
==== Front
101564430
Eur J Prev Cardiol
Eur J Prev Cardiol
European journal of preventive cardiology
2047-4873
2047-4881

38629743
10.1093/eurjpc/zwae147
ems195809
Article
Predictive value of 8-year blood pressure measures in intracerebral haemorrhage risk over 5 years
Zhang Yiqian 1
Ding Yinqi 1
Yu Canqing 123
Sun Dianjianyi 123
Pei Pei 2
Du Huaidong 45
Yang Ling 45
Chen Yiping 45
Schmidt Dan 5
Avery Daniel 5
Chen Jianwei 6
Chen Junshi 7
Chen Zhengming 5
Li Liming 123
Lv Jun 1238*
on behalf of the China Kadoorie Biobank Collaborative Group International Steering Committee Chen Junshi
Chen Zhengming (PI)

Clarke Robert
Collins Rory
Li Liming (PI)

Lv Jun
Peto Richard
Walters Robin

International Co-ordinating Centre, Oxford Avery Daniel
Barnard Maxim
Bennett Derrick
Belbasis Lazaros
Boxall Ruth
Chan Ka Hung
Chen Yiping
Chen Zhengming
Clarke Charlotte
Clarke Johnathan
Clarke Robert
Du Huaidong
Mohamed Ahmed Edris
Fry Hannah
Gilbert Simon
Im Pek Kei
Iona Andri
Kakkoura Maria
Kartsonaki Christiana
Lam Hubert
Lin Kuang
Liu James
Mazidi Mohsen
Millwood Iona
Morris Sam
Nie Qunhua
Pozarickij Alfred
Rahmati Maryanm
Ryder Paul
Said Saredo
Schmidt Dan
Stevens Becky
Turnbull Iain
Walters Robin
Wang Baihan
Wang Lin
Wright Neil
Yang Ling
Yang Xiaoming
Yao Pang

National Co-ordinating Centre, Beijing Han Xiao
Hou Can
Xia Qingmei
Liu Chao
Lv Jun
Pei Pei
Sun Dianjianyi
Yu Canqing
Pan Lang

10 Regional Co-ordinating Centres Qingdao CDC Pang Zengchang
Gao Ruqin
Li Shanpeng
Duan Haiping
Wang Shaojie
Liu Yongmei
Du Ranran
Zang Yajing
Cheng Liang
Tian Xiaocao
Zhang Hua
Zhai Yaoming
Ning Feng
Sun Xiaohui
Li Feifei

Licang CDC Lv Silu
Wang Junzheng
Hou Wei

Heilongjiang Provincial CDC Sun Wei
Yan Shichun
Cui Xiaoming

Nangang CDC Wang Chi
Wu Zhenyuan
Li Yanjie
Kang Quan

Hainan Provincial CDC Luo Huiming
Ou Tingting

Meilan CDC Zheng Xiangyang
Guo Zhendong
Wu Shukuan
Li Yilei
Li Huimei

Jiangsu Provincial CDC Wu Ming
Zhou Yonglin
Zhou Jinyi
Tao Ran
Yang Jie
Su Jian

Suzhou CDC Liu Fang
Zhang Jun
Hu Yihe
Lu Yan
Ma Liangcai
Tang Aiyu
Zhang Shuo
Jin Jianrong
Liu Jingchao

Guangxi Provincial CDC Lin Mei
Lu Zhenzhen

Liuzhou CDC Zhou Lifang
Xie Changping
Lan Jian
Zhu Tingping
Wei Liuping
Zhou Liyuan
Chen Ningyu
Qin Yulu
Wang Sisi

Sichuan Provincial CDC Wu Xianping
Zhang Ningmei
Chen Xiaofang
Chang Xiaoyu

Pengzhou CDC Yuan Mingqiang
Wu Xia
Chen Xiaofang
Jiang Wei
Liu Jiaqiu
Sun Qiang

Gansu Provincial CDC Chen Faqing
Ren Xiaolan
Dong Caixia

Maiji CDC Zhang Hui
Mao Enke
Wang Xiaoping
Wang Tao
zhang Xi

Henan Provincial CDC Kang Kai
Feng Shixian
Tian Huizi
Fan Lei

Huixian CDC Li XiaoLin
Sun Huarong
He Pan
Zhang Xukui

Zhejiang Provincial CDC Yu Min
Hu Ruying
Wang Hao

Tongxiang CDC Zhang Xiaoyi
Cao Yuan
Xie Kaixu
Chen Lingli
Shen Dun

Hunan Provincial CDC Li Xiaojun
Jin Donghui
Yin Li
Liu Huilin
Fu Zhongxi

Liuyang CDC Xu Xin
Zhang Hao
Chen Jianwei
Peng Yuan
Zhang Libo
Qu Chan

Event Adjudication Clinicians Li Shuya
Qin Haiqiang
Wang Yongjun
Beijing Tiantan Hospital,Capital Medical University
Chen Qiling
Wang Jihua
Peking University People’s Hospital
Sun Xiaojia
Wang Lei
Wang Xun
Zhang Liming
Zhou Shanshan
Hospital of Harbin Medical University
Chen Hongyuan
Chen Li
Gou Haiyan
Wang Weizhi
Zhu Yanmei
Zhu Yulan
The 2nd Hospital of Hebei Medical University
Zhang Ning Huashan Hospital

Cheng Xin
Dong Qiang
Dong Yi
Fang Kun
Mao Yiting
Huashan Hospital
An Yu
Chen Peiling
Chen Yinghua
Liu Zhihong
Zhang Lihua
Jinling Hospital
Chen Xiaohong
Jv Naixin
Li Xiaojiu
Liu Liyang
Lu Yun
Xing Xiaona
The People’s Hospital of Liaoning Province
You Shihao Qingdao Fuwai Cardiovascular Hospital

Cheng Xiaoli
Gua Chaojun
Jiang Jinping
Liu Jingyi
Ma Shumei
Shengjing Hospital of China Medical University
Yang Xuefeng Shenyang Military General Hospital

Du Xiaomo
Xu Jian
Yang Xuecheng
Zhao Xiaodi
The First People’s Hospital of Shenyang
Hao Zilong
Liu Ming
Wang Deren
West China Hospital Sichuan University
Li Xiaoting The Second Affiliated Hospital of Suzhou University

Hui Lili
Liao Zhanling
Liu Feng
Qingdao Fuwai Cardiovascular Hospital
Feng Chunning
Ji Dejiang
Qu Fengxia
Yuan Wenwen
The First Affiliated Hospital of Zhengzhou University
Fu Xin
Ding Jing
Du Peng
Jin Lirong
Mao Yueshi
Wang Xin
Zhongshan Hospital

†
1 Department of Epidemiology & Biostatistics, School of Public Health, Peking University, 38 Xueyuan Road, Haidian District, Beijing 100191, China
2 Peking University Center for Public Health and Epidemic Preparedness & Response, 38 Xueyuan Road, Haidian District, Beijing 100191, China
3 Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, 38 Xueyuan Road, Haidian District, Beijing 100191, China
4 Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, United Kingdom
5 Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, United Kingdom
6 Liuyang Centers for Disease Control and Prevention, NO.11 Section 2 Lihua Road, Jili Subdistrict, Liuyang, Changsha, Hunan 410300, China
7 China National Center for Food Safety Risk Assessment, 37 Guangqu Road, Chaoyang District, Beijing 100022, China
8 State Key Laboratory of Vascular Homeostasis and Remodeling, Peking University, 38 Xueyuan Road, Haidian District, Beijing 100191, China
* Corresponding author: Jun Lv, MD, PhD, Department of Epidemiology and Biostatistics, Peking University Health Science Center 38 Xueyuan Road, Beijing 100191, China, Phone: 86-10-82801528, lvjun@bjmu.edu.cn
† The members of steering committee and collaborative group are listed in the online-only supplemental material.

17 4 2024
17 4 2024
29 4 2024
20 9 2024
zwae147https://creativecommons.org/licenses/by/4.0/ This work is licensed under a BY 4.0 International license.
Aims

The relationships between long-term blood pressure (BP) measures and intracerebral haemorrhage (ICH), as well as their predictive ability on ICH, are unclear. In this study, we aim to investigate the independent associations of multiple BP measures with subsequent 5-year ICH risk, as well as the incremental value of these measures over a single-point BP measurement in ICH risk prediction.

Methods and results

We included 12,398 participants from the China Kadoorie Biobank (CKB) who completed three surveys every 4–5 years. The following long-term BP measures were calculated: mean, minimum, maximum, standard deviation, coefficient of variation, average real variability, and cumulative BP exposure (cumBP). Cox proportional hazard models were used to examine the associations between these measures and ICH. The potential incremental value of these measures in ICH risk prediction was assessed using Harrell’s C statistics, continuous net reclassification improvement (cNRI), and relative integrated discrimination improvement (rIDI). The hazard ratios (95% confidence intervals) of incident ICH associated with per standard deviation increase in cumulative systolic BP and cumulative diastolic BP were 1.62 (1.25–2.10) and 1.59 (1.23–2.07), respectively. When cumBP was added to the conventional 5-year ICH risk prediction model, the C-statistic change was 0.009 (-0.001, 0.019), the cNRI was 0.267 (0.070–0.464), and the rIDI was 18.2% (5.8%–30.7%). Further subgroup analyses revealed a consistent increase in cNRI and rIDI in men, rural residents, and participants without diabetes. Other long-term BP measures showed no statistically significant associations with incident ICH and generally did not improve model performance.

Conclusion

The nearly 10-year cumBP was positively associated with an increased 5-year risk of ICH and could significantly improve risk reclassification for the ICH risk prediction model that included single-point BP measurement.

Long term
blood pressure variability
cumulative blood pressure
intracerebral haemorrhage
risk prediction
incremental value
==== Body
pmcIntroduction

Intracerebral haemorrhage (ICH) is one of the most common haemorrhagic stroke subtypes1. In 2019, ICH accounted for 29.5% of all incident strokes in low- and middle-income countries, approximately twice as many as in high-income countries2. Despite accounting for less than one-third of all strokes, the prognosis of ICH is extremely poor, with the case fatality rate as high as 50% at 30 days. Globally, the proportion of stroke deaths attributable to ICH will rise from 44.3% in 2020 to 52.4% in 20503. Because no effective treatment has been demonstrated to significantly reduce mortality or improve neurological deficits following ICH, current guidelines for ICH management focus primarily on managing risk factors to prevent ICH4.

Blood pressure (BP) is one of the most important modifiable risk factors for ICH, and it is more strongly associated with ICH than with subarachnoid haemorrhage (SAH), ischaemic stroke, ischaemic heart disease (IHD), and other vascular diseases5–7. A growing number of studies have found that, in addition to the absolute degree of BP elevation, greater BP variability (BPV) measured over years to decades is associated with a higher risk of cardiovascular disease (CVD)8–10. Long-term BPV measures, such as standard deviation (SD), coefficient of variation (CV), average real variability (ARV), and cumulative BP exposure (cumBP), have been linked to all-cause mortality9–12, myocardial infarction9, 11, coronary heart disease12, and stroke9, 11, 12, independent of single-point BP measurement. Existing research, however, has not consistently demonstrated the incremental value of these long-term BPV measures beyond the single-point measurement in predicting the risk of cardiovascular outcomes10, 13–15. Furthermore, no study has compared the predictive ability of various BPV measures on specific outcomes using the same population data and a unified statistical strategy.

Previous studies have primarily examined the relationships between single-point BP measurement and ICH, with little use of long-term BPV measures7, 16, 17. Only two studies conducted in China and the Netherlands found that visit-to-visit BPV was associated with an increased risk of haemorrhagic stroke18, 19. These two studies did not distinguish between ICH and SAH. Neither study evaluated the additional value of their respective BPV measures over single-point BP measurement in haemorrhagic stroke risk prediction. More potentially valuable BPV measures, such as cumBP, are warranted to be investigated in relation to ICH, as well as a comparison of the incremental values of various BPV measures in ICH risk prediction.

The current study analyzed data from 12 398 Chinese adults who participated in the China Kadoorie Biobank (CKB) and received three surveys every 4–5 years. We aimed to examine the independent associations between multiple long-term BPV measures and the incident risk of ICH. We further assessed the incremental predictive value of these BPV measures over a single-point BP measurement for a 5-year ICH risk prediction model.

Methods

Study design and population

During 2004–08, the CKB baseline survey enrolled more than 0.5 million participants aged 30–79 from five urban (Qingdao, Harbin, Liuzhou, Suzhou, and Haikou; represented by the city name) and five rural (Sichuan, Zhejiang, Hunan, Gansu, and Henan; represented by the provincial name) areas across China. About 5% of surviving participants were cluster sampled at the 2008 and 2013–14 resurveys. The baseline survey and resurvey design were previously described in detail20, 21. Shortly after the baseline survey was completed, all participants were followed up for mortality, morbidity, and hospitalization events through linkages to local disease and mortality registries and the national health insurance database, supplemented with annual active confirmation. All events were coded using the International Classification of Diseases, Tenth Revision (ICD-10), by trained staff who were unaware of the baseline information. All participants provided written, informed consent. The study protocol was approved by the Ethics Review Committee of the Chinese Center for Disease Control and Prevention (Beijing, China) and the Oxford Tropical Research Ethics Committee, University of Oxford (UK).

A total of 14 902 participants attended three surveys, including the 2004–08 baseline survey, the 2008 resurvey, and the 2013–14 resurvey. We excluded participants with a self-reported history of heart disease or stroke at any of three visits (n=1684) or those who were documented with the incidence of IHD (ICD-10: I20-I25) or cerebrovascular disease (I60-I69) from baseline until the 2013–14 resurvey date (n=1551). Participants with missing data for body mass index (BMI; n=1), waist circumference (n=2), and self-reported diseases (n=20) were also excluded, leaving the present study with 12 398 participants (Figure 1). The study outcome of interest was the first incident ICH (I61) from the 2013–14 resurvey until 31 December 2018. The adjudication for ICH showed that the positive predictive value was 90.4% for reporting accuracy and 98.2% for diagnostic accuracy22.

Blood pressure measurement and correction

After at least 5 min of resting, all participants had their BP measured twice from the right upper arm by trained staff using a UA-779 digital sphygmomanometer for the 2004–08 baseline survey and 2008 resurvey and Omron HEM-7430 sphygmomanometer for the 2013–14 resurvey. If the difference between two systolic BP (SBP) readings was >10 mmHg, a third measurement was taken after a 1 min rest. The mean values of the last two readings were used for analyses6. Participants were considered to have hypertension if they had self-reported doctor-diagnosed hypertension, were taking anti-hypertensive medication, or had a mean SBP of ≥140 mmHg or a mean diastolic BP (DBP) of ≥90 mmHg.

In a previous study of the CKB population, outdoor temperature was linked to significant seasonal variations in BP23. We, therefore, corrected the measured BP values for seasonal fluctuations in ambient temperature by standardizing them to mid-season (i.e. April) values in each area with reference to a previous study24. Daily ambient temperatures were obtained from the local Meteorological Services during the CKB baseline survey and 2008 resurvey.

Real-time temperature was recorded using an on-site thermometer during the 2013–14 resurvey. For consistency, we replaced the daily ambient temperatures at the baseline survey and 2008 resurvey with real-time temperature values (precise to the hour) using meteorological data based on historical reanalysis data sets from the National Aeronautics and Space Administration (NASA), provided by https://xihe-energy.com25. All participants were assigned a temperature value based on the start time of the survey as recorded by the laptop for the survey. Based on the known average treatment efficacy of anti-hypertensive drugs, BP values in treated participants were further corrected by adding 15 mmHg to SBP and 10 mmHg to DBP26.

Other covariate assessments

A laptop-based questionnaire administered by an interviewer was used to collect information on socio-demographic characteristics (e.g. age, sex, level of education, occupation, marital status, and annual household income), lifestyle and dietary factors (e.g. tobacco smoking, alcohol consumption, physical activity, and intake of fresh fruits, vegetables, and red meat), and personal and family medical histories (e.g. heart attack, stroke, and diabetes). The assessment and definition of lifestyle and dietary factors have been described elsewhere27–31. Anthropometric measurements were taken using standard procedures and instruments, including height, weight, and waist circumference. Body mass index was calculated as weight in kilograms divided by height in meters squared. A blood sample of 10 mL was collected for storage and on-site glucose testing. Participants with self-reported diabetes or screen-detected diabetes (a fasting time ≥8 h and a plasma glucose concentration of ≥7.0 mmol/L, or a fasting time <8 h and plasma glucose concentration of ≥11.1 mmol/L) were considered to have diabetes32.

Statistical analysis

First, we calculated the mean, minimum (MIN), maximum (MAX), and SD of three longitudinal SBP and DBP measurements for each participant. The CV was calculated as SD divided by mean SBP or mean DBP. The ARV was the average of the absolute difference between consecutive BP measurements. The cumBP was the area under the curve for three BP measurements and was calculated as: cumBP=(BP1+BP22×T12)+(BP2+BP32×T23)

where BPn is the BP value at visit n and Tab is the number of years between visits a and b.

The basic characteristics of the study participants of the 2013–14 resurvey were presented by tertiles of cumSBP and cumDBP. The groups were compared using linear regression for continuous variables and logistic regression for dichotomous variables, with adjustments for age, sex, and 10 study areas as appropriate. The linear trend was tested by treating cumSBP and cumDBP as continuous variables in the model.

Person-years were calculated from the completion of the 2013–14 resurvey to the first diagnosis of ICH, death, loss to follow-up, or 31 December 2018, whichever came first. The Cox proportional hazard model was used to calculate hazard ratios (HRs) and 95% confidence intervals (CIs) for associations of ICH risk with per SD increase in SBP and DBP measures (mean, MIN, MAX, SD, CV, ARV, and cumBP), separately. The models used age as the time scale and were stratified by age (5-year intervals), sex, and study areas (10 groups) as appropriate. Model 1 was adjusted for age (years), education (no formal school, primary school, middle school, high school, technical school or college, and university), occupation (agriculture and related workers, factory worker, administrator/manager, professional/technical, sales and service workers, retired, house wife/husband, self-employed, unemployed, and other/not stated), marital status (married, widowed, separated/divorced, and never married), household income (<¥2500, ¥2500–4999, ¥5000–9999, ¥10 000–19 999, ¥20 000–34 999, ¥35 000–49 999, ¥50 000–74 999, ¥75 000–99 999 and ≥¥100 000), smoking (never, former, and current daily <15, 15–24 or ≥25 cigarettes or equivalent), alcohol drinking (less than weekly, former, weekly, and daily <30, 30–59 or ≥60 g of pure alcohol), intake frequency of fresh fruits, vegetables, and red meat (days/week), total physical activity level (metabolic equivalent task-hour/day), BMI (kg/m2), waist circumference (cm), family history of CVD (yes/no), and prevalence of diabetes (yes/no). Model 2 was further adjusted for single-point SBP and DBP. All covariate information was from the 2013–14 resurvey. To control the probability of reporting false-positive results for multiple hypothesis tests (n=14), we additionally set the cut-off α-value for Bonferroni correction as 0.05/14 = 0.00433.

We further evaluated the potential incremental predictive value of long-term BPV measures in the 5-year ICH risk prediction. We first developed a basic risk prediction model for ICH based on our previous CVD risk prediction model34. The Cox model was used for model development and stratified by sex and 10 study areas, with time since 2013–14 resurvey as the time scale. Predictors included age (years), SBP (mmHg), DBP (mmHg), anti-hypertensive treatment (yes/no), current daily smoking (yes/no), history of diabetes (yes/no), and waist circumference (cm), all based on information from the 2013–14 resurvey. The interactions between age and the other six variables were also included. Since the anti-hypertensive treatment was one of the predictors, no additional 15/10 mmHg were added to SBP and DBP. We additionally added the CV, ARV, mean+SD+MIN+MAX, and cumBP to the basic model separately. The model’s discrimination ability was assessed by Harrell’s C statistics35. We calculated the 95% CIs for C statistics and the difference in C-statistic values between the above four models and the basic model by bootstrapping. A prior power analysis on all participants indicated that a power of 80% could be achieved with a C-statistic change of at least 0.003. The continuous net reclassification improvement (cNRI) and the relative integrated discrimination improvement (rIDI) were also used to assess the reclassification performance of the models36, 37. Analyses were performed on all participants and also stratified by age (≥65 and <65 years), sex (men and women), area of residence (rural and urban), and prevalence of diabetes (yes/no). We only presented results from participants without diabetes at the 2013–14 resurvey because the number of participants with diabetes was small.

We conducted several sensitivity analyses: (i) adjusted for self-reported anti-hypertensive treatment at each visit as covariates instead of adding 15/10 mmHg to SBP/DBP; (ii) additionally adjusted for heart rate variability (SD) and incorporated it into the basic prediction model; (iii) additionally adjusted for menopausal status at 2013–14 resurvey and incorporated it into the basic prediction model for women.

The power analysis was performed using PASS (15.0.5). All other analyses were performed using Stata (version 15.0). Two-sided P-values of <0.05 were considered statistically significant.

Results

Background characteristics of study participants

The mean (SD) age of the 12 398 participants was 50.3 (9.8) in the 2004–08 baseline survey and 58.4 (9.9) in the 2013–14 resurvey. Women made up 62.1% of the participants, and 64.9% lived in rural areas. Between the 2004–08 baseline survey and the 2013–14 resurvey, the average SBP (SD) increased from 131.3 (19.8) to 136.6 (20.2) mmHg, the average DBP (SD) increased from 77.6 (10.8) to 78.6 (11.0) mmHg, and the prevalence of hypertension increased from 31.4% to 52.2%. Table 1 and Table S1 display the basic characteristics of participants based on cumSBP and cumDBP tertiles. Participants with higher cumSBP and cumDBP were more likely to be older, live in rural areas, have a higher BMI and waist circumference, and have a higher prevalence of daily alcohol drinking and diabetes.

Associations between long-term blood pressure measures and intracerebral haemorrhage

Since the 2013–14 resurvey, incident ICH has occurred in 121 participants during a median follow-up of 5 years (59 653 person-years), with an incidence rate of 2.03 per 1000 person-years. After controlling for potential confounders, all long-term SBP measures from the 2004–08 baseline survey to the 2013–14 resurvey were associated with an increased risk of ICH (Model 1 in Table 2). When further adjustments for single-point SBP and DBP from the 2013–14 resurvey were made, all effect sizes were attenuated (Model 2). Nevertheless, associations between 5 long-term SBP measures and ICH persisted except for the Mean and MIN. The HRs (95% CIs) of incident ICH associated with per SD increment in SBP measures were 1.65 (1.15–2.38) for MAX, 1.23 (1.03–1.48) for SD, 1.20 (1.01–1.43) for CV, 1.20 (1.01–1.42) for ARV, and 1.62 (1.25–2.10) for cumSBP. The MAX and cumDBP were the only DBP measures associated with an increased risk of ICH, with HRs of 1.54 (1.08–2.19) and 1.59 (1.23–2.07) for each SD increase, respectively. The cumSBP and cumDBP remained statistically significant after Bonferroni correction at a P-value of <0.004.

Stratification analyses revealed that cumSBP and cumDBP were associated with an increased risk of ICH in all age groups, men, rural residents, and participants without diabetes (Model 2 in Tables S2–S5). Only in individual subgroups did other SBP and DBP measures show statistically significant associations with ICH. Positive associations between cumBP and ICH survived Bonferroni correction in men, rural residents, and participants without diabetes. Using self-reported anti-hypertensive treatment as a covariate rather than adding 15/10 mmHg to SBP/DBP yielded generally consistent results (data not provided). Sensitivity analyses that additionally adjusted for heart rate variability or female menopausal status did not alter the results substantially (Tables S6 and S7).

Discrimination performance of prediction models

When the CV or ARV of both SBP and DBP were included in the conventional 5-year ICH risk prediction models, no statistically significant improvement in C-statistic was observed in either all participants or subgroups (Figure 2). Adding Mean+SD+MIN+MAX of both SBP and DBP improved C-statistic only in men, with an increase (95% CI) of 0.016 (0.004–0.027).

The addition of both cumSBP and cumDBP resulted in a marginal improvement in model performance for all participants, with a C-statistic change of 0.009 (-0.001, 0.019). When restricted to participants without diabetes, adding cumBP to the model resulted in a moderate discriminative improvement, with a C-statistic increase of 0.017 (0.002–0.031). There were no statistically significant increases in the C-statistic in the other subgroups. Sensitivity analyses showed consistent results (Tables S8 and S9).

Reclassification performance of prediction models

In the analyses of all participants and subgroups, most of the additions of CV or ARV to conventional 5-year ICH risk prediction models did not improve the model reclassification performance. Only adding ARV resulted in a slight improvement in cNRI in men (Table 3). There was no improvement in cNRI or rIDI with the Mean+SD+MIN+MAX of both SBP and DBP for all participants. However, cNRI and rIDI increased in certain sex or area of residence subgroups, with cNRIs (95% CI) of 0.294 (0.038–0.551) and 0.621 (0.241–1.001) for men and urban residents, and rIDIs (95% CI) of 20.0% (4.0–36.1%) and 20.1% (4.5–35.7%) for women and rural residents, respectively.

When cumSBP and cumDBP were added to the model, the consistently increased cNRI and rIDI indicated that the model’s reclassification capacity was improved for all participants, as well as for men, rural residents, and participants without diabetes. The largest cNRI and rIDI values were seen for participants without diabetes, with 0.372 (0.157–0.587) and 26.2% (3.5–48.9%), respectively. The increased rIDI alone also suggested improvement in model performance due to the inclusion of cumBP for women and participants aged 65 and over. Sensitivity analyses showed consistent results (Tables S8 and S9).

Discussion

In the current cohort study of 12 398 Chinese adults, the cumBP, based on three repeatedly measured BPs every 4–5 years, was positively associated with subsequent 5-year risk of ICH, independent of the most recent single-point SBP and DBP. The cumBP could improve the risk reclassification of the conventional 5-year ICH risk prediction model that included single-point BP measurement for all participants, as well as for men, rural residents, and participants without diabetes. Other long-term BPV measures, in comparison, showed no associations with incident ICH and generally failed to provide incremental benefits in predicting ICH risk, despite slight improvements in risk reclassification in individual subgroups.

The cumBP has been identified as a marker that allows for a more comprehensive assessment of the duration and intensity of long-term BP exposure. Evidence from Western populations indicated that the cumBP was associated with incident stroke12, 38 and predicted CVD risk13, 38. The Lifetime Risk Pooling Project of American participants aged 45–60 found that every 130 mmHg×year increase in 10-year cumSBP was associated with a 33% increase in the subsequent 12.9-year risk of stroke after adjusting for the most recent single-point SBP and other covariates12. Our study also found robust and strong relationships between almost 10-year cumBP and subsequent 5-year ICH risk, with each SD increase in cumSBP (192 mmHg×year) and cumDBP (104 mmHg×year) associated with increased ICH risks by 62% and 59%, respectively.

One of the primary goals of this study is to assess the incremental value of cumBP and other long-term BPV measures for the conventional ICH risk prediction model with single-point BP measurement. We did not find a statistically significant improvement in the C-statistic when cumSBP and cumDBP were added to the conventional model. However, the C-statistic is a conservative method for assessing changes in model fit and hardly moves once some good risk factors have been included in the model36. Using the widely used NRI and IDI37, we found that adding cumBP could significantly improve the reclassification ability of the overall participant model. The cumBP, in particular, provided a significant incremental benefit in men, rural residents, and participants without diabetes. The efforts to discover new useful biomarkers that could improve risk prediction are generally aimed at the entire population. Perhaps we can direct our efforts towards specific subpopulations.

Using a unified statistical strategy, all BPV measures, except cumBP, had no statistically significant associations with incident ICH after Bonferroni correction. Furthermore, these measures added little to the predictive performance of the conventional 5-year ICH risk prediction model. Certain measures, such as Mean+SD+MIN+MAX, demonstrated statistically significant increases in cNRI or rIDI in individual subgroups but lacked consistency and robustness overall. A Kailuan study of over 50 000 Chinese adults, aged 53 on average at baseline, examined the associations between visit-to-visit BPV and subsequent 3-year risk of stroke, as well as its subtypes (ischaemic and haemorrhagic stroke). The SD, CV, and ARV were calculated using repeatedly measured BPs over three biennial visits. After controlling for mean BP level and other potential confounders, each SD increase in SBP measures was associated with a 26% increase in the risk of haemorrhagic stroke for SD (6.7 mmHg), a 34% increase for CV (4.7%), and a 20% increase for ARV (9.2 mmHg), respectively. The corresponding increased risk of haemorrhagic stroke associated with DBP measures was 28% for SD (4.1 mmHg) and 30% for CV (4.7%), with no association observed for ARV18. Similar associations of ICH with SD, CV, and ARV of SBP were observed in our study before Bonferroni correction, whereas no statistical associations were found for the corresponding DBP measures. Aside from differences in geographic and socio-demographic characteristics, the baseline prevalence of hypertension in the Kailuan study was 44%, which was significantly higher than the 31% in our population. In hypertensive individuals, a larger long-term variation in DBP may have a greater impact on stroke.

To the best of our knowledge, this is the first prospective study that used the same population data and a unified statistical strategy to compare the independent relationships between multiple long-term BPV measures and incident ICH, as well as to assess the potential utility of these measures in the ICH risk prediction model beyond single-point BP measurement. We used data from three regular surveys, as well as a subsequent 5-year outcome surveillance period, for a total of nearly 15 years of observation. This study included participants from 10 geographically diverse urban and rural areas in China, representing various socio-demographic characteristics. We could distinguish two haemorrhagic stroke subtypes: ICH and SAH. Due to a small number of cases, the analysis of SAH was not included. Furthermore, comprehensive information collection from the questionnaire and anthropometric measurements aided in the control of potential confounders.

Several limitations of our study merit consideration. First, BP fluctuations over a shorter period (weeks, months, 1 or 2 years) were not captured. Furthermore, there were only three time points in this study, preventing us from conducting a more in-depth investigation into how long the BP variation was sufficient to predict the subsequent ICH risk. Second, our study population was restricted to those who completed three surveys and had no history of CVD from baseline to the 2013–14 resurvey, which may limit generalizability to those who developed CVD or died prematurely. Nonetheless, these excluded participants may be at a higher risk of CVD and would most likely be recognized by the conventional model based on single-point BP measurement. We emphasized the additional value of long-term BPV rather than substituting for single-point absolute BP level in the conventional model. Third, there may be residual confounding because we did not collect data on personal habits, such as high-salt diets, staying up late, and overworking. Finally, the BP measurements in this study followed guidelines at the time of the surveys39, 40. However, when compared with the updated guideline requirements in recent years, which require taking three measurements with a 1 min interval between each and averaging the last two measurements, our accuracy may be compromised to some extent.

This prospective cohort study of Chinese adults found that the nearly 10-year cumBP was positively associated with an increased subsequent 5-year risk of ICH and could significantly improve risk reclassification for the ICH risk prediction model that included single-point BP measurement. The ICH continues to impose a heavy burden on China. Blood pressure is now a routine measurement for regular health check-ups, establishment and management of resident health records in the Basic Public Health Services project, and hospital visits. The advancement of electronic medical records makes it easier to obtain long-term repeated BP measurement data. To better identify potential high-risk groups for ICH for early intervention, it is necessary to consider BP measured multiple times in the past and use appropriate indicators, such as cumBP, in risk prediction.

Supplementary Material

Supplementary File

Acknowledgments

The most important acknowledgment is to the participants in the study and the members of the survey teams in each of the 10 regional centres, as well as to the project development and management teams based at Beijing, Oxford and the 10 regional centres.

Funding

This work was supported by National Natural Science Foundation of China (82192904, 82192901, 82192900, 82388102). The CKB baseline survey and the first resurvey were supported by a grant from the Kadoorie Charitable Foundation in Hong Kong. The long-term follow-up is supported by grants from the UK Wellcome Trust (212946/Z/18/Z, 202922/Z/16/Z, 104085/Z/14/Z, 088158/Z/09/Z), grants from the National Key Research and Development Program of China (2016YFC0900500), National Natural Science Foundation of China (81390540, 91846303, 81941018), and Chinese Ministry of Science and Technology (2011BAI09B01). The funders had no role in the study design, data collection, data analysis and interpretation, writing of the report, or the decision to submit the article for publication.

Data availability

The CKB is a global resource for the investigation of lifestyle, environmental, blood biochemical and genetic factors as determinants of common diseases. The CKB study group is committed to making the cohort data available to the scientific community in China, the UK and worldwide to advance knowledge about the causes, prevention and treatment of disease. For detailed information on what data is currently available to open access users and how to apply for it, visit: https://www.ckbiobank.org/data-access. Researchers who are interested in obtaining the raw data from the CKB study that underlines this paper should contact ckbaccess@ndph.ox.ac.uk. A research proposal will be requested to ensure that any analysis is performed by bona fide researchers and - where data are not currently available to open access researchers - is restricted to the topic covered in this paper.

Lay Summary This prospective cohort study of Chinese adults investigated the independent associations of multiple blood pressure (BP) measures with subsequent 5-year intracerebral haemorrhage (ICH) risk, as well as the incremental value of these measures over a single-point BP measurement in ICH risk prediction. The cumulative BP exposure (cumBP) was positively associated with subsequent 5-year risk of ICH, independent of the recent single-point systolic BP and diastolic BP levels.

The cumBP could improve the risk reclassification of the conventional 5-year ICH risk prediction model that included single-point BP measurement for all participants, as well as for men, rural residents, and participants without diabetes.

Figure 1 Study design and flowchart of study participants

Figure 2 Harrell’s C change for 5-year intracerebral haemorrhage risk prediction models by including both systolic blood pressure and diastolic blood pressure measures in all participants and by age, sex, area of residence, or prevalence of diabetes

ARV, average real variability; CI, confidence interval; cumBP, cumulative blood pressure; CV, coefficient of variation; MAX, maximum; MIN, minimum; SD, standard deviation.

Basic Cox models included age, systolic blood pressure, diastolic blood pressure, anti-hypertensive treatment, current daily smoking, prevalence of diabetes, and waist circumference at 2013–14 resurvey, and interactions between age and the other six predictors. For the analysis in participants without diabetes, the prevalence of diabetes was removed from the basic model. All models were stratified by sex and 10 study areas, except for the analysis by sex, in which the model was stratified only by 10 study areas.

Table 1 Basic characteristics of participants by tertiles of cumulative blood pressure

	Tertile of cumSBP		Tertile of cumDBP	
Low	Medium	High	P-value for trend		Low	Medium	High	P-value for trend	
Age at 2004-08 baseline, years	47.1 (9.0)	49.7 (9.5)	54.0 (9.7)	<0.001		49.5 (10.3)	50.2 (9.8)	51.1 (9.4)	<0.001	
Age at 2013-14 resurvey, years	54.7 (9.0)	57.8 (9.4)	62.6 (9.6)	<0.001		57.1 (10.3)	58.3 (9.8)	59.6 (9.4)	<0.001	
Women, %	65.4	59.0	62.0	0.826		65.2	61.2	59.9	<0.001	
Menopausal women, %	42.0	45.2	55.7	<0.001		45.7	46.9	50.3	<0.001	
Rural, %	56.4	66.3	72.1	<0.001		56.2	65.6	72.8	<0.001	
Middle school or above, %	45.3	44.9	44.7	0.528		45.0	44.6	45.3	0.980	
Agriculture and related workers, %	34.9	31.9	30.2	<0.001		34.8	31.6	30.6	<0.001	
Married, %	88.9	88.5	88.4	0.248		88.8	88.0	88.9	0.953	
Household income ⩾ ¥ 50 000, %	44.1	44.2	43.0	0.316		43.8	43.2	44.5	0.918	
Current daily smoking, %	22.6	20.7	19.7	<0.001		22.6	20.4	20.1	<0.001	
Current daily alcohol drinking, %	8.3	9.1	9.9	0.014		8.2	9.4	9.8	0.002	
Daily food consumption, %										
   Fresh fruits	31.3	31.9	30.8	0.595		31.3	31.3	31.5	0.735	
   Fresh vegetables	95.8	96.4	96.8	0.006		95.8	96.4	96.9	0.001	
   Red meat	42.6	42.7	41.0	0.043		42.5	42.3	41.5	0.304	
Total physical activity, MET-h/day	19.1 (13.9)	19.2 (14.1)	18.6 (13.6)	0.003		19.5 (13.7)	18.9 (14.0)	18.5 (14.2)	<0.001	
Body mass index, kg/m2	23.0 (3.2)	24.1 (3.4)	24.9 (3.6)	<0.001		23.1 (3.3)	24.0 (3.3)	24.9 (3.6)	<0.001	
Waist circumference, cm	81.2 (9.2)	84.2 (9.5)	86.3 (10.2)	<0.001		81.3 (9.4)	84.1 (9.3)	86.4 (10.1)	<0.001	
Diabetes, %	5.6	9.2	14.2	<0.001		7.1	9.6	12.6	<0.001	
Hypertension, %	16.8	51.8	89.4	<0.001		24.3	50.1	82.4	<0.001	
Anti-hypertensive treatment, %	2.5	12.8	44.8	<0.001		4.3	14.5	42.6	<0.001	
Family history of cardiovascular disease, %	20.3	23.3	24.9	<0.001		20.3	22.5	25.7	<0.001	
SBP at 2004–08 baseline, mmHg	119.2 (12.4)	128.7 (13.8)	145.9 (19.9)	<0.001		121.6 (14.6)	129.6 (16.4)	142.6 (20.8)	<0.001	
SBP at 2013–14 resurvey, mmHg	122.7 (12.7)	135.4 (14.9)	151.7 (19.2)	<0.001		125.8 (16.3)	136.3 (17.5)	147.7 (19.7)	<0.001	
DBP at 2004–08 baseline, mmHg	71.4 (8.4)	76.8 (9.0)	84.6 (11.0)	<0.001		70.1 (7.9)	77.1 (8.2)	85.6 (10.2)	<0.001	
DBP at 2013–14 resurvey, mmHg	72.3 (8.8)	78.5 (9.9)	84.9 (11.4)	<0.001		70.7 (8.2)	78.6 (8.6)	86.5 (10.3)	<0.001	
cumSBP, mmHg×year	905.7 (66.3)	1065.7 (45.4)	1298.1 (135.2)	—		933.6 (104.2)	1071.1 (106.6)	1264.9 (158.9)	<0.001	
cumDBP, mmHg×year	540.8 (52.7)	630.2 (55.9)	743.5 (86.8)	<0.001		532.5 (41.3)	628.8 (26.1)	753.1 (68.9)	—	
cumDBP, cumulative diastolic blood pressure; cumSBP, cumulative systolic blood pressure; MET-h/d, metabolic equivalent task-hour/day.

The basic characteristics at the 2013–14 resurvey are presented in the table unless otherwise stated. Except for age, women, menopausal women, and urban variables, data were presented as mean (SD) or percentage, with adjustments for age, sex, and study areas.

Table 2 Associations between per 1 standard deviation increment in blood pressure measures and incident intracerebral haemorrhage

	Model 1	Model 2	P value*	
SBP				
  Mean, per 19.5 mmHg	1.81 (1.52, 2.16)	1.25 (0.87, 1.79)	0.226	
  MIN, per 17.8 mmHg	1.58 (1.33, 1.88)	0.96 (0.74, 1.25)	0.774	
  MAX, per 23.3 mmHg	1.92 (1.61, 2.28)	1.65 (1.15, 2.38)	0.007	
  SD, per 6.8 mmHg	1.52 (1.30, 1.77)	1.23 (1.03, 1.48)	0.026	
  CV, per 4.5%	1.40 (1.19, 1.64)	1.20 (1.01, 1.43)	0.039	
  ARV, per 8.4 mmHg	1.45 (1.26, 1.68)	1.20 (1.01, 1.42)	0.040	
  cumSBP, per 191.9 mmHg×year	2.01 (1.68, 2.41)	1.62 (1.25, 2.10)	<0.001	
DBP				
  Mean, per 10.4 mmHg	1.81 (1.52, 2.15)	1.26 (0.89, 1.79)	0.188	
  MIN, per 10.2 mmHg	1.65 (1.38, 1.97)	1.04 (0.79, 1.35)	0.791	
  MAX, per 11.8 mmHg	1.85 (1.56, 2.19)	1.54 (1.08, 2.19)	0.018	
  SD, per 3.6 mmHg	1.36 (1.16, 1.58)	1.14 (0.96, 1.36)	0.133	
  CV, per 4.4%	1.21 (1.04, 1.42)	1.11 (0.94, 1.32)	0.213	
  ARV, per 4.6 mmHg	1.31 (1.12, 1.54)	1.11 (0.93, 1.33)	0.240	
  cumDBP, per 104.4 mmHg×year	1.97 (1.65, 2.36)	1.59 (1.23, 2.07)	<0.001	
ARV, average real variability; cumDBP, cumulative diastolic blood pressure; cumSBP, cumulative systolic blood pressure; CV, coefficient of variation; MAX, maximum; MIN, minimum; SD, standard deviation.

The HRs and 95% CIs are presented in the table, with the Cox model stratified by age (5-year intervals), sex, and study areas (10 groups). Model 1 was adjusted for age, education, occupation, marital status, household income, smoking, alcohol drinking, intake frequency of fresh fruits, vegetables, and red meat, total physical activity level, BMI, waist circumference, family history of CVD, and prevalence of diabetes, all from the 2013–14 resurvey. Model 2 was further adjusted for single-point SBP and DBP at 2013–14 resurvey.

* This column displays the P-values of Model 2. The cut-off α-value for Bonferroni correction = 0.05/14 = 0.004.

Table 3 Added predictive ability of both systolic and diastolic blood pressure measures for 5-year intracerebral haemorrhage risk in all participants and by age, sex, area of residence, or prevalence of diabetes

	cNRI (95% CI)	rIDI (%) (95% CI)	
All			
       Basic model+CV	-0.016 (-0.216, 0.185)	5.2 (-1.1, 11.5)	
       Basic model+ARV	0.026 (-0.164, 0.216)	2.0 (-3.0, 6.9)	
       Basic model+Mean+SD+MIN+MAX	0.140 (-0.066, 0.347)	11.8 (-2.7, 26.3)	
       Basic model+cumBP	0.267 (0.070, 0.464)	18.2 (5.8, 30.7)	
Age			
 ⩾65 y			
       Basic model+CV	0.103 (-0.141, 0.347)	6.6 (-2.3, 15.5)	
       Basic model+ARV	0.018 (-0.225, 0.261)	1.4 (-3.2, 5.9)	
       Basic model+Mean+SD+MIN+MAX	0.160 (-0.083, 0.403)	9.0 (-1.3, 19.3)	
       Basic model+cumBP	0.171 (-0.109, 0.450)	15.6 (4.8, 26.5)	
  <65 y			
       Basic model+CV	0.045 (-0.250, 0.339)	3.4 (-3.8, 10.5)	
       Basic model+ARV	0.088 (-0.187, 0.362)	4.7 (-4.2, 13.6)	
       Basic model+Mean+SD+MIN+MAX	0.202 (-0.074, 0.478)	15.6 (-3.3, 34.6)	
       Basic model+cumBP	0.225 (-0.048, 0.498)	15.7 (-7.0, 38.4)	
Sex			
  Men			
       Basic model+CV	0.126 (-0.181, 0.432)	2.2 (-17.8, 22.2)	
       Basic model+ARV	0.281 (0.001, 0.562)	-0.0 (-19.0, 18.9)	
       Basic model+Mean+SD+MIN+MAX	0.294 (0.038, 0.551)	1.3 (-20.3, 23.0)	
       Basic model+cumBP	0.325 (0.067, 0.583)	19.2 (0.4, 38.0)	
  Women			
       Basic model+CV	-0.064 (-0.337, 0.208)	0.6 (-0.3, 1.5)	
       Basic model+ARV	0.151 (-0.139, 0.441)	-0.1 (-2.6, 2.4)	
       Basic model+Mean+SD+MIN+MAX	-0.008 (-0.240, 0.225)	20.0 (4.0, 36.1)	
       Basic model+cumBP	0.147 (-0.098, 0.392)	18.8 (2.7, 34.8)	
Area of residence			
  Rural			
       Basic model+CV	-0.005 (-0.225, 0.215)	4.2 (-2.1, 10.5)	
       Basic model+ARV	0.076 (-0.118, 0.271)	3.9 (-2.1, 9.8)	
       Basic model+Mean+SD+MIN+MAX	0.087 (-0.123, 0.297)	20.1 (4.5, 35.7)	
       Basic model+cumBP	0.271 (0.037, 0.504)	20.2 (2.9, 37.4)	
  Urban			
       Basic model+CV	0.372 (-0.037, 0.781)	20.9 (-16.9, 58.8)	
       Basic model+ARV	0.272 (-0.131, 0.675)	11.2 (-27.8, 50.1)	
       Basic model+Mean+SD+MIN+MAX	0.621 (0.241, 1.001)	33.2 (-32.0, 98.5)	
       Basic model+cumBP	0.257 (-0.119, 0.633)	2.8 (-10.7, 16.2)	
Without diabetes			
       Basic model+CV	-0.069 (-0.250, 0.113)	0.5 (-2.3, 3.2)	
       Basic model+ARV	0.027 (-0.175, 0.230)	0.8 (-1.5, 3.1)	
       Basic model+Mean+SD+MIN+MAX	0.174 (-0.028, 0.376)	10.7 (-6.2, 27.5)	
       Basic model+cumBP	0.372 (0.157, 0.587)	26.2 (3.5, 48.9)	
ARV, average real variability; CI, confidence interval; cNRI, continuous net reclassification improvement; cumBP, cumulative blood pressure; CV, coefficient of variation; MAX, maximum; MIN, minimum; rIDI, relative integrated discrimination improvement; SD, standard deviation.

Basic Cox models included age, SBP, DBP, anti-hypertensive treatment, current daily smoking, prevalence of diabetes, and waist circumference at 2013–14 resurvey, and interactions between age and the other six predictors. For the analysis in participants without diabetes, the prevalence of diabetes was removed from the basic model. All models were stratified by sex and 10 study areas, except for the analysis by sex in which the model was only stratified by 10 study areas.

Conflict of interest

We declare that we have no conflicts of interest.

Authors’ contributions

JL conceived and designed the study. LL, ZC, and JSC, as the members of the China Kadoorie Biobank Steering Committee, designed and supervised the whole study, obtained funding, and, together with CY, DS, PP, HD, LY, YC, DS, DA, and JWC acquired the data. YZ and YD analyzed the data. YZ drafted the manuscript. JL contributed to the interpretation of the results and critical revision of the manuscript for important intellectual content. All authors reviewed and approved the final manuscript. JL is the study guarantor.
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