The importance of CYP1A2 genetic polymorphism in relation to coffee intake and high blood pressure in the Romanian population

doi:10.21203/rs.3.rs-4963863/v1

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Research Article

The importance of CYP1A2 genetic polymorphism in relation to coffee intake and high blood pressure in the Romanian population

https://doi.org/10.21203/rs.3.rs-4963863/v1

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Cytochrome P450 1A2 (CYP1A2) is known to be the main enzyme directly responsible for caffeine metabolism. The rs762551 (NC_000015.10:g.74749576C > A) is a single nucleotide polymorphism characterized by higher enzyme activity in the presence of an inducer such as heavy coffee consumption and its protective role against developing cardiovascular diseases, including high blood pressure. In this comprehensive study, we aim to evaluate any association between coffee intake, CYP1A2 polymorphism, the presence of high blood pressure, and their potential role in cardiovascular outcomes. For 355 individuals, a PCR technique was performed using TaqMan SNP genotyping Assay, analyzing rs762551 on LightCycler 480 (Roche) with Gene Scanning software version 1.5.1 (Roche). Using binomial logistic regression, we found that the individuals who consumed more than three cups of coffee per day are less likely to fit in the slow coffee metabolizers category with an odds ratio of 0.386 and p = 0.033. Another important finding would be the fact that the fast coffee metabolizers (AA genotype) consume larger coffee quantities, although they present the lowest tensional values and the highest values for cholesterol and glucose. When approaching the hypertensive subjects, they show lower coffee consumption and lower caffeine levels, compared to the control group. The main conclusion of this study would be that only coffee intake and genotype are statistically related, although no statistically significant associations regarding the high blood pressure have been found.

CYP1A2

rs762551

high blood pressure

hypertension

CYP1A2 polymorphism

coffee intake

caffeine

Around one billion people globally are impacted by elevated blood pressure, also called hypertension (1), which makes it a major global health problem. (2) Among other risk factors, it can lead to stroke, cardiovascular disease, kidney disease, and other conditions that can cause mortality. (3) In turn, sedentariness, excess body mass index (BMI), a high intake of salt, and an unhealthy lifestyle play a significant role in the onset of hypertension. (4, 5)

Meanwhile, besides water, coffee is one of the most popular beverages in the world (6), with an estimated 2.25 billion cups worldwide consumed daily (7); most likely, this is due to its effect in stimulating the central nervous system and its flavor. (8) Coffee is, in fact, a mixture of more than 800 volatile compounds (8) along with the secondary metabolites; the bioactive components include phenolic compounds (chlorogenic acids, cafestol, and kahweol), alkaloids (caffeine and trigonelline), diterpenes (cafestol and kahweol). (9, 10)

Cytochrome P450 1A2 (CYP1A2) is the primary enzyme responsible for caffeine metabolism (11) and other drugs. Encoded by the CYP1A2 gene, the CYP1A2 enzyme is in control of metabolizing over 95% of ingested caffeine by demethylating caffeine into the primary metabolites. (12)

CYP1A2 -163C > A (rs762551) is a single nucleotide polymorphism (SNP) encoding the CYP1A2*1F allele of the CYP1A2 gene. (13) There are three genotypes: the A/A homozygotes, characterized by higher enzyme activity, also known as the fast caffeine metabolizers; the A/C heterozygotes or the intermediate metabolizers; and the C/C homozygotes, generally known as the slow caffeine metabolizers, characterized by lower enzyme activity. (14) Several studies show that genetic variants affect caffeine metabolism and coffee consumption. (15) Numerous researches indicate that individuals who drink coffee and have common genetic polymorphisms influencing caffeine metabolism may face an increased risk of developing cardiovascular disease. (16) Due to a decreased enzyme action, polymorphism at CYP1A2 rs762551 had been related to impaired caffeine metabolism. (17)

The risk of high blood pressure associated with coffee intake, based on the CYP1A2 rs762551 variant, is widely variable amongst individuals. (18) Regularly, blood pressure increases shortly after coffee or caffeine consumption. (16, 19) Thus, resistance to cardiovascular effects has been observed in some individuals even after acute coffee consumption (20). Still, there is no proof that coffee or caffeine consumption over a long period can determine the appearance of high blood pressure. (21)

According to Timiș County Public Health, the 2016 SEPHAR III study showed that 45.1% of Romanian adults had high blood pressure, translating to about 7.4 million people. High blood pressure is the leading risk factor for cardiovascular diseases. Locally, in Timiș County, in the past three years, the prevalence of arterial hypertension registered at the family physicians’ office has decreased substantially. The number of patients remaining on record in 2016 and 2017 exceeded 9,000 per 100,000 inhabitants, while between 2018 and 2020, the value was around 8,000 per 100,000 inhabitants. (22)

This research aims to determine whether there is any association between coffee intake, high blood pressure, and genotype in an adult population from Timis County, Romania. No other research has been conducted on the Romanian population's CYP1A2, coffee intake, and hypertension.

Given the presented population data, we have calculated the sample size for this study to meet the criteria of 95% confidence level, 5% margins of error, and 50% population proportion using an online sample size calculator. (23)

A total of 355 subjects were enrolled in this study; 238 subjects were diagnosed with hypertension, and 117 represented the control group. Participants belong to the general population of Timișoara who voluntarily participated in this project. This research is part of a much larger study in which patients suffering from obesity, diabetes, metabolic syndrome, or cardiovascular diseases were included. The former research aimed to determine the relationship between different genetic polymorphisms and obesity involving a vast number of subjects (over 1000), adults, and children from the general population of Timisoara.

The inclusion criteria for this research were 1) the subjects had to be over 18 (adults); 2) the subjects from the hypertensive group should have been diagnosed with chronic hypertension; 3) the subjects representing the control group should not be suffering from cardiovascular diseases, including hypertension. For the present study, we selected only the subjects who met our criteria, and we divided them by being hypertensive and non-hypertensive for a better approach.

A board-certified cardiologist diagnosed each hypertension patient, and the medical data was submitted to this study. The blood tests, including the biochemical analysis and genetic testing, were also done in an accredited analysis laboratory. When enrolled in the project, the subjects provided registered medical data, whose confidentiality was preserved.

The present study has a descriptive design, represented by a case series of selected patients for whom we have analyzed the most relevant parameters of this research: coffee intake, genotype, and the documented presence of cardiovascular diseases.

With the help of the research members, each patient filled out a weekly food and beverage inquiry for one month. The amount of caffeine consumed was calculated using the Nutrio web application (Naturalpixel SRL, Bucharest, Romania, https://nutritioapp.com) for all the participants.

Each participant's declaration of the amount of food and drinks consumed daily was converted into energy, macro-, and micro-nutrients. Using this application and the personal inquiry, all patients' caffeine intake was brought to the same level, and the quantity was adjusted. As expected, the primary sources of caffeine were beverages such as coffee, tea, and chocolate.

After being quantified, the coffee consumption has been divided into three groups: low coffee intake (< 1 cup per day), moderate coffee intake (1–3 cups per day), and high coffee intake (> 3 cups per day). One cup of coffee is considered the equivalent of 70 ml.

All participants provided informed consent, and the study received approval from the Ethics Committee of the Medical Faculty at Victor Babeș University (ethical number: 86/2020). The personal data and medical findings were kept confidential.

2.1 Genotyping

We collected two ml of peripheral blood with each patient's consent, extracted DNA, and performed PCR. Automatic genomic DNA extraction using a MagCore extractor, according to the manufacturer’s procedures for the MagCore Nucleic Acid Extraction Kit (RBC Bioscience, New Taipei City, Taiwan), has been performed. Isolated DNA concentration has been measured using an Epoch Microplate Spectrophotometer (Agilent BioTek EPOCHSN). For the present study, we have analyzed one SNP in the CYP1A2 gene, namely rs762551 (assay C_8881221), because, according to previous studies, it is the variant primarily associated with caffeine metabolization and hypertension. To create the PCR mix, we used TaqMan Drug Metabolism Genotyping Assays (Applied Biosystems) and TaqMan Genotyping Master Mix (Applied Biosystems) according to manufacturer protocol. LightCycler 480 (Roche) with Gene Scanning software version 1.5.1 (Roche) was used to amplify the DNA in a PCR reaction. Two different laboratory personnel performed the genotyping technique in a double-blinded manner. Ten percent of the samples were randomly selected for quality control purposes; the genotyping of these samples was repeated with 100% reproducibility.

2.2 Statistical analysis

Continuous variables following a normal distribution were presented as means with standard deviation (SD), while non-normally distributed data were presented as medians with interquartile range (IQR). The normality of the distribution was evaluated using the Shapiro-Wilk test. Differences among groups for normally distributed continuous data were assessed using Welch’s t-test for two groups or ANOVA for more than two groups. Post hoc analyses, when necessary, were performed using the Bonferroni correction to adjust for multiple comparisons. For non-normally distributed continuous data, the Mann-Whitney U test and the Wilcoxon signed-rank test were used for two-group comparisons. In contrast, the Kruskal-Wallis test was applied for comparisons involving three or more groups. Categorical data were analyzed using the χ2 test or Fisher’s exact test, mainly when expected cell counts were below five. Categorical data were reported as frequencies (n) and percentages (%). A prior power analysis was made with at least 80% statistical power with a 95% confidence interval. All statistical analyses were performed using R Studio version 3.6.0, which used the packages stats, dplyr, coin, multcomp, and pwr.

3.1. Descriptive Statistics

Our study sample includes 355 patients with an average age of 53.2(12) years, of whom 173 (48.7%) are men. The average coffee consumption was 129.5 (117.3) ml, while the average caffeine consumption was 99.4 (115.5) mg. 197 participants (55.5%) had one to three cups of coffee, 105 participants (29.6%) have not consumed coffee at all or consumed it very rarely, while 53 participants (14.9%) consumed three or more cups of coffee per day. The genotype distribution of CYP1A2 (rs762551) is represented by 63 participants (17.7%) with the AA genotype, 162 participants (45.6%) with the AC genotype, and 130 participants (36.6%) with CC. Hypertension was diagnosed in 238 (67.0%) patients, and 236 (66.5%) were under treatment with antihypertensive medication. The number of obese patients was 281 (79.2%), and the mean cholesterol value was 313.7 (185.9) mg/dl. Table 1 represents the general characteristics of our group of patients.

Table 1

Baseline characteristics of study participants
Variable	Overall (N = 355)
Demographics
Mean age (SD) (years)	53.24 (12.01)
Gender: Female (%)	182 (51.3)
Gender: Male (%)	173 (48.7)
Coffee mean (SD) (ml)	129.50 (117.33)
Coffee Cups (%)
0 cups	105 (29.6)
1–3 cup	197 (55.5)
>3 cups	53 (14.9)
Caffeine mean (SD) (mg/dl)	99.42 (115.48)
Genotype CYP1A2 rs762551 (%)
AA	63 (17.7)
AC	162 (45.6)
CC	130 (36.6)
Health Indicators
Patients with hypertension (%)	238 (67.0)
Patients without hypertension (%)	117 (33)
Blood glucose mean (SD) (mg/dl)	159.62 (68.14)
Obese patients (%)	281 (79.2)
Patients under HTN treatment (%)	236 (66.5)
Cholesterol mean (SD) (mg/dl)	313.78 (186.95)

Table 2

Demographic and Health Comparisons of Hypertensive and Non-Hypertensive Individuals
	Normotensive (N = 117)	Hypertensive (N = 238)	Total (N = 355)	p-value
Age				0.353¹
Mean (SD)	52.4 (12.8)	53.7 (11.6)	53.2 (12.0)
Range	20.0–71.0	22.0–69.0	20.0–71.0
Gender				0.113²
F	67.0 (57.3%)	115.0 (48.3%)	182.0 (51.3%)
M	50.0 (42.7%)	123.0 (51.7%)	173.0 (48.7%)
Coffee(ml)				0.145¹
Mean (SD)	142.4 (120.3)	123.1 (115.6)	129.5 (117.3)
Range	0.0–585.0	0.0–992.0	0.0–992.0
Caffeine				0.783¹
Mean (SD)	101.9 (128.4)	98.2 (108.9)	99.4 (115.5)
Range	0.0–1000.9	0.0–785.5	0.0–1000.9
Coffee cups				0.212²
0	32.0 (27.4%)	73.0 (30.7%)	105.0 (29.6%)
1–3	62.0 (53.0%)	135.0 (56.7%)	197.0 (55.5%)
>3	23.0 (19.7%)	30.0 (12.6%)	53.0 (14.9%)
Cholesterol				0.039¹
N-Miss	2.0	1.0	3.0
Mean (SD)	343.4 (213.9)	299.4 (171.0)	313.8 (186.9)
Range	22.7–1473.1	39.0–1067.0	22.7–1473.1
Obesity				0.066²
No	31.0 (26.5%)	43.0 (18.1%)	74.0 (20.8%)
Yes	86.0 (73.5%)	195.0 (81.9%)	281.0 (79.2%)
Glucoza [mg / dl]				0.020¹
Mean (SD)	147.6 (59.8)	165.5 (71.3)	159.6 (68.1)
Range	82.0–440.0	70.0–700.0	70.0–700.0
Genotip CYP1A2 rs762551				0.471²
AA	20.0 (17.1%)	43.0 (18.1%)	63.0 (17.7%)
AC	49.0 (41.9%)	113.0 (47.5%)	162.0 (45.6%)
CC	48.0 (41.0%)	82.0 (34.5%)	130.0 (36.6%)
¹= Linear Model ANOVA
²= Pearson's Chi-squared test

Table 2 represents the comparison between the study and the control group regarding demographic and health parameters. There was no significant difference in mean age between the hypertension group (p = 0.3531). Slightly more women were found in the control group, but this was not statistically significant (p = 0.1132). No significant differences were also found between the groups regarding coffee consumption (ml) or caffeine intake. There was a statistically significant difference in mean cholesterol levels (p = 0.0391), with the control group having higher mean values. Obesity was more common in the hypertensive group (195, 81.9%) than in the control group (86, 73.5%), but this was not statistically significant (p = 0.0662). A statistically significant difference was found in mean glucose levels (p = 0.0201), with hypertensive patients having higher mean glucose levels (165.5 [71.3]) than patients without hypertension (147.6 [59.8]).

Associations of the following variables: genotype- coffee intake (p = 0.703), genotype- gender (p = 0780), genotype- obesity (p = 0.990), genotype- cholesterol (p = 0.147), genotype- hypertension (p = 501) provided no significant statistical differences.

Table 3

Demographic and medical parameters according to genotypes
	AA (N = 61)	AC (N = 161)	CC (N = 127)	Total (N = 349)	p value
Gender					0.780¹
F	33.0 (54.1%)	83.0 (51.6%)	62.0 (48.8%)	178.0 (51.0%)
M	28.0 (45.9%)	78.0 (48.4%)	65.0 (51.2%)	171.0 (49.0%)
Caffeine					0.703²
Mean (SD)	106.0 (121.2)	102.5 (127.5)	92.9 (96.9)	99.6 (115.9)
Range	0.0–581.4	0.0–1000.9	0.0–641.4	0.0–1000.9
Coffee quantity (ml)					0.080²
Mean (SD)	153.7 (145.9)	133.6 (117.4)	113.7 (101.2)	129.9 (118.0)
Range	0.0–992.0	0.0–585.0	0.0–506.0	0.0–992.0
Coffee cups					0.172¹
0	12.0 (19.7%)	47.0 (29.2%)	44.0 (34.6%)	103.0 (29.5%)
1–3	36.0 (59.0%)	88.0 (54.7%)	69.0 (54.3%)	193.0 (55.3%)
>3	13.0 (21.3%)	26.0 (16.1%)	14.0 (11.0%)	53.0 (15.2%)
Age					0.089²
Mean (SD)	50.2 (14.1)	53.5 (11.6)	54.3 (11.2)	53.2 (12.0)
Range	20.0–69.0	22.0–71.0	25.0–69.0	20.0–71.0
Obesity					0.990¹
No	13.0 (21.3%)	33.0 (20.5%)	26.0 (20.5%)	72.0 (20.6%)
Yes	48.0 (78.7%)	128.0 (79.5%)	101.0 (79.5%)	277.0 (79.4%)
Cholesterol					0.147²
Mean (SD)	347.0 (233.7)	294.7 (168.3)	321.7 (183.1)	313.7 (187.0)
Range	41.4–1414.9	22.7–930.6	84.6–1473.1	22.7–1473.1
Glucose [mg/dl]					0.705²
Mean (SD)	166.2 (94.6)	158.7 (66.6)	157.6 (54.7)	159.6 (68.4)
Range	91.0–700.0	70.0–440.0	82.0–310.0	70.0–700.0
HTN					0.358¹
No	27.0 (44.3%)	57.0 (35.4%)	43.0 (33.9%)	127.0 (36.4%)
Yes	34.0 (55.7%)	104.0 (64.6%)	84.0 (66.1%)	222.0 (63.6%)

Table 3 represents the shares for each of the three genotypes in terms of demographic parameters, coffee consumption, obesity, cholesterol, glucose, and the presence of hypertension. No significant statistical differences were found between the three genotypes, although it has been observed that the AA genotype tend to consume larger quantities of coffee than the other, while the lowest amount is consumed by the CC genotype. The AA genotype patients also present the highest level of cholesterol and glucose. Regarding the presence of hypertension, statistics show that the CC genotype presents the highest tensional levels whereas the AA genotype presents the lowest.

Table 4

Matrix Correlation of Age, Coffee Consumption, Caffeine Intake, Glucose Levels, and Cholesterol Levels
Correlation Matrix
		Age	Coffee(ml)	Caffeine	Glucose [mg/dl]	Cholesterol
Age	Pearson's r	—
	df	—
	p-value	—
Coffee(ml)	Pearson's r	-0.094	—
	df	353	—
	p-value	0.077	—
Caffeine	Pearson's r	-0.112	0.303	—
	df	350	350	—
	p-value	0.035	< .001	—
Glucoză [mg / dl]	Pearson's r	0.318	-0.097	-0.007	—
	df	353	353	350	—
	p-value	< .001	0.069	0.903	—
Cholesterol	Pearson's r	-0.172	-0.030	0.021	-0.114	—
	df	350	350	347	350	—
	p-value	0.001	0.577	0.694	0.032	—

Table 4 shows how coffee intake, caffeine, glucose and cholesterol are related to each other.

Age:

Coffee(ml): There is a weak, non-significant negative correlation between age and coffee consumption (Pearson's r = -0.094, p = 0.077).
Caffeine: There is a weak, significant negative correlation between age and caffeine intake (Pearson's r = -0.112, p = 0.035).
Glucose [mg/dl]: There is a moderate, significant positive correlation between age and glucose levels (Pearson's r = 0.318, p < 0.001).
Cholesterol: There is a weak, significant negative correlation between age and cholesterol levels (Pearson's r = -0.172, p = 0.001).

Coffee(ml):

Caffeine: There is a moderate, significant positive correlation between coffee consumption and caffeine intake (Pearson's r = 0.303, p < 0.001).
Glucose [mg/dl]: There is a weak, non-significant negative correlation between coffee consumption and glucose levels (Pearson's r = -0.097, p = 0.069).
Cholesterol: There is a very weak, non-significant negative correlation between coffee consumption and cholesterol levels (Pearson's r = -0.030, p = 0.577).

Caffeine:

Glucose [mg/dl]: There is a very weak, non-significant negative correlation between caffeine intake and glucose levels (Pearson's r = -0.007, p = 0.903).
Cholesterol: There is a very weak, non-significant positive correlation between caffeine intake and cholesterol levels (Pearson's r = 0.021, p = 0.694).

Glucose [mg/dl]:

Cholesterol: There is a weak, significant negative correlation between glucose levels and cholesterol levels (Pearson's r = -0.114, p = 0.032).

Table 5

Binomial logistic regression between coffee intake, hypertension and genotype
Model Coefficients - genotype
						95% Confidence Interval
Predictor	Estimate	SE	Z	p	Odds ratio	Lower	Upper
Intercept	2.194	0.379	5.791	< .001	8.967	4.268	18.840
Coffee cups:
1–3–0	-0.619	0.356	-1.738	0.082	0.538	0.268	1.082
> 3–0	-0.951	0.445	-2.137	0.033	0.386	0.162	0.924
Hypertension:
Yes – No	-0.205	0.305	-0.672	0.502	0.815	0.448	1.481
Note. Estimates represent the log odds of "rs = 1 (AC + CC)" vs. "rs = 0 (AA)"

Table 5 shows the impact of coffee consumption and hypertension on the genotypes, with the mention that rs = 1 represents the genotypes AC and CC, or the slow metabolizers, while rs = 0 represents the genotype AA, or the fast metabolizers.

> 3 cups of coffee versus 0 cups: Consuming more than 3 cups of coffee per day further decreases the odds of being a slow coffee metabolizer (AC or CC genotype), with an odds ratio of 0.386 (a 61% decrease in odds). This effect is statistically significant (p = 0.033).

This study aimed to establish whether there is any correlation between the CYP1A2 polymorphism, coffee intake, and high blood pressure in a Romanian population.

Since the number of patients suffering from stroke, cardiovascular surgery, acute myocardial infarction, exertional angina, sleep apnea, or other cardiovascular diseases was deficient, they were not included in the statistical analysis, and we chose to focus our analysis solely on the high blood pressure (hypertension) correlation.

It is already known that caffeine is acutely increasing the blood pressure (25). This association between coffee intake and rising blood pressure has been observed in cross-sectional analyses, strongly suggesting self-regulation behavior. To support an average blood pressure level, each individual is adjusting the coffee intake to maintain biological exposure to caffeine (34). Previously reported in other studies (26, 34), self-regulation of behavior is seen in genetic variants involved in caffeine metabolism, such as CYP1A2 rs762551. The fast metabolizers are known to drink higher amounts of caffeine, while the slow or intermediate metabolizers tend to drink lower amounts. Our study obtained similar results; fast caffeine metabolizers tend to consume higher amounts of caffeine, compared to the slow metabolizers.

Coffee's cardioprotective role could be attributed to its components, which present antioxidant and anti-inflammatory actions (28), possibly acting through epigenetic mechanisms.

An increased CYP1A2 enzyme activity is known to be associated with coffee consumption (25). Also, the association between hypertension and coffee intake varies according to CYP1A2 genotypes (18). A study among the Italian population has proven that moderate and heavy coffee consumption by individuals carrying the C allele is more vulnerable to developing hypertension (18). On the other hand, the Swedish population carrying the A allele and being high caffeine consumers were presenting lower blood pressure and a lower risk of developing hypertension than the C allele carriers (29). In our reaserch, similar results have been found, respectively, the AA genotype subjects tend to drink the highest amount of coffee and yet they present the lowest blood pressure levels; and on the other pole, the CC genotype subjects consume the lowest quantity of coffee and present the highest blood pressure level.

Among other functions, the CYP1A2 gene is also expressed in endothelial cells and has a protective role on reactive oxygen species (30). When reactive oxygen levels are increased, nitric oxide (NO) production decreases by changing the endothelial NO synthase (eNOS) activity (31, 32). As a result, low CYP1A2 activity impacts slow caffeine metabolizer individuals, exposing them to higher levels of reactive oxygen and lower eNOS activity followed by impaired endothelium-dependent vasodilation (33). Following this lead, I. Guessous et al. found that slow caffeine metabolizer individuals, AC individuals, showed higher blood pressure values than fast caffeine metabolizer individuals, respectively, AA genotype (34). In our study, we have found similar associations; respectively, the CC genotype or the slow caffeine metabolizers have presented the highest blood pressure values.

Although some studies demonstrate a directly proportional association between coffee intake and high blood pressure (24, 34) and others show an inverse association between the two (36, 37, 38), our study shows that the hypertensive subjects consumed larger quantities of coffee, respectively a slightly higher caffeine intake, than the control group.

In a study evaluating coffee drinking's associations with mortality by genetic caffeine metabolism score, Loftfield et al. (40) affirmed that common genetic polymorphisms predisposing individuals to slower or faster caffeine metabolism could not influence coffee drinking. The present study shows a difference in coffee intake according to the three genotypes, although there is no statistical difference.

Regarding the association between gender and blood pressure, Yao C et al. observed that the men enrolled in their study had higher systolic and diastolic blood pressure levels than women, but there were no statistically significant differences. (41) Although the proportion of men and women was almost similar in our study, we could not find any statistical differences between genders in association with high blood pressure.

The average age of our group is 53 years old, and the subjects are divided almost equally into two groups- above and below average: 69.1% of the patients above the average presented hypertension, and in the patients below average case who presented hypertension, the percentage was 63.0%. Even though the process of aging is associated with the weakening of cardiovascular function (41), we could not find any statistical differences between the subjects above and below the average in association with high blood pressure. Therefore, we could not prove that population aging is an influential factor.

Previous studies discovered that coffee intake is related to the CYP1A2 polymorphism; namely, the C allele carriers or the slow metabolizers were associated with lower quantities of coffee than other genotypes (16), indicating that caffeine metabolism and blood pressure levels are strongly related. We have obtained similar data regarding the coffee intake; respectively, the AA genotype consumed the highest amount of coffee yet, they presented the lowest tensional values, while the CC genotype subjects consumed the lowest quantity of coffee and presented the highest tensional values .

Previous studies (18, 42) demonstrate that the effect of habitual coffee intake on cardiovascular pathologies, including myocardial infarction and hypertension, is modulated by the CYP1A2 rs762551 variant. However, Chien-Chou Hou et al. have not found any statistical association between CYP1A2 rs762551 and the risk of hypertension. (17) The study that we conducted shows an interesting finding that the subjects who consumed over 3 cups of coffee per day are less likely to belog to the slow metabolizers genotypes, respectively AC or CC.

Because this study is encountering caffeine measured through a daily recall questionnaire, a lack of accuracy might occur. Even though a specialist statistician calculated the caffeine levels, the patients' data might differ. For further investigation, we plan to use a caffeine concentration dosage from saliva or serum for a more accurate measurement.

One of the present study's strengths would have been the ability to provide each patient with biochemical and genetic testing to place the genetic results into a broader context.

The primary limitation of this study is the relatively modest sample size. Although 355 individuals might meet the statistical criteria for validating this study, it is still a small number to generalize the results to the whole population.

Another limitation would be the absence of a similar survey on the Romanian population to compare the results. In addition, since the caffeine measurement was based on a questionnaire, a caffeine dosage test from saliva would have provided results that were more accurate.

In summary, the data presented in this study produced evidence of a statistical association between coffee intake and genotype; nevertheless, we could not establish a statistical difference between coffee, hypertension, and the three CYP1A2 rs762551 polymorphisms, as we expected.

Our results indicate that caffeine intake does not influence blood pressure in patients regardless of the CYP1A2 rs762551 polymorphism in the Romanian population.

Author Contributions: Conceptualization, L.C.P. and N.I.A.; methodology, N.I.A.; software, S.S.F.; validation, N.I.A. and S.S.F.; formal analysis, L.C.P.; investigation, L.C.P.; resources, N.I.A.; data curation, S.S.F.; writing—original draft preparation, L.C.P.; writing—review and editing, N.I.A.; visualization, L.C.P.; supervision, N.I.A.; project administration, S.S.F.; funding acquisition, N.I.A. All authors have read and agreed to the published version of the manuscript.

Funding: We would like to acknowledge VICTOR BABES UNIVERSITY OF MEDICINE AND PHARMACY TIMISOARA for their support in covering the costs of publication for this research paper.

Institutional Review Board Statement: This study was conducted according to the Declaration of Helsinki and approved by the Ethics Committee of Victor Babeș University of Medicine and Pharmacy (86/2020, from 13/03/2013).

Informed Consent Statement: Informed consent was obtained from all subjects involved in the study, and written informed consent was obtained from the patients to publish this paper.

Data Availability Statement: Data is contained within the article or supplementary material.

Acknowledgments: We would like to acknowledge VICTOR BABES UNIVERSITY OF MEDICINE AND PHARMACY TIMISOARA for covering the costs of publishing this research paper.

Conflicts of Interest: The authors declare no conflicts of interest.

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The importance of CYP1A2 genetic polymorphism in relation to coffee intake and high blood pressure in the Romanian population

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Abstract

1. Introduction

2. Materials and Methods

2.1 Genotyping

2.2 Statistical analysis

3. Results

3.1. Descriptive Statistics

4. Discussion

5. Conclusions

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