A Pharmacogenomics‐Guided Antihypertensive Strategy for Blood Pressure Reduction in Rural China: A Cluster‐Randomized Controlled Trial

ABSTRACT

Background

Although pharmacogenomics can effectively guide antihypertensive treatment, relevant evidence from low‑resource primary care settings is lacking. The aim of this study is to evaluate a pharmacogenomic‑guided antihypertensive strategy in real‑world, low‑resource primary care settings.

Methods

We randomly assigned 94 villages (in 1:1:1 ratio, with stratification by villages) to receive a multifaceted intervention that included follow‐up, health education, and medication adjustment guided by pharmacogenomics (treatment) or physician's experience (control), or routine care (observation) over 4 weeks. The primary outcome was the proportion of patients with controlled blood pressure after 4 weeks of treatment. Generalized linear, linear mixed‐effect regression, and sensitivity analysis were conducted to assess the difference among groups. The intracluster correlation coefficient was calculated to assess the heterogeneity with cluster.

Results

This study included 1031 hypertensive patients from 90 villages of Dajie and Jiuzhi towns in Daming County from May 10 to December 31, 2022. Overall, 79 loci on 39 antihypertensive‐related genes were tested for patients in the treatment group. After 4 weeks of treatment, 371 of 377 (98.4%) patients in the treatment group, 256 of 345 (74.2%) patients in the control group, and 232 of 309 (75.1%) patients in the observation group had their blood pressure under control. The between‐group net differences were 24.2% (95% confidence interval [CI]: 19.4%–29.0%) and 23.3% (95% CI: 18.3%–28.3%) for the treatment versus control groups and treatment versus observation groups, respectively. After 4 weeks of treatment, the proportion of patients with overall incident adverse events was no different among the three groups (all p > 0.05), and no serious adverse events or deaths related to antihypertensive treatment were reported during the study period.

Conclusions

A pharmacogenomic‐guided antihypertensive strategy can significantly enhance blood pressure control while maintaining a similar safety level compared to an experience‐guided antihypertensive strategy in low‐resource, primary care settings.

This manuscript reports the findings from the Pharmacogenomic‐guided Antihypertensive Strategy Trial study amidst the deteriorating blood pressure control observed globally during the COVID‐19 pandemic. A pharmacogenomics‐guided strategy achieved a 98.4% blood pressure control rate at 4 weeks in rural China.

Abbreviations

ASCVD

atherosclerotic cardiovascular disease

BMI

body mass index

CI

confidence interval

CJFH

China‐Japan Friendship Hospital

DBP

diastolic blood pressure

ICC

intraclass correlation coefficient

IQR

interquartile range

LDL

low‐density lipoprotein

MMAS‐8

8‐item Morisky Medication Adherence Scale

PAST

Pharmacogenomic‐Guided Antihypertensive Strategy Trial

SBP

systolic blood pressure

1. Background

In 2018, only 10% of hypertensive patients had controlled blood pressure (BP) in rural China [1]. Sun et al. proposed a multifaceted intervention to tackle the lack of treatment adherence and insufficient effort from clinicians, which contributed to a 77.3% hypertension control rate in trial villages [2]. The lack of response to antihypertensive treatment may explain the remaining 22.7% of uncontrolled patients and serve as the “last mile” towards achieving “Zero Hypertension,” the ambitious goal declared by the International Society of Hypertension [3, 4]. Proof‐of‐concept trials and guidelines have supported the potential of using pharmacogenomics to improve treatment responses and reduce costs [3, 5, 6, 7]. Key genes such as ADRB1, AGTR1, ACE, and CYP3A5 have been recommended for consideration in the selection of β‐blockers, angiotensin II receptor blockers, angiotensin‐converting enzyme inhibitors, and calcium channel blockers by prior research and national guidelines, respectively [8, 9]. However, this novel strategy has not been integrated into clinical practice as per the guidelines for hypertension management in China, due to the lack of robust evidence from randomized controlled trials and because its effectiveness in real‐world, primary care settings remains unclear [10].

The Precision Pharmacy Clinic, founded at Beijing Chao‐Yang Hospital and later transferred to the China‐Japan Friendship Hospital (CJFH), has offered pharmacogenomic consultations for treating hypertensive patients since 2015 [11]. From January 2016 to June 2018, 82% of hypertensive patients treated at this outpatient clinic achieved controlled BP, and an average of 562 Chinese Yuan (63 Pounds) was saved in treatment costs per patient per year. However, some primary care physicians in rural areas of China have only received short‐term training programs due to national and historical factors—there is widespread irregularity in their treatment and medication practices, and the current situation cannot be changed in the short term. With these encouraging experiences, policy support of China medical insurance coverage, and as part of the Poverty Relief Program of the China Development Research Foundation, the pharmacogenomic‐guided antihypertensive strategy trial (PAST) aimed to assess the effectiveness and safety of a pharmacogenomic‐guided antihypertensive strategy for hypertension management in a rural primary care setting.

2. Methods

2.1. Trial Design and Participants

This study is a multicenter, open‐label, cluster‐randomized controlled trial conducted by CJFH. In this study, eligible participants were permanent residents aged 55 years and above living in 94 villages of Dajie and Jiuzhi towns in Daming County, Hebei Province. Overall, 2668 patients were recruited for the study, and a total of 1031 eligible hypertensive participants were enrolled between May 10 and December 31, 2022 (Figure 1). Patients with uncontrolled systolic blood pressure (SBP) ≥ 140 mmHg (or ≥ 150 mmHg for those aged above 65 years old), diastolic blood pressure (DBP) ≥ 90 mmHg (or both), and with an expected survival of at least 3 months were recruited from the screening of local patients with primary hypertension. Patients with infectious diseases such as active hepatitis B or C, tuberculosis, malignant tumors, or liver and renal dysfunction were ineligible for this trial. Details of the inclusion and exclusion criteria can be found in the study protocol in the supplementary material. Figure 1 provides the sample selection flowchart for this study. An independent data and safety monitoring board oversaw the trial process. Written informed consent was obtained from each participant. The Institutional Review Board and Ethics Committee of CJFH approved the study (approval number: 2021‐97‐K58). This trial was registered with the Chinese Clinical Trial Registry (ChiCTR2100051226).

Figure 1.

Flow chart of the sample selection process. DBP, diastolic blood pressure; PP, per‐protocol; SBP, systolic blood pressure.

2.2. Randomization and Masking

An independent statistician from CJFH generated randomization schedules stratified by village using SAS 9.4 software (SAS Institute Inc., Cary, United States). The study employed a cluster sampling strategy, with villages serving as clusters, and participants were allocated to the pharmacogenetic‐guided, experience‐guided, and routine‐care groups in a 1:1:1 ratio. The statistician assigned participants to three groups: the pharmacogenetic‐guided group (treatment), the experience‐guided group (control), and the routine‐care group (observation). Given the nature of the cluster trial design and intervention strategies, blinding of physicians, patients, and researchers was not feasible. Nevertheless, the outcome evaluation committee was unaware of participants' allocation groups.

2.3. Trial Procedures

In this study, local researchers consisted of primary care physicians and village doctors. Each primary care physician from township hospitals supervised several village doctors throughout the study period. Participants were recruited through questionnaires. Patients with primary hypertension who were undergoing pharmacological treatment (regularly using medication for at least 2 weeks, with at least one type of medication) were further included in the health examination. Baseline information was collected by village doctors.

Local researchers were uniformly trained by a team of pharmacists from the Precision Pharmacy Clinic of CJFH. The training protocol adhered to “National Clinical Practice Guidelines on the Management of Hypertension in Primary Health Care in China (2020)” and “2018 Chinese Guidelines for the Management of Hypertension” [12, 13]. All local researchers were instructed in standardized BP measurement techniques and in providing health education on maintaining a healthy lifestyle, including low‐fat and low‐salt diets, smoking cessation, reduced alcohol intake, regular physical activity, and treatment adherence. Additionally, all doctors received training on follow‐up protocols and drug selection plans based on the above‐mentioned national guidelines [12, 13]. In pharmacogenetic‐guided villages, doctors were trained to develop drug selection plans based on pharmacogenetic results. The Precision Pharmacy Clinic of CJFH issued certifications upon completion of the training. Blood samples in the pharmacogenetic‐guided group were transferred via cold‐chain transportation to Genergy Bio‐technology (Shanghai) Co. Ltd. (Shanghai, China) for genotyping using Illumina Inc.'s GenomeStudio 2.0 (Illumina Inc., San Diego, United States). Overall, 79 loci across 39 genes were genotyped, and a pharmacogenetic report containing genetic profiles and their responses to 31 antihypertensive drugs was generated for each patient in the pharmacogenetic‐guided group using a pharmacogenetic algorithm.

2.4. Details of Interventions in the Groups

Seated BP was measured for each participant using the Omron J710 device (Omron Corporation, Tokyo, Japan). Two readings were taken with an interval of 1–2 min, and the average of these two readings was recorded. If the difference between the two readings exceeded 10 mmHg, a third measurement was performed, and the mean of the latter two readings was documented. Blood samples were collected exclusively from patients in the pharmacogenetic‐guided group at the time of enrollment during health examinations.

Pharmacogenetic‐guided group: Local researchers drafted medication plans for patient treatment based on recommendations from pharmacogenetic reports, which were then reviewed and approved by researchers from CJFH. For example, when a patient was prescribed metoprolol for hypertension, recommendations (e.g., use of conventional dosage, alerts for insufficient efficacy or adverse reactions, or advice to reduce/increase dosage) were provided based on the genotype of the rs1065852 locus in the CYP2D6 gene. The pharmacogenomic loci of each antihypertensive drug used in this study are listed in Supporting Information: Table S1. Data collection was conducted at baseline and at 1, 2, 3, and 4 weeks, with concurrent follow‐up, medication adjustments, and health education.

Experience‐guided group: Medication plans were developed based on the clinical experience of local researchers. Data collection was performed at baseline and at 1, 2, 3, and 4 weeks, with concurrent follow‐up, medication adjustments, and health education.

Routine‐care group: Experience‐based antihypertensive prescriptions and health education were provided at baseline. No prescription adjustments were made for patients in this group after the baseline visit. Follow‐up was conducted exclusively after 4 weeks of treatment, which included assessments of health‐related quality of life, medication adherence, and adverse events.

2.5. Medications Available in the Groups

The free medications available to the three groups were determined based on local medical insurance coverage and drug availability, with priority given to those included in the national drug catalogue. This selection also adhered to current antihypertensive treatment guidelines and principles of precision medicine. Details of the specific free medications are provided in the supplementary materials (Supporting Information: Table S1).

2.6. Outcome Measures

The primary outcome was the proportion of participants with controlled BP after 4 weeks of treatment, defined as SBP < 140 mmHg (or < 150 mmHg for participants aged > 65 years) and DBP < 90 mmHg. This threshold, aligned with the Consensus on the Clinical Characteristics and Diagnostic/Treatment Process of Geriatric Hypertension, is based on China's epidemiological context and serves as the standard for initiating antihypertensive interventions. Secondary outcomes included: the proportion of participants with controlled BP stratified by atherosclerotic cardiovascular disease (ASCVD) risk level, time to achieve BP control, incidence of adverse events and scores on the 8‐item Morisky Medication Adherence Scale (MMAS‐8) after 4 weeks of treatment [14].

Adverse events were closely monitored throughout the study by local researchers. Details including time of occurrence, clinical manifestations, treatment course, duration, outcomes, and relationship to the study medication were meticulously recorded in the case report forms. Serious adverse events were required to be reported directly to the principal investigator, the ethics committee, the Drug Safety Supervision Department of the National Medical Products Administration of China, and the local health administrative department within 24 h.

2.7. Sample Size Calculation

A pilot study was conducted in Daming County prior to the main trial. However, medication adherence was low due to constraints related to medical insurance policies and the absence of a standardized free drug distribution procedure. Despite this, preliminary results from the 2‐week follow‐up showed that the rates of adequate BP control were 40.7%, 28.6%, and 30.0% in the pharmacogenetic‐guided group, experience‐guided group, and routine‐care group, respectively. In this study, the effect size was defined as the risk difference in BP control rates between groups. The effect sizes were 12.8% (pharmacogenetic‐guided vs. experience‐guided group), 11.2% (pharmacogenetic‐guided vs. routine‐care group), and 1.6% (experience‐guided vs. routine‐care group), respectively. With α set at 0.05 and β at 0.05, a total of 798 participants were required for the comparison between the pharmacogenetic‐guided and experience‐guided groups (399 per group), while 1034 participants were needed for the comparison between the pharmacogenetic‐guided and routine‐care groups (517 per group). The coronavirus disease 2019 (COVID‐19) pandemic necessitated policies such as lockdowns and social distancing, which could potentially complicate study implementation and increase dropout rates. To account for these challenges, the recruitment target was set at 3600 participants, with 1200 allocated to each of the three groups. Sample size calculations were performed using G*Power software (Heinrich Heine University, Düsseldorf, Germany).

2.8. Generalized Linear Model

To evaluate the differences in categorical outcomes (e.g., BP control rates), we employed a generalized linear mixed‐effects model using SAS PROC GLIMMIX. The model was specified with a binary distribution and a logit link function. To account for the cluster randomized design, villages were included as a random effect, and the treatment group was treated as a fixed effect. We used the LSMEANS statement to estimate the marginal proportions for each group at the end of follow‐up. The Net Difference represents the adjusted difference in marginal proportions derived from the LSMEANS statement, which accounts for the covariates (e.g., age, sex, body mass index [BMI]) mentioned in the Methods. This ensures that the reported effect size is a robust estimate of the intervention's impact after controlling for potential confounders and cluster effects.

2.9. Statistical Analysis

Differences among groups were evaluated using ANOVA for continuous variables and chi‐squared tests for categorical variables. Differences in the proportion of patients with controlled BP and other outcomes between the pharmacogenetic‐guided group, experience‐guided group, and routine‐care group were assessed using a univariable generalized linear model. Differences in continuous outcomes were evaluated using linear regression. Multivariable models were also constructed, adjusting for age, sex, duration of hypertension diagnosis, BMI, family history of hypertension, and low‐density lipoprotein (LDL)‐cholesterol. Sensitivity analyses were performed using linear mixed‐effects regression with villages as a random effect, after adjusting for the above covariates. Subgroup analyses of outcomes were conducted by genotype of the four selected antihypertensive genes—targeted for the four major drug classes administered to patients in the pharmacogenetic‐guided group—with reference to previous studies, guidelines, and the availability of genetic testing [8, 9]. The intraclass correlation coefficient (ICC) was calculated to assess heterogeneity within clusters. All statistical analyses were performed using SAS 9.4 software.

2.10. Role of Funding Source

The funders of the PAST study had no role in trial design, data collection, data analysis, or data interpretation. However, several individuals who are current or former officers of the China Development Research Foundation are coauthors of this study.

3. Results

In total, 2668 patients from 94 villages were recruited for the study, and a total of 1031 eligible hypertensive participants from 90 villages were enrolled in this trial (Figure 1). The ICC was 0.024. Generally, the baseline characteristics of patients across the three groups were similar and are reported in Table 1. The mean age of participants was 66.92 ± 7.55 years, with 60.6% being female, and 65.0% were at moderate risk of ASCVD. All included participants had uncontrolled BP, with a median of 154.0/90.5 mmHg. The median duration of hypertension was 8.00 years (interquartile range [IQR]: 5.00–12.00).

Table 1.

Baseline characteristics of the participants in each intervention group.

Variables	Overall (n = 1031)	Pharmacogenetic‐guided (n = 377)	Experience‐guided (n = 345)	Routine‐care (n = 309)	p	Post‐hoc test
Age (years)	66.92 (7.55)	67.51 (7.31)	66.64 (7.93)	66.52 (7.39)	0.165	NA
Sex (female)	625 (60.6)	226 (59.9)	212 (61.4)	187 (60.5)	0.917	NA
Ethnicity (Han)	1023 (99.2)	372 (98.7)	342 (99.1)	309 (100.0)	0.140	NA
Diabetes	71 (6.9)	18 (4.8)	24 (7.0)	29 (9.4)	0.060	NA
With insurance	1027 (99.6)	375 (99.5)	344 (99.7)	308 (99.7)	0.853	NA
Major cardiovascular diseasea	216 (21.0)	94 (24.9)	44 (12.8)	78 (25.2)	< 0.001	g
Alcohol	115 (11.2)	45 (11.9)	35 (10.1)	35 (11.3)	0.742	NA
Current smoker	176 (17.1)	67 (17.8)	63 (18.3)	46 (14.9)	0.468	NA
Education					0.208	NA
Illiterate	285 (27.6)	97 (25.7)	98 (28.4)	90 (29.1)	—	—
Primary school	470 (45.6)	180 (47.7)	153 (44.3)	137 (44.3)	—	—
Junior school	234 (22.7)	91 (24.1)	80 (23.2)	63 (20.4)	—	—
High school and above	42 (4.1)	9 (2.4)	14 (4.1)	19 (6.1)	—	—
Married	861 (83.5)	313 (83.0)	283 (82.0)	265 (85.8)	0.417	NA
Income > 20,000 Yuan	232 (22.5)	86 (22.8)	67 (19.4)	79 (25.6)	0.168	NA
Physically activeb	441 (42.8)	161 (42.7)	155 (44.9)	125 (40.5)	0.623	NA
Healthy dietc	584 (56.6)	208 (55.2)	206 (59.7)	170 (55.0)	0.451	NA
Hypertension diagnosis duration	8.00 (5.00, 12.00)	9.00 (5.00, 13.00)	7.00 (5.00, 11.00)	10.00 (5.00, 12.00)	0.015	f/h
BMI, kg/m2	28.12 (4.32)	28.08 (4.26)	28.15 (4.43)	28.14 (4.27)	0.970	NA
Waist, cm	92.45 (7.04)	92.65 (6.85)	92.08 (7.38)	92.62 (6.87)	0.486	NA
Creatinine clearance, mL/min	92.81 (75.30, 112.69)	97.04 (76.88, 113.51)	90.90 (72.61, 110.88)	93.26 (76.23, 113.44)	0.264	NA
eGFRd, e, mL/min/1.73m2	62.98 (49.23, 81.28)	61.15 (46.76, 81.27)	64.75 (49.91, 82.04)	62.59 (50.11, 80.67)	0.347	NA
AST, U/L	27.79 (9.69)	27.30 (9.55)	28.32 (9.89)	27.78 (9.63)	0.367	NA
ALT, U/L	20.71 (12.36)	20.63 (11.83)	20.31 (12.44)	21.26 (12.90)	0.607	NA
Total cholesterol, mmol/L	5.00 (1.26)	4.97 (1.26)	5.12 (1.14)	4.91 (1.38)	0.075	NA
LDL‐cholesterol, mmol/L	2.73 (0.88)	2.76 (0.83)	2.81 (0.91)	2.60 (0.89)	0.009	NA
HDL‐cholesterol, mmol/L	1.39 (0.39)	1.38 (0.36)	1.40 (0.41)	1.40 (0.38)	0.702	NA
Systolic blood pressure, mmHg	154.00 (147.00, 164.50)	156.50 (150.00, 167.00)	153.50 (146.50, 163.00)	151.50 (143.50, 160.50)	< 0.001	f/g
Diastolic blood pressure, mmHg	90.50 (84.50, 95.00)	90.00 (84.00, 95.00)	89.00 (84.00, 93.50)	91.50 (87.00, 95.50)	0.003	f/h
Heart rate, beat/min	75.00 (70.50, 81.00)	74.00 (70.00, 80.50)	75.00 (70.50, 80.50)	75.00 (71.00, 81.00)	0.200	NA
ASCVD risk level d , e					0.836	NA
Low	147 (14.3%)	54 (14.3%)	44 (12.8%)	49 (15.9%)	—	—
Moderate	670 (65.0%)	249 (66.0%)	226 (65.5%)	195 (63.1%)	—	—
High/Extremely high	214 (20.8%)	74 (19.6%)	75 (21.7%)	65 (21.0%)	—	—

Note: Data are presented as mean (SD), n (%), or median (IQR). Abbreviations: ALT, alanine aminotransferase; ASCVD, atherosclerotic cardiovascular disease; AST, aspartate aminotransferase; BMI, body mass index; eGFR, estimated glomerular filtration rate; HDL, high‐density lipoprotein; LDL, low‐density lipoprotein; NA, not applicable; SD, standard deviation.

^a Patients with history of stroke and/or myocardial infarction and/or heart failure.

^b Patients had physical activity at least 3–5 times per week.

^c Patients had a diet with low fat and salt.

^d eGFR was computed by using 2021 Chronic Kidney Disease Epidemiology Collaboration equation.

^e ASCVD was computed by using pooled population equation of the American College of Cardiology/American Heart Association.

^f Pharmacogenetic‐guided group versus Experience‐guided group.

^g Pharmacogenetic‐guided group versus Routine‐Care group.

^h Experience‐guided group versus Routine‐Care group.

After 4 weeks of treatment, 371 of 377 patients (98.4%) in the pharmacogenetic‐guided group, 256 of 345 patients (74.2%) in the experience‐guided group, and 232 of 309 patients (75.1%) achieved successful BP control. Notably, the between‐group net differences were 24.2% (95% confidence interval [CI]: 19.4%–29.0%, p < 0.0001) for the pharmacogenetic‐guided versus experience‐guided group and 23.3% (95% CI: 18.3%–28.3%, p < 0.0001) for the pharmacogenetic‐guided versus routine‐care group (Table 2). These differences remained consistent after adjustment for potential confounders: 23.8% (95% CI: 19.0%–28.6%, p < 0.0001) and 22.5% (95% CI: 17.6%–27.4%, p < 0.0001), respectively (Table 3). From baseline to week 4, the mean reductions in SBP and DBP were significant across all groups: 25.38/10.29 mmHg (95% CI: 23.82/9.32–26.94/11.26) in the pharmacogenetic‐guided group, 15.83/7.51 mmHg (95% CI: 14.27/6.46–17.40/8.56) in the experience‐guided group, and 13.26/7.36 mmHg (95% CI: 11.59/6.25–14.93/8.48) in the routine‐care group. Additionally, the between‐group net differences in mean reductions were 9.54/2.78 mmHg (95% CI: 7.34/1.34–11.75/4.21, p < 0.0001) for the pharmacogenetic‐guided versus experience‐guided group and 12.12/2.93 mmHg (95% CI: 9.85/1.45–14.39/4.40, p < 0.0001) for the pharmacogenetic‐guided versus routine‐care group. To account for the impact of patient adherence, adherence was included as a covariate in the model; the adjusted outcomes (presented in Supporting Information: Table S3) remained consistent.

Table 2.

Outcomes of patients in three intervention groups after 4 weeks of treatment.

Endpoint	Pharmacogenetic‐guided (n = 377)	Experience‐guided (n = 345)	Routine‐care (n = 309)	Pharmacogenetic‐guided versus Experience‐guidedb	p	Pharmacogenetic‐guided versus Routine careb	p
Proportion of patients with controlled blood pressure	371/377, 98.4% (97.1% to 99.7%)	256/345, 74.2% (69.6% to 78.8%)	232/309, 75.1% (70.3% to 79.9%)	24.2% (19.4% to 29.0%)	< 0.0001	23.3% (18.3% to 28.3%)	< 0.0001
Systolic blood pressure change (mmHg)	−25.38 (−26.94 to −23.82)	−15.83 (−17.40 to −14.27)	−13.26 (−14.93 to −11.59)	−9.54 (−11.75 to −7.34)	< 0.0001	−12.12 ( − 14.39 to −9.85)	< 0.0001
Diastolic blood pressure change (mmHg)	−10.29 (−11.26 to −9.32)	−7.51 (− 8.56 to −6.46)	−7.36 (−8.48 to −6.25)	−2.78 (−4.21 to −1.34)	< 0.001	−2.93 ( − 4.40 to −1.45)	< 0.001
MMAS‐8 score change	0.85 (0.71 to 0.99)	0.49 (0.36 to 0.63)	0.65 (0.46 to 0.83)	0.36 (0.15 to 0.57)	< 0.001	0.21 ( − 0.01 to 0.42)	0.059
Time to achieve blood pressure control (day)	10.95 (10.31 to 11.58)	13.76 (12.74 to 14.78)	—a	−2.81 (−3.83 to −1.80)	< 0.0001	—a	—a
Adverse event	7/377 1.9% (0.5% to 3.2%)	2/345 0.6% (−0.2% to 1.4%)	1/309 0.3% (−0.3% to 1.0%)	1.3% (−0.3% to 2.9%)	0.144	1.5% (0.0% to 3.0%)	0.100

Note: Data are presented as n, % (95% CI), or mean change (95% CI), or net difference (95% CI). Abbreviations: CI, confidence interval; MMAS‐8, Eight‐item Morisky Medication Adherence Scale.

^a The follow‐up time in routine‐care group only at week four, so the time to achieve blood pressure control could not be determined.

^b Net difference, 95% CI.

Table 3.

Adjusted differences of efficacy indicators between Pharmacogenetic‐guided group with experience‐guided and routine‐care groups after 4 weeks of treatment.

Endpoint	Pharmacogenetic‐guided versus experience‐guideda	p‐value	Pharmacogenetic‐guided versus routine‐carea	p‐value
Proportion of patients with controlled BP	23.8% (19.0% to 28.6%)	< 0.0001	22.5% (17.6% to 27.4%)	< 0.0001
Systolic blood pressure change	−9.54 (−11.75 to −7.34)	< 0.0001	−12.12 (−14.39 to −9.85)	< 0.0001
Diastolic blood pressure change	−2.78 (−4.21 to −1.34)	< 0.001	−2.93 (−4.40 to −1.45)	< 0.001
MMAS‐8 score change	0.36 (0.15 to 0.57)	< 0.001	0.21 (−0.01 to 0.42)	0.059
Time to achieve blood pressure control, day	−2.72 (−3.74 to −1.69)	< 0.0001	—	—
Adverse event	0.7% (−0.5% to 1.9%)	0.172	0.9% (−0.3% to 2.1%)	0.132

Note: Adjusted variables include: age, female, hypertension diagnosis duration, BMI, family hypertension history, LDL‐cholesterol. Changes of the outcomes were from baseline to four weeks. Abbreviations: BMI, body mass index; BP, blood pressure; CI, confidence interval; LDL, low‐density lipoprotein; MMAS‐8, Eight‐item Morisky Medication Adherence Scale.

^a Adjusted difference, 95% CI.

The mean increases in MMAS‐8 scores from baseline to week 4 were 0.85 (95% CI: 0.71–0.99) in the pharmacogenetic‐guided group, 0.49 (95% CI: 0.36–0.63) in the experience‐guided group, and 0.65 (95% CI: 0.46–0.83) in the routine‐care group. The increase in adherence scores was significantly greater in the pharmacogenetic‐guided group compared with the experience‐guided group (mean difference: 0.36; 95% CI: 0.15–0.57; p < 0.001). The mean time to achieve BP control was significantly shorter in the pharmacogenetic‐guided group versus the experience‐guided group (mean difference: 2.81 days; 95% CI: 1.80–3.83). In the pharmacogenetic‐guided group, 4 of 7 patients (57.1%) with incident adverse events reported dizziness (Supporting Information: Table S4). However, no significant difference in the proportion of incident adverse events was observed between the three study groups (between‐group net differences, p > 0.05) (Table 2). No serious adverse events or deaths related to antihypertensive treatment occurred during the study period (Supporting Information: Table S4). One death due to coronavirus infection in the experience‐guided group during week 1 was excluded from the analyses. Adjustment for potential confounders did not materially alter the direction or magnitude of the above between‐group differences (Table 3).

The proportion of patients with controlled BP was significantly higher in the pharmacogenetic‐guided group compared with the experience‐guided and routine‐care groups after the first week, with these differences widening thereafter (interaction between interventions and follow‐up time: p < 0.001) (Figure 2a, Supporting Information: Table S5). After the first week of intervention, the mean SBP and DBP in the pharmacogenetic‐guided group were significantly lower than those in the experience‐guided group (interaction between interventions and follow‐up time: p < 0.001) (Figures 2b,c). After 4 weeks of treatment, the mean BP of participants was 133.04/79.38 mmHg (95% CI: 132.08/78.67–134.00/80.09) in the pharmacogenetic‐guided group and 139.84/81.68 mmHg (95% CI: 138.49/80.88–141.20/82.47) in the experience‐guided group (Figure 2, Supporting Information: Table S5). Subgroup analyses were performed to assess variability in intervention effects across populations stratified by age, sex, and ASCVD risk. After 4 weeks of treatment, the proportion of participants with controlled BP in the pharmacogenetic‐guided group was consistently and significantly higher than that in the experience‐guided group across all subgroups (all p < 0.0001). Group differences in the proportion of patients with controlled BP were notably larger in participants aged ≥ 67 years and in male participants. Additionally, these group differences increased with ASCVD risk level, with the largest difference observed in participants at high ASCVD risk (group difference: 29.3%; 95% CI: 18.8%–39.8%) (Figure 3). Sensitivity analyses using a mixed‐effects model did not materially alter the above results (Supporting Information: Table S6A,B).

Figure 2.

Proportion of patients with controlled blood pressure, systolic and diastolic blood pressure throughout the study period in three study groups. (a) Change of the proportion of patients with controlled blood pressure; (b) change of systolic blood pressure; (c) change of diastolic blood pressure.

Figure 3.

Difference in the proportion of patients with controlled hypertension at the fourth week between pharmacogenetic‐guided group and experience‐guided group within each subpopulation. Age group was divided by the median age of 67 years. Abbreviations: ASCVD, atherosclerotic cardiovascular disease; BP, blood pressure.

The frequency of patients advised to adjust their antihypertensive medications was significantly higher in the experience‐guided group than in the pharmacogenetic‐guided group from week 1 to week 3 (all p < 0.01). (Supporting Information: Table S7). Additionally, before week 3, adjustments due to poor effectiveness were less common in the pharmacogenetic‐guided group compared with the experience‐guided group (all p < 0.05) (Supporting Information: Table S8). Prescription changes due to incident adverse events occurred more frequently in the pharmacogenetic‐guided group than in the experience‐guided group before week 2 (all p < 0.05) but less frequently in week 3 (Supporting Information: Table S8).

4. Discussion

In this study, the pharmacogenetic‐guided antihypertensive strategy demonstrated superior efficacy in BP control compared with the experience‐guided approach and routine hypertension care, with a comparable safety profile and better medication adherence. After 4 weeks of treatment, the proportion of patients achieving controlled BP in the pharmacogenetic‐guided group exceeded 40.7%. Over the 4‐week period, the pharmacogenetic‐guided group exhibited a significant reduction in mean SBP and DBP from baseline, with a decrease of 25.38/10.29 mmHg.

The proportion of patients with controlled hypertension in the pharmacogenetic‐guided group was higher than that in any of the previous implementation trials, with a shorter mean treatment time [2]. The net reduction in SBP was also superior to that in any genetic‐based antihypertensive trials targeting a limited number of genes [5, 15]. Notably, SBP has been shown to be substantially harder to control than DBP [16]. An important and nonnegligible reason for this phenomenon is that rural primary healthcare practices lack uniform standardization—with certain regions even failing to adhere to current clinical guidelines. This discrepancy directly contributes to the substantial variability in hypertension control rates across rural areas. Despite the significant BP‐lowering effect, we observed that a slightly larger number of patients in the pharmacogenetic‐guided group experienced dizziness compared with the experience‐guided group. Patients in the pharmacogenetic‐guided group achieved a mean reduction of 25.38/10.29 mmHg within 4 weeks, which may be relatively rapid. Rapid BP reduction may be associated with inadequate cerebral perfusion, leading to dizziness—similar to the mechanism underlying orthostatic hypotension [17]. Older patients receiving the pharmacogenetic‐guided antihypertensive strategy may therefore require additional monitoring to prevent falls. Furthermore, the antihypertensive effects were independent of patient adherence across groups. However, the overall incidence of adverse events did not differ significantly between the pharmacogenetic‐guided group and the experience‐guided group. Thus, the potential prevention of serious adverse outcomes (e.g., stroke and myocardial infarction) is likely to outweigh the risk of dizziness with the pharmacogenetic‐guided strategy.

To our knowledge, the PAST study is the first randomized controlled trial to evaluate the effectiveness and safety of a pharmacogenetic‐guided antihypertensive strategy in a low‐resource primary care setting, utilizing comprehensive genetic testing targeting 39 hypertension‐related genes. Studies have indicated that rural Chinese physicians exhibit suboptimal competence in hypertension management, which has contributed to a notably low BP control rate in rural areas [18]. This may partly stem from limited training among rural doctors, who tend to increase drug dosages rather than adopt rational combination therapy when adjusting hypertension treatment regimens. This practice also leads to heightened side effects due to higher dosages, thereby reducing patient engagement in BP management. Based on recommendations from the pharmacogenetic report, village doctors are enabled to prescribe the most effective medications with fewer side effects to their patients. Furthermore, our results demonstrated a significantly higher rate of prescription adjustments in the experience‐guided group compared with the pharmacogenetic‐guided group, with poor effectiveness being the most frequent reason throughout the study period. As observed in our study, superior clinical competence among physicians may effectively enhance patient trust, which in turn improves medication adherence [19].

The multifaceted hypertension control strategy proposed by Sun et al. demonstrated that the treatment group achieved a BP control rate of 77.3% after 18 months, which was comparable to the 74.2% control rate observed in the experience‐guided group of our study after 4 weeks of treatment [2]. Patients in this group underwent follow‐up, active adjustment with appropriate antihypertensive drugs based on physicians' experience, and received intensive health education from local researchers. Building on our clinical experience and Sun's multifaceted intervention protocol, our innovative pharmacogenetic‐guided drug selection strategy significantly increased the proportion of patients with controlled BP in our treatment group, reaching 98.4% after 4 weeks of treatment. Previous trials have shown that approximately 20%–30% of patients with hypertension have resistant hypertension, which may explain the difficulty in achieving hypertension control in earlier implementation trials [2]. In this context, the PAST study provides preliminary evidence for the effectiveness of pharmacogenomics in managing resistant hypertension. Furthermore, implementing this precision medicine strategy has the potential to optimize regional drug reserves. Guided by local patients' genetic profiles, policymakers can procure antihypertensive medications that are both more effective and associated with fewer adverse effects. Concurrently, this approach allows for reduced supply of less frequently prescribed drugs, thereby enhancing cost‐effectiveness. However, such applications require validation in well‐designed clinical trials.

Currently, antihypertensive treatment strategies based on pharmacogenetics have not been formally integrated into clinical guidelines for hypertension management, including “2018 Chinese Guidelines for the Management of Hypertension,” “National Clinical Practice Guidelines on the Management of Hypertension in Primary Health Care in China (2020),” and “Writing Protocols for the Chinese Clinical Practice Guidelines of Hypertension” [12, 13, 20]. In this context, the present study provides robust clinical evidence to encourage policymakers and clinicians to promote the integration of this novel strategy into clinical practice. Given its significant improvement in BP control within just 4 weeks, the pharmacogenetic‐guided antihypertensive strategy holds promise for helping achieve the “Zero Hypertension” goal—particularly in low‐ and middle‐income settings. These regions often face limited access to antihypertensive drugs and, in rural areas specifically, a shortage of specialized or professionally trained cardiovascular physicians [4]. However, with the implementation of the “Healthy China 2030” national strategy, and comprehensive supportive policies in the healthcare sector, which include robust primary medical insurance coverage, affordable pharmaceutical pricing, and cheap genetic testing, this work aims to offer substantial reference value for international researchers seeking insights into context‐specific pharmacogenomic implementation.

This study has several limitations. Firstly, due to the COVID‐19 pandemic, enrollment fell below the protocol‐specified target. However, the sample size retained over 90% statistical power for key comparisons. Secondly, the 4‐week follow‐up was strategically designed to rapidly evaluate the short‐term effectiveness of pharmacogenetic‐guided antihypertensive treatment in a large, resource‐limited population. This brief trial facilitated close monitoring of adverse events and addressed logistical challenges posed by the pandemic. Consequently, long‐term effects on BP control and cardiovascular outcomes remain unassessed. Longitudinal studies with extended follow‐up are needed to elucidate long‐term benefits. Thirdly, a cost‐effectiveness analysis was not performed; however, the one‐time cost of the pharmacogenetic test (290 Chinese Yuan) is likely to be offset over time by the benefits of improved BP control. Fourthly, while intracluster correlation might have reduced the effective sample size, sensitivity analyses confirmed the robustness of the findings; the ICC was 0.024, which is lower than the 0.19 reported by Sun et al. [2]. Lastly, this study was conducted in rural areas of China, where most medical practitioners are junior‐ or associate‐level physicians. Additionally, the dissemination of clinical guidelines and cutting‐edge treatment strategies is inadequate and delayed, leading to a common practice of increasing drug dosages to manage uncontrolled BP. This also explains why a greater number of antihypertensive drug classes were used in the pharmacogenetic‐guided group. While this represents a limitation, it also underscores the relevance of the study. Potential solutions include incorporating pharmacogenomic testing into primary medical insurance, which can standardize hypertension treatment in rural primary care settings and avoid the abuse and misuse of medications. Additionally, relevant training for primary care providers is necessary to improve the therapeutic effect of hypertension management.

5. Conclusions

The pharmacogenetic‐guided antihypertensive strategy demonstrates effectiveness in reducing BP and holds potential for expansion to other resource‐limited primary care settings in China and globally.

Author Contributions

Wangjun Qin: conceptualization, writing – original draft, investigation, methodology. Mengke Yu: investigation, methodology, validation, software, writing – original draft. Ying Zhu: methodology, validation, software, investigation, writing – original draft. Queran Lin: investigation, methodology, validation, software, data curation, writing – original draft. Limei Yin: methodology, validation, software, data curation, visualization. Guisheng Liu: investigation, methodology, validation, formal analysis, project administration. Yaping Wang: investigation, methodology, validation, software. Guangqiang Lu: methodology, validation, software, formal analysis, data curation. Xiaofei Liu: methodology, validation, visualization, formal analysis, software. Pengmei Li: investigation, validation, software, formal analysis. Xudong Kong: investigation, methodology, validation, software, formal analysis. Li Zhao: validation, visualization, formal analysis, data curation. Kun Tang: methodology, validation, software, formal analysis. Jiangli Jin: validation, visualization, software. Yu Xiong: software, data curation, formal analysis, validation. Yingnan Zhao: methodology, validation, software, formal analysis, data curation. Yan Yan: methodology, validation, software, formal analysis, data curation. Bo Li: methodology, validation, software, formal analysis, data curation. Azeem Majeed: writing – review and editing, supervision. Xianbo Zuo: writing – review and editing, supervision, conceptualization. Yue Qiu: conceptualization, writing – review and editing, supervision. Jiantuo Yu: supervision, conceptualization, writing – review and editing. Lihong Liu: conceptualization, writing – review and editing, funding acquisition, supervision.

Ethics Statement

The Institutional Review Board and Ethics Committee of CJFH approved the study (approval number: 2021‐97‐K58). All other participating subcenters of the study have uniformly authorized the China‐Japan Friendship Hospital to conduct ethical review for this study. This trial was registered with the Chinese Clinical Trial Registry (ChiCTR2100051226).

Consent

Written informed consent was obtained from each participant.

Conflicts of Interest

Xianbo Zuo is the member of the Health Care Science Editorial Board. To minimize bias, he was excluded from all editorial decision‐making related to the acceptance of this article for publication. The remaining authors declare no conflicts of interest.

Supporting information

Supporting File

Qin W., Yu M., Zhu Y., et al., “A Pharmacogenomics‐Guided Antihypertensive Strategy for Blood Pressure Reduction in Rural China: A Cluster‐Randomized Controlled Trial,” Health Care Science 5 (2026): 353–363. 10.1002/hcs2.70084.

Data Availability Statement

Relevant anonymized patient‐level data are available from sending a reasonable request to the corresponding author (Professor Lihong Liu). The data and safety monitoring board of the PAST study will request data sharing consent from each participant for each approved data request.