Skip to main content


Are you in the early stages of your research career? Take our survey!
  • Research article
  • Open Access
  • Open Peer Review

Prehypertension and incidence of cardiovascular disease: a meta-analysis

  • 1,
  • 1,
  • 2,
  • 3,
  • 2,
  • 2 and
  • 1Email author
BMC Medicine201311:177

  • Received: 5 April 2013
  • Accepted: 27 June 2013
  • Published:
Open Peer Review reports



Prospective cohort studies of prehypertension and the incidence of cardiovascular disease (CVD) are controversial after adjusting for other cardiovascular risk factors. This meta-analysis evaluated the association between prehypertension and CVD morbidity.


Databases (PubMed, EMBASE and the Cochrane Library) and conference proceedings were searched for prospective cohort studies with data on prehypertension and cardiovascular morbidity. Two independent reviewers assessed the reports and extracted data. The relative risks (RRs) of CVD, coronary heart disease (CHD) and stroke morbidity were calculated and reported with 95% confidence intervals (95% CIs). Subgroup analyses were conducted on blood pressure, age, gender, ethnicity, follow-up duration, number of participants and study quality.


Pooled data included the results from 468,561 participants from 18 prospective cohort studies. Prehypertension elevated the risks of CVD (RR = 1.55; 95% CI = 1.41 to 1.71); CHD (RR = 1.50; 95% CI = 1.30 to 1.74); and stroke (RR = 1.71; 95% CI = 1.55 to 1.89). In the subgroup analyses, even for low-range prehypertension, the risk of CVD was significantly higher than for optimal BP (RR = 1.46, 95% CI = 1.32 to 1.62), and further increased with high-range prehypertension (RR = 1.80, 95% CI = 1.41 to 2.31). The relative risk was significantly higher in the high-range prehypertensive populations than in the low-range populations (χ2 = 5.69, P = 0.02). There were no significant differences among the other subgroup analyses (P>0.05).


Prehypertension, even in the low range, elevates the risk of CVD after adjusting for multiple cardiovascular risk factors.


  • Prehypertension
  • Cardiovascular diseases
  • Morbidity
  • Meta-analysis


In 2003, the seventh report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure (JNC 7) proposed a new blood pressure (BP) category of 120 to 139 mm Hg systolic blood pressure (SBP) or 80 to 89 mm Hg diastolic blood pressure (DBP) and designated it as “prehypertension” [1]. This proposal was based, at least in part, on a meta-analysis of 61 prospective studies, which indicated that mortality from ischemic heart disease and stroke in individuals aged 40 to 89 years increases in a log-linear relationship with BP, from levels as low as 115 mm Hg systolic and 75 mm Hg diastolic [1, 2].

Since the JNC 7 proposal, epidemiologic studies have shown that prehypertension is a common worldwide condition in up to 30 to 50% of the studied population [3, 4]. Approximately 90% of individuals with prehypertension have at least one other cardiovascular risk factor and 68% have at least one significant clinical risk factor for heart disease or stroke [5]. Some studies have demonstrated that prehypertension is an independent risk factor for cardiovascular disease (CVD) [69], while others have not shown the same results after data were adjusted for baseline cardiovascular risk factors [10, 11]. It remains unclear whether mild BP elevation directly increases the risk of cardiovascular disease or whether other concurrent risk factors are responsible for the increase [12]. Furthermore, arguments against using the term “prehypertension” also include the fact that there is heterogeneity within this category, as the risk of progressing to hypertension and developing CVD is higher in individuals with BP 130 to 139/85 to 89 mm Hg than in those with BP 120 to 129/80 to 84 mm Hg [3, 13].

Given these inconsistent results, a meta-analysis of prospective cohort studies that examines the association of prehypertension with CVD morbidity may help clarify this issue. The objective of the present study was to evaluate the association between prehypertension and composite CVD, coronary heart disease (CHD) and stroke incidence.


Search strategy and selection criteria

We searched the electronic databases (PubMed, EMBASE and the Cochrane Library) up to the third week of December 2012 using the search terms: “prehypertension”, “prehypertensive”, “pre-hypertension”, “pre-hypertensive”, “high-normal blood pressure”, “high normal blood pressure”, “optimal blood pressure”, “borderline hypertension” or “borderline blood pressure”, and “cardiovascular disease”, “cardiovascular events”, “coronary artery disease”, “coronary heart disease”, “ischemic heart disease”, “stroke” or “cerebrovascular disease”. We restricted the search to human studies. Terms were explored whenever possible within each database. There were no language or publication form restrictions. Conference proceedings for the past 10 years from the American College of Cardiology Meeting, American Heart Association Scientific Sessions and the European Society of Cardiology Congress, and the reference lists of potentially relevant studies were also searched manually.

Studies were included if they met the following criteria: (1) prospective cohort studies of participants aged ≥18 years; (2) BP and other cardiovascular risk factors were evaluated at baseline; (3) the follow-up duration was ≥2 years and the study assessed the incidence of composited CVD, CHD or stroke morbidity; (4) they reported the multivariate-adjusted relative risks (RRs, including study-specific relative risk ratios or hazard ratios) and 95% confidence intervals (CIs) for events associated with prehypertension (BP 120 to 139/80 to 89 mm Hg) vs. reference (optimal BP, BP <120/80 mm Hg) or reported RRs and 95% CIs of low-range (BP 120 to 129/80 to 84 mm Hg) and high-range prehypertension (BP 130 to 139/85 to 89 mm Hg) vs. reference, respectively.

Studies were excluded if: (1) enrollment depended on having a particular risk factor condition; (2) they reported only age- and gender-adjusted relative risk; and (3) data were derived from the same cohort or from secondary analysis, or from combined analysis of other cohort studies.

If duplicate studies were derived from the same cohort and offered the same outcome messages, the latest published study was included. However, if duplicate studies offered additional messages for subgroup analysis that could not be derived from the primary included study, they were included in the subgroup analysis.

Data extraction and quality assessment

Two investigators worked independently (YH and XC) to identify potentially relevant articles using the search strategy defined earlier. Full manuscripts of potentially relevant studies were obtained and reviewed according to predefined criteria. Information on study and participant characteristics, follow-up duration, and outcome assessment was abstracted and transferred to specially designed, pretested forms. Discrepancies were resolved by discussion with other investigators (WM, SW). When the primary outcome data were unpublished, we contacted the principal author for additional information.

The quality of each study was evaluated with reference to the US Preventive Task Force guidelines and a modified checklist used in previous studies [1416]. This checklist assessed the following eight characteristics: (1) prospective study design; (2) maintenance of comparable groups; (3) adequate adjustment of potential confounders (at least five of six factors: age; sex; diabetes mellitus (DM); body mass index (BMI) or other measure of overweight/obesity; cholesterol; and smoking); (4) documented loss to follow-up rate; (5) outcome assessed blind to baseline status; (6) clear definition of exposures (prehypertension) and outcomes; (7) temporality (BP measured at baseline, not at the time of outcomes assessment); and (8) follow-up duration ≥2 years. Studies were graded as good quality if they met 7 to 8 criteria, fair for 4 to 6 criteria, and poor for <4 criteria.

Data synthesis and analysis

The primary outcome considered was the risk of composited CVD morbidity, and secondary outcomes were risks of CHD and stroke morbidity associated with prehypertension, respectively. Subgroup analyses of the primary outcome were conducted according to BP (low-range prehypertension vs. high-range prehypertension); participant’s age (average <55 years vs. ≥55 years); gender (men vs. women); ethnicity (Asians vs. non-Asians); follow-up duration (<10 years vs. ≥10 years); participant number (<10,000 vs. ≥10,000); and study quality (good (score 7 to 8) vs. fair (score 4 to 6)).

Study-specific risk ratios or hazard ratios were used as the common measure of association between prehypertension and CVD across studies. Multivariate-adjusted RRs and 95% CIs were used for analysis. We logarithmically transformed these values in every study and calculated the corresponding standard errors (SEs) to stabilize the variance and normalize the distribution [15, 16]. The statistical analysis used the inverse variance approach to combine log relative risks and SEs. When multivariate-adjusted RRs and 95% CIs for events associated with prehypertension were available, these data were used directly in the pooled meta-analysis calculations. For studies that published the RRs and 95% CIs of specific subgroups (for example, men and women, low-range and high-range prehypertension, or age-specific subgroups), but did not report an estimated overall risk, the information for each subgroup was used to calculate the overall RRs and 95% CIs for entry into the meta-analysis calculations.

We used χ2 and I2 statistics to test heterogeneity (25%, 50% and 75% representing low, moderate and high heterogeneity, respectively) [17]. Fixed-effects models were used for comparison with random-effects models on the overall risks estimate and yielded similar findings, but we detected between-study heterogeneity for several outcomes; therefore, results from the random-effects models are presented here. To assess for publication bias, we constructed funnel plots for each outcome in which the ln (RR) was plotted against its SE. Additionally, we conducted sensitivity analyses in which the pooled RR was recalculated by omitting one study at a time. P-values were two-tailed and the statistical significance was set at 0.05. All analyses were performed with RevMan software (version 5.1 for Windows, The Cochrane Collaboration, Copenhagen, Denmark).

We also determined the population-attributable risk (PAR) for prehypertension on the basis of the pooled RR. PAR% expresses the proportion of disease in the study population that is attributable to the exposure (prehypertension) and could be eliminated if the exposure was eliminated. The PAR% was calculated as PAR% = (Pe)(RR - 1)/((Pe)(RR - 1) + 1)) × 100, where Pe is the proportion of the population exposed to the risk factor (prehypertension), and RR indicates multivariate-adjusted relative risk [9].


Selected studies and characteristics

The selection of studies for inclusion in the meta-analysis is shown in Figure 1. Of the initial 22,386 records, two reviewers determined independently that 42 required a review of the full manuscript. Our final primary analysis included 19 articles [711, 1831], with a total of 468,561 participants, derived from 18 prospective cohort studies (two articles were from the Strong Heart Study and reported the risk factors for CHD [19] and stroke [24], respectively). Eighteen of the primary papers were published in full and one was in abstract form [10]. However, study data from this abstract were acquired by correspondence with the main author. One article from the Framingham Heart Study was excluded for primary outcome analysis, because more recent data from the same cohort were available [9]. However, as this article offered additional messages for subgroup analyses according to BP and gender that could not be derived from the article included in the primary group [9], the study data were re-entered for subgroup analyses. Table 1 summarizes the key characteristics of the included studies. All prospective cohort studies were derived from the general population. Of the 18 studies 11 were from Asia (3 from China [7, 25, 29], 6 from Japan [18, 23, 2628, 31], and 2 from Iran [10, 30]); 5 were from the United States [8, 9, 1921, 24]; and 1 each was from Turkey [22] and Germany [11]. The proportion of Asians was 79.6% (n = 372,927).
Figure 1
Figure 1

Flow of selection for studies through review. BP, blood pressure; CIs, confidence intervals; RRs indicates relative risks.* Only the latest of the published duplicate studies from the same cohort was included if they offered the same outcome messages. However, one of these studies offered additional messages for subgroup analysis according to BP and gender [6], which could not be derived from the primary included study [9], so it was re-included when performing the subgroup analyses. Data were derived from 18 prospective cohort studies (two articles were from the Strong Heart Study and reported the risk factors for CHD [19] and stroke [24], respectively).

Table 1

Study characteristics



Prevalence of prehypertension (%)

Sample size (% women)

Age (y), average (range or SD)

Follow-up (y)

Participants with baseline CVD excluded

Events for analysis

Wu, 2002*[7]



27,739 (46.3)

47.4 (35 to 64)


Not report


Asayama, 2004 [18]



1,702 (61)

60.6 (10.7)


Free of stroke


Liszka, 2005 [8]

United States


8,986 (54.6)

NA (≥25)



CVD, stroke, MI

Lee, 2006 [19]

United States


4,372 (60.6)

56.2(45 to 74)




Qureshi, 2005 [9]

United States


5,181 (55.3)

44.0 (8.6)



CAD, stroke, MI

Kshirsagar,2006 [20]

United States


8,960 (55)

53(45 to 64)



CVD, CHD, stroke

Hsia, 2007 [21]

United States


60,785 (100)

62.8 (7.0)



CVD, MI, stroke

Onat, 2008 [22]



3,034 (50.4)

48 (12)


Free of DM and CHD


Kokubo, 2008 [23]



5,494 (53)

55 (30 to 79)



CVD, MI, stroke

Zhang, 2008 [24]

United States


4,507 (60)

56 (45 to 74)


Free of stroke


Gu, 2009 [25]



158,666 (51)

56 (≥40)



CVD, CHD, stroke

Ikeda, 2009 [26]



33,372 (65)

54 (40 to 69)



CHD, stroke

Ishikawa, 2010 [27]



11,000 (61.3)

55.1 (11.5)




Tanaka, 2010 [28]



22,676 (66)

62 (40 to 80)



Ischemic stroke

Wu, 2012 [29]



100,116 (20.1)

49.4 (30 to 70)



CVD, stroke

Hadaegh, 2013 [30]




47.1 (≥30)




Sadeghi, 2012[10]



3,255 (100)

49.7 (≥35)



CVD, CHD, stroke

Fukuhara, 2012 [31]



2,634 (58)

59.1 (≥40)



CVD, CHD, Stroke

Erbel, 2012 [11]



4,181 (53)

59.3 (40 to 75)



MI,stroke , revascularization

* Article in Chinese; Authors contacted for clarification of data.

CHD Coronary heart disease, CVD Cardiovascular disease, DM Diabetes mellitus, MI Myocardial infarction, NA indicates not available.

The prehypertension incidence ranged from 25.2% [28] to 46.0% [18]. The sample size ranged from 1,702 [18] to 158,666 [25]. The follow-up duration ranged from 2.7 years [28] to 31 years [9]. Two studies enrolled women only [10, 21], whereas all others enrolled both genders. All studies adjusted adequately for potential confounders (at least five of six factors: age, sex, DM, BMI or other measure of overweight/obesity, cholesterol and smoking) except for one study that adjusted for age, sex, heart rate, smoking and obesity [22]. Thirteen studies were graded as good quality and five were graded as fair quality. The details of the quality assessment and adjusted confounders are presented in Additional file 1: Table S1.

Primary and secondary outcomes

The data were heterogeneous (I2 = 69%), so we used the random-effects model to combine results from all studies. In this model, the presence of prehypertension was associated with a 55% increase in CVD morbidity after multivariate adjustment for established cardiovascular risk factors (RR = 1.55; 95% CI = 1.41 to 1.71, P<0.00001, Figure 2). A visual inspection of the funnel plot found no evidence of publication bias (Additional file 1: Figure S1).
Figure 2
Figure 2

Forest plot of comparison: prehypertension vs. optimal blood pressure, outcome: cardiovascular morbidity.

Eleven (n = 292,026) and 12 studies (n = 406,539) reported multivariate-adjusted RRs and 95% CIs for CHD and stroke associated with prehypertension, respectively. We used the random-effects model on the pooled data from these studies and calculated a 50% increase in CHD incidence (RR = 1.50; 95% CI = 1.30 to 1.74, P <0.00001, I2 = 67%, Figure 3) and a 71% increase in stroke incidence (RR = 1.71; 95% CI = 1.55 to 1.89, P <0.00001, I2 = 26%, Figure 4). However, the difference between the incidence of CHD and stroke was not significant (χ2 = 2.13, P = 0.14).
Figure 3
Figure 3

Forest plot of comparison: prehypertension vs. optimal blood pressure, outcome: coronary heart disease.

Figure 4
Figure 4

Forest plot of comparison: prehypertension vs. optimal blood pressure, outcome: stroke.

On the basis of the pooled RR, the PARs of CVD, CHD and stroke for prehypertension were 15.9%, 14.6% and 19.6%, respectively.

Subgroup analyses

In the subgroup analyses, prehypertension significantly predicted higher CVD risk across subgroups with analyses conducted according to participant’s age, gender, ethnicity, follow-up duration, participant number and study quality. Even low-range prehypertension increased the risk of CVD compared to optimal BP (RR = 1.46, 95% CI = 1.32 to 1.62), and the risk further increased with high-range prehypertension (RR = 1.80, 95% CI = 1.41 to 2.31). The relative risk was higher in the high-range than in the low-range prehypertensive populations (χ2 = 5.69, P = 0.02, Table 2). We found no significant differences in the other subgroups (Table 2).
Table 2

Subgroup analyses of the association between prehypertension and cardiovascular morbidity


Risk ratio (95% CI)

P-value between subgroups

Blood pressure range


 Low-range prehypertension

1.46 (1.32, 1.62)


 High-range prehypertension

1.63 (1.47, 1.80)




1.80 (1.44, 2.24)



1.46 (1.28, 1.67)




1.54 (1.34, 1.77)



1.55 (1.36, 1.77)

Participant’s average age


 <55 years

1.55 (1.31, 1.84)


 ≥55 years

1.50 (1.35, 1.67)

Follow-up duration


 <10 years

1.45 (1.27, 1.67)


 ≥10 years)

1.63 (1.45, 1.84)

Participant number



1.59 (1.38, 1.84)



1.49 (1.28, 1.73)

Study quality


 Good (score 7 to 8)

1.54 (1.37, 1.73)


 Fair (score 4 to 6)

1.54 (1.21, 1.96)

Sensitivity analyses

Multiple methods were used to test the sensitivity and the primary results were not influenced by the use of fixed-effect models compared with random-effect models, odds ratios compared with RRs or recalculation by omitting one study at a time.


This meta-analysis found, after controlling for multiple cardiovascular risk factors, a robust and significant association between prehypertension and CVD incidence. The results were consistent across age, gender, trial characteristics, follow-up duration and ethnicity. More importantly, even low-range prehypertension increased the risk of CVD compared with optimal BP and the risk was higher with high-range prehypertension. The PARs calculation indicated 15.9% of CVD, 14.6% of CHD and 19.6% of stroke cases could be prevented if prehypertension was eliminated.

The primary strength of this meta-analysis was that the included studies were restricted to prospective cohort studies only and they reported multivariate-adjusted relative risks. It has been reported that prehypertension is associated with other cardiovascular risk factors [3, 32, 33]. In several multivariate analyses, high BMI was the strongest predictor of prehypertension among traditional risk factors [4, 34, 35]. In large populations, individuals with prehypertension are also more likely to have diabetes [5], impaired fasting glucose [4], metabolic syndrome [36], and dyslipidemia than normotensive individuals [4]. After controlling for these risk factors, some prospective studies have demonstrated prehypertension is still an independent risk factor for CVD [69], while others have not shown the same results [10, 11]. In our meta-analysis, all of the included studies adequately adjusted for potential confounders (at least five of six factors: age, sex, DM, BMI or other measure of over-weight/obesity, cholesterol and smoking) except one study which adjusted for age, sex, heart rate, smoking and obesity [22]. This feature probably mitigated the possibility of known confounders influencing the association between prehypertension and CVD.

The term “prehypertension” has been contentious since the JNC 7 proposal [37]. Other national and international hypertension guidelines have adopted neither the term nor the concept behind prehypertension, preferring to retain the older classification systems for BP [3]. For example, the 2007 report from the Task Force for the Management of Arterial Hypertension of the European Society of Hypertension (ESH) and of the European Society of Cardiology (ESC) preferred to term the 120 to 129/80 to 84 mm Hg group as “normal blood pressure” and the 130 to 139/85 to 89 mm Hg group as “high normal” [38].

One of the most important arguments against the term “prehypertension” is that the risks of progressing to hypertension and developing cardiovascular events are different in those with BP 130 to 139/85 to 89 mm Hg than in those with BP in the 120 to 129/80 to 84 mm Hg range. Our meta-analysis reported that even low-range prehypertension increased the risk of composited CVD compared with optimal BP and the risk was higher with high-range prehypertension. In a recently published meta-analysis, Lee et al. reported that prehypertension was associated with a higher risk of incident stroke [16]; however, the association of low-range prehypertension and stroke was not significant (RR = 1.22, 95% CI = 0.95 to 1.57, P = 0.11). Another recently published meta-analysis by Shen et al. had reported that prehypertension was associated with a higher risk of CHD; however, the association of low-range prehypertension and CHD was not significant [39]. In contrast, our analysis found that even low-range prehypertension increases the risk of CVD. One possible cause of these inconsistent findings may be the differences in the events assessed. Lee’s and Shen’s analyses focused on stroke and CHD, respectively [16, 39]. In contrast, our analysis focused primarily on composited cardiovascular morbidity. Also, we used a wider search strategy with more search terms, including “prehypertension”, “prehypertensive”, “high normal blood pressure”, “optimal blood pressure”, “borderline hypertension” or “borderline blood pressure”. We believe that the wider search strategy is important for meta-analyses to avoid missing potentially relevant studies. Meta-analyses may be biased when the literature search fails to identify all relevant studies.

Our analysis is supported by a study by Arima et al. [40], which included 346,570 participants from 36 cohort studies in the Asia-Pacific region, showing that after adjusting for age, sex, cholesterol and smoking, the hazard ratio for CVD was 1.41 (95% CI = 1.31 to 1.53) in prehypertension. However, there are some important differences in the two analyses. First, most of our included studies were adequately adjusted for potential confounders, including BMI and DM, which were the strongest predictors of prehypertension [4, 34, 35]. However, BMI and DM were not adjusted in Arima’s analysis [40]. Second, our analysis used worldwide data, while Arima et al. used data only from the Asia-Pacific region. Our subgroup analysis found no difference between Asians and non-Asians.

Considering that the great incidence of prehypertension is up to 30 to 50% [3, 4], successful intervention in such a large population could, therefore, have a major public health impact. An effective massive public health intervention may be chiefly educational aiming both at patients and physicians. Healthcare professionals should recommend lifestyle changes early to subjects with prehypertension. However, since the incidence of CVD increased across the whole range of prehypertension, physicians should be aware of which subgroup of the population are at high risk for CVD and of steps that should be taken to treat modifiable risk factors in these people, especially in high-range prehypertension [41]. It had been reported that many risk factors, including overweight, dyslipidemia and impaired glucose metabolism were associated with prehypertension and adverse events [33, 42, 43]. These associated CV risk factors are indicators for selection of subpopulations for future controlled trials of pharmacological treatment, and controlling these factors is helpful in clinical management of prehypertension [44].

This meta-analysis has some limitations. First, we had no access to individual patient-level data. However, as discussed previously, most of the included studies were adequately adjusted for potential confounding risk factors and were of good quality; this may have mitigated the possibility of other cardiovascular risk factors influencing the association of prehypertension and CVD. Second, in most included studies, the determination of prehypertension was based on one single-day measurement, albeit with multiple readings. This may misclassify BP levels due to “white coat effect” or “masked hypertension” and lead to a dilution bias. However, our results are indicative, on the basis of a “snapshot” BP measurement, that prehypertension is associated with increased CVD risk. Finally, selection and publication bias are always possible. We used multiple assessors to minimize the likelihood of such bias, including a comprehensive search strategy, two independent reviewers, standardized eligibility criteria, and funnel plot testing for assessment of publication bias. We consider it unlikely that the results and our conclusions were influenced by such bias.


Prehypertension, even at low levels, is associated with a high risk of CVD. This reaffirms the importance of the definition of prehypertension and its importance to health professionals engaged in the primary prevention of CVD. However, because of the significant difference in the risk of CVD for BP between 120 to 129/80 to 84 mm Hg and 130 to 139/85 to 89 mm Hg, we suggest that this category should be subdivided into low- and high-range prehypertension and that lifestyle modification should be advocated earlier than usual in prehypertension. Further studies are needed to reveal better predictors of high-risk subpopulations with prehypertension (especially in high-range prehypertension) to select subpopulations for future controlled trials of pharmacological treatment.



Body mass index


Blood pressure


Coronary heart disease


Confidence intervals


Cardiovascular disease


Diastolic blood pressure


Diabetes mellitus


European Society of Cardiology


European Society of Hypertension

JNCD 7: 

The seventh report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure


Population-attributable risk


Relative risks


Systolic blood pressure


Standard errors.



This project was supported by the Medical Scientific Research Grant of Health Ministry of Guangdong province, China (No: B2011310, A2012663, B2012343), Scientific Research Fund of Foshan, Guangdong, China (No: 201208227, 201208210) and Scientific Research Fund of Shunde, Guangdong, China (No: 201208210).

Authors’ Affiliations

Department of Cardiology, Nanfang Hospital, Southern Medical University, 1838 North Guangzhou Avenue, Guangzhou, 510515, China
Clinical Medicine Research Center, the Affiliated Hospital at Shunde, Southern Medical University (the First People’s Hospital of Shunde), Foshan, China
Department of Cardiology, the First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China


  1. Chobanian AV, Bakris GL, Black HR, Cushman WC, Green LA, Izzo JJ, Jones DW, Materson BJ, Oparil S, Wright JT, Roccella EJ, National Heart, Lung, and Blood Institute Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure; National High Blood Pressure Education Program Coordinating Committee: The seventh report of the joint national committee on prevention, detection, evaluation, and treatment of high blood pressure: the JNC 7 report. JAMA. 2003, 289: 2560-2572. 10.1001/jama.289.19.2560. Erratum in: JAMA 2003, 290:197View ArticlePubMedGoogle Scholar
  2. Lewington S, Clarke R, Qizilbash N, Peto R, Collins R, Prospective Studies Collaboration: Age-specific relevance of usual blood pressure to vascular mortality: a meta-analysis of individual data for one million adults in 61 prospective studies. Lancet. 2002, 360: 1903-1913. Erratum in: Lancet 2003, 361:1060View ArticlePubMedGoogle Scholar
  3. Elliott WJ, Black HR: Prehypertension. Nat Clin Pract Cardiovasc Med. 2007, 4: 538-548. 10.1038/ncpcardio0989.View ArticlePubMedGoogle Scholar
  4. Grotto I, Grossman E, Huerta M, Sharabi Y: Prevalence of prehypertension and associated cardiovascular risk profiles among young Israeli adults. Hypertension. 2006, 48: 254-259. 10.1161/01.HYP.0000227507.69230.fc.View ArticlePubMedGoogle Scholar
  5. Greenlund KJ, Croft JB, Mensah GA: Prevalence of heart disease and stroke risk factors in persons with prehypertension in the United States, 1999–2000. Arch Intern Med. 2004, 164: 2113-2118. 10.1001/archinte.164.19.2113.View ArticlePubMedGoogle Scholar
  6. Vasan RS, Larson MG, Leip EP, Evans JC, O’Donnell CJ, Kannel WB, Levy D: Impact of high-normal blood pressure on the risk of cardiovascular disease. N Engl J Med. 2001, 345: 1291-1297. 10.1056/NEJMoa003417.View ArticlePubMedGoogle Scholar
  7. Wu G, Wu Z, Liu J, Wang W, Zhao D, Ceng Z, Wang W, Liu J, Qin L, Liu S: Impact of high-normal blood pressure on risk of cardiocerebrovascular disease in 11 provinces in China. Zhonghua Yi Xue Za Zhi. 2002, 82: 1083-1085.PubMedGoogle Scholar
  8. Liszka HA, Mainous AR, King DE, Everett CJ, Egan BM: Prehypertension and cardiovascular morbidity. Ann Fam Med. 2005, 3: 294-299. 10.1370/afm.312.View ArticlePubMedPubMed CentralGoogle Scholar
  9. Qureshi AI, Suri MF, Kirmani JF, Divani AA, Mohammad Y: Is prehypertension a risk factor for cardiovascular diseases?. Stroke. 2005, 36: 1859-1863. 10.1161/01.STR.0000177495.45580.f1.View ArticlePubMedGoogle Scholar
  10. Sadeghi M, Aghababaei E, Talaei M, Sarrafzadegan N: Do hypertension and prehypertension count as cardiovascular risk factors among women in an Iranian society?. Int J Cardiol. 2012, 155: S2.View ArticleGoogle Scholar
  11. Erbel R, Lehmann N, Möhlenkamp S, Churzidse S, Bauer M, Kälsch H, Schmermund A, Moebus S, Stang A, Roggenbuck U, Bröcker-Preuss M, Dragano N, Weimar C, Siegrist J, Jöckel KH, Heinz Nixdorf Recall Study Investigators: Subclinical coronary atherosclerosis predicts cardiovascular risk in different stages of hypertension: result of the Heinz Nixdorf Recall Study. Hypertension. 2012, 59: 44-53. 10.1161/HYPERTENSIONAHA.111.180489.View ArticlePubMedGoogle Scholar
  12. Suri MF, Qureshi AI: Prehypertension as a risk factor for cardiovascular diseases. J Cardiovasc Nurs. 2006, 21: 478-482. 10.1097/00005082-200611000-00012. quiz 483–484View ArticlePubMedGoogle Scholar
  13. Gupta P, Nagaraju SP, Gupta A, Mandya ChikkalinqaiahK B: Prehypertension - time to act. Saudi J Kidney Dis Transpl. 2012, 23: 223-233.PubMedGoogle Scholar
  14. Harris RP, Helfand M, Woolf SH, Lohr KN, Mulrow CD, Teutsch SM, Atkins D, Methods Work Group, Third US Preventive Services Task Force: Current methods of the US preventive services task force: a review of the process. Am J Prev Med. 2001, 20: 21-35.View ArticlePubMedGoogle Scholar
  15. Lee M, Saver JL, Chang KH, Liao HW, Chang SC, Ovbiagele B: Low glomerular filtration rate and risk of stroke: meta-analysis. BMJ. 2010, 341: c4249-10.1136/bmj.c4249.View ArticlePubMedPubMed CentralGoogle Scholar
  16. Lee M, Saver JL, Chang B, Chang KH, Hao Q, Ovbiagele B: Presence of baseline prehypertension and risk of incident stroke: a meta-analysis. Neurology. 2011, 77: 1330-1337. 10.1212/WNL.0b013e3182315234.View ArticlePubMedPubMed CentralGoogle Scholar
  17. Higgins JP, Thompson SG, Deeks JJ, Altman DG: Measuring inconsistency in meta-analyses. BMJ. 2003, 327: 557-560. 10.1136/bmj.327.7414.557.View ArticlePubMedPubMed CentralGoogle Scholar
  18. Asayama K, Ohkubo T, Kikuya M, Metoki H, Hoshi H, Hashimoto J, Totsune K, Satoh H, Imai Y: Prediction of stroke by self-measurement of blood pressure at home versus casual screening blood pressure measurement in relation to the Joint National Committee 7 classification: the Ohasama study. Stroke. 2004, 35: 2356-2361. 10.1161/01.STR.0000141679.42349.9f.View ArticlePubMedGoogle Scholar
  19. Lee ET, Howard BV, Wang W, Welty TK, Galloway JM, Best LG, Fabsitz RR, Zhang Y, Yeh J, Devereux RB: Prediction of coronary heart disease in a population with high prevalence of diabetes and albuminuria: the Strong Heart Study. Circulation. 2006, 113: 2897-2905. 10.1161/CIRCULATIONAHA.105.593178.View ArticlePubMedGoogle Scholar
  20. Kshirsagar AV, Carpenter M, Bang H, Wyatt SB, Colindres RE: Blood pressure usually considered normal is associated with an elevated risk of cardiovascular disease. Am J Med. 2006, 119: 133-141. 10.1016/j.amjmed.2005.08.023.View ArticlePubMedGoogle Scholar
  21. Hsia J, Margolis KL, Eaton CB, Wenger NK, Allison M, Wu L, LaCroix AZ, Black HR, Women's Health Initiative Investigators: Prehypertension and cardiovascular disease risk in the Women’s Health Initiative. Circulation. 2007, 115: 855-860. 10.1161/CIRCULATIONAHA.106.656850.View ArticlePubMedGoogle Scholar
  22. Onat A, Yazici M, Can G, Kaya Z, Bulur S, Hergenc G: Predictive value of prehypertension for metabolic syndrome, diabetes, and coronary heart disease among Turks. Am J Hypertens. 2008, 21: 890-895. 10.1038/ajh.2008.212.View ArticlePubMedGoogle Scholar
  23. Kokubo Y, Kamide K, Okamura T, Watanabe M, Higashiyama A, Kawanishi K, Okayama A, Kawano Y: Impact of high-normal blood pressure on the risk of cardiovascular disease in a Japanese urban cohort: the Suita study. Hypertension. 2008, 52: 652-659. 10.1161/HYPERTENSIONAHA.108.118273.View ArticlePubMedGoogle Scholar
  24. Zhang Y, Galloway JM, Welty TK, Wiebers DO, Whisnant JP, Devereux RB, Kizer JR, Howard BV, Cowan LD, Yeh J, Howard WJ, Wang W, Best L, Lee ET: Incidence and risk factors for stroke in American Indians: the Strong Heart Study. Circulation. 2008, 118: 1577-1584. 10.1161/CIRCULATIONAHA.108.772285.View ArticlePubMedPubMed CentralGoogle Scholar
  25. Gu D, Chen J, Wu X, Duan X, Jones DW, Huang JF, Chen CS, Chen JC, Kelly TN, Whelton PK, He J: Prehypertension and risk of cardiovascular disease in Chinese adults. J Hypertens. 2009, 27: 721-729. 10.1097/HJH.0b013e328323ad89.View ArticlePubMedGoogle Scholar
  26. Ikeda A, Iso H, Yamagishi K, Inoue M, Tsugane S: Blood pressure and the risk of stroke, cardiovascular disease, and all-cause mortality among Japanese: the JPHC Study. Am J Hypertens. 2009, 22: 273-280. 10.1038/ajh.2008.356.View ArticlePubMedGoogle Scholar
  27. Ishikawa Y, Ishikawa J, Ishikawa S, Kajii E, Schwartz JE, Pickering TG, Kario K, Jichi Medical School Cohort Investigators Group: Prehypertension and the risk for cardiovascular disease in the Japanese general population: the Jichi Medical School Cohort Study. J Hypertens. 2010, 28: 1630-1637. 10.1097/HJH.0b013e32833a8b9f.View ArticlePubMedGoogle Scholar
  28. Tanaka F, Makita S, Onoda T, Tanno K, Ohsawa M, Itai K, Sakata K, Onodera M, Koeda Y, Kawarura K, Terayama Y, Yoshida Y, Ogawa A, Okayama A, Nakamura M, Iwate-KENCO Study Group: Prehypertension subtype with elevated C-reactive protein: risk of ischemic stroke in a general Japanese population. Am J Hypertens. 2010, 23: 1108-1113. 10.1038/ajh.2010.134.View ArticlePubMedGoogle Scholar
  29. Wu S, Huang Z, Yang X, Li S, Zhao H, Ruan C, Wu Y, Xin A, Li K, Jin C, Cai J: Cardiovascular events in a prehypertensive Chinese population: four-year follow-up study. Int J Cardiol. 2012, Epub ahead of printGoogle Scholar
  30. Hadaegh F, Mohebi R, Khalili D, Hasheminia M, Sheikholeslami F, Azizi F: High normal blood pressure is an independent risk factor for cardiovascular disease among middle-aged but not in elderly populations: 9-year results of a population-based study. J Hum Hypertens. 2013, 27: 18-23. 10.1038/jhh.2011.112.View ArticlePubMedGoogle Scholar
  31. Fukuhara M, Arima H, Ninomiya T, Hata J, Yonemoto K, Doi Y, Hirakawa Y, Matsumura K, Kitazono T, Kiyohara Y: Impact of lower range of prehypertension on cardiovascular events in a general population: the Hisayama Study. J Hypertens. 2012, 30: 893-900. 10.1097/HJH.0b013e328351d380.View ArticlePubMedGoogle Scholar
  32. Egan BM, Lackland DT, Jones DW: Prehypertension: an opportunity for a new public health paradigm. Cardiol Clin. 2010, 28: 561-569. 10.1016/j.ccl.2010.07.008.View ArticlePubMedGoogle Scholar
  33. Pimenta E, Oparil S: Prehypertension: epidemiology, consequences and treatment. Nat Rev Nephrol. 2010, 6: 21-30. 10.1038/nrneph.2009.191.View ArticlePubMedGoogle Scholar
  34. Tsai PS, Ke TL, Huang CJ, Tsai JC, Chen PL, Wang SY, Shyu YK: Prevalence and determinants of prehypertension status in the Taiwanese general population. J Hypertens. 2005, 23: 1355-1360. 10.1097/01.hjh.0000173517.68234.c3.View ArticlePubMedGoogle Scholar
  35. Choi KM, Park HS, Han JH, Lee JS, Lee J, Ryu OH, Lee KW, Cho KH, Yoon D, Baik SH, Choi DS, Kim SM: Prevalence of prehypertension and hypertension in a Korean population: Korean National Health and Nutrition Survey 2001. J Hypertens. 2006, 24: 1515-1521. 10.1097/01.hjh.0000239286.02389.0f.View ArticlePubMedGoogle Scholar
  36. Cordero A, Laclaustra M, León M, Grima A, Casasnovas JA, Luengo E, del Rio A, Ferreira I, Alegria E: Prehypertension is associated with insulin resistance state and not with an initial renal function impairment. A Metabolic Syndrome in Active Subjects in Spain (MESYAS) Registry substudy. Am J Hypertens. 2006, 19: 189-196. 10.1016/j.amjhyper.2005.08.018. discussion 197–198View ArticlePubMedGoogle Scholar
  37. Towfighi A, Kelley G: The (pre)hypertension limbo: is it time to lower the treatment bar?. Neurology. 2011, 77: 1322-1323. 10.1212/WNL.0b013e31823152e9.View ArticlePubMedGoogle Scholar
  38. Mancia G, De Backer G, Dominiczak A, Cifkova R, Fagard R, Germano G, Grassi G, Heagerty AM, Kjeldsen SE, Laurent S, Narkiewicz K, Ruilope L, Rynkiewicz A, Schmieder RE, Boudier HA, Zanchetti A, ESH-ESC Task Force on the Management of Arterial Hypertension: 2007 ESH-ESC practice guidelines for the management of arterial hypertension: ESH-ESC task force on the management of arterial hypertension. J Hypertens. 2007, 25: 1751-1762. 10.1097/HJH.0b013e3282f0580f. Erratum in: J Hypertens 2007, 25:2184View ArticlePubMedGoogle Scholar
  39. Shen L, Ma H, Xiang MX, Wang JA: Meta-analysis of cohort studies of baseline prehypertension and risk of coronary heart disease. Am J Cardiol. 2013, Epub ahead of printGoogle Scholar
  40. Arima H, Murakami Y, Lam TH, Kim HC, Ueshima H, Woo J, Suh I, Fang X, Woodward M, Asia Pacific Cohort Studies Collaboration: Effects of prehypertension and hypertension subtype on cardiovascular disease in the Asia-Pacific Region. Hypertension. 2012, 59: 1118-1123. 10.1161/HYPERTENSIONAHA.111.187252.View ArticlePubMedGoogle Scholar
  41. Egan BM, Julius S: Prehypertension: risk stratification and management considerations. Curr Hypertens Rep. 2008, 10: 359-366. 10.1007/s11906-008-0068-0.View ArticlePubMedGoogle Scholar
  42. Guo X, Zou L, Zhang X, Li J, Zheng L, Sun Z, Hu J, Wong ND, Sun Y: Prehypertension: a meta-analysis of the epidemiology, risk factors, and predictors of progression. Tex Heart Inst J. 2011, 38: 643-652.PubMedPubMed CentralGoogle Scholar
  43. Erdogan D, Ozaydin M, Icli A, Gonul E, Yucel H, Arslan A, Akcay S: Echocardiographic predictors of progression from prehypertension to hypertension. J Hypertens. 2012, 30: 1639-1645. 10.1097/HJH.0b013e3283550faa.View ArticlePubMedGoogle Scholar
  44. Julius S: Prehypertension in China. J Hypertens. 2009, 27: 700-701. 10.1097/HJH.0b013e32832a06ad.View ArticlePubMedGoogle Scholar
  45. Pre-publication history

    1. The pre-publication history for this paper can be accessed here:


© Huang et al.; licensee BioMed Central Ltd. 2013

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.