WITHDRAWN: Deciphering PCSK9 and LDLR Gene Expression in Coronary Artery Disease Patients: Implications for Blood Lipid Regulation

doi:10.21203/rs.3.rs-4684994/v1

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WITHDRAWN: Deciphering PCSK9 and LDLR Gene Expression in Coronary Artery Disease Patients: Implications for Blood Lipid Regulation

https://doi.org/10.21203/rs.3.rs-4684994/v1

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The main purpose of this study to calculate the frequency, risk factors, sedentary lifestyle, prevalence and expression of PCSK9 and LDLR genes showed a substantial role in cholesterol homeostasis involving in prognosis of coronary artery disease patients in Pakistan. Therefore, there is an urgent need to assess the frequency and risk factors involved and expression and regulation of a PCSK9 and LDLR in patients. A demographic survey involving CAD patients from different cardiac Hospitals in Pakistan was conducted and 100 CAD patients and 100 controls were included for the investigation of the expression level of PCSK9 and LDLR gene by using RT-PCR. CAD patients (males = 803, Females = 307) that participated in this study consisted of smokers (55%) and non-smokers (45%), the majority had a family history (62% positive, 38% negative). The participants also had diverse weight profiles (underweight 14%, active or normal 21%, overweight 27%, and obese 34%); while the majority claimed that they suffered stress (stress 85%, no stress 15%). Strong negative correlations (p < 0.01) were observed between CAD & gender, Diet, age of diagnosis, BMI, marital status, stress, different risk factors and family history. A negative correlation (p < 0.05) was seen between CAD and Sleep and with exercise also. The results indicated that the PCSK9 and LDLR expression level were considerably higher P was less than 0.05 in the patient group as compared to the healthy group. The AUC value of expression of PCSK9 (P = 6.58337E-4) and LDLR (P = 0.00111) were significant. The cox model result showed that high expression of LDLR and PCSK9 have significant effect, both the differences and overall survival rate were (P > 0.05) statistically significant. The findings of this study will help improve the management and treatment of CAD worldwide, the expression of PCSK9 and LDLR in CAD’s patient’s with specific implications in under developed countries.

Biological sciences/Microbiology

Biological sciences/Molecular biology

Health sciences/Diseases

Cardiovascular diseases

PCSK9

LDLR

Coronary Artery Disease

AUC

Cardiovascular diseases (CVD) accounted for less than 10% of deaths globally at the start of the 20th century, however by 2001 this figure increased to 31.8 percent¹, and at present CVDs are a foremost cause of death worldwide². Globally, 17.8 million deaths were attributed to CVD’s³. The prevalence of ischemic heart disease (IHD), stroke, and hypertension are high in urban centres, and the death rate in low- and middle-income countries was more than 80%⁴. Coronary illness is a condition that is multi-layered, impacted by economic wellbeing, hereditary qualities, way of life, and natural variables. Risk factors, for example, hypertension, actual latency, tobacco use, and diet are modifiable factors, while hereditary qualities, age, sex and race are non-modifiable risk factors related with coronary conduit sickness⁵. Approximately thirteen million deaths were attributed to stroke and coronary artery disease Asia⁶. Approximately 86% of the CVD worldwide burden is attributed to growing nations, due to a lack of medical intervention and support⁷. Aside from the aforementioned risk factors, haemorrhagic and other non-ischemic causes of stroke, hypertensive heart disease, cardiomyopathy, and myocarditis are also commonly diagnosed CADs associated with high death rates, with ischemic heart disease being the largest contributor⁸.

Furthermore, excessive alcohol consumption, poor diet, physical inactivity, tobacco use, diabetes and psychosocial issues are key factors that lead to CAD. Patients worldwide experience a significant increase in risk of CAD mortality with each poor lifestyle factor⁹. According to a report, 2.1 percent of women and 22.2 percent of men smoke in Pakistan¹⁰. High blood pressure has been associated with high BMI (all p < 0.001)¹¹. The prevalence of hypertension was 80% among people who had a BMI of 30 or greater (p for time interaction = 0.026)¹². A study in Pakistan found that, 57.5% of CVD patients had hypertension, 44.6% had diabetes mellitus, 38% were smokers and 36.7% had a positive family history of CVD¹². It is associated with cardiovascular and renal diseases. The death rate in diabetic patients who are also suffering from CVD is four times greater than patients who only have T2D. In the US, aged patients with T2D die from coronary artery disease (68%) and stroke (15%), even if their glucose levels are controlled¹³. The National Health Survey of Pakistan (1990–1994) showed that 1 in 3 individuals (aged 45) suffer from hypertension¹⁴.

Genetic and environmental factors play a role in the prognosis of the CAD. Obesity, hypercholesterolemia, hypertension, smoking and hyperglycaemia were some of the outmoded risk factors for atherosclerosis¹⁵. The level of gene expression of PCSK9 in CAD with diabetic’s patients were conspicuously higher than the non-diabetic CAD patients. Those who have G allele had higher relative PCSK9 expression than A allele patient¹⁶. These factors lead to a build-up of cholesterol in the body, which leads to a build-up of thrombotic fragments in the blood vessels¹⁷. Cholesterol levels in the circulatory system, specifically raised low-thickness lipoprotein cholesterol (LDL-C), are solid risk factors for cardiovascular illness, and LDL-C decrease lessens mortality in individuals in danger. One of the significant determinants of plasma LDL-C levels is the less thickness LDLR that goes about as scrounger aimed at cholesterol rich lipoprotein particles¹⁸.

PCSK9 propels hardship of low-thickness LDLR and expects a fundamental part in overseeing plasma levels of LDL cholesterol levels, lipoprotein and greasy substance lipoproteins, growing the coincidental of cardiovascular disease¹⁹. Fatty oil bringing down LPL variations and LDL-C-bringing down LDLR variations were related with analogous to the lower hazard of CHD per unit distinction in ApoB²⁰.

The likelihood of developing cardiovascular disease (CVD) is strongly correlated with plasma levels of (LDL-C) low-density lipoprotein cholesterol 20–40% ²¹.

PCSK9 progresses corruption of huge histocompatibility protein class I and inhibits the intratumoral entry of cytotoxic White platelets. The PCSK9 restriction grows enunciation of LDLR, in like manner lessening lipoproteins plasma levels and the bet of heart disorder²². In this way, PCSK9 assumes a focal part in the pathogenicity of CVD and malignant growths. Furthermore, it was accounted for the PCSK9, particularly liver PCSK9, can enroll provocative cells and initiate nearby aggravation²³. Raised Lp(a) levels are a profoundly far and wide risk factors for heart infection, particularly for myocardial localized necrosis, atherosclerotic stenosis and aortic valve stenosis²⁴. Plasma Lp(a) levels are sturdy by its production and autonomy. Through Lp(a) is eliminated from the bloodstream through LDLR, LRP1 and SR-BI, ²⁵. PCSK9 inhibitors powerfully lower the LDL-C levels in plasma in diabetes type 2 patients and decrease the chances for the improvement of cardiovascular sickness. Hostile to PCSK9 mAbs are by and large not pretty much viable in T2DM patients contrasted with a general high-risk population²⁶.The LDLR is a transmembrane glycoprotein that assumes a significant part in the take-up of low-thickness lipoprotein (LDL) from the blood dissemination in a cycle that is interceded by Apolipoprotein B²⁷. The LDLR then releases cholesterol(LDL) at an acidic pH for deprivation by a lysosome, releasing free cholesterol and returning the LDLR to the cell surface²⁸. Patients with a receptor-negative LDLR gene mutation and an Lp(a) level greater than 50 mg/dl have a higher risk of cardiovascular disease (CVD) than those with other less severe mutations²⁹. Transformations in LDLR quality had been accounted to source of familial hypercholesterolemia³⁰. Del2.5 is the main revealed gain-of-capability transformation in LDLR causing an enormous decrease in LDL cholesterol. The information highlight a job for optional polyadenylation of LDLR mRNA as a powerful controller of LDL receptor articulation in people³¹. The expression of PCSK9 and LDLR have not been reported with CAD in Pakistan. So here, we investigated the expression of both genes in term of CAD in Pakistani population.

Pakistan is a developing country facing serious healthcare problems due to the generally poor lifestyle of its people, the lack of medical infrastructure, and awareness. The incidence of CAD is briskly increasing in Pakistan, along with the commonness of diabetes, high BP and other metabolic disorders. Therefore, this study was designed to identify the risk factors (including familial history, genetic etc.) and expression of both genes involved in the development of CADs in the Pakistani population.

Ethics approval of the study

The study received ethical approval from the Punjab Institute of Cardiology's ethical review committee (Reference Number: RTPGME-Research-107) and the Institutional Bioethics Committee of Government College University Lahore (GCU-IIB-956). All methods were performed in accordance with the relevant guidelines and regulations by WMA (Ethical Principles for Medical Research Involving Human Subjects).

Criteria of inclusion and exclusion of patients

The patients involved in this research study were clinically identified with CAD by a physician in according with World Health Organization (WHO) guidelines. The patients were included who have strong incidence of medical symptoms such as hypertension, high BP, chest tightness, Cholesterol and a positive family history. This certifies that the study population involved of individuals who meet the specific medical criteria for CAD diagnosis. The exclusion criteria served to exclude individuals who may possibly have confusing factors or illnesses that could distress the study results or pretence additional risks. These criteria typically exclude patients with other types of tumour diseases, severe functional diseases such as liver, immune disease, cancer, Hepatitis kidney diseases and those who may not fulfil with or cooperate with the study requirements. By cautiously selecting study participants founded on these criteria, it was set apart to guarantee that the review results are reliable, and any noticed impacts can be certifying to the treatment being researched as opposed to other important conditions or elements.

Demographic data collection:

The survey was carried out in different cardiac hospitals in Lahore. Patients were interviewed prior to data collection. Demographic data were collected at the Public Sector Hospital mentioned above between September 2019 to August 2020. Heart disease and all risk factors were diagnosed by physicians. Questionnaires were designed and surveys were completed by interviewed the patient himself/herself or by the person who was patient’s attendant. Consent letters were signed by patient or their guardian (below aged 18) to ensure patient privacy and legitimacy and also have their contact no’s to identify the individuals and patients during and after the data collection for additional information in forthcoming days for study if we required.

The Performa was designed to gather data on various aspects such as gender, age of diagnosis, age family history and different risk factors including hypertension, diabetes, cholesterol, creatinine levels. It also assessed sedentary lifestyle factors like smoking, diet, stress and physical activity. Body Mass Index (BMI) was calculated based on participants' self-reported height (in meters) and weight (in kilograms), following the WHO criteria (BMI = kg/m²). Information related visits to physician and the average income of the family was collected from parents or guardian of patients for determining direct medical costs (total expenditure of treatment only) per patient, per annum and the burden of disease on each family.

Blood sample collection:

100 patients who were clinically diagnosed by coronary artery disease and 100 were healthy control collected in the time frame between September 2019 to August 2020. All patients provided pre informed consent form who were aged 18 or above. Furthermore, patients who were less than 18 years, the pre informed consent form was signed by legal guardians of patients. We have taken their contacts no’s to recognize the individuals and patients during and after the data collection for additional information in prospect and about their repossession or impermanence.

All patients gave 5 mL of blood was collected in EDTA contained blood collection tubes at room temperature. To get serum, the tubes were centrifuged at 3,000 rpm for 10 minutes at 4℃. The serum was then stored in a refrigerator at -80°C.

RNA Extraction

Trizol reagent ((Invitrogen, Carlsbad, CA, USA) was used for the RNA extraction from the blood. To determine RNA concentration and purity, 5µl of RNA samples were diluted with RNase-free water by twenty-fold. A nanodrop was used to read absorbance values at 260 and 280 nm. RNA samples were qualified for subsequent analysis.

cDNA synthesis and quantitation.

After completion of the reverse transcription reaction, cDNA molecules are generated, corresponding to the RNA transcripts present in the original sample. 3–5µg total RNA and molecular grade water to 8 µl final volume is prepared. Then 3ul random primers, 1ul dNTP mix were added. After that vortexed and spin down the PCR tubes. The RNA sample is denatured by heating for 5 minutes at 65°. After denaturation, the sample is cooled rapidly on ice to prevent reannealing of the RNA strands. Then 4 µl 5X Buffer was added in RNA sample, additionally 2 µl DTT and 1µl RNAse Out was also added. Next 1µl Superscript II RNase H- Reverse Transcriptase was added in the mixture. Incubated for 60 min at 42°C and at 70°C for 15 min. Nano drop was used to measure 1000 concentration. Then it was stored at -80°C for further use.

Add 180 µl molecular grade water.

Real-time quantitative PCR (qRT-PCR):

A 5-mL sample of a 10-fold dilution of cDNA was utilized for qRT-PCR of both genes. This was added to thermocycler 480 valves plate containing a 10 mL mixture comprised of 1ul of RNA sample, 0.3 mL of each PCR primer, 3.4 ml of PCR water, and 5 mL of a SYBR Green I. The PCR were done continuously mode on a Roche thermocycler. The temperature for PCR was as followed: 1 cycle of 95°C for 5 min, 35–40 cycles of 95°C for 10 s; 61°C for 15 s; and 72°C for 25s fluorescence was estimated after the extension step of each cycle at72⁰C.(Table.1)

Table 1

List of Primers used in the study.
Genes	Primer
PCSK9	Forward primer: 5′-GAGCCATCACCTAGGACTGA-3′ Reverse primer : 5′-AGTAGGGGTGCGAATGTGTA-3′
LDLR	Forward Primer: 5′-GCCCGCTAGCTTATCAGA CTGATG-3′ Reverse Primer: 5′-GTGCAGGGTCCGAGGT-3′

Statistical analysis:

Demographic data was presented in number and percentages while SPSS correlation tests, Excel, PRISM 5, and ORIGIN statistical software were applied to perform these analyses, visualize the results and to evaluate the significance. The data were articulated in the form of mean value ± standard deviation. A Correlation was found between PCSK9 and LDLR expression level and demographic characteristics. Expression levels were determined using the ΔΔCT method, which is commonly used in qPCR to compare gene expression levels between expression of the target gene to a reference gene and comparing it to a control group. Then the comparison of data between two groups CAD patients and non CAD was done by χ2 test. The ROC curve was used to assess the efficacy of markers in the diagnosis of each group. ROC curves plot the true positive rate against the false positive rate, providing a visual representation of the performance of diagnostic tests. The Cox model regression model used to measure the relationship between covariates (independent variables) and the hazard rate (risk of experiencing the event) over time.

A total of 1110 patients (Men = 803, Women = 307) who visited the Institute of Punjab cardiology, Sheikh Zaid Hospital, participated in the study. Of these patients, 6.1% (n = 68) were under 20 years of age, 48% (n = 534) were aged 21 to 50, while 46% (n = 508) were aged above 50 (Fig. 1a). In our study, 55% (n = 611) of patients were smokers, while 45% (n = 499) were nonsmokers (Fig. 1b). Of the CAD patients, 14% (n = 144) were underweight, 21% (n = 233) were active or normal, 27% (n = 322) were overweight, and 34% (n = 400) were obese (Fig. 1c). More than one third of the patients were married 84% (n = 965), while less than one fourth (13%, n = 145) were unmarried (Fig. 1d). One third (63%, n = 700) of the participants had a positive family history of CAD, while one fourth (37%, n = 410) did not (Fig. 1e). In terms of different risk factors, high blood pressure and diabetes are the most common factors that are associated with CADs. The percentage of different risk factors in our study cohort were as follows; blood pressure (BP) = 31% (n = 344), diabetes = 34% (n = 387), cholesterol = 20% (n = 223), creatinine = 8% (n = 90), kidney problem = 1% (n = 12), others = 5% (n = 54) (Fig. 1f). Amongst the study cohort, 90% (n = 999) were not involved in any physical activity, while 10% (n = 111) were (Fig. 1g). With respects to patient diet, red meat was highly consumed (46%, n = 511), while cereals (8%, n = 89), vegetables (11%, n = 122), and white meat (14%, n = 155) were the least consumed. However, several patients were also found to consume all types of food (21%, n = 233) (Fig. 1h).

More than half of the patients (85%, n = 943) reported suffering from stress, while 15% (n = 167) reported that they had no stress (Fig. 1I). With respects to the incidence of heart diseases, 33% (n = 367) of patients had suffered a heart attack, 8% (n = 89) had suffered stroke, 21% (n = 230) had Angina, 18% (n = 199) had coronary artery disease (CAD), 19% (n = 217) had myocardial infarction (MI), while 0.7% (n = 8) of patients had cardiomyopathy (Fig. 1j). More than one third (70%, n = 777) of patients reported having disruptive sleep, while 30% (n = 333) had regular sleep (Fig. 1k).

Correlation:

Gender and age were found to be negatively correlated (r = − .055, p = .070). The age of diagnosis and gender were also found to have a weak positive correlation (r = .030, p = .206). The gender and BMI were strongly correlated (r = .182, p < 0.01). Family history and gender had no correlation (r = .079, p = 0.09). Gender and the risk factors were found to have no correlation (r = .020, p = .528). Gender and smoking were found to have strong negative correlation (r = − .670, p < 0.01). The gender and exercise were found to have a strong negative correlation (r = − .165, p < 0.01), whereas diet and gender were found to have an s strong positive correlation (r = .31, p < 0.01). There was no correlation between gender and stress (r = .029, p = .39). A negative correlation was observed between sleep and gender (r = − .080, p < 0.01). CAD and gender were found to have no correlation (r = − .055, p = 0.70). Diet and gender also had no correlation (Table 2).

Age of diagnosis and patient age also indicated a resilient positive correlation (r = .212, p < 0.01). Age and BMI were found to have a strong correlation (r = .18, p < 0.01). The age and marital status were found to have a strong positive correlation (r = .67, p < 0.01). Family history and age were also found to have a positive correlation (r = .10, p = .001). Age and the risk factors were found to have strong positive correlation (r = .09, p = .003). Age and smoking were found to have a strong positive correlation (r = .09, p < 0.01). There was no correlation between variable age and exercise (r = .03, p = .19), whereas diet and age were found to have a very strong negative correlation (r = − .19, p < 0.01). Age and stress was positively correlated with each other (r = .54, p < 0.01), even though there was a significant inverse relationship between age and sleep (r = .20, p = .000). CAD and age were found to have a strong direct correlation (r = .82, p = .008 p < 0.01). Diet and age were found to have a negative correlation (Table 2).

The BMI and marital status were found to have a strong positive correlation (r = .265, p = .000). Family history and BMI were also found to have a strong positive correlation (r = .74, p = .016). BMI and the risk factors had no correlation (r = .005, p = .87). BMI and smoking were found to have a negative correlation (r = − .13, p = .00). The variable BMI and exercise had no correlation (r = .005, p = .872), whereas diet and BMI had a negative correlation (r = .58, P < 0.05). A strong positive correlation was found between BMI and stress (r = .20 P < 0.01). Sleep and BMI were found to have a strong negative correlation (r = − .17, p = .000). CAD and BMI were found to have a strong positive correlation (r = .11, p < 0.01) (Table 2).

Family history and Marital status were found to have a moderate positive correlation (r = .113, p = .000). Marital status and the risk factors had a strong positive correlation (r = .16, p=. 000). Marital status and smoking were found to have a strong positive correlation (r = .29, p < 0.01). Variable Marital status and exercise were had no correlation (STAT), while diet and Marital status were found to have a strong negative correlation (r=-.18, P < 0.01). Marital status and stress were found to have a strong positive correlation (r = .69, P < 0.01). Sleep and Marital status were found to have a strong negative correlation (r = .21, p=. 000). CAD and Marital status were found to have a strong positive correlation (r = .031, p < 0.01) (Table 2).

Family history and the risk factors had no correlation (r = .014, p = .67). Family history and smoking were found to have a moderate negative correlation (r = − .068, p = .025). Variable Family history and exercise were found to have a positive correlation (r = .090, p = .003), whereas diet and Family history did not have a correlation. Family history and stress had a correlation (r = .015, p = .000). Sleep and Family history were found to have a moderate negative correlation (r = − .54, p = .097). CAD and Family history were found to have a strong positive correlation (r = .12, p < 0.01) (Table 2).

Risk factors and smoking did not have a correlation (r = .044, p = .9). The variable Risk factors and exercise also had no correlation (r =- .010, p = .76), whereas diet and Risk factors were found to have no correlation (r = .012, p = .71). Risk factors and stress had no correlation (r = .005 p = .8). Sleep and Risk factors were found to have a negative correlation (r = − .07, p = .013). CAD and Risk factors were found to have a strong positive correlation (r = .095, p < 0.01) (Table 2).

Variable diet and Smoking were found to have a strongly negative correlation (r =-.32, p = .00). Smoking and stress had a positive correlation (r = .2, p < 0.01). Sleep and Smoking had no correlation (r = .026, p = .3). CAD and Smoking had a strong positive correlation (r = .16, p < 0.01) (Table 2).

Exercise and diet were found to have a strong negative correlation (r = − .12, p < .001). Exercise and stress had a negative correlation (r = − .05, p < 0.01). Sleep and exercise also had no correlation (r = .41, p = 0.16). CAD and exercise had no correlation (r = .04, p = .16) (Table 1).

Diet and stress had no correlation (r = − .13, p < 0.05). Sleep and Diet had a weak correlation (r = .078, p = .012). CAD and Diet had a strong negative correlation (r = − .17, p = .00) (Table 2).

Variable sleep and stress had a negative correlation (r = − .17, p < 0.01). Stress and CAD had a strong positively correlation (r = .24, p < 0.01) (Table 1). Sleep and CAD had a negative correlation (r = − .11, p < 0.05) as shown in Table 2.

Table 2

Correlations between the different factors of CADs in Pakistani population
	Gender	CAD history	Age	Age of diagnosis	BMI	Marital status	Family history	Risk Factors	Smoking	Exercise	Diet	Stress	Sleep	diet / Meal
Gender	1			^*
CAD history	− .055 .070	1
Age	− .038	.213^**	1
	.008	.000
Age of diagnosis	.030	.082^**	.212^**	1
	.327	.008	.000
BMI	.182^**	.114^**	.189^**	.194^**	1
	.000	.000	.000	.000
Marital status	.001	.314^**	.676^**	.177^**	.265^**	1
	.978	.000	.000	.000	.000
Family history	.079^**	.129^**	.101^**	.005	.074^*	.113^**	1
	.009	.000	.001	.883	.016	.000
Risk factor	.020	.095^**	.096^**	.015	.005	.164^**	.014	1
	.528	.003	.003	.660	.872	.000	.675
smoking	− .670^**	.162^**	.233^**	.093^**	− .136^**	.296^**	− .068^*	.004	1
	.000	.000	.000	.002	.000	.000	.025	.900
	− .165^**	.021	.039	− .010	− .030	.051	.090^**	− .010	.029	1
Exercise	.000	.497	.195	.749	.327	.088	.003	.761	.335
Diet	.313^**	− .171^**	− .192^**	− .200^**	.058	− .184^**	− .034	.012	− .321^**	− .124^**	1
	.000	.000	.000	.000	.061	.000	.277	.716	.000	.000
Sleep	.029	.241^**	.543^**	.147^**	.202^**	.693^**	.005	.150^**	.275^**	− .055	− .136^**	1
	.335	.000	.000	.000	.000	.000	.856	.000	.000	.066	.000
	− .080^**	− .114^**	− .200^**	− .069^*	− .174^**	− .217^**	− .075^*	− .054	.026	.041	.078^*	− .176^**	1
Stress	.008	.000	.000	.024	.000	.000	.013	.097	.389	.168	.012	.000
Diet / meal	.023	− .132^**	− .502^**	− .511^**	− .158^**	− .423^**	.002	− .096^**	− .055	.019	.035	− .282^**	.198^**	1
	.446	.000	.000	.000	.000	.000	.960	.004	.069	.533	.256	.000	.000
**. Correlation is significant at the 0.01 level (2-tailed).
*. Correlation is significant at the 0.05 level (2-tailed).

Expression level of LDLR and PCSK9

The differential expression of LDLR and PCSK9 in coronary artery disease patients was analysed. The Mean ± SD value of PCSK9 = 2.434 ± 0.5336 and LDLR = 4.499 ± 1.149. The CI value of PCSSK9 was 1.328 and LDLR was 2.115. The R value of PCSK9 and LDLR were 0.4862 and 0.4105 respectively. As shown in Fig. 2a, 2b PCSK9 and LDLR were highly expressed in patients than in control group. The results shown in Fig. 2a and Fig. 2b suggested that the expression levels of PCSK9 (P = 0.0002) and LDLR (P = 0.0007) were significantly up-regulated (P < 0.05) in the patient group comparative to the healthy group of people. The χ² tests of PCSK9 and LDLR was performed in the prism 10.

Correlation between expression of LDLR and PCSK9 with demographic characteristics:

The negative correlation showed between gender and PCSK9(p = -0.35). Age and PCSK9 showed positive correlation (p = -0.43). The age of diagnosis and PCSK9 were also found to have a negative correlation (p = -0.40). The PCSK9 and BMI were strongly correlated (p = 0.71). Family history and PCSK9 had negative correlation (p =-0.40). PCSK9 and the risk factors were found negative correlation (p=-0.36). Stress and PCSK9 showed a favorable connection (p = 0.58). There was a found unfavorable relationship between PCSK9 and sleep. CAD and PCSK9 were found to have positive correlation (p = 0.25). Cholesterol and PCSK9 had strong positive correlation (p = 0.37) Fig. 3a.

The strong positive correlation showed between gender and LDLR (p = 0.59). Age and LDLR showed negative correlation (p = -0.39). The age of diagnosis and LDLR were also found to have a weak negative correlation (p = -0.36). The LDLR and BMI were strongly correlated (p = 0.40). Family history and LDLR had negative correlation (p =-0.36). LDLR and the risk factors were found negative correlation (p=-0.36). LDLR and stress showed negative correlation (p = − .35). A non-significant correlation was observed between sleep and LDLR. CAD and LDLR were found to have positive correlation (p = 0.045). Cholesterol and LDLR had strong positive correlation (p = 0.66) Fig. 3b.

ROC analysis of expression of LDLR and PCSK9 in coronary artery disease:

To further evaluate the value expression of LDLR and PCSK9 in CAD patients from normal persons, we performed ROC analysis in CAD patients, and the results were shown in Table 2. The ordinate refers to the sensitivity and the abscissa refers to the specificity. ROC curves were made as Fig. 4a and 4b, the areas under the curve (AUC) were shown in Table 3. The AUC values were tested, and all of them have P > 0.05.

To compare the expression value of LDLR and PCSK9 in CAD patients to non-diseased persons, we conducted ROC analysis in CAD patients, and the findings are presented in Table 3. ROC curve analysis was employed to measure the value of indicators (such as PCSK9 and LDLR expression levels) in detecting each group. ROC curves plot showed the true positive rate against the false positive rate, given that a visual representation. The ordinate denotes the sensitivity, whereas the abscissa represents the specificity. ROC curves were created in Figs. 4a and 4b, and the areas under the curve (AUC) are reported in Table 3. The AUC values of PCSK9(p = 6.58337E-4) and LDLR (p = 0.00111) showed statistically significant.

Table 3

AUC of expression of CAD patients and control group calculated expression of PCSK9 and LDLR area under curve asymptotic prob (p value less than 0.05).
	Area	Std. Error	Asymptotic Prob	95% LCL	95% UCL
PCSK9	0.90972	0.06333	6.58337E-4	0.78559	1.03385
LDLR	0.90152	0.06278	0.00111	0.77847	1.02456

Relationship between the PCSK9 and LDLR in CAD patients Cox- Model survival analysis.

The Cox regression model used to evaluate the relationship between covariates (expression levels of PCSK9 and LDLR) and the hazard rate over time is depicted in Figs. 5A and 5C, respectively. Figures 5B and 5D illustrate the survival rates of patients with high expression levels of PCSK9 and LDLR. The PCSK9 and LDLR survival analysis in CAD patients showed that high expression of LDLR and PCSK9 have significant effect on the overall survival rate, and the differences were statistically significant (both P > 0.05) as shown in Table 4.

Table 4

Cox- Model survival analysis of expression of cad patients and control group
	DF	Estimate	Standard Error	Chi-Square	Pr > ChiSq	Hazard Ratio
PCSK9	1	-0.4975	0.3039	2.6808	0.01016	0.608
LDLR	1	0.1384	0.1552	0.7956	0.03724	1.1485

P value of PCSK9 and LDLR was significant

Cardiovascular diseases (CVDs) are the main cause of mortality globally³². Ischemic heart disease and all types of strokes were estimated to have caused 13 million deaths worldwide in 2010³³. From a CAD point of view, it is predicted that developed societies will experience a reduction in infection-related conditions such as rheumatic heart disease and nutrition deficiency–induced conditions affecting heart muscle in the near future³⁴. Conversely, in developing countries, there is a pronounced increase in age-adjusted incidence of diseases such as CAD induced by changing lifestyles and increasing life expectancies³⁵. There are many types of CVD such as coronary artery disease (CAD), stroke, Myocardial infarction, heart failure, rheumatic, congenital heart disease. In 2011, several risk factors of CVDs such as a sedentary and unhealthy lifestyle, obesity, diabetes mellitus, smoking, family history and stress had already been established³⁶. CVD is an important health distress amongst the aging population. While age is a sovereign risk factor for CAD, other risk factors such as frailty, obesity, and diabetes are strongly associated with aging Resultantly, an increase in body mass index was also observed in such patients³⁷. The prevalence of CAD risk factors, which include smoking, high blood pressure, diabetes, cholesterol, obesity, and diabetes, was 38.2%, 17.4%, 64%, 23.2%, and 22.8%, in that order.

A positive association was observed between family history and CAD. The relationship between a family history of CAD and adopting a healthy lifestyle in relation to perceived risk was not consistent. There is strong epidemiologic evidence for the familial association of CAD³⁹. This Study reported that having CAD in at least one parent doubled the 8- year risk of CAD among men and increased the risk among women by 70%. Risk factors such as age, high-density lipoprotein cholesterol levels, blood pressure, diabetes, BMI, and current smoking status were independent of family history⁴⁰.

In the Ugandan population, normal BMI was observed in 59% of the population, while 23% were overweight and 12% were obese. There was no relation between hypertension, socioeconomic status and smoking, while diabetes was found to be associated with hypertension⁴¹. In the terms of BMI, most of the participants in this study were overweight. A positive correlation was found between CAD and the BP level. Most CAD patients were found to have a high BP level and their lipid profiles were abnormal. Most of the patients had high risk CADs. Patients who ate large amounts of red meat, egg, cheese and oily foods had more low density lipids and triglycerides in their blood, which is a major risk factor associated with CVDs. Conversely, patients who ate fish, chicken, pulse, nuts, fruits, and vegetables in their diet regularly had reduced incidences of CVD’s⁴².

In Asian countries, a major increase in the incidence of diabetes mellitus has been seen (Lim et al., 2015). The risks for stroke and ischemic heart were highest in individuals that had high blood pressure, followed by total cholesterol, obesity, and those who smoked⁴³.

Each lifestyle factor was associated with CAD independently⁴⁴. An inverse association was observed between CVD, CVD medical history and diabetes mellitus⁴⁵.

In the Canadian population, obesity and being overweight are the main risk factors associated with CVDs. The risk of diabetes was increased by 61% among obese individuals and 25% in overweight individuals. More of the risk of hypertension was increased by 80% among obese individuals and 74% in overweight individuals⁴⁶. Depression and CVD’s have a bidirectional relationship⁴⁷. Stressed patients are at a higher risk of cardiac morbidity and mortality⁴⁸.

In South Indian adult’s dyslipidaemia’s is a major risk factor of cardiovascular which has also been highly associated with increased BMI⁴⁹. Similar results were observed in our study. Increased BMI, TC, LDL-C and HDL-C were significant CAD risk factors observed in our study. Similar results have been observed in the people of Karachi Pakistan, where there is a high prevalence of obesity⁵⁰.

In a previous study it was well documented that smoking and high BMI significantly increased the risk of cholesterol and blood pressure⁵¹. Individuals aged 45 or over had the highest levels of cholesterol and diabetes (p < 0.05), therefore hypertension and high cholesterol are the major risk factor of CVDs⁵².

Hypertension and diabetes mellitus had the highest prevalence in both males (2.2%) and females (2.9%). Other diseases such as angina and coronary artery disease were also found in both genders⁵³. The Korean population showed that 61% CAD patients have cholesterol, 15% have positive family history and 21.6% were smokers⁵⁴. The depression and CAD have positive relation. The prevalence of CAD id higher in females rather than males⁵⁵.

PCSK9 inhibition decreases the menace of CHD patients with a high plasma Lp(a) level as compared to CHD patients who has a low level of Lp(a) in plasma 23% vs. 7% respectively⁵⁶.

Therefore, PCSK9 is a auspicious therapeutic target to diminish the risk of the two most important causes of death rate worldwide, cancer and heart disease. Various methods were developed to deter PCSK9⁵⁸. PCSK9 as an enzyme implicated in lipid metabolism and is a substantial genetic risk factor in circulatory diseases amongst many populations⁵⁷.

PCSK9 inhibitors can considerably decrease the LDL cholesterol levels and heart disease events in patients with hypercholesterolemia⁵⁸. The level of PCSK9 was higher as compared to the normal person set when modifying for the perplexing aspects⁵⁹. The level of PCSK9 were positively correlated over-all cholesterol at baseline (all p < 0.05). The level of PCSK9 was greater in patients with heart disease occasions as compared to normal (p < 0.05)⁶⁰. any recent research have demonstrated that PCSK9 can either directly or indirectly contribute to the development of CHD from the beginning to the prognosis by maintaining the inflammatory response, causing endothelial dysfunction, and inhibiting platelet activation, which is a separate component in the control of lipid metabolism.^61,62,63. Human PCSK9 markers evidently lessen the LDL-C levels and showed a decrease in upcoming cardiac events⁶⁴. The low level of PCSK9 plasma levels in stable CAD patients were related with low cholesterol, obesity, insulin resistance, metabolic syndrome, coronary artery disease in addition to diabetes⁶⁵. CAD patients had considerably greater PCSK9 levels (z = 4.559, p < 0.001). The demographic characteristics like age, gender, lipid profiles, hypertension, smoking, diabetic mellitus, and PCSK9 levels endure significantly associated with increased CAD predisposition (OR = 1.002, 95% CI = 1.001–1.002, P < 0.001)⁶⁶. In addition to age, BMI, gender, and HDL-C, were autonomously correlated with the presence of CAD by a multivariable analysis. In conclusion, our results demonstrated that PCSK9 levels might be a marker for assessing the occurrence of CAD⁶⁷. Patients who have LDLR/PCSK9 gene variants were recognised 6% in the study. LDL-C had higher plasma levels (P = 0.04) than those have LDLR gene variants⁶⁸. In Cox regression model, they practised a higher prevalence of nonfatal myocardial infarction hazardous ratio as compared to LDLR patient’s gene variant⁶⁹.

In conclusion, CAD is a major health problem amongst the people of Punjab, and certainly most of the Pakistani population. This study showed that a sedentary lifestyle, family history of disease, other the presence of other diseases (high BP, cholesterol, Diabetes, hypertension) could be the major risk factors of CAD. The prevalence of CAD was more common in males than females. Greater awareness and healthier food choices should be promoted among the population. There is also a need for better education will which will help individuals adapt to challenges. This research provides a baseline study for future survey projects in this area. Pakistan is currently going through economic difficulties. The poverty rate is remarkably high which has increased the incidence of depression and heart problems. By understanding the socioeconomic factors, better healthcare provisions may be introduced.

This study encompasses acquaintance of the relationships of expression of PCSK9 and LDLR with coronary artery disease in patients. Based on the present results, it can be ventured that there could be an association between PCSK9 and low density lipoprotein LDLR in relationship to a particular phenotype (Age, cholesterol, BMI) and the susceptibility of development of the coronary artery disease. Further studies are needed to confirm this association and discover its possible predictive implications. Clinically, the ground-breaking information assembled on the interaction of expression of PCSK9 and LDLR gene variants with individual who has CAD phenotype influence might be beneficial to target individual handling and treatment in the perspective of a modified medicine approach.

Acknowledgment:

The authors are thankful to Division of life science, Hong Kong University of science and technology, Clear water Bay, Hong Kong, China for permitting the use of resources for experimental persistence and analysis. The authors would also like to extend their sincere appreciation to the Researchers Supporting Project Number (RSP2024R350), King Saud University, Riyadh, Saudi Arabia.

Funding:

Researchers Supporting Project Number (RSP2024R350), King Saud University, Riyadh, Saudi Arabia.

Author Contribution:

A.Z conducted research and wrote up the initial draft of the manuscript. A.B gave the concept of research work. A.W supervised during research work. A.U., and M.A.K. made figures and carried out analysis. A.A and A.U. make final corrections in the manuscript. All authors reviewed and approved manuscripts for publication.

Conflicts of Interest

The authors have declared that no competing interests exist.

Ethical approval:

The ethical letter of this research work available. We have obtained consent form all patients and their legal guardians to use this data for research purposes.

Data availability

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

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No competing interests reported.

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WITHDRAWN: Deciphering PCSK9 and LDLR Gene Expression in Coronary Artery Disease Patients: Implications for Blood Lipid Regulation

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Editorial Note

Abstract

Figures

INTRODUCTION

MATERIALS AND METHODS

Ethics approval of the study

Criteria of inclusion and exclusion of patients

Demographic data collection:

Blood sample collection:

RNA Extraction

Real-time quantitative PCR (qRT-PCR):

Statistical analysis:

RESULTS

Correlation:

Expression level of LDLR and PCSK9

Correlation between expression of LDLR and PCSK9 with demographic characteristics:

ROC analysis of expression of LDLR and PCSK9 in coronary artery disease:

DISCUSSION

Conclusion

Declarations

References

Additional Declarations

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