Serum Uric Acid Changes in Relation to Nighttime Blood Pressure Dipping Status in Patients on Antihypertensive Therapy

ABSTRACT

We performed a post hoc exploratory secondary analysis to investigate whether baseline circadian blood pressure (BP) pattern was associated with changes in serum uric acid (SUA) during 8‐week antihypertensive therapy. Of the 494 hypertensive patients who received amlodipine (5–10 mg) or nifedipine GITS (30–60 mg) for 8 weeks, 369 patients with available laboratory data and valid follow‐up ambulatory BP monitoring data were included in the present analysis, including 221 dippers (nocturnal systolic BP decline ≥ 10%) and 148 non‐dippers (nocturnal systolic BP decline < 10%). Analysis of covariance was used to estimate least square mean changes in SUA according to baseline dipping pattern. After 8‐week antihypertensive treatment, SUA decreased significantly in dippers (−12.4 ± 3.4 µmol/L, p = 0.0004) but not in non‐dippers (−3.3 ± 4.2 µmol/L, p = 0.44). In the repeated‐measures analysis, SUA levels decreased significantly over time (p = 0.002), whereas no significant time‐by‐dipping interaction was observed (p = 0.23). Baseline BP dipping pattern may be modestly associated with short‐term SUA changes during antihypertensive therapy. However, the absence of a significant time‐by‐dipping interaction suggests that these findings should be interpreted cautiously and require further confirmation.

1. Introduction

Hypertension is a significant threat to global public health due to its high prevalence, and remains the major preventable cause of cardiovascular diseases [1]. The circadian rhythm of blood pressure (BP) refers to the fluctuations in BP over 24‐h period, typically characterized by a decrease in BP of ≥10% during the night, which is defined as a dipper pattern. When this circadian rhythm is absent or blunted, the pattern is known as non‐dipper [2, 3]. Numerous studies have shown that the abnormal rhythm pattern is associated with increased cardiovascular risk, including a higher incidence of cardiovascular events and target organ damage [4].

Serum uric acid (SUA), a product of purine metabolism, has been shown to be implicated in the pathogenesis of hypertension and cardiovascular disease [5]. SUA metabolism disorder is also common in patients with hypertension [6]. There is some evidence suggesting that increased SUA level is associated with non‐dipping BP patterns [7, 8]. One possible mechanism is that abnormal circadian blood pressure (BP) pattern may lead to alterations in renal hemodynamics, affecting the glomerular filtration rate and tubular reabsorption of SUA, resulting in decreased SUA excretion and increased serum SUA level [9, 10, 11]. Additionally, oxidative stress and inflammation in non‐dipping pattern play a role in the disorder of SUA [8, 12].

Although current studies confirmed the relationship between SUA and dipping pattern, no study has explored the relationship between SUA changes and dipping pattern during antihypertensive therapy. Furthermore, sex differences in this relationship have not been adequately studied. Some studies have suggested that men and women may respond differently to antihypertensive therapy and have varying SUA levels, highlighting the need to consider sex‐specific response in investigating this relationship [13, 14, 15]. Therefore, in this post hoc exploratory secondary analysis of a randomized trial, we aim to investigate the SUA changes in relation to the dipping pattern after 8 weeks of antihypertensive therapy with dihydropyridine calcium channel blockers, and to evaluate whether there were sex differences in SUA levels in patients with different circadian BP pattern.

2. Methods

2.1. Study Design

The present study is a post hoc exploratory analysis based on a previously conducted phase IV, randomized, actively‐controlled, multicenter, parallel‐group clinical trial performed across five hospitals in China from 2009 to 2013, with ethical approval granted by the ethics committee of Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China, and also by the ethics committees of the participating hospitals. The protocol of the study has been described in detail previously [16]. The study enrolled previously untreated or monotherapy‐treated patients with mild‐to‐moderate hypertension, defined as systolic/diastolic blood pressure (SBP/DBP) of 140–179/90‐109 mmHg for untreated patients and 140–159/90‐99 mmHg for previously treated patients. During the subsequent 4‐week run‐in washout period, two clinic visits were conducted, with three consecutive BP measurements taken at each visit, and 24‐h ambulatory blood pressure monitoring (ABPM) at the end of the run‐in period. Eligible participants with a mean of six clinic blood pressure readings of 140–180 mmHg in systolic and/or 90–110 mmHg in diastolic and the 24‐h mean BP ≥130 mmHg in systolic and/or ≥80 mmHg in diastolic were stratified by participating centers and randomly allocated into two equally sized treatment arms. In the amlodipine group: the initial dose of 5 mg/day was up‐titrated to 10 mg/day, and in the nifedipine GITS group, the initial dose of 30 mg/day was up‐titrated to 60 mg/day, if clinic BP remained ≥140/90 mmHg at 4 weeks of follow‐up. All medications were administered once daily between 6:00‐8:00 AM before breakfast. Prior to randomization, participants provided written informed consent and underwent baseline assessments, including 24‐h ABPM, medical history review, clinic BP measurement, electrocardiography, and standardized blood and urine biochemical testing. We performed clinic blood pressure and ambulatory blood pressure monitoring at 4 and 8 weeks of treatment. Laboratory data was obtained again at 8 weeks of treatment.

2.2. Study Population

Inclusion criteria were as follows. Participants aged 40–70 years, and were currently untreated, or treated with one antihypertensive medication but with uncontrolled blood pressure (SBP/DBP: 140–179/90–109 mm Hg or 140–160/90‐100 mm Hg, respectively). For patients previously on monotherapy, a 4‐week washout period was required. Other inclusion criteria included willingness to participate in the trial, adherence to follow‐up visits, and attending the visit independently. Exclusion criteria included presence of life‐threatening diseases, history of myocardial infarction or stroke within the past two years, known contraindications to dihydropyridine calcium channel blockers and participation in other drug trials during the study period.

2.3. Clinic and Ambulatory Blood Pressure Measurements

The study participants underwent both clinic and ambulatory measurements. At baseline and 4‐ and 8‐week treatment, clinic blood pressure was recorded. Prior to each clinic session, individuals rested in the seated position for at least 5 min. Subsequently, three blood pressure readings were taken consecutively, with an interval of 30–60 s between readings. An automated blood pressure device (HEM 705, Omron Healthcare, Kyoto, Japan) with a properly sized cuff was employed throughout the study.

Ambulatory blood pressure monitoring was performed for 24 h at baseline and at 4‐week treatment, and extended to 48 h at the 8‐week treatment. Ambulatory BP monitors including SpaceLabs 90207 and 90217 (SpaceLabs, Redmond, WA, USA), Mobil‐O‐Graph (IEM GmbH, Stolberg, Germany), TM2430 (A&D CO., LTD., Tokyo, Japan), CB (BIOX Instruments CO., LTD., Wuxi, Jiangsu Province, China), and MGY‐ABP1 (Meigaoyi, Beijing, China) were set to record blood pressure at 20‐min intervals during the daytime (06:00–22:00) and 30‐min intervals at night (22:00–06:00). Valid recordings required a minimum duration of 20 h, including at least ten daytime readings (08:00–18:00) and five nighttime readings (23:00–05:00) [17].

The formula for calculating the circadian BP pattern was (mean daytime SBP—mean nighttime SBP) / mean daytime SBP *100%. Participants with a percentage of diurnal difference of SBP ≥10% were defined as dippers. Participants with a percentage of diurnal difference of SBP <10% were defined as non‐dippers [12].

2.4. Epidemiological and Laboratory Data

The researchers collected data on medical history and lifestyle of the participants. Body weight measurements were taken with participants wearing light indoor clothing and without shoes, and were recorded to the nearest 0.1 kg. Body height was measured and recorded to the nearest 0.1 ccm. Body mass index (BMI) was calculated by dividing the body weight in kilograms by the square of body height in meters.

Blood samples were collected after an overnight fast at baseline and at the end of follow‐up. These samples were then analyzed to determine levels of serum uric acid, creatinine, and other biochemical parameters. The serum uric acid level was measured through an oxidation process involving uricase and peroxidase enzymes. Estimated glomerular filtration rate (eGFR) was calculated using the Chronic Kidney Disease Epidemiology Collaboration (CKD‐EPI) formula [18]. Chronic kidney disease (CKD) was defined as an eGFR lower than 60 mL/min/1.73 m². Hyperuricaemia was defined as a serum uric acid concentration ≥420 mmol/L in men and ≥360 mmol/L in women.

2.5. Statistical Analysis

The SAS software version 9.4 (SAS Institute Inc, Cary, NC, USA) was used for data management and statistical analysis. Means and proportions were compared using the Student's t‐test and Chi‐square test, respectively. Sex‐stratified analysis of covariance was performed to calculate the least square mean changes (±standard error [SE]) in SUA from baseline to 8‐week treatment according to the baseline BP dipping pattern. Further adjusted analyses were performed after adjustment for age, treatment group, body mass index, alcohol intake, smoking status, estimated glomerular filtration rate, baseline 24‐h, daytime, and nighttime systolic and diastolic BP, and treatment‐related changes in 24‐h systolic and diastolic blood pressure. Additional sensitivity analyses were further performed with additional adjustment for baseline SUA.

A mixed‐effects model for repeated measures (MMRM) was applied to evaluate longitudinal changes in SUA over time. SUA values at baseline and 8 weeks were treated as repeated outcomes, and the model included fixed‐effects for time, baseline dipping pattern, and the time‐by‐dipping pattern interaction, with adjustment for the same covariates.

Subgroup analyses were performed according to the presence or absence of CKD. In addition, participants were categorized according to transitions in the dipping pattern between baseline and 8 weeks, including persistent dippers, persistent non‐dippers, dippers to non‐dippers, and non‐dippers to dippers. Changes in SUA and BP across these transition groups were compared using the analysis of covariance. All tests were two‐sided, and p < 0.05 was considered statistically significant.

3. Results

3.1. Flow and Baseline Characteristics of the Study Patients

Figure 1 shows the overall flow of the trial. Of the 505 randomized patients, 136 were excluded because of missing laboratory data at baseline (n = 11) or at 8 weeks of follow‐up (n = 98) or because of missing valid 24‐h ABPM data at 8 weeks of follow‐up (n = 27), leaving 369 patients in the present analysis, including 221 dippers and 148 non‐dippers.

FIGURE 1.

Flowchart of patients.

Table 1 presents the baseline characteristics of 369 study patients categorized into dippers (n = 221) and non‐dippers (n = 148). Dippers and non‐dippers were comparable in most of the demographic and clinical characteristics (p > 0.05). As expected, non‐dippers, compared with dippers, had a higher 24‐h systolic (142.7 ± 11.1 vs. 138.3 ± 10.5 mmHg, p = 0.0001) and diastolic blood pressure (91.1 ± 7.5 vs. 88.2 ± 7.0 mmHg, p = 0.0002), higher nighttime systolic (137.5 ± 11.5 vs. 122.2 ± 10.7 mmHg, p < 0.0001) and diastolic blood pressure (87.1 ± 8.5 vs. 77.8 ± 7.6 mmHg, p < 0.0001), but lower daytime systolic (143.9 ± 11.7 vs. 147.1 ± 11.4 mmHg, p = 0.0009) and diastolic blood pressure (92.4 ± 8.1 vs. 93.9 ± 8.0 mmHg, p = 0.09).

TABLE 1.

Baseline characteristics of the study patients by the rhythm pattern of ambulatory blood pressure (n = 369).

Characteristics	Dippers (n = 221)	Non‐dippers (n = 148)	p value
Men, n (%)	127 (57.5)	80 (54.0)	0.52
Age, years	53.3 ± 7.5	54.5 ± 7.7	0.16
Body mass index, kg/m2	25.0 ± 2.7	25.3 ± 3.5	0.38
Current smoking, n (%)	66 (29.9)	41 (27.7)	0.65
Alcohol intake, n (%)	58 (26.2)	46 (31.1)	0.31
Blood biochemistry
Serum uric acid, µmol/L	331.6 ± 75.1	331.3 ± 81.3	0.98
Glucose, mmol/L	5.5 ± 1.2	5.7 ± 1.6	0.32
High‐density lipoprotein cholesterol, mmol/L	1.3 ± 0.3	1.3 ± 0.4	0.52
Triglyceride, mmol/L	1.8 ± 1.3	1.8 ± 1.2	0.79
Cholesterol, mmol/L	5.1 ± 0.9	5.0 ± 1.0	0.72
Serum creatinine, µmol/L	71.5 ± 16.3	71.8 ± 16.6	0.89
Estimated glomerular filtration rate, ml/min/1.73 m2	121.2 ± 25.2	119.6 ± 26.5	0.57
Alanine aminotransferase, U/L	24.8 ± 16.1	24.5 ± 15.1	0.86
Aspartate aminotransferase, U/L	23.8 ± 17.8	22.9 ± 8.3	0.54
Clinic blood pressure, mmHg
Systolic	150.6 ± 9.6	152.2 ± 10.5	0.14
Diastolic	92.2 ± 7.6	92.6 ± 8.2	0.61
Pulse rate, beats/min	75.6 ± 8.2	75.5 ± 8.7	0.88
Ambulatory blood pressure, mmHg
24‐h systolic	138.3 ± 10.5	142.7 ± 11.1	0.0001
24‐h diastolic	88.2 ± 7.0	91.1 ± 7.5	0.0002
Daytime systolic	147.1 ± 11.4	143.9 ± 11.7	0.009
Daytime diastolic	93.9 ± 8.0	92.4 ± 8.1	0.09
Nighttime systolic	122.2 ± 10.7	137.5 ± 11.5	<0.0001
Nighttime diastolic	77.8 ± 7.6	87.1 ± 8.5	<0.0001
Comorbid diseases, n (%)
Chronic kidney disease	29 (13.1)	26 (17.6)	0.24
Diabetes mellitus	18 (8.1)	11 (7.4)	0.80
Hyperuricaemia	33 (14.9)	26 (17.6)	0.50

Values are mean ± SD or number of participants (%).

Baseline characteristics were similar between the included and excluded patients with available baseline data (Table S1), including the distribution of dipping pattern.

3.2. Serum Uric Acid Levels in Dippers and Non‐Dippers at Baseline and at 8 Weeks of Treatment

Figure 2 shows the results of cross‐sectional analysis on the SUA levels according to the circadian BP pattern at baseline and 8 weeks of treatment (n = 369). At baseline, dippers and non‐dippers had similar serum uric acid concentration in all patients (331.6 ± 75.1 vs. 331.3 ± 81.3 µmol/L, p = 0.98) and in men (366.7 ± 63.5 vs. 373.4 ± 77.4 µmol/L, p = 0.52) and women (284.1 ± 62.6 vs. 281.7 ± 79.6 µmol/L, p = 0.84) separately. Similar results were observed at 8 weeks of treatment (Figure 2), and in 314 patients without CKD (Figure S1) and 55 patients with CKD (Figure S2).

FIGURE 2.

Serum uric acid in men and women at baseline and 8 weeks of follow‐up (n = 369).

3.3. Least Square Mean Changes in Serum Uric Acid Over 8 Weeks

Table 2 presents the least square mean changes in SUA at 8 weeks in dippers and non‐dippers in all patients and in men and women separately. Overall, dippers (‐12.4 ± 3.4 µmol/L, p = 0.0004), but not non‐dippers (‐3.3 ± 4.2 µmol/L, p = 0.44), showed significant SUA reductions from baseline. The corresponding changes in serum uric acid concentration were ‐20.3 ± 4.5 µmol/L (p < 0.0001) and ‐5.9 ± 5.7 µmol/L in men (p = 0.30), respectively, and ‐1.5 ± 5.3 µmol/L (p = 0.77) and ‐0.1 ± 6.2 µmol/L in women (p = 0.98), respectively (Table 2 and Figure 2). Although a greater reduction in SUA was observed among male dippers, no significant interaction by sex was detected (P for interaction = 0.23).

TABLE 2.

Least square mean changes from baseline in serum uric acid at 8 weeks of follow‐up according to the baseline rhythm pattern of ambulatory blood pressure in all patients and in men and women separately (n = 369).

Dipping pattern	Least square mean change ± SE, µmol/L	P	P for group	Grouping by sex	Least square mean change ± SE, µmol/L	P	P for difference	P for sex	P for interaction (rhythm pattern*sex)
Dippers (n = 221)	−12.4 ± 3.4	0.0004	0.09	Men (n = 127)	−20.3 ± 4.5	<0.0001	0.007	0.03	0.23
				Women (n = 94)	−1.5 ± 5.3	0.77
Non‐dippers (n = 148)	−3.3 ± 4.2	0.44		Men (n = 80)	−5.9 ± 5.7	0.30	0.49
				Women (n = 68)	−0.1 ± 6.2	0.98

The results were similar in 314 patients without CKD (Table S2 and Figure S1) but not in 55 patients with CKD (Table S3 and Figure S2). In CKD patients, 26 non‐dippers showed significant serum uric acid reductions from baseline in all patients (‐21.9 ± 9.7 µmol/L, p = 0.03), in women (‐28.5 ± 11.7 µmol/L, p = 0.02) but not in men (‐7.9 ± 17.0 µmol/L, p = 0.65). The corresponding changes in 29 dippers were 0.7 ± 9.3 µmol/L (p = 0.94), 9.4 ± 11.7 µmol/L (p = 0.42), and ‐14.1 ± 15.2 µmol/L (p = 0.36), respectively.

After adjustment for age and treatment group (model 1) and additionally for BMI, alcohol intake, smoking status, eGFR, and baseline and treatment‐related BP variables (model 2), the results remained unaltered in dippers as well as non‐dippers in all patients and in men and women separately. Although greater reductions were observed among men than women, these differences were attenuated after adjustment and were no longer statistically significant (Table 3 and Table S4). After further adjustment for baseline SUA, the estimated changes in SUA were attenuated, and the overall pattern differed from that was observed in the primary adjusted model (Table S5).

TABLE 3.

Adjusted analyses for the least square mean changes from baseline in serum uric acid at 8 weeks of follow‐up according to the baseline rhythm pattern of ambulatory blood pressure and sex (n = 369).

		Crude			Model 1			Model 2
Dipping pattern	Grouping by sex	Least square mean change ± SE, µmol/L	P	P for difference	Least square mean change ± SE, µmol/L	P	P for difference	Least square mean change ± SE, µmol/L	P	P for difference
Dippers (n = 221)	Men (n = 127)	−20.3 ± 4.5	<0.0001	0.007	−20.3 ± 4.1	<0.0001	0.003	−17.5 ± 4.7	0.0002	0.08
	Women (n = 94)	−1.5 ± 5.3	0.77		−1.5 ± 4.8	0.75		−5.4 ± 5.7	0.35
Non‐dippers (n = 148)	Men (n = 80)	−5.9 ± 5.7	0.30	0.49	−5.7 ± 6.4	0.37	0.58	−4.3 ± 7.4	0.57	0.96
	Women (n = 68)	−0.1 ± 6.2	0.98		−0.4 ± 7.0	0.96		−3.7 ± 8.2	0.66

Model 1 adjusted for age and treatment group. Model 2 adjusted for age, treatment group, and additionally for BMI, alcohol intake, smoking status, eGFR, 24‐h, daytime and nighttime systolic blood pressure, and 24‐h, daytime and nighttime diastolic blood pressure at baseline, and treatment‐related changes in 24‐h systolic and diastolic blood pressure. Abbreviations: BMI, body mass index; eGFR, estimated glomerular filtration rate.

3.4. Longitudinal Analysis of Serum Uric Acid over 8 Weeks

In the mixed‐effects model for repeated measures (MMRM), SUA levels decreased significantly over time (p = 0.002). Neither the main effect of baseline dipping pattern (p = 0.25) nor the time‐by‐dipping pattern interaction (p = 0.23) was statistically significant.

3.5. Least Square Mean Changes in SUA and SBP According to Transitions in Dipping Pattern

Changes in SUA and daytime and nighttime SBP according to transitions in the dipping pattern are shown in Supplementary Table S6. Although only persistent dippers showed a significant within‐group reduction in SUA (p = 0.01), no overall differences in SUA change were observed across the transition groups (P for group = 0.67). Daytime and nighttime SBP reductions differed significantly across the four transition groups (both p < 0.001).

4. Discussion

In this post hoc exploratory analysis, baseline circadian BP pattern may be associated with modest short‐term changes in SUA during antihypertensive therapy. However, the repeated‐measures analysis did not demonstrate a significant time‐by‐dipping pattern interaction, and subgroup findings should be interpreted with caution.

Prior evidence suggests that alterations in nocturnal BP may influence renal hemodynamics and tubular function, which in turn could affect urate handling. Non‐dipper pattern is characterized by a reduction in nighttime BP levels from daytime <10%. Autonomic nervous system dysfunction, impaired renal sodium handling and endothelial dysfunction have been proposed as underlying mechanisms of this abnormal dipping pattern [19, 20]. Compared with normal dipping pattern (nighttime BP levels from daytime ≥10%), the non‐dipping pattern results in worse prognosis [19, 21]. Previous studies have evaluated the relationship between circadian BP pattern and SUA. Osman Turak et al. demonstrated that non‐dippers had significantly higher SUA levels than dippers (345.1 ± 47.6 vs. 303.5 ± 53.6 µmol/L, p < 0.001) [7]. Tsutomu Koike et al. found that SUA level was higher in non‐dippers than dippers (357.0 ± 101.0 vs. 327.5 ± 89.3 µmol/L, p < 0.05) [12]. These findings suggest that impaired nocturnal BP decline may be associated with elevated SUA levels. One possible explanation is that blunted nighttime BP decline may compromise renal perfusion, leading to reduced uric acid excretion through glomerular hypofiltration [10, 11, 22]. In addition, alterations in renal hemodynamics may influence urate transport processes, including those mediated by transporters such as URAT1, thereby affecting SUA levels [23, 24, 25].

We previously found a significant decrease in SUA after 8‐week treatment with dihydropyridine calcium channel blockers [26]. However, whether this effect differs according to circadian BP patterns remains unclear. In this post hoc analysis, a greater reduction in SUA was observed in dippers, possibly reflecting relatively preserved renal function and tubular handling [3, 4, 27]. The non‐dipper rhythm pattern may be associated with a less favorable uric acid‐lowering effect of response, potentially due to impaired renal hemodynamics [28]. However, these potential mechanisms were not directly assessed in the present study and should be considered speculative. The current findings are based on observational associations and do not allow any causal inference regarding the underlying biological pathways.

In the repeated‐measures analysis, the difference in SUA changes between baseline dippers and non‐dippers did not reach statistical significance, suggesting that the observed SUA reduction may partly reflect overall treatment‐related or temporal effects rather than a clearly differential response according to baseline dipping pattern. Nevertheless, in the analysis based on least square mean change, dippers showed a numerically greater reduction in SUA than non‐dippers, which may indicate a trend requiring further confirmation. Although the observed reduction in SUA was statistically significant, its magnitude was modest and may not be clinically meaningful. Furthermore, although daytime and nighttime SBP reductions differed across various dipping transition groups, SUA changes did not differ significantly among the four groups. The significant within‐group reductions observed in the persistent dippers should therefore be interpreted cautiously and do not indicate a significant association between dipping‐pattern transitions and short‐term SUA changes in this exploratory analysis. Given the short duration of follow‐up and the absence of cardiovascular and renal outcome data, the present study cannot determine whether these modest changes in SUA confer any measurable clinical benefit.

In line with the results of our current study, previous studies have reported that men with hypertension tend to have higher SUA levels compared to women, regardless of circadian BP pattern [15]. This difference may be attributed to sex‐specific hormonal, renal mechanisms, and lifestyle factors [14, 15, 29]. In the present study, numerically different patterns of SUA changes were observed between men and women in the subgroup analyses and may partly reflect higher baseline SUA levels in men. After additional adjustment for baseline SUA, the subgroup estimates were attenuated and differed from that was observed in the primary adjusted models. These findings suggest that baseline SUA differences and regression to the mean may partly contribute to the observed subgroup differences. However, baseline SUA may also reflect an intermediate metabolic state related to both circadian BP pattern and subsequent urate metabolism, rather than acting solely as a conventional confounder. Overall, no statistically significant interaction by sex was detected in the crude or adjusted analyses. Therefore, these findings should be interpreted with caution and are insufficient to support a definitive sex‐specific effect.

Significant SUA reductions in non‐dippers with CKD may reflect vital pharmacodynamics of calcium channel blockers in CKD patients. The greater SUA decline in female non‐dippers with CKD may reflect underlying biological or hormonal differences, potentially including estrogen‐related effects reported in prior studies [30]. However, given the relatively small sample size of the CKD subgroup, particularly after further stratification for sex and the dipping pattern, these findings should be interpreted with considerable caution.

Our study should also be interpreted in the context of its other limitations. First, the 8‐week follow‐up may be insufficient to assess long‐term SUA changes in relation to the dipping pattern with antihypertensive treatment. Second, the relationship between the dipping pattern and serum uric acid was limited when generalizability to the context of other antihypertensive regimens other than CCBs. Third, the CKD subgroup analysis was underpowered, which required further validation in large‐scale studies. Fourth, the analysis incorporating transitions in the dipping pattern was exploratory, and residual influences from unmeasured metabolic or behavioral factors cannot be excluded. Given the post hoc nature of this analysis, the relatively short follow‐up duration, and the presence of missing data, the study may have been underpowered to detect modest between‐group differences in longitudinal SUA change. Therefore, the findings should be considered exploratory and hypothesis‐generating rather than confirmatory.

5. Conclusion

In this post hoc exploratory secondary analysis, modest reductions in SUA were observed during antihypertensive therapy in the least square mean change analysis. However, no significant time‐by‐dipping interaction was observed in the repeated‐measures analysis, and SUA changes were comparable across dipping‐pattern transition groups. Therefore, the findings should be considered exploratory and hypothesis‐generating. These findings should be interpreted cautiously and require confirmation in future prospective studies with longer‐term follow‐up and clinically relevant endpoints to clarify their potential cardiovascular and renal implications.

Funding

The study investigators were supported by the National Natural Science Foundation of China (grants 82370426, 82270469, 82070435, and 82070432), and National Health Commission (grants 2022YFC3601302 and 2024ZD0527304), Beijing, China, and the Shanghai Municipal Health Commission (grants 202340035 and 2024ZZ1028 and a special grant for Leading Academics 2022LJ022), and the Shanghai Talent Work Bureau (an Oriental Talent Program BJWS2024086), Shanghai, China.

Disclosure

Dr Wang reports having received consulting and lecture fees from Pfizer China (Shanghai). The other authors declare no conflicts of interest.

Supporting information

Supporting Information: jch70324‐sup‐0001‐SuppMat.docx