targeted to 20

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performance of the assay is controlled.

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ng/L) and assay file parameters such as Calibrator B/Calibrator A ratio the low end

eGFR was derived from the Chronic Kidney Disease Epidemiology Collaboration

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(CKD-EPI) equation [32]. Measurements of apolipoproteins: apoB and apoAI, sdLDL-C,

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Lp(a) and hs-CRP were performed on the Horiba ABX Pentra 400 analyzer (Horiba ABX, Montpellier, France). Reagents for sd-LDL-C (direct automated sdLDL-C kit) and Lp(a) were

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supplied by Randox Laboratories (Crumlin, UK).

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2.3. Definition of study populations and selection criteria 2.3.1. Presumably healthy population The baseline presumably healthy population comprised of study participants, who confirmed their good health status by questionnaire. For the 99th percentile URL calculation, this population was further stratified by gender. 2.3.2. Healthy cohort Next we defined a healthy cohort within the presumably healthy population, according to stringent selection criteria based on the use of surrogate biomarkers. We progressively performed different classifications according to HbA1c and BNP concentrations and eGFR

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ACCEPTED MANUSCRIPT values. Further, the healthy cohort was most stringently selected using laboratory screening methods and defined as those reference individuals who had HbA1c <48 mmol/mol, BNP <35

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ng/L, and eGFR >60 mL/min/1.73 m2. For the 99th percentile URL calculation, this healthy

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cohort was also further stratified by gender.

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2.4. Statistical analysis

Continuous variables were expressed as medians with 25th–75th percentiles and

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categorical data as numbers and percentages. The Kolmogorov-Smirnov test was used to assess the normality of distribution of investigated parameters. Comparison between the

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groups was performed by using the Chi-square test for categorical variables and the MannWhitney U-test or the Kruskal-Wallis test for continuous variables with non-parametric

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distribution. Correlations were calculated using Spearman’s correlation coefficients. We

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evaluated the impact of other variables as potential sources of hs-cTnI variation using

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multiple regression analysis. Hs-cTnI concentrations were logarithmically (logn) transformed before their introduction in multiple regression analysis in order to improve their adherence to the normal distribution and to decrease/eliminate the presence of outliers. Univariate and

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multivariate logistic regression models were used to identify predictors of detectable hs-cTnI concentrations. Variables with a p-value of <0.1 in the univariate analysis were introduced into the multivariate logistic regression model. Subsequently, variables with no significant impact on the presence of detectable hs-cTnI concentrations were one after another removed from the multivariate model according to their decreasing p-values. Finally, to optimize our multivariate model, it was also adjusted for age and gender. P value <0.05 was considered statistically significant. The hs-cTnI 99th percentile URL values were determined by using the robust method, which has been described and recommended in the CLSI C28-A3c document [17]. Ninety

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ACCEPTED MANUSCRIPT percent confidence intervals (CI) were calculated for the 99th percentile URL values, if possible. We excluded outlier observations from the analyzed population based on the method

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described by Reed et al [33].

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Statistical analysis was performed using Statistica 12.0 for Windows (StatSoft, Tulsa,

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OK, USA) and MedCalc 15.8 (MedCalc Software, Ostend, Belgium). 3. Results

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3.1. Characteristics of the study population and findings of basic statistics

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Baseline characteristics of the presumably healthy population (n=531), including subgroups with detectable (n=370; 70%) and undetectable (n=161; 30%) serum hs-cTnI

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concentrations, are presented in Table 1. The presumably healthy population was

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characterized by a median age of 40 years and a similar distribution of both genders. Of note, almost half of the presumably healthy population had a body mass index ≥25 kg/m2. Among

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the assessed lipid parameters, only median TC, LDL-C and apoB concentrations were above desirable values. Individuals with detectable (≥LoD; i.e. ≥1.9 ng/L) serum hs-cTnI were

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generally older and had lower eGFR than those with undetectable hs-cTnI concentrations. Additionally, comparisons of the investigated laboratory parameters revealed significantly higher values of HbA1c, LDL-C, non-HDL-C, apoB and apoB:apoAI ratio in the former subgroup. Supplemental Table 1 shows baseline characteristics of the healthy cohort (n=408). Detectable serum hs-cTnI concentrations were found in 268 of 408 individuals (66%), which was slightly less than in the presumably healthy population. As with the presumably healthy population, there were significant differences in the healthy cohort between subjects with detectable vs. undetectable serum hs-cTnI concentrations in terms of HbA1c, eGFR, LDL-C, apoB and apoB:apoAI ratio values. Moreover, non-HDL-C concentration tended to be higher

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ACCEPTED MANUSCRIPT in the former subgroup, however a p-value did not reach the threshold of statistical significance. Similarly to the presumably healthy population, rates of smokers within the

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healthy cohort were comparable between the subgroups specified with respect to hs-cTnI

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detectability.

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The prevalence of lipid disorders and elevated hs-CRP concentration in the presumably healthy population is displayed in Figure 1A. In general, the most common disorders were elevated values of apoB, TC, LDL-C and apoB:apoAI ratio. Importantly, individuals with

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detectable vs. undetectable serum hs-cTnI concentrations more often showed substantially

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elevated LDL-C (60% vs. 46%), apoB (73% vs. 61%), apoB:apoAI ratio (53% vs. 40%) and Lp(a) (15% vs. 7%). These findings were concordant with results obtained in the healthy cohort (Figure 1B). Similarly, subjects with serum hs-cTnI concentrations ≥1.9 ng/L vs. <1.9

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ng/L were more frequently classified as having increased values of LDL-C (60% vs. 44%), apoB (72% vs. 59%), apoB:apoAI (53% vs. 39%) and Lp(a) (15% vs. 8%).

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Correlation analysis showed a number of significant relationships between serum hscTnI and other non-lipid and lipid parameters, both in the presumably healthy population and

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in the healthy cohort (Supplemental Table 2). Age, BMI, HbA1c, eGFR and lipid markers, except for Lp(a), but not BNP and hs-CRP, were significantly, though weakly, correlated with serum hs-cTnI concentration. 3.2. The impact of lipid parameters and traditional CV risk factors on log-transformed hscTnI concentration In the course of further analysis, we developed multiple linear regression models. Supplemental Tables 3 and 4 show both the adjusted and unadjusted associations of serum hscTnI concentrations in the presumably healthy population and in the healthy cohort, respectively. Lipid parameters and hs-CRP were separately included in models containing HbA1c, BNP and eGFR, and then adjusted for age and gender, and finally for age, gender,

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ACCEPTED MANUSCRIPT BMI and smoking status. Among the investigated laboratory parameters, only HbA1c, eGFR and apoB:apoAI ratio remained significantly related to log-transformed hs-cTnI

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concentration, even after adjustments for conventional risk factors. Additionally, we also

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observed some indicators of the impact of apoB and apoAI on log-transformed hs-cTnI

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concentration. However, these initial associations became insignificant after adjustments. 3.3. Predictors of detectable serum hs-cTnI concentrations

In univariate logistic regression analysis including all variables listed in Table 1, the

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presence of detectable serum hs-cTnI concentrations in the presumably healthy population

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was associated with older age and higher values of HbA1c, BNP, eGFR, apoB:apoAI ratio, apoB, LDL-C and Lp(a). Furthermore, in multivariate logistic regression analysis adjusted for age and gender, the only independent predictors of serum hs-cTnI concentration ≥1.9 ng/L

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were increased HbA1c, BNP and apoB:apoAI ratio (Table 2). Consistent results were obtained in the healthy cohort (Supplemental Table 5). In

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univariate logistic regression analysis, the presence of detectable serum hs-cTnI concentrations was associated with older age and higher values of

HbA1c, eGFR,

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apoB:apoAI, apoB and LDL-C, while the impact of Lp(a) was borderline. In multivariate logistic regression analysis adjusted for age and gender, high values of HbA1c and apoB:apoAI ratio, but no longer BNP remained as independent predictors of detectable serum hs-cTnI concentrations. 3.4. hs-cTnI 99th percentile URL in the presumably healthy population Values of the 99th percentile URL for hs-cTnI in the presumably healthy population are shown in Table 3. The overall 99th percentile URL (with 90% CI) was 7.5 (6.3-8.6) ng/L with men having higher values than women (7.2 [6.5-8.0] vs. 6.9 [5.2-8.4] ng/L). In general, exclusion of subjects with elevated lipid markers and hs-CRP decreased the overall 99th percentile URL for hs-cTnI. The implementation of apoB- and apoB:apoAI ratio-based

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ACCEPTED MANUSCRIPT selection criteria had the most pronounced effect on the 99th percentile URL, excluding respectively 70% and 49% of the study participants and decreasing its overall value by 24.0%

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for both biomarkers.

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3.5. hs-cTnI 99th percentile URL in the healthy cohort

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Values of the 99th percentile for hs-cTnI in the healthy cohort are presented in Table 4. Subjects were progressively excluded using stringent surrogate biomarkers for diabetes, myocardial and renal dysfunction selection criteria which reduced the population by 14% and

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decreased the 99th percentile URL by 11.4%. As with the presumably healthy population,

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99th percentile URL for women was lower than for men.

The implementation of apoB- and apoB:apoAI ratio-based selection criteria had the most pronounced effect on the 99th percentile URL, excluding 69% and 48% of the subjects

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4. Discussion

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and decreasing its overall value by 14.5% and 12.9%, respectively.

Our comprehensive assessment of the impact of lipid markers and hs-CRP on hs-cTnI

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concentration in the reference population revealed that: i) the apoB:apoAI ratio and to a lesser extent other lipid markers, but not hs-CRP, are positively associated with serum hs-cTnI concentration, and ii) the use of either apoB- or apoB:apoAI ratio-based selection criterion in addition to the questionnaire-based approach, and combined with surrogate biomarkers for diabetes, myocardial and renal dysfunction, further decreases the value of the 99th percentile URL for hs-cTnI. We believe, this is the first paper which demonstrates that atherogenic lipid markers influence the 99th percentile URL for hs-cTnI. Specifically, exclusion of individuals with elevated values of either the apoB:apoAI ratio or apoB lowered the overall value of the 99th

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ACCEPTED MANUSCRIPT percentile URL for hs-cTnI in the presumably healthy population by 24.0% (7.5 vs. 5.7 ng/L for both biomarkers). The corresponding decreases associated with the implementation of

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apoB- or apoB:apoAI ratio-based selection criteria in the healthy cohort were 12.9% (6.2 vs.

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5.4 ng/L) and 14.5% (6.2 vs. 5.3 ng/L), respectively. Importantly, the apoB:apoAI ratio was

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independently associated with serum hs-cTnI concentration in both the presumably healthy population and healthy cohort. Furthermore, consistent results for the apoB:apoAI ratio were achieved in both types of performed analyses (i.e. with absolute hs-cTnI concentration and the

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occurrence of detectable serum hs-cTnI concentrations as end points). In line with these

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findings, our previous study also suggested that the presence of dyslipidemia may be linked with increased hs-cTnI concentrations in the presumably healthy population [11]. However, these results were only hypothesis-generating due to the fact that solely lipid profile, but no

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other lipid markers, was assessed in the previous project and blood samples for lipid profile measurement were only available from 715 out of 1368 (52%) investigated individuals.

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Interestingly, the results of the INTERHEART study, an international, multicenter, project covering 15,152 cases with 14,820 controls, suggested that the apoB:apoAI ratio

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provides the highest odds ratio for MI occurrence, when compared with LDL-C, the TC:HDLC ratio, and other non-lipid classical risk factors [34,35]. Similar findings were obtained in the AMORIS trial, a Swedish cohort study of 69,030 men and 57,168 women, with a mean follow-up of 98 months. In this study, apoB, apoAI, and the apoB:apoAI ratio were stronger predictors for MI than LDL-C and HDL-C [36]. Importantly, in both of these studies elevated apoB:apoA-I ratio was able to identify subjects at high risk even when their LDL-C values were normal [35-37]. Moreover, an individual participant-level meta-analysis by the Emerging Risk Factors Collaboration, comprised 165,544 participants, without baseline cardiovascular disease, from 37 prospective cohorts, showed that the addition of combination of apoB and apoAI to basic lipid profile and conventional risk factors improved 12

ACCEPTED MANUSCRIPT cardiovascular risk stratification, particularly in individuals classified at intermediate risk [37]. These observations and our findings may be explained by the fact that apoB is

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considered an indirect measure of all atherogenic lipoproteins in the bloodstream, while

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apoAI acts as the major apolipoprotein in HDL particles, involved in reverse cholesterol

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transport [29]. Of note, apoB is present not only in LDL particles but also constitutes the main protein component of other atherogenic lipoproteins originating in the liver, such as very lowdensity lipoprotein (VLDL), intermediate-density lipoprotein (IDL), and Lp(a). Therefore, it

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is suggested that the sum of all apoB concentrations in all atherogenic particles may be a

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better risk marker than TC and LDL-C only [36].

In contrast to our findings, Lippi et al. found that HDL-C, but not TC and LDL-C, was independently and inversely associated with high-sensitivity cardiac troponin T (hs-cTnT) and

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contemporary sensitive cTnI in subjects attending an inpatient clinic, as part of routine cardiovascular risk assessment, or in patients who were hospitalized due to various causes

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[38]. In this study, positive correlations between LDL-C or TC and cTns, present in simple linear regression, became non-significant in multivariate linear regression models.

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Importantly, both studies investigated different populations (highly selected healthy individuals vs. all-comers with possible cardiovascular disease). Additionally, other researchers also demonstrated positive associations between cTn and atherogenic lipid markers (i.e. LDL-C, TC and TG) in patients with suspected or diagnosed MI [39,40]. Recent evidence from epidemiological and genetic studies indicates that elevated Lp(a) concentrations are strongly, independently and causally related to premature coronary artery disease (CAD) [41,42]. Nevertheless, in our study, Lp(a) was associated with detectable hscTnI concentrations in some, but not all analyses, with no additional influence on the 99th percentile URL in the healthy cohort.

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ACCEPTED MANUSCRIPT We failed to demonstrate any association between hs-CRP and hs-cTnI concentrations in both the presumably healthy population and the healthy cohort. Similarly, the

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implementation of hs-CRP-based selection criterion did not affect the value of the 99th

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percentile URL for hs-cTnI. In fact, our study is the first report to date addressing a possible

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link between hs-CRP and hs-cTn concentrations in a normal reference population. Although numerous epidemiological studies demonstrated a link between CRP concentration and the risk of developing CAD [43-46], genetic studies conducted so far do not support the causal

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role of CRP in the pathogenesis of CAD [47,48]. Therefore, increased CRP concentration

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accounts for the presence of multiple risk factors and reflects a substantial part of the overall cardiovascular risk rather than being a causal factor in CAD.

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We hypothesize that the presence of subclinical coronary atherosclerosis may be

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responsible for increased hs-cTnI concentrations in individuals with dyslipidemia. In line with this assumption, Segre et al. have recently found that cTnI, but not BNP, myeloperoxidase,

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nitrotyrosine or oxidized LDL, elevation was related to the presence of angiographically documented chronic CAD in 95 diabetic patients with multiple associated cardiovascular risk

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factors [49]. However, the underlying mechanism of our findings needs further investigation. In the present study, we have shown that lipid disorders significantly affect the 99th percentile URL for hs-cTnI. We and others have previously demonstrated that the application of different selection criteria and statistical approaches may result in a large variability in the 99th percentile URL, even despite the use of the same hs-cTnI assay and the same baseline reference population [11-16]. Additionally, the adoption of gender- and age-based 99th percentile URL values have been proposed [1,50]. We believe that all these observations may be responsible for substantial differences in the 99th percentile URL for hs-cTnI in both our study and similar articles or the assay insert and underscore the need for a uniform protocol for the selection of a normal reference population [11-16,18,31]. 14

ACCEPTED MANUSCRIPT We acknowledge that our study has several limitations. Firstly, due to the nature of our single-centre study and its moderate size, our findings warrant confirmation in further studies

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and other ethnic groups. Secondly, it remains unclear how the proposed lower value of the

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99th percentile URL for hs-cTnI, established with the inclusion of lipid selection criteria, will

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affect the final diagnosis of patients with suspected MI. Thirdly, we did not perform cardiac imaging examinations. However, the low cut-off value (35 ng/L) for BNP applied in our study was demonstrated to successfully identify patients with both heart failure and left ventricular

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dysfunction [24,51]. Fourthly, some lipid markers (e.g. oxidized LDL, IDL, VLDL) as well as

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HDL and LDL subfractions were not investigated in our study. Fifthly, values of HbA1c, BNP or eGFR were not available in 55 of 531 individuals from the presumably healthy population. These subjects were analyzed in the presumably healthy population, but were

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excluded from the healthy cohort due to the fact that selection criteria for the healthy cohort could not be verified. Finally, the underlying mechanism of our findings needs further

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investigation.

In conclusion, our study demonstrates that atherogenic lipid markers, particularly apoB

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or apoB:apoAI ratio, but not hs-CRP, influence the 99th percentile URL for hs-cTnI.

Acknowledgments: This study was supported by Collegium Medicum of Nicolaus Copernicus University (NCU CM grant no. 779/2014). Abbott Laboratories partially provided the reagents, calibrators and controls for this study. Authors’ conflict of interest disclosure: The authors stated that there are no conflicts of interest regarding the publication of this article.

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percentile reference value for cardiac troponin assays, Clin. Chem. 58 (2012) 219–225. 13. G. Koerbin, W.P. Abhayaratna, J.M. Potter, et al., Effect of population selection on 99th percentile values for a high sensitivity cardiac troponin I and T assays, Clin. Biochem. 46

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14. T. Keller, F. Ojeda, T. Zeller, et al., Defining a reference population to determine the 99th percentile of a contemporary sensitive cardiac troponin I assay, Int. J. Cardiol. 167 (2013) 1423–1429. 15. M. Franzini, V. Lorenzoni, S. Masotti, et al., The calculation of the cardiac troponin T 99th percentile of the reference population is affected by age, gender, and population selection: a multicenter study in Italy, Clin. Chim. Acta 438 (2015) 376-381.

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ACCEPTED MANUSCRIPT 16. P.E. Hickman, B. Lindahl, J.M. Potter, et al., Is it time to do away with the 99th percentile for cardiac troponin in the diagnosis of acute coronary syndrome and the assessment of

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cardiac risk? Clin. Chem. 60 (2014) 734–736.

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ACCEPTED MANUSCRIPT 24. J.J. McMurray, S. Adamopoulos, S.D. Anker, et al., ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure 2012, Eur. Heart J. 33 (2012) 1787–1847.

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25. The International Expert Committee. International Expert Committee report on the role of

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28. P.M. Ridker, E. Danielson, F.A. Fonseca, et al., Rosuvastatin to prevent vascular events in

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Intern. Med. 259 (2006) 493–519. 30. Randox Laboratories Ltd LDL-EX "SEIKEN" (sLDL). General insert 2015. 562616. 31. Abbott ARCHITECT STAT High Sensitive Troponin-I. Package insert 2013. G10139/R02.

32. A.S. Levey, L.A. Stevens, C.H. Schmid, et al., A new equation to estimate glomerular filtration rate. Ann Intern Med. 150 (2009) 604–612.

33. A.H. Reed, R.J. Henry, W.B. Mason, Influence of statistical method used on the resulting estimate of normal range, Clin. Chem. 17 (1971) 275–284. 19

ACCEPTED MANUSCRIPT 34. S. Yusuf, S. Hawken, S. Ounpuu, et al., Effect of potentially modifiable risk factors associated with myocardial infarction in 52 countries (the INTERHEART study): case control

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study, Lancet 364 (2004) 937–952.

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35. M.J. McQueen, S. Hawken, X. Wang, et al., Lipids, lipoproteins, and apolipoproteins as

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risk markers of myocardial infarction in 52 countries (the INTERHEART study): a case control study, Lancet 372 (2008) 224–233.

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36. G. Walldius, I. Jungner, I. Holme, A.H. Aastveit, W. Kolar, E. Steiner, High apolipoprotein B, low apolipoprotein A-I, and improvement in the prediction of fatal

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myocardial infarction (AMORIS study): a prospective study, Lancet 358 (2001) 2026–2033.

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37. E. Di Angelantonio, P. Gao, L. Pennells, et al., Lipid-related markers and cardiovascular

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disease prediction, JAMA 307 (2012) 2499–2506.

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38. G. Lippi, C. Lo Cascio, G. Brocco, et al., High-density lipoprotein cholesterol values independently and inversely predict cardiac troponin T and I concentration, Ann. Transl. Med. 4 (2016) 188.

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39. S.B. Nayak, L.M. Pinto Pereira, S. Boodoo, et al., Association of troponin T and altered lipid profile in patients admitted with acute myocardial infarction, Arch. Physiol. Biochem. 116 (2010) 21–27. 40. A. Kumar, B. Sathian, Correlation between lipid profile and troponin I test results in patients with chest pain in Nepal, Asian Pac. J. Trop. Biomed. 3 (2013) 487–491. 41. B.G. Nordestgaard, M.J. Chapman, K. Ray, et al., Lipoprotein(a) as a cardiovascular risk factor: current status. Eur. Heart J. 31 (2010) 2844–2853.

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ACCEPTED MANUSCRIPT 42. K. Kotani, M.C. Serban, P. Penson, G. Lippi, M. Banach, Evidence-based assessment of lipoprotein(a) as a risk biomarker for cardiovascular diseases - Some answers and still many

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questions, Crit. Rev. Clin. Lab. Sci. (2016) 1-9. [Epub ahead of print]

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43. P.M. Ridker, M. Cushman, M.J. Stampfer, R.P. Tracy, C.H. Hennekens, Inflammation,

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aspirin, and the risk of cardiovascular disease in apparently healthy men, N. Engl. J. Med. 336 (1997) 973–979.

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reactive protein in risk prediction for cardiovascular disease, Ann. Intern. Med. 145 (2006) 35–42.

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45. S. Kaptoge, E. Di Angelantonio, G. Lowe, et al, C-reactive protein concentration and risk

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of coronary heart disease, stroke, and mortality: an individual participant meta-analysis,

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Lancet 375 (2010) 132–140.

46. S. Kaptoge, E. Di Angelantonio, L. Pennells, et al., C-reactive protein, fibrinogen, and

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cardiovascular disease prediction, N. Engl. J. Med. 367 (2012) 1310–1320. 47. F. Wensley, P. Gao, S. Burgess, et al., Association between C reactive protein and coronary heart disease: mendelian randomisation analysis based on individual participant data, BMJ 342 (2011) d548.

48. P. Elliott, J.C. Chambers, W. Zhang, et al., Genetic Loci associated with C-reactive protein levels and risk of coronary heart disease, JAMA 302 (2009) 37–48.

49. C.A Segre, W. Hueb, R.M. Garcia, et al., Troponin in diabetic patients with and without chronic coronary artery disease, BMC Cardiovasc. Disord. 15 (2015) 72.

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ACCEPTED MANUSCRIPT 50. M. Mueller-Hennessen, B. Lindahl, E. Giannitsis, et al., Diagnostic and prognostic implications using age- and gender-specific cut-offs for high-sensitivity cardiac troponin T -

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Sub-analysis from the TRAPID-AMI study, Int. J. Cardiol. 209 (2016) 26–33.

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51. P. Krishnaswamy, E. Lubien, P. Clopton, et al., Utility of B-natriuretic peptide levels in

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identifying patients with left ventricular systolic or diastolic dysfunction, Am. J. Med. 111

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(2001) 274–279.

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ACCEPTED MANUSCRIPT Figure 1. The prevalence of lipid disorders and elevated hs-CRP concentration in the presumably healthy population (A) and healthy cohort (B) in relation to the presence of

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detectable serum hs-cTnI concentrations. Values of TC >5mmoL/L (190 mg/dL), LDL-C >3

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mmoL/L (115 mg/dL), HDL-C <1.2 mmoL/L (45 mg/dL) for women and <1 mmol/L (40

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mg/dL) for men, TG >1.7 mmoL/L (150 mg/dL), non-HDL-C >3.75 mmol/L (145 mg/dL), the TC:HDL-C ratio <4.00 for women and <4.75 for men, apoAI <1.4 g/L (140 mg/dL) for women and <1.2 g/L (120 mg/dL) for men, apoB >0.8 g/L (80 mg/dL), the apoB:apoAI ratio

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<0.6 for women and <0.7 for men, Lp[a] >500 mg/L (50 mg/dL), sdLDL-C >350 mg/L (35

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mg/dL), and hs-CRP >2 mg/L were considered abnormal and associated with high total cardiovascular risk [6-30]. P-values are provided for comparisons between subjects with

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detectable and undetectable hs-cTnI concentrations.

apoAI, apolipoprotein AI; apoB, apolipoprotein B; apoB:apoAI, apolipoprotein B to

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apolipoprotein AI ratio; hs-CRP, high-sensitivity C-reactive protein; HDL-C, high-density lipoprotein cholesterol; hs-cTnI, high-sensitivity cardiac troponin I; LDL-C, low-density

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lipoprotein cholesterol; Lp(a), lipoprotein (a); non-HDL-C, non-high-density lipoprotein cholesterol; sd-LDL-C, small dense low-density lipoprotein cholesterol; TC, total cholesterol; TC:HDL-C, total cholesterol to high-density lipoprotein cholesterol ratio; TG, triglycerides

Supplemental Figure 1. Health questionnaire for study volunteers. Questionnaire was filled at the presence of general practitioner. Blood pressure and heart rate were performed by a trained nurse, whilst an experienced cardiologist consulted all patients with suspected CAD.

CABG, coronary artery bypass grafting; CAD, coronary artery disease; PCI, percutaneous coronary intervention

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Fig. 1

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ACCEPTED MANUSCRIPT Table 1. Baseline characteristics of the presumably healthy population in relation to the presence of detectable hs-cTnI concentrations. * for comparison between subjects with detectable and undetectable hscTnI concentrations.

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p value*

LDL-C [mmol/L]

3.13 (2.53-3.83)

HDL-C [mmol/L]

1.45 (1.22-1.73)

1.45 (1.22-1.73)

1.45 (1.27-1.76)

0.618

1.06 (0.78-1.62)

1.06 (0.78-1.60)

1.11 (0.80-1.64)

0.685

3.62 (2.97-4.42)

3.70 (3.08-4.45)

3.47 (2.79-4.32)

0.044

eGFR [mL/min/1.73 m2]

TG [mmol/L]

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non-HDL-C [mmol/L]

Female 89 (55%) Male 72 (45%)

<0.001 0.975 0.336 N/A 0.295 <0.001

5.23 (4.58-6.00)

24.7 (21.6-27.9) N/A 14.8 (10.0-20.6) 33.0 (31.0-35.5) 104.0 (94.9112.9) 4.99 (4.35-5.82)

3.18 (2.66-3.85)

2.87 (2.30-3.72)

0.006

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BMI [kg/m2] hs-cTnI [ng/L] BNP [ng/L] HbA1c [mmoL/mol]

99.8 (92.7-109.6)

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Gender

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TC [mmol/L]

40 (33-50) Female 293 (55%) Male 238 (45%) 24.8 (22.0-27.8) 2.4 (1.7-3.2) 14.8 (10.0-23.7) 35.5 (32.2-38.8) 100.9 (93.4110.4) 5.14 (4.53-5.98)

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Age [years]

hs-cTnI <1.9 ng/L (n=161) 36 (30-47)

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Variable

hs-cTnI ≥1.9 ng/L (n=370) 41 (34-52) Female 204 (55%) Male 166 (45%) 24.9 (22.3-27.8) 2.9 (2.3-3.6) 14.8 (10.0-25.0) 36.6 (34.4-38.8)

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Overall (n=531)

0.005 0.070

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TC:HDL-C 3.51 (2.82-4.31) 3.52 (2.91-4.31) 3.45 (2.74-4.31) 0.162 apoB [g/L] 0.93 (0.77-1.11) 0.97 (0.79-1.13) 0.86 (0.72-1.05) 0.002 apoAI [g/L] 1.43 (1.25-1.63) 1.41 (1.24-1.61) 1.46 (1.26-1.65) 0.149 apoB:apoAI 0.66 (0.52-0.81) 0.66 (0.54-0.84) 0.61 (0.49-0.73) 0.001 sd-LDL-C [mg/L] 262 (171-387) 267 (179-390) 251 (166-378) 0.315 Lp(a) [mg/L] 90 (30-218) 90 (34-284) 90 (30-174) 0.520 hs-CRP [mg/L] 0.51 (0.19-1.71) 0.51 (0.17-1.42) 0.55 (0.22-1.92) 0.323 Current or former smoker 192 (36%) 134 (36%) 58 (36%) 0.966 Family history of premature 111 (21%) 79 (21%) 32 (20%) 0.701 CAD apoAI, apolipoprotein AI; apoB, apolipoprotein B; apoB:apoAI, apolipoprotein B to apolipoprotein AI ratio; BMI, body mass index; BNP, B-type natriuretic peptide; CAD, coronary artery disease; eGFR, estimated glomerular filtration rate; HbA1c, glycated hemoglobin; HDL-C, high-density lipoprotein cholesterol; hs-cTnI, high-sensitivity cardiac troponin I; hs-CRP, high-sensitivity C-reactive protein; LDL-C, low-density lipoprotein cholesterol; Lp(a), lipoprotein (a); non-HDL-C, N/A, not applicable; non-high-density lipoprotein cholesterol; sd-LDL-C, small dense low-density lipoprotein cholesterol; TC, total cholesterol; TC:HDL-C, total cholesterol to high-density lipoprotein cholesterol ratio, TG, triglycerides 26

ACCEPTED MANUSCRIPT

Table 2. Predictors of detectable hs-cTnI (≥1.9 ng/L) concentrations in the presumably healthy population in univariate and multivariate logistic regression analysis. Univariate

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analysis shows age, gender and laboratory parameters with a p-value <0.1, whereas

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multivariate analysis is adjusted for age and gender, and restricted to laboratory parameters

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with a p-value <0.05. Results are presented according to decreasing values of odds ratios. Univariate analysis

Odds ratio -95% CI +95% CI

Age

1.410

HbA1c

1.372

<0.001

1.140

0.998

1.303

0.053

1.180

1.077

1.292

<0.001

1.112

1.030

1.199

0.006

1.079

1.002

1.163

0.044

1.242

1.014

1.522

0.036

0.805

0.687

0.943

0.007

0.999

0.689

1.451

0.999

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apoB:apoAI

0.001

1.217

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for a 10 ng/L increase

1.682

1.292

for a 1 mmol/mol increase BNP

1.182

p

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for a 10 year increase

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Variable

for a 0.1 increase apoB

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for a 0.1 g/L increase Lp(a)

for a 0.1 g/L increase LDL-C for a 1 mmol/L increase eGFR for a 10 mL/min/1.73 m2 increase Gender male vs. female Multivariate analysis* 27

HbA1c

1.290

1.209

1.376

<0.001

1.187

1.008

1.398

0.039

1.129

1.021

1.248

0.018

1.004

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0.632

1.594

0.987

1.019

0.804

for a 1 mmol/mol increase BNP

apoB:apoAI for a 0.1 increase Gender

Age

0.997

0.976

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for a 10 year increase

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male vs. female

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for a 10 ng/L increase

*Model adjusted for age and gender: x2=119,9; df=5; p<0.001; R2 Cox and Snell=0.20; R2Nagelkerke=0.29.

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apoB, apolipoprotein B; apoB:apoAI, apolipoprotein B to apolipoprotein AI ratio; BNP, B-

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type natriuretic peptide; CI, confidence interval; HbA1c, glycated hemoglobin; hs-cTnI, high-

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lipoprotein(a)

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sensitivity cardiac troponin I; LDL-C, low-density lipoprotein cholesterol; Lp(a),

Table 3. Values of 99th percentile URL for hs-cTnI with 90% confidence intervals in relation to gender, age, lipid biomarkers and hs-CRP in the presumably healthy population. The percentages relate to individuals in a particular subgroup and all questionnaire screened 28

ACCEPTED MANUSCRIPT individuals with measured HbA1c and BNP concentrations and calculated eGFR. *P value refers for comparison of hs-cTnI concentrations between subjects who were considered for calculation of hs-cTnI 99th percentile URL and all questionnaire screened individuals. Outliers were identified by the MedCalc 15.8 software (MedCalc Software, Ostend,

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#

p value*

hs-cTnI excluded value# [ng/L]

7.5 (6.3-8.6)

N/A

108.5

6.3 (5.5-7.0)

0.319

16.1

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6.0 (5.2-6.6)

0.084

none

2.3 (2.2-2.5)

6.2 (5.7-6.7)

0.757

24.0; 24.3

2.3 (2.2-2.5)

7.1 (6.0-8.2)

0.858

24.3

2.3 (2.1-2.4)

6.0 (5.4-6.6)

0.264

16.1

2.1 (1.9-2.3)

5.7 (4.8-6.5)

0.026

16.1; 10.9

2.4 (2.2-2.5)

Cohort after exclusion of 330 subjects with elevated TC

2.2 (2.0-2.5)

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Questionnaire screened individuals [n=531 (100%), 238 men]

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hs-cTnI 99th percentile URL [ng/L]

Presumably healthy population Median hs-cTnI concentrations (IQR) [ng/L]

[n=201 (37.9%); 89 men]

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2.1 (2.0-2.4)

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Cohort after exclusion of 296 subjects with elevated LDL-C

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Belgium) using the method described by Reed et al. [33].

[n=235 (44.3%); 90 men]

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Cohort after exclusion of 65 subjects with decreased HDL-C

[n=466 (87.8%); 188 men] Cohort after exclusion of 115 subjects with elevated TG [n=416 (78.3%); 160 men] Cohort after exclusion of 244 subjects with elevated non-HDL-C [n=287 (54.0%); 105 men] Cohort after exclusion of 375 subjects with elevated

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ACCEPTED MANUSCRIPT apoB [n=156 (29.4%); 52 men] 2.3 (2.2-2.5)

6.1 (5.6-6.5)

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0.046

none

6.9 (5.7-8.0)

0.637

24.3

5.9 (5.4-6.4)

0.387

16.1; 24.3

2.3 (2.2-2.5)

6.1 (5.6-6.6)

0.609

24.3; 16.1

2.4 (2.2-2.5)

6.5 (5.9-7.1)

0.769

24.3

2.3 (2.2-2.5)

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Cohort after exclusion of 109 subjects with elevated TC:HDL-C ratio [n=422 (79.5%); 167 men]

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2.3 (2.2-2.4)

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Cohort after exclusion of 80 subjects with elevated Lp(a)

5.7 (5.1-6.3)

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[n=269 (50.7%); 107 men]

[n=357 (67.2%); 119 men]

16.1; 24.3

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Cohort after exclusion of 262 subjects with elevated apoB:apoAI ratio

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[n=351 (66.1%); 162 men]

Cohort after exclusion of 174 subjects with elevated sd-LDL-C

0.705

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Cohort after exclusion of 180 subjects with decreased apoAI

[n=451 (84.9%); 203 men] Cohort after exclusion of 118 subjects with elevated hs-CRP [n=413 (77.8%); 191 men] apoAI, apolipoprotein AI; apoB, apolipoprotein B; apoB:apoAI, apolipoprotein B to apolipoprotein AI ratio; HDL-C, high-density lipoprotein cholesterol; hs-cTnI, highsensitivity cardiac troponin I; hs-CRP, high-sensitivity C-reactive protein; IQR, interquartile range; LDL-C, low-density lipoprotein cholesterol; Lp(a), lipoprotein (a); N/A, not applicable; non-HDL-C, non-high-density lipoprotein cholesterol; sd-LDL-C, small dense low-density lipoprotein cholesterol; TC, total cholesterol; TC:HDL-C, total cholesterol to high-density lipoprotein cholesterol ratio; TG, triglycerides; URL, upper reference limit 30

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Table 4. Values of 99th percentile URL for hs-cTnI with 90% confidence intervals in the healthy cohort, classified according to surrogate biomarkers for diabetes, myocardial and

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renal dysfunction, lipid parameters and hs-CRP. The percentages relate to individuals in the particular subgroup and all questionnaire screened individuals with measured HbA1c and BNP concentrations and calculated eGFR. *P value refers for comparison of hs-cTnI concentrations between subjects who were considered for calculation of hs-cTnI 99th percentile URL and all subjects from the healthy cohort. #Outliers were identified by the MedCalc 15.8 software (MedCalc Software, Ostend, Belgium) using the method described by Reed et al. [33]. Population

Questionnaire screened individuals with measured

Median hs-cTnI concentrations (IQR) [ng/L]

hs-cTnI 99th percentile URL [ng/L]

p value*

hs-cTnI excluded value# [ng/L]

2.3 (2.2-2.4)

7.0 (6.0-8.1)

N/A

none

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ACCEPTED MANUSCRIPT HbA1c and BNP concentrations and calculated eGFR

0.451

none

0.164

none

6.1 (5.2-6.9)

0.018

none

2.2 (2.1-2.4)

6.2 (5.6-6.8)

0.117

none

2.2 (2.1-2.4)

5.9 (5.3-6.4)

0.140

none

2.1 (1.9-2.3)

6.1 (5.3-6.8)

0.117

none

1.9 (1.8-2.2)

5.3 (4.4-6.1)

0.007

none

[n=408 (87.0%); 200 men] 2.1 (1.9-2.3)

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2.0 (1.9-2.2)

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[n=187 (45.8%); 82 men]

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[n=168 (41.2%); 82 men] Healthy cohort after exclusion of 221 subjects with elevated LDL-C

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Healthy cohort after exclusion of 49 subjects with decreased HDL-C

6.4 (5.5-7.2)

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Healthy cohort after exclusion of 240 subjects with elevated TC

6.2 (5.6-6.7)

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2.2 (2.1-2.4)

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Healthy cohort (after exclusion of 68 subjects with abnormal HbA1c, BNP and eGFR)

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[n=476; 213 men]

[n=359 (88%); 161 men]

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Healthy cohort after exclusion of 93 subjects with elevated TG [n=315 (77.2%); 134 men]

Healthy cohort after exclusion of 188 subjects with elevated non-HDL-C [n=220 (53.9%); 212 men] Healthy cohort after exclusion of 282 subjects with elevated apoB [n=126 (30.9%); 48 men]

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ACCEPTED MANUSCRIPT Healthy cohort after exclusion of 140 subjects with decreased apoAI

2.2 (2.1-2.4)

6.0 (5.4-6.6)

2.1 (1.9-2.2)

5.4 (4.9-6.4)

0.114

none

none

0.109

none

6.0 (5.4-6.6)

0.077

none

2.2 (2.1-2.4)

6.1 (5.5-6.7)

0.112

none

2.2 (2.1-2.4)

6.5 (5.8-7.1)

0.415

none

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0.018

[n=212 (52%); 96 men] 2.2 (2.1-2.4)

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2.2 (2.1-2.3)

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[n=281 (68.9%); 106 men]

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[n=324 (79.4%); 144 men] Healthy cohort after exclusion of 127 subjects with elevated sd-LDL-C

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Healthy cohort after exclusion of 58 subjects with elevated Lp(a)

5.8 (5.3-6.4)

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Healthy cohort after exclusion of 82 subjects with elevated TC:HDL-C ratio

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Healthy cohort after exclusion of 196 subjects with elevated apoB:apoAI ratio

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[n=268 (65.7%); 139 men]

[n=350 (85.8%); 172 men]

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Healthy cohort after exclusion of 83 subjects with elevated hs-CRP [n=325 (79.6%); 164 men]

apoAI, apolipoprotein AI; apoB, apolipoprotein B; apoB:apoAI, apolipoprotein B to apolipoprotein AI ratio; BNP, B-type natriuretic peptide; eGFR, estimated glomerular filtration rate; HbA1c, glycated hemoglobin ; HDL-C, high-density lipoprotein cholesterol; hs-cTnI, high-sensitivity cardiac troponin I; hs-CRP, high-sensitivity C-reactive protein; IQR, interquartile range; LDL-C, low-density lipoprotein cholesterol; Lp(a), lipoprotein (a); N/A, not applicable; non-HDL-C, non-high-density lipoprotein cholesterol; sd-LDL-C, small dense low-density lipoprotein cholesterol; TC, total cholesterol; TC:HDL-C, total cholesterol to high-density lipoprotein cholesterol ratio; TG, triglycerides; URL, upper reference limit

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ACCEPTED MANUSCRIPT Highlights

AC

CE P

TE

D

MA

NU

SC R

IP

T

• Lipid disorders were more common in subjects with detectable serum hs-cTnI concentrations. • The apoB:apoAI ratio independently correlated with serum hs-cTnI concentration. • Both apoB:apoAI ratio and apoB influenced the 99th percentile URL for hs-cTnI.

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