Elevation of lipoprotein(a) and PCSK9 serum plasma concentration among patients with angiogram-proven premature coronary artery disease in an Asian Cohort

doi:10.21203/rs.3.rs-4942779/v1

Coronary artery disease (CAD) has been associated with elevated Lp(a) levels, yet the underlying mechanism by which Lp(a) mediates atherogenesis and inflammation is still incompletely understood. Proprotein convertase subtilisin/kexin type 9 (PCSK9) known to be during the inflammatory process, thus a potential relationship between Lp(a) and PCSK9 could be established. This study aimed to investigate the correlation and association between Lp (a) and PCSK9 in the angioproven-premature CAD (AP-pCAD) subjects with and without FH. Patients were recruited from Cardiology and Specialist Lipid Clinics were grouped into + pCAD + FH (n = 70), +pCAD -FH (n = 65), and -CAD-FH (G3; n = 69). FH was clinically diagnosed using Dutch Lipid Clinic Network Criteria. Lp(a) and PCSK9 levels were measured using an automated chemistry analyser and ELISA, respectively. Both were higher in + pCAD + FH [27.2 (13.2–72.2), 431.4 (178.0-1008.0)] and + pCAD -FH [34.7 (12.7-100.9), 471.4 (333.1–1188.0] compared to G3 [7.5 (7.0-14.7), 389.7 (147.1-566.2)]. In conclusion, Lp(a) and PCSK9 levels were significantly higher in pCAD compared to G3-normal control (NC) group, regardless the FH clinical diagnosis. A significant correlation was found in all pCAD and NC groups. We suggested that PCSK9 concentration is correlated with Lp(a) levels in pCAD and NC groups, indicating its potential of becoming a CAD predictor.

Health sciences/Cardiology

Health sciences/Medical research

Premature coronary artery disease

Coronary artery disease

Familial hypercholesterolaemia

Proprotein convertase subtilisin/kexin type 9

PCSK9

Lipoprotein (a)

Atherosclerotic cardiovascular disease (ASCVD) in young adults represents a specific and growing challenge. Young people may be less aware of their underlying cardiovascular risk factors and are less likely to discuss lifestyle modification or primary prevention with their healthcare providers [1–2]. The burden of ASCVD in young adults is a significant public health issue because of the potential loss of lifetime productivity and increased lifetime healthcare use. Moreover, recent reports highlight that the myocardial infarction (MI) rate reduction has not extended to young adults but continues to have worse cardiovascular outcomes than men [3, 4]. Premature coronary artery disease (pCAD) is defined as coronary artery disease (CAD) age of onset below 55 in men and 60 in women [5].

According to the European Society of Cardiology/European Atherosclerosis Society (ESC/EAS) and American College of Cardiology/American Heart Association (ACC/AHA) guidelines, lipoprotein (a) (Lp(a)) is a genetically predisposed lipoprotein known as an independent factor in the development of cardiovascular disease [6–10]. Elevated Lp(a), also called hyperlipoproteinemia(a), are relatively independent of age and gender and varies 1000-fold among individuals (including siblings). Diet and nutritional factors have minimal influence on Lp(a) levels [10]. Lp(a) adversely affects endothelial function, inflammation, oxidative stress, fibrinolysis, and plaque stability, leading to accelerated atherothrombosis and pCAD. Lp(a) not only contains all the proatherogenic components of LDL but also of apolipoprotein (a) (apo(a)). Hovingh et al. [11] and Duarte & Giugliano [12], Lp(a) is linked to an increased risk of CAD, and Langston et al. [13] found that a high concentration of Lp(a) is a common genetic risk factor for CAD and MI in the general population. Lp(a) levels have been significantly higher in FH patients, particularly those with an early CAD episode [14]. At the same time, Langsted et al. (2016) investigated the Lp(a) concentration in individuals with clinically diagnosed FH compared to those unlikely to have FH in Denmark’s general populations. Their study highlighted higher Lp(a) concentrations in clinical FH without mutations [13]. In contrast, Perez et al. [15] found that increased Lp(a) concentrations in FH patients were related to LDLR dysfunction, which affects LDL-c clearance. Lp(a) level was found to be variable among different populations [13, 16]. Despite the divergent views, the solid reason why Lp(a) concentrations are higher in FH patients remains unknown.

In 2003, the discovery of PCSK9 marked an extraordinary paradigm shift, as it led to the development of a potent arsenal of inhibitory medicine that effectively lowered LDL-c to previously unattainable levels. Currently, a considerable number of drug candidates that impede the PCSK9 pathway have commenced preclinical or early-phase clinical trials. Notably, two of these drugs, Evolocumab and Alirocumab, were approved by the US FDA in 2015. According to Shapiro & Fazio (2017), preclinical studies demonstrated that PCSK9 is capable of producing pleiotropic effects in addition to its modulation of plasma LDL levels. Before PCSK9 discovery, there were only two known genes (LDLR and ApoB) [17] linked with FH in humans. The tremendous interest in PCSK9 was sparked by the findings that gain-of-function (GOF) mutations are associated with HC and increased coronary events, while loss-of-function (LOF) mutations lead to a reduction of circulating LDL and decreased risk of developing CAD. A GOF mutation in the PCSK9 gene was found to cause FH [19–22].

PCSK9 gain-of-function (GOF) mutation causes hypercholesterolemia, while loss-of-function (LOF) mutations of PCSK9 can result in hypocholesterolemia [23–24]. It has been demonstrated that plasma PCSK9 levels are elevated in acute MI and are associated with the severity of CAD [25–26]. Several demographic and metabolic parameters appear to correlate with serum PCSK9 levels, including plasma LDL cholesterol (LDL-c), high-density lipoprotein (HDL) cholesterol, triglycerides (TG), apolipoprotein B (apo B), insulin, glucose, smoking, and body mass index (BMI) [27–29]. Besides, the correlation between PCSK9 and Lp(a) is poorly established [30–31]. There were few studies concerning the association of PCSK9 with Lp(a) have been reported, such as Tavori et al. [32] found a relationship between PCSK9 and Lp(a), but a small sample size was used together with the absence of a control group. A recent study by Nekaies et al. [31] reported that plasma PCSK9 levels correlated positively with Lp(a) concentrations in 50 patients with type 2 diabetes mellitus (T2DM) but not in non-T2DM subjects in Tunisians. Interestingly, they found no positive association of PCSK9 with total cholesterol (TC) or LDL-c, which is somehow different from previous studies in both T2DM patients and non-T2DM subjects [33–37]. Moreover, both Lp(a) and PCSK9 levels considerably vary by race and other factors [29, 37, 38].

However, to our knowledge, the relationship between Lp (a) and PCSK9 in the angioproven-pCAD (AP-pCAD) subjects with and without FH patients in Asian cohort have not been investigated thus far. Therefore, this study was conducted to investigate the correlation and association between PCSK9 and Lp(a) level in patients with FH pCAD among pCAD patients. Thus, an investigation on the lipid profile, PCSK9 and Lp(a) levels, gender and age-matched subjects with and without CAD were conducted.

Study Design

This cross-sectional study analyses various clinical and biochemical data from study subjects through population-based screening programs via convenient sampling. The community health screening programmes were conducted in East and West Malaysia, incorporating the various states and regions in the north, south, central, east, and west parts of the country to represent the whole population for the normal control (NC) group, group 3, non-CAD and non-FH (CAD- FH-). The recruitment of patients in group 1, angiogram proven-pCAD (AP-pCAD) patients who were clinically identified as FH (pCAD + FH+) and group 2, AP-pCAD patients who were clinically identified as non-FH (pCAD + FH-) were performed in the National Heart Institute (IJN) and UiTM specialist clinics, including Cardiology and Specialist Lipid Clinics, based on the inclusion and exclusion criteria. This study also involved the recruitment of known and undiagnosed hypercholesterolemic individuals in a community setting. The diagnosis of FH was determined using DLCC. Healthy individuals with normal cholesterol levels without CAD nor clinically diagnosed FH were recruited as normal controls.

Ethical Approvals

Ethical approval

s for this study have been obtained from UiTM Research Ethics Committee [REC/02/2023 (PG/MR/45)] and IJN Ethics Committee [IJNEC/03/2012 (6)] prior to the commencement of this study. The study was conducted in accordance with the Declaration of Helsinki. All subjects provided written informed consent prior to enrolment into the study.

Inclusion Criteria

The general inclusion criteria for group 1 (G1) and group 2 (G2) were Malaysian male and female aged ≥ 18 years old with angiogram proven-pCAD (AP-pCAD) and voluntarily consented to participate in this study (Table 1). This study included group 3 (G3), healthy individuals with normal serum cholesterol levels and no known history of CAD nor FH.

Definition of pCAD for this study:

Subject with pCAD was defined as those with the first diagnosis of CAD at < 55 years old in males and < 60 years old in females [5].

Table 1

Inclusion Criteria
GROUP 1 pCAD (+) FH (+)		GROUP 3 CAD (-) FH (-)
Malaysians
Not pregnant/ Absence of physical disabilities
Definite, Probable, or Possible FH by DLCC	Unlikely FH by DLCC
Has undergone at least one of the listed procedures: i) An abnormal coronary angiogram with stenosis of > 50% in at least one major epicardial artery segment [39] and/or, ii) Percutaneous coronary intervention (PCI) and/or, iii) Coronary artery bypass graft surgery (CABG) [40]		a) No past history or record of CAD b) Has no previous history of undergoing any of the following listed procedures: i) Coronary angiogram (stenosis of > 50% in at least one major epicardial artery segment) and/or, ii) Percutaneous coronary intervention (PCI) and/or, iii) Coronary artery bypass graft surgery (CABG).
Statin Treatment		Not on Statin Treatment
No secondary hypercholesterolemia

Exclusion criteria

The exclusion criteria for G1 and G2 were non-Malaysian’s males and females aged ≤ 18 years old, pregnant women, those without angiogram-confirmed PCAD diagnosis (in G1 and G2), history of PCI and CABG, and those with known causes of secondary hypercholesterolaemia were excluded (Table 1). Electronic hospital medical records were reviewed for clinical and medication history of known secondary hypercholesterolaemia which are hypothyroidism, nephrotic syndrome and cholestasis. Control subjects were individuals without history of CAD, selected from the community health screening programmes. Only Malaysian citizen who were age- and gender- matched were selected for this study. Non-Malaysian, those who were pregnant and with known secondary hypercholesterolaemia were excluded from the study. If any documentations, clinical indication, and/or abnormal blood test result are not present, the subjects were considered as not having any secondary hypercholesterolaemia. Those with secondary hypercholesterolaemia may have similar lipid profiles as those with FH. Besides, certain types of drugs may also contribute to the development of secondary hypercholesterolaemia.

Clinical Diagnosis of FH according to DLCC

All eligible subjects were classified into possible FH (3–5 points), probable FH (6–8 points), or definite FH (≥ 8 points) categories by using modified DLCC criteria [41]. The measurement of LDL-c were then calculated from the measurements of TC, HDL and TG values using Friedewald equation [42] as follow:

[TC (mmol/L)] – [HDL-c (mmol/L)] – [TG (mmol/L) /2.2] = [LDL-c (mmol/L)]

The Friedewald equation was verified by comparing its results with those of a reference method known as beta-quantification where total LDL mass is directly measured by analytical ultracentrifugation [43]. Calculated LDL-c were used to reflect the baseline LDL-c level. Their plasma LDL-c was adjusted by a correction factor that depends on the type and dosage of specific statins to estimate pre-treatment LDL-c levels [44].

Subject Recruitment

Written informed consent and a standard validated questionnaire [45] were given to be filled in and signed by the subjects upon recruitment. The questionnaire used in this study includes personal information regarding the personal and family medical history, anthropometric data, and clinical findings of the subjects. Clinical data were obtained by clinicians who were part of the research project to ensure accurate clinical assessment of the subjects.

Demographic data obtained includes age, gender, ethnicity, gross household income, education, marital status, personal and family history of hypertension, hypercholesterolemia, CVD, and other lifestyle habits, such as smoking status, alcohol intake, measurements of body mass index (BMI), waist circumference (WC), hip circumference (HC), Waist-to-Hip Ratio (WHR), and blood pressure. The clinical examination includes examination for lipid stigmata, including the presence of corneal arcus, the skin of eyelids (xanthelasma), and tendon xanthomas. Upon completion of the questionnaire and contentment to participate, the questionnaires were collected, and the information was transferred to the database for further statistical analysis.

Blood Sample Collection

Blood samples were collected into plain blood collection tubes in the morning after an overnight fast (of ≥ 8 hours) and centrifuged at 4000 rpm for 20 minutes to obtain serum after being sat upwards for at least 30 minutes. The serum was transferred using a syringe pipette into a 1.5 mL microcentrifuge tube and was kept in a -20°C freezer till analysis.

Measurement of Lp(a)

Lp(a) was measured using an automated clinical chemistry analyser, Cobas Roche C501 (Roche Diagnostics), configured to nmol/L units. The principle of Lp(a) detection was turbidimetrically determined at wavelength 800/ 660 nm.

Measurement of Total Protein PCSK9

Concentrations of total PCSK9 in the blood serum were performed with a commercially available standard ELISA kit (Elabscience, Texas, United States), according to the manufacturer's instructions. The absorbance was measured at 450 nm using a microplate reader (Agilent, California, United States). All procedures were performed according to the manufacturer's instructions. The absorbance of the samples was measured at 450 nm using a microplate reader (Agilent, California, United States).

Statistical Analysis

Data were analyzed using IBM SPSS Statistics version 26 (IBM, NY, USA). data were presented as means (SD) for continuous variables (after checking the data distribution by performing the Kolmogorov-Smirnov test) and percentages for categorical variables. The common risk factor for pCAD was calculated by combining G1 and G2 [(YES categorical variables /total number of patients) ×100%]. The significance of differences between the numerical variables was determined by using a two-sample t-test and One-Way ANOVA (for parametric tests) or Mann-Whitney and Kruskal Wallis tests (for non-parametric tests), while the significance of association between categorical variables was determined by using a Chi-squared test [46]. The association of Lp(a) and PCSK9 and the coronary risk factors were analysed using Spearman’s rank correlation. All final analyses with p-value < 0.05 were considered statistically significant.

Baseline Characteristics and Coronary Risk Factors of The Study Population

Table 2 shows the distribution of participants into groups based on the presence of pCAD and clinical diagnosis of FH according to the DLCC criteria. Malay was the major ethnic across all groups. G1 and G2 (participants with pCAD) have higher proportions of obesity (BM) and hypertension compared to G3, the normal control group (participants without CAD and FH) (p<0.001). The presence of the family history of pCAD was also significantly higher in G1 compared to the other groups (p<0.001). Similarly, there was a significantly higher percentage of individuals with a family history of HC in G1 compared to G2 and G3 (p<0.001). Only two patients (2.9%) presented with tendon xanthoma (TX), and six patients (8.6%) with corneal arcus in G1. TC, TG, and LDL-c levels of G1 were significantly higher than G2 but significantly lower than G3. The median Lp(a) and PCSK9 level was highest in the pCAD group (G1 and G2), whilst it appeared lowest in the normal control group (G3) (Table 3).

Table 2. Distribution of Individuals Based on the Presence of CAD and Clinical Diagnosis of FH (n=204)

Parameters	GROUP 1 pCAD (+) FH (+) n=70	GROUP 2 pCAD (+) FH (-) n=65	GROUP 3 CAD (-) FH (-) n=69		p-value
Gender
Male	59 (84.3)	57 (87.7)	47 (68.1)		NS ^
Female	11 (15.7)	8 (12.3)	22 (31.9)
Age (years)	52.3 ± 6.9	53.1 ± 8.1	51.13 ± 2.8		NS @
Age range (years)	34 – 64	33 – 72	34 - 53		-
Ethnicity					<0.05^
Malay	48 (68.6)	52 (80.0)	42 (59.4)
Chinese	5 (7.1)	6 (9.2)	3 (2.9)
Indian & Others	17 (24.3)	7 (10.8)	26 (37.7)
BMI Categories					<0.001^
Normal	11 (15.7)	23 (35.4)	11 (15.9)
Overweight	5 (7.1)	15 (23.1)	9 (13.0)
Obese	54 (77.2)	27 (41.5)	49 (71.0)
Central Obesity					<0.001^
Yes	50 (71.4)	28 (43.1)	50 (72.5)
No	20 (28.6)	37 (56.9)	19 (27.5)
Smoker				<0.05^
Current smoker	19 (27.1)	9 (13.8)	16 (23.2)
Ex-smoker	18 (25.7)	28 (43.1)	12 (17.4)
Non-smoker	33 (47.2)	28 (43.1)	41 (59.4)
Diabetes				<0.001^
Yes	39 (55.7)	36 (55.44)	4 (5.8)
No	31 (44.3)	29 (44.6)	65 (94.2)
Hypertension				<0.001^
Yes	46 (65.7)	40 (61.5)	18 (26.1)
No	24 (34.3)	25 (38.5)	51(73.9)
Hypercholesterolaemia				<0.001^
Yes	70 (100.00)	0 (0.0)	40 (58.0)
No	0 (0.0)	65 (100.0)	29 (42.0)
Statin therapy				<0.001^
Yes	70 (100.0)	65 (100.0)	0 (0.0)
No	0 (0.0)	0 (0.0)	69 (100.0)
Family history of pCAD				<0.001^
Yes	27 (38.6)	0 (0.0)	5 (7.2)
No	43 (61.4)	65 (100.0)	65 (92.8)
Family history of HC				<0.001^
Yes	38 (54.3)	14 (21.5)	9 (13.0)
No	32 (45.7)	51 (78.5)	60 (87.0)
Tendon xanthomata				NS^
Yes	2 (2.9)	0 (0.0)	0 (0.0)
No	68 (97.1)	65 (100.0)	67 (100.0)
Corneal arcus (<45 years)				<0.05^
Yes	6 (8.6)	0 (0.0)	0 (0.0)
No	64 (91.4)	65 (100.0)	69 (100.0)

Data are presented as number (n) and percentage (%) for categorical data, mean and standard deviation (SD) for continuous data. Statistical analysis: ^Chi-squared test, @ One-Way ANOVA test, # Kruskal Wallis test. Abbreviation: FH (familial hypercholesterolemia), pCAD (premature coronary artery disease), HC (hypercholesterolemia), BMI (body mass index).

Table 3. Lipid profile, Lp(a), and PCSK9 based on the Presence of CAD and Clinical Diagnosis of FH (n=204)

Parameters	GROUP 1 pCAD (+) FH (+)		GROUP 2 pCAD (+) FH (-)		GROUP 3 CAD (-) FH (-)	p-value
Lipid Profile at Study Entry
Pre-treatment LDL-c (mmol/l)^{**, 1}		5.2 ± 1.8 ^{a, b}		3.3 ± 0.8 ^{a, b}	2.9 ± 0.8 ^a	<0.001*
Post-treatment LDL-c (mmol/l)		2.4 ± 0.8		1.7 ±0.4	-	NS*
TC (mmol/l)		4.4 ±1.0 ^a		3.5 ± 0.7 ^a	5.2 ± 1.1 ^a	<0.001*
TG (mmol/l)		1.6 ± 0.8 ^{a, c}		1.5 ± 0.9 ^{b, c}	2.4 ± 1.6 ^{a, b, c}	<0.001*
HDL-c (mmol/l)		1.2 ± 0.2		1.1 ± 0.2	1.0 ± 0.3	NS*
Lp(a) (mmol/l)		27.2 (13.2 – 72.2) ^a		34.7 (12.7 – 100.9) ^b	7.5(7.0 – 14.7) ^{a, b}	<0.001*
PCSK9 (ng/ml)		431.4 (178.0-1008.0)^a		471.4 (333.1-1188) ^b	389.7(147.1-566.2) ^{a, b}	< 0.05*

Data are presented as mean and standard deviation (SD) and median (interquartile range) [IQR] for continuous data.**Representing baseline LDL-c level prior to lipid-lowering medication and LDL-c level for drug-naïve individuals. 1 Several patients do not have the exact baseline lipid profile – statin conversion was used to calculate the estimated post-treatment LDL-c level. a, b, c = p<0.05. Statistical tests with the same symbols in the same row are significantly different from each other. Abbreviation: LDL-c (low-density lipoprotein cholesterol), TC (total cholesterol), TG (triglyceride), HDL-c (high-density lipoprotein cholesterol).

Conventional Risk Factors in pCAD patients

Patients with pCAD with and without FH from G1 (n = 70) and G2 (n = 65) were combined to determine the most common risk factors among pCAD patients. Figure 4.25 shows that after male gender (86.0%), smoking (54.8%), and hypertension (63.7%), HC (51.9%) was the most common risk factor in this cohort, followed by diabetes (44.4%), central obesity (42.2%), family history of HC (38.5%) and family history of pCAD (20.0%) (Fig.1.)

Correlation between Lp(a) and PCSK9

The correlation between Lp(a) and PCSK9 in each group was determined using Spearman’s rank correlation analysis. No statistically significant correlation between Lp(a) and PCSK9 were found in the pCAD group (G1 or G2). However, the correlation was found in G3 (r = 0.366, p = 0.019) and the combination of G1 and G2 (G1+G2) (r=0.366, p=0.002) (p<0.01, p<0.05) (Table 4).

Table 4. The Correlation between Lp(a) and PCSK9

GROUP		PCSK9 (ng/ml)
		r	p-value*
G1		0.236	0.102
G2		0.257	0.071
G3	Lp(a) (mmol/L)	0.366	0.019
G1+G2		0.566	0.002
G1+G2+G3		0.051	0.532

*Spearman Correlation

Abbreviation: G1 (pCAD+ FH+), G2 (pCAD+ FH-), G3 (CAD- FH-), G1 + G2 (pCAD+ FH+ and pCAD+ FH-), G1 + G2+ G3 (pCAD+ FH+, pCAD+ FH-, and CAD- FH-)

Association between PCSK9 and Lp(a) levels

The association between Lp(a) and PCSK9 in each group were determined using Chi-Square analysis. No statistically significant correlation between Lp(a) and PCSK9 were found in pCAD (G1 or G2), all pCAD as well normal control group (G3) (Table 5).

Table 5. The Association between Lp(a) and PCSK9

				PCSK9 (ng/ml)		X²(df)		p-value
				Low	High
Lp(a) (mmol/l)	G1					0.370 (1)		0.543
		Low		14 (87.5%)	41 (80.4 %)
		High		5 (26.3%)	10 (19.6%)
	G2					0.273 (1)	0.602
		Low		4 (33.3%)	22 (41.5%)
		High		8 (66.7%)	31 (58.5%)
	G3					0.855 (1)	0.355
		Low		14 (87.5%)	41 (94.3%)
		High		2 (12.5%)	3 (5.7%)
	G1+G2					0.063 (1)	0.802
		Low		18 (58.1%)	63 (60.6)
		High		13 (41.9%)	41 (39.4%)
	G1+G2+G3					4.141 (1)	0.606
		Low		32 (68.1%)	113 (72.0%)
			High	15 (31.9%)	44 (28.0%)

*Chi Square test. Data are presented as number (n) and percentage (%) for categorical data. Abbreviation: G1 (pCAD+ FH+), G2 (pCAD+ FH-), G3 (CAD- FH-), G1 + G2 (pCAD+ FH+ and pCAD+ FH-), G1 + G2+ G3 (pCAD+ FH+, pCAD+ FH-, and CAD- FH-)

Lp(a) is a quantitative genetic trait in human plasma that is highly heritable, with plasma level being predominantly (90%) determined by variation in the Lp(a) gene on chromosome 6q26-27, which affected the number of KIV2 copies among individuals, and displaying extreme variation both within and between populations [48-49]. Patients with CAD had a significantly higher level of Lp(a) [50], which is increasingly recognised as the strongest known genetic risk factor for pCAD [10]. In consensus in our studies that involved AP-pCAD as a subject that showed the highest level of Lp(a) in the pCAD group [G1: 27.2 (13.2-72.2) mmol/l, G2: 34.7 (12.7-100.9) mmol/l] and lowest in the normal control group [group 3: 7.5 (7.0-14.7) mmol/l].

PCSK9 variants have variable frequencies in different populations and their impact on cholesterol levels [51-53, 22]. More than 40 amino acid variants of PCSK9 have been shown to affect plasma cholesterol levels in humans [54, 55, 23]. These changes are classified as GOF mutations associated with high levels of LDL-c and LOF mutations associated with low LDL-c. GOF mutations result in mild to severe HC [22]. While Tada et al. [56] recently reported that Lp(a) was elevated in patients with FH caused by PCSK9 GOF mutations to the same level as that in FH caused by LDLR mutations. Almontashiri et al. [25] found that plasma PCSK9 is elevated with acute MI in non-diabetic AP-pCAD without statin therapy. In our study, AP-pCAD patients showed the higher level of PCSK9 in both pCAD group [G1: 4.31.4 (178.0-1008.0) ng/ml and G2: 471.4 (333.1-1188.0) ng/ml] and lowest in the normal control group [group 3: 38.9 (147.1-566.2) ng/ml].

In order to understand the mechanism by which PCSK9 mAb reduce Lp(a) levels, elucidation of the correlation between serum levels of PCSK9 and Lp(a) is required. A recent study by Nekaies et al. [31] reported that PCSK9 correlated positively with Lp(a). In contrast, Yang et al. reported that PCSK9 are not associated with Lp(a) levels [57]. Both reported the correlation between Lp(a) and PCSK9 in DM patients, however, it was conducted in a different population. Therefore, the correlation between PCSK9 and Lp(a) levels remains controversial. This study was the first to report the correlation between Lp(a) and PCSK9 in parallel in a cohort of pCAD with and without clinically diagnosed FH. There was no significant correlation between Lp(a) and PCSK9, neither in G1 (pCAD+ FH-) nor G2 (pCAD+ FH-). However, the significant correlations were observed between Lp(a) and PCSK9 concentration when the G1 and G2 were regarded as one group [pCAD with and without FH group (G1 and G2)]. The NC group (G3) that has been age and gender match with the pCAD group also showed a significant correlation (p<0.05). Our findings suggest that Lp(a) interact with PCSK9 in these two groups indicating the vital role of Lp(a) and PCSK9 in predicting CAD regardless the clinically diagnosed FH. Variations in terms of correlation between Lp(a) and PCSK9 observed between patients may be attributed to differences in race, as both Lp(a) and PCSK9 concentration vary considerably by race and other factors [29]. The current cutoff for Lp(a) is not appropriate and suitable for all individual. As reported by Guan et al. [58], for Caucasian and Hispanic individuals, the Lp(a) cutoff above 50 mg/dl should be considered, while for Black individuals the 30mg/dl cutoff remains. The Lp(a) cutoff need to be race specific [59]. This study was the first to report on Lp(a) and PCSK9 in parallel in a cohort of pCAD with and without clinically diagnosed FH among three different races in Malaysia.

Lp(a) is a hepatically synthesised particle resembling LDL, comprising an apoB100 molecule connected to a significantly large glycoprotein called apo(a) [34,38]. The biological role of Lp(a) remains unknown; yet an increase in Lp(a) concentration has been identified as an independent risk factor for ASCVD [9]. Several recent clinical trials have provided evidence that PCSK9 antibodies are a promising novel candidate drug for lowering LDL-c and Lp(a) [60-61]. Nevertheless, the mechanism by which PCSK9 inhibitors reduce Lp(a) levels remains unclear.

In this cross-sectional study, Lp (a) and PCSK9 concentration were highest in the G2 group (pCAD+ FH-) and lowest in the G3 (NC group). However, no association was found between Lp(a) and PCSK9 in all groups. According to Yang et al. [57], non-significant variations in Lp(a) and PCSK9 levels were discovered among T2DM patients who had greater percentages of hypertension, hyperlipidaemia, and CAD compared to non-T2DM patients. In addition, they found no statistically significant relationship between PCSK9 and Lp(a) in their study sample. In line with their finding, our study also found no association between PCSK9 level and Lp(a) concentration in the pCAD patient with a higher percentage of diabetes mellitus (DM) (55%). Contrary to our findings, Nozue et al. [62] reported that hetero-dimer PCSK9 levels were positively correlated with Lp(a) concentration. These inconsistent results may be attributed to the different forms of PCSK9 being measured. Our study measured all forms of PCSK9, active and not active PCSK9 altogether. However, Nozue et al. [62] measured the active form (hetero-dimer PCSK9) and non-active form (furin-cleaved PCSK9) separately. Hetero-dimer PCSK9 was considered the form with a stronger binding to and degradation of LDLR [63] and furin-cleaved heterodimer with reduced affinity for LDLR [64]. The connection between Lp(a) and furin-cleaved PCSK9 was insignificant in their research. The measurement of PCSK9 levels as a whole may contribute to the weak or no association between PCSK9 and Lp(a). Furthermore, based on Table 2, the ethnicity was significantly different between each group (p<0.05). This may contribute to the no correlation, as previous study reported Lp(a) and PCSK9 concentration vary considerably by race [29].

Coronary risk factors such as obesity [45, 65-66], type 2 diabetes mellitus (T2DM) [66], TC [67], and LDL-c [68] play a role in contributing to the high PCSK9 levels. Numerous current findings also suggest that circulating PCSK9 concentrations are associated with the incidence of CAD [24, 69-70]. In line with our study, the PCSK9 was highest in the pCAD group with the highest proportion of obesity, hypertension and HC subjects with the highest TC, TG, and LDL-c. Traditional predictors of CAD risk, such as age, gender, smoking, hypertension, DM, family history of CAD, HDL-c, TG, LDL-c, and Lp(a) levels, have been studied and reported as potential predictors of atherosclerotic burden and/or CVD prognosis among people with FH [71-72]. In our study, the simple logistic regression was performed in determining the significant predictors of pCAD. All related data such as Lp(a), PCSK9, and CRFs including the lipid profile were included in the analysis

Furthermore, some other considerations may explain our results’ disagreement. First, a comparison of Lp(a) concentration distributions in different ethnic groups reveals significant inter-racial variances. Variations in PCSK9 and Lp(a) correlation between patients may be related to racial differences, as PCSK9 and Lp(a) levels vary significantly by race and other factors. Second, our study includes three major races in Malaysia, which may contribute to the varying amounts of Lp(a) within the study. Third, the measurement of Lp(a) concentration is not standardised, and Lp(a) concentration may be influenced by LDL-c concentration. This could be the difficulty in establishing the link between Lp(a) and PCSK9 in our work.

PCSK9 is significantly correlated with Lp(a) levels in both pCAD patients and the NC group. This correlation highlights the potential role of PCSK9 as a valuable biomarker in predicting CAD risk. Given the established association of elevated Lp(a) with increased cardiovascular risk, the strong correlation with PCSK9 suggests that measuring PCSK9 levels could provide additional insight into an individual’s susceptibility to CAD. This finding underscores the need for further research to explore the mechanisms linking these two biomarkers and to evaluate the potential of PCSK9 as a predictive tool for CAD, especially in populations at high risk of premature disease onset.

Author Contribution

(I) Conceptualization: H.N. and S.A.M. (II) Funding acquisition and supervision: H.N., S.A.M. and S.M.W. (III) Patient recruitment: A.B.M.R., K.S.I. and R.Z. (IV) Investigation, data collection, data analysis, and manuscript writing: R.Z. Final approval of manuscript: All authors.

Acknowledgement

This study was funded by the Fundamental Research Grant Scheme FRGS [Grant code:600-IRMI/FRGS 5/3 (067/2019)] awarded by the Malaysia Ministry of Higher Education (MOHE) to the corresponding author of this study.

Data Availability

All data generated or analysed during this study are included in this published article.

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No competing interests reported.

Elevation of lipoprotein(a) and PCSK9 serum plasma concentration among patients with angiogram-proven premature coronary artery disease in an Asian Cohort

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