Life-course blood pressure trajectories and cardiovascular diseases: A population-based cohort study in China.

BACKGROUND: The patterns of blood pressure trajectory (i.e., change over time) over life-course remain to be explored. In this study, we aim to determine the trajectories of systolic blood pressure (SBP) from adulthood to late life and to assess its impact on the risk of cardiovascular diseases (CVDs).
METHODS: Based on the China Health and Nutrition Survey, a total of 3566 participants aged 20-50 years at baseline (1989) with at least three SBP measurements during 1989-2011 were included. SBP was measured through physical examination, and socio-demographic factors, lifestyles, medications, and CVDs were based on self-reported questionnaire. Latent class growth modeling was performed to examine SBP trajectory. Odds ratio (OR) and 95% confidence interval (CI) from logistic regression was used to determine the association between SBP trajectory and CVDs.
RESULTS: Five trajectory groups of SBP were identified: Class 1: rapid increase (n = 113, 3.2%); Class 2: slight increase (n = 1958, 54.9%); Class 3: stable (n = 614, 17.2%); Class 4: increase (n = 800, 22.4%); Class 5: fluctuant (n = 81, 2.3%). After adjustment of demographic factors, baseline SBP, and lifestyles, compared with the "slight increase" group, the OR (95% CI) of CVDs was 0.65 (0.32, 1.28) for "stable" group, 2.24 (1.40, 3.58) for "increase" group, 3.95 (1.81, 8.62) for "rapid increase" group, and 4.32 (1.76, 10.57) for "fluctuant" group. After stratified by use of antihypertensive drugs, the association was only significant for "rapid increase" group among those using antihypertensive drugs with OR (95% CI) of 2.81 (1.01, 7.77).
CONCLUSIONS: Having a rapidly increasing SBP over life-course is associated with a higher risk of CVDs. This implies the importance of monitoring lifetime change of blood pressure for the prevention of CVDs.

Introduction

Previous studies on the association between high blood pressure and cardiovascular diseases (CVDs) have normally measured blood pressure at one occasion [1-3] or several occasions in a specific life period [4, 5]. However, the blood pressure of each individual varies over different life periods, and one-time value of blood pressure without considering the change of blood pressure levels is less reliable [6]. In addition to actual blood pressure level, the over-time changes of blood pressure (i.e., blood pressure trajectory) should be used to assess the risk of CVDs. Therefore, monitoring the level and trajectory of blood pressure is an essential part of the guidelines for CVD prevention [7, 8].

So far, several studies have assessed the trajectories of blood pressure and examined the association between blood pressure trajectory pattern and the risk of CVDs [6, 9–14]. However, the previous studies on blood pressure trajectories were confined to the change of blood pressure in a certain short period of life, e.g., only in childhood [9], from childhood to adulthood [6, 10], only in adulthood and midlife [11, 12], from midlife to late life [13], or only in late life [14]. There is little evidence about the patterns of blood pressure trajectory over life-course and their impacts on CVDs.

In addition, the previous studies on blood pressure trajectory focused on only systolic blood pressure (SBP) [6, 13], SBP and diastolic blood pressure (DBP) separately [9–12, 14] or in combinations [7, 11, 12, 14]. Some found that SBP trajectory was associated with atherosclerotic biomarkers (e.g., carotid intima–media thickness and left ventricular mass index) where DBP was not [10]. Others found that DBP trajectory groups were less strongly associated with coronary artery atherosclerosis than SBP trajectories [11]. In addition, SBP has been shown to be a better and stronger predictor than other blood pressure components for the risk of cardiovascular events [15, 16]. In regard to the above evidence, we focused only on SBP in this study.

Based on the longitudinal cohort from the China Health and Nutrition Survey (CHNS), this study aimed to examine the trajectory of SBP from young adulthood to old age as well as to determine the association between distinct patterns and CVDs.

Methods

Study design and population

Data from the CHNS was retrieved for this study. The details of CHNS have been described elsewhere [17]. In short, the CHNS is an ongoing cohort study, which was initiated in 1989 with follow-ups in every 2 to 4 years, aiming to assess the health status and risk factors of the Chinese population. Participants were recruited from fifteen provinces and municipal cities in China through a stratified cluster randomization procedure. In CHNS, data were collected by the well-trained examiners through questionnaire investigation, physical examination, and laboratory tests according to a consistent protocol in each wave [17].

For the purpose of this study, 3566 participants (female, 52.0%) aged from 20 to 50 years at baseline (mean age = 33.3 years, standard deviation = 6.8 years) with at least three SBP measurements during the study period were selected for the trajectory analyses. Data from eight waves (i.e. 1989, 1991, 1997, 2000, 2004, 2006, 2009 and 2011) were included. The survey in 1993 was excluded from this study due to the lack of blood pressure measurements. The number of participants (%) were 3566 (100%) in 1989, 3206 (89.9%) in 1991, 2590 (72.6%) in 1997, 2834 (79.5%) in 2000, 2571 (72.1%) in 2004, 2564 (71.9%) in 2006, 2371 (66.5%) in 2009, and 2210 (62.0%) in 2011.

The study protocol of CHNS was approved by the institutional review board from the University of North Carolina at Chapel Hill and the National Institute for Nutrition and Food Safety, China Centre for Disease Control and Prevention. Written informed consent was collected from all participants.

Data collection

During each wave, SBP was measured using a mercury sphygmomanometer on the right arm of each participant after a 10-minute seated rest [18]. SBP was measured three times at each visit, and the average of three measurements was used as the SBP level at each wave.

CVD status was determined based on self-reported information, which was available since the survey in 1997. CVDs were defined as having either myocardial infarction (MI) or stroke. The information of MI and stroke was retrieved based on the following questions: “Has the doctor ever given you the diagnosis of myocardial infarction?”, and “Has the doctor ever given you the diagnosis of stroke?”. Those giving a positive answer to either of the questions above were defined as having CVDs. Since the onset time of CVDs was not reported by the participants, we used the life-time cumulative risk of CVDs (i.e., ever report of having a diagnosis of MI or stroke during 1997–2011). Both recurrent and new cases of CVDs were included in the study.

The covariates included socio-demographic factors (e.g., age, sex, education, and living region), lifestyle behaviors (e.g., smoking, alcohol use, physical activity, and diet), use of anti-hypertensive medication, and body mass index (BMI). Data on socio-demographic factors, life-style behaviors, and use of anti-hypertensive medication were collected through self-reported questionnairres. Data on BMI was collected through physical examination. The socio-demographic factors, diet and BMI were collected at baseline (in 1989), however, data on smoking, alcohol intake and physical activity were used from the survey of 1997 because there was lack of these data at baseline.

Specifically, education was categorized according to the years of eduation: no formal school (i.e., <1 year), primary school (i.e., 1–6 years), middle school and above (i.e., >6 years). Living region was dichotomized into urban (i.e., living in a city) and rural (i.e., living in a village or town). Ever smoking was defined as a positive answer to the question “Have you ever smoked cigarettes or pipe?”. High alcohol consumption was defined as drinking more than 3 times of alcoholic beverage per week regularly [19]. Physically inactive was defined as doing physical exercise less than 150-min of moderate physical activity or less than 75-min vigorous physical activity per week [20]. Data on dietary intake was collected from a questionnaire on 3-day records of household meals. Unfavorable diet was defined as having at least one of three macronutrients not meeting the dietary intake recommendation (i.e., 45–65% for carbohydrate, 20–35% for fat, and 10–35% for protein) [21]. Height and weight were measured, and BMI was calculated as measured weight (kg) divided by height (m) squared.

The use of anti-hypertensive medications was defined as the positive answer to the question “Are you currently taking anti-hypertensive drugs?”. Regarding that the cumulative risk of CVDs during 1989–2011 was used as the outcome, the use of anti-hypertensive drugs was also identified as ever report of the positive answers during the study period.

Statistical analyses

We performed latent class growth modeling (LCGM) to identify the SBP trajectories [22, 23]. Regarding that previous studies showed a nonlinear pattern of blood pressure trajectories [10, 24], so we considered two possible polynomial specifications (a linear and a quadratic) as a function of age to describe SBP trajectories. We used crude models to describe the SBP trajectories. For each of the polynomial models, one to seven class solution was described, starting with one-class linear model which assumed that all subjects follow the same trajectory over time (linear term); then, the number of latent classes and the polynomial term increases sequentially (S1 Table). The optimal number of classes was determined by the model with the lower Bayesian information criterion, high mean posterior class membership probabilities (>0.75), and significant level less than 0.05 of polynomial terms. We chose the final model of SBP trajectory also based on the assumption of SBP change over life-course (e.g., distinct change patterns including fluctuant variations).

The characteristics across trajectory groups were compared using ANOVA and chi-square tests for the continuous and categorical variables, respectively. Multiple imputation (5 times) was performed to impute the missing values of covariates (e.g., lifestyles and use of antihypertensive medications). Then, the analysis of association between SBP trajectory patterns and CVDs was performed through binary logistic regression model in the pooled dataset. Odds ratio (OR) and 95% confidence intervals (CI) were used to describe the associations from three models: model 1 was adjusted for age, sex, living region, education, and baseline SBP; model 2 was additionally adjusted for smoking, alcohol use, physical activities, diet, and BMI; and model 3 was further adjusted for use of anti-hypertensive drugs.

Two sensitivity analyses were performed. We examined the assocaition between SBP trajectory groups and CVDs after stratification by the use of anytihypertensive medication. In addition, to assess the effect of lost to follow-up, the association between SBP trajectory and CVDs was assessed among those who were followed from baseline until the last follow-up.

Stata 14.0 was used to perform the LCGM models and the associations, and R was used to plot the graph of life-course trajectories.

Results

The groups of SBP trajectory

We found five SBP trajectories according to different index of goodness of fit and discrimination (S1 Table). Fig 1 shows the five SBP trajectories over lifetime: Class 1 (red): rapid increase (n = 113, 3.2%); Class 2 (green): slight increase (n = 1958, 54.9%); Class 3 (blue): stable (n = 614, 17.2%); Class 4 (black): increase (n = 800, 22.4%); Class 5 (brown): fluctuant (n = 81, 2.3%). The “stable” group and “slight increase” group had similar patterns that SBP remained at a normal level (<140 mmHg) across adulthood, midlife and late life. The “increase” group was characterized by a normal SBP (<140 mmHg) from adulthood to midlife and a high SBP (≥140 mmHg) in late life. The group with “rapid increasing” had a more rapid increase in SBP with a normal SBP in adulthood and a high SBP during midlife and late life. The “fluctuant” group was characterized by a rapidly increasing SBP in adulthood and a decreasing SBP in midlife and late life. The SBP level in “fluctuant” group reached to the high level (≥140 mmHg) in adulthood and midlife and returned to normal level in late life.

Fig 1

Systolic blood pressure trajectories over lifetime.

Abbreviations: SBP = systolic blood pressure; CVDs = cardiovascular diseases. Class 1 (red): rapid increase (n = 113, 3.2%); Class 2 (green): slight increase (n = 1958, 54.9%); Class 3 (blue): stable (n = 614, 17.2%); Class 4 (black): increase (n = 800, 22.4%); Class 5 (brown): fluctuant (n = 81, 2.3%).

Characteristics across trajectory groups

In Table 1, across the five trajectory groups, the “stable” group were oldest, most likely to be women, and had highest proportion of people with no formal education (all p<0.001); the “fluctuant” group were most likely to live in urban area (p = 0.009), had highest prevalence of use of anti-hypertensive drugs, had highest level of BMI and SBP at baseline (all p<0.001); the “increase” group were most likely to smoke and drink alcohol (both p<0.001). There were no significant differences in the prevalence of physical inactivity (p = 0.44) and unfavorable diet (p = 0.66) across SBP trajectory groups.

Table 1

Characteristics of the study population across trajectory groups (n = 3566).

Characteristics*	Total	Class 1 (rapid increase)	Class 2 (slight increase)	Class 3 (stable)	Class 4 (increase)	Class 5 (fluctuant)	p
	(n = 3566)	(n = 113)	(n = 1958)	(n = 614)	(n = 800)	(n = 81)
Age, years	33.3 (6.8)	32.4 (5.5)	33.1 (6.8)	35.5 (6.5)	32.2 (6.6)	33.9 (7.2)	<0.001
Women	1853 (52.0)	63 (55.8)	1019 (52.0)	407 (66.3)	320 (40.0)	44 (54.3)	<0.001
Urban	975 (27.3)	28 (24.8)	542 (27.7)	172 (28.0)	198 (24.8)	35 (43.2)	0.009
Education
No formal school	1014 (28.6)	30 (26.5)	541 (27.7)	242 (39.7)	174 (21.9)	27 (33.3)	<0.001
Primary school	909 (25.6)	30 (26.5)	476 (24.4)	156 (25.6)	229 (28.8)	18 (22.2)
Middle school or above	1627 (45.8)	53 (46.9)	934 (47.9)	211 (34.6)	393 (49.4)	36 (44.4)
Ever smoking	1005 (34.5)	25 (29.4)	552 (34.5)	133 (24.2)	279 (45.1)	16 (26.7)	<0.001
High alcohol consumption	507 (17.8)	11 (12.9)	281 (17.9)	66 (12.2)	142 (23.6)	7 (11.9)	<0.001
Physical inactivity	2680 (91.4)	77 (88.5)	1484 (91.9)	508 (92.0)	556 (89.8)	55 (91.7)	0.44
Unfavorable diet	2666 (79.5)	85 (81.7)	1466 (79.1)	474 (81.6)	580 (78.7)	61 (78.2)	0.66
Use of anti-hypertensive drugs	498 (14.0)	68 (60.2)	130 (6.6)	15 (2.4)	229 (28.6)	56 (69.1)	<0.001
Body mass index, kg/ m2	21.6 (2.4)	22.5 (2.5)	21.4 (2.3)	20.9 (2.1)	22.2 (2.5)	23.6 (2.7)	<0.001
Systolic blood pressure, mmHg	111.1 (13.2)	115.8 (11.6)	109.6 (10.8)	102.0 (10.9)	118.3 (11.9)	137.0 (21.1)	<0.001

Values are presented as mean (standard deviation) or n (%).

*The number of missing values was 16 for education, 652 for smoking, 710 for alcohol consumption, 634 for physical activity, 212 for diet, and 60 for body mass index. The missing value was imputed through multiple imputation for the subsequent analysis.

The association between SBP trajectories and CVDs

During the study period, 116 participants (3.2%) reported of having CVDs. After the adjustment of age, sex, living region, education, and baseline SBP, compared with “slight increase” group, the OR (95% CI) of CVDs was 0.62 (0.31, 1.22) for “stable” group, 2.32 (1.46, 3.68) for “increase” group, 4.40 (2.03, 9.51) for “rapid increase” group, and 4.79 (1.95, 11.75) for “fluctuant” group (Table 2, model 1). The ORs slightly decreased after additional adjustment of lifestyles and BMI in model 2. After further adjustment of use of anti-hypertensive medication in model 3, the ORs were not significant in any of the trajectory groups.

Table 2

The association between systolic blood pressure trajectories and cardiovascular diseases (n = 3566).

Trajectory group	No. of subjects	No. of CVD cases	Odds ratio (95% confidence interval)*
			Model 1	Model 2	Model 3
Slight increase	1958	46	Ref	Ref	Ref
Stable	614	11	0.62 (0.31, 1.22)	0.65 (0.32, 1.28)	0.79 (0.40, 1.59)
Increase	800	39	2.32 (1.46, 3.68)	2.24 (1.40, 3.58)	1.36 (0.82, 2.26)
Rapid increase	113	9	4.40 (2.03, 9.51)	3.95 (1.81, 8.62)	1.68 (0.72, 3.89)
Fluctuant	81	11	4.79 (1.95, 11.75)	4.32 (1.76, 10.57)	1.82 (0.71, 4.65)

Abbreviations: CVD = cardiovascular disease.

*Model 1 was adjusted for socio-demographic factors (i.e., age, sex, living region and education) and baseline SBP, model 2 was further adjusted for smoking, high alcohol consumption, physical activity, unfavorable diet, and body mass index, and model 3 was additionally adjusted for antihypertensive drugs.

The associations between SBP trajectory and CVDs stratified by the use of antihypertensive drugs were shown in S2 Table. Among those without using any antihypertensive drugs, there was no significant association between SBP trajectory groups and CVDs. Among those using antihypertensive drugs, compared with the “slight increase” group, the fully-adjusted OR was significant only for the “rapid increase” group, and the OR (95% CI) was 2.81 (1.01, 7.77).

Discussion

Main findings

Five distinct patterns of SBP trajectories from young adulthood to late life were identified: “stable”, “slight increase”, “increase”, “rapid increase”, and “fluctuant”. Compared with the persons with “slight increase” SBP, those with “increase”, “rapid increase”, and “fluctuant” SBP over life-course had higher risk of CVDs. However, the antihypertensive drugs play a big role in the associations: only those antihypertensive drug users with “rapid increase” SBP over life-course had higher risk of CVDs.

Comparison with previous studies on trajectory patterns

Since there are very few studies having assessed the life-course trajectory of SBP, it is difficult to directly compare our results to them. However, we can compare with the previous studies on blood pressure trajectory in specific life periods. Our study showed that the patterns of SBP trajectories differed in various age periods. For instance, there was an increasing trajectory of SBP (either gradually or rapidly) during adulthood before midlife, which was in line with the previous studies on trajectory in younger populations [11, 12]. Our findings also showed distinct SBP trajectories after midlife with an increasing, decreasing, or fluctuant pattern from midlife to old age, which is consistent with previous studies focusing on that part of life [13, 14]. The Rotterdam Study identified four SBP trajectories in an older population: gradual increase group, steeper increase group, decreasing group, and a group with modest variation [13], which was similar with our findings on SBP trajectory in late life. However, the findings in our study are notably a combination of the previous studies on blood pressure trajectory showing a bigger picture of SBP trajectory from a life-course perspective.

Explanations of the associations between SBP trajectory and CVDs

The previous studies showed that the more rapid increase of blood pressure, the higher risk of CVDs in adulthood and midlife [10–12, 24, 25]. In line with them, we found a higher risk of CVDs in those with rapidly increasing SBP over lifetime. The possible explanation is that increase in blood pressure tends to cause rupture of an arteriole resulting in the high risk of CVDs (e.g., hemorrhagic stroke) [4]. In addition, the longer duration of hypertension could lead to stiffer vessels or arterial wall, which in turn leads to a resistance to anti-hypertensive medication and finally a higher risk of CVDs [26]. This can explain why the trajectory pattern with a rapid increase in SBP was associated with CVDs among those using antihypertensive medications. Our findings imply the importance of preventing rapid increase in SBP along with age. This is also pointed by a recent review on high blood pressure and CVDs, which showed that it is the time to pay greater attention to the prevention of the typical age-related increase in blood pressure in addition to the intensive treatment of established hypertension to eliminate the population burden of blood pressure related CVDs [27].

In addition, people with “fluctuant” SBP trajectory also had a significantly higher risk of CVDs, but the association disappeared after the adjustment of antihypertensive medications. This fluctuant group had the earliest onset of hypertension and the highest prevalence of using antihypertensive medications. The non-significant association with CVDs could be explained by the therapeutical effect of antihypertensive medications, which showed a decrease in SBP from high level to normal level during midlife and late life. This also emphasizes the need for effective treatment of hypertension and well control of blood pressure to reduce the risk of CVDs.

Strengths and limitations

The major strengths of this study are the longitudinal cohort design, long follow-up period (>20 years), and repeated measurements of SBP which enable us to perform the trajectory analyses. Second, the large number of participants provided us enough statistical power to detect the distinct trajectory patterns. Third, the LCGM allows us to identify the heterogeneity of individual differences in SBP change, and it is more likely to find diverse trajectory which could be easily overlooked by other analysis.

However, the study has several limitations. First, the rate of lost to follow-up was quite high (from 10.1% in 1991 to 38% in 2011). However, the bias due to lost to follow-up might be minimized because the association between SBP trajectory and CVDs remained unchanged among those with the longest follow-up time (S3 Table). Second, statistical power might be reduced for the association between SBP trajectory and CVDs due to the small number of CVD cases especially in the stratified analyses. Third, the self-reported CVDs were used in the study, in which the results could be affected by the recall bias of these variables. Also, it is unable to differentiate the recurrent and new cases of CVDs. In addition, the results cannot rule out the cofounding effect from other factors (e.g., biomarkers) on CVDs due to lack of data.

Conclusion

Having a rapidly increasing SBP over life-course is associated with a higher risk of CVDs. Our study implies the importance of lifetime monitoring the change of blood pressure for the prevention of CVDs.

The parameters of latent class growth models (n = 3566).

Abbreviations: BIC = Bayesian information criterion. *Model in bold font was chosen as the best model and included as the main analysis in the study.

(DOCX)

Click here for additional data file.

The association between systolic blood pressure trajectories and cardiovascular diseases by use of antihypertensive drugs (n = 3566).

Abbreviations: CVD = cardiovascular disease. *Model 1 was adjusted for socio-demographic factors (i.e., age, sex, living region and education) and baseline SBP, and model 2 was further adjusted for smoking, alcohol overconsumption, physical activity, unhealthy dietary and body mass index.

(DOCX)

Click here for additional data file.

The association between systolic blood pressure trajectories and cardiovascular diseases among those followed from baseline to last visit (n = 2210).

Abbreviations: CVD = cardiovascular disease. *Model 1 was adjusted for socio-demographic factors (i.e., age, sex, living region and education) and baseline SBP, model 2 was further adjusted for smoking, alcohol overconsumption, physical activity, unhealthy dietary and body mass index, and model 3 was additionally adjusted for antihypertensive drugs.

(DOCX)

Click here for additional data file.

18 Jun 2020

PONE-D-20-15275

Life-course blood pressure trajectories and cardiovascular diseases: a population-based cohort study in China

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Reviewer #1: The authors identified five SBP blood pressure trajectories in a prospective cohort of 5144 Chinese people and found fluctuant SBP trajectory was associated with increased CV risk of non-lethal stroke and myocardial infarction in a subset of 3750 participants. There are some inertial problems that heavily weaken the credibility of the conclusion and should be properly addressed.

Major comments:

1. The SBP trajectories cannot be deemed as lifelong SBP trajectories. The follow-up duration is shorter compared to the whole lifespan and thus heterogenous participants, the youth and the elderly, are likely to be modelled into the same trajectory. Moreover 95% of total participants aged 17-47 years (Table 1). Modelling within this age range may be appropriate whereas modelling out of the range misleading. A clear trend toward persistent high blood pressure of the "fluctuant" group when compared to other groups is observed within this age range, which is consistent with our common sense that high blood pressure, but not "fluctuant" SBP, increases CV risk.

2. LCGM modelling strategy should be re-evaluated. It takes at least three repeated measures to conduct trajectory analysis, thus participants with 1-2 SBP readings should be excluded in the first place. Besides, each trajectory is expected to have at least 5% of the overall population. In the current analysis, "rapid increase" group and "fluctuant" group both comprise 2% of total participants, which make it hard to interpret at clinical perspective. Mean posterior probabilities are recommended at > 0.7-0.8, why did the authors choose 0.65 as the cutoff? In addition, the analysis of SBP trajectory and CV events is restricted to 3750 participants (73%), thus the BP trajectories should be re-modelled.

3. Since baseline SBP level is a strong predictor of prospective CV events and the authors had emphasized in the introduction that BP trajectory should be considered as an auxiliary factor to BP in CV risk evaluation, baseline SBP should be adjusted in the logistic model.

4. Given the proportion of loss to follow-up is high, and CV events within this study is self-reported, bias exists where a certain proportion of participants who had developed debilitating or lethal CVD, or died due to other cause, were neglected in the analyses due to the study design.

Minor comments:

1. Abstract: The OR and 95%CI of the "rapid increase" group is 2.10 (0.95, 4.64) which is non-significant. Thus it's not in support of the conclusion that "Rapid increase" patterns of SBP trajectory are associated with higher risk of CVDs.

2. Line 48-51: The authors expressed that, "BP change over time should be accounted in CVD risk estimation; monitoring BP with lifetime estimate is essential." The second sentence makes it hard to understand what the authors wanted to emphasize, the BP monitoring or the BP change over time, thus the sentences should be re-phrased.

Reviewer #2: Dr. Xu and colleagues investigated the association between life-course blood pressure trajectories and cardiovascular diseases in a population based cohort study in China. The major finding is that rapid increase and fluctuant patterns of SBP trajectory over life course are associated with higher risk of CVDs.

1. The study showed that the “fluctuant” group started with a low SBP (~70 mmHg) in childhood, which was not consistent with the common sense, please explain the reason.

2. Tables 2 and S2 showed different results between “Rapid increase” group and “Fluctuant” group, which needed to be further explained.

3. The table did not show the characteristics of the study population across trajectory groups (n=3750), which were analyzed in this study.

4. Did the events include both recurrent and new cases of CVD?

**********

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Reviewer #1: No

Reviewer #2: No

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Submitted filename: review(Huang) 20200612.docx

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8 Aug 2020

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Answer: We have added the captions for the supporting materials after references, see pages 20-21, lines 400-403.

Reviewer #1:

Major comments:

1. The SBP trajectories cannot be deemed as lifelong SBP trajectories. The follow-up duration is shorter compared to the whole lifespan and thus heterogenous participants, the youth and the elderly, are likely to be modelled into the same trajectory. Moreover 95% of total participants aged 17-47 years (Table 1). Modelling within this age range may be appropriate whereas modelling out of the range misleading. A clear trend toward persistent high blood pressure of the "fluctuant" group when compared to other groups is observed within this age range, which is consistent with our common sense that high blood pressure, but not "fluctuant" SBP, increases CV risk.

Answer: The age range of study participants was 9-70 years old at baseline. Thus, the 20-year’s SBP trajectory covered the age from 9 years old (youngest at baseline) to 90 years (oldest at the end of follow-up). The modelling of the trajectory was within this age range, which can be considered from a lifetime perspective. We agree that the trajectory in childhood and late life are less reliable due to the small number of participants. We have acknowledged this as a limitation in the revised version (see page 15, lines 274-275).

2. LCGM modelling strategy should be re-evaluated. It takes at least three repeated measures to conduct trajectory analysis, thus participants with 1-2 SBP readings should be excluded in the first place. Besides, each trajectory is expected to have at least 5% of the overall population. In the current analysis, "rapid increase" group and "fluctuant" group both comprise 2% of total participants, which make it hard to interpret at clinical perspective. Mean posterior probabilities are recommended at > 0.7-0.8, why did the authors choose 0.65 as the cutoff? In addition, the analysis of SBP trajectory and CV events is restricted to 3750 participants (73%), thus the BP trajectories should be re-modelled.

Answer: We thank the reviewer for these suggestions. We have re-modelled the trajectory analysis among those with at least 3 SBP measurements (n=3600). Accordingly, the description and results in both abstract and main text have been revised (see pages 2,4,5).

In light of these analyses, we still choose the model with 5 trajectory classes as the final model. The mean posterior probability for each class in the updated model meets the recommended criteria (0.76-0.85). The percentage is still small for two groups: 2.3% for fluctuant group, and 3.1% for rapid increase group. We agree with the reviewer that each class is expected to have at least 5% of the participants, which is good to have enough power for the subsequent analysis. However, this is not a necessary requirement for a model decision as done in the previous studies (Buscot M-J, et al. Distinct child-to-adult body mass index trajectories are associated with different levels of adult cardiometabolic risk. Eur Heart J 2018; 39:2263-2270.). We acknowledged that with the small percent of some group (e.g., 3.1% for rapid increase), we have a reduced power for the subsequent associations between SBP trajectory and CVDs. We have acknowledged this as a limitation (see page 15, lines 275-278).

We know that statistical parameters (e.g., BIC, group membership, mean posterior probability) are very important for the model chosen, but this cannot be considered as the only criteria in the trajectory analysis. We have chosen the model also based on the assumption of SBP change over life course (e.g., distinct change patterns including fluctuant variations). If the model is chosen solely on the statistical parameters, we may not be able to identify those with fluctuant SBP levels, which do exist in reality and is informative from a clinical perspective. To make it more clear, we have modified the description in the statistical analysis (see page 7, lines 149-153).

3. Since baseline SBP level is a strong predictor of prospective CV events and the authors had emphasized in the introduction that BP trajectory should be considered as an auxiliary factor to BP in CV risk evaluation, baseline SBP should be adjusted in the logistic model.

Answer: As the reviewer suggested, we have adjusted baseline SBP in the association between SBP trajectory and CVDs. The associations remain significant after additional adjustment of baseline SBP in model 1, and still remain significant for fluctuant and increase group in the fully-adjusted model. Accordingly, we revised the description in statistical analysis (see page 8, lines 160-161) and the notes under Table 2.

4. Given the proportion of loss to follow-up is high, and CV events within this study is self-reported, bias exists where a certain proportion of participants who had developed debilitating or lethal CVD, or died due to other cause, were neglected in the analyses due to the study design.

Answer: In the study cohort, the rate of lost to follow-up ranged from 9.7% in 1991 to 36.4% in 2011. To minimize the bias due to lost to follow-up, we examined the cumulative risk of CVDs, which was defined as ever report of having CVDs during the study period. In the updated analysis, the information on CVDs were available from all of 3600 study participants in the updated analysis. Thus, the bias due to lost to follow-up for the outcome has been minimized in this study.

Minor comments:

1. Abstract: The OR and 95%CI of the "rapid increase" group is 2.10 (0.95, 4.64) which is non-significant. Thus it's not in support of the conclusion that "Rapid increase" patterns of SBP trajectory are associated with higher risk of CVDs.

Answer: The abstract has been revised based on the new results, see pages 2-3.

2. Line 48-51: The authors expressed that, "BP change over time should be accounted in CVD risk estimation; monitoring BP with lifetime estimate is essential." The second sentence makes it hard to understand what the authors wanted to emphasize, the BP monitoring or the BP change over time, thus the sentences should be re-phrased.

Answer: We have revised the sentence, see page 3, lines 48-50.

Reviewer #2:

1. The study showed that the “fluctuant” group started with a low SBP (~70 mmHg) in childhood, which was not consistent with the common sense, please explain the reason.

Answer: The SBP in childhood was estimated based on the model parameters. We agree that the low SBP of 70 mmHg is not common in reality. This could be due to the less reliable trajectory pattern in childhood because of the small number of participants in childhood. We have now acknowledged this as a limitation in the discussion (see page 15, lines 274-275).

2. Tables 2 and S2 showed different results between “Rapid increase” group and “Fluctuant” group, which needed to be further explained.

Answer: We thank the reviewer for pointing this out. Now, the results have been updated based on the participants with 3 SBP measurements (n=3600) according to #1 Reviewer’s suggestion. The association between SBP trajectory and CVDs are shown in Table 2 and in Results (page 12, lines 199-207).

3. The table did not show the characteristics of the study population across trajectory groups (n=3750), which were analyzed in this study.

Answer: We have updated the analysis in 3600 participants, which is the final sample with at least 3 SBP measurements and information on CVDs. The characteristics across trajectory groups are shown in Table 1 and Results (see page 9, lines 190-198).

4. Did the events include both recurrent and new cases of CVD?

Answer: We used self-reports of ever diagnosed CVDs as the outcome. The question is worded as follows: “Has a doctor ever given you the diagnosis of myocardial infarction (or stroke)?” Hence outcome included all events of CVDs. To make it clear, we have added the information in the methods section (see page 6, lines 108-109). We also revised the discussion to include the new and recurrent cases of CVDs (see page 15, lines 279-280).

Submitted filename: Response to comments_2020Jul28.docx

Click here for additional data file.

26 Aug 2020

PONE-D-20-15275R1

Life-course blood pressure trajectories and cardiovascular diseases: A population-based cohort study in China

PLOS ONE

Dear Dr. Liang,

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, consider the comments of the two Reviewers on the age range of the study population. Both Reviewers were concerned about the small number of subjects in the very young and older age groups.

Please submit your revised manuscript by Oct 10 2020 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

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We look forward to receiving your revised manuscript.

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Yan Li, MD, PhD

Academic Editor

PLOS ONE

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: (No Response)

Reviewer #2: (No Response)

**********

2. 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.

Reviewer #1: Partly

Reviewer #2: Partly

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: 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

Reviewer #2: 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: Yes

Reviewer #2: 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: Three additional major comments should be addressed, on trajectory age range, use of antihypertensives and follow-up lost.

Re. authors' reply #1:

Baseline age for majority of participants falls in "adulthood" as illustrated in figure 1 and calculated from Table 1 (95% fall at 33.3±1.96*7.0 = 19.6-47.0 if normally distributed). After 20 years of follow-up, they turned 40-67 years ("midlife" and "late life"). Extreme young and old participants should be removed as they contributed to less than 5% of total population and trajectory at such age range was not reliable due to small sample size and lower-than-lifetime follow-up. Since removal of such participants would not change the result to a significant extent, the study will still offer information on blood pressure trajectory of participants from early-to-mid adulthood to mid-to-late adulthood, who are the major target for primary prevention of hypertension-related disorders.

The authors may partially rebut my comment by providing a table in the next revision of manuscript demonstrating that there is a consistent difference of CVD prevalence across blood pressure trajectories at different baseline age groups (e.g. grouped according to figure 1, baseline age: 9-18, 19-45, 46-60 and 61-70) , or, alternatively downplay the trajectory coverage, i.e. adulthood but not lifetime.

Interestingly, if the trajectories remain unchanged in the participants at adulthood at baseline after re-analysis, then adding of percentage of cardiovascular disease to the figure (see attachment) and limiting age range from 18-50 (black rectangle) will show us that participants at low level of blood pressure over adulthood had the lowest prevalence of cardiovascular disease and CVD prevalence increases with the increment of blood pressure, which is not surprising. Meanwhile, when limiting age range to 40-70 (red rectangle) then participants with highest CVD prevalence (class 4) had lower blood pressure. Since criteria of CVD were severe but non-lethal, then the result could be explained by the usage of antihypertensives for primary and secondary CVD prevention, that participants at class 2, 4 and 5 had significantly higher proportion of antihypertensive use (28%, 68% and 60%). That could also be confirmed from model 3 of table 2 that the significance nearly diminished after adjustment of antihypertensive treatment. A sensitivity analysis is needed to show if such association was still significant in antihypertensive drug-naïve subjects.

Re. authors' reply #2 & #3:

comments were properly addressed.

Re. authors' reply #4:

To minimize the impact of lost of follow-up due to various reasons, the authors should conduct another sensitivity analysis by re-evaluate their findings in those who participated the first and the last follow-up (i.e. 63.6% according to line 89).

Reviewer #2: Most comments were properly addressed. I have a minor suggestion regarding my previous comment 1:

Because of the small number of participants in childhood and the aged, the trajectory in childhood and later life shown in the figure might be less reliable. It is suggested that the age span in the analyses could be smaller, making the results more reliable.

**********

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

[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: Figure 1_2020Jul28.png

Click here for additional data file.

17 Sep 2020

Reviewer #1:

Three additional major comments should be addressed, on trajectory age range, use of antihypertensives and follow-up lost.

Answer: We thank the reviewer very much for the further comments. We have addressed these three comments, please see the details of revisions as specified below.

Re. authors' reply #1:

Baseline age for majority of participants falls in "adulthood" as illustrated in figure 1 and calculated from Table 1 (95% fall at 33.3±1.96*7.0 = 19.6-47.0 if normally distributed). After 20 years of follow-up, they turned 40-67 years ("midlife" and "late life"). Extreme young and old participants should be removed as they contributed to less than 5% of total population and trajectory at such age range was not reliable due to small sample size and lower-than-lifetime follow-up. Since removal of such participants would not change the result to a significant extent, the study will still offer information on blood pressure trajectory of participants from early-to-mid adulthood to mid-to-late adulthood, who are the major target for primary prevention of hypertension-related disorders.

The authors may partially rebut my comment by providing a table in the next revision of manuscript demonstrating that there is a consistent difference of CVD prevalence across blood pressure trajectories at different baseline age groups (e.g. grouped according to figure 1, baseline age: 9-18, 19-45, 46-60 and 61-70) , or, alternatively downplay the trajectory coverage, i.e. adulthood but not lifetime.

Answer: To address the reviewer’s comments, we have now removed the very young and very old participants by limiting the age range at baseline to 20-50 years (n=3566). We have revised the description of participants in the methods, see page 5. The trajectory pattern of SBP remain unchanged (see Fig 1 and S1 table). In addition, we updated the subsequent analysis on associations between SBP trajectories and cardiovascular diseases, see Table 1, S2-S3 tables and the text in the results (see pages 8-14). In addition, we have updated the abstract (see pages 2-3), main findings (see page 14) and discussion (see pages 15-17).

Interestingly, if the trajectories remain unchanged in the participants at adulthood at baseline after re-analysis, then adding of percentage of cardiovascular disease to the figure (see attachment) and limiting age range from 18-50 (black rectangle) will show us that participants at low level of blood pressure over adulthood had the lowest prevalence of cardiovascular disease and CVD prevalence increases with the increment of blood pressure, which is not surprising. Meanwhile, when limiting age range to 40-70 (red rectangle) then participants with highest CVD prevalence (class 4) had lower blood pressure. Since criteria of CVD were severe but non-lethal, then the result could be explained by the usage of antihypertensives for primary and secondary CVD prevention, that participants at class 2, 4 and 5 had significantly higher proportion of antihypertensive use (28%, 68% and 60%). That could also be confirmed from model 3 of table 2 that the significance nearly diminished after adjustment of antihypertensive treatment. A sensitivity analysis is needed to show if such association was still significant in antihypertensive drug-naïve subjects.

Answer: As the reviewer suggested, we have added the cumulative incidence of CVDs to the figure (see Fig 1). We agree with reviewer that use of antihypertensive medication has an effect on the associations between SBP trajectory and CVDs. Now, we have added a supplementary table on the associations stratified by use of antihypertensive medications, see S2 table. Accordingly, we have added these results (see page 13) and a brief discussion (see page 16).

Re. authors' reply #4:

To minimize the impact of lost of follow-up due to various reasons, the authors should conduct another sensitivity analysis by re-evaluate their findings in those who participated the first and the last follow-up (i.e. 63.6% according to line 89).

Answer: As the reviewer suggested, we have performed the sensitivity analysis among those who were followed from baseline until the last follow-up (n=2210). The results were shown in S3 Table. The associations remained unchanged compared with the main results from total participants. Accordingly, we added a brief discussion on the lost of follow-up, see page 17.

Reviewer #2:

Because of the small number of participants in childhood and the aged, the trajectory in childhood and later life shown in the figure might be less reliable. It is suggested that the age span in the analyses could be smaller, making the results more reliable.

Answer: We thank the reviewer for kindly providing further comments. As the reviewer suggested, we have removed the very young and very old participants (n=34) from baseline. The age range at baseline has been limited to 20-50 years (n=3566). We have described this in the methods, see page 5. Accordingly, we have updated the trajectory analysis (see Fig 1 and S1 Table) and subsequent analysis on associations between trajectory groups and cardiovascular diseases (see Table 1). Please also refer to our response to the first comments of Reviewer #1.

Submitted filename: Response to comments_2020Sept17.docx

Click here for additional data file.

5 Oct 2020

Life-course blood pressure trajectories and cardiovascular diseases: A population-based cohort study in China

PONE-D-20-15275R2

Dear Dr. Liang,

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.

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Kind regards,

Yan Li, MD, PhD

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

**********

2. 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.

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: 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: Yes

Reviewer #2: 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: Yes

Reviewer #2: 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)

**********

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

12 Oct 2020

PONE-D-20-15275R2

Life-course blood pressure trajectories and cardiovascular diseases: A population-based cohort study in China

Dear Dr. Liang:

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. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

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on behalf of

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Academic Editor

PLOS ONE