Advertisement
Browse Subject Areas
?

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here.

  • Loading metrics

Recent development of risk-prediction models for incident hypertension: An updated systematic review

  • Dongdong Sun ,

    Contributed equally to this work with: Dongdong Sun, Jielin Liu

    Roles Conceptualization, Data curation, Formal analysis, Methodology, Writing – original draft

    ‡ These authors are co-first authors on this work.

    Affiliations Department of Hypertension Research, Beijing Anzhen Hospital, Capital Medical University and Beijing Institute of Heart Lung and Blood Vessel Diseases, Beijing, China, Beijing Lab for Cardiovascular Precision Medicine(PXM2017_014226_000037), Beijing, China

  • Jielin Liu ,

    Contributed equally to this work with: Dongdong Sun, Jielin Liu

    Roles Conceptualization, Methodology, Writing – review & editing

    ‡ These authors are co-first authors on this work.

    Affiliations Department of Hypertension Research, Beijing Anzhen Hospital, Capital Medical University and Beijing Institute of Heart Lung and Blood Vessel Diseases, Beijing, China, Beijing Lab for Cardiovascular Precision Medicine(PXM2017_014226_000037), Beijing, China

  • Lei Xiao,

    Roles Formal analysis, Methodology, Writing – review & editing

    Affiliation Program Director & Medical Officer, Lung Cell and Vascular Biology Program, National Heart, Lung, and Blood Institute, Bethesda, Maryland, United States of America

  • Ya Liu,

    Roles Data curation, Resources

    Affiliations Department of Hypertension Research, Beijing Anzhen Hospital, Capital Medical University and Beijing Institute of Heart Lung and Blood Vessel Diseases, Beijing, China, Beijing Lab for Cardiovascular Precision Medicine(PXM2017_014226_000037), Beijing, China

  • Zuoguang Wang,

    Roles Data curation, Methodology, Resources

    Affiliations Department of Hypertension Research, Beijing Anzhen Hospital, Capital Medical University and Beijing Institute of Heart Lung and Blood Vessel Diseases, Beijing, China, Beijing Lab for Cardiovascular Precision Medicine(PXM2017_014226_000037), Beijing, China

  • Chuang Li,

    Roles Data curation, Methodology, Resources, Validation

    Affiliations Department of Hypertension Research, Beijing Anzhen Hospital, Capital Medical University and Beijing Institute of Heart Lung and Blood Vessel Diseases, Beijing, China, Beijing Lab for Cardiovascular Precision Medicine(PXM2017_014226_000037), Beijing, China

  • Yongxin Jin,

    Roles Data curation, Formal analysis

    Affiliations Department of Hypertension Research, Beijing Anzhen Hospital, Capital Medical University and Beijing Institute of Heart Lung and Blood Vessel Diseases, Beijing, China, Beijing Lab for Cardiovascular Precision Medicine(PXM2017_014226_000037), Beijing, China

  • Qiong Zhao ,

    Roles Conceptualization, Data curation, Methodology, Writing – review & editing

    wenshaojun@aliyun.com (WSJ); qiong.zhao@inova.org (ZQ)

    Affiliation Department of Medicine, Division of Cardiology, Virginia Commonwealth University School of Medicine, Inova Campus, Falls Church, Virginia, United States of America

  • Shaojun Wen

    Roles Conceptualization, Funding acquisition, Project administration, Resources, Writing – review & editing

    wenshaojun@aliyun.com (WSJ); qiong.zhao@inova.org (ZQ)

    Affiliations Department of Hypertension Research, Beijing Anzhen Hospital, Capital Medical University and Beijing Institute of Heart Lung and Blood Vessel Diseases, Beijing, China, Beijing Lab for Cardiovascular Precision Medicine(PXM2017_014226_000037), Beijing, China

Recent development of risk-prediction models for incident hypertension: An updated systematic review

  • Dongdong Sun, 
  • Jielin Liu, 
  • Lei Xiao, 
  • Ya Liu, 
  • Zuoguang Wang, 
  • Chuang Li, 
  • Yongxin Jin, 
  • Qiong Zhao, 
  • Shaojun Wen
PLOS
x

Abstract

Background

Hypertension is a leading global health threat and a major cardiovascular disease. Since clinical interventions are effective in delaying the disease progression from prehypertension to hypertension, diagnostic prediction models to identify patient populations at high risk for hypertension are imperative.

Methods

Both PubMed and Embase databases were searched for eligible reports of either prediction models or risk scores of hypertension. The study data were collected, including risk factors, statistic methods, characteristics of study design and participants, performance measurement, etc.

Results

From the searched literature, 26 studies reporting 48 prediction models were selected. Among them, 20 reports studied the established models using traditional risk factors, such as body mass index (BMI), age, smoking, blood pressure (BP) level, parental history of hypertension, and biochemical factors, whereas 6 reports used genetic risk score (GRS) as the prediction factor. AUC ranged from 0.64 to 0.97, and C-statistic ranged from 60% to 90%.

Conclusions

The traditional models are still the predominant risk prediction models for hypertension, but recently, more models have begun to incorporate genetic factors as part of their model predictors. However, these genetic predictors need to be well selected. The current reported models have acceptable to good discrimination and calibration ability, but whether the models can be applied in clinical practice still needs more validation and adjustment.

Introduction

The number of people living with hypertension is predicted to be 1.56 billion worldwide by the year 2025[1]. In addition, hypertension contributes to ~13% of the total mortality worldwide[2] and ~7% of the total disability-adjusted life years, creating a tremendous financial burden for both patients and the health-care system[2]. The association between hypertension and traditional risk factors such as age, body mass index (BMI), blood pressure (BP), smoking and family history have been well studied, whereas the roles of genetic variants associated with the incidence of hypertension are less clearly defined[3,4].

In 2013, Echouffo-Tcheugui JB et al. published a systematic review of 11 articles with 15 models[5]. Most of these models were carried out in Caucasian populations, and the prediction factors used in these studies were almost identical. Noticeably, none of the above models took genetic factors into consideration, whereas in recent years, more study designs of hypertension risk prediction models have tended not only to have larger patient enrollment size with diverse ethnic backgrounds but also to include genetic factors in these models. Therefore, we conducted this systematic review to summarize the current development status and performance of hypertension prediction models, which would provide updates for health-care providers and policy-makers in the field of hypertension research and clinical practice. This review could also help improve hypertension awareness, identify populations at high risk for hypertension, and determine those individuals who could benefit from early interventions.

Method

Search strategy

The research strategy, study selection and analysis methods used in this study followed the guidelines from the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) Statement[6] (S1 Table). We conducted a complete literature search in both PubMed and Embase to retrieve all published reports about hypertension prediction models using the keywords “hypertension”, “high blood pressure”, “prediction model”, and “risk score”. The search strategy was (((prediction model[Title/Abstract]) OR risk score)) AND ((hypertension[Title/Abstract]) OR high blood pressure[Title/Abstract]). The last search was conducted on September 5, 2016. The related references from those retrieved reports were also searched manually to identify any additional published reports. For those identified articles that were not available online, we contacted the authors directly to request copies.

Inclusion and exclusion criteria

All the retrieved reports were screened independently for inclusion by two researchers from this study. The titles and abstracts of retrieved papers were used as the primary review content for inclusion verification. However, if questioned or unclear, the full article was reviewed prior to inclusion decision. The study’s inclusion criteria include: 1. Reporting a risk assessment tool, e.g., an equation or a risk score system; 2. Predicting the risk incidence of essential hypertension; 3. Published in English-language journals; 4. Conducted in subjects 18 years old or older; 5. Reporting quantitative measures of model performance (preferred but not necessarily required). Exclusion criteria include: 1. Studies only describe association between risk factors and incident hypertension; 2. Simulation studies; 3. Studies predict gestation-related hypertension; 4. Unpublished research data.

Data extraction and synthesis

Any discrepancy of the independently collected data from the two researchers was resolved by group discussion among all participating project investigators. The following data were extracted from each study: study design, subject characteristics, number of subjects in derivation and validation cohorts, number of subjects who developed hypertension, number of candidate variables considered, variables included in the final model and statistical method used for development of the model. We extracted the area under the curve (AUC) of the receiver operating characteristic or C-statistic to assess the discrimination ability of each model. We also collected the value of Hosmer–Lemeshow χ2, and the p value of the corresponding test statistic, to assess model calibration ability. Due to the wide spread of differences in risk factors, population, study design, and sufficiency of data, it was impossible to perform meta-analysis in our current study. Instead, we opted to conduct a narrative synthesis of the evidence. However, to provide a nice summary graph, we applied the random effects model meta-analysis to combine the estimates of the AUC from studies with enough data and assessed the between-study heterogeneity, with the use of the Stata statistical software version 12.0(http://www.stata.com/). The data used in meta-analysis was transformed in the way of double arcsine transformations to addresses the problems of confidence limits and variance instability. The potential publication bias was assessed with funnel plot, as well as Begg's and Egger’s test. A P value <.05 indicated significant publication bias.

Results

The process of the literature search and paper selection, according to PRISMA guidelines, is presented in Fig 1. Our initial literature search resulted in 7332 citations; only 26 articles were selected, reporting 48 prediction models. Table 1 shows the characteristics of these 26 studies, of which 5 were conducted in the US[711], 5 in Europe[1216], 7 in China[1723], 4 in Korea[2427], 2 in Japan[28,29], 2 in Iran[30,31], and 1 in India[32]. Among them, only 1 study was carried out in women alone[9]. A total of 162,358 subjects were enrolled in these studies. In the longitudinal studies, participants were followed up for 3 to 30 years. The definition of hypertension among these studies was consistent. Twenty-four studies defined hypertension as either systolic blood pressure (SBP) ≥140 mmHg and/or diastolic blood pressure (DBP) ≥ 90 mmHg, or the use of antihypertensive drugs. Two studies[17,31] defined isolated systolic hypertension as SBP ≥140 mmHg and DBP ≤ 90 mmHg, and isolated diastolic hypertension as DBP ≥ 90 mmHg and SBP ≤ 140 mmHg. Twenty studies used traditional factors only, and 6 studies[11,14,16,19,21,26] also included Genetic Risk Score (GRS) factors (indeed, 2 studies[19,26] used genetic risk factors exclusively). The common predictors included in most models were age, gender, BMI, SBP, DBP, and parental history of hypertension. The SNPs that were used for setting up the GRS system were nearly all derived from the genome-wide association study (GWAS). The number of SNPs used in these studies ranged from 2 to 32 (S2 Table). The AUC or C-statistic of models[11,21] including GRS were superior compared to those without GRS (C-index change = 0.3%–0.5%; p<0.05). Twelve studies proposed to build models with logistic regression, 7 with COX regression, 6 with Weibull regression, and 1 with linear regression.

thumbnail
Fig 1. The process of article search and selection.

From: Moher D, Liberati A, Tetzlaff J, Altman DG, The PRISMA Group (2009). Preferred Reporting Iterns for Systematic Reviews and Meta-Analyses: The PRISMA Statement. PLoS Med 6(6): e1000097. doi:10.1371/joumal.pmed1000097. For more information, visit www.prisma-statement.org.

https://doi.org/10.1371/journal.pone.0187240.g001

Performance of prediction models

The performance of prediction models is shown in Table 2. AUC ranged from 0.64 to 0.97, and C-statistic ranged from 60% to 90%. The results of pooling 35 models in meta-analysis(S1 File) show the value of AUC as 0.767, 95%CI(0.742, 0.792). The calibration was assessed by Hosmer–Lemeshow χ2, suggesting that these models had good calibration ability.

Validation of prediction models

The prediction models of 7 studies[9,10,12,13,15,26,28] were validated in internal cohorts through split samples, with C-statistics ranging from 0.79 to 0.9. Three models were externally validated. The SHIP risk model[15] from northeast Germany was validated by data from the Danish INTER99, comprising 2887 participants, and it performed well, with an AUC of 0.77 (P = 0.74) and the Hosmer–Lemeshow χ2 test of 40.6 (P = 2×10−6). The KoGES risk score from Korea was externally validated by a large nationwide Korean cohort[33]. The discrimination (AUC = 0.733) and calibration (Hosmer–Lemeshow χ2 = 14.85, P = 0.062) of this model were both good. The Framingham model was externally validated by 7 studies[12,15,24,3336] from different countries (S3 Table).

Meta-analysis

Results from pooling 35 models in the meta-analysis showed that the AUC was 0.767, 95% CI(0.742, 0.792) indicating the performance of prediction models was well. Fig 2 shows the forest plots of analysis. As expected, the heterogeneity between studies(I-squared = 99.5%, Estimate of between-study variance Tau-squared = 0.0055) was significant(S1 file). Publication bias was evaluated with Funnel plot (Fig 3). The results(P>0.05) indicated no significant publication bias.

Discussion

This systematic review summarizes the current evidence regarding risk models developed to predict incident hypertension. The prediction models could help identify individuals who are more susceptible to hypertension and prioritize the underlying risk factors that lead to the incidence of hypertension. In addition, it could also help individuals with high risk for hypertension and health-care providers to take preventive interventions earlier.

Population of studies

Most of these models were derived from American, European or East Asian populations; only one study was carried out in India, and the other 2 were in Iran. It is perceivable that systematic underestimation or overestimation of risk may occur when applying a model constructed from one particular cohort to a distinct ethnic population with different characteristics (the selection of predictors and the genetic background). We found that most prediction models were established in developed countries, and only a few were established in developing or undeveloped countries. Thus, it is imperative to establish reliable predictive models in those countries or regions to help reduce the incidence of hypertension and cardiovascular events caused by high blood pressure.

Predictors included

The most commonly used predictors include age, BMI, SBP, DBP, etc., which are easy to obtain in clinical practice. A few studies also take blood biochemistry factors or anthropometric parameters[25,27] as predictors (Table 2), which are also part of the routine lab test results in a general physical examination. The biochemistry factors used as predictors include blood glucose, triglycerides, high-density lipoprotein cholesterol (HDL-C), and fibrinogen. It has been reported that the level of blood glucose is associated with high blood pressure[37]. Triglyceride, cholesterol and HDL-C are also known to contribute to blood hyperviscosity[38] and vascular sclerosis, which could lead to the rise of the BP. Since hypertension is also considered as a metabolic disease, the changes of blood biochemical factors could provide important and valuable information for the accuracy of certain hypertension prediction models.

It is well known that the interaction between environmental and genetic factors contributes to the development of hypertension. Theoretically, the prediction models should contain both environmental and genetic predictors. Most of the SNPs used to construct GRS were from GWAS (S2 Table). In the Finnish study[16], results showed that GRS were significantly associated with BP but weakly associated with BP increase and incident hypertension; in contrast, in Hispanic Americans[11], GRS was constructed by 2 SNPs on chromosome 3 alone, and when GRS was added into the model, the improvement of predicting capability measured by the AUC was minor. In a Korean population[26], GRS was constructed by 4 SNPs based on GWAS, which was independently associated with the risk of incident hypertension. Among the 4 SNPs, rs17249754 was the same predictor as that selected in 2 Chinese genetic studies[19,21], and rs1378942 was the same as that used in the Swedish genetic study[14]. However, adding GRS into models with traditional risk factors did not significantly improve the discrimination ability. In the Swedish study[14], when adding cGRS (derived from a simple, unweighted count method) into the traditional model, AUC was marginally, but not significantly, improved (from 0.662 to 0.664). In the 29 SNPs that constructed cGRS, one (rs1378942) was the same as that selected in Korean study[26] and two (rs16998073, rs11191548) were the same as those selected in 2 Chinese studies[19,21]. A couple of factors may contribute to these unfavorable observations. First, since hypertension is a known multigene disease, a limited number of SNPs as representative predictors may not fully reflect the overall contribution and weight of all genetic variants. Second, it is possible that some of those included SNPs were selected without fully considering their potential interactions with other genetic variants or environmental factors.

In contrast, in a Chinese study[21], adding GRS constructed by 22 carefully selected SNPs to the traditional predictors produced an ideal result, as the C-index value improved significantly (C-index change = 0.3%–0.5%; all p < 0.05). Among the 22 uncorrelated (r2 < 0.5) SNPs, 10 were associated with SBP or DBP from published GWAS data obtained from an East Asian population, and 19 SNPs had been identified and verified in a Chinese population. These results clearly suggested that the contribution and value of GRS to a hypertension prediction model heavily depends on the selection of SNPs. Since hypertension is a disease of polygenic inheritance, the selection of SNPs used for GRS construction is thus critical. Using GWAS results as the only source for SNP selection is inadequate, as the characteristics of SNPs obtained from one particular GWAS may not necessarily be suitable for other ethnic populations. More appropriate SNP selection should come from the genetic research results in the same ethnic group. Other SNP selection considerations for GRS construction should include a sufficient number of SNPs, causal relationship between the select genes and disease development, gene-gene or gene-environment interactions, and proper statistical methods to include or exclude gene loci.

At the present stage, genetic markers for predicting hypertension can be of great interest for researchers and basic scientists (and possibly for drug companies), but may not hold much interest for patients. Once genetic factors are included in prediction models, patients cannot use the model for self-assessment, clinicians could face problems explaining the model, and cost for genetic tests can be high. These problems may be resolved with the development of gene-function and gene-sequencing research.

Model validation

Seven studies validated their prediction models using internal validation. All studies indicated good discriminatory ability and calibration, suggesting that the models could be applied in the original population with satisfactory performance. A Framingham prediction model was validated in external populations by 7 studies (S3 Table). It performed well in a study of African-American and Caucasians in the US[35], a German study[15] and a British study[12]. In a large nationwide Korean cohort,[33]the AUC was acceptable, but this model underestimated hypertension incidence (p<0.001) in Korea. In the Multi-Ethnic Study of Atherosclerosis (MESA)[34], including Caucasian, African-American, Hispanic, and Asian (primarily of Chinese descent) participants, the Framingham model showed better discrimination ability than SBP alone or age-specific DBP categories. However, the difference between the observed and the predicted hypertension risks (Hosmer-Lemeshow goodness of fit p<0.001) in the MESA study was significant. In contrast, the discrimination (C-statistics = 0.5 to 0.6) and calibration ability (p<0.0001) in rural Chinese was poor[36], whereas poor agreement (χ2 = 29.73, p = 0.0002) underestimated the risk of hypertension in Koreans[24]. The distinct performance in different populations was partially attributed to the various levels of risk predictors and inherited variables. These differences suggested that a model derived from one particular population could not be directly applied to a distinct population, and the fittest model for one particular population is that derived from the same population.

Heterogeneity

The meta-analysis showed the heterogeneity was significant. The included variables, study designs, number of participants, populations, statistical methods, and follow-up times were different from each other, which might be the source of heterogeneity. We attribute this to the specialization of prediction models, which need to be built for various populations, because no one model could be applied to all people.

Clinical implications

Currently, a large issue regarding hypertension prediction models/scores is that nobody uses these scores in daily life or clinical practice beyond research publications. Some people even question whether hypertension needs to be predicted, as it can be easily measured with noninvasive, cheap, and accessible methods. The function of these models is not only to predict the occurrence of hypertension but, more meaningfully, to remind patients and physicians to pay attention to BP. What is more, it has been proved that the process of progression from normotensive or prehypertension to hypertension can be delayed or prevented by proper and timely clinical interventions. It is urgent and meaningful for people to conduct timely interventions. The importance of prediction models/scores needs to be widely disseminated by authorities or the media to promote their application.

Strengths and limitations of existing models

Most of the current predictors are data commonly collected in routine clinical practice, which are relatively easier for both health-care providers and patients to access. Some models are in the form of risk scores, which may still have room to improve but are also convenient to use in routine clinical practice. Furthermore, several models took GRS into account, which could contribute significantly to their prediction accuracy of hypertension. Since the performance of all these prediction models was accepted as good, the application of these models in clinical practice is very promising.

In contrast, several limitations of these prediction models are also noted. First, since not all these studies were specifically designed or conducted for generating prediction models, the clinical data collection may not be complete, and quality of data collected to inform these models also varies greatly; thus, prediction accuracy is a concern. Second, the enrollment number of participants was low in some studies and may not represent the true characteristics of the general population. Third, the various levels of risk predictors and inherited variables between populations made the models inapplicable for other general people. Fourth, a justified method in selecting the suitable SNPs is lacking. Fifth, since most of the BP data were obtained in hospital or clinic settings, the “white coat effect” may influence the outcome of the BP measurement. Sixthly, none of these models have been shown to improve outcomes in prospective research. Lastly, only a few models were indeed validated by internal cohorts, and only 3 were validated in external cohorts. The validation in internal cohort is more or less considered as a repeat of the original cohort and thus may be overoptimistic in its prediction performance results.

Conclusion

Recently, more and more hypertension prediction models have been reported in different countries and among various ethnic populations. Most of the reported predictors are commonly used in routine clinical practice, and the role of genetic factors is earning more attention. However, the incorporation of genetic variation does not improve the performance significantly for all models. The selection of gene loci is critical, and a justified method in selecting the suitable SNPs is needed. The current reported models have satisfying discrimination and calibration ability, but the validation of these models is still insufficient, which is a critical and required step prior to their broad application in daily clinical practice.

Perspective of future research

It is obvious that the current prediction models might not be perfect, but they do provide a solid foundation for future studies. Of course, more studies on prediction models of hypertension should be conducted with large enrollment numbers, complete data collection, experienced or well-trained investigators, and appropriate statistical analysis. With the development of genetic research, more hypertension-associated SNPs will be found, and a standard protocol in gene loci selection as a candidate prediction factor will be needed. Indeed, before any models are used as guidelines, they need to be validated in various cohorts and adjusted accordingly.

Supporting information

S1 Fig. Begg’s and Egger’s publication bias plot.

https://doi.org/10.1371/journal.pone.0187240.s001

(TIF)

S2 Table. SNPs of GRS.

SNP: single nucleotide polymorphism; GWAS: Genome Wide Association Study; NR: not reported. Rs1378942 was chosen in both Sweden and Korean studies; rs17249754 in Korean and 2 Chinese studies; rs11191548 and rs16998073 from Sweden were the same in two Chinese studies; in two Chinese studies, 7 SNPs (rs17030613, rs16849225, rs1173766, rs11066280, rs35444, rs880315 and rs17249754) were the same.

https://doi.org/10.1371/journal.pone.0187240.s003

(DOCX)

S3 Table. External validation of the Framingham model.

AUC means the area under the receiver operating characteristic curve; CI means confidence interval; JNC—VII definition means the definition of hypertension is based on the Joint National Committee (JNC)—VII definition of hypertension (i.e., SBP/DBP ≥140/90 mmHg or use of antihypertension medications); NR means not reported. First author and year represent study.

https://doi.org/10.1371/journal.pone.0187240.s004

(DOCX)

S1 File. Results of meta-analysis and publication bias test.

https://doi.org/10.1371/journal.pone.0187240.s005

(DOCX)

References

  1. 1. Kearney PM, Whelton M, Reynolds K, Muntner P, Whelton PK, He J. Global burden of hypertension: analysis of worldwide data. Lancet. 2005; 365: 217–223. pmid:15652604
  2. 2. Mendis S P PN B. editors (2011) Global Atlas on Cardiovascular Disease Prevention and Control. Geneva: World Health Organization, Geneva. 2011.
  3. 3. Levy D, Ehret GB, Rice K, Verwoert GC, Launer LJ, Dehghan A, et al. Genome-wide association study of blood pressure and hypertension. Nat Genet. 2009; 41: 677–687. pmid:19430479
  4. 4. Padmanabhan S. Prospects for genetic risk prediction in hypertension. Hypertension. 2013; 61: 961–963. pmid:23509079
  5. 5. Echouffo-Tcheugui JB, Batty GD, Kivimaki M, Kengne AP. Risk models to predict hypertension: a systematic review. PLoS One. 2013; 8: e67370. pmid:23861760
  6. 6. Liberati A, Altman DG, Tetzlaff J, Mulrow C, Gotzsche PC, Ioannidis JP, et al. The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate healthcare interventions: explanation and elaboration. BMJ. 2009; 339: b2700. pmid:19622552
  7. 7. Pearson TA, LaCroix AZ, Mead LA, Liang KY. The prediction of midlife coronary heart disease and hypertension in young adults: the Johns Hopkins multiple risk equations. Am J Prev Med. 1990; 6: 23–28. pmid:2383409
  8. 8. Parikh NI, Pencina MJ, Wang TJ, Benjamin EJ, Lanier KJ, Levy D, et al. A risk score for predicting near-term incidence of hypertension: the Framingham Heart Study. Ann Intern Med. 2008; 148: 102–110. pmid:18195335
  9. 9. Paynter NP, Cook NR, Everett BM, Sesso HD, Buring JE, Ridker PM. Prediction of incident hypertension risk in women with currently normal blood pressure. Am J Med. 2009; 122: 464–471. pmid:19375556
  10. 10. Kshirsagar AV, Chiu YL, Bomback AS, August PA, Viera AJ, Colindres RE, et al. A hypertension risk score for middle-aged and older adults. J Clin Hypertens (Greenwich). 2010; 12: 800–808.
  11. 11. Choi YH, Chowdhury R, Swaminathan B. Prediction of hypertension based on the genetic analysis of longitudinal phenotypes: a comparison of different modeling approaches for the binary trait of hypertension. BMC Proc. 2014; 8: S78. pmid:25519406
  12. 12. Kivimaki M, Batty GD, Singh-Manoux A, Ferrie JE, Tabak AG, Jokela M, et al. Validating the Framingham Hypertension Risk Score: results from the Whitehall II study. Hypertension. 2009; 54: 496–501. pmid:19597041
  13. 13. Kivimaki M, Tabak AG, Batty GD, Ferrie JE, Nabi H, Marmot MG, et al. Incremental predictive value of adding past blood pressure measurements to the Framingham hypertension risk equation: the Whitehall II Study. Hypertension. 2010; 55: 1058–1062. pmid:20157053
  14. 14. Fava C, Sjogren M, Montagnana M, Danese E, Almgren P, Engstrom G, et al. Prediction of blood pressure changes over time and incidence of hypertension by a genetic risk score in Swedes. Hypertension. 2013; 61: 319–326. pmid:23232644
  15. 15. Volzke H, Fung G, Ittermann T, Yu S, Baumeister SE, Dorr M, et al. A new, accurate predictive model for incident hypertension. J Hypertens. 2013; 31: 2142–2150, 2150. pmid:24077244
  16. 16. Niiranen TJ, Havulinna AS, Langen VL, Salomaa V, Jula AM. Prediction of Blood Pressure and Blood Pressure Change With a Genetic Risk Score. J Clin Hypertens (Greenwich). 2016; 18: 181–186.
  17. 17. Yeh CJ, Pan WH, Jong YS, Kuo YY, Lo CH. Incidence and predictors of isolated systolic hypertension and isolated diastolic hypertension in Taiwan. J Formos Med Assoc. 2001; 100: 668–675. pmid:11760372
  18. 18. Chien KL, Hsu HC, Su TC, Chang WT, Sung FC, Chen MF, et al. Prediction models for the risk of new-onset hypertension in ethnic Chinese in Taiwan. J Hum Hypertens. 2011; 25: 294–303. pmid:20613783
  19. 19. Qi Y, Zhao H, Wang Y, Wang Y, Lu C, Xiao Y, et al. Replication of the top 10 most significant polymorphisms from a large blood pressure genome-wide association study of northeastern Han Chinese East Asians. Hypertens Res. 2014; 37: 134–138. pmid:24196197
  20. 20. Li G, Liu J, Wang W, Wang M, Xie W, Hao Y, et al. [Prediction models for the 15 years risk of new-onset hypertension in Chinese people aged from 35 to 64 years old]. Zhonghua Nei Ke Za Zhi. 2014; 53: 265–268. pmid:24857297
  21. 21. Lu X, Huang J, Wang L, Chen S, Yang X, Li J, et al. Genetic predisposition to higher blood pressure increases risk of incident hypertension and cardiovascular diseases in Chinese. Hypertension. 2015; 66: 786–792. pmid:26283040
  22. 22. Zhang W, Wang L, Chen Y, Tang F, Xue F, Zhang C. Identification of Hypertension Predictors and Application to Hypertension Prediction in an Urban Han Chinese Population: A Longitudinal Study, 2005–2010. Prev Chronic Dis. 2015; 12: E184. pmid:26513440
  23. 23. Chen Y, Wang C, Liu Y, Yuan Z, Zhang W, Li X, et al. Incident hypertension and its prediction model in a prospective northern urban Han Chinese cohort study. J Hum Hypertens. 2016: 1–7.
  24. 24. Lim NK, Son KH, Lee KS, Park HY, Cho MC. Predicting the risk of incident hypertension in a Korean middle-aged population: Korean genome and epidemiology study. J Clin Hypertens (Greenwich). 2013; 15: 344–349.
  25. 25. Lee BJ, Kim JY. A comparison of the predictive power of anthropometric indices for hypertension and hypotension risk. PLoS One. 2014; 9: e84897. pmid:24465449
  26. 26. Lim NK, Lee JY, Lee JY, Park HY, Cho MC. The Role of Genetic Risk Score in Predicting the Risk of Hypertension in the Korean population: Korean Genome and Epidemiology Study. PLoS One. 2015; 10: e131603.
  27. 27. Lee JW, Lim NK, Baek TH, Park SH, Park HY. Anthropometric indices as predictors of hypertension among men and women aged 40–69 years in the Korean population: the Korean Genome and Epidemiology Study. BMC Public Health. 2015; 15: 140. pmid:25886025
  28. 28. Otsuka T, Kachi Y, Takada H, Kato K, Kodani E, Ibuki C, et al. Development of a risk prediction model for incident hypertension in a working-age Japanese male population. Hypertens Res. 2015; 38: 445. pmid:26043932
  29. 29. Yamakado M, Nagao K, Imaizumi A, Tani M, Toda A, Tanaka T, et al. Plasma Free Amino Acid Profiles Predict Four-Year Risk of Developing Diabetes, Metabolic Syndrome, Dyslipidemia, and Hypertension in Japanese Population. Sci Rep. 2015; 5: 11918. pmid:26156880
  30. 30. Bozorgmanesh M, Hadaegh F, Mehrabi Y, Azizi F. A point-score system superior to blood pressure measures alone for predicting incident hypertension: Tehran Lipid and Glucose Study. J Hypertens. 2011; 29: 1486–1493. pmid:21720268
  31. 31. Asgari S, Khalili D, Mehrabi Y, Kazempour-Ardebili S, Azizi F, Hadaegh F. Incidence and risk factors of isolated systolic and diastolic hypertension: a 10 year follow-up of the Tehran Lipids and Glucose Study. Blood Press. 2015: 1–7.
  32. 32. Sathish T, Kannan S, Sarma PS, Razum O, Thrift AG, Thankappan KR. A Risk Score to Predict Hypertension in Primary Care Settings in Rural India. Asia Pac J Public Health. 2016; 28: 26S–31S. pmid:26354334
  33. 33. Lim NK, Lee JW, Park HY. Validation of the Korean Genome Epidemiology Study Risk Score to Predict Incident Hypertension in a Large Nationwide Korean Cohort. Circ J. 2016; 80: 1578–1582. pmid:27238835
  34. 34. Muntner P, Woodward M, Mann DM, Shimbo D, Michos ED, Blumenthal RS, et al. Comparison of the Framingham Heart Study hypertension model with blood pressure alone in the prediction of risk of hypertension: the Multi-Ethnic Study of Atherosclerosis. Hypertension. 2010; 55: 1339–1345. pmid:20439822
  35. 35. Carson AP, Lewis CE, Jacobs DJ, Peralta CA, Steffen LM, Bower JK, et al. Evaluating the Framingham hypertension risk prediction model in young adults: the Coronary Artery Risk Development in Young Adults (CARDIA) study. Hypertension. 2013; 62: 1015–1020. pmid:24041951
  36. 36. Zheng L, Sun Z, Zhang X, Li J, Hu D, Chen J, et al. Predictive value for the rural Chinese population of the Framingham hypertension risk model: results from Liaoning Province. Am J Hypertens. 2014; 27: 409–414. pmid:24308978
  37. 37. Sun NL, Wang HY, Chen XP, Sun YM, Zhao LY, Wang H, et al. [Status of glucose metabolism in Chinese essential hypertensive patients]. Zhonghua Xin Xue Guan Bing Za Zhi. 2013; 41: 333–336. pmid:23906407
  38. 38. Devereux RB, Case DB, Alderman MH, Pickering TG, Chien S, Laragh JH. Possible role of increased blood viscosity in the hemodynamics of systemic hypertension. Am J Cardiol. 2000; 85: 1265–1268. pmid:10802017