Literature DB >> 34329344

Association between blood pressure categories and cardiovascular disease mortality in China.

Jie Guo1, Jun Lv1,2,3, Yu Guo4, Zheng Bian4, Bang Zheng1, Man Wu1, Ling Yang5, Yiping Chen5, Jian Su6, Jianqiang Zhang7, Jvying Yao8, Junshi Chen9, Zhengming Chen5, Canqing Yu1,2, Liming Li1,2.   

Abstract

BACKGROUND: Blood pressure (BP) categories are useful to simplify preventions in public health, and diagnostic and treatment approaches in clinical practice. Updated evidence about the associations of BP categories with cardiovascular diseases (CVDs) and its subtypes is warranted. METHODS AND
FINDINGS: About 0.5 million adults aged 30 to 79 years were recruited from 10 areas in China during 2004-2008. The present study included 430 977 participants without antihypertension treatment, cancer, or CVD at baseline. BP was measured at least twice in a single visit at baseline and CVD deaths during follow-up were collected via registries and the national health insurance databases. Multivariable Cox regression was used to estimate the associations between BP categories and CVD mortality. Overall, 16.3% had prehypertension-low, 25.1% had prehypertension-high, 14.1% had isolated systolic hypertension (ISH), 1.9% had isolated diastolic hypertension (IDH), and 9.1% had systolic-diastolic hypertension (SDH). During a median 10-year follow-up, 9660 CVD deaths were documented. Compared with normal, the hazard ratios (95% CI) of prehypertension-low, prehypertension-high, ISH, IDH, SDH for CVD were 1.10 (1.01-1.19), 1.32 (1.23-1.42), 2.04 (1.91-2.19), 2.20 (1.85-2.61), and 3.81 (3.54-4.09), respectively. All hypertension subtypes were related to the increased risk of CVD subtypes, with a stronger association for hemorrhagic stroke than for ischemic heart disease. The associations were stronger in younger than older adults.
CONCLUSIONS: Prehypertension-high should be considered in CVD primary prevention given its high prevalence and increased CVD risk. All hypertension subtypes were independently associated with CVD and its subtypes mortality, though the strength of associations varied substantially.

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Year:  2021        PMID： 34329344      PMCID： PMC8323908          DOI： 10.1371/journal.pone.0255373

Source DB:  PubMed          Journal:  PLoS One        ISSN： 1932-6203            Impact factor:   3.240


Introduction

Hypertension is the most important risk factor for cardiovascular disease (CVD) [1]. According to single or combined elevations of systolic blood pressure (SBP) and diastolic blood pressure (DBP), hypertension is frequently classified into isolated systolic hypertension (ISH), isolated diastolic hypertension (IDH), and systolic-diastolic hypertension (SDH). The associations with CVD might vary among hypertension subtypes because of their different pathophysiological mechanisms [2,3]. Previous studies had demonstrated definite evidence for the CVD risk of ISH and SDH but the effect of IDH was inconclusive [4-6]. Moreover, a continuous and positive association with CVD had been demonstrated above SBP 115–120 mm Hg [7,8]. There is a growing concern about the CVD risk of SBP 120 mm Hg to 140 mm Hg. The Seventh Report of the Joint National Committee on the Prevention, Detection, Evaluation, and Treatment of High Blood Pressure (JNC-7) introduced a category as prehypertension with BP level of 120 to 139/80 to 89 mm Hg [9]. However, evidence about the CVD risk of prehypertension remains controversial [10,11]. A meta-analysis demonstrated that the increased CVD mortality was largely driven by the high-range of prehypertension [12]. In the 2017 American College of Cardiology/American Heart Association (ACC/AHA) BP guideline, SBP 130–139 and/or DBP 80–89 mm Hg was newly defined as “Hypertension stage 1” [13]. Current evidence found that “Hypertension stage 1” was associated with the increased CVD risk among the younger adults but studies conducted among the older did not find an increased risk, partly due to their small sample size of older adults [14-16]. According to the 2017 ACC/AHA guideline, both the prevalence of hypertension and the number of participants who should take antihypertension treatments increase dramatically [17]. Moreover, during the COVID-19 pandemic, individuals with CVD were more vulnerable to COVID-19 and had a greater risk of developing into a severe condition [18]. Therefore, to clarify the role of “Hypertension stage 1” on the development of CVD, especially among the older adults, is warranted for making prevention strategies about this BP group. Besides, cerebrovascular diseases have been the top leading cause of mortality in China [19]. Moreover, hemorrhagic stroke accounted for a larger proportion in the Chinese population than western populations [20] and the mortality from hemorrhagic stroke was higher than that from ischemic stroke [21]. However, there is limited evidence for the associations of BP categories with the major subtypes of cardiovascular disease (i.e., ischemic heart disease, ischemic stroke, and hemorrhagic stroke) in a Chinese population. We hypothesized that the newly defined hypertension was associated with increased CVD risk across a wide range of age, and the strength of the association of different hypertension subtypes with CVD and its subtypes might vary. We aimed to provide more detailed information about the associations of BP categories with overall and specific CVD mortality based on the China Kadoorie Biobank (CKB) study.

Materials and methods

Study population

Details of the CKB study design and methods have been reported elsewhere [22,23]. The CKB is a community-based prospective cohort study, involving over 0.5 million adults from 10 areas of China between 2004 and 2008. All men and women aged 30–79 years who were permanently resident and without major disability in each administrative unit were identified and invited to participate [22]. Ethics approvals were obtained from the Ethical Review Committee of Oxford University, the China National Center for Disease Control and Prevention (CDC), and from institutional research boards at the local CDCs in the 10 regions, and all participants provided written informed consent. The study was in accordance with the Declaration of Helsinki. For the current study, we excluded participants with missing data for covariates (n = 49), or with implausible censoring date (n = 1), or with CVD or cancer at baseline (n = 25 511). Moreover, we further excluded participants with the antihypertension treatment at baseline (n = 65 168), because both dose and types of antihypertensive medications may influence BP levels and lead to misclassification of BP categories. Finally, we included 430 977 participants in the main analyses. By December 31, 2016, 4434 (1.03%) participants were lost to follow-up.

Assessment of BP categories

BP was measured twice by trained staff using a UA-779 digital monitor after they remained at rest in the seated position for at least 5 minutes [24]. If the difference between the two measurements was >10 mm Hg for SBP, a third measurement was required. Only the last two readings were recorded and used to calculate the average of SBP and DBP [7]. According to the JNC-7, BP categories were defined into five groups 1) normal (SBP <120 and DBP <80 mm Hg); 2) prehypertension (SBP 120−139 and/or DBP 80−89 mm Hg); 3) ISH (SBP ≥140 and DBP <90 mm Hg); 4) IDH (SBP <140 and DBP ≥90 mm Hg); 5) SDH (SBP ≥140 and DBP ≥90 mm Hg) [9]. In the 2017 ACC/AHA hypertension guideline, hypertension was defined as SBP ≥130 mmHg and/or DPB ≥90 mmHg [13]. To estimate the effect of “Elevated” and “Hypertension stage 1” in the 2017 ACC/AHA hypertension guideline, we further divided prehypertension into prehypertension-low (equal to “Elevated”, SBP 120−129 and DBP <80 mm Hg) and prehypertension-high (equal to “Hypertension stage 1”, SBP 130−139 and/or DBP 80−89 mm Hg) [13].

Assessment of covariates

Potential confounding variables in this study included sociodemographic characteristics (age, sex, education, and marital status), lifestyle behaviors (tobacco smoking, alcohol consumption, physical activity, and consumption of red meat, fresh fruits, and vegetables), diabetes, menopausal status for female only, and family medical history (heart attack, stroke). Physical activity was calculated by multiplying the metabolic equivalent tasks (METs) value for a particular type of physical activity by hours spent on that activity per day and summing the MET-hours for all activities for each day. Height, body weight, and heart rate were measured by trained staff. Body mass index (BMI) was calculated as weight in kilograms divided by height in meters squared. Prevalent diabetes was defined as a measured fasting blood glucose concentration of ≥7.0 mmol/L, a measured random blood glucose concentration of ≥11.1 mmol/L, or self-reported diagnoses of diabetes.

Assessment of outcomes

The vital status of participants was collected through linkage with regional disease and death registries, and with the new national health insurance databases. To minimize the under-reporting of deaths, we also carried out active follow-up annually, by reviewing residential records, visiting local communities, or directly contacting participants [23]. All deaths were coded using the 10th International Classification of Diseases (ICD-10). The main outcome measures in our analysis were mortality from CVD (ICD-10 codes I00 to I99), ischemic heart disease (I20 to I25), myocardial infarction (I21 to I23), cerebrovascular disease (I60 to I69), hemorrhagic stroke (I61), and ischemic stroke (I63).

Statistical analysis

Baseline characteristics were described as means and standard errors or percentages in each BP category, with adjustment for age, sex, and survey sites as appropriate, using either multiple linear regression (for continuous variables) or logistic regression (for categorical variables). Person-years at risk were calculated from the baseline to the date of death, loss to follow-up, or 31 December 2016, whichever occurred first. Cox proportional hazard models, stratified by age at risk (in 5-year intervals), sex, and survey sites (10 regions), were used to estimate the hazard ratios (HR) and 95% confidence intervals (CI) for CVD mortality related to BP categories, with age as the timescale. The proportional hazards assumption was checked using the Schoenfield residuals, and no violation was observed. The associations of BP categories with mortality from total CVD and its subtypes were assessed after adjustment for age (continuous) at recruitment; and subsequently adjusted for the level of education (no formal or primary school, middle school or high school, or college or higher), marital status (married, or others), smoking status (5 categories: never or occasional smoker, ex-smoker who quit not because of illness, ex-smoker who quit because of illness and current smoker divided into 3 groups according to cigarette equivalents/day [<15, 15−25, ≥25]), alcohol consumption (7 categories: never or occasional or seasonal drinker, ex-regular drinker of reduced intake, 1 to 5 days/week, almost daily drinker divided into 4 groups according to total grams of alcohol [<15, 15−<30, 30−<60, ≥60]), intake frequencies of red meat, fresh fruits, and vegetables (daily, 4−6 days/week, 1−3 days/week, or monthly, rarely or never), physical activity in MET-hours a day (continuous), survey seasons (spring; summer: June, July, August; autumn; winter: December, January, February), menopausal status (for female only, postmenopausal or others); and finally further adjustment for prevalent diabetes at baseline, family medical history, BMI (continuous), and heart rate (continuous). The Nelson-Aalen method was used to describe the cumulative hazard of CVDs during the follow-up across BP categories. Moreover, we conducted subgroup analyses by age (30−49, 50−59, or 60−79 years), sex (male or female), and survey sites (10 areas) and the interaction effect was estimated by adding a cross-product term (e.g., age groups × BP categories) to the Cox model. In sensitivity analyses, we 1) investigated the associations between BP categories and CVD mortality after excluding the first 2 years of follow-up; or excluding those who had diabetes at baseline; 2) compared the risk of ISH, IDH, and SDH with further controlling SBP or DBP by conducting analyses in the range of SBP/DBP level of 140−159/90−99 mm Hg and of ≥160/100 mm Hg separately. Statistical analyses were performed using SAS 9.3 (SAS Institute, Cary, NC). All P values were two-sided, and we defined statistical significance as P <0.05.

Results

Baseline characteristics of participants by BP categories

Among 430 977 participants, the mean age was 50.6 years, 58.7% were female, and 57.6% were from rural areas. Overall, 16.3% had prehypertension-low, 25.1% had prehypertension-high, 14.1% had ISH, 1.9% had IDH, and 9.1% had SDH. The prevalence of ISH in older participants was higher than that in younger, while the opposite was observed for IDH (). Compared with normal BP, participants with prehypertension-low, prehypertension-high, or hypertension subtypes were older (except for IDH P = 0.10) and were less likely to be female, had a lower level of education (except for IDH P = 0.99), were more likely to live in rural areas (except for IDH P = 0.32), were less likely to smoke but more likely to drink regularly, were more likely to consume red meat (except for prehypertension-low P = 0.33) but less likely to consume fresh fruit, had a higher level of BMI and heart rate, and had a higher prevalence of diabetes (). The distribution of baseline characteristics of the study population without adjustment for age, sex, and survey sites was presented in . Abbreviations: BP, blood pressure; SE, standard error; MET, metabolic equivalent of task; bpm, beat per minute. a All variables were adjusted for age, sex, and survey sites when appropriate. b Average weekly consumptions of red meat, fresh vegetables, and fruits were calculated by assigning participants to the midpoint of their consumption category. c Only for female.

Association of BP categories with CVD mortality

During 4.3 million person-years of follow-up (mean duration of follow-up: 10.0 years; median 10.2 years), there were 9660 deaths from CVD, 3564 from ischemic heart diseases (including 2248 myocardial infarction), and 5168 from cerebrovascular diseases (including 3092 hemorrhagic strokes and 965 ischemic strokes). With normal BP as the reference, prehypertension had a higher risk of overall CVD, cerebrovascular disease, and hemorrhagic stroke (). Similar patterns were also observed for prehypertension-low and prehypertension-high (). Besides, prehypertension-high was related to the increased risk of ischemic heart disease and ischemic stroke. All hypertension subtypes were associated with the increased mortality of overall CVD and its subtypes, after basic- or multi-adjustment for potential confounding factors (). The multi-adjusted HRs for overall CVD were highest for SDH (adjusted HR, 3.81 [95%CI, 3.54 to 4.09]), followed by IDH (2.20 [95%CI, 1.85 to 2.61]), ISH (2.04 [95%CI, 1.91 to 2.19]), prehypertension-high (1.32 [95%CI, 1.23 to 1.42]), and finally prehypertension-low (1.10 [95%CI, 1.01 to 1.19]) (). The Nelson-Aalen curves of the cumulative hazard of CVDs visually showed that SDH had the highest hazard curve (). Abbreviations: BP, blood pressure; CVDs, cardiovascular diseases; ISH, isolated systolic hypertension; IDH, isolated diastolic hypertension; SDH, systolic-diastolic hypertension; HR, hazard ratio; CI, confidence interval. Multi-adjusted hazard ratios were adjusted for age (continuous variable); education level (no formal or primary school, middle school or high school, or college or higher); marital status (married, others); smoking status (never or occasional smoker, ex-smoker who quit not because of illness, ex-smoker who quit because of illness and current smoker divided into 3 groups according to cigarette equivalents/day [<15, 15–25, ≥25]); alcohol consumption (never or occasional or seasonal drinker, ex-regular drinker of reduced intake, 1 to 5 days/week, almost daily drinker divided into 4 groups according to total grams of alcohol [<15, 15-<30, 30-<60, ≥60]); intake frequencies of vegetables, fruits, and red meat (daily, 4 to 6 days/per week, 1 to 3 days/per week, or monthly or rarely/never); physical activity (continuous variable); body mass index (BMI, continuous variable); survey season (spring; summer: June, July, August; autumn; winter: December, January, February); heart rate (continuous variable); diabetes at baseline (2 categories, presence or absence); family history of heart attack, or stroke (2 categories, presence or absence, only adjusted for in corresponding analysis of cause-specific mortality), and were stratified according to age at risk (in 5-year intervals), sex, and survey sites.

Subgroup and sensitivity analyses

Prehypertension-high and all hypertension subtypes were associated with increased CVD mortality among all age groups (). Moreover, we observed stronger associations in younger participants than older ones (all P values for interaction <0.005, except for ischemic stroke P = 0.20). Heterogeneity by sex was observed for overall CVD, ischemic heart disease, cerebrovascular diseases, and hemorrhagic stroke (). shows the associations between BP categories and CVD mortality across 10 survey sites. There was a statistically significant interaction between BP categories and survey sites (P for interaction = 0.016).

Associations of BP categories with mortality from CVDs and its major subtypes by age groups.

Abbreviations: BP, blood pressure; CVDs, cardiovascular diseases; HR, hazard ratios; CI, confidence interval. Reference was normal BP. Multi-adjusted HR were adjusted for age, education level, marital status, smoking status, alcohol consumption, intake frequencies of vegetables, fruits, and red meat, physical activity, body mass index, survey season, heart rate, diabetes at baseline, family history of heart attack, stroke (only adjusted for in corresponding analysis of cause-specific mortality) and were stratified according to sex and survey sites. Statistically significant heterogeneity was observed in the associations between blood pressure categories and CVD mortality across age groups (all P values for interaction <0.005, except for ischemic stroke P = 0.20). Data markers represent the point estimate of hazard ratio. Error bars represent 95% confidence interval. Black arrows represent the confidence intervals exceed the X-axis value. The associations between BP categories and CVD mortality were not materially altered after excluding the first 2 years of follow-up () or excluding participants who had diabetes at baseline (). In hypertension, the multivariable-adjusted HR of SDH for CVD mortality was higher than that of ISH and IDH (P <0.001), while the effect of ISH on CVD mortality was almost similar to that of IDH ().

Discussion

The present study, involving more than 0.4M people living in China, found that both prehypertension-low and prehypertension-high were associated with higher CVD mortality independent of other cardiovascular risk factors. All hypertension subtypes were associated with increased mortality from overall CVD and CVD subtypes, and the CVD risk of SDH was higher than that of ISH and IDH. The associations between BP categories and cerebrovascular diseases were stronger than for ischemic heart diseases. Furthermore, the associations between BP categories and CVD mortality were stronger in younger participants than in older ones. The 2017 ACC/AHA hypertension guideline defined the 130-139/80-89 mm Hg as “Hypertension stage 1” [13]. Based on the guideline, about a quarter of participants (i.e., prehypertension-high) in our study would be newly defined as hypertension. Previous studies found an increased CVD risk of this BP category in younger adults, which was consistent with our results [15,25]. However, the findings were mixed in older adults. Some studies reported that this BP category was not associated with the increased CVD risk [15,25], while others reported a slightly higher CVD mortality for those aged ≥65 years (HR [95%CI]: 1.22 [1.04 to 1.44]) [26]. In our study, the CVD risk of this BP category became weaker in participants aged 60 to 79 years, but there was still a 22% higher CVD mortality than the normal BP. Moreover, this BP category was associated with increased mortality from CVD subtypes, especially for hemorrhagic stroke, with a 69% higher risk compared to normal BP. Given the high prevalence of cerebrovascular diseases, especially hemorrhagic stroke in China [20], managing the BP of that newly defined hypertension in the public health practice may benefit for reducing the CVD burden. Previous studies have reported consistent associations of both ISH and SDH with the CVD mortality [4,6,27], and clinical trial also detected that individuals with ISH or SDH could benefit from antihypertensive treatment [28], but whether IDH was associated with an increased CVD risk was still controversial [4,5,29]. In the current study, all hypertension subtypes were related to the increased mortality from CVD and specific CVDs, and IDH was at least as important as ISH in predicting future CVD mortality. These results also supported current guidelines which recommended pharmacologic treatments based on DBP as well as SBP [9,30]. In line with previous studies [4,6], the present study demonstrated that SDH conferred the highest CVD risk, followed by IDH and ISH. The heterogeneity among hypertension subtypes might be explained by the higher mean level of SBP or DBP of SDH than that of ISH or IDH. However, after controlling for SBP or DBP, the CVD risk of SDH was still higher than that of ISH. Similar results were also observed for cerebrovascular disease, hemorrhagic stroke, and ischemic stroke. Our finding suggested that incorporating SBP and DBP might improve the prediction of CVD risk models, and future studies are needed to clarify potential mechanisms for the heterogeneity among hypertension subtypes. Likewise, we also identified that the strength of the associations between hypertension subtypes and specific CVD mortality varied significantly, with stronger for cerebrovascular disease than for ischemic heart disease, and extreme for hemorrhagic stroke than for ischemic stroke. Previous literature found that the associations between BP categories and CVD deaths varied by age, with a stronger association in younger adults than older ones [6,31]. Consistent with these findings, we also identified stronger associations between BP categories and major CVDs (except ischemic stroke) among younger compared with older. Additionally, the mechanisms of ISH in young are still unclear. Some studies showed that in young and middle-aged adults, ISH was “pseudo” or “spurious” hypertension attributed to amplification of central aortic waveform, while others found ISH might be related to increased stroke volume and aortic stiffness [32-34]. However, the increased CVD risk of ISH in the current study indicated the terms “pseudo” or “spurious” hypertension might be unjustified. To the best of our knowledge, this is the largest prospective study to investigate the associations of BP categories with mortality from CVD and its major subtypes in a Chinese population. The chief strengths of this study included the unified standard methods for collecting information, the sufficient number of CVD outcomes, and the rigorous check of diagnosis of CVD. This study also had several limitations. First, BP was obtained in a single baseline survey, without considering the fluctuation of BP in the daytime, which may misclassify individuals with “white-coat hypertension” or “masked hypertension” [35,36]. However, it is not feasible to monitor ambulatory BP for each participant in a large-scale population study. Second, we excluded participants who taking antihypertensive medicines, which might cause selection bias and limit the extrapolation of our findings. However, antihypertensive medication would affect the patients’ blood pressure level, leading to misclassification of BP categories. Hence, it is more reasonable to restrict study participants without antihypertensive treatment. Third, elevated low-density lipoprotein (or total) cholesterol, a risk factor for CVD [37], was not available in the present study. A meta-analysis reported that total cholesterol (TC) was positively associated with ischemic heart disease mortality, but there was rather a weak association of TC with mortality from cerebrovascular disease and hemorrhagic stroke [38]. Therefore, failing to adjust for TC is unlikely to have any appreciable impact on the associations with cerebrovascular disease in our study. Fourth, under-reporting of cardiovascular deaths might have occurred during follow-up, but the probability of under-reported would not depend on BP categories, and we also used multiple ways to minimize the under-reporting of deaths.

Conclusion

The definition of hypertension is one of the most notable changes in the 2017 ACC/AHA hypertension guideline. The present study provided important evidence about the long-term CVD risk of those new hypertensives (i.e., “Hypertension stage 1” in 2017 guideline and prehypertension-high in the current study) and highlighted its important role in CVD primary prevention, both due to the high prevalence and be associated with higher CVD mortality. All hypertension subtypes were related to the increased mortality from CVDs, especially from hemorrhagic strokes, and should be considered in BP management regardless of age and gender.

STROBE statement for observational studies.

(DOCX) Click here for additional data file.

Nelson-Aalen cumulative hazard for cardiovascular diseases according to the blood pressure categories.

(DOCX) Click here for additional data file.

Associations of ISH, IDH, and SDH with mortality from CVDs and its major subtypes in stage 1 hypertension, stage 2 hypertension and total hypertension.

(DOCX) Click here for additional data file.

Baseline characteristics of the study population by baseline BP categories.

(DOCX) Click here for additional data file.

Associations of blood pressure categories with cardiovascular diseases mortality among 430 977 participants.

(DOCX) Click here for additional data file.

Associations of blood pressure categories with mortality from cardiovascular diseases and its major subtypes.

Values are hazard ratios (95% confidence interval). (DOCX) Click here for additional data file.

Associations of prehypertension and hypertension subtypes with mortality from cardiovascular diseases and its major subtypes by sex.

(DOCX) Click here for additional data file.

Associations of prehypertension and hypertension subtypes with mortality from cardiovascular diseases by survey sites.

(DOCX) Click here for additional data file.

Associations of blood pressure categories with deaths due to cardiovascular diseases among participants excluding the first two years of follow-up.

(DOCX) Click here for additional data file.

Associations of blood pressure categories with deaths of cardiovascular diseases among non-diabetes participants at baseline.

(DOCX) Click here for additional data file.

Baseline questionnaire in the China Kadoorie Biobank study.

(DOCX) Click here for additional data file.

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This paper was transferred from another journal. As a result, its full editorial history (including decision letters, peer reviews and author responses) may not be present. 12 Apr 2021 PONE-D-21-07340 Association between Blood Pressure Categories and Cardiovascular Disease Mortality in China PLOS ONE Dear Dr. Yu, Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process. Please revise the manuscript according to the comments of the three Reviewers. In addition, please consider analyses using blood pressure as a continuous variable. Please submit your revised manuscript by May 27 2021 11:59PM. 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We will update your Data Availability statement on your behalf to reflect the information you provide. 3. One of the noted authors is a group or consortium; China Kadoorie Biobank Collaborative Group. In addition to naming the author group, please list the individual authors and affiliations within this group in the acknowledgments section of your manuscript. Please also indicate clearly a lead author for this group along with a contact email address. [Note: HTML markup is below. Please do not edit.] Reviewers' comments: Reviewer's Responses to Questions Comments to the Author 1. Is the manuscript technically sound, and do the data support the conclusions? The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. 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Is the manuscript presented in an intelligible fashion and written in standard English? PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here. Reviewer #1: Yes Reviewer #2: Yes Reviewer #3: Yes ********** 5. Review Comments to the Author Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters) Reviewer #1: The authors' work with large prospective cohort (n=431,000, 10-year follow-up) provided information on blood pressure (BP) category and prospective mortality risk from cardiovascular (CV) origin. Importantly, prehypertension, along with all subcategories of hypertension, was associated with increased mortality compared to normotensive participants in adult Chinese free of baseline antihypertensive drug, CV disease or cancer. Major comments 1. High BP could aggravate other non-CV diseases. The hazard ratio (HR) of BP categories to all-cause and non-CV mortality should therefore be reported. CV death, as a competing event to death from other causes, should be evaluated by competing risk regression. 2. There could be discrepancies on the mortality rate and HR of BP categories with mortality among the included centers. Center-specific statistics should as well be provided. Minor comments 1. The limitation part in the abstract (line 56-58), albeit modestly written, could lead to a suspicion in the first impression on the credibility of results and conclusion. In the abstract, it's better to put "BP was measured twice in a single visit" into the methods section and the limitation should be omitted. 2. Following the line, in methods part, line 119-120, the authors should rephrase the measurement of blood pressure as "occasion" and "reading" are two distinct entities. The authors may refer to their Lacey 2018 Lancet Global Health paper (PMID 29773120) for more precise description on the method of BP measurement. 3. Standard Error SE used in Table 1 is inversely correlated to sample size, and therefore definitely small when the sample size is huge. Change it to SD to show the distribution of continuous variables. 4. line 88: "the number of hypertension" should be the prevalence of hypertension or the number of participants with hypertension. 5. line 96: there is limited evidence for the associations of 'BP categories' with the.... 6. line 113: Check if the study was performed in accordance with the Declaration of Helsinki, and add to the end of this paragraph. Reviewer #2: “Association between Blood Pressure Categories and Cardiovascular Disease Mortality in China” divided the population into 6 categories based on the systolic and diastolic blood pressure levels among approximately 430,000 general people without antihypertensive drugs or medical history of cardiovascular disease. The study found that compared with people with normal blood pressure, the other 5 categories were all related to compound cardiovascular death after adjustment. At the same time, the study found that cerebrovascular mortality was most closely related to blood pressure categories in the Chinese population. Besides, age was a confounding factor that affected the strength of the association between blood pressure categories and cardiovascular death. The highlight of this study, as the author mentioned, the sample size was very large, and the study covered 10 regions in China. There however remain some points that might be clarified: 1) In the methodology (lines 108-109), please add that this study was a general population study when you briefly described the CKB study. Otherwise, it will be liable to cause misunderstandings. 2) The average blood pressure used in this study is the average of the last two blood pressure measurements. Will there be different results and conclusions if the average of three consecutive blood pressure measurements is taken? 3) This study only analyzed the relationship between cardiovascular death and blood pressure categories. Why is there no further analysis of all-cause death? If feasible, please add results and discussion about the relationship between all-cause death and blood pressure categories. 4) The statistical description of the constituent ratio in Figure 1 is not visualized enough using histograms, and it is recommended to modify it to a pie chart. 5) When the continuous variables in Table 1 were used for statistical description, should the standard deviation rather than the standard error be considered for the degree of dispersion? 6) The incidence rate of hemorrhagic stroke in Table 2 is higher than that of ischemic stroke. Is it different from the constituent ratio of stroke types in the Chinese population? Please explain further. 7) The study concluded that prehypertension-low was associated with higher cardiovascular deaths. However, in Table S2, prehypertension-low is actually only associated with hemorrhagic stroke, HR1.26 (1.07-1.48), and other single-factor deaths were not related to prehypertension-low. Is there more sufficient evidence to make such an extrapolated conclusion cautiously? 8) In Table S2, the HR of most blood pressure types in Model 1, Model 2 and Model 3 were gradually increasing. In the Cox risk regression model, as the number of independent variables added, the contribution of the research factors of interest to the outcome generally decreases. This phenomenon was confusing, please explain further. Reviewer #3: This study, involving 0.4 million people, investigated the association between hypertension subtypes and CVD outcomes. It was well written. There are several comments that might be taken into consideration. 1. It would be better to demonstrated the “inclusion criteria” and “exclusion criteria” of the CKB study. 2. For better understanding, it is recommended to use the definition of hypertension grades according to the 2018 ESC guidelines of hypertension as “optimal, normal, high normal”, rather than using the terms as “normal, prehypertension-low, prehypertension-high”. 3. Please state why patients with antihypertension treatment at baseline were excluded. 4. Please state the proportion of “loss to follow-up” and how it was addressed. ********** 6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files. If you choose “no”, your identity will remain anonymous but your review may still be made public. Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy. Reviewer #1: No Reviewer #2: No Reviewer #3: Yes: Kun Xie [NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.] While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step. Submitted filename: reviewer .docx Click here for additional data file. 10 May 2021 Response to Reviewers’ comments Ms. Ref. No.: PONE-D-21-07340 1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf We thank the editor’s help and we have revised the manuscript based on the PLOS ONE’S requirements carefully. 2. We note that you have indicated that data from this study are available upon request. PLOS only allows data to be available upon request if there are legal or ethical restrictions on sharing data publicly. For information on unacceptable data access restrictions, please see http://journals.plos.org/plosone/s/data-availability#loc-unacceptable-data-access-restrictions. In your revised cover letter, please address the following prompts: a) If there are ethical or legal restrictions on sharing a de-identified data set, please explain them in detail (e.g., data contain potentially identifying or sensitive patient information) and who has imposed them (e.g., an ethics committee). Please also provide contact information for a data access committee, ethics committee, or other institutional body to which data requests may be sent. b) If there are no restrictions, please upload the minimal anonymized data set necessary to replicate your study findings as either Supporting Information files or to a stable, public repository and provide us with the relevant URLs, DOIs, or accession numbers. Please see http://www.bmj.com/content/340/bmj.c181.long for guidelines on how to de-identify and prepare clinical data for publication. For a list of acceptable repositories, please see http://journals.plos.org/plosone/s/data-availability#loc-recommended-repositories. We will update your Data Availability statement on your behalf to reflect the information you provide. We thank the editor’s help, and we would like to revise the Data Availability statement as follows. “The authors do not own the data for this study. Requests for the data should be submitted to the China Kadoorie Biobank (CKB) Data Access Committee (http://www.ckbiobank.org/site/Research/Data+Access+Policy). As stated in the policy, as data custodian, the CKB study group must maintain the integrity of the database for future use and regulate data access to comply with prior conditions agreed with the Chinese government. Data security is an integral part of CKB study protocols. Data can be released outside the CKB research group only with appropriate security safeguards.” 3. One of the noted authors is a group or consortium; China Kadoorie Biobank Collaborative Group. In addition to naming the author group, please list the individual authors and affiliations within this group in the acknowledgments section of your manuscript. Please also indicate clearly a lead author for this group along with a contact email address. Following the editor’s suggestion, we added the members of the China Kadoorie Biobank collaborative group in the acknowledgements (pages 24-25, lines 392-429). Please revise the manuscript according to the comments of the three Reviewers. In addition, please consider analyses using blood pressure as a continuous variable. We thank the reviewers for their valuable comments and thus for the improvement of our paper. All comments and suggestions have been carefully addressed in the revised manuscript. Details of the comments are responded to point-by-point as follows. One previous paper based on CKB study had explored the association between blood pressure as a continuous variable and CVD (Lacey Ben, et al, The Lancet Global Health, 2018), so in this manuscript, we mainly focused on the risk effect of blood pressure categories and aimed to provide more evidence about the newly defined hypertension in 2017 ACC/AHA and hypertension subtypes. Reviewer #1: The authors' work with large prospective cohort (n=431,000, 10-year follow-up) provided information on blood pressure (BP) category and prospective mortality risk from cardiovascular (CV) origin. Importantly, prehypertension, along with all subcategories of hypertension, was associated with increased mortality compared to normotensive participants in adult Chinese free of baseline antihypertensive drug, CV disease or cancer. Major comments 1. High BP could aggravate other non-CV diseases. The hazard ratio (HR) of BP categories to all-cause and non-CV mortality should therefore be reported. CV death, as a competing event to death from other causes, should be evaluated by competing risk regression. Thank the review’s comments. Considering that the burden of high BP on the health system is mainly through CVDs and the heavy burden of CVD, especially cerebrovascular diseases, in the Chinese population, we mainly focused on exploring the effect of BP categories on CVD and CVD subtypes in the current study. We agree that the associations of BP categories with all-cause and non-CVD mortality are important considerations but not within the scope of this study. In the future study, we would explore the role of BP in the development of all-cause and non-CVD mortality. 2. There could be discrepancies on the mortality rate and HR of BP categories with mortality among the included centers. Center-specific statistics should as well be provided. We thank the reviewer’s comments. We agreed with the reviewer that there might be discrepancies in the associations between BP categories and CVD mortality by survey sites. To deal with this issue, we conducted the Cox regression models with stratification according to survey sites (10 regions), as well as age at risk (in 5-year intervals) and sex. Moreover, we conducted subgroup analyses by 10 regions and added the results in the supplementary file (S5 Table) and in the manuscript (page 18, lines 277-279) as follows. “S5 Table shows the associations between BP categories and CVD mortality across 10 survey sites. There was a statistically significant interaction between BP categories and survey sites (P for interaction = 0.016).” Minor comments 1. The limitation part in the abstract (line 56-58), albeit modestly written, could lead to a suspicion in the first impression on the credibility of results and conclusion. In the abstract, it's better to put "BP was measured twice in a single visit" into the methods section and the limitation should be omitted. We thank the reviewer for pointing this out. Following the reviewer’s suggestion, we revised the methods (page 3, lines 46-47) as follows “BP was measured twice in a single visit at baseline”. As the reviewer commented, to avoid confusion, we delete the limitation in the abstract. 2. Following the line, in methods part, line 119-120, the authors should rephrase the measurement of blood pressure as "occasion" and "reading" are two distinct entities. The authors may refer to their Lacey 2018 Lancet Global Health paper (PMID 29773120) for more precise description on the method of BP measurement. We appreciate the reviewer’s careful reading, and we have corrected it (page 7, lines 122-127) as follows. “BP was measured twice by trained staff using a UA-779 digital monitor after they remained at rest in the seated position for at least 5 minutes (Longo D et al, Blood Press Monit 2002). If the difference between the two measurements was >10 mm Hg for SBP, a third measurement was required. Only the last two readings were recorded and used to calculate the average of SBP and DBP (Lacey B et al, Lancet Global Health 2018).” 3. Standard Error SE used in Table 1 is inversely correlated to sample size, and therefore definitely small when the sample size is huge. Change it to SD to show the distribution of continuous variables. We thank the reviewer for the comments. Considering the potential effect of age, sex, and survey sites for the distribution of baseline characteristics across BP categories, we conducted linear regression with adjustment for age, sex, and survey sites to analyse the distribution of continuous variables. Thus, in Table 1, we showed the mean and standard error derived from the adjusted linear model. Following the reviewer’s suggestion, we added one table (S1 Table) in the supplementary file to describe the crude means (standard deviation) and percentages and revised the corresponding content in the manuscript (page 12, lines 229-230). “The distribution of baseline characteristics of the study population without adjustment for age, sex, and survey sites was presented in S1 Table.” 4. line 88: "the number of hypertension" should be the prevalence of hypertension or the number of participants with hypertension. Thank the reviewer for the correction. We have corrected the term (page 5, line 91) as follows. “…the prevalence of hypertension…”. 5. line 96: there is limited evidence for the associations of 'BP categories' with the.... Following the reviewer’s suggestion, we revised the sentence (page 5, lines 99-100) as follows. “there is limited evidence for the associations of BP categories with the major subtypes of cardiovascular disease…” 6. line 113: Check if the study was performed in accordance with the Declaration of Helsinki, and add to the end of this paragraph. As the reviewer commented, we added one sentence in the Study population (page 6, line 116). “The study was in accordance with Declaration of Helsinki.” Reviewer #2: “Association between Blood Pressure Categories and Cardiovascular Disease Mortality in China” divided the population into 6 categories based on the systolic and diastolic blood pressure levels among approximately 430,000 general people without antihypertensive drugs or medical history of cardiovascular disease. The study found that compared with people with normal blood pressure, the other 5 categories were all related to compound cardiovascular death after adjustment. At the same time, the study found that cerebrovascular mortality was most closely related to blood pressure categories in the Chinese population. Besides, age was a confounding factor that affected the strength of the association between blood pressure categories and cardiovascular death. The highlight of this study, as the author mentioned, the sample size was very large, and the study covered 10 regions in China. There however remain some points that might be clarified: 1) In the methodology (lines 108-109), please add that this study was a general population study when you briefly described the CKB study. Otherwise, it will be liable to cause misunderstandings. Thank the reviewer for pointing this out. We have revised this part (page 6, line 111) to avoid misunderstanding. “The CKB is a community-based prospective cohort study, involving over 0.5 million adults aged 30-79 years in 10 areas of China between 2004 and 2008.” 2) The average blood pressure used in this study is the average of the last two blood pressure measurements. Will there be different results and conclusions if the average of three consecutive blood pressure measurements is taken? If the difference between the two measurements was >10 mm Hg for SBP, a third measurement was required. Only the last two readings were recorded so it was not possible to calculate the average BP using three consecutive measurements. To avoid confusion, we have rephrased the assessment of BP as follows (page 7, lines 132-133). “Only the last two readings were recorded and used to calculate the average of SBP and DBP (Lacey B et al, Lancet Global Health 2018).” 3) This study only analyzed the relationship between cardiovascular death and blood pressure categories. Why is there no further analysis of all-cause death? If feasible, please add results and discussion about the relationship between all-cause death and blood pressure categories. Thank the review’s comments. Considering that the burden of high BP on the health system is mainly through CVDs and the heavy burden of CVD, especially cerebrovascular diseases, in the Chinese population, we mainly focused on exploring the effect of BP categories on CVD and CVD subtypes in the current study. Moreover, we want to provide more evidence about the effect of newly defined hypertension in the 2017 ACC/AHA guideline given that previous studies showed mixed results about its effect on CVD mortality and little evidence is available for its effect on CVD subtypes. Thus, in the current study, we focused on CVD but not all-cause death. Future studies would be conducted to explore the associations of BP with all-cause and non-CVD mortality. 4) The statistical description of the constituent ratio in Figure 1 is not visualized enough using histograms, and it is recommended to modify it to a pie chart. Thank the reviewer’s suggestion. Besides showing the proportions of different BP categories, we also want to present the trend of proportions of different BP categories with aging. Therefore, we decided to use the histogram graph to visualise and compare the height of the corresponding bar across age groups. 5) When the continuous variables in Table 1 were used for statistical description, should the standard deviation rather than the standard error be considered for the degree of dispersion? Thank the review for pointing this out. Considering the potential effect of age, sex, and survey sites for the distribution of baseline characteristics across BP categories, we conducted linear regression with adjustment for age, sex, and survey sites to analyse the distribution of continuous variables. Thus, in Table 1, we showed the mean and standard error derived from the adjusted linear model. We added one table (S1 Table) in supplementary file to describe the crude means and standard deviations (page 12, lines 229-230). “The distribution of baseline characteristics of the study population without adjustment for age, sex, and survey sites was presented in S1 Table.” 6) The incidence rate of hemorrhagic stroke in Table 2 is higher than that of ischemic stroke. Is it different from the constituent ratio of stroke types in the Chinese population? Please explain further. Thank the reviewer’s comment. In the current study, the mortality of hemorrhagic stroke is higher than that of ischemic stroke, which is consistent with previous studies conducted among a Chinese population (Yang G, et al, Lancet 2013; Zhou M, et al, Lancet 2016). Zhou M et al reported that the age-standardised death rate per 100 000 people was 62.9 for ischemic stroke vs. 94.4 for hemorrhagic stroke in 2013 among a Chinese population. We added the previous evidence in the manuscript (page 6, lines 99-100). “…and the mortality from hemorrhagic stroke was higher than that from ischemic stroke (Zhou M, et al, Lancet 2016)” 7) The study concluded that prehypertension-low was associated with higher cardiovascular deaths. However, in Table S2, prehypertension-low is actually only associated with hemorrhagic stroke, HR1.26 (1.07-1.48), and other single-factor deaths were not related to prehypertension-low. Is there more sufficient evidence to make such an extrapolated conclusion cautiously? We understand the reviewer’s concern. Our results showed that prehypertension-low was associated with the increased risk of death from hemorrhagic stroke. There were no statistically significant associations between prehypertension-low and mortality from other CVD subtypes. Given that the analyses were conducted with a sufficient sample size and the point estimates of hazard risk for other CVD subtypes were around 1, we are confident about the results that prehypertension-low was not associated with increased CVD risk except for hemorrhagic stroke. However, for prehypertension-high (newly defined as hypertension in 2017 ACC/AHA guideline), it related to all subtypes of CVDs, so we concluded that it should be paid attention to in the primary prevention of CVD. 8) In Table S2, the HR of most blood pressure types in Model 1, Model 2 and Model 3 were gradually increasing. In the Cox risk regression model, as the number of independent variables added, the contribution of the research factors of interest to the outcome generally decreases. This phenomenon was confusing, please explain further. In the Table S2, we presented the associations using the Cox regression model with adjustment for covariates or step by step to explore whether these potential confounders could change the associations. A confounder could be a risk factor (e.g., age), a preventive factor (e.g., being physically active), or a surrogate or a marker of other causes (e.g., marital status and education level) for the CVD outcomes. Thus, confounders might distort the estimate of association in one direction or another. For example, a family history of corresponding diseases might increase the risk of disease, but participants with a family history might be more likely to improve their lifestyles which could further decrease the risk. Moreover, after adjustment, both the point estimates and the confidence intervals did not change substantially. Reviewer #3: This study, involving 0.4 million people, investigated the association between hypertension subtypes and CVD outcomes. It was well written. There are several comments that might be taken into consideration. 1. It would be better to demonstrated the “inclusion criteria” and “exclusion criteria” of the CKB study. We thank the reviewer’s suggestions. We added the inclusion criteria and exclusion criteria in the manuscript as follows (page 6, lines 204-206). “All men and women aged 30-79 years who were permanently resident and without major disability in each administrative unit were identified and invited to participate (Chen Z, et al. Int J Epidemiol 2005).” 2. For better understanding, it is recommended to use the definition of hypertension grades according to the 2018 ESC guidelines of hypertension as “optimal, normal, high normal”, rather than using the terms as “normal, prehypertension-low, prehypertension-high”. We thank the reviewer’s suggestion. In the current study, we further stratified the “prehypertension” into two groups (SBP 120−129 and DBP <80 mm Hg; SBP 130−139 and/or DBP 80−89 mm Hg) using the criteria from the 2017 ACC/AHA hypertension guideline to estimate the risk of the newly defined “Hypertension stage 1”. The cut-off for the “Hypertension stage 1” (SBP 130−139 and/or DBP 80−89 mm Hg) is different from the 2018 ESC guidelines (High-normal BP: SBP 130−139 and/or DBP 85−89 mm Hg). Thus, in the manuscript, we used the terms “prehypertension-low and prehypertension-high” to name those categories instead of using the terms from 2018 ESC guidelines. 3. Please state why patients with antihypertension treatment at baseline were excluded. Thank the reviewer’s comment. We have stated the reason in the manuscript (page 7, lines 123-125) as follows. “Moreover, we further excluded participants with the antihypertension treatment at baseline (n=65 168), because both dose and types of antihypertensive medications may influence BP levels and lead to misclassification of BP categories.” 4. Please state the proportion of “loss to follow-up” and how it was addressed. By December 31, 2016, 4434 (1.03%) participants were lost to follow-up. In the analyses, if participants were lost to follow-up without information about death, the survival time was calculated from baseline to the date of the latest follow-up date. We stated the rate of loss to follow-up in the manuscript as follows (page 7, line 126; page 9, lines 176-177). “By December 31, 2016, 4434 (1.03%) participants were lost to follow-up.” “For the current study, survival time was calculated from baseline to the date of death, loss to follow-up, or 31 December 2016, whichever occurred first.” Submitted filename: Response to Reviewers.docx Click here for additional data file. 3 Jun 2021 PONE-D-21-07340R1 Association between blood pressure categories and cardiovascular disease mortality in China PLOS ONE Dear Dr. Yu, Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process. Please revise the manuscript according to Reviewer 3's minor comments. 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In the current study, anti-hypertensive treatment could be an unmeasured covariate which would affect the results. It should be further discussed in the part of Discussion. 2. It’s a little bit confusing that the author defined “prehypertension-high” as “hypertension stage 1” according to ACC/AHA guideline, while the definitions of IDH and ISH were not consist with ACC/AHA guideline. It should have a much clearer expression. ********** 7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files. If you choose “no”, your identity will remain anonymous but your review may still be made public. Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy. 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Submitted filename: Second round review.docx Click here for additional data file. 14 Jun 2021 There are two minor comments which need to be addressed. We thank the reviewer for the valuable comments. All comments and suggestions have been carefully addressed in the revised manuscript. Details of the comments are responded to point-by-point as follows. 1. It would cause selection bias if those patients with antihypertensive agents were excluded. In the current study, antihypertensive treatment could be an unmeasured covariate which would affect the results. It should be further discussed in the part of Discussion. We thank the reviewer's comments. We agree that excluding patients with antihypertension agents might cause selection bias given that hypertension with treatment might be more aware of their health status and more accessible to health care or might have severe symptom due to high blood pressure compared to those without treatment, which could affect future CVD risk. However, taking antihypertensive medicines would change the level of blood pressure and lead to a misclassification of hypertension subtypes. Given that it is not possible to know their blood pressure level before taking antihypertensive medicines, to exclude participants with antihypertensive medicines is more reasonable for the current study. Thus, our findings could be generalized to the population without taking antihypertensive medicines. We added the corresponding issue in the limitations section of discussion (page 21, lines 359-364). "Second, we excluded participants who taking antihypertensive medicines, which might cause selection bias and limit the extrapolation of our findings. However, antihypertensive medication would affect the patients' blood pressure level, leading to misclassification of BP categories. Hence, it is more reasonable to restrict study participants without antihypertensive treatment." 2. It's a little bit confusing that the author defined "prehypertension-high" as "hypertension stage 1" according to ACC/AHA guideline, while the definitions of IDH and ISH were not consist with ACC/AHA guideline. It should have a much clearer expression. We thank the reviewer's comments. In the current study, we defined the blood pressure categories mainly referred to the JNC-7 guideline, which defined prehypertension and hypertension subtypes (i.e. ISH, IDH, and SDH). The definition was adopted in previous studies (Arima H et al, Hypertension 2012; Kelly TN et al, Circulation 2008). However, the prehypertension (i.e., SBP/DBP 120 to 139/80 to 89 mm Hg) had a broad range of blood pressure, and its effects on CVD risk were heterogeneous within prehypertension (Y Huang et al, American Heart Journal 2014). In addition, the 2017 ACC/AHA guideline newly defined the high-range blood pressure within prehypertension as hypertension. Thus, we combined the prehypertension definition in JNC-7 with the new cut-off in 2017 ACC/AHA guideline, i.e., SBP 120−129 and DBP <80 mm Hg; SBP 130−139 and/or DBP 80−89 mm Hg. We revised the manuscript (page 7, lines 127-132) to clarify our categorisation. "According to the JNC-7, BP categories were defined into five groups 1) normal (SBP <120 and DBP <80 mm Hg); 2) prehypertension (SBP 120−139 and/or DBP 80−89 mm Hg); 3) ISH (SBP ≥140 and DBP <90 mm Hg); 4) IDH (SBP <140 and DBP ≥90 mm Hg); 5) SDH (SBP ≥140 and DBP ≥90 mm Hg) (Chobanian A V., Hypertension 2003). In the 2017 ACC/AHA hypertension guideline, hypertension was defined as SBP ≥130 mmHg and/or DPB ≥90 mmHg (Whelton PK, Hypertension 2018). To estimate the effect of "Elevated" and "Hypertension stage 1", we further divided prehypertension into prehypertension-low (equal to "Elevated", SBP 120−129 and DBP <80 mm Hg) and prehypertension-high (equal to "Hypertension stage 1", SBP 130−139 and/or DBP 80−89 mm Hg) (Whelton PK, Hypertension 2018)." Reviewer's Responses to Questions Have the authors made all data underlying the findings in their manuscript fully available? The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified. Reviewer #1: (No Response) Reviewer #2: Yes Reviewer #3: No For the data availability, we want to state as follows. "According to the Regulation of the People's Republic of China on the Administration of Human Genetic Resources, we are not allowed to provide Chinese human clinical and genetic data abroad without official approval so the data in the present study cannot be shared without restrictions. However, researchers who are interested in accessing and analyzing data collected in the China Kadoorie Biobank (CKB) study may contact the data use and access committee (http://www.ckbiobank.org/site/Research/Data+Access+Policy). As stated in the policy, as data custodian, the CKB study group must maintain the integrity of the database for future use and regulate data access to comply with prior conditions agreed with the Chinese government." Submitted filename: Response to Reviewers.docx Click here for additional data file. 15 Jul 2021 Association between blood pressure categories and cardiovascular disease mortality in China PONE-D-21-07340R2 Dear Dr. Yu, We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements. Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication. An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. 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Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: (No Response) Reviewer #2: Yes Reviewer #3: Yes ********** 4. Have the authors made all data underlying the findings in their manuscript fully available? The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified. Reviewer #1: (No Response) Reviewer #2: Yes Reviewer #3: Yes ********** 5. Is the manuscript presented in an intelligible fashion and written in standard English? PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here. Reviewer #1: (No Response) Reviewer #2: Yes Reviewer #3: Yes ********** 6. Review Comments to the Author Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters) Reviewer #1: (No Response) Reviewer #2: (No Response) Reviewer #3: This study reported CVD endpoints among 0.5 million adults in primary prevention in China. It was well written and should be accepted. ********** 7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files. If you choose “no”, your identity will remain anonymous but your review may still be made public. Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy. Reviewer #1: No Reviewer #2: No Reviewer #3: No 19 Jul 2021 PONE-D-21-07340R2 Association between blood pressure categories and cardiovascular disease mortality in China Dear Dr. Yu: I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department. If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. 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Table 1

Baseline characteristics of the study population by baseline BP categories.

CharacteristicNormalPrehypertension-lowPrehypertension-highHypertension
ISHIDHSDH
Total, No.144 76570 130107 96060 708838739 027
Age, mean (SE), y47.7 (0.03)50.5 (0.04)50.5 (0.03)58.0 (0.04)47.9 (0.11)53.0 (0.05)
Female, No. (%)96 918 (66.1)39 937 (56.9)58 329 (54.0)35 134 (60.2)3794 (44.7)18 784 (49.0)
Education level, No. (%)
    No formal education or primary59 294 (48.5)35 191 (49.8)53 136 (49.2)40 644 (51.4)3246 (48.6)21 588 (50.6)
    Middle or high school73 853 (45.0)31 459 (44.7)49 166 (45.4)18 249 (43.6)4442 (45.6)15 814 (44.9)
    College or higher11 618 (6.5)3480 (5.5)5658 (5.5)1815 (5.0)699 (5.8)1625 (4.5)
Rural area, No. (%)73 840 (50.1)42 291 (60.2)65 486 (60.6)37 856 (64.4)4324 (50.6)24 396 (63.1)
Married, No. (%)134 038 (91.3)64 446 (91.8)99 571 (91.7)52 636 (91.1)7849 (91.1)35 419 (91.0)
Regular smoking, No. (%)33 951 (29.0)20 331 (27.7)32 518 (27.0)16 082 (26.3)2917 (26.8)13 142 (27.2)
    Male31 785 (66.5)19 323 (63.6)31 197 (62.4)15147 (61.3)2852 (61.8)12689 (63.1)
    Female2166 (3.1)1008 (2.4)1321 (2.2)935 (1.9)65 (2.3)453 (2.2)
Regular alcohol intake, No. (%)15 732 (13.5)10 150 (14.1)19 655 (16.0)9628 (16.6)2196 (18.7)9724 (20.1)
    Male13 635 (29.5)9330 (31.4)18 320 (35.6)8875 (37.2)2085 (40.8)9239 (44.4)
    Female2097 (2.3)820 (2.0)1335 (2.2)753 (2.1)111 (3.1)485 (2.7)
Average weekly consumption, mean (SE), day/week b
    Fresh vegetables6.82 (0.002)6.82 (0.002)6.82 (0.002)6.82 (0.003)6.81 (0.008)6.81 (0.004)
    Fresh fruits2.59 (0.006)2.48 (0.008)2.48 (0.007)2.37 (0.009)2.44 (0.024)2.30 (0.011)
    Red meat3.90 (0.006)3.91 (0.008)3.96 (0.006)3.96 (0.009)3.99 (0.023)3.94 (0.010)
Postmenopausal, No. (%) c33 002 (49.7)18 865 (48.5)27 871 (49.1)26 430 (48.0)1447 (50.5)11 023 (49.1)
Physical activity, mean (SE), MET- hr/day21.9 (0.03)22.3 (0.05)21.9 (0.04)21.8 (0.05)21.1 (0.13)21.6 (0.06)
Heart rate, mean (SE), bpm76.2 (0.03)77.7 (0.04)79.7 (0.03)79.8 (0.05)82.9 (0.12)83.1 (0.06)
Body mass index, mean (SE), kg/m222.4 (0.01)23.3 (0.01)23.8 (0.01)24.3 (0.01)24.4(0.03)24.9(0.02)
Diabetes, No. (%)3574 (2.6)2635 (3.9)4847 (4.7)5177 (6.9)366 (4.7)2409 (6.0)
Family medical history, No. (%)25 525 (17.2)12 555 (18.2)21 193 (19.7)11 662 (20.2)2012 (22.3)9347 (23.9)
    Heart attack4707 (3.0)2078 (3.1)3314 (3.1)1613 (3.1)338 (3.5)1329 (3.5)
    Stroke21 875 (14.9)10 918 (15.8)18 684 (17.3)10 445 (17.8)1772 (19.8)8390 (21.3)
SBP, mean (SE), mmHg109.7 (0.02)124.3 (0.03)129.8 (0.03)150.0 (0.04)133.5 (0.09)161.8 (0.04)
DBP, mean (SE), mmHg68.0 (0.02)72.4 (0.02)80.1 (0.02)81.4 (0.03)92.1 (0.07)97.6 (0.03)

Abbreviations: BP, blood pressure; SE, standard error; MET, metabolic equivalent of task; bpm, beat per minute.

a All variables were adjusted for age, sex, and survey sites when appropriate.

b Average weekly consumptions of red meat, fresh vegetables, and fruits were calculated by assigning participants to the midpoint of their consumption category.

c Only for female.

Table 2

Associations of BP categories with mortality from CVDs and its major subtypes among 430 977 participants.

Cause of deathNormalPrehypertension-lowPrehypertension-highHypertension
ISHIDHSDH
No. of participants144 76570 130107 96060 708838739 027
No. of person-years1 462 055705 3101 082 972593 91385 056383 785
Cardiovascular disease
    No. of deaths13301039178731331482223
    Incidence rate (no./1,000 person-y)0.911.471.655.281.745.79
    HR (95%CI)Reference1.10 (1.01–1.19)1.32 (1.23–1.42)2.04 (1.91–2.19)2.20 (1.85–2.61)3.81 (3.54–4.09)
Ischemic heart disease
    No. of deaths586423691115956649
    Incidence rate (no./1,000 person-y)0.400.600.641.950.661.69
    HR (95%CI)Reference1.00 (0.88–1.13)1.14 (1.02–1.28)1.66 (1.50–1.85)1.76 (1.32–2.30)2.48 (2.21–2.80)
Myocardial infarction
    No. of deaths37825844471332423
    Incidence rate (no./1,000 person-y)0.260.370.411.200.381.10
    HR (95%CI)Reference0.94 (0.80–1.11)1.14 (0.99–1.31)1.65 (1.44–1.88)1.58 (1.08–2.24)2.45 (2.11–2.84)
Cerebrovascular disease
    No. of deaths5774999001683701439
    Incidence rate (no./1,000 person-y)0.390.710.832.830.823.75
    HR (95%CI)Reference1.20 (1.06–1.36)1.53 (1.37–1.70)2.52 (2.28–2.78)2.51 (1.94–3.21)5.60 (5.06–6.21)
Hemorrhagic stroke
    No. of deaths31527953096245961
    Incidence rate (no./1,000 person-y)0.220.400.491.620.532.50
    HR (95%CI)Reference1.26 (1.07–1.48)1.69 (1.47–1.95)2.90 (2.53–3.32)2.86 (2.06–3.88)6.91 (6.05–7.92)
Ischemic stroke
    No. of deaths1269318133113221
    Incidence rate (no./1,000 person-y)0.090.130.170.560.150.58
    HR (95%CI)Reference1.01 (0.77–1.33)1.38 (1.10–1.75)2.04 (1.65–2.55)2.13 (1.14–3.65)3.93 (3.12–4.97)

Abbreviations: BP, blood pressure; CVDs, cardiovascular diseases; ISH, isolated systolic hypertension; IDH, isolated diastolic hypertension; SDH, systolic-diastolic hypertension; HR, hazard ratio; CI, confidence interval.

Multi-adjusted hazard ratios were adjusted for age (continuous variable); education level (no formal or primary school, middle school or high school, or college or higher); marital status (married, others); smoking status (never or occasional smoker, ex-smoker who quit not because of illness, ex-smoker who quit because of illness and current smoker divided into 3 groups according to cigarette equivalents/day [<15, 15–25, ≥25]); alcohol consumption (never or occasional or seasonal drinker, ex-regular drinker of reduced intake, 1 to 5 days/week, almost daily drinker divided into 4 groups according to total grams of alcohol [<15, 15-<30, 30-<60, ≥60]); intake frequencies of vegetables, fruits, and red meat (daily, 4 to 6 days/per week, 1 to 3 days/per week, or monthly or rarely/never); physical activity (continuous variable); body mass index (BMI, continuous variable); survey season (spring; summer: June, July, August; autumn; winter: December, January, February); heart rate (continuous variable); diabetes at baseline (2 categories, presence or absence); family history of heart attack, or stroke (2 categories, presence or absence, only adjusted for in corresponding analysis of cause-specific mortality), and were stratified according to age at risk (in 5-year intervals), sex, and survey sites.

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