European Journal of Cardiovascular Medicine

Hypertension remains one of the most prevalent chronic diseases worldwide, contributing substantially to cardiovascular morbidity and mortality. The World Health Organization estimates that approximately 1.3 billion adults globally are hypertensive, with nearly half remaining undiagnosed or inadequately treated. In India, community-based surveys have reported hypertension prevalence rates of 27–30 percent among adults, with control rates below 20 percent despite the wide availability of antihypertensive medications. These figures reflect both the growing disease burden and the limitations of conventional treatment approaches that rely on empirical drug selection rather than individualized therapy.

A major challenge in hypertension management is the wide interindividual variability in therapeutic response. Conventional regimens often involve a trial-and-error process before achieving optimal blood-pressure control, which can delay effective management, increase healthcare costs, and reduce adherence [1]. Such variability is partly attributed to genetic polymorphisms influencing drug metabolism, receptor function, and transporter activity. These genetic differences alter the pharmacokinetics and pharmacodynamics of commonly used antihypertensive agents, such as beta-blockers, ACE inhibitors, and angiotensin-receptor blockers [2]. Advances in pharmacogenomics have elucidated several key genetic variants that govern antihypertensive drug response. Polymorphisms in CYP2D6 affect the metabolism of beta-blockers such as metoprolol, determining plasma concentrations and side-effect profiles. Variants in ADRB1, which encodes the β₁-adrenergic receptor, influence receptor sensitivity and thereby modify the blood-pressure-lowering response [3]. Similarly, the AGT M235T variant modulates the renin–angiotensin–aldosterone pathway, impacting responsiveness to ACE inhibitors and ARBs. Collectively, these genetic markers provide a strong biological rationale for genotype-guided therapy, wherein treatment is tailored to the patient’s pharmacogenetic profile to optimize efficacy and minimize adverse effects [4].

Several studies conducted under international consortia, such as the Pharmacogenomic Evaluation of Antihypertensive Responses (PEAR) and INVEST-GENES projects, have demonstrated that pharmacogenomic-guided therapy improves blood-pressure control and treatment tolerability [5]. However, data from Indian populations remain scarce, despite significant genetic diversity and a growing prevalence of hypertension. Given that allele frequencies of CYP2D6 and ADRB1 variants differ across ethnic groups, region-specific studies are essential for validating and applying pharmacogenetic principles in Indian clinical practice [6].

Integrating pharmacogenomics into hypertension management represents a critical step toward personalized medicine, emphasizing precise, evidence-based, and patient-centered care. Such an approach can reduce therapeutic inertia, prevent avoidable adverse events, and accelerate the achievement of treatment goals [7].

Therefore, it is of interest to evaluate whether genotype-guided antihypertensive therapy improves blood-pressure control, reduces adverse drug reactions, and shortens the time to achieve target blood pressure compared with standard guideline-based treatment among patients with essential hypertension managed at a tertiary-care hospital in India.

Study Design and Setting

This was a prospective, randomized, open-label, controlled clinical study conducted in the Department of Cardiology of a tertiary care teaching hospital in India from March 2023 to April 2024. The study was approved by the Institutional Ethics Committee and conducted in accordance with the Declaration of Helsinki (2013) and Good Clinical Practice (GCP) guidelines. All participants provided written informed consent before enrollment.

Study Population

A total of 380 adults aged 30–65 years with confirmed essential hypertension (systolic blood pressure ≥140 mmHg and/or diastolic blood pressure ≥90 mmHg on two separate visits) were included. Recruitment occurred through outpatient clinics and hospital screening programs.

Inclusion Criteria

1. Adults aged 30–65 years of either sex.
2. Newly diagnosed or previously untreated essential hypertension.
3. Willingness to undergo genotyping and follow-up visits.
4. Provision of written informed consent.

Exclusion Criteria

1. Secondary hypertension (renal, endocrine, or vascular causes).
2. History of major cardiovascular events within the past six months.
3. Chronic kidney disease (eGFR < 60 mL/min/1.73 m²) or hepatic dysfunction.
4. Pregnancy or lactation.
5. Concurrent drugs known to interfere with antihypertensive metabolism.

Randomization and Group Allocation

Participants were randomized in a 1:1 ratio using a computer-generated random sequence into:

• Group A (Genotype-Guided Therapy): Drug selection based on pharmacogenomic profile.
• Group B (Standard Therapy): Empirical guideline-based management without genotype input.

Allocation concealment was maintained with sealed opaque envelopes. Investigators performing data analysis were blinded to group identity.

Genotyping Procedure

Venous blood (3 mL) was collected in EDTA tubes for DNA extraction using the QIAamp DNA Mini Kit (Qiagen, India). Genotyping was performed for three validated markers:

• CYP2D6 alleles (*1, *4, *10, *41) influencing beta-blocker metabolism,
• ADRB1 polymorphisms (Ser49Gly, Arg389Gly) affecting β₁-receptor sensitivity,
• AGT M235T variant associated with renin–angiotensin activity.

Allelic discrimination was performed by real-time PCR using TaqMan assays (Applied Biosystems, India). Ten percent of samples were re-analyzed for quality control, ensuring >99% concordance.

Treatment Protocol

• CYP2D6 poor metabolizers received calcium-channel blockers or angiotensin receptor blockers instead of beta-blockers.
• ADRB1 Arg389Arg carriers received beta-blockers as first-line therapy due to increased receptor responsiveness.
• AGT TT genotype participants were initiated on ACE inhibitors owing to elevated renin–angiotensin system activity.

The standard therapy group received treatment according to the Indian Council of Medical Research (ICMR) 2019 hypertension guidelines, typically starting with ACE inhibitors or calcium-channel blockers.

Follow-Up and Data Collection

Follow-up visits were scheduled at 4, 12, and 24 weeks. At each visit, blood pressure was recorded as the mean of three seated readings taken after a 10-minute rest using a calibrated mercury sphygmomanometer. Drug adherence was monitored by monthly pill counts and patient-maintained compliance diaries. Adverse drug reactions were recorded using standardized forms. Baseline and follow-up biochemical tests (serum creatinine, electrolytes, and liver enzymes) were performed at 6 months.

Outcome Measures

• Primary Outcome: Percentage of patients achieving target blood pressure <130/80 mmHg at 6 months.
• Secondary Outcomes:
1. Mean reduction in systolic and diastolic blood pressure.
2. Time to achieve target blood pressure.
3. Frequency and severity of adverse drug reactions.
4. Correlation between genotype and therapeutic response pattern.

Sample Size Calculation

Based on an anticipated 15% higher control rate in the genotype-guided group, with α = 0.05 and power = 80%, the required sample size was calculated to be 170 per group. Allowing for 10% attrition, 190 participants per group (total 380) were recruited.

Statistical Analysis

Data analysis was carried out using SPSS version 27.0 (IBM Corp., USA). Continuous variables were expressed as mean ± standard deviation (SD) and compared using independent t-tests. Categorical variables were analyzed using chi-square or Fisher’s exact tests. Repeated-measures ANOVA assessed longitudinal blood pressure trends. Logistic regression determined predictors of achieving target blood pressure, adjusting for age, sex, BMI, and baseline values. A p-value < 0.05 was considered statistically significant.

Ethical and Safety Monitoring

All procedures adhered to institutional biosafety and data confidentiality policies. An independent Data and Safety Monitoring Board (DSMB) reviewed adverse events throughout the study. No genetic data were shared outside the institutional database.

Overview
A total of 380 participants were randomized equally into two groups: genotype-guided therapy (n = 190) and standard guideline-based therapy (n = 190). Four participants (two from each group) were lost to follow-up, leaving 376 participants for final analysis. Baseline demographic and biochemical characteristics were comparable between both groups, ensuring internal validity. The genotype frequencies for CYP2D6, ADRB1, and AGT polymorphisms were evenly distributed. Antihypertensive drugs were assigned in accordance with pharmacogenomic profiles in the genotype-guided arm and per standard guidelines in the control group. Blood pressure reduction was observed in both groups, but the magnitude and rate of reduction were significantly greater in the genotype-guided group. The proportion of participants achieving target blood pressure (<130/80 mmHg) was higher in the genotype-guided group, and the mean time to reach target control was shorter. The incidence of adverse drug reactions was lower among patients receiving genotype-guided therapy, while adherence and biochemical safety parameters remained comparable between the groups.

Table 1: Baseline Demographic and Clinical Characteristics

This table presents demographic and clinical parameters of participants in both groups, confirming comparability at baseline.

Table 2: Baseline Biochemical Parameters

This table shows biochemical equivalence between groups before the intervention, confirming matched renal and hepatic function.

Table 3: Distribution of Pharmacogenomic Variants

This table summarizes the genotype distribution for CYP2D6, ADRB1, and AGT polymorphisms across study groups, confirming balanced allocation.

Table 4: First-Line Antihypertensive Allocation

This table shows the distribution of initial drug classes prescribed in each group according to treatment strategy.

Table 5: Mean Blood Pressure at Each Follow-Up

This table presents mean systolic and diastolic blood pressures over 24 weeks, demonstrating greater and faster reduction with genotype-guided therapy.

Table 6: Participants Achieving Target Blood Pressure

This table shows the proportion of participants achieving blood pressure <130/80 mmHg at 12 and 24 weeks.

Table 7: Time to Achieve Target Blood Pressure

This table summarizes the mean and median duration required to achieve target blood pressure in both groups.

Table 8: Adverse Drug Reactions

This table presents the frequency of common adverse reactions observed during the study.

Table 9: Treatment Adherence by Pill Count

This table compares medication adherence between the two groups over six months.

Table 10: Subgroup Analysis of Blood Pressure Control by Genotype

This table illustrates the percentage of participants achieving target blood pressure within specific genotype subgroups.

Table 11: Predictors of Blood Pressure Control at 24 Weeks

This table lists independent predictors of achieving target blood pressure in multivariable logistic regression analysis.

Table 12: Change in Laboratory Safety Parameters from Baseline to 24 Weeks

This table compares the mean change in renal and hepatic parameters, confirming biochemical safety of genotype-guided therapy.

Table 1 shows that age, sex distribution, BMI, baseline systolic and diastolic pressures, smoking, and diabetes were comparable, minimizing confounding at entry. Table 2 confirms biochemical equivalence at baseline, supporting internal validity for subsequent comparisons. Table 3  demonstrates balanced genotype frequencies for CYP2D6, ADRB1, and AGT, indicating successful randomization of pharmacogenomic determinants. Table 4 documents divergent first-line choices driven by strategy: genotype-guided care prioritized beta-blockers for ADRB1 Arg389Arg and avoided them in CYP2D6 poor metabolizers, while standard care leaned on ACE inhibitors and calcium-channel blockers. Table 5 shows greater and earlier reductions in both systolic and diastolic pressures under genotype-guided therapy across all visits. Table 6 indicates a higher proportion achieving the <130/80 mmHg target at 12 and 24 weeks in the genotype-guided arm, with absolute differences of about 21 percentage points at 12 weeks and 17 percentage points at 24 weeks. Table 7 quantifies faster control in the genotype-guided arm, with an average of 8.2 weeks to target versus 12.4 weeks for standard care. Table 8 reports fewer adverse reactions with genotype-guided care, notably less ACE-inhibitor–associated cough and a lower overall proportion with any adverse event. Table 9 shows similarly high adherence in both arms, indicating that efficacy differences are not explained by differential compliance. Table 10 highlights pharmacogenomic signal: ADRB1 Arg389Arg and CYP2D6 poor/intermediate metabolizers showed the largest benefit from genotype-guided selection; AGT TT also favored ACE inhibitor prioritization. Table 11 presents multivariable modeling where genotype-guided therapy and high adherence independently predicted target attainment, while higher baseline systolic pressure modestly reduced the odds of control. Table 12 confirms biochemical safety, with no clinically meaningful differences in kidney function, potassium, or liver enzymes between groups.

This randomized controlled study evaluated the effectiveness of genotype-guided antihypertensive therapy compared with standard guideline-based treatment in adults with essential hypertension managed at a tertiary-care hospital in India. The findings demonstrate that personalized pharmacogenomic therapy achieved significantly greater blood pressure control, reduced the time to reach target levels, and minimized adverse drug reactions without compromising safety or adherence. These results underscore the clinical value of integrating pharmacogenetic profiling into hypertension management within the Indian healthcare context [8]. The baseline demographic and biochemical parameters were comparable between the two groups, confirming successful randomization and internal validity. The observed genotype frequencies for CYP2D6, ADRB1, and AGT were consistent with previously reported Asian allele distributions, indicating representativeness of the study population [9]. The genotype-guided approach influenced therapeutic choices, leading to a higher proportion of beta-blocker prescriptions in ADRB1 Arg389Arg carriers and avoidance of beta-blockers in CYP2D6 poor metabolizers. These pharmacogenomic adaptations were associated with superior treatment outcomes [10]. The primary outcome showed that 77.9 percent of patients in the genotype-guided group achieved target blood pressure (<130/80 mmHg) compared with 61.1 percent in the standard therapy group. This magnitude of benefit parallels international pharmacogenomic hypertension trials such as the Pharmacogenomic Evaluation of Antihypertensive Responses (PEAR) and INVEST-GENES projects, which reported 15–20 percent higher control rates with genotype-informed therapy [11]. The faster response observed in the present study, with mean time to target at 8.2 weeks versus 12.4 weeks for standard care, highlights the potential of pharmacogenomic tailoring to shorten the therapeutic optimization period a key advantage in preventing end-organ complications associated with prolonged uncontrolled hypertension [12].

The greater mean reduction in systolic and diastolic pressures among genotype-guided participants likely reflects improved receptor-level and metabolic alignment between drug mechanism and individual genetic background. In CYP2D6 poor metabolizers, reduced beta-blocker exposure under conventional dosing often leads to suboptimal effect or accumulation-related side effects [13]. By redirecting these individuals toward calcium-channel blockers or ARBs, therapeutic efficacy was maximized while minimizing metabolic inefficiency. Likewise, in ADRB1 Arg389Arg carriers, enhanced β₁-adrenergic receptor responsiveness explains the superior efficacy of beta-blockers observed in genotype-matched patients. The AGT M235T polymorphism’s effect on the renin–angiotensin axis also supports the greater responsiveness to ACE inhibitors among TT homozygotes seen in this study [14].

Adverse drug reactions were nearly halved in the genotype-guided arm (5.3% vs. 10.5%), mainly due to elimination of ACE inhibitor–induced cough in patients without pharmacogenomic suitability. This reduction in adverse effects contributes not only to clinical safety but also to better adherence and long-term persistence with therapy an essential determinant of cardiovascular outcomes [15]. Importantly, medication adherence assessed by pill count remained uniformly high across groups, confirming that improved outcomes were pharmacogenetically, not behaviorally, driven. Laboratory parameters including serum creatinine, potassium, and liver enzymes remained stable, affirming that individualized therapy maintained biochemical safety throughout follow-up [16].

The multivariable regression analysis reinforced the independent role of genotype-guided therapy in achieving blood pressure control even after adjusting for age, sex, baseline values, body mass index, and diabetes [17]. These findings align with the mechanistic rationale that pharmacogenomics refines the therapeutic index by matching the right drug to the right patient, thus improving both efficacy and tolerability. The observed odds ratio of 2.28 for target achievement in genotype-guided participants mirrors trends in pharmacogenomic interventions across other therapeutic domains, such as statin or anticoagulant therapy, supporting its translational reliability [18].

In the Indian setting, pharmacogenomics remains underutilized despite significant ethnic genetic diversity and increasing non-communicable disease burden. Implementing genotype-guided antihypertensive therapy could substantially improve population-level outcomes, particularly in tertiary-care institutions equipped with molecular diagnostic facilities [19]. Cost-effectiveness analyses from comparable international studies have shown that the incremental expense of genetic testing is offset by reductions in clinic visits, adverse event management, and treatment switches. Establishing similar models locally could accelerate adoption and policy integration under personalized medicine initiatives [20].

The study’s strengths include its randomized controlled design, adequate sample size, and use of validated genetic assays. It demonstrates the feasibility of integrating pharmacogenomic testing into clinical workflow in a resource-constrained environment. Limitations include its single-center design, 6-month duration, and lack of pharmacoeconomic evaluation, which may limit generalizability and long-term inference. Additionally, only three genetic loci were analyzed; future studies incorporating broader panels could refine predictive accuracy and uncover gene–gene interactions relevant to drug response.

Overall, the results provide robust evidence that pharmacogenomic-guided therapy enhances blood pressure control and treatment efficiency in Indian hypertensive patients. Wider adoption of such strategies may mark a paradigm shift in cardiovascular therapeutics, moving from population-based empiricism to individualized precision care.

Genotype-guided antihypertensive therapy significantly improved blood pressure control, reduced the time to reach therapeutic targets, and minimized adverse drug reactions compared with standard guideline-based treatment. The approach was safe, feasible, and clinically advantageous in an Indian tertiary-care hospital setting. Incorporating pharmacogenomic profiling into hypertension management protocols can advance precision medicine, optimize drug efficacy, and contribute to better long-term cardiovascular outcomes.

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