Leucine Rich Alpha-2-Glycoprotein 1 Is A Novel Predictor Of Diastolic Dysfunction: A Cross-Sectional Study

doi:10.21203/rs.3.rs-815995/v1

Download PDF

Research

Leucine Rich Alpha-2-Glycoprotein 1 Is A Novel Predictor Of Diastolic Dysfunction: A Cross-Sectional Study

https://doi.org/10.21203/rs.3.rs-815995/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted

You are reading this latest preprint version

Background. Leucine-rich α2-glycoprotein (LRG1) mediates cardiac fibrocyte activation. It is upregulated in inflammatory conditions, atherosclerosis, and fibrosis. Diastolic dysfunction (DD) is due to myocardial fibrosis. This study examined the relationship between LRG1 and DD.

Methods. Patients with symptoms of chronic coronary ischemia were recruited. Exclusion criteria: symptoms of overt heart failure, ejection fraction (EF) < 55%, impaired renal function, infection, recent trauma. Investigations: SYNergy between percutaneous coronary intervention with TAXus and cardiac surgery (SYNTAX) score, Echocardiographic assessment, and LRG1 levels. Binary stepwise logistic regression to evaluate the relationship between LRG1 and DD. ROC analysis with calculation of optimal cut-off values.

Results. 94 patients were included; 47 had diastolic dysfunction. Serum LRG1 was significantly (U = 417.00, p<0.001) higher in the DD group (M=14) compared to the No-DD group (M=8) (U-Mann Whitney Test). There were higher SYNTAX scores in the DD group (M = 24.5) compared with No-DD (M=7). LRG1 had significant predictability of DD (OR =1.30 (95% CI:1.13-1.49)). The ROC for the adjusted model had an AUC=0.89 (95% CI: 0.82-0.95). The cut-off value of “9” LRG1 had a 78% sensitivity (95% CI: 65.3–87.7) and 72.3% specificity (95% CI: 57.4 – 84.4) for predicting DD.

Conclusions. We identified LRG1 as a novel independent predictor of diastolic dysfunction. Further studies are warranted to validate the utility of LRG1 in predicting DD.

Biomaterials

Leucine Rich Alpha-2-Glycoprotein 1 (LRG1)

Diastolic Dysfunction

Biomarker

Heart failure with preserved ejection fraction (HFpEF)

Heart failure with preserved ejection fraction (HFpEF) accounts for about half of all heart failure (HF) diagnoses with a morbidity and mortality that is on par with HF with reduced EF (HFrEF) (1). The presence of diastolic dysfunction is a conditio sine qua non to diagnose HFpEF. Diastolic dysfunction refers to the inability of the ventricle to accommodate blood from the atrium due to increased stiffness and reduced compliance. Asymptomatic diastolic dysfunction commonly progresses to symptomatic HFpEF (2).

Multiple mechanisms have been proposed with regards to the pathogenesis of diastolic dysfunction, including inflammation with increased interstitial deposition of collagen and matricellular proteins and chronic myocardial ischaemia (3–5). Patients diagnosed with coronary artery disease (CAD) have been shown to have a significant higher risk for diastolic dysfunction (6). It might be important to identify patients with early, asymptomatic CAD and potentially early myocardial fibrosis, to halt progression to overt HF. There is no effective treatment for diastolic dysfunction to date. Echocardiographic parameters of diastolic dysfunction are commonly not sensitive enough to detect cardiac fibrosis early in asymptomatic patients.

The leucine-rich α-2-glycoprotein-1 (LRG1) is a 50kDa glycosylated protein consisting of 20–30 amino acid residues that are rich in leucine. Generally, LRG1 expression increases acutely in response to inflammation, thus serving as a biomarker of inflammatory conditions (7). In addition to inflammation, LRG1 expression has been implicated in the pathogenesis of atherosclerosis (8). The mechanistic role of LRG1 in cardiac structural remodeling has recently been elucidated: LRG1 regulates and inhibits cardiac fibrocyte activation and consequently cardiac fibrosis by limiting the profibrotic endothelial signalling cascades of TGF-β1 (transforming growth factor- β1) during cardiac remodeling in animal models (3). There is however paucity of clinical data in humans on the role of LRG1 and cardiac fibrosis.

We hypothesized that LRG1 might be a useful biomarker to identify patients with asymptomatic diastolic dysfunction. Thus, the objective of this study was to determine the association between LRG1 and diastolic dysfunction.

Patient recruitment and sample collection

The study was conducted from 1^st August 2019 until 1^st March 2020 at a tertiary teaching hospital. The study protocol was approved by the institutional review board (MREC ID NO 20171126-5850) and conformed with the principles of the Helsinki Declaration. The study prospectively enrolled patients presenting with chronic ischemic symptoms. Written informed consent was obtained from all patients. Patients were included if they fulfilled the following criteria: presentation with symptoms of chronic coronary ischaemia; adults (> 18 years old), completion of coronary angiography and transthoracic echocardiography. Patients with renal insufficiency (serum creatinine > 150 µmol/L), recent or current infection or physical trauma (< 6 months) were excluded. Patients with symptoms of overt heart failure were excluded. Demographic and clinical characteristics were documented using the hospital Electronic Medical Records (EMR) system. Blood samples were collected prior to angiography.

Angiographic and echocardiographic assessment

Coronary atherosclerosis was classified into 3 categories: “None”: patent, non-diseased coronary arteries; “non-obstructive”: presence of luminal irregularities with a maximum diameter of stenosis of 70% in at least one major epicardial artery; “Obstructive”: luminal narrowing of more than 70% in at least 1 major epicardial artery. The SYNergy between percutaneous coronary intervention with TAXus and cardiac surgery (SYNTAX) score quantifies the extent of coronary vascular disease and was calculated for each patient by 2 cardiologists blinded to the study’s clinical and angiographic outcomes.(9) Diastolic dysfunction was classified and graded according to latest societal guidelines of the American Society of Echocardiography and the European Association of Cardiovascular Imaging. Imaging included apical two- and four-chamber views, from which left atrial (LA) and left ventricular (LV) volumes were measured using the method of discs. LA volume index (LAVi) and LVEF were calculated based on those. Pulsed-wave Doppler of the mitral inflow at the level of valve leaflet tips was used to measure the peak early (E-wave) and late (A-wave) diastolic flow velocities. E/A ratios were calculated. Pulsed-wave Doppler tissue imaging was performed with the sample volume at the lateral and septal mitral annulus to obtain early diastolic annular (e′) velocity (E/e′) ratios were calculated. Peak velocity of the tricuspid regurgitant jet via continuous-wave Doppler and inferior vena cava diameter and respiratory variation as an estimate of right atrial pressure were acquired. Patients with reduced LV systolic function (defined as an ejection fraction of less than 55%) were excluded from the study.

Other measurements

Resting blood pressures were recorded on admission. The cut-off for hypertension was 140/90mmHg. Serum total cholesterol, high-density lipoprotein (HDL) cholesterol, and low-density lipoprotein (LDL) levels were measured using an automated autoanalyzer (Roche Diagnostics). Glycated haemoglobin (HbA1c) was measured by a point-of-care immunoassay analyzer (Siemens). Estimated glomerular filtration rate (eGFR) and urinary creatinine were measured using commercial assays (Immulite, Siemens). Plasma LRG1 levels were measured using commercially available ELISA kits (Immuno-Biological Laboratories) according to the manufacturer’s protocol.

Statistical analysis

Data distribution was tested for normality with the Shapiro-Wilk Test, skewness, and kurtosis. Continuous variables are presented as median and interquartile range (if non-normally distributed data) and as mean ± standard deviation (if normally distributed data). Categorical variables are described as frequencies (percentage). To compare groups with normal distributions, the independent-sample t-test was used, while the Mann–Whitney U-test was used for those with non-normal distributions, respectively. Categorical data were analysed with the χ2 test. Prior to multivariate analysis, univariate analyses were employed to evaluate the association between all variables and DD and potential covariates were selected for adjustment into multivariate analysis. Multicollinearity diagnostics also was done among all covariates using Variance inflation factor (VIF) before including them into multivariate binary logistic regression analyses using forward selection method. Receiver operating characteristic (ROC) curves assessed the predictability of LRG1 in identifying diastolic dysfunction and the predictive performance of the regression models. Estimated area under the ROC curve (ROC-AUC) with 95% CI were used for the evaluation. Youden Index measures the effectiveness of a diagnostic marker and enables the selection of an optimal threshold value for that marker. The Youden index was used to determine the optimum cut-off point from the ROC curve, calculated with the formula YI=(sensitivity±specificity)-1. All statistical tests were performed two-tailed. A p-value less than 0.05 was considered to be statistically significant. All statistical analyses were performed with SPSS software (version 26.0; SPSS Inc., Chicago, IL, USA) and Medcalc Version 20.009.

A total of 94 patients (58 males and 36 females) were enrolled in the study. 47 patients fulfilled the echocardiographic criteria for diastolic dysfunction. Group comparison of the echocardiographic indices using U-Mann Whitney Test confirmed significant differences (p < 0.001) between the no-DD and DD groups. The large effect size for all echocardiographic parameters confirmed the appropriate categorization of patients into the respective groups (Table 1).

Table 1

Comparison of echocardiographic indices between patients without diastolic dysfunction (No-DD) and diastolic dysfunction (DD)
Index	No-DD Median (IQR)	DD Median (IQR)	p value	effect size
LAVI	28(3)	35(2)	< 0.001**	1.54
TR velocity	2.1(0.4)	2.9(0.1)	< 0.001**	2.7
Septal e’	7.8(1.5)	6.7(1.5)	< 0.001**	1.42
Lateral e’	12(3.3)	9.8(1.1)	< 0.001**	1.33
E/e’	12(2)	15(1)	< 0.001**	1.64
EF (%)	67(21)	60(14)	0.069	0.8
LAVI – left atrial volume index, TR velocity – tricuspid valve regurgitant jet velocity, EF – ejection fraction

Patient Demographics And Clinical Characteristics

Prior to testing the relationship between LRG1 and DD in this study, a bivariate approach was applied to evaluate the relationship between other demographic and clinical variables. There was no significant difference between the two groups with regards to demographic variables. Serum LRG1 levels were significantly (U = 417.00, p < 0.001) higher in the DD group (M = 14) compared to the No-DD group (M = 8) (U-Mann Whitney Test). There was also a higher SYNTAX score in the DD group (M = 24.5) compared with the No-DD group (M = 7). A pairwise comparison of the median of serum LRG1 levels between No-DD and DD groups with the Mann Whitney U Test demonstrated highly significant differences between the groups (p < 0.001). Likewise, the pairwise comparison of the median of SYNTAX scores between No-DD and DD groups yielded a p < 0.001 (Fig. 1).

Regression Model Analysis Of Lrg1 For Diastolic Dysfunction

Binary logistic regression was employed to evaluate the relationship between LRG1 and DD. Based on bivariate analysis, several variables including number of coronary lesions, SYNTAX, Urea, Creatinine, Diabetes Mellitus and LDL showed a significant association with DD (Table 2). Therefore, the final model was adjusted considering all those variables using stepwise regression (forward-conditional method). In the final model, only three factors comprising of SYNTAX, creatinine and diabetes mellitus (DM) remained as significant variables. The results of both unadjusted and adjusted model are presented in Table 3. LRG1 had significant predictability of DD with an OR = 1.26 (95% CI: 1.23–1.42) indicating that for an one-unit increase in LRG1, a 26% increase in the odds of DD being present is to be expected. After adjustment, the odd ratio increased to OR = 1.30 (95% CI:1.13–1.49). Furthermore, the accuracy of LRG1 for the prediction of DD was evaluated with ROC-AUC. Figure 2 showed the ROC curves for LRG1 with an AUC of 0.79 (95% CI: 0.71–0.86).

Table 2

Baseline Demographics and clinical data between patients without diastolic dysfunction (No-DD) and diastolic dysfunction (DD)
Variable	Diastolic Function		p-value
Variable	No-DD (n = 47)	DD (n = 47)	p-value
Age (years)	62.04 ± 11.35	63.53 ± 12.16	0.323
Gender ^a
Male	27 (57.40)	31 (66.00)	0.396
Female	20 (42.60)	16 (34.00)
Race ^a
Malay	14 (29.80)	18 (38.3)	0.228
Chinese	21 (44.7)	13 (27.7)
Indian	12 (25.50)	16 (34.0)
Number of coronary lesion(s) ^a
0	8 (17.00)	4 (8.50)	< 0.001**
1	20 (42.60)	3 (6.40)
2	11 (23.40)	10 (21.30)
3	8 (17.00)	30 (63.80)
LRG1 levels ^b (ng/mL)	8(4)	14(8)	< 0.001**
SYNTAX ^b	7(17)	24.5(15)	< 0.001**
Laboratory tests
Hemoglobin ^b (g/dL)	13.2(1.5)	13.3(1.5)	0.758
Urea ^b (mmol/L)	5.5(3.6)	6.6(3.9)	0.037*
Creatinine ^b (mcmol/L)	78(28)	90(48)	0.018*
Risk Factors
Diabetes Mellitus ^a
Non-diabetic	39 (83.00)	27 (57.40)	0.007*
Diabetic	8 (17.00)	20 (42.60)
Lipid Profile
Total cholesterol ^b (mmol/L)	4(1.8)	4.5(1.3)	0.117
Triglyceride ^b (mmol/L)	1(0.8)	1.2(0.7)	0.604
LDL ^b (mmol/L)	2.24(1.33)	3(1.47)	0.031*
HDL ^b (mmol/L)	1.07(0.5)	1.08(0.38)	0.678
Hyperlipidemia ^a
No	40 (85.10)	37 (78.70)	0.421
Yes	7 (14.90)	10 (21.30)
Hypertension ^a
< 140/90mmHg	27 (57.4)	31 (66.0)	0.396
> 140/90mmHg	20 (42.6)	16 (34.0)
Smoking ^a
Non-smoker	24 (51.10)	27 (57.40)	0.204
Current smoker	20 (42.60)	20 (42.60)
Ex-smoker	3 (6.40)	0 (0.00)
Medication use ^a
Perindopril	27 (57.4)	21 (44.7)	0.216
Bisoprolol	19 (40.4)	16 (34.0)	0.522
Carvedilol	7 (14.9)	4 (8.5)	0.336
Frusemide	6 (12.8)	2 (4.3)	0.139
Spironolactone	0 (0)	1 (2.1)	0.315
Metformin	9 (19.1)	19 (40.4)	0.024*
Gliclazide	3 (6.4)	7 (14.9)	0.181
Insulin	6 (12.8)	8 (17.0)	0.562
a Categorical variables are frequency, n (%). b: Continuous variables are median (IQR). Pairwise groups comparison were performed by Pearson's chi-square test for categorical variables, or Mann–Whitney U test for continuous variables, p < 0.05, p < 0.005, **p < 0.001. No-DD = without diastolic dysfunction, DD = with diastolic dysfunction

Table 3

Univariable and multivariable logistic regression analyses for the prediction of DD
Variables	Unadjusted OR [95% CI]	p-value	Adjusted^a OR [95% CI]	p-value
LRG1	1.26*** [1.23–1.42]	< 0.001	1.30*** [1.13–1.49]	< 0.001
^a Adjusted based on SYNTAX, creatinine and diabetes mellitus, p < 0.05, **p < 0.001. The model fit is demonstrated by Nagelkerke R Square and Chi-square. The adjusted model ((χ2(4) = 50.32, p < 0.001) and explained 59.9% of the variance in DD and correctly classified 81.9% of cases. DD = diastolic dysfunction

The highest Youden Index (YI) was calculated as YI = 0.503 (95% CI: 0.351–0.643). The cut-off value of “9” LRG1 obtained the highest Youden index, with 78% sensitivity (95% CI: 65.3–87.7) and 72.3% specificity (95% CI: 57.4–84.4) for predicting DD. The ROC for the adjusted model was calculated and resulted in a significantly improved accuracy: AUC = 0.89 (95% CI: 0.82–0.95) of model (Z = 2.356, p < 0.001). Table 4 summarised the ROC-AUC of the predictive performance of each regression model.

Table 4

Comparison of Predictive performance of regression model
Predictive performance of regression model
Model	AUC [95% CI]	Standard error	p-value	z	p-value
Unadjusted Model	0.79 [0.70–0.87]	0.04	< 0.001***	2.356	0.0185
Adjusted Model	0.89 [0.82–0.95]	0.03	< 0.001***	2.356	0.0185
p < 0.05, **p < 0.001

This study investigated the association of LRG1 and left ventricular diastolic dysfunction (LVDD). We demonstrated that patients with LVDD had significantly higher circulating LRG1 levels compared to patients without LVDD. We found that LRG1 was independently and highly predictive for LVDD. LRG1 could be a suitable marker for the early identification of diastolic dysfunction in at-risk patients as well as for the monitoring of disease progression and treatment effects.

Asymptomatic LVDD increases the risk of adverse cardiovascular events and commonly progresses to symptomatic heart failure with preserved ejection fraction (HFpEF) (10–13). The prevalence of HFpEF is increasing due to an ageing population, increased awareness, refined diagnostic criteria and advances in imaging (14, 15). In fact, symptomatic patients with echocardiographic evidence of diastolic dysfunction, previously subsumed as having “diastolic heart failure” are now being classified as having HFpEF. It is a major public health issue as it accounts for more than half of the total heart failure prevalence (14). Despite its clinical importance, there is no reference strategy for the diagnosis of HFpEF; even more challenging is the identification of asymptomatic patients with LVDD (16). Echocardiographic parameters at early stages of HFpEF are commonly normal at rest and natriuretic peptides are frequently within normal range or only minimally raised (17, 18). Diagnosis at an asymptomatic stage could identify patients that would benefit from early intervention with lifestyle modifications, intensification of treatment regimes, and closer follow-up. Hence, there is an urgent need for a more sensitive marker of diastolic dysfunction.

It has been postulated, that the plethora of comorbidities that is commonly present in HFpEF patients create a proinflammatory state resulting in generalized endothelial inflammation. Reduced nitric oxide bioavailability with negative effects on cyclic GMP content, and protein kinase G activity in adjacent myocytes results in fibrosis, diastolic LV stiffness and HF development (19, 20). Likewise, micro- and macrovascular ischemia has been shown to be a major determinant of diastolic dysfunction (21–25). Inflammation plays a key role in the pathogenesis of atherosclerosis, promotes deposition of LDL in the affected vasculature, and formation of atherosclerotic plaques.

The role of LRG1 as a key regulator of vascular disease associated with inflammation is emerging as studies reported an increased expression of LRG1 in such setting (26–30). LRG1 has been shown to be elevated in animal models of retinopathy (31), in patients with coronary artery disease (26), arterial stiffness (32) and most recently, animal models of cardiac fibrosis (33). It has been proposed, that LRG1 is upregulated in cardiac myocytes during fibrotic cardiac remodelling (31, 33, 34). Liu et al. demonstrated recently, that overexpression of LRG1 attenuated cardiac fibrosis and overt heart failure in the myocardium of heart failure animal models (33). As such, elevated levels of LRG1 likely reflect the physiological compensatory response to a fibrotic process. LRG1 exerts its regulatory anti-fibrotic effect by inhibiting the inflammatory signalling of the transforming growth factor (TGF)-β1 within a complex molecular network involving PPAR (peroxisome proliferator-activated receptor) β/δ, and SMRT (silencing mediator for retinoid and thyroid hormone receptor) (35, 36).

The SYNTAX score is a scoring algorithm that quantifies the extent of coronary atherosclerosis. It is widely used to risk stratify patients in clinical practise and to support decision making (9). We utilised the SYNTAX score as a surrogate marker of the atherosclerotic burden in our study cohort. Pairwise comparison demonstrated, in addition to the elevated LRG1 levels, markedly higher SYNTAX scores in the DD group. The association of LRG1 and new-onset atherosclerotic cardiovascular disease has recently been reported in a cohort of the Framingham Heart Study (37). It has been proposed that the elevation of LRG1 occurs at early, preclinical stages (30, 37).

The potential role of LRG1 in heart failure was first reported by Watson et al (38) who studied a heterogeneous group of patients with overt systolic and diastolic heart failure. He found that LRG1 was consistently overexpressed in high BNP serum and identified heart failure patients independent of BNP. Our study confirmed Watson’s experimental findings in a clinical setting by demonstrating a potentially mechanistic relationship between LRG1, diastolic dysfunction and the extent of vascular disease.

The multivariable logistic regression model showed that LRG1 had significant predictability of DD. The overall high diagnostic accuracy of the regression models in our study was confirmed with ROC analysis. The ROC for the adjusted model demonstrated a high accuracy with an AUC = 0.89 (95% CI: 0.82–0.95). The cut-off value of “9” LRG1 had a 78% sensitivity (95% CI: 65.3–87.7) and 72.3% specificity (95% CI: 57.4–84.4) for predicting DD.

Collectively, our results suggest that LRG1 might be a useful, independent predictor of early, subclinical diastolic dysfunction.

The limitation of our study is the relatively small sample size and the cross-sectional study design. Inclusion of patients with ischemic symptoms at hospital presentation may create a selection bias. The current study did not test for other biomarkers such as brain natriuretic peptides (BNPs) and High Sensitivity C-Reactive Protein (hsCRP) (39). Overall, we consider this research preliminary and encourage replication.

In summary, the progression from subtle impairment of diastolic function to clinically overt heart failure is a slow and insidious process. Hence, the discovery of LRG1 as a sensitive and independent marker of diastolic dysfunction might open up new avenues for earlier and more reliable diagnoses of DD. Further prospective studies with larger cohorts of patient at varying stages of asymptomatic DD and symptomatic heart failure, as well as healthy controls, are warranted to validate the clinical utility of LRG1 as a novel biomarker for risk stratification and prognostic evaluation.

Abbreviation	Full name
LRG1	Leucine-rich α2-glycoprotein
DD	diastolic dysfunction
No-DD	No-Diastolic dysfunction
LVDD	left ventricular diastolic dysfunction
EF	ejection fraction
SYNTAX	SYNergy between percutaneous coronary intervention with TAXus and cardiac surgery
OR	Odd ratio
95% CI	95% Confidence interval
HFpEF	heart failure with preserved ejection fraction
HFrEF	heart failure with reduced ejection fraction
CAD	coronary artery disease
TGF-β1	transforming growth factor- β1
EMR	Electronic Medical Records
LA	left atrial
LV	left ventricular
LAVi	left atrial volume index
E/A ratios	ratios of peak early (E-wave) and late (A-wave)
E/e′	early diastolic annular (e) velocity ratio
HDL	high-density lipoprotein cholesterol
LDL	low-density lipoprotein
HbA1c	glycated haemoglobin
eGFR	estimated glomerular filtration rate
VIF	variance inflation factor
ROC	receiver operating characteristic
AUC	estimated area under the ROC curve
DM	diabetes mellitus
YI	Youden Index
BNPs	brain natriuretic peptides
hsCRP	high Sensitivity C-Reactive Protein

Ethics approval and consent to participate

The study was approved by the institutional review board (MREC ID NO 20171126-5850) and conformed with the principles of the Helsinki Declaration.

Consent for publication

All authors have consented for publication.

Availability of data and materials

The authors agree to open access for this publication

Competing interests

All authors have no competing interest to declare.

Funding

This work was supported by the Ministry of Education Malaysia Fundamental Research Grant Scheme (FRGS) [Grant Number: FRGS/1/2018/SKK02/USM/03/2].

Authors’ contributions

Alexander Loch, Kok Leng Tan and Mei Li Ng wrote the manuscript. Kok Leng Tan and Mahmoud Danaee designed the research, initiated patient enrolment, data collection and

samples analysis. Mei Li Ng and Mahmoud Danaee supervised statistical analysis. Iskandar Idris critically reviewed the manuscript.

Acknowledgements

The authors thank all patients participated in the study, and the Ministry of Higher Education for funding support.

Ponikowski P, Voors AA, Anker SD, Bueno H, Cleland JG, Coats AJ, et al. 2016 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure: The Task Force for the diagnosis and treatment of acute and chronic heart failure of the European Society of Cardiology (ESC). Developed with the special contribution of the Heart Failure Association (HFA) of the ESC. Eur J Heart Fail. 2016;18(8):891–975.
Paulus WJ, Tschope C, Sanderson JE, Rusconi C, Flachskampf FA, Rademakers FE, et al. How to diagnose diastolic heart failure: a consensus statement on the diagnosis of heart failure with normal left ventricular ejection fraction by the Heart Failure and Echocardiography Associations of the European Society of Cardiology. Eur Heart J. 2007;28(20):2539–50.
Liu T, Song D, Dong J, Zhu P, Liu J, Liu W, et al. Current Understanding of the Pathophysiology of Myocardial Fibrosis and Its Quantitative Assessment in Heart Failure. Front Physiol. 2017;8:238-.
Schellings MW, Pinto YM, Heymans S. Matricellular proteins in the heart: possible role during stress and remodeling. Cardiovasc Res. 2004;64(1):24–31.
Burlew BS, Weber KT. Cardiac fibrosis as a cause of diastolic dysfunction. Herz. 2002;27(2):92–8.
Jamiel A, Ahmed AM, Farah I, Al-Mallah MH. Correlation Between Diastolic Dysfunction and Coronary Artery Disease on Coronary Computed Tomography Angiography. Heart Views. 2016;17(1):13–8.
Song W, Wang X. The role of TGFβ1 and LRG1 in cardiac remodelling and heart failure. Biophysical reviews. 2015;7(1):91–104.
Wei G, Runda W, Jiasheng Y, Yuanji M, Junjie G, Xin Z, et al. Research Square. 2020.
Yadav M, Palmerini T, Caixeta A, Madhavan MV, Sanidas E, Kirtane AJ, et al. Prediction of Coronary Risk by SYNTAX and Derived Scores: Synergy Between Percutaneous Coronary Intervention With Taxus and Cardiac Surgery. J Am Coll Cardiol. 2013;62(14):1219–30.
Lam CS, Lyass A, Kraigher-Krainer E, Massaro JM, Lee DS, Ho JE, et al. Cardiac dysfunction and noncardiac dysfunction as precursors of heart failure with reduced and preserved ejection fraction in the community. Circulation. 2011;124(1):24–30.
Greenland P, Alpert JS, Beller GA, Benjamin EJ, Budoff MJ, Fayad ZA, et al. 2010 ACCF/AHA guideline for assessment of cardiovascular risk in asymptomatic adults: a report of the American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines. J Am Coll Cardiol. 2010;56(25):e50–103.
Reynolds HR, Axel L, Hochman JS. Diastolic Dysfunction in Patients With Ischemic Symptoms Without Obstructive Coronary Artery Disease. Circ Cardiovasc Imaging. 2014;7(3):420–1.
Michelsen MM, Pena A, Mygind ND, Høst N, Gustafsson I, Hansen PR, et al. Overlap between angina without obstructive coronary artery disease and left ventricular diastolic dysfunction with preserved ejection fraction. PLoS One. 2019;14(5):e0216240-e.
Vasan RS, Xanthakis V, Lyass A, Andersson C, Tsao C, Cheng S, et al. Epidemiology of Left Ventricular Systolic Dysfunction and Heart Failure in the Framingham Study: An Echocardiographic Study Over 3 Decades. JACC Cardiovasc Imaging. 2018;11(1):1–11.
Reddy YNV, Carter RE, Obokata M, Redfield MM, Borlaug BA. A Simple, Evidence-Based Approach to Help Guide Diagnosis of Heart Failure With Preserved Ejection Fraction. Circulation. 2018;138(9):861–70.
Paulus WJ. H2FPEF Score: At Last, a Properly Validated Diagnostic Algorithm for Heart Failure With Preserved Ejection Fraction. Circulation. 2018;138(9):871–3.
Obokata M, Kane GC, Reddy YN, Olson TP, Melenovsky V, Borlaug BA. Role of Diastolic Stress Testing in the Evaluation for Heart Failure With Preserved Ejection Fraction: A Simultaneous Invasive-Echocardiographic Study. Circulation. 2017;135(9):825–38.
Anjan VY, Loftus TM, Burke MA, Akhter N, Fonarow GC, Gheorghiade M, et al. Prevalence, clinical phenotype, and outcomes associated with normal B-type natriuretic peptide levels in heart failure with preserved ejection fraction. Am J Cardiol. 2012;110(6):870–6.
Paulus WJ, Tschope C. A novel paradigm for heart failure with preserved ejection fraction: comorbidities drive myocardial dysfunction and remodeling through coronary microvascular endothelial inflammation. J Am Coll Cardiol. 2013;62(4):263–71.
Paulus WJ, Tschöpe C. A novel paradigm for heart failure with preserved ejection fraction: comorbidities drive myocardial dysfunction and remodeling through coronary microvascular endothelial inflammation. J Am Coll Cardiol. 2013;62(4):263–71.
Lin FY, Zemedkun M, Dunning A, Gomez M, Labounty TM, Asim M, et al. Extent and severity of coronary artery disease by coronary CT angiography is associated with elevated left ventricular diastolic pressures and worsening diastolic function. J Cardiovasc Comput Tomogr. 2013;7(5):289 – 96.e1.
Amende I, Coltart DJ, Krayenbuehl HP, Rutishauser W. Left ventricular contraction and relaxation in patients with coronary heart disease. European journal of cardiology. 1975;3(1):37–45.
Reduto LA, Wickemeyer WJ, Young JB, Del Ventura LA, Reid JW, Glaeser DH, et al. Left ventricular diastolic performance at rest and during exercise in patients with coronary artery disease. Assessment with first-pass radionuclide angiography. Circulation. 1981;63(6):1228–37.
Mansour MJ, Aljaroudi W, Mroueh A, Hamoui O, Honeine W, Khoury N, et al. Stress-induced Worsening of Left Ventricular Diastolic Function as a Marker of Myocardial Ischemia. J Cardiovasc Echogr. 2017;27(2):45–51.
Mehta SK, Rame JE, Khera A, Murphy SA, Canham RM, Peshock RM, et al Left ventricular hypertrophy, subclinical atherosclerosis, and inflammation. Hypertension. (Dallas, Tex: 1979). 2007;49(6):1385-91.
Feng-Jung Y. MON-056 Leucine-Rich a-2-Glycoprotein 1 increased Peripheral arterial occlusive disease risk in end-stage renal disease. Kidney International Reports. 2019;4(7):327.
Yang F-J, Hsieh C-Y, Shu K-H, Chen IY, Pan S-Y, Chuang Y-F, et al. Plasma Leucine-Rich α-2-Glycoprotein 1 Predicts Cardiovascular Disease Risk in End-Stage Renal Disease. Sci Rep. 2020;10(1):5988.
Serada S, Fujimoto M, Terabe F, Iijima H, Shinzaki S, Matsuzaki S, et al. Serum Leucine-rich Alpha-2 Glycoprotein is a Disease Activity Biomarker in Ulcerative Colitis. Inflamm Bowel Dis. 2012;18(11):2169–79.
Zhang J, Zhu L, Fang J, Ge Z, Li X. LRG1 modulates epithelial-mesenchymal transition and angiogenesis in colorectal cancer via HIF-1α activation. Journal of Experimental Clinical Cancer Research. 2016;35(1):29.
Bos S, Phillips M, Watts GF, Verhoeven AJM, Sijbrands EJG, Ward NC. Novel protein biomarkers associated with coronary artery disease in statin-treated patients with familial hypercholesterolemia. J Clin Lipidol. 2017;11(3):682–93.
Wang X, Abraham S, McKenzie JAG, Jeffs N, Swire M, Tripathi VB, et al. LRG1 promotes angiogenesis by modulating endothelial TGF-β signalling. Nature. 2013;499(7458):306–11.
Celik T, Yuksel UC, Fici F, Celik M, Yaman H, Kilic S, et al. Vascular inflammation and aortic stiffness relate to early left ventricular diastolic dysfunction in prehypertension. Blood Press. 2013;22(2):94–100.
34.
Kumagai S, Nakayama H, Fujimoto M, Honda H, Serada S, Ishibashi-Ueda H, et al. Myeloid cell-derived LRG attenuates adverse cardiac remodelling after myocardial infarction. Cardiovasc Res. 2016;109(2):272–82.
Yang F, Liu YH, Yang XP, Xu J, Kapke A, Carretero OA. Myocardial infarction and cardiac remodelling in mice. Exp Physiol. 2002;87(5):547–55.
Yin X, Subramanian S, Hwang SJ, O'Donnell CJ, Fox CS, Courchesne P, et al. Protein biomarkers of new-onset cardiovascular disease: prospective study from the systems approach to biomarker research in cardiovascular disease initiative. Arterioscler Thromb Vasc Biol. 2014;34(4):939–45.
Watson CJ, Ledwidge MT, Phelan D, Collier P, Byrne JC, Dunn MJ, et al. Proteomic analysis of coronary sinus serum reveals leucine-rich α2-glycoprotein as a novel biomarker of ventricular dysfunction and heart failure. Circ Heart Fail. 2011;4(2):188–97.
Lakhani I, Wong MV, Hung JKF, Gong M, Waleed KB, Xia Y, et al. Diagnostic and prognostic value of serum C-reactive protein in heart failure with preserved ejection fraction: a systematic review and meta-analysis. Heart failure reviews. 2020.
Lakhani I, Wong MV, Hung JKF, Gong M, Waleed KB, Xia Y, et al. Diagnostic and prognostic value of serum C-reactive protein in heart failure with preserved ejection fraction: a systematic review and meta-analysis. Heart failure reviews. 2020.

Download PDF

Version 1

posted

You are reading this latest preprint version

Leucine Rich Alpha-2-Glycoprotein 1 Is A Novel Predictor Of Diastolic Dysfunction: A Cross-Sectional Study

Status:

Version 1

Abstract

Figures

Introduction

Methods

Results

Discussion

Conclusion

Abbreviations

Declarations

References

Status:

Version 1