Effects of Cuisine-Based Chinese Heart-Healthy Diet in Lowering Blood Pressure Among Adults in China: Multicenter, Single-Blind, Randomized, Parallel Controlled Feeding Trial (2024)

More than one-fifth of the world’s population consumes Chinese cuisines regularly, but no evidence-based healthy diets fitting the Chinese food culture are available for implementation.

Clinical Perspective

Study Design

The DECIDE-Diet study design and protocol have been published previously.¹⁶ Briefly, it was a multicenter, randomized, patient- and outcome assessors–blind, parallel controlled feeding trial to evaluate the CHH diet’s efficacy, preference, and cost-effectiveness in lowering blood pressure and other cardiovascular risks among community residents with mild hypertension. The trial was conducted at 4 study centers across China (Beijing, Shanghai, Guangzhou, and Chengdu), representing 4 major Chinese cuisines: Shandong, Huaiyang, Cantonese, and Szechuan. These cuisines represent the dietary styles of >80% of the total population in China.¹⁷ Each center chose a collaborative local catering organization that was able to provide a kitchen with enough space, equipment, and dining rooms to run the study. The chefs were selected from the catering organizations, trained, and overseen by the research staff. The Management Committee designed and oversaw the trial in consultation with the Advisory Committee. The trial was supported by the National Key Research and Development Program, Ministry of Science and Technology of China. The Peking University Institutional Review Board approved the trial protocol. The trial was performed in accordance with the principles of the Declaration of Helsinki. All participants provided written informed consent. The trial was registered (URL: https://www.clincialtrials.gov; Unique identifier: NCT03882645) for the first time on March 18, 2019; the first patient was enrolled on March 22, 2019. The inclusion criteria were updated to ensure the evaluation of the intervention effect on blood pressure by requiring all participants to have SBP ≥130 mm Hg and extending the upper limit of the age range to 75 years because of difficulties in completing the first batch of 8 patients. We also replaced 24-hour urine collection with spot urine collection to increase the feasibility. The registry information on these key modifications was first updated on July 5, 2019, but no significant changes were made afterward. The final follow-up of the last patient was completed on January 31, 2021.

Study Population and Screening

Men and women aged 25 to 75 years old, living in the target communities for at least 6 months before the study, with SBP between 130 to 159 mm Hg, were eligible for this trial. More detailed inclusion and exclusion criteria were presented in the design article. Meetings were held, and advertisem*nt posters were placed in the relevant communities to recruit potential participants. The responders were first screened physically by a trained recruiter using a simple questionnaire and underwent blood pressure measurements. The potential participants were then invited to the study sites for the second screening by trained research staff to confirm their eligibility.

Run-In (7 Days), Baseline Assessment, and Recruitment

The eligible participants were invited to a 7-day run-in phase during which the usual local diets (control diets) were provided and further adjusted to fit best the participants’ usual diet taste and food preference. The baseline data were collected at the end of the run-in phase, including measurement of blood pressure, pulse rate, height, and weight; a questionnaire interview was conducted on demography, lifestyle and health behaviors, medical history, current medication use, and the food preference of the run-in diet. In addition, the spot urine was collected to test for urinary sodium and potassium excretion.

Specifically, blood pressure was measured 3 times within 24 hours: 8:00 to 10:00 am, 2:00 to 4:00 pm, and 6:00 to 8:00 pm. All blood pressure measurements were taken at least half an hour after each meal to ensure quality. Three readings were taken at each time point on the upper right arm with at least a 1-minute interval using an Omron HEM-7136 blood pressure monitor (Omron Health Care Co, Ltd; Matsusaka, Japan). The mean of 9 readings was used in the primary analysis. The pulse rate was measured with the same device for blood pressure measurement. Data on demography, lifestyle and health behaviors, medical history, and current medication use were collected with interviews by trained staff using a questionnaire.¹⁸ Body weight was measured using a standard protocol, and the food preference was measured by the participants using a visual assessment scale with a score of 10 indicating most preferred.

The participants who consumed more than 18 meals in the 7 days and whose baseline mean SBP was still within 130 to 159 mm Hg would be formally enrolled in the trial.

Randomization

All enrolled participants were randomly assigned in a 1:1 ratio to the CHH intervention and control groups using a central randomization procedure stratified by study center and enrollment batch. A statistician based at the study coordinating center in Peking University Clinical Research Institute was responsible for generating the random allocation sequence using SAS V9.4 (SAS Institute, Cary, NC). The allocation sequence was concealed from researchers and participants.

Interventions (28 Days)

Immediately after completing the 7-day run-in phase, participants in the CHH intervention group started to consume the CHH diet, whereas those in the control group continued with the same usual diet as provided during the run-in phase. All participants were asked to consume the study meals at 3 meal times (breakfast, lunch, and dinner) for the next 28 days. No other intervention was provided to the participants in both groups.

Blinding

Participants in both groups were unaware of the intervention allocation and arranged to take their meals in separate dining rooms. Although the nutrients targets differed between the control and CHH diets, each meal’s menu on the same day was kept as similar as possible between intervention groups, with the same dish name (eg, Kongbao chicken) and interchangeable raw food ingredients. In addition, research staff blinded to the intervention allocation conducted outcome measurements. Blood pressure was measured by the trained research staff blinded to the interventions, and laboratory measurements were performed by central laboratory staff without awareness of the randomization codes. Our assessment at the end of the study showed that the blinding was successful with a James’s blinding index of 0.68 (95% CI, 0.59 to 0.77).²¹

Outcomes and Follow-Ups

All the data measurements conducted at baseline were repeated at the end of the trial with the same methods. In addition, morning blood pressure, body weight, and food preference were measured weekly at the end of the intervention’s first, second, and third weeks. Morning blood pressure was measured using the same device and method used at the baseline and the end of the intervention.

The primary outcome was the change in SBP from baseline to the end of the study. The secondary outcomes included the change in diastolic blood pressure (DBP) and food preference. The mean costs of the CHH and control diets per participant during the intervention period (28 days) were calculated, and the incremental cost of the CHH diet was estimated per mm Hg SBP reduction.

The participants were reminded daily to keep their medications unchanged unless their physicians instructed it.

Quality Assurance and Quality Control

During the run-in and randomized intervention periods, the leftover foods of each participant for every meal, as well as the food not provided by the study, were recorded to estimate their exact daily intakes of food and nutrients.

All participants were required to consume all of their meals on-site during the entire study period, with <20% of the study meals allowed to be taken home to eat with the investigators’ permission and to accommodate the participants when they were unable to consume the meal on-site. The amounts of leftovers were assessed by trained study staff after each meal by weighing the leftovers or visual estimation of either real leftovers or photos of leftovers submitted by the participants. The visual estimation method was validated by 19 staff before the study began. For 5 samples of study foods leftover, each had <20%, 20% to 39%, 40% to 59%, 60% to 79% and >80% of the original foods. Mean differences (SD) between estimated and weighted amount were +1.4 (4.2) g, +2.6 (21.0) g, +1.7 (27.5) g, –1.3 (34.3) g, and –9.7 (29.1) g, respectively, for visual estimation of real foods; and were +2.5 (6.4) g, –2.5 (17.8) g, –1.4 (26.0) g, –0.6 (32.1) g, and –16.4 (34.3) g, respectively, for visual estimation of photos. The interobserver correlation coefficient ranged from 0.91 to 0.99 among regions for the real food estimation and 0.98 to 0.99 for the photo estimation.

If a participant’s body weight changed by >2 kg from baseline, their energy intake was adjusted immediately to stabilize weight.

In addition, information on foods outside of the study was collected before every meal. Overall, the participants who consumed <80% of the study meals had their medications (medications to lower blood pressure, glucose, or serum lipids) adjusted by either themselves or their physicians, and body weight changes >2 kg were considered a deviation from the study protocol and hence were excluded from the per-protocol analysis.

We established an electronic data capture system that was centrally managed, allowed prompt data collection as the study moved forward, and enabled remote monitoring of the study implementation quality.

We determined 24-hour urinary sodium and potassium excretion by the measured concentration of the electrolytes in spot urine samples using the method developed in the China Salt Substitute and Stroke Study,²² in which individual spot urine sodium and potassium concentrations were multiplied by a single estimated 24-hour urine volume that represents the average 24-hour urine volume of the study population. Our study obtained the mean volume (2.5 L) of 24-hour urine samples in the first batch of 8 participants in the Beijing study center.

Statistical Analysis

The study was designed to recruit 180 participants in each arm that would allow us to detect an intervention effect size of 3.0 mm Hg in SBP in 4 weeks, assuming a SD change in SBP of 8.0 mm Hg, with 90% power and a type I error rate of 5%.¹⁶ The effect size was conservatively estimated on the basis of data from the DASH trial.⁸

The primary analyses followed the intention-to-treat principle and were conducted among randomized participants with blood pressure taken at baseline and final follow-up (or at withdrawal). The linear regression model was used to estimate the net differences between the 2 groups for primary and secondary outcomes, reported as least square means after adjusting for the center as a fixed effect. We did not impute primary or secondary outcomes for the minimal dropout rate (3.7% in CHH and 1.7% in control).

The baseline characteristics were compared between the 2 groups to examine the comparability. Sensitivity analyses were performed to repeat the analyses in the per-protocol set and adjust for the change in body weight from baseline to end of the intervention, as prespecified in the statistical analysis plan. More sensitivity analyses were conducted to explore further the possible effect of other factors on the intervention effects, including a linear mixed model to adjust for the center’s clustering effect and the baseline blood pressure values, another linear mixed model to adjust further for all possible confounders measured in our study (ie, age, sex, antihypertension medication use, alcohol drinking, and weight change from baseline to the end of study), and pooling the center-specific estimations by the random-effect meta-approach to account for different centers. The per-protocol set excluded participants who consumed <80% of the study meals, had their medications (medications to lower blood pressure, glucose, or serum lipids) changed, or had body weight change >2 kg during the intervention period.

The mean of the 3 morning blood pressure readings at all visits (baseline and end of first, second, third, and last week) was used as the outcome variable to test the temporal trend of effect. A linear mixed model was used to adjust for the center as a random effect, time of visit, and the interaction term of dietary intervention with the time of visit. Prespecified subgroup analyses were performed to identify potential modifiers of the intervention effect, including the type of Chinese cuisine, sex, age, hypertension status, baseline SBP, antihypertension medication use, and estimated 10-year risk of ischemic cardiovascular disease.

Each participant’s everyday nutrient and food intakes were estimated according to the standard daily menus, meal recipes, recorded amount of leftovers, and foods consumed outside of the study, using the China Food Composition Table.²³ The baseline intakes of nutrients for each participant were defined as the means during the last 3 days of run-in, and the intervention intakes were defined as the means during the 28 days of intervention. The same model as for primary analyses was used to compare the differences in change in nutrients and foods between the intervention groups.

Implementation of the Study

Among the 2268 individuals screened, the eligible 332 started the run-in phase. Of 332 initial participants, 265 passed the run-in and were randomized (Figure ​(Figure1).1). Because of the COVID-19 epidemic and the quarantine policy, the study had to be terminated early, achieving 74% of the planned sample size (360). However, a high percentage (97%) of the participants completed the study, and on average, they consumed 97% of the meals provided by the study. The study recruited at least 20% of participants from each cuisine area.

The actual mean nutrients intake and energy composition of participants in the CHH intervention group were similar to the targets of the CHH diet (Table S1). Compared with the baseline intakes, the percentage of energy intake from carbohydrates and protein increased, whereas it decreased for fat in the CHH diet group. There were also increased fiber, potassium, magnesium, and calcium and decreased sodium intakes (all P values <0.05, Table ​Table1).1). Meanwhile, the control group’s major nutrient intakes were almost unchanged compared with the baseline.

The net change in estimated 24-hour urinary sodium and potassium excretion was –2194 mg (95% CI, –2870 to –1517) and 941 mg (95% CI, 363 to 1519), respectively, in line with the changes in dietary sodium and potassium intake.

The mean change in weight from the baseline to the end of the intervention was –0.6 (±1.0) kg and 0.1 (±1.0) kg in the CHH diet and control diet groups, respectively (P<0.001).

Baseline Characteristics of the Participants

The baseline characteristics, including the type of cuisine, demography, lifestyle, blood pressure, history of hypertension, antihypertension medication use, nutrients intake, and food pattern, were compatible (all P values >0.05) between the 2 study groups (Tables ​(Tables11 and ​and2).2). The baseline characteristics of participants in the primary analysis were almost the same as those randomized.

Effect of CHH Diet on Blood Pressure

The primary analysis showed that the changes in SBP from baseline to the end of the intervention in the CHH diet and control diet groups were –15.0 (95% CI, –16.5 to –13.5) mm Hg and –5.0 (95% CI, –6.5 to –3.5) mm Hg, respectively. The net difference in the change of SBP in the CHH diet group from baseline to the end of the intervention was –10.0 mm Hg (95% CI, –12.1 to –7.9) compared with the control diet group (P<0.001). For DBP, the corresponding changes in the CHH diet and control diet groups were –6.7 (95% CI, –7.5 to –5.7) mm Hg and –2.8 (95% CI, –3.7 to –1.9) mm Hg, respectively. The net difference in the CHH diet group was –3.8 mm Hg (95% CI, –5.0 to –2.5) compared with the control diet group (Figure ​(Figure2).2). The sensitivity analyses in the per-protocol set, with weight change adjusted, adjustment for clustering and baseline values, adjustment for all possible confounders, and meta-analysis, showed similar results (Table ​(Table33).

Figure ​Figure33 shows the change in morning blood pressure measured weekly from baseline to the end of the intervention. Morning SBP started to differ between the 2 groups from the end of the first week, and the difference became wider until the end of the intervention. The mean DBP showed a similar trend and a significant difference at the end of the study.

Subgroup Analysis

The prespecified subgroup analyses showed that female participants experienced a greater reduction in SBP (–12.0 [95% CI, –15.0 to –9.0] mm Hg) than the men (–7.6 [95% CI, –10.4 to –4.7] mm Hg), with a P for interaction of 0.037. All other prespecified factors did not significantly influence the effects of the CHH diet on SBP (Figure ​(Figure44).

Effects on Food Preference and Food Variety

The food preference visual analytic score was 9.5 in both groups at baseline. The change from baseline to the end of the trial was 0.2 in both groups, and the net difference in the change between the 2 groups was 0.1 (95% CI, –0.1 to 0.2; P=0.558).

The food variety defined by the number of food items consumed by study participants was, on average, 4.7 (95% CI, 4.4 to 5.1) more in the CHH diet group than in the control diet group.

Cost of the CHH Diet in Comparison With the Control Diet

The average cost for food materials was CNY 24 and CNY 21 per day for the CHH and control diets, respectively. Other costs, such as the expenses of chefs and assistants who worked for the study, were about the same between the 2 groups and were cancelled out between the CHH and local diets. The incremental cost-effectiveness ratio for 1 mm Hg SBP reduction was CNY 0.4 (USD 0.06) per day.

Safety Outcomes

Six adverse events were reported during the run-in phase and 14 during the intervention phase. None of them was a serious adverse event, and none was related to the intervention. No difference in the number of events was found between the 2 groups (P=0.259; Table ​Table4).4). No participant withdrew because of adverse events.

Acknowledgments

Contributor and guarantor information: Y. Wu, Y. Wang, and J.C. developed the research idea and designed the study; Y. Wang, G.Z., H.Z., J.S., X. Li, and Z.Y. developed the CCH diet with advices from J.C. and Y. Wu; Y. Wang, L.F., J.Y., X. Lan, S.L., Y.Z., H.D., S.C., and Z.L. collected data; L.F. and Y.Z. analyzed data with advice from P.G.; X.C., L.F., and G.X. were responsible for the field and data quality control; Y. Wang, L.F., and Y. Wu wrote the first draft of the article, which was reviewed by all authors. Y. Wu and Y. Wang are the guarantors. The corresponding authors attest that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted.

Study Management Committee: Yangfeng Wu (Chair), Yanfang Wang (Cochair), Jianqin Sun, Huilian Zhu, Guo Zeng, Zhenquan Yang.

Study Advisory Committee: Junshi Chen (Chair), Pao-Hwa Lin, Wenyi Niu, Pei Gao, Hai Fang, Guansheng Ma, Ming Li.

Participating centers and staff information:

Peking University Clinical Research Institute (study coordinating center): Yangfeng Wu (Project Principal Investigator), Yanfang Wang (Study Principal Investigator), Huijuan Li (Project Manager), Lin Feng (Study Coordinator), Gaoqiang Xie (Data Management Supervisor), Wenyao Ma (Data Manager), Jiarong Li (Assistant Data Manager), Xiayan Chen (Clinical Research Associate), Yidan Zhu, Wuxiang Xie, Shulan Zhu, Xue Bai, Yuqing Gao, Chenglong Li, Yanjun Ma, Yiyu Hu, Ke Miao, Shujing Zhu, Caiyun Zhao, Yunqing Zhu.

School of Public Health, Sichuan University: Guo Zeng (Key Investigator), Wenya Yin, Ming Li, Yiqi Zhang, Congjie Cai, Xinxin Pang, Hong Sun, Haiying Zhang, Dan Bai.

School of Public Health, Sun Yat-sen University: Huilian Zhu (Key Investigator), Aiping Fang, Rongzhu Huang, Yun Luo, Zhaoyan Liu, Xinlei Lin.

Huadong Hospital Affiliated to Fudan University: Jianqin Sun (Key Investigator), Yanfang Zhao, Huijing Bai, Mengyao Ye, Zhen Li, Qing Fan, Jun Tang, Fei Xiao, Jianming Wang, Yanguo Zhang, Guixiang Zhang, Weiping Chen, Weigang Zhao.

Sichuan Tourism University Culinary College: Xiang Li (Key Investigator), Guangsen Tong, Kun Zhang.

The Center for Disease Control and Prevention of Yuexiu District, Guangzhou: Bin Xu, Ting Zhang.

Yangzhou University College of Tourism and Cuisine: Zhenquan Yang (Key Investigator), Xinchi Wang, Yunlong Zhu, Jing Peng, Haifeng Zhang, Lu Gao, Shengqi Rao.

Chinese Center for Disease Control and Prevention: Jianguo Xu, Jing Yang, Dong Jin, Ji Pu, Juan Zhou, Yuanmeihui Tao, Yifan Jiao.

Qingdao Yilianhua Catering Co, Ltd: Zhiheng Wang, Yiying Xu.

Health Hope (Beijing) Technology Co, Ltd: Yuxin Li, Shi Qiu.

Sources of Funding

The study is funded by the National Key Research and Development Program, Ministry of Science and Technology of China (2016YFC1300201) and the Chinese Nutrition Society Nutrition Science Foundation-Yum China Dietary Health Foundation (CNS-YUM2019B16). The funders have no role in the study design; in the collection, analysis, and interpretation of data; in the writing of the report; or in the decision to submit the article for publication. The researchers are independent from the funders. Y. Wu and Y. Wang had full access to all of the data (including statistical reports and tables) in the study and can take responsibility for the integrity of the data and the accuracy of the data analysis.

Disclosures

None.

Supplemental Material

Table S1 (Targets and means of nutrients intakes consumed by participants in the CHH diet group during the 28-day intervention phase)

DECIDE-Diet Study Group

Circulation is available at www.ahajournals.org/journal/circ

^*Y. Wang and L. Feng contributed equally.

^†A complete list of the investigators in the DECIDE-Diet Study Group is provided in the Appendix.

Supplemental Material is available at https://www.ahajournals.org/doi/suppl/10.1161/CIRCULATIONAHA.122.059045.

For Sources of Funding and Disclosures, see page 314.

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